Resources
Repositories
This is a list of template course repositories. You can use these most easily either by creating a private copy of each repository in the course GitHub Classroom (the copy link) and expanding on the template from there, or by downloading the repository and using whichever parts you find useful (the download link). If you create copies in GitHub classroom, you can keep them private or make them public and move them to your own GitHub account. Some repositories connect to specific lectures (e.g. those for package development) while others are more universally applicable (e.g. the notebooks playground template).
Lectures
Week02 Intro To Data Types
- Jupyter Notebook (rendered HTML)
- RMarkdown Notebook (rendered HTML)
Week03 Intro To Operators And Functions
- Jupyter Notebook (rendered HTML)
- RMarkdown Notebook (rendered HTML)
Week04 Import Structures Plotting
- Jupyter Notebook (rendered HTML)
- RMarkdown Notebook (rendered HTML)
Week05 Numerical Methods
- Jupyter Notebook (rendered HTML)
- RMarkdown Notebook (rendered HTML)
Week06 Numerical Methods 2
- Jupyter Notebook (rendered HTML)
- RMarkdown Notebook (rendered HTML)
Week07 Testing And R Package Development
- Markdown File (rendered HTML)
Week08 Testing And Python Package Development
- Markdown File (rendered HTML)
Week09 Microcontrollers Intro
- Markdown File (rendered HTML)
Week10 Microntrollers Logging
- Markdown File (rendered HTML)
- RMarkdown Notebook (rendered HTML)
Week11 Microcontrollers Communication
- Markdown File (rendered HTML)
Week12 Problem Set Binder Setup
- Markdown File (rendered HTML)
Week13 Parallel Processing Data Structures
- Jupyter Notebook (rendered HTML)
- RMarkdown Notebook (rendered HTML)
Materials
Community & CU Boulder
Quick references (cheatsheets)
- Using Git cheatsheet
- Markdown (GitHub flavored) cheatsheet
- latex math cheatsheet
- Jupyter Notebook cheatsheet
- Python basics cheatsheet
- RStudio cheatsheet
- RMarkdown cheatsheet
- dplyr cheatsheet
- tidyr cheatsheet
- ggplot cheatsheet
- numpy cheatsheet
- pandas cheatsheet
- matplotlib cheatsheet
- R Development Tools
- C++ reference card
- Mapping with Purrr
- Dask cheat sheet
Documentation, Books & Misc.
- Teaching and Learning with Jupyter
- Jupyter Notebook documentation
- RMarkdown: The Definitive Guide
- R for Data Science Book (GitHub repository that generates this book)
- Fundamentals of Data Visualization (GitHub repository that generates this book)
- IPython Interactive Computing and Visualization Cookbook (GitHub repository with the exercises)
- Mastering Software Development in R, especially the chapter on Building R Packages
- R packages
- Post on Testing & Packaging in Python
- Presentation on xarray and dask
- color blind color use
- R color palettes
- Linting in R
Software
- GitHub Desktop
- Python and Jupyter
- recommend installing anaconda for python 3.6 (if you already have anaconda for python 2.7, no need to reinstall!)
- open a terminal and run
conda --versionto check the installed version (if lower than conda 4.4, recommend runningconda update conda) - create a virtual environment for python 3.6 that we will use for the class (that way you can easily keep/run other python versions/configurations) by running
conda create -n class python=3.6 anaconda - run
source activate class(mac & linux) /activate class(windows) to test-activate the new virtual environment - run
conda install -c conda-forge jupyterlabto install jupyter lab - run
jupyter labextension install @jupyterlab/tocto install the Table of Contents extension - run
jupyter labto start jupyter lab - run
source deactivate(mac & linux) /deactivate(windows) to leave the virtual environment again
- R and RStudio
- install the newest version of R (should be 3.5.1), if you have older versions you want to keep you will still have access (see details on version switching)
- install/upgrade to the preview of the newest version of RStudio (should be >= 1.2.907)
- start RStudio
- in the console, run
install.packages(c("devtools", "tidyverse", "rmarkdown", "reticulate", "readxl", "latex2exp"))