rCh10.md

rm(list=ls())
source('runDir.R')

library('ggplot2')
runDir('../CodeExamples/c10_Documentation_and_deployment',
      '../Buzz',last=221)

[1] "############################### start  204 Fri Jun 17 10:43:36 2016"
[1] "#####  running  /Users/johnmount/Documents/work/PracticalDataScienceWithR/zmPDSwR/RunExamples/replacements/00204_informalexample_10.1_of_section_10.2.1.R"
[1] "#####   in directory ../Buzz"

> # can't "knit" inside a knitr document
[1] "############################### end  204 Fri Jun 17 10:43:36 2016"
[1] "############################### start  211 Fri Jun 17 10:43:36 2016"
[1] "#####  running  ../CodeExamples/c10_Documentation_and_deployment/00211_example_10.7_of_section_10.3.1.R"
[1] "#####   in directory ../Buzz"

> # example 10.7 of section 10.3.1 
> # (example 10.7 of section 10.3.1)  : Documentation and deployment : Using comments and version control for running documentation : Writing effective comments 
> # Title: Example code comment 
> 
> #    Return the pseudo logarithm of x, which is close to
> # sign(x)*log10(abs(x)) for x such that abs(x) is large
> # and doesn't "blow up" near zero.  Useful
> # for transforming wide-range variables that may be negative
> # (like profit/loss).
> # See: http://www.win-vector.com/blog
> #  /2012/03/modeling-trick-the-signed-pseudo-logarithm/
> #    NB: This transform has the undesirable property of making most
> # signed distributions appear bimodal around the origin, no matter
> # what the underlying distribution really looks like.
> # The argument x is assumed be numeric and can be a vector.
> pseudoLog10 <- function(x) { asinh(x/2)/log(10) }
[1] "############################### end  211 Fri Jun 17 10:43:36 2016"
[1] "############################### start  212 Fri Jun 17 10:43:36 2016"
[1] "#####  running  ../CodeExamples/c10_Documentation_and_deployment/00212_example_10.8_of_section_10.3.1.R"
[1] "#####   in directory ../Buzz"

> # example 10.8 of section 10.3.1 
> # (example 10.8 of section 10.3.1)  : Documentation and deployment : Using comments and version control for running documentation : Writing effective comments 
> # Title: Useless comment 
> 
> #######################################
> # Function: addone
> # Author: John Mount
> # Version: 1.3.11
> # Location: RSource/helperFns/addone.R
> # Date: 10/31/13
> # Arguments: x
> # Purpose: Adds one
> #######################################
> addone <- function(x) { x + 1 }
[1] "############################### end  212 Fri Jun 17 10:43:36 2016"
[1] "############################### start  213 Fri Jun 17 10:43:36 2016"
[1] "#####  running  ../CodeExamples/c10_Documentation_and_deployment/00213_example_10.9_of_section_10.3.1.R"
[1] "#####   in directory ../Buzz"

> # example 10.9 of section 10.3.1 
> # (example 10.9 of section 10.3.1)  : Documentation and deployment : Using comments and version control for running documentation : Writing effective comments 
> # Title: Worse than useless comment 
> 
> # adds one
> addtwo <- function(x) { x + 2 }
[1] "############################### end  213 Fri Jun 17 10:43:36 2016"

rm(list=ls())
source('runDir.R')

library('ggplot2')
runDir('../CodeExamples/c10_Documentation_and_deployment',
      '../Buzz',first=225)

[1] "############################### start  225 Fri Jun 17 10:43:36 2016"
[1] "#####  running  ../CodeExamples/c10_Documentation_and_deployment/00225_example_10.18_of_section_10.4.2.R"
[1] "#####   in directory ../Buzz"

> # example 10.18 of section 10.4.2 
> # (example 10.18 of section 10.4.2)  : Documentation and deployment : Deploying models : Deploying models by export 
> # Title: Exporting the random forest model 
> 
> load('thRS500.Rdata')     # Note: 1 

> library(randomForest)     # Note: 2 

randomForest 4.6-12

Type rfNews() to see new features/changes/bug fixes.


Attaching package: 'randomForest'

The following object is masked from 'package:ggplot2':

    margin


> extractTrees <- function(rfModel) {   # Note: 3 
    ei <- function(i) {
       ti <- getTree(rfModel,k=i,labelVar=T)
       ti$nodeid <- 1:dim(ti)[[1]]
       ti$treeid <- i
       ti
    }
    nTrees <- rfModel$ntree
    do.call('rbind',sapply(1:nTrees,ei,simplify=F))
 }

> write.table(extractTrees(fmodel),     # Note: 4 
    file='rfmodel.tsv',row.names=F,sep='\t',quote=F)

> # Note 1: 
> #   Load the saved buzz workspace (includes the 
> #   random forest model). 
> 
> # Note 2: 
> #   Load the random forest library (loading the 
> #   workspace doesn’t load the library). 
> 
> # Note 3: 
> #   Define a function that joins the tree tables 
> #   from the random forest getTree() method into one 
> #   large table of trees. 
> 
> # Note 4: 
> #   Write the table of trees as a tab-separated 
> #   values table (easy for other software to 
> #   read). 
> 
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