README.md
In yonicd/foreman: Unpacking, Interrogating and Subsetting R packages
foreman
The goals of foreman are to:
- Unpack a package’s functions to interrogate
relationships of the functions within it.
- Isolate function scripts within a package (including the
documentation for local paths)
- Consolidate a subset of self contained functions
in a file(s) to allow for focused learning on a specific package
functionality.
Given these goals it is important to state that this package is not
meant to replace any parent package.
The package supports both local packages and
compiled libraries.
Installation
remotes::install_github("yonicd/foreman")
Local Packages
This example will use a local fork of purrr.
library(foreman)
library(ggraph)
library(igraph)
Unpacking
Unpack a pacakge into a list
x <- unpack(path = '../forks/purrr/R', warn = FALSE)
Click the triangle to view the contents found in arrays.R
details::details(lapply(x$arrays.R,get_text),summary = 'arrays.R')
arrays.R
$array_branch
[1] "#' Coerce array to list"
[2] "#'"
[3] "#' `array_branch()` and `array_tree()` enable arrays to be"
[4] "#' used with purrr's functionals by turning them into lists. The"
[5] "#' details of the coercion are controlled by the `margin`"
[6] "#' argument. `array_tree()` creates an hierarchical list (a tree)"
[7] "#' that has as many levels as dimensions specified in `margin`,"
[8] "#' while `array_branch()` creates a flat list (by analogy, a"
[9] "#' branch) along all mentioned dimensions."
[10] "#'"
[11] "#' When no margin is specified, all dimensions are used by"
[12] "#' default. When `margin` is a numeric vector of length zero, the"
[13] "#' whole array is wrapped in a list."
[14] "#' @param array An array to coerce into a list."
[15] "#' @param margin A numeric vector indicating the positions of the"
[16] "#' indices to be to be enlisted. If `NULL`, a full margin is"
[17] "#' used. If `numeric(0)`, the array as a whole is wrapped in a"
[18] "#' list."
[19] "#' @name array-coercion"
[20] "#' @export"
[21] "#' @examples"
[22] "#' # We create an array with 3 dimensions"
[23] "#' x <- array(1:12, c(2, 2, 3))"
[24] "#'"
[25] "#' # A full margin for such an array would be the vector 1:3. This is"
[26] "#' # the default if you don't specify a margin"
[27] "#'"
[28] "#' # Creating a branch along the full margin is equivalent to"
[29] "#' # as.list(array) and produces a list of size length(x):"
[30] "#' array_branch(x) %>% str()"
[31] "#'"
[32] "#' # A branch along the first dimension yields a list of length 2"
[33] "#' # with each element containing a 2x3 array:"
[34] "#' array_branch(x, 1) %>% str()"
[35] "#'"
[36] "#' # A branch along the first and third dimensions yields a list of"
[37] "#' # length 2x3 whose elements contain a vector of length 2:"
[38] "#' array_branch(x, c(1, 3)) %>% str()"
[39] "#'"
[40] "#' # Creating a tree from the full margin creates a list of lists of"
[41] "#' # lists:"
[42] "#' array_tree(x) %>% str()"
[43] "#'"
[44] "#' # The ordering and the depth of the tree are controlled by the"
[45] "#' # margin argument:"
[46] "#' array_tree(x, c(3, 1)) %>% str()"
[47] "array_branch <- function(array, margin = NULL) {"
[48] " dims <- dim(array) %||% length(array)"
[49] " margin <- margin %||% seq_along(dims)"
[50] ""
[51] " if (length(margin) == 0) {"
[52] " list(array)"
[53] " } else if (is.null(dim(array))) {"
[54] " if (!identical(as.integer(margin), 1L)) {"
[55] " abort(sprintf("
[56] " \"`margin` must be `NULL` or `1` with 1D arrays, not `%s`\","
[57] " toString(margin)"
[58] " ))"
[59] " }"
[60] " as.list(array)"
[61] " } else {"
[62] " flatten(apply(array, margin, list))"
[63] " }"
[64] "}"
$array_tree
[1] "#' @rdname array-coercion"
[2] "#' @export"
[3] "array_tree <- function(array, margin = NULL) {"
[4] " dims <- dim(array) %||% length(array)"
[5] " margin <- margin %||% seq_along(dims)"
[6] ""
[7] " if (length(margin) > 1) {"
[8] " new_margin <- ifelse(margin[-1] > margin[[1]], margin[-1] - 1, margin[-1])"
[9] " apply(array, margin[[1]], array_tree, new_margin)"
[10] " } else {"
[11] " array_branch(array, margin)"
[12] " }"
[13] "}"
Relationships
x_rel <- relationship(x)
Relationships contained in arrays.R
x_rel$arrays.R
#> $array_branch
#> [1] "flatten"
#>
#> $array_tree
#> [1] "array_branch"
Functions that compose calls
relationship(x,parent = 'compose')
#> $compose.R
#> $compose.R$compose
#> [1] "compose" "map" "fn"
#>
#>
#> attr(,"class")
#> [1] "relationship" "list"
Functions who call flatten
relationship(x,child = 'flatten')
#> $arrays.R
#> $arrays.R$array_branch
#> [1] "flatten"
#>
#>
#> $lmap.R
#> $lmap.R$lmap_at
#> [1] "flatten"
#>
#>
#> $splice.R
#> $splice.R$splice_if
#> [1] "flatten"
#>
#>
#> attr(,"class")
#> [1] "relationship" "list"
Convert relationships to a data.frame
x_rel_df <- as.data.frame(x_rel)
Click the triangle to view the data.frame
details::details(x_rel_df,summary = 'Relatives')
Relatives
child parent file
1 flatten array_branch arrays.R
2 as_mapper as_function as_mapper.R
3 stop_defunct as_function as_mapper.R
4 paste_line as_function as_mapper.R
5 coerce coerce_lgl coerce.R
6 can_simplify as_vector coercion.R
7 warn_deprecated signal_soft_deprecated compat-lifecycle.R
8 compose compose compose.R
9 map compose compose.R
10 fn compose compose.R
11 as_vector lift_vl composition.R
12 what_bad_object stop_bad_type conditions.R
13 friendly_type_of stop_bad_type conditions.R
14 as_mapper cross cross.R
15 as_predicate_friendly_type_of cross cross.R
16 compact cross cross.R
17 is_bool cross cross.R
18 map_int vec_depth depth.R
19 as_predicate detect detect.R
20 index detect detect.R
21 as_predicate every every-some.R
22 detect_index head_while head-tail.R
23 negate head_while head-tail.R
24 as_mapper imap imap.R
25 vec_index imap imap.R
26 map2 imap imap.R
27 probe keep keep.R
28 list_recurse list_modify list-modify.R
29 lmap_at lmap lmap.R
30 as_mapper map map.R
31 as_mapper map2 map2-pmap.R
32 as_mapper modify.default modify.R
33 as_mapper negate negate.R
34 as_mapper safely output.R
35 capture_error safely output.R
36 signal_soft_deprecated partial partial.R
37 stop_defunct partial partial.R
38 map partial partial.R
39 paste_line partial partial.R
40 friendly_type_of partial partial.R
41 assign_in `pluck<-` pluck.R
42 as_mapper insistently rate.R
43 stop_bad_type insistently rate.R
44 capture_error insistently rate.R
45 rate_backoff insistently rate.R
46 is_rate insistently rate.R
47 rate_sleep insistently rate.R
48 rate_reset insistently rate.R
49 f insistently rate.R
50 reduce_impl reduce reduce.R
51 eval_dots rerun rerun.R
52 has_names rerun rerun.R
53 map2 invoke_map retired-invoke.R
54 as_invoke_function invoke_map retired-invoke.R
55 splice_if splice splice.R
56 check_tibble maybe_as_data_frame utils.R
Plotting the relationships
graph <- igraph::graph_from_data_frame(x_rel_df,directed = TRUE)
igraph::V(graph)$parents <- names(igraph::V(graph))
ggraph(graph) +
geom_edge_link(
aes(colour = file),
arrow = grid::arrow(length = unit(0.05, "inches"))) +
geom_node_text(aes(label = parents),size = 3) +
labs(title = 'purrr function map', colour = 'Exported') +
ggplot2::theme(legend.position = 'bottom')
#> Using `nicely` as default layout
Subset
Subsetting Package Functions
sub_x <- subset(x,'compose')
Click the triangle to view the contents found in the subset containing
compose and the functions the it calls.
