# Mindanao State University
# General Santos City
# Introduction to R base commands
# Submitted by: Danzel A. Ocenar
# Math Department
# March 20, 2023
# Lab Exercise 1: How to create Lines in with different styles in R
# Step 1: Create Data
x <- 1:10 # Create example data
y <- c(3, 1, 5, 2, 3, 8, 4, 7, 6, 9) # using the c() function to create an array
# Step 2: Plot the line graph using the base plot() command
plot(x, y, type = "l")
# Step 3: Add Main Title & Change Axis Labels
plot(x, y, type = "l",
main = "Hello: This is my Line Plot",
xlab = "My X-Values",
ylab = "My Y-Values")
# Step 4: Add color to the line using the col command
plot(x, y, type = "l",
main = "This is my Line Plot",
xlab = "My X-Values",
ylab = "My Y-Values",
col = "red")
# Step 5: Modify Thickness of Line using lwd command
plot(x, y, type = "l",
main = "This is my Line Plot",
xlab = "My X-Values",
ylab = "My Y-Values",
lwd=7,
col = "green")
# Step 6: Add points to line graph by changing the type command
plot(x, y, type = "b",
main = "This is my Line Plot",
xlab = "My X-Values",
ylab = "My Y-Values",
lwd=3,
col = "blue")
# Lab Exercise 2: How to Make Lines in Various Styles Using R
# Step1: Assign values for different lines. We enclose the entire line with parenthesis symbol to force R to display the results instantly
# set the same value for the x variable
(x <- 1:10)
## [1] 1 2 3 4 5 6 7 8 9 10
# set different values for y variables
(y1 <- c(3, 1, 5, 2, 3, 8, 4, 7, 6, 9))
## [1] 3 1 5 2 3 8 4 7 6 9
(y2 <- c(5, 1, 4, 6, 2, 3, 7, 8, 2, 8))
## [1] 5 1 4 6 2 3 7 8 2 8
(y3 <- c(3, 3, 3, 3, 4, 4, 5, 5, 7, 7))
## [1] 3 3 3 3 4 4 5 5 7 7
# Plot first the pair x and y1.
plot(x, y1, type = "b",
main = "This is my Line Plot",
xlab = "My X-Values",
ylab = "My Y-Values",
lwd = 3,
col = "blue")
# then Add the two lines (for x,y2) and (x,y3)
lines(x, y2, type = "b", col = "red", lwd = 3)
lines(x, y3, type = "b", col = "green", lwd = 3)
# Add legend to the plot
legend("topleft",
legend = c("Line y1", "Line y2", "Line y3"),
lty = 1,
col = c("blue", "red", "green"))
# Step 2: Create Different Point Symbol for each line using the pch command
plot(x, y1, type = "b", pch = 16,
main = "This is my Line Plot",
xlab = "My X-Values",
ylab = "My Y-Values",
lwd = 3,
col = "blue")
lines(x, y2, type = "b", col = "red", lwd = 3, pch = 15)
lines(x, y3, type ="b", col = "green", lwd = 3, pch = 8)
# Add legend
legend("topleft",
legend = c("Line y1", "Line y2", "Line y3"),
col = c("black", "red", "green"),
lty = 1)
# Lab Exercise 3: Create Line graph without x values
Pupils <- c(3.55 ,3.54 ,3.53 ,3.61 ,3.65 ,3.63 ,3.61
,3.61 ,3.59 ,3.63 ,3.59 ,3.63 ,3.62 ,3.62
,3.59 ,3.63 ,3.62 ,3.65 ,3.65)
# get number of elements of Pupils
length (Pupils)
## [1] 19
# Display the elements of Pupils
Pupils
## [1] 3.55 3.54 3.53 3.61 3.65 3.63 3.61 3.61 3.59 3.63 3.59 3.63 3.62 3.62 3.59
## [16] 3.63 3.62 3.65 3.65
# You can obtain the plot without x values
plot (Pupils, type = 'o')
# Lab Exercise 4: How to Create vertical, horizontal lines
# We will use buit-in cars dataset in R
# display the cars dataset
cars
## speed dist
## 1 4 2
## 2 4 10
## 3 7 4
## 4 7 22
## 5 8 16
## 6 9 10
## 7 10 18
## 8 10 26
## 9 10 34
## 10 11 17
## 11 11 28
## 12 12 14
## 13 12 20
## 14 12 24
## 15 12 28
## 16 13 26
## 17 13 34
## 18 13 34
## 19 13 46
## 20 14 26
## 21 14 36
## 22 14 60
## 23 14 80
## 24 15 20
## 25 15 26
## 26 15 54
## 27 16 32
## 28 16 40
## 29 17 32
## 30 17 40
## 31 17 50
## 32 18 42
## 33 18 56
## 34 18 76
## 35 18 84
## 36 19 36
## 37 19 46
## 38 19 68
## 39 20 32
## 40 20 48
## 41 20 52
## 42 20 56
## 43 20 64
## 44 22 66
## 45 23 54
## 46 24 70
## 47 24 92
## 48 24 93
## 49 24 120
## 50 25 85
# get the number of rows and columns using dim() command
dim(cars)
## [1] 50 2
# display the variable names of the cars dataset
names(cars)
## [1] "speed" "dist"
# display only the first column of the dataset
cars$speed # using the column name
## [1] 4 4 7 7 8 9 10 10 10 11 11 12 12 12 12 13 13 13 13 14 14 14 14 15 15
## [26] 15 16 16 17 17 17 18 18 18 18 19 19 19 20 20 20 20 20 22 23 24 24 24 24 25
cars[,1] # using the column number
## [1] 4 4 7 7 8 9 10 10 10 11 11 12 12 12 12 13 13 13 13 14 14 14 14 15 15
## [26] 15 16 16 17 17 17 18 18 18 18 19 19 19 20 20 20 20 20 22 23 24 24 24 24 25
# Remarks: the following commands will give you the same result
plot (cars) # using the comma after the name
plot (cars[,1], cars[,2]) # using the column index 1 and 2
attach (cars); plot (speed,dist) # using the attach command to load the variables
plot (cars$speed,cars$dist) # using the dollar notation
# combine all 4 plots using the par() command
par (mfrow = c(2, 2)) # set a 2x2 plot output
plot (cars,) # using the comma after the name
plot (cars[,1], cars[,2]) # using the column index 1 and 2
attach (cars); plot(speed,dist) # using the attach command to load the variables
## The following objects are masked from cars (pos = 3):
##
## dist, speed
plot (cars$speed, cars$dist) # using the dollar notation
par (mfrow = c(1,1)) # reset to default plot setting
# Problem: Create vertical lines using the v command
plot (cars)
abline (v = 15, col = "darkgreen", lwd = 3) # vertical line
abline (v = 10, col = "blue", lwd = 3) # vertical line
# Problem: Create horizontal lines using the h command
abline (h = 80, col = "darkgreen", lwd = 3) # vertical line
abline (h = 20, col = "blue", lwd = 3) # vertical line
# Create more lines simultaneously, using a vector of values
plot(cars)
abline(v = c(9, 22,25), col = c("darkgreen", "blue","red"),
lwd = c(1, 3,2), # line thickness
lty = c(2,2,2)) # dashed lines
plot (cars)
abline(v = c(9, 22, 25), col = c("darkgreen", "blue", "red"),
lwd = c(1, 3, 2)) # line thickness and solid lines
# create horizontal lines
plot (cars)
abline (h = 60, col = "red", lty = 1, lwd = 3)
abline (h = 100, col = "red", lty = 2, lwd = 3)
abline (h = 20, col = "red", lty = 3, lwd = 3)
# Lab Exercise 5: How to Plot data by group
# We will use buit-in iris dataset in R
# this dataset is a collection of 4 species of flowers with different
# sepal length and width and also with different petal length and width
dim(iris)
## [1] 150 5
names(iris)
## [1] "Sepal.Length" "Sepal.Width" "Petal.Length" "Petal.Width" "Species"
# two different commands to get the frequency table
table(iris$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\\USER\\Documents\\MAT 108\\Modules and Activities"
filename <- "Cancer.csv"
(file <- paste0(folder,"/",filename))
## [1] "C:\\Users\\USER\\Documents\\MAT 108\\Modules and Activities/Cancer.csv"
cancer <- read.csv(file)
dim(cancer)
## [1] 173 17
names(cancer)
## [1] "country" "incomeperperson" "alcconsumption"
## [4] "armedforcesrate" "breastcancer" "co2emissions"
## [7] "femaleemployrate" "hivrate" "internetuserate"
## [10] "lifeexpectancy" "oilperperson" "polityscore"
## [13] "relectricperperson" "suicideper100th" "employrate"
## [16] "urbanrate" "continent"
# compute mean value for every continent
(means <- round(tapply(cancer$breastcancer, cancer$continent, mean), digits=2))
## AF AS EE LATAM NORAM OC WE
## 24.02 24.51 49.44 36.70 71.73 45.80 74.80
# draw boxplot per continent
boxplot(cancer$breastcancer ~ cancer$continent, main= "Breast cancer by continent (brown dot = mean value)", xlab="continents", ylab="new
cases per 100,00 residents", col=rainbow(7))
# insert the mean value using brown dot
points(means, col="brown", pch=18)
# Lab Exercise 8: How to load external datasets and change data layout
folder <- "C:\\Users\\USER\\Documents\\MAT 108\\Modules and Activities"
filename <- "hsb2.csv"
(file <- paste0(folder,"/",filename))
## [1] "C:\\Users\\USER\\Documents\\MAT 108\\Modules and Activities/hsb2.csv"
hsb2_wide <- read.csv(file)
# display only the top 6 rows
head(hsb2_wide)
## X id female race ses schtyp prog read write math science socst
## 1 1 70 0 4 1 1 1 57 52 41 47 57
## 2 2 121 1 4 2 1 3 68 59 53 63 61
## 3 3 86 0 4 3 1 1 44 33 54 58 31
## 4 4 141 0 4 3 1 3 63 44 47 53 56
## 5 5 172 0 4 2 1 2 47 52 57 53 61
## 6 6 113 0 4 2 1 2 44 52 51 63 61
# display only the last 6 rows
tail(hsb2_wide)
## X id female race ses schtyp prog read write math science socst
## 195 195 179 1 4 2 2 2 47 65 60 50 56
## 196 196 31 1 2 2 2 1 55 59 52 42 56
## 197 197 145 1 4 2 1 3 42 46 38 36 46
## 198 198 187 1 4 2 2 1 57 41 57 55 52
## 199 199 118 1 4 2 1 1 55 62 58 58 61
## 200 200 137 1 4 3 1 2 63 65 65 53 61
# delete redundant first column (run only once)
(hsb2_wide <- hsb2_wide[-1])
