Help for package R6DS

Type:

Package

Title:

R6 Reference Class Based Data Structures

Version:

1.2.0

Description:

Provides reference classes implementing some useful data structures. The package implements these data structures by using the reference class R6. Therefore, the classes of the data structures are also reference classes which means that their instances are passed by reference. The implemented data structures include stack, queue, double-ended queue, doubly linked list, set, dictionary and binary search tree. See for example https://en.wikipedia.org/wiki/Data_structure for more information about the data structures.

Depends:

R (≥ 3.0.0)

License:

GPL-3

Encoding:

UTF-8

LazyData:

true

RoxygenNote:

6.1.1

URL:

https://github.com/yukai-yang/R6DS

BugReports:

https://github.com/yukai-yang/R6DS/issues

Imports:

Suggests:

knitr, rmarkdown

VignetteBuilder:

knitr

NeedsCompilation:

Packaged:

2019-05-21 10:53:14 UTC; yyang

Author:

Yukai Yang

[aut, cre]

Maintainer:

Yukai Yang <yukai.yang@statistik.uu.se>

Repository:

CRAN

Date/Publication:

2019-05-21 11:00:03 UTC

R6DS: provides reference classes implementing some useful data stuctures.

Description

R6DS stands for R6 class based Data Structures. The package provides reference classes implementing some useful data stuctures.

Details

Some data structures are quite useful in solving some programming problems, as they offer great convenience and are the keys to implement some algorithms.

The package implements these data structures by using the reference class R6. Each class defined in the package represents a certain data structure, and it inherits the R6 reference class which means that it is also a reference class.

In order to create an instance of the R6 type reference class, you will have to use its new method as follows:

instance <- RStack$new()

where RStack is an R6 type reference class.

The reference class has the feature that each time when you pass (or assign) an instance of the reference class to somewhere, it is not pass-by-value but pass-by-reference. For example, suppose there is an instance of the reference class x who has an attribute x$y. If you pass it to a function func by using func(x), the function will not copy x but pass it as a reference. Inside the function func, if there is a sentence like

x$y <- 0

then the attribute y of the global x outside the function will be changed to zero.

Of course you can copy the instance of a reference class, but you have to use its clone method:

new_instance <- instance$clone()

Notice that all the classes in the package use instances of some other R6 type reference class as their members. This implies that, according to the rule of the R6 family, you have to add deep = TRUE when you clone their instances:

new_instance <- instance$clone(deep=TRUE)

and then you can successfully copy them.

The classes in the package are designed in the way that you cannot get the access to their members directly, as they are declared to be private. Instead, you have to use their methods (member functions) to get them. In the following, a complete list of these classes and their methods in common are presented. Each class has its own methods, and for details of these class-specific methods please refer to their help documents.

Some methods are declared to be "active", or active method, which means that, when you call them, you do not need to use parenthesis. For example, the size method is a common active method of all classes in the package. When you call it, you do

instance$size

So it looks pretty like a member attribute, but actually not.

How to Use the Package

All classes in the package are declared to be non-portable and non-class (R6 standards), which means that the user of the package cannot inherit them.

The user can create and use instances of these classes, and the instances can contain any R objects (vector, matrix, factor, data.frame, list and etc.) as their values.

The author suggest that the user of the package puts the instances of these classes inside other classes to be their members.

But it is still possible to inherit the classes in the package. To this end, the user can copy and paste the source code of the package.

Author and Maintainer

Yukai Yang

Department of Statistics, Uppsala University

yukai.yang@statistik.uu.se

References

For the details about the data structures, see Data Structure at Wikipedia.

Classes Available in the Package

RStack The RStack reference class implements the data structure stack.

RQueue The RQueue reference class implements the data structure queue.

RDeque The RDeque reference class implements the data structure double-ended queue.

RDLL The RDLL reference class implements the data structure doubly linked list.

RSet The RSet reference class implements the data structure set.

RDict The RDict reference class implements the data structure dictionary.

RBST The RBST reference class implements the data structure binary search tree.

Common Methods of the Classes in the Package

new(..., collapse=NULL)

a method belonging to the class which create an instance of the class. The method creates a new instance of some class in the package containing the values in ... and collapse as its elements.

toList

an active immutable method of an instance which returns a list containing its elements (a copy).

