# Mindanao State University
# General Santos City
# Introduction to R base commands
# Prepared by: Kathleen I. Pena
# Student
# 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
## function (data = NA, dim = length(data), dimnames = NULL)
## {
## if (is.atomic(data) && !is.object(data))
## return(.Internal(array(data, dim, dimnames)))
## data <- as.vector(data)
## if (is.object(data)) {
## dim <- as.integer(dim)
## if (!length(dim))
## stop("'dim' cannot be of length 0")
## vl <- prod(dim)
## if (length(data) != vl) {
## if (vl > .Machine$integer.max)
## stop("'dim' specifies too large an array")
## data <- rep_len(data, vl)
## }
## if (length(dim))
## dim(data) <- dim
## if (is.list(dimnames) && length(dimnames))
## dimnames(data) <- dimnames
## data
## }
## else .Internal(array(data, dim, dimnames))
## }
## <bytecode: 0x00000202deae5240>
## <environment: namespace:base>
# Step 2: Plot the line graph using the base plot() command
plot(x, y, type = "l")
# Step 3: Add Main Title & Change Axis Labels
plot(x, y, type = "l",
main = "Hello: This is my Line Plot",
xlab = "My X-Values",
ylab = "My Y-Values")
# Step 4: Add color to the line using the col command
plot(x, y, type = "l",
main = "This is my Line Plot",
xlab = "My X-Values",
ylab = "My Y-Values",
col = "red")
# Step 5: Modify Thickness of Line using lwd command
plot(x, y, type = "l",
main = "This is my Line Plot",
xlab = "My X-Values",
ylab = "My Y-Values",
lwd=7,
col = "green")
# Step 6: Add points to line graph by changing the type command
plot(x, y, type = "b",
main = "This is my Line Plot",
xlab = "My X-Values",
ylab = "My Y-Values",
lwd=3,
col = "blue")
# Lab Exercise 2: How to create Lines in with different styles in R
# Step1: Assign values for different lines. We enclose the entire
# line with parenthesis symbol to force R to display the results instantly
# set the same value for the x variable
(x <- 1:10)
## [1] 1 2 3 4 5 6 7 8 9 10
# set different values for y variables
(y1 <- c(3, 1, 5, 2, 3, 8, 4, 7, 6, 9))
## [1] 3 1 5 2 3 8 4 7 6 9
(y2 <- c(5, 1, 4, 6, 2, 3, 7, 8, 2, 8))
## [1] 5 1 4 6 2 3 7 8 2 8
(y3 <- c(3, 3, 3, 3, 4, 4, 5, 5, 7, 7))
## [1] 3 3 3 3 4 4 5 5 7 7
# Plot first the pair x and y1.
plot(x, y1, type = "b",
main = "This is my Line Plot",
xlab = "My X-Values",
ylab = "My Y-Values",
lwd=3,
col = "blue")
# then Add the two lines (for x,y2) and (x,y3)
lines(x, y2, type = "b", col = "red",lwd=3)
lines(x, y3, type = "b", col = "green",lwd=3)
# Add legend to the plot
legend("topleft",
legend = c("Line y1", "Line y2", "Line y3"),
col = c("black", "red", "green"),
lty = 1)
# Step 2: Create Different Point Symbol for Each
# Line using the pch command
plot(x, y1, type = "b",pch = 16,
main = "This is my Line Plot",
xlab = "My X-Values",
ylab = "My Y-Values",
lwd=3,
col = "blue")
lines(x, y2, type = "b", col = "red",lwd=3, pch = 15)
lines(x, y3, type = "b", col = "green",lwd=3, pch = 8)
# Add legend
legend("topleft",
legend = c("Line y1", "Line y2", "Line y3"),
col = c("black", "red", "green"),
lty = 1)
# Lab Exercise 3: Create Line graph without x values
Pupils <- c(3.55 ,3.54 ,3.53 ,3.61 ,3.65 ,3.63 ,3.61
,3.61 ,3.59 ,3.63 ,3.59 ,3.63 ,3.62 ,3.62
,3.59 ,3.63 ,3.62 ,3.65 ,3.65)
# get number of elements of Pupils
length(Pupils)
## [1] 19
# Display the elements of Pupils
Pupils
## [1] 3.55 3.54 3.53 3.61 3.65 3.63 3.61 3.61 3.59 3.63 3.59 3.63 3.62 3.62 3.59
## [16] 3.63 3.62 3.65 3.65
# You can obtain the plot without x values
plot(Pupils, type = 'o')
# Lab Exercise 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
# 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("C:/Users/Acer/Downloads/Cancer.csv")
## Rows: 173 Columns: 17
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): country, continent
## dbl (15): incomeperperson, alcconsumption, armedforcesrate, breastcancer, co...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
View(Cancer)
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("C:/Users/Acer/Downloads/hsb2.csv")
