updated

NAMESPACE

@@ -20,7 +20,6 @@ export(strsplitx)
 importFrom(REDCapR,redcap_event_instruments)
 importFrom(REDCapR,redcap_metadata_read)
 importFrom(REDCapR,redcap_read)
-importFrom(dplyr,left_join)
 importFrom(keyring,key_get)
 importFrom(keyring,key_list)
 importFrom(keyring,key_set)

NEWS.md

@@ -4,7 +4,9 @@
 
 * Fix: `ds2dd()`: uses correct default dd column names. Will be deprecated.
 
-* Fix: `easy_redcap()`: fixed to actually allow project naming. also specifically asks for uri.
+* Fix: `easy_redcap()`: fixed to actually allow project naming. also specifically asks for uri. widening updated to work.
+
+* Fix: `redcap_wider()`: updated to accept more formats and allow handling of simple projects without repeating instruments and not longitudinal.
 
 * Fix: `read_redcap_tables()`: now handles non-longitudinal project without repeatable instruments.
 

R/REDCap_split.r

@@ -92,13 +92,13 @@ REDCap_split <- function(records,
   metadata <-
     as.data.frame(process_user_input(metadata))
 
-  # Process repeat instrument names to match the redcap naming
-  records$redcap_repeat_instrument <- clean_redcap_name(records$redcap_repeat_instrument)
-
-
   # Get the variable names in the dataset
   vars_in_data <- names(records)
 
+  # Process repeat instrument names to match the redcap naming
+  if (is.repeated_longitudinal(records)){
+  records$redcap_repeat_instrument <- clean_redcap_name(records$redcap_repeat_instrument)
+
   # Match arg for forms
   forms <- match.arg(forms, c("repeating", "all"))
 
@@ -116,6 +116,7 @@ REDCap_split <- function(records,
       as.character(records$redcap_repeat_instrument)
     )
   }
+  }
 
   # Standardize variable names for metadata
   # names(metadata) <- metadata_names
@@ -138,6 +139,8 @@ REDCap_split <- function(records,
     )
   )
 
+
+
   if ("redcap_repeat_instrument" %in% vars_in_data) {
     # Variables to be at the beginning of each repeating instrument
     repeat_instrument_fields <- grep("^redcap_repeat.*",
@@ -196,5 +199,5 @@ REDCap_split <- function(records,
   }
 
   out
-
 }
+

R/easy_redcap.R

@@ -10,7 +10,7 @@ get_api_key <- function(key.name) {
   if (key.name %in% keyring::key_list()$service) {
     keyring::key_get(service = key.name)
   } else {
-    keyring::key_set(service = key.name, prompt = "Write REDCap API key:")
+    keyring::key_set(service = key.name, prompt = "Provide REDCap API key:")
     keyring::key_get(service = key.name)
   }
 }
@@ -19,41 +19,25 @@ get_api_key <- function(key.name) {
 #' Secure API key storage and data acquisition in one
 #'
 #' @param project.name The name of the current project (for key storage with
-#' `keyring::key_set()`)
+#' `keyring::key_set()`, using the default keyring)
 #' @param widen.data argument to widen the exported data
 #' @param uri REDCap database API uri
 #' @param ... arguments passed on to `REDCapCAST::read_redcap_tables()`
 #'
 #' @return data.frame or list depending on widen.data
-#' @importFrom purrr reduce
-#' @importFrom dplyr left_join
 #' @export
 easy_redcap <- function(project.name, widen.data=TRUE, uri, ...) {
   key <- get_api_key(key.name = paste0(project.name, "_REDCAP_API"))
 
   out <- read_redcap_tables(
-    token = key,
     uri = uri,
+    token = key,
     ...
   )
 
-  all_names <- out |>
-    lapply(names) |>
-    Reduce(c, x = _) |>
-    unique()
-
   if (widen.data){
-    if (!any(c("redcap_event_name", "redcap_repeat_instrument") %in%
-      all_names)) {
-      if (length(out) == 1) {
-        out <- out[[1]]
-      } else {
-        out <- out |> purrr::reduce(dplyr::left_join)
-      }
-    } else {
     out <- out |> redcap_wider()
   }
-  }
 
