# Mindanao State University
# General Santos City
# Introduction to R base commands
# Prepared by: Prof. Carlito O. Daarol
# Submitted by: Ehjie Bob A. Jumaway
# Lab Exercise 1: How to create Lines in with different styles in R
# Step 1: Create Data
x <- 1:10 # Create example data
y <- c(3, 1, 5, 2, 3, 8, 4, 7, 6, 9) # using the c() function to create an array
# Step 2: Plot the line graph using the base plot() command
plot(x, y, type = "l")
# Step 3: Add Main Title & Change Axis Labels
plot(x, y, type = "l",
main = "Main Title",
xlab = "X-Values",
ylab = "Y-Values")
#Step 4: Add color to the line using the col command
plot(x, y, type = "l",
main = "Main Title",
xlab = "X-Values",
ylab = "Y-Values",
col = "blue")
# Step 5: Modify Thickness of Line using lwd command
plot(x, y, type = "l",
main = "Main Title",
xlab = "X-Values",
ylab = "Y-Values",
lwd=7,
col = "red")
# Step 6: Add points to line graph by changing the type command
plot(x, y, type = "b",
main = "Main Title",
xlab = "X-Values",
ylab = "Y-Values",
lwd=3,
col = "green")
# Lab Exercise 2: How to create Lines in with different styles in R
# Step1: Assign values for different lines. We enclose the entire
# line with parenthesis symbol to force R to display the results instantly
# set the same value for the x variable
(x <- 1:10)
## [1] 1 2 3 4 5 6 7 8 9 10
# set different values for y variables
(y1 <- c(3, 1, 5, 2, 3, 8, 4, 7, 6, 9))
## [1] 3 1 5 2 3 8 4 7 6 9
(y2 <- c(5, 1, 4, 6, 2, 3, 7, 8, 2, 8))
## [1] 5 1 4 6 2 3 7 8 2 8
(y3 <- c(3, 3, 3, 3, 4, 4, 5, 5, 7, 7))
## [1] 3 3 3 3 4 4 5 5 7 7
# Plot first the pair x and y1.
plot(x, y1, type = "b",
main = "Main Title",
xlab = "X-Values",
ylab = "Y-Values",
lwd=3,
col = "blue")
# then Add the two lines (for x,y2) and (x,y3)
lines(x, y2, type = "b", col = "red",lwd=3)
lines(x, y3, type = "b", col = "green",lwd=3)
# Add legend to the plot
legend("topleft",
legend = c("Line y1", "Line y2", "Line y3"),
col = c("blue", "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# get summary of all columns
##
## setosa versicolor virginica
## 50 50 50
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/USERPC/Documents"
filename <- "Cancer.csv"
(file <- paste0(folder,"/",filename))
## [1] "C:/Users/USERPC/Documents/Cancer.csv"
#type getwd()
#paste the result in setwd("here")
getwd()
## [1] "C:/Users/USERPC/Desktop/Program/Programs Created/RStudio"
setwd("C:/Users/USERPC/Documents")
cancer <- read.csv("Cancer.csv", header = TRUE, sep = ",")
dim(cancer)
## [1] 173 17
names(cancer)
## [1] "country" "incomeperperson" "alcconsumption"
## [4] "armedforcesrate" "breastcancer" "co2emissions"
## [7] "femaleemployrate" "hivrate" "internetuserate"
## [10] "lifeexpectancy" "oilperperson" "polityscore"
## [13] "relectricperperson" "suicideper100th" "employrate"
## [16] "urbanrate" "continent"
# compute mean value for every continent
(means <- round(tapply(cancer$breastcancer, cancer$continent, mean), digits=2))
## AF AS EE LATAM NORAM OC WE
## 24.02 24.51 49.44 36.70 71.73 45.80 74.80
# draw boxplot per continent
boxplot(cancer$breastcancer ~ cancer$continent, main= "Breast cancer by continent (brown dot = mean value)", xlab="continents", ylab="new cases per 100,00 residents", col=rainbow(7))
# insert the mean value using brown dot
points(means, col="brown", pch=18)
# Lab Exercise 8: How to load external datasets and change data layout
folder <- "C:C:/Users/USERPC/Documents"
filename <- "hsb2.csv"
(file <- paste0(folder,"/",filename))
## [1] "C:C:/Users/USERPC/Documents/hsb2.csv"
#type getwd()
#paste the result in setwd("here")
getwd()
## [1] "C:/Users/USERPC/Documents"
setwd("C:/Users/USERPC/Documents")
hsb2_wide <- read.csv("hsb2.csv", header = TRUE, sep = ",")
# display only the top 6 rows
head(hsb2_wide)
## X id female race ses schtyp prog read write math science socst
## 1 1 70 0 4 1 1 1 57 52 41 47 57
## 2 2 121 1 4 2 1 3 68 59 53 63 61
## 3 3 86 0 4 3 1 1 44 33 54 58 31
## 4 4 141 0 4 3 1 3 63 44 47 53 56
## 5 5 172 0 4 2 1 2 47 52 57 53 61
## 6 6 113 0 4 2 1 2 44 52 51 63 61
# display only the last 6 rows
tail(hsb2_wide)
## X id female race ses schtyp prog read write math science socst
## 195 195 179 1 4 2 2 2 47 65 60 50 56
## 196 196 31 1 2 2 2 1 55 59 52 42 56
## 197 197 145 1 4 2 1 3 42 46 38 36 46
## 198 198 187 1 4 2 2 1 57 41 57 55 52
## 199 199 118 1 4 2 1 1 55 62 58 58 61
## 200 200 137 1 4 3 1 2 63 65 65 53 61
# delete redundant first column (run only once)
(hsb2_wide <- hsb2_wide[-1])
