diff --git a/DESCRIPTION b/DESCRIPTION
index 74bc857c..86fa2b15 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,8 +1,8 @@
 Package: faux
 Title: Simulation Functions
-Version: 0.0.0.9005
-Date: 2019-04-29
-Authors@R: person("Lisa", "DeBruine", email = "debruine@gmail.com", role = c("aut", "cre"))
+Version: 0.0.0.9006
+Date: 2019-05-02
+Authors@R: person(given = "Lisa", family = "DeBruine", role = c("aut", "cre"), email = "debruine@gmail.com", comment = c(ORCID = "0000-0002-7523-5539"))
 Description: Provides functions for simulating multiple variables with specified relationships.
 Depends:
     R (>= 3.2.4)
@@ -19,7 +19,7 @@ Imports:
     ggplot2
 License: MIT + file LICENSE
 Suggests:
-    testthat,
+    testthat (>= 2.1.0),
     knitr,
     rmarkdown
 VignetteBuilder: knitr
diff --git a/NAMESPACE b/NAMESPACE
index a4f021d0..2bada45d 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -5,12 +5,16 @@ export(check_design)
 export(check_sim_stats)
 export(cormat)
 export(cormat_from_triangle)
+export(get_design_long)
 export(is_pos_def)
+export(long2wide)
+export(make_id)
 export(pos_def_limits)
 export(rnorm_multi)
 export(rnorm_pre)
 export(select_num_grp)
 export(sim_design)
-export(simdf)
-export(simdf_mixed)
+export(sim_df)
+export(sim_mixed_df)
+export(wide2long)
 importFrom(magrittr,"%>%")
diff --git a/NEWS.md b/NEWS.md
index fafedd47..940dca9c 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -5,4 +5,9 @@
 
 # faux 0.0.0.9005
 
-* Bug fixes for `sim_design()` (failed when within or between factor number was 0)
\ No newline at end of file
+* Bug fixes for `sim_design()` (failed when within or between factor number was 0)
+
+# faux 0.0.0.9006
+
+* Changes to argument order and names (more consistent, but may break old scripts)
+* Updated vignettes
\ No newline at end of file
diff --git a/R/check_design.R b/R/check_design.R
new file mode 100644
index 00000000..33db726b
--- /dev/null
+++ b/R/check_design.R
@@ -0,0 +1,323 @@
+#' Validate design
+#'
+#' \code{check_design} validates the specified within and between design
+#'
+#' @param within a list of the within-subject factors
+#' @param between a list of the between-subject factors
+#' @param n the number of samples required
+#' @param mu a vector giving the means of the variables (numeric vector of length 1 or vars)
+#' @param sd the standard deviations of the variables (numeric vector of length 1 or vars)
+#' @param r the correlations among the variables (can be a single number, vars\*vars matrix, vars\*vars vector, or a vars\*(vars-1)/2 vector)
+#' 
+#' @return list
+#' 
+#' @examples 
+#' 
+#' within <- list(time = c("day", "night"))
+#' between <- list(pet = c("dog", "cat"))
+#' check_design(within, between)
+#' 
+#' @export
+#' 
+check_design <- function(within = list(), between = list(), 
+                         n = 100, mu = 0, sd = 1, r = 0) {
+  # name anonymous factors
+  if (is.numeric(within) && within %in% 2:10 %>% mean() == 1) { # vector of level numbers
+    within_names <- LETTERS[1:length(within)]
+    within <- purrr::map2(within_names, within, ~paste0(.x, 1:.y))
+    names(within) <- within_names
+  }
+  if (is.numeric(between) && between %in% 2:10 %>% mean() == 1) { # vector of level numbers
+    between_names <- LETTERS[(length(within)+1):(length(within)+length(between))]
+    between <- purrr::map2(between_names, between, ~paste0(.x, 1:.y))
+    names(between) <- between_names
+  }
+  
+  # check factor specification
+  if (!is.list(within) || !is.list(between)) {
+    stop("within and between must be lists")
+  } else if (length(within) == 0 && length(between) == 0) {
+    stop("You must specify at least one factor")
+  }
+  
+  # if within or between factors are named vectors, 
+  # use their names as column names and values as labels for plots
+  between_labels <- purrr::map(between, fix_name_labels)
+  between <- lapply(between_labels, names)
+  within_labels <- purrr::map(within, fix_name_labels)
+  within <- lapply(within_labels, names)
+  
+  within_factors <- names(within)
+  between_factors <- names(between)
+  
+  # handle no w/in or btwn
+  if (length(between_factors) == 0) between_factors <- ".tmpvar."
+  if (length(within_factors) == 0) within_factors <- ".tmpvar." 
+  
+  # check for duplicate factor names
+  factor_overlap <- intersect(within_factors, between_factors)
+  if (length(factor_overlap)) {
+    stop("You have multiple factors with the same name (", 
+         paste(factor_overlap, collapse = ", "),
+         "). Please give all factors unique names.")
+  }
+  
+  # check for duplicate level names within any factor
+  dupes <- c(within, between) %>%
+    lapply(duplicated) %>%
+    lapply(sum) %>%
+    lapply(as.logical) %>%
+    unlist()
+  
+  if (sum(dupes)) {
+    dupelevels <- c(within, between) %>% 
+      names() %>% 
+      magrittr::extract(dupes) %>% 
+      paste(collapse = ", ")
+    stop("You have duplicate levels for factor(s): ", dupelevels)
+  }
+  
+  # define columns
+  if (length(within) == 0) {
+    cells_w = "val"
+  } else {
+    cells_w <- do.call(expand.grid, within) %>%
+      tidyr::unite("b", 1:ncol(.)) %>% dplyr::pull("b")
+  }
+  if (length(between) == 0) {
+    cells_b = ".tmpvar."
+  } else {
+    cells_b <- do.call(expand.grid, between) %>%
+      tidyr::unite("b", 1:ncol(.)) %>% dplyr::pull("b")
+  }
+  
+  # convert n, mu and sd from vector/list formats
+  cell_n  <- convert_param(n,  cells_w, cells_b, "Ns")
+  cell_mu <- convert_param(mu, cells_w, cells_b, "means")
+  cell_sd <- convert_param(sd, cells_w, cells_b, "SDs")
+  
+  # figure out number of subjects and their IDs
+  sub_n <- sum(cell_n[1,])
+  sub_id <- make_id(sub_n)
+  
+  # set up cell correlations from r (number, vector, matrix or list styles)
+  cell_r <- list()
+  if (length(within)) {
+    for (cell in cells_b) {
+      cell_cor <- if(is.list(r)) r[[cell]] else r
+      cell_r[[cell]] <- cormat(cell_cor, length(cells_w)) 
+    }
+  }
+  
+  list(
+    within = within,
+    between = between,
+    within_factors = within_factors,
+    between_factors = between_factors,
+    within_labels = within_labels,
+    between_labels = between_labels,
+    cells_w = cells_w,
+    cells_b = cells_b,
+    cell_n = cell_n,
+    cell_mu = cell_mu,
+    cell_sd = cell_sd,
+    cell_r = cell_r,
+    sub_id = sub_id
+  )
+}
+
+#' Convert parameter
+#' 
+#' Converts parameter specification from vector or list format
+#' 
+#' @param param the parameter (mu, sd, or n)
+#' @param cells_w a list of within-subject cells combinations
+#' @param cells_b a list of between-subject cell combinations
+#' @param type the name of the parameter (for error messages)
+#' 
+#' @return a data frame 
+#' @keywords internal
+#' 
+convert_param <- function (param, cells_w, cells_b, type = "this parameter") {
+  w_n <- length(cells_w)
+  b_n <- length(cells_b)
+  all_n <- b_n*w_n
+  
+  if (is.data.frame(param)) { # convert to list first
+    # check for row/column confusion
+    cols_are_b <- setdiff(names(param), cells_b) %>% length() == 0
+    rows_are_w <- setdiff(rownames(param), cells_w) %>% length() == 0
+    cols_are_w <- setdiff(names(param), cells_w) %>% length() == 0
+    rows_are_b <- setdiff(rownames(param), cells_b) %>% length() == 0
+    if (cols_are_b && rows_are_w) {
+      # check this first in case rows and cols are the same labels
+      param <- as.list(param) %>%  lapply(magrittr::set_names, rownames(param))
+    } else if (cols_are_w && rows_are_b) {
+      param <- t(param) %>% as.data.frame()
+      param <- as.list(param) %>%  lapply(magrittr::set_names, rownames(param))
+    } else {
+      stop("The ", type, " dataframe is misspecified.")
+    }
+  }
+  
+  if (is.list(param)) {
+    param2 <- c()
+    # add param in right order
+    for (f in cells_b) {
+      if (length(param[[f]]) == 1) { 
+        new_param <- rep(param[[f]], w_n)
+      } else if (length(param[[f]]) != w_n) {
+        stop("The number of ", type, " for cell ", f, 
+             " is not correct. Please specify either 1 or a vector of ", 
+             w_n, " per cell")
+      } else if (setdiff(cells_w, names(param[[f]])) %>% length() == 0) {
+        new_param <- param[[f]][cells_w] # add named parameters in the right order
+      } else {
+        new_param <- param[[f]] # parameters are not or incorrectly named, add in this order
+      }
+      param2 <- c(param2, new_param)
+    }
+    
+    if (length(cells_b) == 0) { # no between-subject factors
+      message("no between-subject factors")
+      if (length(param) == 1) { 
+        param2 <- rep(param, w_n)
+      } else if (length(param) != w_n) {
+        stop("The number of ", type, 
+             " is not correct. Please specify either 1 or a vector of ", 
+             w_n, " per cell")
+      } else if (setdiff(cells_w, names(param)) %>% length() == 0) {
+        param2 <- param[cells_w] # add named parameters in the right order
+      } else {
+        param2 <- param # parameters are not or incorrectly named, add in this order
+      }
+    }
+  } else if (is.numeric(param)) {
+    if (length(param) == 1) { 
+      param2 <- rep(param, all_n) 
+    } else if (length(param) == all_n) {
+      param2 <- param
+    } else {
+      stop("The number of ", type, " is not correct. Please specify 1, a vector of ", 
+           all_n , ", or use the list format")
+    }
+  }
+  
+  dd <- matrix(param2, ncol = b_n) %>% as.data.frame()
+  names(dd) <- cells_b
+  rownames(dd) <- cells_w
+  
+  dd
+}
+
+
+#' Get design from long data
+#' 
+#' Makes a best guess at the design of a long-format data frame. 
+#' Finds all columns that contain a single value per unit of analysis (between factors), 
+#' all columns that contain the same values per unit of analysis (within factors), and 
+#' all columns that differ over units of analysis (dv, continuous factors)
+#' 
+#' @param .data the data frame (in long format)
+#' @param id the column name(s) that identify a unit of analysis
+#' @param dv the column name that identifies the DV
+#' 
+#' @return the data frame in long format
+#' 
+#' @export
+#'
+get_design_long <- function(.data, id = "sub_id", dv = "val") {
+  between_factors <- .data %>%
+    dplyr::group_by_at(dplyr::vars(tidyselect::one_of(id))) %>%
+    dplyr::summarise_all(dplyr::n_distinct) %>%
+    dplyr::ungroup() %>%
+    dplyr::select(-tidyselect::one_of(id)) %>%
+    dplyr::summarise_all(max) %>%
+    dplyr::select_if(~ . == 1) %>%
+    names()
+  
+  within_factors <- .data %>%
+    dplyr::select(-tidyselect::one_of(between_factors)) %>%
+    dplyr::group_by_at(dplyr::vars(tidyselect::one_of(id))) %>%
+    dplyr::summarise_all(paste0, collapse = ",") %>%
+    dplyr::ungroup() %>%
+    dplyr::select(-tidyselect::one_of(id)) %>%
+    dplyr::summarise_all(dplyr::n_distinct) %>%
+    dplyr::select_if(~ . == 1) %>%
+    names()
+  
+  within <- .data %>%
+    dplyr::select(tidyselect::one_of(within_factors)) %>%
+    dplyr::mutate_all(as.factor) %>%
+    dplyr::summarise_all(~levels(.) %>% paste0(collapse = ".|.")) %>%
+    as.list() %>%
+    sapply(strsplit, split=".|.", fixed = TRUE)
+  
+  between <- .data %>%
+    dplyr::select(tidyselect::one_of(between_factors)) %>%
+    dplyr::mutate_all(as.factor) %>%
+    dplyr::summarise_all(~levels(.) %>% paste0(collapse = ".|.")) %>%
+    as.list() %>%
+    sapply(strsplit, split=".|.", fixed = TRUE)
+  
+  between_labels <- purrr::map(between, fix_name_labels)
+  within_labels <- purrr::map(within, fix_name_labels)
+  
+  cells_b <- do.call(expand.grid, between) %>%
+    tidyr::unite("b", 1:ncol(.)) %>% dplyr::pull("b")
+  
+  cells_w <- do.call(expand.grid, within) %>%
+    tidyr::unite("b", 1:ncol(.)) %>% dplyr::pull("b")
+  
+  # get n, mu, sd, r per cell
+  chk <- check_sim_stats(.data, between_factors, within_factors, dv, id)
+  
+  n <- chk %>%
+    tidyr::unite(".within", tidyselect::one_of(between_factors)) %>%
+    dplyr::select(.within, var, n) %>%
+    tidyr::spread(var, n) %>%
+    tibble::column_to_rownames(".within")
+  
+  mu <- chk %>%
+    tidyr::unite(".within", tidyselect::one_of(between_factors)) %>%
+    dplyr::select(.within, var, mean) %>%
+    tidyr::spread(var, mean) %>%
+    tibble::column_to_rownames(".within")
+  
+  sd <- chk %>%
+    tidyr::unite(".within", tidyselect::one_of(between_factors)) %>%
+    dplyr::select(.within, var, sd) %>%
+    tidyr::spread(var, sd) %>%
+    tibble::column_to_rownames(".within")
+  
+  cors <- chk %>%
+    tidyr::unite(".between", tidyselect::one_of(between_factors)) %>%
+    dplyr::select(tidyselect::one_of(c(".between", "var", cells_w))) %>%
+    dplyr::mutate(var = forcats::fct_relevel(var, cells_w)) %>%
+    dplyr::arrange(var) %>%
+    dplyr::group_by(.between) %>%
+    tidyr::nest(.key = "r") %>%
+    as.list() 
+  
+  r <- purrr::map(cors$r, ~tibble::column_to_rownames(., "var"))
+  names(r) <- cors$.between
+   
+  design <- list(
+    within = within,
+    between = between,
+    within_factors = within_factors,
+    between_factors = between_factors,
+    within_labels = within_labels,
+    between_labels = between_labels,
+    cells_w = cells_w,
+    cells_b = cells_b,
+    cell_n = n,
+    cell_mu = mu,
+    cell_sd = sd,
+    cell_r = r,
+    sub_id = id
+  )
+  
+  design
+}
+
diff --git a/R/check_sim_stats.R b/R/check_sim_stats.R
index c3839dfa..7f82ad3b 100644
--- a/R/check_sim_stats.R
+++ b/R/check_sim_stats.R
@@ -2,18 +2,30 @@
 #'
 #' \code{check_sim_stats} Generates a table of the correlations and means of numeric columns in a data frame
 #'
-#' @param dat the existing dataframe
-#' @param grp_by an optional list of column names to group by
+#' @param .data the existing tbl
+#' @param between a vector of column names for between-subject factors
+#' @param within a vector of column names for within-subject factors (if data is long)
+#' @param dv the column name of the dv (if data is long)
+#' @param id the column name(s) of the subject ID (if data is long)
 #' @param digits how many digits to round to (default = 2)
 #' @param usekable logical. If TRUE, output with knitr::kable
 #' 
-#' @return tibble or kable
+#' @return a tbl or kable
 #' @examples
 #' check_sim_stats(iris, "Species")
 #' @export
+#' 
 
-check_sim_stats <- function(dat, grp_by = NULL, digits = 2, usekable = FALSE) {
-  grpdat <- select_num_grp(dat, grp_by)
+check_sim_stats <- function(.data, between = c(), within = c(), dv = c(), id = c(),
+                            digits = 2, usekable = FALSE) {
+  
+  if (length(within) && length(dv) && length(id)) {
+    # convert long to wide
+    .data <- long2wide(.data, within, between, dv, id) %>%
+      dplyr::select(-tidyselect::one_of(id))
+  }
+  
+  grpdat <- select_num_grp(.data, between)
   grpvars <- dplyr::group_vars(grpdat)
   numvars <- names(grpdat)[!names(grpdat) %in% grpvars]
   
@@ -27,14 +39,14 @@ check_sim_stats <- function(dat, grp_by = NULL, digits = 2, usekable = FALSE) {
     tidyr::spread(stat, val)
   
   stats <- grpdat %>%
-    tidyr::nest(dplyr::one_of(numvars), .key = "multisim_data") %>%
+    tidyr::nest(tidyselect::one_of(numvars), .key = "multisim_data") %>%
     dplyr::mutate(multisim_cor = purrr::map(multisim_data, function(d) {
       cor(d) %>% round(digits) %>% tibble::as_tibble(rownames = "var")
     })) %>%
     dplyr::select(-multisim_data) %>%
     tidyr::unnest(multisim_cor) %>%
-    dplyr::left_join(descriptives, by = c(grp_by, "var")) %>%
-    dplyr::select(tidyselect::one_of(c(grp_by, "n", "var", numvars, "mean", "sd")))
+    dplyr::left_join(descriptives, by = c(between, "var")) %>%
+    dplyr::select(tidyselect::one_of(c(between, "n", "var", numvars, "mean", "sd")))
     
   if (usekable) {
     return(knitr::kable(stats))
diff --git a/R/long2wide.R b/R/long2wide.R
new file mode 100644
index 00000000..d2c4e7b1
--- /dev/null
+++ b/R/long2wide.R
@@ -0,0 +1,53 @@
+#' Long to wide format
+#' 
+#' Converts data from long format to wide
+#' 
+#' @param .data the tbl in long format
+#' @param within the names of the within column(s)
+#' @param between the names of between column(s) (optional)
+#' @param dv the name of the DV (value) column
+#' @param id the names of the column(s) for grouping observations
+#' 
+#' @return a tbl in wide format
+#' 
+#' @examples 
+#' df_long <- sim_design(2, 2, long = TRUE)
+#' long2wide(df_long, "A", "B", "val", "sub_id")
+#' 
+#' @export
+#' 
+long2wide <- function(.data, within = c(), between = c(), dv = "val", id = "sub_id") {
+  .data %>%
+    dplyr::select(tidyselect::one_of(c(id, between, within, dv))) %>%
+    tidyr::unite(".tmpwithin.", tidyselect::one_of(within))  %>%
+    dplyr::group_by_at(dplyr::vars(tidyselect::one_of(between))) %>%
+    tidyr::spread(".tmpwithin.", !!dplyr::quo(dv)) %>%
+    dplyr::ungroup()
+}
+
+#' Wide to long format
+#' 
+#' Converts data from wide format to long
+#' 
+#' @param .data the tbl in wide format
+#' @param within_factors the names of the within factors
+#' @param within_cols the names (or indices) of the within-subject (value) columns
+#' @param sep Separator for within-columns (see tidyr::separate)
+#' 
+#' @return a tbl in long format
+#' 
+#' @examples 
+#' wide2long(iris, c("Feature", "Measure"), 1:4)
+#' 
+#' @export
+#' 
+wide2long <- function(.data, within_factors = c(), within_cols = c(), sep = "[^[:alnum:]]+") {
+  if (is.numeric(within_cols)) {
+    within_cols <- names(.data)[within_cols]
+  }
+  
+  .data %>%
+    tidyr::gather(".tmpwithin.", "val", tidyselect::one_of(within_cols)) %>%
+    tidyr::separate(".tmpwithin.", within_factors, sep = sep)
+}
+
diff --git a/R/make_id.R b/R/make_id.R
new file mode 100644
index 00000000..2748032f
--- /dev/null
+++ b/R/make_id.R
@@ -0,0 +1,27 @@
+#' Make ID
+#' 
+#' Make IDs with fixed length and a letter prefix for random effects (e.g., S001, S002, ..., S100).
+#' @param n the number of IDs to generate (or a vector of numbers)
+#' @param prefix the letter prefix to the number
+#' @param digits the number of digits to use for the numeric part. Only used if this is larger than the number of digits in n.
+#' 
+#' @return a vector of IDs
+#' @export
+#' 
+#' @examples 
+#' 
+#' make_id(20, "SUBJECT_")
+#' make_id(10:30, digits = 3)
+#' 
+make_id <- function(n = 100, prefix = "S", digits = 0) {
+  # set max digits to the larger of digits in `n`` or `digits`
+  if (length(n) == 1) {
+    max_n <- n
+    n <- 1:max_n
+  } else {
+    max_n <- max(n)
+  }
+  
+  max_digits <- max(floor(log10(max_n))+1, digits)
+  paste0(prefix, formatC(n, width = max_digits, flag = "0"))
+}
diff --git a/R/rnorm_multi.R b/R/rnorm_multi.R
index 6f171d8b..6e44c8ce 100644
--- a/R/rnorm_multi.R
+++ b/R/rnorm_multi.R
@@ -1,26 +1,35 @@
 #' Multiple Normally Distributed Vectors
 #'
-#' \code{rnorm_multi} makes multiple normally distributed vectors with specified relationships
+#' \code{rnorm_multi()} makes multiple normally distributed vectors with specified relationships.
 #'
 #' @param n the number of samples required
 #' @param vars the number of variables to return
-#' @param cors the correlations among the variables (can be a single number, vars\*vars matrix, vars\*vars vector, or a vars\*(vars-1)/2 vector)
 #' @param mu a vector giving the means of the variables (numeric vector of length 1 or vars)
 #' @param sd the standard deviations of the variables (numeric vector of length 1 or vars)
-#' @param varnames optional names for the variables (string vector of length vars) defaults if cors is a matrix with column names
-#' @param empirical logical. If true, mu, sd and cors specify the empirical not population mean, sd and covariance 
+#' @param r the correlations among the variables (can be a single number, vars\*vars matrix, vars\*vars vector, or a vars\*(vars-1)/2 vector)
+#' @param varnames optional names for the variables (string vector of length vars) defaults if r is a matrix with column names
+#' @param empirical logical. If true, mu, sd and r specify the empirical not population mean, sd and covariance 
 #' @param as.matrix logical. If true, returns a matrix
+#' @param cors (deprecated; use r)
+#' 
+#' @return a tbl of vars vectors
 #' 
-#' @return dataframe of vars vectors
 #' @examples
-#' rnorm_multi(100, 3, c(0.2, 0.4, 0.5), varnames=c("A", "B", "C"))
-#' rnorm_multi(100, 3, c(1, 0.2, -0.5, 0.2, 1, 0.5, -0.5, 0.5, 1), varnames=c("A", "B", "C"))
+#' rnorm_multi(100, 3, 0, 1, c(0.2, 0.4, 0.5), varnames=c("A", "B", "C"))
+#' rnorm_multi(100, 3, 0, 1, c(1, 0.2, -0.5, 0.2, 1, 0.5, -0.5, 0.5, 1), varnames=c("A", "B", "C"))
+#' 
 #' @export
 
-rnorm_multi <- function(n, vars = 3, cors = 0, mu = 0, sd = 1, 
+rnorm_multi <- function(n, vars = 3, mu = 0, sd = 1, r = 0,
                        varnames = NULL, 
                        empirical = FALSE, 
-                       as.matrix = FALSE) {
+                       as.matrix = FALSE, 
+                       cors = NULL) {
+  if (!is.null(cors)) {
+    warning("cors is deprecated, please use r")
+    if (r == 0) r = cors # set r to cors if r is not set
+  }
+  
   # error handling
   if ( !is.numeric(n) || n %% 1 > 0 || n < 3 ) {
     stop("n must be an integer > 2")
@@ -42,7 +51,7 @@ rnorm_multi <- function(n, vars = 3, cors = 0, mu = 0, sd = 1,
     stop("the length of sd must be 1 or vars");
   }
   
-  cor_mat <- cormat(cors, vars)
+  cor_mat <- cormat(r, vars)
   
   sigma <- (sd %*% t(sd)) * cor_mat
   bvn <- MASS::mvrnorm(n, mu, sigma, empirical = empirical)
@@ -51,7 +60,7 @@ rnorm_multi <- function(n, vars = 3, cors = 0, mu = 0, sd = 1,
   if (length(varnames) == vars) {
     colnames(bvn) <- varnames
   } else if (!is.null(colnames(cor_mat))) {
-    # if cors was a matrix with names, use that
+    # if r was a matrix with names, use that
     colnames(bvn) <- colnames(cor_mat)
   }
   
diff --git a/R/rnorm_pre.R b/R/rnorm_pre.R
index f8709878..9a1d19fc 100644
--- a/R/rnorm_pre.R
+++ b/R/rnorm_pre.R
@@ -3,18 +3,18 @@
 #' \code{rnorm_pre} Produces a random normally distributed vector with the specified correlation to an existing vector
 #'
 #' @param x the existing vector
-#' @param rho desired correlation between existing and returned vectors
-#' @param ymean desired mean of returned vector
-#' @param ysd desired SD of returned vector
+#' @param mu desired mean of returned vector
+#' @param sd desired SD of returned vector
+#' @param r desired correlation between existing and returned vectors
 #' 
 #' @return vector
 #' @examples
 #' v1 <- rnorm(10)
-#' v2 <- rnorm_pre(v1, 0.5, 0, 1)
+#' v2 <- rnorm_pre(v1, 0, 1, 0.5)
 #' cor(v1, v2)
 #' @export
 
-rnorm_pre <- function (x, rho=0, ymean=0, ysd=1) {
+rnorm_pre <- function (x, mu=0, sd=1, r=0) {
   # error checking
   if (!is.vector(x)) stop("x must be a vector") 
   if (!is.numeric(x)) stop("x must be numeric")
@@ -22,9 +22,9 @@ rnorm_pre <- function (x, rho=0, ymean=0, ysd=1) {
   
   n <- length(x)
   y <- stats::rnorm(n)
-  z <- rho * scale(x)[,1] + sqrt(1 - rho^2) * 
+  z <- r * scale(x)[,1] + sqrt(1 - r^2) * 
     scale(stats::resid(stats::lm(y ~ x)))[,1]
-  yresult <- ymean + ysd * z
+  yresult <- mu + sd * z
   
   return(yresult)
 }
diff --git a/R/select_num_grp.R b/R/select_num_grp.R
index 8a1076e5..ac6c0a50 100644
--- a/R/select_num_grp.R
+++ b/R/select_num_grp.R
@@ -2,52 +2,52 @@
 #'
 #' \code{select_num_grp} Select grouping and (optionally specified) numeric columns and group
 #'
-#' @param dat the existing dataframe
-#' @param grp_by an optional list of column names to group by
+#' @param .data the existing tbl
+#' @param between an optional list of column names to group by
 #' @param cols an optional list of column names to return (default of NULL returns all numeric columns)
 #' 
-#' @return tibble
+#' @return a tbl
 #' @examples
 #' select_num_grp(iris, "Species")
 #' @export
 
-select_num_grp <- function(dat, grp_by = NULL, cols = NULL) {
+select_num_grp <- function(.data, between = c(), cols = NULL) {
   # error checking -----------------
-  if (is.matrix(dat)) {
-    dat = as.data.frame(dat)
-  } else if (!is.data.frame(dat)) {
-    stop("dat must be a data frame or matrix")
+  if (is.matrix(.data)) {
+    .data = as.data.frame(.data)
+  } else if (!is.data.frame(.data)) {
+    stop(".data must be a data frame or matrix")
   }
   
   # select only grouping and numeric columns -----------------
-  if (is.null(grp_by)) {
+  if (is.null(between)) {
     # no grouping, so select all numeric columns
-    numdat <- dplyr::select_if(dat, is.numeric)
+    numdat <- dplyr::select_if(.data, is.numeric)
     grpdat <- numdat
-  } else if (is.numeric(grp_by) || is.character(grp_by)) {
+  } else if (is.numeric(between) || is.character(between)) {
     # get grouping column names if specified by index
-    if (is.numeric(grp_by)) grp_by <- names(dat)[grp_by]
+    if (is.numeric(between)) between <- names(.data)[between]
     
     # numeric columns, excluding grouping columns
-    numdat <- dat %>%
-      dplyr::select(-tidyselect::one_of(grp_by)) %>%
+    numdat <- .data %>%
+      dplyr::select(-tidyselect::one_of(between)) %>%
       dplyr::select_if(is.numeric)
     
     # get grouping columns, add remaining numeric columns, and group
-    grpdat <- dat %>%
-      dplyr::select(tidyselect::one_of(grp_by)) %>%
+    grpdat <- .data %>%
+      dplyr::select(tidyselect::one_of(between)) %>%
       dplyr::bind_cols(numdat) %>%
-      dplyr::group_by_at(dplyr::vars(tidyselect::one_of(grp_by)))
+      dplyr::group_by_at(dplyr::vars(tidyselect::one_of(between)))
   } else {
-    stop("grp_by must be a numeric or character vector")
+    stop("between must be a numeric or character vector")
   }
   
   if (!is.null(cols)) {
     # return only grouping and cols
-    if (is.numeric(cols)) cols <- names(dat)[cols]
+    if (is.numeric(cols)) cols <- names(.data)[cols]
     
     grpdat <- grpdat %>%
-      dplyr::select(tidyselect::one_of(c(grp_by, cols)))
+      dplyr::select(tidyselect::one_of(c(between, cols)))
   }
   
   return(grpdat) 
diff --git a/R/sim_design.R b/R/sim_design.R
index d172da6e..103d530a 100644
--- a/R/sim_design.R
+++ b/R/sim_design.R
@@ -1,27 +1,29 @@
 #' Simulate Data from Design
 #'
-#' \code{sim_design} generates a dataframe with a specified within and between design
+#' \code{sim_design()} generates a data table with a specified within and between design.
 #'
 #' @param within a list of the within-subject factors
 #' @param between a list of the between-subject factors
 #' @param n the number of samples required
-#' @param cors the correlations among the variables (can be a single number, vars\*vars matrix, vars\*vars vector, or a vars\*(vars-1)/2 vector)
 #' @param mu a vector giving the means of the variables (numeric vector of length 1 or vars)
 #' @param sd the standard deviations of the variables (numeric vector of length 1 or vars)
-#' @param empirical logical. If true, mu, sd and cors specify the empirical not population mean, sd and covariance 
-#' @param frame_long Whether the returned dataframe is in wide (default = FALSE) or long (TRUE) format
+#' @param r the correlations among the variables (can be a single number, vars\*vars matrix, vars\*vars vector, or a vars\*(vars-1)/2 vector)
+#' @param empirical logical. If true, mu, sd and r specify the empirical not population mean, sd and covariance 
+#' @param long Whether the returned tbl is in wide (default = FALSE) or long (TRUE) format
 #' 
-#' @return dataframe
+#' @return a tbl
 #' 
 #' @export
 #' 
 sim_design <- function(within = list(), between = list(), 
-                       n = 100, cors = 0, mu = 0, sd = 1, 
-                       empirical = FALSE, frame_long = FALSE) {
+                       n = 100, mu = 0, sd = 1, r = 0, 
+                       empirical = FALSE, long = FALSE) {
   # check the design is specified correctly
-  design <- check_design(within, between, n, cors, mu, sd)
+  design <- check_design(within = within, between = between, 
+                         n = n, mu = mu, sd = sd, r = r)
+  
   # simulate the data
-  sim_design_(design, empirical = empirical, frame_long = frame_long)
+  sim_design_(design, empirical = empirical, long = long)
 }
 
 #' Fix name labels
@@ -41,227 +43,27 @@ fix_name_labels <- function(x) {
   x
 }
 
-
-#' Convert parameter
-#' 
-#' Converts parameter specification from vector or list format
-#' 
-#' @param param the parameter (mu, sd, or n)
-#' @param cells_b a list of between-subject cell combinations
-#' @param cells_w a list of within-subject cells combinations
-#' @param type the name of the parameter (for error messages)
-#' 
-#' @return a data frame 
-#' 
-convert_param <- function (param, cells_b, cells_w, type = "this parameter") {
-  w_n <- length(cells_w)
-  b_n <- length(cells_b)
-  all_n <- b_n*w_n
-  
-  if (is.data.frame(param)) { # convert to list first
-    # check for row/column confusion
-    cols_are_b <- setdiff(names(param), cells_b) %>% length() == 0
-    rows_are_w <- setdiff(rownames(param), cells_w) %>% length() == 0
-    cols_are_w <- setdiff(names(param), cells_w) %>% length() == 0
-    rows_are_b <- setdiff(rownames(param), cells_b) %>% length() == 0
-    if (cols_are_b && rows_are_w) {
-      # check this first in case rows and cols are the same labels
-      param <- as.list(param) %>%  lapply(magrittr::set_names, rownames(param))
-    } else if (cols_are_w && rows_are_b) {
-      param <- t(param) %>% as.data.frame()
-      param <- as.list(param) %>%  lapply(magrittr::set_names, rownames(param))
-    } else {
-      stop("The ", type, " dataframe is misspecified.")
-    }
-  }
-  
-  if (is.list(param)) {
-    param2 <- c()
-    # add param in right order
-    for (f in cells_b) {
-      if (length(param[[f]]) == 1) { 
-        new_param <- rep(param[[f]], w_n)
-      } else if (length(param[[f]]) != w_n) {
-        stop("The number of ", type, " for cell ", f, 
-             " is not correct. Please specify either 1 or a vector of ", 
-             w_n, " per cell")
-      } else if (setdiff(cells_w, names(param[[f]])) %>% length() == 0) {
-        new_param <- param[[f]][cells_w] # add named parameters in the right order
-      } else {
-        new_param <- param[[f]] # parameters are not or incorrectly named, add in this order
-      }
-      param2 <- c(param2, new_param)
-    }
-    
-    if (length(cells_b) == 0) { # no between-subject factors
-      message("no between-subject factors")
-      if (length(param) == 1) { 
-        param2 <- rep(param, w_n)
-      } else if (length(param) != w_n) {
-        stop("The number of ", type, 
-             " is not correct. Please specify either 1 or a vector of ", 
-             w_n, " per cell")
-      } else if (setdiff(cells_w, names(param)) %>% length() == 0) {
-        param2 <- param[cells_w] # add named parameters in the right order
-      } else {
-        param2 <- param # parameters are not or incorrectly named, add in this order
-      }
-    }
-  } else if (is.numeric(param)) {
-    if (length(param) == 1) { 
-      param2 <- rep(param, all_n) 
-    } else if (length(param) == all_n) {
-      param2 <- param
-    } else {
-      stop("The number of ", type, " is not correct. Please specify 1, a vector of ", 
-           all_n , ", or use the list format")
-    }
-  }
-  
-  dd <- matrix(param2, ncol = b_n) %>% as.data.frame()
-  names(dd) <- cells_b
-  rownames(dd) <- cells_w
-  
-  dd
-}
-
-#' Validate design
-#'
-#' \code{check_design} validates the specified within and between design
-#'
-#' @param within a list of the within-subject factors
-#' @param between a list of the between-subject factors
-#' @param n the number of samples required
-#' @param cors the correlations among the variables (can be a single number, vars\*vars matrix, vars\*vars vector, or a vars\*(vars-1)/2 vector)
-#' @param mu a vector giving the means of the variables (numeric vector of length 1 or vars)
-#' @param sd the standard deviations of the variables (numeric vector of length 1 or vars)
-#' 
-#' @return list
-#' 
-#' @examples 
-#' 
-#' within <- list(time = c("day", "night"))
-#' between <- list(pet = c("dog", "cat"))
-#' design <- check_design(within, between)
-#' 
-#' @export
-#' 
-check_design <- function(within = list(), between = list(), 
-                         n = 100, cors = 0, mu = 0, sd = 1) {
-  # error checking
-  if (!is.list(within) || !is.list(between)) {
-    stop("within and between must be lists")
-  } else if (length(within) == 0 && length(between) == 0) {
-    stop("You must specify at least one factor")
-  }
-  
-  # if within or between factors are named vectors, 
-  # use their names as column names and values as labels for plots
-  between_labels <- purrr::map(between, fix_name_labels)
-  between <- lapply(between_labels, names)
-  within_labels <- purrr::map(within, fix_name_labels)
-  within <- lapply(within_labels, names)
-  
-  within_factors <- names(within)
-  between_factors <- names(between)
-  
-  # handle no w/in or btwn
-  if (length(between_factors) == 0) between_factors <- ".tmpvar."
-  if (length(within_factors) == 0) within_factors <- ".tmpvar." 
-  
-  # check for duplicate factor names
-  factor_overlap <- intersect(within_factors, between_factors)
-  if (length(factor_overlap)) {
-    stop("You have multiple factors with the same name (", 
-         paste(factor_overlap, collapse = ", "),
-         "). Please give all factors unique names.")
-  }
-  
-  # check for duplicate level names within any factor
-  dupes <- c(within, between) %>%
-    lapply(duplicated) %>%
-    lapply(sum) %>%
-    lapply(as.logical) %>%
-    unlist()
-  
-  if (sum(dupes)) {
-    dupelevels <- c(within, between) %>% 
-      names() %>% 
-      magrittr::extract(dupes) %>% 
-      paste(collapse = ", ")
-    stop("You have duplicate levels for factor(s): ", dupelevels)
-  }
-  
-  # define columns
-  if (length(within) == 0) {
-    cells_w = "val"
-  } else {
-    cells_w <- do.call(expand.grid, within) %>%
-      tidyr::unite("b", 1:ncol(.)) %>% dplyr::pull("b")
-  }
-  if (length(between) == 0) {
-    cells_b = ".tmpvar."
-  } else {
-    cells_b <- do.call(expand.grid, between) %>%
-      tidyr::unite("b", 1:ncol(.)) %>% dplyr::pull("b")
-  }
-  
-  # convert n, mu and sd from vector/list formats
-  cell_n <-  convert_param(n, cells_b, cells_w, "Ns")
-  cell_mu <- convert_param(mu, cells_b, cells_w, "means")
-  cell_sd <- convert_param(sd, cells_b, cells_w, "SDs")
-  
-  # figure out number of subjects and their IDs
-  sub_n <- sum(cell_n[1,])
-  max_digits <- floor(log10(sub_n))+1
-  sub_id <- paste0("S",formatC(1:sub_n, width = max_digits, flag = "0"))
-  
-  # set up cell correlations from cors (number, vector, matrix or list styles)
-  cell_cors <- list()
-  if (length(within)) {
-    for (cell in cells_b) {
-      cell_cor <- if(is.list(cors)) cors[[cell]] else cors
-      cell_cors[[cell]] <- cormat(cell_cor, length(cells_w)) 
-    }
-  }
-  
-  list(
-    within = within,
-    between = between,
-    within_factors = within_factors,
-    between_factors = between_factors,
-    within_labels = within_labels,
-    between_labels = between_labels,
-    cell_n = cell_n,
-    cell_mu = cell_mu,
-    cell_sd = cell_sd,
-    cell_cors = cell_cors,
-    cells_w = cells_w,
-    cells_b = cells_b,
-    sub_id = sub_id
-  )
-}
-
 #' Simulate Data from Design
 #'
-#' \code{sim_from_design} generates a dataframe with a specified design
+#' \code{sim_design_} generates a data table with a specified design
 #'
 #' @param design A list of design parameters created by check_design()
-#' @param empirical logical. If true, mu, sd and cors specify the empirical not population mean, sd and covariance 
-#' @param frame_long Whether the returned dataframe is in wide (default = FALSE) or long (TRUE) format
+#' @param empirical logical. If true, mu, sd and r specify the empirical not population mean, sd and covariance 
+#' @param long Whether the returned tbl is in wide (default = FALSE) or long (TRUE) format
 #' 
-#' @return dataframe
+#' @return a tbl
 #' @keywords internal
 #' 
-sim_design_ <- function(design, empirical = FALSE, frame_long = FALSE) {
+sim_design_ <- function(design, empirical = FALSE, long = FALSE) {
   list2env(design, envir = environment())
   
   # simulate data for each between-cell
   for (cell in cells_b) {
     if (length(within)) {
       cell_vars <- rnorm_multi(
-        cell_n[1,cell], length(cells_w), cell_cors[[cell]], 
-        cell_mu[[cell]], cell_sd[[cell]], cells_w, empirical
+        cell_n[1,cell], length(cells_w), 
+        cell_mu[[cell]], cell_sd[[cell]], cell_r[[cell]], 
+        cells_w, empirical
       ) %>%
         dplyr::mutate("btwn" = cell)
     } else {
@@ -298,7 +100,7 @@ sim_design_ <- function(design, empirical = FALSE, frame_long = FALSE) {
   #  df_wide[[f]] <- factor(df_wide[[f]], levels = between[[f]])
   #}
   
-  if (frame_long == TRUE && length(within)) {
+  if (long == TRUE && length(within)) {
     # not necessary for fully between designs
     col_order <- c("sub_id", between_factors, within_factors, "val") %>%
       setdiff(".tmpvar.")
diff --git a/R/sim_df.R b/R/sim_df.R
new file mode 100644
index 00000000..a7d222c5
--- /dev/null
+++ b/R/sim_df.R
@@ -0,0 +1,48 @@
+#' Simulate an existing dataframe
+#'
+#' \code{sim_df} Produces a data table with the same distributions and correlations as an existing data table Only returns numeric columns and simulates all numeric variables from a continuous normal distribution (for now).
+#'
+#' @param .data the existing tbl (must be in wide format)
+#' @param n the number of samples to return per group
+#' @param between a list of the between-subject columns
+#' @param empirical logical. Passed on to rnorm_multi
+#' @param grp_by (deprecated; use between)
+#' 
+#' @return a tbl
+#' @examples
+#' iris100 <- sim_df(iris, 100)
+#' iris_species <- sim_df(iris, 100, between = "Species")
+#' @export
+
+sim_df <- function (.data, n = 100, between = c(), empirical = FALSE, grp_by = NULL) {
+  # error checking
+  if ( !is.numeric(n) || n %% 1 > 0 || n < 3 ) {
+    stop("n must be an integer > 2")
+  }
+  
+  if (!is.null(grp_by)) {
+    warning("grp_by is deprecated, please use between")
+    if (between == c()) between = grp_by # set between to grp_by if between is not set
+  }
+  
+  grpdat <- select_num_grp(.data, between)
+  
+  simdat <- grpdat %>%
+    tidyr::nest() %>%
+    dplyr::mutate(newsim = purrr::map(data, function(data) {
+      rnorm_multi(
+        n = n, 
+        vars = ncol(data), 
+        mu = t(dplyr::summarise_all(data, mean)), 
+        sd = t(dplyr::summarise_all(data, stats::sd)), 
+        r = stats::cor(data),
+        varnames = names(data),
+        empirical = empirical
+      )
+    })) %>%
+    dplyr::select(-data) %>%
+    tidyr::unnest(newsim) %>%
+    dplyr::ungroup()
+  
+  return(simdat)
+}
diff --git a/R/simdf_mixed.R b/R/sim_mixed_df.R
similarity index 70%
rename from R/simdf_mixed.R
rename to R/sim_mixed_df.R
index 6b084c31..646b59b1 100644
--- a/R/simdf_mixed.R
+++ b/R/sim_mixed_df.R
@@ -1,36 +1,36 @@
 #' Generate a sample with random intercepts for subjects and items
 #'
-#' \code{simdf_mixed} Produces a dataframe with the same distributions of by-subject and by-item random intercepts as an existing dataframe
+#' \code{sim_mixed_df()} produces a data table with the same distributions of by-subject and by-item random intercepts as an existing data table.
 #'
-#' @param dat the existing dataframe
+#' @param .data the existing tbl
 #' @param sub_n the number of subjects to simulate
 #' @param item_n the number of items to simulate
 #' @param dv the column name or index containing the DV
 #' @param sub_id the column name or index for the subject IDs
 #' @param item_id the column name or index for the item IDs
 #' 
-#' @return tibble
+#' @return a tbl
 #' @examples
-#' \donttest{simdf_mixed(faceratings, 10, 10, "rating", "rater_id", "face_id")}
+#' \donttest{sim_mixed_df(faceratings, 10, 10, "rating", "rater_id", "face_id")}
 #' @export
 
-simdf_mixed <- function(dat, sub_n = 100, item_n = 25, 
+sim_mixed_df <- function(.data, sub_n = 100, item_n = 25, 
                         dv = 1, sub_id = 2, item_id = 3) {
   # error checking -------------------------------------------------------------
-  if (is.matrix(dat)) {
-    dat = as.data.frame(dat)
-  } else if (!is.data.frame(dat)) {
-    stop("dat must be a data frame or matrix")
+  if (is.matrix(.data)) {
+    .data = as.data.frame(.data)
+  } else if (!is.data.frame(.data)) {
+    stop(".data must be a data frame or matrix")
   }
   
   # get column names if specified by index
-  if (is.numeric(dv)) dv <- names(dat)[dv]
-  if (is.numeric(sub_id)) sub_id <- names(dat)[sub_id]
-  if (is.numeric(item_id)) item_id <- names(dat)[item_id]
+  if (is.numeric(dv)) dv <- names(.data)[dv]
+  if (is.numeric(sub_id)) sub_id <- names(.data)[sub_id]
+  if (is.numeric(item_id)) item_id <- names(.data)[item_id]
   
   lmer_formula <- paste0(dv, " ~ 1 + (1 | ", sub_id, ") + (1 | ", item_id, ")") %>%
     stats::as.formula()
-  mod <- lme4::lmer(lmer_formula, data = dat)
+  mod <- lme4::lmer(lmer_formula, data = .data)
   grand_i <- lme4::fixef(mod)
   
   sds <- lme4::VarCorr(mod) %>% as.data.frame()
diff --git a/R/simdf.R b/R/simdf.R
deleted file mode 100644
index 1b02cc2d..00000000
--- a/R/simdf.R
+++ /dev/null
@@ -1,42 +0,0 @@
-#' Simulate an existing dataframe
-#'
-#' \code{simdf} Produces a dataframe with the same distributions and correlations as an existing dataframe. Only returns numeric columns and simulates all numeric variables from a continuous normal distribution (for now).
-#'
-#' @param dat the existing dataframe
-#' @param n the number of samples to return per group
-#' @param grp_by an optional list of column names to group by
-#' @param empirical logical. Passed on to rnorm_multi
-#' 
-#' @return tibble
-#' @examples
-#' iris100 <- simdf(iris, 100)
-#' iris_species <- simdf(iris, 100, "Species")
-#' @export
-
-simdf <- function (dat, n=100, grp_by=NULL, empirical = FALSE) {
-  # error checking
-  if ( !is.numeric(n) || n %% 1 > 0 || n < 3 ) {
-    stop("n must be an integer > 2")
-  }
-  
-  grpdat <- select_num_grp(dat, grp_by)
-  
-  simdat <- grpdat %>%
-    tidyr::nest() %>%
-    dplyr::mutate(newsim = purrr::map(data, function(data) {
-      rnorm_multi(
-        n = n, 
-        vars = ncol(data), 
-        cor = stats::cor(data),
-        mu = t(dplyr::summarise_all(data, mean)), 
-        sd = t(dplyr::summarise_all(data, stats::sd)), 
-        varnames = names(data),
-        empirical = empirical
-      )
-    })) %>%
-    dplyr::select(-data) %>%
-    tidyr::unnest(newsim) %>%
-    dplyr::ungroup()
-  
-  return(simdat)
-}
diff --git a/README.Rmd b/README.Rmd
index d90a152f..c2c1f805 100644
--- a/README.Rmd
+++ b/README.Rmd
@@ -5,192 +5,144 @@ always_allow_html: yes
 
 <!-- README.md is generated from README.Rmd. Please edit that file -->
 
+<!-- badges: start -->
+[![Lifecycle: experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](https://www.tidyverse.org/lifecycle/#experimental)
+<!-- badges: end -->
+
+
 ```{r setup, include = FALSE}
 knitr::opts_chunk$set(
+  fig.width = 8,
+  fig.height = 5,
   collapse = TRUE,
-  comment = "#>",
-  fig.path = "man/figures/README-",
-  out.width = "100%"
+  comment = "#>"
 )
+
+library(ggplot2)
+library(dplyr)
+library(tidyr)
+library(faux)
+theme_set(theme_bw())
+set.seed(200)
 ```
-# faux
+
 
 It is useful to be able to simulate data with a specified structure. The `faux` package provides some functions to make this process easier.
 
-## Installation
+## sim_design
 
-You can install the released version of faux from
-[GitHub](https://github.com/debruine/faux) with:
+This function creates a dataset with a specific between- and within-subjects design. [see vignette](articles/sim_design.html)
 
-``` r
-devtools::install_github("debruine/faux")
-```
+For example, the following creates a 2w*2b design with 100 observations in each cell. The between-subject factor is `pet` with twolevels of `cat` and `dog`. The within-subject factor is `time` with two levels of `day` and `night`. The mean for the `cat_day` cell is 10, the mean for the `cat_night` cell is 20, the mean for the `dog_day` cell is 15, and the mean for the `dog_night` cell is 25. All cells have a SD of 5 and all within-subject cells are correlated <code>r = 0.5</code>. The resulting data has exactly these values (set `empirical = FALSE` to sample from a population with these values).
 
