MSDS Bridge HW 3 Project
Adam Gersowitz
1/15/2020
#Meaningful question for analysis. Which flowers should be planted to attract polinators from a large distance?
##1. Data Exploration:
This should include summary statistics, means, medians, quartiles, or any other relevant information about the data set. Please include some conclusions in the R Markdown text.
iris <- read.csv(file="iris.csv", header=TRUE, sep=",")
summary(iris) ## X Sepal.Length Sepal.Width Petal.Length
## Min. : 1.00 Min. :4.300 Min. :2.000 Min. :1.000
## 1st Qu.: 38.25 1st Qu.:5.100 1st Qu.:2.800 1st Qu.:1.600
## Median : 75.50 Median :5.800 Median :3.000 Median :4.350
## Mean : 75.50 Mean :5.843 Mean :3.057 Mean :3.758
## 3rd Qu.:112.75 3rd Qu.:6.400 3rd Qu.:3.300 3rd Qu.:5.100
## Max. :150.00 Max. :7.900 Max. :4.400 Max. :6.900
## Petal.Width Species
## Min. :0.100 setosa :50
## 1st Qu.:0.300 versicolor:50
## Median :1.300 virginica :50
## Mean :1.199
## 3rd Qu.:1.800
## Max. :2.500IMeanSL <- mean(iris$Sepal.Length, na.rm=TRUE)
IMedianSL <- median(iris$Sepal.Length, na.rm=TRUE)
IMeanSW <- mean(iris$Sepal.Width, na.rm=TRUE)
IMedianSW <- median(iris$Sepal.Width, na.rm=TRUE)
IQSL <- quantile(iris$Sepal.Length, na.rm=TRUE)
IQSW <- quantile(iris$Sepal.Width, na.rm=TRUE)
cat("\nThe Mean of the Sepal Length is ", IMeanSL,"\n")##
## The Mean of the Sepal Length is 5.843333cat("The Median of the Sepal Length is ",IMedianSL,"\n")## The Median of the Sepal Length is 5.8cat("The Mean of the Sepal Width is ",IMeanSW,"\n")## The Mean of the Sepal Width is 3.057333cat("The Median of the Sepal Width is ",IMedianSW,"\n")## The Median of the Sepal Width is 3print("The quantiles of the Sepal Length are: ")## [1] "The quantiles of the Sepal Length are: "print(IQSL) ## 0% 25% 50% 75% 100%
## 4.3 5.1 5.8 6.4 7.9print("The qunatiles of the Sepal Width are: ")## [1] "The qunatiles of the Sepal Width are: "print(IQSW)## 0% 25% 50% 75% 100%
## 2.0 2.8 3.0 3.3 4.4##2. Data wrangling:
Please perform some basic transformations. They will need to make sense but could include column renaming, creating a subset of the data, replacing values, or creating new columns with derived data (for example – if it makes sense you could sum two columns together)
petals <- data.frame("PL" = iris$Petal.Length,
"PW" = iris$Petal.Width,
"S" = iris$Species)
petals$PetalArea <- petals$PL*petals$PW
petals$avgPArea <- mean(petals$PetalArea)
petals$AboveAvgArea <- ifelse(petals$PetalArea > petals$avgPArea, "Above Average", "Below Average")
petals$color <- ifelse(petals$S == "setosa", "red",
ifelse(petals$S == "versicolor", "blue",
ifelse(petals$S == "virginica", "green", "black")
)
)
petals## PL PW S PetalArea avgPArea AboveAvgArea color
