All eyes are on export sales and shipments of soybeans in the midst of the 2018 trade war.
I always have my commodity price analysis students produce those ‘export pace of use’ charts as a lab assignment because it is data that is very actively followed in the commodity marketing business and because I get to teach them how to use pivot tables in Excel. I’ve had a hankering for awhile to produce the same graphs with
tidyverse functions in R, and this year I am especially interested in following the progress of export sales and shipments to see how the trade war is playing out. But I’m not excited about babysitting the excel spreadsheets and manually copy and pasting the new data, so now is a good time to go ahead and do this exercise in R!
First, I’ll just present graphs, and below I will provide the code to reproduce the charts on your own if you want. I’ll be updating them frequently for the next few weeks for my own interest.
I’m going to use the export inspection data from the USDA Federal Grain Inspection Services (FGIS) rather than the more commonly followed Foreign Aricultural Service (FAS) export sales data. Mostly this is because its what I use in my class, the FGIS provides every export inspection as a datapoint in a .csv file, and gives us the opportunity to do a little more intense data work.
Since ‘sales’ and ‘export inspections’ at the port do not happen at the same time, the graphs in this post look different than what you typcially see tweeted after the FAS report comes out on Thursdays. The FAS report gives us information faster because we know about sales within one week (or one day for the big sales). However, sometimes these sales are cancelled before they get shipped, so the FGIS data is exports that we know left the ports (this is my understanding anyway).
First the weekly export inspections for soybeans in 2018 compared to 2013-2017.
The last couple of weeks’ export inspections have been the worst since 2013.