# Mindanao State University
# General Santos City
# Introduction to R base commands
# Prepared by: Prof. Carlito O. Daarol
# Faculty
# Math Department
# Submitted by: Jeshryl R. Sabang
# Lab Exercise 1: How to create Lines in with different styles in R
# Step 1: Create Data
x <- 1:10 # Create example data
y <- c(3, 1, 5, 2, 3, 8, 4, 7, 6, 9) # using the c() function to create an array
# Step 2: Plot the line graph using the base plot() command
plot(x, y, type = "l")
# Step 3: Add Main Title & Change Axis Labels
plot(x, y, type = "l",
main = "Main Title",
xlab = "X-Values",
ylab = "Y-Values")
#Step 4: Add color to the line using the col command
plot(x, y, type = "l",
main = "Main Title",
xlab = "X-Values",
ylab = "Y-Values",
col = "green")
# Step 5: Modify Thickness of Line using lwd command
plot(x, y, type = "l",
main = "Main Title",
xlab = "X-Values",
ylab = "Y-Values",
lwd=7,
col = "yellow")
# Step 6: Add points to line graph by changing the type command
plot(x, y, type = "b",
main = "Main Title",
xlab = "X-Values",
ylab = "Y-Values",
lwd=3,
col = "red")
# Lab Exercise 2: How to create Lines in with different styles in R
# Step1: Assign values for different lines. We enclose the entire
# line with parenthesis symbol to force R to display the results instantly
# set the same value for the x variable
(x <- 1:10)
## [1] 1 2 3 4 5 6 7 8 9 10
# set different values for y variables
(y1 <- c(3, 1, 5, 2, 3, 8, 4, 7, 6, 9))
## [1] 3 1 5 2 3 8 4 7 6 9
(y2 <- c(5, 1, 4, 6, 2, 3, 7, 8, 2, 8))
## [1] 5 1 4 6 2 3 7 8 2 8
(y3 <- c(3, 3, 3, 3, 4, 4, 5, 5, 7, 7))
## [1] 3 3 3 3 4 4 5 5 7 7
# Plot first the pair x and y1.
plot(x, y1, type = "b",
main = "Main Title",
xlab = "X-Values",
ylab = "Y-Values",
lwd=3,
col = "blue")
# then Add the two lines (for x,y2) and (x,y3)
lines(x, y2, type = "b", col = "red",lwd=3)
lines(x, y3, type = "b", col = "green",lwd=3)
# Add legend to the plot
legend("topleft",
legend = c("Line y1", "Line y2", "Line y3"),
col = c("black", "red", "green"),
lty = 1)
# Step 2: Create Different Point Symbol for Each
# Line using the pch command
plot(x, y1, type = "b",pch = 16,
main = "Main Title",
xlab = "X-Values",
ylab = "Y-Values",
lwd=3,
col = "blue")
lines(x, y2, type = "b", col = "red",lwd=3, pch = 15)
lines(x, y3, type = "b", col = "green",lwd=3, pch = 8)
# Add legend
legend("topleft",
legend = c("Line y1", "Line y2", "Line y3"),
col = c("black", "red", "green"),
lty = 1)
# Lab Exercise 3: Create Line graph without x values
Pupils <- c(3.55 ,3.54 ,3.53 ,3.61 ,3.65 ,3.63 ,3.61
,3.61 ,3.59 ,3.63 ,3.59 ,3.63 ,3.62 ,3.62
,3.59 ,3.63 ,3.62 ,3.65 ,3.65)
# get number of elements of Pupils
length(Pupils)
## [1] 19
# Display the elements of Pupils
Pupils
## [1] 3.55 3.54 3.53 3.61 3.65 3.63 3.61 3.61 3.59 3.63 3.59 3.63 3.62 3.62 3.59
## [16] 3.63 3.62 3.65 3.65
# You can obtain the plot without x values
plot(Pupils, type = 'o')
# Lab Exercise 4: How to Create vertical, horizontal lines
# We will use buit-in cars dataset in R
# display the cars dataset
cars
## speed dist
## 1 4 2
## 2 4 10
## 3 7 4
## 4 7 22
## 5 8 16
## 6 9 10
## 7 10 18
## 8 10 26
## 9 10 34
## 10 11 17
## 11 11 28
## 12 12 14
## 13 12 20
## 14 12 24
## 15 12 28
## 16 13 26
## 17 13 34
## 18 13 34
## 19 13 46
## 20 14 26
## 21 14 36
## 22 14 60
## 23 14 80
## 24 15 20
## 25 15 26
## 26 15 54
## 27 16 32
## 28 16 40
## 29 17 32
## 30 17 40
## 31 17 50
## 32 18 42
## 33 18 56
## 34 18 76
## 35 18 84
## 36 19 36
## 37 19 46
## 38 19 68
## 39 20 32
## 40 20 48
## 41 20 52
## 42 20 56
## 43 20 64
## 44 22 66
## 45 23 54
## 46 24 70
## 47 24 92
## 48 24 93
## 49 24 120
## 50 25 85
# get the number of rows and columns using dim() command
dim(cars)
## [1] 50 2
# display the variable names of the cars dataset
names(cars)
## [1] "speed" "dist"
# display only the first column of the dataset
cars$speed # using the column name
## [1] 4 4 7 7 8 9 10 10 10 11 11 12 12 12 12 13 13 13 13 14 14 14 14 15 15
## [26] 15 16 16 17 17 17 18 18 18 18 19 19 19 20 20 20 20 20 22 23 24 24 24 24 25
cars[,1] # using the column number
## [1] 4 4 7 7 8 9 10 10 10 11 11 12 12 12 12 13 13 13 13 14 14 14 14 15 15
## [26] 15 16 16 17 17 17 18 18 18 18 19 19 19 20 20 20 20 20 22 23 24 24 24 24 25
# Remarks: the following commands will give you the same result
plot(cars,) # using the comma after the name
plot(cars[,1],cars[,2]) # using the column index 1 and 2
attach(cars); plot(speed,dist) # using the attach command to load the variables
plot(cars$speed,cars$dist) # using the dollar notation
# combine all 4 plots using the par() command
par(mfrow = c(2,2)) # set a 2x2 plot output
plot(cars,) # using the comma after the name
plot(cars[,1],cars[,2]) # using the column index 1 and 2
attach(cars); plot(speed,dist) # using the attach command to load the variables
## The following objects are masked from cars (pos = 3):
##
## dist, speed
plot(cars$speed,cars$dist) # using the dollar notation
par(mfrow = c(1,1)) # reset to default plot setting
# Problem: Create vertical lines using the v command
plot(cars)
abline(v = 15, col = "darkgreen",lwd=3) # vertical line
abline(v = 10, col = "blue",lwd=3) # vertical line
# Problem: Create horizontal lines using the h command
abline(h = 80, col = "darkgreen",lwd=3) # vertical line
abline(h = 20, col = "blue",lwd=3) # vertical line
# Create more lines simultaneously, using a vector of values
plot(cars)
abline(v = c(9, 22,25), col = c("darkgreen", "blue","red"),
lwd = c(1, 3,2), # line thickness
lty = c(2,2,2)) # dashed lines
plot(cars)
abline(v = c(9, 22,25), col = c("darkgreen", "blue","red"),
lwd = c(1, 3,2)) # line thickness and solid lines
# create horizontal lines
plot(cars)
abline(h = 60, col = "red",lty = 1, lwd = 3)
abline(h = 100, col = "red",lty = 2, lwd = 3)
abline(h = 20, col = "red",lty = 3, lwd = 3)
# Lab Exercise 5: How to Plot data by group
# We will use buit-in iris dataset in R
# this dataset is a collection of 4 species of flowers with different
# sepal length and width and also with different petal length and width
dim(iris) # iris dataset has 150 rows and 5 columns
## [1] 150 5
names(iris)
## [1] "Sepal.Length" "Sepal.Width" "Petal.Length" "Petal.Width" "Species"
# two different commands to get the frequency table
table(iris$Species) # refer to the dataset by variable name
##
## setosa versicolor virginica
## 50 50 50
table(iris[,5]) # refer to the dataset by column number
##
## setosa versicolor virginica
## 50 50 50
# get summary of all columns
summary(iris)
## Sepal.Length Sepal.Width Petal.Length Petal.Width
## Min. :4.300 Min. :2.000 Min. :1.000 Min. :0.100
## 1st Qu.:5.100 1st Qu.:2.800 1st Qu.:1.600 1st Qu.:0.300
## Median :5.800 Median :3.000 Median :4.350 Median :1.300
## Mean :5.843 Mean :3.057 Mean :3.758 Mean :1.199
## 3rd Qu.:6.400 3rd Qu.:3.300 3rd Qu.:5.100 3rd Qu.:1.800
## Max. :7.900 Max. :4.400 Max. :6.900 Max. :2.500
## Species
## setosa :50
## versicolor:50
## virginica :50
##
##
##
#create scatterplot of sepal width vs. sepal length
plot(iris$Sepal.Width, iris$Sepal.Length,
col='steelblue',
main='Scatterplot',
xlab='Sepal Width',
ylab='Sepal Length',
pch=19)
plot(iris$Sepal.Width, iris$Sepal.Length,
col='steelblue',
main='Scatterplot',
xlab='Sepal Width',
ylab='Sepal Length',
pch=1)
# another way to retrieve columns of data
PL <- iris$Petal.Length
PW <- iris$Petal.Width
plot(PL, PW)
# add color by species
plot(PL, PW, col = iris$Species, main= "My Plot")
# draw a line along with the distribution of points
# using the abline and lm commands
abline(lm(PW ~ PL))
# add text annotation
text(5, 0.5, "Regression Line")
legend("topleft", # specify the location of the legend
levels(iris$Species), # specify the levels of species
pch = 1:3, # specify three symbols used for the three species
col = 1:3) # specify three colors for the three species
# Lab Exercise 6: Generate advance scatter plot
pairs(iris, col = rainbow(3)[iris$Species]) # set colors by species
# How to filter data for each flower
(Versicolor <- subset(iris, Species == "versicolor"))
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 51 7.0 3.2 4.7 1.4 versicolor
## 52 6.4 3.2 4.5 1.5 versicolor
## 53 6.9 3.1 4.9 1.5 versicolor
## 54 5.5 2.3 4.0 1.3 versicolor
## 55 6.5 2.8 4.6 1.5 versicolor
## 56 5.7 2.8 4.5 1.3 versicolor
## 57 6.3 3.3 4.7 1.6 versicolor
## 58 4.9 2.4 3.3 1.0 versicolor
## 59 6.6 2.9 4.6 1.3 versicolor
## 60 5.2 2.7 3.9 1.4 versicolor
## 61 5.0 2.0 3.5 1.0 versicolor
## 62 5.9 3.0 4.2 1.5 versicolor
## 63 6.0 2.2 4.0 1.0 versicolor
## 64 6.1 2.9 4.7 1.4 versicolor
## 65 5.6 2.9 3.6 1.3 versicolor
## 66 6.7 3.1 4.4 1.4 versicolor
## 67 5.6 3.0 4.5 1.5 versicolor
## 68 5.8 2.7 4.1 1.0 versicolor
## 69 6.2 2.2 4.5 1.5 versicolor
## 70 5.6 2.5 3.9 1.1 versicolor
## 71 5.9 3.2 4.8 1.8 versicolor
## 72 6.1 2.8 4.0 1.3 versicolor
## 73 6.3 2.5 4.9 1.5 versicolor
## 74 6.1 2.8 4.7 1.2 versicolor
## 75 6.4 2.9 4.3 1.3 versicolor
## 76 6.6 3.0 4.4 1.4 versicolor
## 77 6.8 2.8 4.8 1.4 versicolor
## 78 6.7 3.0 5.0 1.7 versicolor
## 79 6.0 2.9 4.5 1.5 versicolor
## 80 5.7 2.6 3.5 1.0 versicolor
## 81 5.5 2.4 3.8 1.1 versicolor
## 82 5.5 2.4 3.7 1.0 versicolor
## 83 5.8 2.7 3.9 1.2 versicolor
## 84 6.0 2.7 5.1 1.6 versicolor
## 85 5.4 3.0 4.5 1.5 versicolor
## 86 6.0 3.4 4.5 1.6 versicolor
## 87 6.7 3.1 4.7 1.5 versicolor
## 88 6.3 2.3 4.4 1.3 versicolor
## 89 5.6 3.0 4.1 1.3 versicolor
## 90 5.5 2.5 4.0 1.3 versicolor
## 91 5.5 2.6 4.4 1.2 versicolor
## 92 6.1 3.0 4.6 1.4 versicolor
## 93 5.8 2.6 4.0 1.2 versicolor
## 94 5.0 2.3 3.3 1.0 versicolor
## 95 5.6 2.7 4.2 1.3 versicolor
## 96 5.7 3.0 4.2 1.2 versicolor
## 97 5.7 2.9 4.2 1.3 versicolor
## 98 6.2 2.9 4.3 1.3 versicolor
## 99 5.1 2.5 3.0 1.1 versicolor
## 100 5.7 2.8 4.1 1.3 versicolor
(Setosa <- subset(iris, Species == "setosa"))
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa
## 7 4.6 3.4 1.4 0.3 setosa
## 8 5.0 3.4 1.5 0.2 setosa
## 9 4.4 2.9 1.4 0.2 setosa
## 10 4.9 3.1 1.5 0.1 setosa
## 11 5.4 3.7 1.5 0.2 setosa
## 12 4.8 3.4 1.6 0.2 setosa
## 13 4.8 3.0 1.4 0.1 setosa
## 14 4.3 3.0 1.1 0.1 setosa
## 15 5.8 4.0 1.2 0.2 setosa
## 16 5.7 4.4 1.5 0.4 setosa
## 17 5.4 3.9 1.3 0.4 setosa
## 18 5.1 3.5 1.4 0.3 setosa
## 19 5.7 3.8 1.7 0.3 setosa
## 20 5.1 3.8 1.5 0.3 setosa
## 21 5.4 3.4 1.7 0.2 setosa
## 22 5.1 3.7 1.5 0.4 setosa
## 23 4.6 3.6 1.0 0.2 setosa
## 24 5.1 3.3 1.7 0.5 setosa
## 25 4.8 3.4 1.9 0.2 setosa
## 26 5.0 3.0 1.6 0.2 setosa
## 27 5.0 3.4 1.6 0.4 setosa
## 28 5.2 3.5 1.5 0.2 setosa
## 29 5.2 3.4 1.4 0.2 setosa
## 30 4.7 3.2 1.6 0.2 setosa
## 31 4.8 3.1 1.6 0.2 setosa
## 32 5.4 3.4 1.5 0.4 setosa
## 33 5.2 4.1 1.5 0.1 setosa
## 34 5.5 4.2 1.4 0.2 setosa
## 35 4.9 3.1 1.5 0.2 setosa
## 36 5.0 3.2 1.2 0.2 setosa
## 37 5.5 3.5 1.3 0.2 setosa
## 38 4.9 3.6 1.4 0.1 setosa
## 39 4.4 3.0 1.3 0.2 setosa
## 40 5.1 3.4 1.5 0.2 setosa
## 41 5.0 3.5 1.3 0.3 setosa
## 42 4.5 2.3 1.3 0.3 setosa
## 43 4.4 3.2 1.3 0.2 setosa
## 44 5.0 3.5 1.6 0.6 setosa
## 45 5.1 3.8 1.9 0.4 setosa
## 46 4.8 3.0 1.4 0.3 setosa
## 47 5.1 3.8 1.6 0.2 setosa
## 48 4.6 3.2 1.4 0.2 setosa
## 49 5.3 3.7 1.5 0.2 setosa
## 50 5.0 3.3 1.4 0.2 setosa
(Virginica <- subset(iris, Species == "virginica"))
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 101 6.3 3.3 6.0 2.5 virginica
## 102 5.8 2.7 5.1 1.9 virginica
## 103 7.1 3.0 5.9 2.1 virginica
## 104 6.3 2.9 5.6 1.8 virginica
## 105 6.5 3.0 5.8 2.2 virginica
## 106 7.6 3.0 6.6 2.1 virginica
## 107 4.9 2.5 4.5 1.7 virginica
## 108 7.3 2.9 6.3 1.8 virginica
## 109 6.7 2.5 5.8 1.8 virginica
## 110 7.2 3.6 6.1 2.5 virginica
## 111 6.5 3.2 5.1 2.0 virginica
## 112 6.4 2.7 5.3 1.9 virginica
## 113 6.8 3.0 5.5 2.1 virginica
## 114 5.7 2.5 5.0 2.0 virginica
## 115 5.8 2.8 5.1 2.4 virginica
## 116 6.4 3.2 5.3 2.3 virginica
## 117 6.5 3.0 5.5 1.8 virginica
## 118 7.7 3.8 6.7 2.2 virginica
## 119 7.7 2.6 6.9 2.3 virginica
## 120 6.0 2.2 5.0 1.5 virginica
## 121 6.9 3.2 5.7 2.3 virginica
## 122 5.6 2.8 4.9 2.0 virginica
## 123 7.7 2.8 6.7 2.0 virginica
## 124 6.3 2.7 4.9 1.8 virginica
## 125 6.7 3.3 5.7 2.1 virginica
## 126 7.2 3.2 6.0 1.8 virginica
## 127 6.2 2.8 4.8 1.8 virginica
## 128 6.1 3.0 4.9 1.8 virginica
## 129 6.4 2.8 5.6 2.1 virginica
## 130 7.2 3.0 5.8 1.6 virginica
## 131 7.4 2.8 6.1 1.9 virginica
## 132 7.9 3.8 6.4 2.0 virginica
## 133 6.4 2.8 5.6 2.2 virginica
## 134 6.3 2.8 5.1 1.5 virginica
## 135 6.1 2.6 5.6 1.4 virginica
## 136 7.7 3.0 6.1 2.3 virginica
## 137 6.3 3.4 5.6 2.4 virginica
## 138 6.4 3.1 5.5 1.8 virginica
## 139 6.0 3.0 4.8 1.8 virginica
## 140 6.9 3.1 5.4 2.1 virginica
## 141 6.7 3.1 5.6 2.4 virginica
## 142 6.9 3.1 5.1 2.3 virginica
## 143 5.8 2.7 5.1 1.9 virginica
## 144 6.8 3.2 5.9 2.3 virginica
## 145 6.7 3.3 5.7 2.5 virginica
## 146 6.7 3.0 5.2 2.3 virginica
## 147 6.3 2.5 5.0 1.9 virginica
## 148 6.5 3.0 5.2 2.0 virginica
## 149 6.2 3.4 5.4 2.3 virginica
## 150 5.9 3.0 5.1 1.8 virginica
# Draw boxplot for each type of flower
boxplot(Versicolor[,1:4], main="Versicolor, Rainbow Palette",ylim = c(0,8),las=2, col=rainbow(4))
boxplot(Setosa[,1:4], main="Setosa, Heat color Palette",ylim = c(0,8),las=2, col=heat.colors(4))
boxplot(Virginica[,1:4], main="Virginica, Topo colors Palette",ylim = c(0,8),las=2, col=topo.colors(4))
# Lab Exercise 7: How to load external datasets
# From a local directory
# the folder that contains the file should be specified completely
# using the forward slash symbol instead of the backward splash
folder <- "C:/Users/Admin/Desktop/Class Lectures/BLecture 0 Graphics in R"
filename <- "Cancer.csv"
(file <- paste0(folder,"/",filename))
## [1] "C:/Users/Admin/Desktop/Class Lectures/BLecture 0 Graphics in R/Cancer.csv"
#type getwd()
#paste the result in setwd("here")
getwd()
## [1] "C:/Users/Vince/Documents/jeshryl's file/Lab Exercises"
setwd("C:/Users/Vince/Documents/jeshryl's file/Lab Exercises")
cancer <- read.csv("Cancer.csv", header = TRUE, sep = ",")
dim(cancer)
## [1] 173 17
names(cancer)
## [1] "country" "incomeperperson" "alcconsumption"
## [4] "armedforcesrate" "breastcancer" "co2emissions"
## [7] "femaleemployrate" "hivrate" "internetuserate"
## [10] "lifeexpectancy" "oilperperson" "polityscore"
## [13] "relectricperperson" "suicideper100th" "employrate"
## [16] "urbanrate" "continent"
# compute mean value for every continent
(means <- round(tapply(cancer$breastcancer, cancer$continent, mean), digits=2))
## AF AS EE LATAM NORAM OC WE
## 24.02 24.51 49.44 36.70 71.73 45.80 74.80
# draw boxplot per continent
boxplot(cancer$breastcancer ~ cancer$continent, main= "Breast cancer by continent (brown dot = mean value)", xlab="continents", ylab="new cases per 100,00 residents", col=rainbow(7))
# insert the mean value using brown dot
points(means, col="brown", pch=18)
# Lab Exercise 8: How to load external datasets and change data layout
folder <- "C:/Users/Admin/Desktop/Class Lectures/BLecture 0 Graphics in R"
filename <- "hsb2.csv"
(file <- paste0(folder,"/",filename))
## [1] "C:/Users/Admin/Desktop/Class Lectures/BLecture 0 Graphics in R/hsb2.csv"
#type getwd()
#paste the result in setwd("here")
getwd()
## [1] "C:/Users/Vince/Documents/jeshryl's file/Lab Exercises"
setwd("C:/Users/Vince/Documents/jeshryl's file/Lab Exercises")
hsb2_wide <- read.csv("hsb2.csv", header = TRUE, sep = ",")
# display only the top 6 rows
head(hsb2_wide)
## X id female race ses schtyp prog read write math science socst
## 1 1 70 0 4 1 1 1 57 52 41 47 57
## 2 2 121 1 4 2 1 3 68 59 53 63 61
## 3 3 86 0 4 3 1 1 44 33 54 58 31
## 4 4 141 0 4 3 1 3 63 44 47 53 56
## 5 5 172 0 4 2 1 2 47 52 57 53 61
## 6 6 113 0 4 2 1 2 44 52 51 63 61
# display only the last 6 rows
tail(hsb2_wide)
## X id female race ses schtyp prog read write math science socst
## 195 195 179 1 4 2 2 2 47 65 60 50 56
## 196 196 31 1 2 2 2 1 55 59 52 42 56
## 197 197 145 1 4 2 1 3 42 46 38 36 46
## 198 198 187 1 4 2 2 1 57 41 57 55 52
## 199 199 118 1 4 2 1 1 55 62 58 58 61
## 200 200 137 1 4 3 1 2 63 65 65 53 61
# delete redundant first column (run only once)
(hsb2_wide <- hsb2_wide[-1])
