diff --git a/.gitignore b/.gitignore
index 5d68b430..3a2b1941 100644
--- a/.gitignore
+++ b/.gitignore
@@ -57,3 +57,5 @@ rsconnect/
 
 # Markdown generated files
 */README.html
+*/shiny_bookmarks
+teal_app.lock
diff --git a/RNA-seq/app.R b/RNA-seq/app.R
index c5eeaaa1..4e97072e 100644
--- a/RNA-seq/app.R
+++ b/RNA-seq/app.R
@@ -13,7 +13,6 @@ data <- within(data, {
     dplyr::mutate(is_event = CNSR == 0)
   MAE <- hermes::multi_assay_experiment
 })
-datanames(data) <- c("ADTTE", "MAE")
 
 ## App header and footer ----
 nest_logo <- "https://raw.githubusercontent.com/insightsengineering/hex-stickers/main/PNG/nest.png"
diff --git a/early-dev/app.R b/early-dev/app.R
index cb777512..ad2807ac 100644
--- a/early-dev/app.R
+++ b/early-dev/app.R
@@ -15,7 +15,7 @@ data <- within(data, {
   ADSL <- radsl(seed = 1)
 
   # derive ADSL treatment duration
-  adsl_labels <- teal.data::col_labels(ADSL, fill = FALSE)
+  .adsl_labels <- teal.data::col_labels(ADSL, fill = FALSE)
   ADSL <- ADSL %>%
     mutate(
       TRTDURD = as.numeric(as.Date(TRTEDTM) - as.Date(TRTSDTM)) + 1,
@@ -29,12 +29,11 @@ data <- within(data, {
       EOSSTT = "End of Study Status"
     ) %>%
     droplevels()
-  teal.data::col_labels(ADSL)[c(names(adsl_labels))] <- adsl_labels
+  teal.data::col_labels(ADSL)[c(names(.adsl_labels))] <- .adsl_labels
 
   ADAE <- radae(ADSL, seed = 1)
 
   # derive common flags for AEs
-  adae_labels <- teal.data::col_labels(ADAE, fill = FALSE)
   ADAE <- ADAE %>%
     mutate_at(c("AESOC", "AEBODSYS", "AEHLT", "AEDECOD", "AETERM", "AELLT"), as.character) %>%
     mutate(
@@ -93,7 +92,7 @@ data <- within(data, {
   ADTR <- radtr(ADSL, seed = 1)
 
   # process ADTR
-  adtr_labels <- teal.data::col_labels(ADTR, fill = FALSE)
+  .adtr_labels <- teal.data::col_labels(ADTR, fill = FALSE)
   ADTR <- ADTR %>%
     mutate(
       PCHG = ifelse(AVISIT == "BASELINE", 0, PCHG),
@@ -102,7 +101,7 @@ data <- within(data, {
       AVALC = ifelse(AVISIT == "BASELINE", as.character(BASE), AVALC)
     ) %>%
     filter(AVISIT != "SCREENING")
-  teal.data::col_labels(ADTR) <- adtr_labels
+  teal.data::col_labels(ADTR) <- .adtr_labels
 
   ADTRWF <- ADTR %>%
     filter(AVISIT != "BASELINE")
@@ -115,7 +114,6 @@ data <- within(data, {
     arrange(USUBJID)
 
   ADRS <- radrs(ADSL, seed = 1)
-  adrs_labels <- teal.data::col_labels(ADRS, fill = FALSE)
   ADRS <- ADRS %>%
     filter(PARAMCD %in% c("BESRSPI", "INVET")) %>%
     mutate(ADT = as.Date(ADTM)) %>%
@@ -136,12 +134,10 @@ data <- within(data, {
     )
 })
 