-## Examples
 
-```{r libraries, message=FALSE}
-library(tidyverse)
-library(faux)
+```{r}
+between <- list("pet" = c("cat", "dog"))
+within <- list("time" = c("day", "night"))
+mu <- data.frame(
+  cat = c(10, 20),
+  dog = c(15, 25),
+  row.names = within$time
+)
+df <- sim_design(within, between, 
+                 n = 100, mu = mu, sd = 5, r = .5,
+                 empirical = TRUE)
 ```
 
+`r check_sim_stats(df, between = "pet", usekable = TRUE)`
+Table: Sample `sim_design()` stats
 
-### rnorm_multi
+## rnorm_multi
 
-This function makes multiple normally distributed vectors with specified parameters and relationships. 
-
-For example, the following creates a sample that has 100 observations of 3 variables, drawn from a population where where A correlates with B and C with r = 0.5, and B and C correlate with r = 0.25. A has a mean of 0 and SD of 1, while B and C have means of 20 and SDs of 5.
-
-```{r set-seed, include=FALSE}
-set.seed(200)
-```
+This function makes multiple normally distributed vectors with specified parameters and relationships. [see vignette](articles/rnorm_multi.html)
 
+For example, the following creates a sample that has 100 observations of 3 variables, drawn from a population where A has a mean of 0 and SD of 1, while B and C have means of 20 and SDs of 5. A correlates with B and C with r = 0.5, and B and C correlate with r = 0.25.
 
 ```{r multirnorm-example}
 
-dat <- rnorm_multi(n = 100, 
-                  cors = c(0.5, 0.5, 0.25), 
-                  mu = c(0, 20, 20),
-                  sd = c(1, 5, 5),
-                  varnames = c("A", "B", "C"),
-                  empirical = FALSE)
+dat <- rnorm_multi(
+  n = 100, 
+  mu = c(0, 20, 20),
+  sd = c(1, 5, 5),
+  r = c(0.5, 0.5, 0.25), 
+  varnames = c("A", "B", "C"),
+  empirical = FALSE
+)
 
 ```
 
 
 `r check_sim_stats(dat, usekable = T)`
-Table: Sample stats
+Table: Sample `rnorm_multi()` stats
 
-#### Specify `cors`
 
-You can specify the correlations in one of four ways:
+## sim_df
 
-* A single r for all pairs
-* A vars by vars matrix
-* A vars\*vars length vector
-* A vars\*(vars-1)/2 length vector
+This function produces a dataframe with the same distributions and correlations as an existing dataframe. It only returns numeric columns and simulates all numeric variables from a continuous normal distribution (for now). [see vignette](articles/sim_df.html)
 
-##### One Number
-
-If you want all the pairs to have the same correlation, just specify a single number. 
+For example, the following code creates a new sample from the built-in dataset `iris` with 50 observations of each species.
 
 ```{r}
-bvn <- rnorm_multi(100, 5, .3, varnames = letters[1:5])
+new_iris <- sim_df(iris, 50, "Species") 
 ```
 
-`r check_sim_stats(bvn, usekable = T)`
-Table: Sample stats from a single rho
-
-##### Matrix
-
-If you already have a correlation matrix, such as the output of `cor()`, you can specify the simulated data with that.
-
-```{r vvmatrix}
-cmat <- cor(iris[,1:4])
-bvn <- rnorm_multi(100, 4, cmat, 
-                  varnames = colnames(cmat))
+```{r plot-iris-sim, echo = FALSE, fig.cap="Simulated iris dataset"}
+new_iris %>%
+  ggplot(aes(Sepal.Width, Sepal.Length, color = Species)) +
+  geom_point() +
+  geom_smooth(method = "lm")
 ```
 
-`r check_sim_stats(bvn, usekable = T)`
-Table: Sample stats from a correlation matrix
+## Additional functions
 
-##### Vector (vars\*vars)
+### check_sim_stats
 
-You can specify your correlation matrix by hand as a vars\*vars length vector, which will include the correlations of 1 down the diagonal.
+If you want to check your simulated stats or just describe an existing dataset, use `check_sim_stats()`.
 
 ```{r}
-cmat <- c(1, .3, .5,
-          .3, 1, 0,
-          .5, 0, 1)
-bvn <- rnorm_multi(100, 3, cmat, 
-                  varnames = c("first", "second", "third"))
+check_sim_stats(iris)
 ```
 
-`r check_sim_stats(bvn, usekable = T)`
-Table: Sample stats from a vars\*vars vector
-
-##### Vector (vars\*(vars-1)/2)
-
-You can specify your correlation matrix by hand as a vars\*(vars-1)/2 length vector, skipping the diagonal and lower left duplicate values.
+You can also group your data and change the digits to round. Display the table using `knitr::kable()` by setting `usekable` to `TRUE` (remember to set `results='asis'` in the chunk header.
 
-```{r}
-rho1_2 <- .3
-rho1_3 <- .5
-rho1_4 <- .5
-rho2_3 <- .2
-rho2_4 <- 0
-rho3_4 <- -.3
-cmat <- c(rho1_2, rho1_3, rho1_4, rho2_3, rho2_4, rho3_4)
-bvn <- rnorm_multi(100, 4, cmat, 
-                  varnames = letters[1:4])
+```{r, results='asis'}
+check_sim_stats(iris, 
+                between = "Species", 
+                digits = 3, 
+                usekable = TRUE)
 ```
 
-`r check_sim_stats(bvn, usekable = T)`
-Table: Sample stats from a (vars\*(vars-1)/2) vector
-
-#### empirical
+### make_id
 
-If you want your samples to have the *exact* correlations, means, and SDs you entered, set `empirical` to TRUE.
+It is useful for IDs for random effects (e.g., subjects or stimuli) to be character strings (so you don't accidentally include them as fixed effects) with the same length s(o you can sort them in order like S01, S02,..., S10 rather than S1, S10, S2, ...) This function returns a list of IDs that have the same string length and a specified prefix.
 
 ```{r}
-bvn <- rnorm_multi(100, 5, .3, 
-                  varnames = letters[1:5], 
-                  empirical = T)
+make_id(n = 10, prefix = "ITEM_")
 ```
 
-`r check_sim_stats(bvn, usekable = T)`
-Table: Sample stats with empirical = TRUE
-
-### simdf
+You can also manually set the number of digits and set `n` to a range of integers.
 
-This function produces a dataframe with the same distributions and correlations as an existing dataframe. It only returns numeric columns and simulates all numeric variables from a continuous normal distribution (for now).
-
-For example, here is the relationship between speed and distance in the built-in dataset `cars`. 
-
-```{r plot-cars-orig, fig.cap="Original cars dataset"}
-cars %>%
-  ggplot(aes(speed, dist)) + 
-  geom_point() +
-  geom_smooth(method = "lm")
+```{r}
+make_id(n = 10:20, digits = 3)
 ```
 
-You can create a new sample with the same parameters and 500 rows with the code `simdf(cars, 500)`.
 
-```{r plot-cars-sim, fig.cap="Simulated cars dataset"}
-simdf(cars, 500) %>%
-  ggplot(aes(speed, dist)) + 
-    geom_point() +
-    geom_smooth(method = "lm")
-```
-
-#### Grouping Variables
+### long2wide
 
-You can also optionally add grouping variables. For example, here is the relationship between sepal length and width in the built-in dataset `iris`.
+```{r}
+between <- list("pet" = c("cat", "dog"))
+within <- list("time" = c("day", "night"))
+df_long <- sim_design(within, between, long = TRUE)
 
-```{r plot-iris-orig, fig.cap="Original iris dataset"}
-iris %>%
-  ggplot(aes(Sepal.Width, Sepal.Length, color = Species)) +
-  geom_point() +
-  geom_smooth(method = "lm")
+df_wide <- long2wide(df_long, 
+                     within = "time", 
+                     between = "pet", 
+                     dv = "val", 
+                     id = "sub_id")
 ```
 
-And here is a new sample with 50 observations of each species, made with the code `simdf(iris, 100, "Species")`.
 
-```{r plot-iris-sim, fig.cap="Simulated iris dataset"}
-simdf(iris, 50, "Species") %>%
-  ggplot(aes(Sepal.Width, Sepal.Length, color = Species)) +
-  geom_point() +
-  geom_smooth(method = "lm")
-```
 
-For now, the function only creates new variables sampled from a continuous normal distribution. I hope to add in other sampling distributions in the future. So you'd need to do any rounding or truncating yourself.
-
-```{r plot-iris-sim-round, fig.cap="Simulated iris dataset (rounded)"}
-simdf(iris, 50, "Species") %>%
-  mutate_if(is.numeric, round, 1) %>%
-  ggplot(aes(Sepal.Width, Sepal.Length, color = Species)) +
-  geom_point() +
-  geom_smooth(method = "lm")
-```
 
 ### pos_def_limits
 
@@ -216,6 +168,8 @@ lims <- pos_def_limits(.8, .2,  0,
 
 `r knitr::kable(lims)`
 
+### is_pos_def() 
+
 If you have a full matrix and want to know if it is positive definite, you can use the following code:
 
 ```{r is_pos_def}
@@ -235,50 +189,7 @@ matrix(c(1, .3, -.9, .2,
   is_pos_def()
 ```
 
-### check_sim_stats
-
-If you want to check your simulated stats or just describe an existing dataset, use `check_sim_stats()`.
-
-```{r}
-check_sim_stats(iris)
-```
-
-You can also group your data and change the digits to round. Display the table using `knitr::kable()` by setting `usekable` to `TRUE` (remember to set `results='asis'` in the chunk header.
-
-```{r, results='asis'}
-check_sim_stats(iris, 
-                grp_by = "Species", 
-                digits = 3, 
-                usekable = TRUE)
-```
-
-
-### sim_design
 
-Simulate data by specifying a design structure.
 
-**This function is under development and should be carefully checked!**
 
-```{r}
-between <- list("pet" = c("cat", "dog"))
-within <- list("time" = c("day", "night"))
-mu <- data.frame(
-  cat = c(10, 20),
-  dog = c(15, 25),
-  row.names = within$time
-)
-df <- sim_design(within, between, n = 100, cors = 0.5, mu = mu, sd = 5)
-```
-
-```{r echo = FALSE, result='asis'}
-check_sim_stats(df, grp_by = "pet", usekable = TRUE)
-```
 
-```{r, echo = FALSE, fig.width = 8, fig.height = 4}
-gather(df, time, val, day:night) %>%
-  ggplot(aes(pet, val, fill = pet)) +
-  geom_violin(show.legend = FALSE) +
-  geom_boxplot(width = .2, fill = "white", show.legend = FALSE) +
-  facet_grid(~time) +
-  scale_fill_manual(values = c("dodgerblue", "red"))
-```
\ No newline at end of file
diff --git a/README.md b/README.md
index bba184d3..f068d489 100644
--- a/README.md
+++ b/README.md
@@ -1,227 +1,149 @@
 
 <!-- README.md is generated from README.Rmd. Please edit that file -->
-faux
-====
+<!-- badges: start -->
+[![Lifecycle: experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](https://www.tidyverse.org/lifecycle/#experimental) <!-- badges: end -->
 
 It is useful to be able to simulate data with a specified structure. The `faux` package provides some functions to make this process easier.
 
-Installation
-------------
-
-You can install the released version of faux from [GitHub](https://github.com/debruine/faux) with:
+sim\_design
+-----------
 
-``` r
-devtools::install_github("debruine/faux")
-```
+This function creates a dataset with a specific between- and within-subjects design. [see vignette](articles/sim_design.html)
 
-Examples
---------
+For example, the following creates a 2w\*2b design with 100 observations in each cell. The between-subject factor is `pet` with twolevels of `cat` and `dog`. The within-subject factor is `time` with two levels of `day` and `night`. The mean for the `cat_day` cell is 10, the mean for the `cat_night` cell is 20, the mean for the `dog_day` cell is 15, and the mean for the `dog_night` cell is 25. All cells have a SD of 5 and all within-subject cells are correlated <code>r = 0.5</code>. The resulting data has exactly these values (set `empirical = FALSE` to sample from a population with these values).
 
 ``` r
-library(tidyverse)
-library(faux)
+between <- list("pet" = c("cat", "dog"))
+within <- list("time" = c("day", "night"))
+mu <- data.frame(
+  cat = c(10, 20),
+  dog = c(15, 25),
+  row.names = within$time
+)
+df <- sim_design(within, between, 
+                 n = 100, mu = mu, sd = 5, r = .5,
+                 empirical = TRUE)
 ```
 
-### rnorm\_multi
+| pet |    n| var   |  day|  night|  mean|   sd|
+|:----|----:|:------|----:|------:|-----:|----:|
+| cat |  100| day   |  1.0|    0.5|    10|    5|
+| cat |  100| night |  0.5|    1.0|    20|    5|
+| dog |  100| day   |  1.0|    0.5|    15|    5|
+| dog |  100| night |  0.5|    1.0|    25|    5|
+
+rnorm\_multi
+------------
 
-This function makes multiple normally distributed vectors with specified parameters and relationships.
+This function makes multiple normally distributed vectors with specified parameters and relationships. [see vignette](articles/rnorm_multi.html)
 
-For example, the following creates a sample that has 100 observations of 3 variables, drawn from a population where where A correlates with B and C with r = 0.5, and B and C correlate with r = 0.25. A has a mean of 0 and SD of 1, while B and C have means of 20 and SDs of 5.
+For example, the following creates a sample that has 100 observations of 3 variables, drawn from a population where A has a mean of 0 and SD of 1, while B and C have means of 20 and SDs of 5. A correlates with B and C with r = 0.5, and B and C correlate with r = 0.25.
 
 ``` r
 
-dat <- rnorm_multi(n = 100, 
-                  cors = c(0.5, 0.5, 0.25), 
-                  mu = c(0, 20, 20),
-                  sd = c(1, 5, 5),
-                  varnames = c("A", "B", "C"),
-                  empirical = FALSE)
+dat <- rnorm_multi(
+  n = 100, 
+  mu = c(0, 20, 20),
+  sd = c(1, 5, 5),
+  r = c(0.5, 0.5, 0.25), 
+  varnames = c("A", "B", "C"),
+  empirical = FALSE
+)
 ```
 
 |    n| var |     A|     B|     C|   mean|    sd|
 |----:|:----|-----:|-----:|-----:|------:|-----:|
-|  100| A   |  1.00|  0.45|  0.49|   0.03|  0.99|
-|  100| B   |  0.45|  1.00|  0.33|  20.01|  4.89|
-|  100| C   |  0.49|  0.33|  1.00|  19.76|  4.02|
-
-#### Specify `cors`
-
-You can specify the correlations in one of four ways:
+|  100| A   |  1.00|  0.62|  0.46|  -0.05|  1.08|
+|  100| B   |  0.62|  1.00|  0.19|  19.95|  5.38|
+|  100| C   |  0.46|  0.19|  1.00|  19.81|  5.15|
 
--   A single r for all pairs
--   A vars by vars matrix
--   A vars\*vars length vector
--   A vars\*(vars-1)/2 length vector
+sim\_df
+-------
 
-##### One Number
+This function produces a dataframe with the same distributions and correlations as an existing dataframe. It only returns numeric columns and simulates all numeric variables from a continuous normal distribution (for now). [see vignette](articles/sim_df.html)
 
-If you want all the pairs to have the same correlation, just specify a single number.
+For example, the following code creates a new sample from the built-in dataset `iris` with 50 observations of each species.
 
 ``` r
-bvn <- rnorm_multi(100, 5, .3, varnames = letters[1:5])
+new_iris <- sim_df(iris, 50, "Species") 
 ```
 
-|    n| var |     a|     b|     c|     d|     e|   mean|    sd|
-|----:|:----|-----:|-----:|-----:|-----:|-----:|------:|-----:|
-|  100| a   |  1.00|  0.35|  0.22|  0.45|  0.37|  -0.04|  1.09|
-|  100| b   |  0.35|  1.00|  0.19|  0.36|  0.28|  -0.05|  0.83|
-|  100| c   |  0.22|  0.19|  1.00|  0.26|  0.20|   0.01|  1.08|
-|  100| d   |  0.45|  0.36|  0.26|  1.00|  0.24|   0.00|  1.00|
-|  100| e   |  0.37|  0.28|  0.20|  0.24|  1.00|   0.04|  0.97|
+![Simulated iris dataset](README_files/figure-markdown_github/plot-iris-sim-1.png)
 
-##### Matrix
+Additional functions
+--------------------
 
-If you already have a correlation matrix, such as the output of `cor()`, you can specify the simulated data with that.
-
-``` r
-cmat <- cor(iris[,1:4])
-bvn <- rnorm_multi(100, 4, cmat, 
-                  varnames = colnames(cmat))
-```
-
-|    n| var          |  Sepal.Length|  Sepal.Width|  Petal.Length|  Petal.Width|   mean|    sd|
-|----:|:-------------|-------------:|------------:|-------------:|------------:|------:|-----:|
-|  100| Sepal.Length |          1.00|        -0.10|          0.88|         0.83|  -0.01|  1.05|
-|  100| Sepal.Width  |         -0.10|         1.00|         -0.38|        -0.29|  -0.19|  1.09|
-|  100| Petal.Length |          0.88|        -0.38|          1.00|         0.96|  -0.01|  1.02|
-|  100| Petal.Width  |          0.83|        -0.29|          0.96|         1.00|  -0.05|  0.98|
-
-##### Vector (vars\*vars)
-
-You can specify your correlation matrix by hand as a vars\*vars length vector, which will include the correlations of 1 down the diagonal.
-
-``` r
-cmat <- c(1, .3, .5,
-          .3, 1, 0,
-          .5, 0, 1)
-bvn <- rnorm_multi(100, 3, cmat, 
-                  varnames = c("first", "second", "third"))
-```
-
-|    n| var    |  first|  second|  third|   mean|    sd|
-|----:|:-------|------:|-------:|------:|------:|-----:|
-|  100| first  |   1.00|    0.33|   0.45|  -0.12|  1.01|
-|  100| second |   0.33|    1.00|  -0.04|  -0.01|  1.04|
-|  100| third  |   0.45|   -0.04|   1.00|  -0.11|  1.00|
-
-##### Vector (vars\*(vars-1)/2)
+### check\_sim\_stats
 
-You can specify your correlation matrix by hand as a vars\*(vars-1)/2 length vector, skipping the diagonal and lower left duplicate values.
+If you want to check your simulated stats or just describe an existing dataset, use `check_sim_stats()`.
 
 ``` r
-rho1_2 <- .3
-rho1_3 <- .5
-rho1_4 <- .5
-rho2_3 <- .2
-rho2_4 <- 0
-rho3_4 <- -.3
-cmat <- c(rho1_2, rho1_3, rho1_4, rho2_3, rho2_4, rho3_4)
-bvn <- rnorm_multi(100, 4, cmat, 
-                  varnames = letters[1:4])
+check_sim_stats(iris)
+#> # A tibble: 4 x 8
+#>       n var   Sepal.Length Sepal.Width Petal.Length Petal.Width  mean    sd
+#>   <dbl> <chr>        <dbl>       <dbl>        <dbl>       <dbl> <dbl> <dbl>
+#> 1   150 Sepa…         1          -0.12         0.87        0.82  5.84  0.83
+#> 2   150 Sepa…        -0.12        1           -0.43       -0.37  3.06  0.44
+#> 3   150 Peta…         0.87       -0.43         1           0.96  3.76  1.77
+#> 4   150 Peta…         0.82       -0.37         0.96        1     1.2   0.76
 ```
 
-|    n| var |     a|     b|      c|      d|   mean|    sd|
-|----:|:----|-----:|-----:|------:|------:|------:|-----:|
-|  100| a   |  1.00|  0.35|   0.55|   0.50|  -0.13|  1.01|
-|  100| b   |  0.35|  1.00|   0.16|   0.09|  -0.10|  1.05|
-|  100| c   |  0.55|  0.16|   1.00|  -0.21|  -0.19|  0.91|
-|  100| d   |  0.50|  0.09|  -0.21|   1.00|   0.12|  0.97|
-
-#### empirical
-
-If you want your samples to have the *exact* correlations, means, and SDs you entered, set `empirical` to TRUE.
+You can also group your data and change the digits to round. Display the table using `knitr::kable()` by setting `usekable` to `TRUE` (remember to set `results='asis'` in the chunk header.
 
 ``` r
-bvn <- rnorm_multi(100, 5, .3, 
-                  varnames = letters[1:5], 
-                  empirical = T)
+check_sim_stats(iris, 
+                between = "Species", 
+                digits = 3, 
+                usekable = TRUE)
 ```
 
-|    n| var |    a|    b|    c|    d|    e|  mean|   sd|
-|----:|:----|----:|----:|----:|----:|----:|-----:|----:|
-|  100| a   |  1.0|  0.3|  0.3|  0.3|  0.3|     0|    1|
-|  100| b   |  0.3|  1.0|  0.3|  0.3|  0.3|     0|    1|
-|  100| c   |  0.3|  0.3|  1.0|  0.3|  0.3|     0|    1|
-|  100| d   |  0.3|  0.3|  0.3|  1.0|  0.3|     0|    1|
-|  100| e   |  0.3|  0.3|  0.3|  0.3|  1.0|     0|    1|
-
-### simdf
+| Species    |    n| var          |  Sepal.Length|  Sepal.Width|  Petal.Length|  Petal.Width|   mean|     sd|
+|:-----------|----:|:-------------|-------------:|------------:|-------------:|------------:|------:|------:|
+| setosa     |   50| Sepal.Length |         1.000|        0.743|         0.267|        0.278|  5.006|  0.352|
+| setosa     |   50| Sepal.Width  |         0.743|        1.000|         0.178|        0.233|  3.428|  0.379|
+| setosa     |   50| Petal.Length |         0.267|        0.178|         1.000|        0.332|  1.462|  0.174|
+| setosa     |   50| Petal.Width  |         0.278|        0.233|         0.332|        1.000|  0.246|  0.105|
+| versicolor |   50| Sepal.Length |         1.000|        0.526|         0.754|        0.546|  5.936|  0.516|
+| versicolor |   50| Sepal.Width  |         0.526|        1.000|         0.561|        0.664|  2.770|  0.314|
+| versicolor |   50| Petal.Length |         0.754|        0.561|         1.000|        0.787|  4.260|  0.470|
+| versicolor |   50| Petal.Width  |         0.546|        0.664|         0.787|        1.000|  1.326|  0.198|
+| virginica  |   50| Sepal.Length |         1.000|        0.457|         0.864|        0.281|  6.588|  0.636|
+| virginica  |   50| Sepal.Width  |         0.457|        1.000|         0.401|        0.538|  2.974|  0.322|
+| virginica  |   50| Petal.Length |         0.864|        0.401|         1.000|        0.322|  5.552|  0.552|
+| virginica  |   50| Petal.Width  |         0.281|        0.538|         0.322|        1.000|  2.026|  0.275|
 
-This function produces a dataframe with the same distributions and correlations as an existing dataframe. It only returns numeric columns and simulates all numeric variables from a continuous normal distribution (for now).
+### make\_id
 
-For example, here is the relationship between speed and distance in the built-in dataset `cars`.
+It is useful for IDs for random effects (e.g., subjects or stimuli) to be character strings (so you don't accidentally include them as fixed effects) with the same length s(o you can sort them in order like S01, S02,..., S10 rather than S1, S10, S2, ...) This function returns a list of IDs that have the same string length and a specified prefix.
 
 ``` r
-cars %>%
-  ggplot(aes(speed, dist)) + 
-  geom_point() +
-  geom_smooth(method = "lm")
+make_id(n = 10, prefix = "ITEM_")
+#>  [1] "ITEM_01" "ITEM_02" "ITEM_03" "ITEM_04" "ITEM_05" "ITEM_06" "ITEM_07"
+#>  [8] "ITEM_08" "ITEM_09" "ITEM_10"
 ```
 
-<img src="man/figures/README-plot-cars-orig-1.png" alt="Original cars dataset" width="100%" />
-<p class="caption">
-Original cars dataset
-</p>
-
-You can create a new sample with the same parameters and 500 rows with the code `simdf(cars, 500)`.
+You can also manually set the number of digits and set `n` to a range of integers.
 
 ``` r
-simdf(cars, 500) %>%
-  ggplot(aes(speed, dist)) + 
-    geom_point() +
-    geom_smooth(method = "lm")
+make_id(n = 10:20, digits = 3)
+#>  [1] "S010" "S011" "S012" "S013" "S014" "S015" "S016" "S017" "S018" "S019"
+#> [11] "S020"
 ```
 
-<img src="man/figures/README-plot-cars-sim-1.png" alt="Simulated cars dataset" width="100%" />
-<p class="caption">
-Simulated cars dataset
-</p>
-
-#### Grouping Variables
-
-You can also optionally add grouping variables. For example, here is the relationship between sepal length and width in the built-in dataset `iris`.
+### long2wide
 
 ``` r
-iris %>%
-  ggplot(aes(Sepal.Width, Sepal.Length, color = Species)) +
-  geom_point() +
-  geom_smooth(method = "lm")
-```
-
-<img src="man/figures/README-plot-iris-orig-1.png" alt="Original iris dataset" width="100%" />
-<p class="caption">
-Original iris dataset
-</p>
-
-And here is a new sample with 50 observations of each species, made with the code `simdf(iris, 100, "Species")`.
-
-``` r
-simdf(iris, 50, "Species") %>%
-  ggplot(aes(Sepal.Width, Sepal.Length, color = Species)) +
-  geom_point() +
-  geom_smooth(method = "lm")
-```
-
-<img src="man/figures/README-plot-iris-sim-1.png" alt="Simulated iris dataset" width="100%" />
-<p class="caption">
-Simulated iris dataset
-</p>
-
-For now, the function only creates new variables sampled from a continuous normal distribution. I hope to add in other sampling distributions in the future. So you'd need to do any rounding or truncating yourself.
+between <- list("pet" = c("cat", "dog"))
+within <- list("time" = c("day", "night"))
+df_long <- sim_design(within, between, long = TRUE)
 
-``` r
-simdf(iris, 50, "Species") %>%
-  mutate_if(is.numeric, round, 1) %>%
-  ggplot(aes(Sepal.Width, Sepal.Length, color = Species)) +
-  geom_point() +
-  geom_smooth(method = "lm")
+df_wide <- long2wide(df_long, 
+                     within = "time", 
+                     between = "pet", 
+                     dv = "val", 
+                     id = "sub_id")
 ```
 
-<img src="man/figures/README-plot-iris-sim-round-1.png" alt="Simulated iris dataset (rounded)" width="100%" />
-<p class="caption">
-Simulated iris dataset (rounded)
-</p>
-
 ### pos\_def\_limits
 
 Not all correlation matrices are possible. For example, if variables A and B are correlated with r = 1.0, then the correlation between A and C can only be exactly equal to the correlation between B and C.
@@ -250,6 +172,8 @@ lims <- pos_def_limits(.8, .2,  0,
 |:----|:----|
 | NA  | NA  |
 
+### is\_pos\_def()
+
 If you have a full matrix and want to know if it is positive definite, you can use the following code:
 
