no old Rd tags - partial

R/Queue.R

@@ -2,7 +2,9 @@
 
 #' @title R6 Class - A First-In-First-Out Abstract Data Type
 #'
-#' @description `r lifecycle::badge("experimental")`\cr
+#' @description
+#' `r lifecycle::badge("experimental")`
+#'
 #' Abstract data type that stores and returns any number of elements.
 #'
 #' A `Queue` object stores all elements in a single vector,

R/call_utils.R

@@ -1,8 +1,10 @@
 #' Checks `varname` argument and convert to call
 #'
 #' Checks `varname` type and parse if it's a `character`
-#' @param varname (`name`, `call` or `character(1)`)\cr
-#'   name of the variable
+#'
+#' @param varname (`name`, `call` or `character(1)`)
+#' name of the variable
+#'
 #' @keywords internal
 #'
 call_check_parse_varname <- function(varname) {
@@ -373,12 +375,9 @@ call_extract_matrix <- function(dataname = ".", row = NULL, column = NULL) {
 
 #' Compose extract call with `$` operator
 #'
-#' @param dataname (`character(1)` or `name`)
-#' name of the object
-#' @param varname (`character(1)` or `name`)
-#' name of the slot in data
-#' @param dollar (`logical(1)`)\cr
-#' whether returned call should use `$` or `[[` operator
+#' @param dataname (`character(1)` or `name`) name of the object
+#' @param varname (`character(1)` or `name`) name of the slot in data
+#' @param dollar (`logical(1)`) whether returned call should use `$` or `[[` operator
 #'
 #' @return `$` or `[[` call
 #'

R/column_functions.R

@@ -8,7 +8,7 @@
 #'
 #' @return A named character vector with the non-key columns of the `data`.
 #'
-#' @references [data_extract_srv]
+#' @references [data_extract_srv()]
 get_dataset_prefixed_col_names <- function(data) {
   if (!is.null(attr(data, "filter_and_columns")$columns) && attr(data, "filter_and_columns")$columns != "") {
     paste(attr(data, "dataname"), attr(data, "filter_and_columns")$columns, sep = ".")

