diff --git a/.Rbuildignore b/.Rbuildignore
index a546bcda..950b63bf 100644
--- a/.Rbuildignore
+++ b/.Rbuildignore
@@ -30,3 +30,5 @@ vignettes/x0*
^CRAN-SUBMISSION$
^scripts$
\.sass$
+manifest.json
+^\.posit$
\ No newline at end of file
diff --git a/.github/workflows/pkgdown.yaml b/.github/workflows/pkgdown.yaml
index 84edbf33..6cc8e599 100644
--- a/.github/workflows/pkgdown.yaml
+++ b/.github/workflows/pkgdown.yaml
@@ -29,12 +29,9 @@ jobs:
use-public-rspm: true
- uses: r-lib/actions/setup-renv@v2
- with:
- extra-packages: any::pkgdown, local::.
- needs: website
- - name: Install specific pkgdown version
- run: install.packages("remotes") ; remotes::install_version("pkgdown", version = "2.0.3", repos = "cran.rstudio.com", dependencies = FALSE)
+ - name: Install dev dependencies
+ run: install.packages(c("pkgdown", "devtools", "covr"))
shell: Rscript {0}
- name: Install tidyCDISC
@@ -42,7 +39,7 @@ jobs:
run: R CMD INSTALL --preclean .
- name: Build site
- run: pkgdown::build_site_github_pages(new_process = FALSE, install = FALSE)
+ run: pkgdown::build_site_github_pages(new_process = FALSE, install = TRUE)
shell: Rscript {0}
- name: Deploy to GitHub pages 🚀
diff --git a/.github/workflows/test-coverage.yaml b/.github/workflows/test-coverage.yaml
index 880e83bb..ce2bbc04 100644
--- a/.github/workflows/test-coverage.yaml
+++ b/.github/workflows/test-coverage.yaml
@@ -24,8 +24,10 @@ jobs:
use-public-rspm: true
- uses: r-lib/actions/setup-renv@v2
- with:
- extra-packages: covr
+
+ - name: Install dev dependencies
+ run: install.packages(c("devtools", "covr"))
+ shell: Rscript {0}
- name: Install tidyCDISC
shell: bash
diff --git a/.gitignore b/.gitignore
index 4145fd0b..6faaef61 100644
--- a/.gitignore
+++ b/.gitignore
@@ -8,3 +8,4 @@ docs/
revdep/
/doc/
/Meta/
+/.posit
diff --git a/DESCRIPTION b/DESCRIPTION
index 9ee87e15..6e206ab5 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
Package: tidyCDISC
Title: Quick Table Generation & Exploratory Analyses on ADaM-Ish Datasets
-Version: 0.2.1
+Version: 0.2.1.9000
Authors@R: c(
person("Aaron", "Clark", , "clark.aaronchris@gmail.com", role = c("aut", "cre"),
comment = c(ORCID = "0000-0002-0123-0970")),
@@ -10,6 +10,8 @@ Authors@R: c(
person("Maya", "Gans", , "maya.gans@biogen.com", role = "aut"),
person("Robert", "Krajcik", , "robert.krajcik@biogen.com", role = "ctb"),
person("Marly", "Gotti", , "marly.cormar@biogen.com", role = "ctb"),
+ person("Ryan", "Harrison", , "harrison.ryan.w@gmail.com", role = "aut",
+ comment = c(ORCID = "0000-0003-4575-006X")),
person("Biogen", "Inc", role = "cph")
)
Description: Provides users a quick exploratory dive into common
@@ -36,40 +38,56 @@ License: AGPL (>= 3)
URL: https://github.com/Biogen-Inc/tidyCDISC/, https://Biogen-Inc.github.io/tidyCDISC/
BugReports: https://github.com/Biogen-Inc/tidyCDISC/issues
Depends:
- R (>= 2.10)
+ R (>= 4.1)
Imports:
+ arrow,
+ broom.helpers,
cicerone,
+ cli,
config,
dplyr,
DT,
+ fs,
GGally,
ggcorrplot,
ggplot2,
+ ggsurvfit,
glue,
golem,
gt,
+ gtsummary,
haven,
IDEAFilter,
+ pkgload,
plotly,
purrr,
+ readxl,
rlang,
rmarkdown,
shiny,
shinyjs,
shinyWidgets,
sjlabelled,
+ stringdist,
stringr,
survival,
+ tibble,
tidyr,
+ tidyselect,
timevis,
- tippy (== 0.1.0)
+ tippy (== 0.1.0),
+ vroom
Suggests:
+ devtools,
knitr,
+ shinyalert,
spelling,
testthat
VignetteBuilder:
knitr
+Config/Needs/website: pkgdown
+Config/Needs/coverage: covr
Encoding: UTF-8
Language: en-US
LazyData: true
-RoxygenNote: 7.2.3
+RoxygenNote: 7.3.3
diff --git a/NAMESPACE b/NAMESPACE
index 728c434c..e76340f3 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -1,5 +1,8 @@
# Generated by roxygen2: do not edit by hand
+S3method(error_handler,default)
+S3method(error_handler,purrr_error_indexed)
+S3method(error_handler,rlang_error)
export(app_methods)
export(col_for_list_expr)
export(common_rownames)
@@ -36,6 +39,11 @@ importFrom(ggplot2,ggplot)
importFrom(ggplot2,labs)
importFrom(ggplot2,scale_color_manual)
importFrom(ggplot2,scale_x_continuous)
+importFrom(ggsurvfit,add_censor_mark)
+importFrom(ggsurvfit,add_confidence_interval)
+importFrom(ggsurvfit,add_risktable)
+importFrom(ggsurvfit,ggsurvfit)
+importFrom(ggsurvfit,survfit2)
importFrom(glue,glue)
importFrom(golem,activate_js)
importFrom(golem,add_resource_path)
@@ -111,6 +119,7 @@ importFrom(shinyjs,useShinyjs)
importFrom(sjlabelled,get_label)
importFrom(sjlabelled,set_label)
importFrom(stats,as.formula)
+importFrom(stats,reformulate)
importFrom(stringr,str_detect)
importFrom(stringr,str_locate_all)
importFrom(stringr,str_remove)
diff --git a/NEWS.md b/NEWS.md
index 4eb72db7..ca5b273d 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,3 +1,17 @@
+# tidyCDISC (development version)
+
+* Update wordlist
+* Add Ryan Harrison as author
+
+### Population Explorer
+* Refactor KM plots from plotly to ggsurvfit
+* Add estimated median table to KM plot
+* Add estimate survival probabilities table to KM plot
+* Add time-axis ticks configuration to KM plot
+* Enable multiple groups to KM plot
+* Add Cox PH Hazard Ratios table to KM plot
+* Add ADaM validation functions
+* Allow more input file types: csv, parquet, txt, xls, xlsx
# tidyCDISC 0.2.1 (CRAN Release)
diff --git a/R/global.R b/R/global.R
index 3ee7f870..e5c0fafe 100644
--- a/R/global.R
+++ b/R/global.R
@@ -1,6 +1,6 @@
utils::globalVariables(c(
"AEDECOD",
- "AESER",
+ "AESER",
"AESEV",
"AESTDT",
"..prop..",
@@ -29,17 +29,17 @@ utils::globalVariables(c(
"value",
"ymax",
"ymin",
- "stats",
- "as.dist",
- "cor",
+ "stats",
+ "as.dist",
+ "cor",
"hclust",
"PopuExplor",
- "AVAL",
- "AVISIT",
- "AVISITN",
- "CMDECOD",
- "CMSTDT",
- "DECODE",
+ "AVAL",
+ "AVISIT",
+ "AVISITN",
+ "CMDECOD",
+ "CMSTDT",
+ "DECODE",
"DOMAIN",
"END",
"EVENTTYP",
@@ -48,7 +48,7 @@ utils::globalVariables(c(
"LBDY",
"LBSTNRHI",
"LBSTNRLO",
- "MHCAT",
+ "MHCAT",
"MHDECOD",
"MHENDTC",
"MHSTDTC",
@@ -57,8 +57,8 @@ utils::globalVariables(c(
"PARAMCD",
"START",
"USUBJID",
- "VISIT",
- "Visit",
+ "VISIT",
+ "Visit",
"all_of",
"className",
"col_exist",
@@ -74,7 +74,7 @@ utils::globalVariables(c(
"hcl",
"min_lbdt",
"missing_disp",
- "not_exist",
+ "not_exist",
"not_exist_disp",
"ord",
"setNames",
@@ -113,20 +113,73 @@ utils::globalVariables(c(
"adtte",
"warn",
"dy_min",
- "AEACN", "AEACNOTH", "AREL", "CNSR", "FASFL", "ITTFL", "LOWER", "Lower",
- "MEAN", "Mean (SD)",
- "Median", "Min | Max", "N", "PANEL", "Q1 | Q3", "SAFFL", "SEM", "STD",
- "TRTEMFL", "UPPER", "Upper",
- "Variable", "ae_data", "avals_by", "bds_data", "colour", "colour2",
- "corr", "corr_lab",
- "corr_lab_hover", "descr", "grp", "id_block", "id_desc", "id_rn",
- "id_stat", "inner_sort",
- "lab", "n_subgroups", "n_tot", "orig_id_rn", "orig_var_rn", "param_x",
- "param_y", "pt", "pval",
- "pval_hover", "sort_n", "v", "var", "var_rn", "where", "y",
- "type", "title"
- ,":=",
- "DATE", "EVENT_TIME", "DATE_ST", "DECODE_ST", "DATE_EN", "DECODE_EN"
+ "AEACN",
+ "AEACNOTH",
+ "AREL",
+ "CNSR",
+ "FASFL",
+ "ITTFL",
+ "LOWER",
+ "Lower",
+ "MEAN",
+ "Mean (SD)",
+ "Median",
+ "Min | Max",
+ "N",
+ "PANEL",
+ "Q1 | Q3",
+ "SAFFL",
+ "SEM",
+ "STD",
+ "TRTEMFL",
+ "UPPER",
+ "Upper",
+ "Variable",
+ "ae_data",
+ "avals_by",
+ "bds_data",
+ "colour",
+ "colour2",
+ "corr",
+ "corr_lab",
+ "corr_lab_hover",
+ "descr",
+ "grp",
+ "id_block",
+ "id_desc",
+ "id_rn",
+ "id_stat",
+ "inner_sort",
+ "lab",
+ "n_subgroups",
+ "n_tot",
+ "orig_id_rn",
+ "orig_var_rn",
+ "param_x",
+ "param_y",
+ "pt",
+ "pval",
+ "pval_hover",
+ "sort_n",
+ "v",
+ "var",
+ "var_rn",
+ "where",
+ "y",
+ "type",
+ "title",
+ ":=",
+ "DATE",
+ "EVENT_TIME",
+ "DATE_ST",
+ "DECODE_ST",
+ "DATE_EN",
+ "DECODE_EN",
+ "time",
+ "est_ci",
+ "Strata",
+ "0.95LCL",
+ "0.95UCL",
+ "label",
+ "id"
))
-
-
diff --git a/R/mod_dataUpload.R b/R/mod_dataUpload.R
index e247fa3e..7823eeb6 100644
--- a/R/mod_dataUpload.R
+++ b/R/mod_dataUpload.R
@@ -41,7 +41,8 @@ mod_dataUpload_ui <- function(id){
div(style="display: inline-block; ",h3("Data upload")),
div(style="display: inline-block; float:right;",mod_dataComplyRules_ui("dataComplyRules_ui_1")),
HTML("
ADSL file is mandatory & BDS/ OCCDS files are optional"),
- fileInput(ns("file"), "Upload sas7bdat files",accept = c(".sas7bdat"), multiple = TRUE),
+ # fileInput(ns("file"), "Upload sas7bdat files",accept = c(".sas7bdat"), multiple = TRUE),
+ fileInput(ns("file"), "Upload datasets", accept = c(".txt", ".csv", ".xls", ".xlsx", ".sas7bdat"), multiple = TRUE),
uiOutput(ns("radio_test"))
)
),
@@ -91,18 +92,25 @@ mod_dataUpload_server <- function(input, output, session){
data_list <- list()
## data list
- for (i in 1:nrow(input$file)){
- if(length(grep(".sas7bdat", input$file$name[i], ignore.case = TRUE)) > 0){
- data_list[[i]] <- haven::zap_formats(haven::read_sas(input$file$datapath[i])) %>%
- dplyr::mutate(dplyr::across(.cols = where(is.character),
- .fns = na_if, y = ""))
- }else{
- data_list[[i]] <- NULL
- }
- }
+ # for (i in 1:nrow(input$file)){
+ # if(length(grep(".sas7bdat", input$file$name[i], ignore.case = TRUE)) > 0){
+ # data_list[[i]] <- haven::zap_formats(haven::read_sas(input$file$datapath[i])) %>%
+ # dplyr::mutate(dplyr::across(.cols = where(is.character),
+ # .fns = na_if, y = ""))
+ # }else{
+ # data_list[[i]] <- NULL
+ # }
+ # }
+
+ data_list <- input$file$datapath |>
+ purrr::map(\(x) {
+ read_data(x)
+ }) |>
+ purrr::set_names(toupper(tools::file_path_sans_ext(input$file$name)))
+
# names
- names(data_list) <- toupper(stringr::str_remove(input$file$name, ".sas7bdat"))
