# Mindanao State University
# General Santos City
# Introduction to R base commands
# Prepared by: Eugene D. Villaralbo
# March 16, 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 instantly
# set the same value for the x variable
(x <- 1:10)
## [1] 1 2 3 4 5 6 7 8 9 10
# set different values for y variables
(y1 <- c(3, 1, 5, 2, 3, 8, 4, 7, 6, 9))
## [1] 3 1 5 2 3 8 4 7 6 9
(y2 <- c(5, 1, 4, 6, 2, 3, 7, 8, 2, 8))
## [1] 5 1 4 6 2 3 7 8 2 8
(y3 <- c(3, 3, 3, 3, 4, 4, 5, 5, 7, 7))
## [1] 3 3 3 3 4 4 5 5 7 7
# Plot first the pair x and y1.
plot(x, y1, type = "b",
main = "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 3.62 3.62 3.59
## [16] 3.63 3.62 3.65 3.65
# You can obtain the plot without x values
plot(Pupils, type = 'o')
# Lab Exercise 4: How to Create vertical, horizontal lines
# We will use buit-in cars dataset in R
# display the cars dataset
cars
## speed dist
## 1 4 2
## 2 4 10
## 3 7 4
## 4 7 22
## 5 8 16
## 6 9 10
## 7 10 18
## 8 10 26
## 9 10 34
## 10 11 17
## 11 11 28
## 12 12 14
## 13 12 20
## 14 12 24
## 15 12 28
## 16 13 26
## 17 13 34
## 18 13 34
## 19 13 46
## 20 14 26
## 21 14 36
## 22 14 60
## 23 14 80
## 24 15 20
## 25 15 26
## 26 15 54
## 27 16 32
## 28 16 40
## 29 17 32
## 30 17 40
## 31 17 50
## 32 18 42
## 33 18 56
## 34 18 76
## 35 18 84
## 36 19 36
## 37 19 46
## 38 19 68
## 39 20 32
## 40 20 48
## 41 20 52
## 42 20 56
## 43 20 64
## 44 22 66
## 45 23 54
## 46 24 70
## 47 24 92
## 48 24 93
## 49 24 120
## 50 25 85
# get the number of rows and columns using dim() command
dim(cars)
## [1] 50 2
# display the variable names of the cars dataset
names(cars)
## [1] "speed" "dist"
# display only the first column of the dataset
cars$speed # using the column name
## [1] 4 4 7 7 8 9 10 10 10 11 11 12 12 12 12 13 13 13 13 14 14 14 14 15 15
## [26] 15 16 16 17 17 17 18 18 18 18 19 19 19 20 20 20 20 20 22 23 24 24 24 24 25
cars[,1] # using the column number
## [1] 4 4 7 7 8 9 10 10 10 11 11 12 12 12 12 13 13 13 13 14 14 14 14 15 15
## [26] 15 16 16 17 17 17 18 18 18 18 19 19 19 20 20 20 20 20 22 23 24 24 24 24 25
# Remarks: the following commands will give you the same result
plot(cars,) # using the comma after the name
plot(cars[,1],cars[,2]) # using the column index 1 and 2
attach(cars); plot(speed,dist) # using the attach command to load the variables
plot(cars$speed,cars$dist) # using the dollar notation
# combine all 4 plots using the par() command
par(mfrow = c(2,2)) # set a 2x2 plot output
plot(cars,) # using the comma after the name
plot(cars[,1],cars[,2]) # using the column index 1 and 2
attach(cars); plot(speed,dist) # using the attach command to load the variables
## The following objects are masked from cars (pos = 3):
##
## dist, speed
plot(cars$speed,cars$dist) # using the dollar notation
par(mfrow = c(1,1)) # reset to default plot setting
# Problem: Create vertical lines using the v command
plot(cars)
abline(v = 15, col = "darkgreen",lwd=3) # vertical line
abline(v = 10, col = "blue",lwd=3) # vertical line
# Problem: Create horizontal lines using the h command
abline(h = 80, col = "darkgreen",lwd=3) # vertical line
abline(h = 20, col = "blue",lwd=3) # vertical line
# Create more lines simultaneously, using a vector of values
plot(cars)
abline(v = c(9, 22,25), col = c("darkgreen", "blue","red"),
lwd = c(1, 3,2), # line thickness
lty = c(2,2,2)) # dashed lines
plot(cars)
abline(v = c(9, 22,25), col = c("darkgreen", "blue","red"),
lwd = c(1, 3,2)) # line thickness and solid lines
# create horizontal lines
plot(cars)
abline(h = 60, col = "red",lty = 1, lwd = 3)
abline(h = 100, col = "red",lty = 2, lwd = 3)
abline(h = 20, col = "red",lty = 3, lwd = 3)
# Lab Exercise 5: How to Plot data by group
# We will use buit-in iris dataset in R
# this dataset is a collection of 4 species of flowers with different
# sepal length and width and also with different petal length and width
dim(iris) # iris dataset has 150 rows and 5 columns
## [1] 150 5
names(iris)
## [1] "Sepal.Length" "Sepal.Width" "Petal.Length" "Petal.Width" "Species"
# two different commands to get the frequency table
table(iris$Species) # refer to the dataset by variable name
##
## setosa versicolor virginica
## 50 50 50
table(iris[,5]) # refer to the dataset by column number
##
## setosa versicolor virginica
## 50 50 50
# get summary of all columns
summary(iris)
## Sepal.Length Sepal.Width Petal.Length Petal.Width
## Min. :4.300 Min. :2.000 Min. :1.000 Min. :0.100
## 1st Qu.:5.100 1st Qu.:2.800 1st Qu.:1.600 1st Qu.:0.300
## Median :5.800 Median :3.000 Median :4.350 Median :1.300
## Mean :5.843 Mean :3.057 Mean :3.758 Mean :1.199
## 3rd Qu.:6.400 3rd Qu.:3.300 3rd Qu.:5.100 3rd Qu.:1.800
## Max. :7.900 Max. :4.400 Max. :6.900 Max. :2.500
## Species
## setosa :50
## versicolor:50
## virginica :50
##
##
##
#create scatterplot of sepal width vs. sepal length
plot(iris$Sepal.Width, iris$Sepal.Length,
col='steelblue',
main='Scatterplot',
xlab='Sepal Width',
ylab='Sepal Length',
pch=19)
plot(iris$Sepal.Width, iris$Sepal.Length,
col='steelblue',
main='Scatterplot',
xlab='Sepal Width',
ylab='Sepal Length',
pch=1)
# another way to retrieve columns of data
PL <- iris$Petal.Length
PW <- iris$Petal.Width
plot(PL, PW)
# add color by species
plot(PL, PW, col = iris$Species, main= "My Plot")
# draw a line along with the distribution of points
# using the abline and lm commands
abline(lm(PW ~ PL))
# add text annotation
text(5, 0.5, "Regression Line")
legend("topleft", # specify the location of the legend
levels(iris$Species), # specify the levels of species
pch = 1:3, # specify three symbols used for the three species
col = 1:3) # specify three colors for the three species
# Lab Exercise 6: Generate advance scatter plot
pairs(iris, col = rainbow(3)[iris$Species]) # set colors by species
# How to filter data for each flower
(Versicolor <- subset(iris, Species == "versicolor"))
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 51 7.0 3.2 4.7 1.4 versicolor
## 52 6.4 3.2 4.5 1.5 versicolor
## 53 6.9 3.1 4.9 1.5 versicolor
## 54 5.5 2.3 4.0 1.3 versicolor
## 55 6.5 2.8 4.6 1.5 versicolor
## 56 5.7 2.8 4.5 1.3 versicolor
## 57 6.3 3.3 4.7 1.6 versicolor
## 58 4.9 2.4 3.3 1.0 versicolor
## 59 6.6 2.9 4.6 1.3 versicolor
## 60 5.2 2.7 3.9 1.4 versicolor
## 61 5.0 2.0 3.5 1.0 versicolor
## 62 5.9 3.0 4.2 1.5 versicolor
## 63 6.0 2.2 4.0 1.0 versicolor
## 64 6.1 2.9 4.7 1.4 versicolor
## 65 5.6 2.9 3.6 1.3 versicolor
## 66 6.7 3.1 4.4 1.4 versicolor
## 67 5.6 3.0 4.5 1.5 versicolor
## 68 5.8 2.7 4.1 1.0 versicolor
## 69 6.2 2.2 4.5 1.5 versicolor
## 70 5.6 2.5 3.9 1.1 versicolor
## 71 5.9 3.2 4.8 1.8 versicolor
## 72 6.1 2.8 4.0 1.3 versicolor
## 73 6.3 2.5 4.9 1.5 versicolor
## 74 6.1 2.8 4.7 1.2 versicolor
## 75 6.4 2.9 4.3 1.3 versicolor
## 76 6.6 3.0 4.4 1.4 versicolor
## 77 6.8 2.8 4.8 1.4 versicolor
## 78 6.7 3.0 5.0 1.7 versicolor
## 79 6.0 2.9 4.5 1.5 versicolor
## 80 5.7 2.6 3.5 1.0 versicolor
## 81 5.5 2.4 3.8 1.1 versicolor
## 82 5.5 2.4 3.7 1.0 versicolor
## 83 5.8 2.7 3.9 1.2 versicolor
## 84 6.0 2.7 5.1 1.6 versicolor
## 85 5.4 3.0 4.5 1.5 versicolor
## 86 6.0 3.4 4.5 1.6 versicolor
## 87 6.7 3.1 4.7 1.5 versicolor
## 88 6.3 2.3 4.4 1.3 versicolor
## 89 5.6 3.0 4.1 1.3 versicolor
## 90 5.5 2.5 4.0 1.3 versicolor
## 91 5.5 2.6 4.4 1.2 versicolor
## 92 6.1 3.0 4.6 1.4 versicolor
## 93 5.8 2.6 4.0 1.2 versicolor
## 94 5.0 2.3 3.3 1.0 versicolor
## 95 5.6 2.7 4.2 1.3 versicolor
## 96 5.7 3.0 4.2 1.2 versicolor
## 97 5.7 2.9 4.2 1.3 versicolor
## 98 6.2 2.9 4.3 1.3 versicolor
## 99 5.1 2.5 3.0 1.1 versicolor
## 100 5.7 2.8 4.1 1.3 versicolor
(Setosa <- subset(iris, Species == "setosa"))
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa
## 7 4.6 3.4 1.4 0.3 setosa
## 8 5.0 3.4 1.5 0.2 setosa
## 9 4.4 2.9 1.4 0.2 setosa
## 10 4.9 3.1 1.5 0.1 setosa
## 11 5.4 3.7 1.5 0.2 setosa
## 12 4.8 3.4 1.6 0.2 setosa
## 13 4.8 3.0 1.4 0.1 setosa
## 14 4.3 3.0 1.1 0.1 setosa
## 15 5.8 4.0 1.2 0.2 setosa
## 16 5.7 4.4 1.5 0.4 setosa
## 17 5.4 3.9 1.3 0.4 setosa
## 18 5.1 3.5 1.4 0.3 setosa
## 19 5.7 3.8 1.7 0.3 setosa
## 20 5.1 3.8 1.5 0.3 setosa
## 21 5.4 3.4 1.7 0.2 setosa
## 22 5.1 3.7 1.5 0.4 setosa
## 23 4.6 3.6 1.0 0.2 setosa
## 24 5.1 3.3 1.7 0.5 setosa
## 25 4.8 3.4 1.9 0.2 setosa
## 26 5.0 3.0 1.6 0.2 setosa
## 27 5.0 3.4 1.6 0.4 setosa
## 28 5.2 3.5 1.5 0.2 setosa
## 29 5.2 3.4 1.4 0.2 setosa
## 30 4.7 3.2 1.6 0.2 setosa
## 31 4.8 3.1 1.6 0.2 setosa
## 32 5.4 3.4 1.5 0.4 setosa
## 33 5.2 4.1 1.5 0.1 setosa
## 34 5.5 4.2 1.4 0.2 setosa
## 35 4.9 3.1 1.5 0.2 setosa
## 36 5.0 3.2 1.2 0.2 setosa
## 37 5.5 3.5 1.3 0.2 setosa
## 38 4.9 3.6 1.4 0.1 setosa
## 39 4.4 3.0 1.3 0.2 setosa
## 40 5.1 3.4 1.5 0.2 setosa
## 41 5.0 3.5 1.3 0.3 setosa
## 42 4.5 2.3 1.3 0.3 setosa
## 43 4.4 3.2 1.3 0.2 setosa
## 44 5.0 3.5 1.6 0.6 setosa
## 45 5.1 3.8 1.9 0.4 setosa
## 46 4.8 3.0 1.4 0.3 setosa
## 47 5.1 3.8 1.6 0.2 setosa
## 48 4.6 3.2 1.4 0.2 setosa
## 49 5.3 3.7 1.5 0.2 setosa
## 50 5.0 3.3 1.4 0.2 setosa
(Virginica <- subset(iris, Species == "virginica"))
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 101 6.3 3.3 6.0 2.5 virginica
## 102 5.8 2.7 5.1 1.9 virginica
## 103 7.1 3.0 5.9 2.1 virginica
## 104 6.3 2.9 5.6 1.8 virginica
## 105 6.5 3.0 5.8 2.2 virginica
## 106 7.6 3.0 6.6 2.1 virginica
## 107 4.9 2.5 4.5 1.7 virginica
## 108 7.3 2.9 6.3 1.8 virginica
## 109 6.7 2.5 5.8 1.8 virginica
## 110 7.2 3.6 6.1 2.5 virginica
## 111 6.5 3.2 5.1 2.0 virginica
## 112 6.4 2.7 5.3 1.9 virginica
## 113 6.8 3.0 5.5 2.1 virginica
## 114 5.7 2.5 5.0 2.0 virginica
## 115 5.8 2.8 5.1 2.4 virginica
## 116 6.4 3.2 5.3 2.3 virginica
## 117 6.5 3.0 5.5 1.8 virginica
## 118 7.7 3.8 6.7 2.2 virginica
## 119 7.7 2.6 6.9 2.3 virginica
## 120 6.0 2.2 5.0 1.5 virginica
## 121 6.9 3.2 5.7 2.3 virginica
## 122 5.6 2.8 4.9 2.0 virginica
## 123 7.7 2.8 6.7 2.0 virginica
## 124 6.3 2.7 4.9 1.8 virginica
## 125 6.7 3.3 5.7 2.1 virginica
## 126 7.2 3.2 6.0 1.8 virginica
## 127 6.2 2.8 4.8 1.8 virginica
## 128 6.1 3.0 4.9 1.8 virginica
## 129 6.4 2.8 5.6 2.1 virginica
## 130 7.2 3.0 5.8 1.6 virginica
## 131 7.4 2.8 6.1 1.9 virginica
## 132 7.9 3.8 6.4 2.0 virginica
## 133 6.4 2.8 5.6 2.2 virginica
## 134 6.3 2.8 5.1 1.5 virginica
## 135 6.1 2.6 5.6 1.4 virginica
## 136 7.7 3.0 6.1 2.3 virginica
## 137 6.3 3.4 5.6 2.4 virginica
## 138 6.4 3.1 5.5 1.8 virginica
## 139 6.0 3.0 4.8 1.8 virginica
## 140 6.9 3.1 5.4 2.1 virginica
## 141 6.7 3.1 5.6 2.4 virginica
## 142 6.9 3.1 5.1 2.3 virginica
## 143 5.8 2.7 5.1 1.9 virginica
## 144 6.8 3.2 5.9 2.3 virginica
## 145 6.7 3.3 5.7 2.5 virginica
## 146 6.7 3.0 5.2 2.3 virginica
## 147 6.3 2.5 5.0 1.9 virginica
## 148 6.5 3.0 5.2 2.0 virginica
## 149 6.2 3.4 5.4 2.3 virginica
## 150 5.9 3.0 5.1 1.8 virginica
# Draw boxplot for each type of flower
boxplot(Versicolor[,1:4], main="Versicolor, Rainbow Palette",ylim =
c(0,8),las=2, col=rainbow(4))
boxplot(Setosa[,1:4], main="Setosa, Heat color Palette",ylim = c(0,8),las=2,
col=heat.colors(4))
boxplot(Virginica[,1:4], main="Virginica, Topo colors Palette",ylim =
c(0,8),las=2, col=topo.colors(4))
# Lab Exercise 7: How to load external datasets
# From a local directory
# the folder that contains the file should be specified completely
# using the forward slash symbol instead of the backward splash
folder <- "/cloud/project/"
filename <- "Cancer.csv"
(file <- paste0(folder,"/",filename))
## [1] "/cloud/project//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 <- "/cloud/project/"
filename <- "hsb2.csv"
(file <- paste0(folder,"/",filename))
## [1] "/cloud/project//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 socst
## 195 195 179 1 4 2 2 2 47 65 60 50 56
## 196 196 31 1 2 2 2 1 55 59 52 42 56
## 197 197 145 1 4 2 1 3 42 46 38 36 46
## 198 198 187 1 4 2 2 1 57 41 57 55 52
## 199 199 118 1 4 2 1 1 55 62 58 58 61
## 200 200 137 1 4 3 1 2 63 65 65 53 61
# delete redundant first column (run only once)
(hsb2_wide <- hsb2_wide[-1])
