The minimal {rrapply}-package contains a single function rrapply(),
providing an extended implementation of R-base’s rapply() function,
which applies a function f to all elements of a nested list
recursively and controls how to structure the returned result.
rrapply() builds upon rapply()’s native C implementation and for
this reason requires no external R-package dependencies.
# Install latest release from CRAN:
install.packages("rrapply")
# Install the development version from GitHub:
# install.packages("devtools")
devtools::install_github("JorisChau/rrapply")
With base rapply(), there is no convenient way to prune or filter list
elements from the input list object. The rrapply() function adds an
option how = "prune" to prune all list elements not subject to
application of f from a nested list,
library(rrapply)
## data: renewable energy per country in 2016 (% of total energy consumption)
data("renewable_energy_by_country")
## subset countries and areas in Oceania
renewable_oceania <- renewable_energy_by_country[["World"]]["Oceania"]
str(renewable_oceania, list.len = 3, give.attr = FALSE)
#> List of 1
#> $ Oceania:List of 4
#> ..$ Australia and New Zealand:List of 6
#> .. ..$ Australia : num 9.32
#> .. ..$ Christmas Island : logi NA
#> .. ..$ Cocos (Keeling) Islands : logi NA
#> .. .. [list output truncated]
#> ..$ Melanesia :List of 5
#> .. ..$ Fiji : num 24.4
#> .. ..$ New Caledonia : num 4.03
#> .. ..$ Papua New Guinea: num 50.3
#> .. .. [list output truncated]
#> ..$ Micronesia :List of 8
#> .. ..$ Guam : num 3.03
#> .. ..$ Kiribati : num 45.4
#> .. ..$ Marshall Islands : num 11.8
#> .. .. [list output truncated]
#> .. [list output truncated]## drop all logical NA's while preserving list structure
rrapply(
renewable_oceania,
f = \(x) x,
classes = "numeric",
how = "prune"
) |>
str(list.len = 3, give.attr = FALSE)
#> List of 1
#> $ Oceania:List of 4
#> ..$ Australia and New Zealand:List of 2
#> .. ..$ Australia : num 9.32
#> .. ..$ New Zealand: num 32.8
#> ..$ Melanesia :List of 5
#> .. ..$ Fiji : num 24.4
#> .. ..$ New Caledonia : num 4.03
#> .. ..$ Papua New Guinea: num 50.3
#> .. .. [list output truncated]
#> ..$ Micronesia :List of 7
#> .. ..$ Guam : num 3.03
#> .. ..$ Kiribati : num 45.4
#> .. ..$ Marshall Islands : num 11.8
#> .. .. [list output truncated]
#> .. [list output truncated]Instead, use how = "flatten" to return a flattened unnested version of
the pruned list,
## drop all logical NA's and return unnested list
rrapply(
renewable_oceania,
f = \(x) x,
classes = "numeric",
how = "flatten"
) |>
head(n = 10)
#> Australia New Zealand Fiji New Caledonia
#> 9.32 32.76 24.36 4.03
#> Papua New Guinea Solomon Islands Vanuatu Guam
#> 50.34 65.73 33.67 3.03
#> Kiribati Marshall Islands
#> 45.43 11.75Hint: the options argument allows to avoid coercion of the
flattened list to a vector and/or to include all parent list names in
the result.
## flatten to simple list with full names
rrapply(
renewable_oceania,
f = \(x) x,
classes = "numeric",
how = "flatten",
options = list(namesep = ".", simplify = FALSE)
) |>
str(list.len = 5, give.attr = FALSE)
#> List of 22
#> $ Oceania.Australia and New Zealand.Australia : num 9.32
#> $ Oceania.Australia and New Zealand.New Zealand : num 32.8
#> $ Oceania.Melanesia.Fiji : num 24.4
#> $ Oceania.Melanesia.New Caledonia : num 4.03
#> $ Oceania.Melanesia.Papua New Guinea : num 50.3
#> [list output truncated]Or, use how = "melt" to return a melted data.frame of the pruned list
similar in format to reshape2::melt() applied to a nested list.
