Combining Australian Census data with the Same Sex Marriage Postal Survey in R by Miles McBain.
Last week I put out a post that showed you how to tidy the Same Sex Marriage Postal Survey Data in R. In this post we’ll visualise that data in combination with the 2016 Australian Census. Note to people just here for the R — the main challenge here is actually just navigating the ABS’s Census DataPack, but I’ve tried to include a few pearls of wisdom on joining datasets to keep things interesting for you.
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Decoding the “datapack” is an early task:
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The datapack consists of 59 encoded csv files and 3 metadata excel files that will help us decode their meaning. What? You didn’t think this was going to be straight forward did you?When I say encoded, I mean the csv’s have inscrutable names like ‘2016Census_G09C.csv’ and contain column names like ‘Se_d_r_or_t_h_t_Tot_NofB_0_ib’ (H.T. @hughparsonage).
Two of the metadata files in
/Metadata/have useful applications for us. ‘2016Census_geog_desc_1st_and_2nd_release.xlsx’ will help us resolve encoded geographic areas to federal electorate names. ‘Metadata_2016_GCP_DataPack.xlsx’ lists the topics of each of the 59 tables and will allow us to replace a short and uninformative column name with a much longer, and slightly more informative name….
Followed by the joys of joining and analyzing the data sets.
McBain develops original analysis of the data that demonstrates a relationship between having children and opinions on the impact of same sex marriage on children.
No, I won’t repeat his insight. Read his post, it’s quite entertaining.