mirror of
https://github.com/agdamsbo/REDCapCAST.git
synced 2024-10-30 03:21:53 +01:00
267 lines
7.5 KiB
R
267 lines
7.5 KiB
R
|
|
|
|
#' focused_metadata
|
|
#' @description Extracts limited metadata for variables in a dataset
|
|
#' @param metadata A dataframe containing metadata
|
|
#' @param vars_in_data Vector of variable names in the dataset
|
|
#' @return A dataframe containing metadata for the variables in the dataset
|
|
#' @export
|
|
#' @examples
|
|
#'
|
|
focused_metadata <- function(metadata, vars_in_data) {
|
|
|
|
if (any(c("tbl_df", "tbl") %in% class(metadata))) {
|
|
metadata <- data.frame(metadata)
|
|
}
|
|
|
|
field_name <- grepl(".*[Ff]ield[._][Nn]ame$", names(metadata))
|
|
field_type <- grepl(".*[Ff]ield[._][Tt]ype$", names(metadata))
|
|
|
|
fields <-
|
|
metadata[!metadata[, field_type] %in% c("descriptive", "checkbox") &
|
|
metadata[, field_name] %in% vars_in_data,
|
|
field_name]
|
|
|
|
# Process checkbox fields
|
|
if (any(metadata[, field_type] == "checkbox")) {
|
|
# Getting base field names from checkbox fields
|
|
vars_check <-
|
|
sub(pattern = "___.*$", replacement = "", vars_in_data)
|
|
|
|
# Processing
|
|
checkbox_basenames <-
|
|
metadata[metadata[, field_type] == "checkbox" &
|
|
metadata[, field_name] %in% vars_check,
|
|
field_name]
|
|
|
|
fields <- c(fields, checkbox_basenames)
|
|
|
|
}
|
|
|
|
# Process instrument status fields
|
|
form_names <-
|
|
unique(metadata[, grepl(".*[Ff]orm[._][Nn]ame$",
|
|
names(metadata))][metadata[, field_name]
|
|
%in% fields])
|
|
|
|
form_complete_fields <- paste0(form_names, "_complete")
|
|
|
|
fields <- c(fields, form_complete_fields)
|
|
|
|
# Process survey timestamps
|
|
timestamps <-
|
|
intersect(vars_in_data, paste0(form_names, "_timestamp"))
|
|
if (length(timestamps)) {
|
|
timestamp_fields <- timestamps
|
|
|
|
fields <- c(fields, timestamp_fields)
|
|
|
|
}
|
|
|
|
# Process ".*\\.factor" fields supplied by REDCap's export data R script
|
|
if (any(grepl("\\.factor$", vars_in_data))) {
|
|
factor_fields <-
|
|
do.call("rbind",
|
|
apply(fields,
|
|
1,
|
|
function(x, y) {
|
|
field_indices <- grepl(paste0("^", x[1], "\\.factor$"), y)
|
|
if (any(field_indices))
|
|
data.frame(
|
|
field_name = y[field_indices],
|
|
form_name = x[2],
|
|
stringsAsFactors = FALSE,
|
|
row.names = NULL
|
|
)
|
|
},
|
|
y = vars_in_data))
|
|
|
|
fields <- c(fields, factor_fields[, 1])
|
|
|
|
}
|
|
|
|
metadata[metadata[, field_name] %in% fields, ]
|
|
|
|
}
|
|
|
|
|
|
|
|
# function to convert the list of dataframes
|
|
|
|
|
|
#' Sanitize list of data frames
|
|
#'
|
|
#' Removing empty rows
|
|
#' @param l A list of data frames.
|
|
#' @param generic.names A vector of generic names to be excluded.
|
|
#'
|
|
#' @return A list of data frames with generic names excluded.
