# Mindanao State University
# General Santos City
# Introduction to R base commands
# Prepared by: Rojan Jhay A. Hamboy
# March 20, 2023
# Lab Exercise 1: How to create Lines in with different styles in R
# Step 1: Create Data
x <- 1:10 # Create example data
y <- c(3, 1, 5, 2, 3, 8, 4, 7, 6, 9) # using the c() function to create an array
# Step 2: Plot the line graph using the base plot() command
plot(x, y, type = "l")
# Step 3: Add Main Title & Change Axis Labels
plot(x, y, type = "l",
main = "Hello: This is my Line Plot",
xlab = "My X-Values",
ylab = "My Y-Values")
# Step 4: Add color to the line using the col command
plot(x, y, type = "l",
main = "This is my Line Plot",
xlab = "My X-Values",
ylab = "My Y-Values",
col = "blue")
# 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 = "red")
# 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 = "orange")
# 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 = "orange")
# then Add the two lines (for x,y2) and (x,y3)
lines(x, y2, type = "b", col = "blue",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", "blue", "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 = "orange")
lines(x, y2, type = "b", col = "blue",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", "blue", "green"),
lty = 1)
# Lab Exercise 3: Create Line graph without x values
Pupils <- c(3.55 ,3.54 ,3.53 ,3.61 ,3.65 ,3.63 ,3.61
,3.61 ,3.59 ,3.63 ,3.59 ,3.63 ,3.62 ,3.62
,3.59 ,3.63 ,3.62 ,3.65 ,3.65)
# get number of elements of Pupils
length(Pupils)
## [1] 19
# Display the elements of Pupils
Pupils
## [1] 3.55 3.54 3.53 3.61 3.65 3.63 3.61 3.61 3.59 3.63 3.59 3.63 3.62 3.62 3.59
## [16] 3.63 3.62 3.65 3.65
# You can obtain the plot without x values
plot(Pupils, type = 'o')
# Lab Exercise 4: How to Create vertical, horizontal lines
# We will use buit-in cars dataset in R
# display the cars dataset
cars
## speed dist
## 1 4 2
## 2 4 10
## 3 7 4
## 4 7 22
## 5 8 16
## 6 9 10
## 7 10 18
## 8 10 26
## 9 10 34
## 10 11 17
## 11 11 28
## 12 12 14
## 13 12 20
## 14 12 24
## 15 12 28
## 16 13 26
## 17 13 34
## 18 13 34
## 19 13 46
## 20 14 26
## 21 14 36
## 22 14 60
## 23 14 80
## 24 15 20
## 25 15 26
## 26 15 54
## 27 16 32
## 28 16 40
## 29 17 32
## 30 17 40
## 31 17 50
## 32 18 42
## 33 18 56
## 34 18 76
## 35 18 84
## 36 19 36
## 37 19 46
## 38 19 68
## 39 20 32
## 40 20 48
## 41 20 52
## 42 20 56
## 43 20 64
## 44 22 66
## 45 23 54
## 46 24 70
## 47 24 92
## 48 24 93
## 49 24 120
## 50 25 85
# get the number of rows and columns using dim() command
dim(cars)
## [1] 50 2
# display the variable names of the cars dataset
names(cars)
## [1] "speed" "dist"
# display only the first column of the dataset
cars$speed # using the column name
## [1] 4 4 7 7 8 9 10 10 10 11 11 12 12 12 12 13 13 13 13 14 14 14 14 15 15
## [26] 15 16 16 17 17 17 18 18 18 18 19 19 19 20 20 20 20 20 22 23 24 24 24 24 25
cars[,1] # using the column number
## [1] 4 4 7 7 8 9 10 10 10 11 11 12 12 12 12 13 13 13 13 14 14 14 14 15 15
## [26] 15 16 16 17 17 17 18 18 18 18 19 19 19 20 20 20 20 20 22 23 24 24 24 24 25
# Remarks: the following commands will give you the same result
plot(cars,) # using the comma after the name
plot(cars[,1],cars[,2]) # using the column index 1 and 2
attach(cars); plot(speed,dist) # using the attach command to load the variables
plot(cars$speed,cars$dist) # using the dollar notation
# combine all 4 plots using the par() command
par(mfrow = c(2,2)) # set a 2x2 plot output
plot(cars,) # using the comma after the name
plot(cars[,1],cars[,2]) # using the column index 1 and 2
attach(cars); plot(speed,dist) # using the attach command to load the variables
## The following objects are masked from cars (pos = 3):
##
## dist, speed
plot(cars$speed,cars$dist) # using the dollar notation
par(mfrow = c(1,1)) # reset to default plot setting
# Problem: Create vertical lines using the v command
plot(cars)
abline(v = 15, col = "darkgreen",lwd=3) # vertical line
abline(v = 10, col = "blue",lwd=3) # vertical line
# Problem: Create horizontal lines using the h command
abline(h = 80, col = "darkgreen",lwd=3) # vertical line
abline(h = 20, col = "blue",lwd=3) # vertical line
# Create more lines simultaneously, using a vector of values
plot(cars)
abline(v = c(9, 22,25), col = c("darkgreen", "blue","red"),
lwd = c(1, 3,2), # line thickness
lty = c(2,2,2)) # dashed lines
plot(cars)
abline(v = c(9, 22,25), col = c("darkgreen", "blue","red"),
lwd = c(1, 3,2)) # line thickness and solid lines
# create horizontal lines
plot(cars)
abline(h = 60, col = "red",lty = 1, lwd = 3)
abline(h = 100, col = "red",lty = 2, lwd = 3)
abline(h = 20, col = "red",lty = 3, lwd = 3)
# Lab Exercise 5: How to Plot data by group
# We will use buit-in iris dataset in R
# this dataset is a collection of 4 species of flowers with different
# sepal length and width and also with different petal length and width
dim(iris) # iris dataset has 150 rows and 5 columns
## [1] 150 5
names(iris)
## [1] "Sepal.Length" "Sepal.Width" "Petal.Length" "Petal.Width" "Species"
# two different commands to get the frequency table
table(iris$Species) # refer to the dataset by variable name
##
## setosa versicolor virginica
## 50 50 50
table(iris[,5]) # refer to the dataset by column number
##
## setosa versicolor virginica
## 50 50 50
# get summary of all columns
summary(iris)
## Sepal.Length Sepal.Width Petal.Length Petal.Width
## Min. :4.300 Min. :2.000 Min. :1.000 Min. :0.100
## 1st Qu.:5.100 1st Qu.:2.800 1st Qu.:1.600 1st Qu.:0.300
## Median :5.800 Median :3.000 Median :4.350 Median :1.300
## Mean :5.843 Mean :3.057 Mean :3.758 Mean :1.199
## 3rd Qu.:6.400 3rd Qu.:3.300 3rd Qu.:5.100 3rd Qu.:1.800
## Max. :7.900 Max. :4.400 Max. :6.900 Max. :2.500
## Species
## setosa :50
## versicolor:50
## virginica :50
##
##
##
#create scatterplot of sepal width vs. sepal length
plot(iris$Sepal.Width, iris$Sepal.Length,
col='steelblue',
main='Scatterplot',
xlab='Sepal Width',
ylab='Sepal Length',
pch=19)
