Ram-N

one_lm_attempt <- function (df, target) {
  rnd5 <- sample(10,5)
  predictor_variables <- names(df)[rnd5]
  #create the formula, string to give to lm()
  rnd_formula <- formula(paste(target, " ~ ", 
                           paste(predictor_variables, collapse=" + ")))
  m <- lm(rnd_formula, df)
  #print (summary(m)$r.squared)
  #print (summary(m)$adj.r.squared)
  return(c(rnd5, summary(m)$r.squared,summary(m)$adj.r.squared ))

Ram-N

library(ggplot2)
head(diamonds,10)
colList <- c("cut", "clarity") #list of columns to replace. Can also be column numbers.
rowstoReplace <- 1:10 
lapply(colList, function(colname){gsub("I", ".", diamonds[rowstoReplace, colname])}) 
#lapply gives it one column name at a time...
#Use a comma instead of rowstoReplace if you want all rows changed.

Ram-N

library(shiny)

updates <- 0
updater <- function(updates){ updates + 1 }

shinyServer(function(input,output, session){                                       

    
  updateTracker <- reactive( { 
    invalidateLater(as.numeric(input$secPause) * 1000, session)

Ram-N

rm(list=ls())
library(ggplot2)

df <- read.csv("~/RStats/data/ResearchGrants2013.csv", stringsAsFactors=F, strip.white=T)
df <- df[,-6] #get rid of the trailing blank
dim(df)

#quick check
unique(df$Research.Category)
table(df$Research.Category)

Ram-N

# @ Given a data frame slice, this function determines who had more wins in that slice
moreWins <- function(df) {  
  valid.rows <- sum(!is.na(df$GK.won)) # count of 0 or 1 values
  if(valid.rows==0) {
    return("00")
  }
  gk.wins <- sum(na.omit(df$GK.won == "1")) #how many times did GK win
  gk.losses <- sum(na.omit(df$GK.won == "0")) #how many times did GK lose
  
  if(gk.wins > gk.losses) {

Ram-N

  rm(list=ls())
library(ggplot2)
library(plyr)
library(reshape2)


df <- read.csv("~/RStats/data/KvsK_lifetime.csv", stringsAsFactors=F, strip.white=T)
df <- arrange(df, game) #use plyr's arrange to Sort by Game Number

#For ease drop the Opening, Event.Locale columns and  Moves Columns

Ram-N

library(lpSolveAPI)
library(reshape2)
library(ggplot2)


#Problem definition
trim.lengths <- c(44, 57, 86, 86, 40, 88, 88, 41, 40, 40, 62, 62, 54, 54, 55, 55, 63, 63, 75, 75, 100, 100)
avail.lengths <- c(120, 103, rep(100, 9), 98, rep(97, 4), 95, rep(88, 3), 85, 65)
num.trims = length(trim_lengths) #22
num.avail = length(avail.lengths) #22

Ram-N

init(side, num.colors) #df, const.type.vec have been initialized
df <- populate.Amatrix() #see github link for this function
rhs.vector <- create_rhs_vector(rhs.vector, terminal.cells)
const.type.vec <- createConstraintTypeVector(const.type.vec)
length(rhs.vector); length(const.type.vec)

Ram-N

terminal.cells <- read.csv(problemfile, header=T, stringsAsFactors=FALSE)
terminal.cells$tcell <- (terminal.cells$Y -1)* side + terminal.cells$X # cell serial number
num.colors <- length(unique(terminal.cells$color))
colorpalette <- unique(terminal.cells$palette)


#The problemfile
#X,Y,color, palette
#1,5,1,red
#2,4,1,red

Ram-N

# actual problem definition
lpff <- make.lp(nrow=n.row, ncol=n.col)
defineIP()
lpff
solve(lpff)
sol <- get.variables(lpff)

 

defineIP <- function() {