Learning Aims

Introduction
Getting data into R
Accessing variables and managing subsets
An introduction to basic plotting tools
Graphing tools
An introduction to the Lattice Package
Common R mistakes

Part 1 (Introduction)

Help files and newsgroups

?boxplot

## starting httpd help server ... done

help(boxplot)

boxplot(count~spray,data=InsectSprays,col="lightgray")

?InsectSprays

R codes

2+2

## [1] 4

2+2^2

## [1] 6

(2+2)^2

## [1] 16

sqrt(2)

## [1] 1.414214

log(2)

## [1] 0.6931472

x=5
y=10
z <- x+y
z

## [1] 15

seq(1,5, by=0.5)

## [1] 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0

v1 = c(6,5,4,3,2,1)
v1

## [1] 6 5 4 3 2 1

v2 = c(10,9,8,7,6,5)

v3 = v1+v2
v3

## [1] 16 14 12 10  8  6

max(v3);min(v3)

## [1] 16

## [1] 6

length(v3)

## [1] 6

mean(v3)

## [1] 11

sd(v3)

## [1] 3.741657

Packages

A collection of previously programmed functions, often including functions for specific tasks

# Check if MASS package is installed
if (!requireNamespace("MASS", quietly = TRUE)) {
  # Install MASS package
  install.packages("MASS")
}

# Load MASS package
library(MASS)

Quitting R

Citing R and citing packages

citation()

## 
## To cite R in publications use:
## 
##   R Core Team (2023). R: A language and environment for statistical
##   computing. R Foundation for Statistical Computing, Vienna, Austria.
##   URL https://www.R-project.org/.
## 
## A BibTeX entry for LaTeX users is
## 
##   @Manual{,
##     title = {R: A Language and Environment for Statistical Computing},
##     author = {{R Core Team}},
##     organization = {R Foundation for Statistical Computing},
##     address = {Vienna, Austria},
##     year = {2023},
##     url = {https://www.R-project.org/},
##   }
## 
## We have invested a lot of time and effort in creating R, please cite it
## when using it for data analysis. See also 'citation("pkgname")' for
## citing R packages.

citation("lattice")

## 
## To cite the lattice package in publications use:
## 
##   Sarkar, Deepayan (2008) Lattice: Multivariate Data Visualization with
##   R. Springer, New York. ISBN 978-0-387-75968-5
## 
## A BibTeX entry for LaTeX users is
## 
##   @Book{,
##     title = {Lattice: Multivariate Data Visualization with R},
##     author = {Deepayan Sarkar},
##     publisher = {Springer},
##     address = {New York},
##     year = {2008},
##     note = {ISBN 978-0-387-75968-5},
##     url = {http://lmdvr.r-forge.r-project.org},
##   }

Review

Function	Purpose	Example
`help.start`	Access help on getting started	`help.start()`
`?`	Access help files	`?boxplot`
`#`	Add comments	`#add your comments here`
`boxplot`	Makes a boxplot	`boxplot(count \~ spray, data = InsectSprays, col = "lightgray")`
`sqrt`	Square root	`sqrt(2)`
`log`	Natural logarithm	`log(2)`
`seq`	Sequence of numbers	`seq(1,5, by=.5)`
`max`	Maximum	`max(1,2,3,4,5)`
`min`	Minimum	`min(1,2,3,4,5)`
`mean`	Mean	`mean(x)`
`sd`	Standard deviation	`sd(1:2) \^ 2`
`install.packages`	Install package	`install.packages("MASS")`
`library`	load package	`library(MASS)`
`q`	quit R	`q()`
`citation`	citation for R	`citation()`

Part 2 (Getting data into R)

Typing in small datasets

a <- 59
b <- 55
c <- 53.5
d <- 55
e <- 52.5

## [1] 59

Wing1 <- 59
Wing2 <- 55
Wing3 <- 53.5
Wing4 <- 55
Wing5 <- 52.5
sqrt(Wing1)

