# Mindanao State University
# General Santos City
# A0 Basic Graphs Using R
# Submitted by: Princess Joy Angga, 1-BSMATH
# Mat108
# 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")
# 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
# set different values for y variables
(y2<-c(5, 1, 4, 6, 2, 3, 7, 8, 2, 8))
## [1] 5 1 4 6 2 3 7 8 2 8
# set different values for y variables
(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)#create scatterplot of sepal width vs. sepal length
plot(iris$Sepal.Width, iris$Sepal.Length,
col='steelblue',
main='Scatterplot',
xlab='Sepal Width',
ylab='Sepal Length',
pch=19)
plot(iris$Sepal.Width, iris$Sepal.Length,
col='steelblue',
main='Scatterplot',
xlab='Sepal Width',
ylab='Sepal Length',
pch=1)
# another way to retrieve columns of data
PL <-iris$Petal.Length
PW <- iris$Petal.Width
plot(PL, PW)
# add color by species
plot(PL, PW, col = iris$Species, main= "My Plot")
# draw a line along with the distribution of points
# using the abline and lm commands
abline(lm(PW ~ PL))
# add text annotation
text(5, 0.5, "Regression Line")
legend("topleft", # specify the location of the legend
levels(iris$Species), # specify the levels of species
pch = 1:3, # specify three symbols used for the three species
col = 1:3 # specify three colors for the three species
)
# Lab Exercise 6: Generate advance scatter plot
pairs(iris, col = rainbow(3)[iris$Species]) # set colors by species
# How to filter data for each flower
(Versicolor <- subset(iris, Species == "versicolor"))
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 51 7.0 3.2 4.7 1.4 versicolor
## 52 6.4 3.2 4.5 1.5 versicolor
## 53 6.9 3.1 4.9 1.5 versicolor
## 54 5.5 2.3 4.0 1.3 versicolor
## 55 6.5 2.8 4.6 1.5 versicolor
## 56 5.7 2.8 4.5 1.3 versicolor
## 57 6.3 3.3 4.7 1.6 versicolor
## 58 4.9 2.4 3.3 1.0 versicolor
## 59 6.6 2.9 4.6 1.3 versicolor
## 60 5.2 2.7 3.9 1.4 versicolor
## 61 5.0 2.0 3.5 1.0 versicolor
## 62 5.9 3.0 4.2 1.5 versicolor
## 63 6.0 2.2 4.0 1.0 versicolor
## 64 6.1 2.9 4.7 1.4 versicolor
## 65 5.6 2.9 3.6 1.3 versicolor
## 66 6.7 3.1 4.4 1.4 versicolor
## 67 5.6 3.0 4.5 1.5 versicolor
## 68 5.8 2.7 4.1 1.0 versicolor
## 69 6.2 2.2 4.5 1.5 versicolor
## 70 5.6 2.5 3.9 1.1 versicolor
## 71 5.9 3.2 4.8 1.8 versicolor
## 72 6.1 2.8 4.0 1.3 versicolor
## 73 6.3 2.5 4.9 1.5 versicolor
## 74 6.1 2.8 4.7 1.2 versicolor
## 75 6.4 2.9 4.3 1.3 versicolor
## 76 6.6 3.0 4.4 1.4 versicolor
## 77 6.8 2.8 4.8 1.4 versicolor
## 78 6.7 3.0 5.0 1.7 versicolor
## 79 6.0 2.9 4.5 1.5 versicolor
## 80 5.7 2.6 3.5 1.0 versicolor
## 81 5.5 2.4 3.8 1.1 versicolor
## 82 5.5 2.4 3.7 1.0 versicolor
## 83 5.8 2.7 3.9 1.2 versicolor
## 84 6.0 2.7 5.1 1.6 versicolor
## 85 5.4 3.0 4.5 1.5 versicolor
## 86 6.0 3.4 4.5 1.6 versicolor
## 87 6.7 3.1 4.7 1.5 versicolor
## 88 6.3 2.3 4.4 1.3 versicolor
## 89 5.6 3.0 4.1 1.3 versicolor
## 90 5.5 2.5 4.0 1.3 versicolor
## 91 5.5 2.6 4.4 1.2 versicolor
## 92 6.1 3.0 4.6 1.4 versicolor
## 93 5.8 2.6 4.0 1.2 versicolor
## 94 5.0 2.3 3.3 1.0 versicolor
## 95 5.6 2.7 4.2 1.3 versicolor
## 96 5.7 3.0 4.2 1.2 versicolor
## 97 5.7 2.9 4.2 1.3 versicolor
## 98 6.2 2.9 4.3 1.3 versicolor
## 99 5.1 2.5 3.0 1.1 versicolor
## 100 5.7 2.8 4.1 1.3 versicolor
(Setosa <- subset(iris, Species == "setosa"))
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa
## 7 4.6 3.4 1.4 0.3 setosa
## 8 5.0 3.4 1.5 0.2 setosa
## 9 4.4 2.9 1.4 0.2 setosa
## 10 4.9 3.1 1.5 0.1 setosa
## 11 5.4 3.7 1.5 0.2 setosa
## 12 4.8 3.4 1.6 0.2 setosa
## 13 4.8 3.0 1.4 0.1 setosa
## 14 4.3 3.0 1.1 0.1 setosa
## 15 5.8 4.0 1.2 0.2 setosa
## 16 5.7 4.4 1.5 0.4 setosa
## 17 5.4 3.9 1.3 0.4 setosa
## 18 5.1 3.5 1.4 0.3 setosa
## 19 5.7 3.8 1.7 0.3 setosa
## 20 5.1 3.8 1.5 0.3 setosa
## 21 5.4 3.4 1.7 0.2 setosa
## 22 5.1 3.7 1.5 0.4 setosa
## 23 4.6 3.6 1.0 0.2 setosa
## 24 5.1 3.3 1.7 0.5 setosa
## 25 4.8 3.4 1.9 0.2 setosa
## 26 5.0 3.0 1.6 0.2 setosa
## 27 5.0 3.4 1.6 0.4 setosa
## 28 5.2 3.5 1.5 0.2 setosa
## 29 5.2 3.4 1.4 0.2 setosa
## 30 4.7 3.2 1.6 0.2 setosa
## 31 4.8 3.1 1.6 0.2 setosa
## 32 5.4 3.4 1.5 0.4 setosa
## 33 5.2 4.1 1.5 0.1 setosa
## 34 5.5 4.2 1.4 0.2 setosa
## 35 4.9 3.1 1.5 0.2 setosa
## 36 5.0 3.2 1.2 0.2 setosa
## 37 5.5 3.5 1.3 0.2 setosa
## 38 4.9 3.6 1.4 0.1 setosa
## 39 4.4 3.0 1.3 0.2 setosa
## 40 5.1 3.4 1.5 0.2 setosa
## 41 5.0 3.5 1.3 0.3 setosa
## 42 4.5 2.3 1.3 0.3 setosa
## 43 4.4 3.2 1.3 0.2 setosa
## 44 5.0 3.5 1.6 0.6 setosa
## 45 5.1 3.8 1.9 0.4 setosa
## 46 4.8 3.0 1.4 0.3 setosa
## 47 5.1 3.8 1.6 0.2 setosa
## 48 4.6 3.2 1.4 0.2 setosa
## 49 5.3 3.7 1.5 0.2 setosa
## 50 5.0 3.3 1.4 0.2 setosa
(Virginica <- subset(iris, Species == "virginica"))
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 101 6.3 3.3 6.0 2.5 virginica