+ # names(data_list) <- toupper(stringr::str_remove(input$file$name, ".sas7bdat"))
@@ -111,11 +119,19 @@ mod_dataUpload_server <- function(input, output, session){
id = NULL, #"dataComply_ui_1",
datalist = reactive(data_list))
+ # if(length(names(dl_comply)) > 0){
+ # # append to existing reactiveValues list
+ # dd$data <- c(dd$data, dl_comply) # dl_comply #
+
+ # }
if(length(names(dl_comply)) > 0){
- # append to existing reactiveValues list
- dd$data <- c(dd$data, dl_comply) # dl_comply #
+ # Normalize ADaM dataset names based on domain validation
+ dl_normalized <- normalize_adam_datasets(dl_comply)
+ # append to existing reactiveValues list
+ dd$data <- c(dd$data, dl_normalized)
}
+
# set dd$current to FALSE for previous & TRUE for current uploads
dd$current <- c(rep(FALSE, length(dd$current)), rep(TRUE, length(data_list)))
diff --git a/R/mod_indvExp_srv.R b/R/mod_indvExp_srv.R
index b066e235..c2046bc4 100644
--- a/R/mod_indvExp_srv.R
+++ b/R/mod_indvExp_srv.R
@@ -51,7 +51,7 @@ mod_indvExp_server <- function(input, output, session, datafile){
my_loaded_adams <- reactive({
req(!is.null(datafile()))
sasdata0 <- toupper(names(datafile()))
- sasdata <- names(which(sapply(sasdata0,function(df) { return(stringr::str_detect(toupper(df),"^AD[A-Z0-9\\_]+")) })))
+ sasdata <- names(which(sapply(sasdata0,function(df) { return(stringr::str_detect(toupper(df),"^AD")) })))
return(sasdata)
})
diff --git a/R/mod_popExp.R b/R/mod_popExp.R
index 3f15b1ab..8633fc01 100644
--- a/R/mod_popExp.R
+++ b/R/mod_popExp.R
@@ -46,7 +46,15 @@ mod_popExp_server <- function(input, output, session, datafile) {
req(!is.null(datafile()))
# wait until ADSL has been selected
- req("ADSL" %in% names(datafile()) )
+ adsl_datasets <- names(datafile())[sapply(names(datafile()), \(name) {
+ validation <- validate_adamish(datafile()[[name]])
+ return(validation$domain == "ADSL")
+ })]
+
+ req(length(adsl_datasets) > 0)
+ adsl_name <- adsl_datasets[1]
+
+ # req("ADSL" %in% names(datafile()) )
#####################################################################
# The data used by the population explorer is going to be one of:
@@ -56,13 +64,18 @@ mod_popExp_server <- function(input, output, session, datafile) {
######################################################################
# Isolate ADSL
- if ("ADSL" %in% names(datafile())) {
- ADSL <- datafile()$ADSL %>%
+ # if ("ADSL" %in% names(datafile())) {
+ # ADSL <- datafile()$ADSL %>%
+ # haven::zap_formats()
+ # }
+ if (length(adsl_datasets) > 0) {
+ ADSL <- datafile()[[adsl_name]] |>
haven::zap_formats()
}
# split the non-ADSL data into those which have a USUBJID or not
- NOTADSL <- datafile()[names(datafile()) != "ADSL" ]
+ # NOTADSL <- datafile()[names(datafile()) != "ADSL" ]
+ NOTADSL <- datafile()[!names(datafile()) %in% adsl_datasets]
if (!rlang::is_empty(NOTADSL)) {
# zap formats
@@ -138,7 +151,15 @@ mod_popExp_server <- function(input, output, session, datafile) {
my_loaded_adams <- reactive({
req(!is.null(datafile()))
sasdata0 <- toupper(names(datafile()))
- sasdata <- names(which(sapply(sasdata0,function(df) { return(stringr::str_detect(toupper(df),"^AD[A-Z0-9\\_]+")) })))
+ sasdata <- names(
+ which(
+ sapply(
+ sasdata0, function(df) {
+ return(stringr::str_detect(toupper(df),"^AD"))
+ })
+ )
+ )
+
return(sasdata)
})
@@ -151,7 +172,14 @@ mod_popExp_server <- function(input, output, session, datafile) {
observeEvent(my_loaded_adams(), {
# req(any(substr(my_loaded_adams(), 1, 4) == "ADTT"))
# adtt_ <- my_loaded_adams()[substr(my_loaded_adams(), 1, 4) == "ADTT"]
- req(any(purrr::map_lgl(my_loaded_adams(), ~ "CNSR" %in% colnames(datafile()[[.x]]))))
+ # req(any(purrr::map_lgl(my_loaded_adams(), ~ "CNSR" %in% colnames(datafile()[[.x]]))))
+ tte_datasets <- names(datafile())[sapply(names(datafile()), \(name) {
+ validation <- validate_adamish(datafile()[[name]])
+ return(validation$domain == "ADTTE" || "CNSR" %in% names(datafile()[[name]]))
+ })]
+
+ req(length(tte_datasets) > 0)
+
updateRadioButtons(session, "plot_type",
choices = c("Kaplan-Meier Curve",
"Line plot - mean over time",
@@ -272,7 +300,9 @@ mod_popExp_server <- function(input, output, session, datafile) {
# use plot output of the module to create the plot
- output$plot_output <- renderPlotly({
+ # Interactive plotly plot
+ output$plot_output_plotly <- renderPlotly({
+ req(input$plot_type != "Kaplan-Meier Curve")
switch(input$plot_type,
`Scatter Plot` = p_scatter() %>% plotly::ggplotly() %>%
plotly::layout(title = list(yref = "container", y = .95, yanchor = "bottom")),
@@ -281,20 +311,162 @@ mod_popExp_server <- function(input, output, session, datafile) {
`Line plot - mean over time` = p_line$plot() %>%
plotly::ggplotly(tooltip = c("text")) %>%
plotly::layout(title = list(yref = "container", y = .95, yanchor = "bottom")),
- `Heatmap - endpoint correlations` = p_heatmap$plot() %>% plotly::ggplotly(tooltip = c("text"))
- , `Kaplan-Meier Curve` = p_km() %>% plotly::ggplotly()
+ `Heatmap - endpoint correlations` = p_heatmap$plot() %>% plotly::ggplotly(tooltip = c("text"))#,
+ #`Kaplan-Meier Curve` = p_km() %>% plotly::ggplotly(),
+
) %>%
- config(displaylogo = FALSE,
- modeBarButtonsToRemove =
- c("zoom2d", "pan2d", "select2d", "lasso2d", "zoomIn2d", "zoomOut2d", "autoScale2d", "resetScale2d",
- "hoverClosestCartesian", "hoverCompareCartesian", "zoom3d", "pan3d",
- "resetCameraDefault3d", "resetCameraLastSave3d", "hoverClosest3d",
- "orbitRotation", "tableRotation", "zoomInGeo", "zoomOutGeo",
- "resetGeo", "hoverClosestGeo", "sendDataToCloud","hoverClosestGl2d",
- "hoverClosestPie", "toggleHover","resetViews","toggleSpikelines","resetViewMapbox"
- # , 'toImage', 'resetScale2d', 'zoomIn2d', 'zoomOut2d','zoom2d', 'pan2d'
- )
+ config(
+ displaylogo = FALSE,
+ modeBarButtons = list(
+ list("zoom2d"),
+ list("pan2d"),
+ list("toImage")
+ ),
+ toImageButtonOptions = list(
+ format = c("png"),
+ filename = glue::glue("{input$plot_type}_{Sys.Date()}"),
+ width = 1200,
+ height = 800,
+ scale = 2
+ )
+ )
+ })
+
+ # Static plots, for ggsurvfit KM plot
+
+ km_result <- reactive({
+ req(input$plot_type == "Kaplan-Meier Curve")
+ p_km()
+ })
+
+ output$plot_output_gg <- renderPlot({
+ km_result()$plot_km
+ })
+
+ # KM Estimated Median Time table
+ output$km_median_table <- gt::render_gt({
+ df <- km_result()$table_median
+
+ time_cols <- setdiff(names(df), "Strata")
+
+ df |>
+ gt::gt() |>
+ gt::tab_options(
+ table.width = gt::px(700),
+ table.font.size = "small"
+ ) |>
+ gt::cols_label(Strata = "Group") |>
+ gt::fmt_missing(
+ missing_text = "-"
+ ) |>
+ gt::cols_align(
+ align = "center",
+ columns = time_cols
+ ) |>
+ gt::fmt_markdown(columns = everything()) |>
+ gt::tab_style(
+ locations = list(
+ gt::cells_column_labels(columns = everything()),
+ gt::cells_column_spanners(spanners = everything())
+ ),
+ style = list(
+ gt::cell_borders(sides = "bottom", weight = gt::px(1)),
+ gt::cell_text(weight = "bold")
+ )
+ )
+ })
+
+ # Cox Table
+ output$km_cox_table <- gt::render_gt({
+ df <- km_result()$table_cox
+ if (!rlang::is_null(df)) {
+
+ df <- gtsummary::as_gt(df) |>
+ gt::cols_hide(columns = c("p.value")) |>
+ gt::tab_options(
+ table.width = gt::px(700),
+ table.font.size = "small"
+ ) |>
+ gt::fmt_missing(
+ missing_text = "-"
+ ) |>
+ gt::cols_align(
+ align = "center",
+ columns = !label
+ ) |>
+ # gt::fmt_markdown(columns = everything()) |>
+ gt::tab_style(
+ locations = list(
+ gt::cells_column_labels(columns = everything()),
+ gt::cells_column_spanners(spanners = everything())
+ ),
+ style = list(
+ gt::cell_borders(sides = "bottom", weight = gt::px(1)),
+ gt::cell_text(weight = "bold")
)
+ )
+
+ return(df)
+ }
+
+ })
+
+ # KM Estimated Survival probability table
+ output$km_est_table <- gt::render_gt({
+ df <- km_result()$table_est
+
+ time_cols <- setdiff(names(df), "Strata")
+
+ df |>
+ gt::gt() |>
+ gt::tab_options(
+ table.width = gt::px(700),
+ table.font.size = "small"
+ ) |>
+ gt::tab_spanner(
+ label = ("Time"),
+ columns = time_cols
+ ) |>
+ gt::cols_label(Strata = "Group") |>
+ gt::fmt_missing(
+ missing_text = "-"
+ ) |>
+ gt::cols_align(
+ align = "center",
+ columns = time_cols
+ ) |>
+ gt::fmt_markdown(columns = everything()) |>
+ gt::tab_style(
+ locations = list(
+ gt::cells_column_labels(columns = everything()),
+ gt::cells_column_spanners(spanners = everything())
+ ),
+ style = list(
+ gt::cell_borders(sides = "bottom", weight = gt::px(1)),
+ gt::cell_text(weight = "bold")
+ )
+ )
+ })
+
+ # Dynamic UI for plots
+ output$dynamic_plot_ui <- renderUI({
+ if (input$plot_type == "Kaplan-Meier Curve") {
+ req(km_result())
+
+ tagList(
+ plotOutput(ns("plot_output_gg"), height = 700),
+ tags$br(),
+ tags$h4("Estimated Survival Probability (95% CI) over Time"),
+ gt::gt_output(ns("km_est_table")),
+ tags$br(),
+ tags$h4("Median Survival Time (95% CI)"),
+ gt::gt_output(ns("km_median_table")),
+ tags$h4("Hazard Ratios (95% CI)"),
+ gt::gt_output(ns("km_cox_table"))
+ )
+ } else {
+ plotlyOutput(ns("plot_output_plotly"), height = 700)
+ }
})
# Output text string of what was filtered in IDEAFilter widget/ module
diff --git a/R/mod_popExp_fct_km.R b/R/mod_popExp_fct_km.R
index 72194749..3539b52f 100644
--- a/R/mod_popExp_fct_km.R
+++ b/R/mod_popExp_fct_km.R
@@ -1,86 +1,231 @@
#' Kaplan-Meier Curve
-#'
+#'
#' Create scatter plot where if the variables are numeric then they
-#' are plotted, and if they are PARAMCD's then a week and value
+#' are plotted, and if they are PARAMCD's then a week and value
#' must be selected for plotting.
