R for the Rest of Us Podcast Episode #2: Nassos Stylianou and Clara Guibourg Discuss Making a Custom ggplot Theme for the BBC

Nassos Stylianou and Clara Guibourg, data journalists at the BBC, created a package called bbplot. This package contained a custom theme to help journalists at the BBC make plots that follow brand guidelines.

Show Notes

In 2017, BBC data journalist Nassos Stylianou was working with a backend developer on a particularly large data set. Nassos was primarily an Excel user at the time, but this data was too large for Excel. Seeing the developer work through the data with ease, a light bulb went off for Stylianou: if he and his data journalism team learned to use R, they could do this type of analysis on their own.

This realization began a journey into R. This journey, which started with needing to analyze data too large for Excel to handle, would ultimately end up in a very different place. In 2018, Stylianou, his colleague Clara Guibourg, and their team created a custom ggplot theme to create plots that match the BBC style. The code in the bbplot package is a great example of the value of developing a custom theme. But the real story of the creation of bbplot is not just about technical tools. Through learning R and creating a custom theme for others to use, Nassos, Clara and their colleagues would change the culture, remove bottlenecks, and allow the BBC to be more creative with their data viz.

To understand how big these changes were, it’s helpful to understand what things looked like at the BBC before bbplot. In the mid-2010s, journalists at the BBC who wanted to make data visualization had two choices:

1. They could use an internal tool. This tool could create data visualization, but only the predefined charts it had been designed to generate.
   
   
2. They could use Excel to create mockups and then work with a graphic designer to finalize the charts. This approach led to better results, and was way more flexible, but required extensive back-and-forth with a designer. As Stylianou described it, working with a designer “is just a very time-consuming workflow if you think of how many visualizations the BBC does.”

Neither of these choices was ideal. And this limited set of less-than-ideal choices led to a limited output of data viz. 

That would all change when Stylianou, Guibourg, and their colleagues realized that R, the tool they had decided to learn for data analysis, could also do data visualization. As they began playing around with ggplot, they quickly saw its power. Guibourg said she found it “immediately addictive when I started working with ggplot to make charts.” No longer limited by the BBC’s inflexible internal tool, she found that ggplot was “completely flexible in a way that was just completely new to me.”

The biggest change, though, came from not having to work with a designer. Not because the designers were bad (they weren’t), but because ggplot allowed the BBC data journalists to explore different visualizations on their own. Working with a designer required the journalists to have a fully-formed idea that the designer could take and improve upon. Working in ggplot allowed BBC data journalists to explore different data viz ideas.

Clara Guibourg believes this freedom is what explains the addictive quality of ggplot. As she told me, “even before we got anywhere near having a production-ready chart, just trying things out, visualizing things for the first time” was completely captivating. Having learned the basics of ggplot, she saw that “you can make like the simplest chart with just a couple of lines of code.” Being able to explore different types of visualization on her own led Clara and others to produce more data viz than they had previously.

As the BBC data journalism team improved their ggplot skills, they realized that it might be possible produce for more than just exploratory data viz. They had learned to use R for data analysis and they were starting to use it for exploratory data visualization. Could they go all the way and create production-ready charts in R that could go straight onto the BBC website?

Stylianou, Guibourg, and their colleagues set about looking into what would be involved in creating production-ready charts from R. They realized that so much of this work involved small tweaks. What font should they use? Where should the legend go? Should axes have titles? Should charts have grid lines? These questions may seem small but they have a big impact. Having consistent answers to them is what enabled BBC designers to turn Excel mockups into high-quality data viz ready to go on the website. As the BBC data journalism team dug further into ggplot, they realized that they might be able to write code to make their data viz production-ready. They realized that, if making production-ready charts required asking question about fonts, legends, axes, and grid lines, ggplot had the answer. And the answer was to make a custom theme.

Learn More 

• Read Chapter 3 of R Without Statistics, where this conversation is summarized
• bbplot website
• BBC Visual and Data Journalism cookbook for R graphics
• Nassos's website and Twitter
• Clara's website and Twitter 

To view all of our courses, go to https://rfortherestofus.com/

What is R for the Rest of Us?

