ds %>%
filter(location %in% (ds$location %>% unique %>% sample(10))) %>%
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 sized 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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