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Video instructions and help with filling out and completing consignee vs importer of record
So what I want to talk about today is the tidy verse and the goal of the tidy verse is not just individual art packages but kind of an ecosystem of our packages that work together to make data analysis is easy and as fun and as seamless as possible and to me when you're thinking about doing data science there really are these seven main components so the first thing you always have to do is get your data from winner of a crazy format it's currently stored in into your analysis environment of choice you've got it imported in from from disk from a database from the meat from the web and get it into R once you've done that I think it's a really good idea to tidy your data to store it in a consistent format we'll talk about this idea I kind of again and again throughout this talk but this idea of sharing structure of constructing some uniform objects that you can operate within the same way for many different tasks is a really really powerful idea once you've done that once you've tided it you'll often do some transformation that's things like adding new variables that are functions of existing variables creating summaries collapsing your data down or maybe just rearranging the rows or picking out from hundreds of variables just a handful that you're interested in once you've done that in my opinion there really are two main engines of knowledge discovery visualization in modeling now visualizations are fundamentally a human activity and they're great because they can surprise you they can show you something that you did not anticipate visualizations are also really great at helping you make those sort of vague ill-formed questions and your head into precise questions that can be answered quantitatively so visualizations are great because they are fundamentally a human activity but it's also their downfall because if we a humanist look at every visualization and that means they fundamentally do not scale as you get more variables as you get more observations you simply cannot look at every possible visualization so to me the complementary tool to visualization is more and I think a modeling is very broadly but basically whenever you've got a precise question that you can answer with a simple algorithm or some summary statistics I think of that as a model that statistical modeling that's machine learning that's deep learning this data mining and then this is a fundamentally a computational tool right this is something that computers do and again that says it this has its advantages it scales much better and even if it doesn't scale that well you can just throw more and more computers at them all at the problem and it's much much easier than throwing more brains at a problem but every model makes assumptions and a model by its very nature cannot question those assumptions so that means at some fundamental level a