oddsratio v2.0 (#35)

oddsratio v2.0

.travis.yml

@@ -1,51 +1,38 @@
-language: r
-sudo: false
-dist: trusty
-cache: 
-  - packages
-  - ccache
-latex: false
+# DO NOT CHANGE THE CODE BELOW
+before_install:
+  - R -q -e 'if (!requireNamespace("remotes", quietly = TRUE)) install.packages("remotes")'
+  - R -q -e 'if (!requireNamespace("curl", quietly = TRUE)) install.packages("curl")'
+  - R -q -e 'remotes::install_github("ropenscilabs/tic"); tic::prepare_all_stages(); tic::before_install()'
+install: R -q -e 'tic::install()'
+after_install: R -q -e 'tic::after_install()'
+before_script: R -q -e 'tic::before_script()'
+script: R -q -e 'tic::script()'
+after_success: R -q -e 'tic::after_success()'
+after_failure: R -q -e 'tic::after_failure()'
+before_deploy: R -q -e 'tic::before_deploy()'
+deploy:
+  provider: script
+  script: R -q -e 'tic::deploy()'
+  on:
+    all_branches: true
+after_deploy: R -q -e 'tic::after_deploy()'
+after_script: R -q -e 'tic::after_script()'
+# DO NOT CHANGE THE CODE ABOVE
+
 
-# These steps apply to all matrix jobs unless defined differently in tic.R
-before_install: 
-  - R -q -e 'install.packages("remotes"); remotes::install_github("ropenscilabs/tic"); tic::prepare_all_stages(); tic::before_install(); remotes::install_github("tidyverse/ggplot2")'
+# Custom parts:
 
-matrix:
-  fast_finish: true
-  allow_failures:
-    - env: NB='w/ lintr'
-    - env: NB='w/ covr'
-  include:
-    - os: linux
-      r: devel
-      install: R -q -e 'tic::install()'
-      after_install: R -q -e 'tic::after_install()'
-      before_script: R -q -e 'tic::before_script()'
-      script: R -q -e 'tic::script()'
-      before_deploy: R -q -e 'tic::before_deploy()'
-      deploy:
-          provider: script
-          script: R -q -e 'tic::deploy()'
-          on:
-            branch: master
-            condition:
-               - $TRAVIS_PULL_REQUEST = false
-               - $TRAVIS_EVENT_TYPE != cron
-      after_deploy: R -q -e 'tic::after_deploy()'
-    - os: osx
-      r: release
-      install: R -q -e 'tic::install()'
-      after_install: R -q -e 'tic::after_install()'
-      before_script: R -q -e 'tic::before_script()'
-      script: R -q -e 'tic::script()'
-    - os: linux
-      r: devel
-      env: NB='w/ covr' 
-    - os: linux
-      r: devel
-      env: NB='w/ lintr'
+# Header
+language: r
+sudo: false
+dist: xenial
+cache: packages
+latex: true
+pandoc_version: 2.5
 
-notifications:
-  on_success: change # default: always
-  on_failure: change # default: always
-  email: false
+env:
+  global:
+  - _R_CHECK_FORCE_SUGGESTS_=false
+  - MAKEFLAGS="-j 2"
+  - BUILD_PKGDOWN=true
+  - _R_CHECK_TESTS_NLINES_=0

DESCRIPTION

@@ -1,39 +1,52 @@
 Package: oddsratio
-Version: 1.0.3
-Date: 2018-07-15
 Title: Odds Ratio Calculation for GAM(M)s & GLM(M)s
-Authors@R: c(
-   person("Patrick", "Schratz", ,
-   email = "[email protected]", 
-   role = c("aut", "cre"),
-   comment = c(ORCID = '0000-0003-0748-6624'))
-  )
-Description: Simplified odds ratio calculation of GAM(M)s & GLM(M)s. 
-    Provides structured output (data frame) of all predictors and their corresponding odds ratios and confident intervals for further analyses. 
-    It helps to avoid false references of predictors and increments by specifying these parameters in a list instead of using 'exp(coef(model))' (standard approach of odds ratio calculation for GLMs) which just returns a plain numeric output. 
-    For GAM(M)s, odds ratio calculation is highly simplified with this package since it takes care of the multiple 'predict()' calls of the chosen predictor while holding other predictors constant.
-    Also, this package allows odds ratio calculation of percentage steps across the whole predictor distribution range for GAM(M)s. 
-    In both cases, confident intervals are returned additionally.
-    Calculated odds ratio of GAM(M)s can be inserted into the smooth function plot. 
+Version: 2.0.0
+Date: 2019-06-13
+Authors@R: 
+    person(given = "Patrick",
+           family = "Schratz",
+           role = c("aut", "cre"),
+           email = "[email protected]",
+           comment = c(ORCID = "0000-0003-0748-6624"))
+Description: Simplified odds ratio calculation of GAM(M)s &
+    GLM(M)s.  Provides structured output (data frame) of all predictors
+    and their corresponding odds ratios and confident intervals for
+    further analyses.  It helps to avoid false references of predictors
+    and increments by specifying these parameters in a list instead of
+    using 'exp(coef(model))' (standard approach of odds ratio calculation
+    for GLMs) which just returns a plain numeric output.  For GAM(M)s,
+    odds ratio calculation is highly simplified with this package since it
+    takes care of the multiple 'predict()' calls of the chosen predictor
+    while holding other predictors constant.  Also, this package allows
+    odds ratio calculation of percentage steps across the whole predictor
+    distribution range for GAM(M)s.  In both cases, confident intervals
+    are returned additionally.  Calculated odds ratio of GAM(M)s can be
+    inserted into the smooth function plot.
+License: MIT + file LICENSE
 URL: https://github.com/pat-s/oddsratio
 BugReports: https://github.com/pat-s/oddsratio/issues
-Depends: R (>= 3.0.0)
-Imports: mgcv, 
-         MASS, 
-         stats,
-         ggplot2 (>= 3.0.0),
-         cowplot
-License: MIT + file LICENSE
-RoxygenNote: 6.0.1
-Suggests: knitr,
-          rmarkdown,
-          grid,
-          gtable,
-          scales,
-          testthat,
-          pacman,
-          gam
-LazyData: true
+Depends: 
+    R (>= 3.0.0)
+Imports: 
+    ggplot2 (>= 3.0.0),
+    mgcv,
+    stats,
+    stringr,
+    tibble
+Suggests: 
+    cowplot,
+    gam,
+    grid,
+    gtable,
+    knitr,
+    MASS,
+    rmarkdown,
+    scales,
+    testthat
+VignetteBuilder: 
+    knitr
 ByteCompile: true
-VignetteBuilder: knitr
+Encoding: UTF-8
+LazyData: true
 Roxygen: list(markdown = TRUE)
+RoxygenNote: 6.1.1

