• You should choose a color palette based on the types of the data you are plotting.
  • An unordered categorical variable (e.g., ‘Sex’ or ‘Country’) requires distinct colors that won’t be easily confused with one another.
  • An ordered categorical variable (e.g., ‘Level of Education’) requires a graded color scheme of some kind running from less to more or earlier to later.

• We choose color palettes for mappings through one of the scale_ functions for color or fill.

• RColorBrewer package provides a wide range of named color palettes.

  • You can access these colors by specifying the scale_color_brewer() or scale_fill_brewer() functions with palette parameter.

p <- ggplot(data = organdata,
            mapping = aes(x = roads, y = donors, color = world))

p + geom_point(size = 2) + scale_color_brewer(palette = "Set2") +
    theme(legend.position = "top")

p <- ggplot(data = organdata,
            mapping = aes(x = roads, y = donors, color = world))

p + geom_point(size = 2) + scale_color_brewer(palette = "Pastel2") +
    theme(legend.position = "top")

p <- ggplot(data = organdata,
            mapping = aes(x = roads, y = donors, color = world))

p + geom_point(size = 2) + scale_color_brewer(palette = "Dark2") +
    theme(legend.position = "top")

• We can also specify colors manually, via scale_color_manual() or scale_fill_manual().
  • These functions take values argument that can be specified as vector of color names or hex colors.

p <- ggplot(data = organdata,
            mapping = aes(x = roads, y = donors, color = world))

p + geom_point(size = 2) + 
  scale_color_manual(values = c("#3c6ff8", "#afd68d", "#8467ad", "#82857f")) +
  theme_minimal() + theme(legend.position = "top") 

• Even though colorblind people can recognize a wide range of colors, it might be hard to differentiate between certain colors.

  • dichromat package provides a range of safe palettes and some useful functions for helping you approximately see what your current palette might look like to a viewer with one of several different kinds of color blindness.

Default <- brewer.pal(5, "Set2")
types <- c("deutan", "protan", "tritan")
names(types) <- c("Deuteronopia", "Protanopia", "Tritanopia")

color_table <- types %>%
    purrr::map(~ dichromat(Default, .x)) %>%
    as_tibble() %>%
    add_column(Default, .before = TRUE)

color_table

## # A tibble: 5 × 4
##   Default Deuteronopia Protanopia Tritanopia
##   <chr>   <chr>        <chr>      <chr>     
## 1 #66C2A5 #AEAEA7      #BABAA5    #82BDBD   
## 2 #FC8D62 #B6B661      #9E9E63    #F29494   
## 3 #8DA0CB #9C9CCB      #9E9ECB    #92ABAB   
## 4 #E78AC3 #ACACC1      #9898C3    #DA9C9C   
## 5 #A6D854 #CACA5E      #D3D355    #B6C8C8

• Color is very useful when we want to pick out or highlight some aspect of our data.

• Let’s do a simple exploratory analysis on the relationship between politics and race using color.

• First, use county_data with a value of “No” on the flipped variable to draw a scatter plot on the pop-black/100 plane.

party_colors <- c("#2E74C0", "#CB454A") # DEM Blue and REP Red

p0 <- ggplot(data = subset(county_data,
                           flipped == "No"),
             mapping = aes(x = pop, y = black/100) )

p1 <- p0 + geom_point(alpha = 0.15, color = "gray50") +
    scale_x_log10(labels=scales::comma) 
p1

• Next, we add a second geom_point() layer.
  • We choose the data with “Yes” counties on the flipped variable.
  • We map the partywinner16 variable to the color aesthetic using scale_color_manual(values = party_colors).

p2 <- p1 + geom_point(data = subset(county_data,
                                    flipped == "Yes"),
                      mapping = aes(x = pop, y = black/100,
                                    color = partywinner16)) +
    scale_color_manual(values = party_colors)

p2

• Next, we set the y-axis scale and the labels.

p3 <- p2 + scale_y_continuous(labels=scales::percent) +
    labs(color = "County flipped to ... ",
         x = "County Population (log scale)",
         y = "Percent Black Population",
         title = "Flipped counties, 2016",
         caption = "Counties in gray did not flip.")

p3

• Finally, we add a third layer using the geom_text_repel() function.
  • We are interested in the flipped counties that have with a relatively high percentage of African-American residents (black > 25).

p4 <- p3 + geom_text_repel(data = subset(county_data,
                                      flipped == "Yes" &
                                      black  > 25),
                           mapping = aes(x = pop,
                                   y = black/100,
                                   label = state), size = 2)

p4 + theme_minimal() +
    theme(legend.position="top")

• If we want to change the overall look of it all at once, we can do that using ggplot’s theme engine.
  • Themes can be turned on or off using the theme_set() function.

theme_set(theme_economist())
p4 + theme(legend.position="top")

theme_set(theme_wsj())
p4 + theme(plot.title = element_text(size = rel(0.6)),
           legend.title = element_text(size = rel(0.35)),
           plot.caption = element_text(size = rel(0.35)),
           legend.position = "top")

• We can adjust the text size using element_text() function.

theme_set(theme_wsj())
p4 + theme(plot.title = element_text(size = rel(0.6)),
           legend.title = element_text(size = rel(0.35)),
           plot.caption = element_text(size = rel(0.35)),
           legend.position = "top")

• cowplot package provides theme_cowplot().

