The labelled_spss_survey class"

In retroharmonize: Ex Post Survey Data Harmonization

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

library(retroharmonize)

Use the labelled_spss_survey() helper function to create vectors of class retroharmonize_labelled_spss_survey.

sl1 <- labelled_spss_survey (
  x = c(1,1,0,8,8,8), 
  labels = c("yes" =1,
             "no" = 0,
             "declined" = 8),
  label = "Do you agree?",
  na_values = 8, 
  id = "survey1")

print(sl1)

You can check the type:

is.labelled_spss_survey (sl1)

The labelled_spss_survey() class inherits some properties from haven::labelled(), which can be manipulated by the labelled package (See particularly the vignette Introduction to labelled by Joseph Larmarange.)

haven::is.labelled(sl1)

labelled::val_labels(sl1)

labelled::na_values(sl1)

It can also be subsetted:

sl1[3:4]

When used within the modernized version of data.frame, tibble::tibble(), the summary of the variable content prints in an informative way.

df <- tibble::tibble (v1 = sl1)
## Use tibble instead of data.frame(v1=sl1) ...
print(df)
## ... which inherits the methods of a data.frame 
subset(df, v1 == 1)

Coercion rules and type casting

To avoid any confusion with mis-labelled surveys, coercion with double or integer vectors will result in a double or integer vector. The use of vctrs::vec_c is generally safer than base R c().

#double
c(sl1, 1/7)
vctrs::vec_c(sl1, 1/7)

c(sl1, 1:3)

Conversion to character works as expected:

as.character(sl1)

The base as.factor converts to integer and uses the integers as levels, because base R factors are integers with a levels attribute.

as.factor(sl1)

Conversion to factor with as_factor converts the value labels to factor levels:

as_factor(sl1)

Similarly, when converting to numeric types, we have to convert the user-defined missing values to NA values used in the R language. For numerical analysis, convert with as_numeric.

as.numeric(sl1)
as_numeric(sl1)

Arithmetics

The median value is correctly displayed, because user-defined missing values are removed from the calculation. Only a few arithmetic methods are implemented, such as

• median()

median (as.numeric(sl1))
median (sl1)

• quantile()

quantile (as.numeric(sl1), 0.9)
quantile (sl1, 0.9)

• mean()

mean (as.numeric(sl1))
mean (sl1)
mean (sl1, na.rm=TRUE)

• weighted.mean() - always removes NA values.

weights1 <- runif (n = 6, min = 0, max = 1)
weighted.mean(as.numeric(sl1), weights1)
weighted.mean(sl1, weights1)

• sum()

sum (as.numeric(sl1))
sum (sl1, na.rm=TRUE)

The result of the conversion to numeric can be used for other mathematical / statistical function.

as_numeric(sl1)
min ( as_numeric(sl1))
min ( as_numeric(sl1), na.rm=TRUE)

retroharmonize documentation built on Nov. 3, 2021, 1:07 a.m.