| Type: | Package |
| Title: | Vectorised Substitution and Evaluation |
| Version: | 1.0.0 |
| Description: | Provides a clean syntax for vectorising the use of Non-Standard Evaluation (NSE), for example in 'ggplot2', 'dplyr', or 'data.table'. |
| License: | MIT + file LICENSE |
| URL: | https://github.com/KiwiMateo/where |
| BugReports: | https://github.com/KiwiMateo/where/issues |
| Suggests: | data.table, dplyr, ggplot2, knitr, rmarkdown, testthat (≥ 3.0.0) |
| VignetteBuilder: | knitr |
| Config/testthat/edition: | 3 |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.3.2 |
| NeedsCompilation: | no |
| Packaged: | 2024-11-18 06:45:14 UTC; OEM |
| Author: | Matt Hendtlass [aut, cre] |
| Maintainer: | Matt Hendtlass <m.hendtlass@gmail.com> |
| Repository: | CRAN |
| Date/Publication: | 2024-11-19 09:40:02 UTC |
Posterior variable declaration
Description
Posterior variable declaration
Usage
expr %with% variables
Arguments
expr |
expression to evaluate |
variables |
expression with variable assignments |
Value
The value of the evaluated expression.
Examples
(a + b) %with% {
a = 1
b = 2
}
Capture expressions
Description
Capture expressions
Usage
.(...)
Arguments
... |
code |
Value
a list
Examples
.(a = 1, b = x^2, c = filter(iris, Species == "veriscolor"))
Run interpolated code
Description
Vectorised substitution of expressions into a large code block and execution.
Usage
run(expr, ..., e = parent.frame())
expr %for% x
expr %where% pars
Arguments
expr |
the code to run |
... |
named values to be substituted by name into 'expr' |
e |
environment, for evaluation; defaults to 'parent.frame()' |
x |
list of expressions to be substituted for 'x' in 'expr' |
pars |
a named list of values to be substituted by name into 'expr' |
Details
' '
Value
A list.
Examples
library(dplyr)
subgroups = .(all = TRUE,
long_sepal = Sepal.Length > 6,
long_petal = Petal.Length > 5.5)
functions = .(mean, sum, prod)
run(
iris %>%
filter(subgroup) %>%
summarise(across(Sepal.Length:Petal.Width,
summary),
.by = Species),
subgroup = subgroups,
summary = functions
)
library(data.table)
df <- as.data.table(iris)
run(df[subgroup, lapply(.SD, functions), keyby = "Species",
.SDcols = Sepal.Length:Petal.Width],
subgroup = subgroups,
functions = functions)
library(ggplot2)
plots <- run(
ggplot(filter(iris, subgroup),
aes(Sepal.Length, Sepal.Width)) +
geom_point() +
theme_minimal(),
subgroup = subgroups
)
Map(function(plot, name) plot + ggtitle(name), plots, names(plots))
(
iris %>%
filter(subgroup) %>%
summarise(across(Sepal.Length:Petal.Width,
summary),
.by = Species)
) %where%
list(subgroup = subgroups,
summary = functions)
library(ggplot2)
(
ggplot(filter(iris, x),
aes(Sepal.Length, Sepal.Width)) +
geom_point() +
theme_minimal()
) %for% subgroups