Coronavirus Unemployment

Which states have been most impacted by unemployment due to the 2020 Coronavirus pandemic? To investigate this, I use the Bureau of Labor Statistics API and some special R ggplot extensions to visualize this relationship with sparklines and small multiples.

Load Packages

if (!require("pacman")) install.packages("pacman")
## Loading required package: pacman
pacman::p_load(blscrapeR, geofacet, tidyverse, scales, here)

# For Windows computers, load font devices here: 
# extrafont::loadfonts(device = "win", quiet = TRUE)

Now, scrape the 2020 monthly unemployment data with the Bureau of Labor Statistics API. This isn’t pretty, particularly because each of the “LASST” links is somewhat randomly named and does not import the state name.

bls <- 
  bls_api("LASST010000000000003", startyear = 2020, endyear = 2020) %>% mutate(state = "Alabama") %>%
  bind_rows(bls_api("LASST020000000000003", startyear = 2020, endyear = 2020) %>% mutate(state = "Alaska")) %>%
  bind_rows(bls_api("LASST040000000000003", startyear = 2020, endyear = 2020) %>% mutate(state = "Arizona")) %>%
  bind_rows(bls_api("LASST050000000000003", startyear = 2020, endyear = 2020) %>% mutate(state = "Arkansas")) %>%
  bind_rows(bls_api("LASST060000000000003", startyear = 2020, endyear = 2020) %>% mutate(state = "California")) %>%
  bind_rows(bls_api("LASST080000000000003", startyear = 2020, endyear = 2020) %>% mutate(state = "Colorado")) %>%
  bind_rows(bls_api("LASST090000000000003", startyear = 2020, endyear = 2020) %>% mutate(state = "Connecticut")) %>%
  bind_rows(bls_api("LASST100000000000003", startyear = 2020, endyear = 2020) %>% mutate(state = "Delaware")) %>%
  bind_rows(bls_api("LASST110000000000003", startyear = 2020, endyear = 2020) %>% mutate(state = "District of Columbia")) %>%
  bind_rows(bls_api("LASST120000000000003", startyear = 2020, endyear = 2020) %>% mutate(state = "Florida")) %>%
  bind_rows(bls_api("LASST130000000000003", startyear = 2020, endyear = 2020) %>% mutate(state = "Georgia")) %>%
  bind_rows(bls_api("LASST150000000000003", startyear = 2020, endyear = 2020) %>% mutate(state = "Hawaii")) %>%
  bind_rows(bls_api("LASST160000000000003", startyear = 2020, endyear = 2020) %>% mutate(state = "Idaho")) %>%
  bind_rows(bls_api("LASST170000000000003", startyear = 2020, endyear = 2020) %>% mutate(state = "Illinois")) %>%
  bind_rows(bls_api("LASST180000000000003", startyear = 2020, endyear = 2020) %>% mutate(state = "Indiana")) %>%
  bind_rows(bls_api("LASST190000000000003", startyear = 2020, endyear = 2020) %>% mutate(state = "Iowa")) %>%
  bind_rows(bls_api("LASST200000000000003", startyear = 2020, endyear = 2020) %>% mutate(state = "Kansas")) %>%
  bind_rows(bls_api("LASST210000000000003", startyear = 2020, endyear = 2020) %>% mutate(state = "Kentucky")) %>%
  bind_rows(bls_api("LASST220000000000003", startyear = 2020, endyear = 2020) %>% mutate(state = "Louisiana")) %>%
  bind_rows(bls_api("LASST230000000000003", startyear = 2020, endyear = 2020) %>% mutate(state = "Maine")) %>%
  bind_rows(bls_api("LASST240000000000003", startyear = 2020, endyear = 2020) %>% mutate(state = "Maryland")) %>%
  bind_rows(bls_api("LASST250000000000003", startyear = 2020, endyear = 2020) %>% mutate(state = "Massachusetts")) %>%
  bind_rows(bls_api("LASST260000000000003", startyear = 2020, endyear = 2020) %>% mutate(state = "Michigan")) %>%
  bind_rows(bls_api("LASST270000000000003", startyear = 2020, endyear = 2020) %>% mutate(state = "Minnesota")) %>%
  bind_rows(bls_api("LASST280000000000003", startyear = 2020, endyear = 2020) %>% mutate(state = "Mississippi")) %>%
  bind_rows(bls_api("LASST290000000000003", startyear = 2020, endyear = 2020) %>% mutate(state = "Missouri")) %>%
  bind_rows(bls_api("LASST300000000000003", startyear = 2020, endyear = 2020) %>% mutate(state = "Montana")) %>%
  bind_rows(bls_api("LASST310000000000003", startyear = 2020, endyear = 2020) %>% mutate(state = "Nebraska")) %>%
  bind_rows(bls_api("LASST320000000000003", startyear = 2020, endyear = 2020) %>% mutate(state = "Nevada")) %>%
  bind_rows(bls_api("LASST330000000000003", startyear = 2020, endyear = 2020) %>% mutate(state = "New_Hampshire")) %>%
  bind_rows(bls_api("LASST340000000000003", startyear = 2020, endyear = 2020) %>% mutate(state = "New_Jersey")) %>%
  bind_rows(bls_api("LASST350000000000003", startyear = 2020, endyear = 2020) %>% mutate(state = "New_Mexico")) %>%
  bind_rows(bls_api("LASST360000000000003", startyear = 2020, endyear = 2020) %>% mutate(state = "New_York")) %>%
  bind_rows(bls_api("LASST370000000000003", startyear = 2020, endyear = 2020) %>% mutate(state = "North_Carolina")) %>%
  bind_rows(bls_api("LASST380000000000003", startyear = 2020, endyear = 2020) %>% mutate(state = "North_Dakota")) %>%
  bind_rows(bls_api("LASST390000000000003", startyear = 2020, endyear = 2020) %>% mutate(state = "Ohio")) %>%
  bind_rows(bls_api("LASST400000000000003", startyear = 2020, endyear = 2020) %>% mutate(state = "Oklahoma")) %>%
  bind_rows(bls_api("LASST410000000000003", startyear = 2020, endyear = 2020) %>% mutate(state = "Oregon")) %>%
  bind_rows(bls_api("LASST420000000000003", startyear = 2020, endyear = 2020) %>% mutate(state = "Pennsylvania")) %>%
  bind_rows(bls_api("LASST440000000000003", startyear = 2020, endyear = 2020) %>% mutate(state = "Rhode_Island")) %>%
  bind_rows(bls_api("LASST450000000000003", startyear = 2020, endyear = 2020) %>% mutate(state = "South_Carolina")) %>%
  bind_rows(bls_api("LASST460000000000003", startyear = 2020, endyear = 2020) %>% mutate(state = "South_Dakota")) %>%
  bind_rows(bls_api("LASST470000000000003", startyear = 2020, endyear = 2020) %>% mutate(state = "Tennessee")) %>%
  bind_rows(bls_api("LASST480000000000003", startyear = 2020, endyear = 2020) %>% mutate(state = "Texas")) %>%
  bind_rows(bls_api("LASST490000000000003", startyear = 2020, endyear = 2020) %>% mutate(state = "Utah")) %>%
  bind_rows(bls_api("LASST500000000000003", startyear = 2020, endyear = 2020) %>% mutate(state = "Vermont")) %>%
  bind_rows(bls_api("LASST510000000000003", startyear = 2020, endyear = 2020) %>% mutate(state = "Virginia")) %>%
  bind_rows(bls_api("LASST530000000000003", startyear = 2020, endyear = 2020) %>% mutate(state = "Washington")) %>%
  bind_rows(bls_api("LASST540000000000003", startyear = 2020, endyear = 2020) %>% mutate(state = "West_Virginia")) %>%
  bind_rows(bls_api("LASST550000000000003", startyear = 2020, endyear = 2020) %>% mutate(state = "Wisconsin")) %>%
  bind_rows(bls_api("LASST560000000000003", startyear = 2020, endyear = 2020) %>% mutate(state = "Wyoming")) %>%
  dplyr::select(-c(year,period, latest, footnotes,seriesID))

