R is an open source statistical computing platform that is rapidly growing in popularity within academia. It allows for statistical analysis and data visualization. The document provides an introduction to basic R functions and syntax for assigning values, working with data frames, filtering data, plotting, and connecting to databases. More advanced techniques demonstrated include decision trees, random forests, and other data mining algorithms.

1. Introduction to R
Sander Kieft

2. Why R?
• Statistic Computing Platform
• Rapidly growing from academia
• Open Source
• (Analysis can be offloaded to a cluster)

3. Install

4. 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))

5. Booleans
! x
x & y
x && y
x | y
x || y
xor(x, y)
T
TRUE
F
FALSE

6. List comprehension
for(x in d)
for(y in d[x])
if(d[x,y]>100) ...
• vs
d[d > 100]

7. 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

8. Working with Data
csv <- read.csv(csv, header=F)
csv
names(csv) <- c(“orange”,”apple”)
•Data frames:
csv$bm
csv[1]

9. Filtering Data
csv = csv[csv$Cha>100,]
or
subset(impressions, impressions$placement_id = 3599)
or
impressions$good = impressions$placement_id==3599
na.omit(impressions$good)

10. 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 count
309 26049 4825197
2264 22550 1366138
98 22548 648174
2731 39086 566028
2258 22526 559803
99 22551 359716

11. Easy Data inspection
> head(users)
cookie browser
1 a00018e1f34e72deaa4a IE 7.0
2 a00034de71c0724b0380 IE 9.0
3 a0003941ca94dffe699b Firefox 18.0
4 a0004ad296e6e6db2b4f IE 9.0
5 a0005a52a8d123f24487 IE 9.0
> table(users$browser)
IE 7.0 IE 8.0 IE 9.0 Firefox 18.0
150 786 15645 4221
> pie(table(users$browser))

12. Build in plots
•demo(graphics)
•plot(x)

13. Extra
Packages
Provide 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)

14. Build in plots
•x <- stats::rnorm(50)
•hist(x)

15. Build in plots
•x <- c(1,2,2,3,3,3,4,4,5)
•plot(x)

16. Build in plots
•pairs(x)

17. More advanced
graphs
•ggplot2 libary
• Combine line, point and bars in one
graph
• Combine smoothing or regression
function

18. Combine Linear
Model and ggplot2
c <- ggplot(mtcars, aes(qsec, wt))
c + stat_smooth()
c + stat_smooth() + geom_point()
# Adjust parameters
c + 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()

19. Reading data
# read the data from csv
data = read.csv('data.csv', header = F, sep = 't', col.names = c('title',
'count'))
# order the data
data = 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 around
qplot(title, count, data=data)

20. 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/

21. Decision Tree
> head(kyphosis)
Kyphosis Age Number Start
1 absent 71 3 5
2 absent 158 3 14
3 present 128 4 5
4 absent 2 5 1
5 absent 1 4 15
6 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

22. Decision Tree
• Exercise: Build a decision tree to find
clickers and non-clicks in startpagina
data

23. 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) clicks
FROM 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)

24. Random Forest
> rf = randomForest(factor(Species) ~ Sepal.Length + Sepal.Width +
Petal.Length + Petal.Width, data =iris)
> rf$confusion
setosa versicolor virginica class.error
setosa 50 0 0 0.00
versicolor 0 47 3 0.06
virginica 0 4 46 0.08
> set.seed(1)
> iris.rf <- randomForest(iris[,-5], iris[,5], proximity=TRUE)
> plot(outlier(iris.rf), type="h",
> col=c("red", "green", "blue")[as.numeric(iris$Species)])

25. 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

26. Where to start
• R interpreter: http://www.r-project.org
• RStudio: http://www.rstudio.com/
• RForge: http://www.rforge.net/