| Type: | Package |
| Title: | Access the Weekly 'TidyTuesday' Project Dataset |
| Version: | 1.2.1 |
| Description: | 'TidyTuesday' is a project by the 'Data Science Learning Community' in which they post a weekly dataset in a public data repository (https://github.com/rfordatascience/tidytuesday) for people to analyze and visualize. This package provides the tools to easily download this data and the description of the source. |
| License: | MIT + file LICENSE |
| URL: | https://dslc-io.github.io/tidytuesdayR/, https://github.com/dslc-io/tidytuesdayR |
| BugReports: | https://github.com/dslc-io/tidytuesdayR/issues |
| Depends: | R (≥ 4.1.0) |
| Imports: | cli, gh, glue, jsonlite, lubridate (≥ 1.7.0), magrittr, purrr (≥ 1.0.0), readr (≥ 1.0.0), rlang, rvest (≥ 0.3.2), tidyr, tools (≥ 3.1.0), utils, xml2 (≥ 1.2.0) |
| Suggests: | base64enc, covr, dplyr, fs, knitr, openssl, readxl (≥ 1.0.0), rmarkdown, rstudioapi (≥ 0.2), stringr, testthat (≥ 3.0.0), tibble, usethis, vctrs, withr, yaml |
| VignetteBuilder: | knitr |
| Config/Needs/website: | pkgdown |
| Config/testthat/edition: | 3 |
| Encoding: | UTF-8 |
| Language: | en-US |
| RoxygenNote: | 7.3.2 |
| NeedsCompilation: | no |
| Packaged: | 2025-04-29 10:40:10 UTC; jonth |
| Author: | Jon Harmon  [aut,
cre],
Ellis Hughes [aut],
Thomas Mock [ctb],
Data Science Learning Community [dtc] |
| Maintainer: | Jon Harmon <jonthegeek@gmail.com> |
| Repository: | CRAN |
| Date/Publication: | 2025-04-29 14:20:02 UTC |
tidytuesdayR: Access the Weekly 'TidyTuesday' Project Dataset
Description
'TidyTuesday' is a project by the 'Data Science Learning Community' in which they post a weekly dataset in a public data repository (https://github.com/rfordatascience/tidytuesday) for people to analyze and visualize. This package provides the tools to easily download this data and the description of the source.
Author(s)
Maintainer: Jon Harmon jonthegeek@gmail.com (ORCID)
Authors:
Ellis Hughes ellishughes@live.com
Other contributors:
Thomas Mock j.thomasmock@gmail.com [contributor]
Data Science Learning Community tidytuesday@dslc.io [data contributor]
See Also
Useful links:
Report bugs at https://github.com/dslc-io/tidytuesdayR/issues
Pipe operator
Description
See magrittr::%>% for details.
Usage
lhs %>% rhs
Arguments
lhs |
A value or the magrittr placeholder. |
rhs |
A function call using the magrittr semantics. |
Value
The result of calling rhs(lhs).
Get data from the tt github repo.
Description
Get data from the tt github repo.
Usage
gh_get(path, auth = gh::gh_token(), ...)
Arguments
path |
Path within the |
auth |
A GitHub token. See |
... |
Additional parameters passed to |
Value
The GitHub response as parsed by gh::gh().
Find the most recent tuesday
Description
Identify the most recent 'TidyTuesday' date relative to a specified date.
Usage
last_tuesday(date = today(tzone = "America/New_York"))
Arguments
date |
A date as a date object or character string in |
Value
The TidyTuesday date in the same week as the specified date, using Monday as the start of the week.
Examples
last_tuesday() # get last Tuesday relative to today's date
last_tuesday("2020-01-01") # get last Tuesday relative to a specified date
Set up a directory for dataset curation
Description
Set up a directory for dataset curation
Usage
prep_tt_curate(
path = "tt_submission",
ignore = FALSE,
env = rlang::caller_env()
)
Arguments
path |
The relative path to the directory to hold your submission files
( |
ignore |
Should the newly created file be added to |
Value
The resolved path (invisibly).
print methods of the tt objects
Description
In tidytuesdayR there are nice print methods for the objects that were used to download and store the data from the TidyTuesday repo. They will always print the available datasets/files. If there is a readme available, it will try to display the TidyTuesday readme.
Usage
## S3 method for class 'tt_data'
print(x, ...)
## S3 method for class 'tt'
print(x, ...)
Arguments
x |
a tt_data or tt object |
... |
further arguments passed to or from other methods. |
Value
x, invisibly.
