1 Load packages
library(tidyverse) # data wrangling
library(see) # okabeito_colors2 Data
data("mariokart", package = "openintro")3 Unadjusted labels
mario_quantile <-
mariokart %>%
filter(total_pr < 100) %>%
summarise(q25 = quantile(total_pr, .25),
q50 = quantile(total_pr, .50),
q75 = quantile(total_pr, .75))
mario_quantile <-
mariokart %>%
filter(total_pr < 100) %>%
reframe(qs = quantile(total_pr, c(.25, .5, .75)))
mariokart %>%
filter(total_pr < 100) %>%
ggplot(aes(x = total_pr)) +
geom_histogram() +
geom_vline(xintercept = mario_quantile$qs) +
annotate("label", x = mario_quantile$qs, y = 0, label = mario_quantile$qs) +
annotate("label", x = mario_quantile$qs, y = Inf, label = c("Q1", "Median", "Q3")) 
4 Adjusted labels manually
vjust = 2 does the trick:
mario_quantile <-
mariokart %>%
filter(total_pr < 100) %>%
summarise(q25 = quantile(total_pr, .25),
q50 = quantile(total_pr, .50),
q75 = quantile(total_pr, .75))
mario_quantile <-
mariokart %>%
filter(total_pr < 100) %>%
reframe(qs = quantile(total_pr, c(.25, .5, .75)))
mariokart %>%
filter(total_pr < 100) %>%
ggplot(aes(x = total_pr)) +
geom_histogram() +
geom_vline(xintercept = mario_quantile$qs) +
annotate("label", x = mario_quantile$qs, y = 0, label = mario_quantile$qs) +
annotate("label", x = mario_quantile$qs, y = Inf, label = c("Q1", "Median", "Q3"), vjust = 2) 
5 Adjust labels automatically
Using vjust = "top" (or “bottom”) does the trick in an automatic, comfortable way.
mariokart %>%
filter(total_pr < 100) %>%
ggplot(aes(x = total_pr)) +
geom_histogram() +
geom_vline(xintercept = mario_quantile$qs) +
annotate("label", x = mario_quantile$qs, y = 0, label = mario_quantile$qs, vjust = "bottom") +
annotate("label", x = mario_quantile$qs, y = Inf, label = c("Q1", "Median", "Q3"), vjust = "top") 
Similarly, one case use hjust = "left" for horizontal alignment.
6 Expanding the limits
Let’s consider a second example, where it helped to expand the limits of the coordinates.
In this example, we use Weather data from the German DWD i.e., Deutschen Wetterdienst, DWD .
After some data wrangling, which will not discussed here, there’s a nice data set waiting for further scrutiny.
wetter_path <- "https://raw.githubusercontent.com/sebastiansauer/Lehre/main/data/wetter-dwd/precip_temp_DWD.csv"
wetter <- read.csv(wetter_path)Some data preparation:
wetter <-
wetter %>%
mutate(after_1950 = year > 1950) %>%
filter(region != "Deutschland") # ohne Daten für Gesamt-DeutschlandHere’s a linear model for our weather data:
lm_wetter_region <- lm(temp ~ region, data = wetter)wetter_summ <-
wetter %>%
group_by(region) %>%
summarise(temp = mean(temp)) %>%
mutate(id = 0:15) %>%
ungroup() %>%
mutate(grandmean = mean(temp),
delta = temp - grandmean)
wetter_summ %>%
ggplot(aes(y = region, x = temp)) +
theme_minimal() +
geom_label(aes(label = paste0("b", id),
x = grandmean + delta),
vjust = 1,
size = 2,
color = okabeito_colors()[1]) +
geom_vline(xintercept = coef(lm_wetter_region)[1], linetype = "dashed", color = okabeito_colors()[2]) +
#coord_cartesian(xlim = c(7, 10), ylim = c(0, 16)) +
annotate("label",
y = 1,
x = coef(lm_wetter_region)[1],
vjust = 1,
label = paste0("b0"),
#size = 6,
color = okabeito_colors()[2]) +
annotate("point",
y = 1,
x = coef(lm_wetter_region)[1],
color = okabeito_colors()[2],
#vjust = 1,
size = 4) +
geom_segment(aes(yend = region, xend = temp),
x = coef(lm_wetter_region)[1],
color = okabeito_colors()[1]) +
geom_point() +
labs(y = "",
x = "Temperatur")
Behold the label right at the bottom of the diagram, “b0”. It is not displayed properly.
One way to get it back into the picture is bay extending the limits of the y-axis:
wetter_summ %>%
ggplot(aes(y = region, x = temp)) +
theme_minimal() +
geom_label(aes(label = paste0("b", id),
x = grandmean + delta),
vjust = 1,
size = 2,
color = okabeito_colors()[1]) +
geom_vline(xintercept = coef(lm_wetter_region)[1], linetype = "dashed", color = okabeito_colors()[2]) +
coord_cartesian(xlim = c(7, 10), ylim = c(0, 16)) + # back in the game!
annotate("label",
y = 1,
x = coef(lm_wetter_region)[1],
vjust = 1,
label = paste0("b0"),
#size = 6,
color = okabeito_colors()[2]) +
annotate("point",
y = 1,
x = coef(lm_wetter_region)[1],
color = okabeito_colors()[2],
#vjust = 1,
size = 4) +
geom_segment(aes(yend = region, xend = temp),
x = coef(lm_wetter_region)[1],
color = okabeito_colors()[1]) +
geom_point() +
labs(y = "",
x = "Temperatur")
7 Duckdive the problem: tinyfy the label
It may feel like cheating, but, hey, problem solved: just decrease the size of the label.
wetter_summ %>%
ggplot(aes(y = region, x = temp)) +
theme_minimal() +
geom_point() +
annotate("point",
y = 1,
x = coef(lm_wetter_region)[1],
color = okabeito_colors()[2],
size = 4,
alpha = .5) +
geom_label(aes(label = paste0("b", id),
x = grandmean + delta),
vjust = 1,
size = 2,
color = okabeito_colors()[1]) +
geom_vline(xintercept = coef(lm_wetter_region)[1],
linetype = "dashed", color = okabeito_colors()[2]) +
#coord_cartesian(xlim = c(7, 10), ylim = c(0, 16)) +
annotate("label",
y = 1,
x = coef(lm_wetter_region)[1],
vjust = 1,
label = paste0("b0"),
size = 2,
color = okabeito_colors()[2]) +
geom_segment(aes(yend = region, xend = temp),
x = coef(lm_wetter_region)[1],
color = okabeito_colors()[1]) +
labs(y = "",
x = "Temperatur")
8 Reproducibility
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