# Mindanao State University
# General Santos City
# Introduction to R base commands
# Submitted by: Zandra Marie C. Delgado
# Submitted to: Prof. Carlito O. Daarol
# Math Department
# Lab Exercise 1: How to create Lines in with different styles in R
# Step 1: Create Data
x <- 1:10 # Create example data
y <- c(3, 1, 5, 2, 3, 8, 4, 7, 6, 9) # using the c() function to create an array
# Step 2: Plot the line graph using the base plot() command
plot(x, y, type = "l")
# Step 3: Add Main Title & Change Axis Labels
plot(x, y, type = "l",
main = "Hello: This is my Line Plot",
xlab = "My X-Values",
ylab = "My Y-Values")
# Step 4: Add color to the line using the col command
plot(x, y, type = "l",
main = "This is my Line Plot",
xlab = "My X-Values",
ylab = "My Y-Values",
col = "red")
# Step 5: Modify Thickness of Line using lwd command
plot(x, y, type = "l",
main = "This is my Line Plot",
xlab = "My X-Values",
ylab = "My Y-Values",
lwd=7,
col = "green")
# Step 6: Add points to line graph by changing the type command
plot(x, y, type = "b",
main = "This is my Line Plot",
xlab = "My X-Values",
ylab = "My Y-Values",
lwd=3,
col = "blue")
# Lab Exercise 2: How to create Lines in with different styles in R
# Step1: Assign values for different lines. We enclose the entire
# line with parenthesis symbol to force R to display the results instantly
# set the same value for the x variable
(x <- 1:10)
## [1] 1 2 3 4 5 6 7 8 9 10
# set different values for y variables
(y1 <- c(3, 1, 5, 2, 3, 8, 4, 7, 6, 9))
## [1] 3 1 5 2 3 8 4 7 6 9
(y2 <- c(5, 1, 4, 6, 2, 3, 7, 8, 2, 8))
## [1] 5 1 4 6 2 3 7 8 2 8
(y3 <- c(3, 3, 3, 3, 4, 4, 5, 5, 7, 7))
## [1] 3 3 3 3 4 4 5 5 7 7
# Plot first the pair x and y1.
plot(x, y1, type = "b",
main = "This is my Line Plot",
xlab = "My X-Values",
ylab = "My Y-Values",
lwd=3,
col = "blue")
# then Add the two lines (for x,y2) and (x,y3)
lines(x, y2, type = "b", col = "red",lwd=3)
lines(x, y3, type = "b", col = "green",lwd=3)
# Add legend to the plot
legend("topleft",
legend = c("Line y1", "Line y2", "Line y3"),
col = c("black", "red", "green"),
lty = 1)
# Step 2: Create Different Point Symbol for Each
# Line using the pch command
plot(x, y1, type = "b",pch = 16,
main = "This is my Line Plot",
xlab = "My X-Values",
ylab = "My Y-Values",
lwd=3,
col = "blue")
lines(x, y2, type = "b", col = "red",lwd=3, pch = 15)
lines(x, y3, type = "b", col = "green",lwd=3, pch = 8)
# Add legend
legend("topleft",
legend = c("Line y1", "Line y2", "Line y3"),
col = c("black", "red", "green"),
lty = 1)
# Lab Exercise 3: Create Line graph without x values
Pupils <- c(3.55 ,3.54 ,3.53 ,3.61 ,3.65 ,3.63 ,3.61
,3.61 ,3.59 ,3.63 ,3.59 ,3.63 ,3.62 ,3.62
,3.59 ,3.63 ,3.62 ,3.65 ,3.65)
# get number of elements of Pupils
length(Pupils)
## [1] 19
# Display the elements of Pupils
Pupils
## [1] 3.55 3.54 3.53 3.61 3.65 3.63 3.61 3.61 3.59 3.63 3.59 3.63 3.62 3.62 3.59
## [16] 3.63 3.62 3.65 3.65
# You can obtain the plot without x values
plot(Pupils, type = 'o')
# Lab Exercise 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
folder <- "C:/Users/Admin/Desktop/Class Lectures/BLecture 0 Graphics in R"
filename <- "Cancer.csv"
(file <- paste0(folder,"/",filename))
## [1] "C:/Users/Admin/Desktop/Class Lectures/BLecture 0 Graphics in R/Cancer.csv"
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...
##
## 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.
dim(cancer)
## [1] 173 17
names(cancer)
## [1] "country" "incomeperperson" "alcconsumption"
## [4] "armedforcesrate" "breastcancer" "co2emissions"
## [7] "femaleemployrate" "hivrate" "internetuserate"
## [10] "lifeexpectancy" "oilperperson" "polityscore"
## [13] "relectricperperson" "suicideper100th" "employrate"
## [16] "urbanrate" "continent"
# compute mean value for every continent
(means <- round(tapply(cancer$breastcancer, cancer$continent, mean),
digits=2))
## AF AS EE LATAM NORAM OC WE
## 24.02 24.51 49.44 36.70 71.73 45.80 74.80
# draw boxplot per continent
boxplot(cancer$breastcancer ~ cancer$continent, main= "Breast cancer by
continent (brown dot = mean value)", xlab="continents", ylab="new
cases per 100,00 residents", col=rainbow(7))
# insert the mean value using brown dot
points(means, col="brown", pch=18)
# Lab Exercise 8: How to load external datasets and change data layout
folder <- "C:/Users/Admin/Desktop/Class Lectures/BLecture 0 Graphics in R"
filename <- "hsb2.csv"
(file <- paste0(folder,"/",filename))
## [1] "C:/Users/Admin/Desktop/Class Lectures/BLecture 0 Graphics in R/hsb2.csv"
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...
