R for Data Analysis in easy steps: R Programming essentials
Language: English
Published by In Easy Steps Limited, 2017
Series: Book 114 of 231 - In Easy Steps
- Softcover
- Used

Seller: World of Books Inc, Montgomery, IL, U.S.A.World of Books Inc
AbeBooks seller since March 23, 2026
Condition: Used - Good
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R for Data Analysis in easy steps is written using a proven easy-to-follow style for maximum appeal. It will be useful to anyone who wants to begin programming in R, with minimum fuss.
Seller Inventory # CIN1840787953G
- Title
- R for Data Analysis in easy steps: R Programming essentials
- Author
- Mike Mcgrath
- Publisher
- In Easy Steps Limited
- Publication year
- 2017
- Condition
- Good
- Binding
- Paperback
- Language
- English
- ISBN 10
- 1840787953
- ISBN 13
- 9781840787955
- Series
- Book 114 of 231: In Easy Steps
The R language is widely used by statisticians for data analysis, and the popularity of R programming has therefore increased substantially in recent years. The emerging Internet of Things (IoT) gathers increasing amounts of data that can be analyzed to gain useful insights into trends.
R for Data Analysis in easy steps has an easy-to-follow style that will appeal to anyone who wants to produce graphic visualizations to gain insights from gathered data.
R for Data Analysis in easy steps begins by explaining core programming principles of the R programming language, which stores data in “vectors” from which simple graphs can be plotted.
Next, the book describes how to create “matrices” to store and manipulate data from which graphs can be plotted to provide better insights. This book then demonstrates how to create “data frames” from imported data sets, and how to employ the “Grammar of Graphics” to produce advanced visualizations that can best illustrate useful insights from your data.
R for Data Analysis in easy steps contains separate chapters on the major features of the R programming language. There are complete example programs that demonstrate how to create Line graphs, Bar charts, Histograms, Scatter graphs, Box plots, and more. The code for each R script is listed, together with screenshots that illustrate the actual output when that script has been executed. The free, downloadable example R code is provided for clearer understanding.
By the end of this book you will have gained a sound understanding of R programming, and be able to write your own scripts that can be executed to produce graphic visualizations for data analysis. You need have no previous knowledge of any programming language, so it's ideal for the newcomer to computer programming.
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