Package 'comprehenr'

Title: List Comprehensions
Description: Provides 'Python'-style list comprehensions. List comprehension expressions use usual loops (for(), while() and repeat()) and usual if() as list producers. In many cases it gives more concise notation than standard "*apply + filter" strategy.
Authors: Gregory Demin [aut, cre]
Maintainer: Gregory Demin <[email protected]>
License: GPL-2
Version: 0.6.11
Built: 2024-10-31 20:29:26 UTC
Source: https://github.com/gdemin/comprehenr

Help Index

Auxiliary functions for working with lists

Description

  • numerate returns list of lists. Each list consists of two elements: sequential number of element and element. Reverse operation - unumerate.

  • mark returns list of lists. Each list consists of two elements: name of element and element. Reverse operation - unmark.

  • zip_lists combines lists side-by-sidy. Reverse operation - unzip_list.

  • unzip_list is similair to matrix transposition but for list of lists.

  • lag_list converts argument to list of arguments with previous values: x -> list(x[i-1], x[i]).

Usage

numerate(x)

enumerate(x)

unnumerate(x, item = 2)

mark(x)

unmark(x, item = 2)

unzip_list(x)

zip_lists(...)

lag_list(x)

Arguments

x

list, vector or list of lists
item

numeric number of list in which stored values
...

lists which will be zipped

Value

list or list of lists

Examples

cities = c('Chicago', 'Detroit', 'Atlanta')
airports = c('ORD', 'DTW', 'ATL')
pairs = zip_lists(cities, airports)

str(pairs)
str(unzip_list(pairs))

str(numerate(cities))

named_list = c('Chicago' = 'ORD', 'Detroit' = 'DTW', 'Atlanta' = 'ATL')
str(mark(named_list))

set.seed(123)
rand_sequence = runif(20)
# gives only locally increasing values
to_vec(for(`i, j` in lag_list(rand_sequence)) if(j>i) j)

List comprehensions for R

Description

  • to_list converts usual R loops expressions to list producers. Expression should be started with for, while or repeat. You can iterate over multiple lists if you provide several loop variables in backticks. See examples.

  • to_vec is the same as 'to_list' but return vector. See examples.

  • to_df is the same as 'to_list' but return data.frame. All elements of resulted list will be converted to data.frame and combined via rbind.

  • alter returns the same type as its argument but with modified elements. It is useful for altering existing data.frames or lists. See examples.

  • exclude is an auxiliary function for dropping elements in alter. There are no arguments for this function.

Usage

to_list(expr)

to_vec(expr, recursive = TRUE, use.names = FALSE)

alter(expr, data = NULL)

to_df(expr, fill = TRUE)

exclude()

Arguments

expr

expression which starts with for, while or repeat.
recursive

logical. Should unlisting be applied to list components of result? See unlist for details.
use.names

logical. Should names be preserved? See unlist for details.
data

data.frame/list/vector which we want to alter
fill

logical. TRUE by default. Should we combine data.frames with different names in the to_df?

Value

list for to_list and vector for to_vec

Examples

# rather useless expression - squares of even numbers
to_list(for(i in 1:10) if(i %% 2==0) i*i)

# Pythagorean triples
to_list(for (x in 1:30) for (y in x:30) for (z in y:30) if (x^2 + y^2 == z^2) c(x, y, z))

colours = c("red", "green", "yellow", "blue")
things = c("house", "car", "tree")
to_vec(for(x in colours) for(y in things) paste(x, y))

# prime numbers
noprimes = to_vec(for (i in 2:7) for (j in seq(i*2, 99, i)) j)
primes = to_vec(for (x in 2:99) if(!x %in% noprimes) x)
primes

# iteration over multiple lists
to_vec(for(`i, j` in numerate(letters)) if(i %% 2==0) paste(i, j))

set.seed(123)
rand_sequence = runif(20)
# gives only locally increasing values
to_vec(for(`i, j` in lag_list(rand_sequence)) if(j>i) j)

# to_df
to_df(for(`name, x` in mark(mtcars)) list(mean = mean(x), sd = sd(x), var = name))

# 'alter' examples
data(iris)
# scale numeric variables
res = alter(for(i in iris) if(is.numeric(i)) scale(i))
str(res)

# convert factors to characters
res = alter(for(i in iris) if(is.factor(i)) as.character(i))
str(res)

# exclude factors from data.frame
res = alter(for(i in iris) if(is.factor(i)) exclude())
str(res)

# 'data' argument example
# specify which columns to map with a numeric vector of positions:
res = alter(
    for(`i, value` in numerate(mtcars)) if(i %in% c(1, 4, 5)) as.character(value),
    data = mtcars
)
str(res)

# or with a vector of names:
res = alter(
    for(`name, value` in mark(mtcars)) if(name %in% c("cyl", "am")) as.character(value),
    data = mtcars
)
str(res)