mirror of
https://github.com/agdamsbo/REDCapCAST.git
synced 2024-10-30 03:21:53 +01:00
419 lines
12 KiB
R
419 lines
12 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
|
|
#'
|
|
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, ]
|
|
|
|
}
|
|
|
|
#' clean_redcap_name
|
|
#' @description
|
|
#' Stepwise removal on non-alphanumeric characters, trailing white space,
|
|
#' substitutes spaces for underscores and converts to lower case.
|
|
#' Trying to make up for different naming conventions.
|
|
#'
|
|
#' @param x vector or data frame for cleaning
|
|
#'
|
|
#' @return vector or data frame, same format as input
|
|
#' @export
|
|
#'
|
|
clean_redcap_name <- function(x){
|
|
|
|
gsub(" ", "_",
|
|
gsub("[' ']$","",
|
|
gsub("[^a-z0-9' '_]", "",
|
|
tolower(x)
|
|
)))}
|
|
|
|
|
|
#' Sanitize list of data frames
|
|
#'
|
|
#' Removing empty rows
|
|
#' @param l A list of data frames.
|
|
#' @param generic.names A vector of generic names to be excluded.
|
|
#'
|
|
#' @return A list of data frames with generic names excluded.
|
|
#'
|
|
#' @export
|
|
#'
|
|
#'
|
|
sanitize_split <- function(l,
|
|
generic.names = c(
|
|
"record_id",
|
|
"redcap_event_name",
|
|
"redcap_repeat_instrument",
|
|
"redcap_repeat_instance"
|
|
)) {
|
|
lapply(l, function(i) {
|
|
if (ncol(i) > 2) {
|
|
s <- data.frame(i[, !colnames(i) %in% generic.names])
|
|
i[!apply(is.na(s), MARGIN = 1, FUN = all),]
|
|
} else {
|
|
i
|
|
}
|
|
})
|
|
}
|
|
|
|
|
|
#' Match fields to forms
|
|
#'
|
|
#' @param metadata A data frame containing field names and form names
|
|
#' @param vars_in_data A character vector of variable names
|
|
#'
|
|
#' @return A data frame containing field names and form names
|
|
#'
|
|
#' @export
|
|
#'
|
|
#'
|
|
match_fields_to_form <- function(metadata, vars_in_data) {
|
|
|
|
metadata <- data.frame(metadata)
|
|
|
|
field_form_name <- grepl(".*([Ff]ield|[Ff]orm)[._][Nn]ame$",names(metadata))
|
|
field_type <- grepl(".*[Ff]ield[._][Tt]ype$",names(metadata))
|
|
|
|
fields <- metadata[!metadata[,field_type] %in% c("descriptive", "checkbox"),
|
|
field_form_name]
|
|
|
|
names(fields) <- c("field_name", "form_name")
|
|
|
|
# Process instrument status fields
|
|
form_names <- unique(metadata[,grepl(".*[Ff]orm[._][Nn]ame$",
|
|
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)
|
|
|
|
}
|
|
|
|
|
|
#' Extended string splitting
|
|
#'
|
|
#' Can be used as a substitute of the base function. Main claim to fame is
|
|
#' easing the split around the defined delimiter, see example.
|
|
#' @param x data
|
|
#' @param split delimiter
|
|
#' @param type Split type. Can be c("classic", "before", "after", "around")
|
|
#' @param perl perl param from strsplit()
|
|
#' @param ... additional parameters are passed to base strsplit handling splits
|
|
#'
|
|
#' @return list
|
|
#' @export
|
|
#'
|
|
#' @examples
|
|
#' test <- c("12 months follow-up", "3 steps", "mRS 6 weeks", "Counting to 231 now")
|
|
#' strsplitx(test,"[0-9]",type="around")
|
|
strsplitx <- function(x,
|
|
split,
|
|
type = "classic",
|
|
perl = FALSE,
|
|
...) {
|
|
if (type == "classic") {
|
|
# use base::strsplit
|
|
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,
|
|
split = paste0("(?<=.)(?=", split, ")"),
|
|
perl = TRUE,
|
|
...)
|
|
} else if (type == "after") {
|
|
# split after the delimiter and keep it
|
|
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 ~
|
|
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'!")
|
|
}
|
|
|
|
out
|
|
}
|
|
|
|
#' Convert single digits to words
|
|
#'
|
|
#' @param x data. Handle vectors, data.frames and lists
|
|
#' @param lang language. Danish (da) and English (en), Default is "en"
|
|
#' @param neutrum for numbers depending on counted word
|
|
#' @param everything flag to also split numbers >9 to single digits
|
|
#'
|
|
#' @return returns characters in same format as input
|
|
#' @export
|
|
#'
|
|
#' @examples
|
|
#' d2w(c(2:8,21))
|
|
#' d2w(data.frame(2:7,3:8,1),lang="da",neutrum=TRUE)
|
|
#'
|
|
#' ## If everything=T, also larger numbers are reduced.
|
|
#' ## Elements in the list are same length as input
|
|
#' 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(
|
|
num = 0:9,
|
|
en = c(
|
|
'zero',
|
|
'one',
|
|
'two',
|
|
'three',
|
|
'four',
|
|
'five',
|
|
'six',
|
|
'seven',
|
|
'eight',
|
|
'nine'
|
|
),
|
|
da = c(
|
|
"nul",
|
|
paste0("e",nt),
|
|
"to",
|
|
"tre",
|
|
"fire",
|
|
"fem",
|
|
"seks",
|
|
"syv",
|
|
"otte",
|
|
"ni"
|
|
)
|
|
)
|
|
|
|
wrd <- lapply(x, function(i) {
|
|
zero_nine[, tolower(lang)][zero_nine[, 1] == i]
|
|
})
|
|
|
|
sub <- lengths(wrd) == 1
|
|
|
|
x[sub] <- wrd[sub]
|
|
|
|
unlist(x)
|
|
}
|
|
|
|
# Also converts numbers >9 to single digits and writes out
|
|
# Uses strsplitx()
|
|
if (everything) {
|
|
out <- sapply(x,function(y){
|
|
do.call(c,lapply(y,function(z){
|
|
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)
|
|
}
|
|
|
|
if (is.data.frame(x)) out <- data.frame(out)
|
|
|
|
out
|
|
}
|