## 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`
# 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
## # ℹ 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
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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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## 999 118 1 4 2 1 1 socst 61
## 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", "african-
amer","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
#install.packages("gplots")
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
#install.packages("ggplot2")
library(ggplot2)
# we load variable names to memory to avoid the dollar notation
attach(hsb2_long)
# create the plot object p
p <- ggplot(hsb2_long, aes(ses, fill = prog)) + facet_wrap(~race)
p + geom_bar()
## Warning: The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
p + geom_bar(position = "dodge")
## Warning: The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
library(ggplot2)
attach(hsb2_long)
## The following objects are masked from hsb2_long (pos = 3):
##
## female, id, prog, race, schtyp, ses, value, variable
p <- ggplot(hsb2_long, aes(ses, fill = prog)) + facet_wrap(~schtyp)
p + geom_bar()
## Warning: The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
p + geom_bar(position = "dodge")
## Warning: The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
# Lab Exercise 10: Scatter Plots with marginal Distributions
# Advance Scatter plots using libraries
#install.packages("ggExtra")
#install.packages("tidyverse")
library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.1 ✔ stringr 1.5.0
## ✔ forcats 1.0.0 ✔ tibble 3.2.1
## ✔ 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 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] -0.155028582 2.208922861 0.170881735 0.108656811 -0.363087520
## [6] 1.804882542 0.928650633 -0.134423907 0.044976105 -1.699982941
## [11] 0.153022506 -0.681904279 -0.416370540 -1.257946474 0.638934914
## [16] 1.175156472 -0.027954806 -1.420717713 1.020615171 -1.333668766
## [21] -1.053169595 1.260857362 0.202690573 0.412463576 -0.872132990
## [26] -0.652902543 -0.587783477 -0.136087093 -0.248963930 -0.857764357
## [31] -1.785726853 -1.115411505 1.040771810 -0.562934442 0.759742650
## [36] -2.142030608 -0.174475977 -0.666088261 0.945104131 -0.724342684
## [41] 0.295304114 0.191575628 -0.699116075 0.371368803 -0.252765569
## [46] -0.911589413 -0.046322348 -1.348549584 -0.855483241 0.749146258
## [51] 0.485654353 -0.609144219 -2.081105130 -0.907363392 -1.912323768
## [56] -0.818722216 -1.289054412 -0.995328244 -1.204224236 -0.605004143
## [61] 1.240115152 -0.015582942 -1.931407784 -0.948070541 1.356661279
## [66] -0.782543321 -1.236417083 -1.275640549 -1.515160458 -0.683749268
## [71] -0.445331699 -0.628607103 -0.308833193 -0.188850326 -0.309678444
## [76] 1.708317491 -0.690355554 0.146336094 -1.795275020 -0.880603123
## [81] 0.953640058 -0.323013773 -0.688701852 -1.024318436 0.761180372
## [86] 0.272560219 -0.276414107 -1.466785473 0.914511283 1.010988139
## [91] -2.792182864 1.383007645 0.537292275 -0.541279322 -0.855697166
## [96] 1.633718255 0.406766848 0.914717761 0.368407654 -0.274709599
## [101] 0.333203848 -1.171962662 2.571335590 0.622988077 0.034082753
## [106] 1.580614373 0.859004104 -0.245354197 0.820408779 -0.728453632
## [111] 0.148324704 -0.485234976 -1.190783340 -0.217498702 1.450168637
## [116] -0.197409556 -1.073158870 0.483071104 -0.247582878 0.812239924
## [121] -1.418465915 0.154618149 -1.598765208 -2.204247941 -2.017053055
## [126] 0.254732919 -0.840263516 0.998329054 1.853135313 2.713059417
## [131] -1.158191978 -0.193515317 -1.013184915 -0.163921212 1.893988945
## [136] -0.314754307 -0.088082163 -0.598472805 -0.841521775 0.206938315
## [141] 0.257435631 0.476659820 -0.207560484 -0.927694077 -0.309566313
## [146] 0.395798057 0.179952236 -0.412898623 -0.069360065 -1.859269236
## [151] 1.892084426 0.503125917 -1.028791682 -2.715918172 -1.015008805
## [156] -0.463429977 1.546780021 0.280902393 0.268363048 0.985480748
## [161] 0.777581628 -1.398520744 0.106497921 -0.686214255 0.401637537
## [166] -1.044281026 1.008532215 -0.475341120 -0.556533290 -1.869553998
## [171] 0.553663713 0.831992373 0.585651419 0.163164777 -1.195091742
## [176] -0.062031831 1.571951719 0.322209425 0.832534607 0.958174958
## [181] 0.146066739 0.095735983 0.259558195 1.456445106 -1.132521159
## [186] -1.970164962 0.983028106 1.543378591 -0.571995081 0.296507377
## [191] -0.372614367 -0.086923407 0.951250924 0.758236837 1.134700563
## [196] -0.343718840 -1.108046672 -0.751104271 1.758104111 0.428562907
## [201] 0.877356931 -0.175087153 0.103923768 0.914429191 -0.914741025
## [206] 0.196957654 1.069394371 -1.449318241 -0.232985059 -0.635602702
## [211] -0.197789653 