look_for() when no keyword is
provided (#116)user_na_to_tagged_na() (#114)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()