 ``` r
@@ -270,68 +194,3 @@ matrix(c(1, .3, -.9, .2,
   is_pos_def()
 #> [1] FALSE
 ```
-
-### check\_sim\_stats
-
-If you want to check your simulated stats or just describe an existing dataset, use `check_sim_stats()`.
-
-``` r
-check_sim_stats(iris)
-#> # A tibble: 4 x 8
-#>       n var   Sepal.Length Sepal.Width Petal.Length Petal.Width  mean    sd
-#>   <dbl> <chr>        <dbl>       <dbl>        <dbl>       <dbl> <dbl> <dbl>
-#> 1   150 Sepa…         1          -0.12         0.87        0.82  5.84  0.83
-#> 2   150 Sepa…        -0.12        1           -0.43       -0.37  3.06  0.44
-#> 3   150 Peta…         0.87       -0.43         1           0.96  3.76  1.77
-#> 4   150 Peta…         0.82       -0.37         0.96        1     1.2   0.76
-```
-
-You can also group your data and change the digits to round. Display the table using `knitr::kable()` by setting `usekable` to `TRUE` (remember to set `results='asis'` in the chunk header.
-
-``` r
-check_sim_stats(iris, 
-                grp_by = "Species", 
-                digits = 3, 
-                usekable = TRUE)
-```
-
-| Species    |    n| var          |  Sepal.Length|  Sepal.Width|  Petal.Length|  Petal.Width|   mean|     sd|
-|:-----------|----:|:-------------|-------------:|------------:|-------------:|------------:|------:|------:|
-| setosa     |   50| Sepal.Length |         1.000|        0.743|         0.267|        0.278|  5.006|  0.352|
-| setosa     |   50| Sepal.Width  |         0.743|        1.000|         0.178|        0.233|  3.428|  0.379|
-| setosa     |   50| Petal.Length |         0.267|        0.178|         1.000|        0.332|  1.462|  0.174|
-| setosa     |   50| Petal.Width  |         0.278|        0.233|         0.332|        1.000|  0.246|  0.105|
-| versicolor |   50| Sepal.Length |         1.000|        0.526|         0.754|        0.546|  5.936|  0.516|
-| versicolor |   50| Sepal.Width  |         0.526|        1.000|         0.561|        0.664|  2.770|  0.314|
-| versicolor |   50| Petal.Length |         0.754|        0.561|         1.000|        0.787|  4.260|  0.470|
-| versicolor |   50| Petal.Width  |         0.546|        0.664|         0.787|        1.000|  1.326|  0.198|
-| virginica  |   50| Sepal.Length |         1.000|        0.457|         0.864|        0.281|  6.588|  0.636|
-| virginica  |   50| Sepal.Width  |         0.457|        1.000|         0.401|        0.538|  2.974|  0.322|
-| virginica  |   50| Petal.Length |         0.864|        0.401|         1.000|        0.322|  5.552|  0.552|
-| virginica  |   50| Petal.Width  |         0.281|        0.538|         0.322|        1.000|  2.026|  0.275|
-
-### sim\_design
-
-Simulate data by specifying a design structure.
-
-**This function is under development and should be carefully checked!**
-
-``` r
-between <- list("pet" = c("cat", "dog"))
-within <- list("time" = c("day", "night"))
-mu <- data.frame(
-  cat = c(10, 20),
-  dog = c(15, 25),
-  row.names = within$time
-)
-df <- sim_design(within, between, n = 100, cors = 0.5, mu = mu, sd = 5)
-```
-
-| pet |    n| var   |   day|  night|   mean|    sd|
-|:----|----:|:------|-----:|------:|------:|-----:|
-| cat |  100| day   |  1.00|   0.55|  10.39|  4.69|
-| cat |  100| night |  0.55|   1.00|  19.78|  4.67|
-| dog |  100| day   |  1.00|   0.46|  15.02|  4.18|
-| dog |  100| night |  0.46|   1.00|  24.79|  4.74|
-
-<img src="man/figures/README-unnamed-chunk-9-1.png" width="100%" />
diff --git a/README_files/figure-markdown_github/plot-iris-sim-1.png b/README_files/figure-markdown_github/plot-iris-sim-1.png
new file mode 100644
index 00000000..819df6be
Binary files /dev/null and b/README_files/figure-markdown_github/plot-iris-sim-1.png differ
diff --git a/data-raw/speed_tests.R b/data-raw/speed_tests.R
index 4dd0fd85..cfc6dcd5 100644
--- a/data-raw/speed_tests.R
+++ b/data-raw/speed_tests.R
@@ -1,35 +1,60 @@
+library(faux)
+library(tidyverse)
+
+
 # speed test ----
 
-library(tidyverse)
+
 within <- list(
-  "A" = c("A1", "A2")
+  "W" = c("W1", "W2"),
+  "X" = c("X1", "X2")
 )
 
-between <- list(
-  "B" = c("B1", "B2")
-)
+between <- list()
+
+mu <- c(W1_X1 = 10, W1_X2 = 12, W2_X1 = 10, W2_X2 = 10)
 
-mu <- list(
-  "B1" = c(10, 10),
-  "B2" = c(10, 10)
-)
 
-func <- function(i) {
-  utils::setTxtProgressBar(pb, i)
-  df <- sim_design(within, between, n = 20, mu = mu, frame_long = TRUE)
-  suppressMessages(
-    anova <- afex::aov_4(val~B*(A|sub_id), data = df, check_contrasts = TRUE)
-  )
-  anova$anova_table %>%
-    tibble::as_tibble(rownames = "factor")
+anova_func <- function(i, v = "afex") {
+  #utils::setTxtProgressBar(pb, i)
+  df <- sim_design(within, between, n = 20, mu = mu, sd = 4, frame_long = TRUE)
+  
+  if (v == "afex") {
+    afex::aov_4(val~(X*W|sub_id), data = df, return = "aov") %>%
+      broom::tidy()
+  } else if (v == "aov") {
+    aov(val~(X*W)+Error(sub_id/(X*W)), data = df, contrasts = NULL) %>%
+      broom::tidy()
+  }
 }
 
-reps <- 2
-pb <- txtProgressBar(max = reps)
-timestamp()
-sims <- purrr::map_df(1:reps, func)
-timestamp()
-close(pb)
+anova_func2 <- function(i) {
+  df <- sim_design(within, between, n = 20, mu = mu, sd = 4, frame_long = TRUE)
+  
+  aov(val~(X*W)+Error(sub_id/(X*W)), data = df, contrasts = NULL) %>%
+      broom::tidy()
+}
+
+reps <- 10000
+#pb <- utils::txtProgressBar(max = reps)
+system.time(
+  sims_afex <- purrr::map_df(1:reps, anova_func, v = "afex")
+)
+system.time(
+  sims_aov <- purrr::map_df(1:reps, anova_func, v = "aov")
+)
+#close(pb)
+
+sims_afex %>%
+  filter(term != "Residuals") %>%
+  group_by(term) %>%
+  summarise(power = mean(p.value < .05))
+
+sims_aov %>%
+  filter(term != "Residuals") %>%
+  group_by(term) %>%
+  summarise(power = mean(p.value < .05))
+
 
 alpha <- 0.05
 
@@ -48,3 +73,7 @@ sims %>%
 #   df <- purrr::map(1:1e4, ~sim_design(within, between, n = 20))
 ##------ Sun Apr 28 20:44:48 2019 ------##
 
+##------ Mon Apr 29 16:59:53 2019 ------##
+# > sims <- purrr::map_df(1:1e4, anova_func)
+##------ Mon Apr 29 17:02:51 2019 ------##
+
diff --git a/docs/LICENSE-text.html b/docs/LICENSE-text.html
index 8319144c..e30bd682 100644
--- a/docs/LICENSE-text.html
+++ b/docs/LICENSE-text.html
@@ -60,7 +60,7 @@
       </button>
       <span class="navbar-brand">
         <a class="navbar-link" href="index.html">faux</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.0.0.9005</span>
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.0.0.9006</span>
       </span>
     </div>
 
@@ -82,9 +82,6 @@
     <span class="caret"></span>
   </a>
   <ul class="dropdown-menu" role="menu">
-    <li>
-      <a href="articles/intro.html">Introduction to faux</a>
-    </li>
     <li>
       <a href="articles/rnorm_multi.html">Simulate Correlated Variables</a>
     </li>
@@ -92,7 +89,7 @@
       <a href="articles/sim_design.html">Simulate by Design</a>
     </li>
     <li>
-      <a href="articles/simdf.html">Simulate from Existing Data</a>
+      <a href="articles/sim_df.html">Simulate from Existing Data</a>
     </li>
   </ul>
 </li>
diff --git a/docs/articles/index.html b/docs/articles/index.html
index 1b2dde89..edd0368b 100644
--- a/docs/articles/index.html
+++ b/docs/articles/index.html
@@ -60,7 +60,7 @@
       </button>
       <span class="navbar-brand">
         <a class="navbar-link" href="../index.html">faux</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.0.0.9005</span>
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.0.0.9006</span>
       </span>
     </div>
 
@@ -82,9 +82,6 @@
     <span class="caret"></span>
   </a>
   <ul class="dropdown-menu" role="menu">
-    <li>
-      <a href="../articles/intro.html">Introduction to faux</a>
-    </li>
     <li>
       <a href="../articles/rnorm_multi.html">Simulate Correlated Variables</a>
     </li>
@@ -92,7 +89,7 @@
       <a href="../articles/sim_design.html">Simulate by Design</a>
     </li>
     <li>
-      <a href="../articles/simdf.html">Simulate from Existing Data</a>
+      <a href="../articles/sim_df.html">Simulate from Existing Data</a>
     </li>
   </ul>
 </li>
@@ -123,10 +120,9 @@ <h3>All vignettes</h3>
       <p class="section-desc"></p>
 
       <ul>
-        <li><a href="intro.html">Introduction to faux</a></li>
         <li><a href="rnorm_multi.html">Simulate Correlated Variables</a></li>
         <li><a href="sim_design.html">Simulate by Design</a></li>
-        <li><a href="simdf.html">Simulate from Existing Data</a></li>
+        <li><a href="sim_df.html">Simulate from Existing Data</a></li>
       </ul>
     </div>
   </div>
diff --git a/docs/articles/intro.html b/docs/articles/intro.html
index 4dbbf0d0..5a4af1c5 100644
--- a/docs/articles/intro.html
+++ b/docs/articles/intro.html
@@ -62,7 +62,7 @@
       <a href="../articles/sim_design.html">Simulate by Design</a>
     </li>
     <li>
-      <a href="../articles/simdf.html">Simulate from Existing Data</a>
+      <a href="../articles/sim_df.html">Simulate from Existing Data</a>
     </li>
   </ul>
 </li>
@@ -85,7 +85,7 @@
       <h1>Introduction to faux</h1>
                         <h4 class="author">Lisa DeBruine</h4>
             
-            <h4 class="date">2019-04-29</h4>
+            <h4 class="date">2019-05-01</h4>
       
       
       <div class="hidden name"><code>intro.Rmd</code></div>
@@ -217,12 +217,12 @@ <h2 class="hasAnchor">
 </tbody>
 </table>
 </div>
-<div id="simdf" class="section level2">
+<div id="sim_df" class="section level2">
 <h2 class="hasAnchor">
-<a href="#simdf" class="anchor"></a>simdf</h2>
-<p>This function produces a dataframe with the same distributions and correlations as an existing dataframe. It only returns numeric columns and simulates all numeric variables from a continuous normal distribution (for now). <a href="simdf.html">see vignette</a></p>
+<a href="#sim_df" class="anchor"></a>sim_df</h2>
+<p>This function produces a dataframe with the same distributions and correlations as an existing dataframe. It only returns numeric columns and simulates all numeric variables from a continuous normal distribution (for now). <a href="sim_df.html">see vignette</a></p>
 <p>For example, the following code creates a new sample from the built-in dataset <code>iris</code> with 50 observations of each species.</p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">new_iris &lt;-<span class="st"> </span><span class="kw"><a href="../reference/simdf.html">simdf</a></span>(iris, <span class="dv">50</span>, <span class="st">"Species"</span>) </code></pre></div>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">new_iris &lt;-<span class="st"> </span><span class="kw"><a href="../reference/sim_df.html">sim_df</a></span>(iris, <span class="dv">50</span>, <span class="st">"Species"</span>) </code></pre></div>
 <div class="figure">
 <img src="intro_files/figure-html/plot-iris-sim-1.png" alt="Simulated iris dataset" width="768"><p class="caption">
 Simulated iris dataset
@@ -246,7 +246,7 @@ <h3 class="hasAnchor">
 <span class="co">#&gt; 4   150 Peta…         0.82       -0.37         0.96        1     1.2   0.76</span></code></pre></div>
 <p>You can also group your data and change the digits to round. Display the table using <code><a href="https://www.rdocumentation.org/packages/knitr/topics/kable">knitr::kable()</a></code> by setting <code>usekable</code> to <code>TRUE</code> (remember to set <code>results='asis'</code> in the chunk header.</p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw"><a href="../reference/check_sim_stats.html">check_sim_stats</a></span>(iris, 
-                <span class="dt">grp_by =</span> <span class="st">"Species"</span>, 
+                <span class="dt">between =</span> <span class="st">"Species"</span>, 
                 <span class="dt">digits =</span> <span class="dv">3</span>, 
                 <span class="dt">usekable =</span> <span class="ot">TRUE</span>)</code></pre></div>
 <table class="table">
@@ -397,6 +397,14 @@ <h3 class="hasAnchor">
 </tbody>
 </table>
 </div>
+<div id="make_id" class="section level3">
+<h3 class="hasAnchor">
+<a href="#make_id" class="anchor"></a>make_id</h3>
+<p>It is useful for IDs for random effects (e.g., subjects or stimuli) to be character strings (so you don’t accidentally include them as fixed effects) with the same length s(o you can sort them in order like S01, S02,…, S10 rather than S1, S10, S2, …) This function returns a list of IDs that have the same string length and a specified prefix.</p>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw"><a href="../reference/make_id.html">make_id</a></span>(<span class="dt">n =</span> <span class="dv">10</span>, <span class="dt">prefix =</span> <span class="st">"ITEM_"</span>)
+<span class="co">#&gt;  [1] "ITEM_01" "ITEM_02" "ITEM_03" "ITEM_04" "ITEM_05" "ITEM_06" "ITEM_07"</span>
+<span class="co">#&gt;  [8] "ITEM_08" "ITEM_09" "ITEM_10"</span></code></pre></div>
+</div>
 <div id="pos_def_limits" class="section level3">
 <h3 class="hasAnchor">
 <a href="#pos_def_limits" class="anchor"></a>pos_def_limits</h3>
@@ -428,6 +436,10 @@ <h3 class="hasAnchor">
 <td align="left">NA</td>
 </tr></tbody>
 </table>
+</div>
+<div id="is_pos_def" class="section level3">
+<h3 class="hasAnchor">
+<a href="#is_pos_def" class="anchor"></a>is_pos_def()</h3>
 <p>If you have a full matrix and want to know if it is positive definite, you can use the following code:</p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/c">c</a></span>(.<span class="dv">2</span>, .<span class="dv">3</span>, .<span class="dv">4</span>, .<span class="dv">2</span>,
       .<span class="dv">3</span>, <span class="op">-</span>.<span class="dv">1</span>, .<span class="dv">2</span>,
@@ -453,7 +465,7 @@ <h2 class="hasAnchor">
       <ul class="nav nav-pills nav-stacked">
 <li><a href="#sim_design">sim_design</a></li>
       <li><a href="#rnorm_multi">rnorm_multi</a></li>
-      <li><a href="#simdf">simdf</a></li>
+      <li><a href="#sim_df">sim_df</a></li>
       <li><a href="#additional-functions">Additional functions</a></li>
       </ul>
 </div>
diff --git a/docs/articles/rnorm_multi.html b/docs/articles/rnorm_multi.html
index 7e30208c..ee3f4a82 100644
--- a/docs/articles/rnorm_multi.html
+++ b/docs/articles/rnorm_multi.html
@@ -30,7 +30,7 @@
       </button>
       <span class="navbar-brand">
         <a class="navbar-link" href="../index.html">faux</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.0.0.9005</span>
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.0.0.9006</span>
       </span>
     </div>
 
@@ -53,16 +53,13 @@
   </a>
   <ul class="dropdown-menu" role="menu">
 <li>
-      <a href="../articles/intro.html">Introduction to faux</a>
-    </li>
-    <li>
       <a href="../articles/rnorm_multi.html">Simulate Correlated Variables</a>
     </li>
     <li>
       <a href="../articles/sim_design.html">Simulate by Design</a>
     </li>
     <li>
-      <a href="../articles/simdf.html">Simulate from Existing Data</a>
+      <a href="../articles/sim_df.html">Simulate from Existing Data</a>
     </li>
   </ul>
 </li>
@@ -85,7 +82,7 @@
       <h1>Simulate Correlated Variables</h1>
                         <h4 class="author">Lisa DeBruine</h4>
             
-            <h4 class="date">2019-04-29</h4>
+            <h4 class="date">2019-05-03</h4>
       
       
       <div class="hidden name"><code>rnorm_multi.Rmd</code></div>
@@ -98,12 +95,12 @@ <h4 class="date">2019-04-29</h4>
 <div id="quick-example" class="section level2">
 <h2 class="hasAnchor">
 <a href="#quick-example" class="anchor"></a>Quick example</h2>
-<p>For example, the following creates a sample that has 100 observations of 3 variables, drawn from a population where where A correlates with B and C with r = 0.5, and B and C correlate with r = 0.25. A has a mean of 0 and SD of 1, while B and C have means of 20 and SDs of 5.</p>
+<p>For example, the following creates a sample that has 100 observations of 3 variables, drawn from a population where A has a mean of 0 and SD of 1, while B and C have means of 20 and SDs of 5. A correlates with B and C with r = 0.5, and B and C correlate with r = 0.25.</p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">
 dat &lt;-<span class="st"> </span><span class="kw"><a href="../reference/rnorm_multi.html">rnorm_multi</a></span>(<span class="dt">n =</span> <span class="dv">100</span>, 
-                  <span class="dt">cors =</span> <span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/c">c</a></span>(<span class="fl">0.5</span>, <span class="fl">0.5</span>, <span class="fl">0.25</span>), 
                   <span class="dt">mu =</span> <span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/c">c</a></span>(<span class="dv">0</span>, <span class="dv">20</span>, <span class="dv">20</span>),
                   <span class="dt">sd =</span> <span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/c">c</a></span>(<span class="dv">1</span>, <span class="dv">5</span>, <span class="dv">5</span>),
+                  <span class="dt">r =</span> <span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/c">c</a></span>(<span class="fl">0.5</span>, <span class="fl">0.5</span>, <span class="fl">0.25</span>), 
                   <span class="dt">varnames =</span> <span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/c">c</a></span>(<span class="st">"A"</span>, <span class="st">"B"</span>, <span class="st">"C"</span>),
                   <span class="dt">empirical =</span> <span class="ot">FALSE</span>)</code></pre></div>
 <table class="table">
@@ -147,10 +144,9 @@ <h2 class="hasAnchor">
 </tr>
 </tbody>
 </table>
-<div id="spec_cors" class="section level3">
+<div id="spec_r" class="section level3">
 <h3 class="hasAnchor">
-<a href="#spec_cors" class="anchor"></a>Specify <code>cors</code>
-</h3>
+<a href="#spec_r" class="anchor"></a>Specify correlations</h3>
 <p>You can specify the correlations in one of four ways:</p>
 <ul>
 <li>A single r for all pairs</li>
@@ -162,7 +158,7 @@ <h3 class="hasAnchor">
 <h4 class="hasAnchor">
 <a href="#one-number" class="anchor"></a>One Number</h4>
 <p>If you want all the pairs to have the same correlation, just specify a single number.</p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">bvn &lt;-<span class="st"> </span><span class="kw"><a href="../reference/rnorm_multi.html">rnorm_multi</a></span>(<span class="dv">100</span>, <span class="dv">5</span>, .<span class="dv">3</span>, <span class="dt">varnames =</span> letters[<span class="dv">1</span><span class="op">:</span><span class="dv">5</span>])</code></pre></div>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">bvn &lt;-<span class="st"> </span><span class="kw"><a href="../reference/rnorm_multi.html">rnorm_multi</a></span>(<span class="dv">100</span>, <span class="dv">5</span>, <span class="dv">0</span>, <span class="dv">1</span>, .<span class="dv">3</span>, <span class="dt">varnames =</span> letters[<span class="dv">1</span><span class="op">:</span><span class="dv">5</span>])</code></pre></div>
 <table class="table">
 <caption>Sample stats from a single rho</caption>
 <thead><tr class="header">
@@ -240,7 +236,7 @@ <h4 class="hasAnchor">
 <a href="#matrix" class="anchor"></a>Matrix</h4>
 <p>If you already have a correlation matrix, such as the output of <code><a href="https://www.rdocumentation.org/packages/stats/topics/cor">cor()</a></code>, you can specify the simulated data with that.</p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">cmat &lt;-<span class="st"> </span><span class="kw"><a href="https://www.rdocumentation.org/packages/stats/topics/cor">cor</a></span>(iris[,<span class="dv">1</span><span class="op">:</span><span class="dv">4</span>])
-bvn &lt;-<span class="st"> </span><span class="kw"><a href="../reference/rnorm_multi.html">rnorm_multi</a></span>(<span class="dv">100</span>, <span class="dv">4</span>, cmat, 
+bvn &lt;-<span class="st"> </span><span class="kw"><a href="../reference/rnorm_multi.html">rnorm_multi</a></span>(<span class="dv">100</span>, <span class="dv">4</span>, <span class="dv">0</span>, <span class="dv">1</span>, cmat, 
                   <span class="dt">varnames =</span> <span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/colnames">colnames</a></span>(cmat))</code></pre></div>
 <table class="table">
 <caption>Sample stats from a correlation matrix</caption>
@@ -305,7 +301,7 @@ <h4 class="hasAnchor">
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">cmat &lt;-<span class="st"> </span><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/c">c</a></span>(<span class="dv">1</span>, .<span class="dv">3</span>, .<span class="dv">5</span>,
           .<span class="dv">3</span>, <span class="dv">1</span>, <span class="dv">0</span>,
           .<span class="dv">5</span>, <span class="dv">0</span>, <span class="dv">1</span>)
-bvn &lt;-<span class="st"> </span><span class="kw"><a href="../reference/rnorm_multi.html">rnorm_multi</a></span>(<span class="dv">100</span>, <span class="dv">3</span>, cmat, 
+bvn &lt;-<span class="st"> </span><span class="kw"><a href="../reference/rnorm_multi.html">rnorm_multi</a></span>(<span class="dv">100</span>, <span class="dv">3</span>, <span class="dv">0</span>, <span class="dv">1</span>, cmat, 
                   <span class="dt">varnames =</span> <span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/c">c</a></span>(<span class="st">"first"</span>, <span class="st">"second"</span>, <span class="st">"third"</span>))</code></pre></div>
 <table class="table">
 <caption>Sample stats from a vars*vars vector</caption>
@@ -360,7 +356,7 @@ <h4 class="hasAnchor">
 rho2_<span class="dv">4</span> &lt;-<span class="st"> </span><span class="dv">0</span>
 rho3_<span class="dv">4</span> &lt;-<span class="st"> </span><span class="op">-</span>.<span class="dv">3</span>
 cmat &lt;-<span class="st"> </span><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/c">c</a></span>(rho1_<span class="dv">2</span>, rho1_<span class="dv">3</span>, rho1_<span class="dv">4</span>, rho2_<span class="dv">3</span>, rho2_<span class="dv">4</span>, rho3_<span class="dv">4</span>)
-bvn &lt;-<span class="st"> </span><span class="kw"><a href="../reference/rnorm_multi.html">rnorm_multi</a></span>(<span class="dv">100</span>, <span class="dv">4</span>, cmat, 
+bvn &lt;-<span class="st"> </span><span class="kw"><a href="../reference/rnorm_multi.html">rnorm_multi</a></span>(<span class="dv">100</span>, <span class="dv">4</span>, <span class="dv">0</span>, <span class="dv">1</span>, cmat, 
                   <span class="dt">varnames =</span> letters[<span class="dv">1</span><span class="op">:</span><span class="dv">4</span>])</code></pre></div>
 <table class="table">
 <caption>Sample stats from a (vars*(vars-1)/2) vector</caption>
@@ -423,7 +419,7 @@ <h4 class="hasAnchor">
 <h3 class="hasAnchor">
 <a href="#empirical" class="anchor"></a>empirical</h3>
 <p>If you want your samples to have the <em>exact</em> correlations, means, and SDs you entered, set <code>empirical</code> to TRUE.</p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">bvn &lt;-<span class="st"> </span><span class="kw"><a href="../reference/rnorm_multi.html">rnorm_multi</a></span>(<span class="dv">100</span>, <span class="dv">5</span>, .<span class="dv">3</span>, 
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">bvn &lt;-<span class="st"> </span><span class="kw"><a href="../reference/rnorm_multi.html">rnorm_multi</a></span>(<span class="dv">100</span>, <span class="dv">5</span>, <span class="dv">0</span>, <span class="dv">1</span>, .<span class="dv">3</span>, 
                   <span class="dt">varnames =</span> letters[<span class="dv">1</span><span class="op">:</span><span class="dv">5</span>], 
                   <span class="dt">empirical =</span> T)</code></pre></div>
 <table class="table">
@@ -498,6 +494,43 @@ <h3 class="hasAnchor">
 </tbody>
 </table>
 </div>
+</div>
+<div id="pre-existing-variable" class="section level2">
+<h2 class="hasAnchor">
+<a href="#pre-existing-variable" class="anchor"></a>Pre-existing variable</h2>
+<p>Us <code><a href="../reference/rnorm_pre.html">rnorm_pre()</a></code> to create a vector with a specified correlation to a pre-existing variable. The following code creates a vector called <code>sl.5</code> with a mean of 10, SD of 2 and a correaltion of r = 0.5 to the <code>Sepal.Length</code> column in the built-in dataset <code>iris</code>. This function only creates a vector with the <em>exact</em> parameters specified.</p>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">sl &lt;-<span class="st"> </span>iris<span class="op">$</span>Sepal.Length
+
+sl.<span class="fl">5.</span>v1 &lt;-<span class="st"> </span><span class="kw"><a href="../reference/rnorm_pre.html">rnorm_pre</a></span>(sl, <span class="dt">mu =</span> <span class="dv">10</span>, <span class="dt">sd =</span> <span class="dv">2</span>, <span class="dt">r =</span> <span class="fl">0.5</span>)
+sl.<span class="fl">5.</span>v2 &lt;-<span class="st"> </span><span class="kw"><a href="../reference/rnorm_pre.html">rnorm_pre</a></span>(sl, <span class="dt">mu =</span> <span class="dv">10</span>, <span class="dt">sd =</span> <span class="dv">2</span>, <span class="dt">r =</span> <span class="fl">0.5</span>)</code></pre></div>
+<table class="table">
+<thead><tr class="header">
+<th></th>
+<th align="right">sl</th>
+<th align="right">sl.5.v1</th>
+<th align="right">sl.5.v2</th>
+</tr></thead>
+<tbody>
+<tr class="odd">
+<td>sl</td>
+<td align="right">1.0</td>
+<td align="right">0.5000000</td>
+<td align="right">0.5000000</td>
+</tr>
+<tr class="even">
+<td>sl.5.v1</td>
+<td align="right">0.5</td>
+<td align="right">1.0000000</td>
+<td align="right">0.1765035</td>
+</tr>
+<tr class="odd">
+<td>sl.5.v2</td>
+<td align="right">0.5</td>
+<td align="right">0.1765035</td>
+<td align="right">1.0000000</td>
+</tr>
+</tbody>
+</table>
 </div>
   </div>
 
@@ -507,6 +540,7 @@ <h2 class="hasAnchor">
 <a href="#tocnav" class="anchor"></a>Contents</h2>
       <ul class="nav nav-pills nav-stacked">
 <li><a href="#quick-example">Quick example</a></li>
+      <li><a href="#pre-existing-variable">Pre-existing variable</a></li>
       </ul>
 </div>
       </div>
diff --git a/docs/articles/sim_design.html b/docs/articles/sim_design.html
index 2f1a48f0..fe2d0404 100644
--- a/docs/articles/sim_design.html
+++ b/docs/articles/sim_design.html
@@ -30,7 +30,7 @@
       </button>
       <span class="navbar-brand">
         <a class="navbar-link" href="../index.html">faux</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.0.0.9005</span>
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.0.0.9006</span>
       </span>
     </div>
 
@@ -53,16 +53,13 @@
   </a>
   <ul class="dropdown-menu" role="menu">
 <li>
-      <a href="../articles/intro.html">Introduction to faux</a>
-    </li>
-    <li>
       <a href="../articles/rnorm_multi.html">Simulate Correlated Variables</a>
     </li>
     <li>
       <a href="../articles/sim_design.html">Simulate by Design</a>
     </li>
     <li>
-      <a href="../articles/simdf.html">Simulate from Existing Data</a>
+      <a href="../articles/sim_df.html">Simulate from Existing Data</a>
     </li>
   </ul>
 </li>
@@ -85,7 +82,7 @@
       <h1>Simulate by Design</h1>
                         <h4 class="author">Lisa DeBruine</h4>
             
-            <h4 class="date">2019-04-29</h4>
+            <h4 class="date">2019-05-03</h4>
       
       
       <div class="hidden name"><code>sim_design.Rmd</code></div>
@@ -106,7 +103,7 @@ <h2 class="hasAnchor">
   <span class="dt">dog =</span> <span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/c">c</a></span>(<span class="dv">15</span>, <span class="dv">25</span>),
   <span class="dt">row.names =</span> within<span class="op">$</span>time
 )
-df &lt;-<span class="st"> </span><span class="kw"><a href="../reference/sim_design.html">sim_design</a></span>(within, between, <span class="dt">n =</span> <span class="dv">100</span>, <span class="dt">cors =</span> <span class="fl">0.5</span>, <span class="dt">mu =</span> mu, <span class="dt">sd =</span> <span class="dv">5</span>)</code></pre></div>
+df &lt;-<span class="st"> </span><span class="kw"><a href="../reference/sim_design.html">sim_design</a></span>(within, between, <span class="dt">n =</span> <span class="dv">100</span>, <span class="dt">mu =</span> mu, <span class="dt">sd =</span> <span class="dv">5</span>, <span class="dt">r =</span> <span class="fl">0.5</span>, <span class="dt">empirical =</span> <span class="ot">TRUE</span>)</code></pre></div>
 <table class="table">
 <thead><tr class="header">
 <th align="left">pet</th>
@@ -122,37 +119,37 @@ <h2 class="hasAnchor">
 <td align="left">cat</td>
 <td align="right">100</td>
 <td align="left">day</td>
-<td align="right">1.00</td>
-<td align="right">0.56</td>
-<td align="right">10.23</td>
-<td align="right">4.12</td>
+<td align="right">1.0</td>
+<td align="right">0.5</td>
+<td align="right">10</td>
+<td align="right">5</td>
 </tr>
 <tr class="even">
 <td align="left">cat</td>
 <td align="right">100</td>
 <td align="left">night</td>
-<td align="right">0.56</td>
-<td align="right">1.00</td>
-<td align="right">20.02</td>
-<td align="right">4.88</td>
+<td align="right">0.5</td>
+<td align="right">1.0</td>
+<td align="right">20</td>
+<td align="right">5</td>
 </tr>
 <tr class="odd">
 <td align="left">dog</td>
 <td align="right">100</td>
 <td align="left">day</td>
-<td align="right">1.00</td>
-<td align="right">0.55</td>
-<td align="right">15.27</td>
-<td align="right">5.00</td>
+<td align="right">1.0</td>
+<td align="right">0.5</td>
+<td align="right">15</td>
+<td align="right">5</td>
 </tr>
 <tr class="even">
 <td align="left">dog</td>
 <td align="right">100</td>
 <td align="left">night</td>
-<td align="right">0.55</td>
-<td align="right">1.00</td>
-<td align="right">25.31</td>
-<td align="right">5.36</td>
+<td align="right">0.5</td>
+<td align="right">1.0</td>
+<td align="right">25</td>
+<td align="right">5</td>
 </tr>
 </tbody>
 </table>
@@ -164,26 +161,142 @@ <h2 class="hasAnchor">
 <div id="factor-and-level-names" class="section level3">
 <h3 class="hasAnchor">
 <a href="#factor-and-level-names" class="anchor"></a>Factor and level names</h3>
-<p>First, list your between-subject and within-subject factors. You can specify them like this:</p>
+<p>If you don’t feel like naming your factors and levels, you can just put in a vector of levels. So you can make a quick 2w*3w*2b with the following code.</p>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">df &lt;-<span class="st"> </span><span class="kw"><a href="../reference/sim_design.html">sim_design</a></span>(<span class="dt">within =</span> <span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/c">c</a></span>(<span class="dv">2</span>,<span class="dv">3</span>), <span class="dt">between =</span> <span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/c">c</a></span>(<span class="dv">2</span>), <span class="dt">n =</span> <span class="dv">5</span>, <span class="dt">mu =</span> <span class="dv">0</span>, <span class="dt">sd =</span> <span class="dv">1</span>, <span class="dt">r =</span> <span class="fl">0.5</span>)</code></pre></div>
+<table class="table">
+<thead><tr class="header">
+<th align="left">sub_id</th>
+<th align="left">C</th>
+<th align="right">A1_B1</th>
+<th align="right">A2_B1</th>
+<th align="right">A1_B2</th>
+<th align="right">A2_B2</th>
+<th align="right">A1_B3</th>
+<th align="right">A2_B3</th>
+</tr></thead>
+<tbody>
+<tr class="odd">
+<td align="left">S01</td>
+<td align="left">C1</td>
+<td align="right">-2.7038494</td>
+<td align="right">-2.0031140</td>
+<td align="right">-1.2463619</td>
+<td align="right">0.2395364</td>
+<td align="right">-0.3551418</td>
+<td align="right">-0.6613917</td>
+</tr>
+<tr class="even">
+<td align="left">S02</td>
+<td align="left">C1</td>
+<td align="right">-0.2574224</td>
+<td align="right">-2.5189879</td>
+<td align="right">-0.0945284</td>
+<td align="right">-1.0963883</td>
+<td align="right">-0.9231602</td>
+<td align="right">-0.8312929</td>
+</tr>
+<tr class="odd">
+<td align="left">S03</td>
+<td align="left">C1</td>
+<td align="right">0.9326960</td>
+<td align="right">0.5741398</td>
+<td align="right">0.9461607</td>
+<td align="right">1.5174811</td>
+<td align="right">0.1336817</td>
+<td align="right">2.2414371</td>
+</tr>
+<tr class="even">
+<td align="left">S04</td>
+<td align="left">C1</td>
+<td align="right">-0.2846845</td>
+<td align="right">-0.0003843</td>
+<td align="right">-0.4205892</td>
+<td align="right">0.1429045</td>
+<td align="right">0.7309652</td>
+<td align="right">-0.4047417</td>
+</tr>
+<tr class="odd">
+<td align="left">S05</td>
+<td align="left">C1</td>
+<td align="right">0.0261954</td>
+<td align="right">0.5607634</td>
+<td align="right">-1.2736072</td>
+<td align="right">-1.0107932</td>
+<td align="right">1.3337995</td>
+<td align="right">0.2854269</td>
+</tr>
+<tr class="even">
+<td align="left">S06</td>
+<td align="left">C2</td>
+<td align="right">-0.9116783</td>
+<td align="right">-1.0939985</td>
+<td align="right">-1.0655517</td>
+<td align="right">-0.9173210</td>
+<td align="right">-1.4847558</td>
+<td align="right">-2.5493564</td>
+</tr>
+<tr class="odd">
+<td align="left">S07</td>
+<td align="left">C2</td>
+<td align="right">-0.0690060</td>
+<td align="right">0.4741805</td>
+<td align="right">0.3974194</td>
+<td align="right">-0.1888221</td>
+<td align="right">0.0963150</td>
+<td align="right">1.4528341</td>
+</tr>
+<tr class="even">
+<td align="left">S08</td>
+<td align="left">C2</td>
+<td align="right">-0.4591101</td>
+<td align="right">0.8723573</td>
+<td align="right">-0.2708937</td>
+<td align="right">-0.6212891</td>
+<td align="right">1.3480380</td>
+<td align="right">0.9535270</td>
+</tr>
+<tr class="odd">
+<td align="left">S09</td>
+<td align="left">C2</td>
+<td align="right">0.1287775</td>
+<td align="right">-0.3451130</td>
+<td align="right">-0.5767718</td>
+<td align="right">-1.8759247</td>
+<td align="right">-0.8973256</td>
+<td align="right">-1.1960685</td>
+</tr>
+<tr class="even">
+<td align="left">S10</td>
+<td align="left">C2</td>
+<td align="right">1.0623320</td>
+<td align="right">1.0175079</td>
+<td align="right">-0.1675100</td>
+<td align="right">0.5620579</td>
+<td align="right">0.7513321</td>
+<td align="right">0.7814270</td>
+</tr>
+</tbody>
+</table>
+<p>You can specify between-subject and within-subject factors as a list of vectors where the item names are the factor labels and the vectors are the level labels.</p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">between &lt;-<span class="st"> </span><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/list">list</a></span>(
   <span class="st">"pet"</span> =<span class="st"> </span><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/c">c</a></span>(<span class="st">"cat"</span>, <span class="st">"dog"</span>)
 )
 within &lt;-<span class="st"> </span><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/list">list</a></span>(
   <span class="st">"time"</span> =<span class="st"> </span><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/c">c</a></span>(<span class="st">"day"</span>, <span class="st">"night"</span>)
 )</code></pre></div>
-<p>Or like this:</p>
+<p>You can also specify factors as a list of named vectors where the item names are the factor labels, the vector names are the level labels, and the vector items are the long labels you would use in a codebook or plot.</p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">between &lt;-<span class="st"> </span><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/list">list</a></span>(
   <span class="dt">pet =</span> <span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/c">c</a></span>(<span class="dt">cat =</span> <span class="st">"Is a cat person"</span>, <span class="dt">dog =</span> <span class="st">"Is a dog person"</span>)
 )
 within &lt;-<span class="st"> </span><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/list">list</a></span>(
   <span class="dt">time =</span> <span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/c">c</a></span>(<span class="dt">day =</span> <span class="st">"Tested during the day"</span>, <span class="dt">night =</span> <span class="st">"Tested at night"</span>)
 )</code></pre></div>
-<p>The long name for factor levels isn’t used yet, but will be used in future functions that create a codebook or plots.</p>
+<p>The long label for factor levels isn’t used yet, but will be used in future functions that create a codebook or plots.</p>
 </div>
 <div id="n-mean-sd" class="section level3">
 <h3 class="hasAnchor">
 <a href="#n-mean-sd" class="anchor"></a>N, mean, SD</h3>
-<p>You can specify the Ns, means and standard deviations for each cell as a single number, vector, list, or data frame.</p>
+<p>You can specify the Ns, means and standard deviations for each cell as a single number, list of vectors, or data frame.</p>
 <p>You usually want to specify <code>n</code> as a single number. This is N per cell, not total sample size.</p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">n &lt;-<span class="st"> </span><span class="dv">100</span> <span class="co"># n per cell, not total</span></code></pre></div>
 <p>The levels of the between-subject factors are the list names and the levels of the within-subject factors are the vector names.</p>
@@ -221,13 +334,13 @@ <h3 class="hasAnchor">
 <h3 class="hasAnchor">
 <a href="#correlations" class="anchor"></a>Correlations</h3>
 <p>If you have any within-subject factors, you need to set the correlation for each between-cell. Here, we only have two levels of one within-subject factor, so can only set one correlation per between-cell.</p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">cors &lt;-<span class="st"> </span><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/list">list</a></span>(
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">r &lt;-<span class="st"> </span><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/list">list</a></span>(
   <span class="dt">cat =</span> .<span class="dv">5</span>,
   <span class="dt">dog =</span> .<span class="dv">6</span>
 )</code></pre></div>
 <p>If you set <code>empirical = TRUE</code>, you will get the <em>exact</em> means, SDs and correlations you specified. If you set <code>empirical = FALSE</code> or omit that argument, your data will be sampled from a population with those parameters, but your dataset will not have exactly those values (just on average).</p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">df &lt;-<span class="st"> </span><span class="kw"><a href="../reference/sim_design.html">sim_design</a></span>(within, between, <span class="dt">n =</span> <span class="dv">100</span>, 
-                 <span class="dt">cors =</span> cors, <span class="dt">mu =</span> mu, <span class="dt">sd =</span> sd,
+                 <span class="dt">mu =</span> mu, <span class="dt">sd =</span> sd, <span class="dt">r =</span> r,
                  <span class="dt">empirical =</span> <span class="ot">TRUE</span>)</code></pre></div>
 <table class="table">
 <thead><tr class="header">
@@ -304,32 +417,34 @@ <h2 class="hasAnchor">
   <span class="dt">expert_night =</span> <span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/c">c</a></span>(<span class="dv">10</span>, <span class="dv">15</span>, <span class="dv">12</span>, <span class="dv">17</span>, <span class="dv">14</span>, <span class="dv">19</span>)
 )</code></pre></div>
 <p>You can set the correlation for each between-cell to a single number.</p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">cors &lt;-<span class="st"> </span><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/list">list</a></span>(
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">r &lt;-<span class="st"> </span><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/list">list</a></span>(
   <span class="dt">novice_day =</span> <span class="fl">0.3</span>,
   <span class="dt">novice_night =</span> <span class="fl">0.2</span>,
   <span class="dt">expert_day =</span> <span class="fl">0.5</span>,
   <span class="dt">expert_night =</span> <span class="fl">0.4</span>
 )</code></pre></div>
-<p>Or you can set the full correlation matrix with a vector or matrix (see section <a href="intro.html#spec_cors">Specify cors</a>). Since we have 6 within-cells, this is a 6x6 matrix or a vector of the upper right 15 values.</p>
+<p>Or you can set the full correlation matrix with a vector or matrix. Since we have 6 within-cells, this is a 6x6 matrix or a vector of the upper right 15 values.</p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="co"># upper right triangle correlation specification</span>
 <span class="co"># inc and con have r = 0.5 within each difficultly level, 0.2 otherwise</span>
-<span class="co">#   ce,  ie,  cm,  im,  ch,  ih</span>
-r &lt;-<span class="st">  </span><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/c">c</a></span>(<span class="fl">0.5</span>, <span class="fl">0.2</span>, <span class="fl">0.2</span>, <span class="fl">0.2</span>, <span class="fl">0.2</span>, <span class="co">#con_easy</span>
-             <span class="fl">0.2</span>, <span class="fl">0.2</span>, <span class="fl">0.2</span>, <span class="fl">0.2</span>, <span class="co">#inc_easy</span>
-                  <span class="fl">0.5</span>, <span class="fl">0.2</span>, <span class="fl">0.2</span>, <span class="co">#con_med</span>
-                       <span class="fl">0.2</span>, <span class="fl">0.2</span>, <span class="co">#inc_med</span>
-                            <span class="fl">0.5</span>) <span class="co">#con_hard</span>
-                                 <span class="co">#inc_hard</span>
+<span class="co">#          ce,  ie,  cm,  im,  ch,  ih</span>
+triangle &lt;-<span class="st">  </span><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/c">c</a></span>(<span class="fl">0.5</span>, <span class="fl">0.2</span>, <span class="fl">0.2</span>, <span class="fl">0.2</span>, <span class="fl">0.2</span>, <span class="co">#con_easy</span>
+                    <span class="fl">0.2</span>, <span class="fl">0.2</span>, <span class="fl">0.2</span>, <span class="fl">0.2</span>, <span class="co">#inc_easy</span>
+                         <span class="fl">0.5</span>, <span class="fl">0.2</span>, <span class="fl">0.2</span>, <span class="co">#con_med</span>
+                              <span class="fl">0.2</span>, <span class="fl">0.2</span>, <span class="co">#inc_med</span>
+                                   <span class="fl">0.5</span>) <span class="co">#con_hard</span>
+                                        <span class="co">#inc_hard</span>
 
-cors &lt;-<span class="st"> </span><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/list">list</a></span>(
-  <span class="dt">novice_day =</span> r,
-  <span class="dt">novice_night =</span> r,
-  <span class="dt">expert_day =</span> r,
-  <span class="dt">expert_night =</span> r
+r &lt;-<span class="st"> </span><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/list">list</a></span>(
+  <span class="dt">novice_day =</span> triangle,
+  <span class="dt">novice_night =</span> triangle,
+  <span class="dt">expert_day =</span> triangle,
+  <span class="dt">expert_night =</span> triangle
 )</code></pre></div>
-<p>You can set <code>frame_long = TRUE</code> to return the data frame in long format, which is usually easier for plotting.</p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">df &lt;-<span class="st"> </span><span class="kw"><a href="../reference/sim_design.html">sim_design</a></span>(within, between, <span class="dt">n =</span> <span class="dv">100</span>, <span class="dt">cors =</span> cors, <span class="dt">mu =</span> mu, <span class="dt">sd =</span> <span class="dv">2</span>, <span class="dt">frame_long =</span> <span class="ot">TRUE</span>)</code></pre></div>
-<p><img src="sim_design_files/figure-html/unnamed-chunk-16-1.png" width="768"></p>
+<p>You can set <code>long = TRUE</code> to return the data frame in long format, which is usually easier for plotting.</p>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">df &lt;-<span class="st"> </span><span class="kw"><a href="../reference/sim_design.html">sim_design</a></span>(within, between, <span class="dt">n =</span> <span class="dv">100</span>, 
+                 <span class="dt">mu =</span> mu, <span class="dt">sd =</span> <span class="dv">2</span>, <span class="dt">r =</span> r, 
+                 <span class="dt">long =</span> <span class="ot">TRUE</span>)</code></pre></div>
+<p><img src="sim_design_files/figure-html/unnamed-chunk-17-1.png" width="768"></p>
 </div>
   </div>
 
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+    <div class="page-header toc-ignore">
+      <h1>Simulate from Existing Data</h1>
+                        <h4 class="author">Lisa DeBruine</h4>
+            
+            <h4 class="date">2019-05-03</h4>
+      
+      
+      <div class="hidden name"><code>sim_df.Rmd</code></div>
+
+    </div>
+
+    
+    
+<p>The <code><a href="../reference/sim_df.html">sim_df()</a></code> function produces a dataframe with the same distributions and correlations as an existing dataframe. It only returns numeric columns and simulates all numeric variables from a continuous normal distribution (for now).</p>
+<p>For example, here is the relationship between speed and distance in the built-in dataset <code>cars</code>.</p>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">cars <span class="op">%&gt;%</span>
+<span class="st">  </span><span class="kw">ggplot</span>(<span class="kw">aes</span>(speed, dist)) <span class="op">+</span><span class="st"> </span>
+<span class="st">  </span><span class="kw">geom_point</span>() <span class="op">+</span>
+<span class="st">  </span><span class="kw">geom_smooth</span>(<span class="dt">method =</span> <span class="st">"lm"</span>)</code></pre></div>
+<div class="figure">
+<img src="sim_df_files/figure-html/plot-cars-orig-1.png" alt="Original cars dataset" width="768"><p class="caption">
+Original cars dataset
+</p>
+</div>
+<p>You can create a new sample with the same parameters and 500 rows with the code <code><a href="../reference/sim_df.html">sim_df(cars, 500)</a></code>.</p>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw"><a href="../reference/sim_df.html">sim_df</a></span>(cars, <span class="dv">500</span>) <span class="op">%&gt;%</span>
+<span class="st">  </span><span class="kw">ggplot</span>(<span class="kw">aes</span>(speed, dist)) <span class="op">+</span><span class="st"> </span>
+<span class="st">    </span><span class="kw">geom_point</span>() <span class="op">+</span>
+<span class="st">    </span><span class="kw">geom_smooth</span>(<span class="dt">method =</span> <span class="st">"lm"</span>)</code></pre></div>
+<div class="figure">
+<img src="sim_df_files/figure-html/plot-cars-sim-1.png" alt="Simulated cars dataset" width="768"><p class="caption">
+Simulated cars dataset
+</p>
+</div>
+<div id="between-subject-variables" class="section level2">
+<h2 class="hasAnchor">
+<a href="#between-subject-variables" class="anchor"></a>Between-subject variables</h2>
+<p>You can also optionally add between-subject variables. For example, here is the relationship between sepal length and width in the built-in dataset <code>iris</code>.</p>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">iris <span class="op">%&gt;%</span>
+<span class="st">  </span><span class="kw">ggplot</span>(<span class="kw">aes</span>(Sepal.Width, Sepal.Length, <span class="dt">color =</span> Species)) <span class="op">+</span>
+<span class="st">  </span><span class="kw">geom_point</span>() <span class="op">+</span>
+<span class="st">  </span><span class="kw">geom_smooth</span>(<span class="dt">method =</span> <span class="st">"lm"</span>)</code></pre></div>
+<div class="figure">
+<img src="sim_df_files/figure-html/plot-iris-orig-1.png" alt="Original iris dataset" width="768"><p class="caption">
+Original iris dataset
+</p>
+</div>
+<p>And here is a new sample with 50 observations of each species, made with the code <code><a href="../reference/sim_df.html">sim_df(iris, 100, "Species")</a></code>.</p>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw"><a href="../reference/sim_df.html">sim_df</a></span>(iris, <span class="dv">50</span>, <span class="st">"Species"</span>) <span class="op">%&gt;%</span>
+<span class="st">  </span><span class="kw">ggplot</span>(<span class="kw">aes</span>(Sepal.Width, Sepal.Length, <span class="dt">color =</span> Species)) <span class="op">+</span>
+<span class="st">  </span><span class="kw">geom_point</span>() <span class="op">+</span>
+<span class="st">  </span><span class="kw">geom_smooth</span>(<span class="dt">method =</span> <span class="st">"lm"</span>)</code></pre></div>
+<div class="figure">
+<img src="sim_df_files/figure-html/plot-iris-sim-1.png" alt="Simulated iris dataset" width="768"><p class="caption">
+Simulated iris dataset
+</p>
+</div>
+</div>
+<div id="empirical" class="section level2">
+<h2 class="hasAnchor">
+<a href="#empirical" class="anchor"></a>Empirical</h2>
+<p>Set <code>empirical = TRUE</code> to return a data frame with <em>exactly</em> the same means, SDs, and correlations as the original dataset.</p>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">exact_iris &lt;-<span class="st"> </span><span class="kw"><a href="../reference/sim_df.html">sim_df</a></span>(iris, <span class="dv">50</span>, <span class="dt">between =</span> <span class="st">"Species"</span>, <span class="dt">empirical =</span> <span class="ot">TRUE</span>)</code></pre></div>
+</div>
+<div id="rounding" class="section level2">
+<h2 class="hasAnchor">
+<a href="#rounding" class="anchor"></a>Rounding</h2>
+<p>For now, the function only creates new variables sampled from a continuous normal distribution. I hope to add in other sampling distributions in the future. So you’d need to do any rounding or truncating yourself.</p>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw"><a href="../reference/sim_df.html">sim_df</a></span>(iris, <span class="dv">50</span>, <span class="st">"Species"</span>) <span class="op">%&gt;%</span>
+<span class="st">  </span><span class="kw">mutate_if</span>(is.numeric, round, <span class="dv">1</span>) <span class="op">%&gt;%</span>
+<span class="st">  </span><span class="kw">ggplot</span>(<span class="kw">aes</span>(Sepal.Width, Sepal.Length, <span class="dt">color =</span> Species)) <span class="op">+</span>
+<span class="st">  </span><span class="kw">geom_point</span>() <span class="op">+</span>
+<span class="st">  </span><span class="kw">geom_smooth</span>(<span class="dt">method =</span> <span class="st">"lm"</span>)</code></pre></div>
+<div class="figure">
+<img src="sim_df_files/figure-html/plot-iris-sim-round-1.png" alt="Simulated iris dataset (rounded)" width="768"><p class="caption">
+Simulated iris dataset (rounded)
+</p>
+</div>
+</div>
+  </div>
+
+  <div class="col-md-3 hidden-xs hidden-sm" id="sidebar">
+        <div id="tocnav">
+      <h2 class="hasAnchor">
+<a href="#tocnav" class="anchor"></a>Contents</h2>
+      <ul class="nav nav-pills nav-stacked">
+<li><a href="#between-subject-variables">Between-subject variables</a></li>
+      <li><a href="#empirical">Empirical</a></li>
+      <li><a href="#rounding">Rounding</a></li>
+      </ul>
+</div>
+      </div>
+
+</div>
+
+
+      <footer><div class="copyright">
+  <p>Developed by Lisa DeBruine.</p>
+</div>
+
+<div class="pkgdown">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.3.0.</p>
+</div>
+      </footer>
+</div>
+
+  
+
+  </body>
+</html>
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diff --git a/docs/authors.html b/docs/authors.html
index 918f2b40..1c314be5 100644
--- a/docs/authors.html
+++ b/docs/authors.html
@@ -60,7 +60,7 @@
       </button>
       <span class="navbar-brand">
         <a class="navbar-link" href="index.html">faux</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.0.0.9005</span>
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.0.0.9006</span>
       </span>
     </div>
 
@@ -82,9 +82,6 @@
     <span class="caret"></span>
   </a>
   <ul class="dropdown-menu" role="menu">
-    <li>
-      <a href="articles/intro.html">Introduction to faux</a>
-    </li>
     <li>
       <a href="articles/rnorm_multi.html">Simulate Correlated Variables</a>
     </li>
@@ -92,7 +89,7 @@
       <a href="articles/sim_design.html">Simulate by Design</a>
     </li>
     <li>
-      <a href="articles/simdf.html">Simulate from Existing Data</a>
+      <a href="articles/sim_df.html">Simulate from Existing Data</a>
     </li>
   </ul>
 </li>
@@ -120,7 +117,7 @@ <h1>Authors</h1>
 
     <ul class="list-unstyled">
       <li>
-        <p><strong>Lisa DeBruine</strong>. Author, maintainer. 
+        <p><strong>Lisa DeBruine</strong>. Author, maintainer. <a href='https://orcid.org/0000-0002-7523-5539' target='orcid.widget'><img src='https://members.orcid.org/sites/default/files/vector_iD_icon.svg' class='orcid' alt='ORCID' height='16'></a>
         </p>
       </li>
     </ul>
diff --git a/docs/index.html b/docs/index.html
index d494478a..6c9528ff 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -30,7 +30,7 @@
       </button>
       <span class="navbar-brand">
         <a class="navbar-link" href="index.html">faux</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.0.0.9005</span>
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.0.0.9006</span>
       </span>
     </div>
 
@@ -53,16 +53,13 @@
   </a>
   <ul class="dropdown-menu" role="menu">
 <li>
-      <a href="articles/intro.html">Introduction to faux</a>
-    </li>
-    <li>
       <a href="articles/rnorm_multi.html">Simulate Correlated Variables</a>
     </li>
     <li>
       <a href="articles/sim_design.html">Simulate by Design</a>
     </li>
     <li>
-      <a href="articles/simdf.html">Simulate from Existing Data</a>
+      <a href="articles/sim_df.html">Simulate from Existing Data</a>
     </li>
   </ul>
 </li>
@@ -86,532 +83,146 @@
     
     
 <!-- README.md is generated from README.Rmd. Please edit that file -->
-<div id="faux" class="section level1">
-<div class="page-header"><h1 class="hasAnchor">
-<a href="#faux" class="anchor"></a>faux</h1></div>
+<!-- badges: start -->
+
 <p>It is useful to be able to simulate data with a specified structure. The <code>faux</code> package provides some functions to make this process easier.</p>
-<div id="installation" class="section level2">
+<div id="sim_design" class="section level2">
 <h2 class="hasAnchor">
-<a href="#installation" class="anchor"></a>Installation</h2>
-<p>You can install the released version of faux from <a href="https://github.com/debruine/faux">GitHub</a> with:</p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">devtools<span class="op">::</span><span class="kw"><a href="https://www.rdocumentation.org/packages/devtools/topics/reexports">install_github</a></span>(<span class="st">"debruine/faux"</span>)</code></pre></div>
-</div>
-<div id="examples" class="section level2">
-<h2 class="hasAnchor">
-<a href="#examples" class="anchor"></a>Examples</h2>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/library">library</a></span>(tidyverse)
-<span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/library">library</a></span>(faux)</code></pre></div>
-<div id="rnorm_multi" class="section level3">
-<h3 class="hasAnchor">
-<a href="#rnorm_multi" class="anchor"></a>rnorm_multi</h3>
-<p>This function makes multiple normally distributed vectors with specified parameters and relationships.</p>
-<p>For example, the following creates a sample that has 100 observations of 3 variables, drawn from a population where where A correlates with B and C with r = 0.5, and B and C correlate with r = 0.25. A has a mean of 0 and SD of 1, while B and C have means of 20 and SDs of 5.</p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">
