Graph-In

Graph-In
April James Palermo
2023-06-15
# Mindanao State University
# General Santos City
# Introduction to R base commands
# Submitted by: April James Palermo
# Submitted to: Prof. Carlito O. Daarol
# Faculty
# Math Department
# March 28, 2023

# 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 = "Hello: This is my Line Plot",
xlab = "My X-Values",
ylab = "My Y-Values")
# Step 4: Add color to the line using the col command
plot(x, y, type = "l",
main = "This is my Line Plot",
xlab = "My X-Values",
ylab = "My Y-Values",
col = "red")
# Step 5: Modify Thickness of Line using lwd command
plot(x, y, type = "l",
main = "This is my Line Plot",
xlab = "My X-Values",
ylab = "My Y-Values",
lwd=7,
col = "green")
# Step 6: Add points to line graph by changing the type command
plot(x, y, type = "b",
main = "This is my Line Plot",
xlab = "My X-Values",
ylab = "My Y-Values",
lwd=3,
col = "blue")

# 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 instan
tly
# 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 = "This is my Line Plot",
xlab = "My X-Values",
ylab = "My 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 = "This is my Line Plot",
xlab = "My X-Values",
ylab = "My 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 3.6
2 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 v
ariables
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 v
ariables
## 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" "Spe
cies"
# 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),l
as=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\\johnm\\OneDrive\\Documents\\Lecture"
filename <- "cancer.csv"
(file <- paste0(folder,"/",filename))
## [1] "C:\\Users\\johnm\\OneDrive\\Documents\\Lecture/cancer.csv"
cancer <- read.csv(file)
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\\johnm\\OneDrive\\Documents\\Lecture"
filename <- "hsb2.csv"
(file <- paste0(folder,"/",filename))
## [1] "C:\\Users\\johnm\\OneDrive\\Documents\\Lecture/hsb2.csv"
hsb2_wide <- read.csv(file)
# 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 socs
t
## 195 195 179 1 4 2 2 2 47 65 60 50 5
6
## 196 196 31 1 2 2 2 1 55 59 52 42 5
6
## 197 197 145 1 4 2 1 3 42 46 38 36 4
6
## 198 198 187 1 4 2 2 1 57 41 57 55 5
2
## 199 199 118 1 4 2 1 1 55 62 58 58 6
1
## 200 200 137 1 4 3 1 2 63 65 65 53 6
1
# 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
## 408 11 0 1 2 1 2 math 45
## 409 84 0 4 2 1 1 math 54
## 410 48 0 3 2 1 2 math 52
## 411 75 0 4 2 1 3 math 51
## 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
## 415 38 0 3 1 1 2 math 50
## 416 115 0 4 1 1 1 math 43
## 417 76 0 4 3 1 2 math 51
## 418 195 0 4 2 2 1 math 60
## 419 114 0 4 3 1 2 math 62
## 420 85 0 4 2 1 1 math 57
## 421 167 0 4 2 1 1 math 35
## 422 143 0 4 2 1 3 math 75
## 423 41 0 3 2 1 2 math 45
## 424 20 0 1 3 1 2 math 57
## 425 12 0 1 2 1 3 math 45
## 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
## 450 7 0 1 2 1 2 math 59
## 451 27 0 2 2 1 2 math 61
## 452 128 0 4 3 1 2 math 38
## 453 21 0 1 2 1 1 math 61
## 454 183 0 4 2 2 2 math 49
## 455 132 0 4 2 1 2 math 73
## 456 15 0 1 3 1 3 math 44
## 457 67 0 4 1 1 3 math 42
## 458 22 0 1 2 1 3 math 39
## 459 185 0 4 2 2 2 math 55
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## 468 107 0 4 1 1 3 math 47
## 469 81 0 4 1 1 2 math 59
## 470 18 0 1 2 1 3 math 49
## 471 155 0 4 2 1 1 math 46
## 472 97 0 4 3 1 2 math 58
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## 474 157 0 4 2 1 1 math 58
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## 483 174 0 4 2 2 2 math 71
## 484 3 0 1 1 1 2 math 48
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## 486 146 0 4 3 1 2 math 64
## 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
## 491 24 0 2 2 1 2 math 66
## 492 149 0 4 1 1 1 math 49
## 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
## 500 194 1 4 3 2 2 math 69
## 501 88 1 4 3 1 2 math 64
## 502 99 1 4 3 1 1 math 56
## 503 47 1 3 1 1 2 math 49
## 504 120 1 4 3 1 2 math 54
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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 qualitati
ve 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", "vocation
al"))
data$race = factor(data$race, labels=c("hispanic", "asian", "africaname
r","white"))
data$female = factor(data$female, labels=c("female", "male"))
# check data structure again. The former integer variables are now cate
gorical 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 Subje
ct
(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
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 tr
ansformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping s
tructure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a num
erical
## variable into a factor?
## The following aesthetics were dropped during statistical transformat
ion: fill
## ℹ This can happen when ggplot fails to infer the correct grouping s
tructure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a num
erical
## variable into a factor?
## The following aesthetics were dropped during statistical transformat
ion: fill
## ℹ This can happen when ggplot fails to infer the correct grouping s
tructure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a num
erical
## variable into a factor?
## The following aesthetics were dropped during statistical transformat
ion: fill
## ℹ This can happen when ggplot fails to infer the correct grouping s
tructure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a num
erical
## variable into a factor?
p + geom_bar(position = "dodge")
## Warning: The following aesthetics were dropped during statistical tr
ansformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping s
tructure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a num
erical
## variable into a factor?
## The following aesthetics were dropped during statistical transformat
ion: fill
## ℹ This can happen when ggplot fails to infer the correct grouping s
tructure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a num
erical
## variable into a factor?
## The following aesthetics were dropped during statistical transformat
ion: fill
## ℹ This can happen when ggplot fails to infer the correct grouping s
tructure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a num
erical
## variable into a factor?
## The following aesthetics were dropped during statistical transformat
ion: fill
## ℹ This can happen when ggplot fails to infer the correct grouping s
tructure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a num
erical
## 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 tr
ansformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping s
tructure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a num
erical
## variable into a factor?
## The following aesthetics were dropped during statistical transformat
ion: fill
## ℹ This can happen when ggplot fails to infer the correct grouping s
tructure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a num
erical
## variable into a factor?
p + geom_bar(position = "dodge")
