A box plot is a way of summarizing a set of data measured on an interval scale and is often used in explanatory data analysis. This graph is used to visually display the shape of the distribution of your data points, its central value, and its variability. This is done by creating four parts of equal size, also called quartiles or percentiles. You can easily recognize the median, minimum and maximum values.
A huge advantage compared to other graphs is that you can spot the variable's spread and outliers at a glance, and this graph doesn’t take too much space in case you need to compare multiple categories.
There are many use cases like visualizing data observed by sensors, operational data or even sales data.
The box plot can be found underneath the special charts section. Simply drag and drop into the canvas.
When clicking the ‘data’ icon of the graph, you see there are two dataslots you can add data to : the measure slot and the category slot.
You can add a numeric column to the measure slot. The different parts of the box plot will be calculated and created based on this data.
You can add a numeric, hierarchy or date column to the category slot. Based on this column, a separate box plot per category will be created and will show the data for that category only.
By default the tooltip will show 6 values when hovering over a box. The description of each value is:
IQR stands for the Interquartile range (IQR) which is equal to Q3 - Q1.
You can fully customize the look and feel of the box plot. Below an overview of the different options.
Display the box plot vertically or horizontally, with or without the whiskers.
Adapt the colors, width and corners of the box itself
Visualise all observations or outliers only, and change how these data points are styled
Add custom events to the box plot. Want to know more on this topic? Click here.
The box plot is extremely useful if you want to analyze your data. Get your insights at a glance.