# Mindanao State University
# General Santos City
# Introduction to R base commands
# Submitted by: Ron Christian D. Falle
# Submitted to: Prof. Carlito O. Daarol
# Faculty
# Math Department
# April 4, 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 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 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
## 415 38 0 3 1 1 2 math 50
## 416 115 0 4 1 1 1 math 43
## 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
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## 457 67 0 4 1 1 3 math 42
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## 468 107 0 4 1 1 3 math 47
## 469 81 0 4 1 1 2 math 59
## 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
## 475 56 0 4 2 1 3 math 46
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## 483 174 0 4 2 2 2 math 71
## 484 3 0 1 1 1 2 math 48
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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
## 491 24 0 2 2 1 2 math 66
## 492 149 0 4 1 1 1 math 49
## 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
## 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
## 523 73 1 4 2 1 2 math 53
## 524 151 1 4 2 1 3 math 52
## 525 44 1 3 1 1 3 math 45
## 526 152 1 4 3 1 2 math 56
## 527 105 1 4 2 1 2 math 45
## 528 28 1 2 2 1 1 math 54
## 529 91 1 4 3 1 3 math 56
## 530 45 1 3 1 1 3 math 41
## 531 116 1 4 2 1 2 math 54
## 532 33 1 2 1 1 2 math 72
## 533 66 1 4 2 1 3 math 56
## 534 72 1 4 2 1 3 math 47
## 535 77 1 4 1 1 2 math 49
## 536 61 1 4 3 1 2 math 60
## 537 190 1 4 2 2 2 math 54
## 538 42 1 3 2 1 3 math 55
## 539 2 1 1 2 1 3 math 33
## 540 55 1 3 2 2 2 math 49
## 541 19 1 1 1 1 1 math 43
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## 543 142 1 4 2 1 3 math 52
## 544 17 1 1 2 1 2 math 48
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## 546 191 1 4 3 2 2 math 43
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## 553 138 1 4 2 1 3 math 40
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## 557 110 1 4 2 1 3 math 50
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## 559 109 1 4 2 1 1 math 42
## 560 39 1 3 3 1 2 math 67
## 561 147 1 4 1 1 2 math 53
## 562 74 1 4 2 1 2 math 50
## 563 198 1 4 3 2 2 math 51
## 564 161 1 4 1 1 2 math 72
## 565 112 1 4 2 1 2 math 48
## 566 69 1 4 1 1 3 math 40
## 567 156 1 4 2 1 2 math 53
## 568 111 1 4 1 1 1 math 39
## 569 186 1 4 2 2 2 math 63
## 570 98 1 4 1 1 3 math 51
## 571 119 1 4 1 1 1 math 45
## 572 13 1 1 2 1 3 math 39
## 573 51 1 3 3 1 1 math 42
## 574 26 1 2 3 1 2 math 62
## 575 36 1 3 1 1 1 math 44
## 576 135 1 4 1 1 2 math 65
## 577 59 1 4 2 1 2 math 63
## 578 78 1 4 2 1 2 math 54
## 579 64 1 4 3 1 3 math 45
## 580 63 1 4 1 1 1 math 60
## 581 79 1 4 2 1 2 math 49
## 582 193 1 4 2 2 2 math 48
## 583 92 1 4 3 1 1 math 57
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## 1000 137 1 4 3 1 2 socst 61
#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] -0.6265488361 0.5852105464 -1.6882048673 -0.7576481227 0.6062989741
## [6] 0.2587546129 -0.8263937214 -1.1280246314 1.0697588190 -1.1856984553
## [11] -0.5999348875 -0.1775945316 -1.5484685206 -0.0511938322 0.8109910775
## [16] -0.4645852303 -1.1864011420 -0.0267797897 0.0753130059 0.3797701283
## [21] 0.7235531319 0.2679243565 0.4029716398 0.1877555169 0.7255349397
## [26] -0.9629647541 1.0111617313 -0.8682190647 -0.1345755450 -0.9468706133
## [31] 1.4797598023 -1.6024310932 0.6497167887 -0.8731586145 0.0003584022
## [36] 0.9899089276 0.5227731003 -0.1039039722 -0.1585050929 0.2086258849
## [41] -0.3718403590 -0.5931372625 -1.1676011799 0.3594345272 -0.2278052662
## [46] 1.7032563041 -1.2096656894 0.1970698622 -0.4466300810 0.4800976285
## [51] 0.8315188309 0.6998902426 1.5043893796 0.3173491136 1.7989862782
## [56] -1.7979392881 0.3726796375 0.9764404122 -0.7343184864 -1.3290748949
## [61] 1.1678776070 0.3027817660 -1.6934809552 -0.3752791009 0.9697784407
## [66] -0.8867436659 0.5328531319 -0.3823069403 -1.2905252197 1.3571337948
## [71] 0.8178881977 1.1236849311 -0.3944424790 0.9905130698 -0.4781755254
## [76] -0.7516387285 -0.0498317916 -0.1812409835 1.9088448035 -0.5700261931
## [81] 1.1105366189 0.2444452749 1.7751483369 -2.0043199604 -0.9368806916
## [86] -0.1597984655 0.8941456085 -0.6141696928 0.0627637633 0.7664733199
## [91] 0.4947733686 -1.4434453392 0.3401700696 1.5922893810 -0.1664749649
## [96] 0.0804628723 -0.8613404045 2.2456050497 0.1487179239 1.3042204072
## [101] -1.1998618202 -1.1399422664 -1.4031194627 0.6204596856 0.0440640816
## [106] -1.4693555384 0.6787524630 0.9924301231 0.2290539037 0.8161508984
## [111] -0.5111912046 0.7341012050 -2.4141599552 0.1210174138 0.0889690560
## [116] -0.2150083858 -1.1744392381 -0.4973894474 0.4591024683 -0.3864834968
## [121] -1.5230955361 0.3195192491 0.4004536605 0.6113694590 0.7410356199
## [126] -0.7884838855 -1.2081886704 -0.4628469234 0.6369751191 -0.8965722311
## [131] 0.9789476586 0.6188652340 -1.0860178498 -1.4568486752 0.1312970325
## [136] -0.6485784734 0.9165833946 -0.8776053993 -0.0767871962 -0.7979059115
## [141] 0.3569527965 -1.3710965905 0.3488526851 0.0588593620 -1.0528796279
## [146] -0.6188184759 -0.7242671971 -0.9233979795 -0.9299056869 -1.5519548059
## [151] -0.0047955042 0.7740628024 -1.1669098568 -0.0592109839 0.4235678746
## [156] -0.1898012868 1.0768837382 -0.9491459715 0.9132674334 -0.6986173823
## [161] -1.2698138561 -0.3244762449 -0.0608052405 0.2439499199 -0.9607945371
## [166] 0.6592703999 -1.7776227729 0.2127066608 -0.1278463867 0.5242322892
## [171] 0.7452594194 0.2200700809 2.0936065456 -1.2092714197 0.7551778953
## [176] -1.3340311355 -0.1318808727 -0.5619378776 -0.5178855495 0.7839894523
## [181] -0.5484499182 0.3423037505 1.6144636158 0.7316775751 0.4136075501
## [186] -0.7264170714 -0.1328661008 -0.8839530995 1.3841968838 -1.0708380915
## [191] 1.3402998566 2.1247117189 -1.7887556919 0.6457575336 -0.8898226898
## [196] -0.6695059155 -0.0303457804 -0.1609317773 -0.4221547496 -0.8266188354
## [201] -0.8752946417 -0.4885157788 0.7338166939 0.5416745194 -0.9522156156
## [206] 3.4505543298 1.4768919543 -0.0957104815 -0.5489908764 -0.0244069085
## [211] -0.0719362953 0.2480742532 0.8770062596 0.8011165653 0.0142447332
## [216] -0.7785388234 -0.7711849954 0.2071745060 0.4650742430 -0.0179319482
## [221] -1.5216692172 -1.6616954798 0.0448650203 1.4029424731 -1.0505822165
## [226] -1.9335962878 1.7327774785 1.0596895438 0.0910443024 1.8943631239
## [231] -0.4440109984 0.0157430176 -0.2326492756 -1.2579877500 -1.8071446407
## [236] 0.5787816451 -0.9291789855 -0.2392422910 0.1124026487 -0.6076968879
## [241] 1.3697852623 0.4983217248 0.5147326843 -0.0686002412 0.7735347078
## [246] -1.1337673723 -1.2526021439 0.5369509898 0.4726586814 1.1489259431
## [251] 0.7095396612 -1.8093277213 -1.2022952102 1.4595344612 -0.3767386335
## [256] 0.0124512000 -1.5662923993 0.3869532380 0.2197145684 -1.6404480028
## [261] -0.0292205071 -0.4562854820 -0.0328845611 -1.1094630140 0.2947012640
## [266] 0.8886420685 -0.4156403693 1.2858596577 -1.2638691218 -0.7343467335
## [271] 0.2305591427 -1.6414391534 0.2469539925 1.1023901014 0.9402299926
## [276] 1.1614206474 0.2918332583 -0.1225309761 -1.8839391295 -1.1479526316
## [281] 1.5119993443 0.8050943787 1.0476054193 1.2169692150 1.3960106494
## [286] 0.9323016344 0.3027213946 -0.3889303943 -0.0758557521 -2.1160562306
## [291] 1.5424784134 0.5349917439 -1.3123404462 -0.1267611594 -1.0127309269
## [296] 0.8077700474 0.9885231610 0.1180054912 1.9270652751 1.8154358410
## [301] 0.3736091804 -0.4499147265 1.6135360177 0.9133136654 0.7775282765
## [306] 0.6188608084 -0.5888964735 2.0118473994 -0.1283830172 0.7133036313
## [311] 0.5410041151 -1.2083454399 0.7181423894 0.2096608045 0.1389007244
## [316] 0.2008677509 -0.7617633438 1.0492697578 0.7225948501 0.4629090318
## [321] 1.1172834727 -1.5645556741 0.4915865676 0.8673616390 0.2183794038
## [326] -0.0414339593 0.2870823748 -1.0886784841 1.0136859913 1.5788445803
## [331] 1.1157177217 -0.3137983877 -1.0720335855 1.3071484098 0.3506889455
## [336] 0.2659310799 -1.5682828221 -0.7058378089 -1.5198163205 0.5354720192
## [341] -0.3803019689 0.7791009062 1.2721478617 0.3562402101 -0.8188918607
## [346] 1.1029690633 -0.2414048377 -0.5517999831 1.9528222898 0.6598503184
## [351] 0.9115135073 0.7102904102 -0.6266540333 1.1623886273 -1.0954193735
## [356] 0.0462927807 -0.8210314869 2.0456004631 -0.9487493777 0.4578616615
## [361] 0.4835974843 1.2131817994 -0.4445799600 -1.0287044024 0.1659565816
