# Mindanao State University
# General Santos City
# Introduction to R base commands
# Submitted by: Jen Marie B. Talaugon
# Submitted to: Prof. Carlito O. Daarol
# Math Department
# 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 = "red")
# Step 5: Modify Thickness of Line using lwd command
plot(x, y, type = "l",
main = "This is my Line Plot",
xlab = "My X-Values",
ylab = "My Y-Values",
lwd=7,
col = "green")
# Step 6: Add points to line graph by changing the type command
plot(x, y, type = "b",
main = "This is my Line Plot",
xlab = "My X-Values",
ylab = "My Y-Values",
lwd=3,
col = "blue")
# Lab Exercise 2: How to create Lines in with different styles in R
# Step1: Assign values for different lines. We enclose the entire
# line with parenthesis symbol to force R to display the results instantly
# set the same value for the x variable
(x <- 1:10)
## [1] 1 2 3 4 5 6 7 8 9 10
## [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
## [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
## [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
## [1] 3 3 3 3 4 4 5 5 7 7
# Plot first the pair x and y1.
plot(x, y1, type = "b",
main = "This is my Line Plot",
xlab = "My X-Values",
ylab = "My Y-Values",
lwd=3,
col = "blue")
# then Add the two lines (for x,y2) and (x,y3)
lines(x, y2, type = "b", col = "red",lwd=3)
lines(x, y3, type = "b", col = "green",lwd=3)
# Add legend to the plot
legend("topleft",
legend = c("Line y1", "Line y2", "Line y3"),
col = c("black", "red", "green"),
lty = 1)
# Step 2: Create Different Point Symbol for Each
# Line using the pch command
plot(x, y1, type = "b",pch = 16,
main = "This is my Line Plot",
xlab = "My X-Values",
ylab = "My Y-Values",
lwd=3,
col = "blue")
lines(x, y2, type = "b", col = "red",lwd=3, pch = 15)
lines(x, y3, type = "b", col = "green",lwd=3, pch = 8)
# Add legend
legend("topleft",
legend = c("Line y1", "Line y2", "Line y3"),
col = c("black", "red", "green"),
lty = 1)
# Lab Exercise 3: Create Line graph without x values
Pupils <- c(3.55 ,3.54 ,3.53 ,3.61 ,3.65 ,3.63 ,3.61
,3.61 ,3.59 ,3.63 ,3.59 ,3.63 ,3.62 ,3.62
,3.59 ,3.63 ,3.62 ,3.65 ,3.65)
# get number of elements of Pupils
length(Pupils)
## [1] 19
## [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
## [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$dist) # using the dollar notation
par(mfrow = c(1,1)) # reset to default plot setting
# Problem: Create vertical lines using the v command
plot(cars)
abline(v = 15, col = "darkgreen",lwd=3) # vertical line
abline(v = 10, col = "blue",lwd=3) # vertical line
# Problem: Create horizontal lines using the h command
abline(h = 80, col = "darkgreen",lwd=3) # vertical line
abline(h = 20, col = "blue",lwd=3) # vertical line
# Create more lines simultaneously, using a vector of values
plot(cars)
abline(v = c(9, 22,25), col = c("darkgreen", "blue","red"),
lwd = c(1, 3,2), # line thickness
lty = c(2,2,2)) # dashed lines
plot(cars)
abline(v = c(9, 22,25), col = c("darkgreen", "blue","red"),
lwd = c(1, 3,2))
# line thickness and solid lines
# create horizontal lines
plot(cars)
abline(h = 60, col = "red",lty = 1, lwd = 3)
abline(h = 100, col = "red",lty = 2, lwd = 3)
abline(h = 20, col = "red",lty = 3, lwd = 3)
# Lab Exercise 5: How to Plot data by group
# We will use buit-in iris dataset in R
# this dataset is a collection of 4 species of flowers with different
# sepal length and width and also with different petal length and width
dim(iris) # iris dataset has 150 rows and 5 columns
## [1] 150 5
# two different commands to get the frequency table
table(iris$Species) # refer to the dataset by variable name
##
## setosa versicolor virginica
## 50 50 50
table(iris[,5]) # refer to the dataset by column number
##
## setosa versicolor virginica
## 50 50 50
# get summary of all columns
summary(iris)
## Sepal.Length Sepal.Width Petal.Length Petal.Width
## Min. :4.300 Min. :2.000 Min. :1.000 Min. :0.100
## 1st Qu.:5.100 1st Qu.:2.800 1st Qu.:1.600 1st Qu.:0.300
## Median :5.800 Median :3.000 Median :4.350 Median :1.300
## Mean :5.843 Mean :3.057 Mean :3.758 Mean :1.199
## 3rd Qu.:6.400 3rd Qu.:3.300 3rd Qu.:5.100 3rd Qu.:1.800
## Max. :7.900 Max. :4.400 Max. :6.900 Max. :2.500
## Species
## setosa :50
## versicolor:50
## virginica :50
##
##
##
#create scatterplot of sepal width vs. sepal length
plot(iris$Sepal.Width, iris$Sepal.Length,
col='steelblue',
main='Scatterplot',
xlab='Sepal Width',
ylab='Sepal Length',
pch=19)
plot(iris$Sepal.Width, iris$Sepal.Length,
col='steelblue',
main='Scatterplot',
xlab='Sepal Width',
ylab='Sepal Length',
pch=1)
# another way to retrieve columns of data
PL <- iris$Petal.Length
PW <- iris$Petal.Width
plot(PL, PW)
# add color by species
plot(PL, PW, col = iris$Species, main= "My Plot")
# draw a line along with the distribution of points
# using the abline and lm commands
abline(lm(PW ~ PL))
# add text annotation
text(5, 0.5, "Regression Line")
legend("topleft", # specify the location of the legend
levels(iris$Species), # specify the levels of species
pch = 1:3, # specify three symbols used for the three species
col = 1:3 # specify three colors for the three species
)
# Lab Exercise 6: Generate advance scatter plot
pairs(iris, col=rainbow(3)[iris$Species]) # set colors by species
# How to filter data for each flower
(Versicolor<-subset(iris, Species == "versicolor"))
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 51 7.0 3.2 4.7 1.4 versicolor
