 So, hi everyone, I'm Ed Ardmi Cooke, I'm a research associate working at the University of Glasgow, and today I'm just going to talk to you about a Shiny app that I've been working on with my colleague, Joel Pick from the University of Edinburgh. And as you can see on the screen here, it's Shiny Digitized and this is really a graphical user interface or a Shiny app overlay for the fantastic Meta Digitize app that package that came out in 2019. And essentially what this does is it provides a really sort of intuitive and easy to follow graphical user interface for people to go through the entire process of data extraction. So importing the file itself, the image file, and then going through each of the steps from calibrating axes to extracting individual data points. And I'm just going to walk you through that in a second and this is just the GitHub page so you can see the address up here. This is the in development branch, because we're just working on a couple of additional features. And it's quite simple so you can just install via this code here so you can do DevTools package and install GitHub and then essentially you load the library and then you've got two different options and I'll show this interactively, I'll show a demonstration of this in a second, but essentially you either just provide a directory of the folder which you want to actually, which contain the images that you want to extract, or you simply just call the function itself and that will open up the Shiny window and then you can navigate to it through the app itself. And really, it should be stressed that this is building upon all the inner workings of the metadigitized package, which was released in 2019 and if you are interested in that package, then you should really check out this paper by Pickertal, which was released in 2018, 2019. But this package is really building upon. So, I'm now just going to go through a interactive demo of the Shiny app. And for those interested, this is using our version 4.2.2 and I'm obviously using our studio for this. So I've already gone ahead and installed the in-development version of Shiny. Obviously, we're hoping to merge this with the main branch and then in the end put it on CRAN and it's using the DevTools install GitHub function. So you can do is you can load the Shiny digitize package and then you've got a couple of options. So you can either present the directory folder where all the images are located itself. So for instance, there is a folder on my desktop called SimFigs, which has got a whole load of images that I want to get data out of and use the Shiny digitize app to extract them, extract the data from these images. Or if I'm not entirely certain about the folder path, I can simply leave this blank. And when you do it, I'm using external browser. So this will pop up in my web browser and you go presented with a window modal dialog box like this, showing the Shiny digitize logo. And essentially what you do is you're selecting an image file from within the folder of images. So what I want to do is I want to go to desktop, go to SimFigs and then essentially click on one of them. So scatterplot1. And then you get presented with something that looks a bit like this. So you can either go into review mode or extract mode. And review mode is so that you can look through all the stuff that you previously extracted. And we'll come on to that in a little bit. Or for the purpose of this we're going to go to extract mode and then you have an option here we have, you can go through all of them or just the unfinished one so we're able to have a history of the ones that you've already extracted from. You could just solely finish off a an image folder that you might have temporarily started and then come back to, or you can specify, for instance, a specific graph here. But for instance, for this we're just going to go with all. So then we can get extracting and then what you're given is something that looks like this. I mean, normally doesn't take that long to load. And you've got quite a lot of options here. So, along the top here you've got the logo once again and if you click this, it comes up with some further information. So we released recently released a package that was introducing this. And as well as some other tips for reproducibility, then there's also the metadata ties paper that all the internal functions are based upon. You also have an ability to click between review and extract and that will come into play later on when you've already done your extraction and then later on you can change the point size. So, you know, for instance, if you want them to be smaller or larger, you can change the group name and then you can zoom so in order to zoom save for instance you wanted to zoom in on where how close these points were. You can drag a box here and you click zoom, and then it will zoom in on whatever you've created a brush over to get back you just click it again, and then it will revert to how it is so let's try and do this one so you can choose a plot type here and there are currently lots of plots that you can extract from using the meta digitize internal meta digitize function so for this one we're going to click scatter plot, and it will come up with a little tick saying that you've done it correctly click next step, and then orientate figure So, in these cases, there are sometimes when, for instance, you might have taken a screenshot maybe it's not perfect. So you can either flip and this will, you know, flip it for you, or you can rotate and when you do that you can do a whole load of fun rotations and it will remember what you've done. But for this we're fine it's perfectly straight and and aligned properly. So then we click next then we're going to calibrate so we click this and essentially it will