Type:	Package
Title:	Miscellaneous, Analytic R Kernels
Version:	0.8.3
Maintainer:	Jordan Mark Barbone <jmbarbone@gmail.com>
Description:	Miscellaneous functions and wrappers for development in other packages created, maintained by Jordan Mark Barbone.
License:	MIT + file LICENSE
URL:	https://CRAN.R-project.org/package=mark, https://github.com/jmbarbone/mark, https://jmbarbone.github.io/mark/
BugReports:	https://github.com/jmbarbone/mark/issues
Encoding:	UTF-8
Depends:	R (≥ 3.6)
Imports:	cli, fs (≥ 1.6.2), fuj (≥ 0.2.1), magrittr (≥ 2.0.1), rlang, stats (≥ 3.6), tools (≥ 3.6), utils (≥ 3.6)
Suggests:	bench (≥ 1.1.1), bib2df (≥ 1.1.1), crayon (≥ 1.3.4), covr (≥ 3.5.1), desc (≥ 1.3.0), dplyr (≥ 1.0.6), echo (≥ 0.1.0), graphics (≥ 3.6), haven, knitr (≥ 1.30), rcmdcheck (≥ 1.3.3), stringi (≥ 1.5.3), spelling (≥ 2.2), testthat (≥ 3.0.0), tibble (≥ 3.0.4), waldo (≥ 0.2.5), withr (≥ 2.3.0), xopen, yaml, jsonlite, arrow (≥ 16.1.0), readMDTable (≥ 0.2.0), clipr (≥ 0.8.0)
RoxygenNote:	7.3.2
Config/testthat/edition:	3
Config/testthat/parallel:	true
Language:	en-US
NeedsCompilation:	no
Packaged:	2025-04-23 03:17:49 UTC; jordan
Author:	Jordan Mark Barbone [aut, cph, cre]
Repository:	CRAN
Date/Publication:	2025-04-23 03:40:02 UTC

mark

Description

Miscellaneous, Analytic R Kernels

Author(s)

Maintainer: Jordan Mark Barbone jmbarbone@gmail.com (ORCID) [copyright holder]

See Also

Useful links:

• https://CRAN.R-project.org/package=mark

• https://github.com/jmbarbone/mark

• https://jmbarbone.github.io/mark/

• Report bugs at https://github.com/jmbarbone/mark/issues

Add file timestamp

Description

Adds a timestamp to a file

Usage

add_file_timestamp(
  x,
  ts = Sys.time(),
  format = "%Y-%m-%d %H%M%S",
  sep = " "
)

Arguments

Value

The full name paths with the appended time stamp

Examples

file1 <- tempfile(fileext = ".txt")
file2 <- tempfile()

add_file_timestamp(file1)
add_file_timestamp(file2)

file.remove(file1, file2)

Identical extensions

Description

Extensions for the use of base::identical()

Usage

are_identical(..., params = NULL)

Arguments

Value

A logical vector of TRUE/FALSE of equal length of each ... vector

Examples

x <- y <- z <- 1:5
y[2] <- 3L
z[5] <- NA_integer_

identical(x, y)        # compare entire vector
are_identical(x, y)    # element-wise
are_identical(x, y, z) # 3 or more vectors

Array extract

Description

Extract dimensions from an array

Usage

array_extract(.arr, ..., default = "1")

Arguments

Value

A value from the array arr

Examples

x <- array(rep(NA, 27), dim = c(3, 3, 3))
x[1, 2, 3] <- TRUE
x[1, 2, 3]
x
array_extract(x, `2` = 2, `3` = 3)

Ordered

Description

As ordered

Usage

as_ordered(x)

## Default S3 method:
as_ordered(x)

Arguments

Details

Simple implementation of ordered. If x is ordered it is simply returned. If x is a factor the ordered class is added. Otherwise, x is made into a factor with fact() and then the ordered class is added. Unlike just fact, ordered will replace the NA levels with NA_integer_ to work appropriately with other functions.

Value

An ordered vector

See Also

fact()

Other factors: char2fact(), drop_levels(), fact(), fact2char(), fact_na()

Examples

x <- c("a", NA, "b")
x <- fact(x)
str(x) # NA is 3L

y <- x
class(y) <- c("ordered", class(y))
max(y)
max(y, na.rm = TRUE) # returns NA -- bad

# as_ordered() removes the NA level
x <- as_ordered(x)
str(x)
max(x, na.rm = TRUE) # returns b -- correct

Alpha base

Description

Base 26 conversion with letters

Usage

base_alpha(x, base = 26)

Arguments

Value

A vector of integers

Examples

base_alpha("AB")
base_alpha("XFD")
base_alpha(c("JMB", "Jordan Mark", "XKCD"))
sum(base_alpha(c("x", "k", "c", "d")))

Base N conversion

Description

Convert between base numbers

Usage

base_n(x, from = 10, to = 10)

Arguments

Value

The A vector of integers converted from base from to base to

Examples

base_n(c(24, 22, 16), from = 7)

Blank values

Description

Detect blank values; select, remove columns that are entirely blank

Usage

is_blank(x, na_blank = FALSE, ws = TRUE)

select_blank_cols(x, na_blank = FALSE, ws = TRUE)

remove_blank_cols(x, na_blank = FALSE, ws = TRUE)

is_blank_cols(x, names = TRUE, na_blank = FALSE, ws = TRUE)

Arguments

Details

Blank values are values that do not contain any text

Value

• is_blank() a logical vector indicating blank elements in x

• select_blank_cols() x with only columns that are all blank

• remove_blank_cols() x without columns of only blank

• is_blank_cols() a logical vector: TRUE all rows of column are blank, otherwise FALSE

Character to factor

Description

Converts characters to factors

Usage

char2fact(x, n = 5)

## Default S3 method:
char2fact(x, n = 5)

## S3 method for class 'character'
char2fact(x, n = 5)

## S3 method for class 'factor'
char2fact(x, n = 5)

## S3 method for class 'data.frame'
char2fact(x, n = 5)

Arguments

See Also

fact2char()

Other factors: as_ordered(), drop_levels(), fact(), fact2char(), fact_na()

Check options

Description

For each name in x checks the current option value and reports if there is a difference in a message. This does not change the options

Usage

checkOptions(x)

Arguments

Details

Checks and reports on options

Value

Invisible, a list of the current options from options()

Examples

op <- options()

x <- list(width = -20, warning.length = 2, probably_not_a_real_option = 2)
checkOptions(x)
# pointless, but shows that no messages are given
identical(options(), checkOptions(options()))

options(op)

Character split

Description

Split apart a string by each character

Usage

chr_split(x)

Arguments

Value

A character vector of length nchar(x)

Examples

chr_split("split this")

Write to and read from the clipboard

Description

Wrappers for working with the clipboard

Usage

write_clipboard(x, ...)

## Default S3 method:
write_clipboard(x, ...)

## S3 method for class 'data.frame'
write_clipboard(x, sep = "\t", row.names = FALSE, ...)

## S3 method for class 'matrix'
write_clipboard(x, sep = "\t", ...)

## S3 method for class 'list'
write_clipboard(x, sep = "\t", ...)

read_clipboard(method = read_clipboard_methods(), ...)

read_clipboard_methods()

Arguments

Details

As these functions rely on clipr::read_clip() and utils::writeClipboard() they are only available for Windows 10. For copying and pasting floats, there may be some rounding that can occur.