## id female race ses schtyp prog read write math science socst
## 1 70 0 4 1 1 1 57 52 41 47 57
## 2 121 1 4 2 1 3 68 59 53 63 61
## 3 86 0 4 3 1 1 44 33 54 58 31
## 4 141 0 4 3 1 3 63 44 47 53 56
## 5 172 0 4 2 1 2 47 52 57 53 61
## 6 113 0 4 2 1 2 44 52 51 63 61
## 7 50 0 3 2 1 1 50 59 42 53 61
## 8 11 0 1 2 1 2 34 46 45 39 36
## 9 84 0 4 2 1 1 63 57 54 58 51
## 10 48 0 3 2 1 2 57 55 52 50 51
## 11 75 0 4 2 1 3 60 46 51 53 61
## 12 60 0 4 2 1 2 57 65 51 63 61
## 13 95 0 4 3 1 2 73 60 71 61 71
## 14 104 0 4 3 1 2 54 63 57 55 46
## 15 38 0 3 1 1 2 45 57 50 31 56
## 16 115 0 4 1 1 1 42 49 43 50 56
## 17 76 0 4 3 1 2 47 52 51 50 56
## 18 195 0 4 2 2 1 57 57 60 58 56
## 19 114 0 4 3 1 2 68 65 62 55 61
## 20 85 0 4 2 1 1 55 39 57 53 46
## 21 167 0 4 2 1 1 63 49 35 66 41
## 22 143 0 4 2 1 3 63 63 75 72 66
## 23 41 0 3 2 1 2 50 40 45 55 56
## 24 20 0 1 3 1 2 60 52 57 61 61
## 25 12 0 1 2 1 3 37 44 45 39 46
## 26 53 0 3 2 1 3 34 37 46 39 31
## 27 154 0 4 3 1 2 65 65 66 61 66
## 28 178 0 4 2 2 3 47 57 57 58 46
## 29 196 0 4 3 2 2 44 38 49 39 46
## 30 29 0 2 1 1 1 52 44 49 55 41
## 31 126 0 4 2 1 1 42 31 57 47 51
## 32 103 0 4 3 1 2 76 52 64 64 61
## 33 192 0 4 3 2 2 65 67 63 66 71
## 34 150 0 4 2 1 3 42 41 57 72 31
## 35 199 0 4 3 2 2 52 59 50 61 61
## 36 144 0 4 3 1 1 60 65 58 61 66
## 37 200 0 4 2 2 2 68 54 75 66 66
## 38 80 0 4 3 1 2 65 62 68 66 66
## 39 16 0 1 1 1 3 47 31 44 36 36
## 40 153 0 4 2 1 3 39 31 40 39 51
## 41 176 0 4 2 2 2 47 47 41 42 51
## 42 177 0 4 2 2 2 55 59 62 58 51
## 43 168 0 4 2 1 2 52 54 57 55 51
## 44 40 0 3 1 1 1 42 41 43 50 41
## 45 62 0 4 3 1 1 65 65 48 63 66
## 46 169 0 4 1 1 1 55 59 63 69 46
## 47 49 0 3 3 1 3 50 40 39 49 47
## 48 136 0 4 2 1 2 65 59 70 63 51
## 49 189 0 4 2 2 2 47 59 63 53 46
## 50 7 0 1 2 1 2 57 54 59 47 51
## 51 27 0 2 2 1 2 53 61 61 57 56
## 52 128 0 4 3 1 2 39 33 38 47 41
## 53 21 0 1 2 1 1 44 44 61 50 46
## 54 183 0 4 2 2 2 63 59 49 55 71
## 55 132 0 4 2 1 2 73 62 73 69 66
## 56 15 0 1 3 1 3 39 39 44 26 42
## 57 67 0 4 1 1 3 37 37 42 33 32
## 58 22 0 1 2 1 3 42 39 39 56 46
## 59 185 0 4 2 2 2 63 57 55 58 41
## 60 9 0 1 2 1 3 48 49 52 44 51
## 61 181 0 4 2 2 2 50 46 45 58 61
## 62 170 0 4 3 1 2 47 62 61 69 66
## 63 134 0 4 1 1 1 44 44 39 34 46
## 64 108 0 4 2 1 1 34 33 41 36 36
## 65 197 0 4 3 2 2 50 42 50 36 61
## 66 140 0 4 2 1 3 44 41 40 50 26
## 67 171 0 4 2 1 2 60 54 60 55 66
## 68 107 0 4 1 1 3 47 39 47 42 26
## 69 81 0 4 1 1 2 63 43 59 65 44
## 70 18 0 1 2 1 3 50 33 49 44 36
## 71 155 0 4 2 1 1 44 44 46 39 51
## 72 97 0 4 3 1 2 60 54 58 58 61
## 73 68 0 4 2 1 2 73 67 71 63 66
## 74 157 0 4 2 1 1 68 59 58 74 66
## 75 56 0 4 2 1 3 55 45 46 58 51
## 76 5 0 1 1 1 2 47 40 43 45 31
## 77 159 0 4 3 1 2 55 61 54 49 61
## 78 123 0 4 3 1 1 68 59 56 63 66
## 79 164 0 4 2 1 3 31 36 46 39 46
## 80 14 0 1 3 1 2 47 41 54 42 56
## 81 127 0 4 3 1 2 63 59 57 55 56
## 82 165 0 4 1 1 3 36 49 54 61 36
## 83 174 0 4 2 2 2 68 59 71 66 56
## 84 3 0 1 1 1 2 63 65 48 63 56
## 85 58 0 4 2 1 3 55 41 40 44 41
## 86 146 0 4 3 1 2 55 62 64 63 66
## 87 102 0 4 3 1 2 52 41 51 53 56
## 88 117 0 4 3 1 3 34 49 39 42 56
## 89 133 0 4 2 1 3 50 31 40 34 31
## 90 94 0 4 3 1 2 55 49 61 61 56
## 91 24 0 2 2 1 2 52 62 66 47 46
## 92 149 0 4 1 1 1 63 49 49 66 46
## 93 82 1 4 3 1 2 68 62 65 69 61
## 94 8 1 1 1 1 2 39 44 52 44 48
## 95 129 1 4 1 1 1 44 44 46 47 51
## 96 173 1 4 1 1 1 50 62 61 63 51
## 97 57 1 4 2 1 2 71 65 72 66 56
## 98 100 1 4 3 1 2 63 65 71 69 71
## 99 1 1 1 1 1 3 34 44 40 39 41
## 100 194 1 4 3 2 2 63 63 69 61 61
## 101 88 1 4 3 1 2 68 60 64 69 66
## 102 99 1 4 3 1 1 47 59 56 66 61
## 103 47 1 3 1 1 2 47 46 49 33 41
## 104 120 1 4 3 1 2 63 52 54 50 51
## 105 166 1 4 2 1 2 52 59 53 61 51
## 106 65 1 4 2 1 2 55 54 66 42 56
## 107 101 1 4 3 1 2 60 62 67 50 56
## 108 89 1 4 1 1 3 35 35 40 51 33
## 109 54 1 3 1 2 1 47 54 46 50 56
## 110 180 1 4 3 2 2 71 65 69 58 71
## 111 162 1 4 2 1 3 57 52 40 61 56
## 112 4 1 1 1 1 2 44 50 41 39 51
## 113 131 1 4 3 1 2 65 59 57 46 66
## 114 125 1 4 1 1 2 68 65 58 59 56
## 115 34 1 1 3 2 2 73 61 57 55 66
## 116 106 1 4 2 1 3 36 44 37 42 41
## 117 130 1 4 3 1 1 43 54 55 55 46
## 118 93 1 4 3 1 2 73 67 62 58 66
## 119 163 1 4 1 1 2 52 57 64 58 56
## 120 37 1 3 1 1 3 41 47 40 39 51
## 121 35 1 1 1 2 1 60 54 50 50 51
## 122 87 1 4 2 1 1 50 52 46 50 56
## 123 73 1 4 2 1 2 50 52 53 39 56
## 124 151 1 4 2 1 3 47 46 52 48 46
## 125 44 1 3 1 1 3 47 62 45 34 46
## 126 152 1 4 3 1 2 55 57 56 58 61
## 127 105 1 4 2 1 2 50 41 45 44 56
## 128 28 1 2 2 1 1 39 53 54 50 41
## 129 91 1 4 3 1 3 50 49 56 47 46
## 130 45 1 3 1 1 3 34 35 41 29 26
## 131 116 1 4 2 1 2 57 59 54 50 56
## 132 33 1 2 1 1 2 57 65 72 54 56
## 133 66 1 4 2 1 3 68 62 56 50 51
## 134 72 1 4 2 1 3 42 54 47 47 46
## 135 77 1 4 1 1 2 61 59 49 44 66
## 136 61 1 4 3 1 2 76 63 60 67 66
## 137 190 1 4 2 2 2 47 59 54 58 46
## 138 42 1 3 2 1 3 46 52 55 44 56
## 139 2 1 1 2 1 3 39 41 33 42 41
## 140 55 1 3 2 2 2 52 49 49 44 61
## 141 19 1 1 1 1 1 28 46 43 44 51
## 142 90 1 4 3 1 2 42 54 50 50 52
## 143 142 1 4 2 1 3 47 42 52 39 51
## 144 17 1 1 2 1 2 47 57 48 44 41
## 145 122 1 4 2 1 2 52 59 58 53 66
## 146 191 1 4 3 2 2 47 52 43 48 61
## 147 83 1 4 2 1 3 50 62 41 55 31
## 148 182 1 4 2 2 2 44 52 43 44 51
## 149 6 1 1 1 1 2 47 41 46 40 41
## 150 46 1 3 1 1 2 45 55 44 34 41
## 151 43 1 3 1 1 2 47 37 43 42 46
## 152 96 1 4 3 1 2 65 54 61 58 56
## 153 138 1 4 2 1 3 43 57 40 50 51
## 154 10 1 1 2 1 1 47 54 49 53 61
## 155 71 1 4 2 1 1 57 62 56 58 66
## 156 139 1 4 2 1 2 68 59 61 55 71
## 157 110 1 4 2 1 3 52 55 50 54 61
## 158 148 1 4 2 1 3 42 57 51 47 61
## 159 109 1 4 2 1 1 42 39 42 42 41
## 160 39 1 3 3 1 2 66 67 67 61 66
## 161 147 1 4 1 1 2 47 62 53 53 61
## 162 74 1 4 2 1 2 57 50 50 51 58
## 163 198 1 4 3 2 2 47 61 51 63 31
## 164 161 1 4 1 1 2 57 62 72 61 61
## 165 112 1 4 2 1 2 52 59 48 55 61
## 166 69 1 4 1 1 3 44 44 40 40 31
## 167 156 1 4 2 1 2 50 59 53 61 61
## 168 111 1 4 1 1 1 39 54 39 47 36
## 169 186 1 4 2 2 2 57 62 63 55 41
## 170 98 1 4 1 1 3 57 60 51 53 37
## 171 119 1 4 1 1 1 42 57 45 50 43
## 172 13 1 1 2 1 3 47 46 39 47 61
## 173 51 1 3 3 1 1 42 36 42 31 39
## 174 26 1 2 3 1 2 60 59 62 61 51
## 175 36 1 3 1 1 1 44 49 44 35 51
## 176 135 1 4 1 1 2 63 60 65 54 66
## 177 59 1 4 2 1 2 65 67 63 55 71
## 178 78 1 4 2 1 2 39 54 54 53 41
## 179 64 1 4 3 1 3 50 52 45 58 36
## 180 63 1 4 1 1 1 52 65 60 56 51
## 181 79 1 4 2 1 2 60 62 49 50 51
## 182 193 1 4 2 2 2 44 49 48 39 51
## 183 92 1 4 3 1 1 52 67 57 63 61
## 184 160 1 4 2 1 2 55 65 55 50 61
## 185 32 1 2 3 1 3 50 67 66 66 56
## 186 23 1 2 1 1 2 65 65 64 58 71
## 187 158 1 4 2 1 1 52 54 55 53 51
## 188 25 1 2 2 1 1 47 44 42 42 36
## 189 188 1 4 3 2 2 63 62 56 55 61
## 190 52 1 3 1 1 2 50 46 53 53 66
## 191 124 1 4 1 1 3 42 54 41 42 41
## 192 175 1 4 3 2 1 36 57 42 50 41
## 193 184 1 4 2 2 3 50 52 53 55 56
## 194 30 1 2 3 1 2 41 59 42 34 51
## 195 179 1 4 2 2 2 47 65 60 50 56
## 196 31 1 2 2 2 1 55 59 52 42 56
## 197 145 1 4 2 1 3 42 46 38 36 46
## 198 187 1 4 2 2 1 57 41 57 55 52
## 199 118 1 4 2 1 1 55 62 58 58 61
## 200 137 1 4 3 1 2 63 65 65 53 61
# Remarks
# hsb2 dataset consists of 200 selected random samples from senior
# high school students in the US.
# We want to compare the student performance across different subjects
# change data layout by grouping different subjects
# into one column using melt() command. Install first reshape2 package
# install.packages("reshape2")
library(reshape2)
(hsb2_long <- melt(hsb2_wide, measure.vars = c("read","write","math","science","socst")))
