# Mindanao State University
# General Santos City
# Introduction to R base commands
# Prepared by: Prof. Carlito O. Daarol
# Faculty
# Math Department
# Submitted by: Jovel Jade Casidsid
# 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/DELL/Downloads/Jovel"
filename <- "Cancer.csv"
(file <- paste0(folder,"/",filename))
## [1] "C:/Users/DELL/Downloads/Jovel/Cancer.csv"
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/DELL/Downloads/Jovel"
filename <- "hsb2.csv"
(file <- paste0(folder,"/",filename))
## [1] "C:/Users/DELL/Downloads/Jovel/hsb2.csv"
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
## 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
## 424 20 0 1 3 1 2 math 57
## 425 12 0 1 2 1 3 math 45
## 426 53 0 3 2 1 3 math 46
## 427 154 0 4 3 1 2 math 66
## 428 178 0 4 2 2 3 math 57
## 429 196 0 4 3 2 2 math 49
## 430 29 0 2 1 1 1 math 49
## 431 126 0 4 2 1 1 math 57
## 432 103 0 4 3 1 2 math 64
## 433 192 0 4 3 2 2 math 63
## 434 150 0 4 2 1 3 math 57
## 435 199 0 4 3 2 2 math 50
## 436 144 0 4 3 1 1 math 58
## 437 200 0 4 2 2 2 math 75
## 438 80 0 4 3 1 2 math 68
## 439 16 0 1 1 1 3 math 44
## 440 153 0 4 2 1 3 math 40
## 441 176 0 4 2 2 2 math 41
## 442 177 0 4 2 2 2 math 62
## 443 168 0 4 2 1 2 math 57
## 444 40 0 3 1 1 1 math 43
## 445 62 0 4 3 1 1 math 48
## 446 169 0 4 1 1 1 math 63
## 447 49 0 3 3 1 3 math 39
## 448 136 0 4 2 1 2 math 70
## 449 189 0 4 2 2 2 math 63
## 450 7 0 1 2 1 2 math 59
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## 453 21 0 1 2 1 1 math 61
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## 455 132 0 4 2 1 2 math 73
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## 457 67 0 4 1 1 3 math 42
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## 488 117 0 4 3 1 3 math 39
## 489 133 0 4 2 1 3 math 40
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## 493 82 1 4 3 1 2 math 65
## 494 8 1 1 1 1 2 math 52
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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
## 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
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#Remark: Pay extra attention to the last 2 columns
head(hsb2_long)
## id female race ses schtyp prog variable value
## 1 70 0 4 1 1 1 read 57
## 2 121 1 4 2 1 3 read 68
## 3 86 0 4 3 1 1 read 44
## 4 141 0 4 3 1 3 read 63
## 5 172 0 4 2 1 2 read 47
## 6 113 0 4 2 1 2 read 44
tail(hsb2_long)
## id female race ses schtyp prog variable value
## 995 179 1 4 2 2 2 socst 56
## 996 31 1 2 2 2 1 socst 56
## 997 145 1 4 2 1 3 socst 46
## 998 187 1 4 2 2 1 socst 52
## 999 118 1 4 2 1 1 socst 61
## 1000 137 1 4 3 1 2 socst 61
# get thefrequency
table(hsb2_long$variable)
##
## read write math science socst
## 200 200 200 200 200
# This means that the tables hsb2_long and hsb2_wide are the same
# tables displayed in two ways
# display data structure of the hsb2_long dataset
str(hsb2_long)
## 'data.frame': 1000 obs. of 8 variables:
## $ id : int 70 121 86 141 172 113 50 11 84 48 ...
## $ female : int 0 1 0 0 0 0 0 0 0 0 ...
## $ race : int 4 4 4 4 4 4 3 1 4 3 ...
## $ ses : int 1 2 3 3 2 2 2 2 2 2 ...
## $ schtyp : int 1 1 1 1 1 1 1 1 1 1 ...
## $ prog : int 1 3 1 3 2 2 1 2 1 2 ...
## $ variable: Factor w/ 5 levels "read","write",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ value : int 57 68 44 63 47 44 50 34 63 57 ...
# the variables female, race, ses, schtyp, prog are stored as numbers
# for encoding purposes. However these variables are actually qualitative variables
# so we convert each from integer type to categorical type
# defining some variables to become factor variable
# we use another variable to preserve the file hsb2_long
data <- hsb2_long
data$ses = factor(data$ses, labels=c("low", "middle", "high"))
data$schtyp = factor(data$schtyp, labels=c("public", "private"))
data$prog = factor(data$prog, labels=c("general", "academic", "vocational"))
data$race = factor(data$race, labels=c("hispanic", "asian", "african-amer","white"))
data$female = factor(data$female, labels=c("female", "male"))
# check data structure again. The former integer variables are now categorical variable
str(data)
## 'data.frame': 1000 obs. of 8 variables:
## $ id : int 70 121 86 141 172 113 50 11 84 48 ...
## $ female : Factor w/ 2 levels "female","male": 1 2 1 1 1 1 1 1 1 1 ...
## $ race : Factor w/ 4 levels "hispanic","asian",..: 4 4 4 4 4 4 3 1 4 3 ...
## $ ses : Factor w/ 3 levels "low","middle",..: 1 2 3 3 2 2 2 2 2 2 ...
## $ schtyp : Factor w/ 2 levels "public","private": 1 1 1 1 1 1 1 1 1 1 ...
## $ prog : Factor w/ 3 levels "general","academic",..: 1 3 1 3 2 2 1 2 1 2 ...
## $ variable: Factor w/ 5 levels "read","write",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ value : int 57 68 44 63 47 44 50 34 63 57 ...
# we compare student performance by using boxplots
library(gplots)
##
## Attaching package: 'gplots'
## The following object is masked from 'package:stats':
##
## lowess
# compute the average value by group using tapply() command
means <- round(tapply(data$value, data$variable, mean), digits=2)
# create boxplot by group
boxplot(data$value ~ data$variable, main= "Student Performance by Subject (brown dot = mean score)",
xlab="Subject matter", ylab="Percentage Scores", col=rainbow(5))
# insert the average values
points(means, col="brown", pch=18)
# can also compute the median values
medians = round(tapply(data$value, data$variable, median), digits=2)
medians
## read write math science socst
## 50 54 52 53 52
points(medians, col="red", pch=18)
# Lab Exercise 9: How to plot categorical variables
###install.packages("ggplot2")
###install.packages("colorspace", repos = "http://cran.us.r-project.org")
###("hsb2_long")
###install.packages(c("pkg1", "pkg2"))
library(ggplot2)
# we load variable names to memory to avoid the dollar notation
attach(hsb2_long)
# create the plot object p
p <- ggplot(hsb2_long, aes(ses, fill = prog)) + facet_wrap(~race)