details::details(lapply(sub_x,get_text),summary = 'Package subset')
Package subset
$compose.R
[1] "#' Compose multiple functions"
[2] "#'"
[3] "#' @param ... Functions to apply in order (from right to left by"
[4] "#' default). Formulas are converted to functions in the usual way."
[5] "#'"
[6] "#' These dots support [tidy dots][rlang::list2] features. In"
[7] "#' particular, if your functions are stored in a list, you can"
[8] "#' splice that in with `!!!`."
[9] "#' @param .dir If `\"backward\"` (the default), the functions are called"
[10] "#' in the reverse order, from right to left, as is conventional in"
[11] "#' mathematics. If `\"forward\"`, they are called from left to right."
[12] "#' @return A function"
[13] "#' @export"
[14] "#' @examples"
[15] "#' not_null <- compose(`!`, is.null)"
[16] "#' not_null(4)"
[17] "#' not_null(NULL)"
[18] "#'"
[19] "#' add1 <- function(x) x + 1"
[20] "#' compose(add1, add1)(8)"
[21] "#'"
[22] "#' # You can use the formula shortcut for functions:"
[23] "#' fn <- compose(~ paste(.x, \"foo\"), ~ paste(.x, \"bar\"))"
[24] "#' fn(\"input\")"
[25] "#'"
[26] "#' # Lists of functions can be spliced with !!!"
[27] "#' fns <- list("
[28] "#' function(x) paste(x, \"foo\"),"
[29] "#' ~ paste(.x, \"bar\")"
[30] "#' )"
[31] "#' fn <- compose(!!!fns)"
[32] "#' fn(\"input\")"
[33] "compose <- function(..., .dir = c(\"backward\", \"forward\")) {"
[34] " .dir <- arg_match(.dir, c(\"backward\", \"forward\"))"
[35] ""
[36] " fns <- map(list2(...), rlang::as_closure, env = caller_env())"
[37] " if (!length(fns)) {"
[38] " # Return the identity function"
[39] " return(compose(function(x, ...) x))"
[40] " }"
[41] ""
[42] " if (.dir == \"backward\") {"
[43] " n <- length(fns)"
[44] " first_fn <- fns[[n]]"
[45] " fns <- rev(fns[-n])"
[46] " } else {"
[47] " first_fn <- fns[[1]]"
[48] " fns <- fns[-1]"
[49] " }"
[50] ""
[51] " body <- expr({"
[52] " out <- !!fn_body(first_fn)"
[53] ""
[54] " fns <- !!fns"
[55] " for (fn in fns) {"
[56] " out <- fn(out)"
[57] " }"
[58] ""
[59] " out"
[60] " })"
[61] ""
[62] " structure("
[63] " new_function(formals(first_fn), body, fn_env(first_fn)),"
[64] " class = c(\"purrr_function_compose\", \"function\"),"
[65] " first_fn = first_fn,"
[66] " fns = fns"
[67] " )"
[68] "}"
$map.R
[1] "#' Apply a function to each element of a vector"
[2] "#'"
[3] "#' @description"
[4] "#'"
[5] "#' The map functions transform their input by applying a function to"
[6] "#' each element and returning a vector the same length as the input."
[7] "#'"
[8] "#' * `map()`, `map_if()` and `map_at()` always return a list. See the"
[9] "#' [modify()] family for versions that return an object of the same"
[10] "#' type as the input."
[11] "#'"
[12] "#' The `_if` and `_at` variants take a predicate function `.p` that"
[13] "#' determines which elements of `.x` are transformed with `.f`."
[14] "#'"
[15] "#' * `map_lgl()`, `map_int()`, `map_dbl()` and `map_chr()` each return"
[16] "#' an atomic vector of the indicated type (or die trying)."
[17] "#'"
[18] "#' The return value of `.f` must be of length one for each element of"
[19] "#' `.x`. If `.f` uses an extractor function shortcut, `.default`"
[20] "#' can be specified to handle values that are absent or empty. See"
[21] "#' [as_mapper()] for more on `.default`."
[22] "#'"
[23] "#' * `map_dfr()` and `map_dfc()` return data frames created by"
[24] "#' row-binding and column-binding respectively. They require dplyr"
[25] "#' to be installed."
[26] "#'"
[27] "#' * `walk()` calls `.f` for its side-effect and returns the input `.x`."
[28] "#'"
[29] "#' @inheritParams as_mapper"
[30] "#' @param .x A list or atomic vector."
[31] "#' @param .p A single predicate function, a formula describing such a"
[32] "#' predicate function, or a logical vector of the same length as `.x`."
[33] "#' Alternatively, if the elements of `.x` are themselves lists of"
[34] "#' objects, a string indicating the name of a logical element in the"
[35] "#' inner lists. Only those elements where `.p` evaluates to"
[36] "#' `TRUE` will be modified."
[37] "#' @param .at A character vector of names, positive numeric vector of"
[38] "#' positions to include, or a negative numeric vector of positions to"
[39] "#' exlude. Only those elements corresponding to `.at` will be modified."
[40] "#' @param ... Additional arguments passed on to the mapped function."
