---
title: "Lab 5: Spotify Songs"
author: "Your Name Here"
date: "`r Sys.Date()`"
output: html_document
---

```{r}
#Include all libraries here



#Import your data here 



```

## Scenario and Goal

We have been asked by an up and coming music artist to use our advanced data analytic skills to see what has been related to the most popular songs. They have provided us with all of this data for recent popular songs that have been streamed the most.

Our goal is to identify which variable is most related to higher streams. These variables that they are looking at are things like danceability, instrumentalness, etc.

### Variables of Interest

-   `streams`: The number of streams for the individual song

-   Percentage Variables: These variables reflect a rating from 0-100 on the intensity related to that variable

    -   `danceability_%`
    -   `valence_%`
    -   `energy_%`
    -   `acousticness_%`
    -   `instrumentalness_%`
    -   `liveness_%`
    -   `speechiness_%`

------------------------------------------------------------------------

## Lab Exercises

Import your data and use the `clean_names()` function to make the variables a little nicer looking

## Task 1 - Correlation table

Start by creating a correlation table (make sure it looks nice and not just an output from R using `cor()`).

```{r}

```

Examine the correlation table and identify the two largest effects with the outcome (`streams`).

## Task 2 - Visualize Relationships

Visualize the two correlations that you have identified.

```{r}

```

## Task 3 - APA Writeup

Choose one of the relationships that you visualized and write the results in APA format.

*End of Lab. Don't forget to Knit! 🧶*
