primaryobjects

library('caTools')
library('ROCR')

data <- read.csv('quality.csv')

# 98 received good care (0), 33 poor care (1).
table(data$PoorCare)

rbPal <- colorRampPalette(c('red','blue'))
data$col <- rbPal(10)[as.numeric(cut(data$PoorCare, breaks = 10))]

primaryobjects

library(caTools)
library(ROCR)

framingham <- read.csv('framingham.csv')

set.seed(1000)

split <- sample.split(framingham$TenYearCHD, SplitRatio = 0.65)
train <- subset(framingham, split == TRUE)
test <- subset(framingham, split == FALSE)

primaryobjects

primaryobjects

songs <- read.csv('songs.csv')

table(songs$year == 2010)
table(songs$artistname == 'Michael Jackson')
songs[songs$artistname == 'Michael Jackson' & songs$Top10 == 1, 'songtitle']

table(songs$timesignature)
# or
unique(songs$timesignature)
summary(songs$timesignature)

primaryobjects

library(caTools)
library(ROCR)

parole <- read.csv('parole.csv')

table(parole$violator)

unique(parole$state)
unique(parole$crime)

primaryobjects

library(mice)
library(caTools)
library(ROCR)

loans <- read.csv('loans.csv')

# What proportion of the loans in the dataset were not paid in full?
table(loans$not.fully.paid)
table(loans$not.fully.paid)['1'] / nrow(loans)

primaryobjects

library(caTools)
library(rpart)
library(rpart.plot)
library(ROCR)
library(randomForest)
library(caret)
library(e1071)

stevens <- read.csv('stevens.csv')

primaryobjects

library(caTools)
library(rpart)
library(rpart.plot)

claims <- read.csv('claimsdata.csv')

table(claims$bucket2009) / nrow(claims)

set.seed(88)
spl <- sample.split(claims$bucket2009, SplitRatio = 0.6)

primaryobjects

module.exports = function(grunt) {
  // Project configuration.
  grunt.initConfig({
    nwjs: {
        options: {
            platforms: ['win64', 'osx64'],
            buildDir: './builds',
            macZip: true
        },
        src: ['../myapp/**/*']

primaryobjects

library(rpart)
library(rpart.plot)
library(caTools)
library(caret)
library(e1071)

boston <- read.csv('boston.csv')

plot(boston$LON, boston$LAT)