[41] "#' @return All functions return a vector the same length as `.x`."
[42] "#'"
[43] "#' `map()` returns a list, `map_lgl()` a logical vector, `map_int()` an"
[44] "#' integer vector, `map_dbl()` a double vector, and `map_chr()` a character"
[45] "#' vector. The output of `.f` will be automatically typed upwards,"
[46] "#' e.g. logical -> integer -> double -> character."
[47] "#'"
[48] "#' If `.x` has `names()`, the return value preserves those names."
[49] "#'"
[50] "#' `walk()` returns the input `.x` (invisibly). This makes it easy to"
[51] "#' use in pipe."
[52] "#' @export"
[53] "#' @family map variants"
[54] "#' @examples"
[55] "#' 1:10 %>%"
[56] "#' map(rnorm, n = 10) %>%"
[57] "#' map_dbl(mean)"
[58] "#'"
[59] "#' # Or use an anonymous function"
[60] "#' 1:10 %>%"
[61] "#' map(function(x) rnorm(10, x))"
[62] "#'"
[63] "#' # Or a formula"
[64] "#' 1:10 %>%"
[65] "#' map(~ rnorm(10, .x))"
[66] "#'"
[67] "#' # The names of the input are preserved in the output:"
[68] "#' list(foo = 1, bar = 2) %>% map(`+`, 10)"
[69] "#'"
[70] "#' # Using set_names() with character vectors is handy to keep track"
[71] "#' # of the original inputs:"
[72] "#' set_names(c(\"foo\", \"bar\")) %>% map_chr(paste0, \":suffix\")"
[73] "#'"
[74] "#' # Extract by name or position"
[75] "#' # .default specifies value for elements that are missing or NULL"
[76] "#' l1 <- list(list(a = 1L), list(a = NULL, b = 2L), list(b = 3L))"
[77] "#' l1 %>% map(\"a\", .default = \"???\")"
[78] "#' l1 %>% map_int(\"b\", .default = NA)"
[79] "#' l1 %>% map_int(2, .default = NA)"
[80] "#'"
[81] "#' # Supply multiple values to index deeply into a list"
[82] "#' l2 <- list("
[83] "#' list(num = 1:3, letters[1:3]),"
[84] "#' list(num = 101:103, letters[4:6]),"
[85] "#' list()"
[86] "#' )"
[87] "#' l2 %>% map(c(2, 2))"
[88] "#'"
[89] "#' # Use a list to build an extractor that mixes numeric indices and names,"
[90] "#' # and .default to provide a default value if the element does not exist"
[91] "#' l2 %>% map(list(\"num\", 3))"
[92] "#' l2 %>% map_int(list(\"num\", 3), .default = NA)"
[93] "#'"
[94] "#'"
[95] "#' # Use a predicate function to decide whether to map a function:"
[96] "#' map_if(iris, is.factor, as.character)"
[97] "#'"
[98] "#' # Specify an alternative with the `.else` argument:"
[99] "#' map_if(iris, is.factor, as.character, .else = as.integer)"
[100] "#'"
[101] "#' # A more realistic example: split a data frame into pieces, fit a"
[102] "#' # model to each piece, summarise and extract R^2"
[103] "#' mtcars %>%"
[104] "#' split(.$cyl) %>%"
[105] "#' map(~ lm(mpg ~ wt, data = .x)) %>%"
[106] "#' map(summary) %>%"
[107] "#' map_dbl(\"r.squared\")"
[108] "#'"
[109] "#' # Use map_lgl(), map_dbl(), etc to reduce to a vector."
[110] "#' # * list"
[111] "#' mtcars %>% map(sum)"
[112] "#' # * vector"
[113] "#' mtcars %>% map_dbl(sum)"
[114] "#'"
[115] "#' # If each element of the output is a data frame, use"
[116] "#' # map_dfr to row-bind them together:"
[117] "#' mtcars %>%"
[118] "#' split(.$cyl) %>%"
[119] "#' map(~ lm(mpg ~ wt, data = .x)) %>%"
[120] "#' map_dfr(~ as.data.frame(t(as.matrix(coef(.)))))"
[121] "#' # (if you also want to preserve the variable names see"
[122] "#' # the broom package)"
[123] "#'"
[124] "#' # Use `map_depth()` to recursively traverse nested vectors and map"
[125] "#' # a function at a certain depth:"
[126] "#' x <- list(a = list(foo = 1:2, bar = 3:4), b = list(baz = 5:6))"
[127] "#' str(x)"
[128] "#' map_depth(x, 2, paste, collapse = \"/\")"
[129] "#'"
[130] "#' # Equivalent to:"
[131] "#' map(x, map, paste, collapse = \"/\")"
[132] "map <- function(.x, .f, ...) {"
[133] " .f <- as_mapper(.f, ...)"
[134] " .Call(map_impl, environment(), \".x\", \".f\", \"list\")"
[135] "}"
$reduce.R
[1] " fn <- function(x, y, ...) .f(y, x, ...)"
Consolidation
Consolidating Subsetted Functions into a File
pack_path <- repack(sub_x)
#> Functions packed to /var/folders/kx/t4h_mm1910sb7vhm_gnfnx2c0000gn/T//Rtmpgg7cm0/foreman/unpacked.R
Click the triangle to view the contents found in the file containing the
consolidated
functions.
details::details(file.path(pack_path,'unpacked.R'),summary = 'Consolidated Script')
Consolidated Script
#Generated by foreman:
#' Compose multiple functions
#'
#' @param ... Functions to apply in order (from right to left by
#' default). Formulas are converted to functions in the usual way.
#'
#' These dots support [tidy dots][rlang::list2] features. In
#' particular, if your functions are stored in a list, you can
#' splice that in with `!!!`.
#' @param .dir If `"backward"` (the default), the functions are called
#' in the reverse order, from right to left, as is conventional in
#' mathematics. If `"forward"`, they are called from left to right.
#' @return A function
#' @export
#' @examples
#' not_null <- compose(`!`, is.null)
#' not_null(4)
#' not_null(NULL)
#'
#' add1 <- function(x) x + 1
#' compose(add1, add1)(8)
#'
#' # You can use the formula shortcut for functions:
#' fn <- compose(~ paste(.x, "foo"), ~ paste(.x, "bar"))
#' fn("input")
#'
#' # Lists of functions can be spliced with !!!
