Dr. Cedric Scherer is a graduate computational ecologist with a passion for design. In 2020, he combined his expertise in analyzing and visualizing large data sets in R with his passion to become a freelance data visualization specialist.
Cédric has created visualizations across all disciplines, purposes, and styles and regularly teaches data visualization principles, R, and ggplot2. Due to regular participation to social data challenges, he is now well known for complex and visually appealing figures, entirely made with ggplot2, that look as if they have been created with a vector design tool.
Support the Show
This show is completely listener-supported. There are no ads on the show notes page or in the audio. If you would like to financially support the show, please check out my Patreon page, where just for a few bucks a month, you can get a sneak peek at guests, grab stickers, or even a podcast mug. Patrons also have the opportunity to ask questions to guests, so not only will you get a sneak peek at guests but also have the opportunity to submit your own questions. You can also send a one-time donation through PayPal. Your support helps me cover audio editing services, transcription services, and more. You can also support the show by sharing it with others and reviewing it on iTunes or your favorite podcast provider.
Jon Schwabish: Welcome back to the PolicyViz podcast. I’m your host, Jon Schwabish. On this week’s episode of the show, I chat with Cedric Scherer who is, well, he’s awesome at R. Let me just put it that way. He’s awesome at R. He’s awesome at ggplot. He’s awesome at sharing how he creates his data visualizations and so we talk about Cedric’s early use of R. He started with base R. So if you don’t know base R, well, he’s moved past it. We’ve talked about his early work in R. We’ve talked about how he approaches sharing his work, how he approaches making things in R, and what he sees going forward, what are the technologies, what are the areas of R, think Markdown, think notebooks, what is the future of R look like for those of us who are working in the field of data visualization. Speaking of working in the field of data visualization, I am working every other week to bring you this podcast. So if you would like to support the show, please consider heading over to my Patreon page, works for just a couple of bucks a month like a cup of coffee. Well, a cup of coffee is a little bit more than a couple bucks a month these days. But if you would like to support the show with just a few bucks a month, head over to my Patreon page where you can get some goodies and you can help support the show to support all the sound editing and transcription and web support that’s needed to bring the show to you. Or if you’d rather just use a one-time payment, you can head over to my PayPal account. But more generally feel free and please do share the show with your friends, your families, your colleagues, your networks, whoever you think might benefit from learning more about data and data visualization. Alright, having said all that, let me bring you my conversation with Cedric Scherer. Hey Cedric, how are you? Welcome to the podcast. How are things?
Cedric Scherer: Hi John. Everything fine here. How are you?
JS: I mean, you know hanging in there. It’s a sunny, but cold day here in Virginia. So it’s, it’s all good. You’re in? You’re in where again?
CS: I’m in Berlin, Germany and it’s already dark, you know, it’s getting late.
JS: Oh really? That’s right. That’s right. I got to get you before bedtime. So thanks for coming on. I’m excited to talk about your work on R. We’ve got I think a lot of things to kind of talk about. I want to get your story of like how you got into R, maybe some of your other tools that you use. Maybe we can talk about like did you use tools before you got into R and then as we talked about a little bit more, maybe some of the other tools that you use in and around R. I know a lot of people like they’ll make something in R. They’ll pipe it out as like a PDF and they’ll bring it into illustrator to clean it up. And I’m, I’m curious about your, your process on that. So maybe we can just start with how did you get to this point of like being like, I don’t know, I kind of like now I’m like you use like the data of this guy in R which I’m sure there are other people out there who are mad at me about saying that, but you know, what’s your sort of R journey to get to where you are now?
CS: Yeah, it’s a pretty long journey. Good questions. Maybe I like so I’m not a graphics person for anyone who doesn’t know me. So I’m an ecologist by training or biologist first. So when I started looking for my courses, I was thinking about graphics design or biology and I thought like, okay, biology will give me jobs, graphics design will not so I ended up doing biology. A fun story now. So I mean, I still have a biologist job so that’s fine. Here. So yeah, I already had a passion kind of for for design, not so much for coding actually. And then it was the first contact of R was already in my first bachelor semester in 2008 already, but really like just clicking running code for statistical analysis base plots. So yeah, R is very common or became very common around that time for ecologists and biologists to use for analysis, so this was more like really the geeky old R times.
JS: Yeah. (inaudible 04:01) base R times.
CS: Yeah, like linear models old time.
