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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 Barbonejmbarbone@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 athttps://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 ofbase::identical()

Usage

are_identical(..., params = NULL)

Arguments

Value

Alogical vector ofTRUE/FALSE of equal length of each...vector

Examples

x <- y <- z <- 1:5y[2] <- 3Lz[5] <- NA_integer_identical(x, y)        # compare entire vectorare_identical(x, y)    # element-wiseare_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 arrayarr

Examples

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

Ordered

Description

As ordered

Usage

as_ordered(x)## Default S3 method:as_ordered(x)

Arguments

Details

Simple implementation ofordered. Ifx isordered it issimply returned. Ifx is afactor theordered class is added.Otherwise,x is made into afactor withfact() and then theordered class is added. Unlike justfact,ordered will replace theNAlevels withNA_integer_ to work appropriately with other functions.

Value

Anordered 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 3Ly <- xclass(y) <- c("ordered", class(y))max(y)max(y, na.rm = TRUE) # returns NA -- bad# as_ordered() removes the NA levelx <- 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 basefrom to baseto

Examples

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

Blank values

Description

Detectblank values; select, remove columns that are entirelyblank

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() alogical vector indicatingblank elements inx

• select_blank_cols()x with only columns that are allblank

• remove_blank_cols()x without columns of onlyblank

• is_blank_cols() a logical vector:TRUE all rows of column areblank,otherwiseFALSE

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 inx checks the current option value and reports if thereis a difference in amessage. This does not change the options

Usage

checkOptions(x)

Arguments

Details

Checks and reports on options

Value

Invisible, a list of the current options fromoptions()

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 givenidentical(options(), checkOptions(options()))options(op)

Character split

Description

Split apart a string by each character

Usage

chr_split(x)

Arguments

Value

Acharacter vector of lengthnchar(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 onclipr::read_clip() andutils::writeClipboard() they are only available for Windows 10. For copyingand pasting floats, there may be some rounding that can occur.

Value

write_clipboard() None, called for side effectsread_clipboard()Either a vector,data.frame, ortibble depending on themethod chosen.Unlikeutils::readClipboard(), an empty clipboard value returnsNA ratherthan""

Examples

# Will only run on windowsif (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

Adata.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 rowscomplete_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 areshown by their order of appearance in the vector.

There are 2 methods for counting vectors. Thedefault method usesbase::tabulate() (the workhorse forbase::table() with a call topseudo_id() to transform all inputs into integers. Thelogical methodcountsTRUE,FALSE andNA 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 adata.frame

Value

A named vector ofintegers ordoubles (forcounts, andprops,respectively) ordata.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 whenpart of the date string is missing.

Value

A vector ofDates

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 orNULLobjects.

Value

A singleinteger

Examples

a <- c(1, 2, 3)depth(a) # Vectors are 1Lb <- 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 classof the object returned is no longerdifftime (but does print) with thedifftime method. This makes the exporting process easier as the datawill not have to be converted back tonumeric *difftime() computes thedifference oftime1 -time2, but the inverse feels a bit more nature:time difference fromx toy * Additional units can be used (detailedbelow) * Differences can be sensitive to time zones if time zones arepassed to thetz parameter as a character vector

Value

Adiff_time vector, object

Units

Units can be used beyond those available inbase::difftime(). Some of these use assumptions in how units of timeshould be standardized and can be changed in the corresponding options. Anyof these can be calculated withbase::difftime() through usingunits = "days" but thedtime class will print out with these specifications intothe console for less potential confusion.

months

Months by number of daysmark.days_in_month (defaults:30)

years

Years by number of daysmark.days_in_year (defaults:365)

dyears

Years by number of daysmark.days_in_year (defaults:365) (same asyears)

myears

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

wyears

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

Time zones

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

A default timezone can be set withoptions(mark.default_tz = .). Thevalue 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 ofx 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] TRUEfile.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 thanfactor() oras.factor() as it doesn't bother sorting the levels for non-numeric dataor have other checks or features. It simply converts a vector to a factorwith all unique values as levels withNAs included.

fact.factor() will perform several checks on a factor to includeNAlevels and to check if the levels should be reordered to conform with theother methods. Thefact.fact() method simple returnsx.

