How to Compare Against the Previous Period in a Stacked Column Chart

Sometimes you may want to test the responses from a Nominal-Multi variable set against the previous time period, and to plot this in a stacked column chart. This article describes how to go from a table where the years are nested under the responses in the columns:

To a stacked column chart where arrows are shown on the visualization that tests against the previous period for each variable+response:

Requirements

A table with at least two columns and at least one row.
- Where the Rows are a Nominal-Multi variable set, and the Columns are a Date/Time variable (required to do the previous period testing).

Method

Set up stat testing to test against the previous period

Set the statistics testing to compare to the previous period; more detail is in How to Conduct Significance Tests by Comparing to Previous Time Periods if needed. By default, this setting is enabled in all new Displayr documents.

To check the setting and enable it if needed, do the following:

Select the table.
In the object inspector , click the Appearance tab and from the Significance menu, select Advanced > Test Type.
From the Date/Time field, select Compare to previous period
Click Apply to Selection to apply to only this table, or Apply as Default to apply to all tables using the default significance testing settings.

Use R to reshape the table and set up the visualization

There are two ways to do this:

Method 1 - Using the Statistical Assumptions Attributes

Click Calculation > Custom Code from the toolbar.
Click on the page to insert the output.

Copy and paste the following R code into the code editor (you will need to change the table and variable names to match the ones in your file).

# Getting table. Note that this is a four dimensional array. 
x = table.Brand.attitude.by.Interview.Date.5
dims = dim(x)
n.brands = dims[1]
n.statements = dims[2]
n.periods = dims[3]
brand.names = dimnames(x)[[1]]

# Getting stat testing information. Note that this appears as a data frame.
# The stat testing is stored in a data frame, which is structured
# so that the first rows in the data frame represent the first row of the table
# (that is, it is storing the stat testing information according to the rows
# in the table that contains the percentages)
# By happy coincidence in this case it is already in the same order that we will
# want so that it matches up with them modifications that we will make to the 
# table containing the percentages

stat.testing.info =  attr(x, "QStatisticsTestingInfo")

# Rearrangin the percentages to a format that is liked by the visualization
# swapping around dimensions 
x = aperm(x, c(3, 2, 1))

# Replacing a 3D array with a 2D array, by subscripting and then splicing by row
library(abind)
x = abind(x[,,1], x[,,2], x[,,3], along = 1)

# Updating column names
rownames(x) = paste(rep(brand.names, rep(n.periods, n.brands)),
                    rownames(x)[1:n.periods])

# Appending the statistic name, so Displayr recognizes the data in the table
attr(x, "statistic") <- "Column %"

# Appending the stat testing
attr(x,  "QStatisticsTestingInfo") = stat.testing.info
x
rearranged = x

Click Calculate
Either click the Visualization button in the object inspector , or while the calculation is selected, go to Visualization from the toolbar and select Column > Stacked Column.

The results are as follows:

Method 2 - Using the Z-Statistic and Annotations in the Visualization

Click Calculation > Custom Code from the toolbar.
Click on the page to insert the output.

Copy and paste the following R code into the code editor (you will need to change the table and variable names to match the ones in your file).

# Getting table. Note that this is a four dimensional array. 
x = table.Brand.attitude.by.Interview.Date.6
dims = dim(x)[1:3]# Ignoring last dimension as unneeded
n.brands = dims[1]
n.periods = dims[3]
brand.names = dimnames(x)[[1]]

# swapping around dimensions 
x = aperm(x, c(3, 2, 1, 4))

# Replacing a four dimensional array with a 3 dimensional array by 
# subscripting and then splicing together by row

library(abind)
x = abind(x[,,1, ], x[,,2, ], x[,,3, ], along = 1)

# Updating column names
n.months = dim(x)
rep(n.periods, n.brands)
rownames(x) = paste(rep(brand.names, rep(n.periods, n.brands)),
                    rownames(x)[1:n.periods])
rearranged.with.z = x

3. Click Calculate

4. Either click the Visualization button in the object inspector or, while the calculation is selected, select Visualization from the toolbar and select Column > Stacked Column.
The results are as follows: