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R Statistics Cookbook

By Francisco Juretig
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  1. Free Chapter
    Getting Started with R and Statistics
About this book
R is a popular programming language for developing statistical software. This book will be a useful guide to solving common and not-so-common challenges in statistics. With this book, you'll be equipped to confidently perform essential statistical procedures across your organization with the help of cutting-edge statistical tools. You'll start by implementing data modeling, data analysis, and machine learning to solve real-world problems. You'll then understand how to work with nonparametric methods, mixed effects models, and hidden Markov models. This book contains recipes that will guide you in performing univariate and multivariate hypothesis tests, several regression techniques, and using robust techniques to minimize the impact of outliers in data.You'll also learn how to use the caret package for performing machine learning in R. Furthermore, this book will help you understand how to interpret charts and plots to get insights for better decision making. By the end of this book, you will be able to apply your skills to statistical computations using R 3.5. You will also become well-versed with a wide array of statistical techniques in R that are extensively used in the data science industry.
Publication date:
March 2019
Publisher
Packt
Pages
448
ISBN
9781789802566

 

Getting Started with R and Statistics

In this chapter, we will cover the following recipes:

  • Maximum likelihood estimation
  • Calculation densities, quantiles, and CDFs
  • Creating barplots using ggplot
  • Generating random numbers from multiple distributions
  • Complex data processing with dplyr
  • 3D visualization with the plot3d package
  • Formatting tabular data with the formattable package
  • Simple random sampling
  • Creating plots via the DiagrammeR package
  • C++ in R via the Rcpp package
  • Interactive plots with the ggplot GUI package
  • Animations with the gganimate package
  • Using R6 classes
  • Modelling sequences with the TraMineR package
  • Clustering sequences with the TraMineR package
  • Displaying geographical data with the leaflet package
 

Introduction

In this chapter, we will introduce a wide array of topics regarding statistics and data analysis in R. We will use quite a diverse set of packages, most of which have been released over recent years.

We'll start by generating random numbers, fitting distributions to data, and using several packages to plot data. We will then move onto sampling, creating diagrams with the DiagrammeR package, and analyzing sequence data with the TraMineR package. We also present several techniques, not strictly related to statistics, but important for dealing with advanced methods in R—we introduce the Rcpp package (used for embedding highly efficient C++ code into your R scripts) and the R6 package (used for operating with R6 classes, allowing you to code using an object-oriented approach in R).

 

Technical requirements

We will use R and its packages, that can be installed via the install.packages() command, and we will indicate which ones are necessary for each recipe in the corresponding Getting ready section.

                               
About the Author
  • Francisco Juretig

    Francisco Juretig has worked for over a decade in a variety of industries such as retail, gambling and finance deploying data-science solutions. He has written several R packages, and is a frequent contributor to the open source community.

    Browse publications by this author
Latest Reviews (5 reviews total)
Very reasonable price for the contents.
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