cm007 Exercises: Practice with dplyr

suppressPackageStartupMessages(library(tidyverse))
suppressPackageStartupMessages(library(gapminder))
suppressPackageStartupMessages(library(tsibble))

This worksheet contains exercises aimed for practice with dplyr.

1. 1. What’s the minimum life expectancy for each continent and each year? (b) Add the corresponding country to the tibble, too. (c) Arrange by min life expectancy.

gapminder %>% 
  group_by(continent, year) %>% 
  summarize(min_life = min(lifeExp),
            country = country[lifeExp == min_life]) %>% 
  arrange(min_life)

## # A tibble: 60 x 4
## # Groups:   continent [5]
##    continent  year min_life country     
##    <fct>     <int>    <dbl> <fct>       
##  1 Africa     1992     23.6 Rwanda      
##  2 Asia       1952     28.8 Afghanistan 
##  3 Africa     1952     30   Gambia      
##  4 Asia       1957     30.3 Afghanistan 
##  5 Asia       1977     31.2 Cambodia    
##  6 Africa     1957     31.6 Sierra Leone
##  7 Asia       1962     32.0 Afghanistan 
##  8 Africa     1962     32.8 Sierra Leone
##  9 Asia       1967     34.0 Afghanistan 
## 10 Africa     1967     34.1 Sierra Leone
## # … with 50 more rows

2. Calculate the growth in population since the first year on record for each country by rearranging the following lines, and filling in the FILL_THIS_IN. Here’s another convenience function for you: dplyr::first().

gapminder %>%
  group_by(country) %>%
  arrange(year) %>% 
  mutate(rel_growth = pop - first(pop)) %>%
  DT::datatable()

3. Determine the country that experienced the sharpest 5-year drop in life expectancy, in each continent, sorted by the drop, by rearranging the following lines of code. Ensure there are no NA’s. Instead of using lag(), use the convenience function provided by the tsibble package, tsibble::difference():

gapminder %>% 
  group_by(country) %>% 
  arrange(year) %>% 
  mutate(inc_life_exp = difference(lifeExp)) %>% 
  drop_na() %>% 
  ungroup() %>% 
  group_by(continent) %>% 
  filter(inc_life_exp == min(inc_life_exp)) %>% 
  arrange(inc_life_exp) %>% 
  knitr::kable()

country	continent	year	lifeExp	pop	gdpPercap	inc_life_exp
Rwanda	Africa	1992	23.599	7290203	737.0686	-20.421
Cambodia	Asia	1977	31.220	6978607	524.9722	-9.097
El Salvador	Americas	1977	56.696	4282586	5138.9224	-1.511
Montenegro	Europe	2002	73.981	720230	6557.1943	-1.464
Australia	Oceania	1967	71.100	11872264	14526.1246	0.170