Examples
tt <- tt_load_gh("2019-01-15")
print(tt)
tt_data <- tt_download(tt, files = "All")
print(tt_data)
Readme HTML maker and Viewer
Description
Readme HTML maker and Viewer
Usage
readme(tt)
Arguments
tt |
tt_data object for printing |
Value
Null, invisibly. Used to show readme of the downloaded TidyTuesday dataset in the Viewer.
Examples
if (rate_limit_check(quiet = TRUE) > 30) {
tt_output <- tt_load_gh("2019-01-15")
readme(tt_output)
}
Parameters used in multiple functions
Description
Reused parameter definitions are gathered here for easier editing.
Arguments
auth |
A GitHub token. See |
files |
Which file names to download. Default "All" downloads all files for the specified week. |
path |
The relative path to the directory to hold your submission files
( |
tt |
A |
week |
Which week number to use within a given year. Only used when |
x |
The date of data to pull (in "YYYY-MM-dd" format), or the four-digit year as a number. |
year |
What year of TidyTuesday to use |
Decide whether to update the master file
Description
Decide whether to update the master file
Usage
should_update_tt_master_file(force = FALSE, auth = gh::gh_token())
Arguments
force |
force the update to occur even if the SHA matches |
auth |
A GitHub token. See |
Value
Boolean indicating whether the master file should be updated.
Listing all available TidyTuesdays
Description
The TidyTuesday project is a constantly growing repository of data sets. Knowing what type of data is available for each week requires going to the source. However, one of the hallmarks of 'tidytuesdayR' is that you never have to leave your R console. These functions were created to help maintain this philosophy.
Usage
tt_available(auth = gh::gh_token())
tt_datasets(year, auth = gh::gh_token())
Arguments
auth |
A GitHub token. See |
year |
What year of TidyTuesday to use |
Details
To find out the available datasets for a specific year, the user
can use the function tt_datasets(). This function will either populate the
Viewer or print to console all the available data sets and the week/date
they are associated with.
To get the whole list of all the data sets ever released by TidyTuesday, the
function tt_available() was created. This function will either populate the
Viewer or print to console all the available data sets ever made for
TidyTuesday.
Value
tt_available() returns a tt_dataset_table_list, which is a
list of tt_dataset_table. This class has special printing methods to show
the available data sets.
tt_datasets() returns a tt_dataset_table object. This class has
special printing methods to show the available datasets for the year.
Examples
# check to make sure there are requests still available
if (rate_limit_check(quiet = TRUE) > 30) {
## show data available from 2018
tt_datasets(2018)
## show all data available ever
tt_available()
}
Generate valid TidyTuesday URL
Description
Given multiple types of inputs, generate a valid TidyTuesday URL.
Usage
tt_check_date(x, week = NULL, auth = gh::gh_token())
Arguments
x |
The date of data to pull (in "YYYY-MM-dd" format), or the four-digit year as a number. |
week |
Which week number to use within a given year. Only used when |
auth |
A GitHub token. See |
Create and open cleaning.R
Description
The first step of curating a TidyTuesday dataset is cleaning the data. This
function creates a simple cleaning.R file in the specified path (creating
that path if it does not already exist), and (if possible) opens it for
editing.
Usage
tt_clean(
path = "tt_submission",
open = rlang::is_interactive(),
ignore = FALSE
)
Arguments
path |
The relative path to the directory to hold your submission files
( |
open |
Open the newly created file for editing? Happens in RStudio, if
applicable, or via |
ignore |
Should the newly created file be added to |
Value
A logical vector indicating whether the file was created or modified, invisibly.
Examples
tt_clean()
Get TidyTuesday readme and list of files and HTML based on the date
Description
Get TidyTuesday readme and list of files and HTML based on the date
Usage
tt_compile(date, auth = gh::gh_token())
Arguments
date |
date of TidyTuesday of interest |
auth |
A GitHub token. See |
Guidance for TidyTuesday dataset curation
Description
Open an R script to guide you through the process of curating and submitting
a TidyTuesday dataset. See vignette("curating", package = "tidytuesdayR)
for more information.
Usage
tt_curate_data()
Value
The path to the tt_curation.R script, invisibly.
Examples
tt_curate_data()
Get date of TidyTuesday, given the year and week
Description
Sometimes we don't know the date we want, but we do know the week. This function provides the ability to pass the year and week we are interested in to get the correct date
Usage
tt_date(year, week = NULL, auth = gh::gh_token())
Arguments
year |
What year of TidyTuesday to use |
week |
Which week number to use within a given year. Only used when |
auth |
A GitHub token. See |
Download TidyTuesday data
Description
Download all or specific files identified in a TidyTuesday dataset.
