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library(shiny)

shinyServer(function(input, output) {

  output$distPlot <- renderPlot({
   
    library(LearnBayes)
    quantile1 = list(p=.5, x=input$p50)
    quantile2 = list(p=.9, x=input$p90)
    ab = beta.select(quantile1, quantile2)

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# R Script for Jan 6, 2014 blog

# function shrink will compute improved estimates at true batting rates
# for all players with at least Lower.Bound opportunities

# be sure that packages Lahman, LearnBayes, and plyr have been installed
# before running this function

shrink <- function(year, n.var, d.var, Lower.Bound=100){
  # load required packages (each should be installed first)

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library(shiny)
library(Lahman)
library(ggplot2)

# Define server logic required to plot various variables against year
shinyServer(function(input, output) {
  
  # Compute the forumla text in a reactive function since it is 
  # shared by the output$caption and output$mpgPlot functions
  formulaText <- reactive({

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library(shiny)
library(Lahman)
library(ggplot2)

# preliminary work
Master$birthyear <- with(Master, 
                         ifelse(birthMonth >= 7, birthYear + 1, birthYear))
Batting <- merge(Batting, 
                 Master[, c("playerID", "nameFirst", "nameLast", "birthyear")],
                 by="playerID")

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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(

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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

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#######################################################
# trajectory.2014.R
# script and function to plot a career trajectory for a batter
# in major league baseball
# requires installation of packages
# Lahman, dplyr, and ggplot2
#
# once this script is sourced, then graph trajectory of
# Alex Rodriquez's home run rates by typing
# plot.trajectory("Alex Rodriguez", "HR")

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# setup work
library(Lahman)
library(dplyr) 

# create new data frame Batting.new by
# collapsing over stint variable
Batting.new <- summarise(group_by(Batting, playerID, yearID), 
                           AB = sum(AB),
                           H = sum(H),
                           X2B = sum(X2B),

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# Comparing Ryan Howard's with 10 similar players 
# Richie Sexson, Cecil Fiedler, Mo Vaughn, Mark McGwire, Norm Cash
# Jay Buhner, Willie Stargel, Jason Giambi, Frank Howard, David Justice

# uses packages Lahman, dplyr, ggplot2

# setup work and function to plot trajectory
# add argument plot=TRUE or FALSE
# plot=FALSE outputs the data frame with the rate data

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#########################################################
# Ryan Howard platoon advantage R work
#########################################################

# download Ryan.csv from https://docs.google.com/spreadsheets/d/1KRwk03LT4EaRA14oeEJlTE0rqoL88j3B11r1TB36O2U/edit?usp=sharing
# save as a csv file "Ryan.csv" in current working directory

Ryan <- read.csv("Ryan.csv")

library(dplyr)