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 provides control in structuring 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. 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)
## Nested list of renewable energy as a percentage of total energy consumption per country in 2016
data("renewable_energy_by_country")
## Subset values for 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
na_drop_oceania <- rrapply(
renewable_oceania,
f = identity,
classes = "numeric",
how = "prune"
)
str(na_drop_oceania, 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
na_drop_oceania2 <- rrapply(
renewable_oceania,
f = identity,
classes = "numeric",
how = "flatten"
)
head(na_drop_oceania2, 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 11.75Or, 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
na_drop_oceania3 <- rrapply(
renewable_oceania,
f = identity,
classes = "numeric",
how = "melt"
)
head(na_drop_oceania3)
#> 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
na_drop_oceania4 <- rrapply(
na_drop_oceania3,
how = "unmelt"
)
str(na_drop_oceania4, 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.
## 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 as a wide data.frame
pokedex_wide <- rrapply(pokedex, how = "bind")
head(pokedex_wide[, c(1:3, 5:10)], n = 5)
#> pokemon.id pokemon.num pokemon.name pokemon.type pokemon.height
#> 1 1 001 Bulbasaur Grass, Poison 0.71 m
#> 2 2 002 Ivysaur Grass, Poison 0.99 m
#> 3 3 003 Venusaur Grass, Poison 2.01 m
#> 4 4 004 Charmander Fire 0.61 m
#> 5 5 005 Charmeleon Fire 1.09 m
#> pokemon.weight pokemon.candy pokemon.candy_count pokemon.egg
#> 1 6.9 kg Bulbasaur Candy 25 2 km
#> 2 13.0 kg Bulbasaur Candy 100 Not in Eggs
#> 3 100.0 kg Bulbasaur Candy NA Not in Eggs
#> 4 8.5 kg Charmander Candy 25 2 km
#> 5 19.0 kg Charmander Candy 100 Not in EggsBase 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 function
na_drop_oceania3 <- rrapply(
renewable_oceania,
condition = Negate(is.na),
f = function(x) x,
how = "prune"
)
str(na_drop_oceania3, 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 above 85%
renewable_energy_above_85 <- rrapply(
renewable_energy_by_country,
condition = function(x) x > 85,
how = "prune"
)
str(renewable_energy_above_85, 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 86.5.xname, .xpos, .xparents and .xsiblingsIn 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 the f function. rrapply() allows the use of the special arguments .xname, .xpos,.xparents and .xsiblings inside the f and condition functions (in addition to the principal function argument). .xname evaluates to the name of the list element, .xpos evaluates to the position of the element in the nested list structured as an integer vector, .xparents evaluates to a vector of parent node names in the path to the current list element, and .xsiblings evaluates to the parent list containing the current list element and all of its direct siblings.
## Apply a function using the name of the node
renewable_oceania4 <- rrapply(
renewable_oceania,
f = function(x, .xname) sprintf("Renewable energy in %s: %.2f%%", .xname, x),
condition = Negate(is.na),
how = "flatten"
)
head(renewable_oceania4, 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%"
## Extract values based on country names
renewable_benelux <- rrapply(
renewable_energy_by_country,
condition = function(x, .xname) .xname %in% c("Belgium", "Netherlands", "Luxembourg"),
how = "prune"
)
str(renewable_benelux, 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
renewable_europe_above_50 <- rrapply(
renewable_energy_by_country,
condition = function(x, .xpos) identical(head(.xpos, 2), c(1L, 5L)) & x > 50,
how = "prune"
)
str(renewable_europe_above_50, 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
renewable_europe_above_50 <- rrapply(
renewable_energy_by_country,
condition = function(x, .xparents) "Europe" %in% .xparents & x > 50,
how = "prune"
)
str(renewable_europe_above_50, 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 Sweden in list
(xpos_sweden <- rrapply(
renewable_energy_by_country,
condition = function(x, .xname) identical(.xname, "Sweden"),
f = function(x, .xpos) .xpos,
how = "flatten"
))
#> $Sweden
#> [1] 1 5 2 14
renewable_energy_by_country[[xpos_sweden$Sweden]]
#> [1] 51.35
#> attr(,"M49-code")
#> [1] "752"
## Return siblings of Sweden in list
siblings_sweden <- rrapply(
renewable_energy_by_country,
condition = function(x, .xsiblings) "Sweden" %in% names(.xsiblings),
how = "flatten"
)
head(siblings_sweden, n = 5)
#> Aland Islands Denmark Estonia Faroe Islands Finland
#> NA 33.06 26.55 4.24 42.03
## Parse only Pokemon number, name and type columns
pokedex_small <- rrapply(
pokedex,
condition = function(x, .xpos, .xname) length(.xpos) < 4 & .xname %in% c("num", "name", "type"),
how = "bind"
)
head(pokedex_small)
#> pokemon.num pokemon.name pokemon.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. Using rrapply(), we can set classes = "list" to override this behavior and apply f to any list element (i.e. a sublist) that satisfies the condition argument. This is useful to collapse sublists or calculate summary statistics of sublists of a nested list. Together with e.g. the .xname and .xpos arguments, we have a flexible way in deciding which sublists to summarize through the condition function.