## 52 6.4 3.2 4.5 1.5 versicolor
## 53 6.9 3.1 4.9 1.5 versicolor
## 54 5.5 2.3 4.0 1.3 versicolor
## 55 6.5 2.8 4.6 1.5 versicolor
## 56 5.7 2.8 4.5 1.3 versicolor
## 57 6.3 3.3 4.7 1.6 versicolor
## 58 4.9 2.4 3.3 1.0 versicolor
## 59 6.6 2.9 4.6 1.3 versicolor
## 60 5.2 2.7 3.9 1.4 versicolor
## 61 5.0 2.0 3.5 1.0 versicolor
## 62 5.9 3.0 4.2 1.5 versicolor
## 63 6.0 2.2 4.0 1.0 versicolor
## 64 6.1 2.9 4.7 1.4 versicolor
## 65 5.6 2.9 3.6 1.3 versicolor
## 66 6.7 3.1 4.4 1.4 versicolor
## 67 5.6 3.0 4.5 1.5 versicolor
## 68 5.8 2.7 4.1 1.0 versicolor
## 69 6.2 2.2 4.5 1.5 versicolor
## 70 5.6 2.5 3.9 1.1 versicolor
## 71 5.9 3.2 4.8 1.8 versicolor
## 72 6.1 2.8 4.0 1.3 versicolor
## 73 6.3 2.5 4.9 1.5 versicolor
## 74 6.1 2.8 4.7 1.2 versicolor
## 75 6.4 2.9 4.3 1.3 versicolor
## 76 6.6 3.0 4.4 1.4 versicolor
## 77 6.8 2.8 4.8 1.4 versicolor
## 78 6.7 3.0 5.0 1.7 versicolor
## 79 6.0 2.9 4.5 1.5 versicolor
## 80 5.7 2.6 3.5 1.0 versicolor
## 81 5.5 2.4 3.8 1.1 versicolor
## 82 5.5 2.4 3.7 1.0 versicolor
## 83 5.8 2.7 3.9 1.2 versicolor
## 84 6.0 2.7 5.1 1.6 versicolor
## 85 5.4 3.0 4.5 1.5 versicolor
## 86 6.0 3.4 4.5 1.6 versicolor
## 87 6.7 3.1 4.7 1.5 versicolor
## 88 6.3 2.3 4.4 1.3 versicolor
## 89 5.6 3.0 4.1 1.3 versicolor
## 90 5.5 2.5 4.0 1.3 versicolor
## 91 5.5 2.6 4.4 1.2 versicolor
## 92 6.1 3.0 4.6 1.4 versicolor
## 93 5.8 2.6 4.0 1.2 versicolor
## 94 5.0 2.3 3.3 1.0 versicolor
## 95 5.6 2.7 4.2 1.3 versicolor
## 96 5.7 3.0 4.2 1.2 versicolor
## 97 5.7 2.9 4.2 1.3 versicolor
## 98 6.2 2.9 4.3 1.3 versicolor
## 99 5.1 2.5 3.0 1.1 versicolor
## 100 5.7 2.8 4.1 1.3 versicolor
(Setosa<-subset(iris, Species == "setosa"))
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa
## 7 4.6 3.4 1.4 0.3 setosa
## 8 5.0 3.4 1.5 0.2 setosa
## 9 4.4 2.9 1.4 0.2 setosa
## 10 4.9 3.1 1.5 0.1 setosa
## 11 5.4 3.7 1.5 0.2 setosa
## 12 4.8 3.4 1.6 0.2 setosa
## 13 4.8 3.0 1.4 0.1 setosa
## 14 4.3 3.0 1.1 0.1 setosa
## 15 5.8 4.0 1.2 0.2 setosa
## 16 5.7 4.4 1.5 0.4 setosa
## 17 5.4 3.9 1.3 0.4 setosa
## 18 5.1 3.5 1.4 0.3 setosa
## 19 5.7 3.8 1.7 0.3 setosa
## 20 5.1 3.8 1.5 0.3 setosa
## 21 5.4 3.4 1.7 0.2 setosa
## 22 5.1 3.7 1.5 0.4 setosa
## 23 4.6 3.6 1.0 0.2 setosa
## 24 5.1 3.3 1.7 0.5 setosa
## 25 4.8 3.4 1.9 0.2 setosa
## 26 5.0 3.0 1.6 0.2 setosa
## 27 5.0 3.4 1.6 0.4 setosa
## 28 5.2 3.5 1.5 0.2 setosa
## 29 5.2 3.4 1.4 0.2 setosa
## 30 4.7 3.2 1.6 0.2 setosa
## 31 4.8 3.1 1.6 0.2 setosa
## 32 5.4 3.4 1.5 0.4 setosa
## 33 5.2 4.1 1.5 0.1 setosa
## 34 5.5 4.2 1.4 0.2 setosa
## 35 4.9 3.1 1.5 0.2 setosa
## 36 5.0 3.2 1.2 0.2 setosa
## 37 5.5 3.5 1.3 0.2 setosa
## 38 4.9 3.6 1.4 0.1 setosa
## 39 4.4 3.0 1.3 0.2 setosa
## 40 5.1 3.4 1.5 0.2 setosa
## 41 5.0 3.5 1.3 0.3 setosa
## 42 4.5 2.3 1.3 0.3 setosa
## 43 4.4 3.2 1.3 0.2 setosa
## 44 5.0 3.5 1.6 0.6 setosa
## 45 5.1 3.8 1.9 0.4 setosa
## 46 4.8 3.0 1.4 0.3 setosa
## 47 5.1 3.8 1.6 0.2 setosa
## 48 4.6 3.2 1.4 0.2 setosa
## 49 5.3 3.7 1.5 0.2 setosa
## 50 5.0 3.3 1.4 0.2 setosa
(Virginica<-subset(iris, Species == "virginica"))
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 101 6.3 3.3 6.0 2.5 virginica
## 102 5.8 2.7 5.1 1.9 virginica
## 103 7.1 3.0 5.9 2.1 virginica
## 104 6.3 2.9 5.6 1.8 virginica
## 105 6.5 3.0 5.8 2.2 virginica
## 106 7.6 3.0 6.6 2.1 virginica
## 107 4.9 2.5 4.5 1.7 virginica
## 108 7.3 2.9 6.3 1.8 virginica
## 109 6.7 2.5 5.8 1.8 virginica
## 110 7.2 3.6 6.1 2.5 virginica
## 111 6.5 3.2 5.1 2.0 virginica
## 112 6.4 2.7 5.3 1.9 virginica
## 113 6.8 3.0 5.5 2.1 virginica
## 114 5.7 2.5 5.0 2.0 virginica
## 115 5.8 2.8 5.1 2.4 virginica
## 116 6.4 3.2 5.3 2.3 virginica
## 117 6.5 3.0 5.5 1.8 virginica
## 118 7.7 3.8 6.7 2.2 virginica
## 119 7.7 2.6 6.9 2.3 virginica
## 120 6.0 2.2 5.0 1.5 virginica
## 121 6.9 3.2 5.7 2.3 virginica
## 122 5.6 2.8 4.9 2.0 virginica
## 123 7.7 2.8 6.7 2.0 virginica
## 124 6.3 2.7 4.9 1.8 virginica
## 125 6.7 3.3 5.7 2.1 virginica
## 126 7.2 3.2 6.0 1.8 virginica
## 127 6.2 2.8 4.8 1.8 virginica
## 128 6.1 3.0 4.9 1.8 virginica
## 129 6.4 2.8 5.6 2.1 virginica
## 130 7.2 3.0 5.8 1.6 virginica
## 131 7.4 2.8 6.1 1.9 virginica
## 132 7.9 3.8 6.4 2.0 virginica
## 133 6.4 2.8 5.6 2.2 virginica
## 134 6.3 2.8 5.1 1.5 virginica
## 135 6.1 2.6 5.6 1.4 virginica
## 136 7.7 3.0 6.1 2.3 virginica
## 137 6.3 3.4 5.6 2.4 virginica
## 138 6.4 3.1 5.5 1.8 virginica
## 139 6.0 3.0 4.8 1.8 virginica
## 140 6.9 3.1 5.4 2.1 virginica
## 141 6.7 3.1 5.6 2.4 virginica
## 142 6.9 3.1 5.1 2.3 virginica
## 143 5.8 2.7 5.1 1.9 virginica
## 144 6.8 3.2 5.9 2.3 virginica
## 145 6.7 3.3 5.7 2.5 virginica
## 146 6.7 3.0 5.2 2.3 virginica
## 147 6.3 2.5 5.0 1.9 virginica
## 148 6.5 3.0 5.2 2.0 virginica
## 149 6.2 3.4 5.4 2.3 virginica
## 150 5.9 3.0 5.1 1.8 virginica
# Draw boxplot for each type of flower
boxplot(Versicolor[,1:4], main="Versicolor, Rainbow Palette",ylim =
c(0,8),las=2, col=rainbow(4))
boxplot(Setosa[,1:4], main="Setosa, Heat color Palette",ylim = c(0,8),las=2,
col=heat.colors(4))
boxplot(Virginica[,1:4], main="Virginica, Topo colors Palette",ylim =
c(0,8),las=2, col=topo.colors(4))
# Lab Exercise 7: How to load external datasets
# From a local directory
# the folder that contains the file should be specified completely
# using the forward slash symbol instead of the backward splash
library(readr)
cancer<-read_csv("Cancer.csv")
## Rows: 173 Columns: 17
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): country, continent
## dbl (15): incomeperperson, alcconsumption, armedforcesrate, breastcancer, co...
##
## â„ą Use `spec()` to retrieve the full column specification for this data.
## â„ą Specify the column types or set `show_col_types = FALSE` to quiet this message.
dim(cancer)
## [1] 173 17
names(cancer)
## [1] "country" "incomeperperson" "alcconsumption"
## [4] "armedforcesrate" "breastcancer" "co2emissions"
## [7] "femaleemployrate" "hivrate" "internetuserate"
## [10] "lifeexpectancy" "oilperperson" "polityscore"
## [13] "relectricperperson" "suicideper100th" "employrate"
## [16] "urbanrate" "continent"
# compute mean value for every continent
(means <- round(tapply(cancer$breastcancer, cancer$continent, mean),
digits=2))
## AF AS EE LATAM NORAM OC WE
## 24.02 24.51 49.44 36.70 71.73 45.80 74.80
# draw boxplot per continent
boxplot(cancer$breastcancer ~ cancer$continent, main= "Breast cancer by
continent (brown dot = mean value)", xlab="continents", ylab="new
cases per 100,00 residents", col=rainbow(7))
# insert the mean value using brown dot
points(means, col="brown", pch=18)
# Lab Exercise 8: How to load external datasets and change data layout
library(readr)
hsb2 <- read_csv("hsb2.csv")
