gluc

library(data.tree)

#create an example tree
contacts <- Node$new("contacts")
contacts$type <- "root"
jack <- contacts$AddChild("c1")
jack$fullName <- "Jack Miller"
jack$isGoodCustomer <- FALSE
jack$type <- "customer"
jill <- contacts$AddChild("c2")

gluc

#this has now become much simpler:

data(acme)
l <- acme$ToList()
#alternatively, you might prefer the "traditional" generic method:
l <- as.list(acme)

library(rjson)
j <- toJSON(l)
cat(j)

gluc

This gist shows how to convert a nested JSON file to an R data.frame. To do this, it uses jsonlite and data.tree.

The gist contains two examples: one is a bit simpler, the second one a bit more advanced.

Example 1

In the first example, we download all the repos from Hadley Wickham's Github account from https://api.github.com/users/hadley/repos . This JSON contains a nested owner object. The code shows how to convert that in a flat data.frame in three statements:

1. line 5: download
2. line 8: convert to data.tree

gluc

library(data.tree)
library(igraph)
taxonomy <- data.frame(
  children    = LETTERS[2:13],
  parents     = c("A", "A", "B", "B", "C", "C", "C", "E", "E", "G", "H",
                  "H"),
  child.level = c(2, 2, 3, 3, 3, 3, 3,4, 4, 4, 4, 4),
  description = c("w", "i", "z", "v", "j", "u", "q", "g", "b", "c", "t",
                  "o"),
  stringsAsFactors = FALSE

gluc

### This demo calculates and plots a simple decision tree
### It requires the yaml and the ape packages to be installed

library(data.tree)
library(yaml)


#load from file

fileName <- 'jennylind.yaml'

gluc

.Rproj.user
.Rhistory
.RData

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csv <- "
parent,child,share,aDirectShare
A,B,50,50
A,C,69,69
A,D,56,56
B,E,80,50
B,F,72,50
C,G,45,45
D,H,67,56
D,I,35,35

gluc

library(shiny)
library(shinyjs)



# Get table metadata. For now, just the fields
# Further development: also define field types
# and create inputs generically
GetTableMetadata <- function() {
  fields <- c(id = "Id", 

gluc

library(data.tree)

CreateCrossoverRandomTree <- function(n, treeId) {
  tree <- CreateRandomTree(n)
  tree$Set(treeId = treeId)
  tree$Set(id = 1:tree$totalCount)
  return (tree)
}

gluc

#requires data.tree 0.2.2
library(data.tree)
library(yaml)
library(stringr)

fileName <- 'jennylind.yaml'
l <- yaml.load_file(fileName)
jl <- as.Node(l)

#this file contains all the functions referred to from the yaml