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-Functional programming in other languages
- - - Source:vignettes/other-langs.Rmd
- other-langs.Rmdpurrr draws inspiration from many related tools:
--
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List operations defined in the Haskell prelude
-Scala’s list methods.
-Functional programming librarys for javascript: underscore.js, lodash and lazy.js.
-rlist, another R package to support working with lists. Similar goals but somewhat different philosophy.
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However, the goal of purrr is not to try and simulate a purer functional programming language in R; we don’t want to implement a second-class version of Haskell in R. The goal is to give you similar expressiveness to an FP language, while allowing you to write code that looks and works like R:
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Instead of point free (tacit) style, we use the pipe,
%>%, to write code that can be read from left to right.
-Instead of currying, we use
...to pass in extra arguments.
-Anonymous functions are verbose in R, so we provide two convenient shorthands. For unary functions,
~ .x + 1is equivalent tofunction(.x) .x + 1. For chains of transformations functions,. %>% f() %>% g()is equivalent tofunction(.) . %>% f() %>% g()(this shortcut is provided by magrittr).
-R is weakly typed, so we need
mapvariants that describe the output type (likemap_int(),map_dbl(), etc) because we don’t know the return type of.f.
-R has named arguments, so instead of providing different functions for minor variations (e.g.
detect()anddetectLast()) we use a named argument,.right. Type-stable functions are easy to reason about so additional arguments will never change the type of the output.
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diff --git a/docs/favicon.ico b/docs/favicon.ico
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Lionel Henry. Author, maintainer. -
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Hadley Wickham. Author. -
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. Copyright holder, funder.
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-/*
- Sticky-kit v1.1.2 | WTFPL | Leaf Corcoran 2015 | http://leafo.net
-*/
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--Overview
-purrr enhances R’s functional programming (FP) toolkit by providing a complete and consistent set of tools for working with functions and vectors. If you’ve never heard of FP before, the best place to start is the family of map() functions which allow you to replace many for loops with code that is both more succinct and easier to read. The best place to learn about the map() functions is the iteration chapter in R for data science.
-
-
--Usage
-The following example uses purrr to solve a fairly realistic problem: split a data frame into pieces, fit a model to each piece, compute the summary, then extract the R2.
-library(purrr)
-
-mtcars %>%
- split(.$cyl) %>% # from base R
- map(~ lm(mpg ~ wt, data = .)) %>%
- map(summary) %>%
- map_dbl("r.squared")
-#> 4 6 8
-#> 0.5086326 0.4645102 0.4229655This example illustrates some of the advantages of purrr functions over the equivalents in base R:
--
-
The first argument is always the data, so purrr works naturally with the pipe.
-All purrr functions are type-stable. They always return the advertised output type (
map()returns lists;map_dbl()returns double vectors), or they throw an errror.
-All
map()functions either accept function, formulas (used for succinctly generating anonymous functions), a character vector (used to extract components by name), or a numeric vector (used to extract by position).
-
Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.
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- Changelog
- Source:NEWS.md
-
-
-
- -purrr 0.2.4.9000 Unreleased -
--
-
accumulate()andaccumulate_right()now inherit names from their first input (@AshesITR, #446)
-attr_getter()no longer uses partial matching. For example, if anxobject has alabelsattribute but nolabelattribute,attr_getter("label")(x)will no longer extract thelabelsattribute (#460, @huftis).
-flatten_dfr()andflatten_dfc()now aborts if dplyr is not installed. (#454)
-imap_dfr()now works with.idargument is provided (#429)
-map_at(),modify_at()andlmap_at()accept negative values for.at, ignoring elements at those positions.
-map()andmodify()now work with calls and pairlists (#412).
-modify(),modify_if()andmodify_at()now preserve the class of atomic vectors instead of promoting them to lists. New S3 methods are provided for character, logical, double, and integer classes (@t-kalinowski, #417).
-list_modify(),update_list()andlist_merge()now handle duplicate duplicate argument names correctly (#441, @mgirlich).
-map_raw,imap_raw,flatten_raw,invoke_map_raw,map2_rawandpmap_rawadded to support raw vectors. (#455, @romainfrancois)
-
-
- -purrr 0.2.3 2017-08-02 -
-
-
--Breaking changes
-We noticed the following issues during reverse dependencies checks:
--
-
If
reduce()fails with this message:Error: `.x` is empty, and no `.init` supplied, this is becausereduce()now returns.initwhen.xis empty. Fix the problem by supplying an appropriate argument to.init, or by providing special behaviour when.xhas length 0.
-The type predicates have been migrated to rlang. Consequently the
bare-type-predicatesdocumentation topic is no longer in purrr, which might cause a warning if you cross-reference it.
-
-
--Dependencies
-purrr no longer depends on lazyeval or Rcpp (or dplyr, as of the previous version). This makes the dependency graph of the tidyverse simpler, and makes purrr more suitable as a dependency of lower-level packages.
-There have also been two changes to eliminate name conflicts between purrr and dplyr:
--
-
order_by(),sort_by()andsplit_by()have been removed.order_by()conflicted withdplyr::order_by()and the complete family doesn’t feel that useful. Use tibbles instead (#217).
-contains()has been renamed tohas_element()to avoid conflicts with dplyr (#217).
-
-
--pluck()
-The plucking mechanism used for indexing into data structures with map() has been extracted into the function pluck(). Plucking is often more readable to extract an element buried in a deep data structure. Compare this syntax-heavy extraction which reads non-linearly:
accessor(x[[1]])$footo the equivalent pluck:
-x %>% pluck(1, accessor, "foo")
-
--Map helpers
--
-
-
-
-as_function()is nowas_mapper()because it is a tranformation that makes sense primarily for mapping functions, not in general (#298)..nullhas been renamed to.defaultto better reflect its intent (#298)..defaultis returned whenever an element is absent or empty (#231, #254).
-as_mapper()sanitises primitive functions by transforming them to closures with standardised argument names (usingrlang::as_closure()). For instance+is transformed tofunction(.x, .y) .x + .y. This results in proper argument matching so thatmap(1:10, partial(-, .x = 5))produceslist(5 - 1, 5 - 2, ...).
- Recursive indexing can now extract objects out of environments (#213) and S4 objects (#200), as well as lists.
-attr_getter()makes it possible to extract from attributes likemap(list(iris, mtcars), attr_getter("row.names")).
-The argument list for formula-functions has been tweaked so that you can refer to arguments by position with
..1,..2, and so on. This makes it possible to use the formula shorthand for functions with more than two arguments (#289).
-possibly(),safely()and friends no longer capture interrupts: this means that you can now terminate a mapper using one of these with Escape or Ctrl + C (#314)
-
-
--Map functions
--
-
All map functions now treat
NULLthe same way as an empty vector (#199), and return an empty vector if any input is an empty vector.
-All
map()functions now force their arguments in the same way that base R does forlapply()(#191). This makesmap()etc easier to use when generating functions.
-A new family of “indexed” map functions,
imap(),imap_lgl()etc, provide a short-hand formap2(x, names(x))ormap2(x, seq_along(x))(#240).
-The data frame suffix
_dfhas been (soft) deprecated in favour of_dfrto more clearly indicate that it’s a row-bind. All variants now also have a_dfcfor column binding (#167). (These will not be terribly useful untildplyr::bind_rows()/dplyr::bind_cols()have better semantics for vectors.)
-
-
--Modify functions
-A new modify() family returns the same output of the type as the input .x. This is in contrast to the map() family which always returns a list, regardless of the input type.
The modify functions are S3 generics. However their default methods should be sufficient for most classes since they rely on the semantics of [<-. modify.default() is thus a shorthand for x[] <- map(x, f).
-
-
at_depth()has been renamed tomodify_depth().
-modify_depth()gains new.raggedargument, and negative depths are now computed relative to the deepest component of the list (#236).
-
-
--New functions
--
-
auto_browse(f)returns a new function that automatically callsbrowser()iffthrows an error (#281).
-vec_depth()computes the depth (i.e. the number of levels of indexing) or a vector (#243).
-reduce2()andreduce2_right()make it possible to reduce with a 3 argument function where the first argument is the accumulated value, the second argument is.x, and the third argument is.y(#163).
-list_modify()extendsstats::modifyList()to replace by position if the list is not named.(#201).list_merge()operates similarly tolist_modify()but combines instead of replacing (#322).
-The legacy function
update_list()is basically a version oflist_modifythat evaluates formulas within the list. It is likely to be deprecated in the future in favour of a tidyeval interface such as a list method fordplyr::mutate().
-
-
--Minor improvements and bug fixes
--
-
Thanks to @dchiu911, the unit test coverage of purrr is now much greater.
-All predicate functions are re-exported from rlang (#124).
-compact()now works with standard mapper conventions (#282).
-cross_n()has been renamed tocross(). The_nsuffix was removed for consistency withpmap()(originally calledmap_n()at the start of the project) andtranspose()(originally calledzip_n()). Similarly,cross_d()has been renamed tocross_df()for consistency withmap_df().
-every()andsome()now returnNAif present in the input (#174).
-invoke()uses a more robust approach to generate the argument list (#249) It no longer uses lazyeval to figure out which enviroment a characterfcomes from.
-is_numeric()andis_scalar_numeric()are deprecated because they don’t test for what you might expect at first sight.
-reduce()now throws an error if.xis empty and.initis not supplied.
-Deprecated functions
flatmap(),map3(),map_n(),walk3(),walk_n(),zip2(),zip3(),zip_n()have been removed.
-pmap()coerces data frames to lists to avoid the expensive[.data.framewhich provides security that is unneeded here (#220).
-- -
set_names()can now take a function to tranform the names programmatically (#276), and you can supply names in...to reduce typing even more more (#316).set_names()is now powered byrlang::set_names().
-- -
transpose()now matches by name if available (#164). You can override the default choice with the new.namesargument.
-The function argument of
detect()anddetect_index()have been renamed from.pto.f. This is because they have mapper semantics rather than predicate semantics.
-
-
- -purrr 0.2.2.1 Unreleased -
-This is a compatibility release with dplyr 0.6.0.
--
-
- All data-frame based mappers have been removed in favour of new functions and idioms in the tidyverse.
dmap(),dmap_at(),dmap_if(),invoke_rows(),slice_rows(),map_rows(),by_slice(),by_row(), andunslice()have been moved to purrrlyr. This is a bit of an aggresive change but it allows us to make the dependencies much lighter.
-
-
- -purrr 0.2.2 2016-06-18 -
--
-
Fix for dev tibble support.
-as_function()now supports list arguments which allow recursive indexing using either names or positions. They now always stop when encountering the first NULL (#173).
-accumulateandreducecorrectly pass extra arguments to the worker function.
-
-
- -purrr 0.2.1 2016-02-13 -
--
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as_function()gains a.nullargument that for character and numeric values allows you to specify what to return for null/absent elements (#110). This can be used with any map function, e.g.map_int(x, 1, .null = NA)
-as_function()is now generic.
-New
is_function()that returnsTRUEonly for regular functions.
-Fix crash on GCC triggered by
invoke_rows().
-
-
- -purrr 0.2.0 2016-01-04 -
-
-
--New functions
--
-
-
-
There are two handy infix functions:
- -
- accumulate()has been added to handle recursive folding. It is shortand forReduce(f, .x, accumulate = TRUE)and follows a similar syntax toreduce()(#145). A right-hand versionaccumulate_right()was also added.
-map_df()row-binds output together. It’s the equivalent ofplyr::ldply()(#127)
-flatten()is now type-stable and always returns a list. To return a simpler vector, useflatten_lgl(),flatten_int(),flatten_dbl(),flatten_chr(), orflatten_df().
-invoke()has been overhauled to be more useful: it now works similarly tomap_call()when.xis NULL, and hencemap_call()has been deprecated.invoke_map()is a vectorised complement toinvoke()(#125), and comes with typed variantsinvoke_map_lgl(),invoke_map_int(),invoke_map_dbl(),invoke_map_chr(), andinvoke_map_df().
-transpose()replaceszip2(),zip3(), andzip_n()(#128). The name more clearly reflects the intent (transposing the first and second levels of list). It no longer has fields argument or the.simplifyargument; instead use the newsimplify_all()function.
-safely(),quietly(), andpossibly()are experimental functions for working with functions with side-effects (e.g. printed output, messages, warnings, and errors) (#120).safely()is a version oftry()that modifies a function (rather than an expression), and always returns a list with two components,resultanderror.
-list_along()andrep_along()generalise the idea ofseq_along(). (#122).
-is_null()is the snake-case version ofis.null().
-pmap()(parallel map) replacesmap_n()(#132), and has typed-variants suffixedpmap_lgl(),pmap_int(),pmap_dbl(),pmap_chr(), andpmap_df().
-set_names()is a snake-case alternative tosetNames()with stricter equality checking, and more convenient defaults for pipes:x %>% set_names()is equivalent tosetNames(x, x)(#119).
-
-
--Row based functionals
-We are still figuring out what belongs in dplyr and what belongs in purrr. Expect much experimentation and many changes with these functions.
--
-
map()now always returns a list. Data frame support has been moved tomap_df()anddmap(). The latter supports sliced data frames as a shortcut for the combination ofby_slice()anddmap():x %>% by_slice(dmap, fun, .collate = "rows"). The conditional variantsdmap_at()anddmap_if()also support sliced data frames and will recycle scalar results to the slice size.
-map_rows()has been renamed toinvoke_rows(). As other rows-based functionals, it collates results inside lists by default, but with column collation this function is equivalent toplyr::mdply().
-The rows-based functionals gain a
.tooption to name the output column as well as a.collateargument. The latter allows to collate the output in lists (by default), on columns or on rows. This makes these functions more flexible and more predictable.
-
-
-
--Bug fixes and minor changes
--
-
as_function(), which converts formulas etc to functions, is now exported (#123).
-- -
update_list()can now modify an element calledx(#98).
-map*()now use custom C code, rather than relying onlapply(),mapply()etc. The performance characteristcs are very similar, but it allows us greater control over the output (#118).
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accumulate(.x, .f, ..., .init) - -accumulate_right(.x, .f, ..., .init)- -
Arguments
-| .x | -A list or atomic vector. |
-
|---|---|
| .f | -For For |
-
| ... | -Additional arguments passed on to |
-
| .init | -If supplied, will be used as the first value to start
-the accumulation, rather than using |
-
Value
- -A vector the same length of .x with the same names as .x
Examples
--1:3 %>% accumulate(`+`)#> [1] 1 3 61:10 %>% accumulate_right(`*`)#> [1] 3628800 3628800 1814400 604800 151200 30240 5040 720 90 -#> [10] 10-# From Haskell's scanl documentation -1:10 %>% accumulate(max, .init = 5)#> [1] 5 5 5 5 5 5 6 7 8 9 10-# Understanding the arguments .x and .y when .f -# is a lambda function -# .x is the accumulating value -1:10 %>% accumulate(~ .x)#> [1] 1 1 1 1 1 1 1 1 1 1# .y is element in the list -1:10 %>% accumulate(~ .y)#> [1] 1 2 3 4 5 6 7 8 9 10-# Simulating stochastic processes with drift -# NOT RUN { -library(dplyr) -library(ggplot2) - -rerun(5, rnorm(100)) %>% - set_names(paste0("sim", 1:5)) %>% - map(~ accumulate(., ~ .05 + .x + .y)) %>% - map_dfr(~ data_frame(value = .x, step = 1:100), .id = "simulation") %>% - ggplot(aes(x = step, y = value)) + - geom_line(aes(color = simulation)) + - ggtitle("Simulations of a random walk with drift") -# }
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diff --git a/docs/reference/array-coercion.html b/docs/reference/array-coercion.html
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- These functions take the idea of seq_along() and generalise
-it to creating lists (list_along) and repeating values
-(rep_along).
list_along(x) - -rep_along(x, y)- -
Arguments
-| x | -A vector. |
-
|---|---|
| y | -Values to repeat. |
-
Value
- -A vector of the same length as x.