## id female race ses schtyp prog read write math science socst
## 1 70 0 4 1 1 1 57 52 41 47 57
## 2 121 1 4 2 1 3 68 59 53 63 61
## 3 86 0 4 3 1 1 44 33 54 58 31
## 4 141 0 4 3 1 3 63 44 47 53 56
## 5 172 0 4 2 1 2 47 52 57 53 61
## 6 113 0 4 2 1 2 44 52 51 63 61
## 7 50 0 3 2 1 1 50 59 42 53 61
## 8 11 0 1 2 1 2 34 46 45 39 36
## 9 84 0 4 2 1 1 63 57 54 58 51
## 10 48 0 3 2 1 2 57 55 52 50 51
## 11 75 0 4 2 1 3 60 46 51 53 61
## 12 60 0 4 2 1 2 57 65 51 63 61
## 13 95 0 4 3 1 2 73 60 71 61 71
## 14 104 0 4 3 1 2 54 63 57 55 46
## 15 38 0 3 1 1 2 45 57 50 31 56
## 16 115 0 4 1 1 1 42 49 43 50 56
## 17 76 0 4 3 1 2 47 52 51 50 56
## 18 195 0 4 2 2 1 57 57 60 58 56
## 19 114 0 4 3 1 2 68 65 62 55 61
## 20 85 0 4 2 1 1 55 39 57 53 46
## 21 167 0 4 2 1 1 63 49 35 66 41
## 22 143 0 4 2 1 3 63 63 75 72 66
## 23 41 0 3 2 1 2 50 40 45 55 56
## 24 20 0 1 3 1 2 60 52 57 61 61
## 25 12 0 1 2 1 3 37 44 45 39 46
## 26 53 0 3 2 1 3 34 37 46 39 31
## 27 154 0 4 3 1 2 65 65 66 61 66
## 28 178 0 4 2 2 3 47 57 57 58 46
## 29 196 0 4 3 2 2 44 38 49 39 46
## 30 29 0 2 1 1 1 52 44 49 55 41
## 31 126 0 4 2 1 1 42 31 57 47 51
## 32 103 0 4 3 1 2 76 52 64 64 61
## 33 192 0 4 3 2 2 65 67 63 66 71
## 34 150 0 4 2 1 3 42 41 57 72 31
## 35 199 0 4 3 2 2 52 59 50 61 61
## 36 144 0 4 3 1 1 60 65 58 61 66
## 37 200 0 4 2 2 2 68 54 75 66 66
## 38 80 0 4 3 1 2 65 62 68 66 66
## 39 16 0 1 1 1 3 47 31 44 36 36
## 40 153 0 4 2 1 3 39 31 40 39 51
## 41 176 0 4 2 2 2 47 47 41 42 51
## 42 177 0 4 2 2 2 55 59 62 58 51
## 43 168 0 4 2 1 2 52 54 57 55 51
## 44 40 0 3 1 1 1 42 41 43 50 41
## 45 62 0 4 3 1 1 65 65 48 63 66
## 46 169 0 4 1 1 1 55 59 63 69 46
## 47 49 0 3 3 1 3 50 40 39 49 47
## 48 136 0 4 2 1 2 65 59 70 63 51
## 49 189 0 4 2 2 2 47 59 63 53 46
## 50 7 0 1 2 1 2 57 54 59 47 51
## 51 27 0 2 2 1 2 53 61 61 57 56
## 52 128 0 4 3 1 2 39 33 38 47 41
## 53 21 0 1 2 1 1 44 44 61 50 46
## 54 183 0 4 2 2 2 63 59 49 55 71
## 55 132 0 4 2 1 2 73 62 73 69 66
## 56 15 0 1 3 1 3 39 39 44 26 42
## 57 67 0 4 1 1 3 37 37 42 33 32
## 58 22 0 1 2 1 3 42 39 39 56 46
## 59 185 0 4 2 2 2 63 57 55 58 41
## 60 9 0 1 2 1 3 48 49 52 44 51
## 61 181 0 4 2 2 2 50 46 45 58 61
## 62 170 0 4 3 1 2 47 62 61 69 66
## 63 134 0 4 1 1 1 44 44 39 34 46
## 64 108 0 4 2 1 1 34 33 41 36 36
## 65 197 0 4 3 2 2 50 42 50 36 61
## 66 140 0 4 2 1 3 44 41 40 50 26
## 67 171 0 4 2 1 2 60 54 60 55 66
## 68 107 0 4 1 1 3 47 39 47 42 26
## 69 81 0 4 1 1 2 63 43 59 65 44
## 70 18 0 1 2 1 3 50 33 49 44 36
## 71 155 0 4 2 1 1 44 44 46 39 51
## 72 97 0 4 3 1 2 60 54 58 58 61
## 73 68 0 4 2 1 2 73 67 71 63 66
## 74 157 0 4 2 1 1 68 59 58 74 66
## 75 56 0 4 2 1 3 55 45 46 58 51
## 76 5 0 1 1 1 2 47 40 43 45 31
## 77 159 0 4 3 1 2 55 61 54 49 61
## 78 123 0 4 3 1 1 68 59 56 63 66
## 79 164 0 4 2 1 3 31 36 46 39 46
## 80 14 0 1 3 1 2 47 41 54 42 56
## 81 127 0 4 3 1 2 63 59 57 55 56
## 82 165 0 4 1 1 3 36 49 54 61 36
## 83 174 0 4 2 2 2 68 59 71 66 56
## 84 3 0 1 1 1 2 63 65 48 63 56
## 85 58 0 4 2 1 3 55 41 40 44 41
## 86 146 0 4 3 1 2 55 62 64 63 66
## 87 102 0 4 3 1 2 52 41 51 53 56
## 88 117 0 4 3 1 3 34 49 39 42 56
## 89 133 0 4 2 1 3 50 31 40 34 31
## 90 94 0 4 3 1 2 55 49 61 61 56
## 91 24 0 2 2 1 2 52 62 66 47 46
## 92 149 0 4 1 1 1 63 49 49 66 46
## 93 82 1 4 3 1 2 68 62 65 69 61
## 94 8 1 1 1 1 2 39 44 52 44 48
## 95 129 1 4 1 1 1 44 44 46 47 51
## 96 173 1 4 1 1 1 50 62 61 63 51
## 97 57 1 4 2 1 2 71 65 72 66 56
## 98 100 1 4 3 1 2 63 65 71 69 71
## 99 1 1 1 1 1 3 34 44 40 39 41
## 100 194 1 4 3 2 2 63 63 69 61 61
## 101 88 1 4 3 1 2 68 60 64 69 66
## 102 99 1 4 3 1 1 47 59 56 66 61
## 103 47 1 3 1 1 2 47 46 49 33 41
## 104 120 1 4 3 1 2 63 52 54 50 51
## 105 166 1 4 2 1 2 52 59 53 61 51
## 106 65 1 4 2 1 2 55 54 66 42 56
## 107 101 1 4 3 1 2 60 62 67 50 56
## 108 89 1 4 1 1 3 35 35 40 51 33
## 109 54 1 3 1 2 1 47 54 46 50 56
## 110 180 1 4 3 2 2 71 65 69 58 71
## 111 162 1 4 2 1 3 57 52 40 61 56
## 112 4 1 1 1 1 2 44 50 41 39 51
## 113 131 1 4 3 1 2 65 59 57 46 66
## 114 125 1 4 1 1 2 68 65 58 59 56
## 115 34 1 1 3 2 2 73 61 57 55 66
## 116 106 1 4 2 1 3 36 44 37 42 41
## 117 130 1 4 3 1 1 43 54 55 55 46
## 118 93 1 4 3 1 2 73 67 62 58 66
## 119 163 1 4 1 1 2 52 57 64 58 56
## 120 37 1 3 1 1 3 41 47 40 39 51
## 121 35 1 1 1 2 1 60 54 50 50 51
## 122 87 1 4 2 1 1 50 52 46 50 56
## 123 73 1 4 2 1 2 50 52 53 39 56
## 124 151 1 4 2 1 3 47 46 52 48 46
## 125 44 1 3 1 1 3 47 62 45 34 46
## 126 152 1 4 3 1 2 55 57 56 58 61
## 127 105 1 4 2 1 2 50 41 45 44 56
## 128 28 1 2 2 1 1 39 53 54 50 41
## 129 91 1 4 3 1 3 50 49 56 47 46
## 130 45 1 3 1 1 3 34 35 41 29 26
## 131 116 1 4 2 1 2 57 59 54 50 56
## 132 33 1 2 1 1 2 57 65 72 54 56
## 133 66 1 4 2 1 3 68 62 56 50 51
## 134 72 1 4 2 1 3 42 54 47 47 46
## 135 77 1 4 1 1 2 61 59 49 44 66
## 136 61 1 4 3 1 2 76 63 60 67 66
## 137 190 1 4 2 2 2 47 59 54 58 46
## 138 42 1 3 2 1 3 46 52 55 44 56
## 139 2 1 1 2 1 3 39 41 33 42 41
## 140 55 1 3 2 2 2 52 49 49 44 61
## 141 19 1 1 1 1 1 28 46 43 44 51
## 142 90 1 4 3 1 2 42 54 50 50 52
## 143 142 1 4 2 1 3 47 42 52 39 51
## 144 17 1 1 2 1 2 47 57 48 44 41
## 145 122 1 4 2 1 2 52 59 58 53 66
## 146 191 1 4 3 2 2 47 52 43 48 61
## 147 83 1 4 2 1 3 50 62 41 55 31
## 148 182 1 4 2 2 2 44 52 43 44 51
## 149 6 1 1 1 1 2 47 41 46 40 41
## 150 46 1 3 1 1 2 45 55 44 34 41
## 151 43 1 3 1 1 2 47 37 43 42 46
## 152 96 1 4 3 1 2 65 54 61 58 56
## 153 138 1 4 2 1 3 43 57 40 50 51
## 154 10 1 1 2 1 1 47 54 49 53 61
## 155 71 1 4 2 1 1 57 62 56 58 66
## 156 139 1 4 2 1 2 68 59 61 55 71
## 157 110 1 4 2 1 3 52 55 50 54 61
## 158 148 1 4 2 1 3 42 57 51 47 61
## 159 109 1 4 2 1 1 42 39 42 42 41
## 160 39 1 3 3 1 2 66 67 67 61 66
## 161 147 1 4 1 1 2 47 62 53 53 61
## 162 74 1 4 2 1 2 57 50 50 51 58
## 163 198 1 4 3 2 2 47 61 51 63 31
## 164 161 1 4 1 1 2 57 62 72 61 61
## 165 112 1 4 2 1 2 52 59 48 55 61
## 166 69 1 4 1 1 3 44 44 40 40 31
## 167 156 1 4 2 1 2 50 59 53 61 61
## 168 111 1 4 1 1 1 39 54 39 47 36
## 169 186 1 4 2 2 2 57 62 63 55 41
## 170 98 1 4 1 1 3 57 60 51 53 37
## 171 119 1 4 1 1 1 42 57 45 50 43
## 172 13 1 1 2 1 3 47 46 39 47 61
## 173 51 1 3 3 1 1 42 36 42 31 39
## 174 26 1 2 3 1 2 60 59 62 61 51
## 175 36 1 3 1 1 1 44 49 44 35 51
## 176 135 1 4 1 1 2 63 60 65 54 66
## 177 59 1 4 2 1 2 65 67 63 55 71
## 178 78 1 4 2 1 2 39 54 54 53 41
## 179 64 1 4 3 1 3 50 52 45 58 36
## 180 63 1 4 1 1 1 52 65 60 56 51
## 181 79 1 4 2 1 2 60 62 49 50 51
## 182 193 1 4 2 2 2 44 49 48 39 51
## 183 92 1 4 3 1 1 52 67 57 63 61
## 184 160 1 4 2 1 2 55 65 55 50 61
## 185 32 1 2 3 1 3 50 67 66 66 56
## 186 23 1 2 1 1 2 65 65 64 58 71
## 187 158 1 4 2 1 1 52 54 55 53 51
## 188 25 1 2 2 1 1 47 44 42 42 36
## 189 188 1 4 3 2 2 63 62 56 55 61
## 190 52 1 3 1 1 2 50 46 53 53 66
## 191 124 1 4 1 1 3 42 54 41 42 41
## 192 175 1 4 3 2 1 36 57 42 50 41
## 193 184 1 4 2 2 3 50 52 53 55 56
## 194 30 1 2 3 1 2 41 59 42 34 51
## 195 179 1 4 2 2 2 47 65 60 50 56
## 196 31 1 2 2 2 1 55 59 52 42 56
## 197 145 1 4 2 1 3 42 46 38 36 46
## 198 187 1 4 2 2 1 57 41 57 55 52
## 199 118 1 4 2 1 1 55 62 58 58 61
## 200 137 1 4 3 1 2 63 65 65 53 61
# Remarks
# hsb2 dataset consists of 200 selected random samples from senior
# high school students in the US.
# We want to compare the student performance across different subjects
# change data layout by grouping different subjects
# into one column using melt() command. Install first reshape2 package
# install.packages("reshape2")
library(reshape2)
(hsb2_long <- melt(hsb2_wide, measure.vars = c("read","write","math","science","socst")))
