Use picks in modules from teal.modules.general

RNA-seq/app.R

@@ -6,9 +6,10 @@ options(shiny.useragg = FALSE)
 data <- teal_data()
 data <- within(data, {
   library(nestcolor)
+  library(random.cdisc.data)
 
-  ADSL <- random.cdisc.data::cadsl
-  ADTTE <- random.cdisc.data::cadtte %>%
+  ADSL <- radsl(seed = 1)
+  ADTTE <- radtte(ADSL, seed = 1) %>%
     dplyr::mutate(is_event = CNSR == 0)
   MAE <- hermes::multi_assay_experiment
 })

early-dev/app.R

@@ -6,13 +6,12 @@ options(shiny.useragg = FALSE)
 ## Data reproducible code ----
 data <- teal_data()
 data <- within(data, {
-  library(random.cdisc.data)
   library(dplyr)
   library(nestcolor)
   # optional libraries
   library(sparkline)
 
-  ADSL <- radsl(seed = 1)
+  ADSL <- random.cdisc.data::cadsl
 
   # derive ADSL treatment duration
   .adsl_labels <- teal.data::col_labels(ADSL, fill = FALSE)
@@ -31,7 +30,7 @@ data <- within(data, {
     droplevels()
   teal.data::col_labels(ADSL)[c(names(.adsl_labels))] <- .adsl_labels
 
-  ADAE <- radae(ADSL, seed = 1)
+  ADAE <- random.cdisc.data::cadae
 
   # derive common flags for AEs
   ADAE <- ADAE %>%
@@ -63,7 +62,7 @@ data <- within(data, {
       AELLT = "Lowest Level Term"
     )
 
-  ADCM <- radcm(ADSL, seed = 1)
+  ADCM <- random.cdisc.data::cadcm
 
   # process ADCM
   ADCM <- ADCM %>%
@@ -76,7 +75,7 @@ data <- within(data, {
       AENDT = "Analysis End Date"
     )
 
-  ADEX <- radex(ADSL, seed = 1)
+  ADEX <- random.cdisc.data::cadex
 
   # process ADEX
   ADEX <- ADEX %>%
@@ -89,7 +88,7 @@ data <- within(data, {
       AENDT = "Analysis End Date"
     )
 
-  ADTR <- radtr(ADSL, seed = 1)
+  ADTR <- random.cdisc.data::cadtr
 
   # process ADTR
   .adtr_labels <- teal.data::col_labels(ADTR, fill = FALSE)
@@ -109,18 +108,18 @@ data <- within(data, {
 
 
   # process ADRS
-  ADRSSWIM <- radrs(ADSL, seed = 1) %>%
+  ADRSSWIM <- random.cdisc.data::cadrs %>%
     filter(PARAMCD == "OVRINV") %>%
     arrange(USUBJID)
 
-  ADRS <- radrs(ADSL, seed = 1)
+  ADRS <- random.cdisc.data::cadrs
   ADRS <- ADRS %>%
     filter(PARAMCD %in% c("BESRSPI", "INVET")) %>%
     mutate(ADT = as.Date(ADTM)) %>%
     droplevels()
   teal.data::col_labels(ADRS)["ADT"] <- "Analysis Date"
 
-  ADLB <- radlb(ADSL, seed = 1)
+  ADLB <- random.cdisc.data::cadlb
 
   # process ADLB
   ADLB <- ADLB %>%
@@ -157,7 +156,6 @@ ADTRWF <- data[["ADTRWF"]]
 ADRSSWIM <- data[["ADRSSWIM"]]
 ADLB <- data[["ADLB"]]
 
-
 fact_vars_asl <- names(Filter(isTRUE, sapply(ADSL, is.factor)))
 
 arm_vars <- c("ARMCD", "ARM", "ACTARMCD", "ACTARM", "EOSSTT")

efficacy/app.R

@@ -6,12 +6,11 @@ options(shiny.useragg = FALSE)
 data <- teal_data()
 data <- within(data, {
   library(dplyr)
-  library(random.cdisc.data)
   library(nestcolor)
   # optional libraries
   library(sparkline)
 