-#'
+#'
#' @param data Merged data to be used in plot
-#' @param yvar Selected xy-axis
-#' @param resp_var character, the response variable (paramcd)
-#' @param cnsr_var character, the censor variable. Usually CNSR
-#' @param group character, variable name of grouping variable (categorical or factor)
-#' @param points logical, whether to plot + symbols when patients censored
-#' @param ci logical, whether the curve(s) should be accompanied with a 95\% CI
-#'
+#' @param yvar Selected xy-axis
+#' @param response_var character, the response variable (paramcd)
+#' @param censor_var character, the censor variable. Usually CNSR
+#' @param CI logical, whether the curve(s) should be accompanied with a 95\% CI
+#' @param risk_table logical, whether to display risk table below plot
+#' @param censor_mark logical, whether to plot + symbols when patients censored
+#' @param strata character, variable name(s) of strata variable(s) (categorical or factor)
+#' @param covariates character, variable name(s) of covariate variable(s) (categorical or factor)
+#'
#' @importFrom stats as.formula
#' @importFrom GGally ggsurv
#' @importFrom survival survfit Surv
-#'
+#' @importFrom ggsurvfit survfit2 ggsurvfit add_risktable add_confidence_interval add_censor_mark
+#' @importFrom stats reformulate
+#'
#' @family popExp functions
#' @keywords popEx
-#'
+#'
#' @return A ggplot object containing the KM curve plot
-#'
+#'
#' @noRd
-app_km_curve <- function(data, yvar, resp_var, cnsr_var, group = "NONE", points = TRUE, ci = FALSE) {
-
- resp_var_sym <- rlang::sym(resp_var)
-
- # Filter data by param selected
+app_km_curve <- function(
+ data,
+ yvar,
+ response_var,
+ censor_var,
+ CI,
+ risk_table,
+ censor_mark,
+ median_line,
+ strata = NULL,
+ covariates = NULL,
+ time_ticks = NULL
+) {
+ # Symbols
+ response_var <- rlang::sym(response_var)
+ censor_var <- rlang::sym(censor_var)
+
+ # Filter to selected PARAMCD retaining all columns
suppressWarnings(
- d <- data %>%
- dplyr::filter(PARAMCD == yvar) %>%
- dplyr::select(USUBJID, PARAM, PARAMCD, one_of(resp_var), one_of(cnsr_var), one_of(group)) %>%
- filter(!is.na(!!resp_var_sym)) %>%
- dplyr::distinct()
+ d_param <- data |>
+ dplyr::filter(PARAMCD == yvar)
)
- # print(d)
- # if(group != "NONE"){
- # group_counts <-
- # d %>%
- # group_by_at(vars(one_of(group))) %>%
- # summarize(n = n())
- # print(group_counts)
- # }
-
-
- fit_formula <- paste0("survival::Surv(",resp_var,", ",cnsr_var,") ~ ",
- if(group != "NONE" & !rlang::is_empty(group)) group else 1)
- fit <- survival::survfit(as.formula(fit_formula),data = d)
- # print(fit_formula)
- # print(fit)
- # print(summary(fit))
-
- # Initialize title of variables plotted
- # if group used, include those "by" variables in title
- by_title <- case_when(
- group != "NONE" ~ paste("\nby", best_lab(data, group)),
- TRUE ~ ""
+
+ d_model <- d_param |>
+ dplyr::filter(!is.na(!!response_var)) |>
+ dplyr::distinct()
+
+ # Package checks
+ rlang::check_installed("survival", reason = "to use this function.")
+ rlang::check_installed("ggsurvfit", reason = "to use this function.")
+ rlang::check_installed("purrr", reason = "to use this function.")
+
+ if (!is.data.frame(data)) {
+ cli::cli_abort("{.arg data} must be a dataframe.")
+ }
+ if (!rlang::is_symbol(rlang::enexpr(response_var))) {
+ cli::cli_abort("{.arg response_var} must be an unquoted column name.")
+ }
+ if (!rlang::is_symbol(rlang::enexpr(censor_var))) {
+ cli::cli_abort("{.arg censor_var} must be an unquoted column name.")
+ }
+
+ # LHS construction
+ LHS <- glue::glue(
+ "survival::Surv({rlang::ensym(response_var)}, {rlang::ensym(censor_var)})"
)
-
- # generate plot
- p <-
- GGally::ggsurv(fit,
- order.legend = FALSE, # use data order, not survival order
- CI = ci,
- plot.cens = points) +
- # survminer::ggsurvplot(fit,conf.int = ci) + # didn't work
- ggplot2::xlab(glue::glue("{unique(d$PARAM)}")) +
- ggplot2::theme_bw() +
+ if (
+ rlang::as_label(censor_var) == "CNSR" &&
+ rlang::as_label(response_var) == "AVAL"
+ ) {
+ LHS <- glue::glue(
+ "ggsurvfit::Surv_CNSR({rlang::ensym(response_var)}, {rlang::ensym(censor_var)})"
+ )
+ }
+
+ # Remove sentinel values
+ strata <- setdiff(strata, "NONE")
+ covariates <- setdiff(covariates, "NONE")
+
+ # RHS for KM fit (strata only)
+ if (length(strata) == 0) {
+ RHS <- "1"
+ no_strata <- TRUE
+ } else {
+ RHS <- strata
+ no_strata <- FALSE
+ }
+
+ # Ensure required columns exist
+ needed <- unique(c(
+ rlang::as_label(response_var),
+ rlang::as_label(censor_var),
+ strata,
+ covariates
+ ))
+ missing_vars <- setdiff(needed, colnames(d_model))
+ if (length(missing_vars) > 0) {
+ cli::cli_abort("Missing {missing_vars} column{?s} in filtered data.")
+ }
+
+ fit_formula <- stats::reformulate(response = LHS, termlabels = RHS)
+ fit <- ggsurvfit::survfit2(fit_formula, data = d_model)
+
+ by_title <- if (no_strata) "" else {
+ glue::glue(
+ "\nby {glue::glue_collapse(purrr::map_chr(strata, ~ best_lab(d_model, .x)), sep = ', ', last = ' and ')}"
+ )
+ }
+
+ # Build base plot to get automatic breaks
+ base_p <- ggsurvfit::ggsurvfit(fit)
+ auto_breaks <- ggplot2::ggplot_build(base_p)$layout$panel_params[[1]]$x$breaks
+
+ # Determine final ticks (user-specified or defaults). Server parse already
+ # ensures numeric, non-negative, unique values; just range-filter here.
+ ticks <- time_ticks
+ if (is.null(ticks) || length(ticks) == 0) {
+ ticks <- auto_breaks
+ } else {
+ rng <- range(d_model[[rlang::as_label(response_var)]], na.rm = TRUE)
+ # Force lower bound to 0 to allow baseline tick even if earliest observed time > 0
+ rng[1] <- 0
+ # Keep ticks within upper range but never drop 0 if user specified it
+ ticks <- ticks[ticks <= rng[2]]
+ if (0 %in% time_ticks && !(0 %in% ticks)) {
+ ticks <- c(0, ticks)
+ }
+ ticks <- sort(unique(ticks))
+ if (length(ticks) == 0) ticks <- auto_breaks
+ }
+
+ p2 <- base_p +
+ (if (risk_table) ggsurvfit::add_risktable(risktable_stats = c("n.risk"), time_breaks = ticks)) +
+ (if (CI) ggsurvfit::add_confidence_interval()) +
+ (if (censor_mark) ggsurvfit::add_censor_mark()) +
+ (if (median_line) ggsurvfit::add_quantile(y_value = 0.5)) +
ggplot2::theme(
text = ggplot2::element_text(size = 12),
axis.text = ggplot2::element_text(size = 12),
plot.title = ggplot2::element_text(size = 16)
) +
- ggplot2::ggtitle(paste(unique(d$PARAM), by_title)
- # ,subtitle = paste(by_title) # plotly won't automatically accept this
+ ggplot2::ggtitle(glue::glue("{unique(d_model$PARAM)} {by_title}")) +
+ (if (!no_strata)
+ ggplot2::theme(plot.margin = ggplot2::margin(t = 1.2, unit = "cm"))) +
+ ggplot2::scale_x_continuous(breaks = ticks, minor_breaks = NULL)
+
+ table_est <- ggsurvfit::tidy_survfit(
+ fit,
+ times = ticks,
+ type = "survival"
+ ) |>
+ (\(x) {
+ if (!"strata" %in% names(x)) {
+ dplyr::mutate(x, strata = "Overall")
+ } else {
+ x
+ }
+ })() |>
+ dplyr::mutate(
+ est_ci = glue::glue(
+ "{round(estimate, 2)} ({round(conf.low, 2)}, {round(conf.high, 2)})"
+ )
+ ) |>
+ dplyr::select(Strata = strata, time, est_ci) |>
+ dplyr::filter(time > 0) |>
+ dplyr::mutate(
+ est_ci = dplyr::if_else(est_ci == "NA (NA, NA)", "-", est_ci)
+ ) |>
+ tidyr::pivot_wider(names_from = time, values_from = est_ci)
+
+ table_median0 <- summary(fit)$table
+ table_median1 <- if (no_strata) {
+ dplyr::bind_rows(table_median0) |>
+ dplyr::mutate(Strata = "Overall")
+ } else {
+ tibble::as_tibble(table_median0, rownames = "Strata")
+ }
+ table_median <- table_median1 |>
+ dplyr::select(Strata, median, `0.95LCL`, `0.95UCL`) |>
+ dplyr::mutate(
+ est_ci = glue::glue(
+ "{round(median, 2)} ({round(`0.95LCL`, 2)}, {round(`0.95UCL`, 2)})"
+ )
+ ) |>
+ dplyr::select(Strata, `Median (95% CI)` = est_ci) |>
+ dplyr::mutate(
+ Strata = stringr::str_replace_all(
+ Strata,
+ "(^|,\\s*)[^=,]+\\s*=\\s*",
+ "\\1"
+ )
)
-
- # Add in plot layers conditional upon user selection
- if (by_title != "") {
- p <- p + ggplot2::theme(plot.margin = ggplot2::margin(t = 1.2, unit = "cm")) #+
- # survminer::ggsurvplot_group_by(fit, group.by = group)
+
+ # Cox PH model construction
+ no_covar <- length(covariates) == 0
+ termlabels <- NULL
+ if (!no_strata && no_covar) {
+ termlabels <- strata
+ } else if (no_strata && !no_covar) {
+ termlabels <- covariates
+ } else if (!no_strata && !no_covar) {
+ termlabels <- unique(c(strata, covariates))
}
-
- return(p)
+
+ table_cox <- NULL
+ if (!is.null(termlabels) && length(termlabels) > 0) {
+ cox_formula <- stats::reformulate(response = LHS, termlabels = termlabels)
+ cox_fit <- survival::coxph(cox_formula, data = d_model)
+ table_cox <- gtsummary::tbl_regression(
+ cox_fit,
+ exponentiate = TRUE,
+ conf.level = 0.95,
+ add_estimate_to_reference_rows = TRUE
+ )
+ }
+
+ list(
+ plot_km = p2,
+ table_est = table_est,
+ table_median = table_median,
+ table_cox = table_cox
+ )
}
diff --git a/R/mod_popExp_km.R b/R/mod_popExp_km.R
index 439e8bc6..ac9d325b 100644
--- a/R/mod_popExp_km.R
+++ b/R/mod_popExp_km.R
@@ -11,7 +11,7 @@
#'
#' @family popExp Functions
#' @noRd
-#'
+#'
km_ui <- function(id, label = "km") {
ns <- NS(id)
tagList(
@@ -19,18 +19,103 @@ km_ui <- function(id, label = "km") {
wellPanel(
fluidRow(
column(6, selectInput(ns("yvar"), "Select param", choices = NULL)),
- column(6, selectInput(ns("resp_var"), "Response Variable", choices = "AVAL",selected = "AVAL"))
+ column(
+ 6,
+ selectInput(
+ ns("resp_var"),
+ "Response Variable",
+ choices = "AVAL",
+ selected = "AVAL"
+ )
+ )
),
fluidRow(
- column(6, selectInput(ns("group"), "Group By", choices = "NONE", selected = "NONE")),
- column(6, selectInput(ns("cnsr_var"), "Censor Variable (0,1)", choices = "CNSR", selected = "CNSR"))
+ column(
+ 6,
+ selectizeInput(
+ ns("group"),
+ "Group By",
+ choices = "NONE",
+ selected = "NONE",
+ multiple = TRUE,
+ options = list(
+ render = I("{
+ option: function(item, escape){
+ return '' + escape(item.label) + '
';
+ }
+ }")
+ )
+ )
+ ),
+ column(
+ 6,
+ selectInput(
+ ns("cnsr_var"),
+ "Censor Variable (0,1)",
+ choices = "CNSR",
+ selected = "CNSR"
+ )
+ )
+ ),
+ shinyWidgets::materialSwitch(
+ ns("points"),
+ h6("Mark censored observations?"),
+ status = "primary",
+ value = TRUE
+ ),
+ shinyWidgets::materialSwitch(
+ ns("ci"),
+ h6("Include 95% confidence interval?"),
+ status = "primary",
+ value = FALSE
+ ),
+ shinyWidgets::materialSwitch(
+ ns("risk_table"),
+ h6("Include risk table?"),
+ status = "primary",
+ value = TRUE
),
- shinyWidgets::materialSwitch(ns("points"), h6("Mark censored observations?"),
- status = "primary", value = TRUE),
- shinyWidgets::materialSwitch(ns("ci"), h6("Include 95% confidence interval?"),
- status = "primary", value = FALSE)
+ shinyWidgets::materialSwitch(
+ ns("median_line"),
+ h6("Include median line(s)?"),
+ status = "primary",
+ value = TRUE
+ )
+
+ , tags$hr(),
+ h4("Time Axis Tick Marks"),
+ helpText("Enter comma, space, or semicolon separated time values (numeric). Leave blank for defaults."),
+ textInput(ns("time_ticks_raw"), label = NULL, placeholder = "e.g. 0, 12, 24, 36, 48"),
+ actionLink(ns("time_ticks_clear"), "Clear"),
+ tags$small("Invalid or out-of-range values are ignored; blanks use defaults." )
+
# checkboxInput(ns("points"), "Mark censored observations?", value = TRUE),
# checkboxInput(ns("ci"), "Include 95% confidence interval?", value = FALSE)
+ ),
+ h4("Cox Proportional Hazards Model"),
+ wellPanel(
+ fluidRow(
+ column(
+ 12,
+ selectizeInput(
+ ns("covariates"),
+ "Covariates",
+ choices = "NONE",
+ selected = "NONE",
+ multiple = TRUE,
+ options = list(
+ render = I("{
+ option: function(item, escape){
+ return '' + escape(item.label) + '
';
+ }
+ }")
+ )
+ )
+ )
+ ),
+ fluidRow(
+ column(12, uiOutput(ns("ref_group_ui")))
+ )
)
)
}
@@ -39,7 +124,7 @@ km_ui <- function(id, label = "km") {
#' Kaplan-Meier Curve Server Function
#'
#' Using the widgets from the km UI create
-#' a ggplot object which is returned to the
+#' a ggplot object which is returned to the
#' parent Population Explorer module
#'
#' @param input,output,session Internal parameters for {shiny}.