Value

write_clipboard() None, called for side effects read_clipboard() Either a vector, data.frame, or tibble depending on the method chosen. Unlike utils::readClipboard(), an empty clipboard value returns NA rather than ""

Examples

# Will only run on windows
if (Sys.info()[["sysname"]] == "Windows") {
  foo <- function(x) {
    write_clipboard(x)
    y <- read_clipboard()
    res <- all.equal(x, y)
    if (isTRUE(res)) return("All equal")
    print(x)
    print(y)
  }
  foo(1:4)
  foo(seq(-1, 1, .02))
  foo(Sys.Date() + 1:4)

  # May have some rounding issues
  x <- "0.316362437326461129"
  write_clipboard(x)
  res <- as.character(read_clipboard())
  all.equal(x, res)
  x; res
}

Complete cases

Description

Return completed cases of a data.frame

Usage

complete_cases(data, cols = NULL, invert = FALSE)

Arguments

Value

A data.frame

Examples

x <- data.frame(
  a = 1:5,
  b = c(1, NA, 3, 4, 5),
  c = c(1, NA, NA, 4, 5)
)

complete_cases(x)
complete_cases(x, invert = TRUE) # returns the incomplete rows
complete_cases(x, "a")
complete_cases(x, "b")
complete_cases(x, "c")

Count observations by unique values

Description

Variables will be return by the order in which they appear. Even factors are shown by their order of appearance in the vector.

There are 2 methods for counting vectors. The default method uses base::tabulate() (the workhorse for base::table() with a call to pseudo_id() to transform all inputs into integers. The logical method counts TRUE, FALSE and NA values, which is much quicker.

Usage

counts(x, ...)

## S3 method for class 'data.frame'
counts(x, cols, sort = FALSE, ..., .name = "freq")

props(x, ...)

## Default S3 method:
props(x, sort = FALSE, na.rm = FALSE, ...)

## S3 method for class 'data.frame'
props(x, cols, sort = FALSE, na.rm = FALSE, ..., .name = "prop")

Arguments

Details

Get counts or proportions of unique observations in a vector or columns in a data.frame

Value

A named vector of integers or doubles (for counts, and props, respectively) or data.frame with columns for each column chosen and the .name chosen for the summary

Examples

x <- sample(1:5, 10, TRUE)
counts(x)
props(x)

x <- quick_df(list(
  a = c("a", "c", "a", "c", "d", "b"),
  b = c("a", "a", "a", "c", "c", "b"),
  c = c("a", "a", "a", "c", "b", "b")
))

counts(x, "a")
counts(x, c("a", "b", "c"))
props(x, 2)
props(x, 1:3)

props(c(1, 1, 3, NA, 4))
props(c(1, 1, 3, NA, 4), na.rm = TRUE)

Partial dates

Description

Derive a date vector from a partial date string

Usage

date_from_partial(
  x,
  format = "ymd",
  method = c("min", "max"),
  year_replacement = NA_integer_
)

Arguments

Details

Takes a character as an argument and attempts to create a date object when part of the date string is missing.

Value

A vector of Dates

Examples

x <- c("2020-12-17", NA_character_, "", "2020-12-UN", "2020-12-UN",
       "2019-Unknown-00", "UNK-UNK-UNK", "1991-02-UN", "    ",
       "2020January20")
data.frame(
  x = x,
  min = date_from_partial(x),
  max = date_from_partial(x, method = "max"),
  year = date_from_partial(x, year_replacement = 1900)
)

Depth

Description

Functions to extract the 'depth' of an object

Usage

depth(x, ...)

## Default S3 method:
depth(x, ...)

## S3 method for class 'list'
depth(x, ...)

Arguments

Details

This function does not count an empty lists (list()) as a level or NULL objects.

Value

A single integer

Examples

a <- c(1, 2, 3)
depth(a) # Vectors are 1L

b <- list(a = 1, b = list(list(1)))
depth(b)

Details an object

Description

Provides details about an object

Usage

detail(x, ...)

## Default S3 method:
detail(x, factor_n = 5L, ...)

## S3 method for class 'data.frame'
detail(x, factor_n = 5L, ...)

Arguments

Examples

x <- sample(letters[1:4], 10, TRUE)
detail(x)

df <- quick_df(list(
  x = x,
  y = round(runif(10), 2),
  z = Sys.Date() + runif(10) * 100
))

detail(df)

Diff time wrappers

Description

Wrappers for computing diff times

Usage

diff_time(
  x,
  y,
  method = c("secs", "mins", "hours", "days", "weeks", "months", "years", "dyears",
    "wyears", "myears"),
  tzx = NULL,
  tzy = tzx
)

diff_time_days(x, y, ...)

diff_time_weeks(x, y, ...)

diff_time_hours(x, y, ...)

diff_time_mins(x, y, ...)

diff_time_secs(x, y, ...)

diff_time_months(x, y, ...)

diff_time_years(x, y, ...)

diff_time_dyears(x, y, ...)

diff_time_wyears(x, y, ...)

diff_time_myears(x, y, ...)

Arguments

Details

A few significant differences exist with these functions * The class of the object returned is no longer difftime (but does print) with the difftime method. This makes the exporting process easier as the data will not have to be converted back to numeric * difftime() computes the difference of time1 - time2, but the inverse feels a bit more nature: time difference from x to y * Additional units can be used (detailed below) * Differences can be sensitive to time zones if time zones are passed to the tz parameter as a character vector

Value

A diff_time vector, object

Units

Units can be used beyond those available in base::difftime(). Some of these use assumptions in how units of time should be standardized and can be changed in the corresponding options. Any of these can be calculated with base::difftime() through using units = "days" but the dtime class will print out with these specifications into the console for less potential confusion.

months

Months by number of days mark.days_in_month (defaults: 30)

years

Years by number of days mark.days_in_year (defaults: 365)

dyears

Years by number of days mark.days_in_year (defaults: 365) (same as years)

myears

Years by number of days in a month mark.days_in_month (defaults: 30)

wyears

Years by number of weeks in a year mark.weeks_in_year (defaults: 52)

Time zones

Time zones can be passed as either a numeric vector of GMT/UTC offsets (the number of seconds from GMT) or as a character vector. If the letter, these need to conform with values from base::OlsonNames().

A default timezone can be set with options(mark.default_tz = .). The value can either be a numeric

Drop levels

Description

Drop unused levels of a factor

Usage

drop_levels(x, ...)

## S3 method for class 'data.frame'
drop_levels(x, ...)

## S3 method for class 'fact'
drop_levels(x, ...)

## S3 method for class 'factor'
drop_levels(x, ...)

Arguments

See Also

base::droplevels

Other factors: as_ordered(), char2fact(), fact(), fact2char(), fact_na()

Parse and evaluate text

Description

A wrapper for eval(parse(text = .))

Usage

ept(x, envir = parent.frame())

Arguments

Value

The evaluation of x after parsing

Evaluate a Named Chunk

Description

Evaluate a named chunk from an Rmd file.

Usage

eval_named_chunk(rmd_file, label_name)

Arguments

Value

The value from the evaluated code chunk

Examples

temp_rmd <- tempfile(fileext = ".rmd")

text <- '
```{r not this label}
print("that is wrong")
```

```{r hello label}
text <- "hello, world"
print(text)
print(TRUE)
```

```{r another label}
warning("wrong label")
```
'
## Not run: 
writeLines(text, con = temp_rmd)

eval_named_chunk(temp_rmd, "hello label")
# [1] "hello, world"
# [1] TRUE

file.remove(temp_rmd)

## End(Not run)

Expands a vector

Description

Expands vector x by y

Usage

expand_by(x, y, expand = c("x", "y", "intersect", "both"), sort = FALSE)

Arguments

Value

A vector with expanded

Examples

x <- letters[c(3:2, 5, 9)]
y <- letters[c(1:4, 8)]
expand_by(x, y, "x")
expand_by(x, y, "y")
expand_by(x, y, "intersect")
expand_by(x, y, "both")

Factor

Description

Quickly create a factor

Usage

fact(x)

## Default S3 method:
fact(x)

## S3 method for class 'character'
fact(x)

## S3 method for class 'numeric'
fact(x)

## S3 method for class 'integer'
fact(x)

## S3 method for class 'Date'
fact(x)

## S3 method for class 'POSIXt'
fact(x)

## S3 method for class 'logical'
fact(x)

## S3 method for class 'factor'
fact(x)

## S3 method for class 'fact'
fact(x)

## S3 method for class 'pseudo_id'
fact(x)

## S3 method for class 'haven_labelled'
fact(x)

Arguments

Details

fact() can be about 5 times quicker than factor() or as.factor() as it doesn't bother sorting the levels for non-numeric data or have other checks or features. It simply converts a vector to a factor with all unique values as levels with NAs included.

fact.factor() will perform several checks on a factor to include NA levels and to check if the levels should be reordered to conform with the other methods. The fact.fact() method simple returns x.

