# Mindanao State University
# General Santos City
# Introduction to R base commands
# Prepared by: Mohammad L. Dapak
# March 26, 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] 1.291949564 -0.555081660 -1.857351913 1.255459666 -1.329177124
## [6] -0.832493748 0.015429354 0.524770701 1.253751876 0.447863667
## [11] -0.328137075 -0.142306136 -0.487790592 1.511497218 1.108461151
## [16] -1.718632923 0.657274288 -0.444426393 -0.890732370 -0.539126837
## [21] -1.127615587 0.165574254 1.860639326 -1.069225201 0.618567045
## [26] 0.032327769 0.085675578 -0.618622164 0.399622788 -0.897501423
## [31] 1.886034701 -0.633474927 -0.996736670 -0.033725983 0.220712867
## [36] -1.031357533 -1.186809582 1.378554457 2.172390747 0.484454692
## [41] -1.111138737 -0.970244271 1.253515305 0.885258320 0.599756229
## [46] 1.897374265 -0.386698540 -1.048147192 1.618499101 -0.406727969
## [51] -0.713822887 0.823296802 0.627161195 1.624483137 -0.583908865
## [56] -0.325509509 1.343798967 -0.796918735 -0.953693050 -0.446909422
## [61] -0.180376262 -0.429929440 0.766775106 -1.508791633 2.108057538
## [66] 1.127770398 0.564327700 -0.594190746 1.085776660 0.360093169
## [71] -0.981879480 0.264119439 0.765062759 -1.483043865 -0.297808865
## [76] 0.777257328 -0.171283510 -0.330047585 -0.222438349 -0.477471380
## [81] -1.125238372 -1.567897678 -0.415064520 -1.390793939 -1.339213039
## [86] 0.268826804 0.224142659 0.931479401 2.436300986 -0.011412617
## [91] 2.393131066 -1.763933060 -1.467790293 0.726486263 0.129679648
## [96] -0.299218288 0.016765519 -0.562157821 0.734134395 -0.688453747
## [101] 0.816337770 -0.901235898 -1.084359699 -1.973643274 -0.653549627
## [106] -0.527710576 0.376684980 -0.418131915 0.957579923 0.613629175
## [111] -2.011077865 -1.040836843 -0.406440328 0.820203814 1.481763906
## [116] 0.331793907 -0.361550083 -0.471233939 -0.663980402 -1.055302506
## [121] 1.311723779 -1.815527499 0.328364648 -0.191785406 -0.042173071
## [126] 0.597044206 0.104494356 0.928545542 -1.326786113 0.281965782
## [131] 0.708183331 1.952793254 0.279153860 -0.857367229 1.397709425
## [136] 0.951194719 -0.805639403 1.270372344 0.374372703 -0.318213293
## [141] 2.069878735 -0.644029150 -0.694893672 -0.277955337 -1.335388849
## [146] 1.795800843 -0.548207296 -0.205789138 -1.142186601 0.740967630
## [151] 0.478848310 -0.797492482 0.776878848 0.876921474 -0.830977757
## [156] -0.155204687 -0.732688629 0.305035097 -0.525179886 -1.059724511
## [161] 0.194341235 -0.409857617 0.614417518 -0.364119619 -1.052833370
## [166] 0.440840919 0.494152495 -1.191590116 -0.752099711 -0.723420032
## [171] 1.147456351 0.821738483 -0.538196015 0.339623497 0.511255985
## [176] 0.042165008 1.209304489 0.559351991 0.046556486 0.276067619
## [181] -0.412931528 -0.382652088 0.281664405 0.464135976 -0.865803635
## [186] -0.346953653 -2.305778269 1.140890805 0.043911013 -0.196362470
## [191] -0.505470716 -1.988182588 1.300114468 0.969416237 0.251663902
## [196] -1.243650724 -0.854692872 -0.979550499 -0.138811820 1.059406692
## [201] 1.788414077 0.978298943 1.120009884 1.812750287 -0.765259059
## [206] 1.430125637 0.339084863 -1.481299853 -0.830412281 -0.061879850
## [211] 0.211364319 -0.908499553 -0.661356918 0.616282366 0.482268611
## [216] 0.164179969 -0.999607319 -0.311348862 -0.619319245 -0.263510884
## [221] 0.227554289 0.107252644 0.114454625 0.638940336 2.265536872
## [226] 1.151809424 -0.165308849 -1.858760970 1.731019983 0.057276573
## [231] -2.874089601 0.609410227 -0.054246179 -1.791967668 -0.214057593
## [236] 