@@ -53,114 +138,282 @@ km_ui <- function(id, label = "km") {
#'
#' @family popExp Functions
#' @noRd
-#'
+#'
km_srv <- function(input, output, session, data, run) {
ns <- session$ns
-
+
observe({
req(run(), data())
-
+
# get unique paramcd
+
paramcd <- sort(na.omit(unique(data()$PARAMCD)))
- updateSelectInput(session, "yvar",
- choices = as.list(paramcd),
- selected = isolate(input$yvar)
+ updateSelectInput(
+ session,
+ "yvar",
+ choices = as.list(paramcd),
+ selected = isolate(input$yvar)
)
})
-
-
+
# update response variable options for user based on data filtered to a
# certain param
observeEvent(input$yvar, {
req(run(), input$yvar != "")
-
+
d <- data()
- my_vars <- d %>%
- dplyr::filter(PARAMCD == input$yvar) %>%
+ my_vars <- d %>%
+ dplyr::filter(PARAMCD == input$yvar) %>%
dplyr::filter(data_from == "ADTTE") %>% # Numeric visit var has to exist in TTE data
- select(one_of("AVISITN", "VISITNUM"), ends_with("DY")) %>%
- select_if(~!all(is.na(.))) %>% # remove NA cols
+ select(one_of("AVISITN", "VISITNUM"), ends_with("DY")) %>%
+ select_if(~ !all(is.na(.))) %>% # remove NA cols
colnames()
-
- updateSelectInput (
+
+ updateSelectInput(
session = session,
inputId = "resp_var",
choices = c("AVAL", my_vars),
selected = isolate(input$resp_var)
)
})
-
+
# update censor variable options for user based on data filtered to a
# certain param
observeEvent(input$yvar, {
req(run(), input$yvar != "")
-
+
# col <- c(1:4)
# col <- c(0, 1, 1, 1)
# col <- c(1, 1, 1)
-
+
d0 <- data()
- my_cvars <- d0 %>%
- dplyr::filter(PARAMCD == input$yvar) %>%
+ my_cvars <- d0 %>%
+ dplyr::filter(PARAMCD == input$yvar) %>%
dplyr::filter(data_from == "ADTTE") %>% # Numeric visit var has to exist in TTE data
select(where(is.numeric)) %>%
- select(where(function(col) all( unique(col) %in% c(0,1) ))) %>%
- select_if(~!all(is.na(.))) %>% # remove NA cols
+ select(where(function(col) all(unique(col) %in% c(0, 1)))) %>%
+ select_if(~ !all(is.na(.))) %>% # remove NA cols
colnames()
-
- updateSelectInput (
+
+ updateSelectInput(
session = session,
inputId = "cnsr_var",
choices = c("CNSR", my_cvars[my_cvars != "CNSR"]),
selected = isolate(input$cnsr_var)
)
})
-
-
+
observeEvent(input$yvar, {
req(run(), input$yvar != "")
-
+
# yvar paramcd
- group_dat <- data() %>%
- dplyr::filter(PARAMCD == input$yvar) %>%
- select_if(~!all(is.na(.))) # remove NA cols
-
+ group_dat <- data() %>%
+ dplyr::filter(PARAMCD == input$yvar) %>%
+ select_if(~ !all(is.na(.))) # remove NA cols
+
# character and factor columns for grouping or faceting (separating)
char_col <- subset_colclasses(group_dat, is.character)
fac_col <- subset_colclasses(group_dat, is.factor)
- group <- sort(c(fac_col, char_col))
+ log_col <- subset_colclasses(group_dat, is.logical)
+ num_col <- subset_colclasses(group_dat, is.numeric)
+
+ group <- sort(c(fac_col, char_col, log_col))
# print("char_col:")
# print(char_col)
# print("fac_col:")
# print(fac_col)
# print(".")
# print(".")
-
+
# remove some variables...
grp <- group[!(group %in% c("data_from", "PARAM", "PARAMCD", "USUBJID"))]
-
+
# populate dropdowns with choices
- updateSelectInput(session, "group",
- choices = c("NONE", grp),
- selected = isolate(input$group))
-
+ updateSelectizeInput(
+ session,
+ "group",
+ choices = c("NONE", grp),
+ selected = isolate(input$group)
+ )
+
+ covar <- sort(c(fac_col, char_col, log_col, num_col))
+ covar2 <- covar[!(covar %in% c("data_from", "PARAM", "PARAMCD", "USUBJID"))]
+
+ updateSelectizeInput(
+ session,
+ "covariates",
+ choices = c("NONE", covar2),
+ selected = isolate(input$covariates)
+ )
+ })
+
+ observeEvent(input$group, {
+ req(input$group)
+ # Remove sentinel NONE if any real strata selected
+ if ("NONE" %in% input$group & length(input$group) > 1) {
+ cleaned <- setdiff(input$group, "NONE")
+ # Avoid triggering extra invalid intermediate state
+ isolate({
+ updateSelectizeInput(
+ session,
+ "group",
+ selected = cleaned
+ )
+ })
+ return(NULL)
+ } else if (
+ !is.null(input$group) &
+ !("NONE" %in% input$group) &
+ length(input$group) >= 1
+ ) {
+ # If any real variable is selected, remove "NONE"
+ if ("NONE" %in% input$group) {
+ updateSelectizeInput(
+ session,
+ "group",
+ selected = setdiff(input$group, "NONE")
+ )
+ }
+ }
+ })
+ # New covariates observer
+ observeEvent(input$covariates, {
+ req(input$covariates)
+ if ("NONE" %in% input$covariates && length(input$covariates) > 1) {
+ isolate(
+ updateSelectizeInput(
+ session,
+ "covariates",
+ selected = setdiff(input$covariates, "NONE")
+ )
+ )
+ }
+ })
+
+ # helper to create safe ids
+ make_valid_id <- function(x) gsub("[^A-Za-z0-9]", "_", x)
+
+ # reactive: categorical vars among selected strata + covariates
+ categorical_vars <- reactive({
+ req(run(), data(), input$yvar)
+
+ d_sub <- data() |>
+ dplyr::filter(PARAMCD == input$yvar)
+
+ sel <- unique(c(
+ if (!is.null(input$group) & !("NONE" %in% input$group)) input$group else
+ character(),
+ if (!is.null(input$covariates) & !("NONE" %in% input$covariates))
+ input$covariates else character()
+ ))
+
+ if (length(sel) == 0) return(character())
+
+ # keep only categorical (factor / character / logical)
+ keep <- sel[vapply(
+ sel,
+ function(v) {
+ col <- d_sub[[v]]
+ is.factor(col) || is.character(col) || is.logical(col)
+ },
+ logical(1)
+ )]
+ keep
+ })
+
+ # UI for reference selectors
+ output$ref_group_ui <- renderUI({
+ vars <- categorical_vars()
+ if (length(vars) == 0) return(NULL)
+
+ d_sub <- data() |> dplyr::filter(PARAMCD == input$yvar)
+
+ tagList(
+ h5("Reference groups (Cox PH):"),
+ lapply(vars, \(v) {
+ col <- d_sub[[v]]
+ lvls <- if (is.factor(col)) levels(col) else sort(unique(col))
+ # exclude NA
+ lvls <- lvls[!is.na(lvls)]
+ selectInput(
+ inputId = ns(paste0("ref_", make_valid_id(v))),
+ label = v,
+ choices = lvls,
+ selected = lvls[1]
+ )
+ })
+ )
+ })
+
+ # reactive: apply releveling
+ km_model_data <- reactive({
+ req(run(), data(), input$yvar)
+ d0 <- data() |> dplyr::filter(PARAMCD == input$yvar)
+ vars <- categorical_vars()
+ if (length(vars) == 0) return(d0)
+ ids <- paste0("ref_", make_valid_id(vars))
+ # if ref inputs not yet created, just return original
+ if (any(vapply(ids, function(id) is.null(input[[id]]), logical(1)))) {
+ return(d0)
+ }
+ refs <- purrr::map_chr(
+ vars,
+ \(v) input[[paste0("ref_", make_valid_id(v))]]
+ ) |>
+ purrr::set_names(vars)
+
+ for (v in names(refs)) {
+ if (v %in% names(d0)) {
+ ref <- refs[[v]]
+ d0[[v]] <- if (is.factor(d0[[v]])) {
+ stats::relevel(d0[[v]], ref = ref)
+ } else if (is.character(d0[[v]]) || is.logical(d0[[v]])) {
+ lvls <- sort(unique(d0[[v]]))
+ stats::relevel(factor(d0[[v]], levels = lvls), ref = ref)
+ } else {
+ d0[[v]]
+ }
+ }
+ }
+ d0
+ })
+
+ # ---- Time tick parsing ----
+ parse_ticks <- function(x) {
+ if (is.null(x) || !nzchar(trimws(x))) return(NULL)
+ parts <- strsplit(x, "[,;\t\n\r ]+", perl = TRUE)[[1]]
+ nums <- suppressWarnings(as.numeric(parts))
+ nums <- nums[!is.na(nums) & nums >= 0]
+ nums <- sort(unique(nums))
+ if (length(nums) == 0) return(NULL)
+ nums
+ }
+
+ user_ticks <- reactive({
+ parse_ticks(input$time_ticks_raw)
+ })
+
+ observeEvent(input$time_ticks_clear, {
+ updateTextInput(session, "time_ticks_raw", value = "")
})
-
-
- # create plot object using the numeric column on the yaxis
- # or by filtering the data by PARAMCD, then using AVAL or CHG for the yaxis
+
+ # modify reactive p to use km_model_data() instead of data()
p <- reactive({
- req(run(), data(), input$yvar )
- #, input$resp_var,input$group,input$points,input$ci) # can't include these in req
- app_km_curve(data(),
- input$yvar,
- input$resp_var,
- input$cnsr_var,
- input$group,
- input$points,
- input$ci
- )
+ req(run(), km_model_data(), input$yvar)
+ app_km_curve(
+ data = km_model_data(),
+ yvar = input$yvar,
+ response_var = input$resp_var,
+ censor_var = input$cnsr_var,
+ strata = input$group,
+ covariates = input$covariates,
+ censor_mark = input$points,
+ CI = input$ci,
+ risk_table = input$risk_table,
+ median_line = input$median_line,
+ time_ticks = user_ticks()
+ )
})
-
+
return(p)
}
diff --git a/R/mod_popExp_ui.R b/R/mod_popExp_ui.R
index 000bfdac..669f9988 100644
--- a/R/mod_popExp_ui.R
+++ b/R/mod_popExp_ui.R
@@ -81,7 +81,8 @@ mod_popExp_ui <- function(id, label = "Population Explorer"){
column(width = 9,
div(id = "pop_cic_plot",
wellPanel(
- plotlyOutput(ns("plot_output"), height = 700),
+ #plotlyOutput(ns("plot_output_plotly"), height = 700),
+ uiOutput(ns("dynamic_plot_ui")),
div(style = "color: #0275d8; font-size: 12px;", htmlOutput(ns("applied_filters")))
, br(), br()
,DT::dataTableOutput(ns("plot_data"))
diff --git a/R/mod_selectData.R b/R/mod_selectData.R
index 815ee0a4..fc30c191 100644
--- a/R/mod_selectData.R
+++ b/R/mod_selectData.R
@@ -54,7 +54,7 @@ mod_selectData_server <- function(input, output, session, datafile){
sasdata <- toupper(names(datafile()))
# Only select data that starts with AD followed by one or more alphanumerics or underscore
- sasdata <- names(which(sapply(sasdata,function(df) { return(stringr::str_detect(toupper(df),"^AD[A-Z0-9\\_]+")) })))
+ sasdata <- names(which(sapply(sasdata,function(df) { return(stringr::str_detect(toupper(df),"^AD")) })))
# Update the picker input list
updatePickerInput(
diff --git a/R/mod_tableGen.R b/R/mod_tableGen.R
index d9be8e02..86ae4910 100644
--- a/R/mod_tableGen.R
+++ b/R/mod_tableGen.R
@@ -117,7 +117,7 @@ mod_tableGen_server <- function(input, output, session, datafile = reactive(NULL
my_loaded_adams <- reactive({
req(!is.null(datafile()))
sasdata0 <- toupper(names(datafile()))
- sasdata <- names(which(sapply(sasdata0,function(df) { return(stringr::str_detect(toupper(df),"^AD[A-Z0-9\\_]+")) })))
+ sasdata <- names(which(sapply(sasdata0,function(df) { return(stringr::str_detect(toupper(df),"^AD")) })))
return(sasdata)
})
diff --git a/R/sysdata.rda b/R/sysdata.rda
index 34771ede..70d8a4f7 100644
Binary files a/R/sysdata.rda and b/R/sysdata.rda differ
diff --git a/R/utils_adam.R b/R/utils_adam.R
new file mode 100644
index 00000000..c79a571e
--- /dev/null
+++ b/R/utils_adam.R
@@ -0,0 +1,270 @@
+#' Suggest ADaM Domain
+#'
+#' Heuristically suggests the most likely ADaM domain for a dataset
+#' based on the presence of required variables.