Value

A vector of equal length ofx with classfact andfactor. Ifx wasordered, that class is added in between.

level orders

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

character

The order of appearance

numeric/integer/Date/POSIXt

By the numeric order

logical

AsTRUE,FALSE, thenNA if present

factor

Numeric if levels can be safely converted, otherwise asthey 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 adata.frame

Usage

fact2char(data, threshold = 10)

Arguments

Value

Thedata.framedata with factors converted by the rule above

See Also

char2fact()

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

fact withNA

Description

IncludedNA values intofact()

Usage

fact_na(x, remove = FALSE)

Arguments

Details

This re-formats thex value so thatNAs are found immediately within theobject rather than accessed through its attributes.

Value

Afact vector

See Also

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

Fact reverse levels

Description

Reverse the levels of afact

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 formin_lvl andmax_lvl are the minimum and maximumlevels in the ordered vectorx.

Value

Anordered 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 3fct_expand_seq(x, "g", "t", 3L) # same as above# from the first inherit level to the last observedfct_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. Whenoverwrite isNA, the paths will be returned with two addition attributes,"changed",a logical vector indicating whether the file was changed (NA for when thefile is new), and"md5sum", a list of the md5sums of the old and new md5sums.

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 toshell.exec() that can take takemultiple files.list_files andlist_dirs are mostly wrappers forfs::dir_ls() with preferred defaults and pattern searching on the full filepath.

file_open is simply an alias.

Value

• open_file(),shell_exec(): A logical vector whereTRUE successfullyopened,FALSE did not andNA 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 arguethat the quickest way to do it is to just take the work someone else did andutilize that. No reason to reinvent the wheel.

Usage

fizzbuzz(n, show_numbers = TRUE)fizzbuzz_lazy(n).fizzbuzz_vector

Arguments

Format

An object of classcharacter of length 1000000.

Details

Multiples of3 are shown as"Fizz"; multiples of5 as"Buzz";multiple of both (i.e.,15) are"FizzBuzz".fizzbuzz_lazy() subsets the.fizzbuzz_vector object, which is a solutionwith default parameters up to1e6

Value

Acharacter vector of⁠1, 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 forwhen 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 theDESCRIPTION file and to get and adjust theversion

bump_date_version() will not check if the version is actually a date. Whenthe 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 willreturn an error or warning without raising.

Value

The⁠has_*()⁠ functions will returnTRUE/FALSE for if the handleris found in the execution of the code. The⁠get_*()⁠ functions provide thetext of the message

References

Function forcatching has been adapted fromhttps://stackoverflow.com/a/4952908/12126576

Examples

has_warning(c(1, "no"), as.integer)#     1    no# FALSE  TRUEget_warning(c(1, "no"), as.integer)# drop NULLsget_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 FALSEget_error(c(1, 0, 2), foo)# drop NULLsget_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 cautionimport("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 fromutils::file_test() forop = '-d' andop = -f but separated.

Value

Alogical 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(withView() inside Rstudio). Theview_labels() has a call toView()inside and will retrieve the labels and show them in the viewer as adata.frame.

Value

A labelled vector ordata.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 columnlabs0 <- 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 vectorx withlower andupper as the minimum, maximumvalues

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 sumstogether. Files that fail to read are not counted.

Value

Aninteger 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

Unlikebase::list2DF(),list2df() tries to format the data.frame by usingthe names of the list as values rather than variables. This creates alonger form list that may be more tidy.

Value

adata.frame object with columns"name" and"value" for thenames of thelist 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 forbase::search()

ls_all() andobjects_all() can be used retrieved all objects from allenvironments in thesearch() path, which may print out a large resultinto the console.

Value

• environments(): Invisibly, acharacter vector of environment names

• ls_all(),objects_all(): A named list for each of the environmentsthesearch() 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 asinteger or doubles) and return logical values.