Examples
--x <- 1:5 -rep_along(x, 1:2)#> [1] 1 2 1 2 1rep_along(x, 1)#> [1] 1 1 1 1 1list_along(x)#> [[1]] -#> NULL -#> -#> [[2]] -#> NULL -#> -#> [[3]] -#> NULL -#> -#> [[4]] -#> NULL -#> -#> [[5]] -#> NULL -#>
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diff --git a/docs/reference/as_mapper.html b/docs/reference/as_mapper.html
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-used with purrr's functionals by turning them into lists. The
-details of the coercion are controlled by the margin
-argument. array_tree() creates an hierarchical list (a tree)
-that has as many levels as dimensions specified in margin,
-while array_branch() creates a flat list (by analogy, a
-branch) along all mentioned dimensions.
array_branch(array, margin = NULL) - -array_tree(array, margin = NULL)- -
Arguments
-| array | -An array to coerce into a list. |
-
|---|---|
| margin | -A numeric vector indicating the positions of the
-indices to be to be enlisted. If |
-
Details
- -When no margin is specified, all dimensions are used by
-default. When margin is a numeric vector of length zero, the
-whole array is wrapped in a list.
Examples
--# We create an array with 3 dimensions -x <- array(1:12, c(2, 2, 3)) - -# A full margin for such an array would be the vector 1:3. This is -# the default if you don't specify a margin - -# Creating a branch along the full margin is equivalent to -# as.list(array) and produces a list of size length(x): -array_branch(x) %>% str()#> List of 12 -#> $ : int 1 -#> $ : int 2 -#> $ : int 3 -#> $ : int 4 -#> $ : int 5 -#> $ : int 6 -#> $ : int 7 -#> $ : int 8 -#> $ : int 9 -#> $ : int 10 -#> $ : int 11 -#> $ : int 12-# A branch along the first dimension yields a list of length 2 -# with each element containing a 2x3 array: -array_branch(x, 1) %>% str()#> List of 2 -#> $ : int [1:2, 1:3] 1 3 5 7 9 11 -#> $ : int [1:2, 1:3] 2 4 6 8 10 12-# A branch along the first and third dimensions yields a list of -# length 2x3 whose elements contain a vector of length 2: -array_branch(x, c(1, 3)) %>% str()#> List of 6 -#> $ : int [1:2] 1 3 -#> $ : int [1:2] 2 4 -#> $ : int [1:2] 5 7 -#> $ : int [1:2] 6 8 -#> $ : int [1:2] 9 11 -#> $ : int [1:2] 10 12-# Creating a tree from the full margin creates a list of lists of -# lists: -array_tree(x) %>% str()#> List of 2 -#> $ :List of 2 -#> ..$ :List of 3 -#> .. ..$ : int 1 -#> .. ..$ : int 5 -#> .. ..$ : int 9 -#> ..$ :List of 3 -#> .. ..$ : int 3 -#> .. ..$ : int 7 -#> .. ..$ : int 11 -#> $ :List of 2 -#> ..$ :List of 3 -#> .. ..$ : int 2 -#> .. ..$ : int 6 -#> .. ..$ : int 10 -#> ..$ :List of 3 -#> .. ..$ : int 4 -#> .. ..$ : int 8 -#> .. ..$ : int 12-# The ordering and the depth of the tree are controlled by the -# margin argument: -array_tree(x, c(3, 1)) %>% str()#> List of 3 -#> $ :List of 2 -#> ..$ : int [1:2] 1 3 -#> ..$ : int [1:2] 2 4 -#> $ :List of 2 -#> ..$ : int [1:2] 5 7 -#> ..$ : int [1:2] 6 8 -#> $ :List of 2 -#> ..$ : int [1:2] 9 11 -#> ..$ : int [1:2] 10 12
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diff --git a/docs/reference/as_vector.html b/docs/reference/as_vector.html
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- as_mapper is the powerhouse behind the varied function
-specifications that most purrr functions allow. It is an S3
-generic. The default method forwards its arguments to
-rlang::as_function().
as_mapper(.f, ...) - -# S3 method for character -as_mapper(.f, ..., .null, .default = NULL) - -# S3 method for numeric -as_mapper(.f, ..., .null, .default = NULL) - -# S3 method for list -as_mapper(.f, ..., .null, .default = NULL)- -
Arguments
-| .f | -A function, formula, or atomic vector. -If a function, it is used as is. -If a formula, e.g.
This syntax allows you to create very compact anonymous functions. -If character vector, numeric vector, or list, it
-is converted to an extractor function. Character vectors index by name
-and numeric vectors index by position; use a list to index by position
-and name at different levels. Within a list, wrap strings in |
-
|---|---|
| ... | -Additional arguments passed on to methods. |
-
| .default, .null | -Optional additional argument for extractor functions
-(i.e. when |
-
Examples
--as_mapper(~ . + 1)#> function (..., .x = ..1, .y = ..2, . = ..1) -#> . + 1 -#> <environment: 0x7f9d525cf5c8>as_mapper(1)#> function (x, ...) -#> pluck(x, list(1), .default = NULL) -#> <environment: 0x7f9d52109cd0>-as_mapper(c("a", "b", "c"))#> function (x, ...) -#> pluck(x, list("a", "b", "c"), .default = NULL) -#> <environment: 0x7f9d55aa0698># Equivalent to function(x) x[["a"]][["b"]][["c"]] - -as_mapper(list(1, "a", 2))#> function (x, ...) -#> pluck(x, list(1, "a", 2), .default = NULL) -#> <environment: 0x7f9d559eb990>#> function (x, ...) -#> pluck(x, list(1, function (x) -#> attr(x, attr, exact = TRUE)), .default = NULL) -#> <environment: 0x7f9d5384ef90># Equivalent to function(x) attr(x[[1]], "a") - -as_mapper(c("a", "b", "c"), .null = NA)#> function (x, ...) -#> pluck(x, list("a", "b", "c"), .default = NA) -#> <environment: 0x7f9d532a7d80>
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diff --git a/docs/reference/compose.html b/docs/reference/compose.html
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- as_vector() collapses a list of vectors into one vector. It
-checks that the type of each vector is consistent with
-.type. If the list can not be simplified, it throws an error.
-simplify will simplify a vector if possible; simplify_all
-will apply simplify to every element of a list.
as_vector(.x, .type = NULL) - -simplify(.x, .type = NULL) - -simplify_all(.x, .type = NULL)- -
Arguments
-| .x | -A list of vectors |
-
|---|---|
| .type | -A vector mold or a string describing the type of the
-input vectors. The latter can be any of the types returned by
- |
-
Details
- -.type can be a vector mold specifying both the type and the
-length of the vectors to be concatenated, such as numeric(1)
-or integer(4). Alternatively, it can be a string describing
-the type, one of: "logical", "integer", "double", "complex",
-"character" or "raw".
Examples
--# Supply the type either with a string: -as.list(letters) %>% as_vector("character")#> [1] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j" "k" "l" "m" "n" "o" "p" "q" "r" "s" -#> [20] "t" "u" "v" "w" "x" "y" "z"-# Or with a vector mold: -as.list(letters) %>% as_vector(character(1))#> [1] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j" "k" "l" "m" "n" "o" "p" "q" "r" "s" -#> [20] "t" "u" "v" "w" "x" "y" "z"-# Vector molds are more flexible because they also specify the -# length of the concatenated vectors: -list(1:2, 3:4, 5:6) %>% as_vector(integer(2))#> [1] 1 2 3 4 5 6-# Note that unlike vapply(), as_vector() never adds dimension -# attributes. So when you specify a vector mold of size > 1, you -# always get a vector and not a matrix -
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diff --git a/docs/reference/cross.html b/docs/reference/cross.html
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- Compose multiple functions
- -compose(...)- -
Arguments
-| ... | -n functions to apply in order from right to left. |
-
|---|
Value
- -A function
- - -Examples
--not_null <- compose(`!`, is.null) -not_null(4)#> [1] TRUEnot_null(NULL)#> [1] FALSE-add1 <- function(x) x + 1 -compose(add1, add1)(8)#> [1] 10
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- cross2() returns the product set of the elements of
-.x and .y. cross3() takes an additional
-.z argument. cross() takes a list .l and
-returns the cartesian product of all its elements in a list, with
-one combination by element. cross_df() is like
-cross() but returns a data frame, with one combination by
-row.
cross(.l, .filter = NULL) - -cross2(.x, .y, .filter = NULL) - -cross3(.x, .y, .z, .filter = NULL) - -cross_df(.l, .filter = NULL)- -
Arguments
-| .l | -A list of lists or atomic vectors. Alternatively, a data
-frame. |
-
|---|---|
| .filter | -A predicate function that takes the same number of -arguments as the number of variables to be combined. |
-
| .x, .y, .z | -Lists or atomic vectors. |
-
Value
- -cross2(), cross3() and cross()
-always return a list. cross_df() always returns a data
-frame. cross() returns a list where each element is one
-combination so that the list can be directly mapped
-over. cross_df() returns a data frame where each row is one
-combination.
Details
- -cross(), cross2() and cross3() return the
-cartesian product is returned in wide format. This makes it more
-amenable to mapping operations. cross_df() returns the output
-in long format just as expand.grid() does. This is adapted
-to rowwise operations.
When the number of combinations is large and the individual
-elements are heavy memory-wise, it is often useful to filter
-unwanted combinations on the fly with .filter. It must be
-a predicate function that takes the same number of arguments as the
-number of crossed objects (2 for cross2(), 3 for
-cross3(), length(.l) for cross()) and
-returns TRUE or FALSE. The combinations where the
-predicate function returns TRUE will be removed from the
-result.
See also
- -expand.grid()
Examples
--# We build all combinations of names, greetings and separators from our -# list of data and pass each one to paste() -data <- list( - id = c("John", "Jane"), - greeting = c("Hello.", "Bonjour."), - sep = c("! ", "... ") -) - -data %>% - cross() %>% - map(lift(paste))#> [[1]] -#> [1] "John! Hello." -#> -#> [[2]] -#> [1] "Jane! Hello." -#> -#> [[3]] -#> [1] "John! Bonjour." -#> -#> [[4]] -#> [1] "Jane! Bonjour." -#> -#> [[5]] -#> [1] "John... Hello." -#> -#> [[6]] -#> [1] "Jane... Hello." -#> -#> [[7]] -#> [1] "John... Bonjour." -#> -#> [[8]] -#> [1] "Jane... Bonjour." -#>-# cross() returns the combinations in long format: many elements, -# each representing one combination. With cross_df() we'll get a -# data frame in long format: crossing three objects produces a data -# frame of three columns with each row being a particular -# combination. This is the same format that expand.grid() returns. -args <- data %>% cross_df() - -# In case you need a list in long format (and not a data frame) -# just run as.list() after cross_df() -args %>% as.list()#> $id -#> [1] "John" "Jane" "John" "Jane" "John" "Jane" "John" "Jane" -#> -#> $greeting -#> [1] "Hello." "Hello." "Bonjour." "Bonjour." "Hello." "Hello." "Bonjour." -#> [8] "Bonjour." -#> -#> $sep -#> [1] "! " "! " "! " "! " "... " "... " "... " "... " -#>-# This format is often less pratical for functional programming -# because applying a function to the combinations requires a loop -out <- vector("list", length = nrow(args)) -for (i in seq_along(out)) - out[[i]] <- map(args, i) %>% invoke(paste, .) -out#> [[1]] -#> [1] "John! Hello." -#> -#> [[2]] -#> [1] "Jane! Hello." -#> -#> [[3]] -#> [1] "John! Bonjour." -#> -#> [[4]] -#> [1] "Jane! Bonjour." -#> -#> [[5]] -#> [1] "John... Hello." -#> -#> [[6]] -#> [1] "Jane... Hello." -#> -#> [[7]] -#> [1] "John... Bonjour." -#> -#> [[8]] -#> [1] "Jane... Bonjour." -#>-# It's easier to transpose and then use invoke_map() -args %>% transpose() %>% map_chr(~ invoke(paste, .))#> [1] "John! Hello." "Jane! Hello." "John! Bonjour." "Jane! Bonjour." -#> [5] "John... Hello." "Jane... Hello." "John... Bonjour." "Jane... Bonjour."-# Unwanted combinations can be filtered out with a predicate function -filter <- function(x, y) x >= y -cross2(1:5, 1:5, .filter = filter) %>% str()#> List of 10 -#> $ :List of 2 -#> ..$ : int 1 -#> ..$ : int 2 -#> $ :List of 2 -#> ..$ : int 1 -#> ..$ : int 3 -#> $ :List of 2 -#> ..$ : int 2 -#> ..$ : int 3 -#> $ :List of 2 -#> ..$ : int 1 -#> ..$ : int 4 -#> $ :List of 2 -#> ..$ : int 2 -#> ..$ : int 4 -#> $ :List of 2 -#> ..$ : int 3 -#> ..$ : int 4 -#> $ :List of 2 -#> ..$ : int 1 -#> ..$ : int 5 -#> $ :List of 2 -#> ..$ : int 2 -#> ..$ : int 5 -#> $ :List of 2 -#> ..$ : int 3 -#> ..$ : int 5 -#> $ :List of 2 -#> ..$ : int 4 -#> ..$ : int 5-# To give names to the components of the combinations, we map -# setNames() on the product: -seq_len(3) %>% - cross2(., ., .filter = `==`) %>% - map(setNames, c("x", "y"))#> [[1]] -#> [[1]]$x -#> [1] 2 -#> -#> [[1]]$y -#> [1] 1 -#> -#> -#> [[2]] -#> [[2]]$x -#> [1] 3 -#> -#> [[2]]$y -#> [1] 1 -#> -#> -#> [[3]] -#> [[3]]$x -#> [1] 1 -#> -#> [[3]]$y -#> [1] 2 -#> -#> -#> [[4]] -#> [[4]]$x -#> [1] 3 -#> -#> [[4]]$y -#> [1] 2 -#> -#> -#> [[5]] -#> [[5]]$x -#> [1] 1 -#> -#> [[5]]$y -#> [1] 3 -#> -#> -#> [[6]] -#> [[6]]$x -#> [1] 2 -#> -#> [[6]]$y -#> [1] 3 -#> -#>-# Alternatively we can encapsulate the arguments in a named list -# before crossing to get named components: -seq_len(3) %>% - list(x = ., y = .) %>% - cross(.filter = `==`)#> [[1]] -#> [[1]]$x -#> [1] 2 -#> -#> [[1]]$y -#> [1] 1 -#> -#> -#> [[2]] -#> [[2]]$x -#> [1] 3 -#> -#> [[2]]$y -#> [1] 1 -#> -#> -#> [[3]] -#> [[3]]$x -#> [1] 1 -#> -#> [[3]]$y -#> [1] 2 -#> -#> -#> [[4]] -#> [[4]]$x -#> [1] 3 -#> -#> [[4]]$y -#> [1] 2 -#> -#> -#> [[5]] -#> [[5]]$x -#> [1] 1 -#> -#> [[5]]$y -#> [1] 3 -#> -#> -#> [[6]] -#> [[6]]$x -#> [1] 2 -#> -#> [[6]]$y -#> [1] 3 -#> -#>
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diff --git a/docs/reference/detect.html b/docs/reference/detect.html
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- - -depth(x)- -
Arguments
-| x | -A vector |
-
|---|
Value
- -An integer.
- - -Examples
--x <- list( - list(), - list(list()), - list(list(list(1))) -) -depth(x)#> [1] 5#> [1] 1 2 4
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diff --git a/docs/reference/every.html b/docs/reference/every.html
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Arguments
-| .x | -A list or atomic vector. |
-
|---|---|
| .f | -A function, formula, or atomic vector. -If a function, it is used as is. -If a formula, e.g.
This syntax allows you to create very compact anonymous functions. -If character vector, numeric vector, or list, it
-is converted to an extractor function. Character vectors index by name
-and numeric vectors index by position; use a list to index by position
-and name at different levels. Within a list, wrap strings in |
-
| ... | -Additional arguments passed on to |
-
| .right | -If |
-
| .p | -A single predicate function, a formula describing such a
-predicate function, or a logical vector of the same length as |
-
Value
- -detect the value of the first item that matches the
-predicate; detect_index the position of the matching item.
-If not found, detect returns NULL and detect_index
-returns 0.
