The paper is incomplete so I will only be looking at the parts done so far.

• The idea seems interesting and has a lot of potential.
• The Overview section in the README needs to be improved. You can use your abstract to include a more detailed Overview.
• Title needs to be a bit more detailed.
• The abstract can be improved by making the writing more focused. I have got marks deducted for adding extra words like "delve into". Make sure the sentences are not flowery.
• The Introduction is good and adequately sets up rest of the paper.
• The datasets are accurate.

ReadMe:

"Code" button"

• remove the extra " after button, everything else looks good

data:

• for the clean data, use more descriptive names so it's easier to understand what data the cleaned dataset represents

paper.pdf:

• add the "Abstract" label before the abstract
• For the existing tables, the data should be cleaned by adding a larger margin or lowering the padding so the column names and column variables can co-exist without blending into the next one. For example in table 2, some parts of t_const and genre are mashed together. table 4 looks much better in terms of formatting
• The references should have many more references than just 2, as noted within your scripts:

library(tidyverse)
library(dplyr)
library(here)
library(janitor)
library(arrow)
add missing references when possible

• the graphs look great, excited to see the analysis that will come along with them soon

scripts:

• Overall, I like how you label the process you take into categories for example a section for "Label by years" and another one with "Save sample of raw data". However, I think a great addition that can be made would be description inside the actual code. Like docstrings. For example, in the download file, you can add some details about how the data is being scraped
• Add some simple true false tests when possible in the test scripts section

Overall, the data and graphs are very well done. The analysis that comes from the data should be solid as well. Good work.

Since this paper is not finished yet, this peer review will only addressed the parts completed.

Overall

• Super interesting topic! I see a lot of potential, I can't wait to see the finished paper and the results. Remember to delete unused files such as datasheets and paper_files.
• You may want to delete many fancy terms and make the overall writing structure less verbose. Words like "delves into" and "meticulously" can be replaced by simpler words.

Cross reference

• You could use cross reference for your sections when you are introducing them at the introduction

Tables

• Your tables need some polishing.
• Table 2's column width needs to be adjusted. I'm confused what is the column "tconstant"?
• Same with Table 3.
• Table 4's column width needs to be adjusted as well.
• You may want to rename your column titles for all the tables to make them more understandable.

Graphs

• I love your graphs, can't wait to read some verbal descriptions of them later.

Scripts

• Don't forget to create some tests for your data.

Analysis on Movie Success Indicators

The title is good and to the point. Gives a gist of the purpose of the paper

README.md

The README.md is solid and provides a good overview of the repo and the file structure.

paper.qmd

In this study, we delve into the variables contributing to movie success, focusing on the interplay between a movies’ genre, number of theaters showing, the moth of release, and the year of premiere, particularly in the context of the pre and post-COVID-19 era, spanning from 2019 to 2022. This comprehensive analysis aims to shed light on the nuanced relationship between these variables and movie success, highlighting how the cinematic landscape has evolved in response to the COVID-19 pandemic. The findings of this study enrich our understanding of the determinants of cinematic success, providing valuable insights for filmmakers, distributors, and industry strategists in navigating the changing film industry.

The abstract is well written and interests the reader in this study. I look forward to seeing the rest of the paper.

Scripts

For the scripts that are completed, the code is well written. The purpose is clearly described and the files are well documented with appropriate comments.

Overall, your paper is well structured and has great potential. I look forward to reading the final version. Well done! 👍

Abstract:
The last statement, just be more focused on a quick highlight of the results that you found.

Introduction:
Some statements sound a little too fancy, for example, "Employing a multifaceted analytical approach that combines genre classification .. ", could be changed to "Employing an analytical approach that combines genre classification ...".

Data: Remember to remove the TODO comments once you are done or make so that it doesn't appear in your paper. For table, make a caption which mention where you are getting this data since you are using two data sources IMDB and Box Office Mojo. I am confused on what table 2 trying to show and what it is purpose in this paper. Same remark for Table 3 and 4, and I am not sure why you are filtering to show films that have a score greater than 7 like you mention in your introduction. Also, two of your graphs for Movie success (2021 and 2022) are in the model section, so make sure to move it into the data section.

References:
Don't forget to add IMDB and Box Office Mojo.

Readme:
Make sure to add a link to the data sources you are using. I think the description could have a little more detail.

General comments:
Double check spelling and grammar, for example, month is misspelled in the abstract, and you didn't full capitalize IMDB in the introduction. Overall, this has potential to be an interesting paper and I look forward to reading the complete version.