# Mindanao State University
# General Santos City
# A0 Basic Graphs Using R
# Submitted by: Davy D. Dongosa, 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
## # 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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## 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
# 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 3.2.1
## ✔ 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))
## [1] 0.0297457221 -0.3719067033 -0.8253756307 1.2635609301 0.3373823884
## [6] -1.1221804025 -1.2463666417 0.6475856235 1.0462736522 0.1527480433
## [11] -0.6534576853 1.4887153715 2.5621314625 1.5528798009 0.4994329011
## [16] 0.0948725504 -0.1323002499 0.4523541675 1.2768456677 0.6987960709
## [21] 0.0417965938 1.6388258527 1.4461354553 1.0818587624 0.0544531075
## [26] 0.1074881178 3.2339407154 1.8234381284 -3.2454524858 -1.9548676897
## [31] -0.1481428193 -0.6954619085 -0.8007918073 -0.3908060212 -1.8754739715
## [36] 1.2584700185 1.3151973251 0.5317531269 -0.0580682687 0.6921051899
## [41] 0.2346195712 -0.8610615238 0.6633136609 0.6744063999 0.3269978108
## [46] -0.4877755023 -0.1137583627 2.6781180839 0.1278014106 -0.3655744141
## [51] -0.4882098827 2.1734367878 -0.5429270303 -0.0193129758 0.3712298181
## [56] 0.0458684303 0.4391368022 0.5887800685 -0.1299347125 0.5530494818
## [61] 0.5919736062 1.3471604490 0.3072489895 -0.3194096380 -0.6597028712
## [66] 0.7702126840 0.3277050136 -0.9209661583 -0.4250160318 -0.7106530330
## [71] 0.9478641981 1.1902588180 1.1128440764 0.4792808503 -0.6179437288
## [76] -0.8940206173 0.4394460577 1.7830676794 0.6673078420 0.8616651994
## [81] 0.2115773632 0.1174122692 -0.5348682957 -1.2609639858 0.3004198304
## [86] 0.8909041637 -0.6307066462 -1.1581841792 -0.3813005073 -0.6356241906
## [91] 1.7560205216 0.3748407752 -1.0016321722 0.8815161214 -0.4446169157
## [96] 0.8056973043 0.9596901829 0.8839995274 1.1058517812 -1.3385581906
## [101] 0.2915704470 2.0975267880 2.4509721113 0.1523230095 -0.8194487876
## [106] -0.7872714077 0.4300525068 2.6854367340 0.7053059859 1.4962929491
## [111] 0.3158209939 -0.2913716149 1.2130750140 -1.2368420390 0.8569946197
## [116] 0.2882919879 -0.1584598250 -1.5852201440 -1.2602463461 -0.8132881969
## [121] 1.7349570309 -1.3992372867 -0.6953358783 1.0476741407 1.0244923691
## [126] 1.6643829979 -0.7463045863 -0.9935095546 0.6770156484 -0.8655686195
## [131] -0.1308094442 -0.6526466899 -0.9539715941 0.5201950445 0.0618129340
## [136] 0.4789391045 -0.7170856005 1.2520437364 -0.7848649740 0.0717970452
## [141] -1.4215403554 0.3497119775 1.1787481713 -0.3455071408 -0.1528148662
## [146] -0.8737248206 -2.1281153683 -0.8114387539 -1.6834010981 0.7467304858
## [151] -0.8531826868 0.9022617113 0.2724357033 1.3399059299 1.0002050319
## [156] 1.2897714746 -0.2337223301 -0.3529319491 -0.0254069604 -0.1415272495
## [161] 1.9534852377 0.7952436697 1.7059509124 0.2287902451 -0.7197580072
## [166] 1.4320970016 -0.4978682028 0.4033766157 -2.6827907193 -0.6434684961
## [171] 0.2104551201 0.1814535056 0.6256250418 -0.8334662839 -3.2866558340
## [176] -0.1914934496 0.0739430164 1.4013357109 0.4105214421 0.8348844985
## [181] 0.8303169205 1.4810686147 1.5778752152 0.2925599109 0.8528775091
## [186] 0.9956945843 -0.4423267967 1.0819348440 0.7509834580 1.2144061117
## [191] -0.6269285829 -1.9956967214 1.3999964670 -1.0847765252 -0.6366000027
## [196] -0.5343268251 0.0251600175 1.0198586086 -1.9443437091 0.6835100483
## [201] -0.5528511459 -0.6162332787 -0.6576578874 0.0815784655 -0.8171453347
## [206] 1.1364162684 -0.9335120173 0.4429067723 -0.3416493123 -0.6595571092
## [211] -1.3576045662 -0.1557524431 -1.0016997979 -0.5858578885 0.6424769480
## [216] -0.1446774168 0.6413738382 1.2383236686 -0.4187591588 -1.0799796455
## [221] -0.2213393487 0.9521837707 -1.7438350977 0.5400997653 1.1348809726
## [226] 0.4580609373 -0.3531589272 -1.7750748404 0.3808376441 0.1994831970
## [231] 2.0226426827 1.6038290968 0.1313310503 -1.7277859104 -0.3819201738
## [236] 1.9496984806 1.1765250763 -1.3226663181 -0.1918995827 1.9321220005
## [241] -0.5479718485 -0.5162434903 0.6466880213 0.5474881045 1.4852350735
## [246] 0.4599635112 -1.2623146455 1.0102853943 0.1850491089 -0.8903080334
## [251] 0.2763383093 -1.0220096042 1.1534258222 -0.7475686779 -0.9788482946
## [256] 1.3982609353 -1.3545032848 0.0508031229 -0.3559652436 1.8741142624
## [261] -1.2061940744 -0.6637800434 0.4488764355 0.3030192950 1.3982851122
## [266] -0.2512757324 0.5091259813 -0.6001630079 0.2581386892 -0.2329848448
## [271] 1.1489735461 0.5410224450 -0.8741434013 1.0738074060 -0.6522022046
## [276] -0.9285015744 0.7542558450 -0.2571674906 1.4037325119 1.0375238472
## [281] -0.2136570705 0.1853265992 0.1367506063 0.6520719678 2.1692158518
## [286] 1.3490746000 0.4583375324 -1.3220937126 0.1703010627 -0.0554655676
## [291] 0.2614838201 -1.7973645401 0.0384140595 -0.5096118249 -1.2379034206
## [296] -0.3713795580 2.0223192487 -0.6831552790 -0.7054738944 0.8583198419
## [301] -0.5101694671 -2.0772583556 -0.8092578543 0.7106423353 -1.4629320060
## [306] -1.1191629995 -1.3426143607 -0.9688817416 -0.4201186485 0.0849011625
## [311] -0.5009813782 0.5147757125 -0.2512468984 0.8567017527 0.0954278366
## [316] 1.2643845003 -0.5942554966 -0.1959233348 0.8031506208 -2.1043710184
## [321] -0.3513860160 1.5757064583 -1.1412941099 -0.0347549640 0.1347892449
## [326] 0.1820817426 1.8624937573 -0.2947744839 0.2065919606 -1.3272411922
## [331] 1.1415769922 0.7668875238 0.1615193508 -1.3263229136 -0.0769360389
## [336] -0.1828639582 0.6385818443 -1.7151803535 0.4288619325 0.1191084710
## [341] 0.6700526906 0.2790120515 0.2756173916 -0.1502085874 0.4331693172
## [346] 1.1467919103 -1.0165315629 1.1901396094 0.8015312079 0.0005762223
## [351] 2.8416201059 0.0935774062 -1.4909674779 0.5108198901 1.3321489205
## [356] 1.2695250294 -0.1738973419 0.2662572892 -0.3889410945 1.2288827984
## [361] -0.3381186163 -0.2380601220 1.0429621369 0.9803586578 1.3509120593
## [366] -0.7383224784 -0.4854716874 2.5997581769 -1.0815692092 -0.1471275718
## [371] -0.1600268250 -0.5704367848 -0.9198855770 -0.7636672908 1.4153589000
## [376] -0.1659504121 0.5639701711 0.8302977937 0.8173706018 0.1629736719
## [381] -1.5437672226 1.8359009049 -0.0632389876 -0.5539449771 0.9306801528
## [386] 0.4280427344 0.1520541167 -0.6252512522 0.2760306576 -0.5689200104
## [391] 1.2699471743 -0.3633696736 1.3456736697 -0.0522560763 -0.5662884605
## [396] 0.2091842034 -0.7015440325 -0.8046725672 1.3408945290 0.7954213419
## [401] -0.9010721323 0.8619327704 -0.2175228967 0.9659447097 -0.5372789740
## [406] -0.9546615639 -0.6797564966 -1.9018391496 1.6192149852 0.5472612225
## [411] -0.6144420252 1.1888269385 0.5635015544 0.4941279351 1.5312793554
## [416] -0.3020416968 -0.8869475881 0.4751195671 0.7773538511 -0.4089110935
## [421] 0.4281784664 2.0147695821 -0.7697458582 0.2758818202 0.2196724627
## [426] 1.0329571938 -0.8180375092 -0.2362989896 -1.2189605054 -2.1308951576
## [431] 1.0323978957 -0.7860113016 0.4428930284 0.2834588576 0.6802378303
## [436] -1.2289552681 -1.0900406038 2.5966592694 -1.5084017499 -0.9899890909
## [441] 0.3408482172 -0.6781674916 1.3610992777 -0.0210812558 1.0544831482
## [446] 0.3895570468 -0.4363310935 0.2837655650 0.0863493076 2.5997731258
## [451] -0.0011795042 -2.5988743468 -1.2031173728 -1.1261898237 -0.2134853681
## [456] -1.8113182752 -0.3400631182 -1.0931491380 -0.4946962242 0.8942886211
## [461] -0.1596610463 0.1857733330 -1.0956333200 -0.2902149167 -1.6670120006
## [466] -0.6129941763 0.7183107447 0.7351411024 -0.3281944261 -0.7839329164
## [471] 0.5422309355 -1.0422092173 -0.6276356705 1.1562746523 -1.0416456911
## [476] 0.2033987850 0.0202967311 0.1175921262 0.5521406806 -0.1238682333
## [481] -0.5617541845 -0.2866909545 -0.8359714681 0.3890531537 0.4523446737
## [486] 0.0931218427 0.0190845718 0.9280798721 -0.9874708752 0.8783012278
## [491] 0.6375443912 0.6467993809 -0.5677967838 0.1599924919 0.3417400395
## [496] 0.5774724337 -1.2805535592 0.6900678257 0.4480056317 0.4769246725
## [501] -0.5103118726 0.7551541478 0.7522938691 -0.1958805916 1.0059900750
## [506] 0.1900092866 -0.2499413794 -0.8676335234 1.4228207331 -0.2656096563
