# Mindanao State University
# General Santos City
#Submitted by: Roland Fritz C. Adam
# Lab Exercise 1: How to create Lines in with different styles in R
# Step 1: Create Data
x <- 1:10 # Create example data
y <- c(3, 1, 5, 2, 3, 8, 4, 7, 6, 9) # using the c() function to create an
array
## function (data = NA, dim = length(data), dimnames = NULL)
## {
## if (is.atomic(data) && !is.object(data))
## return(.Internal(array(data, dim, dimnames)))
## data <- as.vector(data)
## if (is.object(data)) {
## dim <- as.integer(dim)
## if (!length(dim))
## stop("'dim' cannot be of length 0")
## vl <- prod(dim)
## if (length(data) != vl) {
## if (vl > .Machine$integer.max)
## stop("'dim' specifies too large an array")
## data <- rep_len(data, vl)
## }
## if (length(dim))
## dim(data) <- dim
## if (is.list(dimnames) && length(dimnames))
## dimnames(data) <- dimnames
## data
## }
## else .Internal(array(data, dim, dimnames))
## }
## <bytecode: 0x557583a2f640>
## <environment: namespace:base>
# Step 2: Plot the line graph using the base plot() command
plot(x, y, type = "l")
# Step 3: Add Main Title & Change Axis Labels
plot(x, y, type = "l",
main = "Hello: This is my Line Plot",
xlab = "My X-Values",
ylab = "My Y-Values")
# Step 4: Add color to the line using the col command
plot(x, y, type = "l",
main = "This is my Line Plot",
xlab = "My X-Values",
ylab = "My Y-Values",
col = "red")
# Step 5: Modify Thickness of Line using lwd command
plot(x, y, type = "l",
main = "This is my Line Plot",
xlab = "My X-Values",
ylab = "My Y-Values",
lwd=7,
col = "green")
# Step 6: Add points to line graph by changing the type command
plot(x, y, type = "b",
main = "This is my Line Plot",
xlab = "My X-Values",
ylab = "My Y-Values",
lwd=3,
col = "blue")
# Lab Exercise 2: How to create Lines in with different styles in R
# Step1: Assign values for different lines. We enclose the entire
# line with parenthesis symbol to force R to display the results instantly
# set the same value for the x variable
(x <- 1:10)
## [1] 1 2 3 4 5 6 7 8 9 10
# set different values for y variables
(y1 <- c(3, 1, 5, 2, 3, 8, 4, 7, 6, 9))
## [1] 3 1 5 2 3 8 4 7 6 9
(y2 <- c(5, 1, 4, 6, 2, 3, 7, 8, 2, 8))
## [1] 5 1 4 6 2 3 7 8 2 8
(y3 <- c(3, 3, 3, 3, 4, 4, 5, 5, 7, 7))
## [1] 3 3 3 3 4 4 5 5 7 7
# Plot first the pair x and y1.
plot(x, y1, type = "b",
main = "This is my Line Plot",
xlab = "My X-Values",
ylab = "My Y-Values",
lwd=3,
col = "blue")
# then Add the two lines (for x,y2) and (x,y3)
lines(x, y2, type = "b", col = "red",lwd=3)
lines(x, y3, type = "b", col = "green",lwd=3)
# Add legend to the plot
legend("topleft",
legend = c("Line y1", "Line y2", "Line y3"),
col = c("blue", "red", "green"),
lty = 1)
# Step 2: Create Different Point Symbol for Each
# Line using the pch command
plot(x, y1, type = "b",pch = 16,
main = "This is my Line Plot",
xlab = "My X-Values",
ylab = "My Y-Values",
lwd=3,
col = "blue")
lines(x, y2, type = "b", col = "red",lwd=3, pch = 15)
lines(x, y3, type = "b", col = "green",lwd=3, pch = 8)
# Add legend
legend("topleft",
legend = c("Line y1", "Line y2", "Line y3"),
col = c("black", "red", "green"),
lty = 1)
# Lab Exercise 3: Create Line graph without x values
Pupils <- c(3.55 ,3.54 ,3.53 ,3.61 ,3.65 ,3.63 ,3.61
,3.61 ,3.59 ,3.63 ,3.59 ,3.63 ,3.62 ,3.62
,3.59 ,3.63 ,3.62 ,3.65 ,3.65)
# get number of elements of Pupils
length(Pupils)
## [1] 19
# Display the elements of Pupils
Pupils
## [1] 3.55 3.54 3.53 3.61 3.65 3.63 3.61 3.61 3.59 3.63 3.59 3.63 3.62 3.62 3.59
## [16] 3.63 3.62 3.65 3.65
# You can obtain the plot without x values
plot(Pupils, type = 'o')
# Lab Exercise 4: How to Create vertical, horizontal lines
# We will use buit-in cars dataset in R
# display the cars dataset
cars
## speed dist
## 1 4 2
## 2 4 10
## 3 7 4
## 4 7 22
## 5 8 16
## 6 9 10
## 7 10 18
## 8 10 26
## 9 10 34
## 10 11 17
## 11 11 28
## 12 12 14
## 13 12 20
## 14 12 24
## 15 12 28
## 16 13 26
## 17 13 34
## 18 13 34
## 19 13 46
## 20 14 26
## 21 14 36
## 22 14 60
## 23 14 80
## 24 15 20
## 25 15 26
## 26 15 54
## 27 16 32
## 28 16 40
## 29 17 32
## 30 17 40
## 31 17 50
## 32 18 42
## 33 18 56
## 34 18 76
## 35 18 84
## 36 19 36
## 37 19 46
## 38 19 68
## 39 20 32
## 40 20 48
## 41 20 52
## 42 20 56
## 43 20 64
## 44 22 66
## 45 23 54
## 46 24 70
## 47 24 92
## 48 24 93
## 49 24 120
## 50 25 85
# get the number of rows and columns using dim() command
dim(cars)
## [1] 50 2
# display the variable names of the cars dataset
names(cars)
## [1] "speed" "dist"
# display only the first column of the dataset
cars$speed # using the column name
## [1] 4 4 7 7 8 9 10 10 10 11 11 12 12 12 12 13 13 13 13 14 14 14 14 15 15
## [26] 15 16 16 17 17 17 18 18 18 18 19 19 19 20 20 20 20 20 22 23 24 24 24 24 25
cars[,1] # using the column number
## [1] 4 4 7 7 8 9 10 10 10 11 11 12 12 12 12 13 13 13 13 14 14 14 14 15 15
## [26] 15 16 16 17 17 17 18 18 18 18 19 19 19 20 20 20 20 20 22 23 24 24 24 24 25
# Remarks: the following commands will give you the same result
plot(cars,) # using the comma after the name
plot(cars[,1],cars[,2]) # using the column index 1 and 2
attach(cars); plot(speed,dist) # using the attach command to load the variables
plot(cars$speed,cars$dist) # using the dollar notation
# combine all 4 plots using the par() command
par(mfrow = c(2,2)) # set a 2x2 plot output
plot(cars,) # using the comma after the name
plot(cars[,1],cars[,2]) # using the column index 1 and 2
attach(cars); plot(speed,dist) # using the attach command to load the variables
## The following objects are masked from cars (pos = 3):
##
## dist, speed
plot(cars$speed,cars$dist) # using the dollar notation
par(mfrow = c(1,1)) # reset to default plot setting
# Problem: Create vertical lines using the v command
plot(cars)
abline(v = 15, col = "darkgreen",lwd=3) # vertical line
abline(v = 10, col = "blue",lwd=3) # vertical line
# Problem: Create horizontal lines using the h command
abline(h = 80, col = "darkgreen",lwd=3) # vertical line
abline(h = 20, col = "blue",lwd=3) # vertical line
# Create more lines simultaneously, using a vector of values
plot(cars)
abline(v = c(9, 22,25), col = c("darkgreen", "blue","red"),
lwd = c(1, 3,2), # line thickness
lty = c(2,2,2)) # dashed lines
plot(cars)
abline(v = c(9, 22,25), col = c("darkgreen", "blue","red"),
lwd = c(1, 3,2)) # line thickness and solid lines
# create horizontal lines
plot(cars)
abline(h = 60, col = "red",lty = 1, lwd = 3)
abline(h = 100, col = "red",lty = 2, lwd = 3)
abline(h = 20, col = "red",lty = 3, lwd = 3)