   out
 }

R/read_redcap_tables.R

@@ -31,20 +31,12 @@ read_redcap_tables <- function(uri,
                                events = NULL,
                                forms = NULL,
                                raw_or_label = "label",
-                               split_forms = "all",
+                               split_forms = "all") {
-                               generics = c(
-                                 "redcap_event_name",
-                                 "redcap_repeat_instrument",
-                                 "redcap_repeat_instance"
-                               )) {
-
-
   # Getting metadata
   m <-
     REDCapR::redcap_metadata_read(redcap_uri = uri, token = token)[["data"]]
 
   if (!is.null(fields)) {
-
     fields_test <- fields %in% unique(m$field_name)
 
     if (any(!fields_test)) {
@@ -55,7 +47,6 @@ read_redcap_tables <- function(uri,
 
 
   if (!is.null(forms)) {
-
     forms_test <- forms %in% unique(m$form_name)
 
     if (any(!forms_test)) {
@@ -65,8 +56,10 @@ read_redcap_tables <- function(uri,
   }
 
   if (!is.null(events)) {
-    arm_event_inst <- REDCapR::redcap_event_instruments(redcap_uri = uri,
+    arm_event_inst <- REDCapR::redcap_event_instruments(
-                                                        token = token)
+      redcap_uri = uri,
+      token = token
+    )
 
     event_test <- events %in% unique(arm_event_inst$data$unique_event_name)
 
@@ -95,28 +88,17 @@ read_redcap_tables <- function(uri,
   }
 
   # Processing metadata to reflect focused dataset
-  if (!is.null(c(fields,forms,events))){
   m <- focused_metadata(m, names(d))
-  }
 
 
-
-  if (any(generics %in% names(d))){
     # Splitting
-  l <- REDCap_split(d,
+    out <- REDCap_split(d,
       m,
       forms = split_forms,
-                    primary_table_name = "")
+      primary_table_name = ""
+    )
 
-  # Sanitizing split list by removing completely empty rows apart from colnames
+    sanitize_split(out)
-  # in "generics"
-  sanitize_split(l,c(names(d)[1],generics))
-  } else {
-    # If none of generics are present, the data base is not longitudinal,
-    # and does not have repeatable events, and therefore splitting does not
-    # make sense. But now we handle that as well.
-    d
-  }
 
 }
 

R/redcap_wider.R

@@ -5,40 +5,70 @@ utils::globalVariables(c("redcap_wider",
 #' @title Redcap Wider
 #' @description Converts a list of REDCap data frames from long to wide format.
 #' Handles longitudinal projects, but not yet repeated instruments.
-#' @param list A list of data frames.
+#' @param data A list of data frames.
 #' @param event.glue A dplyr::glue string for repeated events naming
 #' @param inst.glue A dplyr::glue string for repeated instruments naming
 #' @return The list of data frames in wide format.
 #' @export
 #' @importFrom tidyr pivot_wider
 #' @importFrom tidyselect all_of
+#' @importFrom purrr reduce
 #'
 #' @examples
-#' list <- list(data.frame(record_id = c(1,2,1,2),
+#' # Longitudinal
+#' list1 <- list(data.frame(record_id = c(1,2,1,2),
 #' redcap_event_name = c("baseline", "baseline", "followup", "followup"),
 #' age = c(25,26,27,28)),
 #' data.frame(record_id = c(1,2),
 #' redcap_event_name = c("baseline", "baseline"),
 #' gender = c("male", "female")))
-#' redcap_wider(list)
+#' redcap_wider(list1)
+#' # Simpel with two instruments
+#' list2 <- list(data.frame(record_id = c(1,2),
+#' age = c(25,26)),
+#' data.frame(record_id = c(1,2),
+#' gender = c("male", "female")))
+#' redcap_wider(list2)
+#' # Simple with single instrument
+#' list3 <- list(data.frame(record_id = c(1,2),
+#' age = c(25,26)))
+#' redcap_wider(list3)
+#' # Longitudinal with repeatable instruments
+#' list4 <- list(data.frame(record_id = c(1,2,1,2),
+#' redcap_event_name = c("baseline", "baseline", "followup", "followup"),
+#' age = c(25,26,27,28)),
+#' data.frame(record_id = c(1,1,1,1,2,2,2,2),
+#' redcap_event_name = c("baseline", "baseline", "followup", "followup",
+#' "baseline", "baseline", "followup", "followup"),
+#' redcap_repeat_instrument = "walk",
+#' redcap_repeat_instance=c(1,2,1,2,1,2,1,2),
+#' dist = c(40, 32, 25, 33, 28, 24, 23, 36)),
+#' data.frame(record_id = c(1,2),
+#' redcap_event_name = c("baseline", "baseline"),
+#' gender = c("male", "female")))
+#'redcap_wider(list4)
 redcap_wider <-
-  function(list,
+  function(data,
            event.glue = "{.value}_{redcap_event_name}",
            inst.glue = "{.value}_{redcap_repeat_instance}") {
-    all_names <- unique(do.call(c, lapply(list, names)))
 