## New names:
## Rows: 200 Columns: 12
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "," dbl
## (12): ...1, id, female, race, ses, schtyp, prog, read, write, math, scie...
## ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## • `` -> `...1`
View(hsb2)
# display only the top 6 rows
head(hsb2)
## # A tibble: 6 × 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 × 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 × 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
## # … with 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
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#Remark: Pay extra attention to the last 2 columns
head(hsb2)
## # A tibble: 6 × 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
tail(hsb2)
## # A tibble: 6 × 11
## id female race ses schtyp prog read write math science socst
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 179 1 4 2 2 2 47 65 60 50 56
## 2 31 1 2 2 2 1 55 59 52 42 56
## 3 145 1 4 2 1 3 42 46 38 36 46
## 4 187 1 4 2 2 1 57 41 57 55 52
## 5 118 1 4 2 1 1 55 62 58 58 61
## 6 137 1 4 3 1 2 63 65 65 53 61
# get thefrequency
table(hsb2_long$variable)
##
## read write math science socst
## 200 200 200 200 200
# This means that the tables hsb2 and hsb2 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)
# 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)
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 ✔ stringr 1.5.0
## ✔ forcats 1.0.0 ✔ tibble 3.2.0
## ✔ lubridate 1.9.2 ✔ tidyr 1.3.0
## ✔ purrr 1.0.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the ]8;;http://conflicted.r-lib.org/conflicted package]8;; to force all conflicts to become errors
library(ggExtra)
# set theme appearance of grid background
theme_set(theme_bw(1))
# create X and Y vector
(xAxis <- rnorm(1000)) # normal distribution with mean 0 and sd=1
## [1] 0.001077924 -1.630869914 0.801192197 -0.463052970 -0.275248656
## [6] -1.206690837 -0.921710287 0.167326898 -0.841639294 1.451874447
## [11] -0.370221220 0.184417925 -0.245533032 0.942833449 0.072911566
## [16] 0.575972677 0.642687766 -1.417858423 0.245770534 -0.301733496
## [21] -1.025296389 -0.017533598 -0.508099358 1.533833687 -0.774723877
## [26] -0.285227270 -0.416073779 1.010836856 -0.529166254 0.600978932
## [31] -0.649472278 0.594358858 0.486144347 -2.789792000 -0.174556845
## [36] 0.133294365 -0.308711976 1.087565051 0.325160453 -0.995586036
## [41] -0.509719324 -0.550777277 -0.668354608 -0.803313175 0.281208653
## [46] -0.999304954 -0.480106750 -0.626274440 -0.062705181 0.593672812
## [51] 0.557931318 -0.571628917 0.800391252 -0.292954955 -0.036062992
## [56] -0.930970809 -0.050831596 -1.218120750 0.082542338 0.420232454
## [61] 0.183824142 -1.576311651 1.042104431 0.068030824 -0.901354991
## [66] 0.518028692 0.314944468 0.368145410 1.031346698 -0.555650137
## [71] 0.573527458 -0.635001732 0.402726095 0.007714037 0.987597342
## [76] -0.178315054 0.507699141 0.553168827 0.287188510 -0.727512703
## [81] -0.521539222 -1.484248535 -0.179874315 0.795454057 -0.696781486
## [86] 1.158518325 -0.784201611 -0.071498176 -1.383245820 -0.072945463
## [91] -0.044415252 -0.892559363 0.527236687 -2.348370585 -0.603148688
## [96] -1.770116115 2.307338943 2.078054846 -1.387668447 1.569164771
## [101] -0.481541692 0.796703225 0.384873929 0.837024202 -0.670014710
## [106] -0.225509505 1.025755620 0.431043665 -1.309207763 -0.536201674
## [111] 0.440653000 -0.036183785 -0.195678409 -0.819340560 -0.019838093
## [116] -0.143055611 -0.020676929 0.356167350 0.628785161 -1.249840247
## [121] 2.028756527 -1.730672576 0.081417169 0.574100189 1.163213499
## [126] 0.469826343 -0.767106981 1.006892113 -0.751847743 1.084257450
## [131] 0.750250438 0.005224619 -1.335375496 0.809575467 -0.422189987
## [136] -0.624295199 0.394041738 0.978508473 -1.217209108 0.563988619
## [141] 1.377382207 -1.685004641 0.296232400 -0.748013236 0.068999673
## [146] -1.283534750 -0.577523469 0.335687572 -1.366504016 -0.609538361
## [151] -0.209105891 -1.039693268 1.403617314 -1.065186283 -0.893025606
## [156] 0.685929999 0.289820516 -0.570375924 1.340015864 0.715555233
## [161] -0.598122642 0.561358931 0.223262785 -0.688570713 -0.424276956
## [166] -0.607546181 0.707610902 0.012920802 -0.284820049 0.364880902
## [171] 0.019632460 1.908369531 -0.512072911 -0.833541986 1.577207008
## [176] 1.079330094 0.181864957 0.932786507 -1.653031458 -1.560248076
## [181] 0.407524131 -0.388866447 1.929189457 -0.735365940 -1.048676262
## [186] 0.650489546 1.311574404 0.326377556 -0.182917685 0.575070417
## [191] -1.271430118 1.157342566 -0.006461685 -1.100671598 -0.248194421
## [196] 1.152907309 0.160197725 0.484955388 0.882323959 0.106251700
## [201] 0.979028985 -0.104352102 1.479021030 2.043662312 1.792728335
## [206] 1.926467943 0.710391903 -1.200190007 -1.216382100 -0.961411060
## [211] 0.390201626 0.759532495 -1.631118178 -1.089169639 1.581638683
## [216] 0.292060197 -1.002433022 -1.566842057 0.056212457 -0.411830613
## [221] 0.556606511 0.527058283 2.107015891 -2.231925185 -0.618082003
## [226] -1.693706938 -1.153137786 -0.580987438 1.824607741 0.761104493
## [231] 0.362969451 -1.796513947 -0.154302730 -1.851231436 -1.171934910
## [236] -0.153606157 -0.836972420 -0.122730912 0.482090601 -1.577702105
## [241] 0.137863753 -1.127380006 -1.026097292 -0.608173832 1.876082065
## [246] 1.008173928 2.014240912 1.802876181 1.400457551 0.048559571
## [251] -0.165448031 -0.867945582 0.171677754 0.385376363 0.007095654
## [256] -0.777196479 -1.887894082 -0.956896286 0.158931433 -0.966596149
## [261] -0.120483390 0.553026753 -0.473545951 3.485660505 -2.632511277
## [266] 0.825571085 -0.145219124 -0.728566709 -0.609207244 1.486484322
## [271] -0.469533454 0.437968859 -1.432645975 -0.714492073 -0.358755102
## [276] -0.208258492 1.194001408 1.070155225 0.021096655 -0.688713916
## [281] 0.684404327 0.211072696 -1.267782655 -1.545909911 0.826942755
## [286] 0.864762761 -0.806516413 -0.620744523 1.083100857 0.855850930
## [291] -0.208453310 0.223053617 -0.009960012 0.534063757 -0.094706522
## [296] 1.539960064 0.500781858 0.900726711 -1.431724069 2.399037619
## [301] -0.106144819 -1.366962064 -1.461439589 0.093025403 1.381108518
## [306] -2.103395730 0.173398060 -0.962195162 -0.399853329 1.693386721
## [311] -1.942534924 -1.162255677 -1.143817666 1.366440987 -0.051807000
## [316] 0.348706701 2.028410899 -0.067045706 0.087145706 0.894169112
## [321] 0.252476736 -0.563565829 -0.126473261 0.289536151 -0.790231663
## [326] 0.858330148 0.854046655 1.470441640 -0.748311601 -0.770924535
## [331] -1.017278856 3.402903515 0.402872553 0.148690616 1.968101945
## [336] -0.011293051 -1.987367296 0.463437825 -0.387512310 -1.228813761
## [341] -1.807016384 -0.245577216 -0.713319197 -1.200415241 -0.882582341
## [346] 0.974094599 -0.288548956 1.195906512 -0.797201395 1.290066166
## [351] 0.928044452 0.664485058 -0.594463435 0.195060020 0.183231628
## [356] -2.656183285 0.538965249 0.219967607 -1.738009631 0.786397546
## [361] -2.741990071 0.066815464 -0.310762358 -0.594468636 1.809287523
## [366] 0.943781424 0.359142808 0.949853403 -1.242332703 -0.551998860
## [371] 0.372287732 -0.763470966 1.253431432 -1.001713339 0.105656372
## [376] -0.412003383 1.072601521 1.768067806 1.510394588 -1.319438001
## [381] -0.734001878 -0.033124889 -0.110180616 0.145302643 0.156725095
## [386] 0.584125581 -3.137719660 0.882977260 1.474019299 -1.835646786
## [391] -0.563914575 -1.184994405 0.088495666 0.272748332 -1.703224662
## [396] -0.583863540 -0.718602629 -1.378929249 -0.792967636 0.941994280
## [401] -0.369517670 0.299073663 -0.667257128 -1.513618671 0.087907245
## [406] -1.656095635 -0.287941357 -0.335068994 1.364035332 0.796551054
## [411] -0.026084403 1.123234970 0.118696197 1.323144407 -0.387983354
## [416] -0.865876994 -0.870755737 -0.340851149 0.297637308 1.025212044
## [421] -0.372702387 -0.296121702 0.872003897 0.949144777 -0.846578876
## [426] -1.244562414 0.106599779 -0.014364552 -1.259715342 -0.903302338
## [431] 0.774289526 0.468554865 0.166753602 0.787470912 -0.844814746
## [436] 0.814332075 -0.793953231 -0.408634658 0.151186307 0.993645208
## [441] 1.845049607 -0.646928332 -0.338291518 -0.004712909 0.137123789
## [446] -0.824718835 -0.481967523 -1.585273312 -0.500305234 -1.592132738
## [451] 1.664570685 -0.913006261 0.768404849 1.415189202 -1.145562513
## [456] 1.103646310 -0.278578017 -0.862147916 1.594186083 2.056734205
## [461] -0.861103880 -0.831136878 2.488748530 -0.979897596 1.795188592
## [466] -0.813480742 -1.548275661 -0.848512799 -1.294266677 -1.205917630
## [471] 0.737018082 -0.449621481 -1.335474452 -0.556355663 0.277077557
## [476] -0.420815202 -0.514776169 -0.168427725 1.192767917 -1.377812711
## [481] 0.023161367 0.883356122 -0.941463426 -1.399377508 0.369964487
## [486] 1.292232312 1.301755611 0.411529101 1.508613157 -0.382518250
## [491] 0.269392434 0.146742571 0.078098170 0.592047222 1.957477399
## [496] -0.521761508 -1.076746555 0.786890273 1.778374969 1.925947344
## [501] 0.092578011 0.495789091 0.043880813 0.264826594 -1.072719262
## [506] 0.985587029 -0.569121384 0.664794430 -0.703989323 1.121805662
## [511] 0.477412308 -1.277956079 0.110410830 -1.378877035 -1.104366758
## [516] 1.980107287 -0.930233361 -0.490410659 0.755300225 -0.820224304
## [521] -0.314574519 -2.046352285 -1.082622698 -1.388836881 -0.399386421
## [526] -0.832770089 -2.068413948 0.649291167 -0.829191986 -0.374164305
## [531] 0.429561005 -1.027532613 -0.543149942 -0.292333539 -1.184493643
## [536] 1.217960016 -0.241514054 -1.027530336 -0.102916671 -1.252805702
## [541] -0.398339396 -1.155362593 -1.519895756 0.968864314 -0.046015423
## [546] -1.519790272 -2.976375349 0.435975649 -0.402546714 -0.225992591
## [551] -0.154804908 1.962125205 0.497311164 1.481510762 1.263000116
## [556] -0.697145264 -1.585914834 -0.539583099 -0.434972905 1.531388389
## [561] 0.647941654 0.639369709 -0.444891789 0.516655050 -0.386908215
## [566] -0.265805642 0.220539404 -1.357716972 -0.725113488 -1.267622814
## [571] 1.038574709 -0.717671132 1.372306414 0.525834209 -0.061584675
## [576] -0.905363075 0.421950229 0.194194654 -1.355607481 -0.915424217
## [581] -1.063682041 0.653395838 -0.757046003 -1.386568813 -0.160233822
## [586] 0.424618185 1.418368185 0.776831744 0.241368109 -0.395433687
## [591] 1.593129505 0.574447023 -0.597321844 2.344925948 -1.582595657
## [596] 0.463297330 0.406284424 -2.468935314 -0.497662569 1.822125484
## [601] -2.441536907 -0.154692579 0.349984809 1.755859062 -0.108016441
## [606] -0.372219061 -1.172996264 2.077947241 0.829177936 0.952489322
## [611] 0.299018720 -1.064355810 0.985696570 1.398831360 1.361488527
## [616] -0.972409158 0.912542850 2.127756180 0.946964480 -0.618948836
## [621] 0.364453886 -1.321048835 -0.513638347 -1.781110712 -0.479183801
## [626] 0.069866390 -0.397315708 -1.755057413 1.004611990 0.304485180
## [631] -0.473195380 -0.317356740 0.933925940 -0.395110510 -1.997505194
## [636] 0.604980882 -0.113359665 0.806186524 -0.316713495 -1.377052188
## [641] -1.281674443 0.684495910 -0.980231638 1.111467768 0.031569867
## [646] 0.980052854 0.447731088 1.046573472 -0.863266775 0.001795783
## [651] 1.653878450 1.148482692 -0.815017518 -0.954494266 0.744090452
## [656] 0.985687801 -1.044035899 0.055273381 -0.977524285 2.334348986
## [661] 0.468052618 -0.455758963 -0.824347758 1.556192467 -0.467910037
## [666] -0.895871155 0.212615582 -0.113748773 1.807701917 0.033421315
## [671] 0.206690396 -0.538864481 0.050861386 0.948959766 -0.622845814
## [676] 0.879263359 0.466619534 0.868317232 0.495206243 1.846521168
## [681] -1.650049194 0.190116747 1.247671058 -1.793899151 0.519136991
## [686] -0.072658420 0.509525716 0.241638235 -1.838608784 0.838622540
## [691] 0.253688593 -1.357806170 0.409020140 -0.199680144 0.610400044
## [696] -1.642044875 -0.237752514 -1.427906138 -0.714034962 -0.332420428
## [701] -1.210772507 0.247162471 -0.511938210 0.505267936 -0.140541361
## [706] -0.782125950 1.668004939 -0.990455603 -0.805111756 0.967252428
## [711] -0.720192957 0.550018081 0.340568760 -0.004591242 -2.156768874
## [716] 0.618242956 0.020005001 0.689534770 -0.746858908 -0.092871051
## [721] -0.873121716 1.472924389 -0.242151809 0.525023954 -1.507912449
## [726] 1.338254606 1.950040588 1.472508097 0.538646293 -0.623756747
## [731] -0.912360208 0.463860500 -1.722407899 -0.066888419 1.183710395
## [736] 0.630840371 -0.228346327 2.170634615 0.299263547 -0.583008190
## [741] -0.958363965 1.597342908 -0.167060606 -0.706877476 0.512665093
## [746] 0.066437183 0.157042037 0.433331123 0.425728247 -0.229712166
## [751] -0.811874553 1.614674608 -0.389964883 1.654240293 0.498362885
## [756] 0.355977379 0.181620006 -2.066775658 -0.887629250 0.076549278
## [761] 0.306016174 0.407523610 0.065570055 2.203123128 -0.025903145
## [766] 0.077341701 0.417648509 -0.974219571 0.013827649 -0.025273460
## [771] -0.607467052 -0.591275558 1.733164805 0.781144483 0.856296669
## [776] -0.075048615 -1.253113361 0.138834607 -0.022773277 -1.675978029
## [781] 0.626194616 -0.478192393 -0.544286684 -0.247510506 -0.657851135
## [786] 0.171642562 0.149695959 -1.169907736 -0.909512257 0.872538709
## [791] 0.137105024 2.592036516 -1.266557423 1.239556480 0.081549786
## [796] 0.396064015 -1.417899769 -1.704076471 -0.822017317 1.486512387
## [801] -0.031635041 -0.284024958 -1.016307936 0.518748512 0.454742988
## [806] 0.942021037 -0.611611423 0.527102627 -0.565160050 -0.386083242
## [811] 0.684099161 1.278193337 1.391787379 -0.610195504 -0.648404689
## [816] -0.052875991 1.115110365 -0.642646728 -0.329526358 -0.883051275
## [821] 0.703805350 1.218857451 -0.113008126 -0.021897169 -0.137060043
## [826] -0.355718624 -0.667967359 -1.752808239 -0.882549482 1.568041549
## [831] -0.655181126 0.201692855 0.172190493 -0.094737839 1.171066556
## [836] -0.283086338 1.126671004 1.808939576 1.825541933 0.797362766
## [841] 0.182986860 -0.709881799 -0.664680757 2.516431210 1.120517644
## [846] 1.540391614 0.371810178 0.563581344 1.351893154 0.026595585
## [851] -0.201196063 -1.197729700 1.070001901 -0.418279464 1.028969071
## [856] 0.813759571 0.416318781 -2.512680394 0.185109946 -1.020318651
## [861] 2.345925197 -0.021076408 0.496224586 -1.898713443 -0.950571527
## [866] -0.010772066 -0.396625388 -1.070993741 0.511029246 1.473854218
## [871] 0.982265669 -0.958962259 -0.581594272 -0.105563748 0.204213981
## [876] -0.083984765 0.115338986 0.558260308 -0.306684591 0.728338981
## [881] -0.062027886 0.312969373 0.715073927 1.785635335 1.562206062
## [886] -0.307412251 -0.200564040 -0.442372242 -0.122340648 0.501440314
## [891] -1.261328597 -0.314769102 -1.390071997 1.638121638 -1.603698075
## [896] -0.059722663 0.184474230 2.269502090 0.553885160 -1.451345437
## [901] 0.566454270 0.715319302 0.760963089 -0.615469802 0.086929569
## [906] -0.508109454 -0.806884221 0.737092180 0.365585556 -1.254915592