## 1 1.4 0.2 setosa 0.28 5.794067 Below Average red
## 2 1.4 0.2 setosa 0.28 5.794067 Below Average red
## 3 1.3 0.2 setosa 0.26 5.794067 Below Average red
## 4 1.5 0.2 setosa 0.30 5.794067 Below Average red
## 5 1.4 0.2 setosa 0.28 5.794067 Below Average red
## 6 1.7 0.4 setosa 0.68 5.794067 Below Average red
## 7 1.4 0.3 setosa 0.42 5.794067 Below Average red
## 8 1.5 0.2 setosa 0.30 5.794067 Below Average red
## 9 1.4 0.2 setosa 0.28 5.794067 Below Average red
## 10 1.5 0.1 setosa 0.15 5.794067 Below Average red
## 11 1.5 0.2 setosa 0.30 5.794067 Below Average red
## 12 1.6 0.2 setosa 0.32 5.794067 Below Average red
## 13 1.4 0.1 setosa 0.14 5.794067 Below Average red
## 14 1.1 0.1 setosa 0.11 5.794067 Below Average red
## 15 1.2 0.2 setosa 0.24 5.794067 Below Average red
## 16 1.5 0.4 setosa 0.60 5.794067 Below Average red
## 17 1.3 0.4 setosa 0.52 5.794067 Below Average red
## 18 1.4 0.3 setosa 0.42 5.794067 Below Average red
## 19 1.7 0.3 setosa 0.51 5.794067 Below Average red
## 20 1.5 0.3 setosa 0.45 5.794067 Below Average red
## 21 1.7 0.2 setosa 0.34 5.794067 Below Average red
## 22 1.5 0.4 setosa 0.60 5.794067 Below Average red
## 23 1.0 0.2 setosa 0.20 5.794067 Below Average red
## 24 1.7 0.5 setosa 0.85 5.794067 Below Average red
## 25 1.9 0.2 setosa 0.38 5.794067 Below Average red
## 26 1.6 0.2 setosa 0.32 5.794067 Below Average red
## 27 1.6 0.4 setosa 0.64 5.794067 Below Average red
## 28 1.5 0.2 setosa 0.30 5.794067 Below Average red
## 29 1.4 0.2 setosa 0.28 5.794067 Below Average red
## 30 1.6 0.2 setosa 0.32 5.794067 Below Average red
## 31 1.6 0.2 setosa 0.32 5.794067 Below Average red
## 32 1.5 0.4 setosa 0.60 5.794067 Below Average red
## 33 1.5 0.1 setosa 0.15 5.794067 Below Average red
## 34 1.4 0.2 setosa 0.28 5.794067 Below Average red
## 35 1.5 0.2 setosa 0.30 5.794067 Below Average red
## 36 1.2 0.2 setosa 0.24 5.794067 Below Average red
## 37 1.3 0.2 setosa 0.26 5.794067 Below Average red
## 38 1.4 0.1 setosa 0.14 5.794067 Below Average red
## 39 1.3 0.2 setosa 0.26 5.794067 Below Average red
## 40 1.5 0.2 setosa 0.30 5.794067 Below Average red
## 41 1.3 0.3 setosa 0.39 5.794067 Below Average red
## 42 1.3 0.3 setosa 0.39 5.794067 Below Average red
## 43 1.3 0.2 setosa 0.26 5.794067 Below Average red
## 44 1.6 0.6 setosa 0.96 5.794067 Below Average red
## 45 1.9 0.4 setosa 0.76 5.794067 Below Average red
## 46 1.4 0.3 setosa 0.42 5.794067 Below Average red
## 47 1.6 0.2 setosa 0.32 5.794067 Below Average red
## 48 1.4 0.2 setosa 0.28 5.794067 Below Average red
## 49 1.5 0.2 setosa 0.30 5.794067 Below Average red
## 50 1.4 0.2 setosa 0.28 5.794067 Below Average red
## 51 4.7 1.4 versicolor 6.58 5.794067 Above Average blue
## 52 4.5 1.5 versicolor 6.75 5.794067 Above Average blue
## 53 4.9 1.5 versicolor 7.35 5.794067 Above Average blue
## 54 4.0 1.3 versicolor 5.20 5.794067 Below Average blue
## 55 4.6 1.5 versicolor 6.90 5.794067 Above Average blue
## 56 4.5 1.3 versicolor 5.85 5.794067 Above Average blue
## 57 4.7 1.6 