An easy-to-use, dependency-free set of functions for iterating over elements of various input objects. Functions are wrappers around base apply()/lapply()/vapply() functions but designed to have similar functionality to the mapping functions in the 'purrr' package < https://purrr.tidyverse.org/>. Specifically, function names more explicitly communicate the expected class of the output and functions also allow for the convenient shortcut of '~ .x' instead of the more verbose 'function(.x) .x'.
Dependency-free purrr-like apply/map/iterate functions
Install the development version from Github with:
## install remotes pkg if not alreadyif (!requireNamespace("remotes", quietly = TRUE)) {install.packages("remotes")}## install from githubremotes::install_github("mkearney/dapr")
{dapr} provides the ease and consistency of
{purrr}, (see also: simple benchmark
results plot below) including use of ~
and .x
, without all the
dependencies. In other words, use {dapr} when you want a purrr-like
experience but you need a lightweight solution.
Function names use the convention *ap()
where *
is the first
letter of output data type.
vap
for vectorslap
for listsdap
for data framesCommon inputs:
.data
Input object–numeric, character, list, data frame, etc.–over
which elements will be iterated. If matrix or data frame, each
column will be treated as the elements which are to be iterated
over..f
Function to apply to each element of input object. This can be
written as a single function name e.g., mean
, a formula-like
function call where .x
is assumed to be the iterated over element
of input data e.g., ~ mean(.x)
, or an in-line function definition
e.g., function(x) mean(x)
.Functions that apply expressions to input data objects and return atomic vectors e.g., numeric (double), character, logical.
vap_dbl()
Iterate and return numeric vector.vap_int()
Iterate and return integer vector.vap_lgl()
Iterate and return logical vector.vap_chr()
Iterate and return character vector.## create dataset.seed(2018)d <- replicate(5, rnorm(10), simplify = FALSE)e <- replicate(5, sample(letters, 10), simplify = FALSE)## numericvap_dbl(d, ~ mean(.x))#> [1] 0.26934527 -0.55232322 0.05559290 -0.06253258 -0.11183760## integervap_int(d, length)#> [1] 10 10 10 10 10## logicalvap_lgl(d, ~ max(.x) > 3)#> [1] FALSE FALSE FALSE FALSE FALSE## charactervap_chr(e, paste, collapse = "")#> [1] "hizjpgcexk" "rbeovimtxh" "ujrimwgvzs" "euwrlytgbj" "qkrhylgmnx"
Function(s) that apply expressions to input data objects and return lists.
lap()
Iterate and return a list vector.## list of stringslap(e[1:2], ~ paste0(.x, "."))#> [[1]]#> [1] "h." "i." "z." "j." "p." "g." "c." "e." "x." "k."#>#> [[2]]#> [1] "r." "b." "e." "o." "v." "i." "m." "t." "x." "h."
ilap()
Iterate over sequence length .i
(instead of .x
) and
return a list vector.## list of stringsilap(1:4, ~ paste0(letters[.i], rev(LETTERS)[.i]))#> [[1]]#> [1] "aZ"#>#> [[2]]#> [1] "bY"#>#> [[3]]#> [1] "cX"#>#> [[4]]#> [1] "dW"
Functions that apply expressions to input data objects and return data frames.
dap*()
Iterate and return a data frame
dapc()
Iterate over columnsdapr()
Iterate over rowsdap*_if()
Conditionally iterate
dapc_if()
Conditionally iterate over columnsdapr_if()
Conditionally iterate over rows## some datad <- data.frame(a = letters[1:3],b = rnorm(3),c = rnorm(3),stringsAsFactors = FALSE)## column explicit (same as dap)dapc(d[-1], ~ round(.x, 2))#> b c#> 1 -0.50 -0.09#> 2 -1.87 1.08#> 3 0.74 -1.36## rowsdapr(d[-1], round, 3)#> b c#> 1 -0.499 -0.089#> 2 -1.869 1.081#> 3 0.743 -1.365## conditional COLUMNSdapc_if(d, is.numeric, ~ round(.x, 4))#> a b c#> 1 a -0.4994 -0.0892#> 2 b -1.8686 1.0812#> 3 c 0.7434 -1.3646## conditional ROWSdapr_if(d[-1], ~ sum(.x) >= -.7, ~ round(.x, 0))#> b c#> 1 0.000000 0.000000#> 2 -1.868615 1.081164#> 3 1.000000 -1.000000
lapply()
and vapply()
, so functions now return namesNEWS.md
file to track changes to the package.lap()
, vap_*()
, and dap*()
functions.