![]() Note that the plots are smart in aggregating the data. If you have other grain types, such as if you have split out the lithic grain types even more finely, you will need to modify the data frame function ( ternaryValues()) to use your variable names. If you do not have a particular type of grain, you do not need to include that column in your data frame. Note that the ‘feldspar’ and ‘lithic’ columns are for those grains that cannot be assigned more precisely, such as a feldspar that cannot be assigned to kfeldspar or plagioclase. In particular, the functions expect to see these column names (case and spelling matter): quartz, chert, plagioclase, kfeldspar, feldspar, lithic, plutonic, volcanic, metamorphic, sedimentary, and shale. ![]() The functions assume certain names for the grain types, and these must match exactly. The data should be set up in a data frame, with columns for the grain types, and samples in rows. See my previous post on making ternary diagrams to understand how to use those functions. Source code for this project is contained in sandstoneProvenance.r, and the source code for making ternary diagrams ( ternary.r) is also required. A single function, provenancePlot(), is used to create all of these plots. I’ve created R code for making the four most common of these ternary diagram provenance plots. The provenance or plate tectonic source of sandstones is commonly inferred from point counts of their constituent grains (Dickinson and Suczek 1979). 31 October 2016 Updated 7 December 2018 to allow for more grain types and faster plotting.
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