10.44 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.
<- c("date", "location") id
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 Sale 4500 12
## 2 Penrith 4530 12
## 3 Uluru 3069 8
## 4 Albury 4531 12
## 5 Perth 4683 13
## 6 Cobar 4500 12
## 7 Wollongong 4531 12
## 8 Launceston 4531 12
## 9 Ballarat 4531 12
## 10 Albany 4530 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. : 8.00
## 1st Qu.:12.00
## Median :12.00
## Mean :11.86
## 3rd Qu.:12.00
## Max. :13.00
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