atlanta fed wage tracker

gistfile1.txt

library(tidyverse)
library(readxl)

url <- 
  "https://www.atlantafed.org/-/media/documents/datafiles/chcs/wage-growth-tracker/wage-growth-data.xlsx"
destfile <- "wage_growth_data.xlsx"
curl::curl_download(url, destfile)

### Import and visualize

read_excel(destfile, sheet = "Average Wage Quartile", 
    skip = 2) %>% 
  rename(date = 1) %>% 
  slice(-1:-10) %>% 
  mutate(
    across(where(is.character), as.numeric)
    ) %>% 
  pivot_longer(-date) %>% 
  mutate(date = ymd(date)) %>% 
  # filter(account == "Gross domestic product")  %>% 
  arrange(desc(date)) %>% 
  filter(!str_detect(name, "eighted")) %>% 
  ggplot(aes(x = date, y = value, color = name)) + 
  geom_line() +
  theme_minimal() +
  labs(title = "Wage Growth by Wage Level",
       subtitle = "12-month Moving Avg Median Wage Growth",
       color = "Wage Level",
       y = "", 
       x = "")
       
 ### Filter to just 2020-2021 and top bottom wage levels
 read_excel(destfile, sheet = "Average Wage Quartile", 
    skip = 2) %>% 
  rename(date = 1) %>% 
  slice(-1:-10) %>% 
  mutate(
    across(where(is.character), as.numeric)
    ) %>% 
  pivot_longer(-date) %>% 
  mutate(date = ymd(date)) %>% 
  # filter(account == "Gross domestic product")  %>% 
  arrange(desc(date)) %>% 
  filter(!str_detect(name, "eighted"),
         name %in% c("1st", "4th"),
         date >= "2020-01-01") %>% 
  ggplot(aes(x = date, y = value, color = name)) + 
  geom_line() +
  theme_minimal() +
  labs(title = "Wage Growth by Wage Level",
       subtitle = "Zoom in to 2020 and just top/bottom level",
       color = "Wage Level",
       y = "", 
       x = "")