write_csv(bls, path = "bls_July.csv") # save the data, as you have limited daily queries! 

Alternatively, I already have this data saved, so I’ll import it here instead:

bls <- read_csv("bls_July.csv") 
## Parsed with column specification:
## cols(
##   periodName = col_character(),
##   value = col_double(),
##   state = col_character()
## )
bls <- bls %>% 
  pivot_wider(id_cols = "state", 
              names_from = periodName, 
              values_from = value) %>% # change from long for to wide form
  rowwise() %>% 
  mutate(row_max = max(January:June), # calculate maximum unemployment by state
         row_min = min(January:June), # calculate minimum unemployment by state
         diff = (row_max - row_min)/100) %>%  # calculate the difference between max and min for color plotting 
  pivot_longer(cols = -c(state, diff, row_max, row_min), 
               names_to = "month", 
               values_to = "value") %>% # switch back to long form for plotting 
  mutate(month = fct_relevel(as.factor(month), 
                             c("January", "February", "March", "April", "May", "June")), 
         # turn the month into a factor and reorder it 
         state = str_replace(state, "_", " "), # replace the "_" in the state names with spaces
         value = value / 100) # to probably render the percents

And now, plot! Not the faceting over approximate state locations with the geofacet package.

unempl_plot <- ggplot(bls, aes(x=month, y = value, group = state)) +
  geom_line(aes(color = diff), size = 2) +
  scale_color_gradient(name = "Max Rise in\nUnemployment",
                       low = "#fffeea", high = "#c03728", 
                       label = label_percent(accuracy = 1,
                                             trim = FALSE)) +
  theme_minimal(base_family = "Roboto Condensed", base_size = 14) +
  theme(axis.text = element_blank(),
        axis.title = element_text(family = "Roboto Condensed Light"),
        panel.grid = element_blank(),
        panel.border = element_blank(),
        legend.position = c(.9,.3),
        legend.title = element_text(size = 8, family = "Roboto Condensed Light"),
        strip.text = element_text(size = 12, "Roboto Condensed Light"),
        legend.text = element_text(size = 10)) +
  facet_geo(~ state, grid = "us_state_grid2", label = "code") +
  labs(title = "Which states have the sharpest increase in unemployment due to COVID-19?",
       caption = "Source: Bureau of Labor Statistics\ngithub.com/kelseygonzalez",
       x = "Month of 2020 (January - June)",
       y = "Unemployment Rate") 

ggsave("bls_unemployment.png", plot = unempl_plot, width = 10, height = 6)

Kelsey E. Gonzalez
Kelsey E. Gonzalez
PhD Candidate

I’m a computational social scientist interested in using social data in new and innovative ways.