Write FULL R markdown code with comments to solve these steps:

Instructions:
There is 1000 observations, spread across 10 csv files in a zip.
You will need to generate a vector of all of those files with list.files. It works like this:

file_list <- list.files("~/data_wrangling_class/homework/data/", pattern = ".csv", 
                        all.files = TRUE, full.names = TRUE)

My data is located here: "C:\Users\kylek\OneDrive\Desktop\data"

You can use a for loop, an lapply, or a purrr::map statement to bring all of those files together into one data frame. The columns are in the same order, so feel free to bind them together. If you end up with a list of data frames, you can use do.call(rbind, your_object), dplyr::bind_rows, or data.table::rbindlist to bring them together.

Data Prep Tasks:
This part should live in a separate qmd file.
1. Create a function to clean ratings & length variables.
2. Create a total length (as in movie length) column. (There is length hours and length movies so take that into account)
3. Create a date delta between release time and review time.
4. Pick a word from the reviews and count how many times that word appears. (The word should be set so it is easy to change if need be)
5. Create an aggregated data set for the unique movies. There should be movies, average ratings, release year, genre, and total number of reviews. (Some movies will have multiple release years so we need to take that into account)
6. You should have two data frames, so save those objects for the next step.

Analytic Tasks:
The tasks below should live in a separate qmd from your prep work. You will want to load your data from the previous step.
1. Which genre has the highest critic rating? Which genre had the highest viewer rating? (This should not be for just first or second. print the answer and plot it using ggplot2)
2. What is the relationship between movie length and average rating? (this should not be for just first or second. print the answer and plot it using ggplot2)
3. What is the relationship between the date delta and average rating? (this should not be for just first or second. print the answer and plot it using ggplot2)
4. What is the relationship between total number of reviews and average? (print the answer and plot it using ggplot2)
5. Which movie contains your word of interest at the highest frequency. Does that word have any relationship with average rating? (print the answer and plot it using ggplot2)

Here is how the original CSV data is structured:
title	age	children_in_home	education	gender	reviewer_type	work_status	genre	length_minutes	season	total_reviews	review_text	word_count	rating_first_watch	rating_second_watch	release_date	review_date	length_hours
Visions of Valor	27	2	Post-Graduate	female	Viewer	Employed	Action/Adventure	11 mins	Fall	2574	I wanted to like this movie, but it was a letdown. The dialogue was cringe-worthy, and the story felt rushed.	21	2.7 stars	2.9 stars	5-2-1987	1995/1/21	2 hrs
Hilarity Unleashed	43	1	Completed College	female	Viewer	Employed	Comedy	30 mins	Fall	7590	Pure cinematic joy that brought tears to my eyes.	9	4.2 stars	4.2 stars	5-2-2019	2019/6/27	2 hrs
Soulbound	65	0	Post-Graduate	male	Viewer	Employed	Drama	18 mins	Fall	1618	An emotional rollercoaster that left me in awe. The captivating plot and outstanding acting made this film a true gem. I couldn't stop thinking about it long after the credits rolled.	32	3.7 stars	3.6 stars	4-9-2010	2017/1/25	1 hrs
Amour Forever	43	1	Post-Graduate	female	Critic	Employed	Romance	51 mins	Fall	6251	A movie that failed to live up to its potential. Disappointing.	11	2.7 stars	2.7 stars	11-22-2004	2007/7/2	1 hrs