# Lab Exercise 5: How to Plot data by group
# We will use buit-in iris dataset in R
# this dataset is a collection of 4 species of flowers with different
# sepal length and width and also with different petal length and width
dim(iris) # iris dataset has 150 rows and 5 columns
## [1] 150 5
names(iris)
## [1] "Sepal.Length" "Sepal.Width" "Petal.Length" "Petal.Width" "Species"
# two different commands to get the frequency table
table(iris$Species) # refer to the dataset by variable name
##
## setosa versicolor virginica
## 50 50 50
table(iris[,5]) # refer to the dataset by column number
##
## setosa versicolor virginica
## 50 50 50
# get summary of all columns
summary(iris)
## Sepal.Length Sepal.Width Petal.Length Petal.Width
## Min. :4.300 Min. :2.000 Min. :1.000 Min. :0.100
## 1st Qu.:5.100 1st Qu.:2.800 1st Qu.:1.600 1st Qu.:0.300
## Median :5.800 Median :3.000 Median :4.350 Median :1.300
## Mean :5.843 Mean :3.057 Mean :3.758 Mean :1.199
## 3rd Qu.:6.400 3rd Qu.:3.300 3rd Qu.:5.100 3rd Qu.:1.800
## Max. :7.900 Max. :4.400 Max. :6.900 Max. :2.500
## Species
## setosa :50
## versicolor:50
## virginica :50
##
##
##
#create scatterplot of sepal width vs. sepal length
plot(iris$Sepal.Width, iris$Sepal.Length,
col='steelblue',
main='Scatterplot',
xlab='Sepal Width',
ylab='Sepal Length',
pch=19)
plot(iris$Sepal.Width, iris$Sepal.Length,
col='steelblue',
main='Scatterplot',
xlab='Sepal Width',
ylab='Sepal Length',
pch=1)
# another way to retrieve columns of data
PL <- iris$Petal.Length
PW <- iris$Petal.Width
plot(PL, PW)
# add color by species
plot(PL, PW, col = iris$Species, main= "My Plot")
# draw a line along with the distribution of points
# using the abline and lm commands
abline(lm(PW ~ PL))
# add text annotation
text(5, 0.5, "Regression Line")
legend("topleft", # specify the location of the legend
levels(iris$Species), # specify the levels of species
pch = 1:3, # specify three symbols used for the three species
col = 1:3 # specify three colors for the three species
)
# Lab Exercise 6: Generate advance scatter plot
pairs(iris, col = rainbow(3)[iris$Species]) # set colors by species
# How to filter data for each flower
(Versicolor <- subset(iris, Species == "versicolor"))
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 51 7.0 3.2 4.7 1.4 versicolor
## 52 6.4 3.2 4.5 1.5 versicolor
## 53 6.9 3.1 4.9 1.5 versicolor
## 54 5.5 2.3 4.0 1.3 versicolor
## 55 6.5 2.8 4.6 1.5 versicolor
## 56 5.7 2.8 4.5 1.3 versicolor
## 57 6.3 3.3 4.7 1.6 versicolor
## 58 4.9 2.4 3.3 1.0 versicolor
## 59 6.6 2.9 4.6 1.3 versicolor
## 60 5.2 2.7 3.9 1.4 versicolor
## 61 5.0 2.0 3.5 1.0 versicolor
## 62 5.9 3.0 4.2 1.5 versicolor
## 63 6.0 2.2 4.0 1.0 versicolor
## 64 6.1 2.9 4.7 1.4 versicolor
## 65 5.6 2.9 3.6 1.3 versicolor
## 66 6.7 3.1 4.4 1.4 versicolor
## 67 5.6 3.0 4.5 1.5 versicolor
## 68 5.8 2.7 4.1 1.0 versicolor
## 69 6.2 2.2 4.5 1.5 versicolor
## 70 5.6 2.5 3.9 1.1 versicolor
## 71 5.9 3.2 4.8 1.8 versicolor
## 72 6.1 2.8 4.0 1.3 versicolor
## 73 6.3 2.5 4.9 1.5 versicolor
## 74 6.1 2.8 4.7 1.2 versicolor
## 75 6.4 2.9 4.3 1.3 versicolor
## 76 6.6 3.0 4.4 1.4 versicolor
## 77 6.8 2.8 4.8 1.4 versicolor
## 78 6.7 3.0 5.0 1.7 versicolor
## 79 6.0 2.9 4.5 1.5 versicolor
## 80 5.7 2.6 3.5 1.0 versicolor
## 81 5.5 2.4 3.8 1.1 versicolor
## 82 5.5 2.4 3.7 1.0 versicolor
## 83 5.8 2.7 3.9 1.2 versicolor
## 84 6.0 2.7 5.1 1.6 versicolor
## 85 5.4 3.0 4.5 1.5 versicolor
## 86 6.0 3.4 4.5 1.6 versicolor
## 87 6.7 3.1 4.7 1.5 versicolor
## 88 6.3 2.3 4.4 1.3 versicolor
## 89 5.6 3.0 4.1 1.3 versicolor
## 90 5.5 2.5 4.0 1.3 versicolor
## 91 5.5 2.6 4.4 1.2 versicolor
## 92 6.1 3.0 4.6 1.4 versicolor
## 93 5.8 2.6 4.0 1.2 versicolor
## 94 5.0 2.3 3.3 1.0 versicolor
## 95 5.6 2.7 4.2 1.3 versicolor
## 96 5.7 3.0 4.2 1.2 versicolor
## 97 5.7 2.9 4.2 1.3 versicolor
## 98 6.2 2.9 4.3 1.3 versicolor
## 99 5.1 2.5 3.0 1.1 versicolor
## 100 5.7 2.8 4.1 1.3 versicolor
(Setosa <- subset(iris, Species == "setosa"))
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa
## 7 4.6 3.4 1.4 0.3 setosa
## 8 5.0 3.4 1.5 0.2 setosa
## 9 4.4 2.9 1.4 0.2 setosa
## 10 4.9 3.1 1.5 0.1 setosa
## 11 5.4 3.7 1.5 0.2 setosa
## 12 4.8 3.4 1.6 0.2 setosa
## 13 4.8 3.0 1.4 0.1 setosa
## 14 4.3 3.0 1.1 0.1 setosa
## 15 5.8 4.0 1.2 0.2 setosa
## 16 5.7 4.4 1.5 0.4 setosa
## 17 5.4 3.9 1.3 0.4 setosa
## 18 5.1 3.5 1.4 0.3 setosa
## 19 5.7 3.8 1.7 0.3 setosa
## 20 5.1 3.8 1.5 0.3 setosa
## 21 5.4 3.4 1.7 0.2 setosa
## 22 5.1 3.7 1.5 0.4 setosa
## 23 4.6 3.6 1.0 0.2 setosa
## 24 5.1 3.3 1.7 0.5 setosa
## 25 4.8 3.4 1.9 0.2 setosa
## 26 5.0 3.0 1.6 0.2 setosa
## 27 5.0 3.4 1.6 0.4 setosa
## 28 5.2 3.5 1.5 0.2 setosa
## 29 5.2 3.4 1.4 0.2 setosa
## 30 4.7 3.2 1.6 0.2 setosa
## 31 4.8 3.1 1.6 0.2 setosa
## 32 5.4 3.4 1.5 0.4 setosa
## 33 5.2 4.1 1.5 0.1 setosa
## 34 5.5 4.2 1.4 0.2 setosa
## 35 4.9 3.1 1.5 0.2 setosa
## 36 5.0 3.2 1.2 0.2 setosa
## 37 5.5 3.5 1.3 0.2 setosa
## 38 4.9 3.6 1.4 0.1 setosa
## 39 4.4 3.0 1.3 0.2 setosa
## 40 5.1 3.4 1.5 0.2 setosa
## 41 5.0 3.5 1.3 0.3 setosa
## 42 4.5 2.3 1.3 0.3 setosa
## 43 4.4 3.2 1.3 0.2 setosa
## 44 5.0 3.5 1.6 0.6 setosa
## 45 5.1 3.8 1.9 0.4 setosa
## 46 4.8 3.0 1.4 0.3 setosa
## 47 5.1 3.8 1.6 0.2 setosa
## 48 4.6 3.2 1.4 0.2 setosa
## 49 5.3 3.7 1.5 0.2 setosa
## 50 5.0 3.3 1.4 0.2 setosa
(Virginica <- subset(iris, Species == "virginica"))
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 101 6.3 3.3 6.0 2.5 virginica
## 102 5.8 2.7 5.1 1.9 virginica
## 103 7.1 3.0 5.9 2.1 virginica
## 104 6.3 2.9 5.6 1.8 virginica
## 105 6.5 3.0 5.8 2.2 virginica
## 106 7.6 3.0 6.6 2.1 virginica
## 107 4.9 2.5 4.5 1.7 virginica
## 108 7.3 2.9 6.3 1.8 virginica
## 109 6.7 2.5 5.8 1.8 virginica
## 110 7.2 3.6 6.1 2.5 virginica
## 111 6.5 3.2 5.1 2.0 virginica
## 112 6.4 2.7 5.3 1.9 virginica
## 113 6.8 3.0 5.5 2.1 virginica
## 114 5.7 2.5 5.0 2.0 virginica
## 115 5.8 2.8 5.1 2.4 virginica
## 116 6.4 3.2 5.3 2.3 virginica
## 117 6.5 3.0 5.5 1.8 virginica
## 118 7.7 3.8 6.7 2.2 virginica
## 119 7.7 2.6 6.9 2.3 virginica
## 120 6.0 2.2 5.0 1.5 virginica
## 121 6.9 3.2 5.7 2.3 virginica
## 122 5.6 2.8 4.9 2.0 virginica
## 123 7.7 2.8 6.7 2.0 virginica
## 124 6.3 2.7 4.9 1.8 virginica
## 125 6.7 3.3 5.7 2.1 virginica
## 126 7.2 3.2 6.0 1.8 virginica
## 127 6.2 2.8 4.8 1.8 virginica
## 128 6.1 3.0 4.9 1.8 virginica
## 129 6.4 2.8 5.6 2.1 virginica
## 130 7.2 3.0 5.8 1.6 virginica
## 131 7.4 2.8 6.1 1.9 virginica
## 132 7.9 3.8 6.4 2.0 virginica
## 133 6.4 2.8 5.6 2.2 virginica
## 134 6.3 2.8 5.1 1.5 virginica
## 135 6.1 2.6 5.6 1.4 virginica
## 136 7.7 3.0 6.1 2.3 virginica
## 137 6.3 3.4 5.6 2.4 virginica
## 138 6.4 3.1 5.5 1.8 virginica
## 139 6.0 3.0 4.8 1.8 virginica
## 140 6.9 3.1 5.4 2.1 virginica
## 141 6.7 3.1 5.6 2.4 virginica
## 142 6.9 3.1 5.1 2.3 virginica
## 143 5.8 2.7 5.1 1.9 virginica
## 144 6.8 3.2 5.9 2.3 virginica
## 145 6.7 3.3 5.7 2.5 virginica
## 146 6.7 3.0 5.2 2.3 virginica
## 147 6.3 2.5 5.0 1.9 virginica
## 148 6.5 3.0 5.2 2.0 virginica
## 149 6.2 3.4 5.4 2.3 virginica
## 150 5.9 3.0 5.1 1.8 virginica
# Draw boxplot for each type of flower
boxplot(Versicolor[,1:4], main="Versicolor, Rainbow Palette",ylim =