#' fns <- list(
#' function(x) paste(x, "foo"),
#' ~ paste(.x, "bar")
#' )
#' fn <- compose(!!!fns)
#' fn("input")
compose <- function(..., .dir = c("backward", "forward")) {
.dir <- arg_match(.dir, c("backward", "forward"))
fns <- map(list2(...), rlang::as_closure, env = caller_env())
if (!length(fns)) {
# Return the identity function
return(compose(function(x, ...) x))
}
if (.dir == "backward") {
n <- length(fns)
first_fn <- fns[[n]]
fns <- rev(fns[-n])
} else {
first_fn <- fns[[1]]
fns <- fns[-1]
}
body <- expr({
out <- !!fn_body(first_fn)
fns <- !!fns
for (fn in fns) {
out <- fn(out)
}
out
})
structure(
new_function(formals(first_fn), body, fn_env(first_fn)),
class = c("purrr_function_compose", "function"),
first_fn = first_fn,
fns = fns
)
}
#' Apply a function to each element of a vector
#'
#' @description
#'
#' The map functions transform their input by applying a function to
#' each element and returning a vector the same length as the input.
#'
#' * `map()`, `map_if()` and `map_at()` always return a list. See the
#' [modify()] family for versions that return an object of the same
#' type as the input.
#'
#' The `_if` and `_at` variants take a predicate function `.p` that
#' determines which elements of `.x` are transformed with `.f`.
#'
#' * `map_lgl()`, `map_int()`, `map_dbl()` and `map_chr()` each return
#' an atomic vector of the indicated type (or die trying).
#'
#' The return value of `.f` must be of length one for each element of
#' `.x`. If `.f` uses an extractor function shortcut, `.default`
#' can be specified to handle values that are absent or empty. See
#' [as_mapper()] for more on `.default`.
#'
#' * `map_dfr()` and `map_dfc()` return data frames created by
#' row-binding and column-binding respectively. They require dplyr
#' to be installed.
#'
#' * `walk()` calls `.f` for its side-effect and returns the input `.x`.
#'
#' @inheritParams as_mapper
#' @param .x A list or atomic vector.
#' @param .p A single predicate function, a formula describing such a
#' predicate function, or a logical vector of the same length as `.x`.
#' Alternatively, if the elements of `.x` are themselves lists of
#' objects, a string indicating the name of a logical element in the
#' inner lists. Only those elements where `.p` evaluates to
#' `TRUE` will be modified.
#' @param .at A character vector of names, positive numeric vector of
#' positions to include, or a negative numeric vector of positions to
#' exlude. Only those elements corresponding to `.at` will be modified.
#' @param ... Additional arguments passed on to the mapped function.
#' @return All functions return a vector the same length as `.x`.
#'
#' `map()` returns a list, `map_lgl()` a logical vector, `map_int()` an
#' integer vector, `map_dbl()` a double vector, and `map_chr()` a character
#' vector. The output of `.f` will be automatically typed upwards,
#' e.g. logical -> integer -> double -> character.
#'
#' If `.x` has `names()`, the return value preserves those names.
#'
#' `walk()` returns the input `.x` (invisibly). This makes it easy to
#' use in pipe.
#' @export
#' @family map variants
#' @examples
#' 1:10 %>%
#' map(rnorm, n = 10) %>%
#' map_dbl(mean)
#'
#' # Or use an anonymous function
#' 1:10 %>%
#' map(function(x) rnorm(10, x))
#'
#' # Or a formula
#' 1:10 %>%
#' map(~ rnorm(10, .x))
#'
#' # The names of the input are preserved in the output:
#' list(foo = 1, bar = 2) %>% map(`+`, 10)
#'
#' # Using set_names() with character vectors is handy to keep track
#' # of the original inputs:
#' set_names(c("foo", "bar")) %>% map_chr(paste0, ":suffix")
#'
#' # Extract by name or position
#' # .default specifies value for elements that are missing or NULL
#' l1 <- list(list(a = 1L), list(a = NULL, b = 2L), list(b = 3L))
#' l1 %>% map("a", .default = "???")
#' l1 %>% map_int("b", .default = NA)
#' l1 %>% map_int(2, .default = NA)
#'
#' # Supply multiple values to index deeply into a list
#' l2 <- list(
#' list(num = 1:3, letters[1:3]),
#' list(num = 101:103, letters[4:6]),
#' list()
#' )
#' l2 %>% map(c(2, 2))
#'
#' # Use a list to build an extractor that mixes numeric indices and names,
#' # and .default to provide a default value if the element does not exist
#' l2 %>% map(list("num", 3))
#' l2 %>% map_int(list("num", 3), .default = NA)
#'
#'
#' # Use a predicate function to decide whether to map a function:
#' map_if(iris, is.factor, as.character)
#'
#' # Specify an alternative with the `.else` argument:
#' map_if(iris, is.factor, as.character, .else = as.integer)
#'
#' # A more realistic example: split a data frame into pieces, fit a
#' # model to each piece, summarise and extract R^2
#' mtcars %>%
#' split(.$cyl) %>%
#' map(~ lm(mpg ~ wt, data = .x)) %>%
#' map(summary) %>%
#' map_dbl("r.squared")
#'
#' # Use map_lgl(), map_dbl(), etc to reduce to a vector.
#' # * list
#' mtcars %>% map(sum)
#' # * vector
#' mtcars %>% map_dbl(sum)
#'
#' # If each element of the output is a data frame, use
#' # map_dfr to row-bind them together:
#' mtcars %>%
#' split(.$cyl) %>%
#' map(~ lm(mpg ~ wt, data = .x)) %>%
#' map_dfr(~ as.data.frame(t(as.matrix(coef(.)))))
#' # (if you also want to preserve the variable names see
#' # the broom package)
#'
#' # Use `map_depth()` to recursively traverse nested vectors and map
#' # a function at a certain depth:
#' x <- list(a = list(foo = 1:2, bar = 3:4), b = list(baz = 5:6))
#' str(x)
#' map_depth(x, 2, paste, collapse = "/")
#'
#' # Equivalent to:
#' map(x, map, paste, collapse = "/")
map <- function(.x, .f, ...) {
.f <- as_mapper(.f, ...)
.Call(map_impl, environment(), ".x", ".f", "list")
}
fn <- function(x, y, ...) .f(y, x, ...)
Compiled Library
This example will use the installed library future.
Using foreman with an compiled libraries is also simple
library(future)
#>
#> Attaching package: 'future'
#> The following objects are masked from 'package:igraph':
#>
#> %->%, %<-%
unpacked_future <- unpack(ns = 'future')%>%
relationship()%>%
as.data.frame()
graph <- igraph::graph_from_data_frame(unpacked_future,directed = TRUE)
igraph::V(graph)$parents <- names(igraph::V(graph))
igraph::V(graph)$exported <- names(igraph::V(graph))%in%ls('package:future')
ggraph(graph) +
geom_edge_link(
arrow = grid::arrow(length = unit(0.05, "inches")),alpha = 0.05) +
geom_node_text(aes(colour = exported,label = parents),size = 2) +
labs(title = 'future function map', colour = 'Exported') +
ggplot2::theme(legend.position = 'bottom')
#> Using `nicely` as default layout
yonicd/foreman documentation built on Nov. 5, 2019, 1:21 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
foreman
The goals of foreman are to:
- Unpack a package’s functions to interrogate relationships of the functions within it.
