This is a book for the people who are interested in learning R programming language which is used by the data analyst and data scientist to work on data and get the insights of the data.
This book takes the reader quite far into the world of R language, but before that it also presents some elementary topics related to the Statistical Computing and Data Analysis. This book does not assume any previous knowledge about the R language. So, this book is intended for the beginners as well as intermediate in the field of data analysis.
This book is informative because of the examples in the book have been carefully written and selected.
This book contains 8 Chapters. Brief details of each chapter are as follows:
Chapter 1: This chapter sets the stage by emphasizing the importance of statistics in the realm of computer science.
Chapter 2: This chapter delves into the R programming language, exploring its evolution and widespread use in academia and industry.
Chapter 3: This chapter provides an in-depth understanding of the foundational elements of R programming.
Chapter 4: This chapter focuses on data structures and how to work with different data formats in R, including vectors, matrices, data frames, arrays, lists, and factors. It guides readers through importing and exporting data, as well as data manipulation techniques.
Chapter 5: This chapter highlights the importance of data cleaning as a critical step in any data analysis workflow.
Chapter 6: This chapter introduces descriptive statistics as the backbone of data analysis. It covers essential concepts such as measures of central tendency (mean, median, mode), dispersion (range, variance, standard deviation) as well as frequency distributions.
Chapter 7: This chapter focuses on the importance of data visualization in R and its crucial role in making data more understandable and interpretable.
Chapter 8: This chapter demonstrates a full case study which covers all important concepts of R Programming language.
Contents –
Chapter 1: Overview of Statistical Computing and Data Analysis
1.1 Importance of Statistics in Computer Science
1.2 Introduction to Statistical Computing
1.3 Introduction to Data Analysis
1.4 How does Statistical Computing Contribute to Data Analysis?
1.5 Which Software are used for Statistical Computing?
Chapter 2: Overview of Statistical Programming Language R
2.1 Introduction to R
2.1.1 Evolution of R
2.1.2 The R Environment
2.2 Significance of Using R
2.2.1 Advantages of R
2.2.2 Disadvantages of R
2.2.3 Applications of R in Real World
2.3 Installation of R
2.4 Using R Language for Data Analysis
2.4.1 R Command Line Tool
2.4.2 Installation and use of RStudio
2.4.3 Using Visual Studio Code
2.4.4 Using Google Colab
Chapter 3: Basics of R Programming Language
3.1 Data Types in R
3.2 R Variables
3.2.1 Methods for Variables
3.2.2 Scope of R Variables
3.3 R Comments
3.4 R Reserved Words
3.5 R Conditional Statements
3.6 R Looping Statements
3.7 R Boolean Statements
3.8 Handling Null, NaN, NA and Inf Values in R
3.9 Built-in Functions
3.9.1 Math Functions
3.9.2 String Literals and Functions
3.10 Escape Characters
3.11 R Operators
3.12 User-defined Function
Chapter 4: Working with Data in R
4.1 Creating Different Data Structures (Vector, Matrix, Data Frame, Array, List, Factor)
4.2 Importing Data (CSV, EXCEL)
4.3 Exporting Data
4.4 Managing Attribute/Column/Variable Names
4.5 Manipulating Data
4.5.1 Subsetting and Filtering
4.5.2 Reordering
4.5.3 Merging and Joining
4.5.4 Reshaping Data (Pivoting and Melting)
Chapter 5: Preprocessing and Data Cleaning
5.1 Overview of Data Cleaning
5.2 Handling Missing Values
5.3 Data Transformation/Formatting
5.4 Type Conversion
5.4.1 Data Type Conversion
5.4.2 Data Structure Conversion
5.5 Handling Categorical and Continuous Data (Recoding)
5.6 Detecting and Handling Outliers
5.7 Data Normalization
Chapter 6: Descriptive Analysis using R
6.1 Measuring Central Tendency (Mean, Median, Mode)
6.2 Dispersion (Range, Variance and Standard Deviation)
6.3 Frequency Distribution and Cross Tabulation (Normal and Binomial)
6.3.1 One-Way Frequency Tables in R
6.3.2 Cross-Tabulation (Summarizing Two or More Categorical Variables)
6.3.3 Three-Way Cross-Tabulation
Chapter 7: Data Visualization using R
7.1 Importance of Data Visualization
7.2 Visualizing Data using Charts
7.3 Basic Plotting in R
7.3.1 Base R Graphics
7.3.2 Visualize Categorical Data (Bar Chart, Pie Chart)
7.3.3 Visualize Numerical Data (Histogram, Density Plot, Boxplot)
7.3.4 Plotting with plotly Package
Chapter 8: Case Study
Multiple Choice Questions
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