R/data_extract_module.R

@@ -44,36 +44,31 @@ cond_data_extract_single_ui <- function(ns, single_data_extract_spec) {
 #'
 #' @description `r lifecycle::badge("experimental")`
 #' This functionality should be used in the encoding panel of your `teal` app.
-#' It will allow app-developers to specify a [data_extract_spec] object.
+#' It will allow app-developers to specify a [data_extract_spec()] object.
 #' This object should be used to `teal` module variables being filtered data
 #' from `CDISC` datasets. You can use this function in the same way as any
-#' [shiny module](https://shiny.rstudio.com/articles/modules.html) UI.
+#' [`shiny module`](https://shiny.rstudio.com/articles/modules.html) UI.
 #' The corresponding server module can be found in [data_extract_srv()].
 #'
 #' @param id (`character`) shiny input unique identifier
 #' @param label (`character`) Label above the data extract input
 #' @param data_extract_spec (`list` of `data_extract_spec`)
-#'  This is the outcome of listing [data_extract_spec]
-#'  constructor calls.
+#' This is the outcome of listing [data_extract_spec()] constructor calls.
 #' @param is_single_dataset (`logical`) FALSE to display the dataset widget
 #'
 #' @return shiny [shiny::selectInput]`s` that allow to define how to extract data from
 #' a specific dataset. The input elements will be returned inside a [shiny::div] container.
 #'
 #' There are three inputs that will be rendered
-#' \enumerate{
-#'  \item{Dataset select}{ Optional. If more than one [data_extract_spec] is handed over
-#'   to the function, a shiny [shiny::selectInput] will be rendered. Else just the name
-#'   of the dataset is given.
-#'  }
-#'  \item{Filter Panel }{Optional. If the [data_extract_spec] contains a
-#'   filter element a shiny [shiny::selectInput] will be rendered with the options to
-#'   filter the dataset.
-#'  }
-#'  \item{Select panel }{A shiny [shiny::selectInput] to select columns from the dataset to
-#'   go into the analysis.
-#'   }
-#' }
+#'
+#' 1. Dataset select Optional. If more than one [data_extract_spec] is handed over
+#' to the function, a shiny [shiny::selectInput] will be rendered. Else just the name
+#' of the dataset is given.
+#' 2. Filter Panel Optional. If the [data_extract_spec] contains a
+#' filter element a shiny [shiny::selectInput] will be rendered with the options to
+#' filter the dataset.
+#' 3. Select panel A shiny [shiny::selectInput] to select columns from the dataset to
+#' go into the analysis.
 #'
 #' The output can be analyzed using `data_extract_srv(...)`.
 #'
@@ -252,32 +247,30 @@ check_data_extract_spec_react <- function(datasets, data_extract) {
 #' Extraction of the selector(s) details
 #'
 #' @description `r lifecycle::badge("stable")`
+#'
 #' Extracting details of the selection(s) in [data_extract_ui] elements.
 #'
 #' @inheritParams shiny::moduleServer
-#' @param datasets (`FilteredData` or `list` of `reactive` or non-`reactive` `data.frame`)\cr
-#'  object containing data either in the form of `FilteredData` or as a list of `data.frame`.
-#'  When passing a list of non-reactive `data.frame` objects, they are converted to reactive `data.frame`s internally.
-#'  When passing a list of reactive or non-reactive `data.frame` objects, the argument `join_keys` is required also.
-#' @param data_extract_spec (`data_extract_spec` or a list of `data_extract_spec`)\cr
-#'  A list of data filter and select information constructed by [data_extract_spec].
+#' @param datasets (`FilteredData` or `list` of `reactive` or non-`reactive` `data.frame`)
+#' object containing data either in the form of `FilteredData` or as a list of `data.frame`.
+#' When passing a list of non-reactive `data.frame` objects, they are converted to reactive `data.frame`s internally.
+#' When passing a list of reactive or non-reactive `data.frame` objects, the argument `join_keys` is required also.
+#' @param data_extract_spec (`data_extract_spec` or a list of `data_extract_spec`)
+#' A list of data filter and select information constructed by [data_extract_spec].
 #' @param ...
 #' an additional argument `join_keys` is required when `datasets` is a list of `data.frame`.
 #' It shall contain the keys per dataset in `datasets`.
 #'
-#' @return
-#' A reactive `list` containing following fields:
-#'
-#' \itemize{
-#'   \item{`filters`: }{A list with the information on the filters that are applied to the data set.}
-#'   \item{`select`: }{The variables that are selected from the dataset.}
-#'   \item{`always_selected`: }{The column names from the data set that should always be selected.}
-#'   \item{`reshape`: }{Whether reshape long to wide should be applied or not.}
-#'   \item{`dataname`: }{The name of the data set.}
-#'   \item{`internal_id`: }{The `id` of the corresponding shiny input element.}
-#'   \item{`keys`: }{The names of the columns that can be used to merge the data set.}
-#'   \item{`iv`:}{A `shinyvalidate::InputValidator` containing `validator` for this `data_extract`}
-#' }
+#' @return A reactive `list` containing following fields:
+#'
+#' * `filters`: A list with the information on the filters that are applied to the data set.
+#' * `select`: The variables that are selected from the dataset.
+#' * `always_selected`: The column names from the data set that should always be selected.
+#' * `reshape`: Whether reshape long to wide should be applied or not.
+#' * `dataname`: The name of the data set.
+#' * `internal_id`: The `id` of the corresponding shiny input element.
+#' * `keys`: The names of the columns that can be used to merge the data set.
+#' * `iv`: A `shinyvalidate::InputValidator` containing `validator` for this `data_extract`
 #'
 #' @references [data_extract_srv]
 #'

