rrapply is a reimplemented and extended version of rapply to recursively apply a function f to
a set of elements of a list and deciding how the result is structured.
Usage
rrapply(
object,
condition,
f,
classes = "ANY",
deflt = NULL,
how = c("replace", "list", "unlist", "prune", "flatten", "melt", "bind", "recurse",
"unmelt", "names"),
options,
...
)Arguments
- object
a
list,expressionvector, orcallobject, i.e., “list-like”.- condition
a condition
functionof one “principal” argument and optional special arguments.xname,.xpos,.xparentsand/or.xsiblings(see ‘Details’), passing further arguments via....- f
a
functionof one “principal” argument and optional special arguments.xname,.xpos,.xparentsand/or.xsiblings(see ‘Details’), passing further arguments via....- classes
character vector of
classnames, or"ANY"to match the class of any terminal node. Include"list"or"data.frame"to match the class of non-terminal nodes as well.- deflt
the default result (only used if
how = "list"orhow = "unlist").- how
character string partially matching the ten possibilities given: see ‘Details’.
- options
a named
listwith additional optionsnamesep,simplify,namecolsand/orcoldepththat only apply to certain choices ofhow: see ‘Details’.- ...
additional arguments passed to the call to
fandcondition.
Value
If how = "unlist", a vector as in rapply. If how = "list", how = "replace", how = "recurse" or how = "names",
“list-like” of similar structure as object as in rapply. If how = "prune", a pruned “list-like” object
of similar structure as object with pruned list elements based on classes and condition. If how = "flatten", a flattened
pruned vector or list with pruned elements based on classes and condition. If how = "melt", a melted data.frame containing the node paths
and values of the pruned list elements based on classes and condition. If how = "bind", a wide data.frame with repeated list elements
expanded as single data.frame rows and aligned by identical list names using the same coercion rules as how = "unlist". The repeated list elements
are subject to pruning based on classes and condition. If how = "unmelt", a nested list with list names and values defined
in the data.frame object.
Note
rrapply allows the f function argument to be missing, in which case no function is applied to the list
elements.
how = "unmelt" requires as input a data.frame as returned by how = "melt" with character columns to name the nested list components
and a final list- or vector-column containing the values of the nested list elements.
How to structure result
In addition to rapply's modes to set how equal to "replace", "list" or "unlist",
seven choices "prune", "flatten", "melt", "bind", "unmelt", "recurse" and "names" are available:
how = "prune"filters all list elements not subject to application offfrom the listobject. The original list structure is retained, similar to the non-pruned optionshow = "replace"orhow = "list".how = "flatten"is an efficient way to return a flattened unnested version of the pruned list. By defaulthow = "flatten"uses similar coercion rules ashow = "unlist", this can be disabled withsimplify = FALSEin theoptionsargument.how = "melt"returns a melted data.frame of the pruned list, each row contains the path of a single terminal node in the pruned list at depth layersL1,L2, and so on. The column"value"contains the possibly coerced values at the terminal nodes and is equivalent to the result ofhow = "flatten". If no list names are present, the node names in the data.frame default to the indices of the list elements"1","2", etc.how = "bind"is used to unnest a nested list containing repeated sublists into a wide data.frame. Each repeated sublist is expanded as a single row in the data.frame and identical sublist component names are aligned as individual columns. By default, the list layer containing repeated sublists is identified based on the minimal depth detected across leaf nodes, this can be set manually withcoldepthin theoptionsargument.how = "unmelt"is a special case that reconstructs a nested list from a melted data.frame. For this reason,how = "unmelt"only applies to data.frames in the same format as returned byhow = "melt". Internally,how = "unmelt"first reconstructs a nested list from the melted data.frame and second uses the same functional framework ashow = "replace".how = "recurse"is a specialized option that is only useful in combination with e.g.classes = "list"to recurse further into updated “list-like” elements. This is explained in more detail below.how = "names"modifies the names of the nested list elements instead of the list content.how = "names"internally works similar tohow = "list", except that the value offis used to replace the name of the list element under evaluation instead of its content.