CS: And base R graphics and people were using it for statistics, mostly statistics. So nowadays R is very different. You can do all kinds of things like you already crossed it, covered it. It’s kind of like plots, tables, web pages. You can write books. You can do really fancy things, but nowadays back then it was kind of like the yeah the tool to use to do statistics in the proper way. So I also learned about SPSS and Excel so this was basically after that first semester I quickly switched back because yeah I didn’t learn much in R and most people ran out using R in my group so we were just using Excel and didn’t really like the extra graphs, and then during my bachelor I was still using Excel and during the master was more on R courses, then became an ecology course studies and in the ecology field that’s mainly the people driving so many of the R packages and R developments and then I started my masters and I got into it more and more. And then towards the end of my master beginning of my PhD I found out about tidyverse and ggplot2.
CS: So and then it was really a fell off. And during the PhD I just realized that I spent much more time on designing than on writing. So actually we’ve talked to too many people. Many people are like, yeah, you’re so perfectionist about so many details, you need to drop it. If you want to be a successful scientist, you can’t spend three days on the graphic. And I was like, I don’t want to drop it. I know I’m a perfectionist, but I want to make it a good thing and not a bad thing. So yeah, so kind of like moved after my PhD and moved into some more design persons. So I’m currently doing both things being an academic but also being a freelance yeah designer or consultant or having there’s so many rights for these people.
JS: So when was that that you like, that was like right when Padley has sort of come up with tidyverse and ggplot2. So that was like right at the very beginning?
CS: I can’t really recall or think ggplot2 is really common since 2015 maybe.
JS: Yeah, something like that. Yeah.
CS: 2014, 2015.
JS: Yeah. Yeah.
CS: So I should know actually. Anyway, so yeah, it was I think 2016, end of 2016 so maybe a bit late. So I wasn’t really following the packages. So I met someone and he was completely coding in R, everything. He had had his Google page and blog, blog posts. And I was just looking at it and he was like, oh man, I just know base R. I can’t compete. And then we really became friends and we’re doing that together. And he pushed me and yeah, what I mean, the thing why I was so interested about it, but actually I wanted to do a small multiple. So trellis plots and which is called (inaudible 06:49) so easy to do and with base R for everyone who knows base R was so hard to remove all the labelings and putting it together in the right format. And that’s actually one of the best showcases for ggplot, I think the small multiples.
JS: Yeah, I I I totally agree. I mean, whenever I want to do small multiples, I’m going I’m going to R to do it because it’s like, it’s like that one. It’s like one extra line of code. So which kind of brings me to a related question. So like is there something about I mean, I know why I enjoy and I, I mean intro baby R coder, so like is there something particular about R because it sounds like you’ve coded in a couple of other languages too. Is there something about R in particular ggplot? Is it the philosophy of it that you like? Is it just the ease of use? Like, what is it about R versus any other language that like really attracted you to using that particular tool?
CS: Yeah. So even though I’m programming everyday, I don’t see myself really like a programming person, like IT programming person. I’m not really, really a computer nerd. I don’t know much about hardware. I don’t know much about bash command line stuff. Yeah. So it was really also a hard start. But at some point, yeah I also did a lot of my research. I do a lot of my research just on computational data, simulation data. So I I also picked up a few other programming language, but most of them not for visualization. So a bit of Python, but this is really like, yeah, it’s R what we are using, so everyone was using R so there was basically no choice or I didn’t even think about using something else. That was very happy actually. So I learned a bit of C++ actually, which I found super complicated for me as a non IT person and dealing with compilers and bugging. And I had to build my own functions to just get simple jobs done which was really annoying me.
CS: I think R is the perfect combination. I mean, that’s why many true programmers I call them now don’t like R maybe because it’s something in between. It’s easier to to read for nonprogrammers I would say and easier to learn maybe than some of the other programming languages.
JS: Yeah, yeah, I think that’s I think that’s right. I mean, yeah, it’s definitely like there’s stuff going on in the hardware, it’s it’s back there, but I’m not gonna, I’m not gonna worry about it.
CS: Yeah, I think for statistics, let me do something. For a statistic, I think it’s the it’s the programming language or at least was most back then to do statistics in a reproducible coding way. So writing, also, I’m not sure about the state of Python, but C++, we don’t do statistics or at least (inaudible 09:20). I’ve rarely seen someone using C++ for statistics.
