wrapr is an R package that supplies powerful tools for writing and debugging R code.

Introduction

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 assignments

Unpack 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)

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

Installation

Install with:

install.packages("wrapr")

More Information

More details on wrapr capabilities can be found in the following two technical articles:

• let
• R Journal “Dot-Pipe: an S3 Extensible Pipe for R””

Note

Note: wrapr is meant only for “tame names”, that is: variables and column names that are also valid simple (without quotes) R variables names.