## id female race ses schtyp prog read write math science socst
## 1 70 0 4 1 1 1 57 52 41 47 57
## 2 121 1 4 2 1 3 68 59 53 63 61
## 3 86 0 4 3 1 1 44 33 54 58 31
## 4 141 0 4 3 1 3 63 44 47 53 56
## 5 172 0 4 2 1 2 47 52 57 53 61
## 6 113 0 4 2 1 2 44 52 51 63 61
## 7 50 0 3 2 1 1 50 59 42 53 61
## 8 11 0 1 2 1 2 34 46 45 39 36
## 9 84 0 4 2 1 1 63 57 54 58 51
## 10 48 0 3 2 1 2 57 55 52 50 51
## 11 75 0 4 2 1 3 60 46 51 53 61
## 12 60 0 4 2 1 2 57 65 51 63 61
## 13 95 0 4 3 1 2 73 60 71 61 71
## 14 104 0 4 3 1 2 54 63 57 55 46
## 15 38 0 3 1 1 2 45 57 50 31 56
## 16 115 0 4 1 1 1 42 49 43 50 56
## 17 76 0 4 3 1 2 47 52 51 50 56
## 18 195 0 4 2 2 1 57 57 60 58 56
## 19 114 0 4 3 1 2 68 65 62 55 61
## 20 85 0 4 2 1 1 55 39 57 53 46
## 21 167 0 4 2 1 1 63 49 35 66 41
## 22 143 0 4 2 1 3 63 63 75 72 66
## 23 41 0 3 2 1 2 50 40 45 55 56
## 24 20 0 1 3 1 2 60 52 57 61 61
## 25 12 0 1 2 1 3 37 44 45 39 46
## 26 53 0 3 2 1 3 34 37 46 39 31
## 27 154 0 4 3 1 2 65 65 66 61 66
## 28 178 0 4 2 2 3 47 57 57 58 46
## 29 196 0 4 3 2 2 44 38 49 39 46
## 30 29 0 2 1 1 1 52 44 49 55 41
## 31 126 0 4 2 1 1 42 31 57 47 51
## 32 103 0 4 3 1 2 76 52 64 64 61
## 33 192 0 4 3 2 2 65 67 63 66 71
## 34 150 0 4 2 1 3 42 41 57 72 31
## 35 199 0 4 3 2 2 52 59 50 61 61
## 36 144 0 4 3 1 1 60 65 58 61 66
## 37 200 0 4 2 2 2 68 54 75 66 66
## 38 80 0 4 3 1 2 65 62 68 66 66
## 39 16 0 1 1 1 3 47 31 44 36 36
## 40 153 0 4 2 1 3 39 31 40 39 51
## 41 176 0 4 2 2 2 47 47 41 42 51
## 42 177 0 4 2 2 2 55 59 62 58 51
## 43 168 0 4 2 1 2 52 54 57 55 51
## 44 40 0 3 1 1 1 42 41 43 50 41
## 45 62 0 4 3 1 1 65 65 48 63 66
## 46 169 0 4 1 1 1 55 59 63 69 46
## 47 49 0 3 3 1 3 50 40 39 49 47
## 48 136 0 4 2 1 2 65 59 70 63 51
## 49 189 0 4 2 2 2 47 59 63 53 46
## 50 7 0 1 2 1 2 57 54 59 47 51
## 51 27 0 2 2 1 2 53 61 61 57 56
## 52 128 0 4 3 1 2 39 33 38 47 41
## 53 21 0 1 2 1 1 44 44 61 50 46
## 54 183 0 4 2 2 2 63 59 49 55 71
## 55 132 0 4 2 1 2 73 62 73 69 66
## 56 15 0 1 3 1 3 39 39 44 26 42
## 57 67 0 4 1 1 3 37 37 42 33 32
## 58 22 0 1 2 1 3 42 39 39 56 46
## 59 185 0 4 2 2 2 63 57 55 58 41
## 60 9 0 1 2 1 3 48 49 52 44 51
## 61 181 0 4 2 2 2 50 46 45 58 61
## 62 170 0 4 3 1 2 47 62 61 69 66
## 63 134 0 4 1 1 1 44 44 39 34 46
## 64 108 0 4 2 1 1 34 33 41 36 36
## 65 197 0 4 3 2 2 50 42 50 36 61
## 66 140 0 4 2 1 3 44 41 40 50 26
## 67 171 0 4 2 1 2 60 54 60 55 66
## 68 107 0 4 1 1 3 47 39 47 42 26
## 69 81 0 4 1 1 2 63 43 59 65 44
## 70 18 0 1 2 1 3 50 33 49 44 36
## 71 155 0 4 2 1 1 44 44 46 39 51
## 72 97 0 4 3 1 2 60 54 58 58 61
## 73 68 0 4 2 1 2 73 67 71 63 66
## 74 157 0 4 2 1 1 68 59 58 74 66
## 75 56 0 4 2 1 3 55 45 46 58 51
## 76 5 0 1 1 1 2 47 40 43 45 31
## 77 159 0 4 3 1 2 55 61 54 49 61
## 78 123 0 4 3 1 1 68 59 56 63 66
## 79 164 0 4 2 1 3 31 36 46 39 46
## 80 14 0 1 3 1 2 47 41 54 42 56
## 81 127 0 4 3 1 2 63 59 57 55 56
## 82 165 0 4 1 1 3 36 49 54 61 36
## 83 174 0 4 2 2 2 68 59 71 66 56
## 84 3 0 1 1 1 2 63 65 48 63 56
## 85 58 0 4 2 1 3 55 41 40 44 41
## 86 146 0 4 3 1 2 55 62 64 63 66
## 87 102 0 4 3 1 2 52 41 51 53 56
## 88 117 0 4 3 1 3 34 49 39 42 56
## 89 133 0 4 2 1 3 50 31 40 34 31
## 90 94 0 4 3 1 2 55 49 61 61 56
## 91 24 0 2 2 1 2 52 62 66 47 46
## 92 149 0 4 1 1 1 63 49 49 66 46
## 93 82 1 4 3 1 2 68 62 65 69 61
## 94 8 1 1 1 1 2 39 44 52 44 48
## 95 129 1 4 1 1 1 44 44 46 47 51
## 96 173 1 4 1 1 1 50 62 61 63 51
## 97 57 1 4 2 1 2 71 65 72 66 56
## 98 100 1 4 3 1 2 63 65 71 69 71
## 99 1 1 1 1 1 3 34 44 40 39 41
## 100 194 1 4 3 2 2 63 63 69 61 61
## 101 88 1 4 3 1 2 68 60 64 69 66
## 102 99 1 4 3 1 1 47 59 56 66 61
## 103 47 1 3 1 1 2 47 46 49 33 41
## 104 120 1 4 3 1 2 63 52 54 50 51
## 105 166 1 4 2 1 2 52 59 53 61 51
## 106 65 1 4 2 1 2 55 54 66 42 56
## 107 101 1 4 3 1 2 60 62 67 50 56
## 108 89 1 4 1 1 3 35 35 40 51 33
## 109 54 1 3 1 2 1 47 54 46 50 56
## 110 180 1 4 3 2 2 71 65 69 58 71
## 111 162 1 4 2 1 3 57 52 40 61 56
## 112 4 1 1 1 1 2 44 50 41 39 51
## 113 131 1 4 3 1 2 65 59 57 46 66
## 114 125 1 4 1 1 2 68 65 58 59 56
## 115 34 1 1 3 2 2 73 61 57 55 66
## 116 106 1 4 2 1 3 36 44 37 42 41
## 117 130 1 4 3 1 1 43 54 55 55 46
## 118 93 1 4 3 1 2 73 67 62 58 66
## 119 163 1 4 1 1 2 52 57 64 58 56
## 120 37 1 3 1 1 3 41 47 40 39 51
## 121 35 1 1 1 2 1 60 54 50 50 51
## 122 87 1 4 2 1 1 50 52 46 50 56
## 123 73 1 4 2 1 2 50 52 53 39 56
## 124 151 1 4 2 1 3 47 46 52 48 46
## 125 44 1 3 1 1 3 47 62 45 34 46
## 126 152 1 4 3 1 2 55 57 56 58 61
## 127 105 1 4 2 1 2 50 41 45 44 56
## 128 28 1 2 2 1 1 39 53 54 50 41
## 129 91 1 4 3 1 3 50 49 56 47 46
## 130 45 1 3 1 1 3 34 35 41 29 26
## 131 116 1 4 2 1 2 57 59 54 50 56
## 132 33 1 2 1 1 2 57 65 72 54 56
## 133 66 1 4 2 1 3 68 62 56 50 51
## 134 72 1 4 2 1 3 42 54 47 47 46
## 135 77 1 4 1 1 2 61 59 49 44 66
## 136 61 1 4 3 1 2 76 63 60 67 66
## 137 190 1 4 2 2 2 47 59 54 58 46
## 138 42 1 3 2 1 3 46 52 55 44 56
## 139 2 1 1 2 1 3 39 41 33 42 41
## 140 55 1 3 2 2 2 52 49 49 44 61
## 141 19 1 1 1 1 1 28 46 43 44 51
## 142 90 1 4 3 1 2 42 54 50 50 52
## 143 142 1 4 2 1 3 47 42 52 39 51
## 144 17 1 1 2 1 2 47 57 48 44 41
## 145 122 1 4 2 1 2 52 59 58 53 66
## 146 191 1 4 3 2 2 47 52 43 48 61
## 147 83 1 4 2 1 3 50 62 41 55 31
## 148 182 1 4 2 2 2 44 52 43 44 51
## 149 6 1 1 1 1 2 47 41 46 40 41
## 150 46 1 3 1 1 2 45 55 44 34 41
## 151 43 1 3 1 1 2 47 37 43 42 46
## 152 96 1 4 3 1 2 65 54 61 58 56
## 153 138 1 4 2 1 3 43 57 40 50 51
## 154 10 1 1 2 1 1 47 54 49 53 61
## 155 71 1 4 2 1 1 57 62 56 58 66
## 156 139 1 4 2 1 2 68 59 61 55 71
## 157 110 1 4 2 1 3 52 55 50 54 61
## 158 148 1 4 2 1 3 42 57 51 47 61
## 159 109 1 4 2 1 1 42 39 42 42 41
## 160 39 1 3 3 1 2 66 67 67 61 66
## 161 147 1 4 1 1 2 47 62 53 53 61
## 162 74 1 4 2 1 2 57 50 50 51 58
## 163 198 1 4 3 2 2 47 61 51 63 31
## 164 161 1 4 1 1 2 57 62 72 61 61
## 165 112 1 4 2 1 2 52 59 48 55 61
## 166 69 1 4 1 1 3 44 44 40 40 31
## 167 156 1 4 2 1 2 50 59 53 61 61
## 168 111 1 4 1 1 1 39 54 39 47 36
## 169 186 1 4 2 2 2 57 62 63 55 41
## 170 98 1 4 1 1 3 57 60 51 53 37
## 171 119 1 4 1 1 1 42 57 45 50 43
## 172 13 1 1 2 1 3 47 46 39 47 61
## 173 51 1 3 3 1 1 42 36 42 31 39
## 174 26 1 2 3 1 2 60 59 62 61 51
## 175 36 1 3 1 1 1 44 49 44 35 51
## 176 135 1 4 1 1 2 63 60 65 54 66
## 177 59 1 4 2 1 2 65 67 63 55 71
## 178 78 1 4 2 1 2 39 54 54 53 41
## 179 64 1 4 3 1 3 50 52 45 58 36
## 180 63 1 4 1 1 1 52 65 60 56 51
## 181 79 1 4 2 1 2 60 62 49 50 51
## 182 193 1 4 2 2 2 44 49 48 39 51
## 183 92 1 4 3 1 1 52 67 57 63 61
## 184 160 1 4 2 1 2 55 65 55 50 61
## 185 32 1 2 3 1 3 50 67 66 66 56
## 186 23 1 2 1 1 2 65 65 64 58 71
## 187 158 1 4 2 1 1 52 54 55 53 51
## 188 25 1 2 2 1 1 47 44 42 42 36
## 189 188 1 4 3 2 2 63 62 56 55 61
## 190 52 1 3 1 1 2 50 46 53 53 66
## 191 124 1 4 1 1 3 42 54 41 42 41
## 192 175 1 4 3 2 1 36 57 42 50 41
## 193 184 1 4 2 2 3 50 52 53 55 56
## 194 30 1 2 3 1 2 41 59 42 34 51
## 195 179 1 4 2 2 2 47 65 60 50 56
## 196 31 1 2 2 2 1 55 59 52 42 56
## 197 145 1 4 2 1 3 42 46 38 36 46
## 198 187 1 4 2 2 1 57 41 57 55 52
## 199 118 1 4 2 1 1 55 62 58 58 61
## 200 137 1 4 3 1 2 63 65 65 53 61
# Remarks
# hsb2 dataset consists of 200 selected random samples from senior
# high school students in the US.
# We want to compare the student performance across different subjects
# change data layout by grouping different subjects
# into one column using melt() command. Install first reshape2 package
# install.packages("reshape2")
library(reshape2)
