Chapter 5 Aggregating data: sum

5.1 Intro

A common task is aggregating multiple variables (columns in a dataset) into one new variable (column). For example, you may want to compute the sum of the items of a questionnaire.

Note that when creating new variable names, it is important to follow the convention for variable names (see section (software-basics-file-and-variable-name-conventions)).

5.1.1 Example dataset

This example uses the Rosetta Stats example dataset “pp15” (see Chapter 1 for information about the datasets and Chapter 3 for an explanation of how to load datasets).

5.1.2 Variable(s)

From this dataset, this example uses variables highDose_AttGeneral_good, highDose_AttGeneral_prettig, highDose_AttGeneral_slim, highDose_AttGeneral_gezond & highDose_AttGeneral_spannend.

We will aggregate these into the variable highDose_attitude_sum.

5.2 Input: jamovi

In the “Data” tab, click the “Compute” button as shown in Figure 5.1.

Aggregating in jamovi: opening Compute menu

Figure 5.1: Aggregating in jamovi: opening Compute menu

Type in the new variable name in the text field at the top, labelled “COMPUTED VARIABLE”. Then click the function button, marked \(f_x\), select the SUM function from the box labelled “Functions”, and double click all variables for which you want the sum in the box labelled “Variables”, while typing a comma in between each variable name as shown in Figure 5.2.

Aggregating in jamovi: using the function menu to specify a computation

Figure 5.2: Aggregating in jamovi: using the function menu to specify a computation

Alternatively, you can type the function name and list of variables directly without using the function (\(f_x\)) dialog as shown in Figure 5.3.

Aggregating in jamovi: directly typing in a computation

Figure 5.3: Aggregating in jamovi: directly typing in a computation

5.3 Input: R

In R, there are roughly three approaches. Many analyses can be done with base R without installing additional packages. The rosetta package accompanies this book and aims to provide output similar to jamovi and SPSS with simple commands. Finally, the tidyverse is a popular collection of packages that try to work together consistently but implement a different underlying logic that base R (and so, the rosetta package).

5.3.1 R: base R

dat$highdose_attitude <-

5.3.2 R: rosetta

dat$highdose_attitude <-
    data = dat,

5.4 Input: SPSS

For SPSS, there are two approaches: using the Graphical User Interface (GUI) or specify an analysis script, which in SPSS are called “syntax”.

5.4.1 SPSS: GUI

First activate the dat dataset (see 2.4.1).

A screenshot placeholder

Figure 5.4: A screenshot placeholder

5.4.2 SPSS: Syntax

COMPUTE highdose_attitude =

5.5 Output

Aggregating variables is not an analysis, and as such, does not produce output. You can inspect the newly created variable to ensure it has been created properly.