Please add alt text (alternative text) to all of your posted graphics for #TidyTuesday.
Twitter provides guidelines for how to add alt text to your images.
The DataViz Society/Nightingale by way of Amy Cesal has an article on writing good alt text for plots/graphs.
Here's a simple formula for writing alt text for data visualization:
It's helpful for people with partial sight to know what chart type it is and gives context for understanding the rest of the visual. Example: Line graph
What data is included in the chart? The x and y axis labels may help you figure this out. Example: number of bananas sold per day in the last year
Think about why you're including this visual. What does it show that's meaningful. There should be a point to every visual and you should tell people what to look for. Example: the winter months have more banana sales
Don't include this in your alt text, but it should be included somewhere in the surrounding text. People should be able to click on a link to view the source data or dig further into the visual. This provides transparency about your source and lets people explore the data. Example: Data from the USDA
Penn State has an article on writing alt text descriptions for charts and tables.
Charts, graphs and maps use visuals to convey complex images to users. But since they are images, these media provide serious accessibility issues to colorblind users and users of screen readers. See the examples on this page for details on how to make charts more accessible.
The {rtweet} package includes the ability to post tweets with alt text programatically.
Need a reminder? There are extensions that force you to remember to add Alt Text to Tweets with media.
The data in this repo comes from Spotify and Genius. Thank you to the authors of the spotifyr and geniusr packages for making it easy to access data from these platforms!
There are 3 data sets about or related to the Spice Girls:
studio_album_tracks: Audio features of each song from the three studio albums by the Spice Girls. From Spotify.related artists: Artists deemed to be similar to the Spice Girls, with info about each artist including their musical genres and follower numbers. Includes a row with details for the Spice Girls, for comparison purposes. From Spotify.lyrics: Lyrics of each song from the three studio albums by the Spice Girls. From Genius.
Credit: Jacquie Tran
A data dictionary for each data set is provided here.
The R code below uses the studio_album_tracks data set to produce summary statistics for selected audio features.
# Load libraries
library(dplyr)
# Read data into R
studio_album_tracks <- readr::read_csv("https://github.com/jacquietran/spice_girls_data/raw/main/data/studio_album_tracks.csv")
# For each album, calculate mean values for danceability, energy, and valence
studio_album_tracks %>%
group_by(album_name) %>%
summarise(
danceability_mean = mean(danceability),
energy_mean = mean(energy),
valence_mean = mean(valence)) %>%
ungroup() %>%
# Set factor levels of album_name
mutate(
album_name = factor(
album_name, levels = c("Spice", "Spiceworld", "Forever"))) %>%
arrange(album_name)
spotifyr: https://www.rcharlie.com/spotifyr/index.htmlgeniusr: https://ewenme.github.io/geniusr/
# Get the Data
# Read in with tidytuesdayR package
# Install from CRAN via: install.packages("tidytuesdayR")
# This loads the readme and all the datasets for the week of interest
# Either ISO-8601 date or year/week works!
tuesdata <- tidytuesdayR::tt_load('2021-12-14')
tuesdata <- tidytuesdayR::tt_load(2021, week = 51)
studio_album_tracks <- tuesdata$studio_album_tracks
# Or read in the data manually
studio_album_tracks <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2021/2021-12-14/studio_album_tracks.csv')
| variable | class | description |
|---|---|---|
| artist_name | character | Artist name |
| album_name | character | Album name |
| track_number | double | Track number |
| song_id | double | Song ID |
| song_name | character | Song Name |
| line_number | double | Line Number |
| section_name | character | Section name |
| line | character | Line |
| section_artist | character | Section artist |
| variable | class | description |
|---|---|---|
| artist_id | character | Artist ID |
| artist_name | character | Artist name |
| genres | character | Genres |
| popularity | double | Popularity |
| followers_total | double | Followers total |
| variable | class | description |
|---|---|---|
| artist_name | character | Artist name |
| artist_id | character | Artist ID |
| album_id | character | Album ID |
| album_release_date | double | Release date |
| album_release_year | double | Year |
| danceability | double | Danceability |
| energy | double | Energy |
| key | double | Key |
| loudness | double | Loudness |
| mode | double | Mode |
| speechiness | double | Speechiness |
| acousticness | double | Acousticness |
| instrumentalness | double | Instrumentalness |
| liveness | double | Liveness |
| valence | double | Valence |
| tempo | double | Tempo |
| track_id | character | Track ID |
| time_signature | double | Time signature |
| duration_ms | double | Duration in ms |
| track_name | character | track name |
| track_number | double | Track number |
| album_name | character | Album name |
| key_name | character | Key name |
| mode_name | character | Mode name |
| key_mode | character | Key mode |
See: Jacquie Tran's repo