2021_02_23_employment_earnings.Rmd

---
title: "TidyTemplate"
date: 2021-02-23
output: html_output
---

# 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 setup, include=FALSE}

knitr::opts_chunk$set(echo = TRUE)

library(tidyverse)
library(tidytuesdayR)
library(scales)
theme_set(theme_light())

```

# Load the weekly Data

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

```{r Load}

tt <- tt_load("2021-02-23")

earn <- tt$earn

employed <- tt$employed %>%
  mutate(dimension = case_when(
    race_gender == "TOTAL" ~ "Total",
    race_gender %in% c("Men", "Women") ~ "Gender",
    TRUE ~ "Race"
  ))
```

```{r}
employed_cleaned <- employed %>%
  filter(!is.na(employ_n)) %>%
  mutate(industry = fct_lump(industry, 15, w = employ_n),
         industry = fct_reorder(industry, employ_n, sum))

employed_cleaned %>%
  filter(dimension == "Total") %>%
  ggplot(aes(year, employ_n, fill = industry)) +
  geom_col() +
  scale_y_continuous(labels = comma) +
  labs(y = "# employed in industry",
       x = "Year")

employed_cleaned %>%
  filter(dimension == "Gender") %>%
  group_by(industry, year, race_gender) %>%
  summarize(employ_n = sum(employ_n)) %>%
  ggplot(aes(year, employ_n, color = race_gender)) +
  geom_line() +
  facet_wrap(~ industry, scales = "free_y") +
  expand_limits(y = 0) +
  scale_y_continuous(labels = comma) +
  labs(y = "# employed in industry",
       x = "Year",
       color = "Gender")

employed_cleaned %>%
  filter(year == 2020,
         industry == "Wholesale and retail trade") %>%
  View()
```

Not a ton of 2015-2019 variation; let's look at 2019 to 2020

```{r}
comparison <- employed_cleaned %>%
  filter(year %in% c(2019, 2020)) %>%
  mutate(major_occupation = paste(industry, major_occupation, sep = " - "),
         minor_occupation = paste(major_occupation, minor_occupation, sep = " - ")) %>%
  gather(level, occupation, industry, major_occupation, minor_occupation) %>%
  group_by(dimension, race_gender, level, occupation, year) %>%
  summarize(employ_n = sum(employ_n)) %>%
  ungroup() %>%
  arrange(year) %>%
  group_by(dimension, level, occupation, race_gender) %>%
  summarize(ratio = last(employ_n) / first(employ_n),
            change = ratio - 1,
            employed_2019 = first(employ_n)) %>%
  group_by(dimension, level, occupation) %>%
  mutate(total_2019 = sum(employed_2019)) %>%
  ungroup()

comparison %>%
  View()

comparison %>%
  filter(dimension == "Total", level == "industry") %>%
  mutate(occupation = fct_reorder(occupation, change)) %>%
  ggplot(aes(change, occupation)) +
  geom_col() +
  scale_x_continuous(labels = percent) +
  labs(title = "What industries suffered the most in 2020?",
       x = "Shift in # employed from 2019 to 2020 (BLS)",
       y = "")

library(glue)

compare_lollipop <- function(tbl) {
  tbl %>%
    mutate(occupation = glue("{ occupation } ({ comma(total_2019 / 1000) }K)"),
           occupation = fct_reorder(occupation, change)) %>%
    ggplot(aes(change, occupation)) +
    geom_errorbarh(aes(xmin = 0, xmax = change, color = race_gender),
                   height = 0,
                   position = position_dodge(width = .7)) +
    geom_point(aes(size = employed_2019, color = race_gender),
               position = position_dodge(width = .7)) +
    geom_vline(lty = 2, xintercept = 0) +
    scale_x_continuous(labels = percent) +
    scale_color_discrete(guide = guide_legend(reverse = TRUE)) +
    scale_size_continuous(labels = comma, guide = FALSE) +
    labs(x = "Shift in # employed from 2019 to 2020 (BLS)",
         y = "",
         color = "",
         size = "# employed 2019")
}

comparison %>%
  filter(dimension == "Gender", level == "industry") %>%
  compare_lollipop()

