Analyzing Big Data with R Programming

48 Ratings

Course Number: RPROG-112

Duration: 4 days (26 hours)

Format: Live, hands-on

Big Data with R Training Overview

Accelebrate's Analyzing Big Data with R Programming training teaches attendees how to use In-memory/on-disk, distributed analysis using H20, Hadoop, and Apache Spark, and how to integrate Microsoft Machine Learning Server and R.

Location and Pricing

Accelebrate offers instructor-led enterprise training for groups of 3 or more online or at your site. Most Accelebrate classes can be flexibly scheduled for your group, including delivery in half-day segments across a week or set of weeks. To receive a customized proposal and price quote for private corporate training on-site or online, please contact us.

In addition, some Programming courses are available as live, online classes for individuals.

Objectives

  • Understand how R works with big data sets
  • Manage big data in memory with data.table
  • Conduct exploratory data analysis with data.table
  • Learn big data management strategies such as sampling, chunk-and-pull, and pushing compute to the database
  • Run SQL queries directly against R dataframes using DuckDB
  • Use DuckDB as an out-of memory backend for R dataframes
  • Perform machine learning operations using mlr3
  • Interface with Apache Spark using Sparklyr or SparkR
  • Use H2O for data munging and machine learning

Prerequisites

In addition to their professional experience, students who attend this course should have:

  • Programming experience using R, and familiarity with common R packages
  • Knowledge of common statistical methods and data analysis best practices
  • Basic knowledge of the Microsoft Windows operating system and its core functionality

Outline

Expand All | Collapse All

Introduction: 
  • Does R work with big datasets?
  • What challenges does big data introduce when using R?
  • ETL and descriptive data tasks
  • Modeling tasks, optimization challenges
In-memory Big Data: Data.table
  • Why do we need data.table?
  • The i and the j arguments in data.table
  • Renaming columns
  • Adding new columns
  • Binning data (continuous to categorical)
  • Combining categorical values
  • Transforming variables
  • Group-by functions with data.table
  • Chaining commands with data.table
  • Data.table pronouns .N, .SD, SDCols
  • Handling missing data
EDA with Data.table
  • Data subsetting, splitting, and merging
  • Managing datasets
  • Long to wide and back
  • Merging datasets together
  • Stacking datasets together (concatenation)
  • Data summarization
    • Numerical summaries
    • Categorical summaries
    • Multivariate summaries
  • Creating visualizations
Big Three Strategies for dealing with Big Data in R
  • https://rviews.rstudio.com/2019/07/17/3-big-data-strategies-for-r/
  • 1. Sampling
  • 2. Chunk-and-pull
  • 3. Push compute to DB
DuckDB 
  • Overview: DuckDB works nicely with R
  • Basic SQL commands for working with DuckDB
  • Understanding query performance optimizations
  • Using dbplyr to work with DuckDB
mlr3 for Machine Learning in R
  • Overview of mlr3
  • Goals of machine learning
  • mlr3 R6 object-oriented R and methods
  • Defining a task
  • Assigning roles to data
  • Performing a classification
  • Performing a regression
  • Visualization with mlr3
  • Pipelines
  • Model assessment
  • Model optimization
  • Implementing general linear models
  • Establishing and leveraging partitions/clusters
  • Fitting regression models and making predictions
  • Decision trees and random forests
  • Naïve bayes
  • Implementing stacked models via pipelines
  • Implementing an AutoML model via pipelines
  • Managing resource utilization through parallelization
Apache Spark
  • Overview of Spark
  • APIs to use Apache Spark with R
  • Sparklyr versus SparkR
  • R, Python, Java and Scala APIs to Spark
  • Applied Examples using SparkR
  • Spark and H2O together: sparklingwater
  • Data import and manipulation in Spark(R)
  • The Spark machine learning library MLlib:
    • General linear models
    • Random forest
    • Naïve bayes
  • Data Munging and Machine Learning Via H20
    • Intro to H20
    • Launching the cluster, checking status
    • Data Import, manipulation in H20
    • Fitting models in H20
    • Generalized Linear Models
    • Naïve bayes
    • Random forest
    • Gradient boosting machine (GBM)
    • Ensemble model building
    • AutoML
    • Methods for explaining modeling output
Conclusion

Training Materials:

All R training students receive comprehensive courseware.

Software Requirements:

  • A recent release of R 4.x
  • IDE or text editor of your choice (RStudio recommended)


Accelebrate really brought the goods. By the end of the class, every one of the students was solving their own business-specific problem.

 David - National Renewable Energy Laboratory, Golden, CO

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