A common task in the market research world is to collapse two or more categories together to see how the collapsed categories compare to one another. For example, if you asked people to rate their preference on a scale of 1 to 10, you might want to see how the people who provide a rating between 1 and 5 compare to those who rated it between 6 and 10. This goes by a number of names, including “Top Box” or “Nets”, depending on the use case. In Crunch, we call this family of features Subtotals. This vignette shows how to define, manage, and analyze variables with subtotals.
Subtotals can be applied to any Categorical or Categorical Array variable. In R, we can view and set subtotal definitions with the
subtotals() function. If there are no subtotals, the function will return
To add subtotals, we can assign a list of
Subtotal objects. Each
Subtotal object has three things: a
name to identify it; a set of
categories to pool together, referenced either by category name or id; and a location to show it, either
after a given category or with
"bottom" to pin it first or last in the list.
subtotals(ds$DiversityImportant) <- list( Subtotal(name = "Follows closely", categories = c("Strongly closely", "Very closely"), after = "Somewhat closely"), Subtotal(name = "Generally disagree", categories = c("Not very closely", "Not at all"), after = "Not at all") )
Now, if we check
subtotals(), we can see that we have saved them. In this output we see a few different aspects of subtotals: the
anchor is the id of the category to put the subtotal after (matching the
position argument in
Subtotal()), name, aggregation functions and
args, which in the this case are the category ids to include in the subtotal.
## anchor name func args ## 1 2 Follows closely subtotal 2 and 1 ## 2 4 Doesn't Follow Closely subtotal 3 and 4
This shows up in the Categorical variable card on the web app like this:
We can also add headings, which are similar to subtotals in that they are additions to categorical variables that will be displayed in the app, but they don’t sum up any categories. For a variable with many categories, they can help group variables visually. Here we add some guides to Obama’s approval rating.
## anchor name func args ## 1 0 Approves NA NA ## 2 2 Disapprove NA NA ## 3 4 No Answer NA NA
Again, this shows up in the Categorical variable card on the web app:
Subtotals and headings can be removed by assigning a
subtotals(ds$YearsCodedJob) <- NULL
In the Economist survey, there are a number of questions that have the same response categories. If the category names (or ids, if we’re using those) are the same, we can use the same set of subtotals across multiple variables.
approve_subtotals <- list( Subtotal(name = "Approves", categories = c("Somewhat approve", "Strongly approve"), after = "Somewhat approve"), Subtotal(name = "Disapprove", categories = c("Somewhat disapprove", "Strongly disapprove"), after = "Strongly disapprove"))
Notice here, because each of the categories for these variables has slightly different ids, the
args in the output differs slightly. But, because we used category names when we were constructing our list of subtotals, when we store them on the variable itself, Crunch does the right thing and converts them over to the correct ids.
## anchor name func args ## 1 2 Approves subtotal 2 and 1 ## 2 4 Disapprove subtotal 3 and 4
## anchor name func args ## 1 2 Approves subtotal 2 and 1 ## 2 5 Disapprove subtotal 4 and 5
Now that we have defined subtotals on the congressional approval question, if we use it in a crosstab, we can see the subtotals.
crtabs(~congapp + gender, data = ds)
## gender ## congapp Male Female ## Strongly approve 0 0 ## Somewhat approve 2.51012984049177 5.46240518719819 ## Approves 2.51012984049177 5.46240518719819 ## Neither approve nor disapprove 11.2916608660399 18.8687169535911 ## Somewhat disapprove 12.2769754589437 24.3223013661526 ## Strongly disapprove 62.6898938858094 38.2775564880149 ## Disapprove 74.9668693447531 62.5998578541675 ## Not sure 15.2265010614957 25.8714100226452
We can even get just the subtotals as an array from the result if we want to ignore the constituent groups:
## gender ## congapp Male Female ## Approves 2.51013 5.462405 ## Disapprove 74.96687 62.599858
## gender ## congapp Male Female ## Strongly approve 0.00000 0.000000 ## Somewhat approve 2.51013 5.462405 ## Neither approve nor disapprove 11.29166 18.868717 ## Somewhat disapprove 12.27698 24.322301 ## Strongly disapprove 62.68989 38.277556 ## Not sure 15.22650 25.871410
This does not modify the variable—the subtotals are still defined and visible in the web app—but they are removed from the current analysis.