Tidying up, transforming and exploring data is an important part of data analysis, and you can manage many common tasks in this process with the tidyverse or related packages. The sjmisc-package fits into this workflow, especially when you work with labelled data, because it offers functions for data transformation and labelled data utility functions. This vignette describes typical steps when beginning with data exploration.

The examples are based on data from the EUROFAMCARE project, a survey on the situation of family carers of older people in Europe. The sample data set efc is part of this package. Let us see how the family carer’s gender and subjective perception of negative impact of care as well as the cared-for person’s dependency are associated with the family carer’s quality of life.


Find variables in a data frame

Next, let’s look at the distribution of gender by the cared-for person’s dependency. To compute cross tables, you can use flat_table(). It requires the data as first argument, followed by any number of variable names.

But first, we need to know the name of the dependency-variable. This is where find_var() comes into play. It searches for variables in a data frame by

  1. variable names,
  2. variable labels,
  3. value labels
  4. or any combination of these.

By default, it looks for variable name and labels. The function also supports regex-patterns. By default, find_var() returns the column-indices, but you can also print a small “summary”" with the out-argument.

Variable in column 5, named e42dep, is what we are looking for.

Recoding variables

Next, we need the negatice impact of care (neg_c_7) and want to create three groups: low, middle and high negative impact. We can easily recode and label vectors with rec(). This function does not only recode vectors, it also allows direct labelling of categories inside the recode-syntax (this is optional, you can also use the val.labels-argument). We now recode neg_c_7 into a new variable burden. The cut-points are a bit arbitrary, for the sake of demonstration.

You can see the variable burden has a variable label (“Subjective burden”), which was set inside rec(), as well as three values with labels (“low”, “moderate” and “high”). From the lowest value in neg_c_7 to 9 were recoded into 1, values 10 to 12 into 2 and values 13 to the highest value in neg_c_7 into 3. All remaining values are set to missing (else=NA – for details on the recode-syntax, see ?rec).

Nested data frames

Let’s investigate the association between quality of life and burden across the different dependency categories, by fitting linear models for each category of e42dep. We can do this using nested data frames. nest() from the tidyr-package can create subsets of a data frame, based on grouping criteria, and create a new list-variable, where each element itself is a data frame (so it’s nested, because we have data frames inside a data frame).

In the following example, we group the data by e42dep, and “nest” the groups. Now we get a data frame with two columns: First, the grouping variable (e42dep) and second, the datasets (subsets) for each country as data frame, stored in the list-variable data. The data frames in the subsets (in data) all contain the selected variables burden, c161sex and quol_5 (quality of life).

Get coefficients of nested models

Using map() from the purrr-package, we can iterate this list and apply any function on each data frame in the list-variable “data”. We want to apply the lm()-function to the list-variable, to run linear models for all “dependency-datasets”. The results of these linear regressions are stored in another list-variable, models (created with mutate()). To quickly access and look at the coefficients, we can use spread_coef().

We see that higher burden is associated with lower quality of life, for all dependency-groups. The se and p.val-arguments add standard errors and p-values to the output. model.term returns the statistics only for a specific term. If you specify a model.term, arguments se and p.val automatically default to TRUE.