A Workshop on R

Pre- Placement Workshop
in R and Analytics
Delhi School of Economics 2014
Ajay Ohri

Agenda
• Try and learn R in 12 hours

Agenda
• Get an introduction to Analytics

Agenda
• Be better skilled for Analytics as a career

Agenda
• Be better skilled for Analytics as a career (?)

Training Plan
• DAY 1
– Session 1 -2.5 hours
• DAY 2
– Session 1-2.5 hours

Instructor
• Author of R for Business Analytics
• Author of R for Cloud Computing ( An
approach for Data Scientists)
• 10+ yrs in Analytics and 6+ years in R
• Founder, Decisionstats.com

The Audience
Breakup – Demographics and Background

Expectations from each other
• From Instructor
– Your turn to speak

Expectations from each other
• From Instructor
• From Audience
– mobile phones should be kindly switched off
• Yes, this includes Whatsapp
– Ask Questions at end of session
– Take Notes

Day 1 Session 1
– Introductions
• Introduction to Analytics
• Introduction to R
• Interfaces in R
– Demos in R (Maths, Objects,etc)
• Break 1-
– Installation, Trouble Shooting, Questions

Day 1 Session 2
– Recap
• Input of Data
• Inspecting Data Quality
• Investigating Data Issues
– Demos in R
• Data Input,
• Data Quality,
• Data Exploration)
• Break 2-
– Questions

Day 2 Session 1
– Revision
• Exploring Data
• Manipulating Data
• Visualization of Data
• Demos in R
• Data Exploration,
• Data Manipulation,
• Data Visualizations
• Break 1
– Questions

Day 2 Session 2
– Recap
• Data Mining
• Regression Models
• Advanced Topics
• Demos in R
• Data Mining,
• Model Building,
• Advanced Topics
• Summary and Conclusion
• Break 2
– Questions

Analytics
• What is analytics?
• Where is it used?
• How is it used?
• What are some good practices?

Analytics
• What is analytics? – Study of data for helping
with decision making using software
• How is it used?

Analytics
• Where is it used? – Industries (like Pharma,
BFSI, Telecom, Retail)
• How is it used? –Use statistics and software

Analytics
• How is it used?
• What are some good practices? –
– Learn one new thing extra from your
competition every day. This is a fast moving field.
– Etc.

Other Analytics Software
• SAS (Base) et al
• JMP
• SPSS
• Python
• Octave
• Clojure
• Julia(?)

What is R?
http://www.r-project.org/
• Language
– Object oriented
– Open Source
– Free
– Widely used
the concept of "objects" that have data fields(attributes that describe the object)
and associated procedures known as methods. Objects, which are
usually instances of classes, are used to interact with one another to design
applications and computer programs

Pre Requisites
• Installation of R
http://cran.rstudio.com/bin/windows/base/
• R Studio
• R Packages

Pre Requisites
– Rtools
– http://cran.rstudio.com/bin/windows/Rtools/
• R Studio
• R Packages

Pre Requisites
– RTools
• R Studio
http://www.rstudio.com/products/rstudio/download/
• R Packages

Pre Requisites
– RTools
• R Studio
• R Packages
about eight packages supplied with the R distribution and many more are available through the CRAN family of Internet
sites covering a very wide range of modern statistics.

Pre Requisites
– RTools
• R Studio
• R Packages
install.packages(),
update.packages(),
library()
Packages are installed once, updated periodically, but loaded every time

Pre Requisites
• R
• R Studio
• R Tools (for Windows)
• JAVA (JRE)
– R Packages (need Internet connection)
– Rcmdr
• All packages asked at startup
• Epack plugin
• KMggplot2plugin
– rattle
• A few packages that are asked when using rattle
• GTK+ (needs internet)
– Deducer
– ggmap
– Hmisc
– arules
– MASS

Interfaces to R
• Console
Default
Customization
• IDE
• GUI

Demo-
Basic Math on R Console
• +
• -
• Log
• Exp
• *
• /
• ()
• mean
• sum
• sd
• log
• median
• exp