-dat &lt;-<span class="st"> </span><span class="kw"><a href="reference/rnorm_multi.html">rnorm_multi</a></span>(<span class="dt">n =</span> <span class="dv">100</span>, 
-                  <span class="dt">cors =</span> <span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/c">c</a></span>(<span class="fl">0.5</span>, <span class="fl">0.5</span>, <span class="fl">0.25</span>), 
-                  <span class="dt">mu =</span> <span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/c">c</a></span>(<span class="dv">0</span>, <span class="dv">20</span>, <span class="dv">20</span>),
-                  <span class="dt">sd =</span> <span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/c">c</a></span>(<span class="dv">1</span>, <span class="dv">5</span>, <span class="dv">5</span>),
-                  <span class="dt">varnames =</span> <span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/c">c</a></span>(<span class="st">"A"</span>, <span class="st">"B"</span>, <span class="st">"C"</span>),
-                  <span class="dt">empirical =</span> <span class="ot">FALSE</span>)</code></pre></div>
-<table class="table table">
-<caption>Sample stats</caption>
-<thead><tr class="header">
-<th align="right">n</th>
-<th align="left">var</th>
-<th align="right">A</th>
-<th align="right">B</th>
-<th align="right">C</th>
-<th align="right">mean</th>
-<th align="right">sd</th>
-</tr></thead>
-<tbody>
-<tr class="odd">
-<td align="right">100</td>
-<td align="left">A</td>
-<td align="right">1.00</td>
-<td align="right">0.45</td>
-<td align="right">0.49</td>
-<td align="right">0.03</td>
-<td align="right">0.99</td>
-</tr>
-<tr class="even">
-<td align="right">100</td>
-<td align="left">B</td>
-<td align="right">0.45</td>
-<td align="right">1.00</td>
-<td align="right">0.33</td>
-<td align="right">20.01</td>
-<td align="right">4.89</td>
-</tr>
-<tr class="odd">
-<td align="right">100</td>
-<td align="left">C</td>
-<td align="right">0.49</td>
-<td align="right">0.33</td>
-<td align="right">1.00</td>
-<td align="right">19.76</td>
-<td align="right">4.02</td>
-</tr>
-</tbody>
-</table>
-<div id="specify-cors" class="section level4">
-<h4 class="hasAnchor">
-<a href="#specify-cors" class="anchor"></a>Specify <code>cors</code>
-</h4>
-<p>You can specify the correlations in one of four ways:</p>
-<ul>
-<li>A single r for all pairs</li>
-<li>A vars by vars matrix</li>
-<li>A vars*vars length vector</li>
-<li>A vars*(vars-1)/2 length vector</li>
-</ul>
-<div id="one-number" class="section level5">
-<h5 class="hasAnchor">
-<a href="#one-number" class="anchor"></a>One Number</h5>
-<p>If you want all the pairs to have the same correlation, just specify a single number.</p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">bvn &lt;-<span class="st"> </span><span class="kw"><a href="reference/rnorm_multi.html">rnorm_multi</a></span>(<span class="dv">100</span>, <span class="dv">5</span>, .<span class="dv">3</span>, <span class="dt">varnames =</span> letters[<span class="dv">1</span><span class="op">:</span><span class="dv">5</span>])</code></pre></div>
+<a href="#sim_design" class="anchor"></a>sim_design</h2>
+<p>This function creates a dataset with a specific between- and within-subjects design. <a href="articles/sim_design.html">see vignette</a></p>
+<p>For example, the following creates a 2w*2b design with 100 observations in each cell. The between-subject factor is <code>pet</code> with twolevels of <code>cat</code> and <code>dog</code>. The within-subject factor is <code>time</code> with two levels of <code>day</code> and <code>night</code>. The mean for the <code>cat_day</code> cell is 10, the mean for the <code>cat_night</code> cell is 20, the mean for the <code>dog_day</code> cell is 15, and the mean for the <code>dog_night</code> cell is 25. All cells have a SD of 5 and all within-subject cells are correlated <code>r = 0.5</code>. The resulting data has exactly these values (set <code>empirical = FALSE</code> to sample from a population with these values).</p>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">between &lt;-<span class="st"> </span><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/list">list</a></span>(<span class="st">"pet"</span> =<span class="st"> </span><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/c">c</a></span>(<span class="st">"cat"</span>, <span class="st">"dog"</span>))
+within &lt;-<span class="st"> </span><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/list">list</a></span>(<span class="st">"time"</span> =<span class="st"> </span><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/c">c</a></span>(<span class="st">"day"</span>, <span class="st">"night"</span>))
+mu &lt;-<span class="st"> </span><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/data.frame">data.frame</a></span>(
+  <span class="dt">cat =</span> <span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/c">c</a></span>(<span class="dv">10</span>, <span class="dv">20</span>),
+  <span class="dt">dog =</span> <span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/c">c</a></span>(<span class="dv">15</span>, <span class="dv">25</span>),
+  <span class="dt">row.names =</span> within<span class="op">$</span>time
+)
+df &lt;-<span class="st"> </span><span class="kw"><a href="reference/sim_design.html">sim_design</a></span>(within, between, 
+                 <span class="dt">n =</span> <span class="dv">100</span>, <span class="dt">mu =</span> mu, <span class="dt">sd =</span> <span class="dv">5</span>, <span class="dt">r =</span> .<span class="dv">5</span>,
+                 <span class="dt">empirical =</span> <span class="ot">TRUE</span>)</code></pre></div>
 <table class="table table">
-<caption>Sample stats from a single rho</caption>
+<caption>Sample <code><a href="reference/sim_design.html">sim_design()</a></code> stats</caption>
 <thead><tr class="header">
+<th align="left">pet</th>
 <th align="right">n</th>
 <th align="left">var</th>
-<th align="right">a</th>
-<th align="right">b</th>
-<th align="right">c</th>
-<th align="right">d</th>
-<th align="right">e</th>
+<th align="right">day</th>
+<th align="right">night</th>
 <th align="right">mean</th>
 <th align="right">sd</th>
 </tr></thead>
 <tbody>
 <tr class="odd">
+<td align="left">cat</td>
 <td align="right">100</td>
-<td align="left">a</td>
-<td align="right">1.00</td>
-<td align="right">0.35</td>
-<td align="right">0.22</td>
-<td align="right">0.45</td>
-<td align="right">0.37</td>
-<td align="right">-0.04</td>
-<td align="right">1.09</td>
+<td align="left">day</td>
+<td align="right">1.0</td>
+<td align="right">0.5</td>
+<td align="right">10</td>
+<td align="right">5</td>
 </tr>
 <tr class="even">
+<td align="left">cat</td>
 <td align="right">100</td>
-<td align="left">b</td>
-<td align="right">0.35</td>
-<td align="right">1.00</td>
-<td align="right">0.19</td>
-<td align="right">0.36</td>
-<td align="right">0.28</td>
-<td align="right">-0.05</td>
-<td align="right">0.83</td>
+<td align="left">night</td>
+<td align="right">0.5</td>
+<td align="right">1.0</td>
+<td align="right">20</td>
+<td align="right">5</td>
 </tr>
 <tr class="odd">
+<td align="left">dog</td>
 <td align="right">100</td>
-<td align="left">c</td>
-<td align="right">0.22</td>
-<td align="right">0.19</td>
-<td align="right">1.00</td>
-<td align="right">0.26</td>
-<td align="right">0.20</td>
-<td align="right">0.01</td>
-<td align="right">1.08</td>
+<td align="left">day</td>
+<td align="right">1.0</td>
+<td align="right">0.5</td>
+<td align="right">15</td>
+<td align="right">5</td>
 </tr>
 <tr class="even">
+<td align="left">dog</td>
 <td align="right">100</td>
-<td align="left">d</td>
-<td align="right">0.45</td>
-<td align="right">0.36</td>
-<td align="right">0.26</td>
-<td align="right">1.00</td>
-<td align="right">0.24</td>
-<td align="right">0.00</td>
-<td align="right">1.00</td>
-</tr>
-<tr class="odd">
-<td align="right">100</td>
-<td align="left">e</td>
-<td align="right">0.37</td>
-<td align="right">0.28</td>
-<td align="right">0.20</td>
-<td align="right">0.24</td>
-<td align="right">1.00</td>
-<td align="right">0.04</td>
-<td align="right">0.97</td>
+<td align="left">night</td>
+<td align="right">0.5</td>
+<td align="right">1.0</td>
+<td align="right">25</td>
+<td align="right">5</td>
 </tr>
 </tbody>
 </table>
 </div>
-<div id="matrix" class="section level5">
-<h5 class="hasAnchor">
-<a href="#matrix" class="anchor"></a>Matrix</h5>
-<p>If you already have a correlation matrix, such as the output of <code><a href="https://www.rdocumentation.org/packages/stats/topics/cor">cor()</a></code>, you can specify the simulated data with that.</p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">cmat &lt;-<span class="st"> </span><span class="kw"><a href="https://www.rdocumentation.org/packages/stats/topics/cor">cor</a></span>(iris[,<span class="dv">1</span><span class="op">:</span><span class="dv">4</span>])
-bvn &lt;-<span class="st"> </span><span class="kw"><a href="reference/rnorm_multi.html">rnorm_multi</a></span>(<span class="dv">100</span>, <span class="dv">4</span>, cmat, 
-                  <span class="dt">varnames =</span> <span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/colnames">colnames</a></span>(cmat))</code></pre></div>
+<div id="rnorm_multi" class="section level2">
+<h2 class="hasAnchor">
+<a href="#rnorm_multi" class="anchor"></a>rnorm_multi</h2>
+<p>This function makes multiple normally distributed vectors with specified parameters and relationships. <a href="articles/rnorm_multi.html">see vignette</a></p>
+<p>For example, the following creates a sample that has 100 observations of 3 variables, drawn from a population where A has a mean of 0 and SD of 1, while B and C have means of 20 and SDs of 5. A correlates with B and C with r = 0.5, and B and C correlate with r = 0.25.</p>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">
+dat &lt;-<span class="st"> </span><span class="kw"><a href="reference/rnorm_multi.html">rnorm_multi</a></span>(
+  <span class="dt">n =</span> <span class="dv">100</span>, 
+  <span class="dt">mu =</span> <span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/c">c</a></span>(<span class="dv">0</span>, <span class="dv">20</span>, <span class="dv">20</span>),
+  <span class="dt">sd =</span> <span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/c">c</a></span>(<span class="dv">1</span>, <span class="dv">5</span>, <span class="dv">5</span>),
+  <span class="dt">r =</span> <span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/c">c</a></span>(<span class="fl">0.5</span>, <span class="fl">0.5</span>, <span class="fl">0.25</span>), 
+  <span class="dt">varnames =</span> <span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/c">c</a></span>(<span class="st">"A"</span>, <span class="st">"B"</span>, <span class="st">"C"</span>),
+  <span class="dt">empirical =</span> <span class="ot">FALSE</span>
+)</code></pre></div>
 <table class="table table">
-<caption>Sample stats from a correlation matrix</caption>
+<caption>Sample <code><a href="reference/rnorm_multi.html">rnorm_multi()</a></code> stats</caption>
 <thead><tr class="header">
 <th align="right">n</th>
 <th align="left">var</th>
-<th align="right">Sepal.Length</th>
-<th align="right">Sepal.Width</th>
-<th align="right">Petal.Length</th>
-<th align="right">Petal.Width</th>
+<th align="right">A</th>
+<th align="right">B</th>
+<th align="right">C</th>
 <th align="right">mean</th>
 <th align="right">sd</th>
 </tr></thead>
 <tbody>
 <tr class="odd">
 <td align="right">100</td>
-<td align="left">Sepal.Length</td>
-<td align="right">1.00</td>
-<td align="right">-0.10</td>
-<td align="right">0.88</td>
-<td align="right">0.83</td>
-<td align="right">-0.01</td>
-<td align="right">1.05</td>
-</tr>
-<tr class="even">
-<td align="right">100</td>
-<td align="left">Sepal.Width</td>
-<td align="right">-0.10</td>
-<td align="right">1.00</td>
-<td align="right">-0.38</td>
-<td align="right">-0.29</td>
-<td align="right">-0.19</td>
-<td align="right">1.09</td>
-</tr>
-<tr class="odd">
-<td align="right">100</td>
-<td align="left">Petal.Length</td>
-<td align="right">0.88</td>
-<td align="right">-0.38</td>
-<td align="right">1.00</td>
-<td align="right">0.96</td>
-<td align="right">-0.01</td>
-<td align="right">1.02</td>
-</tr>
-<tr class="even">
-<td align="right">100</td>
-<td align="left">Petal.Width</td>
-<td align="right">0.83</td>
-<td align="right">-0.29</td>
-<td align="right">0.96</td>
+<td align="left">A</td>
 <td align="right">1.00</td>
+<td align="right">0.62</td>
+<td align="right">0.46</td>
 <td align="right">-0.05</td>
-<td align="right">0.98</td>
-</tr>
-</tbody>
-</table>
-</div>
-<div id="vector-varsvars" class="section level5">
-<h5 class="hasAnchor">
-<a href="#vector-varsvars" class="anchor"></a>Vector (vars*vars)</h5>
-<p>You can specify your correlation matrix by hand as a vars*vars length vector, which will include the correlations of 1 down the diagonal.</p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">cmat &lt;-<span class="st"> </span><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/c">c</a></span>(<span class="dv">1</span>, .<span class="dv">3</span>, .<span class="dv">5</span>,
-          .<span class="dv">3</span>, <span class="dv">1</span>, <span class="dv">0</span>,
-          .<span class="dv">5</span>, <span class="dv">0</span>, <span class="dv">1</span>)
-bvn &lt;-<span class="st"> </span><span class="kw"><a href="reference/rnorm_multi.html">rnorm_multi</a></span>(<span class="dv">100</span>, <span class="dv">3</span>, cmat, 
-                  <span class="dt">varnames =</span> <span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/c">c</a></span>(<span class="st">"first"</span>, <span class="st">"second"</span>, <span class="st">"third"</span>))</code></pre></div>
-<table class="table table">
-<caption>Sample stats from a vars*vars vector</caption>
-<thead><tr class="header">
-<th align="right">n</th>
-<th align="left">var</th>
-<th align="right">first</th>
-<th align="right">second</th>
-<th align="right">third</th>
-<th align="right">mean</th>
-<th align="right">sd</th>
-</tr></thead>
-<tbody>
-<tr class="odd">
-<td align="right">100</td>
-<td align="left">first</td>
-<td align="right">1.00</td>
-<td align="right">0.33</td>
-<td align="right">0.45</td>
-<td align="right">-0.12</td>
-<td align="right">1.01</td>
-</tr>
-<tr class="even">
-<td align="right">100</td>
-<td align="left">second</td>
-<td align="right">0.33</td>
-<td align="right">1.00</td>
-<td align="right">-0.04</td>
-<td align="right">-0.01</td>
-<td align="right">1.04</td>
-</tr>
-<tr class="odd">
-<td align="right">100</td>
-<td align="left">third</td>
-<td align="right">0.45</td>
-<td align="right">-0.04</td>
-<td align="right">1.00</td>
-<td align="right">-0.11</td>
-<td align="right">1.00</td>
-</tr>
-</tbody>
-</table>
-</div>
-<div id="vector-varsvars-12" class="section level5">
-<h5 class="hasAnchor">
-<a href="#vector-varsvars-12" class="anchor"></a>Vector (vars*(vars-1)/2)</h5>
-<p>You can specify your correlation matrix by hand as a vars*(vars-1)/2 length vector, skipping the diagonal and lower left duplicate values.</p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">rho1_<span class="dv">2</span> &lt;-<span class="st"> </span>.<span class="dv">3</span>
-rho1_<span class="dv">3</span> &lt;-<span class="st"> </span>.<span class="dv">5</span>
-rho1_<span class="dv">4</span> &lt;-<span class="st"> </span>.<span class="dv">5</span>
-rho2_<span class="dv">3</span> &lt;-<span class="st"> </span>.<span class="dv">2</span>
-rho2_<span class="dv">4</span> &lt;-<span class="st"> </span><span class="dv">0</span>
-rho3_<span class="dv">4</span> &lt;-<span class="st"> </span><span class="op">-</span>.<span class="dv">3</span>
-cmat &lt;-<span class="st"> </span><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/c">c</a></span>(rho1_<span class="dv">2</span>, rho1_<span class="dv">3</span>, rho1_<span class="dv">4</span>, rho2_<span class="dv">3</span>, rho2_<span class="dv">4</span>, rho3_<span class="dv">4</span>)
-bvn &lt;-<span class="st"> </span><span class="kw"><a href="reference/rnorm_multi.html">rnorm_multi</a></span>(<span class="dv">100</span>, <span class="dv">4</span>, cmat, 
-                  <span class="dt">varnames =</span> letters[<span class="dv">1</span><span class="op">:</span><span class="dv">4</span>])</code></pre></div>
-<table class="table table">
-<caption>Sample stats from a (vars*(vars-1)/2) vector</caption>
-<thead><tr class="header">
-<th align="right">n</th>
-<th align="left">var</th>
-<th align="right">a</th>
-<th align="right">b</th>
-<th align="right">c</th>
-<th align="right">d</th>
-<th align="right">mean</th>
-<th align="right">sd</th>
-</tr></thead>
-<tbody>
-<tr class="odd">
-<td align="right">100</td>
-<td align="left">a</td>
-<td align="right">1.00</td>
-<td align="right">0.35</td>
-<td align="right">0.55</td>
-<td align="right">0.50</td>
-<td align="right">-0.13</td>
-<td align="right">1.01</td>
+<td align="right">1.08</td>
 </tr>
 <tr class="even">
 <td align="right">100</td>
-<td align="left">b</td>
-<td align="right">0.35</td>
+<td align="left">B</td>
+<td align="right">0.62</td>
 <td align="right">1.00</td>
-<td align="right">0.16</td>
-<td align="right">0.09</td>
-<td align="right">-0.10</td>
-<td align="right">1.05</td>
+<td align="right">0.19</td>
+<td align="right">19.95</td>
+<td align="right">5.38</td>
 </tr>
 <tr class="odd">
 <td align="right">100</td>
-<td align="left">c</td>
-<td align="right">0.55</td>
-<td align="right">0.16</td>
-<td align="right">1.00</td>
-<td align="right">-0.21</td>
-<td align="right">-0.19</td>
-<td align="right">0.91</td>
-</tr>
-<tr class="even">
-<td align="right">100</td>
-<td align="left">d</td>
-<td align="right">0.50</td>
-<td align="right">0.09</td>
-<td align="right">-0.21</td>
+<td align="left">C</td>
+<td align="right">0.46</td>
+<td align="right">0.19</td>
 <td align="right">1.00</td>
-<td align="right">0.12</td>
-<td align="right">0.97</td>
-</tr>
-</tbody>
-</table>
-</div>
-</div>
-<div id="empirical" class="section level4">
-<h4 class="hasAnchor">
-<a href="#empirical" class="anchor"></a>empirical</h4>
-<p>If you want your samples to have the <em>exact</em> correlations, means, and SDs you entered, set <code>empirical</code> to TRUE.</p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">bvn &lt;-<span class="st"> </span><span class="kw"><a href="reference/rnorm_multi.html">rnorm_multi</a></span>(<span class="dv">100</span>, <span class="dv">5</span>, .<span class="dv">3</span>, 
-                  <span class="dt">varnames =</span> letters[<span class="dv">1</span><span class="op">:</span><span class="dv">5</span>], 
-                  <span class="dt">empirical =</span> T)</code></pre></div>
-<table class="table table">
-<caption>Sample stats with empirical = TRUE</caption>
-<thead><tr class="header">
-<th align="right">n</th>
-<th align="left">var</th>
-<th align="right">a</th>
-<th align="right">b</th>
-<th align="right">c</th>
-<th align="right">d</th>
-<th align="right">e</th>
-<th align="right">mean</th>
-<th align="right">sd</th>
-</tr></thead>
-<tbody>
-<tr class="odd">
-<td align="right">100</td>
-<td align="left">a</td>
-<td align="right">1.0</td>
-<td align="right">0.3</td>
-<td align="right">0.3</td>
-<td align="right">0.3</td>
-<td align="right">0.3</td>
-<td align="right">0</td>
-<td align="right">1</td>
-</tr>
-<tr class="even">
-<td align="right">100</td>
-<td align="left">b</td>
-<td align="right">0.3</td>
-<td align="right">1.0</td>
-<td align="right">0.3</td>
-<td align="right">0.3</td>
-<td align="right">0.3</td>
-<td align="right">0</td>
-<td align="right">1</td>
-</tr>
-<tr class="odd">
-<td align="right">100</td>
-<td align="left">c</td>
-<td align="right">0.3</td>
-<td align="right">0.3</td>
-<td align="right">1.0</td>
-<td align="right">0.3</td>
-<td align="right">0.3</td>
-<td align="right">0</td>
-<td align="right">1</td>
-</tr>
-<tr class="even">
-<td align="right">100</td>
-<td align="left">d</td>
-<td align="right">0.3</td>
-<td align="right">0.3</td>
-<td align="right">0.3</td>
-<td align="right">1.0</td>
-<td align="right">0.3</td>
-<td align="right">0</td>
-<td align="right">1</td>
-</tr>
-<tr class="odd">
-<td align="right">100</td>
-<td align="left">e</td>
-<td align="right">0.3</td>
-<td align="right">0.3</td>
-<td align="right">0.3</td>
-<td align="right">0.3</td>
-<td align="right">1.0</td>
-<td align="right">0</td>
-<td align="right">1</td>
+<td align="right">19.81</td>
+<td align="right">5.15</td>
 </tr>
 </tbody>
 </table>
 </div>
-</div>
-<div id="simdf" class="section level3">
-<h3 class="hasAnchor">
-<a href="#simdf" class="anchor"></a>simdf</h3>
-<p>This function produces a dataframe with the same distributions and correlations as an existing dataframe. It only returns numeric columns and simulates all numeric variables from a continuous normal distribution (for now).</p>
-<p>For example, here is the relationship between speed and distance in the built-in dataset <code>cars</code>.</p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">cars <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">ggplot</span>(<span class="kw">aes</span>(speed, dist)) <span class="op">+</span><span class="st"> </span>
-<span class="st">  </span><span class="kw">geom_point</span>() <span class="op">+</span>
-<span class="st">  </span><span class="kw">geom_smooth</span>(<span class="dt">method =</span> <span class="st">"lm"</span>)</code></pre></div>
-<div class="figure">
-<img src="reference/figures/README-plot-cars-orig-1.png" alt="Original cars dataset" width="100%"><p class="caption">
-Original cars dataset
-</p>
-</div>
-<p>You can create a new sample with the same parameters and 500 rows with the code <code><a href="reference/simdf.html">simdf(cars, 500)</a></code>.</p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw"><a href="reference/simdf.html">simdf</a></span>(cars, <span class="dv">500</span>) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">ggplot</span>(<span class="kw">aes</span>(speed, dist)) <span class="op">+</span><span class="st"> </span>
-<span class="st">    </span><span class="kw">geom_point</span>() <span class="op">+</span>
-<span class="st">    </span><span class="kw">geom_smooth</span>(<span class="dt">method =</span> <span class="st">"lm"</span>)</code></pre></div>
-<div class="figure">
-<img src="reference/figures/README-plot-cars-sim-1.png" alt="Simulated cars dataset" width="100%"><p class="caption">
-Simulated cars dataset
-</p>
-</div>
-<div id="grouping-variables" class="section level4">
-<h4 class="hasAnchor">
-<a href="#grouping-variables" class="anchor"></a>Grouping Variables</h4>
-<p>You can also optionally add grouping variables. For example, here is the relationship between sepal length and width in the built-in dataset <code>iris</code>.</p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">iris <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">ggplot</span>(<span class="kw">aes</span>(Sepal.Width, Sepal.Length, <span class="dt">color =</span> Species)) <span class="op">+</span>
-<span class="st">  </span><span class="kw">geom_point</span>() <span class="op">+</span>
-<span class="st">  </span><span class="kw">geom_smooth</span>(<span class="dt">method =</span> <span class="st">"lm"</span>)</code></pre></div>
-<div class="figure">
-<img src="reference/figures/README-plot-iris-orig-1.png" alt="Original iris dataset" width="100%"><p class="caption">
-Original iris dataset
-</p>
-</div>
-<p>And here is a new sample with 50 observations of each species, made with the code <code><a href="reference/simdf.html">simdf(iris, 100, "Species")</a></code>.</p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw"><a href="reference/simdf.html">simdf</a></span>(iris, <span class="dv">50</span>, <span class="st">"Species"</span>) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">ggplot</span>(<span class="kw">aes</span>(Sepal.Width, Sepal.Length, <span class="dt">color =</span> Species)) <span class="op">+</span>
-<span class="st">  </span><span class="kw">geom_point</span>() <span class="op">+</span>
-<span class="st">  </span><span class="kw">geom_smooth</span>(<span class="dt">method =</span> <span class="st">"lm"</span>)</code></pre></div>
+<div id="sim_df" class="section level2">
+<h2 class="hasAnchor">
+<a href="#sim_df" class="anchor"></a>sim_df</h2>
+<p>This function produces a dataframe with the same distributions and correlations as an existing dataframe. It only returns numeric columns and simulates all numeric variables from a continuous normal distribution (for now). <a href="articles/sim_df.html">see vignette</a></p>
+<p>For example, the following code creates a new sample from the built-in dataset <code>iris</code> with 50 observations of each species.</p>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">new_iris &lt;-<span class="st"> </span><span class="kw"><a href="reference/sim_df.html">sim_df</a></span>(iris, <span class="dv">50</span>, <span class="st">"Species"</span>) </code></pre></div>
 <div class="figure">
-<img src="reference/figures/README-plot-iris-sim-1.png" alt="Simulated iris dataset" width="100%"><p class="caption">
+<img src="index_files/figure-html/plot-iris-sim-1.png" alt="Simulated iris dataset" width="768"><p class="caption">
 Simulated iris dataset
 </p>
 </div>
-<p>For now, the function only creates new variables sampled from a continuous normal distribution. I hope to add in other sampling distributions in the future. So you’d need to do any rounding or truncating yourself.</p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw"><a href="reference/simdf.html">simdf</a></span>(iris, <span class="dv">50</span>, <span class="st">"Species"</span>) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">mutate_if</span>(is.numeric, round, <span class="dv">1</span>) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw">ggplot</span>(<span class="kw">aes</span>(Sepal.Width, Sepal.Length, <span class="dt">color =</span> Species)) <span class="op">+</span>
-<span class="st">  </span><span class="kw">geom_point</span>() <span class="op">+</span>
-<span class="st">  </span><span class="kw">geom_smooth</span>(<span class="dt">method =</span> <span class="st">"lm"</span>)</code></pre></div>
-<div class="figure">
-<img src="reference/figures/README-plot-iris-sim-round-1.png" alt="Simulated iris dataset (rounded)" width="100%"><p class="caption">
-Simulated iris dataset (rounded)
-</p>
-</div>
-</div>
-</div>
-<div id="pos_def_limits" class="section level3">
-<h3 class="hasAnchor">
-<a href="#pos_def_limits" class="anchor"></a>pos_def_limits</h3>
-<p>Not all correlation matrices are possible. For example, if variables A and B are correlated with r = 1.0, then the correlation between A and C can only be exactly equal to the correlation between B and C.</p>
-<p>The function <code><a href="reference/pos_def_limits.html">pos_def_limits()</a></code> lets you know what the possible range of values is for the missing value in a correlation matrix with one missing value. The correlation values are entered just from the top right triangle of the matrix, with a single <code>NA</code> for the missing value.</p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">lims &lt;-<span class="st"> </span><span class="kw"><a href="reference/pos_def_limits.html">pos_def_limits</a></span>(.<span class="dv">8</span>, .<span class="dv">2</span>, <span class="ot">NA</span>)</code></pre></div>
-<table class="table table">
-<thead><tr class="header">
-<th align="right">min</th>
-<th align="right">max</th>
-</tr></thead>
-<tbody><tr class="odd">
-<td align="right">-0.427</td>
-<td align="right">0.747</td>
-</tr></tbody>
-</table>
-<p>For example, if r<sub>AB</sub> = 0.8 and r<sub>AC</sub> = 0.2, then -0.427 &lt;= r<sub>BC</sub> &lt;= 0.747.</p>
-<p>If you enter a correlation matrix that contains impossible combinations, your limits will be <code>NA</code>.</p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">lims &lt;-<span class="st"> </span><span class="kw"><a href="reference/pos_def_limits.html">pos_def_limits</a></span>(.<span class="dv">8</span>, .<span class="dv">2</span>,  <span class="dv">0</span>,
-                          <span class="op">-</span>.<span class="dv">5</span>, <span class="ot">NA</span>,
-                               .<span class="dv">2</span>)</code></pre></div>
-<table class="table table">
-<thead><tr class="header">
-<th align="left">min</th>
-<th align="left">max</th>
-</tr></thead>
-<tbody><tr class="odd">
-<td align="left">NA</td>
-<td align="left">NA</td>
-</tr></tbody>
-</table>
-<p>If you have a full matrix and want to know if it is positive definite, you can use the following code:</p>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/c">c</a></span>(.<span class="dv">2</span>, .<span class="dv">3</span>, .<span class="dv">4</span>, .<span class="dv">2</span>,
-      .<span class="dv">3</span>, <span class="op">-</span>.<span class="dv">1</span>, .<span class="dv">2</span>,
-           .<span class="dv">4</span>, .<span class="dv">5</span>,
-               .<span class="dv">3</span>) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw"><a href="reference/cormat_from_triangle.html">cormat_from_triangle</a></span>() <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw"><a href="reference/is_pos_def.html">is_pos_def</a></span>()
-<span class="co">#&gt; [1] TRUE</span></code></pre></div>
-<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/matrix">matrix</a></span>(<span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/c">c</a></span>(<span class="dv">1</span>, .<span class="dv">3</span>, <span class="op">-</span>.<span class="dv">9</span>, .<span class="dv">2</span>,
-        .<span class="dv">3</span>,  <span class="dv">1</span>,  .<span class="dv">4</span>, .<span class="dv">5</span>,
-       <span class="op">-</span>.<span class="dv">9</span>, .<span class="dv">4</span>,   <span class="dv">1</span>, .<span class="dv">3</span>,
-        .<span class="dv">2</span>, .<span class="dv">5</span>,  .<span class="dv">3</span>,  <span class="dv">1</span>), <span class="dv">4</span>) <span class="op">%&gt;%</span>
-<span class="st">  </span><span class="kw"><a href="reference/is_pos_def.html">is_pos_def</a></span>()
-<span class="co">#&gt; [1] FALSE</span></code></pre></div>
 </div>
+<div id="additional-functions" class="section level2">
+<h2 class="hasAnchor">
+<a href="#additional-functions" class="anchor"></a>Additional functions</h2>
 <div id="check_sim_stats" class="section level3">
 <h3 class="hasAnchor">
 <a href="#check_sim_stats" class="anchor"></a>check_sim_stats</h3>
@@ -626,7 +237,7 @@ <h3 class="hasAnchor">
 <span class="co">#&gt; 4   150 Peta…         0.82       -0.37         0.96        1     1.2   0.76</span></code></pre></div>
 <p>You can also group your data and change the digits to round. Display the table using <code><a href="https://www.rdocumentation.org/packages/knitr/topics/kable">knitr::kable()</a></code> by setting <code>usekable</code> to <code>TRUE</code> (remember to set <code>results='asis'</code> in the chunk header.</p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw"><a href="reference/check_sim_stats.html">check_sim_stats</a></span>(iris, 
-                <span class="dt">grp_by =</span> <span class="st">"Species"</span>, 
+                <span class="dt">between =</span> <span class="st">"Species"</span>, 
                 <span class="dt">digits =</span> <span class="dv">3</span>, 
                 <span class="dt">usekable =</span> <span class="ot">TRUE</span>)</code></pre></div>
 <table class="table table">
@@ -777,70 +388,80 @@ <h3 class="hasAnchor">
 </tbody>
 </table>
 </div>
-<div id="sim_design" class="section level3">
+<div id="make_id" class="section level3">
+<h3 class="hasAnchor">
+<a href="#make_id" class="anchor"></a>make_id</h3>
+<p>It is useful for IDs for random effects (e.g., subjects or stimuli) to be character strings (so you don’t accidentally include them as fixed effects) with the same length s(o you can sort them in order like S01, S02,…, S10 rather than S1, S10, S2, …) This function returns a list of IDs that have the same string length and a specified prefix.</p>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw"><a href="reference/make_id.html">make_id</a></span>(<span class="dt">n =</span> <span class="dv">10</span>, <span class="dt">prefix =</span> <span class="st">"ITEM_"</span>)
+<span class="co">#&gt;  [1] "ITEM_01" "ITEM_02" "ITEM_03" "ITEM_04" "ITEM_05" "ITEM_06" "ITEM_07"</span>
+<span class="co">#&gt;  [8] "ITEM_08" "ITEM_09" "ITEM_10"</span></code></pre></div>
+<p>You can also manually set the number of digits and set <code>n</code> to a range of integers.</p>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw"><a href="reference/make_id.html">make_id</a></span>(<span class="dt">n =</span> <span class="dv">10</span><span class="op">:</span><span class="dv">20</span>, <span class="dt">digits =</span> <span class="dv">3</span>)
+<span class="co">#&gt;  [1] "S010" "S011" "S012" "S013" "S014" "S015" "S016" "S017" "S018" "S019"</span>
+<span class="co">#&gt; [11] "S020"</span></code></pre></div>
+</div>
+<div id="long2wide" class="section level3">
 <h3 class="hasAnchor">
-<a href="#sim_design" class="anchor"></a>sim_design</h3>
-<p>Simulate data by specifying a design structure.</p>
-<p><strong>This function is under development and should be carefully checked!</strong></p>
+<a href="#long2wide" class="anchor"></a>long2wide</h3>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">between &lt;-<span class="st"> </span><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/list">list</a></span>(<span class="st">"pet"</span> =<span class="st"> </span><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/c">c</a></span>(<span class="st">"cat"</span>, <span class="st">"dog"</span>))
 within &lt;-<span class="st"> </span><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/list">list</a></span>(<span class="st">"time"</span> =<span class="st"> </span><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/c">c</a></span>(<span class="st">"day"</span>, <span class="st">"night"</span>))
-mu &lt;-<span class="st"> </span><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/data.frame">data.frame</a></span>(
-  <span class="dt">cat =</span> <span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/c">c</a></span>(<span class="dv">10</span>, <span class="dv">20</span>),
-  <span class="dt">dog =</span> <span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/c">c</a></span>(<span class="dv">15</span>, <span class="dv">25</span>),
-  <span class="dt">row.names =</span> within<span class="op">$</span>time
-)
-df &lt;-<span class="st"> </span><span class="kw"><a href="reference/sim_design.html">sim_design</a></span>(within, between, <span class="dt">n =</span> <span class="dv">100</span>, <span class="dt">cors =</span> <span class="fl">0.5</span>, <span class="dt">mu =</span> mu, <span class="dt">sd =</span> <span class="dv">5</span>)</code></pre></div>
+df_long &lt;-<span class="st"> </span><span class="kw"><a href="reference/sim_design.html">sim_design</a></span>(within, between, <span class="dt">long =</span> <span class="ot">TRUE</span>)
+
+df_wide &lt;-<span class="st"> </span><span class="kw"><a href="reference/long2wide.html">long2wide</a></span>(df_long, 
+                     <span class="dt">within =</span> <span class="st">"time"</span>, 
+                     <span class="dt">between =</span> <span class="st">"pet"</span>, 
+                     <span class="dt">dv =</span> <span class="st">"val"</span>, 
+                     <span class="dt">id =</span> <span class="st">"sub_id"</span>)</code></pre></div>
+</div>
+<div id="pos_def_limits" class="section level3">
+<h3 class="hasAnchor">
+<a href="#pos_def_limits" class="anchor"></a>pos_def_limits</h3>
+<p>Not all correlation matrices are possible. For example, if variables A and B are correlated with r = 1.0, then the correlation between A and C can only be exactly equal to the correlation between B and C.</p>
+<p>The function <code><a href="reference/pos_def_limits.html">pos_def_limits()</a></code> lets you know what the possible range of values is for the missing value in a correlation matrix with one missing value. The correlation values are entered just from the top right triangle of the matrix, with a single <code>NA</code> for the missing value.</p>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">lims &lt;-<span class="st"> </span><span class="kw"><a href="reference/pos_def_limits.html">pos_def_limits</a></span>(.<span class="dv">8</span>, .<span class="dv">2</span>, <span class="ot">NA</span>)</code></pre></div>
 <table class="table table">
 <thead><tr class="header">
-<th align="left">pet</th>
-<th align="right">n</th>
-<th align="left">var</th>
-<th align="right">day</th>
-<th align="right">night</th>
-<th align="right">mean</th>
-<th align="right">sd</th>
+<th align="right">min</th>
+<th align="right">max</th>
 </tr></thead>
-<tbody>
-<tr class="odd">
-<td align="left">cat</td>
-<td align="right">100</td>
-<td align="left">day</td>
-<td align="right">1.00</td>
-<td align="right">0.55</td>
-<td align="right">10.39</td>
-<td align="right">4.69</td>
-</tr>
-<tr class="even">
-<td align="left">cat</td>
-<td align="right">100</td>
-<td align="left">night</td>
-<td align="right">0.55</td>
-<td align="right">1.00</td>
-<td align="right">19.78</td>
-<td align="right">4.67</td>
-</tr>
-<tr class="odd">
-<td align="left">dog</td>
-<td align="right">100</td>
-<td align="left">day</td>
-<td align="right">1.00</td>
-<td align="right">0.46</td>
-<td align="right">15.02</td>
-<td align="right">4.18</td>
-</tr>
-<tr class="even">
-<td align="left">dog</td>
-<td align="right">100</td>
-<td align="left">night</td>
-<td align="right">0.46</td>
-<td align="right">1.00</td>
-<td align="right">24.79</td>
-<td align="right">4.74</td>
-</tr>
-</tbody>
+<tbody><tr class="odd">
+<td align="right">-0.427</td>
+<td align="right">0.747</td>
+</tr></tbody>
+</table>
+<p>For example, if r<sub>AB</sub> = 0.8 and r<sub>AC</sub> = 0.2, then -0.427 &lt;= r<sub>BC</sub> &lt;= 0.747.</p>
+<p>If you enter a correlation matrix that contains impossible combinations, your limits will be <code>NA</code>.</p>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">lims &lt;-<span class="st"> </span><span class="kw"><a href="reference/pos_def_limits.html">pos_def_limits</a></span>(.<span class="dv">8</span>, .<span class="dv">2</span>,  <span class="dv">0</span>,
+                          <span class="op">-</span>.<span class="dv">5</span>, <span class="ot">NA</span>,
+                               .<span class="dv">2</span>)</code></pre></div>
+<table class="table table">
+<thead><tr class="header">
+<th align="left">min</th>
+<th align="left">max</th>
+</tr></thead>
+<tbody><tr class="odd">
+<td align="left">NA</td>
+<td align="left">NA</td>
+</tr></tbody>
 </table>
-<p><img src="reference/figures/README-unnamed-chunk-9-1.png" width="100%"></p>
 </div>
+<div id="is_pos_def" class="section level3">
+<h3 class="hasAnchor">
+<a href="#is_pos_def" class="anchor"></a>is_pos_def()</h3>
+<p>If you have a full matrix and want to know if it is positive definite, you can use the following code:</p>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/c">c</a></span>(.<span class="dv">2</span>, .<span class="dv">3</span>, .<span class="dv">4</span>, .<span class="dv">2</span>,
+      .<span class="dv">3</span>, <span class="op">-</span>.<span class="dv">1</span>, .<span class="dv">2</span>,
+           .<span class="dv">4</span>, .<span class="dv">5</span>,
+               .<span class="dv">3</span>) <span class="op">%&gt;%</span>
+<span class="st">  </span><span class="kw"><a href="reference/cormat_from_triangle.html">cormat_from_triangle</a></span>() <span class="op">%&gt;%</span>
+<span class="st">  </span><span class="kw"><a href="reference/is_pos_def.html">is_pos_def</a></span>()
+<span class="co">#&gt; [1] TRUE</span></code></pre></div>
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/matrix">matrix</a></span>(<span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/c">c</a></span>(<span class="dv">1</span>, .<span class="dv">3</span>, <span class="op">-</span>.<span class="dv">9</span>, .<span class="dv">2</span>,
+        .<span class="dv">3</span>,  <span class="dv">1</span>,  .<span class="dv">4</span>, .<span class="dv">5</span>,
+       <span class="op">-</span>.<span class="dv">9</span>, .<span class="dv">4</span>,   <span class="dv">1</span>, .<span class="dv">3</span>,
+        .<span class="dv">2</span>, .<span class="dv">5</span>,  .<span class="dv">3</span>,  <span class="dv">1</span>), <span class="dv">4</span>) <span class="op">%&gt;%</span>
+<span class="st">  </span><span class="kw"><a href="reference/is_pos_def.html">is_pos_def</a></span>()
+<span class="co">#&gt; [1] FALSE</span></code></pre></div>
 </div>
 </div>
   </div>
@@ -857,11 +478,17 @@ <h2>License</h2>
 <div class="developers">
 <h2>Developers</h2>
 <ul class="list-unstyled">
-<li>Lisa DeBruine <br><small class="roles"> Author, maintainer </small>  </li>
+<li>Lisa DeBruine <br><small class="roles"> Author, maintainer </small> <a href="https://orcid.org/0000-0002-7523-5539" target="orcid.widget"><img src="https://members.orcid.org/sites/default/files/vector_iD_icon.svg" class="orcid" alt="ORCID" height="16"></a> </li>
 </ul>
 </div>
 
-      </div>
+      <div class="dev-status">
+<h2>Dev status</h2>
+<ul class="list-unstyled">
+<li><a href="https://www.tidyverse.org/lifecycle/#experimental"><img src="https://img.shields.io/badge/lifecycle-experimental-orange.svg" alt="Lifecycle: experimental"></a></li>
+</ul>
+</div>
+</div>
 
 </div>
 
diff --git a/docs/index_files/figure-html/plot-iris-sim-1.png b/docs/index_files/figure-html/plot-iris-sim-1.png
new file mode 100644
index 00000000..29468f8d
Binary files /dev/null and b/docs/index_files/figure-html/plot-iris-sim-1.png differ
diff --git a/docs/news/index.html b/docs/news/index.html
index 1970a793..b8338901 100644
--- a/docs/news/index.html
+++ b/docs/news/index.html
@@ -60,7 +60,7 @@
       </button>
       <span class="navbar-brand">
         <a class="navbar-link" href="../index.html">faux</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.0.0.9005</span>
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.0.0.9006</span>
       </span>
     </div>
 
@@ -82,9 +82,6 @@
     <span class="caret"></span>
   </a>
   <ul class="dropdown-menu" role="menu">
-    <li>
-      <a href="../articles/intro.html">Introduction to faux</a>
-    </li>
     <li>
       <a href="../articles/rnorm_multi.html">Simulate Correlated Variables</a>
     </li>
@@ -92,7 +89,7 @@
       <a href="../articles/sim_design.html">Simulate by Design</a>
     </li>
     <li>
-      <a href="../articles/simdf.html">Simulate from Existing Data</a>
+      <a href="../articles/sim_df.html">Simulate from Existing Data</a>
     </li>
   </ul>
 </li>
@@ -133,6 +130,14 @@ <h1 class="page-header">
 <ul>
 <li>Bug fixes for <code><a href="../reference/sim_design.html">sim_design()</a></code> (failed when within or between factor number was 0)</li>
 </ul>
+</div>
+    <div id="faux-0-0-0-9006" class="section level1">
+<h1 class="page-header">
+<a href="#faux-0-0-0-9006" class="anchor"></a>faux 0.0.0.9006</h1>
+<ul>
+<li>Changes to argument order and names (more consistent, but may break old scripts)</li>
+<li>Updated vignettes</li>
+</ul>
 </div>
   </div>
 
@@ -142,6 +147,7 @@ <h2>Contents</h2>
       <ul class="nav nav-pills nav-stacked">
         <li><a href="#faux-0-0-0-9004">0.0.0.9004</a></li>
         <li><a href="#faux-0-0-0-9005">0.0.0.9005</a></li>
+        <li><a href="#faux-0-0-0-9006">0.0.0.9006</a></li>
       </ul>
     </div>
   </div>
diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml
index 59781696..ba63c6ac 100644
--- a/docs/pkgdown.yml
+++ b/docs/pkgdown.yml
@@ -2,8 +2,7 @@ pandoc: 1.19.2.1
 pkgdown: 1.3.0
 pkgdown_sha: ~
 articles:
-  intro: intro.html
   rnorm_multi: rnorm_multi.html
   sim_design: sim_design.html
-  simdf: simdf.html
+  sim_df: sim_df.html
 
diff --git a/docs/reference/check_design.html b/docs/reference/check_design.html
index 9743ebb2..b6769873 100644
--- a/docs/reference/check_design.html
+++ b/docs/reference/check_design.html
@@ -63,7 +63,7 @@
       </button>
       <span class="navbar-brand">
         <a class="navbar-link" href="../index.html">faux</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.0.0.9005</span>
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.0.0.9006</span>
       </span>
     </div>
 
@@ -85,9 +85,6 @@
     <span class="caret"></span>
   </a>
   <ul class="dropdown-menu" role="menu">
-    <li>
-      <a href="../articles/intro.html">Introduction to faux</a>
-    </li>
     <li>
       <a href="../articles/rnorm_multi.html">Simulate Correlated Variables</a>
     </li>
@@ -95,7 +92,7 @@
       <a href="../articles/sim_design.html">Simulate by Design</a>
     </li>
     <li>
-      <a href="../articles/simdf.html">Simulate from Existing Data</a>
+      <a href="../articles/sim_df.html">Simulate from Existing Data</a>
     </li>
   </ul>
 </li>
@@ -129,8 +126,8 @@ <h1>Validate design</h1>
     
     </div>
 
-    <pre class="usage"><span class='fu'>check_design</span>(<span class='kw'>within</span> <span class='kw'>=</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/list'>list</a></span>(), <span class='kw'>between</span> <span class='kw'>=</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/list'>list</a></span>(), <span class='kw'>n</span> <span class='kw'>=</span> <span class='fl'>100</span>, <span class='kw'>cors</span> <span class='kw'>=</span> <span class='fl'>0</span>,
-  <span class='kw'>mu</span> <span class='kw'>=</span> <span class='fl'>0</span>, <span class='kw'>sd</span> <span class='kw'>=</span> <span class='fl'>1</span>)</pre>
+    <pre class="usage"><span class='fu'>check_design</span>(<span class='kw'>within</span> <span class='kw'>=</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/list'>list</a></span>(), <span class='kw'>between</span> <span class='kw'>=</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/list'>list</a></span>(), <span class='kw'>n</span> <span class='kw'>=</span> <span class='fl'>100</span>, <span class='kw'>mu</span> <span class='kw'>=</span> <span class='fl'>0</span>,
+  <span class='kw'>sd</span> <span class='kw'>=</span> <span class='fl'>1</span>, <span class='kw'>r</span> <span class='kw'>=</span> <span class='fl'>0</span>)</pre>
     
     <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
     <table class="ref-arguments">
@@ -147,10 +144,6 @@ <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arg
       <th>n</th>
       <td><p>the number of samples required</p></td>
     </tr>
-    <tr>
-      <th>cors</th>
-      <td><p>the correlations among the variables (can be a single number, vars\*vars matrix, vars\*vars vector, or a vars\*(vars-1)/2 vector)</p></td>
-    </tr>
     <tr>
       <th>mu</th>
       <td><p>a vector giving the means of the variables (numeric vector of length 1 or vars)</p></td>
@@ -159,6 +152,10 @@ <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arg
       <th>sd</th>
       <td><p>the standard deviations of the variables (numeric vector of length 1 or vars)</p></td>
     </tr>
+    <tr>
+      <th>r</th>
+      <td><p>the correlations among the variables (can be a single number, vars\*vars matrix, vars\*vars vector, or a vars\*(vars-1)/2 vector)</p></td>
+    </tr>
     </table>
     
     <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
@@ -170,7 +167,90 @@ <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examp
     <pre class="examples"><div class='input'>
 <span class='no'>within</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/list'>list</a></span>(<span class='kw'>time</span> <span class='kw'>=</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='st'>"day"</span>, <span class='st'>"night"</span>))
 <span class='no'>between</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/list'>list</a></span>(<span class='kw'>pet</span> <span class='kw'>=</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='st'>"dog"</span>, <span class='st'>"cat"</span>))
-<span class='no'>design</span> <span class='kw'>&lt;-</span> <span class='fu'>check_design</span>(<span class='no'>within</span>, <span class='no'>between</span>)</div></pre>
+<span class='fu'>check_design</span>(<span class='no'>within</span>, <span class='no'>between</span>)</div><div class='output co'>#&gt; $within
+#&gt; $within$time
+#&gt; [1] "day"   "night"
+#&gt; 
+#&gt; 
+#&gt; $between
+#&gt; $between$pet
+#&gt; [1] "dog" "cat"
+#&gt; 
+#&gt; 
+#&gt; $within_factors
+#&gt; [1] "time"
+#&gt; 
+#&gt; $between_factors
+#&gt; [1] "pet"
+#&gt; 
+#&gt; $within_labels
+#&gt; $within_labels$time
+#&gt;     day   night 
+#&gt;   "day" "night" 
+#&gt; 
+#&gt; 
+#&gt; $between_labels
+#&gt; $between_labels$pet
+#&gt;   dog   cat 
+#&gt; "dog" "cat" 
+#&gt; 
+#&gt; 
+#&gt; $cell_n
+#&gt;       dog cat
+#&gt; day   100 100
+#&gt; night 100 100
+#&gt; 
+#&gt; $cell_mu
+#&gt;       dog cat
+#&gt; day     0   0
+#&gt; night   0   0
+#&gt; 
+#&gt; $cell_sd
+#&gt;       dog cat
+#&gt; day     1   1
+#&gt; night   1   1
+#&gt; 
+#&gt; $cell_r
+#&gt; $cell_r$dog
+#&gt;      [,1] [,2]
+#&gt; [1,]    1    0
+#&gt; [2,]    0    1
+#&gt; 
+#&gt; $cell_r$cat
+#&gt;      [,1] [,2]
+#&gt; [1,]    1    0
+#&gt; [2,]    0    1
+#&gt; 
+#&gt; 
+#&gt; $cells_w
+#&gt; [1] "day"   "night"
+#&gt; 
+#&gt; $cells_b
+#&gt; [1] "dog" "cat"
+#&gt; 
+#&gt; $sub_id
+#&gt;   [1] "S001" "S002" "S003" "S004" "S005" "S006" "S007" "S008" "S009" "S010"
+#&gt;  [11] "S011" "S012" "S013" "S014" "S015" "S016" "S017" "S018" "S019" "S020"
+#&gt;  [21] "S021" "S022" "S023" "S024" "S025" "S026" "S027" "S028" "S029" "S030"
+#&gt;  [31] "S031" "S032" "S033" "S034" "S035" "S036" "S037" "S038" "S039" "S040"
+#&gt;  [41] "S041" "S042" "S043" "S044" "S045" "S046" "S047" "S048" "S049" "S050"
+#&gt;  [51] "S051" "S052" "S053" "S054" "S055" "S056" "S057" "S058" "S059" "S060"
+#&gt;  [61] "S061" "S062" "S063" "S064" "S065" "S066" "S067" "S068" "S069" "S070"
+#&gt;  [71] "S071" "S072" "S073" "S074" "S075" "S076" "S077" "S078" "S079" "S080"
+#&gt;  [81] "S081" "S082" "S083" "S084" "S085" "S086" "S087" "S088" "S089" "S090"
+#&gt;  [91] "S091" "S092" "S093" "S094" "S095" "S096" "S097" "S098" "S099" "S100"
+#&gt; [101] "S101" "S102" "S103" "S104" "S105" "S106" "S107" "S108" "S109" "S110"
+#&gt; [111] "S111" "S112" "S113" "S114" "S115" "S116" "S117" "S118" "S119" "S120"
+#&gt; [121] "S121" "S122" "S123" "S124" "S125" "S126" "S127" "S128" "S129" "S130"
+#&gt; [131] "S131" "S132" "S133" "S134" "S135" "S136" "S137" "S138" "S139" "S140"
+#&gt; [141] "S141" "S142" "S143" "S144" "S145" "S146" "S147" "S148" "S149" "S150"
+#&gt; [151] "S151" "S152" "S153" "S154" "S155" "S156" "S157" "S158" "S159" "S160"
+#&gt; [161] "S161" "S162" "S163" "S164" "S165" "S166" "S167" "S168" "S169" "S170"
+#&gt; [171] "S171" "S172" "S173" "S174" "S175" "S176" "S177" "S178" "S179" "S180"
+#&gt; [181] "S181" "S182" "S183" "S184" "S185" "S186" "S187" "S188" "S189" "S190"
+#&gt; [191] "S191" "S192" "S193" "S194" "S195" "S196" "S197" "S198" "S199" "S200"
+#&gt; </div><div class='input'>
+</div></pre>
   </div>
   <div class="col-md-3 hidden-xs hidden-sm" id="sidebar">
     <h2>Contents</h2>
diff --git a/docs/reference/check_sim_stats.html b/docs/reference/check_sim_stats.html
index d95cc2e0..4ab3c1c6 100644
--- a/docs/reference/check_sim_stats.html
+++ b/docs/reference/check_sim_stats.html
@@ -63,7 +63,7 @@
       </button>
       <span class="navbar-brand">
         <a class="navbar-link" href="../index.html">faux</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.0.0.9005</span>
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.0.0.9006</span>
       </span>
     </div>
 
@@ -85,9 +85,6 @@
     <span class="caret"></span>
   </a>
   <ul class="dropdown-menu" role="menu">
-    <li>
-      <a href="../articles/intro.html">Introduction to faux</a>
-    </li>
     <li>
       <a href="../articles/rnorm_multi.html">Simulate Correlated Variables</a>
     </li>
@@ -95,7 +92,7 @@
       <a href="../articles/sim_design.html">Simulate by Design</a>
     </li>
     <li>
-      <a href="../articles/simdf.html">Simulate from Existing Data</a>
+      <a href="../articles/sim_df.html">Simulate from Existing Data</a>
     </li>
   </ul>
 </li>
@@ -129,18 +126,31 @@ <h1>Check table stats</h1>
     
     </div>
 
-    <pre class="usage"><span class='fu'>check_sim_stats</span>(<span class='no'>dat</span>, <span class='kw'>grp_by</span> <span class='kw'>=</span> <span class='kw'>NULL</span>, <span class='kw'>digits</span> <span class='kw'>=</span> <span class='fl'>2</span>, <span class='kw'>usekable</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>)</pre>
+    <pre class="usage"><span class='fu'>check_sim_stats</span>(<span class='no'>.data</span>, <span class='kw'>between</span> <span class='kw'>=</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(), <span class='kw'>within</span> <span class='kw'>=</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(), <span class='kw'>dv</span> <span class='kw'>=</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(),
+  <span class='kw'>id</span> <span class='kw'>=</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(), <span class='kw'>digits</span> <span class='kw'>=</span> <span class='fl'>2</span>, <span class='kw'>usekable</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>)</pre>
     
     <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
     <table class="ref-arguments">
     <colgroup><col class="name" /><col class="desc" /></colgroup>
     <tr>
-      <th>dat</th>
-      <td><p>the existing dataframe</p></td>
+      <th>.data</th>
+      <td><p>the existing tbl</p></td>
+    </tr>
+    <tr>
+      <th>between</th>
+      <td><p>a vector of column names for between-subject factors</p></td>
+    </tr>
+    <tr>
+      <th>within</th>
+      <td><p>a vector of column names for within-subject factors (if data is long)</p></td>
+    </tr>
+    <tr>
+      <th>dv</th>
+      <td><p>the column name of the dv (if data is long)</p></td>
     </tr>
     <tr>
-      <th>grp_by</th>
-      <td><p>an optional list of column names to group by</p></td>
+      <th>id</th>
+      <td><p>the column name(s) of the subject ID (if data is long)</p></td>
     </tr>
     <tr>
       <th>digits</th>
@@ -154,26 +164,26 @@ <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arg
     
     <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
 
-    <p>tibble or kable</p>
+    <p>a tbl or kable</p>
     
 
     <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='fu'>check_sim_stats</span>(<span class='no'>iris</span>, <span class='st'>"Species"</span>)</div><div class='output co'>#&gt; <span style='color: #555555;'># A tibble: 12 x 9</span><span>
+    <pre class="examples"><div class='input'><span class='fu'>check_sim_stats</span>(<span class='no'>iris</span>, <span class='st'>"Species"</span>)</div><div class='output co'>#&gt; <span style='color: #949494;'># A tibble: 12 x 9</span><span>
 #&gt;    Species     n var   Sepal.Length Sepal.Width Petal.Length Petal.Width  mean
