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Statistical Application Development with R and Python
Statistical Application Development with R and Python

Statistical Application Development with R and Python: Develop applications using data processing, statistical models, and CART , Second Edition

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Table of content icon View table of contents Preview book icon Preview Book

Statistical Application Development with R and Python

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...

Packages and settings – R and Python

First, we load the essential R packages:

Note

Note: To install the required packages, you need to add a code line before loading the packages:

Syntax:

install.packages("Package Name")
  1. Load the R packages foreign, RMySQL, and RSADBE:
    > library(RSADBE)
    > library(foreign)
    > library(RMysSQL)
  2. Import the Python packages as follows:
    Packages and settings – R and Python

Note that you might have to set the working directory path as per your settings.

Understanding data.frame and other formats

Any software comes with its structure and nuances. The Questionnaire and its component section of Chapter 1, Data Characteristics, introduced various facets of data. In the current section, we will go into the details of how R works with data of different characteristics. Depending on the need, we have different formats of the data. In this section, we will begin with simpler objects and move up the ladder toward some of the more complex ones.

Constants, vectors, and matrices

R has five inbuilt objects that store certain constant values. The five objects are LETTERS, letters, month.abb, month.name, and pi. The first two objects contain the letters A-Z in upper and lower cases. The third and fourth objects have months in their abbreviated form and the complete month names. Finally, the object pi contains the value of the famous irrational number. So here, the exercise for the reader is to find the value of the irrational number e. The details about...

Using utils and the foreign packages

Data is generally available in an external file. The types of external files are certainly varied and it is important to learn which of them may be imported into R. The probable spreadsheet files may exist in a comma separated variable (CSV) format, XLS or XLSX (Microsoft Excel) form, or ODS (OpenOffice/LibreOffice Calc) ones. There are more possible formats but we restrict our attention to these described previously. A snapshot of two files, Employ.dat and SCV.csv, in gedit and MS Excel are given in the following screenshot. The brief characteristics of the two files are summarized in the following list:

  • The first row lists the names of the variables of the dataset
  • Each observation begins on a new line
  • In the DAT file, the delimiter is a tab (\t), whereas for the CSV file, it is a comma (,)
  • All three columns of the DAT file are numeric in nature
  • The first five columns of the CSV file are numeric while the last column is character
  • Overall, both the files...

Exporting data/graphs

In the previous section, we learned how to import data from external files. Now, there will be many instances where we would be keen to export data from R into suitable foreign files. The need may arise in automated systems, reporting, and so on, where the other software requires making good use of the R output.

Exporting R objects

The basic R function that exports data is write.table, which is not surprising as we saw the utility of the read.table function. The following screenshot gives a snippet of the write.table function. While reading, we assign the imported file to an R object, and when exporting, we first specify the R object and then mention the filename. By default, R assigns row names while exporting the object. If there are no row names, R will simply choose a serial number beginning with 1. If the user does not need such row names, they need to specify row.names = FALSE in the program:

Exporting R objects

Exporting data using the write.table function

Example 2.3.1. Exporting...

Pop quiz

You have two matrices

Pop quiz

and

Pop quiz

. Obtain the cross-product AB and find the inverse of AB. Next, find (BTAT) then the transpose of its inverse. What will be your observation?

Packages and settings – R and Python


First, we load the essential R packages:

Note

Note: To install the required packages, you need to add a code line before loading the packages:

Syntax:

install.packages("Package Name")
  1. Load the R packages foreign, RMySQL, and RSADBE:

    > library(RSADBE)
    > library(foreign)
    > library(RMysSQL)
  2. Import the Python packages as follows:

Note that you might have to set the working directory path as per your settings.

Left arrow icon Right arrow icon

Key benefits

  • Learn the nature of data through software which takes the preliminary concepts right away using R and Python.
  • Understand data modeling and visualization to perform efficient statistical analysis with this guide.
  • Get well versed with techniques such as regression, clustering, classification, support vector machines and much more to learn the fundamentals of modern statistics.