## Warning: The following aesthetics were dropped during statistical tr
ansformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping s
tructure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a num
erical
## variable into a factor?
## The following aesthetics were dropped during statistical transformat
ion: fill
## ℹ This can happen when ggplot fails to infer the correct grouping s
tructure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a num
erical
## 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 ──────────────────────── tidyve
rse 2.0.0 ──
## ✔ dplyr 1.1.1 ✔ readr 2.1.4
## ✔ forcats 1.0.0 ✔ stringr 1.5.0
## ✔ lubridate 1.9.2 ✔ tibble 3.2.1
## ✔ purrr 1.0.1 ✔ tidyr 1.3.0
## ── Conflicts ────────────────────────────────────────── tidyverse_co
nflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to fo
rce 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] -0.1631453029 0.1393248355 -1.3318035075 -0.7431742789 1.21
96300795
## [6] 0.2527621957 -0.0265420404 0.0242265978 -0.6300441696 0.53
86602949
## [11] -1.1954288002 0.2337001946 0.1297820113 1.7982242043 -1.36
76542218
## [16] -2.6321638564 1.2536656339 0.5565465348 -0.6835805703 0.70
65302125
## [21] 0.4453597115 -0.8641030288 0.2234358218 -1.2024881210 0.00
66958664
## [26] -0.5525051482 -0.3334405401 -0.0532299721 1.0088558827 -0.17
73455437
## [31] 0.9391456638 -1.2714849597 -0.5918975442 0.5022272488 -1.47
28376979
## [36] -1.5226888133 0.2410702856 0.0897615887 0.5586248785 -1.95
87848419
## [41] 1.3030341798 1.3688284329 0.3367408261 -0.0502687960 0.20
19326225
## [46] 1.4158180681 0.0356535792 1.7869630369 -0.0803064743 -0.30
81487712
## [51] 0.7335123634 0.8499819785 -2.4421511988 -0.6744580942 -0.48
94969610
## [56] 0.0961812772 -0.8352657604 0.4289362600 -1.4981267379 0.36
28642398
## [61] -0.5622761926 -1.5283810485 -0.5413620380 -1.8002429987 0.46
70262886
## [66] 0.8021081877 1.8262960925 1.1894513210 0.1564466063 0.40
93766117
## [71] -1.4220289742 1.2816783658 0.5436570410 -0.6963696584 2.07
85097156
## [76] 0.1721415909 -0.7242287868 -1.9600507589 -0.9024000614 1.34
43074615
## [81] 0.4799381685 1.0844215431 -0.6044237467 0.6754399875 0.96
78569518
## [86] 2.1677747322 1.0798739763 -0.6005402307 -1.6823365599 1.63
64588694
## [91] 0.1563613394 -0.2753164368 -1.2849886264 0.8453616040 -1.02
65367715
## [96] -0.3302942206 0.7716177135 1.3602066069 -0.6746802678 -1.33
60118199
## [101] -1.9580063056 -0.5953520371 -0.8430663659 -0.6868415877 -0.26
24194732
## [106] -0.2609887218 1.0922887493 0.3747954888 -0.1307910983 -2.16
06931396
## [111] -0.6286087034 -0.5606862817 -1.0750623093 -0.7164800677 0.73
18213451
## [116] 1.4572611037 1.1532561323 -0.6498436701 -0.5696556674 -0.68
27087075
## [121] -0.2552186885 -2.4117907628 0.2569324209 -0.5951418466 -0.48
94718169
## [126] 1.1261369192 -0.6556438770 -0.1521950387 0.2214185979 -1.31
81933800
## [131] -0.6310719672 -0.1585797102 0.1554964894 0.9817094476 -2.05
57089330
## [136] -0.1096554495 -0.1160208850 -0.0754779999 1.4033210205 1.05
12001496
## [141] 2.5722896717 1.7497416483 -1.1818122553 0.3394303021 2.44
81723770
## [146] -0.3674741401 -0.8421643788 -0.5024031716 -0.7463507693 0.63
20844328
## [151] 0.5008751855 1.1668058281 0.1137640772 0.2427811193 -1.47
25262002
## [156] 0.1090187191 -0.5831008615 -1.4577907254 0.9772205265 0.66
66524167
## [161] 0.2787004632 -0.9005738400 -1.2115353744 0.8854911703 -1.57
20508503
## [166] 0.6851280527 -0.2076696120 0.8185256861 1.0766151678 0.32
31073304
## [171] -0.5972475553 -1.2111491923 -0.4928570284 -1.4328814930 -1.34
59300859
## [176] -1.4806822954 -0.2926441241 -0.6041053316 1.2149917113 -0.22
18933904
## [181] 1.1381964157 0.2841726533 1.4192100241 1.1668497952 0.17
08909698
## [186] 0.1588455755 -0.3896627143 0.7821379914 0.4576157039 -0.80
40277500
## [191] 0.1821686633 0.3257720359 0.0090671486 -0.4211468085 0.94
45531997
## [196] 0.4212658086 0.2266830993 0.1807024402 -1.7354645207 0.56
57937678
## [201] 0.4934078242 -0.4799591773 0.2573426033 -2.0294514942 -0.67
82429291
## [206] 0.4133737031 0.8500263508 1.6993976826 2.1489674476 -0.38
99559928
## [211] 0.3903887905 -0.6864771634 -0.7730762000 -0.8500764425 0.56
25314541
## [216] -0.0401710906 1.6593852260 0.4671132434 -0.3769859129 0.40
78926332
## [221] -0.2743229217 -1.2129441728 -1.6407093778 0.9362792320 -0.06
57865083
## [226] -1.0285565282 0.2696047914 0.1288675517 0.3047802442 -1.84
20852707
## [231] -0.0320253292 -0.0402669247 -1.9517331838 0.9315665396 0.31
19353796
## [236] 0.9895697677 2.8081106922 -0.1954801282 0.1685859699 -0.60
89592421
## [241] -0.0114643485 -0.2320231521 0.0922727846 -1.5125599570 -0.72
95055003
## [246] 1.1186457322 0.9800601360 -0.6267624792 1.0069422552 -0.40
78635418
## [251] 0.8201696331 -1.7407139683 -0.3011145065 1.2440940000 -0.02
89769208
## [256] 1.1775458967 -0.2092520357 0.0100173063 1.5497949717 0.57
78154397
## [261] 0.6183461129 1.1684056009 0.9031227154 -0.8840571717 0.25
45334347
## [266] 0.8720249299 -1.0447287493 1.1311248957 -1.3731162802 -0.06
00481594
## [271] -1.0994171735 0.4228141544 1.2693212227 0.7582997683 0.26
36901262
## [276] 0.1110200252 1.0579417550 -2.6772814830 -0.3706148187 0.74
27929015
## [281] 0.7805430857 0.0397286865 2.1511688763 -1.0176416363 0.27
80327051
## [286] 0.8118120885 0.2058008401 -1.3083673437 -0.0816268495 -0.92
33494631
## [291] 0.4976224093 1.1061122285 -0.4347981355 0.4972779380 -0.61
05163434
## [296] 0.2113275173 -1.4501135438 -1.3071062105 -0.8847757938 0.31
45852387
## [301] -1.6682172335 0.1507838173 -1.5483899088 -0.2539184048 -0.37
27972614
## [306] -0.9930037966 0.7520843150 -1.0875939595 1.7546972944 -0.33
64086319
## [311] 0.2426177825 -0.0564883834 -1.1636024715 1.0379304630 0.48
84872442
## [316] 1.4772974088 0.1567297069 0.5316590664 -0.4882698702 0.40
68998383
## [321] 1.0098129101 0.8015294980 -0.5146077617 0.0055184978 0.52
33641015
## [326] -0.1606091667 0.6652851364 -0.7275071162 -0.1874679188 0.06
21769062
## [331] 0.5017913528 -1.3803122672 -1.9419167221 -0.4988856965 -1.04
13013207
## [336] 0.2191705699 -1.3427693608 -1.7369899853 -0.0302999350 0.35
52982605
## [341] 0.0691371128 -0.9342759105 0.0757565143 0.0064918773 -2.66
89170058
## [346] -0.1938499556 -1.8158261057 0.0609633498 0.0893661367 -0.27
77391240
## [351] -2.3173512366 -0.9767617936 -1.2946434261 -0.0057090828 -1.05
36373412
## [356] 0.0692262277 0.6862625371 -0.2384943861 -1.3515852508 -1.92
42557695
## [361] -0.7896672861 1.4406512611 -0.2651159564 -0.4709988901 3.36
81077648
## [366] -1.4602785558 0.2196233448 1.3803421211 0.9931981813 -0.42
49642836
