Bar-Chart

A bar-chart is defined as “a graph in which bars of varying height with spaces between them are used to display data for variables defined by qualities or categories” (Zedeck, 2014, p. 20).

There are some alternatives for a simple bar-chart, these include pie-chart, (Cleveland) dotplot, and Pareto chart.

In a bar-chart the horizontal axis has the categories, to show what each bar represents. An example is shown in figure 1.

Figure 1
Results of marital status
bar chart example of marital status

Click here to see how to create a bar-chart

with Excel

Excel file from video VI - Bar Chart (Simple).xlsm.

with Python

Jupyter Notebook from video: VI - Bar Chart (Simple).ipynb.
Data file used: GSS2012a.csv.

with R (Studio)

R script from video VI - Bar Chart (Simple).R.
Data file used: GSS2012-Adjusted.sav.

with SPSS

There are a few different ways to obtain a (simple) bar-chart with SPSS. The end result for each will be the same.

using Chart builder

Watch the video below, or follow the instructions in this pdf (via bitly, opens in new window/tab).

Data file used: StudentStatistics.sav.

using Legacy dialogs

Watch the video below, or follow the instructions in this pdf (via bitly, opens in new window/tab).

Data file used: StudentStatistics.sav.

using Frequencies

Watch the video below, or follow the instructions in this pdf (via bitly, opens in new window/tab).

Data file used: StudentStatistics.sav.

using an existing table

Watch the video below, or follow the instructions in this pdf (via bitly, opens in new window/tab)

Data file used: StudentStatistics.sav.

There are some variations, with splitting the bars by another variable, and/or stacking the bars. See the sections below for these variations.

As a guideline for the size of the bar there is a rule of thumb known as the 'three quarter high rule' (Pitts, 1971). It means that the height of the vertical axis should be 3/4 of the length of the horizontal axis. So if the horizontal axis is 20 cm long, the vertical axis should be 3/4 * 20 = 15 cm high.

According to Singh (2009) vertical bars (instead of horizontal bars) are preferred since they are easier on the eye. However if you have long category names some names might become unreadable. A bar chart with the bars placed horizontally might then be preferred. One of the earliest found bar-charts from William Playfair (1786) has the bars placed horizontally. There is an earlier bar chart by Oresme (1486), but that is used more for a theoretical concept, than for descriptive statistics.

Multiple Bar Chart (nominal (or binary) vs. nominal (or binary))

The bars can be split into multiple bars based on another variable. This is then known as a multiple bar-chart (Kemp & Kemp, 2004, p. 150) or clustered bar-chart (Brase, 2009, p. 50; Griffith, 2007, p. 168).

It can be defined as “a bar chart for comparing the frequencies of a categorical variable in two or more situations” (Upton & Cook, 2014, p. 283). An example is shown in Figure 2

Figure 2.
Example of a Clustered Bar-Chart.
Clustered bar-chart of Gender vs Marital status

Click here to see how you can create a clustered bar-chart ...

with Excel

Excel file: VI - Bar Chart - Clustered.xlsm

with Python

Jupyter Notebook: VI - Bar Chart - Multiple.ipynb

Data file: GSS2012a.csv.

with R (Studio)

R script: VI - Bar chart - Clustered.R

Data file: GSS2012a.csv.

with SPSS

There are a two different ways to create a clustered bar chart with SPSS.

using the chart builder

Data file: GSS2012-Adjusted.sav.

using legacy dialogs

Data file: GSS2012-Adjusted.sav.

Alternatives for a clustered bar-chart could be a spine plot, or a waffle chart.

Stacked Bar Chart (single ordinal)

Instead of the bars next to each other, it can also be beneficial to stack them on top of each other. This is called a compound bar chart, or sometimes stacked bar chart (Wilkinson, 2005, p. 157) or component bar chart (Zedeck, 2014, p. 54). It can be defined as: “a bar chart showing multiple bars stacked at each x-axis category, each representing a value of the stacking variable” (Upton & Cook, 2014, p. 88). An example is shown in Figure 3.

Figure 3.
Example of a compound bar-chart of a single ordinal variable
Compound bar chart

Click here to see how to create a stacked bar-chart

with Excel

Excel file from video: VI - Bar Chart (Stacked - Single) (E).xlsm

with Python

Jupyter Notebook: VI - Bar Chart (Stacked Single) (P).ipynb

with stikpetP library:

without stikpetP library:

with R

Jupyter Notebook: VI - Bar Chart (Stacked Single) (R).ipynb

with stikpetR library:

without stikpetR library:

with SPSS

Datafile used in video: GSS2012-Adjusted.sav

From the figure we can immediately see that only about 30% find accounting pretty or very scientific, while the others do not.

An alternative for the compound bar-chart might be a dual axis bar-chart showing both the count and the cumulative percentage. Although this would provide more information it is a bit harder to read for most people.

Dual Axis Bar Chart (single ordinal)

To also make use of the order that is available with an ordinal variable, we can add a secondary vertical axis, that shows the cumulative frequencies, or cumulative percent. This can be called a dual-axis bar-chart. An example is shown in Figure 4.

Figure 4.
Example of a dual-axis bar-chart