Accessibility

Lecture 17

Dr. Mine Çetinkaya-Rundel

Duke University
STA 113 - Fall 2023

Warm-up

Announcements

• Peer eval 1 due today at 5pm, comments will be published later this evening.

• Set up a meeting with Shelby and your entire team for 30 minutes between today and next Tuesday to review your project.

• Due date of last HW: Fri, Dec 8

Setup

library(countdown)
library(tidyverse)
library(scales)
library(ggthemes)
library(coloratio) # devtools::install_github("matt-dray/coloratio")

Do you remember?

Flatten the curve

Source: The New York Times. Flattening the Coronavirus Curve

Exponential spread

Source: The Washington Post. Why outbreaks like coronavirus spread exponentially, and how to “flatten the curve”

JHU COVID-19 Dashboard

Source: COVID-19 Dashboard by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU)

Think back

What do they all have in common?

Accessible COVID-19 statistics tracker

Source: Accessible COVID-19 statistics tracker

Accessibility and screen readers

Alternative text

It is read by screen readers in place of images allowing the content and function of the image to be accessible to those with visual or certain cognitive disabilities.

It is displayed in place of the image in browsers if the image file is not loaded or when the user has chosen not to view images.

It provides a semantic meaning and description to images which can be read by search engines or be used to later determine the content of the image from page context alone.

Source: WebAIM

Alt and surrounding text

"CHART TYPE of TYPE OF DATA where REASON FOR INCLUDING CHART`

+ Link to data source somewhere in the text

• 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.

• TYPE OF DATA: What data is included in the chart? The x and y axis labels may help you figure this out.

• REASON FOR INCLUDING 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.

• Link to data source: Don’t include this in your alt text, but it should be included somewhere in the surrounding text.

Source: Writing Alt Text for Data Visualization

Data

• Registered nurses by state and year
• Number of nurses, salaries, employment
• Source: TidyTuesday

nurses <- read_csv("data/nurses.csv") |> janitor::clean_names()
names(nurses)

 [1] "state"                                       
 [2] "year"                                        
 [3] "total_employed_rn"                           
 [4] "employed_standard_error_percent"             
 [5] "hourly_wage_avg"                             
 [6] "hourly_wage_median"                          
 [7] "annual_salary_avg"                           
 [8] "annual_salary_median"                        
 [9] "wage_salary_standard_error_percent"          
[10] "hourly_10th_percentile"                      
[11] "hourly_25th_percentile"                      
[12] "hourly_75th_percentile"                      
[13] "hourly_90th_percentile"                      
[14] "annual_10th_percentile"                      
[15] "annual_25th_percentile"                      
[16] "annual_75th_percentile"                      
[17] "annual_90th_percentile"                      
[18] "location_quotient"                           
[19] "total_employed_national_aggregate"           
[20] "total_employed_healthcare_national_aggregate"
[21] "total_employed_healthcare_state_aggregate"   
[22] "yearly_total_employed_state_aggregate"       

Alt text for bar charts

• Provide title and axis labels
• Briefly describe the chart and give a summary of any trends it displays
• Convert bar charts to accessible tables or lists
• Avoid describing visual attributes of the bars (e.g., dark blue, gray, yellow) unless there’s an explicit need to do so

Developing the alt text

• Total employed registered nurses in three states over time.

• Total employed registered nurses in California, New York, and North Carolina, in 2000, 2010, and 2020.

• A bar chart of total employed registered nurses in California, New York, and North Carolina, in 2000, 2010, and 2020, showing increasing numbers of nurses over time.

• The figure is a bar chart titled “Total employed Registered Nurses” that displays the numbers of registered nurses in three states (California, New York, and North Carolina) over a 20 year period, with data recorded in three time points (2000, 2010, and 2020). In each state, the numbers of registered nurses increase over time. The following numbers are all approximate. California started off with 200K registered nurses in 2000, 240K in 2010, and 300K in 2020. New York had 150K in 2000, 160K in 2010, and 170K in 2020. Finally North Carolina had 60K in 2000, 90K in 2010, and 100K in 2020.

Alt text for line graphs

Write alt text for the line graph above.

• Provide title and axis labels
• Briefly describe the graph and give a summary of any trends it displays
• Convert data represented in lines to accessible tables or lists where feasible
• Avoid describing visual attributes of the lines (e.g., purple, pink) unless there’s an explicit need to do so

Alt text for scatter plots

Write alt text for the scatter plot above.

Scatter plots are among the more difficult graphs to describe, especially if there is a need to make specific data point accessible.