Condition function
Both rapply and rrapply allow to apply f to list elements of certain classes via the classes argument.
rrapply generalizes this concept via an additional condition argument, which accepts any function to use as a condition
or predicate to select list elements to which f is applied. Conceptually, the f function is applied to all list elements for
which the condition function exactly evaluates to TRUE similar to isTRUE. If the condition function is missing,
f is applied to all list elements.
Since the condition function generalizes the classes argument, it is allowed to use the deflt argument
together with how = "list" or how = "unlist" to set a default value to all list elements for which the condition does
not evaluate to TRUE.
Correct use of ...
The principal argument of the f and condition functions evaluates to the content of the list element. Any further arguments to
f and condition (besides the special arguments .xname, .xpos, etc. discussed below) supplied via the dots ...
argument need to be defined as function arguments in both the f and condition function (if existing), even if they are not
used in the function itself. See also the ‘Examples’ section.
Special arguments .xname, .xpos, .xparents and .xsiblings
The f and condition functions accept four special arguments .xname, .xpos, .xparents and .xsiblings in
addition to the first principal argument. The .xname argument evaluates to the name of the list element. The .xpos argument evaluates
to the position of the element in the nested list structured as an integer vector. That is, if x = list(list("y", "z")), then an .xpos
location of c(1, 2) corresponds to the list element x[[c(1, 2)]]. The .xparents argument evaluates to a vector of all parent
node names in the path to the list element. The .xsiblings argument evaluates to the complete (sub)list that includes the list element as a direct child.
The names .xname, .xpos, .xparents or .xsiblings need to be explicitly included as function arguments in f and
condition (in addition to the principal argument). See also the ‘Examples’ section.
Avoid recursing into list nodes
By default, rrapply recurses into any “list-like” element. If classes = "list", this behavior is overridden and the
f function is also applied to any list element of object that satisfies condition. For expression objects, use
classes = "language", classes = "expression" or classes = "pairlist" to avoid recursing into branches of the abstract
syntax tree of object. If the condition or classes arguments are not satisfied for a “list-like” element,
rrapply will recurse further into the sublist, apply the f function to the nodes that satisfy condition and classes,
and so on. Note that this behavior can only be triggered using the classes argument and not the condition argument.
Recursive list node updating
If classes = "list" and how = "recurse", rrapply applies the f function to any list element of object that satisfies
condition similar to the previous section using how = "replace", but recurses further into the updated list-like element
after application of the f function. A primary use of how = "recurse" in combination with classes = "list" is to
recursively update for instance the class or other attributes of all nodes in a nested list.
Avoid recursing into data.frames
If classes = "ANY" (default), rrapply recurses into all “list-like” objects equivalent to rapply.
Since data.frames are “list-like” objects, the f function will descend into the individual columns of a data.frame.
To avoid this behavior, set classes = "data.frame", in which case the f and condition functions are applied directly to
the data.frame and not its columns. Note that this behavior can only be triggered using the classes argument and not the condition argument.
List attributes
In rapply intermediate list attributes (not located at terminal nodes) are kept when how = "replace", but are dropped when
how = "list". To avoid unexpected behavior, rrapply always preserves intermediate list attributes when using how = "replace",
how = "list", how = "prune" or how = "names". If how = "unlist", how = "flatten", how = "melt" or how = "bind"
intermediate list attributes cannot be preserved as the result is no longer a nested list.
Expressions
Call objects and expression vectors are also accepted as object argument, 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. If object is a
call object or expression vector, how = "replace" always maintains the type of object, whereas how = "list" returns the result
structured as a nested list. 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.
