Monthly State Retail Sales

Load libraries

library(tidyverse)
library(fuzzyjoin)
library(ggstream)
library(colorspace)

Set the theme

theme_set(theme_minimal(base_family = "Roboto Condensed",
                        base_size = 12))

theme_update(
  plot.title = element_text(
    size = 20,
    face = "bold",
    hjust = .5,
    margin = margin(10, 0, 30, 0)
  ),
  plot.caption = element_text(
    size = 9,
    color = "grey40",
    hjust = .5,
    margin = margin(20, 0, 5, 0)
  ),
  axis.text.y = element_blank(),
  axis.title = element_blank(),
  plot.background = element_rect(fill = "grey88", color = NA),
  panel.background = element_rect(fill = NA, color = NA),
  panel.grid = element_blank(),
  panel.spacing.y = unit(0, "lines"),
  strip.text.y = element_text(angle = 0),
  legend.position = "bottom",
  legend.text = element_text(size = 9, color = "grey40"),
  legend.box.margin = margin(t = 30),
  legend.background = element_rect(
    color = "grey40",
    linewidth = .3,
    fill = "grey95"
  ),
  legend.key.height = unit(.25, "lines"),
  legend.key.width = unit(2.5, "lines"),
  plot.margin = margin(rep(20, 4))
)

And the color palette

pal <- c("#FFB400",
         "#C20008",
         "#13AFEF",
         "#8E038E")

Load the data

tuesdata <- tidytuesdayR::tt_load(2022, week = 50)
coverage_codes <- tuesdata$coverage_codes
state_retail <- tuesdata$state_retail

Add the states’ names

fipcodes <- tigris::fips_codes %>%
  select(state, state_name)

Join all sets

my_df <- state_retail %>%
  left_join(fipcodes, by = c("state_abbr" = "state")) %>%
  mutate(state_name = ifelse(state_abbr == "USA", "USA", state_name)) %>%
  distinct() %>%
  merge(coverage_codes, by = "coverage_code") %>%
  arrange()

my_df %>% head

Data wrangling

my_df1 <- my_df %>%
  select(-naics) %>%
  mutate(
    coverage = case_when(
      coverage == "non-imputed coverage is greater than or equal to 10% and less than 25% of the state/NAICS total" ~
        "greater than or equal 10% and less than 25% of the state/NAICS total",
      coverage == "non-imputed coverage is greater than or equal to 25% and less than 50% of the state/NAICS total" ~
        "greater than or equal to 25% and less than 50% of the state/NAICS total",
      coverage == "non-imputed coverage is greater than or equal to 50% of the state/NAICS total." ~
        "greater than or equal to 50% of the state/NAICS total",
      coverage == "non-imputed coverage is less than 10% of the state/NAICS total." ~
        "less than 10% of the state/NAICS total",
      TRUE ~ coverage
    ),
    month = as.character(month),
    year = zoo::as.yearmon(paste0(year, "-", month)),
    change_yoy = ifelse(change_yoy == "S", 0, change_yoy),
    change_yoy_se = ifelse(change_yoy_se == "S", 0, change_yoy_se),
    change_yoy = as.numeric(change_yoy),
    change_yoy_se = as.numeric(change_yoy_se),
    coverage = as.factor(coverage),
    coverage = paste(coverage_code, "-", coverage)
  ) %>%
  filter(state_abbr %in% c("USA", "PA", "MD", "MT")) %>%
  filter(!coverage_code == "S") %>%
  group_by(state_name, coverage, year) %>%
  summarise_if(is.numeric, sum, na.rm = TRUE) %>%
  mutate(change_yoy = scale(change_yoy, center = FALSE)) %>%
  ungroup() %>%
  mutate(year = as.POSIXct(year),
         year = as.Date(year))

Make the plot

my_df1 %>%
  ggplot(aes(
    x = year,
    y = change_yoy,
    color = coverage,
    fill = coverage
  )) +
  geom_stream(
    geom = "contour",
    color = "white",
    linewidth = 1.25,
    bw = .45 # Controls smoothness
  ) +
  geom_stream(geom = "polygon",
              bw = .45,
              linewidth = 0.2) +
  facet_grid(state_name ~ .,
             scales = "free_y",
             space = "free") +
  scale_y_continuous(trans = scales::modulus_trans(0.1, 1)) +
  scale_x_date(date_breaks = "6 months",
               date_labels = "%b-%Y",
               expand = c(0, 0)) +
  scale_color_manual(expand = c(0, 0),
                     values = pal,
                     guide = "none") +
  scale_fill_manual(values = pal,
                    name = NULL) +
  labs(title = "Total Year-Over-Year percent change\nin monthly retail sales value",
       subtitle = "North American Industry Classification System (NAICS) top YoY states",
       caption = "DataSource: #TidyTuesday 2022 Week50 | Monthly State Retail Sales | DataViz: Fgazzelloni") +
  theme(legend.direction = "vertical")

ggsave("w50_retail_sales.png")