-# set datanames
-datanames <- c("ADSL", "ADAE", "ADCM", "ADEX", "ADTR", "ADTRWF", "ADRS", "ADRSSWIM", "ADLB")
-datanames(data) <- datanames
-
 # set join keys
-join_keys(data) <- default_cdisc_join_keys[datanames] # get default keys by name
+join_keys(data) <- default_cdisc_join_keys[
+  c("ADSL", "ADAE", "ADCM", "ADEX", "ADTR", "ADTRWF", "ADRS", "ADRSSWIM", "ADLB")
+] # get default keys by name
 join_keys(data)["ADTR", "ADTR"] <- c("STUDYID", "USUBJID", "PARAMCD", "AVISIT")
 join_keys(data)["ADTRWF", "ADTRWF"] <- c("STUDYID", "USUBJID", "PARAMCD", "AVISIT")
 join_keys(data)["ADRSSWIM", "ADRSSWIM"] <- c("STUDYID", "USUBJID", "PARAMCD", "AVISIT")
@@ -162,13 +158,8 @@ ADRSSWIM <- data[["ADRSSWIM"]]
 ADLB <- data[["ADLB"]]
 
 
-adsl_labels <- teal.data::col_labels(ADSL)
 fact_vars_asl <- names(Filter(isTRUE, sapply(ADSL, is.factor)))
 
-date_vars_asl <-
-  names(ADSL)[vapply(ADSL, function(x) inherits(x, c("Date", "POSIXct", "POSIXlt")), logical(1))]
-demog_vars_asl <- names(ADSL)[!(names(ADSL) %in% c("USUBJID", "STUDYID", date_vars_asl))]
-
 arm_vars <- c("ARMCD", "ARM", "ACTARMCD", "ACTARM", "EOSSTT")
 aeflag_vars <- c("RELFL", "CTC35FL", "SERFL", "RELSERFL")
 facet_vars <- c("SEX", "BMRKR2", "RACE", "STRATA1", "STRATA2")
diff --git a/efficacy/app.R b/efficacy/app.R
index 357c3212..5aa7a4fd 100644
--- a/efficacy/app.R
+++ b/efficacy/app.R
@@ -12,12 +12,12 @@ data <- within(data, {
   library(sparkline)
 
   ADSL <- radsl(seed = 1)
-  adsl_labels <- teal.data::col_labels(ADSL, fill = FALSE)
+  .adsl_labels <- teal.data::col_labels(ADSL, fill = FALSE)
 
-  char_vars_asl <- names(Filter(isTRUE, sapply(ADSL, is.character)))
+  .char_vars_asl <- names(Filter(isTRUE, sapply(ADSL, is.character)))
 
-  adsl_labels <- c(
-    adsl_labels,
+  .adsl_labels <- c(
+    .adsl_labels,
     AGEGR1 = "Age Group"
   )
   ADSL <- ADSL %>%
@@ -27,19 +27,19 @@ data <- within(data, {
         AGE >= 45 ~ ">=45"
       ))
     ) %>%
-    mutate_at(char_vars_asl, factor)
+    mutate_at(.char_vars_asl, factor)
 
-  teal.data::col_labels(ADSL) <- adsl_labels
+  teal.data::col_labels(ADSL) <- .adsl_labels
 
   ADTTE <- radtte(ADSL, seed = 1)
 
   ADRS <- radrs(ADSL, seed = 1)
-  adrs_labels <- teal.data::col_labels(ADRS, fill = FALSE)
+  .adrs_labels <- teal.data::col_labels(ADRS, fill = FALSE)
   ADRS <- filter(ADRS, PARAMCD == "BESRSPI" | AVISIT == "FOLLOW UP")
-  teal.data::col_labels(ADRS) <- adrs_labels
+  teal.data::col_labels(ADRS) <- .adrs_labels
 
   ADQS <- radqs(ADSL, seed = 1)
-  adqs_labels <- teal.data::col_labels(ADQS, fill = FALSE)
+  .adqs_labels <- teal.data::col_labels(ADQS, fill = FALSE)
   ADQS <- ADQS %>%
     filter(ABLFL != "Y" & ABLFL2 != "Y") %>%
     filter(AVISIT %in% c("WEEK 1 DAY 8", "WEEK 2 DAY 15", "WEEK 3 DAY 22")) %>%
@@ -50,15 +50,11 @@ data <- within(data, {
         as.numeric() %>%
         as.factor()
     )
-  teal.data::col_labels(ADQS) <- adqs_labels
+  teal.data::col_labels(ADQS) <- .adqs_labels
 })
 
