Week 4 Discussion: Solution

NOTE: Use the Zoom button to check out your plot

Your plot will appear slightly different in the RStudio plot window vs. a zoom window vs. rendered in a Quarto doc – I recommend checking it out in the zoom window while you iterate.

Importantly, how you theme a data visualization depends on how you plan to package / present it. For example, your plot margin sizes may differ when you prepare a standalone plot vs. one that you plan to embed in a rendered Quarto doc.

##~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
##                                    setup                                 ----
##~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

#..........................load packages.........................
library(tidyverse)

#..........................import data...........................
tuesdata <- tidytuesdayR::tt_load('2021-07-20')

    Downloading file 1 of 1: `drought.csv`

drought <- tuesdata$drought

##~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
##                            wrangle drought data                          ----
##~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

drought_clean <- drought |>

  # select cols of interest & update names for clarity (as needed) ----
  select(date = valid_start, state_abb, drought_lvl, area_pct) |> 

  # add year, month & day cols using {lubridate} fxns ----
  # NOTE: this step isn't necessary for our plot, but I'm including as examples of how to extract different date elements from a object of class Date using {lubridate} ----
  mutate(year = year(date),
         month = month(date, label = TRUE, abbr = TRUE),
         day = day(date)) |>

  # add drought level conditions names ----
  mutate(drought_lvl_long = factor(drought_lvl,
                            levels = c("D4", "D3", "D2", "D1","D0", "None"),
                            labels = c("(D4) Exceptional", "(D3) Extreme",
                                       "(D2) Severe", "(D1) Moderate", 
                                       "(D0) Abnormally Dry", 
                                       "No Drought"))) |>
  
  # reorder cols ----
  relocate(date, year, month, day, state_abb, drought_lvl, drought_lvl_long, area_pct)

##~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
##       create stacked area plot of CA drought conditions through time     ----
##~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

drought_clean |> 
  
  # remove drought_lvl "None" & filter for just CA ----
  filter(drought_lvl != "None",
         state_abb == "CA") |> 
  
  # initialize ggplot ----
  ggplot(mapping = aes(x = date, y = area_pct, fill = drought_lvl_long)) +
  
  # reverse order of groups so level D4 is closest to x-axis ----
  geom_area(position = position_stack(reverse = TRUE)) +
  
  # update colors to match US Drought Monitor ----
 # (colors identified using ColorPick Eyedropper extension on the original USDM data viz) 
  scale_fill_manual(values = c("#853904", "#FF0000", "#FFC100", "#FFD965", "#FFFF00")) +
  
  # set x-axis breaks & remove padding between data and x-axis ----
  scale_x_date(breaks = scales::breaks_pretty(n = 10),
               expand = c(0, 0)) +

  # set y-axis breaks & remove padding between data and y-axis & convert values to percentages ----
  scale_y_continuous(breaks = seq(0, 100, by = 10),
                     expand = c(0, 0),
                     labels = scales::label_percent(scale = 1)) +
  
  # add title ----
  labs(title = "Drought area in California") +
  
##~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
##                                                                            --
##------------------------- NEWLY ADDED THEME CODE!-----------------------------
##                                                                            --
##~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

  # set theme minimal (includes major/minor grid lines, no axes) ----
  theme_minimal() +
  
  # fine-tune adjustments to plot theme ----
  theme(
    
    # update axis lines & ticks color ----
    axis.line = element_line(color = "#5A9CD6"),
    axis.ticks = element_line(color = "#5A9CD6"),
    
    # adjust length of axis ticks ----
    axis.ticks.length = unit(.2, "cm"),
    
    # center plot title ----
    plot.title = element_text(hjust = 0.5, color = "#686868", size = 20,
                              margin = margin(t = 10, r = 0, b = 15, l = 0)),
    
    # remove axis & legend titles ----
    axis.title = element_blank(),
    legend.title = element_blank(),
    
    # axis text color & size ----
    axis.text = element_text(color = "#686868", size = 12),
    legend.text = element_text(color = "#686868", size = 12),
    
    # move legend below plot ----
    legend.position = "bottom",
    legend.key.width = unit(0.4, "cm"),
    legend.key.height = unit(0.25, "cm"),
    
    plot.background = element_rect(color = "#686868"),
    plot.margin = margin(t = 10, r = 10, b = 10, l = 10)
  )