Correspondence analysis is a technique that summarizes the patterns in a table of data as a visualization. Tables with multiple rows and columns can become difficult to read and identify patterns in the data. Correspondence analysis makes it easier to see the story in your data.
This article describes how to go from a data table containing multiple rows and columns:
To a correspondence analysis output in which you can better visualization the patterns in the data:
- A table with multiple rows and columns containing data that are all on the same scale. This includes crosstabs showing counts, percentages, or averages, grids of data created from binary variables, and even raw numeric data.
1. Open the document in Displayr containing the data set that you want to use to create the correspondence analysis.
2. Create a table that you want to use as an input to the correspondence analysis. For this example, we'll use the table shown above which represents device ownership among different income groups.
3. Select Anything > Advanced Analysis > Dimension Reduction > Correspondence Analysis of a Table.
4. From the object inspector on the right, select your table from the Input table(s) drop-down box.
5. OPTIONAL: Remove any additional rows which correspond to 'NET' or 'Total' by adding the corresponding row/column labels in the Rows to ignore and Columns to ignore options. These should typically not be included in the analysis, and Displayr automatically removes 'NET', 'Total', and 'SUM' by default.
6. OPTIONAL: Customize your title, colors, fonts, and gridlines using the settings under Chart.
7. Click the Calculate button to generate the correspondence analysis output which will appear as a scatterplot on your page.
8. OPTIONAL: Instead of a scatterplot, you can create a Moonplot visualization by selecting 'Moonplot' from the Output drop-down box in the object inspector.