6. Color

Author

Camille Seaberry

Modified

February 20, 2024

Code 
library(dplyr)
library(ggplot2)
library(justviz)

source(here::here("utils/plotting_utils.R"))

Warm-up

Color perception

Which row uses a darker color?

Which line uses a darker color?

Which line uses a darker color?

How many purple dots are in each row?

Colors in R

If you don’t already have a color selection tool on your computer 1 you can install the colourpicker package that has a color picker addin for RStudio.

1 Maybe the thing I miss most since switching from Mac to Linux is the color picker app Sip, definitely recommend it

Chapters

We’ll walk through Wilke chapters 4 and 19–I don’t have a ton to add until we get to mapping.

Datawrapper. (2021). What to consider when choosing colors for data visualization. https://academy.datawrapper.de/article/140-what-to-consider-when-choosing-colors-for-data-visualization

Tools

2 I just read a post making fun of Colorgorical for leaning toward puke green colors; haven’t used it in a while but heads up I guess?

Types of color palettes

The main types of color palettes are:

  • sequential / quantitative: values are numeric and continuous; values and colors (saturation, lightness, hue) increase in some way in tandem
  • diverging: values are likely numeric, but colors trend in opposite directions
  • qualitative / categorical: values are not numeric / continuous, and colors should not imply continuity

ColorBrewer and Carto Colors are great because they have options for all three of these.

These are rough examples using ColorBrewer palettes; in practice you might want to make some adjustments to these.

Exercises

local_counties <- c("Baltimore city", "Baltimore County", "Harford County", "Howard County", "Anne Arundel County")
acs_county <- acs |>
  filter(level %in% c("us", "state", "county")) |>
  mutate(local1 = forcats::as_factor(name) |>
           forcats::fct_other(keep = c(local_counties, "United States", "Maryland"), other_level = "Other counties"),
         local2 = forcats::fct_collapse(local1, "Outside Baltimore" = c("Baltimore County", "Harford County", "Howard County", "Anne Arundel County")) |>
           forcats::fct_relevel("Outside Baltimore", "Other counties", after = Inf))

theme_set(theme_nice())
knitr::kable(head(acs_county))
level county name total_pop white black latino asian other_race diversity_idx foreign_born total_hh homeownership total_cost_burden total_severe_cost_burden owner_cost_burden owner_severe_cost_burden renter_cost_burden renter_severe_cost_burden no_vehicle_hh median_hh_income ages25plus less_than_high_school high_school_grad some_college_or_aa bachelors grad_degree pov_status_determined poverty low_income area_sqmi pop_density local1 local2
us NA United States 331097593 0.59 0.12 0.19 0.06 0.05 0.7376547 0.14 125736353 0.65 0.29 0.14 0.21 0.09 0.45 0.23 0.08 75149 226600992 0.11 0.26 0.28 0.21 0.13 323275448 0.13 0.29 NA NA United States United States
state NA Maryland 6161707 0.49 0.29 0.11 0.06 0.05 0.7905359 0.16 2318124 0.67 0.29 0.13 0.21 0.09 0.46 0.23 0.09 98461 4260095 0.09 0.24 0.25 0.22 0.20 6034320 0.09 0.21 NA NA Maryland Maryland
county NA Allegany County 68161 0.87 0.07 0.02 0.01 0.03 0.3379538 0.02 27462 0.70 0.23 0.10 0.16 0.07 0.39 0.17 0.10 55248 47914 0.09 0.41 0.29 0.12 0.09 61098 0.16 0.36 422.19881 161.4429 Other counties Other counties
county NA Anne Arundel County 588109 0.65 0.17 0.09 0.04 0.05 0.6745018 0.09 221704 0.75 0.26 0.11 0.20 0.08 0.44 0.19 0.04 116009 409052 0.06 0.22 0.27 0.25 0.19 577547 0.06 0.14 414.80643 1417.7914 Anne Arundel County Outside Baltimore
county NA Baltimore County 850737 0.54 0.30 0.06 0.06 0.04 0.7241974 0.12 328611 0.67 0.29 0.14 0.20 0.09 0.48 0.24 0.08 88157 589611 0.08 0.24 0.26 0.23 0.18 830921 0.10 0.23 598.35821 1421.7855 Baltimore County Outside Baltimore
county NA Baltimore city 584548 0.27 0.61 0.06 0.03 0.04 0.6483744 0.08 247232 0.48 0.37 0.19 0.26 0.13 0.47 0.26 0.26 58349 410221 0.13 0.28 0.24 0.18 0.17 564634 0.20 0.38 80.94606 7221.4510 Baltimore city Baltimore city

Find some ways to improve the use of color in these charts, including whether color even should be used. Before writing any code, write down:

  • How color is used / what color signifies
  • What’s wrong with that use of color
  • How you want to change it

Here are some charts that are bad because they use color inappropriately.

acs_county |>
  mutate(name = forcats::as_factor(name) |> forcats::fct_reorder(homeownership)) |>
  ggplot(aes(x = name, y = homeownership, fill = name)) +
  geom_col(width = 0.8) +
  coord_flip() +
  labs(x = NULL, y = NULL, 
       title = "Homeownership rate by location", 
       subtitle = "US, Maryland, and Maryland counties, 2022")

acs_county |>
  mutate(name = forcats::as_factor(name) |> forcats::fct_reorder(homeownership)) |>
  ggplot(aes(x = name, y = homeownership, fill = local2)) +
  geom_col(width = 0.7, color = "gray20", linewidth = 0.2) +
  coord_flip() +
  labs(x = NULL, y = NULL, 
       title = "Homeownership rate by location", 
       subtitle = "US, Maryland, and Maryland counties, 2022") +
  scale_fill_brewer(palette = "GnBu")

Here’s a chart that’s okay but not great; it uses color in a way that’s not wrong but not effective either.

acs_county |>
  mutate(name = forcats::as_factor(name) |> forcats::fct_reorder(homeownership)) |>
  ggplot(aes(x = name, y = homeownership, fill = local2)) +
  geom_col(width = 0.8) +
  coord_flip() +
  labs(x = NULL, y = NULL, 
       title = "Homeownership rate by location", 
       subtitle = "US, Maryland, and Maryland counties, 2022")

Here’s one that uses color when it should actually use something else to convey its pattern. What type of chart would be more appropriate?

acs_county |>
  mutate(name = forcats::as_factor(name) |> forcats::fct_reorder(homeownership)) |>
  ggplot(aes(x = name, y = homeownership, fill = median_hh_income)) +
  geom_col(width = 0.8) +
  coord_flip() +
  labs(x = NULL, y = NULL, 
       title = "Homeownership rate vs median household income by location", 
       subtitle = "US, Maryland, and Maryland counties, 2022")

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