#Submitted by: John Jimuel B. Glimada
#Exercise 1
x <- 1:10
y <- c(3, 1, 5, 2, 3, 8, 4, 7, 6, 9)
plot(x, y, type = "l")
plot(x, y, type = "l",
main = "Hello: This is my Line Plot",
xlab = "My X-Values",
ylab = "My Y-Values")
plot(x, y, type = "l",
main = "This is my Line Plot",
xlab = "My X-Values",
ylab = "My Y-Values",
col = "red")
plot(x, y, type = "l",
main = "This is my Line Plot",
xlab = "My X-Values",
ylab = "My Y-Values",
lwd=7,
col = "green")
plot(x, y, type = "b",
main = "This is my Line Plot",
xlab = "My X-Values",
ylab = "My Y-Values",
lwd=3,
col = "blue")
#Exercise2
(x <- 1:10)
## [1] 1 2 3 4 5 6 7 8 9 10
(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(x, y1, type = "b",
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)
lines(x, y3, type = "b", col = "green",lwd=3)
legend("topleft",
legend = c("Line y1", "Line y2", "Line y3"),
col = c("black", "red", "green"),
lty = 1)
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)
legend("topleft",
legend = c("Line y1", "Line y2", "Line y3"),
col = c("black", "red", "green"),
lty = 1)
#Exercise3
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)
length(Pupils)
## [1] 19
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
plot(Pupils, type = 'o')
#Exercise4
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
dim(cars)
## [1] 50 2
names(cars)
## [1] "speed" "dist"
cars$speed
## [1] 4 4 7 7 8 9 10 10 10 11 11 12 12 12 12 13 13 13 13 14 14 14 14 15 15
## [26] 15 16 16 17 17 17 18 18 18 18 19 19 19 20 20 20 20 20 22 23 24 24 24 24 25
cars[,1]
## [1] 4 4 7 7 8 9 10 10 10 11 11 12 12 12 12 13 13 13 13 14 14 14 14 15 15
## [26] 15 16 16 17 17 17 18 18 18 18 19 19 19 20 20 20 20 20 22 23 24 24 24 24 25
plot(cars,)
plot(cars[,1],cars[,2])
attach(cars); plot(speed,dist)
plot(cars$speed,cars$dist)
par(mfrow = c(2,2))
plot(cars,)
plot(cars[,1],cars[,2])
attach(cars); plot(speed,dist)
## The following objects are masked from cars (pos = 3):
##
## dist, speed
plot(cars$speed,cars$dist)
par(mfrow = c(1,1))
plot(cars)
abline(v = 15, col = "darkgreen",lwd=3)
abline(v = 10, col = "blue",lwd=3)
abline(h = 80, col = "darkgreen",lwd=3)
abline(h = 20, col = "blue",lwd=3)
plot(cars)
abline(v = c(9, 22,25), col = c("darkgreen", "blue","red"),
lwd = c(1, 3,2),
lty = c(2,2,2))
plot(cars)
abline(v = c(9, 22,25), col = c("darkgreen", "blue","red"),
lwd = c(1, 3,2))
plot(cars)
abline(h = 60, col = "red",lty = 1, lwd = 3)
abline(h = 100, col = "red",lty = 2, lwd = 3)
abline(h = 20, col = "red",lty = 3, lwd = 3)
#Exercise5
dim(iris)
## [1] 150 5
names(iris)
## [1] "Sepal.Length" "Sepal.Width" "Petal.Length" "Petal.Width" "Species"
table(iris$Species)
##
## setosa versicolor virginica
## 50 50 50
table(iris[,5])
##
## setosa versicolor virginica
## 50 50 50
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
##
##
##
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)
PL <- iris$Petal.Length
PW <- iris$Petal.Width
plot(PL, PW)
plot(PL, PW, col = iris$Species, main= "My Plot")
abline(lm(PW ~ PL))
text(5, 0.5, "Regression Line")
legend("topleft", # specify the location of the legend
levels(iris$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
)
#Exercise6
pairs(iris, col = rainbow(3)[iris$Species]) # set colors by species
(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
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))
#Exercise 7
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.
cancer <- read.csv("Cancer.csv", header = TRUE, sep = ",")
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"
(means <- round(tapply(cancer$breastcancer, cancer$continent, mean), digits=2))
## AF AS EE LATAM NORAM OC WE
## 24.02 24.51 49.44 36.70 71.73 45.80 74.80
boxplot(cancer$breastcancer ~ cancer$continent, main= "Breast cancer by continent (brown dot = mean value)", xlab="continents", ylab="new cases per 100,00 residents", col=rainbow(7))
points(means, col="brown", pch=18)
#Exercise 8
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`
hsb2_wide <- read.csv("hsb2.csv", header = TRUE, sep = ",")
head(hsb2_wide)
## X id female race ses schtyp prog read write math science socst
## 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
tail(hsb2_wide)
## X id female race ses schtyp prog read write math science socst
## 195 195 179 1 4 2 2 2 47 65 60 50 56
## 196 196 31 1 2 2 2 1 55 59 52 42 56
## 197 197 145 1 4 2 1 3 42 46 38 36 46
## 198 198 187 1 4 2 2 1 57 41 57 55 52
## 199 199 118 1 4 2 1 1 55 62 58 58 61
## 200 200 137 1 4 3 1 2 63 65 65 53 61
(hsb2_wide <- hsb2_wide[-1])
## id female race ses schtyp prog read write math science socst
## 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
## 11 75 0 4 2 1 3 60 46 51 53 61
## 12 60 0 4 2 1 2 57 65 51 63 61
## 13 95 0 4 3 1 2 73 60 71 61 71
## 14 104 0 4 3 1 2 54 63 57 55 46
## 15 38 0 3 1 1 2 45 57 50 31 56
## 16 115 0 4 1 1 1 42 49 43 50 56
## 17 76 0 4 3 1 2 47 52 51 50 56
## 18 195 0 4 2 2 1 57 57 60 58 56
## 19 114 0 4 3 1 2 68 65 62 55 61
## 20 85 0 4 2 1 1 55 39 57 53 46
## 21 167 0 4 2 1 1 63 49 35 66 41
## 22 143 0 4 2 1 3 63 63 75 72 66
## 23 41 0 3 2 1 2 50 40 45 55 56
## 24 20 0 1 3 1 2 60 52 57 61 61
## 25 12 0 1 2 1 3 37 44 45 39 46
## 26 53 0 3 2 1 3 34 37 46 39 31
## 27 154 0 4 3 1 2 65 65 66 61 66
## 28 178 0 4 2 2 3 47 57 57 58 46
## 29 196 0 4 3 2 2 44 38 49 39 46
## 30 29 0 2 1 1 1 52 44 49 55 41
## 31 126 0 4 2 1 1 42 31 57 47 51
## 32 103 0 4 3 1 2 76 52 64 64 61
## 33 192 0 4 3 2 2 65 67 63 66 71
## 34 150 0 4 2 1 3 42 41 57 72 31
## 35 199 0 4 3 2 2 52 59 50 61 61
## 36 144 0 4 3 1 1 60 65 58 61 66
## 37 200 0 4 2 2 2 68 54 75 66 66
## 38 80 0 4 3 1 2 65 62 68 66 66
## 39 16 0 1 1 1 3 47 31 44 36 36
## 40 153 0 4 2 1 3 39 31 40 39 51
## 41 176 0 4 2 2 2 47 47 41 42 51
## 42 177 0 4 2 2 2 55 59 62 58 51
## 43 168 0 4 2 1 2 52 54 57 55 51
## 44 