Themes and annotations in ggplot2
Using themes and annotations to surface the story in a chart, with the gapminder data.
Visualisation20234 min readggplot2VisualisationR
On this page
Show code (23 lines)
library(tidyverse) # For ggplot, dplyr, and friends
library(gapminder) # For gapminder data
library(scales) # For nice axis labels
gapminder_filtered <- gapminder %>%
filter(year > 2000)
base_plot <- ggplot(data = gapminder_filtered,
mapping = aes(x = gdpPercap, y = lifeExp,
color = continent, size = pop)) +
geom_point() +
# Use dollars, and get rid of the cents part (i.e. $300 instead of $300.00)
scale_x_log10(labels = dollar_format(accuracy = 1)) +
# Format with commas
scale_size_continuous(labels = comma) +
# Use viridis
scale_color_viridis_d(option = "plasma", end = 0.9) +
labs(x = "GDP per capita", y = "Life expectancy",
color = "Continent", size = "Population",
title = "Here's a cool title",
subtitle = "And here's a neat subtitle",
caption = "Source: The Gapminder Project") +
facet_wrap(vars(year))
Create and save theme (my_pretty_theme)
Show code (23 lines)
my_pretty_theme <- theme_minimal(base_family = "Roboto Condensed", base_size = 12) +
theme(panel.grid.minor = element_blank(),
# Bold, bigger title
plot.title = element_text(face = "bold", size = rel(1.7)),
# Plain, slightly bigger subtitle that is grey
plot.subtitle = element_text(face = "plain", size = rel(1.3), color = "grey70"),
# Italic, smaller, grey caption that is left-aligned
plot.caption = element_text(face = "italic", size = rel(0.7),
color = "grey70", hjust = 0),
# Bold legend titles
legend.title = element_text(face = "bold"),
# Bold, slightly larger facet titles that are left-aligned for the sake of repetition
strip.text = element_text(face = "bold", size = rel(1.1), hjust = 0),
# Bold axis titles
axis.title = element_text(face = "bold"),
# Add some space above the x-axis title and make it left-aligned
axis.title.x = element_text(margin = margin(t = 10), hjust = 0),
# Add some space to the right of the y-axis title and make it top-aligned
axis.title.y = element_text(margin = margin(r = 10), hjust = 1),
# Add a light grey background to the facet titles, with no borders
strip.background = element_rect(fill = "grey90", color = NA),
# Add a thin grey border around all the plots to tie in the facet titles
panel.border = element_rect(color = "grey90", fill = NA))
base_plot +
my_pretty_theme
Lets make this plot even better :D
Show code (64 lines)
library(tidyverse)
library(gapminder)
library(scales)
library(ggtext)
library(thematic)
gapminder_filtered <- gapminder %>%
filter(year > 2000)
colors <- thematic::okabe_ito(5)
title_text <- glue::glue(
' Life Expectancy per GDP for <span style = "color:{colors[1]}">**Africa**</span>, <span style = "color:{colors[2]}">**America**</span>, <span style = "color:{colors[3]}">**Asia**</span>, <br> <span style = "color:{colors[4]}">**Europa**</span> and <span style = "color:{colors[5]}">**Oceania**</span>'
)
plot_nice <- ggplot(data = gapminder_filtered,
mapping = aes(x = gdpPercap, y = lifeExp,
color = continent, size = pop)) +
geom_point() +
# Use dollars, and get rid of the cents part (i.e. $300 instead of $300.00)
#log10 scale = abstände nicht gleich(nicht-linear): x-1*x = x+1
scale_x_log10(labels = dollar_format(accuracy = 1)) +
# Format with commas (anstatt wissenschaftliche Notation für Population)
scale_size_continuous(labels = comma) +
# Use viridis color
scale_color_viridis_d(option = "plasma", end = 0.9) +
labs(x = "GDP per capita", y = "Life expectancy",
color = "Continent", size = "Population",
title = title_text,