-    if (!any(c("redcap_event_name", "redcap_repeat_instrument") %in%
+    if (!is.repeated_longitudinal(data)) {
-             all_names)) {
+      if (is.list(data)) {
-      stop(
+        if (length(data) == 1) {
-        "The dataset does not include a 'redcap_event_name' variable.
+          out <- data[[1]]
-         redcap_wider only handles projects with repeating instruments or
+        } else {
-         longitudinal projects"
+          out <- data |> purrr::reduce(dplyr::left_join)
-      )
+        }
+      } else if (is.data.frame(data)){
+        out <- data
       }
 
-    id.name <- all_names[1]
 
-    l <- lapply(list, function(i) {
+    } else {
+
+    id.name <- do.call(c, lapply(data, names))[[1]]
+
+    l <- lapply(data, function(i) {
       rep_inst <- "redcap_repeat_instrument" %in% names(i)
 
       if (rep_inst) {
@@ -81,13 +111,17 @@ redcap_wider <-
             names_glue = event.glue
           )
           s[colnames(s) != "redcap_event_name"]
-        } else
+        } else {
-          (i[colnames(i) != "redcap_event_name"])
+          i[colnames(i) != "redcap_event_name"]
-      } else
+          }
-        (i)
+      } else {
+        i
+        }
     })
 
-    ## Additional conditioning is needed to handle repeated instruments.
+    out <- data.frame(Reduce(f = dplyr::full_join, x = l))
-
-    data.frame(Reduce(f = dplyr::full_join, x = l))
     }
+
+    out
+  }
+

R/utils.r

@@ -1,5 +1,3 @@
-
-
 #' focused_metadata
 #' @description Extracts limited metadata for variables in a dataset
 #' @param metadata A dataframe containing metadata
@@ -8,7 +6,6 @@
 #' @export
 #'
 focused_metadata <- function(metadata, vars_in_data) {
-
   if (any(c("tbl_df", "tbl") %in% class(metadata))) {
     metadata <- data.frame(metadata)
   }
@@ -17,9 +14,11 @@ focused_metadata <- function(metadata, vars_in_data) {
   field_type <- grepl(".*[Ff]ield[._][Tt]ype$", names(metadata))
 
   fields <-
-    metadata[!metadata[, field_type] %in% c("descriptive", "checkbox") &
+    metadata[
+      !metadata[, field_type] %in% c("descriptive", "checkbox") &
         metadata[, field_name] %in% vars_in_data,
-             field_name]
+      field_name
+    ]
 
   # Process checkbox fields
   if (any(metadata[, field_type] == "checkbox")) {
@@ -29,18 +28,21 @@ focused_metadata <- function(metadata, vars_in_data) {
 
     # Processing
     checkbox_basenames <-
-      metadata[metadata[, field_type] == "checkbox" &
+      metadata[
+        metadata[, field_type] == "checkbox" &
           metadata[, field_name] %in% vars_check,
-               field_name]
+        field_name
+      ]
 
     fields <- c(fields, checkbox_basenames)
-
   }
 
   # Process instrument status fields
   form_names <-
-    unique(metadata[, grepl(".*[Ff]orm[._][Nn]ame$",
+    unique(metadata[, grepl(
-                            names(metadata))][metadata[, field_name]
+      ".*[Ff]orm[._][Nn]ame$",
+      names(metadata)
+    )][metadata[, field_name]
     %in% fields])
 
   form_complete_fields <- paste0(form_names, "_complete")
@@ -54,33 +56,34 @@ focused_metadata <- function(metadata, vars_in_data) {
     timestamp_fields <- timestamps
 