## New names:
## Rows: 200 Columns: 12
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "," dbl
## (12): ...1, id, female, race, ses, schtyp, prog, read, write, math, scie...
## â„ą Use `spec()` to retrieve the full column specification for this data. â„ą
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## • `` -> `...1`
# display only the top 6 rows
head(hsb2)
## # A tibble: 6 Ă— 12
## ...1 id female race ses schtyp prog read write math science socst
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 70 0 4 1 1 1 57 52 41 47 57
## 2 2 121 1 4 2 1 3 68 59 53 63 61
## 3 3 86 0 4 3 1 1 44 33 54 58 31
## 4 4 141 0 4 3 1 3 63 44 47 53 56
## 5 5 172 0 4 2 1 2 47 52 57 53 61
## 6 6 113 0 4 2 1 2 44 52 51 63 61
# display only the last 6 rows
tail(hsb2)
## # A tibble: 6 Ă— 12
## ...1 id female race ses schtyp prog read write math science socst
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 195 179 1 4 2 2 2 47 65 60 50 56
## 2 196 31 1 2 2 2 1 55 59 52 42 56
## 3 197 145 1 4 2 1 3 42 46 38 36 46
## 4 198 187 1 4 2 2 1 57 41 57 55 52
## 5 199 118 1 4 2 1 1 55 62 58 58 61
## 6 200 137 1 4 3 1 2 63 65 65 53 61
# delete redundant first column (run only once)
(hsb2<-hsb2[-1])
## # A tibble: 200 Ă— 11
## id female race ses schtyp prog read write math science socst
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 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
## # â„ą 190 more rows
# Remarks
# hsb2 dataset consists of 200 selected random samples from senior
# high school students in the US.
# We want to compare the student performance across different subjects
# change data layout by grouping different subjects
# into one column using melt() command. Install first reshape2 package
# install.packages("reshape2")
library(reshape2)
(hsb2_long <- melt(hsb2, 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
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## 982 193 1 4 2 2 2 socst 51
## 983 92 1 4 3 1 1 socst 61
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## 996 31 1 2 2 2 1 socst 56
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## 1000 137 1 4 3 1 2 socst 61
#Remark: Pay extra attention to the last 2 columns
head(hsb2_long)
## id female race ses schtyp prog variable value
## 1 70 0 4 1 1 1 read 57
## 2 121 1 4 2 1 3 read 68
## 3 86 0 4 3 1 1 read 44
## 4 141 0 4 3 1 3 read 63
## 5 172 0 4 2 1 2 read 47
## 6 113 0 4 2 1 2 read 44
tail(hsb2_long)
## id female race ses schtyp prog variable value
## 995 179 1 4 2 2 2 socst 56
## 996 31 1 2 2 2 1 socst 56
## 997 145 1 4 2 1 3 socst 46
## 998 187 1 4 2 2 1 socst 52
## 999 118 1 4 2 1 1 socst 61
## 1000 137 1 4 3 1 2 socst 61
# get thefrequency
table(hsb2_long$variable)
##
## read write math science socst
## 200 200 200 200 200
# This means that the tables hsb2_long and hsb2_wide are the same
# tables displayed in two ways
# display data structure of the hsb2_long dataset
str(hsb2_long)
## 'data.frame': 1000 obs. of 8 variables:
## $ id : num 70 121 86 141 172 113 50 11 84 48 ...
## $ female : num 0 1 0 0 0 0 0 0 0 0 ...
## $ race : num 4 4 4 4 4 4 3 1 4 3 ...
## $ ses : num 1 2 3 3 2 2 2 2 2 2 ...
## $ schtyp : num 1 1 1 1 1 1 1 1 1 1 ...
## $ prog : num 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 : num 57 68 44 63 47 44 50 34 63 57 ...
# the variables female, race, ses, schtyp, prog are stored as numbers
# for encoding purposes. However these variables are actually qualitative variables
# so we convert each from integer type to categorical type
# defining some variables to become factor variable
# we use another variable to preserve the file hsb2_long
data <- hsb2_long
data$ses = factor(data$ses, labels=c("low", "middle", "high"))
data$schtyp = factor(data$schtyp, labels=c("public", "private"))
data$prog = factor(data$prog, labels=c("general", "academic", "vocational"))
data$race = factor(data$race, labels=c("hispanic", "asian", "africanamer","white"))
data$female = factor(data$female, labels=c("female", "male"))
# check data structure again. The former integer variables are now categorical variable
str(data)
## 'data.frame': 1000 obs. of 8 variables:
## $ id : num 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 : num 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
## â„ą This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## â„ą 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
## â„ą This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## â„ą 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
## â„ą This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## â„ą 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
## â„ą This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## â„ą 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
## â„ą This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## â„ą 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
## â„ą This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## â„ą 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
## â„ą This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## â„ą 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
## â„ą This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## â„ą 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
## â„ą This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## â„ą 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
## â„ą This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## â„ą 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
## â„ą This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## â„ą 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
## â„ą This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## â„ą 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 ──
## âś” dplyr 1.1.1 âś” stringr 1.5.0
## âś” forcats 1.0.0 âś” tibble 3.2.1
## âś” lubridate 1.9.2 âś” tidyr 1.3.0
## âś” purrr 1.0.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## âś– dplyr::filter() masks stats::filter()
## âś– dplyr::lag() masks stats::lag()
## â„ą Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(ggExtra)
# set theme appearance of grid background
theme_set(theme_bw(1))
# create X and Y vector
(xAxis <- rnorm(1000)) # normal distribution with mean 0 and sd=1
