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: | R6 |
Suggests: | knitr, rmarkdown |
VignetteBuilder: | knitr |
NeedsCompilation: | no |
Packaged: | 2019-05-21 10:53:14 UTC; yyang |
Author: | Yukai Yang |
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
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
...
andcollapse
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 thetoList
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.
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...
andcollapse
as its nodes.The argument
lessthan
takes a function defining the "<" operation, and the argumentequal
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 formlessthan <- function(x, y) return(x$num < y$num)
where
x
andy
are values of two nodes in the tree with the attributenum
.equal
can take, for example, the formequal <- function(x, y) return(x$num == y$num)
where
x
andy
are values of two nodes in the tree with the attributenum
.
Immutable Methods
The immutable methods do not change the nodes of the instance.
toList
,toList_pre
, andtoList_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 usingtoList_pre
, or "traverse-post-order" by usingtoList_post
traverse(mode, callback=function(item){print(item)}, ...)
-
The
traverse
method takes at least two arguments which aremode
andcallback
.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 theprint
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 thecallback
function if any. search_for(val)
-
The method
search_for
uses theequal
function to compareval
with the nodes in BST. It returns the value of the node if the node isequal
to the given value, andNULL
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, andNULL
if the tree is empty. max
-
The active method
min
returns the largest node in the tree, andNULL
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 areequal
to the nodes in the tree, they will not be inserted. delete(val)
-
The method
delete
removes the node which isequal
toval
. If the node is found, then it will be removed and the function returns aTRUE
, and if the node is not found, then it will do nothing and returns aFALSE
,
Author(s)
Yukai Yang, yukai.yang@statistik.uu.se
See Also
R6DS for the introduction of the reference class and some common methods
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 (argumentcallback
) specifying how to handle the elements in the DLL. It also takes...
as the additional arguments for thecallback
function if any.By default, the
show
method prints the elements by using theprint
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 definefunc <- 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 ofRQueue
. The code can bequeue <- 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 ofindex
is out of the bounds of the instance, aNULL
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 positionindex
. It returnsTRUE
if the insertion is successful, andFALSE
if theindex
is out of the bounds. It will push all the elements at and afterindex
rightward.Thus, suppose that
instance
is an instance of the class.insert_at(1, val)
is equivalent to
appendleft
inRDeque
, andinsert_at(instance$size+1, val)
is equivalent to
append
inRDeque
,push
inRStack
, andenqueue
inRQueue
. remove_at(index)
-
This function returns and removes the element at position
index
. It returnsNULL
if theindex
is out of the bounds.Thus, suppose that
instance
is an instance of the class.remove_at(1, val)
is equivalent topopleft
inRDeque
, andremove_at(instance$size, val)
is equivalent topop
inRDeque
andRStack
, anddequeue
inRQueue
. 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
See Also
RDeque, RSet, and R6DS for the introduction of the reference class and some common methods
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$remove_at(1)
The RDeque reference class
Description
The RDeque reference class implements the data structure double-ended queue (deque).
Usage
RDeque
Format
An object of class R6ClassGenerator
of length 24.
Details
A deque is an ordered list of elements generalizing the queue data structure. One can append and pop (return and remove) elements from both sides (left and right, front and rear) of the deque.
The elements in the deque are not necessarily to be of the same type, and they can be any R objects.
References
For the details about the deque data structure, see Deque at Wikipedia.
Immutable Methods
The immutable methods do not change the instance.
peekleft()
-
This method returns the leftmost (front) element of the deque. It returns
NULL
if the deque is empty. peek()
-
This method returns the rightmost (rear) element of the deque. It returns
NULL
if the deque is empty.
Mutable Methods
The mutable methods change the instance.
appendleft(..., collapse=NULL)
-
The
appendleft
method appends the elements in...
andcollapse
into the deque to the left (front).Note that if you append elements in this order:
instance$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...
andcollapse
into the deque to the right (rear). popleft()
-
The
popleft
method returns and removes the leftmost (front) element in the deque. It returnsNULL
if the deque is empty. pop()
-
The
pop
method returns and removes the rightmost (rear) element in the deque. It returnsNULL
if the deque is empty.
Author(s)
Yukai Yang, yukai.yang@statistik.uu.se
See Also
RStack, RQueue, and R6DS for the introduction of the reference class and some common methods
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 returnsNULL
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 formkeyname1 = value2, keyname2 = value2, ...
Therefor, the input can take the form
instance$add(key1=1, key2="hello", key3=list(1))
and the keys of the elements will be strings like
"key1"
,"key2"
, and"key3"
, respectively.If the
keyname
is missing, thevalue
will not be added. delete(key)
-
The method
delete
removes the element with the keykey
in the dictionary.Suppose that the key name of the element that you want to remove is "keyname". Both of the following two sentences are valid:
instance$delete("keyname")
instance$delete(keyname)
It returns a boolean showing if the element is found and deleted.
Author(s)
Yukai Yang, yukai.yang@statistik.uu.se
See Also
R6DS for the introduction of the reference class and some common methods
Examples
### create a new instance
# to create a new instance of the class
dict <- RDict$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$delete(id0001)
The RQueue reference class
Description
The RQueue reference class implements the data structure queue.
Usage
RQueue
Format
An object of class R6ClassGenerator
of length 24.
Details
A queue is an ordered list of elements following the First-In-First-Out (FIFO) principle.
The enqueue
method takes elements and add them to the rear terminal position (right) of the queue,
while the dequeue
method returns and removes the element in the queue from the front terminal position (left).
The elements in the queue are not necessarily to be of the same type, and they can be any R objects.
References
For the details about the queue data structure, see Queue at Wikipedia.
Immutable Methods
The immutable method does not change the instance.
peekleft()
-
This method returns the leftmost (front) element in the queue. It returns
NULL
if the queue is empty.
Mutable Methods
The mutable methods change the instance.
enqueue(..., collapse=NULL)
-
The
enqueue
method enqueues the elements in...
andcollapse
into the queue (to the right or rear).Note that you can input multiple elements.
dequeue()
-
The
dequeue
method dequeues (returns and removes) one element (the leftmost or front) fron the queue. It returnsNULL
if the queue is empty.
Author(s)
Yukai Yang, yukai.yang@statistik.uu.se
See Also
R6DS for the introduction of the reference class and some common methods
Examples
### create a new instance
# to create a new instance of the class
queue <- RQueue$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...
andcollapse
as its elements.The argument
equal
takes a function defining the "=" operation, The function set toequal
takes two values of the elements in the set and return a boolean. It can be, for example, of the formequal <- function(x, y) return(x$num == y$num)
where
x
andy
are values of two elements in the set with the attributenum
.
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 containsval
. union(rset)
-
The method
union
merges the elements inrset
, 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 andrset
, an instance of some class in the package. difference(rset)
-
The method
difference
returns a new difference set (RSet) of the current set andrset
, an instance of some class in the package (current instance minusrset
). subset(rset)
-
The method
subset
returns a boolean indicating if the current set is a subset ofrset
, an instance of some class in the package. contains(rset)
-
The method
contains
returns a boolean indicating if the current set containsrset
, 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...
andcollapse
into the set. delete(val)
-
The method
delete
removes the element which isequal
toval
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
See Also
RDLL and R6DS for the introduction of the reference class and some common methods
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...
andcollapse
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 returnsNULL
if the stack is empty.
Author(s)
Yukai Yang, yukai.yang@statistik.uu.se
See Also
R6DS for the introduction of the reference class and some common methods
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