# Mindanao State University
# General Santos City
# Introduction to R base commands
# Submitted by: Kient Rey L. Zuyco
# Submitted to: Prof. Carlito O. Daarol
# Faculty
# Math Department
# April 11, 2023
# Lab Exercise 1: How to create Lines in with different styles in R
# Step 1: Create Data
x <- 1:10 # Create example data
y <- c(3, 1, 5, 2, 3, 8, 4, 7, 6, 9) # using the c() function to create an array
# Step 2: Plot the line graph using the base plot() command
plot(x, y, type = "l")
# Step 3: Add Main Title & Change Axis Labels
plot(x, y, type = "l",
main = "Hello: This is my Line Plot",
xlab = "My X-Values",
ylab = "My Y-Values")
# Step 4: Add color to the line using the col command
plot(x, y, type = "l",
main = "This is my Line Plot",
xlab = "My X-Values",
ylab = "My Y-Values",
col = "red")
# Step 5: Modify Thickness of Line using lwd command
plot(x, y, type = "l",
main = "This is my Line Plot",
xlab = "My X-Values",
ylab = "My Y-Values",
lwd=7,
col = "green")
# Step 6: Add points to line graph by changing the type command
plot(x, y, type = "b",
main = "This is my Line Plot",
xlab = "My X-Values",
ylab = "My Y-Values",
lwd=3,
col = "blue")
# Lab Exercise 2: How to create Lines in with different styles in R
# Step1: Assign values for different lines. We enclose the entire
# line with parenthesis symbol to force R to display the results instantly
# set the same value for the x variable
(x <- 1:10)
## [1] 1 2 3 4 5 6 7 8 9 10
# set different values for y variables
(y1 <- c(3, 1, 5, 2, 3, 8, 4, 7, 6, 9))
## [1] 3 1 5 2 3 8 4 7 6 9
(y2 <- c(5, 1, 4, 6, 2, 3, 7, 8, 2, 8))
## [1] 5 1 4 6 2 3 7 8 2 8
(y3 <- c(3, 3, 3, 3, 4, 4, 5, 5, 7, 7))
## [1] 3 3 3 3 4 4 5 5 7 7
# Plot first the pair x and y1.
plot(x, y1, type = "b",
main = "This is my Line Plot",
xlab = "My X-Values",
ylab = "My Y-Values",
lwd=3,
col = "blue")
# then Add the two lines (for x,y2) and (x,y3)
lines(x, y2, type = "b", col = "red",lwd=3)
lines(x, y3, type = "b", col = "green",lwd=3)
# Add legend to the plot
legend("topleft",
legend = c("Line y1", "Line y2", "Line y3"),
col = c("black", "red", "green"),
lty = 1)
# Step 2: Create Different Point Symbol for Each
# Line using the pch command
plot(x, y1, type = "b",pch = 16,
main = "This is my Line Plot",
xlab = "My X-Values",
ylab = "My Y-Values",
lwd=3,
col = "blue")
lines(x, y2, type = "b", col = "red",lwd=3, pch = 15)
lines(x, y3, type = "b", col = "green",lwd=3, pch = 8)
# Add legend
legend("topleft",
legend = c("Line y1", "Line y2", "Line y3"),
col = c("black", "red", "green"),
lty = 1)
# Lab Exercise 3: Create Line graph without x values
Pupils <- c(3.55 ,3.54 ,3.53 ,3.61 ,3.65 ,3.63 ,3.61
,3.61 ,3.59 ,3.63 ,3.59 ,3.63 ,3.62 ,3.62
,3.59 ,3.63 ,3.62 ,3.65 ,3.65)
# get number of elements of Pupils
length(Pupils)
## [1] 19
# Display the elements of Pupils
Pupils
## [1] 3.55 3.54 3.53 3.61 3.65 3.63 3.61 3.61 3.59 3.63 3.59 3.63 3.62 3.62 3.59
## [16] 3.63 3.62 3.65 3.65
# You can obtain the plot without x values
plot(Pupils, type = 'o')
# Lab Exercise 4: How to Create vertical, horizontal lines
# We will use buit-in cars dataset in R
# display the cars dataset
cars
## speed dist
## 1 4 2
## 2 4 10
## 3 7 4
## 4 7 22
## 5 8 16
## 6 9 10
## 7 10 18
## 8 10 26
## 9 10 34
## 10 11 17
## 11 11 28
## 12 12 14
## 13 12 20
## 14 12 24
## 15 12 28
## 16 13 26
## 17 13 34
## 18 13 34
## 19 13 46
## 20 14 26
## 21 14 36
## 22 14 60
## 23 14 80
## 24 15 20
## 25 15 26
## 26 15 54
## 27 16 32
## 28 16 40
## 29 17 32
## 30 17 40
## 31 17 50
## 32 18 42
## 33 18 56
## 34 18 76
## 35 18 84
## 36 19 36
## 37 19 46
## 38 19 68
## 39 20 32
## 40 20 48
## 41 20 52
## 42 20 56
## 43 20 64
## 44 22 66
## 45 23 54
## 46 24 70
## 47 24 92
## 48 24 93
## 49 24 120
## 50 25 85
# get the number of rows and columns using dim() command
dim(cars)
## [1] 50 2
# display the variable names of the cars dataset
names(cars)
## [1] "speed" "dist"
# display only the first column of the dataset
cars$speed # using the column name
## [1] 4 4 7 7 8 9 10 10 10 11 11 12 12 12 12 13 13 13 13 14 14 14 14 15 15
## [26] 15 16 16 17 17 17 18 18 18 18 19 19 19 20 20 20 20 20 22 23 24 24 24 24 25
cars[,1] # using the column number
## [1] 4 4 7 7 8 9 10 10 10 11 11 12 12 12 12 13 13 13 13 14 14 14 14 15 15
## [26] 15 16 16 17 17 17 18 18 18 18 19 19 19 20 20 20 20 20 22 23 24 24 24 24 25
# Remarks: the following commands will give you the same result
plot(cars,) # using the comma after the name
plot(cars[,1],cars[,2]) # using the column index 1 and 2
attach(cars); plot(speed,dist) # using the attach command to load the variables
plot(cars$speed,cars$dist) # using the dollar notation
# combine all 4 plots using the par() command
par(mfrow = c(2,2)) # set a 2x2 plot output
plot(cars,) # using the comma after the name
plot(cars[,1],cars[,2]) # using the column index 1 and 2
attach(cars); plot(speed,dist) # using the attach command to load the variables
## The following objects are masked from cars (pos = 3):
##
## dist, speed
plot(cars$speed,cars$dist) # using the dollar notation
par(mfrow = c(1,1)) # reset to default plot setting
# Problem: Create vertical lines using the v command
plot(cars)
abline(v = 15, col = "darkgreen",lwd=3) # vertical line
abline(v = 10, col = "blue",lwd=3) # vertical line
# Problem: Create horizontal lines using the h command
abline(h = 80, col = "darkgreen",lwd=3) # vertical line
abline(h = 20, col = "blue",lwd=3) # vertical line
# Create more lines simultaneously, using a vector of values
plot(cars)
abline(v = c(9, 22,25), col = c("darkgreen", "blue","red"),
lwd = c(1, 3,2), # line thickness
lty = c(2,2,2)) # dashed lines
plot(cars)
abline(v = c(9, 22,25), col = c("darkgreen", "blue","red"),
lwd = c(1, 3,2)) # line thickness and solid lines
# create horizontal lines
plot(cars)
abline(h = 60, col = "red",lty = 1, lwd = 3)
abline(h = 100, col = "red",lty = 2, lwd = 3)
abline(h = 20, col = "red",lty = 3, lwd = 3)
# Lab Exercise 5: How to Plot data by group
# We will use buit-in iris dataset in R
# this dataset is a collection of 4 species of flowers with different
# sepal length and width and also with different petal length and width
dim(iris) # iris dataset has 150 rows and 5 columns
## [1] 150 5
names(iris)
## [1] "Sepal.Length" "Sepal.Width" "Petal.Length" "Petal.Width" "Species"
# two different commands to get the frequency table
table(iris$Species) # refer to the dataset by variable name
##
## setosa versicolor virginica
## 50 50 50
table(iris[,5]) # refer to the dataset by column number
##
## setosa versicolor virginica
## 50 50 50
# get summary of all columns
summary(iris)