## [511] -0.2561761877 0.1743264763 -0.9633970303 -0.9358733832 -0.2153810986
## [516] 0.2402580386 0.1204239625 -0.8141156532 -0.6868357684 -0.7231393496
## [521] -0.6764966187 -0.7176467980 0.5779342896 0.9957480922 -0.0386231676
## [526] -1.7077338635 -1.0723290572 -0.4566252874 -0.5014118220 -1.6812829298
## [531] 0.8963215567 -0.6811605984 -0.6822936553 -1.1768724534 -1.0504688042
## [536] 0.7412883015 -1.1055011654 1.0777936831 -0.2176385345 -0.1563741014
## [541] -0.9383919626 -2.0691509909 0.4129131415 -0.5002915365 -1.1520533293
## [546] 0.7502276950 -0.0159827483 0.4418007043 -1.7671544898 -1.1886650133
## [551] 0.5468990408 1.1789110925 0.3119105528 0.0320808944 -0.5386678752
## [556] -0.6651075227 0.6680940772 0.3845416500 2.4254537112 0.4185386061
## [561] -0.3251533967 0.3859831799 1.7631980880 0.1305918683 0.6274930407
## [566] -0.1630011408 -1.4783170037 -1.6496447308 -0.1247818164 0.0135423654
## [571] 0.3921813355 2.0814343037 -0.3567735748 -1.5747662625 0.2267419106
## [576] 0.3630650998 -1.6970907102 -1.4002838168 -0.7451661069 -0.7884760724
## [581] -0.7337431960 1.1149351186 0.6246632377 -0.4208283521 -0.0915037871
## [586] -0.7235204869 -0.5494415578 0.4224614928 2.6998457919 -1.9868075514
## [591] 0.7640852126 -0.8708582515 -0.1925863419 1.3026112360 -2.6308698377
## [596] -0.1827368551 -2.5155085625 0.5409627272 -0.0715402364 0.2782962569
## [601] -0.2415786741 0.7577619957 -0.2001982408 -0.6578067619 -0.1947296522
## [606] 0.5166947393 1.8450558818 1.2083777089 -0.3832953331 -0.5899266732
## [611] -0.2367193575 0.1320895838 -1.1943478977 -0.1435304511 -0.7595091218
## [616] -0.8476480906 0.5547168586 -1.3211332473 1.2340623425 1.1723823030
## [621] 1.3731175868 1.1379608864 -1.5905774585 0.1789986817 0.4731962955
## [626] -1.6002061336 0.7325575942 -0.6977242626 0.8800582890 -0.3028542113
## [631] -0.5737616821 0.4292194936 0.3628705552 -1.6565033629 -1.5387774487
## [636] 0.7678755282 -1.4654691672 -1.5813663734 -0.7575978718 0.4655437514
## [641] -0.8436281236 -0.3006566746 -0.5174593437 0.7633806412 0.8183168425
## [646] 1.7776842797 0.9144274051 1.2454977872 0.6546916633 0.4648268447
## [651] -0.2243081006 0.3659407346 0.3910939630 0.7383422903 0.4482501930
## [656] 0.6125614051 -0.0985846665 0.5365952806 0.2282276364 -2.6934540057
## [661] 0.3927495104 -1.5331429097 0.3969422139 -0.2560736925 0.5973491979
## [666] 0.5799516753 0.0340022550 -1.3705439507 0.7796662237 1.3527227187
## [671] 0.8880785587 0.4281518747 0.8131003051 0.9514390215 -0.2029615117
## [676] -1.7667811032 0.0516614393 -1.0366619417 0.8693987508 0.9394040445
## [681] 0.2538333669 -0.3675027883 0.0109160205 -0.7277246889 -0.3239529641
## [686] -0.5564472117 1.4464204507 -1.0310062722 0.2602291299 -0.6380003046
## [691] 0.9575259657 -0.6210657048 -0.3857114253 -0.3856911746 -1.4581458731
## [696] -0.7502569402 -1.1340671516 -3.0388001691 -1.5373962817 0.4889541443
## [701] -1.1519935035 0.3791254663 -0.4141287636 1.6749196623 0.0628685597
## [706] 0.9038242126 -0.0099023019 -0.6442807000 2.0975446250 -0.7725449796
## [711] 1.5771951108 0.3979039102 -1.6970415653 0.2535416039 0.1265786375
## [716] 0.2035679537 -0.3778597280 1.1509146882 -0.0602493287 -0.8263212624
## [721] 0.8761536200 0.1918654509 0.5348178766 0.6083988019 -0.4185915200
## [726] 0.6997370166 0.1231148384 1.0838257647 0.1439278046 -0.0471785283
## [731] -0.4565876386 0.9803712607 -1.4154048344 0.3569650564 3.0928633289
## [736] 0.6061368545 -0.8468732328 0.0833718451 -2.2887629438 -1.7820875112
## [741] 1.7080953977 0.2706446745 -1.8567387645 -0.1542795611 -1.5654347877
## [746] -0.7850431414 1.2903500047 -1.3805939354 -0.0187458037 1.9228706924
## [751] -0.4730253561 -0.9719429129 1.3006055581 0.2005076726 0.5652685458
## [756] 0.5558317357 -0.4765681503 0.5476065594 0.3085675840 -0.5541490755
## [761] -1.2552239409 -0.2559452912 -0.1359679495 -1.4033261291 -1.3164641901
## [766] 0.9179817436 -2.2426068872 1.3040162462 1.2552004530 1.1566747740
## [771] 0.9794294313 -0.0497125682 -0.2697065594 -0.9755739772 -0.1374296268
## [776] 0.5459667194 -1.3904980762 0.8396296728 -0.6646680879 -0.8375020993
## [781] 0.7640428809 -2.0124963104 0.7595413493 -0.6578196216 -0.5583651012
## [786] 1.7021310938 -2.1196033392 0.6206077285 -1.0619905347 -0.7030054091
## [791] -0.5309383037 -0.7672069485 0.1931049686 -1.7334763004 0.4143746928
## [796] 0.7209046114 0.2945504691 0.8161347519 -1.0038830181 1.7016408921
## [801] 1.2577022189 1.9260521635 0.1662076754 -1.7577495264 0.6703969429
## [806] -2.1051744017 -0.1667474744 0.0594817727 0.0941378187 0.3543524670
## [811] 0.6269898006 1.0048186730 -0.4769985695 -0.3251766059 -0.5173528620
## [816] -0.4036362353 -0.1551973066 -1.0505020557 -0.0172511929 -0.6919528967
## [821] -0.6448005916 -1.1982603120 -0.9742627991 -0.5993928782 0.9301630581
## [826] -1.2898350836 1.6008091211 0.2915624462 1.4523207364 -0.5389507846
## [831] -0.8963846845 -1.2566753874 1.1631139186 0.2150311787 -0.4459107398
## [836] -0.6777855052 -0.8017529425 0.2954133649 -0.3836601844 0.0342056412
## [841] 0.0550615927 2.6609255614 -0.4417095556 -0.3088007134 -0.6732679582
## [846] -1.3328456295 0.1550246863 -0.7126725320 -0.2103545785 -0.1403886692
## [851] -0.8215777522 -1.2391128403 -0.1465631137 0.4646454770 -0.4522039176
## [856] -0.4375279621 1.6101006014 0.6123861320 -1.3688133574 -1.2244759987
## [861] -0.0322167807 0.2452736495 -0.2469359395 -0.5110601252 -0.4677808825
## [866] -1.0064426915 -0.2748848620 0.1953934872 -0.0606274237 1.5655562888
## [871] 0.6184544484 -0.2990100585 -0.3143566908 -0.7065443872 -0.2037041013
## [876] -1.2895126264 -2.0992743534 0.0124892358 -2.1224452478 1.1393488979
## [881] 0.6879020058 -0.3691033942 -0.6172458888 -1.4996393879 0.2595915139
## [886] 1.3158683091 0.3486132658 0.3398511201 1.0036176314 0.6349044194
## [891] 0.8060742293 -0.7846271708 -1.1550594687 2.1942048994 -0.5963683397
## [896] 0.7546089242 -0.2034354331 -0.4577825019 0.0542870940 0.1590032818
## [901] -1.2113694840 -1.5519424440 -1.3176355257 -0.8022102232 -0.9820196646
## [906] -1.0603902851 -0.4592565251 -1.0464125597 0.4960222822 -1.3693016781
## [911] 0.6615997304 -0.7617800862 0.1655881799 -2.2249045183 -0.9505599853
## [916] -2.4214269169 -0.9147266438 -0.6629722189 -0.9377096007 -0.4965909589
## [921] -0.0534838030 -0.3532319983 -1.0151665209 -1.1587899601 0.2747119836
## [926] 1.6666740704 1.2278629737 -0.2042248009 0.6013494335 0.5511320483
## [931] -0.5579131916 -0.7296543666 0.1774073474 0.0002128264 -0.2026117538
## [936] 0.5113188002 0.3024781680 0.7554330439 -0.3747357267 0.5411469038
## [941] -0.7177320424 -1.0012487218 -1.2199443454 -0.3418202159 2.5526970476
## [946] -0.3443462193 -0.1216373336 1.0536224918 0.4925280066 -1.4238895891
## [951] 0.1276215981 -1.2614858224 0.4449606766 -0.5571041217 -0.8896232860
## [956] -0.1757594582 3.3304936723 -1.5019112649 -1.7893983933 -0.6615357369
## [961] -0.6867815944 1.9066274096 -1.5856863006 -1.3105004204 -0.5981638880
## [966] -0.2792472009 0.3610765637 -0.0291916455 -0.9654784549 0.9216795983
## [971] -0.0634337931 -0.5164548949 2.1105840222 0.6073370371 -0.3054983594
## [976] 0.2459192201 -0.5665018687 0.1203742216 -0.7718147519 -0.9040384209
## [981] -1.1253513692 0.2595276588 0.2444850367 -0.0007143723 1.2971856249
## [986] 0.9383303305 -0.1643244314 -2.1390283857 -0.1057298581 0.6932183281
## [991] -0.2577201448 0.8853583672 -1.6570018606 -0.3205469796 -0.1897882330
## [996] 0.3445636956 -2.4838691184 -0.0950728115 2.0734309450 0.7004286628
yAxis <- rnorm(1000) + xAxis + 10
yAxis
## [1] 9.453977 9.239074 8.934578 12.525743 9.509183 9.284195 8.685609
## [8] 10.270069 11.451555 10.495430 9.532087 11.051496 12.743650 13.741138
## [15] 10.267638 10.099549 10.117929 11.631696 9.729594 8.741232 9.945405
## [22] 10.908883 11.670314 9.433225 8.747644 9.799313 13.151943 13.609822
## [29] 5.681295 7.955493 11.249553 10.535224 9.250472 10.561307 7.244991
## [36] 11.222024 12.749457 11.434454 9.936051 11.302762 12.024915 10.602074
## [43] 10.144157 11.189969 10.720416 10.515124 8.094872 11.555110 10.255558
## [50] 9.538774 8.715178 12.185025 10.507931 8.258384 11.140384 10.690920
## [57] 9.876167 10.669613 10.662628 9.608914 11.903545 12.574498 