Project description

The project aims to analyze Mexico's COVID-19 dataset to identify patterns, relationships, and impacts of the pandemic. The team will clean and preprocess the data, conduct statistical tests to explore associations between variables, and evaluate the performance of statistical models used in the analysis.

The process involves:

• Data Pre-processing: Cleaning and preparing the data for analysis.
• Chi-square Analysis: Testing the relationship between categorical variables.
• Associations Analysis: Using Logistic Regression to explore deeper associations in the data.
• Model Evaluation: Assessing the accuracy and effectiveness of our models.
• Dynamic Report: Compiling all findings into a comprehensive report, tailored to suit different audience needs.

The goal is to provide insights into the COVID-19 impact in Mexico through detailed statistical analysis and modeling.

The Covid-19 report has been broken into two subprojects.

• data/ contains the code, original file and output related to the project
  • data/clean.R produces the data_clean.rds
    • teammate can choose to use the original file or data_clean.rds
• subproject1/ contains all code and output related to the descriptive analysis
  • subproject1/code/descriptive_analysis.R produces tables and plots
    • output should be saved to subproject1/output/descriptive_output/
  • subproject1/code/chi-square.R produces the chi-square results containing tables and plots
    • output should be saved to subproject1/output/chi-square/
  • subproject1/report.Rmd reads in output from subproject1/output/ and creates the report for the descriptive analysis
    • suproject1/render_report.R renders the report for subproject1
    • subproject1/Makefile can help user produce the report for subproject1 using command line
• subproject2/ contains all code and output related to the regression analysis and model evaluation
  • subproject2/code/models.R fits Multinominal Logistic Regression models
    • summary tables and plots should be saved to subproject2/output/model/
  • subproject2/code/model_evaluation.R evaluate the effectiveness of model
    • summary tables and plots should be saved to subproject2/output/model_evaluation/
  • subproject2/report.Rmd reads in output from subproject2/output/ and creates the report for the regression analysis
    • suproject2/render_report.R renders the report for subproject2
    • subproject2/Makefile can help user produce the report for subproject2 using command line.

The subprojects are inserted into dynamic_report.Rmd dynamically. The user can change the params in yaml title by switching the value from subproject1 to subproject2 to show different parts of analysis.

Produce report guildance:

1. Make sure you have installed the renv package (install.packages("renv"))
2. Use make install under the main folder to synchroniz packages
3. Use make under the main folder to get the dynamic report
4. If you want to see different parts analysis, please change the params in yaml title by switching the value from Subproject1 to Subproject2 or change the plaha level.
5. Use make under the main folder to produce the report again.