## Sepal.Length Sepal.Width Petal.Length Petal.Width
## Min. :4.300 Min. :2.000 Min. :1.000 Min. :0.100
## 1st Qu.:5.100 1st Qu.:2.800 1st Qu.:1.600 1st Qu.:0.300
## Median :5.800 Median :3.000 Median :4.350 Median :1.300
## Mean :5.843 Mean :3.057 Mean :3.758 Mean :1.199
## 3rd Qu.:6.400 3rd Qu.:3.300 3rd Qu.:5.100 3rd Qu.:1.800
## Max. :7.900 Max. :4.400 Max. :6.900 Max. :2.500
## Species
## setosa :50
## versicolor:50
## virginica :50
##
##
##
#create scatterplot of sepal width vs. sepal length
plot(iris$Sepal.Width, iris$Sepal.Length,
col='steelblue',
main='Scatterplot',
xlab='Sepal Width',
ylab='Sepal Length',
pch=19)
plot(iris$Sepal.Width, iris$Sepal.Length,
col='steelblue',
main='Scatterplot',
xlab='Sepal Width',
ylab='Sepal Length',
pch=1)
# another way to retrieve columns of data
PL <- iris$Petal.Length
PW <- iris$Petal.Width
plot(PL, PW)
# add color by species
plot(PL, PW, col = iris$Species, main= "My Plot")
# draw a line along with the distribution of points
# using the abline and lm commands
abline(lm(PW ~ PL))
# add text annotation
text(5, 0.5, "Regression Line")
legend("topleft", # specify the location of the legend
levels(iris$Species), # specify the levels of species
pch = 1:3, # specify three symbols used for the three species
col = 1:3 # specify three colors for the three species
)
# Lab Exercise 6: Generate advance scatter plot
pairs(iris, col = rainbow(3)[iris$Species]) # set colors by species
# How to filter data for each flower
(Versicolor <- subset(iris, Species == "versicolor"))
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 51 7.0 3.2 4.7 1.4 versicolor
## 52 6.4 3.2 4.5 1.5 versicolor
## 53 6.9 3.1 4.9 1.5 versicolor
## 54 5.5 2.3 4.0 1.3 versicolor
## 55 6.5 2.8 4.6 1.5 versicolor
## 56 5.7 2.8 4.5 1.3 versicolor
## 57 6.3 3.3 4.7 1.6 versicolor
## 58 4.9 2.4 3.3 1.0 versicolor
## 59 6.6 2.9 4.6 1.3 versicolor
## 60 5.2 2.7 3.9 1.4 versicolor
## 61 5.0 2.0 3.5 1.0 versicolor
## 62 5.9 3.0 4.2 1.5 versicolor
## 63 6.0 2.2 4.0 1.0 versicolor
## 64 6.1 2.9 4.7 1.4 versicolor
## 65 5.6 2.9 3.6 1.3 versicolor
## 66 6.7 3.1 4.4 1.4 versicolor
## 67 5.6 3.0 4.5 1.5 versicolor
## 68 5.8 2.7 4.1 1.0 versicolor
## 69 6.2 2.2 4.5 1.5 versicolor
## 70 5.6 2.5 3.9 1.1 versicolor
## 71 5.9 3.2 4.8 1.8 versicolor
## 72 6.1 2.8 4.0 1.3 versicolor
## 73 6.3 2.5 4.9 1.5 versicolor
## 74 6.1 2.8 4.7 1.2 versicolor
## 75 6.4 2.9 4.3 1.3 versicolor
## 76 6.6 3.0 4.4 1.4 versicolor
## 77 6.8 2.8 4.8 1.4 versicolor
## 78 6.7 3.0 5.0 1.7 versicolor
## 79 6.0 2.9 4.5 1.5 versicolor
## 80 5.7 2.6 3.5 1.0 versicolor
## 81 5.5 2.4 3.8 1.1 versicolor
## 82 5.5 2.4 3.7 1.0 versicolor
## 83 5.8 2.7 3.9 1.2 versicolor
## 84 6.0 2.7 5.1 1.6 versicolor
## 85 5.4 3.0 4.5 1.5 versicolor
## 86 6.0 3.4 4.5 1.6 versicolor
## 87 6.7 3.1 4.7 1.5 versicolor
## 88 6.3 2.3 4.4 1.3 versicolor
## 89 5.6 3.0 4.1 1.3 versicolor
## 90 5.5 2.5 4.0 1.3 versicolor
## 91 5.5 2.6 4.4 1.2 versicolor
## 92 6.1 3.0 4.6 1.4 versicolor
## 93 5.8 2.6 4.0 1.2 versicolor
## 94 5.0 2.3 3.3 1.0 versicolor
## 95 5.6 2.7 4.2 1.3 versicolor
## 96 5.7 3.0 4.2 1.2 versicolor
## 97 5.7 2.9 4.2 1.3 versicolor
## 98 6.2 2.9 4.3 1.3 versicolor
## 99 5.1 2.5 3.0 1.1 versicolor
## 100 5.7 2.8 4.1 1.3 versicolor
(Setosa <- subset(iris, Species == "setosa"))
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa
## 7 4.6 3.4 1.4 0.3 setosa
## 8 5.0 3.4 1.5 0.2 setosa
## 9 4.4 2.9 1.4 0.2 setosa
## 10 4.9 3.1 1.5 0.1 setosa
## 11 5.4 3.7 1.5 0.2 setosa
## 12 4.8 3.4 1.6 0.2 setosa
## 13 4.8 3.0 1.4 0.1 setosa
## 14 4.3 3.0 1.1 0.1 setosa
## 15 5.8 4.0 1.2 0.2 setosa
## 16 5.7 4.4 1.5 0.4 setosa
## 17 5.4 3.9 1.3 0.4 setosa
## 18 5.1 3.5 1.4 0.3 setosa
## 19 5.7 3.8 1.7 0.3 setosa
## 20 5.1 3.8 1.5 0.3 setosa
## 21 5.4 3.4 1.7 0.2 setosa
## 22 5.1 3.7 1.5 0.4 setosa
## 23 4.6 3.6 1.0 0.2 setosa
## 24 5.1 3.3 1.7 0.5 setosa
## 25 4.8 3.4 1.9 0.2 setosa
## 26 5.0 3.0 1.6 0.2 setosa
## 27 5.0 3.4 1.6 0.4 setosa
## 28 5.2 3.5 1.5 0.2 setosa
## 29 5.2 3.4 1.4 0.2 setosa
## 30 4.7 3.2 1.6 0.2 setosa
## 31 4.8 3.1 1.6 0.2 setosa
## 32 5.4 3.4 1.5 0.4 setosa
## 33 5.2 4.1 1.5 0.1 setosa
## 34 5.5 4.2 1.4 0.2 setosa
## 35 4.9 3.1 1.5 0.2 setosa
## 36 5.0 3.2 1.2 0.2 setosa
## 37 5.5 3.5 1.3 0.2 setosa
## 38 4.9 3.6 1.4 0.1 setosa
## 39 4.4 3.0 1.3 0.2 setosa
## 40 5.1 3.4 1.5 0.2 setosa
## 41 5.0 3.5 1.3 0.3 setosa
## 42 4.5 2.3 1.3 0.3 setosa
## 43 4.4 3.2 1.3 0.2 setosa
## 44 5.0 3.5 1.6 0.6 setosa
## 45 5.1 3.8 1.9 0.4 setosa
## 46 4.8 3.0 1.4 0.3 setosa
## 47 5.1 3.8 1.6 0.2 setosa
## 48 4.6 3.2 1.4 0.2 setosa
## 49 5.3 3.7 1.5 0.2 setosa
## 50 5.0 3.3 1.4 0.2 setosa
(Virginica <- subset(iris, Species == "virginica"))
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 101 6.3 3.3 6.0 2.5 virginica
## 102 5.8 2.7 5.1 1.9 virginica
## 103 7.1 3.0 5.9 2.1 virginica
## 104 6.3 2.9 5.6 1.8 virginica
## 105 6.5 3.0 5.8 2.2 virginica
## 106 7.6 3.0 6.6 2.1 virginica
## 107 4.9 2.5 4.5 1.7 virginica
## 108 7.3 2.9 6.3 1.8 virginica
## 109 6.7 2.5 5.8 1.8 virginica
## 110 7.2 3.6 6.1 2.5 virginica
## 111 6.5 3.2 5.1 2.0 virginica
## 112 6.4 2.7 5.3 1.9 virginica
## 113 6.8 3.0 5.5 2.1 virginica
## 114 5.7 2.5 5.0 2.0 virginica
## 115 5.8 2.8 5.1 2.4 virginica
## 116 6.4 3.2 5.3 2.3 virginica
## 117 6.5 3.0 5.5 1.8 virginica
## 118 7.7 3.8 6.7 2.2 virginica
## 119 7.7 2.6 6.9 2.3 virginica
## 120 6.0 2.2 5.0 1.5 virginica
## 121 6.9 3.2 5.7 2.3 virginica
## 122 5.6 2.8 4.9 2.0 virginica
## 123 7.7 2.8 6.7 2.0 virginica
## 124 6.3 2.7 4.9 1.8 virginica
## 125 6.7 3.3 5.7 2.1 virginica
## 126 7.2 3.2 6.0 1.8 virginica
## 127 6.2 2.8 4.8 1.8 virginica
## 128 6.1 3.0 4.9 1.8 virginica
## 129 6.4 2.8 5.6 2.1 virginica
## 130 7.2 3.0 5.8 1.6 virginica
## 131 7.4 2.8 6.1 1.9 virginica
## 132 7.9 3.8 6.4 2.0 virginica
## 133 6.4 2.8 5.6 2.2 virginica
## 134 6.3 2.8 5.1 1.5 virginica
## 135 6.1 2.6 5.6 1.4 virginica
## 136 7.7 3.0 6.1 2.3 virginica
## 137 6.3 3.4 5.6 2.4 virginica
## 138 6.4 3.1 5.5 1.8 virginica
## 139 6.0 3.0 4.8 1.8 virginica
## 140 6.9 3.1 5.4 2.1 virginica
## 141 6.7 3.1 5.6 2.4 virginica
## 142 6.9 3.1 5.1 2.3 virginica
## 143 5.8 2.7 5.1 1.9 virginica
## 144 6.8 3.2 5.9 2.3 virginica
## 145 6.7 3.3 5.7 2.5 virginica
## 146 6.7 3.0 5.2 2.3 virginica
## 147 6.3 2.5 5.0 1.9 virginica
## 148 6.5 3.0 5.2 2.0 virginica
## 149 6.2 3.4 5.4 2.3 virginica
## 150 5.9 3.0 5.1 1.8 virginica
# Draw boxplot for each type of flower
boxplot(Versicolor[,1:4], main="Versicolor, Rainbow Palette",ylim =
c(0,8),las=2, col=rainbow(4))
boxplot(Setosa[,1:4], main="Setosa, Heat color Palette",ylim = c(0,8),las=2,
col=heat.colors(4))
boxplot(Virginica[,1:4], main="Virginica, Topo colors Palette",ylim =
c(0,8),las=2, col=topo.colors(4))
# Lab Exercise 7: How to load external datasets
# From a local directory
# the folder that contains the file should be specified completely
# using the forward slash symbol instead of the backward splash
folder <- "C:\\Users\\johnm\\OneDrive\\Documents\\Lecture"