You may think of R as a tool for complex statistical analysis, but it's much more than that. From data visualization to efficient reporting, to improving your workflow, R can do it all. On this podcast, I talk with people about how they use R in unique and creative ways.

David Keyes: 00:00

Hi. I'm David Keyes, and I run R for the rest of us. You may think of R as a tool for complex statistical analysis, but it's much more than that. From data visualization to efficient reporting to improving your workflow, R can do it all. On this podcast, I talk with people about how they use R in unique and creative ways.

David Keyes: 00:18

Join me and learn how R can help you. I'm joined today by Clara Geborg, who is a multilingual data journalist working at a Swedish public radio, but who used to work at the BBC News, and by Nasser Stylianu, a senior journalist at BBC News who specializes in data driven journalism there. Welcome to both of you, and thanks for being with me today. I appreciate it.

Clara Guibourg: 00:41

Thank you.

David Keyes: 00:42

Yeah. I can try. So I'm excited to talk to you. I came across your work specifically because you released a package called bbplot, which makes it possible which uses a custom theme within ggplot, to make plots take on the BBC style. So I'm excited to talk to you about that.

David Keyes: 01:00

But before we do that, I'd like to know just a bit about your background, specifically as it relates to R. So maybe, Clara, if you don't mind starting, when did you start using r, and what changed for you when you made that switch to r?

Clara Guibourg: 01:12

Sure. I probably started using r exactly around the same time that I started working at the BBC. So previously, I've I've been aware of it for a couple of years. I've been meaning to teach myself. I've been trying to teach myself, and I've been doing a couple of videos online.

Clara Guibourg: 01:25

And that kind of typical thing where you do a tutorial and you follow it perfectly and you think, oh, great. I'm great at this now. I can do anything. And then you try to do even the simplest thing on your own and you can't get it, then you just give up. But once I started at the BBC, I actually committed to it more fully.

Clara Guibourg: 01:40

And basically, 2018. So, yeah, I started then and basically never looked back. And I think that I think that what helped me get started with it then was that there was just a really supportive environment at the BBC for learning internally. There was I had really helpful colleagues, and there was a lot of you really felt like you could take the time to ask questions and learn learn new things.

David Keyes: 02:01

And just out of curiosity, what tool or tools were you using primarily before you made that switch?

Clara Guibourg: 02:06

So I was working I was working previously in in Excel, basically with spreadsheets. And in terms of visualizations, I I would have been using chart tools. Great.

David Keyes: 02:14

What about for you, Nassos? When did you take up R, and how did that change the work that you were doing?

Nassos Stylianou: 02:20

Yeah. I think I probably started in a similar way to class using kind of Excel spreadsheets and for mapping sort of tools like QGIS to visualize things. Then at the PBC, as we started doing more, as the team joined up with kind of data scientists and programmers, the data journalists, the the more sort of editorial journalists side of it, we started seeing a bit wider world out there, really, that was using R, using Python, using these things, and working really closely with a a developer who was at the BBC at the time whose background was back end development, and he was using R for a lot of the data projects we would do. So it would be any task that was too underused for Excel would go to him, and he as you can imagine, he was very popular within the team. And sitting next to him and getting a feel for the work he would do, I slowly started learning our yeah.

Nassos Stylianou: 03:15

Of him and Stack Overflow, really, I would say. And I think it was a slow a a really a weird process. It was really quick at first. It was, oh, this is amazing. And then probably a few months of feeling, I have literally made no progress.

Nassos Stylianou: 03:29

I'm doing the same things over and over again until you discover functions and you discover a few other things, and then, like, you look back and say, god. I I can't imagine doing this, like, manually that I used to do. And then you realize that I have rested really rapidly. But I think it started off from my side very much analysis. I wanna do more involved and more and heavier and work with bigger datasets, use it for programming and analysis purposes, and the visualization came after that.

David Keyes: 04:05

Interesting. So when what was it that led you to realize, oh, we should use this not just for analysis, but also for the visualization pieces?

