Software

Homework assignments in this course are presentations or reports created using quarto, which supports a number of different frameworks for creating visualizations. R or python are the recommended languages for creating visualizations, but it is also possible to use other languages including julia or javascript.

Getting set up

  • quarto is what you will use to author your assignments. Install it if you have not already.
  • positron is a full featured IDE for both python and R that is tuned for data science. It is based on VS Code and is the environment that I recommend you use for this course. RStudio is also an excellent choice, especially if you plan to work primarily in R.
  • R installation if you plan to work in R.
  • Python 3 installation if you plan to work in python. There are many places to install python from; I recommend using anaconda (or the faster mamba).
  • posit.cloud If you have insufficient computing resources, contact me about setting up an account with my course posit.cloud instance.

Guide to Key Packages

R

  • ggplot2 is the workhorse plotting library for this course and implements the grammar of graphics.
  • tidyverse collection of packages for wrangling and visualizing data.
  • sf for working with geospatial data.
  • plotly for R for interactive graphics.
  • shiny for building interactive applications and dashboards.
  • patchwork for composing multiple figures.

Python

  • matplotlib is a powerful plotting library in python.
  • seaborn is another powerful plotting library, technically powered by matplotlib.
  • plotly for interactive graphics.
  • pandas standard python package for wrangling data.
  • geopandas for working with geospatial data.
  • shiny for python for building interactive applications and dashboards.
  • scikit-learn for the dimension reduction and clustering portions of the course.