Extensions to the base logical operations to account forNA values.

base::isTRUE() andbase::isFALSE() will only return single lengthTRUEorFALSE as it checks for valid lengths in the evaluation. When needing tocheck over a vector for the presence ofTRUE orFALSE and not being heldback byNA values,is_true andis_false will always provide aTRUEFALSE when the vector is logical or returnNA is the vectorx is notlogical.

⁠%or%⁠ is just a wrapper forbase::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% yOR(..., 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(): Alogicalvector, equal length ofx (ory or of all... lengths)

• is_boolean():TRUE orFALSE

• none():TRUE,FALSE, orNA

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% TRUETRUE %xor% TRUETRUE %xor% FALSENA %xor% FALSEOR(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

Acharacter 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 ofbase::match.arg() whichproduces a cleaner warning message and does not restrict thetable paramtocharacter vectors only.

Usage

match_arg(x, table)

Arguments

Details

Match arguments

Value

A single value fromx matched ontable

See Also

match_param()

Examples

x <- c("apple", "banana", "orange")match_arg("b", x)# Produces errortry(match_arg("pear", x))foo <- function(x, op = c(1, 2, 3)) {  op <- match_arg(op)  x / op}foo(10, 3)# Errortry(foo(1, 0))

Match params

Description

Much likebase::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 fromparam matched onchoices

See Also

match_arg()

Examples

fruits <- function(x = c("apple", "banana", "orange")) {  match_param(x)}fruits()         # appletry(fruits("b")) # must be exact fruits("banana")pfruits <- function(x = c("apple", "apricot", "banana")) {  match_param(x, partial = TRUE)}pfruits()          # appletry(pfruits("ap")) # matchParamMatchErrorpfruits("app")     # appleafruits <- function(x = c("apple", "banana", "orange")) {  match_param(x, multiple = TRUE)}afruits() # apple, banana, orange# can have multiple responseshow_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 insteadls <- 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 callingtools::md5sum() on objects rather than files.

Usage

md5(x)

Arguments

Details

Allx objects areserialized to a temporaryfile beforetools::md5sum() is called.

Value

Amd5sum 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 formedian2

Value

Seestats::quantile()

See Also

stats::quantile()

Examples

set.seed(42)x <- rnorm(100)median(x)            # 0.08979677median2(x, type = 7) # 0.08979677 - default type is 7median2(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 eithermulti_grepl(x, c("a" = "^[ab]", "b" = "e"))                   # apple matches "a" before "b"multi_grepl(x, c("a" = "^[ab]", "b" = "e"), simplify = FALSE) # shows all matchesmulti_grepl(x, c("^[ab]", "e"))                               # returned as positionsmulti_grepl(x, c("^[ab]", "e"), simplify = FALSE)

NA at positions

Description

Converts select elements of a vector intoNAs

This is how the end results are

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

  • NA_at expectsy (or the result of functiony) to beintegers

  • NA_if expectsy (or the result of functiony) to belogical

• NA_in andNA_out expect some values to match on

  • NA_in checksx[x %in% y] <- NA

  • NA_out checksx[x %out% y] <- NA (seefuj::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 assignedNA values

See Also

Inspired bydplyr::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    eNA_in(let, c("a", "c"))  # [1] <NA> b    <NA> d    eNA_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 allNA

• remove_na_cols()x without columns of onlyNA

• is_na_cols() a logical vector:TRUE all rows of column areNA,otherwiseFALSE

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

Parametersrange andbounds are modified withbase::range().The largest and smallest values are then used to determine theminimum/maximum values and lower/upper bounds. This allows for a vector ofmore than two values to be passed.

The current implementation ofnormalize.data.frame() allows forlist ofparameters passed for each column. However, it is probably best suited fordefault values.

Value

x with transformed values whererange values are transformed tobounds.

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 matrixmat <- 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))matnormalize(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 classDate of length 1.

An object of classPOSIXct (inherits fromPOSIXt) of length 1.

An object of classPOSIXlt (inherits fromPOSIXt) of length 1.

Details

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

Value

A vector ofNA values

Examples

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

Append a note to an object

Description

An alternative to thebase::comment().

