3D Correspondence Analysis Plots in Displayr

In this post I show you to create 3D visualizations of correspondence analysis using Displayr. This allows you to view an extra dimension of your data (and looks great too!).


Explore 3D Correspondence Analysis!

Traditional correspondence analysis

Traditional correspondence analysis plots typically plot the first two dimensions of a correspondence analysis. Sometimes, additional insight can be gained by plotting the first three dimension. Displayr makes it easy to create three-dimensional correspondence analysis plots.

The data

In this post I use a brand association grid which shows perceptions of cola brands.


Creating the correspondence analysis

The first step is to create a correspondence analysis. In Displayr, this is done as follows:

  1. Create a table of the data to be analyzed (e.g., import a data set and then press Insert > Table (Analysis)).
  2. Select Insert > Dimension Reduction > Correspondence Analysis of a Table.
  3. Select the table to be analyzed in the Input table(s) field in the Object Inspector.
  4. Check Automatic (at the top of the Object Inspector).

This should give you a visualization like the one shown below. You can see that in this example the plot shows 86% of the variance from the correspondence analysis. This leads to the question: is the 14% that is not explained interesting?


Create your own Correspondence Analysis!

Creating the interactive three-dimensional visualization

  1. Insert > R Output
  2. Paste in the code below
  3. Replace my.ca with the name of your correspondence analysis. By default it is called correspondence.analysis, but it can have numbers affixed to the end if you have created several correspondence analysis plots. You can find the correct name by clicking on the map and looking for the name in the Object Inspector (Properties > GENERAL).
  4. Check the Automatic option at the top of the object inspector.
 
rc = my.ca$row.coordinates
cc = my.ca$column.coordinates
library(plotly)
p = plot_ly() 
p = add_trace(p, x = rc[,1], y = rc[,2], z = rc[,3],
              mode = 'text', text = rownames(rc),
              textfont = list(color = "red"), showlegend = FALSE) 
p = add_trace(p, x = cc[,1], y = cc[,2], z = cc[,3], 
              mode = "text", text = rownames(cc), 
              textfont = list(color = "blue"), showlegend = FALSE) 
p <- config(p, displayModeBar = FALSE)
p <- layout(p, scene = list(xaxis = list(title = colnames(rc)[1]),
           yaxis = list(title = colnames(rc)[2]),
           zaxis = list(title = colnames(rc)[3]),
           aspectmode = "data"),
           margin = list(l = 0, r = 0, b = 0, t = 0))
p$sizingPolicy$browser$padding <- 0
my.3d.plot = p

You will now have an interactive visualization like the one below. You can click on it and drag with your mouse to rotate, and use the scroll wheel in your mouse (if you have one) to zoom in and zoom out.


Click the button below to see the original dashboard and modify it however you want!

Explore 3D Correspondence Analysis!

Sharing the interactive visualization

You can also share the interactive visualization with others, by using one of the following approaches:

  • Press Export > Public Web Page and share the URL of the web page with colleagues.
  • Press Export > Private Web Page and share the URL of the web page with colleagues, who will require a login to access it.
  • Press Export > Embed, which will give you some code that you can embed in blog posts and other websites, which will make the interactive visualization appear in them.

If you click here you will go into Displayr and into a document containing the code used the create the analyses and visualizations in this chart, which you can then modify to re-use for your own analyses.

Find out what else you can do in Displayr, or if you’re ready to get started, sign up for free!

About Tim Bock

Tim Bock is the founder of Displayr. Tim is a data scientist, who has consulted, published academic papers, and won awards, for problems/techniques as diverse as neural networks, mixture models, data fusion, market segmentation, IPO pricing, small sample research, and data visualization. He has conducted data science projects for numerous companies, including Pfizer, Coca Cola, ACNielsen, KFC, Weight Watchers, Unilever, and Nestle. He is also the founder of Q www.qresearchsoftware.com, a data science product designed for survey research, which is used by all the world’s seven largest market research consultancies. He studied econometrics, maths, and marketing, and has a University Medal and PhD from the University of New South Wales (Australia’s leading research university), where he was an adjunct member of staff for 15 years.

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