## [911] -0.956502285 0.925106124 -1.451558464 1.786560953 -1.230537212
## [916] -0.474135569 1.066971122 -0.345958063 1.811681783 0.518519398
## [921] 0.974195942 0.989789561 0.664136160 -0.415230540 -0.786862526
## [926] -1.332459510 0.556865228 0.413823674 1.312937024 -1.773184619
## [931] 1.000597224 1.565296836 -0.477279661 0.085587758 1.372366971
## [936] -0.248546372 -1.098829127 0.677466680 0.328251908 -0.951368820
## [941] -1.883817004 -0.644709462 -0.966193968 -0.821568050 1.181470410
## [946] 0.157356350 0.500257926 -0.107348773 0.668222077 -1.550614394
## [951] 0.220794915 1.065687151 -0.853515977 0.846346293 -0.733673373
## [956] 1.151136614 1.159105019 0.161076158 -2.266603148 1.670214180
## [961] 0.088193408 0.187784285 -0.201882480 -0.354548164 -0.666353276
## [966] 0.099907993 -0.131183089 1.523832762 -1.896024853 0.263332824
## [971] -0.136004132 -1.056328148 0.993536100 0.125972418 -0.636347471
## [976] -1.055062802 -0.796014652 0.137708805 1.114772347 0.427069705
## [981] -1.573738375 -2.909977566 1.772225758 -0.188183182 -0.300829351
## [986] -1.351418308 -0.847513573 1.019803135 1.078847903 -2.696212853
## [991] 1.105414851 0.952766769 0.204972420 0.910300690 0.822300179
## [996] 0.662749220 0.301601206 -0.386042463 0.142477912 -1.545568549
yAxis <- rnorm(1000) + xAxis + 10
yAxis
## [1] 8.984784 6.700325 11.118479 8.814946 12.273942 7.613248 9.683336
## [8] 9.789410 8.090054 12.302137 8.980494 10.837204 8.855358 10.268559
## [15] 10.868858 11.194294 10.789700 8.514630 10.801223 9.335378 11.599325
## [22] 9.577549 9.339796 9.762563 9.162990 10.375549 9.528601 9.375813
## [29] 9.925400 9.508630 8.807830 11.622365 11.350589 8.157197 8.899755
## [36] 10.347998 9.781617 10.622697 10.764483 9.302356 9.923679 9.594947
## [43] 9.711662 8.143874 11.736669 10.889188 10.049393 10.516435 10.556302
## [50] 9.184864 10.557379 9.585500 12.463173 8.556998 9.503737 10.225983
## [57] 10.426404 7.606686 10.141007 10.113221 10.792460 8.885330 10.457961
## [64] 10.238638 9.312675 9.097014 10.539198 11.592587 10.511587 9.944528
## [71] 10.139505 8.893903 10.868632 9.461556 11.376608 10.331983 10.323855
## [78] 11.572209 9.571436 10.135179 10.782376 7.656187 8.644578 10.176204
## [85] 9.735038 10.539452 9.410417 8.899732 9.471919 8.213742 10.895502
## [92] 9.064712 10.011257 7.032222 9.756339 7.931808 12.997191 11.705673
## [99] 8.983342 11.957163 8.410154 10.517905 8.824796 13.060681 8.612647
## [106] 9.825594 10.463638 10.125512 9.832842 11.207432 9.760561 10.959928
## [113] 8.899034 7.966727 10.059401 9.963121 10.036487 12.260610 12.016405
## [120] 8.637216 12.027002 10.049465 10.222609 10.812837 8.730558 10.843557
## [127] 9.074031 11.243610 7.986200 11.076300 9.331354 11.208792 8.817331
## [134] 9.368861 12.168430 9.987903 12.466551 10.622827 8.852701 9.155186
## [141] 13.206877 7.328309 10.158918 6.844564 9.687365 8.356732 10.082774
## [148] 10.387169 9.609955 9.421872 9.492402 8.022526 13.454631 8.108310
## [155] 10.719413 11.159195 10.943990 10.265060 10.142453 11.193825 9.465217
## [162] 11.314400 10.938592 10.127545 8.545048 10.406270 10.754377 9.913491
## [169] 9.472538 10.888453 10.274572 13.276099 10.194960 8.887124 13.310434
## [176] 12.137836 10.789018 10.222104 8.824952 8.563015 12.020006 10.725329
## [183] 10.614976 8.392410 8.578610 9.449048 10.009989 11.105984 9.295698
## [190] 10.438632 9.133640 12.771188 10.368802 9.597231 10.388339 11.606745
## [197] 10.632810 11.808962 10.941555 10.387435 11.139192 9.817920 10.726035
## [204] 13.066974 12.058362 11.446837 10.001464 10.513427 9.452810 8.436466
## [211] 9.029181 10.524116 7.013840 9.044008 11.047312 8.843637 9.262744
## [218] 8.172594 11.170022 9.795045 11.506833 11.303724 10.963970 7.392117
## [225] 8.524304 7.592546 7.866162 10.933122 12.095249 10.490064 10.465068
## [232] 7.750893 9.955644 6.770406 10.604088 10.861838 10.607539 9.867305
## [239] 10.675445 9.818186 10.497826 7.762387 9.558764 11.284825 11.763780
## [246] 12.184758 11.695939 11.833310 11.136580 9.496410 10.439990 9.147707
## [253] 8.939227 10.826269 9.685344 9.772486 9.858567 10.202468 11.534542
## [260] 9.694993 8.286831 11.837309 8.656031 14.547797 7.860564 11.500954
## [267] 11.071868 7.252576 10.210898 12.911406 9.576215 8.661608 7.853244
## [274] 7.364023 9.999082 10.485564 13.005482 11.246533 9.939399 8.123246
## [281] 9.951502 9.520815 9.096554 8.318737 10.690795 9.618299 8.888650
## [288] 10.455099 9.523523 9.294081 9.285815 10.227273 10.046910 11.583860
## [295] 10.890611 12.625440 8.770276 10.214978 9.724912 12.612627 9.201791
## [302] 8.442953 10.355317 9.550641 9.753374 8.359984 10.526688 7.871034
## [309] 10.030776 12.016682 7.622795 8.008687 11.575875 12.685948 10.832234
## [316] 9.027820 12.838845 10.047154 9.329465 11.884183 10.122245 10.078699
## [323] 10.970858 10.451501 10.240237 10.815012 10.454634 12.183785 8.171511
## [330] 9.059963 10.769928 14.071659 11.546163 10.100302 11.049973 10.682260
