Title:	Extra Binary Relational and Logical Operators
Version:	0.3.0
URL:	https://joshuawiley.com/extraoperators/, https://github.com/JWiley/extraoperators
BugReports:	https://github.com/JWiley/extraoperators/issues
Description:	Speed up common tasks, particularly logical or relational comparisons and routine follow up tasks such as finding the indices and subsetting. Inspired by mathematics, where something like: 3 < x < 6 is a standard, elegant and clear way to assert that x is both greater than 3 and less than 6 (see for example https://en.wikipedia.org/wiki/Relational_operator), a chaining operator is implemented. The chaining operator, %c%, allows multiple relational operations to be used in quotes on the right hand side for the same object, on the left hand side. The %e% operator allows something like set-builder notation (see for example https://en.wikipedia.org/wiki/Set-builder_notation) to be used on the right hand side. All operators have built in prefixes defined for all, subset, and which to reduce the amount of code needed for common tasks, such as return those values that are true.
License:	GPL-3
Encoding:	UTF-8
RoxygenNote:	7.2.3
Suggests:	testthat (≥ 3.0.0), covr, knitr, rmarkdown
Config/testthat/edition:	3
VignetteBuilder:	knitr
NeedsCompilation:	no
Packaged:	2023-12-14 00:27:20 UTC; wileyj
Author:	Joshua F. Wiley [aut, cre]
Maintainer:	Joshua F. Wiley <jwiley.psych@gmail.com>
Repository:	CRAN
Date/Publication:	2023-12-14 01:20:02 UTC

Chain Operator

Description

This operator allows operators on the right hand side to be chained together. The intended use case is when you have a single object on which you want to perform several operations. For example, testing whether a variable is between 1 and 5 or equals special number 9, which might be used to indicate that someone responded to a question (i.e., its not missing per se) but that they preferred not to answer or did not know the answer.

Usage

e1 %c% e2

Arguments

Details

' 'is.na', '!is.na', 'is.nan', and '!is.nan'. These do not need any values supplied but they work as expected to add those logical assessments into the chain of operators.

Value

a logical vector

Examples

## define a variable
sample_data <- c(1, 3, 9, 5, NA, -9)

## suppose that we expect that values should fall in [1, 10]
## unless they are special character, -9 used for unknown / refused
sample_data %c% "( >= 1 & <= 10 ) | == -9"

## we might expect some missing values and be OK as long as
## above conditions are met or values are missing
sample_data %c% "( >= 1 & <= 10 ) | == -9 | is.na"

## equally we might be expecting NO missing values
## and want missing values to come up as FALSE
sample_data %c% "(( >= 1 & <= 10 ) | == -9) & !is.na"

c(1, 3, 9, 5, NA, -9) %c% "is.na & (( >= 1 & <= 10 ) | == -9)"

## clean up
rm(sample_data)

Element In Set Operator

Description

This operator allows use of set notation style definitions

Usage

e1 %e% e2

Arguments

Value

a logical vector

Examples

c(-1, 0, 1, 9, 10, 16, 17, 20) %e% "(-Inf, 0) | [1, 9) | [10, 16] | (17, Inf]"
table(mtcars$mpg %e% "(0, 15.5) | [22.8, 40)")
table(mtcars$mpg %e% "(0, 15) | [16, 18] | [30, 50)")
c(-1, 0, 1) %e% "(-Inf, Inf) & [0, 0] | [1, 1]"

z <- max(mtcars$mpg)
table(mtcars$mpg %e% "(-Inf, z)")

## clean up
rm(z)

Process Sets

Description

This is an internal function and not intended to be used directly. It processes small sets.

Usage

.set1(x, envir)

Arguments

Value

A data frame with the processed set

Examples

## ## below is an example that should generate an (informative) error
## extraoperators:::.set1("(-Inf,x)", envir = environment())

z <- max(mtcars$mpg)
extraoperators:::.set1("(-Inf,z)", envir = environment())

extraoperators:::.set1("(-Inf,30)", envir = environment())

## clean up
rm(z)

Several ways to evaluate whether all values meet logical conditions including logical range comparison helpers

Description

Several ways to evaluate whether all values meet logical conditions including logical range comparison helpers

Usage

e1 %agele% e2

e1 %agel% e2

e1 %agle% e2

e1 %agl% e2

e1 %age% e2

e1 %ag% e2

e1 %ale% e2

e1 %al% e2

e1 %ain% e2

e1 %a!in% e2

e1 %anin% e2

e1 %a==% e2

e1 %a!=% e2

e1 %ac% e2

e1 %ae% e2

e1 %agrepl% e2

e1 %a!grepl% e2

Arguments

Value

A logical value whether all e1 meet the logical conditions.