## id female race ses schtyp prog variable value
## 1 70 0 4 1 1 1 read 57
## 2 121 1 4 2 1 3 read 68
## 3 86 0 4 3 1 1 read 44
## 4 141 0 4 3 1 3 read 63
## 5 172 0 4 2 1 2 read 47
## 6 113 0 4 2 1 2 read 44
## 7 50 0 3 2 1 1 read 50
## 8 11 0 1 2 1 2 read 34
## 9 84 0 4 2 1 1 read 63
## 10 48 0 3 2 1 2 read 57
## 11 75 0 4 2 1 3 read 60
## 12 60 0 4 2 1 2 read 57
## 13 95 0 4 3 1 2 read 73
## 14 104 0 4 3 1 2 read 54
## 15 38 0 3 1 1 2 read 45
## 16 115 0 4 1 1 1 read 42
## 17 76 0 4 3 1 2 read 47
## 18 195 0 4 2 2 1 read 57
## 19 114 0 4 3 1 2 read 68
## 20 85 0 4 2 1 1 read 55
## 21 167 0 4 2 1 1 read 63
## 22 143 0 4 2 1 3 read 63
## 23 41 0 3 2 1 2 read 50
## 24 20 0 1 3 1 2 read 60
## 25 12 0 1 2 1 3 read 37
## 26 53 0 3 2 1 3 read 34
## 27 154 0 4 3 1 2 read 65
## 28 178 0 4 2 2 3 read 47
## 29 196 0 4 3 2 2 read 44
## 30 29 0 2 1 1 1 read 52
## 31 126 0 4 2 1 1 read 42
## 32 103 0 4 3 1 2 read 76
## 33 192 0 4 3 2 2 read 65
## 34 150 0 4 2 1 3 read 42
## 35 199 0 4 3 2 2 read 52
## 36 144 0 4 3 1 1 read 60
## 37 200 0 4 2 2 2 read 68
## 38 80 0 4 3 1 2 read 65
## 39 16 0 1 1 1 3 read 47
## 40 153 0 4 2 1 3 read 39
## 41 176 0 4 2 2 2 read 47
## 42 177 0 4 2 2 2 read 55
## 43 168 0 4 2 1 2 read 52
## 44 40 0 3 1 1 1 read 42
## 45 62 0 4 3 1 1 read 65
## 46 169 0 4 1 1 1 read 55
## 47 49 0 3 3 1 3 read 50
## 48 136 0 4 2 1 2 read 65
## 49 189 0 4 2 2 2 read 47
## 50 7 0 1 2 1 2 read 57
## 51 27 0 2 2 1 2 read 53
## 52 128 0 4 3 1 2 read 39
## 53 21 0 1 2 1 1 read 44
## 54 183 0 4 2 2 2 read 63
## 55 132 0 4 2 1 2 read 73
## 56 15 0 1 3 1 3 read 39
## 57 67 0 4 1 1 3 read 37
## 58 22 0 1 2 1 3 read 42
## 59 185 0 4 2 2 2 read 63
## 60 9 0 1 2 1 3 read 48
## 61 181 0 4 2 2 2 read 50
## 62 170 0 4 3 1 2 read 47
## 63 134 0 4 1 1 1 read 44
## 64 108 0 4 2 1 1 read 34
## 65 197 0 4 3 2 2 read 50
## 66 140 0 4 2 1 3 read 44
## 67 171 0 4 2 1 2 read 60
## 68 107 0 4 1 1 3 read 47
## 69 81 0 4 1 1 2 read 63
## 70 18 0 1 2 1 3 read 50
## 71 155 0 4 2 1 1 read 44
## 72 97 0 4 3 1 2 read 60
## 73 68 0 4 2 1 2 read 73
## 74 157 0 4 2 1 1 read 68
## 75 56 0 4 2 1 3 read 55
## 76 5 0 1 1 1 2 read 47
## 77 159 0 4 3 1 2 read 55
## 78 123 0 4 3 1 1 read 68
## 79 164 0 4 2 1 3 read 31
## 80 14 0 1 3 1 2 read 47
## 81 127 0 4 3 1 2 read 63
## 82 165 0 4 1 1 3 read 36
## 83 174 0 4 2 2 2 read 68
## 84 3 0 1 1 1 2 read 63
## 85 58 0 4 2 1 3 read 55
## 86 146 0 4 3 1 2 read 55
## 87 102 0 4 3 1 2 read 52
## 88 117 0 4 3 1 3 read 34
## 89 133 0 4 2 1 3 read 50
## 90 94 0 4 3 1 2 read 55
## 91 24 0 2 2 1 2 read 52
## 92 149 0 4 1 1 1 read 63
## 93 82 1 4 3 1 2 read 68
## 94 8 1 1 1 1 2 read 39
## 95 129 1 4 1 1 1 read 44
## 96 173 1 4 1 1 1 read 50
## 97 57 1 4 2 1 2 read 71
## 98 100 1 4 3 1 2 read 63
## 99 1 1 1 1 1 3 read 34
## 100 194 1 4 3 2 2 read 63
## 101 88 1 4 3 1 2 read 68
## 102 99 1 4 3 1 1 read 47
## 103 47 1 3 1 1 2 read 47
## 104 120 1 4 3 1 2 read 63
## 105 166 1 4 2 1 2 read 52
## 106 65 1 4 2 1 2 read 55
## 107 101 1 4 3 1 2 read 60
## 108 89 1 4 1 1 3 read 35
## 109 54 1 3 1 2 1 read 47
## 110 180 1 4 3 2 2 read 71
## 111 162 1 4 2 1 3 read 57
## 112 4 1 1 1 1 2 read 44
## 113 131 1 4 3 1 2 read 65
## 114 125 1 4 1 1 2 read 68
## 115 34 1 1 3 2 2 read 73
## 116 106 1 4 2 1 3 read 36
## 117 130 1 4 3 1 1 read 43
## 118 93 1 4 3 1 2 read 73
## 119 163 1 4 1 1 2 read 52
## 120 37 1 3 1 1 3 read 41
## 121 35 1 1 1 2 1 read 60
## 122 87 1 4 2 1 1 read 50
## 123 73 1 4 2 1 2 read 50
## 124 151 1 4 2 1 3 read 47
## 125 44 1 3 1 1 3 read 47
## 126 152 1 4 3 1 2 read 55
## 127 105 1 4 2 1 2 read 50
## 128 28 1 2 2 1 1 read 39
## 129 91 1 4 3 1 3 read 50
## 130 45 1 3 1 1 3 read 34
## 131 116 1 4 2 1 2 read 57
## 132 33 1 2 1 1 2 read 57
## 133 66 1 4 2 1 3 read 68
## 134 72 1 4 2 1 3 read 42
## 135 77 1 4 1 1 2 read 61
## 136 61 1 4 3 1 2 read 76
## 137 190 1 4 2 2 2 read 47
## 138 42 1 3 2 1 3 read 46
## 139 2 1 1 2 1 3 read 39
## 140 55 1 3 2 2 2 read 52
## 141 19 1 1 1 1 1 read 28
## 142 90 1 4 3 1 2 read 42
## 143 142 1 4 2 1 3 read 47
## 144 17 1 1 2 1 2 read 47
## 145 122 1 4 2 1 2 read 52
## 146 191 1 4 3 2 2 read 47
## 147 83 1 4 2 1 3 read 50
## 148 182 1 4 2 2 2 read 44
## 149 6 1 1 1 1 2 read 47
## 150 46 1 3 1 1 2 read 45
## 151 43 1 3 1 1 2 read 47
## 152 96 1 4 3 1 2 read 65
## 153 138 1 4 2 1 3 read 43
## 154 10 1 1 2 1 1 read 47
## 155 71 1 4 2 1 1 read 57
## 156 139 1 4 2 1 2 read 68
## 157 110 1 4 2 1 3 read 52
## 158 148 1 4 2 1 3 read 42
## 159 109 1 4 2 1 1 read 42
## 160 39 1 3 3 1 2 read 66
## 161 147 1 4 1 1 2 read 47
## 162 74 1 4 2 1 2 read 57
## 163 198 1 4 3 2 2 read 47
## 164 161 1 4 1 1 2 read 57
## 165 112 1 4 2 1 2 read 52
## 166 69 1 4 1 1 3 read 44
## 167 156 1 4 2 1 2 read 50
## 168 111 1 4 1 1 1 read 39
## 169 186 1 4 2 2 2 read 57
## 170 98 1 4 1 1 3 read 57
## 171 119 1 4 1 1 1 read 42
## 172 13 1 1 2 1 3 read 47
## 173 51 1 3 3 1 1 read 42
## 174 26 1 2 3 1 2 read 60
## 175 36 1 3 1 1 1 read 44
## 176 135 1 4 1 1 2 read 63
## 177 59 1 4 2 1 2 read 65
## 178 78 1 4 2 1 2 read 39
## 179 64 1 4 3 1 3 read 50
## 180 63 1 4 1 1 1 read 52
## 181 79 1 4 2 1 2 read 60
## 182 193 1 4 2 2 2 read 44
## 183 92 1 4 3 1 1 read 52
## 184 160 1 4 2 1 2 read 55
## 185 32 1 2 3 1 3 read 50
## 186 23 1 2 1 1 2 read 65
## 187 158 1 4 2 1 1 read 52
## 188 25 1 2 2 1 1 read 47
## 189 188 1 4 3 2 2 read 63
## 190 52 1 3 1 1 2 read 50
## 191 124 1 4 1 1 3 read 42
## 192 175 1 4 3 2 1 read 36
## 193 184 1 4 2 2 3 read 50
## 194 30 1 2 3 1 2 read 41
## 195 179 1 4 2 2 2 read 47
## 196 31 1 2 2 2 1 read 55
## 197 145 1 4 2 1 3 read 42
## 198 187 1 4 2 2 1 read 57
## 199 118 1 4 2 1 1 read 55
## 200 137 1 4 3 1 2 read 63
## 201 70 0 4 1 1 1 write 52
## 202 121 1 4 2 1 3 write 59
## 203 86 0 4 3 1 1 write 33
## 204 141 0 4 3 1 3 write 44
## 205 172 0 4 2 1 2 write 52
## 206 113 0 4 2 1 2 write 52
## 207 50 0 3 2 1 1 write 59
## 208 11 0 1 2 1 2 write 46
## 209 84 0 4 2 1 1 write 57
## 210 48 0 3 2 1 2 write 55
## 211 75 0 4 2 1 3 write 46
## 212 60 0 4 2 1 2 write 65
## 213 95 0 4 3 1 2 write 60
## 214 104 0 4 3 1 2 write 63
## 215 38 0 3 1 1 2 write 57
## 216 115 0 4 1 1 1 write 49
## 217 76 0 4 3 1 2 write 52
## 218 195 0 4 2 2 1 write 57
## 219 114 0 4 3 1 2 write 65
## 220 85 0 4 2 1 1 write 39
## 221 167 0 4 2 1 1 write 49
## 222 143 0 4 2 1 3 write 63
## 223 41 0 3 2 1 2 write 40
## 224 20 0 1 3 1 2 write 52
## 225 12 0 1 2 1 3 write 44
## 226 53 0 3 2 1 3 write 37
## 227 154 0 4 3 1 2 write 65
## 228 178 0 4 2 2 3 write 57
## 229 196 0 4 3 2 2 write 38
## 230 29 0 2 1 1 1 write 44
## 231 126 0 4 2 1 1 write 31
## 232 103 0 4 3 1 2 write 52
## 233 192 0 4 3 2 2 write 67
## 234 150 0 4 2 1 3 write 41
## 235 199 0 4 3 2 2 write 59
## 236 144 0 4 3 1 1 write 65
## 237 200 0 4 2 2 2 write 54
## 238 80 0 4 3 1 2 write 62
## 239 16 0 1 1 1 3 write 31
## 240 153 0 4 2 1 3 write 31
## 241 176 0 4 2 2 2 write 47
## 242 177 0 4 2 2 2 write 59
## 243 168 0 4 2 1 2 write 54
## 244 40 0 3 1 1 1 write 41
## 245 62 0 4 3 1 1 write 65
## 246 169 0 4 1 1 1 write 59
## 247 49 0 3 3 1 3 write 40
## 248 136 0 4 2 1 2 write 59
## 249 189 0 4 2 2 2 write 59
## 250 7 0 1 2 1 2 write 54
## 251 27 0 2 2 1 2 write 61
## 252 128 0 4 3 1 2 write 33
## 253 21 0 1 2 1 1 write 44
## 254 183 0 4 2 2 2 write 59
## 255 132 0 4 2 1 2 write 62
## 256 15 0 1 3 1 3 write 39
## 257 67 0 4 1 1 3 write 37
## 258 22 0 1 2 1 3 write 39
## 259 185 0 4 2 2 2 write 57
## 260 9 0 1 2 1 3 write 49
## 261 181 0 4 2 2 2 write 46
## 262 170 0 4 3 1 2 write 62
## 263 134 0 4 1 1 1 write 44
## 264 108 0 4 2 1 1 write 33
## 265 197 0 4 3 2 2 write 42
## 266 140 0 4 2 1 3 write 41
## 267 171 0 4 2 1 2 write 54
## 268 107 0 4 1 1 3 write 39
## 269 81 0 4 1 1 2 write 43
## 270 18 0 1 2 1 3 write 33
## 271 155 0 4 2 1 1 write 44
## 