## [337] 8.954132 11.453180 9.561360 8.742265 8.289112 10.146970 9.386492
## [344] 8.039552 8.914465 10.072671 10.696000 12.447986 9.470572 11.348252
## [351] 10.909260 11.481516 8.365327 10.374632 11.473357 6.749228 8.999364
## [358] 10.904134 8.435945 10.743009 6.852674 9.093485 9.268726 9.019224
## [365] 10.201992 10.520016 11.847206 10.817047 9.620674 10.841913 10.541593
## [372] 9.393086 12.200273 9.845008 10.148219 8.692739 12.301122 11.958052
## [379] 10.814375 6.401286 8.032333 10.496386 8.709828 10.654943 12.603048
## [386] 10.645578 7.177732 10.056118 10.631079 8.317982 10.707773 7.940264
## [393] 9.938016 9.564036 7.973511 9.319872 8.234441 9.070571 9.064745
## [400] 9.242888 9.770944 10.352122 9.806540 9.186066 11.627319 8.587858
## [407] 11.442565 10.790461 11.442013 10.987313 8.664280 12.218324 10.383038
## [414] 10.893095 10.301434 10.836845 10.205778 9.243299 10.992912 9.962394
## [421] 11.081504 9.981955 9.654934 13.983011 8.900236 9.768392 9.348375
## [428] 10.735157 7.800902 7.726110 10.139757 10.800441 9.602247 11.517442
## [435] 7.538177 8.615666 10.715749 11.027472 11.591365 9.581636 11.965620
## [442] 8.818485 10.401841 9.916339 10.427646 9.989783 11.948073 7.962022
## [449] 8.561328 7.693738 12.110985 8.961612 9.812623 11.836962 8.507040
## [456] 12.611362 11.916456 9.735844 11.864648 12.647463 7.063101 7.952670
## [463] 13.592505 8.319063 12.017952 8.277895 8.379202 11.621330 8.618307
## [470] 9.288354 9.873584 7.126201 8.841269 9.412391 9.273510 10.096959
## [477] 8.161682 10.408912 11.032026 8.868971 8.609948 10.156575 9.297268
## [484] 8.235753 10.843325 9.767443 12.472135 11.021959 10.928018 10.384733
## [491] 10.221791 9.514269 9.474409 11.089374 11.697753 8.772538 9.003942
## [498] 11.908526 11.934424 11.888672 10.102465 10.907967 9.283119 10.752258
## [505] 8.377719 12.373238 9.840700 9.748570 7.325687 9.898393 10.278249
## [512] 9.397503 11.471943 7.993012 10.141899 12.614454 6.917763 8.054747
## [519] 9.963533 9.159639 11.217574 7.455922 6.658700 8.456209 9.043905
## [526] 7.539216 9.365364 11.536546 9.966696 8.863066 9.409746 7.946490
## [533] 9.696795 9.612429 7.739120 11.801870 10.793038 8.224660 9.669010
## [540] 9.571127 8.668090 6.885290 8.026682 11.051294 9.733158 8.561926
## [547] 6.761213 11.430086 9.724905 10.915775 9.535991 11.628491 9.113187
## [554] 11.068549 9.936907 10.721670 9.794036 9.561175 10.659810 12.165878
## [561] 10.187705 10.948850 10.127773 8.167046 8.915684 7.806735 10.025573
## [568] 7.217854 9.548902 8.603701 11.155206 8.808956 11.881487 11.014470
## [575] 10.976336 9.943754 10.176609 11.636513 9.786759 8.637870 9.415614
## [582] 11.881767 10.870672 8.774560 8.857423 10.471791 10.608874 11.053825
## [589] 9.113886 9.873393 12.618744 11.457812 10.933680 12.641827 9.294838
## [596] 10.151347 8.600576 8.855225 8.419200 12.148294 6.602888 12.243741
## [603] 10.164616 14.193475 9.099514 10.201535 7.768270 10.300171 10.155415
## [610] 10.576700 10.975301 7.940647 10.574781 11.133195 10.558767 9.393760
## [617] 10.130453 10.785808 11.577227 9.429652 10.621231 8.404698 8.938396
## [624] 7.798813 9.986656 11.494258 9.633669 7.329544 10.651681 9.968928
## [631] 10.124367 9.343363 9.912672 9.122048 7.039590 11.795011 10.237145
## [638] 11.370047 9.923458 9.796140 5.346698 10.569483 8.237295 11.822686
## [645] 8.944316 10.099980 11.129731 11.030863 8.671636 10.148431 11.803714
## [652] 9.879237 10.423058 9.253339 12.516297 10.853303 9.122672 11.127126
## [659] 9.513109 14.048919 11.747505 9.380299 9.837106 10.710972 8.692309
## [666] 8.245206 10.341040 8.787556 12.263379 12.314274 12.709177 11.001235
## [673] 9.850663 11.928324 9.695865 10.556535 10.641112 10.733686 11.886567
## [680] 11.356111 8.349953 11.536640 10.384196 8.225717 11.387124 11.329200
## [687] 11.154594 10.425815 8.750697 11.042454 8.814594 7.602447 10.787543
## [694] 9.827684 9.806586 8.342718 9.080326 8.205754 9.402041 8.757667
## [701] 8.499962 9.589591 8.003281 12.215821 11.785333 10.125051 11.381581
## [708] 10.613053 9.322072 11.071663 9.442115 9.120322 7.926767 9.985293
## [715] 6.990729 11.067822 9.223668 10.748616 10.289160 10.571800 9.926012
## [722] 13.063839 9.751824 10.993458 6.837095 12.395740 12.365621 11.711021
## [729] 11.582504 8.846489 9.317769 10.979087 8.446700 10.367957 12.676996
## [736] 9.724764 8.778603 12.263119 9.238525 7.834121 7.307167 12.640863
## [743] 9.035785 8.449943 10.081918 11.023919 10.939597 11.646358 9.708050
## [750] 9.410256 8.182276 11.049240 10.383818 12.423194 9.728969 10.647606
## [757] 10.255737 7.584948 9.036777 9.479180 9.759835 8.918659 9.386736
## [764] 12.366621 9.530757 10.158095 11.089273 8.202288 10.213876 10.381802
## [771] 7.865663 7.946014 11.349208 8.302748 10.171675 9.701660 7.588552
## [778] 9.379636 8.500283 7.350132 11.398833 9.248220 9.028786 8.852832
## [785] 10.349731 11.500001 10.225988 7.432825 10.464784 10.855860 10.326656
## [792] 10.656819 8.392862 12.055624 10.714002 