## [366] -0.6240426134 0.0844617661 -0.3225152288 0.6568122936 1.1123849683
## [371] 1.4267633715 -0.7696044855 0.4497082037 0.0031956819 -1.9231449694
## [376] -0.8004765439 0.8309296238 -0.0791986250 -1.4090079663 -0.7654794240
## [381] 0.6667862771 1.3092446166 -0.5917045463 -1.2000645289 0.9662433057
## [386] 1.1606765090 -0.1427098055 0.4999930844 -0.3124392681 0.1519908535
## [391] -0.3735156519 1.5806983320 -1.1894563200 1.3920630799 0.2144686918
## [396] 0.2379899158 1.8572477878 0.0243651613 -0.0027410204 0.6853684394
## [401] 0.5534344024 0.9471802364 -0.6617287684 -0.3963729884 -1.5492312537
## [406] 0.1274051716 -0.1103471960 -1.4894515320 -1.0638334888 -0.0589482585
## [411] 1.0364363615 0.2965944348 -0.1941735498 0.6888412312 -0.3784466540
## [416] -0.3709784767 -0.5167302890 -0.4565283651 -0.8605740699 1.1714322122
## [421] 0.3757219106 0.6223609826 -0.7552889795 -1.0654260643 0.4743583308
## [426] 0.4567843790 0.8243152830 1.2737855867 -0.0699571938 0.7178809171
## [431] -0.8404848256 0.0198669054 0.7021518393 -1.2771303397 1.3433430494
## [436] -1.4451185750 -0.9105024291 -1.6962067182 0.1234804528 0.6170957241
## [441] -1.4046490796 0.3493536837 1.1067372307 -0.3777782522 -0.3556540703
## [446] -1.1095402243 -0.8328528186 0.0286526568 -0.4941106370 -1.0786797578
## [451] -0.7509439172 -1.4269924032 0.0588713478 0.8335866529 -0.1528613195
## [456] 0.1538833207 0.0825309276 -1.3566099652 0.1095587614 -0.7147004955
## [461] 0.7783890060 0.5380757869 -0.3622240218 -1.4133052801 0.4468507018
## [466] 0.1285586940 1.3782237798 1.0342051388 1.2318416850 0.5215441386
## [471] -0.0271253370 -0.5338885155 -0.6128869651 -0.5068530147 -1.7635901861
## [476] 0.9884075839 -0.6973711320 0.5013476289 -0.3714057443 -0.1267125226
## [481] 1.3342043188 0.9914562804 -1.1291988687 -1.1859604242 1.2917541034
## [486] 0.0927988300 -1.3295869912 -1.9911376778 -0.8335888467 0.9636413199
## [491] 0.6636750647 -0.3005384687 -0.0290721149 -0.2155979228 -1.0934774452
## [496] -1.7080615608 0.5151424998 0.5034479015 0.1725022593 -1.9002258382
## [501] 0.2949800810 -0.4421798130 1.2531802803 -3.6057987598 0.8943702603
## [506] -0.6927316640 1.8814282369 0.3607170342 -0.4941115148 1.2287542444
## [511] 1.2700270763 -1.8784560591 -1.3449023292 0.0397982013 -1.1318801775
## [516] 1.3685222114 -0.8465393552 -1.7812340271 -0.1595932113 0.6076072035
## [521] 0.3403022021 0.3024781984 -0.7523922918 -0.2004439524 0.6593161386
## [526] 0.0128020234 0.3562829834 0.7601780311 0.9493047395 2.4051577686
## [531] 0.8893298088 0.8751991141 -1.5930343139 0.6971757942 -1.5127937618
## [536] -1.1211803837 0.2902103170 0.3321306517 2.0400679528 -0.6308556488
## [541] 0.2456267983 -0.9690103898 -0.9691345955 -0.4071553943 -1.1446782933
## [546] -0.9263464948 -0.7858449419 0.9883961216 1.3437592803 -0.2576716582
## [551] -1.9364802417 -2.1372829779 2.0140655636 -1.1748587086 1.4888531018
## [556] -0.2981150108 -1.5324303167 -0.2610983567 1.1059300260 -0.0860970802
## [561] 0.3336081147 -0.6941593794 0.3414812564 -0.0191108272 -0.5772969833
## [566] 0.5405550801 -0.3615979872 1.2909214897 1.3497304070 -2.0201312389
## [571] 2.4747706754 -0.9534369405 -0.0676257085 0.3669500539 1.1571433393
## [576] -1.4138615565 -0.1748702409 -0.7004146123 -0.6751360660 1.1129945682
## [581] 1.0911502389 0.1807840308 0.9354788760 1.0057656581 1.4230804544
## [586] 0.3547253223 -1.5950732922 -1.4516343030 -1.2855367707 2.3336158017
## [591] -0.9080335192 -0.8478445547 1.5951448696 1.3187858490 0.8146222183
## [596] -1.5739714358 1.1067414099 -0.2630500775 -0.0071627535 1.9624231098
## [601] -0.6400191947 0.2328369265 0.2756746266 0.6877426519 -0.3990592283
## [606] -0.6682920328 -0.0318990630 0.0491040042 0.6592469553 0.2440840552
## [611] 0.4838759293 0.1331324629 -0.2877836849 -1.9869938791 -1.5502178532
## [616] -0.1537025004 1.6296985523 0.3313102094 1.5511131546 -0.2324216244
## [621] -0.0900421420 -0.4294323696 0.7953270714 -2.1246964727 0.6855700265
## [626] -0.9963546899 1.1520582491 -0.9350701783 -1.9125202612 1.5117526185
## [631] -1.5330476740 1.6836568687 -1.0174797505 0.7862629321 0.7232043248
## [636] -0.2482210230 -0.1270115560 0.2750410319 -0.0662921878 0.8711772782
## [641] 0.6668768784 -0.9233761781 -0.7847291250 1.2408290453 0.4026943174
## [646] 0.6649292786 -1.6510953248 -0.5386110683 0.7574815258 -0.5434203571
## [651] -1.6016998095 -0.8365495670 -2.4781458768 1.9507277829 -2.6043328972
## [656] 1.9635951654 -1.1144467910 0.3241361213 1.9194806699 -0.0442839407
## [661] 0.0820735073 0.3274168601 1.7616660539 -0.6119742360 0.6235815012
## [666] 1.0084325705 0.1769123866 0.2608266053 -1.5935095688 0.0998571345
## [671] 0.9319240881 0.3955432548 0.7683290157 0.9674770232 -0.9864519710
## [676] -0.2990897408 -0.9473035395 0.4739075890 -0.6996575220 -0.3846513687
## [681] -0.2359799881 0.8081304757 -0.0668902459 -0.0236406007 -0.2383770419
## [686] 1.7380466237 1.3217684283 1.5004319348 0.7498078739 -1.2363845743
## [691] -0.6600385888 -2.0274231276 -0.7307893737 1.9770138324 0.3986409071
## [696] 0.6893649998 0.8138485170 1.6916721274 -0.2170085452 0.5662366352
## [701] 0.7158273587 0.1644078626 0.0663822655 -1.8948257865 0.0865675303
## [706] 1.2546045066 -0.9461527071 0.6617343194 -0.7279490766 -1.2226510499
## [711] -0.4335879331 0.9832089000 0.7200415772 -1.6088387258 0.7696432299
## [716] 0.0920166295 1.9694976812 -0.3820867740 -0.1188921035 1.2681035644
## [721] 0.2698211576 -0.6973778334 -0.0279592463 -1.4287800118 -1.4980848754
## [726] -0.0928803973 -0.7811724109 0.1546725466 0.8875239434 -1.0011297819
## [731] -0.2850114018 -0.8904092647 0.5328699448 -0.1723194304 -0.0444861382
## [736] -0.6017137992 0.3496263044 0.3879138843 -0.3243006971 -1.3734065694
## [741] -0.4370619313 -0.0624903931 0.8634486064 -0.6456909414 -0.3351084587
## [746] -0.6289198470 -0.4375701345 -0.1942205744 0.0005034279 -1.3074043349
## [751] 0.0538282641 0.2175097686 2.1993039696 -1.9369361196 -1.1158178258
## [756] 2.6789618528 -0.7302025630 0.2309443685 1.8199145348 1.7191996421
## [761] -0.6211309391 0.3375526440 -0.5748165936 0.0460085597 -1.1943138293
## [766] 0.4080858535 -0.6428518634 -0.7464951739 1.0423121752 1.8186648752
## [771] 0.2844178721 0.1564871852 0.5013142695 -0.0081163936 0.8988899329
## [776] 0.3683553863 -0.2251899543 1.2672072599 0.7368805101 -0.7094412036
## [781] 0.7730227444 0.0583440787 -1.4722120486 1.5390005355 0.5172028656
## [786] -0.2265909180 -0.7658140208 0.7702307468 -0.7585511529 1.5008844868
## [791] -0.2475894330 -0.3816946843 0.8399149413 1.0578479208 1.2263339892
## [796] -0.2030343827 0.0890903138 -2.4286085011 0.1309570260 0.2780318886
## [801] 0.4597111019 0.9535969037 0.7294463648 -0.5340610007 0.7911566077
## [806] -0.5686177370 0.1975190251 -1.7302583989 0.0652797575 -0.0969059823
## [811] 0.4484904228 -0.0449832925 0.2607654309 -3.0730841728 -0.7948676503
## [816] 1.5903957228 0.7126439684 -0.8843936897 -0.9036983258 0.7251764220
## [821] -0.5523592698 -0.8110120875 1.5215201906 1.1769736404 0.4910069460
## [826] -1.0863450964 -0.5419570742 0.5184137084 0.7979183955 -0.9353780764
## [831] 0.4434187173 0.3389206042 -0.3730397224 -0.9293916078 -1.2002420781
## [836] -0.0971855077 -0.0226736770 0.9366332762 -0.2860383502 -0.5922795305
## [841] -1.3203987221 -0.2356130276 1.2272849187 -1.4804399892 0.4132384365
## [846] 0.0804281802 0.9576080865 -1.1218385847 -0.9238769294 1.7528192628
## [851] 0.4856109060 0.4653749096 -1.0190969390 0.8843692991 -0.1907748096
## [856] -1.6551964964 2.1827486340 0.6167465251 -0.3538802188 0.0278627842
## [861] 1.0935679721 -0.5338313172 -0.5734343985 0.1073479684 0.0512896566
## [866] 1.2939548357 -0.5251781153 -0.3085757528 1.1124093557 -0.4358713901
## [871] -1.3899191530 -2.0929478513 -1.0728554019 0.4429075068 0.4700123583
## [876] 0.9072940983 -0.1487204397 0.2175017043 -2.8326845145 -1.5187876001
## [881] -1.0674981910 0.6420213064 0.0059219842 -0.3981377630 -2.2490018745
## [886] -1.5740070771 -0.5053114226 2.3163854690 -0.7272683127 -0.4534127570
## [891] -0.3566301710 -0.9048522795 0.9797482586 -1.3426653321 -0.2848383998
## [896] 0.7488894912 1.5864229068 -0.3115682294 -2.4069338869 1.1449731479
## [901] -0.1066737522 -0.2672256197 -1.2420401988 0.2729555739 -1.8075916825
## [906] -2.3136579288 0.1946846120 0.4632220872 0.9189956835 -0.0594734757
## [911] 2.2762080045 0.4851353204 -1.2189493760 0.6901166198 0.8964411819
## [916] 0.9991039336 -1.2972965758 -1.5878680619 0.9232200208 0.6800799962
