How to Make a Column Chart in Displayr

How to Make a Column Chart in Displayr

The most standard way for graphically representing categorical data is with a column chart. The advantage of using column charts is that you can easily see the differences between categories. While these charts can handle multiple series of data quite well, the ease of making comparisons decreases as more data points are added. This article will outline the steps for generating a column chart visualization in Displayr using either the Chart or Visualization menu.

Data setup

Before we construct a chart, Displayr needs specific data outputs to point to. In building a column chart, we have 2 options: using a raw data variable or variables from our dataset or using a table or “output”. Here, I’ve created a table of the “LearningDataScience” variable.

Creating a column chart

Next, we’ll create a column chart. To do so:

  1. Select Insert > Chart > Column Chart.
  2. Click into Inputs > DATA > Show in the Object Inspector on the right, and choose the variable that you want to chart (alternatively, you can drag the variable directly from the Data Sets section to the chart area).
  3. Tick the Automatic box at the top right. This ensures that whenever you change the source data or the options. The column chart will refresh itself to show the latest version.
  4. Change the options in the Chart section of the Object Inspector to your liking.

Following these steps, I get the following column chart:

We can complicate this chart a bit by basing it on a crosstab format. This is accomplished by adding a question into the By field on the Inputs tab of the Object Inspector. In the image below, I’ve added the “StudentStatus” question so that I can see the difference in responses to the “LearningDataScience” variable based upon a respondent’s student status.

As noted above, we’ll need to change options within the Object Inspector to adjust this chart. On the Inputs tab, we can add filters or weights to the chart data as well as adjust the statistic used to generate the columns or add additional summary statistics either to the right or below the chart. From the Properties tab, we can manually set the dimensions and location of the chart on the page. And from the Chart tab, there are a host of appearance modifications we can make including applying colors to columns and labels, specifying the gap width between the columns, or adding NETs or Sums to the chart.

In the chart below, I’ve reduced the bar gap, bolded and increased the font size of the data labels, and added a NET column.

Creating a column chart visualization

As an alternative, you can also create column charts as visualizations. These visualizations take the same data inputs as a regular column chart (raw variable(s) or an output table) but offer additional customization. The steps to create the visualization are similar to those used for a regular chart:

  1. Select Insert > Visualization > Column Chart.
  2. Click into Inputs > Data Source > Output in ‘Pages’ in the Object Inspector on the right, and choose the output that you want to chart (alternatively, you select individual variables in the Variables in ‘Data’ field).
  3. Tick the Automatic box at the top right. This ensures that whenever you change the source data or the options. The column chart will refresh itself to show the latest version.
  4. Change the options in the Chart section of the Object Inspector to your liking.

In the chart below, I have used a table that charts the same “LearningDataScience” variable by age groups.

As you can see, it is much more difficult to trace the differences between the columns. To better distinguish between different data series, we can format the chart as small multiples.

 

Small Multiples

One of the biggest downfalls of column charts is that they become more difficult to read with larger sets of categories. Small multiples, or a panel chart, create a series of column charts, one for each data series. This selection is available on the Inputs tab of the Object Inspector.

With our data now split into 5 separate charts, or panels, we can more clearly see differences and trends across age groups.

Now that you’re an expert on column charts, check out what else you can do in Displayr!

About Daren Jackson

Daren has worked in market research for over a decade, cutting his teeth at major firms like J.D. Power and Associates and Market Track. In the research supplier function, he has extensive experience in customized and in-depth reporting, working with some of the wold’s top retailers and automotive manufacturers. On the technical side, he has spent over 5 years building databases, providing support and training users on market research software. He holds a Bachelors of Science degree from the University of Southern California.