• Identify the image as a scatter plot
• Provide the title and axis labels
• Focus on the overall trend
• If it’s necessary to be more specific, convert the data into an accessible table

Recommended reading

Accessible Visualization via Natural Language Descriptions: A Four-Level Model of Semantic Content

Alan Lundgard, MIT CSAIL
Arvind Satyanarayan, MIT CSAIL

IEEE Transactions on Visualization & Computer Graphics (Proceedings of IEEE VIS), 2021

To demonstrate how our model can be applied to evaluate the effectiveness of visualization descriptions, we conduct a mixed-methods evaluation with 30 blind and 90 sighted readers, and find that these reader groups differ significantly on which semantic content they rank as most useful. Together, our model and findings suggest that access to meaningful information is strongly reader-specific, and that research in automatic visualization captioning should orient toward descriptions that more richly communicate overall trends and statistics, sensitive to reader preferences.

Adding alt text to plots

• Short:

```{r}
#| fig-alt: Alt text goes here.

# code for plot goes here
```

• Longer:

```{r}
#| fig-alt: |
#|   Longer alt text goes here. Make sure to add line breaks ~roughly
#|   80 characters.

# code for plot goes here
```

Accessibility and colors

Color scales

Use colorblind friendly color scales (e.g., Okabe Ito, viridis)

nurses_subset |>
  ggplot(aes(x = year, y = hourly_wage_median, color = state)) +
  geom_point(size = 2) +
  ggthemes::scale_color_colorblind() +
  scale_y_continuous(labels = label_dollar()) +
  labs(
    x = "Year", y = "Median hourly wage", color = "State",
    title = "Median hourly wage of Registered Nurses"
  ) +
  theme(
    legend.position = c(0.15, 0.75),
    legend.background = element_rect(fill = "white", color = "white")
    )

The default ggplot2 color scale + deuteranopia

Deuteranopia: A type of red-green confusion

Default ggplot2 scale

Default ggplot2 scale with deuteranopia

The default ggplot2 color scale + tritanopia

Tritanopia: A type of yellow-blue confusion

Default ggplot2 scale

Default ggplot2 scale with tritanopia

Testing for colorblind friendliness

• Best way to test is with users (or collaborators) who have these color deficiencies

• Simulation software also helps, e.g. Sim Daltonism for Mac and PC

Color contrast I

• Background and foreground text should have sufficient contrast to be distinguishable by users with different vision

• Web app for checking color contrast checking: Colour Contrast Analyser

Color contrast II

A WIP R package for checking for color contrast: coloratio

cr_get_ratio("black", "white")

[1] 21

cr_get_ratio("#FFFFFF", "#000000")

[1] 21

cr_get_ratio("black", "gray10")

[1] 1.206596

Double encoding

Use shape and color where possible

Default ggplot2 scale

Default ggplot2 scale with deuteranopia

Use direct labeling

• Prefer direct labeling where color is used to display information over a legend

• Quicker to read

• Ensures graph can be understood without reliance on color

Without direct labeling

Default ggplot2 scale

Default ggplot2 scale with deuteranopia

With direct labeling

Default ggplot2 scale

Default ggplot2 scale with deuteranopia

Use whitespace or pattern to separate elements

• Separate elements with whitespace or pattern

• Allows for distinguishing between data without entirely relying on contrast between colors

Without whitespace

Default ggplot2 scale

Default ggplot2 scale with tritanopia

With whitespace

Default ggplot2 scale

Default ggplot2 scale with tritanopia

Find a colorblind friendly color palette other than viridis and those in the colorblindr package. This could be an R package that offers a colorblind friendly color palette or it could be just a palette you find online. Apply it to the scatterplot you previously wrote alt text for.

Accessibility and fonts

Accessibility and fonts

• Use a font that has been tested for accessibility (e.g., Atkinson Hyperlegible)

• Keep plot labels and annotations similarly sized as the rest of your text (e.g., ggplot2::theme_set(ggplot2::theme_minimal(base_size = 16)))

Demo: Using custom fonts in ggplots!

Keep in mind

When you design for accessibility, you benefit everyone

Source: A Comprehensive Guide to Accessible Data Visualization

Other approaches to accessibility

Data sonification

Data sonification is the presentation of data as sound, i.e., auditory equivalent of data visualization.

Example: http://playitbyr.org/gettingstarted.html#Idea

Data physicalization / haptics

Data physicalization is the presentation of data as objects you can touch, i.e., sensory equivalent of data visualization.

Examples: https://datajournalism.com/read/longreads/lets-get-physical-how-to-represent-data-through-touch

Acknowledgements

• COVID visualization examples:

  • The New York Times. Flattening the Coronavirus Curve
  • The Washington Post. Why outbreaks like coronavirus spread exponentially, and how to “flatten the curve”
  • COVID-19 Dashboard by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU)
  • T. Littlefield (2020) COVID-19 Statistics Tracker

• Lundgard, Alan, and Arvind Satyanarayan. “Accessible Visualization via Natural Language Descriptions: A Four-Level Model of Semantic Content.” IEEE transactions on visualization and computer graphics (2021).

• A Comprehensive Guide to Accessible Data Visualization

• Silvia Canelón and Liz Hare. Revealing Room for Improvement in Accessibility within a Social Media Data Visualization Learning Community