Getting Started with REDCapTidieR

In REDCapTidieR: Extract 'REDCap' Databases into Tidy 'Tibble's

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

knitr::knit_exit()

# Load credentials
redcap_uri <- Sys.getenv("REDCAP_URI")
token <- Sys.getenv("SUPERHEROES_REDCAP_API")

REDCap is an electronic data capture software that is widely used in the academic research community. The REDCapR package streamlines calls to the REDCap API from an R environment. One of REDCapR's main uses is to import records from a REDCap project. This works well for simple projects, however becomes ugly when complex databases that include longitudinal structure and/or repeating instruments are involved.

The REDCapTidieR package aims to make the life of analysts who deal with complex REDCap databases easier. It builds upon REDCapR to make its output tidier. Instead of one large data frame that contains all the data from your project, you get to work with a set of tidy tibbles, one for each REDCap instrument.

Case Study: The Superhero Database

Let's look at a REDCap project that has information about some 734 superheroes, derived from the Superhero Database.

Here is a screenshot of the REDCap Record Status Dashboard of this database. It has two instruments, Heroes Information which captures "demographic" data about each individual superhero such as their name, gender, and alignment (good or evil), and Super Hero Powers which captures each one of the superpowers that a specific superhero possesses.

Importing data from REDCap

To import data from REDCap, use the read_redcap() function. read_redcap() requires a REDCap database URI and a REDCap API token. You need to have API access to the REDCap database to use REDCapTidieR. REDCapTidieR does not work with files exported from REDCap. We use it here to import data from the Superheroes database. You can see that it returns a tibble named superheroes. We use rmarkdown::paged_table() so you can explore this tibble.

library(REDCapTidieR)
superheroes <- read_redcap(redcap_uri, token)

superheroes |>
  rmarkdown::paged_table()

You can see that the tibble that read_redcap() returned has only two rows. This may be surprising because you might expect more rows from a database with 734 superheroes. read_redcap() returns data in a special object that we call the supertibble. The supertibble contains, among other things, tibbles with the data and metadata derived from each instrument. We call these the data tibbles and metadata tibbles.

Each row of the supertibble corresponds to one REDCap instrument. The redcap_form_name and redcap_form_label columns identify which instrument the row relates to. The redcap_data column contains the data tibbles. The redcap_metadata column contains the metadata tibbles. Additional columns contain useful information about the data tibble, such as row and column counts, size in memory, and the percentage of missing values in the data.

Exploring the contents of the supertibble

We designed the supertibble so you can explore it with the RStudio Data Viewer. You can click on the table icon in the Environment tab to view of the supertibble in the data viewer. At a glance you see an overview of the instruments in the REDCap project.

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You can drill down into individual tables in the redcap_data and redcap_metadata columns. Note that in the heroes_information data tibble, each row represents a superhero, identified by their record_id.

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In the super_hero_powers data tibble, each row represents a superpower of a specific hero. Each row is identified by the combination of record_id and redcap_form_instance. This difference in granularity is because super_hero_powers is a repeating instrument whereas heroes_information is a nonrepeating instrument.

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You can also explore the metadata tibbles in the redcap_metadata column to find out about field labels, field types, and other field attributes.

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Extracting data tibbles from the supertibble

REDCapTidieR provides three different functions to extract data tibbles from a supertibble.

Binding data tibbles into the environment

The bind_tibbles() function takes a supertibble and binds its data tibbles directly into the global environment. When you use bind_tibbles() while working interactively in the RStudio IDE, you will see data tibbles appear in the Environment pane.

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By default, bind_tibbles() extracts all data tibbles from the supertibble. With the tbls argument you can specify a subset of data tibbles that should be extracted. With the environment argument you can supply your own environment object to which the tibbles will be bound.

