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.
Install the latest version from GitHub:
Install from CRAN:
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.
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.
library(rlist) devs <- list( p1=list(name="Ken",age=24, interest=c("reading","music","movies"), lang=list(r=2,csharp=4)), p2=list(name="James",age=25, interest=c("sports","music"), lang=list(r=3,java=2,cpp=5)), p3=list(name="Penny",age=24, interest=c("movies","reading"), lang=list(r=1,cpp=4,python=2)))
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
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.
In this package, almost all functions that work with expressions accept the following forms of lambda expressions:
- Implicit lambda expression:
- Univariate lambda expressions:
x ~ expression
f(x) ~ expression
- Multivariate lambda expressions:
f(x,i) ~ expression
f(x,i,name) ~ expression
x refers to the list member itself,
i denotes the index,
name denotes the name. If the symbols are not explicitly declared,
.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))
Working with pipeR
Query the name of each developer who likes music and uses R, and put the results in a data frame.
library(pipeR) devs %>>% list.filter("music" %in% interest & "r" %in% names(lang)) %>>% list.select(name,age) %>>% list.stack
name age 1 Ken 24 2 James 25
The example above uses
pipeR(http://renkun.me/pipeR/) package for pipeline operator
%>>% that chains commands in a fluent style.
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$filter("music" %in% interest & "r" %in% names(lang))$ select(name,age)$ stack()$ data
name age 1 Ken 24 2 James 25
help(package = rlist)
or view the documentation on CRAN
This package is under MIT License.