# Mindanao Stinate University
# General Santos City
# Basic Programming in R
# Prepared by: Prof. Carlito O. Daarol
# Faculty
# Math Department
# Submitted by: Czarina L. Genobiagon
# Lab Exercise 1: How to create Lines in with different styles in R
# Step 1: Create Data
x <- 1:10 # Create example data
y <- c(3, 1, 5, 2, 3, 8, 4, 7, 6, 9) # using the c() function to create an array
# Step 2: Plot the line graph using the base plot() command
plot(x, y, type = "l")
# Step 3: Add Main Title & Change Axis Labels
plot(x, y, type = "l",
main = "Main Title",
xlab = "X-Values",
ylab = "Y-Values")
#Step 4: Add color to the line using the col command
plot(x, y, type = "l",
main = "Main Title",
xlab = "X-Values",
ylab = "Y-Values",
col = "green")
# Step 5: Modify Thickness of Line using lwd command
plot(x, y, type = "l",
main = "Main Title",
xlab = "X-Values",
ylab = "Y-Values",
lwd=7,
col = "yellow")
# Step 6: Add points to line graph by changing the type command
plot(x, y, type = "b",
main = "Main Title",
xlab = "X-Values",
ylab = "Y-Values",
lwd=3,
col = "red")
# Lab Exercise 2: How to create Lines in with different styles in R
# Step1: Assign values for different lines. We enclose the entire
# line with parenthesis symbol to force R to display the results instantly
# set the same value for the x variable
(x <- 1:10)
## [1] 1 2 3 4 5 6 7 8 9 10
# set different values for y variables
(y1 <- c(3, 1, 5, 2, 3, 8, 4, 7, 6, 9))
## [1] 3 1 5 2 3 8 4 7 6 9
(y2 <- c(5, 1, 4, 6, 2, 3, 7, 8, 2, 8))
## [1] 5 1 4 6 2 3 7 8 2 8
(y3 <- c(3, 3, 3, 3, 4, 4, 5, 5, 7, 7))
## [1] 3 3 3 3 4 4 5 5 7 7
# Plot first the pair x and y1.
plot(x, y1, type = "b",
main = "Main Title",
xlab = "X-Values",
ylab = "Y-Values",
lwd=3,
col = "blue")
# then Add the two lines (for x,y2) and (x,y3)
lines(x, y2, type = "b", col = "red",lwd=3)
lines(x, y3, type = "b", col = "green",lwd=3)
# Add legend to the plot
legend("topleft",
legend = c("Line y1", "Line y2", "Line y3"),
col = c("black", "red", "green"),
lty = 1)
# Step 2: Create Different Point Symbol for Each
# Line using the pch command
plot(x, y1, type = "b",pch = 16,
main = "Main Title",
xlab = "X-Values",
ylab = "Y-Values",
lwd=3,
col = "blue")
lines(x, y2, type = "b", col = "red",lwd=3, pch = 15)
lines(x, y3, type = "b", col = "green",lwd=3, pch = 8)
# Add legend
legend("topleft",
legend = c("Line y1", "Line y2", "Line y3"),
col = c("black", "red", "green"),
lty = 1)
# Lab Exercise 3: Create Line graph without x values
Pupils <- c(3.55 ,3.54 ,3.53 ,3.61 ,3.65 ,3.63 ,3.61
,3.61 ,3.59 ,3.63 ,3.59 ,3.63 ,3.62 ,3.62
,3.59 ,3.63 ,3.62 ,3.65 ,3.65)
# get number of elements of Pupils
length(Pupils)
## [1] 19
# Display the elements of Pupils
Pupils
## [1] 3.55 3.54 3.53 3.61 3.65 3.63 3.61 3.61 3.59 3.63 3.59 3.63 3.62 3.62 3.59
## [16] 3.63 3.62 3.65 3.65
# You can obtain the plot without x values
plot(Pupils, type = 'o')
# Lab Exercise 4: How to Create vertical, horizontal lines
# We will use buit-in cars dataset in R
# display the cars dataset
cars
## speed dist
## 1 4 2
## 2 4 10
## 3 7 4
## 4 7 22
## 5 8 16
## 6 9 10
## 7 10 18
## 8 10 26
## 9 10 34
## 10 11 17
## 11 11 28
## 12 12 14
## 13 12 20
## 14 12 24
## 15 12 28
## 16 13 26
## 17 13 34
## 18 13 34
## 19 13 46
## 20 14 26
## 21 14 36
## 22 14 60
## 23 14 80
## 24 15 20
## 25 15 26
## 26 15 54
## 27 16 32
## 28 16 40
## 29 17 32
## 30 17 40
## 31 17 50
## 32 18 42
## 33 18 56
## 34 18 76
## 35 18 84
## 36 19 36
## 37 19 46
## 38 19 68
## 39 20 32
## 40 20 48
## 41 20 52
## 42 20 56
## 43 20 64
## 44 22 66
## 45 23 54
## 46 24 70
## 47 24 92
## 48 24 93
## 49 24 120
## 50 25 85
# get the number of rows and columns using dim() command
dim(cars)
## [1] 50 2
# display the variable names of the cars dataset
names(cars)
## [1] "speed" "dist"
# display only the first column of the dataset
cars$speed # using the column name
## [1] 4 4 7 7 8 9 10 10 10 11 11 12 12 12 12 13 13 13 13 14 14 14 14 15 15
## [26] 15 16 16 17 17 17 18 18 18 18 19 19 19 20 20 20 20 20 22 23 24 24 24 24 25
cars[,1] # using the column number
## [1] 4 4 7 7 8 9 10 10 10 11 11 12 12 12 12 13 13 13 13 14 14 14 14 15 15
## [26] 15 16 16 17 17 17 18 18 18 18 19 19 19 20 20 20 20 20 22 23 24 24 24 24 25
# Remarks: the following commands will give you the same result
plot(cars,) # using the comma after the name
plot(cars[,1],cars[,2]) # using the column index 1 and 2
attach(cars); plot(speed,dist) # using the attach command to load the variables
plot(cars$speed,cars$dist) # using the dollar notation
# combine all 4 plots using the par() command
par(mfrow = c(2,2)) # set a 2x2 plot output
plot(cars,) # using the comma after the name
plot(cars[,1],cars[,2]) # using the column index 1 and 2
attach(cars); plot(speed,dist) # using the attach command to load the variables
## The following objects are masked from cars (pos = 3):
##
## dist, speed
plot(cars$speed,cars$dist) # using the dollar notation
par(mfrow = c(1,1)) # reset to default plot setting
# Problem: Create vertical lines using the v command
plot(cars)
abline(v = 15, col = "darkgreen",lwd=3) # vertical line
abline(v = 10, col = "blue",lwd=3) # vertical line
# Problem: Create horizontal lines using the h command
abline(h = 80, col = "darkgreen",lwd=3) # vertical line
abline(h = 20, col = "blue",lwd=3) # vertical line
# Create more lines simultaneously, using a vector of values
plot(cars)
abline(v = c(9, 22,25), col = c("darkgreen", "blue","red"),
lwd = c(1, 3,2), # line thickness
lty = c(2,2,2)) # dashed lines
plot(cars)
abline(v = c(9, 22,25), col = c("darkgreen", "blue","red"),
lwd = c(1, 3,2)) # line thickness and solid lines
# create horizontal lines
plot(cars)
abline(h = 60, col = "red",lty = 1, lwd = 3)
abline(h = 100, col = "red",lty = 2, lwd = 3)
abline(h = 20, col = "red",lty = 3, lwd = 3)
# Lab Exercise 5: How to Plot data by group
# We will use buit-in iris dataset in R
# this dataset is a collection of 4 species of flowers with different
# sepal length and width and also with different petal length and width
dim(iris) # iris dataset has 150 rows and 5 columns
## [1] 150 5
names(iris)
## [1] "Sepal.Length" "Sepal.Width" "Petal.Length" "Petal.Width" "Species"
# two different commands to get the frequency table
table(iris$Species) # refer to the dataset by variable name
##
## setosa versicolor virginica
## 50 50 50
table(iris[,5]) # refer to the dataset by column number
##
## setosa versicolor virginica
## 50 50 50
# get summary of all columns
summary(iris)
## Sepal.Length Sepal.Width Petal.Length Petal.Width
## Min. :4.300 Min. :2.000 Min. :1.000 Min. :0.100
## 1st Qu.:5.100 1st Qu.:2.800 1st Qu.:1.600 1st Qu.:0.300
## Median :5.800 Median :3.000 Median :4.350 Median :1.300
## Mean :5.843 Mean :3.057 Mean :3.758 Mean :1.199
## 3rd Qu.:6.400 3rd Qu.:3.300 3rd Qu.:5.100 3rd Qu.:1.800
## Max. :7.900 Max. :4.400 Max. :6.900 Max. :2.500
## Species
## setosa :50
## versicolor:50
## virginica :50
##
##
##
#create scatterplot of sepal width vs. sepal length
plot(iris$Sepal.Width, iris$Sepal.Length,
col='steelblue',
main='Scatterplot',
xlab='Sepal Width',
ylab='Sepal Length',
pch=19)
plot(iris$Sepal.Width, iris$Sepal.Length,
col='steelblue',
main='Scatterplot',
xlab='Sepal Width',
ylab='Sepal Length',
pch=1)
# another way to retrieve columns of data
PL <- iris$Petal.Length
PW <- iris$Petal.Width
plot(PL, PW)
# add color by species
plot(PL, PW, col = iris$Species, main= "My Plot")
# draw a line along with the distribution of points
# using the abline and lm commands
abline(lm(PW ~ PL))
# add text annotation
text(5, 0.5, "Regression Line")
legend("topleft", # specify the location of the legend
levels(iris$Species), # specify the levels of species
pch = 1:3, # specify three symbols used for the three species
col = 1:3) # specify three colors for the three species
# Lab Exercise 6: Generate advance scatter plot
pairs(iris, col = rainbow(3)[iris$Species]) # set colors by species
# How to filter data for each flower
(Versicolor <- subset(iris, Species == "versicolor"))
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 51 7.0 3.2 4.7 1.4 versicolor
## 52 6.4 3.2 4.5 1.5 versicolor
## 53 6.9 3.1 4.9 1.5 versicolor
## 54 5.5 2.3 4.0 1.3 versicolor
## 55 6.5 2.8 4.6 1.5 versicolor
## 56 5.7 2.8 4.5 1.3 versicolor
## 57 6.3 3.3 4.7 1.6 versicolor
## 58 4.9 2.4 3.3 1.0 versicolor
## 59 6.6 2.9 4.6 1.3 versicolor
## 60 5.2 2.7 3.9 1.4 versicolor
## 61 5.0 2.0 3.5 1.0 versicolor
## 62 5.9 3.0 4.2 1.5 versicolor
## 63 6.0 2.2 4.0 1.0 versicolor
## 64 6.1 2.9 4.7 1.4 versicolor
## 65 5.6 2.9 3.6 1.3 versicolor
## 66 6.7 3.1 4.4 1.4 versicolor
## 67 5.6 3.0 4.5 1.5 versicolor
## 68 5.8 2.7 4.1 1.0 versicolor
## 69 6.2 2.2 4.5 1.5 versicolor
## 70 5.6 2.5 3.9 1.1 versicolor
## 71 5.9 3.2 4.8 1.8 versicolor
## 72 6.1 2.8 4.0 1.3 versicolor
## 73 6.3 2.5 4.9 1.5 versicolor
## 74 6.1 2.8 4.7 1.2 versicolor
## 75 6.4 2.9 4.3 1.3 versicolor
## 76 6.6 3.0 4.4 1.4 versicolor
## 77 6.8 2.8 4.8 1.4 versicolor
## 78 6.7 3.0 5.0 1.7 versicolor
## 79 6.0 2.9 4.5 1.5 versicolor
## 80 5.7 2.6 3.5 1.0 versicolor
## 81 5.5 2.4 3.8 1.1 versicolor
## 82 5.5 2.4 3.7 1.0 versicolor
## 83 5.8 2.7 3.9 1.2 versicolor
## 84 6.0 2.7 5.1 1.6 versicolor
## 85 5.4 3.0 4.5 1.5 versicolor
## 86 6.0 3.4 4.5 1.6 versicolor
## 87 6.7 3.1 4.7 1.5 versicolor
## 88 6.3 2.3 4.4 1.3 versicolor
## 89 5.6 3.0 4.1 1.3 versicolor
## 90 5.5 2.5 4.0 1.3 versicolor
## 91 5.5 2.6 4.4 1.2 versicolor
## 92 6.1 3.0 4.6 1.4 versicolor
## 93 5.8 2.6 4.0 1.2 versicolor
## 94 5.0 2.3 3.3 1.0 versicolor
## 95 5.6 2.7 4.2 1.3 versicolor
## 96 5.7 3.0 4.2 1.2 versicolor
## 97 5.7 2.9 4.2 1.3 versicolor
## 98 6.2 2.9 4.3 1.3 versicolor
## 99 5.1 2.5 3.0 1.1 versicolor
## 100 5.7 2.8 4.1 1.3 versicolor
(Setosa <- subset(iris, Species == "setosa"))