272 97 0 4 3 1 2 write 54
## 273 68 0 4 2 1 2 write 67
## 274 157 0 4 2 1 1 write 59
## 275 56 0 4 2 1 3 write 45
## 276 5 0 1 1 1 2 write 40
## 277 159 0 4 3 1 2 write 61
## 278 123 0 4 3 1 1 write 59
## 279 164 0 4 2 1 3 write 36
## 280 14 0 1 3 1 2 write 41
## 281 127 0 4 3 1 2 write 59
## 282 165 0 4 1 1 3 write 49
## 283 174 0 4 2 2 2 write 59
## 284 3 0 1 1 1 2 write 65
## 285 58 0 4 2 1 3 write 41
## 286 146 0 4 3 1 2 write 62
## 287 102 0 4 3 1 2 write 41
## 288 117 0 4 3 1 3 write 49
## 289 133 0 4 2 1 3 write 31
## 290 94 0 4 3 1 2 write 49
## 291 24 0 2 2 1 2 write 62
## 292 149 0 4 1 1 1 write 49
## 293 82 1 4 3 1 2 write 62
## 294 8 1 1 1 1 2 write 44
## 295 129 1 4 1 1 1 write 44
## 296 173 1 4 1 1 1 write 62
## 297 57 1 4 2 1 2 write 65
## 298 100 1 4 3 1 2 write 65
## 299 1 1 1 1 1 3 write 44
## 300 194 1 4 3 2 2 write 63
## 301 88 1 4 3 1 2 write 60
## 302 99 1 4 3 1 1 write 59
## 303 47 1 3 1 1 2 write 46
## 304 120 1 4 3 1 2 write 52
## 305 166 1 4 2 1 2 write 59
## 306 65 1 4 2 1 2 write 54
## 307 101 1 4 3 1 2 write 62
## 308 89 1 4 1 1 3 write 35
## 309 54 1 3 1 2 1 write 54
## 310 180 1 4 3 2 2 write 65
## 311 162 1 4 2 1 3 write 52
## 312 4 1 1 1 1 2 write 50
## 313 131 1 4 3 1 2 write 59
## 314 125 1 4 1 1 2 write 65
## 315 34 1 1 3 2 2 write 61
## 316 106 1 4 2 1 3 write 44
## 317 130 1 4 3 1 1 write 54
## 318 93 1 4 3 1 2 write 67
## 319 163 1 4 1 1 2 write 57
## 320 37 1 3 1 1 3 write 47
## 321 35 1 1 1 2 1 write 54
## 322 87 1 4 2 1 1 write 52
## 323 73 1 4 2 1 2 write 52
## 324 151 1 4 2 1 3 write 46
## 325 44 1 3 1 1 3 write 62
## 326 152 1 4 3 1 2 write 57
## 327 105 1 4 2 1 2 write 41
## 328 28 1 2 2 1 1 write 53
## 329 91 1 4 3 1 3 write 49
## 330 45 1 3 1 1 3 write 35
## 331 116 1 4 2 1 2 write 59
## 332 33 1 2 1 1 2 write 65
## 333 66 1 4 2 1 3 write 62
## 334 72 1 4 2 1 3 write 54
## 335 77 1 4 1 1 2 write 59
## 336 61 1 4 3 1 2 write 63
## 337 190 1 4 2 2 2 write 59
## 338 42 1 3 2 1 3 write 52
## 339 2 1 1 2 1 3 write 41
## 340 55 1 3 2 2 2 write 49
## 341 19 1 1 1 1 1 write 46
## 342 90 1 4 3 1 2 write 54
## 343 142 1 4 2 1 3 write 42
## 344 17 1 1 2 1 2 write 57
## 345 122 1 4 2 1 2 write 59
## 346 191 1 4 3 2 2 write 52
## 347 83 1 4 2 1 3 write 62
## 348 182 1 4 2 2 2 write 52
## 349 6 1 1 1 1 2 write 41
## 350 46 1 3 1 1 2 write 55
## 351 43 1 3 1 1 2 write 37
## 352 96 1 4 3 1 2 write 54
## 353 138 1 4 2 1 3 write 57
## 354 10 1 1 2 1 1 write 54
## 355 71 1 4 2 1 1 write 62
## 356 139 1 4 2 1 2 write 59
## 357 110 1 4 2 1 3 write 55
## 358 148 1 4 2 1 3 write 57
## 359 109 1 4 2 1 1 write 39
## 360 39 1 3 3 1 2 write 67
## 361 147 1 4 1 1 2 write 62
## 362 74 1 4 2 1 2 write 50
## 363 198 1 4 3 2 2 write 61
## 364 161 1 4 1 1 2 write 62
## 365 112 1 4 2 1 2 write 59
## 366 69 1 4 1 1 3 write 44
## 367 156 1 4 2 1 2 write 59
## 368 111 1 4 1 1 1 write 54
## 369 186 1 4 2 2 2 write 62
## 370 98 1 4 1 1 3 write 60
## 371 119 1 4 1 1 1 write 57
## 372 13 1 1 2 1 3 write 46
## 373 51 1 3 3 1 1 write 36
## 374 26 1 2 3 1 2 write 59
## 375 36 1 3 1 1 1 write 49
## 376 135 1 4 1 1 2 write 60
## 377 59 1 4 2 1 2 write 67
## 378 78 1 4 2 1 2 write 54
## 379 64 1 4 3 1 3 write 52
## 380 63 1 4 1 1 1 write 65
## 381 79 1 4 2 1 2 write 62
## 382 193 1 4 2 2 2 write 49
## 383 92 1 4 3 1 1 write 67
## 384 160 1 4 2 1 2 write 65
## 385 32 1 2 3 1 3 write 67
## 386 23 1 2 1 1 2 write 65
## 387 158 1 4 2 1 1 write 54
## 388 25 1 2 2 1 1 write 44
## 389 188 1 4 3 2 2 write 62
## 390 52 1 3 1 1 2 write 46
## 391 124 1 4 1 1 3 write 54
## 392 175 1 4 3 2 1 write 57
## 393 184 1 4 2 2 3 write 52
## 394 30 1 2 3 1 2 write 59
## 395 179 1 4 2 2 2 write 65
## 396 31 1 2 2 2 1 write 59
## 397 145 1 4 2 1 3 write 46
## 398 187 1 4 2 2 1 write 41
## 399 118 1 4 2 1 1 write 62
## 400 137 1 4 3 1 2 write 65
## 401 70 0 4 1 1 1 math 41
## 402 121 1 4 2 1 3 math 53
## 403 86 0 4 3 1 1 math 54
## 404 141 0 4 3 1 3 math 47
## 405 172 0 4 2 1 2 math 57
## 406 113 0 4 2 1 2 math 51
## 407 50 0 3 2 1 1 math 42
## 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
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## 417 76 0 4 3 1 2 math 51
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## 422 143 0 4 2 1 3 math 75
## 423 41 0 3 2 1 2 math 45
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## 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
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## 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
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## 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
## 505 166 1 4 2 1 2 math 53
## 506 65 1 4 2 1 2 math 66
## 507 101 1 4 3 1 2 math 67
## 508 89 1 4 1 1 3 math 40
## 509 54 1 3 1 2 1 math 46
## 510 180 1 4 3 2 2 math 69
## 511 162 1 4 2 1 3 math 40
## 512 4 1 1 1 1 2 math 41
## 513 131 1 4 3 1 2 math 57
## 514 125 1 4 1 1 2 math 58
## 515 34 1 1 3 2 2 math 57
## 516 106 1 4 2 1 3 math 37
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#Remark: Pay extra attention to the last 2 columns
head(hsb2_long)
## id female race ses schtyp prog variable value
## 1 70 0 4 1 1 1 read 57
## 2 121 1 4 2 1 3 read 68
## 3 86 0 4 3 1 1 read 44
## 4 141 0 4 3 1 3 read 63
## 5 172 0 4 2 1 2 read 47
## 6 113 0 4 2 1 2 read 44
tail(hsb2_long)
## id female race ses schtyp prog variable value
## 995 179 1 4 2 2 2 socst 56
## 996 31 1 2 2 2 1 socst 56
## 997 145 1 4 2 1 3 socst 46
## 998 187 1 4 2 2 1 socst 52
## 999 118 1 4 2 1 1 socst 61
## 1000 137 1 4 3 1 2 socst 61
# get thefrequency
table(hsb2_long$variable)
##
## read write math science socst
## 200 200 200 200 200
# This means that the tables hsb2_long and hsb2_wide are the same
# tables displayed in two ways
# display data structure of the hsb2_long dataset
str(hsb2_long)
## 'data.frame': 1000 obs. of 8 variables:
## $ id : int 70 121 86 141 172 113 50 11 84 48 ...
## $ female : int 0 1 0 0 0 0 0 0 0 0 ...
## $ race : int 4 4 4 4 4 4 3 1 4 3 ...
## $ ses : int 1 2 3 3 2 2 2 2 2 2 ...
## $ schtyp : int 1 1 1 1 1 1 1 1 1 1 ...
## $ prog : int 1 3 1 3 2 2 1 2 1 2 ...
## $ variable: Factor w/ 5 levels "read","write",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ value : int 57 68 44 63 47 44 50 34 63 57 ...
# the variables female, race, ses, schtyp, prog are stored as numbers
# for encoding purposes. However these variables are actually qualitative variables
# so we convert each from integer type to categorical type
# defining some variables to become factor variable
# we use another variable to preserve the file hsb2_long
data <- hsb2_long
data$ses = factor(data$ses, labels=c("low", "middle", "high"))
data$schtyp = factor(data$schtyp, labels=c("public", "private"))
data$prog = factor(data$prog, labels=c("general", "academic", "vocational"))
data$race = factor(data$race, labels=c("hispanic", "asian", "african-amer","white"))
data$female = factor(data$female, labels=c("female", "male"))
# check data structure again. The former integer variables are now categorical variable
str(data)
## 'data.frame': 1000 obs. of 8 variables:
## $ id : int 70 121 86 141 172 113 50 11 84 48 ...
## $ female : Factor w/ 2 levels "female","male": 1 2 1 1 1 1 1 1 1 1 ...
## $ race : Factor w/ 4 levels "hispanic","asian",..: 4 4 4 4 4 4 3 1 4 3 ...
## $ ses : Factor w/ 3 levels "low","middle",..: 1 2 3 3 2 2 2 2 2 2 ...
## $ schtyp : Factor w/ 2 levels "public","private": 1 1 1 1 1 1 1 1 1 1 ...
## $ prog : Factor w/ 3 levels "general","academic",..: 1 3 1 3 2 2 1 2 1 2 ...
## $ variable: Factor w/ 5 levels "read","write",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ value : int 57 68 44 63 47 44 50 34 63 57 ...
