10.45 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    Townsville 4740    13
## 2        Albury 4740    13
## 3   Tuggeranong 4739    13
## 4        Cairns 4740    13
## 5     NorahHead 4704    13
## 6  AliceSprings 4740    13
## 7         Perth 4892    13
## 8    SalmonGums 4662    13
## 9      Brisbane 4893    13
## 10         Nhil 3278     9

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.   : 9.00  
##  1st Qu.:13.00  
##  Median :13.00  
##  Mean   :12.71  
##  3rd Qu.:13.00  
##  Max.   :14.00

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