A VERY BRIEF INTRODUCTION TO R

Transcription

1 CS 432/532 INTRODUCTION TO WEB SCIENCE A VERY BRIEF INTRODUCTION TO R SCOTT G. AINSWORTH OLD DOMINION UNIVERSITY

2 WHO AM I? Scott G. Ainsworth Former sailor Worked for several consulting firms Computer Scientist with U.S. Navy (civilian) Started programming at 13 Know over 20 languages Ph.D. student Temporal Coherence of composite mementos 2

3 WHAT IS R? R is Hard to Google is R a stop word? Try R Stats or R Package or R CRAN Free implementation of S Works on Linux, OS X, Windows Well documented Complex 3

4 GET R Home Page Downloads Documentation Lots and lots of samples and tutorials Google R Tutorials and R subject Samples Lots of discussion forums 4

5 DATA TYPES & MANIPULATION The basic data type is a vector x <- 5 y <- c(3, 4) x + y yields c(8, 9) There are no scalars watch out! Sequences 1:30 yields c(1, 2,, 29, 30) 2 * 1:15 yields c(2, 4,, 28, 30) 5

6 LOGICAL VECTORS Generated by conditions 5 > 6 yields FALSE Operators <, <=, >, >=, ==,!= &,,! Numerical value TRUE == 1, FALSE == TRUE yields 6 6

7 MISSING VALUES NA denotes a missing value v <- c(4, 5, NA) is.na() tests for missing values is.na(v) yields c(false, FALSE, TRUE) 7

8 INDEX VECTORS Logical Vector Index v <- c(3, 4, NA, 5, NA) y <- v[!is.na(v)] yields c(3, 4, 5) Positive Integer Vector i <- c(2, 3, 5) y <- v[i] yields c(4, NA, NA) Negative Integer Vector y <- v[-i] yields c(3, 5) 8

9 OTHER TYPES Matrices Multi-dimensional vectors Factors For categorical data Lists Vectors of multiple types Data Frames Matrix-like, named columns Functions Say what? 9

10 DATA FRAME URI Mementos NA

11 DATA FRAMES Loading a Data Frame setwd(... ) data <- read.table( sample.txt ) data print table Data[ URI ] print URI column data[4,] print row 4 summary(data[ Mementos ]) basic stats 11

12 OCCURANCES Suppose we want a bar plot barplot(data[,"mementos"], names.arg=row.names(data)) Not quite what we were looking for 12

13 OCCURANCES counts <- table(data$mementos) barplot(counts) Better But what about the missing Memento counts? 13

14 OCCURANCES What about missing values? m <- max(row.names(counts)) i <- match(0:m, row.names(counts), NA) counts2 <- counts[i] row.names(counts2) <- 0:m barplot(counts2)

15 ADDING TEXT Label the Axis barplot(counts2, main="timemap Size", xlab="number of Mementos", ylab="number of URIs") Number of URIs Timemap Size Number of Mementos 15

16 FUN STUFF Joan A. Smith's article in D-Lib Magazine 2008 Chuck Cartledge's movie consisting of R- generated pngs 16

17 IMAGE QUALITY (PNG, 100DPI) 17

18 IMAGE QUALITY (JPEG, HIGH ) 18

19 IMAGE QUALITY (PDF, EPS, VECTOR) Drift (Years) 0 1y 2y 3y 4y 5y 6y 7y 8y 9y 1 At least 1 memento At least 8 mementos At least 64 mementos At least 512 mementos At least 4,096 mementos At least 32,768 mementos 19

20 TEMPORAL COHERENCE Root Memento Datetime y 1y 0 1y 2y 3y 4y 5y 6y Mementos by Delta 20

21 TEMPORAL COHERENCE 21

22 CODE WALK THROUGH The data Loading the data Colors Advanced Plotting (scatter.plot) Plotting both PNG & PDF 22

23 COLOR ADDS A THIRD DIMENSION Drift by Step (API) Drift (Years) 0 1y 2y 3y 4y 5y 6y 7y 8y 9y 10y At least 1 memento At least 8 mementos At least 64 mementos At least 512 mementos At least 4,096 mementos At least 32,768 mementos Step Number 23

24 CODE WALK THROUGH Overview Loading and saving data Creating subsets make.heat.plot() y is step, y is drift apply() and y[x==s] hist() groups and counts Log used to select color <run it> 24

25 REFERENCES Martin Klein s Intro Home Page Downloads Documentation Quick-R Site 25