- Isolate function scripts within a package (including the documentation for local paths)
- Consolidate a subset of self contained functions in a file(s) to allow for focused learning on a specific package functionality.
Given these goals it is important to state that this package is not meant to replace any parent package.
The package supports both local packages and compiled libraries.
Installation
remotes::install_github("yonicd/foreman")
Local Packages
This example will use a local fork of purrr.
library(foreman)
library(ggraph)
library(igraph)
Unpacking
Unpack a pacakge into a list
x <- unpack(path = '../forks/purrr/R', warn = FALSE)
Click the triangle to view the contents found in arrays.R
details::details(lapply(x$arrays.R,get_text),summary = 'arrays.R')
$array_branch
[1] "#' Coerce array to list"
[2] "#'"
[3] "#' `array_branch()` and `array_tree()` enable arrays to be"
[4] "#' used with purrr's functionals by turning them into lists. The"
[5] "#' details of the coercion are controlled by the `margin`"
[6] "#' argument. `array_tree()` creates an hierarchical list (a tree)"
[7] "#' that has as many levels as dimensions specified in `margin`,"
[8] "#' while `array_branch()` creates a flat list (by analogy, a"
[9] "#' branch) along all mentioned dimensions."
[10] "#'"
[11] "#' When no margin is specified, all dimensions are used by"
[12] "#' default. When `margin` is a numeric vector of length zero, the"
[13] "#' whole array is wrapped in a list."
[14] "#' @param array An array to coerce into a list."
[15] "#' @param margin A numeric vector indicating the positions of the"
[16] "#' indices to be to be enlisted. If `NULL`, a full margin is"
[17] "#' used. If `numeric(0)`, the array as a whole is wrapped in a"
[18] "#' list."
[19] "#' @name array-coercion"
[20] "#' @export"
[21] "#' @examples"
[22] "#' # We create an array with 3 dimensions"
[23] "#' x <- array(1:12, c(2, 2, 3))"
[24] "#'"
[25] "#' # A full margin for such an array would be the vector 1:3. This is"
[26] "#' # the default if you don't specify a margin"
[27] "#'"
[28] "#' # Creating a branch along the full margin is equivalent to"
[29] "#' # as.list(array) and produces a list of size length(x):"
[30] "#' array_branch(x) %>% str()"
[31] "#'"
[32] "#' # A branch along the first dimension yields a list of length 2"
[33] "#' # with each element containing a 2x3 array:"
[34] "#' array_branch(x, 1) %>% str()"
[35] "#'"
[36] "#' # A branch along the first and third dimensions yields a list of"
[37] "#' # length 2x3 whose elements contain a vector of length 2:"
[38] "#' array_branch(x, c(1, 3)) %>% str()"
[39] "#'"
[40] "#' # Creating a tree from the full margin creates a list of lists of"
[41] "#' # lists:"
[42] "#' array_tree(x) %>% str()"
[43] "#'"
[44] "#' # The ordering and the depth of the tree are controlled by the"
[45] "#' # margin argument:"
[46] "#' array_tree(x, c(3, 1)) %>% str()"
[47] "array_branch <- function(array, margin = NULL) {"
[48] " dims <- dim(array) %||% length(array)"
[49] " margin <- margin %||% seq_along(dims)"
[50] ""
[51] " if (length(margin) == 0) {"
[52] " list(array)"
[53] " } else if (is.null(dim(array))) {"
[54] " if (!identical(as.integer(margin), 1L)) {"
[55] " abort(sprintf("
[56] " \"`margin` must be `NULL` or `1` with 1D arrays, not `%s`\","
[57] " toString(margin)"
[58] " ))"
[59] " }"
[60] " as.list(array)"
[61] " } else {"
[62] " flatten(apply(array, margin, list))"
[63] " }"
[64] "}"
$array_tree
[1] "#' @rdname array-coercion"
[2] "#' @export"
[3] "array_tree <- function(array, margin = NULL) {"
[4] " dims <- dim(array) %||% length(array)"
[5] " margin <- margin %||% seq_along(dims)"
[6] ""
[7] " if (length(margin) > 1) {"
[8] " new_margin <- ifelse(margin[-1] > margin[[1]], margin[-1] - 1, margin[-1])"
[9] " apply(array, margin[[1]], array_tree, new_margin)"
[10] " } else {"
[11] " array_branch(array, margin)"
[12] " }"
[13] "}"
Relationships
x_rel <- relationship(x)
Relationships contained in arrays.R
x_rel$arrays.R
#> $array_branch
#> [1] "flatten"
#>
#> $array_tree
#> [1] "array_branch"
Functions that compose calls
relationship(x,parent = 'compose')
#> $compose.R
#> $compose.R$compose
#> [1] "compose" "map" "fn"
#>
#>
#> attr(,"class")
#> [1] "relationship" "list"
Functions who call flatten
relationship(x,child = 'flatten')
#> $arrays.R
#> $arrays.R$array_branch
#> [1] "flatten"
#>
#>
#> $lmap.R
#> $lmap.R$lmap_at
#> [1] "flatten"
#>
#>
#> $splice.R
#> $splice.R$splice_if
#> [1] "flatten"
#>
#>
#> attr(,"class")
#> [1] "relationship" "list"
Convert relationships to a data.frame
x_rel_df <- as.data.frame(x_rel)
Click the triangle to view the data.frame
details::details(x_rel_df,summary = 'Relatives')
child parent file
1 flatten array_branch arrays.R
2 as_mapper as_function as_mapper.R
3 stop_defunct as_function as_mapper.R
4 paste_line as_function as_mapper.R
5 coerce coerce_lgl coerce.R
6 can_simplify as_vector coercion.R
7 warn_deprecated signal_soft_deprecated compat-lifecycle.R
8 compose compose compose.R
9 map compose compose.R
10 fn compose compose.R
11 as_vector lift_vl composition.R
12 what_bad_object stop_bad_type conditions.R
13 friendly_type_of stop_bad_type conditions.R
14 as_mapper cross cross.R
15 as_predicate_friendly_type_of cross cross.R
16 compact cross cross.R
17 is_bool cross cross.R
18 map_int vec_depth depth.R
19 as_predicate detect detect.R
20 index detect detect.R
21 as_predicate every every-some.R
22 detect_index head_while head-tail.R
23 negate head_while head-tail.R
24 as_mapper imap imap.R
25 vec_index imap imap.R
26 map2 imap imap.R
27 probe keep keep.R
28 list_recurse list_modify list-modify.R
29 lmap_at lmap lmap.R
30 as_mapper map map.R
31 as_mapper map2 map2-pmap.R
32 as_mapper modify.default modify.R
33 as_mapper negate negate.R
34 as_mapper safely output.R
35 capture_error safely output.R
36 signal_soft_deprecated partial partial.R
37 stop_defunct partial partial.R
38 map partial partial.R
39 paste_line partial partial.R
40 friendly_type_of partial partial.R
41 assign_in `pluck<-` pluck.R
42 as_mapper insistently rate.R
43 stop_bad_type insistently rate.R
44 capture_error insistently rate.R
45 rate_backoff insistently rate.R
46 is_rate insistently rate.R
47 rate_sleep insistently rate.R
48 rate_reset insistently rate.R
49 f insistently rate.R
50 reduce_impl reduce reduce.R
51 eval_dots rerun rerun.R
52 has_names rerun rerun.R
53 map2 invoke_map retired-invoke.R
54 as_invoke_function invoke_map retired-invoke.R
55 splice_if splice splice.R
56 check_tibble maybe_as_data_frame utils.R
Plotting the relationships
graph <- igraph::graph_from_data_frame(x_rel_df,directed = TRUE)
igraph::V(graph)$parents <- names(igraph::V(graph))
ggraph(graph) +
geom_edge_link(
aes(colour = file),
arrow = grid::arrow(length = unit(0.05, "inches"))) +
geom_node_text(aes(label = parents),size = 3) +
labs(title = 'purrr function map', colour = 'Exported') +
ggplot2::theme(legend.position = 'bottom')
#> Using `nicely` as default layout
Subset
Subsetting Package Functions
sub_x <- subset(x,'compose')
Click the triangle to view the contents found in the subset containing
compose and the functions the it calls.