## [1] -0.847746816 -0.973672464 1.438488878 -1.149828861 -1.903317733
## [6] -0.408559651 -0.519378236 0.042483923 0.440007488 -0.806491369
## [11] -0.979978372 -0.992636917 0.333540560 -1.190053490 -0.363316592
## [16] 2.404956351 0.615876824 0.707116377 -2.272786791 -1.218772713
## [21] 0.419925436 0.224061179 0.624664922 -0.197298556 1.446387933
## [26] 0.252869813 0.929401527 -1.392003623 0.186749381 0.315598205
## [31] -0.622532493 1.335772410 -0.922243545 0.607210814 0.703031573
## [36] 1.915605498 -0.152911046 -0.470333111 -0.787460461 0.505115277
## [41] 0.078301886 0.326358267 0.812588460 0.175233727 0.063181989
## [46] 1.386046979 0.095748856 0.471645134 -0.382303194 0.818682794
## [51] -0.930640484 0.364539641 -1.321948101 -1.420917467 0.236637393
## [56] -0.090044579 -0.236522752 -1.424998993 -1.794329617 -1.228590423
## [61] 0.429838291 0.420509834 -0.079547149 0.454790159 0.391310791
## [66] -0.698624766 0.245148903 0.557331367 -1.704597622 1.842008031
## [71] 0.292001877 0.656895337 -1.133390965 1.680053976 0.390779797
## [76] 1.083102839 0.031718171 -0.297416378 -0.265401241 0.500061474
## [81] 0.144933997 -0.101657402 -0.310750191 2.369217847 0.193558575
## [86] -0.266720152 1.842375898 0.436992844 -2.107140180 0.921451837
## [91] -1.443199492 -1.259280925 1.168948562 -1.400093737 -1.493423841
## [96] 0.240554916 0.929220924 -0.147424491 -0.330296623 -2.404237667
## [101] 1.325864256 1.612995351 0.589659874 -0.356241071 -0.069218987
## [106] -0.271277614 -2.135090708 -1.063282323 -1.100805953 0.726562167
## [111] -0.308393444 0.437126597 0.744107313 0.816337005 1.305705125
## [116] -0.604394079 -0.469884817 -0.362656143 0.235207781 0.883369625
## [121] -1.080943647 -0.343776234 0.344705052 0.983501586 0.319536523
## [126] 0.560882783 -2.002778053 1.596490970 -0.142226876 -1.691725826
## [131] -1.399875559 0.858847974 1.100182254 -0.568346400 1.190809110
## [136] 1.735915511 0.308226762 -0.299454342 0.952663131 -0.284358596
## [141] -0.345903779 1.681571403 0.315696904 -1.768032613 0.512739705
## [146] -0.432805038 0.295547622 -0.831185681 -0.008678897 -0.985295681
## [151] 0.783130051 -0.581287706 0.565651932 0.497060090 -0.645546562
## [156] 1.597077942 -0.622908351 -0.119987942 -0.110278347 -1.084526729
## [161] 0.184083433 -0.412329937 -1.215105901 -0.100664527 -0.616664493
## [166] -0.471998740 0.412882711 0.323061772 -0.336431513 0.029653457
## [171] -0.908765608 -0.388894180 0.083319818 0.809772470 -0.037311007
## [176] 0.667105136 0.625243022 -1.210428454 -0.682584107 0.937527671
## [181] 0.275650427 -0.802202289 2.152013691 -0.711082236 -2.426108407
## [186] -0.141981163 -0.385894495 1.033141634 -0.725281584 1.528247995
## [191] 0.844186534 -0.203047015 1.812980233 -0.156528430 -0.243234207
## [196] -1.042026577 -2.148776131 0.283730829 -0.080963176 0.718236508
## [201] 0.699167449 1.197567852 0.236978005 -0.142255444 0.124224749
## [206] 0.233103311 0.253601818 0.267090187 -0.743589175 0.225727034
## [211] 0.520095786 -1.034098028 1.180296572 1.149738521 0.299046041
## [216] -0.025618328 -0.140349508 1.647998278 -1.573398647 -0.146809297
## [221] -0.784994965 -2.078095963 0.951818054 0.742050405 0.080866746
## [226] -0.027138213 1.116797782 0.013118347 -2.128213120 0.585469566
## [231] 0.178799417 0.353180874 -0.965876151 0.597561145 1.584423885
## [236] -0.965816228 0.070747318 -1.453946023 -1.024131391 -1.436501053
## [241] 0.610540903 -0.566883482 -0.275417241 -0.043623997 0.384406242
## [246] -1.894122161 -0.419363397 -1.196465313 -2.279605640 -0.030283731
## [251] 0.616197852 0.034743928 0.324883703 -0.571726772 -0.139579741
## [256] -0.302012073 -0.232962575 0.084269605 1.888942858 1.268045566
## [261] 1.260346726 0.935398356 -1.066718864 -1.645549669 1.773273580
## [266] 1.334080280 0.144835476 0.353125329 0.690150446 0.986830934
## [271] -0.144660610 1.465607229 0.751796381 1.127380837 0.496916603
## [276] -0.458891514 0.001734249 0.353226984 0.896313869 -0.392856940
## [281] 0.644852745 -0.183069939 0.737663608 0.669432610 -1.821819973
## [286] -1.212829983 1.270555525 0.225323007 0.658852023 0.916922920
## [291] -0.050458978 0.296613362 -1.084995767 -0.199672481 -1.910844958
## [296] 1.111970967 -0.216503571 2.086061260 0.221199357 -1.984811730
## [301] -0.530404446 -1.871712696 2.475993181 0.699027693 0.890163818
## [306] 1.445210845 -0.450965603 1.087141074 1.073115323 -0.751178739
## [311] -0.992166198 -0.949136061 -0.742255218 0.512141553 -0.062106752
## [316] 0.383306429 0.015646350 -1.342080527 0.312020253 0.698069388
## [321] -0.265736849 0.534117159 0.295306387 -2.159759638 -0.659950046
## [326] 1.626115030 0.022959977 -1.881897741 -1.274864507 -1.141493210
## [331] -0.014985856 -1.253141842 1.124578591 2.192730067 -1.827533360
## [336] 1.662890425 -0.903482423 0.071076779 0.186146575 0.047505574
## [341] -0.138490887 0.749836863 0.756601371 -0.232946006 -0.625378123
## [346] -0.047536998 0.764094091 1.044368716 0.103121368 0.846588833
## [351] 0.037616161 0.922099134 0.976579672 -0.692303359 -0.172997115
## [356] 1.873874078 0.172863007 -0.204621745 0.340694161 0.354886552
## [361] -1.061958063 0.557822764 -2.032168955 1.580954029 -0.651900070
## [366] 0.932253022 -0.189916762 0.917025766 0.263212071 0.121681325
## [371] -1.040937788 -1.512782288 1.413168784 0.562745247 0.444740382
## [376] -0.116340316 0.917972206 -1.661092050 -0.471210665 0.027034975
## [381] -0.757035998 1.852826786 -0.765112581 -1.142201340 -1.051729678
## [386] -0.495694017 -1.376030313 -0.036482424 -2.229061535 1.468551576
## [391] 2.162417089 -0.207828754 1.659738192 -0.527419907 0.232784194
## [396] -0.705801915 -0.615541018 -1.468357881 1.330016407 0.285687543
## [401] -0.691096040 -0.105030903 -1.506809791 1.528094845 0.656897795
## [406] 0.215376277 -0.617422566 -0.945594292 0.044732657 -1.000449059
## [411] 0.538243477 -0.529315801 0.811987070 -0.491934840 0.593705108
## [416] 0.533391452 0.126435717 0.218093559 -2.005049885 -0.111156900
## [421] 0.607950769 -0.291684217 -1.798137257 1.181735183 -0.161056306
## [426] -1.190690467 0.187499843 0.761868160 -1.850219070 1.780883633
## [431] -1.286159095 -0.338455962 -0.685114404 -0.475045393 1.253617724
## [436] -1.989567586 0.684563234 1.698598575 0.831584950 0.590609999
## [441] 0.618182217 0.672022189 0.550686522 -0.191541509 -0.111029483
## [446] -1.616358144 -0.168304514 1.148904196 0.035163958 0.105531403
## [451] -0.832729603 -0.032659696 1.152490672 0.794169254 -0.214780783
## [456] -0.033116195 -0.052917447 0.465160143 -0.257381005 0.226821264
## [461] -0.203856596 -0.468130041 1.224535118 -1.081192542 0.200836417
## [466] -0.008017600 -0.398864888 -0.704027178 0.717398870 1.079533690
## [471] -0.326217257 0.258989173 1.753412715 0.862897857 1.067314292
## [476] -1.082330167 0.579659589 -0.248373463 -0.964265267 1.426909605
## [481] -0.089918083 -0.495761895 -0.031696739 0.278655161 0.259474605
## [486] 0.546793978 1.458662928 1.529467088 -0.974187108 -0.791216221
## [491] 0.652102062 0.123557634 1.922323327 -0.106827586 