Note that RBST has multiple versions of the toList methods.

is_empty()

a method which returns a boolean showing if the instance is empty.

size

an active immutable method of an instance to return its size (like the length of an R vector).

release()

a method of an instance which does the garbage collection and releases the redundant memory occupation.

Note that RDict RBST do not have this methhod.

The RBST reference class

Description

The RBST reference class implements the data structure binary search tree (BST).

Usage

RBST

Format

An object of class R6ClassGenerator of length 24.

Details

A BST is a particular type of container storing elements in nodes by following a binary tree structure. So the element is the value of the corresponding node in the tree.

The BST has one root on top, which is the first node of the tree, and each node in the BST has at most two sub-nodes (left sub-node and right sub-node) which can be the roots of their sub-trees.

The BST should be equipped with the "<" and "=" operations such that any two nodes in the tree can be compared. Note that, by the definitions of the "<" and "=" operations, the operation ">" is also defined.

The BST structure follows strictly the rules that, for a certain node in the tree, any nodes in its left sub-tree must be strictly smaller ("<") than it, any nodes in its right sub-tree must be strictly larger (">") than it, and any two nodes in the tree must not be equal (no "=").

Therefore, the BST is a special set or dictionary equipped with "<", ">" operations.

When you create a new RBST instance, you have to input two functions which defines the bodies of the two private methods lessthan and equal. The RBST instance then will use them to make comparison and decide where to put new nodes (build the BST).

Each time a new node is inserted, the BST algorithm finds its location on the tree. Then you can imagine, the BST is efficient in maintaining (inserting and deleting), searching and traversing the tree. An average O(log n) time complexity can be achieved by applying the BST algorithm.

A very important fact is that, the RBST only compares the nodes by using the function equal. So it will regard any two nodes identical if equal returns TRUE, even though they are different.

We see that the BST can also be regarded as a dictionary, as the key of the dictionary is actually the value input into insert, delete and search_for.

The traversals of the BST (in-order, pre-order, and post-order) are implemented as well. A callback function can be input into the traverse function to specify how to treat the traversed nodes. By default (if you do not input anything here) the traverse function prints the traversed nodes. But of course you can, for example, store them by changing the callback function, see the examples below.

The elements in the BST are not necessarily to be of the same type, and they can even contain functions.

References

For the details about the BST data structure, see BST at Wikipedia.

Class Method

The class method belongs to the class.

new(lessthan, equal, ..., collapse=NULL)

The new method creates a new instance of the RBST class containing the values in ... and collapse as its nodes.

The argument lessthan takes a function defining the "<" operation, and the argument equal takes a function defining the "=" operation. Both of the functions takes two values of the nodes in the tree and return a boolean.

lessthan can take, for example, the form

lessthan <- function(x, y) return(x$num < y$num)

where x and y are values of two nodes in the tree with the attribute num.

equal can take, for example, the form

equal <- function(x, y) return(x$num == y$num)

where x and y are values of two nodes in the tree with the attribute num.

Immutable Methods

The immutable methods do not change the nodes of the instance.

toList, toList_pre, and toList_post

The active method toList returns a list containing its elements (a copy).

The order of the list can be "traverse-in-order" by using toList, "traverse-pre-order" by using toList_pre, or "traverse-post-order" by using toList_post

traverse(mode, callback=function(item){print(item)}, ...)

The traverse method takes at least two arguments which are mode and callback.

The mode takes a value in one of the three strings "in", "pre", and "post" which indicate traverse-in-order, traverse-pre-order, and traverse-post-order, respectively.

The callback takes a function specifying how to handle the value of each node in the tree. By default, callback prints the nodes by using the print function.

Note that the first argument of the callback function must be the value of the node but not the node itself!

callback can have two or more arguments. The method also takes ... as the additional arguments for the callback function if any.

search_for(val)

The method search_for uses the equal function to compare val with the nodes in BST. It returns the value of the node if the node is equal to the given value, and NULL otherwise.

As the tree has been structured strictly by following the rules introduced above, there is no need to search the whole tree in most cases, and the maintaining and searching are efficient.

min

The active method min returns the smallest node in the tree, and NULL if the tree is empty.

max

The active method min returns the largest node in the tree, and NULL if the tree is empty.