9.556155
## [64] 8.174616 12.161662 9.921142 10.430268 10.370481 8.788717 9.125051
## [71] 9.933326 12.232248 12.181600 8.042256 8.592068 10.188963 12.626654
## [78] 11.715015 11.808902 9.991671 9.864414 9.765506 9.670371 8.015212
## [85] 11.190050 11.032440 8.660235 8.293803 8.573428 11.332472 10.538030
## [92] 8.960662 7.177706 12.120763 9.275724 12.223843 10.798274 10.250420
## [99] 12.396547 8.011546 9.243631 13.087319 10.814846 9.630633 8.573007
## [106] 9.508982 11.159354 13.036523 12.082295 11.416104 12.186637 7.363956
## [113] 10.535066 8.742677 9.464906 9.793261 9.190397 9.444054 8.432466
## [120] 8.640899 11.090482 7.005171 10.077834 12.128834 12.556016 10.735983
## [127] 8.581836 11.483329 11.098689 8.914656 10.187653 10.111956 10.271029
## [134] 9.761257 10.178292 12.799087 8.747296 11.769882 9.334794 9.658767
## [141] 9.268787 10.373545 11.307271 9.698254 9.128445 7.686615 7.620257
## [148] 7.572256 6.307194 9.589956 7.467497 10.996517 11.528833 10.780836
## [155] 10.932635 11.749219 10.610544 9.718818 12.076556 9.909437 13.183351
## [162] 7.442172 10.376130 9.903405 9.129734 10.495736 9.132305 11.870084
## [169] 7.976982 9.477272 11.408120 8.455681 10.559582 9.763914 6.893826
## [176] 8.795818 9.495852 10.566155 8.745284 10.729021 11.041582 11.612691
## [183] 10.738843 11.553614 9.482287 10.567418 9.362975 11.531980 10.234234
## [190] 10.231721 9.733713 8.045294 12.786436 9.357128 10.750265 10.488705
## [197] 10.117537 10.405390 7.256452 11.648672 10.596191 9.402079 9.666902
## [204] 10.591137 8.580359 10.147043 9.192565 8.524973 8.304412 9.218174
## [211] 8.838642 8.789215 7.969412 8.522022 11.637406 10.113470 8.893417
## [218] 10.754529 9.233649 9.594366 10.351329 10.743913 7.927042 10.115025
## [225] 12.148059 12.179157 10.784021 8.212278 11.130464 10.364592 11.620614
## [232] 11.339242 9.341435 7.195250 8.330650 12.351507 10.481267 7.932837
## [239] 10.812078 11.614247 8.539142 10.459865 9.918622 7.753849 12.180473
## [246] 10.570968 9.545812 11.518425 8.645588 10.420645 9.769995 8.232035
## [253] 12.456708 7.623977 7.578276 11.092797 7.729065 10.649937 10.356220
## [260] 12.949696 7.918587 10.271219 11.220810 9.575969 13.428994 8.884329
## [267] 10.289314 10.265825 9.604150 10.213750 11.257015 11.573971 9.683439
## [274] 11.965759 9.154427 8.349757 11.649629 9.183719 9.904275 11.108445
## [281] 9.791304 12.507152 10.853708 10.328175 12.991986 12.037280 8.488311
## [288] 9.956862 12.215851 10.496268 9.795027 8.579310 10.969953 11.214230
## [295] 9.015577 7.568631 11.142255 11.199421 8.160060 10.121936 9.716368
## [302] 8.639236 9.222355 10.168601 9.710971 10.210685 8.223630 8.402594
## [309] 8.941203 10.087638 10.416331 8.006993 10.609145 10.902514 8.891210
## [316] 11.644846 9.864319 10.981086 10.470505 5.970141 10.132353 12.066213
## [323] 7.646080 9.845150 8.855918 8.810322 11.953430 10.578748 9.120276
## [330] 10.321347 9.935359 10.443771 9.427451 8.969488 9.683240 10.133429
## [337] 11.478430 8.905310 9.197546 10.263742 11.240898 11.918584 10.976689
## [344] 10.148683 9.344365 11.351561 8.258376 10.436985 10.259799 11.756580
## [351] 13.231096 8.611064 8.485799 11.234891 11.811525 11.193468 10.566011
## [358] 10.199050 11.671322 10.382145 10.646544 9.143901 11.631043 10.792654
## [365] 11.247553 9.968890 9.061755 13.487286 10.872045 10.759334 11.265467
## [372] 10.187659 9.613908 9.293601 10.920138 8.465347 12.485995 11.444308
## [379] 8.952600 8.319204 8.783507 11.644908 10.805600 9.460759 12.243261
## [386] 9.648155 9.710594 10.355361 9.429137 9.311973 9.456671 9.792069
## [393] 11.331636 10.296542 9.273401 9.495275 9.312412 7.903524 12.740962
## [400] 11.292401 8.496637 10.238764 9.387828 9.989543 10.484603 8.117885
## [407] 8.598078 7.881863 11.817668 9.188350 10.454189 11.017408 9.203277
## [414] 11.790521 10.580694 9.151308 9.508735 9.953474 11.648079 9.766623
## [421] 9.746793 14.125984 9.062812 9.093786 11.514893 9.251347 10.089108
## [428] 10.346711 8.864413 7.476873 11.051503 9.872299 10.572560 9.870005
## [435] 9.420738 8.929287 7.354173 12.395216 8.180989 8.515345 9.787178
## [442] 7.541471 10.765931 