Extracting a list of data tibbles

The extract_tibbles() function takes a supertibble and returns a named list of data tibbles. The default is to extract all data tibbles. We use str here to show the structure of the list returned by extract_tibbles().

superheroes_list <- superheroes |>
  extract_tibbles()

superheroes_list |>
  str(max.level = 1)

You can use tidyselect selectors to select specific data tibbles.

superheroes |>
  extract_tibbles(ends_with("powers")) |>
  str(max.level = 1)

Extracting a single data tibble

The extract_tibble() takes a supertibble and returns a single data tibble.

superheroes |>
  extract_tibble("heroes_information") |>
  rmarkdown::paged_table()

Memory considerations

You might wonder if it's memory efficient to have both the supertibble and the extracted tibbles in your environment. Because of R's copy-on-modify behavior, extracted data tibbles actually use very little additional memory. To demonstrate this, here we check the size of the superheroes supertibble:

lobstr::obj_size(superheroes)

If we bind the data tibbles into the environment and then check the combined size of the supertibble and the two data tibbles we get the following:

superheroes |>
  bind_tibbles()

lobstr::obj_size(superheroes, heroes_information, super_hero_powers)

The same is true if we use the extract_tibble() or extract_tibbles() functions:

a <- superheroes |> extract_tibble("heroes_information")
b <- superheroes |> extract_tibbles()

lobstr::obj_size(superheroes, a, b)

Adding variable labels with the labelled package

REDCapTidieR integrates with the labelled package to allow you to attach labels to variables in the supertibble. Variable labels can make data exploration easier. An increasing number of R packages support labelled data, including ggplot2 (via ggeasy) and gtsummary. The RStudio Data Viewer shows variable labels below variable names.

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The make_labelled() function takes a supertibble and returns a supertibble with variable labels applied to the variables of the supertibble as well as to the variables of all data and metadata tibbles in the redcap_data and redcap_metadata columns of the supertibble.

You can use the labelled::look_for() function to explore the variable labels of a tibble.

superheroes |>
  make_labelled() |>
  bind_tibbles()

labelled::look_for(heroes_information)

Where did these labels come from? These labels are actually the REDCap field labels that prompt data entry in the REDCap instrument! REDCapTidieR places them into the field_label variable of the instrument's metadata tibble. Below you can see that the field labels of the REDCap instrument for heroes_information are the same as the labels above.

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Note that the label for name has a trailing colon :. This won't look good as a variable label so let's remove it. The make_labelled() function has a format_labels argument that you can use to preprocess labels before applying them to variables.

superheroes |>
  make_labelled(format_labels = ~ gsub(":", "", .)) |>
  bind_tibbles()

labelled::look_for(heroes_information, "hero")

Removing trailing : characters from a field label is a fairly common operation, so REDCapTidieR provides a format helper function that you can pass to the format_labels argument:

fmt_strip_trailing_colon("Hero name:")

To find out about other helpers included with REDCapTidieR, see ?`format-helpers`.

The format_labels argument will also accept multiple functions in a vector or list. You can pass any function that takes a character vector and returns a modified character vector to format_labels. make_labelled() will process the variable labels in the order that these functions are supplied. In the following example, we remove the trailing colon with fmt_strip_trailing_colon() and then make the labels lower case with base::tolower().

superheroes |>
  make_labelled(
    format_labels = c(
      fmt_strip_trailing_colon,
      base::tolower
    )
  ) |>
  bind_tibbles()

labelled::look_for(heroes_information)

Adding summary statistics to the metadata with the skimr package

REDCapTidieR provides the add_skimr_metadata() function to make it easy to compute summary statistics for fields of the project using the skimr package. The summary statistics are added to metadata tibbles. Below is a simple example showing some of the summaries including count of missing values (n_missing), proportion of non-missing values (complete_rate), and various numeric statistics:

# Extract the heroes_information metadata tibble and add metadata
heroes_information_metadata <-
  superheroes |>
  add_skimr_metadata() |>
  dplyr::select(redcap_metadata) |>
  purrr::pluck(1, 1)

# Highlight the numeric summaries created by add_skimr_metadata()
heroes_information_metadata |>
  dplyr::select(field_name, skim_type:complete_rate, starts_with("numeric")) |>
  rmarkdown::paged_table()

This enables quick insights into data content and supports exploratory data analytics. The columns added by add_skimr_metadata() can also be labelled.

Package Options

REDCapTidieR allows you to set a couple options globally to avoid passing extra arguments to read_redcap.

Globally allow mixed structure instruments:

options(redcaptidier.allow.mixed.structure = TRUE)

Globally silence warnings related to Missing Data Codes (MDCs):

options(redcaptidier.allow.mdc = TRUE)

As of v1.1.0, REDCapTidieR has partial support for MDCs. MDCs in logical and categorical fields are converted to NA with a warning. MDCs in all other field types remain in the output. If you need greater support for MDCs, consider opening an issue!

REDCapTidieR documentation built on April 3, 2025, 10:50 p.m.