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Merge pull request #26 from aghaynes/main
better handling of single/multiple choice variables, function to split data by form
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,69 @@ | ||
| #' Deprecated functions | ||
| #' These functions have been renamed to be more consistent with the rest of the package. They may be removed in a future version. | ||
| #' @name deprecated | ||
| #' @param data dataframe | ||
| #' @param metadata data dictionary from REDCap | ||
| #' @param rep replace variables. If FALSE, encoded versions of the variable will be created | ||
| #' @param rep_date,rep_datetime,rep_singlechoice,rep_multichoice replace the indicated variable type | ||
| #' @param app_date,app_datetime,app_singlechoice,app_multichoice text to append to the newly generated variables name (if \code{rep_*} is FALSE) | ||
| #' @param append text to append to the newly generated variables name (if \code{replace} is TRUE) | ||
| #' @param replace indicator of whether to replace original variables or not | ||
| #' @param ... options passed to/from other methods | ||
| NULL | ||
|
|
||
| #' @describeIn deprecated original function name for \code{redcap_prep} | ||
| #' @export | ||
| rc_prep <- function(data, metadata, | ||
| rep = FALSE, | ||
| rep_date = rep, rep_datetime = rep, | ||
| rep_singlechoice = rep, rep_multichoice = rep, | ||
| app_date = "_date", app_datetime = "_datetime", | ||
| app_singlechoice = "_factor", app_multichoice = "_factor", | ||
| ...){ | ||
|
|
||
| warning("rc_prep is deprecated, please use redcap_prep") | ||
| redcap_prep(data, metadata, | ||
| rep = rep, | ||
| rep_date = rep_date, rep_datetime = rep_datetime, | ||
| rep_singlechoice = rep_singlechoice, rep_multichoice = rep_multichoice, | ||
| app_date = app_date, app_datetime = app_datetime, | ||
| app_singlechoice = app_singlechoice, app_multichoice = app_multichoice, | ||
| ...) | ||
|
|
||
| } | ||
|
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||
| #' @describeIn deprecated original function name for \code{redcap_dates} | ||
| #' @export | ||
| rc_dates <- function(data, metadata, replace = FALSE, append = "_date"){ | ||
| warning("rc_dates is deprecated, use redcap_prep_dates instead") | ||
| redcap_prep_dates(data, metadata, replace, append) | ||
| } | ||
|
|
||
| #' @describeIn deprecated original function name for \code{redcap_datetimes} | ||
| #' @export | ||
| rc_datetimes <- function(data, metadata, replace = FALSE, append = "_datetime", ...){ | ||
| warning("rc_datetimes is deprecated, use redcap_prep_datetimes instead") | ||
| redcap_prep_datetimes(data, metadata, replace, append, ...) | ||
| } | ||
|
|
||
| #' @describeIn deprecated deprecated in favour of \code{redcap_toform} | ||
| #' Split a manually exported REDCap dataset into forms | ||
| #' | ||
| #' @param data dataframe | ||
| #' @param metadata datadictionary as exported from REDCap or downloaded from the API | ||
| #' | ||
| #' @return list of dataframes | ||
| #' @export | ||
| split_by_form <- function(data, metadata){ | ||
| warning("split_by_form is deprecated, use redcap_toform instead") | ||
| metadata$regex <- ifelse(metadata$field_type == "checkbox", | ||
| paste0(metadata$field_name, "___"), | ||
| metadata$field_name) | ||
| sapply(unique(metadata$form_name), function(x){ | ||
| regex <- paste(metadata$field_name[1], "^redcap", | ||
| paste0("^", metadata$regex[metadata$form_name == x], | ||
| collapse = "|"), sep = "|") | ||
| dd <- data[, grepl(regex, names(data))] | ||
| remove_empty_rows(dd) | ||
| }) | ||
| } |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,125 @@ | ||
| #' Convert manually downloaded REDCap data into a list of forms | ||
| #' | ||
| #' Similar to \code{redcap_export_byform}, this function tries to split a | ||
| #' manually downloaded dataset into it's constituent forms. While use of the API | ||
| #' allows individual forms to be downloaded, with a manual download, only the data | ||
| #' dictionary is available as auxillary information. If no data dictionary is | ||
| #' available, the function will use the variable names to guess the forms (see | ||
| #' details). | ||
| #' | ||
| #' @param data imported REDCap data | ||
| #' @param datadict data dictionary downloaded manually from REDCap | ||
| #' @param metadata metadata downloaded from REDCap API | ||
| #' @param guess_events restrict forms to events (rows) where data exists (see details) | ||
| #' @param ... additional arguments passed to other functions (currently unused) | ||
| #' | ||