Examples
--is_even <- function(x) x %% 2 == 0 - -3:10 %>% detect(is_even)#> [1] 43:10 %>% detect_index(is_even)#> [1] 2-3:10 %>% detect(is_even, .right = TRUE)#> [1] 103:10 %>% detect_index(is_even, .right = TRUE)#> [1] 8- -# Since `.f` is passed to as_mapper(), you can supply a -# lambda-formula or a pluck object: -x <- list( - list(1, foo = FALSE), - list(2, foo = TRUE), - list(3, foo = TRUE) -) - -detect(x, "foo")#> [[1]] -#> [1] 2 -#> -#> $foo -#> [1] TRUE -#>detect_index(x, "foo")#> [1] 2
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diff --git a/docs/reference/extract.html b/docs/reference/extract.html
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Arguments
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-
|---|---|
| .p | -A single predicate function, a formula describing such a
-predicate function, or a logical vector of the same length as |
-
| ... | -Additional arguments passed on to |
-
Value
- -A logical vector of length 1.
- - -Examples
--x <- list(0, 1, TRUE) -x %>% every(identity)#> [1] TRUEx %>% some(identity)#> [1] TRUE-y <- list(0:10, 5.5) -y %>% every(is.numeric)#> [1] TRUEy %>% every(is.integer)#> [1] FALSE
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-Extract an element from a vector or environment
- - -extract(x, index, default = NULL)- -
Arguments
-| x | -A vector or environment |
-
|---|---|
| index | -A list indexing into the object |
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diff --git a/docs/reference/get-attr.html b/docs/reference/get-attr.html
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-unlist(), but they only ever remove a single layer of hierarchy and they
-are type-stable, so you always know what the type of the output is.
flatten(.x) - -flatten_lgl(.x) - -flatten_int(.x) - -flatten_dbl(.x) - -flatten_chr(.x) - -flatten_raw(.x) - -flatten_dfr(.x, .id = NULL) - -flatten_dfc(.x)- -
Arguments
-| .x | -A list of flatten. The contents of the list can be anything for
- |
-
|---|---|
| .id | -Either a string or Only applies to |
-
Value
- -flatten() returns a list, flatten_lgl() a logical
-vector, flatten_int() an integer vector, flatten_dbl() a
-double vector, and flatten_chr() a character vector.
flatten_dfr() and flatten_dfc() return data frames created by
-row-binding and column-binding respectively. They require dplyr to
-be installed.
Examples
--#> [[1]] -#> [1] 1 3 2 4 -#> -#> [[2]] -#> [1] 1 2 4 3 -#>x %>% flatten()#> [[1]] -#> [1] 1 -#> -#> [[2]] -#> [1] 3 -#> -#> [[3]] -#> [1] 2 -#> -#> [[4]] -#> [1] 4 -#> -#> [[5]] -#> [1] 1 -#> -#> [[6]] -#> [1] 2 -#> -#> [[7]] -#> [1] 4 -#> -#> [[8]] -#> [1] 3 -#>x %>% flatten_int()#> [1] 1 3 2 4 1 2 4 3#> [1] 1 1#> [1] 1 1
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Arguments
-| .x | -A list or atomic vector. |
-
|---|---|
| .p | -A single predicate function, a formula describing such a
-predicate function, or a logical vector of the same length as |
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| ... | -Additional arguments passed on to |
-
Value
- -A vector the same type as .x.
Examples
--pos <- function(x) x >= 0 -head_while(5:-5, pos)#> [1] 5 4 3 2 1 0#> [1] -1 -2 -3 -4 -5-big <- function(x) x > 100 -head_while(0:10, big)#> integer(0)tail_while(0:10, big)#> integer(0)
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- imap_xxx(x, ...), an indexed map, is short hand for
-map2(x, names(x), ...) if x has names, or map2(x, seq_along(x), ...)
-if it does not. This is useful if you need to compute on both the value
-and the position of an element.
imap(.x, .f, ...) - -imap_lgl(.x, .f, ...) - -imap_chr(.x, .f, ...) - -imap_int(.x, .f, ...) - -imap_dbl(.x, .f, ...) - -imap_raw(.x, .f, ...) - -imap_dfr(.x, .f, ..., .id = NULL) - -imap_dfc(.x, .f, ...) - -iwalk(.x, .f, ...)- -
Arguments
-| .x | -A list or atomic vector. |
-
|---|---|
| .f | -A function, formula, or atomic vector. -If a function, it is used as is. -If a formula, e.g.
This syntax allows you to create very compact anonymous functions. -If character vector, numeric vector, or list, it
-is converted to an extractor function. Character vectors index by name
-and numeric vectors index by position; use a list to index by position
-and name at different levels. Within a list, wrap strings in |
-
| ... | -Additional arguments passed on to |
-
| .id | -Either a string or Only applies to |
-
Value
- -A vector the same length as .x.
See also
- - - - -Examples
--# Note that when using the formula shortcut, the first argument -# is the value, and the second is the position -imap_chr(sample(10), ~ paste0(.y, ": ", .x))#> [1] "1: 8" "2: 7" "3: 9" "4: 2" "5: 1" "6: 10" "7: 5" "8: 6" "9: 3" -#> [10] "10: 4"iwalk(mtcars, ~ cat(.y, ": ", median(.x), "\n", sep = ""))#> mpg: 19.2 -#> cyl: 6 -#> disp: 196.3 -#> hp: 123 -#> drat: 3.695 -#> wt: 3.325 -#> qsec: 17.71 -#> vs: 0 -#> am: 0 -#> gear: 4 -#> carb: 2
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diff --git a/docs/reference/invoke.html b/docs/reference/invoke.html
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- Apply a function to each element of a vector |
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| - - | -Convert an object into a mapper function |
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- Map variants-A rich set of variants builds on the basic idea of |
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- Map over multiple inputs simultaneously. |
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| - - | -Modify elements selectively |
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- Apply a function to each element of a vector, and its index |
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| - - | -Apply a function to list-elements of a list |
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- Invoke functions. |
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- Predicate functionals-A predicate function is a function that either returns |
- |
| - - | -Find the value or position of the first match. |
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| - - | -Do every or some elements of a list satisfy a predicate? |
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| - - | -Does a list contain an object? |
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| - - | -Find head/tail that all satisfies a predicate. |
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| - - | -Keep or discard elements using a predicate function. |
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| - - | -Negate a predicate function. |
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| - - | -Prepend a vector |
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- Other vector transforms-A grab bag of useful tools for manipulating vectors. - |
- |
| - - | -Accumulate recursive folds across a list |
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| - - | -Produce all combinations of list elements |
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| - - | -Modify a list |
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| - - | -Reduce a list to a single value by iteratively applying a binary function. |
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| - - | -Splice objects and lists of objects into a list |
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| - - | -Transpose a list. |
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- Adverbs-Adverbs modify the action of a function; taking a function as input and returning a function with modified action as output. - |
- |
| - - | -Compose multiple functions |
-
|
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|
- Lift the domain of a function |
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| - - | -Negate a predicate function. |
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| - - | -Partial apply a function, filling in some arguments. |
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| - - | -Capture side effects. |
-
- Misc- - |
- |
| - - | -Default value for |
-
| - - | -Infix attribute accessor |
-
| - - | -Coerce array to list |
-
| - - | -Coerce a list to a vector |
-
| - - | -Generate random sample from a Bernoulli distribution |
-
| - - | -Generate random sample from a discrete uniform distribution |
-
| - - | -Re-run expressions multiple times. |
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| - - | -Set names in a vector |
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| - - | -Compute the depth of a vector |
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diff --git a/docs/reference/is_numeric.html b/docs/reference/is_numeric.html
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- This pair of functions make it easier to combine a function and list
-of parameters to get a result. invoke is a wrapper around
-do.call that makes it easy to use in a pipe. invoke_map
-makes it easier to call lists of functions with lists of parameters.
invoke(.f, .x = NULL, ..., .env = NULL) - -invoke_map(.f, .x = list(NULL), ..., .env = NULL) - -invoke_map_lgl(.f, .x = list(NULL), ..., .env = NULL) - -invoke_map_int(.f, .x = list(NULL), ..., .env = NULL) - -invoke_map_dbl(.f, .x = list(NULL), ..., .env = NULL) - -invoke_map_chr(.f, .x = list(NULL), ..., .env = NULL) - -invoke_map_raw(.f, .x = list(NULL), ..., .env = NULL) - -invoke_map_dfr(.f, .x = list(NULL), ..., .env = NULL) - -invoke_map_dfc(.f, .x = list(NULL), ..., .env = NULL)- -
Arguments
-| .f | -For |
-
|---|---|
| .x | -For |
-
| ... | -Additional arguments passed to each function. |
-
| .env | -Environment in which |
-
See also
- - - - -Examples
--# Invoke a function with a list of arguments -invoke(runif, list(n = 10))#> [1] 0.38870131 0.97554784 0.28989230 0.67838043 0.73531960 0.19595673 -#> [7] 0.98053967 0.74152153 0.05144628 0.53021246# Invoke a function with named arguments -invoke(runif, n = 10)#> [1] 0.69582388 0.68855600 0.03123033 0.22556253 0.30083081 0.63646561 -#> [7] 0.47902455 0.43217126 0.70643384 0.94857658-# Combine the two: -invoke(paste, list("01a", "01b"), sep = "-")#> [1] "01a-01b"# That's more natural as part of a pipeline: -list("01a", "01b") %>% - invoke(paste, ., sep = "-")#> [1] "01a-01b"-# Invoke a list of functions, each with different arguments -invoke_map(list(runif, rnorm), list(list(n = 10), list(n = 5)))#> [[1]] -#> [1] 0.18033877 0.21689988 0.68016292 0.49884561 0.64167935 0.66028435 -#> [7] 0.09602416 0.76560016 0.76967480 0.99071231 -#> -#> [[2]] -#> [1] 1.88850493 -0.09744510 -0.93584735 -0.01595031 -0.82678895 -#># Or with the same inputs: -invoke_map(list(runif, rnorm), list(list(n = 5)))#> [[1]] -#> [1] 0.06521611 0.35420680 0.82519942 0.27381825 0.57004495 -#> -#> [[2]] -#> [1] -0.4241750 -0.8723159 0.1066846 -0.5870140 -0.3278536 -#>invoke_map(list(runif, rnorm), n = 5)#> [[1]] -#> [1] 0.46598719 0.39003139 0.02006522 0.37697093 0.55991284 -#> -#> [[2]] -#> [1] 1.06730788 0.07003485 -0.63912332 -0.04996490 -0.25148344 -#># Or the same function with different inputs: -invoke_map("runif", list(list(n = 5), list(n = 10)))#> [[1]] -#> [1] 0.67176682 0.05861411 0.99706914 0.14903547 0.51855664 -#> -#> [[2]] -#> [1] 0.84612005 0.71826972 0.24131402 0.54704337 0.83480182 0.02795603 -#> [7] 0.46938430 0.80568003 0.81405131 0.40391100 -#>-# Or as a pipeline -list(m1 = mean, m2 = median) %>% invoke_map(x = rcauchy(100))#> $m1 -#> [1] -17.1555 -#> -#> $m2 -#> [1] -0.2478571 -#>list(m1 = mean, m2 = median) %>% invoke_map_dbl(x = rcauchy(100))#> m1 m2 -#> -1.0554082 -0.1491601-# Note that you can also match by position by explicitly omitting `.x`. -# This can be useful when the argument names of the functions are not -# identical -list(m1 = mean, m2 = median) %>% - invoke_map(, rcauchy(100))#> $m1 -#> [1] -0.634837 -#> -#> $m2 -#> [1] -0.1482912 -#>-# If you have pairs of function name and arguments, it's natural -# to store them in a data frame. Here we use a tibble because -# it has better support for list-columns -df <- tibble::tibble( - f = c("runif", "rpois", "rnorm"), - params = list( - list(n = 10), - list(n = 5, lambda = 10), - list(n = 10, mean = -3, sd = 10) - ) -) -df#> # A tibble: 3 x 2 -#> f params -#> <chr> <list> -#> 1 runif <list [1]> -#> 2 rpois <list [2]> -#> 3 rnorm <list [3]>invoke_map(df$f, df$params)#> [[1]] -#> [1] 0.204329063 0.471755611 0.385183100 0.392751929 0.218338430 0.007208941 -#> [7] 0.363712143 0.526106730 0.844163393 0.135991171 -#> -#> [[2]] -#> [1] 9 9 5 6 9 -#> -#> [[3]] -#> [1] -3.240840 12.844975 -2.943244 -9.482045 15.586632 5.283032 -#> [7] 8.990353 3.000512 -2.412399 -15.897635 -#>
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diff --git a/docs/reference/keep.html b/docs/reference/keep.html
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-
-
as an alias for double (as in
as.numeric())
-to mean either integer or double (as in
mode())
-for something representable as numeric (as in
as.numeric()) -This function tests for the second, which is often not what you want -so these functions are deprecated.
-
is_numeric(x) - -is_scalar_numeric(x)- - -
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diff --git a/docs/reference/lift.html b/docs/reference/lift.html
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- keep() and discard() are opposites. compact() is a handy
-wrapper that removes all empty elements.
keep(.x, .p, ...) - -discard(.x, .p, ...) - -compact(.x, .p = identity)- -
Arguments
-| .x | -A list or vector. |
-
|---|---|
| .p | -A single predicate function, a formula describing such a
-predicate function, or a logical vector of the same length as |
-
| ... | -Additional arguments passed on to |
-
Details
- -These are usually called select or filter and reject or
-drop, but those names are already taken. keep() is similar to
-Filter(), but the argument order is more convenient, and the
-evaluation of the predicate function .p is stricter.
Examples
--#> [[1]] -#> [1] 9 6 10 2 7 -#> -#> [[2]] -#> [1] 2 9 8 10 6 -#> -#> [[3]] -#> [1] 1 8 4 10 9 -#> -#> [[4]] -#> [1] 10 8 5 9 3 -#>#> list()-# Using a string instead of a function will select all list elements -# where that subelement is TRUE -x <- rerun(5, a = rbernoulli(1), b = sample(10)) -x#> [[1]] -#> [[1]]$a -#> [1] TRUE -#> -#> [[1]]$b -#> [1] 6 4 5 1 10 9 8 2 3 7 -#> -#> -#> [[2]] -#> [[2]]$a -#> [1] TRUE -#> -#> [[2]]$b -#> [1] 7 4 3 6 8 1 9 10 5 2 -#> -#> -#> [[3]] -#> [[3]]$a -#> [1] TRUE -#> -#> [[3]]$b -#> [1] 7 10 9 2 8 1 3 4 6 5 -#> -#> -#> [[4]] -#> [[4]]$a -#> [1] FALSE -#> -#> [[4]]$b -#> [1] 1 4 6 3 9 10 5 7 2 8 -#> -#> -#> [[5]] -#> [[5]]$a -#> [1] TRUE -#> -#> [[5]]$b -#> [1] 1 7 10 8 5 3 6 2 4 9 -#> -#>x %>% keep("a")#> [[1]] -#> [[1]]$a -#> [1] TRUE -#> -#> [[1]]$b -#> [1] 6 4 5 1 10 9 8 2 3 7 -#> -#> -#> [[2]] -#> [[2]]$a -#> [1] TRUE -#> -#> [[2]]$b -#> [1] 7 4 3 6 8 1 9 10 5 2 -#> -#> -#> [[3]] -#> [[3]]$a -#> [1] TRUE -#> -#> [[3]]$b -#> [1] 7 10 9 2 8 1 3 4 6 5 -#> -#> -#> [[4]] -#> [[4]]$a -#> [1] TRUE -#> -#> [[4]]$b -#> [1] 1 7 10 8 5 3 6 2 4 9 -#> -#>x %>% discard("a")#> [[1]] -#> [[1]]$a -#> [1] FALSE -#> -#> [[1]]$b -#> [1] 1 4 6 3 9 10 5 7 2 8 -#> -#>-# compact() discards elements that are NULL or that have length zero -list(a = "a", b = NULL, c = integer(0), d = NA, e = list()) %>% - compact()#> $a -#> [1] "a" -#> -#> $d -#> [1] NA -#>
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diff --git a/docs/reference/list_modify.html b/docs/reference/list_modify.html
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- lift_xy() is a composition helper. It helps you compose
-functions by lifting their domain from a kind of input to another
-kind. The domain can be changed from and to a list (l), a vector
-(v) and dots (d). For example, lift_ld(fun) transforms a
-function taking a list to a function taking dots.
lift(..f, ..., .unnamed = FALSE) - -lift_dl(..f, ..., .unnamed = FALSE) - -lift_dv(..f, ..., .unnamed = FALSE) - -lift_vl(..f, ..., .type) - -lift_vd(..f, ..., .type) - -lift_ld(..f, ...) - -lift_lv(..f, ...)- -
Arguments
-| ..f | -A function to lift. |
-
|---|---|
| ... | -Default arguments for |
-
| .unnamed | -If |
-
| .type | -A vector mold or a string describing the type of the
-input vectors. The latter can be any of the types returned by
- |
-
Value
- -A function.
