data.tree: Hierarchical Data Structures

Description

data.tree is to hierarchical data what data.frame is to tabular data: An extensible, general purpose structure to store, manipulate, and display hierarchical data.

Introduction

Hierarchical data is ubiquitous in statistics and programming (XML, search trees, family trees, classification, file system, etc.). However, no general-use tree data structure is available in R. Where tabular data has data.frame, hierarchical data is often modeled in lists of lists or similar makeshifts. These structures are often difficult to manage. This is where the data.tree package steps in. It lets you build trees of hierarchical data for various uses: to print, to rapid prototype search algorithms, to test out new classification algorithms, and much more.

Tree Traversal

data.tree allows to Traverse trees in various orders (pre-order, post-order, level, etc.), and it lets you run operations on Nodes via Do. Similarly, you can collect and store data while traversing a tree using the Get and the Set methods.

Methods

The package also contains utility functions to Sort, to Prune, to Aggregate and Cumulate and to print in custom formats.

Construction and Conversion

The package also contains many conversions from and to data.tree structures. Check out the see also section of as.Node.

You can construct a tree from a data.frame using as.Node.data.frame, and convert it back using as.data.frame.Node. Similar options exist for list of lists. For more specialized conversions, see as.dendrogram.Node, as.Node.dendrogram, as.phylo.Node and as.Node.phylo

Finally, easy conversion options from and to list, dataframe, JSON, YAML, igraph, ape, rpart, party and more exist:

• list: both directions

• dataframe: both directions

• JSON, YAML: both directions, via lists

• igraph: from igraph to data.tree

• ape: both directions

• rpart: from rpart to data.tree

• party: from party to data.tree

Node and Reference Semantics

The entry point to the package is Node. Each tree is composed of a number of Nodes, referencing each other.

One of most important things to note about data.tree is that it exhibits reference semantics. In a nutshell, this means that you can modify your tree along the way, without having to reassign it to a variable after each modification. By and large, this is a rather exceptional behavior in R, where value-semantics is king most of the time.

Applications

data.tree is not optimised for computational speed, but for implementation speed. Namely, its memory footprint is relatively large compared to traditional R data structures. However, it can easily handle trees with several thousand nodes, and once a tree is constructed, operations on it are relatively fast. data.tree is always useful when

• you want to develop and test a new algorithm

• you want to import and convert tree structures (it imports and exports to list-of-list, data.frame, yaml, json, igraph, dendrogram, phylo and more)

• you want to play around with data, display it and get an understanding

• you want to test another package, to compare it with your own results

• you need to do homework

For a quick overview of the features, read the data.tree vignette by running vignette("data.tree"). For stylized applications, see vignette("applications", package='data.tree')

Author(s)

Maintainer: Christoph Glur christoph.glur@powerpartners.pro (R interface)

Other contributors:

• Russ Hyde (improve dependencies) [contributor]

• Chris Hammill (improve getting) [contributor]

• Facundo Munoz (improve list conversion) [contributor]

• Markus Wamser (fixed some typos) [contributor]

• Pierre Formont (additional features) [contributor]

• Kent Russel (documentation) [contributor]

• Noam Ross (fixes) [contributor]

• Duncan Garmonsway (fixes) [contributor]

See Also

Node

For more details, see the data.tree vignette by running: vignette("data.tree")

Examples

data(acme)
print(acme)
acme$attributesAll
acme$count
acme$totalCount
acme$isRoot
acme$height
print(acme, "p", "cost")

outsource <- acme$IT$Outsource
class(outsource)
print(outsource)
outsource$attributes
outsource$isLeaf
outsource$level
outsource$path
outsource$p
outsource$parent$name
outsource$root$name
outsource$expCost <- outsource$p * outsource$cost
print(acme, "expCost")

acme$Get("p")
acme$Do(function(x) x$expCost <- x$p * x$cost)
acme$Get("expCost", filterFun = isLeaf)

ToDataFrameTable(acme, "name", "p", "cost", "level", "pathString")
ToDataFrameTree(acme, "name", "p", "cost", "level")
ToDataFrameNetwork(acme, "p", "cost")

Aggregate child values of a Node, recursively.

Description

The Aggregate method lets you fetch an attribute from a Node's children, and then aggregate them using aggFun. For example, you can aggregate cost by summing costs of child Nodes. This is especially useful in the context of tree traversal, when using post-order traversal mode.

Usage

Aggregate(node, attribute, aggFun, ...)

Arguments

Details

As with Get, the attribute can be a field, a method or a function. If the attribute on a child is NULL, Aggregate is called recursively on its children.

See Also

Node

Examples

data(acme)

#Aggregate on a field
Aggregate(acme, "cost", sum)

#This is the same as:
HomeRolledAggregate <- function(node) {
  sum(sapply(node$children, function(child) {
    if (!is.null(child$cost)) child$cost
    else HomeRolledAggregate(child)
  }))
}
HomeRolledAggregate(acme)

#Aggregate using Get
print(acme, "cost", minCost = acme$Get(Aggregate, "cost", min))

#use Aggregate with a function:
Aggregate(acme, function(x) x$cost * x$p, sum)

#cache values along the way
acme$Do(function(x) x$cost <- Aggregate(x, "cost", sum), traversal = "post-order")
acme$IT$cost

Test whether all node names are unique.

Description

This can be useful for some conversions.

Usage

AreNamesUnique(node)

Arguments

Value

TRUE if all Node$name == TRUE for all nodes in the tree

See Also

as.igraph.Node

Examples

data(acme)
AreNamesUnique(acme)
acme$name <- "IT"
AreNamesUnique(acme)

Checks whether name is a reserved word, as defined in NODE_RESERVED_NAMES_CONST.

Description

Checks whether name is a reserved word, as defined in NODE_RESERVED_NAMES_CONST.

Usage

CheckNameReservedWord(name, check = c("check", "no-warn", "no-check"))

Arguments

Climb a tree from parent to children, by provided criteria.

Description

This method lets you climb the tree, from crutch to crutch. On each Node, the Climb finds the first child having attribute value equal to the the provided argument.

Usage

#node$Climb(...)
Climb(node, ...)

Arguments

Value

the Node having path ..., or NULL if such a path does not exist

See Also

Node

Navigate

Examples

data(acme)

#the following are all equivalent
Climb(acme, 'IT', 'Outsource')
Climb(acme, name = 'IT', name = 'Outsource')
Climb(acme, 'IT')$Climb('Outsource')
Navigate(acme, path = "IT/Outsource")

Climb(acme, name = 'IT')

Climb(acme, position = c(2, 1))
#or, equivalent:
Climb(acme, position = 2, position = 1)
Climb(acme, name = "IT", cost = 250000)

tree <- CreateRegularTree(5, 2)
tree$Climb(c("1", "1"), position = c(2, 2))$path

Clone a tree (creates a deep copy)

Description

The method also clones object attributes (such as the formatters), if desired. If the method is called on a non-root, then the parent relationship is not cloned, and the resulting Node will be a root.