## 102 5.8 2.7 5.1 1.9 virginica
## 103 7.1 3.0 5.9 2.1 virginica
## 104 6.3 2.9 5.6 1.8 virginica
## 105 6.5 3.0 5.8 2.2 virginica
## 106 7.6 3.0 6.6 2.1 virginica
## 107 4.9 2.5 4.5 1.7 virginica
## 108 7.3 2.9 6.3 1.8 virginica
## 109 6.7 2.5 5.8 1.8 virginica
## 110 7.2 3.6 6.1 2.5 virginica
## 111 6.5 3.2 5.1 2.0 virginica
## 112 6.4 2.7 5.3 1.9 virginica
## 113 6.8 3.0 5.5 2.1 virginica
## 114 5.7 2.5 5.0 2.0 virginica
## 115 5.8 2.8 5.1 2.4 virginica
## 116 6.4 3.2 5.3 2.3 virginica
## 117 6.5 3.0 5.5 1.8 virginica
## 118 7.7 3.8 6.7 2.2 virginica
## 119 7.7 2.6 6.9 2.3 virginica
## 120 6.0 2.2 5.0 1.5 virginica
## 121 6.9 3.2 5.7 2.3 virginica
## 122 5.6 2.8 4.9 2.0 virginica
## 123 7.7 2.8 6.7 2.0 virginica
## 124 6.3 2.7 4.9 1.8 virginica
## 125 6.7 3.3 5.7 2.1 virginica
## 126 7.2 3.2 6.0 1.8 virginica
## 127 6.2 2.8 4.8 1.8 virginica
## 128 6.1 3.0 4.9 1.8 virginica
## 129 6.4 2.8 5.6 2.1 virginica
## 130 7.2 3.0 5.8 1.6 virginica
## 131 7.4 2.8 6.1 1.9 virginica
## 132 7.9 3.8 6.4 2.0 virginica
## 133 6.4 2.8 5.6 2.2 virginica
## 134 6.3 2.8 5.1 1.5 virginica
## 135 6.1 2.6 5.6 1.4 virginica
## 136 7.7 3.0 6.1 2.3 virginica
## 137 6.3 3.4 5.6 2.4 virginica
## 138 6.4 3.1 5.5 1.8 virginica
## 139 6.0 3.0 4.8 1.8 virginica
## 140 6.9 3.1 5.4 2.1 virginica
## 141 6.7 3.1 5.6 2.4 virginica
## 142 6.9 3.1 5.1 2.3 virginica
## 143 5.8 2.7 5.1 1.9 virginica
## 144 6.8 3.2 5.9 2.3 virginica
## 145 6.7 3.3 5.7 2.5 virginica
## 146 6.7 3.0 5.2 2.3 virginica
## 147 6.3 2.5 5.0 1.9 virginica
## 148 6.5 3.0 5.2 2.0 virginica
## 149 6.2 3.4 5.4 2.3 virginica
## 150 5.9 3.0 5.1 1.8 virginica
# Draw boxplot for each type of flower
boxplot(Versicolor[,1:4], main="Versicolor, Rainbow Palette",ylim =
c(0,8),las=2, col=rainbow(4))
boxplot(Setosa[,1:4], main="Setosa, Heat color Palette",ylim = c(0,8),las=2,
col=heat.colors(4))
boxplot(Virginica[,1:4], main="Virginica, Topo colors Palette",ylim =
c(0,8),las=2, col=topo.colors(4))
# Lab Exercise 7: How to load external datasets
# From a local directory
# the folder that contains the file should be specified completely
# using the forward slash symbol instead of the backward splash
library(readr)
cancer <- read_csv("Cancer.csv")
## Rows: 173 Columns: 17
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): country, continent
## dbl (15): incomeperperson, alcconsumption, armedforcesrate, breastcancer, co...
##
## 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
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
## # … 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
## 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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## 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
# 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
library(gplots)
##
## Attaching package: 'gplots'
##
## The following object is masked from 'package:stats':
##
## lowess
# compute the average value by group using tapply() command
means <- round(tapply(data$value, data$variable, mean), digits=2)
# create boxplot by group
boxplot(data$value ~ data$variable, main= "Student Performance by Subject
(brown dot = mean score)",
xlab="Subject matter", ylab="Percentage Scores", col=rainbow(5))
# insert the average values
points(means, col="brown", pch=18)
# can also compute the median values
medians = round(tapply(data$value, data$variable, median), digits=2)
medians
## read write math science socst
## 50 54 52 53 52
points(medians, col="red", pch=18)
# Lab Exercise 9: How to plot categorical variables
library(ggplot2)
# we load variable names to memory to avoid the dollar notation
attach(hsb2_long)
# create the plot object p
p <- ggplot(hsb2_long, aes(ses, fill = prog)) + facet_wrap(~race)
p + geom_bar()
## Warning: The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
p + geom_bar(position = "dodge")
## Warning: The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
library(ggplot2)
attach(hsb2_long)
## The following objects are masked from hsb2_long (pos = 3):
##
## female, id, prog, race, schtyp, ses, value, variable
p <- ggplot(hsb2_long, aes(ses, fill = prog)) + facet_wrap(~schtyp)
p + geom_bar()
## Warning: The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
p + geom_bar(position = "dodge")
## Warning: The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
# Lab Exercise 10: Scatter Plots with marginal Distributions
# Advance Scatter plots using libraries
# install.packages("ggExtra")
# install.packages("tidyverse")
library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.1 ✔ 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))
## [1] 0.3044354095 -0.5257994308 0.1001440564 -1.0395980015 -0.4204357791
## [6] 1.2321759764 -0.9057734260 0.4610606369 -0.5480279750 -0.7405994042
## [11] -2.1788703494 0.9737466971 -0.7115629209 -2.0343911261 -1.5409624759
## [16] 2.7180454128 1.1159574683 0.0912110842 0.4024839962 -0.0124221087
## [21] 1.6032554767 0.9378537948 -0.4175160432 0.8838681706 -0.0170671585
## [26] 1.7146988674 -1.8565786483 -2.2861629761 0.0471002195 -1.3828450934
## [31] 1.2728576626 -0.3245453488 0.7262553774 -0.2494040895 2.0491318156
## [36] 0.3276720122 1.5152625813 -0.6999958393 -0.1918170948 1.3772500943
## [41] 0.3062699462 -0.9330012236 -0.3861351026 -0.5335455646 1.0532717454
## [46] -1.5708218398 1.8380984469 -0.4532271014 -0.7283387249 0.8581755323
## [51] -0.4204010006 1.0778569444 0.9895192634 0.4267555750 -1.3829155256
## [56] -0.8570834825 -0.2640844935 0.2161320079 0.1547657917 -0.0659525389
## [61] 0.2270387793 -2.4730291126 1.9810059355 0.0466660567 -2.4708005569
## [66] -2.8658457773 -1.3225337869 -0.8709826551 0.2914939939 0.2145210159
## [71] 0.2383878020 -2.6328561752 -0.4682606636 0.3495380054 -1.5958955689
## [76] -1.0257860632 -0.0141401703 1.6544979157 -2.6600212874 -0.8256373144
## [81] 0.1959102371 -3.0920288767 -0.0812324934 0.7517704440 0.4349359553
## [86] 1.8998608769 -0.6694894942 1.9408410206 0.1775329935 -0.7941748106
## [91] -0.0275808490 -1.1492469470 -0.5633729004 0.5937611549 0.5961760626
## [96] 0.5242191765 -0.6191658246 1.2969949297 0.0773046878 0.8157126131
## [101] -0.8583404811 -0.3507176282 0.6130602023 -0.4252623016 -0.0559297642
## [106] 1.8298331496 0.6216792311 -0.0316617515 -0.7729496334 0.4637217647
## [111] 0.5315629854 -0.3709727278 0.3754108385 -0.3186548792 0.7069561511
## [116] 1.1725859796 -2.0130684206 -0.0819044653 0.8584935622 -0.4543546066
## [121] -0.6507421087 1.8434104251 -0.7911273413 -1.1923899564 -0.9974582251
## [126] -0.4683656014 1.2967259667 0.6087507051 -0.2959396859 -0.7466421320
## [131] -0.0921995913 -0.4378725635 -0.6725085430 0.6615921484 -0.8625573553
## [136] -1.1341667570 -1.4807916361 -0.5900618935 -1.2814524762 0.3165258468
## [141] 0.6570339570 -0.0187409771 0.8713660843 1.9337368363 0.4407674585
## [146] -0.4926425691 1.2413481853 -0.2863585661 -0.5717866603 -0.2001002136
## [151] -2.2862388769 1.5369567345 0.8590783467 -0.2508246623 -0.3436209794
## [156] 0.0708539794 0.1057754965 -0.3495602385 0.1647855834 1.0066792947
## [161] 0.4118245832 -0.4222314673 0.1226717692 0.5888377409 -1.2227264738
## [166] -1.0949851057 0.7671869020 1.6579167049 0.3591573208 -0.3423587698
## [171] 1.6758210356 0.7571943424 -0.5905553114 -1.1855388416 -0.4514117014
## [176] -0.8317408578 -2.0355597791 -0.6335060466 -0.0116526964 1.3518112849
## [181] 0.9396288977 -0.0289942595 0.9876101455 0.3560384389 0.0195971892
## [186] 1.4599568519 -0.4032293453 -1.9154368819 -0.6825644029 0.1306103457
## [191] 0.9900845016 -0.1173402319 -1.1497179175 1.3266665655 0.0509583907
## [196] -0.2780046335 1.9082034155 -1.6474274562 0.5578982253 0.2343240211
## [201] -0.9866085058 2.1144904223 -0.6561648753 -0.4882981192 -0.6891631292
## [206] 1.1485101370 -0.0368217709 -0.4482192833 1.7662423337 1.8318457065
## [211] 1.6299453599 0.4039753764 -0.4323547310 -0.3212190598 -0.7726922907
## [216] -0.3278116510 0.2903329010 1.9053303001 -0.9036802221 -1.6418718324
## [221] 0.8989258532 -0.4405764510 -0.6748831379 0.2563531318 0.2959482831
## [226] -0.5704049786 0.9537437444 -0.1709895947 -0.7814028246 -0.6570829603
## [231] -0.8513756706 0.4276390316 -0.0480373312 -0.7621423827 -0.0124026827
## [236] 1.0952554663 0.3626934746 -1.1614660137 -0.8868120683 -0.0071122342
## [241] -1.6222453488 -1.0230407129 0.5572974153 0.9811392672 0.4726665092
## [246] -0.0157631846 -0.2204658103 -0.5510020135 -0.4300207068 -0.2972517232
## [251] 1.5838150240 -0.1440928292 -1.7271017310 1.1219165229 1.5413988778
## [256] 0.6988414185 -1.2468740813 0.3998193333 -0.7659441779 1.2454253381
## [261] 0.4968513736 -0.6072435429 0.9941786042 -1.2194661352 -1.8121795380
## [266] 0.8040428487 -0.2358109608 -1.5000207090 0.1617245629 -1.1200164706
## [271] 0.5782271977 -0.2068637653 0.5173912330 -0.3918875133 -0.0732250530
## [276] 0.0649235005 0.5245246965 1.4790381070 1.4389844664 0.1789289404
## [281] 0.5625881513 0.8898773764 0.5783732229 -0.2268677604 0.0800202229
## [286] -0.3250521754 -1.1155902322 1.0969066979 1.8898705138 2.3055786579
## [291] 1.1264740272 -0.1474970935 0.6237443200 -0.1385101814 0.6735048424
## [296] 0.6300389331 0.1195697075 0.0707124969 0.2167006690 0.4443547095
## [301] -2.2590308851 -0.7649771711 0.0014415263 0.6547275101 0.2597045856
## [306] 1.7620456148 -0.3859593307 1.3870001189 -0.0212235496 -0.6509638841
## [311] 1.1992377046 0.1796186321 0.3393748795 1.7817576675 -0.5855739239
## [316] 0.3566708001 0.7590523123 -0.8928728542 -0.2701804463 -0.2067931617
## [321] 0.0983887926 0.6205497950 -1.2695908216 -0.6421355139 -0.0110093413
## [326] -1.2853332461 -0.6223737074 -1.1531115767 -0.0866847932 0.3617493875
## [331] -0.8626707174 0.5864998081 -0.8836900325 0.4974136395 0.5498395158
## [336] -0.3855654826 1.3996948855 0.6862772463 0.1193969514 -1.5027219306
## [341] -0.7495437147 0.5353136045 -0.8040252416 -1.6174667078 1.0563548809
## [346] 2.2453849829 0.3869034468 0.5452057061 0.5549635686 0.3070380479
## [351] -0.0637277611 -0.6171690331 -0.6741083703 0.2158482075 1.2959892970
## [356] 2.3558405175 0.7383800664 -0.2940762680 -1.5287312858 -2.1069477725
## [361] -0.4324332263 1.2345735392 0.6735513045 0.9532267769 1.1469006855
## [366] -1.2687625129 -0.6400328881 -0.0393060386 2.7236428784 -2.4062742146
## [371] -0.9998979355 -0.2469892611 -1.1620974581 0.6151039376 -0.2196046119
## [376] -0.8220412104 0.4631410779 -0.8612027503 -0.5555126311 -1.1557635273
## [381] 1.0882650044 -0.4936636106 -2.3497721672 1.2097264522 1.5921487499
## [386] -1.9752284122 -0.9056651212 -0.7332260623 0.8504593232 1.3014646741
## [391] 0.1944703368 -1.6586209104 -0.1709489867 -0.7670248205 -1.0664236762
## [396] -0.6710911270 -2.0218142271 1.9594122221 0.0835354430 -1.3902492799
## [401] -1.4035383492 0.6258769224 0.2046417638 1.0992480713 0.7770462944
## [406] -0.8588747045 0.5659782409 -0.6725976789 -0.6806048998 -1.0784618740
## [411] 0.5837478802 -0.5580063964 0.3467836423 0.7216386909 -0.2362065467
## [416] 0.2966296366 -0.0395267660 0.4308669103 0.2285468965 