p + geom_bar()
## Warning: The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
p + geom_bar(position = "dodge")
## Warning: The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
library(ggplot2)
attach(hsb2_long)
## The following objects are masked from hsb2_long (pos = 3):
##
## female, id, prog, race, schtyp, ses, value, variable
p <- ggplot(hsb2_long, aes(ses, fill = prog)) + facet_wrap(~schtyp)
p + geom_bar()
## Warning: The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
p + geom_bar(position = "dodge")
## Warning: The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
# Lab Exercise 10: Scatter Plots with marginal Distributions
# Advance Scatter plots using libraries
# install.packages("ggExtra")
# install.packages("tidyverse")
library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.1 ✔ readr 2.1.4
## ✔ forcats 1.0.0 ✔ stringr 1.5.0
## ✔ lubridate 1.9.2 ✔ tibble 3.2.1
## ✔ purrr 1.0.1 ✔ tidyr 1.3.0
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) 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] -7.453866e-01 1.112834e+00 1.616667e+00 -2.225926e-01 1.419856e+00
## [6] -1.283909e+00 1.109258e+00 1.212121e+00 -1.089389e+00 1.140458e+00
## [11] -9.635149e-01 -9.564921e-01 -2.191163e-01 6.851901e-01 1.242714e+00
## [16] -1.041953e-01 -4.893366e-01 -8.601536e-01 1.610345e+00 1.475209e+00
## [21] 1.601949e-01 4.644350e-01 5.314498e-02 2.550895e+00 1.257231e+00
## [26] -2.370791e-01 4.262925e-01 3.426237e-01 -5.149847e-01 5.920164e-01
## [31] 7.149082e-01 1.432975e+00 -7.916888e-02 1.676181e+00 1.160458e-01
## [36] -1.721663e+00 -8.892661e-01 1.144037e+00 2.445141e-01 -1.407634e+00
## [41] 1.244238e+00 -5.099116e-01 8.915904e-01 9.707900e-01 1.950343e-01
## [46] -2.133363e+00 1.115985e+00 2.234185e-02 -1.462171e+00 8.735605e-02
## [51] -1.297105e+00 3.651586e-01 -6.722992e-01 -7.930440e-01 -3.334374e-01
## [56] -2.027157e+00 3.586543e-01 -2.510933e-01 -1.217642e+00 -8.401360e-02
## [61] 2.388805e+00 1.478646e+00 -5.381292e-01 -3.636397e-01 -1.002196e+00
## [66] 5.146007e-01 -5.731214e-01 7.063590e-01 -4.836548e-01 -2.031780e+00
## [71] 1.018322e+00 1.372891e+00 8.778598e-01 1.555501e+00 -7.092181e-01
## [76] 2.284654e-01 5.650148e-01 -1.421210e+00 8.320104e-01 -3.538334e-01
## [81] -1.262031e+00 -1.184659e+00 1.594753e+00 2.016534e+00 5.705928e-01
## [86] -6.856967e-01 -2.349545e-01 5.584347e-01 -1.230237e+00 1.554731e+00
## [91] -1.152217e+00 -6.114856e-01 -7.024153e-01 -2.056660e+00 -1.319453e+00
## [96] -6.447146e-01 1.546029e-01 -6.576738e-01 4.065794e-01 -4.402328e-01
## [101] -1.103099e+00 -6.703175e-01 1.533863e+00 -8.298023e-01 3.636126e-01
## [106] -6.213304e-01 -6.168832e-01 -1.158119e-01 1.213172e+00 9.829543e-01
## [111] -7.113613e-01 5.969266e-01 7.578510e-01 5.743317e-01 -1.030368e+00
## [116] 1.455029e+00 -1.042592e+00 2.666930e+00 -1.613161e-01 -3.850002e-01
## [121] 5.045856e-01 -5.742047e-02 6.506845e-01 1.127286e+00 -6.369325e-01
## [126] -2.778223e-01 1.823947e+00 1.488190e+00 -4.235818e-01 -2.591146e+00
## [131] 1.483227e+00 -1.769383e+00 -2.108717e-01 -4.725348e-01 1.091790e+00
## [136] -1.869665e+00 1.730909e+00 -3.189835e-01 1.858014e-01 5.056291e-01
## [141] 2.717584e-01 1.709411e+00 1.554871e+00 1.221901e+00 -3.122473e-01
## [146] -1.144604e-01 5.472037e-01 -1.367172e+00 -1.673499e+00 1.822256e+00
## [151] 1.258035e+00 1.481877e-01 -1.107107e+00 -2.101725e-01 -9.355808e-01
## [156] -3.637922e-01 -1.428628e+00 1.279208e+00 3.262582e-01 6.165129e-01
## [161] 9.398759e-01 4.507299e-01 3.158073e-01 -2.281957e+00 -3.561705e-02
## [166] 2.946156e-01 -1.563486e-01 1.721763e+00 4.373044e-01 -1.821234e+00
## [171] 1.550344e-01 1.002583e+00 -6.329229e-01 -7.735131e-02 3.861550e-01
## [176] 2.740864e-03 5.157061e-02 6.709318e-01 -3.627891e-01 9.742347e-01
## [181] 1.040428e+00 -5.367105e-01 9.760422e-01 -6.478758e-01 6.498341e-01
## [186] 2.125393e-02 3.090123e-01 1.096940e+00 4.142528e-01 -9.324995e-01
## [191] -7.785797e-01 1.399382e-01 -2.449736e-01 1.014444e-02 1.054445e+00
## [196] -5.923229e-01 -8.059927e-01 5.790845e-01 -2.328233e-01 5.700291e-01
## [201] 1.477026e-01 4.017317e-01 1.871720e+00 -7.458580e-01 -5.679769e-01
## [206] 9.466068e-03 2.048355e+00 1.985207e+00 -1.769419e+00 8.261416e-01
## [211] 1.203314e+00 -1.136579e+00 1.625516e+00 -7.449527e-01 -8.090581e-01
## [216] -3.872437e-01 5.420848e-01 5.558915e-01 3.139446e-01 7.548533e-01
## [221] 6.390620e-01 2.599574e-01 5.594768e-01 7.111423e-02 -9.435378e-02
## [226] -8.787231e-01 -3.543012e-02 -8.450144e-01 1.229080e-01 -1.107785e-01
## [231] 1.064520e+00 -7.587317e-01 6.923660e-01 5.671310e-01 -2.086828e+00
## [236] 8.894948e-01 -9.588017e-01 4.341573e-03 7.326553e-01 4.577658e-01
## [241] -1.378582e+00 -2.000853e-02 -1.505652e+00 9.485944e-02 3.887853e-01
## [246] 8.700351e-01 1.106425e-01 -6.694163e-02 -3.664279e-01 1.290545e+00
## [251] 5.216271e-01 1.039771e+00 2.177906e-01 -5.523703e-01 1.798185e+00
## [256] 9.030856e-02 9.036563e-01 -7.345701e-01 -1.908699e-01 1.386649e+00
## [261] -1.692437e-01 -4.580138e-01 8.226337e-01 -5.863992e-01 -5.012582e-01
## [266] 7.235030e-02 -6.962808e-01 3.286573e-02 -2.145390e-01 -2.189593e+00
## [271] -7.810447e-01 6.747315e-01 -6.416264e-01 6.789903e-01 2.364652e+00
## [276] 2.184058e-01 6.604410e-01 -5.863318e-01 1.626649e+00 -9.992452e-02
## [281] -5.475719e-01 4.050502e-01 -8.558470e-01 -1.894257e+00 1.552237e+00
## [286] -3.626194e-01 4.619869e-01 -1.006956e+00 -1.281014e+00 -1.072515e+00
## [291] -6.798579e-01 1.328946e+00 1.840073e+00 -1.996479e-01 1.386929e-01
## [296] 9.753225e-01 4.900117e-01 2.806392e-01 2.609490e-02 -1.229037e+00
## [301] 4.520452e-01 -3.918335e-01 1.838862e+00 -6.521304e-02 -1.624003e+00
## [306] -1.272129e+00 1.280230e+00 3.468056e-01 2.790492e-01 1.454619e+00
## [311] -1.345795e+00 -4.873028e-01 -9.184097e-01 -1.467760e+00 3.192311e-01
## [316] -2.787185e+00 3.813286e+00 