comparison %>%
  filter(dimension == "Race", level == "industry") %>%
  compare_lollipop() +
  labs(title = "What industries suffered the most in 2020?",
       subtitle = "Size of point represents # employed in industry in 2019")

comparison %>%
  filter(dimension == "Race", level == "major_occupation") %>%
  separate(occupation, c("industry", "occupation"), sep = " - ") %>%
  filter(industry == "Construction",
         employed_2019 >= 10000) %>%
  compare_lollipop()

comparison %>%
  filter(dimension == "Gender", level == "major_occupation") %>%
  separate(occupation, c("industry", "occupation"), sep = " - ") %>%
  filter(industry == "Construction",
         employed_2019 >= 10000) %>%
  compare_lollipop()

comparison %>%
  filter(dimension == "Race",
         level == "major_occupation") %>%
  separate(occupation, c("industry", "occupation"), sep = " - ") %>%
  filter(industry == "Leisure and hospitality",
         employed_2019 >= 10000) %>%
  compare_lollipop() +
  labs(title = "Within 'Leisure and hospitality', what occupations suffered?")

compare_2019_2020 %>%
  filter(dimension == "Gender") %>%
  mutate(industry = fct_reorder(industry, change)) %>%
  ggplot(aes(change, industry, fill = race_gender)) +
  geom_col(position = "dodge") +
  scale_x_continuous(labels = percent) +
  scale_fill_discrete(guide = guide_legend(reverse = TRUE)) +
  labs(title = "What industries suffered the most in 2020?",
       x = "Shift in # employed from 2019 to 2020 (BLS)",
       y = "",
       fill = "Gender")

compare_2019_2020 %>%
  filter(dimension == "Race") %>%
  mutate(industry = fct_reorder(industry, change)) %>%
  ggplot(aes(change, industry, fill = race_gender)) +
  geom_col(position = "dodge") +
  scale_x_continuous(labels = percent) +
  scale_fill_discrete(guide = guide_legend(reverse = TRUE)) +
  labs(title = "What industries suffered the most in 2020?",
       x = "Shift in # employed from 2019 to 2020 (BLS)",
       y = "",
       fill = "Race")

compare_2019_2020 %>%
  filter(dimension == "Race")

compare_2019_2020 %>%
  filter(dimension == "Gender") %>%
  mutate(industry = fct_reorder(industry, change)) %>%
  ggplot(aes(change, industry)) +
  geom_errorbarh(aes(xmin = 0, xmax = change, color = race_gender),
                 height = 0,
                 position = position_dodge(width = .7)) +
  geom_point(aes(size = employed_2019, color = race_gender),
             position = position_dodge(width = .7)) +
  geom_vline(lty = 2, xintercept = 0) +
  scale_x_continuous(labels = percent) +
  scale_color_discrete(guide = guide_legend(reverse = TRUE)) +
  scale_size_continuous(labels = comma, guide = FALSE) +
  labs(title = "What industries suffered the most in 2020?",
       subtitle = "Size of point represents # employed in industry in 2019",
       x = "Shift in # employed from 2019 to 2020 (BLS)",
       y = "",
       color = "Gender",
       size = "# employed 2019")
```

```{r}
library(ggrepel)

compare_2019_2020 %>%
  filter(dimension == "Total") %>%
  mutate(industry = fct_reorder(industry, change)) %>%
  ggplot(aes(employed_2019, change)) +
  geom_point() +
  geom_text_repel(aes(label = industry)) +
  geom_hline(lty = 2, yintercept = 0) +
  scale_x_continuous(labels = comma) +
  scale_y_continuous(labels = percent)
```

```{r}
employed_cleaned %>%
  filter(year %in% c(2019, 2020)) %>%
  filter(industry == "Construction") %>%
  group_by(dimension, race_gender, major_occupation, year) %>%
  summarize(employ_n = sum(employ_n)) %>%
  arrange(year) %>%
  summarize(ratio = last(employ_n) / first(employ_n),
            change = ratio - 1,
            employed_2019 = first(employ_n)) %>%
  ungroup()
```