---
title: "IS5 in R: Stats Starts Here (Chapter 1)"
author: "Nicholas Horton (nhorton@amherst.edu)"
date: "2025-01-02"
date-format: iso
format: pdf
toc: true
editor: source
---

```{r}
#| label: setup
#| include: false
library(mosaic)   
library(tidyverse)
```


## Introduction and background 

This document is intended to help describe how to undertake analyses introduced as examples in the Fifth Edition of *Intro Stats* (2018) by De Veaux, Velleman, and Bock.
This file as well as the associated Quarto reproducible analysis source file used to create it can be found at http://nhorton.people.amherst.edu/is5.

This work leverages initiatives undertaken by Project MOSAIC (http://www.mosaic-web.org), an NSF-funded effort to improve the teaching of statistics, calculus, science and computing in the undergraduate curriculum.
In particular, we utilize the `mosaic` package, which was written to simplify the use of R for introductory statistics courses.
A short summary of the R needed to teach introductory statistics can be found in the mosaic package vignettes (https://cran.r-project.org/web/packages/mosaic).
A paper describing the mosaic approach was published in the *R Journal*: https://journal.r-project.org/archive/2017/RJ-2017-024.

We begin by loading packages that will be required for our analyses.

```{r}
library(mosaic)
library(tidyverse)
```


## Chapter 1: Stats Starts Here

### Section 1.1: What is Statistics?
### Section 1.2: Data
### Section 1.3: Variables

See table on page 7.

```{r}
library(mosaic)
options(digits = 3)
Tour <- read_csv(
  "http://nhorton.people.amherst.edu/is5/data/Tour_de_France_2016.csv"
) |>
  janitor::clean_names()
```

By default, `read_csv()` prints the variable names as it reads the online file.
These messages can be suppressed using the `message: false` code chunk option to save space and improve readability. 
(I'll ask you to do this in future as you develop more familiarity with these systems.)

The variable names coming out of spreadsheet files can sometimes be quite wonky.
The results from `read_csv()` are passed to the `clean_names()` function from the `janitor` package to make them more consistent.

```{r}
names(Tour)
glimpse(Tour)
head(Tour, 3)
tail(Tour, 8) |>
  select(winner, year, country)
```

Piping (`|>`) takes the output of the line of code and passes it along to the next command.
We will use this to "chain" together commands to do useful things.
(`%>%` is an alternative pipe operator that you may sometimes see: it works in almost the same manner.)

#### Let's find who was the winner in 1998

We use the `filter()` command.

```{r}
filter(Tour, year == 1998) |>
  select(winner, year, country)
```

Several things are noteworthy here:

1. Two equal signs are used for "comparison" (we will see that one equal sign is used for options to commands).
2. Nothing is saved from this pipeline: the output is displayed.
(Later we will "assign" the output to an object that can be reused.)

#### How many stages were there in the tour in the year that Alberto Contador won?

We can also use the `filter()` command.

```{r}
filter(Tour, winner == "Contador Alberto") |>
  select(winner, year, stages)
```

Note that the following command generates the same output.

```{r}
Tour |>
  filter(winner == "Contador Alberto") |>
  select(winner, year, stages)
```

As does:

```{r}
select(filter(Tour, winner == "Contador Alberto"), winner, year, stages)
```

The pipe operator (`|>`) can help improve the readability of code, since each step is clearly indicated.

#### What was the slowest average speed of any tour?  Fastest?

Again, we use `filter()` but this time in conjunction with the `min()` function.

```{r}
Tour |>
filter(average_speed == min(average_speed)) |>
  select(year, average_speed)
Tour |>
filter(average_speed == max(average_speed)) |>
  select(year, average_speed)
```

#### How can we summarize the distribution of Average Speeds?


```{r}
df_stats(~ average_speed, data = Tour)
```

Note that `~ x` denotes the simplest form of the general modelling language (used to indicate a single variable in using the `mosaic` package).