# Mindanao State University
# General Santos City
# Introduction to R base commands
# Submitted by: Elvie Mae P. Cadungog
# 1st Year BS MATH
# Math Department
# March 16, 2023
# Lab Exercise 1: How to create Lines in with different styles in R
# Step 1: Create Data
x <- 1:10 # Create example data
y <- c(3, 1, 5, 2, 3, 8, 4, 7, 6, 9) # using the c() function to create an
array
## function (data = NA, dim = length(data), dimnames = NULL)
## {
## if (is.atomic(data) && !is.object(data))
## return(.Internal(array(data, dim, dimnames)))
## data <- as.vector(data)
## if (is.object(data)) {
## dim <- as.integer(dim)
## if (!length(dim))
## stop("'dim' cannot be of length 0")
## vl <- prod(dim)
## if (length(data) != vl) {
## if (vl > .Machine$integer.max)
## stop("'dim' specifies too large an array")
## data <- rep_len(data, vl)
## }
## if (length(dim))
## dim(data) <- dim
## if (is.list(dimnames) && length(dimnames))
## dimnames(data) <- dimnames
## data
## }
## else .Internal(array(data, dim, dimnames))
## }
## <bytecode: 0x55d45a04b508>
## <environment: namespace:base>
# Step 2: Plot the line graph using the base plot() command
plot(x, y, type = "l")
# Step 3: Add Main Title & Change Axis Labels
plot(x, y, type = "l",
main = "Hello: This is my Line Plot",
xlab = "My X-Values",
ylab = "My Y-Values")
# Step 4: Add color to the line using the col command
plot(x, y, type = "l",
main = "This is my Line Plot",
xlab = "My X-Values",
ylab = "My Y-Values",
col = "red")
# Step 5: Modify Thickness of Line using lwd command
plot(x, y, type = "l",
main = "This is my Line Plot",
xlab = "My X-Values",
ylab = "My Y-Values",
lwd=7,
col = "green")
# Step 6: Add points to line graph by changing the type command
plot(x, y, type = "b",
main = "This is my Line Plot",
xlab = "My X-Values",
ylab = "My Y-Values",
lwd=3,
col = "blue")
# Lab Exercise 2: How to create Lines in with different styles in R
# Step1: Assign values for different lines. We enclose the entire
# line with parenthesis symbol to force R to display the results instantly
# set the same value for the x variable
(x <- 1:10)
## [1] 1 2 3 4 5 6 7 8 9 10
# set different values for y variables
(y1 <- c(3, 1, 5, 2, 3, 8, 4, 7, 6, 9))
## [1] 3 1 5 2 3 8 4 7 6 9
(y2 <- c(5, 1, 4, 6, 2, 3, 7, 8, 2, 8))
## [1] 5 1 4 6 2 3 7 8 2 8
(y3 <- c(3, 3, 3, 3, 4, 4, 5, 5, 7, 7))
## [1] 3 3 3 3 4 4 5 5 7 7
# Plot first the pair x and y1.
plot(x, y1, type = "b",
main = "This is my Line Plot",
xlab = "My X-Values",
ylab = "My Y-Values",
lwd=3,
col = "blue")
# then Add the two lines (for x,y2) and (x,y3)
lines(x, y2, type = "b", col = "red",lwd=3)
lines(x, y3, type = "b", col = "green",lwd=3)
# Add legend to the plot
legend("topleft",
legend = c("Line y1", "Line y2", "Line y3"),
col = c("black", "red", "green"),
lty = 1)
# Step 2: Create Different Point Symbol for Each
# Line using the pch command
plot(x, y1, type = "b",pch = 16,
main = "This is my Line Plot",
xlab = "My X-Values",
ylab = "My Y-Values",
lwd=3,
col = "blue")
lines(x, y2, type = "b", col = "red",lwd=3, pch = 15)
lines(x, y3, type = "b", col = "green",lwd=3, pch = 8)
# Add legend
legend("topleft",
legend = c("Line y1", "Line y2", "Line y3"),
col = c("black", "red", "green"),
lty = 1)
# Lab Exercise 3: Create Line graph without x values
Pupils <- c(3.55 ,3.54 ,3.53 ,3.61 ,3.65 ,3.63 ,3.61
,3.61 ,3.59 ,3.63 ,3.59 ,3.63 ,3.62 ,3.62
,3.59 ,3.63 ,3.62 ,3.65 ,3.65)
# get number of elements of Pupils
length(Pupils)
## [1] 19
# Display the elements of Pupils
Pupils
## [1] 3.55 3.54 3.53 3.61 3.65 3.63 3.61 3.61 3.59 3.63 3.59 3.63 3.62 3.62 3.59
## [16] 3.63 3.62 3.65 3.65
# You can obtain the plot without x values
plot(Pupils, type = 'o')
# Lab Exercise 4: How to Create vertical, horizontal lines
# We will use buit-in cars dataset in R
# display the cars dataset
cars
## speed dist
## 1 4 2
## 2 4 10
## 3 7 4
## 4 7 22
## 5 8 16
## 6 9 10
## 7 10 18
## 8 10 26
## 9 10 34
## 10 11 17
## 11 11 28
## 12 12 14
## 13 12 20
## 14 12 24
## 15 12 28
## 16 13 26
## 17 13 34
## 18 13 34
## 19 13 46
## 20 14 26
## 21 14 36
## 22 14 60
## 23 14 80
## 24 15 20
## 25 15 26
## 26 15 54
## 27 16 32
## 28 16 40
## 29 17 32
## 30 17 40
## 31 17 50
## 32 18 42
## 33 18 56
## 34 18 76
## 35 18 84
## 36 19 36
## 37 19 46
## 38 19 68
## 39 20 32
## 40 20 48
## 41 20 52
## 42 20 56
## 43 20 64
## 44 22 66
## 45 23 54
## 46 24 70
## 47 24 92
## 48 24 93
## 49 24 120
## 50 25 85
# get the number of rows and columns using dim() command
dim(cars)
## [1] 50 2
# display the variable names of the cars dataset
names(cars)
## [1] "speed" "dist"
# display only the first column of the dataset
cars$speed # using the column name
## [1] 4 4 7 7 8 9 10 10 10 11 11 12 12 12 12 13 13 13 13 14 14 14 14 15 15
## [26] 15 16 16 17 17 17 18 18 18 18 19 19 19 20 20 20 20 20 22 23 24 24 24 24 25
cars[,1] # using the column number
## [1] 4 4 7 7 8 9 10 10 10 11 11 12 12 12 12 13 13 13 13 14 14 14 14 15 15
## [26] 15 16 16 17 17 17 18 18 18 18 19 19 19 20 20 20 20 20 22 23 24 24 24 24 25
# Remarks: the following commands will give you the same result
plot(cars,) # using the comma after the name
plot(cars[,1],cars[,2]) # using the column index 1 and 2
attach(cars); plot(speed,dist) # using the attach command to load the variables
plot(cars$speed,cars$dist) # using the dollar notation
# Problem: Create vertical lines using the v command
plot(cars)
abline(v = 15, col = "darkgreen",lwd=3) # vertical line
abline(v = 10, col = "blue",lwd=3) # vertical line
# Problem: Create horizontal lines using the h command
abline(h = 80, col = "darkgreen",lwd=3) # vertical line
abline(h = 20, col = "blue",lwd=3) # vertical line
# Create more lines simultaneously, using a vector of values
plot(cars)
abline(v = c(9, 22,25), col = c("darkgreen", "blue","red"),
lwd = c(1, 3,2), # line thickness
lty = c(2,2,2)) # dashed lines
plot(cars)
abline(v = c(9, 22,25), col = c("darkgreen", "blue","red"),
lwd = c(1, 3,2)) # line thickness and solid lines
# create horizontal lines
plot(cars)
abline(h = 60, col = "red",lty = 1, lwd = 3)
abline(h = 100, col = "red",lty = 2, lwd = 3)
abline(h = 20, col = "red",lty = 3, lwd = 3)
# Lab Exercise 5: How to Plot data by group
# We will use buit-in iris dataset in R
# this dataset is a collection of 4 species of flowers with different
# sepal length and width and also with different petal length and width
dim(iris) # iris dataset has 150 rows and 5 columns
## [1] 150 5
names(iris)
## [1] "Sepal.Length" "Sepal.Width" "Petal.Length" "Petal.Width" "Species"
# two different commands to get the frequency table
table(iris$Species) # refer to the dataset by variable name
##
## setosa versicolor virginica
## 50 50 50
table(iris[,5]) # refer to the dataset by column number
##
## setosa versicolor virginica
## 50 50 50
# get summary of all columns
summary(iris)
## Sepal.Length Sepal.Width Petal.Length Petal.Width
## Min. :4.300 Min. :2.000 Min. :1.000 Min. :0.100
## 1st Qu.:5.100 1st Qu.:2.800 1st Qu.:1.600 1st Qu.:0.300
## Median :5.800 Median :3.000 Median :4.350 Median :1.300
## Mean :5.843 Mean :3.057 Mean :3.758 Mean :1.199
## 3rd Qu.:6.400 3rd Qu.:3.300 3rd Qu.:5.100 3rd Qu.:1.800
## Max. :7.900 Max. :4.400 Max. :6.900 Max. :2.500
## Species
## setosa :50
## versicolor:50
## virginica :50
##
##
##
#create scatterplot of sepal width vs. sepal length