## id female race ses schtyp prog variable value
## 1 70 0 4 1 1 1 read 57
## 2 121 1 4 2 1 3 read 68
## 3 86 0 4 3 1 1 read 44
## 4 141 0 4 3 1 3 read 63
## 5 172 0 4 2 1 2 read 47
## 6 113 0 4 2 1 2 read 44
## 7 50 0 3 2 1 1 read 50
## 8 11 0 1 2 1 2 read 34
## 9 84 0 4 2 1 1 read 63
## 10 48 0 3 2 1 2 read 57
## 11 75 0 4 2 1 3 read 60
## 12 60 0 4 2 1 2 read 57
## 13 95 0 4 3 1 2 read 73
## 14 104 0 4 3 1 2 read 54
## 15 38 0 3 1 1 2 read 45
## 16 115 0 4 1 1 1 read 42
## 17 76 0 4 3 1 2 read 47
## 18 195 0 4 2 2 1 read 57
## 19 114 0 4 3 1 2 read 68
## 20 85 0 4 2 1 1 read 55
## 21 167 0 4 2 1 1 read 63
## 22 143 0 4 2 1 3 read 63
## 23 41 0 3 2 1 2 read 50
## 24 20 0 1 3 1 2 read 60
## 25 12 0 1 2 1 3 read 37
## 26 53 0 3 2 1 3 read 34
## 27 154 0 4 3 1 2 read 65
## 28 178 0 4 2 2 3 read 47
## 29 196 0 4 3 2 2 read 44
## 30 29 0 2 1 1 1 read 52
## 31 126 0 4 2 1 1 read 42
## 32 103 0 4 3 1 2 read 76
## 33 192 0 4 3 2 2 read 65
## 34 150 0 4 2 1 3 read 42
## 35 199 0 4 3 2 2 read 52
## 36 144 0 4 3 1 1 read 60
## 37 200 0 4 2 2 2 read 68
## 38 80 0 4 3 1 2 read 65
## 39 16 0 1 1 1 3 read 47
## 40 153 0 4 2 1 3 read 39
## 41 176 0 4 2 2 2 read 47
## 42 177 0 4 2 2 2 read 55
## 43 168 0 4 2 1 2 read 52
## 44 40 0 3 1 1 1 read 42
## 45 62 0 4 3 1 1 read 65
## 46 169 0 4 1 1 1 read 55
## 47 49 0 3 3 1 3 read 50
## 48 136 0 4 2 1 2 read 65
## 49 189 0 4 2 2 2 read 47
## 50 7 0 1 2 1 2 read 57
## 51 27 0 2 2 1 2 read 53
## 52 128 0 4 3 1 2 read 39
## 53 21 0 1 2 1 1 read 44
## 54 183 0 4 2 2 2 read 63
## 55 132 0 4 2 1 2 read 73
## 56 15 0 1 3 1 3 read 39
## 57 67 0 4 1 1 3 read 37
## 58 22 0 1 2 1 3 read 42
## 59 185 0 4 2 2 2 read 63
## 60 9 0 1 2 1 3 read 48
## 61 181 0 4 2 2 2 read 50
## 62 170 0 4 3 1 2 read 47
## 63 134 0 4 1 1 1 read 44
## 64 108 0 4 2 1 1 read 34
## 65 197 0 4 3 2 2 read 50
## 66 140 0 4 2 1 3 read 44
## 67 171 0 4 2 1 2 read 60
## 68 107 0 4 1 1 3 read 47
## 69 81 0 4 1 1 2 read 63
## 70 18 0 1 2 1 3 read 50
## 71 155 0 4 2 1 1 read 44
## 72 97 0 4 3 1 2 read 60
## 73 68 0 4 2 1 2 read 73
## 74 157 0 4 2 1 1 read 68
## 75 56 0 4 2 1 3 read 55
## 76 5 0 1 1 1 2 read 47
## 77 159 0 4 3 1 2 read 55
## 78 123 0 4 3 1 1 read 68
## 79 164 0 4 2 1 3 read 31
## 80 14 0 1 3 1 2 read 47
## 81 127 0 4 3 1 2 read 63
## 82 165 0 4 1 1 3 read 36
## 83 174 0 4 2 2 2 read 68
## 84 3 0 1 1 1 2 read 63
## 85 58 0 4 2 1 3 read 55
## 86 146 0 4 3 1 2 read 55
## 87 102 0 4 3 1 2 read 52
## 88 117 0 4 3 1 3 read 34
## 89 133 0 4 2 1 3 read 50
## 90 94 0 4 3 1 2 read 55
## 91 24 0 2 2 1 2 read 52
## 92 149 0 4 1 1 1 read 63
## 93 82 1 4 3 1 2 read 68
## 94 8 1 1 1 1 2 read 39
## 95 129 1 4 1 1 1 read 44
## 96 173 1 4 1 1 1 read 50
## 97 57 1 4 2 1 2 read 71
## 98 100 1 4 3 1 2 read 63
## 99 1 1 1 1 1 3 read 34
## 100 194 1 4 3 2 2 read 63
## 101 88 1 4 3 1 2 read 68
## 102 99 1 4 3 1 1 read 47
## 103 47 1 3 1 1 2 read 47
## 104 120 1 4 3 1 2 read 63
## 105 166 1 4 2 1 2 read 52
## 106 65 1 4 2 1 2 read 55
## 107 101 1 4 3 1 2 read 60
## 108 89 1 4 1 1 3 read 35
## 109 54 1 3 1 2 1 read 47
## 110 180 1 4 3 2 2 read 71
## 111 162 1 4 2 1 3 read 57
## 112 4 1 1 1 1 2 read 44
## 113 131 1 4 3 1 2 read 65
## 114 125 1 4 1 1 2 read 68
## 115 34 1 1 3 2 2 read 73
## 116 106 1 4 2 1 3 read 36
## 117 130 1 4 3 1 1 read 43
## 118 93 1 4 3 1 2 read 73
## 119 163 1 4 1 1 2 read 52
## 120 37 1 3 1 1 3 read 41
## 121 35 1 1 1 2 1 read 60
## 122 87 1 4 2 1 1 read 50
## 123 73 1 4 2 1 2 read 50
## 124 151 1 4 2 1 3 read 47
## 125 44 1 3 1 1 3 read 47
## 126 152 1 4 3 1 2 read 55
## 127 105 1 4 2 1 2 read 50
## 128 28 1 2 2 1 1 read 39
## 129 91 1 4 3 1 3 read 50
## 130 45 1 3 1 1 3 read 34
## 131 116 1 4 2 1 2 read 57
## 132 33 1 2 1 1 2 read 57
## 133 66 1 4 2 1 3 read 68
## 134 72 1 4 2 1 3 read 42
## 135 77 1 4 1 1 2 read 61
## 136 61 1 4 3 1 2 read 76
## 137 190 1 4 2 2 2 read 47
## 138 42 1 3 2 1 3 read 46
## 139 2 1 1 2 1 3 read 39
## 140 55 1 3 2 2 2 read 52
## 141 19 1 1 1 1 1 read 28
## 142 90 1 4 3 1 2 read 42
## 143 142 1 4 2 1 3 read 47
## 144 17 1 1 2 1 2 read 47
## 145 122 1 4 2 1 2 read 52
## 146 191 1 4 3 2 2 read 47
## 147 83 1 4 2 1 3 read 50
## 148 182 1 4 2 2 2 read 44
## 149 6 1 1 1 1 2 read 47
## 150 46 1 3 1 1 2 read 45
## 151 43 1 3 1 1 2 read 47
## 152 96 1 4 3 1 2 read 65
## 153 138 1 4 2 1 3 read 43
## 154 10 1 1 2 1 1 read 47
## 155 71 1 4 2 1 1 read 57
## 156 139 1 4 2 1 2 read 68
## 157 110 1 4 2 1 3 read 52
## 158 148 1 4 2 1 3 read 42
## 159 109 1 4 2 1 1 read 42
## 160 39 1 3 3 1 2 read 66
## 161 147 1 4 1 1 2 read 47
## 162 74 1 4 2 1 2 read 57
## 163 198 1 4 3 2 2 read 47
## 164 161 1 4 1 1 2 read 57
## 165 112 1 4 2 1 2 read 52
## 166 69 1 4 1 1 3 read 44
## 167 156 1 4 2 1 2 read 50
## 168 111 1 4 1 1 1 read 39
## 169 186 1 4 2 2 2 read 57
## 170 98 1 4 1 1 3 read 57
## 171 119 1 4 1 1 1 read 42
## 172 13 1 1 2 1 3 read 47
## 173 51 1 3 3 1 1 read 42
## 174 26 1 2 3 1 2 read 60
## 175 36 1 3 1 1 1 read 44
## 176 135 1 4 1 1 2 read 63
## 177 59 1 4 2 1 2 read 65
## 178 78 1 4 2 1 2 read 39
## 179 64 1 4 3 1 3 read 50
## 180 63 1 4 1 1 1 read 52
## 181 79 1 4 2 1 2 read 60
## 182 193 1 4 2 2 2 read 44
## 183 92 1 4 3 1 1 read 52
## 184 160 1 4 2 1 2 read 55
## 185 32 1 2 3 1 3 read 50
## 186 23 1 2 1 1 2 read 65
## 187 158 1 4 2 1 1 read 52
## 188 25 1 2 2 1 1 read 47
## 189 188 1 4 3 2 2 read 63
## 190 52 1 3 1 1 2 read 50
## 191 124 1 4 1 1 3 read 42
## 192 175 1 4 3 2 1 read 36
## 193 184 1 4 2 2 3 read 50
## 194 30 1 2 3 1 2 read 41
## 195 179 1 4 2 2 2 read 47
## 196 31 1 2 2 2 1 read 55
## 197 145 1 4 2 1 3 read 42
## 198 187 1 4 2 2 1 read 57
## 199 118 1 4 2 1 1 read 55
## 200 137 1 4 3 1 2 read 63
## 201 70 0 4 1 1 1 write 52
## 202 121 1 4 2 1 3 write 59
## 203 86 0 4 3 1 1 write 33
## 204 141 0 4 3 1 3 write 44
## 205 172 0 4 2 1 2 write 52
## 206 113 0 4 2 1 2 write 52
## 207 50 0 3 2 1 1 write 59
## 208 11 0 1 2 1 2 write 46
## 209 84 0 4 2 1 1 write 57
## 210 48 0 3 2 1 2 write 55
## 211 75 0 4 2 1 3 write 46
## 212 60 0 4 2 1 2 write 65
## 213 95 0 4 3 1 2 write 60
## 214 104 0 4 3 1 2 write 63
## 215 38 0 3 1 1 2 write 57
## 216 115 0 4 1 1 1 write 49
## 217 76 0 4 3 1 2 write 52
## 218 195 0 4 2 2 1 write 57
## 219 114 0 4 3 1 2 write 65
## 220 85 0 4 2 1 1 write 39
## 221 167 0 4 2 1 1 write 49
## 222 143 0 4 2 1 3 write 63
## 223 41 0 3 2 1 2 write 40
## 224 20 0 1 3 1 2 write 52
## 225 12 0 1 2 1 3 write 44
## 226 53 0 3 2 1 3 write 37
## 227 154 0 4 3 1 2 write 65
## 228 178 0 4 2 2 3 write 57
## 229 196 0 4 3 2 2 write 38
## 230 29 0 2 1 1 1 write 44
## 231 126 0 4 2 1 1 write 31
## 232 103 0 4 3 1 2 write 52
## 233 192 0 4 3 2 2 write 67
## 234 150 0 4 2 1 3 write 41
## 235 199 0 4 3 2 2 write 59
## 236 144 0 4 3 1 1 write 65
## 237 200 0 4 2 2 2 write 54
## 238 80 0 4 3 1 2 write 62
## 239 16 0 1 1 1 3 write 31
## 240 153 0 4 2 1 3 write 31
## 241 176 0 4 2 2 2 write 47
## 242 177 0 4 2 2 2 write 59
## 243 168 0 4 2 1 2 write 54
## 244 40 0 3 1 1 1 write 41
## 245 62 0 4 3 1 1 write 65
## 246 169 0 4 1 1 1 write 59
## 247 49 0 3 3 1 3 write 40
## 248 136 0 4 2 1 2 write 59
## 249 189 0 4 2 2 2 write 59
## 250 7 0 1 2 1 2 write 54
## 251 27 0 2 2 1 2 write 61
## 252 128 0 4 3 1 2 write 33
## 253 21 0 1 2 1 1 write 44
## 254 183 0 4 2 2 2 write 59
## 255 132 0 4 2 1 2 write 62
## 256 15 0 1 3 1 3 write 39
## 257 67 0 4 1 1 3 write 37
## 258 22 0 1 2 1 3 write 39
## 259 185 0 4 2 2 2 write 57
## 260 9 0 1 2 1 3 write 49
## 261 181 0 4 2 2 2 write 46
## 262 170 0 4 3 1 2 write 62
## 263 134 0 4 1 1 1 write 44
## 264 108 0 4 2 1 1 write 33
## 265 197 0 4 3 2 2 write 42
## 266 140 0 4 2 1 3 write 41
## 267 171 0 4 2 1 2 write 54
## 268 107 0 4 1 1 3 write 39
## 269 81 0 4 1 1 2 write 43
## 270 18 0 1 2 1 3 write 33
## 271 155 0 4 2 1 1 write 44
## 272 97 0 4 3 1 2 write 54
## 273 68 0 4 2 1 2 write 67
## 274 157 0 4 2 1 1 write 59
## 275 56 0 4 2 1 3 write 45
## 276 5 0 1 1 1 2 write 40
## 277 159 0 4 3 1 2 write 61
## 278 123 0 4 3 1 1 write 59
## 279 164 0 4 2 1 3 write 36
## 280 14 0 1 3 1 2 write 41
## 281 127 0 4 3 1 2 write 59
## 282 165 0 4 1 1 3 write 49
## 283 174 0 4 2 2 2 write 59
## 284 3 0 1 1 1 2 write 65
## 285 58 0 4 2 1 3 write 41
## 286 146 0 4 3 1 2 write 62
## 287 102 0 4 3 1 2 write 41
## 288 117 0 4 3 1 3 write 49
## 289 133 0 4 2 1 3 write 31
## 290 94 0 4 3 1 2 write 49
## 291 24 0 2 2 1 2 write 62
## 292 149 0 4 1 1 1 write 49
## 293 82 1 4 3 1 2 write 62
## 294 8 1 1 1 1 2 write 44
## 295 129 1 4 1 1 1 write 44
## 296 173 1 4 1 1 1 write 62
## 297 57 1 4 2 1 2 write 65
## 298 100 1 4 3 1 2 write 65
## 299 1 1 1 1 1 3 write 44
## 300 194 1 4 3 2 2 write 63
## 301 88 1 4 3 1 2 write 60
## 302 99 1 4 3 1 1 write 59
## 303 47 1 3 1 1 2 write 46
## 304 120 1 4 3 1 2 write 52
## 305 166 1 4 2 1 2 write 59
## 306 65 1 4 2 1 2 write 54
## 307 101 1 4 3 1 2 write 62
## 308 89 1 4 1 1 3 write 35
## 309 54 1 3 1 2 1 write 54
## 310 180 1 4 3 2 2 write 65
## 311 162 1 4 2 1 3 write 52
## 312 4 1 1 1 1 2 write 50
## 313 131 1 4 3 1 2 write 59
## 314 125 1 4 1 1 2 write 65
## 315 34 1 1 3 2 2 write 61
## 316 106 1 4 2 1 3 write 44
## 317 130 1 4 3 1 1 write 54
## 318 93 1 4 3 1 2 write 67
## 319 163 1 4 1 1 2 write 57
## 320 37 1 3 1 1 3 write 47
## 321 35 1 1 1 2 1 write 54
## 322 87 1 4 2 1 1 write 52
## 323 73 1 4 2 1 2 write 52
## 324 151 1 4 2 1 3 write 46
## 325 44 1 3 1 1 3 write 62
## 326 152 1 4 3 1 2 write 57
## 327 105 1 4 2 1 2 write 41
## 328 28 1 2 2 1 1 write 53
## 329 91 1 4 3 1 3 write 49
## 330 45 1 3 1 1 3 write 35
## 331 116 1 4 2 1 2 write 59
## 332 33 1 2 1 1 2 write 65
## 333 66 1 4 2 1 3 write 62
## 334 72 1 4 2 1 3 write 54
## 335 77 1 4 1 1 2 write 59
## 336 61 1 4 3 1 2 write 63
## 337 190 1 