c(0,8),las=2, col=rainbow(4))
boxplot(Setosa[,1:4], main="Setosa, Heat color Palette",ylim = c(0,8),las=2,
col=heat.colors(4))
boxplot(Virginica[,1:4], main="Virginica, Topo colors Palette",ylim =
c(0,8),las=2, col=topo.colors(4))
# Lab Exercise 7: How to load external datasets
# From a local directory
# the folder that contains the file should be specified completely
# using the forward slash symbol instead of the backward splash
folder <- "C:\\Users\\Administrator\\Documents\\MAT108.R"
filename <- "Cancer.csv"
(file <- paste0(folder,"/",filename))
## [1] "C:\\Users\\Administrator\\Documents\\MAT108.R/Cancer.csv"
library(readr)
cancer <- read_csv("Cancer.csv")
## Rows: 173 Columns: 17
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): country, continent
## dbl (15): incomeperperson, alcconsumption, armedforcesrate, breastcancer, co...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
dim(cancer)
## [1] 173 17
names(cancer)
## [1] "country" "incomeperperson" "alcconsumption"
## [4] "armedforcesrate" "breastcancer" "co2emissions"
## [7] "femaleemployrate" "hivrate" "internetuserate"
## [10] "lifeexpectancy" "oilperperson" "polityscore"
## [13] "relectricperperson" "suicideper100th" "employrate"
## [16] "urbanrate" "continent"
# compute mean value for every continent
(means <- round(tapply(cancer$breastcancer, cancer$continent, mean),
digits=2))
## AF AS EE LATAM NORAM OC WE
## 24.02 24.51 49.44 36.70 71.73 45.80 74.80
# draw boxplot per continent
boxplot(cancer$breastcancer ~ cancer$continent, main= "Breast cancerbycontinent (brown dot = mean value)", xlab="continents", ylab="new
cases per 100,00 residents", col=rainbow(7))
# insert the mean value using brown dot
points(means, col="brown", pch=18)
# Lab Exercise 8: How to load external datasets and change data layout
folder <- "C:/home/student/Downloads"
filename <- "hsb2.csv"
(file <- paste0(folder,"/",filename))
## [1] "C:/home/student/Downloads/hsb2.csv"
library(readr)
hsb2 <- read_csv("hsb2.csv")
## New names:
## Rows: 200 Columns: 12
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "," dbl
## (12): ...1, id, female, race, ses, schtyp, prog, read, write, math, scie...
## ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## • `` -> `...1`
# display only the top 6 rows
head(hsb2)
## # A tibble: 6 × 12
## ...1 id female race ses schtyp prog read write math science socst
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 70 0 4 1 1 1 57 52 41 47 57
## 2 2 121 1 4 2 1 3 68 59 53 63 61
## 3 3 86 0 4 3 1 1 44 33 54 58 31
## 4 4 141 0 4 3 1 3 63 44 47 53 56
## 5 5 172 0 4 2 1 2 47 52 57 53 61
## 6 6 113 0 4 2 1 2 44 52 51 63 61
# display only the last 6 rows
tail(hsb2)
## # A tibble: 6 × 12
## ...1 id female race ses schtyp prog read write math science socst
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 195 179 1 4 2 2 2 47 65 60 50 56
## 2 196 31 1 2 2 2 1 55 59 52 42 56
## 3 197 145 1 4 2 1 3 42 46 38 36 46
## 4 198 187 1 4 2 2 1 57 41 57 55 52
## 5 199 118 1 4 2 1 1 55 62 58 58 61
## 6 200 137 1 4 3 1 2 63 65 65 53 61
# delete redundant first column (run only once)
(hsb2<- hsb2 [-1])
## # A tibble: 200 × 11
## id female race ses schtyp prog read write math science socst
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 70 0 4 1 1 1 57 52 41 47 57
## 2 121 1 4 2 1 3 68 59 53 63 61
## 3 86 0 4 3 1 1 44 33 54 58 31
## 4 141 0 4 3 1 3 63 44 47 53 56
## 5 172 0 4 2 1 2 47 52 57 53 61
## 6 113 0 4 2 1 2 44 52 51 63 61
## 7 50 0 3 2 1 1 50 59 42 53 61
## 8 11 0 1 2 1 2 34 46 45 39 36
## 9 84 0 4 2 1 1 63 57 54 58 51
## 10 48 0 3 2 1 2 57 55 52 50 51
## # … 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
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#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 the frequency
table(hsb2_long$variable)
##
## read write math science socst
## 200 200 200 200 200
# This means that the tables hsb2_long and hsb2_wide are the same
# tables displayed in two ways
# display data structure of the hsb2_long dataset
str(hsb2_long)
## 'data.frame': 1000 obs. of 8 variables:
## $ id : num 70 121 86 141 172 113 50 11 84 48 ...
## $ female : num 0 1 0 0 0 0 0 0 0 0 ...
## $ race : num 4 4 4 4 4 4 3 1 4 3 ...
## $ ses : num 1 2 3 3 2 2 2 2 2 2 ...
## $ schtyp : num 1 1 1 1 1 1 1 1 1 1 ...
## $ prog : num 1 3 1 3 2 2 1 2 1 2 ...
## $ variable: Factor w/ 5 levels "read","write",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ value : num 57 68 44 63 47 44 50 34 63 57 ...
# the variables female, race, ses, schtyp, prog are stored as numbers
# for encoding purposes. However these variables are actually qualitative variables
# so we convert each from integer type to categorical type
# defining some variables to become factor variable
# we use another variable to preserve the file hsb2_long
data <- hsb2_long
data$ses = factor(data$ses, labels=c("low", "middle", "high"))
data$schtyp = factor(data$schtyp, labels=c("public", "private"))
data$prog = factor(data$prog, labels=c("general", "academic", "vocational"))
data$race = factor(data$race, labels=c("hispanic", "asian", "african-
amer","white"))
data$female = factor(data$female, labels=c("female", "male"))
# check data structure again. The former integer variables are now categorical variable
str(data)
## 'data.frame': 1000 obs. of 8 variables:
## $ id : num 70 121 86 141 172 113 50 11 84 48 ...
## $ female : Factor w/ 2 levels "female","male": 1 2 1 1 1 1 1 1 1 1 ...
## $ race : Factor w/ 4 levels "hispanic","asian",..: 4 4 4 4 4 4 3 1 4 3 ...
## $ ses : Factor w/ 3 levels "low","middle",..: 1 2 3 3 2 2 2 2 2 2 ...
## $ schtyp : Factor w/ 2 levels "public","private": 1 1 1 1 1 1 1 1 1 1 ...
## $ prog : Factor w/ 3 levels "general","academic",..: 1 3 1 3 2 2 1 2 1 2 ...
## $ variable: Factor w/ 5 levels "read","write",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ value : num 57 68 44 63 47 44 50 34 63 57 ...