plot(iris$Sepal.Width, iris$Sepal.Length,
col='steelblue',
main='Scatterplot',
xlab='Sepal Width',
ylab='Sepal Length',
pch=19)
plot(iris$Sepal.Width, iris$Sepal.Length,
col='steelblue',
main='Scatterplot',
xlab='Sepal Width',
ylab='Sepal Length',
pch=1)
# another way to retrieve columns of data
PL <- iris$Petal.Length
PW <- iris$Petal.Width
plot(PL, PW)
# add color by species
plot(PL, PW, col = iris$Species, main= "My Plot")
# draw a line along with the distribution of points
# using the abline and lm commands
abline(lm(PW ~ PL))
# add text annotation
text(5, 0.5, "Regression Line")
legend("topleft", # specify the location of the legend
levels(iris$Species), # specify the levels of species
pch = 1:3, # specify three symbols used for the three species
col = 1:3 # specify three colors for the three species
)
# Lab Exercise 6: Generate advance scatter plot
pairs(iris, col = rainbow(3)[iris$Species]) # set colors by species
# How to filter data for each flower
(Versicolor <- subset(iris, Species == "versicolor"))
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 51 7.0 3.2 4.7 1.4 versicolor
## 52 6.4 3.2 4.5 1.5 versicolor
## 53 6.9 3.1 4.9 1.5 versicolor
## 54 5.5 2.3 4.0 1.3 versicolor
## 55 6.5 2.8 4.6 1.5 versicolor
## 56 5.7 2.8 4.5 1.3 versicolor
## 57 6.3 3.3 4.7 1.6 versicolor
## 58 4.9 2.4 3.3 1.0 versicolor
## 59 6.6 2.9 4.6 1.3 versicolor
## 60 5.2 2.7 3.9 1.4 versicolor
## 61 5.0 2.0 3.5 1.0 versicolor
## 62 5.9 3.0 4.2 1.5 versicolor
## 63 6.0 2.2 4.0 1.0 versicolor
## 64 6.1 2.9 4.7 1.4 versicolor
## 65 5.6 2.9 3.6 1.3 versicolor
## 66 6.7 3.1 4.4 1.4 versicolor
## 67 5.6 3.0 4.5 1.5 versicolor
## 68 5.8 2.7 4.1 1.0 versicolor
## 69 6.2 2.2 4.5 1.5 versicolor
## 70 5.6 2.5 3.9 1.1 versicolor
## 71 5.9 3.2 4.8 1.8 versicolor
## 72 6.1 2.8 4.0 1.3 versicolor
## 73 6.3 2.5 4.9 1.5 versicolor
## 74 6.1 2.8 4.7 1.2 versicolor
## 75 6.4 2.9 4.3 1.3 versicolor
## 76 6.6 3.0 4.4 1.4 versicolor
## 77 6.8 2.8 4.8 1.4 versicolor
## 78 6.7 3.0 5.0 1.7 versicolor
## 79 6.0 2.9 4.5 1.5 versicolor
## 80 5.7 2.6 3.5 1.0 versicolor
## 81 5.5 2.4 3.8 1.1 versicolor
## 82 5.5 2.4 3.7 1.0 versicolor
## 83 5.8 2.7 3.9 1.2 versicolor
## 84 6.0 2.7 5.1 1.6 versicolor
## 85 5.4 3.0 4.5 1.5 versicolor
## 86 6.0 3.4 4.5 1.6 versicolor
## 87 6.7 3.1 4.7 1.5 versicolor
## 88 6.3 2.3 4.4 1.3 versicolor
## 89 5.6 3.0 4.1 1.3 versicolor
## 90 5.5 2.5 4.0 1.3 versicolor
## 91 5.5 2.6 4.4 1.2 versicolor
## 92 6.1 3.0 4.6 1.4 versicolor
## 93 5.8 2.6 4.0 1.2 versicolor
## 94 5.0 2.3 3.3 1.0 versicolor
## 95 5.6 2.7 4.2 1.3 versicolor
## 96 5.7 3.0 4.2 1.2 versicolor
## 97 5.7 2.9 4.2 1.3 versicolor
## 98 6.2 2.9 4.3 1.3 versicolor
## 99 5.1 2.5 3.0 1.1 versicolor
## 100 5.7 2.8 4.1 1.3 versicolor
(Setosa <- subset(iris, Species == "setosa"))
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa
## 7 4.6 3.4 1.4 0.3 setosa
## 8 5.0 3.4 1.5 0.2 setosa
## 9 4.4 2.9 1.4 0.2 setosa
## 10 4.9 3.1 1.5 0.1 setosa
## 11 5.4 3.7 1.5 0.2 setosa
## 12 4.8 3.4 1.6 0.2 setosa
## 13 4.8 3.0 1.4 0.1 setosa
## 14 4.3 3.0 1.1 0.1 setosa
## 15 5.8 4.0 1.2 0.2 setosa
## 16 5.7 4.4 1.5 0.4 setosa
## 17 5.4 3.9 1.3 0.4 setosa
## 18 5.1 3.5 1.4 0.3 setosa
## 19 5.7 3.8 1.7 0.3 setosa
## 20 5.1 3.8 1.5 0.3 setosa
## 21 5.4 3.4 1.7 0.2 setosa
## 22 5.1 3.7 1.5 0.4 setosa
## 23 4.6 3.6 1.0 0.2 setosa
## 24 5.1 3.3 1.7 0.5 setosa
## 25 4.8 3.4 1.9 0.2 setosa
## 26 5.0 3.0 1.6 0.2 setosa
## 27 5.0 3.4 1.6 0.4 setosa
## 28 5.2 3.5 1.5 0.2 setosa
## 29 5.2 3.4 1.4 0.2 setosa
## 30 4.7 3.2 1.6 0.2 setosa
## 31 4.8 3.1 1.6 0.2 setosa
## 32 5.4 3.4 1.5 0.4 setosa
## 33 5.2 4.1 1.5 0.1 setosa
## 34 5.5 4.2 1.4 0.2 setosa
## 35 4.9 3.1 1.5 0.2 setosa
## 36 5.0 3.2 1.2 0.2 setosa
## 37 5.5 3.5 1.3 0.2 setosa
## 38 4.9 3.6 1.4 0.1 setosa
## 39 4.4 3.0 1.3 0.2 setosa
## 40 5.1 3.4 1.5 0.2 setosa
## 41 5.0 3.5 1.3 0.3 setosa
## 42 4.5 2.3 1.3 0.3 setosa
## 43 4.4 3.2 1.3 0.2 setosa
## 44 5.0 3.5 1.6 0.6 setosa
## 45 5.1 3.8 1.9 0.4 setosa
## 46 4.8 3.0 1.4 0.3 setosa
## 47 5.1 3.8 1.6 0.2 setosa
## 48 4.6 3.2 1.4 0.2 setosa
## 49 5.3 3.7 1.5 0.2 setosa
## 50 5.0 3.3 1.4 0.2 setosa
(Virginica <- subset(iris, Species == "virginica"))
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 101 6.3 3.3 6.0 2.5 virginica
## 102 5.8 2.7 5.1 1.9 virginica
## 103 7.1 3.0 5.9 2.1 virginica
## 104 6.3 2.9 5.6 1.8 virginica
## 105 6.5 3.0 5.8 2.2 virginica
## 106 7.6 3.0 6.6 2.1 virginica
## 107 4.9 2.5 4.5 1.7 virginica
## 108 7.3 2.9 6.3 1.8 virginica
## 109 6.7 2.5 5.8 1.8 virginica
## 110 7.2 3.6 6.1 2.5 virginica
## 111 6.5 3.2 5.1 2.0 virginica
## 112 6.4 2.7 5.3 1.9 virginica
## 113 6.8 3.0 5.5 2.1 virginica
## 114 5.7 2.5 5.0 2.0 virginica
## 115 5.8 2.8 5.1 2.4 virginica
## 116 6.4 3.2 5.3 2.3 virginica
## 117 6.5 3.0 5.5 1.8 virginica
## 118 7.7 3.8 6.7 2.2 virginica
## 119 7.7 2.6 6.9 2.3 virginica
## 120 6.0 2.2 5.0 1.5 virginica
## 121 6.9 3.2 5.7 2.3 virginica
## 122 5.6 2.8 4.9 2.0 virginica
## 123 7.7 2.8 6.7 2.0 virginica
## 124 6.3 2.7 4.9 1.8 virginica
## 125 6.7 3.3 5.7 2.1 virginica
## 126 7.2 3.2 6.0 1.8 virginica
## 127 6.2 2.8 4.8 1.8 virginica
## 128 6.1 3.0 4.9 1.8 virginica
## 129 6.4 2.8 5.6 2.1 virginica
## 130 7.2 3.0 5.8 1.6 virginica
## 131 7.4 2.8 6.1 1.9 virginica
## 132 7.9 3.8 6.4 2.0 virginica
## 133 6.4 2.8 5.6 2.2 virginica
## 134 6.3 2.8 5.1 1.5 virginica
## 135 6.1 2.6 5.6 1.4 virginica
## 136 7.7 3.0 6.1 2.3 virginica
## 137 6.3 3.4 5.6 2.4 virginica
## 138 6.4 3.1 5.5 1.8 virginica
## 139 6.0 3.0 4.8 1.8 virginica
## 140 6.9 3.1 5.4 2.1 virginica
## 141 6.7 3.1 5.6 2.4 virginica
## 142 6.9 3.1 5.1 2.3 virginica
## 143 5.8 2.7 5.1 1.9 virginica
## 144 6.8 3.2 5.9 2.3 virginica
## 145 6.7 3.3 5.7 2.5 virginica
## 146 6.7 3.0 5.2 2.3 virginica
## 147 6.3 2.5 5.0 1.9 virginica
## 148 6.5 3.0 5.2 2.0 virginica
## 149 6.2 3.4 5.4 2.3 virginica
## 150 5.9 3.0 5.1 1.8 virginica
# Draw boxplot for each type of flower
boxplot(Versicolor[,1:4], main="Versicolor, Rainbow Palette",ylim =
c(0,8),las=2, col=rainbow(4))
boxplot(Setosa[,1:4], main="Setosa, Heat color Palette",ylim = c(0,8),las=2,
col=heat.colors(4))
boxplot(Virginica[,1:4], main="Virginica, Topo colors Palette",ylim =
c(0,8),las=2, col=topo.colors(4))
# Lab Exercise 7: How to load external datasets
# From a local directory
# the folder that contains the file should be specified completely
# using the forward slash symbol instead of the backward splash
library(readr)
cancer <- read_csv("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
## 33 192 0 4 3 2 2 read 65
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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 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", "africanamer","
white"))
data$female = factor(data$female, labels=c("female", "male"))
# check data structure again. The former integer variables are now categorical variable
str(data)