4 2 2 2 write 59
## 338 42 1 3 2 1 3 write 52
## 339 2 1 1 2 1 3 write 41
## 340 55 1 3 2 2 2 write 49
## 341 19 1 1 1 1 1 write 46
## 342 90 1 4 3 1 2 write 54
## 343 142 1 4 2 1 3 write 42
## 344 17 1 1 2 1 2 write 57
## 345 122 1 4 2 1 2 write 59
## 346 191 1 4 3 2 2 write 52
## 347 83 1 4 2 1 3 write 62
## 348 182 1 4 2 2 2 write 52
## 349 6 1 1 1 1 2 write 41
## 350 46 1 3 1 1 2 write 55
## 351 43 1 3 1 1 2 write 37
## 352 96 1 4 3 1 2 write 54
## 353 138 1 4 2 1 3 write 57
## 354 10 1 1 2 1 1 write 54
## 355 71 1 4 2 1 1 write 62
## 356 139 1 4 2 1 2 write 59
## 357 110 1 4 2 1 3 write 55
## 358 148 1 4 2 1 3 write 57
## 359 109 1 4 2 1 1 write 39
## 360 39 1 3 3 1 2 write 67
## 361 147 1 4 1 1 2 write 62
## 362 74 1 4 2 1 2 write 50
## 363 198 1 4 3 2 2 write 61
## 364 161 1 4 1 1 2 write 62
## 365 112 1 4 2 1 2 write 59
## 366 69 1 4 1 1 3 write 44
## 367 156 1 4 2 1 2 write 59
## 368 111 1 4 1 1 1 write 54
## 369 186 1 4 2 2 2 write 62
## 370 98 1 4 1 1 3 write 60
## 371 119 1 4 1 1 1 write 57
## 372 13 1 1 2 1 3 write 46
## 373 51 1 3 3 1 1 write 36
## 374 26 1 2 3 1 2 write 59
## 375 36 1 3 1 1 1 write 49
## 376 135 1 4 1 1 2 write 60
## 377 59 1 4 2 1 2 write 67
## 378 78 1 4 2 1 2 write 54
## 379 64 1 4 3 1 3 write 52
## 380 63 1 4 1 1 1 write 65
## 381 79 1 4 2 1 2 write 62
## 382 193 1 4 2 2 2 write 49
## 383 92 1 4 3 1 1 write 67
## 384 160 1 4 2 1 2 write 65
## 385 32 1 2 3 1 3 write 67
## 386 23 1 2 1 1 2 write 65
## 387 158 1 4 2 1 1 write 54
## 388 25 1 2 2 1 1 write 44
## 389 188 1 4 3 2 2 write 62
## 390 52 1 3 1 1 2 write 46
## 391 124 1 4 1 1 3 write 54
## 392 175 1 4 3 2 1 write 57
## 393 184 1 4 2 2 3 write 52
## 394 30 1 2 3 1 2 write 59
## 395 179 1 4 2 2 2 write 65
## 396 31 1 2 2 2 1 write 59
## 397 145 1 4 2 1 3 write 46
## 398 187 1 4 2 2 1 write 41
## 399 118 1 4 2 1 1 write 62
## 400 137 1 4 3 1 2 write 65
## 401 70 0 4 1 1 1 math 41
## 402 121 1 4 2 1 3 math 53
## 403 86 0 4 3 1 1 math 54
## 404 141 0 4 3 1 3 math 47
## 405 172 0 4 2 1 2 math 57
## 406 113 0 4 2 1 2 math 51
## 407 50 0 3 2 1 1 math 42
## 408 11 0 1 2 1 2 math 45
## 409 84 0 4 2 1 1 math 54
## 410 48 0 3 2 1 2 math 52
## 411 75 0 4 2 1 3 math 51
## 412 60 0 4 2 1 2 math 51
## 413 95 0 4 3 1 2 math 71
## 414 104 0 4 3 1 2 math 57
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## 417 76 0 4 3 1 2 math 51
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## 422 143 0 4 2 1 3 math 75
## 423 41 0 3 2 1 2 math 45
## 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 the frequency
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", "africanamer","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
library(ggplot2)
# we load variable names to memory to avoid the dollar notation
attach(hsb2_long)
# create the plot object p
p <- ggplot(hsb2_long, aes(ses, fill = prog)) + facet_wrap(~race)
p + geom_bar ()
## Warning: The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
p + geom_bar(position = "dodge")
## Warning: The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
library(ggplot2)
attach(hsb2_long)
## The following objects are masked from hsb2_long (pos = 3):
##
## female, id, prog, race, schtyp, ses, value, variable
p <- ggplot(hsb2_long, aes(ses, fill = prog)) + facet_wrap(~schtyp)
p + geom_bar()
## Warning: The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
p + geom_bar(position = "dodge")
## Warning: The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
# Lab Exercise 10: Scatter Plots with marginal Distributions
# Advance Scatter plots using libraries
# install.packages("ggExtra")
# install.packages("tidyverse")
library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.0 ✔ readr 2.1.4
## ✔ forcats 1.0.0 ✔ stringr 1.5.0
## ✔ lubridate 1.9.2 ✔ tibble 3.2.0
## ✔ purrr 1.0.1 ✔ tidyr 1.3.0
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the ]8;;http://conflicted.r-lib.org/conflicted package]8;; to force all conflicts to become errors
library(ggExtra)
# set theme appearance of grid background
theme_set(theme_bw(1))
# create X and Y vector
(xAxis <- rnorm(1000)) # normal distribution with mean 0 and sd=1
## [1] 0.236313906 0.021528537 -0.766066565 0.015096718 -0.852067624
## [6] 1.275920067 -2.095233581 -0.374409413 0.932909204 3.023079813
## [11] -0.172264293 -0.513736821 1.959305352 0.055131951 -0.423013195
## [16] 0.111311644 -0.914290754 -1.595724432 1.753024932 -0.106774664
## [21] -1.204024715 -0.293050896 1.282573274 0.786776490 -0.234791584
## [26] 0.592798830 -0.545526295 0.612753508 -0.745687185 -0.351572081
## [31] -0.123318856 0.181099276 0.762580867 0.932323420 0.037701725
## [36] 0.087841264 -1.168239252 -0.938475589 -0.197457145 0.933775325
## [41] -1.146121991 0.831904666 -0.526713357 -0.569325432 1.097300733
## [46] 0.111510083 -1.018719294 -0.990189152 -0.215174576 -1.267201471
## [51] -0.012936045 -2.289739445 0.757478403 0.028837045 0.165394426
## [56] 0.270514270 1.267110091 0.750033504 0.719639041 -0.369628269
## [61] -0.962742500 -0.644419524 -1.292057651 1.260061429 1.335023059
## [66] 0.489960317 -1.366475381 -1.656504657 0.259147545 1.333116312
## [71] -0.060296017 -0.190682041 0.389402591 0.940468939 -0.746412299
## [76] -0.473450785 0.523295706 -1.343780966 -0.641221239 0.762278245
## [81] -1.101348000 -1.706687663 0.145986926 0.676686963 -1.212278998
## [86] -0.384591040 1.114436084 0.261829155 0.843261332 -1.047757757
## [91] -0.978814016 -1.546665417 -0.880573668 1.113212330 -2.112074574
## [96] -0.680440886 -0.643867344 1.124545927 -1.699043394 1.162801304
## [101] 1.683908155 -0.964989654 -0.556436173 0.886760179 -0.699291105
## [106] 0.560865498 0.380925348 -0.887469172 0.162354957 0.514725297
## [111] -1.614169489 -0.223466834 0.091125346 -1.498458271 1.458387256
## [116] 0.806430393 1.241361734 -0.898690105 1.596056410 -0.026016652
## [121] 1.890898796 1.694272772 -0.435737808 0.875102032 0.102744084
## [126] -0.397323170 1.410899240 1.177807746 1.114876095 -0.783782909
## [131] 0.560221678 1.394541365 -0.378635702 -1.349552424 2.937387463
## [136] -0.725488305 -0.310877936 -0.196832605 1.549496268 0.516922759
## [141] 0.588586592 0.787365114 1.247514421 -0.840803326 -0.333783401
## [146] -0.787072176 1.827869028 0.579089732 -0.074574596 0.906682458
## [151] -2.433063773 -1.875649133 0.012980119 0.087403859 0.641455193
## [156] 0.885665380 1.296489361 -2.332444091 0.231940019 0.316425175
## [161] 0.848068366 0.332597373 -0.297999727 1.005398003 2.077693762
## [166] -0.470951565 1.103031773 -0.803147345 0.130947526 -0.790739781
## [171] -1.740285909 -0.443879937 0.260174337 -0.962884024 -0.192205658
## [176] -0.014461824 -0.105357872 -1.240555751 -1.472981724 -2.138560660
## [181] -0.508498854 -0.686003418 -0.166367777 -2.415818164 -0.803018769
## [186] -2.557494665 -1.783459653 0.294128640 -1.051390815 -0.656129364
## [191] 0.025875730 -1.480481934 -0.130011800 0.323258389 0.421236434
## [196] -0.738085895 0.281747183 0.064247954 -0.747636175 -0.466246689
## [201] 0.853242208 -1.511811149 -0.826783802 0.923162524 1.600186333
## [206] -0.427062919 0.048735791 0.835278179 -0.915963965 1.860648177
## [211] 0.251120262 0.275066003 -0.287546266 0.493520579 0.379653770
## [216] 0.511893964 0.518336019 -1.473556676 -0.202329990 -0.594806662
## [221] 1.952346251 0.735018157 -2.442253901 2.624582773 0.338914327
## [226] 0.294466622 0.144994273 0.772885443 1.987328511 -0.526096922
## [231] 0.373252259 -0.909624522 0.744851361 2.246050256 -1.358546300
## [236] 1.072507913 0.910341540 0.145026672 0.709928608 0.185245752
## [241] -0.737000914 -0.557353950 -0.214028078 -0.932596650 0.703641202
## [246] 0.598457538 1.086969961 -0.424003498 1.309660695 0.544096967
## [251] 0.093668507 -0.026187860 -0.964297863 1.000706025 0.012151387
## [256] 0.781365256 -1.157308019 1.382136225 0.177987477 0.838339265
## [261] 1.004432640 -0.268637208 0.472592937 -0.670208934 -0.318320637
## [266] -0.197845316 -0.915108336 -1.172531118 0.236911626 -0.554527539
## [271] -0.427541468 -0.227091220 0.378222871 0.676090076 0.271311919
## [276] -0.358598792 -1.106126431 0.182234876 -0.281199169 -1.418170091
## [281] -0.536822163 0.214647598 0.915242863 -1.033106954 2.171179430
## [286] 1.419462574 -1.584105683 -0.771039440 0.988264113 0.427062872
## [291] 0.663020478 -1.044598087 0.650672146 -1.156593866 -1.328919752
## [296] -1.487605474 -0.854157606 -0.072132731 -2.827235865 0.301097622
## [301] -0.267931974 0.368080098 0.647520323 2.215925396 0.476985977
## [306] 0.166201457 0.184393165 0.239032964 1.226911321 -0.350991660
## [311] -0.798067585 1.626338555 0.611435793 -0.985546179 -1.215998962
## [316] -0.976048827 -0.481803572 -0.484849995 1.874716433 -0.929679405
## [321] -0.079487842 -0.779484637 0.625336419 1.629035011 1.246842516
## [326] 0.211404610 0.707484719 -1.362056724 0.654993708 0.904052345
## [331] -1.009015228 -0.625779036 0.130671631 -0.992975890 -1.267334861
## [336] -0.575911758 1.600377191 -0.532642556 -0.939055475 -0.556781495
## [341] 0.451758558 -0.591347255 -1.025209484 -1.529046287 1.645269857
## [346] -0.438130158 -0.324105628 -0.858069380 -0.843268995 -0.540093189
## [351] 2.085215148 -1.181558819 -0.893420290 -0.774567445 -0.047439013
## [356] 0.213804495 0.373908615 1.133535590 -2.340610827 -0.988349293
## [361] -1.999988646 -0.420565533 0.657430747 1.417548647 -1.671491740
## [366] -0.558807346 -1.083572024 0.175926090 0.933052828 0.913639618
## [371] -0.176003562 0.162992453 1.170789284 0.505302759 0.021020047
## [376] 2.490249066 0.031253090 0.029307622 -1.533170251 0.666052502
## [381] 0.764961620 -0.285758704 -0.180501226 2.112722445 -1.230724844
## [386] -1.615000602 -0.191347964 0.298003782 0.249752863 -0.881435906
## [391] 1.228737148 0.070476032 -0.395633175 0.981479538 0.320329690
## [396] -0.495727976 -0.420994136 1.575723340 0.514057048 -0.204926071
## [401] -1.141870270 3.543700973 -0.082506456 0.256294562 0.449633774
## [406] -0.498875307 0.688403590 -0.685238064 0.382774262 0.655281087
## [411] -0.230392796 -0.535353658 -1.713148150 -1.699900205 0.012855652
## [416] 0.115546795 1.701421613 0.198732047 -0.541702851 1.653444190
## [421] 0.181143393 0.546444362 -0.054053672 0.128758226 1.162662492
## [426] 1.038529993 3.024576846 -0.420634467 -1.464236569 -1.135094712
## [431] -0.200494811 -1.480724686 -1.089434126 -1.430313057 0.697611509
## [436] -1.431876383 -1.558340332 1.067552562 -0.404371474 -2.658683649
## [441] -1.954581154 -1.573976664 -0.390619807 -0.229698033 -1.679441834
## [446] 2.895850196 1.397693373 0.024420574 -1.072721809 -0.122880602
## [451] 1.497932941 1.117051419 -0.225635589 -0.609344958 0.774004218
## [456] 0.501401035 -1.617285015 0.715877514 -0.888307603 1.375319215
## [461] -0.342829194 -1.094444056 1.559457240 -0.407632023 -0.173241299
## [466] 0.738641778 -0.666552702 -0.198149479 0.014048944 -1.138441045
## [471] 3.359344709 0.078688659 0.438742025 -0.731509459 -0.468002180
## [476] 0.937324821 -1.431472049 -0.380063685 -0.239495019 -0.401849562
## [481] -0.928523807 -1.454131260 -1.693760758 0.911053418 -0.896411752
## [486] -0.694650944 1.637782853 1.508353317 2.123606208 1.998016321
## [491] -0.434746023 0.462206099 -1.631722832 -0.929955194 2.310557355
## [496] -0.422035710 1.121564832 -0.111852707 1.023887410 -0.209957273
## [501] 2.638236547 0.138321055 0.281941665 1.165642333 -0.317343024
## [506] -0.701486508 -0.362084741 0.257185360 0.774735142 0.086935720
## [511] 0.449280289 -0.051196326 -0.549512053 0.076331603 0.675697338
## [516] 1.245366594 -0.443271524 -1.241361590 -0.460003974 -0.844530013
## [521] -0.094800840 0.010005919 -1.397003089 -0.895881263 -2.083343557
## [526] -1.734826562 1.023713447 0.043834501 -0.149688965 -1.461906829
## [531] 0.446340216 -0.764103409 0.275647009 -0.347200868 -0.944843062
## [536] 0.639502916 0.565455392 0.353791006 0.861027757 0.847704695
## [541] -0.829835145 -0.825481293 -2.090862000 0.659263575 -1.479925339
## [546] -0.071736134 -0.480710098 -1.182521785 -0.570355879 0.065303883
## [551] 0.901663505 0.275585327 0.116220619 -0.350248430 1.920836406
## [556] -0.431602284 -1.192300588 1.733521558 1.408461610 1.628163296
## [561] 0.773839755 -0.026948162 -0.744789712 -0.594236886 1.153837609
## [566] 0.315343032 0.619528131 0.965627589 -0.082084084 -0.158860565
## [571] 1.176193192 -0.464065257 -0.088169788 0.713547597 1.184896305
## [576] -2.564012670 0.235301683 1.170303496 0.299734242 0.309975848
## [581] -0.014690348 0.278054200 1.681878230 0.264298366 0.440020259
## [586] 0.221390967 0.591556526 0.904216448 -1.474257989 -1.079490560
## [591] -1.326372764 0.814134633 -0.221724202 -0.804902468 0.938603002
## [596] 0.477989475 0.084929808 -0.612730159 0.774403077 1.219371326
## [601] -1.091269971 -1.082300378 -0.023090143 0.146287197 0.839086312
## [606] 0.317849948 -0.561133044 0.257117769 -0.017377645 0.409825309
## [611] -1.872555568 -1.225906656 -0.415901786 -0.330684908 0.794577864
## [616] -0.507628936 -0.378360506 -0.047526103 -0.976914726 0.139776092
## [621] 1.138583461 0.918334694 0.681056312 -0.767629683 0.107560982
## [626] 0.744868265 1.953253105 -0.702560820 -0.554610796 -0.241091702
## [631] -0.358731111 0.008393770 1.963405606 -1.820515390 -0.465771256
## [636] 1.176405405 1.071811044 0.087454741 0.292820991 -0.732099085
## [641] -0.441238541 1.083132097 -0.452782501 0.547879614 -1.171036123
## [646] 0.420728514 -1.236014394 -0.021706819 1.302214560 -0.879595996
## [651] -0.279355521 0.806758106 -0.665054011 -0.330634624 0.953010915
## [656] -1.236453955 1.019577350 0.800260854 0.117251574 -0.443007691
## [661] -1.203914010 -1.126581476 0.445452169 -0.506931378 0.533025986
## [666] 0.930424370 -0.239355397 0.215580734 0.347907219 0.114092669
## [671] 0.776031491 0.960531951 0.413277635 1.704940552 0.024170849
## [676] -1.249050495 0.798632327 -1.217570355 1.138327730 -0.145770647
## [681] -1.706684616 -0.995779238 -0.291075416 -1.936546256 -0.180655311
## [686] -0.650518748 0.963719882 0.070117004 -0.486632776 -0.965022859
## [691] 0.952471288 -1.459684322 -0.892798705 2.180171918 -0.679617645
## [696] 0.468532321 0.651244728 0.958486629 1.405300364 1.145531469
## [701] 0.615111488 -0.446065653 -0.569546819 0.544872875 0.613867096
## [706] 0.187255329 0.613157431 0.150806494 0.006522281 0.652157856
## [711] 0.486715424 -1.365779309 1.749668882 -0.502213386 -1.402148199
## [716] -0.367201460 0.735437573 0.274404567 0.086357278 1.068907106
## [721] -0.265966243 0.674405509 0.589515721 -0.554286695 0.500099407
## [726] 0.338309002 0.795871390 0.729384873 0.811739204 -1.113149317
## [731] 1.201461241 0.259473180 -0.779814394 -0.699388290 0.885719464
## [736] 0.440059434 -2.002619351 1.187861712 0.670453622 0.344119830
## [741] -0.198215169 0.556730442 1.920312912 0.538650581 1.234955572
## [746] -1.192449087 -2.771662678 1.402823639 0.173941612 1.424565551
## [751] 0.528149564 -0.758810009 0.206512040 0.313694776 -0.143377407
## [756] 0.198701894 0.266055730 0.727427537 -0.376477016 0.354253438
## [761] 0.567423055 0.945810514 -0.143878050 0.924129275 1.142370630
## [766] -1.295188446 -0.272843008 -1.092284679 -0.446110998 -0.744948727
## [771] 0.446196425 0.317770329 1.219113530 1.327418699 -0.628874653
## [776] 0.577104775 -0.388291672 -0.405803920 1.736534551 0.969187762
## [781] 0.361691798 0.236109725 -0.216283597 1.390671192 -0.042697045
## [786] -0.048160394 -0.926028191 -0.140323663 0.259664597 0.473761908
## [791] 0.986259938 0.959039708 1.403105827 0.472263207 -0.206821249
## [796] -0.625912045 1.048038932 -0.573692647 0.908242722 -0.581177003
## [801] -0.320105024 -0.610693637 -0.384527707 -1.740428029 0.321156374
## [806] 0.559379399 0.189430625 -0.237455120 0.942578272 1.528752622
## [811] 0.190759146 -0.216304682 1.051407403 1.329326477 -0.300186368
## [816] 0.013864949 1.355100020 0.095714602 0.721531996 -0.176963166
## [821] -2.553819950 1.648658379 1.208358775 -0.058731211 -1.933316059
## [826] 2.491913101 0.446781234 -0.867074539 0.354468840 1.309161283
## [831] -1.107282504 0.810452749 1.471731310 -0.012021434 2.329959018
## [836] -1.731189973 -0.517429479 -0.928367573 1.777502643 -1.035863534
## [841] 0.121295555 0.048642942 0.657877122 -1.554496169 -0.068558297
## [846] 1.171665858 0.329926506 1.703276385 0.908425135 -0.282166035
## [851] -0.234682691 1.871792066 0.153607753 1.749103296 0.113201980
## [856] -0.171896979 1.386984576 -0.105912750 0.517798681 -0.400688141
## [861] -0.093468790 0.783826160 0.999141492 -1.230431040 -0.555558693
## [866] -0.449362280 -1.961536672 -0.825609472 -0.729163517 -0.303888307
## [871] -0.573960257 1.346624992 -0.733375232 0.012500700 -1.556227916
## [876] 0.972272073 -1.215621065 -0.063184264 -0.227843473 -2.149660199
## [881] 0.538534068 1.483380812 1.260766908 -0.327846274 0.466594466
## [886] 0.048387211 -0.525984164 1.857400843 1.308254583 -0.063889466
## [891] -1.529603528 0.063616472 -0.265728129 1.070325653 0.952745133
## [896] 0.394709640 1.382283291 -2.269839737 0.089016052 -0.911835122
## [901] -1.009066222 1.066109637 1.055312206 -1.096224678 -1.400229897
## [906] 0.705596459 1.012472959 -0.297168071 -1.918863581 -0.454241029
## [911] 1.213589638 0.474084802 -1.283937232 0.419244256 1.318712590
## [916] -0.178887711 -0.859910607 0.537351567 0.495265638 1.318755136
## [921] 1.055051118 0.740807330 -0.168265596 0.426427163 0.120711182
## [926] -3.746261805 -0.151497544 0.753246547 -0.833145340 1.648803652
## [931] 1.270059822 -1.463013750 -1.117191977 -0.337270095 0.342815649
## [936] -1.449933918 -0.666168150 -0.231158591 1.142036266 1.623030576
## [941] -0.379409248 1.274067033 -0.110451938 0.094372594 1.506424394
## [946] 0.188455170 -0.045534529 -1.747871366 -0.303039751 0.308779433
## [951] -0.180517990 0.772479612 -0.528007986 -1.111491462 -0.023511850
## [956] -0.445385573 -0.051192981 -0.483825733 0.031036481 0.951177008
## [961] -0.883022921 0.836949948 -0.691045144 -0.555913212 -0.147118489
## [966] 0.842213755 1.372799946 0.091908748 -0.900434552 0.483265886
## [971] 0.261075060 0.256036914 -0.701198490 0.187570918 2.271100026
## [976] 0.042843381 0.568108568 1.392632756 0.766673180 -0.818240850
## [981] 0.202853881 -0.091594036 -0.829545862 0.734656353 -1.814602974
## [986] -0.724088203 0.540317916 -0.604249820 0.110345508 -2.852083687
## [991] -0.077396268 1.974795123 0.164105292 0.153070344 1.137758937
## [996] -1.261574388 1.295167812 0.461063387 0.392999737 -0.997408665
yAxis <- rnorm(1000) + xAxis + 10