# we compare student performance by using boxplots
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?
# 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)) # normal distribution with mean 0 and sd=1
## [1] 0.6648893499 0.1425949744 -0.6097850027 -0.4479946025 0.1038701263
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## [111] 0.4670671822 1.1469484592 -0.1563618176 0.3030876799 1.3997512306
## [116] -0.5894495442 -1.2111001806 1.2900413499 1.0178437336 0.6576149846
## [121] 1.0334496763 0.0654639568 0.1931400574 1.2032798869 -0.3106075242
## [126] -2.1729542052 0.2137026270 0.4519802147 -1.0378567532 -2.7389969842
## [131] 0.4042552464 0.4754810335 -1.8936493291 -0.5155464476 -1.0179374492
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## [146] 0.3561619640 1.5423985558 -0.2612839332 -0.1147744638 0.9146363216
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## [161] -0.0237347395 0.7556173235 -0.0781669277 -0.0663338389 0.6284689573
## [166] 1.3330186731 -0.9052111233 0.7314217744 1.6061095605 -0.9919881930
## [171] 0.3391731476 -1.9795214572 0.3358804074 -0.0424228502 -0.4992510657
## [176] 1.4715150182 1.1201341980 -1.2518588549 -1.5159484175 0.8526025312
## [181] 1.4639926063 0.0598577791 0.2815485204 0.6556959285 0.0108300842
## [186] 0.7023689595 0.0241170882 1.8760471680 -1.7213246538 1.5076607757
## [191] 0.4637702565 0.0884645417 -0.9217097001 -0.3737010385 1.4074451138
## [196] 0.8964411487 0.4822319471 1.0022647929 0.1016552782 0.3843729757
## [201] -1.5295763912 0.1742463923 -1.5697574733 -0.4099754080 0.0838274926
## [206] 2.0837263036 0.4046104948 0.6401476923 -0.2305895061 0.9621803729
## [211] 0.8875922246 -0.4295266604 -1.9888072940 0.2588205830 -1.3956623414
## [216] -1.3966405930 1.0681594442 0.4709537748 0.2133468259 1.3033050799
## [221] -0.8311146063 -0.6019360687 0.8760407423 0.1149546358 -1.2742850724
## [226] 0.1256963265 -0.6404611416 -1.1445616547 1.0877225998 -1.1293910578
## [231] 0.5614665214 0.5432494385 -1.0684462942 -0.3497118899 0.5444466846
## [236] -0.3812851987 -0.8147563973 -0.4029791089 -0.6055311482 -0.6996157652
## [241] 1.5693942821 0.2635519784 0.5512476641 0.9605309338 -0.8019756491
## [246] 0.6530891332 0.4143287425 2.1763700655 -0.0676964669 -0.3895240780
## [251] 0.7886223261 -0.6402408218 1.0568633802 0.2864111005 -0.0458681410
## [256] 1.2319393006 1.1443198091 -1.1116132672 -0.5050918775 -1.1924504566
## [261] 1.7372525598 2.5709166454 0.1391079692 1.2724169245 -0.3666587341
## [266] -0.1908726522 1.6700941822 -0.7158070916 0.0409969078 -0.7666668648
## [271] 0.9217442747 -1.2509755604 -0.1578075153 -0.1742785741 2.4639479117
## [276] -1.0650952557 -0.5523525457 0.2129181151 -0.5490023833 0.3757220401
## [281] -1.6561477350 -0.8466254880 -0.8387047435 -0.3897152552 0.2204317928
## [286] 1.0377779274 0.1324104744 0.5213332574 0.6661806043 0.7784964193
## [291] -0.4423386227 -1.3332015588 -1.5036569775 -0.6190954477 0.7717888129
## [296] -0.2921388010 -1.1052256590 0.7257791566 -0.0139572079 1.4755086167
## [301] 0.2459432297 -1.7961178491 0.0008695500 2.0229630242 0.1699466600
## [306] -1.1083214220 0.6227166716 -0.7723486260 1.4925604048 -0.7623717875
## [311] 0.3595851194 -0.6768563976 2.1710312265 -0.8060127063 -0.2093327341
## [316] -0.0222088917 0.9499768428 2.0121563693 0.0965820812 -0.4663069876
## [321] 0.4502742164 0.4077532843 -0.3553932773 -2.4881381130 0.3257397557
## [326] -1.3620331160 0.2193474023 0.1456060638 0.2104952864 2.0012850108
## [331] 1.4682670436 -1.1271609620 0.3956081937 -0.8087874841 -1.0191761932
## [336] -1.6301057390 0.4429205162 1.1528953552 -1.8390157970 0.5041871950
## [341] 0.8933541942 0.0084817093 -1.3703590434 -0.2786322117 0.7009839327
## [346] 1.4793721211 -0.3786203522 2.2311575182 -1.1270081447 -1.2067705107
## [351] 0.8368583206 0.6490829997 -0.3252379405 -1.8196033219 -0.6105184497
## [356] -1.1328467987 0.8677627633 0.6549373376 0.6631979993 -0.2170791529
## [361] -1.5621642464 -0.4681387284 -0.9259160322 0.6908057168 0.5324428644
## [366] -0.7339052540 1.4836873705 1.5632137216 1.1039423740 -0.4301062703
## [371] -0.4125407777 -0.2188687387 -0.6210636095 -0.2300838741 0.8197010781
## [376] -1.6400828007 2.5619577514 1.4653628047 -0.8874813581 -0.1951500441
## [381] -0.5034332947 0.3894078901 -1.8386144060 -0.6814015993 0.3642835491
## [386] 0.9006033872 -1.0118646688 -0.1242881433 0.6397691502 0.9553354155
## [391] -0.7105556200 -0.6932133763 0.6865203005 0.4482167842 -0.8301825530
## [396] -0.0190666849 0.1688009367 -0.9182069785 -0.3642774219 0.2130913510
## [401] 0.1358073016 -0.3529762414 -1.2851507611 0.0226005721 -0.6691497288
## [406] 1.3379801117 1.7778107302 0.6929440138 2.4180439534 0.7114788764
## [411] 1.1667047493 -0.5977446514 -0.9393527611 -0.4999755489 -1.3977054167
## [416] -0.3225692394 -1.3805510910 -1.2888502001 0.6187459030 1.3421046292
## [421] -0.1569045742 -0.3065727456 1.5126754376 0.0998314838 -1.5516506483
## [426] 0.5686351238 -0.5603467801 0.3121618333 -1.0522458974 -0.1175242483
## [431] -1.2414570136 0.8043303081 0.7246005816 1.3296907247 -0.9154637581
## [436] -1.2421185812 0.6065207567 0.2863742323 -0.0614799957 0.1495888496
## [441] -0.2976655925 1.1519641292 2.1501530743 1.1215703057 1.1681946999
## [446] 1.0717425079 1.1294060793 2.0965559869 1.6177802268 -0.0230148285
## [451] 0.4657571056 0.7278132510 0.5111825055 1.2644731232 0.8851587850
## [456] -1.1784903618 -1.2169878911 -1.2717145729 -1.0854311794 -1.5937272919
## [461] 1.8376864881 1.0774551466 -0.4106619412 -0.1303941516 1.2613497567
## [466] -0.0273952224 -0.7375977303 0.2366314317 -0.9068819829 -2.0635724489
## [471] 0.2714019201 -0.7577536269 1.3987089287 -0.1583863603 1.1022753376
## [476] 0.8510139488 0.3122541036 1.2931536074 -0.6001357244 0.7192746495
## [481] 1.0956520893 0.1892607557 -0.0366942920 -0.6405858811 -0.7004375584
## [486] 0.3215100587 0.5007042659 -1.4309554471 0.8760865430 0.6618406072
## [491] -0.5996213762 -0.0486156896 2.5025333535 1.3619843587 -1.4410738116
## [496] 0.6772963000 0.1934795848 0.2980398111 -0.7341763394 1.3979112998
## [501] 0.4042770773 -1.8506413042 0.2294801800 1.3359934722 1.3902670302
## [506] -1.5298662971 -0.3654286351 1.2686595822 -0.2529187566 0.2848728815
## [511] 1.0833698438 1.2955785686 1.1593090954 0.9554438368 -0.9416436369
## [516] -0.8577009510 -1.3700083307 -0.5063068907 1.2901457791 -0.0195128257
## [521] 0.4909767280 -2.5995352830 -1.1809997356 0.1629575227 -0.5190012061