details::details(lapply(sub_x,get_text),summary = 'Package subset')
$compose.R
[1] "#' Compose multiple functions"
[2] "#'"
[3] "#' @param ... Functions to apply in order (from right to left by"
[4] "#' default). Formulas are converted to functions in the usual way."
[5] "#'"
[6] "#' These dots support [tidy dots][rlang::list2] features. In"
[7] "#' particular, if your functions are stored in a list, you can"
[8] "#' splice that in with `!!!`."
[9] "#' @param .dir If `\"backward\"` (the default), the functions are called"
[10] "#' in the reverse order, from right to left, as is conventional in"
[11] "#' mathematics. If `\"forward\"`, they are called from left to right."
[12] "#' @return A function"
[13] "#' @export"
[14] "#' @examples"
[15] "#' not_null <- compose(`!`, is.null)"
[16] "#' not_null(4)"
[17] "#' not_null(NULL)"
[18] "#'"
[19] "#' add1 <- function(x) x + 1"
[20] "#' compose(add1, add1)(8)"
[21] "#'"
[22] "#' # You can use the formula shortcut for functions:"
[23] "#' fn <- compose(~ paste(.x, \"foo\"), ~ paste(.x, \"bar\"))"
[24] "#' fn(\"input\")"
[25] "#'"
[26] "#' # Lists of functions can be spliced with !!!"
[27] "#' fns <- list("
[28] "#' function(x) paste(x, \"foo\"),"
[29] "#' ~ paste(.x, \"bar\")"
[30] "#' )"
[31] "#' fn <- compose(!!!fns)"
[32] "#' fn(\"input\")"
[33] "compose <- function(..., .dir = c(\"backward\", \"forward\")) {"
[34] " .dir <- arg_match(.dir, c(\"backward\", \"forward\"))"
[35] ""
[36] " fns <- map(list2(...), rlang::as_closure, env = caller_env())"
[37] " if (!length(fns)) {"
[38] " # Return the identity function"
[39] " return(compose(function(x, ...) x))"
[40] " }"
[41] ""
[42] " if (.dir == \"backward\") {"
[43] " n <- length(fns)"
[44] " first_fn <- fns[[n]]"
[45] " fns <- rev(fns[-n])"
[46] " } else {"
[47] " first_fn <- fns[[1]]"
[48] " fns <- fns[-1]"
[49] " }"
[50] ""
[51] " body <- expr({"
[52] " out <- !!fn_body(first_fn)"
[53] ""
[54] " fns <- !!fns"
[55] " for (fn in fns) {"
[56] " out <- fn(out)"
[57] " }"
[58] ""
[59] " out"
[60] " })"
[61] ""
[62] " structure("
[63] " new_function(formals(first_fn), body, fn_env(first_fn)),"
[64] " class = c(\"purrr_function_compose\", \"function\"),"
[65] " first_fn = first_fn,"
[66] " fns = fns"
[67] " )"
[68] "}"
$map.R
[1] "#' Apply a function to each element of a vector"
[2] "#'"
[3] "#' @description"
[4] "#'"
[5] "#' The map functions transform their input by applying a function to"
[6] "#' each element and returning a vector the same length as the input."
[7] "#'"
[8] "#' * `map()`, `map_if()` and `map_at()` always return a list. See the"
[9] "#' [modify()] family for versions that return an object of the same"
[10] "#' type as the input."
[11] "#'"
[12] "#' The `_if` and `_at` variants take a predicate function `.p` that"
[13] "#' determines which elements of `.x` are transformed with `.f`."
[14] "#'"
[15] "#' * `map_lgl()`, `map_int()`, `map_dbl()` and `map_chr()` each return"
[16] "#' an atomic vector of the indicated type (or die trying)."
[17] "#'"
[18] "#' The return value of `.f` must be of length one for each element of"
[19] "#' `.x`. If `.f` uses an extractor function shortcut, `.default`"
[20] "#' can be specified to handle values that are absent or empty. See"
[21] "#' [as_mapper()] for more on `.default`."
[22] "#'"
[23] "#' * `map_dfr()` and `map_dfc()` return data frames created by"
[24] "#' row-binding and column-binding respectively. They require dplyr"
[25] "#' to be installed."
[26] "#'"
[27] "#' * `walk()` calls `.f` for its side-effect and returns the input `.x`."
[28] "#'"
[29] "#' @inheritParams as_mapper"
[30] "#' @param .x A list or atomic vector."
[31] "#' @param .p A single predicate function, a formula describing such a"
[32] "#' predicate function, or a logical vector of the same length as `.x`."
[33] "#' Alternatively, if the elements of `.x` are themselves lists of"
[34] "#' objects, a string indicating the name of a logical element in the"
[35] "#' inner lists. Only those elements where `.p` evaluates to"
[36] "#' `TRUE` will be modified."
[37] "#' @param .at A character vector of names, positive numeric vector of"
[38] "#' positions to include, or a negative numeric vector of positions to"
[39] "#' exlude. Only those elements corresponding to `.at` will be modified."
[40] "#' @param ... Additional arguments passed on to the mapped function."