1.0817841324
## [421] -1.3906412911 1.3827336381 -0.6196222472 -0.1687698645 -1.2992493621
## [426] 1.3485594590 -0.5220623280 0.5388095302 -0.2029742870 -0.1070416025
## [431] 0.3000520659 0.5142745754 -0.0131140144 0.0081605493 0.0314392541
## [436] -1.6140224937 0.0323870357 -1.1553315229 -0.6797376128 -0.4047066774
## [441] 0.5518970864 0.1601441343 0.9208949423 -1.0509480050 0.4103655327
## [446] 2.0838656421 -0.9773133847 -1.0887862489 -0.0456768059 1.2790416608
## [451] -1.2439546392 0.8681935485 0.0004165961 -0.8424970396 -0.1366589215
## [456] 1.0075975689 -0.8149423678 -0.1804258984 -0.4782596928 -0.9157929760
## [461] 0.4743673728 0.2903182386 2.6936152215 -0.6241890542 -0.5932364072
## [466] -2.1954217419 0.3416443933 0.6588293304 1.3580356180 1.3093821533
## [471] 1.6943061570 0.5066448992 -0.1639529172 0.9505804702 -0.4391926823
## [476] -0.5515109130 -0.3535123179 0.8984509772 -0.7576813719 -0.2013262307
## [481] 0.8765478590 -0.5930716508 0.5376197013 -2.2833607983 -0.5665643453
## [486] -2.5175735711 0.4674481367 0.1484072046 2.0046736809 -0.3701488336
## [491] 0.6037550685 -0.2661934059 1.1076782959 0.8712294617 -0.8096749762
## [496] 0.4462783859 -0.6825766159 -0.4338845915 0.6890134072 -0.9179980187
## [501] -1.6547014403 -1.5238281156 -0.3721998479 0.5632036307 1.7636313234
## [506] 0.5581978936 0.1130866256 0.8981849329 -0.0223334701 -0.8257424155
## [511] 0.2809081331 0.4519724951 0.1254043582 -0.8734934287 0.7920636624
## [516] -0.8042922927 0.2821748510 0.2181134373 -1.1992943443 -0.0698397737
## [521] 0.2181216875 -1.2614165261 -0.4218155919 1.0622895583 0.2927909827
## [526] 0.9776700988 -0.3678728590 -0.9060383353 1.8378613419 -1.3420177501
## [531] -2.5716438421 -0.9614307452 2.1405490274 0.4634153167 0.9710616012
## [536] -1.4623905617 0.5302932872 0.5993767043 0.1900882672 0.2747808996
## [541] -0.2303585407 -1.8946609583 -1.0078136433 0.4148923554 1.0055989461
## [546] -1.4917699664 -0.5688061902 -1.1719447675 1.4693149028 0.0306152556
## [551] 0.0868409660 1.3131401206 -2.6115878926 -0.4762225617 -0.2732732267
## [556] 0.3691516420 1.7653314046 0.7953141879 -0.1995916026 0.4604098373
## [561] -1.9989875509 -1.7152457733 1.3660584639 -1.1020931426 2.0638171753
## [566] -1.5059143353 -0.4816523563 0.4301156782 -0.0254881779 0.9268030539
## [571] 0.7147917734 0.4690025482 -0.6420311531 1.8533791301 0.7560689070
## [576] 2.7252830368 -2.1335539258 -1.9335499325 0.5109130534 0.6077045138
## [581] 0.6904450521 -1.1458096885 -0.6151437103 0.6416006764 0.3934823842
## [586] -0.0340365885 1.6313773831 -1.6834126200 -0.5287084634 2.0577313520
## [591] -1.1218550804 -0.4788391315 -0.4048853987 -0.1984804241 -0.1368749547
## [596] -0.1687722949 -0.3944023705 -0.0419429031 -1.3496454674 -2.4051717538
## [601] 0.3849683663 -0.1847447280 1.7821931678 3.8736036332 1.3950807628
## [606] -2.0192245269 -0.7579872143 0.4009934554 0.1583627808 -0.3977596369
## [611] -0.9485669640 -0.6519827207 -1.0153151763 -0.2411425060 -0.5914468672
## [616] -2.9225169825 0.0904655425 1.2208694237 -0.0650637853 1.3050220653
## [621] 1.6146147430 0.7262409081 0.8641373814 -0.0076603394 1.7452277709
## [626] 0.4200491623 0.6117914433 -0.0879303276 0.5272890851 0.0912368363
## [631] -0.0680976755 -0.6990107875 0.1128058774 1.3899057363 -0.6767311560
## [636] -0.0409704943 -0.5063299729 0.5583916330 0.5698679923 -1.1836435929
## [641] -0.6316584266 1.3829609427 1.9399015154 0.6594982443 1.9247236926
## [646] 0.4943696895 0.1469820137 -0.3582001441 1.6442175141 -0.5151975055
## [651] 0.4554274028 -1.9532840748 -0.8040065375 -0.2315330012 -0.6754039859
## [656] 0.2004805910 2.9566794532 0.2330555754 1.5004812406 0.3904121387
## [661] -0.0446824182 -2.6183540737 0.5084847584 1.1406234493 0.8226365473
## [666] -0.2301524392 1.8496914247 1.1356215152 -0.8043080373 0.5718986927
## [671] -1.9418579444 0.5711040466 0.7811939965 0.5193180401 1.5820247070
## [676] 0.6664716787 -0.6928428370 -0.0200042272 -0.2649072439 1.0335199465
## [681] -1.2958232839 -0.4131638405 -0.1498142302 0.4975603201 -0.1930084770
## [686] -0.4553572922 0.7002130674 -0.8982692481 1.5014775170 0.6215705884
## [691] 0.7190416058 1.3410819740 -0.5033569029 0.1379233138 0.5394154396
## [696] -1.4737882858 -0.3327955358 -0.5867387356 1.3285528390 0.2303312023
## [701] 0.5525194744 -1.2251910332 1.4238366596 1.6096234772 -0.2428775836
## [706] 0.5563062279 0.6408575531 -0.8987349193 1.0480288959 -1.1940280425
## [711] -2.7526032074 -0.7962699759 1.4959329295 2.2779429382 -0.5039770800
## [716] -2.4303420678 -0.2952992241 -0.0875116712 -0.1455336948 0.0841564450
## [721] -0.0430578366 -1.4099519188 0.5768896150 -0.7034708169 0.6935589461
## [726] 0.7530442626 -1.2317635485 1.8693710377 0.7040078468 0.4657245206
## [731] -1.6069932105 -0.7480674319 0.6313046402 -2.7011588156 -1.1336424795
## [736] -0.7597805843 -0.0412386598 -0.3922271759 -0.2671558511 0.1156187796
## [741] 0.5072246640 -0.7238991596 0.4515196706 0.9493823458 -0.0880718200
## [746] -0.1328836450 -0.3850641696 0.6558220229 -0.4865468502 0.8055596544
## [751] -0.6072135574 0.5808996661 -0.2023625421 0.0269433810 -0.5448533803
## [756] 0.3292024551 0.3420162818 -0.1023588189 -0.3866013407 0.3811430681
## [761] -1.8404672930 -0.3651212697 -0.5116591008 -0.2758232924 1.4431735184
## [766] 2.4826745643 1.1915298552 -1.9590835062 0.8940636419 1.0442507484
## [771] 0.8381218952 -0.6866196404 -1.1293395781 0.0486396924 1.2513697372
## [776] -0.3851750403 0.3403672930 0.2313801731 0.2962049246 0.7974252762
## [781] 0.0196525164 -0.9017674504 -1.5301727088 0.9453588982 0.2538537420
## [786] -0.2714035757 -1.1237543258 0.6145796109 -1.4509824499 -0.9076845374
## [791] 1.0554106397 2.2338708756 -1.6727588522 1.3336382184 -1.9181149179
## [796] -1.7384659670 0.4554047871 -0.6249910373 1.5048623023 -1.3198650563
## [801] -0.6292501522 0.1507951817 0.2443209148 0.6502040856 -1.1461184023
## [806] -0.8782332382 1.1759163469 0.9322235785 0.2313600051 -0.5510304413
## [811] -1.5491137171 -1.0500159807 1.3141175090 -1.2035852816 -0.7451525057
## [816] -0.8403065910 0.1776485060 -0.0259211802 -0.8465602934 1.2111135502
## [821] 0.0749671916 -0.1482556314 0.2862388561 -0.7260533364 0.6685652696
## [826] -0.5934586708 0.7741104723 -0.2071697039 -0.2027011109 0.9574258410
## [831] 1.1625529437 0.8282061298 1.5394247810 -1.2711274511 -0.8819446183
## [836] 0.1080830706 0.3232959626 -1.1754397012 1.0270313519 -0.1925211983
## [841] -0.0078458596 -0.7123281862 0.3432004174 1.3814302165 0.9352477989
## [846] -0.4455522893 -0.9872652103 1.3039829242 0.8265714364 -0.6831877820
## [851] 1.0712223914 0.1467048658 -0.8347984154 0.8891916794 1.3302737988
## [856] 1.0451120633 -0.0878990602 -1.4195983286 1.7428816690 -0.0657558142
## [861] -0.0851362869 -0.8186930220 1.2421110364 -0.5132890015 0.2227091101
## [866] 0.4248075724 0.4160292456 0.6910758665 -0.3359379788 0.7844051419
## [871] 0.8261360931 1.7432365285 -0.1818973270 -0.5686144128 -0.4998774983
## [876] -0.3157733439 0.6585815186 0.2067717775 0.0421699982 -0.1058709243
## [881] 0.0277246618 0.7650888592 1.5252716819 -0.7176551837 -0.9755821637
## [886] -0.0330482997 0.0977162092 -0.6363849515 -0.6889267249 -0.8682171960
## [891] 0.6750329224 1.1526755818 0.2269823268 -0.7534459285 0.0509386844
## [896] -0.8941927833 -0.3035743650 -0.7953182003 -0.8877574175 -1.3273414574
## [901] -0.6184444623 1.9446205013 -0.6749742823 1.7472481932 0.2470832609
## [906] 0.1069184812 -0.7376557730 -1.3414254382 0.9828555081 -1.7646199047
## [911] 1.1776814440 -1.8107520704 0.9273769426 -1.0463020024 1.3245428420
## [916] 1.1594046945 1.1091730417 -1.9449355354 0.4027791147 -0.9831681418
## [921] -1.1026730892 0.4609630184 -0.9766358132 0.7007430820 0.5424184682
## [926] 0.7041373338 0.0979209161 0.7460258401 1.0629764360 0.8927189656
## [931] 1.5068338769 0.9386205494 1.9126978519 1.4112336656 0.2138877896
## [936] 0.9393453642 1.5584366710 -0.2952126376 -1.9220160094 0.5019714280
## [941] -0.8570468197 -0.2071753360 -0.7575767804 -0.3420006926 -0.1554809950
## [946] 1.6226443631 -0.5733961116 -0.3702574542 1.1127211947 0.3005587360
## [951] 1.3177064309 0.7402832173 0.5280264429 -0.7754564336 0.5202949291
## [956] 0.3084680731 -1.4616793874 0.1366492501 -0.2625022056 0.7440344795
## [961] -0.9590344488 -0.5492616668 -0.7694232771 1.1191090728 0.5837538544
## [966] 0.5248929037 1.1233175510 -0.6180340960 0.3407282801 0.2921614620
## [971] 1.8793070411 0.4306817400 0.0328452187 2.0481189568 2.2039995112
## [976] 0.5245273583 1.3761740197 -0.8689012275 -0.4070391836 -1.4937511102
## [981] -1.4710307181 2.3156743348 1.3322466674 -1.0613288287 0.2158994140
## [986] 0.7053118039 -0.0002193419 -2.1311719605 -1.6368908415 0.6407822349
## [991] -0.1671230467 -1.6676212031 -0.8982684155 -0.2902440495 1.2919413147
## [996] 1.8405514218 0.6238500008 0.2378931056 0.1123142635 0.0901884313
yAxis <- rnorm(1000) + xAxis + 10
yAxis
## [1] 10.210596 12.353595 11.461889 9.312477 11.016835 12.508212 8.645871
## [8] 10.253232 9.265846 9.909380 7.962929 10.650548 9.936466 8.649797
## [15] 7.255993 13.654710 11.743999 9.498961 10.618655 10.001271 8.522253
## [22] 11.461540 8.930194 10.868724 10.693370 10.456113 8.253330 7.186649
## [29] 10.298254 10.566010 10.756913 10.946407 10.450464 9.114150 10.064172
## [36] 10.544246 11.615695 10.385596 10.956902 11.841118 10.359209 9.468785
## [43] 9.641030 8.358789 10.966168 8.084911 12.814348 10.654698 8.885468
## [50] 10.735924 8.089043 11.243113 11.079470 11.441018 8.589329 7.569627
## [57] 8.622487 10.411994 10.168802 9.508330 10.069193 5.790723 12.218257
## [64] 9.671807 7.454286 8.101722 7.969104 9.551389 10.755886 9.791133
## [71] 8.701286 6.147145 9.850825 9.583269 7.734690 11.053949 9.437949
## [78] 13.065258 7.987069 9.365756 10.706552 8.761431 8.169531 10.493889
## [85] 8.121641 11.050972 8.520256 13.342697 9.406518 8.409629 11.041181
## [92] 9.606813 10.015176 8.782437 11.318083 10.353466 9.505557 10.892798
## [99] 10.876243 10.541534 8.785981 9.765015 7.794735 8.382135 9.305425
## [106] 11.287993 11.075281 10.916271 6.891907 11.088427 9.034340 10.153138
## [113] 10.389584 11.247210 11.676459 10.452521 7.943397 10.359271 9.209663
## [120] 9.491503 10.637826 10.008347 9.043622 9.647121 8.345704 9.012067
## [127] 12.404547 10.175222 9.885600 7.651816 11.719004 10.442220 9.415203
## [134] 9.599734 10.085324 8.843963 7.518584 9.710594 10.022011 10.178536
## [141] 10.019757 8.873744 12.406416 12.998641 11.146378 9.510803 11.080339
## [148] 