plot(iris$Sepal.Width, iris$Sepal.Length,
col='steelblue',
main='Scatterplot',
xlab='Sepal Width',
ylab='Sepal Length',
pch=1)
# another way to retrieve columns of data
PL <- iris$Petal.Length
PW <- iris$Petal.Width
plot(PL, PW)
# add color by species
plot(PL, PW, col = iris$Species, main= "My Plot")
# draw a line along with the distribution of points
# using the abline and lm commands
abline(lm(PW ~ PL))
# add text annotation
text(5, 0.5, "Regression Line")
legend("topleft", # specify the location of the legend
levels(iris$Species), # specify the levels of species
pch = 1:3, # specify three symbols used for the three species
col = 1:3 # specify three colors for the three species
)
# Lab Exercise 6: Generate advance scatter plot
pairs(iris, col = rainbow(3)[iris$Species]) # set colors by species
# How to filter data for each flower
(Versicolor <- subset(iris, Species == "versicolor"))
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 51 7.0 3.2 4.7 1.4 versicolor
## 52 6.4 3.2 4.5 1.5 versicolor
## 53 6.9 3.1 4.9 1.5 versicolor
## 54 5.5 2.3 4.0 1.3 versicolor
## 55 6.5 2.8 4.6 1.5 versicolor
## 56 5.7 2.8 4.5 1.3 versicolor
## 57 6.3 3.3 4.7 1.6 versicolor
## 58 4.9 2.4 3.3 1.0 versicolor
## 59 6.6 2.9 4.6 1.3 versicolor
## 60 5.2 2.7 3.9 1.4 versicolor
## 61 5.0 2.0 3.5 1.0 versicolor
## 62 5.9 3.0 4.2 1.5 versicolor
## 63 6.0 2.2 4.0 1.0 versicolor
## 64 6.1 2.9 4.7 1.4 versicolor
## 65 5.6 2.9 3.6 1.3 versicolor
## 66 6.7 3.1 4.4 1.4 versicolor
## 67 5.6 3.0 4.5 1.5 versicolor
## 68 5.8 2.7 4.1 1.0 versicolor
## 69 6.2 2.2 4.5 1.5 versicolor
## 70 5.6 2.5 3.9 1.1 versicolor
## 71 5.9 3.2 4.8 1.8 versicolor
## 72 6.1 2.8 4.0 1.3 versicolor
## 73 6.3 2.5 4.9 1.5 versicolor
## 74 6.1 2.8 4.7 1.2 versicolor
## 75 6.4 2.9 4.3 1.3 versicolor
## 76 6.6 3.0 4.4 1.4 versicolor
## 77 6.8 2.8 4.8 1.4 versicolor
## 78 6.7 3.0 5.0 1.7 versicolor
## 79 6.0 2.9 4.5 1.5 versicolor
## 80 5.7 2.6 3.5 1.0 versicolor
## 81 5.5 2.4 3.8 1.1 versicolor
## 82 5.5 2.4 3.7 1.0 versicolor
## 83 5.8 2.7 3.9 1.2 versicolor
## 84 6.0 2.7 5.1 1.6 versicolor
## 85 5.4 3.0 4.5 1.5 versicolor
## 86 6.0 3.4 4.5 1.6 versicolor
## 87 6.7 3.1 4.7 1.5 versicolor
## 88 6.3 2.3 4.4 1.3 versicolor
## 89 5.6 3.0 4.1 1.3 versicolor
## 90 5.5 2.5 4.0 1.3 versicolor
## 91 5.5 2.6 4.4 1.2 versicolor
## 92 6.1 3.0 4.6 1.4 versicolor
## 93 5.8 2.6 4.0 1.2 versicolor
## 94 5.0 2.3 3.3 1.0 versicolor
## 95 5.6 2.7 4.2 1.3 versicolor
## 96 5.7 3.0 4.2 1.2 versicolor
## 97 5.7 2.9 4.2 1.3 versicolor
## 98 6.2 2.9 4.3 1.3 versicolor
## 99 5.1 2.5 3.0 1.1 versicolor
## 100 5.7 2.8 4.1 1.3 versicolor
(Setosa <- subset(iris, Species == "setosa"))
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa
## 7 4.6 3.4 1.4 0.3 setosa
## 8 5.0 3.4 1.5 0.2 setosa
## 9 4.4 2.9 1.4 0.2 setosa
## 10 4.9 3.1 1.5 0.1 setosa
## 11 5.4 3.7 1.5 0.2 setosa
## 12 4.8 3.4 1.6 0.2 setosa
## 13 4.8 3.0 1.4 0.1 setosa
## 14 4.3 3.0 1.1 0.1 setosa
## 15 5.8 4.0 1.2 0.2 setosa
## 16 5.7 4.4 1.5 0.4 setosa
## 17 5.4 3.9 1.3 0.4 setosa
## 18 5.1 3.5 1.4 0.3 setosa
## 19 5.7 3.8 1.7 0.3 setosa
## 20 5.1 3.8 1.5 0.3 setosa
## 21 5.4 3.4 1.7 0.2 setosa
## 22 5.1 3.7 1.5 0.4 setosa
## 23 4.6 3.6 1.0 0.2 setosa
## 24 5.1 3.3 1.7 0.5 setosa
## 25 4.8 3.4 1.9 0.2 setosa
## 26 5.0 3.0 1.6 0.2 setosa
## 27 5.0 3.4 1.6 0.4 setosa
## 28 5.2 3.5 1.5 0.2 setosa
## 29 5.2 3.4 1.4 0.2 setosa
## 30 4.7 3.2 1.6 0.2 setosa
## 31 4.8 3.1 1.6 0.2 setosa
## 32 5.4 3.4 1.5 0.4 setosa
## 33 5.2 4.1 1.5 0.1 setosa
## 34 5.5 4.2 1.4 0.2 setosa
## 35 4.9 3.1 1.5 0.2 setosa
## 36 5.0 3.2 1.2 0.2 setosa
## 37 5.5 3.5 1.3 0.2 setosa
## 38 4.9 3.6 1.4 0.1 setosa
## 39 4.4 3.0 1.3 0.2 setosa
## 40 5.1 3.4 1.5 0.2 setosa
## 41 5.0 3.5 1.3 0.3 setosa
## 42 4.5 2.3 1.3 0.3 setosa
## 43 4.4 3.2 1.3 0.2 setosa
## 44 5.0 3.5 1.6 0.6 setosa
## 45 5.1 3.8 1.9 0.4 setosa
## 46 4.8 3.0 1.4 0.3 setosa
## 47 5.1 3.8 1.6 0.2 setosa
## 48 4.6 3.2 1.4 0.2 setosa
## 49 5.3 3.7 1.5 0.2 setosa
## 50 5.0 3.3 1.4 0.2 setosa
(Virginica <- subset(iris, Species == "virginica"))
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 101 6.3 3.3 6.0 2.5 virginica
## 102 5.8 2.7 5.1 1.9 virginica
## 103 7.1 3.0 5.9 2.1 virginica
## 104 6.3 2.9 5.6 1.8 virginica
## 105 6.5 3.0 5.8 2.2 virginica
## 106 7.6 3.0 6.6 2.1 virginica
## 107 4.9 2.5 4.5 1.7 virginica
## 108 7.3 2.9 6.3 1.8 virginica
## 109 6.7 2.5 5.8 1.8 virginica
## 110 7.2 3.6 6.1 2.5 virginica
## 111 6.5 3.2 5.1 2.0 virginica
## 112 6.4 2.7 5.3 1.9 virginica
## 113 6.8 3.0 5.5 2.1 virginica
## 114 5.7 2.5 5.0 2.0 virginica
## 115 5.8 2.8 5.1 2.4 virginica
## 116 6.4 3.2 5.3 2.3 virginica
## 117 6.5 3.0 5.5 1.8 virginica
## 118 7.7 3.8 6.7 2.2 virginica
## 119 7.7 2.6 6.9 2.3 virginica
## 120 6.0 2.2 5.0 1.5 virginica
## 121 6.9 3.2 5.7 2.3 virginica
## 122 5.6 2.8 4.9 2.0 virginica
## 123 7.7 2.8 6.7 2.0 virginica
## 124 6.3 2.7 4.9 1.8 virginica
## 125 6.7 3.3 5.7 2.1 virginica
## 126 7.2 3.2 6.0 1.8 virginica
## 127 6.2 2.8 4.8 1.8 virginica
## 128 6.1 3.0 4.9 1.8 virginica
## 129 6.4 2.8 5.6 2.1 virginica
## 130 7.2 3.0 5.8 1.6 virginica
## 131 7.4 2.8 6.1 1.9 virginica
## 132 7.9 3.8 6.4 2.0 virginica
## 133 6.4 2.8 5.6 2.2 virginica
## 134 6.3 2.8 5.1 1.5 virginica
## 135 6.1 2.6 5.6 1.4 virginica
## 136 7.7 3.0 6.1 2.3 virginica
## 137 6.3 3.4 5.6 2.4 virginica
## 138 6.4 3.1 5.5 1.8 virginica
## 139 6.0 3.0 4.8 1.8 virginica
## 140 6.9 3.1 5.4 2.1 virginica
## 141 6.7 3.1 5.6 2.4 virginica
## 142 6.9 3.1 5.1 2.3 virginica
## 143 5.8 2.7 5.1 1.9 virginica
## 144 6.8 3.2 5.9 2.3 virginica
## 145 6.7 3.3 5.7 2.5 virginica
## 146 6.7 3.0 5.2 2.3 virginica
## 147 6.3 2.5 5.0 1.9 virginica
## 148 6.5 3.0 5.2 2.0 virginica
## 149 6.2 3.4 5.4 2.3 virginica
## 150 5.9 3.0 5.1 1.8 virginica
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/PC/Downloads/Documents"
filename <- "Cancer.csv"
(file <- paste0(folder,"/",filename))
## [1] "C:/Users/PC/Downloads/Documents/Cancer.csv"
setwd("C:/Users/PC/Downloads/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)