filename <- "cancer.csv"
(file <- paste0(folder,"/",filename))
## [1] "C:\\Users\\johnm\\OneDrive\\Documents\\Lecture/cancer.csv"
cancer <- read.csv(file)
dim(cancer)
## [1] 173 17
names(cancer)
## [1] "country" "incomeperperson" "alcconsumption"
## [4] "armedforcesrate" "breastcancer" "co2emissions"
## [7] "femaleemployrate" "hivrate" "internetuserate"
## [10] "lifeexpectancy" "oilperperson" "polityscore"
## [13] "relectricperperson" "suicideper100th" "employrate"
## [16] "urbanrate" "continent"
# compute mean value for every continent
(means <- round(tapply(cancer$breastcancer, cancer$continent, mean),
digits=2))
## AF AS EE LATAM NORAM OC WE
## 24.02 24.51 49.44 36.70 71.73 45.80 74.80
# draw boxplot per continent
boxplot(cancer$breastcancer ~ cancer$continent, main= "Breast cancer by
continent (brown dot = mean value)", xlab="continents", ylab="new
cases per 100,00 residents", col=rainbow(7))
# insert the mean value using brown dot
points(means, col="brown", pch=18)
# Lab Exercise 8: How to load external datasets and change data layout
folder <- "C:\\Users\\johnm\\OneDrive\\Documents\\Lecture"
filename <- "hsb2.csv"
(file <- paste0(folder,"/",filename))
## [1] "C:\\Users\\johnm\\OneDrive\\Documents\\Lecture/hsb2.csv"
hsb2_wide <- read.csv(file)
# display only the top 6 rows
head(hsb2_wide)
## X id female race ses schtyp prog read write math science socst
## 1 1 70 0 4 1 1 1 57 52 41 47 57
## 2 2 121 1 4 2 1 3 68 59 53 63 61
## 3 3 86 0 4 3 1 1 44 33 54 58 31
## 4 4 141 0 4 3 1 3 63 44 47 53 56
## 5 5 172 0 4 2 1 2 47 52 57 53 61
## 6 6 113 0 4 2 1 2 44 52 51 63 61
# display only the last 6 rows
tail(hsb2_wide)
## X id female race ses schtyp prog read write math science 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
## 418 195 0 4 2 2 1 math 60
## 419 114 0 4 3 1 2 math 62
## 420 85 0 4 2 1 1 math 57
## 421 167 0 4 2 1 1 math 35
## 422 143 0 4 2 1 3 math 75
## 423 41 0 3 2 1 2 math 45
## 424 20 0 1 3 1 2 math 57
## 425 12 0 1 2 1 3 math 45
## 426 53 0 3 2 1 3 math 46
## 427 154 0 4 3 1 2 math 66
## 428 178 0 4 2 2 3 math 57
## 429 196 0 4 3 2 2 math 49
## 430 29 0 2 1 1 1 math 49
## 431 126 0 4 2 1 1 math 57
## 432 103 0 4 3 1 2 math 64
## 433 192 0 4 3 2 2 math 63
## 434 150 0 4 2 1 3 math 57
## 435 199 0 4 3 2 2 math 50
## 436 144 0 4 3 1 1 math 58
## 437 200 0 4 2 2 2 math 75
## 438 80 0 4 3 1 2 math 68
## 439 16 0 1 1 1 3 math 44
## 440 153 0 4 2 1 3 math 40
## 441 176 0 4 2 2 2 math 41
## 442 177 0 4 2 2 2 math 62
## 443 168 0 4 2 1 2 math 57
## 444 40 0 3 1 1 1 math 43
## 445 62 0 4 3 1 1 math 48
## 446 169 0 4 1 1 1 math 63
## 447 49 0 3 3 1 3 math 39
## 448 136 0 4 2 1 2 math 70
## 449 189 0 4 2 2 2 math 63
## 450 7 0 1 2 1 2 math 59
## 451 27 0 2 2 1 2 math 61
## 452 128 0 4 3 1 2 math 38
## 453 21 0 1 2 1 1 math 61
## 454 183 0 4 2 2 2 math 49
## 455 132 0 4 2 1 2 math 73
## 456 15 0 1 3 1 3 math 44
## 457 67 0 4 1 1 3 math 42
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## 467 171 0 4 2 1 2 math 60
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## 470 18 0 1 2 1 3 math 49
## 471 155 0 4 2 1 1 math 46
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## 474 157 0 4 2 1 1 math 58
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## 486 146 0 4 3 1 2 math 64
## 487 102 0 4 3 1 2 math 51
## 488 117 0 4 3 1 3 math 39
## 489 133 0 4 2 1 3 math 40
## 490 94 0 4 3 1 2 math 61
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## 493 82 1 4 3 1 2 math 65
## 494 8 1 1 1 1 2 math 52
## 495 129 1 4 1 1 1 math 46
## 496 173 1 4 1 1 1 math 61
## 497 57 1 4 2 1 2 math 72
## 498 100 1 4 3 1 2 math 71
## 499 1 1 1 1 1 3 math 40
## 500 194 1 4 3 2 2 math 69
## 501 88 1 4 3 1 2 math 64
## 502 99 1 4 3 1 1 math 56
## 503 47 1 3 1 1 2 math 49
## 504 120 1 4 3 1 2 math 54
## 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
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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", "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 1.1.1 ✔ 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] 0.837742366 1.673231319 -0.083168010 -0.105056786 0.041821146
## [6] 1.454725196 0.254140655 0.186547678 -0.902218302 -1.297398853
## [11] -0.170471688 0.060800402 -0.178322046 -1.098683010 0.735289235
## [16] 0.255678126 -0.050995874 0.285876595 -0.277859759 1.399465074
## [21] 1.748566326 -0.845957473 2.157610941 -1.124274766 2.061106359
## [26] -0.189109705 1.435079363 1.527670848 0.392261096 -0.404733383
## [31] -1.691287832 0.541073946 -0.141716158 0.080403677 -2.023998320
## [36] -0.753290552 -0.485594542 -0.575977746 0.892485769 0.993532890
## [41] 0.926856298 0.906277149 0.571362779 -0.980438593 0.681608369
## [46] 0.824501273 -1.432022611 -0.111535344 -0.932028777 -0.574925022
## [51] -1.279032885 -0.510863748 1.154433751 -0.239683087 -0.006947736
## [56] -0.421044027 1.393304552 -1.442959232 -0.840090067 0.028874533
## [61] -0.291074163 -1.916063912 -0.413899282 0.420120843 -2.265721137
## [66] -0.664993778 -0.630188141 0.828017123 0.983135794 0.023356704
## [71] 0.458473195 -0.657536014 1.682514611 -0.745848069 -0.004462471
## [76] -0.164988509 0.120314698 -1.656330368 0.577133011 -2.833976985
## [81] -0.008984706 0.515293357 0.027955799 0.828687347 -0.073376727
## [86] 0.662947566 0.687595294 -0.668305934 -0.514888184 1.705380377
## [91] -0.275398337 0.959543216 -0.255951783 -0.079709616 -1.630506502
## [96] -0.278678394 0.758595644 0.136630104 0.188434771 -0.298126737
## [101] -0.490934330 -0.063416566 2.487205238 0.072979287 -0.527816382
## [106] -0.541955316 0.539368741 1.497412270 -0.225570440 -0.212876299
## [111] -1.403130313 0.385549817 1.610337076 -1.137023928 0.072432466
## [116] 1.137507879 -1.034633202 -1.080526176 -0.609980694 0.342606637
## [121] 0.181285143 -0.850980287 -0.982618492 -0.302002152 0.072323969
## [126] -0.401347396 -0.982416190 -2.095897348 -1.291236638 0.399677790
## [131] 0.013188482 -0.968202401 -0.150982763 -0.392867921 0.394150012
## [136] -0.617503051 -0.230628436 1.624094934 1.554291421 -0.551084738
## [141] -0.895289175 0.524532130 -0.887788137 0.148108996 1.537621756
## [146] 1.392378610 1.515498416 1.145254217 1.572840057 -1.779515829
## [151] -0.362088683 0.794214282 1.489669909 -1.479195278 -0.381121834
## [156] 1.464386336 0.088401591 1.455391572 0.040277511 -0.027433818
## [161] 1.199870326 0.544408040 -0.148346755 1.013734192 -0.076080959
## [166] 0.502283617 -0.484279596 1.862504930 0.609080209 -0.120908340
## [171] -0.196223036 1.123583841 -0.304185333 1.205701543 -0.268135355
## [176] -0.987912064 -1.194175662 -0.129259291 0.422043749 0.416843128
## [181] -1.188956075 1.576088519 0.825624297 -0.676403873 -0.033464916
## [186] -0.898077497 0.135771629 -0.062318407 -0.137769953 -0.365481750
## [191] -0.434817894 0.089908007 -0.508587842 -1.236682235 1.550711947
## [196] -1.356164895 1.825079793 0.546691346 1.742704823 0.750645349
## [201] 0.231140413 0.039989571 2.045573407 0.703375398 -0.454281069
## [206] 0.945673007 0.144075080 -0.981423391 1.252677932 0.336074653
## [211] -1.111498850 -0.417139136 2.680362972 0.642122169 0.513333882
## [216] -0.007576573 -0.595489762 1.167739983 -0.273175993 -0.842044922
## [221] -0.863052656 0.639985412 1.348609136 -0.572972894 -0.500351596
## [226] 0.967435546 -0.143495942 0.214941073 -0.037637991 0.120938431
## [231] 0.308177294 1.601632545 -0.240577442 -0.561940539 -1.067659877
## [236] 1.268892316 2.117816912 -0.856045437 0.710842610 0.073635016
## [241] -0.492426229 0.672558158 1.122136860 -0.340154490 0.537083083
## [246] 1.466926255 2.006332550 1.940121874 2.579415983 1.252659064
## [251] 1.019315882 -1.462034185 0.478604958 1.212570384 -0.631904581
## [256] -1.022155765 -1.682820965 -0.413090657 0.595202200 0.727438469
## [261] -1.038539332 -0.075697713 -0.897098617 -0.659452531 -0.611567942
## [266] 1.060638943 -0.410334849 -0.650347940 1.434850252 -0.181780687
## [271] -0.242975199 -1.348615183 -0.329474017 0.492839736 -0.324242410
## [276] 0.443990416 0.494697149 -0.039958179 -0.163342055 0.555966298
## [281] 0.409820305 0.129008349 -0.760433723 0.591974832 -2.890768131
## [286] 0.546734747 -0.012934528 0.530970801 1.569674565 1.113730763
## [291] -0.857313828 2.023737121 0.355336215 0.080141110 -0.438626593
## [296] -0.718983273 -0.328548312 -1.815478890 0.103239856 -1.185364275
## [301] -1.521472830 -1.410950025 -1.642081861 0.273059707 1.244532217
## [306] 1.283323285 2.525607267 -0.992897877 -1.050246135 1.788592085
## [311] -0.267013046 1.480450967 -0.950103732 0.119637287 0.720603890
## [316] -1.415130752 -0.845097692 0.175664462 -0.832709999 0.189764110
## [321] -1.191126024 0.086883023 1.413052328 -0.087164139 0.009116239
## [326] -1.568299347 -0.196782963 0.504235565 -0.082926232 0.828098723
## [331] 0.553942692 1.697392416 -1.482692386 -0.823568771 1.002815125
## [336] -0.599332499 -0.009359186 0.420676706 1.781756246 -0.114793419
## [341] 0.047680612 -0.536596536 0.233331009 -1.382241683 0.387493405
## [346] -0.154118969 0.727144911 -1.117927832 -0.848989592 -1.120197332
## [351] 0.948683066 -1.712382530 1.380532208 -0.013406546 0.241951271
## [356] -1.956034263 0.955706694 -0.348185462 -1.050849852 -1.487579368
## [361] -0.859017126 0.895223917 -0.238925000 -0.747607629 1.863819930
## [366] -0.833273356 -0.685722293 0.248370834 -2.005568184 1.799767737
## [371] -0.973515064 1.418233229 0.074561862 -1.049010017 -1.389126411
## [376] -0.256370181 0.056472145 0.069652840 0.050058646 1.705205107
## [381] 0.190113975 -0.704657829 -1.007212303 0.415308509 0.284646859
## [386] -0.281493175 0.020556430 -1.702276703 0.384792283 -1.108288684
## [391] -0.833838380 -1.504705554 0.244218264 -0.393751995 1.360735132
## [396] -0.490291970 -1.082930545 0.632942125 -0.165429494 1.123351886
## [401] 0.862105132 -0.527117894 -1.070078495 0.350918054 -1.268577673
## [406] -0.107495887 -2.112272619 0.227142176 1.003800492 0.387564685
## [411] -0.068603577 0.444626047 1.486460321 -0.622969733 1.315887587
## [416] -1.001012775 -0.183758289 -0.873332937 -1.780905045 0.674350548
## [421] 0.222786934 1.159896512 -1.745700541 0.629109963 0.525183854
## [426] -0.923687951 0.873303765 0.240700922 1.942794452 -0.483868498
## [431] 0.109727636 1.090774414 1.684753809 -0.823308723 -0.157023774
## [436] -0.113105970 0.880856505 0.011122988 -0.721249094 -0.125126024
## [441] 0.164000739 -1.676396830 -1.096337987 0.769012759 1.004020714
## [446] -1.590694471 -0.706758051 0.737479460 1.350188851 0.626576104
## [451] 1.808203110 -1.490250448 -1.267698817 0.707928631 0.686351886
## [456] -0.995321844 1.103924532 -0.918080680 0.122203138 -1.541268141
## [461] 0.498639900 -0.735556112 -0.492679438 1.513943626 -0.257521487
## [466] 0.816328004 1.369510796 -1.590796163 0.430488425 -0.340437585
## [471] -0.108134790 -0.596044656 -0.343137724 -2.080001469 -0.556908294
## [476] -0.494131288 -0.154345161 1.269523826 -1.111429228 -1.109998160
## [481] 0.085523736 0.024772644 0.349173620 0.209609521 -0.858322319
## [486] -0.103443564 -1.471854196 -0.498298290 0.998146999 -0.696821147
## [491] 0.662250366 1.391228927 0.112922273 1.020242858 1.546715428
## [496] 1.255286251 0.582250870 1.508225610 0.018130806 0.259325058
## [501] -0.072708056 -0.408401676 -0.163571932 1.369492771 2.346917657
## [506] 0.108634144 -1.718490088 0.398080226 -0.029988640 -0.928142530
## [511] -0.761668303 0.442136262 0.487142256 0.932619259 0.833224444
## [516] -1.230648002 0.085721252 -0.157238473 1.015048208 -0.203744531
## [521] -1.860555526 -0.645280165 -0.275876773 0.045831773 -0.862420780
## [526] 0.021305858 -0.262498869 -0.837347177 0.247123187 0.762946186
## [531] -0.578508063 -0.712590423 -0.270517226 -0.074746426 1.350928733
## [536] -0.134732198 -2.108639519 0.054148157 -0.263009287 0.392547039
## [541] 1.291369556 -0.061957888 0.902223536 -0.914711697 0.020859461
## [546] 0.050169047 -0.644490110 0.807037508 1.344656049 -1.191290604
## [551] 0.314995656 1.288293545 1.508847016 1.216061655 1.466001396
## [556] -0.043653554 0.112903162 0.365087140 -1.199704632 -0.954334488
## [561] 0.854586629 -0.545769593 -1.549239763 -0.992570468 0.376871809
## [566] 1.414187124 0.993406107 -0.365909941 0.533198255 0.315403865
## [571] -0.520357731 -0.128689058 -0.876258101 -0.271416559 0.378948065
## [576] -1.591292269 1.339097164 -0.016685820 0.272886468 1.126412582
## [581] 1.187023068 0.127356486 -0.451304197 0.091372468 1.245446151
## [586] -1.148516498 -0.408097360 -1.819991359 -0.211288549 -1.479734003
## [591] -0.997847697 -0.500388634 1.436867377 2.161437097 0.880163582
## [596] -2.443699572 0.758320128 -1.962072429 -0.822232636 0.732016834
## [601] -0.007577071 0.821495872 0.141378076 -0.799026088 0.432279533
## [606] -0.367707756 0.086186767 0.677635489 1.171804927 1.160289953
## [611] -0.396707548 -2.210287955 -0.479569554 -0.079335198 -0.998848517
## [616] 0.707858026 -0.699862728 0.598164137 -1.861126586 0.331997759
## [621] 0.754428575 0.371827138 0.140464699 -0.374420313 -0.905257205
## [626] -0.645143251 -0.504617012 -0.714968956 -1.952829582 -1.693120069
## [631] 0.412157518 0.081342635 -0.924333572 -0.629532864 -0.568414503
## [636] 0.195853091 0.850927534 -0.690108661 0.076350369 1.477183831
## [641] -2.796652050 -2.677361306 -0.533991209 -0.229042074 1.785684549
## [646] 2.055460578 -0.739967862 -0.804413878 -0.235564047 0.853314393
## [651] 0.560879893 0.630590989 0.072058181 0.184593499 -1.481060958
## [656] 0.632319119 0.346505691 -0.047265431 0.518371430 