[41] "#' @return All functions return a vector the same length as `.x`."
[42] "#'"
[43] "#' `map()` returns a list, `map_lgl()` a logical vector, `map_int()` an"
[44] "#' integer vector, `map_dbl()` a double vector, and `map_chr()` a character"
[45] "#' vector. The output of `.f` will be automatically typed upwards,"
[46] "#' e.g. logical -> integer -> double -> character."
[47] "#'"
[48] "#' If `.x` has `names()`, the return value preserves those names."
[49] "#'"
[50] "#' `walk()` returns the input `.x` (invisibly). This makes it easy to"
[51] "#' use in pipe."
[52] "#' @export"
[53] "#' @family map variants"
[54] "#' @examples"
[55] "#' 1:10 %>%"
[56] "#' map(rnorm, n = 10) %>%"
[57] "#' map_dbl(mean)"
[58] "#'"
[59] "#' # Or use an anonymous function"
[60] "#' 1:10 %>%"
[61] "#' map(function(x) rnorm(10, x))"
[62] "#'"
[63] "#' # Or a formula"
[64] "#' 1:10 %>%"
[65] "#' map(~ rnorm(10, .x))"
[66] "#'"
[67] "#' # The names of the input are preserved in the output:"
[68] "#' list(foo = 1, bar = 2) %>% map(`+`, 10)"
[69] "#'"
[70] "#' # Using set_names() with character vectors is handy to keep track"
[71] "#' # of the original inputs:"
[72] "#' set_names(c(\"foo\", \"bar\")) %>% map_chr(paste0, \":suffix\")"
[73] "#'"
[74] "#' # Extract by name or position"
[75] "#' # .default specifies value for elements that are missing or NULL"
[76] "#' l1 <- list(list(a = 1L), list(a = NULL, b = 2L), list(b = 3L))"
[77] "#' l1 %>% map(\"a\", .default = \"???\")"
[78] "#' l1 %>% map_int(\"b\", .default = NA)"
[79] "#' l1 %>% map_int(2, .default = NA)"
[80] "#'"
[81] "#' # Supply multiple values to index deeply into a list"
[82] "#' l2 <- list("
[83] "#' list(num = 1:3, letters[1:3]),"
[84] "#' list(num = 101:103, letters[4:6]),"
[85] "#' list()"
[86] "#' )"
[87] "#' l2 %>% map(c(2, 2))"
[88] "#'"
[89] "#' # Use a list to build an extractor that mixes numeric indices and names,"
[90] "#' # and .default to provide a default value if the element does not exist"
[91] "#' l2 %>% map(list(\"num\", 3))"
[92] "#' l2 %>% map_int(list(\"num\", 3), .default = NA)"
[93] "#'"
[94] "#'"
[95] "#' # Use a predicate function to decide whether to map a function:"
[96] "#' map_if(iris, is.factor, as.character)"
[97] "#'"
[98] "#' # Specify an alternative with the `.else` argument:"
[99] "#' map_if(iris, is.factor, as.character, .else = as.integer)"
[100] "#'"
[101] "#' # A more realistic example: split a data frame into pieces, fit a"
[102] "#' # model to each piece, summarise and extract R^2"
[103] "#' mtcars %>%"
[104] "#' split(.$cyl) %>%"
[105] "#' map(~ lm(mpg ~ wt, data = .x)) %>%"
[106] "#' map(summary) %>%"
[107] "#' map_dbl(\"r.squared\")"
[108] "#'"
[109] "#' # Use map_lgl(), map_dbl(), etc to reduce to a vector."
[110] "#' # * list"
[111] "#' mtcars %>% map(sum)"
[112] "#' # * vector"
[113] "#' mtcars %>% map_dbl(sum)"
[114] "#'"
[115] "#' # If each element of the output is a data frame, use"
[116] "#' # map_dfr to row-bind them together:"
[117] "#' mtcars %>%"
[118] "#' split(.$cyl) %>%"
[119] "#' map(~ lm(mpg ~ wt, data = .x)) %>%"
[120] "#' map_dfr(~ as.data.frame(t(as.matrix(coef(.)))))"
[121] "#' # (if you also want to preserve the variable names see"
[122] "#' # the broom package)"
[123] "#'"
[124] "#' # Use `map_depth()` to recursively traverse nested vectors and map"
[125] "#' # a function at a certain depth:"
[126] "#' x <- list(a = list(foo = 1:2, bar = 3:4), b = list(baz = 5:6))"
[127] "#' str(x)"
[128] "#' map_depth(x, 2, paste, collapse = \"/\")"
[129] "#'"
[130] "#' # Equivalent to:"
[131] "#' map(x, map, paste, collapse = \"/\")"
[132] "map <- function(.x, .f, ...) {"
[133] " .f <- as_mapper(.f, ...)"
[134] " .Call(map_impl, environment(), \".x\", \".f\", \"list\")"
[135] "}"
$reduce.R
[1] " fn <- function(x, y, ...) .f(y, x, ...)"
Consolidation
Consolidating Subsetted Functions into a File
pack_path <- repack(sub_x)
#> Functions packed to /var/folders/kx/t4h_mm1910sb7vhm_gnfnx2c0000gn/T//Rtmpgg7cm0/foreman/unpacked.R
Click the triangle to view the contents found in the file containing the consolidated functions.
details::details(file.path(pack_path,'unpacked.R'),summary = 'Consolidated Script')
#Generated by foreman:
#' Compose multiple functions
#'
#' @param ... Functions to apply in order (from right to left by
#' default). Formulas are converted to functions in the usual way.
#'
#' These dots support [tidy dots][rlang::list2] features. In
#' particular, if your functions are stored in a list, you can
#' splice that in with `!!!`.
#' @param .dir If `"backward"` (the default), the functions are called
#' in the reverse order, from right to left, as is conventional in
#' mathematics. If `"forward"`, they are called from left to right.
#' @return A function
#' @export
#' @examples
#' not_null <- compose(`!`, is.null)
#' not_null(4)
#' not_null(NULL)
#'
#' add1 <- function(x) x + 1
#' compose(add1, add1)(8)
#'
#' # You can use the formula shortcut for functions:
#' fn <- compose(~ paste(.x, "foo"), ~ paste(.x, "bar"))
#' fn("input")
#'
#' # Lists of functions can be spliced with !!!
