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# StatFiles
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[](http://www.repostatus.org/#active)
The call to ``load`` returns a ``struct`` that is an [IterableTable.jl](https://github.com/davidanthoff/IterableTables.jl), so it can be passed to any function that can handle iterable tables, i.e. all the sinks in [IterableTable.jl](https://github.com/davidanthoff/IterableTables.jl). Here are some examples of materializing a Stata, SPSS, or SAS file into data structures that are not a ``DataFrame``:
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The call to ``load`` returns a ``struct`` that is an [IterableTable.jl](https://github.com/queryverse/IterableTables.jl), so it can be passed to any function that can handle iterable tables, i.e. all the sinks in [IterableTable.jl](https://github.com/queryverse/IterableTables.jl). Here are some examples of materializing a Stata, SPSS, or SAS file into data structures that are not a ``DataFrame``:
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````julia
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using StatFiles, DataTables, IndexedTables, TimeSeries, Temporal, Gadfly
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df =load("data.dta") |> DataFrame
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````
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The pipe syntax is especially useful when combining it with [Query.jl](https://github.com/davidanthoff/Query.jl) queries, for example one can easily load a Stata, SPSS, or SAS file, pipe it into a query, then pipe it to the ``save`` function to store the results in a new file.
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The pipe syntax is especially useful when combining it with [Query.jl](https://github.com/queryverse/Query.jl) queries, for example one can easily load a Stata, SPSS, or SAS file, pipe it into a query, then pipe it to the ``save`` function to store the results in a new file.
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