40 0 3 1 1 1 42 41 43 50 41
## 45 62 0 4 3 1 1 65 65 48 63 66
## 46 169 0 4 1 1 1 55 59 63 69 46
## 47 49 0 3 3 1 3 50 40 39 49 47
## 48 136 0 4 2 1 2 65 59 70 63 51
## 49 189 0 4 2 2 2 47 59 63 53 46
## 50 7 0 1 2 1 2 57 54 59 47 51
## 51 27 0 2 2 1 2 53 61 61 57 56
## 52 128 0 4 3 1 2 39 33 38 47 41
## 53 21 0 1 2 1 1 44 44 61 50 46
## 54 183 0 4 2 2 2 63 59 49 55 71
## 55 132 0 4 2 1 2 73 62 73 69 66
## 56 15 0 1 3 1 3 39 39 44 26 42
## 57 67 0 4 1 1 3 37 37 42 33 32
## 58 22 0 1 2 1 3 42 39 39 56 46
## 59 185 0 4 2 2 2 63 57 55 58 41
## 60 9 0 1 2 1 3 48 49 52 44 51
## 61 181 0 4 2 2 2 50 46 45 58 61
## 62 170 0 4 3 1 2 47 62 61 69 66
## 63 134 0 4 1 1 1 44 44 39 34 46
## 64 108 0 4 2 1 1 34 33 41 36 36
## 65 197 0 4 3 2 2 50 42 50 36 61
## 66 140 0 4 2 1 3 44 41 40 50 26
## 67 171 0 4 2 1 2 60 54 60 55 66
## 68 107 0 4 1 1 3 47 39 47 42 26
## 69 81 0 4 1 1 2 63 43 59 65 44
## 70 18 0 1 2 1 3 50 33 49 44 36
## 71 155 0 4 2 1 1 44 44 46 39 51
## 72 97 0 4 3 1 2 60 54 58 58 61
## 73 68 0 4 2 1 2 73 67 71 63 66
## 74 157 0 4 2 1 1 68 59 58 74 66
## 75 56 0 4 2 1 3 55 45 46 58 51
## 76 5 0 1 1 1 2 47 40 43 45 31
## 77 159 0 4 3 1 2 55 61 54 49 61
## 78 123 0 4 3 1 1 68 59 56 63 66
## 79 164 0 4 2 1 3 31 36 46 39 46
## 80 14 0 1 3 1 2 47 41 54 42 56
## 81 127 0 4 3 1 2 63 59 57 55 56
## 82 165 0 4 1 1 3 36 49 54 61 36
## 83 174 0 4 2 2 2 68 59 71 66 56
## 84 3 0 1 1 1 2 63 65 48 63 56
## 85 58 0 4 2 1 3 55 41 40 44 41
## 86 146 0 4 3 1 2 55 62 64 63 66
## 87 102 0 4 3 1 2 52 41 51 53 56
## 88 117 0 4 3 1 3 34 49 39 42 56
## 89 133 0 4 2 1 3 50 31 40 34 31
## 90 94 0 4 3 1 2 55 49 61 61 56
## 91 24 0 2 2 1 2 52 62 66 47 46
## 92 149 0 4 1 1 1 63 49 49 66 46
## 93 82 1 4 3 1 2 68 62 65 69 61
## 94 8 1 1 1 1 2 39 44 52 44 48
## 95 129 1 4 1 1 1 44 44 46 47 51
## 96 173 1 4 1 1 1 50 62 61 63 51
## 97 57 1 4 2 1 2 71 65 72 66 56
## 98 100 1 4 3 1 2 63 65 71 69 71
## 99 1 1 1 1 1 3 34 44 40 39 41
## 100 194 1 4 3 2 2 63 63 69 61 61
## 101 88 1 4 3 1 2 68 60 64 69 66
## 102 99 1 4 3 1 1 47 59 56 66 61
## 103 47 1 3 1 1 2 47 46 49 33 41
## 104 120 1 4 3 1 2 63 52 54 50 51
## 105 166 1 4 2 1 2 52 59 53 61 51
## 106 65 1 4 2 1 2 55 54 66 42 56
## 107 101 1 4 3 1 2 60 62 67 50 56
## 108 89 1 4 1 1 3 35 35 40 51 33
## 109 54 1 3 1 2 1 47 54 46 50 56
## 110 180 1 4 3 2 2 71 65 69 58 71
## 111 162 1 4 2 1 3 57 52 40 61 56
## 112 4 1 1 1 1 2 44 50 41 39 51
## 113 131 1 4 3 1 2 65 59 57 46 66
## 114 125 1 4 1 1 2 68 65 58 59 56
## 115 34 1 1 3 2 2 73 61 57 55 66
## 116 106 1 4 2 1 3 36 44 37 42 41
## 117 130 1 4 3 1 1 43 54 55 55 46
## 118 93 1 4 3 1 2 73 67 62 58 66
## 119 163 1 4 1 1 2 52 57 64 58 56
## 120 37 1 3 1 1 3 41 47 40 39 51
## 121 35 1 1 1 2 1 60 54 50 50 51
## 122 87 1 4 2 1 1 50 52 46 50 56
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## 159 109 1 4 2 1 1 42 39 42 42 41
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## 200 137 1 4 3 1 2 63 65 65 53 61
library(reshape2)
(hsb2_long <- melt(hsb2_wide, 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
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## 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
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## 32 103 0 4 3 1 2 read 76
## 33 192 0 4 3 2 2 read 65
## 34 150 0 4 2 1 3 read 42
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## 36 144 0 4 3 1 1 read 60
## 37 200 0 4 2 2 2 read 68
## 38 80 0 4 3 1 2 read 65
## 39 16 0 1 1 1 3 read 47
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## 42 177 0 4 2 2 2 read 55
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## 76 5 0 1 1 1 2 read 47
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## 78 123 0 4 3 1 1 read 68
## 79 164 0 4 2 1 3 read 31
## 80 14 0 1 3 1 2 read 47
## 81 127 0 4 3 1 2 read 63
## 82 165 0 4 1 1 3 read 36
## 83 174 0 4 2 2 2 read 68
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## 107 101 1 4 3 1 2 read 60
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## 111 162 1 4 2 1 3 read 57
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## 201 70 0 4 1 1 1 write 52
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## 212 60 0 4 2 1 2 write 65
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## 214 104 0 4 3 1 2 write 63
## 215 38 0 3 1 1 2 write 57
## 216 115 0 4 1 1 1 write 49
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## 219 114 0 4 3 1 2 write 65
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## 235 199 0 4 3 2 2 write 59
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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
table(hsb2_long$variable)
##
## read write math science socst
## 200 200 200 200 200
str(hsb2_long)
## 'data.frame': 1000 obs. of 8 variables:
## $ id : int 70 121 86 141 172 113 50 11 84 48 ...
## $ female : int 0 1 0 0 0 0 0 0 0 0 ...
## $ race : int 4 4 4 4 4 4 3 1 4 3 ...
## $ ses : int 1 2 3 3 2 2 2 2 2 2 ...
## $ schtyp : int 1 1 1 1 1 1 1 1 1 1 ...
## $ prog : int 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 : int 57 68 44 63 47 44 50 34 63 57 ...
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"))
str(data)
## 'data.frame': 1000 obs. of 8 variables:
## $ id : int 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 : int 57 68 44 63 47 44 50 34 63 57 ...
library(gplots)
##
## Attaching package: 'gplots'
##
## The following object is masked from 'package:stats':
##
## lowess
means <- round(tapply(data$value, data$variable, mean), digits=2)
boxplot(data$value ~ data$variable, main= "Student Performance by Subject (brown dot = mean score)",
xlab="Subject matter", ylab="Percentage Scores", col=rainbow(5))
points(means, col="brown", pch=18)
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)
#Exercise 9
library(ggplot2)
attach(hsb2_long)
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?