-#&gt;    </span><span style='color: #555555;font-style: italic;'>&lt;fct&gt;</span><span>   </span><span style='color: #555555;font-style: italic;'>&lt;dbl&gt;</span><span> </span><span style='color: #555555;font-style: italic;'>&lt;chr&gt;</span><span>        </span><span style='color: #555555;font-style: italic;'>&lt;dbl&gt;</span><span>       </span><span style='color: #555555;font-style: italic;'>&lt;dbl&gt;</span><span>        </span><span style='color: #555555;font-style: italic;'>&lt;dbl&gt;</span><span>       </span><span style='color: #555555;font-style: italic;'>&lt;dbl&gt;</span><span> </span><span style='color: #555555;font-style: italic;'>&lt;dbl&gt;</span><span>
-#&gt; </span><span style='color: #555555;'> 1</span><span> setosa     50 Sepa…         1           0.74         0.27        0.28  5.01
-#&gt; </span><span style='color: #555555;'> 2</span><span> setosa     50 Sepa…         0.74        1            0.18        0.23  3.43
-#&gt; </span><span style='color: #555555;'> 3</span><span> setosa     50 Peta…         0.27        0.18         1           0.33  1.46
-#&gt; </span><span style='color: #555555;'> 4</span><span> setosa     50 Peta…         0.28        0.23         0.33        1     0.25
-#&gt; </span><span style='color: #555555;'> 5</span><span> versic…    50 Sepa…         1           0.53         0.75        0.55  5.94
-#&gt; </span><span style='color: #555555;'> 6</span><span> versic…    50 Sepa…         0.53        1            0.56        0.66  2.77
-#&gt; </span><span style='color: #555555;'> 7</span><span> versic…    50 Peta…         0.75        0.56         1           0.79  4.26
-#&gt; </span><span style='color: #555555;'> 8</span><span> versic…    50 Peta…         0.55        0.66         0.79        1     1.33
-#&gt; </span><span style='color: #555555;'> 9</span><span> virgin…    50 Sepa…         1           0.46         0.86        0.28  6.59
-#&gt; </span><span style='color: #555555;'>10</span><span> virgin…    50 Sepa…         0.46        1            0.4         0.54  2.97
-#&gt; </span><span style='color: #555555;'>11</span><span> virgin…    50 Peta…         0.86        0.4          1           0.32  5.55
-#&gt; </span><span style='color: #555555;'>12</span><span> virgin…    50 Peta…         0.28        0.54         0.32        1     2.03
-#&gt; </span><span style='color: #555555;'># … with 1 more variable: sd </span><span style='color: #555555;font-style: italic;'>&lt;dbl&gt;</span><span></div></span></pre>
+#&gt;    </span><span style='color: #949494;font-style: italic;'>&lt;fct&gt;</span><span>   </span><span style='color: #949494;font-style: italic;'>&lt;dbl&gt;</span><span> </span><span style='color: #949494;font-style: italic;'>&lt;chr&gt;</span><span>        </span><span style='color: #949494;font-style: italic;'>&lt;dbl&gt;</span><span>       </span><span style='color: #949494;font-style: italic;'>&lt;dbl&gt;</span><span>        </span><span style='color: #949494;font-style: italic;'>&lt;dbl&gt;</span><span>       </span><span style='color: #949494;font-style: italic;'>&lt;dbl&gt;</span><span> </span><span style='color: #949494;font-style: italic;'>&lt;dbl&gt;</span><span>
+#&gt; </span><span style='color: #BCBCBC;'> 1</span><span> setosa     50 Sepa…         1           0.74         0.27        0.28  5.01
+#&gt; </span><span style='color: #BCBCBC;'> 2</span><span> setosa     50 Sepa…         0.74        1            0.18        0.23  3.43
+#&gt; </span><span style='color: #BCBCBC;'> 3</span><span> setosa     50 Peta…         0.27        0.18         1           0.33  1.46
+#&gt; </span><span style='color: #BCBCBC;'> 4</span><span> setosa     50 Peta…         0.28        0.23         0.33        1     0.25
+#&gt; </span><span style='color: #BCBCBC;'> 5</span><span> versic…    50 Sepa…         1           0.53         0.75        0.55  5.94
+#&gt; </span><span style='color: #BCBCBC;'> 6</span><span> versic…    50 Sepa…         0.53        1            0.56        0.66  2.77
+#&gt; </span><span style='color: #BCBCBC;'> 7</span><span> versic…    50 Peta…         0.75        0.56         1           0.79  4.26
+#&gt; </span><span style='color: #BCBCBC;'> 8</span><span> versic…    50 Peta…         0.55        0.66         0.79        1     1.33
+#&gt; </span><span style='color: #BCBCBC;'> 9</span><span> virgin…    50 Sepa…         1           0.46         0.86        0.28  6.59
+#&gt; </span><span style='color: #BCBCBC;'>10</span><span> virgin…    50 Sepa…         0.46        1            0.4         0.54  2.97
+#&gt; </span><span style='color: #BCBCBC;'>11</span><span> virgin…    50 Peta…         0.86        0.4          1           0.32  5.55
+#&gt; </span><span style='color: #BCBCBC;'>12</span><span> virgin…    50 Peta…         0.28        0.54         0.32        1     2.03
+#&gt; </span><span style='color: #949494;'># … with 1 more variable: sd </span><span style='color: #949494;font-style: italic;'>&lt;dbl&gt;</span><span></div></span></pre>
   </div>
   <div class="col-md-3 hidden-xs hidden-sm" id="sidebar">
     <h2>Contents</h2>
diff --git a/docs/reference/convert_param.html b/docs/reference/convert_param.html
index 92e13ce4..ee54cc32 100644
--- a/docs/reference/convert_param.html
+++ b/docs/reference/convert_param.html
@@ -63,7 +63,7 @@
       </button>
       <span class="navbar-brand">
         <a class="navbar-link" href="../index.html">faux</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.0.0.9005</span>
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.0.0.9006</span>
       </span>
     </div>
 
@@ -85,9 +85,6 @@
     <span class="caret"></span>
   </a>
   <ul class="dropdown-menu" role="menu">
-    <li>
-      <a href="../articles/intro.html">Introduction to faux</a>
-    </li>
     <li>
       <a href="../articles/rnorm_multi.html">Simulate Correlated Variables</a>
     </li>
@@ -95,7 +92,7 @@
       <a href="../articles/sim_design.html">Simulate by Design</a>
     </li>
     <li>
-      <a href="../articles/simdf.html">Simulate from Existing Data</a>
+      <a href="../articles/sim_df.html">Simulate from Existing Data</a>
     </li>
   </ul>
 </li>
@@ -129,7 +126,7 @@ <h1>Convert parameter</h1>
     
     </div>
 
-    <pre class="usage"><span class='fu'>convert_param</span>(<span class='no'>param</span>, <span class='no'>cells_b</span>, <span class='no'>cells_w</span>, <span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"this parameter"</span>)</pre>
+    <pre class="usage"><span class='fu'>convert_param</span>(<span class='no'>param</span>, <span class='no'>cells_w</span>, <span class='no'>cells_b</span>, <span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"this parameter"</span>)</pre>
     
     <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
     <table class="ref-arguments">
@@ -138,14 +135,14 @@ <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arg
       <th>param</th>
       <td><p>the parameter (mu, sd, or n)</p></td>
     </tr>
-    <tr>
-      <th>cells_b</th>
-      <td><p>a list of between-subject cell combinations</p></td>
-    </tr>
     <tr>
       <th>cells_w</th>
       <td><p>a list of within-subject cells combinations</p></td>
     </tr>
+    <tr>
+      <th>cells_b</th>
+      <td><p>a list of between-subject cell combinations</p></td>
+    </tr>
     <tr>
       <th>type</th>
       <td><p>the name of the parameter (for error messages)</p></td>
diff --git a/docs/reference/cormat.html b/docs/reference/cormat.html
index c6b0a676..6abdb450 100644
--- a/docs/reference/cormat.html
+++ b/docs/reference/cormat.html
@@ -63,7 +63,7 @@
       </button>
       <span class="navbar-brand">
         <a class="navbar-link" href="../index.html">faux</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.0.0.9005</span>
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.0.0.9006</span>
       </span>
     </div>
 
@@ -85,9 +85,6 @@
     <span class="caret"></span>
   </a>
   <ul class="dropdown-menu" role="menu">
-    <li>
-      <a href="../articles/intro.html">Introduction to faux</a>
-    </li>
     <li>
       <a href="../articles/rnorm_multi.html">Simulate Correlated Variables</a>
     </li>
@@ -95,7 +92,7 @@
       <a href="../articles/sim_design.html">Simulate by Design</a>
     </li>
     <li>
-      <a href="../articles/simdf.html">Simulate from Existing Data</a>
+      <a href="../articles/sim_df.html">Simulate from Existing Data</a>
     </li>
   </ul>
 </li>
diff --git a/docs/reference/cormat_from_triangle.html b/docs/reference/cormat_from_triangle.html
index f2797f34..d7d2db61 100644
--- a/docs/reference/cormat_from_triangle.html
+++ b/docs/reference/cormat_from_triangle.html
@@ -63,7 +63,7 @@
       </button>
       <span class="navbar-brand">
         <a class="navbar-link" href="../index.html">faux</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.0.0.9005</span>
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.0.0.9006</span>
       </span>
     </div>
 
@@ -85,9 +85,6 @@
     <span class="caret"></span>
   </a>
   <ul class="dropdown-menu" role="menu">
-    <li>
-      <a href="../articles/intro.html">Introduction to faux</a>
-    </li>
     <li>
       <a href="../articles/rnorm_multi.html">Simulate Correlated Variables</a>
     </li>
@@ -95,7 +92,7 @@
       <a href="../articles/sim_design.html">Simulate by Design</a>
     </li>
     <li>
-      <a href="../articles/simdf.html">Simulate from Existing Data</a>
+      <a href="../articles/sim_df.html">Simulate from Existing Data</a>
     </li>
   </ul>
 </li>
diff --git a/docs/reference/faceratings.html b/docs/reference/faceratings.html
index 481c039d..8ed382bd 100644
--- a/docs/reference/faceratings.html
+++ b/docs/reference/faceratings.html
@@ -63,7 +63,7 @@
       </button>
       <span class="navbar-brand">
         <a class="navbar-link" href="../index.html">faux</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.0.0.9005</span>
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.0.0.9006</span>
       </span>
     </div>
 
@@ -85,9 +85,6 @@
     <span class="caret"></span>
   </a>
   <ul class="dropdown-menu" role="menu">
-    <li>
-      <a href="../articles/intro.html">Introduction to faux</a>
-    </li>
     <li>
       <a href="../articles/rnorm_multi.html">Simulate Correlated Variables</a>
     </li>
@@ -95,7 +92,7 @@
       <a href="../articles/sim_design.html">Simulate by Design</a>
     </li>
     <li>
-      <a href="../articles/simdf.html">Simulate from Existing Data</a>
+      <a href="../articles/sim_df.html">Simulate from Existing Data</a>
     </li>
   </ul>
 </li>
diff --git a/docs/reference/fix_name_labels.html b/docs/reference/fix_name_labels.html
index fec99c75..a6041bfe 100644
--- a/docs/reference/fix_name_labels.html
+++ b/docs/reference/fix_name_labels.html
@@ -63,7 +63,7 @@
       </button>
       <span class="navbar-brand">
         <a class="navbar-link" href="../index.html">faux</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.0.0.9005</span>
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.0.0.9006</span>
       </span>
     </div>
 
@@ -85,9 +85,6 @@
     <span class="caret"></span>
   </a>
   <ul class="dropdown-menu" role="menu">
-    <li>
-      <a href="../articles/intro.html">Introduction to faux</a>
-    </li>
     <li>
       <a href="../articles/rnorm_multi.html">Simulate Correlated Variables</a>
     </li>
@@ -95,7 +92,7 @@
       <a href="../articles/sim_design.html">Simulate by Design</a>
     </li>
     <li>
-      <a href="../articles/simdf.html">Simulate from Existing Data</a>
+      <a href="../articles/sim_df.html">Simulate from Existing Data</a>
     </li>
   </ul>
 </li>
diff --git a/docs/reference/get_design_long.html b/docs/reference/get_design_long.html
new file mode 100644
index 00000000..5518b325
--- /dev/null
+++ b/docs/reference/get_design_long.html
@@ -0,0 +1,186 @@
+<!-- Generated by pkgdown: do not edit by hand -->
+<!DOCTYPE html>
+<html lang="en">
+  <head>
+  <meta charset="utf-8">
+<meta http-equiv="X-UA-Compatible" content="IE=edge">
+<meta name="viewport" content="width=device-width, initial-scale=1.0">
+
+<title>Get design from long data — get_design_long • faux</title>
+
+<!-- jquery -->
+<script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.3.1/jquery.min.js" integrity="sha256-FgpCb/KJQlLNfOu91ta32o/NMZxltwRo8QtmkMRdAu8=" crossorigin="anonymous"></script>
+<!-- Bootstrap -->
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+
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+
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+
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+<script src="https://cdnjs.cloudflare.com/ajax/libs/sticky-kit/1.1.3/sticky-kit.min.js" integrity="sha256-c4Rlo1ZozqTPE2RLuvbusY3+SU1pQaJC0TjuhygMipw=" crossorigin="anonymous"></script>
+
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+<script src="../pkgdown.js"></script>
+
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+
+<meta property="og:title" content="Get design from long data — get_design_long" />
+
+<meta property="og:description" content="Makes a best guess at the design of a long-format data frame. 
+Finds all columns that contain a single value per unit of analysis (between factors), 
+all columns that contain the same values per unit of analysis (within factors), and 
+all columns that differ over units of analysis (dv, continuous factors)" />
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+<script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script>
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+      <a href="../articles/sim_df.html">Simulate from Existing Data</a>
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+</li>
+<li>
+  <a href="../news/index.html">Changelog</a>
+</li>
+      </ul>
+      
+      <ul class="nav navbar-nav navbar-right">
+        
+      </ul>
+      
+    </div><!--/.nav-collapse -->
+  </div><!--/.container -->
+</div><!--/.navbar -->
+
+      
+      </header>
+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Get design from long data</h1>
+    
+    <div class="hidden name"><code>get_design_long.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    
+    <p>Makes a best guess at the design of a long-format data frame. 
+Finds all columns that contain a single value per unit of analysis (between factors), 
+all columns that contain the same values per unit of analysis (within factors), and 
+all columns that differ over units of analysis (dv, continuous factors)</p>
+    
+    </div>
+
+    <pre class="usage"><span class='fu'>get_design_long</span>(<span class='no'>.data</span>, <span class='kw'>id</span> <span class='kw'>=</span> <span class='st'>"sub_id"</span>, <span class='kw'>dv</span> <span class='kw'>=</span> <span class='st'>"val"</span>)</pre>
+    
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>.data</th>
+      <td><p>the data frame (in long format)</p></td>
+    </tr>
+    <tr>
+      <th>id</th>
+      <td><p>the column name(s) that identify a unit of analysis</p></td>
+    </tr>
+    <tr>
+      <th>dv</th>
+      <td><p>the column name that identifies the DV</p></td>
+    </tr>
+    </table>
+    
+    <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
+
+    <p>the data frame in long format</p>
+    
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="sidebar">
+    <h2>Contents</h2>
+    <ul class="nav nav-pills nav-stacked">
+      <li><a href="#arguments">Arguments</a></li>
+      
+      <li><a href="#value">Value</a></li>
+          </ul>
+
+  </div>
+</div>
+
+      <footer>
+      <div class="copyright">
+  <p>Developed by Lisa DeBruine.</p>
+</div>
+
+<div class="pkgdown">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.3.0.</p>
+</div>
+      </footer>
+   </div>
+
+  
+
+  </body>
+</html>
+
diff --git a/docs/reference/index.html b/docs/reference/index.html
index 2e76c849..bedc5427 100644
--- a/docs/reference/index.html
+++ b/docs/reference/index.html
@@ -60,7 +60,7 @@
       </button>
       <span class="navbar-brand">
         <a class="navbar-link" href="../index.html">faux</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.0.0.9005</span>
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.0.0.9006</span>
       </span>
     </div>
 
@@ -82,9 +82,6 @@
     <span class="caret"></span>
   </a>
   <ul class="dropdown-menu" role="menu">
-    <li>
-      <a href="../articles/intro.html">Introduction to faux</a>
-    </li>
     <li>
       <a href="../articles/rnorm_multi.html">Simulate Correlated Variables</a>
     </li>
@@ -92,7 +89,7 @@
       <a href="../articles/sim_design.html">Simulate by Design</a>
     </li>
     <li>
-      <a href="../articles/simdf.html">Simulate from Existing Data</a>
+      <a href="../articles/sim_df.html">Simulate from Existing Data</a>
     </li>
   </ul>
 </li>
@@ -147,12 +144,6 @@ <h2 id="section-all-functions" class="hasAnchor"><a href="#section-all-functions
         <td><p>Check table stats</p></td>
       </tr><tr>
         
-        <td>
-          <p><code><a href="convert_param.html">convert_param()</a></code> </p>
-        </td>
-        <td><p>Convert parameter</p></td>
-      </tr><tr>
-        
         <td>
           <p><code><a href="cormat.html">cormat()</a></code> </p>
         </td>
@@ -171,12 +162,30 @@ <h2 id="section-all-functions" class="hasAnchor"><a href="#section-all-functions
         <td><p>Attractiveness ratings of faces</p></td>
       </tr><tr>
         
+        <td>
+          <p><code><a href="get_design_long.html">get_design_long()</a></code> </p>
+        </td>
+        <td><p>Get design from long data</p></td>
+      </tr><tr>
+        
         <td>
           <p><code><a href="is_pos_def.html">is_pos_def()</a></code> </p>
         </td>
         <td><p>Check a Matrix is Positive Definite</p></td>
       </tr><tr>
         
+        <td>
+          <p><code><a href="long2wide.html">long2wide()</a></code> </p>
+        </td>
+        <td><p>Long to wide format</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="make_id.html">make_id()</a></code> </p>
+        </td>
+        <td><p>Make ID</p></td>
+      </tr><tr>
+        
         <td>
           <p><code><a href="pos_def_limits.html">pos_def_limits()</a></code> </p>
         </td>
@@ -208,15 +217,21 @@ <h2 id="section-all-functions" class="hasAnchor"><a href="#section-all-functions
       </tr><tr>
         
         <td>
-          <p><code><a href="simdf.html">simdf()</a></code> </p>
+          <p><code><a href="sim_df.html">sim_df()</a></code> </p>
         </td>
         <td><p>Simulate an existing dataframe</p></td>
       </tr><tr>
         
         <td>
-          <p><code><a href="simdf_mixed.html">simdf_mixed()</a></code> </p>
+          <p><code><a href="sim_mixed_df.html">sim_mixed_df()</a></code> </p>
         </td>
         <td><p>Generate a sample with random intercepts for subjects and items</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="wide2long.html">wide2long()</a></code> </p>
+        </td>
+        <td><p>Wide to long format</p></td>
       </tr>
     </tbody>
     </table>
diff --git a/docs/reference/is_pos_def.html b/docs/reference/is_pos_def.html
index 375742c3..cc7e1e4c 100644
--- a/docs/reference/is_pos_def.html
+++ b/docs/reference/is_pos_def.html
@@ -63,7 +63,7 @@
       </button>
       <span class="navbar-brand">
         <a class="navbar-link" href="../index.html">faux</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.0.0.9005</span>
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.0.0.9006</span>
       </span>
     </div>
 
@@ -85,9 +85,6 @@
     <span class="caret"></span>
   </a>
   <ul class="dropdown-menu" role="menu">
-    <li>
-      <a href="../articles/intro.html">Introduction to faux</a>
-    </li>
     <li>
       <a href="../articles/rnorm_multi.html">Simulate Correlated Variables</a>
     </li>
@@ -95,7 +92,7 @@
       <a href="../articles/sim_design.html">Simulate by Design</a>
     </li>
     <li>
-      <a href="../articles/simdf.html">Simulate from Existing Data</a>
+      <a href="../articles/sim_df.html">Simulate from Existing Data</a>
     </li>
   </ul>
 </li>
diff --git a/docs/reference/long2wide.html b/docs/reference/long2wide.html
new file mode 100644
index 00000000..654b00f5
--- /dev/null
+++ b/docs/reference/long2wide.html
@@ -0,0 +1,208 @@
+<!-- Generated by pkgdown: do not edit by hand -->
+<!DOCTYPE html>
+<html lang="en">
+  <head>
+  <meta charset="utf-8">
+<meta http-equiv="X-UA-Compatible" content="IE=edge">
+<meta name="viewport" content="width=device-width, initial-scale=1.0">
+
+<title>Long to wide format — long2wide • faux</title>
+
+<!-- jquery -->
+<script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.3.1/jquery.min.js" integrity="sha256-FgpCb/KJQlLNfOu91ta32o/NMZxltwRo8QtmkMRdAu8=" crossorigin="anonymous"></script>
+<!-- Bootstrap -->
+
+<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.3.7/css/bootstrap.min.css" integrity="sha256-916EbMg70RQy9LHiGkXzG8hSg9EdNy97GazNG/aiY1w=" crossorigin="anonymous" />
+<script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.3.7/js/bootstrap.min.js" integrity="sha256-U5ZEeKfGNOja007MMD3YBI0A3OSZOQbeG6z2f2Y0hu8=" crossorigin="anonymous"></script>
+
+<!-- Font Awesome icons -->
+<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/4.7.0/css/font-awesome.min.css" integrity="sha256-eZrrJcwDc/3uDhsdt61sL2oOBY362qM3lon1gyExkL0=" crossorigin="anonymous" />
+
+<!-- clipboard.js -->
+<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.4/clipboard.min.js" integrity="sha256-FiZwavyI2V6+EXO1U+xzLG3IKldpiTFf3153ea9zikQ=" crossorigin="anonymous"></script>
+
+<!-- sticky kit -->
+<script src="https://cdnjs.cloudflare.com/ajax/libs/sticky-kit/1.1.3/sticky-kit.min.js" integrity="sha256-c4Rlo1ZozqTPE2RLuvbusY3+SU1pQaJC0TjuhygMipw=" crossorigin="anonymous"></script>
+
+<!-- pkgdown -->
+<link href="../pkgdown.css" rel="stylesheet">
+<script src="../pkgdown.js"></script>
+
+
+
+<meta property="og:title" content="Long to wide format — long2wide" />
+
+<meta property="og:description" content="Converts data from long format to wide" />
+<meta name="twitter:card" content="summary" />
+
+
+
+<!-- mathjax -->
+<script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script>
+<script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script>
+
+<!--[if lt IE 9]>
+<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
+<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
+<![endif]-->
+
+
+  </head>
+
+  <body>
+    <div class="container template-reference-topic">
+      <header>
+      <div class="navbar navbar-default navbar-fixed-top" role="navigation">
+  <div class="container">
+    <div class="navbar-header">
+      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
+        <span class="sr-only">Toggle navigation</span>
+        <span class="icon-bar"></span>
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+        <span class="icon-bar"></span>
+      </button>
+      <span class="navbar-brand">
+        <a class="navbar-link" href="../index.html">faux</a>
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.0.0.9006</span>
+      </span>
+    </div>
+
+    <div id="navbar" class="navbar-collapse collapse">
+      <ul class="nav navbar-nav">
+        <li>
+  <a href="../index.html">
+    <span class="fa fa-home fa-lg"></span>
+     
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+<li>
+  <a href="../reference/index.html">Reference</a>
+</li>
+<li class="dropdown">
+  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false">
+    Articles
+     
+    <span class="caret"></span>
+  </a>
+  <ul class="dropdown-menu" role="menu">
+    <li>
+      <a href="../articles/rnorm_multi.html">Simulate Correlated Variables</a>
+    </li>
+    <li>
+      <a href="../articles/sim_design.html">Simulate by Design</a>
+    </li>
+    <li>
+      <a href="../articles/sim_df.html">Simulate from Existing Data</a>
+    </li>
+  </ul>
+</li>
+<li>
+  <a href="../news/index.html">Changelog</a>
+</li>
+      </ul>
+      
+      <ul class="nav navbar-nav navbar-right">
+        
+      </ul>
+      
+    </div><!--/.nav-collapse -->
+  </div><!--/.container -->
+</div><!--/.navbar -->
+
+      
+      </header>
+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Long to wide format</h1>
+    
+    <div class="hidden name"><code>long2wide.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    
+    <p>Converts data from long format to wide</p>
+    
+    </div>
+
+    <pre class="usage"><span class='fu'>long2wide</span>(<span class='no'>.data</span>, <span class='kw'>within</span> <span class='kw'>=</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(), <span class='kw'>between</span> <span class='kw'>=</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(), <span class='kw'>dv</span> <span class='kw'>=</span> <span class='st'>"val"</span>,
+  <span class='kw'>id</span> <span class='kw'>=</span> <span class='st'>"sub_id"</span>)</pre>
+    
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>.data</th>
+      <td><p>the tbl in long format</p></td>
+    </tr>
+    <tr>
+      <th>within</th>
+      <td><p>the names of the within column(s)</p></td>
+    </tr>
+    <tr>
+      <th>between</th>
+      <td><p>the names of between column(s) (optional)</p></td>
+    </tr>
+    <tr>
+      <th>dv</th>
+      <td><p>the name of the DV (value) column</p></td>
+    </tr>
+    <tr>
+      <th>id</th>
+      <td><p>the names of the column(s) for grouping observations</p></td>
+    </tr>
+    </table>
+    
+    <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
+
+    <p>a tbl in wide format</p>
+    
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='no'>df_long</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='sim_design.html'>sim_design</a></span>(<span class='fl'>2</span>, <span class='fl'>2</span>, <span class='kw'>long</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)
+<span class='fu'>long2wide</span>(<span class='no'>df_long</span>, <span class='st'>"A"</span>, <span class='st'>"B"</span>, <span class='st'>"val"</span>, <span class='st'>"sub_id"</span>)</div><div class='output co'>#&gt; <span style='color: #949494;'># A tibble: 200 x 4</span><span>
+#&gt;    sub_id B         A1       A2
+#&gt;    </span><span style='color: #949494;font-style: italic;'>&lt;chr&gt;</span><span>  </span><span style='color: #949494;font-style: italic;'>&lt;fct&gt;</span><span>  </span><span style='color: #949494;font-style: italic;'>&lt;dbl&gt;</span><span>    </span><span style='color: #949494;font-style: italic;'>&lt;dbl&gt;</span><span>
+#&gt; </span><span style='color: #BCBCBC;'> 1</span><span> S001   B1     0.387 -</span><span style='color: #BB0000;'>1.40</span><span>   
+#&gt; </span><span style='color: #BCBCBC;'> 2</span><span> S002   B1     0.785  0.255  
+#&gt; </span><span style='color: #BCBCBC;'> 3</span><span> S003   B1     1.06  -</span><span style='color: #BB0000;'>2.44</span><span>   
+#&gt; </span><span style='color: #BCBCBC;'> 4</span><span> S004   B1     0.796 -</span><span style='color: #BB0000;'>0.005</span><span style='color: #BB0000;text-decoration: underline;'>57</span><span>
+#&gt; </span><span style='color: #BCBCBC;'> 5</span><span> S005   B1     1.76   0.622  
+#&gt; </span><span style='color: #BCBCBC;'> 6</span><span> S006   B1     0.691  1.15   
+#&gt; </span><span style='color: #BCBCBC;'> 7</span><span> S007   B1     0.559 -</span><span style='color: #BB0000;'>1.82</span><span>   
+#&gt; </span><span style='color: #BCBCBC;'> 8</span><span> S008   B1     0.537 -</span><span style='color: #BB0000;'>0.247</span><span>  
+#&gt; </span><span style='color: #BCBCBC;'> 9</span><span> S009   B1    -</span><span style='color: #BB0000;'>0.227</span><span> -</span><span style='color: #BB0000;'>0.244</span><span>  
+#&gt; </span><span style='color: #BCBCBC;'>10</span><span> S010   B1    -</span><span style='color: #BB0000;'>0.978</span><span> -</span><span style='color: #BB0000;'>0.283</span><span>  
+#&gt; </span><span style='color: #949494;'># … with 190 more rows</span><span></div><div class='input'>
+</div></span></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="sidebar">
+    <h2>Contents</h2>
+    <ul class="nav nav-pills nav-stacked">
+      <li><a href="#arguments">Arguments</a></li>
+      
+      <li><a href="#value">Value</a></li>
+      
+      <li><a href="#examples">Examples</a></li>
+    </ul>
+
+  </div>
+</div>
+
+      <footer>
+      <div class="copyright">
+  <p>Developed by Lisa DeBruine.</p>
+</div>
+
+<div class="pkgdown">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.3.0.</p>
+</div>
+      </footer>
+   </div>
+
+  
+
+  </body>
+</html>
+
diff --git a/docs/reference/make_id.html b/docs/reference/make_id.html
new file mode 100644
index 00000000..999c632b
--- /dev/null
+++ b/docs/reference/make_id.html
@@ -0,0 +1,191 @@
+<!-- Generated by pkgdown: do not edit by hand -->
+<!DOCTYPE html>
+<html lang="en">
+  <head>
+  <meta charset="utf-8">
+<meta http-equiv="X-UA-Compatible" content="IE=edge">
+<meta name="viewport" content="width=device-width, initial-scale=1.0">
+
+<title>Make ID — make_id • faux</title>
+
+<!-- jquery -->
+<script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.3.1/jquery.min.js" integrity="sha256-FgpCb/KJQlLNfOu91ta32o/NMZxltwRo8QtmkMRdAu8=" crossorigin="anonymous"></script>
+<!-- Bootstrap -->
+
+<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.3.7/css/bootstrap.min.css" integrity="sha256-916EbMg70RQy9LHiGkXzG8hSg9EdNy97GazNG/aiY1w=" crossorigin="anonymous" />
+<script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.3.7/js/bootstrap.min.js" integrity="sha256-U5ZEeKfGNOja007MMD3YBI0A3OSZOQbeG6z2f2Y0hu8=" crossorigin="anonymous"></script>
+
+<!-- Font Awesome icons -->
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+
+<!-- clipboard.js -->
+<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.4/clipboard.min.js" integrity="sha256-FiZwavyI2V6+EXO1U+xzLG3IKldpiTFf3153ea9zikQ=" crossorigin="anonymous"></script>
+
+<!-- sticky kit -->
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+
+<meta property="og:description" content="Make IDs with fixed length and a letter prefix for random effects (e.g., S001, S002, ..., S100)." />
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+<script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script>
+
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+
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+      <span class="navbar-brand">
+        <a class="navbar-link" href="../index.html">faux</a>
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.0.0.9006</span>
+      </span>
+    </div>
+
+    <div id="navbar" class="navbar-collapse collapse">
+      <ul class="nav navbar-nav">
+        <li>
+  <a href="../index.html">
+    <span class="fa fa-home fa-lg"></span>
+     
+  </a>
+</li>
+<li>
+  <a href="../reference/index.html">Reference</a>
+</li>
+<li class="dropdown">
+  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false">
+    Articles
+     
+    <span class="caret"></span>
+  </a>
+  <ul class="dropdown-menu" role="menu">
+    <li>
+      <a href="../articles/rnorm_multi.html">Simulate Correlated Variables</a>
+    </li>
+    <li>
+      <a href="../articles/sim_design.html">Simulate by Design</a>
+    </li>
+    <li>
+      <a href="../articles/sim_df.html">Simulate from Existing Data</a>
+    </li>
+  </ul>
+</li>
+<li>
+  <a href="../news/index.html">Changelog</a>
+</li>
+      </ul>
+      
+      <ul class="nav navbar-nav navbar-right">
+        
+      </ul>
+      
+    </div><!--/.nav-collapse -->
+  </div><!--/.container -->
+</div><!--/.navbar -->
+
+      
+      </header>
+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Make ID</h1>
+    
+    <div class="hidden name"><code>make_id.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    
+    <p>Make IDs with fixed length and a letter prefix for random effects (e.g., S001, S002, ..., S100).</p>
+    
+    </div>
+
+    <pre class="usage"><span class='fu'>make_id</span>(<span class='kw'>n</span> <span class='kw'>=</span> <span class='fl'>100</span>, <span class='kw'>prefix</span> <span class='kw'>=</span> <span class='st'>"S"</span>, <span class='kw'>digits</span> <span class='kw'>=</span> <span class='fl'>0</span>)</pre>
+    
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>n</th>
+      <td><p>the number of IDs to generate (or a vector of numbers)</p></td>
+    </tr>
+    <tr>
+      <th>prefix</th>
+      <td><p>the letter prefix to the number</p></td>
+    </tr>
+    <tr>
+      <th>digits</th>
+      <td><p>the number of digits to use for the numeric part. Only used if this is larger than the number of digits in n.</p></td>
+    </tr>
+    </table>
+    
+    <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
+
+    <p>a vector of IDs</p>
+    
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'>
+<span class='fu'>make_id</span>(<span class='fl'>20</span>, <span class='st'>"SUBJECT_"</span>)</div><div class='output co'>#&gt;  [1] "SUBJECT_01" "SUBJECT_02" "SUBJECT_03" "SUBJECT_04" "SUBJECT_05"
+#&gt;  [6] "SUBJECT_06" "SUBJECT_07" "SUBJECT_08" "SUBJECT_09" "SUBJECT_10"
+#&gt; [11] "SUBJECT_11" "SUBJECT_12" "SUBJECT_13" "SUBJECT_14" "SUBJECT_15"
+#&gt; [16] "SUBJECT_16" "SUBJECT_17" "SUBJECT_18" "SUBJECT_19" "SUBJECT_20"</div><div class='input'><span class='fu'>make_id</span>(<span class='fl'>10</span>:<span class='fl'>30</span>, <span class='kw'>digits</span> <span class='kw'>=</span> <span class='fl'>3</span>)</div><div class='output co'>#&gt;  [1] "S010" "S011" "S012" "S013" "S014" "S015" "S016" "S017" "S018" "S019"
+#&gt; [11] "S020" "S021" "S022" "S023" "S024" "S025" "S026" "S027" "S028" "S029"
+#&gt; [21] "S030"</div><div class='input'>
+</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="sidebar">
+    <h2>Contents</h2>
+    <ul class="nav nav-pills nav-stacked">
+      <li><a href="#arguments">Arguments</a></li>
+      
+      <li><a href="#value">Value</a></li>
+      
+      <li><a href="#examples">Examples</a></li>
+    </ul>
+
+  </div>
+</div>
+
+      <footer>
+      <div class="copyright">
+  <p>Developed by Lisa DeBruine.</p>
+</div>
+
+<div class="pkgdown">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.3.0.</p>
+</div>
+      </footer>
+   </div>
+
+  
+
+  </body>
+</html>
+
diff --git a/docs/reference/pipe.html b/docs/reference/pipe.html
index 9592e06d..3fa0d792 100644
--- a/docs/reference/pipe.html
+++ b/docs/reference/pipe.html
@@ -63,7 +63,7 @@
       </button>
       <span class="navbar-brand">
         <a class="navbar-link" href="../index.html">faux</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.0.0.9005</span>
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.0.0.9006</span>
       </span>
     </div>
 
@@ -85,9 +85,6 @@
     <span class="caret"></span>
   </a>
   <ul class="dropdown-menu" role="menu">
-    <li>
-      <a href="../articles/intro.html">Introduction to faux</a>
-    </li>
     <li>
       <a href="../articles/rnorm_multi.html">Simulate Correlated Variables</a>
     </li>
@@ -95,7 +92,7 @@
       <a href="../articles/sim_design.html">Simulate by Design</a>
     </li>
     <li>
-      <a href="../articles/simdf.html">Simulate from Existing Data</a>
+      <a href="../articles/sim_df.html">Simulate from Existing Data</a>
     </li>
   </ul>
 </li>
diff --git a/docs/reference/pos_def_limits.html b/docs/reference/pos_def_limits.html
index 2c4957db..628cd5a6 100644
--- a/docs/reference/pos_def_limits.html
+++ b/docs/reference/pos_def_limits.html
@@ -63,7 +63,7 @@
       </button>
       <span class="navbar-brand">
         <a class="navbar-link" href="../index.html">faux</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.0.0.9005</span>
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.0.0.9006</span>
       </span>
     </div>
 
@@ -85,9 +85,6 @@
     <span class="caret"></span>
   </a>
   <ul class="dropdown-menu" role="menu">
-    <li>
-      <a href="../articles/intro.html">Introduction to faux</a>
-    </li>
     <li>
       <a href="../articles/rnorm_multi.html">Simulate Correlated Variables</a>
     </li>
@@ -95,7 +92,7 @@
       <a href="../articles/sim_design.html">Simulate by Design</a>
     </li>
     <li>
-      <a href="../articles/simdf.html">Simulate from Existing Data</a>
+      <a href="../articles/sim_df.html">Simulate from Existing Data</a>
     </li>
   </ul>
 </li>
diff --git a/docs/reference/rnorm_multi.html b/docs/reference/rnorm_multi.html
index 4b74b783..da60ab24 100644
--- a/docs/reference/rnorm_multi.html
+++ b/docs/reference/rnorm_multi.html
@@ -32,7 +32,7 @@
 
 <meta property="og:title" content="Multiple Normally Distributed Vectors — rnorm_multi" />
 
-<meta property="og:description" content="rnorm_multi makes multiple normally distributed vectors with specified relationships" />
+<meta property="og:description" content="rnorm_multi() makes multiple normally distributed vectors with specified relationships." />
 <meta name="twitter:card" content="summary" />
 
 
@@ -63,7 +63,7 @@
       </button>
       <span class="navbar-brand">
         <a class="navbar-link" href="../index.html">faux</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.0.0.9005</span>
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.0.0.9006</span>
       </span>
     </div>
 
@@ -85,9 +85,6 @@
     <span class="caret"></span>
   </a>
   <ul class="dropdown-menu" role="menu">
-    <li>
-      <a href="../articles/intro.html">Introduction to faux</a>
-    </li>
     <li>
       <a href="../articles/rnorm_multi.html">Simulate Correlated Variables</a>
     </li>
@@ -95,7 +92,7 @@
       <a href="../articles/sim_design.html">Simulate by Design</a>
     </li>
     <li>
-      <a href="../articles/simdf.html">Simulate from Existing Data</a>
+      <a href="../articles/sim_df.html">Simulate from Existing Data</a>
     </li>
   </ul>
 </li>
@@ -125,12 +122,12 @@ <h1>Multiple Normally Distributed Vectors</h1>
 
     <div class="ref-description">
     
-    <p><code>rnorm_multi</code> makes multiple normally distributed vectors with specified relationships</p>
+    <p><code>rnorm_multi()</code> makes multiple normally distributed vectors with specified relationships.</p>
     
     </div>
 
-    <pre class="usage"><span class='fu'>rnorm_multi</span>(<span class='no'>n</span>, <span class='kw'>vars</span> <span class='kw'>=</span> <span class='fl'>3</span>, <span class='kw'>cors</span> <span class='kw'>=</span> <span class='fl'>0</span>, <span class='kw'>mu</span> <span class='kw'>=</span> <span class='fl'>0</span>, <span class='kw'>sd</span> <span class='kw'>=</span> <span class='fl'>1</span>,
-  <span class='kw'>varnames</span> <span class='kw'>=</span> <span class='kw'>NULL</span>, <span class='kw'>empirical</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>, <span class='kw'>as.matrix</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>)</pre>
+    <pre class="usage"><span class='fu'>rnorm_multi</span>(<span class='no'>n</span>, <span class='kw'>vars</span> <span class='kw'>=</span> <span class='fl'>3</span>, <span class='kw'>mu</span> <span class='kw'>=</span> <span class='fl'>0</span>, <span class='kw'>sd</span> <span class='kw'>=</span> <span class='fl'>1</span>, <span class='kw'>r</span> <span class='kw'>=</span> <span class='fl'>0</span>, <span class='kw'>varnames</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
+  <span class='kw'>empirical</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>, <span class='kw'>as.matrix</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>, <span class='kw'>cors</span> <span class='kw'>=</span> <span class='kw'>NULL</span>)</pre>
     
     <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
     <table class="ref-arguments">
@@ -143,10 +140,6 @@ <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arg
       <th>vars</th>
       <td><p>the number of variables to return</p></td>
     </tr>
-    <tr>
-      <th>cors</th>
-      <td><p>the correlations among the variables (can be a single number, vars\*vars matrix, vars\*vars vector, or a vars\*(vars-1)/2 vector)</p></td>
-    </tr>
     <tr>
       <th>mu</th>
       <td><p>a vector giving the means of the variables (numeric vector of length 1 or vars)</p></td>
@@ -155,227 +148,236 @@ <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arg
       <th>sd</th>
       <td><p>the standard deviations of the variables (numeric vector of length 1 or vars)</p></td>
     </tr>
+    <tr>
+      <th>r</th>
+      <td><p>the correlations among the variables (can be a single number, vars\*vars matrix, vars\*vars vector, or a vars\*(vars-1)/2 vector)</p></td>
+    </tr>
     <tr>
       <th>varnames</th>
-      <td><p>optional names for the variables (string vector of length vars) defaults if cors is a matrix with column names</p></td>
+      <td><p>optional names for the variables (string vector of length vars) defaults if r is a matrix with column names</p></td>
     </tr>
     <tr>
       <th>empirical</th>
-      <td><p>logical. If true, mu, sd and cors specify the empirical not population mean, sd and covariance</p></td>
+      <td><p>logical. If true, mu, sd and r specify the empirical not population mean, sd and covariance</p></td>
     </tr>
     <tr>
       <th>as.matrix</th>
       <td><p>logical. If true, returns a matrix</p></td>
     </tr>
+    <tr>
+      <th>cors</th>
+      <td><p>(deprecated; use r)</p></td>
+    </tr>
     </table>
     
     <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
 
-    <p>dataframe of vars vectors</p>
+    <p>a tbl of vars vectors</p>
     
 
     <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='fu'>rnorm_multi</span>(<span class='fl'>100</span>, <span class='fl'>3</span>, <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='fl'>0.2</span>, <span class='fl'>0.4</span>, <span class='fl'>0.5</span>), <span class='kw'>varnames</span><span class='kw'>=</span><span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='st'>"A"</span>, <span class='st'>"B"</span>, <span class='st'>"C"</span>))</div><div class='output co'>#&gt;                A            B           C
-#&gt; 1    0.750792572  1.424873995  1.01619162
-#&gt; 2   -1.042243673 -0.275816637  0.52639710
-#&gt; 3    0.878491981  2.434684102  2.13943193
-#&gt; 4   -0.500509231  0.536184495 -0.07283162
-#&gt; 5   -1.099839859  1.395668514 -1.63782640
-#&gt; 6   -1.268203415 -0.516346650 -0.90254750
-#&gt; 7    1.062341096  2.085361364  1.05389880
-#&gt; 8   -0.072670487  0.713209683 -0.06669068
-#&gt; 9    0.799007438  0.832138301 -0.84904571
-#&gt; 10   0.274439429 -1.305844900  1.50703213
-#&gt; 11   0.483758950  0.961135166 -0.09054153
-#&gt; 12  -1.335560745  0.410231530 -0.60767290
-#&gt; 13  -1.365394967 -1.989742102 -1.39808947
-#&gt; 14   1.058685530  1.942674898  0.79418602
-#&gt; 15  -0.059286958 -0.896242839 -0.20963410
-#&gt; 16   2.732876265  0.225408869  1.48006414
-#&gt; 17   0.520040403 -0.045099167  0.69894364
-#&gt; 18  -1.261009283  0.795477058  0.38156161
-#&gt; 19   0.152387337 -0.001507871 -1.21894494
-#&gt; 20   1.153452339 -0.243123670  1.18059995
-#&gt; 21   1.221036402 -1.329257135 -0.72696920
-#&gt; 22   0.097150590 -0.488381489 -0.38403747
-#&gt; 23   1.847044170  0.215193119  1.03746265
-#&gt; 24  -0.213594069 -0.833126883 -0.60370775
-#&gt; 25  -0.944882567 -1.108280341 -2.13611782
-#&gt; 26  -0.203627401 -0.399418526  0.70486850
-#&gt; 27   0.971899030  0.251539268  0.93124695
-#&gt; 28  -0.067710098  0.330687769 -0.20521675
-#&gt; 29   0.008234813  0.643985743  1.10851032
-#&gt; 30   1.491364279  1.606960145  0.51407350
-#&gt; 31  -1.491314723 -0.173569149 -0.59828848
-#&gt; 32   0.788400822 -0.100235777 -0.87821239
-#&gt; 33  -0.275325098 -0.506920585  0.16149729
-#&gt; 34  -1.646030985  0.066253801 -2.08692166
-#&gt; 35  -0.763940091  0.400133152  0.01055207
-#&gt; 36   0.559861711  0.094147624 -0.24183194
-#&gt; 37   0.851096610  2.217705492  1.27999821
-#&gt; 38  -0.174916339  0.113335133  0.60261481
-#&gt; 39   0.579792609 -0.678784282  0.78461866
-#&gt; 40  -0.270889155 -0.210612089 -1.77436354
-#&gt; 41   0.045616422  0.372504963 -0.50383699
-#&gt; 42  -0.469471970  0.436848174  1.27704375
-#&gt; 43   1.116862858 -1.388382465  0.45742838
-#&gt; 44   0.435540978 -0.674785328  0.76644032
-#&gt; 45   0.733199928 -0.330524626 -1.17879303
-#&gt; 46  -1.202678340 -3.307972897 -1.77046080
-#&gt; 47  -0.829440876  0.569422843  0.04572102
-#&gt; 48   0.001784459 -0.684587716 -0.57477015
-#&gt; 49  -1.255359394  0.562293801  0.23466705
-#&gt; 50   2.329409361  0.563363357  1.59377323
-#&gt; 51  -0.074278634 -0.571594535 -1.19274011
-#&gt; 52   0.364196656  0.305387342 -0.05428873
-#&gt; 53  -0.399415335  1.216792632 -0.33993885
-#&gt; 54  -0.237192644  0.568221498 -0.35410471
-#&gt; 55   0.395099319 -1.135493621  0.63054444
-#&gt; 56   0.309387409 -0.471324983 -0.94209682
-#&gt; 57   1.467751541  1.584594817  2.09891298
-#&gt; 58  -1.921116085 -1.649349291 -2.52231252
-#&gt; 59   0.936534595  0.074720566 -0.05360963
-#&gt; 60   0.095504143 -1.141376090  0.49330431
-#&gt; 61  -2.100395036  0.162246518 -0.70544430
-#&gt; 62   1.437719944  0.530497597 -0.22212529
-#&gt; 63  -0.956070367 -0.668411888 -0.93985501
-#&gt; 64   0.399297618  0.122294316  0.08451302
-#&gt; 65  -0.286806047  2.194503989  0.68953463
-#&gt; 66   0.569197516  1.947087832 -0.16302798
-#&gt; 67  -1.949290727 -0.897080260  0.12529717
-#&gt; 68   0.394865364  0.065765899 -0.62937094
-#&gt; 69  -0.503900977 -0.175240568 -0.45048740
-#&gt; 70   1.439840970  0.910978267  1.54359768
-#&gt; 71   0.887303297  1.141197444  1.30315927
-#&gt; 72  -0.380841256  1.126287256 -1.27061154
-#&gt; 73   0.046357782 -1.651026981 -1.30496243
-#&gt; 74   0.501969183 -0.740051843 -0.21905098
-#&gt; 75  -0.327677152 -1.412160664 -1.18738515
-#&gt; 76  -0.041519929 -1.656652359  0.41972821
-#&gt; 77  -1.237010922 -1.007396285  0.60385664
-#&gt; 78  -0.999395609  0.507528583  0.54872639
-#&gt; 79  -0.508546869 -0.250979903  0.26216982
-#&gt; 80   1.706916012 -1.013138961 -0.24417856
-#&gt; 81  -2.250638781 -1.264743365 -1.06547039
-#&gt; 82  -1.465750791 -0.014902364 -1.49434831
-#&gt; 83  -1.819797525  0.131454869 -0.23351058
-#&gt; 84   0.644464430 -1.365010318 -0.43015447
-#&gt; 85   0.673776094 -0.023604894  0.61492934
-#&gt; 86   0.424058255 -2.337033827 -0.53427314
-#&gt; 87   1.838236640  2.403650450  1.78253998
-#&gt; 88   1.880351381 -1.351154782  0.05113393
-#&gt; 89   0.143202959 -1.430192202  0.26174046
-#&gt; 90   1.155769129 -0.186903327  0.04880420
-#&gt; 91   0.559813595 -0.862499677 -0.53711912
-#&gt; 92  -0.936341076 -0.503527111 -0.99627787
-#&gt; 93  -1.049096344  0.032194348 -1.34962850
-#&gt; 94  -0.497978396  0.162235981  0.31899526
-#&gt; 95   0.565120969 -1.919780662  0.25948394
-#&gt; 96  -0.664895124 -0.766542929 -2.21344800
-#&gt; 97   0.829280403  0.592403315 -0.43854944
-#&gt; 98  -0.017677656 -1.656619109 -0.45664659
-#&gt; 99  -0.131923116 -1.896881557 -0.91048949
-#&gt; 100  0.146219686 -0.896684910  1.27558673</div><div class='input'><span class='fu'>rnorm_multi</span>(<span class='fl'>100</span>, <span class='fl'>3</span>, <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>0.2</span>, -<span class='fl'>0.5</span>, <span class='fl'>0.2</span>, <span class='fl'>1</span>, <span class='fl'>0.5</span>, -<span class='fl'>0.5</span>, <span class='fl'>0.5</span>, <span class='fl'>1</span>), <span class='kw'>varnames</span><span class='kw'>=</span><span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='st'>"A"</span>, <span class='st'>"B"</span>, <span class='st'>"C"</span>))</div><div class='output co'>#&gt;               A             B           C
-#&gt; 1    1.39081618  1.2987554842 -0.54274184
-#&gt; 2    0.12474394 -1.4341962212 -0.83385076
-#&gt; 3   -1.31234622 -0.1773139308  1.11548187
-#&gt; 4    0.13764915  1.4386412100  1.18934079
-#&gt; 5   -1.55271429 -0.7649622047 -0.14994138
-#&gt; 6   -0.69104513 -1.1444681510 -0.39532881
-#&gt; 7    0.28331723  0.6930376611  0.78522733
-#&gt; 8    0.62698282  2.6282635812  2.09300595
-#&gt; 9   -1.35745434  0.4066573669  2.08806190
-#&gt; 10  -0.06698887 -2.4653818490 -1.20034841
-#&gt; 11   0.02483736 -0.0410126675 -0.86813242
-#&gt; 12   0.79142685 -1.8084817841 -0.37123506
-#&gt; 13  -0.88409828 -0.1531384713  0.49194054
-#&gt; 14   0.60358179 -1.3450087188 -1.86485444
-#&gt; 15   0.74467121  1.2994217211  0.32121665
-#&gt; 16   0.26231671  0.7238420836 -0.21253993
-#&gt; 17  -0.08841355  0.8852067209  1.38717738
-#&gt; 18  -1.18692758 -1.5262910813 -0.22392502
-#&gt; 19   1.02473345  0.3596218704 -1.36911133
-#&gt; 20   0.29173164  0.0537979231  0.48062427
-#&gt; 21  -0.10151671  1.0013139089  0.41741153
-#&gt; 22  -0.38409912  0.1086193588  0.27613213
-#&gt; 23  -0.17473214  1.3226328277  1.56677087
-#&gt; 24  -0.68841302 -0.8568191519  0.22462907
-#&gt; 25  -3.40268201 -1.1503624457  2.19509962
-#&gt; 26   1.27476703 -0.1608727574 -1.60243175
-#&gt; 27  -0.76774044 -0.3136043209  0.40748355
-#&gt; 28  -2.93463796  0.0002708164  0.54330948
-#&gt; 29  -0.36309562  0.4812979048  0.08769210
-#&gt; 30   0.11235349  1.2436686101  0.40667721
-#&gt; 31  -0.42112919  1.1241955619  0.82149954
-#&gt; 32   0.65132497 -1.9692499386 -1.72175518
-#&gt; 33  -0.39001734  1.2671731043  1.01569959
-#&gt; 34   1.05444567  0.7305534127 -0.08594560
-#&gt; 35  -0.65195625 -0.5195396901  0.41514849
-#&gt; 36  -0.95833910  0.2003833523  1.41935582
-#&gt; 37  -0.09312194 -1.7133185727 -0.91550462
-#&gt; 38   0.15254009 -0.8956400361 -1.25958623
-#&gt; 39   0.07535271  0.1033439817  0.16790440
-#&gt; 40  -1.61236803 -1.1266807862  1.13697372
-#&gt; 41  -0.53493316 -1.6457716779  0.28787366
-#&gt; 42  -0.69921708  1.7978716255  1.97787573
-#&gt; 43  -0.58102874 -1.3412768754  0.28024767
-#&gt; 44   0.39080373 -0.9614140080 -1.51340506
-#&gt; 45   0.98098214 -0.2651307385 -1.15693664
-#&gt; 46  -1.41159592  0.9114650277  1.44179604
-#&gt; 47  -1.05225142 -0.9418931935 -0.20252229
-#&gt; 48  -0.40068116 -1.1950503292 -0.24799347
-#&gt; 49   1.38669506  0.9589220188  0.06210057
-#&gt; 50  -0.35464447  0.8920651923  0.55778884
-#&gt; 51   1.53904431  1.2176546707 -0.77686030
-#&gt; 52   0.16578814  0.4331779127 -0.40893746
-#&gt; 53  -0.60914155 -0.8140949898  1.01178545
-#&gt; 54  -2.16814062 -0.2863029111  1.76151931
-#&gt; 55  -0.85887053  0.7286470557  1.46281376
-#&gt; 56  -2.69493701 -0.5352409200  1.22842833
-#&gt; 57   0.25329846 -0.2477709459 -0.33847285
-#&gt; 58  -1.42870286  0.8775450724  1.65829026
-#&gt; 59   1.59072274  0.1852101502 -0.06485567
-#&gt; 60   0.19047954 -0.5331432083 -1.01246796
-#&gt; 61   1.12633124  0.4510249139 -0.16385936
-#&gt; 62  -0.86502391  0.4564717361  0.97700377
-#&gt; 63   0.41198377  2.1574189238  0.29625434
-#&gt; 64  -1.33266567  0.1334556024  2.13446398
-#&gt; 65  -1.26258869 -0.1687697156 -0.84856895
-#&gt; 66  -1.02980626  0.6029517902  0.65050954
-#&gt; 67   0.38682167 -1.3061125155 -1.84341380
-#&gt; 68   0.23417755 -0.4236220335 -0.99612575
-#&gt; 69   0.38936213  1.4383517669  1.20765999
-#&gt; 70   0.52613652  0.4669543053 -0.25615525
-#&gt; 71  -0.78625593 -0.1890983901 -0.06583813
-#&gt; 72  -0.52607091 -0.7252667231 -1.05297080
-#&gt; 73  -0.28890582 -1.0441594449 -1.15239430
-#&gt; 74  -1.25168046 -2.1088896749 -1.11497680
-#&gt; 75  -0.67847213 -1.4529646435 -0.09410188
-#&gt; 76  -0.99112777  0.2271702323  1.19094153
-#&gt; 77  -0.27328045 -0.6278109929  0.89942994
-#&gt; 78  -0.05788188 -0.2011886425 -0.70444974
-#&gt; 79   0.44426016  0.3086371578 -0.14094071
-#&gt; 80  -1.86391621 -0.2017716705  1.04344653
-#&gt; 81  -0.52027246  0.1144370542  0.17047033
-#&gt; 82   0.68790768  0.3227648343 -0.35982484
-#&gt; 83   0.06379928  1.0963218557 -0.79995582
-#&gt; 84   0.71240232  0.6765428974  0.08052656
-#&gt; 85   1.00680982  1.3004044786  0.46945908
-#&gt; 86  -0.19307208 -0.8284714031  0.42528039
-#&gt; 87   0.58798451  0.2443710099  0.22379468
-#&gt; 88   0.29882724  0.8581150980  1.22292625
-#&gt; 89   1.84074363  1.0970181676 -0.69553217
-#&gt; 90  -1.91299092 -1.2784569227  0.17807915
-#&gt; 91   0.64255687 -0.5425659342  0.03429620
-#&gt; 92  -0.83135155  0.4205869612  0.80329005
-#&gt; 93  -1.44298017  0.7499570038  1.75007779
-#&gt; 94  -0.80721771 -1.3782307639 -0.83537525
-#&gt; 95  -0.56849692 -0.2160438395 -0.20213552
-#&gt; 96  -1.43237796  1.0144647169  1.92039006
-#&gt; 97  -0.17925759 -1.5018571840 -1.44550197
-#&gt; 98  -0.35894894  0.9200034445  1.16485148
-#&gt; 99   0.03204885 -0.5520698956  0.01677568
-#&gt; 100  0.83799420  1.2748026410 -0.90440295</div></pre>
+    <pre class="examples"><div class='input'><span class='fu'>rnorm_multi</span>(<span class='fl'>100</span>, <span class='fl'>3</span>, <span class='fl'>0</span>, <span class='fl'>1</span>, <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='fl'>0.2</span>, <span class='fl'>0.4</span>, <span class='fl'>0.5</span>), <span class='kw'>varnames</span><span class='kw'>=</span><span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='st'>"A"</span>, <span class='st'>"B"</span>, <span class='st'>"C"</span>))</div><div class='output co'>#&gt;                A            B           C
+#&gt; 1   -1.711924939 -1.011599824 -1.32018668
+#&gt; 2    0.887134792  0.043794457  0.99462051
+#&gt; 3    0.748427556  0.410991509  1.01598343
+#&gt; 4   -0.988547303 -1.612324193  0.10827445
+#&gt; 5    0.250415810  1.967522573  1.12127447
+#&gt; 6    1.012473136  1.329047164  0.46137529
+#&gt; 7    0.290391321 -0.492076253 -1.07257786
+#&gt; 8   -0.999779485 -2.143044059 -1.61909580
+#&gt; 9    1.296369128  0.223297040  0.05172819
+#&gt; 10   1.844675802  0.525323939  1.43792983
+#&gt; 11  -0.594332509  1.134701125 -0.51513681
+#&gt; 12   2.892055704 -0.024653246 -0.87388294
+#&gt; 13   0.674912545  1.145791522 -0.16442269
+#&gt; 14   0.114929596  0.751160451  0.22516804
+#&gt; 15  -0.798672240 -0.534844472 -1.59558698
+#&gt; 16  -1.031496700  0.049687627 -0.54144674
+#&gt; 17  -0.004310488 -1.375989084  0.16455645
+#&gt; 18   1.415566785  1.598106413  1.06560667
+#&gt; 19  -1.642731803 -0.241490494 -0.33724621
+#&gt; 20   0.910502722 -0.191788473 -0.98662517
+#&gt; 21  -1.175150978 -0.479527388  0.14625695
+#&gt; 22   0.305901881  0.761645013 -0.54205693
+#&gt; 23  -0.367942472 -1.256019905 -0.12063644
+#&gt; 24   0.892308000  0.193255995  1.16876289
+#&gt; 25   2.538452134  2.240282666  2.04947504
+#&gt; 26  -0.695068465  0.222697327 -1.11980217
+#&gt; 27   0.512021718  0.508770162  0.57718656
+#&gt; 28  -0.932274424  2.643562030  2.24879422
+#&gt; 29  -0.585604074  0.622505249 -0.24917585
+#&gt; 30  -1.355941412 -0.590585765 -0.21519908
+#&gt; 31  -0.733846362  0.082328508 -0.42830930
+#&gt; 32   1.264872249  0.715577491  0.12590367
+#&gt; 33  -0.518786456  0.201122493 -0.92638734
+#&gt; 34  -0.637506919 -1.122500780 -0.86513473
+#&gt; 35   0.632266473 -0.010632207  1.05787745
+#&gt; 36   1.104769095  0.406299810 -0.21379747
+#&gt; 37   1.472185167  0.971669943  0.38215346
+#&gt; 38   0.208642678  1.232615573 -0.26677942
+#&gt; 39   0.194660642 -0.021702220 -0.36527459
+#&gt; 40   1.781764439  0.408108369  1.86009067
+#&gt; 41   2.042761937 -0.215878860  1.47377857
+#&gt; 42  -0.490815530 -0.425984391 -0.68877135
+#&gt; 43   1.655781000 -0.033659668  1.27356903
+#&gt; 44  -0.025382344  0.905039301 -0.02548762
+#&gt; 45  -0.665268785 -0.563017316  0.06761990
+#&gt; 46  -0.276302646  0.608159422  0.33566043
+#&gt; 47   0.484250987  1.126346311  1.25406657
+#&gt; 48   1.282907646  0.720836758  0.47160057
+#&gt; 49  -0.277030402 -1.021860386 -1.96611414
+#&gt; 50  -0.819145743 -0.037232249 -0.04063296
+#&gt; 51  -1.564663610 -0.636464908 -1.85042764
+#&gt; 52  -0.898058218  0.284764624 -0.34268128
+#&gt; 53   1.805225599 -0.408926161  0.83297444
+#&gt; 54   1.365198223  1.113118376  1.19018362
+#&gt; 55   0.216231182 -0.115908105  0.10565315
+#&gt; 56   0.641593299  1.738105080  2.21714184
+#&gt; 57   0.213674643  0.168441446 -0.31953324
+#&gt; 58   0.253013272  0.862077906 -0.22789514
+#&gt; 59   0.287070174 -1.951729555 -0.83911657
