EPL Scraping
In a previous post, I scraped some NFL data and learned the structure of Sportrac. Now, I want to scrape the available data on the EPL. The EPL data is organized in a few distinct but potentially linked tables. The basic structure is organized around team folders. Let me begin by isolating those URLs.
library(rvest)
library(tidyverse)
base_url <- "http://www.spotrac.com/epl/"
read.base <- read_html(base_url)
team.URL <- read.base %>% html_nodes(".team-name") %>% html_attr('href')
team.
Variance in the Outcome: The Black Box
Regression models engage an exercise in variance accounting. How much of the outcome is explained by the inputs, individually (slope divided by standard error is t) and collectively (Average explained/Average unexplained with averaging over degrees of freedom is F). This, of course, assumes normal errors. This document provides a function for making use of the black box. Just as in common parlance, a black box is the unexplained.
Correlations and the Impact on Sums and Differences
I will use a simple R function to illustrate the effect of correlation on sums and differences of random variables. In general, the variance [and standard deviation] of a sum of random variables is the variance of the individual variables plus twice the covariance; the variance [and standard deviation] of a difference in random variables is the variance of the individual variables minus twice the (signed) covariance.
Presidential Press
The language of presidential communications is interesting and I know very little about text as data. I have a number of applications in mind for these tools but I have to learn how to use them. What does the website look like?
White House News
The site is split in four parts: all news, articles, presidential actions, and briefings and statements. The first one is a catch all and the second is news links.