Merge branch 'release/0.1.0.9001'

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: redcapfiller
 Type: Package
 Title: Fill a REDCap Project with Generated Data
-Version: 0.1.0
+Version: 0.1.0.9001
 Authors@R: c(
   person("Philip", "Chase",
         email = "[email protected]",
@@ -37,6 +37,8 @@ Suggests:
 Config/testthat/edition: 3
 Imports: 
     dplyr,
+    lorem,
+    purrr,
     rlang,
     tidyr
 RoxygenNote: 7.3.2

NAMESPACE

@@ -2,5 +2,7 @@
 
 export(get_long_categorical_field_response_values)
 export(get_long_categorical_field_responses)
+export(get_long_text_field_values)
+export(get_long_text_fields)
 export(get_one_rectangle_of_values)
 importFrom(rlang,.data)

NEWS.md

@@ -0,0 +1,5 @@
+# redcapfiller (0.1.0.9001)
+
+* Supports filling of checkbox, dropdown, radio, yes/no, True/False and unvalidated text fields on classic projects.
+* Provides a proof_of_concept.R script with write-back to a demonstration project and properly managed secrets.
+

R/get_long_categorical_field_response_values.R

@@ -16,13 +16,22 @@
 #' }
 get_long_categorical_field_response_values <- function(long_categorical_field_responses) {
   single_value_responses <- long_categorical_field_responses |>
+    # Filter for any categorical field_type
+    dplyr::filter(.data$field_type %in% c(
+      "checkbox",
+      "dropdown",
+      "radio",
+      "yesno"
+    )) |>
+    # Filter for anything but checkbox fields
     dplyr::filter(.data$field_type != "checkbox") |>
     dplyr::group_by(.data$field_name) |>
     dplyr::slice_sample(n = 1, weight_by = .data$weight) |>
     dplyr::ungroup()
 
   multi_value_responses <-
     long_categorical_field_responses |>
+    # Filter for checkbox fields
     dplyr::filter(.data$field_type == "checkbox") |>
     dplyr::group_by(.data$field_group) |>
     dplyr::slice_sample(prop = 0.5, weight_by = .data$weight) |>

R/get_long_categorical_field_responses.R

@@ -25,11 +25,16 @@ get_long_categorical_field_responses <- function(metadata) {
   balanced_responses <-
     metadata |>
     # include only categorical field types
-    dplyr::filter(.data$field_type %in% c("checkbox", "radio", "dropdown")) |>
+    dplyr::filter(.data$field_type %in% c("checkbox", "radio", "dropdown", "truefalse", "yesno")) |>
     # excluding anything displayed by branching logic
     dplyr::filter(is.na(.data$branching_logic)) |>
     # narrow our focus to the required columns
     dplyr::select(c("field_name", "form_name", "field_type", "select_choices_or_calculations")) |>
+    dplyr::mutate(select_choices_or_calculations = dplyr::case_when(
+      .data$field_type == "truefalse" ~ "1, True|0, False",
+      .data$field_type == "yesno" ~ "1, Yes|0, No",
+      TRUE ~ .data$select_choices_or_calculations)
+      ) |>
     # separate responses
     tidyr::separate_longer_delim("select_choices_or_calculations", delim = stringr::regex("\\s?\\|\\s?")) |>
     # separate response_codes from response_labels

R/get_long_text_field_values.R

@@ -0,0 +1,48 @@
+#' @title generate text field values
+#' @description
+#' Provide a set of values for each field in the output of
+#' `get_long_text_fields`
+#'
+#' @param long_text_fields a long data set of text
+#'   fields, their parameters, and weights.
+#'
+#' @return a long dataframe of text field values with one row for each value set.
+#' @export
+#'
+#' @examples
+#' \dontrun{
+#' get_long_text_field_values(long_text_fields)
+#' }
+get_long_text_field_values <- function(long_text_fields) {
+  tvt_na <- function(df) {
+    df |>
+      dplyr::filter(.data$tvt == "tvt_na") |>
+      dplyr::mutate(value = replicate(length(.data$field_name), lorem::ipsum_words(round(stats::rnorm(mean = .data$mean, sd = .data$sd, n = 1)))))
+    # |>
+    #   dplyr::slice_sample(prop = 0.5, weight_by = .data$weight)
+  }
+
+  tvt_types <- c(
+    "tvt_na"
+  )
+
+  process_one_text_validation_type <- function(my_tvt, df) {
+    # exec (run) the function named in `alert` on the rows of data that
+    #  have an alert_type of `alert`
+    result <- rlang::exec(my_tvt, df |>
+      dplyr::filter(.data$tvt == my_tvt))
+    return(result)
+  }
+
+  text_field_values <-
+    purrr::map(tvt_types,
+               process_one_text_validation_type,
+               long_text_fields |> dplyr::filter(.data$field_type == "text")
+               ) |>
+    dplyr::bind_rows()
+
+  result <- text_field_values |>
+    dplyr::select("field_name", "value")
+
+  return(result)
+}