- -Details
- -The most important of those helpers is probably lift_dl()
-because it allows you to transform a regular function to one that
-takes a list. This is often essential for composition with purrr
-functional tools. Since this is such a common function,
-lift() is provided as an alias for that operation.
from ... to list(...) or c(...)
-
-
- Here dots should be taken here in a figurative way. The lifted
-functions does not need to take dots per se. The function is
-simply wrapped a function in do.call(), so instead
-of taking multiple arguments, it takes a single named list or
-vector which will be interpreted as its arguments. This is
-particularly useful when you want to pass a row of a data frame
-or a list to a function and don't want to manually pull it apart
-in your function.
from c(...) to list(...) or ...
-
-
- These factories allow a function taking a vector to take a list
-or dots instead. The lifted function internally transforms its
-inputs back to an atomic vector. purrr does not obey the usual R
-casting rules (e.g., c(1, "2") produces a character
-vector) and will produce an error if the types are not
-compatible. Additionally, you can enforce a particular vector
-type by supplying .type.
from list(...) to c(...) or ...
- - -lift_ld() turns a function that takes a list into a
-function that takes dots. lift_vd() does the same with a
-function that takes an atomic vector. These factory functions are
-the inverse operations of lift_dl() and lift_dv().
lift_vd() internally coerces the inputs of ..f to
-an atomic vector. The details of this coercion can be controlled
-with .type.
See also
- - - - -Examples
--### Lifting from ... to list(...) or c(...) - -x <- list(x = c(1:100, NA, 1000), na.rm = TRUE, trim = 0.9) -lift_dl(mean)(x)#> [1] 51#> [1] 51-# You can also use the lift() alias for this common operation: -lift(mean)(x)#> [1] 51-# Default arguments can also be specified directly in lift_dl() -list(c(1:100, NA, 1000)) %>% lift_dl(mean, na.rm = TRUE)()#> [1] 59.90099-# lift_dl() and lift_ld() are inverse of each other. -# Here we transform sum() so that it takes a list -fun <- sum %>% lift_dl() -fun(list(3, NA, 4, na.rm = TRUE))#> [1] 7-# Now we transform it back to a variadic function -fun2 <- fun %>% lift_ld() -fun2(3, NA, 4, na.rm = TRUE)#> [1] 7-# It can sometimes be useful to make sure the lifted function's -# signature has no named parameters, as would be the case for a -# function taking only dots. The lifted function will take a list -# or vector but will not match its arguments to the names of the -# input. For instance, if you give a data frame as input to your -# lifted function, the names of the columns are probably not -# related to the function signature and should be discarded. -lifted_identical <- lift_dl(identical, .unnamed = TRUE) -mtcars[c(1, 1)] %>% lifted_identical()#> [1] TRUEmtcars[c(1, 2)] %>% lifted_identical()#> [1] FALSE# - - -### Lifting from c(...) to list(...) or ... - -# In other situations we need the vector-valued function to take a -# variable number of arguments as with pmap(). This is a job for -# lift_vd(): -pmap(mtcars, lift_vd(mean))#> [[1]] -#> [1] 29.90727 -#> -#> [[2]] -#> [1] 29.98136 -#> -#> [[3]] -#> [1] 23.59818 -#> -#> [[4]] -#> [1] 38.73955 -#> -#> [[5]] -#> [1] 53.66455 -#> -#> [[6]] -#> [1] 35.04909 -#> -#> [[7]] -#> [1] 59.72 -#> -#> [[8]] -#> [1] 24.63455 -#> -#> [[9]] -#> [1] 27.23364 -#> -#> [[10]] -#> [1] 31.86 -#> -#> [[11]] -#> [1] 31.78727 -#> -#> [[12]] -#> [1] 46.43091 -#> -#> [[13]] -#> [1] 46.5 -#> -#> [[14]] -#> [1] 46.35 -#> -#> [[15]] -#> [1] 66.23273 -#> -#> [[16]] -#> [1] 66.05855 -#> -#> [[17]] -#> [1] 65.97227 -#> -#> [[18]] -#> [1] 19.44091 -#> -#> [[19]] -#> [1] 17.74227 -#> -#> [[20]] -#> [1] 18.81409 -#> -#> [[21]] -#> [1] 24.88864 -#> -#> [[22]] -#> [1] 47.24091 -#> -#> [[23]] -#> [1] 46.00773 -#> -#> [[24]] -#> [1] 58.75273 -#> -#> [[25]] -#> [1] 57.37955 -#> -#> [[26]] -#> [1] 18.92864 -#> -#> [[27]] -#> [1] 24.77909 -#> -#> [[28]] -#> [1] 24.88027 -#> -#> [[29]] -#> [1] 60.97182 -#> -#> [[30]] -#> [1] 34.50818 -#> -#> [[31]] -#> [1] 63.15545 -#> -#> [[32]] -#> [1] 26.26273 -#>-# lift_vd() will collect the arguments and concatenate them to a -# vector before passing them to ..f. You can add a check to assert -# the type of vector you expect: -lift_vd(tolower, .type = character(1))("this", "is", "ok")#> [1] "this" "is" "ok"# - - -### Lifting from list(...) to c(...) or ... - -# cross() normally takes a list of elements and returns their -# cartesian product. By lifting it you can supply the arguments as -# if it was a function taking dots: -cross_dots <- lift_ld(cross) -out1 <- cross(list(a = 1:2, b = c("a", "b", "c"))) -out2 <- cross_dots(a = 1:2, b = c("a", "b", "c")) -identical(out1, out2)#> [1] TRUE-# This kind of lifting is sometimes needed for function -# composition. An example would be to use pmap() with a function -# that takes a list. In the following, we use some() on each row of -# a data frame to check they each contain at least one element -# satisfying a condition: -mtcars %>% pmap(lift_ld(some, partial(`<`, 200)))#> [[1]] -#> [1] FALSE -#> -#> [[2]] -#> [1] FALSE -#> -#> [[3]] -#> [1] FALSE -#> -#> [[4]] -#> [1] TRUE -#> -#> [[5]] -#> [1] TRUE -#> -#> [[6]] -#> [1] TRUE -#> -#> [[7]] -#> [1] TRUE -#> -#> [[8]] -#> [1] FALSE -#> -#> [[9]] -#> [1] FALSE -#> -#> [[10]] -#> [1] FALSE -#> -#> [[11]] -#> [1] FALSE -#> -#> [[12]] -#> [1] TRUE -#> -#> [[13]] -#> [1] TRUE -#> -#> [[14]] -#> [1] TRUE -#> -#> [[15]] -#> [1] TRUE -#> -#> [[16]] -#> [1] TRUE -#> -#> [[17]] -#> [1] TRUE -#> -#> [[18]] -#> [1] FALSE -#> -#> [[19]] -#> [1] FALSE -#> -#> [[20]] -#> [1] FALSE -#> -#> [[21]] -#> [1] FALSE -#> -#> [[22]] -#> [1] TRUE -#> -#> [[23]] -#> [1] TRUE -#> -#> [[24]] -#> [1] TRUE -#> -#> [[25]] -#> [1] TRUE -#> -#> [[26]] -#> [1] FALSE -#> -#> [[27]] -#> [1] FALSE -#> -#> [[28]] -#> [1] FALSE -#> -#> [[29]] -#> [1] TRUE -#> -#> [[30]] -#> [1] FALSE -#> -#> [[31]] -#> [1] TRUE -#> -#> [[32]] -#> [1] FALSE -#>-# Default arguments for ..f can be specified in the call to -# lift_ld() -lift_ld(cross, .filter = `==`)(1:3, 1:3) %>% str()#> List of 6 -#> $ :List of 2 -#> ..$ : int 2 -#> ..$ : int 1 -#> $ :List of 2 -#> ..$ : int 3 -#> ..$ : int 1 -#> $ :List of 2 -#> ..$ : int 1 -#> ..$ : int 2 -#> $ :List of 2 -#> ..$ : int 3 -#> ..$ : int 2 -#> $ :List of 2 -#> ..$ : int 1 -#> ..$ : int 3 -#> $ :List of 2 -#> ..$ : int 2 -#> ..$ : int 3- -# Here is another function taking a list and that we can update to -# take a vector: -glue <- function(l) { - if (!is.list(l)) stop("not a list") - l %>% invoke(paste, .) -}# NOT RUN { -letters %>% glue() # fails because glue() expects a list -# }-letters %>% lift_lv(glue)() # succeeds#> [1] "a b c d e f g h i j k l m n o p q r s t u v w x y z"
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diff --git a/docs/reference/lmap.html b/docs/reference/lmap.html
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-elements either by name or position. If a sub-element is present in
-both lists list_modify() takes the value from y, and list_merge()
-concatenates the values together.
update_list() handles formulas and quosures that can refer to
-values existing within the input list. Note that this function
-might be deprecated in the future in favour of a dplyr::mutate()
-method for lists.
list_modify(.x, ...) - -list_merge(.x, ...)- -
Arguments
-| .x | -List to modify. |
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|---|---|
| ... | -New values of a list. Use |
-
Examples
--x <- list(x = 1:10, y = 4, z = list(a = 1, b = 2)) -str(x)#> List of 3 -#> $ x: int [1:10] 1 2 3 4 5 6 7 8 9 10 -#> $ y: num 4 -#> $ z:List of 2 -#> ..$ a: num 1 -#> ..$ b: num 2-# Update values -str(list_modify(x, a = 1))#> List of 4 -#> $ x: int [1:10] 1 2 3 4 5 6 7 8 9 10 -#> $ y: num 4 -#> $ z:List of 2 -#> ..$ a: num 1 -#> ..$ b: num 2 -#> $ a: num 1# Replace values -str(list_modify(x, z = 5))#> List of 3 -#> $ x: int [1:10] 1 2 3 4 5 6 7 8 9 10 -#> $ y: num 4 -#> $ z: num 5str(list_modify(x, z = list(a = 1:5)))#> List of 3 -#> $ x: int [1:10] 1 2 3 4 5 6 7 8 9 10 -#> $ y: num 4 -#> $ z:List of 2 -#> ..$ a: int [1:5] 1 2 3 4 5 -#> ..$ b: num 2# Remove values -str(list_modify(x, z = NULL))#> List of 2 -#> $ x: int [1:10] 1 2 3 4 5 6 7 8 9 10 -#> $ y: num 4-# Combine values -str(list_merge(x, x = 11, z = list(a = 2:5, c = 3)))#> List of 3 -#> $ x: num [1:11] 1 2 3 4 5 6 7 8 9 10 ... -#> $ y: num 4 -#> $ z:List of 3 -#> ..$ a: num [1:5] 1 2 3 4 5 -#> ..$ b: num 2 -#> ..$ c: num 3- -# All these functions take dots with splicing. Use !!! or UQS() to -# splice a list of arguments: -l <- list(new = 1, y = NULL, z = 5) -str(list_modify(x, !!! l))#> List of 3 -#> $ x : int [1:10] 1 2 3 4 5 6 7 8 9 10 -#> $ z : num 5 -#> $ new: num 1-# In update_list() you can also use quosures and formulas to -# compute new values. This function is likely to be deprecated in -# the future -str(update_list(x, z1 = ~z[[1]]))#> List of 4 -#> $ x : int [1:10] 1 2 3 4 5 6 7 8 9 10 -#> $ y : num 4 -#> $ z :List of 2 -#> ..$ a: num 1 -#> ..$ b: num 2 -#> $ z1: num 1#> List of 3 -#> $ x: int [1:10] 1 2 3 4 5 6 7 8 9 10 -#> $ y: num 4 -#> $ z: num [1:10] 5 6 7 8 9 10 11 12 13 14
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diff --git a/docs/reference/map.html b/docs/reference/map.html
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- lmap(), lmap_at() and lmap_if() are similar to
-map(), map_at() and map_if(), with the
-difference that they operate exclusively on functions that take
-and return a list (or data frame). Thus, instead of mapping
-the elements of a list (as in .x[[i]]), they apply a
-function .f to each subset of size 1 of that list (as in
-.x[i]). We call those elements list-elements).
lmap(.x, .f, ...) - -lmap_if(.x, .p, .f, ...) - -lmap_at(.x, .at, .f, ...)- -
Arguments
-| .x | -A list or data frame. |
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|---|---|
| .f | -A function that takes and returns a list or data frame. |
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| ... | -Additional arguments passed on to |
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| .p | -A single predicate function, a formula describing such a
-predicate function, or a logical vector of the same length as |
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| .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 |
-
Value
- -If .x is a list, a list. If .x is a data
-frame, a data frame.
Details
- -Mapping the list-elements .x[i] has several advantages. It
-makes it possible to work with functions that exclusively take a
-list or data frame. It enables .f to access the attributes
-of the encapsulating list, like the name of the components it
-receives. It also enables .f to return a larger list than
-the list-element of size 1 it got as input. Conversely, .f
-can also return empty lists. In these cases, the output list is
-reshaped with a different size than the input list .x.
See also
- - - - -Examples
--# Let's write a function that returns a larger list or an empty list -# depending on some condition. This function also uses the names -# metadata available in the attributes of the list-element -maybe_rep <- function(x) { - n <- rpois(1, 2) - out <- rep_len(x, n) - if (length(out) > 0) { - names(out) <- paste0(names(x), seq_len(n)) - } - out -} - -# The output size varies each time we map f() -x <- list(a = 1:4, b = letters[5:7], c = 8:9, d = letters[10]) -x %>% lmap(maybe_rep)#> $a1 -#> [1] 1 2 3 4 -#> -#> $b1 -#> [1] "e" "f" "g" -#> -#> $b2 -#> [1] "e" "f" "g" -#> -#> $c1 -#> [1] 8 9 -#> -#> $d1 -#> [1] "j" -#> -#> $d2 -#> [1] "j" -#> -#> $d3 -#> [1] "j" -#>-# We can apply f() on a selected subset of x -x %>% lmap_at(c("a", "d"), maybe_rep)#> $a1 -#> [1] 1 2 3 4 -#> -#> $a2 -#> [1] 1 2 3 4 -#> -#> $a3 -#> [1] 1 2 3 4 -#> -#> $a4 -#> [1] 1 2 3 4 -#> -#> $b -#> [1] "e" "f" "g" -#> -#> $c -#> [1] 8 9 -#> -#> $d1 -#> [1] "j" -#> -#> $d2 -#> [1] "j" -#> -#> $d3 -#> [1] "j" -#>-# Or only where a condition is satisfied -x %>% lmap_if(is.character, maybe_rep)#> $a -#> [1] 1 2 3 4 -#> -#> $b1 -#> [1] "e" "f" "g" -#> -#> $b2 -#> [1] "e" "f" "g" -#> -#> $b3 -#> [1] "e" "f" "g" -#> -#> $b4 -#> [1] "e" "f" "g" -#> -#> $c -#> [1] 8 9 -#> -#> $d1 -#> [1] "j" -#>- -# A more realistic example would be a function that takes discrete -# variables in a dataset and turns them into disjunctive tables, a -# form that is amenable to fitting some types of models. - -# A disjunctive table contains only 0 and 1 but has as many columns -# as unique values in the original variable. Ideally, we want to -# combine the names of each level with the name of the discrete -# variable in order to identify them. Given these requirements, it -# makes sense to have a function that takes a data frame of size 1 -# and returns a data frame of variable size. -disjoin <- function(x, sep = "_") { - name <- names(x) - x <- as.factor(x[[1]]) - - out <- lapply(levels(x), function(level) { - as.numeric(x == level) - }) - - names(out) <- paste(name, levels(x), sep = sep) - tibble::as_tibble(out) -} - -# Now, we are ready to map disjoin() on each categorical variable of a -# data frame: -iris %>% lmap_if(is.factor, disjoin)#> # A tibble: 150 x 7 -#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species_setosa -#> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 5.1 3.5 1.4 0.2 1 -#> 2 4.9 3 1.4 0.2 1 -#> 3 4.7 3.2 1.3 0.2 1 -#> 4 4.6 3.1 1.5 0.2 1 -#> 5 5 3.6 1.4 0.2 1 -#> 6 5.4 3.9 1.7 0.4 1 -#> 7 4.6 3.4 1.4 0.3 1 -#> 8 5 3.4 1.5 0.2 1 -#> 9 4.4 2.9 1.4 0.2 1 -#> 10 4.9 3.1 1.5 0.1 1 -#> # ... with 140 more rows, and 2 more variables: Species_versicolor <dbl>, -#> # Species_virginica <dbl>mtcars %>% lmap_at(c("cyl", "vs", "am"), disjoin)#> # A tibble: 32 x 15 -#> mpg cyl_4 cyl_6 cyl_8 disp hp drat wt qsec vs_0 vs_1 am_0 -#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 21 0 1 0 160 110 3.9 2.62 16.5 1 0 0 -#> 2 21 0 1 0 160 110 3.9 2.88 17.0 1 0 0 -#> 3 22.8 1 0 0 108 93 3.85 2.32 18.6 0 1 0 -#> 4 21.4 0 1 0 258 110 3.08 3.22 19.4 0 1 1 -#> 5 18.7 0 0 1 360 175 3.15 3.44 17.0 1 0 1 -#> 6 18.1 0 1 0 225 105 2.76 3.46 20.2 0 1 1 -#> 7 14.3 0 0 1 360 245 3.21 3.57 15.8 1 0 1 -#> 8 24.4 1 0 0 147. 62 3.69 3.19 20 0 1 1 -#> 9 22.8 1 0 0 141. 95 3.92 3.15 22.9 0 1 1 -#> 10 19.2 0 1 0 168. 123 3.92 3.44 18.3 0 1 1 -#> # ... with 22 more rows, and 3 more variables: am_1 <dbl>, gear <dbl>, -#> # carb <dbl>
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diff --git a/docs/reference/map2.html b/docs/reference/map2.html
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- The map functions transform their input by applying a function to -each element and returning a vector the same length as the input.
