![]() Ggplot(mpg_sum, aes(x = n, y = manufacturer)) + Perc = if_else(row_number() = 1, paste(perc, "of all car models"), perc) So let’s add the prepared percentage label to our bar graph with geom_text(): ggplot(mpg_sum, aes(x = n, y = manufacturer)) +Īnd in case you want to add some more description to one of the bars, you can use an if_else() (or an ifelse()) statement like this: mpg_sum % # add percentage label with `scales::percent()`ĭplyr::mutate(perc = scales::percent(n / sum(n), accuracy =. 1) and we can similarly add the leading white space by setting trim to FALSE. The accuracy determines the number of digits (here. ![]() And this short tutorial shows you multiple ways how to do so.Ī few days ago, I got a request on some code creating bar charts with individual colors and percentage labels with the package. It is pretty easy to improve your ggplot with a few lines of code. In addition, one can highlight specific bars with use of custom colors. Ordering your bar charts make sense in case the categorical value has no internal order and helps focusing on the largest and smallest groups. Most notably, direct labels can increase accessibility of a bar graph and reduce the “chart junk” since grid lines, axis labels, and even axis titles become obsolete. The function is smart enough to try to break after word boundaries-that is, if you tell it to break after 5 characters, it won’t split something like “Approved” into “Appro” and “ved” it’ll break after the end of the word.Bar charts are likely the most common chart type out there and come in several varieties. This will automatically add line breaks after X characters in labels with lots of text-you just have to tell it how many characters to use. ![]() The easiest and quickest and nicest way to fix these long labels, though, is to use the label_wrap() function from the scales package. Verdict: 7/10, no manual work needed, labels easy to read, but there’s extra whitespace that can sometimes feel unbalanced. Maybe it would look better if the corresponding axis ticks were a little longer, idk. But I’m not a huge fan of the gaps above the second-row labels. The text is all horizontal so it’s more readable. Again, this is all automatic and we don’t have to manually adjust any labels. If we add hjust = 0.5 (horizontal justification), the rotated labels will be centered in the columns, and vjust (vertical justification) will center the labels vertically.Īes(x = CATEGORY, y = total)) + geom_col() + scale_x_discrete(guide = guide_axis(n.dodge = 2)) + scale_y_continuous(labels = comma) + labs(x = NULL, y = "Total projects") Here we rotate the labels 30°, but we could also do 45°, 90°, or whatever we want. Since we want to avoid manually recoding categories, we can do some visual tricks to make the labels readable without changing any of the lable text. Verdict: 6/10, we have more control over the labels, but too much abbreviation reduces readibility, and it’s not automatic. If a new longer category gets added in a later iteration of the data, this code won’t automatically shorten it. It also requires more manual work and a lot of extra typing. Hous.” mean affordable housing? affluent housing? affable housing?). That works great! However, it reduces readibility (does “Aff. ![]() "Homeless Shelter" = "Homeless\nShelter"))Īes(x = CATEGORY, y = total)) + geom_col() + scale_y_continuous(labels = comma) + labs(x = NULL, y = "Total projects") "Hospital / Health Care" = "Hosp./Health", Essential_by_category_shorter % mutate(CATEGORY = recode(CATEGORY,
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