## [1] 7.681146

2*Wing1

## [1] 118

Wing1 + Wing2 + Wing3 + Wing4 + Wing5

## [1] 275

(Wing1 + Wing2 + Wing3 + Wing4 + Wing5)/5

## [1] 55

Concatenating data with the `c()` function

Wingcrd <- c(59,55,53.5,55,52.5,57.5,53,55)
Wingcrd

## [1] 59.0 55.0 53.5 55.0 52.5 57.5 53.0 55.0

Wingcrd[1]

## [1] 59

Wingcrd[1:5]

## [1] 59.0 55.0 53.5 55.0 52.5

Wingcrd[-2]

## [1] 59.0 53.5 55.0 52.5 57.5 53.0 55.0

sum(Wingcrd)

## [1] 440.5

S.win <-sum(Wingcrd)
S.win

## [1] 440.5

Tarsus <- c(22.3,19.7,20.8,20.3,20.8,21.5,20.6,21.5)
Head <- c(31.2,30.4,30.6,30.3,30.3,30.8,32.5,NA)
Wt <- c(9.5,13.8,14.8,15.2,15.5,15.6,15.6,15.7)

sum(Head)

## [1] NA

sum(Head,na.rm=TRUE)

## [1] 216.1

Combining variables with the `c`, `cbind`, `rbind`

BirdData <- c(Wingcrd, Tarsus, Head, Wt) #contains the original variables in vectors

BirdData

##  [1] 59.0 55.0 53.5 55.0 52.5 57.5 53.0 55.0 22.3 19.7 20.8 20.3 20.8 21.5 20.6
## [16] 21.5 31.2 30.4 30.6 30.3 30.3 30.8 32.5   NA  9.5 13.8 14.8 15.2 15.5 15.6
## [31] 15.6 15.7

BirdData[3]

## [1] 53.5

BirdData is a single vector of length 32

[1] is the index number of the first element

[3] is the third element

 Z <- cbind(Wingcrd,Tarsus,Head,Wt) #contains the original variables in columns
 Z

##      Wingcrd Tarsus Head   Wt
## [1,]    59.0   22.3 31.2  9.5
## [2,]    55.0   19.7 30.4 13.8
## [3,]    53.5   20.8 30.6 14.8
## [4,]    55.0   20.3 30.3 15.2
## [5,]    52.5   20.8 30.3 15.5
## [6,]    57.5   21.5 30.8 15.6
## [7,]    53.0   20.6 32.5 15.6
## [8,]    55.0   21.5   NA 15.7

 dim(Z)

## [1] 8 4

Z2 <- rbind(Wingcrd,Tarsus,Head,Wt) #combines the data in rows
Z2

##         [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
## Wingcrd 59.0 55.0 53.5 55.0 52.5 57.5 53.0 55.0
## Tarsus  22.3 19.7 20.8 20.3 20.8 21.5 20.6 21.5
## Head    31.2 30.4 30.6 30.3 30.3 30.8 32.5   NA
## Wt       9.5 13.8 14.8 15.2 15.5 15.6 15.6 15.7

dim(Z2)

## [1] 4 8

Combining data using a matrix

Dmat <- matrix(nrow=8,ncol=4)
Dmat

##      [,1] [,2] [,3] [,4]
## [1,]   NA   NA   NA   NA
## [2,]   NA   NA   NA   NA
## [3,]   NA   NA   NA   NA
## [4,]   NA   NA   NA   NA
## [5,]   NA   NA   NA   NA
## [6,]   NA   NA   NA   NA
## [7,]   NA   NA   NA   NA
## [8,]   NA   NA   NA   NA

Dmat[,1] <- c(59,55,53.5,55,52.5,57.5,53,55)
Dmat[,2] <- c(22.3,19.7,20.8,20.3,20.8,21.5,20.6,21.5)
Dmat[,3] <- c(31.2,30.4,30.6,30.3,30.3,30.8,32.5,NA)
Dmat[,4] <- c(9.5,13.8,14.8,15.2,15.5,15.6,15.6,15.7)
Dmat

