Introduction to Oracle R for Big Data Analysis

Introduction to R
For Big Data Analysis
Sunday,
October

25,
2015

2:30pm
–
3:15
pm

Moscone
South
–
303

Raastech, Inc.
2201 Cooperative Way, Suite 600
Herndon, VA 20171
+1-703-884-2223
info@raastech.com

SIG Meetings at Oracle OpenWorld
All meetings will be held in User Group Pavilion,
Meeting Room, Moscone South
Monday, October 26
•  IOUG Cloud Computing SIG Meet-Up: 10:00 a.m.—11:00 a.m.
•  IOUG Oracle Enterprise Manager SIG: 5:00 p.m.—6:00 p.m. *Location: OTN Lounge, Moscone South*
Tuesday, October 27
•  IOUG IoT SIG: 10:30 a.m.—11:30 a.m.
•  IOUG BIWA SIG: 11:30 a.m.—12:30 p.m.
Wednesday, October 28
•  IOUG Exadata SIG: 10:30 a.m.—11:30 a.m.
•  IOUG RAC SIG: 1:00 p.m.—2:00 p.m.
•  IOUG Oracle 12c SIG: 2:00 p.m.—3:00 p.m.

© Raastech, Inc. 2015 | All rights reserved. Slide 3 of 51@Raastech
About Me
§  Harold Dost III @hdost
§  7+ years of Oracle Middleware experience
§  OCE (SOA Foundation Practitioner)
§  Oracle ACE Associate
§  From Michigan
§  blog.raastech.com

About Raastech
§  Small systems integrator founded in 2009
§  Headquartered in the Washington DC area
§  Specializes in Oracle Fusion Middleware
§  Oracle Platinum Partner – 1 in 3,000 worldwide
§  Oracle SOA Specialized – 1 in 1,500 worldwide
§  Oracle ACE – 2 of 500 worldwide
§  100% of consultants are Oracle certified
§  100% of consultants present at major Oracle conferences
§  100% of consultants have published books, whitepapers, or articles

Outline
1.  Getting Started
§  Installing R
§  Installing Tools
§  Getting Data
2.  Understanding R
§  Data Types
§  Functions
§  Data Import Mechanisms

Outline (Cont.)
3.  Manipulating Data (Large Data Sets)
§  Deriving Simple Statistics
§  Graphing
4.  Demo
5.  Incorporating into an Enterprise
§  Using Enterprise Data Sources
§  Running R in your environment.
§  Familiarize with Oracle's R offerings

Why use R?
§  Strong Community
§  Including packages
§  Targeted for Statistical Analysis
§  Less code

Know CRAN
§  Comprehensive
§  R
§  Archive
§  Network

Installing R
§  Windows
§  Mac
§  Linux

Installing R
§  Windows https://cran.r-project.org/bin/windows/
§  Mac https://cran.r-project.org/bin/macosx/
§  Linux https://cran.r-project.org/bin/linux/

Development Tools
§  Rstudio - http://www.rstudio.com/products/rstudio/
§  Open Source Edition
§  Commercial License - $995
§  Eclipse
§  Sublime, TextPad, Other Simple Text Editors,…

Installing Packages
§  Anything From CRAN
§  Anywhere
install.packages(c(“first”, “second”))
> sudo R CMD INSTALL package-version.tar.gz

Data Types
§  Vectors
§  Matrices
§  Arrays
§  Data Frames
§  Lists
§  Factors

Everything is a Vector
§  Scalars are vectors of 1
§  Basic Types (Atomic Vectors)
§  Logical
§  Integer
§  Real
§  Complex
§  String/Character
§  Raw

Matrices and Arrays
§  Require All the Same Type
§  Matrix Functions:
§  det(), t() , solve(), diag()

Data Frames
§  Special List
§  Columns can have different types

Special Values
§  Infinity, Positive and Negative: Inf and –Inf
§  Not A Number: NaN
§  Not Available: NA
§  Complex Numbers, 1+9i

Use Case for Infinities
§  Finding Maximums and Minimums
§  Placeholder values when others won’t work

Not a Number (NaN)
§  In means something went wrong somewhere
§  A missing argument
§  Invalid number
§  Check for with is.nan(x) to prevent leaking
§  Don’t use “==“ to find NaN, it will only give more NaN

Assigning NaN
> a = NaN
> a
[1] NaN

Adding NaN
Adding NaN
> b = 1
> c = a + b
> c
[1] NaN
When
adding
a
number
to
NaN
“Not
a
Number”
you
will
get

NaN.

Comparing NaN to Regular Number
> d = b == c
> d
[1] NA
When
comparing
a
number
to
NaN
“Not
a
Number”
you
will

get
NA.

Comparing NaN to NaN
> e = c == a
> e
[1] NA
When
comparing
NaN
“Not
a
Number”
to
NaN
you
will
get
NA.

Detecting NaN
> a
[1] NaN
> is.nan(a)
[1] TRUE
> is.na(a)
[1] TRUE
Since
NaN
aren’t
proper
numbers,
special
funcHons
must
be

used
to
detect
them.
They
are
the
result
of
math
gone
wrong.