+#'
+#' @param df A data.frame to evaluate.
+#'
+#' @return A character string fo the most likely domain name (e.g., "ADLB"), or NULL.
+#' @keywords internal
+suggest_adam_domain <- function(df) {
+ scores <- purrr::map_int(adam_requirements, \(def) {
+ sum(def$required %in% names(df))
+ })
+
+ best <- which.max(scores)
+ if (scores[best] > 1) {
+ names(adam_requirements)[best]
+ } else {
+ NULL
+ }
+}
+
+#' Validate ADaM Structure
+#'
+#' Validates that a dataset conforms to required variables for a given ADaM domain.
+#' If no domain is supplied, it will attempt to infer one based on variable names.
+#'
+#' @param df A data.frame to validate
+#' @param domain Optional. A string naming the ADaM domain to validate against.
+#'
+#' @returns A list with:
+#' - `domain`: inferred or provided domain
+#' - `is_valid`: logical
+#' - `missing_required`: character vector
+#' - `missing_recommended`: character vector
+#' - `error`: character or NULL
+#'
+#' @keywords internal
+validate_adamish <- function(df, domain = NULL) {
+ if (is.null(domain)) {
+ domain <- suggest_adam_domain(df)
+ }
+
+ if (is.null(domain) || !domain %in% names(adam_requirements)) {
+ out_list <- structure(
+ list(
+ domain = NULL,
+ is_valid = FALSE,
+ missing_required = character(0),
+ missing_recommended = character(0),
+ error = "Unknown or unspecified ADaM domain."
+ ),
+ class = "adam_validation"
+ )
+ return(out_list)
+ }
+
+ req_vars <- adam_requirements[[domain]]$required
+ rec_vars <- adam_requirements[[domain]]$recommended
+
+ missing_required <- setdiff(req_vars, names(df))
+ missing_recommended <- setdiff(rec_vars, names(df))
+ is_valid <- length(missing_required) == 0
+
+ out_list <- structure(
+ list(
+ domain = domain,
+ is_valid = is_valid,
+ missing_required = missing_required,
+ missing_recommended = missing_recommended,
+ error = NULL
+ ),
+ class = "adam_validation"
+ )
+
+ return(out_list)
+}
+
+#' Suggest Column Mappings for ADaM
+#'
+#' Based on validation results, attempt to suggest which columns to rename to standard ADaM variable names.
+#'
+#' @param df A data.frame to evaluate
+#' @param validation A list returned from `validate_adamish()`
+#' @param string_distance Logical. Use string distance matching to provide suggestions, requires `stringdist` package
+#' @param session A Shiny session object (optional).
+#' @param input_ids A named list of input IDs corresponding to ADaM variable names (optional)
+#'
+#' @returns A named list with best-guess mappings for missing variables
+#' @keywords internal
+suggest_adam_column_mapping <- function(
+ df,
+ validation,
+ string_distance = FALSE,
+ session = NULL,
+ input_ids = NULL
+) {
+ if (!inherits(validation, "adam_validation")) {
+ stop("Invalid validation object passed. Must be from validate_adamish()")
+ }
+
+ if (validation$is_valid) {
+ return(stats::setNames(vector("list", 0), character(0)))
+ }
+
+ candidates <- names(df)
+ suggestions <- list()
+
+ # Alias dictionary
+ aliases <- list(
+ USUBJID = c("subject_id", "subjid", "usrid"),
+ STUDYID = c("study", "study_id"),
+ PARAM = c("test", "variable", "paramname"),
+ PARAMCD = c("testcd", "param_code"),
+ AVAL = c("value", "avalval", "score"),
+ AVISIT = c("visit", "timepoint"),
+ AVISITN = c("visitnum", "visitn"),
+ CNSR = c("censor", "cnsrflag"),
+ ADT = c("date", "adtm", "obsdt"),
+ QNAM = c("question", "qname", "item"),
+ AEDECOD = c("event", "aeterm"),
+ AESTDTC = c("startdt", "aestdt", "start_date")
+ )
+
+ for (var in c(validation$missing_required, validation$missing_recommended)) {
+ # browser()
+ lc_candidates <- tolower(candidates)
+ lc_var <- tolower(var)
+
+ # 1. Exact match
+ exact_match <- candidates[lc_candidates == lc_var]
+
+ # 2. Alias match
+ alias_match <- NULL
+ if (var %in% names(aliases)) {
+ alias_match <- candidates[lc_candidates %in% tolower(aliases[[var]])]
+ }
+
+ # 3. Fuzzy contains match
+ fuzzy_match <- candidates[grepl(lc_var, lc_candidates)]
+
+ # 4. String distance match
+ stringdist_match <- NULL
+
+ if (rlang::is_true(string_distance)) {
+ rlang::check_installed(
+ "stringdist",
+ reason = "is required to perform string distance matching."
+ )
+
+ # Create set of targets for expected var names and aliases
+ targets <- tolower(c(var, aliases[[var]] %||% character(0)))
+
+ # Compute distances between each target and column name
+ dist_matrix <- stringdist::stringdistmatrix(
+ targets,
+ lc_candidates,
+ method = "jw"
+ )
+ min_idx <- which(dist_matrix == min(dist_matrix), arr.ind = TRUE)
+
+ # Get best column candidate if distance is small enough
+ if (nrow(min_idx) > 0) {
+ min_val <- dist_matrix[min_idx[1, 1], min_idx[1, 2]]
+ if (min_val < 0.25) {
+ stringdist_match <- candidates[min_idx[1, 2]]
+ }
+ }
+ }
+
+ # 5. Select suggestion
+ suggestion <- dplyr::first(
+ c(exact_match, alias_match, fuzzy_match, stringdist_match),
+ default = NULL
+ )
+ suggestions[[var]] <- suggestion
+
+ # If session + input_ids provided, update selectizeInput()
+ if (
+ !rlang::is_null(session) &&
+ !rlang::is_null(input_ids) &&
+ !rlang::is_null(input_ids[[var]]) &&
+ !rlang::is_null(suggestion)
+ ) {
+ shiny::updateSelectizeInput(
+ session = session,
+ inputId = input_ids[[var]],
+ choices = candidates,
+ selected = suggestion
+ )
+ }
+ }
+
+ return(suggestions)
+}
+
+
+#' Show ADaM Compatibility Alert
+#'
+#' Display a shinyalert-style popup based on the results of `validate_adamish()`
+#'
+#' @param validation A list returned from `validate_adamish()`
+#' @param session The Shiny session (typically `session` from server)
+#' @keywords internal
+show_adamish_alert <- function(validation, session) {
+ rlang::check_installed("shinyalert", reason = "to use this function.")
+
+ if (isTRUE(validation$is_valid)) {
+ shinyalert::shinyalert(
+ title = "\u2705 ADaM Compatibility Passed",
+ text = glue::glue("Domain: {validation$domain}"),
+ type = "success",
+ session = session
+ )
+ } else {
+ message <- if (!is.null(validation$error)) {
+ validation$error
+ } else {
+ domain_msg <- glue::glue("Domain: {validation$domain}")
+
+ req_msg <- glue::glue(
+ "\u274C Missing required: {glue::glue_collapse(validation$missing_required, sep = ', ')}"
+ )
+ rec_msg <- glue::glue(
+ "\u26A0\uFE0F Missing recommended: {glue::glue_collapse(validation$missing_recommended, sep = ', ')}"
+ )
+
+ message <- dplyr::if_else(
+ length(validation$missing_recommended) > 0,
+ glue::glue("{domain_msg}\n{req_msg}\n{rec_msg}"),
+ req_msg
+ )
+ }
+
+ shinyalert::shinyalert(
+ title = "\u274C ADaM Compatibility Failed",
+ text = message,
+ type = "error",
+ session = session
+ )
+ }
+}
+
+#' Normalize ADaM datasets
+#'
+#' @description Checks ADaM domain of uploaded datasets and renames based on the domain. Returns filename if no ADaM domain.
+#'
+#' @param datafile_list list A list of data.frames dataset to normalize
+#' @return A list of normalized data.frames.
+#'
+#' @noRd
+#'
+normalize_adam_datasets <- function(datafile_list) {
+ normalized_list <- list()
+
+ for (name in names(datafile_list)) {
+ df <- datafile_list[[name]]
+ validation <- validate_adamish(df)
+
+ if (!is.null(validation$domain)) {
+ # Rename to standard domain name
+ normalized_list[[validation$domain]] <- df
+ } else {
+ # Keep original name if can't validate
+ normalized_list[[name]] <- df
+ }
+ }
+
+ return(normalized_list)
+}
diff --git a/R/utils_helpers.R b/R/utils_helpers.R
index 76d052b9..aee3fb0d 100644
--- a/R/utils_helpers.R
+++ b/R/utils_helpers.R
@@ -1,34 +1,38 @@
#' GT Column Names
-#'
+#'
#' @param col_names A vector of column names
#' @param col_total A vector of column totals
-#'
+#'
#' @description The function creates the labels for each column using the total function so the columns are now NAME N= X
#' @export
#' @keywords tabGen_repro
-#'
+#'
#' @return A character object of class \code{from_markdown}.