Demo-
Basic Math on R Console
• +
• -
• Log
• Exp
• *
• /
• ()
Hint- Ctrl +L clears screen

Demo-
Basic Objects on R Console
• +
• -
• Log
• Exp
• *
• /
• ()
Functions-ls()
– what objects are here
rm(“foo”) removes object named foo
Assignment
Using = or -> assigns object names to values
Hint- Up arrow gives you last
typed command

Functions and Loops
• Loops
for (number in 1:5){ print (number) }

Functions and Loops
• Function
functionajay=function(a)(a^2+2*a+1)
Hint: Always match brackets
Each ( deserves a )
Each { deserves a }
Each [ deserves a ]

Demo-
Basic Objects on R Console
• +
• -
• Log
• Exp
• *
This is made more clear in
next slide
Functions-class()
gives class
dim() gives dimensions
nrow() gives rows
ncol() gives columns
length() gives length
str() gives structure
Hint- Up arrow gives you last
typed command

Demo-
Datasets on R Console
•
Hint- use data() to list all loaded
datasets

Demo-
Datasets on R Console
•
Hint- use data() to list all loaded
datasets
library(FOO) loads package “FOO”

R- Basic Functions
– ls()
– rm()
– str()
– summary()
– getwd()
– setwd()
– dir()
– read.csv()

Statistical formats
• read.spss from foreign package
• read.sas7bdat from sas7bdat package

From Databases
The RODBC package provides access to databases through
an ODBC interface.
The primary functions are
• odbcConnect(dsn, uid="", pwd="") Open a connection
to an ODBC database
• sqlFetch(channel, sqltable) Read a table from an ODBC
database into a data frame
Hint- a good site to learn R
http://www.statmethods.net

From Web (aka Web Scraping)
• readlines Hint : R is case sensitive
readlines is not the same as readLines
Hint : Use head() and tail() to inspect objects
Other packages are XML and Curl
Case Study- http://decisionstats.com/2013/04/14/using-r-for-cricket-analysis-rstats/

Inspecting Data Quality
• head()
• tail()
• names()
• str()
• objectname[I,m]
• objectname$variable
Hint- Try this code please
data(mtcars)
head(mtcars,10)
tail(mtcars,5)
names(mtcars)
str(mtcars)
mtcars[1,]
mtcars[,2]
mtcars[2,3]
mtcars$cyl

Inspecting Data Quality: Demo
•

Data Selection
• object[l,m] gives the value in l row and m
column
• object[l,] will give all the values in l row
• object$varname gives all values of varname
• subset helps in selection

Data Selection: Demo
Questions- How do I use multiple conditions (AND OR)
Can I do away with subset function
How do I select random sample
Useful Link- http://decisionstats.com/2013/11/24/50-functions-to-clear-a-basic-interview-for-business-
analytics-rstats/

Good coding practices
• Use # for comment
• Use git for version control
• Use Rstudio for multiple lines of code

Functions in R
• custom functions
• source code for a function
• Understanding help ? , ??

Packages in R
• CRAN
• CRAN Views
• R Documentation

Documentation in R
• Help ? And ??
• CRAN Views
• Package Help
• Tips for Googling
– Stack Overflow
– Email Lists
– Twitter
– R Bloggers

Interfaces to R
• Console
• IDE
R Studio
• GUI
Graphical User
Interface

Graphical Interfaces to R
• R Commander
• Rattle
• Deducer

Demo
R Commander – 3D Graphs

Installation of Rattle
• GTK+ Installation Necessary
• Install other packages when prompted

Installation Deducer (with JGR)

Overview of Deducer (with JGR)

Demo Deducer
• data()
• data(mtcars)

Data Exploration
• summary()
• table()
• describe() (Hmisc)
• summarize()(Hmisc)
Hint- Try this code please
summary(mtcars)
table(mtcars$cyl)
library(Hmisc)
describe(mtcars)
summarize(mtcars$mpg,mtcars$cyl,mean)
CLASS WORK-
•Use table command for two variables
•Summarize mtcars$mpg for two variables (cyl , gear)
•Try and find min and max for the same

