2022-11-08

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.

Radio Stations

The data this week comes from Wikipedia.

The dataset included was mined from all 50 states, tidying column names, binding and aggregating.

Erin's blogpost on Visualizing the Geography of FM Radio. Data sourced from FCC.

Credit: Frank Hull

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('2022-11-08')
tuesdata <- tidytuesdayR::tt_load(2022, week = 45)

state_stations <- tuesdata$state_stations

# Or read in the data manually

state_stations <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2022/2022-11-08/state_stations.csv')

Data Dictionary

state_stations.csv

variable	class	description
call_sign	character	Call Sign
frequency	character	frequency
city	character	city
licensee	character	licensee
format	character	format
state	character	state

fm_service_contour_current.zip

A large Zip file (~200 Mb on disk when unzipped) - see cleaning script for use.

variable	class	description
application_id	character	application_id
service	character	service
lms_application_id	character	lms_application_id
dts_site_number	character	dts_site_number
site_lat	double	Site of station latitude
site_long	double	Site of station longitude
angle	integer	angle (0 to 360)
deg_lat	double	Latitude for angle
deg_lng	double	Longitude for angle

station_info.csv

Can be joined:

state_stations |> dplyr::right_join(station_info, by = c("call_sign"))

variable	class	description
call_sign	character	Call sign
facility_id	double	Facility id
service	character	Service
licensee	character	Licensee
status	character	Status
details	character	Details

Cleaning Script

library(purrr)
library(dplyr)
library(tidyr)
library(ggplot2)
library(stringr)
library(janitor)
library(rvest)


all_states <- datasets::state.name
all_states <- gsub(" ", "_", all_states)


get_stations <- function(state){

root <- paste0("https://en.wikipedia.org/wiki/List_of_radio_stations_in_", state) 
 tables <- read_html(root) %>% html_nodes("table")
  stations <- tables[1] %>% # ideally all pages will have same format
              html_table(header = TRUE) %>% 
              do.call(rbind, .) %>% 
              clean_names() %>% 
              mutate(frequency = as.character(frequency)) %>%                    # handling Ohio special case (frequency / band split)
              rename_if(startsWith(names(.), "city"), ~ ("city")) %>%            # handling naming issues, citation handler
              rename_at(vars(matches("format")), ~ "format") %>%                 # handling naming issues, Oklahoma handler
              rename_if(startsWith(names(.), "licensee"), ~ ("licensee")) %>%    # handling naming issues, citation handler
              rename_if(startsWith(names(.), "owner"), ~ ("licensee")) %>%       # South Dakota handler
              select(call_sign, frequency, city, licensee, format) %>% 
              mutate(
                state = state
              )
  
  return(stations)
}

state_stations <- map_dfr(all_states, get_stations)

For the Contour cleaning:

library(tidyverse)

raw_contour <- read_delim(
  "2022/2022-11-08/FM_service_contour_current.txt",
  delim = "|"
)

conv_contour <- raw_contour |>
  select(-last_col()) |>
  set_names(nm = c(
    "application_id", "service", "lms_application_id", "dts_site_number", "transmitter_site",
    glue::glue("deg_{0:360}")
  ))

lng_contour <- conv_contour |>
  separate(
    transmitter_site, 
    into = c("site_lat", "site_long"), 
    sep = " ,") |>
  pivot_longer(
    names_to = "angle",
    values_to = "values",
    cols = deg_0:deg_360
  ) |>
  mutate(
    angle = str_remove(angle, "deg_"),
    angle = as.integer(angle)
  ) |>
  separate(
    values,
    into = c("deg_lat", "deg_lng"),
    sep = " ,"
  )