## 'data.frame': 1000 obs. of 8 variables:
## $ id : num 70 121 86 141 172 113 50 11 84 48 ...
## $ female : Factor w/ 2 levels "female","male": 1 2 1 1 1 1 1 1 1 1 ...
## $ race : Factor w/ 4 levels "hispanic","asian",..: 4 4 4 4 4 4 3 1 4 3 ...
## $ ses : Factor w/ 3 levels "low","middle",..: 1 2 3 3 2 2 2 2 2 2 ...
## $ schtyp : Factor w/ 2 levels "public","private": 1 1 1 1 1 1 1 1 1 1 ...
## $ prog : Factor w/ 3 levels "general","academic",..: 1 3 1 3 2 2 1 2 1 2 ...
## $ variable: Factor w/ 5 levels "read","write",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ value : num 57 68 44 63 47 44 50 34 63 57 ...
# we compare student performance by using boxplots
library(gplots)
##
## Attaching package: 'gplots'
##
## The following object is masked from 'package:stats':
##
## lowess
# compute the average value by group using tapply() command
means <- round(tapply(data$value, data$variable, mean), digits=2)
# create boxplot by group
boxplot(data$value ~ data$variable, main= "Student Performance by Subject
(brown dot = mean score)",
xlab="Subject matter", ylab="Percentage Scores", col=rainbow(5))
# insert the average values
points(means, col="brown", pch=18)
# can also compute the median values
medians = round(tapply(data$value, data$variable, median), digits=2)
medians
## read write math science socst
## 50 54 52 53 52
points(medians, col="red", pch=18)
# Lab Exercise 9: How to plot categorical variables
library(ggplot2)
# we load variable names to memory to avoid the dollar notation
attach(hsb2_long)
# create the plot object p
p <- ggplot(hsb2_long, aes(ses, fill = prog)) + facet_wrap(~race)
p + geom_bar()
## Warning: The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
p + geom_bar(position = "dodge")
## Warning: The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
library(ggplot2)
attach(hsb2_long)
## The following objects are masked from hsb2_long (pos = 3):
##
## female, id, prog, race, schtyp, ses, value, variable
p <- ggplot(hsb2_long, aes(ses, fill = prog)) + facet_wrap(~schtyp)
p + geom_bar()
## Warning: The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
p + geom_bar(position = "dodge")
## Warning: The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
# Lab Exercise 10: Scatter Plots with marginal Distributions
# Advance Scatter plots using libraries
# install.packages("ggExtra")
# install.packages("tidyverse")
library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.1 ✔ stringr 1.5.0
## ✔ forcats 1.0.0 ✔ tibble 3.2.1
## ✔ lubridate 1.9.2 ✔ tidyr 1.3.0
## ✔ purrr 1.0.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(ggExtra)
# set theme appearance of grid background
theme_set(theme_bw(1))
# create X and Y vector
(xAxis <- rnorm(1000)) # normal distribution with mean 0 and sd=1
## [1] 3.409575e-01 -7.719201e-01 4.876172e-01 3.991219e-01 1.878852e+00
## [6] -2.121204e+00 1.035551e-02 2.179876e-01 -1.000403e+00 -4.487063e-01
## [11] 1.581279e+00 4.188315e-01 -6.791913e-01 1.659934e+00 7.553677e-01
## [16] -1.667321e+00 6.574069e-01 -4.869845e-01 -1.249671e+00 -1.590320e-01
## [21] -5.782027e-01 -8.943598e-01 6.245035e-01 -1.093621e+00 2.373496e-01
## [26] 2.689920e-01 4.942085e-01 -3.297741e-01 -1.367781e+00 2.928795e-01
## [31] -1.425571e+00 -1.783196e+00 -1.290991e+00 -1.400121e-01 -1.117545e+00
## [36] 1.829056e+00 -8.282749e-02 1.994940e-02 -1.240645e-01 6.586247e-03
## [41] -1.536574e+00 7.053144e-02 7.334283e-01 -6.769525e-01 1.469061e-01
## [46] 7.066507e-01 3.745086e-01 7.165542e-01 2.253530e+00 2.265302e-01
## [51] 3.839332e-01 -1.023759e-01 9.144695e-01 -1.127231e+00 -1.070486e+00
## [56] -5.809122e-01 1.417153e+00 5.027462e-01 5.846934e-01 -2.049763e-03
## [61] -4.135235e-01 6.522196e-01 6.659892e-01 -9.109032e-01 -2.030678e+00
## [66] -3.869109e-01 -1.299242e+00 -8.259640e-02 -5.462009e-02 1.341195e+00
## [71] 2.764819e-01 -1.807248e+00 1.564987e+00 -1.381861e+00 -1.050401e-01
## [76] 7.875416e-01 3.318146e-01 -2.326054e-01 2.689871e+00 -1.270241e+00
## [81] 8.592305e-01 -6.859279e-01 2.220620e+00 -1.290481e+00 -2.761709e-01
## [86] -1.114385e+00 -1.910441e+00 7.458022e-01 -5.603938e-01 1.113525e-01
## [91] 9.577619e-01 3.873006e-01 -5.995250e-01 -6.049432e-01 -7.088252e-02
## [96] -5.938226e-02 1.761465e+00 1.530876e+00 -2.099453e+00 8.429981e-02
## [101] -1.242658e+00 2.088927e-01 -1.352475e+00 1.077401e+00 4.340448e-02
## [106] 1.777176e+00 -4.066184e-01 1.435004e+00 7.951172e-02 -1.129640e-01
## [111] 8.741924e-01 -5.148990e-01 -1.479696e-01 9.906909e-01 1.096519e+00
## [116] 2.434663e+00 -2.913619e-01 -9.871906e-01 4.511395e-01 -2.202277e-01
## [121] -6.979903e-01 -1.445276e+00 3.862502e-01 1.584769e+00 6.803168e-01
## [126] -1.682292e+00 6.878002e-02 -6.574444e-01 1.209443e+00 -1.079798e+00
## [131] -1.422287e+00 7.189521e-02 -2.525578e-01 -7.882570e-01 2.076565e+00
## [136] -1.305280e+00 -1.209629e+00 -6.035099e-01 2.088116e-01 -6.667146e-02
## [141] 1.086865e+00 -7.959841e-01 -1.556256e+00 -1.492581e+00 -1.118105e-01
## [146] -1.669966e+00 8.791722e-01 -3.296513e-01 9.326564e-01 -7.043529e-01
## [151] 1.648896e+00 -1.076650e+00 9.009351e-01 5.682331e-01 -2.809782e-01
## [156] 6.244678e-01 2.195397e+00 -3.790542e-01 -3.245761e-01 9.337869e-01
## [161] -1.971571e+00 -1.033280e+00 3.427030e-01 -1.004823e+00 1.901582e+00
## [166] -1.232107e+00 -1.047757e-01 1.308674e+00 6.631255e-01 -4.952800e-01
## [171] 1.460605e+00 -5.702497e-01 1.046362e+00 1.403625e+00 1.089381e+00
## [176] 1.370614e-01 -8.852995e-01 6.574533e-01 -8.096115e-01 -4.305182e-01
## [181] -3.179791e-01 -7.457510e-01 -4.605317e-01 -1.056466e+00 -1.139446e+00
## [186] 3.868106e-01 -7.859772e-01 2.384253e-01 -8.961429e-01 -1.593211e+00
## [191] -3.738927e-01 7.735259e-01 6.857871e-01 -5.223749e-01 8.010797e-01
## [196] -9.413153e-01 -5.669490e-01 -4.746584e-01 -1.638148e-01 1.515446e-01
## [201] -7.273367e-02 -2.011138e-01 1.050540e+00 1.200810e+00 9.815205e-01
## [206] -8.332721e-01 -2.503941e-02 3.829635e-01 -1.115403e-01 3.058218e-01
## [211] -5.118443e-01 1.423254e+00 2.794278e-02 -1.283295e+00 -2.569362e-01
## [216] -1.130067e+00 8.388776e-03 9.044386e-02 -5.563117e-01 -1.695991e+00
## [221] -5.706962e-01 6.248616e-01 -9.524885e-02 -8.769316e-01 6.294927e-01
## [226] 1.281731e-01 1.316090e+00 1.290378e+00 -1.137059e+00 -2.067914e-01
## [231] -8.330314e-01 5.004598e-01 -1.540153e+00 -2.741267e-02 1.865387e-01
## [236] 1.115644e+00 -1.361765e-01 2.450819e+00 -3.740874e-01 -9.424560e-01
## [241] -2.248322e+00 -4.886354e-01 1.092209e-01 9.874453e-04 -4.527194e-01
## [246] 1.611906e-01 3.620933e-01 -9.289031e-01 -1.259060e+00 1.691572e-01
## [251] -2.323643e-01 -5.670137e-01 -7.511098e-01 7.084574e-01 -5.284639e-01
## [256] 5.092486e-02 -3.241424e-01 9.909194e-01 1.929576e-01 3.739242e-01
## [261] 1.530022e+00 4.611246e-01 -1.810379e+00 -1.238104e+00 -5.004199e-01
## [266] -5.112005e-01 -1.982114e-01 -7.322820e-01 -9.797301e-01 -3.086885e-01
## [271] 1.161829e+00 7.339281e-01 3.765491e-01 -5.336119e-01 -3.650638e-01
## [276] 3.562810e-01 1.719492e+00 -4.002897e-01 7.861289e-01 -2.905187e+00
## [281] 5.889372e-02 -4.531388e-01 2.922541e-01 2.262458e-01 -3.311195e-01
## [286] -1.175555e+00 -4.844587e-01 -3.526646e-01 6.753127e-01 -2.232440e-01
## [291] -5.984553e-01 1.192218e+00 1.006098e+00 -1.164808e+00 -9.114231e-01
## [296] -5.474136e-01 1.388172e+00 -1.198035e-01 -8.760456e-01 -1.413709e+00
## [301] -8.775808e-01 -1.562521e+00 -2.415780e-01 -1.592944e-01 9.298013e-01
## [306] -1.487598e+00 -1.375478e+00 -1.150258e+00 3.734889e-01 -1.641655e+00
## [311] 2.090085e+00 1.241919e+00 2.018898e+00 -1.244763e+00 8.686362e-01
## [316] 5.951364e-01 -1.537980e-01 -5.609926e-01 1.004828e+00 -1.374475e+00
## [321] -1.889149e+00 2.591793e+00 1.467393e+00 1.861938e-01 -1.760868e+00
## [326] -6.162767e-01 -5.995633e-01 5.449664e-01 -1.303450e-01 1.761425e+00
## [331] -8.098125e-01 -1.729015e-01 3.384362e-01 -2.536018e-03 2.427281e-01
## [336] 1.256939e-01 3.774665e-01 9.251033e-01 9.977532e-02 6.632399e-02
## [341] -2.495618e-01 -1.445335e+00 3.649235e-01 -3.770405e-01 9.257322e-01
## [346] -1.504689e+00 3.794436e-01 -1.691665e+00 8.244697e-01 -9.771159e-01
## [351] 1.358224e-01 1.177632e+00 5.076344e-01 2.731869e-01 -1.760787e-01
## [356] -6.432038e-01 -2.873483e-01 5.863002e-02 1.014129e+00 -7.591230e-01
## [361] -6.625472e-01 2.928904e-02 9.680263e-01 -4.535334e-01 1.830489e-01
## [366] -1.548943e+00 -1.690957e+00 1.266109e+00 1.871802e+00 6.784542e-01
## [371] 1.046214e+00 8.970366e-01 3.194762e-01 -9.493115e-02 -4.227833e-01
## [376] 7.006012e-01 6.290820e-01 -4.461346e-01 -2.078600e-01 -5.945440e-01
## [381] -1.356801e+00 7.350038e-01 7.502912e-01 -3.737877e-01 1.009162e+00
## [386] -5.926768e-01 1.954754e+00 -1.098446e+00 -1.942397e+00 1.667034e+00
## [391] -9.524117e-01 2.137054e+00 -5.459332e-01 2.729063e+00 -4.296521e-01
## [396] -3.638107e-01 6.815228e-01 -1.628109e+00 1.445356e+00 2.246554e+00
## [401] -1.407752e+00 3.975670e-01 -1.667417e+00 2.211537e+00 1.215376e+00
## [406] -8.592549e-01 -1.156635e+00 1.110529e+00 -1.507332e+00 -1.938236e+00
## [411] 9.916233e-01 -8.664058e-01 7.320542e-01 5.035988e-01 -1.330849e+00
## [416] -2.449426e-01 1.132481e+00 4.598998e-01 1.570867e-01 4.875234e-01
## [421] -1.069531e+00 1.265634e+00 1.345252e+00 2.860358e-01 -1.103901e-01
## [426] -1.088520e+00 5.087606e-01 9.509454e-01 8.404597e-01 -1.639187e+00
## [431] -5.378148e-01 -8.032560e-01 3.341928e-01 -1.185620e-01 1.041088e+00
## [436] 3.788275e-02 7.211497e-01 2.270019e+00 3.664618e-02 5.633037e-01
## [441] -6.148231e-01 4.683675e-03 -1.046138e+00 -2.282173e-01 4.084029e-01
## [446] 3.266178e-01 -1.368726e+00 1.402579e+00 -1.802245e+00 1.130855e+00
## [451] 6.600765e-01 1.091589e+00 7.738603e-01 -1.133310e+00 -4.452571e-01
## [456] 7.039486e-01 3.130652e-01 -9.612895e-01 -2.522741e-01 -1.989946e-01
## [461] 1.706204e+00 6.652973e-01 2.642765e-01 -4.485797e-01 -4.195383e-01
## [466] -1.067415e-01 7.373909e-01 -2.088068e-01 -1.696900e+00 1.410234e+00
## [471] 3.200339e-02 9.583779e-01 -4.903723e-01 2.283348e+00 5.068375e-01
## [476] 5.536835e-01 -4.187955e-01 1.055986e+00 -7.790599e-01 1.847680e-01
## [481] 3.071071e-01 -2.179237e+00 1.253352e+00 1.316307e+00 -1.612764e-01
## [486] -1.811290e-01 -5.152022e-01 -9.876129e-01 1.574241e+00 -3.122014e-01
## [491] -5.577358e-01 1.060582e+00 9.615360e-01 4.895078e-03 -1.072402e-01
## [496] 3.270672e-01 -1.041616e+00 -7.131226e-01 -4.035470e-01 -3.978707e-01
## [501] -2.959602e+00 -2.849276e-01 7.345552e-01 9.245570e-01 -8.548432e-01
## [506] -2.730342e-02 4.693053e-01 9.565311e-01 7.173685e-01 1.032761e+00
## [511] -1.364626e+00 -4.033207e-01 -1.071182e+00 1.598844e+00 -2.845634e-01
## [516] 2.573061e-01 -3.744480e-01 9.086362e-01 -7.604490e-01 7.852267e-01
## [521] 1.707014e+00 -2.031403e+00 4.747758e-01 1.151432e+00 4.242852e-01
## [526] -6.339310e-01 8.712061e-01 -1.006350e+00 -2.409523e+00 -1.634676e+00
## [531] -1.569678e+00 -8.052392e-01 5.489853e-01 -5.816327e-01 1.571224e+00
## [536] 7.001274e-01 -7.765119e-01 2.784401e+00 2.329353e-01 -6.181795e-01
## [541] -1.415938e+00 3.849437e-01 4.883491e-01 -3.660169e-01 -2.578184e-01
## [546] 8.707405e-01 -4.299221e-01 -1.266613e+00 1.629829e+00 -1.218538e+00
## [551] -1.542836e+00 -1.238951e+00 3.237242e-01 1.880632e-01 -4.003935e-01
## [556] 8.023049e-01 -1.532100e-01 1.810440e-01 -1.272760e+00 -1.066830e+00
## [561] 1.280447e+00 -1.186110e+00 5.863203e-05 4.584896e-01 -8.955855e-01
## [566] -1.255165e+00 -4.739914e-01 -6.007936e-01 -7.505299e-01 -3.623925e-01
## [571] -8.241837e-01 7.778733e-04 -1.522984e+00 -3.962287e-01 -1.535087e+00
## [576] -4.057073e-01 -5.862881e-01 4.185755e-01 -7.345549e-01 2.385410e-01
## [581] -1.442348e-01 4.341262e-02 6.762230e-01 1.165168e+00 2.738523e+00
## [586] 5.179207e-01 -7.014055e-01 -9.156144e-01 9.455999e-01 5.096008e-01
## [591] -6.627273e-01 3.897679e-01 3.585609e-01 -7.743672e-01 2.484510e-01
## [596] 1.165633e+00 -6.364062e-01 -1.786430e+00 2.307636e-01 -2.601488e-01
## [601] 7.190922e-01 3.616583e-01 3.104218e-02 2.759193e-01 1.370825e-01
## [606] 3.080248e-01 1.362376e+00 1.802387e+00 -3.514487e-01 -5.853853e-01
## [611] -3.665441e-01 1.601780e+00 -2.475328e-02 -3.280880e-01 6.935167e-01
## [616] 3.247416e-02 1.236982e+00 1.169434e+00 -1.189060e+00 1.145508e+00
## [621] -2.783449e-01 -1.791495e+00 9.010615e-01 -2.029156e+00 2.123552e+00
## [626] 4.904401e-01 1.660082e+00 8.696916e-01 -3.226574e-01 -1.162466e+00
## [631] 2.316559e-02 4.385162e-01 7.194273e-01 -1.044595e+00 1.005509e+00
## [636] 1.221441e-01 5.297728e-01 -1.617265e+00 -5.258901e-01 3.764792e-01
## [641] 9.464863e-01 -1.428222e+00 4.694239e-01 -7.500929e-01 2.907662e-01
## [646] -5.759592e-01 -5.696566e-01 -1.731151e-01 -4.328340e-01 1.611089e-01
## [651] -2.148214e-01 2.362618e+00 6.472600e-01 1.103584e+00 7.241580e-02
## [656] 1.992861e+00 -6.113006e-01 -1.067434e+00 6.110102e-01 -4.397162e-01
## [661] 3.319870e-01 5.481909e-01 -4.550486e-01 -5.203283e-01 5.447056e-01
## [666] -1.180524e-01 3.322038e-01 7.255522e-01 -5.301118e-01 -9.452194e-01
## [671] 1.996763e+00 -2.805795e-01 -5.893873e-02 -1.152007e+00 2.864881e-01
## [676] -5.075160e-01 5.303191e-01 5.997789e-01 1.063368e+00 -1.187970e+00
## [681] -9.573954e-02 -6.867736e-02 4.934703e-01 6.346203e-01 1.657042e+00
## [686] -5.342873e-01 4.030736e-01 6.430032e-01 4.621802e-01 -6.629698e-01
## [691] 2.426747e-01 6.753601e-01 -2.903990e-01 7.357490e-01 -7.186730e-01
## [696] -1.388407e-01 -7.056514e-01 4.137548e-02 -3.565859e-01 9.441206e-01
## [701] -5.012408e-01 2.717150e-01 3.749520e-01 -1.474104e+00 -1.051264e+00
## [706] 2.500415e-01 -1.971542e-01 -7.895032e-01 -7.208512e-01 -4.952971e-01
## [711] -2.062995e-01 -4.759524e-01 -4.178306e-01 1.332582e+00 -2.295608e+00
## [716] -1.363704e+00 -2.164797e-01 1.693665e+00 3.988694e-01 -1.236076e+00
## [721] -7.506897e-01 -1.211024e+00 -7.647874e-01 -9.856615e-01 1.609288e+00
## [726] 8.441458e-01 1.012514e+00 -1.826996e-01 -5.556780e-01 -8.616292e-01
## [731] -2.563663e+00 8.634105e-01 4.506151e-02 2.215987e+00 -5.931609e-01
## [736] -5.259580e-01 -8.885156e-01 1.902544e-01 5.837953e-01 7.773104e-01
## [741] 6.273919e-01 -1.502877e+00 1.005311e+00 -1.746908e+00 -2.650310e-01
## [746] 7.116609e-01 -3.212287e-01 -5.030847e-02 7.715189e-01 2.725811e-01
## [751] -6.826053e-01 2.252843e-01 -1.426949e+00 -6.073178e-01 4.193971e-01
## [756] 1.260885e+00 -5.630185e-01 -1.387677e+00 1.240208e+00 -1.367099e+00
## [761] 8.576436e-01 1.235896e+00 1.312439e-01 -4.438057e-01 -6.236640e-01
## [766] 1.771232e+00 -6.077922e-01 -1.349894e-01 3.454136e-01 -4.582448e-01
## [771] -2.910404e-01 -1.554030e+00 -2.466181e+00 -9.949888e-01 -1.487724e+00
## [776] -1.039775e-01 8.149550e-01 1.522509e+00 -1.007803e+00 -1.262925e+00
## [781] -2.110408e+00 6.517618e-01 7.238002e-01 1.431935e-01 -9.776515e-01
## [786] -1.303081e-01 -1.461183e+00 6.284861e-01 -1.108837e+00 -3.810098e-01
## [791] 1.981811e+00 -4.473148e-01 2.217736e-01 -8.764978e-02 1.149344e+00
## [796] -1.844002e+00 5.988403e-01 -1.720555e+00 -7.366785e-01 -1.114084e-01
## [801] -5.347304e-01 1.127710e+00 1.521446e+00 -4.837819e-01 -8.874225e-01
## [806] 2.785573e-01 5.463433e-04 -9.747812e-01 -8.918059e-01 1.271791e+00
## [811] 1.817452e+00 6.914880e-01 2.548714e-01 -7.441122e-01 -6.528096e-01
## [816] -1.299401e-01 1.611301e+00 -1.367432e+00 -3.830471e-01 1.632605e-01
## [821] 9.908760e-01 5.558035e-01 -1.858030e+00 -1.557793e+00 -7.832210e-01
## [826] 1.683863e+00 4.571530e-01 1.037675e+00 6.085304e-01 -4.551881e-01
## [831] -2.627435e-01 6.757000e-01 -8.355696e-01 -1.719740e+00 -1.384501e+00
## [836] -1.016026e+00 -1.024886e+00 -1.249492e+00 -1.570872e+00 1.101574e-01
## [841] -8.615660e-01 -1.398329e-01 -1.242791e+00 8.381966e-02 -4.801935e-01
## [846] -6.982082e-01 -1.361053e+00 -1.314153e+00 1.265492e+00 -1.457447e-01
## [851] 2.281221e-01 -1.202463e+00 -5.528430e-01 1.192652e+00 6.315029e-01
## [856] -8.092317e-01 9.350428e-01 2.097894e+00 -8.632397e-02 -2.013994e-01
## [861] 4.836859e-01 3.103468e+00 3.235659e-01 -8.804952e-02 1.449323e+00
## [866] 5.105417e-01 -3.868537e-01 -5.036138e-01 7.133995e-01 5.787124e-01
## [871] 8.042879e-01 3.543449e-01 -8.433190e-01 -4.357954e-03 3.912083e-01
## [876] 1.076239e+00 2.787701e+00 -4.423350e-01 -7.047497e-02 -5.685607e-01
## [881] -1.613962e-01 1.606455e+00 2.641425e-01 8.916688e-01 6.791757e-01
## [886] -2.858623e-02 -7.402281e-01 1.258176e-01 1.271132e-02 9.267894e-01
## [891] -2.587824e-02 -8.448512e-02 9.041815e-01 1.734598e-01 -4.374525e-01
## [896] -1.369398e-01 -9.206628e-01 -9.109012e-01 4.059263e-02 5.093732e-01
## [901] 1.733256e+00 3.969522e-01 1.482538e+00 -3.624031e-01 7.276882e-01
## [906] -1.174352e+00 9.163876e-01 -1.408021e+00 -1.004358e+00 -8.426190e-01
## [911] -1.547401e+00 -6.604427e-01 -1.149441e+00 -9.787213e-01 -7.247790e-01
## [916] -1.162991e-01 -8.715543e-01 2.084079e+00 6.895942e-01 1.425751e-01
## [921] -2.242896e-01 6.519925e-01 -2.253090e+00 6.352372e-01 -6.458876e-01
## [926] -1.809039e+00 3.593689e-01 -1.444949e+00 1.997993e+00 -9.630709e-01
## [931] -1.172982e+00 -1.277992e+00 1.635831e+00 -8.222604e-01 -3.362282e-01
## [936] -2.117293e-02 2.840844e-01 1.134588e-01 -7.953834e-01 9.737984e-01
## [941] -1.727439e-01 -6.157031e-01 8.342797e-02 -1.602411e+00 -1.355857e+00
## [946] 1.452195e+00 1.403837e+00 3.117169e-01 4.102366e-01 -9.258744e-01
## [951] -4.100004e-01 6.509562e-01 1.393049e-01 6.767576e-01 -8.357439e-01
## [956] -5.544964e-02 8.289160e-01 -2.754559e-01 -2.874954e-01 5.221841e-01
## [961] 3.005028e-01 1.682822e+00 1.708991e-01 1.478940e+00 -1.193102e+00
## [966] -8.565334e-01 1.730042e-02 6.378157e-01 -4.718942e-01 5.241351e-01
## [971] 3.540228e-01 -2.319803e+00 -2.466931e-02 7.198521e-01 -1.103903e-01
## [976] -1.922731e+00 -1.262346e+00 -2.766504e-01 -4.794508e-01 6.075831e-01
## [981] -5.357607e-01 -2.458623e-01 -5.549597e-01 8.732257e-02 1.568078e+00
## [986] -2.256177e-01 9.868531e-01 -8.563744e-01 8.045794e-01 6.141468e-01
## [991] -7.616397e-01 -2.695376e-01 -9.618311e-01 -1.191909e+00 4.339510e-01
## [996] 1.457446e+00 1.583324e+00 2.895092e-01 1.123415e+00 -2.773339e-01
yAxis <- rnorm(1000) + xAxis + 10
yAxis