## [371] -0.3688220273 0.1622048281 2.0085571223 -0.3237244396 0.44
91631070
## [376] -1.5327069555 -0.4025077155 0.5583144585 -0.0454651129 -0.97
89489112
## [381] 0.3568130568 1.8865834442 0.8494111365 -1.2783978225 0.01
80924361
## [386] -1.2680089844 -0.4767437806 -0.7910824606 0.8788586153 0.34
65868362
## [391] -0.6414045249 2.2418146323 1.2095714107 -1.1705546965 -1.18
51357556
## [396] -1.7026633570 -0.0520404663 0.3123416459 -1.1470936908 -1.08
07436240
## [401] 0.0497059290 0.9319608049 0.5220603836 -1.1730775552 -0.18
12173211
## [406] 0.7042062311 -0.0259374210 1.2408386454 -0.4518528184 -0.40
12135328
## [411] 2.3847890330 -2.2696666755 -0.8345925386 0.9904547977 0.46
00398435
## [416] 1.6405839213 1.0387141065 0.9618115561 -0.9378277082 -0.02
91272008
## [421] 0.6439071619 -1.3825047444 -0.4311920370 -1.0040159187 1.23
63260112
## [426] 0.2538680499 -0.2383026887 -0.1107355749 0.1442558142 0.60
41360192
## [431] 1.6166371255 -0.3793654548 0.3109111455 -0.3443240577 0.12
31743427
## [436] -1.2200645920 0.4906216509 -0.2912032065 -0.4173116982 1.55
80431596
## [441] -1.1217728814 -0.2140928770 -0.4915634932 -1.3286692015 -0.10
80660007
## [446] 0.2166687723 -0.2016113643 1.5546020488 -0.8304256524 -0.44
58391352
## [451] -0.4150744586 -0.9940058327 0.0959310812 -0.8868059839 -0.71
80736382
## [456] 0.6005079373 -1.1546566161 -0.9980016005 -0.5350576729 0.30
94961285
## [461] -1.2077545935 -1.7816824276 0.1876536490 -0.6354533959 1.40
27996150
## [466] 0.6382374797 -0.4982610389 -3.0057995936 1.1398617488 0.16
69393708
## [471] 1.1334000026 0.0706258881 0.4383021196 -1.1208402512 -1.18
20379690
## [476] -0.8005383221 0.2470545556 -1.9159574349 0.3898971715 1.52
19061072
## [481] 1.0146741922 -2.4008028299 1.4280306839 -0.8046799250 1.81
66612462
## [486] -0.6432864523 0.3670601850 -0.0853404399 0.5566899257 0.63
55886286
## [491] -1.9911527689 -0.2093912991 3.0625126875 -0.9243056027 0.47
42212130
## [496] 1.0743669767 0.7415746814 -1.5488009187 -0.2055551978 1.01
50084900
## [501] -0.7665423371 0.7416751812 2.3243526519 0.5877506255 -1.24
01647049
## [506] 0.1752737473 1.1187712945 0.2844334128 -0.9818408706 0.81
22507576
## [511] -0.2523147449 -0.2310626217 1.4823694970 -0.0004548197 1.69
00540236
## [516] 1.2247477640 0.2665582573 -1.2203857797 0.1900554172 0.80
34381016
## [521] 0.0978253827 -1.3971946163 -0.0852159518 -0.0658266755 -0.91
24711562
## [526] -0.7008018209 0.0181465934 -0.9734789183 0.8671931910 -0.83
85653646
## [531] -0.3990775891 -1.4781790209 -0.6475435220 1.1475938806 0.71
35953413
## [536] -0.8446974962 0.8170304545 1.3311991817 -0.0391120897 -0.50
33335371
## [541] -1.0167068383 1.2027682092 -0.7127774772 0.2547324214 0.10
13955418
## [546] 0.4573552320 0.4559480202 0.7439493773 -0.6012321742 2.10
15289350
## [551] 1.6287211683 0.6056577465 0.7185431284 -1.0235315408 1.04
69959186
## [556] 1.2992232521 -0.1236850058 1.8330627849 0.3337728250 1.05
63282637
## [561] 1.7399638495 -1.3535531809 -0.5916995273 0.5125344370 0.08
81971753
## [566] 0.1858545633 -0.4554517316 2.0686618509 -0.3820931367 -1.05
84027513
## [571] -0.2102838698 -0.5582258533 0.0135225320 -1.5926206563 0.40
14490591
## [576] -0.9021471479 -0.7735556407 -1.1889016258 -0.8990333736 -0.49
81680363
## [581] -0.5632952655 0.2633882047 -1.9420326749 -0.4417559210 0.15
66872441
## [586] -0.0068846169 -1.0624413650 0.1835858566 -0.5403687672 0.27
80953644
## [591] -0.6463251434 2.6425590636 -0.7947573148 -1.4552281812 -0.68
29896593
## [596] 0.1651190484 -0.1794388819 -1.1617802821 -0.2527376678 1.29
73398871
## [601] 2.3139956622 -0.6592041734 0.2404057841 0.2669128864 0.30
27375755
## [606] -0.5589043927 -1.0142812853 -0.5378662107 0.9377169405 0.38
11859938
## [611] 0.6151531313 -2.1160272092 0.5063498957 -0.2191195389 -0.16
72095614
## [616] -1.1197690897 0.5011989317 0.1448679123 -1.4801680987 -0.02
83083744
## [621] -0.6537280331 -0.9101045655 -1.2448785143 1.7112676048 0.57
93436400
## [626] -0.9669718150 0.3030023768 -1.1999289972 0.5382605319 -1.16
27467225
## [631] 1.1919680535 -0.8092995198 -0.4007458262 0.9585053558 -0.37
62431294
## [636] -0.7391306712 0.9614994735 0.5523733139 0.4238216812 -0.15
59859742
## [641] -0.4107676819 0.1211210988 0.3495107232 1.6046699327 -0.74
93333524
## [646] 0.0321392620 -0.6398297542 -0.5726197880 0.5341436268 1.81
15446712
## [651] -0.2805765219 -1.3074477202 0.4189830153 -0.1387100924 -0.93
29365591
## [656] 0.3047824289 0.0454647644 -0.1502262949 0.9272540075 0.53
92389716
## [661] 0.2703515090 0.7376521941 0.2674142956 0.3847527688 -0.58
55077512
## [666] -0.2622784502 -0.0128259012 -0.0950599183 -0.2184703474 0.68
85527647
## [671] 0.4506511425 -1.3838527886 1.7248968603 -0.5458739216 -1.30
17271033
## [676] -0.8529912875 1.0550714904 0.8094027312 0.7293800042 -0.11
51259035
## [681] 0.0583845312 -0.8496950737 -0.7333250442 -0.7498092207 0.04
34511125
## [686] 0.3933115879 -0.9942821113 -1.5958621880 1.3772120082 -0.97
72982887
## [691] 0.8948894811 0.7767711853 2.5379448056 1.5075520347 -0.33
71770574
## [696] -0.9068076067 0.3761986503 1.4739695934 0.8746207397 0.00
47382269
## [701] 0.3635303892 -0.6581732162 -0.9241840135 -0.7100945651 -0.02
29754709
## [706] 1.2968606405 0.9262211356 -0.9026779735 -0.6557789793 0.54
44192488
## [711] 0.9184345937 -1.1040839393 -0.6156133041 -0.6528362467 -1.75
38973917
## [716] 0.3891676416 0.7400331621 0.1976833560 0.5913512591 -0.69
19371003
## [721] -0.3224527520 -1.1973309508 2.1133689580 -1.3168126426 -0.38
48473717
## [726] 1.1171356310 -1.3258443300 0.1366880572 -1.1404152741 1.50
85066794
## [731] -0.5427187683 -0.0404445019 -0.4379227542 -1.8324818167 0.34
26191439
## [736] 0.1273841017 1.5764893078 -0.5060485098 -0.5530303276 -0.97
85322791
## [741] 0.7616199134 -0.3807059395 0.1513732545 0.7556494667 0.16
61400470
## [746] -0.8181876848 -0.1120617165 0.5363989309 -0.6244760199 -0.34
48038668
## [751] -0.8700461533 0.1633637069 1.8093271386 0.2106275161 0.46
75903132
## [756] -0.0661858525 0.2811247591 -0.8154772890 1.1236353072 2.41
47981169
## [761] 0.1280225410 0.1708505072 1.3693529991 0.3308574196 -0.01
51063233
## [766] 2.0871135332 0.0715068874 -0.8525372945 0.8354300215 0.43
78222875
## [771] 0.2337647850 -0.0414902535 0.1774087655 -1.3048088576 -0.17
56527954
## [776] -0.9790421910 -0.1889691549 0.4931946613 -0.9621695549 -0.34
71498779
## [781] 2.0135673195 0.3905246176 -1.0389517893 1.1696960481 0.75
19137858
## [786] -2.0062293429 -0.9327467577 1.1968189143 -0.0603601078 1.74
84838719
## [791] -0.7570787248 -1.1706059059 -0.6379218725 -0.6858682638 -0.11
20494462
