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.
The first thing that may be of interest is probably the distribution of gender. You can plot frequencies for labelled data with
frq(). This function requires either a vector or data frame as input and prints the variable label as first line, followed by a frequency-table with values, labels, counts and percentages of the vector.
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
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
Variable in column 5, named e42dep, is what we are looking for.
Now we can look at the distribution of gender by dependency:
Since the distribution of male and female carers is skewed, let’s see the proportions. To compute crosstables with row or column percentages, use the
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.
efc$burden <- rec( efc$neg_c_7, rec = c("min:9=1 [low]; 10:12=2 [moderate]; 13:max=3 [high]; else=NA"), var.label = "Subjective burden", as.num = FALSE # we want a factor ) # print frequencies frq(efc$burden) #> #> Subjective burden (x) <categorical> #> # total N=908 valid N=892 mean=2.03 sd=0.81 #> #> val label frq raw.prc valid.prc cum.prc #> 1 low 280 30.84 31.39 31.39 #> 2 moderate 301 33.15 33.74 65.13 #> 3 high 311 34.25 34.87 100.00 #> NA <NA> 16 1.76 NA NA
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
How is burden distributed by gender? We can group the data and print frequencies using
frq() for this as well, as this function also accepts grouped data frames. Frequencies for grouped data frames first print the group-details (variable name and category), followed by the frequency table. Thanks to labelled data, the output is easy to understand.
efc %>% select(burden, c161sex) %>% group_by(c161sex) %>% frq() #> #> Subjective burden (burden) <categorical> #> # grouped by: Male #> # total N=215 valid N=212 mean=1.91 sd=0.81 #> #> val label frq raw.prc valid.prc cum.prc #> 1 low 80 37.21 37.74 37.74 #> 2 moderate 72 33.49 33.96 71.70 #> 3 high 60 27.91 28.30 100.00 #> NA <NA> 3 1.40 NA NA #> #> #> Subjective burden (burden) <categorical> #> # grouped by: Female #> # total N=686 valid N=679 mean=2.08 sd=0.81 #> #> val label frq raw.prc valid.prc cum.prc #> 1 low 199 29.01 29.31 29.31 #> 2 moderate 229 33.38 33.73 63.03 #> 3 high 251 36.59 36.97 100.00 #> NA <NA> 7 1.02 NA NA
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).
# convert variable to labelled factor, because we then # have the labels as factor levels in the output efc$e42dep <- to_label(efc$e42dep, drop.levels = T) efc %>% select(e42dep, burden, c161sex, quol_5) %>% group_by(e42dep) %>% tidyr::nest() #> # A tibble: 5 x 2 #> # Groups: e42dep  #> e42dep data #> <fct> <list<df[,3]>> #> 1 moderately dependent [306 x 3] #> 2 severely dependent [304 x 3] #> 3 independent [66 x 3] #> 4 slightly dependent [225 x 3] #> 5 <NA> [7 x 3]
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
efc %>% select(e42dep, burden, c161sex, quol_5) %>% group_by(e42dep) %>% tidyr::nest() %>% na.omit() %>% # remove nested group for NA arrange(e42dep) %>% # arrange by order of levels mutate(models = purrr::map( data, ~ lm(quol_5 ~ burden + c161sex, data = .)) ) %>% spread_coef(models) #> # A tibble: 4 x 7 #> # Groups: e42dep  #> e42dep data models `(Intercept)` burden2 burden3 c161sex #> <fct> <list<df[,3> <list> <dbl> <dbl> <dbl> <dbl> #> 1 independent [66 x 3] <lm> 18.8 -3.16 -4.94 -0.709 #> 2 slightly depen~ [225 x 3] <lm> 19.8 -2.20 -2.48 -1.14 #> 3 moderately dep~ [306 x 3] <lm> 17.9 -1.82 -5.29 -0.637 #> 4 severely depen~ [304 x 3] <lm> 19.1 -3.66 -7.92 -0.746
We see that higher burden is associated with lower quality of life, for all dependency-groups. The
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
p.val automatically default to
efc %>% select(e42dep, burden, c161sex, quol_5) %>% group_by(e42dep) %>% tidyr::nest() %>% na.omit() %>% # remove nested group for NA arrange(e42dep) %>% # arrange by order of levels mutate(models = purrr::map( data, ~ lm(quol_5 ~ burden + c161sex, data = .)) ) %>% spread_coef(models, burden3) #> # A tibble: 4 x 6 #> # Groups: e42dep  #> e42dep data models burden3 std.error p.value #> <fct> <list<df[,3]>> <list> <dbl> <dbl> <dbl> #> 1 independent [66 x 3] <lm> -4.94 2.20 2.84e- 2 #> 2 slightly dependent [225 x 3] <lm> -2.48 0.694 4.25e- 4 #> 3 moderately dependent [306 x 3] <lm> -5.29 0.669 5.22e-14 #> 4 severely dependent [304 x 3] <lm> -7.92 0.875 2.10e-17