Mutable Methods

The mutable methods changes the nodes of the instance.

insert(..., collapse=NULL): The method insert inserts new nodes into the tree. If some nodes are equal to the nodes in the tree, they will not be inserted.
delete(val): The method delete removes the node which is equal to val. If the node is found, then it will be removed and the function returns a TRUE, and if the node is not found, then it will do nothing and returns a FALSE,

Author(s)

Yukai Yang, yukai.yang@statistik.uu.se

Examples


### create a new instance

# you have to define two functions for "<" and "="
lessthan <- function(x, y) return(x$key < y$key)
equal <- function(x, y) return(x$key == y$key)
# remember that the nodes in the BST have the "key" variable
# and it is numeric

# to create a new instance of the class
bst <- RBST$new(lessthan=lessthan, equal=equal)

# of course you can start to push elements when creating the instance
bst <- RBST$new(lessthan=lessthan, equal=equal,
    list(key=5, val="5"), collapse=list(list(key=3,val="3"), list(key=9,val="9")))
# the following sentence is equivalent to the above
bst <- RBST$new(lessthan=lessthan, equal=equal,
    list(key=5, val="5"), list(key=3,val="3"), list(key=9,val="9"))
# where the three lists are inserted into the BST

### maintaining

bst$insert(list(key=5, val="6"))
bst$insert(list(key=6, val="5"))

bst$delete(list(key=7, val="7"))
# FALSE
bst$delete(list(key=6, val="7"))
# TRUE and delete list(key=6, val="5")
# though val are different

### searching

bst$search_for(list(key=0, val="0"))
# NULL
bst$search_for(list(key=5, val="0"))
# the BST has a node whose key is 5

### min and max

# min and max are two active functions
# so the parenthesis is not needed
bst$min
bst$max

### toList

bst$toList
bst$toList_pre
bst$toList_post

### traversing

# by default, the callback function prints the nodes
# but you can re-define the callback function
queue <- RQueue$new()
callback <- function(item)queue$enqueue(item)
# remember that RQueue is a reference class
# so the new callback will store the traversed nodes

bst$traverse(mode = "in", callback=callback)
tmp = queue$dequeue(); print(tmp)
while(!is.null(tmp)) {tmp = queue$dequeue(); print(tmp)}
bst$traverse(mode = "in", callback=callback)
tmp = queue$dequeue(); print(tmp)
while(!is.null(tmp)) {tmp = queue$dequeue(); print(tmp)}

# pre-order traversing
bst$traverse(mode = "pre", callback=callback)
tmp = queue$dequeue(); print(tmp)
while(!is.null(tmp)) {tmp = queue$dequeue(); print(tmp)}

# post-order traversing
bst$traverse(mode = "post", callback=callback)
tmp = queue$dequeue(); print(tmp)
while(!is.null(tmp)) {tmp = queue$dequeue(); print(tmp)}

The RDLL reference class

Description

The RDLL reference class implements the data structure doubly linked list (DLL).

Usage

RDLL

Format

An object of class R6ClassGenerator of length 24.

Details

A doubly linked list is an ordered list of elements with multiple operations. The DLL is a powerful sequantial data structure in the sense that it can be regarded as the generalized version of the data structures stack, queue, deque.

The class RDLL inherits the RDeque class, and therefor it has all the methods that RDeque has.

The DLL is much more friendly and flexible as it offers more useful methods to help the user get access to its elements than RStack, RQueue and RDeque. See below its immutable methods and mutable methods.

It is worth noting that the classes RSet inherits the RDLL class, and therefor it has all the methods that the RDLL has.

The elements in the DLL are not necessarily to be of the same type, and they can be any R objects.

References

For the details about the DLL data structure, see DLL at Wikipedia.

Immutable Methods

The immutable methods do not change the instance.

show(callback=function(val){print(val)}, ...)

The show method takes a funtion input (argument callback) specifying how to handle the elements in the DLL. It also takes ... as the additional arguments for the callback function if any.