yAxis
## [1] 10.261907 10.435646 12.115552 9.062436 9.258501 9.518178 8.858271
## [8] 9.509541 11.231692 10.358001 10.508750 9.698263 13.228659 10.002940
## [15] 11.198849 8.536116 8.702535 8.059952 10.049605 9.198537 8.441697
## [22] 8.109314 11.931279 11.521268 10.655081 9.212135 8.993825 10.273680
## [29] 9.354769 9.691121 10.245535 9.471905 10.456198 11.817269 9.058133
## [36] 9.387490 7.081202 9.317950 9.805540 10.307097 10.400787 11.610969
## [43] 10.540473 10.359741 10.589011 10.598980 7.026737 8.620457 8.705125
## [50] 7.941441 8.346406 6.686558 10.923367 9.234302 9.305950 9.740923
## [57] 12.473384 10.159163 10.384563 9.370755 9.030475 11.545449 10.541775
## [64] 10.524575 11.448677 11.654638 10.598646 7.778341 8.752501 11.316632
## [71] 10.734112 9.969199 10.777420 11.055755 8.012743 11.091096 10.287984
## [78] 9.433451 7.470392 13.163373 7.931438 8.711046 11.195192 9.721415
## [85] 8.809787 10.564831 11.648575 10.303493 8.649638 7.198187 9.147945
## [92] 9.081599 8.694530 12.399308 10.242617 8.656104 9.292522 10.641956
## [99] 8.506824 11.586902 10.941222 7.374557 8.526646 10.812165 8.427465
## [106] 9.612765 10.748179 8.752123 9.180965 9.356451 8.538688 10.389863
## [113] 9.581212 11.162774 11.300335 10.552470 11.315786 10.230593 12.750885
## [120] 9.364774 12.803217 10.828945 9.311646 11.106210 12.486898 8.936509
## [127] 11.821657 11.766894 11.376658 8.173365 10.005675 11.487335 9.403857
## [134] 7.584422 12.696755 8.237782 9.439544 7.980927 13.253472 11.570574
## [141] 9.737232 9.423618 11.052959 9.913894 8.890260 7.685485 13.671975
## [148] 8.815985 10.196132 11.758114 6.106133 7.471224 9.619921 10.014683
## [155] 10.731994 8.490889 12.649390 8.923611 10.083421 10.921047 12.344936
## [162] 9.452480 10.252122 10.887615 12.409159 10.396809 10.419240 9.779557
## [169] 10.519353 6.984750 9.533537 8.700142 9.070934 9.234337 10.033564
## [176] 10.418099 9.938248 10.626654 8.464819 8.212908 9.103618 8.041437
## [183] 8.634049 9.499333 10.020334 8.068748 8.308110 9.804037 8.560659
## [190] 9.772276 10.137949 7.781357 11.625440 11.635805 10.206315 9.068726
## [197] 9.698898 10.229973 8.600963 8.175189 11.475110 9.172921 9.263040
## [204] 11.164457 12.622955 9.938178 7.609039 11.078359 10.501117 12.243263
## [211] 11.253992 8.478279 10.948606 11.486570 9.442760 10.625319 12.149591
## [218] 7.523850 12.141199 8.737021 9.538987 9.783761 7.357645 12.040992
## [225] 11.326416 10.045856 8.535257 11.729145 10.730526 10.472629 10.459979
## [232] 8.169677 12.516305 12.106396 8.594674 11.253813 10.873697 9.353890
## [239] 11.393035 10.472286 9.816849 9.788547 9.116531 8.929299 10.341420
## [246] 9.523641 14.097105 10.039395 12.248979 10.536558 10.206832 10.746838
## [253] 8.390048 9.128411 10.014586 10.181544 7.146505 11.044279 8.943215
## [260] 9.234705 11.389800 10.583173 10.048056 9.789453 11.160366 8.056367
## [267] 8.481279 7.851169 10.822467 10.481075 8.260047 8.593872 10.407911
## [274] 11.779930 9.779375 9.340963 8.903844 11.018485 9.869707 7.637370
## [281] 10.281462 9.932106 11.412832 8.260437 11.661319 9.529128 8.072627
## [288] 8.554468 10.555710 13.157952 10.677340 8.749630 11.250378 9.150811
## [295] 8.658487 7.094350 9.868148 8.517128 7.235418 10.976222 10.341597
## [302] 10.581648 11.332530 11.191901 9.066956 9.811476 9.651247 10.802334
## [309] 10.372333 9.285761 9.402028 12.955086 10.694136 10.007280 8.158258
## [316] 8.892960 10.169228 9.920869 12.597693 9.333108 11.030201 8.402384
## [323] 10.447704 10.284853 11.099904 11.510400 11.207878 7.852518 11.404652
## [330] 10.618299 9.335453 8.942560 10.532232 10.185399 9.861652 9.206591
## [337] 13.201860 8.924446 9.450764 9.109061 12.042685 10.352753 7.025600
## [344] 7.793097 10.912563 10.081263 9.284083 8.954350 9.180836 9.934201
## [351] 12.347628 9.370083 8.258305 7.395650 10.323754 11.340060 10.846475
## [358] 10.224583 6.997906 8.707721 8.252971 8.931688 10.182162 10.968018
## [365] 8.224622 10.602786 6.917162 9.761049 11.368859 9.988642 10.655604
## [372] 10.816811 11.313003 11.273307 10.937393 13.351634 9.083183 10.244407
## [379] 9.832858 10.546006 11.822121 10.501739 11.661693 12.196198 9.190655
## [386] 9.469117 11.586599 10.677359 9.380207 8.599048 10.851426 10.990720
## [393] 11.447835 9.841840 10.767422 8.682671 9.015041 12.615907 8.986318
## [400] 9.255590 8.960800 14.489060 9.827072 10.797048 11.477306 9.773449
## [407] 11.974930 9.204141 9.950166 11.429429 10.021405 9.342433 8.228958
## [414] 9.435865 9.647888 10.377272 12.066311 9.783177 10.236717 12.807970
## [421] 8.374572 11.736502 10.567969 10.084587 11.839657 10.380083 12.872679
## [428] 8.841706 8.891112 8.866974 10.673342 8.342420 8.286283 8.855964
## [435] 11.085123 9.888174 7.382777 11.363432 9.488423 6.189270 6.728737
## [442] 9.473488 9.713093 10.282181 7.062526 12.757386 10.857627 11.798920
## [449] 10.963677 10.106719 11.865161 10.161749 8.817307 10.037323 11.043101
## [456] 9.258638 8.544563 11.333290 8.627520 10.774187 8.338504 9.955805
## [463] 12.026920 8.609003 10.198912 10.867411 8.842118 10.223353 10.874980
## [470] 8.331342 14.512319 8.558337 10.647056 9.382280 9.309796 10.105536
## [477] 10.429795 10.144694 10.458401 11.751676 9.198607 9.634043 6.963974
## [484] 11.443414 9.055110 9.747851 11.642582 13.434934 15.143854 13.784114
## [491] 10.398071 9.628637 7.559706 9.874695 12.703417 8.723772 11.207569
## [498] 9.883180 13.290626 8.264606 12.447384 10.287392 9.892864 11.191296
## [505] 11.267203 9.923210 8.800051 10.433764 10.840916 9.415840 12.317672
## [512] 9.809748 8.714992 11.058272 10.668304 10.828052 11.104614 9.752048
## [519] 9.685878 9.474048 9.431424 9.902352 7.092543 8.306007 8.586368
## [526] 9.299517 10.704291 9.590699 9.619382 6.113806 11.752150 10.474794
## [533] 9.407665 10.440195 9.306341 9.974241 10.296637 10.639420 9.606005
## [540] 9.438019 10.514709 8.466316 7.651368 12.861814 7.545894 8.974118
## [547] 9.900622 9.383029 8.232650 10.528685 9.101617 9.860811 9.192396
## [554] 10.655203 12.726129 10.828554 8.312185 11.844820 11.849526 10.268747
## [561] 9.529554 11.087497 8.917625 10.042119 11.003026 10.017506 11.414247
## [568] 11.315829 10.645150 8.311743 10.574209 9.302538 9.488441 12.467050
## [575] 10.252458 7.297034 8.461702 10.540705 10.500422 12.775641 10.902441
## [582] 10.736413 11.593846 10.421331 11.491868 11.823836 11.108542 10.182393
## [589] 10.600954 8.593987 8.944220 10.418911 11.542763 9.112985 10.254064
## [596] 10.386259 11.280530 9.239665 10.438009 12.173229 9.256025 8.122546
## [603] 10.060838 8.245521 9.937599 12.040114 8.289382 10.788378 8.103294
## [610] 11.056209 6.307066 10.082501 8.452391 7.296699 11.763110 8.790606
## [617] 11.279780 11.624468 10.128176 11.894517 11.268328 10.781293 9.616397
## [624] 8.644616 11.575315 11.314520 12.336798 8.602244 10.394990 10.807910
## [631] 9.457050 8.332331 10.565888 8.671771 9.785438 10.377649 10.519344
## [638] 10.235473 10.639010 7.728743 12.066718 10.959068 8.892804 10.580536
## [645] 9.298171 11.584113 9.494272 9.107705 10.947030 8.454684 8.172729
## [652] 8.971343 10.052201 9.368278 9.386690 10.418538 10.533200 11.680736
## [659] 10.379309 9.255651 7.736262 8.506023 10.855238 8.350087 11.177701
## [666] 9.999046 11.470649 10.695248 8.789086 9.412709 11.367321 12.013535
## [673] 10.048335 10.930565 8.760610 8.243298 9.351434 8.240819 