The Data

The data this week comes from the U.S. Drought Monitor. Many more datasets including county-level data can be accessed there. In the interest of size we stuck with state-level data.

Please reference the data as seen below:

The U.S. Drought Monitor is jointly produced by the National Drought Mitigation Center at the University of Nebraska-Lincoln, the United States Department of Agriculture, and the National Oceanic and Atmospheric Administration. Map courtesy of NDMC.

drought <- read_csv("drought.csv") 

CA_drought <- drought %>%
  filter(state_abb == "CA")

The data as given are cumulative percentages (and areas and counts). To create information about the percentage (or area or count), we need a lagged difference.

drought <- drought %>%
  group_by(state_abb, map_date) %>%
  mutate(drought_lvl = factor(drought_lvl, 
                               levels = c("None", "D0", "D1", "D2", "D3", "D4"))) %>%
  arrange(state_abb, map_date, drought_lvl) %>%
  mutate(area_pct_lvl =  area_pct - lead(area_pct)) %>%
  mutate(area_pct_lvl = case_when(
    is.na(area_pct_lvl) ~ area_pct, 
    drought_lvl == "None" ~ area_pct,
    TRUE ~ area_pct_lvl))

CA_drought <- CA_drought %>%
  group_by(state_abb, map_date) %>%
  mutate(drought_lvl = factor(drought_lvl, 
                               levels = c("None", "D0", "D1", "D2", "D3", "D4"))) %>%
  arrange(map_date, drought_lvl) %>%
  mutate(area_pct_lvl =  area_pct - lead(area_pct)) %>%
  mutate(area_pct_lvl = case_when(
    is.na(area_pct_lvl) ~ area_pct, 
    drought_lvl == "None" ~ area_pct,
    TRUE ~ area_pct_lvl))

Viz

Viewing CA droughts over time. As we know, CA is bracing for terrible drought conditions in the coming years.

CA_drought %>%
  filter(drought_lvl != "None", drought_lvl != "D0") %>%
  ggplot(aes(x = valid_start, y = area_pct_lvl, color = drought_lvl,
             fill = drought_lvl))+
  geom_col() +
  scale_color_brewer(palette = "OrRd") +
  scale_fill_brewer(palette = "OrRd") +
  xlab("date") +
  ylab("percent of state in drought")

A plot of California only. A bar plot with time on the x-axis and percent of state in drought on the y-axis. Only the highest 4 states of drought are shown, and we can see that from about 2012 to 2017, almost 100% of the area of California was in one of the drought levels with 2015 and 2016 being close to 50% of the state being in the highest level of drought.

And all 50 states. It is interesting to see that many of the (mostly western) states have had serious drought positions over the years.

drought %>%
  filter(drought_lvl != "None", drought_lvl != "D0") %>%
  ggplot(aes(x = valid_start, y = area_pct_lvl, color = drought_lvl,
             fill = drought_lvl)) +
  geom_col() +
  scale_color_brewer(palette = "OrRd") +
  scale_fill_brewer(palette = "OrRd") +
  xlab("date") +
  ylab("") +
  facet_geo(~state_abb, grid = "us_state_grid2") +
  scale_x_continuous(breaks = c(2000, 2010, 2020)) +
  scale_y_continuous(breaks = c(0, 100)) +
  labs(
    title = "Percent of state in drought",
    captions = "Tidy Tuesday Plot: @hardin47 | Data: US Drought Monitor"
  ) +
  theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1),
        axis.title=element_text(size=15,face="bold"),
        plot.title = element_text(size = 20, face = "bold"),
        plot.caption = element_text(size = 15))

Each of the 50 states has a separate plot, and the separate plots are faceted / laid out roughly according to the US map. Each of the 50 plots is a bar plot with time on the x-axis and percent of state in drought on the y-axis. The western states have suffered from server drought for much of the last 10 years.

praise()
## [1] "You are priceless!"