# 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/PC/Downloads/DocumentsR"
filename <- "hsb2.csv"
(file <- paste0(folder,"/",filename))
## [1] "C:/Users/PC/Downloads/DocumentsR/hsb2.csv"
setwd("C:/Users/PC/Downloads/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
# 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
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## 107 101 1 4 3 1 2 60 62 67 50 56
## 108 89 1 4 1 1 3 35 35 40 51 33
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## 139 2 1 1 2 1 3 39 41 33 42 41
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## 143 142 1 4 2 1 3 47 42 52 39 51
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## 159 109 1 4 2 1 1 42 39 42 42 41
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## 200 137 1 4 3 1 2 63 65 65 53 61
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
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## 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
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## 101 88 1 4 3 1 2 read 68
## 102 99 1 4 3 1 1 read 47
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## 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
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## 156 139 1 4 2 1 2 read 68
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## 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
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## 167 156 1 4 2 1 2 read 50
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## 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
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## 200 137 1 4 3 1 2 read 63
## 201 70 0 4 1 1 1 write 52
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## 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
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## 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
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## 244 40 0 3 1 1 1 write 41
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## 246 169 0 4 1 1 1 write 59
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## 250 7 0 1 2 1 2 write 54
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## 256 15 0 1 3 1 3 write 39
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## 363 198 1 4 3 2 2 write 61
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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
# get thefrequency
table(hsb2_long$variable)
##
## read write math science socst
## 200 200 200 200 200
# defining some variables to become factor variable
# we use another variable to preserve the file hsb2_long
data <- hsb2_long
data$ses = factor(data$ses, labels=c("low", "middle", "high"))
data$schtyp = factor(data$schtyp, labels=c("public", "private"))
data$prog = factor(data$prog, labels=c("general", "academic", "vocational"))
data$race = factor(data$race, labels=c("hispanic", "asian", "africanamer","white"))
data$female = factor(data$female, labels=c("female", "male"))
# check data structure again. The former integer variables are now categorical variable
str(data)
## 'data.frame': 1000 obs. of 8 variables:
## $ id : int 70 121 86 141 172 113 50 11 84 48 ...
## $ female : Factor w/ 2 levels "female","male": 1 2 1 1 1 1 1 1 1 1 ...
## $ race : Factor w/ 4 levels "hispanic","asian",..: 4 4 4 4 4 4 3 1 4 3 ...
## $ ses : Factor w/ 3 levels "low","middle",..: 1 2 3 3 2 2 2 2 2 2 ...
## $ schtyp : Factor w/ 2 levels "public","private": 1 1 1 1 1 1 1 1 1 1 ...
## $ prog : Factor w/ 3 levels "general","academic",..: 1 3 1 3 2 2 1 2 1 2 ...
## $ variable: Factor w/ 5 levels "read","write",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ value : int 57 68 44 63 47 44 50 34 63 57 ...
# we compare student performance by using boxplots
library(gplots)
##
## Attaching package: 'gplots'
## The following object is masked from 'package:stats':
##
## lowess
# compute the average value by group using tapply() command
means <- round(tapply(data$value, data$variable, mean), digits=2)
# create boxplot by group
boxplot(data$value ~ data$variable, main= "Student Performance by Subject
(brown dot = mean score)",
xlab="Subject matter", ylab="Percentage Scores", col=rainbow(5))
# insert the average values
points(means, col="brown", pch=18)
# can also compute the median values
medians = round(tapply(data$value, data$variable, median), digits=2)
medians
## read write math science socst
## 50 54 52 53 52
points(medians, col="red", pch=18)
# Lab Exercise 9: How to plot categorical variables
library(ggplot2)
# we load variable names to memory to avoid the dollar notation
attach(hsb2_long)
# create the plot object p
p <- ggplot(hsb2_long, aes(ses, fill = prog)) + facet_wrap(~race)
p + geom_bar()
## Warning: The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
p + geom_bar(position = "dodge")
## Warning: The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
library(ggplot2)
attach(hsb2_long)
## The following objects are masked from hsb2_long (pos = 3):
##
## female, id, prog, race, schtyp, ses, value, variable
p <- ggplot(hsb2_long, aes(ses, fill = prog)) + facet_wrap(~schtyp)
p + geom_bar()
## Warning: The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
p + geom_bar(position = "dodge")
## Warning: The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
# Lab Exercise 10: Scatter Plots with marginal Distributions
# Advance Scatter plots using libraries
# install.packages("ggExtra")
# install.packages("tidyverse")
library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.0 ✔ readr 2.1.4
## ✔ forcats 1.0.0 ✔ stringr 1.5.0
## ✔ lubridate 1.9.2 ✔ tibble 3.2.0
## ✔ purrr 1.0.1 ✔ tidyr 1.3.0
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the ]8;;http://conflicted.r-lib.org/conflicted package]8;; to force all conflicts to become errors
tidyverse_conflicts()
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the ]8;;http://conflicted.r-lib.org/conflicted package]8;; to force all conflicts to become errors
