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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.

Dr. Who

The data this week comes from the datardis package by way of Jonathan Kitt.

They have a short blogpost on the package at: https://randomics.netlify.app/posts/2021-11-16-datardis/.

Additional brief articlet from the Independent.ie

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('2021-11-23')
tuesdata <- tidytuesdayR::tt_load(2021, week = 48)

directors <- tuesdata$directors

# Or read in the data manually

directors <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2021/2021-11-23/directors.csv')
episodes <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2021/2021-11-23/episodes.csv')
writers <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2021/2021-11-23/writers.csv')
imdb <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2021/2021-11-23/imdb.csv')

Data Dictionary

directors.csv

variable class description
story_number character Story number
director character Director

episodes.csv

variable class description
era character Era = Classic or revived
season_number double Season number
serial_title character Serial title
story_number character Storu number
episode_number integer Episode number
episode_title character Episode title
type character Type
first_aired double First aired date
production_code character Production code
uk_viewers double UK Viewership in Millions
rating double Rating
duration double Duration in minutes

writers.csv

variable class description
story_number character Story number
writer character Writer

imdb.csv

variable class description
season integer Season number
ep_num double Episode number
air_date character Air date
rating double Rating
rating_n double Number of ratings
desc character Episode description

Cleaning Script

library(tidyverse)
library(rvest)

season <- 1

get_imdb <- function(season){
  url <- glue::glue("https://www.imdb.com/title/tt0436992/episodes?season={season}")
  
  raw_html <- read_html(url)
  
  raw_div <- raw_html %>% 
    html_elements("div.list.detail.eplist") %>% 
    html_elements("div.info")
  
  ep_num <- raw_div %>% 
    html_elements("meta") %>% 
    html_attr("content")
  
  air_date <- raw_div %>% 
    html_elements("div.airdate") %>% 
    html_text() %>% 
    str_squish()
  
  ratings <- raw_div %>% 
    html_elements("div.ipl-rating-star.small > span.ipl-rating-star__rating") %>% 
    html_text()
  rate_ct <- raw_div %>% 
    html_elements("div.ipl-rating-star.small > span.ipl-rating-star__total-votes")%>% 
    html_text() %>% 
    str_remove_all("\\(|\\)|,")
  
  descrip <- raw_div %>% 
    html_elements("div.item_description") %>% 
    html_text() %>% 
    str_squish()
  
  tibble(
    season = season,
    ep_num = ep_num,
    air_date = air_date,
    rating = ratings, rating_n = rate_ct, desc = descrip)
  
}

all_season <- 1:12 %>% 
  map_dfr(get_imdb)

clean_season <- all_season %>% 
  type_convert()

clean_season %>% 
  write_csv("2021/2021-11-23/imdb.csv")