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Statistical Application Development with R and Python

You're reading from Statistical Application Development with R and Python Develop applications using data processing, statistical models, and CART

Product type Paperback

Published in Aug 2017

Publisher

ISBN-13 9781788621199

Length 432 pages

Edition 2nd Edition

Languages

Python

Concepts

Application Development

Author (1):

Prabhanjan Narayanachar Tattar

View More author details

Table of Contents (12) Chapters

Preface

1. Data Characteristics FREE CHAPTER

2. Import/Export Data

3. Data Visualization

4. Exploratory Analysis

5. Statistical Inference

6. Linear Regression Analysis

7. Logistic Regression Model

8. Regression Models with Regularization

9. Classification and Regression Trees

10. CART and Beyond

Index

Chapter 2. Import/Export Data

The main goals of this chapter are to familiarize the reader with the various classes of objects in R, help the reader extract data from various popular formats, connect R with popular databases such as MySQL, and finally, the best export options of the R output. The main purpose is that the practitioner frequently has data available in a fixed format, and sometimes the dataset is available in popular database systems. This chapter helps the reader to extract data from various sources, and also recommends the export options of the R output.

We will begin by gaining a better understanding of the various formats in which R stores the data. Updated information about the import/export options is maintained on http://cran.r-project.org/doc/manuals/R-data.html. On the Python front, we will be using the Jupyter Notebook throughout the book. Here, we will deal with the basic operations and give indications to import the data from external sources. Python session...

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