#Exercise 10
library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.0 ✔ 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)
theme_set(theme_bw(1))
(xAxis <- rnorm(1000))
## [1] 0.078851110 0.207236931 0.266564075 -1.179809719 2.217820430
## [6] -0.388101041 0.148447250 0.658248739 0.410168641 1.010368162
## [11] 0.889917795 2.234645962 0.500489334 -0.648395621 -0.478805517
## [16] -0.599988795 1.575278080 0.367251105 0.295268708 0.501077096
## [21] 1.207058353 1.045319986 -0.552236474 -1.226106994 0.620730079
## [26] -0.483302168 0.759622981 -0.465452498 1.164475433 1.070889414
## [31] 0.051988709 0.628534529 -1.382741888 0.998710133 -0.301864316
## [36] -0.019393037 0.071385139 0.763858681 -1.224909947 1.523476844
## [41] 2.297298190 -1.582388714 -1.214610867 1.294106428 -0.150348900
## [46] -0.543401839 0.237567103 0.801194163 -1.369415804 1.425382851
## [51] 1.027037130 1.393158952 0.248897224 -0.182607718 -0.103887415
## [56] 0.552136786 1.019911549 -0.058922937 -0.153955036 0.839754149
## [61] -0.139060713 0.806065989 -0.746025934 -1.411305506 -0.203170380
## [66] 1.259983633 1.043015863 1.469638193 0.874900697 -0.682832022
## [71] 1.099357422 -0.368067957 -1.274610148 -1.632366207 -1.036249405
## [76] 0.116532480 0.070074480 1.435346383 -0.635996696 -2.375641762
## [81] 0.649779714 -0.236052528 -0.708855165 -0.005104886 1.088636986
## [86] 0.766035170 0.785952359 0.092027943 -1.439622725 -0.461897201
## [91] 1.977552134 -2.389820609 0.062971688 0.505893637 0.305153316
## [96] 0.047832228 0.265194383 -0.813758243 1.138777369 0.804176590
## [101] 1.328779748 -0.663854843 -1.596267516 -1.770082369 -0.127883700
## [106] -1.697274608 -0.790462287 0.509354921 1.575021919 0.100433920
## [111] -0.443755783 -1.014887732 0.411194955 -0.201970636 -2.144000167
## [116] 0.898877821 0.657970886 -1.133488631 -1.060756160 0.321977075
## [121] -1.676574198 1.886184843 0.838791400 -1.485084595 -1.222320892
## [126] -1.082185694 1.138941469 -0.174466641 0.720263950 -0.736768008
## [131] 0.633333587 0.614652897 -1.226193296 0.578446630 0.286926318
## [136] -0.889574831 1.166654399 -0.607300320 0.512605505 -1.528711877
## [141] -0.385540102 0.874121434 0.426118284 -0.531356679 0.450420509
## [146] -0.964294308 -2.523803900 -0.830442478 0.195401582 -0.258153372
## [151] 0.824995106 -0.216832155 1.274920161 -0.897993282 -0.569519636
## [156] 1.069212158 -0.680368232 -0.540418367 -0.254833054 -1.920046212
## [161] 0.978023149 1.260398291 0.439578349 -0.775999954 1.461716811
## [166] 0.944792522 0.639646312 0.321546361 0.403525052 0.844058330
## [171] 0.160572442 0.611266907 -0.890782292 -1.202285546 0.667264440
## [176] -1.488091456 0.216149156 -0.860124751 2.089020073 2.596590585
## [181] 0.199226972 -0.438801848 0.713782921 0.399197289 1.598626272
## [186] 0.319647536 0.595202137 -0.356687837 1.037080996 -2.473354574
## [191] 0.717446072 0.226379480 0.558984807 -0.999716377 -1.174778228
## [196] -0.182826379 0.767178529 -0.792458830 0.496292124 -0.914880960
## [201] -0.399405003 0.229286355 -0.587134006 0.918693112 0.901984872
## [206] 0.682500303 -0.035264964 0.800791122 2.521629321 -1.407234479
## [211] -1.534117889 0.491666277 -1.005940153 -1.019574613 -0.824725592
## [216] 0.217678622 0.055305009 0.228505171 -0.310622260 1.372595200
## [221] -1.011735333 -0.833268846 -0.108558587 1.470576755 -0.556256037
## [226] -0.175468092 -0.570613044 -0.870107414 -0.217580028 1.092969296
## [231] -0.275270694 0.119484945 -1.020406971 0.873489751 -0.888973145
## [236] 0.959627069 -0.287243468 -0.740700443 0.424320123 -1.869986443
## [241] -0.736191517 -0.561190754 -1.627972120 0.230264849 0.152386325
## [246] 0.211117821 0.392054252 0.525999181 0.339995114 0.663537384
## [251] 0.058090803 0.930875714 0.153121094 -0.632607652 0.924915679
## [256] -1.424831407 0.641854179 -0.269442633 -0.977382389 0.367398527
## [261] -1.163914555 -0.060702864 -0.208064815 -0.162307923 -1.233944372
## [266] 0.841561717 0.919453367 0.167749815 0.185217178 0.419223888
## [271] -0.220210497 -0.454262894 2.121741533 1.837242626 0.538073934
## [276] 0.212430440 1.980831705 -0.605783430 -0.896374335 -0.793184775
## [281] 0.413484659 -0.589413382 -0.927515407 -0.850360063 0.701133938
## [286] -1.523492188 -0.413304834 0.802651065 -1.689815498 0.310740535
## [291] 0.120338757 -0.566877389 0.396923836 1.091102732 -1.420698851
## [296] 1.295362845 1.441012580 1.257515925 -1.635309408 -1.356469783
## [301] -1.312777786 0.817872053 -0.517181386 -0.978109880 -0.424150474
## [306] 0.440191122 0.418210666 0.890571802 -1.252126557 -0.866935258
## [311] -0.456530056 -0.574192435 -0.196906259 1.468982026 1.644913133
## [316] -0.067893829 0.762903615 -0.653730559 -2.005481328 1.321776843
## [321] 0.247241210 -0.193498368 -0.003508127 1.415428662 -0.613112722
## [326] -0.087233253 -1.485444347 1.017702930 -0.804955677 0.943473649
## [331] 0.667271378 -0.116027793 -0.261134173 -0.808968815 1.171817143
## [336] -0.799876897 -0.366824279 1.432184307 -0.065591071 0.744218311
## [341] 0.546258277 -2.088940668 1.299829596 -1.145560438 -0.037889135
## [346] -0.612148923 0.996759210 -1.399190253 -0.601753768 1.202485819
## [351] 0.094600116 -0.838400552 -0.958986959 -0.005322433 -1.432954720
## [356] 0.693812991 0.841358724 1.203360435 -1.147773344 -0.041000143
## [361] -0.761800245 -0.813752184 -0.870396274 -1.442706936 0.727548025
## [366] 1.270006285 -1.019845528 -0.226097901 -0.082577485 -0.611205391
## [371] 1.520417024 0.732337999 -0.531355719 0.578523492 -1.003403039
## [376] -0.820938209 -0.254331565 -0.208294428 -1.591013166 1.618402400
## [381] 2.346817480 -0.530469921 -0.488033098 -1.178976387 0.230566857
## [386] 0.859331093 -1.027566209 -0.625405682 -0.110674241 0.578675340
## [391] -1.107681365 0.447683694 -0.652693495 1.310007321 -0.069803785
## [396] -0.372086707 -0.444686334 1.470530220 -0.779139317 0.812004137
## [401] -0.486578580 0.580387190 0.567533369 0.879957753 0.922518421
## [406] -1.113760947 -2.751763115 -0.377424648 0.334951996 -0.906739117
## [411] -0.847337369 2.343121829 -1.173443183 0.492449850 -0.625837809
## [416] 1.559656605 0.600260185 0.443634913 1.574154090 0.947378746
## [421] 1.231916129 -0.857049633 0.156928264 0.442045292 0.812533158
## [426] 0.760401175 0.420280563 -0.474119683 -0.657734508 -0.350227268
## [431] -2.570644874 -1.022162527 -0.554121438 -0.903925797 -0.654305242
## [436] 0.794262297 0.511323133 0.800908495 0.020867882 1.361642583
## [441] 0.374034880 0.670823195 0.387415732 -1.722306775 0.122439216
## [446] -1.172036978 1.750148666 1.232137593 1.190071474 1.410176760
## [451] 0.126298359 0.473681135 0.331212330 1.185009655 0.544131072
## [456] 0.489885588 0.186742497 1.241550721 0.914165786 -1.163887725
## [461] 1.237892096 0.190402624 0.725445994 -0.760730542 1.251084140
## [466] -0.824402167 -0.264413051 0.689570884 0.527238559 -0.935647600
## [471] -0.379098608 0.622633200 -0.281951447 0.942760809 -0.117588063
## [476] -0.295921396 -0.367198518 0.653673897 0.180762526 -0.346493286
## [481] -0.844285083 0.675910695 0.659228686 1.187374145 -1.140503206
## [486] -1.488277507 -0.646397221 -0.121657729 0.484629285 -0.637740850
## [491] -0.267020199 1.931078830 -0.934720899 0.461201876 -1.416576591
## [496] -0.314626365 -0.132343556 0.847715621 -1.259981687 -0.285465923
## [501] 0.553843234 -1.426813590 -0.526158228 -0.685055777 -0.163137722
## [506] -0.623694905 -0.231230759 0.322173758 0.176599153 0.847238754
## [511] -0.065213277 -1.201546010 -1.438699145 0.350922013 0.086559109
## [516] 0.168208684 -0.064634147 0.400979146 0.749211781 -1.251594101
## [521] 1.159424466 -0.294210561 -1.264340440 0.935307197 0.676660804
## [526] -0.986497741 -1.465427651 0.752886644 -1.205622216 -1.301158668
## [531] 1.247532259 -0.170921546 -0.011360053 1.387512458 -0.104037967
## [536] 1.198235514 -0.445694641 2.696632316 -0.625916167 0.566499822
## [541] -0.144601356 -0.193082315 0.465128081 -2.569178281 0.146326303
## [546] 0.121021408 -0.295626255 -0.644978924 -0.660450779 1.110632025
## [551] -0.162977073 0.114974196 0.065005418 -0.504424582 0.960291013
## [556] -1.165909405 -1.662296160 0.371603485 -1.525722795 0.400030632
## [561] -0.369324138 0.856523401 -0.216344255 0.384288197 0.023807727
## [566] 0.697065819 0.132939191 1.224781771 0.987888202 -1.299626063
## [571] 1.382909403 0.559954614 -0.078044373 -0.822133395 0.071808977
## [576] 1.176842432 1.086355785 -0.633020678 0.121874181 1.100357825
## [581] -0.255913761 0.161505388 1.156824830 0.772345045 -0.536946553
## [586] 1.364416709 -0.560557723 -0.180456608 0.283349924 0.053052268
## [591] -0.002312381 -0.015709166 -0.537548386 -0.887967945 -0.261111068
## [596] 1.319258169 1.195555458 -0.680681749 -0.904597874 0.208495321
## [601] -0.458357749 -1.217399511 0.523930703 -1.038881622 0.214182525
## [606] 0.372425876 -1.237711855 0.249362803 -0.851608077 0.624288132
## [611] 0.667439568 0.250029720 1.054803670 -0.824638611 0.286835781
## [616] 0.147335339 -0.231154710 -0.635166072 -0.672519224 0.112894335
## [621] 0.015593940 -1.881649700 1.090134231 0.710517115 -1.300450998
## [626] -0.291300433 -0.885652769 1.913632978 -0.066293698 -1.376473911
## [631] 0.393575202 0.561312473 0.155310968 0.889259151 0.205430128
## [636] 0.794563283 0.806851657 -0.145599557 -0.346845312 -1.151285737
## [641] -1.383591732 2.788420352 -0.257490971 0.046016514 -0.394687449
## [646] 0.149987046 0.501333832 0.771710453 -0.474125140 -0.589143230
## [651] 1.079667259 -0.576095854 0.515843907 -0.964374819 -0.509831535
## [656] 0.430445497 -1.124137008 1.531604722 0.921268433 -0.365223607
## [661] 1.075194399 0.342087372 -0.381929798 0.425529095 -0.335901285
## [666] -0.563976142 -0.607136784 0.604673777 -1.359942673 -1.545162984
## [671] 0.589551557 1.123792650 0.052392037 0.552803091 -0.584541445
## [676] -1.406506340 -0.122834303 2.633902668 -0.217197268 -1.308039969
## [681] 0.449357965 -0.699493690 -0.561648244 -1.072547218 -0.608180256
## [686] 0.473564396 0.258922242 0.393806165 1.074958992 -0.914596118
## [691] 0.108036690 0.986118756 -0.222531950 0.559425864 -0.641741067
## [696] 1.586559049 -0.160390777 0.290459162 -1.825372642 -2.172246819
## [701] 0.057828758 -0.376296599 0.467482881 1.073695329 0.276158654
## [706] -1.007955651 -0.010296420 -0.483169602 1.316713800 1.097762040
## [711] 1.101094825 1.416952812 0.917749368 0.683175445 -1.399482744
## [716] 0.384199535 1.499889110 1.381758681 0.944604713 1.195917586
## [721] 0.991263670 -1.202217561 -0.131122589 -0.361252792 -0.674538455
## [726] 0.821163885 1.965516466 -0.403676602 1.132877101 -0.439467541
## [731] 0.821331907 -1.076848824 0.201989217 -0.358933399 -0.526402458
## [736] -0.574909114 -0.288400746 -1.575919027 -1.409309197 -1.301897982
## [741] 0.153222336 0.141698908 0.113884648 -1.053902391 0.459124542
## [746] 0.157837784 -0.715118517 0.329943327 0.594392281 -1.302225714
## [751] -0.390497164 1.155288736 1.066687049 -1.591954870 -2.656989877
## [756] 0.829180014 -0.449566122 -1.336445985 0.709060939 0.748121745
## [761] 0.630170977 -0.020196597 -0.414512592 0.671442494 0.075326740
## [766] 2.157740250 -0.712103535 -0.656734013 -1.517742686 -0.734543588
## [771] 2.158426143 -1.007864696 -1.654743242 -0.418411146 0.163357658
## [776] -0.783569474 -1.183409496 1.118885106 -0.116473692 0.798403793
## [781] -1.374142601 1.268675598 0.708316293 -0.117229499 -0.861885201
## [786] 1.948426417 0.364019675 -1.022518048 0.818402280 -0.709596106
## [791] 1.771966323 -1.231294346 1.947318604 -0.855098060 -0.461567775
## [796] -0.627892263 -0.557040773 -1.051489025 0.528697292 -0.662964992
## [801] -0.235297411 1.034172179 -1.888192233 0.866185986 -0.258201642
## [806] 0.001982145 0.023479584 -2.300533666 0.987556557 2.041029039
## [811] 0.066928260 -0.929611288 -2.289120437 0.518223228 -0.110993802
## [816] -0.045844599 0.812009021 -0.204170150 -0.062640290 -0.506651994
## [821] -0.205670818 1.245110591 0.008960183 -0.652263566 -0.710943938
## [826] -1.339120570 1.156008954 -0.297985623 0.684075094 0.788417326
## [831] -0.360540851 -0.295306919 0.691851328 0.869421728 -0.070804515
## [836] 0.880895972 0.357156280 -0.250826379 -1.274947947 -0.632649356
## [841] 0.085283547 0.584288741 1.721374569 0.967328793 -0.451279897
## [846] 2.208066603 0.322948068 -1.820907622 0.204019266 0.363344060
## [851] -0.137781578 0.171151871 0.542146285 0.961209430 -0.236429352
## [856] 0.156232967 0.487460507 0.057082076 0.754317764 -0.235258988
## [861] 0.888592098 1.220381850 -0.099947226 0.473596221 1.293158316
## [866] 0.096091376 0.166198355 0.661808047 -1.450428481 1.521236333
## [871] 0.782595013 1.389445375 0.153122791 1.212307327 0.435339958
## [876] 0.414562630 -0.982314605 -0.171353507 -0.070504517 2.107491821
## [881] 0.873406796 1.814614224 -1.253699888 0.147222454 0.553855524
## [886] -0.365922281 -0.033117125 1.269978837 -0.061553082 0.628042030
## [891] 0.049922879 -1.331312289 -1.523724804 1.293445108 -0.708788044
## [896] 0.211591766 0.977899003 -0.497506549 -1.117565756 -0.588910217
## [901] -1.321516599 0.899191272 -0.564491606 -0.148552678 1.587033596
## [906] -0.702333730 1.709220757 -0.030430958 -0.636070109 0.056987013
## [911] -1.896993636 -0.086987995 1.642512639 -0.977005699 -0.005974806
## [916] -2.015505892 -0.350945486 2.154746682 0.211474053 1.164322901
## [921] 1.317960944 -0.541470251 -0.279054363 -0.248246981 0.461706100
## [926] 1.637638163 -1.656644786 0.383842891 1.345428101 0.423400940
## [931] -0.493747682 1.147867679 0.019226812 -1.326654849 1.472809787
## [936] 1.165142518 0.253212829 -0.371741063 0.216820400 0.383448141
## [941] 2.170133095 -0.316440152 1.578328330 -0.277700138 0.354615193
## [946] 1.985851085 1.520661638 0.234161524 0.503753426 -0.734253039
## [951] -0.240291147 -0.313768989 1.135382569 -1.019419061 -0.528065436
## [956] 1.743319721 1.028775569 -1.037098234 0.515127124 -0.340134143
## [961] -0.368864767 -1.154644269 1.189303283 0.001687232 -1.509154815
## [966] 0.699650656 0.006912015 1.176347938 -0.026454588 0.057929431
## [971] -0.563217758 1.139208494 1.422784053 0.179010457 -0.027292529
## [976] -0.562562726 1.145394959 -0.431207043 -0.728215178 -0.417393235
## [981] -0.762183288 -1.060725116 -0.791348236 1.632692447 0.265971343
## [986] -0.476996034 -1.613893717 1.576257921 -0.474461183 -0.539392476
## [991] 1.666322311 -0.712730144 1.860631910 -0.005827979 1.088636895
## [996] -0.916816044 -0.274415828 1.417412200 -1.443408796 -1.147004253
yAxis <- rnorm(1000) + xAxis + 10
yAxis
## [1] 11.130246 9.855063 10.017304 8.674430 10.590724 9.251500 10.765930
## [8] 10.930852 10.350644 11.343779 9.343643 11.105355 9.867647 8.577967
## [15] 8.346096 9.079866 12.873983 10.767298 11.037333 9.327367 11.212516
## [22] 10.964682 8.167271 8.916474 8.925917 9.344061 9.774186 10.464889
## [29] 11.084526 9.134452 7.784890 9.196602 9.473469 10.723557 10.262077
## [36] 8.431591 10.235598 8.581930 9.743565 10.837518 12.224231 7.693967
## [43] 8.868371 10.261894 7.777590 9.475423 10.228703 10.024436 9.203461
## [50] 10.962537 10.951171 10.682464 8.930178 9.418700 10.009073 11.327402
## [57] 11.329509 9.679896 9.663579 11.371254 9.936662 12.760635 10.383674
## [64] 8.241218 10.004719 10.865626 12.689408 11.047943 10.299866 10.390123
## [71] 12.099454 10.145658 11.333309 9.033914 11.275208 10.492838 11.301800
## [78] 11.474873 8.402740 6.159612 10.704725 9.308609 11.425707 9.284349
## [85] 12.534477 10.274860 11.676200 10.304620 9.356969 8.776851 13.795486
## [92] 7.325712 9.511915 9.492109 9.716845 11.626299 9.697560 10.160055
## [99] 11.568934 11.553091 11.278778 8.927256 10.994322 10.153971 9.963620
## [106] 8.056735 9.893627 10.829706 9.890846 10.523358 11.235634 9.519592
## [113] 10.712606 9.230704 7.585528 12.365458 10.687409 8.453201 10.553491
## [120] 10.085633 9.142766 11.898605 12.268735 8.754872 8.910023 9.939571
## [127] 10.748969 