## [1] 12.892625 11.173296 10.175672 10.308664 12.160531 8.417629 11.046510
## [8] 8.839149 8.837632 9.345194 10.661556 10.056582 8.729314 13.554631
## [15] 10.555042 9.259935 9.234248 8.957486 8.194139 11.704306 10.702909
## [22] 9.657230 11.738335 8.527644 10.042653 9.693401 10.211137 9.203718
## [29] 9.978710 10.493051 8.398914 7.676048 7.777014 8.654656 8.772878
## [36] 12.578867 8.686168 10.300881 8.213982 7.580450 7.988360 8.622610
## [43] 9.524330 8.850089 12.564320 11.021498 10.440161 11.648839 11.987670
## [50] 10.746997 9.787251 9.640866 11.878266 9.238145 7.616859 9.914876
## [57] 11.221256 11.342597 10.725963 10.459142 11.185811 11.469862 12.543374
## [64] 8.186019 8.440229 10.284443 7.426495 9.766200 9.682551 11.779807
## [71] 9.650926 6.690981 11.093230 8.394739 8.723273 9.966412 10.548530
## [78] 11.654665 14.617199 8.311213 10.386806 8.667801 12.618485 9.185743
## [85] 9.653822 8.212596 6.698118 10.490690 10.798787 9.084677 13.740030
## [92] 9.364291 7.866137 10.127910 10.576488 8.259794 11.092027 12.702329
## [99] 9.425831 10.706165 8.467866 11.403658 8.026764 11.204583 9.605109
## [106] 10.728297 9.848181 11.481361 11.979739 12.914399 10.729113 9.969755
## [113] 11.004299 10.108346 10.415779 11.996905 8.692926 9.194785 9.866129
## [120] 9.243707 8.277330 8.999043 11.831247 10.616584 10.807493 8.525035
## [127] 10.379733 10.133338 10.881087 8.206357 9.460271 10.464816 9.580811
## [134] 8.257852 11.752051 8.953424 7.649440 8.420750 11.656184 9.828466
## [141] 8.516517 9.035826 8.910192 8.974078 8.649192 8.599475 10.376352
## [148] 10.026643 12.138227 10.733934 12.262438 8.626191 10.795087 10.594027
## [155] 10.819404 10.221354 12.316327 8.777980 11.397147 10.944873 7.210773
## [162] 10.769828 10.253372 7.464330 11.148918 8.010420 8.837086 11.222564
## [169] 8.591933 9.725245 12.480563 10.219494 11.238329 10.331374 10.696265
## [176] 11.224772 9.926861 11.372845 9.137210 10.022522 8.683944 8.449150
## [183] 9.366595 6.697981 8.973977 10.206729 8.743858 10.582804 8.904925
## [190] 8.059808 10.662433 10.205395 9.894897 8.867486 11.475806 9.918846
## [197] 10.288948 8.857025 8.839124 9.961463 8.049242 10.280401 11.206947
## [204] 13.585279 11.123224 8.612142 10.563722 10.245548 10.646689 9.738789
## [211] 8.708668 11.284799 8.993306 8.632134 10.517476 9.411578 10.148909
## [218] 9.771651 9.280209 8.699431 8.929193 8.447479 8.537282 7.677566
## [225] 11.197624 9.567884 13.406143 11.802406 10.917544 10.235615 11.672771
## [232] 10.581569 7.851378 9.577973 9.497651 10.522383 10.612819 10.228507
## [239] 9.075845 8.193641 8.780104 8.774857 10.444103 12.016545 8.015640
## [246] 10.658478 10.387375 9.425360 9.214457 10.474577 8.860022 11.190823
## [253] 10.603936 9.492520 9.523786 10.327303 10.507218 11.281701 10.498232
## [260] 9.575227 10.962047 11.144562 7.685182 6.557255 8.554512 12.513912
## [267] 8.387771 9.288357 9.752984 9.591454 10.529840 10.082747 10.175962
## [274] 10.134024 11.814467 9.777667 10.675741 8.480834 10.596306 8.364720
## [281] 8.863333 9.696045 10.061210 10.350772 8.913524 8.442173 11.353871
## [288] 8.902919 11.623149 8.693585 10.972060 10.456362 9.498321 8.959776
## [295] 9.179153 8.282276 13.519750 11.152992 7.280464 9.507550 7.582267
## [302] 8.605865 9.939469 11.488075 11.824876 8.807065 7.955255 8.623602
## [309] 10.996858 8.569321 12.542108 11.292896 12.216863 8.238455 10.516459
## [316] 10.894670 10.075690 8.420886 11.361274 9.252027 9.255990 13.087473
## [323] 10.568339 8.083356 7.246026 10.566936 10.290827 9.000862 7.891355
## [330] 12.524960 8.842356 9.469790 9.831609 11.149858 9.392395 10.078429
## [337] 9.289625 10.316251 10.362854 10.389445 10.254542 8.073540 10.730560
## [344] 9.974695 11.025247 8.296802 10.548531 6.964184 12.211146 10.304183
## [351] 10.465728 9.988001 10.075623 12.000657 9.856213 8.751250 9.703439
## [358] 10.070620 11.385667 9.006097 9.342215 9.335508 11.827200 8.979954
## [365] 10.156229 8.673724 7.736622 11.454045 10.341247 11.880950 9.918257
## [372] 10.342329 9.313431 9.745759 10.270981 11.059335 8.459238 8.045430
## [379] 9.719529 8.954733 9.913903 8.782587 9.934287 10.539918 9.466127
## [386] 10.288010 9.737623 9.665147 7.415041 12.044373 8.946035 12.645331
## [393] 9.771325 13.134929 10.100385 10.779023 11.343287 6.783610 10.076339
## [400] 10.255794 9.761192 9.401942 8.644151 10.982655 11.488020 10.910782
## [407] 7.911283 11.573348 6.379789 8.858610 12.591544 8.902608 9.981642
## [414] 9.433385 8.577985 9.942465 11.089416 11.853922 9.551877 10.418123
## [421] 6.853700 11.480948 11.509571 10.235357 10.088006 9.973733 11.121410
## [428] 10.794233 8.981918 8.335456 9.007717 9.329114 10.811885 11.504149
## [435] 11.530183 9.675236 12.237933 12.038731 10.104681 11.083763 9.498179
## [442] 9.890344 10.757290 8.607113 9.316943 10.910340 8.544103 10.004224
## [449] 8.104598 8.895699 8.831625 12.587992 8.053071 9.996973 9.299589
## [456] 12.753960 9.256647 9.364809 10.168343 9.222600 10.676237 10.924931
## [463] 9.069445 11.792655 8.767678 10.518399 10.691468 10.262996 8.312270
## [470] 12.136209 10.344651 11.545743 9.573209 12.992881 11.036733 10.782293
## [477] 7.506896 8.987665 9.613765 9.497083 9.369096 6.214975 10.171382
## [484] 11.436190 10.262285 10.301168 11.018970 7.972591 11.627048 8.707828
## [491] 10.034211 9.915433 10.947481 10.677295 8.931912 11.835029 9.334289
## [498] 9.674532 10.314979 9.536909 6.821658 10.875510 9.218992 10.172447
## [505] 9.302330 7.361110 9.950485 10.057364 10.812881 12.594364 9.030714
## [512] 11.155835 8.647064 8.602104 10.279869 10.371634 10.211516 11.162380
## [519] 7.747094 10.065442 9.708688 9.961398 10.723624 11.347602 8.346943
## [526] 9.381799 9.893754 7.775311 6.545063 6.407268 9.330381 9.849711
## [533] 12.598262 9.703255 10.170175 11.226495 9.459410 12.390867 12.362525
## [540] 9.631010 8.776812 11.088282 10.890665 9.809008 11.051228 11.666779
## [547] 9.098147 9.088695 10.814431 10.299546 7.531063 8.829695 9.732160
## [554] 10.079685 9.137025 11.161015 9.673467 8.464822 9.450513 11.110122
## [561] 11.023003 9.647683 9.644044 