# QFC short course_An Introduction to R by everthingwithus.blog

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```									An Introduction to
Web resources

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

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

• R manuals: 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
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“)
Entering data into R
• Vectors:
• Sequences of numbers
c()
seq()
years<-c(1990:2007)

x<-seq(0,100,10)

x<-seq(0, 200, length=100)
Entering data into R
• Arrays:
array()
matrix()

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

m2<-matrix(1:20, ncol=5, nrow=4)
Entering data into R
• 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

df1
df[3,5]            C1    C2    C3    C4    C5
R1   25   Mon     56   45    Cat
df[,3]
R2   2    Tues    84   2     Dog
R3   24   Wed     7    15    Dog
df[5,]
R4   15   Thurs 56     236   Cat
R5   26    Fri    89   6     Cat
df[2:5,]
R6   25   Sat     23   58    Dog
R7   2    Sun     11   8     Dog
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

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:

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
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
Graphing basics
Plotting commands
1. High-level functions: Create a new plot
on the graphics device
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
Common high-level functions
plot()

plot(x)

plot(x,y) : scatter plot

plot(y~x) : scatter plot

plot(group, x) : box plot
Common high-level functions
hist(x)

boxplot(x~group)

pairs(z)

pairs(df1[,3:7])
Common high-level functions
Script:
QFC R course Graphing Basics_1.R
Exercise 3
END OF DAY 1
Lower-level graphing functions
Length (mm) histogram                                                             Boxplot of length

170
0.04

Length (mm)

150
Density

0.02

130
110
0.00

100             120            140             160          180

Chinook slamon lengths

Chinook triglyceride levels for three hatcheries                                        Scatter plot of length-weight
2000

50
Triglycerides (mg/dL)

1500

DWOR
MCCA

40
Weight (g)
RAPH
1000

30                                                  Big Fish
500

20
0

DWOR                MCCA                    RAPH                             110   120     130      140        150   160       170

Length (mm)
Lower-level graphing functions
• Axis scales and labels
xlim=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
Lower-level graphing functions
Symbol shapes and colors

25
24
pch = symbol types                                                               23
22
col = color types                                                     21
20
19
18
17
16
15
14
13
12
11
10
9
8
7
6
5
4
3
2
1

0                       5                    10                       15                       20                       25
Lower-level graphing functions
• 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)
Lower-level graphing functions
Scripts:
QFC R course Graphing Basics_2.R
QFC R course Graphing Basics_3.R
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:

yi   0  1 xi  ei   i = 1, 2,…n
Linear regression

 y1        1         x1   e1          1        x1 
                                                  
 y2        1         x2   e 2         1        x2    0 
 ...  = 0  ...+ 1  ...  +  ...  =
 
 ...          1 
...  
                                                  
y          1         x  e             1        xn 
 n                   n  n                         

Design matrix X
Linear regression
We can solve for      0 and 1 by


 X X T
1    T
X Y

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
Nonlinear regression
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

Lt  L 1  e        k[t  t 0 ]
 error
Lt 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
t 0 is the hypothetical age which the species has
zero length
Nonlinear regression
               
900

Lt  L 1  e k[t  t 0 ]  error
800
500 600 700
Length (mm)
400

0                    5                     10   15
Age (yrs)
Nonlinear regression
nls()
Least-squares estimates of the parameters
of a nonlinear model
Nonlinear regression
              
Lt  L 1  e k[t  t 0 ]  error

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

Script:
QFC R course Von Bertalanffy Nonlinear
regression.R

Exercise 6

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