Fundamental chart types (part II)

Artwork by Allison Horst

Pre-class Prep

Please be sure to complete the following before class:

Install required packages

install.packages("ggalt") # extra coordinate systems, geoms, statistical transformations, scales and fonts for 'ggplot2' (includes `geom_lollipop()`)
install.packages("ggExtra") # {ggplot2} extension2222 which can be used to add marginal histograms/boxplots/density plots to ggplot2 scatterplots
install.packages("ggdensity") # {ggplot2} extension providing more interpretable visualizations of density estimates based on highest density regions (HDRs)

Review data wrangling code

In lecture 4.1, we’ll be exploring income data from the Bureau of Labor Statistics and the Census Bureau, which has been moderately pre-processed by TidyTuesday organizers for the March 5, 2021 data set.

Please review the necessary background information (see slide 4) and walk through the data wrangling code on slide 5 ahead of class. Come prepared with any questions you may have about the code – we’ll set aside ~10 minutes to discuss before jumping into building our data visualizations.

Lecture Materials

Week 4 instruction is broken down into two lessons:

Visualizing rankings

lecture 4.1 slides

Visualizing relationships

lecture 4.2 slides

Discussion Materials

Last week’s discussion focused on recreating the data layers of the U.S. Drought Monitor’s visualization of CA drought conditions through time. This week, you’ll focus on refining the theme (non-data plot elements) to match the original visualization as closely as possible.

Week 4 discussion slides

Background

Themes are used to modify the non-data components of plots (e.g. titles, labels, fonts, background, gridlines, legend). In addition to using pre-built themes (available via the {ggplot2} package and also from extension packages), you can fine-tune the appearance of your plots theme by making adjustments using the theme() function.

Applying pre-built themes:

{ggplot2} comes with eight complete themes, which can be applied as-is, or further modified using theme() (see next section, below). There are also many additional themes which can be applied via ggplot extension packages. A small handful of packages are listed in the expandable note, below:

A non-exhaustive list of ggplot theme extension packages

• {ggthemes}: a collection of popular themes, some of which also come with corresponding color scales
• {hrbrthemes}: typographic-centric themes and theme components
• {ggthemr}: a collection of color palettes and plot layouts for quickly setting a ggplot theme
• {ggtech}: themes based off large tech company color palettes
• {ggdark}: dark mode for ggplot complete themes
• {firatheme}: a ggplot theme with Fira font
• {urbnthemes}: tools for creating Urban Institute-themed plots and maps in R (also check out the Urban Institute Data Visualization Style Guide for a really great example of how an organization enforces a cohesive style across all published data viz content)
• {bbplot}: tools for creating ggplots in the style used by the BBC News data team
• {ggpomological}: a ggplot2 theme based on the USDA Pomological Watercolor Collection
• {tvthemes}: a collection of ggplot themes and color / fill palettes based on everybody’s favorite TV shows

Keep your eye out for extension packages that supply both a geom(s) and a pre-build theme(s) designed specifically to work with that geom. For example, the {ggridges} package provides both a few different ridgeline plot geoms and a pre-built theme_ridges() theme to pair with them.

Using ggplot2::theme() to control plot elements:

Use this (non-exhaustive) list to start tweaking elements of your plot’s appearance. It’s most common to start with a pre-built theme (see above) and modify from there. For a complete list of elements, explore the documentation for ggplot::theme().

Adapted from Reproducible Science for Busy Researchers: How to Save Time using Literate Programming, by Andrew P. Lapointe

Solution

You’ll get the most out of discussion section if you physically type out the code yourself (rather than copying / pasting)!

Note: Much of the following code (data wrangling and ggplot code) was copied over from last week’s discussion materials. Newly added theme code begins below the comment, NEWLY ADDED THEME CODE!.

Complete code for week 4 discussion

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

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

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

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

drought_clean <- drought |>

  # select cols of interest & update names 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 ----
  select(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") |> 
  
  # create 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 selected using ColorPick Eyedropper from 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 & convert values to percentages & & remove padding between data and y-axis----
  scale_y_continuous(breaks = seq(0, 100, by = 10),
                     labels = scales::label_percent(scale = 1),
                     expand = c(0, 0)) +
  
  # 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 = 25,
                              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 = 15),
    legend.text = element_text(color = "#686868", size = 15),
    
    # move legend below plot ----
    legend.position = "bottom",
  )

Assignment Reminders

Assignment Type	Assignment Title	Date Assigned	Date Due
EOC	EOC (week 4)	Mon 01/29/2024	Mon 01/29/2024, 11:55pm PT
HW	Homework Assignment #2	Mon 01/22/2024	Sat 02/03/2024, 11:59pm PT