versicolor 7.52 5.794067 Above Average blue
## 58 3.3 1.0 versicolor 3.30 5.794067 Below Average blue
## 59 4.6 1.3 versicolor 5.98 5.794067 Above Average blue
## 60 3.9 1.4 versicolor 5.46 5.794067 Below Average blue
## 61 3.5 1.0 versicolor 3.50 5.794067 Below Average blue
## 62 4.2 1.5 versicolor 6.30 5.794067 Above Average blue
## 63 4.0 1.0 versicolor 4.00 5.794067 Below Average blue
## 64 4.7 1.4 versicolor 6.58 5.794067 Above Average blue
## 65 3.6 1.3 versicolor 4.68 5.794067 Below Average blue
## 66 4.4 1.4 versicolor 6.16 5.794067 Above Average blue
## 67 4.5 1.5 versicolor 6.75 5.794067 Above Average blue
## 68 4.1 1.0 versicolor 4.10 5.794067 Below Average blue
## 69 4.5 1.5 versicolor 6.75 5.794067 Above Average blue
## 70 3.9 1.1 versicolor 4.29 5.794067 Below Average blue
## 71 4.8 1.8 versicolor 8.64 5.794067 Above Average blue
## 72 4.0 1.3 versicolor 5.20 5.794067 Below Average blue
## 73 4.9 1.5 versicolor 7.35 5.794067 Above Average blue
## 74 4.7 1.2 versicolor 5.64 5.794067 Below Average blue
## 75 4.3 1.3 versicolor 5.59 5.794067 Below Average blue
## 76 4.4 1.4 versicolor 6.16 5.794067 Above Average blue
## 77 4.8 1.4 versicolor 6.72 5.794067 Above Average blue
## 78 5.0 1.7 versicolor 8.50 5.794067 Above Average blue
## 79 4.5 1.5 versicolor 6.75 5.794067 Above Average blue
## 80 3.5 1.0 versicolor 3.50 5.794067 Below Average blue
## 81 3.8 1.1 versicolor 4.18 5.794067 Below Average blue
## 82 3.7 1.0 versicolor 3.70 5.794067 Below Average blue
## 83 3.9 1.2 versicolor 4.68 5.794067 Below Average blue
## 84 5.1 1.6 versicolor 8.16 5.794067 Above Average blue
## 85 4.5 1.5 versicolor 6.75 5.794067 Above Average blue
## 86 4.5 1.6 versicolor 7.20 5.794067 Above Average blue
## 87 4.7 1.5 versicolor 7.05 5.794067 Above Average blue
## 88 4.4 1.3 versicolor 5.72 5.794067 Below Average blue
## 89 4.1 1.3 versicolor 5.33 5.794067 Below Average blue
## 90 4.0 1.3 versicolor 5.20 5.794067 Below Average blue
## 91 4.4 1.2 versicolor 5.28 5.794067 Below Average blue
## 92 4.6 1.4 versicolor 6.44 5.794067 Above Average blue
## 93 4.0 1.2 versicolor 4.80 5.794067 Below Average blue
## 94 3.3 1.0 versicolor 3.30 5.794067 Below Average blue
## 95 4.2 1.3 versicolor 5.46 5.794067 Below Average blue
## 96 4.2 1.2 versicolor 5.04 5.794067 Below Average blue
## 97 4.2 1.3 versicolor 5.46 5.794067 Below Average blue
## 98 4.3 1.3 versicolor 5.59 5.794067 Below Average blue
## 99 3.0 1.1 versicolor 3.30 5.794067 Below Average blue
## 100 4.1 1.3 versicolor 5.33 5.794067 Below Average blue
## 101 6.0 2.5 virginica 15.00 5.794067 Above Average green
## 102 5.1 1.9 virginica 9.69 5.794067 Above Average green
## 103 5.9 2.1 virginica 12.39 5.794067 Above Average green
## 104 5.6 1.8 virginica 10.08 5.794067 Above Average green
## 105 5.8 2.2 virginica 12.76 5.794067 Above Average green
## 106 6.6 2.1 