
---
title: "SDS/MTH 220 Project Proposal Example"
author: "WRITE YOUR GROUP NAME HERE"
date: "Last updated on `r Sys.Date()`"
output:
  html_document:
    fig_caption: yes
    theme: lumen
    toc: yes
    toc_depth: 2
    df_print: kable
    toc_float:
      collapsed: no
---

```{r, message=FALSE}
# Load all packages here:
library(readr)
library(dplyr)
library(ggplot2)
library(janitor)

# Set seed value of random number generator to get "replicable" random numbers.
# The choice of seed value of 76 was an arbitrary one on my part.
set.seed(76)
```


# Data

## Load data into R

_Include the code to load your data here. If your data is not confidential nor is it private in nature, consider publishing it as a `.csv` file on Google Sheets as in the code chunk below; instructions on how to do this are in Steps 1-6 [here](https://twitter.com/rudeboybert/status/1055821846539591680). If the data shouldn't be published online, then please submit the spreadsheet file on Moodle._

```{r, message=FALSE}

```

## Clean variable names

_Pipe your data frame into the `clean_names()` function from the `janitor` package. Then be sure to overwrite the original data frame with this new data frame using the `<-` assignment operator. This will clean your variable names, making them easier to work with._

```{r}

```

## Data wrangling

_Complete your data wrangling here_: 

```{r}

```



***


# Preview of data

## Pare down variables

_`select()` the following variables **in this order** and drop all others. Eliminating all unnecessary variables will making visually exploring the raw values less taxing mentally, as we'll have less data to look at._

1. _First: The identification variable (if any)_
1. _Second: The outcome variable $y$_
1. _Third: The numerical explanatory variable_
1. _Fourth: The categorical explanatory variable_
1. _Rest: any other variable you find interesting_

```{r}

```

## Look at your data using glimpse

_Look at your data using the `glimpse()` function._

```{r}

```

## Show a preview of your data

_Look at your data another way by displaying a random sample of 5 rows of your data frame by piping it into the `sample_n(5)` function from the `dplyr` package._

```{r}

```



***



# Exploratory data analysis

_Let's do an little exploratory data analysis._

## Inspect for missing values

_Address missing values._


```{r}

```


## Summary statistics

_Compute some quick summary statistics of the outcome variable and comment._

```{r}

```


## Histogram of outcome variable 

_Visualize the distribution of the outcome variable using a histogram and comment._

```{r, fig.cap = "Figure 1. WRITE A TITLE HERE", fig.align = "center", fig.width=16/2, fig.height=9/2}
```



## Scatterplot 

_Visualize the relationship of the outcome variable and the numerical explanatory variable using a scatterplot and comment._

```{r, fig.cap = "Figure 2. WRITE A TITLE HERE", fig.align = "center", fig.width=16/2, fig.height=9/2}

```


## Boxplot

_Visualize the relationship of the outcome variable and the categorical explanatory variable using a scatterplot and comment._

```{r, fig.cap = "Figure 3. WRITE A TITLE HERE", fig.align = "center", fig.width=16/2, fig.height=9/2}

```

## Colored scatterplot

_Visualize the relationship of the outcome variable and both explanatory variables using a colored scatterplot and comment._

```{r, fig.cap = "Figure 4. WRITE A TITLE HERE", fig.align = "center", fig.width=16/2, fig.height=9/2}

```