0.888504918 0.251564479 0.195106644 -1.182005669 -0.622178158
## [241] 0.137765276 0.586992866 1.362429429 -0.074118839 1.800636409
## [246] -0.461118133 -0.855788035 1.071681841 -0.715474810 0.678813593
## [251] 0.615904617 -0.672363294 -0.273095783 -0.659546818 2.888601639
## [256] 0.234184062 -0.705050157 -1.236870508 -1.897845993 -0.766022038
## [261] -1.252472439 -0.291714456 0.332481833 0.325373865 0.282577836
## [266] 0.238963270 1.232753563 -1.005543973 -1.687197144 -0.577414039
## [271] 1.554603697 -0.331575616 2.754712389 1.743005124 -0.046982342
## [276] -0.980709521 -1.653294174 -1.412894734 -1.778493754 0.527859433
## [281] 1.218689125 -1.685069844 -1.339215972 -1.586163273 -1.307765150
## [286] 0.799696711 2.009363007 -0.399469862 0.677739606 0.577826223
## [291] 0.833454964 -0.830979200 -0.380350815 -0.806327766 -0.293782433
## [296] -0.240807534 0.714435129 0.668777754 -0.192935650 -0.094252389
## [301] 0.876676279 -0.505536927 -1.128418373 0.031560968 -1.533078231
## [306] -0.747294415 0.620409666 0.767370963 0.819413326 -0.046997780
## [311] -1.520477892 -0.080267155 -0.390729830 0.001480952 1.351435170
## [316] -2.018000399 -0.540980245 -0.606080866 -1.207808600 -0.059102146
## [321] 0.099273978 -0.832557345 -0.200317996 -0.716957272 1.451102869
## [326] 0.464444721 1.330530387 -1.192709319 -1.196721382 -0.088206074
## [331] 0.324300537 -0.794757639 1.544684014 0.175118713 -0.936720410
## [336] -0.233254880 0.349089402 -0.300030763 1.232024374 -1.920730433
## [341] 0.325675929 0.647387592 -1.041028930 -0.556967568 -0.950250398
## [346] -0.073047960 0.452070901 -2.128846191 0.807255576 -1.631295827
## [351] -0.008711641 -1.099913603 0.937384045 -0.751476767 1.597704509
## [356] 1.258462290 -0.037764578 -1.116389465 -0.306672065 -0.465971805
## [361] -0.410994466 -0.830458365 2.037400866 0.604066279 -1.555911024
## [366] 0.612724814 0.144165725 -0.564330669 0.590041234 1.607870685
## [371] 0.559109035 -2.524330897 1.576863076 1.981576609 0.192962451
## [376] 0.027569980 2.371102005 0.392045747 1.399863044 0.488011306
## [381] 0.249984329 -0.394807530 1.284491787 -1.595509096 -0.362334615
## [386] -0.069935785 -0.722383214 -1.176131839 1.755680781 0.861353668
## [391] 1.064698634 0.153354856 -1.361048836 0.386290186 0.512209107
## [396] 0.991699898 -0.305124060 -0.541466939 -0.250724508 1.080707336
## [401] -0.670784915 0.659240634 -1.696629065 -1.275788424 0.819573230
## [406] -1.301981995 0.488048591 1.909106305 -0.153120659 -0.746827898
## [411] 0.427127602 1.754424483 -2.758430521 0.611604808 -0.443917770
## [416] -0.686701963 0.105433651 0.529571395 -1.013027899 0.033268976
## [421] -0.375538289 -1.893309407 -0.128722625 -0.863474157 -0.835917810
## [426] 0.603319495 -0.704920716 -0.884573844 -0.158211648 0.159107091
## [431] -0.038400301 0.005806783 -1.506772570 -1.318454492 -0.464580648
## [436] -2.436780419 -0.169501768 0.427638240 0.375513583 -0.329045532
## [441] -0.931618245 -0.325640864 2.118946554 0.054327366 -0.340922076
## [446] -0.520793083 1.537764501 0.267820144 -0.194942923 -1.995247405
## [451] 1.540266787 1.180420002 -0.137482400 1.107865711 0.761508467
## [456] -0.426846286 -1.092677838 0.843444720 -2.044271611 1.558700877
## [461] 0.340064460 -0.220962339 -0.104577471 -0.731722120 -0.879765638
## [466] -0.645574772 -0.664899193 0.471690383 0.265600601 0.531185669
## [471] 0.537924935 -0.792913700 -1.723902833 0.455704798 0.746730362
## [476] 1.399215747 -0.055386817 -0.007230308 -1.641638274 -0.502888756
## [481] 0.462789035 