Usage

Clone(node, pruneFun = NULL, attributes = FALSE)

Arguments

Value

the clone of the tree or sub-tree

See Also

SetFormat

Examples

data(acme)
acmeClone <- Clone(acme)
acmeClone$name <- "New Acme"
# acmeClone does not point to the same reference object anymore:
acme$name

#cloning a subtree
data(acme)
itClone <- Clone(acme$IT)
itClone$isRoot

Create a tree for demo and testing

Description

Create a tree for demo and testing

Usage

CreateRandomTree(nodes = 100, root = Node$new("1"), id = 1)

Arguments

Create a tree for demo and testing

Description

Create a tree for demo and testing

Usage

CreateRegularTree(height = 5, branchingFactor = 3, parent = Node$new("1"))

Arguments

Cumulate values among siblings

Description

For example, you can sum up values of siblings before this Node.

Usage

Cumulate(node, attribute, aggFun, ...)

Arguments

Examples

data(acme)
acme$Do(function(x) x$cost <- Aggregate(x, "cost", sum), traversal = "post-order")
acme$Do(function(x) x$cumCost <- Cumulate(x, "cost", sum))
print(acme, "cost", "cumCost")

Calculates the height of a Node given the height of the root.

Description

This function puts leafs at the bottom (not hanging), and makes edges equally long. Useful for easy plotting with third-party packages, e.g. if you have no specific height attribute, e.g. with as.dendrogram.Node, ToNewick, and as.phylo.Node

Usage

DefaultPlotHeight(node, rootHeight = 100)

Arguments

Examples

data(acme)
dacme <- as.dendrogram(acme, heightAttribute = function(x) DefaultPlotHeight(x, 200))
plot(dacme, center = TRUE)

Find the distance between two nodes of the same tree

Description

The distance is measured as the number of edges that need to be traversed to reach node2 when starting from node1.

Usage

Distance(node1, node2)

Arguments

Examples

data(acme)
Distance(FindNode(acme, "Outsource"), FindNode(acme, "Research"))

Executes a function on a set of nodes

Description

Executes a function on a set of nodes

Usage

# OO-style:
# node$Do(fun, 
#         ..., 
#         traversal = c("pre-order", "post-order", "in-order", "level", "ancestor"), 
#         pruneFun = NULL, 
#         filterFun = NULL)
         
# traditional:
Do(nodes, fun, ...)

Arguments

See Also

Node

Get

Set

Traverse

Examples

data(acme)
traversal <- Traverse(acme)
Do(traversal, function(node) node$expectedCost <- node$p * node$cost)
print(acme, "expectedCost")

Find a node by name in the (sub-)tree

Description

Scans the entire sub-tree spanned by node and returns the first Node having the name specified. This is mainly useful for trees whose name is unique. If AreNamesUnique is FALSE, i.e. if there is more than one Node called name in the tree, then it is undefined which one will be returned. Also note that this method is not particularly fast. See examples for a faster way to index large trees, if you need to do multiple searches. See Traverse if you need to find multiple Nodes.

Usage

FindNode(node, name)

Arguments

Value

The first Node whose name matches, or NULL if no such Node is found.

See Also

AreNamesUnique, Traverse

Examples

data(acme)
FindNode(acme, "Outsource")

#re-usable hashed index for multiple searches:
if(!AreNamesUnique(acme)) stop("Hashed index works for unique names only!")
trav <- Traverse(acme, "level")
names(trav) <- Get(trav, "name")
nameIndex <- as.environment(trav)
#you could also use hash from package hash instead!
#nameIndex <- hash(trav)
nameIndex$Outsource
nameIndex$IT

Format a Number as a Decimal

Description

Simple function that can be used as a format function when converting trees to a data.frame

Usage

FormatFixedDecimal(x, digits = 3)

Arguments

Value

A string corresponding to x, suitable for printing

Examples

data(acme)
print(acme, prob = acme$Get("p", format = function(x) FormatFixedDecimal(x, 4)))

Format a Number as a Percentage

Description

This utility method can be used as a format function when converting trees to a data.frame

Usage

FormatPercent(x, digits = 2, format = "f", ...)

Arguments

Value

A string corresponding to x, suitable for printing

See Also

formatC

Examples

data(acme)
print(acme, prob = acme$Get("p", format = FormatPercent))

Traverse a Tree and Collect Values

Description

The Get method is one of the most important ones of the data.tree package. It lets you traverse a tree and collect values along the way. Alternatively, you can call a method or a function on each Node.

Usage

# OO-style:
#node$Get(attribute, 
#        ..., 
#        traversal = c("pre-order", "post-order", "in-order", "level", "ancestor"), 
#        pruneFun = NULL, 
#        filterFun = NULL, 
#        format = FALSE, 
#        inheritFromAncestors = FALSE)
         
# traditional:
Get(nodes, 
    attribute, 
    ..., 
    format = FALSE, 
    inheritFromAncestors = FALSE, 
    simplify = c(TRUE, FALSE, "array", "regular"))

Arguments

Value

a vector containing the atrributes collected during traversal, in traversal order. NULL is converted to NA, such that length(Node$Get) == Node$totalCount

See Also

Node

Set

Do

Traverse

Examples

data(acme)
acme$Get("level")
acme$Get("totalCount")
 

acme$Get(function(node) node$cost * node$p,
         filterFun = isLeaf)

#This is equivalent:
nodes <- Traverse(acme, filterFun = isLeaf)
Get(nodes, function(node) node$cost * node$p)

   
#simplify = "regular" will preserve names
acme$Get(function(x) c(position = x$position, level = x$level), simplify = "regular")
 

Get an attribute from a Node.

Description

Get an attribute from a Node.

Usage

GetAttribute(
  node,
  attribute,
  ...,
  format = FALSE,
  inheritFromAncestors = FALSE,
  nullAsNa = TRUE
)

Arguments

Examples

data(acme)
GetAttribute(acme$IT$Outsource, "cost")

Determine the number a Node has after conversion to a phylo object

Description

Use this function when plotting a Node as a phylo, e.g. to set custom labels to plot.

Usage

GetPhyloNr(x, type = c("node", "edge"))

Arguments

Value

an integer representing the node

See Also

Other ape phylo conversions: as.Node.phylo(), as.phylo.Node()

Examples

library(ape)
library(data.tree)
data(acme)
ap <- as.phylo(acme)
#plot(ap)
#nodelabels("IT Dep.", GetPhyloNr(Climb(acme, "IT")))
#edgelabels("Good!", GetPhyloNr(Climb(acme, "IT", "Switch to R"), "edge"))

Names that are reserved by the Node class.