R/get_long_text_fields.R

@@ -0,0 +1,85 @@
+#' @title Get every text field response from a REDCap data dictionary
+#'
+#' @description
+#' Given a REDCap data dictionary, enumerate every text field in that data dictionary and return a dataset with default weights
+#'
+#' @param metadata A REDCap data dictionary
+#'
+#' @returns a dataframe with these columns
+#' \describe{
+#'   \item{field_name}{REDCap field name}
+#'   \item{form_name}{REDCap form name}
+#'   \item{field_type}{REDCap field type}
+#'   \item{text_validation_type}{REDCap text validation type}
+#'   \item{text_validation_min}{REDCap text validation min}
+#'   \item{text_validation_max}{REDCap text validation max}
+#'   \item{tvt}{text validation type function name}
+#'   \item{weight}{a default weight for the field}
+#'   \item{mean}{mean of data to be generated}
+#'   \item{sd}{standard deviation of data to be generated}
+#' }
+#' @export
+#'
+#' @examples
+#' \dontrun{
+#' long_text_fields <-
+#'   get_long_text_fields(metadata_to_populate)
+#' }
+get_long_text_fields <- function(metadata) {
+  text_fields <-
+    metadata |>
+    # include only categorical field types
+    dplyr::filter(.data$field_type %in% c("text")) |>
+    # excluding anything displayed by branching logic
+    dplyr::filter(is.na(.data$branching_logic)) |>
+    # narrow our focus to the required columns
+    dplyr::select(c(
+      "field_name",
+      "form_name",
+      "field_type",
+      "text_validation_type_or_show_slider_number",
+      "text_validation_min",
+      "text_validation_max"
+    )) |>
+    dplyr::rename(text_validation_type = "text_validation_type_or_show_slider_number") |>
+    dplyr::mutate(tvt = dplyr::case_when(
+      is.na(.data$text_validation_type) ~ "tvt_na",
+      TRUE ~ "tvt_unsupported"
+    )) |>
+    # set weights for each response
+    dplyr::mutate(weight = 100)
+
+  tvt_na <- function(text_fields) {
+    result <-
+      text_fields |>
+      dplyr::filter(is.na(.data$text_validation_type)) |>
+      dplyr::mutate(mean = 1.5, sd = 0.8)
+    return(result)
+  }
+
+  tvt_unsupported <- function(text_fields) {
+    result <-
+      text_fields |>
+      dplyr::filter(F)
+    return(result)
+  }
+
+  tvt_types <- c(
+    "tvt_na",
+    "tvt_unsupported"
+  )
+
+  process_one_text_validation_type <- function(my_tvt, df) {
+    # exec (run) the function named in `alert` on the rows of data that
+    #  have an alert_type of `alert`
+    result <- rlang::exec(my_tvt, df |>
+      dplyr::filter(.data$tvt == my_tvt))
+    return(result)
+  }
+
+  text_fields_and_weights <-
+    purrr::map(tvt_types, process_one_text_validation_type, text_fields) |>
+    dplyr::bind_rows()
+
+  return(text_fields_and_weights)
+}

R/get_one_rectangle_of_values.R

@@ -3,7 +3,8 @@
 #' @param one_record_id a single record_id
 #' @param record_id_name the column name the record_id should be returned in
 #' @param forms_to_fill the forms to fill for this rectangle
-#' @param long_categorical_field_responses the output of `get_long_categorical_field_responses()`
+#' @param long_fields_and_responses the output of `get_long_*_fields` and
+#'   `get_long_categorical_field_responses_responses` functions
 #'
 #' @returns a rectangle of data with appropriate REDCap identifiers ready to write to REDCap
 #' @export
@@ -17,7 +18,7 @@ get_one_rectangle_of_values <- function(
     one_record_id = 1,
     record_id_name,
     forms_to_fill,
-    long_categorical_field_responses) {
+    long_fields_and_responses) {
   # Build tibble of static REDCap identifiers
   redcap_identifiers <- dplyr::tibble(
     record_id = one_record_id
@@ -26,14 +27,25 @@ get_one_rectangle_of_values <- function(
   # fix the first column name
   names(redcap_identifiers) <- record_id_name
 
+  value_getter_functions <- c(
+    "get_long_categorical_field_response_values",
+    "get_long_text_field_values"
+  )
+
+  process_one_value_getter <- function(value_getter, df) {
+    rlang::exec(value_getter, df)
+  }
+
   # pick values for one record on one event
   #    ...by binding the output of each field_type / field_validation function
-  all_responses <- dplyr::bind_rows(
-    get_long_categorical_field_response_values(
-      long_categorical_field_responses |>
+  all_responses <-
+    purrr::map(
+      value_getter_functions,
+      process_one_value_getter,
+      long_fields_and_responses |>
         dplyr::filter(.data$form_name %in% forms_to_fill)
-    )
-  )
+    ) |>
+    dplyr::bind_rows()
 
   # prefix responses with redcap fields
   long_result <- dplyr::bind_cols(

README.md

@@ -19,7 +19,7 @@ Copy [proof_of_concept.R](https://github.com/ctsit/redcapfiller/blob/main/proof_
 
 ### Limitations
 
-REDCap Filler does not yet understand all the dimensions of a modern REDCap project. It can fill the categorical fields checkbox, dropdown, and radio. It ignores all other field types and will not attempt to fill them. It doesn't even know how to fill a yesno field. :-( Filler only knows how to fill classic projects without repeating forms or events. It does not honor form display logic and ignores all fields governed by branching logic.
+REDCap Filler does not yet understand all the dimensions of a modern REDCap project. It can fill the categorical fields. It can fill unvalidated text fields. It ignores all other field types and will not attempt to fill them. It doesn't even know how to fill a date field. :-( Filler only knows how to fill classic projects without repeating forms or events. It does not honor form display logic and ignores all fields governed by branching logic.
 
 Focusing more on what Filler _can_ do, the first release milestone will support these features:
 

example.env

@@ -1,2 +1,2 @@
 filler_demo_pid=15321
-path_credential = ~/credentials.csv
+path_credential=~/credentials.csv