
---
title: "SDS/MTH 220 Project Data Proposal"
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)
```


# Big-picture

## Research question

_What is your research question?_


## Description of data

_Please give a very short description of the data set along with it's original source._


## 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

_Piping your data frame into the `clean_names()` function from the `janitor` package will clean your variable names, making them easier to work with._

```{r}

```


## Explore your data

_Be sure to explore your data. Note that `eval=FALSE` is set so that R Markdown doesn't "evaluate" this code chunk, i.e. it will ignore it in the ultimate `.html` report. You should run this code on your own, but not in the ultimate `.html` report._

```{r, eval = FALSE}
glimpse()
```



***



# Variables

## Identification variable

_What is your identification (ID) variable (if you have one)?_


## Outcome variable

_What is your outcome variable $y$? What are its units of measurement?_


## Numerical explantory variable

_What is your numeric explanatory variable? What are its units of measurement?_


## Categorical explantory variable

_What is your categorical explanatory variable? Does it have between 3 and 5 levels. Please list the different levels._



***


# Rows/observations

## Observational units

_What is the observational unit of your data? In other words, what does each row in your data represent?_


## Sample size

_How many rows/cases are in the data i.e. what is the sample size? Is the sample size at least 50?_



***


# 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. _The identification variable_
1. _The outcome variable $y$_
1. _The numerical explanatory variable_
1. _The categorical explanatory variable_
1. _Optional: any other variable you find interesting_


```{r}

```


## Preview data

_Display a random sample of 5 rows of your data frame by piping it into the `sample_n(5)` function from the `dplyr` package . You'll get the same 5 rows everytime you knit this document and hence replicable results because we set the seed value of the random number generator in the first code chunk above._

```{r}

```