1.044444507
## [496] -0.607258287 -0.734220290 -2.535923896 -0.301613236 -0.700806180
## [501] 0.210799816 -0.951021444 0.089195771 1.150926432 0.357878553
## [506] -1.009358872 -0.115000713 1.521505830 -0.139245787 1.128850090
## [511] 2.158315614 -1.695848812 -2.173734285 -0.511169006 1.821302614
## [516] -0.689905271 -0.038706583 0.213974679 0.303943911 -0.401531825
## [521] -0.418750285 -1.407363297 -1.528896158 0.321245018 -0.476733610
## [526] 1.111549073 -0.047828235 -0.074066359 0.485884345 0.267775666
## [531] 0.264569004 0.494813351 0.493035855 0.155170960 -0.131484144
## [536] 1.119328348 0.763477205 1.052156680 0.611230614 -0.946803625
## [541] 1.377126720 -1.598313397 0.119277918 -0.346859833 0.382357002
## [546] -0.431930750 -0.022138220 0.399247983 0.214422870 -1.225029964
## [551] 1.390246366 -0.410472973 1.138708615 -0.604659288 0.578246994
## [556] 0.173074178 -1.528266226 1.324059090 1.746672628 -1.138514837
## [561] -0.310416639 0.953213065 1.645476186 1.163358620 1.413534017
## [566] -1.584963992 0.648622875 -1.459446790 0.473120353 -1.524023880
## [571] 1.462597725 -0.130901835 -0.161351120 -0.459101372 1.313216727
## [576] -0.026472460 -0.217983604 0.681121734 0.153916341 0.915086264
## [581] 0.417066454 -0.701309583 -1.764838758 0.260045129 -0.678242885
## [586] -1.387375648 -1.795903704 -0.061949532 0.512384656 -0.424743288
## [591] 0.868888402 -1.157654221 1.362861861 0.580116298 0.837740251
## [596] -0.638405415 -1.106070165 0.245811480 0.877106227 -0.526682883
## [601] -0.611203986 -0.245734439 -0.772154664 0.951529628 -0.828210164
## [606] -0.928114457 -0.258098351 -0.995971253 -0.118556305 -0.184043031
## [611] 1.297284982 -2.898571560 -0.894707744 0.132679173 -0.495971694
## [616] -1.067890819 1.720932986 -0.215125887 -0.278238450 1.270595959
## [621] 1.673301077 -0.620138071 -0.355602590 0.300952680 0.592623851
## [626] -1.426781802 0.103952881 0.902866394 -0.385400370 -1.818795300
## [631] -0.379295341 -0.542324656 -0.809846641 1.019418286 0.346524297
## [636] -0.811607803 -0.277246970 0.165275335 0.367494220 -0.039501090
## [641] 1.254240363 0.434622498 0.313841325 0.950711053 -1.485653858
## [646] 2.664467772 0.654931939 -0.901152430 -1.279496098 -1.153785367
## [651] 0.408978434 -0.588757176 -0.662686550 0.812853258 0.141428546
## [656] -2.211587193 0.596832213 -1.206077034 1.481725539 1.829634286
## [661] 0.645831293 0.412613296 -0.660631155 2.168805486 -0.287088248
## [666] -0.879590698 -0.537996899 -0.170094859 0.247151735 0.562479959
## [671] -0.278777535 -0.340782330 -0.601931651 0.278765907 -0.394655635
## [676] -0.297013796 0.929543354 -0.220808034 0.794599571 0.184820071
## [681] 0.277420366 1.070199284 0.730011145 -1.300414032 -0.350699738
## [686] 1.640829583 -1.881501202 0.704815217 0.715791762 1.428278261
## [691] 1.198899158 -0.229420435 -1.216960917 0.315442733 1.749463046
## [696] -0.309339014 1.478064156 0.389331274 -0.033826894 1.449022293
## [701] 0.157791210 -0.775027975 0.203843362 1.268895353 1.275215653
## [706] -1.595700381 -1.327076434 0.249812439 -1.820643061 0.495943826
## [711] 0.479368883 0.722963121 0.529813284 -0.893173495 0.508781961
## [716] 1.208872015 1.212849484 0.044711979 -0.589089126 -0.665494610
## [721] 0.140755832 -1.092220602 0.794877764 0.893617150 0.402013271
## [726] -0.094616007 -0.691172507 -0.194658917 -1.017144458 0.883507074
## [731] -0.524749434 -1.277411951 -1.779131979 0.185484213 0.940640638
## [736] 0.935087252 0.227247070 -0.403411200 1.807261932 0.742328351
## [741] -1.532682264 1.118501828 0.725862032 1.329618323 -1.112148415
## [746] -0.209506247 -0.264907758 1.546935950 -0.084579747 -0.348701316
## [751] 1.255538486 -1.301077642 0.603153916 0.700908973 -0.142217979
## [756] 0.368547266 1.211487981 -1.129946978 -1.169088624 -0.771795440
## [761] 0.996794769 -1.171898941 -0.892547526 -0.593423310 0.157865314
## [766] 0.270841776 0.018843117 1.498278931 -1.059461716 -1.333549145
## [771] -0.440378206 -0.731142433 -1.273038425 0.226390606 0.944092784
## [776] 0.768668971 -0.068420798 1.378195000 -0.428683555 1.226100908
## [781] 1.076147289 1.071576718 1.245778260 -1.441376843 2.592046396
## [786] 0.042894333 -0.152429454 -1.290053949 -1.340636151 -0.176033874
## [791] -2.400918864 -1.205146651 -0.654305267 -0.257274095 1.493205256
## [796] 2.870063930 0.387575609 0.201574753 -0.334123327 0.969950858
## [801] -0.651939055 0.561661958 -0.040138229 -0.583810750 1.710840696
## [806] 0.107303007 -0.622157237 1.056959252 -0.014683246 -0.132782951
## [811] -0.973452654 -1.095612727 -0.127753655 -1.623386074 -0.272429960
## [816] 0.141448282 0.789798838 -1.188886305 2.531866848 -0.528085060
## [821] 0.076030320 -1.472345807 1.467788186 1.580832444 -1.732536962
## [826] 1.683829391 0.349679633 -1.439967842 0.347376254 -0.638982141
## [831] 1.142397800 0.829397544 -1.063413216 -0.690775973 -1.239039418
## [836] 1.796237735 0.108056380 -0.510582278 -0.312114745 -0.228324880
## [841] 0.472998632 1.260248069 -0.124240282 -0.324142016 1.006363257
## [846] -0.524916406 -0.314542085 -0.133535016 0.063528972 -1.216684318
## [851] 1.250213192 1.003491370 -2.405543423 -0.907923490 -1.230257774
## [856] 0.915677360 -0.788063589 0.356620641 -0.148655719 -3.619609191
## [861] -1.332087920 1.053458795 0.465125834 0.717526262 -0.153379698
## [866] -0.119857948 1.017087200 0.010634876 -0.888529601 0.928853443
## [871] 0.664669769 -1.171249414 1.368106704 0.627456967 0.724084905
## [876] 0.681549910 -0.171463837 -0.146103509 -0.482395919 0.291635169
## [881] -0.655579558 -0.056605953 -0.159829765 -1.042203799 -1.558316148
## [886] -0.761681786 -1.762632796 0.687454473 -0.666851122 -2.070962837
## [891] 1.602127322 -0.527655821 1.268392065 -1.111297307 -0.215827614
## [896] -0.099938057 0.569236136 1.109334614 0.654588429 -0.259636407
## [901] 1.418241079 -0.475751230 -0.026448043 -0.755137422 0.376080131
## [906] 1.589550435 1.278104039 -0.167353405 -1.295376434 -0.671827464
## [911] 0.234589543 0.718877833 0.897938007 -1.753654574 1.855122566
## [916] 0.344541746 0.486776974 -1.449509569 0.479397758 0.287080007
## [921] 0.825973152 0.499423469 -1.642559020 0.468787520 1.648802061
## [926] -0.013620237 -0.777188299 -1.521909449 -0.428069986 -0.161633613
## [931] 0.115677890 -0.195651972 -1.013945037 2.440049127 0.407477529
## [936] -0.558110397 0.767608176 0.247028137 0.517452118 -0.081810297
## [941] 0.712348190 -0.002505624 -1.039118586 1.052349085 -0.698226271
## [946] 1.149809544 0.051341807 -0.690695978 0.487722287 -0.038945992
## [951] -0.218343743 -0.032553320 1.712867928 -1.984243089 1.180480336
## [956] -1.145709218 0.901554314 0.510850263 -0.805734732 -0.043316157
## [961] 2.045597977 0.028865032 -0.498150700 0.047229054 1.055278989
## [966] -0.973397502 0.449500234 0.309134217 1.271281907 -0.001905288
## [971] -0.315496942 1.538920594 0.860399447 -0.665085436 -1.310156994
## [976] -0.653193755 1.316597211 1.620060559 -1.314652416 -0.922062103
## [981] 2.181028855 -1.466852374 -0.866386646 -1.475432119 -0.883477558
## [986] -0.291936253 -0.152724594 -1.521451654 0.409533781 0.014060854
## [991] -0.668308047 -0.290148150 1.025716753 -0.444231988 0.704734960
## [996] -0.716321085 1.510508215 -1.578881493 0.692309804 -0.157106216
yAxis <- rnorm(1000) + xAxis + 10