Value

A vector of equal length of x with class fact and factor. If x was ordered, that class is added in between.

level orders

The order of the levels may be adjusted to these rules depending on the class of x:

character

The order of appearance

numeric/integer/Date/POSIXt

By the numeric order

logical

As TRUE, FALSE, then NA if present

factor

Numeric if levels can be safely converted, otherwise as they are

See Also

as_ordered()

Other factors: as_ordered(), char2fact(), drop_levels(), fact2char(), fact_na()

Factor to character

Description

Convert factor columns to characters in a data.frame

Usage

fact2char(data, threshold = 10)

Arguments

Value

The data.frame data with factors converted by the rule above

See Also

char2fact()

Other factors: as_ordered(), char2fact(), drop_levels(), fact(), fact_na()

fact with NA

Description

Included NA values into fact()

Usage

fact_na(x, remove = FALSE)

Arguments

Details

This re-formats the x value so that NAs are found immediately within the object rather than accessed through its attributes.

Value

A fact vector

See Also

Other factors: as_ordered(), char2fact(), drop_levels(), fact(), fact2char()

Fact reverse levels

Description

Reverse the levels of a fact

Usage

fact_reverse(x)

Arguments

Factor Expand by Sequence

Description

Expands an ordered factor from one level to another

Usage

fct_expand_seq(
  x,
  min_lvl = min(x, na.rm = TRUE),
  max_lvl = max(x, na.rm = TRUE),
  by = 1L
)

Arguments

Details

Defaults for min_lvl and max_lvl are the minimum and maximum levels in the ordered vector x.

Value

An ordered vector

Examples

x <- ordered(letters[c(5:15, 2)], levels = letters)
fct_expand_seq(x)
fct_expand_seq(x, "g", "s", 3L) # from "g" to "s" by 3
fct_expand_seq(x, "g", "t", 3L) # same as above

# from the first inherit level to the last observed
fct_expand_seq(x, min(levels(x)))

File copy with md5 hash check

Description

File copy with md5 hash check

Usage

file_copy_md5(path, new_path, overwrite = NA, quiet = FALSE)

Arguments

Value

The path(s) of the new file(s), invisibly. When overwrite is NA, the paths will be returned with two addition attributes, "changed", a logical vector indicating whether the file was changed (NA for when the file is new), and "md5sum", a list of the md5sums of the old and new md5 sums.

File information utils

Description

Other utility functions for dealing with files

Usage

newest_file(x)

newest_dir(x)

oldest_file(x)

oldest_dir(x)

largest_file(x)

smallest_file(x)

Arguments

Value

A full file path

File name

Description

Basename of file without extension

Usage

file_name(x, compression = FALSE)

Arguments

Value

The file name of the path without the extension

Open a file using windows file associations

Description

Opens the given files(s)

Usage

open_file(x)

file_open(x)

shell_exec(x)

list_files(
  x = ".",
  pattern = utils::glob2rx(glob),
  glob = NULL,
  ignore_case = FALSE,
  all = FALSE,
  negate = FALSE,
  basename = FALSE
)

list_dirs(
  x = ".",
  pattern = NULL,
  ignore_case = FALSE,
  all = FALSE,
  basename = FALSE,
  negate = FALSE
)

Arguments

Details

open_file is an alternative to shell.exec() that can take take multiple files. list_files and list_dirs are mostly wrappers for fs::dir_ls() with preferred defaults and pattern searching on the full file path.

file_open is simply an alias.

Value

• open_file(), shell_exec(): A logical vector where TRUE successfully opened, FALSE did not and NA did not try to open (file not found)

• list_files(), list_dirs(): A vector of full paths

Fizz Buzz

Description

For when someone asked you to do something you've done before, you can argue that the quickest way to do it is to just take the work someone else did and utilize that. No reason to reinvent the wheel.

Usage

fizzbuzz(n, show_numbers = TRUE)

fizzbuzz_lazy(n)

.fizzbuzz_vector

Arguments

Format

An object of class character of length 1000000.

Details

Multiples of 3 are shown as "Fizz"; multiples of 5 as "Buzz"; multiple of both (i.e., 15) are "FizzBuzz". fizzbuzz_lazy() subsets the .fizzbuzz_vector object, which is a solution with default parameters up to 1e6

Value

A character vector of ⁠1, 2, Fizz, 3, Buzz⁠, etc

Examples

fizzbuzz(15)
fizzbuzz(30, show_numbers = FALSE)
cat(fizzbuzz(30), sep = "\n")


# show them how fast your solution is:
if (package_available("bench")) {
  bench::mark(fizzbuzz(1e5), fizzbuzz_lazy(1e5))
}

Get recent directory by number name

Description

Finds the directory where the number is the greatest. This can be useful for when folders are created as run IDs.

Usage

get_dir_max_number(x)

Arguments

Value

A full path to a directory

Get recent directory by date

Description

Looks at the directories and assumes the date

Usage

get_dir_recent_date(x = ".", dt_pattern = NULL, dt_format = NULL, all = FALSE)

Arguments

Value

A full path to a directory

Get recent directory

Description

Finds the recent subdirectory in a directory.

Usage

get_recent_dir(x = ".", ...)

Arguments

Value

The full path of the most recent directory

Get recent file

Description

A function where you can detect the most recent file from a directory.

Usage

get_recent_file(x, exclude_temp = TRUE, ...)

Arguments

Value

The full name of the most recent file from the stated directory

Get and bump version

Description

Will read the DESCRIPTION file and to get and adjust the version

bump_date_version() will not check if the version is actually a date. When the current version is the same as today's date(equal by character strings) it will append a .1.

Usage

get_version()

bump_version(version = NULL)

bump_date_version(version = NULL)

update_version(version = NULL, date = FALSE)

Arguments

Details

Get and bump package version for dates

Value

• get_version(): A package_version

• bump_version(): None, called for its side-effects

• bump_date_version(): None, called for its side-effects

• update_version(): None, called for its side-effects

Wildcard globbing

Description

Helper function for globbing character vectors

Usage

glob(x, pattern = NULL, value = TRUE, ...)

Arguments

Examples

x <- c("apple", "banana", "peach", "pear", "orange")
glob(x, "*e")
glob(x, "pea*", value = FALSE)
glob(x, "*an*", value = NA)

path <- system.file("R", package = "mark")
glob(list.files(path), "r*")

Handlers

Description

Catch and report handlers

Usage

has_warning(x, FUN, ...)

has_error(x, FUN, ...)

has_message(x, FUN, ...)

get_warning(x, FUN, ..., .null = TRUE)

get_message(x, FUN, ..., .null = TRUE)

get_error(x, FUN, ..., .null = TRUE)

Arguments

Details

These functions can be used to catch whether an evaluation will return an error or warning without raising.

Value

The ⁠has_*()⁠ functions will return TRUE/FALSE for if the handler is found in the execution of the code. The ⁠get_*()⁠ functions provide the text of the message

References

Function for catching has been adapted from https://stackoverflow.com/a/4952908/12126576

Examples

has_warning(c(1, "no"), as.integer)
#     1    no
# FALSE  TRUE

get_warning(c(1, "no"), as.integer)

# drop NULLs
get_warning(c(1, "no"), as.integer, .null = FALSE)

foo <- function(x) {
  stopifnot(x > 0)
  x
}

has_error(c(1, 0, 2), foo)
#     1     0     2
# FALSE  TRUE FALSE

get_error(c(1, 0, 2), foo)

# drop NULLs
get_error(c(1, 0, 2), foo, .null = FALSE)

Import

Description

Import a single function from a package

Usage

import(pkg, fun, overwrite = FALSE)

Arguments

Value

None, called for side effects

Examples

# assigns `add` -- test with caution
import("magrittr", "add")

Insert

Description

Insert values at a position

Usage

insert(x, positions, values)

Arguments

Value

A vector with the intended values inserted

Examples

insert(letters[1:5], c(2, 4), c("X", "Y"))

Is File/Directory

Description

Is the path a file/directory?