1.235674e+00 -5.553115e-01 -4.710029e-01
## [321] 1.342099e+00 2.310624e+00 2.908265e-01 -1.169536e+00 8.431659e-01
## [326] 2.720174e-01 -7.366049e-01 2.943440e-03 4.043566e-01 7.857545e-01
## [331] -5.155689e-01 3.543866e-01 -6.046720e-01 -1.276038e+00 1.825193e+00
## [336] 4.349259e-01 -3.042715e-01 9.788263e-01 4.540006e-01 -1.387786e-01
## [341] 6.440301e-02 -6.060368e-01 1.807651e+00 1.031066e+00 1.154838e+00
## [346] -1.499626e+00 1.544044e+00 -6.078782e-01 1.956845e+00 1.175726e-02
## [351] 1.101671e+00 -1.217688e+00 1.074303e+00 1.261407e+00 -1.888037e-01
## [356] -1.245512e-01 -1.030838e+00 7.136078e-02 1.493058e+00 -7.580163e-02
## [361] 1.395794e+00 -4.425042e-03 3.524778e-01 9.976257e-01 -5.761477e-01
## [366] -7.979090e-01 6.034775e-01 8.467141e-01 -5.651816e-01 8.678440e-01
## [371] 7.813719e-01 1.403112e+00 -4.018013e-01 -1.832053e+00 -5.016720e-01
## [376] -1.284668e+00 -1.160363e+00 -4.805715e-01 2.349930e+00 7.352166e-01
## [381] 9.952863e-01 -2.063796e+00 7.249803e-01 -6.083485e-01 -8.836375e-01
## [386] -6.785198e-01 -1.785065e+00 -1.175574e+00 -1.260075e+00 -1.372752e+00
## [391] -8.544240e-02 2.148036e-01 2.632585e+00 -1.742711e-01 7.056003e-01
## [396] 5.631258e-01 -6.711755e-01 -4.260963e-01 5.415663e-01 9.129252e-01
## [401] -1.325143e+00 -9.195963e-01 -1.981137e+00 -2.351535e-01 3.902046e-01
## [406] 4.771339e-01 -2.317892e-01 -6.326844e-02 1.109953e+00 -1.611200e-01
## [411] -1.319748e+00 9.274746e-01 -4.523098e-01 -9.131801e-01 8.357096e-01
## [416] 1.191035e+00 1.040848e+00 1.919328e+00 1.317134e+00 5.513623e-01
## [421] 1.385674e+00 7.042458e-01 -6.335099e-01 -1.097234e+00 3.117945e-01
## [426] 1.207208e-01 2.577963e-01 4.656152e-01 1.141608e+00 2.780770e-01
## [431] -1.976580e-01 -2.736220e-01 7.289467e-01 3.407103e-05 -7.955817e-01
## [436] -3.195429e-01 1.496836e+00 2.281232e-02 8.408991e-01 3.315204e-02
## [441] -2.744511e-01 -3.412512e-01 -3.551205e-01 -1.986225e+00 2.539891e-01
## [446] -1.283060e+00 1.898690e+00 1.340395e+00 -8.143855e-01 2.208023e+00
## [451] 1.671334e+00 -1.351936e+00 -1.309473e+00 5.598968e-01 1.837312e+00
## [456] 1.007364e+00 1.413166e+00 -7.242097e-01 -7.878230e-01 7.611733e-01
## [461] -1.838927e-01 -1.074008e+00 1.016348e+00 -2.954021e-02 5.100529e-02
## [466] -1.773142e-01 -1.585617e+00 3.035388e-01 -4.405536e-01 -1.097164e+00
## [471] -6.016776e-01 1.270628e-01 1.612118e+00 5.878236e-01 2.198050e+00
## [476] -1.058010e+00 -5.538441e-01 -6.657593e-01 3.045495e-02 5.322840e-01
## [481] -1.614665e+00 -6.359571e-01 -1.087218e+00 1.667336e+00 -5.856542e-01
## [486] 1.765257e-01 5.484440e-01 7.317504e-01 3.842707e-01 -5.504229e-01
## [491] -8.627341e-01 3.427963e-01 5.998346e-01 8.489699e-01 1.866641e-01
## [496] -2.308914e+00 6.911431e-02 -6.392120e-01 -1.029967e+00 -1.870535e-01
## [501] -1.416423e+00 -1.194343e+00 6.522040e-01 -1.608601e+00 5.430024e-01
## [506] -5.970726e-01 -3.981069e-01 -8.640866e-01 -1.375261e+00 1.856045e-01
## [511] 3.942520e-01 -1.967611e-01 -4.075934e-01 -2.659703e+00 6.238578e-01
## [516] -1.079414e-01 1.286482e+00 5.170036e-01 1.544796e+00 2.464385e-01
## [521] -2.507366e-01 -1.900775e-01 6.260044e-01 8.657299e-02 3.727433e-01
## [526] 5.638835e-01 2.405130e-01 1.980853e+00 -1.835639e-01 1.009117e+00
## [531] -1.332237e-01 -6.132746e-02 1.391566e+00 1.045953e+00 7.960689e-01
## [536] 3.782006e-01 -1.467454e-01 -7.762739e-02 -1.822285e+00 1.051350e+00
## [541] -7.409250e-01 -1.083309e+00 -5.018198e-02 1.995957e-01 -1.752615e-01
## [546] 6.976332e-01 -1.025411e+00 -6.527884e-01 5.776265e-02 -1.168361e+00
## [551] -1.744549e+00 1.259602e+00 2.128025e-01 -1.989940e+00 -4.067171e-01
## [556] -9.122721e-01 2.172850e+00 -3.273446e-01 3.370847e-01 -1.568226e+00
## [561] -1.803821e+00 -2.066289e+00 -6.915430e-01 -5.312701e-01 -1.767708e+00
## [566] -8.371508e-01 1.414185e+00 -1.317232e-01 -3.191340e-01 9.111925e-01
## [571] 6.905848e-01 2.933732e-02 -1.515478e-01 6.123437e-02 -1.013023e+00
## [576] -1.285411e+00 2.498249e-01 3.852755e-01 -1.610294e+00 1.360470e+00
## [581] 3.633989e-01 4.071961e-01 -1.637146e+00 -4.149858e-01 8.208128e-01
## [586] 2.185504e-01 2.173266e-01 -4.391002e-01 4.233881e-01 5.516786e-01
## [591] 1.195593e+00 -2.988455e-01 -1.881210e-01 -6.187651e-01 -7.585610e-01
## [596] -1.063053e+00 5.620482e-01 1.677956e+00 -5.107823e-01 1.220644e-01
## [601] 2.780861e+00 -1.748187e-01 -2.234627e+00 5.352869e-01 9.197955e-01
## [606] 7.651977e-01 8.750396e-01 -5.432937e-01 1.656860e+00 1.387238e+00
## [611] -3.232436e-01 6.794640e-01 2.128985e-01 2.383808e+00 6.280461e-01
## [616] -2.303065e+00 -2.255251e-01 -1.678195e-01 -1.676204e-01 3.016705e+00
## [621] -3.521560e-01 9.065903e-01 3.494033e-02 2.204366e-01 -4.223367e-02
## [626] 9.298566e-01 -6.044167e-01 5.026650e-01 2.590250e-01 1.573947e+00
## [631] 1.254129e-01 9.865922e-01 -7.109148e-01 -4.490787e-01 4.596137e-01
## [636] 1.291004e-01 -2.876346e-01 9.732824e-02 3.999675e-01 -9.442289e-01
## [641] 1.321935e-02 -2.484569e-02 -1.384443e-01 7.077439e-01 8.885819e-01
## [646] 1.043716e+00 -6.256274e-01 -1.315972e+00 8.699282e-01 -1.127547e+00
## [651] -7.177590e-01 -4.109965e-01 -1.016052e+00 4.223836e-01 1.068513e+00
## [656] 1.702245e+00 1.797604e+00 1.410089e+00 -1.325436e-01 -3.303413e-01
## [661] -8.834004e-01 -2.398547e+00 -7.447074e-01 -7.143844e-02 5.504062e-01
## [666] 8.812388e-02 -4.648650e-01 -5.627257e-02 -1.858388e+00 -1.974284e-01
## [671] -1.759545e-01 -2.858867e-01 3.730540e-01 -3.701750e-01 -1.253012e+00
## [676] -4.200456e-03 7.076939e-01 1.390657e+00 1.991217e-01 2.502238e-01
## [681] -4.486011e-01 2.029661e+00 -1.025927e+00 -1.376765e+00 1.105262e-02
## [686] -8.879166e-01 -3.574904e-01 5.317290e-01 -2.683132e-01 -4.632763e-01
## [691] -5.848226e-01 1.321859e+00 -3.317120e-01 4.916288e-01 -9.963007e-01
## [696] -7.957444e-01 1.345099e+00 -2.949213e-01 6.361718e-01 -1.865831e-01
## [701] 5.960941e-01 -2.492535e+00 -5.720274e-01 -9.299961e-01 2.771238e-02
## [706] 1.712809e-01 -1.893382e-01 -1.039090e+00 -3.836865e-01 3.185967e-01
## [711] 5.942795e-01 -2.038742e-02 1.664832e+00 2.579355e-01 -1.720989e-01
## [716] 5.954415e-01 2.124171e+00 -6.292406e-01 -2.181905e-02 2.706172e-01
## [721] -2.979217e-01 1.226676e-01 -1.421041e-01 -5.599233e-01 1.790038e-01
## [726] 5.582927e-01 -1.487139e+00 -8.238220e-01 -2.123579e-01 1.402247e+00
## [731] 1.387456e-01 -1.076832e-01 1.489678e-01 1.124060e+00 -9.153814e-01
## [736] 4.918533e-01 2.236266e-01 -8.128747e-01 -2.939347e-02 4.112873e-01
## [741] 5.727292e-01 -1.199186e+00 1.795407e+00 1.160463e+00 -1.675886e+00
## [746] 1.107955e+00 7.695033e-02 3.120904e-01 -3.780104e-01 -2.025238e+00
## [751] 3.073344e-01 2.381941e+00 1.958416e+00 -6.386216e-01 -2.319449e-01
## [756] 1.061183e+00 5.882715e-01 -1.996565e-01 -8.080914e-01 1.948361e-01
## [761] -2.947283e-01 -1.284086e+00 3.373282e-01 -7.593353e-01 -7.851464e-01
## [766] -1.542263e+00 1.245403e+00 -9.107140e-01 -5.642634e-01 -1.197827e-01
## [771] -5.035659e-02 8.848279e-01 1.762071e+00 8.840537e-01 -4.818814e-01
## [776] 1.084283e+00 -3.723960e-01 1.692947e+00 9.268209e-01 -1.854877e+00
## [781] -2.117591e+00 -9.513453e-02 -6.919926e-01 1.665709e+00 -9.170727e-01
## [786] -5.085484e-01 8.817051e-01 1.289167e+00 -2.437056e-01 7.514952e-01
## [791] 6.558274e-02 -5.214759e-01 -2.151035e+00 5.934145e-02 -1.457156e+00
## [796] -1.671423e+00 1.655692e-01 -1.627525e+00 1.956489e-01 7.669289e-02
## [801] -8.799911e-02 -1.165850e-01 1.550042e+00 9.971772e-01 -4.528075e-01
## [806] -1.341697e+00 -8.066392e-01 1.410708e+00 1.011574e+00 -1.703507e-02
## [811] -2.214877e+00 7.732646e-01 -2.320884e+00 3.978922e-01 6.807064e-01
## [816] -1.144395e+00 -1.210759e+00 1.125275e+00 -1.624556e-01 -3.777205e-01
## [821] -3.774974e-01 -5.192506e-01 3.503157e-01 1.935237e-01 8.289884e-01
## [826] -3.720243e-01 1.323859e+00 -7.194201e-01 -3.424078e-01 -6.222357e-01
## [831] 8.232502e-02 -1.665826e+00 -3.558142e-02 1.835353e-01 -3.575913e-01
## [836] -3.099848e+00 -2.096606e+00 8.834061e-01 2.868116e-02 2.002519e+00
## [841] -7.981134e-01 -2.639936e-01 2.831548e-01 -4.500767e-01 7.471683e-01
## [846] 5.371686e-01 2.346453e-01 9.966191e-01 8.074197e-01 3.424824e-01
## [851] -5.606274e-01 -1.130891e+00 -5.218228e-02 -1.645221e+00 -1.854702e+00
## [856] 4.080465e-01 7.363594e-01 -7.023675e-01 -1.324885e-01 -7.226099e-01
## [861] -1.248974e+00 7.604897e-02 1.491068e+00 -1.270238e-01 -1.038759e+00
## [866] -8.613548e-01 5.770915e-01 2.806348e+00 -7.124078e-01 -2.758894e-01
## [871] 1.754313e+00 -3.647237e-01 -9.225332e-01 -6.608860e-01 -8.548815e-01
## [876] -1.046144e+00 -1.743899e+00 1.289683e+00 -8.923418e-02 1.059473e+00
## [881] 1.902116e-01 4.895418e-01 -5.444616e-01 5.360445e-02 -6.588850e-01
## [886] 9.225844e-01 -7.700629e-01 -3.429862e-02 -1.145784e-01 2.178118e+00
## [891] 1.517065e+00 3.497549e-01 1.605523e+00 4.039780e-01 -1.972468e+00
## [896] -1.746884e+00 2.823252e-02 -1.290942e+00 -1.869807e+00 2.416963e-01
## [901] -2.318906e-01 -4.612933e-01 -1.237266e+00 -5.225361e-01 -5.093796e-01
## [906] 1.194512e+00 -5.532268e-01 3.453956e-01 -1.108513e+00 8.593090e-01
## [911] 1.066352e+00 -1.482601e-01 1.560534e+00 6.290531e-02 3.905750e-01
## [916] 8.066026e-01 1.598578e+00 -2.197104e-01 5.760951e-01 1.438479e+00
## [921] -1.435191e-01 -1.262140e+00 -1.119035e+00 -4.517699e-01 5.377967e-01
## [926] -1.271970e+00 1.445020e+00 -7.716825e-01 -7.063988e-01 5.105339e-01
## [931] -4.956215e-01 -3.889266e-01 6.177498e-01 8.990596e-02 -9.089675e-01
## [936] -7.363344e-01 2.316818e+00 1.867951e-02 -1.127902e+00 -3.866387e-01
## [941] 4.459477e-01 6.322589e-01 -5.318610e-01 3.236107e-01 -4.333344e-02
## [946] 1.518864e+00 1.258928e+00 -9.314948e-02 -7.423906e-01 1.095894e+00
## [951] 1.291698e+00 1.409417e+00 -1.894593e+00 7.949878e-03 3.443205e-02
## [956] -6.729222e-01 1.953668e+00 -8.271314e-01 3.739943e-01 8.216360e-01
## [961] -5.391788e-01 -2.426084e-01 1.075731e+00 -6.902877e-01 4.342457e-01
## [966] 5.416647e-01 1.273949e-01 3.449651e-01 2.123759e-01 -4.045707e-02
## [971] 1.109399e-02 -7.928521e-01 1.393740e+00 -2.564181e-02 5.933520e-01
## [976] 7.802044e-01 -3.448022e-01 7.033642e-02 -2.619055e-01 -9.198238e-02
## [981] -6.910569e-01 -6.606563e-01 5.171710e-01 1.717944e+00 -9.015686e-01
## [986] -3.035348e-01 -9.203473e-01 -3.171462e+00 -6.144295e-01 5.729294e-01
## [991] -1.841060e+00 3.757458e-01 -2.122550e-01 -3.165982e-02 -1.184063e-01
## [996] 1.315052e+00 7.698425e-01 -4.680503e-01 1.298723e-01 -2.567159e+00
yAxis <- rnorm(1000) + xAxis + 10
yAxis
## [1] 9.397657 12.286828 12.753188 9.909517 10.726787 9.561486 9.720841
## [8] 10.642103 9.582655 12.506351 8.939211 8.376955 9.878450 11.277611
## [15] 11.872275 9.997038 9.863265 9.535250 11.066817 11.931874 9.469595
## [22] 10.330704 8.598460 11.734031 11.898245 8.468433 9.244084 11.036601
## [29] 7.874644 11.327873 10.827382 11.983886 10.167238 12.717787 10.902013
## [36] 7.311760 10.753398 14.232733 10.321356 8.599660 11.094622 8.057322
## [43] 11.366223 10.833686 8.934612 8.837575 12.795874 10.435951 8.696305
## [50] 9.218425 7.996232 10.814483 8.749454 10.346406 9.890548 7.498144
## [57] 9.459383 9.659895 9.494136 10.025525 12.040110 12.671305 9.183806
## [64] 9.328427 8.074814 8.780084 10.807595 11.252928 9.984762 7.825037
## [71] 10.913757 11.284598 9.220568 11.436904 9.578551 9.675925 9.418627
## [78] 8.420433 11.880817 10.939512 8.074703 7.599984 10.780876 11.966795
## [85] 12.195765 9.873300 10.502501 11.709075 9.102284 11.183830 7.603867
## [92] 8.380409 10.884013 8.431401 9.612767 9.208543 9.979935 8.799780
## [99] 9.028685 9.303691 7.809927 9.634175 11.416347 8.838739 12.276325
## [106] 9.063364 9.319757 11.378771 9.846260 9.715327 10.673942 12.192406
## [113] 9.753946 11.107637 8.785098 12.421373 8.618081 11.937210 11.030811
## [120] 9.683722 8.947197 