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R Data Analysis Cookbook, Second Edition

R Data Analysis Cookbook, Second Edition - Second Edition

By : Kuntal Ganguly, Viswanathan, Viswa Viswanathan
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R Data Analysis Cookbook, Second Edition

R Data Analysis Cookbook, Second Edition

3.3 (4)
By: Kuntal Ganguly, Viswanathan, Viswa Viswanathan

Overview of this book

Data analytics with R has emerged as a very important focus for organizations of all kinds. R enables even those with only an intuitive grasp of the underlying concepts, without a deep mathematical background, to unleash powerful and detailed examinations of their data. This book will show you how you can put your data analysis skills in R to practical use, with recipes catering to the basic as well as advanced data analysis tasks. Right from acquiring your data and preparing it for analysis to the more complex data analysis techniques, the book will show you how you can implement each technique in the best possible manner. You will also visualize your data using the popular R packages like ggplot2 and gain hidden insights from it. Starting with implementing the basic data analysis concepts like handling your data to creating basic plots, you will master the more advanced data analysis techniques like performing cluster analysis, and generating effective analysis reports and visualizations. Throughout the book, you will get to know the common problems and obstacles you might encounter while implementing each of the data analysis techniques in R, with ways to overcoming them in the easiest possible way. By the end of this book, you will have all the knowledge you need to become an expert in data analysis with R, and put your skills to test in real-world scenarios.
Table of Contents (14 chapters)
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Reading data from fixed-width formatted files

In fixed-width formatted files, columns have fixed widths; if a data element does not use up the entire allotted column width, then the element is padded with spaces to make up the specified width. To read fixed-width text files, specify the columns either by column widths or by starting positions.

Getting ready

Download the files for this chapter and store the student-fwf.txt file in your R working directory.

How to do it...

Read the fixed-width formatted file as follows:

> student  <- read.fwf("student-fwf.txt",     widths=c(4,15,20,15,4),       col.names=c("id","name","email","major","year")) 

How it works...

In the student-fwf.txt file, the first column occupies 4 character positions, the second 15, and so on. The c(4,15,20,15,4) expression specifies the widths of the 5 columns in the data file.

We can use the optional col.names argument to supply our own variable names.

There's more...

The read.fwf() function has several optional arguments that come in handy. We discuss a few of these, as follows:

Files with headers

Files with headers use the following command:

> student  <- read.fwf("student-fwf-header.txt",     widths=c(4,15,20,15,4), header=TRUE, sep="t",skip=2) 

If header=TRUE, the first row of the file is interpreted as having the column headers. Column headers, if present, need to be separated by the specified sep argument. The sep argument only applies to the header row.

The skip argument denotes the number of lines to skip; in this recipe, the first two lines are skipped.

Excluding columns from data

To exclude a column, make the column width negative. Thus, to exclude the email column, we will specify its width as -20 and also remove the column name from the col.names vector, as follows:

> student <- read.fwf("student-fwf.txt",widths=c(4,15,-20,15,4),     col.names=c("id","name","major","year")) 
CONTINUE READING
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R Data Analysis Cookbook, Second Edition
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