Examples

1:5 %agele% c(2, 4)
1:5 %agele% c(4, 2) # order does not matter uses min / max

1:5 %agel% c(2, 4)
1:5 %agel% c(4, 2) # order does not matter uses min / max

1:5 %agle% c(2, 4)
1:5 %agle% c(4, 2) # order does not matter uses min / max

1:5 %agl% c(2, 4)
1:5 %agl% c(4, 2) # order does not matter uses min / max

1:5 %age% 2
1:5 %age% 4

1:5 %ag% 2
1:5 %ag% 4

1:5 %ale% 2
1:5 %ale% 4

1:5 %al% 2
1:5 %al% 4

1:5 %ain% c(2, 99)
c("jack", "jill", "john", "jane") %ain% c("jill", "jane", "bill")

1:5 %a!in% c(2, 99)
c("jack", "jill", "john", "jane") %a!in% c("jill", "jane", "bill")

1:5 %a==% 1:5
1:5 %a==% 5:1

1:5 %a!=% 1:5
1:5 %a!=% 5:1
1:5 %a!=% c(5, 4, 1, 3, 2)
## define a variable
sample_data <- c(1, 3, 9, 5, NA, -9)

## suppose that we expect that values should fall in [1, 10]
## unless they are special character, -9 used for unknown / refused
sample_data %ac% "( >= 1 & <= 10 ) | == -9"

## we might expect some missing values and be OK as long as
## above conditions are met or values are missing
sample_data %ac% "( >= 1 & <= 10 ) | == -9 | is.na"

## equally we might be expecting NO missing values
## and want missing values to come up as FALSE
sample_data %ac% "(( >= 1 & <= 10 ) | == -9) & !is.na"

## clean up
rm(sample_data)
## define a variable
sample_data <- c(1, 3, 9, 5, -9)

sample_data %ae% "(-8, 1] | [2, 9)"
sample_data %ae% "(-Inf, Inf)"

## clean up
rm(sample_data)

c("jack", "jane", "ajay") %agrepl% "ja"
c("jack", "jill", "john", "jane", "sill", "ajay") %agrepl% "^ja"

c("jack", "jane", "ajay") %a!grepl% "ja"
c("jack", "jill", "john", "jane", "sill", "ajay") %a!grepl% "^ja"
c("jack", "jill", "john", "jane", "sill", "ajay") %a!grepl% "ja$"

Several ways to return an index based on logical range comparison helpers

Description

Several ways to return an index based on logical range comparison helpers

Usage

e1 %?gele% e2

e1 %?gel% e2

e1 %?gle% e2

e1 %?gl% e2

e1 %?ge% e2

e1 %?g% e2

e1 %?le% e2

e1 %?l% e2

e1 %?in% e2

e1 %?!in% e2

e1 %?nin% e2

e1 %?==% e2

e1 %?!=% e2

e1 %?c% e2

e1 %?e% e2

e1 %?grepl% e2

e1 %?!grepl% e2

Arguments

Value

A vector of the indices identifying which values of e1 meet the logical conditions.

Examples

1:5 %?gele% c(2, 4)
1:5 %?gele% c(4, 2) # order does not matter uses min / max

1:5 %?gel% c(2, 4)
1:5 %?gel% c(4, 2) # order does not matter uses min / max

1:5 %?gle% c(2, 4)
1:5 %?gle% c(4, 2) # order does not matter uses min / max

1:5 %?gl% c(2, 4)
1:5 %?gl% c(4, 2) # order does not matter uses min / max

1:5 %?ge% 2
1:5 %?ge% 4

1:5 %?g% 2
1:5 %?g% 4

1:5 %?le% 2
1:5 %?le% 4

1:5 %?l% 2
1:5 %?l% 4

1:5 %?in% c(2, 99)
c("jack", "jill", "john", "jane") %?in% c("jill", "jane", "bill")

1:5 %?!in% c(2, 99)
c("jack", "jill", "john", "jane") %?!in% c("jill", "jane", "bill")

1:5 %?nin% c(2, 99)
c("jack", "jill", "john", "jane") %snin% c("jill", "jane", "bill")

11:15 %?==% c(11, 1, 13, 15, 15)

11:15 %?!=% c(11, 1, 13, 15, 15)
## define a variable
sample_data <- c(1, 3, 9, 5, NA, -9)

## suppose that we expect that values should fall in [1, 10]
## unless they are special character, -9 used for unknown / refused
sample_data %?c% "( >= 1 & <= 10 ) | == -9"

## we might expect some missing values and be OK as long as
## above conditions are met or values are missing
sample_data %?c% "( >= 1 & <= 10 ) | == -9 | is.na"

## equally we might be expecting NO missing values
## and want missing values to come up as FALSE
sample_data %?c% "(( >= 1 & <= 10 ) | == -9) & !is.na"

## clean up
rm(sample_data)
## define a variable
sample_data <- c(1, 3, 9, 5, -9)

sample_data %?e% "(-8, 1] | [2, 9)"

