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Learning Bayesian Models with R

You're reading from  Learning Bayesian Models with R

Product type Book
Published in Oct 2015
Publisher Packt
ISBN-13 9781783987603
Pages 168 pages
Edition 1st Edition
Languages
Author (1):
Hari Manassery Koduvely Hari Manassery Koduvely
Profile icon Hari Manassery Koduvely

Table of Contents (16) Chapters

Learning Bayesian Models with R
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
1. Introducing the Probability Theory 2. The R Environment 3. Introducing Bayesian Inference 4. Machine Learning Using Bayesian Inference 5. Bayesian Regression Models 6. Bayesian Classification Models 7. Bayesian Models for Unsupervised Learning 8. Bayesian Neural Networks 9. Bayesian Modeling at Big Data Scale Index

SparkR


Similar to RHadoop, SparkR is an R package that allows R users to use Spark APIs through the RDD class. For example, using SparkR, users can run jobs on Spark from RStudio. SparkR can be evoked from RStudio. To enable this, include the following lines in your .Rprofile file that R uses at startup to initialize the environments:

Sys.setenv(SPARK_HOME/.../spark-1.5.0-bin-hadoop2.6")
#provide the correct path where spark downloaded folder is kept for SPARK_HOME 
.libPaths(c(file.path(Sys.getenv("SPARK_HOME"),""R",""lib"),".libPaths()))

Once this is done, start RStudio and enter the following commands to start using SparkR:

>library(SparkR)
>sc <- sparkR.init(master="local")

As mentioned, as of the latest version 1.5 when this chapter is in writing, SparkR supports limited functionalities of R. This mainly includes data slicing and dicing and summary stat functions. The current version does not support the use of contributed R packages; however, it is planned for a future release...

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