0.568397664 -0.292850551 -1.715705266 -0.398067343
## [216] -0.437196309 -0.786758723 -0.483338588 0.354802914 -0.722448185
## [221] 1.525750551 -0.672976370 1.270271150 -0.063856072 1.010116375
## [226] 0.785416347 0.233025169 1.149894511 1.622269596 0.834835924
## [231] 0.047527106 1.180337761 1.173556689 -0.681048598 0.200329311
## [236] -1.339036621 -0.355560176 0.321452723 0.230216621 1.116193331
## [241] 0.266154267 0.231357320 -0.475202828 0.075195741 0.076399103
## [246] -0.074048782 0.780322548 -1.005520567 -1.872631771 1.131086907
## [251] -0.219866086 0.750954829 -0.122783612 -1.703776764 -0.914033572
## [256] 0.677071622 -1.681143489 -0.492807771 2.200873910 0.580000843
## [261] -0.159288758 -1.296878504 -0.514852536 -0.266360079 0.384664616
## [266] -1.660384658 0.783596345 1.254265970 0.884533169 -0.430179256
## [271] 1.830098737 -1.231879167 -0.608257467 0.469396559 -0.776693071
## [276] 1.117166444 -0.920988261 -0.132083699 1.192898430 -2.878583670
## [281] -0.592846395 -0.296777091 -0.389013351 -0.215322191 -0.994537022
## [286] 0.408754743 -0.659195369 0.119576760 -0.811635657 -0.098050940
## [291] 0.428408170 0.439474311 -1.966096071 -0.041996164 -0.709289741
## [296] -1.192959114 2.071779644 1.203190318 1.225259239 0.407813557
## [301] 1.567990912 1.172500447 -0.321551830 0.357636623 1.433137724
## [306] 2.534201377 -0.126875950 0.544901965 -0.574877818 -0.468529103
## [311] -2.319969024 -0.056668696 1.103934643 -0.584960674 0.930552124
## [316] -0.899458366 0.638716850 0.277054158 0.653562486 0.295539225
## [321] -1.089602058 1.560273795 0.598732080 0.132945048 0.069700460
## [326] 1.342566085 -1.330451964 -0.091785385 0.519319513 0.710125980
## [331] 0.112736220 0.027337147 -1.530230476 0.844420417 0.416278633
## [336] 0.915221035 0.408666359 1.067413947 -1.314192860 -0.968797730
## [341] -1.141929295 -0.676117836 1.663199062 -0.840444493 -2.311289535
## [346] -0.705074935 1.596542606 0.496894976 0.176405742 -1.183106053
## [351] -0.765861108 -1.744091153 1.407640942 0.539037565 -1.262275970
## [356] -0.819021386 0.084443333 1.655635182 -0.761144085 0.599587992
## [361] -0.431243229 0.169278800 -1.229609031 -1.727879812 -0.859348961
## [366] 0.617012363 1.405670380 0.852964853 -1.084351536 -2.312713492
## [371] -1.271537317 0.994059960 2.446990352 1.666516610 0.200622292
## [376] -0.235105152 0.796149904 -0.629276506 1.388243836 1.305648316
## [381] -0.107646397 -0.257038540 0.950245831 0.855006915 -0.548905776
## [386] -0.190902912 -1.142174906 -1.319857325 -0.384108095 -0.582369241
## [391] -0.658055891 -0.484060528 -0.522190761 -0.364898624 -0.924955433
## [396] 0.475980806 -0.683692133 -1.340226865 -0.504438542 0.753078720
## [401] -1.129252132 1.242615405 1.331265939 -1.283259400 1.507197353
## [406] 0.023014617 0.086194306 1.021427376 -0.449875859 -0.135652849
## [411] 0.601783418 -0.957280807 -0.827221785 1.264890218 -0.287232629
## [416] 1.231255358 -0.061742348 0.589316466 0.893516661 0.753376301
## [421] 1.528673401 2.795093324 -0.152730481 0.830246665 -1.111604286
## [426] -1.701777384 0.052971574 2.033798101 0.321112006 1.703520185
## [431] -0.003977174 1.446035817 -0.172326783 -0.227928345 -0.402167141
## [436] 0.070201048 1.317654461 -0.217847416 -0.200442983 0.953767508
## [441] 1.021959712 -0.484104176 -1.248013354 -0.326895707 -1.231188549
## [446] -0.448098855 -0.250595344 0.016752005 -1.483712782 0.686055133
## [451] -0.942894647 -0.562974983 -0.790681121 -0.135641938 -0.214893590
## [456] -0.083525928 -0.491255573 -0.371755545 -0.391293525 0.465290938
## [461] 0.305214557 0.607022758 0.567897217 -1.179229813 -0.704388871
## [466] 0.084594814 -0.421357666 0.924070618 -0.348904618 1.425227315
## [471] -0.972579206 -0.352570908 0.594964916 1.685407215 -0.036400862
## [476] 1.675701066 -0.864271870 1.263539288 0.440005765 -2.558970051
## [481] -1.093068412 -0.339532443 0.489177538 -0.159935432 1.357078831
## [486] 0.777851334 1.050886356 -0.094045104 0.924013584 1.367836837
## [491] 0.013751444 0.810689110 1.149571815 -0.529036026 0.204814654
## [496] 0.632226440 -0.621015633 -0.056263209 -0.226515654 0.175037131
## [501] 0.214737894 0.746883154 -1.064052909 -1.012590716 0.425173276
## [506] -0.213387952 -0.082697923 -0.655666424 0.333483202 1.192386051
## [511] 0.632495785 1.138908739 -0.728106188 0.594988295 0.102243129
## [516] 0.969534437 0.632077922 -1.054075014 0.552455547 -0.399187509
## [521] 1.817422498 -0.344210913 -0.951993187 0.189964396 0.845948629
## [526] 1.364955245 1.442541637 -1.252624813 0.606982665 0.542609212
## [531] -0.352452469 0.257585721 -1.811964869 1.370155680 -0.277362115
## [536] -1.105841409 0.117762217 0.118535581 1.550854759 0.712507029
## [541] -0.481973990 1.103079795 -1.604011345 0.630526343 -0.962670880
## [546] -1.222529102 -0.067705589 -0.749491102 1.490778490 0.795756489
## [551] -1.167724862 1.176040709 -0.435005038 1.025811998 -0.785072134
## [556] 0.939796301 0.346281597 1.313103029 -1.240198850 -0.397915485
## [561] 1.132404617 1.211370570 -0.507909760 -1.373562680 0.811508616
## [566] 0.243696522 0.063083724 0.213362309 0.536639225 -0.347343326
## [571] 0.619716144 1.354796953 0.514620791 -0.164318791 -0.173541581
## [576] 1.330500873 0.738546762 -0.179670105 -0.339337679 -1.030940059
## [581] 1.222362431 -2.304045090 -0.778704563 -1.102305016 -0.827276577
## [586] 1.438359654 0.238448356 0.180222322 -0.511689604 0.685601853
## [591] -0.173736975 1.393242352 0.268567472 -1.636987901 1.835401450
## [596] -0.452340186 -0.799696683 0.342219809 -0.353939015 1.977588803
## [601] 0.156095028 1.330314077 0.373039571 0.039292500 -0.735188560
## [606] -0.568704789 0.910888117 -1.039822198 -0.446081135 0.339036768
## [611] 2.041368495 0.422898863 -0.736497930 -0.733804134 0.482995879
## [616] 0.696803489 3.633343526 0.511877324 -0.708571127 -0.022713203
## [621] -2.393468733 0.877525064 0.047464191 -1.238311704 -0.169411845
## [626] -0.026160161 -0.246515797 -0.849054433 1.220447980 -1.510251887
## [631] -0.649352107 -0.320965500 -0.696965277 -0.304541229 0.764456235
## [636] 0.289808745 1.221231701 -1.496836064 0.311338887 -1.000608106
## [641] 0.178684627 -0.624976282 0.866190328 0.344259276 1.157662672
## [646] -0.862599037 -0.080423045 0.451994064 -0.526567937 0.537115310
## [651] -0.097080570 0.804204989 0.738336536 -0.430079887 -1.535874838
## [656] 1.216348927 1.454712323 -0.313291418 -0.836432773 -0.295746759
## [661] 0.993337166 -1.401550426 -1.125641573 -0.711517253 -0.486156265
## [666] 0.502995136 2.614270345 -0.576199784 -0.857397592 0.137195258
## [671] 0.318882336 0.024703052 0.158303803 -1.425814196 0.371423749
## [676] 0.318076130 0.430468549 0.545856328 -0.012467912 -1.447402800
## [681] 0.311743071 2.856327646 -0.457657321 -1.150473592 0.149492379
## [686] 2.423869254 0.395327997 -1.527483685 1.365710510 -0.673812145
## [691] 1.515602878 -0.358122716 1.842390844 0.954318140 -0.537679316
## [696] 1.024942002 -0.640428791 0.014079927 -0.394273722 -0.848514690
## [701] -0.882087076 0.488054416 -1.117098200 -0.236689028 1.229212626
## [706] 1.214564766 0.417955752 -1.029026208 -0.790772210 -0.233274513
## [711] -0.178465186 -0.611748988 0.297573889 0.351467912 0.950206323
## [716] 0.619830891 0.652940729 1.609661899 -1.112584895 0.301157915
## [721] 0.185421612 -0.386202397 -1.509704250 -0.402616598 -1.349040948
## [726] -0.566518178 -0.810069881 -0.866561669 -1.015456617 0.672428938
## [731] 1.681489902 0.219160618 -1.115863464 -2.197214400 0.222240021
## [736] -0.526806820 -0.705000571 -0.773870395 -0.082765624 0.428134207
## [741] -0.761585464 0.511173322 0.098474513 -1.067026430 -0.189400718
## [746] -0.209701091 1.318980008 -0.695208127 1.565795656 0.646710027
## [751] 2.062260897 -1.122716193 1.367181151 0.347070939 0.666595959
## [756] -0.471986713 -1.119960991 1.186151053 0.110329404 -0.296145520
## [761] 0.019476223 0.642921601 1.153550042 0.217569800 0.194562245
## [766] 0.574655626 0.445753531 -0.001587512 -0.266466115 0.440174315
## [771] 1.605834445 -2.184962862 0.053045413 0.252361299 1.384573894
## [776] -0.016832347 0.339376285 0.999618311 -1.118477120 0.038661747
## [781] 0.420254572 1.345955007 1.723048285 0.127475974 1.458209851
## [786] 0.963515546 -0.588626672 0.444490832 -1.032276210 -0.668430393
## [791] 0.873407289 0.833224729 2.271878466 0.255736117 0.376063997
## [796] 1.947897616 0.868894272 0.492040811 0.354914300 1.535273189
## [801] -0.943101462 -0.127551883 -0.374099223 0.030379884 -1.184353212
## [806] -1.170239614 -1.476686808 0.929672907 -0.351656971 -0.078793027
## [811] 0.346331255 1.609488250 -0.353341447 -1.505750407 1.205623116
## [816] -0.175405449 1.008333337 1.119052917 -0.084413981 -0.487624889
## [821] -0.896476785 -0.104155564 -1.546350502 -1.381647879 -1.086599099
## [826] 0.274759667 0.067845835 -1.378202034 -0.627744015 0.133071924
## [831] -0.974501201 0.117196628 0.927324581 -1.410320812 0.799335537
## [836] 1.587728636 0.147760748 3.194282906 1.051718456 0.188189825
## [841] -0.515968383 -0.792191341 -1.981952785 -0.350479395 -0.064674821
## [846] -0.286263985 0.494415545 0.112288515 -0.467552607 1.586948329
## [851] -0.126201740 0.051947111 0.087883797 -1.590680247 0.860790330
## [856] -1.640144348 -1.171312894 0.444754685 -0.062342041 1.116424241
## [861] -0.502700124 1.237276928 -0.843897488 0.483690078 -1.565059812
## [866] 0.069694073 -0.076104262 0.816691816 1.312575851 -0.374282934
## [871] 0.688907145 -0.591798927 -2.266263362 0.539052145 0.899224022
## [876] -1.929334546 -1.587267416 0.277486776 -0.771515305 -0.032668146
## [881] -0.468886125 -0.260407292 -0.697860868 