Usage

is_dir(x)

is_file(x)

Arguments

Details

These are essentially taken from utils::file_test() for op = '-d' and op = -f but separated.

Value

A logical vector

Dataframe labels

Description

Assign labels to a vector or data.frame.

Usage

assign_labels(x, ...)

## Default S3 method:
assign_labels(x, label, ...)

## S3 method for class 'data.frame'
assign_labels(
  x,
  ...,
  .missing = c("error", "warn", "skip"),
  .ls = rlang::list2(...)
)

get_labels(x)

## Default S3 method:
get_labels(x)

## S3 method for class 'data.frame'
get_labels(x)

view_labels(x, title)

remove_labels(x, ...)

## Default S3 method:
remove_labels(x, ...)

## S3 method for class 'data.frame'
remove_labels(x, cols, ...)

Arguments

Details

When labels are assigned to a data.frame they can make viewing the object (with View() inside Rstudio). The view_labels() has a call to View() inside and will retrieve the labels and show them in the viewer as a data.frame.

Value

A labelled vector or data.frame

Examples

labs <- assign_labels(
  iris,
  Sepal.Length = "cms",
  Sepal.Width  = "cms",
  Petal.Length = "cms",
  Petal.Width  = "cms",
  Species      = "Iris ..."
)

labs$dummy <- ""
get_labels(labs) # shows label as <NA> for dummy column

labs0 <- remove_labels(labs, c("Sepal.Length", "Sepal.Width"))
get_labels(labs0) # No labels for Sepal.Length and Sepal.Width

Limit

Description

Limit a numeric vector by lower and upper bounds

Usage

limit(x, lower = min(x), upper = max(x))

Arguments

Value

The vector x with lower and upper as the minimum, maximum values

Lines of R code

Description

Find the total number of lines of R code

Usage

lines_of_r_code(x = ".", skip_empty = TRUE)

Arguments

Details

Tries to read each file in the directory that ends in .R or .r and sums together. Files that fail to read are not counted.

Value

An integer for the number of lines in all applicable files

Examples

lines_of_r_code(system.file())
lines_of_r_code(system.file(), skip_empty = FALSE)

List to data.frame

Description

Converts a list object into a data.frame

Usage

list2df(x, name = "name", value = "value", show_NA, warn = TRUE)

Arguments

Details

Unlike base::list2DF(), list2df() tries to format the data.frame by using the names of the list as values rather than variables. This creates a longer form list that may be more tidy.

Value

a data.frame object with columns "name" and "value" for the names of the list and the values in each

Examples

x <- list(a = 1, b = 2:4, c = letters[10:20], "unnamed", "unnamed2")
list2df(x, "col1", "col2", warn = FALSE)

if (getRversion() >= as.package_version('4.0')) {
# contrast with `base::list2DF()` and `base::as.data.frame()`
  x <- list(a = 1:3, b = 2:4, c = letters[10:12])
  list2df(x, warn = FALSE)
  list2DF(x)
  as.data.frame(x)
}

List all environments and objects

Description

Functions to list out all environments and objects

Usage

environments()

ls_all(all.names = FALSE)

objects_all(all.names = FALSE)

Arguments

Details

environments() is basically a printing wrapper for base::search()

ls_all() and objects_all() can be used retrieved all objects from all environments in the search() path, which may print out a large result into the console.

Value

• environments(): Invisibly, a character vector of environment names

• ls_all(), objects_all(): A named list for each of the environments the search() path with all the objects found in that environment

Logic - Extension'

Description

All functions take logical or logical-like (i.e., 1, 0, or NA as integer or doubles) and return logical values.

Extensions to the base logical operations to account for NA values.

base::isTRUE() and base::isFALSE() will only return single length TRUE or FALSE as it checks for valid lengths in the evaluation. When needing to check over a vector for the presence of TRUE or FALSE and not being held back by NA values, is_true and is_false will always provide a TRUE FALSE when the vector is logical or return NA is the vector x is not logical.

⁠%or%⁠ is just a wrapper for base::xor()

Usage

is_true(x)

## Default S3 method:
is_true(x)

## S3 method for class 'logical'
is_true(x)

is_false(x)

## Default S3 method:
is_false(x)

## S3 method for class 'logical'
is_false(x)

x %xor% y

OR(..., na.rm = FALSE)

AND(..., na.rm = FALSE)

either(x, y)

is_boolean(x)

none(..., na.rm = FALSE)

Arguments

Details

Logical operations, extended

Value

• is_true(), is_false(), either(), ⁠%or%⁠, AND(), OR(): A logical vector, equal length of x (or y or of all ... lengths)

• is_boolean(): TRUE or FALSE

• none(): TRUE, FALSE, or NA

Examples

x <- c(TRUE, FALSE, NA)
y <- c(FALSE, FALSE, TRUE)
z <- c(TRUE, NA, TRUE)
isTRUE(x)
is_true(x)
isFALSE(x)
is_false(x)
x %xor% TRUE
TRUE %xor% TRUE
TRUE %xor% FALSE
NA %xor% FALSE
OR(x, y, z)
OR(x, y, z, na.rm = TRUE)
AND(x, y, z)
AND(x, y, z, na.rm = TRUE)
either(x, FALSE)
either(TRUE, FALSE)
either(FALSE, NA)
either(TRUE, NA)
none(x)
none(x & y, na.rm = TRUE)
is_boolean(x)
is_boolean(c(1L, NA_integer_, 0L))
is_boolean(c(1.01, 0, -1))

List Objects - extensions

Description

List Objects - extensions

Usage

ls_dataframe(pattern, all.names = FALSE, envir = parent.frame())

ls_function(pattern, all.names = FALSE, envir = parent.frame())

ls_object(pattern, all.names = FALSE, envir = parent.frame())

Arguments

Value

A character vector of names

Make system file function

Description

Simple wrapper for package specific function for internal packages

Usage

make_sf(package)

Arguments

Match arguments

Description

This function is essentially a clear version of base::match.arg() which produces a cleaner warning message and does not restrict the table param to character vectors only.

Usage

match_arg(x, table)

Arguments

Details

Match arguments

Value

A single value from x matched on table

See Also

match_param()

Examples

x <- c("apple", "banana", "orange")
match_arg("b", x)

# Produces error
try(match_arg("pear", x))

foo <- function(x, op = c(1, 2, 3)) {
  op <- match_arg(op)
  x / op
}

foo(10, 3)

# Error
try(foo(1, 0))

Match params

Description

Much like base::match.arg() with a few key differences:

• Will not perform partial matching

• Will not return error messages with ugly quotation marks

Usage

match_param(
  param,
  choices,
  null = TRUE,
  partial = getOption("mark.match_param.partial", FALSE),
  multiple = FALSE,
  simplify = TRUE
)

Arguments

Details

Param matching for an argument

Value

A single value from param matched on choices

See Also

match_arg()

Examples

fruits <- function(x = c("apple", "banana", "orange")) {
  match_param(x)
}

fruits()         # apple
try(fruits("b")) # must be exact fruits("banana")

pfruits <- function(x = c("apple", "apricot", "banana")) {
  match_param(x, partial = TRUE)
}
pfruits()          # apple
try(pfruits("ap")) # matchParamMatchError
pfruits("app")     # apple

afruits <- function(x = c("apple", "banana", "orange")) {
  match_param(x, multiple = TRUE)
}

afruits() # apple, banana, orange

# can have multiple responses
how_much <- function(x = list(too_few = 0:2, ok = 3:5, too_many = 6:10)) {
  match_param(x)
}

how_much(1)
how_much(3)
how_much(9)

# use a list of formulas instead
ls <- list(1L ~ 0:1, 2L, 3L ~ 3:5)
sapply(0:5, match_param, choices = ls)

Compute the MD5 hash of an object

Description

Wrapper for calling tools::md5sum() on objects rather than files.