## [796] -0.5741321987 -1.1643604241 -2.3000223514 -1.0028003592 1.00
08858114
## [801] 1.3440319490 0.9559293908 0.9618371233 -0.4474723135 0.49
68618865
## [806] 1.7876440595 -2.1270001805 0.6392416918 0.9903405956 1.44
35487611
## [811] -0.2088417411 -0.1714843417 -0.1374574025 -0.4561486607 -0.35
16667466
## [816] 1.1546822048 0.1940360483 0.9441066662 0.4979252382 -0.45
37096745
## [821] -1.6112474080 1.3522108491 1.1233006264 -1.0811950214 -0.36
38012223
## [826] -0.8291919863 -1.5662149611 1.3987917400 0.1447824388 0.20
50272752
## [831] 1.0866896202 -0.4821316361 -0.2015943858 -0.2609891006 1.47
09171358
## [836] -1.4607735218 -1.8556819906 -1.7673762704 -0.1227570425 -0.26
85573008
## [841] 1.1196520838 0.1843318693 0.9443361096 1.6830938287 -0.05
52703941
## [846] 0.5005076702 1.9524815748 0.2873150557 0.8320645617 -0.60
75245233
## [851] 1.0586420386 -0.7612847736 -1.7807578283 1.1466720813 1.72
74397886
## [856] 1.2201035919 -1.8880369574 0.7140372677 -1.1338212540 1.78
10546254
## [861] -1.1855738487 -0.3710075125 -1.1727502185 -2.2466055012 -0.53
98567072
## [866] -0.8625186031 1.2463568124 0.1688507887 -0.2050870389 -1.32
45383759
## [871] -1.2454303623 0.3579898577 -0.6110634767 -0.4025163727 0.57
38382618
## [876] -1.4955512857 -1.5868943144 0.1663860045 -0.9103485839 0.33
00036211
## [881] -0.6741423004 0.8287109470 0.5352426259 0.5218784733 1.30
77246201
## [886] 0.2018852406 0.8326900131 -0.7243671406 -1.0634555533 2.06
65704337
## [891] 0.6784046396 -0.1353564159 2.3966672203 -1.2581352843 1.12
01432187
## [896] 0.2462760344 -1.8984778450 -1.6638065885 0.8198775590 0.07
13406597
## [901] 0.2144056268 -0.2364180905 1.0791744883 -0.0878766434 1.22
74762889
## [906] -0.9857937243 -0.0498994252 0.6311069966 0.0557924777 -2.78
11353539
## [911] 0.8728912797 0.1428594758 0.0922495209 -0.7242929197 1.68
93869586
## [916] -1.6930887909 -0.8366529223 0.3028817288 -1.8913475377 0.11
26484004
## [921] -0.6832131285 -0.0850776189 -0.6084226164 -1.4540338613 -0.36
24831670
## [926] 0.8448538259 0.5863170900 -0.3964756882 0.9783725317 -0.44
04421156
## [931] -0.1185526936 -1.5133560890 -1.4094521478 -0.9353887713 -1.50
23280700
## [936] -0.8930168506 1.1859268121 -2.0994834280 -0.5416746534 1.06
83535778
## [941] 2.3981219578 -1.0803460292 2.4936292242 -1.7589728855 -0.73
14968202
## [946] 0.9125487709 -0.1985592709 -0.9572092811 -0.7759020328 -1.44
96369961
## [951] -0.9060952251 -0.1076058094 -0.1509132965 -0.6731964297 0.40
81600938
## [956] -1.0360690541 -1.5033635194 1.0320527879 -1.1445380930 -0.94
96242631
## [961] -0.1579522478 1.3682427157 0.0351867732 -0.4151135651 -0.87
35543269
## [966] -0.9536408953 0.6061789356 1.0363022977 -0.2096072517 -1.19
32271909
## [971] -1.5091530099 -0.6391486056 1.5375661037 -0.6089585602 -0.37
52071054
## [976] -0.1660625594 1.4738735038 1.5410318691 0.6518996171 -0.68
62958519
## [981] 0.8460487509 0.0627597034 -0.3028202515 0.0825875380 -0.26
32516734
## [986] -0.9443586815 0.1885684348 1.1800889081 0.4966564042 -0.77
44242192
## [991] 0.0106326207 -1.3812097628 0.9479158910 1.3372607042 0.04
09462198
## [996] 0.2792401958 0.7505123824 -0.1316762636 -0.3977614354 0.08
53124180
yAxis <- rnorm(1000) + xAxis + 10
yAxis
## [1] 8.820437 10.520537 10.270916 10.568159 12.331528 9.601970 1
0.355212
## [8] 10.104649 9.930907 10.745631 9.395428 11.308412 10.765784 1
0.721925
## [15] 8.297477 7.185039 12.840707 10.838762 9.424193 9.323654
9.196618
## [22] 10.789761 10.813581 8.358889 9.909564 10.651432 8.413567
8.517230
## [29] 11.885530 10.346885 11.681231 7.426259 10.269831 11.352058
8.508741
## [36] 9.883149 9.860698 10.851346 12.191081 9.085533 10.599319 1
1.298881
## [43] 10.125332 10.372868 11.260438 11.210220 11.057666 10.852537
8.192904
## [50] 9.844941 11.011674 11.160907 7.161585 9.951401 10.501517 1
1.386644
## [57] 10.035498 11.240723 8.308849 11.516583 9.349347 8.486068
9.574707
## [64] 6.985101 10.216207 13.789966 11.059583 11.708491 10.230653
9.817235
## [71] 7.248083 11.155906 11.236782 7.692956 11.368294 9.507139
9.492971
## [78] 6.406638 10.018295 10.073035 12.894377 11.002040 8.229832 1
0.971630
## [85] 10.644928 12.853437 10.866079 9.001161 8.630431 10.616927
9.402760
## [92] 8.060536 10.110419 10.127189 7.922079 8.936381 9.052050 1
1.695048
## [99] 9.253208 9.093663 8.244776 9.689098 10.431324 10.338293 1
0.059035
## [106] 8.502765 10.862242 8.401266 9.835705 5.978207 9.827016
9.025912
## [113] 9.289201 9.210249 11.335090 9.879986 11.912047 8.658320
8.050536
## [120] 10.126352 10.796530 5.881552 10.044980 9.908443 9.943513
9.723616
## [127] 10.139707 10.984521 8.246711 9.792599 9.785477 10.507227 1
0.681310
## [134] 10.535537 6.759769 8.339766 10.196997 9.372173 12.029347
9.919186
## [141] 13.081195 12.036581 8.765074 10.290335 11.331262 8.772896
9.322390
## [148] 10.262642 8.778736 10.456736 10.340454 10.667527 10.930202 1
1.033928
## [155] 6.642511 10.827453 9.589177 7.847026 11.382262 10.546154
9.449354
## [162] 8.273590 8.045953 10.623613 6.476594 10.030741 8.967706 1
1.469153
## [169] 10.219962 10.869922 10.375776 9.137593 9.428025 7.728841
8.409209
## [176] 6.838520 9.403054 9.194603 12.273943 11.922698 10.581859 1
1.432101
## [183] 12.120231 12.046086 10.051203 10.855585 7.582225 11.155122 1
0.805070
## [190] 7.480780 10.377472 9.768535 10.517881 7.851945 9.862810
9.710882
## [197] 8.834005 10.126063 10.186496 10.444827 11.974323 9.351462
9.151193
## [204] 7.314165 9.643933 10.458269 11.762806 9.640251 11.838827
9.292351
## [211] 8.844872 10.853533 9.951350 9.099545 10.665698 9.295064 1
3.010983
## [218] 10.718153 9.896269 8.735945 10.127248 10.212534 9.037217 1
1.671649
## [225] 11.401410 9.962735 10.969480 11.824635 10.266412 8.256420
9.108635
## [232] 9.232507 7.161282 10.058300 12.076134 10.355621 13.189578
9.657435
## [239] 9.222655 7.336384 10.450546 8.397644 10.365858 9.463097 1
0.930497
## [246] 10.536622 11.519200 8.913155 9.921831 10.147543 12.180557
7.164752
## [253] 10.184978 12.166750 9.234978 11.111946 10.299542 11.306555 1
1.133338
## [260] 11.574461 10.295416 12.237735 10.336717 8.525692 9.675094 1
0.248935
## [267] 8.694791 12.035586 8.987283 11.520413 8.757420 9.613015 1
1.869916
## [274] 9.279532 9.526722 9.927490 10.184394 5.466692 9.253748 1
0.936675
## [281] 9.708664 8.831257 11.068776 10.716753 10.340492 9.587860 1
1.591515
## [288] 9.235690 8.379373 7.847895 10.683391 10.336818 10.204946
9.688213
## [295] 9.716560 9.061870 8.520398 7.965571 9.998480 11.115560
8.259249
## [302] 10.154029 7.751302 8.877611 10.237629 9.203811 11.357609
8.567277