0.557115515 0.496068227
## [886] -0.087442385 -1.048683653 -0.986622766 0.971358240 -1.814334416
## [891] 1.535161166 1.899382008 -0.175227059 -0.359634567 0.611845755
## [896] 0.999593162 0.467231144 -1.605175509 -0.377313254 -0.178539511
## [901] -0.195058964 0.085191133 -0.291610254 -0.033744288 -0.798038918
## [906] 1.403306854 0.082348305 1.807967426 1.318182064 -0.618966352
## [911] -1.410583695 0.774142672 0.054344103 0.210064889 -1.761873394
## [916] -1.994768236 -1.253102214 0.246012840 -0.095518180 0.672101908
## [921] -0.720362286 1.041246838 -0.907743325 -0.954030346 -0.464865883
## [926] -0.831112181 -1.719333878 -0.529865230 0.069169634 1.938121500
## [931] -0.766743801 -0.053625169 1.671530581 -0.308250139 0.389268530
## [936] -1.402964733 1.341743219 -1.683303067 1.929079119 1.221879440
## [941] 1.883072266 0.656096562 -0.844346400 0.874849359 -0.147104935
## [946] 0.399214411 -1.678881792 0.675934449 0.434027638 0.943389646
## [951] 0.658900519 0.233482301 0.595119424 -1.570496768 0.060599542
## [956] -0.313366595 1.420448070 -0.310187279 0.560290759 -0.081891366
## [961] -0.910669904 -0.726287992 1.120339395 -0.799183906 1.308598132
## [966] -1.263226872 -1.034056381 0.118231469 0.628168597 -0.681237335
## [971] -0.819949284 -1.271380526 1.396863907 -2.334221090 -1.843780431
## [976] 0.833667310 0.231888285 -1.387196784 1.018121197 -0.604045816
## [981] 0.385617493 -0.316093351 -1.272109874 -1.602642187 0.670563997
## [986] 0.484104046 -1.546103890 0.326592883 0.435211345 0.858593482
## [991] 0.318474272 -0.399719724 -0.363564173 1.185831416 -1.391081983
## [996] 0.133193482 0.257032225 1.687801740 -0.958605890 -0.351586614
yAxis <- rnorm(1000) + xAxis + 10
yAxis
## [1] 9.750155 13.104507 8.996033 11.147891 8.149292 12.025218 13.304326
## [8] 8.897192 9.481206 8.217555 9.741676 7.333804 10.320227 7.935584
## [15] 10.036874 11.124679 9.418852 7.956847 11.371119 8.680243 8.703816
## [22] 12.052154 9.755605 9.821180 8.385890 9.354205 9.791223 11.424547
## [29] 10.812874 8.832205 9.440706 10.391366 10.653324 9.590935 10.804253
## [36] 6.879106 8.635284 8.628554 12.821082 5.620303 10.326903 11.399695
## [43] 11.769888 9.068200 10.899725 9.719059 10.316025 9.441790 9.887422
## [50] 11.533724 10.541930 8.980886 7.215192 10.087672 9.691778 9.245021
## [57] 7.550562 7.270466 7.749321 8.656972 10.803994 9.401961 9.114226
## [64] 9.535293 11.353747 9.490201 7.623853 8.738544 8.389946 9.274118
## [71] 9.536341 8.374417 8.325803 9.506638 9.846191 11.611072 8.183614
## [78] 10.129385 8.020511 7.475398 10.335822 10.598625 9.432273 8.855001
## [85] 12.052765 10.161479 8.759742 9.044128 10.462298 9.451920 5.342545
## [92] 12.586777 10.245986 9.382789 10.473890 10.825021 10.990099 10.475948
## [99] 10.908866 9.855189 10.304862 9.356206 10.987957 12.247468 9.704770
## [106] 11.041568 11.816178 10.061964 10.070202 9.798294 9.580124 9.363662
## [113] 8.261100 11.421028 10.864255 10.531513 8.747188 11.166837 9.403810
## [120] 9.618064 7.491411 10.213503 10.406680 7.907770 9.157060 8.835585
## [127] 8.399849 11.400940 13.203964 13.013078 8.729743 10.955493 9.029416
## [134] 8.363676 9.667519 10.044654 9.684563 10.733382 8.219613 9.021054
## [141] 9.685964 8.681353 8.592991 7.665586 11.069013 10.749040 8.285453
## [148] 9.028834 10.411081 8.228147 12.000226 8.178472 8.220224 5.999581
## [155] 9.926917 11.586390 11.238996 10.252901 11.719350 11.238928 11.666701
## [162] 8.283939 10.863527 7.582434 8.348577 8.297230 12.187906 10.035416
## [169] 10.471427 6.833436 10.660144 10.717658 10.705939 9.853059 9.496669
## [176] 8.609673 10.635864 9.868432 7.904131 12.286457 10.187018 11.379614
## [183] 11.365162 12.751979 9.722127 8.647252 12.466512 11.053757 10.414391
## [190] 11.287816 9.097938 10.501825 11.450471 12.003442 12.737243 9.784834
## [197] 9.752620 9.514236 12.248908 9.570944 10.643108 9.490487 8.951493
## [204] 9.140549 9.097628 8.722146 11.680465 9.418769 11.211224 10.447175
## [211] 10.250779 11.011298 8.049642 8.962410 10.778146 11.858127 9.403653
## [218] 9.706241 12.136141 8.699294 10.201928 9.520901 11.289440 12.125443
## [225] 9.020947 12.575919 6.538257 11.374715 10.483309 11.977090 10.201080
## [232] 10.719268 10.620088 9.659804 10.406953 7.864280 9.312387 9.617595
## [239] 10.892095 12.403322 10.385336 13.061986 9.217389 10.053365 10.850267
## [246] 10.642185 11.237522 8.964389 9.553085 11.021174 10.833211 9.908978
## [253] 9.259604 8.885497 8.660511 10.747754 9.503562 9.210095 12.007206
## [260] 8.510831 9.061853 9.203409 9.775782 10.334496 8.673586 8.086048
## [267] 10.136250 9.387125 10.263004 9.522951 10.970589 9.473602 8.669649
## [274] 11.051231 9.788867 9.353879 12.331941 9.852171 10.205129 7.533713
## [281] 10.713775 8.827367 11.059878 9.561270 7.717949 11.410304 8.796306
## [288] 