Hao Zhu haozhu233

Goal

Install working tensorflow or pytorch via standard conda environment workflow.

The recommended conda-based install process works smoothly:

$ # Create a fresh environment

	library(tidyverse)
	library(glue)
	library(gt)
	library(kableExtra)
	library(espnscrapeR)

	# use espnscrapeR to get NFL standings + QBR ratings
	nfl_qbr <- get_nfl_qbr(2020)
	nfl_standings <- get_nfl_standings(2020)

	import requests
	from tqdm import tqdm


	def download(url: str, fname: str, chunk_size=1024):
	resp = requests.get(url, stream=True)
	total = int(resp.headers.get('content-length', 0))
	with open(fname, 'wb') as file, tqdm(
	desc=fname,
	total=total,

	fastROC <- function(probs, class) {
	class_sorted <- class[order(probs, decreasing=T)]
	TPR <- cumsum(class_sorted) / sum(class)
	FPR <- cumsum(class_sorted == 0) / sum(class == 0)
	return(list(tpr=TPR, fpr=FPR))
	}

	# Helpful function adapted from: https://stat.ethz.ch/pipermail/r-help/2005-September/079872.html
	fastAUC <- function(probs, class) {
	x <- probs

	<html>
	<head>
	<title>
	[Visualizing Movement Data with D3.js]
	</title>
	<script src="https://d3js.org/d3.v4.min.js"></script>
	<script src="https://omid.al/Mova-Viz/MovaViz-v0.1.2.js"></script>
	<style>
	* {
	box-sizing: border-box

	library(serial)

	rm(list = ls()) # clear environment
	graphics.off() # close all graphic windows



	### establish a serial connection

	con <- serialConnection(name = "get_temps",

	#!/bin/bash
	# Install Spark on CentOS 7
	yum install java -y
	java -version

	yum install wget -y
	wget http://downloads.typesafe.com/scala/2.11.7/scala-2.11.7.tgz
	tar xvf scala-2.11.7.tgz
	sudo mv scala-2.11.7 /usr/lib
	sudo ln -s /usr/lib/scala-2.11.7 /usr/lib/scala

	---
	title: HTML Dependencies
	output: html_document
	---

	This example explains how HTML dependencies work in htmltools. Sometimes an HTML fragment may have additional dependencies to work correctly, such as JavaScript and/or CSS files. In R Markdown documents, you have to let rmarkdown know these dependencies, so they can be added to the HTML header when the document is rendered through Pandoc.

	Another thing that you need to pay attention is you have to "protect" your HTML output so that Pandoc does not touch it, e.g. when you have four leading spaces, Pandoc may think this line is supposed to be a `<pre>` block whereas you only meant to indent the line for cosmetic purposes. In this case, the function `htmltools::htmlPreserve()` will be _automatically_ applied to your HTML content in R Markdown if the content is generated from `htmltools::tags` or wrapped in `htmltools::HTML()`.

	Now we use a random CSS file in the knitr package to illustrate how dependencies work. The goal here is to generate a