smerge() bug introduced by Base R changes to set
operation behavior.tabl() bug affecting use of wide.col option. Bug
was introduced with the labeled.data.frame construct in
0.1.7 and affected internal call to stats::reshape().added the labeled.data.frame class, including an
as_labeled_data_frame() function that assigns that class to
a data.frame. Now any data.frame that is assigned frame, name, or value
labels is assigned the class labeled.data.frame (while
continuing to inherit the data.frame class).
added a [ method so that subsetting and filtering of
labeled.data.frame objects preserves labelr label
attributes. Note that, unlike with Base R data.frames, this
labeled.data.frame [ method always returns a
data.frame.
modified as_base_data_frame() so that
labeled.data.frames are quietly coerced to Base R
data.frames, unlike other augmented data.frames, which are (still)
noisily converted to Base R data.frames.
improved ability of add_lab_col*() and
use_val_lab*() functions to detect quantity-labeled
variables that are no longer numeric.
bug fixed in greml() and gremlr() so
that these handle cases where character vector passed to x argument is a
single element (1L).
added two convenience functions: fact2char()
converts all factor data.frame variables to character variables, and
irregular2() converts all irregular (see
check_irregular() and irregular2v()) values in
a data.frame to some other single value (NA, by default).
irregular2().
ssort() code is streamlined (and no longer issues a
warning when it recycles the descending argument).
added fact.to.char (defaults to FALSE) and irreg.to.na (defaults
to FALSE) arguments to as_base_data_frame().
removed extraneous code from
transfer_labs().
Corrected argument specification error in example
flab() and slab() calls in the Introduction
vignette (call was supplying argument “Female” when it should have
supplied “F”) . Vignette call syntax error was introduced in v1.0.5 and
was limited to the syntax of the specific example calls in the vignette.
The functions themselves were not at fault and are, thus,
unchanged.
Added function v() to allow passing of an “unquoted
character vector” (of labels or column names).
Updated make_demo_data() and associated gender
identity label examples to be more inclusive.
Fixed some typos and copy-editing errors in documentation.
Functions has_val_labs(),
has_avl_labs(), has_m1_labs(), and
has_quant_labs() added to facilitate logical check of a
single data.frame variable (column) for the presence of, respectively:
(1) any sort of value labels, (2) add_val_labs() -style
value labels specifically, (3) add_m1_lab()-style value
labels specifically, or (4) add_quant_labs()-style value
labels specifically.
Function all_uniquev() (alias
all_univ()) added. This checks where the length of a vector
is equal to the length of the unique values in the vector (is equivalent
to length(x) == length(unique(x)). It is
anticipated that this will be used internally in future iterations of
labelr.
Improved add_val_labs() (and
add_val1()) to better detect and prevent their use on
variables that already have add_m1_lab() -style many-to-one
values labels. Previously, it was possible in some cases to modify
select add_m1_lab()-style value labels using
add_val_labs(). Now, add_val_labs() (and
add_val1()) will detect and prohibit this behavior,
redirecting the user to use add_m1_lab() for such
variables. This reinforces the distinction between
add_val_labs()-style (one-to-one) and
add_m1_lab()-style (many-values-to-one-label) value labels,
whereas prior behavior blurred this line.
Modified select *1() functions (e.g.,
add_lab_col1(), use_val_lab1()) so that they
explicitly dis-allow indirection (e.g., passing an arbitrarily named
character vector containing column names) and instead require that the
supplied variable name (the var argument) be the literal name of a
single variable present in the supplied data.frame. The functions permit
users to pass that var name unquoted or quoted, although the
documentation advises the user to supply unquoted variable
names.
Two shared, vestigial code chunks removed from the following:
use_val_labs(), use_val_lab1(),
add_lab_cols(), add_lab_col1(), and
val_labs_vec() documentation. Code involved checking for
all values NA from an earlier iteration of use_val_labs().
Code is more concise; functions, arguments, and outputs are
unchanged.
val_labs_vec() now strips any lingering attributes
from the returned character vector.
Improved handling of errant and varying var arg specifications in
val_labs_vec(), add_lab_col1(), and
use_val_lab1() to be more flexible and informative if a
non-existent var is supplied by the user.
Move internal functions to the beginning of the the body of
add_val_labs(), add_val1(),
add_m1_lab(), and add1m1().
Copy edit fixes and improved argument explanations in
documentation items (e.g., an outdated comment in README, an
infelicitous example in use_val1() and
add_lab_col1() documentation).
function get_val_lab1() (with alias
gvl1()) added, providing a variant of
get_val_labs() that follows the conventions of other
*1() functions (i.e., allowing only a single variable,
whose name may be passed unquoted to the var argument). In addition,
get_val_lab1() includes a simplify argument (FALSE by
default) that allows the user to return the value-label mapping as a
named vector.
functions add_lab_dummies() and
add_lab_dumm1() (with aliases ald() and
ald1()) added. These generate a dummy variable for each
label of a value-labeled variable and return the supplied data.frame
with these dummy variables added.
functions factor_to_lab_int() (with alias
f2int()) and lab_int_to_factor() (with alias
int2f()) added to allow easy conversion of labeled integer
variables to factors and of factors to labeled integer
variables.
function axis_lab() (with alias alb())
added - helper that allows one to easily use variable name labels as y
or x axis labels with Base R plots or ggplot2.
function all_quant_labs() (with alias
allq()) added: automatically add quantile-based numerical
range value labels for all numeric variables that meet
specifications.
function tabl() updated. labs.on = TRUE is now the
default. qtiles argument added that allows for non-value-labeled,
many-valued numeric variables to be converted temporarily and on the fly
to quantile category variables, so that they can be included in returned
table results. Tabulation “engine” now relies on base
table(), which makes select tabulations much
faster.
function use_val_lab1() (with alias
uvl1()) added. This provides the same functionality as
use_val_labs(), but allows the user to supply only one
unquoted variable, whereas use_val_labs() requires quoted
variable name arguments but allows the user to pass a vector of multiple
variable names in one call.
function add_lab_col1() (with alias
alc1()) added. This provides the same functionality as
add_lab_cols(), but allows the user to supply only one
unquoted variable, whereas add_lab_cols() requires quoted
variable name arguments but allows the user to pass a vector of multiple
variable names in one call.
updated add_name_labs(),
drop_name_labs(), convert_labs(), and
clean_data_atts() functions, so that changes (add or drop)
of name.labs attributes are applied to native labels() attribute and, in
case of convert_labs(), so that any incoming
variable/column level labels() attributes (e.g., column name labels from
a haven-imported tibble) are converted to labelr name.labs. Primary
benefit of this is that RStudio View() will now show name labels
underneath column names.
use_val_labs(), add_lab_cols(), and
val_labs_vec() fixed to convert any irregular character
values to NA labels (was converting to “NA”).
tabl() wide.cols (pivot, cross-tab) functionality
improved to show 0 instead of NA for empty cross-tab cell
counts.
add_quant_labs() and add_quant1() now
round non-integer auto-generated quantile value labels to avoid long,
repeating-decimal value labels (e.g., “q067” instead of
“q066.66666…”).
updated ssort() to preserve rownames.
updated gremlr(), greml(),
slab(), sbrac(), somel(),
taill(), sfilter(), and ssort()
to handle situations involving (or producing) one-column
data.frames.
Defensive programming improvements made to various value-label-related functions, so that they respond more gracefully and informatively to non-valid variable name arguments.
split up original one vignette into two respective vignettes: Introduction and Special Topics.
light copy-editing updates to README, vignette, and documentation.