## Calculate mean value of Europe
rrapply(
renewable_energy_by_country,
classes = "list",
condition = function(x, .xname) .xname == "Europe",
f = function(x) mean(unlist(x), na.rm = TRUE),
how = "flatten"
)
#> Europe
#> 22.36565
## Calculate mean value for each continent
renewable_continent_summary <- rrapply(
renewable_energy_by_country,
classes = "list",
condition = function(x, .xpos) length(.xpos) == 2,
f = function(x) mean(unlist(x), na.rm = TRUE)
)
## Antarctica's value is missing
str(renewable_continent_summary, 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 evolution sublists to character vectors
pokedex_wide2 <- rrapply(
pokedex,
classes = c("character", "list"),
condition = function(x, .xname) .xname %in% c("name", "next_evolution", "prev_evolution"),
f = function(x) if(is.list(x)) sapply(x, `[[`, "name") else x,
how = "bind"
)
head(pokedex_wide2, n = 9)
#> pokemon.name pokemon.next_evolution pokemon.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, WartortleIf 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 the f function. Using how = "recurse", we can for instance recursively update all node names in a nested list:
## Replace country names by M-49 attributes
renewable_M49 <- rrapply(
list(renewable_energy_by_country),
classes = "list",
f = function(x) {
names(x) <- vapply(x, attr, character(1L), which = "M49-code")
return(x)
},
how = "recurse"
)
str(renewable_M49[[1]], max.level = 3, list.len = 3, give.attr = FALSE)
#> List of 1
#> $ 001:List of 6
#> ..$ 002:List of 2
#> .. ..$ 015:List of 7
#> .. ..$ 202:List of 4
#> ..$ 019:List of 2
#> .. ..$ 419:List of 3
#> .. ..$ 021:List of 5
#> ..$ 010: logi NA
#> .. [list output truncated]rrapply() on data.framesSince base rapply() recurses into all list-like objects, and data.frames are list-like objects, rapply() always descends into the individual columns of a data.frame. To avoid this behavior using rrapply(), set classes = "data.frame", in which case the f and condition functions are applied to complete data.frame objects instead of individual data.frame columns.
