Error: stat_summary requires the following missing aesthetics: y

Question

I'm trying to create a barplot with confidence interval error bars using ggplot. Essentially, I have a variable, Q1, with 7 answer options, and I want to plot the percent of respondents for each option, as a factor of two groups (One and Two) - the percent of subjects in each group that selected each of the 7 answer option.

I've tried adding y= count, y=prop or y=..prop.. to aes in ggplot, but it neither seem to work. Any suggestions are appreciated.

df5 <- filter(df, Q1!="-99",df$Group=="One"|df$Group=="Two") 

ggplot(data = df5, aes(x = Q1)) + 
 stat_summary(fun.y = mean, geom = "bar") +
 stat_summary(fun.data = mean_cl_boot, geom = "errorbar", fun.args = list(mult = 1)) +
    geom_bar(aes(label= scales::percent(..prop..),
                   y= ..prop..,fill = df5$Group), position = "dodge")

Error: stat_summary requires the following missing aesthetics: y.

I'm essentially trying to get something that looks like this, with the error bars representing confidence intervals.

Answer 1

Please note that there is a better way to write your first selection:

df5 <- df %>% filter(Q1!="-99", Group %in% c("One", "Two"))

I recommend you to compute the stats explicitly before making the graph. function DescTools::MultinomCI() will do the job (cf documentation)

# Reproducible example: random
library(tidyverse)
n <- 1000
df5 <- tibble(
    Q1 = sample(letters[1:7], n, replace=TRUE),
    Group = sample(c("One","Two"), n, replace=TRUE)
    )

library(DescTools)
df_stats <- df5 %>% 
    count(Group, Q1) %>% 
    group_by(Group) %>% 
    do({
        df_grp <- .
        df_grp %>% 
            select(Q1, n) %>%
            bind_cols(as_tibble(MultinomCI(df_grp$n))) %>% 
            rename(prop = est)
    })

If you want to use bar plots:

df_stats %>% 
    ggplot(aes(Q1, y=prop, ymin=lwr.ci, ymax=upr.ci, fill=Group)) + 
    geom_col(position="dodge") + 
    geom_errorbar(position="dodge") + 
    ylim(0, NA)

(Note that axes of barplots should always start from zero, hence the use of ylim)

However, in order to underline between-group differences in the answers, a line plot will be much more readable:

df_stats %>% 
    ggplot(aes(Q1, y=prop, ymin=lwr.ci, ymax=upr.ci, color=Group, group=Group)) + 
    geom_line() + 
    geom_errorbar(position="dodge", width=.2) + 
    ylim(0, NA)