+#&gt; 60  -0.481794003  0.239976721  0.61024139
+#&gt; 61   0.025132801 -0.628056375 -1.53689844
+#&gt; 62  -0.167673231 -0.101515541  0.75379782
+#&gt; 63  -2.425362627 -0.498203692 -1.06272155
+#&gt; 64   1.350643220 -0.486047718  0.95719724
+#&gt; 65  -1.242626738  2.175710297  0.92752367
+#&gt; 66  -0.338836433  1.113640640 -0.15518463
+#&gt; 67  -0.026841819  0.966138768  0.37921477
+#&gt; 68  -0.453608406  0.413598279  0.23549706
+#&gt; 69  -0.493723033 -1.334332953 -0.84630857
+#&gt; 70  -1.221646818 -1.238780109  0.72514498
+#&gt; 71   0.321048652  1.743009432 -0.49620029
+#&gt; 72  -0.527149031  1.184322665  1.00890307
+#&gt; 73   0.070016525  1.224225492  0.62085618
+#&gt; 74   1.134493406  2.219461421  1.58638671
+#&gt; 75   1.504749960  0.614156332  1.09643083
+#&gt; 76   0.446530671  0.052052719  0.61180283
+#&gt; 77   1.818863849 -0.302817368  0.04494236
+#&gt; 78  -0.791160513  0.179908256  0.79171348
+#&gt; 79  -0.300914823 -0.253138072 -0.53330713
+#&gt; 80   0.926355041  1.873512137  0.35236855
+#&gt; 81   0.144573957  1.043953709 -0.49422615
+#&gt; 82  -0.027810676  0.325938318 -1.58919459
+#&gt; 83  -2.100930284  1.066836105 -0.83668769
+#&gt; 84  -0.444333571 -0.715766312 -0.85121894
+#&gt; 85  -0.968875193 -1.687613536 -0.79915130
+#&gt; 86   0.959440062 -1.088246737  1.55349882
+#&gt; 87  -0.345251762 -1.686853560  0.65311349
+#&gt; 88   0.572082195 -0.813048594 -1.24514160
+#&gt; 89  -1.365521552 -1.373149134 -1.59569125
+#&gt; 90   1.176097394  2.119269863  1.42040520
+#&gt; 91   1.221033387 -0.577310879 -0.56619463
+#&gt; 92   0.108998277  0.648942202 -0.11418245
+#&gt; 93   0.101151686  0.111384203  0.73298987
+#&gt; 94   0.586259342  1.453725946  1.13935521
+#&gt; 95   0.153452555  1.348353614 -0.27134221
+#&gt; 96   0.235523130  0.480340620 -0.05456858
+#&gt; 97   0.785445485  1.876836130 -0.04612489
+#&gt; 98  -0.198978331 -0.525346365 -0.12181418
+#&gt; 99   0.629380511 -0.357590811  0.51006913
+#&gt; 100 -2.276017019 -0.001639159 -1.01436632</div><div class='input'><span class='fu'>rnorm_multi</span>(<span class='fl'>100</span>, <span class='fl'>3</span>, <span class='fl'>0</span>, <span class='fl'>1</span>, <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>0.2</span>, -<span class='fl'>0.5</span>, <span class='fl'>0.2</span>, <span class='fl'>1</span>, <span class='fl'>0.5</span>, -<span class='fl'>0.5</span>, <span class='fl'>0.5</span>, <span class='fl'>1</span>), <span class='kw'>varnames</span><span class='kw'>=</span><span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='st'>"A"</span>, <span class='st'>"B"</span>, <span class='st'>"C"</span>))</div><div class='output co'>#&gt;               A            B           C
+#&gt; 1    0.98053619  1.397345080  1.24702308
+#&gt; 2    0.28494917 -0.929721452  0.11231993
+#&gt; 3   -0.48926432 -0.317158657  0.91387543
+#&gt; 4    0.62746163  0.690257228 -0.04676857
+#&gt; 5    0.52497027 -0.006770402 -0.54982277
+#&gt; 6   -0.08113113 -1.203695112 -0.38591526
+#&gt; 7    0.10950230 -0.448304595 -0.72749899
+#&gt; 8   -0.98054763 -1.413085775  0.44500300
+#&gt; 9   -0.22717869  0.588496717  1.47955055
+#&gt; 10  -0.08167748  0.129810359  0.62297867
+#&gt; 11  -1.06794579  1.622348860  2.17266535
+#&gt; 12   1.23713517  1.254934760  0.21802500
+#&gt; 13   2.34268548 -0.127564780 -1.84686336
+#&gt; 14  -0.79906111 -0.670100344 -0.78681222
+#&gt; 15   1.14762821  0.425896059 -0.19353105
+#&gt; 16  -0.68792927  0.746387469  1.17342419
+#&gt; 17  -0.42239464 -2.279769415 -1.45252677
+#&gt; 18  -0.71983873 -0.782993158 -0.48339888
+#&gt; 19  -0.27644209 -0.078365574 -0.15548499
+#&gt; 20   0.65702277 -2.167247198 -2.28768427
+#&gt; 21   1.36346504  2.807540375  0.61058213
+#&gt; 22   2.26453757 -0.107450280 -1.03851429
+#&gt; 23   1.07057295  1.036447960  0.77105891
+#&gt; 24  -0.75535093  0.189251094  1.72491550
+#&gt; 25  -0.68100214 -0.665149580 -0.04357655
+#&gt; 26  -0.47017350 -0.314580812 -0.27934568
+#&gt; 27   0.73476477  1.147051694  0.19216533
+#&gt; 28  -0.09149070  0.876867711 -0.08339957
+#&gt; 29  -2.05839553 -1.187120062 -0.84369069
+#&gt; 30  -0.62779679  0.680592120  0.13823150
+#&gt; 31   0.82896856  0.492073064 -0.10702775
+#&gt; 32   1.12230254  0.029101542 -0.69550195
+#&gt; 33   1.41109327 -0.547954608 -1.55760838
+#&gt; 34  -1.28998785 -0.644308963  0.45818445
+#&gt; 35  -0.74524292  0.423205419  2.23457494
+#&gt; 36   0.18163205  0.475178614  0.81660170
+#&gt; 37  -1.29214617  0.507173875  0.60586872
+#&gt; 38  -0.52453660 -0.422831247  0.14219949
+#&gt; 39   2.71082498  1.025301778 -0.77900169
+#&gt; 40   0.67234061 -0.927892656 -0.94160161
+#&gt; 41  -0.22932465 -0.452949466 -0.75393801
+#&gt; 42   0.79662820  0.012013809 -0.66456985
+#&gt; 43  -1.48598921 -1.485862845 -0.34075299
+#&gt; 44  -2.73794234 -0.333565773  1.35801078
+#&gt; 45   1.04730267 -0.239862856 -0.94911295
+#&gt; 46   0.79570220  0.409234356 -0.02512850
+#&gt; 47  -0.29797799 -0.960151155  0.32858669
+#&gt; 48   1.32398457  0.086940658 -0.84502893
+#&gt; 49   0.70645034  0.763352966  0.66553042
+#&gt; 50  -1.19011584 -0.898273386  0.01265418
+#&gt; 51   0.17313328  0.375553597 -0.01675357
+#&gt; 52   1.04061188 -0.219130525 -0.77337525
+#&gt; 53  -0.43883826 -0.042922039  1.27360295
+#&gt; 54   0.67987637 -1.058120498 -0.27502560
+#&gt; 55  -0.51468288  0.062507127  0.76999444
+#&gt; 56  -0.80241295 -0.945210621  0.34889021
+#&gt; 57  -0.47060134 -1.268959331  0.70686003
+#&gt; 58   2.49959376  0.682354301 -1.46377249
+#&gt; 59  -0.76711289 -0.641906992  0.26363487
+#&gt; 60   0.84037060 -0.130252667 -0.61136080
+#&gt; 61  -0.47759972  0.405378724  0.06637905
+#&gt; 62  -1.35795459  0.303024258  1.33414293
+#&gt; 63  -0.86120089 -1.445283701  0.98588190
+#&gt; 64   0.65405904 -1.262137582 -1.45782179
+#&gt; 65  -1.52487511 -1.185720620 -0.29528001
+#&gt; 66  -0.60310623  0.543202595  0.16109147
+#&gt; 67  -1.34640940  0.111464903  0.03001872
+#&gt; 68  -1.20726090 -0.211677096  0.84891723
+#&gt; 69  -0.07838011  0.191885581  0.98564804
+#&gt; 70   1.13262307  0.785543693 -0.06059885
+#&gt; 71   1.36490199 -0.252005899 -2.00968693
+#&gt; 72   1.18092139 -0.345183489 -0.80804496
+#&gt; 73  -1.50057211 -0.143484529  0.56351319
+#&gt; 74  -0.67605220 -0.161655297  0.70612018
+#&gt; 75  -0.39630979 -0.711680593 -0.01193046
+#&gt; 76  -1.06729240  0.790110596  0.42953674
+#&gt; 77   0.25135374  0.004217116 -1.46584325
+#&gt; 78  -0.62606452 -0.500831364  0.35233621
+#&gt; 79   0.22130814 -1.054521838 -0.13425086
+#&gt; 80  -0.82249462 -0.106502250  1.12587401
+#&gt; 81   1.13659660  0.694685870 -0.63335312
+#&gt; 82   0.63912932 -0.346001998 -0.15632373
+#&gt; 83   0.50735668 -0.121686372 -0.35290927
+#&gt; 84   0.95787156  0.663581444 -0.85623386
+#&gt; 85  -0.91227314 -1.085103286  0.84931769
+#&gt; 86  -0.23904032  0.820166860  0.16160248
+#&gt; 87  -0.71341295 -0.149361316  0.56590330
+#&gt; 88   1.64368977 -1.175172449 -2.12371033
+#&gt; 89  -0.35317370  0.697507425 -0.12301334
+#&gt; 90  -1.24671366 -0.243868992  1.74687093
+#&gt; 91  -0.64148189  0.722090091  1.47996273
+#&gt; 92   0.22026898  1.174067582  1.19864762
+#&gt; 93  -0.88325604  0.271478223  0.35463827
+#&gt; 94   0.07313435  1.405022266  1.00772946
+#&gt; 95  -1.47150701  0.560249257  1.03483886
+#&gt; 96  -1.10044697 -1.187067944  0.66307881
+#&gt; 97  -2.23696083  0.102519789  2.21376158
+#&gt; 98  -0.92624102 -1.158465566  0.37674556
+#&gt; 99  -1.39758231  0.256387690  1.30028559
+#&gt; 100 -0.21576305 -0.512530549 -0.64474625</div><div class='input'>
+</div></pre>
   </div>
   <div class="col-md-3 hidden-xs hidden-sm" id="sidebar">
     <h2>Contents</h2>
diff --git a/docs/reference/rnorm_pre.html b/docs/reference/rnorm_pre.html
index 4bc19a03..69bf328e 100644
--- a/docs/reference/rnorm_pre.html
+++ b/docs/reference/rnorm_pre.html
@@ -63,7 +63,7 @@
       </button>
       <span class="navbar-brand">
         <a class="navbar-link" href="../index.html">faux</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.0.0.9005</span>
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.0.0.9006</span>
       </span>
     </div>
 
@@ -85,9 +85,6 @@
     <span class="caret"></span>
   </a>
   <ul class="dropdown-menu" role="menu">
-    <li>
-      <a href="../articles/intro.html">Introduction to faux</a>
-    </li>
     <li>
       <a href="../articles/rnorm_multi.html">Simulate Correlated Variables</a>
     </li>
@@ -95,7 +92,7 @@
       <a href="../articles/sim_design.html">Simulate by Design</a>
     </li>
     <li>
-      <a href="../articles/simdf.html">Simulate from Existing Data</a>
+      <a href="../articles/sim_df.html">Simulate from Existing Data</a>
     </li>
   </ul>
 </li>
@@ -129,7 +126,7 @@ <h1>Correlated Normal Vector</h1>
     
     </div>
 
-    <pre class="usage"><span class='fu'>rnorm_pre</span>(<span class='no'>x</span>, <span class='kw'>rho</span> <span class='kw'>=</span> <span class='fl'>0</span>, <span class='kw'>ymean</span> <span class='kw'>=</span> <span class='fl'>0</span>, <span class='kw'>ysd</span> <span class='kw'>=</span> <span class='fl'>1</span>)</pre>
+    <pre class="usage"><span class='fu'>rnorm_pre</span>(<span class='no'>x</span>, <span class='kw'>mu</span> <span class='kw'>=</span> <span class='fl'>0</span>, <span class='kw'>sd</span> <span class='kw'>=</span> <span class='fl'>1</span>, <span class='kw'>r</span> <span class='kw'>=</span> <span class='fl'>0</span>)</pre>
     
     <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
     <table class="ref-arguments">
@@ -139,17 +136,17 @@ <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arg
       <td><p>the existing vector</p></td>
     </tr>
     <tr>
-      <th>rho</th>
-      <td><p>desired correlation between existing and returned vectors</p></td>
-    </tr>
-    <tr>
-      <th>ymean</th>
+      <th>mu</th>
       <td><p>desired mean of returned vector</p></td>
     </tr>
     <tr>
-      <th>ysd</th>
+      <th>sd</th>
       <td><p>desired SD of returned vector</p></td>
     </tr>
+    <tr>
+      <th>r</th>
+      <td><p>desired correlation between existing and returned vectors</p></td>
+    </tr>
     </table>
     
     <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
@@ -159,7 +156,7 @@ <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
 
     <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
     <pre class="examples"><div class='input'><span class='no'>v1</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/stats/topics/Normal'>rnorm</a></span>(<span class='fl'>10</span>)
-<span class='no'>v2</span> <span class='kw'>&lt;-</span> <span class='fu'>rnorm_pre</span>(<span class='no'>v1</span>, <span class='fl'>0.5</span>, <span class='fl'>0</span>, <span class='fl'>1</span>)
+<span class='no'>v2</span> <span class='kw'>&lt;-</span> <span class='fu'>rnorm_pre</span>(<span class='no'>v1</span>, <span class='fl'>0</span>, <span class='fl'>1</span>, <span class='fl'>0.5</span>)
 <span class='fu'><a href='https://www.rdocumentation.org/packages/stats/topics/cor'>cor</a></span>(<span class='no'>v1</span>, <span class='no'>v2</span>)</div><div class='output co'>#&gt; [1] 0.5</div></pre>
   </div>
   <div class="col-md-3 hidden-xs hidden-sm" id="sidebar">
diff --git a/docs/reference/select_num_grp.html b/docs/reference/select_num_grp.html
index f2ba3ed7..6541896e 100644
--- a/docs/reference/select_num_grp.html
+++ b/docs/reference/select_num_grp.html
@@ -63,7 +63,7 @@
       </button>
       <span class="navbar-brand">
         <a class="navbar-link" href="../index.html">faux</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.0.0.9005</span>
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.0.0.9006</span>
       </span>
     </div>
 
@@ -85,9 +85,6 @@
     <span class="caret"></span>
   </a>
   <ul class="dropdown-menu" role="menu">
-    <li>
-      <a href="../articles/intro.html">Introduction to faux</a>
-    </li>
     <li>
       <a href="../articles/rnorm_multi.html">Simulate Correlated Variables</a>
     </li>
@@ -95,7 +92,7 @@
       <a href="../articles/sim_design.html">Simulate by Design</a>
     </li>
     <li>
-      <a href="../articles/simdf.html">Simulate from Existing Data</a>
+      <a href="../articles/sim_df.html">Simulate from Existing Data</a>
     </li>
   </ul>
 </li>
@@ -129,17 +126,17 @@ <h1>Select grouping and numeric columns and group</h1>
     
     </div>
 
-    <pre class="usage"><span class='fu'>select_num_grp</span>(<span class='no'>dat</span>, <span class='kw'>grp_by</span> <span class='kw'>=</span> <span class='kw'>NULL</span>, <span class='kw'>cols</span> <span class='kw'>=</span> <span class='kw'>NULL</span>)</pre>
+    <pre class="usage"><span class='fu'>select_num_grp</span>(<span class='no'>.data</span>, <span class='kw'>between</span> <span class='kw'>=</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(), <span class='kw'>cols</span> <span class='kw'>=</span> <span class='kw'>NULL</span>)</pre>
     
     <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
     <table class="ref-arguments">
     <colgroup><col class="name" /><col class="desc" /></colgroup>
     <tr>
-      <th>dat</th>
-      <td><p>the existing dataframe</p></td>
+      <th>.data</th>
+      <td><p>the existing tbl</p></td>
     </tr>
     <tr>
-      <th>grp_by</th>
+      <th>between</th>
       <td><p>an optional list of column names to group by</p></td>
     </tr>
     <tr>
@@ -150,25 +147,25 @@ <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arg
     
     <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
 
-    <p>tibble</p>
+    <p>a tbl</p>
     
 
     <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='fu'>select_num_grp</span>(<span class='no'>iris</span>, <span class='st'>"Species"</span>)</div><div class='output co'>#&gt; <span style='color: #555555;'># A tibble: 150 x 5</span><span>
-#&gt; </span><span style='color: #555555;'># Groups:   Species [3]</span><span>
+    <pre class="examples"><div class='input'><span class='fu'>select_num_grp</span>(<span class='no'>iris</span>, <span class='st'>"Species"</span>)</div><div class='output co'>#&gt; <span style='color: #949494;'># A tibble: 150 x 5</span><span>
+#&gt; </span><span style='color: #949494;'># Groups:   Species [3]</span><span>
 #&gt;    Species Sepal.Length Sepal.Width Petal.Length Petal.Width
-#&gt;    </span><span style='color: #555555;font-style: italic;'>&lt;fct&gt;</span><span>          </span><span style='color: #555555;font-style: italic;'>&lt;dbl&gt;</span><span>       </span><span style='color: #555555;font-style: italic;'>&lt;dbl&gt;</span><span>        </span><span style='color: #555555;font-style: italic;'>&lt;dbl&gt;</span><span>       </span><span style='color: #555555;font-style: italic;'>&lt;dbl&gt;</span><span>
-#&gt; </span><span style='color: #555555;'> 1</span><span> setosa           5.1         3.5          1.4         0.2
-#&gt; </span><span style='color: #555555;'> 2</span><span> setosa           4.9         3            1.4         0.2
-#&gt; </span><span style='color: #555555;'> 3</span><span> setosa           4.7         3.2          1.3         0.2
-#&gt; </span><span style='color: #555555;'> 4</span><span> setosa           4.6         3.1          1.5         0.2
-#&gt; </span><span style='color: #555555;'> 5</span><span> setosa           5           3.6          1.4         0.2
-#&gt; </span><span style='color: #555555;'> 6</span><span> setosa           5.4         3.9          1.7         0.4
-#&gt; </span><span style='color: #555555;'> 7</span><span> setosa           4.6         3.4          1.4         0.3
-#&gt; </span><span style='color: #555555;'> 8</span><span> setosa           5           3.4          1.5         0.2
-#&gt; </span><span style='color: #555555;'> 9</span><span> setosa           4.4         2.9          1.4         0.2
-#&gt; </span><span style='color: #555555;'>10</span><span> setosa           4.9         3.1          1.5         0.1
-#&gt; </span><span style='color: #555555;'># … with 140 more rows</span><span></div></span></pre>
+#&gt;    </span><span style='color: #949494;font-style: italic;'>&lt;fct&gt;</span><span>          </span><span style='color: #949494;font-style: italic;'>&lt;dbl&gt;</span><span>       </span><span style='color: #949494;font-style: italic;'>&lt;dbl&gt;</span><span>        </span><span style='color: #949494;font-style: italic;'>&lt;dbl&gt;</span><span>       </span><span style='color: #949494;font-style: italic;'>&lt;dbl&gt;</span><span>
+#&gt; </span><span style='color: #BCBCBC;'> 1</span><span> setosa           5.1         3.5          1.4         0.2
+#&gt; </span><span style='color: #BCBCBC;'> 2</span><span> setosa           4.9         3            1.4         0.2
+#&gt; </span><span style='color: #BCBCBC;'> 3</span><span> setosa           4.7         3.2          1.3         0.2
+#&gt; </span><span style='color: #BCBCBC;'> 4</span><span> setosa           4.6         3.1          1.5         0.2
+#&gt; </span><span style='color: #BCBCBC;'> 5</span><span> setosa           5           3.6          1.4         0.2
+#&gt; </span><span style='color: #BCBCBC;'> 6</span><span> setosa           5.4         3.9          1.7         0.4
+#&gt; </span><span style='color: #BCBCBC;'> 7</span><span> setosa           4.6         3.4          1.4         0.3
+#&gt; </span><span style='color: #BCBCBC;'> 8</span><span> setosa           5           3.4          1.5         0.2
+#&gt; </span><span style='color: #BCBCBC;'> 9</span><span> setosa           4.4         2.9          1.4         0.2
+#&gt; </span><span style='color: #BCBCBC;'>10</span><span> setosa           4.9         3.1          1.5         0.1
+#&gt; </span><span style='color: #949494;'># … with 140 more rows</span><span></div></span></pre>
   </div>
   <div class="col-md-3 hidden-xs hidden-sm" id="sidebar">
     <h2>Contents</h2>
diff --git a/docs/reference/sim_design.html b/docs/reference/sim_design.html
index fbf40eaf..9758a981 100644
--- a/docs/reference/sim_design.html
+++ b/docs/reference/sim_design.html
@@ -32,7 +32,7 @@
 
 <meta property="og:title" content="Simulate Data from Design — sim_design" />
 
-<meta property="og:description" content="sim_design generates a dataframe with a specified within and between design" />
+<meta property="og:description" content="sim_design() generates a data table with a specified within and between design." />
 <meta name="twitter:card" content="summary" />
 
 
@@ -63,7 +63,7 @@
       </button>
       <span class="navbar-brand">
         <a class="navbar-link" href="../index.html">faux</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.0.0.9005</span>
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.0.0.9006</span>
       </span>
     </div>
 
@@ -85,9 +85,6 @@
     <span class="caret"></span>
   </a>
   <ul class="dropdown-menu" role="menu">
-    <li>
-      <a href="../articles/intro.html">Introduction to faux</a>
-    </li>
     <li>
       <a href="../articles/rnorm_multi.html">Simulate Correlated Variables</a>
     </li>
@@ -95,7 +92,7 @@
       <a href="../articles/sim_design.html">Simulate by Design</a>
     </li>
     <li>
-      <a href="../articles/simdf.html">Simulate from Existing Data</a>
+      <a href="../articles/sim_df.html">Simulate from Existing Data</a>
     </li>
   </ul>
 </li>
@@ -125,12 +122,12 @@ <h1>Simulate Data from Design</h1>
 
     <div class="ref-description">
     
-    <p><code>sim_design</code> generates a dataframe with a specified within and between design</p>
+    <p><code>sim_design()</code> generates a data table with a specified within and between design.</p>
     
     </div>
 
-    <pre class="usage"><span class='fu'>sim_design</span>(<span class='kw'>within</span> <span class='kw'>=</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/list'>list</a></span>(), <span class='kw'>between</span> <span class='kw'>=</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/list'>list</a></span>(), <span class='kw'>n</span> <span class='kw'>=</span> <span class='fl'>100</span>, <span class='kw'>cors</span> <span class='kw'>=</span> <span class='fl'>0</span>,
-  <span class='kw'>mu</span> <span class='kw'>=</span> <span class='fl'>0</span>, <span class='kw'>sd</span> <span class='kw'>=</span> <span class='fl'>1</span>, <span class='kw'>empirical</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>, <span class='kw'>frame_long</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>)</pre>
+    <pre class="usage"><span class='fu'>sim_design</span>(<span class='kw'>within</span> <span class='kw'>=</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/list'>list</a></span>(), <span class='kw'>between</span> <span class='kw'>=</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/list'>list</a></span>(), <span class='kw'>n</span> <span class='kw'>=</span> <span class='fl'>100</span>, <span class='kw'>mu</span> <span class='kw'>=</span> <span class='fl'>0</span>,
+  <span class='kw'>sd</span> <span class='kw'>=</span> <span class='fl'>1</span>, <span class='kw'>r</span> <span class='kw'>=</span> <span class='fl'>0</span>, <span class='kw'>empirical</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>, <span class='kw'>long</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>)</pre>
     
     <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
     <table class="ref-arguments">
@@ -147,10 +144,6 @@ <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arg
       <th>n</th>
       <td><p>the number of samples required</p></td>
     </tr>
-    <tr>
-      <th>cors</th>
-      <td><p>the correlations among the variables (can be a single number, vars\*vars matrix, vars\*vars vector, or a vars\*(vars-1)/2 vector)</p></td>
-    </tr>
     <tr>
       <th>mu</th>
       <td><p>a vector giving the means of the variables (numeric vector of length 1 or vars)</p></td>
@@ -159,19 +152,23 @@ <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arg
       <th>sd</th>
       <td><p>the standard deviations of the variables (numeric vector of length 1 or vars)</p></td>
     </tr>
+    <tr>
+      <th>r</th>
+      <td><p>the correlations among the variables (can be a single number, vars\*vars matrix, vars\*vars vector, or a vars\*(vars-1)/2 vector)</p></td>
+    </tr>
     <tr>
       <th>empirical</th>
-      <td><p>logical. If true, mu, sd and cors specify the empirical not population mean, sd and covariance</p></td>
+      <td><p>logical. If true, mu, sd and r specify the empirical not population mean, sd and covariance</p></td>
     </tr>
     <tr>
-      <th>frame_long</th>
-      <td><p>Whether the returned dataframe is in wide (default = FALSE) or long (TRUE) format</p></td>
+      <th>long</th>
+      <td><p>Whether the returned tbl is in wide (default = FALSE) or long (TRUE) format</p></td>
     </tr>
     </table>
     
     <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
 
-    <p>dataframe</p>
+    <p>a tbl</p>
     
 
   </div>
diff --git a/docs/reference/sim_design_.html b/docs/reference/sim_design_.html
index 5b6d3a0e..997e7e70 100644
--- a/docs/reference/sim_design_.html
+++ b/docs/reference/sim_design_.html
@@ -32,7 +32,7 @@
 
 <meta property="og:title" content="Simulate Data from Design — sim_design_" />
 
-<meta property="og:description" content="sim_from_design generates a dataframe with a specified design" />
+<meta property="og:description" content="sim_design_ generates a data table with a specified design" />
 <meta name="twitter:card" content="summary" />
 
 
@@ -63,7 +63,7 @@
       </button>
       <span class="navbar-brand">
         <a class="navbar-link" href="../index.html">faux</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.0.0.9005</span>
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.0.0.9006</span>
       </span>
     </div>
 
@@ -85,9 +85,6 @@
     <span class="caret"></span>
   </a>
   <ul class="dropdown-menu" role="menu">
-    <li>
-      <a href="../articles/intro.html">Introduction to faux</a>
-    </li>
     <li>
       <a href="../articles/rnorm_multi.html">Simulate Correlated Variables</a>
     </li>
@@ -95,7 +92,7 @@
       <a href="../articles/sim_design.html">Simulate by Design</a>
     </li>
     <li>
-      <a href="../articles/simdf.html">Simulate from Existing Data</a>
+      <a href="../articles/sim_df.html">Simulate from Existing Data</a>
     </li>
   </ul>
 </li>
@@ -125,11 +122,11 @@ <h1>Simulate Data from Design</h1>
 
     <div class="ref-description">
     
-    <p><code>sim_from_design</code> generates a dataframe with a specified design</p>
+    <p><code>sim_design_</code> generates a data table with a specified design</p>
     
     </div>
 
-    <pre class="usage"><span class='fu'>sim_design_</span>(<span class='no'>design</span>, <span class='kw'>empirical</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>, <span class='kw'>frame_long</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>)</pre>
+    <pre class="usage"><span class='fu'>sim_design_</span>(<span class='no'>design</span>, <span class='kw'>empirical</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>, <span class='kw'>long</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>)</pre>
     
     <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
     <table class="ref-arguments">
@@ -140,17 +137,17 @@ <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arg
     </tr>
     <tr>
       <th>empirical</th>
-      <td><p>logical. If true, mu, sd and cors specify the empirical not population mean, sd and covariance</p></td>
+      <td><p>logical. If true, mu, sd and r specify the empirical not population mean, sd and covariance</p></td>
     </tr>
     <tr>
-      <th>frame_long</th>
-      <td><p>Whether the returned dataframe is in wide (default = FALSE) or long (TRUE) format</p></td>
+      <th>long</th>
+      <td><p>Whether the returned tbl is in wide (default = FALSE) or long (TRUE) format</p></td>
     </tr>
     </table>
     
     <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
 
-    <p>dataframe</p>
+    <p>a tbl</p>
     
 
   </div>
diff --git a/docs/reference/sim_df.html b/docs/reference/sim_df.html
new file mode 100644
index 00000000..e3ea6eb6
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+    </li>
+    <li>
+      <a href="../articles/sim_df.html">Simulate from Existing Data</a>
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+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Simulate an existing dataframe</h1>
+    
+    <div class="hidden name"><code>sim_df.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    
+    <p><code>sim_df</code> Produces a data table with the same distributions and correlations as an existing data table Only returns numeric columns and simulates all numeric variables from a continuous normal distribution (for now).</p>
+    
+    </div>
+
+    <pre class="usage"><span class='fu'>sim_df</span>(<span class='no'>.data</span>, <span class='kw'>n</span> <span class='kw'>=</span> <span class='fl'>100</span>, <span class='kw'>between</span> <span class='kw'>=</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(), <span class='kw'>empirical</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>,
+  <span class='kw'>grp_by</span> <span class='kw'>=</span> <span class='kw'>NULL</span>)</pre>
+    
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>.data</th>
+      <td><p>the existing tbl (must be in wide format)</p></td>
+    </tr>
+    <tr>
+      <th>n</th>
+      <td><p>the number of samples to return per group</p></td>
+    </tr>
+    <tr>
+      <th>between</th>
+      <td><p>a list of the between-subject columns</p></td>
+    </tr>
+    <tr>
+      <th>empirical</th>
+      <td><p>logical. Passed on to rnorm_multi</p></td>
+    </tr>
+    <tr>
+      <th>grp_by</th>
+      <td><p>(deprecated; use between)</p></td>
+    </tr>
+    </table>
+    
+    <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
+
+    <p>a tbl</p>
+    
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='no'>iris100</span> <span class='kw'>&lt;-</span> <span class='fu'>sim_df</span>(<span class='no'>iris</span>, <span class='fl'>100</span>)
+<span class='no'>iris_species</span> <span class='kw'>&lt;-</span> <span class='fu'>sim_df</span>(<span class='no'>iris</span>, <span class='fl'>100</span>, <span class='kw'>between</span> <span class='kw'>=</span> <span class='st'>"Species"</span>)</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="sidebar">
+    <h2>Contents</h2>
+    <ul class="nav nav-pills nav-stacked">
+      <li><a href="#arguments">Arguments</a></li>
+      
+      <li><a href="#value">Value</a></li>
+      
+      <li><a href="#examples">Examples</a></li>
+    </ul>
+
+  </div>
+</div>
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+      <div class="copyright">
+  <p>Developed by Lisa DeBruine.</p>
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diff --git a/docs/reference/sim_mixed_df.html b/docs/reference/sim_mixed_df.html
new file mode 100644
index 00000000..36852235
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+++ b/docs/reference/sim_mixed_df.html
@@ -0,0 +1,297 @@
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+
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+      <a href="../articles/sim_df.html">Simulate from Existing Data</a>
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+    <div class="page-header">
+    <h1>Generate a sample with random intercepts for subjects and items</h1>
+    
+    <div class="hidden name"><code>sim_mixed_df.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    
+    <p><code>sim_mixed_df()</code> produces a data table with the same distributions of by-subject and by-item random intercepts as an existing data table.</p>
+    
+    </div>
+
+    <pre class="usage"><span class='fu'>sim_mixed_df</span>(<span class='no'>.data</span>, <span class='kw'>sub_n</span> <span class='kw'>=</span> <span class='fl'>100</span>, <span class='kw'>item_n</span> <span class='kw'>=</span> <span class='fl'>25</span>, <span class='kw'>dv</span> <span class='kw'>=</span> <span class='fl'>1</span>, <span class='kw'>sub_id</span> <span class='kw'>=</span> <span class='fl'>2</span>,
+  <span class='kw'>item_id</span> <span class='kw'>=</span> <span class='fl'>3</span>)</pre>
+    
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>.data</th>
+      <td><p>the existing tbl</p></td>
+    </tr>
+    <tr>
+      <th>sub_n</th>
+      <td><p>the number of subjects to simulate</p></td>
+    </tr>
+    <tr>
+      <th>item_n</th>
+      <td><p>the number of items to simulate</p></td>
+    </tr>
+    <tr>
+      <th>dv</th>
+      <td><p>the column name or index containing the DV</p></td>
+    </tr>
+    <tr>
+      <th>sub_id</th>
+      <td><p>the column name or index for the subject IDs</p></td>
+    </tr>
+    <tr>
+      <th>item_id</th>
+      <td><p>the column name or index for the item IDs</p></td>
+    </tr>
+    </table>
+    
+    <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
+
+    <p>a tbl</p>
+    
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='fu'>sim_mixed_df</span>(<span class='no'>faceratings</span>, <span class='fl'>10</span>, <span class='fl'>10</span>, <span class='st'>"rating"</span>, <span class='st'>"rater_id"</span>, <span class='st'>"face_id"</span>)</div><div class='output co'>#&gt;     sub_id item_id       sub_i     item_i        dv
+#&gt; 1        1       1  0.15336871  0.1152507 3.4263347
+#&gt; 2        2       1 -0.07546968  0.1152507 2.6552210
+#&gt; 3        3       1 -0.09464565  0.1152507 2.5473771
+#&gt; 4        4       1  0.13680669  0.1152507 4.5736075
+#&gt; 5        5       1  0.46710613  0.1152507 4.6470300
+#&gt; 6        6       1  0.71713231  0.1152507 5.0470723
+#&gt; 7        7       1 -0.16377959  0.1152507 3.0286129
+#&gt; 8        8       1 -0.03648029  0.1152507 3.0203508
+#&gt; 9        9       1 -0.44490238  0.1152507 2.4007496
+#&gt; 10      10       1 -0.50294582  0.1152507 1.4482325
+#&gt; 11       1       2  0.15336871  0.9694343 4.2646276
+#&gt; 12       2       2 -0.07546968  0.9694343 3.5117228
+#&gt; 13       3       2 -0.09464565  0.9694343 1.9205807
+#&gt; 14       4       2  0.13680669  0.9694343 3.3332175
+#&gt; 15       5       2  0.46710613  0.9694343 4.1037056
+#&gt; 16       6       2  0.71713231  0.9694343 4.8340290
+#&gt; 17       7       2 -0.16377959  0.9694343 4.3931885
+#&gt; 18       8       2 -0.03648029  0.9694343 3.5224828
+#&gt; 19       9       2 -0.44490238  0.9694343 2.7403387
+#&gt; 20      10       2 -0.50294582  0.9694343 4.9209943
+#&gt; 21       1       3  0.15336871 -0.1809222 2.3955324
+#&gt; 22       2       3 -0.07546968 -0.1809222 5.0649247
+#&gt; 23       3       3 -0.09464565 -0.1809222 3.0032075
+#&gt; 24       4       3  0.13680669 -0.1809222 2.4135067
+#&gt; 25       5       3  0.46710613 -0.1809222 4.5656069
+#&gt; 26       6       3  0.71713231 -0.1809222 2.8650467
+#&gt; 27       7       3 -0.16377959 -0.1809222 3.6126806
+#&gt; 28       8       3 -0.03648029 -0.1809222 0.9722492
+#&gt; 29       9       3 -0.44490238 -0.1809222 0.6125408
+#&gt; 30      10       3 -0.50294582 -0.1809222 3.8909402
+#&gt; 31       1       4  0.15336871  1.0979794 5.9448229
+#&gt; 32       2       4 -0.07546968  1.0979794 3.9574235
+#&gt; 33       3       4 -0.09464565  1.0979794 4.8079004
+#&gt; 34       4       4  0.13680669  1.0979794 5.0826263
+#&gt; 35       5       4  0.46710613  1.0979794 3.3817108
+#&gt; 36       6       4  0.71713231  1.0979794 3.2445244
+#&gt; 37       7       4 -0.16377959  1.0979794 4.2746244
+#&gt; 38       8       4 -0.03648029  1.0979794 4.7967650
+#&gt; 39       9       4 -0.44490238  1.0979794 4.3737837
+#&gt; 40      10       4 -0.50294582  1.0979794 4.2923638
+#&gt; 41       1       5  0.15336871  0.2255086 5.9170173
+#&gt; 42       2       5 -0.07546968  0.2255086 2.7538325
+#&gt; 43       3       5 -0.09464565  0.2255086 3.1842397
+#&gt; 44       4       5  0.13680669  0.2255086 4.2260520
+#&gt; 45       5       5  0.46710613  0.2255086 1.5543152
+#&gt; 46       6       5  0.71713231  0.2255086 3.9556215
+#&gt; 47       7       5 -0.16377959  0.2255086 3.8447343
+#&gt; 48       8       5 -0.03648029  0.2255086 4.5385200
+#&gt; 49       9       5 -0.44490238  0.2255086 2.6456965
+#&gt; 50      10       5 -0.50294582  0.2255086 2.2025837
+#&gt; 51       1       6  0.15336871 -0.5688417 3.5177725
+#&gt; 52       2       6 -0.07546968 -0.5688417 2.9838297
+#&gt; 53       3       6 -0.09464565 -0.5688417 3.5923231
+#&gt; 54       4       6  0.13680669 -0.5688417 3.1022826
+#&gt; 55       5       6  0.46710613 -0.5688417 2.7535176
+#&gt; 56       6       6  0.71713231 -0.5688417 5.4940446
+#&gt; 57       7       6 -0.16377959 -0.5688417 2.5234260
+#&gt; 58       8       6 -0.03648029 -0.5688417 0.3511046
+#&gt; 59       9       6 -0.44490238 -0.5688417 1.8498874
+#&gt; 60      10       6 -0.50294582 -0.5688417 3.5379786
+#&gt; 61       1       7  0.15336871 -0.1003512 4.2409599
+#&gt; 62       2       7 -0.07546968 -0.1003512 3.5556705
+#&gt; 63       3       7 -0.09464565 -0.1003512 2.6899611
+#&gt; 64       4       7  0.13680669 -0.1003512 2.0439883
+#&gt; 65       5       7  0.46710613 -0.1003512 5.0354057
+#&gt; 66       6       7  0.71713231 -0.1003512 3.5521496
+#&gt; 67       7       7 -0.16377959 -0.1003512 2.3525066
+#&gt; 68       8       7 -0.03648029 -0.1003512 3.5415480
+#&gt; 69       9       7 -0.44490238 -0.1003512 4.1344729
+#&gt; 70      10       7 -0.50294582 -0.1003512 2.3269392
+#&gt; 71       1       8  0.15336871  1.6989795 6.4250556
+#&gt; 72       2       8 -0.07546968  1.6989795 5.7450023
+#&gt; 73       3       8 -0.09464565  1.6989795 3.8872425
+#&gt; 74       4       8  0.13680669  1.6989795 4.5973502
+#&gt; 75       5       8  0.46710613  1.6989795 5.7349937
+#&gt; 76       6       8  0.71713231  1.6989795 2.6852695
+#&gt; 77       7       8 -0.16377959  1.6989795 3.7919020
+#&gt; 78       8       8 -0.03648029  1.6989795 4.7810577
+#&gt; 79       9       8 -0.44490238  1.6989795 5.1999969
+#&gt; 80      10       8 -0.50294582  1.6989795 5.4375538
+#&gt; 81       1       9  0.15336871  1.2452224 5.3180720
+#&gt; 82       2       9 -0.07546968  1.2452224 2.7003936
+#&gt; 83       3       9 -0.09464565  1.2452224 4.3759073
+#&gt; 84       4       9  0.13680669  1.2452224 6.5338902
+#&gt; 85       5       9  0.46710613  1.2452224 3.8544295
+#&gt; 86       6       9  0.71713231  1.2452224 5.2503336
+#&gt; 87       7       9 -0.16377959  1.2452224 4.6527757
+#&gt; 88       8       9 -0.03648029  1.2452224 3.6883031
+#&gt; 89       9       9 -0.44490238  1.2452224 2.8378979
+#&gt; 90      10       9 -0.50294582  1.2452224 4.8626384
+#&gt; 91       1      10  0.15336871 -0.2064970 4.7460439
+#&gt; 92       2      10 -0.07546968 -0.2064970 1.8389187
+#&gt; 93       3      10 -0.09464565 -0.2064970 1.3021962
+#&gt; 94       4      10  0.13680669 -0.2064970 2.4226626
+#&gt; 95       5      10  0.46710613 -0.2064970 3.0711292
+#&gt; 96       6      10  0.71713231 -0.2064970 2.2198113
+#&gt; 97       7      10 -0.16377959 -0.2064970 0.9575364
+#&gt; 98       8      10 -0.03648029 -0.2064970 2.6135275
+#&gt; 99       9      10 -0.44490238 -0.2064970 1.2924662
+#&gt; 100     10      10 -0.50294582 -0.2064970 0.8947152</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="sidebar">
+    <h2>Contents</h2>
+    <ul class="nav nav-pills nav-stacked">
+      <li><a href="#arguments">Arguments</a></li>
+      
+      <li><a href="#value">Value</a></li>
+      
+      <li><a href="#examples">Examples</a></li>
+    </ul>
+
+  </div>
+</div>
+
+      <footer>
+      <div class="copyright">
+  <p>Developed by Lisa DeBruine.</p>
+</div>
+
+<div class="pkgdown">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.3.0.</p>
+</div>
+      </footer>
+   </div>
+
+  
+
+  </body>
+</html>
+
diff --git a/docs/reference/simdf_mixed.html b/docs/reference/simdf_mixed.html
index 1515559f..2dfb5c19 100644
--- a/docs/reference/simdf_mixed.html
+++ b/docs/reference/simdf_mixed.html
@@ -85,9 +85,6 @@
     <span class="caret"></span>
   </a>
   <ul class="dropdown-menu" role="menu">
-    <li>
-      <a href="../articles/intro.html">Introduction to faux</a>
-    </li>
     <li>
       <a href="../articles/rnorm_multi.html">Simulate Correlated Variables</a>
     </li>
@@ -95,7 +92,7 @@
       <a href="../articles/sim_design.html">Simulate by Design</a>
     </li>
     <li>
-      <a href="../articles/simdf.html">Simulate from Existing Data</a>
+      <a href="../articles/sim_df.html">Simulate from Existing Data</a>
     </li>
   </ul>
 </li>
@@ -167,107 +164,107 @@ <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
     
 
     <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='fu'>simdf_mixed</span>(<span class='no'>faceratings</span>, <span class='fl'>10</span>, <span class='fl'>10</span>, <span class='st'>"rating"</span>, <span class='st'>"rater_id"</span>, <span class='st'>"face_id"</span>)</div><div class='output co'>#&gt;     sub_id item_id      sub_i      item_i         dv
-#&gt; 1        1       1  0.7171917 -0.16440417  2.7076926
-#&gt; 2        2       1  1.5872296 -0.16440417  4.0497225
-#&gt; 3        3       1 -0.7289360 -0.16440417  2.8124829
-#&gt; 4        4       1  0.6003483 -0.16440417  1.6454961
-#&gt; 5        5       1  0.5163209 -0.16440417  2.8399589
-#&gt; 6        6       1  0.2563833 -0.16440417  3.0883100
-#&gt; 7        7       1 -0.7781445 -0.16440417  3.0922434
-#&gt; 8        8       1  1.1424562 -0.16440417  3.7438529
-#&gt; 9        9       1  0.1711736 -0.16440417  1.9164980
-#&gt; 10      10       1  1.0828005 -0.16440417  2.1094020
-#&gt; 11       1       2  0.7171917  0.37636537  6.5942167
-#&gt; 12       2       2  1.5872296  0.37636537  3.8486785
-#&gt; 13       3       2 -0.7289360  0.37636537  3.4411992
-#&gt; 14       4       2  0.6003483  0.37636537  5.3235771
-#&gt; 15       5       2  0.5163209  0.37636537  4.2771207
-#&gt; 16       6       2  0.2563833  0.37636537  3.3479907
-#&gt; 17       7       2 -0.7781445  0.37636537  2.7002602
-#&gt; 18       8       2  1.1424562  0.37636537  4.1520841
-#&gt; 19       9       2  0.1711736  0.37636537  5.1249236
-#&gt; 20      10       2  1.0828005  0.37636537  4.4809776
-#&gt; 21       1       3  0.7171917 -0.82422093  3.2861012
-#&gt; 22       2       3  1.5872296 -0.82422093  2.8786693
-#&gt; 23       3       3 -0.7289360 -0.82422093  1.9902052
-#&gt; 24       4       3  0.6003483 -0.82422093  2.0458923
-#&gt; 25       5       3  0.5163209 -0.82422093  1.5216236
-#&gt; 26       6       3  0.2563833 -0.82422093  3.3659935
-#&gt; 27       7       3 -0.7781445 -0.82422093  2.9983831
-#&gt; 28       8       3  1.1424562 -0.82422093  3.4864340
-#&gt; 29       9       3  0.1711736 -0.82422093  2.7199936
-#&gt; 30      10       3  1.0828005 -0.82422093  4.6086848
-#&gt; 31       1       4  0.7171917 -0.91128136  1.6789966
-#&gt; 32       2       4  1.5872296 -0.91128136  5.6968101
-#&gt; 33       3       4 -0.7289360 -0.91128136 -1.0487718
-#&gt; 34       4       4  0.6003483 -0.91128136  2.0597989
-#&gt; 35       5       4  0.5163209 -0.91128136  2.3732315
-#&gt; 36       6       4  0.2563833 -0.91128136  4.2745678
-#&gt; 37       7       4 -0.7781445 -0.91128136  1.8469632
-#&gt; 38       8       4  1.1424562 -0.91128136  4.1014591
-#&gt; 39       9       4  0.1711736 -0.91128136  1.0318854
-#&gt; 40      10       4  1.0828005 -0.91128136  4.2240532
-#&gt; 41       1       5  0.7171917  1.66678147  5.0273318
-#&gt; 42       2       5  1.5872296  1.66678147  3.5821303
-#&gt; 43       3       5 -0.7289360  1.66678147  5.4538830
-#&gt; 44       4       5  0.6003483  1.66678147  7.9807309
-#&gt; 45       5       5  0.5163209  1.66678147  3.7473337
-#&gt; 46       6       5  0.2563833  1.66678147  7.1439931
-#&gt; 47       7       5 -0.7781445  1.66678147  4.8426279
-#&gt; 48       8       5  1.1424562  1.66678147  6.6205912
-#&gt; 49       9       5  0.1711736  1.66678147  6.7093920
-#&gt; 50      10       5  1.0828005  1.66678147  4.3002833
-#&gt; 51       1       6  0.7171917 -0.32253158  3.5070761
-#&gt; 52       2       6  1.5872296 -0.32253158  4.5057448
-#&gt; 53       3       6 -0.7289360 -0.32253158  0.9024044
-#&gt; 54       4       6  0.6003483 -0.32253158  3.9435972
-#&gt; 55       5       6  0.5163209 -0.32253158  4.3432880
-#&gt; 56       6       6  0.2563833 -0.32253158  1.5616119
-#&gt; 57       7       6 -0.7781445 -0.32253158  1.5701492
-#&gt; 58       8       6  1.1424562 -0.32253158  1.8015241
-#&gt; 59       9       6  0.1711736 -0.32253158  2.9668905
-#&gt; 60      10       6  1.0828005 -0.32253158  4.5541003
-#&gt; 61       1       7  0.7171917  0.06766548  3.6839723
-#&gt; 62       2       7  1.5872296  0.06766548  3.5298722
-#&gt; 63       3       7 -0.7289360  0.06766548  1.3879482
-#&gt; 64       4       7  0.6003483  0.06766548  3.0134067
-#&gt; 65       5       7  0.5163209  0.06766548  3.8805034
-#&gt; 66       6       7  0.2563833  0.06766548  2.8650564
-#&gt; 67       7       7 -0.7781445  0.06766548  3.9055132
-#&gt; 68       8       7  1.1424562  0.06766548  5.1346467
-#&gt; 69       9       7  0.1711736  0.06766548  2.2637057
-#&gt; 70      10       7  1.0828005  0.06766548  3.8728360
-#&gt; 71       1       8  0.7171917 -0.41971766  4.1334290
-#&gt; 72       2       8  1.5872296 -0.41971766  4.1028115
-#&gt; 73       3       8 -0.7289360 -0.41971766  2.0302724
-#&gt; 74       4       8  0.6003483 -0.41971766  2.8500859
-#&gt; 75       5       8  0.5163209 -0.41971766  1.2627937
-#&gt; 76       6       8  0.2563833 -0.41971766  2.0276961
-#&gt; 77       7       8 -0.7781445 -0.41971766  0.9953581
-#&gt; 78       8       8  1.1424562 -0.41971766  4.0599506
-#&gt; 79       9       8  0.1711736 -0.41971766  3.1317760
-#&gt; 80      10       8  1.0828005 -0.41971766  3.4296845
-#&gt; 81       1       9  0.7171917 -0.09253167  2.7561118
-#&gt; 82       2       9  1.5872296 -0.09253167  4.7486546
-#&gt; 83       3       9 -0.7289360 -0.09253167  0.9585535
-#&gt; 84       4       9  0.6003483 -0.09253167  3.6791317
-#&gt; 85       5       9  0.5163209 -0.09253167  2.7224103
-#&gt; 86       6       9  0.2563833 -0.09253167  2.6014336
-#&gt; 87       7       9 -0.7781445 -0.09253167  0.6113194
-#&gt; 88       8       9  1.1424562 -0.09253167  5.3929322
-#&gt; 89       9       9  0.1711736 -0.09253167  3.5604510
-#&gt; 90      10       9  1.0828005 -0.09253167  3.8776255
-#&gt; 91       1      10  0.7171917  1.38999715  5.8347359
-#&gt; 92       2      10  1.5872296  1.38999715  6.5293892
-#&gt; 93       3      10 -0.7289360  1.38999715  4.4470914
-#&gt; 94       4      10  0.6003483  1.38999715  5.8856564
-#&gt; 95       5      10  0.5163209  1.38999715  4.7294257
-#&gt; 96       6      10  0.2563833  1.38999715  5.8323700
-#&gt; 97       7      10 -0.7781445  1.38999715  3.5409873
-#&gt; 98       8      10  1.1424562  1.38999715  6.7181566
-#&gt; 99       9      10  0.1711736  1.38999715  4.5778140
-#&gt; 100     10      10  1.0828005  1.38999715  4.5562784</div></pre>
+    <pre class="examples"><div class='input'><span class='fu'>simdf_mixed</span>(<span class='no'>faceratings</span>, <span class='fl'>10</span>, <span class='fl'>10</span>, <span class='st'>"rating"</span>, <span class='st'>"rater_id"</span>, <span class='st'>"face_id"</span>)</div><div class='output co'>#&gt;     sub_id item_id       sub_i     item_i        dv
+#&gt; 1        1       1  0.15336871  0.1152507 3.4263347
+#&gt; 2        2       1 -0.07546968  0.1152507 2.6552210
+#&gt; 3        3       1 -0.09464565  0.1152507 2.5473771
+#&gt; 4        4       1  0.13680669  0.1152507 4.5736075
+#&gt; 5        5       1  0.46710613  0.1152507 4.6470300
+#&gt; 6        6       1  0.71713231  0.1152507 5.0470723
+#&gt; 7        7       1 -0.16377959  0.1152507 3.0286129
+#&gt; 8        8       1 -0.03648029  0.1152507 3.0203508
+#&gt; 9        9       1 -0.44490238  0.1152507 2.4007496
+#&gt; 10      10       1 -0.50294582  0.1152507 1.4482325
+#&gt; 11       1       2  0.15336871  0.9694343 4.2646276
+#&gt; 12       2       2 -0.07546968  0.9694343 3.5117228
+#&gt; 13       3       2 -0.09464565  0.9694343 1.9205807
+#&gt; 14       4       2  0.13680669  0.9694343 3.3332175
+#&gt; 15       5       2  0.46710613  0.9694343 4.1037056
+#&gt; 16       6       2  0.71713231  0.9694343 4.8340290
+#&gt; 17       7       2 -0.16377959  0.9694343 4.3931885
+#&gt; 18       8       2 -0.03648029  0.9694343 3.5224828
+#&gt; 19       9       2 -0.44490238  0.9694343 2.7403387
+#&gt; 20      10       2 -0.50294582  0.9694343 4.9209943
+#&gt; 21       1       3  0.15336871 -0.1809222 2.3955324
+#&gt; 22       2       3 -0.07546968 -0.1809222 5.0649247
+#&gt; 23       3       3 -0.09464565 -0.1809222 3.0032075
+#&gt; 24       4       3  0.13680669 -0.1809222 2.4135067
+#&gt; 25       5       3  0.46710613 -0.1809222 4.5656069
+#&gt; 26       6       3  0.71713231 -0.1809222 2.8650467
+#&gt; 27       7       3 -0.16377959 -0.1809222 3.6126806
+#&gt; 28       8       3 -0.03648029 -0.1809222 0.9722492
+#&gt; 29       9       3 -0.44490238 -0.1809222 0.6125408
+#&gt; 30      10       3 -0.50294582 -0.1809222 3.8909402
+#&gt; 31       1       4  0.15336871  1.0979794 5.9448229
+#&gt; 32       2       4 -0.07546968  1.0979794 3.9574235
+#&gt; 33       3       4 -0.09464565  1.0979794 4.8079004
+#&gt; 34       4       4  0.13680669  1.0979794 5.0826263
+#&gt; 35       5       4  0.46710613  1.0979794 3.3817108
+#&gt; 36       6       4  0.71713231  1.0979794 3.2445244
+#&gt; 37       7       4 -0.16377959  1.0979794 4.2746244
+#&gt; 38       8       4 -0.03648029  1.0979794 4.7967650
+#&gt; 39       9       4 -0.44490238  1.0979794 4.3737837
+#&gt; 40      10       4 -0.50294582  1.0979794 4.2923638
+#&gt; 41       1       5  0.15336871  0.2255086 5.9170173
+#&gt; 42       2       5 -0.07546968  0.2255086 2.7538325
+#&gt; 43       3       5 -0.09464565  0.2255086 3.1842397
+#&gt; 44       4       5  0.13680669  0.2255086 4.2260520
+#&gt; 45       5       5  0.46710613  0.2255086 1.5543152
+#&gt; 46       6       5  0.71713231  0.2255086 3.9556215
+#&gt; 47       7       5 -0.16377959  0.2255086 3.8447343
+#&gt; 48       8       5 -0.03648029  0.2255086 4.5385200
+#&gt; 49       9       5 -0.44490238  0.2255086 2.6456965
+#&gt; 50      10       5 -0.50294582  0.2255086 2.2025837
+#&gt; 51       1       6  0.15336871 -0.5688417 3.5177725
+#&gt; 52       2       6 -0.07546968 -0.5688417 2.9838297
+#&gt; 53       3       6 -0.09464565 -0.5688417 3.5923231
+#&gt; 54       4       6  0.13680669 -0.5688417 3.1022826
+#&gt; 55       5       6  0.46710613 -0.5688417 2.7535176
+#&gt; 56       6       6  0.71713231 -0.5688417 5.4940446
+#&gt; 57       7       6 -0.16377959 -0.5688417 2.5234260
+#&gt; 58       8       6 -0.03648029 -0.5688417 0.3511046
+#&gt; 59       9       6 -0.44490238 -0.5688417 1.8498874
+#&gt; 60      10       6 -0.50294582 -0.5688417 3.5379786
+#&gt; 61       1       7  0.15336871 -0.1003512 4.2409599
+#&gt; 62       2       7 -0.07546968 -0.1003512 3.5556705
+#&gt; 63       3       7 -0.09464565 -0.1003512 2.6899611
+#&gt; 64       4       7  0.13680669 -0.1003512 2.0439883
+#&gt; 65       5       7  0.46710613 -0.1003512 5.0354057
+#&gt; 66       6       7  0.71713231 -0.1003512 3.5521496
+#&gt; 67       7       7 -0.16377959 -0.1003512 2.3525066
+#&gt; 68       8       7 -0.03648029 -0.1003512 3.5415480
+#&gt; 69       9       7 -0.44490238 -0.1003512 4.1344729
+#&gt; 70      10       7 -0.50294582 -0.1003512 2.3269392
+#&gt; 71       1       8  0.15336871  1.6989795 6.4250556
+#&gt; 72       2       8 -0.07546968  1.6989795 5.7450023
+#&gt; 73       3       8 -0.09464565  1.6989795 3.8872425
+#&gt; 74       4       8  0.13680669  1.6989795 4.5973502
+#&gt; 75       5       8  0.46710613  1.6989795 5.7349937
+#&gt; 76       6       8  0.71713231  1.6989795 2.6852695
+#&gt; 77       7       8 -0.16377959  1.6989795 3.7919020
+#&gt; 78       8       8 -0.03648029  1.6989795 4.7810577
+#&gt; 79       9       8 -0.44490238  1.6989795 5.1999969
+#&gt; 80      10       8 -0.50294582  1.6989795 5.4375538
+#&gt; 81       1       9  0.15336871  1.2452224 5.3180720
+#&gt; 82       2       9 -0.07546968  1.2452224 2.7003936
+#&gt; 83       3       9 -0.09464565  1.2452224 4.3759073
+#&gt; 84       4       9  0.13680669  1.2452224 6.5338902
+#&gt; 85       5       9  0.46710613  1.2452224 3.8544295
+#&gt; 86       6       9  0.71713231  1.2452224 5.2503336
+#&gt; 87       7       9 -0.16377959  1.2452224 4.6527757
+#&gt; 88       8       9 -0.03648029  1.2452224 3.6883031
+#&gt; 89       9       9 -0.44490238  1.2452224 2.8378979
+#&gt; 90      10       9 -0.50294582  1.2452224 4.8626384
+#&gt; 91       1      10  0.15336871 -0.2064970 4.7460439
+#&gt; 92       2      10 -0.07546968 -0.2064970 1.8389187
+#&gt; 93       3      10 -0.09464565 -0.2064970 1.3021962
+#&gt; 94       4      10  0.13680669 -0.2064970 2.4226626
+#&gt; 95       5      10  0.46710613 -0.2064970 3.0711292
+#&gt; 96       6      10  0.71713231 -0.2064970 2.2198113
+#&gt; 97       7      10 -0.16377959 -0.2064970 0.9575364
+#&gt; 98       8      10 -0.03648029 -0.2064970 2.6135275
+#&gt; 99       9      10 -0.44490238 -0.2064970 1.2924662
+#&gt; 100     10      10 -0.50294582 -0.2064970 0.8947152</div></pre>
   </div>
   <div class="col-md-3 hidden-xs hidden-sm" id="sidebar">
     <h2>Contents</h2>
diff --git a/docs/reference/wide2long.html b/docs/reference/wide2long.html
new file mode 100644
index 00000000..52178b8b
--- /dev/null
+++ b/docs/reference/wide2long.html
@@ -0,0 +1,441 @@
+<!-- Generated by pkgdown: do not edit by hand -->
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+    <div class="page-header">
+    <h1>Wide to long format</h1>
+    
+    <div class="hidden name"><code>wide2long.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    
+    <p>Converts data from wide format to long</p>
+    
+    </div>
+
+    <pre class="usage"><span class='fu'>wide2long</span>(<span class='no'>.data</span>, <span class='kw'>within_factors</span> <span class='kw'>=</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(), <span class='kw'>within_cols</span> <span class='kw'>=</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(),
+  <span class='kw'>sep</span> <span class='kw'>=</span> <span class='st'>"[^[:alnum:]]+"</span>)</pre>
+    
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>.data</th>
+      <td><p>the tbl in wide format</p></td>
+    </tr>
+    <tr>
+      <th>within_factors</th>
+      <td><p>the names of the within factors</p></td>
+    </tr>
+    <tr>
+      <th>within_cols</th>
+      <td><p>the names (or indices) of the within-subject (value) columns</p></td>
+    </tr>
+    <tr>
+      <th>sep</th>
+      <td><p>Separator for within-columns (see tidyr::separate)</p></td>
+    </tr>
+    </table>
+    
+    <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
+
+    <p>a tbl in long format</p>
+    
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='fu'>wide2long</span>(<span class='no'>iris</span>, <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='st'>"Feature"</span>, <span class='st'>"Measure"</span>), <span class='fl'>1</span>:<span class='fl'>4</span>)</div><div class='output co'>#&gt;        Species Feature Measure val
+#&gt; 1       setosa   Sepal  Length 5.1
+#&gt; 2       setosa   Sepal  Length 4.9
+#&gt; 3       setosa   Sepal  Length 4.7
+#&gt; 4       setosa   Sepal  Length 4.6
+#&gt; 5       setosa   Sepal  Length 5.0
+#&gt; 6       setosa   Sepal  Length 5.4
+#&gt; 7       setosa   Sepal  Length 4.6
+#&gt; 8       setosa   Sepal  Length 5.0
+#&gt; 9       setosa   Sepal  Length 4.4
+#&gt; 10      setosa   Sepal  Length 4.9
+#&gt; 11      setosa   Sepal  Length 5.4
+#&gt; 12      setosa   Sepal  Length 4.8
+#&gt; 13      setosa   Sepal  Length 4.8
+#&gt; 14      setosa   Sepal  Length 4.3
+#&gt; 15      setosa   Sepal  Length 5.8
+#&gt; 16      setosa   Sepal  Length 5.7
+#&gt; 17      setosa   Sepal  Length 5.4
+#&gt; 18      setosa   Sepal  Length 5.1
+#&gt; 19      setosa   Sepal  Length 5.7
+#&gt; 20      setosa   Sepal  Length 5.1
+#&gt; 21      setosa   Sepal  Length 5.4
+#&gt; 22      setosa   Sepal  Length 5.1
+#&gt; 23      setosa   Sepal  Length 4.6
+#&gt; 24      setosa   Sepal  Length 5.1
+#&gt; 25      setosa   Sepal  Length 4.8
+#&gt; 26      setosa   Sepal  Length 5.0
+#&gt; 27      setosa   Sepal  Length 5.0
+#&gt; 28      setosa   Sepal  Length 5.2
+#&gt; 29      setosa   Sepal  Length 5.2
+#&gt; 30      setosa   Sepal  Length 4.7
+#&gt; 31      setosa   Sepal  Length 4.8
+#&gt; 32      setosa   Sepal  Length 5.4
+#&gt; 33      setosa   Sepal  Length 5.2
+#&gt; 34      setosa   Sepal  Length 5.5
+#&gt; 35      setosa   Sepal  Length 4.9
+#&gt; 36      setosa   Sepal  Length 5.0
+#&gt; 37      setosa   Sepal  Length 5.5
+#&gt; 38      setosa   Sepal  Length 4.9
+#&gt; 39      setosa   Sepal  Length 4.4
+#&gt; 40      setosa   Sepal  Length 5.1
+#&gt; 41      setosa   Sepal  Length 5.0
+#&gt; 42      setosa   Sepal  Length 4.5
+#&gt; 43      setosa   Sepal  Length 4.4
+#&gt; 44      setosa   Sepal  Length 5.0
+#&gt; 45      setosa   Sepal  Length 5.1
+#&gt; 46      setosa   Sepal  Length 4.8
+#&gt; 47      setosa   Sepal  Length 5.1
+#&gt; 48      setosa   Sepal  Length 4.6
+#&gt; 49      setosa   Sepal  Length 5.3
+#&gt; 50      setosa   Sepal  Length 5.0
+#&gt; 51  versicolor   Sepal  Length 7.0
+#&gt; 52  versicolor   Sepal  Length 6.4
+#&gt; 53  versicolor   Sepal  Length 6.9
+#&gt; 54  versicolor   Sepal  Length 5.5
+#&gt; 55  versicolor   Sepal  Length 6.5
+#&gt; 56  versicolor   Sepal  Length 5.7
+#&gt; 57  versicolor   Sepal  Length 6.3
+#&gt; 58  versicolor   Sepal  Length 4.9
+#&gt; 59  versicolor   Sepal  Length 6.6
+#&gt; 60  versicolor   Sepal  Length 5.2