-# set datanames
-datanames <- c("ADSL", "ADTTE", "ADRS", "ADQS")
-datanames(data) <- datanames
-
 # set join_keys
-join_keys(data) <- default_cdisc_join_keys[datanames]
+join_keys(data) <- default_cdisc_join_keys[c("ADSL", "ADTTE", "ADRS", "ADQS")]
 
 ## Reusable Configuration For Modules
 ADSL <- data[["ADSL"]]
diff --git a/exploratory/app.R b/exploratory/app.R
index 51bfd15a..92e5746a 100644
--- a/exploratory/app.R
+++ b/exploratory/app.R
@@ -39,10 +39,7 @@ data <- within(data, {
     )
 })
 
-datanames <- c("ADSL", "ADRS", "ADLB", "ADLBPCA")
-datanames(data) <- datanames
-
-join_keys(data) <- default_cdisc_join_keys[datanames]
+join_keys(data) <- default_cdisc_join_keys[c("ADSL", "ADRS", "ADLB", "ADLBPCA")]
 
 ## Reusable Configuration For Modules
 ADSL <- data[["ADSL"]]
diff --git a/longitudinal/app.R b/longitudinal/app.R
index a82c904b..2c3f04ff 100644
--- a/longitudinal/app.R
+++ b/longitudinal/app.R
@@ -31,12 +31,12 @@ data <- within(data, {
   # for example if biomarker is already log2 transformed then exclude
   # value of AVAL will then be assigned to XXXXXL2 variables. e.g. AVALL2
   # e.g. if x assay is already log2 transformed then assigns AVAL to AVALL2
-  exclude_l2 <- c("")
+  .exclude_l2 <- c("")
 
   # for example if study specific biomarker represents a CHG value then exclude
   # value NA is assigned to XXXXXL2. e.g. AVALL2
   # e.g. if x assay are CHG values then assigns NA to AVALL2
-  exclude_chg <- c("")
+  .exclude_chg <- c("")
 
   # the app expects ARM to be the main treatment variable
   # to use other treatment variables it is easiest to rename to ARM and process as ARM
@@ -64,13 +64,13 @@ data <- within(data, {
 
   # convenience operator: create an operator that keeps the variable attributes
   # variable labels often get clobbered and we would like to keep them)
-  `%keep_label%` <- function(lhv, rhv) {
+  .keep_label <- function(lhv, rhv) {
     attributes(lhv) <- attributes(rhv)
     lhv
   }
 
   # convenience operator: create an operator that adds a label to newly created or re-processed variables
-  `%make_label%` <- function(lhv, label) {
+  .make_label <- function(lhv, label) {
     attr(lhv, "label") <- label
     lhv
   }
@@ -86,10 +86,10 @@ data <- within(data, {
         TRT01P == "B: Placebo" ~ 3,
         TRUE ~ as.numeric(NA)
       ),
-      TRTORD = TRTORD %make_label% "Treatment Order",
+      TRTORD = .make_label(TRTORD, "Treatment Order"),
       TRT01P = as.character(arm_mapping[match(TRT01P, names(arm_mapping))]),
       TRT01P = factor(ARM) %>% reorder(TRTORD),
-      TRT01P = TRT01P %make_label% "Planned Treatment for Period 01"
+      TRT01P = .make_label(TRT01P, "Planned Treatment for Period 01")
     )
 
   # capture state of variable labels to apply back onto variables after data filtering
@@ -198,56 +198,60 @@ data <- within(data, {
     select(USUBJID, PARAMCD, AVAL) %>%
     group_by(PARAMCD) %>%
     summarise(AVAL_MIN = min(AVAL, na.rm = TRUE), .groups = "drop") %>%
-    mutate(PARAMCD = PARAMCD %make_label% "Parameter Code")
+    mutate(PARAMCD = .make_label(PARAMCD, "Parameter Code"))
 