Usage

md5(x)

Arguments

Details

All x objects are serialized to a temporary file before tools::md5sum() is called.

Value

A md5sum object

Examples

md5("hello")
md5(1:10)
md5(data.frame(a = 1:10, b = letters[1:10]))

Median (Q 50)

Description

Median as the 50th quantile with an option to select quantile algorithm

Usage

median2(x, type = 7, na.rm = FALSE)

q50(x, type = 7, na.rm = FALSE)

Arguments

Details

q50 is an alias for median2

Value

See stats::quantile()

See Also

stats::quantile()

Examples

set.seed(42)
x <- rnorm(100)
median(x)            # 0.08979677
median2(x, type = 7) # 0.08979677 - default type is 7
median2(x, type = 3) # 0.08976065

Merge lists

Description

Merge lists with different or intersecting names

Usage

merge_list(x, y, keep = c("x", "y"), null = c("ignore", "drop", "keep")[1:2])

Arguments

Examples

x <- list(a = 1, b = 2,    c = NULL, d = NULL)
y <- list(a = 2, b = NULL, c = 3)

# compared to:
utils::modifyList(x, y)
utils::modifyList(x, y, keep.null = TRUE)

merge_list(x, y)
merge_list(x, y, keep = "y")
merge_list(x, y, null = "drop")

Multiple searching

Description

Multiple search pattern searches

Usage

multi_grepl(x, patterns, ..., simplify = TRUE)

multi_grep(x, patterns, ..., simplify = TRUE)

Arguments

Value

The name or position of the pattern that is matched

Examples

x <- c("apple", "banana", "lemon")
multi_grepl(x, c("a" = "^[ab]", "b" = "lem"))
multi_grepl(x, c("a" = "^[ab]", "b" = "q"))                   # lemon not matches on either
multi_grepl(x, c("a" = "^[ab]", "b" = "e"))                   # apple matches "a" before "b"
multi_grepl(x, c("a" = "^[ab]", "b" = "e"), simplify = FALSE) # shows all matches
multi_grepl(x, c("^[ab]", "e"))                               # returned as positions
multi_grepl(x, c("^[ab]", "e"), simplify = FALSE)

NA at positions

Description

Converts select elements of a vector into NAs

This is how the end results are

• NA_at and NA_if require a suitable index value (x[y] <- NA)

  • NA_at expects y (or the result of function y) to be integers

  • NA_if expects y (or the result of function y) to be logical

• NA_in and NA_out expect some values to match on

  • NA_in checks x[x %in% y] <- NA

  • NA_out checks x[x %out% y] <- NA (see fuj::match_ext)

Usage

NA_at(x, y, ...)

NA_if(x, y, ...)

NA_in(x, y, ...)

NA_out(x, y, ...)

Arguments

Details

Convert specific values to NA

Value

x with assigned NA values

See Also

Inspired by dplyr::na_if()

Examples

let <- ordered(letters[1:5])
NA_at(let, c(1, 3, 5))   # [1] <NA> b    <NA> d    <NA>
NA_if(let, let <= "b")   # [1] <NA> <NA> c    d    e
NA_in(let, c("a", "c"))  # [1] <NA> b    <NA> d    e
NA_out(let, c("a", "c")) # [1] a    <NA> c    <NA> <NA>

Selecting NA columns

Description

Select or remove columns that are entirely NA

Usage

select_na_cols(x)

remove_na_cols(x)

is_na_cols(x, names = TRUE)

Arguments

Value

• select_na_cols() x with only columns that are all NA

• remove_na_cols() x without columns of only NA

• is_na_cols() a logical vector: TRUE all rows of column are NA, otherwise FALSE

Normalize paths

Description

Normalize and check a vector of paths

Usage

norm_path(x = ".", check = FALSE, remove = check)

file_path(..., check = FALSE, remove = check)

user_file(..., check = FALSE, remove = check)

Arguments

Value

A vector of full file paths

Normalize values

Description

Normalizes values based on possible range and new bounds

Usage

normalize(x, ...)

## Default S3 method:
normalize(x, range = base::range(x, na.rm = TRUE), bounds = 0:1, ...)

## S3 method for class 'data.frame'
normalize(x, ...)

Arguments

Details

Parameters range and bounds are modified with base::range(). The largest and smallest values are then used to determine the minimum/maximum values and lower/upper bounds. This allows for a vector of more than two values to be passed.

The current implementation of normalize.data.frame() allows for list of parameters passed for each column. However, it is probably best suited for default values.

Value

x with transformed values where range values are transformed to bounds.

Examples

x <- c(0.23, 0.32, 0.12, 0.61, 0.26, 0.24, 0.23, 0.32, 0.29, 0.27)
data.frame(
  x  = normalize(x),
  v  = normalize(x, range = 0:2),
  b  = normalize(x, bounds = 0:10),
  vb = normalize(x, range = 0:2, bounds = 0:10)
)

# maintains matrix
mat <- structure(c(0.24, 0.92, 0.05, 0.37, 0.19, 0.69, 0.43, 0.22, 0.85,
0.73, 0.89, 0.68, 0.57, 0.89, 0.61, 0.98, 0.75, 0.37, 0.24, 0.24,
0.34, 0.8, 0.25, 0.46, 0.03, 0.71, 0.79, 0.56, 0.83, 0.97), dim = c(10L, 3L))

mat
normalize(mat, bounds = -1:1)
normalize(as.data.frame(mat), bounds = -1:1)

Make not available

Description

Create NA vectors

Usage

not_available(type = "logical", length = 0L)

set_not_available(type, value)

NA_Date_

NA_POSIXct_

NA_POSIXlt_

Arguments

Format

An object of class Date of length 1.

An object of class POSIXct (inherits from POSIXt) of length 1.

An object of class POSIXlt (inherits from POSIXt) of length 1.

Details

If length is a text it will search for an appropriate match.

Value

A vector of NA values

Examples

x <- not_available("Date", 3)
x
class(x)

Append a note to an object

Description

An alternative to the base::comment().

Usage

note(x) <- value

set_note(x, value)

note(x)

Arguments

Details

When the note is assigned to an object a new class will be added, note, so that a print function can call an S3 method. The print for this can be adjusted for it's width by using the option mark.note.width which defaults to the option width when not set.

The type of object assigned to the note is not restricted, so user beware of odd prints or additional features added to the notes fun.

When assigning a note (with ⁠note<-⁠, and its alias set_note()) the noted class is added to the object. This allows the print.noted class to be dispatched and for the note to be printed every time the object is called/printed and the next print method used. However, it will not be called when not interactive()

Value

• ⁠note<-⁠, set_note() will return x (with the "note" attribute assigned)

• note() will retrieve the "note" attribute

Examples

x <- c("x", "k", "c", "d")
comment(x) <- "This is just a comment"
comment(x)

# Comment is intentionally hidden
x
note(x) <- "Just some random letters"
note(x)

# Note is now present every time
x

# Assigning `NULL` will remove note (and class)
note(x) <- NULL
note(x) # NULL
x       # No more note

Omit NA values

Description

Omit NA values

Usage

omit_na(x)

Arguments

Value

x which NA values removes and two attributes of integers: na which is the position of NA values, and valid for the position of non-NA values; empty positions reported as integer(0)

Examples

# Like stats::na.omit but always provides
x <- letters[1:5]
omit_na(x)
x[c(3, 5)] <- NA
omit_na(x)

Check if package is available

Description

A wrapped requireNamespace

Usage

package_available(namespace)

Arguments

Value

• require_namespace(): None, called for side effects

• package_available(): Visibly, TRUE or FALSE

Percentile rank

Description

The bounds of the percentile rank are > 0 and < 1 (see Boundaries)

A percentile rank here is the proportion of scores that are less than the current score.

PR = (c_L + 0.5 f_i) / N

Where

c_L is the frequency of scores less than the score of interest

f_i is the frequency of the score of interest

Usage

percentile_rank(x, weights = times, times)

Arguments

Details

Computes a percentile rank for each score in a set.