## id female race ses schtyp prog read write math science socst
## 1 70 0 4 1 1 1 57 52 41 47 57
## 2 121 1 4 2 1 3 68 59 53 63 61
## 3 86 0 4 3 1 1 44 33 54 58 31
## 4 141 0 4 3 1 3 63 44 47 53 56
## 5 172 0 4 2 1 2 47 52 57 53 61
## 6 113 0 4 2 1 2 44 52 51 63 61
## 7 50 0 3 2 1 1 50 59 42 53 61
## 8 11 0 1 2 1 2 34 46 45 39 36
## 9 84 0 4 2 1 1 63 57 54 58 51
## 10 48 0 3 2 1 2 57 55 52 50 51
## 11 75 0 4 2 1 3 60 46 51 53 61
## 12 60 0 4 2 1 2 57 65 51 63 61
## 13 95 0 4 3 1 2 73 60 71 61 71
## 14 104 0 4 3 1 2 54 63 57 55 46
## 15 38 0 3 1 1 2 45 57 50 31 56
## 16 115 0 4 1 1 1 42 49 43 50 56
## 17 76 0 4 3 1 2 47 52 51 50 56
## 18 195 0 4 2 2 1 57 57 60 58 56
## 19 114 0 4 3 1 2 68 65 62 55 61
## 20 85 0 4 2 1 1 55 39 57 53 46
## 21 167 0 4 2 1 1 63 49 35 66 41
## 22 143 0 4 2 1 3 63 63 75 72 66
## 23 41 0 3 2 1 2 50 40 45 55 56
## 24 20 0 1 3 1 2 60 52 57 61 61
## 25 12 0 1 2 1 3 37 44 45 39 46
## 26 53 0 3 2 1 3 34 37 46 39 31
## 27 154 0 4 3 1 2 65 65 66 61 66
## 28 178 0 4 2 2 3 47 57 57 58 46
## 29 196 0 4 3 2 2 44 38 49 39 46
## 30 29 0 2 1 1 1 52 44 49 55 41
## 31 126 0 4 2 1 1 42 31 57 47 51
## 32 103 0 4 3 1 2 76 52 64 64 61
## 33 192 0 4 3 2 2 65 67 63 66 71
## 34 150 0 4 2 1 3 42 41 57 72 31
## 35 199 0 4 3 2 2 52 59 50 61 61
## 36 144 0 4 3 1 1 60 65 58 61 66
## 37 200 0 4 2 2 2 68 54 75 66 66
## 38 80 0 4 3 1 2 65 62 68 66 66
## 39 16 0 1 1 1 3 47 31 44 36 36
## 40 153 0 4 2 1 3 39 31 40 39 51
## 41 176 0 4 2 2 2 47 47 41 42 51
## 42 177 0 4 2 2 2 55 59 62 58 51
## 43 168 0 4 2 1 2 52 54 57 55 51
## 44 40 0 3 1 1 1 42 41 43 50 41
## 45 62 0 4 3 1 1 65 65 48 63 66
## 46 169 0 4 1 1 1 55 59 63 69 46
## 47 49 0 3 3 1 3 50 40 39 49 47
## 48 136 0 4 2 1 2 65 59 70 63 51
## 49 189 0 4 2 2 2 47 59 63 53 46
## 50 7 0 1 2 1 2 57 54 59 47 51
## 51 27 0 2 2 1 2 53 61 61 57 56
## 52 128 0 4 3 1 2 39 33 38 47 41
## 53 21 0 1 2 1 1 44 44 61 50 46
## 54 183 0 4 2 2 2 63 59 49 55 71
## 55 132 0 4 2 1 2 73 62 73 69 66
## 56 15 0 1 3 1 3 39 39 44 26 42
## 57 67 0 4 1 1 3 37 37 42 33 32
## 58 22 0 1 2 1 3 42 39 39 56 46
## 59 185 0 4 2 2 2 63 57 55 58 41
## 60 9 0 1 2 1 3 48 49 52 44 51
## 61 181 0 4 2 2 2 50 46 45 58 61
## 62 170 0 4 3 1 2 47 62 61 69 66
## 63 134 0 4 1 1 1 44 44 39 34 46
## 64 108 0 4 2 1 1 34 33 41 36 36
## 65 197 0 4 3 2 2 50 42 50 36 61
## 66 140 0 4 2 1 3 44 41 40 50 26
## 67 171 0 4 2 1 2 60 54 60 55 66
## 68 107 0 4 1 1 3 47 39 47 42 26
## 69 81 0 4 1 1 2 63 43 59 65 44
## 70 18 0 1 2 1 3 50 33 49 44 36
## 71 155 0 4 2 1 1 44 44 46 39 51
## 72 97 0 4 3 1 2 60 54 58 58 61
## 73 68 0 4 2 1 2 73 67 71 63 66
## 74 157 0 4 2 1 1 68 59 58 74 66
## 75 56 0 4 2 1 3 55 45 46 58 51
## 76 5 0 1 1 1 2 47 40 43 45 31
## 77 159 0 4 3 1 2 55 61 54 49 61
## 78 123 0 4 3 1 1 68 59 56 63 66
## 79 164 0 4 2 1 3 31 36 46 39 46
## 80 14 0 1 3 1 2 47 41 54 42 56
## 81 127 0 4 3 1 2 63 59 57 55 56
## 82 165 0 4 1 1 3 36 49 54 61 36
## 83 174 0 4 2 2 2 68 59 71 66 56
## 84 3 0 1 1 1 2 63 65 48 63 56
## 85 58 0 4 2 1 3 55 41 40 44 41
## 86 146 0 4 3 1 2 55 62 64 63 66
## 87 102 0 4 3 1 2 52 41 51 53 56
## 88 117 0 4 3 1 3 34 49 39 42 56
## 89 133 0 4 2 1 3 50 31 40 34 31
## 90 94 0 4 3 1 2 55 49 61 61 56
## 91 24 0 2 2 1 2 52 62 66 47 46
## 92 149 0 4 1 1 1 63 49 49 66 46
## 93 82 1 4 3 1 2 68 62 65 69 61
## 94 8 1 1 1 1 2 39 44 52 44 48
## 95 129 1 4 1 1 1 44 44 46 47 51
## 96 173 1 4 1 1 1 50 62 61 63 51
## 97 57 1 4 2 1 2 71 65 72 66 56
## 98 100 1 4 3 1 2 63 65 71 69 71
## 99 1 1 1 1 1 3 34 44 40 39 41
## 100 194 1 4 3 2 2 63 63 69 61 61
## 101 88 1 4 3 1 2 68 60 64 69 66
## 102 99 1 4 3 1 1 47 59 56 66 61
## 103 47 1 3 1 1 2 47 46 49 33 41
## 104 120 1 4 3 1 2 63 52 54 50 51
## 105 166 1 4 2 1 2 52 59 53 61 51
## 106 65 1 4 2 1 2 55 54 66 42 56
## 107 101 1 4 3 1 2 60 62 67 50 56
## 108 89 1 4 1 1 3 35 35 40 51 33
## 109 54 1 3 1 2 1 47 54 46 50 56
## 110 180 1 4 3 2 2 71 65 69 58 71
## 111 162 1 4 2 1 3 57 52 40 61 56
## 112 4 1 1 1 1 2 44 50 41 39 51
## 113 131 1 4 3 1 2 65 59 57 46 66
## 114 125 1 4 1 1 2 68 65 58 59 56
## 115 34 1 1 3 2 2 73 61 57 55 66
## 116 106 1 4 2 1 3 36 44 37 42 41
## 117 130 1 4 3 1 1 43 54 55 55 46
## 118 93 1 4 3 1 2 73 67 62 58 66
## 119 163 1 4 1 1 2 52 57 64 58 56
## 120 37 1 3 1 1 3 41 47 40 39 51
## 121 35 1 1 1 2 1 60 54 50 50 51
## 122 87 1 4 2 1 1 50 52 46 50 56
## 123 73 1 4 2 1 2 50 52 53 39 56
## 124 151 1 4 2 1 3 47 46 52 48 46
## 125 44 1 3 1 1 3 47 62 45 34 46
## 126 152 1 4 3 1 2 55 57 56 58 61
## 127 105 1 4 2 1 2 50 41 45 44 56
## 128 28 1 2 2 1 1 39 53 54 50 41
## 129 91 1 4 3 1 3 50 49 56 47 46
## 130 45 1 3 1 1 3 34 35 41 29 26
## 131 116 1 4 2 1 2 57 59 54 50 56
## 132 33 1 2 1 1 2 57 65 72 54 56
## 133 66 1 4 2 1 3 68 62 56 50 51
## 134 72 1 4 2 1 3 42 54 47 47 46
## 135 77 1 4 1 1 2 61 59 49 44 66
## 136 61 1 4 3 1 2 76 63 60 67 66
## 137 190 1 4 2 2 2 47 59 54 58 46
## 138 42 1 3 2 1 3 46 52 55 44 56
## 139 2 1 1 2 1 3 39 41 33 42 41
## 140 55 1 3 2 2 2 52 49 49 44 61
## 141 19 1 1 1 1 1 28 46 43 44 51
## 142 90 1 4 3 1 2 42 54 50 50 52
## 143 142 1 4 2 1 3 47 42 52 39 51
## 144 17 1 1 2 1 2 47 57 48 44 41
## 145 122 1 4 2 1 2 52 59 58 53 66
## 146 191 1 4 3 2 2 47 52 43 48 61
## 147 83 1 4 2 1 3 50 62 41 55 31
## 148 182 1 4 2 2 2 44 52 43 44 51
## 149 6 1 1 1 1 2 47 41 46 40 41
## 150 46 1 3 1 1 2 45 55 44 34 41
## 151 43 1 3 1 1 2 47 37 43 42 46
## 152 96 1 4 3 1 2 65 54 61 58 56
## 153 138 1 4 2 1 3 43 57 40 50 51
## 154 10 1 1 2 1 1 47 54 49 53 61
## 155 71 1 4 2 1 1 57 62 56 58 66
## 156 139 1 4 2 1 2 68 59 61 55 71
## 157 110 1 4 2 1 3 52 55 50 54 61
## 158 148 1 4 2 1 3 42 57 51 47 61
## 159 109 1 4 2 1 1 42 39 42 42 41
## 160 39 1 3 3 1 2 66 67 67 61 66
## 161 147 1 4 1 1 2 47 62 53 53 61
## 162 74 1 4 2 1 2 57 50 50 51 58
## 163 198 1 4 3 2 2 47 61 51 63 31
## 164 161 1 4 1 1 2 57 62 72 61 61
## 165 112 1 4 2 1 2 52 59 48 55 61
## 166 69 1 4 1 1 3 44 44 40 40 31
## 167 156 1 4 2 1 2 50 59 53 61 61
## 168 111 1 4 1 1 1 39 54 39 47 36
## 169 186 1 4 2 2 2 57 62 63 55 41
## 170 98 1 4 1 1 3 57 60 51 53 37
## 171 119 1 4 1 1 1 42 57 45 50 43
## 172 13 1 1 2 1 3 47 46 39 47 61
## 173 51 1 3 3 1 1 42 36 42 31 39
## 174 26 1 2 3 1 2 60 59 62 61 51
## 175 36 1 3 1 1 1 44 49 44 35 51
## 176 135 1 4 1 1 2 63 60 65 54 66
## 177 59 1 4 2 1 2 65 67 63 55 71
## 178 78 1 4 2 1 2 39 54 54 53 41
## 179 64 1 4 3 1 3 50 52 45 58 36
## 180 63 1 4 1 1 1 52 65 60 56 51
## 181 79 1 4 2 1 2 60 62 49 50 51
## 182 193 1 4 2 2 2 44 49 48 39 51
## 183 92 1 4 3 1 1 52 67 57 63 61
## 184 160 1 4 2 1 2 55 65 55 50 61
## 185 32 1 2 3 1 3 50 67 66 66 56
## 186 23 1 2 1 1 2 65 65 64 58 71
## 187 158 1 4 2 1 1 52 54 55 53 51
## 188 25 1 2 2 1 1 47 44 42 42 36
## 189 188 1 4 3 2 2 63 62 56 55 61
## 190 52 1 3 1 1 2 50 46 53 53 66
## 191 124 1 4 1 1 3 42 54 41 42 41
## 192 175 1 4 3 2 1 36 57 42 50 41
## 193 184 1 4 2 2 3 50 52 53 55 56
## 194 30 1 2 3 1 2 41 59 42 34 51
## 195 179 1 4 2 2 2 47 65 60 50 56
## 196 31 1 2 2 2 1 55 59 52 42 56
## 197 145 1 4 2 1 3 42 46 38 36 46
## 198 187 1 4 2 2 1 57 41 57 55 52
## 199 118 1 4 2 1 1 55 62 58 58 61
## 200 137 1 4 3 1 2 63 65 65 53 61
# Remarks
# hsb2 dataset consists of 200 selected random samples from senior
# high school students in the US.
# We want to compare the student performance across different subjects
# change data layout by grouping different subjects
# into one column using melt() command. Install first reshape2 package
# install.packages("reshape2")
library(reshape2)
(hsb2_long <- melt(hsb2_wide, measure.vars = c("read","write","math","science","socst")))