Additional options
The options argument accepts a named list to configure several default options that only apply to certain choices of how. The options
list can contain (any of) the named components namesep, simplify, namecols and/or coldepth:
namesep, a character separator used to combine parent and child list names inhow = "flatten"andhow = "bind". Ifnamesep = NA(default), no parent names are included inhow = "flatten"and the default separator"."is used inhow = "bind". Note thatnamesepcannot be used withhow = "unlist"for which the name separator always defaults to".".simplify, a logical value indicating whether the flattened unnested list inhow = "flatten"andhow = "melt"is simplified according to standard coercion rules similar tohow = "unlist". The default issimplify = TRUE. Ifsimplify = FALSE,objectis flattened to a single-layer list and returned as is.namecols, a logical value that only applies tohow = "bind"indicating whether the parent node names associated to the each expanded sublist should be included as columnsL1,L2, etc. in the wide data.frame returned byhow = "bind".coldepth, an integer value indicating the depth (starting from depth 1) at which list elements should be mapped to individual columns in the wide data.frame returned byhow = "bind". Ifcoldepth = 0(default), this depth layer is identified automatically based on the minimal depth detected across all leaf nodes. This option only applies tohow = "bind".
Examples
# Example data
## Renewable energy shares per country (% of total consumption) in 2016
data("renewable_energy_by_country")
## Renewable energy shares in Oceania
renewable_oceania <- renewable_energy_by_country[["World"]]["Oceania"]
## Pokemon properties in Pokemon GO
data("pokedex")
# List pruning and unnesting
## Drop logical NA's while preserving list structure
na_drop_oceania <- rrapply(
renewable_oceania,
f = function(x) x,
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]
## Drop logical NA's and return unnested list
na_drop_oceania2 <- rrapply(
renewable_oceania,
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 11.75
## Flatten to simple list with full names
na_drop_oceania3 <- rrapply(
renewable_oceania,
classes = "numeric",
how = "flatten",
options = list(namesep = ".", simplify = FALSE)
)
str(na_drop_oceania3, list.len = 10, 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
#> $ Oceania.Melanesia.Solomon Islands : num 65.7
#> $ Oceania.Melanesia.Vanuatu : num 33.7
#> $ Oceania.Micronesia.Guam : num 3.03
#> $ Oceania.Micronesia.Kiribati : num 45.4
#> $ Oceania.Micronesia.Marshall Islands : num 11.8
#> [list output truncated]
## Drop logical NA's and return melted data.frame
na_drop_oceania4 <- rrapply(
renewable_oceania,
classes = "numeric",
how = "melt"
)
head(na_drop_oceania4)
#> 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 65.73
## Reconstruct nested list from melted data.frame
na_drop_oceania5 <- rrapply(
na_drop_oceania4,
how = "unmelt"
)
str(na_drop_oceania5, 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]
## Unnest list to wide data.frame
pokedex_wide <- rrapply(pokedex, how = "bind")
head(pokedex_wide)
#> id num name img
#> 1 1 001 Bulbasaur http://www.serebii.net/pokemongo/pokemon/001.png
#> 2 2 002 Ivysaur http://www.serebii.net/pokemongo/pokemon/002.png
#> 3 3 003 Venusaur http://www.serebii.net/pokemongo/pokemon/003.png
#> 4 4 004 Charmander http://www.serebii.net/pokemongo/pokemon/004.png
#> 5 5 005 Charmeleon http://www.serebii.net/pokemongo/pokemon/005.png
#> 6 6 006 Charizard http://www.serebii.net/pokemongo/pokemon/006.png
#> type height weight candy candy_count egg
#> 1 Grass, Poison 0.71 m 6.9 kg Bulbasaur Candy 25 2 km
#> 2 Grass, Poison 0.99 m 13.0 kg Bulbasaur Candy 100 Not in Eggs