## drop all logical NA's and return melted data.frame
oceania_melt <- rrapply(
renewable_oceania,
f = \(x) x,
classes = "numeric",
how = "melt"
)
head(oceania_melt)
#> L1 L2 L3 value
#> 1 Oceania Australia and New Zealand Australia 9.32
#> 2 Oceania Australia and New Zealand New Zealand 32.76
#> 3 Oceania Melanesia Fiji 24.36
#> 4 Oceania Melanesia New Caledonia 4.03
#> 5 Oceania Melanesia Papua New Guinea 50.34
#> 6 Oceania Melanesia Solomon Islands 65.73A melted data.frame can be used to reconstruct a nested list with
how = "unmelt",
## reconstruct nested list from melted data.frame
rrapply(oceania_melt, how = "unmelt") |>
str(list.len = 3, give.attr = FALSE)
#> List of 1
#> $ Oceania:List of 4
#> ..$ Australia and New Zealand:List of 2
#> .. ..$ Australia : num 9.32
#> .. ..$ New Zealand: num 32.8
#> ..$ Melanesia :List of 5
#> .. ..$ Fiji : num 24.4
#> .. ..$ New Caledonia : num 4.03
#> .. ..$ Papua New Guinea: num 50.3
#> .. .. [list output truncated]
#> ..$ Micronesia :List of 7
#> .. ..$ Guam : num 3.03
#> .. ..$ Kiribati : num 45.4
#> .. ..$ Marshall Islands : num 11.8
#> .. .. [list output truncated]
#> .. [list output truncated]Nested lists containing repeated observations can be unnested with
how = "bind", which returns a wide data.frame similar in format to
dplyr::bind_rows() applied to a list of data.frames or repeated
application of tidyr::unnest_wider() to a nested data.frame.
## data: nested list of Pokemon properties in Pokemon GO
data("pokedex")
str(pokedex, list.len = 3)
#> List of 1
#> $ pokemon:List of 151
#> ..$ :List of 16
#> .. ..$ id : int 1
#> .. ..$ num : chr "001"
#> .. ..$ name : chr "Bulbasaur"
#> .. .. [list output truncated]
#> ..$ :List of 17
#> .. ..$ id : int 2
#> .. ..$ num : chr "002"
#> .. ..$ name : chr "Ivysaur"
#> .. .. [list output truncated]
#> ..$ :List of 15
#> .. ..$ id : int 3
#> .. ..$ num : chr "003"
#> .. ..$ name : chr "Venusaur"
#> .. .. [list output truncated]
#> .. [list output truncated]## unnest list to wide data.frame
rrapply(pokedex, how = "bind")[, c(1:3, 5:8)] |>
head(n = 9)
#> id num name type height weight candy
#> 1 1 001 Bulbasaur Grass, Poison 0.71 m 6.9 kg Bulbasaur Candy
#> 2 2 002 Ivysaur Grass, Poison 0.99 m 13.0 kg Bulbasaur Candy
#> 3 3 003 Venusaur Grass, Poison 2.01 m 100.0 kg Bulbasaur Candy
#> 4 4 004 Charmander Fire 0.61 m 8.5 kg Charmander Candy
#> 5 5 005 Charmeleon Fire 1.09 m 19.0 kg Charmander Candy
#> 6 6 006 Charizard Fire, Flying 1.70 m 90.5 kg Charmander Candy
#> 7 7 007 Squirtle Water 0.51 m 9.0 kg Squirtle Candy
#> 8 8 008 Wartortle Water 0.99 m 22.5 kg Squirtle Candy
#> 9 9 009 Blastoise Water 1.60 m 85.5 kg Squirtle CandyHint: set options = list(namecols = TRUE) to include the parent
list names associated to each row in the wide data.frame as individual
columns L1, L2, etc.
## bind to data.frame including parent columns
rrapply(pokedex, how = "bind", options = list(namecols = TRUE))[, c(1:5, 7:10)] |>
head(n = 6)
#> L1 L2 id num name type height weight candy
#> 1 pokemon 1 1 001 Bulbasaur Grass, Poison 0.71 m 6.9 kg Bulbasaur Candy
#> 2 pokemon 2 2 002 Ivysaur Grass, Poison 0.99 m 13.0 kg Bulbasaur Candy
#> 3 pokemon 3 3 003 Venusaur Grass, Poison 2.01 m 100.0 kg Bulbasaur Candy
#> 4 pokemon 4 4 004 Charmander Fire 0.61 m 8.5 kg Charmander Candy
#> 5 pokemon 5 5 005 Charmeleon Fire 1.09 m 19.0 kg Charmander Candy
#> 6 pokemon 6 6 006 Charizard Fire, Flying 1.70 m 90.5 kg Charmander CandyBase rapply() allows to apply a function f to list elements of
certain types or classes via the classes argument. rrapply()
generalizes this option via an additional condition argument, which
accepts any function to use as a condition or predicate to apply f to
a subset of list elements.
