Variable label is human readable description of the variable. R supports rather long variable names and these names can contain even spaces and punctuation but short variables names make coding easier. Variable label can give a nice, long description of variable. With this description it is easier to remember what those variable names refer to.
Value labels are similar to variable labels, but value labels are descriptions of the values a variable can take. Labeling values means we don’t have to remember if 1=Extremely poor and 7=Excellent or vice-versa. We can easily get dataset description and variables summary with info
function.
The usual way to connect numeric data to labels in R is factor variables. However, factors miss important features which the value labels provide. Factors only allow for integers to be mapped to a text label, these integers have to be a count starting at 1 and every value need to be labelled. Also, we can’t calculate means or other numeric statistics on factors.
With labels we can manipulate short variable names and codes when we analyze our data but in the resulting tables and graphs we will see human-readable text.
It is easy to store labels as variable attributes in R but most R functions cannot use them or even drop them. expss
package integrates value labels support into base R functions and into functions from other packages. Every function which internally converts variable to factor will utilize labels. Labels will be preserved during variables subsetting and concatenation. Additionally, there is a function (use_labels
) which greatly simplify variable labels usage. See examples below.
First, apply value and variables labels to dataset:
library(expss) data(mtcars) mtcars = apply_labels(mtcars, mpg = "Miles/(US) gallon", cyl = "Number of cylinders", disp = "Displacement (cu.in.)", hp = "Gross horsepower", drat = "Rear axle ratio", wt = "Weight (1000 lbs)", qsec = "1/4 mile time", vs = "Engine", vs = c("V-engine" = 0, "Straight engine" = 1), am = "Transmission", am = c("Automatic" = 0, "Manual"=1), gear = "Number of forward gears", carb = "Number of carburetors" )
In addition to apply_labels
we have SPSS-style var_lab
and val_lab
functions:
nps = c(-1, 0, 1, 1, 0, 1, 1, -1) var_lab(nps) = "Net promoter score" val_lab(nps) = num_lab(" -1 Detractors 0 Neutralists 1 Promoters ")
We can read, add or remove existing labels:
var_lab(nps) # get variable label val_lab(nps) # get value labels # add new labels add_val_lab(nps) = num_lab(" 98 Other 99 Hard to say ") # remove label by value # %d% - diff, %n_d% - names diff val_lab(nps) = val_lab(nps) %d% 98 # or, remove value by name val_lab(nps) = val_lab(nps) %n_d% "Other"
Additionaly, there are some utility functions. They can applied on one variable as well as on the entire dataset.
drop_val_labs(nps) drop_var_labs(nps) unlab(nps) drop_unused_labels(nps) prepend_values(nps)
There is also prepend_names
function but it can be applied only to data.frame.
Base table
and plotting with value labels:
with(mtcars, table(am, vs)) with(mtcars, barplot( table(am, vs), beside = TRUE, legend = TRUE) )
There is a special function for variables labels support - use_labels
. By now variables labels support available only for expression which will be evaluated inside data.frame.
# table with dimension names use_labels(mtcars, table(am, vs)) # linear regression use_labels(mtcars, lm(mpg ~ wt + hp + qsec)) %>% summary # boxplot with variable labels use_labels(mtcars, boxplot(mpg ~ am))
And, finally, ggplot2
graphics with variables and value labels. Note that with ggplot2 version 3.2.0 and higher you need to explicitly convert labelled variables to factors in the facet_grid
formula:
library(ggplot2, warn.conflicts = FALSE) use_labels(mtcars, { # '..data' is shortcut for all 'mtcars' data.frame inside expression ggplot(..data) + geom_point(aes(y = mpg, x = wt, color = qsec)) + facet_grid(factor(am) ~ factor(vs)) })
We have an option for extreme values lables support: expss_enable_value_labels_support_extreme()
. With this option factor
/as.factor
will take into account empty levels. However, unique
will give weird result for labelled variables: labels without values will be added to unique values. That's why it is recommended to turn off this option immediately after usage. See examples.
We have label 'Hard to say' for which there are no values in nps
:
nps = c(-1, 0, 1, 1, 0, 1, 1, -1) var_lab(nps) = "Net promoter score" val_lab(nps) = num_lab(" -1 Detractors 0 Neutralists 1 Promoters 99 Hard to say ")
Here we disable labels support and get results without labels:
expss_disable_value_labels_support() table(nps) # there is no labels in the result unique(nps)
Results with default value labels support - three labels are here but "Hard to say" is absent.
expss_enable_value_labels_support() # table with labels but there are no label "Hard to say" table(nps) unique(nps)
And now extreme value labels support - we see "Hard to say" with zero counts. Note the weird unique
result.
expss_enable_value_labels_support_extreme() # now we see "Hard to say" with zero counts table(nps) # weird 'unique'! There is a value 99 which is absent in 'nps' unique(nps)
Return immediately to defaults to avoid issues:
expss_enable_value_labels_support()
There are special methods for subsetting and concatenating labelled variables. These methods preserve labels during common operations. We don't need to restore labels on subsetted or sorted data.frame.
mtcars
with labels:
str(mtcars)
Make subset of the data.frame:
mtcars_subset = mtcars[1:10, ]
Labels are here, nothing is lost:
str(mtcars_subset)
To use expss
with haven
you need to load expss
strictly after haven
(or other package with implemented 'labelled' class) to avoid conflicts. And it is better to use read_spss
with explict package specification: haven::read_spss
. See example below.
haven
package doesn't set 'labelled' class for variables which have variable label but don't have value labels. It leads to labels losing during subsetting and other operations. We have a special function to fix this: add_labelled_class
. Apply it to dataset loaded by haven
.
# we need to load packages strictly in this order to avoid conflicts library(haven) library(expss) spss_data = haven::read_spss("spss_file.sav") # add missing 'labelled' class spss_data = add_labelled_class(spss_data)
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