# we compare student performance by using boxplots
library(gplots)
##
## Attaching package: 'gplots'
## The following object is masked from 'package:stats':
##
## lowess
# compute the average value by group using tapply() command
means <- round(tapply(data$value, data$variable, mean), digits=2)
# create boxplot by group
boxplot(data$value ~ data$variable, main= "Student Performance by Subject (brown dot = mean score)",
xlab="Subject matter", ylab="Percentage Scores", col=rainbow(5))
# insert the average values
points(means, col="brown", pch=18)
# can also compute the median values
medians = round(tapply(data$value, data$variable, median), digits=2)
medians
## read write math science socst
## 50 54 52 53 52
points(medians, col="red", pch=18)
# Lab Exercise 9: How to plot categorical variables
#install.packages("ggplot2")
library(ggplot2)
# we load variable names to memory to avoid the dollar notation
attach(hsb2_long)
# create the plot object p
p <- ggplot(hsb2_long, aes(ses, fill = prog)) + facet_wrap(~race)
p + geom_bar()
## Warning: The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
p + geom_bar(position = "dodge")
## Warning: The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
library(ggplot2)
attach(hsb2_long)
## The following objects are masked from hsb2_long (pos = 3):
##
## female, id, prog, race, schtyp, ses, value, variable
p <- ggplot(hsb2_long, aes(ses, fill = prog)) + facet_wrap(~schtyp)
p + geom_bar()
## Warning: The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
p + geom_bar(position = "dodge")
## Warning: The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
# Lab Exercise 10: Scatter Plots with marginal Distributions
# Advance Scatter plots using libraries
#install.packages("ggExtra")
#install.packages("tidyverse")
library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.0 ✔ readr 2.1.4
## ✔ forcats 1.0.0 ✔ stringr 1.5.0
## ✔ lubridate 1.9.2 ✔ tibble 3.2.0
## ✔ purrr 1.0.1 ✔ tidyr 1.3.0
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the ]8;;http://conflicted.r-lib.org/conflicted package]8;; to force all conflicts to become errors
library(ggExtra)
# set theme appearance of grid background
theme_set(theme_bw(1))
# create X and Y vector
(xAxis <- rnorm(1000)) # normal distribution with mean 0 and sd=1
## [1] 0.4220170611 -0.5500644018 1.5009173184 -0.7830299876 -1.0627401881
## [6] -1.6585592044 1.3386225366 -2.2152248806 0.2330858651 -0.6543021531
## [11] -0.9251839129 -1.4439462803 0.0160599572 0.1638618319 0.1531886761
## [16] 1.0269126967 0.1262699482 1.1204524881 -1.5406507670 -0.4892703818
## [21] 0.0361436359 1.5920139549 -0.3782213845 1.4243820070 -0.9984079256
## [26] -1.6029476366 -0.6233727913 3.2097115485 0.3742329085 -0.1740244358
## [31] 0.8212261513 -0.7672291346 1.0482471970 -1.0724887391 -2.5655936002
## [36] -0.6551341980 0.8249175264 0.0138276502 -1.1241932036 0.2080098842
## [41] -0.7200242230 0.0854807763 0.0755564585 -0.6607930056 0.5153107006
## [46] -1.2512969837 0.3988682674 -1.3122045355 0.7891026419 -0.3698297574
## [51] -0.4212465224 0.4121567538 0.8999344194 0.0797827389 -0.3362990420
## [56] -0.1016296824 -0.5606800615 1.7770252067 -0.3379241151 1.0334328224
## [61] 0.6834846238 0.0046101834 0.5194252596 2.2280491136 0.3960544872
## [66] -0.3327111481 1.2603301074 0.0375280961 -0.0151091690 0.3828209680
## [71] 0.0667180393 1.7040569668 -1.3665296948 0.7620081395 1.2223866239
## [76] -0.3614973811 0.9036577998 -0.4793861747 -0.5442960976 0.1675238569
## [81] 0.7143891307 0.4329522598 1.0743376205 0.2910351965 1.0938902237
## [86] 1.6638679080 0.4455428707 0.9010125766 0.5324868197 -0.5215936797
## [91] 0.4290614294 1.0397437839 -0.9767938798 1.3975540299 0.6703107131
## [96] 0.0073341736 0.8714274775 0.7399452733 -0.1434880328 -1.5937799697
## [101] -0.3801254334 -2.0540118177 -0.2700619193 -0.5809492050 -1.0558475188
## [106] 1.1925246035 0.4319949157 -0.1968657568 1.5018223238 -0.4838885837
## [111] 0.0353800929 -0.2268864026 0.2808694481 -0.6272509380 0.7410210804
## [116] -0.0045236650 0.6436108293 0.6573016862 0.6235360199 -2.0903744801
## [121] 0.6134374062 -0.7655919092 -0.1667745907 0.0313833455 0.8049236292
## [126] -0.9577129734 1.1693542290 0.1207732099 -1.3335260909 1.9324089893
## [131] 0.1748329745 -0.9195905377 0.6985241571 0.8681954702 0.1712874268
## [136] -0.3475754050 2.0331757860 -0.7439520613 -0.4999260264 -0.2467294416
## [141] 0.9842817695 1.0735243984 0.4503634052 0.7188632475 1.9163148125
## [146] 0.0444551518 -0.6127526564 -0.5421053086 -1.5349775648 1.1185333629
## [151] 0.4197422268 -1.1723672726 0.9005994620 1.4631547730 -0.4002449844
## [156] -2.1745607533 0.6800351083 -0.8381024181 0.3096602735 0.4810390993
## [161] 0.7958601113 0.9944962235 -0.3802654397 -0.3581726005 0.6710985423
## [166] 0.3005795780 -0.0744702222 -1.2297269517 -2.1723981154 1.1516477871
## [171] -0.0562135677 -0.3494687171 1.3254900804 -1.6174825557 1.0132016398
## [176] -0.1465844243 1.3069986625 -0.0711079974 -0.9738562484 -1.0550922432
## [181] -0.7868754248 0.2920796903 -0.9125250381 -0.8157219891 0.5148139115
## [186] 0.2126681396 -0.1882885430 -0.4051862215 1.0475909052 0.8357940545
## [191] -0.9754252262 0.2339021093 0.6191400804 0.2722021574 -0.2676996086
## [196] -0.3748608029 0.0410494280 1.7890287743 0.6254522164 -1.4903294744
## [201] -0.4009106080 0.6724728410 0.5494727966 -1.0211986529 0.3828538018
## [206] 0.9275837180 -1.9117121285 -0.1937270565 0.8124991796 -0.7795581369
## [211] -0.0725741173 0.8153320857 0.4714919469 0.0323409940 0.6910820112
## [216] -0.6797613742 1.1952255112 1.4580357102 0.7852301416 0.3770832718
## [221] -0.0413460607 -1.4739367452 -1.1826148645 1.9309516906 -0.1917256348
## [226] -0.1262809127 0.0718984333 0.6961787048 0.4639727723 0.6933578183
## [231] -1.1898071605 -0.8915840942 1.1688687068 -1.1649507466 0.0278385876
## [236] 0.7941373370 0.4424571278 -0.9362896335 -0.0140914776 -1.5914703470
## [241] 0.8495724106 -0.4729089364 1.1092671206 -0.1979585792 1.7157060644
## [246] 0.8180285480 1.0296285852 -0.5201634634 1.1330133270 -0.1389533205
## [251] 0.2521393985 0.0315715109 -0.0027800939 0.4899703615 -1.4429957381
## [256] 0.5025567294 -0.4941060492 0.0769154761 -1.3669792786 1.7276042730
## [261] -1.5799927539 -1.5400536923 -0.2554768795 -0.4273265796 0.0864887698
## [266] -1.0010270358 -0.0343246241 -1.2062514795 0.8817518517 0.8469034232
## [271] -0.6889664670 1.1855504994 -0.5455501630 0.5501766049 0.3608863161
## [276] 0.8210334219 0.9876362248 -0.6734900091 0.0621669571 0.6069859426
## [281] -0.1578477724 -0.6042131450 -0.9895545740 -1.0076651140 -1.1338960697
## [286] 0.9150858718 1.2229972423 -0.4256497172 1.0085464076 0.9254499993
## [291] -0.2912522332 -0.9672405573 0.4080203941 -0.7860302334 -0.2076716629
## [296] -0.1894425026 0.4801206408 -0.5688544281 -0.5893799129 0.0625857604
## [301] -1.3754482237 -1.0014856908 -0.4224353452 -1.7647570377 1.1109561646
## [306] 0.2166575142 -0.0462518563 1.2229468612 0.0138611647 -1.0818304442
## [311] -0.1955086855 -1.3302608687 0.8584939290 -0.6546503073 -0.5709198958
## [316] 1.5740658296 -0.1200297192 1.9020329964 0.7149431925 -1.0886464137
## [321] 1.6999178915 -0.8237579495 1.1783499449 0.4448398769 -1.1503657146
## [326] -0.5208979788 -0.2756770851 0.7168427611 -1.3726378474 -1.8909223480
## [331] 0.7514375618 0.8252125838 1.0783776071 1.8084637370 -0.8644770881
## [336] 0.2331602481 0.9880870261 -0.3179269918 -0.9398860953 -1.1163921832
## [341] 1.3743821835 0.2155774086 -0.4737859199 1.2746829824 -1.9064839296
## [346] 1.2183565667 -0.1818478980 1.0297193329 0.9531757890 0.2019199946
## [351] 0.4542674539 0.9213126252 0.1756401066 -0.0844257522 -1.6724668835
## [356] -0.7171521669 -0.1694118905 -0.4510674281 1.0818291667 0.8653970023
## [361] -0.4150562949 0.0252619341 1.2570973027 1.4524227567 -0.7794675950
## [366] -2.1506935126 0.5788703199 1.3025578996 2.2301305081 -1.0882218957
## [371] 0.0451178721 -0.2931957305 0.4342754496 0.4030455559 1.9345420395
## [376] 0.8521538746 -0.2673931921 0.2841572545 -1.5989245475 -1.1482807808
## [381] -0.4855194002 -0.6200266492 -0.7034742002 2.5608119450 -0.9095881875
## [386] -1.3085442237 0.9824771032 -0.9333841526 1.7282469727 -1.2161988499
## [391] 1.3878686963 0.6884815417 -0.7892830530 0.0934441859 0.0886352905
## [396] -0.3662558863 0.3173344737 -0.5051452837 -0.3088319988 -1.5962553779
## [401] 1.9028815210 -0.3709830715 -0.6924881295 0.1828066392 -1.2274081560
## [406] -0.2698388575 0.7980104536 2.2792464261 0.0320571337 -0.6809961566
## [411] 0.9432349317 -1.6043807581 0.1940725088 0.1309293686 -0.0062122604
## [416] -0.8547618525 -0.6305703384 -0.5288150826 -0.9999300537 -0.0096375876
## [421] 1.6397940196 -0.0009915764 -0.1276869087 0.0165663931 0.6976787743
## [426] -1.4608338642 1.4878436716 1.0006423320 1.5514174086 0.1254183041
## [431] 0.6719297774 -0.6999267029 -0.8170745259 1.0931083890 -0.8364616799
## [436] 0.2224741825 -0.3606035048 -0.5554722890 1.0585003335 0.7019386972
## [441] 0.2917775403 1.6993124408 -0.0598380951 -0.9570203352 1.3533668418
## [446] -0.6408691579 -1.2402828939 0.9561975941 -1.4081012715 -1.1753821755
## [451] 0.7492521089 -1.8640877064 -0.4030012847 -0.7587711360 -0.3399311222
## [456] 0.4635679598 -1.2737962648 1.5243537047 -2.7513221503 -0.6671143274
## [461] 1.0081318223 -0.9686828764 0.3025156417 -0.2150991953 -0.6631026713
## [466] -0.7658307614 0.6941006403 -1.6772870802 0.9865949052 -0.7149136941
## [471] -0.9282324020 0.4685592347 -0.3112834781 -0.0883659734 -0.9373949752
## [476] -1.1594402998 0.0288656864 1.1421921866 -1.8050475670 0.4492079285
## [481] -1.1567948465 -1.6184991504 -1.3391382527 1.7451799723 -2.2109382151
## [486] 1.5645265515 -1.3416397212 0.7830060013 -0.5550025181 1.1555981173
## [491] -0.0641943518 -0.3442386589 1.2530106739 2.6179522671 -0.7270956496
## [496] -0.0289871433 -0.5635131266 -1.0642795505 -1.1478780851 0.9268607808
## [501] -0.5459474895 -0.5967860034 -0.6791565170 0.1864768614 1.1454088851
## [506] 0.3397904481 -0.3305331697 -0.1185129347 0.8179562019 1.3086136495
## [511] -1.2506505038 -0.6281781407 -0.0966830309 -1.3596845166 1.7215502233
## [516] -0.2297018640 0.5964706199 0.2508069186 1.0315502432 0.2093627820
## [521] 0.5841249982 -0.7929477011 -1.2096258854 -1.4534449284 -1.1532647057
## [526] -1.0594131737 1.6141518753 0.5545340195 -0.0675560144 -0.0263988208
## [531] -2.1483922138 -1.2531533133 0.2909201211 0.6481275409 -1.3362224575
## [536] 1.8068444314 -0.1881231202 0.5134880367 0.5475884220 0.8245145188
## [541] 1.4908292584 1.2231332272 -1.8065527559 -0.3574495326 -0.5566069352
## [546] 0.3778163283 -1.4804648808 -0.2533216920 0.0376328881 0.7473832654
## [551] -1.0604839249 1.0221017856 -1.1506618792 -0.4352269347 0.6333781221
## [556] -0.1986589859 -1.2075488867 -1.0599096370 0.0963750219 -0.1702792122
## [561] 0.3400664100 0.7635489189 -0.7406409039 0.3004401904 0.4858858830
## [566] -0.9460026631 1.8275196200 0.6183711651 