R/data_extract_spec.R

@@ -12,23 +12,21 @@
 #' @rdname data_extract_spec
 #'
 #' @section Module Development:
-#' \describe{
 #' `teal.transform` uses this object to construct a UI element in a module.
-#' }
 #'
-#' @param dataname (`character`)\cr
-#'  The name of the dataset to be extracted.
-#' @param select (`NULL`, `select_spec`-S3 class or `delayed_select_spec`)\cr
-#'  Columns to be selected from the input dataset
-#'  mentioned in `dataname`. The setup can be created using [select_spec] function.
-#' @param filter (`NULL` or `filter_spec` or its respective delayed version)\cr
-#'  Setup of the filtering of key columns inside the dataset.
-#'  This setup can be created using the [filter_spec] function.
-#'  Please note that if both select and filter are set to NULL, then the result will be a filter spec UI with all
-#'  variables as possible choices and a select spec with multiple set to `TRUE`.
-#' @param reshape (`logical`)\cr
-#'  whether reshape long to wide. Note that it will be used only in case of long dataset with multiple
-#'  keys selected in filter part.
+#' @param dataname (`character`)
+#' The name of the dataset to be extracted.
+#' @param select (`NULL`, `select_spec`-S3 class or `delayed_select_spec`)
+#' Columns to be selected from the input dataset
+#' mentioned in `dataname`. The setup can be created using [select_spec] function.
+#' @param filter (`NULL` or `filter_spec` or its respective delayed version)
+#' Setup of the filtering of key columns inside the dataset.
+#' This setup can be created using the [filter_spec] function.
+#' Please note that if both select and filter are set to NULL, then the result will be a filter spec UI with all
+#' variables as possible choices and a select spec with multiple set to `TRUE`.
+#' @param reshape (`logical`)
+#' whether reshape long to wide. Note that it will be used only in case of long dataset with multiple
+#' keys selected in filter part.
 #'
 #' @examples
 #' adtte_filters <- filter_spec(

R/get_dplyr_call.R

@@ -4,8 +4,8 @@
 #' same selector - same dataset, same filter configuration and same reshape status
 #' @inheritParams get_merge_call
 #'
-#' @return (\code{list}) simplified selectors with aggregated set of filters,
-#'   selections, reshapes etc. All necessary data for merging
+#' @return (`list`) simplified selectors with aggregated set of filters,
+#' selections, reshapes etc. All necessary data for merging
 #' @keywords internal
 #'
 get_dplyr_call_data <- function(selector_list, join_keys = teal.data::join_keys()) {
@@ -121,12 +121,12 @@ get_dplyr_call_data <- function(selector_list, join_keys = teal.data::join_keys(
 #'
 #' Parse filter, select, rename and reshape call
 #' @inheritParams get_dplyr_call_data
-#' @param idx optional (\code{integer}) current selector index in all selectors list
-#' @param dplyr_call_data (\code{list}) simplified selectors with aggregated set of filters,
-#'   selections, reshapes etc. All necessary data for merging
+#' @param idx optional (`integer`) current selector index in all selectors list
+#' @param dplyr_call_data (`list`) simplified selectors with aggregated set of filters,
+#' selections, reshapes etc. All necessary data for merging
 #' @param data (`NULL` or named `list`).
 #'
-#' @return (\code{call}) filter, select, rename and reshape call
+#' @return (`call`) filter, select, rename and reshape call
 #'
 #' @examples
 #' # use non-exported function from teal.transform
@@ -246,11 +246,11 @@ get_dplyr_call <- function(selector_list,
   return(final_call)
 }
 
-#' Parse \code{dplyr} select call
+#' Parse `dplyr` select call
 #'
-#' @param select (\code{character}) vector of selected column names
+#' @param select (`character`) vector of selected column names
 #'
-#' @return (\code{call}) \code{dplyr} select call
+#' @return (`call`) `dplyr` select call
 #'
 #' @examples
 #' # use non-exported function from teal.transform
@@ -269,12 +269,12 @@ get_select_call <- function(select) {
   as.call(c(list(quote(dplyr::select)), lapply(select, as.name)))
 }
 
-#' Returns \code{dplyr} filter call
+#' Returns `dplyr` filter call
 #'
-#' @param filter (\code{list}) Either list of lists or list with \code{select} and \code{selected} items.
-#' @param dataname (\code{NULL} or \code{character}) name of dataset.
-#' @param datasets (\code{NULL} or \code{named `list`}).
-#' @return (\code{call}) \code{dplyr} filter call
+#' @param filter (`list`) Either list of lists or list with `select` and `selected` items.
+#' @param dataname (`NULL` or `character`) name of dataset.
+#' @param datasets (`NULL` or named `list`).
+#' @return (`call`) `dplyr` filter call
 #'
 #' @examples
 #' # use non-exported function from teal.transform
@@ -408,13 +408,14 @@ rename_duplicated_cols <- function(x, internal_id, selected_cols, all_cols) {
   )
 }
 
-#' Returns \code{dplyr} rename call
+#' Returns `dplyr` rename call
 #'
 #' Rename is used only if there are duplicated columns
 #'
 #' @inheritParams get_dplyr_call
 #'
-#' @return (\code{call}) \code{dplyr} rename call
+#' @return (`call`) `dplyr` rename call
+#'
 #' @references get_rename_dict
 #'
 #' @examples
@@ -489,11 +490,11 @@ get_rename_call <- function(selector_list = list(),
   as.call(append(quote(dplyr::rename), internal))
 }
 
