Introduction to R - Urfist · Introduction Data types and structures Control structures Data...

Introduction Data types and structures Control structures Data Handling Basic Analysis

Introduction to RUrfist

Moritz Muller, Maıtre de Conference at FSEG, Universit’e deStrasbourg

05.-06.12.2019

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Introduction

Data types and structures

Control structures

Data Handling

Basic Analysis

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OverviewIntroduction

What is R?First stepsExamplesData typesVectorMatrixListsData.framesConditional expressionsLoopsRead/WriteData cleaning/formattingData Merge/SelectionPlottingStatisticsBeyond this course

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References

Shorted list

• Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statisticswith S. Fourth Edition. Springer, New York. ISBN 0-387-95457-0(downloadable)

• Uwe Ligges (2009): Programmieren mit R, 3.,Uberarb. u.erweiterte Auflage, Springer Verlag, Heidelberg.

• Venables, W.N., Smith, D.M., and the R core team (2015) AnIntroduction to R. Notes on R: A Programming Environment forData Analysis and Graphics. Version 3.2.0 (2015-04-16). Downloadhttp://cran.r-project.org/doc/manuals/R-intro.pdf

• Daalgard, P (2008) Introductory Statistics with R. Second Edition.Springer Science+Business Media LLC, New York. Downloadhttp://www.academia.dk/BiologiskAntropologi/Epidemiologi/PDF/Introductory Statistics with R 2nd ed.pdf.

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Why (not) use R?

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What is R?

What is R?Object oriented, interpreted, language and programmingenvironment for Data analysis and graphics.

• huge collection of tools for statistics and data analysis

• a language for expressing statistical models and tools

• graphical facilities

• effective object-oriented programming language that can beextended

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Short History of R

Short history

1976 S language developed by Bell Labs at AT&T

1988 S-PLUS, commercial version of S (Statistical Sciences Inc.)

1995 R, open source (GPL) version of S (by Ross Ihaka and RobertGentleman)

1997 R Development Core Team (in short: R Core Team)

1998 Comprehensive R Archive Network (CRAN) founded

2000 R-1.0.0 first version compatible with S

2001 R News journal appears

2004 R version R-2.0.0 (S4 methods in package methods)

2015 R consortium (industry support)

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Short History of R - submitted packages over time1

1Gergely Daroczi, source: https://gist.github.com/daroczig8 / 109

Why (not) use R?

Interpreted language

• programming on the fly

• flexible handling

• slower than a compiled language such as C, C++ (manybuilt-in functions are in C)

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Why (not) use R?

Open Source

• No black box (in principle)

• New methods early available

• MCMC with Stan (https://github.com/stan-dev/rstan/wiki/RStan-Getting-Started),

• Webscraping with rSelenium(https://www.seleniumhq.org/projects/webdriver/),

• Deep learning with Keras(https://www.tensorflow.org/guide/keras),

• Natural language processing with WordNet(https://wordnet.princeton.edu/), and many more.

• Support from the community

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Why (not) use R?

Open Source & Programming needed

• No central authority

• No GUI (Graphical User Interface)

• e.g. offered by SPSS, SAS

Do you want to travel all-inclusive or as back-packer?

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Why (not) use R?

Open Source & Programming needed

• No central authority

• No GUI (Graphical User Interface)

• e.g. offered by SPSS, SAS

Do you want to travel all-inclusive or as back-packer?

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Today’s topicsIntroduction

What is R?First stepsExamples

Data types and structuresData typesVectorMatrixListsData.frames

Control structuresConditional expressionsLoops

Data HandlingRead/WriteData cleaning/formattingData Merge/Selection

Basic AnalysisPlottingStatisticsBeyond this course

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Installation on your own device:

Install R

• go to http://www.r-project.org/

• Download R for Windows / Mac / Linux

• Install ‘Base distribution’ with default settings

Install RStudio

• go to http://www.rstudio.org/

• Download and install with default

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Start R Studio

Explain R vs. ‘R Studio’. Small tour through RStudio.

