Package index • labelled

Manipulating variable labels

Functions to set, manipulate and remove variable labels

var_label() `var_label<-`() get_variable_labels() set_variable_labels() label_attribute() get_label_attribute() set_label_attribute() `label_attribute<-`(): Get / Set a variable label

remove_labels() remove_var_label() remove_val_labels() remove_user_na(): Remove variable label, value labels and user defined missing values

copy_labels() copy_labels_from(): Copy variable and value labels and SPSS-style missing value

update_variable_labels_with() update_value_labels_with(): Update variable/value labels with a function

dictionary_to_variable_labels() dictionary_to_value_labels(): Convert a dictionary data frame into a list of variable / value labels

Manipulating value labels

Functions to set, manipulate and remove value labels

labelled() is.labelled(): Create a labelled vector. (from haven)

val_labels() `val_labels<-`() val_label() `val_label<-`() get_value_labels() set_value_labels() add_value_labels() remove_value_labels(): Get / Set value labels

remove_labels() remove_var_label() remove_val_labels() remove_user_na(): Remove variable label, value labels and user defined missing values

sort_val_labels(): Sort value labels

val_labels_to_na(): Recode value labels to NA

nolabel_to_na(): Recode values with no label to NA

drop_unused_value_labels(): Drop unused value labels

copy_labels() copy_labels_from(): Copy variable and value labels and SPSS-style missing value

update_variable_labels_with() update_value_labels_with(): Update variable/value labels with a function

dictionary_to_variable_labels() dictionary_to_value_labels(): Convert a dictionary data frame into a list of variable / value labels

Data dictionary

Functions to look for keywords variable names / labels and create a data dictionary.

look_for() lookfor() generate_dictionary() print(<look_for>) look_for_and_select() convert_list_columns_to_character() lookfor_to_long_format() to_gt(): Look for keywords variable names and descriptions / Create a data dictionary

Manipulating SPSS style missing values

Functions to set, manipulate and remove SPSS style missing values

labelled_spss(): Labelled vectors for SPSS (from haven)

na_values() `na_values<-`() na_range() `na_range<-`() get_na_values() get_na_range() set_na_values() set_na_range() is_user_na() is_regular_na() user_na_to_na() user_na_to_regular_na() user_na_to_tagged_na(): Get / Set SPSS missing values

copy_labels() copy_labels_from(): Copy variable and value labels and SPSS-style missing value

remove_labels() remove_var_label() remove_val_labels() remove_user_na(): Remove variable label, value labels and user defined missing values

Tagged missing values

tagged_na() na_tag() is_tagged_na() format_tagged_na() print_tagged_na(): "Tagged" missing values (from haven)

unique_tagged_na() duplicated_tagged_na() order_tagged_na() sort_tagged_na(): Unique elements, duplicated, ordering and sorting with tagged NAs

tagged_na_to_user_na() tagged_na_to_regular_na(): Convert tagged NAs into user NAs

Converting

Functions to convert from and to labelled classes

look_for() lookfor() generate_dictionary() print(<look_for>) look_for_and_select() convert_list_columns_to_character() lookfor_to_long_format() to_gt(): Look for keywords variable names and descriptions / Create a data dictionary

to_character(): Convert input to a character vector

to_factor() unlabelled(): Convert input to a factor.

to_labelled() foreign_to_labelled() memisc_to_labelled(): Convert to labelled data

update_labelled(): Update labelled data to last version

Other functions and methods

is_prefixed(): Check if a factor is prefixed

recode_if(): Recode some values based on condition

recode(<haven_labelled>): Recode values

remove_attributes(): Remove attributes

names_prefixed_by_values(): Turn a named vector into a vector of names prefixed by values

Internal datasets for testing

x_haven_2.0 x_spss_haven_2.0 spss_file dta_file: Datasets for testing