0.238138817 0.926024476 -1.591513626 0.129651728
## [486] 2.226044023 0.285354883 1.757314536 -0.125058438 -0.518876799
## [491] -0.496047459 1.008231306 -0.851880893 -0.290393417 1.522493356
## [496] -0.579774502 -1.212711171 -0.798465090 0.865934761 -0.985130502
## [501] -0.541733697 -0.201493399 0.506319890 1.101875145 0.234696781
## [506] 0.556861473 -0.419517942 1.683778459 -0.716284748 -2.925697450
## [511] -0.299474837 0.136905103 3.117207713 -0.983188769 -0.857564533
## [516] -1.966853382 -0.156499896 0.755161179 -0.428158453 -0.638101825
## [521] -0.694585861 -0.520964959 0.371371979 -0.269608131 -0.700998403
## [526] 0.324528153 0.501480117 0.126281854 0.775063408 1.575385479
## [531] -0.194346905 1.000775693 -0.391067678 2.699347627 -0.476465678
## [536] 0.594498725 1.083059652 1.599480620 0.521905866 0.149940418
## [541] -2.047238173 -1.349431925 -0.169646354 0.744841480 -1.719670146
## [546] 0.054190972 -0.573491026 0.164451805 0.318613097 -0.698602213
## [551] -0.296839939 -0.221050026 0.168909024 0.145004090 0.044156211
## [556] -0.684834888 -1.065887686 1.378397643 -1.576700100 0.151967317
## [561] -1.746829469 -0.385289498 0.648060873 -0.374307368 -0.140076656
## [566] -1.298986225 -0.636408618 0.229688432 -0.657829796 -0.401173748
## [571] -0.196498469 0.552217340 0.829622163 0.976689891 0.059946055
## [576] 2.896575244 1.238091679 0.141440152 0.275150924 0.918953497
## [581] -0.453062769 1.253300418 -0.562666384 1.136123388 -0.322836610
## [586] -2.305292843 -0.037372775 0.721674709 -0.271845227 -1.089339526
## [591] 0.800737780 -0.186154092 -0.557383933 -0.711675534 -0.197919650
## [596] 0.327507069 -0.881713474 0.682890761 0.184732458 -0.342944842
## [601] -1.177049735 0.143985989 0.154050244 0.707987889 -0.221866452
## [606] 0.485841674 -0.025717645 0.027331414 0.756100139 2.898607774
## [611] 0.720487150 0.144748051 0.272483073 -0.215258402 -0.385103780
## [616] -0.807190136 1.159205086 1.305115782 -0.429969868 1.090791142
## [621] 0.783696281 -0.172149161 -1.182090872 -2.073155319 -0.168128247
## [626] 0.664707439 -0.933958963 -0.833575015 0.167495740 -1.407018504
## [631] 0.497940895 -0.064864128 0.513082674 -0.416080059 -0.014695742
## [636] -0.299199289 -0.304488162 -0.744527784 -0.542698603 0.149520487
## [641] 0.772492578 0.272156319 0.892455167 1.123440318 -1.121335058
## [646] -1.276320613 -0.084699178 0.198689051 1.375798302 -0.364617884
## [651] 1.768948773 1.254016023 -0.442938519 0.654417357 0.393038346
## [656] -0.222740972 -0.214030037 -1.238369986 -0.750520392 -1.465293639
## [661] -1.151290988 1.546668587 1.640407752 -2.258655942 2.033500400
## [666] -0.326422840 1.156286291 -0.154173547 1.226455879 1.299308106
## [671] 2.194302838 0.678803808 -0.039442916 -0.469349989 0.335306388
## [676] -0.333770061 0.081237838 -0.017957185 0.145940737 -0.026534879
## [681] -0.897338538 -0.723624864 1.241969285 0.226568165 0.509122381
## [686] 0.680071114 -1.212856728 -0.831687427 -0.165037004 0.267128358
## [691] 1.148424137 -0.197459040 -1.051992987 -0.813614061 0.573918356
## [696] -0.806623999 0.766548547 -0.142490536 1.164977232 -0.155292034
## [701] 0.607858744 1.081753380 -1.496491731 -0.415960249 -0.207922339
## [706] 0.456402360 0.096547554 0.205674947 -0.209865172 -0.611213884
## [711] -0.289443793 1.778943611 -1.116655070 -0.390479027 1.518407831
## [716] -0.496069440 -1.206290726 -0.253507226 1.039807864 -1.339813187
## [721] -0.379816902 1.118380373 -1.072641582 -1.928411510 -0.603431750
## [726] 0.042125871 0.009271142 1.003030440 