## drop all NA elements using condition
rrapply(
renewable_oceania,
condition = \(x) !is.na(x),
f = \(x) x,
how = "prune"
) |>
str(list.len = 3, give.attr = FALSE)
#> List of 1
#> $ Oceania:List of 4
#> ..$ Australia and New Zealand:List of 2
#> .. ..$ Australia : num 9.32
#> .. ..$ New Zealand: num 32.8
#> ..$ Melanesia :List of 5
#> .. ..$ Fiji : num 24.4
#> .. ..$ New Caledonia : num 4.03
#> .. ..$ Papua New Guinea: num 50.3
#> .. .. [list output truncated]
#> ..$ Micronesia :List of 7
#> .. ..$ Guam : num 3.03
#> .. ..$ Kiribati : num 45.4
#> .. ..$ Marshall Islands : num 11.8
#> .. .. [list output truncated]
#> .. [list output truncated]## filter all countries with values > 85%
rrapply(
renewable_energy_by_country,
condition = \(x) x > 85,
how = "prune"
) |>
str(give.attr = FALSE)
#> List of 1
#> $ World:List of 1
#> ..$ Africa:List of 1
#> .. ..$ Sub-Saharan Africa:List of 3
#> .. .. ..$ Eastern Africa:List of 7
#> .. .. .. ..$ Burundi : num 89.2
#> .. .. .. ..$ Ethiopia : num 91.9
#> .. .. .. ..$ Rwanda : num 86
#> .. .. .. ..$ Somalia : num 94.7
#> .. .. .. ..$ Uganda : num 88.6
#> .. .. .. ..$ United Republic of Tanzania: num 86.1
#> .. .. .. ..$ Zambia : num 88.5
#> .. .. ..$ Middle Africa :List of 2
#> .. .. .. ..$ Chad : num 85.3
#> .. .. .. ..$ Democratic Republic of the Congo: num 97
#> .. .. ..$ Western Africa:List of 1
#> .. .. .. ..$ Guinea-Bissau: num 86.5With base rapply(), the f function only has access to the content
of the list element under evaluation, and there is no convenient way to
access its name or location in the nested list from inside f.
rrapply() defines the special arguments .xname, .xpos,
.xparents, .xsiblings inside the f and condition functions (in
addition to the principal function argument):
.xnameevaluates to the name of the list element;.xposevaluates to the position of the element in the nested list structured as an integer vector;.xparentsevaluates to a vector of parent list names in the path to the current list element;.xsiblingsevaluates to the parent list containing the current list element and its direct siblings.
## apply f based on element's name
rrapply(
renewable_oceania,
condition = \(x) !is.na(x),
f = \(x, .xname) sprintf("Renewable energy in %s: %.2f%%", .xname, x),
how = "flatten"
) |>
head(n = 5)
#> Australia
#> "Renewable energy in Australia: 9.32%"
#> New Zealand
#> "Renewable energy in New Zealand: 32.76%"
#> Fiji
#> "Renewable energy in Fiji: 24.36%"
#> New Caledonia
#> "Renewable energy in New Caledonia: 4.03%"
#> Papua New Guinea
#> "Renewable energy in Papua New Guinea: 50.34%"
## filter elements by name
rrapply(
renewable_energy_by_country,
condition = \(x, .xname) .xname %in% c("Belgium", "Netherlands", "Luxembourg"),
how = "prune"
) |>
str(give.attr = FALSE)
#> List of 1
#> $ World:List of 1
#> ..$ Europe:List of 1
#> .. ..$ Western Europe:List of 3
#> .. .. ..$ Belgium : num 9.14
#> .. .. ..$ Luxembourg : num 13.5
#> .. .. ..$ Netherlands: num 5.78
## filter European countries > 50% using .xpos
rrapply(
renewable_energy_by_country,
condition = \(x, .xpos) identical(.xpos[1:2], c(1L, 5L)) && x > 50,
how = "prune"
) |>
str(give.attr = FALSE)
#> List of 1
#> $ World:List of 1
#> ..$ Europe:List of 2
#> .. ..$ Northern Europe:List of 3
#> .. .. ..$ Iceland: num 78.1
