as.Node.data.frame: Convert a 'data.frame' to a 'data.tree' structure

In data.tree: General Purpose Hierarchical Data Structure

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")
)

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

Arguments

x	The data.frame in the required format.
...	Any other argument implementations of this might need
mode	Either "table" (if x is a data.frame in tree or table format) or "network"
pathName	The name of the column in x containing the path of the row
pathDelimiter	The delimiter used to separate nodes in pathName
colLevels	Nested list of column names, determining on what node levels the attributes are written to.
na.rm	If TRUE, then NA's are treated as NULL and values will not be set on nodes
table	a data.frame in table or tree format, i.e. having a row for each leaf (and optionally for additional nodes). There should be a column called pathName, separated by pathDelimiter, describing the path of each row.
check	Either "check": if the name conformance should be checked and warnings should be printed in case of non-conformance (the default) "no-warn": if the name conformance should be checked, but no warnings should be printed in case of non-conformance (if you expect non-conformance) "no-check" or FALSE: if the name conformance should not be checked; use this if performance is critical. However, in case of non-conformance, expect cryptic follow-up errors
network	A data.frame in network format, i.e. it must adhere to the following requirements: It must contain as many rows as there are nodes (excluding the root, there is no row for the root) Its first and second columns contain the network relationships. This can be either climbing (from parent to children) or descending (from child to parent) Its subsequent columns contain the attributes to be set on the nodes It must contain a single root There are no cycles in the network

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")

data.tree documentation built on Nov. 13, 2023, 1:08 a.m.