|
|
#'
|
|
#' @export
|
|
#'
|
|
#' @examples
|
|
#'
|
|
sanitize_split <- function(l,
|
|
generic.names = c(
|
|
"record_id",
|
|
"redcap_event_name",
|
|
"redcap_repeat_instrument",
|
|
"redcap_repeat_instance"
|
|
)) {
|
|
lapply(l, function(i) {
|
|
if (ncol(i) > 2) {
|
|
s <- data.frame(i[, !colnames(i) %in% generic.names])
|
|
i[!apply(is.na(s), MARGIN = 1, FUN = all),]
|
|
} else {
|
|
i
|
|
}
|
|
})
|
|
}
|
|
|
|
|
|
#' Match fields to forms
|
|
#'
|
|
#' @param metadata A data frame containing field names and form names
|
|
#' @param vars_in_data A character vector of variable names
|
|
#'
|
|
#' @return A data frame containing field names and form names
|
|
#'
|
|
#' @export
|
|
#'
|
|
#' @examples
|
|
#'
|
|
#'
|
|
match_fields_to_form <- function(metadata, vars_in_data) {
|
|
|
|
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"),
|
|
field_form_name]
|
|
|
|
names(fields) <- c("field_name", "form_name")
|
|
|
|
# Process instrument status fields
|
|
form_names <- unique(metadata[,grepl(".*[Ff]orm[._][Nn]ame$",names(metadata))])
|
|
form_complete_fields <- data.frame(
|
|
field_name = paste0(form_names, "_complete"),
|
|
form_name = form_names,
|
|
stringsAsFactors = FALSE
|
|
)
|
|
|
|
fields <- rbind(fields, form_complete_fields)
|
|
|
|
# Process survey timestamps
|
|
timestamps <-
|
|
intersect(vars_in_data, paste0(form_names, "_timestamp"))
|
|
if (length(timestamps)) {
|
|
timestamp_fields <- data.frame(
|
|
field_name = timestamps,
|
|
form_name = sub("_timestamp$", "", timestamps),
|
|
stringsAsFactors = FALSE
|
|
)
|
|
|
|
fields <- rbind(fields, timestamp_fields)
|
|
|
|
}
|
|
|
|
# Process checkbox fields
|
|
if (any(metadata[,field_type] == "checkbox")) {
|
|
checkbox_basenames <- metadata[metadata[,field_type] == "checkbox",
|
|
field_form_name]
|
|
|
|
checkbox_fields <-
|
|
do.call("rbind",
|
|
apply(checkbox_basenames,
|
|
1,
|
|
function(x, y)
|
|
data.frame(
|
|
field_name =
|
|
y[grepl(paste0("^", x[1], "___((?!\\.factor).)+$"),
|
|
y, perl = TRUE)],
|
|
form_name = x[2],
|
|
stringsAsFactors = FALSE,
|
|
row.names = NULL
|
|
),
|
|
y = vars_in_data))
|
|
|
|
fields <- rbind(fields, checkbox_fields)
|
|
|
|
}
|
|
|
|
# Process ".*\\.factor" fields supplied by REDCap's export data R script
|
|
if (any(grepl("\\.factor$", vars_in_data))) {
|
|
factor_fields <-
|
|
do.call("rbind",
|
|
apply(fields,
|
|
1,
|
|
function(x, y) {
|
|
field_indices <- grepl(paste0("^", x[1], "\\.factor$"), y)
|
|
if (any(field_indices))
|
|
data.frame(
|
|
field_name = y[field_indices],
|
|
form_name = x[2],
|
|
stringsAsFactors = FALSE,
|
|
row.names = NULL
|
|
)
|
|
},
|
|
y = vars_in_data))
|
|
|
|
fields <- rbind(fields, factor_fields)
|
|
|
|
}
|
|
|
|
fields
|
|
|
|
}
|
|
|
|
#' Split a data frame into separate tables for each form
|
|
#'
|
|
#' @param table A data frame
|
|
#' @param universal_fields A character vector of fields that should be included
|
|
#' in every table
|
|
#' @param fields A two-column matrix containing the names of fields that should
|
|
#' be included in each form
|
|
#'
|
|
#' @return A list of data frames, one for each non-repeating form
|
|
#'
|
|
#' @export
|
|
#'
|
|
#' @examples
|
|
#' # Create a table
|
|
#' table <- data.frame(
|
|
#' id = c(1, 2, 3, 4, 5),
|
|
#' form_a_name = c("John", "Alice", "Bob", "Eve", "Mallory"),
|
|
#' form_a_age = c(25, 30, 25, 15, 20),
|
|
#' form_b_name = c("John", "Alice", "Bob", "Eve", "Mallory"),
|
|
#' form_b_gender = c("M", "F", "M", "F", "F")
|
|
#' )
|
|
#'
|
|
#' # Create the universal fields
|
|
#' universal_fields <- c("id")
|
|
#'
|
|
#' # Create the fields
|
|
#' fields <- matrix(
|
|
#' c("form_a_name", "form_a",
|
|
#' "form_a_age", "form_a",
|
|
#' "form_b_name", "form_b",
|
|
#' "form_b_gender", "form_b"),
|
|
#' ncol = 2, byrow = TRUE
|
|
#' )
|
|
#'
|
|
#' # Split the table
|
|
#' split_non_repeating_forms(table, universal_fields, fields)
|
|
split_non_repeating_forms <-
|
|
function(table, universal_fields, fields) {
|
|
forms <- unique(fields[[2]])
|
|
|
|
x <- lapply(forms,
|
|
function (x) {
|
|
table[names(table) %in% union(universal_fields,
|
|
fields[fields[, 2] == x, 1])]
|
|
})
|
|
|
|
structure(x, names = forms)
|
|
|
|
}
|