library(ggExtra)
# set theme appearance of grid background
theme_set(theme_bw(1))
# create X and Y vector
(xAxis <- rnorm(1000)) # normal distribution with mean 0 and sd=1
## [1] 0.884747453 0.637572335 1.003354399 1.168721426 -1.770404521
## [6] 0.965641689 1.156639314 -1.208729286 1.904501212 -0.073919964
## [11] -1.376376949 0.191702683 0.295157929 0.465072356 0.782444853
## [16] 0.635499180 0.197483086 -2.069945915 0.280227890 0.427623901
## [21] -0.146360031 -0.662421063 0.583164486 -0.586725212 0.513316960
## [26] 0.987629963 0.223589540 0.388961904 1.448094461 -0.881486357
## [31] -1.128787017 -0.945781548 0.814769058 -2.147031762 0.884254915
## [36] 0.035525953 0.974478914 0.789117715 0.618967513 -1.486747998
## [41] 2.039153592 0.941299777 2.463959340 0.450347754 -0.893421990
## [46] -1.299239732 -1.460008158 -0.959110401 -0.148678877 0.007356467
## [51] 1.014618967 0.686718484 0.066742770 0.998092987 -1.789994198
## [56] 1.264108859 0.166383783 -1.337358481 0.419890811 -0.130278212
## [61] 0.049752714 -0.751912704 -0.330837877 0.096482235 -0.242967987
## [66] -0.455324017 1.257637406 1.671804280 -1.055976217 1.630890714
## [71] -2.464693654 0.038536259 -0.064743627 -0.396218643 -2.554731844
## [76] -1.338546941 0.561017440 0.715463116 -0.426431531 -0.231667955
## [81] 0.257627151 0.152130420 -1.491324687 0.319080604 0.479106201
## [86] 0.347734518 1.936570184 1.900745530 -0.562902733 -0.754554529
## [91] -0.310337762 1.568315458 0.385818715 -0.578306771 0.189944865
## [96] -0.275824007 -0.690143621 -1.570918927 -0.695615012 -0.658414430
## [101] -0.698287364 2.818770212 0.958898496 1.944607840 1.192628496
## [106] -0.706604892 -1.712607695 -0.510242862 0.138612063 1.179171986
## [111] -3.263903040 0.642158209 -1.320250418 1.024751829 0.267277253
## [116] -1.999974065 1.623092193 -0.407914429 -0.415010709 0.031217196
## [121] 0.487310171 -0.540447824 -0.909588889 -0.444743104 0.854313330
## [126] 0.193604112 -0.186533215 -1.224838845 0.260039242 -0.771818053
## [131] -0.668879249 -0.801207703 -0.088382051 2.032412227 -0.677060416
## [136] 0.064652513 -0.834578720 -0.339309403 0.410410616 1.918339786
## [141] -0.676996305 -0.234133688 1.131215564 0.285393130 0.942948863
## [146] 0.259904735 0.306332945 -0.408991454 -0.538311712 -0.747720650
## [151] 0.749197025 0.225298871 0.580986133 -1.484431551 0.413871454
## [156] 1.667888072 -0.169566092 0.594963998 -0.069583615 0.945903248
## [161] 0.495156599 0.015198723 0.006290152 -0.107822940 -0.114157449
## [166] -0.145989157 -1.045339068 -0.026378798 -0.042865550 -0.071140285
## [171] 0.392551633 0.971614679 0.313791995 -1.679363680 -0.134341020
## [176] 1.863579010 -0.742369754 0.971272608 0.743208869 0.778763896
## [181] 0.873697595 0.728152557 0.583272396 1.789650489 -0.294352671
## [186] -1.182811884 -1.174105050 0.001260155 0.425495748 0.592528997
## [191] 0.864118373 1.598643084 -0.459906747 -1.676340348 -0.663161515
## [196] 0.382389174 0.769718625 -0.502306736 -0.740350859 0.809436931
## [201] -0.028214019 0.677140589 -0.005395385 -0.107779469 0.799532558
## [206] 0.825789983 0.531787355 -0.905180640 1.395049622 -0.978197412
## [211] 0.250790842 -1.821798431 1.056781919 0.007797304 -0.607244801
## [216] 1.441310212 -0.420064850 0.858595249 -0.822110915 0.871084142
## [221] 0.149800942 0.504278564 1.546258411 0.662294300 0.485571828
## [226] 2.321423276 -0.025327197 -0.493559069 1.482031618 -0.074979657
## [231] -0.651041726 0.446272746 1.556999495 0.253792866 0.595908100
## [236] 0.741198724 0.743122169 0.056033816 -0.024289872 0.323598516
## [241] -0.162214789 -1.573629323 0.923293656 1.643966742 -0.366055003
## [246] -2.347553892 0.253456561 1.535242650 -0.869562847 -0.268069369
## [251] 0.388266978 0.673593239 -0.415534563 -0.245858915 0.271407475
## [256] 0.888800278 0.785349408 -0.391433987 -0.399049971 0.220428709
## [261] -0.470569994 -1.421935663 0.500208477 -0.761988507 0.155538238
## [266] 0.096681827 -0.649958441 0.515970212 0.134255967 0.829442667
## [271] 0.138079393 -1.418952618 0.727886951 0.308638870 0.391145709
## [276] -0.640148871 0.748407498 0.712738042 -0.104179129 -0.221371548
## [281] -0.054651551 -1.718959365 -1.423144933 -2.036539432 -0.122550233
## [286] 1.863172010 -1.142976071 0.610112509 -0.496062883 1.381083657
## [291] 1.029629764 -0.754806944 0.755832351 -0.048420837 1.947941513
## [296] 0.981937311 -1.322737114 -1.055056192 0.437697051 1.491985133
## [301] 0.844221202 -0.675083709 -1.000352478 -0.679641776 -1.826324062
## [306] 0.622481935 -0.676763734 -1.141580440 0.130268156 -0.544707510
## [311] 0.118583163 -0.344374743 -0.356815588 0.516526919 1.206979275
## [316] 0.341693504 -0.226389925 -0.478992563 -1.757585584 -0.065184822
## [321] 0.898386167 1.396345216 1.041573695 -1.442500020 1.212767076
## [326] 1.287700365 1.819782671 0.912231426 -0.998617632 -2.642456787
## [331] -0.285898681 0.974677978 1.379152854 -0.537980223 -0.098460706
## [336] -0.359699121 0.745279907 -1.941988196 -0.159725389 0.465381661
## [341] -0.817248750 -0.679933432 -0.845871418 1.217278292 -0.389097086
## [346] -0.598033355 0.214302807 1.023217989 0.678571967 0.369378364
## [351] -1.067063736 -0.757962380 -0.778212057 0.674970114 0.869049128
## [356] -0.676330116 0.904729286 -0.082585351 0.441140486 1.296722277
## [361] -0.181390840 -1.106496106 -0.461348150 -0.636214552 0.851986764
## [366] 0.173265673 -1.922743251 -1.144528971 0.480196713 -0.551742422
## [371] 0.193860807 -0.244693633 -0.085976109 0.541882727 0.213353337
## [376] 1.448951249 0.476103126 0.780124660 -0.338063765 2.249222909
## [381] -1.866448593 -0.190766312 -0.868848706 -1.523845728 0.250915446
## [386] -0.033574983 -1.305626411 0.258612942 -0.163213605 0.583365699
## [391] 0.680759540 -0.430077300 -2.182213588 0.808359639 1.092128836
## [396] -0.650577644 0.545894169 -1.531183333 -1.165352587 -0.145318688
## [401] 0.693570030 1.117064662 -1.166029487 -1.987649044 0.799186717
