Mean/SEM plots
# calculate means, standard deviations,
# standard errors, and 95% confidence
# intervals by rank
library(dplyr)
plotdata <- Salaries %>%
group_by(rank) %>%
summarize(n = n(),
mean = mean(salary),
sd = sd(salary),
se = sd / sqrt(n),
ci = qt(0.975, df = n - 1) * sd /
sqrt(n))
# plot the means and standard errors
ggplot(plotdata,
aes(x = rank,
y = mean,
group = 1)) +
geom_point(size = 3) +
geom_line() +
geom_errorbar(aes(ymin = mean - se,
ymax = mean + se),
width = .1)
# calculate means and standard errors by rank and sex
plotdata <- Salaries %>%
group_by(rank, sex) %>%
summarize(n = n(),
mean = mean(salary),
sd = sd(salary),
se = sd/sqrt(n))
## `summarise()` has grouped output by 'rank'. You can override using the
## `.groups` argument.
# plot the means and standard errors by sex
ggplot(plotdata, aes(x = rank,
y = mean,
group=sex,
color=sex)) +
geom_point(size = 3) +
geom_line(size = 1) +
geom_errorbar(aes(ymin =mean - se,
ymax = mean+se),
width = .1)
- Unfortunately, the error bars overlap. We can dodge the horizontal positions a bit to overcome this.
# plot the means and standard errors by sex (dodged)
pd <- position_dodge(0.2)
ggplot(plotdata,
aes(x = factor(rank,
labels = c("Assistant\nProfessor",
"Associate\nProfessor",
"Full\nProfessor")),
y = mean,
group=sex,
color=sex)) +
geom_point(position=pd,
size = 3) +
geom_line(position = pd,
size = 1) +
geom_errorbar(aes(ymin = mean - se,
ymax = mean + se),
width = .1,
position = pd,
size = 1) +
scale_y_continuous(label = scales::dollar) +
scale_color_brewer(palette="Set1") +
theme_minimal() +
labs(title = "Mean salary by rank and sex",
subtitle = "(mean +/- standard error)",
x = "",
y = "",
color = "Gender")