#' fns <- list(
#' function(x) paste(x, "foo"),
#' ~ paste(.x, "bar")
#' )
#' fn <- compose(!!!fns)
#' fn("input")
compose <- function(..., .dir = c("backward", "forward")) {
.dir <- arg_match(.dir, c("backward", "forward"))
fns <- map(list2(...), rlang::as_closure, env = caller_env())
if (!length(fns)) {
# Return the identity function
return(compose(function(x, ...) x))
}
if (.dir == "backward") {
n <- length(fns)
first_fn <- fns[[n]]
fns <- rev(fns[-n])
} else {
first_fn <- fns[[1]]
fns <- fns[-1]
}
body <- expr({
out <- !!fn_body(first_fn)
fns <- !!fns
for (fn in fns) {
out <- fn(out)
}
out
})
structure(
new_function(formals(first_fn), body, fn_env(first_fn)),
class = c("purrr_function_compose", "function"),
first_fn = first_fn,
fns = fns
)
}
#' Apply a function to each element of a vector
#'
#' @description
#'
#' The map functions transform their input by applying a function to
#' each element and returning a vector the same length as the input.
#'
#' * `map()`, `map_if()` and `map_at()` always return a list. See the
#' [modify()] family for versions that return an object of the same
#' type as the input.
#'
#' The `_if` and `_at` variants take a predicate function `.p` that
#' determines which elements of `.x` are transformed with `.f`.
#'
#' * `map_lgl()`, `map_int()`, `map_dbl()` and `map_chr()` each return
#' an atomic vector of the indicated type (or die trying).
#'
#' The return value of `.f` must be of length one for each element of
#' `.x`. If `.f` uses an extractor function shortcut, `.default`
#' can be specified to handle values that are absent or empty. See
#' [as_mapper()] for more on `.default`.
#'
#' * `map_dfr()` and `map_dfc()` return data frames created by
#' row-binding and column-binding respectively. They require dplyr
#' to be installed.
#'
#' * `walk()` calls `.f` for its side-effect and returns the input `.x`.
#'
#' @inheritParams as_mapper
#' @param .x A list or atomic vector.
#' @param .p A single predicate function, a formula describing such a
#' predicate function, or a logical vector of the same length as `.x`.
#' Alternatively, if the elements of `.x` are themselves lists of
#' objects, a string indicating the name of a logical element in the
#' inner lists. Only those elements where `.p` evaluates to
#' `TRUE` will be modified.
#' @param .at A character vector of names, positive numeric vector of
#' positions to include, or a negative numeric vector of positions to
#' exlude. Only those elements corresponding to `.at` will be modified.
#' @param ... Additional arguments passed on to the mapped function.
#' @return All functions return a vector the same length as `.x`.
#'
#' `map()` returns a list, `map_lgl()` a logical vector, `map_int()` an
#' integer vector, `map_dbl()` a double vector, and `map_chr()` a character
#' vector. The output of `.f` will be automatically typed upwards,
#' e.g. logical -> integer -> double -> character.
#'
#' If `.x` has `names()`, the return value preserves those names.
#'
#' `walk()` returns the input `.x` (invisibly). This makes it easy to
#' use in pipe.
#' @export
#' @family map variants
#' @examples
#' 1:10 %>%
#' map(rnorm, n = 10) %>%
#' map_dbl(mean)
#'
#' # Or use an anonymous function
#' 1:10 %>%
#' map(function(x) rnorm(10, x))
#'
#' # Or a formula
#' 1:10 %>%
#' map(~ rnorm(10, .x))
#'
#' # The names of the input are preserved in the output:
#' list(foo = 1, bar = 2) %>% map(`+`, 10)
#'
#' # Using set_names() with character vectors is handy to keep track
#' # of the original inputs:
#' set_names(c("foo", "bar")) %>% map_chr(paste0, ":suffix")
#'
#' # Extract by name or position
#' # .default specifies value for elements that are missing or NULL
#' l1 <- list(list(a = 1L), list(a = NULL, b = 2L), list(b = 3L))
#' l1 %>% map("a", .default = "???")
#' l1 %>% map_int("b", .default = NA)
#' l1 %>% map_int(2, .default = NA)
#'
#' # Supply multiple values to index deeply into a list
#' l2 <- list(
#' list(num = 1:3, letters[1:3]),
#' list(num = 101:103, letters[4:6]),
#' list()
#' )
#' l2 %>% map(c(2, 2))
#'
#' # Use a list to build an extractor that mixes numeric indices and names,
#' # and .default to provide a default value if the element does not exist
#' l2 %>% map(list("num", 3))
#' l2 %>% map_int(list("num", 3), .default = NA)
#'
#'
#' # Use a predicate function to decide whether to map a function:
#' map_if(iris, is.factor, as.character)
#'
#' # Specify an alternative with the `.else` argument:
#' map_if(iris, is.factor, as.character, .else = as.integer)
#'
#' # A more realistic example: split a data frame into pieces, fit a
#' # model to each piece, summarise and extract R^2
#' mtcars %>%
#' split(.$cyl) %>%
#' map(~ lm(mpg ~ wt, data = .x)) %>%
#' map(summary) %>%
#' map_dbl("r.squared")
#'
#' # Use map_lgl(), map_dbl(), etc to reduce to a vector.
#' # * list
#' mtcars %>% map(sum)
#' # * vector
#' mtcars %>% map_dbl(sum)
#'
#' # If each element of the output is a data frame, use
#' # map_dfr to row-bind them together:
#' mtcars %>%
#' split(.$cyl) %>%
#' map(~ lm(mpg ~ wt, data = .x)) %>%
#' map_dfr(~ as.data.frame(t(as.matrix(coef(.)))))
#' # (if you also want to preserve the variable names see
#' # the broom package)
#'
#' # Use `map_depth()` to recursively traverse nested vectors and map
#' # a function at a certain depth:
#' x <- list(a = list(foo = 1:2, bar = 3:4), b = list(baz = 5:6))
#' str(x)
#' map_depth(x, 2, paste, collapse = "/")
#'
#' # Equivalent to:
#' map(x, map, paste, collapse = "/")
map <- function(.x, .f, ...) {
.f <- as_mapper(.f, ...)
.Call(map_impl, environment(), ".x", ".f", "list")
}
fn <- function(x, y, ...) .f(y, x, ...)
Compiled Library
This example will use the installed library future.
Using foreman with an compiled libraries is also simple
library(future)
#>
#> Attaching package: 'future'
#> The following objects are masked from 'package:igraph':
#>
#> %->%, %<-%
unpacked_future <- unpack(ns = 'future')%>%
relationship()%>%
as.data.frame()
graph <- igraph::graph_from_data_frame(unpacked_future,directed = TRUE)
igraph::V(graph)$parents <- names(igraph::V(graph))
igraph::V(graph)$exported <- names(igraph::V(graph))%in%ls('package:future')
ggraph(graph) +
geom_edge_link(
arrow = grid::arrow(length = unit(0.05, "inches")),alpha = 0.05) +
geom_node_text(aes(colour = exported,label = parents),size = 2) +
labs(title = 'future function map', colour = 'Exported') +
ggplot2::theme(legend.position = 'bottom')
#> Using `nicely` as default layout
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