subtitle = "Visible Correlation between GDP (per capita) and Life expecanty",
caption = "Source: The Gapminder Project") +
facet_wrap(vars(year))+
theme_minimal(base_family = 'Source Sans Pro', base_size = 12) +
#guide = none, legend wird ausgelassen
scale_color_manual(values = thematic::okabe_ito(5), guide = "none")+
#grauere Hintergrund verschwindet+
theme(panel.grid.minor = element_blank(),
# Plain, slightly bigger subtitle that is grey
plot.subtitle = element_text(face = "plain", size = rel(1.3), color = "grey70"),
# Italic, smaller, grey caption that is left-aligned
plot.caption = element_text(face = "italic", size = rel(0.7),
color = "grey70", hjust = 0),
#Bold legend titles
legend.title = element_text(face = "bold"),
# Bold, slightly larger facet titles that are left-aligned for the sake of repetition
strip.text = element_text(face = "bold", size = rel(1.1), hjust = 0, color = "white"),
plot.title = ggtext::element_markdown(
color = 'grey20', family = 'Merriweather', size = rel(1.7),face = "bold"
),
plot.title.position = "plot",
# Bold axis titles
axis.title = element_text(face = "bold"),
# Add some space above the x-axis title and make it left-aligned
axis.title.x = element_text(margin = margin(t = 10), hjust = 0),
# Add some space to the right of the y-axis title and make it top-aligned
axis.title.y = element_text(margin = margin(r = 10), hjust = 1, margin (0,4,0,0)),
# Add a light grey background to the facet titles, with no borders
strip.background = element_rect(fill = "grey20", color = NA),
# Add a thin grey border around all the plots to tie in the facet titles
panel.border = element_rect(color = "grey20", fill = NA),
legend.position = "top",
legend.justification = 1)
plot_nice
We can add some annotations
Annotionations can be great to highlight information that gets lost otherwise. For example we see that South Africa increases its GDP per capita from 2002 to 2007 but life expectancy decreases by 4 years.
#find african country with dip in lifeexp: South Africa(2002: x = 7711 ,y= 53.4; 2007: x = 9270, y = 49.3)
gapminder_filtered |>
filter(continent == "Africa" & between(gdpPercap,3000,10000) & between(lifeExp,48,55)) |> arrange(desc(pop))
# A tibble: 6 × 6
country continent year lifeExp pop gdpPercap
<fct> <fct> <int> <dbl> <int> <dbl>
1 South Africa Africa 2002 53.4 44433622 7711.
2 South Africa Africa 2007 49.3 43997828 9270.
3 Congo, Rep. Africa 2002 53.0 3328795 3484.
4 Namibia Africa 2007 52.9 2055080 4811.
5 Namibia Africa 2002 51.5 1972153 4072.
6 Equatorial Guinea Africa 2002 49.3 495627 7703.
Show code (39 lines)
# creat df for different arrow coordinations
df_2002 <- gapminder_filtered %>%
filter(year == 2002) %>%
mutate(
start_x = 12000,
end_x = 9000,
start_y = 55,
end_y = 53.4
)
# creat df for different arrow coordinations
df_2007 <- gapminder_filtered %>%
filter(year == 2007) %>%
mutate(
start_x = 12000,
end_x = 10000,
start_y = 55,
end_y = 49.3
)
plot_nice_anno <- plot_nice + geom_label(aes(x = 12000, y = 55, label = "South Africa"),
hjust = 0,
vjust = 0.5,
lineheight = 0.8,
colour = colors[1],
fill = "white",
label.size = NA,
size = 5) +
geom_curve(data = df_2002, aes(x = start_x, y = start_y, xend = end_x, yend = end_y),
colour = "grey20",
size=0.8,
curvature = -0.2,
arrow = arrow(length = unit(0.03, "npc"))) +
geom_curve(data = df_2007, aes(x = start_x, y = start_y, xend = end_x, yend = end_y),
colour = "grey20",
size=0.8,
curvature = -0.2,
arrow = arrow(length = unit(0.03, "npc")))
plot_nice_anno
Lets keep playing around
plot_nice_anno +
scale_y_continuous(
breaks = c(4:7*10, seq(70, 80, by = 2))
) + coord_cartesian(clip = "off")
Back to top