9.597984 9.165756 7.821919 12.064879 10.746980 7.216644 10.397127
## [295] 7.685632 10.158131 9.590801 10.437931 9.621523 12.152181 10.202260
## [302] 12.794688 8.960848 10.395990 11.923899 14.395995 9.159228 10.893301
## [309] 7.712228 10.104771 7.047884 9.304316 12.807543 8.458234 10.556386
## [316] 8.853793 11.008628 11.359530 9.944939 10.750933 10.455724 12.481169
## [323] 9.062878 11.590007 10.522503 11.795377 7.911905 8.889611 11.270920
## [330] 10.273720 10.916578 9.434960 5.852508 11.775338 11.656264 9.780581
## [337] 8.748385 10.609405 8.982722 8.373326 7.602466 8.367506 12.768169
## [344] 9.470809 6.963773 8.959454 11.410654 9.872479 11.305556 9.336345
## [351] 7.974286 6.903040 10.035708 9.712760 8.611073 9.762323 11.466727
## [358] 11.750452 10.268400 10.785572 10.396007 8.907319 8.892100 10.112504
## [365] 8.971061 11.838970 11.858656 9.627901 9.337348 7.527780 6.066960
## [372] 13.391127 12.344379 11.214903 10.972005 8.867331 11.469944 10.568384
## [379] 12.207091 11.046726 11.478804 9.810857 10.240439 10.977199 9.886558
## [386] 8.864351 7.078065 8.828687 10.796343 7.958320 12.139472 9.161661
## [393] 11.451706 8.148984 10.315236 10.816695 8.839829 9.290958 9.295412
## [400] 12.134553 8.055043 12.292466 11.125181 8.073672 10.493905 8.445574
## [407] 10.768051 12.243423 9.666155 10.065225 10.076909 8.024411 10.111769
## [414] 9.713765 10.324611 10.331420 10.347420 9.984822 10.722834 8.772753
## [421] 12.098128 13.679311 10.332527 10.760365 9.976705 7.908665 9.431424
## [428] 11.256797 9.834050 12.372530 10.051197 10.041493 10.320952 10.270083
## [435] 10.765569 9.356329 13.392141 8.362899 7.872689 10.041995 9.923325
## [442] 9.774246 8.543134 9.493121 7.905285 11.078051 9.575527 9.106483
## [449] 8.366521 9.646669 10.398356 10.873289 10.605158 9.154703 9.121472
## [456] 10.994492 10.693452 9.409262 10.713829 9.972363 11.141290 10.992861
## [463] 9.876650 7.967628 8.240005 11.225062 9.909064 10.532264 10.447527
## [470] 13.147958 7.707965 10.337607 11.168726 11.481029 10.329662 11.671007
## [477] 9.467700 13.162093 10.465028 8.710950 8.820819 10.409847 10.092411
## [484] 8.952898 11.184659 10.994684 11.469251 9.650554 11.768263 11.546650
## [491] 10.611465 11.457199 11.374605 9.775579 8.436483 12.056218 9.126379
## [498] 9.071476 11.233276 10.974702 11.419345 12.384360 9.062416 9.788668
## [505] 10.684802 10.005126 8.488677 8.620350 10.540273 10.061680 13.198406
## [512] 10.743986 10.911648 9.826184 11.025116 11.069260 10.408438 8.947493
## [519] 9.304245 10.868556 13.339092 11.136936 7.419198 9.501921 10.435858
## [526] 11.657430 11.409151 10.648867 10.888368 11.191315 10.060782 9.726270
## [533] 8.737779 11.009612 8.136827 9.736647 10.649201 11.640859 9.315512
## [540] 11.345422 9.943945 10.910801 8.780855 9.053180 11.536861 7.843455
## [547] 11.249383 9.477040 11.429913 10.445752 8.982761 11.840644 8.783217
## [554] 11.364053 9.578009 12.898945 8.849220 12.060871 8.424914 10.550608
## [561] 11.834295 12.420962 8.207255 7.827062 11.761251 10.203006 10.051239
## [568] 9.713337 10.229620 8.904122 9.881136 11.131929 10.482300 11.141203
## [575] 9.139562 10.580514 11.946220 9.407565 11.274290 10.069357 12.363877
## [582] 8.100223 8.728576 11.003491 8.477681 11.444028 10.075390 9.902681
## [589] 9.002705 9.465013 11.552403 11.026206 8.917892 8.199546 12.681166
## [596] 9.815489 9.252606 10.090585 10.245691 11.975860 9.654226 11.846375
## [603] 10.078217 11.492283 8.028287 9.301923 10.219764 8.907092 9.714929
## [610] 12.474010 10.819858 12.223897 9.913714 9.964526 9.727539 10.176896
## [617] 13.019397 10.850425 9.307165 10.450980 7.688616 11.811493 11.065506
## [624] 9.362130 8.330338 9.269468 9.382262 7.136076 11.014648 9.175019
## [631] 8.589935 10.018683 10.445505 11.270258 11.349821 9.719309 12.524224
## [638] 7.442716 10.790022 9.680948 10.486747 9.548832 9.133469 10.696756
## [645] 11.834561 8.534549 9.059114 10.359601 9.551346 10.324670 9.064945
## [652] 11.189868 10.740445 10.238990 8.545631 11.712319 12.204567 8.407766
## [659] 8.052433 9.271798 10.410425 8.823774 9.227253 10.528583 8.452535
## [666] 10.903379 15.071731 9.311928 6.997753 11.285416 10.463431 10.702481
## [673] 8.773425 6.994759 9.011063 10.177514 12.464415 12.406933 9.316919
## [680] 8.916007 9.045722 12.979599 10.434997 8.223449 8.944800 13.813703
## [687] 10.472259 9.843736 11.095547 10.127107 13.557439 9.275831 11.391560
## [694] 10.636711 10.100710 11.120377 7.780269 9.796239 9.496444 11.143255
## [701] 9.237568 10.559141 8.488661 10.045898 9.078606 11.830064 12.594175
## [708] 8.087597 9.132502 9.989098 10.857012 10.265914 10.594799 9.562976
## [715] 11.493958 9.889379 9.298894 11.555792 10.551086 11.603649 8.669364
## [722] 10.148311 10.460288 9.652828 7.615671 8.727064 8.163192 8.175029
## [729] 7.271877 10.618652 