Nassos Stylianou: 04:17

Workflow issues, I guess. It it just we'd come to a stage where this might be the disadvantage in a way of a larger team in that there are, like, there at the BBC, we had designers who would work on graphics, and we would get a certain distance in R and work it, like, analyzing data and then exploring it using ggplot and using the sort of custom themes, and then we would sort of work with a a designer or someone who's a bit more specialist on the design side of things and export something for them, and then they would make it seem really and and work on it. And but that is just a very time consuming workflow. If you think how many kind of basic charts or how many visualizations you wanna like the BBC does, especially another thing to bear in mind with the BBC is there's 40 odd world service languages as well who also kinda reversion things. So you wouldn't really want thinking back.

Nassos Stylianou: 05:13

It was workflow issues that were like, there has to be a better way rather than us working in our doing the analysis, doing the visualizations as exploratory data visualizations, exporting as a PDF, then getting it to someone in Illustrator who would then spend time cleaning that up. So it was that final bit of the workflow, though. We could this be done in our altogether? And it was more of an exploration, and I guess that's around the time sort of Karim and then we started properly looking into how far can we go with getting templates, making all this stuff that we're doing production ready just in R.

David Keyes: 05:51

Yeah. How many kind of folks were there on your team who were at that point? It sounds like you were both, like, getting into r at the same time. How many other folks were there who were doing that as well?

Nassos Stylianou: 06:08

Trying to think back. Probably 5 around 5 of us are material from there. Yeah.

Clara Guibourg: 06:12

Yeah. I think that sounds about right. I would say that most of the data team was already using R for analysis. So it's a question of a few of us being a handful of us, yeah, probably that were curious about trying to trying to solve these problems that that Nasus was talking about of trying to figure out a way of getting overcoming these, like, individual little obstacles that we had in order to to get, like, production ready graphics from start to finish in our Yeah.

David Keyes: 06:37

That makes sense. I I guess I'm just curious because I don't as an outsider, I hear BBC, and I think thousands of people, but, obviously, it's gonna be a small team who's doing that that one particular piece.

Nassos Stylianou: 06:48

Yeah. I think there was a couple of people who knew R really well, but, again, from the data science side, from the back end side, nobody came into the team at that time being a specialist in ggplot that then moved us on. It was we were all learning together and researching together and adding components together and, like, how far can we go with this? It was a collaboration where we were all at a similar level, really, but then each person might have the final answer to, here's how we make the footer left aligned and really kind of very specific things like the it's crazy how important they were at the time.

Clara Guibourg: 07:27

Yeah. But Yeah.

Nassos Stylianou: 07:27

That it was. Like, if you're gonna generate, like, hundreds of graphics, you want the footer to always be the same proportion, and you want the BBC logo, the blogs to always be in exactly the same place. It's yeah.

Clara Guibourg: 07:39

And I remember long discussions about the exact, should the thought be should it be 9 or 10 Yeah. Spaces about yeah. No. I definitely agree with that. But I think that it was real like, it was a really big benefit for us that it was just an exploration where we were, like you're saying else is that we were learning together and trying to trying things out.

Clara Guibourg: 07:57

I think that kind of collaborative and experimental approach really was why I think that kind of brought far like our the group's level, like then our our knowledge forward faster than it would have been otherwise, I think, if that makes sense.

David Keyes: 08:13

Yeah. Definitely. How did you you've talked about how you were all, like, exploring R at the beginning. How did you get from that point to the point where you realized, hey, we could actually take what we've learned and make a custom theme and create this package? Do you remember?

David Keyes: 08:27

Was there a light bulb moment, or how did that come about?

Clara Guibourg: 08:32

I don't think that there was a currently, if you disagree, Nasus, but I would say that it was such a it was just, like, constantly evolving thing. Like, it was started out at the point where where we would just start firstly displaying around with ggplot, trying out how can we get the right font for it to look like a BBC chart, and then the next the the footer of all the charts, things like that. And then once we could develop, like, functions that work for this, trying to work out how do you actually create a package that we can that was, like, the next step, which I don't think that any of it was, oh, this is, like, this one big light bulb moment where we brought it forward. It was just constantly making little fixes and taking the next step.