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R - Calculator

Math expressions

> 1 + 2 * 3

> 2 * 5^2 - 10 * 5 # a comment

> 4 * sin(pi / 2)

> 0 / 0 # not defined (Not a Number)

[1] NaN

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Arithmetic Operators, Functions, Values2

Operators, Functions, Values Description

∗, / multiplication, division+, - addition, subtraction%% modulo

max(), min() extremaabs() absolute valueround(), floor(), ceiling() round (up, down)sum(), prod() sum, productlog() logarithmsin(), cos() sinus, cosinus

pi the value of piInf, -Inf infinityNaN not defined (Not a Number)NA Not AvailableNULL empty set

2See R Development Core Team (2008): R: A Language and Environmentfor Statistical Computing. R Foundation For Statistical Computing, Vienna,Austria. URL http://www.R-project.org.

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Assignments

How to ‘remember’ calculations

> x1 <- 3.25 # assign the value 3.25 to object x1

[1] 3.25

# please don’t use ‘=’ or ‘->’ for assignments

# <<- ‘special’ assignment - kept for later

Declaration and initialisation at once.

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Assignments

How to ‘remember’ calculations

> x1 <- 3.25 # assign the value 3.25 to object x1

[1] 3.25

# please don’t use ‘=’ or ‘->’ for assignments

# <<- ‘special’ assignment - kept for later

Declaration and initialisation at once.

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Print function - print()

What appears on screen? Try

> x1 <- 3.25

> (x1 <- 3.25)

> print(x1 <- 3.25)

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Objects

R is an object oriented language

• Everything in R is an object (data, functions, etc.),

• objects have names (start with letter, upper-/lower-case),

• every object has a length(),

• every object is of a certain class - class(),

• for each class, there are special (generic) functions (e.g.print()),

• every object has a mode (data type) - mode(),

• an object may have attributes - attributes(),

• classes may inherit from other classes.

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Find Help

Integrated help

• help.start()

• help(”cor”), ?cor

• help.search(”correlation”), ??correlation

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Find Help

Internet

• check r-cran

• cran taskview

• cran discussions/mailing lists/examples

• manuals / vignettes

• R news/journal

• search for snippets - anything

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Find Help

• see literature first slide and many more.

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Faithful - example

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Workspace and working directory

Workspace - save what?

• workspace keeps your R-objects

• workspace is located in your RAM

• different environments in your workspace

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Workspace - try out

> ls()

> search()

> ls(name="package:datasets")

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Working directory - default folder for save/load etc. - try out

> getwd()

[1] "/Users/moritz"

> setwd("/Users/moritz/Documents/Teaching

/2019_2020/Urfist")

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OverviewWhat is R?First stepsExamples

Data types and structuresData typesVectorMatrixListsData.framesConditional expressionsLoopsRead/WriteData cleaning/formattingData Merge/SelectionPlottingStatisticsBeyond this course

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Data types - mode()

Description Example Data type

Basic data types (hierarchical)

empty set NULL NULLboolean TRUE logical

integers & reals 3.14 numericcomplex numbers 2.13+1i complex

characters and strings ”Hello” character

Composite data type

factors blue & red factor

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Logical values in R

Try and explain

> 4 < 3

> (3+1) !=3

> -3<-2

> -3 < -2

> (3 >= 2) & (4 == (3+1))

> c(TRUE, FALSE) & c(TRUE, TRUE)

> x <- c(-4, -5, -1, 0, 2, 4, 5)

> x > 0

> sum(x > 0)

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Logical values in R

Function, Operator, Value Description

==, != equal, not equal>, >= greater, greater equal<, <= smaller, smaller equal! not

&, && and (vector, non-vector)|, || or (vector, non-vector)xor() exclusive or

TRUE, FALSE true, false

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Logical values in R

Try - any(), all(), which()

> x <- c(2, 0, 3, 1, 6, 9, 7)

> someTrue <- x > 4

> allTrue <- x >= 0

> any(someTrue)

> all(allTrue)

> any(allTrue)

> all(someTrue)

