List Comprehension and Tools.

eList

Write list comprehensions in R!

The eList package allows users to write vectorized for loops and contains a variety of tools for working with lists and other vectors. Just wrap a normal for loop within one of the comprehension functions, such as List(), and let the package do the rest.

Features include, but are not limited to:

• Multiple variables within the loop using "." to separate names.
• Multiple returns types: list, numeric vectors, character vectors, matrices, environments, etc.
• if, else statements to filter results.
• Name assignment within the loop using =.
• Parallelization by adding the option , clust = and the cluster.
• Nested loops.
• Helper functions such as enum and items to access the index or name within the loop, or zip objects together.
• Summary comprehensions that calculate sums, means, etc. from for loops.
• Use either List(...) or List[...] for the comprehension.
• Higher order functions, such as map or filter, that accept functions built from formulas (using . notation for variables) or calls.

Installation

You can install the released version of eList from CRAN with:

install.packages("eList")

Examples

A simple “list” comprehension that accumulates all integer sequences to 4 using the List function. Though it looks like a for loop, it is actually using lapply behind the scenes.

library(eList)
#> 
#> Attaching package: 'eList'
#> The following object is masked from 'package:stats':
#> 
#>     filter
#> The following object is masked from 'package:utils':
#> 
#>     zip
List(for (i in 1:4) 1:i)
#> [[1]]
#> [1] 1
#> 
#> [[2]]
#> [1] 1 2
#> 
#> [[3]]
#> [1] 1 2 3
#> 
#> [[4]]
#> [1] 1 2 3 4

Loops can be nested and filtered using if statements. The example below uses Num to produce a numeric vector rather than a list. Other comprehensions include Chr for character vectors, Logical for logical vectors, Vec for flat (non-list) vectors, etc.

Num(for (i in 1:4) for (j in 3:6) if (i == j) i^2)
#> [1]  9 16

Use the “dot” notation to use multiple variables within the loop.

Chr(for (i.j in zip(1:4, 5:8)) paste0(i, j))
#> [1] "15" "26" "37" "48"

Use = within the loop to assign a name to each item within the list, or other item.

values <- zip(letters[1:4], 5:8)

List(for (i.j in values) i = j)
#> $a
#> [1] "5"
#> 
#> $b
#> [1] "6"
#> 
#> $c
#> [1] "7"
#> 
#> $d
#> [1] "8"

Parallelization is also very easy. Just create a cluster and add it to the comprehension with the clust argument.

my_cluster <- auto_cluster()

x <- Num(for (i in sample(1:100, 50)) sqrt(i), clust = my_cluster)

# Close the cluster if not needed!
close_cluster(my_cluster)

x
#>  [1] 3.872983 9.949874 3.316625 6.633250 7.000000 9.165151 9.899495 9.539392
#>  [9] 9.055385 3.464102 5.830952 9.486833 3.741657 8.660254 4.898979 8.000000
#> [17] 4.690416 5.477226 5.385165 5.000000 8.485281 8.246211 4.358899 7.416198
#> [25] 8.717798 9.591663 9.433981 7.549834 3.162278 9.797959 7.615773 3.000000
#> [33] 9.746794 2.236068 5.291503 8.124038 9.219544 9.695360 7.483315 9.643651
#> [41] 3.605551 4.000000 1.732051 5.196152 2.828427 6.855655 7.681146 7.348469
#> [49] 7.211103 7.280110

Want a statistical summary using a comprehension? eList contains a variety of summary functions for that purpose. Stats is a general summary comprehension that computes many different values.

Stats(for (i in sample(1:100, 50)) sqrt(i))
#> $min
#> [1] 2
#> 
#> $q1
#> [1] 6.143211
#> 
#> $med
#> [1] 7.483016
#> 
#> $q3
#> [1] 8.587745
#> 
#> $max
#> [1] 10
#> 
#> $mean
#> [1] 7.172598
#> 
#> $sd
#> [1] 2.028831

eList also contains functional programming style functions for working with lists and other vectors. These functions perform an operation using a function on another object. They are similar to the higher order functions in Base R, but are pipe-friendly, handle a wide ranger of object types, and allow for different methods of specifying functions.

x <- list(1:4, 5:8, 9:12)
map(x, mean)
#> [[1]]
#> [1] 2.5
#> 
#> [[2]]
#> [1] 6.5
#> 
#> [[3]]
#> [1] 10.5

This can also be calculated using formula notation. Formulas can be be written as either a two-sided formula or a one-sided formula by prefixing variables with dots.

# Two-sided Formula
map(x, i ~ sqrt(i) + 1)
#> [[1]]
#> [1] 2.000000 2.414214 2.732051 3.000000
#> 
#> [[2]]
#> [1] 3.236068 3.449490 3.645751 3.828427
#> 
#> [[3]]
#> [1] 4.000000 4.162278 4.316625 4.464102

# One-sided Formula
map(x, ~ sqrt(.i) + 1)
#> [[1]]
#> [1] 2.000000 2.414214 2.732051 3.000000
#> 
#> [[2]]
#> [1] 3.236068 3.449490 3.645751 3.828427
#> 
#> [[3]]
#> [1] 4.000000 4.162278 4.316625 4.464102

The higher order functions also accept unevaluated “calls”.

round2 <- substitute(round(digits=2))
map(rnorm(5), round2)
#> [[1]]
#> [1] 0.16
#> 
#> [[2]]
#> [1] -3.18
#> 
#> [[3]]
#> [1] -0.17
#> 
#> [[4]]
#> [1] -0.13
#> 
#> [[5]]
#> [1] -0.27