Here I loaded in the Data in regards to alcohol comsumption in varies countries
tidy <-read_csv("https://raw.githubusercontent.com/fivethirtyeight/data/master/alcohol-consumption/drinks.csv")
## Parsed with column specification:
## cols(
## country = col_character(),
## beer_servings = col_integer(),
## spirit_servings = col_integer(),
## wine_servings = col_integer(),
## total_litres_of_pure_alcohol = col_double()
## )
tidy
## # A tibble: 193 x 5
## country beer_servings spirit_servings wine_servings total_litres_of_~
## <chr> <int> <int> <int> <dbl>
## 1 Afghanis~ 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 ~ 102 128 45 4.9
## 7 Argentina 193 25 221 8.3
## 8 Armenia 21 179 11 3.8
## 9 Australia 261 72 212 10.4
## 10 Austria 279 75 191 9.7
## # ... with 183 more rows
To create this visualization, I chose to use ggplot which is part of the Tidyverse libarary
The scatter plot reveals to us the serving of beer from different countries.
tidy1 <- ggplot(data = tidy) +
geom_point(mapping = aes(x= tidy$country, y = tidy$beer_servings, color= tidy$total_litres_of_pure_alcohol))
tidy1
tidy2 <- ggplot(data = tidy) +
geom_bar(mapping = aes(x = tidy$total_litres_of_pure_alcohol))
tidy2