8.655321 9.992046 11.095646 7.556230 11.195552 11.303871 8.441916
## [155] 9.051813 10.451025 11.048283 12.502942 11.099279 11.823481 9.541571
## [162] 9.820721 8.604365 10.570744 8.409239 10.095374 9.746991 11.905541
## [169] 9.741000 11.358473 11.931747 9.814660 8.359635 8.952681 11.459300
## [176] 8.775225 8.333072 9.954088 10.193181 8.906373 9.654982 7.662104
## [183] 13.057865 10.898087 8.158449 11.413359 10.198464 7.527617 9.784141
## [190] 9.892802 10.690463 11.858704 7.341721 12.376857 9.299176 10.401437
## [197] 11.959978 7.576640 10.043497 9.449248 9.270105 13.381249 9.828220
## [204] 9.204397 10.856038 12.121183 10.404713 9.160810 12.395978 10.842269
## [211] 10.906759 10.500310 8.890992 8.675026 9.464821 10.154436 9.663888
## [218] 10.806429 9.467695 8.607901 11.647449 7.885839 10.479173 11.124822
## [225] 10.809389 8.902100 12.638514 9.454843 8.893200 9.464514 9.577151
## [232] 10.027758 9.988006 10.750895 10.900282 12.324721 11.088647 7.752174
## [239] 9.846824 11.049875 9.270017 8.008291 8.950866 10.603924 10.796012
## [246] 9.264330 9.178406 9.319806 10.494996 9.226641 12.979236 10.486879
## [253] 8.243650 11.572769 11.368885 10.466726 9.823586 9.527618 8.985306
## [260] 11.698071 11.703286 7.957428 13.267657 9.634624 6.691032 11.932861
## [267] 10.829213 7.751456 9.370613 9.315124 11.491947 8.221433 12.104285
## [274] 8.096910 9.606480 9.009623 10.214672 8.891459 11.817774 12.180843
## [281] 8.797096 9.944163 10.496469 9.783186 9.713252 10.402036 9.680429
## [288] 12.120682 11.881353 12.556028 11.852683 10.681688 10.106830 10.204091
## [295] 10.632913 11.753382 12.107130 10.694915 10.584636 9.474966 6.933220
## [302] 7.928919 10.292778 11.318140 11.226648 14.223297 8.886984 11.518076
## [309] 9.047643 9.469302 10.642678 9.508072 8.516938 10.362675 9.142158
## [316] 10.698386 11.444383 7.629542 9.308323 10.475137 10.294516 10.010427
## [323] 9.050006 7.630330 10.084986 9.258041 8.405460 8.481041 8.603163
## [330] 9.371600 10.904628 10.518428 10.458256 11.160399 12.193918 11.350784
## [337] 12.600032 9.872288 7.886148 7.483723 7.876052 8.984196 7.696817
## [344] 8.639686 11.851907 12.792633 9.035101 10.852862 10.235359 10.838151
## [351] 9.557754 9.429256 8.370274 9.517855 13.994708 11.035241 10.333792
## [358] 9.451015 7.817080 6.615459 10.625511 11.122152 12.162078 12.092896
## [365] 10.759544 8.605084 9.986629 9.721086 12.554893 5.018708 7.898955
## [372] 10.094131 8.953288 11.392107 10.547035 8.595551 11.907931 9.846651
## [379] 8.962984 8.962341 11.793037 9.710364 6.581234 12.380656 11.347578
## [386] 7.521080 10.620167 10.501034 8.717257 11.370018 8.073487 8.918060
## [393] 10.194802 9.493902 7.369297 10.306222 9.019665 10.969929 11.375680
## [400] 8.375988 9.431931 11.251910 10.413024 11.917019 10.371534 10.336133
## [407] 10.156971 9.692828 10.084126 10.099691 10.621615 10.319100 9.290819
## [414] 10.059285 10.636376 9.835304 8.879543 8.536724 10.279228 11.438543
## [421] 9.171205 9.890947 9.505066 11.398249 10.535380 9.964714 9.867109
## [428] 9.051113 10.168118 11.696324 10.144906 9.046578 10.695633 10.918980
## [435] 10.110298 9.273833 10.871583 8.129817 9.173858 9.767589 8.197673
## [442] 9.461821 10.078482 10.702960 9.193808 11.337227 8.655333 8.687756
## [449] 9.313206 11.431532 9.595247 11.116252 8.706184 9.675697 10.686977
## [456] 10.530646 8.276056 10.724350 10.613826 8.180681 10.720386 9.165656
## [463] 12.051516 9.373018 7.345606 6.653213 9.340471 11.194557 11.109835
## [470] 12.107501 10.099307 10.424255 10.525801 9.538010 8.590873 9.032078
## [477] 8.309119 9.857916 8.667682 9.945640 10.622581 10.425465 10.268018
## [484] 8.786352 8.066870 6.020643 11.605890 10.482485 14.320862 8.171678
## [491] 10.344422 8.480261 12.692975 10.498217 8.184208 10.028762 10.955983
## [498] 9.036407 9.876749 6.816224 8.149449 10.402079 8.984990 9.575950
## [505] 11.470094 10.045108 10.772841 8.756899 10.614921 8.697102 9.518644
## [512] 9.571675 7.687062 9.572785 10.602925 10.692605 11.310919 10.629442
## [519] 8.851376 9.536466 8.868015 9.315631 9.450166 12.414173 11.637977
## [526] 10.126388 8.838054 8.596238 9.393880 9.629862 6.285149 8.523096
## [533] 13.859726 11.291741 10.304364 8.004580 10.022104 11.487728 10.269298
## [540] 9.073241 10.069313 7.132209 8.929287 11.745386 10.009653 7.891127
## [547] 9.433571 8.714861 11.709438 9.592145 11.305894 13.582523 7.627641
## [554] 8.332939 9.356058 9.992157 11.273953 9.778666 10.773779 11.498401
## [561] 7.546535 7.618239 9.137784 8.480585 12.468446 7.959604 9.770100
## [568] 10.544603 11.042130 11.201544 10.661670 11.799355 10.209999 10.456756
## [575] 11.823310 13.091238 8.288899 8.545957 12.093723 11.910642 11.355229
## [582] 9.176477 9.106053 11.867998 10.311299 9.071923 10.479440 7.420354
## [589] 10.494515 11.727607 8.911063 11.409247 9.556768 9.295895 11.227245
## [596] 9.199496 9.157616 11.507345 8.485738 8.468796 10.367574 9.189556
## [603] 10.886602 13.422383 10.885736 8.227385 9.764621 11.577316 10.361125
## [610] 9.263815 8.112932 8.120759 9.146203 10.555664 9.945945 7.485870
## [617] 9.839753 10.945922 9.584475 11.439328 10.701057 10.823835 9.858998
## [624] 10.553159 12.157615 10.025359 11.059639 9.279732 8.852569 12.010735
## [631] 9.681555 10.585619 9.062348 9.301960 