yAxis
## [1] 11.104017 9.563424 10.942701 9.255974 7.850297 11.688375 8.709075
## [8] 11.701084 10.920682 9.664040 8.875743 10.489226 9.915243 8.955952
## [15] 9.728615 13.392249 9.962836 11.091583 9.061199 8.306231 10.423832
## [22] 9.111808 10.686416 8.090090 12.466222 10.860366 13.684902 7.970076
## [29] 9.784152 10.921457 10.160145 11.156238 9.557583 12.520606 9.901411
## [36] 10.538652 8.051696 10.646968 9.790341 11.570238 10.854431 9.668684
## [43] 10.162964 10.225439 10.148620 11.438123 10.141977 9.098905 8.477768
## [50] 9.315370 9.398593 10.157634 8.046069 8.447152 10.396749 10.387873
## [57] 8.546161 6.795835 8.288021 6.888686 9.945261 10.886470 10.200172
## [64] 10.274842 9.723253 9.579097 10.199494 9.771840 9.691887 12.717675
## [71] 10.951009 11.792724 6.975161 12.725518 10.122984 11.534593 8.850604
## [78] 8.935795 10.342856 11.846763 11.091578 9.415821 10.637637 12.604238
## [85] 10.368647 7.822467 10.861246 9.791512 7.281298 11.491955 8.486573
## [92] 7.580323 10.565082 8.264634 10.181820 9.816984 10.905337 10.658323
## [99] 8.515944 7.572689 10.068034 13.007443 8.795907 10.168338 10.969473
## [106] 11.314316 9.278932 7.125187 9.984268 11.479884 9.633348 10.249820
## [113] 10.177092 10.580851 10.805860 9.270765 7.694566 9.776989 9.556397
## [120] 12.001410 8.864283 8.827804 9.997741 11.193404 10.790325 10.826553
## [127] 9.348155 11.667828 8.166706 9.586932 9.222384 11.080419 12.483958
## [134] 8.873619 10.676780 11.970936 9.106648 9.579528 9.404070 9.033104
## [141] 11.118561 13.169843 9.906941 8.112125 10.139536 8.810799 11.696008
## [148] 8.864305 8.156210 8.582582 10.727531 10.433167 9.184110 12.175507
## [155] 9.662851 12.953522 8.353722 9.854977 8.667774 6.170009 9.117681
## [162] 8.575157 10.108883 10.467226 10.766994 8.071470 11.198952 8.578497
## [169] 8.120622 10.673373 8.695323 8.980661 10.282288 10.868955 10.595549
## [176] 10.203938 9.080518 8.693023 11.425207 11.921803 9.297769 9.382209
## [183] 12.473563 8.196159 7.685830 9.206253 10.097703 10.771180 10.224854
## [190] 11.638242 10.997969 8.989681 12.049739 9.768455 10.257592 7.943773
## [197] 8.005254 11.825248 11.497778 10.338387 8.787915 11.196052 9.817217
## [204] 8.745392 8.468992 9.456668 10.614795 10.371216 9.679121 9.377198
## [211] 10.896973 10.451637 12.271912 11.267965 11.798062 9.516106 8.763355
## [218] 11.199070 8.955935 10.380714 9.436869 8.048074 11.828806 10.507095
## [225] 9.737267 9.973105 10.752616 9.926903 10.735320 10.448762 10.250891
## [232] 8.690833 7.462640 10.817145 11.130208 8.473512 8.811459 7.773162
## [239] 8.358744 6.363218 9.985910 9.030196 10.138250 8.593219 12.726640
## [246] 7.517553 9.830323 8.649400 8.410369 8.385722 10.713032 8.730914
## [253] 9.827848 9.874624 8.956076 8.271644 7.498034 10.335473 10.342530
## [260] 13.487218 10.010835 10.615158 8.806965 6.761690 12.995261 12.662477
## [267] 9.051597 9.961861 10.919663 11.433672 9.854505 12.149687 11.391672
## [274] 13.302333 9.767680 9.434444 11.850216 10.398683 8.514637 9.246034
## [281] 10.952255 10.759816 11.597618 10.027882 8.511067 8.304915 10.879067
## [288] 9.679725 10.781462 10.182201 9.529417 10.371762 9.384520 9.455964
## [295] 6.704020 10.131944 9.220976 12.970611 10.239365 7.754985 10.916955
## [302] 8.417612 11.487970 9.688691 9.540136 10.425046 8.935184 10.174498
## [309] 10.038470 8.534505 9.236374 9.101285 8.439178 9.219225 7.038622
## [316] 9.612312 7.331914 7.944604 10.259625 8.948916 8.101592 8.905968
## [323] 8.745677 6.296006 9.616523 10.666986 10.114357 8.305515 8.419758
## [330] 9.815325 11.739554 10.034330 9.787654 12.134336 7.661294 12.353445
## [337] 10.273784 9.613011 9.877584 9.926100 8.841025 11.009550 11.099912
## [344] 11.776105 8.736062 8.705428 12.096958 12.006821 10.425182 11.342534
## [351] 9.506210 11.254302 11.666824 9.251634 11.217790 12.583921 12.374861
## [358] 10.577143 10.373739 10.003776 8.549966 10.779358 7.731260 8.853684
## [365] 9.038718 10.357881 10.742674 11.490979 11.431506 10.303309 9.796848
## [372] 10.183780 12.208448 10.565989 8.397453 8.258158 10.110785 8.519331
## [379] 9.830133 9.612356 9.731655 11.665957 6.815330 9.303802 7.601140
## [386] 10.115331 10.480159 7.869640 8.129313 10.212610 12.410191 7.341166
## [393] 11.573849 9.630379 9.007312 8.782602 8.916676 6.731178 12.306217
## [400] 10.187481 10.331727 9.378138 7.709080 13.388390 7.696218 11.563055
## [407] 10.063251 10.350038 11.396428 8.720285 10.918093 8.905028 11.320458
## [414] 10.211678 11.471724 11.020077 11.315394 9.497804 6.565173 10.759135
## [421] 10.939579 8.818280 6.656656 12.462514 7.675186 8.866034 8.510661
## [428] 9.666211 7.704857 12.278702 7.759058 10.386449 8.199451 9.541014
## [435] 10.837673 8.290038 10.038818 11.660091 11.270101 12.140039 11.675095
## [442] 10.553942 10.408302 10.024979 10.022454 9.028161 10.421901 11.769340
## [449] 9.244789 10.244013 7.611707 9.052735 11.423800 12.003462 9.767432
## [456] 9.154603 9.635475 9.276149 7.696482 9.372386 7.512691 8.639925
## [463] 12.412699 7.630581 10.562617 9.770476 10.766221 9.873389 12.125125
## [470] 10.319677 9.234421 8.622607 11.573110 8.099514 10.919307 7.632766
## [477] 11.030822 9.279847 8.365892 9.883443 10.988093 8.599395 8.964210
## [484] 10.690475 10.518304 9.912837 10.938748 11.373332 8.062468 9.562609
## [491] 11.968292 10.200743 14.153279 11.411590 12.511153 8.473872 10.179534
## [498] 7.892262 9.430990 8.891728 10.931930 9.294324 9.445388 11.312953
## [505] 10.451354 9.634296 7.150581 10.872572 11.298584 11.514386 10.634919
## [512] 6.498786 8.861740 8.224258 13.230733 9.227924 9.829630 9.785047
## [519] 8.502567 11.570062 10.459727 8.892663 9.038559 8.716333 10.219933
## [526] 10.272607 10.428285 9.640247 11.217574 10.433502 10.507631 8.516749
## [533] 10.991845 8.242672 9.632762 13.280852 10.423038 11.921607 10.242209
## [540] 9.507374 10.587548 7.297961 9.133828 9.848214 10.758784 8.517581
## [547] 9.390691 11.293213 10.964314 11.073572 11.676868 10.143997 12.154982
## [554] 8.322327 10.719938 10.550908 9.020160 