Final Project Guidance

Welcome to Siyu's repo🏠! This repository will be updated with any changes or new information regarding my final project—Lung Cancer Data Analysis. All relevant materials are included in this repository, including the patient data set and all R code used to generate tables, figures, and reports.

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📝Contents of report

This report will follow a formal data analysis workflow involving variable description, exploratory analysis and model establishment to explore the main cause of lung cancer, and finally generate suggestions for preventing lung cancer efficiently.

📊Obtaining the data

Data is stored in the folder of data and you can directly download it if need.
This data set contains information on 1000 patients with lung cancer, including their demographics (age, gender), levels of a series of exposure (air pollution exposure, alcohol use, dust allergy, occupational hazards, genetic risk, chronic lung disease, balanced diet, obesity, smoking, passive smoker) and specific body symptoms (chest pain, coughing of blood, fatigue, weight loss ,shortness of breath ,wheezing ,swallowing difficulty ,clubbing of finger nails and snoring), 26 columns in total.

💻Initial code description

code/01_make_tables.R

• generates variable description table, saves as table1_variable.rds
• generates descriptive statistics table, saves as table2_descriptive.rds
• generates table of smoking level, saves as table3_smoking.rds
• generates table of shortness of breath level, saves as table4_breath.rds
• all tables are output in output/ folder

code/02_make_figures.R

• generates a correlation heat map, saves as figure1_heatmap.png
• generates a stacked bar chart for variable of exposure, saves as figure2_barExposure.png
• generates a stacked bar chart for variable of symptom, saves as figure3_barSymptom.png
• all figures are output in output/ folder

code/03_render_report.R

• renders Interim_Report.Rmd

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📄 How to Generate the Final Report

To generate the final report, follow these steps:

1. Download Project folder from GitHub

2. Ensure Required Packages are Installed in Your Rstudio
   knitr, kableExtra, dplyr,labelled, gtsummary, ggplot2, reshape2, gt

   Additionally, to make your preparation tasks easier, renv library is installed here to help you synchronize your local package repository. Please type renv::activate() renv::status() renv::restore() in turn in your R console.

   If you still feel a little bit cumbersome to synchronize packages, don't worry! Here is another much easier way. Please just run make install in your terminal, and everything is done!

3. Change terminal path to the project and click make in your terminal

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🔗 Instructions for DockerHub and Building Image

Way 1:
Directly pull the image from DockerHub to your local:
docker pull livzhaidocker/project_image:final_project_tag

then run the image and make report in the container:
make report

Link to The Image on DockerHub: https://hub.docker.com/u/livzhaidocker

Way 2:
Since actually the project folder includes Dockerfile, you could rebuild the image and run the automated version of the image with typing the following instruction in your terminal:
docker build -t project_image . （or use Makefile to do so: make project_image)
make report/Report.html