   # post process the data to create several new variables and adjust existing record specific values per specification
   # - adjust existing BASELINE record values where values are missing
   ADLB_SUPED1 <- ADLB_SUBSET %>%
-    mutate(BASE2 = ifelse(toupper(AVISIT) == "SCREENING" & is.na(BASE2), AVAL, BASE2) %keep_label% BASE2) %>%
-    mutate(CHG2 = ifelse(toupper(AVISIT) == "SCREENING" & is.na(CHG2), 0, CHG2) %keep_label% CHG2) %>%
-    mutate(PCHG2 = ifelse(toupper(AVISIT) == "SCREENING" & is.na(PCHG2), 0, PCHG2) %keep_label% PCHG2) %>%
-    mutate(BASE = ifelse(toupper(AVISIT) == "BASELINE" & is.na(BASE), AVAL, BASE) %keep_label% BASE) %>%
-    mutate(CHG = ifelse(toupper(AVISIT) == "BASELINE" & is.na(CHG), 0, CHG) %keep_label% CHG) %>%
-    mutate(PCHG = ifelse(toupper(AVISIT) == "BASELINE" & is.na(PCHG), 0, PCHG) %keep_label% PCHG) %>%
-    mutate(TRTORD = TRTORD %make_label% "Treatment Order")
+    mutate(BASE2 = .keep_label(ifelse(toupper(AVISIT) == "SCREENING" & is.na(BASE2), AVAL, BASE2), BASE2)) %>%
+    mutate(CHG2 = .keep_label(ifelse(toupper(AVISIT) == "SCREENING" & is.na(CHG2), 0, CHG2), CHG2)) %>%
+    mutate(PCHG2 = .keep_label(ifelse(toupper(AVISIT) == "SCREENING" & is.na(PCHG2), 0, PCHG2), PCHG2)) %>%
+    mutate(BASE = .keep_label(ifelse(toupper(AVISIT) == "BASELINE" & is.na(BASE), AVAL, BASE), BASE)) %>%
+    mutate(CHG = .keep_label(ifelse(toupper(AVISIT) == "BASELINE" & is.na(CHG), 0, CHG), CHG)) %>%
+    mutate(PCHG = .keep_label(ifelse(toupper(AVISIT) == "BASELINE" & is.na(PCHG), 0, PCHG), PCHG)) %>%
+    mutate(TRTORD = .make_label(TRTORD, "Treatment Order"))
 
