flower {cluster} | R Documentation |
Flower Characteristics
Description
8 characteristics for 18 popular flowers.
Usage
data(flower)
Format
A data frame with 18 observations on 8 variables:
[ , "V1"] | factor | winters |
[ , "V2"] | factor | shadow |
[ , "V3"] | factor | tubers |
[ , "V4"] | factor | color |
[ , "V5"] | ordered | soil |
[ , "V6"] | ordered | preference |
[ , "V7"] | numeric | height |
[ , "V8"] | numeric | distance |
- V1
winters, is binary and indicates whether the plant may be left in the garden when it freezes.
- V2
shadow, is binary and shows whether the plant needs to stand in the shadow.
- V3
tubers, is asymmetric binary and distinguishes between plants with tubers and plants that grow in any other way.
- V4
color, is nominal and specifies the flower's color (1 = white, 2 = yellow, 3 = pink, 4 = red, 5 = blue).
- V5
soil, is ordinal and indicates whether the plant grows in dry (1), normal (2), or wet (3) soil.
- V6
preference, is ordinal and gives someone's preference ranking going from 1 to 18.
- V7
height, is interval scaled, the plant's height in centimeters.
- V8
distance, is interval scaled, the distance in centimeters that should be left between the plants.
References
Struyf, Hubert and Rousseeuw (1996), see agnes
.
Examples
data(flower)
str(flower) # factors, ordered, numeric
## "Nicer" version (less numeric more self explainable) of 'flower':
flowerN <- flower
colnames(flowerN) <- c("winters", "shadow", "tubers", "color",
"soil", "preference", "height", "distance")
for(j in 1:3) flowerN[,j] <- (flowerN[,j] == "1")
levels(flowerN$color) <- c("1" = "white", "2" = "yellow", "3" = "pink",
"4" = "red", "5" = "blue")[levels(flowerN$color)]
levels(flowerN$soil) <- c("1" = "dry", "2" = "normal", "3" = "wet")[levels(flowerN$soil)]
flowerN
## ==> example(daisy) on how it is used