0.543131952 -0.335538824
## [731] 0.303072368 -0.131060696 -0.040491243 0.863012323 -0.055277144
## [736] 0.778727844 0.755911588 0.305725414 0.115475600 0.077836231
## [741] -0.189165271 -0.098644574 0.643277386 1.378557646 0.342383021
## [746] -0.724808400 0.559431489 -0.006399898 -0.825400070 0.921778713
## [751] 0.525449146 -0.075069485 1.331036047 -0.293746743 -0.681810028
## [756] 0.039572792 2.285291288 1.572421829 -0.980094677 -1.337241197
## [761] -1.134792105 -0.697968424 0.387200503 1.459453040 -0.798839391
## [766] -1.903434354 1.996125658 1.163955424 -1.164687177 2.593660304
## [771] -0.316886609 0.395474973 -0.748927581 -1.626144320 -1.423985996
## [776] 0.504370724 2.631170237 -0.943489139 -0.274429773 0.125716510
## [781] -0.078593430 0.049362636 0.403413798 -1.021963221 0.900734744
## [786] -0.918325479 -0.439644188 -0.243711469 -0.791578309 -0.681434639
## [791] 1.201448755 -1.402977964 -0.741762285 -0.001563869 -0.226503074
## [796] 0.250142910 -1.166182939 -0.072581687 -1.152785973 -0.338187130
## [801] 0.995790745 1.614342601 0.635322204 -0.597522832 0.255008322
## [806] -0.111691682 -0.176648746 0.839812489 0.206990781 1.136445657
## [811] -1.606925926 -0.102533822 1.874925215 -0.925816247 0.473533540
## [816] 1.215567938 1.379829610 -1.896258395 0.261035493 -0.863463676
## [821] -0.345846543 -1.817332563 -0.742122319 0.989284586 -0.089077716
## [826] -0.964793398 -1.344454508 -0.289280930 -1.505765403 -0.180118109
## [831] 0.875413786 0.206127857 -0.294846947 1.098993515 -1.737425940
## [836] -0.156629408 1.521639215 0.284306523 0.819416799 -0.567494311
## [841] -0.128860575 -0.549147056 1.120023838 -1.224481234 1.493491134
## [846] -0.590200302 -0.452972813 0.498788618 -0.360569618 0.314891810
## [851] -0.674415682 0.273852578 0.220025155 0.455515226 0.614970195
## [856] -0.004541995 0.388330151 -0.406156103 -0.052054652 0.021794663
## [861] -0.983135337 0.990548675 -0.170011447 0.412295539 2.419054294
## [866] -0.685464773 -1.841226595 0.814237322 -2.538082233 -0.403567149
## [871] 0.280674806 -1.321851761 -2.735200600 0.422932389 1.540050089
## [876] -0.642136684 -0.550870735 1.024961926 0.606179772 -0.571087155
## [881] -0.974412497 -0.081652057 -0.583543227 0.344113318 1.008992792
## [886] 0.188354515 -0.419570517 0.254637550 -1.304959422 0.924579907
## [891] 0.087116163 0.509634638 -0.600553568 0.083521938 -0.032943361
## [896] 0.133567333 0.830510275 -0.234798899 -0.480342019 -0.559149245
## [901] 0.073805995 -1.029061209 0.066330478 0.465068728 -0.800606750
## [906] -0.192384459 -0.053284176 -2.835180718 0.613734872 1.479038590
## [911] -1.401692324 0.192999903 -1.725226551 0.282659679 -1.846768548
## [916] 0.545194628 0.536155820 0.227709333 0.704771835 -1.367372066
## [921] -0.707516537 1.082269620 1.898090320 -0.646792335 -0.893793656
## [926] -0.236772950 -0.459130349 0.748952342 1.275212439 0.186440991
## [931] 0.281991612 1.907158327 -1.153114028 -1.397358030 0.902146097
## [936] -1.014003813 -0.098982316 -0.276393559 -0.130611971 0.060160984
## [941] -0.301873601 -0.615171489 0.815476201 2.342348863 2.168970486
## [946] 1.003977260 -0.929939270 0.547536509 -0.550557768 0.596913390
## [951] 0.261148964 1.712150993 0.597066141 1.232678678 0.308330698
## [956] -0.654288163 -1.387323526 0.114306315 -3.168570437 0.448218908
## [961] 0.764319930 0.123087926 -0.644714210 -1.218195553 0.431747515
## [966] 0.837265771 -1.020698428 1.343837047 -0.498682027 -0.589123600
## [971] -0.550604407 0.050407054 -1.909901268 0.087495609 1.133963864
## [976] 1.120667168 0.891399315 0.477820807 -0.053838662 0.562547245
## [981] 1.062860701 0.949208348 0.465163124 1.668586418 -0.429390368
## [986] 0.934443749 0.016747506 1.419153947 0.953661220 0.034252729
## [991] -1.224074261 -0.225162402 0.901667803 0.358697651 0.341813725
## [996] 0.267400736 0.820103724 1.030431151 -1.258115158 -2.896682345
yAxis <- rnorm(1000) + xAxis + 10