## [309] 12.525146 10.825481 9.052289 9.447870 9.857127 11.104568 1
1.767580
## [316] 8.869570 9.375034 7.663119 12.383538 11.242374 9.846153
9.798853
## [323] 9.237508 10.374363 11.233334 8.157789 10.692945 8.575894 1
2.126765
## [330] 10.574187 10.176089 8.845809 7.963375 10.732067 8.579937 1
1.739856
## [337] 9.734966 8.806684 10.391578 10.221977 10.489048 8.941345
8.276038
## [344] 10.556011 6.661919 10.102796 7.408311 8.818884 10.100961 1
0.291252
## [351] 8.209895 8.451122 7.358714 10.834220 9.404335 11.926147 1
0.787574
## [358] 10.256799 7.829833 7.037775 10.090106 12.001627 10.002622 1
1.061695
## [365] 13.149222 8.435755 11.445586 10.662447 11.643199 8.845799 1
0.770617
## [372] 10.256330 13.452709 10.173414 10.602186 7.511122 8.823511 1
1.361309
## [379] 10.224143 10.806936 10.456612 9.971677 10.974993 7.462535 1
1.490276
## [386] 7.450754 9.822232 9.336099 12.182732 9.231654 10.638699 1
1.427453
## [393] 11.490334 8.504300 8.878915 9.368196 10.656747 11.563759
7.599745
## [400] 9.712492 9.847250 11.450022 10.205877 10.783597 8.410119 1
1.363148
## [407] 9.218328 10.607692 9.160151 8.044574 13.976731 5.475440 1
0.703396
## [414] 11.998503 8.246935 11.497199 10.132563 13.917524 7.927742 1
1.303467
## [421] 11.904789 8.439578 9.040474 8.526685 9.988438 10.840683 1
0.912642
## [428] 9.961922 10.614041 11.689439 9.674724 9.087496 10.071288 1
1.717182
## [435] 10.027378 9.098085 13.816536 9.895531 9.831376 10.617205
8.228389
## [442] 10.267454 8.891489 8.591275 9.202757 10.477516 8.370165 1
0.779312
## [449] 8.986922 9.888433 8.721767 11.081291 11.918186 9.178719
9.619939
## [456] 12.082079 9.865129 9.645830 8.958386 11.100543 8.000217
9.100196
## [463] 10.831550 9.747410 9.740874 11.046137 10.636901 8.217511 1
0.624519
## [470] 11.505944 11.080780 9.576902 10.351662 8.127010 9.568303
9.164067
## [477] 10.109611 8.825197 10.085386 9.831935 10.082830 7.412375 1
2.703745
## [484] 8.128682 12.430903 8.078461 12.063327 9.809798 10.913177 1
0.939285
## [491] 8.388809 10.703856 14.690914 9.852257 10.576473 12.658584 1
2.175527
## [498] 7.866166 10.871089 10.900139 8.764774 10.620557 11.941110
8.927199
## [505] 9.479887 8.758383 13.183208 9.992972 7.997417 11.525238
9.080165
## [512] 10.335750 11.095647 11.503585 11.975332 10.854363 10.131513 1
0.910208
## [519] 10.527559 10.770968 12.070753 8.191230 11.034595 8.265900
9.266263
## [526] 9.756314 10.411983 9.613169 12.853243 8.274314 6.450447
8.125011
## [533] 7.843728 11.448398 10.908820 8.309511 10.552419 10.948992 1
0.104960
## [540] 9.141212 8.251990 9.861613 8.478070 8.905190 10.510335 1
0.203995
## [547] 9.930767 11.340981 11.173195 11.625199 9.329142 10.625128 1
1.416483
## [554] 7.338604 9.511828 11.408207 10.812501 11.798271 9.751684 1
2.758656
## [561] 11.237307 9.652483 12.283672 9.790457 9.560858 9.122648
8.301004
## [568] 12.782648 8.912709 7.451891 8.979297 11.295947 11.450336
8.801757
## [575] 9.778483 6.735006 8.323682 8.816316 8.822841 8.114335
9.899953
## [582] 8.655522 6.499905 10.146437 10.762032 9.187959 9.789917 1
0.285350
## [589] 9.782854 9.371822 9.872152 12.845835 8.419223 9.293002
9.485384
## [596] 9.511456 8.634667 9.913964 7.444876 12.199062 11.231162
6.573477
## [603] 10.486392 10.689628 9.404406 7.432084 8.144812 10.802359 1
0.395178
## [610] 11.270015 10.046538 7.299825 9.394531 9.553683 10.848507 1
0.605334
## [617] 11.681811 9.149820 8.739899 10.241795 10.439855 8.673287
8.389702
## [624] 11.470133 10.209385 7.636006 9.493727 8.103574 11.406563
9.514956
## [631] 10.404760 9.193598 10.254690 10.118510 9.361469 10.582088 1
0.427611
## [638] 11.595653 10.910699 9.785917 8.946950 10.141491 9.655789 1
2.163916
## [645] 8.238291 10.253562 9.814251 8.064515 10.110921 11.468099 1
1.311721
## [652] 9.940715 9.710076 11.736529 8.908054 10.051977 7.194273
8.640754
## [659] 9.916136 11.736051 9.863280 9.753209 10.293521 9.408452
8.127758
## [666] 9.589314 9.672935 8.940100 11.866729 10.584685 10.494877
9.679005
## [673] 10.016298 8.070829 7.704340 7.241956 12.642068 10.286630
9.361555
## [680] 7.954953 10.519632 8.321736 9.808992 9.170423 11.807387 1
0.288713
## [687] 6.675098 7.983275 10.363911 7.101106 10.821729 9.664523 1
2.401268
## [694] 11.845856 8.789938 10.474898 9.688161 11.602772 11.935554 1
0.251697
## [701] 8.655172 9.642264 10.990866 10.213913 10.023263 10.464278 1
2.082694
## [708] 9.061742 11.419804 9.960228 12.024442 7.383749 8.383176
8.434888
## [715] 9.381353 10.166046 9.883568 9.228611 11.339344 10.398459
9.358463
## [722] 8.035880 12.562852 6.591981 9.037940 11.588178 7.381819 1
1.365214
## [729] 9.367038 8.234777 10.132899 10.647642 10.581874 7.665655
9.951965
## [736] 11.241923 12.950923 10.314123 9.239541 9.620816 11.003726
8.707720
## [743] 10.795678 12.180423 10.465835 9.037145 8.781975 9.639737
8.894952
## [750] 10.800686 10.836926 10.661999 12.318151 9.880077 9.565005 1
0.526452
## [757] 11.856237 7.505835 12.230593 10.710581 7.464390 10.805120 1
0.369893
## [764] 9.114831 10.276963 11.336521 9.339726 10.436628 12.567767
9.199398
## [771] 9.986595 9.518532 10.059215 9.201815 10.789543 8.185708
9.097297
## [778] 9.768851 9.152197 7.888739 13.571437 11.735936 7.468258 1
2.048176
## [785] 11.637632 9.009484 8.479004 11.529901 11.058705 13.026647 1
0.176329
## [792] 8.630733 7.674221 6.985050 7.942303 8.258326 9.372321
7.465089
## [799] 8.662025 11.596266 11.841908 12.331155 12.200363 8.762586 1
0.772719
## [806] 12.250688 9.691244 11.283028 11.842076 11.490693 9.721019
8.578639
## [813] 8.901133 8.442465 10.400009 11.937088 11.144762 11.417930 1
0.859644
## [820] 9.458336 8.152258 11.602753 12.466922 8.135954 9.707595 1
0.119742
## [827] 7.011226 10.010772 10.006929 8.411434 10.645421 9.025571
8.793655
## [834] 10.738868 11.665133 9.188635 7.832535 5.891702 9.640623
9.256599
## [841] 12.262190 9.419402 10.454088 11.292128 9.824736 10.995353 1
2.665888
## [848] 10.386121 10.330217 9.017544 10.972948 10.764570 8.115521 1
0.811796
## [855] 12.061379 11.432833 7.408228 11.459574 8.597576 11.863501
8.855435
## [862] 10.454570 8.808184 7.935362 7.681621 9.078466 12.214777 1
0.746052
## [869] 10.021618 7.888916 7.598638 11.349969 8.334540 10.299622 1
0.473238
## [876] 8.193109 8.168545 12.007970 7.372406 9.455066 10.038723 1
0.275082
## [883] 9.232562 10.473731 13.900788 10.156349 9.576093 10.978360
9.183811
## [890] 13.809379 8.937820 9.660513 13.436121 10.075291 9.878247 1
2.021096
## [897] 7.602768 8.822410 10.451870 10.200650 11.288078 8.670828 1
0.726648
## [904] 12.659297 12.087105 8.489647 9.618557 8.502412 10.985832
7.564197
## [911] 10.667694 11.682680 10.341130 9.509659 12.223594 8.518920 1
0.026591