## id female race ses schtyp prog variable value
## 1 70 0 4 1 1 1 read 57
## 2 121 1 4 2 1 3 read 68
## 3 86 0 4 3 1 1 read 44
## 4 141 0 4 3 1 3 read 63
## 5 172 0 4 2 1 2 read 47
## 6 113 0 4 2 1 2 read 44
## 7 50 0 3 2 1 1 read 50
## 8 11 0 1 2 1 2 read 34
## 9 84 0 4 2 1 1 read 63
## 10 48 0 3 2 1 2 read 57
## 11 75 0 4 2 1 3 read 60
## 12 60 0 4 2 1 2 read 57
## 13 95 0 4 3 1 2 read 73
## 14 104 0 4 3 1 2 read 54
## 15 38 0 3 1 1 2 read 45
## 16 115 0 4 1 1 1 read 42
## 17 76 0 4 3 1 2 read 47
## 18 195 0 4 2 2 1 read 57
## 19 114 0 4 3 1 2 read 68
## 20 85 0 4 2 1 1 read 55
## 21 167 0 4 2 1 1 read 63
## 22 143 0 4 2 1 3 read 63
## 23 41 0 3 2 1 2 read 50
## 24 20 0 1 3 1 2 read 60
## 25 12 0 1 2 1 3 read 37
## 26 53 0 3 2 1 3 read 34
## 27 154 0 4 3 1 2 read 65
## 28 178 0 4 2 2 3 read 47
## 29 196 0 4 3 2 2 read 44
## 30 29 0 2 1 1 1 read 52
## 31 126 0 4 2 1 1 read 42
## 32 103 0 4 3 1 2 read 76
## 33 192 0 4 3 2 2 read 65
## 34 150 0 4 2 1 3 read 42
## 35 199 0 4 3 2 2 read 52
## 36 144 0 4 3 1 1 read 60
## 37 200 0 4 2 2 2 read 68
## 38 80 0 4 3 1 2 read 65
## 39 16 0 1 1 1 3 read 47
## 40 153 0 4 2 1 3 read 39
## 41 176 0 4 2 2 2 read 47
## 42 177 0 4 2 2 2 read 55
## 43 168 0 4 2 1 2 read 52
## 44 40 0 3 1 1 1 read 42
## 45 62 0 4 3 1 1 read 65
## 46 169 0 4 1 1 1 read 55
## 47 49 0 3 3 1 3 read 50
## 48 136 0 4 2 1 2 read 65
## 49 189 0 4 2 2 2 read 47
## 50 7 0 1 2 1 2 read 57
## 51 27 0 2 2 1 2 read 53
## 52 128 0 4 3 1 2 read 39
## 53 21 0 1 2 1 1 read 44
## 54 183 0 4 2 2 2 read 63
## 55 132 0 4 2 1 2 read 73
## 56 15 0 1 3 1 3 read 39
## 57 67 0 4 1 1 3 read 37
## 58 22 0 1 2 1 3 read 42
## 59 185 0 4 2 2 2 read 63
## 60 9 0 1 2 1 3 read 48
## 61 181 0 4 2 2 2 read 50
## 62 170 0 4 3 1 2 read 47
## 63 134 0 4 1 1 1 read 44
## 64 108 0 4 2 1 1 read 34
## 65 197 0 4 3 2 2 read 50
## 66 140 0 4 2 1 3 read 44
## 67 171 0 4 2 1 2 read 60
## 68 107 0 4 1 1 3 read 47
## 69 81 0 4 1 1 2 read 63
## 70 18 0 1 2 1 3 read 50
## 71 155 0 4 2 1 1 read 44
## 72 97 0 4 3 1 2 read 60
## 73 68 0 4 2 1 2 read 73
## 74 157 0 4 2 1 1 read 68
## 75 56 0 4 2 1 3 read 55
## 76 5 0 1 1 1 2 read 47
## 77 159 0 4 3 1 2 read 55
## 78 123 0 4 3 1 1 read 68
## 79 164 0 4 2 1 3 read 31
## 80 14 0 1 3 1 2 read 47
## 81 127 0 4 3 1 2 read 63
## 82 165 0 4 1 1 3 read 36
## 83 174 0 4 2 2 2 read 68
## 84 3 0 1 1 1 2 read 63
## 85 58 0 4 2 1 3 read 55
## 86 146 0 4 3 1 2 read 55
## 87 102 0 4 3 1 2 read 52
## 88 117 0 4 3 1 3 read 34
## 89 133 0 4 2 1 3 read 50
## 90 94 0 4 3 1 2 read 55
## 91 24 0 2 2 1 2 read 52
## 92 149 0 4 1 1 1 read 63
## 93 82 1 4 3 1 2 read 68
## 94 8 1 1 1 1 2 read 39
## 95 129 1 4 1 1 1 read 44
## 96 173 1 4 1 1 1 read 50
## 97 57 1 4 2 1 2 read 71
## 98 100 1 4 3 1 2 read 63
## 99 1 1 1 1 1 3 read 34
## 100 194 1 4 3 2 2 read 63
## 101 88 1 4 3 1 2 read 68
## 102 99 1 4 3 1 1 read 47
## 103 47 1 3 1 1 2 read 47
## 104 120 1 4 3 1 2 read 63
## 105 166 1 4 2 1 2 read 52
## 106 65 1 4 2 1 2 read 55
## 107 101 1 4 3 1 2 read 60
## 108 89 1 4 1 1 3 read 35
## 109 54 1 3 1 2 1 read 47
## 110 180 1 4 3 2 2 read 71
## 111 162 1 4 2 1 3 read 57
## 112 4 1 1 1 1 2 read 44
## 113 131 1 4 3 1 2 read 65
## 114 125 1 4 1 1 2 read 68
## 115 34 1 1 3 2 2 read 73
## 116 106 1 4 2 1 3 read 36
## 117 130 1 4 3 1 1 read 43
## 118 93 1 4 3 1 2 read 73
## 119 163 1 4 1 1 2 read 52
## 120 37 1 3 1 1 3 read 41
## 121 35 1 1 1 2 1 read 60
## 122 87 1 4 2 1 1 read 50
## 123 73 1 4 2 1 2 read 50
## 124 151 1 4 2 1 3 read 47
## 125 44 1 3 1 1 3 read 47
## 126 152 1 4 3 1 2 read 55
## 127 105 1 4 2 1 2 read 50
## 128 28 1 2 2 1 1 read 39
## 129 91 1 4 3 1 3 read 50
## 130 45 1 3 1 1 3 read 34
## 131 116 1 4 2 1 2 read 57
## 132 33 1 2 1 1 2 read 57
## 133 66 1 4 2 1 3 read 68
## 134 72 1 4 2 1 3 read 42
## 135 77 1 4 1 1 2 read 61
## 136 61 1 4 3 1 2 read 76
## 137 190 1 4 2 2 2 read 47
## 138 42 1 3 2 1 3 read 46
## 139 2 1 1 2 1 3 read 39
## 140 55 1 3 2 2 2 read 52
## 141 19 1 1 1 1 1 read 28
## 142 90 1 4 3 1 2 read 42
## 143 142 1 4 2 1 3 read 47
## 144 17 1 1 2 1 2 read 47
## 145 122 1 4 2 1 2 read 52
## 146 191 1 4 3 2 2 read 47
## 147 83 1 4 2 1 3 read 50
## 148 182 1 4 2 2 2 read 44
## 149 6 1 1 1 1 2 read 47
## 150 46 1 3 1 1 2 read 45
## 151 43 1 3 1 1 2 read 47
## 152 96 1 4 3 1 2 read 65
## 153 138 1 4 2 1 3 read 43
## 154 10 1 1 2 1 1 read 47
## 155 71 1 4 2 1 1 read 57
## 156 139 1 4 2 1 2 read 68
## 157 110 1 4 2 1 3 read 52
## 158 148 1 4 2 1 3 read 42
## 159 109 1 4 2 1 1 read 42
## 160 39 1 3 3 1 2 read 66
## 161 147 1 4 1 1 2 read 47
## 162 74 1 4 2 1 2 read 57
## 163 198 1 4 3 2 2 read 47
## 164 161 1 4 1 1 2 read 57
## 165 112 1 4 2 1 2 read 52
## 166 69 1 4 1 1 3 read 44
## 167 156 1 4 2 1 2 read 50
## 168 111 1 4 1 1 1 read 39
## 169 186 1 4 2 2 2 read 57
## 170 98 1 4 1 1 3 read 57
## 171 119 1 4 1 1 1 read 42
## 172 13 1 1 2 1 3 read 47
## 173 51 1 3 3 1 1 read 42
## 174 26 1 2 3 1 2 read 60
## 175 36 1 3 1 1 1 read 44
## 176 135 1 4 1 1 2 read 63
## 177 59 1 4 2 1 2 read 65
## 178 78 1 4 2 1 2 read 39
## 179 64 1 4 3 1 3 read 50
## 180 63 1 4 1 1 1 read 52
## 181 79 1 4 2 1 2 read 60
## 182 193 1 4 2 2 2 read 44
## 183 92 1 4 3 1 1 read 52
## 184 160 1 4 2 1 2 read 55
## 185 32 1 2 3 1 3 read 50
## 186 23 1 2 1 1 2 read 65
## 187 158 1 4 2 1 1 read 52
## 188 25 1 2 2 1 1 read 47
## 189 188 1 4 3 2 2 read 63
## 190 52 1 3 1 1 2 read 50
## 191 124 1 4 1 1 3 read 42
## 192 175 1 4 3 2 1 read 36
## 193 184 1 4 2 2 3 read 50
## 194 30 1 2 3 1 2 read 41
## 195 179 1 4 2 2 2 read 47
## 196 31 1 2 2 2 1 read 55
## 197 145 1 4 2 1 3 read 42
## 198 187 1 4 2 2 1 read 57
## 199 118 1 4 2 1 1 read 55
## 200 137 1 4 3 1 2 read 63
## 201 70 0 4 1 1 1 write 52
## 202 121 1 4 2 1 3 write 59
## 203 86 0 4 3 1 1 write 33
## 204 141 0 4 3 1 3 write 44
## 205 172 0 4 2 1 2 write 52
## 206 113 0 4 2 1 2 write 52
## 207 50 0 3 2 1 1 write 59
## 208 11 0 1 2 1 2 write 46
## 209 84 0 4 2 1 1 write 57
## 210 48 0 3 2 1 2 write 55
## 211 75 0 4 2 1 3 write 46
## 212 60 0 4 2 1 2 write 65
## 213 95 0 4 3 1 2 write 60
## 214 104 0 4 3 1 2 write 63
## 215 38 0 3 1 1 2 write 57
## 216 115 0 4 1 1 1 write 49
## 217 76 0 4 3 1 2 write 52
## 218 195 0 4 2 2 1 write 57
## 219 114 0 4 3 1 2 write 65
## 220 85 0 4 2 1 1 write 39
## 221 167 0 4 2 1 1 write 49
## 222 143 0 4 2 1 3 write 63
## 223 41 0 3 2 1 2 write 40
## 224 20 0 1 3 1 2 write 52
## 225 12 0 1 2 1 3 write 44
## 226 53 0 3 2 1 3 write 37
## 227 154 0 4 3 1 2 write 65
## 228 178 0 4 2 2 3 write 57
## 229 196 0 4 3 2 2 write 38
## 230 29 0 2 1 1 1 write 44
## 231 126 0 4 2 1 1 write 31
## 232 103 0 4 3 1 2 write 52
## 233 192 0 4 3 2 2 write 67
## 234 150 0 4 2 1 3 write 41
## 235 199 0 4 3 2 2 write 59
## 236 144 0 4 3 1 1 write 65
## 237 200 0 4 2 2 2 write 54
## 238 80 0 4 3 1 2 write 62
## 239 16 0 1 1 1 3 write 31
## 240 153 0 4 2 1 3 write 31
## 241 176 0 4 2 2 2 write 47
## 242 177 0 4 2 2 2 write 59
## 243 168 0 4 2 1 2 write 54
## 244 40 0 3 1 1 1 write 41
## 245 62 0 4 3 1 1 write 65
## 246 169 0 4 1 1 1 write 59
## 247 49 0 3 3 1 3 write 40
## 248 136 0 4 2 1 2 write 59
## 249 189 0 4 2 2 2 write 59
## 250 7 0 1 2 1 2 write 54
## 251 27 0 2 2 1 2 write 61
## 252 128 0 4 3 1 2 write 33
## 253 21 0 1 2 1 1 write 44
## 254 183 0 4 2 2 2 write 59
## 255 132 0 4 2 1 2 write 62
## 256 15 0 1 3 1 3 write 39
## 257 67 0 4 1 1 3 write 37
## 258 22 0 1 2 1 3 write 39
## 259 185 0 4 2 2 2 write 57
## 260 9 0 1 2 1 3 write 49
## 261 181 0 4 2 2 2 write 46
## 262 170 0 4 3 1 2 write 62
## 263 134 0 4 1 1 1 write 44
## 264 108 0 4 2 1 1 write 33
## 265 197 0 4 3 2 2 write 42
## 266 140 0 4 2 1 3 write 41
## 267 171 0 4 2 1 2 write 54
## 268 107 0 4 1 1 3 write 39
## 269 81 0 4 1 1 2 write 43
## 270 18 0 1 2 1 3 write 33
## 271 155 0 4 2 1 1 write 44
## 272 97 0 4 3 1 2 write 54
## 273 68 0 4 2 1 2 write 67
## 274 157 0 4 2 1 1 write 59
## 275 56 0 4 2 1 3 write 45
## 276 5 0 1 1 1 2 write 40
## 277 159 0 4 3 1 2 write 61
## 278 123 0 4 3 1 1 write 59
## 279 164 0 4 2 1 3 write 36
## 280 14 0 1 3 1 2 write 41
## 281 127 0 4 3 1 2 write 59
## 282 165 0 4 1 1 3 write 49
## 283 174 0 4 2 2 2 write 59
## 284 3 0 1 1 1 2 write 65
## 285 58 0 4 2 1 3 write 41
## 286 146 0 4 3 1 2 write 62
## 287 102 0 4 3 1 2 write 41
## 288 117 0 4 3 1 3 write 49
## 289 133 0 4 2 1 3 write 31
## 290 94 0 4 3 1 2 write 49
## 291 24 0 2 2 1 2 write 62
## 292 149 0 4 1 1 1 write 49