By default, the show method prints the elements by using the print function.

callback=function(val){print(val)}

You can see that show is powerful as it makes it possible to freely manipulate the elements in the DLL. For example, you can define

func <- function(val, arg1, arg2){ do something here on val with arg1 and arg2 }

and then

instance$show(func, arg1, arg2)

And you can also store the elements by using instances of reference classes. For example,

func <- function(val, queue){ queue$enqueue(val) }

where queue is an instance of RQueue. The code can be

queue <- RQueue$new()

instance$show(func, queue)

elem_at(index)

It returns the element (a copy) at position index (a positive integer). index must be a scalar, and if it is a vector of more than one element, only the first element will be considered. If the value of index is out of the bounds of the instance, a NULL will be returned.

peekleft()

See RDeque.

peek()

See RDeque.

Mutable Methods

The mutable methods change the instance.

insert_at(index, val)

This function inserts a new element val at position index. It returns TRUE if the insertion is successful, and FALSE if the index is out of the bounds. It will push all the elements at and after index rightward.

Thus, suppose that instance is an instance of the class.

insert_at(1, val)

is equivalent to appendleft in RDeque, and

insert_at(instance$size+1, val)

is equivalent to append in RDeque, push in RStack, and enqueue in RQueue.

remove_at(index)

This function returns and removes the element at position index. It returns NULL if the index is out of the bounds.

Thus, suppose that instance is an instance of the class.

remove_at(1, val) is equivalent to popleft in RDeque, and

remove_at(instance$size, val) is equivalent to pop in RDeque and RStack, and dequeue in RQueue.

appendleft(..., collapse=NULL)

See RDeque.

append(..., collapse=NULL)

See RDeque.

popleft()

See RDeque.

pop()

See RDeque.

Author(s)

Yukai Yang, yukai.yang@statistik.uu.se

Examples


### create a new instance

# to create a new instance of the class
dll <- RDLL$new()

# the previous RDLL instance will be removed if you run
dll <- RDLL$new(0, 1, 2, collapse=list(3, 4))
# the following sentence is equivalent to the above
dll <- RDLL$new(0, 1, 2, 3, 4)
# where the numbers 0, 1, 2, 3, 4 are appended into the DLL

### immutable methods

# show
dll$show()

# elem_at
dll$elem_at(1)

# toList
tmp <- dll$toList

### mutable methods

# insert_at
dll$insert_at(1, -1)
dll$insert_at(dll$size+1, "end")

# remove_at
for(iter in 1:dll$size) dll$remove_at(1)

The RDeque reference class

Description

The RDeque reference class implements the data structure double-ended queue (deque).

Usage

RDeque

Format

An object of class R6ClassGenerator of length 24.

Details

A deque is an ordered list of elements generalizing the queue data structure. One can append and pop (return and remove) elements from both sides (left and right, front and rear) of the deque.

The elements in the deque are not necessarily to be of the same type, and they can be any R objects.

References

For the details about the deque data structure, see Deque at Wikipedia.

Immutable Methods

The immutable methods do not change the instance.

peekleft(): This method returns the leftmost (front) element of the deque. It returns NULL if the deque is empty.
peek(): This method returns the rightmost (rear) element of the deque. It returns NULL if the deque is empty.

Mutable Methods

The mutable methods change the instance.

appendleft(..., collapse=NULL)

The appendleft method appends the elements in ... and collapse into the deque to the left (front).

Note that if you append elements in this order:

instance$appendleft(elem1, elem2, elem3)

The order of them inside the deque will be

elem3, elem2, elem1, ...

and elem3 will be the new front of the deque.

append(..., collapse=NULL)

The append method appends the elements in ... and collapse into the deque to the right (rear).

popleft()

The popleft method returns and removes the leftmost (front) element in the deque. It returns NULL if the deque is empty.

pop()

The pop method returns and removes the rightmost (rear) element in the deque. It returns NULL if the deque is empty.