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa
## 7 4.6 3.4 1.4 0.3 setosa
## 8 5.0 3.4 1.5 0.2 setosa
## 9 4.4 2.9 1.4 0.2 setosa
## 10 4.9 3.1 1.5 0.1 setosa
## 11 5.4 3.7 1.5 0.2 setosa
## 12 4.8 3.4 1.6 0.2 setosa
## 13 4.8 3.0 1.4 0.1 setosa
## 14 4.3 3.0 1.1 0.1 setosa
## 15 5.8 4.0 1.2 0.2 setosa
## 16 5.7 4.4 1.5 0.4 setosa
## 17 5.4 3.9 1.3 0.4 setosa
## 18 5.1 3.5 1.4 0.3 setosa
## 19 5.7 3.8 1.7 0.3 setosa
## 20 5.1 3.8 1.5 0.3 setosa
## 21 5.4 3.4 1.7 0.2 setosa
## 22 5.1 3.7 1.5 0.4 setosa
## 23 4.6 3.6 1.0 0.2 setosa
## 24 5.1 3.3 1.7 0.5 setosa
## 25 4.8 3.4 1.9 0.2 setosa
## 26 5.0 3.0 1.6 0.2 setosa
## 27 5.0 3.4 1.6 0.4 setosa
## 28 5.2 3.5 1.5 0.2 setosa
## 29 5.2 3.4 1.4 0.2 setosa
## 30 4.7 3.2 1.6 0.2 setosa
## 31 4.8 3.1 1.6 0.2 setosa
## 32 5.4 3.4 1.5 0.4 setosa
## 33 5.2 4.1 1.5 0.1 setosa
## 34 5.5 4.2 1.4 0.2 setosa
## 35 4.9 3.1 1.5 0.2 setosa
## 36 5.0 3.2 1.2 0.2 setosa
## 37 5.5 3.5 1.3 0.2 setosa
## 38 4.9 3.6 1.4 0.1 setosa
## 39 4.4 3.0 1.3 0.2 setosa
## 40 5.1 3.4 1.5 0.2 setosa
## 41 5.0 3.5 1.3 0.3 setosa
## 42 4.5 2.3 1.3 0.3 setosa
## 43 4.4 3.2 1.3 0.2 setosa
## 44 5.0 3.5 1.6 0.6 setosa
## 45 5.1 3.8 1.9 0.4 setosa
## 46 4.8 3.0 1.4 0.3 setosa
## 47 5.1 3.8 1.6 0.2 setosa
## 48 4.6 3.2 1.4 0.2 setosa
## 49 5.3 3.7 1.5 0.2 setosa
## 50 5.0 3.3 1.4 0.2 setosa
(Virginica <- subset(iris, Species == "virginica"))
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 101 6.3 3.3 6.0 2.5 virginica
## 102 5.8 2.7 5.1 1.9 virginica
## 103 7.1 3.0 5.9 2.1 virginica
## 104 6.3 2.9 5.6 1.8 virginica
## 105 6.5 3.0 5.8 2.2 virginica
## 106 7.6 3.0 6.6 2.1 virginica
## 107 4.9 2.5 4.5 1.7 virginica
## 108 7.3 2.9 6.3 1.8 virginica
## 109 6.7 2.5 5.8 1.8 virginica
## 110 7.2 3.6 6.1 2.5 virginica
## 111 6.5 3.2 5.1 2.0 virginica
## 112 6.4 2.7 5.3 1.9 virginica
## 113 6.8 3.0 5.5 2.1 virginica
## 114 5.7 2.5 5.0 2.0 virginica
## 115 5.8 2.8 5.1 2.4 virginica
## 116 6.4 3.2 5.3 2.3 virginica
## 117 6.5 3.0 5.5 1.8 virginica
## 118 7.7 3.8 6.7 2.2 virginica
## 119 7.7 2.6 6.9 2.3 virginica
## 120 6.0 2.2 5.0 1.5 virginica
## 121 6.9 3.2 5.7 2.3 virginica
## 122 5.6 2.8 4.9 2.0 virginica
## 123 7.7 2.8 6.7 2.0 virginica
## 124 6.3 2.7 4.9 1.8 virginica
## 125 6.7 3.3 5.7 2.1 virginica
## 126 7.2 3.2 6.0 1.8 virginica
## 127 6.2 2.8 4.8 1.8 virginica
## 128 6.1 3.0 4.9 1.8 virginica
## 129 6.4 2.8 5.6 2.1 virginica
## 130 7.2 3.0 5.8 1.6 virginica
## 131 7.4 2.8 6.1 1.9 virginica
## 132 7.9 3.8 6.4 2.0 virginica
## 133 6.4 2.8 5.6 2.2 virginica
## 134 6.3 2.8 5.1 1.5 virginica
## 135 6.1 2.6 5.6 1.4 virginica
## 136 7.7 3.0 6.1 2.3 virginica
## 137 6.3 3.4 5.6 2.4 virginica
## 138 6.4 3.1 5.5 1.8 virginica
## 139 6.0 3.0 4.8 1.8 virginica
## 140 6.9 3.1 5.4 2.1 virginica
## 141 6.7 3.1 5.6 2.4 virginica
## 142 6.9 3.1 5.1 2.3 virginica
## 143 5.8 2.7 5.1 1.9 virginica
## 144 6.8 3.2 5.9 2.3 virginica
## 145 6.7 3.3 5.7 2.5 virginica
## 146 6.7 3.0 5.2 2.3 virginica
## 147 6.3 2.5 5.0 1.9 virginica
## 148 6.5 3.0 5.2 2.0 virginica
## 149 6.2 3.4 5.4 2.3 virginica
## 150 5.9 3.0 5.1 1.8 virginica
# Draw boxplot for each type of flower
boxplot(Versicolor[,1:4], main="Versicolor, Rainbow Palette",ylim = c(0,8),las=2, col=rainbow(4))
boxplot(Setosa[,1:4], main="Setosa, Heat color Palette",ylim = c(0,8),las=2, col=heat.colors(4))
boxplot(Virginica[,1:4], main="Virginica, Topo colors Palette",ylim = c(0,8),las=2, col=topo.colors(4))
# Lab Exercise 7: How to load external datasets
# From a local directory
# the folder that contains the file should be specified completely
# using the forward slash symbol instead of the backward splash
folder <- "C:/Users/Admin/Desktop/Class Lectures/BLecture 0 Graphics in R"
filename <- "Cancer.csv"
(file <- paste0(folder,"/",filename))
## [1] "C:/Users/Admin/Desktop/Class Lectures/BLecture 0 Graphics in R/Cancer.csv"
getwd()
## [1] "C:/Users/Czarina Genobiagon/Desktop/mat108"
setwd("C:/Users/Czarina Genobiagon/Desktop/mat108")
cancer <- read.csv("Cancer.csv", header = TRUE, sep = ",")
dim(cancer)
## [1] 173 17
names(cancer)
## [1] "country" "incomeperperson" "alcconsumption"
## [4] "armedforcesrate" "breastcancer" "co2emissions"
## [7] "femaleemployrate" "hivrate" "internetuserate"
## [10] "lifeexpectancy" "oilperperson" "polityscore"
## [13] "relectricperperson" "suicideper100th" "employrate"
## [16] "urbanrate" "continent"
# compute mean value for every continent
(means <- round(tapply(cancer$breastcancer, cancer$continent, mean), digits=2))
## AF AS EE LATAM NORAM OC WE
## 24.02 24.51 49.44 36.70 71.73 45.80 74.80
# draw boxplot per continent
boxplot(cancer$breastcancer ~ cancer$continent, main= "Breast cancer by continent (brown dot = mean value)", xlab="continents", ylab="new cases per 100,00 residents", col=rainbow(7))
# insert the mean value using brown dot
points(means, col="brown", pch=18)
# Lab Exercise 8: How to load external datasets and change data layout
folder <- "C:/Users/Admin/Desktop/Class Lectures/BLecture 0 Graphics in R"
filename <- "hsb2.csv"
(file <- paste0(folder,"/",filename))
## [1] "C:/Users/Admin/Desktop/Class Lectures/BLecture 0 Graphics in R/hsb2.csv"
getwd()
## [1] "C:/Users/Czarina Genobiagon/Desktop/mat108"
setwd("C:/Users/Czarina Genobiagon/Desktop/mat108")
hsb2_wide <- read.csv("hsb2.csv", header = TRUE, sep = ",")
# display only the top 6 rows
head(hsb2_wide)
## X id female race ses schtyp prog read write math science socst
## 1 1 70 0 4 1 1 1 57 52 41 47 57
## 2 2 121 1 4 2 1 3 68 59 53 63 61
## 3 3 86 0 4 3 1 1 44 33 54 58 31
## 4 4 141 0 4 3 1 3 63 44 47 53 56
## 5 5 172 0 4 2 1 2 47 52 57 53 61
## 6 6 113 0 4 2 1 2 44 52 51 63 61
# display only the last 6 rows
tail(hsb2_wide)
## X id female race ses schtyp prog read write math science socst
## 195 195 179 1 4 2 2 2 47 65 60 50 56
## 196 196 31 1 2 2 2 1 55 59 52 42 56
## 197 197 145 1 4 2 1 3 42 46 38 36 46
## 198 198 187 1 4 2 2 1 57 41 57 55 52
## 199 199 118 1 4 2 1 1 55 62 58 58 61
## 200 200 137 1 4 3 1 2 63 65 65 53 61
# delete redundant first column (run only once)
(hsb2_wide <- hsb2_wide[-1])
## id female race ses schtyp prog read write math science socst
## 1 70 0 4 1 1 1 57 52 41 47 57
## 2 121 1 4 2 1 3 68 59 53 63 61
## 3 86 0 4 3 1 1 44 33 54 58 31
## 4 141 0 4 3 1 3 63 44 47 53 56
## 5 172 0 4 2 1 2 47 52 57 53 61
## 6 113 0 4 2 1 2 44 52 51 63 61
## 7 50 0 3 2 1 1 50 59 42 53 61
## 8 11 0 1 2 1 2 34 46 45 39 36
## 9 84 0 4 2 1 1 63 57 54 58 51
## 10 48 0 3 2 1 2 57 55 52 50 51
## 11 75 0 4 2 1 3 60 46 51 53 61
## 12 60 0 4 2 1 2 57 65 51 63 61
## 13 95 0 4 3 1 2 73 60 71 61 71
## 14 104 0 4 3 1 2 54 63 57 55 46
## 15 38 0 3 1 1 2 45 57 50 31 56
## 16 115 0 4 1 1 1 42 49 43 50 56
## 17 76 0 4 3 1 2 47 52 51 50 56
## 18 195 0 4 2 2 1 57 57 60 58 56
## 19 114 0 4 3 1 2 68 65 62 55 61
## 20 85 0 4 2 1 1 55 39 57 53 46
## 21 167 0 4 2 1 1 63 49 35 66 41
## 22 143 0 4 2 1 3 63 63 75 72 66
## 23 41 0 3 2 1 2 50 40 45 55 56
## 24 20 0 1 3 1 2 60 52 57 61 61
## 25 12 0 1 2 1 3 37 44 45 39 46
## 26 53 0 3 2 1 3 34 37 46 39 31
## 27 154 0 4 3 1 2 65 65 66 61 66
## 28 178 0 4 2 2 3 47 57 57 58 46
## 29 196 0 4 3 2 2 44 38 49 39 46
## 30 29 0 2 1 1 1 52 44 49 55 41
## 31 126 0 4 2 1 1 42 31 57 47 51
## 32 103 0 4 3 1 2 76 52 64 64 61
## 33 192 0 4 3 2 2 65 67 63 66 71
## 34 150 0 4 2 1 3 42 41 57 72 31
## 35 199 0 4 3 2 2 52 59 50 61 61
## 36 144 0 4 3 1 1 60 65 58 61 66
## 37 200 0 4 2 2 2 68 54 75 66 66
## 38 80 0 4 3 1 2 65 62 68 66 66
## 39 16 0 1 1 1 3 47 31 44 36 36
## 40 153 0 4 2 1 3 39 31 40 39 51
## 41 176 0 4 2 2 2 47 47 41 42 51
## 42 177 0 4 2 2 2 55 59 62 58 51
## 43 168 0 4 2 1 2 52 54 57 55 51
## 44 40 0 3 1 1 1 42 41 43 50 41
## 45 62 0 4 3 1 1 65 65 48 63 66
## 46 169 0 4 1 1 1 55 59 63 69 46
## 47 49 0 3 3 1 3 50 40 39 49 47
## 48 136 0 4 2 1 2 65 59 70 63 51
## 49 189 0 4 2 2 2 47 59 63 53 46
## 50 7 0 1 2 1 2 57 54 59 47 51
## 51 27 0 2 2 1 2 53 61 61 57 56
## 52 128 0 4 3 1 2 39 33 38 47 41
## 53 21 0 1 2 1 1 44 44 61 50 46
## 54 183 0 4 2 2 2 63 59 49 55 71
## 55 132 0 4 2 1 2 73 62 73 69 66
## 56 15 0 1 3 1 3 39 39 44 26 42
## 57 67 0 4 1 1 3 37 37 42 33 32
## 58 22 0 1 2 1 3 42 39 39 56 46
## 59 185 0 4 2 2 2 63 57 55 58 41
## 60 9 0 1 2 1 3 48 49 52 44 51
## 61 181 0 4 2 2 2 50 46 45 58 61
## 62 170 0 4 3 1 2 47 62 61 69 66
## 63 134 0 4 1 1 1 44 44 39 34 46
## 64 108 0 4 2 1 1 34 33 41 36 36
## 65 197 0 4 3 2 2 50 42 50 36 61
## 66 140 0 4 2 1 3 44 41 40 50 26
## 67 171 0 4 2 1 2 60 54 60 55 66
## 68 107 0 4 1 1 3 47 39 47 42 26
## 69 81 0 4 1 1 2 63 43 59 65 44
## 70 18 0 1 2 1 3 50 33 49 44 36
## 71 155 0 4 2 1 1 44 44 46 39 51
## 72 97 0 4 3 1 2 60 54 58 58 61
## 73 68 0 4 2 1 2 73 67 71 63 66
## 74 157 0 4 2 1 1 68 59 58 74 66
## 75 56 0 4 2 1 3 55 45 46 58 51
## 76 5 0 1 1 1 2 47 40 43 45 31
## 77 159 0 4 3 1 2 55 61 54 49 61
## 78 123 0 4 3 1 1 68 59 56 63 66
## 79 164 0 4 2 1 3 31 36 46 39 46
## 80 14 0 1 3 1 2 47 41 54 42 56
## 81 127 0 4 3 1 2 63 59 57 55 56
## 82 165 0 4 1 1 3 36 49 54 61 36
## 83 174 0 4 2 2 2 68 59 71 66 56
## 84 3 0 1 1 1 2 63 65 48 63 56
## 85 58 0 4 2 1 3 55 41 40 44 41
## 86 146 0 4 3 1 2 55 62 64 63 66
## 87 102 0 4 3 1 2 52 41 51 53 56
## 88 117 0 4 3 1 3 34 49 39 42 56
## 89 133 0 4 2 1 3 50 31 40 34 31
## 90 94 0 4 3 1 2 55 49 