8.966503 12.834603 9.075432
## [638] 11.061763 10.883793 9.759455 10.546438 11.724186 9.762125 11.328714
## [645] 12.453072 10.019893 10.294661 8.428577 12.806615 8.919816 9.776160
## [652] 8.750302 7.399454 8.387959 9.056653 12.309405 12.195130 10.825902
## [659] 12.223331 11.970764 10.197888 6.698197 10.857745 9.843221 12.095590
## [666] 8.584181 12.034144 12.235321 8.800824 12.539011 7.678443 10.160349
## [673] 12.184220 9.465367 13.472249 8.108502 10.029886 10.766088 8.547368
## [680] 10.853690 8.133302 9.686704 8.709148 9.894223 8.228504 9.379794
## [687] 9.522125 7.523817 9.736284 10.272120 11.762900 11.638370 11.322309
## [694] 11.782858 10.720305 9.669853 10.236653 10.252677 11.297898 10.605793
## [701] 10.541456 7.761525 10.435349 12.769067 10.988717 11.212568 9.477209
## [708] 10.816058 10.677142 9.333266 6.511452 9.909505 10.804825 12.900944
## [715] 10.419821 8.066186 9.243752 9.471444 9.415149 9.791693 9.318329
## [722] 9.319651 10.176757 9.923952 11.763548 10.488715 7.533070 11.915664
## [729] 9.819327 13.156396 7.964745 8.343178 9.704943 8.306539 9.569963
## [736] 9.460775 8.625166 9.832853 9.366086 10.200508 10.166793 8.208432
## [743] 10.307126 12.162452 11.922458 8.007573 10.828904 9.563520 10.337159
## [750] 10.171508 8.797527 11.844624 8.678305 10.389478 8.638703 11.345956
## [757] 10.155709 9.551244 10.004508 10.108197 7.827652 8.412968 7.396276
## [764] 10.864443 12.648068 13.287597 12.023897 7.269725 11.413331 11.711454
## [771] 10.447399 9.719881 9.320116 9.344588 13.122954 9.837440 10.296858
## [778] 9.846751 10.944296 10.818438 10.884802 8.095523 10.022494 12.243978
## [785] 12.080162 10.072993 8.422876 8.797938 9.840499 9.021853 11.731108
## [792] 10.836846 8.733469 11.722954 6.969056 7.751288 11.827723 8.986067
## [799] 10.463209 10.332966 10.356028 11.083439 9.365232 9.711897 9.571499
## [806] 9.410307 12.501326 10.529198 11.904325 11.035816 8.953027 8.490545
## [813] 10.143454 8.760837 8.763153 11.014745 8.745919 10.253972 10.177695
## [820] 10.510165 11.615732 9.061894 9.273357 10.482821 11.062204 8.841807
## [827] 10.150744 11.425259 11.483874 8.083649 11.051120 11.536913 11.718185
## [834] 10.189620 11.251054 10.492476 8.153517 7.643379 10.299563 9.479634
## [841] 9.894569 11.327696 10.968803 12.078102 11.422350 9.586708 9.471784
## [848] 9.555854 9.317070 10.607264 10.096709 9.029833 8.500326 11.015413
## [855] 12.293916 11.204299 9.380729 8.293773 11.058358 10.523798 11.075112
## [862] 8.782519 9.853857 8.973474 10.107876 10.883766 12.464368 9.624853
## [869] 9.807099 12.143115 10.361576 11.819670 10.639571 8.807458 9.567694
## [876] 8.807538 11.421253 8.901512 9.232286 9.830938 10.560969 10.697327
## [883] 11.211984 8.352406 8.342637 8.142232 10.004682 8.068866 9.643723
## [890] 8.852515 9.678260 10.332284 10.227370 8.658715 10.086426 8.623448
## [897] 11.235337 9.756456 9.244488 8.779606 9.158653 11.929046 8.748112
## [904] 13.520534 11.157915 9.683011 11.056500 9.346001 8.803132 6.159399
## [911] 11.168906 7.545532 11.632012 8.897538 12.364534 9.885277 12.022190
## [918] 7.226332 9.723884 9.916594 9.616156 9.840017 9.486020 11.706083
## [925] 10.849688 9.727788 8.726750 9.423563 10.853697 10.203390 12.680632
## [932] 10.769765 11.642365 11.399085 8.902467 11.114223 12.046326 9.887918
## [939] 8.709129 9.303385 10.317544 10.496511 9.630862 8.970870 11.209697
## [946] 11.093967 10.367768 10.157209 11.231056 10.317586 11.101738 12.208788
## [953] 9.735136 7.528456 10.037142 12.063344 8.855988 11.152651 7.809616
## [960] 11.302984 9.980733 7.785311 9.860789 11.701677 8.968769 8.890814
## [967] 12.866414 8.513824 10.689600 11.330831 12.017570 11.586248 7.868906
## [974] 11.252953 12.916176 9.688919 10.262980 8.836083 8.931737 9.140624
## [981] 9.948583 12.939756 9.995379 8.410884 9.322166 11.207073 9.902761
## [988] 6.628176 8.878935 9.703855 6.668871 7.542124 9.187686 11.578682
## [995] 11.661723 13.286581 8.797466 8.816749 9.212998 11.599605
# create groups for different values of X
(group <- rep(1,1000))
## [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 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
# a vector consisting of 1000 elements
group[xAxis > -1.5] <- 2
group[xAxis > -.5] <- 3
group[xAxis > .5] <- 4
group[xAxis > 1.5] <- 5
group
## [1] 3 2 3 2 3 4 2 3 2 2 1 4 2 1 1 5 4 3 3 3 5 4 3 4 3 5 1 1 3 2 4 3 4 3 5 3 5
## [38] 2 3 4 3 2 3 2 4 1 5 3 2 4 3 4 4 3 2 2 3 3 3 3 3 1 5 3 1 1 2 2 3 3 3 1 3 3
## [75] 1 2 3 5 1 2 3 1 3 4 3 5 2 5 3 2 3 2 2 4 4 4 2 4 3 4 2 3 4 3 3 5 4 3 2 3 4
## [112] 3 3 3 4 4 1 3 4 3 2 5 2 2 2 3 4 4 3 2 3 3 2 4 2 2 2 2 2 3 4 3 4 5 3 3 4 3
## [149] 2 3 1 5 4 3 3 3 3 3 3 4 3 3 3 4 2 2 4 5 3 3 5 4 2 2 3 2 1 2 3 4 4 3 4 3 3
## [186] 4 3 1 2 3 4 3 2 4 3 3 5 1 4 3 2 5 2 3 2 4 3 3 5 5 5 3 3 3 2 3 3 5 2 1 4 3
## [223] 2 3 3 2 4 3 2 2 2 3 3 2 3 4 3 2 2 3 1 2 4 4 3 3 3 2 3 3 5 3 1 4 5 4 2 3 2
## [260] 4 3 2 4 2 1 4 3 1 3 2 4 3 4 3 3 3 4 4 4 3 4 4 4 3 3 3 2 4 5 5 4 3 4 3 4 4
## [297] 3 3 3 3 1 2 3 4 3 5 3 4 3 2 4 3 3 5 2 3 4 2 3 3 3 4 2 2 3 2 2 2 3 3 2 4 2
## [334] 3 4 3 4 4 3 1 2 4 2 1 4 5 3 4 4 3 3 2 2 3 4 5 4 3 1 1 3 4 4 4 4 2 2 3 5 1
## [371] 2 3 2 4 3 2 3 2 2 2 4 3 1 4 5 1 2 2 4 4 3 1 3 2 2 2 1 5 3 2 2 4 3 4 4 2 4
## [408] 2 2 2 4 2 3 4 3 3 3 3 3 4 2 4 2 3 2 4 2 4 3 3 3 4 3 3 3 1 3 2 