     fields <- c(fields, timestamp_fields)
-
   }
 
   # Process ".*\\.factor" fields supplied by REDCap's export data R script
   if (any(grepl("\\.factor$", vars_in_data))) {
     factor_fields <-
-      do.call("rbind",
+      do.call(
+        "rbind",
         apply(fields,
           1,
           function(x, y) {
             field_indices <- grepl(paste0("^", x[1], "\\.factor$"), y)
-                      if (any(field_indices))
+            if (any(field_indices)) {
               data.frame(
                 field_name = y[field_indices],
                 form_name = x[2],
                 stringsAsFactors = FALSE,
                 row.names = NULL
               )
+            }
           },
-                    y = vars_in_data))
+          y = vars_in_data
+        )
+      )
 
     fields <- c(fields, factor_fields[, 1])
-
   }
 
   metadata[metadata[, field_name] %in% fields, ]
-
 }
 
 #' clean_redcap_name
@@ -95,12 +98,17 @@ focused_metadata <- function(metadata, vars_in_data) {
 #' @export
 #'
 clean_redcap_name <- function(x) {
-
+  gsub(
-  gsub(" ", "_",
+    " ", "_",
-       gsub("[' ']$","",
+    gsub(
-           gsub("[^a-z0-9' '_]", "",
+      "[' ']$", "",
+      gsub(
+        "[^a-z0-9' '_]", "",
         tolower(x)
-       )))}
+      )
+    )
+  )
+}
 
 
 #' Sanitize list of data frames
@@ -116,11 +124,14 @@ clean_redcap_name <- function(x){
 #'
 sanitize_split <- function(l,
                            generic.names = c(
-                             "record_id",
                              "redcap_event_name",
                              "redcap_repeat_instrument",
                              "redcap_repeat_instance"
                            )) {
+  generic.names <- c(get_id_name(l),
+                     generic.names,
+                     paste0(names(l), "_complete"))
+
   lapply(l, function(i) {
     if (ncol(i) > 2) {
       s <- data.frame(i[, !colnames(i) %in% generic.names])
@@ -132,6 +143,19 @@ sanitize_split <- function(l,
 }
 
 
+#' Get the id name
+#'
+#' @param data data frame or list
+#'
+#' @return character vector
+get_id_name <- function(data) {
+  if ("list" %in% class(data)) {
+    do.call(c, lapply(data, names))[[1]]
+  } else {
+    names(data)[[1]]
+  }
+}
+
 #' Match fields to forms
 #'
 #' @param metadata A data frame containing field names and form names
@@ -143,20 +167,23 @@ sanitize_split <- function(l,
 #'
 #'
 match_fields_to_form <- function(metadata, vars_in_data) {
-
   metadata <- data.frame(metadata)
 
   field_form_name <- grepl(".*([Ff]ield|[Ff]orm)[._][Nn]ame$", names(metadata))
   field_type <- grepl(".*[Ff]ield[._][Tt]ype$", names(metadata))
 
-  fields <- metadata[!metadata[,field_type] %in% c("descriptive", "checkbox"),
+  fields <- metadata[
-                     field_form_name]
+    !metadata[, field_type] %in% c("descriptive", "checkbox"),
+    field_form_name
+  ]
 
   names(fields) <- c("field_name", "form_name")
 
   # Process instrument status fields
-  form_names <- unique(metadata[,grepl(".*[Ff]orm[._][Nn]ame$",
+  form_names <- unique(metadata[, grepl(
-                                       names(metadata))])
+    ".*[Ff]orm[._][Nn]ame$",
+    names(metadata)
+  )])
   form_complete_fields <- data.frame(
     field_name = paste0(form_names, "_complete"),
     form_name = form_names,
@@ -176,57 +203,65 @@ match_fields_to_form <- function(metadata, vars_in_data) {
     )
 
     fields <- rbind(fields, timestamp_fields)
-
   }
 
   # Process checkbox fields
   if (any(metadata[, field_type] == "checkbox")) {
-    checkbox_basenames <- metadata[metadata[,field_type] == "checkbox",
+    checkbox_basenames <- metadata[
-                                   field_form_name]
+      metadata[, field_type] == "checkbox",
+      field_form_name
+    ]
 
     checkbox_fields <-
-      do.call("rbind",
+      do.call(
+        "rbind",
         apply(checkbox_basenames,
           1,
-                    function(x, y)
+          function(x, y) {
             data.frame(
               field_name =
                 y[grepl(paste0("^", x[1], "___((?!\\.factor).)+$"),
-                                  y, perl = TRUE)],
+                  y,
+                  perl = TRUE
+                )],
               form_name = x[2],
               stringsAsFactors = FALSE,
               row.names = NULL
-                      ),
+            )
-                    y = vars_in_data))
+          },
+          y = vars_in_data
+        )
+      )
 