virginica 13.86 5.794067 Above Average green
## 107 4.5 1.7 virginica 7.65 5.794067 Above Average green
## 108 6.3 1.8 virginica 11.34 5.794067 Above Average green
## 109 5.8 1.8 virginica 10.44 5.794067 Above Average green
## 110 6.1 2.5 virginica 15.25 5.794067 Above Average green
## 111 5.1 2.0 virginica 10.20 5.794067 Above Average green
## 112 5.3 1.9 virginica 10.07 5.794067 Above Average green
## 113 5.5 2.1 virginica 11.55 5.794067 Above Average green
## 114 5.0 2.0 virginica 10.00 5.794067 Above Average green
## 115 5.1 2.4 virginica 12.24 5.794067 Above Average green
## 116 5.3 2.3 virginica 12.19 5.794067 Above Average green
## 117 5.5 1.8 virginica 9.90 5.794067 Above Average green
## 118 6.7 2.2 virginica 14.74 5.794067 Above Average green
## 119 6.9 2.3 virginica 15.87 5.794067 Above Average green
## 120 5.0 1.5 virginica 7.50 5.794067 Above Average green
## 121 5.7 2.3 virginica 13.11 5.794067 Above Average green
## 122 4.9 2.0 virginica 9.80 5.794067 Above Average green
## 123 6.7 2.0 virginica 13.40 5.794067 Above Average green
## 124 4.9 1.8 virginica 8.82 5.794067 Above Average green
## 125 5.7 2.1 virginica 11.97 5.794067 Above Average green
## 126 6.0 1.8 virginica 10.80 5.794067 Above Average green
## 127 4.8 1.8 virginica 8.64 5.794067 Above Average green
## 128 4.9 1.8 virginica 8.82 5.794067 Above Average green
## 129 5.6 2.1 virginica 11.76 5.794067 Above Average green
## 130 5.8 1.6 virginica 9.28 5.794067 Above Average green
## 131 6.1 1.9 virginica 11.59 5.794067 Above Average green
## 132 6.4 2.0 virginica 12.80 5.794067 Above Average green
## 133 5.6 2.2 virginica 12.32 5.794067 Above Average green
## 134 5.1 1.5 virginica 7.65 5.794067 Above Average green
## 135 5.6 1.4 virginica 7.84 5.794067 Above Average green
## 136 6.1 2.3 virginica 14.03 5.794067 Above Average green
## 137 5.6 2.4 virginica 13.44 5.794067 Above Average green
## 138 5.5 1.8 virginica 9.90 5.794067 Above Average green
## 139 4.8 1.8 virginica 8.64 5.794067 Above Average green
## 140 5.4 2.1 virginica 11.34 5.794067 Above Average green
## 141 5.6 2.4 virginica 13.44 5.794067 Above Average green
## 142 5.1 2.3 virginica 11.73 5.794067 Above Average green
## 143 5.1 1.9 virginica 9.69 5.794067 Above Average green
## 144 5.9 2.3 virginica 13.57 5.794067 Above Average green
## 145 5.7 2.5 virginica 14.25 5.794067 Above Average green
## 146 5.2 2.3 virginica 11.96 5.794067 Above Average green
## 147 5.0 1.9 virginica 9.50 5.794067 Above Average green
## 148 5.2 2.0 virginica 10.40 5.794067 Above Average green
## 149 5.4 2.3 virginica 12.42 5.794067 Above Average green
## 150 5.1 1.8 virginica 9.18 5.794067 Above Average green##3. Graphics:
Please make sure to display at least one scatter plot, box plot and histogram. Don’t be limited to this. Please explore the many other options in R packages such as ggplot2.