10.287783 8.003916 8.357487 8.442567
## [568] 9.872626 9.412088 8.952996 9.107682 9.424309 8.280070 9.498506
## [575] 8.137316 7.878893 10.301666 11.500162 9.331377 9.767313 8.503024
## [582] 10.820453 10.573519 10.667023 10.282958 9.355924 7.667892 8.566640
## [589] 11.913459 11.857641 9.692941 9.576401 10.964118 9.355961 8.611937
## [596] 12.627223 8.297621 8.743443 10.588798 10.872855 12.334264 9.975152
## [603] 10.972742 11.595387 10.294823 9.591306 12.162307 12.516812 9.277234
## [610] 9.871296 10.028509 11.216439 9.801575 9.994297 10.315537 10.769338
## [617] 11.150474 12.605585 9.872171 11.115188 10.686546 9.246281 11.929814
## [624] 7.110565 12.075868 11.397097 9.764757 11.273909 9.200955 7.561078
## [631] 8.188674 9.699515 11.860673 8.528765 10.804951 10.276444 10.887474
## [638] 7.710259 10.096973 9.868124 11.331875 8.307545 11.891557 9.050659
## [645] 10.422171 8.849218 10.411450 8.169808 9.256204 10.994578 9.418178
## [652] 12.098431 10.662307 10.522527 9.852391 12.776043 7.788473 8.076756
## [659] 9.458226 8.893548 8.221535 10.601381 9.951680 9.430545 11.719252
## [666] 9.780045 11.335393 11.147364 9.334466 8.780500 11.524449 10.869991
## [673] 9.524490 9.858144 10.255462 8.302025 13.030194 10.554643 10.343505
## [680] 8.150104 9.442867 8.574101 9.406789 10.738651 11.249250 9.243971
## [687] 10.587369 12.420145 11.100449 8.260564 9.835390 9.849407 9.251334
## [694] 10.837619 8.304840 7.496706 9.266948 11.957366 9.949717 10.164374
## [701] 9.544662 10.765319 10.405766 9.562969 7.917882 9.625845 8.721998
## [708] 8.672963 11.204748 10.044869 9.165892 10.140330 9.703105 9.439547
## [715] 7.516689 9.726198 8.236137 12.269310 11.072918 9.984116 7.980114
## [722] 8.032713 8.435795 8.513265 12.811519 11.482047 12.016486 10.855574
## [729] 8.901694 10.209606 8.072525 11.255014 10.911354 13.449565 7.061876
## [736] 10.364511 10.879163 11.133383 11.030095 10.381266 10.833155 10.551556
## [743] 10.131775 8.665072 9.721435 10.636928 9.643548 8.336889 11.307883
## [750] 10.745270 9.553494 9.065902 8.592743 8.896115 11.962629 11.790768
## [757] 9.329712 8.873421 10.739439 10.219024 11.669655 12.252619 8.998969
## [764] 9.891125 10.176155 11.599346 9.570402 9.423609 10.584435 9.131257
## [771] 10.435051 8.476986 6.580153 10.934983 9.062307 11.794344 10.751097
## [778] 13.506624 8.095798 9.131595 8.776538 10.445262 9.923498 10.164614
## [785] 10.037622 9.696690 7.604815 8.760753 7.627036 11.049394 11.389872
## [792] 9.140792 8.356305 10.018462 11.223244 8.003984 9.598308 9.127753
## [799] 9.743800 11.150249 10.659722 10.311183 9.803898 10.336694 8.649564
## [806] 10.333851 10.241874 9.941883 8.660743 11.692332 12.467363 10.079548
## [813] 8.483794 9.198587 8.550607 9.798883 13.085279 7.924250 8.747510
## [820] 8.893779 11.786345 12.560868 8.287064 6.788992 8.355824 11.971588
## [827] 12.235746 11.248636 11.347832 9.329526 10.023637 12.563320 8.982155
## [834] 9.127559 9.037511 9.155316 8.739751 8.694574 9.192018 11.265938
## [841] 9.787492 9.399260 8.980915 9.131105 9.262152 9.587596 8.629297
## [848] 9.575951 10.565030 9.968207 10.371368 9.735645 9.075109 11.755552
## [855] 10.849598 7.918041 10.974510 14.409513 10.317066 10.093052 11.275130
## [862] 13.253864 8.786692 10.046705 10.283063 11.190567 10.837267 8.518021
## [869] 11.277277 12.400169 10.467251 9.300778 10.079714 8.675262 8.426540
## [876] 10.543903 11.998281 10.929945 8.864531 9.559048 10.366356 10.888196
## [883] 11.075263 10.714603 11.833268 12.287166 9.413204 11.570862 9.504760
## [890] 11.101216 8.697302 9.807795 11.832683 9.546642 8.481585 10.412156
## [897] 7.772764 9.042225 10.465463 10.240496 13.794785 10.449261 14.292966
## [904] 8.730615 11.514269 8.334469 10.240121 8.589517 8.827704 7.833993
## [911] 8.314930 8.617287 8.972013 7.333772 9.171106 9.269357 9.074314
## [918] 11.050305 10.511208 11.211924 9.558152 11.439671 7.109118 10.760237
## [925] 10.623632 8.197919 12.002887 7.511979 11.679438 10.723981 9.267747
## [932] 8.826215 12.354086 7.306411 7.771697 8.426660 8.838292 11.130172
## [939] 9.681233 11.271871 11.026748 8.486573 10.640725 8.367940 7.793282
## [946] 11.337829 12.593998 9.601023 10.280593 9.379596 8.445986 9.558165
## [953] 10.778719 11.784470 8.743859 11.249599 11.044894 8.812599 8.966387
## [960] 11.456542 10.260938 12.189612 10.665839 11.953277 11.199459 8.790109
## [967] 8.770245 11.896063 7.617126 10.441971 9.681973 9.573541 9.771224
## [974] 10.517743 11.662839 8.730057 8.340625 8.325394 9.322599 10.878099
## [981] 8.145823 10.112760 8.426379 8.775478 13.238994 9.444306 10.711431
## [988] 9.729620 9.570518 11.224007 10.596599 10.620578 9.511671 7.571940
## [995] 9.625180 10.798317 12.534500 9.348679 10.589137 9.766532
# create groups for different values of X
(group <- rep(1,1000)) # a vector consisting of 1000 elements
## [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [186] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [223] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [260] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [297] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [334] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [371] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [408] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [445] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [482] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [519] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [556] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [593] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [630] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [667] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [704] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [741] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [778] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [815] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [852] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [889] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [926] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [963] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1000] 1
group[xAxis > -1.5] <- 2
