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.
The data this week comes from Simon Couch's detectors R package. containing predictions from various GPT detectors.
The data is based on the pre-print:
GPT Detectors Are Biased Against Non-Native English Writers. Weixin Liang, Mert Yuksekgonul, Yining Mao, Eric Wu, James Zou. arXiv: 2304.02819
The study authors carried out a series of experiments passing a number of essays to different GPT detection models. Juxtaposing detector predictions for papers written by native and non-native English writers, the authors argue that GPT detectors disproportionately classify real writing from non-native English writers as AI-generated.
# 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('2023-07-18')
tuesdata <- tidytuesdayR::tt_load(2023, week = 29)
detectors <- tuesdata$detectors
# Or read in the data manually
detectors <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2023/2023-07-18/detectors.csv')
| variable | class | description |
|---|---|---|
| kind | character | Whether the essay was written by a "Human" or "AI". |
| .pred_AI | double | The class probability from the GPT detector that the inputted text was written by AI. |
| .pred_class | character | The uncalibrated class prediction, encoded as if_else(.pred_AI > .5, "AI", "Human") |
| detector | character | The name of the detector used to generate the predictions. |
| native | character | For essays written by humans, whether the essay was written by a native English writer or not. These categorizations are coarse; values of "Yes" may actually be written by people who do not write with English natively. NA indicates that the text was not written by a human. |
| name | character | A label for the experiment that the predictions were generated from. |
| model | character | For essays that were written by AI, the name of the model that generated the essay. |
| document_id | double | A unique identifier for the supplied essay. Some essays were supplied to multiple detectors. Note that some essays are AI-revised derivatives of others. |
| prompt | character | For essays that were written by AI, a descriptor for the form of "prompt engineering" passed to the model. |
csv file was generated from the detectors tibble in the 'detectors' R package.