gluc

library(data.tree)

yaml <- "
campus:
  south:
     label: campussouth
  north:
     label: campusnorth
"

gluc

library(yaml)
library(data.tree)
yaml <- "
functions:
  currbalfun: |
    function(node) {
      Aggregate(deal, 'currbal', function(bal) sum(bal, na.rm = TRUE))
    } 
  
TC_1:

gluc

library(data.tree)
library(yaml)


yaml <- "
node1:
  value: 2
  fnct: >
    function(x) {
      2 * x

gluc

#How to plot data.tree to PDF?
#This was tested with data.tree 0.3.6

#Various options

library(data.tree)
data(acme)

# 1. ape package
library(ape)

gluc

tmp <- tempfile(fileext = ".log")

x <- runif(10e5)
f <- function(x) c(mean(x), median(x), sd(x))
f2 <- function(x) {
  c(sum(x) / length(x),
    median.default(x),
    sqrt(sum((x - sum(x) / length(x))^2L) / (length(x) - 1L)))
}
Rprof(tmp, interval = 0.01)

gluc

---
title: "Untitled"
author: "cg"
date: "Monday, October 19, 2015"
output: html_document
---



```{r}

gluc

#CRUD for multiple tables
library(shiny)
library(shinyjs)



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

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

gluc

library(data.tree)

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

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",