## clean up
rm(sample_data)

c("jack", "jill", "john", "jane", "sill", "ajay") %?grepl% "ja"
c("jack", "jill", "john", "jane", "sill", "ajay") %?grepl% "^ja"

c("jack", "jill", "john", "jane", "sill", "ajay") %?!grepl% "ja"
c("jack", "jill", "john", "jane", "sill", "ajay") %?!grepl% "^ja"

Several logical range comparison helpers

Description

Several logical range comparison helpers

Usage

e1 %gele% e2

e1 %gel% e2

e1 %gle% e2

e1 %gl% e2

e1 %g% e2

e1 %ge% e2

e1 %l% e2

e1 %le% e2

e1 %!in% e2

e1 %nin% e2

e1 %flipIn% e2

e1 %grepl% e2

e1 %!grepl% e2

Arguments

Value

A logical vector of the same length as e1.

Examples

1:5 %gele% c(2, 4)
1:5 %gele% c(4, 2) # order does not matter uses min / max

1:5 %gel% c(2, 4)
1:5 %gel% c(4, 2) # order does not matter uses min / max

1:5 %gle% c(2, 4)
1:5 %gle% c(4, 2) # order does not matter uses min / max

1:5 %gl% c(2, 4)
1:5 %gl% c(4, 2) # order does not matter uses min / max

1:5 %g% c(2)

1:5 %ge% c(2)

1:5 %l% c(2)

1:5 %le% c(2)

1:5 %!in% c(2, 99)
c("jack", "jill", "john", "jane") %!in% c("jill", "jane", "bill")

c("jack", "jill", "john", "jane", "sill", "ajay") %grepl% "ja"
c("jack", "jill", "john", "jane", "sill", "ajay") %grepl% "^ja"

c("jack", "jill", "john", "jane", "sill", "ajay") %!grepl% "ja"
c("jack", "jill", "john", "jane", "sill", "ajay") %!grepl% "^ja"

Several ways to subset based on logical range comparison helpers

Description

Several ways to subset based on logical range comparison helpers

Usage

e1 %sgele% e2

e1 %sgel% e2

e1 %sgle% e2

e1 %sgl% e2

e1 %sge% e2

e1 %sg% e2

e1 %sle% e2

e1 %sl% e2

e1 %sin% e2

e1 %s!in% e2

e1 %snin% e2

e1 %s==% e2

e1 %s!=% e2

e1 %sc% e2

e1 %se% e2

e1 %sgrepl% e2

e1 %s!grepl% e2

Arguments

Value

A subset of e1 that meets the logical conditions.

Examples

1:5 %sgele% c(2, 4)
1:5 %sgele% c(4, 2) # order does not matter uses min / max

1:5 %sgel% c(2, 4)
1:5 %sgel% c(4, 2) # order does not matter uses min / max

1:5 %sgle% c(2, 4)
1:5 %sgle% c(4, 2) # order does not matter uses min / max

1:5 %sgl% c(2, 4)
1:5 %sgl% c(4, 2) # order does not matter uses min / max

1:5 %sge% 2
1:5 %sge% 4

1:5 %sg% 2
1:5 %sg% 4

1:5 %sle% 2
1:5 %sle% 4

1:5 %sl% 2
1:5 %sl% 4

1:5 %sin% c(2, 99)
c("jack", "jill", "john", "jane") %sin% c("jill", "jane", "bill")

1:5 %s!in% c(2, 99)
c("jack", "jill", "john", "jane") %s!in% c("jill", "jane", "bill")

1:5 %s==% 1:5
1:5 %s==% c(1:4, 1)

1:5 %s!=% 1:5
1:5 %s!=% c(1:4, 1)
## define a variable
sample_data <- c(1, 3, 9, 5, NA, -9)

## suppose that we expect that values should fall in [1, 10]
## unless they are special character, -9 used for unknown / refused
sample_data %sc% "( >= 1 & <= 10 ) | == -9"

## we might expect some missing values and be OK as long as
## above conditions are met or values are missing
sample_data %sc% "( >= 1 & <= 10 ) | == -9 | is.na"

## equally we might be expecting NO missing values
## and want missing values to come up as FALSE
sample_data %sc% "(( >= 1 & <= 10 ) | == -9) & !is.na"

## clean up
rm(sample_data)
## define a variable
sample_data <- c(1, 3, 9, 5, -9)

sample_data %se% "(-8, 1] | [2, 9)"

## clean up
rm(sample_data)

c("jack", "jill", "john", "jane", "sill", "ajay") %sgrepl% "ja"
c("jack", "jill", "john", "jane", "sill", "ajay") %sgrepl% "^ja"

c("jack", "jill", "john", "jane", "sill", "ajay") %s!grepl% "ja"
c("jack", "jill", "john", "jane", "sill", "ajay") %s!grepl% "^ja"