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Report Writing for Data Science in R Roger D. Peng This book is for sale at http://leanpub.com/reportwriting This version was published on 2016-07-20 This is a Leanpub book. Leanpub empowers authors and publishers with the Lean Publishing process. Lean Publishing is the act of publishing an in-progress ebook using lightweight tools and many iterations to get reader feedback, pivot until you have the right book and build traction once you do. © 2015 - 2016 Roger D. Peng

Also By Roger D. Peng R Programming for Data Science The Art of Data Science Exploratory Data Analysis with R Executive Data Science Conversations On Data Science

Contents What is Reproducible Reporting? . The Data Science Pipeline . . . . . . Literate Statistical Programming . Reproducibility Check List . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 8 12 15

What is Reproducible Reporting? Watch a video of this chapter.1 This chapter will be about reproducible reporting, and I want to take the opportunity to cover some basic concepts and ideas that are related to reproducible reporting, just in case you haven’t heard about it or don’t know what it is. Before we get to reproducibility, we need to cover a little background with respect to how science works (even if you’re not a scientist, this is important). The basic idea is that in science, replication is the most important element of verifying and validating findings. So if you claim that X causes Y, or that Vitamin C improves disease, or that something causes a problem, what happens is that other scientists that are independent of you will try to investigate that same question and see if they come up with a similar result. If lots of different people come up with the same result and replicate the original finding, then we tend to think that the original finding was probably true and that this is a real relationship or real finding. The ultimate standard in strengthening scientific evidence is replication. The goal is to have independent people to do independent things with different data, different methods, and different laboratories and see if you get the same result. There’s a sense that if a relationship in nature is truly there, then it should be robust to having different people discover it in different ways. Replication is particularly important 1https://www.youtube.com/watch?v=4rBX6r5emgQ 1

What is Reproducible Reporting? 2 in areas where findings can have big policy impacts or can influence regulatory types of decisions. What’s Wrong with Replication? What’s wrong with replication? There’s really nothing wrong with it. This is what science has been doing for a long time, through hundreds of years. And there’s nothing wrong with it today. But the problem is that it’s becoming more and more challenging to do replication or to replicate other studies. Part of the reason is because studies are getting bigger and bigger. In order to do big studies you need a lot of money and so, well, there’s a lot of money involved! If you want to do ten versions of the same study, you need ten times as much money and there’s not as much money around as there used to be. Sometimes it’s difficult to replicate a study because if the original study took 20 years to do, it’s difficult to wait around another 20 years for replication. Some studies are just plain unique, such as studying the impact of a massive earthquake in a very specific location and time. If you’re looking at a unique situation in time or a unique population, you can’t readily replicate that situation. There are a lot of good reasons why you can’t replicate a study. If you can’t replicate a study, is the alternative just to do nothing, just let that study stand by itself? The idea behind a reproducible reporting is to create a kind of minimum standard or a middle ground where we won’t be replicating a study, but maybe we can do something in between. The basic problem is that you have the gold standard, which is replication, and then you have the worst standard which is doing nothing. What can we do that’s in between the gold standard and diong nothing? That is

What is Reproducible Reporting? 3 where reproducibility comes in. That’s how we can kind of bridge the gap between replication and nothing. In non-research settings, often full replication isn’t even the point. Often the goal is to preserve something to the point where anybody in an organization can repeat what you did (for example, after you leave the organization). In this case, reproducibility is key to maintaining the history of a project and making sure that every step along the way is clear. Reproducibility to the Rescue Why do we need this kind of middle ground? I haven’t clearly defined reproducibility yet, but the basic idea is that you need to make the data available for the original study and the computational methods available so that other people can look at your data and run the kind of analysis that you’ve run, and come to the same findings that you found. What reproducible reporting is about is a validation of the data analysis. Because you’re not collecting independent data using independent methods, it’s a little bit more dif- ficult to validate the scientific question itself. But if you can take someone’s data and reproduce their findings, then you can, in some sense, validate the data analysis. This involves having the data and the code because more likely than not, the analysis will have been done on the computer using some sort of programming language, like R. So you can take their code and their data and reproduce the findings that they come up with. Then you can at least have confidence that the analysis was done appropriately and that the correct methods were used. Recently, there’s been a lot of discussion of reproducibility in the media and in the scientific literature. The journal

What is Reproducible Reporting? 4 Science had a special issue on reproducibility and data repli- cation. Other journals of updated policies on publication to encourage reproducibility. In 2012, a feature on the TV show 60 minutes looked at a major incident at Duke Univer- sity where many results involving a promising cancer test were found to be not reproducible. This led to a number of studies and clinical trials having to be stopped, followed by an investigation which is still ongoing. Finally, the Institute of Medicine, in response to a lot of recent events involving reproducibility of scientific studies, issued a report saying that best practices should be done to promote and encourage reproducibility, particularly in what’s called ‘omics based research, such as genomics, pro- teomics, other similar areas involving high-throughput bi- ological measurements. This was a very important report. Of the many recommendations that the IOM made, the key ones were that • Data and metadata need to be made available; • Computer code should be fully specified, so that peo- ple can examine it to see what was done; • All the steps of the computational analysis, including any preprocessing of data, should be fully described so that people can study it and reproduce it. From “X” to “Computational X” What is driving this need for a “reproducibility middle ground” between replication and doing nothing? For starters, there are a lot of new technologies on the scene and in many different fields of study including, biology, chemistry and environmental science. These technologies allow us to collect data at a much higher throughput so we end up

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