-#'
+#'
#' @importFrom purrr map2
#' @importFrom gt md
#' @importFrom glue glue
#' @importFrom rlang set_names
-#'
-#' @examples
+#'
+#' @examples
#' data(example_dat2, package = "tidyCDISC")
-#'
+#'
#' labels <- col_for_list_expr(example_dat2$col_names, example_dat2$col_totals)
#' labels
-#'
+#'
#' if (interactive()) {
#' # TG table without nice column labels or totals
#' example_dat2$TG_table
-#'
+#'
#' # TG table with nice column labels and totals
#' gt::cols_label(example_dat2$TG_table, .list = labels)
#' }
col_for_list_expr <- function(col_names, col_total) {
- purrr::map2(col_names, col_total, ~ gt::md(glue::glue("**{.x}**
N={.y}"))) %>%
+ purrr::map2(
+ col_names,
+ col_total,
+ ~ gt::md(glue::glue("**{.x}**
N={.y}"))
+ ) %>%
rlang::set_names(col_names)
}
@@ -43,14 +47,14 @@ col_for_list_expr <- function(col_names, col_total) {
#'
#' @family indvExp Functions
#' @noRd
-#'
+#'
my_gg_color_hue <- function(n) {
- hues = seq(15, 375, length = n +1)
+ hues = seq(15, 375, length = n + 1)
hcl(h = hues, l = 65, c = 100)[1:n]
}
#' Standard Error Calculation
-#'
+#'
#' Calculates the square root of variance divided by n
#'
#' @param x A numeric vector
@@ -60,78 +64,84 @@ my_gg_color_hue <- function(n) {
#'
#' @family tableGen Functions
#' @noRd
-#'
-std_err <- function(x, na.rm=FALSE) {
- if (na.rm) x <- na.omit(x)
- sqrt(var(x)/length(x))
+#'
+std_err <- function(x, na.rm = FALSE) {
+ if (na.rm) {
+ x <- na.omit(x)
+ }
+ sqrt(var(x) / length(x))
}
#' Translate the position of into a integer needed for ggplot2
#' @param dir the strings to capitalize
#' @noRd
-#'
-translate_pos <- function(dir){
- if(dir %in% c("left","bottom")) -1
- else if(dir %in% c("right","top")) 1
- else 0 # middle
+#'
+translate_pos <- function(dir) {
+ if (dir %in% c("left", "bottom")) {
+ -1
+ } else if (dir %in% c("right", "top")) {
+ 1
+ } else {
+ 0
+ } # middle
}
#' Capitalize the first letter of a string
#' @param y the strings to capitalize
#' @noRd
-#'
+#'
CapStr <- function(y) {
c <- strsplit(y, " ")[[1]]
- paste(toupper(substring(c, 1,1)), substring(c, 2),
- sep="", collapse=" ")
+ paste(toupper(substring(c, 1, 1)), substring(c, 2), sep = "", collapse = " ")
}
-#' transpose dataframes so they can all
+#' transpose dataframes so they can all
#' be used with rbind to generate
#' the gt tables
-#'
+#'
#' @param df the dataframe to transpose
#' @param num the number of rows to return
#' @importFrom dplyr mutate rename
#' @importFrom tidyr pivot_longer pivot_wider
#' @noRd
-#'
+#'
transpose_df <- function(df, num) {
t_df <- df %>%
dplyr::mutate("rowname" = rownames(.), .before = 1) %>%
tidyr::pivot_longer(-"rowname") %>%
tidyr::pivot_wider(names_from = "rowname") %>%
dplyr::rename("rowname" = "name")
- return(t_df[-num,])
+ return(t_df[-num, ])
}
#' Identify Names of Columns
-#'
+#'
#' @description A function to transform the \code{gt} row names from generics to the column name and the total N of
#' each column
#'
#' @param data the data to create columns with
#' @param group whether to group the data to calculate Ns
-#'
+#'
#' @export
#' @keywords tabGen_repro
-#'
+#'
#' @return A character vector
-#'
-#' @examples
+#'
+#' @examples
#' data(adsl, package = "tidyCDISC")
-#'
+#'
#' # Values of TRT01P
#' unique(adsl$TRT01P)
-#'
+#'
#' # Common row names based on TRT01P
#' common_rownames(adsl, "TRT01P")
-common_rownames <- function(data, group) {
- if (is.null(group) ) { #| group == "NONE"
+common_rownames <- function(data, group) {
+ if (is.null(group)) {
+ #| group == "NONE"
vars <- c("Variable", "Total")
} else {
- if(is.factor(data[[group]])){
+ if (is.factor(data[[group]])) {
# droplevels() get's rid of levels that no longer exist in the data post filtering
lvls <- levels(data[[group]]) # removed droplevels() to retain all trt grps
} else {
@@ -165,8 +175,6 @@ common_rownames <- function(data, group) {
# # if (x == "ADAE") ae_data() else all_data()
# # }
-
-
#' Convert actions performed on from an IDEAFilter output dataframe into text
#'
#' Function accepts a filtered data object from IDEAFilter (shiny_data_filter
@@ -186,53 +194,82 @@ common_rownames <- function(data, group) {
#' @importFrom utils capture.output
#' @importFrom tidyr as_tibble
#' @importFrom shiny HTML
-#'
+#'
#' @return An HTML string
#' @noRd
-#'
-filters_in_english <- function(filtered_data, filter_header = "Filters Applied:"){
-
+#'
+filters_in_english <- function(
+ filtered_data,
+ filter_header = "Filters Applied:"
+) {
# grab the output
- orig_code <- paste(utils::capture.output(attr(filtered_data, "code")),collapse = "")
+ orig_code <- paste(
+ utils::capture.output(attr(filtered_data, "code")),
+ collapse = ""
+ )
# orig_code <- 'processed_data %>% filter(ABIFN1 %in% c(NA, "NEGATIVE")) %>% filter(ABIFN1 %in% c(NA, "POSITIVE"))'
# convert double quotes to single quotes
code_text <- orig_code %>%
stringr::str_remove("^.*?\\%>\\%") %>%
stringr::str_replace_all('\"', "\'")
-
+
# find the character position for the end of the string
len <- nchar(code_text)
-
+
# find the start of the variable expressions using position of "filter"
- f_loc <- str_locate_all(code_text,"filter\\(")
+ f_loc <- str_locate_all(code_text, "filter\\(")
filter_loc <- tidyr::as_tibble(f_loc[[1]])
var_st <- filter_loc$end + 1
-
+
# find the end of variable expression susing position of "%>%"
- p_loc <- str_locate_all(code_text,"\\%\\>\\%") # have to use this
+ p_loc <- str_locate_all(code_text, "\\%\\>\\%") # have to use this
pipe_loc <- tidyr::as_tibble(p_loc[[1]])
num_pipes <- nrow(pipe_loc)
- var_end <- c(pipe_loc$start[ifelse(num_pipes == 1, 1, 2):num_pipes] - 3, len - 1) # ifelse(num_pipes == 1, 1, 2)
-
+ var_end <- c(
+ pipe_loc$start[ifelse(num_pipes == 1, 1, 2):num_pipes] - 3,
+ len - 1
+ ) # ifelse(num_pipes == 1, 1, 2)
+
# use map2, to apply multiple arguments to the substr function, returing a list
- filter_vectors <- map2(.x = var_st, .y = var_end, function(x,y) substr(code_text,x,y))
+ filter_vectors <- map2(.x = var_st, .y = var_end, function(x, y) {
+ substr(code_text, x, y)
+ })
my_msgs <- filter_vectors[!(is.na(filter_vectors) | filter_vectors == "")] # get rid of NA msgs
-
+
# clean up messages to read more naturally
- disp_msg <- gsub("\\%in\\%","IN",
- gsub("c\\(","\\(",
- gsub("\\(NA","\\(Missing",
- gsub(".na",".Missing",
- gsub(" "," ", # 3 spaces
- gsub(" "," ", # 2 spaces
- gsub("\\|","OR",
- gsub("\\&","AND",
- my_msgs
- ))))))))
-
+ disp_msg <- gsub(
+ "\\%in\\%",
+ "IN",
+ gsub(
+ "c\\(",
+ "\\(",
+ gsub(
+ "\\(NA",
+ "\\(Missing",
+ gsub(
+ ".na",
+ ".Missing",
+ gsub(
+ " ",
+ " ", # 3 spaces
+ gsub(
+ " ",
+ " ", # 2 spaces
+ gsub("\\|", "OR", gsub("\\&", "AND", my_msgs))
+ )
+ )
+ )
+ )
+ )
+ )
+
# format as html in a specific format, with indentation
- return(HTML(paste0("",filter_header,"
"
- ,paste(disp_msg, collapse = "
"))))
+ return(HTML(paste0(
+ "",
+ filter_header,
+ "
",
+ paste(disp_msg, collapse = "
")
+ )))
}
#' Get Factor Levels
@@ -240,29 +277,30 @@ filters_in_english <- function(filtered_data, filter_header = "Filters Applied:"
#' Extracts the factor levels of a vector or returns the unique values if the vector is not a factor.
#'
#' @param x a vector
-#'
-#' @return x vector
-#'
+#'
+#' @return x vector
+#'
#' @export
#' @keywords helpers
-#'
+#'
#' @references A character vector containing the levels of the factor/vector
-#'
-#' @examples
+#'
+#' @examples
#' data(adae, package = "tidyCDISC")
-#'
+#'
#' # Create levels based on VARN
#' varN_fctr_adae <- varN_fctr_reorder(adae)
-#'
+#'
#' # `adae` does not have factor but `varN_fctr_adae` does
#' levels(adae$RACE)
#' levels(varN_fctr_adae$RACE)
-#'
+#'
#' # `get_levels()` either creates the factor or retrieves it
#' get_levels(adae$RACE)
#' get_levels(varN_fctr_adae$RACE)
-get_levels <- function(x) {if(is.factor(x)) levels(x) else sort(unique(x), na.last = TRUE) }
-
+get_levels <- function(x) {
+ if (is.factor(x)) levels(x) else sort(unique(x), na.last = TRUE)
+}
#' %quote%
@@ -271,41 +309,40 @@ get_levels <- function(x) {if(is.factor(x)) levels(x) else sort(unique(x), na.la
#' @return either y or a string
#' @noRd
#'
-`%quote%` <- function(x,y) {
+`%quote%` <- function(x, y) {
if (is.null(x)) {
y
} else {
- paste0("\'",x,"\'") #sQuote(x) # old
+ paste0("\'", x, "\'") #sQuote(x) # old
}
}
-
#' Re-order Factor Levels by VARN
-#'
+#'
#' Function to that looks for VARN counterparts to any character or factor VAR
#' variables in any dataframe and re-orders there factor levels, taking the lead
#' from VARN's numeric guide.
-#'
+#'
#' @param data a dataframe, including one enriched with SAS labels attributes
-#'
+#'
#' @importFrom sjlabelled get_label set_label
-#' @importFrom purrr walk2
-#'
+#' @importFrom purrr walk2
+#'
#' @export
#' @keywords helpers
-#'
+#'
#' @return The data frame after having factor levels re-ordered by VARN
-#'
-#' @examples
+#'
+#' @examples
#' data(adae, package = "tidyCDISC")
-#'
+#'
#' varN_fctr_adae <- varN_fctr_reorder(adae)
-#'
+#'
#' unique(adae[,c("AGEGR1", "AGEGR1N")])
#' levels(adae$AGEGR1)
#' levels(varN_fctr_adae$AGEGR1)
-#'
+#'
#' unique(adae[,c("RACE", "RACEN")])
#' levels(adae$RACE)
#' levels(varN_fctr_adae$RACE)
@@ -315,24 +352,34 @@ varN_fctr_reorder <- function(data) {
# Now to refactor levels in VARN order, if they exist:
# save the variable labels into savelbls vector
savelbls <- sjlabelled::get_label(data)
-
+
# identify all char - numeric variables pairs that need factor re-ordering
cols <- colnames(data)
- non_num_cols <- c(subset_colclasses(data, is.factor),
- subset_colclasses(data, is.character))
- varn <- paste0(non_num_cols,"N")[paste0(non_num_cols,"N") %in% cols]
- varc <- substr(varn,1,nchar(varn) - 1)
-
- if(!rlang::is_empty(varn)){
- for(i in 1:length(varn)){
+ non_num_cols <- c(
+ subset_colclasses(data, is.factor),
+ subset_colclasses(data, is.character)
+ )
+ varn <- paste0(non_num_cols, "N")[paste0(non_num_cols, "N") %in% cols]
+ varc <- substr(varn, 1, nchar(varn) - 1)
+
+ if (!rlang::is_empty(varn)) {
+ for (i in 1:length(varn)) {
this_varn <- as.character(varn[i])
this_varc <- varc[i]
this_varn_sym <- rlang::sym(this_varn)
- this_varc_sym <-rlang::sym(this_varc)
- pref_ord <- data %>% select(one_of(this_varc, this_varn)) %>% distinct() %>% arrange(!!this_varn_sym)
+ this_varc_sym <- rlang::sym(this_varc)
+ pref_ord <- data %>%
+ select(one_of(this_varc, this_varn)) %>%
+ distinct() %>%
+ arrange(!!this_varn_sym)
data <-
- data %>% mutate(!!this_varc_sym := factor(!!this_varc_sym,
- levels = unique(pref_ord[[this_varc]])))
+ data %>%
+ mutate(
+ !!this_varc_sym := factor(
+ !!this_varc_sym,
+ levels = unique(pref_ord[[this_varc]])
+ )
+ )
# return(data)
}
}
@@ -346,15 +393,18 @@ error_handler <- function(e) {
UseMethod("error_handler")
}
+#' @export
error_handler.default <- function(e) {
conditionMessage(e)
}
+#' @export
error_handler.purrr_error_indexed <-
`error_handler.dplyr:::mutate_error` <- function(e) {
- e$parent$message
-}
+ e$parent$message
+ }
+#' @export
error_handler.rlang_error <- function(e) {
stringr::str_replace_all(rlang::cnd_message(e), "\n.*? ", " ")
}
@@ -367,7 +417,7 @@ error_handler.rlang_error <- function(e) {
#' @param data a data.frame, hopefully containing variable label attributes
#' @param var_str you guessed it, the name of a variable inside of `data`, in
#' the form of a string
-#'
+#'
#' @return a string containing a useful label
#'
#' @noRd
@@ -375,8 +425,9 @@ best_lab <- function(data, var_str) {
attr(data[[var_str]], "label") %||% var_str
}
-
-
-
-
-
+#' Dummy function to remove note from R CMD check
+#' @noRd
+dummy <- function() {
+ pkgload::check_dep_version
+ cli::cli_abort
+}
diff --git a/R/utils_read_data.R b/R/utils_read_data.R
new file mode 100644
index 00000000..96a6dc83
--- /dev/null
+++ b/R/utils_read_data.R
@@ -0,0 +1,59 @@
+#' Reads datasets in various formats
+#'
+#' @description Imports data in various formats and cleans column names. Accommodates .txt, .csv, .xls, .xlsx, .sas7bdat
+#'
+#' @param path Character Path to a tabular dataset in .txt, .csv, .xls, .xlsx, or .sas7bdat format
+#' @return A data.frame of the imported dataset.