   # Inconsequential Warning issued: Warning: "PARAMCD" has different attributes on LHS and RHS of join.
   # merge minimum AVAL value onto the ADLB data to calculate the log2 variables. preserve the variable order
   ADLB_SUPED2 <- inner_join(PARAM_MINS, ADLB_SUPED1, by = "PARAMCD")[, union(names(ADLB_SUPED1), names(PARAM_MINS))] %>%
     # visit values
     # excludes biomarkers where log2 is not appropriate: for example assay value already log2
-    mutate(AVALL2 = ifelse(PARAMCD %in% exclude_l2, AVAL,
-      # excludes biomarkers where log2 is not appropriate: for example CHG type assay
-      ifelse(PARAMCD %in% exclude_chg, NA,
-        ifelse(AVAL == 0 & AVAL_MIN > 0, log2(AVAL_MIN / 2),
-          # would be taking log2 of 0 or negative value so set to NA
-          ifelse(AVAL == 0 & AVAL_MIN <= 0, NA,
-            ifelse(AVAL > 0, log2(AVAL), NA)
+    mutate(AVALL2 = .make_label(
+      ifelse(PARAMCD %in% .exclude_l2, AVAL,
+        # excludes biomarkers where log2 is not appropriate: for example CHG type assay
+        ifelse(PARAMCD %in% .exclude_chg, NA,
+          ifelse(AVAL == 0 & AVAL_MIN > 0, log2(AVAL_MIN / 2),
+            # would be taking log2 of 0 or negative value so set to NA
+            ifelse(AVAL == 0 & AVAL_MIN <= 0, NA,
+              ifelse(AVAL > 0, log2(AVAL), NA)
+            )
           )
         )
-      )
-    ) %make_label% "Log2 of AVAL") %>%
+      ), "Log2 of AVAL"
+    )) %>%
     # baseline values
-    mutate(BASEL2 = ifelse(PARAMCD %in% exclude_l2, BASE,
-      ifelse(PARAMCD %in% exclude_chg, NA,
+    mutate(BASEL2 = .make_label(ifelse(PARAMCD %in% .exclude_l2, BASE,
+      ifelse(PARAMCD %in% .exclude_chg, NA,
         ifelse(BASE == 0 & AVAL_MIN > 0, log2(AVAL_MIN / 2),
           ifelse(BASE == 0 & AVAL_MIN <= 0, NA,
             ifelse(BASE > 0, log2(BASE), NA)
           )
         )
       )
-    ) %make_label% "Log2 of BASE") %>%
+    ), "Log2 of BASE")) %>%
     # screening
-    mutate(BASE2L2 = ifelse(PARAMCD %in% exclude_l2, BASE2,
-      ifelse(PARAMCD %in% exclude_chg, NA,
-        ifelse(BASE2 == 0 & AVAL_MIN > 0, log2(AVAL_MIN / 2),
-          ifelse(BASE2 == 0 & AVAL_MIN <= 0, NA,
-            ifelse(BASE2 > 0, log2(BASE2), NA)
+    mutate(BASE2L2 = .make_label(
+      ifelse(PARAMCD %in% .exclude_l2, BASE2,
+        ifelse(PARAMCD %in% .exclude_chg, NA,
+          ifelse(BASE2 == 0 & AVAL_MIN > 0, log2(AVAL_MIN / 2),
+            ifelse(BASE2 == 0 & AVAL_MIN <= 0, NA,
+              ifelse(BASE2 > 0, log2(BASE2), NA)
+            )
           )
         )
-      )
-    ) %make_label% "Log2 of BASE2") %>%
-    mutate(AVAL_MIN = AVAL_MIN %make_label% "Minimum AVAL Within PARAMCD")
+      ), "Log2 of BASE2"
+    )) %>%
+    mutate(AVAL_MIN = .make_label(AVAL_MIN, "Minimum AVAL Within PARAMCD"))
 
   # create final data set used by goshawk
   # all data set passed into a goshawk app must have all of the columns
@@ -255,15 +259,15 @@ data <- within(data, {
   ADLB <- ADLB_SUPED2 %>%
     mutate(
       TRT01P = as.character(arm_mapping[match(TRT01P, names(arm_mapping))]),
-      TRT01P = factor(TRT01P) %>% reorder(TRTORD) %make_label% "Planned Treatment for Period 01",
+      TRT01P = .make_label(factor(TRT01P) %>% reorder(TRTORD), "Planned Treatment for Period 01"),
       TRT01A = as.character(arm_mapping[match(TRT01A, names(arm_mapping))]),
-      TRT01A = factor(TRT01A) %>% reorder(TRTORD) %make_label% "Actual Treatment for Period 01",
-      LOQFL = LOQFL %make_label% "Limit of Quantification",
-      AVISITCD = factor(AVISITCD) %>% reorder(AVISITCDN) %make_label% "Analysis Visit Window Code",
-      AVISITCDN = AVISITCDN %make_label% "Analysis Visit Window Code (N)",
-      BASE2 = BASE2 %make_label% "Screening Value",
-      CHG2 = CHG2 %make_label% "Absolute Change from Screening",
-      PCHG2 = PCHG2 %make_label% "Percent Change from Screening"
+      TRT01A = .make_label(factor(TRT01A) %>% reorder(TRTORD), "Actual Treatment for Period 01"),
+      LOQFL = .make_label(LOQFL, "Limit of Quantification"),
+      AVISITCD = .make_label(factor(AVISITCD) %>% reorder(AVISITCDN), "Analysis Visit Window Code"),
+      AVISITCDN = .make_label(AVISITCDN, "Analysis Visit Window Code (N)"),
+      BASE2 = .make_label(BASE2, "Screening Value"),
+      CHG2 = .make_label(CHG2, "Absolute Change from Screening"),
+      PCHG2 = .make_label(PCHG2, "Percent Change from Screening")
     )
 