10.667681 10.547369 10.047579 9.817979 12.049414 9.249473
## [134] 9.982866 10.951012 7.303826 10.954243 7.503795 12.146467 9.685200
## [141] 9.771975 10.048169 12.169428 9.997019 11.339862 8.409191 7.894897
## [148] 8.947485 7.380818 9.172825 10.423635 8.993273 11.070226 9.416828
## [155] 9.134481 11.542227 9.472525 10.360207 10.576469 8.379839 11.510930
## [162] 10.911630 11.367063 9.416786 11.063125 11.388534 10.976280 10.783239
## [169] 10.654770 12.004104 9.600421 11.247847 9.955240 9.114297 9.167211
## [176] 6.632893 10.745768 9.501198 12.130107 12.442004 9.199983 9.713463
## [183] 10.247449 8.229451 11.708355 9.027285 13.566383 10.605015 11.371866
## [190] 7.119164 10.778423 10.384309 11.274073 9.749209 8.441257 9.323684
## [197] 11.459850 9.489534 9.659527 8.282836 9.650959 9.668253 10.388941
## [204] 11.056745 11.096599 10.356758 8.781246 10.122761 13.367643 8.482716
## [211] 7.930093 9.294322 8.496137 8.833537 9.262411 10.004087 9.537393
## [218] 10.389052 9.852228 11.111994 9.539276 8.651377 9.388454 11.393990
## [225] 10.139031 9.308548 9.426095 9.104139 7.832896 11.699532 11.108430
## [232] 12.168292 7.366442 10.822915 8.774236 9.725264 10.316696 8.353465
## [239] 10.172629 8.605104 8.257873 9.417641 8.431326 10.918960 9.930948
## [246] 9.304391 10.578796 10.410719 10.832830 10.433296 9.594034 11.806696
## [253] 9.747393 9.814715 12.298500 8.146126 11.250078 7.612012 10.287014
## [260] 10.418950 8.695335 10.545657 10.801748 9.498798 9.944120 11.428592
## [267] 10.735903 9.607433 11.725099 11.760966 10.613132 9.973294 13.462136
## [274] 9.491171 11.006851 9.325920 12.165520 9.745144 8.005157 11.119805
## [281] 9.708387 10.163424 9.005075 9.107801 11.460850 9.292421 7.883849
## [288] 10.680829 6.824265 8.832253 9.228441 11.422691 9.360705 10.489208
## [295] 9.449359 11.308895 10.094937 10.266777 10.494633 9.055637 10.711392
## [302] 10.387034 9.891418 9.327303 9.204269 9.293639 11.594619 9.966564
## [309] 8.724717 9.666075 10.012114 10.854476 8.487266 11.887828 11.734212
## [316] 8.504634 12.939123 9.758618 7.907818 11.087443 10.238260 9.017868
## [323] 11.036428 12.600408 8.832731 11.704011 8.350999 10.335685 9.803593
## [330] 10.470017 12.103536 8.484613 9.467815 9.779673 11.205880 10.602784
## [337] 8.483669 10.732951 9.966667 10.318716 9.126019 7.928849 10.756258
## [344] 9.698255 9.497449 10.015263 9.542834 8.372486 8.473750 10.031178
## [351] 10.218893 8.487079 9.109890 10.688524 10.565063 9.559785 12.117008
## [358] 10.425097 8.147366 10.382241 9.657087 9.934305 9.080317 8.866755
## [365] 11.216336 11.846637 8.965106 8.980717 10.537743 10.071025 13.128629
## [372] 10.278562 8.911468 10.234463 10.206785 7.470899 10.764234 8.016511
## [379] 7.678693 11.045995 10.976833 9.781910 8.923168 9.050845 10.980334
## [386] 10.114483 9.584546 10.184898 10.807030 9.544556 9.889493 11.385201
## [393] 9.986024 12.545742 11.056319 11.040102 9.437798 13.970769 10.661581
## [400] 8.740407 9.913936 12.233971 10.304538 10.334991 11.518422 9.551593
## [407] 6.091567 9.021866 10.121990 8.537417 8.890463 11.933622 7.166898
## [414] 10.418156 10.615998 12.236023 10.050035 11.944172 10.284757 11.224619
## [421] 9.665801 9.569439 9.489001 10.449733 9.707502 10.931495 10.009688
## [428] 8.925927 7.491869 8.942036 6.622317 8.542655 9.993512 8.258008
## [435] 10.173399 10.835971 10.415119 10.813702 7.571158 12.156998 10.929884
## [442] 10.422687 10.141662 5.576995 10.669104 7.692629 11.341669 12.033693
## [449] 11.287870 12.752789 12.138763 11.520517 10.837645 12.552302 9.776696
## [456] 10.589043 11.050292 9.279361 10.303207 10.195405 11.730536 9.566203
## [463] 10.804036 10.164384 11.100698 8.890735 8.761778 11.362299 10.345281
## [470] 8.035441 10.141997 9.930729 8.835353 10.534161 9.031774 8.543276
## [477] 9.627613 10.110853 9.922701 9.352801 9.195192 11.234477 10.150362
## [484] 9.318835 10.355063 7.857363 10.388740 9.348450 11.051116 8.314815
## [491] 9.905434 13.941954 9.888789 10.752905 7.953235 11.651356 9.143079
## [498] 9.688908 7.185762 10.938313 11.529347 9.919502 9.469321 9.764095
## [505] 8.994802 6.670138 8.027754 9.824724 10.765037 11.635400 10.000576
## [512] 7.337284 7.454869 10.528477 11.444991 10.332916 10.005719 10.869027
## [519] 10.190803 8.201809 12.400386 11.243472 8.526006 12.084240 11.044648
## [526] 9.653525 9.774985 11.925562 9.746962 9.082006 11.729935 7.632400
## [533] 9.056106 10.830610 10.013266 13.707960 9.706600 11.741880 12.006907
## [540] 10.474648 10.312309 8.456894 9.442112 7.171883 9.371498 9.265251
## [547] 8.877791 8.736100 8.197039 11.844513 9.974712 8.539793 10.859403
## [554] 10.489847 11.023763 8.650722 8.294372 10.628738 9.080575 7.163976
## [561] 10.688037 11.323841 9.851067 10.457595 9.509667 10.666838 10.651108
## [568] 11.599242 9.455642 8.644443 11.302332 10.768573 11.474677 10.014027
## [575] 13.973045 12.221563 10.703639 9.783101 12.039011 11.412239 11.195919
## [582] 11.809348 11.481853 11.823102 8.003400 9.468635 8.266512 10.623250
## [589] 9.264336 9.844333 8.448743 10.904098 11.505683 10.045731 10.401266
## [596] 11.241750 12.156143 9.875861 8.076646 9.898680 9.746007 9.858238
## [603] 9.127243 8.727076 11.353493 11.232253 8.656629 9.447721 9.019377
## [610] 10.682442 11.230051 9.410522 10.746177 8.474614 8.066390 10.577236
## [617] 