High-Performance Open-Source Archive
rec() now keeps the labels of the old values with
the new ones when there are no labels specified and there is a 1 to 1
correspondence between old and new values.
format() for frq() was revised, and now
allows to format the frequency table to prepare for printing in text,
markdown and HTML-format. To do so, use the methods
print(), print_md() or
print_html().
Address changes in forthcoming update of sjstats.
format() for frq() was
implemented.merge_df() preserves more attributes related to
labelled data.
to.factor is an alias for the argument
as.num.
move_columns() (using a variable as value
for argument .after didn’t work).flat_table() gains a
weights-argument.descr() calculated wrong percentage of missing values
for weighted data.rec() when min,
max, lo or hi was used to recode
a numeric into a character vector, and the new recode string contained
one of these four strings as pattern.rec() when max
or hi was used to recode a value which maximum values was
lower than a defined range, e.g. 4:max when the maximum
values was lower than 4.descr() now also calculates the IQR.print()-method for frq().as.data.frame() for frq().typical_value() now returns the median for
integer-values (instead of mean), to preserve the integer-type of a
variable.rec() now also works for
character variables with whitespaces.rec() now warns explicitely for possible non-intended
multiple assignment of identical new recode-values.frq().merge_imputations() now returns the plot-object as
well.to_numeric() as alias for to_value().find_variables() (alias for
find_var()) was renamed to find_in_data(), to
avoid conflicts with package insight.rename_variables() and rename_columns()
are aliases for var_rename().frq() now also prints frequencies of logical
conditions, e.g. how many values are lower or greater than a certain
threshold.frq() gets a min.frq-argument, indicating
the minimum frequency for which a value will be shown in the
output.descr() gets a show argument to show
selected columns only.descr() gets a file-argument to write the
output as HTML file.var_rename() now also accepts a named vector with
multiple elements as ellipses-argument.de_mean().merge_df() now removes columns with identical column
names inside a data frame before merging, to avoid errors.frq(),
where first element was empty, and vectors were not provided as data
frame argument.word_wrap() when processing
expressions.rec() with token
rec = "rev", when reversing labelled vectors with more
value labels than values.find_variables() as alias for
find_var().reshape_longer(), as alternative to
to_long(), probably easier to remember (function and
argument-names).frq() displayed labels as NA in some
situations for grouped data frames with more than one group, when data
were not labelled.str_pos() was renamed into
str_find().typical_value(), which was formerly located in package
sjstats.is_whole() now automatically removes missing values
from vectors.is_empty() now also checks lists with only
NULL-elements.merge_imputations(),
which previously could result in NA-values when merging
imputed values into one variable.is_empty() in case the vector had
non-missing values, but first element of vector was
NA.frq() for grouped data frame, when
grouping variable was a character vector. In this case, group titles
were mixed up.tidy_values() to “clean” values (i.e. remove special
chars) of character vectors or levels of factors.add_id() to quickly add an ID variable to (grouped)
data frames.frq() gets a show.na-argument, to
(automatically) show or hide the information for NA-values
from the output.weights-argument in frq() now also
accepts vectors, and is not limited to variable names. Note that these
vectors must be part of a data frame.rec(), dicho(),
…), if suffix = "" and append = TRUE, existing
variables will be replaced by the new, recoded variables.group_str().var_rename() now supports quasi-quotation (see
Examples).row_sums() and row_means() now return the
input data frame when this data frame only had one column and no row
means or sums were calculated. The returned data frame still gets the
new variable name defined in var.complete_cases() returned an empty vector instead of
all indexes if all cases (rows) of a data frame were complete.to_dummy() for character-vector
input.group_str().frq() when
grp.strings = TRUE.frq() gets a file and
encoding argument, to save the HTML output as file.add_variables() and move_columns() now
preserve the attributes of a data frame.de_mean() to compute group-meaned and de-meaned
variables.add_variables() and add_case() to add
columns or rows in a convenient way to a data frame.move_columns() to move one or more columns to another
position in a data frame.is_num_chr() to check whether a character vector has
only numeric strings.seq_col() and seq_row() as convenient
wrapper to create a regular sequence for column or row numbers.descr() gets a weights-argument, to print
weighted descriptive statistics.n-argument in row_means() and
row_sums() now also may be Inf, to compute
means or sums only if all values in a row are valid
(i.e. non-missing).weight.by in frq() was renamed
into weights.frq() gets a title-argument, to specify an
alternative title to the variable label.round_num() preserves data frame attributes.frq() printed frequencies of grouping-variable for
grouped data frames, when weights was not
NULL.frq() for grouped data
frames, when grouping variable was an unlabelled factor.has_na() to check if variables or observations in a
data frame contain NA, NaN or Inf
values. Convenient shortcuts for this function are
complete_cases(), incomplete_cases(),
complete_vars() and incomplete_vars().total_mean() to compute the overall mean of all values
from all columns in a data frame.prcn() to convert numeric scalars between 0 and 1 into
a character-percentage value.numeric_to_factor() to convert numeric variables into
factors, using associated value labels as factor levels.set_na() now also replaces different values per
variable into NA.row_sums() and missing values.
