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- Author: Jim Albert
- Publisher: Springer
- ISBN-10: 3031760735
- ISBN-13: 9783031760730
- Format: 15.6 x 23.4 x 2.4 cm, minkšti viršeliai
- Language: English
- SAVE -10% with code: EXTRA
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Description
Now in its second edition, R by Example is an example-based introduction to the statistical computing environment that does not assume any previous familiarity with R or other software packages. R functions are presented in the context of interesting applications with real data.
The purpose of this book is to illustrate a range of statistical and probability computations using R for people who are learning, teaching, or using statistics. Specifically, it is written for users who have covered at least the equivalent of (or are currently studying) undergraduate level calculus-based courses in statistics. These users are learning or applying exploratory and inferential methods for analyzing data, and this book is intended to be a useful resource for learning how to implement these procedures in R.
The new edition includes updated and expanded coverage of Rstudio, knitr, ggplot2, and text mining, as well as a new chapter on data frames.
EXTRA 10 % discount with code: EXTRA
The promotion ends in 23d.06:20:42
The discount code is valid when purchasing from 10 €. Discounts do not stack.
- Author: Jim Albert
- Publisher: Springer
- ISBN-10: 3031760735
- ISBN-13: 9783031760730
- Format: 15.6 x 23.4 x 2.4 cm, minkšti viršeliai
- Language: English English
Now in its second edition, R by Example is an example-based introduction to the statistical computing environment that does not assume any previous familiarity with R or other software packages. R functions are presented in the context of interesting applications with real data.
The purpose of this book is to illustrate a range of statistical and probability computations using R for people who are learning, teaching, or using statistics. Specifically, it is written for users who have covered at least the equivalent of (or are currently studying) undergraduate level calculus-based courses in statistics. These users are learning or applying exploratory and inferential methods for analyzing data, and this book is intended to be a useful resource for learning how to implement these procedures in R.
The new edition includes updated and expanded coverage of Rstudio, knitr, ggplot2, and text mining, as well as a new chapter on data frames.
Reviews