An Introduction to. Web resources R home page: R Archive:

An Introduction to

Web resources

• R home page: http://www.r-project.org/

• R Archive: http://cran.r-project.org/

• R FAQ (frequently asked questions about R): http://cran.r-project.org/doc/FAQ/R-FAQ.html

• R manuals: http://cran.r-project.org/manuals.html

http://www.r-project.org/

http://cran.r-project.org/

http://cran.r-project.org/doc/FAQ/R-FAQ.html

http://cran.r-project.org/manuals.html

The R environment• R command window (console) or

Graphical User Interface (GUI)

– Used for entering commands, data manipulations, analyses, graphing

– Output: results of analyses, queries, etc. are written here

– Toggle through previous commands by using the up and down arrow keys

The R environment

• The R workspace

– Current working environment

– Comprised primarily of variables, datasets, functions

The R environment

• R scripts– A text file containing commands that you

would enter on the command line of R

– To place a comment in a R script, use a hash mark (#) at the beginning of the line

Some tips for getting started in R

1. Create a new folder on your hard drive for your current R session

2. Open R and set the working directory to that folder

3. Save the workspace with a descriptive name and date

4. Open a new script and save the script with a descriptive name and date

Executing simple commands

• The assignment operator <-• x <- 5 assigns the value of 5 to the variable x

• y <- 2*x assigns the value of 2 times x (10 in this case) to the variable y

• r <- 4

• area.circle <- pi*r^2

• NOTE: R is case-sensitive (y ≠ Y)

●

R object types

• Vector

• Matrix

• Array

• Data frame

• Function

• List

Vectors and arrays

• Vector: a one-dimensional array, all elements of a vector must be of the same type (numerical, character, etc)

• Matrix: a two-dimensional array with rows and columns

• Array: as a matrix, but of arbitrary dimension

Entering data into R

• Vectors:• The “c” command

– combine or concatenate data

• Data can be character or numeric

v1 <- c(12, 5, 6, 8, 24)

v2 <- c("Yellow perch", "Largemouth bass", "Rainbow trout", "Lake whitefish“)


• Vectors:• Sequences of numbers

c()seq()

years<-c(1990:2007)

x<-seq(0, 200, length=100)

x<-seq(0,100,10)


• Arrays:

array()

matrix()

m1<-array(1:20, dim=c(4,5))

m2<-matrix(1:20, ncol=5, nrow=4)


• Arrays:

• Combine vectors as columns or rows

cbind()

rbind()

Matrix1 <- cbind(v1, v2)

Matrix2 <- rbind(v1, v2)

Data frames

• A data frame is a list of variables of the same length with unique row names

• A collection of variables which share many of the properties of matrices and of lists

• Used as the fundamental data structure by most of R's modeling software

Data frames

• Convert vectors or matrices into a data frame

data.frame()

df1<-data.frame(v1, v2)

df2<-data.frame(matrix1)

Data frames

• Editing data frames in spreadsheet-like view

edit()

df2<-edit(df1)

• Let’s go to R and enter some vectors, arrays, and data frames

Script : QFC R short course R object types_1_vectors and arrays.R

Placing variables in the R search path

• When variables in a data frame are used in R, the data frame name followed by a $ sign and then the variable name is required

query1<-df1$v3 > 20

Placing variables in the R search path

• Alternatively, the attach() function can be used

attach(df1)

query1 <- v3 > 20

detach(df1)

Accessing data from an array, vector, or data frame

• Subscripts are used to extract data from objects in R

• Subscripts appear in square brackets and reference rows and columns, respectively

Subscripts

C1 C2 C3 C4 C5

R1 25 Mon 56 45 Cat

R2 2 Tues 84 2 Dog

R3 24 Wed 7 15 Dog

R4 15 Thurs 56 236 Cat

R5 26 Fri 89 6 Cat

R6 25 Sat 23 58 Dog

R7 2 Sun 11 8 Dog

df1df[3,5]

df[,3]

df[5,]

df[2:5,]