library(ggplot2)
plot(petals$PW, petals$PL, main="Petals Area",
xlab="Petal Width ", ylab="Petal Length", col=petals$color)
legend("topleft", legend = c("Setosa","Versicolor","Virginica"), col = c("red","blue","green"),pch = 1, cex=0.8,
title="Species", text.font=4)
boxplot(petals$PL~petals$PW,data=mtcars, main="Petals Area",
xlab="Petals Width", ylab="Petals Length")
hist(petals$PetalArea,
main="Petal Area",
xlab="Area",
col="gray"
)
pdf <- as.data.frame(petals)
pdfa <- aggregate(PetalArea~ S, pdf, mean )
pdfa## S PetalArea
## 1 setosa 0.3656
## 2 versicolor 5.7204
## 3 virginica 11.2962ggplot(pdfa, aes(S,PetalArea, colour = S, fill = S)) +
geom_col()
##4. Meaningful question for analysis:
Please state at the beginning a meaningful question for analysis. Use the first three steps and anything else that would be helpful to answer the question you are posing from the data set you chose. Please write a brief conclusion paragraph in R markdown at the end.
Larger petals attract potential pollinators from further away (https://en.wikipedia.org/wiki/Petal#Shape_and_size). One way to attract pollinators from further away would be to plant flowers with larger then average petals. Of the 3 flowers in the iris dataset it would be best to plant primarily Virginica, who have an average petal area of 11.3. It would be best avoid plating setosa and only the largest versicolor as they have a much smaller avereage petal area, 0.4 and 5.7 respectively, then the virginica plant.
##5. BONUS –
place the original .csv in a github file and have R read from the link. This will be a very useful skill as you progress in your data science education and career.
library(RCurl)
x <- getURL("https://raw.githubusercontent.com/agersowitz/ADG/master/iris.csv")
bonus <- read.csv(text=x)
print(bonus)## X Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 1 5.1 3.5 1.4 0.2 setosa
## 2 2 4.9 3.0 1.4 0.2 setosa
## 3 3 4.7 3.2 1.3 0.2 setosa
## 4 4 4.6 3.1 1.5 0.2 setosa
## 5 5 5.0 3.6 1.4 0.2 setosa
## 6 6 5.4 3.9 1.7 0.4 setosa
## 7 7 4.6 3.4 1.4 0.3 setosa
## 8 8 5.0 3.4 1.5 0.2 setosa
## 9 9 4.4 2.9 1.4 0.2 setosa
## 10 10 4.9 3.1 1.5 0.1 setosa
## 11 11 5.4 3.7 1.5 0.2 setosa
## 12 12 4.8 3.4 1.6 0.2 setosa
## 13 13 4.8 3.0 1.4 0.1 setosa
## 14 14 4.3 3.0 1.1 0.1 setosa
## 15 15 5.8 4.0 1.2 0.2 setosa
## 16 16 5.7 4.4 1.5 0.4 setosa
## 17 17 5.4 3.9 