Author(s)

Yukai Yang, yukai.yang@statistik.uu.se

Examples


### create a new instance

# to create a new instance of the class
deque <- RDeque$new()

# the previous RDeque instance will be removed if you run
deque <- RDeque$new(0, 1, 2, collapse=list(3, 4))
# the following sentence is equivalent to the above
deque <- RDeque$new(0, 1, 2, 3, 4)
# where the numbers 0, 1, 2, 3, 4 are enqueued into the deque

### append and appendleft

# it can be one single element
deque$append(5)
# it can be several elements separated by commas
# note the whole list will be one element of the deque
# because it is not passed through the collapse argument
deque$append(list(a=10,b=20), "Hello world!")
# the collapse argument takes a list whose elements will be collapsed
# but the elements' names will not be saved
deque$append(collapse = list(x=100,y=200))
# they can be used together
deque$append("hurrah", collapse = list("RDeque",300))

# this string will be the new head
deque$appendleft("a string")
# we can update the head by
deque$appendleft("string3","string2","string1")
# "string1" will be the leftmost

### peekleft and peek
deque$peekleft()
# "string1"
deque$peek()
# 300

### popleft and pop

val <- deque$popleft()
# "string1"
val <- deque$pop()
# 300

# then we keep dequeuing!
while(!is.null(val)) val <- deque$pop()

The RDict reference class

Description

The RDict reference class implements the data structure dictionary.

Usage

RDict

Format

An object of class R6ClassGenerator of length 24.

Details

A dictionary is a collection of (key, value) pairs as its elements such that each possible key appears at most once in the collection. The dictionary data structure does not care the order of the elements.

The keys of the elements in the dictionary are stored as strings. The values in the dictionary are not necessarily to be of the same type, and they can be any R objects.

References

For the details about the dictionary data structure, see Dictionary at Wikipedia.

Immutable Methods

The immutable methods do not change the instance.

has(key)

The method has returns a boolean indicating if the dictionary contains the element with the key "key".

Both of the following two sentences are equivalent:

instance$has("keyname")

instance$has(keyname)

get(key)

The method get returns the value of the element whose key is "key". It returns NULL if no element is found.

keys

The method keys returns a vector of the keys in the dictionary.

values

The method values returns a list of the values in the dictionary (unnamed list).

Mutable Methods

The mutable methods change the instance.

add(key, val)

The method add adds a new element (the pair key and val) into the dictionary. It will not add element with the key which exists already in the dictionary. It returns a boolean showing if the adding is successful.

Note that any element with the key "" (empty string) will not be added.

add_multiple(..., collapse=NULL)

The method add_multiple adds new elements into the dictionary. It will not add element with the key which exists already in the dictionary.

The argument ... stands for any input with the form

keyname1 = value2, keyname2 = value2, ...

Therefor, the input can take the form

instance$add(key1=1, key2="hello", key3=list(1))

and the keys of the elements will be strings like "key1", "key2", and "key3", respectively.

If the keyname is missing, the value will not be added.

delete(key)

The method delete removes the element with the key key in the dictionary.

Suppose that the key name of the element that you want to remove is "keyname". Both of the following two sentences are valid:

instance$delete("keyname")

instance$delete(keyname)

It returns a boolean showing if the element is found and deleted.

Author(s)

Yukai Yang, yukai.yang@statistik.uu.se

Examples


### create a new instance

# to create a new instance of the class
dict <- RDict$new()

# of course you can start to add elements when creating the instance
dict <- RDict$new(id0001=1, id0002=2, collapse=list(id0003=3, id0004=4))
# the following sentence is equivalent to the above
dict <- RDict$new(id0001=1, id0002=2, id0003=3, id0004=4)
# where the three lists are inserted into the dictionary

### immutable methods

dict$keys
dict$values

dict$has(id0001)
dict$has("id0005")
# TRUE as it has the key attribute

dict$get(id0006)
dict$get("id0002")

### mutable methods

dict$add(id0005, 5)

dict$add(key="id0006", val=6)

dict$delete(id0001)

The RQueue reference class

Description

The RQueue reference class implements the data structure queue.

Usage

RQueue

Format

An object of class R6ClassGenerator of length 24.

Details

A queue is an ordered list of elements following the First-In-First-Out (FIFO) principle. The enqueue method takes elements and add them to the rear terminal position (right) of the queue, while the dequeue method returns and removes the element in the queue from the front terminal position (left).

The elements in the queue are not necessarily to be of the same type, and they can be any R objects.

References

For the details about the queue data structure, see Queue at Wikipedia.

Immutable Methods

The immutable method does not change the instance.

peekleft(): This method returns the leftmost (front) element in the queue. It returns NULL if the queue is empty.