(hsb2_long <- melt(hsb2_wide, measure.vars = c("read","write","math","science","socst")))
## id female race ses schtyp prog variable value
## 1 70 0 4 1 1 1 read 57
## 2 121 1 4 2 1 3 read 68
## 3 86 0 4 3 1 1 read 44
## 4 141 0 4 3 1 3 read 63
## 5 172 0 4 2 1 2 read 47
## 6 113 0 4 2 1 2 read 44
## 7 50 0 3 2 1 1 read 50
## 8 11 0 1 2 1 2 read 34
## 9 84 0 4 2 1 1 read 63
## 10 48 0 3 2 1 2 read 57
## 11 75 0 4 2 1 3 read 60
## 12 60 0 4 2 1 2 read 57
## 13 95 0 4 3 1 2 read 73
## 14 104 0 4 3 1 2 read 54
## 15 38 0 3 1 1 2 read 45
## 16 115 0 4 1 1 1 read 42
## 17 76 0 4 3 1 2 read 47
## 18 195 0 4 2 2 1 read 57
## 19 114 0 4 3 1 2 read 68
## 20 85 0 4 2 1 1 read 55
## 21 167 0 4 2 1 1 read 63
## 22 143 0 4 2 1 3 read 63
## 23 41 0 3 2 1 2 read 50
## 24 20 0 1 3 1 2 read 60
## 25 12 0 1 2 1 3 read 37
## 26 53 0 3 2 1 3 read 34
## 27 154 0 4 3 1 2 read 65
## 28 178 0 4 2 2 3 read 47
## 29 196 0 4 3 2 2 read 44
## 30 29 0 2 1 1 1 read 52
## 31 126 0 4 2 1 1 read 42
## 32 103 0 4 3 1 2 read 76
## 33 192 0 4 3 2 2 read 65
## 34 150 0 4 2 1 3 read 42
## 35 199 0 4 3 2 2 read 52
## 36 144 0 4 3 1 1 read 60
## 37 200 0 4 2 2 2 read 68
## 38 80 0 4 3 1 2 read 65
## 39 16 0 1 1 1 3 read 47
## 40 153 0 4 2 1 3 read 39
## 41 176 0 4 2 2 2 read 47
## 42 177 0 4 2 2 2 read 55
## 43 168 0 4 2 1 2 read 52
## 44 40 0 3 1 1 1 read 42
## 45 62 0 4 3 1 1 read 65
## 46 169 0 4 1 1 1 read 55
## 47 49 0 3 3 1 3 read 50
## 48 136 0 4 2 1 2 read 65
## 49 189 0 4 2 2 2 read 47
## 50 7 0 1 2 1 2 read 57
## 51 27 0 2 2 1 2 read 53
## 52 128 0 4 3 1 2 read 39
## 53 21 0 1 2 1 1 read 44
## 54 183 0 4 2 2 2 read 63
## 55 132 0 4 2 1 2 read 73
## 56 15 0 1 3 1 3 read 39
## 57 67 0 4 1 1 3 read 37
## 58 22 0 1 2 1 3 read 42
## 59 185 0 4 2 2 2 read 63
## 60 9 0 1 2 1 3 read 48
## 61 181 0 4 2 2 2 read 50
## 62 170 0 4 3 1 2 read 47
## 63 134 0 4 1 1 1 read 44
## 64 108 0 4 2 1 1 read 34
## 65 197 0 4 3 2 2 read 50
## 66 140 0 4 2 1 3 read 44
## 67 171 0 4 2 1 2 read 60
## 68 107 0 4 1 1 3 read 47
## 69 81 0 4 1 1 2 read 63
## 70 18 0 1 2 1 3 read 50
## 71 155 0 4 2 1 1 read 44
## 72 97 0 4 3 1 2 read 60
## 73 68 0 4 2 1 2 read 73
## 74 157 0 4 2 1 1 read 68
## 75 56 0 4 2 1 3 read 55
## 76 5 0 1 1 1 2 read 47
## 77 159 0 4 3 1 2 read 55
## 78 123 0 4 3 1 1 read 68
## 79 164 0 4 2 1 3 read 31
## 80 14 0 1 3 1 2 read 47
## 81 127 0 4 3 1 2 read 63
## 82 165 0 4 1 1 3 read 36
## 83 174 0 4 2 2 2 read 68
## 84 3 0 1 1 1 2 read 63
## 85 58 0 4 2 1 3 read 55
## 86 146 0 4 3 1 2 read 55
## 87 102 0 4 3 1 2 read 52
## 88 117 0 4 3 1 3 read 34
## 89 133 0 4 2 1 3 read 50
## 90 94 0 4 3 1 2 read 55
## 91 24 0 2 2 1 2 read 52
## 92 149 0 4 1 1 1 read 63
## 93 82 1 4 3 1 2 read 68
## 94 8 1 1 1 1 2 read 39
## 95 129 1 4 1 1 1 read 44
## 96 173 1 4 1 1 1 read 50
## 97 57 1 4 2 1 2 read 71
## 98 100 1 4 3 1 2 read 63
## 99 1 1 1 1 1 3 read 34
## 100 194 1 4 3 2 2 read 63
## 101 88 1 4 3 1 2 read 68
## 102 99 1 4 3 1 1 read 47
## 103 47 1 3 1 1 2 read 47
## 104 120 1 4 3 1 2 read 63
## 105 166 1 4 2 1 2 read 52
## 106 65 1 4 2 1 2 read 55
## 107 101 1 4 3 1 2 read 60
## 108 89 1 4 1 1 3 read 35
## 109 54 1 3 1 2 1 read 47
## 110 180 1 4 3 2 2 read 71
## 111 162 1 4 2 1 3 read 57
## 112 4 1 1 1 1 2 read 44
## 113 131 1 4 3 1 2 read 65
## 114 125 1 4 1 1 2 read 68
## 115 34 1 1 3 2 2 read 73
## 116 106 1 4 2 1 3 read 36
## 117 130 1 4 3 1 1 read 43
## 118 93 1 4 3 1 2 read 73
## 119 163 1 4 1 1 2 read 52
## 120 37 1 3 1 1 3 read 41
## 121 35 1 1 1 2 1 read 60
## 122 87 1 4 2 1 1 read 50
## 123 73 1 4 2 1 2 read 50
## 124 151 1 4 2 1 3 read 47
## 125 44 1 3 1 1 3 read 47
## 126 152 1 4 3 1 2 read 55
## 127 105 1 4 2 1 2 read 50
## 128 28 1 2 2 1 1 read 39
## 129 91 1 4 3 1 3 read 50
## 130 45 1 3 1 1 3 read 34
## 131 116 1 4 2 1 2 read 57
## 132 33 1 2 1 1 2 read 57
## 133 66 1 4 2 1 3 read 68
## 134 72 1 4 2 1 3 read 42
## 135 77 1 4 1 1 2 read 61
## 136 61 1 4 3 1 2 read 76
## 137 190 1 4 2 2 2 read 47
## 138 42 1 3 2 1 3 read 46
## 139 2 1 1 2 1 3 read 39
## 140 55 1 3 2 2 2 read 52
## 141 19 1 1 1 1 1 read 28
## 142 90 1 4 3 1 2 read 42
## 143 142 1 4 2 1 3 read 47
## 144 17 1 1 2 1 2 read 47
## 145 122 1 4 2 1 2 read 52
## 146 191 1 4 3 2 2 read 47
## 147 83 1 4 2 1 3 read 50
## 148 182 1 4 2 2 2 read 44
## 149 6 1 1 1 1 2 read 47
## 150 46 1 3 1 1 2 read 45
## 151 43 1 3 1 1 2 read 47
## 152 96 1 4 3 1 2 read 65
## 153 138 1 4 2 1 3 read 43
## 154 10 1 1 2 1 1 read 47
## 155 71 1 4 2 1 1 read 57
## 156 139 1 4 2 1 2 read 68
## 157 110 1 4 2 1 3 read 52
## 158 148 1 4 2 1 3 read 42
## 159 109 1 4 2 1 1 read 42
## 160 39 1 3 3 1 2 read 66
## 161 147 1 4 1 1 2 read 47
## 162 74 1 4 2 1 2 read 57
## 163 198 1 4 3 2 2 read 47
## 164 161 1 4 1 1 2 read 57
## 165 112 1 4 2 1 2 read 52
## 166 69 1 4 1 1 3 read 44
## 167 156 1 4 2 1 2 read 50
## 168 111 1 4 1 1 1 read 39
## 169 186 1 4 2 2 2 read 57
## 170 98 1 4 1 1 3 read 57
## 171 119 1 4 1 1 1 read 42
## 172 13 1 1 2 1 3 read 47
## 173 51 1 3 3 1 1 read 42
## 174 26 1 2 3 1 2 read 60
## 175 36 1 3 1 1 1 read 44
## 176 135 1 4 1 1 2 read 63
## 177 59 1 4 2 1 2 read 65
## 178 78 1 4 2 1 2 read 39
## 179 64 1 4 3 1 3 read 50
## 180 63 1 4 1 1 1 read 52
## 181 79 1 4 2 1 2 read 60
## 182 193 1 4 2 2 2 read 44
## 183 92 1 4 3 1 1 read 52
## 184 160 1 4 2 1 2 read 55
## 185 32 1 2 3 1 3 read 50
## 186 23 1 2 1 1 2 read 65
## 187 158 1 4 2 1 1 read 52
## 188 25 1 2 2 1 1 read 47
## 189 188 1 4 3 2 2 read 63
## 190 52 1 3 1 1 2 read 50
## 191 124 1 4 1 1 3 read 42
## 192 175 1 4 3 2 1 read 36
## 193 184 1 4 2 2 3 read 50
## 194 30 1 2 3 1 2 read 41
## 195 179 1 4 2 2 2 read 47
## 196 31 1 2 2 2 1 read 55
## 197 145 1 4 2 1 3 read 42
## 198 187 1 4 2 2 1 read 57
## 199 118 1 4 2 1 1 read 55
## 200 137 1 4 3 1 2 read 63
## 201 70 0 4 1 1 1 write 52
## 202 121 1 4 2 1 3 write 59
## 203 86 0 4 3 1 1 write 33
## 204 141 0 4 3 1 3 write 44
## 205 172 0 4 2 1 2 write 52
## 206 113 0 4 2 1 2 write 52
## 207 50 0 3 2 1 1 write 59
## 208 11 0 1 2 1 2 write 46
## 209 84 0 4 2 1 1 write 57
## 210 48 0 3 2 1 2 write 55
## 211 75 0 4 2 1 3 write 46
## 212 60 0 4 2 1 2 write 65
## 213 95 0 4 3 1 2 write 60
## 214 104 0 4 3 1 2 write 63
## 215 38 0 3 1 1 2 write 57
## 216 115 0 4 1 1 1 write 49
## 217 76 0 4 3 1 2 write 52
## 218 195 0 4 2 2 1 write 57
## 219 114 0 4 3 1 2 write 65
## 220 85 0 4 2 1 1 write 39
## 221 167 0 4 2 1 1 write 49
## 222 143 0 4 2 1 3 write 63
## 223 41 0 3 2 1 2 write 40
## 224 20 0 1 3 1 2 write 52
## 225 12 0 1 2 1 3 write 44
## 226 53 0 3 2 1 3 write 37
## 227 154 0 4 3 1 2 write 65
## 228 178 0 4 2 2 3 write 57
## 229 196 0 4 3 2 2 write 38
## 230 29 0 2 1 1 1 write 44
## 231 126 0 4 2 1 1 write 31
## 232 103 0 4 3 1 2 write 52
## 233 192 0 4 3 2 2 write 67
## 234 150 0 4 2 1 3 write 41
## 235 199 0 4 3 2 2 write 59
## 236 144 0 4 3 1 1 write 65
## 237 200 0 4 2 2 2 write 54
## 238 80 0 4 3 1 2 write 62
## 239 16 