wrapr
is an
R
package that supplies powerful tools
for writing and debugging R
code.
Primary wrapr
services include:
%.>%
(dot arrow pipe)unpack
/to
(assign to multiple values)as_named_list
(build up a named list quickly)build_frame()
/
draw_frame()
( data.frame
builders and formatters )bc()
(blank concatenate)qc()
(quoting
concatenate):=
(named map builder)%?%
(coalesce)%.|%
(reduce/expand args)uniques()
(safe unique()
replacement)partition_tables()
/
execute_parallel()
DebugFnW()
(function debug wrappers)λ()
(anonymous function builder)let()
(let
block)evalb()
/si()
(evaluate with bquote
/ string interpolation)sortv()
(sort a data.frame by a set of columns).stop_if_dot_args()
(check for unexpected arguments)library(wrapr)
packageVersion("wrapr")
# [1] '2.1.0'
date()
# [1] "Sat Aug 19 09:06:13 2023"
%.>%
(dot pipe or dot arrow)%.>%
dot arrow pipe is a pipe with intended semantics:
“
a %.>% b
” is to be treated approximately as if the user had written “{ . <- a; b };
” with “%.>%
” being treated as left-associative.
Other R
pipes include
magrittr
and
pipeR
.
The following two expressions should be equivalent:
cos(exp(sin(4)))
# [1] 0.8919465
4 %.>% sin(.) %.>% exp(.) %.>% cos(.)
# [1] 0.8919465
The notation is quite powerful as it treats pipe stages as expression
parameterized over the variable “.
”. This means you do not need to
introduce functions to express stages. The following is a valid
dot-pipe:
1:4 %.>% .^2
# [1] 1 4 9 16
The notation is also very regular as we show below.
1:4 %.>% sin
# [1] 0.8414710 0.9092974 0.1411200 -0.7568025
1:4 %.>% sin(.)
# [1] 0.8414710 0.9092974 0.1411200 -0.7568025
1:4 %.>% base::sin
# [1] 0.8414710 0.9092974 0.1411200 -0.7568025
1:4 %.>% base::sin(.)
# [1] 0.8414710 0.9092974 0.1411200 -0.7568025
1:4 %.>% function(x) { x + 1 }
# [1] 2 3 4 5
1:4 %.>% (function(x) { x + 1 })
# [1] 2 3 4 5
1:4 %.>% { .^2 }
# [1] 1 4 9 16
1:4 %.>% ( .^2 )
# [1] 1 4 9 16
Regularity can be a big advantage in teaching and comprehension. Please see “In Praise of Syntactic Sugar” for more details. Some formal documentation can be found here.
The dot pipe is also user configurable through standard S3
/S4
methods.
The dot pipe has been formally written up in the R Journal.
@article{RJ-2018-042,
author = {John Mount and Nina Zumel},
title = {{Dot-Pipe: an S3 Extensible Pipe for R}},
year = {2018},
journal = {{The R Journal}},
url = {https://journal.r-project.org/archive/2018/RJ-2018-042/index.html}
}
unpack
/to
multiple assignmentsUnpack a named list into the current environment by name (for a
positional based multiple assignment operator please see
zeallot
, for another
named base multiple assigment please see
vadr::bind
).
d <- data.frame(
x = 1:9,
group = c('train', 'calibrate', 'test'),
stringsAsFactors = FALSE)
unpack[
train_data = train,
calibrate_data = calibrate,
test_data = test
] := split(d, d$group)
knitr::kable(train_data)
| | x | group | |:----|----:|:------| | 1 | 1 | train | | 4 | 4 | train | | 7 | 7 | train |
as_named_list
Build up named lists. Very convenient for managing workspaces when used
with used with
unpack
/to
.
as_named_list(train_data, calibrate_data, test_data)
# $train_data
# x group
# 1 1 train
# 4 4 train
# 7 7 train
#
# $calibrate_data
# x group
# 2 2 calibrate
# 5 5 calibrate
# 8 8 calibrate
#
# $test_data
# x group
# 3 3 test
# 6 6 test
# 9 9 test
build_frame()
/ draw_frame()
build_frame()
is a convenient way to type in a small example data.frame
in natural
row order. This can be very legible and saves having to perform a
transpose in one’s head.
draw_frame()
is the complimentary function that formats a given data.frame
(and is
a great way to produce neatened examples).
x <- build_frame(
"measure" , "training", "validation" |
"minus binary cross entropy", 5 , -7 |
"accuracy" , 0.8 , 0.6 )
print(x)
# measure training validation
# 1 minus binary cross entropy 5.0 -7.0
# 2 accuracy 0.8 0.6
str(x)
# 'data.frame': 2 obs. of 3 variables:
# $ measure : chr "minus binary cross entropy" "accuracy"
# $ training : num 5 0.8
# $ validation: num -7 0.6
cat(draw_frame(x))
# x <- wrapr::build_frame(
# "measure" , "training", "validation" |
# "minus binary cross entropy", 5 , -7 |
# "accuracy" , 0.8 , 0.6 )
qc()
(quoting concatenate)qc()
is a quoting variation on R
’s concatenate operator c()
. This
code such as the following:
qc(a = x, b = y)
# a b
# "x" "y"
qc(one, two, three)
# [1] "one" "two" "three"
qc()
also allows bquote()
driven .()
-style argument escaping.
aname <- "I_am_a"
yvalue <- "six"
qc(.(aname) := x, b = .(yvalue))
# I_am_a b
# "x" "six"
Notice the :=
notation is required for syntacitic reasons.