yAxis
## [1] 10.417822 8.891954 9.796713 10.726390 8.824093 9.434549 8.897032
## [8] 10.279565 9.844772 10.069699 8.192616 9.811317 10.322196 12.610479
## [15] 10.172067 7.129264 10.673576 9.796545 10.177584 8.070933 8.134738
## [22] 9.923182 12.074891 9.472256 9.587148 10.969780 8.720672 8.491404
## [29] 10.245815 9.957292 10.928443 9.319534 8.636083 11.961046 9.186870
## [36] 8.434455 7.233130 11.229949 12.181959 8.785769 8.731586 10.051008
## [43] 10.022642 11.059512 9.852573 11.417565 9.450175 9.122290 10.954169
## [50] 8.522573 8.506879 10.105776 9.232426 12.479780 9.518169 9.091626
## [57] 11.372070 8.682580 9.026448 9.241484 9.906923 8.678784 11.582397
## [64] 7.558410 11.803327 12.042285 10.893970 9.430837 11.499454 9.717523
## [71] 10.972096 10.161734 11.204729 9.298866 7.867899 9.681539 9.128067
## [78] 10.869148 10.791048 9.734209 8.910529 7.978737 10.695581 7.746797
## [85] 9.743386 9.692182 10.825896 9.255137 13.263055 10.590406 11.599833
## [92] 6.114403 8.590514 11.454436 10.195286 8.807863 9.328494 10.529290
## [99] 11.602468 9.805806 11.147696 8.779975 9.056534 8.731541 9.395090
## [106] 11.836642 10.316332 12.014057 10.224018 10.943338 7.870377 9.083971
## [113] 9.959867 9.608689 12.140551 10.715377 9.491932 8.750698 10.553653
## [120] 8.956242 9.850246 7.618734 9.389930 8.146307 10.200323 10.580286
## [127] 8.489361 10.277181 7.465994 11.295204 10.098256 11.342127 10.403045
## [134] 8.383566 12.550355 12.615680 9.012462 11.091400 9.973748 8.556520
## [141] 11.908687 9.467826 9.066797 9.765143 7.630233 11.660024 11.084672
## [148] 8.420858 9.950327 11.104885 8.770168 10.244076 9.788431 11.681438
## [155] 9.000395 10.927758 9.425358 9.215865 9.115545 8.624540 11.337911
## [162] 9.012735 11.453275 7.522052 8.259787 11.631177 10.015561 8.310918
## [169] 9.555925 8.654298 9.705008 11.691259 9.863977 10.553652 9.750551
## [176] 10.787946 10.725921 10.851788 9.545363 8.935158 7.429570 10.780699
## [183] 11.236576 13.107203 8.322155 11.133026 6.965692 12.430746 9.682688
## [190] 9.096580 5.948611 9.225381 10.384543 8.511460 10.349821 9.322783
## [197] 6.629100 8.757074 9.471203 12.016796 9.974057 11.531037 12.036362
## [204] 10.635480 8.387312 10.546473 10.461718 9.408786 11.523174 11.342932
## [211] 8.997865 9.139285 9.774075 9.792008 10.121665 11.197545 8.960660
## [218] 8.439917 10.213764 11.766892 10.164310 11.183628 10.388305 9.752348
## [225] 11.827739 10.088487 9.930626 8.341602 10.087377 11.677144 6.095180
## [232] 11.085542 9.776496 8.630828 9.708879 11.194882 10.914620 10.937790
## [239] 6.948714 10.426981 9.473445 9.661085 11.105924 11.285369 11.814755
## [246] 7.942189 9.361803 10.732952 9.141028 10.764252 10.775732 8.650411
## [253] 8.698826 9.397460 10.776370 9.547254 10.774268 5.885481 9.180458
## [260] 9.322776 8.270904 12.410139 11.129483 9.414172 11.104087 9.212406
## [267] 11.645125 8.378227 7.688985 7.796285 11.689802 11.562602 13.403411
## [274] 12.150090 10.065811 8.352237 7.893276 8.318235 7.249362 10.738451
## [281] 9.279506 8.264180 8.762240 7.734594 7.474643 12.201518 12.956508
## [288] 5.990728 9.938485 12.129418 11.204478 9.371537 11.854696 9.397304