     fields <- rbind(fields, checkbox_fields)
-
   }
 
   # Process ".*\\.factor" fields supplied by REDCap's export data R script
   if (any(grepl("\\.factor$", vars_in_data))) {
     factor_fields <-
-      do.call("rbind",
+      do.call(
+        "rbind",
         apply(fields,
           1,
           function(x, y) {
             field_indices <- grepl(paste0("^", x[1], "\\.factor$"), y)
-                      if (any(field_indices))
+            if (any(field_indices)) {
               data.frame(
                 field_name = y[field_indices],
                 form_name = x[2],
                 stringsAsFactors = FALSE,
                 row.names = NULL
               )
+            }
           },
-                    y = vars_in_data))
+          y = vars_in_data
+        )
+      )
 
     fields <- rbind(fields, factor_fields)
-
   }
 
   fields
-
 }
 
 #' Split a data frame into separate tables for each form
@@ -256,10 +291,12 @@ match_fields_to_form <- function(metadata, vars_in_data) {
 #'
 #' # Create the fields
 #' fields <- matrix(
-#'   c("form_a_name", "form_a",
+#'   c(
+#'     "form_a_name", "form_a",
 #'     "form_a_age", "form_a",
 #'     "form_b_name", "form_b",
-#'     "form_b_gender", "form_b"),
+#'     "form_b_gender", "form_b"
+#'   ),
 #'   ncol = 2, byrow = TRUE
 #' )
 #'
@@ -269,14 +306,17 @@ split_non_repeating_forms <-
   function(table, universal_fields, fields) {
     forms <- unique(fields[[2]])
 
-    x <- lapply(forms,
+    x <- lapply(
+      forms,
       function(x) {
-                  table[names(table) %in% union(universal_fields,
+        table[names(table) %in% union(
-                                                fields[fields[, 2] == x, 1])]
+          universal_fields,
-                })
+          fields[fields[, 2] == x, 1]
+        )]
+      }
+    )
 
     structure(x, names = forms)
-
   }
 
 
@@ -306,26 +346,33 @@ strsplitx <- function(x,
     out <- base::strsplit(x = x, split = split, perl = perl, ...)
   } else if (type == "before") {
     # split before the delimiter and keep it
-    out <- base::strsplit(x = x,
+    out <- base::strsplit(
+      x = x,
       split = paste0("(?<=.)(?=", split, ")"),
       perl = TRUE,
-                          ...)
+      ...
+    )
   } else if (type == "after") {
     # split after the delimiter and keep it
-    out <- base::strsplit(x = x,
+    out <- base::strsplit(
+      x = x,
       split = paste0("(?<=", split, ")"),
       perl = TRUE,
-                          ...)
+      ...
+    )
   } else if (type == "around") {
     # split around the defined delimiter
 
-    out <- base::strsplit(gsub("~~", "~", # Removes double ~
+    out <- base::strsplit(gsub(
-                               gsub("^~", "", # Removes leading ~
+      "~~", "~", # Removes double ~
+      gsub(
+        "^~", "", # Removes leading ~
         gsub(
           # Splits and inserts ~ at all delimiters
           paste0("(", split, ")"), "~\\1~", x
-                                    ))), "~")
+        )
-
+      )
+    ), "~")
   } else {
     # wrong type input
     stop("type must be 'classic', 'after', 'before' or 'around'!")
@@ -353,25 +400,24 @@ strsplitx <- function(x,
 #' d2w(list(2:8, c(2, 6, 4, 23), 2), everything = TRUE)
 #'
 d2w <- function(x, lang = "en", neutrum = FALSE, everything = FALSE) {
-
   # In Danish the written 1 depends on the counted word
   if (neutrum) nt <- "t" else nt <- "n"
 
