%>% ds filter(location %in% (ds$location %>% unique %>% sample(20))) %>% mutate(location=factor(location, levels=location %>% unique() %>% sort() %>% rev())) %>% ggplot(aes(location, temp_3pm, fill=location)) + stat_summary(fun="mean", geom="bar") + stat_summary(fun.data="mean_cl_normal", geom="errorbar", width=0.35) + theme(legend.position="none") + labs(x=vnames["temp_3pm"], y=vnames["location"]) + coord_flip()
Various annotations can be added to plots. In this example we include a confidence interval around the average values.
Exercise: review the confidence intervals—do they make sense?
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