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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.

US Populated Places

While we embark on a road trip for summer vacation, the data this week comes from the National Map Staged Products Directory from the US Board of Geographic Names.

Note: Quite a lot of more data is available from the GNIS. See the cleaning script for clues for downloading the additional data.

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('2023-06-27')
tuesdata <- tidytuesdayR::tt_load(2023, week = 26)

us_place_names <- tuesdata$`us_place_names`
us_place_history <- tuesdata$`us_place_history`

# Or read in the data manually

us_place_names <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2023/2023-06-27/us_place_names.csv')
us_place_history <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2023/2023-06-27/us_place_history.csv')

Data Dictionary

us_place_names.csv

variable class description
feature_id double Permanent, unique feature record identifier.
feature_name character Official feature name.
state_name character The name of the state containing the primary coordinates.
county_name character The name of the county containing the primary coordinates.
county_numeric double The 3-digit code for the county containing the primary coordinates.
date_created date The date the record was initially entered into the
Geographic Names Information System.
date_edited date The date any attribute of an existing record was
edited.
prim_lat_dec double The latitude of the official feature location. Note that some values are unknown.
prim_long_dec double The longitude of the official feature location. Note that some values are unknown.

us_place_history.csv

variable class description
feature_id double Permanent, unique feature record identifier.
description character Characteristics or information about a feature or the feature data
history character Refers to the name origin, and/or cultural history of a feature.

Cleaning Script

See Jon Harmon's cleaning and enriching scripts for most of the (extensive) cleaning.

library(tidyverse)
library(withr)
library(fs)
library(here)

url <- "https://prd-tnm.s3.amazonaws.com/StagedProducts/GeographicNames/DomesticNames/DomesticNames_National_Text.zip"

path_zip <- local_tempfile(fileext = ".zip")
download.file(url, path_zip)

us_place_names <- read_delim(path_zip, "|") |> 
  mutate(county_numeric = as.integer(county_numeric)) |> 
  # We'll just use populated places, you might want to keep more!
  filter(feature_class == "Populated Place") |> 
  select(-feature_class, -starts_with("source_")) |> 
  # We also don't keep some redundant or less useful features.
  select(
    -state_numeric,
    -map_name, 
    -starts_with("bgn"), 
    -ends_with("_dms")) |> 
  mutate(
    across(
      ends_with("_dec"),
      ~ na_if(.x, 0)
    ),
    across(
      starts_with("date_"),
      lubridate::mdy
    )
  ) |> 
  distinct()

glimpse(us_place_names)

url_history <- "https://prd-tnm.s3.amazonaws.com/StagedProducts/GeographicNames/Topical/FeatureDescriptionHistory_National_Text.zip"

path_history <- local_tempfile(fileext = ".zip")
download.file(url_history, path_history)

us_place_history <- read_delim(path_history, "|") |> 
  semi_join(us_place_names)

# Data dictionary: 
# https://prd-tnm.s3.amazonaws.com/StagedProducts/GeographicNames/GNIS_file_format.pdf

write_csv(
  us_place_names,
  here::here(
    "data",
    "2023",
    "2023-06-27",
    "us_place_names.csv"
  )
)
write_csv(
  us_place_history,
  here::here(
    "data",
    "2023",
    "2023-06-27",
    "us_place_history.csv"
  )
)