##      [,1] [,2] [,3] [,4]
## [1,] 59.0 22.3 31.2  9.5
## [2,] 55.0 19.7 30.4 13.8
## [3,] 53.5 20.8 30.6 14.8
## [4,] 55.0 20.3 30.3 15.2
## [5,] 52.5 20.8 30.3 15.5
## [6,] 57.5 21.5 30.8 15.6
## [7,] 53.0 20.6 32.5 15.6
## [8,] 55.0 21.5   NA 15.7

colnames(Dmat) <- c("Wingcard","Tarsus","Head","Wt")
Dmat

##      Wingcard Tarsus Head   Wt
## [1,]     59.0   22.3 31.2  9.5
## [2,]     55.0   19.7 30.4 13.8
## [3,]     53.5   20.8 30.6 14.8
## [4,]     55.0   20.3 30.3 15.2
## [5,]     52.5   20.8 30.3 15.5
## [6,]     57.5   21.5 30.8 15.6
## [7,]     53.0   20.6 32.5 15.6
## [8,]     55.0   21.5   NA 15.7

Dmat2 <- as.matrix(cbind(Wingcrd,Tarsus,Head,Wt))
Dmat2

##      Wingcrd Tarsus Head   Wt
## [1,]    59.0   22.3 31.2  9.5
## [2,]    55.0   19.7 30.4 13.8
## [3,]    53.5   20.8 30.6 14.8
## [4,]    55.0   20.3 30.3 15.2
## [5,]    52.5   20.8 30.3 15.5
## [6,]    57.5   21.5 30.8 15.6
## [7,]    53.0   20.6 32.5 15.6
## [8,]    55.0   21.5   NA 15.7

Combining data with data.frame

Dfrm <- data.frame(WC=Wingcrd,TS=Tarsus,HD=Head,W=Wt)
Dfrm

##     WC   TS   HD    W
## 1 59.0 22.3 31.2  9.5
## 2 55.0 19.7 30.4 13.8
## 3 53.5 20.8 30.6 14.8
## 4 55.0 20.3 30.3 15.2
## 5 52.5 20.8 30.3 15.5
## 6 57.5 21.5 30.8 15.6
## 7 53.0 20.6 32.5 15.6
## 8 55.0 21.5   NA 15.7

Dfrm <- data.frame(WC=Wingcrd,TS=Tarsus,HD=Head,W=Wt,Wsq=sqrt(Wt))
Dfrm

##     WC   TS   HD    W      Wsq
## 1 59.0 22.3 31.2  9.5 3.082207
## 2 55.0 19.7 30.4 13.8 3.714835
## 3 53.5 20.8 30.6 14.8 3.847077
## 4 55.0 20.3 30.3 15.2 3.898718
## 5 52.5 20.8 30.3 15.5 3.937004
## 6 57.5 21.5 30.8 15.6 3.949684
## 7 53.0 20.6 32.5 15.6 3.949684
## 8 55.0 21.5   NA 15.7 3.962323

Importing data

Prepare the data in Excel
Export it to a .csv file format
Close Excel
Use read.table or read.csv function
Exercise!

#test <- read.csv("test1.csv",header=T,sep=",")

Review

Function	Purpose	Example
`sum`	calculated the sum	`sum(x, na.rm= TRUE)`
`median`	calculated the median	`median(x, na.rm= TRUE)`
`max`	calculated the maximum	`max((x, na.rm= TRUE)`
`min`	calculated the minimum	`min(x, na.rm= TRUE)`
`c`	concatenate data	`c(1,2,3)`
`cbind`	combine variables in columns	`cbind(x,y,z)`
`rbind`	combine variables in rows	`rbind(x,y,z)`
`vector`	combine data in a vector	`vector(length=10)`
`matrix`	combine data in a matrix	`matrix(nrow=5,ncol=10)`
`data.frame`	combine data in a data frame	`data.frame(x=x,y=y,z=z)`
`rep`	repeat values or variables	`rep(c(1,2,3),each=10)`
`seq`	create a sequence of numbers	`seq(1,10)`
`dim`	dimension of a matrix or cbind output	`dim(Mydata)`
`colnames`	column names of a matrix or cbind output	`colnames(Mydata)`
`row names`	row names of a matrix or cbind output	`rownames(MyData)`
`read.csv`	read data from a .csv file	`read.csv(file="test.txt",header=T,sep=",")`