0 1 1 1 3 write 31
## 240 153 0 4 2 1 3 write 31
## 241 176 0 4 2 2 2 write 47
## 242 177 0 4 2 2 2 write 59
## 243 168 0 4 2 1 2 write 54
## 244 40 0 3 1 1 1 write 41
## 245 62 0 4 3 1 1 write 65
## 246 169 0 4 1 1 1 write 59
## 247 49 0 3 3 1 3 write 40
## 248 136 0 4 2 1 2 write 59
## 249 189 0 4 2 2 2 write 59
## 250 7 0 1 2 1 2 write 54
## 251 27 0 2 2 1 2 write 61
## 252 128 0 4 3 1 2 write 33
## 253 21 0 1 2 1 1 write 44
## 254 183 0 4 2 2 2 write 59
## 255 132 0 4 2 1 2 write 62
## 256 15 0 1 3 1 3 write 39
## 257 67 0 4 1 1 3 write 37
## 258 22 0 1 2 1 3 write 39
## 259 185 0 4 2 2 2 write 57
## 260 9 0 1 2 1 3 write 49
## 261 181 0 4 2 2 2 write 46
## 262 170 0 4 3 1 2 write 62
## 263 134 0 4 1 1 1 write 44
## 264 108 0 4 2 1 1 write 33
## 265 197 0 4 3 2 2 write 42
## 266 140 0 4 2 1 3 write 41
## 267 171 0 4 2 1 2 write 54
## 268 107 0 4 1 1 3 write 39
## 269 81 0 4 1 1 2 write 43
## 270 18 0 1 2 1 3 write 33
## 271 155 0 4 2 1 1 write 44
## 272 97 0 4 3 1 2 write 54
## 273 68 0 4 2 1 2 write 67
## 274 157 0 4 2 1 1 write 59
## 275 56 0 4 2 1 3 write 45
## 276 5 0 1 1 1 2 write 40
## 277 159 0 4 3 1 2 write 61
## 278 123 0 4 3 1 1 write 59
## 279 164 0 4 2 1 3 write 36
## 280 14 0 1 3 1 2 write 41
## 281 127 0 4 3 1 2 write 59
## 282 165 0 4 1 1 3 write 49
## 283 174 0 4 2 2 2 write 59
## 284 3 0 1 1 1 2 write 65
## 285 58 0 4 2 1 3 write 41
## 286 146 0 4 3 1 2 write 62
## 287 102 0 4 3 1 2 write 41
## 288 117 0 4 3 1 3 write 49
## 289 133 0 4 2 1 3 write 31
## 290 94 0 4 3 1 2 write 49
## 291 24 0 2 2 1 2 write 62
## 292 149 0 4 1 1 1 write 49
## 293 82 1 4 3 1 2 write 62
## 294 8 1 1 1 1 2 write 44
## 295 129 1 4 1 1 1 write 44
## 296 173 1 4 1 1 1 write 62
## 297 57 1 4 2 1 2 write 65
## 298 100 1 4 3 1 2 write 65
## 299 1 1 1 1 1 3 write 44
## 300 194 1 4 3 2 2 write 63
## 301 88 1 4 3 1 2 write 60
## 302 99 1 4 3 1 1 write 59
## 303 47 1 3 1 1 2 write 46
## 304 120 1 4 3 1 2 write 52
## 305 166 1 4 2 1 2 write 59
## 306 65 1 4 2 1 2 write 54
## 307 101 1 4 3 1 2 write 62
## 308 89 1 4 1 1 3 write 35
## 309 54 1 3 1 2 1 write 54
## 310 180 1 4 3 2 2 write 65
## 311 162 1 4 2 1 3 write 52
## 312 4 1 1 1 1 2 write 50
## 313 131 1 4 3 1 2 write 59
## 314 125 1 4 1 1 2 write 65
## 315 34 1 1 3 2 2 write 61
## 316 106 1 4 2 1 3 write 44
## 317 130 1 4 3 1 1 write 54
## 318 93 1 4 3 1 2 write 67
## 319 163 1 4 1 1 2 write 57
## 320 37 1 3 1 1 3 write 47
## 321 35 1 1 1 2 1 write 54
## 322 87 1 4 2 1 1 write 52
## 323 73 1 4 2 1 2 write 52
## 324 151 1 4 2 1 3 write 46
## 325 44 1 3 1 1 3 write 62
## 326 152 1 4 3 1 2 write 57
## 327 105 1 4 2 1 2 write 41
## 328 28 1 2 2 1 1 write 53
## 329 91 1 4 3 1 3 write 49
## 330 45 1 3 1 1 3 write 35
## 331 116 1 4 2 1 2 write 59
## 332 33 1 2 1 1 2 write 65
## 333 66 1 4 2 1 3 write 62
## 334 72 1 4 2 1 3 write 54
## 335 77 1 4 1 1 2 write 59
## 336 61 1 4 3 1 2 write 63
## 337 190 1 4 2 2 2 write 59
## 338 42 1 3 2 1 3 write 52
## 339 2 1 1 2 1 3 write 41
## 340 55 1 3 2 2 2 write 49
## 341 19 1 1 1 1 1 write 46
## 342 90 1 4 3 1 2 write 54
## 343 142 1 4 2 1 3 write 42
## 344 17 1 1 2 1 2 write 57
## 345 122 1 4 2 1 2 write 59
## 346 191 1 4 3 2 2 write 52
## 347 83 1 4 2 1 3 write 62
## 348 182 1 4 2 2 2 write 52
## 349 6 1 1 1 1 2 write 41
## 350 46 1 3 1 1 2 write 55
## 351 43 1 3 1 1 2 write 37
## 352 96 1 4 3 1 2 write 54
## 353 138 1 4 2 1 3 write 57
## 354 10 1 1 2 1 1 write 54
## 355 71 1 4 2 1 1 write 62
## 356 139 1 4 2 1 2 write 59
## 357 110 1 4 2 1 3 write 55
## 358 148 1 4 2 1 3 write 57
## 359 109 1 4 2 1 1 write 39
## 360 39 1 3 3 1 2 write 67
## 361 147 1 4 1 1 2 write 62
## 362 74 1 4 2 1 2 write 50
## 363 198 1 4 3 2 2 write 61
## 364 161 1 4 1 1 2 write 62
## 365 112 1 4 2 1 2 write 59
## 366 69 1 4 1 1 3 write 44
## 367 156 1 4 2 1 2 write 59
## 368 111 1 4 1 1 1 write 54
## 369 186 1 4 2 2 2 write 62
## 370 98 1 4 1 1 3 write 60
## 371 119 1 4 1 1 1 write 57
## 372 13 1 1 2 1 3 write 46
## 373 51 1 3 3 1 1 write 36
## 374 26 1 2 3 1 2 write 59
## 375 36 1 3 1 1 1 write 49
## 376 135 1 4 1 1 2 write 60
## 377 59 1 4 2 1 2 write 67
## 378 78 1 4 2 1 2 write 54
## 379 64 1 4 3 1 3 write 52
## 380 63 1 4 1 1 1 write 65
## 381 79 1 4 2 1 2 write 62
## 382 193 1 4 2 2 2 write 49
## 383 92 1 4 3 1 1 write 67
## 384 160 1 4 2 1 2 write 65
## 385 32 1 2 3 1 3 write 67
## 386 23 1 2 1 1 2 write 65
## 387 158 1 4 2 1 1 write 54
## 388 25 1 2 2 1 1 write 44
## 389 188 1 4 3 2 2 write 62
## 390 52 1 3 1 1 2 write 46
## 391 124 1 4 1 1 3 write 54
## 392 175 1 4 3 2 1 write 57
## 393 184 1 4 2 2 3 write 52
## 394 30 1 2 3 1 2 write 59
## 395 179 1 4 2 2 2 write 65
## 396 31 1 2 2 2 1 write 59
## 397 145 1 4 2 1 3 write 46
## 398 187 1 4 2 2 1 write 41
## 399 118 1 4 2 1 1 write 62
## 400 137 1 4 3 1 2 write 65
## 401 70 0 4 1 1 1 math 41
## 402 121 1 4 2 1 3 math 53
## 403 86 0 4 3 1 1 math 54
## 404 141 0 4 3 1 3 math 47
## 405 172 0 4 2 1 2 math 57
## 406 113 0 4 2 1 2 math 51
## 407 50 0 3 2 1 1 math 42
## 408 11 0 1 2 1 2 math 45
## 409 84 0 4 2 1 1 math 54
## 410 48 0 3 2 1 2 math 52
## 411 75 0 4 2 1 3 math 51
## 412 60 0 4 2 1 2 math 51
## 413 95 0 4 3 1 2 math 71
## 414 104 0 4 3 1 2 math 57
## 415 38 0 3 1 1 2 math 50
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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
## 458 22 0 1 2 1 3 math 39
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## 486 146 0 4 3 1 2 math 64
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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
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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
## 515 34 1 1 3 2 2 math 57
## 516 106 1 4 2 1 3 math 37
## 517 130 1 4 3 1 1 math 55
## 518 93 1 4 3 1 2 math 62
## 519 163 1 4 1 1 2 math 64
## 520 37 1 3 1 1 3 math 40
## 521 35 1 1 1 2 1 math 50
## 522 87 1 4 2 1 1 math 46
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#Remark: Pay extra attention to the last 2 columns
head(hsb2_long)
## id female race ses schtyp prog variable value
## 1 70 0 4 1 1 1 read 57
## 2 121 1 4 2 1 3 read 68
## 3 86 0 4 3 1 1 read 44
## 4 141 0 4 3 1 3 read 63
## 5 172 0 4 2 1 2 read 47
## 6 113 0 4 2 1 2 read 44
tail(hsb2_long)
## id female race ses schtyp prog variable value
## 995 179 1 4 2 2 2 socst 56
## 996 31 1 2 2 2 1 socst 56
## 997 145 1 4 2 1 3 socst 46
## 998 187 1 4 2 2 1 socst 52
## 999 118 1 4 2 1 1 socst 61
## 1000 137 1 4 3 1 2 socst 61
# get thefrequency
table(hsb2_long$variable)
##
## read write math science socst
## 200 200 200 200 200
# This means that the tables hsb2_long and hsb2_wide are the same
# tables displayed in two ways
# display data structure of the hsb2_long dataset
str(hsb2_long)
## 'data.frame': 1000 obs. of 8 variables:
## $ id : int 70 121 86 141 172 113 50 11 84 48 ...
## $ female : int 0 1 0 0 0 0 0 0 0 0 ...
## $ race : int 4 4 4 4 4 4 3 1 4 3 ...
## $ ses : int 1 2 3 3 2 2 2 2 2 2 ...
## $ schtyp : int 1 1 1 1 1 1 1 1 1 1 ...
## $ prog : int 1 3 1 3 2 2 1 2 1 2 ...
## $ variable: Factor w/ 5 levels "read","write",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ value : int 57 68 44 63 47 44 50 34 63 57 ...
