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Please add alt text to your posts

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:

Chart type

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

Type of data

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

Reason for including the chart

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

Link to data or source

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.

Registered Nurses

The data this week comes from Data.World.

The BLS also wrote about Registered Nurses by state.

Get the data here

# 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('2021-10-05')
tuesdata <- tidytuesdayR::tt_load(2021, week = 41)

nurses <- tuesdata$nurses

# Or read in the data manually

nurses <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2021/2021-10-05/nurses.csv')

Data Dictionary

nurses.csv

variable class description
State character State
Year double Year
Total Employed RN double Total Employed Registered Nurses
Employed Standard Error (%) double Employed standard error (%)
Hourly Wage Avg double Hourly wage average
Hourly Wage Median double Hourly wage median
Annual Salary Avg double Annual salary average
Annual Salary Median double Annual salary median
Wage/Salary standard error (%) double Wage/salary standard error %
Hourly 10th Percentile double Hourly 10th Percentile
Hourly 25th Percentile double Hourly 25th Percentile
Hourly 75th Percentile double Hourly 75th Percentile
Hourly 90th Percentile double Hourly 90th Percentile
Annual 10th Percentile double Annual 10th Percentile
Annual 25th Percentile double Annual 25th Percentile
Annual 75th Percentile double Annual 75th Percentile
Annual 90th Percentile double Annual 90th Percentile
Location Quotient double Location Quotient
Total Employed (National)_Aggregate double Total Employed (National)_Aggregate
Total Employed (Healthcare, National)_Aggregate double Total Employed (Healthcare, National)_Aggregate
Total Employed (Healthcare, State)_Aggregate double Total Employed (Healthcare, State)_Aggregate
Yearly Total Employed (State)_Aggregate double Yearly Total Employed (State)_Aggregate

Cleaning Script

No cleaning script but definitely explore:

tidyr::pivot_longer()