8.873230 9.583067 8.138694 9.461518 8.959076 7.781518 12.448826
## [624] 10.510506 10.166830 10.536538 8.815990 10.863545 8.261053 8.549684
## [631] 10.191404 10.696957 11.779648 11.952572 10.438697 11.236233 11.988952
## [638] 10.049297 7.946995 8.223351 9.134875 12.740981 10.782729 8.643606
## [645] 9.267096 10.787680 11.338545 10.930258 9.656798 9.329381 11.116846
## [652] 10.731039 11.155267 8.076176 8.914534 9.479686 7.978471 11.216103
## [659] 10.783023 8.057923 11.312303 9.560419 7.382126 8.147952 11.400764
## [666] 9.194472 10.138846 10.133066 8.617794 7.429764 10.288581 11.146467
## [673] 7.960496 10.709420 8.987870 7.826760 9.695440 12.134808 8.331280
## [680] 8.027941 9.383548 9.497984 9.188154 9.026247 8.830813 11.258212
## [687] 9.313100 10.002657 9.950639 8.246739 9.385608 8.169162 9.342062
## [694] 10.917706 9.749895 12.704029 9.713234 10.936411 8.447691 6.102413
## [701] 9.217668 12.785717 11.441706 11.362295 11.776919 8.962801 10.877152
## [708] 9.500104 9.835388 11.861063 10.622396 12.691023 11.041807 12.159902
## [715] 9.656315 10.582912 11.768379 12.147289 10.915357 12.220035 11.042831
## [722] 9.412017 8.712854 8.665620 10.477982 11.499129 10.445052 9.768250
## [729] 10.313800 9.095054 10.741364 9.756786 10.137785 8.615833 9.054844
## [736] 8.867823 12.131605 9.184419 9.049355 9.315354 11.408203 11.321028
## [743] 11.049441 10.538679 10.015702 12.699616 11.672282 10.720643 11.667585
## [750] 8.588708 9.195277 10.964600 11.379320 7.947784 8.676852 11.071953
## [757] 9.785032 9.567986 10.871269 10.408685 11.216781 9.877599 9.600082
## [764] 10.457479 10.361223 12.287339 7.845021 9.597983 7.087915 7.755431
## [771] 12.606936 7.133669 9.702506 9.714149 11.283351 10.202212 8.718494
## [778] 8.655743 10.128618 11.121110 10.577664 11.092790 9.205118 10.038211
## [785] 9.523515 12.347191 11.015595 8.905853 10.700782 9.477413 11.638271
## [792] 10.178580 12.785981 8.807601 9.046492 8.946079 10.025607 9.162399
## [799] 10.195725 10.605608 10.649943 9.404441 7.920131 11.300710 10.039027
## [806] 10.845190 10.994049 7.896941 12.707434 11.110706 10.257052 9.175412
## [813] 7.644462 11.137899 8.870608 11.462360 11.516271 10.607537 9.657904
## [820] 8.262530 8.757756 12.313828 9.068798 9.483725 10.670844 8.740685
## [827] 11.563900 8.127661 11.163412 11.019809 8.891603 8.058106 10.948107
## [834] 10.796481 9.572239 11.568650 10.998370 7.345407 7.707305 9.769720
## [841] 9.220671 10.049185 12.244454 10.380078 8.113759 13.145144 9.490245
## [848] 8.016174 10.348382 10.626431 8.931506 9.675632 7.662342 10.877054
## [855] 8.328857 9.287338 11.007537 10.948415 12.372672 9.869568 12.623210
## [862] 10.273452 9.453685 9.174229 11.721107 10.722221 11.061095 8.906494
## [869] 9.500913 12.035266 9.170877 11.023109 11.200825 10.706279 10.358206
## [876] 10.378976 7.446480 11.535150 9.126568 12.471485 11.846612 12.037028
## [883] 8.848047 9.856081 10.145735 10.709134 9.193347 11.976012 10.791627
## [890] 9.756085 9.579624 6.626244 9.000327 9.621456 10.138552 9.587946
## [897] 10.938062 8.860220 8.980478 8.404807 10.089368 11.950986 8.541459
## [904] 10.326272 12.434810 9.051468 12.691316 9.542086 8.859415 10.258527
## [911] 8.351877 10.864483 13.845341 9.239707 7.982025 8.843278 11.473286
## [918] 12.511697 8.456745 11.333417 12.566863 11.001916 10.485777 11.398234
## [925] 11.183859 9.811835 8.987648 10.130548 10.101604 10.155335 9.500495
## [932] 12.213568 9.068392 10.116052 13.062087 9.844155 13.253222 10.017572
## [939] 8.967357 10.759631 11.953164 9.444536 10.699463 8.555767 11.139418
## [946] 10.567964 10.942135 8.044406 11.424771 9.983647 10.443782 8.891746
## [953] 11.540090 11.232820 11.142847 11.393280 13.322402 7.476767 10.940269
## [960] 10.245031 8.875629 10.122456 10.192161 11.805002 8.713014 10.142274
## [967] 9.821271 11.659149 9.162946 7.839372 10.213673 10.853876 10.788139
## [974] 11.260491 10.362715 8.858718 12.400322 9.484755 9.118325 10.092585
## [981] 7.579238 8.109179 8.518269 9.822709 10.337233 8.566396 8.908849
## [988] 13.552618 9.661541 9.373928 10.945048 8.021930 12.286347 8.953378
## [995] 9.880967 10.311157 11.148664 10.303740 9.106071 10.043427
(group <- rep(1,1000))
## [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [186] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [223] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [260] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [297] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [334] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [371] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [408] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [445] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [482] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [519] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [556] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [593] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [630] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [667] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [704] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [741] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [778] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [815] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [852] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [889] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [926] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [963] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1000] 1
group[xAxis > -1.5] <- 2
