data analysis in r for beginners

Data Analysis in .

for BeginnersAlton Alexander

Data Science Consultant

Why R? R is open source like python not like SAS Out of the box R is single machine, in memory

statistical computing engine Download from https://www.r-project.org/

Use an IDE R Studio https://www.rstudio.com/ Revolution Analytics (MSFT) Jython (ipython)

https://www.r-project.org/https://www.rstudio.com/

R studio

Download

Overview

Essential Learning Resources

A new book for learning R

Q: What have you tried and what works?

Topics Data ingestion Manipulation Summary and exploration Writing Reports Interactive visualization and dashboarding Predictive Modeling & Forecasting Big Data Integrations

Demo

Options data

R studio

Data ingestion

Load data Load.csv() library(RJDBC) library(RODBC)

Data Structures and Manipulation

Another major reason for using R Ability to work with data in Data Frames Like pandas in python and data tables in SAS

Reasons for doing data manipulation (munging) Feature extraction ETL Data cleansing Pivots, stack/unstack, aggregate, groupby, reshape

Set Theory

SQL joins and their results

merge, sqldf in Rhttp://www.r-bloggers.com/manipulating-

data-frames-using-sqldf-a-brief-overview/

http://www.r-bloggers.com/manipulating-data-frames-using-sqldf-a-brief-overview/http://www.r-bloggers.com/manipulating-data-frames-using-sqldf-a-brief-overview/http://www.r-bloggers.com/manipulating-data-frames-using-sqldf-a-brief-overview/

Summary and Exploration

Powerful summary functions for programmatically quantifying datasets

Functions include: Summary(), hist(), levels(), aggregate()

Interactive Visualizationand Dashboarding

Shiny from Rstudio Like tableau

Local and server options Much more customizable, more coding, no GUI or

click to edit But you can bring in powerful libraries to build

web apps comparatively fast

Predictive Modeling & Forecasting Examples

Customer segmentation Unsupervised classification

Marketing mix models Explain the coefficients

Attribution modeling Supervised time series of events

Multivariate testing (AB tests with statistical significance, ANOVA)

Lead scoring P2B Models, topic of interest, propensity to buy, expected spend

5 Libraries for Machine LearningAllowing the machine to capture complexity:1. gbm [Gradient Boosting Machine]2. randomForest [Random Forest]3. e1071 [Support Vector Machines]

Taking advantage of high-cardinality categorical or text-data:4. glmnet [Lasso and Elastic-Net Regularized Generalized Linear Models]5. tau [Text Analysis Utilities]

http://cran.r-project.org/web/packages/gbm/http://cran.r-project.org/web/packages/gbm/http://cran.r-project.org/web/packages/randomForest/http://cran.r-project.org/web/packages/randomForest/http://cran.r-project.org/web/packages/e1071/http://cran.r-project.org/web/packages/e1071/http://cran.r-project.org/web/packages/glmnet/http://cran.r-project.org/web/packages/glmnet/http://cran.r-project.org/web/packages/Matrix/http://cran.r-project.org/web/packages/Matrix/

Big Data Integration

Single laptop is often sufficient Millions of rows on a 32GB i7 laptop

Scale using a larger server Often sufficient but has limitations (100s of GB)

Clustered compute engine Algorithm considerations to affect performance

RServer

For datasets that dont fit in memory or for convenience there is a SERVER option A shared compute engine Shares resources Think +100 GB of RAM

Big Data Integration - Frameworks

H2O.ai SparkR Revolution Analytics In DB processing

Applying lead score or segmentation model in real time

Spark, teradata, vertica

Why R? In High Demand Nationally

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