## [295] 11.334870 10.235742 10.413482 9.121429 10.124227 9.148692 13.006056
## [302] 8.099411 8.724344 9.662042 9.094519 8.473819 11.591563 10.315101
## [309] 12.240949 9.133096 7.793825 9.497912 10.105098 9.925381 12.109387
## [316] 8.140085 10.134756 10.364296 8.292059 8.333134 8.434361 10.457061
## [323] 9.797632 10.064054 12.186021 9.476022 11.917190 8.328681 9.905574
## [330] 10.465267 11.114155 8.597550 11.846146 9.512762 9.839932 9.217301
## [337] 9.030561 8.680831 13.070117 6.785401 10.955961 10.572404 9.952404
## [344] 11.265278 9.051457 8.435046 10.186195 8.129361 11.410330 6.751382
## [351] 10.989741 8.363786 8.929640 9.327761 12.406272 10.892369 9.100620
## [358] 9.608513 8.490786 9.747698 10.621075 10.820743 13.531101 10.994681
## [365] 9.313542 12.394052 9.164118 10.518174 11.651578 11.241520 10.670770
## [372] 8.257238 11.428275 11.533407 9.249156 9.556560 14.291225 10.569004
## [379] 10.310323 11.158465 10.678137 10.410803 11.007604 9.135700 9.465463
## [386] 10.040493 10.126103 9.029904 12.917785 10.064352 11.387076 10.344368
## [393] 6.953606 11.131321 10.259821 12.160199 9.192902 11.162549 10.436374
## [400] 9.103179 9.080871 11.332827 9.557347 10.287416 10.862921 10.641749
## [407] 10.420787 11.761398 9.411049 9.218700 10.130067 12.395386 7.004955
## [414] 10.154569 9.718716 9.027227 9.643266 10.089223 8.521009 8.668497
## [421] 11.672299 7.859337 10.981180 8.737681 8.612905 9.000565 10.239672
## [428] 8.256575 11.581789 10.516647 10.821301 10.828694 8.327743 8.345526
## [435] 9.117227 7.512084 9.331777 9.358968 9.053457 8.235558 7.839651
## [442] 10.470561 10.780616 10.415913 9.258135 10.309346 11.843347 11.583459
## [449] 11.033464 7.335309 8.872175 13.004110 11.625807 11.338608 12.174141
## [456] 9.060897 9.722204 9.999976 6.786853 12.466771 9.531343 10.933224
## [463] 8.714656 7.991140 9.164851 9.129708 10.860568 10.194502 10.208229
## [470] 10.687780 11.926426 9.965222 6.882043 10.827845 12.179437 11.266646
## [477] 9.152858 9.707317 8.802960 10.003593 9.740359 9.312831 10.025559
## [484] 8.198005 9.349980 11.839523 12.053951 11.969965 10.230904 9.616769
## [491] 9.479424 12.587702 8.672738 11.022945 13.087338 9.135811 9.483540
## [498] 10.722444 10.618174 9.084657 8.935930 8.897109 9.724278 10.583376
## [505] 9.464441 11.160243 9.818544 11.311152 10.431105 6.675627 10.280578
## [512] 10.580221 12.147518 7.892245 9.957741 9.789358 9.403079 9.584239
## [519] 10.888257 8.717945 9.279078 9.053179 11.284019 12.451378 8.887099
## [526] 9.482000 10.291025 10.110524 12.106939 11.861249 9.643348 11.050188
## [533] 9.099128 12.376846 10.393046 10.712758 12.205166 11.999305 9.704953
## [540] 9.779474 8.927603 7.283450 7.700335 9.804784 9.710561 9.908998
## [547] 9.895354 9.699555 11.011068 9.941079 9.727873 11.896799 10.119808
## [554] 9.669662 9.601679 10.418067 8.108810 8.781096 10.140346 9.746640
## [561] 6.623583 9.202413 10.740744 8.153043 10.031725 8.206052 9.528611
## [568] 10.524178 8.962082 8.763390 10.042861 11.245679 10.283159 8.743058
## [575] 7.640916 12.300116 10.350114 8.951530 11.255688 9.493795 8.907656
## [582] 11.631617 10.068804 12.649081 10.245317 6.322465 9.218497 10.406868
## [589] 9.194821 9.921868 10.479572 10.384058 8.800065 9.464462 8.567888
## [596] 10.855923 8.150384 10.189142 10.081231 9.013334 6.636572 10.290546
## [603] 10.013158 12.361755 10.882773 11.019894 11.169550 8.267598 11.456463
## [610] 12.540113 10.915058 7.910858 10.122106 9.104555 10.454336 9.859929
## [617] 12.796919 10.196658 9.619342 13.757786 12.281088 