61 61 56
## 91 24 0 2 2 1 2 52 62 66 47 46
## 92 149 0 4 1 1 1 63 49 49 66 46
## 93 82 1 4 3 1 2 68 62 65 69 61
## 94 8 1 1 1 1 2 39 44 52 44 48
## 95 129 1 4 1 1 1 44 44 46 47 51
## 96 173 1 4 1 1 1 50 62 61 63 51
## 97 57 1 4 2 1 2 71 65 72 66 56
## 98 100 1 4 3 1 2 63 65 71 69 71
## 99 1 1 1 1 1 3 34 44 40 39 41
## 100 194 1 4 3 2 2 63 63 69 61 61
## 101 88 1 4 3 1 2 68 60 64 69 66
## 102 99 1 4 3 1 1 47 59 56 66 61
## 103 47 1 3 1 1 2 47 46 49 33 41
## 104 120 1 4 3 1 2 63 52 54 50 51
## 105 166 1 4 2 1 2 52 59 53 61 51
## 106 65 1 4 2 1 2 55 54 66 42 56
## 107 101 1 4 3 1 2 60 62 67 50 56
## 108 89 1 4 1 1 3 35 35 40 51 33
## 109 54 1 3 1 2 1 47 54 46 50 56
## 110 180 1 4 3 2 2 71 65 69 58 71
## 111 162 1 4 2 1 3 57 52 40 61 56
## 112 4 1 1 1 1 2 44 50 41 39 51
## 113 131 1 4 3 1 2 65 59 57 46 66
## 114 125 1 4 1 1 2 68 65 58 59 56
## 115 34 1 1 3 2 2 73 61 57 55 66
## 116 106 1 4 2 1 3 36 44 37 42 41
## 117 130 1 4 3 1 1 43 54 55 55 46
## 118 93 1 4 3 1 2 73 67 62 58 66
## 119 163 1 4 1 1 2 52 57 64 58 56
## 120 37 1 3 1 1 3 41 47 40 39 51
## 121 35 1 1 1 2 1 60 54 50 50 51
## 122 87 1 4 2 1 1 50 52 46 50 56
## 123 73 1 4 2 1 2 50 52 53 39 56
## 124 151 1 4 2 1 3 47 46 52 48 46
## 125 44 1 3 1 1 3 47 62 45 34 46
## 126 152 1 4 3 1 2 55 57 56 58 61
## 127 105 1 4 2 1 2 50 41 45 44 56
## 128 28 1 2 2 1 1 39 53 54 50 41
## 129 91 1 4 3 1 3 50 49 56 47 46
## 130 45 1 3 1 1 3 34 35 41 29 26
## 131 116 1 4 2 1 2 57 59 54 50 56
## 132 33 1 2 1 1 2 57 65 72 54 56
## 133 66 1 4 2 1 3 68 62 56 50 51
## 134 72 1 4 2 1 3 42 54 47 47 46
## 135 77 1 4 1 1 2 61 59 49 44 66
## 136 61 1 4 3 1 2 76 63 60 67 66
## 137 190 1 4 2 2 2 47 59 54 58 46
## 138 42 1 3 2 1 3 46 52 55 44 56
## 139 2 1 1 2 1 3 39 41 33 42 41
## 140 55 1 3 2 2 2 52 49 49 44 61
## 141 19 1 1 1 1 1 28 46 43 44 51
## 142 90 1 4 3 1 2 42 54 50 50 52
## 143 142 1 4 2 1 3 47 42 52 39 51
## 144 17 1 1 2 1 2 47 57 48 44 41
## 145 122 1 4 2 1 2 52 59 58 53 66
## 146 191 1 4 3 2 2 47 52 43 48 61
## 147 83 1 4 2 1 3 50 62 41 55 31
## 148 182 1 4 2 2 2 44 52 43 44 51
## 149 6 1 1 1 1 2 47 41 46 40 41
## 150 46 1 3 1 1 2 45 55 44 34 41
## 151 43 1 3 1 1 2 47 37 43 42 46
## 152 96 1 4 3 1 2 65 54 61 58 56
## 153 138 1 4 2 1 3 43 57 40 50 51
## 154 10 1 1 2 1 1 47 54 49 53 61
## 155 71 1 4 2 1 1 57 62 56 58 66
## 156 139 1 4 2 1 2 68 59 61 55 71
## 157 110 1 4 2 1 3 52 55 50 54 61
## 158 148 1 4 2 1 3 42 57 51 47 61
## 159 109 1 4 2 1 1 42 39 42 42 41
## 160 39 1 3 3 1 2 66 67 67 61 66
## 161 147 1 4 1 1 2 47 62 53 53 61
## 162 74 1 4 2 1 2 57 50 50 51 58
## 163 198 1 4 3 2 2 47 61 51 63 31
## 164 161 1 4 1 1 2 57 62 72 61 61
## 165 112 1 4 2 1 2 52 59 48 55 61
## 166 69 1 4 1 1 3 44 44 40 40 31
## 167 156 1 4 2 1 2 50 59 53 61 61
## 168 111 1 4 1 1 1 39 54 39 47 36
## 169 186 1 4 2 2 2 57 62 63 55 41
## 170 98 1 4 1 1 3 57 60 51 53 37
## 171 119 1 4 1 1 1 42 57 45 50 43
## 172 13 1 1 2 1 3 47 46 39 47 61
## 173 51 1 3 3 1 1 42 36 42 31 39
## 174 26 1 2 3 1 2 60 59 62 61 51
## 175 36 1 3 1 1 1 44 49 44 35 51
## 176 135 1 4 1 1 2 63 60 65 54 66
## 177 59 1 4 2 1 2 65 67 63 55 71
## 178 78 1 4 2 1 2 39 54 54 53 41
## 179 64 1 4 3 1 3 50 52 45 58 36
## 180 63 1 4 1 1 1 52 65 60 56 51
## 181 79 1 4 2 1 2 60 62 49 50 51
## 182 193 1 4 2 2 2 44 49 48 39 51
## 183 92 1 4 3 1 1 52 67 57 63 61
## 184 160 1 4 2 1 2 55 65 55 50 61
## 185 32 1 2 3 1 3 50 67 66 66 56
## 186 23 1 2 1 1 2 65 65 64 58 71
## 187 158 1 4 2 1 1 52 54 55 53 51
## 188 25 1 2 2 1 1 47 44 42 42 36
## 189 188 1 4 3 2 2 63 62 56 55 61
## 190 52 1 3 1 1 2 50 46 53 53 66
## 191 124 1 4 1 1 3 42 54 41 42 41
## 192 175 1 4 3 2 1 36 57 42 50 41
## 193 184 1 4 2 2 3 50 52 53 55 56
## 194 30 1 2 3 1 2 41 59 42 34 51
## 195 179 1 4 2 2 2 47 65 60 50 56
## 196 31 1 2 2 2 1 55 59 52 42 56
## 197 145 1 4 2 1 3 42 46 38 36 46
## 198 187 1 4 2 2 1 57 41 57 55 52
## 199 118 1 4 2 1 1 55 62 58 58 61
## 200 137 1 4 3 1 2 63 65 65 53 61
# Remarks
# hsb2 dataset consists of 200 selected random samples from senior
# high school students in the US.
# We want to compare the student performance across different subjects
# change data layout by grouping different subjects
# into one column using melt() command. Install first reshape2 package
#install.packages("reshape2")
library(reshape2)
(hsb2_long <- melt(hsb2_wide, measure.vars = c("read","write","math","science","socst")))
## id female race ses schtyp prog variable value
## 1 70 0 4 1 1 1 read 57
## 2 121 1 4 2 1 3 read 68
## 3 86 0 4 3 1 1 read 44
## 4 141 0 4 3 1 3 read 63
## 5 172 0 4 2 1 2 read 47
## 6 113 0 4 2 1 2 read 44
## 7 50 0 3 2 1 1 read 50
## 8 11 0 1 2 1 2 read 34
## 9 84 0 4 2 1 1 read 63
## 10 48 0 3 2 1 2 read 57
## 11 75 0 4 2 1 3 read 60
## 12 60 0 4 2 1 2 read 57
## 13 95 0 4 3 1 2 read 73
## 14 104 0 4 3 1 2 read 54
## 15 38 0 3 1 1 2 read 45
## 16 115 0 4 1 1 1 read 42
## 17 76 0 4 3 1 2 read 47
## 18 195 0 4 2 2 1 read 57
## 19 114 0 4 3 1 2 read 68
## 20 85 0 4 2 1 1 read 55
## 21 167 0 4 2 1 1 read 63
## 22 143 0 4 2 1 3 read 63
## 23 41 0 3 2 1 2 read 50
## 24 20 0 1 3 1 2 read 60
## 25 12 0 1 2 1 3 read 37
## 26 53 0 3 2 1 3 read 34
## 27 154 0 4 3 1 2 read 65
## 28 178 0 4 2 2 3 read 47
## 29 196 0 4 3 2 2 read 44
## 30 29 0 2 1 1 1 read 52
## 31 126 0 4 2 1 1 read 42
## 32 103 0 4 3 1 2 read 76
## 33 192 0 4 3 2 2 read 65
## 34 150 0 4 2 1 3 read 42
## 35 199 0 4 3 2 2 read 52
## 36 144 0 4 3 1 1 read 60
## 37 200 0 4 2 2 2 read 68
## 38 80 0 4 3 1 2 read 65
## 39 16 0 1 1 1 3 read 47
## 40 153 0 4 2 1 3 read 39
## 41 176 0 4 2 2 2 read 47
## 42 177 0 4 2 2 2 read 55
## 43 168 0 4 2 1 2 read 52
## 44 40 0 3 1 1 1 read 42
## 45 62 0 4 3 1 1 read 65
## 46 169 0 4 1 1 1 read 55
## 47 49 0 3 3 1 3 read 50
## 48 136 0 4 2 1 2 read 65
## 49 189 0 4 2 2 2 read 47
## 50 7 0 1 2 1 2 read 57
## 51 27 0 2 2 1 2 read 53
## 52 128 0 4 3 1 2 read 39
## 53 21 0 1 2 1 1 read 44
## 54 183 0 4 2 2 2 read 63
## 55 132 0 4 2 1 2 read 73
## 56 15 0 1 3 1 3 read 39
## 57 67 0 4 1 1 3 read 37
## 58 22 0 1 2 1 3 read 42
## 59 185 0 4 2 2 2 read 63
## 60 9 0 1 2 1 3 read 48
## 61 181 0 4 2 2 2 read 50
## 62 170 0 4 3 1 2 read 47
## 63 134 0 4 1 1 1 read 44
## 64 108 0 4 2 1 1 read 34
## 65 197 0 4 3 2 2 read 50
## 66 140 0 4 2 1 3 read 44
## 67 171 0 4 2 1 2 read 60
## 68 107 0 4 1 1 3 read 47
## 69 81 0 4 1 1 2 read 63
## 70 18 0 1 2 1 3 read 50
## 71 155 0 4 2 1 1 read 44
## 72 97 0 4 3 1 2 read 60
## 73 68 0 4 2 1 2 read 73
## 74 157 0 4 2 1 1 read 68
## 75 56 0 4 2 1 3 read 55
## 76 5 0 1 1 1 2 read 47
## 77 159 0 4 3 1 2 read 55
## 78 123 0 4 3 1 1 read 68
## 79 164 0 4 2 1 3 read 31
## 80 14 0 1 3 1 2 read 47
## 81 127 0 4 3 1 2 read 63
## 82 165 0 4 1 1 3 read 36
## 83 174 0 4 2 2 2 read 68
## 84 3 0 1 1 1 2 read 63
## 85 58 0 4 2 1 3 read 55
## 86 146 0 4 3 1 2 read 55
## 87 102 0 4 3 1 2 read 52
## 88 117 0 4 3 1 3 read 34
## 89 133 0 4 2 1 3 read 50
## 90 94 0 4 3 1 2 read 55
## 91 24 0 2 2 1 2 read 52
## 92 149 0 4 1 1 1 read 63
## 93 82 1 4 3 1 2 read 68
## 94 8 1 1 1 1 2 read 39
## 95 129 1 4 1 1 1 read 44
## 96 173 1 4 1 1 1 read 50
## 97 57 1 4 2 1 2 read 71
## 98 100 1 4 3 1 2 read 63
## 99 1 1 1 1 1 3 read 34
## 100 194 1 4 3 2 2 read 63
## 101 88 1 4 3 1 2 read 68
## 102 99 1 4 3 1 1 read 47
## 103 47 1 3 1 1 2 read 47
## 104 120 1 4 3 1 2 read 63
## 105 166 1 4 2 1 2 read 52
## 106 65 1 4 2 1 2 read 55
## 107 101 1 4 3 1 2 read 60
## 108 89 1 4 1 1 3 read 35
## 109 54 1 3 1 2 1 read 47
## 110 180 1 4 3 2 2 read 71
## 111 162 1 4 2 1 3 read 57
## 112 4 1 1 1 1 2 read 44
## 113 131 1 4 3 1 2 read 65
## 114 125 1 4 1 1 2 read 68
## 115 34 1 1 3 2 2 read 73
## 116 106 1 4 2 1 3 read 36
## 117 130 1 4 3 1 1 read 43
## 118 93 1 4 3 1 2 read 73
## 119 163 1 4 1 1 2 read 52
## 120 37 1 3 1 1 3 read 41
## 121 35 1 1 1 2 1 read 60
## 122 87 1 4 2 1 1 read 50
## 123 73 1 4 2 1 2 read 50
## 124 151 1 4 2 1 3 read 47
## 125 44 1 3 1 1 3 read 47
## 126 152 1 4 3 1 2 read 55
## 127 105 1 4 2 1 2 read 50
## 128 28 1 2 2 1 1 read 39
## 129 91 1 4 3 1 3 read 50
## 130 45 1 3 1 1 3 read 34
## 131 116 1 4 2 1 2 read 57
## 132 33 1 2 1 1 2 read 57
## 133 66 1 4 2 1 3 read 68
## 134 72 1 4 2 1 3 read 42
## 135 77 1 4 1 1 2 read 61
## 136 61 1 4 3 1 2 read 76
## 137 190 1 4 2 2 2 read 47
## 138 42 1 3 2 1 3 read 46
## 139 2 1 1 2 1 3 read 39
## 140 55 1 3 2 2 2 read 52
## 141 19 1 1 1 1 1 read 28
## 142 90 1 4 3 1 2 read 42
## 143 142 1 4 2 1 3 read 47
## 144 17 1 1 2 1 2 read 47
## 145 122 1 4 2 1 2 read 52
## 146 191 1 4 3 2 2 read 47
## 147 83 1 4 2 1 3 read 50
## 148 182 1 4 2 2 2 read 44
## 149 6 1 1 1 1 2 read 47
## 150 46 1 3 1 1 2 read 45
## 151 43 1 3 1 1 2 read 47
## 152 96 1 4 3 1 2 read 65
## 153 138 1 4 2 1 3 read 43
## 154 10 1 1 2 1 1 read 47
## 155 71 1 4 2 1 1 read 57
## 156 139 1 4 2 1 2 read 68
## 157 110 1 4 2 1 3 read 52
## 158 148 1 4 2 1 3 read 42
## 159 109 1 4 2 1 1 read 42
## 160 39 1 3 3 1 2 read 66
## 161 147 1 4 1 1 2 read 47
## 162 74 1 4 2 1 2 read 57
## 163 198 1 4 3 2 2 read 47
## 164 161 1 4 1 1 2 read 57
## 165 112 1 4 2 1 2 read 52
## 166 69 1 4 1 1 3 read 44
## 167 156 1 4 2 1 2 read 50
## 168 111 1 4 1 1 1 read 39
## 169 186 1 4 2 2 2 read 57
## 170 98 1 4 1 1 3 read 57
## 171 119 1 4 1 1 1 read 42
## 172 13 1 1 2 1 3 read 47
## 173 51 1 3 3 1 1 read 42
## 174 26 1 2 3 1 2 read 60
## 175 36 1 3 1 1 1 read 44
## 176 135 1 4 1 1 2 read 63
## 177 59 1 4 2 1 2 read 65
## 178 78 1 4 2 1 2 read 39
## 179 64 1 4 3 1 3 read 50
## 180 63 1 4 1 1 1 read 52
## 181 79 1 4 2 1 2 read 60
## 182 193 1 4 2 2 2 read 44
## 183 92 1 4 3 1 1 