#R bar plot rename x values how to
#R bar plot rename x values code

Png ( file = 'barchart.png' ) # Plot the bar chart H <- c ( 7, 12, 28, 3, 41 ) # Give the chart file a name Live Demo # Create the data for the chart The below script will create and save the bar chart in the current R workingĭirectory. col is used to give colors to the bars in the graph.Ī simple bar chart is created using just the input vector and the name of.names.arg is a vector of names appearing under each bar.H is a vector or matrix containing numeric values used in bar.The basic syntax to create a bar-chart in R is − barplot(H,xlab,ylab,main, names.arg,col)įollowing is the description of the parameters used − In bar chart each of the bars can be given different colors. R can draw both vertical and Horizontal bars in the barĬhart. Proportional to the value of the variable. Increase space on the right via theme(plot.A bar chart represents data in rectangular bars with length of the bar 💁 Expand to see examples with labels next to the bars. You can make sure that labels are not truncated by the panel by adding clip = 'off' to any coordinate system. Thus, I prefer to use the first approach. The drawback of using limits is that you have to define them manually. In case you want to put the next to the bars, you often need to adjust the plot margin and/or the limits to avoid that the labels are cut off. ggplot(mpg_sum, aes(x = n, y = manufacturer)) + We also add some negative horizontal adjustment via nudge_x = -.5 to add some spacing between the end of the bar and the label. To put the labels inside, we first need to right-align the labels with hjust = 1. nudge_x and nudge_y: the horizontal and vertical adjustment to offset text from points.hjust and vjust: the horizontal and vertical justification to align text.The geom_text() function comes with arguments that help you to align and position text labels:

#R bar plot rename x values how to

How to Position the Percentage Labels Inside the Bars To make it look good, we need to adjust the positioning of the labels. Manufacturer = forcats::fct_relevel(manufacturer, 'Other', after = 0)Īs you can see, the default settings of geom_text() place the labels exactly on the border. Manufacturer = forcats::fct_rev(forcats::fct_infreq(manufacturer)), # order factor levels by number, put 'Other' to end Manufacturer = forcats::fct_lump(manufacturer, n = 10), Manufacturer = stringr::str_to_title(manufacturer), # turn into lumped factors with capitalized names # add count to calculate percentages later # prepare non-aggregated data set with lumped and ordered factors To add the labels, we again use geom_text() but this time we overwrite the default statistical transformation stat = 'identity' with stat = 'count' (the same as the default for geom_bar()). We use geom_bar() instead of geom_col() which takes not two but only one variable and calculates counts by default. To illustrate how to create and place the labels on the fly, here is an example with labels showing counts per manufacturer (with percentage labels it gets a bit more complicated). 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.

#R bar plot rename x values code

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. Bar charts are likely the most common chart type out there and come in several varieties.