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-map(),map_if()andmap_at()always return a list. See the -modify()family for versions that return an object of the same -type as the input.The
_ifand_atvariants take a predicate function.pthat -determines which elements of.xare transformed with.f.
-map_lgl(),map_int(),map_dbl()andmap_chr()return -vectors of the corresponding type (or die trying).
-map_dfr()andmap_dfc()return data frames created by -row-binding and column-binding respectively. They require dplyr -to be installed.
-walk()calls.ffor its side-effect and returns the input.x.
-
map(.x, .f, ...) - -map_if(.x, .p, .f, ...) - -map_at(.x, .at, .f, ...) - -map_lgl(.x, .f, ...) - -map_chr(.x, .f, ...) - -map_int(.x, .f, ...) - -map_dbl(.x, .f, ...) - -map_raw(.x, .f, ...) - -map_dfr(.x, .f, ..., .id = NULL) - -map_dfc(.x, .f, ...) - -walk(.x, .f, ...)- -
Arguments
-| .x | -A list or atomic vector. |
-
|---|---|
| .f | -A function, formula, or atomic vector. -If a function, it is used as is. -If a formula, e.g.
This syntax allows you to create very compact anonymous functions. -If character vector, numeric vector, or list, it
-is converted to an extractor function. Character vectors index by name
-and numeric vectors index by position; use a list to index by position
-and name at different levels. Within a list, wrap strings in |
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| ... | -Additional arguments passed on to |
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| .p | -A single predicate function, a formula describing such a
-predicate function, or a logical vector of the same length as |
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| .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 |
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| .id | -Either a string or Only applies to |
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Value
- -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.
walk() returns the input .x (invisibly). This makes it easy to
-use in pipe.
See also
- - - - -Examples
--1:10 %>% - map(rnorm, n = 10) %>% - map_dbl(mean)#> [1] 0.9714714 2.3044667 3.3578648 4.0606456 5.2247875 6.6328310 -#> [7] 6.6803245 8.1763451 9.2655661 10.2225539-# Or use an anonymous function -1:10 %>% - map(function(x) rnorm(10, x))#> [[1]] -#> [1] -0.4960537 -0.1848187 1.6302344 3.1012525 0.3862632 -0.6346383 -#> [7] 0.9895589 0.3434939 0.3304666 0.5214110 -#> -#> [[2]] -#> [1] 3.3194563 2.6365628 2.5143278 0.2486249 2.8935975 2.2230384 2.5808166 -#> [8] 1.8221786 2.7409667 1.0025569 -#> -#> [[3]] -#> [1] 0.06102244 3.71901566 2.30199496 1.10587416 3.07629925 3.87530850 -#> [7] 3.45382739 2.14928309 3.56620161 4.15221195 -#> -#> [[4]] -#> [1] 3.243803 3.510742 2.833948 3.520331 4.115348 2.231952 2.592361 4.709178 -#> [9] 2.759157 3.631673 -#> -#> [[5]] -#> [1] 5.462080 4.677167 3.712785 3.969960 6.514089 5.346904 6.779442 5.386631 -#> [9] 4.081305 3.415664 -#> -#> [[6]] -#> [1] 5.915941 3.914929 6.003568 5.644229 7.146360 5.778812 7.018179 5.736281 -#> [9] 7.658542 5.225913 -#> -#> [[7]] -#> [1] 6.076062 6.724467 6.406600 6.877714 8.179784 7.641037 6.370411 6.192265 -#> [9] 6.139510 4.830761 -#> -#> [[8]] -#> [1] 6.624163 7.506868 7.418348 7.832771 8.485993 6.666604 7.738034 8.652386 -#> [9] 8.748855 8.896560 -#> -#> [[9]] -#> [1] 10.489300 8.340597 9.537283 9.746803 10.896317 6.939929 9.064544 -#> [8] 8.734853 8.552655 7.589299 -#> -#> [[10]] -#> [1] 9.493581 9.730238 8.914845 10.362159 9.664328 11.363804 9.288476 -#> [8] 10.662179 10.291130 10.197958 -#>-# Or a formula -1:10 %>% - map(~ rnorm(10, .x))#> [[1]] -#> [1] -0.2035661 0.9601830 1.6869825 1.7052670 1.9914417 2.1442490 -#> [7] -0.2389102 3.6548983 0.8430828 0.5765099 -#> -#> [[2]] -#> [1] 1.8016129 1.1051976 2.9042691 2.0796492 0.7411728 3.0256851 1.2692214 -#> [8] 1.8098545 2.5288647 2.5502105 -#> -#> [[3]] -#> [1] 3.5496843 2.3404576 3.0574217 0.1919895 2.0877402 2.2176208 2.3358951 -#> [8] 3.6263098 2.4927518 3.2703613 -#> -#> [[4]] -#> [1] 4.467477 4.723995 4.613837 3.382131 4.220725 5.127927 5.813454 3.916174 -#> [9] 5.367707 3.372565 -#> -#> [[5]] -#> [1] 4.783371 4.316286 4.555297 5.606490 5.624183 4.304569 4.216361 4.046876 -#> [9] 6.792756 5.348977 -#> -#> [[6]] -#> [1] 6.259104 5.194048 6.105665 5.666400 7.641848 5.356094 6.587021 5.849597 -#> [9] 4.289178 7.431033 -#> -#> [[7]] -#> [1] 4.354788 5.967543 6.292534 6.299440 7.537885 6.683668 6.160377 5.645072 -#> [9] 6.182432 6.365600 -#> -#> [[8]] -#> [1] 8.815949 8.302796 9.807087 7.105973 7.953572 7.528821 7.473307 7.904865 -#> [9] 5.504635 8.166889 -#> -#> [[9]] -#> [1] 9.350492 10.433701 9.765907 10.167521 8.863057 8.485098 10.519744 -#> [8] 8.671508 8.946328 8.436475 -#> -#> [[10]] -#> [1] 9.256091 9.890958 9.439171 10.188002 10.748851 8.083462 10.236096 -#> [8] 10.628953 10.417926 11.976758 -#>-# 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 = "???")#> [[1]] -#> [1] 1 -#> -#> [[2]] -#> [1] "???" -#> -#> [[3]] -#> [1] "???" -#>l1 %>% map_int("b", .default = NA)#> [1] NA 2 3l1 %>% map_int(2, .default = NA)#> [1] NA 2 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))#> [[1]] -#> [1] "b" -#> -#> [[2]] -#> [1] "e" -#> -#> [[3]] -#> NULL -#>-# 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))#> [[1]] -#> [1] 3 -#> -#> [[2]] -#> [1] 103 -#> -#> [[3]] -#> NULL -#>l2 %>% map_int(list("num", 3), .default = NA)#> [1] 3 103 NA-# 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")#> 4 6 8 -#> 0.5086326 0.4645102 0.4229655-# Use map_lgl(), map_dbl(), etc to reduce to a vector. -# * list -mtcars %>% map(sum)#> $mpg -#> [1] 642.9 -#> -#> $cyl -#> [1] 198 -#> -#> $disp -#> [1] 7383.1 -#> -#> $hp -#> [1] 4694 -#> -#> $drat -#> [1] 115.09 -#> -#> $wt -#> [1] 102.952 -#> -#> $qsec -#> [1] 571.16 -#> -#> $vs -#> [1] 14 -#> -#> $am -#> [1] 13 -#> -#> $gear -#> [1] 118 -#> -#> $carb -#> [1] 90 -#># * vector -mtcars %>% map_dbl(sum)#> mpg cyl disp hp drat wt qsec vs -#> 642.900 198.000 7383.100 4694.000 115.090 102.952 571.160 14.000 -#> am gear carb -#> 13.000 118.000 90.000-# If each element of the output is a data frame, use -# map_dfr to row-bind them together: -mtcars %>% - split(.$cyl) %>% - map(~ lm(mpg ~ wt, data = .x)) %>% - map_dfr(~ as.data.frame(t(as.matrix(coef(.)))))#> (Intercept) wt -#> 1 39.57120 -5.647025 -#> 2 28.40884 -2.780106 -#> 3 23.86803 -2.192438# (if you also want to preserve the variable names see -# the broom package) -
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diff --git a/docs/reference/modify.html b/docs/reference/modify.html
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-simultaneously. They are parallel in the sense that each input is processed
-in parallel with the others, not in the sense of multicore computing. They
-share the same notion of "parallel" as base::pmax() and base::pmin().
-map2() and walk2() are specialised for the two argument case; pmap()
-and pwalk() allow you to provide any number of arguments in a list.
map2(.x, .y, .f, ...) - -map2_lgl(.x, .y, .f, ...) - -map2_int(.x, .y, .f, ...) - -map2_dbl(.x, .y, .f, ...) - -map2_chr(.x, .y, .f, ...) - -map2_raw(.x, .y, .f, ...) - -map2_dfr(.x, .y, .f, ..., .id = NULL) - -map2_dfc(.x, .y, .f, ...) - -walk2(.x, .y, .f, ...) - -pmap(.l, .f, ...) - -pmap_lgl(.l, .f, ...) - -pmap_int(.l, .f, ...) - -pmap_dbl(.l, .f, ...) - -pmap_chr(.l, .f, ...) - -pmap_raw(.l, .f, ...) - -pmap_dfr(.l, .f, ..., .id = NULL) - -pmap_dfc(.l, .f, ...) - -pwalk(.l, .f, ...)- -
Arguments
-| .x, .y | -Vectors of the same length. A vector of length 1 will -be recycled. |
-
|---|---|
| .f | -A function, formula, or atomic vector. -If a function, it is used as is. -If a formula, e.g.
This syntax allows you to create very compact anonymous functions. -If character vector, numeric vector, or list, it
-is converted to an extractor function. Character vectors index by name
-and numeric vectors index by position; use a list to index by position
-and name at different levels. Within a list, wrap strings in |
-
| ... | -Additional arguments passed on to |
-
| .id | -Either a string or Only applies to |
-
| .l | -A list of lists. The length of |
-
Value
- -An atomic vector, list, or data frame, depending on the suffix.
-Atomic vectors and lists will be named if .x or the first
-element of .l is named.
If all input is length 0, the output will be length 0. If any -input is length 1, it will be recycled to the length of the longest.
- -Details
- -Note that arguments to be vectorised over come before the .f,
-and arguments that are supplied to every call come after .f.
See also
- - - - -Examples
--x <- list(1, 10, 100) -y <- list(1, 2, 3) -z <- list(5, 50, 500) - -map2(x, y, ~ .x + .y)#> [[1]] -#> [1] 2 -#> -#> [[2]] -#> [1] 12 -#> -#> [[3]] -#> [1] 103 -#># Or just -map2(x, y, `+`)#> [[1]] -#> [1] 2 -#> -#> [[2]] -#> [1] 12 -#> -#> [[3]] -#> [1] 103 -#>-pmap(list(x, y, z), sum)#> [[1]] -#> [1] 7 -#> -#> [[2]] -#> [1] 62 -#> -#> [[3]] -#> [1] 603 -#>-# Matching arguments by position -pmap(list(x, y, z), function(a, b, c) a / (b + c))#> [[1]] -#> [1] 0.1666667 -#> -#> [[2]] -#> [1] 0.1923077 -#> -#> [[3]] -#> [1] 0.1988072 -#>-# Matching arguments by name -l <- list(a = x, b = y, c = z) -pmap(l, function(c, b, a) a / (b + c))#> [[1]] -#> [1] 0.1666667 -#> -#> [[2]] -#> [1] 0.1923077 -#> -#> [[3]] -#> [1] 0.1988072 -#>-# Split into pieces, fit model to each piece, then predict -by_cyl <- mtcars %>% split(.$cyl) -mods <- by_cyl %>% map(~ lm(mpg ~ wt, data = .)) -map2(mods, by_cyl, predict)#> $`4` -#> Datsun 710 Merc 240D Merc 230 Fiat 128 Honda Civic -#> 26.47010 21.55719 21.78307 27.14774 30.45125 -#> Toyota Corolla Toyota Corona Fiat X1-9 Porsche 914-2 Lotus Europa -#> 29.20890 25.65128 28.64420 27.48656 31.02725 -#> Volvo 142E -#> 23.87247 -#> -#> $`6` -#> Mazda RX4 Mazda RX4 Wag Hornet 4 Drive Valiant Merc 280 -#> 21.12497 20.41604 19.47080 18.78968 18.84528 -#> Merc 280C Ferrari Dino -#> 18.84528 20.70795 -#> -#> $`8` -#> Hornet Sportabout Duster 360 Merc 450SE Merc 450SL -#> 16.32604 16.04103 14.94481 15.69024 -#> Merc 450SLC Cadillac Fleetwood Lincoln Continental Chrysler Imperial -#> 15.58061 12.35773 11.97625 12.14945 -#> Dodge Challenger AMC Javelin Camaro Z28 Pontiac Firebird -#> 16.15065 16.33700 15.44907 15.43811 -#> Ford Pantera L Maserati Bora -#> 16.91800 16.04103 -#>-# Vectorizing a function over multiple arguments -df <- data.frame( - x = c("apple", "banana", "cherry"), - pattern = c("p", "n", "h"), - replacement = c("x", "f", "q"), - stringsAsFactors = FALSE - ) -pmap(df, gsub)#> [[1]] -#> [1] "axxle" -#> -#> [[2]] -#> [1] "bafafa" -#> -#> [[3]] -#> [1] "cqerry" -#>pmap_chr(df, gsub)#> [1] "axxle" "bafafa" "cqerry"-# Use `...` to absorb unused components of input list .l -df <- data.frame( - x = 1:3 + 0.1, - y = 3:1 - 0.1, - z = letters[1:3] -) -plus <- function(x, y) x + y# NOT RUN { -# this won't work -pmap(df, plus) -# }# but this will -plus2 <- function(x, y, ...) x + y -pmap_dbl(df, plus2)#> [1] 4 4 4-# The "p" for "parallel" in pmap() is the same as in base::pmin() -# and base::pmax() -df <- data.frame( - x = c(1, 2, 5), - y = c(5, 4, 8) -) -# all produce the same result -pmin(df$x, df$y)#> [1] 1 2 5map2_dbl(df$x, df$y, min)#> [1] 1 2 5pmap_dbl(df, min)#> [1] 1 2 5
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diff --git a/docs/reference/negate.html b/docs/reference/negate.html
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- modify() is a short-cut for x[] <- map(x, .f); return(x). modify_if()
-only modifies the elements of x that satisfy a predicate and leaves the
-others unchanged. modify_at() only modifies elements given by names or
-positions. modify_depth() only modifies elements at a given level of a
-nested data structure.
modify(.x, .f, ...) - -# S3 method for default -modify(.x, .f, ...) - -modify_if(.x, .p, .f, ...) - -# S3 method for default -modify_if(.x, .p, .f, ...) - -modify_at(.x, .at, .f, ...) - -# S3 method for default -modify_at(.x, .at, .f, ...) - -modify_depth(.x, .depth, .f, ..., .ragged = .depth < 0) - -# S3 method for default -modify_depth(.x, .depth, .f, ..., .ragged = .depth < 0)- -
Arguments
-| .x | -A list or atomic vector. |
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|---|---|
| .f | -A function, formula, or atomic vector. -If a function, it is used as is. -If a formula, e.g.
