readme.md

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.

Emmy Awards and Nominees

The data this week comes from emmys.com.

Susie Lu has a nice visualization of similar data. Statista also has a nice visualization looking at Netflix specifically, and an older one segmented by Network.

The Emmy Award:

An Emmy Award, or simply Emmy, is a trophy presented at one of the numerous annual American events or competitions that each recognize achievements in a particular sector of the television industry. The Emmy is considered one of the four major entertainment awards in the United States, the others being the Grammy (for music), the Oscar (Academy Award) (for film), and the Tony (for theatre). The two events that receive the most media coverage are the Primetime Emmy Awards and the Daytime Emmy Awards, which recognize outstanding work in American primetime and daytime entertainment programming, respectively.

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('2021-09-21')
tuesdata <- tidytuesdayR::tt_load(2021, week = 39)

nominees <- tuesdata$nominees

# Or read in the data manually

nominees <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2021/2021-09-21/nominees.csv')

Data Dictionary

nominees.csv

variable	class	description
category	character	Award Category
logo	character	Logo or headshot
production	character	Production Team
type	character	Type - either nominee or winner
title	character	Title of show
distributor	character	Distributor of show
producer	character	Producer of show
year	integer	Year of award
page	integer	Page number
page_id	integer	Page ID (unique count/page)

Cleaning Script

library(tidyverse)
library(rvest)

clean_nominee <- function(x){
  
  trimmed_nom <- str_trim(x) %>% 
    str_split("\n") %>% 
    pluck(1) %>%
    str_trim()
  
  out_len <- length(trimmed_nom)
  
  names_add <- c(
    c("type", "title", "distributor", "producer"),rep("blank", out_len - 4)
  )
  
  trimmed_nom %>% 
    set_names(nm = names_add)
}

clean_production <- function(raw_html){
  
  data_out <- map2(
    1:10, rep(c("odd", "even"), 5),
    ~raw_html %>% 
      html_nodes(glue::glue("div.views-row.views-row-{.x}.views-row-{.y}")) %>% 
      html_nodes(".winner-list") %>% 
      map(~html_nodes(.x, "li")) %>% 
      map(html_text) 
  ) %>% 
    map(pluck, 1)
  
  data_out
  
}


scrape_pages <- function(page_num){
  
  cat(scales::percent_format()(page_num/1000), "\n")
  
  url <- glue::glue("https://www.emmys.com/awards/nominations/award-search?page={page_num}")
  
  raw_html <- url %>% 
    read_html() %>% 
    html_nodes("#block-system-main > div > div > section:nth-child(3) > div > div")
  
  category <- raw_html %>% 
    # html_node("#block-system-main > div > div > section:nth-child(3) > div > div") %>% 
    html_nodes("h5") %>% 
    html_text()
  
  logos <- raw_html %>% 
    html_nodes("div.image.img.col-4.col-md-3.col-xl-2 > a > img") %>% 
    html_attr("src")
  
  outcome <- raw_html %>% 
    html_nodes("ul.nominee, ul.winner") %>% 
    html_text() %>% 
    map(clean_nominee)
  
  out_len <- map(outcome, length)
  
  production <- clean_production(raw_html)
  
  tibble(
    category = category,
    logo = logos,
    production = production,
    outcome = outcome
  ) %>%
    mutate(year = str_sub(category, -4, -1) %>% as.integer(),
           page = page_num,
           page_id = row_number())
  
}

safe_scrape <- safely(scrape_pages)

map_all <- 0:2362 %>% map(safe_scrape)

all_results <- map_all %>% 
  map_dfr("result") 

clean_df <- all_results %>%
  rowwise() %>% 
  mutate(
    fix = ifelse(str_detect(title, ", as"), 1, 0),
    title = ifelse(fix == 1, distributor, title),
    distributor = ifelse(fix == 1 && !is.na(producer), producer, distributor),
    producer = ifelse(fix == 1 && !is.na(blank), blank, producer),
    producer = ifelse(!is.na(blank), blank, producer)
  ) 

final_df <- clean_df %>% 
  select(-contains("blank"), -fix)