11.284080 8.205125
## [624] 7.081156 11.296076 9.725752 9.288263 9.533286 10.902774 8.955137
## [631] 9.767108 9.780851 11.282035 11.431000 10.246295 7.683265 9.741303
## [638] 8.354910 7.774915 11.073205 11.769248 11.315137 11.452971 10.381605
## [645] 8.046913 9.625168 11.305225 8.845981 12.491290 9.840545 12.557018
## [652] 10.836278 10.876828 10.175392 11.106748 9.810433 10.982255 9.561833
## [659] 11.318694 8.556406 9.237468 12.151974 12.586315 8.444787 12.635871
## [666] 8.381737 11.794432 12.233468 11.267006 12.181257 13.077094 9.952302
## [673] 8.654879 7.993792 11.180171 9.309846 9.941756 7.709133 11.108635
## [680] 11.156229 8.218341 10.911012 10.647210 10.497310 10.265833 11.139282
## [687] 9.077826 7.144442 7.551286 10.786945 10.546839 10.499696 7.840142
## [694] 8.904582 11.420466 9.149924 9.909235 9.762870 12.588605 11.230897
## [701] 9.525763 13.177216 8.481075 10.720755 11.248070 11.418790 9.784976
## [708] 8.560991 10.530037 8.861023 10.013888 11.934580 7.566265 9.945755
## [715] 11.328595 9.355205 9.419868 11.356481 12.027203 10.010775 11.027463
## [722] 11.716999 7.268969 10.099031 7.990155 10.193616 9.103840 11.745624
## [729] 10.313051 9.992588 9.163826 9.876245 10.532768 11.167946 8.370823
## [736] 9.849059 9.898308 9.723545 9.572471 9.905257 10.396183 10.626221
## [743] 11.061387 11.501204 9.031467 7.882357 11.599437 10.230200 10.651845
## [750] 12.169662 10.566994 9.347554 11.839588 9.796079 8.765627 7.066052
## [757] 10.562243 12.599296 10.497066 8.671959 9.761591 7.354103 10.195784
## [764] 13.133236 8.734880 7.815359 13.728980 11.027464 9.617826 11.815991
## [771] 9.389260 11.941690 8.760292 9.010829 8.531923 8.932541 12.371889
## [778] 9.283608 10.411153 9.433822 10.195170 10.176257 11.950729 7.523664
## [785] 9.694202 9.883879 10.012662 10.826080 7.392231 9.530937 11.824101
## [792] 9.332166 9.959256 9.335129 11.045742 8.858476 9.391945 9.887566
## [799] 10.876456 11.785758 10.148272 11.823386 10.264259 9.407835 9.187756
## [806] 10.025460 11.000959 11.780756 9.585567 11.358245 7.945035 10.283059
## [813] 11.133471 9.175844 10.459500 12.215542 12.279831 8.153478 10.168234
## [820] 6.843177 8.571559 8.163251 9.208785 11.999782 9.825692 10.180915
## [827] 7.845691 11.361759 8.625008 8.863226 10.941582 10.378365 10.966278
## [834] 11.289225 9.099134 11.390989 11.740394 8.603530 10.336244 9.233612
## [841] 10.740661 8.582763 10.988714 9.664261 11.630561 11.003003 8.618647
## [848] 11.300963 10.257953 11.161850 7.842001 10.491902 9.795134 9.350940
## [855] 12.479555 9.311299 9.877285 10.317017 10.364574 9.794870 9.221100
## [862] 10.886902 11.519340 8.734999 12.008271 10.777798 6.882805 10.431280
## [869] 7.804305 10.368256 10.404721 8.110673 5.558447 10.027924 12.517053
## [876] 10.306966 9.968578 11.648826 9.733284 10.748149 9.925599 8.980794
## [883] 7.895224 8.590864 9.910292 8.915015 8.562587 9.284918 8.986030
## [890] 11.266499 11.682557 10.059206 7.743917 9.593823 9.180189 9.239145
## [897] 9.993296 9.398299 10.207152 9.322932 10.006293 9.801250 9.214939
## [904] 9.694562 9.813196 9.586336 10.475332 6.545851 10.804434 11.785867
## [911] 8.503011 9.189522 7.381899 9.375311 7.565995 9.531182 10.571017
## [918] 10.645797 12.423748 7.839492 10.147444 12.089749 13.799995 9.483284
## [925] 7.437060 8.031609 8.771517 12.574482 11.851917 10.269166 10.158822
## [932] 12.267444 8.768148 8.368293 10.816231 9.359801 9.482082 11.721120
## [939] 8.469067 10.566424 9.122434 8.839548 10.544826 13.547948 11.540911
## [946] 10.136633 8.993507 11.425358 