read 52
## 184 160 1 4 2 1 2 read 55
## 185 32 1 2 3 1 3 read 50
## 186 23 1 2 1 1 2 read 65
## 187 158 1 4 2 1 1 read 52
## 188 25 1 2 2 1 1 read 47
## 189 188 1 4 3 2 2 read 63
## 190 52 1 3 1 1 2 read 50
## 191 124 1 4 1 1 3 read 42
## 192 175 1 4 3 2 1 read 36
## 193 184 1 4 2 2 3 read 50
## 194 30 1 2 3 1 2 read 41
## 195 179 1 4 2 2 2 read 47
## 196 31 1 2 2 2 1 read 55
## 197 145 1 4 2 1 3 read 42
## 198 187 1 4 2 2 1 read 57
## 199 118 1 4 2 1 1 read 55
## 200 137 1 4 3 1 2 read 63
## 201 70 0 4 1 1 1 write 52
## 202 121 1 4 2 1 3 write 59
## 203 86 0 4 3 1 1 write 33
## 204 141 0 4 3 1 3 write 44
## 205 172 0 4 2 1 2 write 52
## 206 113 0 4 2 1 2 write 52
## 207 50 0 3 2 1 1 write 59
## 208 11 0 1 2 1 2 write 46
## 209 84 0 4 2 1 1 write 57
## 210 48 0 3 2 1 2 write 55
## 211 75 0 4 2 1 3 write 46
## 212 60 0 4 2 1 2 write 65
## 213 95 0 4 3 1 2 write 60
## 214 104 0 4 3 1 2 write 63
## 215 38 0 3 1 1 2 write 57
## 216 115 0 4 1 1 1 write 49
## 217 76 0 4 3 1 2 write 52
## 218 195 0 4 2 2 1 write 57
## 219 114 0 4 3 1 2 write 65
## 220 85 0 4 2 1 1 write 39
## 221 167 0 4 2 1 1 write 49
## 222 143 0 4 2 1 3 write 63
## 223 41 0 3 2 1 2 write 40
## 224 20 0 1 3 1 2 write 52
## 225 12 0 1 2 1 3 write 44
## 226 53 0 3 2 1 3 write 37
## 227 154 0 4 3 1 2 write 65
## 228 178 0 4 2 2 3 write 57
## 229 196 0 4 3 2 2 write 38
## 230 29 0 2 1 1 1 write 44
## 231 126 0 4 2 1 1 write 31
## 232 103 0 4 3 1 2 write 52
## 233 192 0 4 3 2 2 write 67
## 234 150 0 4 2 1 3 write 41
## 235 199 0 4 3 2 2 write 59
## 236 144 0 4 3 1 1 write 65
## 237 200 0 4 2 2 2 write 54
## 238 80 0 4 3 1 2 write 62
## 239 16 0 1 1 1 3 write 31
## 240 153 0 4 2 1 3 write 31
## 241 176 0 4 2 2 2 write 47
## 242 177 0 4 2 2 2 write 59
## 243 168 0 4 2 1 2 write 54
## 244 40 0 3 1 1 1 write 41
## 245 62 0 4 3 1 1 write 65
## 246 169 0 4 1 1 1 write 59
## 247 49 0 3 3 1 3 write 40
## 248 136 0 4 2 1 2 write 59
## 249 189 0 4 2 2 2 write 59
## 250 7 0 1 2 1 2 write 54
## 251 27 0 2 2 1 2 write 61
## 252 128 0 4 3 1 2 write 33
## 253 21 0 1 2 1 1 write 44
## 254 183 0 4 2 2 2 write 59
## 255 132 0 4 2 1 2 write 62
## 256 15 0 1 3 1 3 write 39
## 257 67 0 4 1 1 3 write 37
## 258 22 0 1 2 1 3 write 39
## 259 185 0 4 2 2 2 write 57
## 260 9 0 1 2 1 3 write 49
## 261 181 0 4 2 2 2 write 46
## 262 170 0 4 3 1 2 write 62
## 263 134 0 4 1 1 1 write 44
## 264 108 0 4 2 1 1 write 33
## 265 197 0 4 3 2 2 write 42
## 266 140 0 4 2 1 3 write 41
## 267 171 0 4 2 1 2 write 54
## 268 107 0 4 1 1 3 write 39
## 269 81 0 4 1 1 2 write 43
## 270 18 0 1 2 1 3 write 33
## 271 155 0 4 2 1 1 write 44
## 272 97 0 4 3 1 2 write 54
## 273 68 0 4 2 1 2 write 67
## 274 157 0 4 2 1 1 write 59
## 275 56 0 4 2 1 3 write 45
## 276 5 0 1 1 1 2 write 40
## 277 159 0 4 3 1 2 write 61
## 278 123 0 4 3 1 1 write 59
## 279 164 0 4 2 1 3 write 36
## 280 14 0 1 3 1 2 write 41
## 281 127 0 4 3 1 2 write 59
## 282 165 0 4 1 1 3 write 49
## 283 174 0 4 2 2 2 write 59
## 284 3 0 1 1 1 2 write 65
## 285 58 0 4 2 1 3 write 41
## 286 146 0 4 3 1 2 write 62
## 287 102 0 4 3 1 2 write 41
## 288 117 0 4 3 1 3 write 49
## 289 133 0 4 2 1 3 write 31
## 290 94 0 4 3 1 2 write 49
## 291 24 0 2 2 1 2 write 62
## 292 149 0 4 1 1 1 write 49
## 293 82 1 4 3 1 2 write 62
## 294 8 1 1 1 1 2 write 44
## 295 129 1 4 1 1 1 write 44
## 296 173 1 4 1 1 1 write 62
## 297 57 1 4 2 1 2 write 65
## 298 100 1 4 3 1 2 write 65
## 299 1 1 1 1 1 3 write 44
## 300 194 1 4 3 2 2 write 63
## 301 88 1 4 3 1 2 write 60
## 302 99 1 4 3 1 1 write 59
## 303 47 1 3 1 1 2 write 46
## 304 120 1 4 3 1 2 write 52
## 305 166 1 4 2 1 2 write 59
## 306 65 1 4 2 1 2 write 54
## 307 101 1 4 3 1 2 write 62
## 308 89 1 4 1 1 3 write 35
## 309 54 1 3 1 2 1 write 54
## 310 180 1 4 3 2 2 write 65
## 311 162 1 4 2 1 3 write 52
## 312 4 1 1 1 1 2 write 50
## 313 131 1 4 3 1 2 write 59
## 314 125 1 4 1 1 2 write 65
## 315 34 1 1 3 2 2 write 61
## 316 106 1 4 2 1 3 write 44
## 317 130 1 4 3 1 1 write 54
## 318 93 1 4 3 1 2 write 67
## 319 163 1 4 1 1 2 write 57
## 320 37 1 3 1 1 3 write 47
## 321 35 1 1 1 2 1 write 54
## 322 87 1 4 2 1 1 write 52
## 323 73 1 4 2 1 2 write 52
## 324 151 1 4 2 1 3 write 46
## 325 44 1 3 1 1 3 write 62
## 326 152 1 4 3 1 2 write 57
## 327 105 1 4 2 1 2 write 41
## 328 28 1 2 2 1 1 write 53
## 329 91 1 4 3 1 3 write 49
## 330 45 1 3 1 1 3 write 35
## 331 116 1 4 2 1 2 write 59
## 332 33 1 2 1 1 2 write 65
## 333 66 1 4 2 1 3 write 62
## 334 72 1 4 2 1 3 write 54
## 335 77 1 4 1 1 2 write 59
## 336 61 1 4 3 1 2 write 63
## 337 190 1 4 2 2 2 write 59
## 338 42 1 3 2 1 3 write 52
## 339 2 1 1 2 1 3 write 41
## 340 55 1 3 2 2 2 write 49
## 341 19 1 1 1 1 1 write 46
## 342 90 1 4 3 1 2 write 54
## 343 142 1 4 2 1 3 write 42
## 344 17 1 1 2 1 2 write 57
## 345 122 1 4 2 1 2 write 59
## 346 191 1 4 3 2 2 write 52
## 347 83 1 4 2 1 3 write 62
## 348 182 1 4 2 2 2 write 52
## 349 6 1 1 1 1 2 write 41
## 350 46 1 3 1 1 2 write 55
## 351 43 1 3 1 1 2 write 37
## 352 96 1 4 3 1 2 write 54
## 353 138 1 4 2 1 3 write 57
## 354 10 1 1 2 1 1 write 54
## 355 71 1 4 2 1 1 write 62
## 356 139 1 4 2 1 2 write 59
## 357 110 1 4 2 1 3 write 55
## 358 148 1 4 2 1 3 write 57
## 359 109 1 4 2 1 1 write 39
## 360 39 1 3 3 1 2 write 67
## 361 147 1 4 1 1 2 write 62
## 362 74 1 4 2 1 2 write 50
## 363 198 1 4 3 2 2 write 61
## 364 161 1 4 1 1 2 write 62
## 365 112 1 4 2 1 2 write 59
## 366 69 1 4 1 1 3 write 44
## 367 156 1 4 2 1 2 write 59
## 368 111 1 4 1 1 1 write 54
## 369 186 1 4 2 2 2 write 62
## 370 98 1 4 1 1 3 write 60
## 371 119 1 4 1 1 1 write 57
## 372 13 1 1 2 1 3 write 46
## 373 51 1 3 3 1 1 write 36
## 374 26 1 2 3 1 2 write 59
## 375 36 1 3 1 1 1 write 49
## 376 135 1 4 1 1 2 write 60
## 377 59 1 4 2 1 2 write 67
## 378 78 1 4 2 1 2 write 54
## 379 64 1 4 3 1 3 write 52
## 380 63 1 4 1 1 1 write 65
## 381 79 1 4 2 1 2 write 62
## 382 193 1 4 2 2 2 write 49
## 383 92 1 4 3 1 1 write 67
## 384 160 1 4 2 1 2 write 65
## 385 32 1 2 3 1 3 write 67
## 386 23 1 2 1 1 2 write 65
## 387 158 1 4 2 1 1 write 54
## 388 25 1 2 2 1 1 write 44
## 389 188 1 4 3 2 2 write 62
## 390 52 1 3 1 1 2 write 46
## 391 124 1 4 1 1 3 write 54
## 392 175 1 4 3 2 1 write 57
## 393 184 1 4 2 2 3 write 52
## 394 30 1 2 3 1 2 write 59
## 395 179 1 4 2 2 2 write 65
## 396 31 1 2 2 2 1 write 59
## 397 145 1 4 2 1 3 write 46
## 398 187 1 4 2 2 1 write 41
## 399 118 1 4 2 1 1 write 62
## 400 137 1 4 3 1 2 write 65
## 401 70 0 4 1 1 1 math 41
## 402 121 1 4 2 1 3 math 53
## 403 86 0 4 3 1 1 math 54
## 404 141 0 4 3 1 3 math 47
## 405 172 0 4 2 1 2 math 57
## 406 113 0 4 2 1 2 math 51
## 407 50 0 3 2 1 1 math 42
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## 410 48 0 3 2 1 2 math 52
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## 412 60 0 4 2 1 2 math 51
## 413 95 0 4 3 1 2 math 71
## 414 104 0 4 3 1 2 math 57
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## 422 143 0 4 2 1 3 math 75
## 423 41 0 3 2 1 2 math 45
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## 426 53 0 3 2 1 3 math 46
## 427 154 0 4 3 1 2 math 66
## 428 178 0 4 2 2 3 math 57
## 429 196 0 4 3 2 2 math 49
## 430 29 0 2 1 1 1 math 49
## 431 126 0 4 2 1 1 math 57
## 432 103 0 4 3 1 2 math 64
## 433 192 0 4 3 2 2 math 63
## 434 150 0 4 2 1 3 math 57
## 435 199 0 4 3 2 2 math 50
## 436 144 0 4 3 1 1 math 58
## 437 200 0 4 2 2 2 math 75
## 438 80 0 4 3 1 2 math 68
## 439 16 0 1 1 1 3 math 44
## 440 153 0 4 2 1 3 math 40
## 441 176 0 4 2 2 2 math 41
## 442 177 0 4 2 2 2 math 62
## 443 168 0 4 2 1 2 math 57
## 444 40 0 3 1 1 1 math 43
## 445 62 0 4 3 1 1 math 48
## 446 169 0 4 1 1 1 math 63
## 447 49 0 3 3 1 3 math 39
## 448 136 0 4 2 1 2 math 70
## 449 189 0 4 2 2 2 math 63
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## 455 132 0 4 2 1 2 math 73
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## 488 117 0 4 3 1 3 math 39
## 489 133 0 4 2 1 3 math 40
## 490 94 0 4 3 1 2 math 61
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## 493 82 1 4 3 1 2 math 65
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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
## 501 88 1 4 3 1 2 math 64
## 502 99 1 4 3 1 1 math 56
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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
#install.packages("gplots")
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] -1.025398453 -1.474182426 -1.476609694 0.150203991 1.299979272