   # A sapply() call with nested lapply() to handle vectors, data.frames and lists
   convert <- function(x, lang, neutrum) {
-    zero_nine = data.frame(
+    zero_nine <- data.frame(
       num = 0:9,
       en = c(
-        'zero',
+        "zero",
-        'one',
+        "one",
-        'two',
+        "two",
-        'three',
+        "three",
-        'four',
+        "four",
-        'five',
+        "five",
-        'six',
+        "six",
-        'seven',
+        "seven",
-        'eight',
+        "eight",
-        'nine'
+        "nine"
       ),
       da = c(
         "nul",
@@ -406,7 +452,6 @@ d2w <- function(x, lang = "en", neutrum=FALSE, everything=FALSE) {
         v <- strsplitx(z, "[0-9]", type = "around")
         Reduce(paste, sapply(v, convert, lang = lang, neutrum = neutrum))
       }))
-
     })
   } else {
     out <- sapply(x, convert, lang = lang, neutrum = neutrum)
@@ -416,3 +461,31 @@ d2w <- function(x, lang = "en", neutrum=FALSE, everything=FALSE) {
 
   out
 }
+
+#' Test if repeatable or longitudinal
+#'
+#' @param data data set
+#' @param generics default is "redcap_event_name", "redcap_repeat_instrument"
+#' and "redcap_repeat_instance"
+#'
+#' @return logical
+#' @examples
+#' is.repeated_longitudinal(c("record_id", "age", "record_id", "gender"))
+#' is.repeated_longitudinal(redcapcast_data)
+#' is.repeated_longitudinal(list(redcapcast_data))
+is.repeated_longitudinal <- function(data, generics = c(
+                                       "redcap_event_name",
+                                       "redcap_repeat_instrument",
+                                       "redcap_repeat_instance"
+                                     )) {
+  if ("list" %in% class(data)) {
+    names <- data |>
+      lapply(names) |>
+      purrr::list_c()
+  } else if ("data.frame" %in% class(data)) {
+    names <- names(data)
+  } else if ("character" %in% class(data)) {
+    names <- data
+  }
+  any(generics %in% names)
+}

data-raw/redcapcast_data.R

@@ -3,6 +3,10 @@
 redcapcast_data <- REDCapR::redcap_read(redcap_uri = keyring::key_get("DB_URI"),
                                         token = keyring::key_get("cast_api"),
                                         raw_or_label = "label"
-                                        )$data
+                                        )$data |> dplyr::tibble()
+
+# redcapcast_data <- easy_redcap(project.name = "redcapcast_pacakge",
+#                                uri = keyring::key_get("DB_URI"),
+#                                widen.data = FALSE)
 
 usethis::use_data(redcapcast_data, overwrite = TRUE)

man/easy_redcap.Rd

@@ -8,7 +8,7 @@ easy_redcap(project.name, widen.data = TRUE, uri, ...)
 }
 \arguments{
 \item{project.name}{The name of the current project (for key storage with
-`keyring::key_set()`)}
+`keyring::key_set()`, using the default keyring)}
 
 \item{widen.data}{argument to widen the exported data}
 

man/get_id_name.Rd

@@ -0,0 +1,17 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/utils.r
+\name{get_id_name}
+\alias{get_id_name}
+\title{Get the id name}
+\usage{
+get_id_name(data)
+}
+\arguments{
+\item{data}{data frame or list}
+}
+\value{
+character vector
+}
+\description{
+Get the id name
+}

man/is.repeated_longitudinal.Rd

@@ -0,0 +1,28 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/utils.r
+\name{is.repeated_longitudinal}
+\alias{is.repeated_longitudinal}
+\title{Test if repeatable or longitudinal}
+\usage{
+is.repeated_longitudinal(
+  data,
+  generics = c("redcap_event_name", "redcap_repeat_instrument", "redcap_repeat_instance")
+)
+}
+\arguments{
+\item{data}{data set}
+
+\item{generics}{default is "redcap_event_name", "redcap_repeat_instrument"
+and "redcap_repeat_instance"}
+}
+\value{
+logical
+}
+\description{
+Test if repeatable or longitudinal
+}
+\examples{
+is.repeated_longitudinal(c("record_id", "age", "record_id", "gender"))
+is.repeated_longitudinal(redcapcast_data)
+is.repeated_longitudinal(list(redcapcast_data))
+}

man/read_redcap_tables.Rd

@@ -12,8 +12,7 @@ read_redcap_tables(
   events = NULL,
   forms = NULL,
   raw_or_label = "label",
-  split_forms = "all",
+  split_forms = "all"
-  generics = c("redcap_event_name", "redcap_repeat_instrument", "redcap_repeat_instance")
 )
 }
 \arguments{

man/redcap_wider.Rd

@@ -5,13 +5,13 @@
 \title{Redcap Wider}
 \usage{
 redcap_wider(
-  list,
+  data,
   event.glue = "{.value}_{redcap_event_name}",
   inst.glue = "{.value}_{redcap_repeat_instance}"
 )
 }
 \arguments{
-\item{list}{A list of data frames.}
+\item{data}{A list of data frames.}
 