This syntax allows you to create very compact anonymous functions. -If character vector, numeric vector, or list, it
-is converted to an extractor function. Character vectors index by name
-and numeric vectors index by position; use a list to index by position
-and name at different levels. Within a list, wrap strings in |
-
| ... | -Additional arguments passed on to |
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| .p | -A single predicate function, a formula describing such a
-predicate function, or a logical vector of the same length as |
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| .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 |
-
| .depth | -Level of
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| .ragged | -If |
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Value
- -An object the same class as .x
Details
- -Since the transformation can alter the structure of the input; it's
-your responsibility to ensure that the transformation produces a
-valid output. For example, if you're modifying a data frame, .f
-must preserve the length of the input.
Genericity
- - -All these functions are S3 generic. However, the default method is
-sufficient in many cases. It should be suitable for any data type
-that implements the subset-assignment method [<-. Methods are provided
-for character, logical, integer and double classes (counterparts to map_chr,
-map_lgl, map_int, and map_dbl)
In some cases it may make sense to provide a custom implementation
-with a method suited to your S3 class. For example, a grouped_df
-method might take into account the grouped nature of a data frame.
See also
- - - - -Examples
--# Convert factors to characters -iris %>% - modify_if(is.factor, as.character) %>% - str()#> 'data.frame': 150 obs. of 5 variables: -#> $ Sepal.Length: num 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ... -#> $ Sepal.Width : num 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ... -#> $ Petal.Length: num 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ... -#> $ Petal.Width : num 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ... -#> $ Species : chr "setosa" "setosa" "setosa" "setosa" ...-# Specify which columns to map with a numeric vector of positions: -mtcars %>% modify_at(c(1, 4, 5), as.character) %>% str()#> 'data.frame': 32 obs. of 11 variables: -#> $ mpg : chr "21" "21" "22.8" "21.4" ... -#> $ cyl : num 6 6 4 6 8 6 8 4 4 6 ... -#> $ disp: num 160 160 108 258 360 ... -#> $ hp : chr "110" "110" "93" "110" ... -#> $ drat: chr "3.9" "3.9" "3.85" "3.08" ... -#> $ wt : num 2.62 2.88 2.32 3.21 3.44 ... -#> $ qsec: num 16.5 17 18.6 19.4 17 ... -#> $ vs : num 0 0 1 1 0 1 0 1 1 1 ... -#> $ am : num 1 1 1 0 0 0 0 0 0 0 ... -#> $ gear: num 4 4 4 3 3 3 3 4 4 4 ... -#> $ carb: num 4 4 1 1 2 1 4 2 2 4 ...-# Or with a vector of names: -mtcars %>% modify_at(c("cyl", "am"), as.character) %>% str()#> 'data.frame': 32 obs. of 11 variables: -#> $ mpg : num 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ... -#> $ cyl : chr "6" "6" "4" "6" ... -#> $ disp: num 160 160 108 258 360 ... -#> $ hp : num 110 110 93 110 175 105 245 62 95 123 ... -#> $ drat: num 3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ... -#> $ wt : num 2.62 2.88 2.32 3.21 3.44 ... -#> $ qsec: num 16.5 17 18.6 19.4 17 ... -#> $ vs : num 0 0 1 1 0 1 0 1 1 1 ... -#> $ am : chr "1" "1" "1" "0" ... -#> $ gear: num 4 4 4 3 3 3 3 4 4 4 ... -#> $ carb: num 4 4 1 1 2 1 4 2 2 4 ...-list(x = rbernoulli(100), y = 1:100) %>% - transpose() %>% - modify_if("x", ~ update_list(., y = ~ y * 100)) %>% - transpose() %>% - simplify_all()#> $x -#> [1] FALSE TRUE FALSE FALSE FALSE TRUE TRUE TRUE FALSE TRUE TRUE TRUE -#> [13] FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE -#> [25] TRUE FALSE FALSE TRUE TRUE TRUE FALSE TRUE TRUE TRUE FALSE TRUE -#> [37] TRUE TRUE TRUE FALSE FALSE FALSE FALSE TRUE FALSE FALSE TRUE FALSE -#> [49] FALSE FALSE TRUE FALSE FALSE FALSE FALSE TRUE FALSE TRUE TRUE FALSE -#> [61] TRUE TRUE FALSE TRUE FALSE TRUE FALSE FALSE FALSE FALSE TRUE FALSE -#> [73] TRUE FALSE FALSE TRUE TRUE TRUE FALSE FALSE TRUE FALSE TRUE FALSE -#> [85] TRUE TRUE FALSE TRUE FALSE TRUE TRUE TRUE FALSE TRUE FALSE FALSE -#> [97] TRUE FALSE FALSE TRUE -#> -#> $y -#> [1] 1 200 3 4 5 600 700 800 9 1000 1100 1200 -#> [13] 13 14 15 1600 17 18 19 20 21 22 23 24 -#> [25] 2500 26 27 2800 2900 3000 31 3200 3300 3400 35 3600 -#> [37] 3700 3800 3900 40 41 42 43 4400 45 46 4700 48 -#> [49] 49 50 5100 52 53 54 55 5600 57 5800 5900 60 -#> [61] 6100 6200 63 6400 65 6600 67 68 69 70 7100 72 -#> [73] 7300 74 75 7600 7700 7800 79 80 8100 82 8300 84 -#> [85] 8500 8600 87 8800 89 9000 9100 9200 93 9400 95 96 -#> [97] 9700 98 99 10000 -#>-# Modify at specified depth --------------------------- -l1 <- list( - obj1 = list( - prop1 = list(param1 = 1:2, param2 = 3:4), - prop2 = list(param1 = 5:6, param2 = 7:8) - ), - obj2 = list( - prop1 = list(param1 = 9:10, param2 = 11:12), - prop2 = list(param1 = 12:14, param2 = 15:17) - ) -) - -# In the above list, "obj" is level 1, "prop" is level 2 and "param" -# is level 3. To apply sum() on all params, we map it at depth 3: -l1 %>% modify_depth(3, sum) %>% str()#> List of 2 -#> $ obj1:List of 2 -#> ..$ prop1:List of 2 -#> .. ..$ param1: int 3 -#> .. ..$ param2: int 7 -#> ..$ prop2:List of 2 -#> .. ..$ param1: int 11 -#> .. ..$ param2: int 15 -#> $ obj2:List of 2 -#> ..$ prop1:List of 2 -#> .. ..$ param1: int 19 -#> .. ..$ param2: int 23 -#> ..$ prop2:List of 2 -#> .. ..$ param1: int 39 -#> .. ..$ param2: int 48-# modify() lets us pluck the elements prop1/param2 in obj1 and obj2: -l1 %>% modify(c("prop1", "param2")) %>% str()#> List of 2 -#> $ obj1: int [1:2] 3 4 -#> $ obj2: int [1:2] 11 12-# But what if we want to pluck all param2 elements? Then we need to -# act at a lower level: -l1 %>% modify_depth(2, "param2") %>% str()#> List of 2 -#> $ obj1:List of 2 -#> ..$ prop1: int [1:2] 3 4 -#> ..$ prop2: int [1:2] 7 8 -#> $ obj2:List of 2 -#> ..$ prop1: int [1:2] 11 12 -#> ..$ prop2: int [1:3] 15 16 17-# modify_depth() can be with other purrr functions to make them operate at -# a lower level. Here we ask pmap() to map paste() simultaneously over all -# elements of the objects at the second level. paste() is effectively -# mapped at level 3. -l1 %>% modify_depth(2, ~ pmap(., paste, sep = " / ")) %>% str()#> List of 2 -#> $ obj1:List of 2 -#> ..$ prop1:List of 2 -#> .. ..$ : chr "1 / 3" -#> .. ..$ : chr "2 / 4" -#> ..$ prop2:List of 2 -#> .. ..$ : chr "5 / 7" -#> .. ..$ : chr "6 / 8" -#> $ obj2:List of 2 -#> ..$ prop1:List of 2 -#> .. ..$ : chr "9 / 11" -#> .. ..$ : chr "10 / 12" -#> ..$ prop2:List of 3 -#> .. ..$ : chr "12 / 15" -#> .. ..$ : chr "13 / 16" -#> .. ..$ : chr "14 / 17"
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- Negate a predicate function.
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-| .p | -A single predicate function, a formula describing such a
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| .default | -Optional additional argument for extractor functions
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- -A new predicate function.
- - -Examples
--negate("x")#> function (x, ...) -#> { -#> !pluck(x, list("x"), .default = NULL) -#> } -#> <environment: 0x7f9d53c00600>negate(is.null)#> function (x) -#> { -#> !is.null(x = x) -#> } -#> <environment: base>negate(~ .x > 0)#> function (..., .x = ..1, .y = ..2, . = ..1) -#> { -#> !.x > 0 -#> } -#> <environment: 0x7f9d53fcf190>#> [1] 5#> [1] 5#> [1] 5
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diff --git a/docs/reference/partial.html b/docs/reference/partial.html
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- This infix function makes it easy to replace NULLs with a
-default value. It's inspired by the way that Ruby's or operation (||)
-works.
x %||% y- -
Arguments
-| x, y | -If |
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|---|
Examples
--1 %||% 2#> [1] 1NULL %||% 2#> [1] 2
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diff --git a/docs/reference/pipe.html b/docs/reference/pipe.html
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- Partial function application allows you to modify a function by pre-filling -some of the arguments. It is particularly useful in conjunction with -functionals and other function operators.
- -partial(...f, ..., .env = parent.frame(), .lazy = TRUE, .first = TRUE)- -
Arguments
-| ...f | -a function. For the output source to read well, this should be a -named function. |
-
|---|---|
| ... | -named arguments to |
-
| .env | -the environment of the created function. Defaults to
- |
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| .lazy | -If |
-
| .first | -If |
-
Design choices
- - -There are many ways to implement partial function application in R.
-(see e.g. dots in https://github.com/crowding/ptools for another
-approach.) This implementation is based on creating functions that are as
-similar as possible to the anonymous functions that you'd create by hand,
-if you weren't using partial.
Examples
--# Partial is designed to replace the use of anonymous functions for -# filling in function arguments. Instead of: -compact1 <- function(x) discard(x, is.null) - -# we can write: -compact2 <- partial(discard, .p = is.null) - -# and the generated source code is very similar to what we made by hand -compact1#> function(x) discard(x, is.null) -#> <environment: 0x7f9d52624630>compact2#> function (...) -#> discard(.p = is.null, ...) -#> <environment: 0x7f9d52624630>-# Note that the evaluation occurs "lazily" so that arguments will be -# repeatedly evaluated -f <- partial(runif, n = rpois(1, 5)) -f#> function (...) -#> runif(n = rpois(1, 5), ...) -#> <environment: 0x7f9d52624630>f()#> [1] 0.17635011 0.11075900 0.93834241 0.84647114 0.57114358 0.67909341 0.08932224f()#> [1] 0.2269574 0.4481785 0.1612276 0.1761117 0.1982281 0.3576113 0.1813332-# You can override this by saying .lazy = FALSE -f <- partial(runif, n = rpois(1, 5), .lazy = FALSE) -f#> function (...) -#> runif(n = 5L, ...) -#> <environment: 0x7f9d52624630>f()#> [1] 0.659083010 0.660253409 0.002400788 0.993446034 0.627489231f()#> [1] 0.01463034 0.20517822 0.66307666 0.46374403 0.36034816-# This also means that partial works fine with functions that do -# non-standard evaluation -my_long_variable <- 1:10 -plot2 <- partial(plot, my_long_variable) -plot2()plot2(runif(10), type = "l")
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diff --git a/docs/reference/pluck.html b/docs/reference/pluck.html
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diff --git a/docs/reference/prepend.html b/docs/reference/prepend.html
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- This is a generalised form of [[ which allows you to index deeply
-and flexibly into data structures. It supports R standard accessors
-like integer positions and string names, and also accepts arbitrary
-accessor functions, i.e. functions that take an object and return
-some internal piece.
pluck() is often more readable than a mix of operators and
-accessors because it reads linearly and is free of syntactic
-cruft. Compare: accessor(x[[1]])$foo to pluck(x, 1, accessor, "foo").
Furthermore, pluck() never partial-matches unlike $ which will
-select the disp object if you write mtcars$di.
attr_getter() generates an attribute accessor function;
-i.e., it generates a function for extracting an attribute with
-a given name. Unlike the base R attr() function with default
-options, it doesn't use partial matching.
pluck(.x, ..., .default = NULL) - -attr_getter(attr)- -
Arguments
-| .x | -A vector or environment |
-
|---|---|
| ... | -A list of accessors for indexing into the object. Can be -an integer position, a string name, or an accessor function. If -the object being indexed is an S4 object, accessing it by name -will return the corresponding slot. -These dots splice lists automatically. This -means you can supply arguments and lists of arguments -indistinctly. |
-
| .default | -Value to use if target is empty or absent. |
-
| attr | -An attribute name as string. |
-
Details
- -Since it handles arbitrary accessor functions, pluck() is a type
-of composition operator. However, it is indexing-oriented thanks to
-its handling of strings and integers. By the same token is also
-explicit regarding the intent of the composition (e.g. extraction).
Examples
--# pluck() supports integer positions, string names, and functions. -# Using functions, you can easily extend pluck(). Let's create a -# list of data structures: -obj1 <- list("a", list(1, elt = "foobar")) -obj2 <- list("b", list(2, elt = "foobaz")) -x <- list(obj1, obj2) - -# And now an accessor for these complex data structures: -my_element <- function(x) x[[2]]$elt - -# The accessor can then be passed to pluck: -pluck(x, 1, my_element)#> [1] "foobar"pluck(x, 2, my_element)#> [1] "foobaz"-# Even for this simple data structure, this is more readable than -# the alternative form because it requires you to read both from -# right-to-left and from left-to-right in different parts of the -# expression: -my_element(x[[1]])#> [1] "foobar"- -# This technique is used for plucking into attributes with -# attr_getter(). It takes an attribute name and returns a function -# to access the attribute: -obj1 <- structure("obj", obj_attr = "foo") -obj2 <- structure("obj", obj_attr = "bar") -x <- list(obj1, obj2) - -# pluck() is handy for extracting deeply into a data structure. -# Here we'll first extract by position, then by attribute: -pluck(x, 1, attr_getter("obj_attr")) # From first object#> [1] "foo"pluck(x, 2, attr_getter("obj_attr")) # From second object#> [1] "bar"- -# pluck() splices lists of arguments automatically. The following -# pluck is equivalent to the one above: -idx <- list(1, attr_getter("obj_attr")) -pluck(x, idx)#> [1] "foo"
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diff --git a/docs/reference/purrr-package.html b/docs/reference/purrr-package.html
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- This is a companion to append() to help merging two
-lists or atomic vectors. prepend() is a clearer semantic
-signal than c() that a vector is to be merged at the beginning of
-another, especially in a pipe chain.
prepend(x, values, before = 1)- -
Arguments
-| x | -the vector to be modified. |
-
|---|---|
| values | -to be included in the modified vector. |
-
| before | -a subscript, before which the values are to be appended. |
-
Value
- -A merged vector.