## [921] -0.5000272304 0.3098560591 0.2148344807 -1.7639959502 1.1822631949
## [926] 2.4169900798 -0.0371018002 -1.8068199168 1.6073776741 1.0020075351
## [931] -0.8304909684 0.3173857188 0.1436416326 -0.4444227515 -1.0198865572
## [936] -0.4272750076 -0.2335656067 0.9575362944 1.1088796550 0.0080398780
## [941] 0.7690191552 0.9674820687 2.0703971243 0.1223576871 -1.4772460981
## [946] -0.4345212559 0.8701982755 1.3201411035 0.4732068250 -1.0418752752
## [951] -1.0060439082 -0.9380208327 0.8428570375 -0.5340682227 0.9220287611
## [956] 0.6926531112 0.9987463801 -0.3153971589 0.5588633543 -2.3359470312
## [961] 2.1206968137 -1.0180380938 -0.4873120882 0.4026253424 0.3697566534
## [966] -0.4130020986 -1.2334777059 1.0747269891 0.3511997221 -0.4697713909
## [971] -0.1903520784 0.1371140124 -0.0972997897 0.3166041250 0.1441846059
## [976] -0.4902422010 -0.9347420640 0.0613046024 1.5725182969 -2.1036492346
## [981] -0.0022533393 0.1964389701 0.0641095844 -1.0686637759 -0.8066281307
## [986] 1.4773539671 3.0990505724 0.2036366174 -1.6938666422 -0.2505213062
## [991] -0.5105983464 -1.1741600360 -0.0722632313 -1.0132207412 -0.2748489329
## [996] 0.2880494761 0.0702764236 -0.6553295735 0.0321416120 0.1064956762
yAxis <- rnorm(1000) + xAxis + 10
yAxis
## [1] 8.522268 10.854986 7.490998 9.906732 10.969379 10.520422 9.486757
## [8] 9.489415 8.986783 10.400869 10.864068 8.221574 7.219385 9.734272
## [15] 10.047971 9.182907 9.647541 10.522998 11.154547 10.222265 12.010424
## [22] 10.436834 10.641470 9.302345 10.113536 8.568998 12.632464 9.772230
## [29] 9.073964 8.957029 11.397687 8.258300 10.731236 8.985972 11.806499
## [36] 11.746045 10.860421 10.678370 10.338449 10.080996 11.661360 8.184672
## [43] 10.109213 10.541586 9.330442 12.251946 7.589882 10.717387 9.797201
## [50] 12.238169 10.473037 10.160632 10.548415 11.094594 12.689971 11.458050
## [57] 10.730575 11.369185 8.517366 9.027193 11.748668 10.733790 8.559766
## [64] 10.328638 10.741204 10.299953 11.928326 10.235672 9.166115 13.183279
## [71] 11.473456 10.388337 10.344187 13.325607 9.037390 9.604254 11.344096
## [78] 9.938149 12.947163 9.792116 9.284445 9.762520 11.968675 9.161998
## [85] 9.417494 9.242612 9.613765 9.874595 10.401162 12.884864 9.645774
## [92] 8.715287 10.511162 12.224581 10.357942 10.426790 10.084488 13.237084
## [99] 11.477803 10.949361 8.708567 8.879181 9.690069 8.693966 10.702581
## [106] 8.357192 10.785724 10.888792 11.183891 11.310436 8.675234 10.609945
## [113] 7.172502 10.417450 11.511491 9.371018 9.376624 9.862105 11.777554
## [120] 11.305411 9.543334 10.127389 12.062783 12.412741 10.171341 8.445467
## [127] 10.029873 9.714610 10.609298 9.695477 11.795501 12.773365 9.540243
## [134] 8.222443 8.984878 7.683987 11.205844 9.535627 10.626352 10.895054
## [141] 10.956407 8.251883 12.318445 11.178431 7.372149 9.620385 8.177175
## [148] 9.506948 10.009121 8.017572 8.911311 9.576207 8.394689 10.701555
## [155] 10.851311 9.773439 11.293261 9.026253 10.579925 9.058269 8.279183
## [162] 8.666322 11.828635 10.907028 10.646345 10.063168 7.306679 8.834884
## [169] 10.360692 11.096075 11.215663 10.731216 13.122086 9.926401 9.850961
## [176] 9.306229 9.836445 8.184790 8.608903 10.540248 9.538642 11.053460
## [183] 11.590805 10.770632 10.774054 8.209691 10.911593 10.319148 11.969173
## [190] 7.786829 12.068271 11.720612 9.816565 9.867651 8.881909 8.270074
## [197] 9.568256 7.139243 8.216284 8.039719 10.280170 10.672388 11.095291
## [204] 10.356034 9.207603 14.456968 10.824040 10.222937 8.976705 10.358379
## [211] 9.631275 11.100310 9.125986 7.937924 12.992834 7.888739 9.364362
## [218] 11.552187 9.628505 10.534686 7.428102 9.288627 10.496532 11.274338
## [225] 9.098140 5.750468 11.850364 10.330122 11.466486 9.916577 9.042845
## [232] 10.705066 8.787809 8.234411 7.563293 10.121361 9.162784 8.421822
## [239] 10.724625 9.360358 10.899246 9.918402 9.886513 10.259888 11.330949
## [246] 8.349670 8.723978 11.695137 9.181365 10.555352 10.698458 8.356169
## [253] 8.834386 12.110385 8.718165 8.432268 8.975537 10.558340 10.706947
## [260] 10.333027 11.388360 9.635977 9.340330 7.247907 10.449745 9.014520
## [267] 9.457624 12.536127 10.608202 10.618873 11.557027 8.647509 9.169321
## [274] 9.747226 9.911996 12.180244 