Often, we’ll want to show not only the mean of a value but also its standard error. This tells us how much variation there is around the mean – are most values close to the averages shown, or is there a wide range of values above and below the average?

To add error bars, we need to first calculate our standard errors. Continuing with our msleep dataset, the code below calculates means and standard errors for hours asleep by diet. We compute new variables mean.hours representing the mean of hours, mean.se representing the standard error of our means, and se.min and se.max representing the lower and upper bounds of our error range.

msleep_error_df <- msleep %>%
  subset(status = "asleep") %>%
  na.omit() %>%
  group_by(diet) %>%
  summarize(mean.hours = mean(hours), 
            mean.se = std.error(hours)) %>%
  mutate(se.min = mean.hours - mean.se, 
         se.max = mean.hours + mean.se)

We can then create our bar plot showing means and standard errors as follows. We add a geom_errorbar() layer specifying ymin and ymax variables for our error bar and set its width to 0.2 of the bar’s width. Because we’ve already calculated mean values in our summary data frame, we specify stat = "identity" to display the mean.hours values as is.

msleep_sebar <- 
  ggplot(msleep_error_df, 
         aes(x = diet, 
             y = mean.hours)) + 
  geom_bar(stat = "identity") + 
  geom_errorbar(aes(ymin = se.min, 
                    ymax = se.max), 
                width = 0.2) + 
  labs(title = "Mean Hours Asleep by Diet")

This produces the following plot, which should look familiar from the last exercise! Now that we have error bars, we can see how much variation there is around the mean hours asleep for each diet group.