The goal of
retroharmonize is to facilitate retrospective (ex-post)
harmonization of data, particularly survey data, in a reproducible manner.
The package provides tools for organizing the metadata, standardizing the
coding of variables, variable names and value labels, including missing
values, and for documenting all transformations, with the help of
comprehensive s3 classes.
Read data stored in formats with rich metadata, such as SPSS (.sav) files,
and make them usable in a programmatic context.
read_spss: read an SPSS file and record metadata for reproducibility
read_rds: read an rds file and record metadata for reproducibility
read_surveys: programmatically read a list of surveys
subset_save_surveys: programmatically read a list of surveys,
and subset them (pre-harmonize the same variables.)
pull_survey: pull a single survey from a survey list.
Create consistent coding and labelling.
merge_waves: Create a list of surveys with harmonized names and variable labels.
harmonize_waves: Create a list of surveys with harmonized value labels.
label_normalize removes special characters, whitespace,
and other typical typing errors and helps the uniformization of labels
and variable names.
na_range_to_values: Make the
as imported from SPSS, consistent with the
Make the workflow reproducible by recording the harmonization process.
Consistently treat labels and SPSS-style user-defined missing
values in the R language.
survey helps constructing a valid survey data frame, and
labelled_spss_survey helps creating a vector for a
as_numeric: convert to numeric values.
as_factor: convert to labels to factor levels.
as_character: convert to labels to characters.
as_labelled_spss_survey: convert labelled and labelled_spss
vectors to labelled_spss_survey vectors.