Description

These are reserved by the Node class, you cannot use these as attribute names. Note also that all attributes starting with a . are reserved.

Usage

NODE_RESERVED_NAMES_CONST

Format

An object of class character of length 41.

Navigate to another node by relative path.

Description

Navigate to another node by relative path.

Usage

Navigate(node, path)

Arguments

Details

The path is always relative to the node. Navigation to the parent is defined by .., whereas navigation to a child is defined via the child's name. If path is provided as a string, then the navigation steps are separated by '/'.

See Also

Climb

Examples

data(acme)
Navigate(acme$Research, "../IT/Outsource")
Navigate(acme$Research, c("..", "IT", "Outsource"))

Create a data.tree Structure With Nodes

Description

Node is at the very heart of the data.tree package. All trees are constructed by tying together Node objects.

Usage

# n1 <- Node$new("Node 1")

Format

An R6Class generator object

Details

Assemble Node objects into a data.tree structure and use the traversal methods to set, get, and perform operations on it. Typically, you construct larger tree structures by converting from data.frame, list, or other formats.

Most methods (e.g. node$Sort()) also have a functional form (e.g. Sort(node))

Active bindings

Methods

Public methods

• Node$new()

• Node$AddChild()

• Node$AddChildNode()

• Node$AddSibling()

• Node$AddSiblingNode()

• Node$RemoveChild()

• Node$RemoveAttribute()

• Node$Sort()

• Node$Revert()

• Node$Prune()

• Node$Climb()

• Node$Navigate()

• Node$Get()

• Node$Do()

• Node$Set()

• Node$clone()

Method new()

Create a new Node object. This is often used to create the root of a tree when creating a tree programmatically.

Usage

Node$new(name, check = c("check", "no-warn", "no-check"), ...)

Arguments

Returns

A new 'Node' object

Examples

node <- Node$new("mynode", x = 2, y = "value of y")
node$y

Method AddChild()

Creates a Node and adds it as the last sibling as a child to the Node on which this is called.

Usage

Node$AddChild(name, check = c("check", "no-warn", "no-check"), ...)

Arguments

Returns

The new Node (invisibly)

Examples

root <- Node$new("myroot", myname = "I'm the root")
root$AddChild("child1", myname = "I'm the favorite child")
child2 <- root$AddChild("child2", myname = "I'm just another child")
child3 <- child2$AddChild("child3", myname = "Grandson of a root!")
print(root, "myname")

Method AddChildNode()

Adds a Node as a child to this node.

Usage

Node$AddChildNode(child)

Arguments

Returns

the child node added (this lets you chain calls)

Examples

root <- Node$new("myroot")
child <- Node$new("mychild")
root$AddChildNode(child)

Method AddSibling()

Creates a new Node called name and adds it after this Node as a sibling.

Usage

Node$AddSibling(name, check = c("check", "no-warn", "no-check"), ...)

Arguments

Returns

the sibling node (this lets you chain calls)

Examples

#' root <- Node$new("myroot")
child <- root$AddChild("child1")
sibling <- child$AddSibling("sibling1")

Method AddSiblingNode()

Adds a Node after this Node, as a sibling.

Usage

Node$AddSiblingNode(sibling)

Arguments

Returns

the added sibling node (this lets you chain calls, as in the examples)

Examples

root <- Node$new("myroot")
child <- Node$new("mychild")
sibling <- Node$new("sibling")
root$AddChildNode(child)$AddSiblingNode(sibling)

Method RemoveChild()

Remove the child Node called name from a Node and returns it.

Usage

Node$RemoveChild(name)

Arguments

Returns

the subtree spanned by the removed child.

Examples

node <- Node$new("myroot")$AddChild("mychild")$root
node$RemoveChild("mychild")

Method RemoveAttribute()

Removes attribute called name from this Node.

Usage

Node$RemoveAttribute(name, stopIfNotAvailable = TRUE)

Arguments

Examples

node <- Node$new("mynode")
node$RemoveAttribute("age", stopIfNotAvailable = FALSE)
node$age <- 27
node$RemoveAttribute("age")
node

Method Sort()

Sort children of a Node or an entire data.tree structure

Usage

Node$Sort(attribute, ..., decreasing = FALSE, recursive = TRUE)

Arguments

Details

You can sort with respect to any argument of the tree. But note that sorting has side-effects, meaning that you modify the underlying, original data.tree object structure.

See also Sort for the equivalent function.

Returns

Returns the node on which Sort is called, invisibly. This can be useful to chain Node methods.

Examples

data(acme)
acme$Do(function(x) x$totalCost <- Aggregate(x, "cost", sum), traversal = "post-order")
Sort(acme, "totalCost", decreasing = FALSE)
print(acme, "totalCost")

Method Revert()

Reverts the sort order of a Node's children.

See also Revert for the equivalent function.

Usage

Node$Revert(recursive = TRUE)

Arguments

Returns

returns the Node invisibly (for chaining)

Method Prune()

Prunes a tree.

Pruning refers to removing entire subtrees. This function has side-effects, it modifies your data.tree structure!

See also Prune for the equivalent function.

Usage

Node$Prune(pruneFun)

Arguments

Returns

the number of nodes removed

Examples

data(acme)
acme$Do(function(x) x$cost <- Aggregate(x, "cost", sum))
Prune(acme, function(x) x$cost > 700000)
print(acme, "cost")

Method Climb()

Climb a tree from parent to children, by provided criteria.

Usage

Node$Climb(...)

Arguments

Details

This method lets you climb the tree, from crutch to crutch. On each Node, the Climb finds the first child having attribute value equal to the the provided argument.

See also Climb and Navigate

Climb(node, ...)

Returns

the Node having path ..., or NULL if such a path does not exist

Examples

data(acme)

#the following are all equivalent
Climb(acme, 'IT', 'Outsource')
Climb(acme, name = 'IT', name = 'Outsource')
Climb(acme, 'IT')$Climb('Outsource')
Navigate(acme, path = "IT/Outsource")

Climb(acme, name = 'IT')

Climb(acme, position = c(2, 1))
#or, equivalent:
Climb(acme, position = 2, position = 1)
Climb(acme, name = "IT", cost = 250000)

tree <- CreateRegularTree(5, 2)
tree$Climb(c("1", "1"), position = c(2, 2))$path

Method Navigate()

Navigate to another node by relative path.

Usage

Node$Navigate(path)

Arguments

Details

The path is always relative to the Node. Navigation to the parent is defined by .., whereas navigation to a child is defined via the child's name. If path is provided as a string, then the navigation steps are separated by '/'.