11.006633 8.923483 8.266478 9.436936
## [281] 11.440897 11.721406 11.982466 11.216396 11.453757 10.937466 8.768955
## [288] 8.149050 10.653890 7.263044 11.440512 12.146571 9.711291 8.752949
## [295] 8.813017 11.865628 10.991812 10.302460 12.641228 11.512673 9.967335
## [302] 9.792352 13.209227 10.082169 13.099347 10.635619 8.663425 10.956341
## [309] 8.606907 8.504713 9.038616 7.597848 9.832973 10.796305 9.712959
## [316] 9.800591 8.845212 9.348192 11.594531 10.760830 11.276452 9.503957
## [323] 10.941059 12.616073 9.874378 10.396633 9.459557 7.437870 10.168055
## [330] 10.726281 9.415533 10.137287 8.613359 11.470318 10.881608 9.862597
## [337] 10.532101 9.805012 8.550872 10.936882 10.320703 11.440956 10.553076
## [344] 10.595669 10.848838 10.647179 8.190440 7.914958 13.583840 10.406116
## [351] 10.743225 12.208995 10.256530 10.372523 9.781042 9.877326 6.471694
## [358] 11.052142 8.225911 10.573924 9.010136 10.054284 10.495241 9.735656
## [365] 9.688595 7.812533 10.589078 9.943432 8.700893 11.893210 12.175225
## [372] 10.844148 12.436402 9.018606 8.725631 9.043787 13.210606 10.109840
## [379] 9.071538 7.306074 9.991601 11.762456 9.524199 9.262411 13.043127
## [386] 11.319532 9.415002 10.295544 10.346690 9.383270 8.847598 13.845829
## [393] 9.575081 12.278276 7.346419 8.338457 12.480025 9.758537 7.544167
## [400] 8.925951 12.144832 11.660906 9.818178 10.224032 8.833892 9.629558
## [407] 10.438921 7.179178 8.677849 11.864481 10.930856 10.028971 9.086537
## [414] 10.789641 10.086145 9.553124 8.498764 9.195513 10.121970 12.607129
## [421] 10.193978 10.644037 10.144208 5.755813 9.540053 10.592541 10.192894
## [428] 11.140045 10.118787 10.268775 9.822225 9.498872 10.162210 7.877644
## [435] 11.273848 10.163631 10.857495 8.571574 8.978312 10.504681 8.956745
## [442] 9.426371 11.395497 10.191085 8.761077 9.727452 9.988711 9.494967
## [449] 8.752134 9.356533 9.460894 9.206910 9.761639 10.755667 8.762471
## [456] 8.323671 9.675898 8.107169 10.358929 10.550717 10.600001 10.620022
## [463] 10.456763 8.719699 10.069383 10.810459 12.685615 12.999134 11.298318
## [470] 9.880394 8.866560 8.805974 9.224861 10.116612 8.618700 11.152091
## [477] 10.196120 9.040757 9.796535 10.500770 10.945106 11.195612 9.050830
## [484] 8.327387 10.520074 10.056976 10.177760 9.976530 9.669598 11.501148
## [491] 12.984207 8.035121 10.373990 10.389864 8.306932 7.980237 11.412743
## [498] 10.789199 9.762531 9.583834 11.005057 7.141980 11.264954 6.398224
## [505] 11.864593 10.290920 11.177017 11.562774 10.225105 10.497973 10.795995
## [512] 8.187292 9.549873 10.745498 8.229617 10.743069 9.261219 8.241375
## [519] 9.695674 11.088265 10.021581 11.407345 10.155311 10.143395 12.076192
## [526] 9.797703 8.732319 9.992703 11.299913 11.616765 13.352474 12.314168
## [533] 8.592185 11.797146 8.514920 9.687068 11.996179 11.631313 13.563659
## [540] 8.783280 9.353857 8.555124 9.341014 10.782235 9.298074 6.978967
## [547] 8.280438 11.400101 12.632172 9.201639 8.551316 8.437719 11.028201
## [554] 9.046300 11.946980 11.326955 9.875384 9.083415 14.135302 10.263147
## [561] 11.112230 8.444581 11.061326 11.218515 8.586803 9.751310 9.263380
## [568] 10.220993 12.411033 9.815005 10.925513 10.292192 10.433358 10.674542
## [575] 9.362373 8.995380 12.713031 7.392555 7.292005 12.005790 11.349742
## [582] 10.648443 10.566038 11.597770 8.911143 10.005187 9.102645 9.479791
## [589] 8.972401 12.614922 9.784790 9.672438 11.305029 11.317883 10.861356
## [596] 9.870295 12.084967 9.932701 8.544078 12.232573 9.106967 9.079567
## [603] 10.981522 11.334624 9.670224 10.141774 10.351605 11.739609 11.222074
## [610] 9.350649 11.459947 11.427389 10.766935 7.785757 9.317399 9.213967
## [617] 11.612600 9.746689 10.435992 9.445498 10.848801 8.703265 11.293135
## [624] 9.160704 11.055498 7.576128 11.795412 10.247425 8.376136 10.855952
## [631] 8.434543 12.052551 8.424718 12.018623 10.340278 9.803540 10.185125
## [638] 10.723919 10.665958 11.267985 12.816312 9.841081 9.836832 10.776474
## [645] 9.165870 10.392908 7.372393 9.234327 10.718560 8.414359 6.907769
## [652] 9.085410 7.778274 11.155368 8.351463 13.003644 10.013464 10.266502
## [659] 11.257643 11.458620 10.899473 6.737287 11.451322 8.123900 10.244297
## [666] 11.879917 9.493117 10.104390 