## [406] -0.809059815 -0.422761822 0.069000948 0.511167035 -0.577776483
## [411] -0.253858430 1.120527340 0.616775441 -0.450539844 1.776129385
## [416] -0.478499478 0.567685803 0.353166799 0.460914912 -0.563496560
## [421] 0.237715299 0.331595611 -1.490045172 0.127668506 -0.443687295
## [426] 1.168778934 0.001322990 -0.572987974 -1.978740392 -0.990541969
## [431] 0.676873664 -0.252182655 -0.481316748 -0.072946205 1.864431690
## [436] 0.155956041 -0.402540696 1.259767903 0.552031447 0.179069924
## [441] 0.375211656 0.252468643 -0.714264047 0.989725573 -1.734925811
## [446] 0.038070282 -0.491335864 0.179607271 -0.578140581 -0.536203879
## [451] 2.553037054 0.618101064 -2.336742439 -1.746122683 0.692221636
## [456] 1.321431502 0.654677474 -0.258125869 1.239727625 -0.948891296
## [461] -0.470825831 0.056914118 0.690472269 3.374445790 0.786220896
## [466] 0.545702876 0.201901424 -0.890098448 -0.368977535 0.015478799
## [471] -2.615984952 0.280118038 -0.651148869 0.178657789 -0.760821505
## [476] -0.822605499 0.036912109 0.240767354 1.634072491 0.511369122
## [481] 0.135342258 -0.104128316 -1.683346223 -0.306141705 1.519412804
## [486] -1.283889640 0.271202570 1.821847690 -0.287720308 1.060967276
## [491] -0.126087142 -1.272153455 0.308582350 -0.567753390 -0.493678501
## [496] -0.673761537 -0.590422452 -0.992689765 -0.492170604 1.372742939
## [501] -0.574572255 -0.342092840 1.304375210 0.930761330 -0.500877335
## [506] -0.009676890 -0.804716515 0.333573626 -0.646785575 -1.829034628
## [511] -0.443587298 1.139927773 -0.048922416 0.256202590 0.105106092
## [516] 0.701731711 -0.121305765 -1.563071341 -0.699534678 0.399973594
## [521] -0.105693190 -1.679231082 1.631093735 -0.350069280 1.144902233
## [526] 1.029337708 1.298346268 1.411686260 -0.163424325 -0.963666496
## [531] -1.975492208 -1.233978076 1.071563719 -0.587198168 -0.831335293
## [536] -2.119388754 -0.032724850 -1.775316447 1.690513733 -0.150304951
## [541] -1.000578196 -2.650095052 0.132865155 0.671466974 1.841879559
## [546] -1.444016330 1.837232935 -1.350149780 -0.635622865 0.032601237
## [551] 0.522766150 -1.015489787 -2.060886957 -2.321931610 -0.476333740
## [556] 0.082861115 0.886828717 -0.323841572 -1.875804791 0.928019797
## [561] -0.497225075 1.872496568 1.934768034 0.729655993 0.028478713
## [566] 1.271216030 0.643516027 -2.079328790 0.067132826 0.894290656
## [571] 2.186576414 0.533905957 0.489192011 -0.005591133 0.549068134
## [576] 0.418831678 -0.170663180 0.544710501 -0.406007695 0.532016069
## [581] -2.388162197 0.273068382 -0.118037176 -0.265480932 -0.817848713
## [586] -0.246696893 0.855393967 -0.959192601 1.076576143 -0.832874316
## [591] -1.440872948 0.571175228 0.720916716 0.171825058 0.569566113
## [596] -1.148425481 1.249238870 -1.648281910 -0.387744987 -0.483842417
## [601] -0.547160366 1.627741042 0.314551454 0.548601785 0.080385686
## [606] 1.289278578 -0.028022501 0.189055216 2.399591877 -1.371176953
## [611] 0.312621289 -0.184367929 -0.625613989 -0.845672433 -0.305290491
## [616] -0.609071848 1.149753929 -0.550508416 1.262264260 -1.109833325
## [621] -0.769723464 0.016327971 0.225352204 0.020580409 -0.094092442
## [626] -0.152209628 -0.254846636 0.257163538 -2.265627472 -0.468917346
## [631] 0.347942259 0.662708204 1.374311326 -0.224616052 -2.866322455
## [636] -0.192295833 1.654735310 0.229324503 -2.113320974 0.121091006
## [641] 0.362717752 0.068888303 0.073468804 1.530473812 -0.837888864
## [646] -0.702215173 0.927939022 -1.526064921 -0.359065572 -0.712265584
## [651] -1.678263669 -0.092487499 1.140116267 -0.396405168 -0.663677908
## [656] -0.934539430 0.208881662 0.938591863 0.826436605 -0.648145285
## [661] 0.860871961 -0.408449910 0.180939630 0.694920428 -1.105375302
## [666] -0.558316564 -0.791031314 -0.296780221 -0.284368106 2.074604722
## [671] 1.222963996 -0.950324693 -0.804019290 -0.576197633 1.183717348
## [676] 0.991992508 0.003385549 -0.059661053 0.246429293 0.168134802
## [681] -0.568173959 -0.601714190 -0.404672855 2.441806327 -0.295991538
## [686] 0.841382347 -0.497719748 1.141958529 -0.376377946 0.131832035
## [691] 0.274810104 0.453268916 -0.573506431 0.279364267 -1.321467940
## [696] -0.407040400 0.755015076 1.125947421 -0.429689761 0.110610629
## [701] -1.479764365 0.988898732 0.354236694 -0.208836985 -1.440482304
## [706] -1.423875206 0.757707480 0.395904335 0.529071035 -1.360110893
## [711] -0.966965716 -0.492696407 0.366601664 0.358571981 -0.338393895
## [716] 0.754961211 0.240022406 -0.404515621 1.410247831 -0.317012033
## [721] 0.484218969 0.448976037 0.383963471 -1.527827761 -0.390294046
## [726] -2.373839457 -1.457610249 -0.641970936 0.362664564 -0.166286924
## [731] -0.322115472 -0.151339180 -0.891615103 -0.819338231 0.602434372
## [736] 1.092413409 1.188331403 2.487019741 -0.618629118 0.610810003
## [741] 0.468561767 -0.231252478 -0.704978446 -0.112068307 0.002467652
## [746] -0.463114425 -1.696636901 1.062577226 -1.732516368 -1.469793384
## [751] -0.068870001 -1.889948060 -0.234777298 -2.258899127 0.920757702
## [756] -0.137111338 -0.378697208 0.864322759 0.688112321 0.277137558
## [761] -0.154276778 -1.032232846 -0.714042399 1.265556175 -0.445439861
## [766] -0.745181754 -0.120758845 0.365735872 -2.090054699 0.798190870
## [771] -0.524911253 -0.823715523 0.483547605 -1.275511265 -1.378397559
## [776] 0.187523515 -0.224594592 0.793773215 0.115834600 -0.231639076
## [781] -0.687366444 0.371874814 0.816506422 1.046148830 2.032360447
## [786] -2.603429747 -0.297359846 0.399348279 0.133436191 -1.489455683
## [791] 0.236759546 -0.653190519 0.800091729 -0.759964856 -1.015574171
## [796] 0.221843974 -0.042632722 -1.123448622 -0.633629739 -0.746179595
## [801] -0.940551878 -0.301566341 -0.010792247 2.285065894 -1.503352888
## [806] 0.728702280 0.042293313 -0.784736773 -0.638226038 -0.406394080
## [811] -0.442487256 0.457407440 1.689296071 -0.237148053 -0.839261868
## [816] -1.661543993 -1.185732694 0.380233693 0.745231215 0.349511133
## [821] -0.744982067 -0.493811584 -0.822389579 -0.749455723 -0.011037812
## [826] 0.304710451 -0.720238146 -0.357511870 -0.563020351 -0.891895888
## [831] 0.117309455 -0.219075290 1.400340474 -1.727763531 -0.274416703
## [836] 1.586319205 -2.434651340 0.527573571 -0.305767770 -0.664857137
## [841] 0.947622305 -2.886512570 -0.580239521 -0.387819946 -0.210754324
## [846] 0.298669255 0.448412351 0.355510763 0.751039311 -2.214466089
## [851] -0.694094897 1.018716675 0.309726778 -0.654363478 0.550302298
## [856] 0.175442831 0.011098178 0.580045971 -1.923148944 0.451378553
## [861] -0.441162696 0.732014502 0.478062897 1.168320843 -0.555792704
## [866] 0.057980828 -0.229074586 -1.093309779 -0.101212647 -0.352285975
## [871] -0.243347117 -0.867004677 0.796415723 -0.075953821 0.923973434
## [876] -0.024992600 0.481132996 0.436529888 -0.316654127 -0.116283119
## [881] 0.796929538 1.353389984 -0.338703744 0.552290528 -0.371707922
## [886] -1.218669441 1.058852504 -0.374343976 -2.011333312 -3.032999669
## [891] 0.655237029 -0.996040890 -0.938759733 -1.957552173 -1.312407577
## [896] 0.567159407 2.704850843 -0.136410775 2.283525636 -1.170487942
## [901] 1.247452054 0.351557028 2.118631835 -0.376138668 0.190496086
## [906] 0.313593979 -0.058708720 -0.114077741 -0.371075268 0.172853356
## [911] -1.494079962 -0.458892874 0.939942374 0.644990881 1.328700656
## [916] 0.525226339 0.725182435 1.200614745 -0.343059099 1.579902834
## [921] -0.841904590 0.550669608 1.173689991 0.115798984 -0.659514155
## [926] -1.166788535 0.609365499 -0.976275028 -0.102515880 0.586728402
## [931] 0.362450573 -1.293088232 -0.602325083 0.503753606 0.748650898
## [936] -0.127014833 -0.270794200 0.610326577 0.245864460 -0.210101269
## [941] 0.287091423 -2.152296212 0.324341736 0.857262000 -0.179326457
## [946] 0.463836383 -0.747085878 -1.519572139 0.116900787 -0.126644709
## [951] 0.378391587 0.370910613 1.644312014 1.463369001 0.887022444
## [956] -0.187659996 -0.659162206 2.113620376 1.878640141 1.580735522
## [961] 0.045767579 -1.598987696 -0.934600440 -0.651262068 -1.056899916
## [966] 1.053783479 1.269293805 -0.228186089 -0.688815036 1.399595927
## [971] 2.712462117 1.661538021 -0.241974118 -0.205557603 0.195073512
## [976] -0.580571955 -1.390302518 -0.031968049 2.978351506 -1.890061378
## [981] -0.886108102 -2.154469922 -0.783436917 -1.477523127 -1.919585534
## [986] -2.772226410 0.429998931 0.111567530 -0.263303112 -0.261026281
## [991] -1.081960325 -0.218363267 -0.131924880 0.347891161 -0.874717075
## [996] 1.492563464 0.339569504 -0.499670702 1.080988284 1.681267751
yAxis <- rnorm(1000) + xAxis + 10
yAxis
## [1] 9.965785 11.097389 10.583532 11.816382 9.892415 10.733102 12.415440
## [8] 9.818994 11.620734 9.892642 8.735845 10.078550 9.818135 11.205249
## [15] 11.759373 11.973911 10.245957 7.484825 10.264528 13.167904 8.253824
## [22] 8.771327 10.285150 11.366786 11.723904 12.019089 11.334758 8.713531
## [29] 11.633526 11.755775 7.858493 9.435939 11.156359 8.725274 10.524343
## [36] 9.988429 10.537262 11.015148 11.616706 7.765830 12.211670 11.506655
## [43] 11.579348 11.293884 10.077761 10.556156 9.242392 8.936510 10.168355
## [50] 10.128959 11.511498 9.479977 10.687714 10.123940 8.769292 11.659657
## [57] 10.110708 7.433887 10.957450 9.648757 11.249532 8.874452 9.759046
## [64] 9.710389 8.024191 9.857400 10.280954 13.600269 7.421368 10.543410
## [71] 7.392870 10.447620 10.120574 10.313039 7.682375 9.357229 9.017633
## [78] 11.400523 13.300015 10.800792 8.553475 9.706250 9.586030 10.660150
## [85] 11.492602 7.869865 12.130673 11.902656 10.229648 9.920921 8.306709
## [92] 11.750420 11.526650 9.058110 9.393223 7.234460 10.501067 7.505502
## [99] 11.198930 8.375184 9.067047 12.718564 9.440315 11.870246 11.639660
## [106] 7.267127 8.868196 7.968879 10.916040 9.898198 7.682776 8.478238
## [113] 9.423382 12.805875 11.133369 8.044422 10.560965 9.134257 9.010985
## [120] 9.550128 10.295750 9.579484 8.409605 10.029471 10.576209 10.896252
## [127] 9.372512 7.883379 12.277890 10.852538 8.694381 10.438101 11.884383
## [134] 12.453562 10.224248 9.184095 7.767367 9.868182 12.201348 12.764570
## [141] 7.912561 8.860856 10.284830 11.687311 11.214887 11.015666 8.101605
## [148] 8.776006 8.279442 9.608806 11.535143 9.069068 9.502332 7.724572
## [155] 12.868834 11.803690 8.039258 12.894132 11.204364 12.910098 12.017674
## [162] 10.406691 11.497188 9.792471 11.627854 10.310922 9.177884 10.198444
## [169] 8.921125 9.646868 9.813111 11.275260 11.278003 8.055194 12.654246
## [176] 12.481125 10.460975 11.073612 10.662407 10.613799 11.477861 10.251898
## [183] 10.688471 12.966661 9.614621 9.691704 9.543251 10.498793 11.061779
## [190] 9.560645 11.985962 12.435108 10.314781 9.938955 8.949865 10.309352
## [197] 10.953163 10.754377 10.445214 10.804739 9.078993 8.826838 9.950929
## [204] 7.974005 10.842867 9.578926 11.024451 8.358667 12.409102 9.462977
## [211] 11.520812 9.340829 9.934669 9.482541 7.445998 12.797506 10.267400
## [218] 9.474839 10.040416 10.676024 10.741066 10.579838 11.638741 8.250860
## [225] 11.607672 13.199318 10.548952 8.603963 11.208988 9.906587 8.159811
## [232] 10.026552 12.166409 9.043220 10.312598 11.702434 10.375700 9.353198
## [239] 10.220992 8.341958 11.213898 10.065999 11.673095 12.180836 9.017714
## [246] 8.034855 10.164220 11.950609 8.714357 10.748317 8.756764 10.896441
## [253] 6.608516 11.361731 10.922957 12.720216 9.380908 9.670505 9.737291
## [260] 11.872831 9.644304 8.442439 10.661554 7.581337 9.212417 9.585760
## [267] 9.637142 10.428419 11.159521 10.009481 9.076950 8.393082 10.364832
## [274] 9.038970 11.642854 7.354589 10.406523 9.798801 11.473782 9.019529
## [281] 10.322512 7.880087 9.787460 7.306351 10.012709 12.320928 9.190447
## [288] 10.719515 8.836064 10.671652 9.874085 8.694285 9.289797 9.754787
## [295] 11.133799 10.852272 11.251579 8.876885 9.218992 12.465156 11.690777
## [302] 9.910319 10.311106 10.963260 7.477731 10.139307 10.194382 7.606830
## [309] 9.802328 10.443164 8.701391 8.681423 9.372776 