   # add LLOQ and ULOQ variables to support adding horizontal/vertical range lines
@@ -279,15 +283,13 @@ data <- within(data, {
   ################################################################################
 })
 
-datanames <- c("ADSL", "ADLB")
-datanames(data) <- datanames
-join_keys(data) <- default_cdisc_join_keys[datanames]
+join_keys(data) <- default_cdisc_join_keys[c("ADSL", "ADLB")]
 
 ## App configuration ----
 ADSL <- data[["ADSL"]]
 ADLB <- data[["ADLB"]]
-exclude_l2 <- data[["exclude_l2"]]
-exclude_chg <- data[["exclude_chg"]]
+.exclude_l2 <- data[[".exclude_l2"]]
+.exclude_chg <- data[[".exclude_chg"]]
 
 # study information: used to label app and in "Example Info Page" module
 ATYPE <- "Exploratory" # define here and in the "# code >" section below
@@ -408,7 +410,7 @@ paramcd_list <- paramDict$PARAMCD
 
 # create list of biomarkers excluded from log2 transformation
 paramexcldDict <- paramDict %>%
-  filter(PARAMCD %in% c(exclude_l2, exclude_chg))
+  filter(PARAMCD %in% c(.exclude_l2, .exclude_chg))
 paramexcld_list <- paramexcldDict$PARAM
 paramcdexcld_list <- paramexcldDict$PARAMCD
 
@@ -775,29 +777,4 @@ app <- teal::init(
   )
 )
 
-# Add context sensitive help code
-body(app$server)[[length(body(app$server)) + 1]] <- quote(
-  observeEvent(input$showAnlVarLegendModal, {
-    showModal(modalDialog(
-      title = "Analysis Variable Pull Down Menu",
-      tags$p(
-        "These variables will appear in the Visualizations 'Analysis Variable' pull down menu. This legend
-            provides the variable labels to help clarify the short analysis variable names displayed in the pull down
-          menu."
-      ),
-      p("BASE2 = Screening Visit Value"),
-      p("BASE2L2 = Log2(BASE2)"),
-      p("CHG2 = Change from Screening"),
-      p("PCHG2 =  % Change from Screening"),
-      p("BASE = Baseline Visit Value"),
-      p("BASEL2 = Log2(BASE)"),
-      p("CHG = Change from Baseline"),
-      p("PCHG =  % Change from Baseline"),
-      p("AVAL = Visit Values"),
-      p("AVALL2 = Log2(AVAL)"),
-      easyClose = TRUE
-    ))
-  })
-)
-
 shinyApp(app$ui, app$server)
diff --git a/patient-profile/app.R b/patient-profile/app.R
index 2c7538e6..87ffe2fa 100644
--- a/patient-profile/app.R
+++ b/patient-profile/app.R
@@ -39,9 +39,7 @@ data <- within(data, {
   teal.data::col_labels(ADMH[c("MHDISTAT")]) <- c("Status of Disease")
 })
 
-datanames <- c("ADSL", "ADMH", "ADAE", "ADCM", "ADVS", "ADLB")
-datanames(data) <- datanames
-join_keys(data) <- default_cdisc_join_keys[datanames]
+join_keys(data) <- default_cdisc_join_keys[c("ADSL", "ADMH", "ADAE", "ADCM", "ADVS", "ADLB")]
 