9.353259 8.571568 11.376727 8.191260
## [673] 8.572119 11.676881 7.038058 10.823791 7.732151 12.380997 8.223483
## [680] 10.069305 10.612893 9.142032 8.729275 9.251152 10.491255 11.819598
## [687] 11.240160 11.336816 7.902365 8.821582 10.638304 8.512980 8.488405
## [694] 11.501732 10.334926 11.334686 12.087738 12.383790 10.193840 10.838702
## [701] 11.308288 10.754159 9.407065 9.061624 9.691084 12.494076 9.622602
## [708] 9.141766 9.636932 7.031106 10.982598 11.013510 11.415678 9.387717
## [715] 12.067862 11.621230 11.304820 9.171715 10.712534 9.571352 10.538771
## [722] 9.305506 10.146031 7.689967 8.796758 10.202611 9.562474 11.023589
## [729] 11.159285 9.641884 9.002300 9.604514 10.856691 9.525258 12.661273
## [736] 8.239364 9.165801 10.182728 9.582287 9.553583 10.010871 8.990044
## [743] 11.616827 7.578278 8.619156 10.104928 9.239781 9.581989 9.442881
## [750] 9.545993 9.453457 11.230081 11.581395 6.973432 9.100504 13.536882
## [757] 10.661391 9.575802 11.932829 11.495228 9.083819 9.368420 10.868442
## [764] 8.558023 7.615289 9.148079 10.572416 9.125473 10.812039 12.065945
## [771] 8.813684 10.183149 10.366779 10.398623 9.409873 11.507335 10.887262
## [778] 13.271497 12.228932 8.402741 10.200152 10.502807 7.586903 11.139079
## [785] 9.525989 10.973026 12.097155 11.512829 8.211325 13.316382 10.886829
## [792] 11.648147 11.924378 12.672192 11.670242 9.821717 9.183759 8.089478
## [799] 11.520960 10.317231 9.793755 10.720923 10.887357 10.031418 9.570251
## [806] 8.563678 11.319059 7.207283 10.116688 10.189361 9.413223 11.887861
## [813] 12.119407 6.670495 8.626490 11.385616 10.953969 10.998414 10.213152
## [820] 9.305681 9.601334 9.598621 12.391228 11.480579 12.651271 8.697391
## [827] 9.175844 10.734671 12.277606 9.124744 10.228008 10.714174 8.307192
## [834] 7.609687 9.504851 8.247589 10.933903 13.293334 11.112027 8.374102
## [841] 8.978234 9.432317 11.402120 9.524108 11.699917 10.535208 11.056785
## [848] 9.278948 9.987777 11.867039 12.575886 9.374333 8.847451 11.740844
## [855] 10.718512 8.341495 12.657589 11.140801 7.892808 9.192712 9.911951
## [862] 9.334365 9.186605 11.476404 8.600016 11.289234 10.196331 9.392795
## [869] 12.139539 8.877659 8.758636 7.905875 9.690462 12.334329 11.445272
## [876] 11.338044 10.722505 11.566305 6.263881 10.052121 9.974528 11.155789
## [883] 11.262125 10.010423 6.336547 7.359998 7.613341 13.602320 10.033385
## [890] 8.288065 9.315304 8.697939 10.711138 8.789991 8.040376 11.166618
## [897] 13.715433 8.938970 6.579395 11.177739 8.694277 9.616830 8.310180
## [904] 10.178241 7.872542 7.762600 12.168495 9.720875 11.456923 8.173585
## [911] 12.750845 9.482377 8.758867 10.973227 10.933977 10.573750 9.031562
## [918] 9.882998 11.642751 12.134848 9.847881 10.222111 12.128893 9.846740
## [925] 11.833835 12.043300 11.988893 7.315829 10.302178 9.658181 9.061051
## [932] 10.139343 10.076311 9.287984 8.786067 8.032722 10.072321 10.036442
## [939] 12.033965 11.970290 10.183013 9.975655 13.655968 9.223828 7.767480
## [946] 10.659254 11.355401 12.037628 11.216680 9.247786 8.757841 9.291676
## [953] 12.350256 10.572018 10.879255 10.607289 10.470156 10.735282 11.283603
## [960] 7.033358 13.418886 8.279778 10.874542 10.215783 10.170295 7.957464
## [967] 8.919091 9.417588 11.639175 8.171839 8.392182 11.052531 10.160785
## [974] 11.734653 10.998028 10.367948 10.110513 10.230229 12.821232 8.463259
## [981] 8.277600 7.870553 8.594514 9.288592 9.117847 12.419560 13.543694
## [988] 8.792710 8.121904 10.074929 9.402495 8.556287 8.108680 8.287337
## [995] 11.355246 8.806932 10.038909 7.604714 9.205280 11.020471
# create groups for different values of X
(group <- rep(1,1000)) # a vector consisting of 1000 elements
