Beer Craft Awards

The data set that I’ll use is a data set on craft beer awards from 2016 to 2020. Variables include:

Using the usmap package, I will make a plot of the United States where states are coloured a darker colour if they have more beer awards in 2020 and a lighter colour if they have fewer beer awards in 2020.

The following usmap tutorial is a useful starting point:

https://cran.r-project.org/web/packages/usmap/vignettes/mapping.html

beer_awards <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-10-20/beer_awards.csv')
install.packages("usmap")
library(usmap)
library(tidyverse)
beer_awards
beer_awards <- beer_awards %>% mutate(awards= fct_collapse(medal, award= c("Gold", "Bronze", "Silver")))
##3 min

beer_2020 <- beer_awards %>% filter(year ==2020) # 10 min

awards_df <- beer_2020 %>% group_by(state, awards) %>% summarise(count= n()) #4 min 
## `summarise()` regrouping output by 'state' (override with `.groups` argument)
plot_usmap(data = awards_df, values = "count") + scale_fill_continuous(name = "Award (2020)", label = scales::comma) + 
  theme(legend.position = "right") #7 min

Questions to be answered:

Answer:I would go to California.

Answer:Colorado and Oregon seem to be trending up.

Answer:Boston Beer Co. appears several times.