endolith

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Ram-N

rm(list=ls())
library(lpSolveAPI)

#utility functions
getColnum <- function(cell, color, edge) {
#  return(4*(cell-1)+edge)
  celloffset <- (cell-1)*4*num.colors
  coloroffset <- (color-1)*4
  return(celloffset+coloroffset+edge)
}

bayesball

parse.retrosheet2.pbp = function(season){
# ADJUSTED FOR MAC -- function will work for WINDOWS and MAC
# download, unzip, append retrosheet data
# assume current directory has a folder download.folder
# download.folder has two subfolders unzipped and zipped
# program cwevent.exe is in unzipped folder (for windows)

download.retrosheet <- function(season){
  # get zip file from retrosheet website
  download.file(

bayesball

compute.runs.expectancy <- function(season){
  # changed -- plyr function replaced with dplyr
  # (increases speed from 114 to 30 sec for 2013 data)
  
  # assume that files "allseason.csv" and "fields.csv"
  # are in current working folder
  # for example, if season = 1961, all1961.csv should be
  # available
  
  # returns play-by-play matrix with new variables

massenz

#!/bin/bash
#
# Failure reporter, logs to a well-known log file failure messages
#
# Created 2014-04-30, M. Massenzio (m.massenzio@gmail.com)

declare -r LOGFILE="/var/log/fail.log"
declare -r ALERT_ICON="/usr/share/icons/ubudao-style/actions/dialog-warning.png"
declare -r ERROR_ICON="/usr/share/icons/ubudao-style/actions/stock_dialog-error.png"

aammd

Apparently, there is a simple problem called Fizz buzz which is sometimes used to identify competent programmers. A good opportunity to practice some dplyr and magrittr tricks.

library(dplyr)
library(magrittr)
library(knitr)

1:100 %>%
 data.frame %>%

bayesball

# loading in Retrosheet data for the seasons 1960 through 2013 from my website 
load(url("http://bayes.bgsu.edu/baseball/pbp.1960.1979.Rdata"))
load(url("http://bayes.bgsu.edu/baseball/pbp.1980.1999.Rdata"))
load(url("http://bayes.bgsu.edu/baseball/pbp.2000.2013.Rdata"))

batter.matchup.ggplot <- function(Name, graph=TRUE, retroid=FALSE){
  # this function assumes the data frames pbp.60.79, pbp.80.99, and pbp.00.13 are
  # in the workspace
  fit.model <- function(y, n){
    require(LearnBayes)

datalove

identical          <- function(x) UseMethod("identical") 
identical.my_class <- function(x) do_something(x)
identical.default  <- base::identical

rich-iannone

# install.packages("downloader")
# devtools::install_github("rich-iannone/DiagrammeR")
# devtools::install_github("jeroenooms/V8")

library("downloader")
library("DiagrammeR")
library("V8")

# Create a function that uses the 'chromatography' JS library to generate a random
# set of colors from the CIE Lab color space 

yihui

---
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