In this first short set of exercises for the second data wrangling session, we will focus on creating and transforming variables using functions from the dplyr package.

NB: For the following exercises, it will be helpful to look at the bespoke (mini-)codebook we have created for this workshop.

Before we can start, we need to load the packages we have seen on the slides for this session so far.

library(sjlabelled)
library(tidyverse)
library(haven)

To avoid potential issues due to different data set versions produced by coding along during the session, we will start with a blank slate. Hence, we will import the ALLBUS 2021 .sav file (again),

1

Before we start with the variable creation & transformation, let’s import the ALLBUS 2021 .sav file and assign it to an object named allbus_2021. We want the user-defined missing values from the SPSS file to be converted to NA in the data import and also remove all labels.

For importing the .sav file we can use a function from the haven package. The conversion of SPSS user-defined missings to NA is the default in this function (meaning we do not need to specify that optional argument). For getting rid of all labels we can use a function from the sjlabelled package.

2

To start with the wrangling, let’s use the dplyrfunction for creating and transforming variables to create a new variable representing political orientation (on the left-right spectrum) named pol_view_new that ranges from 0 to 9 instead of from 1 to 10 as is the case for the original variable.

The name of the original variable is pa01. For this transformation, we we simply need to subtract 1 from the existing variable.

3

By combining the function used for the previous task with another one from the dplyr package, recode the values of the variable measuring trust in the federal government into a new variable named distrust_gov that captures distrust instead of trust.

The name of the variable we want to transform is pt12 and it ranges from 1 to 7. Remember that the correct syntax for recoding values with the corresponding dplyr function is old value (enclosed in backticks) = new value.