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 this week comes from gender-pay-gap.service.gov.uk. The online tool reports by gender and occupation. The online quiz lets you test your knowledge/guesses.
# 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('2022-06-28')
tuesdata <- tidytuesdayR::tt_load(2022, week = 26)
paygap <- tuesdata$paygap
# Or read in the data manually
paygap <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2022/2022-06-28/paygap.csv')
| Field | Description | Source |
|---|---|---|
| EmployerName | The name of the employer at the time of reporting | Via CoHo API or manually entered by user when adding an employer to their account |
| EmployerID | Unique ID assigned to each employer that is consistent across every reporting year | Generated by the system |
| Address | The current registered address of the employer | Via CoHo API or manually entered by user when adding an employer to their account |
| PostCode | The postal code of the current registered address of the employer | Via CoHo API or manually entered by user when adding an employer to their account |
| CompanyNumber | The Company Number of the employer as listed on Companies House (null for public sector) | Via CoHo API |
| SicCodes | List of comma-separated SIC codes used to describe the employer's purpose and sectors of work at the time of reporting | Via CoHo API or manually entered by user when adding an employer to their account |
| DiffMeanHourlyPercent | Mean % difference between male and female hourly pay (negative = women's mean hourly pay is higher) | Entered by a user when reporting GPG data |
| DiffMedianHourlyPercent | Median % difference between male and female hourly pay (negative = women's median hourly pay is higher) | Entered by a user when reporting GPG data |
| DiffMeanBonusPercent | Mean % difference between male and female bonus pay (negative = women's mean bonus pay is higher) | Entered by a user when reporting GPG data |
| DiffMedianBonusPercent | Median % difference between male and female bonus pay (negative = women's median bonus pay is higher) | Entered by a user when reporting GPG data |
| MaleBonusPercent | Percentage of male employees paid a bonus | Entered by a user when reporting GPG data |
| FemaleBonusPercent | Percentage of female employees paid a bonus | Entered by a user when reporting GPG data |
| MaleLowerQuartile | Percentage of males in the lower hourly pay quarter | Entered by a user when reporting GPG data |
| FemaleLowerQuartile | Percentage of females in the lower hourly pay quarter | Entered by a user when reporting GPG data |
| MaleLowerMiddleQuartile | Percentage of males in the lower middle hourly pay quarter | Entered by a user when reporting GPG data |
| FemaleLowerMiddleQuartile | Percentage of females in the lower middle hourly pay quarter | Entered by a user when reporting GPG data |
| MaleUpperMiddleQuartile | Percentage of males in the upper middle hourly pay quarter | Entered by a user when reporting GPG data |
| FemaleUpperMiddleQuartile | Percentage of females in the upper middle hourly pay quarter | Entered by a user when reporting GPG data |
| MaleTopQuartile | Percentage of males in the top hourly pay quarter | Entered by a user when reporting GPG data |
| FemaleTopQuartile | Percentage of females in the top hourly pay quarter | Entered by a user when reporting GPG data |
| CompanyLinkToGPGInfo | Voluntary link to additional GPG data published by the reporting employer | Entered by a user when reporting GPG data |
| ResponsiblePerson | The name of the responsible person who confirms that the published information is accurate - Employers covered by the private sector regulations only | Entered by a user when reporting GPG data |
| EmployerSize | Number of employees employed by an employer | Entered by a user when reporting GPG data |
| CurrentName | The current name of the employer | Via CoHo API or manually entered by user when adding an employer to their account |
| SubmittedAfterTheDeadline | TRUE/FALSE value showing whether the employee submitted their GPG data after the relevant reporting deadline. If a report is updated after the initial submission, it is marked as late only if the figures are changed | Generated by the system |
| DueDate | The date that the GPG data should have been submitted by. Format: dd/MM/yyyy HH:mm:ss | Generated by the system |
| DateSubmitted | Date that GPG data was submitted (if this was updated after the initial submission, this date also changes). Format: dd/MM/yyyy HH:mm:ss | Generated by the system |
library(tidyverse)
library(vroom)
library(fs)
pay_files <- fs::dir_ls(path = "2022/2022-06-28/", glob = "*csv")
raw_df <- vroom::vroom(pay_files)
clean_df <- raw_df |>
janitor::clean_names() |>
mutate(
across(c(due_date, date_submitted),lubridate::as_datetime),
employer_name = str_remove_all(employer_name, "\""),
employer_name = str_replace_all(employer_name, ", |,", ", ")
)
clean_df |>
glimpse()
clean_df |>
write_csv("2022/2022-06-28/paygap.csv")