2 3 4 3 4 2
## [445] 3 5 2 2 3 4 2 4 3 2 3 4 2 3 3 2 3 3 5 2 2 1 3 4 4 4 5 4 3 4 3 2 3 4 2 3 4
## [482] 2 4 1 2 1 3 3 5 3 4 3 4 4 2 3 2 3 4 2 1 1 3 4 5 4 3 4 3 2 3 3 3 2 4 2 3 3
## [519] 2 3 3 2 3 4 3 4 3 2 5 2 1 2 5 3 4 2 4 4 3 3 3 1 2 3 4 2 2 2 4 3 3 4 1 3 3
## [556] 3 5 4 3 3 1 1 4 2 5 1 3 3 3 4 4 3 2 5 4 5 1 1 4 4 4 2 2 4 3 3 5 1 2 5 2 3
## [593] 3 3 3 3 3 3 2 1 3 3 5 5 4 1 2 3 3 3 2 2 2 3 2 1 3 4 3 4 5 4 4 3 5 3 4 3 4
## [630] 3 3 2 3 4 2 3 2 4 4 2 2 4 5 4 5 3 3 3 5 2 3 1 2 3 2 3 5 3 5 3 3 1 4 4 4 3
## [667] 5 4 2 4 1 4 4 4 5 4 2 3 3 4 2 3 3 3 3 3 4 2 5 4 4 4 2 3 4 2 3 2 4 3 4 2 4
## [704] 5 3 4 4 2 4 2 1 2 4 5 2 1 3 3 3 3 3 2 4 2 4 4 2 5 4 3 1 2 4 1 2 2 3 3 3 3
## [741] 4 2 3 4 3 3 3 4 3 4 2 4 3 3 2 3 3 3 3 3 1 3 2 3 4 5 4 1 4 4 4 2 2 3 4 3 3
## [778] 3 3 4 3 2 1 4 3 3 2 4 2 2 4 5 1 4 1 1 3 2 5 2 2 3 3 4 2 2 4 4 3 2 1 2 4 2
## [815] 2 2 3 3 2 4 3 3 3 2 4 2 4 3 3 4 4 4 5 2 2 3 3 2 4 3 3 2 3 4 4 3 2 4 4 2 4
## [852] 3 2 4 4 4 3 2 5 3 3 2 4 2 3 3 3 4 3 4 4 5 3 2 3 3 4 3 3 3 3 4 5 2 2 3 3 2
## [889] 2 2 4 4 3 2 3 2 3 2 2 2 2 5 2 5 3 3 2 2 4 1 4 1 4 2 4 4 4 1 3 2 2 3 2 4 4
## [926] 4 3 4 4 4 5 4 5 4 3 4 5 3 1 4 2 3 2 3 3 5 2 3 4 3 4 4 4 2 4 3 2 3 3 4 2 2
## [963] 2 4 4 4 4 2 3 3 5 3 3 5 5 4 4 2 3 2 2 5 4 2 3 4 3 1 1 4 3 1 2 3 4 5 4 3 3
## [1000] 3
# create sample dataframe by joining variables
sample_data <- data.frame(xAxis,yAxis,group)
sample_data
## xAxis yAxis group
## 1 0.3044354095 10.210596 3
## 2 -0.5257994308 12.353595 2
## 3 0.1001440564 11.461889 3
## 4 -1.0395980015 9.312477 2
## 5 -0.4204357791 11.016835 3
## 6 1.2321759764 12.508212 4
## 7 -0.9057734260 8.645871 2
## 8 0.4610606369 10.253232 3
## 9 -0.5480279750 9.265846 2
## 10 -0.7405994042 9.909380 2
## 11 -2.1788703494 7.962929 1
## 12 0.9737466971 10.650548 4
## 13 -0.7115629209 9.936466 2
## 14 -2.0343911261 8.649797 1
## 15 -1.5409624759 7.255993 1
## 16 2.7180454128 13.654710 5
## 17 1.1159574683 11.743999 4
## 18 0.0912110842 9.498961 3
## 19 0.4024839962 10.618655 3
## 20 -0.0124221087 10.001271 3
## 21 1.6032554767 8.522253 5
## 22 0.9378537948 11.461540 4
## 23 -0.4175160432 8.930194 3
## 24 0.8838681706 10.868724 4
## 25 -0.0170671585 10.693370 3
## 26 1.7146988674 10.456113 5
## 27 -1.8565786483 8.253330 1
## 28 -2.2861629761 7.186649 1
## 29 0.0471002195 10.298254 3
## 30 -1.3828450934 10.566010 2
## 31 1.2728576626 10.756913 4
## 32 -0.3245453488 10.946407 3
## 33 0.7262553774 10.450464 4
## 34 -0.2494040895 9.114150 3
## 35 2.0491318156 10.064172 5
## 36 0.3276720122 10.544246 3
## 37 1.5152625813 11.615695 5
## 38 -0.6999958393 10.385596 2
## 39 -0.1918170948 10.956902 3
## 40 1.3772500943 11.841118 4
## 41 0.3062699462 10.359209 3
## 42 -0.9330012236 9.468785 2
## 43 -0.3861351026 9.641030 3
## 44 -0.5335455646 8.358789 2
## 45 1.0532717454 10.966168 4
## 46 -1.5708218398 8.084911 1
## 47 1.8380984469 12.814348 5
## 48 -0.4532271014 10.654698 3
## 49 -0.7283387249 8.885468 2
## 50 0.8581755323 10.735924 4
## 51 -0.4204010006 8.089043 3
## 52 1.0778569444 11.243113 4
## 53 0.9895192634 11.079470 4
## 54 0.4267555750 11.441018 3
## 55 -1.3829155256 8.589329 2
## 56 -0.8570834825 7.569627 2
## 57 -0.2640844935 8.622487 3
## 58 0.2161320079 10.411994 3
## 59 0.1547657917 10.168802 3
## 60 -0.0659525389 9.508330 3
## 61 0.2270387793 10.069193 3
## 62 -2.4730291126 5.790723 1
## 63 1.9810059355 12.218257 5
## 64 0.0466660567 9.671807 3
## 65 -2.4708005569 7.454286 1
## 66 -2.8658457773 8.101722 1
## 67 -1.3225337869 7.969104 2
## 68 -0.8709826551 9.551389 2
## 69 0.2914939939 10.755886 3
## 70 0.2145210159 9.791133 3
## 71 0.2383878020 8.701286 3
## 72 -2.6328561752 6.147145 1
## 73 -0.4682606636 9.850825 3
## 74 0.3495380054 9.583269 3
## 75 -1.5958955689 7.734690 1
## 76 -1.0257860632 11.053949 2
## 77 -0.0141401703 9.437949 3
## 78 1.6544979157 13.065258 5
## 79 -2.6600212874 7.987069 1
## 80 -0.8256373144 9.365756 2
## 81 0.1959102371 10.706552 3
## 82 -3.0920288767 8.761431 1
## 83 -0.0812324934 8.169531 3
## 84 0.7517704440 10.493889 4
## 85 0.4349359553 8.121641 3
## 86 1.8998608769 11.050972 5
## 87 -0.6694894942 8.520256 2
## 88 1.9408410206 13.342697 5
## 89 0.1775329935 9.406518 3
## 90 -0.7941748106 8.409629 2
## 91 -0.0275808490 11.041181 3
## 92 -1.1492469470 9.606813 2
## 93 -0.5633729004 10.015176 2
## 94 0.5937611549 8.782437 4
## 95 0.5961760626 11.318083 4
## 96 0.5242191765 10.353466 4
## 97 -0.6191658246 9.505557 2
## 98 1.2969949297 10.892798 4
## 99 0.0773046878 10.876243 3
## 100 0.8157126131 10.541534 4
## 101 -0.8583404811 8.785981 2
## 102 -0.3507176282 9.765015 3
## 103 0.6130602023 7.794735 4
## 104 -0.4252623016 8.382135 3
## 105 -0.0559297642 9.305425 3
## 106 1.8298331496 11.287993 5
## 107 0.6216792311 11.075281 4
## 108 -0.0316617515 10.916271 3
## 109 -0.7729496334 6.891907 2
## 110 0.4637217647 11.088427 3
## 111 0.5315629854 9.034340 4
## 112 -0.3709727278 10.153138 3
## 113 0.3754108385 10.389584 3
## 114 -0.3186548792 11.247210 3
## 115 0.7069561511 11.676459 4
## 116 1.1725859796 10.452521 4
## 117 -2.0130684206 7.943397 1
## 118 -0.0819044653 10.359271 3
## 119 0.8584935622 9.209663 4
## 120 -0.4543546066 9.491503 3
## 121 -0.6507421087 10.637826 2
## 122 1.8434104251 10.008347 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)