10.052778
## [680] 10.421756 7.349617 9.737799 9.788388 7.154310 11.018521 9.124187
## [687] 11.248454 9.726229 10.203658 8.905146 9.919312 7.829832 9.532232
## [694] 13.062038 10.928101 13.282248 9.875520 10.044962 10.979311 10.986673
## [701] 10.110765 10.758826 9.833003 10.274821 12.203579 9.807042 10.014904
## [708] 9.063218 9.209170 11.222982 10.879808 8.442330 11.473227 8.228842
## [715] 8.396323 10.668024 8.060267 12.125037 10.772402 11.927174 10.003577
## [722] 12.047625 11.886649 9.187442 10.227848 9.862060 10.477892 10.771015
## [729] 13.460948 10.199011 12.629908 9.923031 9.380699 8.288141 10.788078
## [736] 10.312645 6.370583 11.468229 11.242604 10.256851 7.848532 8.347963
## [743] 11.236949 9.186216 11.025275 8.533669 8.606189 11.686667 10.542381
## [750] 12.188789 11.166509 8.087601 8.701972 11.502075 10.581199 11.038662
## [757] 10.346365 10.186665 9.237747 9.731804 10.216404 9.975248 9.331044
## [764] 11.242720 13.098057 8.570317 9.166341 9.606693 9.836943 10.668721
## [771] 10.070293 11.132094 10.361925 11.273226 9.953326 11.967828 7.028258
## [778] 8.671370 11.660745 11.708766 9.535167 12.276721 9.822689 9.559146
## [785] 11.494841 11.296549 9.037655 10.416524 10.113842 9.831769 11.593785
## [792] 11.443890 14.015623 9.879677 8.890445 8.052788 10.682496 8.968140
## [799] 9.027781 9.135297 8.946787 9.046543 9.838941 7.199683 11.529534
## [806] 10.827514 9.743869 7.703399 12.143087 10.992372 9.449845 11.566188
## [813] 12.425209 11.961006 9.853930 8.693210 11.366847 10.432990 11.981173
## [820] 8.599833 9.411419 12.162325 10.494701 9.553213 8.478589 12.976770
## [827] 12.890357 9.586104 10.237019 11.514635 8.673123 11.045603 13.015473
## [834] 10.444853 12.532577 7.768329 9.975878 8.252025 11.715472 9.430483
## [841] 10.823430 9.544981 10.165218 9.937126 10.956747 11.362839 10.097018
## [848] 12.113298 10.721901 9.272368 10.682156 10.212009 11.682121 12.360426
## [855] 10.778851 9.706174 10.941538 10.570189 11.046223 10.785331 10.863891
## [862] 9.036087 10.853956 7.603214 9.048325 11.131425 10.223314 10.794812
## [869] 10.162152 11.684658 9.741594 12.120192 8.995063 10.241990 8.344892
## [876] 10.928993 6.822072 7.958412 10.523303 8.815465 9.693721 11.332929
## [883] 10.343082 9.980093 11.706917 9.174514 8.859749 12.489726 9.362588
## [890] 9.902789 9.250690 10.617502 9.555919 11.514728 12.441984 10.621902
## [897] 12.459069 8.036490 10.099751 9.303732 8.801456 11.017395 11.524788
## [904] 8.371630 9.611595 10.635810 11.708740 10.008550 6.998799 8.337815
## [911] 11.749605 9.340802 7.384034 10.713482 10.759123 10.289911 8.807014
## [918] 10.073359 11.529181 11.252949 12.415107 10.603191 10.198714 9.851392
## [925] 8.957839 6.781761 10.665559 10.636885 10.734344 11.356791 12.504408
## [932] 10.748152 8.944109 8.940001 8.994535 9.640082 9.781231 10.257378
## [939] 10.764712 11.902758 8.978106 11.403518 9.209383 8.333570 12.789645
## [946] 9.718240 10.757078 7.433449 12.655330 9.912693 9.659066 10.380550
## [953] 12.304579 8.433176 9.888383 10.514772 9.944597 8.965848 11.629569
## [960] 12.545287 8.709373 10.387240 10.041464 9.892800 9.868737 10.460868
## [967] 10.323983 10.581378 8.903114 9.115914 9.852853 10.968542 10.190223
## [974] 8.866713 14.193600 9.485641 11.661258 11.706821 10.651965 10.164334
## [981] 9.855125 9.224130 10.148415 11.399800 8.313843 9.518534 11.863038
## [988] 8.228723 9.339145 8.251412 8.864664 12.901227 10.430930 10.470148
## [995] 12.929524 7.673578 9.477696 10.868212 12.179558 8.044031
# create groups for different values of X
(group <- rep(1,1000)) # a vector consisting of 1000 elements
## [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [186] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [223] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [260] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [297] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [334] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [371] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [408] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [445] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [482] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [519] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [556] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [593] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [630] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [667] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [704] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [741] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [778] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [815] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [852] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [889] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [926] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [963] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1000] 1
group[xAxis > -1.5] <- 2
group[xAxis > -.5] <- 3
group[xAxis > .5] <- 4
group[xAxis > 1.5] <- 5
group
## [1] 3 3 2 3 2 4 1 3 4 5 3 2 5 3 3 3 2 1 5 3 2 3 4 4 3 4 2 4 2 3 3 3 4 4 3 3 2
## [38] 2 3 4 2 4 2 2 4 3 2 2 3 2 3 1 4 3 3 3 4 4 4 3 2 2 2 4 4 3 2 1 3 4 3 3 3 4
## [75] 2 3 4 2 2 4 2 1 3 4 2 3 4 3 4 2 2 1 2 4 1 2 2 4 1 4 5 2 2 4 2 4 3 2 3 4 1
## [112] 3 3 2 4 4 4 2 5 3 5 5 3 4 3 3 4 4 4 2 4 4 3 2 5 2 3 3 5 4 4 4 4 2 3 2 5 4
## [149] 3 4 1 1 3 3 4 4 4 1 3 3 4 3 3 4 5 3 4 2 3 2 1 3 3 2 3 3 3 2 2 1 2 2 3 1 2
## [186] 1 1 3 2 2 3 2 3 3 3 2 3 3 2 3 4 1 2 4 5 3 3 4 2 5 3 3 3 3 3 4 4 2 3 2 5 4
## [223] 1 5 3 3 3 4 5 2 3 2 4 5 2 4 4 3 4 3 2 2 3 2 4 4 4 3 4 4 3 3 2 4 3 4 2 4 3
## [260] 4 4 3 3 2 3 3 2 2 3 2 3 3 3 4 3 3 2 3 3 2 2 3 4 2 5 4 1 2 4 3 4 2 4 2 2 2
## [297] 2 3 1 3 3 3 4 5 3 3 3 3 4 3 2 5 4 2 2 2 3 3 5 2 3 2 4 5 4 3 4 2 4 4 2 2 3
## [334] 2 2 2 5 2 2 2 3 2 2 1 5 3 3 2 2 2 5 2 2 2 3 3 3 4 1 2 1 3 4 4 1 2 2 3 4 4
## [371] 3 3 4 4 3 5 3 3 1 4 4 3 3 5 2 1 3 3 3 2 4 3 3 4 3 3 3 5 4 3 2 5 3 3 3 3 4
## [408] 2 3 4 3 2 1 1 3 3 5 3 2 5 3 4 3 3 4 4 5 3 2 2 3 2 2 2 4 2 1 4 3 1 1 1 3 3
## [445] 1 5 4 3 2 3 4 4 3 2 4 4 1 4 2 4 3 2 5 3 3 4 2 3 3 2 5 3 3 2 3 4 2 3 3 3 2
## [482] 2 1 4 2 2 5 5 5 5 3 3 1 2 5 3 4 3 4 3 5 3 3 4 3 2 3 3 4 3 3 3 2 3 4 4 3 2
## [519] 3 2 3 3 2 2 1 1 4 3 3 2 3 2 3 3 2 4 4 3 4 4 2 2 1 4 2 3 3 2 2 3 4 3 3 3 5
## [556] 3 2 5 4 5 4 3 2 2 4 3 4 4 3 3 4 3 3 4 4 1 3 4 3 3 3 3 5 3 3 3 4 4 2 2 2 4
## [593] 3 2 4 3 3 2 4 4 2 2 3 3 4 3 2 3 3 3 1 2 3 3 4 2 3 3 2 3 4 4 4 2 3 4 5 2 2
## [630] 3 3 3 5 1 3 4 4 3 3 2 3 4 3 4 2 3 2 3 4 2 3 4 2 3 4 2 4 4 3 3 2 2 3 2 4 4
## [667] 3 3 3 3 4 4 3 5 3 2 4 2 4 3 1 2 3 1 3 2 4 3 3 2 4 2 2 5 2 3 4 4 4 4 4 3 2
## [704] 4 4 3 4 3 3 4 3 2 5 2 2 3 4 3 3 4 3 4 4 2 4 3 4 4 4 2 4 3 2 2 4 3 1 4 4 3
## [741] 3 4 5 4 4 2 1 4 3 4 4 2 3 3 3 3 3 4 3 3 4 4 3 4 4 2 3 2 3 2 3 3 4 4 2 4 3
## [778] 3 5 4 3 3 3 4 3 3 2 3 3 3 4 4 4 3 3 2 4 2 4 2 3 2 3 1 3 4 3 3 4 5 3 3 4 4
## [815] 3 3 4 3 4 3 1 5 4 3 1 5 3 2 3 4 2 4 4 3 5 1 2 2 5 2 3 3 4 1 3 4 3 5 4 3 3
## [852] 5 3 5 3 3 4 3 4 3 3 4 4 2 2 3 1 2 2 3 2 4 2 3 1 4 2 3 3 1 4 4 4 3 3 3 2 5
## [889] 4 3 1 3 3 4 4 3 4 1 3 2 2 4 4 2 2 4 4 3 1 3 4 3 2 3 4 3 2 4 3 4 4 4 3 3 3
## [926] 1 3 4 2 5 4 2 2 3 3 2 2 3 4 5 3 4 3 3 5 3 3 1 3 3 3 4 2 2 3 3 3 3 3 4 2 4
## [963] 2 2 3 4 4 3 2 3 3 3 2 3 5 3 4 4 4 2 3 3 2 4 1 2 4 2 3 1 3 5 3 3 4 2 4 3 3
## [1000] 2
# create sample dataframe by joining variables
sample_data <- data.frame(xAxis,yAxis,group)
sample_data
