Database Reference
In-Depth Information
class(sales$gender) # returns "factor"
is.ordered(sales$gender) # returns FALSE
Included with the ggplot2 package, the diamonds data frame contains three
ordered factors. Examining the cut factor, there are five levels in order of
improving cut: Fair, Good, Very Good, Premium, and Ideal. Thus, sales$gender
contains nominal data, and diamonds$cut contains ordinal data.
head(sales$gender) # display first six values and the levels
F F M M F F
Levels: F M
library(ggplot2)
data(diamonds)
# load the data frame into the R workspace
str(diamonds)
'data.frame': 53940 obs. of 10 variables:
$ carat : num 0.23 0.21 0.23 0.29 0.31 0.24 0.24 0.26 0.22
$ cut : Ord.factor w/ 5 levels "Fair"<"Good"<..: 5 4 2 4 2
3 …
$ color : Ord.factor w/ 7 levels "D"<"E"<"F"<"G"<..: 2 2 2
6 7 7 …
$ clarity: Ord.factor w/ 8 levels "I1"<"SI2"<"SI1"<..: 2 3
5 4 2 …
$ depth : num 61.5 59.8 56.9 62.4 63.3 62.8 62.3 61.9 65.1
59.4 …
$ table : num 55 61 65 58 58 57 57 55 61 61 …
$ price : int 326 326 327 334 335 336 336 337 337 338 …
$ x : num 3.95 3.89 4.05 4.2 4.34 3.94 3.95 4.07 3.87 4 …
$ y : num 3.98 3.84 4.07 4.23 4.35 3.96 3.98 4.11 3.78
4.05 …
$ z : num 2.43 2.31 2.31 2.63 2.75 2.48 2.47 2.53 2.49
2.39 …
head(diamonds$cut) # display first six values and the
levels
Ideal Premium Good Premium Good Very Good
Levels: Fair < Good < Very Good < Premium < Ideal
Suppose it is decided to categorize sales$sales_totals into three
groups—small, medium, and big—according to the amount of the sales with the
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