See also Navigate and Climb

Examples

data(acme)
Navigate(acme$Research, "../IT/Outsource")
Navigate(acme$Research, c("..", "IT", "Outsource"))

Method Get()

Traverse a Tree and Collect Values

Usage

Node$Get(
  attribute,
  ...,
  traversal = c("pre-order", "post-order", "in-order", "level", "ancestor"),
  pruneFun = NULL,
  filterFun = NULL,
  format = FALSE,
  inheritFromAncestors = FALSE,
  simplify = c(TRUE, FALSE, "array", "regular")
)

Arguments

Details

The Get method is one of the most important ones of the data.tree package. It lets you traverse a tree and collect values along the way. Alternatively, you can call a method or a function on each Node.

See also Get, Node, Set, Do, Traverse

Returns

a vector containing the atrributes collected during traversal, in traversal order. NULL is converted to NA, such that length(Node$Get) == Node$totalCount

Examples

data(acme)
acme$Get("level")
acme$Get("totalCount")
 

acme$Get(function(node) node$cost * node$p,
         filterFun = isLeaf)

#This is equivalent:
nodes <- Traverse(acme, filterFun = isLeaf)
Get(nodes, function(node) node$cost * node$p)

   
#simplify = "regular" will preserve names
acme$Get(function(x) c(position = x$position, level = x$level), simplify = "regular")
 

Method Do()

Executes a function on a set of nodes

Usage

Node$Do(
  fun,
  ...,
  traversal = c("pre-order", "post-order", "in-order", "level", "ancestor"),
  pruneFun = NULL,
  filterFun = NULL
)

Arguments

Details

See also Node, Get, Set, Traverse

Examples

data(acme)
acme$Do(function(node) node$expectedCost <- node$p * node$cost)
print(acme, "expectedCost")

Method Set()

Traverse a Tree and Assign Values

Usage

Node$Set(
  ...,
  traversal = c("pre-order", "post-order", "in-order", "level", "ancestor"),
  pruneFun = NULL,
  filterFun = NULL
)

Arguments

Details

The method takes one or more vectors as an argument. It traverses the tree, whereby the values are picked from the vector. Also available as OO-style method on Node.

See also Node, Get, Do, Traverse

Returns

invisibly returns the nodes (useful for chaining)

Examples

data(acme)
acme$Set(departmentId = 1:acme$totalCount, openingHours = NULL, traversal = "post-order")
acme$Set(head = c("Jack Brown", 
                  "Mona Moneyhead", 
                  "Dr. Frank N. Stein", 
                  "Eric Nerdahl"
                  ),
         filterFun = function(x) !x$isLeaf
        )
print(acme, "departmentId", "head")
 

Method clone()

The objects of this class are cloneable with this method.

Usage

Node$clone(deep = FALSE)

Arguments

See Also

For more details see the data.tree documentations, or the data.tree vignette: vignette("data.tree")

Node

Sort

Examples

library(data.tree)
acme <- Node$new("Acme Inc.")
accounting <- acme$AddChild("Accounting")$
              AddSibling("Research")$
              AddChild("New Labs")$
              parent$
              AddSibling("IT")$
              AddChild("Outsource")
print(acme)



## ------------------------------------------------
## Method `Node$new`
## ------------------------------------------------

node <- Node$new("mynode", x = 2, y = "value of y")
node$y


## ------------------------------------------------
## Method `Node$AddChild`
## ------------------------------------------------

root <- Node$new("myroot", myname = "I'm the root")
root$AddChild("child1", myname = "I'm the favorite child")
child2 <- root$AddChild("child2", myname = "I'm just another child")
child3 <- child2$AddChild("child3", myname = "Grandson of a root!")
print(root, "myname")


## ------------------------------------------------
## Method `Node$AddChildNode`
## ------------------------------------------------

root <- Node$new("myroot")
child <- Node$new("mychild")
root$AddChildNode(child)


## ------------------------------------------------
## Method `Node$AddSibling`
## ------------------------------------------------

#' root <- Node$new("myroot")
child <- root$AddChild("child1")
sibling <- child$AddSibling("sibling1")


## ------------------------------------------------
## Method `Node$AddSiblingNode`
## ------------------------------------------------

root <- Node$new("myroot")
child <- Node$new("mychild")
sibling <- Node$new("sibling")
root$AddChildNode(child)$AddSiblingNode(sibling)


## ------------------------------------------------
## Method `Node$RemoveChild`
## ------------------------------------------------

node <- Node$new("myroot")$AddChild("mychild")$root
node$RemoveChild("mychild")


## ------------------------------------------------
## Method `Node$RemoveAttribute`
## ------------------------------------------------

node <- Node$new("mynode")
node$RemoveAttribute("age", stopIfNotAvailable = FALSE)
node$age <- 27
node$RemoveAttribute("age")
node


## ------------------------------------------------
## Method `Node$Sort`
## ------------------------------------------------

data(acme)
acme$Do(function(x) x$totalCost <- Aggregate(x, "cost", sum), traversal = "post-order")
Sort(acme, "totalCost", decreasing = FALSE)
print(acme, "totalCost")


## ------------------------------------------------
## Method `Node$Prune`
## ------------------------------------------------

data(acme)
acme$Do(function(x) x$cost <- Aggregate(x, "cost", sum))
Prune(acme, function(x) x$cost > 700000)
print(acme, "cost")


## ------------------------------------------------
## Method `Node$Climb`
## ------------------------------------------------

data(acme)

#the following are all equivalent
Climb(acme, 'IT', 'Outsource')
Climb(acme, name = 'IT', name = 'Outsource')
Climb(acme, 'IT')$Climb('Outsource')
Navigate(acme, path = "IT/Outsource")

Climb(acme, name = 'IT')

Climb(acme, position = c(2, 1))
#or, equivalent:
Climb(acme, position = 2, position = 1)
Climb(acme, name = "IT", cost = 250000)

tree <- CreateRegularTree(5, 2)
tree$Climb(c("1", "1"), position = c(2, 2))$path



## ------------------------------------------------
## Method `Node$Navigate`
## ------------------------------------------------

data(acme)
Navigate(acme$Research, "../IT/Outsource")
Navigate(acme$Research, c("..", "IT", "Outsource"))


## ------------------------------------------------
## Method `Node$Get`
## ------------------------------------------------

data(acme)
acme$Get("level")
acme$Get("totalCount")
 

acme$Get(function(node) node$cost * node$p,
         filterFun = isLeaf)

#This is equivalent:
nodes <- Traverse(acme, filterFun = isLeaf)
Get(nodes, function(node) node$cost * node$p)

   
#simplify = "regular" will preserve names
acme$Get(function(x) c(position = x$position, level = x$level), simplify = "regular")
 

## ------------------------------------------------
## Method `Node$Do`
## ------------------------------------------------

data(acme)
acme$Do(function(node) node$expectedCost <- node$p * node$cost)
print(acme, "expectedCost")


## ------------------------------------------------
## Method `Node$Set`
## ------------------------------------------------

data(acme)
acme$Set(departmentId = 1:acme$totalCount, openingHours = NULL, traversal = "post-order")
acme$Set(head = c("Jack Brown", 
                  "Mona Moneyhead", 
                  "Dr. Frank N. Stein", 
                  "Eric Nerdahl"
                  ),
         filterFun = function(x) !x$isLeaf
        )
print(acme, "departmentId", "head")
 

Prunes a tree.