10.475273 10.908677 11.772313 9.455262 9.511325
## [127] 10.867531 11.154996 10.951501 6.689374 9.448467 8.485869 10.001512
## [134] 9.912602 12.990014 8.031592 11.329261 10.233779 10.557650 9.891666
## [141] 10.026738 11.787643 11.685091 11.470751 9.558366 9.728222 10.856178
## [148] 9.325661 9.329701 11.142564 11.422249 8.929261 9.883799 9.881130
## [155] 9.457361 9.772439 9.615179 10.791367 9.242434 10.657452 11.087064
## [162] 10.916789 11.503039 9.305078 9.831784 9.944372 10.786990 12.612059
## [169] 10.569143 9.328736 10.730852 10.955701 8.285148 11.612601 9.783285
## [176] 8.927757 9.976025 10.657572 8.476652 11.670741 11.718555 10.543682
## [183] 11.121688 10.310019 10.252183 11.001850 10.076708 10.968186 10.536872
## [190] 11.345910 7.525418 9.469018 9.984130 9.964342 11.144654 8.005990
## [197] 9.027509 10.545320 8.477654 10.315606 10.373918 9.314303 12.027633
## [204] 9.111311 9.514396 10.805220 11.549030 11.089254 8.229090 11.064231
## [211] 11.091902 6.110981 12.272492 7.451623 7.781318 10.303336 9.954987
## [218] 11.030897 10.586493 10.486827 9.488090 11.397400 9.340429 10.132317
## [225] 10.131398 9.739258 9.304762 7.217564 11.245642 9.927822 12.452590
## [232] 8.432347 12.342047 11.305545 8.591578 12.894898 8.331524 11.146749
## [239] 10.239605 10.115978 9.374034 9.436505 9.683124 10.046880 9.282360
## [246] 10.868274 9.409167 11.188529 8.642929 11.473288 10.299299 10.796384
## [253] 10.936399 9.422769 11.618907 10.624702 9.835349 9.610789 10.127857
## [260] 12.099043 11.069817 9.487461 12.137589 9.637543 11.194130 8.279908
## [267] 6.165545 9.671005 9.353402 10.515099 9.452654 10.398973 8.662920
## [274] 13.117922 11.324613 10.611426 12.120456 7.234062 11.659836 10.827574
## [281] 9.544322 11.661286 8.665173 6.194470 12.104489 8.227866 12.457786
## [288] 8.985029 7.006338 9.351578 7.808920 10.786623 10.226031 9.415843
## [295] 8.768759 10.882284 11.751835 10.252953 11.159902 7.706597 10.278702
## [302] 9.562681 11.996602 8.100405 7.952850 8.820156 12.704013 11.402318
## [309] 11.264592 10.763288 10.361750 9.672158 9.348912 10.278864 11.505662
## [316] 7.456899 16.410352 9.932736 9.593427 10.413922 10.600920 9.630801
## [323] 9.956214 9.717754 10.154049 10.761170 9.778346 10.302335 11.852260
## [330] 10.204824 10.411170 12.455960 9.612179 7.745655 13.955643 10.433221
## [337] 8.699200 12.038551 10.476543 8.296138 9.376160 11.013940 12.472740
## [344] 9.351451 11.950635 9.271778 12.601852 10.166421 12.284025 9.713452
## [351] 12.192560 8.786408 11.166670 10.387179 9.275057 11.095840 8.377889
## [358] 10.088957 11.940452 9.956613 11.239149 9.331609 10.937910 9.233480
## [365] 7.577479 8.022659 10.416494 9.646041 10.226297 10.225045 10.385751
## [372] 10.767481 6.642462 9.742253 10.257435 9.635868 8.981818 9.953647
## [379] 12.307267 10.355089 10.807739 7.256855 10.650139 9.274488 8.740999
## [386] 9.027029 8.968293 7.271994 9.034479 11.608361 9.961545 8.881254
## [393] 12.169192 10.064741 11.123001 10.103401 9.249524 10.508341 10.370040
## [400] 11.284229 8.353023 10.127949 9.509117 9.268254 10.799904 11.195104
## [407] 8.490195 8.367274 12.046377 10.333764 8.670433 11.620539 10.607377
## [414] 7.129231 11.695925 11.580341 11.598790 12.687147 12.247257 11.764561
## [421] 11.772139 12.904745 9.161934 9.502334 9.609373 8.677415 10.413835
## [428] 12.393213 12.106348 10.620752 9.974738 8.711481 9.507162 7.675423
## [435] 10.120515 10.954456 10.927821 10.952550 10.340100 11.185907 9.533980
## [442] 10.844760 9.395342 8.961715 10.857685 8.496432 12.686986 12.850438
## [449] 10.082243 12.747697 12.987711 8.778279 7.577558 11.512102 12.562748
## [456] 10.933309 11.189053 10.198207 9.296836 9.970881 9.436692 9.112828
## [463] 8.782926 11.115024 10.916646 12.055238 7.905268 11.260634 9.280415
## [470] 7.068245 10.105864 10.134365 12.027772 10.562797 12.303483 8.274839
## [477] 8.549585 9.066867 9.316041 8.381126 7.375532 9.329749 8.843852
## [484] 10.239877 9.697300 9.919953 11.360241 10.882515 9.953045 10.712456
## [491] 8.209747 9.160144 11.079170 9.361288 10.779991 8.501930 11.438196
## [498] 10.556294 8.864944 11.107175 9.176280 9.643363 10.154591 6.236221
## [505] 10.132072 9.483205 9.661690 9.668282 8.501511 9.894912 8.944912
## [512] 9.571497 9.508673 7.764153 10.952868 9.018822 10.703040 10.256724
## [519] 12.359628 12.087261 11.904006 11.322111 11.776776 11.422627 9.347899
## [526] 10.060736 10.918215 13.455648 10.264657 11.886987 8.463624 10.018840
## [533] 11.620789 11.937372 10.906560 11.319731 10.073569 10.148033 8.027459
## [540] 10.433612 8.772845 9.443192 11.231531 9.936066 10.194809 10.865416
## [547] 7.149828 10.093987 8.697430 7.990332 6.862537 10.972840 11.082733
## [554] 8.275102 9.992310 11.051724 11.785766 10.268406 10.885972 8.549391
## [561] 8.855129 7.917723 8.883889 10.571324 8.850071 9.573025 10.982628
## [568] 11.058179 10.307068 12.097165 11.796372 8.610615 9.486002 10.880438
## [575] 10.257544 8.420215 9.863619 10.555712 8.682071 11.375283 9.268080
## [582] 10.632157 5.426306 10.429439 10.670579 11.899691 9.126577 10.574866
## [589] 10.252403 9.161787 10.657023 9.886760 9.337623 11.171768 9.852604
## [596] 8.791179 11.835236 10.859762 8.419603 10.625026 14.380261 9.737829
## [603] 6.216254 10.485240 9.991620 10.537504 9.972301 9.971491 12.813821
## [610] 13.744831 11.319945 12.521164 10.901639 12.915493 11.103940 5.492951
## [617] 9.878360 11.585712 11.212991 11.331233 8.095340 11.870355 9.721558
## [624] 9.692347 10.056477 11.669248 9.643051 8.941019 8.537595 10.283843
## [631] 10.222504 10.990258 9.048341 10.039786 8.234315 10.533284 10.242641
## [638] 12.866644 9.922510 8.740943 9.347524 10.770844 7.817778 11.884503
## [645] 10.116321 10.415240 11.180581 6.928919 9.836136 8.486396 8.405628
## [652] 8.654821 8.786014 12.136807 12.742610 10.605085 12.526859 11.513964
## [659] 11.973160 10.161418 10.025750 8.643429 8.541357 8.911117 11.216532
## [666] 