-  ADSL <- radsl(seed = 1)
+  ADSL <- random.cdisc.data::cadsl
   .adsl_labels <- teal.data::col_labels(ADSL, fill = FALSE)
 
   .char_vars_asl <- names(Filter(isTRUE, sapply(ADSL, is.character)))
@@ -31,14 +30,14 @@ data <- within(data, {
 
   teal.data::col_labels(ADSL) <- .adsl_labels
 
-  ADTTE <- radtte(ADSL, seed = 1)
+  ADTTE <- random.cdisc.data::cadtte
 
-  ADRS <- radrs(ADSL, seed = 1)
+  ADRS <- random.cdisc.data::cadrs
   .adrs_labels <- teal.data::col_labels(ADRS, fill = FALSE)
   ADRS <- filter(ADRS, PARAMCD == "BESRSPI" | AVISIT == "FOLLOW UP")
   teal.data::col_labels(ADRS) <- .adrs_labels
 
-  ADQS <- radqs(ADSL, seed = 1)
+  ADQS <- random.cdisc.data::cadqs
   .adqs_labels <- teal.data::col_labels(ADQS, fill = FALSE)
   ADQS <- ADQS %>%
     filter(ABLFL != "Y" & ABLFL2 != "Y") %>%
@@ -61,7 +60,7 @@ ADSL <- data[["ADSL"]]
 ADTTE <- data[["ADTTE"]]
 ADRS <- data[["ADRS"]]
 ADQS <- data[["ADQS"]]
-char_vars_asl <- data[["char_vars_asl"]]
+char_vars_asl <- data[[".char_vars_asl"]]
 
 arm_vars <- c("ARMCD", "ARM")
 strata_vars <- c("STRATA1", "STRATA2")
@@ -231,20 +230,8 @@ app <- init(
     ),
     tm_t_crosstable(
       "Cross Table",
-      x = data_extract_spec(
-        dataname = "ADSL",
-        select = select_spec(
-          choices = variable_choices(ADSL, fact_vars_asl_orig),
-          selected = fact_vars_asl_orig[1]
-        )
-      ),
-      y = data_extract_spec(
-        dataname = "ADSL",
-        select = select_spec(
-          choices = variable_choices(ADSL, fact_vars_asl_orig),
-          selected = fact_vars_asl_orig[4]
-        )
-      )
+      x = picks(datasets("ADSL"), variables(fact_vars_asl_orig, 1L)),
+      y = picks(datasets("ADSL"), variables(fact_vars_asl_orig, 4L))
     ),
     tm_t_coxreg(
       label = "Cox Reg",

exploratory/app.R

@@ -7,6 +7,7 @@ options(
 ## Data reproducible code ----
 data <- teal_data()
 data <- within(data, {
+  library(random.cdisc.data)
   library(dplyr)
   library(tidyr)
   library(ggExtra)
@@ -25,9 +26,10 @@ data <- within(data, {
   library(colourpicker)
   library(sparkline)
 
-  ADSL <- random.cdisc.data::cadsl
-  ADRS <- random.cdisc.data::cadrs
-  ADLB <- random.cdisc.data::cadlb
+
+  ADSL <- radsl(seed = 1)
+  ADRS <- radrs(ADSL, seed = 1)
+  ADLB <- radlb(ADSL, seed = 1)
   ADLBPCA <- ADLB %>%
     dplyr::select(USUBJID, STUDYID, SEX, ARMCD, AVAL, AVISIT, PARAMCD) %>%
     tidyr::pivot_wider(

longitudinal/app.R

@@ -451,7 +451,7 @@ app <- teal::init(
     ),
     module(
       "User Guide",
-      server = function(input, output, session, datasets) {},
+      server = function(input, output, session, data) {},
       ui = function(id, ...) {
         div(
           h5(
@@ -604,7 +604,7 @@ app <- teal::init(
     ),
     module(
       "Example Info Page",
-      server = function(input, output, session, datasets) {},
+      server = function(input, output, session, data) {},
       ui = function(id, ...) {
         div(
           h5(strong("Molecule:"), MOLECULE),

python/app.R

@@ -8,12 +8,19 @@ library(ggExtra)
 library(ggpmisc)
 library(sparkline)
 
