10.36 Location
20180723 From our review of the data so far we start to
make some observations about the character variables. The first is
location. We note that several locations were reported in
the above exploration of the dataset. We can confirm the number of
locations by counting the number of data.table::unique() values
the variable has in the original dataset.
# How many locations are represented in the dataset.
ds$location %>%
unique() %>%
length()## [1] 49We may not know in general what other locations we will come across in related datasets and we already have quite a collection of 49 locations. We will retain this variable as a character data type.
Here is a list of locations and their frequencies in the dataset.
ds$location %>%
table()## .
## Adelaide Albany Albury AliceSprings
## 3924 4739 4740 4740
## BadgerysCreek Ballarat Bendigo Brisbane
## 4690 4738 4731 4893
## Cairns Canberra Cobar CoffsHarbour
## 4740 5136 4709 4709
## Dartmoor Darwin GoldCoast Hobart
## 4709 4893 4740 4893
## Katherine Launceston Melbourne MelbourneAirport
## 3278 4740 4893 4709
## Mildura Moree MountGambier MountGinini
## 4709 4709 4739 4728
## Newcastle Nhil NorahHead NorfolkIsland
## 4030 3278 4704 4709
## Nuriootpa PearceRAAF Penrith Perth
## 4708 4708 4739 4892
## PerthAirport Portland Richmond Sale
## 4708 4709 4709 4709
## SalmonGums Sydney SydneyAirport Townsville
## 4662 5044 4709 4740
## Tuggeranong Uluru WaggaWagga Walpole
## 4739 3278 4709 4705
## Watsonia Williamtown Witchcliffe Wollongong
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