This book covers some introductory steps in using R programming language as a Data Science tool. The data science field has evolved so much recently with incredible quantities of generated data. To extract value from those data, one needs to be trained in the proper data science skills like statistical analysis, data cleaning, data visualization, and machine learning. R is now considered the centerpiece language for doing all these data science skills because it has many useful packages that not only can perform all the previous skills, but also, has additional packages that was developed by different scientists in diverse fields. These fields include, but are not limited to, business, marketing, microbiology, social science, geography, genomics, environmental science, etc. Furthermore, R is free software and can run on all major Windows, Mac Os, and UNIX/Linux. The first two chapters involve installing and using R and RStudio. RStudio is an IDE (integrated development environment) that makes R easier to use and is more similar to SPSS or Stata. Chapters 3–8 covers the different R objects and how to manipulate them including the very popular one, dataframes. Chapter 9 is about importing different files into your R working session like text or excel files. Chapters 10 and 11 are dealing with different tidyverse packages that can do interesting summaries of different dataframes including different types of data visualizations. In the last chapter, it introduces how functions are created in R along with some control structures and useful functions. In all these chapters, many examples along with different codes and outputs are given to help your understanding of this powerful programming language. I hope this book will be great addition to your future data analysis projects.
Author(s): Mohsen Nady
Publisher: Arcler Press
Year: 2023
Language: English
Pages: 448
Cover
Title Page
Copyright
ABOUT THE AUTHOR
TABLE OF CONTENTS
List of Abbreviations
Preface
Chapter 1 Installing R and Rstudio
1.1 Installing R
1.2 Installing Rstudio
Chapter 2 Getting Started with R and Rstudio
2.1 The R Console
2.2 Rstudio
Chapter 3 Objects and Files
3.1 Working at R Console
3.2 R Objects
3.3 Files and Workspaces
Chapter 4 Vectors and Lists
4.1 Numeric Vectors
4.2 Integer Vectors
4.3 Character Vectors
4.4 Logical Vectors
4.5 Complex Vectors
4.6 Implicit Coercion
4.7 Explicit Coercion
4.8 Lists
Chapter 5 Matrices and Dataframes
5.1 Building Matrices with Matrix() Function
5.2 cbind() and rbind() Functions
5.4 data.frame() Function
5.5 Examining the Structure of Built In R Dataframes
Chapter 6 Factors and Missing Values
6.1 Factor() Function
6.2 Table() and prop.table() Functions
6.3 Cut() Function
6.4. Split() Function
6.5 Quantile() Function
6.6 Missing Values
Chapter 7 Subsetting Objects
7.1 Subsetting Vectors
7.2 Subsetting Matrices
7.3 Subsetting Lists
7.4 Subsetting Dataframes
7.5 Sorting Objects
7.6 Removing Na Values
Chapter 8 Dates and Times
8.1 Dates
8.2 Times
8.3 Lubridate Package
8.4 Making Dates from Individual Components
Chapter 9 Importing Data
9.1. Importing Comma Separated Value Files (.csv extension) into R
9.2 Importing Excel Files (.xlx, .xlsx Extensions) into R
9.3 Importing Tab Separated Files (.txt Extension) into R
Chapter 10 Basic Data Wrangling With Tidyverse
10.1 Tidy Datasets
10.2 The “Tidyverse” Package
10.3 dplyr Package
10.4 Tidyr Package
Chapter 11 Data Visualization Using GGPLOT2
11.1 Introduction
11.2 Univariate Analysis: Continuous Data
11.3 Univariate Analysis: Categorical Data
11.4 Bivariate Analysis: Continuous-Continuous Data
11.5 Bivariate Analysis: Continuous-Categorical Data
11.6 Bivariate Analysis: Categorical-Categorical Data
Chapter 12 Functions
12.1 Functions
12.2 Control Structures
12.3 Loop Functions
Bibliography
Index
Back Cover
