10.64 Missing Targets

20180726 Sometimes there may be further operations to perform on the dataset prior to modelling. A common task is to deal with missing values. Here we remove observations with a missing target. As with any missing data we should also analyse whether there is any pattern to the missing targets. This may be indicative of a systemic data issue rather than simply randomly missing values.

# Check the dimensions to start with.

dim(ds)

## [1] 275410     24

# Identify observations with a missing target.

ds %>%
  pull(target) %>%
  is.na() ->
missing.target

# Check how many are found.

sum(missing.target)

## [1] 8093

# Remove observations with a missing target.

ds %<>% filter(!missing.target)

# Confirm the filter delivered the expected dataset.

dim(ds)

## [1] 267317     24

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