11.492191 8.889547 8.847777
## [799] 9.700667 11.390159 10.775471 9.322121 7.253196 9.258393 9.894357
## [806] 12.351662 9.294048 10.991597 10.452825 8.384148 10.831056 10.330869
## [813] 11.451032 8.728909 9.023295 11.544023 12.339630 11.911523 9.072166
## [820] 8.336106 10.563075 10.675474 8.098852 10.481394 10.861506 10.094960
## [827] 8.711487 8.076675 8.804658 11.758939 8.245167 11.600840 9.184036
## [834] 12.543777 9.623159 10.374473 9.744546 12.098816 11.126575 9.949521
## [841] 10.275360 10.962297 9.735631 12.747066 11.437870 11.651103 8.686535
## [848] 11.746910 11.249326 9.219739 9.472024 7.499361 10.316907 9.644327
## [855] 11.141298 11.522665 11.526858 7.976835 8.861137 9.164876 11.576915
## [862] 9.660766 9.007329 9.615257 10.611669 11.553071 8.612791 8.693776
## [869] 11.258892 13.618803 12.804018 10.939437 10.726555 10.054316 10.962498
## [876] 11.051440 12.333788 9.646670 9.431180 9.286578 10.067625 11.681930
## [883] 9.715805 10.224939 13.724767 12.279691 9.496859 9.329736 8.727838
## [890] 11.056363 8.616760 9.598864 7.215738 11.566543 7.446868 9.814717
## [897] 10.798028 10.832557 9.330817 9.209948 10.733505 11.017264 10.958267
## [904] 9.599542 11.457333 8.414126 8.957749 12.009328 10.405115 9.107448
## [911] 9.093949 10.143036 7.149481 11.555270 10.255003 9.499160 11.128639
## [918] 7.158757 11.152294 9.903146 11.032954 9.568849 12.266367 8.660444
## [925] 8.583312 8.409802 9.809807 10.348939 9.181033 9.671457 10.932281
## [932] 11.454503 8.877483 11.036353 11.074686 9.559910 8.427883 11.186308
## [939] 11.275577 9.401156 9.244987 8.638729 8.477686 9.364069 9.335608
## [946] 10.271207 11.635929 9.676485 12.493398 7.371118 8.143091 10.445803
## [953] 9.843700 10.905391 10.378495 8.162185 10.768036 10.944931 7.208008
## [960] 11.936578 10.888311 10.776751 10.306143 8.863324 7.541529 9.539837
## [967] 11.083625 11.815626 7.177838 8.701289 9.924350 8.818110 11.226740
## [974] 9.115142 10.814696 10.417167 9.627613 9.950472 11.370896 11.922489
## [981] 7.738907 7.922421 10.801073 8.672432 10.381830 5.645012 8.966473
## [988] 10.655435 10.531512 7.938439 9.913044 10.472313 10.910439 9.010875
## [995] 11.933051 10.510978 11.881657 8.756952 10.291801 9.325882
# create groups for different values of X
(group <- rep(1,1000)) # a vector consisting of 1000 elements
## [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [186] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [223] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [260] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [297] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [334] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [371] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [408] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [445] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [482] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [519] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [556] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [593] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [630] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [667] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [704] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [741] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [778] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [815] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [852] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [889] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [926] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [963] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1000] 1
group[xAxis > -1.5] <- 2
group[xAxis > -.5] <- 3
group[xAxis > .5] <- 4
group[xAxis > 1.5] <- 5
group
## [1] 3 1 4 3 3 2 2 3 2 4 3 3 3 4 3 4 4 2 3 3 2 3 2 5 2 3 3 4 2 4 2 4 3 1 3 3 3
## [38] 4 3 2 2 2 2 2 3 2 3 2 3 4 4 2 4 3 3 2 3 2 3 3 3 1 4 3 2 4 3 3 4 2 4 2 3 3
## [75] 4 3 4 4 3 2 2 2 3 4 2 4 2 3 2 3 3 2 4 1 2 1 5 5 2 5 3 4 3 4 2 3 4 3 2 2 3
## [112] 3 3 2 3 3 3 3 4 2 5 1 3 4 4 3 2 4 2 4 4 3 2 4 3 2 3 4 2 4 4 1 3 2 3 2 2 3
## [149] 2 2 3 2 4 2 2 4 3 2 4 4 2 4 3 2 3 2 4 3 3 3 3 5 2 2 5 4 3 4 1 1 3 3 5 2 2
## [186] 4 4 3 3 4 2 4 3 2 3 4 3 3 4 3 4 3 4 5 5 5 4 2 2 2 3 4 1 2 5 3 2 1 3 3 4 4
## [223] 5 1 2 1 2 2 5 4 3 1 3 1 2 3 2 3 3 1 3 2 2 2 5 4 5 5 4 3 3 2 3 3 3 2 1 2 3
## [260] 2 3 4 3 5 1 4 3 2 2 4 3 3 2 2 3 3 4 4 3 2 4 3 2 1 4 4 2 2 4 4 3 3 3 4 3 5
## [297] 4 4 2 5 3 2 2 3 4 1 3 2 3 5 1 2 2 4 3 3 5 3 3 4 3 2 3 3 2 4 4 4 2 2 2 5 3
## [334] 3 5 3 1 3 3 2 1 3 2 2 2 4 3 4 2 4 4 4 2 3 3 1 4 3 1 4 1 3 3 2 5 4 3 4 2 2
## [371] 3 2 4 2 3 3 4 5 5 2 2 3 3 3 3 4 1 4 4 1 2 2 3 3 1 2 2 2 2 4 3 3 2 1 3 1 3
## [408] 3 4 4 3 4 3 4 3 2 2 3 3 4 3 3 4 4 2 2 3 3 2 2 4 3 3 4 2 4 2 3 3 4 5 2 3 3
## [445] 3 2 3 1 2 1 5 2 4 4 2 4 3 2 5 5 2 2 5 2 5 2 1 2 2 2 4 3 2 2 3 3 2 3 4 2 3
## [482] 4 2 2 3 4 4 3 5 3 3 3 3 4 5 2 2 4 5 5 3 3 3 3 2 4 2 4 2 4 3 2 3 2 2 5 2 3
## [519] 4 2 3 1 2 2 3 2 1 4 2 