1.2 Aggregating Data
Suppose we wanted to calculate the average price of the diamonds for each level of cut, i.e. the average price for all diamonds of “Ideal” quality, for all diamonds of “Premium” quality and so on, this could be done using aggregate() like this:
ave_price_cut <- aggregate(diamonds$price, by = list(diamonds$cut),
FUN = mean)
ave_price_cut## Group.1 x
## 1 Fair 4358.758
## 2 Good 3928.864
## 3 Very Good 3981.760
## 4 Premium 4584.258
## 5 Ideal 3457.542Note that by = ... needs a list of grouping variables, even if there is only one entry. Unless a named list is specified (see ‘Value’ in ?aggregate), the original column names are not carried over to the newly created table of averaged values. Instead, these get the generic names Group.1 and x.
Group.1 already indicates that we are not limited to aggregate just over one factorial variable, more are also possible. Furthermore, any function to compute the summary statistics which can be applied to all data subsets is allowed, e.g. to compute the number of items per category we could use length():
ave_n_cut_color <- aggregate(diamonds$price,
by = list(diamonds$cut,
diamonds$color),
FUN = length)
ave_n_cut_color## Group.1 Group.2 x
## 1 Fair D 163
## 2 Good D 662
## 3 Very Good D 1513
## 4 Premium D 1603
## 5 Ideal D 2834
## 6 Fair E 224
## 7 Good E 933
## 8 Very Good E 2400
## 9 Premium E 2337
## 10 Ideal E 3903
## 11 Fair F 312
## 12 Good F 909
## 13 Very Good F 2164
## 14 Premium F 2331
## 15 Ideal F 3826
## 16 Fair G 314
## 17 Good G 871
## 18 Very Good G 2299
## 19 Premium G 2924
## 20 Ideal G 4884
## 21 Fair H 303
## 22 Good H 702
## 23 Very Good H 1824
## 24 Premium H 2360
## 25 Ideal H 3115
## 26 Fair I 175
## 27 Good I 522
## 28 Very Good I 1204
## 29 Premium I 1428
## 30 Ideal I 2093
## 31 Fair J 119
## 32 Good J 307
## 33 Very Good J 678
## 34 Premium J 808
## 35 Ideal J 896Given that as a result of aggregating this way we loose our variable names, it makes sense to set them afterwards, so that we can easily refer to them later on.
names(ave_n_cut_color) <- c("cut", "color", "n")
str(ave_n_cut_color)## 'data.frame': 35 obs. of 3 variables:
## $ cut : Ord.factor w/ 5 levels "Fair"<"Good"<..: 1 2 3 4 5 1 2 3 4 5 ...
## $ color: Ord.factor w/ 7 levels "D"<"E"<"F"<"G"<..: 1 1 1 1 1 2 2 2 2 2 ...
## $ n : int 163 662 1513 1603 2834 224 933 2400 2337 3903 ...So, hopefully you see how useful aggregate() is for calculating summary statistics of your data.