Value

The percentile rank of x between 0 and 1 (see Boundaries)

Boundaries

While the percentile rank of a score in a set must be exclusively within the boundaries of 0 and 1, this function may produce a percentile rank that is exactly 0 or 1. This may occur when the number of values are so large that the value within the boundaries is too small to be differentiated.

Additionally, when using the weights parameter, if the lowest or highest number has a value of 0, the number will then have a theoretical 0 or 1, as these values are not actually within the set.

Examples

percentile_rank(0:9)
x <- c(1, 2, 1, 7, 5, NA_integer_, 7, 10)
percentile_rank(x)

if (package_available("dplyr")) {
  dplyr::percent_rank(x)
}

# with times
percentile_rank(7:1, c(1, 0, 2, 2, 3, 1, 1))

Print bib data frame

Description

Print bib dataframe, or as list

Usage

## S3 method for class 'mark_bib_df'
print(x, list = FALSE, ...)

Arguments

Value

x, invisibly, called for its side effects

Print pseudo_id

Description

Print pseudo_id

Usage

## S3 method for class 'pseudo_id'
print(x, ..., all = FALSE)

Arguments

Value

x, invisibly. Called for its side effects.

See Also

pseudo_id()

Print as c

Description

Prints a vector to paste into an R script

Usage

print_c(x = read_clipboard(), sorted = TRUE, null = TRUE)

Arguments

Details

This sorts (if set) and provides unique values for each element in x and prints then as a call to c. This can be useful for copying data that you want to save as a vector in an R script. The result is both called in cat() as well as copied to the clipboard.

Value

Invisibly, as a character vector, the object printed to the console

Examples

print_c(1:10)
print_c(letters[1:3])
print_c(month.abb)

Process bib values

Description

Generates a data frame of values from bibs

Usage

process_bib_dataframe(categories, values, fields, keys)

Arguments

Value

A wide data.frame with explicit NAs

Create an ID for a vector

Description

Transforms a vector into an integer of IDs.

Usage

pseudo_id(x, ...)

## S3 method for class 'pseudo_id'
pseudo_id(x, ...)

## Default S3 method:
pseudo_id(x, na_last = TRUE, ...)

## S3 method for class 'factor'
pseudo_id(x, ...)

Arguments

Value

A pseudo_id object where the integer value of the vector correspond to the position of the unique values in the attribute "uniques".

Examples

set.seed(42)
(x <- sample(letters, 10, TRUE))
(pid <- pseudo_id(x))
attr(pid, "uniques")[pid]

Quiet stop

Description

Quietly calls stop

Usage

quiet_stop()

Value

None, called for side effects

Range 2

Description

Employs min() and max(). However, base::range(), there is no argument for removing Inf values.

Usage

range2(x, na.rm = FALSE)

Arguments

Value

A numeric vector of length 2 of the minimum and maximum values, respectively

Examples

x <- rep(1:1e5, 100)
system.time(rep(range(x),  100))
system.time(rep(range2(x), 100))
x[sample(x, 1e5)] <- NA

system.time(rep(range(x, na.rm = TRUE), 100))
system.time(rep(range2(x, na.rm = TRUE), 100))

Read Bib file

Description

Read a bib file into a data.frame

Usage

read_bib(file, skip = 0L, max_lines = NULL, encoding = "UTF-8")

Arguments

Details

Inspired and partially credited to bib2df::bib2df() although this has no dependencies outside of base functions and much quicker. This speed seems to come from removing stringr functions and simplifying a few *apply functions. This will also include as many categories as possible from the entry.

Value

A data.frame with each row as a bib entry and each column as a field

See Also

?bib2df::bib2df

Examples

file <- "https://raw.githubusercontent.com/jmbarbone/bib-references/master/references.bib"
bibdf <- read_bib(file, max_lines = 51L)

if (package_available("tibble")) {
  tibble::as_tibble(bibdf)
} else {
  head(bibdf)
}

if (package_available("bib2df") & package_available("bench")) {
  file <- system.file("extdata", "bib2df_testfile_3.bib", package = "bib2df")

  # Doesn't include the 'tidying' up
  foo <- function(file) {
    bib <- ("bib2df" %colons% "bib2df_read")(file)
    ("bib2df" %colons% "bib2df_gather")(bib)
  }


  bench::mark(
    read_bib = read_bib(file),
    bib2df = bib2df::bib2df(file),
    foo = foo(file),
    check = FALSE
  )[1:9]

}

Recode by

Description

A simple implementation of recoding

Usage

recode_by(x, by, vals = NULL, mode = "any")

recode_only(x, by, vals = NULL)

Arguments

Details

This can be comparable to dplyr::recode() expect that the values are arranged as new = old rather than old = new and allows for a separate vector to be passed for new.

recode_only() will only recode the values matches in by/val. The mode is automatically set according to mode(x). This functions more like base::replace() but with extra features

Value

A vector of values from x

See Also

dplyr::recode()

Examples

recode_by(1:3, c(a = 1, b = 2))
recode_by(letters[1:3], c(`1` = "a", `2` = "b"))                   # will not guess mode
recode_by(letters[1:3], c(`1` = "a", `2` = "b"), mode = "integer") # make as integer
recode_by(letters[1:3], c("a", "b"), vals = 1:2)                   # or pass to vals

recode_only(letters[1:3], c("zzz" = "a"))
recode_only(letters[1:3], c(`1` = "a")) # returns as "1"
recode_only(1:3, c("a" = 1))            # coerced to NA

# Pass list for multiples
recode_only(letters[1:10], list(abc = c("a", "b", "c"), ef = c("e", "f")))

Objects exported from other packages

Description

These objects are imported from other packages. Follow the links below to see their documentation.

fuj

%:::%, %::%, %||%, %colons%, %len%, %names%, %out%, %wi%, %wo%, any_match, exattr, flip, is_linux, is_macos, is_named, is_windows, muffle, no_match, quick_df, quick_dfl, remove_names, require_namespace, set_names, struct, wuffle

magrittr

%>%

Reindex a data.frame

Description

Reindexes a data.frame with a reference

Usage

reindex(
  x,
  index = NULL,
  new_index,
  expand = c("intersect", "both"),
  sort = FALSE
)

Arguments

Value

A data.frame with rows of index

Examples

iris1 <- head(iris, 5)
iris1$index <- 1:5
reindex(iris1, "index", seq(2, 8, 2))
reindex(iris1, "index", seq(2, 8, 2), expand = "both")

# Using letters will show changes in rownames
iris1$index <- letters[1:5]
reindex(iris1, "index", letters[seq(2, 8, 2)])
reindex(iris1, "index", seq(2, 8, 2))
reindex(iris1, "index", seq(2, 8, 2), expand = "both")

Remove NA

Description

Remove NAs from a vector

Usage

remove_na(x)

## Default S3 method:
remove_na(x)

## S3 method for class 'list'
remove_na(x)

## S3 method for class 'factor'
remove_na(x)

## S3 method for class 'fact'
remove_na(x)

Arguments

Details

remove_na.factor will remove NA values as identified by the levels() or by the integer value of the level. factors are recreated with all NA values and, if present, the NA level removed.

Value

x without values where is.na(x) is TRUE For factors, a new factor (ordered if is.ordered(x))

Examples

remove_na(c(4, 1, 2, NA, 4, NA, 3, 2))

# removes based on levels
remove_na(fact(c("b", NA, "a", "c")))

# removes based on values
x <- as_ordered(c("b", "d", "a", "c"))
x[2:3] <- NA
str(remove_na(x))

Remove NULL

Description

Remove NULL results from a list

Usage

remove_null(x)

Arguments

Value

The list x without NULL

Examples

x <- list(a = letters[1:5], b = NULL, c = complex(3))
x
remove_null(x)

Rounding by a specific interval.

Description

Rounds a number or vector of numbers by another

Usage

round_by(x, by = 1, method = c("round", "ceiling", "floor"), include0 = TRUE)

Arguments

Value

A vector of doubles of the same length of x

Examples

x <- seq(1, 13, by = 4/3)

cbind(
  x,
  by_1 = round_by(x, 1),
  by_2 = round_by(x, 2),
  by_3 = round_by(x, 3)
)

Row bind

Description

Bind a list of data.frames

Usage

row_bind(...)