8.937680 12.471659
## [316] 9.730778 9.729955 8.959448 8.870252 10.436707 11.003114 11.633895
## [323] 13.972038 8.173877 10.354051 10.278892 10.228602 11.475358 9.358907
## [330] 8.026060 9.827570 11.625598 11.765091 8.507114 9.641963 8.180271
## [337] 12.808319 7.499320 10.619418 11.267204 8.954111 9.339763 10.386129
## [344] 10.386231 9.346267 8.784547 11.930643 9.280740 10.578570 9.900392
## [351] 11.313022 8.896108 8.984436 9.960826 11.663515 9.176715 10.042245
## [358] 9.587900 10.334405 11.545963 10.078082 8.423675 10.861969 11.258923
## [365] 10.505356 9.420851 9.678423 9.641404 10.395064 8.030084 9.375150
## [372] 10.195257 7.499755 11.920662 10.110501 11.892722 10.595907 12.538560
## [379] 8.324807 11.493635 6.628152 9.823157 7.502764 9.323265 10.388546
## [386] 8.998760 7.884448 10.413843 11.421071 8.786658 11.434672 9.686655
## [393] 5.176285 11.249642 11.789099 9.528503 10.389494 9.407128 8.363338
## [400] 10.756327 9.297200 12.597295 8.317240 8.968924 11.944776 7.742922
## [407] 10.876477 11.339657 9.090950 9.054493 9.426251 10.251064 10.167728
## [414] 9.630041 10.710327 10.120368 11.862153 9.998768 10.477957 10.356810
## [421] 10.457023 8.917603 8.384809 10.204756 10.213049 11.474349 11.653728
## [428] 8.318413 9.572652 9.994912 10.452459 10.541996 8.362785 10.699380
## [435] 12.044756 11.623852 8.265126 12.001190 9.847815 9.174683 10.857313
## [442] 11.702458 9.922819 11.441738 8.775234 9.292633 9.777939 8.312568
## [449] 9.295276 9.397432 13.483097 10.072923 8.220184 7.986156 10.822938
## [456] 9.438172 10.369711 9.358832 12.897222 7.931712 9.821548 9.625537
## [463] 11.377412 12.320506 10.678833 12.123554 10.843365 7.235461 11.654646
## [470] 10.610074 9.313196 9.920107 8.791398 10.069318 10.669549 9.647614
## [477] 10.461017 9.575684 11.065454 9.835424 8.357638 9.637306 8.724359
## [484] 8.213934 11.310117 7.495725 11.748916 13.934783 10.509230 11.763163
## [491] 11.434495 7.178306 11.366848 8.072649 11.575382 10.631734 11.099198
## [498] 8.034334 9.261052 10.422832 8.303766 8.343392 10.357984 10.712542
## [505] 10.039920 9.073851 8.346919 9.568580 9.860641 6.460396 9.664252
## [512] 10.585819 8.864602 10.487881 10.901316 10.482471 8.311324 7.679623
## [519] 9.069862 8.858991 11.153850 7.639758 12.323262 8.735436 11.406863
## [526] 9.952019 10.784759 12.316504 9.112571 8.682109 6.760842 7.354724
## [533] 11.306699 9.236909 8.350596 6.243281 10.499052 7.386947 12.137359
## [540] 9.487165 9.845570 9.489709 10.538154 10.675217 9.772878 9.712053
## [547] 11.918070 8.451803 7.648524 11.095073 11.697297 8.661969 7.698840
## [554] 9.196033 8.396619 10.417051 8.828315 9.337928 8.960444 10.805652
## [561] 10.148842 12.906854 12.449175 9.187335 11.044775 13.802382 10.202277
## [568] 7.847262 8.431224 10.606792 12.866552 10.516194 11.550908 8.843272
## [575] 9.891498 9.973213 10.512732 11.213422 9.541598 11.354168 8.725089
## [582] 9.590910 9.936735 9.869838 7.902942 9.863362 10.110858 8.668051
## [589] 12.434882 9.952641 7.597235 9.684785 11.334279 10.062371 10.942533
## [596] 8.735385 13.138593 8.584525 10.291145 8.710259 9.625618 12.371033
## [603] 11.180790 10.812585 8.505190 11.659085 8.749875 10.506402 12.122386
## [610] 8.780519 11.130641 8.640282 9.710441 10.218904 8.958613 8.439210
## [617] 11.339522 9.392496 10.594565 6.734438 8.757754 11.298988 10.500191
## [624] 10.296562 10.206405 8.624407 9.866016 10.819190 8.154044 9.232885
## [631] 10.861001 10.462195 11.184900 9.596602 8.307326 8.944396 12.367561
## [638] 11.268689 7.923989 11.819181 10.255399 9.031763 9.464613 12.383938
## [645] 9.360382 10.500639 12.607026 9.749476 9.334499 8.065507 8.906846
## [652] 9.970904 12.628386 9.768108 10.651291 10.337306 10.369554 9.983083
## [659] 10.279373 9.340277 10.262108 8.869139 10.794539 12.216764 9.343556
## [666] 9.201476 8.824943 9.189507 11.739467 11.554517 11.434288 9.948278
## [673] 9.528953 8.882986 12.243624 13.424419 8.681822 11.481442 8.752744
## [680] 9.700611 8.761394 8.711380 10.019346 11.033716 9.021689 11.349864
## [687] 7.238364 9.992272 10.042990 10.369597 10.188283 10.995324 8.657694
## [694] 10.522617 8.270323 9.719047 10.414213 12.204037 6.791686 9.437568
## [701] 7.150346 11.504883 11.673174 10.959790 8.815378 8.777786 11.339491
## [708] 11.060148 10.384577 7.729660 8.044402 10.168695 10.930533 11.933394
## [715] 7.172877 10.777700 9.520852 8.920348 10.871578 9.428748 9.034270
## [722] 10.397644 9.542030 7.575860 9.248206 7.417256 9.240784 10.248121
## [729] 8.979526 10.724822 12.789548 9.411722 10.233243 6.837930 9.856423
## [736] 12.289729 9.243756 9.804100 9.878418 10.209586 10.777218 8.466646
## [743] 7.518378 10.263735 10.171234 9.589561 9.084739 11.570453 7.587744
## [750] 9.996369 9.869737 9.153621 9.384629 6.966560 11.157126 10.292609
## [757] 8.728997 10.889717 10.275642 10.479644 7.891564 9.801753 9.331846
## [764] 11.992786 8.669529 8.599352 10.320155 12.042138 7.083247 11.576292
## [771] 8.039994 8.955258 11.260267 7.591292 9.631860 11.759455 11.702033
## [778] 9.263404 10.926994 10.137030 10.210578 11.209264 11.377999 11.436111
## [785] 11.151667 8.879240 10.111994 10.059869 8.439184 7.669634 11.604530
## [792] 9.516748 10.036351 8.432998 8.226796 8.758274 9.397315 9.186858
## [799] 8.047564 9.679199 10.911367 10.331203 9.760520 12.236845 9.062686
## [806] 9.873080 9.173410 9.775842 10.103536 8.885665 10.192224 9.706183
## [813] 11.628272 10.581059 9.790377 8.446702 7.796395 10.724587 11.354212
## [820] 10.529049 7.866812 10.746773 9.192141 9.231498 11.113818 10.636328
## [827] 10.699458 8.915816 11.117810 10.546239 8.280000 10.410896 11.430434
## [834] 8.027131 10.045170 10.115471 7.532700 10.211869 9.767351 10.228022
## [841] 10.791457 6.593709 10.325759 9.054909 10.332556 11.103502 12.463727
## [848] 9.805134 9.147588 8.431368 6.713351 11.976984 10.708494 10.095996
## [855] 10.993487 9.507647 9.519677 10.122381 7.273467 10.570683 9.272883
## [862] 10.210242 10.641845 10.943562 10.992372 10.930278 9.385123 