Mutable Methods

The mutable methods change the instance.

enqueue(..., collapse=NULL)

The enqueue method enqueues the elements in ... and collapse into the queue (to the right or rear).

Note that you can input multiple elements.

dequeue()

The dequeue method dequeues (returns and removes) one element (the leftmost or front) fron the queue. It returns NULL if the queue is empty.

Author(s)

Yukai Yang, yukai.yang@statistik.uu.se

Examples


### create a new instance

# to create a new instance of the class
queue <- RQueue$new()

# the previous RQueue instance will be removed if you run
queue <- RQueue$new(0, 1, 2, collapse=list(3, 4))
# the following sentence is equivalent to the above
queue <- RQueue$new(0, 1, 2, 3, 4)
# where the numbers 0, 1, 2, 3, 4 are enqueued into the queue

### enqueue elements

# it can be one single element
queue$enqueue(5)
# it can be several elements separated by commas
# note the whole list will be one element of the queue
# because it is not passed through the collapse argument
queue$enqueue(list(a=10,b=20), "Hello world!")
# the collapse argument takes a list whose elements will be collapsed
# but the elements' names will not be saved
queue$enqueue(collapse = list(x=100,y=200))
# they can be used together
queue$enqueue("hurrah", collapse = list("RQueue",300))

### dequeue an element

# dequeue only one element at a time
val <- queue$dequeue()
# then we keep dequeuing!
while(!is.null(val)) val <- queue$dequeue()

The RSet reference class

Description

The RSet reference class implements the data structure set.

Usage

RSet

Format

An object of class R6ClassGenerator of length 24.

Details

A set is a collection of items or elements equipped with the "=" operators such that any two elements in the set cannot be equal. The set data structure does not care the order of the elements.

It should be noticed that, in your design, if any two elements in the set can be easily compared, by simply, for example, keys, numbers, and etc., the RSet should not be recommended due to efficiency reason. The RSet is suitable for the cases when you have a relatively complex "=" operation between two elements in the set.

The class RSet inherits the RDLL class, and therefor it has all the methods that RDLL has.

Note that the methods insert_at, appendleft, append in the super class still works without checking if the new element equals any other elements in the set. Normally they should be depreciated in the RSet class, but this is not done in the current version of the package. It is strongly recommended that the user should use the add method to add a new element when using the RSet class.

The elements in the set are not necessarily to be of the same type, and they can be any R objects.

References

For the details about the set data structure, see Set at Wikipedia.

Class Method

The class method belongs to the class.

new(equal, ..., collapse=NULL)

The new method creates a new instance of the RSet class containing the values in ... and collapse as its elements.

The argument equal takes a function defining the "=" operation, The function set to equal takes two values of the elements in the set and return a boolean. It can be, for example, of the form

equal <- function(x, y) return(x$num == y$num)

where x and y are values of two elements in the set with the attribute num.

Immutable Methods

The immutable methods do not change the elements of the instance.

has(val): The method has returns a boolean indicating if the set contains val.
union(rset): The method union merges the elements in rset, an instance of some class in the package, with its elements, and returns a new union set of the two.
intersection(rset): The method intersection returns a new intersection set (RSet) of the current set and rset, an instance of some class in the package.
difference(rset): The method difference returns a new difference set (RSet) of the current set and rset, an instance of some class in the package (current instance minus rset).
subset(rset): The method subset returns a boolean indicating if the current set is a subset of rset, an instance of some class in the package.
contains(rset): The method contains returns a boolean indicating if the current set contains rset, an instance of some class in the package.

Mutable Methods

The mutable methods change the instance.

add(val): The method add adds a new element into the set and returns a booleank showing if the insertion is successful.
add_multiple(..., collapse=NULL): The method add_multiple adds new elements in ... and collapse into the set.
delete(val): The method delete removes the element which is equal to val in the set. It returns a boolean showing if the deletion is successful (if the element is not found in the set).

