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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:

Get the data here

# Get the Data

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

# Or read in with tidytuesdayR package (https://github.com/thebioengineer/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 devtools::install_github("thebioengineer/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's district
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"))