However, it can also be useful to exploit the property that a data.frame is a list-like object and use base rapply() to apply a function f to data.frame columns of certain classes via the classes argument. Using the condition argument in rrapply(), we can apply a function f to a subset of data.frame columns using a general predicate function,
## Scale only Sepal columns in iris dataset
iris_standard_sepal <- rrapply(
iris,
condition = function(x, .xname) grepl("Sepal", .xname),
f = scale
)
head(iris_standard_sepal)
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#> 1 -0.8976739 1.01560199 1.4 0.2 setosa
#> 2 -1.1392005 -0.13153881 1.4 0.2 setosa
#> 3 -1.3807271 0.32731751 1.3 0.2 setosa
#> 4 -1.5014904 0.09788935 1.5 0.2 setosa
#> 5 -1.0184372 1.24503015 1.4 0.2 setosa
#> 6 -0.5353840 1.93331463 1.7 0.4 setosa
## Scale and keep only numeric columns
iris_standard_transmute <- rrapply(
iris,
f = scale,
classes = "numeric",
how = "prune"
)
head(iris_standard_transmute)
#> Sepal.Length Sepal.Width Petal.Length Petal.Width
#> 1 -0.8976739 1.01560199 -1.335752 -1.311052
#> 2 -1.1392005 -0.13153881 -1.335752 -1.311052
#> 3 -1.3807271 0.32731751 -1.392399 -1.311052
#> 4 -1.5014904 0.09788935 -1.279104 -1.311052
#> 5 -1.0184372 1.24503015 -1.335752 -1.311052
#> 6 -0.5353840 1.93331463 -1.165809 -1.048667
## Summarize only numeric columns with how = "flatten"
iris_standard_summarize <- rrapply(
iris,
f = summary,
classes = "numeric",
how = "flatten"
)
iris_standard_summarize
#> $Sepal.Length
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 4.300 5.100 5.800 5.843 6.400 7.900
#>
#> $Sepal.Width
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 2.000 2.800 3.000 3.057 3.300 4.400
#>
#> $Petal.Length
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 1.000 1.600 4.350 3.758 5.100 6.900
#>
#> $Petal.Width
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 0.100 0.300 1.300 1.199 1.800 2.500rrapply() on expressionsIn contrast to base rapply(), rrapply() supports recursion of call objects and expression vectors, which are treated as nested lists based on their internal 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":
call_old <- quote(y <- x <- 1 + TRUE)
str(call_old)
#> language y <- x <- 1 + TRUE
## Replace logicals by integers
call_new <- rrapply(call_old, classes = "logical", f = as.numeric, how = "replace")
str(call_new)
#> language y <- x <- 1 + 1To update the abstract syntax tree and return it as a nested list, use how = "list":
## Update and decompose call object
call_ast <- rrapply(call_old, f = function(x) ifelse(is.logical(x), as.numeric(x), x), how = "list")
str(call_ast)
#> List of 3
#> $ : symbol <-
#> $ : symbol y
#> $ :List of 3
#> ..$ : symbol <-
#> ..$ : symbol x
#> ..$ :List of 3
#> .. ..$ : symbol +
#> .. ..$ : num 1
#> .. ..$ : num 1The choices 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 are not part of base R:
expr <- expression(y <- x <- 1, f(g(2 * pi)))
is_new_name <- function(x) !exists(as.character(x), envir = baseenv())
## Prune and decompose expression
expr_prune <- rrapply(expr, classes = "name", condition = is_new_name, how = "prune")
str(expr_prune)
#> 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
expr_flatten <- rrapply(expr, classes = "name", condition = is_new_name, how = "flatten")
str(expr_flatten)
#> List of 4
#> $ : symbol y
#> $ : symbol x
#> $ : symbol f
#> $ : symbol g
## Prune and melt expression
expr_melt <- rrapply(expr, classes = "name", condition = is_new_name, f = as.character, how = "melt")
expr_melt
#> L1 L2 L3 value
#> 1 1 2 <NA> y
#> 2 1 3 2 x
#> 3 2 1 <NA> f
#> 4 2 2 1 gAdditional demonstrating examples from https://jorischau.github.io/rrapply/articles/articles/3-calls-and-expressions.html include replacing any logical abbreviations in an expression by their non-abbreviated counterparts:
## Expand logical abbreviation
logical_abbr_expand <- function(x) {
if(identical(x, quote(T))) {
TRUE
} else if(identical(x, quote(F))) {
FALSE
} else {
x
}
}
rrapply(expression(f(x = c(T, F)), any(T, FALSE)), f = logical_abbr_expand, how = "replace")
#> expression(f(x = c(TRUE, FALSE)), any(TRUE, FALSE))Or, collecting all names created by assignment and returning them as a character vector:
## Check if variable is created by assignment
is_assign <- function(x, .xpos, .xsiblings) {
identical(.xpos[length(.xpos)], 2L) &&
as.character(.xsiblings[[1]]) %in% c("<-", "=", "for", "assign", "delayedAssign")
}
unique(rrapply(body(lm), condition = is_assign, f = as.character, how = "unlist"))
#> [1] "ret.x" "ret.y" "cl" "mf" "m" "mt" "y" "w"
#> [9] "offset" "mlm" "ny" "x" "z"For 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/.