Nassos Stylianou: 09:14

Yeah. Yeah. Definitely. I guess an important thing to bear in mind is that the starting point was not how can we make a BBC sharp package. The starting point was yeah.

Nassos Stylianou: 09:26

I think the starting point was literally, can we do make a production ready graphic that does not need to go that does not need any modifications, that can go straight from our onto the BBC website. That was the starting point. And then I think I I yeah. I would agree. Sorry.

Nassos Stylianou: 09:43

It wasn't a light bulb moment, but the more of those we ended up doing and publishing on the site, the more we're like, hey. What about packaging this up and sharing it even more widely outside of the 5 of us? And I guess even before the step where it's a package that you can download and use yourself, there was a stage where the kind of big ambition the thought was like, how can we actually get other people in the BBC using it, and then there aren't super experts in our or don't use it day to day and might know a little bit. So that that was the other internal thing going on is how can we get other people outside of the team to use it.

David Keyes: 10:26

That makes sense. And so you talked a bit just now about how creating this custom theme and putting in a package allowed you to create graphics directly from R and put them straight onto the BBC website. You touched on this a bit earlier, but I'm wondering if you can talk in a bit more depth about what that process was like prior to working prior to having the custom theme. You talked about how you worked with a design team to finish the graphs. So can you walk me through step by step so we can get the contrast of what it looked like prior to having this theme?

Clara Guibourg: 10:59

Sure. Before we have this before we have this theme and before we were working with the g two platform visualization, you basically had 2 options for graphics. We could either use the, there was there or there is, I presume, there is in house chart tool, which you'd be using for quick turnaround things. Basically, just very easy to use, very rigid, and it's it doesn't really offer any flexibility in terms of what you could you get your you can put your data and you get an online chart that looks just so basically the other option was to, would be to to order, to work together with the designers on something, which obviously means, that so that takes longer time. So it only really used for a longer term piece.

Clara Guibourg: 11:38

It was good.

David Keyes: 11:39

Yeah. And so that in that case, you would make something in Excel, I'm assuming, and then have to do a bunch of back and forth with the designer. Is that how it would typically work, or was it a different process?

Clara Guibourg: 11:49

Yeah. Something, basically, either yeah. Yeah. That's great.

David Keyes: 11:53

Okay. Yeah.

Nassos Stylianou: 11:54

It was either yeah. I guess it was either export a PDF from our here's how we want it to look or an SVG with kind of the standard ggplot stuff, either something in Excel, either something in the chart tool saying, here's the base of it. Can we add stuff onto that? Can we add a line for a forecast, make the sort of side of it, half of it, gray. Can we do this thing which wasn't in the standard chart at all?

Nassos Stylianou: 12:18

Was a little harder. Can we add an annotation? Can we add an arrow pointing to? So anything that was additional, which is it sounds you've got a chart tool, you've got ways to do it, but I think a lot of these little additions, like an annotation, are fundamental to storytelling, really. So whilst they feel like, you know, extras, for us, we really wanted to get there from the to have a way to get there.

Nassos Stylianou: 12:44

And I think what's also important in terms of kind of thinking of the wider BBC and this sort of design team is that it wasn't really a fun job for a designer to get a brief that was, can you recreate this or can you add something like this? As opposed to them really putting their creative skills to infographics, to to design pages, to do much more creative stuff than slightly monotonous reversioning a graphic that's almost there, but not quite

David Keyes: 13:11

from the

Nassos Stylianou: 13:12

data journalist. So it helps benefit both sides. It freed up and it has freed up designers to do much more creative and much more illustrative stuff as opposed to recreating charts all day,

David Keyes: 13:24

I guess. That's really interesting. I've never thought about that as a benefit in an organization like yours. And I don't know if for me being able to create everything in ggplot actually makes me more creative, I feel like, because I can as opposed to having to sit down and think, okay. I want this here.