3 3 2 2 3 2 4 3 2 3 2 3 2 1 4 3 1 1 3 3 3 3 5 3 4 4
## [556] 2 1 2 3 5 4 4 3 4 3 3 3 2 2 2 4 2 4 4 3 2 3 3 2 2 2 4 2 2 3 3 4 4 3 3 5 4
## [593] 2 5 1 3 3 1 3 5 1 3 3 5 3 3 2 5 4 4 3 2 4 4 4 2 4 5 4 2 3 2 2 1 3 3 3 1 4
## [630] 3 3 3 4 3 1 4 3 4 3 2 2 4 2 4 3 4 3 4 2 3 5 4 2 2 4 4 2 3 2 5 3 3 2 5 3 2
## [667] 3 3 5 3 3 2 3 4 2 4 3 4 3 5 1 3 4 1 4 3 4 3 1 4 3 2 3 3 4 1 3 2 2 3 2 3 2
## [704] 4 3 2 5 2 2 4 2 4 3 3 1 4 3 4 2 3 2 4 3 4 1 4 5 4 4 2 2 3 1 3 4 4 3 5 3 2
## [741] 2 5 3 2 4 3 3 3 3 3 2 5 3 5 3 3 3 1 2 3 3 3 3 5 3 3 3 2 3 3 2 2 5 4 4 3 2
## [778] 3 3 1 4 3 2 3 2 3 3 2 2 4 3 5 2 4 3 3 2 1 2 4 3 3 2 4 3 4 2 4 2 3 4 4 4 2
## [815] 2 3 4 2 3 2 4 4 3 3 3 3 2 1 2 5 2 3 3 3 4 3 4 5 5 4 3 2 2 5 4 5 3 4 4 3 3
## [852] 2 4 3 4 4 3 1 3 2 5 3 3 1 2 3 3 2 4 4 4 2 2 3 3 3 3 4 3 4 3 3 4 5 5 3 3 3
## [889] 3 4 2 3 2 5 1 3 3 5 4 2 4 4 4 2 3 2 2 4 3 2 2 4 2 5 2 3 4 3 5 4 4 4 4 3 2
## [926] 2 4 3 4 1 4 5 3 3 4 3 2 4 3 2 1 2 2 2 4 3 4 3 4 1 3 4 2 4 2 4 4 3 1 5 3 3
## [963] 3 3 2 3 3 5 1 3 3 2 4 3 2 2 2 3 4 3 1 1 5 3 3 2 2 4 4 1 4 4 3 4 4 4 3 3 3
## [1000] 1
# create sample dataframe by joining variables
sample_data <- data.frame(xAxis,yAxis,group)
sample_data
## xAxis yAxis group
## 1 0.001077924 8.984784 3
## 2 -1.630869914 6.700325 1
## 3 0.801192197 11.118479 4
## 4 -0.463052970 8.814946 3
## 5 -0.275248656 12.273942 3
## 6 -1.206690837 7.613248 2
## 7 -0.921710287 9.683336 2
## 8 0.167326898 9.789410 3
## 9 -0.841639294 8.090054 2
## 10 1.451874447 12.302137 4
## 11 -0.370221220 8.980494 3
## 12 0.184417925 10.837204 3
## 13 -0.245533032 8.855358 3
## 14 0.942833449 10.268559 4
## 15 0.072911566 10.868858 3
## 16 0.575972677 11.194294 4
## 17 0.642687766 10.789700 4
## 18 -1.417858423 8.514630 2
## 19 0.245770534 10.801223 3
## 20 -0.301733496 9.335378 3
## 21 -1.025296389 11.599325 2
## 22 -0.017533598 9.577549 3
## 23 -0.508099358 9.339796 2
## 24 1.533833687 9.762563 5
## 25 -0.774723877 9.162990 2
## 26 -0.285227270 10.375549 3
## 27 -0.416073779 9.528601 3
## 28 1.010836856 9.375813 4
## 29 -0.529166254 9.925400 2
## 30 0.600978932 9.508630 4
## 31 -0.649472278 8.807830 2
## 32 0.594358858 11.622365 4
## 33 0.486144347 11.350589 3
## 34 -2.789792000 8.157197 1
## 35 -0.174556845 8.899755 3
## 36 0.133294365 10.347998 3
## 37 -0.308711976 9.781617 3
## 38 1.087565051 10.622697 4
## 39 0.325160453 10.764483 3
## 40 -0.995586036 9.302356 2
## 41 -0.509719324 9.923679 2
## 42 -0.550777277 9.594947 2
## 43 -0.668354608 9.711662 2
## 44 -0.803313175 8.143874 2
## 45 0.281208653 11.736669 3
## 46 -0.999304954 10.889188 2
## 47 -0.480106750 10.049393 3
## 48 -0.626274440 10.516435 2
## 49 -0.062705181 10.556302 3
## 50 0.593672812 9.184864 4
## 51 0.557931318 10.557379 4
## 52 -0.571628917 9.585500 2
## 53 0.800391252 12.463173 4
## 54 -0.292954955 8.556998 3
## 55 -0.036062992 9.503737 3
## 56 -0.930970809 10.225983 2
## 57 -0.050831596 10.426404 3
## 58 -1.218120750 7.606686 2
## 59 0.082542338 10.141007 3
## 60 0.420232454 10.113221 3
## 61 0.183824142 10.792460 3
## 62 -1.576311651 8.885330 1
## 63 1.042104431 10.457961 4
## 64 0.068030824 10.238638 3
## 65 -0.901354991 9.312675 2
## 66 0.518028692 9.097014 4
## 67 0.314944468 10.539198 3
## 68 0.368145410 11.592587 3
## 69 1.031346698 10.511587 4
## 70 -0.555650137 9.944528 2
## 71 0.573527458 10.139505 4
## 72 -0.635001732 8.893903 2
## 73 0.402726095 10.868632 3
## 74 0.007714037 9.461556 3
## 75 0.987597342 11.376608 4
## 76 -0.178315054 10.331983 3
## 77 0.507699141 10.323855 4
## 78 0.553168827 11.572209 4
## 79 0.287188510 9.571436 3
## 80 -0.727512703 10.135179 2
## 81 -0.521539222 10.782376 2
## 82 -1.484248535 7.656187 2
## 83 -0.179874315 8.644578 3
## 84 0.795454057 10.176204 4
## 85 -0.696781486 9.735038 2
## 86 1.158518325 10.539452 4
## 87 -0.784201611 9.410417 2
## 88 -0.071498176 8.899732 3
## 89 -1.383245820 9.471919 2
## 90 -0.072945463 8.213742 3
## 91 -0.044415252 10.895502 3
## 92 -0.892559363 9.064712 2
## 93 0.527236687 10.011257 4
## 94 -2.348370585 7.032222 1
## 95 -0.603148688 9.756339 2
## 96 -1.770116115 7.931808 1
## 97 2.307338943 12.997191 5
## 98 2.078054846 11.705673 5
## 99 -1.387668447 8.983342 2
## 100 1.569164771 11.957163 5
## 101 -0.481541692 8.410154 3
## 102 0.796703225 10.517905 4
## 103 0.384873929 8.824796 3
## 104 0.837024202 13.060681 4
## 105 -0.670014710 8.612647 2
## 106 -0.225509505 9.825594 3
## 107 1.025755620 10.463638 4
## 108 0.431043665 10.125512 3
## 109 -1.309207763 9.832842 2
## 110 -0.536201674 11.207432 2
## 111 0.440653000 9.760561 3
## 112 -0.036183785 10.959928 3
## 113 -0.195678409 8.899034 3
## 114 -0.819340560 7.966727 2
## 115 -0.019838093 10.059401 3
## 116 -0.143055611 9.963121 3
## 117 -0.020676929 10.036487 3
## 118 0.356167350 12.260610 3
## 119 0.628785161 12.016405 4
## 120 -1.249840247 8.637216 2
## 121 2.028756527 12.027002 5
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# 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)