1.1848963810 -0.0053682393
## [571] 1.0270789837 0.9248182457 -1.8737645699 0.9885164406 3.3005414670
## [576] -1.1917099417 -2.4978896544 0.5043121814 -0.0866552681 0.9128253674
## [581] 0.6343640392 -0.5466214035 -0.5373812348 0.1657942069 -0.1280055769
## [586] -0.7013896600 -0.9072948705 2.2029348216 -0.8447683037 -0.0149010257
## [591] 0.6302035971 -0.0974937687 -0.8291672981 -0.9606677052 -2.8213332062
## [596] 0.8450425603 -1.1178496555 -0.0843946081 0.0903049639 -0.8860868854
## [601] 0.0481340220 -0.8269008734 0.1094207558 -2.1944225880 2.7802025174
## [606] 1.2818797297 0.9221498560 0.6362121527 1.1622972275 -1.3887309150
## [611] -0.4316969010 0.0629569401 0.0929491571 0.6700349935 0.3746361304
## [616] 0.7618539727 0.4930390006 -1.3730170522 -1.2056628654 -2.3700034872
## [621] 0.8692339515 0.8863789635 -0.6752880648 -0.5544808616 -0.1417245785
## [626] -0.2598563344 -1.0579948745 1.3895036703 -0.6978669218 1.1772457202
## [631] -0.1790838461 0.2733097812 -0.9335425608 0.9617737498 0.2213141319
## [636] -1.7410676742 0.2349082205 0.0480908130 1.7044073833 -0.9652801100
## [641] -0.3800382656 0.3590493790 0.1362565579 -0.1423165703 -1.2628293157
## [646] 0.8910510465 -0.0735333560 1.2171246956 1.6277987873 0.6581922397
## [651] 0.3407329392 1.0001579117 -0.4933298945 0.5478667435 1.5696686998
## [656] -0.0794332822 -0.0666897142 2.7692208236 -0.8183728220 0.7831652624
## [661] -0.1450378933 1.4366638013 0.3093967389 2.6718081316 -0.1353635519
## [666] -0.1669938001 0.5612558825 1.9756483434 -0.7592811847 1.2199624393
## [671] 0.2125991948 -2.6877560701 -2.1790775427 0.8273123274 0.7940076231
## [676] 0.4899195677 2.2043978205 1.4402431919 0.9037932562 -1.2684408358
## [681] 0.3900009806 1.8817135271 1.2709984599 0.1824552692 -0.1556556615
## [686] 1.3088730961 0.6645527611 1.0732380005 0.6287714362 1.4412161602
## [691] -0.1602381788 0.6369338597 -0.0677375017 -0.3285009854 -1.9385689583
## [696] -1.8112017242 -0.3531190606 1.0715573201 -0.6098758836 1.4318295353
## [701] -0.2178906178 0.2098933139 0.8937105763 -1.3956474726 1.1225569004
## [706] -0.6749134099 -0.3114977482 1.7004981057 0.1027846433 1.6717760837
## [711] 0.5804267133 -0.0978312873 0.2723197593 -1.9154686660 0.2126665446
## [716] -2.3799697793 -1.1341760466 0.1654001042 -2.0147494602 -1.0143754517
## [721] -0.1571491885 0.8442107413 1.3886713943 0.1760727706 -0.9764674995
## [726] -0.7416945719 -0.6942633211 0.0358307453 -0.5060487780 -0.7291455159
## [731] 0.4347822157 -0.3622593674 -1.6299361828 0.0369936364 -1.9065213339
## [736] -1.3040025353 -0.4970047924 -1.2426581933 0.4070569115 0.0696583164
## [741] -0.2106602460 1.2789991596 1.9350387878 1.0602993890 -0.4276838495
## [746] 2.1292336683 0.2366225550 1.3794196826 1.9067253387 0.1904886014
## [751] -0.9856967468 1.9120839812 -0.5501563781 0.0341529140 -0.9509176802
## [756] 0.1135620989 0.4737619840 0.1272941539 0.8789325630 -1.9936685670
## [761] -0.5259875442 1.1362628809 0.9860888823 0.5853371643 1.1070577878
## [766] -1.0052809298 0.3408189157 -1.3917704819 -0.7261136074 1.0201518857
## [771] 1.2670070551 -0.3078647669 1.6111990359 -1.5948962647 -0.6969068024
## [776] -1.3328330624 0.7659196268 -1.4752363234 -0.6048988017 -0.1411030379
## [781] 2.0358428539 -0.2419732025 -0.6547747437 0.6825872793 1.2404807148
## [786] -0.6546237401 0.6320061908 0.9398674726 -0.4485696721 -0.8138498096
## [791] -0.0638438140 0.3791300936 0.4423652153 -0.2989306593 0.4905837568
## [796] -0.9910456284 1.2619831533 -0.5618259306 -0.9990414830 0.8295551058
## [801] -0.4119733518 1.0256725498 0.3729473066 -0.2863041976 0.7752611695
## [806] 1.5536433825 -0.5069254576 -0.5473158185 0.5196089152 -1.8168545584
## [811] 0.5229303316 0.0933292944 0.2672763068 -2.1462385561 0.2877334214
## [816] -2.6502718795 -0.2571429312 -0.5446976257 -0.4593739592 1.8332295087
## [821] 0.2884197120 -0.4817443493 -0.2680264549 -0.6789268765 0.9422562336
## [826] -0.7064025602 0.1860977884 -0.3600032280 0.5155261176 0.4020062122
## [831] -1.5426904124 -1.6757142161 1.7963265886 -0.1984369760 1.5094333612
## [836] -0.6876584697 0.3639928285 -0.8430754775 0.4742160214 0.2922931842
## [841] 0.6726225405 -0.1356875942 -0.4442619859 -0.2488958146 -0.6372635290
## [846] -2.7688610112 0.6851612218 -0.7107569076 -0.1604743252 -0.9458749416
## [851] -2.5657407028 -0.7703392376 0.0467966186 1.8432664715 1.3409376146
## [856] -0.5770366945 1.1829196541 1.4560444820 -2.5143143377 0.6231608852
## [861] -1.6120682541 0.0228198173 -0.5919543178 -0.7376036928 0.9281116018
## [866] 2.3040070041 -0.9628340993 -0.1389687983 -0.0506122125 0.8867735740
## [871] -0.5707846622 0.2249495373 -1.0803698436 -0.5490571172 -1.0943030357
## [876] 0.0224920286 -0.8649218333 -1.4234755295 0.0757805478 0.5848014054
## [881] -0.1677247115 1.0842614546 -0.1289362210 -0.1823227785 -2.3641716522
## [886] 1.0204188888 1.1414996109 -0.6222006565 1.0030806018 0.0885881552
## [891] 1.2236080883 -0.1554123242 0.5515711590 -0.2709039754 -0.1391859034
## [896] -0.0154609128 0.1646875434 0.0118355677 -0.4704823064 -0.0344051270
## [901] -0.4687987105 1.2597432671 -0.5156764481 0.2883163295 -0.6751177211
## [906] 0.6265409344 0.4879110173 0.6106519048 -0.7120120317 0.4809631487
## [911] -0.2750249786 0.9493628261 1.9649084510 0.1729209964 0.9174380508
## [916] -0.3705490885 -0.6548275520 0.4102670885 1.7088642455 -0.7579695841
## [921] 0.0141430278 2.8994103320 0.7601667926 0.8452088498 0.7862089364
## [926] 0.7948331515 0.2007707307 0.1894445307 1.8522225431 1.5687639229
## [931] -1.0112526448 1.3950533073 -0.5927595166 0.2417262405 0.1716780715
## [936] -0.0676664142 2.1626374819 0.9043272576 -0.3665135155 -0.3899572250
## [941] 1.0529423602 -0.3025892489 -2.0196495117 0.1078174824 -0.6436246317
## [946] 0.8095692288 0.5482780595 0.7139372088 -0.1686933842 -0.4087731138
## [951] 1.1530110093 0.0090757462 -0.9634560752 1.1094728732 0.0815761469
## [956] 0.9316050497 -0.2862212716 -1.4661495095 -0.3089154095 -1.2068333009
## [961] 1.2444354236 1.2026925797 1.3299916667 0.8543583472 -1.4888393497
## [966] -1.4505931595 1.0344394229 -1.8602581461 0.9258684136 -0.0350664136
## [971] 1.1716411063 -1.9173863075 0.7427380005 -0.1296272837 -0.3010670269
## [976] 1.2235251574 -0.4420982960 0.5235453957 0.4700149036 -0.7827815802
## [981] -0.4807818046 1.3973317616 0.1838800697 1.6123983299 0.2051387406
## [986] -0.4437023973 -0.5215894463 0.1962541605 -0.8900413140 0.7336317823
## [991] -0.7858299495 0.7327520538 -1.2677465234 -0.6390822677 -0.2174956604
## [996] -0.7100899234 0.4521476062 -1.5212756906 -0.0490463401 1.2861696219
yAxis <- rnorm(1000) + xAxis + 10
yAxis
## [1] 10.894740 8.023241 11.801498 7.936635 10.046659 8.484353 12.721783
## [8] 8.045755 9.630640 7.640092 9.405860 8.993352 9.382809 11.197035
## [15] 9.651443 11.219973 8.331520 11.247774 7.386233 9.645517 8.075094
## [22] 12.351687 11.102947 12.981941 8.585375 10.307537 11.166635 12.387371
## [29] 9.515474 9.306674 11.588662 9.810828 11.719496 9.255441 9.288707
## [36] 8.545520 11.203524 10.038663 9.123912 9.246809 7.232683 9.346951
## [43] 10.168777 9.376722 9.846958 8.224264 9.727125 8.749983 10.832239
## [50] 10.837185 6.989907 12.119637 10.162648 10.572537 10.465028 9.037392
## [57] 8.991495 11.814424 10.131941 12.924691 12.006471 10.054979 9.010935
## [64] 9.986913 10.588297 9.255058 11.614497 8.654628 9.581464 9.934734
## [71] 10.270841 12.601555 7.987613 9.298477 8.582023 9.646491 9.610303
## [78] 9.960588 10.353215 10.208432 10.294722 11.455134 10.548265 9.139622
## [85] 11.899661 10.165261 10.600040 13.236125 10.697417 8.722958 11.344309
## [92] 11.016107 9.612280 11.983960 12.439944 11.167087 12.324185 12.078665
## [99] 10.253697 9.792491 9.143977 6.753109 9.898251 9.092289 5.740956
## [106] 11.980708 9.695434 10.078158 11.090699 7.888950 8.378184 7.871765
## [113] 11.243392 9.854445 11.848268 9.571998 10.590877 10.616723 10.609874
## [120] 7.649626 10.253704 8.663545 9.538775 10.396347 12.736262 9.565159
## [127] 11.666846 9.503339 5.846901 12.027882 9.278535 9.286313 11.155622
## [134] 10.738934 10.812240 9.046641 10.587887 9.696294 10.171994 10.110816
## [141] 11.977494 10.324179 8.827118 11.279496 10.782714 10.467801 8.713592
## [148] 9.945581 8.680992 11.306668 9.566810 10.257110 10.695451 10.648998
## [155] 10.976499 9.684363 12.340129 8.208906 10.999804 9.423882 12.204653
## [162] 11.727517 9.697298 10.632846 10.571250 8.212009 9.663045 7.768280
## [169] 6.962917 14.440968 8.999297 9.228849 11.980000 9.230495 12.044962
## [176] 10.315750 11.617773 9.343595 8.128885 9.848123 9.850900 12.331724
## [183] 10.633479 9.253924 10.747286 10.169747 9.210779 11.723030 10.275025
## [190] 11.863663 8.574881 10.921051 12.050839 10.650970 10.159200 9.566463
## [197] 12.598215 11.680348 8.023988 8.583289 11.065402 10.137141 11.204691
## [204] 9.190565 8.616936 12.455035 8.648944 9.711085 10.099810 8.916607
## [211] 9.974386 10.314654 11.000483 11.174967 10.020999 9.492703 11.726037
## [218] 9.759716 11.948009 10.974269 10.202417 9.098257 6.979849 12.264527
## [225] 11.309281 9.782165 11.893146 9.991748 9.852221 9.912181 9.727115
## [232] 9.324716 12.581477 10.212182 9.923814 11.238655 12.254122 8.043634
## [239] 8.600290 7.824567 10.382430 9.926750 10.837768 8.358914 10.751334
## [246] 10.548631 10.695013 10.054571 9.940993 8.860966 11.394648 9.190904
## [253] 11.308378 9.771168 9.626608 10.587901 7.526832 11.133601 6.984979
## [260] 11.691831 9.978876 7.483875 10.645402 10.441605 11.109231 9.864061
## [267] 10.235894 7.929915 11.477476 11.133078 9.250708 12.499288 10.171212
## [274] 8.887771 9.492831 9.933396 10.985547 9.248735 8.252881 10.848699
## [281] 9.974573 8.221657 8.919304 8.518981 7.750931 10.675135 12.400517
## [288] 10.557358 11.031319 9.051224 10.160136 9.596774 11.521431 7.912475
## [295] 11.529316 9.322446 11.146122 8.844370 11.067982 9.903705 10.519917
## [302] 10.592820 11.203401 6.271557 11.233343 10.183044 11.097006 12.825508
## [309] 10.339720 10.328850 10.588929 7.986430 9.864748 9.193666 9.344105
## [316] 9.996223 10.220752 12.618881 11.848026 8.921265 11.436032 11.336906
## [323] 10.254236 10.281981 11.298664 9.715662 9.816279 10.834319 9.477792
## [330] 7.034652 9.878249 10.890436 10.763744 11.524932 10.393296 10.640180
## [337] 9.608356 8.911474 9.009094 8.095231 11.844958 11.387404 9.045301
## [344] 10.893987 8.476265 11.688543 11.054977 13.021585 10.534607 9.316752
## [351] 10.407831 10.544216 10.705309 12.360015 9.425568 7.894238 9.539167
## [358] 9.175299 10.153929 10.409966 9.739135 9.887302 10.790217 9.596087
## [365] 11.313895 7.604780 8.844927 11.983142 13.409012 10.393775 10.798528
## [372] 10.015598 9.927367 11.008013 11.873145 9.684599 8.638937 9.023122
## [379] 10.189580 7.786612 9.164063 9.934243 9.789974 12.897242 8.451330
## [386] 8.655548 10.413722 10.767789 10.576297 9.386673 12.273538 11.302521
## [393] 8.566562 9.532922 8.788627 8.926477 10.153548 7.707882 8.513494
## [400] 8.191343 