## [918] 10.124224 7.122943 10.435299 9.601425 9.794174 10.959428
7.705446
## [925] 8.347216 12.847515 11.105470 9.475392 11.200669 9.174039
8.489161
## [932] 8.192370 8.836489 7.957317 8.605057 6.853802 11.423193
8.184668
## [939] 8.286364 9.912572 12.771047 9.466644 11.616765 8.133244
8.771430
## [946] 11.439101 9.567163 7.705156 9.502654 8.657350 8.041778
9.741495
## [953] 8.458689 10.506855 12.188696 8.728569 8.712421 11.778682 1
0.177897
## [960] 8.847760 9.215856 13.276527 10.796054 9.711702 8.633899 1
0.094841
## [967] 10.054413 11.729689 11.307009 7.502718 8.683755 9.904932 1
1.033848
## [974] 11.254455 9.437435 10.884165 12.950944 11.324107 8.792047 1
0.490995
## [981] 10.791221 8.722996 10.001359 11.641214 11.434950 8.660219
9.755777
## [988] 9.931073 9.247104 8.585087 10.092695 6.479742 12.617105 1
1.466250
## [995] 9.351398 11.275152 12.161850 10.326830 11.056197 10.131986
# 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 3 2 2 4 3 3 3 2 4 2 3 3 5 2 1 4 4 2 4 3 2 3 2 3 2 3 3 4 3 4
2 2 4 2 1 3
## [38] 3 4 1 4 4 3 3 3 4 3 5 3 3 4 4 1 2 3 3 2 3 2 3 2 1 2 1 3 4 5 4
3 3 2 4 4 2
## [75] 5 3 2 1 2 4 3 4 2 4 4 5 4 2 1 5 3 3 2 4 2 3 4 4 2 2 1 2 2 2 3
3 4 3 3 1 2
## [112] 2 2 2 4 4 4 2 2 2 3 1 3 2 3 4 2 3 3 2 2 3 3 4 1 3 3 3 4 4 5 5
2 3 5 3 2 2
## [149] 2 4 4 4 3 3 2 3 2 2 4 4 3 2 2 4 1 4 3 4 4 3 2 2 3 2 2 2 3 2 4
3 4 3 4 4 3
## [186] 3 3 4 3 2 3 3 3 3 4 3 3 3 1 4 3 3 3 1 2 3 4 5 5 3 3 2 2 2 4 3
5 3 3 3 3 2
## [223] 1 4 3 2 3 3 3 1 3 3 1 4 3 4 5 3 3 2 3 3 3 1 2 4 4 2 4 3 4 1 3
4 3 4 3 3 5
## [260] 4 4 4 4 2 3 4 2 4 2 3 2 3 4 4 3 3 4 1 3 4 4 3 5 2 3 4 3 2 3 2
3 4 3 3 2 3
## [297] 2 2 2 3 1 3 1 3 3 2 4 2 5 3 3 3 2 4 3 4 3 4 3 3 4 4 2 3 4 3 4
2 3 3 4 2 1
## [334] 3 2 3 2 1 3 3 3 2 3 3 1 3 1 3 3 3 1 2 2 3 2 3 4 3 2 1 2 4 3 3
5 2 3 4 4 3
## [371] 3 3 5 3 3 1 3 4 3 2 3 5 4 2 3 2 3 2 4 3 2 5 4 2 2 1 3 3 2 2 3
4 4 2 3 4 3
## [408] 4 3 3 5 1 2 4 3 5 4 4 2 3 4 2 3 2 4 3 3 3 3 4 5 3 3 3 3 2 3 3
3 5 2 3 3 2
## [445] 3 3 3 5 2 3 3 2 3 2 2 4 2 2 2 3 2 1 3 2 4 4 3 1 4 3 4 3 3 2 2
2 3 1 3 5 4
## [482] 1 4 2 5 2 3 3 4 4 1 3 5 2 3 4 4 1 3 4 2 4 5 4 2 3 4 3 2 4 3 3
4 3 5 4 3 2
## [519] 3 4 3 2 3 3 2 2 3 2 4 2 3 2 2 4 4 2 4 4 3 2 2 4 2 3 3 3 3 4 2
5 5 4 4 2 4
## [556] 4 3 5 3 4 5 2 2 4 3 3 3 5 3 2 3 2 3 1 3 2 2 2 2 3 2 3 1 3 3 3
2 3 2 3 2 5
## [593] 2 2 2 3 3 2 3 4 5 2 3 3 3 2 2 2 4 3 4 1 4 3 3 2 4 3 2 3 2 2 2
5 4 2 3 2 4
## [630] 2 4 2 3 4 3 2 4 4 3 3 3 3 3 5 2 3 2 2 4 5 3 2 3 3 2 3 3 3 4 4
3 4 3 3 2 3
## [667] 3 3 3 4 3 2 5 2 2 2 4 4 4 3 3 2 2 2 3 3 2 1 4 2 4 4 5 5 3 2 3
4 4 3 3 2 2
## [704] 2 3 4 4 2 2 4 4 2 2 2 1 3 4 3 4 2 3 2 5 2 3 4 2 3 2 5 2 3 3 1
3 3 5 2 2 2
## [741] 4 3 3 4 3 2 3 4 2 3 2 3 5 3 3 3 3 2 4 5 3 3 4 3 3 5 3 2 4 3 3
3 3 2 3 2 3
## [778] 3 2 3 5 3 2 4 4 1 2 4 3 5 2 2 2 2 3 2 2 1 2 4 4 4 4 3 3 5 1 4
4 4 3 3 3 3
## [815] 3 4 3 4 3 3 1 4 4 2 3 2 1 4 3 3 4 3 3 3 4 2 1 1 3 3 4 3 4 5 3
4 5 3 4 2 4
## [852] 2 1 4 5 4 1 4 2 5 2 3 2 1 2 2 4 3 3 2 2 3 2 3 4 2 1 3 2 3 2 4
4 4 4 3 4 2
## [889] 2 5 4 3 5 2 4 3 1 1 4 3 3 3 4 3 4 2 3 4 3 1 4 3 3 2 5 1 2 3 1
3 2 3 2 2 3
## [926] 4 4 3 4 3 3 1 2 2 1 2 4 1 2 4 5 2 5 1 2 4 3 2 2 2 2 3 3 2 3 2
1 4 2 2 3 4
## [963] 3 3 2 2 4 4 3 2 1 2 5 2 3 3 4 5 4 2 4 3 3 3 3 2 3 4 3 2 3 2 4
4 3 3 4 3 3
## [1000] 3
# create sample dataframe by joining variables
sample_data <- data.frame(xAxis,yAxis,group)
sample_data
## xAxis yAxis group
## 1 -0.1631453029 8.820437 3
## 2 0.1393248355 10.520537 3
## 3 -1.3318035075 10.270916 2
## 4 -0.7431742789 10.568159 2
## 5 1.2196300795 12.331528 4
## 6 0.2527621957 9.601970 3
## 7 -0.0265420404 10.355212 3
## 8 0.0242265978 10.104649 3
## 9 -0.6300441696 9.930907 2
## 10 0.5386602949 10.745631 4
## 11 -1.1954288002 9.395428 2
## 12 0.2337001946 11.308412 3
## 13 0.1297820113 10.765784 3
## 14 1.7982242043 10.721925 5
## 15 -1.3676542218 8.297477 2
## 16 -2.6321638564 7.185039 1
## 17 1.2536656339 12.840707 4
## 18 0.5565465348 10.838762 4
## 19 -0.6835805703 9.424193 2
## 20 0.7065302125 9.323654 4
## 21 0.4453597115 9.196618 3
## 22 -0.8641030288 10.789761 2
## 23 0.2234358218 10.813581 3
## 24 -1.2024881210 8.358889 2
## 25 0.0066958664 9.909564 3
## 26 -0.5525051482 10.651432 2
## 27 -0.3334405401 8.413567 3
## 28 -0.0532299721 8.517230 3
## 29 1.0088558827 11.885530 4
## 30 -0.1773455437 10.346885 3
## 31 0.9391456638 11.681231 4
## 32 -1.2714849597 7.426259 2
## 33 -0.5918975442 10.269831 2
## 34 0.5022272488 11.352058 4
## 35 -1.4728376979 8.508741 2
## 36 -1.5226888133 9.883149 1
## 37 0.2410702856 9.860698 3
## 38 0.0897615887 10.851346 3
## 39 0.5586248785 12.191081 4
## 40 -1.9587848419 9.085533 1
## 41 1.3030341798 10.599319 4
## 42 1.3688284329 11.298881 4
## 43 0.3367408261 10.125332 3
## 44 -0.0502687960 10.372868 3
## 45 0.2019326225 11.260438 3
## 46 1.4158180681 11.210220 4
## 47 0.0356535792 11.057666 3
## 48 1.7869630369 10.852537 5
## 49 -0.0803064743 8.192904 3
## 50 -0.3081487712 9.844941 3
## 51 0.7335123634 11.011674 4
## 52 0.8499819785 11.160907 4
## 53 -2.4421511988 7.161585 1
## 54 -0.6744580942 9.951401 2
## 55 -0.4894969610 10.501517 3
## 56 0.0961812772 11.386644 3
## 57 -0.8352657604 10.035498 2
## 58 0.4289362600 11.240723 3
## 59 -1.4981267379 8.308849 2
## 60 0.3628642398 11.516583 3
## 61 -0.5622761926 9.349347 2
## 62 -1.5283810485 8.486068 1
## 63 -0.5413620380 9.574707 2
## 64 -1.8002429987 6.985101 1
## 65 0.4670262886 10.216207 3
## 66 0.8021081877 13.789966 4
## 67 1.8262960925 11.059583 5
## 68 1.1894513210 11.708491 4
## 69 0.1564466063 10.230653 3
## 70 0.4093766117 9.817235 3
## 71 -1.4220289742 7.248083 2
## 72 1.2816783658 11.155906 4
## 73 0.5436570410 11.236782 4
## 74 -0.6963696584 7.692956 2
## 75 2.0785097156 11.368294 5
## 76 0.1721415909 9.507139 3
## 77 -0.7242287868 9.492971 2
## 78 -1.9600507589 6.406638 1
## 79 -0.9024000614 10.018295 2
## 80 1.3443074615 10.073035 4
## 81 0.4799381685 12.894377 3
## 82 1.0844215431 11.002040 4
## 83 -0.6044237467 8.229832 2
## 84 0.6754399875 10.971630 4
## 85 0.9678569518 10.644928 4
## 86 2.1677747322 12.853437 5
## 87 1.0798739763 10.866079 4
## 88 -0.6005402307 9.001161 2
## 89 -1.6823365599 8.630431 1
## 90 1.6364588694 10.616927 5
## 91 0.1563613394 9.402760 3
## 92 -0.2753164368 8.060536 3
## 93 -1.2849886264 10.110419 2
## 94 0.8453616040 10.127189 4
## 95 -1.0265367715 7.922079 2
## 96 -0.3302942206 8.936381 3
## 97 0.7716177135 9.052050 4
## 98 1.3602066069 11.695048 4
## 99 -0.6746802678 9.253208 2
## 100 -1.3360118199 9.093663 2
## 101 -1.9580063056 8.244776 1
## 102 -0.5953520371 9.689098 2
## 103 -0.8430663659 10.431324 2
## 104 -0.6868415877 10.338293 2
## 105 -0.2624194732 10.059035 3
## 106 -0.2609887218 