#> .. .. ..$ Norway : num 59.5
#> .. .. ..$ Sweden : num 51.4
#> .. ..$ Western Europe :List of 1
#> .. .. ..$ Liechtenstein: num 62.9
## filter European countries > 50% using .xparents
rrapply(
renewable_energy_by_country,
condition = \(x, .xparents) "Europe" %in% .xparents && x > 50,
how = "prune"
) |>
str(give.attr = FALSE)
#> List of 1
#> $ World:List of 1
#> ..$ Europe:List of 2
#> .. ..$ Northern Europe:List of 3
#> .. .. ..$ Iceland: num 78.1
#> .. .. ..$ Norway : num 59.5
#> .. .. ..$ Sweden : num 51.4
#> .. ..$ Western Europe :List of 1
#> .. .. ..$ Liechtenstein: num 62.9
## return position of element in list
rrapply(
renewable_energy_by_country,
condition = \(x, .xname) .xname == "Sweden",
f = \(x, .xpos) .xpos,
how = "flatten"
)
#> $Sweden
#> [1] 1 5 2 14
## return siblings of element in list
rrapply(
renewable_energy_by_country,
condition = \(x, .xsiblings) "Sweden" %in% names(.xsiblings),
how = "flatten"
) |>
head(n = 5)
#> Aland Islands Denmark Estonia Faroe Islands Finland
#> NA 33.06 26.55 4.24 42.03
## filter elements and unnest list
rrapply(
pokedex,
condition = \(x, .xpos, .xname) length(.xpos) < 4 & .xname %in% c("num", "name", "type"),
how = "bind"
) |>
head()
#> num name type
#> 1 001 Bulbasaur Grass, Poison
#> 2 002 Ivysaur Grass, Poison
#> 3 003 Venusaur Grass, Poison
#> 4 004 Charmander Fire
#> 5 005 Charmeleon Fire
#> 6 006 Charizard Fire, FlyingBy default, both base rapply() and rrapply() recurse into any
“list-like” element. Set classes = "list" in rrapply() to override
and apply f to any list element (i.e. a sublist) that satisfies the
condition argument. This can be useful to e.g. collapse sublists or
calculate summary statistics across elements in a nested list:
## calculate mean value of Europe
rrapply(
renewable_energy_by_country,
condition = \(x, .xname) .xname == "Europe",
f = \(x) mean(unlist(x), na.rm = TRUE),
classes = "list",
how = "flatten"
)
#> Europe
#> 22.36565
## calculate mean value for each continent
## (Antartica's value is missing)
rrapply(
renewable_energy_by_country,
condition = \(x, .xpos) length(.xpos) == 2,
f = \(x) mean(unlist(x), na.rm = TRUE),
classes = "list"
) |>
str(give.attr = FALSE)
#> List of 1
#> $ World:List of 6
#> ..$ Africa : num 54.3
#> ..$ Americas : num 18.2
#> ..$ Antarctica: logi NA
#> ..$ Asia : num 17.9
#> ..$ Europe : num 22.4
#> ..$ Oceania : num 17.8
## simplify pokemon evolutions to character vectors
rrapply(
pokedex,
condition = \(x, .xname) .xname %in% c("name", "next_evolution", "prev_evolution"),
f = \(x) if(is.list(x)) sapply(x, `[[`, "name") else x,
classes = c("character", "list"),
how = "bind"
) |>
head(n = 9)
#> name next_evolution prev_evolution
#> 1 Bulbasaur Ivysaur, Venusaur NA
#> 2 Ivysaur Venusaur Bulbasaur
#> 3 Venusaur NA Bulbasaur, Ivysaur
#> 4 Charmander Charmeleon, Charizard NA
#> 5 Charmeleon Charizard Charmander
#> 6 Charizard NA Charmander, Charmeleon
#> 7 Squirtle Wartortle, Blastoise NA
#> 8 Wartortle Blastoise Squirtle
#> 9 Blastoise NA Squirtle, WartortleHint: as data.frames are also list-like objects, rrapply() applies
f to individual data.frame columns by default. Set
classes = "data.frame" to avoid this behavior and apply the f and
condition functions to complete data.frame objects instead of
individual data.frame columns.