11.657514 10.602468 10.729973 10.585160 12.232231
## [449] 10.286677 11.508160 9.346587 6.900095 7.833641 9.336389 9.299367
## [456] 9.006955 9.079644 8.323343 7.671516 12.635052 9.895812 10.157234
## [463] 10.731041 9.033175 8.321139 8.599102 10.424110 11.224493 10.810104
## [470] 8.538251 9.421635 7.545463 9.855759 9.389395 9.771780 11.764845
## [477] 10.313209 11.519728 11.532286 10.298180 9.102949 10.673277 9.507400
## [484] 10.027527 9.001736 9.748892 10.511648 11.203580 7.736921 12.055085
## [491] 11.187460 8.629239 10.565413 9.482929 10.460369 10.882234 9.011925
## [498] 9.802983 9.317664 10.674292 8.914051 10.687780 10.848717 10.142867
## [505] 10.526016 11.269865 10.196727 7.831304 9.573548 8.929114 9.833303
## [512] 10.437599 9.426122 9.570107 9.777722 11.269118 11.504992 9.632226
## [519] 8.915231 9.242460 8.617729 10.402351 9.646076 10.828696 9.138549
## [526] 9.307227 8.301201 9.790534 10.016987 9.362249 10.515228 9.971570
## [533] 8.462087 9.542522 11.303971 11.975821 8.292499 11.429558 10.920586
## [540] 10.181982 7.050046 7.467770 10.692255 8.709334 7.817622 10.265599
## [547] 11.538511 10.180593 6.810624 7.450217 11.305160 12.299671 11.731279
## [554] 11.323876 9.613083 8.218513 9.460680 9.287316 10.928254 10.934533
## [561] 9.739560 9.321227 12.305381 8.617101 7.825448 9.505827 10.183059
## [568] 9.214365 9.484299 10.031861 9.308477 12.994322 8.517365 6.195398
## [575] 11.745982 11.557786 8.615114 9.079472 7.669773 9.679420 10.442340
## [582] 10.104407 10.633507 7.885585 8.775698 8.712617 8.159045 9.237422
## [589] 13.157305 8.133054 10.626233 9.668621 8.893848 10.271972 6.151951
## [596] 8.004368 5.573782 11.276755 11.628538 11.221920 9.912229 9.979735
## [603] 8.881402 11.885333 10.615009 11.223574 12.496979 11.047250 10.004565
## [610] 6.783518 9.578117 9.560775 10.064334 9.780033 8.260277 9.585861
## [617] 10.713068 10.213374 8.866621 11.724391 11.944886 12.154821 9.300528
## [624] 9.884967 9.917218 8.730692 10.068817 9.044606 11.085177 8.520640
## [631] 9.248731 8.892292 10.196532 9.578561 8.089182 11.866728 8.425290
## [638] 8.705251 10.152008 10.528637 7.924505 9.515210 10.656964 10.074095
## [645] 9.773296 12.653464 10.740055 10.780509 10.039008 10.901971 10.102928
## [652] 9.791358 10.503048 9.041625 11.559882 8.162128 11.348592 10.655467
## [659] 8.269740 5.636452 10.458425 8.763986 9.276993 8.272660 10.842370
## [666] 10.435161 10.639479 9.535979 11.809626 12.340023 10.218518 10.705843
## [673] 9.755214 11.588213 8.664631 8.662769 10.730605 7.845545 11.229735
## [680] 10.336399 9.418976 7.775643 10.684369 9.240709 9.259642 10.044383
## [687] 12.283522 9.105747 11.074378 9.199947 10.907456 10.599504 10.741537
## [694] 10.933206 10.352066 8.421155 8.577236 6.937556 6.714388 9.196153
## [701] 8.050341 11.007279 9.169395 11.121235 9.712279 10.946698 10.725676
## [708] 8.221735 12.125425 8.328009 13.335515 9.995661 8.714171 9.991026
## [715] 8.994569 10.526351 9.630893 9.710638 11.018728 11.000993 9.928009
## [722] 10.619520 10.555067 10.716664 8.773580 9.202677 9.287867 10.782624
## [729] 12.749107 9.701834 10.034987 10.599686 9.463782 8.932511 13.900336
## [736] 10.636148 9.416227 10.753809 6.820454 7.081683 11.016251 10.322426
## [743] 8.423540 9.886447 7.404879 9.221767 11.548995 9.661386 10.528383
## [750] 12.647914 10.185613 10.126399 12.123633 11.960577 10.946945 10.502471
## [757] 9.525026 11.511243 10.027917 8.781929 8.850079 7.454801 8.657842
## [764] 9.103546 9.894381 11.474208 7.045449 11.624869 9.739154 11.176716
## [771] 11.791820 9.562302 10.261262 10.093164 8.646884 10.880643 10.458081
## [778] 9.874506 9.316668 9.452255 11.575891 9.865737 11.234724 8.323238
## [785] 10.568493 12.253681 7.164357 10.830505 10.046062 10.156462 8.984396
## [792] 7.066213 10.255289 10.473874 9.056138 11.349230 10.191731 10.155980
## [799] 9.365147 12.023196 12.762360 9.847179 9.047819 9.619459 10.961039
## [806] 8.833970 10.160492 9.247590 11.482467 12.751812 10.036136 10.862830
## [813] 8.879963 11.192393 10.308868 10.538003 9.977676 9.030002 9.171539
## [820] 10.469054 8.793721 8.363003 9.946520 10.131828 9.792111 