Roger D. Peng - Report writing for data science in R

The first thing you need to do to get started with R is to install iton your computer. R works on pretty much every platform available,including the widely available Windows, Mac OS X, and Linuxsystems. If you want to watch a step-by-step tutorial on how to installR for Mac or Windows, you can watch these videos:

• Installing R on Windows
• Installing R on the Mac

There is also an integrated development environment available for Rthat is built by RStudio. I really like this IDEit has a niceeditor with syntax highlighting, there is an R object viewer, andthere are a number of other nice features that are integrated. You cansee how to install RStudio here

• Installing RStudio

The RStudio IDE is available from RStudios website.

After you install R you will need to launch it and start writing Rcode. Before we get to exactly how to write R code, its useful to geta sense of how the system is organized. In these two videos I talkabout where to write code and how set your working directory, whichlets R know where to find all of your files.

• Writing code and setting your working directory on the Mac
• Writing code and setting your working directory on Windows

Watch a video of this chapter.

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 havent heard about it or dont know what it is.

Before we get to reproducibility, we need to cover a little background with respect to how science works (even if youre 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. Theres 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 in areas where findings can have big policy impacts or can influence regulatory types of decisions.

Whats wrong with replication? Theres really nothing wrong with it. This is what science has been doing for a long time, through hundreds of years. And theres nothing wrong with it today. But the problem is that its 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, theres a lot of money involved! If you want to do ten versions of the same study, you need ten times as much money and theres not as much money around as there used to be. Sometimes its difficult to replicate a study because if the original study took 20 years to do, its 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 youre looking at a unique situation in time or a unique population, you cant readily replicate that situation.

There are a lot of good reasons why you cant replicate a study. If you cant 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 wont 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 thats in between the gold standard and diong nothing? That is where reproducibility comes in. Thats how we can kind of bridge the gap between replication and nothing.

In non-research settings, often full replication isnt 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.

Why do we need this kind of middle ground? I havent 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 youve run, and come to the same findings that you found.

What reproducible reporting is about is a validation of the data analysis. Because youre not collecting independent data using independent methods, its a little bit more difficult to validate the scientific question itself. But if you can take someones 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.