install.packages("fivethirtyeight")
## Installing package into '/home/rstudio-user/R/x86_64-pc-linux-gnu-library/4.0'
## (as 'lib' is unspecified)
library(fivethirtyeight)
## Some larger datasets need to be installed separately, like senators and
## house_district_forecast. To install these, we recommend you install the
## fivethirtyeightdata package by running:
## install.packages('fivethirtyeightdata', repos =
## 'https://fivethirtyeightdata.github.io/drat/', type = 'source')
library(tidyverse)
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──
## ✓ ggplot2 3.3.2     ✓ purrr   0.3.4
## ✓ tibble  3.0.4     ✓ dplyr   1.0.2
## ✓ tidyr   1.1.2     ✓ stringr 1.4.0
## ✓ readr   1.4.0     ✓ forcats 0.5.0
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()
drinks <- drinks
head(drinks)
## # A tibble: 6 x 5
##   country      beer_servings spirit_servings wine_servings total_litres_of_pure…
##   <chr>                <int>           <int>         <int>                 <dbl>
## 1 Afghanistan              0               0             0                   0  
## 2 Albania                 89             132            54                   4.9
## 3 Algeria                 25               0            14                   0.7
## 4 Andorra                245             138           312                  12.4
## 5 Angola                 217              57            45                   5.9
## 6 Antigua & B…           102             128            45                   4.9
drinks_clean <- drinks %>% 
  filter(country %in% c("USA", "Seychelles", "Iceland", "Greece")) %>% 
  select(-total_litres_of_pure_alcohol) %>% 
  rename(beer = beer_servings, spirit = spirit_servings, wine = wine_servings)
drinks_clean
## # A tibble: 4 x 4
##   country     beer spirit  wine
##   <chr>      <int>  <int> <int>
## 1 Greece       133    112   218
## 2 Iceland      233     61    78
## 3 Seychelles   157     25    51
## 4 USA          249    158    84
drinks_final <- drinks_clean %>% 
  pivot_longer(names_to = "type", 
               values_to = "servings", 
               cols = -country)
head(drinks_final)
## # A tibble: 6 x 3
##   country type   servings
##   <chr>   <chr>     <int>
## 1 Greece  beer        133
## 2 Greece  spirit      112
## 3 Greece  wine        218
## 4 Iceland beer        233
## 5 Iceland spirit       61
## 6 Iceland wine         78
ggplot(drinks_final, aes(x = country, y = servings, fill = type)) +
  geom_col(position = "dodge")+ 
  coord_flip()