Description

Pruning refers to removing entire subtrees. This function has side-effects, it modifies your data.tree structure!

Usage

Prune(node, pruneFun)

Arguments

Value

the number of nodes removed

See Also

Node

Examples

data(acme)
acme$Do(function(x) x$cost <- Aggregate(x, "cost", sum))
Prune(acme, function(x) x$cost > 700000)
print(acme, "cost")

Reverts the sort order of a Node's children.

Description

Reverts the sort order of a Node's children.

Usage

Revert(node, recursive = TRUE)

Arguments

Value

returns the Node invisibly (for chaining)

See Also

Node

Sort

Traverse a Tree and Assign Values

Description

The method takes one or more vectors as an argument. It traverses the tree, whereby the values are picked from the vector. Also available as OO-style method on Node.

Usage

#OO-style:
# node$Set(..., 
#          traversal = c("pre-order", "post-order", "in-order", "level", "ancestor"),  
#          pruneFun = NULL, 
#          filterFun = NULL)
#traditional:
Set(nodes, ...)

Arguments

Value

invisibly returns the nodes (useful for chaining)

See Also

Node

Get

Do

Traverse

Examples

data(acme)
acme$Set(departmentId = 1:acme$totalCount, openingHours = NULL, traversal = "post-order")
acme$Set(head = c("Jack Brown", 
                  "Mona Moneyhead", 
                  "Dr. Frank N. Stein", 
                  "Eric Nerdahl"
                  ),
         filterFun = function(x) !x$isLeaf
        )
print(acme, "departmentId", "head")
 

Set a formatter function on a specific node

Description

Formatter functions set on a Node act as a default formatter when printing and using the Get method. The formatter is inherited, meaning that whenever Get fetches an attribute from a Node, it checks on the Node or on any of its ancestors whether a formatter is set.

Usage

SetFormat(node, name, formatFun)

Arguments

See Also

Get

print.Node

Examples

data(acme)
acme$Set(id = 1:(acme$totalCount))
SetFormat(acme, "id", function(x) FormatPercent(x, digits = 0))
SetFormat(Climb(acme, "IT"), "id", FormatFixedDecimal)
print(acme, "id")
# Calling Get with an explicit formatter will overwrite the default set on the Node:
print(acme, id = acme$Get("id", format = function(x) paste0("id:", x)))

# Or, to avoid formatters, even though you set them on a Node:
print(acme, id = acme$Get("id", format = identity))

Sort children of a Node or an entire data.tree structure

Description

You can sort with respect to any argument of the tree. But note that sorting has side-effects, meaning that you modify the underlying, original data.tree object structure.

Usage

Sort(node, attribute, ..., decreasing = FALSE, recursive = TRUE)

Arguments

Value

Returns the node on which Sort is called, invisibly. This can be useful to chain Node methods.

See Also

Node

Revert

Examples

data(acme)
acme$Do(function(x) x$totalCost <- Aggregate(x, "cost", sum), traversal = "post-order")
Sort(acme, "totalCost", decreasing = FALSE)
print(acme, "totalCost")

Write a data.tree structure to Newick notation

Description

To read from Newick, you can use the ape package, and convert the resulting phylo object to a data.tree structure.

Usage

ToNewick(node, heightAttribute = DefaultPlotHeight, ...)

Arguments

See Also

Other Conversions from Node: as.dendrogram.Node()

Examples

data(acme)
ToNewick(acme)
ToNewick(acme, heightAttribute = NULL)
ToNewick(acme, heightAttribute = function(x) DefaultPlotHeight(x, 200))
ToNewick(acme, rootHeight = 200)

Traverse a tree or a sub-tree

Description

Traverse takes the root of a tree or a sub-tree, and "walks" the tree in a specific order. It returns a list of Node objects, filtered and pruned by filterFun and pruneFun.

Usage

Traverse(
  node,
  traversal = c("pre-order", "post-order", "in-order", "level", "ancestor"),
  pruneFun = NULL,
  filterFun = NULL
)

Arguments

Details

The traversal order is as follows. (Note that these descriptions are not precise and complete. They are meant for quick reference only. See the data.tree vignette for a more detailed description).

pre-order

Go to first child, then to its first child, etc.

post-order

Go to the first branch's leaf, then to its siblings, and work your way back to the root

in-order

Go to the first branch's leaf, then to its parent, and only then to the leaf's sibling

level

Collect root, then level 2, then level 3, etc.

ancestor

Take a node, then the node's parent, then that node's parent in turn, etc. This ignores the pruneFun

function

You can also provide a function, whose sole parameter is a Node object. The function is expected to return the node's next node, a list of the node's next nodes, or NULL.

Value

a list of Nodes

See Also

Node

Get

Set

Do

Sample Data: A Simple Company with Departments

Description

acme's tree representation is accessed through its root, acme.

Usage

data(acme)

Format

A data.tree root Node

Details

• cost, only available for leaf nodes. Cost of the project.

• p probability that a project will be undertaken.

Convert an object to a data.tree data structure

Description

Convert an object to a data.tree data structure

Usage

as.Node(x, ...)

Arguments

See Also

Other as.Node: as.Node.data.frame(), as.Node.dendrogram(), as.Node.list(), as.Node.phylo(), as.Node.rpart()

Convert a a SplitNode from the party package to a data.tree structure.

Description

Convert a a SplitNode from the party package to a data.tree structure.

Usage

## S3 method for class 'BinaryTree'
as.Node(x, ...)

Arguments

Examples

library(party)
airq <- subset(airquality, !is.na(Ozone))
airct <- ctree(Ozone ~ ., data = airq, 
               controls = ctree_control(maxsurrogate = 3))
               
tree <- as.Node(airct)
tree

print(tree, 
      "label", 
      criterion = function(x) round(x$criterion$maxcriterion, 3),
      statistic = function(x) round(max(x$criterion$statistic), 3)
      )

FindNode(tree, 6)$path

Convert a data.frame to a data.tree structure

Description

Convert a data.frame to a data.tree structure

Usage

## S3 method for class 'data.frame'
as.Node(
  x,
  ...,
  mode = c("table", "network"),
  pathName = "pathString",
  pathDelimiter = "/",
  colLevels = NULL,
  na.rm = TRUE
)

FromDataFrameTable(
  table,
  pathName = "pathString",
  pathDelimiter = "/",
  colLevels = NULL,
  na.rm = TRUE,
  check = c("check", "no-warn", "no-check"),
  suffix = "_attr"
)