2.009430228
## [661] 0.639463610 -0.508861573 -0.008197064 0.115051707 -0.041671809
## [666] 0.228514912 0.478118489 -0.428512758 -0.154830595 -1.006781855
## [671] 2.001607682 -0.876724594 -0.987775608 -2.497205433 1.648069649
## [676] -0.824246652 0.191053720 -0.757348992 -0.456885377 -1.127200355
## [681] 0.602962197 -0.444964543 1.373038057 -1.035597757 -0.314942865
## [686] -0.477266684 1.740381356 -0.008913413 0.040228274 -0.452178111
## [691] 0.485630702 -0.038757714 0.950873340 0.558139973 0.437437360
## [696] 0.602117219 1.075183346 -1.087855131 0.195565390 0.969479656
## [701] -1.264569500 -0.737378116 0.378258761 -0.644122011 0.430184436
## [706] -1.205716352 0.068667337 -2.526767079 -0.176797223 -1.471620551
## [711] 0.011547364 0.563764969 0.551988490 0.545547600 1.600189298
## [716] 2.362790384 0.237153128 0.594143905 -0.288070333 -0.989249247
## [721] -0.716518779 0.461091634 1.276137350 1.930212577 2.103594242
## [726] -1.505806365 -0.381682316 -0.248045352 -0.861419982 1.050393788
## [731] 0.535198197 0.322389549 -0.371629667 -0.590088637 -0.852594382
## [736] 0.567004908 0.699047632 0.024741850 1.495259784 -1.649265960
## [741] 0.500023464 -0.618391725 0.641064840 1.055078984 -1.493693514
## [746] 1.225006560 1.151810717 1.411216255 1.829387857 -0.092249666
## [751] -0.203614615 1.268942721 -0.542625480 -0.341466570 -1.159837421
## [756] -0.686506411 0.293226977 -1.298918052 -1.107623996 0.049523814
## [761] 2.251938977 0.477762365 -1.238803405 -0.013849962 -0.973147685
## [766] -1.157797825 1.296640670 -0.493494927 -0.591641747 0.594014221
## [771] -1.572623059 0.330179316 1.894132421 1.327223890 -0.454604717
## [776] 1.188710989 -0.946379067 -1.215847706 0.415404061 0.661702095
## [781] 1.479085564 -0.431204591 -0.162686339 -0.349041688 -0.206240497
## [786] -1.453182155 0.761606322 -0.330031988 -0.067944972 1.330996815
## [791] 0.317995333 2.032204399 -0.790533417 -1.368367151 0.814576753
## [796] 0.852348167 1.920896670 1.246016633 1.819583841 2.083967670
## [801] 0.672785628 -1.012290066 0.020934755 0.449311357 0.572500787
## [806] 1.227053357 -0.901055281 0.400491083 1.827916141 -0.424399398
## [811] -0.198023219 -1.625097597 -0.578888831 2.077617199 0.262707413
## [816] -0.603295112 0.766750439 -1.864116487 0.879745081 0.860272593
## [821] -0.999050306 1.002211122 0.170924121 -0.665960550 -1.311380953
## [826] 0.411304457 0.659419512 -0.570884839 0.674052383 -0.334987883
## [831] -2.621530016 -1.571158654 -0.433687268 0.404851878 0.669501291
## [836] -0.353706002 -2.088077477 1.112884938 -0.290059335 0.295701887
## [841] -2.236714664 -1.494889130 0.524339352 0.141770595 0.978574211
## [846] 1.706787954 -0.250533182 -0.181738395 -0.239961950 -0.434246146
## [851] -0.699269737 0.507119571 1.539731944 1.251588848 0.553351718
## [856] 0.472305394 -0.068863872 0.378786954 1.824782809 -0.826139709
## [861] -0.098981649 0.923866480 0.943183478 -1.891039839 1.457097425
## [866] -0.140832663 0.644510479 0.028902386 0.954818859 0.441425373
## [871] 0.343583111 -0.094925251 -0.239692808 0.120436560 0.012416451
## [876] 1.696752061 0.477672010 -1.000675833 0.329176347 0.720609200
## [881] -0.333964181 0.091870340 0.298542892 -0.498479539 0.615623424
## [886] -0.531739190 -0.354662642 1.851818283 0.933824503 -0.969052719
## [891] -1.287934097 2.307760242 -1.732195316 0.378876721 0.824492553
## [896] 0.292644551 1.557176735 -0.479095695 -0.904251997 -2.796585158
## [901] 0.168608826 1.405827276 0.274837633 0.614516713 -0.077406664
## [906] -0.979159658 -1.768389164 -0.751283904 -0.285641524 0.886652185
## [911] 0.937344633 -0.420679617 -0.527819865 -1.045556853 1.815751140
## [916] 0.080444617 -0.956159884 0.887032382 1.004391465 0.436896368
## [921] -0.338629063 -0.047475279 2.345933576 -0.902010462 -0.569280742
## [926] -0.220711180 -0.380907945 0.743463918 0.821958776 1.692013207
## [931] 0.368520488 0.627824750 0.459784782 -1.519372930 -0.266182331
## [936] -0.907760098 0.407843871 -1.629839822 0.867153439 0.666950824
## [941] 0.558877903 1.793612105 -0.104389833 -0.598153070 -1.422519480
## [946] 0.295539415 -0.606137639 0.957397566 -0.025964956 1.086632981
## [951] 0.907919934 0.282009099 1.281018279 -1.328183220 0.323677461
## [956] 0.252063003 0.072546110 -0.861154590 0.003383661 1.509779730
## [961] -0.813532340 0.049665092 -0.440779604 -0.903574243 -0.660413981
## [966] -2.580464641 -0.321901542 -0.491427223 0.026823697 0.009393902
## [971] -1.035147740 -1.025522648 0.700490959 0.813886074 1.395475238
## [976] 0.999292444 0.419092862 0.951554983 -0.471086833 -0.198013785
## [981] -0.073031772 1.051293143 -1.162148026 0.102989414 -0.227215819
## [986] -1.578500797 -0.406742510 -0.994548783 -1.374015182 -1.178588369
## [991] -1.577072245 0.781487184 -0.058640086 -0.296308001 -0.376874573
## [996] 0.992699430 -0.507190633 0.333298919 0.382998167 0.907094239
yAxis <- rnorm(1000) + xAxis + 10
yAxis
## [1] 10.476864 11.034747 11.146869 11.990419 10.697540 10.022841 11.061498
## [8] 10.560293 10.348449 7.122756 10.131059 10.407334 7.771872 9.844471
## [15] 10.710089 10.227245 8.491657 11.513336 9.369598 9.697708 12.054450
## [22] 9.164749 12.458930 9.927240 13.541168 9.585767 11.660869 13.219034
## [29] 11.627572 9.984597 7.042258 8.392365 9.791693 10.870100 6.333331
## [36] 10.141544 9.504031 9.993503 10.959298 10.845047 12.396257 11.270230
## [43] 11.973311 9.769935 11.479126 11.969286 8.006949 9.488677 10.500289
## [50] 8.808673 8.820498 7.260303 11.037234 9.813779 8.295256 10.262511
## [57] 12.691188 8.004732 8.792233 10.140344 9.462033 7.410821 8.467866
## [64] 8.908909 7.660626 9.376452 10.011479 12.088650 10.711041 10.431711
## [71] 10.206041 9.537001 11.132778 9.698510 8.728542 8.435678 11.371911
## [78] 7.524637 9.380837 7.038543 10.655432 9.971567 9.919812 10.170562
## [85] 8.300242 10.353849 10.558089 8.937652 10.241021 11.328439 8.514736
## [92] 10.262662 9.025427 10.305715 7.662979 10.692133 11.644796 9.452598
## [99] 10.466522 9.239931 9.764645 10.813775 12.549297 8.681596 10.100652
## [106] 8.384182 11.602428 10.906942 9.936111 10.171831 9.352552 11.560623
## [113] 10.890348 9.413904 10.264476 9.942563 10.019617 9.753838 10.856277
## [120] 10.205264 9.203736 7.974501 9.261740 9.626589 9.873783 8.041292
## [127] 7.887864 8.607684 10.359889 12.812456 8.731912 8.588821 12.417648
## [134] 10.838550 10.963821 7.589838 11.340708 11.391427 11.548927 10.407531
## [141] 9.865163 9.920037 9.184231 11.428037 10.944024 12.419101 12.319993
## [148] 10.886355 11.343671 9.086233 10.537014 10.424479 12.062719 8.304187
## [155] 10.080050 11.998232 8.935846 12.933675 8.332491 11.565250 10.769336
## [162] 10.514082 11.055429 12.408478 8.722828 10.756730 8.382621 12.350377
## [169] 8.275242 9.689844 10.725145 11.509058 