> !any(allTrue)

> all(!someTrue)

> all(!allTrue)

> which(someTrue)

> which(!someTrue) 33 / 109

Logical values in R

Not available - NA - conversion into other data types

> x <- NA

> mode(x)

> y <- c(3, x)

> mode(y)

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Logical values in R

Not available - NA - is special

> # convert NA

> x <- NA

> mode(x)

> y <- c(3, x)

> mode(y)

> # comparison and calculation

> # - (if a part is unknown - result will be unknown)

> y > 2

> sum(y)

> # do not consider (remove) NA elements

> sum(y, na.rm=TRUE)

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Numeric values in R

Try - mode(), typeof()

> (x <- pi)

[1] 3.141593

> mode(x)

[1] "numeric"

> typeof(x)

[1] "double"

> (y <- as.integer(x)) # information loss

> typeof(y)

[1] "integer"

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Numeric values in R

Try - conversion

> is.character(y)

[1] FALSE

> x <- -1

> -1^0.5

[1] -1

> sqrt(as.complex(x))

[1] 0+1i

> (z <- as.character(y))

[1] "3"

> as.numeric("z")

[1] NA

Warning message:

NAs introduced by coercion

> (y <- as.numeric("-1"))

[1] -1

[1] -5

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Character values in R

There are just strings. Manipulating them is another story. Nextcourse.

"hello world"

a <- "hello world"

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Factors in R

(ordered) factors

gulliver <- factor(c("dwarf","giant", "giant",

"giant","dwarf"))

gulliver <- factor(c("dwarf","giant", "giant",

"giant","dwarf"),

levels=c("giant","dwarf","gulliver"))

gulliver <- ordered(c("dwarf","giant", "giant",

"giant","dwarf"),

levels=c("dwarf","gulliver","giant"))

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Factors in R

(ordered) factors

> class(gulliver)

[1] "ordered" "factor"

> str(gulliver)

Ord.factor w/ 3 levels "dwarf"<"gulliver"<..: 1 3 3 3 1

> mode(gulliver)

[1] "numeric"

> length(gulliver)

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Data structures

Data structure is the representation of data, which defines how weoperate on the data.

Data structures in R

• vector

• matrix

• array

• list

• data.frame

For each data structure you have a function to declare & initialisean object, e.g. vector() .

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Vector

characteristics of vectors

> x <- c(4.1, 4.3, 2, 54, 34) # c() for concatenate

> class(x)

[1] "numeric"

> mode(x)

[1] "numeric"

> typeof(x)

[1] "double"

> str(x)

num [1:5] 4.1 4.3 2 54 34

> is.vector(x)

[1] TRUE

> length(x)

> attributes(x)

NULL43 / 109

Vector

> # Only of one data type

> c(x, FALSE, TRUE)

[1] 4.1 4.3 2.0 54.0 34.0 0.0 1.0

> c(x, FALSE, TRUE, "Hello")

[1] "4.1" "4.3" "2" "54" "34" "FALSE" "TRUE" "Hello"

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Vector

> x <- c(height=10, width=4)

height width

> attributes(x)

$names

[1] "height" "width"

> names(x)

[1] "height" "width"

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Vector

sequences

[1] 2 3 4 5

> seq(2,5,by=2)

[1] 2 4

> rep(2:4,times=3)

[1] 2 3 4 2 3 4 2 3 4

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Vector

Calculate with vectors

> x <- c(2, 4, 8)

[1] 4 8 16

[1] -1 1 5

> x * c(3,2,1)

[1] 6 8 8

> x * c(3,2)

[1] 6 8 24

Warning message:

In x * c(3, 2) :

longer object length is not a multiple of shorter object length

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Vector

Calculate with vectors

> t(2:4) %*% 1:3

[1,] 20

> 2:4 %*% 1:3

[1,] 20

> 2:4 %*% t(1:3)

[,1] [,2] [,3]