1.3 0.4 setosa
## 18 18 5.1 3.5 1.4 0.3 setosa
## 19 19 5.7 3.8 1.7 0.3 setosa
## 20 20 5.1 3.8 1.5 0.3 setosa
## 21 21 5.4 3.4 1.7 0.2 setosa
## 22 22 5.1 3.7 1.5 0.4 setosa
## 23 23 4.6 3.6 1.0 0.2 setosa
## 24 24 5.1 3.3 1.7 0.5 setosa
## 25 25 4.8 3.4 1.9 0.2 setosa
## 26 26 5.0 3.0 1.6 0.2 setosa
## 27 27 5.0 3.4 1.6 0.4 setosa
## 28 28 5.2 3.5 1.5 0.2 setosa
## 29 29 5.2 3.4 1.4 0.2 setosa
## 30 30 4.7 3.2 1.6 0.2 setosa
## 31 31 4.8 3.1 1.6 0.2 setosa
## 32 32 5.4 3.4 1.5 0.4 setosa
## 33 33 5.2 4.1 1.5 0.1 setosa
## 34 34 5.5 4.2 1.4 0.2 setosa
## 35 35 4.9 3.1 1.5 0.2 setosa
## 36 36 5.0 3.2 1.2 0.2 setosa
## 37 37 5.5 3.5 1.3 0.2 setosa
## 38 38 4.9 3.6 1.4 0.1 setosa
## 39 39 4.4 3.0 1.3 0.2 setosa
## 40 40 5.1 3.4 1.5 0.2 setosa
## 41 41 5.0 3.5 1.3 0.3 setosa
## 42 42 4.5 2.3 1.3 0.3 setosa
## 43 43 4.4 3.2 1.3 0.2 setosa
## 44 44 5.0 3.5 1.6 0.6 setosa
## 45 45 5.1 3.8 1.9 0.4 setosa
## 46 46 4.8 3.0 1.4 0.3 setosa
## 47 47 5.1 3.8 1.6 0.2 setosa
## 48 48 4.6 3.2 1.4 0.2 setosa
## 49 49 5.3 3.7 1.5 0.2 setosa
## 50 50 5.0 3.3 1.4 0.2 setosa
## 51 51 7.0 3.2 4.7 1.4 versicolor
## 52 52 6.4 3.2 4.5 1.5 versicolor
## 53 53 6.9 3.1 4.9 1.5 versicolor
## 54 54 5.5 2.3 4.0 1.3 versicolor
## 55 55 6.5 2.8 4.6 1.5 versicolor
## 56 56 5.7 2.8 4.5 1.3 versicolor
## 57 57 6.3 3.3 4.7 1.6 versicolor
## 58 58 4.9 2.4 3.3 1.0 versicolor
## 59 59 6.6 2.9 4.6 1.3 versicolor
## 60 60 5.2 2.7 3.9 1.4 versicolor
## 61 61 5.0 2.0 3.5 1.0 versicolor
## 62 62 5.9 3.0 4.2 1.5 versicolor
## 63 63 6.0 2.2 4.0 1.0 versicolor
## 64 64 6.1 2.9 4.7 1.4 versicolor
## 65 65 5.6 2.9 3.6 1.3 versicolor
## 66 66 6.7 3.1 4.4 1.4 versicolor
## 67 67 5.6 3.0 4.5 1.5 versicolor
## 68 68 5.8 2.7 4.1 1.0 versicolor
## 69 69 6.2 2.2 4.5 1.5 versicolor
## 70 70 5.6 2.5 3.9 1.1 versicolor
## 71 71 5.9 3.2 4.8 1.8 versicolor
## 72 72 6.1 2.8 4.0 1.3 versicolor
## 73 73 6.3 2.5 4.9 1.5 versicolor
## 74 74 6.1 2.8 4.7 1.2 versicolor
## 75 75 6.4 2.9 4.3 1.3 versicolor
## 76 76 6.6 3.0 4.4 1.4 versicolor
## 77 77 6.8 2.8 4.8 1.4 versicolor
## 78 78 6.7 3.0 5.0 1.7 versicolor
## 79 79 6.0 2.9 4.5 1.5 versicolor
## 80 80 5.7 2.6 3.5 1.0 versicolor
## 81 81 5.5 2.4 3.8 1.1 versicolor
## 82 82 5.5 2.4 3.7 1.0 versicolor
## 83 83 5.8 2.7 3.9 1.2 versicolor
## 84 84 6.0 2.7 5.1 1.6 versicolor
## 85 85 5.4 3.0 4.5 1.5 versicolor
## 86 86 6.0 3.4 4.5 1.6 