9.994001 8.004629 10.268656 8.704222 8.310614 8.884436 10.590074
## [673] 8.789420 11.258459 8.730089 10.033097 11.936156 12.694702 9.240838
## [680] 11.359042 7.548895 9.267697 9.134170 7.554140 8.558620 9.535132
## [687] 8.973215 9.694824 8.031855 10.322179 10.085708 9.943213 9.816070
## [694] 10.881079 8.629347 9.986253 11.099079 9.017537 11.533663 8.618910
## [701] 11.122250 8.727052 9.161918 9.520413 9.708862 10.020254 8.354750
## [708] 10.412074 9.427108 9.865565 9.559117 11.166338 12.449780 12.240562
## [715] 9.885163 10.231886 10.747824 8.462172 8.555562 10.406804 11.241618
## [722] 8.009242 11.097890 11.091005 9.953211 8.090522 10.126900 7.886413
## [729] 10.561540 9.997420 10.004236 9.146284 10.046041 12.585516 7.467424
## [736] 9.619932 10.086332 9.095641 10.713982 13.169559 11.845707 9.077724
## [743] 10.927847 10.863915 6.776511 11.495714 8.848615 10.824363 7.609257
## [750] 6.550002 10.782579 14.044113 11.341075 10.357382 10.379807 11.198703
## [757] 11.373346 9.413066 11.651786 9.242842 10.869712 9.799141 10.175905
## [764] 10.958831 8.486094 8.489135 11.591068 8.751166 8.594380 9.663922
## [771] 9.612711 10.806447 11.385389 12.132396 8.832952 11.088618 8.866103
## [778] 10.048570 11.776865 7.103144 10.190013 9.233964 10.503478 10.881979
## [785] 9.021092 8.418997 9.422154 11.228048 10.893914 11.485835 9.502987
## [792] 9.200741 8.070318 11.250826 8.763631 5.937013 11.634661 8.214942
## [799] 10.623175 11.426590 9.161143 10.708198 12.430655 11.751725 9.835423
## [806] 9.189357 8.506069 10.699358 12.818004 8.000124 8.440672 10.083457
## [813] 9.581189 9.993988 9.887652 8.923159 8.926214 9.836474 9.511551
## [820] 8.727596 9.112331 9.067065 9.540022 8.922699 11.439045 8.760270
## [827] 12.073511 9.312325 11.401046 10.496590 11.050563 7.574727 9.164732
## [834] 10.851458 9.440328 6.028087 7.366086 10.311326 9.918013 12.601687
## [841] 9.111533 10.839449 9.301620 10.443411 11.318927 10.120828 10.677324
## [848] 11.372259 11.144152 10.273289 8.898539 8.740851 10.171329 9.875304
## [855] 7.869081 10.170082 12.712402 9.011808 9.627938 8.805059 9.378606
## [862] 10.901853 10.905541 10.372331 9.246965 7.580660 11.017848 13.194576
## [869] 9.034856 8.757563 10.839197 10.199562 8.995581 9.181102 9.501389
## [876] 7.403393 9.986301 11.573569 10.718041 11.815361 10.426839 13.072381
## [883] 10.842479 9.839678 9.467805 10.782318 9.759565 9.279065 10.876929
## [890] 12.885649 10.276141 10.608095 10.713586 10.316702 7.291407 8.616271
## [897] 12.909021 8.928049 7.730981 10.272965 9.047989 11.038148 8.716153
## [904] 10.446061 9.069086 10.486117 9.190051 9.566768 9.283072 11.754889
## [911] 10.017446 9.593054 11.682339 8.391632 12.082505 11.338198 11.562308
## [918] 10.209742 7.794814 10.569404 9.678113 9.235823 9.934393 10.048938
## [925] 10.584269 10.170975 11.045406 8.184619 9.418572 11.106189 10.305632
## [932] 9.583918 9.122125 8.289522 8.544493 8.912851 13.817081 11.922607
## [939] 6.996310 10.599572 11.844302 9.874190 10.387772 10.453229 9.974750
## [946] 13.572997 10.640680 11.838814 10.518162 12.377984 11.488467 12.491248
## [953] 9.562009 8.312112 9.893911 8.789243 12.204492 6.902053 11.096227
## [960] 11.146718 7.918577 9.591925 10.588429 10.370174 11.927039 10.877609
## [967] 9.178977 10.125326 11.514974 9.126538 9.364390 10.134233 12.530720
## [974] 8.397403 10.400525 10.597885 8.138517 12.126303 10.575584 11.786305
## [981] 10.103662 8.210393 11.231572 11.712951 10.792983 9.621980 9.196309
## [988] 8.865451 9.674481 11.395642 7.768379 9.613179 8.813821 11.037504
## [995] 8.430390 12.444669 9.809678 9.711649 8.720496 6.018329
# 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 4 5 3 4 2 4 4 2 4 2 2 3 4 4 3 3 2 5 4 3 3 3 5 4 3 3 3 2 4 4 4 3 5 3 1 2
## [38] 4 3 2 4 2 4 4 3 1 4 3 2 3 2 3 2 2 3 1 3 3 2 3 5 4 2 3 2 4 2 4 3 1 4 4 4 5
## [75] 2 3 4 2 4 3 2 2 5 5 4 2 3 4 2 5 2 2 2 1 2 2 3 2 3 3 2 2 5 2 3 2 2 3 4 4 2
## [112] 4 4 4 2 4 2 5 3 3 4 3 4 4 2 3 5 4 3 1 4 1 3 3 4 1 5 3 3 4 3 5 5 4 3 3 4 2
## [149] 1 5 4 3 2 3 2 3 2 4 3 4 4 3 3 1 3 3 3 5 3 1 3 4 2 3 3 3 3 4 3 4 4 2 4 2 4
## [186] 3 3 4 3 2 2 3 3 3 4 2 2 4 3 4 3 3 5 2 2 3 5 5 1 4 4 2 5 2 2 3 4 4 3 4 4 3
## [223] 4 3 3 2 3 2 3 3 4 2 4 4 1 4 2 3 4 3 2 3 1 3 3 4 3 3 3 4 4 4 3 2 5 3 4 2 3
## [260] 4 3 3 4 2 2 3 2 3 3 1 2 4 2 4 5 3 4 2 5 3 2 3 2 1 5 3 3 2 2 2 2 4 5 3 3 4
## [297] 3 3 3 2 3 3 5 3 1 2 4 3 3 4 2 3 2 2 3 1 5 4 2 3 4 5 3 2 4 3 2 3 3 4 2 3 2
## [334] 2 5 3 3 4 3 3 3 2 5 4 4 2 5 2 5 3 4 2 4 4 3 3 2 3 4 3 4 3 3 4 2 2 4 4 2 4
## [371] 4 4 3 1 2 2 2 3 5 4 4 1 4 2 2 2 1 2 2 2 3 3 5 3 4 4 2 3 4 4 2 2 1 3 3 3 3
## [408] 3 4 3 2 4 3 2 4 4 4 5 4 4 4 4 2 2 3 3 3 3 4 3 3 3 4 3 2 3 4 3 4 3 3 3 3 1
## [445] 3 2 5 4 2 5 5 2 2 4 5 4 4 2 2 4 3 2 4 3 3 3 1 3 3 2 2 3 5 4 5 2 2 2 3 4 1
## [482] 2 2 5 2 3 4 4 3 2 2 3 4 4 3 1 3 2 2 3 2 2 4 1 4 2 3 2 2 3 3 3 3 1 4 3 4 4
## [519] 5 3 3 3 4 3 3 4 3 5 3 4 3 3 4 4 4 3 3 3 1 4 2 2 3 3 3 4 2 2 3 2 1 4 3 1 3
## [556] 2 5 3 3 1 1 1 2 2 1 2 4 3 3 4 4 3 3 3 2 2 3 3 1 4 3 3 1 3 4 3 3 3 3 4 4 3
## [593] 3 2 2 2 4 5 2 3 5 3 1 4 4 4 4 2 5 4 3 4 3 5 4 1 3 3 3 5 3 4 3 3 3 4 2 4 3
## [630] 5 3 4 2 3 3 3 3 3 3 2 3 3 3 4 4 4 2 2 4 2 2 3 2 3 4 5 5 4 3 3 2 1 2 3 4 3
## [667] 3 3 1 3 3 3 3 3 2 3 4 4 3 3 3 5 2 2 3 2 3 4 3 3 2 4 3 3 2 2 4 3 4 3 4 1 2
## [704] 2 3 3 3 2 3 3 4 3 5 3 3 4 5 2 3 3 3 3 3 2 3 4 2 2 3 4 3 3 3 4 2 3 3 2 3 3
## [741] 4 2 5 4 1 4 3 3 3 1 3 5 5 2 3 4 4 3 2 3 3 2 3 2 2 1 4 2 2 3 3 4 5 4 3 4 3
## [778] 5 4 1 1 3 2 5 2 2 4 4 3 4 3 2 1 3 2 1 3 1 3 3 3 3 5 4 3 2 2 4 4 3 1 4 1 3
## [815] 4 2 2 4 3 3 3 2 3 3 4 3 4 2 3 2 3 1 3 3 3 1 1 4 3 5 2 3 3 3 4 4 3 4 4 3 2
## [852] 2 3 1 1 3 4 2 3 2 2 3 4 3 2 2 4 5 2 3 5 3 2 2 2 2 1 4 3 4 3 3 2 3 2 4 2 3
## [889] 3 5 5 3 5 3 1 1 3 2 1 3 3 3 2 2 2 4 2 3 2 4 4 3 5 3 3 4 5 3 4 4 3 2 2 3 4
## [926] 2 4 2 2 4 3 3 4 3 2 2 5 3 2 3 3 4 2 3 3 5 4 3 2 4 4 4 1 3 3 2 5 2 3 4 2 3
## [963] 4 2 3 4 3 3 3 3 3 2 4 3 4 4 3 3 3 3 2 2 4 5 2 3 2 1 2 4 1 3 3 3 3 4 4 3 3
## [1000] 1
# create sample dataframe by joining variables
sample_data <- data.frame(xAxis,yAxis,group)