Arguments

Value

A data.frame combining all the rows from data.frames in ... and all the columns, as they appear. An empty data.frame with 0 columns and 0 rows is returned if ... has no length

See Also

dplyr::bind_rows() base::rbind()

Rscript

Description

Implements Rscript with system2

Usage

rscript(x, ops = NULL, args = NULL, ...)

Arguments

Value

A character vector of the result from calling Rscript via system2()

See Also

source_to_env

Save source

Description

Source a file and save as file

Usage

save_source(env = parent.frame(), file = mark_temp("Rds"), name = NULL)

Arguments

Value

A source_env/environment object, created from env

Set names

Description

Sets or removes names

Usage

set_names0(x, nm = x)

names_switch(x)

Arguments

Value

• set_names0(): x with nm values assigned to names (if x is NULL, NULL is returned)

• remove_names(): x without names

• names_switch(): character vector of equal length x where names and values are switched

Time reports

Description

This function can be used to evaluate an expression line-by-line to capture outputs, errors, messages, and evaluation time.

Usage

simpleTimeReport(title = NULL, expr, envir = parent.frame())

Arguments

Details

Evaluate code and report on the time difference

Value

A reported_results/list object containing results, outputs, messages, warnings, and errors

Examples

simpleTimeReport("example", {
  print("1")
  Sys.sleep(1)
  warning("this is a warning")
  for (i in 1:5) {
    Sys.sleep(0.5)
  }
  sample(1e6, 1e6, TRUE)
})

Sort by

Description

Sort an object by another object

Usage

sort_by(x, by, ...)

Arguments

Value

The values of x, resorted

Examples

l3 <- letters[1:3]
sort_by(l3, c(3, 2, 1))
# make a factor object with the reversed order
f <- factor(l3, levels = rev(l3))
sort_by(f, l3)
sort_by(1:3, rev(l3))

Sort by names

Description

Sort a vector by it's name

Usage

sort_names(x, numeric = FALSE)

Arguments

Value

x sorted by its names()

Source file from directory

Description

Walk through files in a directory and output them. Files are sources in order of names

Usage

source_r_dir(dir, echo = FALSE, quiet = FALSE, ...)

source_r_file(path, echo = FALSE, quiet = FALSE, ...)

Arguments

Value

None, called for side effects

Source to environment

Description

Source an R script to an environment

Usage

source_to_env(x, ops = NULL)

Arguments

Value

Invisibly, and environment variable of the objects/results created from x

Sourcing extensions

Description

Functions for extending sourcing features

Usage

ksource(file, ..., quiet = TRUE, cd = FALSE, env = parent.frame())

try_source(file, cd = FALSE, ...)

try_ksource(file, ...)

Arguments

Details

try_source() will output an error message rather than completely preventing the execution. This can be useful for when a script calls on multiple, independent files to be sourced and a single failure shouldn't prevent the entire run to fail as well.

Value

• ksource(): Invisibly, the result of calling source() on the .R file conversion of file

• try_source(), try_ksource(): attempts of source() and ksource() but converts errors to warnings

Extract date from string

Description

Extract date from string

Usage

str_extract_date(x, format = "%Y-%m-%d")

str_extract_datetime(x, format = "%Y-%m-%d %H%M%S")

Arguments

Value

A Date (if found) or NA

Examples

str_extract_date("This is a file name 2020-02-21.csv")
str_extract_date(c("This is a file name 2020-02-21.csv",
                   "Date of 2012-06-15 here"))
str_extract_date(c("This is a file name 2020-02-21.csv", "No date"))
str_extract_date("Last saved 17 December 2019", format = "%d %B %Y")

str_extract_datetime(c("2020-02-21 235033", "2012-12-12 121212"))
str_extract_datetime("This is a file name 2020-02-21 235033.csv")

String Slice

Description

Slice/split a string into multiple lines by the desired length of the line.

Usage

str_slice(x, n = 80L)

str_slice_by_word(x, n = 80L)

Arguments

Value

A character vector

Examples

if (requireNamespace("stringi")) {
  x <- stringi::stri_rand_lipsum(1)
  str_slice(x)
  str_slice_by_word(x, n = 50L)
}

Switch with a list of parameters

Description

switch_params() is a vectorized version of switch switch_case() uses a formula syntax to return the value to the right of the tilde (~) when x is TRUE switch_in_case() is a special case of switch_case() for match()-ing x in the values on the left to return the value on the right.

Usage

switch_params(x, ...)

switch_in_case(x, ..., .default = NULL, .envir = parent.frame())

switch_case(..., .default = NULL, .envir = parent.frame())

Arguments

Details

Switch with a list of params

Value

A named vector of values of same length x; or for switch_case, an unnamed vector of values matching the rhs of ...

Inspired from:

• https://stackoverflow.com/a/32835930/12126576

• https://github.com/tidyverse/dplyr/issues/5811

Examples

# by single
switch_params(c("j", "m", "b"), j = 10, b = 2, m = 13)


# match with TRUE
switch_case(
  1:10 == 9      ~ NA_integer_,
  1:10 %% 3 == 0 ~ 1:10,
  1:10 %% 4 == 0 ~ 11:20,
  1:10 %% 5 == 0 ~ 21:30,
  1:10 %% 2 == 0 ~ 31:40,
  .default = -1L
)

# match within a vector
switch_in_case(
  c(1, 2, 12, 4, 20, 21),
  1:10  ~ 1,
  11:20 ~ 2
)

switch_in_case(
  c("a", "b", "d", "e", "g", "j"),
  letters[1:3] ~ "a",
  letters[5:6] ~ "e"
)

use_these <- c(1, 3, 2, 5)
switch_in_case(
  1:10,
  use_these ~ TRUE,
  .default = FALSE
)

ne <- new.env()
ne$use_these2 <- use_these
# error
try(switch_in_case(
  1:10,
  use_these2 ~ TRUE
))
switch_in_case(
  1:10,
  use_these2 ~ TRUE,
  .envir = ne
)

switch_in_case(
  seq.int(1, 60, 6),
  1:10          ~ "a",
  11:20         ~ "b",
  c(22, 24, 26) ~ "c",
  30:Inf        ~ "d"
)

# Use functions
switch_in_case(
  1:6,
  c(1, 3, 5) ~ exp,
  c(2, 4) ~ log
)

Data frame transpose

Description

This transposes a data.frame with t() but transforms back into a data.frame with column and row names cleaned up. Because the data types may be mixed and reduced to characters, this may only be useful for a visual viewing of the data.frame.

Usage

t_df(x, id = NULL)

Arguments

Details

Transposes a data.frame as a data.frame

Value

A transposed data.frame with columns ("colname", "row_1", ..., for each row in x.

Examples

x <- data.frame(col_a = Sys.Date() + 1:5, col_b = letters[1:5], col_c = 1:5)
t_df(x)

Table NA values

Description

Tables out whether data are NAs are not

Usage

tableNA(..., .list = FALSE)

Arguments

Details

All data are checked with is.na() and the resulting TRUE or FALSE is are tabulated.

Value

table() returns a contingency table, an object of class "table", an array of integer values. Note that unlike S the result is always an array, a 1D array if one factor is given.

as.table and is.table coerce to and test for contingency table, respectively.

The as.data.frame method for objects inheriting from class "table" can be used to convert the array-based representation of a contingency table to a data frame containing the classifying factors and the corresponding entries (the latter as component named by responseName). This is the inverse of xtabs.

References

Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) The New S Language. Wadsworth & Brooks/Cole.

See Also

tabulate is the underlying function and allows finer control.

Use ftable for printing (and more) of multidimensional tables. margin.table, prop.table, addmargins.

addNA for constructing factors with NA as a level.

xtabs for cross tabulation of data frames with a formula interface.