## [6] -0.289438902 0.476283231 -0.822720954 -0.050076762 1.400693259
## [11] -0.587085215 1.449962369 3.202953153 0.828407651 -1.817114685
## [16] 1.168017813 1.576217857 -1.158445183 0.415547760 0.626267811
## [21] 0.100334576 1.622122232 0.362834337 1.719582828 0.724655967
## [26] 1.218712499 -0.869032104 -0.433728359 0.926386690 0.979356297
## [31] -1.531793787 -1.229055370 -0.179304566 0.341271430 0.053262437
## [36] 0.034597955 1.651271088 1.545020597 1.193734487 -0.521850821
## [41] -0.242081948 0.916736269 -0.058137036 0.435942549 0.789987562
## [46] -1.964119101 -1.668699280 -0.059867318 -1.227650661 -0.760676392
## [51] 2.534390668 -1.520670082 2.541771299 0.753743863 0.424884140
## [56] 0.337364795 0.155694148 -0.920881763 0.655195052 -0.122000348
## [61] -0.812247127 -0.313845656 -1.531035614 -0.719900928 -0.377205875
## [66] -0.203321327 -0.512975276 -0.287021520 0.260954754 -0.221538146
## [71] 0.180409384 1.732923576 -0.630092150 -0.620791203 -1.339640202
## [76] -0.308525700 -1.171149606 -1.971037778 -0.759309682 -0.514345997
## [81] 1.611440549 -0.857661440 -0.474021781 0.332704680 0.584064605
## [86] 1.018917408 -0.429538657 -1.187963664 -0.309268626 -0.597994186
## [91] 2.016998701 -0.754257439 0.462811901 0.639630648 0.840011437
## [96] -1.044551467 0.520873199 -2.023256630 -0.527211057 -0.678838666
## [101] 0.536009709 2.153183764 -0.885061932 -0.683061476 -0.570523359
## [106] -0.040096371 -0.880402898 1.508726920 -0.130283034 -0.450010850
## [111] 0.267407582 0.309327277 0.330654652 -0.118997868 0.529072306
## [116] -2.055496071 0.113443457 -0.963867305 -1.703536803 -0.248746036
## [121] -1.824324589 0.036433295 1.782860721 0.799837374 1.314775058
## [126] -0.321769313 -0.082057231 -1.175023567 -0.085828159 -1.611410878
## [131] -1.408999824 -0.187997262 -0.324178507 1.305311841 -0.620672035
## [136] -1.438240461 -0.171825371 -2.008098696 0.385190609 -0.287464711
## [141] -0.515117253 -0.029637083 0.773795380 -1.062080966 -0.157384731
## [146] -0.413196941 1.639929489 0.613677696 -0.143962036 -0.562589789
## [151] 0.936852061 0.556554726 -0.945371486 -2.125538379 0.805546647
## [156] -1.908240311 1.034460167 0.096313516 -0.703501230 1.219365681
## [161] 0.496109272 -0.389406826 -0.774962577 1.957232335 0.774577183
## [166] -0.737309377 0.728917954 1.127472610 0.033805409 -0.234743743
## [171] -0.001382401 -0.302367408 1.243002794 -2.916847584 0.987124576
## [176] 0.342593123 2.000639438 -0.686171009 -1.281725301 1.314423703
## [181] -0.231955141 -1.373960527 1.438462306 -0.264670696 1.362227200
## [186] 0.975961324 0.625337717 -1.031108144 0.347015747 -0.280042709
## [191] 0.186882987 1.451268694 -1.180001487 -0.744203407 1.148315316
## [196] 0.825217981 -1.058560886 0.450367063 1.345168048 -1.854383875
## [201] -1.311182081 -0.021931909 0.681514155 -0.065342232 0.178652950
## [206] 0.337094790 0.347459150 -1.045718778 -0.666954288 -1.510798064
## [211] 0.904312685 -0.368503331 1.253727629 0.283150947 1.188160026
## [216] -0.418936433 -0.118228066 0.584997594 -0.344060679 -0.540594392
## [221] 0.427997584 -0.152140866 -0.062382472 -1.296756831 1.296269686
## [226] 0.036453838 -0.234528002 0.359829512 -0.367261384 0.920984684
## [231] 0.045058319 1.428097503 -1.594350467 -0.399727574 0.422033471
## [236] 0.663645530 0.628681678 -0.150709698 0.989467899 0.207722515
## [241] -0.222830727 -0.034987751 -1.491640970 1.246811516 -1.021443198
## [246] 1.514296069 0.083475337 -1.617185939 0.714590992 -0.695359828
## [251] 0.215209696 0.728179435 1.035077795 -1.168581696 0.169314986
## [256] -1.725108385 -1.988714150 -1.557693586 -0.053780483 -1.177205451
## [261] 1.524264861 0.815901614 0.410312543 1.145218612 1.445678960
## [266] 0.924902680 0.578638346 0.028800149 -0.200945129 0.899084725
## [271] -0.513356695 -0.648363024 1.245295806 -1.107291465 -0.364000297
## [276] 0.977607056 -0.166264319 -0.558896134 0.846919703 -0.280191073
## [281] -0.182362884 1.212133385 0.935561871 1.570140108 0.515410759
## [286] -1.666691834 -0.275426261 1.080459060 1.806009293 1.820286365
## [291] 1.256613301 -1.457760307 -0.223283024 -0.373150025 -0.136204225
## [296] 0.168884535 1.305709467 -0.454454868 1.736631770 1.105645283
## [301] -0.108726028 -0.488338866 0.865048690 0.800780639 0.076066327
## [306] -1.183732292 -1.708872120 -0.296471353 1.131830782 0.354868892
## [311] 0.898645695 0.125340931 -2.227549011 -0.530288270 0.233166447
## [316] -0.360766089 1.703224560 -2.062890406 -0.676582940 -1.521002235
## [321] 1.449239080 0.504569268 0.650370831 -1.167354018 0.107219650
## [326] -0.307883061 -0.617081818 0.840392013 1.488485220 0.436284147
## [331] -0.354919922 -0.481946421 1.649505609 -0.049940678 0.420927126
## [336] -0.663007181 -0.578777243 -0.607313654 -1.156062398 -1.437798175
## [341] 1.637966524 0.098448107 -0.980470585 0.587792979 -1.644296295
## [346] -0.591282354 0.005866297 -0.203294803 0.100035947 -1.883588769
## [351] -2.815785886 0.542592114 -1.217038827 -1.692103850 0.945604161
## [356] -0.216058903 0.614514221 -1.132085143 -0.722923076 -1.517111352
## [361] 0.924553585 -1.218282342 -1.443391189 1.226810812 0.130052320
## [366] -0.267460101 0.650981082 -0.271229009 -0.347582211 1.237015450
## [371] 0.601978736 -0.193936989 0.960376221 -0.161965022 1.259897327
## [376] -1.260927464 -0.599194246 -0.659990773 0.070810541 -0.104210423
## [381] -0.483784575 -0.330455185 1.239072508 -0.637159903 0.355088793
## [386] 0.665811680 -1.955203310 0.676612472 -0.061803938 -0.381734693
## [391] 0.886771834 -0.504472783 -0.710472070 0.246567241 -0.652067101
## [396] -0.354775111 1.866840918 -1.694573004 -0.082270619 -0.334933949
## [401] 0.675210386 0.999829075 -0.620363775 -0.366700369 0.577952323
## [406] -2.134570142 -0.474189860 0.137352864 0.298338144 1.050344080
## [411] -0.272004763 -0.203452255 0.016154335 1.474504167 0.794387220
## [416] 1.290639250 -0.181572718 -2.194603297 0.286374081 -0.064819699
## [421] -0.697306532 -0.441594436 0.037686887 0.790281553 -1.044879708
## [426] 0.923329705 -1.726730213 -0.690642289 -1.076603787 -0.076994833
## [431] -0.459555181 0.741215629 -1.631928099 -0.017555218 -0.310768272
## [436] 0.327174042 -0.317997704 1.095326998 0.977108006 0.740608916
## [441] 0.837383042 1.086030618 -0.092909714 -0.154089554 -2.266190082
## [446] -1.485651303 0.970705772 -0.042872255 1.560133458 -0.827983425
## [451] 0.345976296 0.100685530 -0.930754257 -0.539912451 0.913441234
## [456] 0.959385078 0.932978120 -0.815028355 -1.038221113 1.623888931
## [461] -0.269445827 0.785691278 -0.974114116 0.452294894 -0.528140261
## [466] -1.072843483 -1.986044945 0.293166449 -2.110561285 -0.222152190
## [471] -0.320492842 -0.835703937 -1.140100547 0.079520714 1.979430614
## [476] -0.017223838 -0.772644948 2.583192902 -0.537401901 0.290569002
## [481] -1.064375738 -0.946616268 -0.157522944 1.935221606 1.470046907
## [486] -0.090713887 -0.388573822 0.180764158 0.706028126 -0.382015226
## [491] -0.312187295 -0.602646131 2.250427610 1.880351830 -0.752399273
## [496] -0.638476255 1.415735008 1.399047008 -0.375248595 -0.326794956
## [501] -0.427386903 -0.585046173 -0.173202309 0.802463172 1.116392928
## [506] 0.571790826 -0.953692393 2.131599928 -0.738309756 0.642385552
## [511] -0.547438905 -0.653724649 0.153731787 -0.760008009 -0.634123695
## [516] -0.699686181 -0.702315083 -0.601903202 -0.966356311 -0.322208820
## [521] 0.771981408 -0.698091585 1.564240897 2.228351991 -0.379477515
## [526] -1.385925625 2.009457315 -0.059095492 -0.072116363 -0.635411231
## [531] -0.320344343 0.155595018 -0.410059703 -0.523257191 0.443285192
## [536] -0.345684021 -1.240797541 -1.277232684 -0.345723332 -2.069675105
## [541] 2.598282266 0.070489845 -0.425834126 -0.473275084 0.425025184
## [546] -1.181646876 -0.147502591 0.612141443 0.129633455 0.587011083
## [551] 0.670344668 -1.590820500 -0.740804488 -0.335731583 0.811569756
## [556] 2.646376269 -0.716106468 -0.704560784 -0.393398139 -0.624625559
## [561] -0.186635346 -1.103105551 0.076325177 1.280375889 0.644376282
## [566] -0.323653074 -0.078954558 -1.235043441 -1.470902862 1.468350993
## [571] -0.419337460 0.602522549 0.635471149 1.045639848 1.317210941
## [576] 0.968742960 0.302289736 0.407622889 1.167373987 1.016536849
## [581] 0.037215197 2.147067227 -0.421030132 0.574223037 -0.006879004
## [586] 0.026751982 0.965770394 1.152558167 -0.427600180 -0.729086515
## [591] -0.190201549 -1.217124638 -0.315069807 0.863364076 -0.232419404
## [596] 1.110303241 -0.416095154 0.418129550 0.768506671 -0.659887020
## [601] -0.302840425 1.041568038 -1.920898292 0.418332074 -0.662244268
## [606] 0.841797245 0.229414573 -0.007957340 -0.962522194 -0.993274978
## [611] 0.793495095 0.199602421 0.118495469 -0.356114632 0.147170892
## [616] -0.031864063 0.729393290 -0.009391592 -0.541216606 1.492674679
## [621] 0.271719846 -0.186879807 -1.206332898 1.438773229 0.541839871
## [626] -0.812409627 -0.683376334 -0.164467566 -0.348911389 1.284590196
## [631] 0.346540833 1.333501707 -0.424511721 0.129034401 0.164488436
## [636] 0.190898770 2.016767746 -0.203487831 -0.064499465 -0.708326218
## [641] 0.391001350 -1.102752089 0.620302122 -1.474827391 -0.302916535
## [646] -0.446582078 -0.555013351 -0.626448394 -0.801063183 -0.399725068
## [651] -1.886780041 0.286589891 0.422855717 1.669178702 -0.843782759
## [656] 0.753207397 -0.198721236 -1.662982069 0.868330843 0.062508275
## [661] -0.657845876 0.246549075 -0.815470784 -1.120097958 -0.393847726
## [666] -0.086725208 -1.116124147 -0.251796997 -0.132397688 -0.968091474
## [671] 0.902035662 -0.039496750 0.550712387 -1.650242160 -1.199457041
## [676] 0.174651477 0.369545589 -2.450528219 0.253417140 -0.825409162
## [681] 0.865533846 0.369976246 0.536730101 0.212226120 0.317640891
## [686] -0.197343378 0.091280614 -1.057076762 1.311028669 0.599218405
## [691] -1.202380207 -0.275069918 2.447637081 -0.970942188 -0.307853951
## [696] -0.424936523 -0.777651067 -0.157815442 -0.015638610 0.344796398
## [701] -1.098734793 2.188826984 -0.340839365 -1.003929397 -0.952156528
## [706] -1.685803545 0.207515277 2.122020272 0.812267102 -0.976897648
## [711] -0.674146731 0.599162251 -0.127902553 1.874084232 2.205758521
## [716] 0.134056094 -0.408435022 -1.666191609 0.812939722 0.540005206
## [721] 0.535637988 -2.139501301 -0.504658475 1.269959450 -0.447303613
## [726] -1.290226348 0.430688485 -0.819529559 -0.223502924 0.155945906
## [731] 0.211738440 0.322645009 -0.167072757 -0.316582890 0.544526393
## [736] -1.040941413 -0.110521073 -0.906319646 0.876727511 -2.258022893
## [741] -1.974292410 1.360293869 -0.745309637 -0.834320663 -0.228723113
## [746] 0.527221495 1.883973441 0.039000788 0.272102983 -0.198307995
## [751] -0.230965690 -1.254079487 0.412216558 1.508010853 -0.446617384
## [756] -0.256279390 1.757075002 0.111358330 1.271273043 0.344114313
## [761] 1.489493723 -0.025334728 -0.225231768 -0.251068165 -0.147367340
## [766] -0.046375523 -1.791048077 0.585815420 0.173289045 1.352168045
## [771] -0.774622543 0.269699054 -1.280017534 -0.143945826 1.043294160
## [776] -0.637244579 0.331292313 -0.205555262 0.197942164 -0.144755011
## [781] -1.567377893 0.349275817 -0.102057614 -0.739579941 -0.818020987
## [786] -0.018225742 -0.787509332 -1.998497839 0.715802128 -0.976310140
## [791] 0.834488335 -1.138672005 -0.417563087 1.757147573 0.452822655
## [796] -1.123782517 0.073935649 0.559241726 -1.498881616 -0.754123703
## [801] 0.915027394 1.101895169 -1.445680289 0.998494077 -1.421970017
## [806] 0.322134715 1.019555618 -0.772307822 -0.459071710 0.765094202
## [811] -0.222170063 -1.090553602 -0.739048129 1.877098398 -0.430256091
## [816] -0.420032624 0.192543878 1.968727280 0.331970258 -0.217268689
## [821] 0.153695879 0.592456053 -0.158584113 -2.255460216 0.809000860
## [826] -0.732948377 0.050254346 -1.271035926 -0.359237134 -1.393080408
## [831] 0.395811440 2.244090383 0.920820082 0.961511722 -0.253955898
## [836] 1.080089088 0.021757972 0.335486303 -1.114402044 0.204573661
## [841] 0.829661772 0.191442102 -1.130505706 -0.810098722 0.101519415
## [846] 0.029682668 -1.203022078 -2.456608496 -1.594477321 0.964181941
## [851] -2.641707833 -0.086179755 0.595205066 -1.496061840 -0.241683189
## [856] 1.272932023 0.191122845 -1.707650975 0.390981387 -0.534403031
## [861] -0.879065507 -0.744608551 -1.153723780 -0.333160377 1.599143462
## [866] 0.457879930 -1.688516575 0.319037587 -1.928436008 0.732307842
## [871] 1.266100241 0.162448671 1.227958077 0.311682466 1.333801571
## [876] -0.498732652 0.762276213 -1.670750536 -0.157534252 -0.142537837
## [881] 1.241927214 0.499235415 0.119596542 0.925087743 -1.436604528
## [886] 2.257242910 1.320624155 0.732645699 -0.469818673 0.928640504
## [891] -0.426641902 0.152039828 0.693159572 -0.733481226 -0.065383026
## [896] 0.196742020 0.252880717 0.655638471 0.556802114 -1.699610570
## [901] -0.630787952 -0.509901618 -0.698046000 -0.977858849 -2.091188385
## [906] -0.192642427 -0.343357051 1.328006004 -0.438429185 -0.793350881
## [911] -1.642601953 1.027936932 1.012256648 0.547745954 0.476162783
## [916] 0.019633086 0.511622555 1.595100343 -1.256923820 -1.031708842
## [921] -0.095008453 0.810722831 2.099260629 0.732254396 0.148294608
## [926] 1.392323752 0.958625106 0.836797141 -1.472965234 -0.473168126
## [931] -0.591409041 -2.283720141 0.698450214 0.159836185 -0.336227550
## [936] -0.153879244 1.975890096 0.403467204 1.072636575 1.355725820
## [941] 0.607613446 0.908535698 0.291741783 -0.442813695 -0.220803884
## [946] -0.601581913 0.185875722 -0.480634828 -0.536093412 0.268631133
## [951] 0.896302630 -0.883735146 0.527615194 -0.517299762 1.099042567
## [956] -0.258662328 0.074268993 -0.854118537 0.059049483 1.242096957
## [961] 0.192878469 -0.580785052 -0.471748197 0.235144050 -1.589602557
## [966] -0.144913412 -0.621467133 -0.641274773 0.026794762 -1.409286710
## [971] 1.577205819 -0.586056840 1.554219814 -1.116401631 -0.924522178
## [976] 1.375423765 -0.778556377 -0.222592854 -0.015670314 -0.431301682
## [981] 0.222239121 -0.042214328 1.234167013 1.535339330 0.369920430
## [986] -0.190937232 -1.144833853 0.644723506 -0.249354702 -1.328098550
## [991] 1.922532262 0.214859389 -1.316236919 0.959188620 1.242625488
## [996] -1.111074265 1.687646622 0.563846279 1.634786067 0.740075601
yAxis <- rnorm(1000) + xAxis + 10
yAxis
## [1] 10.218951 9.765552 8.063624 11.307525 12.411353 10.123184 10.985069
## [8] 9.322369 7.799070 10.555441 8.396929 12.319558 10.678203 9.708382
## [15] 9.102500 13.182593 11.018043 8.958487 10.946939 9.750881 10.830008
## [22] 11.562347 7.792762 12.104715 10.160673 11.437493 11.447821 8.782731
## [29] 11.557595 11.256142 8.551305 9.550858 8.229614 10.065581 9.653125
## [36] 10.248710 12.085718 11.733991 12.449735 8.822752 8.925063 11.809449
## [43] 8.821935 9.939490 9.732519 8.947578 9.063998 10.418721 11.249104
## [50] 9.050816 12.437769 9.653869 11.375472 11.672862 9.903802 10.597984
## [57] 11.711777 7.926808 8.784194 10.712075 8.714797 9.835119 6.737335
## [64] 9.198953 8.344283 8.869362 9.747794 10.608292 8.929138 11.266172
## [71] 8.817787 12.403102 10.675990 9.786269 7.768979 10.761701 9.418079
## [78] 8.883633 9.306427 10.013132 11.446612 8.754994 9.747842 10.613429
## [85] 10.984164 11.643620 10.854527 10.709544 9.850680 9.326868 12.018265
## [92] 11.622430 10.428342 12.282033 11.028032 8.826413 9.938202 6.832172
## [99] 10.151624 9.341462 9.379063 12.959795 9.777015 10.236472 10.686720
## [106] 11.524792 9.530798 11.710877 10.134233 8.758560 9.895687 9.669975
## [113] 12.602021 9.956440 8.772177 6.047677 10.353285 7.969921 7.211032
## [120] 11.059351 7.340122 10.458222 11.462140 9.953527 11.781096 10.185861
## [127] 10.089767 8.676199 10.913178 8.215562 8.928038 9.702751 9.235861
## [134] 11.876492 8.895568 9.543477 9.022315 7.959959 10.127656 8.229318
## [141] 8.675376 9.956919 11.590231 9.242223 8.384576 10.482368 10.896381
## [148] 11.682029 10.316431 9.483306 10.854881 12.146913 7.865689 6.165426
## [155] 11.441384 8.173807 9.852708 9.139250 8.269547 11.508317 11.661506
## [162] 9.247942 8.273026 11.143613 9.770364 9.523257 11.116911 11.660247
## [169] 9.514774 10.687507 12.127056 9.018651 13.784616 5.054193 11.877269
## [176] 11.452439 11.018179 7.646665 11.336323 11.226661 10.531520 8.184018
## [183] 11.531680 10.408249 11.259858 11.861114 11.788587 9.086431 10.384363
## [190] 8.260233 9.227782 10.942817 10.711734 9.175089 10.240948 11.653212
## [197] 9.081989 10.304171 10.415347 8.347630 7.942835 9.199497 12.049587
## [204] 10.173557 12.301855 10.253258 10.520400 8.697857 9.538196 9.591106
## [211] 10.676262 12.040871 12.734765 10.600965 11.102280 9.963064 8.120887
## [218] 10.413589 10.099171 9.027799 10.315799 8.302895 9.143806 7.812587
## [225] 10.856575 10.053626 7.770800 8.760397 9.711484 11.345264 9.770105
## [232] 10.086699 7.045024 10.694734 11.661467 10.851126 11.974858 9.144714
## [239] 11.275497 8.481845 9.724552 11.377385 8.103106 12.327723 9.566184
## [246] 13.567741 11.964988 8.678974 11.340168 8.555210 11.049466 10.024174
## [253] 11.341156 7.567757 10.336745 8.472840 8.286600 7.673521 9.972867
## [260] 7.140949 10.506945 9.403708 9.605971 11.805868 10.325366 11.993302
## [267] 8.983476 10.736653 9.050441 11.398020 9.689793 9.809190 12.560799
## [274] 8.018350 7.175010 10.609520 9.028263 9.451884 11.009217 8.495049
## [281] 9.454714 10.094717 10.398992 12.445668 11.839824 7.527169 9.394286
## [288] 11.802845 11.413896 12.289665 11.146606 6.992299 9.803671 9.434675
## [295] 10.263378 11.350056 10.596315 7.863957 11.983051 11.987226 10.503908
## [302] 8.866519 12.123801 10.687986 11.018520 9.323650 7.776495 7.043665
## [309] 7.592064 8.628263 9.856469 9.781375 8.447621 10.534057 8.592446
## [316] 10.175169 11.816352 7.979435 10.035013 9.345380 10.533386 10.604764
## [323] 11.664049 11.874437 10.075663 8.239249 8.853594 13.027079 9.163605
## [330] 10.627207 8.889720 9.800261 12.031165 9.145214 11.388046 8.755848
## [337] 8.836544 9.438252 11.329523 7.278923 10.805065 11.339423 10.331689
## [344] 9.902063 5.989004 9.117231 9.629895 10.210534 8.406868 7.058690
## [351] 6.538703 10.290024 7.122438 8.009248 9.069763 8.728071 9.958895
## [358] 9.420900 9.882143 6.911203 10.381507 9.814893 8.929561 11.252969
## [365] 9.107579 10.730762 12.621412 9.691064 8.898820 12.725554 9.797143
## [372] 8.771979 11.064503 9.748617 11.381325 6.827354 9.026538 10.059815
## [379] 11.357759 9.296484 8.770270 7.526256 11.865251 9.183372 9.796806
## [386] 9.612973 7.106052 9.020777 11.120124 9.931049 12.699442 9.985740
## [393] 9.464949 10.670301 9.279923 9.561587 11.655299 7.657171 10.633889
## [400] 8.564727 11.033812 11.176761 11.180923 10.661126 10.274468 8.223907
## [407] 9.216062 10.021687 9.892078 10.676490 9.446951 8.963996 8.701315
## [414] 13.176413 10.715892 12.474396 9.019340 9.153294 8.287523 10.980791
## [421] 8.517939 10.093475 8.832135 10.657213 10.307450 11.014782 9.146858
## [428] 7.411147 10.169737 8.424481 10.088422 11.110064 9.010727 11.757814
## [435] 10.376495 8.966828 10.966545 11.525786 11.939405 12.774863 11.332314
## [442] 12.033659 7.267260 9.677861 7.031464 9.974060 10.216665 9.420184
## [449] 12.010169 9.909120 11.299820 9.124191 7.891521 8.439646 10.812022
## [456] 9.813312 10.475940 10.022631 8.665496 11.770125 10.091494 11.130652
## [463] 9.501823 11.534352 8.299168 8.796561 9.287105 9.479230 7.650553
## [470] 9.612083 9.382755 8.381605 8.583148 11.611663 12.833585 9.340124
## [477] 8.348881 13.139447 9.724121 11.520152 8.536433 10.040883 10.203649
## [484] 11.450699 11.746505 11.076188 8.416751 11.754492 12.631701 7.322829
## [491] 10.481559 8.767789 12.425178 12.138645 10.146594 10.281062 12.176081
## [498] 13.358545 11.670219 10.685109 8.939419 10.117022 9.332648 11.240309
## [505] 11.919399 10.854401 10.011349 11.699285 9.730068 8.052469 9.022649
## [512] 8.556193 11.346595 7.680039 8.897575 8.749215 9.665223 8.979601
## [519] 8.577528 9.693884 9.315864 9.034910 12.402032 13.668997 9.086480
## [526] 8.080440 11.101083 10.704566 9.624364 7.935938 9.690668 10.603410
## [533] 9.549709 9.420830 7.841338 10.207646 9.308791 8.405181 