10.386317 10.886910 7.966014
## [561] 9.779111 7.971538 11.547099 11.880175 11.406661 9.188289 10.384112
## [568] 7.945834 9.522394 6.740324 12.138714 9.451033 11.049005 10.465042
## [575] 10.664481 9.029925 11.131749 9.466493 7.411531 11.356076 11.123008
## [582] 9.229285 8.778783 11.501825 8.971065 8.254278 9.398977 9.500012
## [589] 9.594965 10.893348 11.059215 8.344220 9.959639 11.276393 11.552414
## [596] 7.840020 8.045854 9.854458 12.164092 11.503310 10.170166 9.328864
## [603] 9.966425 11.329216 9.316017 9.197207 9.820306 7.438200 10.541700
## [610] 9.076254 11.406120 7.999242 7.905594 11.717360 9.529600 9.530735
## [617] 12.875052 9.929926 9.471242 10.076249 10.942165 9.161438 9.421044
## [624] 9.792042 9.903304 9.069383 9.749918 11.084344 7.689373 7.971834
## [631] 10.164458 9.406301 9.546327 11.589220 10.697000 7.742830 9.199306
## [638] 8.276332 9.552114 10.848243 11.964812 10.901804 10.407054 10.362381
## [645] 7.868340 14.088448 12.154119 7.488335 8.874876 9.631833 9.675600
## [652] 9.615039 7.931711 11.135841 9.952938 6.887556 10.258697 10.331787
## [659] 11.789794 11.958438 12.763124 9.521719 8.768589 12.486737 11.311630
## [666] 8.385218 9.986488 8.842186 10.446503 12.342892 10.170717 10.236487
## [673] 9.346016 9.897734 9.716944 9.969853 9.294423 11.337588 10.651438
## [680] 10.534799 10.939656 10.939990 10.431707 7.987071 8.097201 10.483826
## [687] 8.828070 10.537958 11.490136 12.266818 9.262466 8.425632 9.898786
## [694] 10.365589 12.070509 11.078522 11.546436 12.521274 10.226824 12.082891
## [701] 7.618441 9.973987 12.005376 9.133411 10.616534 10.496218 7.232357
## [708] 10.487271 8.808717 11.706938 10.819703 12.758131 9.254093 9.967964
## [715] 12.069387 11.534930 11.125930 7.845240 8.663778 7.573718 9.815134
## [722] 9.221334 10.220453 10.907327 10.462602 10.720209 9.947394 11.279869
## [729] 8.115832 11.075524 9.588279 9.785753 9.831344 10.301159 12.475901
## [736] 10.793157 12.693258 9.888757 12.270419 11.462019 8.024996 9.747248
## [743] 12.253110 10.691901 8.211885 9.926970 9.951811 11.636292 10.153787
## [750] 11.171817 12.360288 8.334304 13.161721 11.085350 9.776529 10.278687
## [757] 12.118751 9.940008 9.428477 9.317667 11.564291 9.023791 9.945505
## [764] 8.194072 9.390538 9.781126 10.859826 10.780935 10.056618 7.045570
## [771] 8.586529 8.049217 8.570441 10.799193 10.478611 10.624860 10.840523
## [778] 13.561891 10.701922 11.778258 12.065744 12.054658 10.616121 8.746071
## [785] 13.599597 10.452446 9.878782 8.446587 8.643640 9.570353 7.274933
## [792] 10.061870 11.135781 9.952399 11.328274 10.859665 9.130300 9.595582
## [799] 9.835757 11.610399 10.385011 7.955488 10.319055 7.775240 11.361045
## [806] 10.694103 8.691419 11.645778 10.119511 8.469047 9.906335 10.020155
## [813] 11.003934 8.491596 9.893401 9.606310 10.509759 9.072980 12.446291
## [820] 8.493981 11.549834 9.629369 11.479254 13.155681 8.116724 13.765864
## [827] 8.648359 7.557750 9.475202 8.183122 12.246419 9.252966 9.213992
## [834] 9.471875 6.645658 11.079406 9.330475 11.041679 8.516304 9.730366
## [841] 9.684983 11.702501 10.834529 9.733107 11.132017 8.616781 9.028719
## [848] 9.177529 9.808973 8.602675 10.931435 12.200244 6.828507 8.200386
## [855] 8.984502 10.390794 8.155598 9.928130 10.036859 7.307517 9.623079
## [862] 11.480242 10.509057 11.800411 10.000091 11.098127 10.435305 10.715513
## [869] 7.899996 10.220732 11.186004 9.915909 10.931707 10.303261 9.595975
## [876] 10.154645 9.033150 8.161512 9.880547 12.144441 10.018868 10.318345
## [883] 10.264993 11.037867 8.937188 9.175875 9.581914 11.266429 7.770630
## [890] 8.938924 10.820097 10.594363 11.104573 9.060212 10.691044 8.370756
## [897] 11.667237 13.423054 10.170226 8.544988 11.775301 9.836139 9.239517
## [904] 8.417629 9.985326 11.003358 11.022303 9.998231 7.325754 7.686299
## [911] 11.707801 9.724372 9.516040 8.594275 11.937264 10.592560 9.360163
## [918] 8.476951 9.427984 11.296742 10.781022 8.445325 9.137894 8.954292
## [925] 12.144069 10.570140 9.348728 10.436689 10.114735 9.669073 10.553840
## [932] 10.873896 8.892642 13.091620 10.267426 8.940508 11.476061 8.181548
## [939] 11.429271 8.155042 11.094986 11.491403 9.892320 12.030777 8.811764
## [946] 11.431300 9.979456 8.016114 10.511588 9.828258 10.942400 11.331899
## [953] 13.087664 8.167945 10.254114 10.076803 12.295272 9.276637 8.683427
## [960] 10.849073 13.060583 11.993534 9.422058 10.328134 11.428246 9.095745
## [967] 9.284581 12.220689 12.698380 9.161380 8.312330 10.289472 11.404078
## [974] 11.226375 7.820255 9.104839 13.384545 12.610123 8.959881 10.107875
## [981] 11.971824 9.313071 9.222255 7.747717 8.286040 10.077344 11.476787
## [988] 8.832259 11.069272 9.570278 8.472812 9.589594 10.331432 10.293931
## [995] 10.669512 10.340380 12.336807 9.112024 10.801032 9.170572
# create groups for different values of X
(group <- rep(1,1000)) # a vector consisting of 1000 elements
## [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [186] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [223] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [260] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [297] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [334] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [371] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [408] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [445] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [482] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [519] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [556] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [593] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [630] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [667] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [704] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [741] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [778] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [815] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [852] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [889] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [926] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [963] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1000] 1
group[xAxis > -1.5] <- 2
group[xAxis > -.5] <- 3
group[xAxis > .5] <- 4
group[xAxis > 1.5] <- 5
group