## 293 82 1 4 3 1 2 write 62
## 294 8 1 1 1 1 2 write 44
## 295 129 1 4 1 1 1 write 44
## 296 173 1 4 1 1 1 write 62
## 297 57 1 4 2 1 2 write 65
## 298 100 1 4 3 1 2 write 65
## 299 1 1 1 1 1 3 write 44
## 300 194 1 4 3 2 2 write 63
## 301 88 1 4 3 1 2 write 60
## 302 99 1 4 3 1 1 write 59
## 303 47 1 3 1 1 2 write 46
## 304 120 1 4 3 1 2 write 52
## 305 166 1 4 2 1 2 write 59
## 306 65 1 4 2 1 2 write 54
## 307 101 1 4 3 1 2 write 62
## 308 89 1 4 1 1 3 write 35
## 309 54 1 3 1 2 1 write 54
## 310 180 1 4 3 2 2 write 65
## 311 162 1 4 2 1 3 write 52
## 312 4 1 1 1 1 2 write 50
## 313 131 1 4 3 1 2 write 59
## 314 125 1 4 1 1 2 write 65
## 315 34 1 1 3 2 2 write 61
## 316 106 1 4 2 1 3 write 44
## 317 130 1 4 3 1 1 write 54
## 318 93 1 4 3 1 2 write 67
## 319 163 1 4 1 1 2 write 57
## 320 37 1 3 1 1 3 write 47
## 321 35 1 1 1 2 1 write 54
## 322 87 1 4 2 1 1 write 52
## 323 73 1 4 2 1 2 write 52
## 324 151 1 4 2 1 3 write 46
## 325 44 1 3 1 1 3 write 62
## 326 152 1 4 3 1 2 write 57
## 327 105 1 4 2 1 2 write 41
## 328 28 1 2 2 1 1 write 53
## 329 91 1 4 3 1 3 write 49
## 330 45 1 3 1 1 3 write 35
## 331 116 1 4 2 1 2 write 59
## 332 33 1 2 1 1 2 write 65
## 333 66 1 4 2 1 3 write 62
## 334 72 1 4 2 1 3 write 54
## 335 77 1 4 1 1 2 write 59
## 336 61 1 4 3 1 2 write 63
## 337 190 1 4 2 2 2 write 59
## 338 42 1 3 2 1 3 write 52
## 339 2 1 1 2 1 3 write 41
## 340 55 1 3 2 2 2 write 49
## 341 19 1 1 1 1 1 write 46
## 342 90 1 4 3 1 2 write 54
## 343 142 1 4 2 1 3 write 42
## 344 17 1 1 2 1 2 write 57
## 345 122 1 4 2 1 2 write 59
## 346 191 1 4 3 2 2 write 52
## 347 83 1 4 2 1 3 write 62
## 348 182 1 4 2 2 2 write 52
## 349 6 1 1 1 1 2 write 41
## 350 46 1 3 1 1 2 write 55
## 351 43 1 3 1 1 2 write 37
## 352 96 1 4 3 1 2 write 54
## 353 138 1 4 2 1 3 write 57
## 354 10 1 1 2 1 1 write 54
## 355 71 1 4 2 1 1 write 62
## 356 139 1 4 2 1 2 write 59
## 357 110 1 4 2 1 3 write 55
## 358 148 1 4 2 1 3 write 57
## 359 109 1 4 2 1 1 write 39
## 360 39 1 3 3 1 2 write 67
## 361 147 1 4 1 1 2 write 62
## 362 74 1 4 2 1 2 write 50
## 363 198 1 4 3 2 2 write 61
## 364 161 1 4 1 1 2 write 62
## 365 112 1 4 2 1 2 write 59
## 366 69 1 4 1 1 3 write 44
## 367 156 1 4 2 1 2 write 59
## 368 111 1 4 1 1 1 write 54
## 369 186 1 4 2 2 2 write 62
## 370 98 1 4 1 1 3 write 60
## 371 119 1 4 1 1 1 write 57
## 372 13 1 1 2 1 3 write 46
## 373 51 1 3 3 1 1 write 36
## 374 26 1 2 3 1 2 write 59
## 375 36 1 3 1 1 1 write 49
## 376 135 1 4 1 1 2 write 60
## 377 59 1 4 2 1 2 write 67
## 378 78 1 4 2 1 2 write 54
## 379 64 1 4 3 1 3 write 52
## 380 63 1 4 1 1 1 write 65
## 381 79 1 4 2 1 2 write 62
## 382 193 1 4 2 2 2 write 49
## 383 92 1 4 3 1 1 write 67
## 384 160 1 4 2 1 2 write 65
## 385 32 1 2 3 1 3 write 67
## 386 23 1 2 1 1 2 write 65
## 387 158 1 4 2 1 1 write 54
## 388 25 1 2 2 1 1 write 44
## 389 188 1 4 3 2 2 write 62
## 390 52 1 3 1 1 2 write 46
## 391 124 1 4 1 1 3 write 54
## 392 175 1 4 3 2 1 write 57
## 393 184 1 4 2 2 3 write 52
## 394 30 1 2 3 1 2 write 59
## 395 179 1 4 2 2 2 write 65
## 396 31 1 2 2 2 1 write 59
## 397 145 1 4 2 1 3 write 46
## 398 187 1 4 2 2 1 write 41
## 399 118 1 4 2 1 1 write 62
## 400 137 1 4 3 1 2 write 65
## 401 70 0 4 1 1 1 math 41
## 402 121 1 4 2 1 3 math 53
## 403 86 0 4 3 1 1 math 54
## 404 141 0 4 3 1 3 math 47
## 405 172 0 4 2 1 2 math 57
## 406 113 0 4 2 1 2 math 51
## 407 50 0 3 2 1 1 math 42
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## 410 48 0 3 2 1 2 math 52
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## 412 60 0 4 2 1 2 math 51
## 413 95 0 4 3 1 2 math 71
## 414 104 0 4 3 1 2 math 57
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## 417 76 0 4 3 1 2 math 51
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## 419 114 0 4 3 1 2 math 62
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## 422 143 0 4 2 1 3 math 75
## 423 41 0 3 2 1 2 math 45
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## 426 53 0 3 2 1 3 math 46
## 427 154 0 4 3 1 2 math 66
## 428 178 0 4 2 2 3 math 57
## 429 196 0 4 3 2 2 math 49
## 430 29 0 2 1 1 1 math 49
## 431 126 0 4 2 1 1 math 57
## 432 103 0 4 3 1 2 math 64
## 433 192 0 4 3 2 2 math 63
## 434 150 0 4 2 1 3 math 57
## 435 199 0 4 3 2 2 math 50
## 436 144 0 4 3 1 1 math 58
## 437 200 0 4 2 2 2 math 75
## 438 80 0 4 3 1 2 math 68
## 439 16 0 1 1 1 3 math 44
## 440 153 0 4 2 1 3 math 40
## 441 176 0 4 2 2 2 math 41
## 442 177 0 4 2 2 2 math 62
## 443 168 0 4 2 1 2 math 57
## 444 40 0 3 1 1 1 math 43
## 445 62 0 4 3 1 1 math 48
## 446 169 0 4 1 1 1 math 63
## 447 49 0 3 3 1 3 math 39
## 448 136 0 4 2 1 2 math 70
## 449 189 0 4 2 2 2 math 63
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## 453 21 0 1 2 1 1 math 61
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## 455 132 0 4 2 1 2 math 73
## 456 15 0 1 3 1 3 math 44
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## 487 102 0 4 3 1 2 math 51
## 488 117 0 4 3 1 3 math 39
## 489 133 0 4 2 1 3 math 40
## 490 94 0 4 3 1 2 math 61
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## 493 82 1 4 3 1 2 math 65
## 494 8 1 1 1 1 2 math 52
## 495 129 1 4 1 1 1 math 46
## 496 173 1 4 1 1 1 math 61
## 497 57 1 4 2 1 2 math 72
## 498 100 1 4 3 1 2 math 71
## 499 1 1 1 1 1 3 math 40
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#Remark: Pay extra attention to the last 2 columns
head(hsb2_long)
## id female race ses schtyp prog variable value
## 1 70 0 4 1 1 1 read 57
## 2 121 1 4 2 1 3 read 68
## 3 86 0 4 3 1 1 read 44
## 4 141 0 4 3 1 3 read 63
## 5 172 0 4 2 1 2 read 47
## 6 113 0 4 2 1 2 read 44
tail(hsb2_long)
## id female race ses schtyp prog variable value
## 995 179 1 4 2 2 2 socst 56
## 996 31 1 2 2 2 1 socst 56
## 997 145 1 4 2 1 3 socst 46
## 998 187 1 4 2 2 1 socst 52
## 999 118 1 4 2 1 1 socst 61
## 1000 137 1 4 3 1 2 socst 61
# get thefrequency
table(hsb2_long$variable)
##
## read write math science socst
## 200 200 200 200 200
# This means that the tables hsb2_long and hsb2_wide are the same
# tables displayed in two ways
# display data structure of the hsb2_long dataset
str(hsb2_long)
## 'data.frame': 1000 obs. of 8 variables:
## $ id : int 70 121 86 141 172 113 50 11 84 48 ...
## $ female : int 0 1 0 0 0 0 0 0 0 0 ...
## $ race : int 4 4 4 4 4 4 3 1 4 3 ...
## $ ses : int 1 2 3 3 2 2 2 2 2 2 ...
## $ schtyp : int 1 1 1 1 1 1 1 1 1 1 ...
## $ prog : int 1 3 1 3 2 2 1 2 1 2 ...
## $ variable: Factor w/ 5 levels "read","write",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ value : int 57 68 44 63 47 44 50 34 63 57 ...
# the variables female, race, ses, schtyp, prog are stored as numbers
# for encoding purposes. However these variables are actually qualitative variables
# so we convert each from integer type to categorical type
# defining some variables to become factor variable
# we use another variable to preserve the file hsb2_long
data <- hsb2_long
data$ses = factor(data$ses, labels=c("low", "middle", "high"))
data$schtyp = factor(data$schtyp, labels=c("public", "private"))
data$prog = factor(data$prog, labels=c("general", "academic", "vocational"))
data$race = factor(data$race, labels=c("hispanic", "asian", "african-amer","white"))
data$female = factor(data$female, labels=c("female", "male"))
# check data structure again. The former integer variables are now categorical variable
str(data)
## 'data.frame': 1000 obs. of 8 variables:
## $ id : int 70 121 86 141 172 113 50 11 84 48 ...
## $ female : Factor w/ 2 levels "female","male": 1 2 1 1 1 1 1 1 1 1 ...
## $ race : Factor w/ 4 levels "hispanic","asian",..: 4 4 4 4 4 4 3 1 4 3 ...
## $ ses : Factor w/ 3 levels "low","middle",..: 1 2 3 3 2 2 2 2 2 2 ...
## $ schtyp : Factor w/ 2 levels "public","private": 1 1 1 1 1 1 1 1 1 1 ...
## $ prog : Factor w/ 3 levels "general","academic",..: 1 3 1 3 2 2 1 2 1 2 ...
## $ variable: Factor w/ 5 levels "read","write",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ value : int 57 68 44 63 47 44 50 34 63 57 ...
