beer-production.Rmd

---
title: "Beer Production"
output: html_document
name: metrics_beer_production
owner: drob
metrics:
  nb_pounds:
    title: "# of Pounds Produced"
    description: "Number of pounds used in beer production in the US"
dimensions:
  material_type:
    title: Type
    description: Grain / non-grain
  material:
    title: Material
    description: Wheat, hops, corn, etc
---

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

```{r}
library(tidyverse)
library(lubridate)
theme_set(theme_light())

brewing_materials <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-03-31/brewing_materials.csv') %>%
  mutate(date = ymd(paste(year, month, 1))) %>%
  filter(year < 2016)

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

What ingredients are used in US beer production?

```{r}
brewing_materials %>%
  filter(date == max(date)) %>%
  filter(!str_detect(material_type, "Total")) %>%
  mutate(type = fct_reorder(type, month_current)) %>%
  ggplot(aes(type, month_current, fill = material_type)) +
  geom_col() +
  coord_flip()

library(lubridate)

brewing_materials %>%
  filter(!str_detect(material_type, "Total")) %>%
  mutate(type = fct_reorder(type, month_current, sum)) %>%
  ggplot(aes(date, month_current, fill = type)) +
  geom_col() +
  scale_y_continuous(labels = scales::comma) +
  labs(x = "Time",
       y = "Pounds used in beer production",
       fill = "Material")

brewing_materials %>%
  filter(str_detect(material_type, "Total.*products")) %>%
  mutate(type = fct_reorder(type, month_current, sum)) %>%
  ggplot(aes(date, month_current, fill = type)) +
  geom_col() +
  scale_y_continuous(labels = scales::comma) +
  labs(x = "Time",
       y = "Pounds used in beer production",
       fill = "Material")
```

Tidymetrics

```{r}
# devtools::install_github("ramnathv/tidymetrics")
library(tidymetrics)

brewing_summarized <- brewing_materials %>%
  rename(material = type) %>%
  filter(!str_detect(material_type, "Total")) %>%
  cross_by_dimensions(material, material_type) %>%
  cross_by_periods(c("month", "quarter", "year")) %>%
  summarize(nb_pounds = sum(month_current)) %>%
  ungroup()

brewing_summarized %>%
  filter(material_type == "All", material != "All", period == "quarter") %>%
  ggplot(aes(date, nb_pounds, fill = material)) +
  geom_col()
```

```{r}
brewing_metrics <- create_metrics(brewing_summarized)

library(shinymetrics)

preview_metric(brewing_metrics$beer_production_nb_pounds)
```

### Brewer size distribution

```{r}
brewer_size %>%
  filter(brewer_size != "Total", !is.na(total_barrels)) %>%
  mutate(brewer_size = fct_lump(brewer_size, 5, w = total_barrels),
         barrel_number = coalesce(parse_number(as.character(brewer_size)), 1),
         brewer_size = fct_reorder(brewer_size, barrel_number)) %>%
  ggplot(aes(year, total_barrels, fill = brewer_size)) +
  geom_col()
```

```{r}
brewing_materials %>%
  filter(material_type == "Total Used") %>%
  ggplot(aes(month, month_current, color = factor(year))) +
  geom_line() +
  expand_limits(y = 0)
```

Where is beer produced?

```{r}
beer_states %>%
  count(type, sort = TRUE, wt = barrels)

beer_states %>%
  group_by(year) %>%
  summarize(barrels = sum(barrels, na.rm = TRUE))

# Who consumes beer on premises
state_percents_2019 <- beer_states %>%
  filter(year == max(year), state != "total") %>%
  group_by(state, year) %>%
  mutate(percent = barrels / sum(barrels)) %>%
  ungroup()

state_percents_2019 %>%
  filter(type == "On Premises") %>%
  arrange(desc(percent))
```

```{r}
library(maps)
library(sf)

states <- st_as_sf(map("state", plot = FALSE, fill = TRUE))

states_joined <- state_percents_2019 %>%
  mutate(ID = str_to_lower(state.name[match(state, state.abb)])) %>%
  inner_join(states, by = "ID")

states_joined %>%
  filter(type == "On Premises") %>%
  ggplot(aes(geometry = geom, fill = percent)) +
  geom_sf() +
  scale_fill_gradient2(low = "blue", high = "orange", midpoint = .5,
                       labels = scales::percent) +
  ggthemes::theme_map() +
  labs(title = "In which states is a lot of beer produced consumed on premises?",
       fill = "% consumed on prem.")

states_joined %>%
  ggplot(aes(geometry = geom, fill = percent)) +
  geom_sf() +
  facet_wrap(~ type, nrow = 2) +
  scale_fill_gradient2(low = "blue", high = "orange", midpoint = .5,
                       labels = scales::percent) +
  ggthemes::theme_map() +
  theme(legend.position = "right") +
  labs(title = "How is beer consumed within each state?",
       fill = "% consumed")
```

```{r}
library(gganimate)

beer_states %>%
  filter(state != "total") %>%
  group_by(state, year) %>%
  mutate(percent = barrels / sum(barrels)) %>%
  ungroup() %>%
  filter(type == "On Premises") %>%
  mutate(ID = str_to_lower(state.name[match(state, state.abb)])) %>%
  inner_join(states, by = "ID") %>%
  ggplot(aes(geometry = geom, fill = percent)) +
  geom_sf() +
  transition_time(year) +
  facet_wrap(~ type, nrow = 2) +
  scale_fill_gradient2(low = "blue", high = "orange", midpoint = .5,
                       labels = scales::percent) +
  ggthemes::theme_map() +
  theme(legend.position = "right") +
  labs(title = "How is beer consumed within each state? ({ as.integer(frame_time) })",
       fill = "% consumed")
```