Description

Statistical Analysis involves collecting and examining data to describe the nature of data that needs to be analyzed. It helps you explore the relation of data and build models to make better decisions. This book explores statistical concepts along with R and Python, which are well integrated from the word go. Almost every concept has an R code going with it which exemplifies the strength of R and applications. The R code and programs have been further strengthened with equivalent Python programs. Thus, you will first understand the data characteristics, descriptive statistics and the exploratory attitude, which will give you firm footing of data analysis. Statistical inference will complete the technical footing of statistical methods. Regression, linear, logistic modeling, and CART, builds the essential toolkit. This will help you complete complex problems in the real world. You will begin with a brief understanding of the nature of data and end with modern and advanced statistical models like CART. Every step is taken with DATA and R code, and further enhanced by Python. The data analysis journey begins with exploratory analysis, which is more than simple, descriptive, data summaries. You will then apply linear regression modeling, and end with logistic regression, CART, and spatial statistics. By the end of this book you will be able to apply your statistical learning in major domains at work or in your projects.

Who is this book for?

If you want to have a brief understanding of the nature of data and perform advanced statistical analysis using both R and Python, then this book is what you need. No prior knowledge is required. Aspiring data scientist, R users trying to learn Python and vice versa

What you will learn

  • • Learn the nature of data through software with preliminary concepts right away in R
  • • Read data from various sources and export the R output to other software
  • • Perform effective data visualization with the nature of variables and rich alternative options
  • • Do exploratory data analysis for useful first sight understanding building up to the right attitude towards effective inference
  • • Learn statistical inference through simulation combining the classical inference and modern computational power
  • • Delve deep into regression models such as linear and logistic for continuous and discrete regressands for forming the fundamentals of modern statistics
  • • Introduce yourself to CART – a machine learning tool which is very useful when the data has an intrinsic nonlinearity

Product Details

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Publication date : Aug 31, 2017
Length: 432 pages
Edition : 2nd
Language : English
ISBN-13 : 9781788621199
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Product Details

Publication date : Aug 31, 2017
Length: 432 pages
Edition : 2nd
Language : English
ISBN-13 : 9781788621199
Category :
Languages :

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Table of Contents

11 Chapters
1. Data Characteristics Chevron down icon Chevron up icon
2. Import/Export Data Chevron down icon Chevron up icon
3. Data Visualization Chevron down icon Chevron up icon
4. Exploratory Analysis Chevron down icon Chevron up icon
5. Statistical Inference Chevron down icon Chevron up icon
6. Linear Regression Analysis Chevron down icon Chevron up icon
7. Logistic Regression Model Chevron down icon Chevron up icon
8. Regression Models with Regularization Chevron down icon Chevron up icon
9. Classification and Regression Trees Chevron down icon Chevron up icon
10. CART and Beyond Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Full star icon Half star icon 4.3
(4 Ratings)
5 star 25%
4 star 75%
3 star 0%
2 star 0%
1 star 0%
Antonio Amodeo Feb 05, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I'm really impressed by this book, as a Data Scientist, I deal in daily basis with the contests between R and Python, but the author could make all the chapters in a very friendly way showing the codes in R and Python at same time.In terms of content, the book is a very well wrote book, where it serves as a great guide to any levels of data analysts or data scientists.The main challenge of a good data scientist is to make exploratory analysis and the author explore it very well, covering the more usual statistical tools and algorithms.I did try to read the book as a no very deep learner and it really impressed me because if a beginner navigates through the pages, certainly the knowledge of him/her will raise a great level.
Amazon Verified review Amazon
Reynald Francisco Oct 08, 2017
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
I received a free advanced copy of the book and I find it as a great reference. This is good balance between statistical theory and R coding.The book is for those that have basic understanding of statistics and needs some guidance on how to implement it using R or Python. I am glad to have this as one of my reference kits.
Amazon Verified review Amazon
Shaun.Ngai Dec 30, 2017
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
Received a free copy from the owner to review this book. Generally this book provides the reader as to how to apply statistics using R & Python. Would recommend any young aspiring data scientist to have this book as data science 101.
Amazon Verified review Amazon
Pavithra Sep 25, 2017
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
Happy to receive a copy on Statistical Application Development with R and Python. This covers widely on basics as well as advanced 'R and Python' - Application perspective. Quite a lot of examples are provided to the users to achieve good amount of understanding. But the flow at which the book is developed is random and ambiguous, which is difficult for us to gain a continuity on chapters.Thanks
Amazon Verified review Amazon
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