 Hi, my name is Yuliana Malaskanda. I'm a bioinformatician and the data scientist. I love our programming, data visualization, and I'm also one of the founders of the local Our Lady's chapter. And today I will tell you something about one of the projects I'm involved in. You can see here the logo of the project, which symbolizes that we would like to focus on neurological diseases. And we want to find new drugs that would treat the neurological diseases. And we do it by so-called drug repurposing using data from Norwegian health registries. I will tell you a bit about what is drug repurposing. But first, first, how do we define the problem we want to solve when we want to find a new drug? Well, we want to find a protein first that is central to the disease. And then we want to find a small molecule drug that would fit into this protein. And this is a little bit like a puzzle. And we have millions of possibilities. So traditionally, we would first maybe through biological experiments find this protein that is a cause of the disease, or we know that is very important in the disease progression. Then we would go to a bioinformatician lab and try to do some simulations to find the drug that best fit to this protein. And next, we can go again to an experimental lab to check whether these drugs that were found computationally to bind to the protein really bind in a test tube. And if we have success in all these experiments and simulations, if we find some molecules that really bind, then we can go to the clinics and test those on humans. Problem with this approach is that it is very timely. It is very costly and lots, lots, lots of trials fail. So it's a bit like diving in this old fashioned suit. We have better ways to do it now. And one of the ways these ways is so-called drug repurposing. So nowadays in the market, there are all these drugs available already been in use by many, many people in many countries and throughout many years. So we need to use this data. And Norway has a unique position here because there are lots of data collected on Norwegians through the so-called health registries. So we have all this data. We can retrospectively look at information about drug usage and history of illnesses. Then we can maybe find which drugs are significantly changing the risk of a specific disease. We can take these drugs and test them on the cells or on the animals. And then if we find some positive answers, we can explore these drugs even more computationally. So the project is called the drone drug repurposing for neurological diseases. And we have lots of data. We have the entire prescription registry of Norway that contains more than 600 million prescriptions on more than 4 million Norwegians. And this data contains more than 1800 various drugs. In addition, we have also information about the clinical diagnosis for the Norwegian patients and demographic information from statistics Norway. So it's a huge amount of data. We're doing advanced statistical analysis on these data to check which drugs change the risk of developing a specific disease here. I'm showing, for example, Parkinson's disease, but we are also pursuing other diseases. We can take these drugs that seem to lower the risk of the disease, take them to the lab, perform experiments in cells or in animals. And hopefully we can find a new treatment then. We can also take the drugs that showed to increase the risk of Parkinson's disease and do some experiments on them, but also do some bioinformatical analysis. And hopefully we can understand more of how the disease progresses. So we have lots of people. We have lots of ideas. We have lots of data. We need to have a system to manage this research. And first we are using R apart from using the packages that implement strictly statistical methods that we are using in analysis. We have also created an internal R package to ease access to the data for all of the group members. And so this was done with the help of use this, the fantastic tool. We have also created custom templates for R Markdown reports. And we have enabled easy access to data through R SQ Lite and DB Liar Aquaries right within R to a database containing all the data. And of course we are very focused on how to communicate the results well so that we are doing lots of data visualizations, both passive and interactive. We're using ggplot2. We are using flex dashboard to create nice HTML reports directly from R Markdown. We're using plot.ly and crosstalk to create the interactiveness in these reports. And as I've mentioned, we are reporting in R Markdown and I'm also a fan of Zaringan and all the extra packages that extend Zaringan to create presentations. So my take home message is R helps us to manage the research group, manage the research itself because we can create reproducible research. We can track easily any changes in the project. We can easily collaborate on the code or on the presentations. And we can create fantastic presentations of visualizations of the results.