# install.packages("cowplot")
library(cowplot)
theme_set(theme_cowplot())
p4 + theme(legend.position="top")

• hrbrthemes package provides theme_ipsum(), which is similar to the theme used in our textbook.

# install.packages("hrbrthemes")
library(hrbrthemes)
theme_set(theme_ipsum())
p4 + theme(legend.position="top")

• We can also change font and color of the text.

p4 + theme(legend.position = "top",
           plot.title = element_text(size=rel(2),
                                     lineheight=.5,
                                     family="Times",
                                     face="bold.italic",
                                     colour="orange"),
           axis.text.x = element_text(size=rel(1.1),
                                      family="Courier",
                                      face="bold",
                                      color="purple"))

• We can also use element_blank() to remove a number of elements by naming them and making them disappear.

p4 + theme(legend.position = "top",
           plot.title = element_text(size=rel(2),
                                     lineheight=.5,
                                     family="Times",
                                     face="bold.italic",
                                     colour="orange"),
           axis.text.x = element_blank())

• Let’s create an effective small multiple of the age distribution of General Social Survey (GSS) respondents over the years using gss_lon data.

• First, we need to calculate the mean age of the respondents for each year of interest.

• We will look at distributions about every four years since the beginning.

yrs <- c(seq(1972, 1988, 4), 1993, seq(1996, 2016, 4))

mean_age <- gss_lon %>%
    filter(age %nin% NA && year %in% yrs) %>%
    group_by(year) %>%
    summarize(xbar = round(mean(age, na.rm = TRUE), 0))

mean_age$y <- 0.3 # to position the age as a text label. 

yr_labs <- data.frame(x = 85, y = 0.8,
                      year = yrs)

p <- ggplot(data = subset(gss_lon, year %in% yrs),
            mapping = aes(x = age))
p1 <- p + geom_density(fill = "black", color = FALSE,
                       alpha = 0.9, mapping = aes(y = ..scaled..)) +
    geom_vline(data = subset(mean_age, year %in% yrs),
               aes(xintercept = xbar), color = "white", size = 0.5) +
    geom_text(data = subset(mean_age, year %in% yrs),
              aes(x = xbar, y = y, label = xbar), nudge_x = 7.5,
              color = "white", size = 3.5, hjust = 1) +
    geom_text(data = subset(yr_labs, year %in% yrs),
              aes(x = x, y = y, label = year)) +
    facet_grid(year ~ ., switch = "y")
p1

• The first geom_text() call uses a nudge argument to push the label slightly to the right of its x-value (nudge_x).

• In the facet_grid() we use the switch argument to move the labels to the left.

• With the structure of the plot in place, we then style the elements in the way that we want, using a series of instructions to theme().

p2a <- p1 + theme(plot.title = element_text(size = 16),
           axis.text.x= element_text(size = 12),
           axis.line.x = NULL,
           axis.title.y=element_blank(),
           axis.text.y=element_blank(),
           axis.ticks.y = element_blank(),
           strip.background = element_blank(),
           strip.text.y = element_blank(),
           panel.grid.major = element_blank(),
           panel.grid.minor = element_blank()) +
  labs(x = "Age",
       y = NULL,
       title = "Age Distribution of\nGSS Respondents")
p2a

• ggridges allows the distributions to overlap vertically.
  • It is especially useful for repeated distributional measures that change in a clear direction.

p <- ggplot(data = gss_lon,
            mapping = aes(x = age, y = factor(year, levels = rev(unique(year)),
                                     ordered = TRUE)))
p2b <- p + geom_density_ridges(alpha = 0.6, fill = "lightblue", scale = 1.5) +
    scale_x_continuous(breaks = c(25, 50, 75)) +
    scale_y_discrete(expand = c(0.01, 0)) + 
    labs(x = "Age", y = NULL,
         title = "Age Distribution of\nGSS Respondents") +
    theme_ridges() +
    theme(title = element_text(size = 16, face = "bold"))
p2b

• gridExtra package provides grid.arrange() function.
  • grid.arrange() arranges multiple ggplot objects on a page, and draw tables of figures.

library(gridExtra)
grid.arrange(p2a, p2b, nrow = 1)   # sub-figures