David Keyes: 13:40

Let's try this here. But then you get it back from the designer. I imagine there were times in your, oh, that isn't quite what I wanted. But then you feel bad if you keep asking them to redo it. Whereas if you can do it all in ggplot, you can explore, okay.

David Keyes: 13:52

Let me try making the gray slightly lighter or whatever the thing is. It it probably allows you to do more creative things on your end because you don't have to constantly go back and forth.

Clara Guibourg: 14:03

Yeah. Exactly.

Nassos Stylianou: 14:04

It was exactly that. Yeah. I can't imagine that designers would. No. I'm sure that designers are much happier since as opposed to us standing over their shoulder, moving things around live.

Nassos Stylianou: 14:14

Whereas Right. Yeah. That is 80% of making a chart in ggplot, moving things around till you get it right.

Clara Guibourg: 14:20

Yeah. I was thinking about this because I I definitely found it like immediately addictive when I started working with jdplot to make charts. And even before we got anywhere near having a production ready chart, just trying things out, visualizing things for the first time. And I was trying to find out why that was. And I think it is that, that you were saying that you can make like the simplest chart, which is like a couple of lines of code, and you've got something that's just there.

Clara Guibourg: 14:42

And then you realize that, like, the possibilities to tweak this are limitless. Like, I could do anything now, and you can go as in-depth as you want moving thick moving the tiniest things around, and it just completely flexible in a way that was just completely new to me coming from having worked with chart tool basically previously to to visualize things.

David Keyes: 15:04

That makes a lot of sense. One last question before we dig into the code. Nasus, you talked about how, obviously, the BBC has the BBC World Service, and they're organ are produced in different languages. Do you do anything now where our charts produced in different languages at all? In other words, for example, can you, like, make a chart in r in English and then iterate to make versions in different languages?

David Keyes: 15:27

Is that a thing at all?

Nassos Stylianou: 15:29

Yeah. Yeah. Charts in Russian, charts in a few other languages. We also had a kind of, yeah, a hurricane map making kind of script. It wasn't didn't quite make it to package the we could also do it into different languages.

Nassos Stylianou: 15:45

So it would take the data based on kind of which side of the world the hurricane was from and Yeah. It needed a lot of dedication from world service language colleagues

David Keyes: 15:56

whose

Nassos Stylianou: 15:59

lives was made significantly easier by again, it's that reversioning process that we're talking about. But on their end to get a graphic done from English into 30 languages. What so we did 2 things to make that happen. 1, it was a lot of experimentation with kind of Unicode and character sense and all that in a that a lot of the light light went over my head, but was perfectly understood by people who have these issues with character sets every day. So a lot of world service language colleagues that were interested in data and graphics took a lot of that on.

Nassos Stylianou: 16:33

And then the second bit was trying to for example, the Russian service or kind of other colleagues in the Americas Hub, which is in visualisations for BBC Mundo and BBC, which the Spanish version and Brazilian as well. So also kind of part of the tools and the tutorials and everything that we developed, we also use it as an opportunity to that they joined. We put together a sort of course that was how they could get to a stage in R where they can make graphics. So it worked also, Yeah. Part of the work that we've done was also to try and skill people up in R as well as that next step into using it for graphics, if that makes sense.

David Keyes: 17:19

Yeah. Definitely. I know you also made a sort of cookbook to help people learn about how to make different types of visualizations. I'm wondering if you can talk briefly about how that worked.

Nassos Stylianou: 17:30

I think the cookbook was kind of as important, I think, for us in terms of here's a repository of all this information, and people could add their new chart styles to it. People could add tips and tricks to it. And it has been, over the years, added to, again, internally as well in the and new joiners straight away have something, and we've had a few new people joining over the years. It's okay. So I think someone recently is very much a Python person, but because of the cookbook and because of the package could really quickly come into despite kind of being mainly Python literate, they could within week 2 of being at the BBC, given that they knew how to code and everything was there, they could produce graphics in R.

