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by Graham Williams
Duck Duck Go


Pie Chart



ds %>%
  group_by(rain_tomorrow) %>%
  count() %>%
  ungroup()  %>%
  mutate(per=round(`n`/sum(`n`), 2)) %>%
  mutate(label=paste(rain_tomorrow, percent(per))) %>%
  arrange(per) %>%
  ggplot(aes(x=1, y=per, fill=rain_tomorrow)) +
  geom_bar(stat="identity") +
  coord_polar(theta='y') +
  theme_void() +
  theme(legend.position="none") +
  geom_text(aes(x=1, y=cumsum(per)-per/2, label=label), size=8)

A pie chart is a popular circular plot showing the relative proportions through angular slices. Generally, pie charts are not recommended, particularly for multiple wedges, because humans generally have difficulty perceiving the relative angular differences between slices. For two or three slices it may be argued that the pie chart is just fine, particularly if further information is provided, such as labelling the slices with their sizes.

See also the illustrated tour and pie chart papers for explorations of the utility of pie charts.

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Other online resources include the GNU/Linux Desktop Survival Guide.
Books available on Amazon include Data Mining with Rattle and Essentials of Data Science.
Popular open source software includes rattle and wajig.
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