Module 1 Lesson 3 Application

library(tidyverse)

A Real-life Example Dataset

The data comes from the 2014 World Health Organization Global Tuberculosis Report, available at http://www.who.int/tb/country/data/download/en/.

There’s a wealth of epidemiological information in this dataset, but it’s challenging to work with the data in the form that it’s provided:

who

# A tibble: 7,240 × 60
   country  iso2  iso3   year new_sp_m014 new_sp_m1524 new_sp_m2534 new_sp_m3544
   <chr>    <chr> <chr> <dbl>       <dbl>        <dbl>        <dbl>        <dbl>
 1 Afghani… AF    AFG    1980          NA           NA           NA           NA
 2 Afghani… AF    AFG    1981          NA           NA           NA           NA
 3 Afghani… AF    AFG    1982          NA           NA           NA           NA
 4 Afghani… AF    AFG    1983          NA           NA           NA           NA
 5 Afghani… AF    AFG    1984          NA           NA           NA           NA
 6 Afghani… AF    AFG    1985          NA           NA           NA           NA
 7 Afghani… AF    AFG    1986          NA           NA           NA           NA
 8 Afghani… AF    AFG    1987          NA           NA           NA           NA
 9 Afghani… AF    AFG    1988          NA           NA           NA           NA
10 Afghani… AF    AFG    1989          NA           NA           NA           NA
# ℹ 7,230 more rows
# ℹ 52 more variables: new_sp_m4554 <dbl>, new_sp_m5564 <dbl>,
#   new_sp_m65 <dbl>, new_sp_f014 <dbl>, new_sp_f1524 <dbl>,
#   new_sp_f2534 <dbl>, new_sp_f3544 <dbl>, new_sp_f4554 <dbl>,
#   new_sp_f5564 <dbl>, new_sp_f65 <dbl>, new_sn_m014 <dbl>,
#   new_sn_m1524 <dbl>, new_sn_m2534 <dbl>, new_sn_m3544 <dbl>,
#   new_sn_m4554 <dbl>, new_sn_m5564 <dbl>, new_sn_m65 <dbl>, …

This is a very typical real-life example dataset. It contains redundant columns, odd variable codes, and many missing values. In short, who is messy, and we’ll need multiple steps to tidy it. Like dplyr, tidyr is designed so that each function does one thing well. That means in real-life situations you’ll usually need to string together multiple verbs into a pipeline.

tidywho <- who %>%
  pivot_longer(
    cols = new_sp_m014:newrel_f65, 
    names_to = "key", 
    values_to = "cases", 
    values_drop_na = TRUE
  ) %>% 
  mutate(
    key = stringr::str_replace(key, "newrel", "new_rel")
  ) %>%
  separate(key, c("new", "var", "sexage")) %>% 
  select(-new, -iso2, -iso3) %>% 
  separate(sexage, c("sex", "age"), sep = 1)

Saving the dataset as csv

write.csv(tidywho, "tidywho.csv", row.names = FALSE)