jpush1773 / dsci_522_pm2.5_analysis

Analyzing PM 2.5 of Beijing and Shanghai.

DSCI 522 Analysis of PM 2.5 in Beijing and Shanghai

Data analysis project for DSCI 522 - Analyzing PM 2.5 in Beijing and Shanghai

Team members

Wilson Deng

Ting Pan

Question

• Question: Is the average PM2.5 in Beijing same as that in Shanghai?

• Type of Question: Inferential.

Data

• Source: PM2.5 Data of Five Chinese Cities from Kaggle.com

• Context
  PM2.5 readings are often included in air quality reports from environmental authorities and companies. PM2.5 refers to atmospheric particulate matter (PM) that have a diameter less than 2.5 micrometers. In other words, it's used as a measure of pollution.

• Content
  The time period for this data is between Jan 1st, 2010 to Dec 31st, 2015. Missing data are denoted as NA.

Plan

Null Hypothesis: The average PM2.5 in Beijing is the same as the average PM2.5 in Shanghai.

Alternative Hypothesis: The average PM2.5 in Beijing is NOT the same as the average PM2.5 in Shanghai.

To effectively compare the average PM2.5 in Beijing and Shanghai, we plan to do a hypothesis test over the two datasets. Also, two-sample t test will be applied to our question. We have learnt about how to do this in our DSCI 552 class.

First of all, we will select the columns of the time information and the place information, as well as the PM2.5 values, which will be used in our project. Then, for each day, we will calculate the average PM2.5 of all places and all hours as an observation of the specific day.

How to Summarize

Create a visualization of the data that shows the estimate, confidence intervals and distribution for each sample.
For our t-test, we will also visualize the density curve of the corresponding t-distribution, 95% threshold and test statistic.

Usage:

1. Clone this repo, and using the command line, navigate to the root of this project.

2. Run the following commands:

bash run_all.sh

OR

Rscript src/tidy_data.R data/Beijing_PM.csv data/Shanghai_PM.csv data/tidy_data.csv
Rscript src/viz_data.R data/tidy_data.csv results/histogram.png results/boxplot.png
Rscript src/summarize_data.R data/tidy_data.csv results/summarized_data.csv
Rscript src/analyze_data.R data/tidy_data.csv results/results.csv results/testplot.png
Rscript -e "rmarkdown::render('doc/Report.Rmd', output_format = 'github_document')"
Rscript -e "rmarkdown::render('doc/Report.Rmd', output_format = 'pdf_document')"
Rscript -e "rmarkdown::render('doc/Report.Rmd', output_format = 'html_document')"

Dependencies:

• R & R libraries:
  • rmarkdown
  • knitr
  • tidyverse
  • broom