[1,] 2 4 6

[2,] 3 6 9

[3,] 4 8 12

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Vector

Indexing vectors

[1] 2 4 8

> x[2] # 2nd element by position

> x[c(FALSE,TRUE,FALSE)] # 2nd element with logical value

> names(x) <- c("Height","Length","Width")

> x["Length"] # access by name

Length

> x[-2] # without 2nd element

[1] 2 8

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Vector

Indexing vectors

# vector x

x <- c(2, 4, 8)

# extract by boolean vector

idx <- x < 5

print(idx)

x[idx]

# overwrite

x[idx] <- 2

# extract by position vector

pos <- which(x < 5)

print(pos)

x[pos]

# overwrite

x[pos] <- 150 / 109

Vector

Indexing vectors

> x[3,2,1] # reverse order?

Error in x[3, 2, 1] : incorrect number of dimensions

> x[c(3,2,1)] # reverse order!

[1] 8 4 2

> x[] <- -2 # replace every element by -2

[1] -2 -2 -2

> x <- -2 # overwrite x by -2

[1] -2

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Matrix

initialise a Matrix - try and explain

x <- 1:3

y <- 4:6

cbind(x,y)

rbind(x,y)

matrix(1:6, nrow=3, ncol=2)

matrix(1:6, nrow=3, byrow=TRUE)

matrix(1:6, ncol=2, byrow=TRUE)

matrix(1:6, nrow=3, ncol=4, byrow=TRUE)

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Matrix

Operate on matrices - try and explain

A <- matrix(c(1,1,2,2), nrow=2, byrow=TRUE)

B <- t(A) # transpose

solve(A) # inverse

# elementwise operations

A == B

A == t(B)

any(A==B)

# matrix multiplication

A %*% B54 / 109

Matrix

Access matrix elements - exercise

>X <- matrix(1:6, nrow=3)

>rownames(X) <- c("length", "width", "height")

>colnames(X) <- c("shelf","chair")

• read length of shelf by [row pos., column pos.]

• read length of shelf by [row name, column name]

• read length of shelf by [row name, column pos.]

• overwrite length of shelf with 14

• get all dimensions of shelf

• get length of all mobiliar

• overwrite length of mobiliar by prior length times 10055 / 109

Matrix

Matrix features

> class(X)

[1] "matrix"

> typeof(X)

[1] "integer"

> mode(X)

[1] "numeric"

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Matrix

Matrix features

> attributes(X)

[1] 3 2

$dimnames

$dimnames[[1]]

[1] "length" "width" "height"

$dimnames[[2]]

[1] "table" "chair"

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Matrix

Matrix features

> str(X)

int [1:3, 1:2] 1 2 3 4 5 6

- attr(*, "dimnames")=List of 2

..$ : chr [1:3] "length" "width" "height"

..$ : chr [1:2] "table" "chair"

Matrices are special cases of vectors in R (the opposite in math ofcourse).

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Array - like matrix but possibly more dimensions

Avoid arrays.Don’t use arrays with more than 3 dimensions if not absolutelynecessary.Use map of array.

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List - features

• very flexible,

• may contain different data types,

• also lists (recursive)

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List - features

> (L1 <- list("this is a", 100, matrix(1:4,nrow=2),

list(TRUE,FALSE)))

[1] "this is a"

[1] 100

[,1] [,2]

[1,] 1 3

[2,] 2 4

[[4]][[1]]

[1] TRUE

[[4]][[2]]

[1] FALSE

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List - features

> (L1 <- list(a="this is a", b=100, c=matrix(1:4,nrow=2),

truth=list(TRUE,FALSE)))

[1] "this is a"

[1] 100

[,1] [,2]

[1,] 1 3

[2,] 2 4

$truth

$truth[[1]]

[1] TRUE

$truth[[2]]

[1] FALSE

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List - features

> L1[[1]] # access by position

[1] "this is a"

> L1[["a"]] # access by name

[1] "this is a"

> L1$b # access by name (but different)

[1] 100

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List - features

> L1[[1]] <- NULL

[1] 100

[,1] [,2]

[1,] 1 3

[2,] 2 4

$truth

$truth[[1]]