Detecting NA
> e = c == a
> e
[1] NA
> is.nan(e)
[1] FALSE
> is.na(e)
[1] TRUE
Just
as
with
NaN
special
funcHons
must
be
used,
but
NA

generally
indicates
that
there
is
missing
informaHon

Operators
§  Assignment ( ->, <-)
§  Addition (+)
§  Subtraction (–)
§  Division (/)
§  Multiplication (*)
§  Exponent (^)
§  Parentheses ( (, ) )

Math Functions
§  max()
§  min()
§  log()
§  sqrt()

Deriving Simple Statistics
§  Minimum
§  Maximum
§  Median
§  Arithmetic Mean
§  Function estimation
§  Linear
§  Log
§  Exponential
§  R-Values
§  Standard Deviation

How to define your own functions
firstfunction <- function(arg1, arg2, ... ){
statements
return(someoutput)
}

Twitter Example
§  First Install the Package
install.packages(c("twitteR”))

Twitter Example
§  Authenticate
consumer = "CONSUMER KEY"
secret = "SECRET KEY"
setup_twitter_oauth(consumer,secret)

Twitter Example
§  Get Trend Locations
§  The resulting WOEID (Where on Earth ID) can be
chosen
availableTrendLocations()

Twitter Example
§  Get Trends
trends = getTrends(SOMEWOEID)

Twitter Example
§  Retrieve Tweets
tweets <- searchTwitter(trends[XX,XX],n=1500)
tweetdf <- do.call("rbind",lapply(tweets,as.data.frame))

Twitter Example
§  Filter
§  complete.cases is used to check for NA and NaN
numbers
tweetdf <- tweetdf[complete.cases(tweetdf[,15]),]
tweetdf <- tweetdf[tweetdf[,15] != 0,]

Twitter Example
§  Simplify the dataframe
simpledf <- tweetdf[c("screenName","longitude","latitude")]

Twitter Example
§  Create Matrix from Dataframe
tweetMatrix <- data.matrix(simpledf[2:3],rownames.force = FALSE)

Twitter Example
§  Plot the Latitude and Longitude
plot(tweetMatrix)

Graphing
§  Image
§  Contour
§  Box Chart

K-Means
§  Essentially a search algorithm
§  Divides a dataset into k-clusters

Time Series
§  Stock Quotes
§  Infection Incidents
§  Gas Prices
§  Audio
§  Etc.
Source:
hKp://www.loc.gov/pictures/resource/hec.23488/

Data Sources
NASA - https://data.nasa.gov
Quandl - https://www.quandl.com

Time Series Analysis
§  Regression
§  Forecasting
§  Time Frequency (FFTs)
Source:
hKp://groups.csail.mit.edu/netmit/sFFT/algorithm.html

Using Enterprise Data Sources
§  Database
§  Streams
§  Files
§  Etc.

Oracle R Distribution
§  Available on Oracle Public Yum
§  Enhanced dynamic Library loading
§  Enterprise Support Available
§  Oracle Advanced Analytics
§  Oracle Linux
§  Oracle Big Data Appliance
§  http://www.oracle.com/technetwork/database/database-technologies/r/r-
distribution/overview/index.html

ROracle
§  Open Source Package
§  Maintained by Oracle
§  Uses OCI Interface to interact with databases
§  http://www.oracle.com/technetwork/database/database-technologies/r/r-
technologies/overview/index.html

Oracle R Advanced Analytics for Hadoop
§  Component of the Oracle Big Data
Software Connectors Suite, an
option for the BDA
§  Provides abstraction from HiveQL
through R just as in Oracle R
Enterprise does for SQL
§  http://www.oracle.com/technetwork/database/
database-technologies/bdc/r-advanalytics-for-
hadoop/overview/index.html

Oracle R Enterprise
§  Component of the Oracle Advanced
Analytics Option on Oracle Database EE
§  Allows use of R in the database without SQL
§  Save R Objects in the database
§  Easily Integrate with OBIEE
§  http://www.oracle.com/technetwork/database/database-
technologies/r/r-enterprise/overview/index.html

Contact Information
§  Harold Dost III
§  Principal Consultant
§  @hdost
§  harold.dost@raastech.com

Resources
§  https://en.wikibooks.org/wiki/Statistical_Analysis:_an_Introduction_using_R/R_basics
§  http://www.r-project.org/
§  https://docs.oracle.com/cd/E57012_01/doc.141/e56973/toc.htm
§  http://cran.r-project.org/web/packages/akmeans/index.html
§  http://cran.r-project.org/web/packages/twitteR/index.html
§  http://en.wikipedia.org/wiki/K-means_clustering
§  http://www.rdatamining.com/examples/kmeans-clustering
§  http://blog.revolutionanalytics.com/2009/02/how-to-choose-a-random-number-in-r.html
§  https://www.packtpub.com/books/content/text-mining-r-part-2
§  http://www.eia.gov/totalenergy/data/monthly/index.cfm#consumption

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