- - -Examples
--x <- as.list(1:3) - -x %>% append("a")#> [[1]] -#> [1] 1 -#> -#> [[2]] -#> [1] 2 -#> -#> [[3]] -#> [1] 3 -#> -#> [[4]] -#> [1] "a" -#>x %>% prepend("a")#> [[1]] -#> [1] "a" -#> -#> [[2]] -#> [1] 1 -#> -#> [[3]] -#> [1] 2 -#> -#> [[4]] -#> [1] 3 -#>x %>% prepend(list("a", "b"), before = 3)#> [[1]] -#> [1] 1 -#> -#> [[2]] -#> [1] 2 -#> -#> [[3]] -#> [1] "a" -#> -#> [[4]] -#> [1] "b" -#> -#> [[5]] -#> [1] 3 -#>
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diff --git a/docs/reference/rbernoulli.html b/docs/reference/rbernoulli.html
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diff --git a/docs/reference/rdunif.html b/docs/reference/rdunif.html
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- Generate random sample from a Bernoulli distribution
- -rbernoulli(n, p = 0.5)- -
Arguments
-| n | -Number of samples |
-
|---|---|
| p | -Probability of getting |
-
Value
- -A logical vector
- - -Examples
--rbernoulli(10)#> [1] TRUE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSErbernoulli(100, 0.1)#> [1] FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE FALSE FALSE TRUE FALSE -#> [13] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE -#> [25] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE -#> [37] FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE -#> [49] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE -#> [61] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE -#> [73] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE -#> [85] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE -#> [97] FALSE FALSE FALSE FALSE
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diff --git a/docs/reference/reduce.html b/docs/reference/reduce.html
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- Generate random sample from a discrete uniform distribution
- -rdunif(n, b, a = 1)- -
Arguments
-| n | -Number of samples to draw. |
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|---|---|
| a, b | -Range of the distribution (inclusive). |
-
Examples
--table(rdunif(1e3, 10))#> -#> 1 2 3 4 5 6 7 8 9 10 -#> 109 86 106 105 103 89 109 93 95 105table(rdunif(1e3, 10, -5))#> -#> -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 -#> 50 63 69 60 67 54 66 66 69 54 81 67 61 55 72 46
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diff --git a/docs/reference/reexports.html b/docs/reference/reexports.html
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- Reduce a list to a single value by iteratively applying a binary function.
- Source:R/reduce.R
- reduce.Rd
-
-
-
- reduce() combines from the left, reduce_right() combines from
-the right. reduce(list(x1, x2, x3), f) is equivalent to
-f(f(x1, x2), x3); reduce_right(list(x1, x2, x3), f) is equivalent to
-f(f(x3, x2), x1).
reduce(.x, .f, ..., .init) - -reduce_right(.x, .f, ..., .init) - -reduce2(.x, .y, .f, ..., .init) - -reduce2_right(.x, .y, .f, ..., .init)- -
Arguments
-| .x | -A list or atomic vector. |
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|---|---|
| .f | -For For |
-
| ... | -Additional arguments passed on to |
-
| .init | -If supplied, will be used as the first value to start
-the accumulation, rather than using |
-
| .y | -For |
-
Examples
--1:3 %>% reduce(`+`)#> [1] 61:10 %>% reduce(`*`)#> [1] 3628800-paste2 <- function(x, y, sep = ".") paste(x, y, sep = sep) -letters[1:4] %>% reduce(paste2)#> [1] "a.b.c.d"letters[1:4] %>% reduce2(c("-", ".", "-"), paste2)#> [1] "a-b.c-d"#> [[1]] -#> [1] 5 8 6 10 9 -#> -#> [[2]] -#> [1] 8 10 6 4 1 -#>reduce(samples, union)#> [1] 5 8 6 10 9 4 1reduce(samples, intersect)#> [1] 8 6 10-x <- list(c(0, 1), c(2, 3), c(4, 5)) -x %>% reduce(c)#> [1] 0 1 2 3 4 5x %>% reduce_right(c)#> [1] 4 5 2 3 0 1# Equivalent to: -x %>% rev() %>% reduce(c)#> [1] 4 5 2 3 0 1
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diff --git a/docs/reference/rerun.html b/docs/reference/rerun.html
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- These objects are imported from other packages. Follow the links -below to see their documentation.
--
-
- rlang
is_bare_list,is_bare_atomic,is_bare_vector,is_bare_double,is_bare_integer,is_bare_numeric,is_bare_character,is_bare_logical,is_list,is_atomic,is_vector,is_integer,is_double,is_character,is_logical,is_null,is_function,is_scalar_list,is_scalar_atomic,is_scalar_vector,is_scalar_double,is_scalar_character,is_scalar_logical,is_scalar_integer,is_empty,is_formula
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diff --git a/docs/reference/safely.html b/docs/reference/safely.html
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rerun(.n, ...)- -
Arguments
-| .n | -Number of times to run expressions |
-
|---|---|
| ... | -Expressions to re-run. |
-
Value
- -A list of length .n. Each element of ... will be
-re-run once for each .n.
There is one special case: if there's a single unnamed input, the second
-level list will be dropped. In this case, rerun(n, x) behaves like
-replicate(n, x, simplify = FALSE).
Examples
--10 %>% rerun(rnorm(5))#> [[1]] -#> [1] 0.5802748 -0.5941795 -0.3718553 -1.4230352 -1.1404838 -#> -#> [[2]] -#> [1] 1.2685047 -0.2801044 0.9110540 2.0043945 0.2471404 -#> -#> [[3]] -#> [1] -0.4290761 -2.1837551 -0.1236349 0.2873839 -0.3715993 -#> -#> [[4]] -#> [1] 1.6265623 0.3099083 -1.5640407 -1.2306235 -1.3701975 -#> -#> [[5]] -#> [1] 0.1065558 -0.3988255 -0.3534061 0.2345011 1.7697899 -#> -#> [[6]] -#> [1] 0.45787638 1.14375811 -1.30772262 -0.94254960 -0.09496125 -#> -#> [[7]] -#> [1] -1.130047 -1.456208 -1.698718 -1.240827 -1.058299 -#> -#> [[8]] -#> [1] -0.75437857 -0.58510694 0.55814208 -0.01900658 -0.16935602 -#> -#> [[9]] -#> [1] 1.295331 -1.426060 1.754104 -2.115575 1.009702 -#> -#> [[10]] -#> [1] 0.2545351 -0.3775541 0.3724693 -1.4626811 0.9037093 -#>#> [1] 0.3884734 -0.2797106 -0.1499915 -0.1213636 0.1875155 -0.1596498 -#> [7] 0.1293135 0.5927749 0.7689696 -0.4564721
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diff --git a/docs/reference/set_names.html b/docs/reference/set_names.html
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- These functions wrap functions so that instead of generating side effects -through printed output, messages, warnings, and errors, they return enhanced -output. They are all adverbs because they modify the action of a verb (a -function).
- -safely(.f, otherwise = NULL, quiet = TRUE) - -quietly(.f) - -possibly(.f, otherwise, quiet = TRUE) - -auto_browse(.f)- -
Arguments
-| .f | -A function, formula, or atomic vector. -If a function, it is used as is. -If a formula, e.g.
This syntax allows you to create very compact anonymous functions. -If character vector, numeric vector, or list, it
-is converted to an extractor function. Character vectors index by name
-and numeric vectors index by position; use a list to index by position
-and name at different levels. Within a list, wrap strings in |
-
|---|---|
| otherwise | -Default value to use when an error occurs. |
-
| quiet | -Hide errors ( |
-
Value
- -safely: wrapped function instead returns a list with
-components result and error. One value is always NULL.
quietly: wrapped function instead returns a list with components
-result, output, messages and warnings.
possibly: wrapped function uses a default value (otherwise)
-whenever an error occurs.
Examples
--safe_log <- safely(log) -safe_log(10)#> $result -#> [1] 2.302585 -#> -#> $error -#> NULL -#>safe_log("a")#> $result -#> NULL -#> -#> $error -#> <simpleError in log(x = x, base = base): non-numeric argument to mathematical function> -#>#> $result -#> $result[[1]] -#> NULL -#> -#> $result[[2]] -#> [1] 2.302585 -#> -#> $result[[3]] -#> [1] 4.60517 -#> -#> -#> $error -#> $error[[1]] -#> <simpleError in log(x = x, base = base): non-numeric argument to mathematical function> -#> -#> $error[[2]] -#> NULL -#> -#> $error[[3]] -#> NULL -#> -#>-# This is a bit easier to work with if you supply a default value -# of the same type and use the simplify argument to transpose(): -safe_log <- safely(log, otherwise = NA_real_) -list("a", 10, 100) %>% - map(safe_log) %>% - transpose() %>% - simplify_all()#> $result -#> [1] NA 2.302585 4.605170 -#> -#> $error -#> $error[[1]] -#> <simpleError in log(x = x, base = base): non-numeric argument to mathematical function> -#> -#> $error[[2]] -#> NULL -#> -#> $error[[3]] -#> NULL -#> -#>-# To replace errors with a default value, use possibly(). -list("a", 10, 100) %>% - map_dbl(possibly(log, NA_real_))#> [1] NA 2.302585 4.605170-# For interactive usage, auto_browse() is useful because it automatically -# starts a browser() in the right place. -f <- function(x) { - y <- 20 - if (x > 5) { - stop("!") - } else { - x - } -} -if (interactive()) { - map(1:6, auto_browse(f)) -}#> Called from: eval_bare(quote(browser()), env = frame$env)#> Error in .f(...): !-# It doesn't make sense to use auto_browse with primitive functions, -# because they are implemented in C so there's no useful environment -# for you to interact with. -
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diff --git a/docs/reference/splice.html b/docs/reference/splice.html
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- These objects are imported from other packages. Follow the links -below to see their documentation.
--
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- -
set_names(x, nm = x, ...)- -
Arguments
-| x | -Vector to name |
-
|---|---|
| nm, ... | -Vector of names, the same length as You can specify names in three ways:
|
-
Value
- -.x with the names attribute set.
Details
- -This is a snake case wrapper for stats::setNames(), with
-tweaked defaults, and stricter argument checking.
Examples
--set_names(1:4, c("a", "b", "c", "d"))#> a b c d -#> 1 2 3 4set_names(1:4, letters[1:4])#> a b c d -#> 1 2 3 4set_names(1:4, "a", "b", "c", "d")#> a b c d -#> 1 2 3 4-# If the second argument is ommitted a vector is named with itself -set_names(letters[1:5])#> a b c d e -#> "a" "b" "c" "d" "e"-# Alternatively you can supply a function -set_names(1:10, ~ letters[seq_along(.)])#> a b c d e f g h i j -#> 1 2 3 4 5 6 7 8 9 10set_names(head(mtcars), toupper)#> MPG CYL DISP HP DRAT WT QSEC VS AM GEAR CARB -#> Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4 -#> Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4 -#> Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1 -#> Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1 -#> Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2 -#> Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1
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diff --git a/docs/reference/transpose.html b/docs/reference/transpose.html
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- -splice(...)- -
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-| ... | -Objects to concatenate. |
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Value
- -A list.
- - -Examples
--inputs <- list(arg1 = "a", arg2 = "b") - -# splice() concatenates the elements of inputs with arg3 -splice(inputs, arg3 = c("c1", "c2")) %>% str()#> List of 3 -#> $ arg1: chr "a" -#> $ arg2: chr "b" -#> $ arg3: chr [1:2] "c1" "c2"list(inputs, arg3 = c("c1", "c2")) %>% str()#> List of 2 -#> $ :List of 2 -#> ..$ arg1: chr "a" -#> ..$ arg2: chr "b" -#> $ arg3: chr [1:2] "c1" "c2"c(inputs, arg3 = c("c1", "c2")) %>% str()#> List of 4 -#> $ arg1 : chr "a" -#> $ arg2 : chr "b" -#> $ arg31: chr "c1" -#> $ arg32: chr "c2"
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diff --git a/docs/reference/vec_depth.html b/docs/reference/vec_depth.html
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-list of pairs, or a list of pairs into pair of lists. For example,
-if you had a list of length n where each component had values a and
-b, transpose() would make a list with elements a and
-b that contained lists of length n. It's called transpose because
-x[[1]][[2]] is equivalent to transpose(x)[[2]][[1]].
transpose(.l, .names = NULL)- -
Arguments
-| .l | -A list of vectors to transpose. The first element is used as the -template; you'll get a warning if a subsequent element has a different -length. |
-
|---|---|
| .names | -For efficiency, |
-
Value
- -A list with indexing transposed compared to .l.
Details
- -Note that transpose() is its own inverse, much like the
-transpose operation on a matrix. You can get back the original
-input by transposing it twice.
