The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

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

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.