# the variables female, race, ses, schtyp, prog are stored as numbers
# for encoding purposes. However these variables are actually qualitative variables
# so we convert each from integer type to categorical type
# defining some variables to become factor variable
# we use another variable to preserve the file hsb2_long
data <- hsb2_long
data$ses = factor(data$ses, labels=c("low", "middle", "high"))
data$schtyp = factor(data$schtyp, labels=c("public", "private"))
data$prog = factor(data$prog, labels=c("general", "academic", "vocational"))
data$race = factor(data$race, labels=c("hispanic", "asian", "african-amer","white"))
data$female = factor(data$female, labels=c("female", "male"))
# check data structure again. The former integer variables are now categorical variable
str(data)
## 'data.frame': 1000 obs. of 8 variables:
## $ id : int 70 121 86 141 172 113 50 11 84 48 ...
## $ female : Factor w/ 2 levels "female","male": 1 2 1 1 1 1 1 1 1 1 ...
## $ race : Factor w/ 4 levels "hispanic","asian",..: 4 4 4 4 4 4 3 1 4 3 ...
## $ ses : Factor w/ 3 levels "low","middle",..: 1 2 3 3 2 2 2 2 2 2 ...
## $ schtyp : Factor w/ 2 levels "public","private": 1 1 1 1 1 1 1 1 1 1 ...
## $ prog : Factor w/ 3 levels "general","academic",..: 1 3 1 3 2 2 1 2 1 2 ...
## $ variable: Factor w/ 5 levels "read","write",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ value : int 57 68 44 63 47 44 50 34 63 57 ...
# we compare student performance by using boxplots
library(gplots)
##
## Attaching package: 'gplots'
## The following object is masked from 'package:stats':
##
## lowess
# compute the average value by group using tapply() command
means <- round(tapply(data$value, data$variable, mean), digits=2)
# create boxplot by group
boxplot(data$value ~ data$variable, main= "Student Performance by Subject (brown dot = mean score)",
xlab="Subject matter", ylab="Percentage Scores", col=rainbow(5))
# insert the average values
points(means, col="brown", pch=18)
# can also compute the median values
medians = round(tapply(data$value, data$variable, median), digits=2)
medians
## read write math science socst
## 50 54 52 53 52
points(medians, col="red", pch=18)
# Lab Exercise 9: How to plot categorical variables
###install.packages("ggplot2")
###install.packages("colorspace", repos = "http://cran.us.r-project.org")
###("hsb2_long")
###install.packages(c("pkg1", "pkg2"))
library(ggplot2)
# we load variable names to memory to avoid the dollar notation
attach(hsb2_long)
# create the plot object p
p <- ggplot(hsb2_long, aes(ses, fill = prog)) + facet_wrap(~race)
p + geom_bar()
## Warning: The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
p + geom_bar(position = "dodge")
## Warning: The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
library(ggplot2)
attach(hsb2_long)
## The following objects are masked from hsb2_long (pos = 3):
##
## female, id, prog, race, schtyp, ses, value, variable
p <- ggplot(hsb2_long, aes(ses, fill = prog)) + facet_wrap(~schtyp)
p + geom_bar()
## Warning: The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
p + geom_bar(position = "dodge")
## Warning: The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
# Lab Exercise 10: Scatter Plots with marginal Distributions
# Advance Scatter plots using libraries
# install.packages("ggExtra")
# install.packages("tidyverse")
library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.1 ✔ readr 2.1.4
## ✔ forcats 1.0.0 ✔ stringr 1.5.0
## ✔ lubridate 1.9.2 ✔ tibble 3.2.1
## ✔ purrr 1.0.1 ✔ tidyr 1.3.0
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(ggExtra)
# set theme appearance of grid background
theme_set(theme_bw(1))
# create X and Y vector
(xAxis <- rnorm(1000)) # normal distribution with mean 0 and sd=1
## [1] 0.090732960 -0.132814440 0.569418089 -0.027871414 0.061088314
## [6] 0.727897612 -0.438308681 -0.013616007 -0.397243016 -0.743331531
## [11] 0.214581253 -0.877713589 -0.496868782 -1.032001253 0.092550958
## [16] -0.802827388 -0.358191357 -0.353468216 -0.902770409 0.264447008
## [21] 1.192857571 1.249897021 0.287063464 0.132007308 0.109514838
## [26] -0.303155340 0.917532810 2.869395703 0.070578641 -2.217118788
## [31] -0.322222000 0.471841278 0.254532521 1.962747469 -0.837013631
## [36] 0.093667022 0.928973611 0.840395206 -0.804859997 1.002006472
## [41] -0.791998411 0.264015389 0.637352416 0.789785627 -0.239193203
## [46] -1.280285889 -0.520348829 0.680114422 -0.137492697 1.135293131
## [51] 0.052461150 0.042995301 0.394707111 -1.134998925 -0.416237186
## [56] 1.002274905 -0.434770606 -1.065141597 1.605871003 -0.588165571
## [61] 0.870333850 -0.124888152 2.060867014 1.016201317 -0.956060492
## [66] -0.075592856 0.438101824 -0.417509046 0.992382545 1.620198824
## [71] -0.291108351 0.488655017 -0.328110915 1.031125587 0.905740585
## [76] -0.957109435 0.937765456 -0.071262821 -0.136046711 0.223469389
## [81] -1.312718640 -0.123063219 -2.684264197 0.599073869 -0.223799073
## [86] -0.019514360 0.632440067 0.611066071 -0.037349198 1.614425719
## [91] 0.688150230 0.412478151 -1.461987112 -0.981353454 0.896550944
## [96] -0.108348140 -0.163257127 0.089180620 -0.677881150 -0.706320630
## [101] 0.232515154 -0.169405206 -0.987960999 0.389568405 -0.230219487
## [106] -1.154018103 -0.277444809 0.511408337 0.163635134 0.301912052
## [111] -0.678659171 0.913163973 -0.224645161 1.455088633 -1.761435532
## [116] 0.666978688 -1.576792152 -0.565479492 0.332785770 -0.183000273
## [121] 0.817405029 0.798616153 -0.416676826 1.908521901 -1.239861325
## [126] 1.644896674 -0.362035377 -0.121630939 0.268463047 0.688450355
## [131] 0.335333181 -1.496237566 -0.625889502 0.801479591 1.381181315
## [136] 1.278744090 0.251264582 0.962076725 -0.071278468 -0.733542527
## [141] -0.260703024 2.193693112 0.903245104 -0.818600606 0.132213777
## [146] -1.215238961 -0.327886663 0.027095863 -1.621593275 -1.395042376
## [151] 1.878631842 0.011247459 0.212625970 -0.380705611 -1.781790082
## [156] 1.346209358 0.215747075 0.259682688 -2.153726203 1.392187389
## [161] 0.370675215 0.179990368 -0.265121488 -0.059573401 0.554179418
## [166] -0.582980812 -0.856754095 1.386923897 0.714009307 -0.505241068
## [171] -1.072263623 -1.464433236 -0.239950782 2.196434201 0.460544594
## [176] 2.711454092 2.750935978 -1.676269299 -1.858927440 -0.339400738
## [181] 0.791477500 -0.643856013 1.062253072 0.102809549 0.395614120
## [186] 0.517579453 -0.087146970 -2.258385018 1.639240196 0.462324329
## [191] 1.288944252 0.487488021 -0.484689918 -0.228869941 0.918061333
## [196] 0.801068969 0.323907263 1.288686413 -1.762045021 1.176215422
## [201] -0.419581348 -0.303861621 1.254275999 0.726245077 -0.285775476
## [206] 0.576318089 0.530969720 -0.526109445 0.301634393 -0.310114379
## [211] 1.577623504 0.419879786 -0.515748590 1.823347310 0.327337591
## [216] -0.096475955 -0.719354278 0.918223258 0.753789438 -0.095527302
## [221] 1.879486642 1.192482883 -1.500276916 -0.303339984 0.657919001
## [226] 0.346296866 -0.600924050 -1.678330316 -0.238406405 0.230058299
## [231] -0.862585020 -0.982315876 -0.689066222 0.998489062 -0.393470402
## [236] -1.044057625 -1.046662782 2.146516144 1.632960477 0.716075325
## [241] -1.154060843 -1.496350725 -2.318750482 0.612398730 -0.649363692
## [246] 1.302802967 -0.006826436 1.729681864 -0.981894118 -0.734600371
## [251] -0.882204052 0.085335772 -0.098218621 1.861468830 -0.442920456
## [256] 1.136428396 0.471558815 0.378898074 2.680386865 -1.661310090
## [261] 1.538286971 0.138195206 1.487786901 1.327379937 -0.224426603
## [266] -0.175657817 -0.371820886 -0.967262031 -1.313503315 -0.105943388
## [271] 0.332945960 -0.508890578 -0.153828562 0.717392047 -0.016121004
## [276] -1.331514353 0.599808402 0.084273165 -0.858711676 -0.349316055
## [281] -0.101879717 -1.028041343 0.817604835 1.386388904 0.360603177
## [286] 0.581192676 0.881870182 0.241811523 -0.148462624 -0.792274606
## [291] -0.525644912 -0.198908516 0.456926596 0.778456961 -0.441039854
## [296] -0.320650101 -0.196439425 1.132002833 -2.252203599 -2.190727085
## [301] 1.055899831 -0.374556545 -0.165366438 -1.231998048 0.093926528
## [306] 1.030980028 -1.170774111 3.224828440 0.322251892 1.535050600
## [311] -1.728413552 -1.637394739 -0.337431674 -0.754276302 0.392144935
## [316] 0.131489834 0.290359775 -0.756151083 0.044636112 0.495540719
## [321] -0.150527172 0.875283270 -0.580172768 -0.792764289 -1.004570208
## [326] -0.496968662 -1.232495588 -0.430883832 1.901723883 0.618035774
## [331] 1.396021004 0.848542690 1.160091570 -0.273450192 0.568488090
## [336] 1.296993788 0.448721962 0.071313458 -1.787222903 0.228353323
## [341] -1.745018866 0.154862582 0.533753825 1.248326385 1.144142319
## [346] 1.025000241 0.190480885 0.070265120 0.876216633 -2.234751138
## [351] 0.830596756 -0.086063188 -0.702948144 0.493149284 -0.267399214
## [356] 1.342810461 -0.026680477 -0.913157251 0.518598021 1.215833707
## [361] -0.594065633 0.805809907 2.400619725 -2.069922938 0.030861177
## [366] 0.892546137 -0.304577599 0.493532276 0.421667975 1.343080521
## [371] -1.670289372 0.833747834 0.695386358 -0.945891339 -0.475497025
## [376] 0.451940432 0.059930010 0.767585393 -1.096789550 -0.165146932
## [381] -0.073184606 0.203816863 -0.140941419 0.257425317 0.684311022
## [386] 1.028858838 0.039413856 -1.659880068 -0.640611450 0.748051919
## [391] 1.077241821 0.026460197 0.635528006 1.574600550 0.304555438
## [396] -1.093183696 -0.106676135 0.937977597 0.240378842 0.168528318
## [401] 1.670095912 0.418494615 0.411821563 0.416434649 -0.576917491
## [406] 0.195390939 1.071738457 -1.344034319 1.346156280 -1.397719610
## [411] -0.891544730 0.245749534 1.130929173 1.058141772 -0.681403168
## [416] -0.475587540 2.173780534 -0.384249752 -0.317344520 0.999535476
## [421] -1.949722724 1.106203189 0.432145434 0.540308806 -1.625003927
## [426] -0.216173621 -0.926808628 -1.703070300 0.889000667 0.595281600
## [431] -1.380295308 -0.007070503 -1.680140734 0.831795784 0.068230289
## [436] 1.350838235 -0.800470954 0.468623141 0.415734573 0.450093850
## [441] 0.544495989 -1.612814769 0.821390064 0.419870029 0.118339503
## [446] 0.184803491 1.411311971 -1.235818552 -0.372307820 -1.251150207
## [451] -0.790608702 2.008136118 0.557934787 -0.274565305 -0.841833078
## [456] 1.353334483 0.266773418 1.340842488 -0.123444791 1.440254496
## [461] -0.922073935 -1.171785057 0.374854479 1.032599394 0.712881372
## [466] 0.494344974 -0.141915257 -0.163534499 -0.900381364 2.569013921
## [471] 0.101682450 -0.562113734 1.612613575 -1.278868008 1.105009060
## [476] 0.292666272 0.351621225 1.198010817 -0.312115670 -0.550339549
## [481] -0.224084785 -0.903497723 0.887681401 0.649500688 1.384632978
## [486] -0.616459983 -0.070808580 -1.260656792 0.234070823 -1.324568478
## [491] 0.327675143 0.171509991 0.388956922 1.182977905 -0.195326487
## [496] -0.562076594 -1.994548622 0.242771756 1.206108631 -0.891635164
## [501] -0.166117917 -0.252040991 0.167720187 0.025996694 -0.813976169
## [506] 0.602359189 1.158831973 -1.437729441 -0.494416019 1.065316684
## [511] -1.739719344 0.174339833 1.857056130 0.593834815 2.998730134
## [516] -1.459131243 1.053971648 -1.088361982 1.699327629 -0.513257488
## [521] 0.758643757 -1.423773376 0.811894383 -0.545795398 0.820532739
## [526] 0.861070349 0.087630906 -1.000595208 -1.072853308 -0.772063791
## [531] 0.353536711 2.434466482 0.255357188 -0.436752241 0.927684879
## [536] 1.513319123 -0.367624639 -1.977744463 -1.725071044 -2.038339464
## [541] -0.059103163 0.879686464 0.167469046 -0.158393251 -1.733360612
## [546] 0.015992039 1.407819478 0.800045292 0.841603157 -0.992171652
## [551] -1.751602012 0.539010362 -0.407112722 0.837313855 1.209208483
## [556] 0.186312223 1.008798844 0.025554937 -0.432974623 0.304167158
## [561] 0.104948440 2.268718590 0.524703347 0.559715341 -1.883887056
## [566] 0.127834222 -0.238197921 0.669718098 0.212153393 1.728620311
## [571] -0.582355282 0.276284783 -0.712502660 -0.413130831 0.830239598
## [576] -0.683926441 -2.296900053 0.912922846 -0.178245997 0.622145752
## [581] -0.828850082 0.957766879 1.229872362 1.946017292 0.670500184
## [586] 0.204247110 0.985440544 1.197158156 -1.322075099 0.370550609
## [591] 0.354080601 -0.426606452 -0.675921322 -1.267010395 0.332614516
## [596] -0.639957636 -1.087061024 -1.081791952 0.468915696 -1.296158622
## [601] 0.181384917 -1.196466574 -0.371917395 -1.824777292 0.825301494
## [606] -0.520099686 0.910187267 -1.105959287 0.887847443 -1.051732674
## [611] -0.580502921 0.437574637 -0.527425049 -1.934705926 1.260373089
## [616] 2.068536303 -0.184446330 0.726269701 -1.325426815 1.142093089
## [621] -0.111703476 0.793724814 1.617574903 1.735169508 -1.391904409
## [626] 0.294739375 -1.005820883 0.317583788 0.464545077 -0.077852810
## [631] 1.523424094 -0.213356255 -1.633460935 1.090949678 -0.753175421
## [636] -1.062343405 0.093319217 -1.465051155 -0.675845112 0.221404852
## [641] -0.428080579 -0.442412712 0.486172868 -0.253786025 0.143800665
## [646] -0.602027533 -1.474217582 0.314391855 0.739864424 -0.231619740
## [651] -0.449942713 -0.819703437 0.072503596 2.061934846 -1.815753681
## [656] -0.894645179 0.347172035 0.646367246 -1.106599299 0.388481312
## [661] -0.193857503 1.611058321 0.561626587 1.490088329 0.161026220
## [666] -2.113512030 0.633549551 0.986415189 -1.759587527 0.042149570
## [671] -1.283296229 0.385548717 -0.007754906 -0.995815113 0.049151833
## [676] 0.592462342 0.335054170 0.875727603 -1.609750899 0.875586447
## [681] 0.116983958 0.472556685 0.594885544 1.109307991 1.324993754
## [686] 0.217789440 0.334448385 -0.108652671 0.162763155 -0.662860417
## [691] 0.172225819 0.056277281 0.416176810 0.910298120 -0.812008914
## [696] 1.911558028 0.242193872 -1.439923398 -0.136787399 1.237234950
## [701] -0.276552845 0.464988568 0.980225781 0.086404183 -3.695201760
## [706] 1.106196771 0.581333073 -2.309585761 -0.273377323 -0.733663692
## [711] -1.721328954 -0.597182511 1.181245593 -2.272302766 0.639359401
## [716] 0.406828041 1.365353689 -0.270788894 -1.210724728 0.616492022
## [721] 0.497105069 1.691893550 -1.154294525 0.433895765 0.348637271
## [726] -0.584467435 -0.439709043 0.445410771 -1.380830716 -2.016070770
## [731] -0.936458962 0.227003951 0.787275723 0.757213962 -1.068057393
## [736] 0.378057039 2.011104485 0.911800661 1.480066344 0.710938931
## [741] -0.736774632 -1.416965808 -0.222574626 -0.154203937 -0.355174332
## [746] 1.245613815 0.528152162 0.377423991 -0.351865567 -0.680493059
## [751] -2.002845160 0.532957975 0.724063164 -0.540449506 0.226487572
## [756] 1.080920749 -0.473467656 0.265946787 0.022946019 0.258946809
## [761] -0.787272829 -0.296524247 0.406419668 0.607694391 -0.429026235
## [766] -0.346256711 0.750354599 0.508796105 -2.416646590 1.677013858
## [771] -0.749936433 0.595763431 -0.254559709 1.537579302 2.226512428
## [776] -1.404657391 0.100388234 -0.056609681 -0.736420561 0.976642826
## [781] -0.879734175 0.474328785 -1.515475953 0.567529986 0.114438519
## [786] -0.736476076 1.554187433 0.136530282 0.632541862 0.458440551
## [791] -0.549402214 -0.790906547 0.060823881 -0.329937276 -1.798349231
## [796] 0.824245401 -0.585078095 -0.652958301 -1.075885521 1.084805584
## [801] -0.738579981 1.057623969 -1.254415742 -1.857010939 0.500123068
## [806] -0.438887262 -0.056202441 1.053645929 0.428478008 -0.163341756
## [811] 1.557267252 -0.680810175 0.790661362 -2.879630270 0.647662832
## [816] -0.708545001 -1.063475291 -0.230948561 0.849835651 0.038119469
## [821] -0.191019488 -2.018831047 -1.639397429 -1.646544264 -1.007075930
## [826] 0.225601246 0.126110589 0.684648289 -1.284968757 -0.595222752
## [831] 0.392040133 -0.135886420 -1.461072538 -0.639396659 -1.208439913
## [836] 0.371032047 -0.113620957 0.157978554 0.868656195 -0.161035194