Examples
--#> List of 5 -#> $ :List of 2 -#> ..$ x: num 0.475 -#> ..$ y: num [1:5] 0.206 0.707 0.449 0.714 0.358 -#> $ :List of 2 -#> ..$ x: num 0.17 -#> ..$ y: num [1:5] 0.47 0.26 0.136 0.633 0.833 -#> $ :List of 2 -#> ..$ x: num 0.375 -#> ..$ y: num [1:5] 0.8264 0.1672 0.0548 0.1037 0.5826 -#> $ :List of 2 -#> ..$ x: num 0.944 -#> ..$ y: num [1:5] 0.869 0.426 0.257 0.557 0.138 -#> $ :List of 2 -#> ..$ x: num 0.42 -#> ..$ y: num [1:5] 0.2382 0.7582 0.0865 0.0568 0.3083x %>% transpose() %>% str()#> List of 2 -#> $ x:List of 5 -#> ..$ : num 0.475 -#> ..$ : num 0.17 -#> ..$ : num 0.375 -#> ..$ : num 0.944 -#> ..$ : num 0.42 -#> $ y:List of 5 -#> ..$ : num [1:5] 0.206 0.707 0.449 0.714 0.358 -#> ..$ : num [1:5] 0.47 0.26 0.136 0.633 0.833 -#> ..$ : num [1:5] 0.8264 0.1672 0.0548 0.1037 0.5826 -#> ..$ : num [1:5] 0.869 0.426 0.257 0.557 0.138 -#> ..$ : num [1:5] 0.2382 0.7582 0.0865 0.0568 0.3083# Back to where we started -x %>% transpose() %>% transpose() %>% str()#> List of 5 -#> $ :List of 2 -#> ..$ x: num 0.475 -#> ..$ y: num [1:5] 0.206 0.707 0.449 0.714 0.358 -#> $ :List of 2 -#> ..$ x: num 0.17 -#> ..$ y: num [1:5] 0.47 0.26 0.136 0.633 0.833 -#> $ :List of 2 -#> ..$ x: num 0.375 -#> ..$ y: num [1:5] 0.8264 0.1672 0.0548 0.1037 0.5826 -#> $ :List of 2 -#> ..$ x: num 0.944 -#> ..$ y: num [1:5] 0.869 0.426 0.257 0.557 0.138 -#> $ :List of 2 -#> ..$ x: num 0.42 -#> ..$ y: num [1:5] 0.2382 0.7582 0.0865 0.0568 0.3083-# transpose() is useful in conjunction with safely() & quietly() -x <- list("a", 1, 2) -y <- x %>% map(safely(log)) -y %>% str()#> List of 3 -#> $ :List of 2 -#> ..$ result: NULL -#> ..$ error :List of 2 -#> .. ..$ message: chr "non-numeric argument to mathematical function" -#> .. ..$ call : language log(x = x, base = base) -#> .. ..- attr(*, "class")= chr [1:3] "simpleError" "error" "condition" -#> $ :List of 2 -#> ..$ result: num 0 -#> ..$ error : NULL -#> $ :List of 2 -#> ..$ result: num 0.693 -#> ..$ error : NULLy %>% transpose() %>% str()#> List of 2 -#> $ result:List of 3 -#> ..$ : NULL -#> ..$ : num 0 -#> ..$ : num 0.693 -#> $ error :List of 3 -#> ..$ :List of 2 -#> .. ..$ message: chr "non-numeric argument to mathematical function" -#> .. ..$ call : language log(x = x, base = base) -#> .. ..- attr(*, "class")= chr [1:3] "simpleError" "error" "condition" -#> ..$ : NULL -#> ..$ : NULL-# Use simplify_all() to reduce to atomic vectors where possible -x <- list(list(a = 1, b = 2), list(a = 3, b = 4), list(a = 5, b = 6)) -x %>% transpose()#> $a -#> $a[[1]] -#> [1] 1 -#> -#> $a[[2]] -#> [1] 3 -#> -#> $a[[3]] -#> [1] 5 -#> -#> -#> $b -#> $b[[1]] -#> [1] 2 -#> -#> $b[[2]] -#> [1] 4 -#> -#> $b[[3]] -#> [1] 6 -#> -#>#> $a -#> [1] 1 3 5 -#> -#> $b -#> [1] 2 4 6 -#>-# Provide explicit component names to prevent loss of those that don't -# appear in first component -ll <- list( - list(x = 1, y = "one"), - list(z = "deux", x = 2) -) -ll %>% transpose()#> $x -#> $x[[1]] -#> [1] 1 -#> -#> $x[[2]] -#> [1] 2 -#> -#> -#> $y -#> $y[[1]] -#> [1] "one" -#> -#> $y[[2]] -#> NULL -#> -#>#> $x -#> $x[[1]] -#> [1] 1 -#> -#> $x[[2]] -#> [1] 2 -#> -#> -#> $y -#> $y[[1]] -#> [1] "one" -#> -#> $y[[2]] -#> NULL -#> -#> -#> $z -#> $z[[1]] -#> NULL -#> -#> $z[[2]] -#> [1] "deux" -#> -#>
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diff --git a/docs/reference/when.html b/docs/reference/when.html
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diff --git a/docs/tidyverse.css b/docs/tidyverse.css
deleted file mode 100644
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+++ /dev/null
@@ -1,6496 +0,0 @@
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- .input-group-sm > .input-group-btn > select.form-control.btn {
- font-size: 13px; }
- select.input-lg, .input-group-lg > select.form-control,
- .input-group-lg > select.input-group-addon,
- .input-group-lg > .input-group-btn > select.btn,
- select.form-control.input-lg,
- .input-group-lg > select.form-control,
- .input-group-lg > .input-group-btn > select.form-control.btn {
- font-size: 19px; }
- select:focus,
- select.form-control:focus {
- -webkit-box-shadow: inset 0 -2px 0 #5a9ddb;
- box-shadow: inset 0 -2px 0 #5a9ddb;
- background-image: url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABoAAAAaCAMAAACelLz8AAAAJ1BMVEUhISEhISEhISEhISEhISEhISEhISEhISEhISEhISEhISEhISEhISF8S9ewAAAADHRSTlMAAgMJC0uWpKa6wMxMdjkoAAAANUlEQVR4AeXJyQEAERAAsNl7Hf3X6xt0QL6JpZWq30pdvdadme+0PMdzvHm8YThHcT1H7K0BtOMDniZhWOgAAAAASUVORK5CYII=); }
- select[multiple],
- select.form-control[multiple] {
- background: none; }
-
-.radio label,
-.radio-inline label,
-.checkbox label,
-.checkbox-inline label {
- padding-left: 25px; }
-.radio input[type="radio"],
-.radio input[type="checkbox"],
-.radio-inline input[type="radio"],
-.radio-inline input[type="checkbox"],
-.checkbox input[type="radio"],
-.checkbox input[type="checkbox"],
-.checkbox-inline input[type="radio"],
-.checkbox-inline input[type="checkbox"] {
- margin-left: -25px; }
-
-input[type="radio"],
-.radio input[type="radio"],
-.radio-inline input[type="radio"] {
- position: relative;
- margin-top: 6px;
- margin-right: 4px;
- vertical-align: top;
- border: none;
- background-color: transparent;
- -webkit-appearance: none;
- appearance: none;
- cursor: pointer; }
- input[type="radio"]:focus,
- .radio input[type="radio"]:focus,
- .radio-inline input[type="radio"]:focus {
- outline: none; }
- input[type="radio"]:before, input[type="radio"]:after,
- .radio input[type="radio"]:before,
- .radio input[type="radio"]:after,
- .radio-inline input[type="radio"]:before,
- .radio-inline input[type="radio"]:after {
- content: "";
- display: block;
- width: 18px;
- height: 18px;
- border-radius: 50%;
- -webkit-transition: 240ms;
- -o-transition: 240ms;
- transition: 240ms; }
- input[type="radio"]:before,
- .radio input[type="radio"]:before,
- .radio-inline input[type="radio"]:before {
- position: absolute;
- left: 0;
- top: -3px;
- background-color: #5a9ddb;
- -webkit-transform: scale(0);
- -ms-transform: scale(0);
- -o-transform: scale(0);
- transform: scale(0); }
- input[type="radio"]:after,
- .radio input[type="radio"]:after,
- .radio-inline input[type="radio"]:after {
- position: relative;
- top: -3px;
- border: 2px solid #666; }
- input[type="radio"]:checked:before,
- .radio input[type="radio"]:checked:before,
- .radio-inline input[type="radio"]:checked:before {
- -webkit-transform: scale(0.5);
- -ms-transform: scale(0.5);
- -o-transform: scale(0.5);
- transform: scale(0.5); }
- input[type="radio"]:disabled:checked:before,
- .radio input[type="radio"]:disabled:checked:before,
- .radio-inline input[type="radio"]:disabled:checked:before {
- background-color: #bbb; }
- input[type="radio"]:checked:after,
- .radio input[type="radio"]:checked:after,
- .radio-inline input[type="radio"]:checked:after {
- border-color: #5a9ddb; }
- input[type="radio"]:disabled:after, input[type="radio"]:disabled:checked:after,
- .radio input[type="radio"]:disabled:after,
- .radio input[type="radio"]:disabled:checked:after,
- .radio-inline input[type="radio"]:disabled:after,
- .radio-inline input[type="radio"]:disabled:checked:after {
- border-color: #bbb; }
-
-input[type="checkbox"],
-.checkbox input[type="checkbox"],
-.checkbox-inline input[type="checkbox"] {
- position: relative;
- border: none;
- margin-bottom: -4px;
- -webkit-appearance: none;
- appearance: none;
- cursor: pointer; }
- input[type="checkbox"]:focus,
- .checkbox input[type="checkbox"]:focus,
- .checkbox-inline input[type="checkbox"]:focus {
- outline: none; }
- input[type="checkbox"]:focus:after,
- .checkbox input[type="checkbox"]:focus:after,
- .checkbox-inline input[type="checkbox"]:focus:after {
- border-color: #5a9ddb; }
- input[type="checkbox"]:after,
- .checkbox input[type="checkbox"]:after,
- .checkbox-inline input[type="checkbox"]:after {
- content: "";
- display: block;
- width: 18px;
- height: 18px;
- margin-top: -2px;
- margin-right: 5px;
- border: 2px solid #666;
- border-radius: 2px;
- -webkit-transition: 240ms;
- -o-transition: 240ms;
- transition: 240ms; }
- input[type="checkbox"]:checked:before,
- .checkbox input[type="checkbox"]:checked:before,
- .checkbox-inline input[type="checkbox"]:checked:before {
- content: "";
- position: absolute;
- top: 0;
- left: 6px;
- display: table;
- width: 6px;
- height: 12px;
- border: 2px solid #fff;
- border-top-width: 0;
- border-left-width: 0;
- -webkit-transform: rotate(45deg);
- -ms-transform: rotate(45deg);
- -o-transform: rotate(45deg);
- transform: rotate(45deg); }
- input[type="checkbox"]:checked:after,
- .checkbox input[type="checkbox"]:checked:after,
- .checkbox-inline input[type="checkbox"]:checked:after {
- background-color: #5a9ddb;
- border-color: #5a9ddb; }
- input[type="checkbox"]:disabled:after,
- .checkbox input[type="checkbox"]:disabled:after,
- .checkbox-inline input[type="checkbox"]:disabled:after {
- border-color: #bbb; }
- input[type="checkbox"]:disabled:checked:after,
- .checkbox input[type="checkbox"]:disabled:checked:after,
- .checkbox-inline input[type="checkbox"]:disabled:checked:after {
- background-color: #bbb;
- border-color: transparent; }
-
-.has-warning input:not([type=checkbox]),
-.has-warning .form-control,
-.has-warning input.form-control[readonly],
-.has-warning input[type=text][readonly],
-.has-warning [type=text].form-control[readonly],
-.has-warning input:not([type=checkbox]):focus,
-.has-warning .form-control:focus {
- border-bottom: none;
- -webkit-box-shadow: inset 0 -2px 0 #ff9800;
- box-shadow: inset 0 -2px 0 #ff9800; }
-
-.has-error input:not([type=checkbox]),
-.has-error .form-control,
-.has-error input.form-control[readonly],
-.has-error input[type=text][readonly],
-.has-error [type=text].form-control[readonly],
-.has-error input:not([type=checkbox]):focus,
-.has-error .form-control:focus {
- border-bottom: none;
- -webkit-box-shadow: inset 0 -2px 0 #e51c23;
- box-shadow: inset 0 -2px 0 #e51c23; }
-
-.has-success input:not([type=checkbox]),
-.has-success .form-control,
-.has-success input.form-control[readonly],
-.has-success input[type=text][readonly],
-.has-success [type=text].form-control[readonly],
-.has-success input:not([type=checkbox]):focus,
-.has-success .form-control:focus {
- border-bottom: none;
- -webkit-box-shadow: inset 0 -2px 0 #4CAF50;
- box-shadow: inset 0 -2px 0 #4CAF50; }
-
-.has-warning .input-group-addon, .has-error .input-group-addon, .has-success .input-group-addon {
- color: #666;
- border-color: transparent;
- background-color: transparent; }
-
-.form-group-lg select,
-.form-group-lg select.form-control {
- line-height: 1.5; }
-
-.nav-tabs > li > a,
-.nav-tabs > li > a:focus {
- margin-right: 0;
- background-color: transparent;
- border: none;
- color: #444;
- -webkit-box-shadow: inset 0 -1px 0 #ddd;
- box-shadow: inset 0 -1px 0 #ddd;
- -webkit-transition: all 0.2s;
- -o-transition: all 0.2s;
- transition: all 0.2s; }
- .nav-tabs > li > a:hover,
- .nav-tabs > li > a:focus:hover {
- background-color: transparent;
- -webkit-box-shadow: inset 0 -2px 0 #5a9ddb;
- box-shadow: inset 0 -2px 0 #5a9ddb;
- color: #5a9ddb; }
-.nav-tabs > li.active > a, .nav-tabs > li.active > a:focus {
- border: none;
- -webkit-box-shadow: inset 0 -2px 0 #5a9ddb;
- box-shadow: inset 0 -2px 0 #5a9ddb;
- color: #5a9ddb; }
- .nav-tabs > li.active > a:hover, .nav-tabs > li.active > a:focus:hover {
- border: none;
- color: #5a9ddb; }
-.nav-tabs > li.disabled > a {
- -webkit-box-shadow: inset 0 -1px 0 #ddd;
- box-shadow: inset 0 -1px 0 #ddd; }
-.nav-tabs.nav-justified > li > a, .nav-tabs.nav-justified > li > a:hover, .nav-tabs.nav-justified > li > a:focus, .nav-tabs.nav-justified > .active > a, .nav-tabs.nav-justified > .active > a:hover, .nav-tabs.nav-justified > .active > a:focus {
- border: none; }
-.nav-tabs .dropdown-menu {
- margin-top: 0; }
-
-.dropdown-menu {
- margin-top: 0;
- border: none;
- -webkit-box-shadow: 0 1px 4px rgba(0, 0, 0, 0.3);
- box-shadow: 0 1px 4px rgba(0, 0, 0, 0.3); }
-
-.alert {
- border: none;
- color: #fff; }
- .alert-success {
- background-color: #4CAF50; }
- .alert-info {
- background-color: #9C27B0; }
- .alert-warning {
- background-color: #ff9800; }
- .alert-danger {
- background-color: #e51c23; }
- .alert a:not(.close):not(.btn),
- .alert .alert-link {
- color: #fff;
- font-weight: bold; }
- .alert .close {
- color: #fff; }
-
-.badge {
- padding: 4px 6px 4px; }
-
-.progress {
- position: relative;
- z-index: 1;
- height: 6px;
- border-radius: 0;
- -webkit-box-shadow: none;
- box-shadow: none; }
- .progress-bar {
- -webkit-box-shadow: none;
- box-shadow: none; }
- .progress-bar:last-child {
- border-radius: 0 3px 3px 0; }
- .progress-bar:last-child:before {
- display: block;
- content: "";
- position: absolute;
- width: 100%;
- height: 100%;
- left: 0;
- right: 0;
- z-index: -1;
- background-color: #edf4fb; }
- .progress-bar-success:last-child.progress-bar:before {
- background-color: #c7e7c8; }
- .progress-bar-info:last-child.progress-bar:before {
- background-color: #edc9f3; }
- .progress-bar-warning:last-child.progress-bar:before {
- background-color: #ffe0b3; }
- .progress-bar-danger:last-child.progress-bar:before {
- background-color: #f28e92; }
-
-.close {
- font-size: 34px;
- font-weight: 300;
- line-height: 24px;
- opacity: 0.6;
- -webkit-transition: all 0.2s;
- -o-transition: all 0.2s;
- transition: all 0.2s; }
- .close:hover {
- opacity: 1; }
-
-.list-group-item {
- padding: 15px; }
-.list-group-item-text {
- color: #bbb; }
-
-.well {
- border-radius: 0;
- -webkit-box-shadow: none;
- box-shadow: none; }
-
-.panel {
- border: none;
- border-radius: 2px;
- -webkit-box-shadow: 0 1px 4px rgba(0, 0, 0, 0.3);
- box-shadow: 0 1px 4px rgba(0, 0, 0, 0.3); }
- .panel-heading {
- border-bottom: none; }
- .panel-footer {
- border-top: none; }
-
-.popover {
- border: none;
- -webkit-box-shadow: 0 1px 4px rgba(0, 0, 0, 0.3);
- box-shadow: 0 1px 4px rgba(0, 0, 0, 0.3); }
-
-.carousel-caption h1, .carousel-caption h2, .carousel-caption h3, .carousel-caption h4, .carousel-caption h5, .carousel-caption h6 {
- color: inherit; }
-
-/*# sourceMappingURL=tidyverse.css.map */
diff --git a/docs/tocBullet.svg b/docs/tocBullet.svg
deleted file mode 100644
index 29abb514f..000000000
--- a/docs/tocBullet.svg
+++ /dev/null
@@ -1,11 +0,0 @@
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- Match/validate a set of conditions for an object and continue with the action -associated with the first valid match.
- Source:R/when.R
- when.Rd
-
-
-
- when is a flavour of pattern matching (or an if-else abstraction) in
-which a value is matched against a sequence of condition-action sets. When a
-valid match/condition is found the action is executed and the result of the
-action is returned.
when(., ...)- -
Arguments
-| . | -the value to match against |
-
|---|---|
| ... | -formulas; each containing a condition as LHS and an action as RHS. -named arguments will define additional values. |
-
Value
- -The value resulting from the action of the first valid -match/condition is returned. If no matches are found, and no default is -given, NULL will be returned.
-Validity of the conditions are tested with isTRUE, or equivalently
-with identical(condition, TRUE).
-In other words conditions resulting in more than one logical will never
-be valid. Note that the input value is always treated as a single object,
-as opposed to the ifelse function.
Examples
--1:10 %>% - when( - sum(.) <= 50 ~ sum(.), - sum(.) <= 100 ~ sum(.)/2, - ~ 0 - )#> [1] 27.5-1:10 %>% - when( - sum(.) <= x ~ sum(.), - sum(.) <= 2*x ~ sum(.)/2, - ~ 0, - x = 60 - )#> [1] 55-iris %>% - subset(Sepal.Length > 10) %>% - when( - nrow(.) > 0 ~ ., - ~ iris %>% head(10) - )#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species -#> 1 5.1 3.5 1.4 0.2 setosa -#> 2 4.9 3.0 1.4 0.2 setosa -#> 3 4.7 3.2 1.3 0.2 setosa -#> 4 4.6 3.1 1.5 0.2 setosa -#> 5 5.0 3.6 1.4 0.2 setosa -#> 6 5.4 3.9 1.7 0.4 setosa -#> 7 4.6 3.4 1.4 0.3 setosa -#> 8 5.0 3.4 1.5 0.2 setosa -#> 9 4.4 2.9 1.4 0.2 setosa -#> 10 4.9 3.1 1.5 0.1 setosa-iris %>% - head %>% - when(nrow(.) < 10 ~ ., - ~ stop("Expected fewer than 10 rows."))#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species -#> 1 5.1 3.5 1.4 0.2 setosa -#> 2 4.9 3.0 1.4 0.2 setosa -#> 3 4.7 3.2 1.3 0.2 setosa -#> 4 4.6 3.1 1.5 0.2 setosa -#> 5 5.0 3.6 1.4 0.2 setosa -#> 6 5.4 3.9 1.7 0.4 setosa