Concluding Remarks
Spatial Data Programming with R
Learning Outcomes: Overview
Some of the learning outcomes in this course focused on:
- executing basic programming tasks in R (e.g. loops, conditional statements, while statements, etc.)
- understanding basic GIS (Geographic Information Systems) terms and concepts
- utilizing GIS for conducting spatial analyses.
- appreciating the design and structure of GIS as a decision-making tool.
- producing maps
Skills Acquired
- clean and process data
- visualize data
- create interactive web-apps
- typeset: write visually appealing articles and presentations (R Quarto)
Jobs where these skills are valued
Data Scientist/Data Analyst
GIS Analyst/GIS Specialist
Environmental Scientist
Market Research Analyst
Remote Sensing Specialist
Transportation Planner
Use of R
R is also (more commonly) used in a variety of fields:
- Finance
- Academic research
- Government
- Retail
- Data Journalism
- Healthcare
Companies that use R
Examples of companies which use R include
- Airbnb
- Microsoft
- Uber
Additional Good resources for learning R
- An Introduction to R
https://cran.r-project.org/doc/manuals/r-release/R-intro.pdf
Complete introduction to base R
- R for Data Science
http://r4ds.had.co.nz/
Introduction to data analysis using R, focused on the tidyverse packages
Good substitute for Stata
- Advanced R
https://adv-r.hadley.nz/
Programming in R
Good resources for learning R
- Programming interactive R-apps using Shiny
https://mastering-shiny.org/index.html
Useful for including interactive visualizations in web-pages
- Markdown Basics
https://quarto.org/docs/authoring/markdown-basics.html
Useful to create professionally-looking presentations and documents
Books to Use: Data Analysis and Visualization
Books to Use: GIS
Other useful Sources
Overview of Processing Vector Layers
sf was the main library that we worked with
It helped us deal with:
- Numerical Operations to calculate: Areas, Length, Distances, etc.
- GIS Logical Operations: Overlaps, Equals, Intersects, etc.
- Geometry Operations: Centroid, Buffer, Intersection, Union, Difference, etc.
Overview of Processing Raster Layers
We performed geometric operation on rasters (pictures) with the stars package:
- Accessing cell values - as a matrix or as a dataframe, extracting pixels to points
- Performing Raster algebra: raster arirthmentic and logic
- Changing the resolution and extent: cropping, mosaicing, resampling, and reprojecting
- Transforming Rasters: to points and polygons
Processing Raster Layers stars
Temperature in 1901
Processing Raster Layers stars
Temperature in 2022