You’re looking for isnan:
using DataFrames
x = randn(10)
x[5] = NaN
df = DataFrame(x=x)
filter(row -> ! isnan(row.x), df)
Or, using DataFramesMeta,
using DataFramesMeta
@where(df, .! isnan.(:x))
@linq df |> where(.! isnan.(:x))
In Julia, missing is the equivalent of R’s NA, and is used for any value which exist in theory but are not available or weren’t measured. In contrast NaN (not-a-number) only exists for Floats. For most data analysis, missing is more generic and will be easier to work with in Julia–depending on your workflow, it might make sense to convert NaNs to missings first.
(For more than you (probably) want to know on this topic, please this blog post and this Discourse discussion… 
)