This project created an automated R-based workflow for processing, analyzing, and visualizing well data. The R script specifically computes nonparametric Kendall's tau-b correlations and creates time series plots of chloride concentrations and water levels for each monitoring and production well, including dual-axis plots as needed.

R-based project that automates the processing, analysis, and visualization of an Excel-based report.

The main tasks for the third lab are: 1. Plot at least two high-quality static maps with one using the COVID-19 data and one using a related factor. You can use either plot method for sf or ggplot method. 2. Use ggplot2 and other ggplot-compatible packages to create a multi-map figure illustrating the possible relationship between COVID-19 confirmed cases or rate and another factor (e.g., the number of nursing homes, number of food stores, neighborhood racial composition, elderly population, etc.). The maps should be put side by side on one single page. Add graticule to at least one of those maps and label some of the feature on the map where applicable and appropriate. 3. Create a web-based interactive map for COIVD-19 data using tmap, mapview, or leaflet package and save it as a HTML file.

The main tasks for the second lab are: 1. Join the COVID-19 data to the NYC zip code area data (sf or sp polygons). 2. Aggregate the NYC food retails store data (points) to the zip code data, so that we know how many retail stores in each zip code area. And we need to choose the specific types according to the data. 3. Aggregate the NYC health facilities (points) to the zip code data. Similarly, choose appropriate subtypes such as nursing homes from the facilities. 4. Join the Census ACS population, race, and age data to the NYC Planning Census Tract Data. 5. Aggregate the ACS census data to zip code area data. In the end, we will have a single sf object that contains all the information, including polygons of postal zones (geometry), the number of retail food stores, the number of health facilities like nursing homes, the total population, the elderly population, and other columns/attributes.

The tasks for the first lab are: 1. Read the NYC postal areas in Shapefiles into sf objects. 2. Read and process the NYS health facilities spreadsheet data. Create sf objects from geographic coordinates. 3. Read and process the NYS retail food stores data. Create sf objects from geographic coordinates for NYC. 4. Use simple mapping method such as mapview with a basemap to verify the above datasets in terms of their geometry locations. 5. Save the three sf objects in a RData file or in a single GeoPackage file/database.