Makefile

# NYC Restaurants Data Analysis Pipeline
# Author: Nikita Susan Easow, Sneha Sunil, Edward (Yukun) Zhang, Lauren Zung
# Date: 2022-12-01

# This driver script completes the analysis of grading prediction for restaurants in NYC
# and creates 6 figures and tables for the EDA and 9 plots for the results to generate the final html report. 
# This script takes no arguments.

# Example usage:
# make all
# make clean

# export the report
all : doc/ny_rest_report.html

# download the data via url
data/raw/nyc_restaurants.csv : src/download_csv.py
	python src/download_csv.py --input_url="https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2018/2018-12-11/nyc_restaurants.csv" --output_file="./data/raw/nyc_restaurants.csv"

# preprocess the data (train test split)
data/processed/ : src/pre_process_nyc_rest.py data/raw/nyc_restaurants.csv
	python src/pre_process_nyc_rest.py --input_file="./data/raw/nyc_restaurants.csv" --output_train_file="./data/processed/train_df.csv" --output_test_file="./data/processed/test_df.csv"

# run the exploratory data analysis and generate intermediate tables and plots
src/nyc_rest_eda_script_visuals/ : src/nyc_rest_eda.py data/processed/
	python src/nyc_rest_eda.py --train_set='./data/processed/train_df.csv' --visual_dir='src/nyc_rest_eda_script_visuals'

# build the model and generate results tables and plots
results/ : src/nyc_rest_analysis.py data/processed/
	python src/nyc_rest_analysis.py --train_data='./data/processed/train_df.csv' --test_data='./data/processed/test_df.csv' --output_dir='./results'

# write the report
doc/ny_rest_report.html : doc/ny_rest_report.Rmd src/nyc_rest_eda_script_visuals/ results/
	Rscript -e "rmarkdown::render('doc/ny_rest_report.Rmd', output_format = 'html_document')"

# remove all exported files
clean :
	rm -f results/*
	rm -f data/processed/*.csv
	rm -f data/raw/*.csv
	rm -f src/nyc_rest_eda_script_visuals/*
	rm -f doc/*.html