## 10.42 ID Variables

20180723 From our observations so far we note that the variable (date) acts as an identifier as does the variable (location). Given a date and a location we have an observation of the remaining variables. Thus we note that these two variables are so-called identifiers. Identifiers would not usually be used as independent variables for building predictive analytics models.

# Note any identifiers.

id <- c("date", "location")

We might get a sense of how this works with the following which will list a random sample of locations and how long the observations for that location have been collected.

ds[id] %>%
group_by(location) %>%
count() %>%
rename(days=n) %>%
mutate(years=round(days/365)) %>%
as.data.frame() %>%
sample_n(10)
##            location days years
## 1      AliceSprings 3984    11
## 2        WaggaWagga 3953    11
## 3      MountGambier 3983    11
## 4          Ballarat 3984    11
## 5          Dartmoor 3953    11
## 6  MelbourneAirport 3953    11
## 7         Melbourne 4137    11
## 9     NorfolkIsland 3953    11
## 10         Canberra 4380    12

The data for each location ranges in length from 4 years up to 9 years, though most have 8 years of data.

ds[id] %>%
group_by(location) %>%
count() %>%
rename(days=n) %>%
mutate(years=round(days/365)) %>%
ungroup() %>%
select(years) %>%
summary()
##      years
##  Min.   : 7.0
##  1st Qu.:11.0
##  Median :11.0
##  Mean   :10.8
##  3rd Qu.:11.0
##  Max.   :12.0

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