Introduction to RSander Kieft

Why R?

• Statistic Computing Platform

• Rapidly growing from academia

• Open Source

• (Analysis can be offloaded to a cluster)

Install

Assignment

x <-7

x <- c(1,2,3,4)

x = c(1,2,3,4)

c(1,2,3,4) -> x

assign(“x”,c(1,2,3,4))

Booleans

! x

x & y

x && y

x | y

x || y

xor(x, y)

TTRUE

FFALSE

List comprehension

for(x in d)

for(y in d[x])

if(d[x,y]>100) ...

• vs

d[d > 100]

Vector Arithmetic

x <- c(1,2,3,4,5)

x*2

y <- c(1,2,3,4,5)

x+y

x <- c(1,2,3,4,5)

m <- max(x)

x/m

Working with Data

csv <- read.csv(csv, header=F)csvnames(csv) <- c(“orange”,”apple”)

•Data frames:

csv$bmcsv[1]

Filtering Data

csv = csv[csv$Cha>100,]

or

subset(impressions, impressions$placement_id = 3599)

or

impressions$good = impressions$placement_id==3599na.omit(impressions$good)

Easy Data inspection> summary(data)

title count Min. : 1 Min. : 1 1st Qu.:22660 1st Qu.: 6 Median :28430 Median : 44 Mean :28587 Mean : 4184 3rd Qu.:41069 3rd Qu.: 290 Max. :44886 Max. :4825197

> head(data)

title count309 26049 48251972264 22550 136613898 22548 6481742731 39086 5660282258 22526 55980399 22551 359716

title count

Easy Data inspection

> head(users)

cookie browser1 a00018e1f34e72deaa4a IE 7.02 a00034de71c0724b0380 IE 9.03 a0003941ca94dffe699b Firefox 18.04 a0004ad296e6e6db2b4f IE 9.05 a0005a52a8d123f24487 IE 9.0

> table(users$browser)

IE 7.0 IE 8.0 IE 9.0 Firefox 18.0150 786 15645 4221

> pie(table(users$browser))

Build in plots

•demo(graphics)

•plot(x)

Extra

PackagesProvide extra functionalities and

algorithms, you can install them from the

interface. Or add them to your script:

install.packages("RJDBC",dep=TRUE)install.packages("ggplot2",dep=TRUE)

Build in plots

•x <- stats::rnorm(50)

•hist(x)

Build in plots

•x <- c(1,2,2,3,3,3,4,4,5)

•plot(x)

Build in plots

•pairs(x)

More advanced

graphs

•ggplot2 libary

• Combine line, point and bars in one

graph

• Combine smoothing or regression

function

Combine Linear

Model and ggplot2

c <- ggplot(mtcars, aes(qsec, wt))c + stat_smooth()c + stat_smooth() + geom_point()

# Adjust parametersc + stat_smooth(se = FALSE) + geom_point()

c + stat_smooth(span = 0.9) + geom_point()c + stat_smooth(level = 0.99) + geom_point()c + stat_smooth(method = "lm") + geom_point()

Reading data

# read the data from csvdata = read.csv('data.csv', header = F, sep = '\t', col.names = c('title', 'count'))

# order the datadata = data[order(data$count, decreasing=T),]data$title = factor(data$title, levels=unique(as.character(data$title)))head(data)qplot(count, title, data=data)

# the other way aroundqplot(title, count, data=data)

Database

connections• Install:

install.packages("RJDBC",dep=TRUE)install.packages("DBI",dep=TRUE)install.packages("rJava",dep=TRUE)

• Code:library(RJDBC)drv <- JDBC("com.mysql.jdbc.Driver",

"/etc/jdbc/mysql-connector-java-3.1.14-bin.jar",identifier.quote="`")

conn <- dbConnect(drv, "jdbc:mysql://localhost/test", "user", "pwd")

dbGetQuery(conn, "select count(*) from iris")

d <- dbReadTable(conn, "iris")

data(iris)

dbWriteTable(conn, "iris", iris, overwrite=TRUE)

• Docs: http://www.rforge.net/RJDBC/

Decision Tree> head(kyphosis)

Kyphosis Age Number Start1 absent 71 3 52 absent 158 3 143 present 128 4 54 absent 2 5 15 absent 1 4 156 absent 1 2 16

> fit <- rpart(Kyphosis ~ Age + Number + Start, data=kyphosis)> par(mfrow=c(1,2), xpd=NA) # prevent text clipping> plot(fit)> text(fit, use.n=TRUE)

summary(fit)

Predict this, given that

Decision Tree

• Exercise: Build a decision tree to find

clickers and non-clicks in startpagina

data

Decision Tree• Create feature vector with Hive

SELECT v.cookie, COUNT(DISTINCT v.day) dagen, browser_with_version(v.user_agent) bwv, device_type(v.user_agent) dt, v.screen, COUNT(c.day) clicksFROM at_views v LEFT OUTER JOIN at_clicks c ON v.cookie = c.cookie

WHERE v.day > '2013-01-12' AND v.site = 470027 AND v.site_section = 16 AND v.cookie LIKE "a%"GROUP BY v.cookie,browser_with_version(v.user_agent), device_type(v.user_agent), v.screen

Load the output CSV into R

clicklog <- read.csv("~/Downloads/query_result-2.csv", header=T, sep = ',') clicklog$clickers <- (clicklog$clicks > 0)fit <- rpart(clickers ~ screen + dt + bwv + dagen, data=clicklog)plot(fit)text(fit, use.n=TRUE)

Random Forest

> rf = randomForest(factor(Species) ~ Sepal.Length + Sepal.Width + Petal.Length + Petal.Width, data =iris)> rf$confusion

setosa versicolor virginica class.errorsetosa 50 0 0 0.00versicolor 0 47 3 0.06virginica 0 4 46 0.08> set.seed(1)

> iris.rf <- randomForest(iris[,-5], iris[,5], proximity=TRUE)

> plot(outlier(iris.rf), type="h",

> col=c("red", "green", "blue")[as.numeric(iris$Species)])

Data mining

algorithmsExamples of tasks Algorithms to use

Predicting a discrete attribute

• Flag the customers in a prospective buyers list as good or poor prospects.

• Calculate the probability that a server will fail within the next 6 months.

• Categorize patient outcomes and explore related factors.

Decision Trees

Naive Bayes

Clustering

Neural Network

Logistic Regression

Predicting a continuous attribute

• Forecast next year's sales.

• Predict site visitors given past historical and seasonal trends.

• Generate a risk score given demographics.

Decision Trees

Time Series

Linear Regression

Predicting a sequence

• Perform clickstream analysis of a company's Web site.

• Analyze the factors leading to server failure.

• Capture and analyze sequences of activities during outpatient visits, to formulate best practices around common

activities

Sequence Clustering

Finding groups of common items in transactions

Where to start

• R interpreter: http://www.r-project.org

• RStudio: http://www.rstudio.com/

• RForge: http://www.rforge.net/