group[xAxis > -.5] <- 3
group[xAxis > .5] <- 4
group[xAxis > 1.5] <- 5
group
## [1] 3 3 3 2 5 3 3 4 3 4 4 5 4 2 3 2 5 3 3 4 4 4 2 2 4 3 4 3 4 4 3 4 2 4 3 3 3
## [38] 4 2 5 5 1 2 4 3 2 3 4 2 4 4 4 3 3 3 4 4 3 3 4 3 4 2 2 3 4 4 4 4 2 4 3 2 1
## [75] 2 3 3 4 2 1 4 3 2 3 4 4 4 3 2 3 5 1 3 4 3 3 3 2 4 4 4 2 1 1 3 1 2 4 5 3 3
## [112] 2 3 3 1 4 4 2 2 3 1 5 4 2 2 2 4 3 4 2 4 4 2 4 3 2 4 2 4 1 3 4 3 2 3 2 1 2
## [149] 3 3 4 3 4 2 2 4 2 2 3 1 4 4 3 2 4 4 4 3 3 4 3 4 2 2 4 2 3 2 5 5 3 3 4 3 5
## [186] 3 4 3 4 1 4 3 4 2 2 3 4 2 3 2 3 3 2 4 4 4 3 4 5 2 1 3 2 2 2 3 3 3 3 4 2 2
## [223] 3 4 2 3 2 2 3 4 3 3 2 4 2 4 3 2 3 1 2 2 1 3 3 3 3 4 3 4 3 4 3 2 4 2 4 3 2
## [260] 3 2 3 3 3 2 4 4 3 3 3 3 3 5 5 4 3 5 2 2 2 3 2 2 2 4 1 3 4 1 3 3 2 3 4 2 4
## [297] 4 4 1 2 2 4 2 2 3 3 3 4 2 2 3 2 3 4 5 3 4 2 1 4 3 3 3 4 2 3 2 4 2 4 4 3 3
## [334] 2 4 2 3 4 3 4 4 1 4 2 3 2 4 2 2 4 3 2 2 3 2 4 4 4 2 3 2 2 2 2 4 4 2 3 3 2
## [371] 5 4 2 4 2 2 3 3 1 5 5 2 3 2 3 4 2 2 3 4 2 3 2 4 3 3 3 4 2 4 3 4 4 4 4 2 1
## [408] 3 3 2 2 5 2 3 2 5 4 3 5 4 4 2 3 3 4 4 3 3 2 3 1 2 2 2 2 4 4 4 3 4 3 4 3 1
## [445] 3 2 5 4 4 4 3 3 3 4 4 3 3 4 4 2 4 3 4 2 4 2 3 4 4 2 3 4 3 4 3 3 3 4 3 3 2
## [482] 4 4 4 2 2 2 3 3 2 3 5 2 3 2 3 3 4 2 3 4 2 2 2 3 2 3 3 3 4 3 2 2 3 3 3 3 3
## [519] 4 2 4 3 2 4 4 2 2 4 2 2 4 3 3 4 3 4 3 5 2 4 3 3 3 1 3 3 3 2 2 4 3 3 3 2 4
## [556] 2 1 3 1 3 3 4 3 3 3 4 3 4 4 2 4 4 3 2 3 4 4 2 3 4 3 3 4 4 2 4 2 3 3 3 3 3
## [593] 2 2 3 4 4 2 2 3 3 2 4 2 3 3 2 3 2 4 4 3 4 2 3 3 3 2 2 3 3 1 4 4 2 3 2 5 3
## [630] 2 3 4 3 4 3 4 4 3 3 2 2 5 3 3 3 3 4 4 3 2 4 2 4 2 2 3 2 5 4 3 4 3 3 3 3 2
## [667] 2 4 2 1 4 4 3 4 2 2 3 5 3 2 3 2 2 2 2 3 3 3 4 2 3 4 3 4 2 5 3 3 1 1 3 3 3
## [704] 4 3 2 3 3 4 4 4 4 4 4 2 3 4 4 4 4 4 2 3 3 2 4 5 3 4 3 4 2 3 3 2 2 3 1 2 2
## [741] 3 3 3 2 3 3 2 3 4 2 3 4 4 1 1 4 3 2 4 4 4 3 3 4 3 5 2 2 1 2 5 2 1 3 3 2 2
## [778] 4 3 4 2 4 4 3 2 5 3 2 4 2 5 2 5 2 3 2 2 2 4 2 3 4 1 4 3 3 3 1 4 5 3 2 1 4
## [815] 3 3 4 3 3 2 3 4 3 2 2 2 4 3 4 4 3 3 4 4 3 4 3 3 2 2 3 4 5 4 3 5 3 1 3 3 3
## [852] 3 4 4 3 3 3 3 4 3 4 4 3 3 4 3 3 4 2 5 4 4 3 4 3 3 2 3 3 5 4 5 2 3 4 3 3 4
## [889] 3 4 3 2 1 4 2 3 4 3 2 2 2 4 2 3 5 2 5 3 2 3 1 3 5 2 3 1 3 5 3 4 4 2 3 3 3
## [926] 5 1 3 4 3 3 4 3 2 4 4 3 3 3 3 5 3 5 3 3 5 5 3 4 2 3 3 4 2 2 5 4 2 4 3 3 2
## [963] 4 3 1 4 3 4 3 3 2 4 4 3 3 2 4 3 2 3 2 2 2 5 3 3 1 5 3 2 5 2 5 3 4 2 3 4 2
## [1000] 2
sample_data <- data.frame(xAxis,yAxis,group)
sample_data
## xAxis yAxis group
## 1 0.078851110 11.130246 3
## 2 0.207236931 9.855063 3
## 3 0.266564075 10.017304 3
## 4 -1.179809719 8.674430 2
## 5 2.217820430 10.590724 5
## 6 -0.388101041 9.251500 3
## 7 0.148447250 10.765930 3
## 8 0.658248739 10.930852 4
## 9 0.410168641 10.350644 3
## 10 1.010368162 11.343779 4
## 11 0.889917795 9.343643 4
## 12 2.234645962 11.105355 5
## 13 0.500489334 9.867647 4
## 14 -0.648395621 8.577967 2
## 15 -0.478805517 8.346096 3
## 16 -0.599988795 9.079866 2
## 17 1.575278080 12.873983 5
## 18 0.367251105 10.767298 3
## 19 0.295268708 11.037333 3
## 20 0.501077096 9.327367 4
## 21 1.207058353 11.212516 4
## 22 1.045319986 10.964682 4
## 23 -0.552236474 8.167271 2
## 24 -1.226106994 8.916474 2
## 25 0.620730079 8.925917 4
## 26 -0.483302168 9.344061 3
## 27 0.759622981 9.774186 4
## 28 -0.465452498 10.464889 3
## 29 1.164475433 11.084526 4
## 30 1.070889414 9.134452 4
## 31 0.051988709 7.784890 3
## 32 0.628534529 9.196602 4
## 33 -1.382741888 9.473469 2
## 34 0.998710133 10.723557 4
## 35 -0.301864316 10.262077 3
## 36 -0.019393037 8.431591 3
## 37 0.071385139 10.235598 3
## 38 0.763858681 8.581930 4
## 39 -1.224909947 9.743565 2
## 40 1.523476844 10.837518 5
## 41 2.297298190 12.224231 5
## 42 -1.582388714 7.693967 1
## 43 -1.214610867 8.868371 2
## 44 1.294106428 10.261894 4
## 45 -0.150348900 7.777590 3
## 46 -0.543401839 9.475423 2
## 47 0.237567103 10.228703 3
## 48 0.801194163 10.024436 4
## 49 -1.369415804 9.203461 2
## 50 1.425382851 10.962537 4
## 51 1.027037130 10.951171 4
## 52 1.393158952 10.682464 4
## 53 0.248897224 8.930178 3
## 54 -0.182607718 9.418700 3
## 55 -0.103887415 10.009073 3
## 56 0.552136786 11.327402 4
## 57 1.019911549 11.329509 4
## 58 -0.058922937 9.679896 3
## 59 -0.153955036 9.663579 3
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## 975 -0.027292529 10.362715 3
## 976 -0.562562726 8.858718 2
## 977 1.145394959 12.400322 4
## 978 -0.431207043 9.484755 3
## 979 -0.728215178 9.118325 2
## 980 -0.417393235 10.092585 3
## 981 -0.762183288 7.579238 2
## 982 -1.060725116 8.109179 2
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## 985 0.265971343 10.337233 3
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## 987 -1.613893717 8.908849 1
## 988 1.576257921 13.552618 5
## 989 -0.474461183 9.661541 3
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## 998 1.417412200 10.303740 4
## 999 -1.443408796 9.106071 2
## 1000 -1.147004253 10.043427 2
plot <- ggplot(sample_data, aes(x = xAxis, y= yAxis, col = as.factor(group)))+
geom_point()+theme(legend.position = "none")
plot
ggMarginal(plot,type= 'histogram',groupColour=TRUE,groupFill=TRUE)