 ## App configuration ----
 ADSL <- data[["ADSL"]]
diff --git a/python/app.R b/python/app.R
index 114e0185..f3fe1398 100644
--- a/python/app.R
+++ b/python/app.R
@@ -25,20 +25,20 @@ data <- teal_data_module(
           teal_data(),
           "
             library(reticulate)
-            python_dependencies <- c(\"pip\", \"numpy\", \"pandas\") # @linksto py_dict
-            virtualenv_dir <- Sys.getenv(\"VIRTUALENV_NAME\", \"example_env_name\") # @linksto py_dict
-            python_path <- Sys.getenv(\"PYTHON_PATH\") # @linksto py_dict
-            if (python_path == \"\") python_path <- NULL
+            .python_dependencies <- c(\"pip\", \"numpy\", \"pandas\") # @linksto .py_dict
+            .virtualenv_dir <- Sys.getenv(\"VIRTUALENV_NAME\", \"example_env_name\") # @linksto .py_dict
+            .python_path <- Sys.getenv(\"PYTHON_PATH\") # @linksto .py_dict
+            if (.python_path == \"\") .python_path <- NULL
             reticulate::virtualenv_create(
-              envname = virtualenv_dir, python = python_path
-            ) # @linksto py_dict
+              envname = .virtualenv_dir, python = .python_path
+            ) # @linksto .py_dict
             reticulate::virtualenv_install(
-              virtualenv_dir,
-              packages = python_dependencies,
+              .virtualenv_dir,
+              packages = .python_dependencies,
               ignore_installed = TRUE
-            ) # @linksto py_dict
-            reticulate::use_virtualenv(virtualenv_dir, required = TRUE) # @linksto py_dict
-            iris_raw <- cbind(id = seq_len(nrow(iris)), iris) # @linksto py_dict
+            ) # @linksto .py_dict
+            reticulate::use_virtualenv(.virtualenv_dir, required = TRUE) # @linksto .py_dict
+            iris_raw <- cbind(id = seq_len(nrow(iris)), iris) # @linksto .py_dict
           "
         )
 
@@ -47,35 +47,34 @@ data <- teal_data_module(
           data,
           {
             # python code needs to be un-indented
-            python_code <- "
-import pandas as pd
-data = r.iris_raw
-def svd_whiten(dat):
-  import numpy as np
-  X = np.matrix(dat)
-  U, s, Vt = np.linalg.svd(X, full_matrices=False)
-  X_white = np.dot(U, Vt)
-  return X_white
+            .python_code <- "
+              import pandas as pd
+              data = r.iris_raw
+              def svd_whiten(dat):
+                import numpy as np
+                X = np.matrix(dat)
+                U, s, Vt = np.linalg.svd(X, full_matrices=False)
+                X_white = np.dot(U, Vt)
+                return X_white
 
-data_columns = data.columns
-global numeric_cols_ix
-global numeric_cols
-numeric_cols_ix = list(range(5))[1:]
-numeric_cols = [x for i,x in enumerate(data_columns) if i in numeric_cols_ix]
-svd_res = svd_whiten(data.iloc[:, numeric_cols_ix])
-data_new = pd.concat([data, pd.DataFrame(svd_res)], axis = 1)
-data_new.columns = list(data_columns) + [i + '.whiten' for i in numeric_cols]
-data_new = data_new.round(10)
-data_new
+              data_columns = data.columns
+              global numeric_cols_ix
+              global numeric_cols
+              numeric_cols_ix = list(range(5))[1:]
+              numeric_cols = [x for i,x in enumerate(data_columns) if i in numeric_cols_ix]
+              svd_res = svd_whiten(data.iloc[:, numeric_cols_ix])
+              data_new = pd.concat([data, pd.DataFrame(svd_res)], axis = 1)
+              data_new.columns = list(data_columns) + [i + '.whiten' for i in numeric_cols]
+              data_new = data_new.round(10)
+              data_new
             "
             withr::with_options(
               list(reticulate.engine.environment = environment()),
-              py_dict <- py_run_string(python_code)
+              .py_dict <- py_run_string(.python_code)
             )
-            IRIS <- py_dict$data_new
+            IRIS <- .py_dict$data_new
           }
         )
-        datanames(data) <- c("IRIS")
         data
       })
     })
diff --git a/safety/app.R b/safety/app.R
index aaa3ec12..3585fd26 100644
--- a/safety/app.R
+++ b/safety/app.R
@@ -25,21 +25,18 @@ data <- within(data, {
     )) %>%
     teal.data::col_relabel(is_event = "Is an Event") %>%
     teal.data::col_relabel(n_events = "Number of Events")
-  ADAETTE_AE <-
-    filter(ADAETTE, grepl("TOT", .data$PARAMCD, fixed = TRUE)) %>% select(-"AVAL")
-  ADAETTE_OTH <-
-    filter(ADAETTE, !(grepl("TOT", .data$PARAMCD, fixed = TRUE)))
+  .ADAETTE_AE <- filter(ADAETTE, grepl("TOT", .data$PARAMCD, fixed = TRUE)) %>% select(-"AVAL")
+  .ADAETTE_OTH <- filter(ADAETTE, !(grepl("TOT", .data$PARAMCD, fixed = TRUE)))
 
