Pokedex of Pokemon GO

Description

A nested list containing property values of the original 151 Pokemon present in Pokemon GO. The data is available in JSON format from GitHub (credits to Gianluca Bonifazi).

Usage

pokedex

Format

A nested list containing 151 sublists with up to 17 list elements:

id

integer, Identification number

num

character, Pokemon number in official Pokedex

name

character, Pokemon name

img

character, URL to png image of the Pokemon

type

character, Pokemon type

height

character, Pokemon height

weight

character, Pokemon weight

candy

character, type of candy used to evolve Pokemon or given when transfered

candy_count

integer, amount of candies required to evolve

egg

character, travel distance to hatch the egg

spawn_change

numeric, spawn change percentage

avg_spawns

integer, number of spawns per 10.000 spawns

spawn_time

character, local time at which spawns are most active

multipliers

numeric, multiplier of Combat Power (CP) after evolution

weakness

character, types of Pokemon this Pokemon is weak to

next_evolution

list, numbers (num) and names (name) with successive evolutions

prev_evolution

list, numbers (num) and names (name) with previous evolutions

Source

PokemonGO-Pokedex

UNSD renewable energy share by country in 2016

Description

A nested list containing renewable energy shares as a percentage in the total energy consumption per country in 2016. The dataset is publicly available at the United Nations Open SDG Data Hub.

Usage

renewable_energy_by_country

Format

The 249 countries and areas are structured as a nested list based on their geographical location according to the United Nations M49 Standard (UNSD-M49). The numeric values listed for each country or area are percentages, if no data is available the value is NA. Each list element contains an "M49-code" attribute with the UN Standard Country or Area Codes for Statistical Use (Series M, No. 49).

Source

UNSD_SDG07

Reimplementation of base-R's rapply

Description

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

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.

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 of f from the list object. The original list structure is retained, similar to the non-pruned options how = "replace" or how = "list".

• how = "flatten" is an efficient way to return a flattened unnested version of the pruned list. By default how = "flatten" uses similar coercion rules as how = "unlist", this can be disabled with simplify = FALSE in the options argument.

• 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 layers L1, L2, and so on. The column "value" contains the possibly coerced values at the terminal nodes and is equivalent to the result of how = "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 with coldepth in the options argument.

• 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 by how = "melt". Internally, how = "unmelt" first reconstructs a nested list from the melted data.frame and second uses the same functional framework as how = "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 to how = "list", except that the value of f is 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 in how = "flatten" and how = "bind". If namesep = NA (default), no parent names are included in how = "flatten" and the default separator "." is used in how = "bind". Note that namesep cannot be used with how = "unlist" for which the name separator always defaults to ".".

• simplify, a logical value indicating whether the flattened unnested list in how = "flatten" and how = "melt" is simplified according to standard coercion rules similar to how = "unlist". The default is simplify = TRUE. If simplify = FALSE, object is flattened to a single-layer list and returned as is.

• namecols, a logical value that only applies to how = "bind" indicating whether the parent node names associated to the each expanded sublist should be included as columns L1, L2, etc. in the wide data.frame returned by how = "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 by how = "bind". If coldepth = 0 (default), this depth layer is identified automatically based on the minimal depth detected across all leaf nodes. This option only applies to how = "bind".

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.

See Also

rapply

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)

## Drop logical NA's and return unnested list
na_drop_oceania2 <- rrapply(
  renewable_oceania,
  classes = "numeric",
  how = "flatten"
)
head(na_drop_oceania2, n = 10)

## 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)

## Drop logical NA's and return melted data.frame
na_drop_oceania4 <- rrapply(
  renewable_oceania,
  classes = "numeric",
  how = "melt"
)
head(na_drop_oceania4)

## 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)

## Unnest list to wide data.frame
pokedex_wide <- rrapply(pokedex, how = "bind")
head(pokedex_wide)

## Unnest to data.frame including parent columns
pokemon_evolutions <- rrapply(
  pokedex, 
  how = "bind", 
  options = list(namecols = TRUE, coldepth = 5)
) 
head(pokemon_evolutions, n = 10)

# 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)

## 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)

## 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)

# 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)

## 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)

## 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)

## 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)

## 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"
))
renewable_energy_by_country[[xpos_sweden$Sweden]]

## 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)

## 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)

# 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"
)

## 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)

## 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"
)

# 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)

## 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 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)

## 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)

# 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)

## 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)

## 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)

## Prune and flatten expression
expr_flatten <- rrapply(expr, 
  classes = "name", 
  condition = is_new_name, 
  how = "flatten"
)
str(expr_flatten)

## Prune and melt expression
rrapply(expr, 
  classes = "name", 
  condition = is_new_name, 
  f = as.character,
  how = "melt"
)

## Avoid recursing into call objects
rrapply(
  expr, 
  classes = "language", 
  condition = function(x) !any(sapply(x, is.call)),
  how = "flatten"
)