sample_data
## xAxis yAxis group
## 1 -7.453866e-01 9.397657 2
## 2 1.112834e+00 12.286828 4
## 3 1.616667e+00 12.753188 5
## 4 -2.225926e-01 9.909517 3
## 5 1.419856e+00 10.726787 4
## 6 -1.283909e+00 9.561486 2
## 7 1.109258e+00 9.720841 4
## 8 1.212121e+00 10.642103 4
## 9 -1.089389e+00 9.582655 2
## 10 1.140458e+00 12.506351 4
## 11 -9.635149e-01 8.939211 2
## 12 -9.564921e-01 8.376955 2
## 13 -2.191163e-01 9.878450 3
## 14 6.851901e-01 11.277611 4
## 15 1.242714e+00 11.872275 4
## 16 -1.041953e-01 9.997038 3
## 17 -4.893366e-01 9.863265 3
## 18 -8.601536e-01 9.535250 2
## 19 1.610345e+00 11.066817 5
## 20 1.475209e+00 11.931874 4
## 21 1.601949e-01 9.469595 3
## 22 4.644350e-01 10.330704 3
## 23 5.314498e-02 8.598460 3
## 24 2.550895e+00 11.734031 5
## 25 1.257231e+00 11.898245 4
## 26 -2.370791e-01 8.468433 3
## 27 4.262925e-01 9.244084 3
## 28 3.426237e-01 11.036601 3
## 29 -5.149847e-01 7.874644 2
## 30 5.920164e-01 11.327873 4
## 31 7.149082e-01 10.827382 4
## 32 1.432975e+00 11.983886 4
## 33 -7.916888e-02 10.167238 3
## 34 1.676181e+00 12.717787 5
## 35 1.160458e-01 10.902013 3
## 36 -1.721663e+00 7.311760 1
## 37 -8.892661e-01 10.753398 2
## 38 1.144037e+00 14.232733 4
## 39 2.445141e-01 10.321356 3
## 40 -1.407634e+00 8.599660 2
## 41 1.244238e+00 11.094622 4
## 42 -5.099116e-01 8.057322 2
## 43 8.915904e-01 11.366223 4
## 44 9.707900e-01 10.833686 4
## 45 1.950343e-01 8.934612 3
## 46 -2.133363e+00 8.837575 1
## 47 1.115985e+00 12.795874 4
## 48 2.234185e-02 10.435951 3
## 49 -1.462171e+00 8.696305 2
## 50 8.735605e-02 9.218425 3
## 51 -1.297105e+00 7.996232 2
## 52 3.651586e-01 10.814483 3
## 53 -6.722992e-01 8.749454 2
## 54 -7.930440e-01 10.346406 2
## 55 -3.334374e-01 9.890548 3
## 56 -2.027157e+00 7.498144 1
## 57 3.586543e-01 9.459383 3
## 58 -2.510933e-01 9.659895 3
## 59 -1.217642e+00 9.494136 2
## 60 -8.401360e-02 10.025525 3
## 61 2.388805e+00 12.040110 5
## 62 1.478646e+00 12.671305 4
## 63 -5.381292e-01 9.183806 2
## 64 -3.636397e-01 9.328427 3
## 65 -1.002196e+00 8.074814 2
## 66 5.146007e-01 8.780084 4
## 67 -5.731214e-01 10.807595 2
## 68 7.063590e-01 11.252928 4
## 69 -4.836548e-01 9.984762 3
## 70 -2.031780e+00 7.825037 1
## 71 1.018322e+00 10.913757 4
## 72 1.372891e+00 11.284598 4
## 73 8.778598e-01 9.220568 4
## 74 1.555501e+00 11.436904 5
## 75 -7.092181e-01 9.578551 2
## 76 2.284654e-01 9.675925 3
## 77 5.650148e-01 9.418627 4
## 78 -1.421210e+00 8.420433 2
## 79 8.320104e-01 11.880817 4
## 80 -3.538334e-01 10.939512 3
## 81 -1.262031e+00 8.074703 2
## 82 -1.184659e+00 7.599984 2
## 83 1.594753e+00 10.780876 5
## 84 2.016534e+00 11.966795 5
## 85 5.705928e-01 12.195765 4
## 86 -6.856967e-01 9.873300 2
## 87 -2.349545e-01 10.502501 3
## 88 5.584347e-01 11.709075 4
## 89 -1.230237e+00 9.102284 2
## 90 1.554731e+00 11.183830 5
## 91 -1.152217e+00 7.603867 2
## 92 -6.114856e-01 8.380409 2
## 93 -7.024153e-01 10.884013 2
## 94 -2.056660e+00 8.431401 1
## 95 -1.319453e+00 9.612767 2
## 96 -6.447146e-01 9.208543 2
## 97 1.546029e-01 9.979935 3
## 98 -6.576738e-01 8.799780 2
## 99 4.065794e-01 9.028685 3
## 100 -4.402328e-01 9.303691 3
## 101 -1.103099e+00 7.809927 2
## 102 -6.703175e-01 9.634175 2
## 103 1.533863e+00 11.416347 5
## 104 -8.298023e-01 8.838739 2
## 105 3.636126e-01 12.276325 3
## 106 -6.213304e-01 9.063364 2
## 107 -6.168832e-01 9.319757 2
## 108 -1.158119e-01 11.378771 3
## 109 1.213172e+00 9.846260 4
## 110 9.829543e-01 9.715327 4
## 111 -7.113613e-01 10.673942 2
## 112 5.969266e-01 12.192406 4
## 113 7.578510e-01 9.753946 4
## 114 5.743317e-01 11.107637 4
## 115 -1.030368e+00 8.785098 2
## 116 1.455029e+00 12.421373 4
## 117 -1.042592e+00 8.618081 2
## 118 2.666930e+00 11.937210 5
## 119 -1.613161e-01 11.030811 3
## 120 -3.850002e-01 9.683722 3
## 121 5.045856e-01 8.947197 4
## 122 -5.742047e-02 10.475273 3
## 123 6.506845e-01 10.908677 4
## 124 1.127286e+00 11.772313 4
## 125 -6.369325e-01 9.455262 2
## 126 -2.778223e-01 9.511325 3
## 127 1.823947e+00 10.867531 5
## 128 1.488190e+00 11.154996 4
## 129 -4.235818e-01 10.951501 3
## 130 -2.591146e+00 6.689374 1
## 131 1.483227e+00 9.448467 4
## 132 -1.769383e+00 8.485869 1
## 133 -2.108717e-01 10.001512 3
## 134 -4.725348e-01 9.912602 3
## 135 1.091790e+00 12.990014 4
## 136 -1.869665e+00 8.031592 1
## 137 1.730909e+00 11.329261 5
## 138 -3.189835e-01 10.233779 3
## 139 1.858014e-01 10.557650 3
## 140 5.056291e-01 9.891666 4
## 141 2.717584e-01 10.026738 3
## 142 1.709411e+00 11.787643 5
## 143 1.554871e+00 11.685091 5
## 144 1.221901e+00 11.470751 4
## 145 -3.122473e-01 9.558366 3
## 146 -1.144604e-01 9.728222 3
## 147 5.472037e-01 10.856178 4
## 148 -1.367172e+00 9.325661 2
## 149 -1.673499e+00 9.329701 1
## 150 1.822256e+00 11.142564 5
## 151 1.258035e+00 11.422249 4
## 152 1.481877e-01 8.929261 3
## 153 -1.107107e+00 9.883799 2
## 154 -2.101725e-01 9.881130 3
## 155 -9.355808e-01 9.457361 2
## 156 -3.637922e-01 9.772439 3
## 157 -1.428628e+00 9.615179 2
## 158 1.279208e+00 10.791367 4
## 159 3.262582e-01 9.242434 3
## 160 6.165129e-01 10.657452 4
## 161 9.398759e-01 11.087064 4
## 162 4.507299e-01 10.916789 3
## 163 3.158073e-01 11.503039 3
## 164 -2.281957e+00 9.305078 1
## 165 -3.561705e-02 9.831784 3
## 166 2.946156e-01 9.944372 3
## 167 -1.563486e-01 10.786990 3
## 168 1.721763e+00 12.612059 5
## 169 4.373044e-01 10.569143 3
## 170 -1.821234e+00 9.328736 1
## 171 1.550344e-01 10.730852 3
## 172 1.002583e+00 10.955701 4
## 173 -6.329229e-01 8.285148 2
## 174 -7.735131e-02 11.612601 3
## 175 3.861550e-01 9.783285 3
## 176 2.740864e-03 8.927757 3
## 177 5.157061e-02 9.976025 3
## 178 6.709318e-01 10.657572 4
## 179 -3.627891e-01 8.476652 3
## 180 9.742347e-01 11.670741 4
## 181 1.040428e+00 11.718555 4
## 182 -5.367105e-01 10.543682 2
## 183 9.760422e-01 11.121688 4
## 184 -6.478758e-01 