# R's na.rm equivalent in Julia for ignoring missing values during inference?

**URL:** <https://discourse.julialang.org/t/rs-na-rm-equivalent-in-julia-for-ignoring-missing-values-during-inference/99000>\
**Category:** New to Julia\
**Tags:** question\
**Created:** [May 17, 2023, 1:44pm UTC](https://discourse.julialang.org/t/rs-na-rm-equivalent-in-julia-for-ignoring-missing-values-during-inference/99000 "2023-05-17T13:44:23Z")\
**Posts on this page:** 5\
**Page:** 1

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**Author:** ![Sahil\_Khan](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/sahil_khan/32/47573_2.png) [@Sahil\_Khan](https://discourse.julialang.org/u/Sahil_Khan)\
**Post date:** [May 17, 2023, 1:44pm UTC](https://discourse.julialang.org/t/rs-na-rm-equivalent-in-julia-for-ignoring-missing-values-during-inference/99000/1 "2023-05-17T13:44:23Z")

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Do we have anything like r’s na.rm attribute in Julia for Turing.jl for ignoring missing values? I have a data with missing values which is causing issue during sampling.

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**Author:** ![sylvaticus](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/sylvaticus/32/203883_2.png) [@sylvaticus](https://discourse.julialang.org/u/sylvaticus)\
**Post date:** [May 17, 2023, 1:48pm UTC](https://discourse.julialang.org/t/rs-na-rm-equivalent-in-julia-for-ignoring-missing-values-during-inference/99000/2 "2023-05-17T13:48:25Z")

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Don’t know `Turing.jl`, but the julia way to “ignore” missing values is to “skip” them:

```julia
julia> x = [1,missing,3]
3-element Vector{Union{Missing, Int64}}:
 1
  missing
 3

julia> sum(x)
missing

julia> sum(skipmissing(x))
4

```

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<div class="post-metadata">

**Author:** ![Sahil\_Khan](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/sahil_khan/32/47573_2.png) [@Sahil\_Khan](https://discourse.julialang.org/u/Sahil_Khan)\
**Post date:** [May 17, 2023, 1:55pm UTC](https://discourse.julialang.org/t/rs-na-rm-equivalent-in-julia-for-ignoring-missing-values-during-inference/99000/3 "2023-05-17T13:55:18Z")

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This I know but I’m looking for something like this as shown below.

```julia
 for i in 1:n
      for j in 1:size(data[i])[2]
         
        data[i][:,j] ~ MvNormal(predicted[i] , σ)
   
      end 
    end

```

Thanks for reply though 🙂

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<div class="post-metadata">

**Author:** ![Sahil\_Khan](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/sahil_khan/32/47573_2.png) [@Sahil\_Khan](https://discourse.julialang.org/u/Sahil_Khan)\
**Post date:** [May 17, 2023, 2:36pm UTC](https://discourse.julialang.org/t/rs-na-rm-equivalent-in-julia-for-ignoring-missing-values-during-inference/99000/4 "2023-05-17T14:36:46Z")

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How valid is this ?

```julia
for j in 1:size(data)[2]
         for k in 1:size(data)[1]

             if ismissing(data[k,j]) == true
                 continue
             end
          
             data[k,j] ~ Normal(predicted[k] , σ*(data[k,j] .+10^-5))

        end
end

```

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<div class="post-metadata">

**Author:** ![Raf](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/raf/32/3383_2.png) [@Raf](https://discourse.julialang.org/u/Raf)\
**Post date:** [May 17, 2023, 3:10pm UTC](https://discourse.julialang.org/t/rs-na-rm-equivalent-in-julia-for-ignoring-missing-values-during-inference/99000/5 "2023-05-17T15:10:14Z")

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Basically yes, but the more idiomatic way may be with a guard like this:

```julia
for j in axes(data, 2)     
    for k in axes(data, 1)
        ismissing(data[k, j]) && continue 
      
        data[k,j] ~ Normal(predicted[k] , σ*(data[k,j] .+10^-5))
    end
end

```