Data Science Desktop Survival Guide by Graham Williams
Desktop Survival Project Home Preface Data Science Introducing R R Constructs R Tasks R Strings R Read, Write, and Create Data Template Data Exploration Data Wrangling Data Visualisation Statistics ML Template ML Scenarios ML Activities ML Applications ML Algorithms Cluster Analysis Decision Trees Computer Vision Graph Data Privacy Literate Data Science Coding with Style Resources Bibliography Index

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Flipped Text Annotations Commas

20200823

ds %>%
  filter(location %in% (ds$location %>% unique %>% sample(20))) %>%
  mutate(location=factor(location,
                         levels=(location %>% unique() %>%
                                   sort() %>% rev()))) %>%
  ggplot(aes(location, fill=location)) +
  geom_bar(width=1, colour="white") +
  theme(legend.position="none") +
  coord_flip() +
  geom_text(stat="count", color="white", hjust=1.0, size=3,
            aes(y=as.integer(..count..),
                label=scales::comma(..count.., accuracy=1)))

Using scales::comma() for large numbers is essential in any plot to avoid misreading the magnitude.