[1] TRUE

$truth[[2]]

[1] FALSE65 / 109

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data.frames

• the most typical structure for data sets

• data.frames are lists - but entries are vectors with same length

• data.frame()

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Data.frame

initialise Data.frame

> shopping <- data.frame(Product=c("cheese","wine","bread"),

Unit=c("grams","bottles","loaf"), Amount=c(300,2,2))

> str(shopping)

’data.frame’: 3 obs. of 3 variables:

$ Product: Factor w/ 3 levels "bread","cheese",..: 2 3 1

$ Unit : Factor w/ 3 levels "bottles","grams",..: 2 1 3

$ Amount : num 300 2 2

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Data.frame

Data.frame - access elements

> shopping[2,]

Product Unit Amount

2 wine bottles 2

> shopping[2,3]

> shopping[2,"Amount"]

> shopping$Amount[2]

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Data.frame

Plot two regimes of old faithful

40 50 60 70 80 90 100

waiting

eruptions

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Data.frame

Plot two regimes of old faithful (by subsetting data.frame)

# plot geysir data

plot(NA, xlim=c(40,100),ylim=c(1,6), xlab="waiting",

ylab="eruptions")

points(regime1[,"waiting"],regime1[,"eruptions"],col="red")

points(regime2[,"waiting"],regime2[,"eruptions"],col="blue")

points(rest[,"waiting"],rest[,"eruptions"],col="grey")

lines(x=c(70,70),y=c(0,7),lty=3)

lines(x=c(40,100),y=c(3.5,3.5),lty=3)

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Data.frame

Plot two regimes of old faithful (by adding variables todata.frame)

# plot geysir data

plot(faithful$waiting,faithful$eruptions, col=faithful$color,

xlim=c(40,100),ylim=c(1,6),

xlab="waiting", ylab="eruptions")

lines(x=c(70,70),y=c(0,7),lty=3)

lines(x=c(40,100),y=c(3.5,3.5),lty=3)

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OverviewWhat is R?First stepsExamplesData typesVectorMatrixListsData.frames

Control structuresConditional expressionsLoopsRead/WriteData cleaning/formattingData Merge/SelectionPlottingStatisticsBeyond this course

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Conditional expressions

If...else

> x <- 1

> if(x > 0){

+ print("x is pos.")

+ }else{

+ print("x is neg.")

[1] "x is pos."

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Ifelse

# vector-oriented, use for simple expr.

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

> ifelse(x > 2, "A", "B")

[1] "B" "B" "A" "A"

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switch

> switch(2, a=11, b=12, cc=13, d=14)

[1] 12

> (switch("c", a=11, b=12, cc=13, d=14))

> switch("cc", a=11, b=12, cc=13, d=14)

[1] 13

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OverviewWhat is R?First stepsExamplesData typesVectorMatrixListsData.frames

Control structuresConditional expressionsLoopsRead/WriteData cleaning/formattingData Merge/SelectionPlottingStatisticsBeyond this course

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> for(i in 1:3){

+ print(i)

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repeat

> i <- 0

> repeat{

+ i <- i+1

+ if(i < 3){

+ next # start next turn

+ print(i)

+ if(i == 3){

+ break # exit loop

[1] 379 / 109

> i <- 0

> while(i < 3){

+ print(i)

+ i <- i + 1

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Loops in R

Short note on loops and efficiency

• R is executed by an interpreter (not compiled). Interpreterneeds to translate each loop into machine code.

• Vectorized code is typically faster (once translated, fetch,calculate write of each value ‘compressed’).

• maybe slower (if the size of the vector becomes too large),

• maybe difficult to read (if several objects are handled),

• maybe not possible (if sequential combination).

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Excercise

• Exercise ‘R shaked’

• Exercise ‘Normal Distribution uncommented’

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OverviewWhat is R?First stepsExamplesData typesVectorMatrixListsData.framesConditional expressionsLoops

Data HandlingRead/WriteData cleaning/formattingData Merge/SelectionPlottingStatisticsBeyond this course

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Read/Write

Import/export, read/write

• R objects (load(), save())

• text-files (read.table() / write.table(), scan() / cat())

• operate on excel/access/data bases (e.g. RODBC, orRMySQL)

• SAS/SPSS/Stata (e.g. foreign)

See “R data import / export”.

