2019-12-10

You can make it in R

This week's data is a meta collection of data sources and code from Rafael Irizarry - his recent blog post You can replicate almost any plot with R covered a few news articles and how to replicate them in R. I included 4 datasets, but please note that there are many more datasets in his DS Labs package.

I'd recommend checking out his blog post for plot examples and metadata.

Get the Data

murders <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2019/2019-12-10/international_murders.csv")

gun_murders <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2019/2019-12-10/gun_murders.csv")

diseases <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2019/2019-12-10/diseases.csv")

nyc_regents <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2019/2019-12-10/nyc_regents.csv")

# Or read in with tidytuesdayR package (https://github.com/dslc-io/tidytuesdayR)
# Either ISO-8601 date or year/week works!
# Install via pak::pak("dslc-io/tidytuesdayR")

tuesdata <- tidytuesdayR::tt_load("2019-12-10")
tuesdata <- tidytuesdayR::tt_load(2019, week = 50)

diseases <- tuesdata$diseases

Dictionary

diseases.csv

variable	class	description
disease	integer	Disease name
state	character	State
year	double	Year
weeks_reporting	integer	N of weeks reporting
count	double	Count of disease observed
population	double	Total population

nyc_regents.csv

variable	class	description
score	double	Grading score (0 - 100)
integrated_algebra	double	Total observations
global_history	double	Total observations
living_environment	double	Total observations
english	double	Total observations
us_history	double	Total observations

international_murders.csv

variable	class	description
country	character	country
count	double	Total observations
label	character	Total observations
code	character	2 letter country code

gun_murders.csv

variable	class	description
country	character	country
count	double	gun related homicides per 100,000 people

Cleaning

pak::pak("rafalab/dslabs")

library(dslabs)
library(tidyverse)
library(here)

murders <- tibble(country = toupper(c("US", "Italy", "Canada", "UK", "Japan", "Germany", "France", "Russia")),
              count = c(3.2, 0.71, 0.5, 0.1, 0, 0.2, 0.1, 0),
              label = c(as.character(c(3.2, 0.71, 0.5, 0.1, 0, 0.2, 0.1)), "No Data"),
              code = c("us", "it", "ca", "gb", "jp", "de", "fr", "ru"))

gun_murders <- tibble(country = toupper(c("United States", "Canada", "Portugal", "Ireland", "Italy", "Belgium", "Finland", "France", "Netherlands", "Denmark", "Sweden", "Slovakia", "Austria", "New Zealand", "Australia", "Spain", "Czech Republic", "Hungry", "Germany", "United Kingdom", "Norway", "Japan", "Republic of Korea")),
              count = c(3.61, 0.5, 0.48, 0.35, 0.35, 0.33, 0.26, 0.20, 0.20, 0.20, 0.19, 0.19, 0.18, 0.16,
                        0.16, 0.15, 0.12, 0.10, 0.06, 0.04, 0.04, 0.01, 0.01))

diseases <- dslabs::us_contagious_diseases

nyc <- dslabs::nyc_regents_scores


murders %>% 
  write_csv(here("2019", "2019-12-10","international_murders.csv"))

gun_murders %>% 
  write_csv(here("2019", "2019-12-10","gun_murders.csv"))


diseases %>% 
  write_csv(here("2019", "2019-12-10","diseases.csv"))

nyc %>% 
  write_csv(here("2019", "2019-12-10","nyc_regents.csv"))