+#'
+#' @noRd
+#'
+read_data <- function(path) {
+ ext <- fs::path_ext(path)
+
+ data1 <- switch(
+ ext,
+ txt = vroom::vroom(path) |> as.data.frame() |> coerce_ids_to_char(),
+ csv = vroom::vroom(path, delim = ",") |>
+ as.data.frame() |>
+ coerce_ids_to_char(),
+ xls = readxl::read_xls(path) |> as.data.frame() |> coerce_ids_to_char(),
+ xlsx = readxl::read_xlsx(path) |> as.data.frame() |> coerce_ids_to_char(),
+ sas7bdat = haven::read_sas(path) |>
+ haven::zap_formats() |>
+ coerce_ids_to_char(),
+ parquet = arrow::read_parquet(path) |> coerce_ids_to_char(),
+ stop("Unsupported file format")
+ )
+
+ return(data1)
+}
+
+#' Converts known ID variables to characters
+#'
+#' @description Certain file formats have no column type metadata. This function converts known ID variables to character.
+#'
+#' @param data data.frame A dataset to transform
+#' @return A data.frame of the modified dataset.
+#'
+#' @noRd
+#'
+coerce_ids_to_char <- function(data) {
+ data |>
+ dplyr::mutate(
+ dplyr::across(
+ .cols = tidyselect::matches(
+ "STUDYID|USUBJID|SUBJID|SITEID",
+ ignore.case = TRUE
+ ),
+ .fns = \(x) as.character(x)
+ )
+ )
+}
+
+#' Dummy function to remove note from R CMD check
+#' @noRd
+dummy <- function() {
+ broom.helpers::model_get_assign
+ pkgload::check_dep_version
+}
+
diff --git a/_pkgdown.yml b/_pkgdown.yml
index 3f05bcb2..4bd221db 100644
--- a/_pkgdown.yml
+++ b/_pkgdown.yml
@@ -2,11 +2,14 @@ articles:
- title: Getting Started
contents:
- tidyCDISC
+ - getting_started
- title: Blog Posts
navbar: ~
desc: News and Announcements
contents:
- Blog
+ - announcing-tidycdisc-0-0-1-1
+ - announcing-tidycdisc-0-2-1
- title: User Guides
navbar: User Guides
desc: Step-by-step tutorials for each module in the application
diff --git a/data-raw/internal-data.R b/data-raw/internal-data.R
index ff2efab0..07739e88 100644
--- a/data-raw/internal-data.R
+++ b/data-raw/internal-data.R
@@ -1,34 +1,96 @@
pretty_blocks <- tidyr::tibble(
- Pattern = c("MEAN", "FREQ", "CHG", "Y_FREQ", "MAX_FREQ", "NON_MISSING",
- "NESTED_FREQ_DSC", "NESTED_FREQ_ABC"),
- Replacement = c("Descriptive Statistics",
- "Summary Counts",
- "Descriptive Statistics of Change from Baseline",
- "Subject Count for those with 'Y' values",
- "Subject Count for maximum",
- "Subject Count for those with Non Missing values",
- "Subject Count at each variable level, sorted descending by total counts",
- "Subject Count at each variable level, sorted alphabetically by name")
+ Pattern = c(
+ "MEAN",
+ "FREQ",
+ "CHG",
+ "Y_FREQ",
+ "MAX_FREQ",
+ "NON_MISSING",
+ "NESTED_FREQ_DSC",
+ "NESTED_FREQ_ABC"
+ ),
+ Replacement = c(
+ "Descriptive Statistics",
+ "Summary Counts",
+ "Descriptive Statistics of Change from Baseline",
+ "Subject Count for those with 'Y' values",
+ "Subject Count for maximum",
+ "Subject Count for those with Non Missing values",
+ "Subject Count at each variable level, sorted descending by total counts",
+ "Subject Count at each variable level, sorted alphabetically by name"
+ )
)
tg_data <- tidyr::tibble(
USUBJID = 1:10,
- AGE = c(20,30,40,30,40,60,20,30,50,40),
+ AGE = c(20, 30, 40, 30, 40, 60, 20, 30, 50, 40),
PARAMCD = "DIABP",
PARAM = "Diastolic",
SEX = c(rep("F", 5), rep("M", 5)),
COUNTRY = c(rep(c("Canada", "USA"), 5)),
AVISIT = c(rep(c("Week 1", "Week 2"), 5)),
- AVISITN = c(rep(c(1,2), 5)),
- AVAL = c(20,30,40,30,40,60,20,30,50,40),
- CHG = c(20,30,40,30,40,60,20,30,50,40),
- ITTFL = c('Y','Y',rep(NA_character_,8)),
- SAFFL = c('Y','Y','N',rep(NA_character_,7)),
- DTHDT = c(as.Date("2020-01-01"),rep(NA,9)),
- AVISITf1 = factor(c(rep(c("Week 1", "Week 2"), 5)),c("Week 1","Week 2")),
- AVISITf2 = factor(c(rep(c("Week 1", "Week 2"), 5)),c("Week 2","Week 1")),
+ AVISITN = c(rep(c(1, 2), 5)),
+ AVAL = c(20, 30, 40, 30, 40, 60, 20, 30, 50, 40),
+ CHG = c(20, 30, 40, 30, 40, 60, 20, 30, 50, 40),
+ ITTFL = c('Y', 'Y', rep(NA_character_, 8)),
+ SAFFL = c('Y', 'Y', 'N', rep(NA_character_, 7)),
+ DTHDT = c(as.Date("2020-01-01"), rep(NA, 9)),
+ AVISITf1 = factor(c(rep(c("Week 1", "Week 2"), 5)), c("Week 1", "Week 2")),
+ AVISITf2 = factor(c(rep(c("Week 1", "Week 2"), 5)), c("Week 2", "Week 1")),
AVISITf2N = AVISITN
)
-usethis::use_data(pretty_blocks, tg_data, internal = TRUE, overwrite = TRUE)
+# #' ADaM domain variable requirements
+# #'
+# #' This internal object defines required and recommended variables for common ADaM datasets.
+# #' Used by `suggest_adam_domain()` and `validate_adamish()`.
+# #' @keywords internal
+# "adam_requirements"
+
+adam_requirements <- list(
+ ADSL = list(
+ required = c("STUDYID", "USUBJID"),
+ recommended = c("AGE", "SEX", "RACE", "ARM", "TRT01P")
+ ),
+ ADLB = list(
+ required = c("STUDYID", "USUBJID", "PARAM", "PARAMCD", "AVAL", "AVISIT"),
+ recommended = c("AVISITN", "DTYPE", "ABLFL", "ADTM")
+ ),
+ ADVS = list(
+ required = c("STUDYID", "USUBJID", "PARAM", "PARAMCD", "AVAL", "AVISIT"),
+ recommended = c("AVISITN", "ADY", "DTYPE")
+ ),
+ ADQS = list(
+ required = c("STUDYID", "USUBJID", "PARAM", "PARAMCD", "AVAL", "AVISIT"),
+ recommended = c("AVISITN", "ADY")
+ ),
+ ADAE = list(
+ required = c("STUDYID", "USUBJID", "AEDECOD", "AESTDTC"),
+ recommended = c("AESER", "AESEV", "AEACN", "AEOUT")
+ ),
+ ADEG = list(
+ required = c("STUDYID", "USUBJID", "PARAM", "PARAMCD", "AVAL", "AVISIT"),
+ recommended = c("AVISITN", "ADTM")
+ ),
+ ADTTE = list(
+ required = c(
+ "STUDYID",
+ "USUBJID",
+ "PARAMCD",
+ "PARAM",
+ "AVAL",
+ "CNSR",
+ "ADT"
+ ),
+ recommended = c("STARTDT", "ADTM", "AVISIT")
+ )
+)
+
+usethis::use_data(
+ pretty_blocks,
+ tg_data,
+ adam_requirements,
+ internal = TRUE,
+ overwrite = TRUE
+)
diff --git a/dev/03_deploy.R b/dev/03_deploy.R
index 4e36df69..5be9d88a 100644
--- a/dev/03_deploy.R
+++ b/dev/03_deploy.R
@@ -1,12 +1,12 @@
# Building a Prod-Ready, Robust Shiny Application.
-#
-# README: each step of the dev files is optional, and you don't have to
-# fill every dev scripts before getting started.
-# 01_start.R should be filled at start.
+#
+# README: each step of the dev files is optional, and you don't have to
+# fill every dev scripts before getting started.
+# 01_start.R should be filled at start.
# 02_dev.R should be used to keep track of your development during the project.
# 03_deploy.R should be used once you need to deploy your app.
-#
-#
+#
+#
######################################
#### CURRENT FILE: DEPLOY SCRIPT #####
######################################
@@ -35,3 +35,5 @@ golem::add_dockerfile_shinyproxy()
## If you want to deploy to Heroku
golem::add_dockerfile_heroku()
+
+rsconnect::writeManifest()
diff --git a/dev/adam_requirements.R b/dev/adam_requirements.R
new file mode 100644
index 00000000..877f7d4e
--- /dev/null
+++ b/dev/adam_requirements.R
@@ -0,0 +1,47 @@
+# #' ADaM domain variable requirements
+# #'
+# #' This internal object defines required and recommended variables for common ADaM datasets.
+# #' Used by `suggest_adam_domain()` and `validate_adamish()`.