Nassos Stylianou: 18:14

And it just having the cookbook there to talk through how this all works and why and what bit of code does what, rather than having to sit and explain it with someone really helped, I think.

Clara Guibourg: 18:27

Yeah. I think that's totally right. And I think that it it was a really useful, full way of sharing information internally. It did feel like everybody was adding in their own. The idea was that it and it did actually grow organically.

Clara Guibourg: 18:39

The more people that kind of started using R and started adding their own little kind of tips tricks that they discovered along the way. I think everybody was contributing to it. Yeah.

David Keyes: 18:49

Because I know you've written that having both the theme as well as the cookbook really spurred additional interest in art, and I think those are giving people that ownership and letting people contribute. It seems like it would really help with that as well as, of course, like you said, giving people something to start with since and some examples that they can use when they're getting started. Last question is if you have any advice for people, say someone is at an organization and they're thinking, oh, I should make a custom theme for my organization. Any advice on how to do that, both both from a code perspective as well as from a organizational perspective?

Clara Guibourg: 19:25

I guess the thing that I would have to say based on our experiences is to see the value of and not be afraid to experiment and just not to be afraid to to just sort of try things out. And but it's okay if you don't have a huge plan from day 1 with everything set in stone. And thinking, okay, so I'm gonna I'm gonna do all these things. I'm gonna make this package. You can just start by just being curious, just having some one little problem that you have to solve and go going, okay.

Clara Guibourg: 19:51

I figured that out. What would be the next step to improve? And then they maybe you go on from there. You solve the nice problem. One day you realize that you have a custom theme set.

Clara Guibourg: 20:00

It doesn't it doesn't need to be more complicated than that.

Nassos Stylianou: 20:05

Yeah. I guess it's I guess the first question would be to anyone thinking about it is do you have a need for it rather than start? And we yeah. Exactly as Clara said. So we didn't start with let's make a package.

Nassos Stylianou: 20:19

It it just built onto that. So it's kind of break things down into here's what I want to do, and here's what would benefit my organization and do that rather than start off with the solution is I want to write a package or I want to do a theme or yeah. It's what do you actually want to do, want to produce? What sort of what do you want your output to be? Because your output isn't a mess.

Nassos Stylianou: 20:47

You're in software development. Your output isn't, I wanna do a package. So what is your output, and then find the building blocks that would provide a solution to that, really. And, yeah, I guess from my experience is keep it super simple. I think what we ended up doing was having lots of people.

Nassos Stylianou: 21:08

In a weird way, this helped other parts of our skillset development as well, in that it was probably the first time we were new as a sort of team, well, 2018 sort of thing, using GitHub and in a way that everyone was pushing to the same GitHub repo and doing changes and commenting, and it really helped having loads of people working on one thing collaboratively to kind of really it wasn't it was never a task for 1 person go out and build a package. And then by little things that everyone added, we also worked better collaboratively, worked better in terms of documentation, worked better we learn GitHub pull request and all that stuff a little better. So it it benefited a lot of things in parallel.

David Keyes: 21:55

Yeah. Yeah. Yeah. That makes a ton of sense. Great.

David Keyes: 21:59

Is there anything I didn't ask you about the theme or the process or anything like that you think would be useful?

Nassos Stylianou: 22:05

Yeah. So a lot of the coronavirus lookup pages that have that that we've been running run off our and produce graphics. I could send you a link to them, but it's this whole process of plugging in data, getting it getting the data and using the package to produce graphics has lived on to a massive degree even since then and continues. Like, it's just it's not something that we developed, we released, and we let go. It's we've used it.

Nassos Stylianou: 22:37

I don't think there's been a day where someone at the BBC hasn't used the package to produce a graphic. I don't think and definitely over the last sort of 2 years, we're been running coronavirus graphics every day, really. And it was only due yeah. We can only do it in this way because of how kind of one step of it, which is building something as a custom made BDC theme using us, is no longer something we think about. It's something that just happens because it's all there.

Nassos Stylianou: 23:10

Yeah. So yeah. Yeah. I think that I don't think it's too big a statement to say that there's not been a day gone that someone hasn't produced. Yeah.