# we compare student performance by using boxplots
library(gplots)
##
## Attaching package: 'gplots'
## The following object is masked from 'package:stats':
##
## lowess
# compute the average value by group using tapply() command
means <- round(tapply(data$value, data$variable, mean), digits=2)
# create boxplot by group
boxplot(data$value ~ data$variable, main= "Student Performance by Subject (brown dot = mean score)",
xlab="Subject matter", ylab="Percentage Scores", col=rainbow(5))
# insert the average values
points(means, col="brown", pch=18)
# can also compute the median values
medians = round(tapply(data$value, data$variable, median), digits=2)
medians
## read write math science socst
## 50 54 52 53 52
points(medians, col="red", pch=18)
# Lab Exercise 9: How to plot categorical variables
###install.packages("ggplot2")
###install.packages("colorspace", repos = "http://cran.us.r-project.org")
###("hsb2_long")
###install.packages(c("pkg1", "pkg2"))
library(ggplot2)
# we load variable names to memory to avoid the dollar notation
attach(hsb2_long)
# create the plot object p
p <- ggplot(hsb2_long, aes(ses, fill = prog)) + facet_wrap(~race)
p + geom_bar()
## Warning: The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
p + geom_bar(position = "dodge")
## Warning: The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
library(ggplot2)
attach(hsb2_long)
## The following objects are masked from hsb2_long (pos = 3):
##
## female, id, prog, race, schtyp, ses, value, variable
p <- ggplot(hsb2_long, aes(ses, fill = prog)) + facet_wrap(~schtyp)
p + geom_bar()
## Warning: The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
p + geom_bar(position = "dodge")
## Warning: The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
# Lab Exercise 10: Scatter Plots with marginal Distributions
# Advance Scatter plots using libraries
# install.packages("ggExtra")
# install.packages("tidyverse")
library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.0 ✔ readr 2.1.4
## ✔ forcats 1.0.0 ✔ stringr 1.5.0
## ✔ lubridate 1.9.2 ✔ tibble 3.2.0
## ✔ purrr 1.0.1 ✔ tidyr 1.3.0
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the ]8;;http://conflicted.r-lib.org/conflicted package]8;; to force all conflicts to become errors
library(ggExtra)
# set theme appearance of grid background
theme_set(theme_bw(1))
# create X and Y vector
(xAxis <- rnorm(1000)) # normal distribution with mean 0 and sd=1
## [1] -0.3131904408 -0.5910442591 0.0003854115 0.7680988207 0.8899781685
## [6] 0.9867551167 -0.5293468388 -0.2664746148 -0.6868798124 0.5984826210
## [11] -0.0408882110 -1.9754892271 0.3766778877 -0.6939261469 1.3275672121
## [16] 1.2366457094 -1.3053079165 -1.0367059358 0.7651872103 1.6651661735
## [21] -1.3770880189 -0.7675218865 -0.1573490571 1.0780230423 -1.8729743348
## [26] -1.4195269992 0.3066233021 -0.8097009260 1.2452124486 -0.2763685917
## [31] -1.2194689633 -0.7667198407 -0.5077105992 0.2128486604 -0.4983191564
## [36] -0.0587811277 1.1204280795 -1.8759354290 0.0416061921 -1.7504746100
## [41] -1.9610173019 -2.3444925146 2.5272392535 -0.8664767676 0.7746889042
## [46] -0.3047788131 0.7839556095 1.2819931416 1.1411561607 -0.0823007449
## [51] -0.0759022405 1.9324567105 1.0045676777 0.5640413638 0.1894948021
## [56] -1.1624616692 0.0369874915 -0.8054466342 0.9854997734 0.6911381513
## [61] -1.5004715009 1.0546851006 -0.4935699281 0.1341350839 0.6021621924
## [66] 2.2577287972 0.1443726192 1.4221407251 1.4324143043 -0.9546756903
## [71] 1.1204145142 -0.5741875234 -1.4171645627 -0.3554999472 0.7467802129
## [76] -1.0727504528 1.4138592565 -0.2688161241 -1.6612691300 -0.1436638188
## [81] 0.0972024314 -0.7397512918 -1.3081604708 -0.3411345369 1.0164044683
## [86] -1.3216540030 0.7606409434 1.2348735399 -0.6854503711 0.0290952715
## [91] -0.2082335914 -1.0252797127 -1.1240285713 -0.2208533973 1.5439905120
## [96] -0.4314723319 0.9909158036 -1.3679998326 0.1680662977 0.3216664155
## [101] -0.6290541379 0.3624089869 0.1006459471 -0.0394765233 0.5832710198
## [106] 1.2019723123 1.0305654159 0.7109014786 -1.5422662252 -0.6882244398
## [111] -0.5709698712 -1.2560143978 -0.0490585859 -0.1088509499 -0.4105129495
## [116] -0.7488415269 -1.0704781856 -0.7276478827 0.1566961891 -1.0099370219
## [121] 1.0824254454 1.4261035631 -0.7700513969 0.6807905792 0.6507583360
## [126] -0.9855009469 -0.4577304820 0.5688533592 0.6164668430 0.7283982338
## [131] -0.7790895684 0.7406203229 -0.3991236702 -0.5526091636 -0.5911035101
## [136] -0.7989611036 -0.9530011127 -0.0382910114 1.0063120243 0.7786539553
## [141] 0.0665510473 -0.8149780965 -1.2678541734 -1.0417354422 0.7964336672
## [146] 0.1878872908 0.8300517086 1.4843578612 0.5827980362 -0.3076654856
## [151] 0.1077109013 0.6211902389 1.4038993712 -0.4259441699 0.4943170669
## [156] -0.0454036640 0.8319610844 -0.5282662545 -1.6655366854 -1.0318516128
## [161] 0.8412668052 0.8112281712 -0.6330524646 -0.8037766503 0.5866991158
## [166] -1.4376635736 0.7148597747 -1.9068478566 0.0790766714 -0.3319216702
## [171] 0.7811521693 1.1209276097 0.8662527890 -0.1818354978 -0.8957349328
## [176] -0.1766628128 1.8149319302 1.1977259183 -0.1034218820 0.8915496615
## [181] 1.0692021446 -0.3248502990 0.3488216089 0.7169293866 0.8343333987
## [186] -1.1027275671 1.0781836324 -0.7020110385 0.8702444454 -0.4150912450
## [191] 0.6513111595 1.1933608649 -0.9801474814 0.4074065398 0.1225101576
## [196] -0.6709969220 1.1705450777 0.3950491497 -0.2381713285 -0.0185637671
## [201] -1.2899989563 -2.9067073150 1.3076466564 -0.1467963574 0.3057206500
## [206] 0.2114775696 0.9738514883 2.4517310318 0.9559078748 -0.5915301249
## [211] 0.4920580635 0.2066019134 -1.0054099969 -0.0342468541 -0.1599872659
## [216] -0.6542441353 -0.0955416357 0.1435517749 -2.1006593431 1.7358952776
## [221] -1.8567776608 -0.6791628807 0.9005391902 1.2554436539 1.3811295650
## [226] 0.3024203022 -2.5911058219 -0.3085306639 1.1907073182 1.4286336269
## [231] 0.3702117697 -1.5702683854 0.6150222814 0.4607653680 1.1292732456
## [236] 0.6961315177 -0.9874453279 -0.3357946827 0.4189955641 1.1262094814
## [241] -0.9692896631 -1.7306590555 0.8645490411 1.8269446214 -0.9030113322
## [246] 0.1959869359 -0.4536729755 0.8775063656 -0.8299145222 1.5976700532
## [251] 0.7817198004 0.5917019733 -1.2923433924 0.8383441580 -1.4810163153
## [256] 0.8694137972 2.2310275627 0.1675337119 -0.8048887933 -1.5001426623
## [261] -0.5167800411 1.0405436198 0.5953639805 0.3798124316 -0.5948031736
## [266] -0.4205728851 1.8563428479 0.2171292544 -1.3083829845 -1.9691093568
## [271] -0.3432996163 -1.8621852737 0.7295425049 1.1596317303 0.4022014173
## [276] -1.7363413938 0.3041560271 0.0249166231 -0.3801076088 -0.5417172331
## [281] -0.6621784240 0.4474761033 1.5224264104 -0.2016406561 -0.6134709421
## [286] 2.8649218992 -2.0120818774 0.8432552637 -0.9094350743 1.0926981293
## [291] -2.5174994571 1.5262299234 0.3627112913 -1.2848346569 -0.2651783657
## [296] 1.2588525332 -0.8090025453 0.4585745529 -0.0865584997 1.7219165584
## [301] 0.4615690796 1.3245404046 -0.8177222541 -0.7204302465 0.9045337393
## [306] 0.9301673012 -0.1893500916 0.1702090842 0.0938139936 2.0858290543
## [311] 0.1044583641 0.5593864951 -0.1272209320 -1.1030006504 -1.1523610543
## [316] -0.4967033792 -0.5013536129 -2.3633705738 0.8951122270 -0.6489392084
## [321] 1.9061785445 -0.1894400504 -0.3760638016 -0.6199714345 -2.0391308705
## [326] -1.2685309572 -0.3477139161 -0.1194027226 0.6624272255 -0.4357310245
## [331] -0.9732199552 -3.1244983506 -0.7365329543 0.1033540413 1.5841260165
## [336] -1.3437192097 -0.6550753357 1.3820215448 1.6139011665 -0.1318842287
## [341] -0.0528541227 0.9736174674 0.3992267314 -0.0150350595 -0.1974949132
## [346] 2.8119558056 0.9633238347 -0.7586849255 -2.0763972640 -0.0028254127
## [351] 0.0822678961 1.3715978895 0.9980676692 -1.1811230837 -1.2065625641
## [356] 0.5538707821 0.0313324036 -0.1936353836 -0.1059585778 2.2648111413
## [361] 1.4877225811 0.2438040384 0.9722382809 -0.5677774113 0.7779392885
## [366] -1.0611941204 1.8950158451 -0.3768265104 -0.3002938565 0.1701028942
## [371] 0.9002159703 -0.7549107635 -1.4438069630 0.3063366348 1.7773160219
## [376] 0.9777252568 -1.1633058138 0.4200942413 -0.7175604971 0.5435106850
## [381] -0.9656978002 -0.2908900004 -2.5898689252 -0.9807379407 0.4692310598
## [386] -1.2966762017 0.4083462227 -2.4427487271 -2.2604484319 -1.1423699438
## [391] -1.1581848268 0.0790107362 0.2674757890 -0.8289599831 -0.2441300492
## [396] 0.9855038943 -1.7032853797 -0.2982857347 -0.0520263716 -2.0474783503
## [401] 0.2540018872 0.9625694602 -0.3429026641 -1.3641124621 0.5507331174
## [406] 0.6315262355 -0.6558542973 -0.6758739073 0.1003485974 1.3126489390
## [411] 0.2933444653 0.4398708196 0.4046310935 0.2137015075 0.8209663634
## [416] 0.6351722879 0.8635312475 2.4706574453 -0.3329985999 0.8541433738
## [421] 0.4401296654 -0.4459549355 -0.1256796080 -0.5173803245 0.2332732005
## [426] -1.8077855864 0.4126887001 -0.1258216829 1.5020495528 -0.5841694163
## [431] 0.0008916633 0.5119321929 -1.5699426383 2.9830346111 0.7805290662
## [436] 1.0790707613 0.0683817402 0.5756679881 0.2403299411 0.3864191864
## [441] -0.6835300995 -0.3527942378 -0.3633784491 1.1415399995 -0.3332989178
## [446] -1.3324645090 -2.1584637306 -0.2529489879 1.2501799107 1.3482104006
## [451] 2.3109335179 2.3779723394 0.6274880262 0.2260836226 -2.0061950123
## [456] -0.7664783486 -0.6357182022 0.9003421545 -0.1852774458 -1.6261262715
## [461] -0.9789028045 1.1678032246 -1.5167954559 0.8939803636 1.0346027246
## [466] 0.0960770955 0.3735140465 0.7105479675 0.3898715152 -0.4023133776
## [471] -0.5296134792 0.4838314435 1.1742445786 0.4718000917 0.0637095247
## [476] 0.2890470128 -1.8633268208 1.3565674441 0.2861907924 -0.2420468913
## [481] 0.9454387894 0.7918707145 1.8038213535 -0.5104878758 -0.9555872893
## [486] -0.1438464362 -0.2084331967 0.5892646345 -1.4001553140 1.0617700150
## [491] 0.1034825102 0.6253069367 -0.1698873234 2.6920529170 -0.8463920194
## [496] -1.0962619058 2.2583884938 -1.0393846414 1.3902622959 -0.6650725310
## [501] -0.6007261858 -0.8485996692 -0.4456157630 -1.0630384226 0.2541574067
## [506] 1.4367959562 -0.1700954116 0.3226487303 0.9175761724 1.4365355430
## [511] -0.2387956087 0.1492426198 0.2636805821 -0.1410225590 -0.2133285646
## [516] -0.6048300423 -0.1759210617 0.5750758707 0.6442908863 1.0271281019
## [521] 0.4934332562 0.1045184790 1.0736067596 -0.8419748784 0.9564602892
## [526] 1.7731277324 -0.1024343018 0.4334090923 -0.8390858517 1.0553400184
## [531] 1.5332182009 -1.1876445811 0.9733102993 0.6832940718 -0.2158094651
## [536] -0.2329166771 -0.4333615913 0.3795448519 0.1460030811 0.0102680730
## [541] -0.1512883149 -0.8603693782 -1.2282484262 0.3856246879 0.2838194611
## [546] 0.5765841032 1.5088784305 0.5595125936 0.3937265247 0.0839166658
## [551] -1.3362509974 1.0171166865 0.0495502100 -0.3539054553 1.2702743313
## [556] -0.5578176700 -0.9397509213 0.2673266398 -2.4607642299 -0.4894721380
## [561] 0.8061216822 0.3956273966 0.7986129116 -1.0455205310 0.3474340378
## [566] 0.1788156283 -0.4239308593 -0.4423577186 1.7332296539 -1.3310308633
## [571] 0.2987206803 0.4536548579 -0.3729017953 -0.5864641568 -1.0144525869
## [576] -1.3709294750 0.0114594534 -0.6765405889 1.5126928322 -0.0103748247
## [581] 0.6282925110 0.4150624698 -0.3297334844 -0.7640113516 -0.6605033704
## [586] 0.6734532156 1.7147644818 1.8264017515 -0.1505630832 -0.1807260375
## [591] 0.4021780264 1.7399613083 0.6518980857 0.2679937738 -0.6490572528
## [596] -0.4901826221 -0.0416229449 1.6993280448 -1.4858376011 -2.2074679658
## [601] 1.5413708855 -0.0308316475 -0.2995783499 -0.5047684554 -1.9766447347
## [606] 0.2378891147 -0.5954138407 -0.9101283474 1.3444397700 -0.1168455068