7.927098 9.408020 11.286892
## [176] 8.743237 10.275272 11.276182 9.936366 10.864570 8.068392 11.917992
## [183] 11.320654 10.961399 8.305286 7.983089 10.554170 9.201386 9.652353
## [190] 9.954493 9.724373 9.467530 8.289127 8.266745 9.970367 8.505365
## [197] 12.416592 9.556124 12.109758 10.327544 9.587461 9.184861 11.714603
## [204] 12.543602 9.138270 12.165151 10.124831 8.135860 11.489728 11.345354
## [211] 9.251762 9.965991 12.848307 9.588487 10.196376 8.284289 8.928792
## [218] 10.017227 10.165217 10.372020 9.452896 10.940487 11.090309 9.161572
## [225] 8.153033 10.340369 9.287420 9.673987 12.017760 8.811467 9.846309
## [232] 11.599714 11.336173 8.248978 7.746931 10.886542 11.969930 8.514453
## [239] 10.850056 10.846661 9.218303 11.364998 11.613388 9.643257 10.413909
## [246] 9.860298 11.675617 11.970966 13.022344 11.494775 12.845763 7.227910
## [253] 9.620640 11.934207 9.707699 10.336197 8.203648 9.745313 10.690687
## [260] 10.196404 8.156282 10.133220 10.528407 10.422170 9.142526 11.795582
## [267] 9.878147 9.298456 12.462902 10.686630 9.463881 9.304912 9.354897
## [274] 9.963685 9.933630 10.429309 10.171214 7.840928 7.518892 11.286147
## [281] 7.870855 10.289717 8.122242 11.038850 6.223985 9.388997 10.418154
## [288] 9.884821 9.512883 10.753081 9.604190 9.008227 11.434632 8.770086
## [295] 9.404588 9.644702 8.380944 7.872561 10.023961 8.552057 9.119360
## [302] 8.366192 6.613031 10.224173 10.806903 10.899070 13.650725 7.703436
## [309] 9.139748 12.546981 10.622401 12.333748 8.392847 12.238454 11.491196
## [316] 8.309380 10.047842 11.682729 8.305004 10.356348 9.472686 9.554134
## [323] 12.216039 10.263588 10.575244 6.305566 9.416086 9.548667 9.272377
## [330] 13.456276 10.628834 11.379413 7.762894 9.000866 10.444245 10.093790
## [337] 11.082491 8.991719 13.482589 9.884692 9.546585 11.107536 11.005729
## [344] 8.376813 9.848265 9.220567 11.559426 10.309276 9.047142 9.520177
## [351] 11.870699 7.120820 10.091528 11.777928 10.263353 8.326974 10.639963
## [358] 9.563573 9.377876 7.486057 8.272883 11.285554 11.422561 9.298383
## [365] 9.968366 8.642436 9.778087 10.307686 7.516893 11.815831 9.973312
## [372] 10.529729 8.274227 10.236672 8.913789 9.231280 9.392171 8.504766
## [379] 8.673665 10.942427 10.680253 9.802853 6.256424 9.240545 10.550302
## [386] 9.987994 10.027473 10.350704 10.421162 9.665055 7.447563 9.021131
## [393] 11.205865 10.519161 11.934383 9.982411 8.930795 9.956101 8.952720
## [400] 10.505712 11.417104 10.060881 7.387165 10.909788 8.858291 12.784020
## [407] 8.218217 9.751878 10.788191 10.256064 10.863055 10.744274 10.114192
## [414] 8.763647 10.771611 9.311629 9.532609 7.138693 7.335427 9.932227
## [421] 9.960654 10.542083 6.662081 10.891712 9.763900 9.460308 12.225145
## [428] 9.526041 11.208596 9.687614 10.288757 12.153281 10.812949 10.545194
## [435] 9.130489 8.321302 11.746037 8.838683 9.821688 11.401903 10.087852
## [442] 8.118613 9.701181 10.200097 10.886802 8.617312 10.816732 8.778249
## [449] 10.684719 10.878065 12.494290 6.863467 8.848296 10.582016 11.284193
## [456] 9.169056 10.699842 9.096677 9.338400 8.833297 11.458833 8.155136
## [463] 7.443081 12.110967 9.602179 10.847064 11.784073 7.404267 10.621370
## [470] 9.868195 9.838862 8.490890 8.975603 9.219550 9.479308 8.987408
## [477] 10.618428 11.577482 10.638191 8.545765 6.907581 10.515721 8.908399
## [484] 9.030533 8.914793 9.242300 10.061667 7.682367 11.668413 9.353873
## [491] 12.443216 11.509899 11.298045 11.617718 11.729182 10.974961 10.692292
## [498] 12.833390 10.449229 10.414431 10.498033 9.862660 10.110468 11.522081
## [505] 12.584075 11.250413 8.559717 12.002942 10.862500 8.824870 8.406452
## [512] 11.432582 9.575209 11.294354 12.183383 6.974828 9.094343 9.605713
## [519] 11.249367 11.357378 9.668912 9.414618 9.853179 8.935917 7.754248
## [526] 7.801301 10.418394 8.199157 10.787646 12.187325 10.393947 9.917638
## [533] 9.975031 8.881796 10.981360 10.451008 8.103337 9.854579 9.625178
## [540] 11.474601 11.283065 9.542002 11.083582 9.797320 8.543809 7.595454
## [547] 9.209015 11.436386 11.183446 8.906611 9.717821 11.594168 9.725805
## [554] 12.670821 10.894916 11.202493 9.359571 9.930775 7.628046 8.344641
## [561] 12.545171 10.729359 8.517706 9.665715 12.194390 11.000391 8.971535
## [568] 11.157692 11.011741 9.917372 9.463716 10.376027 10.370886 12.493277
## [575] 11.296352 9.093225 12.269606 9.804354 11.377540 11.553145 9.893353
## [582] 8.193473 9.978879 8.529307 10.957316 9.472762 9.397838 9.266784
## [589] 11.301171 9.353736 9.371153 9.042458 10.779122 13.323050 11.321398
## [596] 8.367389 10.716493 9.553816 9.431777 11.446806 10.143627 8.876779
## [603] 9.157411 7.788174 10.240343 8.826691 10.301955 8.539033 11.313225
## [610] 11.153917 8.680002 8.491153 7.786868 11.198743 8.870074 11.452085
## [617] 8.847951 11.188813 6.422357 10.214076 10.995785 10.255077 9.868829
## [624] 8.229067 10.974451 7.931024 10.006780 9.162225 8.071286 9.681594
## [631] 10.264760 10.993196 9.925685 10.741852 8.370300 9.518401 9.660315
## [638] 7.783110 9.300959 11.589517 8.277844 6.408703 9.310879 10.553641
## [645] 14.160081 11.880941 9.529436 9.280685 9.790443 8.612408 10.152338
## [652] 10.258587 9.644440 10.714983 6.659455 11.052072 10.330190 10.955028
## [659] 10.394248 10.903759 11.720253 7.948443 11.256683 9.062547 9.150937
## [666] 9.543110 10.943665 9.613385 8.757050 10.785484 10.561903 9.638292
## [673] 8.216905 6.914868 11.343581 8.732399 11.329993 8.660053 8.362476
## [680] 8.145104 9.942537 9.496240 11.106165 8.393808 9.974022 10.981302
## [687] 12.676077 10.760951 9.410967 11.979004 10.272095 8.598480 10.728968
## [694] 9.758856 10.987189 9.761715 11.542216 9.204734 10.415196 11.688901
## [701] 8.529505 8.615010 11.749045 8.540920 9.765615 10.379915 8.999281
## [708] 10.630139 10.368562 9.138407 11.615241 10.453042 9.022804 12.062578
## [715] 11.318130 11.250654 9.485836 10.158274 9.642002 6.495867 9.482529
## [722] 7.366278 11.906719 11.789345 12.301664 7.544829 9.643581 9.658302
## [729] 7.296752 10.268965 11.204260 10.763462 7.450041 10.164874 9.116246
## [736] 9.702126 10.788924 10.449128 11.277612 7.488610 12.028321 9.230070
## [743] 9.956999 10.339896 7.820028 11.372829 14.691249 12.293669 10.680572
## [750] 10.332420 9.966409 8.311446 9.973450 11.247629 8.937098 10.702384
## [757] 11.268776 8.278792 8.029445 10.854941 14.353429 12.216054 7.829182
## [764] 8.151568 7.530689 9.477869 11.837708 10.062436 9.044028 11.014729
## [771] 8.958769 11.668974 11.869571 10.647045 9.532052 11.729982 8.891718
## [778] 8.535831 8.894625 11.122362 10.815224 7.465656 8.908494 10.131918
## [785] 9.833540 7.741382 11.676069 9.028485 10.554004 11.873036 10.656041
## [792] 13.472748 8.306386 5.422578 10.325776 11.327045 12.089043 10.104157
## [799] 