## [526] 0.2412919866 0.0868984277 2.0196842979 -0.1021846472 0.9188882958
## [531] -2.5178238138 0.1390964407 -0.7840506043 -0.1142796281 -1.1511912571
## [536] 0.6544844695 -1.7466744116 0.5874990128 -0.0815086315 -1.0593831684
## [541] 1.2740413416 0.6112845826 -0.0614939812 -0.2629205536 -1.7788825512
## [546] -0.0220891881 0.1784449046 -0.3200994727 -0.8445387039 -1.1582601700
## [551] 0.2187845759 -0.8854236310 -0.1551179261 1.0644716282 0.6468565925
## [556] 0.2690865088 -0.0420922594 -1.8568647765 1.3044201801 -0.1484706072
## [561] 0.5217052725 -0.5236074191 -0.4514704243 0.8177651580 -2.1955671620
## [566] 0.6142675610 0.5488406443 0.0875441762 -0.6631806222 0.9201616698
## [571] -1.3341576414 -0.3381447698 1.4451525433 0.2887833019 -1.5292947686
## [576] -0.5856326433 0.7829829135 0.3853259873 0.0597105682 0.5256592066
## [581] 0.7329351149 0.7425080858 0.5387042857 0.8196188785 -0.2940430479
## [586] -0.0967502860 0.6370733122 -0.6946254202 -0.2584930739 -1.7852746424
## [591] 1.7587256661 2.0206768794 2.2505682200 1.6958618482 0.8676610034
## [596] 0.9384550159 -1.9291679917 -0.1022513578 -0.0629946520 0.7444018990
## [601] -0.2109460610 -0.5446051576 -0.6826602977 0.0546542457 -0.3816638401
## [606] -0.6163724822 0.3094399743 0.7847321393 -0.1982618532 0.7279213639
## [611] -1.0916000004 0.4038083240 -0.0402223091 0.2380980131 0.8128676714
## [616] -0.1653130400 -1.2753067311 1.8296988298 0.6125042111 1.6195574010
## [621] 0.5479991560 1.0129568898 0.3903419556 0.9574885976 0.1391521479
## [626] -0.7507744102 -0.8168836025 -0.8816876682 -0.9198228148 1.8863566525
## [631] -0.0382198447 0.8730698496 -1.6669003772 0.2411364939 0.1355427148
## [636] 0.7452033456 0.5814303619 0.0161978627 -2.9895651727 0.1815873879
## [641] 0.1866407379 -0.8981964456 1.1848770294 0.1887869973 1.1693182432
## [646] 0.0034339342 0.4289096081 -1.3452239932 1.2095419653 -1.2845012121
## [651] -0.9062313476 -1.5650065172 0.7750426587 -0.5491484167 -2.0395877442
## [656] -1.5173781859 0.5087505989 0.7535229576 -0.1745995407 1.0763299732
## [661] -0.2744736371 -0.7946493386 1.1354170030 -0.1317124636 -0.8532127922
## [666] -0.5979157314 0.1995947839 0.6135571172 -0.2228998649 1.4591461132
## [671] 0.9120105967 1.4005630132 -0.4018939579 0.4682566014 -0.3805426451
## [676] -1.1621669438 -0.5443899482 0.5484421357 -0.1558856903 0.1674892267
## [681] 0.1824778396 -0.2388240238 -2.2903684299 -0.4653842249 -2.5541052591
## [686] -0.7901994295 -0.8804860595 0.4960325976 -0.8748324376 0.0609017743
## [691] 0.6707523799 0.0444938274 0.6491962808 0.1982760267 -0.7433119945
## [696] -0.4726693092 -1.2381104767 0.4068029403 -0.1160895960 -1.6254140883
## [701] 1.7291522627 -0.0323955937 -0.1069542434 0.8810726773 1.0417311232
## [706] -1.3374337581 1.2512426584 -1.1901619642 -1.0299279793 0.5757481883
## [711] 0.0777718617 1.0933657512 0.4513003702 0.9971088998 -1.6761055655
## [716] -0.4894400003 -0.6286058932 -0.0704395569 -0.8344044849 0.1674704952
## [721] -2.4461678137 0.0063437927 0.6321357544 -0.4856027344 -0.1142957628
## [726] 0.1063455202 0.0758423731 0.8732329563 -0.2495234083 0.6742408899
## [731] 0.6018793440 -0.6206144307 -0.5711342663 -0.1716880439 0.3126344892
## [736] 0.2442003797 0.7858132197 -0.1647085325 -0.1139848209 0.2904716538
## [741] 0.0174290208 0.1925096525 0.3509686898 2.2096987780 -0.9523792592
## [746] -0.7889943653 -0.6196207620 1.1259161138 -2.0684186102 -0.9897762185
## [751] 0.4680183193 1.6824123832 -1.3676924527 0.1929016468 -1.6708976527
## [756] 0.1319048762 -0.5464020833 0.9291683585 -0.7119499668 -0.2046532020
## [761] 1.4462491055 -0.2335374918 1.3420254478 -0.7830916669 0.7182158322
## [766] -0.4258629994 -0.6015614314 1.5663689070 1.1648374530 -0.7998682888
## [771] -1.7246739653 0.6121706645 0.2750801509 -0.5366135479 -0.6129472678
## [776] 0.5232466673 0.7163058896 0.7617284824 1.4279673052 -1.2376036013
## [781] -0.4691783502 -1.2580298959 -0.7971099128 1.1711888798 -1.7753031347
## [786] -0.2805140506 0.6758422525 -0.4841535317 -0.1597659599 -0.5541900110
## [791] 0.0663820044 -1.3978208684 0.9801285346 1.0790624790 1.7731662985
## [796] 0.4252030188 0.0002311626 -2.2727877689 -0.0316406137 -0.0704895844
## [801] 0.3777340450 -0.5609573393 -1.0785203589 1.3631891563 -0.5652799496
## [806] 0.4263444657 0.3859052857 -0.6541305508 0.3516097472 0.5914289715
## [811] 0.1515369053 0.0716279285 0.9523859854 -0.0139630544 0.8442532817
## [816] -0.9382775015 -0.6258939022 0.0305720726 1.6161987809 -2.6041258147
## [821] -0.2303667164 0.0167464324 -0.7428094605 -2.0559127278 0.2642629261
## [826] -0.4417555144 0.3308497051 1.4006935312 0.2634087341 -3.1001010939
## [831] -0.1600340954 0.3224968360 -1.9797346545 0.6952330722 -2.1940966255
## [836] 0.9915649931 -1.9183143077 -1.9236785506 0.2931374497 1.3246777849
## [841] 0.2693774839 -0.8391685560 1.5607570613 0.3216175628 -0.3657060979
## [846] -0.1912745796 0.2598966262 -0.9341918184 0.9277121091 0.7406477860
## [851] -0.0506716844 -0.0366714024 -1.1861656902 0.4679080751 1.7152733761
## [856] 1.5100728253 1.6888408048 -2.1938460461 1.9656662405 1.8153149682
## [861] -1.8267623952 0.7210656445 -0.2825491969 0.0573803946 1.4440686468
## [866] -0.9117814241 -0.0645962650 -0.3745494627 0.0645090115 -1.4528748046
## [871] -0.5618488457 2.1145493607 -0.2986497274 -2.5117900344 -1.3245245152
## [876] -0.1697452757 -0.1571494777 -1.6938614227 0.4817938402 -0.3267084349
## [881] -1.4735441573 -0.0017428298 -0.1638184877 0.2440872254 -0.1978737346
## [886] 1.2313909530 0.1394545108 0.9554461968 -0.5973045152 0.6015576902
## [891] 0.3158453817 -0.7016700252 0.5645990284 -1.3461325192 -0.6765984853
## [896] 0.2301010312 -0.4666426126 0.1734639229 -0.5560670489 -1.0872797538
## [901] 0.6012918380 -1.9391356057 2.0840499850 0.4019817076 1.2123026223
## [906] -0.6552553084 1.0344581174 -0.0493781271 -0.1563516714 -0.7404552495
## [911] 0.2948036013 -0.4963082589 0.0145574138 1.0231933410 -0.8848121429
## [916] 0.9504572153 0.4521270434 -0.4515904879 -0.7724584673 -0.3433723759
## [921] 0.5689150201 1.0467692061 0.5662310134 -0.6097537355 0.2998165241
## [926] 1.2251850460 0.4673856523 0.0287484318 0.2549328673 0.2101653409
## [931] -0.3768375310 -0.3932682222 -0.1656235495 -1.6857184268 1.7479346495
## [936] 0.3648381007 0.7011182662 0.3427865854 -0.0931421109 0.0870059581
## [941] -0.5623778348 -0.1764335980 0.1914037051 -0.0562478440 -0.2529714945
## [946] 0.4915247388 0.6061583107 1.1652037575 -0.8385439315 -1.5576709618
## [951] -2.0046145573 -0.6770862720 -2.2849486032 -0.2712422824 0.1483728671
## [956] 0.7223291524 0.4373768289 1.2006918799 -0.5290604292 -0.0452444414
## [961] -0.1263953994 0.8607999905 2.5257654098 -2.2456299644 -0.4173315245
## [966] -0.0692077803 -0.0794555102 1.3651913140 -0.9699635845 0.1043085014
## [971] -0.7542150921 -1.3566109168 1.5440882967 -0.0556951245 0.2587367042
## [976] -1.5073723639 0.3490798431 -0.4012519975 -1.1295931920 0.9653611722
## [981] -1.4617739336 -0.3192242576 -0.2200964643 -0.1086651174 -0.2957829436
## [986] -0.7546362949 -0.2712218888 -0.3342016026 0.5066071875 -0.7410342389
## [991] -2.1264734465 -0.0432725155 0.6434993796 -0.8995395184 -0.3513906569
## [996] -0.4948546938 -0.6543419833 -0.1847511774 -0.8971128591 0.6001090845
yAxis <- rnorm(1000) + xAxis + 10