-#' Returns \code{dplyr} reshape call
+#' Returns `dplyr` reshape call
 #'
 #' @inheritParams get_dplyr_call
 #'
-#' @return (\code{list}) list of multiple \code{dplyr} calls that reshape data
+#' @return (`list`) list of multiple `dplyr` calls that reshape data
 #'
 #' @examples
 #' # use non-exported function from teal.transform
@@ -573,7 +574,7 @@ get_reshape_call <- function(selector_list = list(),
 #' Get pivot longer  columns
 #'
 #' Get values names which are spread into columns.
-#' @param selector one element of selector_list obtained by \code{get_dplyr_call_data}.
+#' @param selector one element of selector_list obtained by `get_dplyr_call_data`.
 #' @keywords internal
 #'
 get_pivot_longer_col <- function(selector) {
@@ -584,7 +585,7 @@ get_pivot_longer_col <- function(selector) {
 #' Get unite columns
 #'
 #' Get key names which spreads values into columns. Reshape is done only
-#' on keys which are in \code{filter_spec}.
+#' on keys which are in `filter_spec`.
 #' @inheritParams get_pivot_longer_col
 #' @keywords internal
 #'

R/get_merge_call.R

@@ -145,18 +145,16 @@ get_merge_key_grid <- function(selector_list, join_keys = teal.data::join_keys()
 #' @param selector_to (`list`) `data_extract_srv`
 #' @param key_from (`character`) keys used in the first selector while joining
 #'
-#' @details This function covers up to now 4 cases
-#' \itemize{
-#'  \item{dataset without parent }{ Primary keys are returned}
-#'  \item{dataset source = dataset target}{
-#'    The primary keys subtracted of all key columns that
-#'    get purely filtered. This means just one value would
-#'    be left after filtering inside this column. Then it
-#'    can be taken out.
-#'  }
-#'  \item{target `dataname` is parent }{ foreign keys}
-#'  \item{any other case }{foreign keys}
-#' }
+#' @details
+#' This function covers up to now 4 cases
+#'
+#' * dataset without parent: Primary keys are returned
+#' * dataset source = dataset target:
+#' The primary keys subtracted of all key columns that get purely filtered.
+#' This means just one value would be left after filtering inside this column.
+#' Then it can be taken out.
+#' * target `dataname` is parent foreign keys
+#' * any other case foreign keys
 #'
 #' @return (`character`)
 #' @keywords internal
@@ -335,10 +333,11 @@ get_dropped_filters <- function(selector) {
 
 #' Gets the relabel call
 #'
-#' @description `r lifecycle::badge("stable")`
+#' `r lifecycle::badge("stable")`
+#'
 #' @inheritParams merge_datasets
-#' @param columns_source \code{named list}\cr
-#'  where names are column names, values are labels + additional attribute `dataname`
+#' @param columns_source named `list`
+#' where names are column names, values are labels + additional attribute `dataname`
 #'
 #' @return (`call`) to relabel `dataset` and assign to `anl_name`.
 #'
@@ -404,9 +403,11 @@ get_anl_relabel_call <- function(columns_source, datasets, anl_name = "ANL") {
 #' Create relabel call from named character
 #'
 #' @description `r lifecycle::badge("stable")`
+#'
 #' Function creates relabel call from named character.
-#' @param labels (`named character`)\cr
-#'   where name is name is function argument name and value is a function argument value.
+#'
+#' @param labels (`named character`)
+#' where name is name is function argument name and value is a function argument value.
 #'
 #' @return (`call`) object with relabel step
 #' @examples

R/merge_datasets.R

@@ -261,9 +261,12 @@ merge_selectors <- function(selector_list) {
 #' Validate data_extracts in merge_datasets
 #'
 #' Validate selected inputs from data_extract before passing to data_merge to avoid
-#' \code{dplyr} errors or unexpected results
+#' `dplyr` errors or unexpected results
+#'
 #' @inheritParams merge_datasets
-#' @return \code{NULL} if check is successful
+#'
+#' @return `NULL` if check is successful
+#'
 #' @keywords internal
 #'
 check_data_merge_selectors <- function(selector_list) {