13.050612 12.565557 11.179926 7.359569 9.430831 10.913860 10.422498
## [806] 11.342337 9.560037 11.857282 12.514273 8.711242 10.982010 9.530606
## [813] 9.916662 10.481571 11.349660 12.414072 9.369359 8.806265 11.193455
## [820] 11.321484 7.577715 11.275936 10.395819 9.239658 9.232635 11.567932
## [827] 11.599434 9.849213 11.312268 10.564112 7.665118 9.750695 9.069836
## [834] 12.083163 11.411210 10.083303 9.551920 10.142094 9.009907 10.878911
## [841] 7.703846 9.523764 9.415061 10.397200 11.162185 10.616959 11.484792
## [848] 8.423287 8.549441 9.421588 8.528508 9.992724 10.232820 11.026677
## [855] 9.126505 10.583911 10.208278 10.752406 9.709657 7.618044 9.707470
## [862] 11.780000 12.470313 8.737734 10.532042 9.789449 9.299603 9.872660
## [869] 10.953724 9.064648 12.688584 9.297367 8.496096 10.762867 9.507573
## [876] 11.369512 9.494166 10.499866 11.166710 11.234731 9.922233 9.011804
## [883] 10.105512 8.282864 10.084710 9.093411 9.291697 11.644650 11.386791
## [890] 8.905093 8.883614 14.168048 8.348380 10.356509 10.480524 9.677967
## [897] 12.372161 9.758298 10.492932 7.397638 10.150325 13.670315 11.057131
## [904] 10.550158 7.655013 8.949560 8.364077 10.012934 10.409577 12.693343
## [911] 10.753811 10.992789 8.515302 7.427341 12.866358 10.976179 9.185686
## [918] 10.306329 10.728982 9.233326 10.207025 10.096856 11.487087 8.810634
## [925] 8.842296 10.488691 9.952138 12.419541 11.598372 13.080719 11.612651
## [932] 12.093785 10.879064 7.284123 8.914613 8.559620 8.909016 7.258150
## [939] 11.406656 10.749949 10.394724 10.127546 10.245496 9.651648 7.881701
## [946] 10.242655 9.148620 10.539696 9.117681 11.410280 10.842106 12.264162
## [953] 9.914598 8.665787 10.438797 10.939477 8.192135 8.461639 7.939600
## [960] 11.246139 9.410986 9.920419 9.709418 10.838445 9.358779 7.799490
## [967] 9.791569 10.089112 9.831307 11.499480 9.426745 9.624275 10.146794
## [974] 10.850669 12.207407 11.183734 10.821799 12.280008 10.893873 10.759068
## [981] 9.878026 12.204751 9.413278 9.920831 11.388847 8.354371 10.274875
## [988] 8.337335 7.328874 6.294963 7.858419 10.095218 7.771373 8.902570
## [995] 8.498960 11.926743 9.996597 10.940765 9.918630 11.530144
# 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] 4 5 3 3 3 4 3 3 2 2 3 3 3 2 4 3 3 3 3 4 5 2 5 2 5 3 4 5 3 3 1 4 3 3 1 2 3
## [38] 2 4 4 4 4 4 2 4 4 2 3 2 2 2 2 4 3 3 3 4 2 2 3 3 1 3 3 1 2 2 4 4 3 3 2 5 2
## [75] 3 3 3 1 4 1 3 4 3 4 3 4 4 2 2 5 3 4 3 3 1 3 4 3 3 3 3 3 5 3 2 2 4 4 3 3 2
## [112] 3 5 2 3 4 2 2 2 3 3 2 2 3 3 3 2 1 2 3 3 2 3 3 3 2 3 5 5 2 2 4 2 3 5 4 5 4
## [149] 5 1 3 4 4 2 3 4 3 4 3 3 4 4 3 4 3 4 3 5 4 3 3 4 3 4 3 2 2 3 3 3 2 5 4 2 3
## [186] 2 3 3 3 3 3 3 2 2 5 2 5 4 5 4 3 3 5 4 3 4 3 2 4 3 2 3 5 4 4 3 2 4 3 2 2 4
## [223] 4 2 2 4 3 3 3 3 3 5 3 2 2 4 5 2 4 3 3 4 4 3 4 4 5 5 5 4 4 2 3 4 2 2 1 3 4
## [260] 4 2 3 2 2 2 4 3 2 4 3 3 2 3 3 3 3 3 3 3 4 3 3 2 4 1 4 3 4 5 4 2 5 3 3 3 2
## [297] 3 1 3 2 1 2 1 3 4 4 5 2 2 5 3 4 2 3 4 2 2 3 2 3 2 3 4 3 3 1 3 4 3 4 4 5 2
## [334] 2 4 2 3 3 5 3 3 2 3 2 3 3 4 2 2 2 4 1 4 3 3 1 4 3 2 2 2 4 3 2 5 2 2 3 1 5
## [371] 2 4 3 2 2 3 3 3 3 5 3 2 2 3 3 3 3 1 3 2 2 1 3 3 4 3 2 4 3 4 4 2 2 3 2 3 1
## [408] 3 4 3 3 3 4 2 4 2 3 2 1 4 3 4 1 4 4 2 4 3 5 3 3 4 5 2 3 3 4 3 2 3 3 1 2 4
## [445] 4 1 2 4 4 4 5 2 2 4 4 2 4 2 3 1 3 2 3 5 3 4 4 1 3 3 3 2 3 1 2 3 3 4 2 2 3
## [482] 3 3 3 2 3 2 3 4 2 4 4 3 4 5 4 4 5 3 3 3 3 3 4 5 3 1 3 3 2 2 3 3 4 4 2 3 3
## [519] 4 3 1 2 3 3 2 3 3 2 3 4 2 2 3 3 4 3 1 3 3 3 4 3 4 2 3 3 2 4 4 2 3 4 5 4 4
## [556] 3 3 3 2 2 4 2 1 2 3 4 4 3 4 3 2 3 2 3 3 1 4 3 3 4 4 3 3 3 4 2 3 1 3 2 2 2
## [593] 4 5 4 1 4 1 2 4 3 4 3 2 3 3 3 4 4 4 3 1 3 3 2 4 2 4 1 3 4 3 3 3 2 2 2 2 1
## [630] 1 3 3 2 2 2 3 4 2 3 4 1 1 2 3 5 5 2 2 3 4 4 4 3 3 2 4 3 3 4 5 4 2 3 3 3 3
## [667] 3 3 3 2 5 2 2 1 5 2 3 2 3 2 4 3 4 2 3 3 5 3 3 3 3 3 4 4 3 4 4 2 3 4 2 2 3
## [704] 2 3 2 3 1 3 2 3 4 4 4 5 5 3 4 3 2 2 3 4 5 5 1 3 3 2 4 4 3 3 2 2 4 4 3 4 1
## [741] 4 2 4 4 2 4 4 4 5 3 3 4 2 3 2 2 3 2 2 3 5 3 2 3 2 2 4 3 2 4 1 3 5 4 3 4 2
## [778] 2 3 4 4 3 3 3 3 2 4 3 3 4 3 5 2 2 4 4 5 4 5 5 4 2 3 3 4 4 2 3 5 3 3 1 2 5
## [815] 3 2 4 1 4 4 2 4 3 2 2 3 4 2 4 3 1 1 3 3 4 3 1 4 3 3 1 2 4 3 4 5 3 3 3 3 2
## [852] 4 5 4 4 3 3 3 5 2 3 4 4 1 4 3 4 3 4 3 3 3 3 3 3 5 3 2 3 4 3 3 3 3 4 2 3 5
## [889] 4 2 2 5 1 3 4 3 5 3 2 1 3 4 3 4 3 2 1 2 3 4 4 3 2 2 5 3 2 4 4 3 3 3 5 2 2
## [926] 3 3 4 4 5 3 4 3 1 3 2 3 1 4 4 4 5 3 2 2 3 2 4 3 4 4 3 4 2 3 3 3 2 3 5 2 3
## [963] 3 2 2 1 3 3 3 3 2 2 4 4 4 4 3 4 3 3 3 4 2 3 3 1 3 2 2 2 1 4 3 3 3 4 2 3 3
## [1000] 4
# create sample dataframe by joining variables
sample_data <- data.frame(xAxis,yAxis,group)
sample_data
## xAxis yAxis group
## 1 0.837742366 10.476864 4
## 2 1.673231319 11.034747 5
## 3 -0.083168010 11.146869 3
## 4 -0.105056786 11.990419 3
## 5 0.041821146 10.697540 3
## 6 1.454725196 10.022841 4
## 7 0.254140655 11.061498 3
## 8 0.186547678 10.560293 3
## 9 -0.902218302 10.348449 2
## 10 -1.297398853 7.122756 2
## 11 -0.170471688 10.131059 3
## 12 0.060800402 10.407334 3
## 13 -0.178322046 7.771872 3
## 14 -1.098683010 9.844471 2
## 15 0.735289235 10.710089 4
## 16 0.255678126 10.227245 3
## 17 -0.050995874 8.491657 3
## 18 0.285876595 11.513336 3
## 19 -0.277859759 9.369598 3
## 20 1.399465074 9.697708 4
## 21 1.748566326 12.054450 5
## 22 -0.845957473 9.164749 2
## 23 2.157610941 12.458930 5
## 24 -1.124274766 9.927240 2
## 25 2.061106359 13.541168 5
## 26 -0.189109705 9.585767 3
## 27 1.435079363 11.660869 4
## 28 1.527670848 13.219034 5
## 29 0.392261096 11.627572 3
## 30 -0.404733383 9.984597 3
## 31 -1.691287832 7.042258 1
## 32 0.541073946 8.392365 4
## 33 -0.141716158 9.791693 3
## 34 0.080403677 10.870100 3
## 35 -2.023998320 6.333331 1
## 36 -0.753290552 10.141544 2
## 37 -0.485594542 9.504031 3
## 38 -0.575977746 9.993503 2
## 39 0.892485769 10.959298 4
## 40 0.993532890 10.845047 4
## 41 0.926856298 12.396257 4
## 42 0.906277149 11.270230 4
## 43 0.571362779 11.973311 4
## 44 -0.980438593 9.769935 2
## 45 0.681608369 11.479126 4
## 46 0.824501273 11.969286 4
## 47 -1.432022611 8.006949 2
## 48 -0.111535344 9.488677 3
## 49 -0.932028777 10.500289 2
## 50 -0.574925022 8.808673 2
## 51 -1.279032885 8.820498 2
## 52 -0.510863748 7.260303 2
## 53 1.154433751 11.037234 4
## 54 -0.239683087 9.813779 3
## 55 -0.006947736 8.295256 3
## 56 -0.421044027 10.262511 3
## 57 1.393304552 12.691188 4
## 58 -1.442959232 8.004732 2
## 59 -0.840090067 8.792233 2
## 60 0.028874533 10.140344 3
## 61 -0.291074163 9.462033 3
## 62 -1.916063912 7.410821 1
## 63 -0.413899282 8.467866 3
## 64 0.420120843 8.908909 3
## 65 -2.265721137 7.660626 1
## 66 -0.664993778 9.376452 2
## 67 -0.630188141 10.011479 2
## 68 0.828017123 12.088650 4
## 69 0.983135794 10.711041 4
## 70 0.023356704 10.431711 3
## 71 0.458473195 10.206041 3
## 72 -0.657536014 