| #' @details | ||
| #' In a longitudinal data collection with many forms, a REDCap dataset will have | ||
| #' a large degree of empty cells. The \code{guess_events} argument uses missingness | ||
| #' as an indicator of a row not being part of the form in question. If all user | ||
| #' variables (i.e. those that do not start with \code{redcap}) are empty, the row | ||
| #' will be removed from the dataset. | ||
| #' | ||
| #' If neither \code{datadict} nor \code{metadata} are provided, the function will | ||
| #' attempt to guess the forms based on the variable names, specifically the | ||
| #' \code{form_complete} variables which denote the state of the form. This is | ||
| #' not a foolproof method: there may be other variables in the data that end with | ||
| #' \code{_complete}. | ||
| #' | ||
| #' @importFrom dplyr if_else pull filter mutate select everything across where slice | ||
| #' @importFrom tidyr fill | ||
| #' @importFrom stringr str_detect str_extract | ||
| #' @export | ||
| #' @examples | ||
| #' data <- readRDS(system.file("extdata/test.rda", package = "redcaptools")) | ||
| #' metadata <- readRDS(system.file("extdata/meta.rda", package = "redcaptools")) | ||
| #' dd <- read.csv(system.file("extdata/DataDictionary.csv", package = "redcaptools")) | ||
| #' redcap_toform(data, dd) | ||
| #' redcap_toform(data, metadata = metadata) | ||
| #' redcap_toform(data) | ||
| redcap_toform <- function(data, | ||
| datadict = NULL, | ||
| metadata = NULL, | ||
| guess_events = TRUE, | ||
| ...){ | ||
| if(!is.null(datadict)){ | ||
| # a manually downloaded data dictionary has different variable names to the | ||
| # API version | ||
| metadata <- harmonize_datadict(datadict) | ||
| } | ||
| if(is.null(datadict) & is.null(metadata)){ | ||
| warning("No metadata provided, guessing forms based on variable names") | ||
| metadata <- data.frame(field_name = names(data)) |> | ||
| mutate(complete = str_detect(field_name, "_complete$"), | ||
| form_name = str_extract(field_name, ".*(?=_complete)"), | ||
| field_type = "") |> | ||
| fill(form_name, .direction = "up") | ||
|
|
||
| metadata <- metadata |> | ||
| filter(!grepl("^redcap", field_name)) | ||
| } | ||
|
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|
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| forms <- metadata$form_name |> unique() | ||
| names(forms) <- forms | ||
| idvar <- metadata$field_name[1] | ||
| redcap_vars <- names(data)[grepl("^redcap", names(data))] | ||
|
|
||
| generic_regex <- regex_many(c(idvar, redcap_vars)) | ||
|
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| lapply(forms, | ||
| function(form){ | ||
| formmeta <- metadata |> slice(-1) |> filter(form_name == form) | ||
| # construct regex to select variables | ||
| form_regex <- formmeta |> | ||
| mutate(regex = if_else(field_type == "checkbox", | ||
| paste0("^", field_name, "___"), | ||
| regex_single(field_name))) |> | ||
| pull(regex) |> paste(collapse = "|") | ||
|
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||
| # extract forms | ||
| tmp <- data |> | ||
| select(matches(generic_regex), matches(form_regex)) | ||
|
|
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| if(guess_events){ | ||
| tmp2 <- tmp |> | ||
| select(matches(form_regex)) |> | ||
| mutate(across( | ||
| everything(), | ||
| ~ !is.na(.x) & .x != ""), | ||
|
|
||
| ) |> rowSums(na.rm = TRUE) | ||
| tmp <- tmp |> filter(tmp2 > 0) | ||
| } | ||
| tmp | ||
| }) | ||
|
|
||
| } | ||
|
|
||
| regex_single <- function(x){ | ||
| paste0("^", x, "$") | ||
| } | ||
| regex_many <- function(x){ | ||
| paste0(regex_single(x), collapse = "|") | ||
| } | ||
|
|
||
| # regex_single("redcap_event_name") | ||
| # regex_many(c("redcap_event_name", "redcap_repeat_instance")) | ||
|
|
||
| harmonize_datadict <- function(datadict){ | ||
| if(!ncol(datadict) == 18) | ||
| stop("data dictionary must have 18 columns") | ||
| # convert factors to character | ||
| datadict <- datadict |> | ||
| mutate(across(where(is.factor), as.character)) | ||
| # set names | ||
| names(datadict) <- c("field_name", "form_name", "section_header", "field_type", | ||
| "field_label", "select_choices_or_calculations", "field_note", | ||
| "text_validation_type_or_show_slider_number", | ||
| "text_validation_min", "text_validation_max", "identifier", | ||
| "branching_logic", "required_field", "custom_alignment", | ||
| "question_number", "matrix_group_name", "matrix_ranking", | ||
| "field_annotation") | ||
| return(datadict) | ||
| } |
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