FromDataFrameNetwork(network, check = c("check", "no-warn", "no-check"))

Arguments

Value

The root Node of the data.tree structure

See Also

as.data.frame.Node

Other as.Node: as.Node.dendrogram(), as.Node.list(), as.Node.phylo(), as.Node.rpart(), as.Node()

Examples

data(acme)

#Tree
x <- ToDataFrameTree(acme, "pathString", "p", "cost")
x
xN <- as.Node(x)
print(xN, "p", "cost")

#Table
x <- ToDataFrameTable(acme, "pathString", "p", "cost")
x
xN <- FromDataFrameTable(x)
print(xN, "p", "cost")

#More complex Table structure, using colLevels
acme$Set(floor = c(1, 2, 3),  filterFun = function(x) x$level == 2)
x <- ToDataFrameTable(acme, "pathString", "floor", "p", "cost")
x
xN <- FromDataFrameTable(x, colLevels = list(NULL, "floor", c("p", "cost")), na.rm = TRUE)
print(xN, "floor", "p", "cost")

#Network
x <- ToDataFrameNetwork(acme, "p", "cost", direction = "climb")
x
xN <- FromDataFrameNetwork(x)
print(xN, "p", "cost")

Convert a dendrogram to a data.tree Node

Description

Convert a dendrogram to a data.tree Node

Usage

## S3 method for class 'dendrogram'
as.Node(
  x,
  name = "Root",
  heightName = "plotHeight",
  check = c("check", "no-warn", "no-check"),
  ...
)

Arguments

Value

The root Node of a data.tree

See Also

Other as.Node: as.Node.data.frame(), as.Node.list(), as.Node.phylo(), as.Node.rpart(), as.Node()

Examples

hc <- hclust(dist(USArrests), "ave")
dend1 <- as.dendrogram(hc)
tree1 <- as.Node(dend1)
tree1$attributesAll
tree1$totalCount
tree1$leafCount
tree1$height
  

Convert a nested list structure to a data.tree structure

Description

Convert a nested list structure to a data.tree structure

Usage

## S3 method for class 'list'
as.Node(
  x,
  mode = c("simple", "explicit"),
  nameName = "name",
  childrenName = "children",
  nodeName = NULL,
  interpretNullAsList = FALSE,
  check = c("check", "no-warn", "no-check"),
  ...
)

FromListExplicit(
  explicitList,
  nameName = "name",
  childrenName = "children",
  nodeName = NULL,
  check = c("check", "no-warn", "no-check")
)

FromListSimple(
  simpleList,
  nameName = "name",
  nodeName = NULL,
  interpretNullAsList = FALSE,
  check = c("check", "no-warn", "no-check")
)

Arguments

See Also

Other as.Node: as.Node.data.frame(), as.Node.dendrogram(), as.Node.phylo(), as.Node.rpart(), as.Node()

Examples

kingJosephs <- list(name = "Joseph I",
                    spouse = "Mary",
                    born = "1818-02-23",
                    died = "1839-08-29",
                    children = list(
                                    list(name = "Joseph II",
                                         spouse = "Kathryn",
                                         born = "1839-03-28",
                                         died = "1865-12-19"),
                                    list(name = "Helen",
                                         born = "1840-17-08",
                                         died = "1845-01-01")
                                    )
                   )
FromListExplicit(kingJosephs)

kingJosephs <- list(head = "Joseph I",
                    spouse = "Mary",
                    born = "1818-02-23",
                    died = "1839-08-29",
                    list(head = "Joseph II",
                         spouse = "Kathryn",
                         born = "1839-03-28",
                         died = "1865-12-19"),
                    list(head = "Helen",
                         born = "1840-17-08",
                         died = "1845-01-01")       
                   )
FromListSimple(kingJosephs, nameName = "head")

kingJosephs <- list(spouse = "Mary",
                    born = "1818-02-23",
                    died = "1839-08-29",
                    `Joseph II` = list(spouse = "Kathryn",
                                       born = "1839-03-28",
                                       died = "1865-12-19"),
                    Helen = list(born = "1840-17-08",
                                 died = "1845-01-01")
                                 
                   )
FromListSimple(kingJosephs, nodeName = "Joseph I")
  

Convert a a party from the partykit package to a data.tree structure.

Description

Convert a a party from the partykit package to a data.tree structure.

Usage

## S3 method for class 'party'
as.Node(x, ...)

Arguments

Examples

library(partykit)
data("WeatherPlay", package = "partykit")
### splits ###
# split in overcast, humidity, and windy
sp_o <- partysplit(1L, index = 1:3)
sp_h <- partysplit(3L, breaks = 75)
sp_w <- partysplit(4L, index = 1:2)

## query labels
character_split(sp_o)

### nodes ###
## set up partynode structure
pn <- partynode(1L, split = sp_o, kids = list(
  partynode(2L, split = sp_h, kids = list(
      partynode(3L, info = "yes"),
      partynode(4L, info = "no"))),
  partynode(5L, info = "yes"),
  partynode(6L, split = sp_w, kids = list(
      partynode(7L, info = "yes"),
      partynode(8L, info = "no")))))
pn
### tree ###
## party: associate recursive partynode structure with data
py <- party(pn, WeatherPlay)
tree <- as.Node(py)

print(tree, 
      "splitname",
      count = function(node) nrow(node$data), 
      "splitLevel")

SetNodeStyle(tree, 
             label = function(node) paste0(node$name, ": ", node$splitname), 
             tooltip = function(node) paste0(nrow(node$data), " observations"),
             fontname = "helvetica")
SetEdgeStyle(tree, 
             arrowhead = "none", 
             label = function(node) node$splitLevel,
             fontname = "helvetica",
             penwidth = function(node) 12 * nrow(node$data)/nrow(node$root$data),
             color = function(node) {
               paste0("grey", 
                      100 - as.integer( 100 * nrow(node$data)/nrow(node$root$data))
                      )
             }
             )
Do(tree$leaves, 
   function(node) {
     SetNodeStyle(node, 
                  shape = "box", 
                  color = ifelse(node$splitname == "yes", "darkolivegreen4", "lightsalmon4"),
                  fillcolor = ifelse(node$splitname == "yes", "darkolivegreen1", "lightsalmon"),
                  style = "filled,rounded",
                  penwidth = 2
                  )
   }
   )

plot(tree)

Convert a phylo object from the ape package to a Node

Description

Convert a phylo object from the ape package to a Node

Usage

## S3 method for class 'phylo'
as.Node(
  x,
  heightName = "plotHeight",
  replaceUnderscores = TRUE,
  namesNotUnique = FALSE,
  ...
)

Arguments

See Also

Other ape phylo conversions: GetPhyloNr(), as.phylo.Node()

Other as.Node: as.Node.data.frame(), as.Node.dendrogram(), as.Node.list(), as.Node.rpart(), as.Node()

Examples

#which bird familes have the max height?
library(ape)
data(bird.families)
bf <- as.Node(bird.families)
height <- bf$height
t <- Traverse(bf, filterFun = function(x) x$level == 25)
Get(t, "name")

Convert an rpart object to a data.tree structure

Description

Convert an rpart object to a data.tree structure

Usage

## S3 method for class 'rpart'
as.Node(x, digits = getOption("digits") - 3, use.n = FALSE, ...)

Arguments

Value

a data.tree object. The tree contains a field rpart.id which references back to the original node id in the row names of the rpart object.

