,

Beginning R

An Introduction to Statistical Programming

Paperback Engels 2015 2e druk 9781484203743
Verwachte levertijd ongeveer 9 werkdagen

Samenvatting

Beginning R, Second Edition is a hands-on book showing how
to use the R language, write and save R scripts, read in data files, and write
custom statistical functions as well as use built in functions. This book shows
the use of R in specific cases such as one-way ANOVA analysis, linear and
logistic regression, data visualization, parallel processing, bootstrapping,
and more. It takes a hands-on, example-based approach incorporating best
practices with clear explanations of the statistics being done. It has been
completely re-written since the first edition to make use of the latest
packages and features in R version 3.

R is a powerful open-source language and programming
environment for statistics and has become the de facto standard for doing,
teaching, and learning computational statistics.  R is both an object-oriented language and a
functional language that is easy to learn, easy to use, and completely free. A
large community of dedicated R users and programmers provides an excellent
source of R code, functions, and data sets, with a constantly evolving
ecosystem of packages providing new functionality for data analysis. R has also
become popular in commercial use at companies such as Microsoft, Google, and
Oracle. Your investment in learning R is sure to pay off in the long term as R
continues to grow into the go to language for data analysis and research.

What You Will Learn:

How to acquire and install R
Hot to import and export data and scripts
How to analyze data and generate graphics
How to program in R to write custom functions
Hot to use R for interactive statistical explorations
How to conduct bootstrapping and other advanced
techniques

Specificaties

ISBN13:9781484203743
Taal:Engels
Bindwijze:paperback
Aantal pagina's:350
Uitgever:Apress
Druk:2

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Inhoudsopgave

<p>Part I. Learning the R Language</p><p>1. Getting Started</p><p>2. Dealing with Dates, Strings, and Data Frames</p><p>3. Input and Output</p><p>4. Control Structures</p><p>Part II. Using R for Descriptive Statistics </p><p>5. Functional Programming</p><p>6. Probability Distributions</p><p>7. Working with Tables</p><p>Part III. Using R for Inferential Statistics </p><p>8. Descriptive Statistics and Exploratory Data Analysis</p><p>9. Working with Graphics</p><p>10. Traditional Statistical Methods</p><p>11. Modern Statistical Methods</p><p>12. Analysis of Variance</p><p>13. Correlation and Regression</p><p>14. Multiple Regression</p><p>15. Logistic Regression</p><p>16. Modern Statistical Methods II</p><p>Part IV. Taking R to the Next Level </p><p>17. Data Visualization Cookbook</p><p>18. High-performance Computing</p><p>19. Text Mining</p>

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        Beginning R