Part 3 (Accessing variables and managing subsets)

# Check if pls package is installed
if (!requireNamespace("pls", quietly = TRUE)) {
  # Install pls package
  install.packages("pls")
}

# Load pls package
library(pls)

## 
## Attaching package: 'pls'

## The following object is masked from 'package:stats':
## 
##     loadings

gasoline

gasoline - A data set consisting of octane number (octane) and NIR spectra (NIR) of 60 gasoline samples. Each NIR spectrum consists of 401 diffuse reflectance measurements from 900 to 1700 nm

dim(gasoline)

## [1] 60  2

names(gasoline)

## [1] "octane" "NIR"

The `str` function

str(gasoline)

## 'data.frame':    60 obs. of  2 variables:
##  $ octane: num  85.3 85.2 88.5 83.4 87.9 ...
##  $ NIR   : 'AsIs' num [1:60, 1:401] -0.0502 -0.0442 -0.0469 -0.0467 -0.0509 ...
##   ..- attr(*, "dimnames")=List of 2
##   .. ..$ : chr [1:60] "1" "2" "3" "4" ...
##   .. ..$ : chr [1:401] "900 nm" "902 nm" "904 nm" "906 nm" ...

The `$` sign

gasoline$octane

##  [1] 85.30 85.25 88.45 83.40 87.90 85.50 88.90 88.30 88.70 88.45 88.75 88.25
## [13] 87.30 88.00 88.70 85.50 88.65 88.75 85.40 88.60 87.00 87.15 87.05 87.25
## [25] 86.85 88.65 86.60 86.00 86.10 86.50 86.30 84.40 84.70 84.60 84.50 88.10
## [37] 85.25 88.40 88.20 88.40 88.55 88.35 88.20 85.30 88.50 88.25 88.00 88.85
## [49] 88.45 88.70 88.10 87.60 88.35 85.10 85.10 84.70 87.20 86.60 89.60 87.10

attach adds gasoline to the search path of R

attach(gasoline)
octane

##  [1] 85.30 85.25 88.45 83.40 87.90 85.50 88.90 88.30 88.70 88.45 88.75 88.25
## [13] 87.30 88.00 88.70 85.50 88.65 88.75 85.40 88.60 87.00 87.15 87.05 87.25
## [25] 86.85 88.65 86.60 86.00 86.10 86.50 86.30 84.40 84.70 84.60 84.50 88.10
## [37] 85.25 88.40 88.20 88.40 88.55 88.35 88.20 85.30 88.50 88.25 88.00 88.85
## [49] 88.45 88.70 88.10 87.60 88.35 85.10 85.10 84.70 87.20 86.60 89.60 87.10

removes adds gasoline to the search path of R

detach(gasoline)

octane

Error: object 'octane' not found

Exporting the data

writes the gasoline file in a .csv format and will be saved in the directory of choice

#write.csv(gasoline,file="gasoline.csv",quote=FALSE)

Review

Function	Purpose	Example
`write.csv`	writes and saves the variables in .csv	`write.csv(gasoline,file=\"test.csv",quote=FALSE)`
`attach`	make a variable inside a data frame available	`attach(gasoline`
`str`	shows the internal structure of an object	`str(gasoline)`
`$`	used to access a variable	`gasoline\$octane`
`names`	provides the names of the variables	`names(gasoline)`
`dim`	provides the dimensions of the variables	`dim(x)`
`detach`	make a variable inside a data frame unavailable	`detach(gasoline)`

Part 4 (An introduction to basic plotting tools)

The plot function

plot(cars)
lines(lowess(cars))

Changing plotting characters

plot(cars,pch=17,main="distance vs time", xlim=c(5,15), ylim=c(0,80))
lines(lowess(cars))

Changing plotting characters (pch)

Changing the color of plotting symbols

plot(cars,pch=17,main="distance vs time", xlim=c(5,15), ylim=c(0,80),col=4)
lines(lowess(cars),col=2)