12.103066 11.450964 9.081102 7.022454 10.493002
## [736] 10.470843 9.095891 9.052765 10.547792 10.357840 10.585478 9.605057
## [743] 12.156613 7.754746 10.543373 8.314623 11.430427 9.692359 12.684276
## [750] 9.596862 10.067107 8.484594 11.810593 8.491581 11.278247 8.238620
## [757] 9.355622 11.476648 9.826685 8.168130 11.017276 9.737289 11.037234
## [764] 9.240368 11.839535 10.965067 13.404731 10.112906 8.635663 10.895380
## [771] 13.305036 7.381334 10.850331 9.038404 11.981798 9.829934 10.322054
## [778] 10.446503 8.380640 10.062248 10.678152 11.108574 11.925439 9.628064
## [785] 11.787096 10.400436 9.560403 11.489886 7.379175 11.319282 12.247187
## [792] 12.360997 11.412067 11.410415 10.400289 12.202441 12.144919 10.290248
## [799] 9.313601 11.303707 9.855678 10.759481 11.254170 8.225531 10.158240
## [806] 7.811101 9.366942 10.920415 9.860297 11.583371 9.720313 11.443166
## [813] 11.506478 9.510469 11.015020 9.699391 10.997322 11.773492 10.334790
## [820] 9.898777 8.100210 11.012343 7.434018 8.954092 10.225131 9.818568
## [827] 9.966354 9.084129 8.242704 9.946897 10.177354 8.488433 10.287727
## [834] 7.327594 10.576108 11.358532 10.733016 14.982182 11.830982 10.036710
## [841] 11.218291 9.032089 10.005177 11.017484 8.665182 8.672658 11.341519
## [848] 10.812344 10.046133 11.230429 10.346817 10.495213 9.643246 8.774723
## [855] 11.621867 8.482963 7.786355 10.014583 8.471249 11.648936 9.480058
## [862] 10.859979 9.786859 9.697075 9.932802 10.588391 11.697944 10.341865
## [869] 11.287815 8.839471 10.208274 9.075006 6.340023 11.827315 10.301752
## [876] 8.087192 8.220668 10.902335 10.106298 8.931037 10.334087 7.695363
## [883] 7.857187 10.323482 10.294313 10.261645 9.391015 8.602027 11.987372
## [890] 8.316403 10.790542 11.523205 9.266662 10.474661 10.170352 11.444453
## [897] 10.957611 7.855888 9.873629 10.582727 9.247469 9.108741 10.638910
## [904] 9.710543 9.485555 12.118528 9.745099 11.854030 12.030871 8.283995
## [911] 8.140877 11.471567 11.020107 10.321634 8.777337 7.587117 7.882229
## [918] 11.666532 10.652013 10.863807 10.229232 10.600186 10.704549 10.102389
## [925] 9.277763 6.798473 6.718707 9.046435 10.350717 11.987272 9.406434
## [932] 9.382576 12.988683 7.747107 10.487333 8.783498 10.663062 8.095103
## [939] 11.235099 10.881206 10.872247 9.148822 8.724568 9.853004 10.604037
## [946] 11.887905 9.301167 10.348077 10.785635 11.485176 9.833480 9.333621
## [953] 10.043656 8.002941 8.976663 9.033830 12.643838 8.643599 10.570244
## [960] 9.220717 9.729632 8.684801 11.137318 8.299191 10.184550 10.634919
## [967] 8.981974 8.713351 9.144875 7.741870 8.572559 9.128785 10.100143
## [974] 7.861970 9.022187 10.039965 9.635857 7.127667 12.168637 10.127696
## [981] 11.103067 10.278076 9.900749 8.718282 10.469986 11.087765 8.640413
## [988] 9.111453 8.549869 10.414574 9.403956 9.480204 10.991421 11.856783
## [995] 8.580385 10.730682 10.778513 11.517225 7.126223 8.357242
# 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 5 3 3 3 5 4 3 3 1 3 2 3 2 4 4 3 2 4 2 2 4 3 3 2 2 2 3 3 2 1 2 4 2 4 1 3
## [38] 2 4 2 3 3 2 3 3 2 3 2 2 4 3 2 1 2 1 2 2 2 2 2 4 3 1 2 4 2 2 2 1 2 3 2 3 3
## [75] 3 5 2 3 1 2 4 3 2 2 4 3 3 2 4 4 1 4 4 2 2 5 3 4 3 3 3 2 5 4 3 5 4 3 4 2 3
## [112] 3 2 3 4 3 2 3 3 4 2 3 1 1 1 3 2 4 5 5 2 3 2 3 5 3 3 2 2 3 3 3 3 2 3 3 3 3
## [149] 3 1 5 4 2 1 2 3 5 3 3 4 4 2 3 2 3 2 4 3 2 1 4 4 4 3 2 3 5 3 4 4 3 3 3 4 2
## [186] 1 4 5 2 3 3 3 4 4 4 3 2 2 5 3 4 3 3 4 2 3 4 2 3 2 3 4 3 1 3 3 2 3 3 2 5 2
## [223] 4 3 4 4 3 4 5 4 3 4 4 2 3 2 3 3 3 4 3 3 3 3 3 3 4 2 1 4 3 4 3 1 2 4 1 3 5
## [260] 4 3 2 2 3 3 1 4 4 4 3 5 2 2 3 2 4 2 3 4 1 2 3 3 3 2 3 2 3 2 3 3 3 1 3 2 2
## [297] 5 4 4 3 5 4 3 3 4 5 3 4 2 3 1 3 4 2 4 2 4 3 4 3 2 5 4 3 3 4 2 3 4 4 3 3 1
## [334] 4 3 4 3 4 2 2 2 2 5 2 1 2 5 3 3 2 2 1 4 4 2 2 3 5 2 4 3 3 2 1 2 4 4 4 2 1
## [371] 2 4 5 5 3 3 4 2 4 4 3 3 4 4 2 3 2 2 3 2 2 3 2 3 2 3 2 2 2 4 2 4 4 2 5 3 3
## [408] 4 3 3 4 2 2 4 3 4 3 4 4 4 5 5 3 4 2 1 3 5 3 5 3 4 3 3 3 3 4 3 3 4 4 3 2 3
## [445] 2 3 3 3 2 4 2 2 2 3 3 3 3 3 3 3 3 4 4 2 2 3 3 4 3 4 2 3 4 5 3 5 2 4 3 1 2
## [482] 3 3 3 4 4 4 3 4 4 3 4 4 2 3 4 2 3 3 3 3 4 2 2 3 3 3 2 3 4 4 4 2 4 3 4 4 2
## [519] 4 3 5 3 2 3 4 4 4 2 4 4 3 3 1 4 3 2 3 3 5 4 3 4 1 4 2 2 3 2 4 4 2 4 3 4 2
## [556] 4 3 4 2 3 4 4 2 2 4 3 3 3 4 3 4 4 4 3 3 4 4 3 3 2 4 1 2 2 2 4 3 3 2 4 3 4
## [593] 3 1 5 3 2 3 3 5 3 4 3 3 2 2 4 2 3 3 5 3 2 2 3 4 5 4 2 3 1 4 3 2 3 3 3 2 4
## [630] 1 2 3 2 3 4 3 4 2 3 2 3 2 4 3 4 2 3 3 2 4 3 4 4 3 1 4 4 3 2 3 4 2 2 2 3 4
## [667] 5 2 2 3 3 3 3 2 3 3 3 4 3 2 3 5 3 2 3 5 3 1 4 2 5 3 5 4 2 4 2 3 3 2 2 3 2
## [704] 3 4 4 3 2 2 3 3 2 3 3 4 4 4 5 2 3 3 3 1 3 2 2 2 2 2 4 5 3 2 1 3 2 2 2 3 3
## [741] 2 4 3 2 3 3 4 2 5 4 5 2 4 3 4 3 2 4 3 3 3 4 4 3 3 4 3 3 3 3 5 1 3 3 4 3 3
## [778] 4 2 3 3 4 5 3 4 4 2 3 2 2 4 4 5 3 3 5 4 3 3 5 2 3 3 3 2 2 2 4 3 3 3 5 3 1
## [815] 4 3 4 4 3 3 2 3 1 2 2 3 3 2 2 3 2 3 4 2 4 5 3 5 4 3 2 2 1 3 3 3 3 3 3 5 3
## [852] 3 3 1 4 1 2 3 3 4 2 4 2 3 1 3 3 4 4 3 4 2 1 4 4 1 1 3 2 3 3 3 2 4 3 3 2 2
## [889] 4 1 5 5 3 3 4 4 3 1 3 3 3 3 3 3 2 4 3 5 4 2 2 4 3 3 1 1 2 3 3 4 2 4 2 2 3
## [926] 2 1 2 3 5 2 3 5 3 3 2 4 1 5 4 5 4 2 4 3 3 1 4 3 4 4 3 4 1 3 3 4 3 4 3 2 2
## [963] 4 2 4 2 2 3 4 2 2 2 4 1 1 4 3 2 4 2 3 3 2 1 4 3 1 3 3 4 3 3 3 4 2 3 3 5 2
## [1000] 3
# create sample dataframe by joining variables
sample_data <- data.frame(xAxis,yAxis,group)