yAxis
## [1] 10.345382 10.991549 7.113603 8.758842 9.922003 7.910039 12.823151
## [8] 11.019196 10.948736 9.099431 8.054706 5.196158 11.290232 9.624425
## [15] 9.814793 9.571054 11.192175 9.479928 10.622662 8.475371 7.135398
## [22] 9.502756 8.848730 12.044551 8.438593 9.418253 10.929341 10.218786
## [29] 8.830767 11.119107 9.318963 8.791979 10.710025 11.435187 10.318606
## [36] 7.165713 9.083699 9.960657 9.973772 8.232280 10.455889 13.055045
## [43] 10.123002 10.534186 9.403370 10.592058 10.882617 9.583051 10.094124
## [50] 10.669661 11.627259 9.692349 10.548553 10.370327 10.085402 9.855896
## [57] 11.204737 10.421919 11.472319 11.288301 7.455514 10.857363 10.633385
## [64] 9.492649 10.792458 10.291696 8.708621 9.020014 11.269972 11.122693
## [71] 8.740815 8.589727 10.527478 9.757308 10.456063 7.630734 10.343195
## [78] 9.409981 10.866775 10.631961 7.869148 8.869121 12.392474 12.056546
## [85] 9.655623 11.878959 11.920230 9.088146 8.580275 8.930984 8.884215
## [92] 12.535664 10.808998 10.619171 10.416533 9.209911 11.280304 9.868686
## [99] 9.913107 6.713357 10.537710 11.082962 9.382522 10.087835 9.594989
## [106] 7.259303 11.499982 11.442209 7.613775 9.296615 9.209185 11.212915
## [113] 9.289988 10.699340 10.367144 9.675120 7.145616 11.691285 11.260135
## [120] 9.321303 9.532345 10.649315 11.240842 11.011403 8.194432 7.512244
## [127] 10.692242 8.600844 8.526048 6.134601 9.001001 10.136478 6.719937
## [134] 9.385502 8.752713 10.814082 10.958306 10.504529 10.676486 11.106514
## [141] 11.933570 10.073447 10.137378 10.632785 8.736710 10.763758 10.953498
## [148] 10.735757 10.471043 10.366408 12.297880 11.135703 7.416839 10.487239
## [155] 8.013763 10.476812 10.310059 10.311356 8.261195 8.263326 11.334814
## [162] 12.519047 11.147241 10.303974 11.205077 11.117468 8.536467 9.595443
## [169] 8.441267 9.894806 9.868233 7.695852 10.727128 11.258785 9.361975
## [176] 11.505569 11.481012 7.922277 8.467514 10.031536 9.185685 9.020615
## [183] 12.170446 10.733393 9.185466 12.456689 9.872760 12.580453 8.511068
## [190] 11.640079 9.503166 10.493385 8.348740 8.880586 10.852106 11.714491
## [197] 10.189988 11.777189 10.048882 9.197428 11.102384 9.879761 6.462651
## [204] 8.887924 10.327002 11.653929 10.837927 11.052325 10.545473 10.899917
## [211] 10.102566 10.656274 7.578942 11.072406 8.072561 9.778902 10.298954
## [218] 9.995101 10.933719 10.827360 7.331102 9.756615 11.171206 8.251393
## [225] 10.227909 11.019829 10.197546 8.909166 10.793604 9.887910 10.594192
## [232] 10.781096 7.509914 9.041392 11.402295 10.101359 8.029709 10.088903
## [239] 10.102005 11.643404 10.546387 9.560403 12.038010 9.929319 9.988323
## [246] 11.913372 10.641987 10.821386 9.605405 10.608734 9.328015 8.586200
## [253] 10.821867 11.499149 11.350562 11.051659 13.459353 9.496805 10.083160
## [260] 9.259240 10.097891 12.971844 9.032554 11.677847 9.353242 11.335528
## [267] 10.560317 8.760875 9.660301 10.411797 10.274065 8.464397 10.083462
## [274] 8.340191 11.606952 9.817553 8.219101 11.966706 9.281999 14.388796
## [281] 7.954189 9.830495 8.478764 10.453561 9.948160 10.328099 10.188442
## [288] 9.287155 9.743844 11.486728 10.628404 9.014790 9.970114 10.889111
## [295] 11.398258 9.317425 8.469999 11.979180 8.851626 12.964401 10.735186
## [302] 8.644026 9.361855 11.735747 9.585871 8.245849 11.108927 7.956584
## [309] 11.480460 9.363161 10.065317 9.637358 13.187886 8.887749 10.000692
## [316] 8.020580 11.334403 12.282947 8.588021 8.829482 9.358123 10.363116
## [323] 10.896697 8.754580 9.870221 9.239600 11.057557 9.845912 10.215259
## [330] 9.228843 10.696238 9.851285 11.419685 8.762807 7.755629 9.444577
## [337] 10.215875 11.856578 8.739710 11.413565 10.762690 11.932090 9.680826
## [344] 8.344056 11.763204 10.541991 10.967007 11.859964 8.990826 10.880690
## [351] 11.422497 8.634471 11.505381 8.206756 9.582154 8.939322 9.925703
## [358] 11.098327 10.573163 9.369464 10.278720 10.057269 10.325856 10.274820
## [365] 11.254521 7.871841 8.779082 12.055401 11.080669 9.278628 11.063887
## [372] 7.291397 9.640868 10.443409 8.996183 7.681740 11.421162 14.230162
## [379] 9.594289 9.227122 8.387636 9.584541 9.194747 10.336520 10.042781
## [386] 10.973469 10.024763 9.963670 11.690917 9.687274 9.967111 11.236945
## [393] 10.883641 9.281550 9.152911 11.217492 9.036036 8.096012 9.853283
## [400] 9.964276 10.236979 8.581134 9.201050 11.451714 11.010429 11.283925
## [407] 12.323820 10.594231 13.159197 9.592293 11.256788 8.764523 9.313223
## [414] 7.962487 7.557899 9.483668 6.761344 10.348418 11.206457 10.466278
## [421] 10.350421 9.614425 13.600001 10.456596 7.696414 10.800423 7.972471
## [428] 9.701894 9.579200 8.530145 9.325962 10.814107 11.223764 11.032261
## [435] 6.747306 10.869063 10.901802 11.714550 7.496370 10.087138 9.803332
## [442] 9.786890 10.237732 12.659980 11.111614 12.105374 10.868604 11.464928
## [449] 9.801949 9.063096 10.032117 10.503111 11.436742 11.416309 10.176419
## [456] 8.471196 8.806400 10.136520 7.843670 9.659654 10.492194 11.356041
## [463] 9.008321 9.387742 12.879300 10.354726 10.091152 10.201590 8.468318
## [470] 7.865161 9.699032 10.124066 9.715272 10.815250 11.932178 10.648771
## [477] 9.214830 12.421978 10.215505 11.025615 12.163722 10.948908 9.701441
## [484] 7.546375 10.206086 10.977314 11.088265 8.184591 11.766473 10.013579
## [491] 8.658799 9.628254 13.056667 13.858110 8.578776 9.901709 9.467942
## [498] 9.045431 7.978757 11.796312 8.965319 8.526997 9.670220 11.764647
## [505] 10.801550 8.086620 8.903256 11.851233 11.150812 10.573532 11.760899
## [512] 12.291467 11.619000 9.502111 9.546802 9.819147 8.531661 8.734297
## [519] 13.458026 8.274898 10.386693 