## [841] 0.287836271 -0.344663471 -0.297779941 0.768418008 -0.195410328
## [846] 0.549535627 0.843162913 1.605506376 -1.008870924 -0.552752937
## [851] -0.334709589 1.004192949 -0.310090322 -0.447201623 0.365572615
## [856] 0.828753167 -1.308782039 0.260382588 -1.421930679 -0.001867305
## [861] -0.782891905 0.895055691 0.164247588 -2.841519863 0.161501930
## [866] -2.721493308 0.733690758 1.291424858 -0.676621644 0.168540671
## [871] -0.071394328 1.889408795 1.062981292 -1.554612857 0.451422268
## [876] -0.988297825 2.115831804 0.641897699 0.239162953 0.652068480
## [881] -1.453693070 -1.112820847 0.739276117 1.600601432 0.510015828
## [886] -0.256054220 0.657089178 0.049842636 -0.396376301 0.851528131
## [891] 1.819909707 0.828789131 0.141835977 0.460172316 1.620573485
## [896] 0.868997711 -0.286177214 -1.615653450 0.539524083 -0.057310640
## [901] -0.020375659 -0.255137108 -0.987289252 0.198656369 -0.164501298
## [906] -1.349044049 0.150196206 0.489163345 -0.662086752 0.814420763
## [911] 1.128076744 0.236002630 -0.411221707 -0.807511420 1.819830981
## [916] -0.319602510 -2.340277903 -0.998037078 1.357461863 -0.289821032
## [921] 0.989786496 1.531215831 -1.011700180 2.249126996 1.475616694
## [926] -0.293883564 0.009767896 -1.540445586 1.202689077 0.200066533
## [931] 3.143737249 0.648697335 1.108867280 0.283881570 -0.228552120
## [936] 0.023703628 0.419004423 -0.685818387 0.680111068 1.314815776
## [941] -0.777731151 -0.374809855 0.281028701 0.008073915 -0.420775407
## [946] 0.847980300 0.140539830 -0.630714683 0.494593491 1.319415194
## [951] -0.172505700 0.010674772 -0.781569170 -0.902374976 0.616294311
## [956] -1.591852116 3.039713380 1.454996533 -0.376418371 -1.288317996
## [961] 1.513824391 -0.358062187 -1.236463178 1.032282114 1.585646237
## [966] 0.609874563 -1.088543566 -1.393078223 -1.040091976 -1.657360845
## [971] -0.675819061 0.484147642 0.470099806 0.502763904 -0.644469202
## [976] 0.096437792 -1.542587795 1.279999622 -0.907334078 -1.112828079
## [981] -2.538163852 -0.122996251 0.102213029 -0.727747860 1.370976157
## [986] 0.556224832 -0.188131989 -1.131253153 -0.913597064 -0.047930975
## [991] -0.848473862 -0.770057473 -0.240330574 -0.042075724 -0.887388763
## [996] -0.118548403 0.855150145 -0.152866995 0.269961199 -0.922740058
yAxis <- rnorm(1000) + xAxis + 10
yAxis
## [1] 11.230305 10.047265 10.245618 10.237488 9.485841 10.129334 9.439268
## [8] 12.313307 9.370681 7.995398 11.610234 7.740096 10.431089 7.988688
## [15] 10.285300 10.715081 7.775683 8.610053 9.437502 9.955396 10.618600
## [22] 10.859165 9.645927 10.675693 9.004331 10.099931 11.742167 13.651220
## [29] 11.267898 9.337454 9.411906 9.436947 10.901987 12.746993 8.586238
## [36] 10.545365 9.244705 11.727755 9.694221 10.022693 9.500846 10.062878
## [43] 10.363395 9.823220 8.731168 9.327753 8.174635 11.476263 9.153211
## [50] 11.095271 9.467454 9.504252 10.495917 10.138997 8.541305 11.395594
## [57] 9.106399 8.646193 11.830241 7.862040 10.547079 10.635253 12.838052
## [64] 11.952958 9.752421 8.864918 9.696601 8.971825 9.902675 12.471945
## [71] 9.782495 9.491373 10.995007 11.560880 8.682164 8.801614 11.359837
## [78] 10.624171 9.205630 11.141258 6.954964 10.326738 7.587123 11.333552
## [85] 9.735959 8.485382 10.401560 10.001653 9.481980 9.609452 11.222024
## [92] 9.917060 8.935538 9.947428 12.141269 9.869988 9.813186 7.885379
## [99] 8.852936 8.301648 9.949323 7.620798 8.239886 11.719583 8.822015
## [106] 6.705430 9.388260 10.475426 10.617575 11.468068 9.489352 11.677208
## [113] 10.402760 10.438963 8.486061 9.840386 8.700798 8.408902 9.119009
## [120] 9.279818 12.937524 10.482308 9.739869 11.484588 9.431695 11.010444
## [127] 8.270626 10.436436 9.472126 10.324013 10.660872 8.318065 9.632806
## [134] 10.276690 10.563365 11.968178 9.188031 12.533315 10.428699 8.022880
## [141] 9.413052 10.867339 11.510171 8.902081 10.971352 8.939961 10.524954
## [148] 8.140853 9.987177 9.792714 10.787222 9.218048 10.936823 10.393066
## [155] 6.530591 13.176380 10.474139 10.256020 7.057226 14.174377 11.277975
## [162] 11.274716 9.914924 8.034255 9.787533 10.098527 8.393910 10.923254
## [169] 10.878786 10.525363 8.650831 8.324122 7.496072 14.035526 11.117681
## [176] 13.062783 14.250383 8.648368 8.543172 9.097514 10.291276 10.094971
## [183] 11.740741 12.418241 9.961177 10.851463 11.183659 8.160805 12.998144
## [190] 10.763841 11.164835 10.603790 10.402386 10.432070 10.083875 11.350815
## [197] 8.590012 12.619279 9.273120 11.862725 7.038096 9.287760 13.652398
## [204] 10.321032 11.584627 9.694493 10.364986 10.110645 9.013157 9.530104
## [211] 10.359312 10.685797 10.216063 11.651003 11.051876 11.306237 8.923922
## [218] 12.296120 11.128386 12.032954 11.369029 9.383679 8.435847 11.518999
## [225] 9.399344 9.059043 8.720864 10.038678 10.664206 8.479668 9.115311
## [232] 9.408033 9.767873 12.169180 8.084079 7.964365 10.795382 12.993866
## [239] 10.755222 10.869843 6.374964 9.005926 8.514593 10.282900 10.247739
## [246] 12.009100 8.663202 10.476928 7.500163 10.274184 9.524905 9.906517
## [253] 7.954451 13.175698 8.146285 12.456489 9.027938 11.662226 12.961615
## [260] 8.707553 11.877604 9.039983 10.384250 13.686406 9.669121 8.347173
## [267] 9.999153 10.671202 9.348721 9.539641 10.991545 9.317667 10.639276
## [274] 10.683499 9.875884 7.926182 11.554559 10.760107 8.530321 9.833773
## [281] 9.705183 8.550782 12.606179 12.260775 11.070742 11.336804 10.512515
## [288] 9.163561 10.371127 10.046848 9.471943 9.722667 10.816204 11.375155
## [295] 10.211192 9.356111 9.033225 11.872576 7.393544 8.043338 10.890747
## [302] 8.079992 8.931514 9.230466 10.416875 10.896028 10.694060 13.613914
## [309] 9.741044 12.174451 7.855904 7.715511 9.808458 10.081526 11.420322
## [316] 9.088897 8.807530 8.746939 10.766223 10.540155 10.881083 10.611823
## [323] 9.481324 10.547186 9.859811 9.977827 10.052025 10.511780 11.566792
## [330] 8.977268 12.512024 9.428011 12.063670 7.934635 11.227537 11.270531
## [337] 12.412587 10.469634 7.016865 10.707588 8.694570 11.607084 11.014516
## [344] 12.119379 10.558818 11.253049 11.163114 10.295446 8.996066 7.546112
## [351] 8.910857 10.928307 6.907872 11.242535 10.015211 10.522772 9.477438
## [358] 8.957593 11.773531 12.028857 9.895609 9.050314 12.816681 9.293135
## [365] 8.359537 11.528173 10.279330 11.288647 11.450621 12.608041 9.394023
## [372] 10.161079 10.022493 8.915771 8.304492 10.317765 10.175038 11.847947
## [379] 8.362782 10.046775 7.556131 9.281009 10.601852 10.905057 9.783903
## [386] 11.634466 9.998408 9.124060 8.164187 10.756862 10.982996 8.942574
## [393] 10.947454 11.327203 11.828409 9.799109 11.415116 11.179659 10.056640
## [400] 8.495745 13.351704 7.758064 10.694090 9.583176 8.314789 9.499263
## [407] 10.707314 9.039984 12.627636 9.758281 7.316955 10.641532 10.741897
## [414] 11.484002 8.272465 8.142867 11.761155 10.768218 8.411538 10.163959
## [421] 8.056184 9.539436 11.712064 10.430549 8.017601 9.844678 7.803780
## [428] 9.420371 11.657272 11.045148 9.565902 12.210286 7.898097 9.863595
## [435] 10.056810 10.572510 9.279515 10.478896 11.248633 10.307686 9.989923
## [442] 8.080563 9.946841 9.508957 9.902323 10.529780 12.773266 10.225382
## [449] 9.744819 8.969010 8.927459 12.471536 12.254168 10.566529 10.328727
## [456] 9.873393 11.664504 11.384291 10.893540 11.194010 8.259615 8.498213
## [463] 10.339276 10.248994 11.010423 10.721041 9.038901 7.832090 10.031738
## [470] 14.833975 11.153732 6.993175 10.889099 7.727685 9.090212 9.311258
## [477] 9.639110 10.612575 9.036660 7.641404 9.391504 11.257367 9.406389
## [484] 10.137823 12.486692 9.068805 8.580962 10.253150 10.735128 8.170912
## [491] 10.136549 11.831328 12.894295 12.786581 9.314838 10.029626 7.818355
## [498] 9.882909 11.336285 8.424201 9.833380 10.552979 9.735887 10.181760
## [505] 9.393146 10.197991 10.432623 9.335541 9.508615 11.572240 7.441842
