Why view Distributions? Cairo Ch. 7 & Wilke Ch. 7 - 9
ggplot(df, aes(x, y)) + geom_boxplot()ggplot(df, aes(x, y)) + geom_violin()
Uncertainty: Cairo Ch. 10 & Wilke Ch. 16
xkcd
Bootstrapping: Within ggplot2
organdata %>% ggplot(aes(x = country, y = donors)) + {{stat_summary(fun.y = mean, geom = "point", size = 3) +}} stat_summary(fun.data = mean_cl_boot, geom = "errorbar", width = 0.5) + coord_flip() + cowplot::theme_cowplot() + labs(x = " ", y = "Organ Donations in 000's", title = "Avg Organ Donations (000s) by Country")
Bootstrapping: Outside ggplot2
organdata %>% group_by(country) %>% {{do(as_tibble(bind_rows(Hmisc::smean.cl.boot(.$donors)))) %>%}} # bootstrapping by country ggplot(aes(x = reorder(country, Mean), y = Mean)) + {{geom_point(size = 3) + }} # Plot means as geom_point geom_errorbar(aes(ymin = Lower, ymax = Upper), width = 0.5) + # Error as geom_errorbar coord_flip() + cowplot::theme_cowplot() + labs(x = " ", y = "Organ Donations in 000's", title = "Avg Organ Donations (000s) by Country")
Unemployment Rate
df %>% ggplot(aes(x = date, y = unemployment)) + geom_line() + coord_cartesian(ylim = c(0, .11), expand = FALSE), scale_y_continuous(labels = scales::percent) + labs(x = NULL, y = NULL, subtitle = "US unemployment over time")