8.502765 3
## 107 1.0922887493 10.862242 4
## 108 0.3747954888 8.401266 3
## 109 -0.1307910983 9.835705 3
## 110 -2.1606931396 5.978207 1
## 111 -0.6286087034 9.827016 2
## 112 -0.5606862817 9.025912 2
## 113 -1.0750623093 9.289201 2
## 114 -0.7164800677 9.210249 2
## 115 0.7318213451 11.335090 4
## 116 1.4572611037 9.879986 4
## 117 1.1532561323 11.912047 4
## 118 -0.6498436701 8.658320 2
## 119 -0.5696556674 8.050536 2
## 120 -0.6827087075 10.126352 2
## 121 -0.2552186885 10.796530 3
## 122 -2.4117907628 5.881552 1
## 123 0.2569324209 10.044980 3
## 124 -0.5951418466 9.908443 2
## 125 -0.4894718169 9.943513 3
## 126 1.1261369192 9.723616 4
## 127 -0.6556438770 10.139707 2
## 128 -0.1521950387 10.984521 3
## 129 0.2214185979 8.246711 3
## 130 -1.3181933800 9.792599 2
## 131 -0.6310719672 9.785477 2
## 132 -0.1585797102 10.507227 3
## 133 0.1554964894 10.681310 3
## 134 0.9817094476 10.535537 4
## 135 -2.0557089330 6.759769 1
## 136 -0.1096554495 8.339766 3
## 137 -0.1160208850 10.196997 3
## 138 -0.0754779999 9.372173 3
## 139 1.4033210205 12.029347 4
## 140 1.0512001496 9.919186 4
## 141 2.5722896717 13.081195 5
## 142 1.7497416483 12.036581 5
## 143 -1.1818122553 8.765074 2
## 144 0.3394303021 10.290335 3
## 145 2.4481723770 11.331262 5
## 146 -0.3674741401 8.772896 3
## 147 -0.8421643788 9.322390 2
## 148 -0.5024031716 10.262642 2
## 149 -0.7463507693 8.778736 2
## 150 0.6320844328 10.456736 4
## 151 0.5008751855 10.340454 4
## 152 1.1668058281 10.667527 4
## 153 0.1137640772 10.930202 3
## 154 0.2427811193 11.033928 3
## 155 -1.4725262002 6.642511 2
## 156 0.1090187191 10.827453 3
## 157 -0.5831008615 9.589177 2
## 158 -1.4577907254 7.847026 2
## 159 0.9772205265 11.382262 4
## 160 0.6666524167 10.546154 4
## 161 0.2787004632 9.449354 3
## 162 -0.9005738400 8.273590 2
## 163 -1.2115353744 8.045953 2
## 164 0.8854911703 10.623613 4
## 165 -1.5720508503 6.476594 1
## 166 0.6851280527 10.030741 4
## 167 -0.2076696120 8.967706 3
## 168 0.8185256861 11.469153 4
## 169 1.0766151678 10.219962 4
## 170 0.3231073304 10.869922 3
## 171 -0.5972475553 10.375776 2
## 172 -1.2111491923 9.137593 2
## 173 -0.4928570284 9.428025 3
## 174 -1.4328814930 7.728841 2
## 175 -1.3459300859 8.409209 2
## 176 -1.4806822954 6.838520 2
## 177 -0.2926441241 9.403054 3
## 178 -0.6041053316 9.194603 2
## 179 1.2149917113 12.273943 4
## 180 -0.2218933904 11.922698 3
## 181 1.1381964157 10.581859 4
## 182 0.2841726533 11.432101 3
## 183 1.4192100241 12.120231 4
## 184 1.1668497952 12.046086 4
## 185 0.1708909698 10.051203 3
## 186 0.1588455755 10.855585 3
## 187 -0.3896627143 7.582225 3
## 188 0.7821379914 11.155122 4
## 189 0.4576157039 10.805070 3
## 190 -0.8040277500 7.480780 2
## 191 0.1821686633 10.377472 3
## 192 0.3257720359 9.768535 3
## 193 0.0090671486 10.517881 3
## 194 -0.4211468085 7.851945 3
## 195 0.9445531997 9.862810 4
## 196 0.4212658086 9.710882 3
## 197 0.2266830993 8.834005 3
## 198 0.1807024402 10.126063 3
## 199 -1.7354645207 10.186496 1
## 200 0.5657937678 10.444827 4
## 201 0.4934078242 11.974323 3
## 202 -0.4799591773 9.351462 3
## 203 0.2573426033 9.151193 3
## 204 -2.0294514942 7.314165 1
## 205 -0.6782429291 9.643933 2
## 206 0.4133737031 10.458269 3
## 207 0.8500263508 11.762806 4
## 208 1.6993976826 9.640251 5
## 209 2.1489674476 11.838827 5
## 210 -0.3899559928 9.292351 3
## 211 0.3903887905 8.844872 3
## 212 -0.6864771634 10.853533 2
## 213 -0.7730762000 9.951350 2
## 214 -0.8500764425 9.099545 2
## 215 0.5625314541 10.665698 4
## 216 -0.0401710906 9.295064 3
## 217 1.6593852260 13.010983 5
## 218 0.4671132434 10.718153 3
## 219 -0.3769859129 9.896269 3
## 220 0.4078926332 8.735945 3
## 221 -0.2743229217 10.127248 3
## 222 -1.2129441728 10.212534 2
## 223 -1.6407093778 9.037217 1
## 224 0.9362792320 11.671649 4
## 225 -0.0657865083 11.401410 3
## 226 -1.0285565282 9.962735 2
## 227 0.2696047914 10.969480 3
## 228 0.1288675517 11.824635 3
## 229 0.3047802442 10.266412 3
## 230 -1.8420852707 8.256420 1
## 231 -0.0320253292 9.108635 3
## 232 -0.0402669247 9.232507 3
## 233 -1.9517331838 7.161282 1
## 234 0.9315665396 10.058300 4
## 235 0.3119353796 12.076134 3
## 236 0.9895697677 10.355621 4
## 237 2.8081106922 13.189578 5
## 238 -0.1954801282 9.657435 3
## 239 0.1685859699 9.222655 3
## 240 -0.6089592421 7.336384 2
## 241 -0.0114643485 10.450546 3
## 242 -0.2320231521 8.397644 3
## 243 0.0922727846 10.365858 3
## 244 -1.5125599570 9.463097 1
## 245 -0.7295055003 10.930497 2
## 246 1.1186457322 10.536622 4
## 247 0.9800601360 11.519200 4
## 248 -0.6267624792 8.913155 2
## 249 1.0069422552 9.921831 4
## 250 -0.4078635418 10.147543 3
## 251 0.8201696331 12.180557 4
## 252 -1.7407139683 7.164752 1
## 253 -0.3011145065 10.184978 3
## 254 1.2440940000 12.166750 4
## 255 -0.0289769208 9.234978 3
## 256 1.1775458967 11.111946 4
## 257 -0.2092520357 10.299542 3
## 258 0.0100173063 11.306555 3
## 259 1.5497949717 11.133338 5
## 260 0.5778154397 11.574461 4
## 261 0.6183461129 10.295416 4
## 262 1.1684056009 12.237735 4
## 263 0.9031227154 10.336717 4
## 264 -0.8840571717 8.525692 2
## 265 0.2545334347 9.675094 3
## 266 0.8720249299 10.248935 4
## 267 -1.0447287493 8.694791 2
## 268 1.1311248957 12.035586 4
## 269 -1.3731162802 8.987283 2
## 270 -0.0600481594 11.520413 3
## 271 -1.0994171735 8.757420 2
## 272 0.4228141544 9.613015 3
## 273 1.2693212227 11.869916 4
## 274 0.7582997683 9.279532 4
## 275 0.2636901262 9.526722 3
## 276 0.1110200252 9.927490 3
## 277 1.0579417550 10.184394 4
## 278 -2.6772814830 5.466692 1
## 279 -0.3706148187 9.253748 3
## 280 0.7427929015 10.936675 4
## 281 0.7805430857 9.708664 4
## 282 0.0397286865 8.831257 3
## 283 2.1511688763 11.068776 5
## 284 -1.0176416363 10.716753 2
## 285 0.2780327051 10.340492 3
## 286 0.8118120885 9.587860 4
## 287 0.2058008401 11.591515 3
## 288 -1.3083673437 9.235690 2
## 289 -0.0816268495 8.379373 3
## 290 -0.9233494631 7.847895 2
## 291 0.4976224093 10.683391 3
## 292 1.1061122285 10.336818 4
## 293 -0.4347981355 10.204946 3
## 294 0.4972779380 9.688213 3
## 295 -0.6105163434 9.716560 2
## 296 0.2113275173 9.061870 3
## 297 -1.4501135438 8.520398 2
## 298 -1.3071062105 7.965571 2
## 299 -0.8847757938 9.998480 2
## 300 0.3145852387 11.115560 3
## 301 -1.6682172335 8.259249 1
## 302 0.1507838173 10.154029 3
## 303 -1.5483899088 7.751302 1
## 304 -0.2539184048 8.877611 3
## 305 -0.3727972614 10.237629 3
## 306 -0.9930037966 9.203811 2
## 307 0.7520843150 11.357609 4
## 308 -1.0875939595 8.567277 2
## 309 1.7546972944 12.525146 5
## 310 -0.3364086319 10.825481 3
## 311 0.2426177825 9.052289 3
## 312 -0.0564883834 