look_for() improvements:
look_for_and_select() (#87)look_for() can now search within factor levels and value labels (#104)improvements for tagged NAs:
user_na_to_tagged_na(), tagged_na_to_user_na() and tagged_na_to_regular_na()explicit_tagged_na in to_factor() and to_character()unique_tagged_na(), duplicated_tagged_na(), order_tagged_na(), sort_tagged_na() (#90, #91)other improvements:
is_user_na() and is_regular_na()na_range() or na_values() to a factor will now produce an errorforeign_to_labelled() for Stata files (#100)recode_if() for recoding values based on condition, variable and value labels being preserved (#82)look_for() could be time consuming for big data frames. Now, by default, only basic details of each variable are computed. You can compute all details with details = "full" (#77)look_for() results has been updated and do not rely anymore on pillar (#85)to_labelled() can properly manage factors whose levels are coded as “[code] level”, as produced by to_factor(levels = "prefixed") (#74 @courtiol)is_prefixed() to check if a factor is prefixedna_range<- and na_values<- when applied to a data.frame (#80).values argument has been added to set_na_values() and set_na_range(), allowing to pass a list of values.strict option has been added to set_variable_labels(), set_value_labels(), add_value_labels(), remove_value_labels(), set_na_values() and set_na_range(), allowing to pass values for columns not observed in the data (it could be useful for using a same list of labels for several data.frame sharing some variables) (#70)copy_labels() is less restrictive for non labelled vectors, copying variable label even if the two vectors are not of the same type (#71).strict option has been added to copy_labels() (#71)look_for() has been redesigned:
look_for() now returns a tibblelookfor_to_long_format() to convert results with one row per factor level and per value labelconvert_list_columns_to_character() to convert list columns to simpler character vectorsgenerate_dictionary() is an equivalent of look_for()set_variable_labels, set_value_labels, add_value_labels, and remove_value_labels now accept “tidy dots” (#67 @psanker)names_prefixed_by_values() to get the names of a vector prefixed by their corresponding value.keep_value_labels argument for recode.haven_labelled().combine_value_labels argument for recode.haven_labelled() (#61)drop_unused_value_labels() method.labels argument for set_value_labels()user_na_to_na argument has been added to to_character.haven_labelled()%>% is now imported from dplyrhavenupdate_labelled() has been improved to allow to reconstruct all labelled vectors created with a previous version of havenkeep_var_label for remove_labels()unlabelled() when applied on a vectorunclass = TRUE with to_factor(), attributes are not removed anymoreunlabelled()look_for() (#52 by @NoahMarconi)val_labels_to_na() documentationna_range() and na_values(): variable labels are now preserved (#48, thanks to @mspittler)copy_labels_from(), compliant with dplyr syntaxupdate_labelled() is now more strict (#42 by @iago-pssjd)look_for() and lookfor() imported from questionr (#44)unlist option for var_label()tagged_na() and similar functions are now imported from havenvar_label(), applied to a data.frame, now accepts a character vector of same length as the number of columns.set_variable_labels has a new .labels argument.unclass option in to_factor(), to be used when strict = TRUE (#36)haven version 2.1.0, it is not mandatory anymore to define a value label before defining a SPSS style missing value. labelled_spss(), na_values() and na_range() have been updated accordingly (#37)to_factor() bug fix then applied on a data.frame (#33)update_labelled() bug fix then applied on a data.frame (#31)haven, labelled() and labelled_spss() now produce objects with class “haven_labelled” and “haven_labelled_spss”, due to conflict between the previous “labelled” class and the “labelled” class used by Hmisc.update_labelled() could be used to convert data imported with an older version of haven to the new classes.user_na_to_na option added to to_factor()foreign_to_labelled() now import SPSS missing values (#27)strict argument added to to_factor() (#25)remove_attributes() preserve character vectors (#30)dplyr::recode() method to be compatible with labelled vectors.copy_labels() now copy also na_range and na_values attributes.remove_attributes()drop_unused_labels could now be used with to_factor.data.frame()to_labelled() method when applied to a factordata.frame (#20)havenna_values(), na_range(), set_na_values(), set_na_values(), remove_user_na(), user_na_to_na().remove_labels() has been updated.set_variable_labels(), set_value_labels(), add_value_labels() and remove_value_labels() compatible with %>%.remove_val_labels and remove_var_label().to_character.labelled() when applied to data frames.to_factor(), to_character() and to_labelled.factor() now preserves variable label.to_factor() when applied to data frames.haven, labelled doesn’t support missing values anymore (cf. https://github.com/hadley/haven/commit/4b12ff9d51ddb9e7486966b85e0bcff44992904d)to_character() (cf. https://github.com/larmarange/labelled/commit/3d32852587bb707d06627e56407eed1c9d5a49de)to_factor() could now be applied to a data.frame (cf. https://github.com/larmarange/labelled/commit/ce1d750681fe0c9bcd767cb83a8d72ed4c5fc5fb)data.table is available, labelled attribute are now changed by reference (cf. https://github.com/larmarange/labelled/commit/c8b163f706122844d798e6625779e8a65e5bbf41)zap_labels() added as a synonym of remove_labels()