Measles

The data this week comes from The Wallstreet Journal. They recently published an article around 46,412 schools across 32 US States.

“This repository contains immunization rate data for schools across the U.S., as compiled by The Wall Street Journal. The dataset includes the overall and MMR-specific vaccination rates for 46,412 schools in 32 states. As used in”What’s the Measles Vaccination Rate at Your Child’s School?“.

Vaccination rates are for the 2017-18 school year for Colorado, Connecticut, Minnesota, Montana, New Jersey, New York, North Dakota, Pennsylvania, South Dakota, Utah and Washington. Rates for other states are 2018-19.”

Additional data sources are available at: * CDC Child immunization
* World Bank
* CDC General Immunization
* WSJ Measles

Get the data here

# Get the Data

measles <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2020/2020-02-25/measles.csv')

# Or read in with tidytuesdayR package (https://github.com/dslc-io/tidytuesdayR)
# PLEASE NOTE TO USE 2020 DATA YOU NEED TO USE tidytuesdayR version ? from GitHub

# Either ISO-8601 date or year/week works!

# Install via pak::pak("dslc-io/tidytuesdayR")

tuesdata <- tidytuesdayR::tt_load('2020-02-25')
tuesdata <- tidytuesdayR::tt_load(2020, week = 9)


measles <- tuesdata$measles

Data Dictionary

measles.csv

variable	class	description
index	double	Index ID
state	character	School’s state
year	character	School academic year
name	character	School name
type	character	Whether a school is public, private, charter
city	character	City
county	character	County
district	logical	School district
enroll	double	Enrollment
mmr	double	School’s Measles, Mumps, and Rubella (MMR) vaccination rate
overall	double	School’s overall vaccination rate
xrel	logical	Percentage of students exempted from vaccination for religious reasons
xmed	double	Percentage of students exempted from vaccination for medical reasons
xper	double	Percentage of students exempted from vaccination for personal reasons

Cleaning Script

library(tidyverse)
library(rvest)

url_wsj <- "https://raw.githubusercontent.com/WSJ/measles-data/master/all-measles-rates.csv"

wsj <- read_csv(url_wsj)

list_of_urls <- "https://github.com/WSJ/measles-data/tree/master/individual-states"

raw_states <- list_of_urls %>% 
  read_html() %>% 
  html_table() %>% 
  .[[1]] %>% 
  select(Name) %>% 
  mutate(Name = str_remove(Name, "\\.csv")) %>% 
  filter(str_length(Name) > 3, str_length(Name) < 20) %>% 
  pull(Name)

all_states <- glue::glue("https://raw.githubusercontent.com/WSJ/measles-data/master/individual-states/{raw_states}.csv") %>% 
  map(read_csv)

clean_states <- all_states %>% 
  map(~select(., state, name, lat, lng)) %>% 
  map(~mutate_at(., vars(lat, lng), as.numeric)) %>% 
  bind_rows() %>% 
  filter(!is.na(lat))

wsj %>% 
  left_join(clean_states, by = c("name", "state")) %>% 
  write_csv(here::here("2020","2020-02-25","measles.csv"))