Changing the color of plotting symbols (col)

Altering the size of plotting symbols

plot(cars,pch=17,cex=5,main="distance vs time", xlim=c(5,15), ylim=c(0,80),col=4)

Review

Function	Purpose	Example
`plot`	plots y versus x	`plot(x,y,xlab="x label",xlim=c(0,1),ylim=c(0,2),pch=1,main="Main",ylab="Y label",col=1)`
`lines`	adds lines to an existing graph	`lines(x,y,col=1)`
`loess`	apply LOESS smoothing	`loess(y\~x)`

Part 5 (Graphing tools)

Pie chart

# Check if plotrix package is installed
if (!requireNamespace("plotrix", quietly = TRUE)) {
  # Install plotrix package
  install.packages("plotrix")
}

# Load plotrix package
library(plotrix)


slices <- c(10, 12, 4, 16, 8) 
lbls <- c("US", "UK", "Australia", "Germany", "France")
pie3D(slices,labels=lbls,explode=0.1,
    main="Pie Chart of Countries ")

pct <- round(slices/sum(slices)*100)
lbls <- paste(lbls, pct) # add percents to labels 
lbls <- paste(lbls,"%",sep="") # ad % to labels 
pie3D(slices,labels=lbls,explode=0.1,
        main="Pie Chart of Countries ")

Bar chart

counts <- table(mtcars$gear)
barplot(counts, main="Car Distribution", 
        xlab="Number of Gears")

Boxplot

# Boxplot of MPG by Car Cylinders 
boxplot(mpg~cyl,data=mtcars, main="Car Milage Data", 
        xlab="Number of Cylinders", ylab="Miles Per Gallon")

Cleveland Dotplots

dotchart(VADeaths, main = "Death Rates in Virginia - 1940")

op <- par(xaxs = "i")  # 0 -- 100%
dotchart(t(VADeaths), xlim = c(0,100),
        main = "Death Rates in Virginia - 1940")

par(op) #used to set or query graphical parameters.

Scatterplot

pairs(iris[1:4], main = "Anderson's Iris Data -- 3 species",
       pch = 21, bg = c("red", "green3", "blue")[unclass(iris$Species)])

Coplot

library(graphics)
coplot(lat ~ long | depth, data = quakes)

given.depth <- co.intervals(quakes$depth, number = 4, overlap = .1)
coplot(lat ~ long | depth, data = quakes, given.v = given.depth, rows = 1)

Review

Function

	Purpose	Example
`pie3D`	displays a 3D pie chart with optional labels	`pie3D(x,...)`
`barplot`	creates a bar plot with vertical or horizontal bars	`barplot(height, \...)`
`boxplot`	produces box-and-whisker plot(s) of the given (grouped) values	`boxplot(x, \...)`
`dotchart`	draws a Cleveland dot plot	`dotchart(x,...)`
`pairs`	a matrix of scatterplots is produced	`pairs(x, \...)`
`coplot`	produces two variants of the conditioning plots	`coplot(formula, data,...)`

Part 6 (An introduction to the Lattice Package)

The Lattice Package

Lattice is an add-on package that implements Trellis graphics (originally developed for S and S-PLUS) in R.

A powerful and elegant high-level data visualization system, with an emphasis on multivariate data.

Sufficient for typical graphics needs.

Flexible enough to handle most nonstandard requirements

# Check if lattice package is installed
if (!requireNamespace("lattice", quietly = TRUE)) {
  # Install lattice package
  install.packages("lattice")
}

# Load lattice package
library(lattice)

# Check if mlmRev package is installed
if (!requireNamespace("mlmRev", quietly = TRUE)) {
  # Install mlmRev package
  install.packages("mlmRev")
}

# Load mlmRev package
library(mlmRev)

## Loading required package: lme4

## Loading required package: Matrix

data(Chem97)
head(Chem97)

