What this book covers
Chapter 1, Getting Started with R, introduces the basics of R programming, including basic data structures such as vectors, matrices, factors, DataFrames, and lists and control logic such as loops, function writing, and so on.
Chapter 2, Data Processing with dplyr, introduces common data manipulation and processing techniques using the dplyr library, covering data transformation, aggregation, selection, and merging.
Chapter 3, Intermediate Data Processing, introduces common data processing challenges such as converting data types, filling in missing values, and matching for strings. This chapter also covers advanced techniques to ensure data quality, including categorical and textual data.
Chapter 4, Data Visualization with ggplot2, introduces common plotting techniques using ggplot2, covering the beginning-level aesthetics, geometrics, and themes of the library, as well as intermediate techniques such as overlaying the graphics with statistical models, coordinate systems, and facets.
Chapter 5, Exploratory Data Analysis, introduces different ways to work with and explore different types of data, including categorical data and numerical data, and different ways to summarize the data. This chapter also covers a case study that starts from data cleaning all the way to different visualization and analysis.
Chapter 6, Effective Reporting with R Markdown, introduces dynamic documents using R Markdown. Different from static contents, the outputs built using the R Markdown ecosystem offer interactivity covering graphs and tables. This chapter covers the fundamentals of R Markdown reports, including how to add, fine-tune, and customize figures and tables to make interactive and effective reports.
Chapter 7, Linear Algebra in R, covers beginner-level linear algebra with illustrated examples in R, including linear equations, vector spaces, and matrix basics such as common matrix operations, such as multiplication, inversion, and transposition.
Chapter 8, Intermediate Linear Algebra in R, introduces intermediate topics in linear algebra and implementations in R, including determinants of a matrix – the norm, rank, and trace of a matrix, and the eigenvalues and eigenvectors values.
Chapter 9, Calculus in R, introduces the basics of calculus and implementations in R, including fitting a function to data and plotting, derivatives and numerical differentiation, and integrals and integration.
Chapter 10, Probability Basics, introduces the basic concepts of probability and implementation in R, including common discrete probability distributions such as geometric distribution, binomial distribution, and Poisson distribution, as well as common continuous distributions such as normal distribution and exponential distribution.
Chapter 11, Statistics Estimation, introduces common statistical estimation and inference procedures for both numerical and categorical data. Key concepts such as hypothesis testing and confidence intervals will also be covered.
Chapter 12, Linear Regression in R, introduces simple and multiple linear regression models, covering topics such as model estimation, closed-form solutions, evaluation, and linear regression assumptions.
Chapter 13, Logistic Regression in R, introduces logistic regression and its connection to linear regression and the loss function and its application to modeling imbalanced datasets.
Chapter 14, Bayesian Statistics, introduces the Bayesian inference framework, covering topics such as posterior updates and uncertainty quantification.