If classes = "list" and how = "recurse", rrapply() applies the f
function to any list element that satisfies the condition argument
similar to the previous section, but recurses further into any updated
list-like element after application of f. This can be useful to
e.g. recursively update the class or other attributes of all elements in
a nested list:
## recursively remove all list attributes
rrapply(
renewable_oceania,
f = \(x) c(x),
classes = c("list", "ANY"),
how = "recurse"
) |>
str(list.len = 3, give.attr = TRUE)
#> List of 1
#> $ Oceania:List of 4
#> ..$ Australia and New Zealand:List of 6
#> .. ..$ Australia : num 9.32
#> .. ..$ Christmas Island : logi NA
#> .. ..$ Cocos (Keeling) Islands : logi NA
#> .. .. [list output truncated]
#> ..$ Melanesia :List of 5
#> .. ..$ Fiji : num 24.4
#> .. ..$ New Caledonia : num 4.03
#> .. ..$ Papua New Guinea: num 50.3
#> .. .. [list output truncated]
#> ..$ Micronesia :List of 8
#> .. ..$ Guam : num 3.03
#> .. ..$ Kiribati : num 45.4
#> .. ..$ Marshall Islands : num 11.8
#> .. .. [list output truncated]
#> .. [list output truncated]The option how = "names" is a special case of how = "recurse", where
the value of f is used to replace the name of the evaluated list
element instead of its content (as with all other how options). By
default, how = "names" uses classes = c("list", "ANY") in order to
allow updating of all names in the nested list.
## recursively replace all names by M49-codes
rrapply(
renewable_oceania,
f = \(x) attr(x, "M49-code"),
how = "names"
) |>
str(list.len = 3, give.attr = FALSE)
#> List of 1
#> $ 009:List of 4
#> ..$ 053:List of 6
#> .. ..$ 036: num 9.32
#> .. ..$ 162: logi NA
#> .. ..$ 166: logi NA
#> .. .. [list output truncated]
#> ..$ 054:List of 5
#> .. ..$ 242: num 24.4
#> .. ..$ 540: num 4.03
#> .. ..$ 598: num 50.3
#> .. .. [list output truncated]
#> ..$ 057:List of 8
#> .. ..$ 316: num 3.03
#> .. ..$ 296: num 45.4
#> .. ..$ 584: num 11.8
#> .. .. [list output truncated]
#> .. [list output truncated]Base rapply() does not include recursion for expression objects. In
contrast rrapply() supports recursion of call objects and expression
vectors, which are treated as nested lists based on their abstract
syntax trees. As such, all functionality that applies to nested lists
extends directly to call objects and expression vectors.
To update the abstract syntax tree of a call object, use
how = "replace":
## language object
(lang <- quote(y <- x <- 1 + TRUE))
#> y <- x <- 1 + TRUE
## replace logicals by integers
rrapply(lang, classes = "logical", f = as.numeric, how = "replace")
#> y <- x <- 1 + 1To update the abstract syntax tree and return it as a nested list, use
how = "list":
## update and decompose call object
rrapply(lang, f = function(x) ifelse(is.logical(x), as.numeric(x), x), how = "list") |>
str()
#> List of 3
#> $ : symbol <-
#> $ : symbol y
#> $ :List of 3
#> ..$ : symbol <-
#> ..$ : symbol x
#> ..$ :List of 3
#> .. ..$ : symbol +
#> .. ..$ : num 1
#> .. ..$ : num 1The modes how = "prune", how = "flatten" and how = "melt" return
the pruned abstract syntax tree as: a nested list, a flattened list and
a melted data.frame respectively. This is identical to application of
rrapply() to the abstract syntax tree formatted as a nested list.
To illustrate, we return all names (i.e. symbols) in the abstract syntax tree that not part of base R:
## expression vector
expr <- expression(y <- x <- 1, f(g(2 * pi)))
is_new_name <- function(x) !exists(as.character(x), envir = baseenv())
## prune and decompose expression
rrapply(expr, classes = "name", condition = is_new_name, how = "prune") |>
str()
#> List of 2
#> $ :List of 2
#> ..$ : symbol y
#> ..$ :List of 1
#> .. ..$ : symbol x
#> $ :List of 2
#> ..$ : symbol f
#> ..$ :List of 1
#> .. ..$ : symbol g
## prune and flatten expression
rrapply(expr, classes = "name", condition = is_new_name, how = "flatten") |>
str()
#> List of 4
#> $ : symbol y
#> $ : symbol x
#> $ : symbol f
#> $ : symbol g
## prune and melt expression
rrapply(expr, classes = "name", condition = is_new_name, f = as.character, how = "melt")
#> L1 L2 L3 value
#> 1 1 2 <NA> y
#> 2 1 3 2 x
#> 3 2 1 <NA> f
#> 4 2 2 1 gFor more details and examples on how to use the rrapply() function see
the accompanying package vignette in the vignettes folder or the
Articles section at https://jorischau.github.io/rrapply/.