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Character string handling

Function Description

scan() / cat() Read from / print to file (or console)parse() / deparse() convert char into expression (and vice-versa)grep() regular expressionsnchar() number of chars in a stringstrsplit() / paste() (dis-)connect strings

See e.g. Sanchez, G. (2013) Handling and Processing Strings in RTrowchez Editions. Berkeley, 2013.http://www.gastonsanchez.com/Handling and Processing Stringsin R.pdf

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Data Merge/Selection

How to join and split data.frames?

• merge(),

• rbind()

• cbind()

• subset()

• idx < − x=y; df[idx,c(”A”,”B”)]

Note: Merge/split of very large tables (millions of entries) better ina data base.

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Data Merge/Selection

How to join and split data.frames?

• merge(),

• rbind()

• cbind()

• subset()

• idx < − x=y; df[idx,c(”A”,”B”)]

Note: Merge/split of very large tables (millions of entries) better ina data base.

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OverviewWhat is R?First stepsExamplesData typesVectorMatrixListsData.framesConditional expressionsLoopsRead/WriteData cleaning/formattingData Merge/Selection

89 / 109

R plotting

How to make good graphs

1. Excel plots

2. R plots - various devices - pdf, ps etc.

3. TikZ plots (vector graphics in LaTeX) - TikzDevice - NEW

4. Plotly - interactive web-based graphs via the open sourceJavaScript graphing library NEWER

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R plotting

Devices

• Device defines where to plot, similar to paper, pergament etc.

• the same graph gets different ‘look and feels’ on differentdevices

• (transparent) colours differ by device

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R plotting - pdf-device

●●

● ● ●

●●

●●●● ●●● ●

●● ●●●

2000 2003 2006 2009

30Regional spread of Greek GDP

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Scientific plotting - tikzDevice

2000 2002 2004 2006 2008 2010

Regional spread of Greek GDP

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R plotting

Typical processStep 0: try out on the default device.

f <- paste(getwd(),"/GreekGDP.pdf",sep="") # define file

pdf(file=f, width=4, height=3) # open device

# plot into device

boxplot(gdp2[idx,"EUR_HAB"]/1000~gdp2[idx,"TIME"],notch=TRUE, xlab="Year", ylab="k Euro per capita",

main="Regional spread of Greek GDP")

dev.off() # close device

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R plotting

Plot functions

• plot() - context dependent / generic function - depend on theclass to plot.

• add - points(), lines(), text() in the plot.

• barplot(), boxplot(), contour(), hist(), etc.

• plot functions from special packages

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R plotting

• options in the function, e.g. cex

• options through par()

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R plotting

Arguments frequently used in plot functions and par()Argument Description

axes (not) draw axes - later fine tunedcex size (multiply) of nodes and letterslty, lwd line type, line widthpch point symbolxlab, ylab axes labelsxlim, ylim axes dimensions

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R plotting - regions

par()-ameters oma/mar3

3http://rgraphics.limnology.wisc.edu/rmargins sf.php98 / 109

R plotting - regions

par()-ameters mfcol/mfrow4

4http://rgraphics.limnology.wisc.edu/rmargins mfcol.php99 / 109

R plotting

General remarks

• Everything can be done,

• But may cost time to figure out how, and to code.

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Descriptives

Checkoutsummary(), table(), prop.table(), cor(), rcorr(), etc.

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Descriptives - free work

What’s the chance of a happy end? Use the ‘Titanic’ data inthe library ‘datasets’.

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Descriptives - free work

Bring your project to the class room

YOUR PROJECT HERE.

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Beyond this course

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Inductive stats

Checkoutlm(), glm(), task view

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Non-parametrics

Checkoutdensity() in R base, np-package, task view

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Bayesian

CheckoutrStan package, task view

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