## xAxis yAxis group
## 1 0.236313906 10.261907 3
## 2 0.021528537 10.435646 3
## 3 -0.766066565 12.115552 2
## 4 0.015096718 9.062436 3
## 5 -0.852067624 9.258501 2
## 6 1.275920067 9.518178 4
## 7 -2.095233581 8.858271 1
## 8 -0.374409413 9.509541 3
## 9 0.932909204 11.231692 4
## 10 3.023079813 10.358001 5
## 11 -0.172264293 10.508750 3
## 12 -0.513736821 9.698263 2
## 13 1.959305352 13.228659 5
## 14 0.055131951 10.002940 3
## 15 -0.423013195 11.198849 3
## 16 0.111311644 8.536116 3
## 17 -0.914290754 8.702535 2
## 18 -1.595724432 8.059952 1
## 19 1.753024932 10.049605 5
## 20 -0.106774664 9.198537 3
## 21 -1.204024715 8.441697 2
## 22 -0.293050896 8.109314 3
## 23 1.282573274 11.931279 4
## 24 0.786776490 11.521268 4
## 25 -0.234791584 10.655081 3
## 26 0.592798830 9.212135 4
## 27 -0.545526295 8.993825 2
## 28 0.612753508 10.273680 4
## 29 -0.745687185 9.354769 2
## 30 -0.351572081 9.691121 3
## 31 -0.123318856 10.245535 3
## 32 0.181099276 9.471905 3
## 33 0.762580867 10.456198 4
## 34 0.932323420 11.817269 4
## 35 0.037701725 9.058133 3
## 36 0.087841264 9.387490 3
## 37 -1.168239252 7.081202 2
## 38 -0.938475589 9.317950 2
## 39 -0.197457145 9.805540 3
## 40 0.933775325 10.307097 4
## 41 -1.146121991 10.400787 2
## 42 0.831904666 11.610969 4
## 43 -0.526713357 10.540473 2
## 44 -0.569325432 10.359741 2
## 45 1.097300733 10.589011 4
## 46 0.111510083 10.598980 3
## 47 -1.018719294 7.026737 2
## 48 -0.990189152 8.620457 2
## 49 -0.215174576 8.705125 3
## 50 -1.267201471 7.941441 2
## 51 -0.012936045 8.346406 3
## 52 -2.289739445 6.686558 1
## 53 0.757478403 10.923367 4
## 54 0.028837045 9.234302 3
## 55 0.165394426 9.305950 3
## 56 0.270514270 9.740923 3
## 57 1.267110091 12.473384 4
## 58 0.750033504 10.159163 4
## 59 0.719639041 10.384563 4
## 60 -0.369628269 9.370755 3
## 61 -0.962742500 9.030475 2
## 62 -0.644419524 11.545449 2
## 63 -1.292057651 10.541775 2
## 64 1.260061429 10.524575 4
## 65 1.335023059 11.448677 4
## 66 0.489960317 11.654638 3
## 67 -1.366475381 10.598646 2
## 68 -1.656504657 7.778341 1
## 69 0.259147545 8.752501 3
## 70 1.333116312 11.316632 4
## 71 -0.060296017 10.734112 3
## 72 -0.190682041 9.969199 3
## 73 0.389402591 10.777420 3
## 74 0.940468939 11.055755 4
## 75 -0.746412299 8.012743 2
## 76 -0.473450785 11.091096 3
## 77 0.523295706 10.287984 4
## 78 -1.343780966 9.433451 2
## 79 -0.641221239 7.470392 2
## 80 0.762278245 13.163373 4
## 81 -1.101348000 7.931438 2
## 82 -1.706687663 8.711046 1
## 83 0.145986926 11.195192 3
## 84 0.676686963 9.721415 4
## 85 -1.212278998 8.809787 2
## 86 -0.384591040 10.564831 3
## 87 1.114436084 11.648575 4
## 88 0.261829155 10.303493 3
## 89 0.843261332 8.649638 4
## 90 -1.047757757 7.198187 2
## 91 -0.978814016 9.147945 2
## 92 -1.546665417 9.081599 1
## 93 -0.880573668 8.694530 2
## 94 1.113212330 12.399308 4
## 95 -2.112074574 10.242617 1
## 96 -0.680440886 8.656104 2
## 97 -0.643867344 9.292522 2
## 98 1.124545927 10.641956 4
## 99 -1.699043394 8.506824 1
## 100 1.162801304 11.586902 4
## 101 1.683908155 10.941222 5
## 102 -0.964989654 7.374557 2
## 103 -0.556436173 8.526646 2
## 104 0.886760179 10.812165 4
## 105 -0.699291105 8.427465 2
## 106 0.560865498 9.612765 4
## 107 0.380925348 10.748179 3
## 108 -0.887469172 8.752123 2
## 109 0.162354957 9.180965 3
## 110 0.514725297 9.356451 4
## 111 -1.614169489 8.538688 1
## 112 -0.223466834 10.389863 3
## 113 0.091125346 9.581212 3
## 114 -1.498458271 11.162774 2
## 115 1.458387256 11.300335 4
## 116 0.806430393 10.552470 4
## 117 1.241361734 11.315786 4
## 118 -0.898690105 10.230593 2
## 119 1.596056410 12.750885 5
## 120 -0.026016652 9.364774 3
## 121 1.890898796 12.803217 5
## 122 1.694272772 10.828945 5
## 123 -0.435737808 9.311646 3
## 124 0.875102032 11.106210 4
## 125 0.102744084 12.486898 3
## 126 -0.397323170 8.936509 3
## 127 1.410899240 11.821657 4
## 128 1.177807746 11.766894 4
## 129 1.114876095 11.376658 4
## 130 -0.783782909 8.173365 2
## 131 0.560221678 10.005675 4
## 132 1.394541365 11.487335 4
## 133 -0.378635702 9.403857 3
## 134 -1.349552424 7.584422 2
## 135 2.937387463 12.696755 5
## 136 -0.725488305 8.237782 2
## 137 -0.310877936 9.439544 3
## 138 -0.196832605 7.980927 3
## 139 1.549496268 13.253472 5
## 140 0.516922759 11.570574 4
## 141 0.588586592 9.737232 4
## 142 0.787365114 9.423618 4
## 143 1.247514421 11.052959 4
## 144 -0.840803326 9.913894 2
## 145 -0.333783401 8.890260 3
## 146 -0.787072176 7.685485 2
## 147 1.827869028 13.671975 5
## 148 0.579089732 8.815985 4
## 149 -0.074574596 10.196132 3
## 150 0.906682458 11.758114 4
## 151 -2.433063773 6.106133 1
## 152 -1.875649133 7.471224 1
## 153 0.012980119 9.619921 3
## 154 0.087403859 10.014683 3
## 155 0.641455193 10.731994 4
## 156 0.885665380 8.490889 4
## 157 1.296489361 12.649390 4
## 158 -2.332444091 8.923611 1
## 159 0.231940019 10.083421 3
## 160 0.316425175 10.921047 3
## 161 0.848068366 12.344936 4
## 162 0.332597373 9.452480 3
## 163 -0.297999727 10.252122 3
## 164 1.005398003 10.887615 4
## 165 2.077693762 12.409159 5
## 166 -0.470951565 10.396809 3
## 167 1.103031773 10.419240 4
## 168 -0.803147345 9.779557 2
## 169 0.130947526 10.519353 3
## 170 -0.790739781 6.984750 2
## 171 -1.740285909 9.533537 1
## 172 -0.443879937 8.700142 3
## 173 0.260174337 9.070934 3
## 174 -0.962884024 9.234337 2
## 175 -0.192205658 10.033564 3
## 176 -0.014461824 10.418099 3
## 177 -0.105357872 9.938248 3
## 178 -1.240555751 10.626654 2
## 179 -1.472981724 8.464819 2
## 180 -2.138560660 8.212908 1
## 181 -0.508498854 9.103618 2
## 182 -0.686003418 8.041437 2
## 183 -0.166367777 8.634049 3
## 184 -2.415818164 9.499333 1
## 185 -0.803018769 10.020334 2
## 186 -2.557494665 8.068748 1
## 187 -1.783459653 8.308110 1
## 188 0.294128640 9.804037 3
## 189 -1.051390815 8.560659 2
## 190 -0.656129364 9.772276 2
## 191 0.025875730 10.137949 3
## 192 -1.480481934 7.781357 2
## 193 -0.130011800 11.625440 3
## 194 0.323258389 11.635805 3
## 195 0.421236434 10.206315 3
## 196 -0.738085895 9.068726 2
## 197 0.281747183 9.698898 3
## 198 0.064247954 10.229973 3
## 199 -0.747636175 8.600963 2
## 200 -0.466246689 8.175189 3
## 201 0.853242208 11.475110 4
## 202 -1.511811149 9.172921 1
## 203 -0.826783802 9.263040 2
## 204 0.923162524 11.164457 4
## 205 1.600186333 12.622955 5
## 206 -0.427062919 9.938178 3
## 207 0.048735791 7.609039 3
## 208 0.835278179 11.078359 4
## 209 -0.915963965 10.501117 2
## 210 1.860648177 12.243263 5
## 211 0.251120262 11.253992 3
## 212 0.275066003 8.478279 3
## 213 -0.287546266 10.948606 3
## 214 0.493520579 11.486570 3
## 215 0.379653770 9.442760 3
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## 218 -1.473556676 7.523850 2
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## 222 0.735018157 9.783761 4
## 223 -2.442253901 7.357645 1
## 224 2.624582773 12.040992 5
## 225 0.338914327 11.326416 3
## 226 0.294466622 10.045856 3
## 227 0.144994273 8.535257 3
## 228 0.772885443 11.729145 4
## 229 1.987328511 10.730526 5
## 230 -0.526096922 10.472629 2
## 231 0.373252259 10.459979 3
## 232 -0.909624522 8.169677 2
## 233 0.744851361 12.516305 4
## 234 2.246050256 12.106396 5
## 235 -1.358546300 8.594674 2
## 236 1.072507913 11.253813 4
## 237 0.910341540 10.873697 4
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## 240 0.185245752 10.472286 3
## 241 -0.737000914 9.816849 2
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## 247 1.086969961 14.097105 4
## 248 -0.424003498 10.039395 3
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## 250 0.544096967 10.536558 4
## 251 0.093668507 10.206832 3
## 252 -0.026187860 10.746838 3
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## 255 0.012151387 10.014586 3
## 256 0.781365256 10.181544 4
## 257 -1.157308019 7.146505 2
## 258 1.382136225 11.044279 4
## 259 0.177987477 8.943215 3
## 260 0.838339265 9.234705 4
## 261 1.004432640 11.389800 4
## 262 -0.268637208 10.583173 3
## 263 0.472592937 10.048056 3
## 264 -0.670208934 9.789453 2
## 265 -0.318320637 11.160366 3
## 266 -0.197845316 8.056367 3
## 267 -0.915108336 8.481279 2
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## 270 -0.554527539 10.481075 2
## 271 -0.427541468 8.260047 3
## 272 -0.227091220 8.593872 3
## 273 0.378222871 10.407911 3
## 274 0.676090076 11.779930 4
## 275 0.271311919 9.779375 3
## 276 -0.358598792 9.340963 3
## 277 -1.106126431 8.903844 2
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## 280 -1.418170091 7.637370 2
## 281 -0.536822163 10.281462 2
## 282 0.214647598 9.932106 3
## 283 0.915242863 11.412832 4
## 284 -1.033106954 8.260437 2
## 285 2.171179430 11.661319 5
## 286 1.419462574 9.529128 4
## 287 -1.584105683 8.072627 1
## 288 -0.771039440 8.554468 2
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## 290 0.427062872 13.157952 3
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## 292 -1.044598087 8.749630 2
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## 294 -1.156593866 9.150811 2
## 295 -1.328919752 8.658487 2
## 296 -1.487605474 7.094350 2
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## 300 0.301097622 10.976222 3
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## 303 0.647520323 11.332530 4
## 304 2.215925396 11.191901 5
## 305 0.476985977 9.066956 3
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## 328 -1.362056724 7.852518 2
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## 990 -2.852083687 8.251412 1
## 991 -0.077396268 8.864664 3
## 992 1.974795123 12.901227 5
## 993 0.164105292 10.430930 3
## 994 0.153070344 10.470148 3
## 995 1.137758937 12.929524 4
## 996 -1.261574388 7.673578 2
## 997 1.295167812 9.477696 4
## 998 0.461063387 10.868212 3
## 999 0.392999737 12.179558 3
## 1000 -0.997408665 8.044031 2
# 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)