-  ADAETTE_TTE <- ADAETTE %>%
+  .ADAETTE_TTE <- ADAETTE %>%
     filter(PARAMCD == "AEREPTTE") %>%
     select(USUBJID, ARM, ARMCD, AVAL)
 
-  ADAETTE_AE <-
-    full_join(ADAETTE_AE, ADAETTE_TTE, by = c("USUBJID", "ARM", "ARMCD"))
-  ADAETTE <- rbind(ADAETTE_AE, ADAETTE_OTH)
+  .ADAETTE_AE <- full_join(.ADAETTE_AE, .ADAETTE_TTE, by = c("USUBJID", "ARM", "ARMCD"))
+  ADAETTE <- rbind(.ADAETTE_AE, .ADAETTE_OTH)
 
   ADEX <- radex(ADSL, seed = 1)
-  ADEX_labels <- teal.data::col_labels(ADEX, fill = FALSE)
+  .ADEX_labels <- teal.data::col_labels(ADEX, fill = FALSE)
   # Below steps are done to simulate data with TDURD parameter as it is not in the ADEX data from `random.cdisc.data` package
   set.seed(1, kind = "Mersenne-Twister")
   ADEX <- ADEX %>%
@@ -59,7 +56,7 @@ data <- within(data, {
   ADEX <- ADEX %>%
     filter(PARCAT1 == "OVERALL" &
       PARAMCD %in% c("TDOSE", "TNDOSE", "TDURD"))
-  teal.data::col_labels(ADEX) <- ADEX_labels
+  teal.data::col_labels(ADEX) <- .ADEX_labels
 
   ADLB <- radlb(ADSL, seed = 1)
 
@@ -95,7 +92,7 @@ data <- within(data, {
   #   tern::df_explicit_na(omit_columns = setdiff(names(ADEX), c("PARAM", "PARAMCD", "PARCAT2") ))
 
   # define study-specific analysis subgroups and baskets from ADAE
-  add_event_flags <- function(dat) {
+  .add_event_flags <- function(dat) {
     dat %>%
       dplyr::mutate(
         TMPFL_SER = AESER == "Y",
@@ -116,12 +113,10 @@ data <- within(data, {
   }
 
   ADAE <- ADAE %>%
-    add_event_flags()
+    .add_event_flags()
 })
 
-datanames <- c("ADSL", "ADAE", "ADAETTE", "ADEX", "ADLB", "ADEG", "ADVS", "ADCM")
-datanames(data) <- datanames
-join_keys(data) <- default_cdisc_join_keys[datanames]
+join_keys(data) <- default_cdisc_join_keys[c("ADSL", "ADAE", "ADAETTE", "ADEX", "ADLB", "ADEG", "ADVS", "ADCM")]
 
 ## App configuration ----
 ADSL <- data[["ADSL"]]