versicolor
## 87 87 6.7 3.1 4.7 1.5 versicolor
## 88 88 6.3 2.3 4.4 1.3 versicolor
## 89 89 5.6 3.0 4.1 1.3 versicolor
## 90 90 5.5 2.5 4.0 1.3 versicolor
## 91 91 5.5 2.6 4.4 1.2 versicolor
## 92 92 6.1 3.0 4.6 1.4 versicolor
## 93 93 5.8 2.6 4.0 1.2 versicolor
## 94 94 5.0 2.3 3.3 1.0 versicolor
## 95 95 5.6 2.7 4.2 1.3 versicolor
## 96 96 5.7 3.0 4.2 1.2 versicolor
## 97 97 5.7 2.9 4.2 1.3 versicolor
## 98 98 6.2 2.9 4.3 1.3 versicolor
## 99 99 5.1 2.5 3.0 1.1 versicolor
## 100 100 5.7 2.8 4.1 1.3 versicolor
## 101 101 6.3 3.3 6.0 2.5 virginica
## 102 102 5.8 2.7 5.1 1.9 virginica
## 103 103 7.1 3.0 5.9 2.1 virginica
## 104 104 6.3 2.9 5.6 1.8 virginica
## 105 105 6.5 3.0 5.8 2.2 virginica
## 106 106 7.6 3.0 6.6 2.1 virginica
## 107 107 4.9 2.5 4.5 1.7 virginica
## 108 108 7.3 2.9 6.3 1.8 virginica
## 109 109 6.7 2.5 5.8 1.8 virginica
## 110 110 7.2 3.6 6.1 2.5 virginica
## 111 111 6.5 3.2 5.1 2.0 virginica
## 112 112 6.4 2.7 5.3 1.9 virginica
## 113 113 6.8 3.0 5.5 2.1 virginica
## 114 114 5.7 2.5 5.0 2.0 virginica
## 115 115 5.8 2.8 5.1 2.4 virginica
## 116 116 6.4 3.2 5.3 2.3 virginica
## 117 117 6.5 3.0 5.5 1.8 virginica
## 118 118 7.7 3.8 6.7 2.2 virginica
## 119 119 7.7 2.6 6.9 2.3 virginica
## 120 120 6.0 2.2 5.0 1.5 virginica
## 121 121 6.9 3.2 5.7 2.3 virginica
## 122 122 5.6 2.8 4.9 2.0 virginica
## 123 123 7.7 2.8 6.7 2.0 virginica
## 124 124 6.3 2.7 4.9 1.8 virginica
## 125 125 6.7 3.3 5.7 2.1 virginica
## 126 126 7.2 3.2 6.0 1.8 virginica
## 127 127 6.2 2.8 4.8 1.8 virginica
## 128 128 6.1 3.0 4.9 1.8 virginica
## 129 129 6.4 2.8 5.6 2.1 virginica
## 130 130 7.2 3.0 5.8 1.6 virginica
## 131 131 7.4 2.8 6.1 1.9 virginica
## 132 132 7.9 3.8 6.4 2.0 virginica
## 133 133 6.4 2.8 5.6 2.2 virginica
## 134 134 6.3 2.8 5.1 1.5 virginica
## 135 135 6.1 2.6 5.6 1.4 virginica
## 136 136 7.7 3.0 6.1 2.3 virginica
## 137 137 6.3 3.4 5.6 2.4 virginica
## 138 138 6.4 3.1 5.5 1.8 virginica
## 139 139 6.0 3.0 4.8 1.8 virginica
## 140 140 6.9 3.1 5.4 2.1 virginica
## 141 141 6.7 3.1 5.6 2.4 virginica
## 142 142 6.9 3.1 5.1 2.3 virginica
## 143 143 5.8 2.7 5.1 1.9 virginica
## 144 144 6.8 3.2 5.9 2.3 virginica
## 145 145 6.7 3.3 5.7 2.5 virginica
## 146 146 6.7 3.0 5.2 2.3 virginica
## 147 147 6.3 2.5 5.0 1.9 virginica
## 148 148 6.5 3.0 5.2 2.0 virginica
## 149 149 6.2 3.4 5.4 2.3 virginica
## 150 150 5.9 3.0 5.1 1.8 virginica