Saghir Bashir

Do you need to find an R dataset that you can use for teaching, presentations or reprex? WhatData is there to help you. You can try it out here.

A Dataset of R Datasets

We start by creating a dataset of the available datasets in R on your system (i.e. from both base R and from the packages installed on your system). The following code creates a tidy dataset (all_ds) with package name, dataset name, title and the object class.

library(tidyverse)
library(stringr)
library(DT)

# Function to catch the error for data that is not exported.
unexportedData <- function (x) {
  out <- tryCatch(class(eval(parse(text = x))), error = function(e) "NOT EXPORTED")
  return(out)
}

all_ds <- data(package = .packages(all.available = TRUE)) %>% 
  .$results %>%
  tibble::as_tibble() %>%
  dplyr::mutate(DataOrig = stringr::word(Item, 1)) %>%
  dplyr::mutate(pkgData = paste(Package, DataOrig, sep="::")) %>% 
  dplyr::arrange(pkgData) %>% 
  dplyr::mutate(Class = purrr::invoke_map(unexportedData, pkgData)) %>% 
  tidyr::unnest(Class) %>% 
  dplyr::filter(!str_detect(Class, "NOT EXPORTED")) %>% 
  dplyr::select(pkgData, Package, DataOrig, Title, Class) %>%
  dplyr::arrange(pkgData, Class) %>%
  dplyr::mutate(Val = Class) %>%
  tidyr::spread(key = Class, value=Val, fill = "") %>%
  tidyr::unite(Classes, c(-pkgData, -Package, -DataOrig, -Title), sep= " ")

Some key points for the code above:

data(package = .packages(all.available = TRUE)) identifies all the data objects in all the packages installed on your system including the those that are not exported.
Function unexportedData() catches the errors from using class() and labels them as “NOT EXPORTED” (although the errors could be due to other reasons but we ignore the reason).
Observations are dropped when Class is “NOT EXPORTED”.
The remaining code is to create a tidy dataset.

Find R Datasets

Interactive Data Table

DT::datatable() can be used to search for datasets and searches can be refined by including a search box on top of each column (using the option filter = "top").

all_ds %>% 
  select(-pkgData) %>% 
  DT::datatable(filter = "top")

Using dplyr::filter()

dplyr::filter() can also be used to search for datasets. Searches can be refined using regular expressions (regex).

# Find all tibbles.
all_ds %>% 
  filter(str_detect(Classes, "tbl_df"))

# Filtering out the rows with class "ts" and not things like "datasets".
all_ds %>% 
  filter(str_detect(Classes, regex("\\b(ts)\\b")))

# Find datasets related with any form of "sleep" in dataset names or descriptions.
all_ds %>% 
  filter(str_detect(Title, regex("sleep", ignore_case = TRUE)))

# Time series data for econ(omic), stock or share data.
all_ds %>% 
  filter(str_detect(Title, regex("econ|stock|share", ignore_case = TRUE)) &
                  str_detect(Classes, regex("\\b(ts)\\b")))

Summary

We have presented one approach to finding R datasets that you can use for teaching, presentations or reprex. It is a useful way to discover (or be reminded about) datasets in R.

Finding Datasets in R

A Dataset of R Datasets

Find R Datasets

Interactive Data Table

Using dplyr::filter()

Summary