Data Exploration
• missing values are represented by NA in R
• Demo
– is.na
– na.omit
– na.rm

Data Visualization
Notes-
Explaining Basic Types of Graphs
Customizing Graphs
Graph Output
Advanced Graphs
Facets,
Grammar of Graphics
Data Visualization Rules

Data Manipulation Demo
Notes-
1. gsub
2. gsub with
escape
3. as operator
4. is operator

Text Manipulation
Functions-nchar
substr
paste

Date Manipulation
Use ? help generously
Hit escape to escape the + signs
+ signs occur due to unclosed quotes or brackets
Class Work
What is your age in days as of today?
What is your age in weeks as of today?
Hint-
> age2=difftime(Sys.Date(),dob2,units='weeks')
> age2
Time difference of 1959.286 weeks

Data Output
• Graphical Output
• Numerical Output (aggregation)

Data Output
• Graphical Output

Data Output
• Use objects to summarize
• Use write.csv
• Use setwd() to set location of output

Econometrics
Coming up
Regression

Regression
Notes-
Correlation is not causation
How do we determine which is dependent
and which are independent variables

Lies True Lies and Statistics
• Anscombe -case study

Regression Recap
• cor
• lm
• anova
• summary and plot of lm object
• residuals
• p value
– vif
– heteroskedascity
– outliers

Propensity Modeling in Industry
• Response Rates
• Lift
• Test and Control groups

Data Mining
• Rattle
– association analysis
– cluster analysis
– modeling

Data Mining
• Brief Introduction
– Affinity analysis is a data analysis and data mining technique that
discovers co-occurrence relationships among activities performed by (or
recorded about) specific individuals or groups. In general, this can be
applied to any process where agents can be uniquely identified and
information about their activities can be recorded. In retail, affinity
analysis is used to perform market basket analysis, in which retailers seek
to understand the purchase behavior of customers. This information can
then be used for purposes of cross-selling and up-selling,

Rattle
– market basket analysis
– Market basket analysis might tell a retailer that customers often
purchase shampoo and conditioner together, so putting both items on
promotion at the same time would not create a significant increase in
revenue, while a promotion involving just one of the items would likely
drive sales of the other

Rattle
– association rules
– if butter and bread are bought, customers also buy milk
Example database with 4 items and 5 transactions
transactio
n ID
milk bread butter beer
1 1 1 0 0
2 0 0 1 0
3 0 0 0 1
4 1 1 1 0
5 0 1 0 0

Rattle
– the itemset (milk,bread->butter) has a support of 20% since it occurs in 20% of all
transactions (1 out of 5 transactions).
– the itemset (milk,bread->butter) has a confidence of 50% since it occurs in 50% of all
such transactions (1 out of 2 transactions).
–

Rattle

Regression Models
• lm function
• Understanding output
• Diagnostics
– homoskedasticity
– Multicollinearity
– p value
– Residuals

Advanced Topics :Demos
• Time Series Analysis (use epack plugin)
http://decisionstats.com/2010/10/22/doing-time-series-using-a-r-gui/

• Advanced Data Visualization ( kmggplot2
plugin)
http://decisionstats.com/2012/05/21/new-rcommander-with-ggplot-rstats/

Social Network Analysis (sna)
Facebook
http://decisionstats.com/2014/05/10/analyzing-facebook-networks-using-rstats/
Twitter
http://www.slideshare.net/ajayohri/twitter-analysis-by-kaify-rais

• Spatial Analysis
• ggmap demo
• http://decisionstats.com/2013/08/19/the-wonderful-ggmap-package-for-spatial-analysis-in-r-rstats/
• rmaps
• http://rcharts.io/viewer/?9223554#.Uw4hOPmSySp

Thank You
• http://linkedin.com/in/ajayohri
• ohri2007@gmail.com

A Workshop on R

Recomendados

Recomendados

Mais conteúdo relacionado

Mais procurados

Mais procurados (20)

Semelhante a A Workshop on R

Semelhante a A Workshop on R (20)

Mais de Ajay Ohri

Mais de Ajay Ohri (20)

Último

Último (20)

A Workshop on R