-options(shiny.useragg = FALSE)
-
 data <- teal_data_module(
   ui = function(id) {
     ns <- NS(id)
-    actionButton(ns("submit"), label = "Load data")
+    tagList(
+      h5(
+        icon("fas fa-info-circle"),
+        "Click on the button below to load the IRIS dataset and run the Python code to generate the whitened dataset."
+      ),
+      p("This will take a few seconds to run the Python code and generate the whitened dataset."),
+      p("Once the data is loaded, you can explore the data using the modules on the teal app."),
+      hr(),
+      actionButton(ns("submit"), label = "Load data"),
+    )
   },
   server = function(id) {
     moduleServer(id, function(input, output, session, python_code) {
@@ -28,7 +35,7 @@ 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
+if (is.null(.python_path) || !nzchar(.python_path) || !file.exists(.python_path)) .python_path <- NULL
 reticulate::virtualenv_create(
   envname = .virtualenv_dir, python = .python_path
 ) # @linksto .py_dict
@@ -38,7 +45,7 @@ reticulate::virtualenv_install(
   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
+.iris_raw <- cbind(id = seq_len(nrow(iris)), iris) # @linksto .py_dict
           "
         )
 
@@ -49,7 +56,7 @@ iris_raw <- cbind(id = seq_len(nrow(iris)), iris) # @linksto .py_dict
             # python code needs to be un-indented
             .python_code <- "
 import pandas as pd
-data = r.iris_raw
+data = r['.iris_raw']
 def svd_whiten(dat):
   import numpy as np
   X = np.matrix(dat)
@@ -110,46 +117,31 @@ app <- teal::init(
     tm_variable_browser("Variable Browser"),
     tm_g_scatterplot(
       "Scatterplot",
-      x = data_extract_spec(
-        dataname = "IRIS",
-        select = select_spec(
-          label = "Select variable:",
-          choices = variable_choices("IRIS", c(
+      x = picks(
+        datasets("IRIS"),
+        variables(
+          c(
             "Sepal.Length", "Sepal.Width",
             "Petal.Length", "Petal.Width",
             "Sepal.Length.whiten", "Sepal.Width.whiten",
             "Petal.Length.whiten", "Petal.Width.whiten"
-          )),
-          selected = "Petal.Length.whiten",
-          multiple = FALSE,
-          fixed = FALSE
+          ),
+          "Petal.Length.whiten"
         )
       ),
-      y = data_extract_spec(
-        dataname = "IRIS",
-        select = select_spec(
-          label = "Select variable:",
-          choices = variable_choices("IRIS", c(
+      y = picks(
+        datasets("IRIS"),
+        variables(
+          c(
             "Sepal.Length", "Sepal.Width",
             "Petal.Length", "Petal.Width",
             "Sepal.Length.whiten", "Sepal.Width.whiten",
             "Petal.Length.whiten", "Petal.Width.whiten"
-          )),
-          selected = "Petal.Width.whiten",
-          multiple = FALSE,
-          fixed = FALSE
+          ),
+          "Petal.Width.whiten"
         )
       ),
-      color_by = data_extract_spec(
-        dataname = "IRIS",
-        select = select_spec(
-          label = "Select variable:",
-          choices = variable_choices("IRIS", c("Species")),
-          selected = "Species",
-          multiple = FALSE,
-          fixed = FALSE
-        )
-      )
+      color_by = picks(datasets("IRIS"), variables("Species", "Species", fixed = FALSE))
     )
   )
 ) |>