+#&gt; 61  versicolor   Sepal  Length 5.0
+#&gt; 62  versicolor   Sepal  Length 5.9
+#&gt; 63  versicolor   Sepal  Length 6.0
+#&gt; 64  versicolor   Sepal  Length 6.1
+#&gt; 65  versicolor   Sepal  Length 5.6
+#&gt; 66  versicolor   Sepal  Length 6.7
+#&gt; 67  versicolor   Sepal  Length 5.6
+#&gt; 68  versicolor   Sepal  Length 5.8
+#&gt; 69  versicolor   Sepal  Length 6.2
+#&gt; 70  versicolor   Sepal  Length 5.6
+#&gt; 71  versicolor   Sepal  Length 5.9
+#&gt; 72  versicolor   Sepal  Length 6.1
+#&gt; 73  versicolor   Sepal  Length 6.3
+#&gt; 74  versicolor   Sepal  Length 6.1
+#&gt; 75  versicolor   Sepal  Length 6.4
+#&gt; 76  versicolor   Sepal  Length 6.6
+#&gt; 77  versicolor   Sepal  Length 6.8
+#&gt; 78  versicolor   Sepal  Length 6.7
+#&gt; 79  versicolor   Sepal  Length 6.0
+#&gt; 80  versicolor   Sepal  Length 5.7
+#&gt; 81  versicolor   Sepal  Length 5.5
+#&gt; 82  versicolor   Sepal  Length 5.5
+#&gt; 83  versicolor   Sepal  Length 5.8
+#&gt; 84  versicolor   Sepal  Length 6.0
+#&gt; 85  versicolor   Sepal  Length 5.4
+#&gt; 86  versicolor   Sepal  Length 6.0
+#&gt; 87  versicolor   Sepal  Length 6.7
+#&gt; 88  versicolor   Sepal  Length 6.3
+#&gt; 89  versicolor   Sepal  Length 5.6
+#&gt; 90  versicolor   Sepal  Length 5.5
+#&gt; 91  versicolor   Sepal  Length 5.5
+#&gt; 92  versicolor   Sepal  Length 6.1
+#&gt; 93  versicolor   Sepal  Length 5.8
+#&gt; 94  versicolor   Sepal  Length 5.0
+#&gt; 95  versicolor   Sepal  Length 5.6
+#&gt; 96  versicolor   Sepal  Length 5.7
+#&gt; 97  versicolor   Sepal  Length 5.7
+#&gt; 98  versicolor   Sepal  Length 6.2
+#&gt; 99  versicolor   Sepal  Length 5.1
+#&gt; 100 versicolor   Sepal  Length 5.7
+#&gt; 101  virginica   Sepal  Length 6.3
+#&gt; 102  virginica   Sepal  Length 5.8
+#&gt; 103  virginica   Sepal  Length 7.1
+#&gt; 104  virginica   Sepal  Length 6.3
+#&gt; 105  virginica   Sepal  Length 6.5
+#&gt; 106  virginica   Sepal  Length 7.6
+#&gt; 107  virginica   Sepal  Length 4.9
+#&gt; 108  virginica   Sepal  Length 7.3
+#&gt; 109  virginica   Sepal  Length 6.7
+#&gt; 110  virginica   Sepal  Length 7.2
+#&gt; 111  virginica   Sepal  Length 6.5
+#&gt; 112  virginica   Sepal  Length 6.4
+#&gt; 113  virginica   Sepal  Length 6.8
+#&gt; 114  virginica   Sepal  Length 5.7
+#&gt; 115  virginica   Sepal  Length 5.8
+#&gt; 116  virginica   Sepal  Length 6.4
+#&gt; 117  virginica   Sepal  Length 6.5
+#&gt; 118  virginica   Sepal  Length 7.7
+#&gt; 119  virginica   Sepal  Length 7.7
+#&gt; 120  virginica   Sepal  Length 6.0
+#&gt; 121  virginica   Sepal  Length 6.9
+#&gt; 122  virginica   Sepal  Length 5.6
+#&gt; 123  virginica   Sepal  Length 7.7
+#&gt; 124  virginica   Sepal  Length 6.3
+#&gt; 125  virginica   Sepal  Length 6.7
+#&gt; 126  virginica   Sepal  Length 7.2
+#&gt; 127  virginica   Sepal  Length 6.2
+#&gt; 128  virginica   Sepal  Length 6.1
+#&gt; 129  virginica   Sepal  Length 6.4
+#&gt; 130  virginica   Sepal  Length 7.2
+#&gt; 131  virginica   Sepal  Length 7.4
+#&gt; 132  virginica   Sepal  Length 7.9
+#&gt; 133  virginica   Sepal  Length 6.4
+#&gt; 134  virginica   Sepal  Length 6.3
+#&gt; 135  virginica   Sepal  Length 6.1
+#&gt; 136  virginica   Sepal  Length 7.7
+#&gt; 137  virginica   Sepal  Length 6.3
+#&gt; 138  virginica   Sepal  Length 6.4
+#&gt; 139  virginica   Sepal  Length 6.0
+#&gt; 140  virginica   Sepal  Length 6.9
+#&gt; 141  virginica   Sepal  Length 6.7
+#&gt; 142  virginica   Sepal  Length 6.9
+#&gt; 143  virginica   Sepal  Length 5.8
+#&gt; 144  virginica   Sepal  Length 6.8
+#&gt; 145  virginica   Sepal  Length 6.7
+#&gt; 146  virginica   Sepal  Length 6.7
+#&gt; 147  virginica   Sepal  Length 6.3
+#&gt; 148  virginica   Sepal  Length 6.5
+#&gt; 149  virginica   Sepal  Length 6.2
+#&gt; 150  virginica   Sepal  Length 5.9
+#&gt; 151     setosa   Sepal   Width 3.5
+#&gt; 152     setosa   Sepal   Width 3.0
+#&gt; 153     setosa   Sepal   Width 3.2
+#&gt; 154     setosa   Sepal   Width 3.1
+#&gt; 155     setosa   Sepal   Width 3.6
+#&gt; 156     setosa   Sepal   Width 3.9
+#&gt; 157     setosa   Sepal   Width 3.4
+#&gt; 158     setosa   Sepal   Width 3.4
+#&gt; 159     setosa   Sepal   Width 2.9
+#&gt; 160     setosa   Sepal   Width 3.1
+#&gt; 161     setosa   Sepal   Width 3.7
+#&gt; 162     setosa   Sepal   Width 3.4
+#&gt; 163     setosa   Sepal   Width 3.0
+#&gt; 164     setosa   Sepal   Width 3.0
+#&gt; 165     setosa   Sepal   Width 4.0
+#&gt; 166     setosa   Sepal   Width 4.4
+#&gt; 167     setosa   Sepal   Width 3.9
+#&gt; 168     setosa   Sepal   Width 3.5
+#&gt; 169     setosa   Sepal   Width 3.8
+#&gt; 170     setosa   Sepal   Width 3.8
+#&gt; 171     setosa   Sepal   Width 3.4
+#&gt; 172     setosa   Sepal   Width 3.7
+#&gt; 173     setosa   Sepal   Width 3.6
+#&gt; 174     setosa   Sepal   Width 3.3
+#&gt; 175     setosa   Sepal   Width 3.4
+#&gt; 176     setosa   Sepal   Width 3.0
+#&gt; 177     setosa   Sepal   Width 3.4
+#&gt; 178     setosa   Sepal   Width 3.5
+#&gt; 179     setosa   Sepal   Width 3.4
+#&gt; 180     setosa   Sepal   Width 3.2
+#&gt; 181     setosa   Sepal   Width 3.1
+#&gt; 182     setosa   Sepal   Width 3.4
+#&gt; 183     setosa   Sepal   Width 4.1
+#&gt; 184     setosa   Sepal   Width 4.2
+#&gt; 185     setosa   Sepal   Width 3.1
+#&gt; 186     setosa   Sepal   Width 3.2
+#&gt; 187     setosa   Sepal   Width 3.5
+#&gt; 188     setosa   Sepal   Width 3.6
+#&gt; 189     setosa   Sepal   Width 3.0
+#&gt; 190     setosa   Sepal   Width 3.4
+#&gt; 191     setosa   Sepal   Width 3.5
+#&gt; 192     setosa   Sepal   Width 2.3
+#&gt; 193     setosa   Sepal   Width 3.2
+#&gt; 194     setosa   Sepal   Width 3.5
+#&gt; 195     setosa   Sepal   Width 3.8
+#&gt; 196     setosa   Sepal   Width 3.0
+#&gt; 197     setosa   Sepal   Width 3.8
+#&gt; 198     setosa   Sepal   Width 3.2
+#&gt; 199     setosa   Sepal   Width 3.7
+#&gt; 200     setosa   Sepal   Width 3.3
+#&gt; 201 versicolor   Sepal   Width 3.2
+#&gt; 202 versicolor   Sepal   Width 3.2
+#&gt; 203 versicolor   Sepal   Width 3.1
+#&gt; 204 versicolor   Sepal   Width 2.3
+#&gt; 205 versicolor   Sepal   Width 2.8
+#&gt; 206 versicolor   Sepal   Width 2.8
+#&gt; 207 versicolor   Sepal   Width 3.3
+#&gt; 208 versicolor   Sepal   Width 2.4
+#&gt; 209 versicolor   Sepal   Width 2.9
+#&gt; 210 versicolor   Sepal   Width 2.7
+#&gt; 211 versicolor   Sepal   Width 2.0
+#&gt; 212 versicolor   Sepal   Width 3.0
+#&gt; 213 versicolor   Sepal   Width 2.2
+#&gt; 214 versicolor   Sepal   Width 2.9
+#&gt; 215 versicolor   Sepal   Width 2.9
+#&gt; 216 versicolor   Sepal   Width 3.1
+#&gt; 217 versicolor   Sepal   Width 3.0
+#&gt; 218 versicolor   Sepal   Width 2.7
+#&gt; 219 versicolor   Sepal   Width 2.2
+#&gt; 220 versicolor   Sepal   Width 2.5
+#&gt; 221 versicolor   Sepal   Width 3.2
+#&gt; 222 versicolor   Sepal   Width 2.8
+#&gt; 223 versicolor   Sepal   Width 2.5
+#&gt; 224 versicolor   Sepal   Width 2.8
+#&gt; 225 versicolor   Sepal   Width 2.9
+#&gt; 226 versicolor   Sepal   Width 3.0
+#&gt; 227 versicolor   Sepal   Width 2.8
+#&gt; 228 versicolor   Sepal   Width 3.0
+#&gt; 229 versicolor   Sepal   Width 2.9
+#&gt; 230 versicolor   Sepal   Width 2.6
+#&gt; 231 versicolor   Sepal   Width 2.4
+#&gt; 232 versicolor   Sepal   Width 2.4
+#&gt; 233 versicolor   Sepal   Width 2.7
+#&gt; 234 versicolor   Sepal   Width 2.7
+#&gt; 235 versicolor   Sepal   Width 3.0
+#&gt; 236 versicolor   Sepal   Width 3.4
+#&gt; 237 versicolor   Sepal   Width 3.1
+#&gt; 238 versicolor   Sepal   Width 2.3
+#&gt; 239 versicolor   Sepal   Width 3.0
+#&gt; 240 versicolor   Sepal   Width 2.5
+#&gt; 241 versicolor   Sepal   Width 2.6
+#&gt; 242 versicolor   Sepal   Width 3.0
+#&gt; 243 versicolor   Sepal   Width 2.6
+#&gt; 244 versicolor   Sepal   Width 2.3
+#&gt; 245 versicolor   Sepal   Width 2.7
+#&gt; 246 versicolor   Sepal   Width 3.0
+#&gt; 247 versicolor   Sepal   Width 2.9
+#&gt; 248 versicolor   Sepal   Width 2.9
+#&gt; 249 versicolor   Sepal   Width 2.5
+#&gt; 250 versicolor   Sepal   Width 2.8
+#&gt;  [ reached 'max' / getOption("max.print") -- omitted 350 rows ]</div><div class='input'>
+</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="sidebar">
+    <h2>Contents</h2>
+    <ul class="nav nav-pills nav-stacked">
+      <li><a href="#arguments">Arguments</a></li>
+      
+      <li><a href="#value">Value</a></li>
+      
+      <li><a href="#examples">Examples</a></li>
+    </ul>
+
+  </div>
+</div>
+
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+      <div class="copyright">
+  <p>Developed by Lisa DeBruine.</p>
+</div>
+
+<div class="pkgdown">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.3.0.</p>
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+
diff --git a/man/check_design.Rd b/man/check_design.Rd
index 1301abd9..140a0c8e 100644
--- a/man/check_design.Rd
+++ b/man/check_design.Rd
@@ -1,11 +1,11 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/sim_design.R
+% Please edit documentation in R/check_design.R
 \name{check_design}
 \alias{check_design}
 \title{Validate design}
 \usage{
-check_design(within = list(), between = list(), n = 100, cors = 0,
-  mu = 0, sd = 1)
+check_design(within = list(), between = list(), n = 100, mu = 0,
+  sd = 1, r = 0)
 }
 \arguments{
 \item{within}{a list of the within-subject factors}
@@ -14,11 +14,11 @@ check_design(within = list(), between = list(), n = 100, cors = 0,
 
 \item{n}{the number of samples required}
 
-\item{cors}{the correlations among the variables (can be a single number, vars\*vars matrix, vars\*vars vector, or a vars\*(vars-1)/2 vector)}
-
 \item{mu}{a vector giving the means of the variables (numeric vector of length 1 or vars)}
 
 \item{sd}{the standard deviations of the variables (numeric vector of length 1 or vars)}
+
+\item{r}{the correlations among the variables (can be a single number, vars\*vars matrix, vars\*vars vector, or a vars\*(vars-1)/2 vector)}
 }
 \value{
 list
@@ -30,6 +30,6 @@ list
 
 within <- list(time = c("day", "night"))
 between <- list(pet = c("dog", "cat"))
-design <- check_design(within, between)
+check_design(within, between)
 
 }
diff --git a/man/check_sim_stats.Rd b/man/check_sim_stats.Rd
index 56b38f6c..88c46544 100644
--- a/man/check_sim_stats.Rd
+++ b/man/check_sim_stats.Rd
@@ -4,19 +4,26 @@
 \alias{check_sim_stats}
 \title{Check table stats}
 \usage{
-check_sim_stats(dat, grp_by = NULL, digits = 2, usekable = FALSE)
+check_sim_stats(.data, between = c(), within = c(), dv = c(),
+  id = c(), digits = 2, usekable = FALSE)
 }
 \arguments{
-\item{dat}{the existing dataframe}
+\item{.data}{the existing tbl}
 
-\item{grp_by}{an optional list of column names to group by}
+\item{between}{a vector of column names for between-subject factors}
+
+\item{within}{a vector of column names for within-subject factors (if data is long)}
+
+\item{dv}{the column name of the dv (if data is long)}
+
+\item{id}{the column name(s) of the subject ID (if data is long)}
 
 \item{digits}{how many digits to round to (default = 2)}
 
 \item{usekable}{logical. If TRUE, output with knitr::kable}
 }
 \value{
-tibble or kable
+a tbl or kable
 }
 \description{
 \code{check_sim_stats} Generates a table of the correlations and means of numeric columns in a data frame
diff --git a/man/convert_param.Rd b/man/convert_param.Rd
index 142de26e..78446714 100644
--- a/man/convert_param.Rd
+++ b/man/convert_param.Rd
@@ -1,18 +1,18 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/sim_design.R
+% Please edit documentation in R/check_design.R
 \name{convert_param}
 \alias{convert_param}
 \title{Convert parameter}
 \usage{
-convert_param(param, cells_b, cells_w, type = "this parameter")
+convert_param(param, cells_w, cells_b, type = "this parameter")
 }
 \arguments{
 \item{param}{the parameter (mu, sd, or n)}
 
-\item{cells_b}{a list of between-subject cell combinations}
-
 \item{cells_w}{a list of within-subject cells combinations}
 
+\item{cells_b}{a list of between-subject cell combinations}
+
 \item{type}{the name of the parameter (for error messages)}
 }
 \value{
@@ -21,3 +21,4 @@ a data frame
 \description{
 Converts parameter specification from vector or list format
 }
+\keyword{internal}
diff --git a/man/get_design_long.Rd b/man/get_design_long.Rd
new file mode 100644
index 00000000..9560b4ae
--- /dev/null
+++ b/man/get_design_long.Rd
@@ -0,0 +1,24 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/check_design.R
+\name{get_design_long}
+\alias{get_design_long}
+\title{Get design from long data}
+\usage{
+get_design_long(.data, id = "sub_id", dv = "val")
+}
+\arguments{
+\item{.data}{the data frame (in long format)}
+
+\item{id}{the column name(s) that identify a unit of analysis}
+
+\item{dv}{the column name that identifies the DV}
+}
+\value{
+the data frame in long format
+}
+\description{
+Makes a best guess at the design of a long-format data frame. 
+Finds all columns that contain a single value per unit of analysis (between factors), 
+all columns that contain the same values per unit of analysis (within factors), and 
+all columns that differ over units of analysis (dv, continuous factors)
+}
diff --git a/man/long2wide.Rd b/man/long2wide.Rd
new file mode 100644
index 00000000..717b59a3
--- /dev/null
+++ b/man/long2wide.Rd
@@ -0,0 +1,31 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/long2wide.R
+\name{long2wide}
+\alias{long2wide}
+\title{Long to wide format}
+\usage{
+long2wide(.data, within = c(), between = c(), dv = "val",
+  id = "sub_id")
+}
+\arguments{
+\item{.data}{the tbl in long format}
+
+\item{within}{the names of the within column(s)}
+
+\item{between}{the names of between column(s) (optional)}
+
+\item{dv}{the name of the DV (value) column}
+
+\item{id}{the names of the column(s) for grouping observations}
+}
+\value{
+a tbl in wide format
+}
+\description{
+Converts data from long format to wide
+}
+\examples{
+df_long <- sim_design(2, 2, long = TRUE)
+long2wide(df_long, "A", "B", "val", "sub_id")
+
+}
diff --git a/man/make_id.Rd b/man/make_id.Rd
new file mode 100644
index 00000000..55894a0e
--- /dev/null
+++ b/man/make_id.Rd
@@ -0,0 +1,27 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/make_id.R
+\name{make_id}
+\alias{make_id}
+\title{Make ID}
+\usage{
+make_id(n = 100, prefix = "S", digits = 0)
+}
+\arguments{
+\item{n}{the number of IDs to generate (or a vector of numbers)}
+
+\item{prefix}{the letter prefix to the number}
+
+\item{digits}{the number of digits to use for the numeric part. Only used if this is larger than the number of digits in n.}
+}
+\value{
+a vector of IDs
+}
+\description{
+Make IDs with fixed length and a letter prefix for random effects (e.g., S001, S002, ..., S100).
+}
+\examples{
+
+make_id(20, "SUBJECT_")
+make_id(10:30, digits = 3)
+
+}
diff --git a/man/rnorm_multi.Rd b/man/rnorm_multi.Rd
index bb9703df..b1a8ef80 100644
--- a/man/rnorm_multi.Rd
+++ b/man/rnorm_multi.Rd
@@ -4,33 +4,36 @@
 \alias{rnorm_multi}
 \title{Multiple Normally Distributed Vectors}
 \usage{
-rnorm_multi(n, vars = 3, cors = 0, mu = 0, sd = 1,
-  varnames = NULL, empirical = FALSE, as.matrix = FALSE)
+rnorm_multi(n, vars = 3, mu = 0, sd = 1, r = 0, varnames = NULL,
+  empirical = FALSE, as.matrix = FALSE, cors = NULL)
 }
 \arguments{
 \item{n}{the number of samples required}
 
 \item{vars}{the number of variables to return}
 
-\item{cors}{the correlations among the variables (can be a single number, vars\*vars matrix, vars\*vars vector, or a vars\*(vars-1)/2 vector)}
-
 \item{mu}{a vector giving the means of the variables (numeric vector of length 1 or vars)}
 
 \item{sd}{the standard deviations of the variables (numeric vector of length 1 or vars)}
 
-\item{varnames}{optional names for the variables (string vector of length vars) defaults if cors is a matrix with column names}
+\item{r}{the correlations among the variables (can be a single number, vars\*vars matrix, vars\*vars vector, or a vars\*(vars-1)/2 vector)}
+
+\item{varnames}{optional names for the variables (string vector of length vars) defaults if r is a matrix with column names}
 
-\item{empirical}{logical. If true, mu, sd and cors specify the empirical not population mean, sd and covariance}
+\item{empirical}{logical. If true, mu, sd and r specify the empirical not population mean, sd and covariance}
 
 \item{as.matrix}{logical. If true, returns a matrix}
+
+\item{cors}{(deprecated; use r)}
 }
 \value{
-dataframe of vars vectors
+a tbl of vars vectors
 }
 \description{
-\code{rnorm_multi} makes multiple normally distributed vectors with specified relationships
+\code{rnorm_multi()} makes multiple normally distributed vectors with specified relationships.
 }
 \examples{
-rnorm_multi(100, 3, c(0.2, 0.4, 0.5), varnames=c("A", "B", "C"))
-rnorm_multi(100, 3, c(1, 0.2, -0.5, 0.2, 1, 0.5, -0.5, 0.5, 1), varnames=c("A", "B", "C"))
+rnorm_multi(100, 3, 0, 1, c(0.2, 0.4, 0.5), varnames=c("A", "B", "C"))
+rnorm_multi(100, 3, 0, 1, c(1, 0.2, -0.5, 0.2, 1, 0.5, -0.5, 0.5, 1), varnames=c("A", "B", "C"))
+
 }
diff --git a/man/rnorm_pre.Rd b/man/rnorm_pre.Rd
index 10aa7f81..48f2b4e6 100644
--- a/man/rnorm_pre.Rd
+++ b/man/rnorm_pre.Rd
@@ -4,16 +4,16 @@
 \alias{rnorm_pre}
 \title{Correlated Normal Vector}
 \usage{
-rnorm_pre(x, rho = 0, ymean = 0, ysd = 1)
+rnorm_pre(x, mu = 0, sd = 1, r = 0)
 }
 \arguments{
 \item{x}{the existing vector}
 
-\item{rho}{desired correlation between existing and returned vectors}
+\item{mu}{desired mean of returned vector}
 
-\item{ymean}{desired mean of returned vector}
+\item{sd}{desired SD of returned vector}
 
-\item{ysd}{desired SD of returned vector}
+\item{r}{desired correlation between existing and returned vectors}
 }
 \value{
 vector
@@ -23,6 +23,6 @@ vector
 }
 \examples{
 v1 <- rnorm(10)
-v2 <- rnorm_pre(v1, 0.5, 0, 1)
+v2 <- rnorm_pre(v1, 0, 1, 0.5)
 cor(v1, v2)
 }
diff --git a/man/select_num_grp.Rd b/man/select_num_grp.Rd
index 2a0a11dd..3831209a 100644
--- a/man/select_num_grp.Rd
+++ b/man/select_num_grp.Rd
@@ -4,17 +4,17 @@
 \alias{select_num_grp}
 \title{Select grouping and numeric columns and group}
 \usage{
-select_num_grp(dat, grp_by = NULL, cols = NULL)
+select_num_grp(.data, between = c(), cols = NULL)
 }
 \arguments{
-\item{dat}{the existing dataframe}
+\item{.data}{the existing tbl}
 
-\item{grp_by}{an optional list of column names to group by}
+\item{between}{an optional list of column names to group by}
 
 \item{cols}{an optional list of column names to return (default of NULL returns all numeric columns)}
 }
 \value{
-tibble
+a tbl
 }
 \description{
 \code{select_num_grp} Select grouping and (optionally specified) numeric columns and group
diff --git a/man/sim_design.Rd b/man/sim_design.Rd
index f17e348f..4d88a826 100644
--- a/man/sim_design.Rd
+++ b/man/sim_design.Rd
@@ -4,8 +4,8 @@
 \alias{sim_design}
 \title{Simulate Data from Design}
 \usage{
-sim_design(within = list(), between = list(), n = 100, cors = 0,
-  mu = 0, sd = 1, empirical = FALSE, frame_long = FALSE)
+sim_design(within = list(), between = list(), n = 100, mu = 0,
+  sd = 1, r = 0, empirical = FALSE, long = FALSE)
 }
 \arguments{
 \item{within}{a list of the within-subject factors}
@@ -14,19 +14,19 @@ sim_design(within = list(), between = list(), n = 100, cors = 0,
 
 \item{n}{the number of samples required}
 
-\item{cors}{the correlations among the variables (can be a single number, vars\*vars matrix, vars\*vars vector, or a vars\*(vars-1)/2 vector)}
-
 \item{mu}{a vector giving the means of the variables (numeric vector of length 1 or vars)}
 
 \item{sd}{the standard deviations of the variables (numeric vector of length 1 or vars)}
 
-\item{empirical}{logical. If true, mu, sd and cors specify the empirical not population mean, sd and covariance}
+\item{r}{the correlations among the variables (can be a single number, vars\*vars matrix, vars\*vars vector, or a vars\*(vars-1)/2 vector)}
+
+\item{empirical}{logical. If true, mu, sd and r specify the empirical not population mean, sd and covariance}
 
-\item{frame_long}{Whether the returned dataframe is in wide (default = FALSE) or long (TRUE) format}
+\item{long}{Whether the returned tbl is in wide (default = FALSE) or long (TRUE) format}
 }
 \value{
-dataframe
+a tbl
 }
 \description{
-\code{sim_design} generates a dataframe with a specified within and between design
+\code{sim_design()} generates a data table with a specified within and between design.
 }
diff --git a/man/sim_design_.Rd b/man/sim_design_.Rd
index dbbccf36..b27f96ac 100644
--- a/man/sim_design_.Rd
+++ b/man/sim_design_.Rd
@@ -4,19 +4,19 @@
 \alias{sim_design_}
 \title{Simulate Data from Design}
 \usage{
-sim_design_(design, empirical = FALSE, frame_long = FALSE)
+sim_design_(design, empirical = FALSE, long = FALSE)
 }
 \arguments{
 \item{design}{A list of design parameters created by check_design()}
 
-\item{empirical}{logical. If true, mu, sd and cors specify the empirical not population mean, sd and covariance}
+\item{empirical}{logical. If true, mu, sd and r specify the empirical not population mean, sd and covariance}
 
-\item{frame_long}{Whether the returned dataframe is in wide (default = FALSE) or long (TRUE) format}
+\item{long}{Whether the returned tbl is in wide (default = FALSE) or long (TRUE) format}
 }
 \value{
-dataframe
+a tbl
 }
 \description{
-\code{sim_from_design} generates a dataframe with a specified design
+\code{sim_design_} generates a data table with a specified design
 }
 \keyword{internal}
diff --git a/man/sim_df.Rd b/man/sim_df.Rd
new file mode 100644
index 00000000..ef95ffc0
--- /dev/null
+++ b/man/sim_df.Rd
@@ -0,0 +1,30 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/sim_df.R
+\name{sim_df}
+\alias{sim_df}
+\title{Simulate an existing dataframe}
+\usage{
+sim_df(.data, n = 100, between = c(), empirical = FALSE,
+  grp_by = NULL)
+}
+\arguments{
+\item{.data}{the existing tbl (must be in wide format)}
+
+\item{n}{the number of samples to return per group}
+
+\item{between}{a list of the between-subject columns}
+
+\item{empirical}{logical. Passed on to rnorm_multi}
+
+\item{grp_by}{(deprecated; use between)}
+}
+\value{
+a tbl
+}
+\description{
+\code{sim_df} Produces a data table with the same distributions and correlations as an existing data table Only returns numeric columns and simulates all numeric variables from a continuous normal distribution (for now).
+}
+\examples{
+iris100 <- sim_df(iris, 100)
+iris_species <- sim_df(iris, 100, between = "Species")
+}
diff --git a/man/simdf_mixed.Rd b/man/sim_mixed_df.Rd
similarity index 53%
rename from man/simdf_mixed.Rd
rename to man/sim_mixed_df.Rd
index 8b6aeaae..6b4d73de 100644
--- a/man/simdf_mixed.Rd
+++ b/man/sim_mixed_df.Rd
@@ -1,14 +1,14 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/simdf_mixed.R
-\name{simdf_mixed}
-\alias{simdf_mixed}
+% Please edit documentation in R/sim_mixed_df.R
+\name{sim_mixed_df}
+\alias{sim_mixed_df}
 \title{Generate a sample with random intercepts for subjects and items}
 \usage{
-simdf_mixed(dat, sub_n = 100, item_n = 25, dv = 1, sub_id = 2,
+sim_mixed_df(.data, sub_n = 100, item_n = 25, dv = 1, sub_id = 2,
   item_id = 3)
 }
 \arguments{
-\item{dat}{the existing dataframe}
+\item{.data}{the existing tbl}
 
 \item{sub_n}{the number of subjects to simulate}
 
@@ -21,11 +21,11 @@ simdf_mixed(dat, sub_n = 100, item_n = 25, dv = 1, sub_id = 2,
 \item{item_id}{the column name or index for the item IDs}
 }
 \value{
-tibble
+a tbl
 }
 \description{
-\code{simdf_mixed} Produces a dataframe with the same distributions of by-subject and by-item random intercepts as an existing dataframe
+\code{sim_mixed_df()} produces a data table with the same distributions of by-subject and by-item random intercepts as an existing data table.
 }
 \examples{
-\donttest{simdf_mixed(faceratings, 10, 10, "rating", "rater_id", "face_id")}
+\donttest{sim_mixed_df(faceratings, 10, 10, "rating", "rater_id", "face_id")}
 }
diff --git a/man/simdf.Rd b/man/simdf.Rd
deleted file mode 100644
index 4ed8880a..00000000
--- a/man/simdf.Rd
+++ /dev/null
@@ -1,27 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/simdf.R
-\name{simdf}
-\alias{simdf}
-\title{Simulate an existing dataframe}
-\usage{
-simdf(dat, n = 100, grp_by = NULL, empirical = FALSE)
-}
-\arguments{
-\item{dat}{the existing dataframe}
-
-\item{n}{the number of samples to return per group}
-
-\item{grp_by}{an optional list of column names to group by}
-
-\item{empirical}{logical. Passed on to rnorm_multi}
-}
-\value{
-tibble
-}
-\description{
-\code{simdf} Produces a dataframe with the same distributions and correlations as an existing dataframe. Only returns numeric columns and simulates all numeric variables from a continuous normal distribution (for now).
-}
-\examples{
-iris100 <- simdf(iris, 100)
-iris_species <- simdf(iris, 100, "Species")
-}
diff --git a/man/wide2long.Rd b/man/wide2long.Rd
new file mode 100644
index 00000000..0574bb41
--- /dev/null
+++ b/man/wide2long.Rd
@@ -0,0 +1,28 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/long2wide.R
+\name{wide2long}
+\alias{wide2long}
+\title{Wide to long format}
+\usage{
+wide2long(.data, within_factors = c(), within_cols = c(),
+  sep = "[^[:alnum:]]+")
+}
+\arguments{
+\item{.data}{the tbl in wide format}
+
+\item{within_factors}{the names of the within factors}
+
+\item{within_cols}{the names (or indices) of the within-subject (value) columns}
+
+\item{sep}{Separator for within-columns (see tidyr::separate)}
+}
+\value{
+a tbl in long format
+}
+\description{
+Converts data from wide format to long
+}
+\examples{
+wide2long(iris, c("Feature", "Measure"), 1:4)
+
+}
diff --git a/tests/testthat/test-check_design.R b/tests/testthat/test-check_design.R
index 61876cc6..d0e7906c 100644
--- a/tests/testthat/test-check_design.R
+++ b/tests/testthat/test-check_design.R
@@ -1,5 +1,6 @@
 context("test-check_design")
 
+# basic ----
 test_that("basic", {
   within <- list(time = c("day", "night"))
   between <- list(pet = c("dog", "cat"))
@@ -13,20 +14,21 @@ test_that("basic", {
     magrittr::set_rownames(c("day", "night"))
     
   
-  testthat::expect_equal(design$within, within)
-  testthat::expect_equal(design$between, between)
-  testthat::expect_equal(design$within_labels, list(time = c(day = "day", night = "night")))
-  testthat::expect_equal(design$between_labels, list(pet = c(dog = "dog", cat = "cat")))
+  expect_equal(design$within, within)
+  expect_equal(design$between, between)
+  expect_equal(design$within_labels, list(time = c(day = "day", night = "night")))
+  expect_equal(design$between_labels, list(pet = c(dog = "dog", cat = "cat")))
   
-  testthat::expect_equal(design$cell_n, cell_n)
-  testthat::expect_equal(design$cell_mu, cell_mu)
-  testthat::expect_equal(design$cell_sd, cell_sd)
+  expect_equal(design$cell_n, cell_n)
+  expect_equal(design$cell_mu, cell_mu)
+  expect_equal(design$cell_sd, cell_sd)
   
-  testthat::expect_equal(design$cells_w, c("day", "night"))
-  testthat::expect_equal(design$cells_b, c("dog", "cat"))
-  testthat::expect_equal(design$sub_id, c(paste0("S0", 1:9), paste0("S", 10:20)))
+  expect_equal(design$cells_w, c("day", "night"))
+  expect_equal(design$cells_b, c("dog", "cat"))
+  expect_equal(design$sub_id, c(paste0("S0", 1:9), paste0("S", 10:20)))
 })
 
+# design spec ----
 test_that("design spec", {
   between <- list(
     "B" = c("B1", "B2")
@@ -51,11 +53,51 @@ test_that("design spec", {
     "B2" = .5
   )
   
-  design <- check_design(within, between, n, r, mu, sd)
+  design <- check_design(within, between, n, mu, sd, r)
   
   design_elements <- c("within", "between", "within_factors", "between_factors", 
-                       "within_labels", "between_labels", "cell_n", "cell_mu", "cell_sd", 
-                       "cell_cors", "cells_w", "cells_b", "sub_id")
+                       "within_labels", "between_labels", "cells_w", "cells_b", 
+                       "cell_n", "cell_mu", "cell_sd", "cell_r", "sub_id")
   
   expect_equal(names(design), design_elements)
 })
+
+# anon factors ----
+test_that("anon factors", {
+  design <- check_design(c(2, 4), c(2, 2))
+  
+  w <- list(
+    A = c("A1", "A2"),
+    B = c("B1", "B2", "B3", "B4")
+  )
+  
+  b <- list(
+    C = c("C1","C2"),
+    D = c("D1", "D2")
+  )
+  
+  expect_equal(design$within, w)
+  expect_equal(design$between, b)
+})
+
+# make_id ----
+test_that("make_id", {
+  expect_equal(make_id(10), c("S01", "S02", "S03", "S04", "S05", 
+                              "S06", "S07", "S08", "S09", "S10"))
+  
+  expect_equal(make_id(10, "SUB"), c("SUB01", "SUB02", "SUB03", "SUB04", "SUB05", 
+                                     "SUB06", "SUB07", "SUB08", "SUB09", "SUB10"))
+  
+  expect_equal(make_id(100)[[1]], "S001")
+  expect_equal(make_id(1000)[[1]], "S0001")
+  expect_equal(make_id(1000, "pokemon_")[[1]], "pokemon_0001")
+  expect_equal(make_id(100, digits = 4)[[1]], "S0001")
+  
+  # named arguments
+  expect_equal(make_id(n = 100, prefix = "A", digits = 4)[[1]], "A0001")
+  expect_equal(make_id(digits = 4, prefix = "A", n = 100)[[1]], "A0001")
+  
+  # vector
+  expect_equal(make_id(2:4), c("S2", "S3", "S4"))
+  expect_equal(make_id(100:200)[[1]], "S100")
+})
diff --git a/tests/testthat/test-check_sim_stats.R b/tests/testthat/test-check_sim_stats.R
index c60bafb2..bd48d53b 100644
--- a/tests/testthat/test-check_sim_stats.R
+++ b/tests/testthat/test-check_sim_stats.R
@@ -1,11 +1,13 @@
 context("check_sim_stats")
 
+# error messages ----
 test_that("error messages", {
-  expect_error(check_sim_stats("A"), "dat must be a data frame or matrix")
-  expect_error(check_sim_stats(iris, FALSE), "grp_by must be a numeric or character vector")
+  expect_error(check_sim_stats("A"), ".data must be a data frame or matrix")
+  expect_error(check_sim_stats(iris, FALSE), "between must be a numeric or character vector")
 })
 
-test_that("correct defaults", {
+# defaults ----
+test_that("defaults", {
   checkiris <- check_sim_stats(iris)
   irisnames <- c("n", "var", "Sepal.Length", "Sepal.Width", "Petal.Length", "Petal.Width", "mean", "sd")
   
@@ -14,7 +16,8 @@ test_that("correct defaults", {
   expect_equal(names(checkiris), irisnames)
 })
 
-test_that("correct defaults with group", {
+# defaults with between ----
+test_that("defaults with between", {
   checkiris <- check_sim_stats(iris, "Species")
   irisnames <- c("Species", "n", "var", "Sepal.Length", "Sepal.Width", "Petal.Length", "Petal.Width", "mean", "sd")
   
@@ -23,6 +26,7 @@ test_that("correct defaults with group", {
   expect_equal(names(checkiris), irisnames)
 })
 
+# is_pos_def ----
 test_that("is_pos_def", {
   expect_equal(is_pos_def(matrix(c(1, .5, .5, 1), 2)), TRUE)
   
@@ -31,3 +35,35 @@ test_that("is_pos_def", {
                         .9, -.2, 1), 3)
   expect_equal(is_pos_def(bad_matrix), FALSE)
 })
+
+
+# get_design_long ----
+test_that("get_design_long", {
+  df_long <- sim_design(2, 2, n = 10, mu = 5, sd = 2, r = 0.5, long = TRUE, empirical = TRUE)
+  d <- get_design_long(df_long)
+  
+  n <- data.frame(A1 = c(10, 10), A2 = c(10, 10), row.names = c("B1", "B2"))
+  mu <- data.frame(A1 = c(5, 5), A2 = c(5, 5), row.names = c("B1", "B2"))
+  sd <- data.frame(A1 = c(2, 2), A2 = c(2, 2), row.names = c("B1", "B2"))
+  r <- data.frame(A1 = c(1, .5), A2 = c(.5, 1), row.names = c("A1", "A2"))
+  
+  expect_equal(d$within, list(A = c("A1", "A2")))
+  expect_equal(d$between, list(B = c("B1", "B2")))
+  expect_equal(d$within_factors, "A")
+  expect_equal(d$between_factors, "B")
+  expect_equal(d$cell_n, n)
+  expect_equal(d$cell_mu, mu)
+  expect_equal(d$cell_sd, sd)
+  expect_equal(d$cell_r$B1, r)
+  expect_equal(d$cell_r$B2, r)
+  
+  df_long <- sim_design(c(2, 2, 2), c(2, 2, 2), long = TRUE, empirical = TRUE)
+  d <- get_design_long(df_long)
+  
+  expect_equal(d$within_factors, c("A", "B", "C"))
+  expect_equal(d$between_factors, c("D", "E", "F"))
+  expect_equal(d$cell_n  %>% sum(), 6400)
+  expect_equal(d$cell_mu %>% sum(), 0)
+  expect_equal(d$cell_sd %>% sum(), 64)
+  expect_equal(d$cell_r[[1]] %>% sum(), 8)
+})
diff --git a/tests/testthat/test-convert_param.R b/tests/testthat/test-convert_param.R
index 8f486b83..01170574 100644
--- a/tests/testthat/test-convert_param.R
+++ b/tests/testthat/test-convert_param.R
@@ -6,7 +6,7 @@ cells_b <- c("B1_C1", "B2_C1", "B1_C2", "B2_C2")
 # single number ---
 testthat::test_that("single number", {
   param <- 3
-  cp <- convert_param(param, cells_b, cells_w) %>%
+  cp <- convert_param(param, cells_w, cells_b) %>%
     unlist() %>% magrittr::equals(param) %>% sum()
   expect_equal(cp, 16)
 })
@@ -19,7 +19,7 @@ testthat::test_that("list of single numbers", {
     "B1_C2" = 3, 
     "B2_C2" = 4
   )
-  cp <- convert_param(param, cells_b, cells_w) %>% unlist() %>% unname()
+  cp <- convert_param(param, cells_w, cells_b) %>% unlist() %>% unname()
   expect_equal(cp, rep(1:4, each = 4))
 })
 
@@ -27,7 +27,7 @@ testthat::test_that("list of single numbers", {
 # unnamed vector of cells ----
 testthat::test_that("unnamed vector of cells", {
   param <- rep(1:4, each = 4)
-  cp <- convert_param(param, cells_b, cells_w) %>% unlist() %>% unname()
+  cp <- convert_param(param, cells_w, cells_b) %>% unlist() %>% unname()
   expect_equal(cp, param)
 })
 
@@ -40,7 +40,7 @@ testthat::test_that("list of unnamed vectors", {
     "B1_C2" = 9:12,
     "B2_C2" = 13:16
   )
-  cp <- convert_param(param, cells_b, cells_w) %>% unlist() %>% unname()
+  cp <- convert_param(param, cells_w, cells_b) %>% unlist() %>% unname()
   expect_equal(cp, 1:16)
 })
 
@@ -53,7 +53,7 @@ testthat::test_that("list of named vectors", {
     "B1_C2" = c("W1_X1" = 9, "W2_X1" = 10, "W1_X2" = 11, "W2_X2" = 12),
     "B2_C2" = c("W1_X1" = 13, "W2_X1" = 14, "W1_X2" = 15, "W2_X2" = 16)
   )
-  cp <- convert_param(param, cells_b, cells_w) %>% unlist() %>% unname()
+  cp <- convert_param(param, cells_w, cells_b) %>% unlist() %>% unname()
   expect_equal(cp, 1:16)
 })
 
@@ -65,7 +65,7 @@ testthat::test_that("list of disordered named vectors", {
     "B2_C1" = c("W1_X1" = 5, "W1_X2" = 7, "W2_X1" = 6, "W2_X2" = 8),
     "B2_C2" = c("W1_X1" = 13, "W1_X2" = 15, "W2_X1" = 14, "W2_X2" = 16)
   )
-  cp <- convert_param(param, cells_b, cells_w) %>% unlist() %>% unname()
+  cp <- convert_param(param, cells_w, cells_b) %>% unlist() %>% unname()
   expect_equal(cp, 1:16)
 })
 
@@ -83,7 +83,7 @@ testthat::test_that("as data frame", {
   
   as.list(param) %>%  lapply(magrittr::set_names, rownames(param))
   
-  cp <- convert_param(param, cells_b, cells_w) %>% unlist() %>% unname()
+  cp <- convert_param(param, cells_w, cells_b) %>% unlist() %>% unname()
   expect_equal(cp, 1:16)
   
 })
@@ -99,7 +99,7 @@ testthat::test_that("backwards data frame", {
     magrittr::set_names(cells_w) %>%
     magrittr::set_rownames(cells_b)
   
-  cp <- convert_param(param, cells_b, cells_w) %>% unlist() %>% unname()
+  cp <- convert_param(param, cells_w, cells_b) %>% unlist() %>% unname()
   expect_equal(cp, 1:16)
   
 })
diff --git a/tests/testthat/test-long2wide.R b/tests/testthat/test-long2wide.R
new file mode 100644
index 00000000..9932d1f5
--- /dev/null
+++ b/tests/testthat/test-long2wide.R
@@ -0,0 +1,51 @@
+context("long2wide")
+
+# 2w*2b ----
+test_that("2w*2b", {
+  df_long <- sim_design(2, 2, long = TRUE)
+  df_wide <- long2wide(df_long, "A", "B", "val", "sub_id")
+  
+  expect_equal(names(df_wide), c("sub_id", "B", "A1", "A2"))
+  expect_equal(nrow(df_wide), 200)
+})
+
+# named arguments ----
+test_that("named arguments", {
+  df_long <- sim_design(2, 2, long = TRUE)
+  df_wide <- long2wide(dv = "val", id = "sub_id", .data = df_long, between = "B", within = "A")
+  
+  expect_equal(names(df_wide), c("sub_id", "B", "A1", "A2"))
+  expect_equal(nrow(df_wide), 200)
+})
+
+# 2w*2w*2b*2b ----
+test_that("2w*2w*2b*2b", {
+  df_long <- sim_design(c(2, 2), c(2, 2), long = TRUE)
+  df_wide <- long2wide(df_long, c("A", "B"), c("C","D"), "val", "sub_id")
+  
+  expect_equal(names(df_wide), c("sub_id", "C", "D", "A1_B1", "A1_B2", "A2_B1", "A2_B2"))
+  expect_equal(nrow(df_wide), 400)
+})
+
+
+# iris ----
+test_that("iris", {
+  iris_long <- iris %>%
+    dplyr::mutate(id = make_id(nrow(.), "I")) %>%
+    tidyr::gather(var, val, Sepal.Length:Petal.Width) %>%
+    tidyr::separate(var, c("Feature", "Measure"))
+  
+  iris_wide <- long2wide(iris_long, within = c("Feature", "Measure"), 
+                         between = "Species", dv = "val", id = "id")
+  inames <- c("id", "Species", "Petal_Length", "Petal_Width", "Sepal_Length", "Sepal_Width")
+  expect_equal(names(iris_wide), inames)
+  expect_equal(nrow(iris_wide), 150)
+  
+  long <- check_sim_stats(iris_long, within = c("Feature", "Measure"), 
+                          between = "Species", dv = "val", id = "id")
+  
+  wide <- check_sim_stats(iris, between = "Species")
+  
+  
+  expect_equal(nrow(long), nrow(wide))
+})
diff --git a/tests/testthat/test-rnorm_multi.R b/tests/testthat/test-rnorm_multi.R
index 77cd1146..d56d4122 100644
--- a/tests/testthat/test-rnorm_multi.R
+++ b/tests/testthat/test-rnorm_multi.R
@@ -6,44 +6,47 @@ test_that("error messages", {
   expect_error(rnorm_multi(10.3), "n must be an integer > 2")
   expect_error(rnorm_multi("A"), "n must be an integer > 2")
   
+  expect_warning(rnorm_multi(100, 3, cors = .5), "cors is deprecated, please use r")
+  
   expect_error(
-    rnorm_multi(10, 3, 0, 1:2),
+    rnorm_multi(10, 3, 1:2),
     "the length of mu must be 1 or vars"
   )
   
   expect_error(
-    rnorm_multi(10, empirical = NA),
-    "empirical must be TRUE or FALSE"
+    rnorm_multi(10, 3, 0, 1:2),
+    "the length of sd must be 1 or vars"
   )
   
   expect_error(
-    rnorm_multi(10, 3, 0, 1, 1:2),
-    "the length of sd must be 1 or vars"
+    rnorm_multi(10, empirical = NA),
+    "empirical must be TRUE or FALSE"
   )
+  
   expect_error(
-    rnorm_multi(10, 3, matrix("A", 3, 3)),
+    rnorm_multi(10, 3, 0, 1, matrix("A", 3, 3)),
     "cors matrix not numeric" 
   )
   expect_error(
-    rnorm_multi(10, 3, matrix(0.5, 4, 2)),
+    rnorm_multi(10, 3, 0, 1, matrix(0.5, 4, 2)),
     "cors matrix wrong dimensions" 
   )
   
   m <- matrix(c(1, .5, .5, .5, 1, .5, .5, .75, 1), 3)
   expect_error( 
-    rnorm_multi(10, 3, m), 
+    rnorm_multi(10, 3, 0, 1, m), 
     "cors matrix not symmetric"
   )
   
   m <- matrix(c(1, .5, .5, .5, 1, .5, .5, .5, 0), 3)
   expect_error(
-    rnorm_multi(10, 3, m),
+    rnorm_multi(10, 3, 0, 1, m),
     "correlation matrix not positive definite"
   )
   
   cors <- c(-0.06826927, -0.89756943, -0.45636273)
   expect_error(
-    rnorm_multi(10, 3, cors),
+    rnorm_multi(10, 3, 0, 1, cors),
     "correlation matrix not positive definite"
   )
 })
@@ -51,9 +54,9 @@ test_that("error messages", {
 test_that("correct default parameters", {
   n <- 1e5
   dat <- rnorm_multi(n)
-  cors <- cor(dat)
+  r <- cor(dat)
   means <- dplyr::summarise_all(dat, mean)
-  sds <- dplyr::summarise_all(dat, sd)
+  sds <- dplyr::summarise_all(dat, stats::sd)
   
   expect_equal(nrow(dat), n)
   expect_equal(ncol(dat), 3)
@@ -64,9 +67,9 @@ test_that("correct default parameters", {
 test_that("correct default parameters with empirical = TRUE", {
   n <- 50
   dat <- rnorm_multi(n, empirical = TRUE)
-  cors <- cor(dat)
+  r <- cor(dat)
   means <- dplyr::summarise_all(dat, mean)
-  sds <- dplyr::summarise_all(dat, sd)
+  sds <- dplyr::summarise_all(dat, stats::sd)
   
   expect_equal(nrow(dat), n)
   expect_equal(ncol(dat), 3)
@@ -76,7 +79,7 @@ test_that("correct default parameters with empirical = TRUE", {
 
 test_that("default matrix names", {
   cmat <- cor(iris[,1:4])
-  dat <- rnorm_multi(10, vars = 4, cors = cmat)
+  dat <- rnorm_multi(10, vars = 4, r = cmat)
   
   expect_equal(colnames(dat), colnames(cmat))
 })
\ No newline at end of file
diff --git a/tests/testthat/test-rnorm_pre.R b/tests/testthat/test-rnorm_pre.R
index 264b1ed7..14cc2979 100644
--- a/tests/testthat/test-rnorm_pre.R
+++ b/tests/testthat/test-rnorm_pre.R
@@ -26,7 +26,7 @@ test_that("correct specified parameters", {
   rho <- runif(1) * sample(c(-1, 1), 1)
   ymean <- rnorm(1, 0, 100)
   ysd <- runif(1, 0.001, 100)
-  v2 <- rnorm_pre(v1, rho, ymean, ysd)
+  v2 <- rnorm_pre(v1, ymean, ysd, rho)
   
   testrho <- cor(v1, v2)
   testymean <- mean(v2)
diff --git a/tests/testthat/test-select_num_grp.R b/tests/testthat/test-select_num_grp.R
index 892e45d4..a8c1a7af 100644
--- a/tests/testthat/test-select_num_grp.R
+++ b/tests/testthat/test-select_num_grp.R
@@ -1,8 +1,8 @@
 context("select_num_grp")
 
 test_that("error messages", {
-  expect_error(select_num_grp("A"), "dat must be a data frame or matrix")
-  expect_error(select_num_grp(iris, FALSE), "grp_by must be a numeric or character vector")
+  expect_error(select_num_grp("A"), ".data must be a data frame or matrix")
+  expect_error(select_num_grp(iris, FALSE), "between must be a numeric or character vector")
 })
 
 test_that("correct defaults", {
diff --git a/tests/testthat/test-sim_design.R b/tests/testthat/test-sim_design.R
index 8e86bf03..cf743942 100644
--- a/tests/testthat/test-sim_design.R
+++ b/tests/testthat/test-sim_design.R
@@ -82,7 +82,7 @@ test_that("2b", {
   within <- list()
   
   df <- sim_design(within, between, n = 100, empirical = TRUE)
-  chk <- check_sim_stats(df, grp_by = "B")
+  chk <- check_sim_stats(df, between = "B")
   
   comp <- tibble::tribble(
     ~B, ~n, ~var, ~val, ~mean, ~sd,
@@ -106,7 +106,7 @@ test_that("2b*2b", {
   within <- list()
   
   df <- sim_design(within, between, n = 100, empirical = TRUE)
-  chk <- check_sim_stats(df, grp_by = c("A","B"))
+  chk <- check_sim_stats(df, between = c("A","B"))
   
   comp <- tibble::tribble(
     ~A, ~B, ~n, ~var, ~val, ~mean, ~sd,
@@ -149,8 +149,8 @@ test_that("2w*2b basic", {
     "B2" = .5
   )
   
-  df <- sim_design(within, between, n, r, mu, sd, TRUE)
-  chk <- check_sim_stats(df, grp_by = "B")
+  df <- sim_design(within, between, n, mu, sd, r, TRUE)
+  chk <- check_sim_stats(df, between = "B")
   
   comp <- tibble::tribble(
     ~B, ~n, ~var, ~W1, ~W2, ~mean, ~sd,
@@ -189,13 +189,13 @@ test_that("2w*2b alt", {
     B1 = c(3, 4),
     B2 = c(5, 6)
   )
-  cors <- list(
+  r <- list(
     B1 = .2,
     B2 = .5
   )
   
-  df <- sim_design(within, between, n, cors, mu, sd, TRUE)
-  chk <- check_sim_stats(df, grp_by = "B")
+  df <- sim_design(within, between, n, mu, sd, r, TRUE)
+  chk <- check_sim_stats(df, between = "B")
   
   comp <- tibble::tribble(
     ~B, ~n, ~var, ~W1, ~W2, ~mean, ~sd,
@@ -233,10 +233,10 @@ test_that("2w*2b within order", {
     "B2" = c(W2 = 6, W1 = 5)
   )
   
-  df <- sim_design(within, between, 50, .5, mu, sd, TRUE)
-  check_sim_stats(df, grp_by = "B")
+  df <- sim_design(within, between, 50, mu, sd, .5, TRUE)
+  check_sim_stats(df, between = "B")
   
-  chk <- check_sim_stats(df, grp_by = "B")
+  chk <- check_sim_stats(df, between = "B")
   comp <- tibble::tribble(
     ~B, ~n, ~var, ~W1, ~W2, ~mean, ~sd,
     "B1", 50, "W1", 1,     0.5,    10,     3,
@@ -281,10 +281,10 @@ test_that("2w*2b order", {
     "B1" = .2
   )
   
-  df <- sim_design(within, between, n, r, mu, sd, TRUE)
-  check_sim_stats(df, grp_by = "B")
+  df <- sim_design(within, between, n, mu, sd, r, TRUE)
+  check_sim_stats(df, between = "B")
   
-  chk <- check_sim_stats(df, grp_by = "B")
+  chk <- check_sim_stats(df, between = "B")
   comp <- tibble::tribble(
     ~B, ~n, ~var, ~W1, ~W2, ~mean, ~sd,
     "B1", 60, "W1", 1,     0.2,    10,     3,
@@ -333,8 +333,8 @@ test_that("2w*2b*2b", {
     A2_B2 = .4
   )
   
-  df <- sim_design(within, between, n, r, mu, sd, TRUE)
-  check_sim_stats(df, grp_by = c("A", "B"))
+  df <- sim_design(within, between, n, mu, sd, r, TRUE)
+  check_sim_stats(df, between = c("A", "B"))
   
   expect_equal(nrow(df), 200)
   expect_equal(ncol(df), 5)
@@ -353,7 +353,7 @@ test_that("long", {
     "N" = c("N2", "N1")
   )
   
-  df <- sim_design(within, between, 100, .5, 0, 1, TRUE, TRUE)
+  df <- sim_design(within, between, 100, 0, 1, .5, TRUE, TRUE)
   
   expect_equal(nrow(df), 3200)
   expect_equal(ncol(df), 7)
@@ -371,8 +371,8 @@ test_that("complex names", {
     "My second within factor" = c("Factor_W2_L1", "Factor_W2_L2")
   )
   
-  df_long <- sim_design(within, between, 10, .5, 0, 1, TRUE, TRUE)
-  df_wide <- sim_design(within, between, 10, .5, 0, 1, TRUE, FALSE)
+  df_long <- sim_design(within, between, 10, 0, 1, .5, TRUE, TRUE)
+  df_wide <- sim_design(within, between, 10, 0, 1, .5, TRUE, FALSE)
   
   long_names <- c("sub_id", "My first between factor", "My second between factor", 
                   "My first within factor", "My second within factor",  "val")
@@ -394,8 +394,8 @@ test_that("complex names", {
     time2 = c("day", "night")
   )
   
-  df_long <- sim_design(within, between, 10, .5, 0, 1, TRUE, TRUE)
-  df_wide <- sim_design(within, between, 10, .5, 0, 1, TRUE, FALSE)
+  df_long <- sim_design(within, between, 10, 0, 1, .5, TRUE, TRUE)
+  df_wide <- sim_design(within, between, 10, 0, 1, .5, TRUE, FALSE)
                         
   long_names <- c("sub_id", "pets", "pets2", "time", "time2",  "val")
   wide_names <- c("sub_id", "pets", "pets2", "day_day", "night_day", "day_night", "night_night")
@@ -436,7 +436,7 @@ test_that("works", {
                .3, .5, .6,  1)
   mat <- matrix(long_cor, nrow = 4)
   
-  cors = list(
+  r = list(
     "B1_A2" = triangle,
     "B1_A1" = long_cor,
     "B2_A1" = mat,
@@ -446,7 +446,7 @@ test_that("works", {
   n = 100
   empirical = TRUE
 
-  df <- sim_design(within, between, n, cors, mu, sd, empirical)
+  df <- sim_design(within, between, n, mu, sd, r, empirical)
   check_sim_stats(df, c("B", "A"))
   
   expect_equal(nrow(df), 400)
diff --git a/tests/testthat/test-simdf.R b/tests/testthat/test-sim_df.R
similarity index 56%
rename from tests/testthat/test-simdf.R
rename to tests/testthat/test-sim_df.R
index 679adb64..6bf668e8 100644
--- a/tests/testthat/test-simdf.R
+++ b/tests/testthat/test-sim_df.R
@@ -1,21 +1,24 @@
-context("simdf")
+context("sim_df")
 
+# error messages ----
 test_that("error messages", {
-  expect_error( simdf("A"), "dat must be a data frame or matrix" )
-  expect_error( simdf(iris, "A"), "n must be an integer > 2" )
-  expect_error( simdf(iris, 2), "n must be an integer > 2" )
-  expect_error( simdf(iris, 10, FALSE), "grp_by must be a numeric or character vector" )
+  expect_error( sim_df("A"), ".data must be a data frame or matrix" )
+  expect_error( sim_df(iris, "A"), "n must be an integer > 2" )
+  expect_error( sim_df(iris, 2), "n must be an integer > 2" )
+  expect_error( sim_df(iris, 10, FALSE), "between must be a numeric or character vector" )
 })
 
-test_that("correct default parameters", {
-  newdf <- simdf(iris)
+# default parameters ----
+test_that("default parameters", {
+  newdf <- sim_df(iris)
 
   expect_equal(nrow(newdf), 100)
   expect_equal(ncol(newdf), 4)
   expect_equal(names(newdf), names(iris)[1:4])
 })
 
-test_that("correct specified parameters", {
+# specified parameters ----
+test_that("specified parameters", {
   n <- 100
   dat <- tibble::as_tibble(iris) %>%
     dplyr::select_if(is.numeric)
@@ -25,7 +28,8 @@ test_that("correct specified parameters", {
   sds <- dplyr::summarise_all(dat, sd) %>%
     as.data.frame()
   
-  newdf <- simdf(iris, n, NULL, TRUE)
+  # unnamed arguments in order
+  newdf <- sim_df(iris, n, c(), TRUE)
   newdat <- dplyr::select_if(newdf, is.numeric)
   newcors <- cor(newdat)
   newmeans <- dplyr::summarise_all(newdat, mean) %>%
@@ -40,18 +44,38 @@ test_that("correct specified parameters", {
   expect_equal(cors, newcors)
   expect_equal(means, newmeans)
   expect_equal(sds, newsds)
+  
+  # named arguments out of order
+  newdf <- sim_df(between = c(), empirical = TRUE, .data = iris, n = n)
+  newdat <- dplyr::select_if(newdf, is.numeric)
+  newcors <- cor(newdat)
+  newmeans <- dplyr::summarise_all(newdat, mean) %>%
+    as.data.frame()
+  newsds <- dplyr::summarise_all(newdat, sd) %>%
+    as.data.frame()
+  
+  expect_equal(nrow(newdf), n)
+  expect_equal(ncol(newdf), 4)
+  expect_equal(names(newdf), names(iris)[1:4])
+  
+  expect_equal(cors, newcors)
+  expect_equal(means, newmeans)
+  expect_equal(sds, newsds)
+  
 })
 
-test_that("grouping by col name", {
-  newdf <- simdf(iris, 20, "Species")
+# grouping by name ----
+test_that("grouping by name", {
+  newdf <- sim_df(iris, 20, "Species")
   
   expect_equal(nrow(newdf), 60)
   expect_equal(ncol(newdf), 5)
   expect_equal(names(newdf) %>% sort(), names(iris) %>% sort())
 })
 
+# grouping by col number ----
 test_that("grouping by col number", {
-  newdf <- simdf(iris, 20, 5)
+  newdf <- sim_df(iris, 20, 5)
   
   expect_equal(nrow(newdf), 60)
   expect_equal(ncol(newdf), 5)
@@ -63,7 +87,7 @@ test_that("grouping by col number", {
 #   
 #   simiris <- purrr::map_df(1:1000, function(i) {
 #     iris %>%
-#       simdf(100) %>%
+#       sim_df(100) %>%
 #       check_sim_stats(digits = 10)
 #   })
 #   
diff --git a/tests/testthat/test-simdf_mixed.R b/tests/testthat/test-sim_mixed_df.R
similarity index 64%
rename from tests/testthat/test-simdf_mixed.R
rename to tests/testthat/test-sim_mixed_df.R
index 475aec61..22814e6a 100644
--- a/tests/testthat/test-simdf_mixed.R
+++ b/tests/testthat/test-sim_mixed_df.R
@@ -1,8 +1,8 @@
-context("simdf_mixed")
+context("sim_mixed_df")
 
 test_that("default settings", {
   testthat::skip_on_cran()
-  s10 <- simdf_mixed(faceratings, 10, 10)
+  s10 <- sim_mixed_df(faceratings, 10, 10)
   
   expect_equal(nrow(s10), 100)
   expect_equal(ncol(s10), 5)
@@ -10,7 +10,7 @@ test_that("default settings", {
 
 test_that("specified dv and IDs", {
   testthat::skip_on_cran()
-  s10 <- simdf_mixed(faceratings, 10, 10, "rating", "rater_id", "face_id")
+  s10 <- sim_mixed_df(faceratings, 10, 10, "rating", "rater_id", "face_id")
   
   expect_equal(nrow(s10), 100)
   expect_equal(ncol(s10), 5)
diff --git a/vignettes/intro.Rmd b/vignettes/intro.Rmd
deleted file mode 100644
index d480e12a..00000000
--- a/vignettes/intro.Rmd
+++ /dev/null
@@ -1,161 +0,0 @@
----
-title: "Introduction to faux"
-author: "Lisa DeBruine"
-date: "`r Sys.Date()`"
-output: rmarkdown::html_vignette
-vignette: >
-  %\VignetteIndexEntry{Introduction to faux}
-  %\VignetteEngine{knitr::rmarkdown}
-  %\VignetteEncoding{UTF-8}
----
-
-```{r setup, include = FALSE}
-knitr::opts_chunk$set(
-  fig.width = 8,
-  fig.height = 5,
-  collapse = TRUE,
-  comment = "#>"
-)
-
-library(ggplot2)
-library(dplyr)
-library(tidyr)
-library(faux)
-theme_set(theme_bw())
-set.seed(200)
-```
-
-
-It is useful to be able to simulate data with a specified structure. The `faux` package provides some functions to make this process easier.
-
-## sim_design
-
-This function creates a dataset with a specific between- and within-subjects design. [see vignette](sim_design.html)
-
-For example, the following creates a 2w*2b design with 100 observations in each cell. The between-subject factor is `pet` with twolevels of `cat` and `dog`. The within-subject factor is `time` with two levels of `day` and `night`. The mean for the `cat_day` cell is 10, the mean for the `cat_night` cell is 20, the mean for the `dog_day` cell is 15, and the mean for the `dog_night` cell is 25. All cells have a SD of 5 and all within-subject cells are correlated <code>r = 0.5</code>. The resulting data has exactly these values (set `empirical = FALSE` to sample from a population with these values).
-
-
-```{r}
-between <- list("pet" = c("cat", "dog"))
-within <- list("time" = c("day", "night"))
-mu <- data.frame(
-  cat = c(10, 20),
-  dog = c(15, 25),
-  row.names = within$time
-)
-df <- sim_design(within, between, 
-                 n = 100, cors = 0.5, mu = mu, sd = 5,
-                 empirical = TRUE)
-```
-
-`r check_sim_stats(df, grp_by = "pet", usekable = TRUE)`
-Table: Sample `sim_design()` stats
-
-## rnorm_multi
-
-This function makes multiple normally distributed vectors with specified parameters and relationships.[see vignette](rnorm_multi.html)
-
-For example, the following creates a sample that has 100 observations of 3 variables, drawn from a population where where A correlates with B and C with r = 0.5, and B and C correlate with r = 0.25. A has a mean of 0 and SD of 1, while B and C have means of 20 and SDs of 5.
-
-```{r multirnorm-example}
-
-dat <- rnorm_multi(
-  n = 100, 
-  cors = c(0.5, 0.5, 0.25), 
-  mu = c(0, 20, 20),
-  sd = c(1, 5, 5),
-  varnames = c("A", "B", "C"),
-  empirical = FALSE
-)
-
-```
-
-
-`r check_sim_stats(dat, usekable = T)`
-Table: Sample `rnorm_multi()` stats
-
-
-## simdf
-
-This function produces a dataframe with the same distributions and correlations as an existing dataframe. It only returns numeric columns and simulates all numeric variables from a continuous normal distribution (for now). [see vignette](simdf.html)
-
-For example, the following code creates a new sample from the built-in dataset `iris` with 50 observations of each species.
-
-```{r}
-new_iris <- simdf(iris, 50, "Species") 
-```
-
-```{r plot-iris-sim, echo = FALSE, fig.cap="Simulated iris dataset"}
-new_iris %>%
-  ggplot(aes(Sepal.Width, Sepal.Length, color = Species)) +
-  geom_point() +
-  geom_smooth(method = "lm")
-```
-
-## Additional functions
-
-### check_sim_stats
-
-If you want to check your simulated stats or just describe an existing dataset, use `check_sim_stats()`.
-
-```{r}
-check_sim_stats(iris)
-```
-
-You can also group your data and change the digits to round. Display the table using `knitr::kable()` by setting `usekable` to `TRUE` (remember to set `results='asis'` in the chunk header.
-
-```{r, results='asis'}
-check_sim_stats(iris, 
-                grp_by = "Species", 
-                digits = 3, 
-                usekable = TRUE)
-```
-
-
-### pos_def_limits
-
-Not all correlation matrices are possible. For example, if variables A and B are correlated with r = 1.0, then the correlation between A and C can only be exactly equal to the correlation between B and C.
-
-The function `pos_def_limits()` lets you know what the possible range of values is for the missing value in a correlation matrix with one missing value. The correlation values are entered just from the top right triangle of the matrix, with a single `NA` for the missing value.
-
-```{r pos_def_limits1}
-lims <- pos_def_limits(.8, .2, NA)
-```
-
-`r knitr::kable(lims)`
-
-For example, if r~AB~ = 0.8 and r~AC~ = 0.2, then `r lims$min` <= r~BC~ <= `r lims$max`.
-
-If you enter a correlation matrix that contains impossible combinations, your limits will be `NA`.
-
-```{r pos_def_limits-na}
-lims <- pos_def_limits(.8, .2,  0,
-                          -.5, NA,
-                               .2)
-```
-
-`r knitr::kable(lims)`
-
-If you have a full matrix and want to know if it is positive definite, you can use the following code:
-
-```{r is_pos_def}
-c(.2, .3, .4, .2,
-      .3, -.1, .2,
-           .4, .5,
-               .3) %>%
-  cormat_from_triangle() %>%
-  is_pos_def()
-```
-
-```{r not_pos_def}
-matrix(c(1, .3, -.9, .2,
-        .3,  1,  .4, .5,
-       -.9, .4,   1, .3,
-        .2, .5,  .3,  1), 4) %>%
-  is_pos_def()
-```
-
-
-
-
-
diff --git a/vignettes/rnorm_multi.Rmd b/vignettes/rnorm_multi.Rmd
index 497706c9..463d7ef9 100644
--- a/vignettes/rnorm_multi.Rmd
+++ b/vignettes/rnorm_multi.Rmd
@@ -29,15 +29,15 @@ The `rnorm_multi()` function makes multiple normally distributed vectors with sp
 
 ## Quick example
 
-For example, the following creates a sample that has 100 observations of 3 variables, drawn from a population where where A correlates with B and C with r = 0.5, and B and C correlate with r = 0.25. A has a mean of 0 and SD of 1, while B and C have means of 20 and SDs of 5.
+For example, the following creates a sample that has 100 observations of 3 variables, drawn from a population where A has a mean of 0 and SD of 1, while B and C have means of 20 and SDs of 5. A correlates with B and C with r = 0.5, and B and C correlate with r = 0.25.
 
 
 ```{r multirnorm-example}
 