Queries in R: Common logical arguments

> Greater than

< Less than

== Equals

!x ! Indicates logical negation (not), not x

x & y Logical and, x and y

x | y Logical or, x or y

Queries in R• The use of logical tests

query1<-df1$v3 > 20

df1[query1,]

query2<-df1$v3 > 20 & df1$v4 < 30 (&=and)

query2<-df1$v3 > 20 | df1$v4 < 30 (| = or)

Queries in R

Script : QFC R short course R object types_2_query arrays data frames.R

Exercise 1

Importing data from Excel (or other database management programs)

• Export as text file (.txt)

• Tips– Avoid spaces in variable and character

names, use a period (e.g., fish.weight, not fish weight and Round.lake not Round lake)

– Replace missing data with “NA”– See Excel example (MI STORET data

RAW.xls)

Importing data from Excel

• read.table()data.frame.name <- read.table(“file path”,

na.strings=”NA”, header=TRUE)

df1<-read.table("C:\\R\\Example\\datafile1.txt", na.strings="NA", header=TRUE)

Note the use of \\ instead of \ in path name

Importing data from Excel

• If your working directory is set, R will automatically look for the data text file there.

• So, the read.table syntax can be simplified by excluding the file path name:

• read.table(“data.txt”, na.strings=“NA”, header=T)

Exporting data from R

write.table()

write.table(df, file = "Path Name\\file_name.csv", sep = ",", col.names = NA)

Introduction to R functions

• R has many built-in functions and many more that can be downloaded from CRAN sites (Comprehensive R Archive Network)

• User-defined functions can also be created

The R base package

Introduction to R functions

• Common functions

names(): obtain variable names of a df

summary(): summary of all variables in a df

mean(): Mean

var(): Variance

sd(): standard deviation

Script: QFC R short course R functions_1.R

Introduction to R functions, cont

head(): print first few rows of data frame

sapply() and tapply(): column-wise summaries

levels(): obtain levels of a character variable

by(): produce summaries by group

Introduction to R functions, cont

tapply(variable, list(group1, group2), mean)Applies function to each element in ragged arrays

sapply(variable, FUN=)

Applies a function to elements in a list

by(data, INDICES, FUN)

Introduction to R loops

Basin syntax:

for (i in 1:n){

some code

}

*Excel example

Script: QFC R short course R functions_2.R

User-defined functions

Function name <- function(x){

argument }

Script: QFC R short course R user defined functions_3.R

Exercise 2 (Part 1)

R functions part 2: subset data

• subset() function

sub<- subset(data frame, criteria)

sub1<-subset(fish, no.fish > 50)

sub2<-subset(fish, no.fish>50 & position=="Below")

R functions part 2: subset data

• Select specific columns

sub3<-subset(fish, select=c(stream, site, no.fish))

Script: QFC R short course R subset_4.R

Exercise 2 (Part 2)

Introduction to basic graphing

http://addictedtor.free.fr/graphiques/

Graphing basics

Plotting commands1. High-level functions: Create a new plot

on the graphics device2. Low-level functions: Add more

information to an already existing plot, such as extra points, lines, and labels

3. Interactive graphing functions: Allow you to interactively add information to a graph

Common high-level functions

• plot(): A generic function that produces a type of plot that is dependent on the type of the first arguement

• hist(): Creates a histogram of frequencies

• barplot(): Creates a histogram of values

• boxplot(): Creates a boxplot

• pairs(): Creates a scatter plot matrix


plot()

plot(x)

plot(x,y) : scatter plot

plot(y~x) : scatter plot

plot(group, x) : box plot


hist(x)

boxplot(x~group)

pairs(z)

pairs(df1[,3:7])


Script:

QFC R course Graphing Basics_1.R

Exercise 3

END OF DAY 1

Lower-level graphing functionsLength (mm) histogram

Chinook slamon lengths

Den

sity

100 120 140 160 180

0.00

0.02

0.04

110

130

150

170

Boxplot of length

Leng

th (

mm

)

DWOR MCCA RAPH

050

010

0015

0020

00

Chinook triglyceride levels for three hatcheries

Trig

lyce

rides

(m

g/dL

)

110 120 130 140 150 160 170

2030

4050

Scatter plot of length-weight

Length (mm)

Wei

ght

(g)