13.000839 8.609434 9.044567 12.543959 8.496610 11.282790
## [407] 9.534379 12.622084 10.671138 7.755926 10.163021 9.893255 8.499633
## [414] 9.034802 11.576270 8.465993 10.093346 8.644144 9.365280 11.657953
## [421] 11.890487 10.127877 8.933899 9.657073 10.561645 9.391821 13.435954
## [428] 10.636864 14.449632 9.281979 8.807119 9.805776 7.897499 9.999658
## [435] 10.697327 11.864022 11.922659 11.004635 10.882952 11.363448 10.905541
## [442] 11.369482 10.519692 9.467641 11.982278 8.630149 8.711653 10.466323
## [449] 7.017869 10.444177 9.865132 7.607944 9.104199 9.074345 7.245076
## [456] 10.855452 9.837531 11.844939 6.590460 8.998189 10.810940 8.814514
## [463] 10.890133 10.719684 9.387028 8.920321 10.526443 7.278630 9.258459
## [470] 10.493556 7.503500 11.416825 9.507457 11.723903 8.400653 7.141857
## [477] 8.849814 11.320094 7.328194 11.380871 9.755676 9.414188 11.037223
## [484] 11.179509 6.544258 11.971701 7.882066 11.670843 8.545574 11.061296
## [491] 11.247648 8.861787 11.114743 13.340163 9.515672 8.800770 9.267457
## [498] 10.601686 6.861406 11.168258 9.719979 8.604579 7.861315 10.224889
## [505] 10.434761 12.092362 8.248288 10.292411 10.457109 12.092168 8.243318
## [512] 9.172037 10.204400 10.210824 12.197708 8.340548 10.720434 12.298713
## [519] 10.799325 11.665259 10.470551 9.721510 8.615821 8.607456 9.932565
## [526] 9.802251 12.973040 10.679900 11.086978 10.238188 9.602999 9.997167
## [533] 9.861673 10.994607 8.182474 11.374060 8.960696 11.381751 9.039150
## [540] 12.030622 12.438544 8.856709 7.770210 10.940776 10.449102 11.218538
## [547] 9.032529 10.586484 10.132764 11.457838 8.936703 10.677114 8.284922
## [554] 8.671387 9.519956 9.942790 8.902810 8.533266 8.751560 11.668850
## [561] 10.064274 10.593842 10.089610 11.942040 9.944695 9.783043 12.577679
## [568] 10.779827 10.447890 11.304532 11.362285 11.935284 7.813676 10.531505
## [575] 12.908771 7.816377 6.476478 10.019606 10.492750 9.801798 10.661026
## [582] 9.940352 10.296980 9.035818 11.391946 10.066454 10.238060 10.412232
## [589] 9.314539 9.550672 9.292512 11.671799 10.386897 9.992867 8.479257
## [596] 11.979703 7.602863 8.885486 9.839781 7.308558 9.844835 9.142829
## [603] 10.692929 7.838514 12.024501 11.418323 10.963884 9.109955 11.607550
## [610] 8.181201 9.878205 11.313085 10.131062 8.905963 11.936653 12.564755
## [617] 9.466513 9.392334 8.854162 8.009107 11.996068 11.401598 8.599252
## [624] 10.534051 10.330387 11.038551 7.501141 9.321822 8.724443 11.123249
## [631] 8.360020 11.971065 9.958747 14.021951 11.577734 6.996401 10.275788
## [638] 11.259493 12.085639 7.929719 8.834893 10.783741 8.975353 10.988075
## [645] 7.745304 12.377699 10.644616 9.736787 9.967109 10.389604 10.813903
## [652] 11.361297 10.405603 11.320716 12.481216 9.186120 9.057705 12.953991
## [659] 8.651149 10.406668 8.290338 12.275394 9.397634 12.544913 8.635621
## [666] 9.060144 10.834887 12.526321 10.079996 12.472659 10.497795 8.950106
## [673] 8.263245 8.180740 10.531701 9.789893 12.721671 11.687994 10.189171
## [680] 9.498918 10.443342 12.311171 12.009534 9.473666 9.168460 10.851095
## [687] 10.293935 10.745475 9.139549 11.033266 10.680968 9.204838 8.690577
## [694] 7.422469 8.250558 7.476394 9.274222 11.553239 8.722644 11.103788
## [701] 10.049922 10.207257 10.291866 6.889254 10.972499 9.751235 7.882332
## [708] 12.902103 8.759845 12.773987 12.045823 10.214980 9.345410 8.917520
## [715] 11.822055 7.975298 9.659429 10.404520 8.919678 9.414646 8.774587
## [722] 10.638995 13.078783 11.563441 9.179884 8.999623 8.932874 7.986415
## [729] 8.701902 9.890734 11.572472 9.668587 9.004876 10.807642 7.597371
## [736] 9.059605 8.820728 9.077478 11.377428 9.631383 10.564315 12.272140
## [743] 13.196191 12.571644 10.968038 12.565278 9.166406 10.024135 10.602312
## [750] 10.792507 9.675561 11.450861 10.277884 10.610133 9.733599 10.075257
## [757] 9.709396 9.496026 9.638727 7.617907 7.599747 13.134291 11.328678
## [764] 11.011013 9.803846 8.649085 11.195178 7.927589 9.530196 11.319243
## [771] 12.611895 10.770189 11.137701 7.888814 8.436475 9.402230 10.968750
## [778] 7.064235 9.051049 11.084237 12.522008 9.456828 8.892652 10.659377
## [785] 12.059424 8.551674 8.769025 10.399772 8.407387 7.897280 9.244537
## [792] 9.423970 9.085779 10.514339 9.230518 9.811841 11.733605 9.067809
## [799] 8.637775 9.511062 8.753102 10.558557 10.641180 8.438615 9.924310
## [806] 11.645008 9.276264 9.804555 10.313113 8.839557 11.947016 9.213687
## [813] 11.091625 6.041905 9.827362 8.132678 11.222309 10.933804 8.738886
## [820] 13.306587 10.930299 10.440371 8.193879 9.131581 11.891227 8.286642
## [827] 10.379484 9.768957 10.093825 10.233315 8.050164 9.257142 12.722425
## [834] 9.229074 11.006871 8.262945 13.063818 10.842591 11.342042 10.732169
## [841] 8.816137 8.604864 8.234897 10.144778 10.056644 9.514539 12.034372
## [848] 10.443467 9.435332 8.882681 8.173714 8.704370 10.238416 11.059070
## [855] 10.683000 10.180604 9.036771 11.049907 7.627050 8.592279 9.327008
## [862] 9.937341 10.141113 9.719053 7.253869 12.382669 7.800304 9.198946
## [869] 9.749944 10.610358 9.164272 9.711942 7.655997 9.380352 8.639351
## [876] 10.125083 7.873588 9.608866 9.025520 8.384730 10.044339 11.011525
## [883] 9.801381 9.291706 6.872739 9.844598 12.891873 9.796393 10.931756
## [890] 10.983747 11.879148 9.282283 11.255073 9.013138 10.481437 9.397710
## [897] 10.002786 10.693577 10.362334 9.748701 9.009355 11.805049 8.749875
## [904] 10.753936 8.105468 11.500626 10.375787 11.988589 8.905497 11.003818
## [911] 8.234351 9.063366 12.341416 7.727027 11.171628 10.898355 9.483046
## [918] 11.990697 11.461257 9.599946 8.559044 11.980858 10.767002 8.643046
## [925] 9.447040 12.305467 10.392018 9.463581 11.767280 10.248318 8.248219
## [932] 11.470180 9.460919 10.017078 10.533760 8.636922 11.712473 10.031522
## [939] 8.316269 9.184337 12.183074 9.243752 7.442619 10.860270 11.303357
## [946] 10.389106 9.892447 11.650561 10.414597 10.033666 9.953021 10.183605
## [953] 9.238667 12.208852 9.911400 10.792216 10.129338 7.293508 9.363625
## [960] 7.982173 11.500019 12.204433 11.069106 8.407013 8.903847 8.077247
## [967] 11.203189 9.400668 11.314184 10.568733 11.370582 10.644465 12.050436
## [974] 9.964779 10.690292 11.617714 10.184647 9.634665 12.688304 8.413812
## [981] 8.342944 10.438732 10.316363 11.484449 9.151383 9.420712 10.178981
## [988] 9.562501 10.367239 10.760535 8.786843 10.415541 8.503771 10.288884
## [995] 9.627151 8.676657 11.757345 9.063236 11.153948 12.275079
# create groups for different values of X
(group <- rep(1,1000)) # a vector consisting of 1000 elements
## [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [186] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [223] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [260] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [297] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [334] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [371] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [408] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [445] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [482] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [519] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [556] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [593] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [630] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [667] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [704] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [741] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [778] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [815] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [852] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [889] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [926] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [963] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1000] 1
group[xAxis > -1.5] <- 2
group[xAxis > -.5] <- 3
group[xAxis > .5] <- 4
group[xAxis > 1.5] <- 5
group
## [1] 3 2 5 2 2 1 4 1 3 2 2 2 3 3 3 4 3 4 1 3 3 5 3 4 2 1 2 5 3 3 4 2 4 2 1 2 4
## [38] 3 2 3 2 3 3 2 4 2 3 2 4 3 3 3 4 3 3 3 2 5 3 4 4 3 4 5 3 3 4 3 3 3 3 5 2 4
## [75] 4 3 4 3 2 3 4 3 4 3 4 5 3 4 4 2 3 4 2 4 4 3 4 4 3 1 3 1 3 2 2 4 3 3 5 3 3
## [112] 3 3 2 4 3 4 4 4 1 4 2 3 3 4 2 4 3 2 5 3 2 4 4 3 3 5 2 3 3 4 4 3 4 5 3 2 2
## [149] 1 4 3 2 4 4 3 1 4 2 3 3 4 4 3 3 4 3 3 2 1 4 3 3 4 1 4 3 4 3 2 2 2 3 2 2 4
## [186] 3 3 3 4 4 2 3 4 3 3 3 3 5 4 2 3 4 4 2 3 4 1 3 4 2 3 4 3 3 4 2 4 4 4 3 3 2
## [223] 2 5 3 3 3 4 3 4 2 2 4 2 3 4 3 2 3 1 4 3 4 3 5 4 4 2 4 3 3 3 3 3 2 4 3 3 2
## [260] 5 1 1 3 3 3 2 3 2 4 4 2 4 2 4 3 4 4 2 3 4 3 2 2 2 2 4 4 3 4 4 3 2 3 2 3 3
## [297] 3 2 2 3 2 2 3 1 4 3 3 4 3 2 3 2 4 2 2 5 3 5 4 2 5 2 4 3 2 2 3 4 2 1 4 4 4
## [334] 5 2 3 4 3 2 2 4 3 3 4 1 4 3 4 4 3 3 4 3 3 1 2 3 3 4 4 3 3 4 4 2 1 4 4 5 2
## [371] 3 3 3 3 5 4 3 3 1 2 3 2 2 5 2 2 4 2 5 2 4 4 2 3 3 3 3 2 3 1 5 3 2 3 2 3 4
## [408] 5 3 2 4 1 3 3 3 2 2 2 2 3 5 3 3 3 4 2 4 4 5 3 4 2 2 4 2 3 3 2 4 4 3 5 3 2
## [445] 4 2 2 4 2 2 4 1 3 2 3 3 2 5 1 2 4 2 3 3 2 2 4 1 4 2 2 3 3 3 2 2 3 4 1 3 2
## [482] 1 2 5 1 5 2 4 2 4 3 3 4 5 2 3 2 2 2 4 2 2 2 3 4 3 3 3 4 4 2 2 3 2 5 3 4 3
## [519] 4 3 4 2 2 2 2 2 5 4 3 3 1 2 3 4 2 5 3 4 4 4 4 4 1 3 2 3 2 3 3 4 2 4 2 3 4
## [556] 3 2 2 3 3 3 4 2 3 3 2 5 4 4 3 4 4 1 4 5 2 1 4 3 4 4 2 2 3 3 2 2 5 2 3 4 3
## [593] 2 2 1 4 2 3 3 2 3 2 3 1 5 4 4 4 4 2 3 3 3 4 3 4 3 2 2 1 4 4 2 2 3 3 2 4 2
## [630] 4 3 3 2 4 3 1 3 3 5 2 3 3 3 3 2 4 3 4 5 4 3 4 3 4 5 3 3 5 2 4 3 4 3 5 3 3
## [667] 4 5 2 4 3 1 1 4 4 3 5 4 4 2 3 5 4 3 3 4 4 4 4 4 3 4 3 3 1 1 3 4 2 4 3 3 4
## [704] 2 4 2 3 5 3 5 4 3 3 1 3 1 2 3 1 2 3 4 4 3 2 2 2 3 2 2 3 3 1 3 1 2 3 2 3 3
## [741] 3 4 5 4 3 5 3 4 5 3 2 5 2 3 2 3 3 3 4 1 2 4 4 4 4 2 3 2 2 4 4 3 5 1 2 2 4
## [778] 2 2 3 5 3 2 4 4 2 4 4 3 2 3 3 3 3 3 2 4 2 2 4 3 4 3 3 4 5 2 2 4 1 4 3 3 1
## [815] 3 1 3 2 3 5 3 3 3 2 4 2 3 3 4 3 1 1 5 3 5 2 3 2 3 3 4 3 3 3 2 1 4 2 3 2 1
## [852] 2 3 5 4 2 4 4 1 4 1 3 2 2 4 5 2 3 3 4 2 3 2 2 2 3 2 2 3 4 3 4 3 3 1 4 4 2
## [889] 4 3 4 3 4 3 3 3 3 3 3 3 3 4 2 3 2 4 3 4 2 3 3 4 5 3 4 3 2 3 5 2 3 5 4 4 4
## [926] 4 3 3 5 5 2 4 2 3 3 3 5 4 3 3 4 3 1 3 2 4 4 4 3 3 4 3 2 4 3 4 3 2 3 2 4 4
## [963] 4 4 2 2 4 1 4 3 4 1 4 3 3 4 3 4 3 2 3 4 3 5 3 3 2 3 2 4 2 4 2 2 3 2 3 1 3
## [1000] 4
# create sample dataframe by joining variables
sample_data <- data.frame(xAxis,yAxis,group)
sample_data