 dat <- rnorm_multi(n = 100, 
-                  cors = c(0.5, 0.5, 0.25), 
                   mu = c(0, 20, 20),
                   sd = c(1, 5, 5),
+                  r = c(0.5, 0.5, 0.25), 
                   varnames = c("A", "B", "C"),
                   empirical = FALSE)
 
@@ -47,7 +47,7 @@ dat <- rnorm_multi(n = 100,
 `r check_sim_stats(dat, usekable = T)`
 Table: Sample stats
 
-### Specify `cors` {#spec_cors}
+### Specify correlations {#spec_r}
 
 You can specify the correlations in one of four ways:
 
@@ -61,7 +61,7 @@ You can specify the correlations in one of four ways:
 If you want all the pairs to have the same correlation, just specify a single number. 
 
 ```{r}
-bvn <- rnorm_multi(100, 5, .3, varnames = letters[1:5])
+bvn <- rnorm_multi(100, 5, 0, 1, .3, varnames = letters[1:5])
 ```
 
 `r check_sim_stats(bvn, usekable = T)`
@@ -73,7 +73,7 @@ If you already have a correlation matrix, such as the output of `cor()`, you can
 
 ```{r vvmatrix}
 cmat <- cor(iris[,1:4])
-bvn <- rnorm_multi(100, 4, cmat, 
+bvn <- rnorm_multi(100, 4, 0, 1, cmat, 
                   varnames = colnames(cmat))
 ```
 
@@ -88,7 +88,7 @@ You can specify your correlation matrix by hand as a vars\*vars length vector, w
 cmat <- c(1, .3, .5,
           .3, 1, 0,
           .5, 0, 1)
-bvn <- rnorm_multi(100, 3, cmat, 
+bvn <- rnorm_multi(100, 3, 0, 1, cmat, 
                   varnames = c("first", "second", "third"))
 ```
 
@@ -107,7 +107,7 @@ rho2_3 <- .2
 rho2_4 <- 0
 rho3_4 <- -.3
 cmat <- c(rho1_2, rho1_3, rho1_4, rho2_3, rho2_4, rho3_4)
-bvn <- rnorm_multi(100, 4, cmat, 
+bvn <- rnorm_multi(100, 4, 0, 1, cmat, 
                   varnames = letters[1:4])
 ```
 
@@ -119,10 +119,28 @@ Table: Sample stats from a (vars\*(vars-1)/2) vector
 If you want your samples to have the *exact* correlations, means, and SDs you entered, set `empirical` to TRUE.
 
 ```{r}
-bvn <- rnorm_multi(100, 5, .3, 
+bvn <- rnorm_multi(100, 5, 0, 1, .3, 
                   varnames = letters[1:5], 
                   empirical = T)
 ```
 
 `r check_sim_stats(bvn, usekable = T)`
 Table: Sample stats with empirical = TRUE
+
+## Pre-existing variable
+
+Us `rnorm_pre()` to create a vector with a specified correlation to a pre-existing variable. The following code creates a vector called `sl.5` with a mean of 10, SD of 2 and a correaltion of r = 0.5 to the `Sepal.Length` column in the built-in dataset `iris`. This function only creates a vector with the *exact* parameters specified.
+
+```{r}
+sl <- iris$Sepal.Length
+
+sl.5.v1 <- rnorm_pre(sl, mu = 10, sd = 2, r = 0.5)
+sl.5.v2 <- rnorm_pre(sl, mu = 10, sd = 2, r = 0.5)
+```
+
+```{r, echo = FALSE, results='asis'}
+data.frame(sl, sl.5.v1, sl.5.v2) %>%
+  cor() %>% knitr::kable()
+  
+```
+
diff --git a/vignettes/sim_design.Rmd b/vignettes/sim_design.Rmd
index d452c745..77261c76 100644
--- a/vignettes/sim_design.Rmd
+++ b/vignettes/sim_design.Rmd
@@ -40,11 +40,11 @@ mu <- data.frame(
   dog = c(15, 25),
   row.names = within$time
 )
-df <- sim_design(within, between, n = 100, cors = 0.5, mu = mu, sd = 5)
+df <- sim_design(within, between, n = 100, mu = mu, sd = 5, r = 0.5, empirical = TRUE)
 ```
 
 ```{r echo = FALSE, result='asis'}
-check_sim_stats(df, grp_by = "pet", usekable = TRUE)
+check_sim_stats(df, between = "pet", usekable = TRUE)
 ```
 
 ```{r, echo = FALSE, fig.width = 8, fig.height = 4}
@@ -60,7 +60,15 @@ gather(df, time, val, day:night) %>%
 
 ### Factor and level names
 
-First, list your between-subject and within-subject factors. You can specify them like this:
+If you don't feel like naming your factors and levels, you can just put in a vector of levels. So you can make a quick 2w\*3w\*2b with the following code.
+
+```{r}
+df <- sim_design(within = c(2,3), between = c(2), n = 5, mu = 0, sd = 1, r = 0.5)
+```
+
+`r knitr::kable(df)`
+
+You can specify between-subject and within-subject factors as a list of vectors where the item names are the factor labels and the vectors are the level labels.
 
 ```{r}
 between <- list(
@@ -71,7 +79,7 @@ within <- list(
 )
 ```
 
-Or like this:
+You can also specify factors as a list of named vectors where the item names are the factor labels, the vector names are the level labels, and the vector items are the long labels you would use in a codebook or plot.
 
 ```{r}
 between <- list(
@@ -82,11 +90,12 @@ within <- list(
 )
 ```
 
-The long name for factor levels isn't used yet, but will be used in future functions that create a codebook or plots.
+The long label for factor levels isn't used yet, but will be used in future functions that create a codebook or plots.
+
 
 ### N, mean, SD
 
-You can specify the Ns, means and standard deviations for each cell as a single number, vector, list, or data frame. 
+You can specify the Ns, means and standard deviations for each cell as a single number, list of vectors, or data frame. 
 
 You usually want to specify `n` as a single number. This is N per cell, not total sample size.
 
@@ -123,7 +132,7 @@ knitr::kable(sd)
 If you have any within-subject factors, you need to set the correlation for each between-cell. Here, we only have two levels of one within-subject factor, so can only set one correlation per between-cell.
 
 ```{r}
-cors <- list(
+r <- list(
   cat = .5,
   dog = .6
 )
@@ -133,11 +142,11 @@ If you set `empirical = TRUE`, you will get the *exact* means, SDs and correlati
 
 ```{r}
 df <- sim_design(within, between, n = 100, 
-                 cors = cors, mu = mu, sd = sd,
+                 mu = mu, sd = sd, r = r,
                  empirical = TRUE)
 ```
 
-`r check_sim_stats(df, grp_by = "pet", usekable = TRUE)`
+`r check_sim_stats(df, between = "pet", usekable = TRUE)`
 
 ## More factors
 
@@ -170,7 +179,7 @@ mu <- data.frame(
 You can set the correlation for each between-cell to a single number.
 
 ```{r}
-cors <- list(
+r <- list(
   novice_day = 0.3,
   novice_night = 0.2,
   expert_day = 0.5,
@@ -178,31 +187,33 @@ cors <- list(
 )
 ```
 
-Or you can set the full correlation matrix with a vector or matrix (see section [Specify cors](intro.html#spec_cors)). Since we have 6 within-cells, this is a 6x6 matrix or a vector of the upper right 15 values.
+Or you can set the full correlation matrix with a vector or matrix. Since we have 6 within-cells, this is a 6x6 matrix or a vector of the upper right 15 values.
 
 ```{r}
 # upper right triangle correlation specification
 # inc and con have r = 0.5 within each difficultly level, 0.2 otherwise
-#   ce,  ie,  cm,  im,  ch,  ih
-r <-  c(0.5, 0.2, 0.2, 0.2, 0.2, #con_easy
-             0.2, 0.2, 0.2, 0.2, #inc_easy
-                  0.5, 0.2, 0.2, #con_med
-                       0.2, 0.2, #inc_med
-                            0.5) #con_hard
-                                 #inc_hard
-
-cors <- list(
-  novice_day = r,
-  novice_night = r,
-  expert_day = r,
-  expert_night = r
+#          ce,  ie,  cm,  im,  ch,  ih
+triangle <-  c(0.5, 0.2, 0.2, 0.2, 0.2, #con_easy
+                    0.2, 0.2, 0.2, 0.2, #inc_easy
+                         0.5, 0.2, 0.2, #con_med
+                              0.2, 0.2, #inc_med
+                                   0.5) #con_hard
+                                        #inc_hard
+
+r <- list(
+  novice_day = triangle,
+  novice_night = triangle,
+  expert_day = triangle,
+  expert_night = triangle
 )
 ```
 
-You can set `frame_long = TRUE` to return the data frame in long format, which is usually easier for plotting. 
+You can set `long = TRUE` to return the data frame in long format, which is usually easier for plotting. 
 
 ```{r}
-df <- sim_design(within, between, n = 100, cors = cors, mu = mu, sd = 2, frame_long = TRUE)
+df <- sim_design(within, between, n = 100, 
+                 mu = mu, sd = 2, r = r, 
+                 long = TRUE)
 ```
 
 
diff --git a/vignettes/simdf.Rmd b/vignettes/sim_df.Rmd
similarity index 68%
rename from vignettes/simdf.Rmd
rename to vignettes/sim_df.Rmd
index 89eab38b..31385c04 100644
--- a/vignettes/simdf.Rmd
+++ b/vignettes/sim_df.Rmd
@@ -24,7 +24,7 @@ theme_set(theme_bw())
 set.seed(200)
 ```
 
-The `simdf()` function produces a dataframe with the same distributions and correlations as an existing dataframe. It only returns numeric columns and simulates all numeric variables from a continuous normal distribution (for now).
+The `sim_df()` function produces a dataframe with the same distributions and correlations as an existing dataframe. It only returns numeric columns and simulates all numeric variables from a continuous normal distribution (for now).
 
 For example, here is the relationship between speed and distance in the built-in dataset `cars`. 
 
@@ -35,18 +35,18 @@ cars %>%
   geom_smooth(method = "lm")
 ```
 
-You can create a new sample with the same parameters and 500 rows with the code `simdf(cars, 500)`.
+You can create a new sample with the same parameters and 500 rows with the code `sim_df(cars, 500)`.
 
 ```{r plot-cars-sim, fig.cap="Simulated cars dataset"}
-simdf(cars, 500) %>%
+sim_df(cars, 500) %>%
   ggplot(aes(speed, dist)) + 
     geom_point() +
     geom_smooth(method = "lm")
 ```
 
-#### Grouping Variables
+## Between-subject variables
 
-You can also optionally add grouping variables. For example, here is the relationship between sepal length and width in the built-in dataset `iris`.
+You can also optionally add between-subject variables. For example, here is the relationship between sepal length and width in the built-in dataset `iris`.
 
 ```{r plot-iris-orig, fig.cap="Original iris dataset"}
 iris %>%
@@ -55,21 +55,33 @@ iris %>%
   geom_smooth(method = "lm")
 ```
 
-And here is a new sample with 50 observations of each species, made with the code `simdf(iris, 100, "Species")`.
+And here is a new sample with 50 observations of each species, made with the code `sim_df(iris, 100, "Species")`.
 
 ```{r plot-iris-sim, fig.cap="Simulated iris dataset"}
-simdf(iris, 50, "Species") %>%
+sim_df(iris, 50, "Species") %>%
   ggplot(aes(Sepal.Width, Sepal.Length, color = Species)) +
   geom_point() +
   geom_smooth(method = "lm")
 ```
 
+## Empirical
+
+Set `empirical = TRUE` to return a data frame with *exactly* the same means, SDs, and correlations as the original dataset.
+
+```{r}
+exact_iris <- sim_df(iris, 50, between = "Species", empirical = TRUE)
+```
+
+
+## Rounding
+
 For now, the function only creates new variables sampled from a continuous normal distribution. I hope to add in other sampling distributions in the future. So you'd need to do any rounding or truncating yourself.
 
 ```{r plot-iris-sim-round, fig.cap="Simulated iris dataset (rounded)"}
-simdf(iris, 50, "Species") %>%
+sim_df(iris, 50, "Species") %>%
   mutate_if(is.numeric, round, 1) %>%
   ggplot(aes(Sepal.Width, Sepal.Length, color = Species)) +
   geom_point() +
   geom_smooth(method = "lm")
 ```
+