For within-group comparisons, the primary categorical variableвЂ™s levels determine a location for a cluster of bars to be plotted. In order to facilitate these comparisons, bars in a grouped bar chart are plotted systematically. In the example above, a within-group comparison would focus on the bars for a single quarter, while a between-group comparison would focus on bars for a single representative across quarters. WeвЂ™ll call comparisons of the first type вЂњwithin-groupвЂќ comparisons, and comparisons of the second type вЂњbetween-groupвЂќ.

A grouped bar chart is used when you want to look at how the second category variable changes within each level of the first, or when you want to look at how the first category variable changes across levels of the second. Where the grouped bar chart differs is its division of data points across two different categorical variables, not just one. Like a standard bar chart, the grouped bar chart is built for showing a distribution of data points or making comparisons across different categories of data. We can also check individual performances such as MerseyвЂ™s relatively stable performance across the year, or YorkвЂ™s major bump in Q4 after a slide from Q1 through Q3. We can see from the plot that Lincoln had the best performance in Q1 with Kent best in all remaining quarters. Colors and positions are consistent within each cluster: for example, we can see that Kent is always in blue and plotted first.

One bar cluster is plotted for each quarter, and in each cluster, one bar for each representative. The grouped bar chart above compares new quarterly revenue for four sales representatives across a year. Bars are grouped by position for levels of one categorical variable, with color indicating the secondary category level within each group. A grouped bar chart (aka clustered bar chart, multi-series bar chart) extends the bar chart, plotting numeric values for levels of two categorical variables instead of one.

Veusz error bar