---
title: "Reproducibility and EDA Examples using R Markdown and the `tidyverse`"
output: 
  html_document:
    toc: true
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(fig.align = 'center', 
                      out.width = "25%",
                      warning = FALSE
                      )
```

# Introduction & Purpose

This document serves to provide some basic information about the `tidyverse` followed by an example Exploratory Data Analysis.

The `tidyverse` is an opinionated collection of R packages designed for data science. All packages share an underlying design philosophy, grammar, and data structures.

Install the complete `tidyverse` with:

```{r, eval = FALSE}
install.packages("tidyverse")
```


# `tidyverse` Core Packages

The four core packages that we'll use the most are given below along with their purpose and a quick example of some functionality.

`dplyr`

```{r}
knitr::include_graphics("https://github.com/jbpost2/R4Reproducibility/raw/main/exercises/img/dplyr.png")
```  

`dplyr` is a grammar of data manipulation https://dplyr.tidyverse.org/, providing a consistent set of verbs that help you solve the most common data manipulation challenges:

- `mutate()` adds new variables that are functions of existing variables
- `select()` picks variables based on their names.
- `filter()` picks cases based on their values.
- `summarise()` reduces multiple values down to a single summary.
- `arrange()` changes the ordering of the rows.

These all combine naturally with `group_by()` which allows you to perform any operation “by group”. You can learn more about them by typing `vignette("dplyr")` into the console after reading the `dplyr` package in via `library(dplyr)`. As well as these single-table verbs, `dplyr` also provides a variety of two-table verbs, which you can learn about in `vignette("two-table")`.

If you are new to `dplyr`, the best place to start is the data transformation chapter in R for data science https://r4ds.had.co.nz/.

```{r}
library(dplyr)

starwars %>% 
  filter(species == "Droid")
```

`ggplot2`  

```{r}
knitr::include_graphics("https://github.com/jbpost2/R4Reproducibility/raw/main/exercises/img/ggplot2.png")
```  

`ggplot2` is a system for declaratively creating graphics, based on The Grammar of Graphics https://ggplot2.tidyverse.org/. You provide the data, tell `ggplot2` how to map variables to aesthetics, what graphical primitives to use, and it takes care of the details.

```{r}
library(ggplot2)

ggplot(mpg, aes(displ, hwy, colour = class)) + 
  geom_point()
```

`readr`

```{r}
knitr::include_graphics("https://github.com/jbpost2/R4Reproducibility/raw/main/exercises/img/readr.png")
```  

The goal of `readr` https://readr.tidyverse.org/ is to provide a fast and friendly way to read rectangular data (like csv, tsv, and fwf). It is designed to flexibly parse many types of data found in the wild, while still cleanly failing when data unexpectedly changes. If you are new to ``readr``, the best place to start is the data import chapter in R for data science https://r4ds.had.co.nz/.

`tidyr`  

```{r}
knitr::include_graphics("https://github.com/jbpost2/R4Reproducibility/raw/main/exercises/img/tidyr.png")
```  

The goal of `tidyr` https://tidyr.tidyverse.org/ is to help you create tidy data. Tidy data is data where:

1. Every column is variable.
2. Every row is an observation.
3. Every cell is a single value.

Tidy data describes a standard way of storing data that is used wherever possible throughout the tidyverse. If you ensure that your data is tidy, you’ll spend less time fighting with the tools and more time working on your analysis. Learn more about tidy data in vignette("tidy-data").

```{r}
library(tidyr)

relig_income

relig_income %>% 
  pivot_longer(-religion, names_to = "income", values_to = "frequency")
```