+# #' @keywords internal
+# "adam_requirements"
+
+adam_requirements <- list(
+ ADSL = list(
+ required = c("STUDYID", "USUBJID"),
+ recommended = c("AGE", "SEX", "RACE", "ARM", "TRT01P")
+ ),
+ ADLB = list(
+ required = c("STUDYID", "USUBJID", "PARAM", "PARAMCD", "AVAL", "AVISIT"),
+ recommended = c("AVISITN", "DTYPE", "ABLFL", "ADTM")
+ ),
+ ADVS = list(
+ required = c("STUDYID", "USUBJID", "PARAM", "PARAMCD", "AVAL", "AVISIT"),
+ recommended = c("AVISITN", "ADY", "DTYPE")
+ ),
+ ADQS = list(
+ required = c("STUDYID", "USUBJID", "PARAM", "PARAMCD", "AVAL", "AVISIT"),
+ recommended = c("AVISITN", "ADY")
+ ),
+ ADAE = list(
+ required = c("STUDYID", "USUBJID", "AEDECOD", "AESTDTC"),
+ recommended = c("AESER", "AESEV", "AEACN", "AEOUT")
+ ),
+ ADEG = list(
+ required = c("STUDYID", "USUBJID", "PARAM", "PARAMCD", "AVAL", "AVISIT"),
+ recommended = c("AVISITN", "ADTM")
+ ),
+ ADTTE = list(
+ required = c(
+ "STUDYID",
+ "USUBJID",
+ "PARAMCD",
+ "PARAM",
+ "AVAL",
+ "CNSR",
+ "ADT"
+ ),
+ recommended = c("STARTDT", "ADTM", "AVISIT")
+ )
+)
+
+usethis::use_data(adam_requirements, internal = TRUE, overwrite = TRUE)
\ No newline at end of file
diff --git a/dev/renv_setup.R b/dev/renv_setup.R
new file mode 100644
index 00000000..7b991faa
--- /dev/null
+++ b/dev/renv_setup.R
@@ -0,0 +1,37 @@
+# Fix renv
+Sys.setenv(RENV_CONFIG_PAK_ENABLED = TRUE)
+Sys.setenv(RENV_CONFIG_PPM_ENABLED = TRUE)
+
+Sys.setenv(RENV_CONFIG_PAK_ENABLED = FALSE)
+Sys.setenv(RENV_CONFIG_PPM_ENABLED = FALSE)
+
+renv::init()
+renv::update()
+#pak::pak("usethis")
+usethis::use_package("ggsurvfit")
+#utils::install.packages("https://packagemanager.posit.co/cran/latest/bin/windows/contrib/4.4/jsonlite_2.0.0.zip")
+#utils::install.packages("https://packagemanager.posit.co/cran/latest/bin/windows/contrib/4.4/httpuv_1.6.16.zip")
+#utils::install.packages("https://packagemanager.posit.co/cran/latest/bin/windows/contrib/4.4/ggsurvfit_1.1.0.zip")
+#utils::install.packages("https://packagemanager.posit.co/cran/latest/bin/windows/contrib/4.4/ggplot2_3.5.2.zip")
+pak::pak("devtools")
+usethis::use_package("devtools", type = "Suggests")
+usethis::use_package("arrow")
+usethis::use_package("readxl")
+usethis::use_package("cli")
+usethis::use_package("fs")
+usethis::use_package("data.table")
+usethis::use_package("gtsummary")
+usethis::use_package("broom.helpers")
+usethis::use_package("vroom")
+usethis::use_package("tidyselect")
+# usethis::use_package("arcusTStidy")
+usethis::use_package("tibble")
+usethis::use_package("stringdist")
+
+
+renv::snapshot(type = "explicit")
+
+renv::install("pkgload")
+renv::restore()
+
+rsconnect::writeManifest(appMode = "shiny")
diff --git a/dev/run_dev.R b/dev/run_dev.R
index c8767797..efcd6907 100644
--- a/dev/run_dev.R
+++ b/dev/run_dev.R
@@ -1,5 +1,9 @@
# Sass code compilation
-sass::sass(input = sass::sass_file("inst/app/www/timevis.sass"), output = "inst/app/www/timevis.css", cache = NULL)
+sass::sass(
+ input = sass::sass_file("inst/app/www/timevis.sass"),
+ output = "inst/app/www/timevis.css",
+ cache = NULL
+)
# Set options here
options(golem.app.prod = FALSE) # TRUE = production mode, FALSE = development mode
@@ -12,11 +16,16 @@ golem::detach_all_attached()
# Document and reload your package, which runs these three functions...
golem::document_and_reload()
-# Run the application
-run_app()
+# Run the application
+run_app(launch.browser = TRUE)
# # turn off any options
# options(shiny.autoload.r=NULL)
-
-
+# options(shiny.logLevel = "trace")
+# Profile
+# profvis::profvis({
+# print(
+# run_app(launch.browser = TRUE)
+# )
+# })
diff --git a/inst/WORDLIST b/inst/WORDLIST
index 62b34a48..f41f1073 100644
--- a/inst/WORDLIST
+++ b/inst/WORDLIST
@@ -54,6 +54,7 @@ VARN
VARN's
YYYY
adae
+adamish
adlbc
adsl
adtte
@@ -65,6 +66,7 @@ boxPlot
callModule
checkGroup
checkboxInput
+csv
dev
df
draggable
@@ -75,7 +77,9 @@ dropzone
eventtype
expr
fluidRow
+fo
ggplot
+ggsurvfit
gitHub
github
grey
@@ -96,6 +100,7 @@ pharma
pharmaverse
phuse
pkgs
+plotly
png
pre
px
@@ -105,9 +110,11 @@ riskmetric
sas
selectInput
selectable
+shinyalert
shinyapps
snagit
stan
+stringdist
submodule
tableGen
tibble
@@ -117,6 +124,9 @@ to’s
ui
un
vline
+wordlist
www
+xls
+xlsx
xpt
’s
diff --git a/man/show_adamish_alert.Rd b/man/show_adamish_alert.Rd
new file mode 100644
index 00000000..c2a5c375
--- /dev/null
+++ b/man/show_adamish_alert.Rd
@@ -0,0 +1,17 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/utils_adam.R
+\name{show_adamish_alert}
+\alias{show_adamish_alert}
+\title{Show ADaM Compatibility Alert}
+\usage{
+show_adamish_alert(validation, session)
+}
+\arguments{
+\item{validation}{A list returned from `validate_adamish()`}
+
+\item{session}{The Shiny session (typically `session` from server)}
+}
+\description{
+Display a shinyalert-style popup based on the results of `validate_adamish()`
+}
+\keyword{internal}
diff --git a/man/suggest_adam_column_mapping.Rd b/man/suggest_adam_column_mapping.Rd
new file mode 100644
index 00000000..5cb71afd
--- /dev/null
+++ b/man/suggest_adam_column_mapping.Rd
@@ -0,0 +1,32 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/utils_adam.R
+\name{suggest_adam_column_mapping}
+\alias{suggest_adam_column_mapping}
+\title{Suggest Column Mappings for ADaM}
+\usage{
+suggest_adam_column_mapping(
+ df,
+ validation,
+ string_distance = FALSE,
+ session = NULL,
+ input_ids = NULL
+)
+}
+\arguments{
+\item{df}{A data.frame to evaluate}
+
+\item{validation}{A list returned from `validate_adamish()`}
+
+\item{string_distance}{Logical. Use string distance matching to provide suggestions, requires `stringdist` package}
+
+\item{session}{A Shiny session object (optional).}
+
+\item{input_ids}{A named list of input IDs corresponding to ADaM variable names (optional)}
+}
+\value{
+A named list with best-guess mappings for missing variables
+}
+\description{
+Based on validation results, attempt to suggest which columns to rename to standard ADaM variable names.
+}
+\keyword{internal}
diff --git a/man/suggest_adam_domain.Rd b/man/suggest_adam_domain.Rd
new file mode 100644
index 00000000..f84c849d
--- /dev/null
+++ b/man/suggest_adam_domain.Rd
@@ -0,0 +1,19 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/utils_adam.R
+\name{suggest_adam_domain}
+\alias{suggest_adam_domain}
+\title{Suggest ADaM Domain}
+\usage{
+suggest_adam_domain(df)
+}
+\arguments{
+\item{df}{A data.frame to evaluate.}
+}
+\value{
+A character string fo the most likely domain name (e.g., "ADLB"), or NULL.
+}
+\description{
+Heuristically suggests the most likely ADaM domain for a dataset
+based on the presence of required variables.
+}
+\keyword{internal}
diff --git a/man/validate_adamish.Rd b/man/validate_adamish.Rd
new file mode 100644
index 00000000..a4476ef3
--- /dev/null
+++ b/man/validate_adamish.Rd
@@ -0,0 +1,26 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/utils_adam.R
+\name{validate_adamish}
+\alias{validate_adamish}
+\title{Validate ADaM Structure}
+\usage{
+validate_adamish(df, domain = NULL)
+}
+\arguments{
+\item{df}{A data.frame to validate}
+
+\item{domain}{Optional. A string naming the ADaM domain to validate against.}
+}
+\value{
+A list with:
+- `domain`: inferred or provided domain
+- `is_valid`: logical
+- `missing_required`: character vector
+- `missing_recommended`: character vector
+- `error`: character or NULL
+}
+\description{
+Validates that a dataset conforms to required variables for a given ADaM domain.
+If no domain is supplied, it will attempt to infer one based on variable names.
+}
+\keyword{internal}
diff --git a/manifest.json b/manifest.json
new file mode 100644
index 00000000..a6782d62
--- /dev/null
+++ b/manifest.json
@@ -0,0 +1,6135 @@
+{
+ "version": 1,
+ "locale": "en_US",
+ "platform": "4.4.0",
+ "metadata": {
+ "appmode": "shiny",
+ "primary_rmd": null,
+ "primary_html": null,
+ "content_category": null,
+ "has_parameters": false
+ },
+ "environment": {
+ "r": {
+ "requires": ">= 4.1"
+ }
+ },
+ "packages": {
+ "DT": {
+ "Source": "CRAN",
+ "Repository": "https://packagemanager.posit.co/cran/latest",
+ "description": {
+ "Package": "DT",
+ "Type": "Package",
+ "Title": "A Wrapper of the JavaScript Library 'DataTables'",
+ "Version": "0.33",
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+ "URL": "https://biogen-inc.github.io/IDEAFilter/,\nhttps://github.com/Biogen-Inc/IDEAFilter",
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+ "License": "GPL (>= 2) | file LICENCE",
+ "URL": "https://Matrix.R-forge.R-project.org",
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+ "Date": "2024-10-03",
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+ "Title": "Bitwise Operations",
+ "Description": "Functions for bitwise operations on integer vectors.",
+ "License": "GPL (>= 2)",
+ "URL": "https://github.com/mmaechler/R-bitops",
+ "BugReports": "https://github.com/mmaechler/R-bitops/issues",
+ "NeedsCompilation": "yes",
+ "Packaged": "2024-10-03 09:17:35 UTC; maechler",
+ "Author": "Steve Dutky [aut] (S original; then (after MM's port) revised and\n modified),\n Martin Maechler [cre, aut] (Initial R port; tweaks,\n )",
+ "Maintainer": "Martin Maechler ",
+ "Repository": "RSPM",
+ "Date/Publication": "2024-10-03 23:00:54 UTC",
+ "Encoding": "UTF-8",
+ "Built": "R 4.4.0; x86_64-pc-linux-gnu; 2026-01-29 00:21:35 UTC; unix",
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+ "Title": "Convert Statistical Objects into Tidy Tibbles",
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+ "Description": "Summarizes key information about statistical\n objects in tidy tibbles. This makes it easy to report results, create\n plots and consistently work with large numbers of models at once.\n Broom provides three verbs that each provide different types of\n information about a model. tidy() summarizes information about model\n components such as coefficients of a regression. glance() reports\n information about an entire model, such as goodness of fit measures\n like AIC and BIC. augment() adds information about individual\n observations to a dataset, such as fitted values or influence\n measures.",
+ "License": "MIT + file LICENSE",
+ "URL": "https://broom.tidymodels.org/, https://github.com/tidymodels/broom",
+ "BugReports": "https://github.com/tidymodels/broom/issues",
+ "Depends": "R (>= 3.5)",
+ "Imports": "backports, cli, dplyr (>= 1.0.0), generics (>= 0.0.2), glue,\nlifecycle, purrr, rlang (>= 1.1.0), stringr, tibble (>= 3.0.0),\ntidyr (>= 1.0.0)",
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+ "VignetteBuilder": "knitr",
+ "Config/Needs/website": "tidyverse/tidytemplate",
+ "Encoding": "UTF-8",
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+ "URL": "https://docs.ropensci.org/commonmark/\nhttps://ropensci.r-universe.dev/commonmark",
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+ "URL": "https://cpp11.r-lib.org, https://github.com/r-lib/cpp11",
+ "BugReports": "https://github.com/r-lib/cpp11/issues",
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+ "Description": "The crayon package is now superseded. Please use the 'cli'\n package for new projects. Colored terminal output on terminals that\n support 'ANSI' color and highlight codes. It also works in 'Emacs'\n 'ESS'. 'ANSI' color support is automatically detected. Colors and\n highlighting can be combined and nested. New styles can also be\n created easily. This package was inspired by the 'chalk' 'JavaScript'\n project.",
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+ "BugReports": "https://github.com/r-lib/crayon/issues",
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+ "RemoteEtag": "\"d4491336725fd316a289bc584db363ee\"",
+ "RemotePackaged": "TRUE",
+ "RemoteRef": "isoband",
+ "RemoteRepos": "https://packagemanager.posit.co/cran/latest",
+ "RemotePkgPlatform": "source",
+ "RemoteSha": "0.2.7"
+ }
+ },
+ "jquerylib": {
+ "Source": "CRAN",
+ "Repository": "https://packagemanager.posit.co/cran/latest",
+ "description": {
+ "Package": "jquerylib",
+ "Title": "Obtain 'jQuery' as an HTML Dependency Object",
+ "Version": "0.1.4",
+ "Authors@R": "c(\n person(\"Carson\", \"Sievert\", role = c(\"aut\", \"cre\"), email = \"carson@rstudio.com\", comment = c(ORCID = \"0000-0002-4958-2844\")),\n person(\"Joe\", \"Cheng\", role = \"aut\", email = \"joe@rstudio.com\"),\n person(family = \"RStudio\", role = \"cph\"),\n person(family = \"jQuery Foundation\", role = \"cph\",\n comment = \"jQuery library and jQuery UI library\"),\n person(family = \"jQuery contributors\", role = c(\"ctb\", \"cph\"),\n comment = \"jQuery library; authors listed in inst/lib/jquery-AUTHORS.txt\")\n )",
+ "Description": "Obtain any major version of 'jQuery' (