us_beer_production.Rmd

---
title: "Beer Production"
author: "Dean Marchiori"
date: "4/3/2020"
output: html_document
editor_options: 
  chunk_output_type: console
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

```{r}
library(tidyverse)
library(forcats)
library(brolgar)
library(tsibble)
library(fabletools)
library(patchwork)

options(scipen = 999)

brewer_size <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-03-31/brewer_size.csv')
beer_states <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-03-31/beer_states.csv')

```

```{r}
size <- brewer_size %>% 
  select(year, brewer_size, n_of_brewers) %>% 
  filter(brewer_size != "Total",
         brewer_size != "Zero Barrels",
          brewer_size != "Under 1 Barrel") %>% 
  mutate(brewer_size = fct_recode(brewer_size, 
                                  `> 1,000,000 Barrels` = "1,000,001 to 6,000,000 Barrels",
                                  `> 1,000,000 Barrels` = "1,000,001 to 1,999,999 Barrels",
                                  `> 1,000,000 Barrels` = "1,000,000 to 6,000,000 Barrels",
                                  `> 1,000,000 Barrels` = "6,000,001 Barrels and Over",
                                  `> 1,000,000 Barrels` = "2,000,000 to 6,000,000 Barrels",
                                  ),
         brewer_size = fct_relevel(brewer_size,
                                   "1 to 1,000 Barrels",
                                   "1,001 to 7,500 Barrels",
                                   "7,501 to 15,000 Barrels",
                                   "15,001 to 30,000 Barrels",
                                   "30,001 to 60,000 Barrels",
                                   "60,001 to 100,000 Barrels",
                                   "100,001 to 500,000 Barrels",
                                   "500,001 to 1,000,000 Barrels"
                                   )) %>% 
  group_by(year, brewer_size) %>% 
  summarise(n_of_brewers = sum(n_of_brewers)) %>% 
  ungroup() %>% 
  as_tsibble(key = brewer_size, index = year)

a <- size %>% 
  features(n_of_brewers, feat_brolgar) %>% 
  left_join(size) %>% 
  ggplot() +
  geom_line(aes(year,
                n_of_brewers, 
                group = brewer_size,
                alpha = sd), 
            show.legend = FALSE,
            colour = "#BD5B0F") +
  geom_text(data = size %>% filter(year == last(year)), 
                  aes(label = brewer_size, 
                      x = year + 0.5, 
                      y = n_of_brewers),
            check_overlap = TRUE,
                  size = 3,
            colour = "#533505") +
    scale_x_continuous(breaks = c(2009:2019)) +
    scale_colour_viridis_c(direction = 1) +
    theme_minimal() +
    labs(title = "Number of brewers",
         x = "",
         y = "Number of Brewers")

```


```{r}
cans <- beer_states %>% 
  filter(!state == "total", 
         type == "Bottles and Cans", 
         !is.na(barrels)) %>% 
  as_tsibble(key = c(state), index = year) 

cans_feat <- cans %>% 
  features(barrels, feat_brolgar) %>% 
  left_join(cans)  
  
b <- ggplot(cans_feat) +
  geom_line(aes(year, 
                barrels, 
                group = state, 
                alpha = sd), 
            colour = "#F17F0F",
            show.legend = FALSE) +
  geom_text(data = cans_feat %>% filter(year == last(year)) %>% top_n(5, sd), 
            aes(label = state, 
                x = year + 0.5, 
                y = barrels),
            show.legend = F,
            check_overlap = TRUE,
            size = 3,
            colour = "#533505") +
  theme_minimal() +
  scale_y_continuous(labels = scales::comma) +
  scale_x_continuous(breaks = c(2009:2019)) +
  labs(title = "Bottles and Cans",
       x = "", 
       y = "Barrels Produced")
```


```{r}
onprem <- beer_states %>% 
  filter(!state == "total", 
         type == "On Premises", 
         !is.na(barrels)) %>% 
  as_tsibble(key = c(state), index = year) 

onprem_feat <- onprem %>% 
  features(barrels, feat_brolgar) %>% 
  left_join(onprem)  
  
c <- ggplot(onprem_feat) +
  geom_line(aes(year, 
                barrels,
                group = state, 
                alpha = sd),
            colour = "#AE430B",
            show.legend = FALSE) +
  geom_text(data = onprem_feat %>% filter(year == last(year)) %>% top_n(5, sd), 
            aes(label = state, 
                x = year + 0.5, 
                y = barrels),
            show.legend = F,
            check_overlap = TRUE,
            size = 3,
            colour = "#533505") +
    scale_y_continuous(labels = scales::comma) +
  theme_minimal() +
  scale_x_continuous(breaks = c(2009:2019)) +
    labs(title = "On Premises",
       x = "", 
       y = "")

```


```{r}
patchwork <- a / (b + c)

patchwork + plot_annotation(
  title = 'The rise of craft beer in the US',
  subtitle = 'Over the last 10 years there has been huge growth in small boutique brewers. 
With bottle and can consumption decreasing, more beer is being consumed on premises, particularly in California.',
  caption = '@deanmarchiori | Data: Alcohol and Tobacco Tax and Trade Bureau (TTB)',
  theme = theme(plot.title = element_text(face = 'bold', size = 42))) &
  theme(text = element_text(colour = '#533505', family =  'Noto Mono'),
        plot.background = element_rect(fill = "#D9D1AF", colour = NA)
        )
```