Examples

x <- list(
  a = c(1, 2, NA, 3),
  b = c("A", NA, "B", "C"),
  c = as.Date(c("2020-01-02", NA, NA, "2020-03-02"))
)
tableNA(x) # entire list
tableNA(x, .list = TRUE) # counts for each
tableNA(x[1], x[2])
tableNA(x[1], x[2], x[3]) # equivalent ot tableNA(x, .list = TRUE)

That

Description

Grammatical correctness

Usage

that(x, arr.ind = FALSE, useNames = TRUE)

Arguments

Details

See fortunes::fortune(175).

Value

see base::which()

See Also

base::which()

To Boolean

Description

Convert a vector to boolean/logical

Usage

to_boolean(x, ...)

## S3 method for class 'logical'
to_boolean(x, ...)

## S3 method for class 'numeric'
to_boolean(x, true = 1L, false = 0L, ...)

## S3 method for class 'character'
to_boolean(x, true = NULL, false = NULL, ...)

## S3 method for class 'factor'
to_boolean(x, true = NULL, false = NULL, ...)

Arguments

Value

A logical vector of equal length as x

To row names

Description

Converts a column to row names

Usage

to_row_names(data, row_names = 1L)

Arguments

Value

A data.frame

Examples

x <- data.frame(
  a = 1:4,
  b = letters[1:4]
)

to_row_names(x)
to_row_names(x, "b")

Get TODOs

Description

Search for ⁠#`` TODO⁠ tags

Usage

todos(
  pattern = NULL,
  path = ".",
  force = getOption("mark.todos.force"),
  ext = getOption("mark.todos.ext"),
  ignore = NULL,
  ...
)

fixmes(
  pattern = NULL,
  path = ".",
  force = getOption("mark.todos.force"),
  ext = getOption("mark.todos.ext"),
  ignore = NULL,
  ...
)

Arguments

Details

Searches for TODO comments in files. Extensions with md, Rmd, and qmd specifically search for a ⁠<-- TODO * -->⁠ string, whereas everything else is found with ⁠# TODO⁠.

Value

NULL if none are found, otherwise a data.frame with the line number, file name, and TODO comment.

Examples

## Not run: 
file <- tempfile()
writeLines(c(
  "# TODO make x longer",
  "x <- 1:10",
  "length(x)",
  "# TODO add another example",
  "# FIXME This is a fixme"
  ), file)
todos(path = file)
todos("example", path = file)
fixmes(path = file)
file.remove(file)

## End(Not run)

Try an expression a set number of times

Description

Try an expression a set number of times

Usage

tryn(expr, n = 10, silent = TRUE)

Arguments

Value

result of expr

Examples

foo <- function() stop("I added an error")
try(tryn(n = 10, foo()))

Unique rows

Description

Drops duplicated rows

Usage

unique_rows(data, cols = NULL, from_last = FALSE, invert = FALSE)

Arguments

Value

data will duplicates removes

Examples

df <- quick_dfl(
  i = 1:4,
  a = rep(1:2, 2L),
  b = rep("a", 4L),
)

unique_rows(df, 2:3)
unique_rows(df, c("a", "b"), from_last = TRUE, invert = TRUE)

Unlist and squash

Description

Unlist without unique names; combine names for unique values

Usage

unlist0(x)

squash_vec(x, sep = ".")

Arguments

Details

• unlist0() is much like unlist() expect that name are not made to be unique.

• squash_vec() works differently

Value

• unlist0(): a vector with the possibility of non-unique names

• squash_vec(): A vector of unique values and names

Examples

x <- list(a = 1:3, b = 2, c = 2:4)
y <- c(a = 1, b = 1, c = 1, d = 2, e = 3, f = 3)

# unlist0() doesn't force unique names
unlist(x)   # names: a1 a2 a3  b c1 c2 c3
unlist0(x)  # names: a a a  b c c c
unlist0(y)  # no change

# squash_vec() is like the inverse of unlist0() because it works on values
squash_vec(x)
squash_vec(y)

Add author to DESCRIPTION

Description

Adds author to description

Usage

use_author(author_info = find_author())

Arguments

Details

Only valid for a single author.

Value

None, called for side effects

Paste combine

Description

Paste and combine

Usage

paste_c(x, y, collate = TRUE, sep = "")

paste_combine(..., collate = TRUE, sep = "")

collapse0(..., sep = "")

Arguments

Value

A character vector

Examples

x <- letters[1:5]
y <- 1:3
z <- month.abb[c(1, 12)]
paste_combine(x, y)
paste_combine(x, y, z)
paste_combine(x, y, z, sep = ".")
paste_combine(x, y, sep = "_")
paste_combine(x, y, collate = FALSE)
collapse0(list(1:3, letters[1:3]), 5:7, letters[5:7])
collapse0(1:3, letters[5:7], sep = "_")

Vaps!

Description

Wrappers for vapply

Usage

vap_int(.x, .f, ..., .nm = FALSE)

vap_dbl(.x, .f, ..., .nm = FALSE)

vap_chr(.x, .f, ..., .nm = FALSE)

vap_lgl(.x, .f, ..., .nm = FALSE)

vap_cplx(.x, .f, ..., .nm = FALSE)

vap_date(.x, .f, ..., .nm = FALSE)

Arguments

Details

These are simply wrappers for base::vapply() to shorten lines.

Each function is designed to use specific vector types:

vap_int

integer

vap_dbl

double

vap_chr

character

vap_lgl

logical

vap_cplx

complex

vap_date

Date

Value

A vector of type matching the intended value in the function name.

See Also

base::vapply()

Vector to data.frame

Description

Transforms a vector (named) to a data.frame

Usage

vector2df(x, name = "name", value = "value", show_NA)

Arguments

Value

A data.frame with name (optional) and value columns

Temporary plotting

Description

Reset par() after running

Usage

with_par(..., ops = NULL)

Arguments

Value

Invisibly, the result of ...

Examples

with_par(
  plot(lm(Sepal.Length ~ Sepal.Width, data = iris)),
  plot(lm(Petal.Length ~ Petal.Width, data = iris)),
  ops = list(mfrow = c(2, 4))
)

within boundaries

Description

Compare a vector within (between) other values

Usage

between_more(x, left, right, type = c("gele", "gel", "gle", "gl"))

within(x, left = NULL, right = NULL, bounds = c("[]", "[)", "(]", "()"))

Arguments

Details

⁠type``, ⁠bounds“ can be one of the below:

g,(

is greater than (>)

ge,[

greater than or equal to (>=)

l,))

less than (<)

le,[]

less than or equal to (<=)

Note: between_more() may be deprecated in the future in favor of just within()

Value

A logical vector

Examples

between_more(2:10, 2, 10, "gl")
within(2:10, 2, bounds = "()")
between_more(10, 2, 10, "gle")
within(2:10, bounds = "(]")
within(1:5, c(3, 3, 2, 2, 1), 5)

Function within

Description

Returns the function call you are within

Usage

within_call()

within_fun()

outer_call(n = 0)

outer_fun(n = 0)

Arguments

Value

The string of the call/function

Write file with md5 hash check

Description

Write file with md5 hash check

Usage

write_file_md5(
  x,
  path = NULL,
  method = mark_write_methods(),
  overwrite = NA,
  quiet = FALSE,
  encoding = "UTF-8",
  compression = getOption("mark.compress.method", mark_compress_methods()),
  ...
)

mark_write_methods()

mark_compress_methods()

Arguments

Value

• write_file_md5(): x, invisibly. When path is not the stdout(), x is returned with the attribute "path" set to the result of file_copy_md5().

• mark_write_methods(): A list of applicable methods and their aliases

• mark_compress_methods(): A character vector of applicable compression methods

options()

• mark.compress.method: compression method to use when writing files

• mark.list.hook: when a data.frame contains a list column, this function is applied to each element of the list. The default "auto" uses toJSON() if the package jsonlite is available, otherwise

Examples

# just writes to stdout()
df <- data.frame(a = 1, b = 2)
write_file_md5(df)

temp <- tempfile()
write_file_md5(df, temp) # new
write_file_md5(df, temp) # same
df$c <- 3
write_file_md5(df, temp) # changes
fs::file_delete(temp)