Usage

note(x) <- valueset_note(x, value)note(x)

Arguments

Details

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

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

When assigning a note (with⁠note<-⁠, and its aliasset_note()) thenoted class is added to the object. This allows theprint.noted classto be dispatched and for the note to be printed every time the object iscalled/printed and the next print method used. However, it will not becalled when notinteractive()

Value

• ⁠note<-⁠,set_note() will returnx (with the"note" attributeassigned)

• 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 hiddenxnote(x) <- "Just some random letters"note(x)# Note is now present every timex# Assigning `NULL` will remove note (and class)note(x) <- NULLnote(x) # NULLx       # No more note

Omit NA values

Description

Omit NA values

Usage

omit_na(x)

Arguments

Value

x whichNA values removes and two attributes ofintegers:nawhich is the position ofNA values, andvalid for the position ofnon-NA values; empty positions reported asinteger(0)

Examples

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

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 thecurrent 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 ofx between 0 and 1 (see Boundaries)

Boundaries

While the percentile rank of a score in a set must be exclusively within theboundaries of0 and1, this function may produce a percentile rank thatis exactly0 or1. This may occur when the number of values are so largethat the value within the boundaries is too small to be differentiated.

Additionally, when using theweights parameter, if the lowest or highestnumber has a value of0, the number will then have a theoretical0 or1, 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 timespercentile_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

Printpseudo_id

Description

Printpseudo_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 inx andprints then as a call toc. This can be useful for copying data that youwant to save as a vector in an R script.The result is both called incat() as well as copied to the clipboard.

Value

Invisibly, as acharacter 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 widedata.frame with explicitNAs

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

Apseudo_id object where theinteger value of the vectorcorrespond 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

Employsmin() andmax(). However,base::range(), there is no argumentfor removingInf values.

Usage

range2(x, na.rm = FALSE)

Arguments

Value

Anumeric 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)] <- NAsystem.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 tobib2df::bib2df() although this has nodependencies outside of base functions and much quicker. This speed seemsto come from removingstringr functions and simplifying a few *applyfunctions.This will also include as many categories as possible from the entry.

Value

Adata.frame with each row as a bib entry and each column as afield

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 todplyr::recode() expect that the values arearranged asnew = old rather thanold = new and allows for a separatevector to be passed fornew.

recode_only() will only recode the values matches inby/val.Themode is automatically set according tomode(x). This functionsmore likebase::replace() but with extra features

Value

A vector of values fromx

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 moderecode_by(letters[1:3], c(`1` = "a", `2` = "b"), mode = "integer") # make as integerrecode_by(letters[1:3], c("a", "b"), vals = 1:2)                   # or pass to valsrecode_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 multiplesrecode_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 linksbelow 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

Adata.frame with rows ofindex

Examples

iris1 <- head(iris, 5)iris1$index <- 1:5reindex(iris1, "index", seq(2, 8, 2))reindex(iris1, "index", seq(2, 8, 2), expand = "both")# Using letters will show changes in rownamesiris1$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 removeNA values as identified by thelevels()or by the integer value of the level.factors are recreated with allNA values and, if present, theNAlevel removed.

Value

x without values whereis.na(x) isTRUEFor factors, a new factor (ordered ifis.ordered(x))

Examples

remove_na(c(4, 1, 2, NA, 4, NA, 3, 2))# removes based on levelsremove_na(fact(c("b", NA, "a", "c")))# removes based on valuesx <- as_ordered(c("b", "d", "a", "c"))x[2:3] <- NAstr(remove_na(x))

Remove NULL

Description

Remove NULL results from a list

Usage

remove_null(x)

Arguments

Value

The listx withoutNULL

Examples

x <- list(a = letters[1:5], b = NULL, c = complex(3))xremove_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 ofdoubles of the same length ofx

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 ofdata.frames

Usage

row_bind(...)