Author(s)

Yukai Yang, yukai.yang@statistik.uu.se

Examples


### create a new instance

# you have to define "="
equal <- function(x, y) return(x$key == y$key)
# remember that the elements in the set must have the "key" attribute

# to create a new instance of the class
set <- RSet$new(equal=equal)

# of course you can start to add elements when creating the instance
set <- RSet$new(equal=equal,
    list(key=5, val="5"), collapse=list(list(key=3,val="3"), list(key=9,val="9")))
# the following sentence is equivalent to the above
set <- RSet$new(equal=equal,
    list(key=5, val="5"), list(key=3,val="3"), list(key=9,val="9"))
# where the three lists are inserted into the set

### immutable methods

set$has(list(key=5, num=10))
# TRUE as it has the key attribute

### mutable methods

set$add(list(key=5, num=10))
# FALSE

set$add(list(key=10, val="10"))
# TRUE

set$delete(list(key=10))
# TRUE and list(key=10, val="10") is removed

# union
another_set <- RSet$new(equal=equal,
    list(key=5, val="5"), list(key=11,val="11"))
set$union(another_set)$show()

# intersection
set$intersection(another_set)$show()

# difference
set$difference(another_set)$show()

# subset
set$subset(another_set)

# contains
set$contains(another_set)

The RStack reference class

Description

The RStack reference class implements the data structure stack.

Usage

RStack

Format

An object of class R6ClassGenerator of length 24.

Details

A stack is an ordered list of elements following the Last-In-First-Out (LIFO) principle. The push method takes elements and add them to the top position (right) of the stack, while the pop method returns and removes the last "pushed" (top or rightmost) element in the stack.

The elements in the stack are not necessarily to be of the same type, and they can be any R objects.

References

For the details about the stack data structure, see Stack at Wikipedia.

Immutable Methods

The immutable method does not change the instance.

peek(): This method returns the last pushed (top or rightmost) element in the stack. It returns NULL if the stack is empty.

Mutable Methods

The mutable methods change the instance.

push(..., collapse=NULL)

The push method pushes the elements in ... and collapse into the stack (to the top or right).

Note that you can input multiple elements.

pop()

The pop method pops (returns and removes) the last pushed (rightmost) element in the stack. It returns NULL if the stack is empty.

Author(s)

Yukai Yang, yukai.yang@statistik.uu.se

Examples


### create a new instance

# to create a new instance of the class
stack <- RStack$new()

# the previous RStack instance will be removed if you run
stack <- RStack$new(0, 1, 2, collapse=list(3, 4))
# the following sentence is equivalent to the above
stack <- RStack$new(0, 1, 2, 3, 4)
# where the numbers 0, 1, 2, 3, 4 are pushed into the stack

### push elements

# it can be one single element
stack$push(5)
# it can be several elements separated by commas
# note the whole list will be one element of the stack
# because it is not passed through the collapse argument
stack$push(list(a=10,b=20), "Hello world!")
# the collapse argument takes a list whose elements will be collapsed
# but the elements' names will not be saved
stack$push(collapse = list(x=100,y=200))
# they can be used together
stack$push("hurrah", collapse = list("RStack",300))

### pop an element

# pop only one element at a time
val <- stack$pop()
# then we keep poping!
while(!is.null(val)) val <- stack$pop()

Show the version number of some information.

Description

This function shows the version number and some information of the package.

Usage

version()

Author(s)

Yukai Yang, yukai.yang@statistik.uu.se

R6DS: provides reference classes implementing some useful data stuctures.

Description

Details

How to Use the Package

Author and Maintainer

References

Classes Available in the Package

Common Methods of the Classes in the Package

The RBST reference class

Description

Usage

Format

Details

References

Class Method

Immutable Methods

Mutable Methods

Author(s)

See Also

Examples

The RDLL reference class

Description

Usage

Format

Details

References

Immutable Methods

Mutable Methods

Author(s)

See Also

Examples

The RDeque reference class

Description

Usage

Format

Details

References

Immutable Methods

Mutable Methods

Author(s)

See Also

Examples

The RDict reference class

Description

Usage

Format

Details

References

Immutable Methods

Mutable Methods

Author(s)

See Also

Examples

The RQueue reference class

Description

Usage

Format

Details

References

Immutable Methods

Mutable Methods

Author(s)

See Also

Examples

The RSet reference class

Description

Usage

Format

Details

References

Class Method

Immutable Methods

Mutable Methods

Author(s)

See Also

Examples

The RStack reference class

Description

Usage

Format