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Hands-On Data Science with R

You're reading from   Hands-On Data Science with R Techniques to perform data manipulation and mining to build smart analytical models using R

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Product type Paperback
Published in Nov 2018
Publisher Packt
ISBN-13 9781789139402
Length 420 pages
Edition 1st Edition
Languages
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Authors (4):
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Nataraj Dasgupta Nataraj Dasgupta
Author Profile Icon Nataraj Dasgupta
Nataraj Dasgupta
Vitor Bianchi Lanzetta Vitor Bianchi Lanzetta
Author Profile Icon Vitor Bianchi Lanzetta
Vitor Bianchi Lanzetta
Doug Ortiz Doug Ortiz
Author Profile Icon Doug Ortiz
Doug Ortiz
Ricardo Anjoleto Farias Ricardo Anjoleto Farias
Author Profile Icon Ricardo Anjoleto Farias
Ricardo Anjoleto Farias
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Table of Contents (16) Chapters Close

Preface 1. Getting Started with Data Science and R FREE CHAPTER 2. Descriptive and Inferential Statistics 3. Data Wrangling with R 4. KDD, Data Mining, and Text Mining 5. Data Analysis with R 6. Machine Learning with R 7. Forecasting and ML App with R 8. Neural Networks and Deep Learning 9. Markovian in R 10. Visualizing Data 11. Going to Production with R 12. Large Scale Data Analytics with Hadoop 13. R on Cloud 14. The Road Ahead 15. Other Books You May Enjoy

Data extraction, transformation, and load

The previous section looked at the high-level characteristics of data as defined by data types, formats and sources. Data Extraction-Transformation-Load (ETL) is a generic term that signifies the process of extracting (retrieving) data, applying transformations such as cleansing operations and aggregations, and finally loading the data onto a target system, if such is needed.

Basic tools of data wrangling

In this section, we're going to share some of the common data mining and aggregation operations that can be performed on data.frame, dplyr, and data.table.

First, we are going to learn how to use a few functions in Base R to perform basic manipulation operations. We'll then...

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