8.425021
## [540] 8.424610 11.983769 9.290327 10.351832 9.336707 12.744046 8.329163
## [547] 9.453051 11.225513 10.420111 9.839788 9.320001 7.569443 9.663102
## [554] 9.093244 12.938827 12.906930 10.409994 9.194042 11.056576 10.815208
## [561] 10.116605 6.878488 9.853685 9.718605 12.307271 9.011715 10.060335
## [568] 8.929953 7.868801 11.947935 10.108709 9.762364 11.509615 11.861815
## [575] 10.674255 9.747852 10.176322 11.587433 10.416996 11.474197 11.107541
## [582] 12.110311 10.218633 9.369105 8.644540 9.735501 10.719864 11.585687
## [589] 11.193202 10.136875 9.308427 9.407772 8.722140 11.771221 9.264235
## [596] 11.777252 11.143150 8.419394 9.602148 10.088258 7.248191 10.232601
## [603] 10.310103 10.511741 8.736266 11.205548 10.662922 8.911956 8.519441
## [610] 8.367729 9.947398 10.002117 12.491282 9.766374 10.670091 9.757212
## [617] 11.832336 11.257483 8.896175 10.923403 9.659536 9.152510 11.345125
## [624] 9.922215 10.592784 7.481802 6.830438 8.521150 9.745329 11.449797
## [631] 11.111649 11.319147 9.826367 11.325775 9.922215 10.793994 12.416216
## [638] 9.251409 8.375765 9.910965 11.171644 10.290825 9.121977 8.260489
## [645] 9.164581 11.765900 9.885455 10.640503 9.228435 11.778646 9.460834
## [652] 10.931824 11.682226 10.836265 9.362158 12.225157 10.230760 6.835660
## [659] 11.374620 9.591643 8.385166 11.812474 7.724478 8.848522 8.753931
## [666] 9.697859 7.619435 6.918133 9.881495 11.102059 12.660073 11.572368
## [673] 9.221209 8.584014 8.350343 10.204525 9.769480 9.929740 10.006704
## [680] 7.897679 10.974991 11.074376 11.923082 9.761767 12.296429 7.964391
## [687] 11.134010 9.608424 11.486814 10.469540 10.325308 9.737638 12.166659
## [694] 9.149988 10.539428 8.713793 10.150569 10.855043 10.952715 10.276023
## [701] 10.535048 12.008592 8.749444 9.096086 9.886814 7.645517 10.455169
## [708] 11.676199 10.632318 9.178898 9.191903 11.183060 9.935801 10.360269
## [715] 11.649608 10.938933 8.891351 8.764130 11.591885 10.411570 9.238114
## [722] 8.057891 7.912614 10.705066 9.441487 9.091253 10.900130 9.849589
## [729] 8.897349 10.162203 11.271473 9.756270 9.249271 10.695441 11.487825
## [736] 8.060407 9.835766 9.511484 10.989625 8.614634 9.012688 11.146722
## [743] 10.236506 10.220784 9.464337 8.528767 11.077048 9.177453 10.320505
## [750] 9.257074 8.312554 6.907431 9.567203 8.962888 9.619194 9.574222
## [757] 10.889782 11.238611 11.719243 9.713890 12.763174 9.924836 9.239429
## [764] 6.922432 10.082472 11.832868 6.803857 9.947026 10.029146 12.777576
## [771] 9.208199 10.917717 9.834789 9.582184 9.981740 8.788163 8.674745
## [778] 10.291211 11.069358 10.613153 7.411629 9.982713 9.716193 9.052663
## [785] 8.256063 9.604244 11.645166 8.069397 11.244719 10.231937 11.395095
## [792] 9.100796 11.759602 10.798650 11.293918 8.775672 9.340159 11.725111
## [799] 7.866608 8.941209 9.730208 12.117093 7.708738 9.368727 9.085507
## [806] 10.792644 10.622175 9.130315 8.705513 10.763488 9.351754 7.479975
## [813] 8.174954 9.878857 10.050636 10.296518 10.129647 11.700685 11.168244
## [820] 10.618218 11.027089 10.624408 9.788782 9.208818 10.463004 8.847930
## [827] 12.263584 8.533096 8.185426 9.092505 11.249992 10.910364 11.630468
## [834] 8.939904 10.418977 10.895552 11.249651 11.051478 10.443419 10.495702
## [841] 10.830376 9.424420 6.667860 7.985056 10.734115 9.734234 9.500233
## [848] 7.501215 7.542027 10.307579 7.108836 9.347974 12.386903 8.568692
## [855] 11.611868 10.869587 10.131020 8.152334 10.734990 10.676848 6.745561
## [862] 10.409988 7.606131 9.684389 14.067261 11.491842 8.052465 10.482471
## [869] 9.557165 10.452812 11.868503 10.290432 11.183520 9.588270 11.973005
## [876] 9.121010 9.927224 9.125481 11.448277 9.609388 10.160412 11.546565
## [883] 10.213731 9.843778 10.151632 12.757230 11.923114 9.918724 11.234897
## [890] 10.252773 7.760081 10.575325 9.438540 8.549111 11.194002 12.902520
## [897] 10.795619 9.760751 11.832434 9.207241 8.531487 11.087676 9.436922
## [904] 8.629439 8.622425 11.504379 10.427591 11.817179 10.451682 11.079034
## [911] 7.718247 11.285373 11.121995 12.071780 9.975425 9.042433 9.399760
## [918] 11.177228 7.893077 9.010705 8.349443 9.101802 12.408356 11.259809
## [925] 10.251408 11.395787 11.637693 11.726247 8.981758 9.900053 9.189884
## [932] 9.000476 10.695075 10.526891 10.934017 8.847542 12.535430 10.994426
## [939] 10.990803 11.460842 12.691284 10.908085 11.314731 10.161946 10.844253
## [946] 9.486443 11.130369 9.354160 9.634155 9.543276 11.421119 9.835035
## [953] 10.357419 11.182234 9.399940 10.014231 8.076813 9.313128 9.717038
## [960] 11.931758 9.993017 9.871532 10.313942 10.824090 7.944797 9.730379
## [967] 8.988492 9.739815 9.554448 7.441839 12.355401 8.709509 11.894018
## [974] 8.989253 7.696633 11.964908 9.138715 9.589090 9.085086 9.501466
## [981] 10.574338 12.267170 11.463356 11.198105 9.814042 9.555950 9.161946
## [988] 11.939742 10.544179 10.433563 13.384157 10.182345 9.695509 11.748260
## [995] 11.568284 7.403428 11.622200 10.215012 10.958420 10.595377
# 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] 2 2 2 3 4 3 3 2 3 4 2 4 5 4 1 4 5 2 3 4 3 5 3 5 4 4 2 3 4 4 1 2 3 3 3 3 5
## [38] 5 4 2 3 4 3 3 4 1 1 3 2 2 5 1 5 4 3 3 3 2 4 3 2 3 1 2 3 3 2 3 3 3 3 5 2 2
## [75] 2 3 2 1 2 2 5 2 3 3 4 4 3 2 3 2 5 2 3 4 4 2 4 1 2 2 4 5 2 2 2 3 2 5 3 3 3
## [112] 3 3 3 4 1 3 2 1 3 1 3 5 4 4 3 3 2 3 1 2 3 3 4 2 2 3 1 3 3 2 3 4 2 3 3 5 4
## [149] 3 2 4 4 2 1 4 1 4 3 2 4 3 3 2 5 4 2 4 4 3 3 3 3 4 1 4 3 5 2 2 4 3 2 4 3 4
## [186] 4 4 2 3 3 3 4 2 2 4 4 2 3 4 1 2 3 4 3 3 3 3 2 2 1 4 3 4 3 4 3 3 4 3 2 3 3
## [223] 3 2 4 3 3 3 3 4 3 4 1 3 3 4 4 3 4 3 3 3 2 4 2 5 3 1 4 2 3 4 4 2 3 1 1 1 3
## [260] 2 5 4 3 4 4 4 4 3 3 4 2 2 4 2 3 4 3 2 4 3 3 4 4 5 4 1 3 4 5 5 4 2 3 3 3 3
## [297] 4 3 5 4 3 3 4 4 3 2 1 3 4 3 4 3 1 2 3 3 5 1 2 1 4 4 4 2 3 3 2 4 4 3 3 3 5
## [334] 3 3 2 2 2 2 2 5 3 2 4 1 2 3 3 3 1 1 4 2 1 4 3 4 2 2 1 4 2 2 4 3 3 4 3 3 4
## [371] 4 3 4 3 4 2 2 2 3 3 3 3 4 2 3 4 1 4 3 3 4 2 2 3 2 3 5 1 3 3 4 4 2 3 4 1 3
## [408] 3 3 4 3 3 3 4 4 4 3 1 3 3 2 3 3 4 2 4 1 2 2 3 3 4 1 3 3 3 3 4 4 4 4 4 3 3
## [445] 1 2 4 3 5 2 3 3 2 2 4 4 4 2 2 5 3 4 2 3 2 2 1 3 1 3 3 2 2 3 5 3 2 5 2 3 2
## [482] 2 3 5 4 3 3 3 4 3 3 2 5 5 2 2 4 4 3 3 3 2 3 4 4 4 2 5 2 4 2 2 3 2 2 2 2 2
## [519] 2 3 4 2 5 5 3 2 5 3 3 2 3 3 3 2 3 3 2 2 3 1 5 3 3 3 3 2 3 4 3 4 4 1 2 3 4
## [556] 5 2 2 3 2 3 2 3 4 4 3 3 2 2 4 3 4 4 4 4 4 3 3 4 4 3 5 3 4 3 3 4 4 3 2 3 2
## [593] 3 4 3 4 3 3 4 2 3 4 1 3 2 4 3 3 2 2 4 3 3 3 3 3 4 3 2 4 3 3 2 4 4 2 2 3 3
## [630] 4 3 4 3 3 3 3 5 3 3 2 3 2 4 2 3 3 2 2 2 3 1 3 3 5 2 4 3 1 4 3 2 3 2 2 3 3
## [667] 2 3 3 2 4 3 4 1 2 3 3 1 3 2 4 3 4 3 3 3 3 2 4 4 2 3 5 2 3 3 2 3 3 3 2 5 3
## [704] 2 2 1 3 5 4 2 2 4 3 5 5 3 3 1 4 4 4 1 2 4 3 2 3 2 3 3 3 3 3 3 4 2 3 2 4 1
## [741] 1 4 2 2 3 4 5 3 3 3 3 2 3 5 3 3 5 3 4 3 4 3 3 3 3 3 1 4 3 4 2 3 2 3 4 2 3
## [778] 3 3 3 1 3 3 2 2 3 2 1 4 2 4 2 3 5 3 2 3 4 2 2 4 4 2 4 2 3 4 2 3 4 3 2 2 5
## [815] 3 3 3 5 3 3 3 4 3 1 4 2 3 2 3 2 3 5 4 4 3 4 3 3 2 3 4 3 2 2 3 3 2 1 1 4 1
## [852] 3 4 2 3 4 3 1 3 2 2 2 2 3 5 3 1 3 1 4 4 3 4 3 4 3 4 1 3 3 4 3 3 4 2 5 4 4
## [889] 3 4 3 3 4 2 3 3 3 4 4 1 2 2 2 2 1 3 3 4 3 2 1 4 4 4 3 3 4 5 2 2 3 4 5 4 3
## [926] 4 4 4 2 3 2 1 4 3 3 3 5 3 4 4 4 4 3 3 3 2 3 3 2 3 4 2 4 2 4 3 3 2 3 4 3 2
## [963] 3 3 1 3 2 2 3 2 5 2 5 2 2 4 2 3 3 3 3 3 4 5 3 3 2 4 3 2 5 3 2 4 4 2 5 4 5
## [1000] 4
# create sample dataframe by joining variables
sample_data <- data.frame(xAxis,yAxis,group)
sample_data
## xAxis yAxis group
## 1 -1.025398453 10.218951 2
## 2 -1.474182426 9.765552 2
## 3 -1.476609694 8.063624 2
## 4 0.150203991 11.307525 3
## 5 1.299979272 12.411353 4
## 6 -0.289438902 10.123184 3
## 7 0.476283231 10.985069 3
## 8 -0.822720954 9.322369 2
## 9 -0.050076762 7.799070 3
## 10 1.400693259 10.555441 4
## 11 -0.587085215 8.396929 2
## 12 1.449962369 12.319558 4
## 13 3.202953153 10.678203 5
## 14 0.828407651 9.708382 4
## 15 -1.817114685 9.102500 1
## 16 1.168017813 13.182593 4
## 17 1.576217857 11.018043 5
## 18 -1.158445183 8.958487 2
## 19 0.415547760 10.946939 3
## 20 0.626267811 9.750881 4
## 21 0.100334576 10.830008 3
## 22 1.622122232 11.562347 5
## 23 0.362834337 7.792762 3
## 24 1.719582828 12.104715 5
## 25 0.724655967 10.160673 4
## 26 1.218712499 11.437493 4
## 27 -0.869032104 11.447821 2
## 28 -0.433728359 8.782731 3
## 29 0.926386690 11.557595 4
## 30 0.979356297 11.256142 4
## 31 -1.531793787 8.551305 1
## 32 -1.229055370 9.550858 2
## 33 -0.179304566 8.229614 3
## 34 0.341271430 10.065581 3
## 35 0.053262437 9.653125 3
## 36 0.034597955 10.248710 3
## 37 1.651271088 12.085718 5
## 38 1.545020597 11.733991 5
## 39 1.193734487 12.449735 4
## 40 -0.521850821 8.822752 2
## 41 -0.242081948 8.925063 3
## 42 0.916736269 11.809449 4
## 43 -0.058137036 8.821935 3
## 44 0.435942549 9.939490 3
## 45 0.789987562 9.732519 4
## 46 -1.964119101 8.947578 1
## 47 -1.668699280 9.063998 1
## 48 -0.059867318 10.418721 3
## 49 -1.227650661 11.249104 2
## 50 -0.760676392 9.050816 2
## 51 2.534390668 12.437769 5
## 52 -1.520670082 9.653869 1
## 53 2.541771299 11.375472 5
## 54 0.753743863 11.672862 4
## 55 0.424884140 9.903802 3
## 56 0.337364795 10.597984 3
## 57 0.155694148 11.711777 3
## 58 -0.920881763 7.926808 2
## 59 0.655195052 8.784194 4
## 60 -0.122000348 10.712075 3
## 61 -0.812247127 8.714797 2
## 62 -0.313845656 9.835119 3
## 63 -1.531035614 6.737335 1
## 64 -0.719900928 9.198953 2
## 65 -0.377205875 8.344283 3
## 66 -0.203321327 8.869362 3
## 67 -0.512975276 9.747794 2
## 68 -0.287021520 10.608292 3
## 69 0.260954754 8.929138 3
## 70 -0.221538146 11.266172 3
## 71 0.180409384 8.817787 3
## 72 1.732923576 12.403102 5
## 73 -0.630092150 10.675990 2
## 74 -0.620791203 9.786269 2
## 75 -1.339640202 7.768979 2
## 76 -0.308525700 10.761701 3
## 77 -1.171149606 9.418079 2
## 78 -1.971037778 8.883633 1
## 79 -0.759309682 9.306427 2
## 80 -0.514345997 10.013132 2
## 81 1.611440549 11.446612 5
## 82 -0.857661440 8.754994 2
## 83 -0.474021781 9.747842 3
## 84 0.332704680 10.613429 3
## 85 0.584064605 10.984164 4
## 86 1.018917408 11.643620 4
## 87 -0.429538657 10.854527 3
## 88 -1.187963664 10.709544 2
## 89 -0.309268626 9.850680 3
## 90 -0.597994186 9.326868 2
## 91 2.016998701 12.018265 5
## 92 -0.754257439 11.622430 2
## 93 0.462811901 10.428342 3
## 94 0.639630648 12.282033 4
## 95 0.840011437 11.028032 4
## 96 -1.044551467 8.826413 2
## 97 0.520873199 9.938202 4
## 98 -2.023256630 6.832172 1
## 99 -0.527211057 10.151624 2
## 100 -0.678838666 9.341462 2
## 101 0.536009709 9.379063 4
## 102 2.153183764 12.959795 5
## 103 -0.885061932 9.777015 2
## 104 -0.683061476 10.236472 2
## 105 -0.570523359 10.686720 2
## 106 -0.040096371 11.524792 3
## 107 -0.880402898 9.530798 2
## 108 1.508726920 11.710877 5
## 109 -0.130283034 10.134233 3
## 110 -0.450010850 8.758560 3
## 111 0.267407582 9.895687 3
## 112 0.309327277 9.669975 3
## 113 0.330654652 12.602021 3
## 114 -0.118997868 9.956440 3
## 115 0.529072306 8.772177 4
## 116 -2.055496071 6.047677 1
## 117 0.113443457 10.353285 3
## 118 -0.963867305 7.969921 2
## 119 -1.703536803 7.211032 1
## 120 -0.248746036 11.059351 3
## 121 -1.824324589 7.340122 1
## 122 0.036433295 10.458222 3
## 123 1.782860721 11.462140 5
## 124 0.799837374 9.953527 4
## 125 1.314775058 11.781096 4
## 126 -0.321769313 10.185861 3
## 127 -0.082057231 10.089767 3
## 128 -1.175023567 8.676199 2
## 129 -0.085828159 10.913178 3
## 130 -1.611410878 8.215562 1
## 131 -1.408999824 8.928038 2
## 132 -0.187997262 9.702751 3
## 133 -0.324178507 9.235861 3
## 134 1.305311841 11.876492 4
## 135 -0.620672035 8.895568 2
## 136 -1.438240461 9.543477 2
## 137 -0.171825371 9.022315 3
## 138 -2.008098696 7.959959 1
## 139 0.385190609 10.127656 3
## 140 -0.287464711 8.229318 3
## 141 -0.515117253 8.675376 2
## 142 -0.029637083 9.956919 3
## 143 0.773795380 11.590231 4
## 144 -1.062080966 9.242223 2
## 145 -0.157384731 8.384576 3
## 146 -0.413196941 10.482368 3
## 147 1.639929489 10.896381 5
## 148 0.613677696 11.682029 4
## 149 -0.143962036 10.316431 3
## 150 -0.562589789 9.483306 2
## 151 0.936852061 10.854881 4
## 152 0.556554726 12.146913 4
## 153 -0.945371486 7.865689 2
## 154 -2.125538379 6.165426 1
## 155 0.805546647 11.441384 4
## 156 -1.908240311 8.173807 1
## 157 1.034460167 9.852708 4
## 158 0.096313516 9.139250 3
## 159 -0.703501230 8.269547 2
## 160 1.219365681 11.508317 4
## 161 0.496109272 11.661506 3
## 162 -0.389406826 9.247942 3
## 163 -0.774962577 8.273026 2
## 164 1.957232335 11.143613 5
## 165 0.774577183 9.770364 4
## 166 -0.737309377 9.523257 2
## 167 0.728917954 11.116911 4
## 168 1.127472610 11.660247 4
## 169 0.033805409 9.514774 3
## 170 -0.234743743 10.687507 3
## 171 -0.001382401 12.127056 3
## 172 -0.302367408 9.018651 3
## 173 1.243002794 13.784616 4
## 174 -2.916847584 5.054193 1
## 175 0.987124576 11.877269 4
## 176 0.342593123 11.452439 3
## 177 2.000639438 11.018179 5
## 178 -0.686171009 7.646665 2
## 179 -1.281725301 11.336323 2
## 180 1.314423703 11.226661 4
## 181 -0.231955141 10.531520 3
## 182 -1.373960527 8.184018 2
## 183 1.438462306 11.531680 4
## 184 -0.264670696 10.408249 3
## 185 1.362227200 11.259858 4
## 186 0.975961324 11.861114 4
## 187 0.625337717 11.788587 4
## 188 -1.031108144 9.086431 2
## 189 0.347015747 10.384363 3
## 190 -0.280042709 8.260233 3
## 191 0.186882987 9.227782 3
## 192 1.451268694 10.942817 4
## 193 -1.180001487 10.711734 2
## 194 -0.744203407 9.175089 2
## 195 1.148315316 10.240948 4
## 196 0.825217981 11.653212 4
## 197 -1.058560886 9.081989 2
## 198 0.450367063 10.304171 3
## 199 1.345168048 10.415347 4
## 200 -1.854383875 8.347630 1
## 201 -1.311182081 7.942835 2
## 202 -0.021931909 9.199497 3
## 203 0.681514155 12.049587 4
## 204 -0.065342232 10.173557 3
## 205 0.178652950 12.301855 3
## 206 0.337094790 10.253258 3
## 207 0.347459150 10.520400 3
## 208 -1.045718778 8.697857 2
## 209 -0.666954288 9.538196 2
## 210 -1.510798064 9.591106 1
## 211 0.904312685 10.676262 4
## 212 -0.368503331 12.040871 3
## 213 1.253727629 12.734765 4
## 214 0.283150947 10.600965 3
## 215 1.188160026 11.102280 4
## 216 -0.418936433 9.963064 3
## 217 -0.118228066 8.120887 3
## 218 0.584997594 10.413589 4
## 219 -0.344060679 10.099171 3
## 220 -0.540594392 9.027799 2
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## 222 -0.152140866 8.302895 3
## 223 -0.062382472 9.143806 3
## 224 -1.296756831 7.812587 2
## 225 1.296269686 10.856575 4
## 226 0.036453838 10.053626 3
## 227 -0.234528002 7.770800 3
## 228 0.359829512 8.760397 3
## 229 -0.367261384 9.711484 3
## 230 0.920984684 11.345264 4
## 231 0.045058319 9.770105 3
## 232 1.428097503 10.086699 4
## 233 -1.594350467 7.045024 1
## 234 -0.399727574 10.694734 3
## 235 0.422033471 11.661467 3
## 236 0.663645530 10.851126 4
## 237 0.628681678 11.974858 4
## 238 -0.150709698 9.144714 3
## 239 0.989467899 11.275497 4
## 240 0.207722515 8.481845 3
## 241 -0.222830727 9.724552 3
## 242 -0.034987751 11.377385 3
## 243 -1.491640970 8.103106 2
## 244 1.246811516 12.327723 4
## 245 -1.021443198 9.566184 2
## 246 1.514296069 13.567741 5
## 247 0.083475337 11.964988 3
## 248 -1.617185939 8.678974 1
## 249 0.714590992 11.340168 4
## 250 -0.695359828 8.555210 2
## 251 0.215209696 11.049466 3
## 252 0.728179435 10.024174 4
## 253 1.035077795 11.341156 4
## 254 -1.168581696 7.567757 2
## 255 0.169314986 10.336745 3
## 256 -1.725108385 8.472840 1
## 257 -1.988714150 8.286600 1
## 258 -1.557693586 7.673521 1
## 259 -0.053780483 9.972867 3
## 260 -1.177205451 7.140949 2
## 261 1.524264861 10.506945 5
## 262 0.815901614 9.403708 4
## 263 0.410312543 9.605971 3
## 264 1.145218612 11.805868 4
## 265 1.445678960 10.325366 4
## 266 0.924902680 11.993302 4
## 267 0.578638346 8.983476 4
## 268 0.028800149 10.736653 3
## 269 -0.200945129 9.050441 3
## 270 0.899084725 11.398020 4
## 271 -0.513356695 9.689793 2
## 272 -0.648363024 9.809190 2
## 273 1.245295806 12.560799 4
## 274 -1.107291465 8.018350 2
## 275 -0.364000297 7.175010 3
## 276 0.977607056 10.609520 4
## 277 -0.166264319 9.028263 3
## 278 -0.558896134 9.451884 2
## 279 0.846919703 11.009217 4
## 280 -0.280191073 8.495049 3
## 281 -0.182362884 9.454714 3
## 282 1.212133385 10.094717 4
## 283 0.935561871 10.398992 4
## 284 1.570140108 12.445668 5
## 285 0.515410759 11.839824 4
## 286 -1.666691834 7.527169 1
## 287 -0.275426261 9.394286 3
## 288 1.080459060 11.802845 4
## 289 1.806009293 11.413896 5
## 290 1.820286365 12.289665 5
## 291 1.256613301 11.146606 4
## 292 -1.457760307 6.992299 2
## 293 -0.223283024 9.803671 3
## 294 -0.373150025 9.434675 3
## 295 -0.136204225 10.263378 3
## 296 0.168884535 11.350056 3
## 297 1.305709467 10.596315 4
## 298 -0.454454868 7.863957 3
## 299 1.736631770 11.983051 5
## 300 1.105645283 11.987226 4
## 301 -0.108726028 10.503908 3
## 302 -0.488338866 8.866519 3
## 303 0.865048690 12.123801 4
## 304 0.800780639 10.687986 4
## 305 0.076066327 11.018520 3
## 306 -1.183732292 9.323650 2
## 307 -1.708872120 7.776495 1
## 308 -0.296471353 7.043665 3
## 309 1.131830782 7.592064 4
## 310 0.354868892 8.628263 3
## 311 0.898645695 9.856469 4
## 312 0.125340931 9.781375 3
## 313 -2.227549011 8.447621 1
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## 315 0.233166447 8.592446 3
## 316 -0.360766089 10.175169 3
## 317 1.703224560 11.816352 5
## 318 -2.062890406 7.979435 1
## 319 -0.676582940 10.035013 2
## 320 -1.521002235 9.345380 1
## 321 1.449239080 10.533386 4
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## 323 0.650370831 11.664049 4
## 324 -1.167354018 11.874437 2
## 325 0.107219650 10.075663 3
## 326 -0.307883061 8.239249 3
## 327 -0.617081818 8.853594 2
## 328 0.840392013 13.027079 4
## 329 1.488485220 9.163605 4
## 330 0.436284147 10.627207 3
## 331 -0.354919922 8.889720 3
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## 333 1.649505609 12.031165 5
## 334 -0.049940678 9.145214 3
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## 336 -0.663007181 8.755848 2
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## 339 -1.156062398 11.329523 2
## 340 -1.437798175 7.278923 2
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## 342 0.098448107 11.339423 3
## 343 -0.980470585 10.331689 2
## 344 0.587792979 9.902063 4
## 345 -1.644296295 5.989004 1
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## 978 -0.222592854 9.589090 3
## 979 -0.015670314 9.085086 3
## 980 -0.431301682 9.501466 3
## 981 0.222239121 10.574338 3
## 982 -0.042214328 12.267170 3
## 983 1.234167013 11.463356 4
## 984 1.535339330 11.198105 5
## 985 0.369920430 9.814042 3
## 986 -0.190937232 9.555950 3
## 987 -1.144833853 9.161946 2
## 988 0.644723506 11.939742 4
## 989 -0.249354702 10.544179 3
## 990 -1.328098550 10.433563 2
## 991 1.922532262 13.384157 5
## 992 0.214859389 10.182345 3
## 993 -1.316236919 9.695509 2
## 994 0.959188620 11.748260 4
## 995 1.242625488 11.568284 4
## 996 -1.111074265 7.403428 2
## 997 1.687646622 11.622200 5
## 998 0.563846279 10.215012 4
## 999 1.634786067 10.958420 5
## 1000 0.740075601 10.595377 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)