## [611] -0.5564397750 -0.4955572724 1.0539351816 -0.2796286318 -1.3369613546
## [616] 0.6752724755 1.0571502605 -0.2670743819 1.0689077612 0.7114199379
## [621] -0.3083870189 0.7574405305 0.2295091313 0.6381707938 0.6803085536
## [626] 0.7518939787 -0.6861501129 1.3329763494 -0.3682543413 1.0373342604
## [631] -2.7854135798 0.3902935921 -0.5701862827 0.1978593121 -1.0848667517
## [636] 0.6206243751 1.3053832840 -0.8989082451 0.2114277494 1.9060400990
## [641] 0.5288544839 -1.0212606258 -0.2520886114 0.1152262803 0.0749682069
## [646] 0.3925383218 -0.8984144035 1.6510082069 -1.8437862960 -0.1305718112
## [651] 0.8930307930 -0.2864560340 0.2635013779 0.9887980341 -0.2841815266
## [656] 0.1947866081 0.0256641559 -0.3647561115 -0.3026499572 1.1989799775
## [661] 0.4468734608 -0.6282180931 -0.2598764145 -0.6568340266 0.1536951653
## [666] 1.6076095455 0.4291020697 -1.4327839380 -1.3165583574 -0.5949825446
## [671] 0.1927254919 0.7073035599 -0.2374845871 0.5843606496 0.9058712149
## [676] 0.0708557220 1.2356706725 -0.0134828991 0.4201026739 -0.2494027687
## [681] -1.0327506941 -1.1670171160 -1.2338019771 -0.8537168764 1.9848854834
## [686] 1.1764802343 0.4322907149 -0.1274027808 0.0887529894 0.1984473030
## [691] 0.6743048414 -0.9253538357 -1.5625866037 0.1077325423 1.1628490464
## [696] -1.6559275647 1.1666541389 -1.5548418734 -0.7777799112 0.4708989601
## [701] 1.7668663269 -0.5994055216 0.8431785733 0.4751361747 0.5384909435
## [706] 0.4447882770 -0.4057204990 0.1518165115 0.9082403705 -0.1577098749
## [711] 0.1989658116 0.3218006444 0.6446460700 0.8921289460 -1.5378843854
## [716] 0.8620889265 1.2287498368 0.6574383624 -0.4658919469 1.1219461806
## [721] 0.5815498848 0.3351215439 0.3920935681 -1.4412026129 -1.2459904181
## [726] -0.6516126479 -0.2627947248 -1.3862876165 0.8491006970 -1.5404331707
## [731] 0.0008000802 -0.5795582510 1.3333531242 1.1901122772 1.0068594904
## [736] -0.9921092671 -0.2554498583 0.6505475897 2.1052785150 0.6471617475
## [741] -1.0313220663 -0.2637318989 0.0660506584 -0.0070639651 0.7867339934
## [746] 0.5724872236 1.1837343430 0.8502812022 -0.8252541648 -0.6835229503
## [751] 1.9009338847 0.1169699654 -1.5696837164 -2.2828307716 0.3458004327
## [756] 0.0750322614 -2.7535473363 -0.2149692886 -0.5912424220 1.2981676113
## [761] 1.0963510698 -0.4021054291 0.5228587098 0.1929809181 0.0791177734
## [766] -1.0772676591 -0.4561189184 -0.5597675326 -0.9749539711 0.4301909770
## [771] -0.0043159667 1.7672863901 -0.6626559687 1.9442933476 -0.7817210387
## [776] 0.2534875405 0.0183391747 0.5368295789 -0.4432371784 1.1153504784
## [781] -1.2229558172 0.4069538003 0.6862210401 -0.4314817309 -0.0975679915
## [786] 0.5141189783 -0.3893859324 0.1047324650 0.3445110994 -1.3928889179
## [791] 0.8153055349 1.0263608166 -0.5410758701 -1.8934380147 0.7542177275
## [796] -0.4384326610 0.7957311236 -0.1349801972 -0.2418192416 -0.1287621011
## [801] -0.5396253089 -0.2497829005 0.2826175111 -0.6212198877 -1.8294854065
## [806] -0.0241708551 0.7063278427 -0.5656228294 0.5571603855 0.3480807703
## [811] -1.4537695324 -1.6880114119 -0.9047417960 0.9395047353 -0.6108305214
## [816] -0.5881834797 -1.3709948955 0.5455469922 -0.6186154753 1.0830263145
## [821] -1.6018593105 -1.2226414952 -0.1643133799 -0.5231982949 -0.4602048762
## [826] 2.0117912507 1.1949358402 -1.0394982591 0.1847104197 -0.1894196889
## [831] 0.0925721064 -1.7189629326 -0.9357976660 0.3256937817 1.3300631985
## [836] -0.1142333464 -0.6301300321 1.2261968336 1.3929100260 -0.3108494455
## [841] 0.8851900231 0.4264953237 -0.2631674232 0.8525127062 -0.0704629582
## [846] 0.0500785908 -0.3434773381 -0.2403770073 0.6870813588 -1.6000213579
## [851] 0.3816120641 -1.2544365036 -0.4451656240 1.0682849062 0.8806411005
## [856] -0.7903936373 -0.0877671045 -0.5292937352 -0.6101864047 -0.7299356567
## [861] 0.7644204514 -0.4867253832 -0.6462870589 -0.6615390890 0.2407250913
## [866] -0.2669427747 0.7036762000 1.2517113425 0.0573070372 0.2696847224
## [871] 0.2762980193 -1.0104507197 -0.5292878935 0.3500997531 -0.1761402048
## [876] -0.1523312112 -1.3178080802 -0.1559144893 -0.5651256288 0.5617879628
## [881] -0.2198102398 -1.3202324319 1.0852813706 0.7762967472 -0.1066849958
## [886] 1.1006827768 0.8738416814 -0.1402888035 -0.4864883073 -0.1656933841
## [891] 1.5821249258 2.2228142315 0.0164480299 -0.9869810771 -0.4020464506
## [896] -0.7065630621 1.8351360922 -1.5408106242 0.4541621068 0.5259240073
## [901] 0.4784758158 -0.4085425787 0.7386314107 -1.4035025068 -0.9236303830
## [906] 1.3236618313 -0.0386319265 -0.8071917034 0.0905570886 0.3613736027
## [911] 1.7442346077 0.4158439875 -0.3375953129 -0.7761465513 0.1228445069
## [916] -0.5844316329 -0.6271097304 0.2344871907 -0.7278953583 0.3673243763
## [921] 0.9653723797 1.3857757331 -1.4243236270 -1.1129188225 1.5319024124
## [926] -0.8734283146 0.1969213306 -0.7526581816 1.1609426827 -0.9285496301
## [931] 0.5174419833 -0.3122379988 -0.4064670034 -0.6376382234 0.6248993902
## [936] 0.9174534934 0.2691769725 -0.3881434146 -0.5649234904 -1.8158937932
## [941] -0.2116268890 -1.3340586485 -0.7628632127 0.3883215840 0.0644220643
## [946] -0.6204899184 1.0973430538 0.5467568781 -0.6653649675 -0.7928334913
## [951] 1.7485040856 1.6597521865 0.0856362940 -0.4449163350 -0.6607425185
## [956] 0.5573677053 -0.2233245702 0.1610599335 -0.9594076820 1.2589109238
## [961] -0.3702296129 0.1906757902 -0.9871640732 0.6575956863 0.0232165287
## [966] 0.3352266020 0.7330584385 0.0714962471 -0.5282843183 0.6608283876
## [971] -0.9988080227 -0.7067545029 -0.1778412188 0.8770021084 -0.3988458819
## [976] 1.1770048240 0.1741996329 -0.7222558965 -0.8828410562 0.3965409479
## [981] -1.1172080943 -1.6473136395 0.5235542142 1.7491255741 1.5798774936
## [986] 2.1671440439 0.5920510646 0.0636905400 1.7233813811 0.4287068962
## [991] -0.4397115337 -0.5333392789 -2.1363029251 -0.7033457093 0.0914746049
## [996] -2.0539943499 1.3013846144 1.4540983736 -0.7696012315 0.3636405408
yAxis <- rnorm(1000) + xAxis + 10
yAxis
## [1] 9.434404 8.079323 9.869881 12.332151 11.100700 9.525093 7.592299
## [8] 7.707508 7.624858 9.999597 11.556035 7.098362 11.643305 9.397716
## [15] 10.108833 11.146562 8.142802 8.911684 11.220742 12.705276 10.634171
## [22] 10.152539 9.465497 10.569824 7.914699 8.326207 10.101591 8.848611
## [29] 11.936089 7.910833 9.785766 11.208878 8.511698 10.287839 7.996248
## [36] 10.720783 8.359629 9.031012 11.016833 9.451013 8.889140 7.653760
## [43] 13.265348 8.427073 11.647759 9.320966 10.456048 11.436670 11.283118
## [50] 9.094744 10.321153 12.016496 9.539236 10.885316 10.678508 8.805682
## [57] 10.689512 7.574970 12.169825 12.229843 8.182116 11.167967 9.114715
## [64] 9.060503 10.350743 13.288838 10.516541 12.269908 11.283385 6.784057
## [71] 11.652246 10.310266 9.526146 10.466706 9.501866 8.246820 11.387205
## [78] 8.059732 9.103616 8.160740 10.086960 9.215879 8.210986 8.375583
## [85] 10.761201 8.488004 11.060976 11.330837 7.952613 11.192008 8.732643
## [92] 8.464366 9.122723 10.300623 9.496257 8.686948 10.818094 7.807011
## [99] 10.246026 9.528913 7.564176 10.965948 10.484206 10.617407 11.242186
## [106] 11.587840 9.654020 10.159228 8.201107 10.269865 9.478307 6.363978
## [113] 9.377983 8.556449 9.696035 8.779347 9.604098 9.621399 10.567828
## [120] 8.452577 11.886576 12.002683 9.979573 10.443004 12.152865 8.560197
## [127] 8.751410 10.444091 10.885368 10.908492 10.653188 11.315040 9.585146
## [134] 8.023119 10.560828 6.925309 8.704587 10.331634 11.896846 11.687970
## [141] 9.231672 9.338539 9.354624 10.498745 11.241640 11.332346 9.964795
## [148] 12.382144 9.031848 10.405501 9.408733 11.361986 11.689568 10.368665
## [155] 9.610124 8.928417 10.484857 10.375520 8.788880 9.541317 11.394228
## [162] 9.938432 8.366551 9.446831 9.815961 8.798485 9.370409 8.763098
## [169] 8.407084 10.424841 10.511737 11.405658 11.074164 10.508658 10.145220
## [176] 11.241579 12.540035 10.740687 9.513470 10.072735 12.465673 9.134809
## [183] 11.209778 8.269292 11.402963 10.897281 12.437195 9.690998 10.883555
## [190] 10.428257 9.237466 10.698665 7.630650 11.445804 10.527779 9.180886
## [197] 9.578853 11.752139 9.277984 10.325884 8.440269 7.917657 10.602568
## [204] 10.268303 11.371678 10.859719 12.818876 12.595046 11.703719 9.563077
## [211] 10.933032 11.092139 8.368991 11.052802 10.187888 9.353808 10.342500
## [218] 9.925403 8.063643 12.625780 6.759366 9.046088 11.166904 11.740368
## [225] 12.689321 10.072691 5.986881 7.646566 10.250979 9.506774 10.002311
## [232] 9.309023 8.878288 10.867557 9.396850 12.324416 9.348999 8.137990
## [239] 9.770990 12.267481 9.226285 6.761238 11.722527 10.793859 7.902419
## [246] 11.433898 9.123738 9.739647 9.835411 12.189699 9.714850 10.989229
## [253] 7.941030 10.989319 9.751571 11.071108 11.920206 10.550370 8.680413
## [260] 10.188394 10.027473 11.966664 10.922559 9.289883 9.026406 7.792765
## [267] 11.606512 10.386711 7.475658 8.125143 9.990061 8.417040 11.440478
## [274] 10.963543 11.645025 9.369663 11.501925 7.642656 10.884414 10.348127
## [281] 8.486456 9.930168 11.741966 11.540542 8.372885 14.613235 9.087149
## [288] 9.568133 6.654456 11.892880 7.487323 11.916419 11.015038 10.114973
## [295] 10.097810 11.972908 9.410080 10.119323 10.700708 13.759525 9.872972
## [302] 11.013703 7.369773 8.277115 10.381991 10.501086 9.109530 12.079634
## [309] 11.463394 11.995164 10.291691 9.677513 10.163476 7.967849 7.772912
## [316] 10.070002 10.584525 7.393081 10.992332 8.364170 12.594005 9.371372
## [323] 10.893658 9.530856 8.894932 8.633053 8.226644 9.719486 10.021005
## [330] 9.576036 10.201047 6.917274 11.718633 8.739442 10.002975 6.673013
## [337] 8.529721 11.285490 11.127722 9.914874 9.676545 11.333905 11.407523
## [344] 9.765397 8.583711 12.525002 10.200358 8.865248 7.544676 9.777233
## [351] 7.412783 11.618722 10.905073 8.385154 8.078422 11.598708 9.559868
## [358] 10.716641 11.469565 13.211667 10.552734 10.205534 12.084148 9.619270
## [365] 8.988256 8.241570 11.884233 9.021444 10.181661 8.669658 14.076221
## [372] 7.963555 9.361390 10.446573 12.206434 11.507225 6.415906 11.103013
## [379] 8.456116 9.790721 9.916047 9.334710 8.934042 8.046904 10.907194
## [386] 8.890137 9.586099 6.734986 8.165499 10.034859 9.067959 7.623190
## [393] 10.592078 8.605515 9.716396 8.345919 7.105395 10.810152 8.573464
## [400] 8.411732 9.711055 11.651504 11.181223 7.547555 10.643170 9.687441
## [407] 9.707759 10.272604 9.666123 12.255549 8.992884 9.511885 11.024968
## [414] 9.089546 10.483753 10.173231 10.948227 11.074198 9.709007 9.383650
## [421] 10.225745 8.944712 9.872263 10.837890 11.061451 8.963606 11.559188
## [428] 11.010065 12.202043 10.688444 9.699042 11.878316 8.326763 12.531205
## [435] 9.509706 11.405621 11.100704 10.489255 10.500365 9.750322 10.206102
## [442] 9.587733 9.575795 12.382153 10.798795 9.729037 6.195836 10.536467
## [449] 10.518467 9.969724 12.447961 11.550325 10.011501 10.380162 8.610051
## [456] 8.202553 9.414785 10.114074 8.743592 8.074011 9.389647 9.975242
## [463] 8.265410 11.207931 11.405949 9.819200 10.479384 10.669928 9.629969
## [470] 9.110364 9.111317 10.483768 10.453888 12.179397 9.705816 12.086175
## [477] 8.358797 12.864688 10.116919 10.372835 11.001091 10.605424 11.045444
## [484] 9.568550 9.011047 10.127832 9.983655 12.205975 9.677697 12.425518
## [491] 9.152891 9.723139 9.466921 12.228982 9.847414 10.107918 13.187777
## [498] 10.278194 12.301876 8.306947 8.714106 8.762410 10.063651 8.451717
## [505] 9.196030 11.188802 9.103441 10.306881 11.887172 12.309872 10.509887
## [512] 10.378098 10.480142 11.758854 9.786086 7.655194 7.184130 9.617225
## [519] 9.916582 10.445264 13.719536 10.918254 10.926946 9.841794 10.203691
## [526] 12.120281 11.450686 11.094745 8.874277 11.889702 12.053507 