#> 3 Grass, Poison 2.01 m 100.0 kg Bulbasaur Candy NA Not in Eggs
#> 4 Fire 0.61 m 8.5 kg Charmander Candy 25 2 km
#> 5 Fire 1.09 m 19.0 kg Charmander Candy 100 Not in Eggs
#> 6 Fire, Flying 1.70 m 90.5 kg Charmander Candy NA Not in Eggs
#> spawn_chance avg_spawns spawn_time multipliers weaknesses
#> 1 0.6900 69.00 20:00 1.58 Fire, Ice, Flying, Psychic
#> 2 0.0420 4.20 07:00 1.2, 1.6 Fire, Ice, Flying, Psychic
#> 3 0.0170 1.70 11:30 NA Fire, Ice, Flying, Psychic
#> 4 0.2530 25.30 08:45 1.65 Water, Ground, Rock
#> 5 0.0120 1.20 19:00 1.79 Water, Ground, Rock
#> 6 0.0031 0.31 13:34 NA Water, Electric, Rock
#> next_evolution.1.num next_evolution.1.name next_evolution.2.num
#> 1 002 Ivysaur 003
#> 2 003 Venusaur <NA>
#> 3 <NA> <NA> <NA>
#> 4 005 Charmeleon 006
#> 5 006 Charizard <NA>
#> 6 <NA> <NA> <NA>
#> next_evolution.2.name prev_evolution.1.num prev_evolution.1.name
#> 1 Venusaur <NA> <NA>
#> 2 <NA> 001 Bulbasaur
#> 3 <NA> 001 Bulbasaur
#> 4 Charizard <NA> <NA>
#> 5 <NA> 004 Charmander
#> 6 <NA> 004 Charmander
#> prev_evolution.2.num prev_evolution.2.name next_evolution.3.num
#> 1 <NA> <NA> <NA>
#> 2 <NA> <NA> <NA>
#> 3 002 Ivysaur <NA>
#> 4 <NA> <NA> <NA>
#> 5 <NA> <NA> <NA>
#> 6 005 Charmeleon <NA>
#> next_evolution.3.name
#> 1 <NA>
#> 2 <NA>
#> 3 <NA>
#> 4 <NA>
#> 5 <NA>
#> 6 <NA>
## Unnest to data.frame including parent columns
pokemon_evolutions <- rrapply(
pokedex,
how = "bind",
options = list(namecols = TRUE, coldepth = 5)
)
head(pokemon_evolutions, n = 10)
#> L1 L2 L3 L4 num name
#> 1 pokemon 1 next_evolution 1 002 Ivysaur
#> 2 pokemon 1 next_evolution 2 003 Venusaur
#> 3 pokemon 2 prev_evolution 1 001 Bulbasaur
#> 4 pokemon 2 next_evolution 1 003 Venusaur
#> 5 pokemon 3 prev_evolution 1 001 Bulbasaur
#> 6 pokemon 3 prev_evolution 2 002 Ivysaur
#> 7 pokemon 4 next_evolution 1 005 Charmeleon
#> 8 pokemon 4 next_evolution 2 006 Charizard
#> 9 pokemon 5 prev_evolution 1 004 Charmander
#> 10 pokemon 5 next_evolution 1 006 Charizard
# Condition function
## Drop all NA elements using condition function
na_drop_oceania6 <- rrapply(
renewable_oceania,
condition = Negate(is.na),
how = "prune"
)
str(na_drop_oceania6, 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]
## Replace NA elements by a new value via the ... argument
## NB: the 'newvalue' argument should be present as function
## argument in both 'f' and 'condition', even if unused.
na_zero_oceania <- rrapply(
renewable_oceania,
condition = function(x, newvalue) is.na(x),
f = function(x, newvalue) newvalue,
newvalue = 0,
how = "replace"
)
str(na_zero_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 : num 0
#> .. ..$ Cocos (Keeling) Islands : num 0
#> .. .. [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]
## 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
# Special arguments .xname, .xpos, .xparents and .xsiblings
## Apply a function using the name of the node
renewable_oceania_text <- rrapply(
renewable_oceania,
condition = Negate(is.na),
f = function(x, .xname) sprintf("Renewable energy in %s: %.2f%%", .xname, x),
how = "flatten"
)
head(renewable_oceania_text, n = 10)
#> 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%"
#> Solomon Islands
#> "Renewable energy in Solomon Islands: 65.73%"
#> Vanuatu
#> "Renewable energy in Vanuatu: 33.67%"
#> Guam
#> "Renewable energy in Guam: 3.03%"
#> Kiribati
#> "Renewable energy in Kiribati: 45.43%"
#> Marshall Islands
#> "Renewable energy in Marshall Islands: 11.75%"
## 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 with value above 50%
renewable_europe_above_50 <- rrapply(
renewable_energy_by_country,
condition = function(x, .xpos) identical(.xpos[c(1, 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 with value above 50%
