Alex Gable alex-gable

nflscrapR Python Guide

This is an introduction to working with nflscrapR data in Python. This is inspired by this guide by Ben Baldwin.

Using Jupyter Notebooks which come pre-installed with Anaconda is typically the best way to work with data in Python. This guide assumes you are using the Ananconda distribution and therefore already have the required packages installed. If you are not using the Anaconda distribution, install numpy, pandas, and matplotlib.

Once Anaconda has been downloaded and installed, open the Anaconda Navigator. Click launch on the Jupyter Notebook section which will open in your browser.

Collecting and Cleaning Data

There are a couple ways to get nflscrapR data. While you don't necessarily need R for historical data, it is necessary for getting data that has not been uploaded to github. My preferred process is to get data u

These links no longer work. Springer have pulled the free plug.

Graduate texts in mathematics

duplicates = multiple editions

A Classical Introduction to Modern Number Theory, Kenneth Ireland Michael Rosen

Multiple SSH Keys settings for different github account

create different public key

create different ssh key according the article Mac Set-Up Git

$ ssh-keygen -t rsa -C "[email protected]"

Deploy your site with git

This gist assumes:

you have a local git repo
with an online remote repository (github / bitbucket etc)
and a cloud server (Rackspace cloud / Amazon EC2 etc)
- your (PHP) scripts are served from /var/www/html/
- your webpages are executed by apache
apache's home directory is /var/www/

	############################################################################
	#### Grab airyards data from airyards.com and test the year-to-year
	#### stability of WOPR (Weighted Opportunity Rating)
	############################################################################

	library(tidyverse)
	library(jsonlite)

	## define a function that uses jsonlite to call the airyards.com
	## API and returns the processed JSON as a dataframe.