9.447870 3
## 313 -1.1636024715 9.857127 2
## 314 1.0379304630 11.104568 4
## 315 0.4884872442 11.767580 3
## 316 1.4772974088 8.869570 4
## 317 0.1567297069 9.375034 3
## 318 0.5316590664 7.663119 4
## 319 -0.4882698702 12.383538 3
## 320 0.4068998383 11.242374 3
## 321 1.0098129101 9.846153 4
## 322 0.8015294980 9.798853 4
## 323 -0.5146077617 9.237508 2
## 324 0.0055184978 10.374363 3
## 325 0.5233641015 11.233334 4
## 326 -0.1606091667 8.157789 3
## 327 0.6652851364 10.692945 4
## 328 -0.7275071162 8.575894 2
## 329 -0.1874679188 12.126765 3
## 330 0.0621769062 10.574187 3
## 331 0.5017913528 10.176089 4
## 332 -1.3803122672 8.845809 2
## 333 -1.9419167221 7.963375 1
## 334 -0.4988856965 10.732067 3
## 335 -1.0413013207 8.579937 2
## 336 0.2191705699 11.739856 3
## 337 -1.3427693608 9.734966 2
## 338 -1.7369899853 8.806684 1
## 339 -0.0302999350 10.391578 3
## 340 0.3552982605 10.221977 3
## 341 0.0691371128 10.489048 3
## 342 -0.9342759105 8.941345 2
## 343 0.0757565143 8.276038 3
## 344 0.0064918773 10.556011 3
## 345 -2.6689170058 6.661919 1
## 346 -0.1938499556 10.102796 3
## 347 -1.8158261057 7.408311 1
## 348 0.0609633498 8.818884 3
## 349 0.0893661367 10.100961 3
## 350 -0.2777391240 10.291252 3
## 351 -2.3173512366 8.209895 1
## 352 -0.9767617936 8.451122 2
## 353 -1.2946434261 7.358714 2
## 354 -0.0057090828 10.834220 3
## 355 -1.0536373412 9.404335 2
## 356 0.0692262277 11.926147 3
## 357 0.6862625371 10.787574 4
## 358 -0.2384943861 10.256799 3
## 359 -1.3515852508 7.829833 2
## 360 -1.9242557695 7.037775 1
## 361 -0.7896672861 10.090106 2
## 362 1.4406512611 12.001627 4
## 363 -0.2651159564 10.002622 3
## 364 -0.4709988901 11.061695 3
## 365 3.3681077648 13.149222 5
## 366 -1.4602785558 8.435755 2
## 367 0.2196233448 11.445586 3
## 368 1.3803421211 10.662447 4
## 369 0.9931981813 11.643199 4
## 370 -0.4249642836 8.845799 3
## 371 -0.3688220273 10.770617 3
## 372 0.1622048281 10.256330 3
## 373 2.0085571223 13.452709 5
## 374 -0.3237244396 10.173414 3
## 375 0.4491631070 10.602186 3
## 376 -1.5327069555 7.511122 1
## 377 -0.4025077155 8.823511 3
## 378 0.5583144585 11.361309 4
## 379 -0.0454651129 10.224143 3
## 380 -0.9789489112 10.806936 2
## 381 0.3568130568 10.456612 3
## 382 1.8865834442 9.971677 5
## 383 0.8494111365 10.974993 4
## 384 -1.2783978225 7.462535 2
## 385 0.0180924361 11.490276 3
## 386 -1.2680089844 7.450754 2
## 387 -0.4767437806 9.822232 3
## 388 -0.7910824606 9.336099 2
## 389 0.8788586153 12.182732 4
## 390 0.3465868362 9.231654 3
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## 881 -0.6741423004 10.038723 2
## 882 0.8287109470 10.275082 4
## 883 0.5352426259 9.232562 4
## 884 0.5218784733 10.473731 4
## 885 1.3077246201 13.900788 4
## 886 0.2018852406 10.156349 3
## 887 0.8326900131 9.576093 4
## 888 -0.7243671406 10.978360 2
## 889 -1.0634555533 9.183811 2
## 890 2.0665704337 13.809379 5
## 891 0.6784046396 8.937820 4
## 892 -0.1353564159 9.660513 3
## 893 2.3966672203 13.436121 5
## 894 -1.2581352843 10.075291 2
## 895 1.1201432187 9.878247 4
## 896 0.2462760344 12.021096 3
## 897 -1.8984778450 7.602768 1
## 898 -1.6638065885 8.822410 1
## 899 0.8198775590 10.451870 4
## 900 0.0713406597 10.200650 3
## 901 0.2144056268 11.288078 3
## 902 -0.2364180905 8.670828 3
## 903 1.0791744883 10.726648 4
## 904 -0.0878766434 12.659297 3
## 905 1.2274762889 12.087105 4
## 906 -0.9857937243 8.489647 2
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## 915 1.6893869586 12.223594 5
## 916 -1.6930887909 8.518920 1
## 917 -0.8366529223 10.026591 2
## 918 0.3028817288 10.124224 3
## 919 -1.8913475377 7.122943 1
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## 921 -0.6832131285 9.601425 2
## 922 -0.0850776189 9.794174 3
## 923 -0.6084226164 10.959428 2
## 924 -1.4540338613 7.705446 2
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## 926 0.8448538259 12.847515 4
## 927 0.5863170900 11.105470 4
## 928 -0.3964756882 9.475392 3
## 929 0.9783725317 11.200669 4
## 930 -0.4404421156 9.174039 3
## 931 -0.1185526936 8.489161 3
## 932 -1.5133560890 8.192370 1
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## 934 -0.9353887713 7.957317 2
## 935 -1.5023280700 8.605057 1
## 936 -0.8930168506 6.853802 2
## 937 1.1859268121 11.423193 4
## 938 -2.0994834280 8.184668 1
## 939 -0.5416746534 8.286364 2
## 940 1.0683535778 9.912572 4
## 941 2.3981219578 12.771047 5
## 942 -1.0803460292 9.466644 2
## 943 2.4936292242 11.616765 5
## 944 -1.7589728855 8.133244 1
## 945 -0.7314968202 8.771430 2
## 946 0.9125487709 11.439101 4
## 947 -0.1985592709 9.567163 3
## 948 -0.9572092811 7.705156 2
## 949 -0.7759020328 9.502654 2
## 950 -1.4496369961 8.657350 2
## 951 -0.9060952251 8.041778 2
## 952 -0.1076058094 9.741495 3
## 953 -0.1509132965 8.458689 3
## 954 -0.6731964297 10.506855 2
## 955 0.4081600938 12.188696 3
## 956 -1.0360690541 8.728569 2
## 957 -1.5033635194 8.712421 1
## 958 1.0320527879 11.778682 4
## 959 -1.1445380930 10.177897 2
## 960 -0.9496242631 8.847760 2
## 961 -0.1579522478 9.215856 3
## 962 1.3682427157 13.276527 4
## 963 0.0351867732 10.796054 3
## 964 -0.4151135651 9.711702 3
## 965 -0.8735543269 8.633899 2
## 966 -0.9536408953 10.094841 2
## 967 0.6061789356 10.054413 4
## 968 1.0363022977 11.729689 4
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## 970 -1.1932271909 7.502718 2
## 971 -1.5091530099 8.683755 1
## 972 -0.6391486056 9.904932 2
## 973 1.5375661037 11.033848 5
## 974 -0.6089585602 11.254455 2
## 975 -0.3752071054 9.437435 3
## 976 -0.1660625594 10.884165 3
## 977 1.4738735038 12.950944 4
## 978 1.5410318691 11.324107 5
## 979 0.6518996171 8.792047 4
## 980 -0.6862958519 10.490995 2
## 981 0.8460487509 10.791221 4
## 982 0.0627597034 8.722996 3
## 983 -0.3028202515 10.001359 3
## 984 0.0825875380 11.641214 3
## 985 -0.2632516734 11.434950 3
## 986 -0.9443586815 8.660219 2
## 987 0.1885684348 9.755777 3
## 988 1.1800889081 9.931073 4
## 989 0.4966564042 9.247104 3
## 990 -0.7744242192 8.585087 2
## 991 0.0106326207 10.092695 3
## 992 -1.3812097628 6.479742 2
## 993 0.9479158910 12.617105 4
## 994 1.3372607042 11.466250 4
## 995 0.0409462198 9.351398 3
## 996 0.2792401958 11.275152 3
## 997 0.7505123824 12.161850 4
## 998 -0.1316762636 10.326830 3
## 999 -0.3977614354 11.056197 3
## 1000 0.0853124180 10.131986 3
# creates plot object using ggplot
plot <- ggplot(sample_data, aes(x = xAxis, y= yAxis, col = as.factor(gr
oup)))+
geom_point()+theme(legend.position = "none")
# Display plot
plot
# Insert marginal ditribution using marginal function
ggMarginal(plot,type= 'histogram',groupColour=TRUE,groupFill=TRUE)