##   lea school student score gender age gcsescore   gcsecnt
## 1   1      1       1     4      F   3     6.625 0.3393157
## 2   1      1       2    10      F  -3     7.625 1.3393157
## 3   1      1       3    10      F  -4     7.250 0.9643157
## 4   1      1       4    10      F  -2     7.500 1.2143157
## 5   1      1       5     8      F  -1     6.444 0.1583157
## 6   1      1       6    10      F   4     7.750 1.4643157

The dataset records information on students appearing in the 1997 A-level chemistry examination in Britain. We are only interested in the following variables:

score: point score in the A-level exam, with six possible values (0, 2, 4, 6, 8).
gcsescore: average score in GCSE exams. This is a continuous score that may be used as a predictor of the A-level score.
gender: gender of the student.

Histogram

histogram(~ gcsescore | factor(score), data = Chem97)

Density plot

densityplot(~ gcsescore | factor(score), Chem97, groups = gender,
              plot.points = FALSE, auto.key = TRUE)

qqplot

qqmath(~ gcsescore | factor(score), Chem97, groups = gender,
       f.value = ppoints(100), auto.key = TRUE,
       type = c("p", "g"), aspect = "xy")

Box-and-whisker plots

bwplot(factor(score) ~ gcsescore | gender, Chem97)

Strip plot

stripplot(depth ~ factor(mag), data = quakes,
          jitter.data = TRUE, alpha = 0.6,
          main = "Depth of earthquake epicenters by magnitude",
          xlab = "Magnitude (Richter)",
          ylab = "Depth (km)")

Trivariate plot

cloud(depth ~ lat * long, data = quakes,
       zlim = rev(range(quakes$depth)),
       screen = list(z = 105, x = -70), panel.aspect = 0.75,
       xlab = "Longitude", ylab = "Latitude", zlab = "Depth")

dotplot

dotplot(VADeaths, type = "o",
        auto.key = list(points = TRUE, lines = TRUE, space = "right"))

Review

Function	Purpose	Example
`histogram`	draw histograms	`histogram(x, data, \...)`
`densityplot`	draw Kernel Density Plots	`densityplot(x, data, \...)`
`qqmath`	draw quantile-Quantile plots of a sample against a theoretical distribution	`qqmath(x, data, \...)`
`bwplot`	produces box-and-whisker plots	`bwplot(x, data, \...)`
`stripplot`	produces strip plot	`stripplot(\...)`
`cloud`	generic functions to draw 3d scatter plots and surfaces.	`cloud(x, data, \...)`
`dotplot`	produces dot plot	`dotplot(\...)`

Part 7 (Common R mistakes)

Errors in the source file (no space)
- No spaces in variable names
Decimal point or comma separation
- Set the style of separation
Directory names and folder location
- Incorrect directory names and folder location
- Avoid non-English alphabetical characters
Did not save R workspace

# save your command history

savehistory(file="myfile") # default is ".Rhistory"

# save the workspace to the file .RData in the cwd

save.image()

Basics of R Language

Gerard G. Dumancas

2023-06-29

Learning Aims

Part 1 (Introduction)

Help files and newsgroups

R codes

Packages

Quitting R

Citing R and citing packages

Review

Part 2 (Getting data into R)

Typing in small datasets

Concatenating data with the c() function

Combining variables with the c, cbind, rbind

Combining data using a matrix

Combining data with data.frame

Importing data

Review

Part 3 (Accessing variables and managing subsets)

The str function

The $ sign

Exporting the data

Review

Part 4 (An introduction to basic plotting tools)

The plot function

Changing plotting characters

Changing plotting characters (pch)

Changing the color of plotting symbols

Changing the color of plotting symbols (col)

Altering the size of plotting symbols

Review

Part 5 (Graphing tools)

Pie chart

Bar chart

Boxplot

Cleveland Dotplots

Scatterplot

Coplot

Review

Part 6 (An introduction to the Lattice Package)

The Lattice Package

Histogram

Density plot

qqplot

Box-and-whisker plots

Strip plot

Trivariate plot

dotplot

Review

Part 7 (Common R mistakes)

Concatenating data with the `c()` function

Combining variables with the `c`, `cbind`, `rbind`

The `str` function

The `$` sign