The whole game of statistical Inference

Whole game

Our goal in this part of the book is to give you a rapid overview of the main tools of data science: importing, tidying, transforming, and visualizing data, as shown in ?fig-ds-whole-game. We want to show you the “whole game” of data science giving you just enough of all the major pieces so that you can tackle real, if simple, data sets. The later parts of the book, will hit each of these topics in more depth, increasing the range of data science challenges that you can tackle.

---
title: "The whole game of statistical Inference"
---

# Whole game {#sec-whole-game-intro .unnumbered}

```{r}
#| results: "asis"
#| echo: false
set.seed(1014)

knitr::opts_chunk$set(
  comment = "#>",
  collapse = TRUE,
  # cache = TRUE,
  fig.retina = 2,
  fig.width = 6,
  fig.asp = 2/3,
  fig.show = "hold"
)

options(
  dplyr.print_min = 6,
  dplyr.print_max = 6,
  stringr.view_n = 10,
  # Activate crayon output - temporarily disabled for quarto
  # crayon.enabled = TRUE,
  pillar.bold = TRUE,
  width = 77 # 80 - 3 for #> comment
)

ggplot2::theme_set(ggplot2::theme_gray(12))

status <- function(type) {
  status <- switch(type,
    polishing = "should be readable but is currently undergoing final polishing",
    restructuring = "is undergoing heavy restructuring 
      and may be confusing or incomplete",
    drafting = "is currently a dumping ground for ideas, 
      and we don't recommend reading it",
    complete = "is largely complete and just needs final proof reading",
    stop("Invalid `type`", call. = FALSE)
  )

  class <- switch(type,
    polishing = "note",
    restructuring = "important",
    drafting = "important",
    complete = "note"
  )

  cat(paste0(
    "\n",
    ":::: status\n",
    "::: callout-", class, " \n",
    "You are reading the work-in-progress second edition
      of R for Data Science. ",
    "This chapter ", status, ". ",
    "You can find the complete first edition at <https://r4ds.had.co.nz>.\n",
    ":::\n",
    "::::\n"
  ))
}


```

Our goal in this part of the book is to give you a rapid overview of the main
tools of data science: **importing**, **tidying**, **transforming**, and
**visualizing data**, as shown in @fig-ds-whole-game. We want to show you the
"whole game" of data science giving you just enough of all the major pieces so
that you can tackle real, if simple, data sets. The later parts of the book,
will hit each of these topics in more depth, increasing the range of data
science challenges that you can tackle.