DWOR

MCCA

RAPHBig Fish

Lower-level graphing functions

• Axis scales and labelsxlim=c(0,50)ylim=c(0,100)xlab=“text”ylab=“text”main=“text”cex= <1 will make font smaller than default, >1 will increase font size


0 5 10 15 20 25

05

1015

2025 pch = symbol types

col = color types

12

34

56

78

910

1112

1314

1516

1718

1920

2122

2324

25

Symbol shapes and colors


• Adding lines and text, and points

abline()

abline(a,b) a= intercept, b = slope

abline(h=mean(x, na.rm=T)

text()

text(x,y, “text”, options)

points()

points(x,y, options)


Scripts:



Exercise 4

Introduction to statistical analyses

• R provides many functions for statistical analyses– Descriptive– Univariate– Multivariate– Mixed models– Spatial– Bayesian

Introduction to statistical analyses

•Descriptive statistics

Correlations: cor()

cor(df1[,2:6])

t-tests: t.test()

t.test(y~group)

Script:

QFC R short course correlations and t-test.R

Basic model structure in R

response variable ~ predictor variable(s)

Symbols in model statements are used differently compared to arithmetic expressions

Symbol Meaning

+ Indicates inclusion of a predictor variable, not addition

- Indicates the deletion of a predictor variable, not subtraction

* Indicates inclusion of a predictor variable and an interaction, not multiplication

/ Indicates nesting of predictor variables, not division

| Indicates conditioning

: Indicates an interaction (e.g., A:B is a two-way interaction between A and B)

Specifying models in R

• Linear regression example:

iii exy 10 i = 1, 2,…n

Linear regression

ny

y

y

...2

1

= 0

1

...

1

1

+ 1

nx

x

x

...2

1

+

ne

e

e

...2

1

=

nx

x

x

1

......

1

1

2

1

1

0

Design matrix X

Linear regression

YXXX TT 1

We can solve for and by0 1

Script:

QFC R short course simple linear regression example 1.R

Simple linear regression lm()Examples

lm(y~x), with intercept and where x and y are continuous

lm(y~1+x), with intercept

lm(y~0+x), regression through the origin (no intercept)

lm(y~A), where A is a categorical variable

lm(y~x + A)

lm(y~A*B) = lm(y~A+B+A:B)

Simple linear regression lm()Example:

Model1<-lm(length~weight, data=reg)

Model1<-lm(log(length)~log(weight), data=reg)

Linear regression

• Model diagnostics– summary()– residual()– fitted()– plot()

Script:QFC R short course simple linear regression

example_2.R

Linear regression

• Subset data for regression

• Model1<-lm(length ~ wgt, subset=hatchery=="DWOR", data=reg1)

• Running models through a loop

Script:QFC R short course simple linear regression

example_3.R

Analysis of variance

• aov()

• Categorical explanatory variables

• Compare the mean values of multiple group

anova<-aov(y~groups)

Script:

QFC short course ANOVA 1.R

Exercise 5

Nonlinear regression

• Estimating parameters is more tricky compared to linear regression models

• Iterative search procedure required

• Must provide starting values of parameters

• Convergence issues


Differences in R between linear and nonlinear regression

1.For nonlinear regression models the user must specify the exact equation as part of the model statement

2.The user must specify initial guesses as to the value of the parameters that are being estimated

Nonlinear regression• Von Bertalanffy growth model

erroreLL ttkt

][ 01

tL is length at age t

L is the asymptotic average maximum length

k is the growth rate coefficient that determines how quickly the maximum size is attained

0t is the hypothetical age which the species has zero length


0 5 10 15

400

500

600

700

800

900

Age (yrs)

Leng

th (

mm

)

erroreLL ttkt

][ 01


nls()

Least-squares estimates of the parameters of a nonlinear model


vonB1<- nls(length~Linf*(1-exp(-k*(age-to))), data=length.age, start=list(Linf=1000, k=0.05, t0=-2))

erroreLL ttkt

][ 01

Nonlinear regressionGraphing fitted lines:1. Generate a sequence of numbers that cover

the range of the x-axis

2. Generate predicted values for the sequence of x-values

3. Plot original data

4. Overlay predicted values


Script:

QFC R course Von Bertalanffy Nonlinear regression.R

Exercise 6

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