sample_data
## xAxis yAxis group
## 1 -0.155028582 9.750155 3
## 2 2.208922861 13.104507 5
## 3 0.170881735 8.996033 3
## 4 0.108656811 11.147891 3
## 5 -0.363087520 8.149292 3
## 6 1.804882542 12.025218 5
## 7 0.928650633 13.304326 4
## 8 -0.134423907 8.897192 3
## 9 0.044976105 9.481206 3
## 10 -1.699982941 8.217555 1
## 11 0.153022506 9.741676 3
## 12 -0.681904279 7.333804 2
## 13 -0.416370540 10.320227 3
## 14 -1.257946474 7.935584 2
## 15 0.638934914 10.036874 4
## 16 1.175156472 11.124679 4
## 17 -0.027954806 9.418852 3
## 18 -1.420717713 7.956847 2
## 19 1.020615171 11.371119 4
## 20 -1.333668766 8.680243 2
## 21 -1.053169595 8.703816 2
## 22 1.260857362 12.052154 4
## 23 0.202690573 9.755605 3
## 24 0.412463576 9.821180 3
## 25 -0.872132990 8.385890 2
## 26 -0.652902543 9.354205 2
## 27 -0.587783477 9.791223 2
## 28 -0.136087093 11.424547 3
## 29 -0.248963930 10.812874 3
## 30 -0.857764357 8.832205 2
## 31 -1.785726853 9.440706 1
## 32 -1.115411505 10.391366 2
## 33 1.040771810 10.653324 4
## 34 -0.562934442 9.590935 2
## 35 0.759742650 10.804253 4
## 36 -2.142030608 6.879106 1
## 37 -0.174475977 8.635284 3
## 38 -0.666088261 8.628554 2
## 39 0.945104131 12.821082 4
## 40 -0.724342684 5.620303 2
## 41 0.295304114 10.326903 3
## 42 0.191575628 11.399695 3
## 43 -0.699116075 11.769888 2
## 44 0.371368803 9.068200 3
## 45 -0.252765569 10.899725 3
## 46 -0.911589413 9.719059 2
## 47 -0.046322348 10.316025 3
## 48 -1.348549584 9.441790 2
## 49 -0.855483241 9.887422 2
## 50 0.749146258 11.533724 4
## 51 0.485654353 10.541930 3
## 52 -0.609144219 8.980886 2
## 53 -2.081105130 7.215192 1
## 54 -0.907363392 10.087672 2
## 55 -1.912323768 9.691778 1
## 56 -0.818722216 9.245021 2
## 57 -1.289054412 7.550562 2
## 58 -0.995328244 7.270466 2
## 59 -1.204224236 7.749321 2
## 60 -0.605004143 8.656972 2
## 61 1.240115152 10.803994 4
## 62 -0.015582942 9.401961 3
## 63 -1.931407784 9.114226 1
## 64 -0.948070541 9.535293 2
## 65 1.356661279 11.353747 4
## 66 -0.782543321 9.490201 2
## 67 -1.236417083 7.623853 2
## 68 -1.275640549 8.738544 2
## 69 -1.515160458 8.389946 1
## 70 -0.683749268 9.274118 2
## 71 -0.445331699 9.536341 3
## 72 -0.628607103 8.374417 2
## 73 -0.308833193 8.325803 3
## 74 -0.188850326 9.506638 3
## 75 -0.309678444 9.846191 3
## 76 1.708317491 11.611072 5
## 77 -0.690355554 8.183614 2
## 78 0.146336094 10.129385 3
## 79 -1.795275020 8.020511 1
## 80 -0.880603123 7.475398 2
## 81 0.953640058 10.335822 4
## 82 -0.323013773 10.598625 3
## 83 -0.688701852 9.432273 2
## 84 -1.024318436 8.855001 2
## 85 0.761180372 12.052765 4
## 86 0.272560219 10.161479 3
## 87 -0.276414107 8.759742 3
## 88 -1.466785473 9.044128 2
## 89 0.914511283 10.462298 4
## 90 1.010988139 9.451920 4
## 91 -2.792182864 5.342545 1
## 92 1.383007645 12.586777 4
## 93 0.537292275 10.245986 4
## 94 -0.541279322 9.382789 2
## 95 -0.855697166 10.473890 2
## 96 1.633718255 10.825021 5
## 97 0.406766848 10.990099 3
## 98 0.914717761 10.475948 4
## 99 0.368407654 10.908866 3
## 100 -0.274709599 9.855189 3
## 101 0.333203848 10.304862 3
## 102 -1.171962662 9.356206 2
## 103 2.571335590 10.987957 5
## 104 0.622988077 12.247468 4
## 105 0.034082753 9.704770 3
## 106 1.580614373 11.041568 5
## 107 0.859004104 11.816178 4
## 108 -0.245354197 10.061964 3
## 109 0.820408779 10.070202 4
## 110 -0.728453632 9.798294 2
## 111 0.148324704 9.580124 3
## 112 -0.485234976 9.363662 3
## 113 -1.190783340 8.261100 2
## 114 -0.217498702 11.421028 3
## 115 1.450168637 10.864255 4
## 116 -0.197409556 10.531513 3
## 117 -1.073158870 8.747188 2
## 118 0.483071104 11.166837 3
## 119 -0.247582878 9.403810 3
## 120 0.812239924 9.618064 4
## 121 -1.418465915 7.491411 2
## 122 0.154618149 10.213503 3
## 123 -1.598765208 10.406680 1
## 124 -2.204247941 7.907770 1
## 125 -2.017053055 9.157060 1
## 126 0.254732919 8.835585 3
## 127 -0.840263516 8.399849 2
## 128 0.998329054 11.400940 4
## 129 1.853135313 13.203964 5
## 130 2.713059417 13.013078 5
## 131 -1.158191978 8.729743 2
## 132 -0.193515317 10.955493 3
## 133 -1.013184915 9.029416 2
## 134 -0.163921212 8.363676 3
## 135 1.893988945 9.667519 5
## 136 -0.314754307 10.044654 3
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## 1000 -0.351586614 8.357242 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)