9.537001 2
## 73 1.682514611 11.132778 5
## 74 -0.745848069 9.698510 2
## 75 -0.004462471 8.728542 3
## 76 -0.164988509 8.435678 3
## 77 0.120314698 11.371911 3
## 78 -1.656330368 7.524637 1
## 79 0.577133011 9.380837 4
## 80 -2.833976985 7.038543 1
## 81 -0.008984706 10.655432 3
## 82 0.515293357 9.971567 4
## 83 0.027955799 9.919812 3
## 84 0.828687347 10.170562 4
## 85 -0.073376727 8.300242 3
## 86 0.662947566 10.353849 4
## 87 0.687595294 10.558089 4
## 88 -0.668305934 8.937652 2
## 89 -0.514888184 10.241021 2
## 90 1.705380377 11.328439 5
## 91 -0.275398337 8.514736 3
## 92 0.959543216 10.262662 4
## 93 -0.255951783 9.025427 3
## 94 -0.079709616 10.305715 3
## 95 -1.630506502 7.662979 1
## 96 -0.278678394 10.692133 3
## 97 0.758595644 11.644796 4
## 98 0.136630104 9.452598 3
## 99 0.188434771 10.466522 3
## 100 -0.298126737 9.239931 3
## 101 -0.490934330 9.764645 3
## 102 -0.063416566 10.813775 3
## 103 2.487205238 12.549297 5
## 104 0.072979287 8.681596 3
## 105 -0.527816382 10.100652 2
## 106 -0.541955316 8.384182 2
## 107 0.539368741 11.602428 4
## 108 1.497412270 10.906942 4
## 109 -0.225570440 9.936111 3
## 110 -0.212876299 10.171831 3
## 111 -1.403130313 9.352552 2
## 112 0.385549817 11.560623 3
## 113 1.610337076 10.890348 5
## 114 -1.137023928 9.413904 2
## 115 0.072432466 10.264476 3
## 116 1.137507879 9.942563 4
## 117 -1.034633202 10.019617 2
## 118 -1.080526176 9.753838 2
## 119 -0.609980694 10.856277 2
## 120 0.342606637 10.205264 3
## 121 0.181285143 9.203736 3
## 122 -0.850980287 7.974501 2
## 123 -0.982618492 9.261740 2
## 124 -0.302002152 9.626589 3
## 125 0.072323969 9.873783 3
## 126 -0.401347396 8.041292 3
## 127 -0.982416190 7.887864 2
## 128 -2.095897348 8.607684 1
## 129 -1.291236638 10.359889 2
## 130 0.399677790 12.812456 3
## 131 0.013188482 8.731912 3
## 132 -0.968202401 8.588821 2
## 133 -0.150982763 12.417648 3
## 134 -0.392867921 10.838550 3
## 135 0.394150012 10.963821 3
## 136 -0.617503051 7.589838 2
## 137 -0.230628436 11.340708 3
## 138 1.624094934 11.391427 5
## 139 1.554291421 11.548927 5
## 140 -0.551084738 10.407531 2
## 141 -0.895289175 9.865163 2
## 142 0.524532130 9.920037 4
## 143 -0.887788137 9.184231 2
## 144 0.148108996 11.428037 3
## 145 1.537621756 10.944024 5
## 146 1.392378610 12.419101 4
## 147 1.515498416 12.319993 5
## 148 1.145254217 10.886355 4
## 149 1.572840057 11.343671 5
## 150 -1.779515829 9.086233 1
## 151 -0.362088683 10.537014 3
## 152 0.794214282 10.424479 4
## 153 1.489669909 12.062719 4
## 154 -1.479195278 8.304187 2
## 155 -0.381121834 10.080050 3
## 156 1.464386336 11.998232 4
## 157 0.088401591 8.935846 3
## 158 1.455391572 12.933675 4
## 159 0.040277511 8.332491 3
## 160 -0.027433818 11.565250 3
## 161 1.199870326 10.769336 4
## 162 0.544408040 10.514082 4
## 163 -0.148346755 11.055429 3
## 164 1.013734192 12.408478 4
## 165 -0.076080959 8.722828 3
## 166 0.502283617 10.756730 4
## 167 -0.484279596 8.382621 3
## 168 1.862504930 12.350377 5
## 169 0.609080209 8.275242 4
## 170 -0.120908340 9.689844 3
## 171 -0.196223036 10.725145 3
## 172 1.123583841 11.509058 4
## 173 -0.304185333 7.927098 3
## 174 1.205701543 9.408020 4
## 175 -0.268135355 11.286892 3
## 176 -0.987912064 8.743237 2
## 177 -1.194175662 10.275272 2
## 178 -0.129259291 11.276182 3
## 179 0.422043749 9.936366 3
## 180 0.416843128 10.864570 3
## 181 -1.188956075 8.068392 2
## 182 1.576088519 11.917992 5
## 183 0.825624297 11.320654 4
## 184 -0.676403873 10.961399 2
## 185 -0.033464916 8.305286 3
## 186 -0.898077497 7.983089 2
## 187 0.135771629 10.554170 3
## 188 -0.062318407 9.201386 3
## 189 -0.137769953 9.652353 3
## 190 -0.365481750 9.954493 3
## 191 -0.434817894 9.724373 3
## 192 0.089908007 9.467530 3
## 193 -0.508587842 8.289127 2
## 194 -1.236682235 8.266745 2
## 195 1.550711947 9.970367 5
## 196 -1.356164895 8.505365 2
## 197 1.825079793 12.416592 5
## 198 0.546691346 9.556124 4
## 199 1.742704823 12.109758 5
## 200 0.750645349 10.327544 4
## 201 0.231140413 9.587461 3
## 202 0.039989571 9.184861 3
## 203 2.045573407 11.714603 5
## 204 0.703375398 12.543602 4
## 205 -0.454281069 9.138270 3
## 206 0.945673007 12.165151 4
## 207 0.144075080 10.124831 3
## 208 -0.981423391 8.135860 2
## 209 1.252677932 11.489728 4
## 210 0.336074653 11.345354 3
## 211 -1.111498850 9.251762 2
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## 214 0.642122169 9.588487 4
## 215 0.513333882 10.196376 4
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## 224 -0.572972894 9.161572 2
## 225 -0.500351596 8.153033 2
## 226 0.967435546 10.340369 4
## 227 -0.143495942 9.287420 3
## 228 0.214941073 9.673987 3
## 229 -0.037637991 12.017760 3
## 230 0.120938431 8.811467 3
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## 232 1.601632545 11.599714 5
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## 234 -0.561940539 8.248978 2
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## 238 -0.856045437 8.514453 2
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## 244 -0.340154490 9.643257 3
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## 247 2.006332550 11.675617 5
## 248 1.940121874 11.970966 5
## 249 2.579415983 13.022344 5
## 250 1.252659064 11.494775 4
## 251 1.019315882 12.845763 4
## 252 -1.462034185 7.227910 2
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## 255 -0.631904581 9.707699 2
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## 257 -1.682820965 8.203648 1
## 258 -0.413090657 9.745313 3
## 259 0.595202200 10.690687 4
## 260 0.727438469 10.196404 4
## 261 -1.038539332 8.156282 2
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## 270 -0.181780687 10.686630 3
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## 278 -0.039958179 7.840928 3
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## 281 0.409820305 7.870855 3
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## 285 -2.890768131 6.223985 1
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## 296 -0.718983273 9.644702 2
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## 987 -0.406742510 10.274875 3
## 988 -0.994548783 8.337335 2
## 989 -1.374015182 7.328874 2
## 990 -1.178588369 6.294963 2
## 991 -1.577072245 7.858419 1
## 992 0.781487184 10.095218 4
## 993 -0.058640086 7.771373 3
## 994 -0.296308001 8.902570 3
## 995 -0.376874573 8.498960 3
## 996 0.992699430 11.926743 4
## 997 -0.507190633 9.996597 2
## 998 0.333298919 10.940765 3
## 999 0.382998167 9.918630 3
## 1000 0.907094239 11.530144 4
# creates plot object using ggplot
plot <- ggplot(sample_data, aes(x = xAxis, y= yAxis, col = as.factor(group)))+
geom_point()+theme(legend.position = "none")
# Display plot
plot
# Insert marginal ditribution using marginal function
ggMarginal(plot,type= 'histogram',groupColour=TRUE,groupFill=TRUE)
