ds %>%
filter(location %in% (ds$location %>% unique %>% sample(20))) %>%
ggplot(aes(location, temp_3pm, fill=location)) +
stat_summary(fun="mean", geom="bar") +
theme(legend.position="none") +
labs(x=vnames["temp_3pm"], y=vnames["location"]) +
coord_flip()
We can flip the coordinates and produce a horizontal histogram as in
Figure . Note that
for clarity of presentation here we have also reduced the number of
locations but would note that for an actual data science report we
would include full plots. Rotating the plot is a more useful solution
than simply rotating the labels as we can then add more bars down the
page than we might across the page and it is easier for us to read the
labels left to right rather than bottom up.
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