Arguments

Value

Adata.frame combining all the rows fromdata.frames in...and all the columns, as they appear. An emptydata.frame with0 columnsand0 rows is returned if... has no length

See Also

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

Rscript

Description

ImplementsRscript withsystem2

Usage

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

Arguments

Value

Acharacter vector of the result from callingRscript viasystem2()

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

Asource_env/environment object, created fromenv

Set names

Description

Sets or removes names

Usage

set_names0(x, nm = x)names_switch(x)

Arguments

Value

• set_names0():x withnm values assigned to names (ifx isNULL,NULL is returned)

• remove_names():x without names

• names_switch():character vector of equal lengthx where names andvalues are switched

Time reports

Description

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

Usage

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

Arguments

Details

Evaluate code and report on the time difference

Value

Areported_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 ofx, resorted

Examples

l3 <- letters[1:3]sort_by(l3, c(3, 2, 1))# make a factor object with the reversed orderf <- 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 itsnames()

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 createdfromx

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 preventingthe execution.This can be useful for when a script calls on multiple, independent files tobe sourced and a single failure shouldn't prevent the entire run to fail aswell.

Value

• ksource(): Invisibly, the result of callingsource() on the.R file conversion offile

• try_source(),try_ksource(): attempts ofsource() andksource() 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

ADate (if found) orNA

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

Acharacter 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 ofswitchswitch_case() uses a formula syntax to return the value to the right of thetilde (~) whenx isTRUEswitch_in_case() is a special case ofswitch_case() formatch()-ingxin 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 lengthx; or forswitch_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 singleswitch_params(c("j", "m", "b"), j = 10, b = 2, m = 13)# match with TRUEswitch_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 vectorswitch_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# errortry(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 functionsswitch_in_case(  1:6,  c(1, 3, 5) ~ exp,  c(2, 4) ~ log)

Data frame transpose

Description

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

Usage

t_df(x, id = NULL)

Arguments

Details

Transposes a data.frame as a data.frame

Value

A transposeddata.frame with columns ("colname","row_1", ...,for each row inx.

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 withis.na() and the resultingTRUE orFALSE isare tabulated.

Value

table() returns acontingency table, an object ofclass"table", an array of integer values.Note that unlike S the result is always anarray, a 1Darray if one factor is given.

as.table andis.table coerce to and test for contingencytable, respectively.

Theas.data.frame method for objects inheriting from class"table" can be used to convert the array-based representationof a contingency table to a data frame containing the classifyingfactors and the corresponding entries (the latter as componentnamed byresponseName). This is the inverse ofxtabs.

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 finercontrol.

Useftable for printing (and more) ofmultidimensional tables.margin.table,prop.table,addmargins.

addNA for constructing factors withNA asa level.

xtabs for cross tabulation of data frames with aformula 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 listtableNA(x, .list = TRUE) # counts for eachtableNA(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

Seefortunes::fortune(175).

Value

seebase::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

Alogical vector of equal length asx

To row names

Description

Converts a column to row names

Usage

to_row_names(data, row_names = 1L)

Arguments

Value

Adata.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 forTODO comments in files. Extensions withmd,Rmd,andqmd specifically search for a⁠<-- TODO * -->⁠ string, whereaseverything else is found with⁠# TODO⁠.

Value

NULL if none are found, otherwise adata.frame with the linenumber, 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 ofexpr

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 likeunlist() expect that name are not made to beunique.

• 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 namesunlist(x)   # names: a1 a2 a3  b c1 c2 c3unlist0(x)  # names: a a a  b c c cunlist0(y)  # no change# squash_vec() is like the inverse of unlist0() because it works on valuessquash_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

Acharacter vector

Examples

x <- letters[1:5]y <- 1:3z <- 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 forbase::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

Adata.frame withname (optional) andvalue 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 justwithin()

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. Whenpath is not thestdout(),xis returned with the attribute"path" set to the result offile_copy_md5().

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

• mark_compress_methods(): A character vector of applicable compressionmethods

options()

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

• mark.list.hook: when adata.frame contains alist column, thisfunction is applied to each element of the list. The default"auto"usestoJSON() if the packagejsonlite 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) # newwrite_file_md5(df, temp) # samedf$c <- 3write_file_md5(df, temp) # changesfs::file_delete(temp)