## [512] 8.128703 12.070490 10.307950 14.289536 7.506660 11.651285 7.635235
## [519] 11.292850 9.208569 10.524522 7.383968 12.288595 10.357698 11.354958
## [526] 11.950436 9.832347 8.115151 7.261645 9.097707 9.906661 12.171373
## [533] 10.284055 10.017889 11.269635 12.542320 9.627974 7.943044 8.838017
## [540] 8.240397 10.191534 12.141317 11.559481 10.538252 6.541946 7.892423
## [547] 11.249403 12.223616 10.449952 7.452419 9.002825 10.834408 11.300153
## [554] 9.497760 12.491381 12.382514 10.253886 10.474646 11.133835 11.376951
## [561] 9.636735 12.905199 8.452848 11.323035 7.514276 9.690463 9.773507
## [568] 10.819119 9.369381 11.155418 8.549698 11.216180 7.870238 8.886197
## [575] 10.392080 9.203362 7.065244 11.518364 9.273207 8.689043 8.392449
## [582] 11.775917 9.627319 11.958877 10.452288 9.133544 10.883052 11.349296
## [589] 7.990914 9.552562 11.262178 10.099409 7.475271 7.268359 10.858144
## [596] 10.813934 8.087330 5.357452 12.011223 8.144344 8.458921 7.579995
## [603] 9.692022 7.722364 11.446296 8.603517 11.446998 8.693790 12.728342
## [610] 8.290574 10.660178 10.464481 10.439580 7.278295 12.776249 10.659413
## [617] 10.565442 9.845618 9.576247 12.592575 8.434378 9.045432 9.945205
## [624] 12.618106 9.412638 11.863011 8.964733 9.455652 10.252889 9.720195
## [631] 10.029850 10.383765 8.889515 11.646700 10.959287 8.334258 10.964937
## [638] 10.263857 9.871349 11.107756 9.513349 10.137563 10.221877 9.493058
## [645] 9.133711 9.015332 7.697901 10.261518 10.095383 9.893923 8.759514
## [652] 10.377000 10.394158 10.502207 6.231828 6.539270 9.846686 11.119291
## [659] 9.805800 9.798343 10.041043 11.494500 10.984904 12.798709 11.446248
## [666] 7.713693 10.546222 10.406478 9.699667 11.487373 6.534482 9.561078
## [673] 10.485976 8.282545 9.691087 12.620927 10.343219 8.969820 9.582747
## [680] 12.655212 8.621884 12.370614 10.632222 10.656153 13.419068 7.853774
## [687] 9.071284 8.547381 8.883734 9.526139 11.949737 10.663449 11.270133
## [694] 10.947934 9.707301 9.886121 9.554215 9.013699 11.317830 10.718247
## [701] 9.868793 10.824973 12.961030 11.633684 6.514454 9.408336 12.588228
## [708] 8.007679 10.896934 9.486333 7.174369 10.663467 8.933342 7.538712
## [715] 11.310083 9.650077 12.562008 8.243814 9.369482 9.081604 11.043205
## [722] 11.261197 9.058941 10.924212 9.684301 9.175164 6.574237 9.960180
## [729] 8.615983 7.675456 9.533658 11.099908 11.966398 12.334338 10.106948
## [736] 12.233842 13.681975 12.692443 12.993300 10.101414 9.410490 8.391454
## [743] 11.288081 8.028072 7.843797 14.077987 9.180013 9.822569 10.048236
## [750] 8.810766 8.283188 11.690817 10.759162 9.911404 11.300939 10.322852
## [757] 10.014091 10.390558 10.232888 9.013842 9.427452 9.970320 11.269184
## [764] 11.252344 9.748665 10.239624 11.976560 11.419804 7.809143 12.401073
## [771] 8.758513 8.638921 10.164730 11.095477 10.019761 8.428410 10.559037
## [778] 10.000558 10.348506 10.967651 10.614132 11.368753 10.662531 12.083643
## [785] 11.122757 9.223809 13.029667 10.233350 9.811240 9.724889 7.816759
## [792] 7.389770 10.405097 10.531716 8.137066 12.489652 9.083246 9.398774
## [799] 10.429055 10.532009 10.128117 10.316110 8.384110 7.210601 10.862302
## [806] 11.002793 9.704107 10.290511 10.543085 9.989065 10.259195 8.469966
## [813] 12.100799 8.466502 11.238343 10.059763 10.132960 8.809273 10.074808
## [820] 9.595247 9.912349 7.282734 8.672248 7.565313 10.004362 10.326710
## [827] 9.613073 8.786517 9.303790 9.889786 9.343603 9.656462 8.830057
## [834] 8.746617 8.522532 11.133789 9.216723 9.843652 10.626785 10.920069
## [841] 10.017090 10.295658 11.379027 10.415362 10.420526 10.831683 9.944107
## [848] 10.932944 8.760821 9.389789 8.907710 9.393145 9.392285 12.466304
## [855] 9.181698 12.266164 7.541665 11.108441 8.091350 10.129107 10.609497
## [862] 9.642544 7.514390 6.360269 11.766826 8.305231 10.208629 12.290462
## [869] 8.923651 9.677270 9.961923 12.974389 10.402494 8.860936 11.217266
## [876] 7.747699 12.453116 10.610370 10.686966 11.021415 9.063711 8.903440
## [883] 11.432776 10.829280 9.609373 9.033011 11.142084 9.335300 8.204415
## [890] 9.398530 12.213371 11.775598 10.620661 10.978457 10.501980 10.335594
## [897] 10.441796 6.848890 11.523714 10.076823 9.827779 9.435531 10.316159
## [904] 10.108197 9.940746 7.744261 10.365212 11.195055 8.290926 10.814307
## [911] 10.456395 8.859066 9.581791 9.840426 10.346232 8.972242 6.260041
## [918] 10.799853 12.395354 10.246736 11.265348 12.185950 7.933901 12.950428
## [925] 11.631057 9.864220 9.940752 8.980641 12.972159 11.319171 11.283039
## [932] 11.139289 10.861236 9.786039 8.852534 10.600184 9.559113 9.151257
## [939] 11.003046 11.848792 11.631501 11.051680 11.656500 11.370738 10.447621
## [946] 11.234934 8.603447 10.702244 9.245221 10.562069 10.724071 12.237491
## [953] 8.966694 9.314463 10.056557 7.552415 12.500994 12.271508 12.499486
## [960] 10.308962 10.334684 10.668234 8.982034 11.507186 11.941436 12.072840
## [967] 9.126484 8.222910 9.136903 7.998963 8.539606 10.552337 9.337080
## [974] 10.535744 7.568874 11.842656 7.213622 10.850049 8.421157 10.195377
## [981] 8.505363 8.055587 10.276390 9.748483 10.736537 12.105391 11.285322
## [988] 8.361293 8.901541 9.691313 9.282082 8.401677 10.553089 9.847148
## [995] 10.229571 9.576474 12.972997 9.404812 10.673408 8.926400
# 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 4 3 3 4 3 3 3 2 3 2 3 2 3 2 3 3 2 3 4 4 3 3 3 3 4 5 3 1 3 3 3 5 2 3 4
## [38] 4 2 4 2 3 4 4 3 2 2 4 3 4 3 3 3 2 3 4 3 2 5 2 4 3 5 4 2 3 3 3 4 5 3 3 3 4
## [75] 4 2 4 3 3 3 2 3 1 4 3 3 4 4 3 5 4 3 2 2 4 3 3 3 2 2 3 3 2 3 3 2 3 4 3 3 2
## [112] 4 3 4 1 4 1 2 3 3 4 4 3 5 2 5 3 3 3 4 3 2 2 4 4 4 3 4 3 2 3 5 4 2 3 2 3 3
## [149] 1 2 5 3 3 3 1 4 3 3 1 4 3 3 3 3 4 2 2 4 4 2 2 2 3 5 3 5 5 1 1 3 4 2 4 3 3
## [186] 4 3 1 5 3 4 3 3 3 4 4 3 4 1 4 3 3 4 4 3 4 4 2 3 3 5 3 2 5 3 3 2 4 4 3 5 4
## [223] 1 3 4 3 2 1 3 3 2 2 2 4 3 2 2 5 5 4 2 2 1 4 2 4 3 5 2 2 2 3 3 5 3 4 3 3 5
## [260] 1 5 3 4 4 3 3 3 2 2 3 3 2 3 4 3 2 4 3 2 3 3 2 4 4 3 4 4 3 3 2 2 3 3 4 3 3
## [297] 3 4 1 1 4 3 3 2 3 4 2 5 3 5 1 1 3 2 3 3 3 2 3 3 3 4 2 2 2 3 2 3 5 4 4 4 4
## [334] 3 4 4 3 3 1 3 1 3 4 4 4 4 3 3 4 1 4 3 2 3 3 4 3 2 4 4 2 4 5 1 3 4 3 3 3 4
## [371] 1 4 4 2 3 3 3 4 2 3 3 3 3 3 4 4 3 1 2 4 4 3 4 5 3 2 3 4 3 3 5 3 3 3 2 3 4
## [408] 2 4 2 2 3 4 4 2 3 5 3 3 4 1 4 3 4 1 3 2 1 4 4 2 3 1 4 3 4 2 3 3 3 4 1 4 3
## [445] 3 3 4 2 3 2 2 5 4 3 2 4 3 4 3 4 2 2 3 4 4 3 3 3 2 5 3 2 5 2 4 3 3 4 3 2 3
## [482] 2 4 4 4 2 3 2 3 2 3 3 3 4 3 2 1 3 4 2 3 3 3 3 2 4 4 2 3 4 1 3 5 4 5 2 4 2
## [519] 5 2 4 2 4 2 4 4 3 2 2 2 3 5 3 3 4 5 3 1 1 1 3 4 3 3 1 3 4 4 4 2 1 4 3 4 4
## [556] 3 4 3 3 3 3 5 4 4 1 3 3 4 3 5 2 3 2 3 4 2 1 4 3 4 2 4 4 5 4 3 4 4 2 3 3 3
## [593] 2 2 3 2 2 2 3 2 3 2 3 1 4 2 4 2 4 2 2 3 2 1 4 5 3 4 2 4 3 4 5 5 2 3 2 3 3
## [630] 3 5 3 1 4 2 2 3 2 2 3 3 3 3 3 3 2 2 3 4 3 3 2 3 5 1 2 3 4 2 3 3 5 4 4 3 1
## [667] 4 4 1 3 2 3 3 2 3 4 3 4 1 4 3 3 4 4 4 3 3 3 3 2 3 3 3 4 2 5 3 2 3 4 3 3 4
## [704] 3 1 4 4 1 3 2 1 2 4 1 4 3 4 3 2 4 3 5 2 3 3 2 3 3 2 1 2 3 4 4 2 3 5 4 4 4
## [741] 2 2 3 3 3 4 4 3 3 2 1 4 4 2 3 4 3 3 3 3 2 3 3 4 3 3 4 4 1 5 2 4 3 5 5 2 3
## [778] 3 2 4 2 3 1 4 3 2 5 3 4 3 2 2 3 3 1 4 2 2 2 4 2 4 2 1 4 3 3 4 3 3 5 2 4 1
## [815] 4 2 2 3 4 3 3 1 1 1 2 3 3 4 2 2 3 3 2 2 2 3 3 3 4 3 3 3 3 4 3 4 4 5 2 2 3
## [852] 4 3 3 3 4 2 3 2 3 2 4 3 1 3 1 4 4 2 3 3 5 4 1 3 2 5 4 3 4 2 2 4 5 4 3 4 3
## [889] 3 4 5 4 3 3 5 4 3 1 4 3 3 3 2 3 3 2 3 3 2 4 4 3 3 2 5 3 1 2 4 3 4 5 2 5 4
## [926] 3 3 1 4 3 5 4 4 3 3 3 3 2 4 4 2 3 3 3 3 4 3 2 3 4 3 3 2 2 4 1 5 4 3 2 5 3
## [963] 2 4 5 4 2 2 2 1 2 3 3 4 2 3 1 4 2 2 1 3 3 2 4 4 3 2 2 3 2 2 3 3 2 3 4 3 3
## [1000] 2
# create sample dataframe by joining variables
sample_data <- data.frame(xAxis,yAxis,group)
sample_data
## xAxis yAxis group
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## 1000 -0.922740058 8.926400 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)