group[xAxis > -.5] <- 3
group[xAxis > .5] <- 4
group[xAxis > 1.5] <- 5
group
## [1] 3 2 3 3 5 1 3 3 2 3 5 3 2 5 4 1 4 3 2 3 2 2 4 2 3 3 3 3 2 3 2 1 2 3 2 5 3
## [38] 3 3 3 1 3 4 2 3 4 3 4 5 3 3 3 4 2 2 2 4 4 4 3 3 4 4 2 1 3 2 3 3 4 3 1 5 2
## [75] 3 4 3 3 5 2 4 2 5 2 3 2 1 4 2 3 4 3 2 2 3 3 5 5 1 3 2 3 2 4 3 5 3 4 3 3 4
## [112] 2 3 4 4 5 3 2 3 3 2 2 3 5 4 1 3 2 4 2 2 3 3 2 5 2 2 2 3 3 4 2 1 2 3 1 4 3
## [149] 4 2 5 2 4 4 3 4 5 3 3 4 1 2 3 2 5 2 3 4 4 3 4 2 4 4 4 3 2 4 2 3 3 2 3 2 2
## [186] 3 2 3 2 1 3 4 4 2 4 2 2 3 3 3 3 3 4 4 4 2 3 3 3 3 2 4 3 2 3 2 3 3 2 1 2 4
## [223] 3 2 4 3 4 4 2 3 2 4 1 3 3 4 3 5 3 2 1 3 3 3 3 3 3 2 2 3 3 2 2 4 2 3 3 4 3
## [260] 3 5 3 1 2 2 2 3 2 2 3 4 4 3 2 3 3 5 3 4 1 3 3 3 3 3 2 3 3 4 3 2 4 4 2 2 2
## [297] 4 3 2 2 2 1 3 3 4 2 2 2 3 1 5 4 5 2 4 4 3 2 4 2 1 5 4 3 1 2 2 4 3 5 2 3 3
## [334] 3 3 3 3 4 3 3 3 2 3 3 4 1 3 1 4 2 3 4 4 3 3 2 3 3 4 2 2 3 4 3 3 1 1 4 5 4
## [371] 4 4 3 3 3 4 4 3 3 2 2 4 4 3 4 2 5 2 1 5 2 5 2 5 3 3 4 1 4 5 2 3 1 5 4 2 2
## [408] 4 1 1 4 2 4 4 2 3 4 3 3 3 2 4 4 3 3 2 4 4 4 1 2 2 3 3 4 3 4 5 3 4 2 3 2 3
## [445] 3 3 2 4 1 4 4 4 4 2 3 4 3 2 3 3 5 4 3 3 3 3 4 3 1 4 3 4 3 5 4 4 3 4 2 3 3
## [482] 1 4 4 3 3 2 2 5 3 2 4 4 3 3 3 2 2 3 3 1 3 4 4 2 3 3 4 4 4 2 3 2 5 3 3 3 4
## [519] 2 4 5 1 3 4 3 2 4 2 1 1 1 2 4 2 5 4 2 5 3 2 2 3 3 3 3 4 3 2 5 2 1 2 3 3 3
## [556] 4 3 3 2 2 4 2 3 3 2 2 3 2 2 3 2 3 1 3 1 3 2 3 2 3 3 3 4 4 5 4 2 2 4 4 2 3
## [593] 3 2 3 4 2 1 3 3 4 3 3 3 3 3 4 5 3 2 3 5 3 3 4 3 4 4 2 4 3 1 4 1 5 3 5 4 3
## [630] 2 3 3 4 2 4 3 4 1 2 3 4 2 3 2 3 2 2 3 3 3 3 5 4 4 3 5 2 2 4 3 3 4 3 2 4 3
## [667] 3 4 2 2 5 3 3 2 3 2 4 4 4 2 3 3 3 4 5 2 3 4 3 2 3 4 3 4 2 3 2 3 3 4 2 3 3
## [704] 2 2 3 3 2 2 3 3 3 3 4 1 2 3 5 3 2 2 2 2 2 5 4 4 3 2 2 1 4 3 5 2 2 2 3 4 4
## [741] 4 1 4 1 3 4 3 3 4 3 2 3 2 2 3 4 2 2 4 2 4 4 3 3 2 5 2 3 3 3 3 1 1 2 2 3 4
## [778] 5 2 2 1 4 4 3 2 3 2 4 2 3 5 3 3 3 4 1 4 1 2 3 2 4 5 3 2 3 3 2 2 4 5 4 3 2
## [815] 2 3 5 2 3 3 4 4 1 1 2 5 3 4 4 3 3 4 2 1 2 2 2 2 1 3 2 3 2 3 3 2 2 2 4 3 3
## [852] 2 2 4 4 2 4 5 3 3 3 5 3 3 4 4 3 2 4 4 4 3 2 3 3 4 5 3 3 2 3 5 3 4 4 3 2 3
## [889] 3 4 3 3 4 3 3 3 2 2 3 4 5 3 4 3 4 2 4 2 2 2 1 2 2 2 2 3 2 5 4 3 3 4 1 4 2
## [926] 1 3 2 5 2 2 2 5 2 3 3 3 3 2 4 3 2 3 1 2 4 4 3 3 2 3 4 3 4 2 3 4 3 3 4 3 5
## [963] 3 4 2 2 3 4 3 4 3 1 3 4 3 1 2 3 3 4 2 3 2 3 5 3 4 2 4 4 2 3 2 2 3 4 5 3 4
## [1000] 3
# create sample dataframe by joining variables
sample_data <- data.frame(xAxis,yAxis,group)
sample_data
## xAxis yAxis group
## 1 3.409575e-01 12.892625 3
## 2 -7.719201e-01 11.173296 2
## 3 4.876172e-01 10.175672 3
## 4 3.991219e-01 10.308664 3
## 5 1.878852e+00 12.160531 5
## 6 -2.121204e+00 8.417629 1
## 7 1.035551e-02 11.046510 3
## 8 2.179876e-01 8.839149 3
## 9 -1.000403e+00 8.837632 2
## 10 -4.487063e-01 9.345194 3
## 11 1.581279e+00 10.661556 5
## 12 4.188315e-01 10.056582 3
## 13 -6.791913e-01 8.729314 2
## 14 1.659934e+00 13.554631 5
## 15 7.553677e-01 10.555042 4
## 16 -1.667321e+00 9.259935 1
## 17 6.574069e-01 9.234248 4
## 18 -4.869845e-01 8.957486 3
## 19 -1.249671e+00 8.194139 2
## 20 -1.590320e-01 11.704306 3
## 21 -5.782027e-01 10.702909 2
## 22 -8.943598e-01 9.657230 2
## 23 6.245035e-01 11.738335 4
## 24 -1.093621e+00 8.527644 2
## 25 2.373496e-01 10.042653 3
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## 928 -1.444949e+00 7.511979 2
## 929 1.997993e+00 11.679438 5
## 930 -9.630709e-01 10.723981 2
## 931 -1.172982e+00 9.267747 2
## 932 -1.277992e+00 8.826215 2
## 933 1.635831e+00 12.354086 5
## 934 -8.222604e-01 7.306411 2
## 935 -3.362282e-01 7.771697 3
## 936 -2.117293e-02 8.426660 3
## 937 2.840844e-01 8.838292 3
## 938 1.134588e-01 11.130172 3
## 939 -7.953834e-01 9.681233 2
## 940 9.737984e-01 11.271871 4
## 941 -1.727439e-01 11.026748 3
## 942 -6.157031e-01 8.486573 2
## 943 8.342797e-02 10.640725 3
## 944 -1.602411e+00 8.367940 1
## 945 -1.355857e+00 7.793282 2
## 946 1.452195e+00 11.337829 4
## 947 1.403837e+00 12.593998 4
## 948 3.117169e-01 9.601023 3
## 949 4.102366e-01 10.280593 3
## 950 -9.258744e-01 9.379596 2
## 951 -4.100004e-01 8.445986 3
## 952 6.509562e-01 9.558165 4
## 953 1.393049e-01 10.778719 3
## 954 6.767576e-01 11.784470 4
## 955 -8.357439e-01 8.743859 2
## 956 -5.544964e-02 11.249599 3
## 957 8.289160e-01 11.044894 4
## 958 -2.754559e-01 8.812599 3
## 959 -2.874954e-01 8.966387 3
## 960 5.221841e-01 11.456542 4
## 961 3.005028e-01 10.260938 3
## 962 1.682822e+00 12.189612 5
## 963 1.708991e-01 10.665839 3
## 964 1.478940e+00 11.953277 4
## 965 -1.193102e+00 11.199459 2
## 966 -8.565334e-01 8.790109 2
## 967 1.730042e-02 8.770245 3
## 968 6.378157e-01 11.896063 4
## 969 -4.718942e-01 7.617126 3
## 970 5.241351e-01 10.441971 4
## 971 3.540228e-01 9.681973 3
## 972 -2.319803e+00 9.573541 1
## 973 -2.466931e-02 9.771224 3
## 974 7.198521e-01 10.517743 4
## 975 -1.103903e-01 11.662839 3
## 976 -1.922731e+00 8.730057 1
## 977 -1.262346e+00 8.340625 2
## 978 -2.766504e-01 8.325394 3
## 979 -4.794508e-01 9.322599 3
## 980 6.075831e-01 10.878099 4
## 981 -5.357607e-01 8.145823 2
## 982 -2.458623e-01 10.112760 3
## 983 -5.549597e-01 8.426379 2
## 984 8.732257e-02 8.775478 3
## 985 1.568078e+00 13.238994 5
## 986 -2.256177e-01 9.444306 3
## 987 9.868531e-01 10.711431 4
## 988 -8.563744e-01 9.729620 2
## 989 8.045794e-01 9.570518 4
## 990 6.141468e-01 11.224007 4
## 991 -7.616397e-01 10.596599 2
## 992 -2.695376e-01 10.620578 3
## 993 -9.618311e-01 9.511671 2
## 994 -1.191909e+00 7.571940 2
## 995 4.339510e-01 9.625180 3
## 996 1.457446e+00 10.798317 4
## 997 1.583324e+00 12.534500 5
## 998 2.895092e-01 9.348679 3
## 999 1.123415e+00 10.589137 4
## 1000 -2.773339e-01 9.766532 3
# creates plot object using ggplot
plot <- ggplot(sample_data, aes(x = xAxis, y= yAxis, col = as.factor(group)))+
geom_point()+theme(legend.position = "none")
# Display plot
plot
# Insert marginal ditribution using marginal function
ggMarginal(plot,type= 'histogram',groupColour=TRUE,groupFill=TRUE)