:=
(named map builder):=
is the “named map builder”. It allows code such as the following:
'a' := 'x'
# a
# "x"
The important property of named map builder is it accepts values on the left-hand side allowing the following:
name <- 'variableNameFromElsewhere'
name := 'newBinding'
# variableNameFromElsewhere
# "newBinding"
A nice property is :=
commutes (in the sense of algebra or category
theory) with R
’s concatenation function c()
. That is the following
two statements are equivalent:
c('a', 'b') := c('x', 'y')
# a b
# "x" "y"
c('a' := 'x', 'b' := 'y')
# a b
# "x" "y"
The named map builder is designed to synergize with
seplyr
.
%?%
(coalesce)The coalesce operator tries to replace elements of its first argument
with elements from its second argument. In particular %?%
replaces
NULL vectors and NULL/NA entries of vectors and lists.
Example:
c(1, NA) %?% list(NA, 20)
# [1] 1 20
%.|%
(reduce/expand args)x %.|% f
stands for f(x[[1]], x[[2]], ..., x[[length(x)]])
.
v %|.% x
also stands for f(x[[1]], x[[2]], ..., x[[length(x)]])
. The
two operators are the same, the variation just allowing the user to
choose the order they write things. The mnemonic is: “data goes on the
dot-side of the operator.”
args <- list('prefix_', c(1:3), '_suffix')
args %.|% paste0
# [1] "prefix_1_suffix" "prefix_2_suffix" "prefix_3_suffix"
# prefix_1_suffix" "prefix_2_suffix" "prefix_3_suffix"
paste0 %|.% args
# [1] "prefix_1_suffix" "prefix_2_suffix" "prefix_3_suffix"
# prefix_1_suffix" "prefix_2_suffix" "prefix_3_suffix"
DebugFnW()
DebugFnW()
wraps a function for debugging. If the function throws an
exception the execution context (function arguments, function name, and
more) is captured and stored for the user. The function call can then be
reconstituted, inspected and even re-run with a step-debugger. Please
see our free debugging video
series
and vignette('DebugFnW', package='wrapr')
for examples.
λ()
(anonymous function builder)λ()
is a concise abstract function creator or “lambda
abstraction”. It is a
placeholder that allows the use of the -character for very concise
function abstraction.
Example:
# Make sure lambda function builder is in our enironment.
wrapr::defineLambda()
# square numbers 1 through 4
sapply(1:4, λ(x, x^2))
# [1] 1 4 9 16
let()
let()
allows execution of arbitrary code with substituted variable
names (note this is subtly different than binding values for names as
with base::substitute()
or base::with()
).
The function is simple and powerful. It treats strings as variable names and re-writes expressions as if you had used the denoted variables. For example the following block of code is equivalent to having written “a + a”.
a <- 7
let(
c(VAR = 'a'),
VAR + VAR
)
# [1] 14
This is useful in re-adapting non-standard evaluation interfaces (NSE interfaces) so one can script or program over them.
We are trying to make let()
self teaching and self documenting (to the
extent that makes sense). For example try the arguments “eval=FALSE
”
prevent execution and see what would have been executed, or
debug=TRUE
to have the replaced code printed in addition to being
executed:
let(
c(VAR = 'a'),
eval = FALSE,
{
VAR + VAR
}
)
# {
# a + a
# }
let(
c(VAR = 'a'),
debugPrint = TRUE,
{
VAR + VAR
}
)
# $VAR
# [1] "a"
#
# {
# a + a
# }
# [1] 14
Please see vignette('let', package='wrapr')
for more examples. Some
formal documentation can be found
here.
wrapr::let()
was inspired by gtools::strmacro()
and
base::bquote()
, please see
here
for some notes on macro methods in R
.
evalb()
/si()
(evaluate with bquote
/ string interpolation)wrapr
supplies unified notation for quasi-quotation and string
interpolation.
angle = 1:10
variable <- "angle"
# # execute code
# evalb(
# plot(x = .(-variable), y = sin(.(-variable)))
# )
# alter string
si("plot(x = .(variable), y = .(variable))")
# [1] "plot(x = \"angle\", y = \"angle\")"
The extra .(-x)
form is a shortcut for .(as.name(x))
.
sortv()
(sort a data.frame by a set of columns)This is the sort command that is missing from R
: sort a data.frame
by a chosen set of columns specified in a variable.
d <- data.frame(
x = c(2, 2, 3, 3, 1, 1),
y = 6:1,
z = 1:6)
order_cols <- c('x', 'y')
sortv(d, order_cols)
# x y z
# 6 1 1 6
# 5 1 2 5
# 2 2 5 2
# 1 2 6 1
# 4 3 3 4
# 3 3 4 3
Install with:
install.packages("wrapr")
More details on wrapr
capabilities can be found in the following two
technical articles:
Note: wrapr
is meant only for “tame names”, that is: variables and
column names that are also valid simple (without quotes) R
variables
names.
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