See Also

Other as.Node: as.Node.data.frame(), as.Node.dendrogram(), as.Node.list(), as.Node.phylo(), as.Node()

Examples

if (require(rpart)) {
   fit <- rpart(Kyphosis ~ Age + Number + Start, data = kyphosis)
   as.Node(fit)
}

Convert a data.tree structure to a data.frame

Description

If a node field contains data of length > 1, then that is converted into a string in the data.frame.

Usage

## S3 method for class 'Node'
as.data.frame(
  x,
  row.names = NULL,
  optional = FALSE,
  ...,
  traversal = c("pre-order", "post-order", "in-order", "level", "ancestor"),
  pruneFun = NULL,
  filterFun = NULL,
  format = FALSE,
  inheritFromAncestors = FALSE
)

ToDataFrameTree(x, ..., pruneFun = NULL)

ToDataFrameTable(x, ..., pruneFun = NULL)

ToDataFrameNetwork(
  x,
  ...,
  direction = c("climb", "descend"),
  pruneFun = NULL,
  format = FALSE,
  inheritFromAncestors = FALSE
)

ToDataFrameTypeCol(x, ..., type = "level", prefix = type, pruneFun = NULL)

Arguments

Value

ToDataFrameTree: a data.frame, where each row represents a Node in the tree or sub-tree spanned by x, possibly pruned according to pruneFun.

ToDataFrameTable: a data.frame, where each row represents a leaf Node in the tree or sub-tree spanned by x, possibly pruned according to pruneFun.

ToDataFrameNetwork: a data.frame, where each row represents a Node in the tree or sub-tree spanned by x, possibly pruned according to pruneFun. The first column is called 'from', while the second is called 'to', describing the parent to child edge (for direction "climb") or the child to parent edge (for direction "descend"). If AreNamesUnique is TRUE, then the Network is based on the Node$name, otherwise on the Node$pathString

ToDataFrameTypeCol: a data.frame in table format (i.e. where each row represents a leaf in the tree or sub-tree spanned by x), possibly pruned according to pruneFun. In addition to ..., each distinct type is output to a column.

Examples

data(acme)
acme$attributesAll
as.data.frame(acme, row.names = NULL, optional = FALSE, "cost", "p")

ToDataFrameTree(acme, "cost", "p")
ToDataFrameNetwork(acme, "cost", "p", direction = "climb")
ToDataFrameTable(acme, "cost", "p")
ToDataFrameTypeCol(acme)

#use the pruneFun:
acme$Do(function(x) x$totalCost <- Aggregate(x, "cost", sum), traversal = "post-order")
ToDataFrameTree(acme, "totalCost", pruneFun = function(x) x$totalCost > 300000)

#inherit
acme$Set(floor = c(1, 2, 3), filterFun = function(x) x$level == 2)
as.data.frame(acme, row.names = NULL, optional = FALSE, "floor", inheritFromAncestors = FALSE)
as.data.frame(acme, row.names = NULL, optional = FALSE, "floor", inheritFromAncestors = TRUE)

#using a function as an attribute:
acme$Accounting$Head <- "Mrs. Numright"
acme$Research$Head <- "Mr. Stein"
acme$IT$Head <- "Mr. Squarehead"
ToDataFrameTable(acme, department = function(x) x$parent$name, "name", "Head", "cost")

#complex TypeCol
acme$IT$Outsource$AddChild("India")
acme$IT$Outsource$AddChild("Poland")
acme$Set(type = c('company', 'department', 'project', 'project', 'department',
                  'project', 'project', 'department', 'program', 'project',
                  'project', 'project', 'project'
                  )
        )
print(acme, 'type')
ToDataFrameTypeCol(acme, type = 'type')

Convert a Node to a dendrogram

Description

Convert a data.tree structure to a dendrogram

Usage

## S3 method for class 'Node'
as.dendrogram(
  object,
  heightAttribute = DefaultPlotHeight,
  edgetext = FALSE,
  ...
)

Arguments

Value

An object of class dendrogram

See Also

Other Conversions from Node: ToNewick()

Examples

data(acme)
acmed <- as.dendrogram(acme)
plot(acmed, center = TRUE)

#you can take an attribute for the height:
acme$Do( function(x) x$myPlotHeight <- (10 - x$level))
acmed <- as.dendrogram(acme, heightAttribute = "myPlotHeight")
plot(acmed, center = TRUE)

#or directly a function
acmed <- as.dendrogram(acme, heightAttribute = function(x) 10 - x$level)
plot(acmed)

Convert a data.tree structure to an igraph network

Description

This requires the igraph package to be installed. Also, this requires the names of the Nodes to be unique within the data.tree structure.

Usage

as.igraph.Node(
  x,
  vertexAttributes = character(),
  edgeAttributes = character(),
  directed = FALSE,
  direction = c("climb", "descend"),
  ...
)

Arguments

Value

an igraph object

See Also

AreNamesUnique

Examples

data(acme)
library(igraph)
ig <- as.igraph(acme, "p", c("level", "isLeaf"))
plot(ig)

Convert a data.tree structure to a list-of-list structure

Description

Convert a data.tree structure to a list-of-list structure

Usage

## S3 method for class 'Node'
as.list(
  x,
  mode = c("simple", "explicit"),
  unname = FALSE,
  nameName = ifelse(unname, "name", ""),
  childrenName = "children",
  rootName = "",
  keepOnly = NULL,
  pruneFun = NULL,
  ...
)

ToListSimple(x, nameName = "name", pruneFun = NULL, ...)

ToListExplicit(
  x,
  unname = FALSE,
  nameName = ifelse(unname, "name", ""),
  childrenName = "children",
  pruneFun = NULL,
  ...
)

Arguments

Examples

data(acme)

str(ToListSimple(acme))
str(ToListSimple(acme, keepOnly = "cost"))

str(ToListExplicit(acme))
str(ToListExplicit(acme, unname = TRUE))
str(ToListExplicit(acme, unname = TRUE, nameName = "id", childrenName = "descendants"))

Convert a Node to a phylo object from the ape package.

Description

This method requires the ape package to be installed and loaded.

Usage

as.phylo.Node(x, heightAttribute = DefaultPlotHeight, ...)

