The iris dataset is a dataset in R that contains measurements on 4 different attributes (in centimeters) for 50 flowers from 3 different species.
In 1935, Edgar Anderson collected data to quantify the geographic variations of iris flowers. The data set consists of 50 samples from each of the three sub-species ( iris setosa, iris virginica, and iris versicolor).
# iris
head(iris)## 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 setosastr(iris)## 'data.frame': 150 obs. of 5 variables:
## $ Sepal.Length: num 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
## $ Sepal.Width : num 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
## $ Petal.Length: num 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
## $ Petal.Width : num 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
## $ Species : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...attributes(iris)## $names
## [1] "Sepal.Length" "Sepal.Width" "Petal.Length" "Petal.Width" "Species"
##
## $class
## [1] "data.frame"
##
## $row.names
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
## [19] 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
## [37] 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54
## [55] 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72
## [73] 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90
## [91] 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108
## [109] 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126
## [127] 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144
## [145] 145 146 147 148 149 150summary(iris)## Sepal.Length Sepal.Width Petal.Length Petal.Width
## Min. :4.300 Min. :2.000 Min. :1.000 Min. :0.100
## 1st Qu.:5.100 1st Qu.:2.800 1st Qu.:1.600 1st Qu.:0.300
## Median :5.800 Median :3.000 Median :4.350 Median :1.300
## Mean :5.843 Mean :3.057 Mean :3.758 Mean :1.199
## 3rd Qu.:6.400 3rd Qu.:3.300 3rd Qu.:5.100 3rd Qu.:1.800
## Max. :7.900 Max. :4.400 Max. :6.900 Max. :2.500
## Species
## setosa :50
## versicolor:50
## virginica :50
##
##
## dim(iris)## [1] 150 5# display column names
names(iris)## [1] "Sepal.Length" "Sepal.Width" "Petal.Length" "Petal.Width" "Species"We can clearly see Iris dataSet is [150 5]. Species is the only one categorical variable
df <- iris[, 1:4]
boxplot(df)
pairs(df)
stars(df)
PL <- df$Petal.Length
barplot(PL)
hist(PL)
SP <- iris$Species
pie(table(SP))
boxplot(PL ~ SP)
summary(aov(PL ~ SP))## Df Sum Sq Mean Sq F value Pr(>F)
## SP 2 437.1 218.55 1180 <2e-16 ***
## Residuals 147 27.2 0.19
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1PW <- df$Petal.Width
plot(PL, PW, col = SP)
abline(lm(PW ~ PL))
summary(PL)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 1.600 4.350 3.758 5.100 6.900This produces a lot of useful information about the distribution of petal length:
The minimum is 1.000, and the maximum is 6.900.
Average petal length is 3.758.
The mid-point, or median, is 4.350, as about half of the numbers are smaller than 4.350. Why the median is different from the mean? What happens if there is a typo and one number is entered 340cm instead of 3.40cm?
The 3rd quartile, or 75th percentile, is 5.100, as 75% of the flowers have petals shorter than 5.100. If a student’s GPA ranks 5th in a class of 25, he/she is at 80th percentile. The minimum is the 0th percentile and the maximum is the 100th percentile.
The 1st quartile, or 25th percentile, is 1.600. Only 25% of the flowers have petals shorter than 1.600. These summary statistics are graphically represented as a boxplot. Boxplots are more useful when multiple sets of numbers are compared.
# View(iris) # show as a spreadsheet
SL <- df$Sepal.Length
hist(SL, main="Histogram of Sepal Length", xlab="Sepal Length",
ylab='Frequency', xlim=c(4,8), col="blue", freq=FALSE)
SW <- df$Sepal.Width
hist(SW, main="Histogram of Sepal Width", xlab="Sepal Width",
ylab='Frequency', xlim=c(2,5), col="darkorchid", freq=FALSE)
Box Plot shows 5 statistically significant numbers- the minimum, the 25th percentile, the median, the 75th percentile and the maximum. It is thus useful for visualizing the spread of the data is and deriving inferences accordingly.
irisVer <- subset(iris, Species == "versicolor")
irisSet <- subset(iris, Species == "setosa")
irisVir <- subset(iris, Species == "virginica")
par(mfrow=c(1,3),mar=c(6,3,2,1))
boxplot(irisVer[,1:4], main="Versicolor, Rainbow Palette",ylim = c(0,8),las=2, col=rainbow(4))
boxplot(irisSet[,1:4], main="Setosa, Heat color Palette",ylim = c(0,8),las=2, col=heat.colors(4))
boxplot(irisVir[,1:4], main="Virginica, Topo colors Palette",ylim = c(0,8),las=2, col=topo.colors(4))
# create boxplot of sepal width vs. sepal length
boxplot(Sepal.Length~Species,
data=iris,
main='Sepal Length by Species',
xlab='Species',
ylab='Sepal Length',
col='steelblue',
border='black')
# 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)
Scatterplot Matrices
When we have two or more variables and we want to correlate between one variable and others so we use a scatterplot matrix.
pairs() function is used to create matrices of scatterplots.
x <- iris[, 1:4]
pairs(x)
sessionInfo()## R version 4.2.1 (2022-06-23 ucrt)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 22000)
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## Matrix products: default
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## [3] LC_MONETARY=English_United States.utf8
## [4] LC_NUMERIC=C
## [5] LC_TIME=English_United States.utf8
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## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
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## loaded via a namespace (and not attached):
## [1] bookdown_0.28 codetools_0.2-18 digest_0.6.29 R6_2.5.1
## [5] jsonlite_1.8.0 magrittr_2.0.3 evaluate_0.15 rmdformats_1.0.4
## [9] highr_0.9 stringi_1.7.8 cachem_1.0.6 rlang_1.0.4
## [13] cli_3.3.0 rstudioapi_0.13 jquerylib_0.1.4 bslib_0.4.0
## [17] rmarkdown_2.14 tools_4.2.1 stringr_1.4.0 xfun_0.31
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## [25] knitr_1.39 sass_0.4.2