During the discussion that followed the ggplot2 posts from David and I last week we started talking about tidy data and the man himself noted that matrices are often useful instead of "tidy data" and I mentioned there might be other data that are usefully "non tidy". Here I will be using tidy/non-tidy according to Hadley's definition. So
I read a lot of articles about what is going wrong with science: The reproducibility/replicability crisis Lack of jobs for PhDs The pressure on the families (or potential families) of scientists Hype around specific papers and a more general abundance of BS Consortia and their potential evils Peer review not working well Research parasites Not enough room for applications/public
A few years ago, if you asked me what the top skills I got asked about for students going into industry, I'd definitely have said things like data cleaning, data transformation, database pulls, and other non-traditional statistical tasks. But as companies have progressed from the point of storing data to actually wanting to do something with
The other day on Twitter Amelia requested a guide for reading papers I love @jtleek’s github guides to reviewing papers, writing R packages, giving talks, etc. Would love one on reading papers, for students. — Amelia McNamara (@AmeliaMN) February 5, 2016 So I came up with a guide which you can find here: Leek
Update: I realize this may seem like I'm picking on people. I really don't mean to, I have for sure made all of these mistakes and many more. I can give many examples, but the one I always remember is the time Rafa saved me from "I got a big one here" when I made
A couple of days ago one of the editors of the New England Journal of Medicine posted an editorial showing some moderate level of support for data sharing but also introducing the term "research parasite": A second concern held by some is that a new class of research person will emerge — people who had nothing
Editor's Note: This is the third year I'm making a list of awesome things other people did this year. Just like the lists for 2013 and 2014 I am doing this off the top of my head. I have avoided talking about stuff I worked on or that people here at Hopkins are doing
Right now reproducibility, replicability, false positive rates, biases in methods, and other problems with science are the hot topic. As I mentioned in a previous post pointing out a flaw with a scientific study is way easier to do correctly than generating a new scientific study. Some folks have noticed that right now there is
Nick Carchedi is back visiting from DataCamp and for fun we came up with a dplyr Rubik's cube puzzle. Here is how it works. To solve the puzzle you have to make a 4 x 3 data frame that spells Thanksgiving like this: View the code on Gist. To solve the puzzle you need to pipe this
Roger and I were just talking about all the discussion around the Case and Deaton paper on death rates for middle class people. Andrew Gelman discussed it among many others. They noticed a potential bias in the analysis and did some re-analysis. Just yesterday an economist blogger wrote a piece about academics versus blogs and