come up with a sort of small diagram about which order you should click them in. So for the purpose of this we're going to click this and so you double click to get the points up. So six and nine, and then 1.5 and four. So you can fill in the y variable so that in this case it's a root length and then the x variable separate some for those that have already spelled that wrong but that's fine because it's quite easy to change it. And for those I've used meta digitize this should be very, very familiar. So what you can do is here you can adjust things on the fly so you can adjust that just quite simply and it will just do it as soon as possible, you can click these but right now it doesn't really have an impact. And then what you need to do is so maybe okay so that in this case the axes limit is is the axes text is too big so I'm now going to reduce it so I can actually see what's happening. So it goes from six to nine, and the numbers will appear quite quickly on the graph and then this goes from 1.5 to 4.5, and then click next and that's it calibrated. And then what you want to do here is you know I'm going to extract the data. So what you want to do is you add a group. I'm going to go with a group name a box that you can drag. So for instance, this is called on this graph control and treatment so I'm going to write in control. I currently don't know the size but this isn't always needed. And then for this for this one I'm going to go with a square, and it's going to be blue. The point comes up in the box here, the shape is given numerically here so this is a square and the color is blue. So then I click on the group that I want to start clicking on, and I can start clicking points so for this case I'm going to do controls that's the gray. So you do one, two I'm not going to do all of them here. I'm going to do as many as possible, but not all of them, because we don't have loads of time. Okay, fine that's that that looks good. And then what I'm going to do is, I'm now going to add another group. So for instance let's do treatment. The sample size again not really needed I'm now going to do circle and orange. So then that appears down here as well so then you can click points again, and then you can do that. And then the points will appear and you can obviously adjust this at any point that you want but maybe, you know, okay actually I'm only really interested in the control treatment so then you can just click delete. And that will just get rid of anything that you've done. But for now, you can then click next step and then you can enter in any comments. So for instance, you could write something about the control versus treatment graph. And then you can click continue. And then what will happen is it will come up with the next one so in this case it's another scatter plot. But as you can see, it's come up iteratively with someone the second one. So then what you could do is, you can then click on review mode, and obviously you've not done anything here. So you can't really see anything but we go previously, and have a look at the extraction that we've done. And we can also click download extraction figure. And what this will do is you can as you can see here is it will create an image record of the scatter plot with complete with the extraction data here it's not necessary for reproducibility but it's quite a nice sort of touch that you can add on. So now one of the most important things that shine digitize and meta digitize actually produces is what happens after you've done the data extraction. So I'm going to in the in the folder of figures that we've simulated, you have to sort of important files obviously you've got the extracted data, which is obviously the process data now if you're interested in the raw value say the raw data points that I was showing on the screen, you can then access that within our, but essentially if you finished it you get a process data file like this, which for instance gives you the mean standard deviation the number of points, and a whole host of added information. And this obviously will differ depending on what type use, but one of the most important things that it does actually produce is a cal dot file. And whilst you won't be able to actually use it without, you won't actually be able to look inside the file without actually using meta digitize or shiny digitize, essentially what it is is is a historical record of the data extraction. And that that you've already done so essentially what happens is anyone, if I upload this scatterplot one cal dot file, along with the scatterplot, anyone can look at the data extraction that I've conducted to see how accurate and how reliable it is to essentially try to try and recreate what I, what I extracted myself. And when you go back to shiny digitize and opened up. You can look at the finished files that you've already done and that is using the cal dot files of essentially bring in the historical historical historical data extraction record. And then you can actually look. So, not only is shine digitize and meta digitize a data extraction tool. They can also be useful at looking at how other people have done their data extraction and it's all revolving around the use of this cal dot file and that's the paper that I showed that was about shiny digitize is really showing the benefits of providing a historical record or a cal dot file, which are readily produced from these data extraction packages, alongside the images that you've used and this will really help with reproducibility. And with that, I just want to say thank you very much. And this should be produced into a main branch and then not to distant future. Hopefully, will be on CRAN and also not to not to distant future, but obviously I'll keep everyone updated. Thank you very much. Thanks.