Usage
tt_download(tt, files = "All", ..., auth = gh::gh_token())
Arguments
tt |
A |
files |
Which file names to download. Default "All" downloads all files for the specified week. |
... |
Additional parameters to pass to the parsing functions. Note: These arguments will be passed for all filetypes. |
auth |
A GitHub token. See |
Value
A list of tibbles from the downloaded files.
Examples
# Get the list of files for a week.
tt_output <- tt_load_gh("2019-01-15")
# Download a specific file.
agencies <- tt_download(tt_output, files = "agencies.csv")
Download a TidyTuesday dataset file
Description
Download an actual data file from the TidyTuesday github repository.
Usage
tt_download_file(tt, x, ..., auth = gh::gh_token())
Arguments
tt |
A |
x |
Index or name of file to download. |
... |
Additional parameters to pass to the parsing functions. Note: These arguments will be passed for all filetypes. |
auth |
A GitHub token. See |
Value
tibble containing the contents of the file downloaded from git
Examples
tt_gh <- tt_load_gh("2019-01-15")
agencies <- tt_download_file(tt_gh, 1)
launches <- tt_download_file(tt_gh, "launches.csv")
Create and open intro.md
Description
When curating a TidyTuesday dataset, you need to introduce the dataset. This
function creates a simple intro.md file in the specified path (creating
that path if it does not already exist), and (if possible) opens it for
editing.
Usage
tt_intro(
path = "tt_submission",
open = rlang::is_interactive(),
ignore = FALSE
)
Arguments
path |
The relative path to the directory to hold your submission files
( |
open |
Open the newly created file for editing? Happens in RStudio, if
applicable, or via |
ignore |
Should the newly created file be added to |
Value
A logical vector indicating whether the file was created or modified, invisibly.
Examples
tt_intro()
Load TidyTuesday data from Github
Description
Load TidyTuesday data from Github
Usage
tt_load(x, week = NULL, files = "All", ..., auth = gh::gh_token())
Arguments
x |
The date of data to pull (in "YYYY-MM-dd" format), or the four-digit year as a number. |
week |
Which week number to use within a given year. Only used when |
files |
Which file names to download. Default "All" downloads all files for the specified week. |
... |
Additional parameters to pass to the parsing functions. Note: These arguments will be passed for all filetypes. |
auth |
A GitHub token. See |
Value
tt_data object, which contains data that can be accessed via $,
and the readme for the week's TidyTuesday, which can be viewed by printing
the object or calling readme().
Examples
tt_output <- tt_load("2019-01-15")
tt_output
agencies <- tt_output$agencies
Load TidyTuesday data from Github
Description
Pulls the readme and URLs of the data from the TidyTuesday github folder based on the date provided
Usage
tt_load_gh(x, week = NULL, auth = gh::gh_token())
Arguments
x |
The date of data to pull (in "YYYY-MM-dd" format), or the four-digit year as a number. |
week |
Which week number to use within a given year. Only used when |
auth |
A GitHub token. See |
Value
A tt object. This contains the files available for the week,
readme html, and the date of the TidyTuesday.
Examples
# check to make sure there are requests still available
if (rate_limit_check(quiet = TRUE) > 30) {
tt_gh <- tt_load_gh("2019-01-15")
## readme attempts to open the readme for the weekly dataset
readme(tt_gh)
agencies <- tt_download(
tt_gh,
files = "agencies.csv"
)
}
Get Master List of Files from TidyTuesday
Description
Import or update dataset from github that records the entire list of objects from TidyTuesday
Usage
tt_master_file(force = FALSE, auth = gh::gh_token())
Arguments
force |
force the update to occur even if the SHA matches |
auth |
A GitHub token. See |
Value
The tt master file, updated if necessary.
Create and open meta.yaml
Description
We need a set of metadata information about each TidyTuesday dataset. Use
this function to set up the meta.yaml file for your submission (and create
the submission directory if it does not already exist). If you do not provide
values for the parameters, you will be prompted to enter them in an
interactive session.