8.261823
## [533] 12.610664 10.645755 9.179452 9.127507 8.747887 10.394424 10.229761
## [540] 9.803505 11.132467 8.357747 8.682928 10.430973 10.658386 10.541427
## [547] 11.791369 9.683677 10.185759 8.884993 7.766476 10.018082 10.739814
## [554] 10.325521 12.281132 10.021040 10.977164 8.772640 7.612923 8.034168
## [561] 10.785402 11.260438 9.314992 8.743050 11.033220 9.215874 9.399060
## [568] 8.998725 12.968481 8.405409 11.770460 9.267395 11.640475 7.824750
## [575] 8.790112 8.069601 10.465538 10.871879 12.763610 9.408589 9.187348
## [582] 9.934240 10.515007 9.576213 11.330618 11.805840 11.160494 12.417292
## [589] 8.555823 10.591922 11.033364 12.906546 13.188622 10.615272 8.277006
## [596] 10.947036 10.188687 12.413761 7.340452 7.096948 10.639145 10.392163
## [603] 10.344673 7.879907 8.104044 10.113773 8.621606 9.214662 11.945407
## [610] 9.538068 10.135822 8.688746 10.914544 9.607328 9.479972 9.589026
## [617] 10.660309 8.694795 10.582048 11.666180 10.074081 11.084555 11.352038
## [624] 11.950589 9.612203 12.163611 9.174219 12.263822 9.400332 10.355291
## [631] 6.093252 11.782330 9.442695 9.022843 8.696705 10.272552 13.077023
## [638] 8.529581 9.624617 11.475661 10.786673 11.219281 10.286782 12.043320
## [645] 8.927532 10.081627 10.680541 13.509582 8.090502 10.889469 12.004392
## [652] 9.835795 9.941194 9.911199 9.786786 10.222463 7.739178 9.100173
## [659] 8.731903 10.050933 10.444313 11.716488 12.893607 8.786548 10.279218
## [666] 11.946732 9.383627 9.033875 10.037481 8.110606 10.389112 10.089411
## [673] 9.553510 10.269738 9.482356 9.776214 10.246363 9.931447 10.058177
## [680] 11.330266 9.483299 8.514422 9.230069 8.868161 13.735287 9.471504
## [687] 10.182978 10.542751 10.384164 9.903149 11.867328 6.971074 8.352857
## [694] 7.959546 10.262146 8.142033 10.103050 8.562540 9.037660 10.877211
## [701] 10.596377 11.630722 9.060471 11.675573 8.779805 10.397245 9.236975
## [708] 10.205326 13.062425 9.200303 9.710543 9.660248 9.442748 8.008140
## [715] 7.663436 10.338755 8.840830 9.947861 10.126540 11.035768 9.639640
## [722] 12.436313 10.765513 8.215965 8.007908 9.180242 9.213840 7.541109
## [729] 11.330545 9.325434 9.876843 6.932811 11.529380 10.426859 10.191389
## [736] 11.454159 9.732720 10.190859 12.374803 10.409096 9.788191 6.367915
## [743] 10.534154 10.638225 6.770609 9.419414 12.068038 10.155715 7.008099
## [750] 10.279628 12.088721 10.438124 9.211219 8.145456 10.056827 8.703143
## [757] 8.088024 10.475658 10.033093 11.005486 13.099461 10.414188 9.349322
## [764] 10.975318 10.529019 9.184905 11.510813 9.023883 9.827025 7.859777
## [771] 10.112945 10.457089 9.475061 10.896694 11.228808 10.327715 11.471453
## [778] 11.064706 9.011913 14.391381 11.581296 11.383065 10.602992 9.595614
## [785] 9.454428 10.696032 8.814885 10.853423 8.849583 8.969166 10.248063
## [792] 11.937431 9.435593 7.120157 8.699366 10.487586 9.388306 9.767351
## [799] 9.683107 8.350662 7.961753 9.181210 10.030194 9.272875 6.834026
## [806] 10.380900 10.579902 9.616392 11.319356 11.274785 9.035479 10.102049
## [813] 9.284365 11.503978 8.366763 9.184976 9.091899 10.803802 10.355637
## [820] 12.268075 8.655722 9.078300 10.051047 11.138484 8.807564 12.684521
## [827] 11.966956 7.396932 9.174085 8.693748 11.176373 8.903554 8.033118
## [834] 9.752946 12.568837 10.202476 10.176273 12.979527 10.222000 9.446599
## [841] 11.151014 10.166462 9.138896 10.703279 10.446991 10.883994 8.563529
## [848] 9.706215 10.215204 7.705449 10.185618 8.782096 8.597346 11.742675
## [855] 10.078670 9.191865 8.131248 10.290765 9.344415 9.634504 11.780148
## [862] 8.743213 10.302313 11.551768 11.202639 9.007472 10.918609 12.310346
## [869] 11.968101 10.477825 12.952557 10.490330 10.591069 10.835990 10.500965
## [876] 10.849751 8.509460 10.513421 9.607593 10.896140 10.743066 8.177163
## [883] 9.247942 11.921356 10.319060 11.131599 12.014428 9.697161 10.385621
## [890] 10.913940 10.195206 12.918936 9.822421 9.647699 10.813047 10.471756
## [897] 10.157874 8.121493 10.745191 11.095177 12.936657 8.908264 11.606003
## [904] 7.312544 7.873224 10.641124 11.969314 10.997833 9.118810 10.177943
## [911] 10.352580 11.006430 8.373077 7.399424 10.949370 8.774408 9.908633
## [918] 10.107110 9.077002 8.672550 11.002225 11.548464 9.819681 9.561978
## [925] 11.651164 9.322361 9.342142 10.419709 10.881327 9.431679 12.330011
## [932] 12.252100 10.674945 10.648728 11.479636 10.925646 10.296749 10.023493
## [939] 11.544973 7.537245 10.372544 9.184722 10.350139 10.946292 9.851126
## [946] 9.458679 11.321516 8.948129 9.063003 10.179712 12.308101 12.030636
## [953] 10.837458 9.008559 8.843259 10.937915 8.642084 9.991816 9.631185
## [960] 10.492916 10.456942 9.967626 9.884364 11.297926 9.967199 10.289328
## [967] 9.317700 11.082529 10.078385 11.236971 8.084485 9.891287 9.566994
## [974] 9.816319 8.138206 9.743763 10.096460 10.346188 8.996799 11.211403
## [981] 8.878533 7.317358 9.542742 12.785761 11.478764 11.470669 10.580803
## [988] 8.407235 11.785723 10.912682 9.350272 12.096594 6.839403 6.991724
## [995] 10.795881 8.915912 11.848156 10.324796 9.353689 12.147881
# create groups for different values of X
(group <- rep(1,1000)) # a vector consisting of 1000 elements
## [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [186] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [223] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [260] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [297] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [334] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [371] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [408] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [445] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [482] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [519] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [556] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [593] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [630] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [667] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [704] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [741] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [778] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [815] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [852] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [889] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [926] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [963] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1000] 1
group[xAxis > -1.5] <- 2
group[xAxis > -.5] <- 3
group[xAxis > .5] <- 4
group[xAxis > 1.5] <- 5
group
## [1] 3 2 3 4 4 4 2 3 2 4 3 1 3 2 4 4 2 2 4 5 2 2 3 4 1 2 3 2 4 3 2 2 2 3 3 3 4
## [38] 1 3 1 1 1 5 2 4 3 4 4 4 3 3 5 4 4 3 2 3 2 4 4 1 4 3 3 4 5 3 4 4 2 4 2 2 3
## [75] 4 2 4 3 1 3 3 2 2 3 4 2 4 4 2 3 3 2 2 3 5 3 4 2 3 3 2 3 3 3 4 4 4 4 1 2 2
## [112] 2 3 3 3 2 2 2 3 2 4 4 2 4 4 2 3 4 4 4 2 4 3 2 2 2 2 3 4 4 3 2 2 2 4 3 4 4
## [149] 4 3 3 4 4 3 3 3 4 2 1 2 4 4 2 2 4 2 4 1 3 3 4 4 4 3 2 3 5 4 3 4 4 3 3 4 4
## [186] 2 4 2 4 3 4 4 2 3 3 2 4 3 3 3 2 1 4 3 3 3 4 5 4 2 3 3 2 3 3 2 3 3 1 5 1 2
## [223] 4 4 4 3 1 3 4 4 3 1 4 3 4 4 2 3 3 4 2 1 4 5 2 3 3 4 2 5 4 4 2 4 2 4 5 3 2
## [260] 1 2 4 4 3 2 3 5 3 2 1 3 1 4 4 3 1 3 3 3 2 2 3 5 3 2 5 1 4 2 4 1 5 3 2 3 4
## [297] 2 3 3 5 3 4 2 2 4 4 3 3 3 5 3 4 3 2 2 3 2 1 4 2 5 3 3 2 1 2 3 3 4 3 2 1 2
## [334] 3 5 2 2 4 5 3 3 4 3 3 3 5 4 2 1 3 3 4 4 2 2 4 3 3 3 5 4 3 4 2 4 2 5 3 3 3
## [371] 4 2 2 3 5 4 2 3 2 4 2 3 1 2 3 2 3 1 1 2 2 3 3 2 3 4 1 3 3 1 3 4 3 2 4 4 2
## [408] 2 3 4 3 3 3 3 4 4 4 5 3 4 3 3 3 2 3 1 3 3 5 2 3 4 1 5 4 4 3 4 3 3 2 3 3 4
## [445] 3 2 1 3 4 4 5 5 4 3 1 2 2 4 3 1 2 4 1 4 4 3 3 4 3 3 2 3 4 3 3 3 1 4 3 3 4
## [482] 4 5 2 2 3 3 4 2 4 3 4 3 5 2 2 5 2 4 2 2 2 3 2 3 4 3 3 4 4 3 3 3 3 3 2 3 4
## [519] 4 4 3 3 4 2 4 5 3 3 2 4 5 2 4 4 3 3 3 3 3 3 3 2 2 3 3 4 5 4 3 3 2 4 3 3 4
## [556] 2 2 3 1 3 4 3 4 2 3 3 3 3 5 2 3 3 3 2 2 2 3 2 5 3 4 3 3 2 2 4 5 5 3 3 3 5
## [593] 4 3 2 3 3 5 2 1 5 3 3 2 1 3 2 2 4 3 2 3 4 3 2 4 4 3 4 4 3 4 3 4 4 4 2 4 3
## [630] 4 1 3 2 3 2 4 4 2 3 5 4 2 3 3 3 3 2 5 1 3 4 3 3 4 3 3 3 3 3 4 3 2 3 2 3 5
## [667] 3 2 2 2 3 4 3 4 4 3 4 3 3 3 2 2 2 2 5 4 3 3 3 3 4 2 1 3 4 1 4 1 2 3 5 2 4
## [704] 3 4 3 3 3 4 3 3 3 4 4 1 4 4 4 3 4 4 3 3 2 2 2 3 2 4 1 3 2 4 4 4 2 3 4 5 4
## [741] 2 3 3 3 4 4 4 4 2 2 5 3 1 1 3 3 1 3 2 4 4 3 4 3 3 2 3 2 2 3 3 5 2 5 2 3 3
## [778] 4 3 4 2 3 4 3 3 4 3 3 3 2 4 4 2 1 4 3 4 3 3 3 2 3 3 2 1 3 4 2 4 3 2 1 2 4
## [815] 2 2 2 4 2 4 1 2 3 2 3 5 4 2 3 3 3 1 2 3 4 3 2 4 4 3 4 3 3 4 3 3 3 3 4 1 3
## [852] 2 3 4 4 2 3 2 2 2 4 3 2 2 3 3 4 4 3 3 3 2 2 3 3 3 2 3 2 4 3 2 4 4 3 4 4 3
## [889] 3 3 5 5 3 2 3 2 5 1 3 4 3 3 4 2 2 4 3 2 3 3 5 3 3 2 3 2 2 3 2 3 4 4 2 2 5
## [926] 2 3 2 4 2 4 3 3 2 4 4 3 3 2 1 3 2 2 3 3 2 4 4 2 2 5 5 3 3 2 4 3 3 2 4 3 3
## [963] 2 4 3 3 4 3 2 4 2 2 3 4 3 4 3 2 2 3 2 1 4 5 5 5 4 3 5 3 3 2 1 2 3 1 4 4 2
## [1000] 3
# create sample dataframe by joining variables
sample_data <- data.frame(xAxis,yAxis,group)
sample_data
## xAxis yAxis group
## 1 -0.3131904408 9.434404 3
## 2 -0.5910442591 8.079323 2
## 3 0.0003854115 9.869881 3
## 4 0.7680988207 12.332151 4
## 5 0.8899781685 11.100700 4
## 6 0.9867551167 9.525093 4
## 7 -0.5293468388 7.592299 2
## 8 -0.2664746148 7.707508 3
## 9 -0.6868798124 7.624858 2
## 10 0.5984826210 9.999597 4
## 11 -0.0408882110 11.556035 3
## 12 -1.9754892271 7.098362 1
## 13 0.3766778877 11.643305 3
## 14 -0.6939261469 9.397716 2
## 15 1.3275672121 10.108833 4
## 16 1.2366457094 11.146562 4
## 17 -1.3053079165 8.142802 2
## 18 -1.0367059358 8.911684 2
## 19 0.7651872103 11.220742 4
## 20 1.6651661735 12.705276 5
## 21 -1.3770880189 10.634171 2
## 22 -0.7675218865 10.152539 2
## 23 -0.1573490571 9.465497 3
## 24 1.0780230423 10.569824 4
## 25 -1.8729743348 7.914699 1
## 26 -1.4195269992 8.326207 2
## 27 0.3066233021 10.101591 3
## 28 -0.8097009260 8.848611 2
## 29 1.2452124486 11.936089 4
## 30 -0.2763685917 7.910833 3
## 31 -1.2194689633 9.785766 2
## 32 -0.7667198407 11.208878 2
## 33 -0.5077105992 8.511698 2
## 34 0.2128486604 10.287839 3
## 35 -0.4983191564 7.996248 3
## 36 -0.0587811277 10.720783 3
## 37 1.1204280795 8.359629 4
## 38 -1.8759354290 9.031012 1
## 39 0.0416061921 11.016833 3
## 40 -1.7504746100 9.451013 1
## 41 -1.9610173019 8.889140 1
## 42 -2.3444925146 7.653760 1
## 43 2.5272392535 13.265348 5
## 44 -0.8664767676 8.427073 2
## 45 0.7746889042 11.647759 4
## 46 -0.3047788131 9.320966 3
## 47 0.7839556095 10.456048 4
## 48 1.2819931416 11.436670 4
## 49 1.1411561607 11.283118 4
## 50 -0.0823007449 9.094744 3
## 51 -0.0759022405 10.321153 3
## 52 1.9324567105 12.016496 5
## 53 1.0045676777 9.539236 4
## 54 0.5640413638 10.885316 4
## 55 0.1894948021 10.678508 3
## 56 -1.1624616692 8.805682 2
## 57 0.0369874915 10.689512 3
## 58 -0.8054466342 7.574970 2
## 59 0.9854997734 12.169825 4
## 60 0.6911381513 12.229843 4
## 61 -1.5004715009 8.182116 1
## 62 1.0546851006 11.167967 4
## 63 -0.4935699281 9.114715 3
## 64 0.1341350839 9.060503 3
## 65 0.6021621924 10.350743 4
## 66 2.2577287972 13.288838 5
## 67 0.1443726192 10.516541 3
## 68 1.4221407251 12.269908 4
## 69 1.4324143043 11.283385 4
## 70 -0.9546756903 6.784057 2
## 71 1.1204145142 11.652246 4
## 72 -0.5741875234 10.310266 2
## 73 -1.4171645627 9.526146 2
## 74 -0.3554999472 10.466706 3
## 75 0.7467802129 9.501866 4
## 76 -1.0727504528 8.246820 2
## 77 1.4138592565 11.387205 4
## 78 -0.2688161241 8.059732 3
## 79 -1.6612691300 9.103616 1
## 80 -0.1436638188 8.160740 3
## 81 0.0972024314 10.086960 3
## 82 -0.7397512918 9.215879 2
## 83 -1.3081604708 8.210986 2
## 84 -0.3411345369 8.375583 3
## 85 1.0164044683 10.761201 4
## 86 -1.3216540030 8.488004 2
## 87 0.7606409434 11.060976 4
## 88 1.2348735399 11.330837 