10.310019 2
## 185 6.498341e-01 10.252183 4
## 186 2.125393e-02 11.001850 3
## 187 3.090123e-01 10.076708 3
## 188 1.096940e+00 10.968186 4
## 189 4.142528e-01 10.536872 3
## 190 -9.324995e-01 11.345910 2
## 191 -7.785797e-01 7.525418 2
## 192 1.399382e-01 9.469018 3
## 193 -2.449736e-01 9.984130 3
## 194 1.014444e-02 9.964342 3
## 195 1.054445e+00 11.144654 4
## 196 -5.923229e-01 8.005990 2
## 197 -8.059927e-01 9.027509 2
## 198 5.790845e-01 10.545320 4
## 199 -2.328233e-01 8.477654 3
## 200 5.700291e-01 10.315606 4
## 201 1.477026e-01 10.373918 3
## 202 4.017317e-01 9.314303 3
## 203 1.871720e+00 12.027633 5
## 204 -7.458580e-01 9.111311 2
## 205 -5.679769e-01 9.514396 2
## 206 9.466068e-03 10.805220 3
## 207 2.048355e+00 11.549030 5
## 208 1.985207e+00 11.089254 5
## 209 -1.769419e+00 8.229090 1
## 210 8.261416e-01 11.064231 4
## 211 1.203314e+00 11.091902 4
## 212 -1.136579e+00 6.110981 2
## 213 1.625516e+00 12.272492 5
## 214 -7.449527e-01 7.451623 2
## 215 -8.090581e-01 7.781318 2
## 216 -3.872437e-01 10.303336 3
## 217 5.420848e-01 9.954987 4
## 218 5.558915e-01 11.030897 4
## 219 3.139446e-01 10.586493 3
## 220 7.548533e-01 10.486827 4
## 221 6.390620e-01 9.488090 4
## 222 2.599574e-01 11.397400 3
## 223 5.594768e-01 9.340429 4
## 224 7.111423e-02 10.132317 3
## 225 -9.435378e-02 10.131398 3
## 226 -8.787231e-01 9.739258 2
## 227 -3.543012e-02 9.304762 3
## 228 -8.450144e-01 7.217564 2
## 229 1.229080e-01 11.245642 3
## 230 -1.107785e-01 9.927822 3
## 231 1.064520e+00 12.452590 4
## 232 -7.587317e-01 8.432347 2
## 233 6.923660e-01 12.342047 4
## 234 5.671310e-01 11.305545 4
## 235 -2.086828e+00 8.591578 1
## 236 8.894948e-01 12.894898 4
## 237 -9.588017e-01 8.331524 2
## 238 4.341573e-03 11.146749 3
## 239 7.326553e-01 10.239605 4
## 240 4.577658e-01 10.115978 3
## 241 -1.378582e+00 9.374034 2
## 242 -2.000853e-02 9.436505 3
## 243 -1.505652e+00 9.683124 1
## 244 9.485944e-02 10.046880 3
## 245 3.887853e-01 9.282360 3
## 246 8.700351e-01 10.868274 4
## 247 1.106425e-01 9.409167 3
## 248 -6.694163e-02 11.188529 3
## 249 -3.664279e-01 8.642929 3
## 250 1.290545e+00 11.473288 4
## 251 5.216271e-01 10.299299 4
## 252 1.039771e+00 10.796384 4
## 253 2.177906e-01 10.936399 3
## 254 -5.523703e-01 9.422769 2
## 255 1.798185e+00 11.618907 5
## 256 9.030856e-02 10.624702 3
## 257 9.036563e-01 9.835349 4
## 258 -7.345701e-01 9.610789 2
## 259 -1.908699e-01 10.127857 3
## 260 1.386649e+00 12.099043 4
## 261 -1.692437e-01 11.069817 3
## 262 -4.580138e-01 9.487461 3
## 263 8.226337e-01 12.137589 4
## 264 -5.863992e-01 9.637543 2
## 265 -5.012582e-01 11.194130 2
## 266 7.235030e-02 8.279908 3
## 267 -6.962808e-01 6.165545 2
## 268 3.286573e-02 9.671005 3
## 269 -2.145390e-01 9.353402 3
## 270 -2.189593e+00 10.515099 1
## 271 -7.810447e-01 9.452654 2
## 272 6.747315e-01 10.398973 4
## 273 -6.416264e-01 8.662920 2
## 274 6.789903e-01 13.117922 4
## 275 2.364652e+00 11.324613 5
## 276 2.184058e-01 10.611426 3
## 277 6.604410e-01 12.120456 4
## 278 -5.863318e-01 7.234062 2
## 279 1.626649e+00 11.659836 5
## 280 -9.992452e-02 10.827574 3
## 281 -5.475719e-01 9.544322 2
## 282 4.050502e-01 11.661286 3
## 283 -8.558470e-01 8.665173 2
## 284 -1.894257e+00 6.194470 1
## 285 1.552237e+00 12.104489 5
## 286 -3.626194e-01 8.227866 3
## 287 4.619869e-01 12.457786 3
## 288 -1.006956e+00 8.985029 2
## 289 -1.281014e+00 7.006338 2
## 290 -1.072515e+00 9.351578 2
## 291 -6.798579e-01 7.808920 2
## 292 1.328946e+00 10.786623 4
## 293 1.840073e+00 10.226031 5
## 294 -1.996479e-01 9.415843 3
## 295 1.386929e-01 8.768759 3
## 296 9.753225e-01 10.882284 4
## 297 4.900117e-01 11.751835 3
## 298 2.806392e-01 10.252953 3
## 299 2.609490e-02 11.159902 3
## 300 -1.229037e+00 7.706597 2
## 301 4.520452e-01 10.278702 3
## 302 -3.918335e-01 9.562681 3
## 303 1.838862e+00 11.996602 5
## 304 -6.521304e-02 8.100405 3
## 305 -1.624003e+00 7.952850 1
## 306 -1.272129e+00 8.820156 2
## 307 1.280230e+00 12.704013 4
## 308 3.468056e-01 11.402318 3
## 309 2.790492e-01 11.264592 3
## 310 1.454619e+00 10.763288 4
## 311 -1.345795e+00 10.361750 2
## 312 -4.873028e-01 9.672158 3
## 313 -9.184097e-01 9.348912 2
## 314 -1.467760e+00 10.278864 2
## 315 3.192311e-01 11.505662 3
## 316 -2.787185e+00 7.456899 1
## 317 3.813286e+00 16.410352 5
## 318 1.235674e+00 9.932736 4
## 319 -5.553115e-01 9.593427 2
## 320 -4.710029e-01 10.413922 3
## 321 1.342099e+00 10.600920 4
## 322 2.310624e+00 9.630801 5
## 323 2.908265e-01 9.956214 3
## 324 -1.169536e+00 9.717754 2
## 325 8.431659e-01 10.154049 4
## 326 2.720174e-01 10.761170 3
## 327 -7.366049e-01 9.778346 2
## 328 2.943440e-03 10.302335 3
## 329 4.043566e-01 11.852260 3
## 330 7.857545e-01 10.204824 4
## 331 -5.155689e-01 10.411170 2
## 332 3.543866e-01 12.455960 3
## 333 -6.046720e-01 9.612179 2
## 334 -1.276038e+00 7.745655 2
## 335 1.825193e+00 13.955643 5
## 336 4.349259e-01 10.433221 3
## 337 -3.042715e-01 8.699200 3
## 338 9.788263e-01 12.038551 4
## 339 4.540006e-01 10.476543 3
## 340 -1.387786e-01 8.296138 3
## 341 6.440301e-02 9.376160 3
## 342 -6.060368e-01 11.013940 2
## 343 1.807651e+00 12.472740 5
## 344 1.031066e+00 9.351451 4
## 345 1.154838e+00 11.950635 4
## 346 -1.499626e+00 9.271778 2
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## 894 4.039780e-01 10.316702 3
## 895 -1.972468e+00 7.291407 1
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## 949 -7.423906e-01 10.518162 2
## 950 1.095894e+00 12.377984 4
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## 952 1.409417e+00 12.491248 4
## 953 -1.894593e+00 9.562009 1
## 954 7.949878e-03 8.312112 3
## 955 3.443205e-02 9.893911 3
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## 988 -3.171462e+00 8.865451 1
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## 998 -4.680503e-01 9.711649 3
## 999 1.298723e-01 8.720496 3
## 1000 -2.567159e+00 6.018329 1
# 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)