row_sums() gets a n-argument and now computes
row sums if a row has at least n non-missing values.CITATION to the publication in the
Journal of Open Source Software.is_cross_classified() to check whether two factors are
partially crossed.ref_lvl() now also accepts value labels as value for
the lvl-argument. Additionally, ref_lvl() now
also works for factor with non-numeric factor levels and simply returns
relevel(x, ref = lvl) in such cases.rec() with direct labelling
for certain locales.count_na(), which did not print labels
of tagged NA values since the last revision of
frq().merge_imputation() for cases where
original data frame had less columns than imputed data frames.find_var() for fuzzy-matching in all
elements (i.e. when fuzzy = TRUE and
search = "all").round_num() to round only numeric values in a data
frame.merge_df(). Furthermore,
add_rows() was added as alias for
merge_df().merge_df() resp. add_rows() now create a
unique id-name instead of dropping the ID-variable, in case
id has the same name of any existing variables in the
provided data frames.descr() and minor changes to
the output.mids-objects (package mice)Following functions now also work on mids-objects, as
returned by the mice()-function: *
row_count(), row_sums(),
row_means(), rec(), dicho(),
center(), std(), recode_to() and
to_long().
weight.by-argument in frq() now should
be a variable name from a variable in x, and no longer a
separate vector.descr() does not work with character vectors, so these
are being removed now.The recoding and transformation functions get scoped variants, allowing to select variables based on logical conditions described in a function:
rec_if() as scoped variant of rec().dicho_if() as scoped variant of
dicho().center_if() as scoped variant of
center().std_if() as scoped variant of std().split_var_if() as scoped variant of
split_var().group_var_if() and group_label_if() as
scoped variant of group_var() and
group_label().recode_to_if() as scoped variant of
recode_to().set_na_if() as scoped variant of
set_na().remove_cols() as alias for
remove_var().std() gets a new robust-option,
robust = "2sd", which divides the centered variables by two
standard deviations.set_na().frq() now removes empty columns before computing
frequencies, because applying frq() on empty vectors caused
an error.empty_cols() and empty_rows() (and hence,
remove_empty_cols() and remove_empty_rows())
caused an error for data frames with only one column resp. row, or if
x was a vector and no data frame.frq() now removes missing values from input when
weights are applied, to ensure that input and weights have same
length.append-argument in
recode and transformation functions like rec(),
dicho(), split_var(),
group_var(), center(), std(),
recode_to(), row_sums(),
row_count(), col_count() and
row_means() now defaults to TRUE.print()-method for descr() now accepts
a digits-argument, to specify the rounding of the
output.dplyr::select_helpers were updated
to tidyselect::select_helpers.is_whole() as counterpart to
is_float().frq() now prints variable names for non-labelled data,
adds variable names in braces for labelled data and omits the
label column for non-labelled data.frq() now prints mean and standard deviation in the
header line of the output.frq() now gets a auto.grp-argument to
automatically group variables with many unique values.frq() now gets a show.strings-argument to
omit string variables (character vectors) from being printed as
frequency table.frq() now gets a grp.strings-argument to
group similar string values in the frequency table.frq() gets an out-argument, to print
output to console, or as HTML table in the viewer or web browser.descr() gets an out-argument, to print
output to console, or as HTML table in the viewer or web browser.is_empty() returned TRUE for single
vectors with NA being the first element.remove_empty_rows() did no longer remove empty rows, but
columns.frq(),
descr() and flat_table().rec() now also recodes doubles with floating points, if
a range of values is specified.std() and center() now use
include.fac = FALSE as default option.std() gets a robust-argument, to divide
variables either by standard deviation, or - in case of asymmetrically
distributed variables - median absolute deviation or Gini’s mean
difference.frq() now shows total and valid N in output.center(), std(), dicho(),
split_var() and group_var() did not work
correctly for grouped data frames.frq() did not print multiple variables when applied on
grouped data frames.as.df and as.varlab in function
find_var() are now deprecated. Please use out
instead.rotate_df() preserves attributes.is_float() is now exported as function.to_label(), when x was a
character vector and argument drop.levels was
TRUE.frq() did not correctly calculate valid and cumulative
percentages when using weights.remove_var() as pipe-friendly function to remove
variables from data frames.var_type() as pipe-friendly function to determine the
type of variables.all_na() to check whether a vector only consists of NA
values.rotate_df() to rotate data frames (switch columns and
rows).shorten_string(), to shorten strings to a certain
maxium number of chars.dicho(), split_var(),
group_var(), std() and
center().groupcount in split_var(),
group_var() and group_labels() is now named
n.groupsize in group_var() and
group_labels() is now named size.frq() gets a revised print-method, which does not print
the result to console when captured in an object (i.e.,
x <- frq(x) no longer prints the result).frq() no longer prints (redundant) labels for factors
w/o value label attributes.frq() adds information about the variable type in the
table caption (only for variables with variable labels).frq() adds information about groups when printing
grouped, non-labelled variables.descr() now also prints information about the variable
type.to_character() now preserves variable labels.one_of() or contains()) no longer need to be
prefixed with a ~ when used as argument within
sjmisc-functions.row_count() to count specific values in a data frame
per observation.col_count() to count specific values in a data frame
per variable.str_start() and str_end() to find starting
and end indices of patterns inside strings.frq() now always includes a
NA-row, but no longer prints a value for the
NA-row.merge_imputations() gets a
summary-argument to plot a graphical summary of the quality
of the merging process.add_columns() and replace_columns()
crashed R when no data frame was specified in ...-ellipses
argument.descr() and frq() used wrong variable
labels when processing grouped data frames for specific situations,
where the grouping variable had no sequences values.descr() did not work for large data frames, because
internally, because psych::describe() switched to fast mode
by default then (removing columns from the output).
Need mirroring services?
Contact our team at info@vpspulse.com.
Mirror powered by VPSpulse
Infrastructure sponsored by VPSPulse & Secure Payments by ArionPay.