Statistiken außerhalb von Geoms

Fortgeschrittene Datenvisualisierung mit ggplot2

Rick Scavetta

Founder, Scavetta Academy

Basis-Plot

ggplot(iris, aes(x = Species, 
                 y = Sepal.Length)) +
  geom_jitter(width = 0.2)

Statistiken berechnen

set.seed(123)
xx <- rnorm(100)
mean(xx)

[1] 0.09040591

mean(xx) + (sd(xx) * c(-1, 1))

[1] -0.822410  1.003222

Statistiken berechnen

set.seed(123)
xx <- rnorm(100)

# Hmisc
library(Hmisc)
smean.sdl(xx, mult = 1)

       Mean       Lower       Upper 
 0.09040591 -0.82240997  1.00322179

# ggplot2
mean_sdl(xx, mult = 1)

           y     ymin     ymax
1 0.09040591 -0.82241 1.003222

stat_summary()

ggplot(iris, aes(x = Species, 
                 y = Sepal.Length)) +
 stat_summary(fun.data = mean_sdl, 
                fun.args = list(mult = 1))

Verwendet standardmäßig geom_pointrange()

stat_summary()

ggplot(iris, aes(x = Species, 
                 y = Sepal.Length)) +
  stat_summary(fun = mean,
               geom = "point") +
  stat_summary(fun.data = mean_sdl,
               fun.args = list(mult = 1),
               geom = "errorbar",
               width = 0.1)

Nicht empfohlen!

95%-Konfidenzintervall

ERR <- qt(0.975, length(xx) - 1) * (sd(xx) / sqrt(length(xx)))
mean(xx)

0.09040591

mean(xx) + (ERR * c(-1, 1)) # 95% CI

-0.09071657  0.27152838

mean_cl_normal(xx)

           y        ymin      ymax
0.09040591 -0.09071657 0.2715284

Weitere stat_-Funktionen

`stat_`	Beschreibung
`stat_summary()`	fasst y-Werte je x zusammen.
`stat_function()`	berechnet y aus einer Funktion von x.
`stat_qq()`	Berechnungen für ein Quantil-Quantil-Diagramm.

MASS::mammals

Normalverteilung

mam.new <- data.frame(body = log10(mammals$body))

ggplot(mam.new, aes(x = body)) + 
  geom_histogram(aes( y = ..density..)) +
  geom_rug() +
  stat_function(fun = dnorm, color = "red", 
                args = list(mean = mean(mam.new$body),
                            sd = sd(mam.new$body)))

QQ-Plot

ggplot(mam.new, aes(sample = body)) + 
  stat_qq() +
  geom_qq_line(col = "red")

Du bist dran!

Fortgeschrittene Datenvisualisierung mit ggplot2