setdiff_: Extra utilities

Description

Extra utilities

Usage

setdiff_(x, y, dups = TRUE)
intersect_(x, y, dups = TRUE)
cut_numeric(
  x,
  breaks,
  labels = NULL,
  include.lowest = FALSE,
  right = TRUE,
  dig.lab = 3L,
  ordered_result = FALSE,
  ...
)
# S3 method for integer64
cut(x, ...)
x %in_% table
x %!in_% table
enframe_(x, name = "name", value = "value")
deframe_(x)
sample_(x, size = vector_length(x), replace = FALSE, prob = NULL)
val_insert(x, value, n = NULL, prop = NULL)
na_insert(x, n = NULL, prop = NULL)
vector_length(x)

Value

enframe()_ converts a vector to a data frame.

deframe()_ converts a 1-2 column data frame to a vector.

intersect_() returns a vector of common values between x and y.

setdiff_() returns a vector of values in x but not y.

cut_numeric() places values of a numeric vector into buckets, defined through the breaks argument and returns a factor unless labels = FALSE, in which case an integer vector of break indices is returned.

%in_% and %!in_% both return a logical vector signifying if the values of x exist or don't exist in table respectively.

sample_() is an alternative to sample() that natively samples data frame rows through sset(). It also does not have a special case when length(x) is 1.

val_insert inserts scalar values randomly into your vector. Useful for replacing lots of data with a single value.

na_insert inserts NA values randomly into your vector. Useful for generating missing data.

vector_length behaves mostly like NROW() except for matrices in which it matches length().

Arguments

x: A vector or data frame.
y: A vector or data frame.
dups: Should duplicates be kept? Default is TRUE.
breaks: See ?cut.
labels: See ?cut.
include.lowest: See ?cut.
right: See ?cut.
dig.lab: See ?cut.
ordered_result: See ?cut.
...: Further arguments passed onto cut or set.seed.
table: See ?collapse::fmatch
name: The column name to assign the names of a vector.
value: The column name to assign the values of a vector.
size: See ?sample.
replace: See ?sample.
prob: See ?sample.
n: Number of scalar values (or NA) to insert randomly into your vector.
prop: Proportion of scalar values (or NA) values to insert randomly into your vector.

Details

intersect_() and setdiff_() are faster and more efficient alternatives to intersect() and setdiff() respectively.
enframe_() and deframe_() are faster alternatives to tibble::enframe() and tibble::deframe() respectively.
cut_numeric() is a faster and more efficient alternative to cut.default().