## [1] 2 2 4 2 1 3 2 3 3 2 2 2 3 2 3 5 4 4 1 2 3 3 4 3 4 3 4 2 3 3 2 4 2 4 4 5 3
## [38] 3 2 4 3 3 4 3 3 4 3 3 3 4 2 3 2 2 3 3 3 2 1 2 3 3 3 3 3 2 3 4 1 5 3 4 2 5
## [75] 3 4 3 3 3 4 3 3 3 5 3 3 5 3 1 4 2 2 4 2 2 3 4 3 3 1 4 5 4 3 3 3 1 2 2 4 3
## [112] 3 4 4 4 2 3 3 3 4 2 3 3 4 3 4 1 5 3 1 2 4 4 2 4 5 3 3 4 3 3 5 3 1 4 3 3 2
## [149] 3 2 4 2 4 3 2 5 2 3 3 2 3 3 2 3 2 3 3 3 3 3 2 3 3 4 3 4 4 2 2 4 3 2 5 2 1
## [186] 3 3 4 2 5 4 3 5 3 3 2 1 3 3 4 4 4 3 3 3 3 3 3 2 3 4 2 4 4 3 3 3 5 1 3 2 1
## [223] 4 4 3 3 4 3 1 4 3 3 2 4 5 2 3 2 2 2 4 2 3 3 3 1 3 2 1 3 4 3 3 2 3 3 3 3 5
## [260] 4 4 4 2 1 5 4 3 3 4 4 3 4 4 4 3 3 3 3 4 3 4 3 4 4 1 2 4 3 4 4 3 3 2 3 1 4
## [297] 3 5 3 1 2 1 5 4 4 4 3 4 4 2 2 2 2 4 3 3 3 2 3 4 3 4 3 1 2 5 3 1 2 2 3 2 4
## [334] 5 1 5 2 3 3 3 3 4 4 3 2 3 4 4 3 4 3 4 4 2 3 5 3 3 3 3 2 4 1 5 2 4 3 4 3 3
## [371] 2 1 4 4 3 3 4 1 3 3 2 5 2 2 2 3 2 3 1 4 5 3 5 2 3 2 2 2 4 3 2 3 1 5 4 3 2
## [408] 2 3 2 4 2 4 3 4 4 3 3 1 3 4 3 1 4 3 2 3 4 1 5 2 3 2 3 4 1 4 5 4 4 4 4 4 3
## [445] 3 1 3 4 3 3 2 3 4 4 3 3 3 3 3 3 3 3 4 2 3 3 3 2 4 4 3 3 5 4 4 2 4 3 2 4 3
## [482] 3 3 3 3 4 4 5 2 2 4 3 5 3 4 2 2 1 3 2 3 2 3 4 3 2 3 5 3 4 5 1 1 2 5 2 3 3
## [519] 3 3 3 2 1 3 3 4 3 3 3 3 3 3 3 3 3 4 4 4 4 2 4 1 3 3 3 3 3 3 3 2 4 3 4 2 4
## [556] 3 1 4 5 2 3 4 5 4 4 1 4 2 3 1 4 3 3 3 4 3 3 4 3 4 3 2 1 3 2 2 1 3 4 3 4 2
## [593] 4 4 4 2 2 3 4 2 2 3 2 4 2 2 3 2 3 3 4 1 2 3 3 2 5 3 3 4 5 2 3 3 4 2 3 4 3
## [630] 1 3 2 2 4 3 2 3 3 3 3 4 3 3 4 2 5 4 2 2 2 3 2 2 4 3 1 4 2 4 5 4 3 2 5 3 2
## [667] 2 3 3 4 3 3 2 3 3 3 4 3 4 3 3 4 4 2 3 5 1 4 4 4 4 3 2 3 5 3 4 3 3 4 3 2 3
## [704] 4 4 1 2 3 1 3 3 4 4 2 4 4 4 3 2 2 3 2 4 4 3 3 2 3 2 4 2 2 1 3 4 4 3 3 5 4
## [741] 1 4 4 4 2 3 3 5 3 3 4 2 4 4 3 3 4 2 2 2 4 2 2 2 3 3 3 4 2 2 3 2 2 3 4 4 3
## [778] 4 3 4 4 4 4 2 5 3 3 2 2 3 1 2 2 3 4 5 3 3 3 4 2 4 3 2 5 3 2 4 3 3 2 2 3 1
## [815] 3 3 4 2 5 2 3 2 4 5 1 5 3 2 3 2 4 4 2 2 2 5 3 2 3 3 3 4 3 3 4 2 3 3 3 2 4
## [852] 4 1 2 2 4 2 3 3 1 2 4 3 4 3 3 4 3 2 4 4 2 4 4 4 4 3 3 3 3 2 3 3 2 1 2 1 4
## [889] 2 1 5 2 4 2 3 3 4 4 4 3 4 3 3 2 3 5 4 3 2 2 3 4 4 1 5 3 3 2 3 3 4 3 1 3 5
## [926] 3 2 1 3 3 3 3 2 5 3 2 4 3 4 3 4 3 2 4 2 4 3 2 3 3 3 3 5 1 4 2 4 4 2 3 5 3
## [963] 3 3 4 2 3 3 4 3 3 5 4 2 2 2 4 5 2 2 5 2 2 2 2 3 3 1 3 3 2 3 4 3 4 2 5 1 4
## [1000] 3
# create sample dataframe by joining variables
sample_data <- data.frame(xAxis,yAxis,group)
sample_data
## xAxis yAxis group
## 1 -0.847746816 11.104017 2
## 2 -0.973672464 9.563424 2
## 3 1.438488878 10.942701 4
## 4 -1.149828861 9.255974 2
## 5 -1.903317733 7.850297 1
## 6 -0.408559651 11.688375 3
## 7 -0.519378236 8.709075 2
## 8 0.042483923 11.701084 3
## 9 0.440007488 10.920682 3
## 10 -0.806491369 9.664040 2
## 11 -0.979978372 8.875743 2
## 12 -0.992636917 10.489226 2
## 13 0.333540560 9.915243 3
## 14 -1.190053490 8.955952 2
## 15 -0.363316592 9.728615 3
## 16 2.404956351 13.392249 5
## 17 0.615876824 9.962836 4
## 18 0.707116377 11.091583 4
## 19 -2.272786791 9.061199 1
## 20 -1.218772713 8.306231 2
## 21 0.419925436 10.423832 3
## 22 0.224061179 9.111808 3
## 23 0.624664922 10.686416 4
## 24 -0.197298556 8.090090 3
## 25 1.446387933 12.466222 4
## 26 0.252869813 10.860366 3
## 27 0.929401527 13.684902 4
## 28 -1.392003623 7.970076 2
## 29 0.186749381 9.784152 3
## 30 0.315598205 10.921457 3
## 31 -0.622532493 10.160145 2
## 32 1.335772410 11.156238 4
## 33 -0.922243545 9.557583 2
## 34 0.607210814 12.520606 4
## 35 0.703031573 9.901411 4
## 36 1.915605498 10.538652 5
## 37 -0.152911046 8.051696 3
## 38 -0.470333111 10.646968 3
## 39 -0.787460461 9.790341 2
## 40 0.505115277 11.570238 4
## 41 0.078301886 10.854431 3
## 42 0.326358267 9.668684 3
## 43 0.812588460 10.162964 4
## 44 0.175233727 10.225439 3
## 45 0.063181989 10.148620 3
## 46 1.386046979 11.438123 4
## 47 0.095748856 10.141977 3
## 48 0.471645134 9.098905 3
## 49 -0.382303194 8.477768 3
## 50 0.818682794 9.315370 4
## 51 -0.930640484 9.398593 2
## 52 0.364539641 10.157634 3
## 53 -1.321948101 8.046069 2
## 54 -1.420917467 8.447152 2
## 55 0.236637393 10.396749 3
## 56 -0.090044579 10.387873 3
## 57 -0.236522752 8.546161 3
## 58 -1.424998993 6.795835 2
## 59 -1.794329617 8.288021 1
## 60 -1.228590423 6.888686 2
## 61 0.429838291 9.945261 3
## 62 0.420509834 10.886470 3
## 63 -0.079547149 10.200172 3
## 64 0.454790159 10.274842 3
## 65 0.391310791 9.723253 3
## 66 -0.698624766 9.579097 2
## 67 0.245148903 10.199494 3
## 68 0.557331367 9.771840 4
## 69 -1.704597622 9.691887 1
## 70 1.842008031 12.717675 5
## 71 0.292001877 10.951009 3
## 72 0.656895337 11.792724 4
## 73 -1.133390965 6.975161 2
## 74 1.680053976 12.725518 5
## 75 0.390779797 10.122984 3
## 76 1.083102839 11.534593 4
## 77 0.031718171 8.850604 3
## 78 -0.297416378 8.935795 3
## 79 -0.265401241 10.342856 3
## 80 0.500061474 11.846763 4
## 81 0.144933997 11.091578 3
## 82 -0.101657402 9.415821 3
## 83 -0.310750191 10.637637 3
## 84 2.369217847 12.604238 5
## 85 0.193558575 10.368647 3
## 86 -0.266720152 7.822467 3
## 87 1.842375898 10.861246 5
## 88 0.436992844 9.791512 3
## 89 -2.107140180 7.281298 1
## 90 0.921451837 11.491955 4
## 91 -1.443199492 8.486573 2
## 92 -1.259280925 7.580323 2
## 93 1.168948562 10.565082 4
## 94 -1.400093737 8.264634 2
## 95 -1.493423841 10.181820 2
## 96 0.240554916 9.816984 3
## 97 0.929220924 10.905337 4
## 98 -0.147424491 10.658323 3
## 99 -0.330296623 8.515944 3
## 100 -2.404237667 7.572689 1
## 101 1.325864256 10.068034 4
## 102 1.612995351 13.007443 5
## 103 0.589659874 8.795907 4
## 104 -0.356241071 10.168338 3
## 105 -0.069218987 10.969473 3
## 106 -0.271277614 11.314316 3
## 107 -2.135090708 9.278932 1
## 108 -1.063282323 7.125187 2
## 109 -1.100805953 9.984268 2
## 110 0.726562167 11.479884 4
## 111 -0.308393444 9.633348 3
## 112 0.437126597 10.249820 3
## 113 0.744107313 10.177092 4
## 114 0.816337005 10.580851 4
## 115 1.305705125 10.805860 4
## 116 -0.604394079 9.270765 2
## 117 -0.469884817 7.694566 3
## 118 -0.362656143 9.776989 3
## 119 0.235207781 9.556397 3
## 120 0.883369625 12.001410 4
## 121 -1.080943647 8.864283 2
## 122 -0.343776234 8.827804 3
## 123 0.344705052 9.997741 3
## 124 0.983501586 11.193404 4
## 125 0.319536523 10.790325 3
## 126 0.560882783 10.826553 4
## 127 -2.002778053 9.348155 1
## 128 1.596490970 11.667828 5
## 129 -0.142226876 8.166706 3
## 130 -1.691725826 9.586932 1
## 131 -1.399875559 9.222384 2
## 132 0.858847974 11.080419 4
## 133 1.100182254 12.483958 4
## 134 -0.568346400 8.873619 2
## 135 1.190809110 10.676780 4
## 136 1.735915511 11.970936 5
## 137 0.308226762 9.106648 3
## 138 -0.299454342 9.579528 3
## 139 0.952663131 9.404070 4
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## 1000 -0.157106216 9.170572 3
# creates plot object using ggplot
plot <- ggplot(sample_data, aes(x = xAxis, y= yAxis, col = as.factor(group)))+
geom_point()+theme(legend.position = "none")
# Display plot
plot
# Insert marginal ditribution using marginal function
ggMarginal(plot,type= 'histogram',groupColour=TRUE,groupFill=TRUE)