4
## 89 -0.6854503711 7.952613 2
## 90 0.0290952715 11.192008 3
## 91 -0.2082335914 8.732643 3
## 92 -1.0252797127 8.464366 2
## 93 -1.1240285713 9.122723 2
## 94 -0.2208533973 10.300623 3
## 95 1.5439905120 9.496257 5
## 96 -0.4314723319 8.686948 3
## 97 0.9909158036 10.818094 4
## 98 -1.3679998326 7.807011 2
## 99 0.1680662977 10.246026 3
## 100 0.3216664155 9.528913 3
## 101 -0.6290541379 7.564176 2
## 102 0.3624089869 10.965948 3
## 103 0.1006459471 10.484206 3
## 104 -0.0394765233 10.617407 3
## 105 0.5832710198 11.242186 4
## 106 1.2019723123 11.587840 4
## 107 1.0305654159 9.654020 4
## 108 0.7109014786 10.159228 4
## 109 -1.5422662252 8.201107 1
## 110 -0.6882244398 10.269865 2
## 111 -0.5709698712 9.478307 2
## 112 -1.2560143978 6.363978 2
## 113 -0.0490585859 9.377983 3
## 114 -0.1088509499 8.556449 3
## 115 -0.4105129495 9.696035 3
## 116 -0.7488415269 8.779347 2
## 117 -1.0704781856 9.604098 2
## 118 -0.7276478827 9.621399 2
## 119 0.1566961891 10.567828 3
## 120 -1.0099370219 8.452577 2
## 121 1.0824254454 11.886576 4
## 122 1.4261035631 12.002683 4
## 123 -0.7700513969 9.979573 2
## 124 0.6807905792 10.443004 4
## 125 0.6507583360 12.152865 4
## 126 -0.9855009469 8.560197 2
## 127 -0.4577304820 8.751410 3
## 128 0.5688533592 10.444091 4
## 129 0.6164668430 10.885368 4
## 130 0.7283982338 10.908492 4
## 131 -0.7790895684 10.653188 2
## 132 0.7406203229 11.315040 4
## 133 -0.3991236702 9.585146 3
## 134 -0.5526091636 8.023119 2
## 135 -0.5911035101 10.560828 2
## 136 -0.7989611036 6.925309 2
## 137 -0.9530011127 8.704587 2
## 138 -0.0382910114 10.331634 3
## 139 1.0063120243 11.896846 4
## 140 0.7786539553 11.687970 4
## 141 0.0665510473 9.231672 3
## 142 -0.8149780965 9.338539 2
## 143 -1.2678541734 9.354624 2
## 144 -1.0417354422 10.498745 2
## 145 0.7964336672 11.241640 4
## 146 0.1878872908 11.332346 3
## 147 0.8300517086 9.964795 4
## 148 1.4843578612 12.382144 4
## 149 0.5827980362 9.031848 4
## 150 -0.3076654856 10.405501 3
## 151 0.1077109013 9.408733 3
## 152 0.6211902389 11.361986 4
## 153 1.4038993712 11.689568 4
## 154 -0.4259441699 10.368665 3
## 155 0.4943170669 9.610124 3
## 156 -0.0454036640 8.928417 3
## 157 0.8319610844 10.484857 4
## 158 -0.5282662545 10.375520 2
## 159 -1.6655366854 8.788880 1
## 160 -1.0318516128 9.541317 2
## 161 0.8412668052 11.394228 4
## 162 0.8112281712 9.938432 4
## 163 -0.6330524646 8.366551 2
## 164 -0.8037766503 9.446831 2
## 165 0.5866991158 9.815961 4
## 166 -1.4376635736 8.798485 2
## 167 0.7148597747 9.370409 4
## 168 -1.9068478566 8.763098 1
## 169 0.0790766714 8.407084 3
## 170 -0.3319216702 10.424841 3
## 171 0.7811521693 10.511737 4
## 172 1.1209276097 11.405658 4
## 173 0.8662527890 11.074164 4
## 174 -0.1818354978 10.508658 3
## 175 -0.8957349328 10.145220 2
## 176 -0.1766628128 11.241579 3
## 177 1.8149319302 12.540035 5
## 178 1.1977259183 10.740687 4
## 179 -0.1034218820 9.513470 3
## 180 0.8915496615 10.072735 4
## 181 1.0692021446 12.465673 4
## 182 -0.3248502990 9.134809 3
## 183 0.3488216089 11.209778 3
## 184 0.7169293866 8.269292 4
## 185 0.8343333987 11.402963 4
## 186 -1.1027275671 10.897281 2
## 187 1.0781836324 12.437195 4
## 188 -0.7020110385 9.690998 2
## 189 0.8702444454 10.883555 4
## 190 -0.4150912450 10.428257 3
## 191 0.6513111595 9.237466 4
## 192 1.1933608649 10.698665 4
## 193 -0.9801474814 7.630650 2
## 194 0.4074065398 11.445804 3
## 195 0.1225101576 10.527779 3
## 196 -0.6709969220 9.180886 2
## 197 1.1705450777 9.578853 4
## 198 0.3950491497 11.752139 3
## 199 -0.2381713285 9.277984 3
## 200 -0.0185637671 10.325884 3
## 201 -1.2899989563 8.440269 2
## 202 -2.9067073150 7.917657 1
## 203 1.3076466564 10.602568 4
## 204 -0.1467963574 10.268303 3
## 205 0.3057206500 11.371678 3
## 206 0.2114775696 10.859719 3
## 207 0.9738514883 12.818876 4
## 208 2.4517310318 12.595046 5
## 209 0.9559078748 11.703719 4
## 210 -0.5915301249 9.563077 2
## 211 0.4920580635 10.933032 3
## 212 0.2066019134 11.092139 3
## 213 -1.0054099969 8.368991 2
## 214 -0.0342468541 11.052802 3
## 215 -0.1599872659 10.187888 3
## 216 -0.6542441353 9.353808 2
## 217 -0.0955416357 10.342500 3
## 218 0.1435517749 9.925403 3
## 219 -2.1006593431 8.063643 1
## 220 1.7358952776 12.625780 5
## 221 -1.8567776608 6.759366 1
## 222 -0.6791628807 9.046088 2
## 223 0.9005391902 11.166904 4
## 224 1.2554436539 11.740368 4
## 225 1.3811295650 12.689321 4
## 226 0.3024203022 10.072691 3
## 227 -2.5911058219 5.986881 1
## 228 -0.3085306639 7.646566 3
## 229 1.1907073182 10.250979 4
## 230 1.4286336269 9.506774 4
## 231 0.3702117697 10.002311 3
## 232 -1.5702683854 9.309023 1
## 233 0.6150222814 8.878288 4
## 234 0.4607653680 10.867557 3
## 235 1.1292732456 9.396850 4
## 236 0.6961315177 12.324416 4
## 237 -0.9874453279 9.348999 2
## 238 -0.3357946827 8.137990 3
## 239 0.4189955641 9.770990 3
## 240 1.1262094814 12.267481 4
## 241 -0.9692896631 9.226285 2
## 242 -1.7306590555 6.761238 1
## 243 0.8645490411 11.722527 4
## 244 1.8269446214 10.793859 5
## 245 -0.9030113322 7.902419 2
## 246 0.1959869359 11.433898 3
## 247 -0.4536729755 9.123738 3
## 248 0.8775063656 9.739647 4
## 249 -0.8299145222 9.835411 2
## 250 1.5976700532 12.189699 5
## 251 0.7817198004 9.714850 4
## 252 0.5917019733 10.989229 4
## 253 -1.2923433924 7.941030 2
## 254 0.8383441580 10.989319 4
## 255 -1.4810163153 9.751571 2
## 256 0.8694137972 11.071108 4
## 257 2.2310275627 11.920206 5
## 258 0.1675337119 10.550370 3
## 259 -0.8048887933 8.680413 2
## 260 -1.5001426623 10.188394 1
## 261 -0.5167800411 10.027473 2
## 262 1.0405436198 11.966664 4
## 263 0.5953639805 10.922559 4
## 264 0.3798124316 9.289883 3
## 265 -0.5948031736 9.026406 2
## 266 -0.4205728851 7.792765 3
## 267 1.8563428479 11.606512 5
## 268 0.2171292544 10.386711 3
## 269 -1.3083829845 7.475658 2
## 270 -1.9691093568 8.125143 1
## 271 -0.3432996163 9.990061 3
## 272 -1.8621852737 8.417040 1
## 273 0.7295425049 11.440478 4
## 274 1.1596317303 10.963543 4
## 275 0.4022014173 11.645025 3
## 276 -1.7363413938 9.369663 1
## 277 0.3041560271 11.501925 3
## 278 0.0249166231 7.642656 3
## 279 -0.3801076088 10.884414 3
## 280 -0.5417172331 10.348127 2
## 281 -0.6621784240 8.486456 2
## 282 0.4474761033 9.930168 3
## 283 1.5224264104 11.741966 5
## 284 -0.2016406561 11.540542 3
## 285 -0.6134709421 8.372885 2
## 286 2.8649218992 14.613235 5
## 287 -2.0120818774 9.087149 1
## 288 0.8432552637 9.568133 4
## 289 -0.9094350743 6.654456 2
## 290 1.0926981293 11.892880 4
## 291 -2.5174994571 7.487323 1
## 292 1.5262299234 11.916419 5
## 293 0.3627112913 11.015038 3
## 294 -1.2848346569 10.114973 2
## 295 -0.2651783657 10.097810 3
## 296 1.2588525332 11.972908 4
## 297 -0.8090025453 9.410080 2
## 298 0.4585745529 10.119323 3
## 299 -0.0865584997 10.700708 3
## 300 1.7219165584 13.759525 5
## 301 0.4615690796 9.872972 3
## 302 1.3245404046 11.013703 4
## 303 -0.8177222541 7.369773 2
## 304 -0.7204302465 8.277115 2
## 305 0.9045337393 10.381991 4
## 306 0.9301673012 10.501086 4
## 307 -0.1893500916 9.109530 3
## 308 0.1702090842 12.079634 3
## 309 0.0938139936 11.463394 3
## 310 2.0858290543 11.995164 5
## 311 0.1044583641 10.291691 3
## 312 0.5593864951 9.677513 4
## 313 -0.1272209320 10.163476 3
## 314 -1.1030006504 7.967849 2
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## 316 -0.4967033792 10.070002 3
## 317 -0.5013536129 10.584525 2
## 318 -2.3633705738 7.393081 1
## 319 0.8951122270 10.992332 4
## 320 -0.6489392084 8.364170 2
## 321 1.9061785445 12.594005 5
## 322 -0.1894400504 9.371372 3
## 323 -0.3760638016 10.893658 3
## 324 -0.6199714345 9.530856 2
## 325 -2.0391308705 8.894932 1
## 326 -1.2685309572 8.633053 2
## 327 -0.3477139161 8.226644 3
## 328 -0.1194027226 9.719486 3
## 329 0.6624272255 10.021005 4
## 330 -0.4357310245 9.576036 3
## 331 -0.9732199552 10.201047 2
## 332 -3.1244983506 6.917274 1
## 333 -0.7365329543 11.718633 2
## 334 0.1033540413 8.739442 3
## 335 1.5841260165 10.002975 5
## 336 -1.3437192097 6.673013 2
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## 338 1.3820215448 11.285490 4
## 339 1.6139011665 11.127722 5
## 340 -0.1318842287 9.914874 3
## 341 -0.0528541227 9.676545 3
## 342 0.9736174674 11.333905 4
## 343 0.3992267314 11.407523 3
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## 346 2.8119558056 12.525002 5
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## 351 0.0822678961 7.412783 3
## 352 1.3715978895 11.618722 4
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## 354 -1.1811230837 8.385154 2
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## 357 0.0313324036 9.559868 3
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## 360 2.2648111413 13.211667 5
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## 364 -0.5677774113 9.619270 2
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## 366 -1.0611941204 8.241570 2
## 367 1.8950158451 11.884233 5
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## 377 -1.1633058138 6.415906 2
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## 379 -0.7175604971 8.456116 2
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## 928 -0.7526581816 10.419709 2
## 929 1.1609426827 10.881327 4
## 930 -0.9285496301 9.431679 2
## 931 0.5174419833 12.330011 4
## 932 -0.3122379988 12.252100 3
## 933 -0.4064670034 10.674945 3
## 934 -0.6376382234 10.648728 2
## 935 0.6248993902 11.479636 4
## 936 0.9174534934 10.925646 4
## 937 0.2691769725 10.296749 3
## 938 -0.3881434146 10.023493 3
## 939 -0.5649234904 11.544973 2
## 940 -1.8158937932 7.537245 1
## 941 -0.2116268890 10.372544 3
## 942 -1.3340586485 9.184722 2
## 943 -0.7628632127 10.350139 2
## 944 0.3883215840 10.946292 3
## 945 0.0644220643 9.851126 3
## 946 -0.6204899184 9.458679 2
## 947 1.0973430538 11.321516 4
## 948 0.5467568781 8.948129 4
## 949 -0.6653649675 9.063003 2
## 950 -0.7928334913 10.179712 2
## 951 1.7485040856 12.308101 5
## 952 1.6597521865 12.030636 5
## 953 0.0856362940 10.837458 3
## 954 -0.4449163350 9.008559 3
## 955 -0.6607425185 8.843259 2
## 956 0.5573677053 10.937915 4
## 957 -0.2233245702 8.642084 3
## 958 0.1610599335 9.991816 3
## 959 -0.9594076820 9.631185 2
## 960 1.2589109238 10.492916 4
## 961 -0.3702296129 10.456942 3
## 962 0.1906757902 9.967626 3
## 963 -0.9871640732 9.884364 2
## 964 0.6575956863 11.297926 4
## 965 0.0232165287 9.967199 3
## 966 0.3352266020 10.289328 3
## 967 0.7330584385 9.317700 4
## 968 0.0714962471 11.082529 3
## 969 -0.5282843183 10.078385 2
## 970 0.6608283876 11.236971 4
## 971 -0.9988080227 8.084485 2
## 972 -0.7067545029 9.891287 2
## 973 -0.1778412188 9.566994 3
## 974 0.8770021084 9.816319 4
## 975 -0.3988458819 8.138206 3
## 976 1.1770048240 9.743763 4
## 977 0.1741996329 10.096460 3
## 978 -0.7222558965 10.346188 2
## 979 -0.8828410562 8.996799 2
## 980 0.3965409479 11.211403 3
## 981 -1.1172080943 8.878533 2
## 982 -1.6473136395 7.317358 1
## 983 0.5235542142 9.542742 4
## 984 1.7491255741 12.785761 5
## 985 1.5798774936 11.478764 5
## 986 2.1671440439 11.470669 5
## 987 0.5920510646 10.580803 4
## 988 0.0636905400 8.407235 3
## 989 1.7233813811 11.785723 5
## 990 0.4287068962 10.912682 3
## 991 -0.4397115337 9.350272 3
## 992 -0.5333392789 12.096594 2
## 993 -2.1363029251 6.839403 1
## 994 -0.7033457093 6.991724 2
## 995 0.0914746049 10.795881 3
## 996 -2.0539943499 8.915912 1
## 997 1.3013846144 11.848156 4
## 998 1.4540983736 10.324796 4
## 999 -0.7696012315 9.353689 2
## 1000 0.3636405408 12.147881 3
# creates plot object using ggplot
plot <- ggplot(sample_data, aes(x = xAxis, y= yAxis, col = as.factor(group)))+
geom_point()+theme(legend.position = "none")
# Display plot
plot
# Insert marginal ditribution using marginal function
ggMarginal(plot,type= 'histogram',groupColour=TRUE,groupFill=TRUE)