## [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [186] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [223] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [260] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [297] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [334] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [371] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [408] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [445] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [482] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [519] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [556] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [593] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [630] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [667] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [704] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [741] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [778] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [815] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [852] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [889] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [926] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [963] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1000] 1
group[xAxis > -1.5] <- 2
group[xAxis > -.5] <- 3
group[xAxis > .5] <- 4
group[xAxis > 1.5] <- 5
group
## [1] 2 4 1 2 4 3 2 2 4 2 2 3 1 3 4 3 2 3 3 3 4 3 3 3 4 2 4 2 3 2 4 1 4 2 3 4 4
## [38] 3 3 3 3 2 2 3 3 5 2 3 3 3 4 4 5 3 5 1 3 4 2 2 4 3 1 3 4 2 4 3 2 4 4 4 3 4
## [75] 3 2 3 3 5 2 4 3 5 1 2 3 4 2 3 4 3 2 3 5 3 3 2 5 3 4 2 2 2 4 3 2 4 4 3 4 2
## [112] 4 1 3 3 3 2 3 3 3 1 3 3 4 4 2 2 3 4 2 4 4 2 2 3 2 4 2 3 2 3 2 3 3 2 2 2 2
## [149] 2 1 3 4 2 3 3 3 4 2 4 2 2 3 3 3 2 4 1 3 3 4 4 3 5 2 4 2 3 2 2 4 2 3 5 4 3
## [186] 2 3 2 4 2 4 5 1 4 2 2 3 3 3 2 2 3 4 4 2 5 4 3 2 3 3 3 4 4 3 2 2 3 3 3 1 1
## [223] 3 4 2 1 5 4 3 5 3 3 3 2 1 4 2 3 3 2 4 3 4 3 4 2 2 4 3 4 4 1 2 4 3 3 1 3 3
## [260] 1 3 3 3 2 3 4 3 4 2 2 3 1 3 4 4 4 3 3 1 2 5 4 4 4 4 4 3 3 3 1 5 4 2 3 2 4
## [297] 4 3 5 5 3 3 5 4 4 4 2 5 3 4 4 2 4 3 3 3 2 4 4 3 4 1 3 4 3 3 3 2 4 5 4 3 2
## [334] 4 3 3 1 2 1 4 3 4 4 3 2 4 3 2 5 4 4 4 2 4 2 3 2 5 2 3 3 4 3 2 3 2 3 3 4 4
## [371] 4 2 3 3 1 2 4 3 2 2 4 4 2 2 4 4 3 3 3 3 3 5 2 4 3 3 5 3 3 4 4 4 2 3 1 3 3
## [408] 2 2 3 4 3 3 4 3 3 2 3 2 4 3 4 2 2 3 3 4 4 3 4 2 3 4 2 4 2 2 1 3 4 2 3 4 3
## [445] 3 2 2 3 3 2 2 2 3 4 3 3 3 2 3 2 4 4 3 2 3 3 4 4 4 4 3 2 2 2 1 4 2 4 3 3 4
## [482] 4 2 2 4 3 2 1 2 4 4 3 3 3 2 1 4 4 3 1 3 3 4 1 4 2 5 3 3 4 4 1 2 3 2 4 2 1
## [519] 3 4 3 3 2 3 4 3 3 4 4 5 4 4 1 4 1 2 3 3 5 2 3 2 2 3 2 2 2 4 4 3 1 1 5 2 4
## [556] 3 1 3 4 3 3 2 3 3 2 4 3 4 4 1 5 2 3 3 4 2 3 2 2 4 4 3 4 4 4 3 1 2 2 5 2 2
## [593] 5 4 4 1 4 3 3 5 2 3 3 4 3 2 3 3 4 3 3 3 3 1 1 3 5 3 5 3 3 3 4 1 4 2 4 2 1
## [630] 5 1 5 2 4 4 3 3 3 3 4 4 2 2 4 3 4 1 2 4 2 1 2 1 5 1 5 2 3 5 3 3 3 5 2 4 4
## [667] 3 3 1 3 4 3 4 4 2 3 2 3 2 3 3 4 3 3 3 5 4 5 4 2 2 1 2 5 3 4 4 5 3 4 4 3 3
## [704] 1 3 4 2 4 2 2 3 4 4 1 4 3 5 3 3 4 3 2 3 2 2 3 2 3 4 2 3 2 4 3 3 2 3 3 3 2
## [741] 3 3 4 2 3 2 3 3 3 2 3 3 5 1 2 5 2 3 5 5 2 3 2 3 2 3 2 2 4 5 3 3 4 3 4 3 3
## [778] 4 4 2 4 3 2 5 4 3 2 4 2 5 3 3 4 4 4 3 3 1 3 3 3 4 4 2 4 2 3 1 3 3 3 3 3 1
## [815] 2 5 4 2 2 4 2 2 5 4 3 2 2 4 4 2 3 3 3 2 2 3 3 4 3 2 2 3 4 2 3 3 4 2 2 5 3
## [852] 3 2 4 3 1 5 4 3 3 4 2 2 3 3 4 2 3 4 3 2 1 2 3 3 4 3 3 1 1 2 4 3 3 1 1 2 5
## [889] 2 3 3 2 4 2 3 4 5 3 1 4 3 3 2 3 1 1 3 3 4 3 5 3 2 4 4 4 2 1 4 4 2 3 3 1 4
## [926] 5 3 1 5 4 2 3 3 3 2 3 3 4 4 3 4 4 5 3 2 3 4 4 3 2 2 2 4 2 4 4 4 3 4 1 5 2
## [963] 3 3 3 3 2 4 3 3 3 3 3 3 3 3 2 3 5 1 3 3 3 2 2 4 5 3 1 3 2 2 3 2 3 3 3 2 3
## [1000] 3
# create sample dataframe by joining variables
sample_data <- data.frame(xAxis,yAxis,group)
sample_data
## xAxis yAxis group
## 1 -0.6265488361 8.522268 2
## 2 0.5852105464 10.854986 4
## 3 -1.6882048673 7.490998 1
## 4 -0.7576481227 9.906732 2
## 5 0.6062989741 10.969379 4
## 6 0.2587546129 10.520422 3
## 7 -0.8263937214 9.486757 2
## 8 -1.1280246314 9.489415 2
## 9 1.0697588190 8.986783 4
## 10 -1.1856984553 10.400869 2
## 11 -0.5999348875 10.864068 2
## 12 -0.1775945316 8.221574 3
## 13 -1.5484685206 7.219385 1
## 14 -0.0511938322 9.734272 3
## 15 0.8109910775 10.047971 4
## 16 -0.4645852303 9.182907 3
## 17 -1.1864011420 9.647541 2
## 18 -0.0267797897 10.522998 3
## 19 0.0753130059 11.154547 3
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## 992 -1.1741600360 8.556287 2
## 993 -0.0722632313 8.108680 3
## 994 -1.0132207412 8.287337 2
## 995 -0.2748489329 11.355246 3
## 996 0.2880494761 8.806932 3
## 997 0.0702764236 10.038909 3
## 998 -0.6553295735 7.604714 2
## 999 0.0321416120 9.205280 3
## 1000 0.1064956762 11.020471 3
# 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)