Usage
tt_meta(
path = "tt_submission",
title,
article_title,
article_url,
source_title,
source_url,
image_filename,
image_alt,
attribution,
github = gh::gh_whoami()$login,
bluesky = NULL,
linkedin = NULL,
mastodon = NULL,
open = rlang::is_interactive(),
ignore = FALSE
)
Arguments
path |
The relative path to the directory to hold your submission files
( |
title |
A short title for your submission. It should fit into the
sentence "This week we're exploring |
article_title |
The title of an article or other website that has something to do with the data. This should usually be an article that uses or describes the dataset, but any related website is acceptable. |
article_url |
The URL of the article whose title is |
source_title |
The title of the source of the dataset. This is usually a website, but might be an R package or a journal article, for example. |
source_url |
A URL associated with the source. Ideally this should be a URL where users can download the data, but, if that isn't possible, provide a URL that is somehow related to the source of the data. |
image_filename |
A character vector with at least one file name for an image to accompany the post. This might be a plot of the data, or some othe image somehow connected to the data. |
image_alt |
Text that can take the place of the image for a visually impaired user or anybody else who cannot see the image. Don't just say "A plot of the data", but rather describe what information you can glean from the plot, such as "A map of the continental United States, with each state colored in shades of blue by population as of 1975. California and New York are the lightest, indicating the highest population. Maine, New Hampshire, Vermont, and the Plains States are all quite dark, indicating low population." |
attribution |
Your name as you would like it to appear when we credit you in the post for this dataset. You can include a title and/or affiliation if you like, such as "Jon Harmon, Executive Director, Data Science Learning Community". |
github |
Your GitHub username, or a link to your profile on GitHub. |
bluesky |
Your Bluesky username, or a link to your profile on Bluesky.
Leave as |
linkedin |
Your LinkedIn username, or a link to your profile on LinkedIn
Leave as |
mastodon |
Your mastodon server and username, or a link to your profile
on a mastodon server. Leave as |
open |
Open the newly created file for editing? Happens in RStudio, if
applicable, or via |
ignore |
Should the newly created file be added to |
Value
A logical vector indicating whether the file was created or modified, invisibly.
Examples
tt_meta()
Printing Utilities for Listing Available Datasets
Description
printing utilities for showing the available datasets for a specific year or all time
Usage
## S3 method for class 'tt_dataset_table'
print(x, ..., is_interactive = interactive())
## S3 method for class 'tt_dataset_table_list'
print(x, ..., is_interactive = interactive())
Arguments
x |
an object used to select a method. |
... |
further arguments passed to or from other methods. |
is_interactive |
Whether the function is being used interactively. |
Value
x, invisibly
Examples
# check to make sure there are requests still available
if (rate_limit_check(quiet = TRUE) > 30) {
available_datasets_2018 <- tt_datasets(2018)
print(available_datasets_2018)
all_available_datasets <- tt_available()
print(all_available_datasets)
}
Save datasets for submission
Description
Datasets for TidyTuesday submissions should be saved in a specific format,
with an accompanying data dictionary dataset_name.md file. This function
saves the dataset as a CSV file in your submission directory (creating the
submission directory if it does not already exist), and creates a data
dictionary file for you to fill out. If you're in an interactive session, the
dictionary file is opened for editing.
Usage
tt_save_dataset(
dataset,
path = "tt_submission",
dataset_name = rlang::caller_arg(dataset),
open = rlang::is_interactive(),
ignore = FALSE
)
Arguments
dataset |
The clean dataset to save. The dataset must be a data.frame. |
path |
The relative path to the directory to hold your submission files
( |
dataset_name |
The name to save the dataset as. By default, the name of the dataset variable is used. |
open |
Open the newly created file for editing? Happens in RStudio, if
applicable, or via |
ignore |
Should the newly created file be added to |
Value
A logical vector indicating whether the file was created or modified, invisibly.
Examples
tt_save_dataset(mtcars)
Submit a TidyTuesday dataset
Description
Submit a curated dataset for review by uploading it to GitHub and creating a
pull request. The dataset should be prepared using tt_clean(),
tt_save_dataset(), tt_intro(), and tt_meta(). You can also use this
function to submit changes to your local copies of the files.
Usage
tt_submit(
path = "tt_submission",
auth = gh::gh_token(),
open = rlang::is_interactive()
)
Arguments
path |
The relative path to the directory to hold your submission files
( |
auth |
A GitHub token. See |
open |
Whether to open the pull request in a browser. Defaults to |
Value
The URL of the pull request, invisibly.
Examples
# First set up a dataset in the "tt_submission" folder.
tt_submit()
Create and open the tidytemplate
Description
Use the tidytemplate Rmd for starting your analysis with a leg up for processing
Usage
use_tidytemplate(
name = NULL,
open = rlang::is_interactive(),
refdate = today(),
ignore = FALSE
)
Arguments
name |
A name for your generated TidyTuesday analysis Rmd, such as "My_TidyTuesday.Rmd". |
open |
Open the newly created file for editing? Happens in RStudio, if
applicable, or via |
refdate |
Date to use as reference to determine which TidyTuesday to use for the template. Either date object or character string in YYYY-MM-DD format. |
ignore |
Should the newly created file be added to |
Value
A logical vector indicating whether the file was created or modified, invisibly.
Examples
use_tidytemplate(name = "My_Awesome_TidyTuesday.Rmd")