## xAxis yAxis group
## 1 0.4220170611 10.894740 3
## 2 -0.5500644018 8.023241 2
## 3 1.5009173184 11.801498 5
## 4 -0.7830299876 7.936635 2
## 5 -1.0627401881 10.046659 2
## 6 -1.6585592044 8.484353 1
## 7 1.3386225366 12.721783 4
## 8 -2.2152248806 8.045755 1
## 9 0.2330858651 9.630640 3
## 10 -0.6543021531 7.640092 2
## 11 -0.9251839129 9.405860 2
## 12 -1.4439462803 8.993352 2
## 13 0.0160599572 9.382809 3
## 14 0.1638618319 11.197035 3
## 15 0.1531886761 9.651443 3
## 16 1.0269126967 11.219973 4
## 17 0.1262699482 8.331520 3
## 18 1.1204524881 11.247774 4
## 19 -1.5406507670 7.386233 1
## 20 -0.4892703818 9.645517 3
## 21 0.0361436359 8.075094 3
## 22 1.5920139549 12.351687 5
## 23 -0.3782213845 11.102947 3
## 24 1.4243820070 12.981941 4
## 25 -0.9984079256 8.585375 2
## 26 -1.6029476366 10.307537 1
## 27 -0.6233727913 11.166635 2
## 28 3.2097115485 12.387371 5
## 29 0.3742329085 9.515474 3
## 30 -0.1740244358 9.306674 3
## 31 0.8212261513 11.588662 4
## 32 -0.7672291346 9.810828 2
## 33 1.0482471970 11.719496 4
## 34 -1.0724887391 9.255441 2
## 35 -2.5655936002 9.288707 1
## 36 -0.6551341980 8.545520 2
## 37 0.8249175264 11.203524 4
## 38 0.0138276502 10.038663 3
## 39 -1.1241932036 9.123912 2
## 40 0.2080098842 9.246809 3
## 41 -0.7200242230 7.232683 2
## 42 0.0854807763 9.346951 3
## 43 0.0755564585 10.168777 3
## 44 -0.6607930056 9.376722 2
## 45 0.5153107006 9.846958 4
## 46 -1.2512969837 8.224264 2
## 47 0.3988682674 9.727125 3
## 48 -1.3122045355 8.749983 2
## 49 0.7891026419 10.832239 4
## 50 -0.3698297574 10.837185 3
## 51 -0.4212465224 6.989907 3
## 52 0.4121567538 12.119637 3
## 53 0.8999344194 10.162648 4
## 54 0.0797827389 10.572537 3
## 55 -0.3362990420 10.465028 3
## 56 -0.1016296824 9.037392 3
## 57 -0.5606800615 8.991495 2
## 58 1.7770252067 11.814424 5
## 59 -0.3379241151 10.131941 3
## 60 1.0334328224 12.924691 4
## 61 0.6834846238 12.006471 4
## 62 0.0046101834 10.054979 3
## 63 0.5194252596 9.010935 4
## 64 2.2280491136 9.986913 5
## 65 0.3960544872 10.588297 3
## 66 -0.3327111481 9.255058 3
## 67 1.2603301074 11.614497 4
## 68 0.0375280961 8.654628 3
## 69 -0.0151091690 9.581464 3
## 70 0.3828209680 9.934734 3
## 71 0.0667180393 10.270841 3
## 72 1.7040569668 12.601555 5
## 73 -1.3665296948 7.987613 2
## 74 0.7620081395 9.298477 4
## 75 1.2223866239 8.582023 4
## 76 -0.3614973811 9.646491 3
## 77 0.9036577998 9.610303 4
## 78 -0.4793861747 9.960588 3
## 79 -0.5442960976 10.353215 2
## 80 0.1675238569 10.208432 3
## 81 0.7143891307 10.294722 4
## 82 0.4329522598 11.455134 3
## 83 1.0743376205 10.548265 4
## 84 0.2910351965 9.139622 3
## 85 1.0938902237 11.899661 4
## 86 1.6638679080 10.165261 5
## 87 0.4455428707 10.600040 3
## 88 0.9010125766 13.236125 4
## 89 0.5324868197 10.697417 4
## 90 -0.5215936797 8.722958 2
## 91 0.4290614294 11.344309 3
## 92 1.0397437839 11.016107 4
## 93 -0.9767938798 9.612280 2
## 94 1.3975540299 11.983960 4
## 95 0.6703107131 12.439944 4
## 96 0.0073341736 11.167087 3
## 97 0.8714274775 12.324185 4
## 98 0.7399452733 12.078665 4
## 99 -0.1434880328 10.253697 3
## 100 -1.5937799697 9.792491 1
## 101 -0.3801254334 9.143977 3
## 102 -2.0540118177 6.753109 1
## 103 -0.2700619193 9.898251 3
## 104 -0.5809492050 9.092289 2
## 105 -1.0558475188 5.740956 2
## 106 1.1925246035 11.980708 4
## 107 0.4319949157 9.695434 3
## 108 -0.1968657568 10.078158 3
## 109 1.5018223238 11.090699 5
## 110 -0.4838885837 7.888950 3
## 111 0.0353800929 8.378184 3
## 112 -0.2268864026 7.871765 3
## 113 0.2808694481 11.243392 3
## 114 -0.6272509380 9.854445 2
## 115 0.7410210804 11.848268 4
## 116 -0.0045236650 9.571998 3
## 117 0.6436108293 10.590877 4
## 118 0.6573016862 10.616723 4
## 119 0.6235360199 10.609874 4
## 120 -2.0903744801 7.649626 1
## 121 0.6134374062 10.253704 4
## 122 -0.7655919092 8.663545 2
## 123 -0.1667745907 9.538775 3
## 124 0.0313833455 10.396347 3
## 125 0.8049236292 12.736262 4
## 126 -0.9577129734 9.565159 2
## 127 1.1693542290 11.666846 4
## 128 0.1207732099 9.503339 3
## 129 -1.3335260909 5.846901 2
## 130 1.9324089893 12.027882 5
## 131 0.1748329745 9.278535 3
## 132 -0.9195905377 9.286313 2
## 133 0.6985241571 11.155622 4
## 134 0.8681954702 10.738934 4
## 135 0.1712874268 10.812240 3
## 136 -0.3475754050 9.046641 3
## 137 2.0331757860 10.587887 5
## 138 -0.7439520613 9.696294 2
## 139 -0.4999260264 10.171994 3
## 140 -0.2467294416 10.110816 3
## 141 0.9842817695 11.977494 4
## 142 1.0735243984 10.324179 4
## 143 0.4503634052 8.827118 3
## 144 0.7188632475 11.279496 4
## 145 1.9163148125 10.782714 5
## 146 0.0444551518 10.467801 3
## 147 -0.6127526564 8.713592 2
## 148 -0.5421053086 9.945581 2
## 149 -1.5349775648 8.680992 1
## 150 1.1185333629 11.306668 4
## 151 0.4197422268 9.566810 3
## 152 -1.1723672726 10.257110 2
## 153 0.9005994620 10.695451 4
## 154 1.4631547730 10.648998 4
## 155 -0.4002449844 10.976499 3
## 156 -2.1745607533 9.684363 1
## 157 0.6800351083 12.340129 4
## 158 -0.8381024181 8.208906 2
## 159 0.3096602735 10.999804 3
## 160 0.4810390993 9.423882 3
## 161 0.7958601113 12.204653 4
## 162 0.9944962235 11.727517 4
## 163 -0.3802654397 9.697298 3
## 164 -0.3581726005 10.632846 3
## 165 0.6710985423 10.571250 4
## 166 0.3005795780 8.212009 3
## 167 -0.0744702222 9.663045 3
## 168 -1.2297269517 7.768280 2
## 169 -2.1723981154 6.962917 1
## 170 1.1516477871 14.440968 4
## 171 -0.0562135677 8.999297 3
## 172 -0.3494687171 9.228849 3
## 173 1.3254900804 11.980000 4
## 174 -1.6174825557 9.230495 1
## 175 1.0132016398 12.044962 4
## 176 -0.1465844243 10.315750 3
## 177 1.3069986625 11.617773 4
## 178 -0.0711079974 9.343595 3
## 179 -0.9738562484 8.128885 2
## 180 -1.0550922432 9.848123 2
## 181 -0.7868754248 9.850900 2
## 182 0.2920796903 12.331724 3
## 183 -0.9125250381 10.633479 2
## 184 -0.8157219891 9.253924 2
## 185 0.5148139115 10.747286 4
## 186 0.2126681396 10.169747 3
## 187 -0.1882885430 9.210779 3
## 188 -0.4051862215 11.723030 3
## 189 1.0475909052 10.275025 4
## 190 0.8357940545 11.863663 4
## 191 -0.9754252262 8.574881 2
## 192 0.2339021093 10.921051 3
## 193 0.6191400804 12.050839 4
## 194 0.2722021574 10.650970 3
## 195 -0.2676996086 10.159200 3
## 196 -0.3748608029 9.566463 3
## 197 0.0410494280 12.598215 3
## 198 1.7890287743 11.680348 5
## 199 0.6254522164 8.023988 4
## 200 -1.4903294744 8.583289 2
## 201 -0.4009106080 11.065402 3
## 202 0.6724728410 10.137141 4
## 203 0.5494727966 11.204691 4
## 204 -1.0211986529 9.190565 2
## 205 0.3828538018 8.616936 3
## 206 0.9275837180 12.455035 4
## 207 -1.9117121285 8.648944 1
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## 209 0.8124991796 10.099810 4
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## 213 0.4714919469 11.000483 3
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## 215 0.6910820112 10.020999 4
## 216 -0.6797613742 9.492703 2
## 217 1.1952255112 11.726037 4
## 218 1.4580357102 9.759716 4
## 219 0.7852301416 11.948009 4
## 220 0.3770832718 10.974269 3
## 221 -0.0413460607 10.202417 3
## 222 -1.4739367452 9.098257 2
## 223 -1.1826148645 6.979849 2
## 224 1.9309516906 12.264527 5
## 225 -0.1917256348 11.309281 3
## 226 -0.1262809127 9.782165 3
## 227 0.0718984333 11.893146 3
## 228 0.6961787048 9.991748 4
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## 230 0.6933578183 9.912181 4
## 231 -1.1898071605 9.727115 2
## 232 -0.8915840942 9.324716 2
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## 234 -1.1649507466 10.212182 2
## 235 0.0278385876 9.923814 3
## 236 0.7941373370 11.238655 4
## 237 0.4424571278 12.254122 3
## 238 -0.9362896335 8.043634 2
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## 240 -1.5914703470 7.824567 1
## 241 0.8495724106 10.382430 4
## 242 -0.4729089364 9.926750 3
## 243 1.1092671206 10.837768 4
## 244 -0.1979585792 8.358914 3
## 245 1.7157060644 10.751334 5
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## 248 -0.5201634634 10.054571 2
## 249 1.1330133270 9.940993 4
## 250 -0.1389533205 8.860966 3
## 251 0.2521393985 11.394648 3
## 252 0.0315715109 9.190904 3
## 253 -0.0027800939 11.308378 3
## 254 0.4899703615 9.771168 3
## 255 -1.4429957381 9.626608 2
## 256 0.5025567294 10.587901 4
## 257 -0.4941060492 7.526832 3
## 258 0.0769154761 11.133601 3
## 259 -1.3669792786 6.984979 2
## 260 1.7276042730 11.691831 5
## 261 -1.5799927539 9.978876 1
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## 263 -0.2554768795 10.645402 3
## 264 -0.4273265796 10.441605 3
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## 266 -1.0010270358 9.864061 2
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## 268 -1.2062514795 7.929915 2
## 269 0.8817518517 11.477476 4
## 270 0.8469034232 11.133078 4
## 271 -0.6889664670 9.250708 2
## 272 1.1855504994 12.499288 4
## 273 -0.5455501630 10.171212 2
## 274 0.5501766049 8.887771 4
## 275 0.3608863161 9.492831 3
## 276 0.8210334219 9.933396 4
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## 278 -0.6734900091 9.248735 2
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## 287 1.2229972423 12.400517 4
## 288 -0.4256497172 10.557358 3
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## 290 0.9254499993 9.051224 4
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## 320 -1.0886464137 8.921265 2
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## 940 -0.3899572250 9.184337 3
## 941 1.0529423602 12.183074 4
## 942 -0.3025892489 9.243752 3
## 943 -2.0196495117 7.442619 1
## 944 0.1078174824 10.860270 3
## 945 -0.6436246317 11.303357 2
## 946 0.8095692288 10.389106 4
## 947 0.5482780595 9.892447 4
## 948 0.7139372088 11.650561 4
## 949 -0.1686933842 10.414597 3
## 950 -0.4087731138 10.033666 3
## 951 1.1530110093 9.953021 4
## 952 0.0090757462 10.183605 3
## 953 -0.9634560752 9.238667 2
## 954 1.1094728732 12.208852 4
## 955 0.0815761469 9.911400 3
## 956 0.9316050497 10.792216 4
## 957 -0.2862212716 10.129338 3
## 958 -1.4661495095 7.293508 2
## 959 -0.3089154095 9.363625 3
## 960 -1.2068333009 7.982173 2
## 961 1.2444354236 11.500019 4
## 962 1.2026925797 12.204433 4
## 963 1.3299916667 11.069106 4
## 964 0.8543583472 8.407013 4
## 965 -1.4888393497 8.903847 2
## 966 -1.4505931595 8.077247 2
## 967 1.0344394229 11.203189 4
## 968 -1.8602581461 9.400668 1
## 969 0.9258684136 11.314184 4
## 970 -0.0350664136 10.568733 3
## 971 1.1716411063 11.370582 4
## 972 -1.9173863075 10.644465 1
## 973 0.7427380005 12.050436 4
## 974 -0.1296272837 9.964779 3
## 975 -0.3010670269 10.690292 3
## 976 1.2235251574 11.617714 4
## 977 -0.4420982960 10.184647 3
## 978 0.5235453957 9.634665 4
## 979 0.4700149036 12.688304 3
## 980 -0.7827815802 8.413812 2
## 981 -0.4807818046 8.342944 3
## 982 1.3973317616 10.438732 4
## 983 0.1838800697 10.316363 3
## 984 1.6123983299 11.484449 5
## 985 0.2051387406 9.151383 3
## 986 -0.4437023973 9.420712 3
## 987 -0.5215894463 10.178981 2
## 988 0.1962541605 9.562501 3
## 989 -0.8900413140 10.367239 2
## 990 0.7336317823 10.760535 4
## 991 -0.7858299495 8.786843 2
## 992 0.7327520538 10.415541 4
## 993 -1.2677465234 8.503771 2
## 994 -0.6390822677 10.288884 2
## 995 -0.2174956604 9.627151 3
## 996 -0.7100899234 8.676657 2
## 997 0.4521476062 11.757345 3
## 998 -1.5212756906 9.063236 1
## 999 -0.0490463401 11.153948 3
## 1000 1.2861696219 12.275079 4
# creates plot object using ggplot
plot <- ggplot(sample_data, aes(x = xAxis, y= yAxis, col = as.factor(group)))+
geom_point()+theme(legend.position = "none")
# Display plot
plot
# Insert marginal ditribution using marginal function
ggMarginal(plot,type= 'histogram',groupColour=TRUE,groupFill=TRUE)