 \item{event.glue}{A dplyr::glue string for repeated events naming}
 
@@ -25,11 +25,36 @@ Converts a list of REDCap data frames from long to wide format.
 Handles longitudinal projects, but not yet repeated instruments.
 }
 \examples{
-list <- list(data.frame(record_id = c(1,2,1,2),
+# Longitudinal
+list1 <- list(data.frame(record_id = c(1,2,1,2),
 redcap_event_name = c("baseline", "baseline", "followup", "followup"),
 age = c(25,26,27,28)),
 data.frame(record_id = c(1,2),
 redcap_event_name = c("baseline", "baseline"),
 gender = c("male", "female")))
-redcap_wider(list)
+redcap_wider(list1)
+# Simpel with two instruments
+list2 <- list(data.frame(record_id = c(1,2),
+age = c(25,26)),
+data.frame(record_id = c(1,2),
+gender = c("male", "female")))
+redcap_wider(list2)
+# Simple with single instrument
+list3 <- list(data.frame(record_id = c(1,2),
+age = c(25,26)))
+redcap_wider(list3)
+# Longitudinal with repeatable instruments
+list4 <- list(data.frame(record_id = c(1,2,1,2),
+redcap_event_name = c("baseline", "baseline", "followup", "followup"),
+age = c(25,26,27,28)),
+data.frame(record_id = c(1,1,1,1,2,2,2,2),
+redcap_event_name = c("baseline", "baseline", "followup", "followup",
+"baseline", "baseline", "followup", "followup"),
+redcap_repeat_instrument = "walk",
+redcap_repeat_instance=c(1,2,1,2,1,2,1,2),
+dist = c(40, 32, 25, 33, 28, 24, 23, 36)),
+data.frame(record_id = c(1,2),
+redcap_event_name = c("baseline", "baseline"),
+gender = c("male", "female")))
+redcap_wider(list4)
 }

man/sanitize_split.Rd

@@ -6,7 +6,7 @@
 \usage{
 sanitize_split(
   l,
-  generic.names = c("record_id", "redcap_event_name", "redcap_repeat_instrument",
+  generic.names = c("redcap_event_name", "redcap_repeat_instrument",
     "redcap_repeat_instance")
 )
 }

man/split_non_repeating_forms.Rd

@@ -36,10 +36,12 @@ universal_fields <- c("id")
 
 # Create the fields
 fields <- matrix(
-  c("form_a_name", "form_a",
+  c(
+    "form_a_name", "form_a",
     "form_a_age", "form_a",
     "form_b_name", "form_b",
-    "form_b_gender", "form_b"),
+    "form_b_gender", "form_b"
+  ),
   ncol = 2, byrow = TRUE
 )
 

test-data/test_splitr/debug.r

@@ -3,10 +3,11 @@
 #devtools::install_github("pegeler/REDCapRITS/R@longitudinal-data")
 
 # Debugging reading in longitudinal datasets ------------------------------
+# setwd(here::here(""))
 
 # Reading in the files
 file_paths <- file.path(
-  "../test-data/test_splitr/",
+  "test-data/test_splitr",
   c(
     records = "WARRIORtestForSoftwa_DATA_2018-06-21_1431.csv",
     metadata = "WARRIORtestForSoftwareUpgrades_DataDictionary_2018-06-21.csv"

vignettes/Introduction.Rmd

@@ -35,7 +35,7 @@ redcapcast_meta |> gt::gt()
 list <-
   REDCap_split(records = redcapcast_data,
                            metadata = redcapcast_meta,
-                           forms = "repeating")
+                           forms = "repeating")|> sanitize_split()
 str(list)
 ```
 
@@ -43,7 +43,7 @@ str(list)
 list <-
   REDCap_split(records = redcapcast_data,
                            metadata = redcapcast_meta,
-                           forms = "all")
+                           forms = "all") |> sanitize_split()
 str(list)
 ```
 
@@ -60,7 +60,7 @@ The function works very similar to the `REDCapR::redcap_read()` in allowing to s
 ## Pivotting to wider format
 
 ```{r}
-# redcap_wider(ds)
+redcap_wider(list) |> str()
 ```