Nassos Stylianou: 23:19

I think that is probably true over the last 2, 3 years, definitely. So it it has become a fundamental part of the BBC data journalism team's output release. And the other thing is how many other people outside of the strictly data journalism team have taken to use it as well. I think that's a really yeah. If I guess if we'd be proud as a team of anything, it's that, from my point of view, is how it has basically, if you tell someone, like, would you like to learn r, everyone would say yes, but then how many people get lost along the journey because Sure.

Nassos Stylianou: 23:56

There is no why am I doing this in their mind, and what can I get out of it that is worth the pain? Because it is a pain at first, but having showing someone, like or someone seeing, like, someone from another team is, you know what? This graphic that a few kind of months ago, you would have to do in this old process, if you learn a few things or devote a bit of time a day, Here's the 5 lines of code that you can run, and you can do that, and you don't need help from you can do it yourself. It's all contained within this. So I think that kind of spurred people a lot of people outside kind of the data journalism team itself to take a real interest and kind of continue to keep on going because there is that, here's what I will produce at the end of it, that graphic.

Nassos Stylianou: 24:48

And here's and you could see the code and here's 6 lines of code. You can do that. Obviously, they slowly learned the the tricky bit is the bits in between, which is, like, how do I get my data to a stage for interlactic? And then it's, okay. I've done the graphic.

Nassos Stylianou: 25:01

How do I add all these layers onto it that make it sing? But, yeah, still, yeah, it really helped.

David Keyes: 25:07

Yeah. That's great. I was gonna ask where folks can learn more about you. Obviously, folks can learn more about the or see the package in action by going to the BBC website and seeing everything produced there. If they wanna learn more about you and your work, where would be the places to go to do that?

Nassos Stylianou: 25:25

Yeah. I guess the stuff that the data team does, we don't have a sort of page as a kind of data team page, but, yeah, if and anything to learn about the package or the cookbook, we did a medium post at the time which talks about the process. So if there's anything that we might not have covered here that would be useful, a bit of a deep dive into the process and the code itself. It should be in that Medium post as well as links to the GitHub and the cookbook. And in terms of kind of work, yeah, yeah, stuff that we do will show up across the BBC website.

David Keyes: 25:59

Yeah. And I know so just if they wanna also find out more about you personally, Nasos, I know your website is nasos stiliano dotcom.

Nassos Stylianou: 26:11

Yeah. Sure. Yeah. Yeah. Yeah.

Nassos Stylianou: 26:15

Twitter. Although I don't, yeah, I don't use it too much recently. Yeah. Yeah. Twitter.

Nassos Stylianou: 26:19

And I've got a link to my website and the projects I've worked on, and I've detailed, yeah, what work's been done in our for each project as well. So that might be useful to see from the projects. Yeah. Especially if someone who's interested in learning our there there is a little bit at the bottom of each section which says what I did and then what yeah. Language.

Nassos Stylianou: 26:36

Yeah.

David Keyes: 26:37

Okay. And then, Clara, it looks like yours your website is

Clara Guibourg: 26:41

Yeah. I do. I do have a website as well. I've not been very good at keeping it updated, but I'll I'll need to look into that. But, yeah, it is there.

Clara Guibourg: 26:47

I can see some examples of I'm pretty sure you can there are some links to to, with charts that that use the slots in there.

David Keyes: 26:57

Yeah. Great.

Clara Guibourg: 26:58

And you can find me on Twitter as well.

David Keyes: 27:01

Okay. So I'll make sure yeah. I'll include links to all those places so folks can find out more about you, the work you've done, and connect. Great. Thanks, Clara.

David Keyes: 27:11

Thanks, Nasos. I appreciate both of you spending some time with me today. And yeah. Thank you.

Nassos Stylianou: 27:17

Hope you're

David Keyes: 27:17

doing it. Thanks again for listening. I hope you found this conversation interesting. If you have any feedback, I'd love to hear it. David@rfortherestofus dotcom.

David Keyes: 27:31

Thanks.