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 neighbors 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 = 10)
#> Aland Islands Denmark Estonia Faroe Islands Finland
#> NA 33.06 26.55 4.24 42.03
#> Iceland Ireland Isle of Man Latvia Lithuania
#> 78.07 8.65 4.30 38.48 31.42
## Unnest selected columns in Pokedex list
pokedex_small <- rrapply(
pokedex,
condition = function(x, .xpos, .xname) length(.xpos) < 4 & .xname %in% c("num", "name", "type"),
how = "bind"
)
head(pokedex_small)
#> 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, Flying
# Modifying list elements
## Calculate mean value of Europe
rrapply(
renewable_energy_by_country,
condition = function(x, .xname) .xname == "Europe",
f = function(x) mean(unlist(x), na.rm = TRUE),
classes = "list",
how = "flatten"
)
#> Europe
#> 22.36565
## Calculate mean value for each continent
## (Antarctica's value is missing)
renewable_continent_summary <- rrapply(
renewable_energy_by_country,
condition = function(x, .xpos) length(.xpos) == 2,
f = function(x) mean(unlist(x), na.rm = TRUE),
classes = "list"
)
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
## Filter country or region by M49-code
rrapply(
renewable_energy_by_country,
condition = function(x) attr(x, "M49-code") == "155",
f = function(x, .xname) .xname,
classes = c("list", "ANY"),
how = "unlist"
)
#> World.Europe.Western Europe
#> "Western Europe"
# Recursive list updating
## Recursively remove list attributes
renewable_no_attrs <- rrapply(
renewable_oceania,
f = function(x) c(x),
classes = c("list", "ANY"),
how = "recurse"
)
str(renewable_no_attrs, 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]
## recursively replace all names by M49-codes
renewable_m49_names <- rrapply(
renewable_oceania,
f = function(x) attr(x, "M49-code"),
how = "names"
)
str(renewable_m49_names, 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]
# List attributes
## how = "list" preserves all list attributes
na_drop_oceania_attr <- rrapply(
renewable_oceania,
f = function(x) replace(x, is.na(x), 0),
how = "list"
)
str(na_drop_oceania_attr, max.level = 2)
#> List of 1
#> $ Oceania:List of 4
#> ..$ Australia and New Zealand:List of 6
#> .. ..- attr(*, "M49-code")= chr "053"
#> ..$ Melanesia :List of 5
#> .. ..- attr(*, "M49-code")= chr "054"
#> ..$ Micronesia :List of 8
#> .. ..- attr(*, "M49-code")= chr "057"
#> ..$ Polynesia :List of 10
#> .. ..- attr(*, "M49-code")= chr "061"
#> ..- attr(*, "M49-code")= chr "009"
## how = "prune" also preserves list attributes
na_drop_oceania_attr2 <- rrapply(
renewable_oceania,
condition = Negate(is.na),
how = "prune"
)
str(na_drop_oceania_attr2, max.level = 2)
#> List of 1
#> $ Oceania:List of 4
#> ..$ Australia and New Zealand:List of 2
#> .. ..- attr(*, "M49-code")= chr "053"
#> ..$ Melanesia :List of 5
#> .. ..- attr(*, "M49-code")= chr "054"
#> ..$ Micronesia :List of 7
#> .. ..- attr(*, "M49-code")= chr "057"
#> ..$ Polynesia :List of 8
#> .. ..- attr(*, "M49-code")= chr "061"
#> ..- attr(*, "M49-code")= chr "009"
# Expressions
## Replace logicals by integers
call_old <- quote(y <- x <- 1 + TRUE)
call_new <- rrapply(call_old,
f = as.numeric,
how = "replace",
classes = "logical"
)
str(call_new)
#> language y <- x <- 1 + 1
## 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 1
## Prune and decompose expression
expr <- expression(y <- x <- 1, f(g(2 * pi)))
is_new_name <- function(x) !exists(as.character(x), envir = baseenv())
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
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 g
## Avoid recursing into call objects
rrapply(
expr,
classes = "language",
condition = function(x) !any(sapply(x, is.call)),
how = "flatten"
)
#> [[1]]
#> x <- 1
#>
#> [[2]]
#> 2 * pi
#>