Arguments

See Also

Other ape phylo conversions: GetPhyloNr(), as.Node.phylo()

Examples

library(ape)
data(acme)
acmephylo <- as.phylo(acme)
#plot(acmephylo)

Calculate the average number of branches each non-leaf has

Description

Calculate the average number of branches each non-leaf has

Usage

averageBranchingFactor(node)

Arguments

Check if a Node is a leaf

Description

Check if a Node is a leaf

Usage

isLeaf(node)

Arguments

Value

TRUE if the Node is a leaf, FALSE otherwise

Check if a Node is not a leaf

Description

Check if a Node is not a leaf

Usage

isNotLeaf(node)

Arguments

Value

FALSE if the Node is a leaf, TRUE otherwise

Check if a Node is not a root

Description

Check if a Node is not a root

Usage

isNotRoot(node)

Arguments

Value

FALSE if the Node is the root, TRUE otherwise

Check if a Node is the root

Description

Check if a Node is the root

Usage

isRoot(node)

Arguments

Value

TRUE if the Node is the root, FALSE otherwise

Sample Data: Data Used by the ID3 Vignette

Description

mushroom contains attributes of mushrooms. We can use this data to predict a mushroom's toxicity based on its attributes. The attributes available in the data set are:

Usage

data(mushroom)

Format

data.frame

Details

• color the color of a mushroom

• size whether a mushroom is small or large

• points whether a mushroom has points

• edibility whether a mushroom is edible or toxic

Plot a graph, or get a graphviz dot representation of the tree

Description

Use these methods to style your graph, and to plot it. The functionality is built around the DiagrammeR package, so for anything that goes beyond simple plotting, it is recommended to read its documentation at https://rich-iannone.github.io/DiagrammeR/docs.html. Note that DiagrammeR is only suggested by data.tree, so 'plot' only works if you have installed it on your system.

Usage

## S3 method for class 'Node'
plot(
  x,
  ...,
  direction = c("climb", "descend"),
  pruneFun = NULL,
  output = "graph"
)

ToDiagrammeRGraph(root, direction = c("climb", "descend"), pruneFun = NULL)

SetNodeStyle(node, inherit = TRUE, keepExisting = FALSE, ...)

SetEdgeStyle(node, inherit = TRUE, keepExisting = FALSE, ...)

SetGraphStyle(root, keepExisting = FALSE, ...)

GetDefaultTooltip(node)

Arguments

Details

Use SetNodeStyle and SetEdgeStyle to define the style of your plot. Use plot to display a graphical representation of your tree.

The most common styles that can be set on the nodes are:

• color

• fillcolor

• fixedsize true or false

• fontcolor

• fontname

• fontsize

• height

• penwidth

• shape box, ellipse, polygon, circle, box, etc.

• style

• tooltip

• width

The most common styles that can be set on the edges are:

• arrowhead e.g. normal, dot, vee

• arrowsize

• arrowtail

• color

• dir forward, back, both, none

• fontcolor

• fontname

• fontsize

• headport

• label

• minlen

• penwidth

• tailport

• tooltip

A good source to understand the attributes is https://graphviz.org/Documentation.php. Another good source is the DiagrammeR package documentation, or more specifically: https://rich-iannone.github.io/DiagrammeR/docs.html

In addition to the standard GraphViz functionality, the data.tree plotting infrastructure takes advantage of the fact that data.tree structure are always hierarchic. Thus, style attributes are inherited from parents to children on an individual basis. For example, you can set the fontcolor to red on a parent, and then all children will also have red font, except if you specifically disallow inheritance. Labels and tooltips are never inherited.

Another feature concerns functions: Instead of setting a fixed value (e.g. SetNodeStyle(acme, label = "Acme. Inc"), you can set a function (e.g. SetNodeStyle(acme, label = function(x) x$name)). The function must take a Node as its single argument. Together with inheritance, this becomes a very powerful tool.

The GetDefaultTooltip method is a utility method that can be used to print all attributes of a Node.

There are some more examples in the 'applications' vignette, see vignette('applications', package = "data.tree")

Examples

data(acme)
SetGraphStyle(acme, rankdir = "TB")
SetEdgeStyle(acme, arrowhead = "vee", color = "blue", penwidth = 2)
#per default, Node style attributes will be inherited:
SetNodeStyle(acme, style = "filled,rounded", shape = "box", fillcolor = "GreenYellow", 
             fontname = "helvetica", tooltip = GetDefaultTooltip)
SetNodeStyle(acme$IT, fillcolor = "LightBlue", penwidth = "5px")
#inheritance can be avoided:
SetNodeStyle(acme$Accounting, inherit = FALSE, fillcolor = "Thistle", 
             fontcolor = "Firebrick", tooltip = "This is the accounting department")
SetEdgeStyle(acme$Research$`New Labs`, 
             color = "red", 
             label = "Focus!", 
             penwidth = 3, 
             fontcolor = "red")
#use Do to set style on specific nodes:
Do(acme$leaves, function(node) SetNodeStyle(node, shape = "egg"))
plot(acme)

#print p as label, where available:
SetNodeStyle(acme, label = function(node) node$p)
plot(acme)

Print a Node in a human-readable fashion.

Description

Print a Node in a human-readable fashion.

Usage

## S3 method for class 'Node'
print(
  x,
  ...,
  pruneMethod = c("simple", "dist", NULL),
  limit = 100,
  pruneFun = NULL,
  row.names = T
)

Arguments

Examples

data(acme)
print(acme, "cost", "p")
print(acme, "cost", probability = "p")
print(acme, expectedCost = function(x) x$cost * x$p)
do.call(print, c(acme, acme$attributesAll))

tree <- CreateRegularTree(4, 5)
# print entire tree:
print(tree, pruneMethod = NULL)
# print first 20 nodes:
print(tree, pruneMethod = "simple", limit = 20)
# print 20 nodes, removing leafs first:
print(tree, pruneMethod = "dist", limit = 20)
# provide your own pruning function:
print(tree, pruneFun = function(node) node$position != 2)

Register a method for a suggested dependency

Description

Code copied into data.tree from 'vctrs' (authors Wickham H, Henry L, Vaughan D; https://github.com/r-lib/vctrs)

Usage

s3_register(generic, class, method = NULL)

Arguments

Details

For R 3.5.0 and later, 's3_register()' is useful when demonstrating class creation in a vignette, since method lookup no longer always involves the lexical scope. For R 3.6.0 and later, you can achieve a similar effect by using "delayed method registration".

Examples

# A typical use case is to dynamically register tibble/pillar methods
# for your class. That way you avoid creating a hard depedency on packages
# that are not essential, while still providing finer control over
# printing when they are used.

.onLoad <- function(...) {
  s3_register("pillar::pillar_shaft", "vctrs_vctr")
  s3_register("tibble::type_sum", "vctrs_vctr")
}