7.728814 9.856164 10.366086 9.035621
## [526] 9.270386 10.078091 10.252372 10.475016 9.928079 7.983977 11.461552
## [533] 10.192782 8.982649 8.909397 10.485333 8.377580 10.549011 9.431331
## [540] 9.040752 11.595115 11.859177 10.080131 11.035040 9.017391 10.593497
## [547] 10.766281 9.922871 8.181967 8.356849 9.690012 8.828404 9.167723
## [554] 10.954836 12.984413 9.084093 8.920495 8.303905 11.225418 10.836063
## [561] 10.946952 10.155144 8.958221 11.111254 8.099370 11.564460 10.955574
## [568] 8.937826 9.182142 11.061466 9.285254 9.339855 11.427941 10.621187
## [575] 8.646278 9.636312 12.343355 10.257580 8.244182 9.972719 10.893100
## [582] 11.845846 7.502498 11.364358 9.273549 10.334713 10.137773 9.456664
## [589] 10.155790 8.085608 11.579064 11.469027 12.444030 11.943547 10.553502
## [596] 9.813485 7.795279 10.906092 9.215645 12.552941 9.987109 10.757613
## [603] 9.758136 10.434533 10.741468 8.348730 8.926676 11.890936 10.335611
## [610] 10.131506 9.718113 10.795433 11.820794 11.411177 11.262319 9.896693
## [617] 10.106756 10.591771 11.225887 11.843390 9.827616 10.507305 10.365436
## [624] 11.230791 8.199514 9.767298 10.232548 7.821455 8.412523 12.039916
## [631] 10.205491 12.085181 9.455761 11.588603 9.451608 11.172804 10.582050
## [638] 10.423376 7.021761 10.991778 10.550470 8.676794 9.690195 9.927681
## [645] 10.736299 11.037907 9.343260 8.449958 11.236751 9.918634 9.814576
## [652] 7.898390 12.110289 10.770123 7.950817 6.025177 11.341326 11.227527
## [659] 11.591837 11.489366 9.868010 9.975491 12.457654 10.726951 8.987377
## [666] 9.656669 10.932126 10.463815 9.115948 10.845432 9.269170 11.710150
## [673] 10.327226 11.197442 8.239497 9.554747 9.533494 9.127942 7.598718
## [680] 10.161479 11.806122 9.896866 8.081487 8.997004 7.891209 8.041542
## [687] 8.538128 9.273795 8.426295 10.438261 9.723971 10.739286 9.966289
## [694] 10.708917 9.476256 10.368541 9.883813 12.084161 11.633119 8.518521
## [701] 12.029303 11.137037 11.335706 9.158663 11.155862 8.913532 11.004822
## [708] 5.288823 8.622612 9.012525 9.276659 10.597105 10.009602 11.136263
## [715] 8.161188 8.250960 8.892059 9.756281 8.443332 10.316877 6.972580
## [722] 9.667041 10.074546 9.461949 9.300794 8.713794 10.265906 11.048223
## [729] 9.920728 12.789041 10.485710 9.271931 8.301082 8.695551 9.977809
## [736] 10.944046 9.899699 9.944009 10.714303 10.580386 9.969440 11.060991
## [743] 12.073481 14.096668 9.530204 8.099533 9.835901 11.746744 9.129236
## [750] 7.420553 10.783172 11.900421 9.312903 9.911901 6.252965 10.395649
## [757] 9.086056 10.436574 10.272619 9.380168 10.573807 8.763795 11.583798
## [764] 9.841138 12.897027 10.230566 9.863732 9.749767 12.291155 9.435981
## [771] 7.735847 9.329448 9.823244 10.862968 8.859952 11.198536 10.826742
## [778] 9.298661 13.009439 8.797805 8.990990 9.802446 10.626647 10.295494
## [785] 7.202452 9.842137 10.102973 6.648965 10.393615 9.163456 12.646388
## [792] 8.401017 10.384346 11.312302 12.530046 10.701757 10.377753 8.041406
## [799] 11.274179 8.082906 9.472167 10.423787 9.712724 10.380710 8.118304
## [806] 9.948939 10.134730 9.477488 10.826955 8.001958 9.306149 11.458928
## [813] 10.857098 10.215738 11.306813 8.595244 10.035622 10.376834 12.915691
## [820] 7.038659 9.604150 9.433144 8.389563 7.800856 9.977314 9.507476
## [827] 9.654107 11.209924 10.413655 8.856391 10.131741 10.137189 9.533962
## [834] 12.468823 6.464525 11.126009 7.898102 8.001282 7.552951 10.983872
## [841] 9.999615 10.012600 12.781634 10.428875 9.979935 10.683912 7.556508
## [848] 8.277412 10.847330 10.181023 11.073363 10.122075 8.416430 10.243070
## [855] 12.251143 11.394940 11.692751 5.579363 12.528779 11.543966 7.594553
## [862] 11.697643 8.876538 9.892503 12.892797 8.784623 9.780577 9.810764
## [869] 9.580515 7.321641 7.791199 10.038161 9.945795 6.682191 8.412511
## [876] 8.840673 10.159830 9.242261 11.260254 11.091308 9.901109 8.626555
## [883] 9.605631 8.788259 8.999570 9.569291 9.355312 9.921542 11.641591
## [890] 11.352046 10.861438 11.014649 10.123134 9.711245 9.902821 9.397631
## [897] 8.744136 9.356034 9.666529 7.821095 11.162349 8.810094 12.624417
## [904] 10.609011 10.114931 6.432085 10.850391 8.998887 8.444220 9.661949
## [911] 11.004746 9.289029 8.124507 9.525105 10.154956 11.485685 12.157230
## [918] 9.525829 10.349470 8.525555 10.901094 9.179634 9.758738 10.073697
## [925] 9.654057 11.700561 11.259702 10.949491 11.351142 11.284636 9.476844
## [932] 10.161947 9.310682 9.939617 10.943729 9.716865 11.293805 10.053952
## [939] 10.225475 9.465795 8.200138 9.436734 11.822850 10.920422 8.381545
## [946] 11.271099 11.522153 9.495087 7.522580 8.278749 8.676740 9.378034
## [953] 5.760154 9.311849 10.227899 12.046126 10.169983 11.886568 9.378313
## [960] 10.628530 9.134384 12.382433 12.745557 7.769090 11.152007 8.054814
## [967] 9.945275 10.757302 10.384944 11.372867 9.747740 10.186810 13.670094
## [974] 8.816586 10.093037 9.041354 8.116257 9.653625 8.414037 11.311746
## [981] 9.976714 8.914089 9.650568 10.446444 8.202551 9.593645 9.416969
## [988] 8.299729 10.716683 8.150360 8.157643 9.440337 9.383930 8.954136
## [995] 10.896999 11.089421 9.502371 11.091316 9.138657 11.689018
# create groups for different values of X
(group <- rep(1,1000)) # a vector consisting of 1000 elementsgroup[xAxis > -1.5] <- 2
## [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [186] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [223] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [260] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [297] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [334] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [371] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [408] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [445] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [482] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [519] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [556] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [593] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [630] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [667] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [704] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [741] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [778] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [815] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [852] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [889] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [926] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [963] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1000] 1
group[xAxis > -.5] <- 3