NAMESPACE

@@ -6,13 +6,14 @@ export(no_plot)
 export(or_gam)
 export(or_glm)
 export(plot_gam)
-import(MASS)
 import(ggplot2)
 import(mgcv)
-importFrom(cowplot,background_grid)
 importFrom(grDevices,dev.off)
 importFrom(grDevices,png)
 importFrom(graphics,plot)
 importFrom(stats,coefficients)
 importFrom(stats,confint)
 importFrom(stats,predict)
+importFrom(stringr,str_remove_all)
+importFrom(tibble,as_tibble)
+importFrom(tibble,tibble)

NEWS.md

@@ -1,3 +1,10 @@
+# oddsratio 2.0.0 (June 13 2019)
+
+* return a `tibble` instead of a `data.frame`
+* clean up code base
+* don't use `cowplot` ggplot theme by default
+* optimize wording in vignette
+
 # oddsratio 1.0.3 (June 19 2018)
 
 * update functions to work with ggplot2 v3.0.0

R/data.R

@@ -10,7 +10,7 @@
 #'
 #' @docType data
 #'
-#' @format a `data.frame` randomly created numerical and non-numerical variables
+#' @format a [tibble] randomly created numerical and non-numerical variables
 NULL
 
 #' data_glm
@@ -23,8 +23,8 @@ NULL
 #' @keywords datasets
 #' @keywords internal
 #'
-#' @format a `data.frame` randomly created numerical and non-numerical variables
+#' @format a [tibble] randomly created numerical and non-numerical variables
 #'
 #' @source Taken from \url{http://www.ats.ucla.edu/stat/r/dae/logit.htm}, direct download
 #' link: \url{http://www.ats.ucla.edu/stat/data/binary.csv}
-NULL
+NULL

R/helper_funs.R

@@ -24,10 +24,12 @@
 #' n <- 200
 #' sig <- 2
 #' dat <- gamSim(1, n = n, scale = sig, verbose = FALSE)
-#' dat$x4 <- as.factor(c(rep("A", 50), rep("B", 50), rep("C", 50),
-#'                     rep("D", 50)))
+#' dat$x4 <- as.factor(c(
+#'   rep("A", 50), rep("B", 50), rep("C", 50),
+#'   rep("D", 50)
+#' ))
 #' fit_gam <- gam(y ~ s(x0) + s(I(x1^2)) + s(x2) +
-#'                offset(x3) + x4, data = dat) # fit model
+#'   offset(x3) + x4, data = dat) # fit model
 #'
 #' tmp <- plot(fit_gam, pages = 1) # plot output
 #' tmp <- no_plot(fit_gam) # no plot output
@@ -65,10 +67,12 @@ no_plot <- function(model = NULL) {
 #' n <- 200
 #' sig <- 2
 #' dat <- gamSim(1, n = n, scale = sig, verbose = FALSE)
-#' dat$x4 <- as.factor(c(rep("A", 50), rep("B", 50), rep("C", 50),
-#'                     rep("D", 50)))
+#' dat$x4 <- as.factor(c(
+#'   rep("A", 50), rep("B", 50), rep("C", 50),
+#'   rep("D", 50)
+#' ))
 #' fit_gam <- gam(y ~ s(x0) + s(I(x1^2)) + s(x2) +
-#'                offset(x3) + x4, data = dat) # fit model
+#'   offset(x3) + x4, data = dat) # fit model
 #'
 #' tmp <- gam_to_df(fit_gam, "x2")
 #' @export
@@ -80,9 +84,11 @@ gam_to_df <- function(model = NULL, pred = NULL) {
   # get list index of spec. predictor
   set_pred <- which(grepl(pred, plot_df))
 
-  df <- data.frame(x = plot_df[[set_pred]]$x,
-                   se_upr = plot_df[[set_pred]]$fit + plot_df[[set_pred]]$se,
-                   se_lwr = plot_df[[set_pred]]$fit - plot_df[[set_pred]]$se,
-                   y = plot_df[[set_pred]]$fit)
+  df <- tibble(
+    x = plot_df[[set_pred]]$x,
+    se_upr = plot_df[[set_pred]]$fit + plot_df[[set_pred]]$se,
+    se_lwr = plot_df[[set_pred]]$fit - plot_df[[set_pred]]$se,
+    y = plot_df[[set_pred]]$fit
+  )
   return(df)
 }