10.33 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 base::unique() values the variable has in the original dataset.

# How many locations are represented in the dataset.

ds$location %>% 
  unique() %>%
  length()
## [1] 49

We may not know in general what other locations we will come across in related datasets and we already have quite a collection of \Sexpr{ds\(location%>%unique()%>%length()} %\) 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             3983             3984             3984 
##    BadgerysCreek         Ballarat          Bendigo         Brisbane 
##             3936             3984             3975             4137 
##           Cairns         Canberra            Cobar     CoffsHarbour 
##             3984             4380             3953             3953 
##         Dartmoor           Darwin        GoldCoast           Hobart 
##             3953             4137             3984             4137 
##        Katherine       Launceston        Melbourne MelbourneAirport 
##             2522             3984             4137             3953 
##          Mildura            Moree     MountGambier      MountGinini 
##             3953             3953             3983             3984 
##        Newcastle             Nhil        NorahHead    NorfolkIsland 
##             3984             2522             3948             3953 
##        Nuriootpa       PearceRAAF          Penrith            Perth 
##             3952             3952             3983             4136 
##     PerthAirport         Portland         Richmond             Sale 
##             3952             3953             3953             3953 
##       SalmonGums           Sydney    SydneyAirport       Townsville 
##             3906             4288             3953             3984 
##      Tuggeranong            Uluru       WaggaWagga          Walpole 
##             3983             2522             3953             3949 
##         Watsonia      Williamtown      Witchcliffe       Wollongong 
##             3953             3953             3952             3984 
##          Woomera 
##             3953


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