8.009001
## [827] 10.832898 8.063563 10.223027 10.356820 8.879845 8.694306 10.587912
## [834] 11.640776 9.270999 11.847172 8.771181 10.374984 10.625844 11.466886
## [841] 9.588701 10.591316 9.123177 9.713419 8.412168 8.212848 10.099760
## [848] 10.753719 9.896123 10.166165 9.239637 9.672952 10.506769 10.119877
## [855] 9.322932 9.251234 10.542353 10.237556 7.545981 7.569029 9.129448
## [862] 8.193194 10.388631 9.100610 9.028805 8.817724 9.664374 10.823007
## [869] 9.952578 12.309893 9.995596 9.706025 10.620885 7.990322 9.681915
## [876] 8.390979 6.592356 11.475712 6.905249 12.864397 9.447940 8.705479
## [883] 8.704129 8.422429 9.575632 9.348573 9.751189 11.269724 10.915746
## [890] 10.129732 10.498727 9.102381 8.197206 12.687056 9.523146 12.486343
## [897] 9.486853 9.369335 10.269033 10.387574 8.640959 8.392967 7.929035
## [904] 10.247707 10.144272 9.000007 8.316906 9.848845 10.813193 8.092104
## [911] 11.338022 7.771764 11.076424 8.046193 8.284352 7.710484 10.320974
## [918] 9.459052 9.001695 7.919460 10.052615 8.935708 8.034828 9.513697
## [925] 9.796135 12.150492 11.364774 8.896390 9.840744 11.170569 9.259643
## [932] 9.729197 8.629687 10.316767 8.244609 10.962467 11.748269 11.834186
## [939] 10.522091 11.089672 9.474423 8.052357 10.064427 8.814902 11.959787
## [946] 9.464402 10.454899 10.581217 11.706605 9.916064 9.858317 8.810747
## [953] 8.834629 9.920361 10.123302 10.234505 15.680570 9.903128 8.738286
## [960] 8.765967 9.405496 11.863345 8.361876 9.616801 8.817522 10.529861
## [967] 9.752964 9.888208 9.483509 11.197902 9.209518 8.639374 10.742357
## [974] 11.803449 10.696162 8.098121 9.638224 8.691978 9.492734 9.779748
## [981] 8.530997 10.891975 10.316937 7.929715 9.531347 9.880865 12.012456
## [988] 7.606808 9.258915 13.449103 10.412024 10.757834 7.289370 11.512605
## [995] 8.174937 9.579570 8.351182 9.490471 11.656934 10.362739
# 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 3 2 4 3 2 2 4 4 3 2 4 5 5 3 3 3 3 4 4 3 5 4 4 3 3 5 5 1 1 3 2 2 3 1 4 4
## [38] 4 3 4 3 2 4 4 3 3 3 5 3 3 3 5 2 3 3 3 3 4 3 4 4 4 3 3 2 4 3 2 3 2 4 4 4 3
## [75] 2 2 3 5 4 4 3 3 2 2 3 4 2 2 3 2 5 3 2 4 3 4 4 4 4 2 3 5 5 3 2 2 3 5 4 4 3
## [112] 3 4 2 4 3 3 1 2 2 5 2 2 4 4 5 2 2 4 2 3 2 2 4 3 3 2 4 2 3 2 3 4 3 3 2 1 2
## [149] 1 4 2 4 3 4 4 4 3 3 3 3 5 4 5 3 2 4 3 3 1 2 3 3 4 2 1 3 3 4 3 4 4 4 5 3 4
## [186] 4 3 4 4 4 2 1 4 2 2 2 3 4 1 4 2 2 2 3 2 4 2 3 3 2 2 3 2 2 4 3 4 4 3 2 3 4
## [223] 1 4 4 3 3 1 3 3 5 5 3 1 3 5 4 2 3 5 2 2 4 4 4 3 2 4 3 2 3 2 4 2 2 4 2 3 3
## [260] 5 2 2 3 3 4 3 4 2 3 3 4 4 2 4 2 2 4 3 4 4 3 3 3 4 5 4 3 2 3 3 3 1 3 2 2 3
## [297] 5 2 2 4 2 1 2 4 2 2 2 2 3 3 2 4 3 4 3 4 2 3 4 1 3 5 2 3 3 3 5 3 3 2 4 4 3
## [334] 2 3 3 4 1 3 3 4 3 3 3 3 4 2 4 4 3 5 3 2 4 4 4 3 3 3 4 3 3 4 4 4 2 3 5 2 3
## [371] 3 2 2 2 4 3 4 4 4 3 1 5 3 2 4 3 3 2 3 2 4 3 4 3 2 3 2 2 4 4 2 4 3 4 2 2 2
## [408] 1 5 4 2 4 4 3 5 3 2 3 4 3 3 5 2 3 3 4 2 3 2 1 4 2 3 3 4 2 2 5 1 2 3 2 4 3
## [445] 4 3 3 3 3 5 3 1 2 2 3 1 3 2 3 4 3 3 2 3 1 2 4 4 3 2 4 2 2 4 2 3 3 3 4 3 2
## [482] 3 2 3 3 3 3 4 2 4 4 4 2 3 3 4 2 4 3 3 2 4 4 3 4 3 3 2 4 3 3 3 2 2 3 3 3 2
## [519] 2 2 2 2 4 4 3 1 2 3 2 1 4 2 2 2 2 4 2 4 3 3 2 1 3 2 2 4 3 3 1 2 4 4 3 3 2
## [556] 2 4 3 5 3 3 3 5 3 4 3 2 1 3 3 3 5 3 1 3 3 1 2 2 2 2 4 4 3 3 2 2 3 5 1 4 2
## [593] 3 4 1 3 1 4 3 3 3 4 3 2 3 4 5 4 3 2 3 3 2 3 2 2 4 2 4 4 4 4 1 3 3 1 4 2 4
## [630] 3 2 3 3 1 1 4 2 1 2 3 2 3 2 4 4 5 4 4 4 3 3 3 3 4 3 4 3 4 3 1 3 1 3 3 4 4
## [667] 3 2 4 4 4 3 4 4 3 1 3 2 4 4 3 3 3 2 3 2 4 2 3 2 4 2 3 3 2 2 2 1 1 3 2 3 3
## [704] 5 3 4 3 2 5 2 5 3 1 3 3 3 3 4 3 2 4 3 4 4 3 4 3 4 3 3 3 4 2 3 5 4 2 3 1 1
## [741] 5 3 1 3 1 2 4 2 3 5 3 2 4 3 4 4 3 4 3 2 2 3 3 2 2 4 1 4 4 4 4 3 3 2 3 4 2
## [778] 4 2 2 4 1 4 2 2 5 1 4 2 2 2 2 3 1 3 4 3 4 2 5 4 5 3 1 4 1 3 3 3 3 4 4 3 3
## [815] 2 3 3 2 3 2 2 2 2 2 4 2 5 3 4 2 2 2 4 3 3 2 2 3 3 3 3 5 3 3 2 2 3 2 3 3 2
## [852] 2 3 3 3 3 5 4 2 2 3 3 3 2 3 2 3 3 3 5 4 3 3 2 3 2 1 3 1 4 4 3 2 2 3 4 3 3
## [889] 4 4 4 2 2 5 2 4 3 3 3 3 2 1 2 2 2 2 3 2 3 2 4 2 3 1 2 1 2 2 2 3 3 3 2 2 3
## [926] 5 4 3 4 4 2 2 3 3 3 4 3 4 3 4 2 2 2 3 5 3 3 4 3 2 3 2 3 2 2 3 5 1 1 2 2 5
## [963] 1 2 2 3 3 3 2 4 3 2 5 4 3 3 2 3 2 2 2 3 3 3 4 4 3 1 3 4 3 4 1 3 3 3 1 3 5
## [1000] 4
# create sample dataframe by joining variables
sample_data <- data.frame(xAxis,yAxis,group)