9.391518 11.812675 10.866689 12.708656
## [953] 8.919754 11.393472 8.924600 8.998206 9.787677 10.344993 6.950948
## [960] 10.014938 12.324510 10.832458 8.614930 8.197952 10.026080 11.440410
## [967] 8.095068 13.157242 10.256615 9.956512 8.429419 9.510870 7.081044
## [974] 9.598949 11.805438 12.063455 12.380869 10.685901 9.240838 10.170167
## [981] 9.240340 11.747324 10.650385 13.550129 10.096262 6.984516 11.684713
## [988] 11.062334 10.015794 8.872361 9.062901 9.540098 11.074181 9.269467
## [995] 12.496101 9.076274 12.104588 11.224587 8.512122 5.438818
# 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 2 1 4 2 2 3 4 4 3 3 3 3 5 4 1 4 3 2 2 2 3 5 2 4 3 3 2 3 2 5 2 2 3 3 2 2
## [38] 4 5 3 2 2 4 4 4 5 3 2 5 3 2 4 4 5 2 3 4 2 2 3 3 3 4 1 5 4 4 2 4 3 2 3 4 2
## [75] 3 4 3 3 3 3 2 1 3 2 2 3 3 4 5 3 5 1 2 4 3 3 3 2 4 2 4 2 2 1 2 2 3 3 4 4 1
## [112] 2 3 4 4 3 3 3 2 2 4 1 3 3 3 4 3 4 2 3 4 5 3 2 4 4 2 4 3 3 5 2 2 3 2 5 2 3
## [149] 2 4 3 2 4 4 2 3 2 3 2 2 3 3 4 3 2 3 3 2 2 2 4 4 2 3 4 3 4 4 3 3 3 3 3 3 2
## [186] 3 1 4 3 3 2 1 4 4 3 2 2 2 3 4 5 4 4 5 2 4 3 2 2 3 3 2 2 4 3 3 2 3 2 3 3 3
## [223] 3 4 5 4 3 1 5 3 1 4 3 1 3 4 3 3 2 2 3 4 4 3 5 3 2 4 2 4 4 2 3 2 5 3 2 2 1
## [260] 2 2 3 3 3 3 3 4 2 1 2 5 3 5 5 3 2 1 2 1 4 4 1 2 1 2 4 5 3 4 4 4 2 3 2 3 3
## [297] 4 4 3 3 4 2 2 3 1 2 4 4 4 3 1 3 3 3 4 1 2 2 2 3 3 2 3 2 4 3 4 2 2 3 3 2 5
## [334] 3 2 3 3 3 4 1 3 4 2 2 2 3 3 1 4 1 3 2 4 2 5 4 3 2 3 3 3 2 5 4 1 4 3 2 4 5
## [371] 4 1 5 5 3 3 5 3 4 3 3 3 4 1 3 3 2 2 5 4 4 3 2 3 4 4 3 2 3 4 2 4 1 2 4 2 3
## [408] 5 3 2 3 5 1 4 3 2 3 4 2 3 3 1 3 2 2 4 2 2 3 3 3 3 1 2 3 1 3 3 3 3 2 3 5 3
## [445] 3 2 5 3 3 1 5 4 3 4 4 3 2 4 1 5 3 3 3 2 2 2 2 3 3 4 4 2 1 3 4 4 3 3 1 2 3
## [482] 3 4 1 3 5 3 5 3 2 3 4 2 3 5 2 2 2 4 2 2 3 4 4 3 4 3 5 2 1 3 3 5 2 2 1 3 4
## [519] 3 2 2 2 3 3 2 3 4 3 4 5 3 4 3 5 3 4 4 5 4 3 1 2 3 4 1 3 2 3 3 2 3 3 3 3 3
## [556] 2 2 4 1 3 1 3 4 3 3 2 2 3 2 3 3 4 4 4 3 5 4 3 3 4 3 4 2 4 3 1 3 4 3 2 4 3
## [593] 2 2 3 3 2 4 3 3 2 3 3 4 3 3 3 3 4 5 4 3 3 3 3 2 4 4 3 4 4 3 2 1 3 4 2 2 3
## [630] 2 3 3 4 3 3 3 3 2 2 3 4 3 4 4 2 2 3 3 4 3 5 4 3 4 3 3 3 2 2 2 2 5 5 1 5 3
## [667] 4 3 4 4 5 4 3 3 3 3 3 3 3 3 2 2 4 3 4 4 2 2 3 3 4 3 2 2 4 2 4 3 4 3 4 4 2
## [704] 3 3 3 3 3 3 2 3 5 2 3 5 3 2 3 4 2 3 4 2 1 2 3 3 4 4 3 3 3 3 4 3 4 4 3 3 3
## [741] 3 3 4 4 3 2 4 3 2 4 4 3 4 3 2 3 5 5 2 2 2 2 3 4 2 1 5 4 2 5 3 3 2 1 2 4 5
## [778] 2 3 3 3 3 3 2 4 2 3 3 2 2 4 2 2 3 3 3 2 3 2 3 4 5 4 2 3 3 3 4 3 4 1 3 5 2
## [815] 3 4 4 1 3 2 3 1 2 4 3 2 2 3 1 3 4 3 3 4 1 3 5 3 4 2 3 2 4 2 4 2 3 3 3 3 2
## [852] 3 3 3 4 3 3 3 3 3 2 4 3 3 5 2 1 4 1 3 3 2 1 3 5 2 2 4 4 2 2 3 2 3 4 3 3 3
## [889] 2 4 3 4 2 3 3 3 4 3 3 2 3 2 3 3 2 3 3 1 4 4 2 3 1 3 1 4 4 3 4 2 2 4 5 2 2
## [926] 3 3 4 4 3 3 5 2 2 4 2 3 3 3 3 3 2 4 5 5 4 2 4 2 4 3 5 4 4 3 2 2 3 1 3 4 3
## [963] 2 2 3 4 2 4 3 2 2 3 1 3 4 4 4 3 3 4 4 4 3 5 3 4 3 4 4 3 2 3 4 3 3 3 4 4 2
## [1000] 1
# create sample dataframe by joining variables
sample_data <- data.frame(xAxis,yAxis,group)
sample_data
## xAxis yAxis group
## 1 1.291949564 10.417822 4
## 2 -0.555081660 8.891954 2
## 3 -1.857351913 9.796713 1
## 4 1.255459666 10.726390 4
## 5 -1.329177124 8.824093 2
## 6 -0.832493748 9.434549 2
## 7 0.015429354 8.897032 3
## 8 0.524770701 10.279565 4
## 9 1.253751876 9.844772 4
## 10 0.447863667 10.069699 3
## 11 -0.328137075 8.192616 3
## 12 -0.142306136 9.811317 3
## 13 -0.487790592 10.322196 3
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## 999 -1.258115158 8.512122 2
## 1000 -2.896682345 5.438818 1
# 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)