group[xAxis > .5] <- 4
group[xAxis > 1.5] <- 5
group
## [1] 4 3 1 3 3 3 5 4 4 1 1 1 4 3 3 3 5 3 1 1 1 1 1 5 3 3 4 3 1 3 1 3 3 4 3 3 4
## [38] 1 3 1 3 5 1 4 1 4 4 1 3 3 3 4 3 1 3 3 3 3 4 4 1 4 4 3 3 4 3 1 4 5 3 3 3 4
## [75] 4 1 4 3 1 4 1 3 4 4 3 4 4 4 1 1 1 4 4 4 3 3 3 3 3 1 3 3 1 1 4 1 4 5 1 1 3
## [112] 4 3 3 4 1 1 4 4 4 4 3 3 4 3 1 3 3 1 1 3 3 1 1 1 4 3 3 3 1 3 1 1 3 3 3 5 3
## [149] 3 4 4 4 4 5 1 4 3 3 3 3 3 4 3 3 4 4 1 4 5 1 3 1 3 3 3 4 4 1 1 4 4 3 3 4 3
## [186] 4 3 5 1 5 3 3 1 3 4 4 3 4 3 3 1 3 1 3 3 5 3 4 3 4 4 3 1 3 1 1 4 3 3 4 1 1
## [223] 4 3 1 3 1 1 4 1 4 4 1 3 4 3 1 3 1 1 5 3 4 4 1 4 3 5 3 3 4 1 4 3 3 4 4 1 1
## [260] 1 5 5 3 4 3 3 5 1 3 1 4 1 3 3 5 1 1 3 1 3 1 1 1 3 3 4 3 4 4 4 3 1 1 1 4 3
## [297] 1 4 3 4 3 1 3 5 3 1 4 1 4 1 3 1 5 1 3 3 4 5 3 3 3 3 3 1 3 1 3 3 3 5 4 1 3
## [334] 1 1 1 3 4 1 4 4 3 1 3 4 4 3 5 1 1 4 4 3 1 1 1 4 4 4 3 1 3 1 4 4 1 4 5 4 3
## [371] 3 3 1 3 4 1 5 4 1 3 1 3 1 1 3 4 1 3 4 4 1 1 4 3 1 3 3 1 3 3 3 3 1 3 1 4 5
## [408] 4 5 4 4 1 1 3 1 3 1 1 4 4 3 3 5 3 1 4 1 3 1 3 1 4 4 4 1 1 4 3 3 3 3 4 5 4
## [445] 4 4 4 5 5 3 3 4 4 4 4 1 1 1 1 1 5 4 3 3 4 3 1 3 1 1 3 1 4 3 4 4 3 4 1 4 4
## [482] 3 3 1 1 3 4 1 4 4 1 3 5 4 1 4 3 3 1 4 3 1 3 4 4 1 3 4 3 3 4 4 4 4 1 1 1 1
## [519] 4 3 3 1 1 3 1 3 3 5 3 4 1 3 1 3 1 4 1 4 3 1 4 4 3 3 1 3 3 3 1 1 3 1 3 4 4
## [556] 3 3 1 4 3 4 1 3 4 1 4 4 3 1 4 1 3 4 3 1 1 4 3 3 4 4 4 4 4 3 3 4 1 3 1 5 5
## [593] 5 5 4 4 1 3 3 4 3 1 1 3 3 1 3 4 3 4 1 3 3 3 4 3 1 5 4 5 4 4 3 4 3 1 1 1 1
## [630] 5 3 4 1 3 3 4 4 3 1 3 3 1 4 3 4 3 3 1 4 1 1 1 4 1 1 1 4 4 3 4 3 1 4 3 1 1
## [667] 3 4 3 4 4 4 3 3 3 1 1 4 3 3 3 3 1 3 1 1 1 3 1 3 4 3 4 3 1 3 1 3 3 1 5 3 3
## [704] 4 4 1 4 1 1 4 3 4 3 4 1 3 1 3 1 3 1 3 4 3 3 3 3 4 3 4 4 1 1 3 3 3 4 3 3 3
## [741] 3 3 3 5 1 1 1 4 1 1 3 5 1 3 1 3 1 4 1 3 4 3 4 1 4 3 1 5 4 1 1 4 3 1 1 4 4
## [778] 4 4 1 3 1 1 4 1 3 4 3 3 1 3 1 4 4 5 3 3 1 3 3 3 1 1 4 1 3 3 1 3 4 3 3 4 3
## [815] 4 1 1 3 5 1 3 3 1 1 3 3 3 4 3 1 3 3 1 4 1 4 1 1 3 4 3 1 5 3 3 3 3 1 4 4 3
## [852] 3 1 3 5 5 5 1 5 5 1 4 3 3 4 1 3 3 3 1 1 5 3 1 1 3 3 1 3 3 1 3 3 3 3 4 3 4
## [889] 1 4 3 1 4 1 1 3 3 3 1 1 4 1 5 3 4 1 4 3 3 1 3 3 3 4 1 4 3 3 1 3 4 4 4 1 3
## [926] 4 3 3 3 3 3 3 3 1 5 3 4 3 3 3 1 3 3 3 3 3 4 4 1 1 1 1 1 3 3 4 3 4 1 3 3 4
## [963] 5 1 3 3 3 4 1 3 1 1 5 3 3 1 3 3 1 4 1 3 3 3 3 1 3 3 4 1 1 3 4 1 3 3 1 3 1
## [1000] 4
# create sample data frame by joining variables
sample_data <- data.frame(xAxis,yAxis,group)
sample_data
## xAxis yAxis group
## 1 0.6648893499 10.345382 4
## 2 0.1425949744 10.991549 3
## 3 -0.6097850027 7.113603 1
## 4 -0.4479946025 8.758842 3
## 5 0.1038701263 9.922003 3
## 6 0.1949834213 7.910039 3
## 7 2.2210867570 12.823151 5
## 8 0.8934661506 11.019196 4
## 9 0.5497949043 10.948736 4
## 10 -1.5519172996 9.099431 1
## 11 -1.6489623263 8.054706 1
## 12 -1.7908626445 5.196158 1
## 13 0.8324284823 11.290232 4
## 14 0.4645562274 9.624425 3
## 15 0.3942514370 9.814793 3
## 16 -0.0857478482 9.571054 3
## 17 1.6366654897 11.192175 5
## 18 -0.0555474577 9.479928 3
## 19 -1.8510986985 10.622662 1
## 20 -1.6881907195 8.475371 1
## 21 -1.4850571604 7.135398 1
## 22 -0.5934563989 9.502756 1
## 23 -0.6576157529 8.848730 1
## 24 1.8819452668 12.044551 5
## 25 -0.1696504966 8.438593 3
## 26 0.1908334017 9.418253 3
## 27 0.7539273944 10.929341 4
## 28 0.3483641850 10.218786 3
## 29 -0.7505360524 8.830767 1
## 30 -0.2838691299 11.119107 3
## 31 -0.6841569649 9.318963 1
## 32 0.2443627996 8.791979 3
## 33 0.4585161357 10.710025 3
## 34 0.7405650191 11.435187 4
## 35 0.3035392710 10.318606 3
## 36 0.4792666266 7.165713 3
## 37 0.5025791590 9.083699 4
## 38 -0.8968920172 9.960657 1
## 39 0.2476003112 9.973772 3
## 40 -1.0977310148 8.232280 1
## 41 -0.0495063763 10.455889 3
## 42 2.9917518147 13.055045 5
## 43 -1.6571531215 10.123002 1
## 44 1.4372383847 10.534186 4
## 45 -0.5226136380 9.403370 1
## 46 0.7346023347 10.592058 4
## 47 0.5885803622 10.882617 4
## 48 -0.7028729396 9.583051 1
## 49 0.0229671179 10.094124 3
## 50 -0.3689663440 10.669661 3
## 51 0.2906497015 11.627259 3
## 52 0.7714357096 9.692349 4
## 53 0.1149725465 10.548553 3
## 54 -0.6118037733 10.370327 1
## 55 0.0743402007 10.085402 3
## 56 -0.3688042616 9.855896 3
## 57 0.3063901939 11.204737 3
## 58 0.3496666757 10.421919 3
## 59 0.5987023317 11.472319 4
## 60 0.6908598849 11.288301 4
## 61 -1.4321695727 7.455514 1
## 62 0.7782637389 10.857363 4
## 63 1.1590958817 10.633385 4
## 64 0.3960993330 9.492649 3
## 65 0.2881286593 10.792458 3
## 66 0.6046127855 10.291696 4
## 67 -0.4658113466 8.708621 3
## 68 -0.9541577903 9.020014 1
## 69 1.2786299209 11.269972 4
## 70 1.8243840979 11.122693 5
## 71 -0.1297311333 8.740815 3
## 72 0.2553533882 8.589727 3
## 73 -0.4173477701 10.527478 3
## 74 1.2508779043 9.757308 4
## 75 0.9686922341 10.456063 4
## 76 -0.9715509101 7.630734 1
## 77 0.6176050881 10.343195 4
## 78 -0.2984659522 9.409981 3
## 79 -0.6895717575 10.866775 1
## 80 1.4628964821 10.631961 4
## 81 -0.9724311132 7.869148 1
## 82 -0.2377497872 8.869121 3
## 83 1.2029333303 12.392474 4
## 84 1.2927256831 12.056546 4
## 85 -0.0184685856 9.655623 3
## 86 1.4903727698 11.878959 4
## 87 1.0971249502 11.920230 4
## 88 0.8224832329 9.088146 4
## 89 -1.2411207532 8.580275 1
## 90 -1.4474676503 8.930984 1
## 91 -1.4728752796 8.884215 1
## 92 0.9365192139 12.535664 4
## 93 0.8791523612 10.808998 4
## 94 0.8707505082 10.619171 4
## 95 0.0991374443 10.416533 3
## 96 0.0191239777 9.209911 3
## 97 -0.0303244111 11.280304 3
## 98 0.4459533811 9.868686 3
## 99 0.1115810673 9.913107 3
## 100 -1.1640000397 6.713357 1
## 101 -0.3584604443 10.537710 3
## 102 -0.2681756927 11.082962 3
## 103 -0.5744052196 9.382522 1
## 104 -1.0480983460 10.087835 1
## 105 0.6729773712 9.594989 4
## 106 -1.3770839046 7.259303 1
## 107 1.2752210608 11.499982 4
## 108 2.1008729638 11.442209 5
## 109 -1.0285740770 7.613775 1
## 110 -1.5937301183 9.296615 1
## 111 0.4670671822 9.209185 3
## 112 1.1469484592 11.212915 4
## 113 -0.1563618176 9.289988 3
## 114 0.3030876799 10.699340 3
## 115 1.3997512306 10.367144 4
## 116 -0.5894495442 9.675120 1
## 117 -1.2111001806 7.145616 1
## 118 1.2900413499 11.691285 4
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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 diStribution using marginal function
ggMarginal(plot,type= 'histogram',groupColour=TRUE,groupFill=TRUE)
