Preface

I (not an author) am compiling this book for myself as a learning exercise for both the contents of ISLR and the use of the bookdown package.

From the book’s official website:

This book provides an introduction to statistical learning methods. It is aimed for upper level undergraduate students, masters students and Ph.D. students in the non-mathematical sciences. The book also contains a number of R labs with detailed explanations on how to implement the various methods in real life settings, and should be a valuable resource for a practicing data scientist.

Resources used in making this book

The first place you should go to learn about bookdown.
- bookdown: Authoring Books and Technical Documents with R Markdown
Getting code folding to work using a variety of StackOverflow posts

Custom functions used throughout the book

In order to make some of the tables and plots, I wrote some custom functions. They are contained in the file visualization_functions.R, but here is a description of each function and why it is used.

`theme_islr()`

This is a plot theming function to mimic the figure style found in the book.

theme_islr = function(){ 
  
  theme_bw(base_size = 14, base_family = "Roboto") %+replace%    #replace elements we want to change
    
    theme(
      
      #grid elements
      panel.grid.major = element_blank(),    #strip major gridlines
      panel.grid.minor = element_blank(),    #strip minor gridlines
      
      #text elements
      plot.title = element_text(             #title
        face = 'bold',            #bold typeface
        hjust = 0,                #left align
        vjust = 2),               #raise slightly
      
      plot.caption = element_text(           #caption
        hjust = 1),               #right align
      
      
      axis.text.x = element_text(            #margin for axis text
        margin=margin(5, b = 10))
      
      #since the legend often requires manual tweaking 
      #based on plot content, don't define it here
    )
}

`prep_reg_table()`

This function takes the output from an lm() object and formats it for regression tables.

prep_reg_table = function(reg_model){
  # Select the coefficients object from the summary of the regression model
  summary(reg_model)$coefficients %>% 
    # Convert it to a tibble, and make the rownames a column
    as_tibble(rownames = "term") %>% 
    # Rename the columns to match ISLR's tables
    rename(Coefficient = Estimate, 
           `t-statistic` = `t value`, 
           pval = `Pr(>|t|)`) %>% 
    
    mutate(
      # Replace the shorthand formulas given to lm() with regression publishing style
      term = str_replace_all(term, c("\\:" = " X ", # Interaction terms
                                     "I(?=\\()|[\\(\\)]" = "")), # Identity function
      # Create a p-value column which shows a less than sign in cases of very small p-values
      `p-value` = case_when(pval < .0001 ~ "<0.0001",
                            T ~ str_c(round(pval, 4)))) %>%
    select(-pval)
}