7.113012
## [869] 11.623250 8.549130 10.614752 8.093225 11.594006 9.937393 9.883425
## [876] 9.680989 10.952550 9.810976 10.278068 9.125988 12.658177 10.420292
## [883] 9.142976 11.141788 9.034905 10.535963 9.154244 10.050314 7.607606
## [890] 5.448928 9.377373 9.808031 9.132459 7.517715 9.159345 11.840324
## [897] 13.590894 10.160555 12.167340 8.034984 10.201859 10.856833 11.192851
## [904] 9.296004 10.914400 10.922717 10.852673 9.442013 8.761515 10.455572
## [911] 10.282781 9.971050 9.929935 7.791467 10.876021 9.795212 11.603008
## [918] 12.227967 7.447380 10.502311 9.002827 10.346498 11.498987 10.368955
## [925] 9.676544 8.668242 10.919986 10.047883 8.910268 9.961823 10.242202
## [932] 10.232732 8.054935 10.382637 13.395085 9.614188 9.069478 9.826892
## [939] 10.343022 11.371923 10.178830 6.516965 10.610684 10.948270 10.775271
## [946] 10.423417 10.347142 7.714856 9.585882 9.457895 8.895832 9.866010
## [953] 12.090066 12.378242 10.694877 9.724494 8.687354 14.024290 12.761336
## [960] 10.742259 8.654099 8.588160 10.399779 9.106007 8.765593 11.364528
## [967] 10.696149 12.577831 9.772235 11.079300 13.522797 9.730673 9.050719
## [974] 10.821007 9.589542 10.656233 7.807980 11.015148 12.106142 8.762984
## [981] 8.114807 7.931556 10.367179 9.574472 9.459743 7.381772 11.405496
## [988] 10.019543 8.887327 10.261943 8.839346 9.462293 8.496055 10.080217
## [995] 11.257396 13.080669 12.138867 10.752148 12.361171 10.654109
# create groups for different values of X
(group <- rep(1,1000)) # a vector consisting of 1000 elements
## [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [186] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [223] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [260] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [297] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [334] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [371] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [408] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [445] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [482] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [519] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [556] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [593] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [630] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [667] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [704] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [741] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [778] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [815] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [852] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [889] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [926] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [963] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1000] 1
group[xAxis > -1.5] <- 2
group[xAxis > -.5] <- 3
group[xAxis > .5] <- 4
group[xAxis > 1.5] <- 5
group
## [1] 4 4 4 4 1 4 4 2 5 3 2 3 3 3 4 4 3 1 3 3 3 2 4 2 4 4 3 3 4 2 2 2 4 1 4 3 4
## [38] 4 4 2 5 4 5 3 2 2 2 2 3 3 4 4 3 4 1 4 3 2 3 3 3 2 3 3 3 3 4 5 2 5 1 3 3 3
## [75] 1 2 4 4 3 3 3 3 2 3 3 3 5 5 2 2 3 5 3 2 3 3 2 1 2 2 2 5 4 5 4 2 1 2 3 4 1
## [112] 4 2 4 3 1 5 3 3 3 3 2 2 3 4 3 3 2 3 2 2 2 3 5 2 3 2 3 3 5 2 3 4 3 4 3 3 3
## [149] 2 2 4 3 4 2 3 5 3 4 3 4 3 3 3 3 3 3 2 3 3 3 3 4 3 1 3 5 2 4 4 4 4 4 4 5 3
## [186] 2 2 3 3 4 4 5 3 1 2 3 4 2 2 4 3 4 3 3 4 4 4 2 4 2 3 1 4 3 2 4 3 4 2 4 3 4
## [223] 5 4 3 5 3 3 4 3 2 3 5 3 4 4 4 3 3 3 3 1 4 5 3 1 3 5 2 3 3 4 3 3 3 4 4 3 3
## [260] 3 3 2 4 2 3 3 2 4 3 4 3 2 4 3 3 2 4 4 3 3 3 1 2 1 3 5 2 4 3 4 4 2 4 3 5 4
## [297] 2 2 3 4 4 2 2 2 1 4 2 2 3 2 3 3 3 4 4 3 3 3 1 3 4 4 4 2 4 4 5 4 2 1 3 4 4
## [334] 2 3 3 4 1 3 3 2 2 2 4 3 2 3 4 4 3 2 2 2 4 4 2 4 3 3 4 3 2 3 2 4 3 1 2 3 2
## [371] 3 3 3 4 3 4 3 4 3 5 1 3 2 1 3 3 2 3 3 4 4 3 1 4 4 2 4 1 2 3 4 4 2 1 4 2 3
## [408] 3 4 2 3 4 4 3 5 3 4 3 3 2 3 3 2 3 3 4 3 2 1 2 4 3 3 3 5 3 3 4 4 3 3 3 2 4
## [445] 1 3 3 3 2 2 5 4 1 1 4 4 4 3 4 2 3 3 4 5 4 4 3 2 3 3 1 3 2 3 2 2 3 3 5 4 3
## [482] 3 1 3 5 2 3 5 3 4 3 2 3 2 3 2 2 2 3 4 2 3 4 4 2 3 2 3 2 1 3 4 3 3 3 4 3 1
## [519] 2 3 3 1 5 3 4 4 4 4 3 2 1 2 4 2 2 1 3 1 5 3 2 1 3 4 5 2 5 2 2 3 4 2 1 1 3
## [556] 3 4 3 1 4 3 5 5 4 3 4 4 1 3 4 5 4 3 3 4 3 3 4 3 4 1 3 3 3 2 3 4 2 4 2 2 4
## [593] 4 3 4 2 4 1 3 3 2 5 3 4 3 4 3 3 5 2 3 3 2 2 3 2 4 2 4 2 2 3 3 3 3 3 3 3 1
## [630] 3 3 4 4 3 1 3 5 3 1 3 3 3 3 5 2 2 4 1 3 2 1 3 4 3 2 2 3 4 4 2 4 3 3 4 2 2
## [667] 2 3 3 5 4 2 2 2 4 4 3 3 3 3 2 2 3 5 3 4 3 4 3 3 3 3 2 3 2 3 4 4 3 3 2 4 3
## [704] 3 2 2 4 3 4 2 2 3 3 3 3 4 3 3 4 3 3 3 3 1 3 1 2 2 3 3 3 3 2 2 4 4 4 5 2 4
## [741] 3 3 2 3 3 3 1 4 1 2 3 1 3 1 4 3 3 4 4 3 3 2 2 4 3 2 3 3 1 4 2 2 3 2 2 3 3
## [778] 4 3 3 2 3 4 4 5 1 3 3 3 2 3 2 4 2 2 3 3 2 2 2 2 3 3 5 1 4 3 2 2 3 3 3 5 3
## [815] 2 1 2 3 4 3 2 3 2 2 3 3 2 3 2 2 3 3 4 1 3 5 1 4 3 2 4 1 2 3 3 3 3 3 4 1 2
## [852] 4 3 2 4 3 3 4 1 3 3 4 3 4 2 3 3 2 3 3 3 2 4 3 4 3 3 3 3 3 4 4 3 4 3 2 4 3
## [889] 1 1 4 2 2 1 2 4 5 3 5 2 4 3 5 3 3 3 3 3 3 3 2 3 4 4 4 4 4 4 3 5 2 4 4 3 2
## [926] 2 4 2 3 4 3 2 2 4 4 3 3 4 3 3 3 1 3 4 3 3 2 1 3 3 3 3 5 4 4 3 2 5 5 5 3 1
## [963] 2 2 2 4 4 3 2 4 5 5 3 3 3 2 2 3 5 1 2 1 2 2 1 1 3 3 3 3 2 3 3 3 2 4 3 3 4
## [1000] 5
# create sample dataframe by joining variables
sample_data <- data.frame(xAxis,yAxis,group)
sample_data
## xAxis yAxis group
## 1 0.884747453 9.965785 4
## 2 0.637572335 11.097389 4
## 3 1.003354399 10.583532 4
## 4 1.168721426 11.816382 4
## 5 -1.770404521 9.892415 1
## 6 0.965641689 10.733102 4
## 7 1.156639314 12.415440 4
## 8 -1.208729286 9.818994 2
## 9 1.904501212 11.620734 5
## 10 -0.073919964 9.892642 3
## 11 -1.376376949 8.735845 2
## 12 0.191702683 10.078550 3
## 13 0.295157929 9.818135 3
## 14 0.465072356 11.205249 3
## 15 0.782444853 11.759373 4
## 16 0.635499180 11.973911 4
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## 1000 1.681267751 10.654109 5
# 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)