sample_data
## xAxis yAxis group
## 1 0.0297457221 9.453977 3
## 2 -0.3719067033 9.239074 3
## 3 -0.8253756307 8.934578 2
## 4 1.2635609301 12.525743 4
## 5 0.3373823884 9.509183 3
## 6 -1.1221804025 9.284195 2
## 7 -1.2463666417 8.685609 2
## 8 0.6475856235 10.270069 4
## 9 1.0462736522 11.451555 4
## 10 0.1527480433 10.495430 3
## 11 -0.6534576853 9.532087 2
## 12 1.4887153715 11.051496 4
## 13 2.5621314625 12.743650 5
## 14 1.5528798009 13.741138 5
## 15 0.4994329011 10.267638 3
## 16 0.0948725504 10.099549 3
## 17 -0.1323002499 10.117929 3
## 18 0.4523541675 11.631696 3
## 19 1.2768456677 9.729594 4
## 20 0.6987960709 8.741232 4
## 21 0.0417965938 9.945405 3
## 22 1.6388258527 10.908883 5
## 23 1.4461354553 11.670314 4
## 24 1.0818587624 9.433225 4
## 25 0.0544531075 8.747644 3
## 26 0.1074881178 9.799313 3
## 27 3.2339407154 13.151943 5
## 28 1.8234381284 13.609822 5
## 29 -3.2454524858 5.681295 1
## 30 -1.9548676897 7.955493 1
## 31 -0.1481428193 11.249553 3
## 32 -0.6954619085 10.535224 2
## 33 -0.8007918073 9.250472 2
## 34 -0.3908060212 10.561307 3
## 35 -1.8754739715 7.244991 1
## 36 1.2584700185 11.222024 4
## 37 1.3151973251 12.749457 4
## 38 0.5317531269 11.434454 4
## 39 -0.0580682687 9.936051 3
## 40 0.6921051899 11.302762 4
## 41 0.2346195712 12.024915 3
## 42 -0.8610615238 10.602074 2
## 43 0.6633136609 10.144157 4
## 44 0.6744063999 11.189969 4
## 45 0.3269978108 10.720416 3
## 46 -0.4877755023 10.515124 3
## 47 -0.1137583627 8.094872 3
## 48 2.6781180839 11.555110 5
## 49 0.1278014106 10.255558 3
## 50 -0.3655744141 9.538774 3
## 51 -0.4882098827 8.715178 3
## 52 2.1734367878 12.185025 5
## 53 -0.5429270303 10.507931 2
## 54 -0.0193129758 8.258384 3
## 55 0.3712298181 11.140384 3
## 56 0.0458684303 10.690920 3
## 57 0.4391368022 9.876167 3
## 58 0.5887800685 10.669613 4
## 59 -0.1299347125 10.662628 3
## 60 0.5530494818 9.608914 4
## 61 0.5919736062 11.903545 4
## 62 1.3471604490 12.574498 4
## 63 0.3072489895 9.556155 3
## 64 -0.3194096380 8.174616 3
## 65 -0.6597028712 12.161662 2
## 66 0.7702126840 9.921142 4
## 67 0.3277050136 10.430268 3
## 68 -0.9209661583 10.370481 2
## 69 -0.4250160318 8.788717 3
## 70 -0.7106530330 9.125051 2
## 71 0.9478641981 9.933326 4
## 72 1.1902588180 12.232248 4
## 73 1.1128440764 12.181600 4
## 74 0.4792808503 8.042256 3
## 75 -0.6179437288 8.592068 2
## 76 -0.8940206173 10.188963 2
## 77 0.4394460577 12.626654 3
## 78 1.7830676794 11.715015 5
## 79 0.6673078420 11.808902 4
## 80 0.8616651994 9.991671 4
## 81 0.2115773632 9.864414 3
## 82 0.1174122692 9.765506 3
## 83 -0.5348682957 9.670371 2
## 84 -1.2609639858 8.015212 2
## 85 0.3004198304 11.190050 3
## 86 0.8909041637 11.032440 4
## 87 -0.6307066462 8.660235 2
## 88 -1.1581841792 8.293803 2
## 89 -0.3813005073 8.573428 3
## 90 -0.6356241906 11.332472 2
## 91 1.7560205216 10.538030 5
## 92 0.3748407752 8.960662 3
## 93 -1.0016321722 7.177706 2
## 94 0.8815161214 12.120763 4
## 95 -0.4446169157 9.275724 3
## 96 0.8056973043 12.223843 4
## 97 0.9596901829 10.798274 4
## 98 0.8839995274 10.250420 4
## 99 1.1058517812 12.396547 4
## 100 -1.3385581906 8.011546 2
## 101 0.2915704470 9.243631 3
## 102 2.0975267880 13.087319 5
## 103 2.4509721113 10.814846 5
## 104 0.1523230095 9.630633 3
## 105 -0.8194487876 8.573007 2
## 106 -0.7872714077 9.508982 2
## 107 0.4300525068 11.159354 3
## 108 2.6854367340 13.036523 5
## 109 0.7053059859 12.082295 4
## 110 1.4962929491 11.416104 4
## 111 0.3158209939 12.186637 3
## 112 -0.2913716149 7.363956 3
## 113 1.2130750140 10.535066 4
## 114 -1.2368420390 8.742677 2
## 115 0.8569946197 9.464906 4
## 116 0.2882919879 9.793261 3
## 117 -0.1584598250 9.190397 3
## 118 -1.5852201440 9.444054 1
## 119 -1.2602463461 8.432466 2
## 120 -0.8132881969 8.640899 2
## 121 1.7349570309 11.090482 5
## 122 -1.3992372867 7.005171 2
## 123 -0.6953358783 10.077834 2
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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)
