Agenda

• Introduction
• What is R?
• Capabilities of R
• R Packages
• How we used R in our project
• R Visualisations
• A Bit About Shiny
• A Bit About Slidify
• Questions

About Ourselves

About our Project

Definition

• "R is a free software environment for statistical computing and graphics. It compiles and runs on a wide variety of UNIX platforms, Windows and MacOS" - The R Project Organisation

• "R is a data analysis software; R is a programming language; R is an environment for statistical analysis; R is open-source; R is a Community" - www.inside-r.org

• "While R was initially a statistical computing language, in 2012 you could call it a complete analytical environment" - R for Business Analytics by Ajay Ohri

• "R is extremely powerful" - Creators of R also called Revolution Analytics

The Original GUI

The Advanced GUI

How to Install

For Installation instructions:
Please Visit: http://theRblabber.wordpress.com/2013/05/21/introduction/

R Features

• The R Language is widely used among statisticians and data miners for developing statistical software and analysing data.

• The capabilities of R are extended through user-created packages, which allow statistical techniques, graphical devices, import/export, reporting tools etc

R Data Types

R has a wide variety of data types including scalars, vectors (numerical, character, logical), matrices, data frames, and lists.

R Data Types - Vectors

a <- c(1, 2, 5.3, 6, -2, 4)  # numeric vector
a

## [1]  1.0  2.0  5.3  6.0 -2.0  4.0

b <- c("one", "two", "three")  # character vector
b

## [1] "one"   "two"   "three"

R Data Types - Matrices

y <- matrix(1:8, nrow = 2, ncol = 4)  # generates 2 x 4 numeric matrix

##      [,1] [,2] [,3] [,4]
## [1,]    1    3    5    7
## [2,]    2    4    6    8

z <- matrix(1:6, nrow = 2, ncol = 3, byrow = TRUE)  # generates 2 x 3 numeric matrix

##      [,1] [,2] [,3]
## [1,]    1    2    3
## [2,]    4    5    6

R Data Types - Data Frames

d <- c(1, 2, 3, 4)  # Assigning numerical vector to d
e <- c("red", "white", "red", NA)  # Assigning character vector to e
f <- c(TRUE, TRUE, TRUE, FALSE)  # Assigning logical vector to f
mydata <- data.frame(d, e, f)  # Creating Data Frame from vectors

##   ID Color Passed
## 1  1   red   TRUE
## 2  2 white   TRUE
## 3  3   red   TRUE
## 4  4  <NA>  FALSE

R Functions

Almost everything in R is done through functions. Some of the built-in functions are:

Introduction

• More than 5000 packages available today.

• More than one package for one type of Analytical Task

• There is a high probability that the algorithm you are looking for is already in the repository.

• You can build your own algorithm into a package.

• Packages: Packages are collection of R functions, data and compiled code in a well-defined format. The directory where packages are stored is called the library. R comes with a standard set of packages. Others are available for download and installation.

Most Common Packages

Some of the most used packages.

What we used?

• Rattle
• JGR
• RMySQL and ROracle
• googleVis
• shiny
• slidify
• Dependent Packages

How to Install?

To learn how to install a package
Please Visit: http://theRblabber.wordpress.com/2013/05/21/getting-started/

Why?

We chose R over other analytical softwares because:

• R is an Open Source Project.

• Good integration with Programming Language.

• Graphics and Data Visualisation.

• New and Upcoming.

• Frequent Package Releases and Upgrades.

How?

Introduction - googleVis

• googleVis is a package for R and provides an interface between R and the Google Chart Tools

• The functions of the package allow users to visualise data with the Google Chart Tools without uploading their data to Google

• The output of googleVis functions is html code that contains the data and references to JavaScript functions hosted by Google

• Create wrapper functions in R which generate html files with references to Google's Chart Tools API

• Run demo(googleVis) to see examples of all charts and read the vignette for more details.

Example - googleVisTable - Tabular Chart

A simple Table with Data.

require(googleVis)  ##Load Library and Data Sets

## Loading required package: googleVis

## Welcome to googleVis version 0.4.3
## 
## Please read the Google API Terms of Use before you use the package:
## https://developers.google.com/terms/
## 
## Type ?googleVis to access the overall documentation and
## vignette('googleVis') for the package vignette. You can execute a demo of
## the package via: demo(googleVis)
## 
## More information is available on the googleVis project web-site:
## http://code.google.com/p/google-motion-charts-with-r/
## 
## Contact: <rvisualisation@gmail.com>
## 
## To suppress the this message use:
## suppressPackageStartupMessages(library(googleVis))

table1 <- gvisTable(Population, options = list(width = 1000, height = 250))  ## Assign gvisTable function to table1
print(table1, tag = "chart")  ## Plot table1 (only for slidify)

Example - gvisColumnChart - Column Chart

A Simple Column Chart - Suncorp Case Study

print(tableRC, "chart")

aa <- read.csv("Minmaxriskperstate.csv", header = TRUE, sep = ",")

Example - gvisColumnChart - Column Chart

print(RiskChart, "chart")

Example - gvisScatterChart - Scatter Chart

Example - gvisScatterChart - Scatter Chart

ScatterWomen <- gvisScatterChart(women, options = list(pointSize = 4, vAxis = "{title:'weight (lbs)'}", 
    hAxis = "{title:'height (in)'}", width = 500, height = 430))

Example - Motion Chart (Time Variance)

M5 <- gvisMotionChart(Fruits, "Fruit", timevar = "Year", options = list(height = 350))

Example - gvisTreeMap - Tree Map Chart (Heirarchical View)

Example - gvisTreeMap - Tree Map Chart (Heirarchical View)

treeRegions <- gvisTreeMap(Regions, idvar = "Region", parentvar = "Parent", 
    sizevar = "Val", colorvar = "Fac", options = list(showScale = TRUE, width = 600, 
        height = 350))

Example - gvisOrgChart - Organizational Chart (Heirarchical View)

Org <- gvisOrgChart(Regions, idvar = "Region", parentvar = "Parent", tipvar = "TipVal", 
    options = list(allowCollapse = TRUE, allowHTML = TRUE))

Example - gvisGeoMap - Map Chart (Markers)

Example - gvisGeoChart - Map Chart (Regions)

GeoMap <- gvisGeoChart(am, locationvar = "STATE", colorvar = "TOTALCOUNT", options = list(region = "AU", 
    displayMode = "regions", resolution = "provinces", height = 300, width = 1000, 
    gvis.editor = "Edit Me!"))

Build Web Applications

• Easily build your reports into a dynamic web application.

• Customize your reports.

• Let users choose input parameters using sliders, drop-downs etc.

• No HTML or Java Script necessary. Only a little bit of R knowledge required to turn your analysis into interactive applications.

A package to create HTML5 Slides from R

• Create Dynamic Powerful Slideshows.

• Automatically include your dynamic charts and maps without using another tool.

• Publish directly on the web.

• HTML Coding

• In its development stage, but a very powerful tool.

  
  This presentation was completely built on R

Big Thanks to-

Ms. Richi Nayak (Project Supervisor, Motivator)

Suncorp Board (Industry Partners)

Eric Tang (Big Data Lab Expert)

Ajay Ohri (Author of R for Business Analytics)

Ramnath Vaidyanathan (Creator of Package Slidify)

Rest of the Team Lin Chen, Sejung (Group Members)

Thank you for attending our presentation