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rlist is a set of tools for working with list objects. Its goal is to make it easier to work with lists by providing a wide range of functions that operate on non-tabular data stored in them.

This package supports list mapping, filtering, grouping, sorting, updating, searching, file input/output, and many other functions. Most functions in the package are designed to be pipeline friendly so that data processing with lists can be chained.

rlist Tutorial is a highly recommended complete guide to rlist.

This document is also translated into 日本語 (by @teramonagi).


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In R, there are numerous powerful tools to deal with structured data stored in tabular form such as data frame. However, a variety of data is non-tabular: different records may have different fields; for each field they may have different number of values.

It is hard or no longer straightforward to store such data in data frame, but the list object in R is flexible enough to represent such records of diversity. rlist is a toolbox to deal with non-structured data stored in list objects, providing a collection of high-level functions which are pipeline friendly.

Getting started

Suppose we have a list of developers, each of whom has a name, age, a few interests, a list of programming languages they use and the number of years they have been using them.

devs <- 

This type of data is non-relational since it does not well fit the shape of a data frame, yet it can be easily stored in JSON or YAML format. In R, list objects are flexible enough to represent a wide range of non-relational datasets like this. This package provides a wide range of functions to query and manipulate this type of data.

The following examples use str() to show the structure of the output.


Filter those who like music and has been using R for more than 3 years.

str( list.filter(devs, "music" %in% interest & lang$r >= 3) )
List of 1
 $ p2:List of 4
  ..$ name    : chr "James"
  ..$ age     : num 25
  ..$ interest: chr [1:2] "sports" "music"
  ..$ lang    :List of 3
  .. ..$ r   : num 3
  .. ..$ java: num 2
  .. ..$ cpp : num 5


Select their names and ages.

str( list.select(devs, name, age) )
List of 3
 $ p1:List of 2
  ..$ name: chr "Ken"
  ..$ age : num 24
 $ p2:List of 2
  ..$ name: chr "James"
  ..$ age : num 25
 $ p3:List of 2
  ..$ name: chr "Penny"
  ..$ age : num 24


Map each of them to the number of interests.

str( list.map(devs, length(interest)) )
List of 3
 $ p1: int 3
 $ p2: int 2
 $ p3: int 2

More functions

In addition to these basic functions, rlist also supports various types of grouping, joining, searching, sorting, updating, etc. For the introduction to more functionality, please go through the rlist Tutorial.

Lambda expression

In this package, almost all functions that work with expressions accept the following forms of lambda expressions:

where x refers to the list member itself, i denotes the index, name denotes the name. If the symbols are not explicitly declared, ., .i and .name will by default be used to represent them, respectively.

nums <- list(a=c(1,2,3),b=c(2,3,4),c=c(3,4,5))
list.map(nums, c(min=min(.),max=max(.)))
list.filter(nums, x ~ mean(x)>=3)
list.map(nums, f(x,i) ~ sum(x,i))

Using pipeline

Working with pipe syntax

Query the name of each developer who likes music and uses R, and put the results in a data frame.

devs |> 
  list.filter("music" %in% interest & "r" %in% names(lang)) |>
  list.select(name,age) |>
   name age
1   Ken  24
2 James  25

The example above uses the pipe syntax |> introduced in R 4.1 that chains commands in a fluent style.

List environment

List() function wraps a list within an environment where almost all list functions are defined. Here is the List-environment version of the previous example.

ldevs <- List(devs)
ldevs$filter("music" %in% interest & "r" %in% names(lang))$
   name age
1   Ken  24
2 James  25


This package is under MIT License.