JS: Right, right. I mean, and if you are in like a language, I mean, I, I tried to learn C++ back in college and you know that’s too long ago. But like, in Fortran, for example, like, if you want to do statistics, you need to code the entire thing. Like it’s not like you, you know it’s not like you load a package or type regression y x and it runs a regression, like you need to actually invert the matrix and do the whole thing which is valuable as a learning process. I remember like coding in MATLAB as in statistics class at one point because you had to invert the matrix and like that’s super useful to understand statistics, but like on a day to day like doing a job is like, I just want to get this done. I need to write yeah. So what does your like workflow look like? Is it simply like just bring the dataset into R, do everything in R, export the visualization as a PNG, JPG, and you’re and you’re done or there do you have like a whole bunch of things sort of surrounding your, your workflow?
CS: Yeah, the one after that. So during my PhD, I was mostly programming in R, so I wasn’t really touching any other tools first of all because it should be reproducible. So we’ll be clicking the code and then the image is returned. So now for my design work, it changed a bit. So I think, well, for these two charts, like academic, scientific figures, and some maps and stuff, you really can go the whole route in in R and ggplot. You could also do it for more complex things. I mean, we have shown that the TidyTuesday, you also had Tom Mock on in your podcast, so people maybe know about TidyTuesday. So this is where I really started getting crazy with ggplot kind of like really tricking the system, like finding ways to manipulate to code to get whatever I want but it can be tedious at some point. So I still do most of the work in R so really depends also on the client on the use case what we what we have as a final product. So the really nice thing about R still is that it’s yeah, it’s reproducible. So if you have something, some project where the data is likely to get updated, I definitely will use R. If I have a client who wants to produce 1000s, 100s or 1000s of plots every day, every week with like updating data and we need to find a clever algorithm to place labels nicely and luckily, there are so many packages for that. And then we do it definitely in R. I also like the notebooks like the reporting style that you can directly include it or if it’s really about more artsy, more complex data visualizations. So I don’t really bother around if I have a one static graphic and I need to play some annotations. I do it usually in Figma because I don’t have really other tools I know. So that was not what I started with. So it’s really I’m not coming from the design world. So I’m just learning the old designs. I say it’s simple. So I think it’s not so simple for me because I kind of need to find the point where I stopped in R and then move on.
JS: Right. Right.
CS: And yeah, usually I use vector graphics anyway. So yeah, so I guess 90 to 100%. So it’s not really like if somebody’s using R and others and they really kind of like spent maybe 5% 10% the time in R and then they move on. And for me, it’s definitely much more. So the final thing, it’s really like already defining the colors or almost defining the colors (inaudible 12:41).
JS: Yeah. So you mentioned the notebooks. Can you talk a little bit about that because I would suspect that many R users are familiar with ggplot, they’re familiar with R Markdown. But I’m curious about like how you think of notebooks and sort of maybe also your view of like what does the future of R look like? And maybe that’s a broader question of the future of, of coding. Yeah, can you sort of walk me through a little bit like when how you use notebooks in R and in particular like your, your specific process especially when you’re working with a client and maybe you’re sharing things back and forth?
CS: Yes, yes. So, yeah, totally notebooks and (inaudible 13:24) R Markdown. I mean, it’s basically R Markdown reports. And, and the nice thing about it that you really can use the R Markdown language or the Markdown language to write text surrounding it. And you can also, I mean, they’re, they’re pretty neat themings and styles to create your reports. You can also write your own CSS and customize it as much as you want so you can also script it, but I always end up using a Markdown notebook usually. And yeah, I mean really depends on what you want to do. So even if I’m just drafting one finger for some challenge or some personal project, I’m using these because it’s just became my workflow. I also like that I have kind of like a HTML and you could also knit it or render it to a PDF but I really also liked it, I have like a copy of my code. So if you go get lost, I still have this HTML template or also you can add the session info to to the end so I know which package versions were used and so on which is a bit more difficult if you do it for an R script for example. Then you might want to use some images also, but it’s not super easy then to really use later to come back to the same setup but at least I would have the option to kind of like get back to the package version which made it possible and I mean some people write full scientific publications in R. I tried it not to get lucky with it but also because I mean you need to (inaudible 14:49) some people then kind of like go to Word and go back to R but this kind of like got a bit fuzzy and also depends really on data. If you have very big datasets, I still find little bit of pain if you need to knit and it takes a long, long time. But yeah, I think it’s, it’s pretty neat. And you asked for about clients, so it’s the same basically. So you can hide the code and just show the report. You can also have buttons where you can show the code if you want to, so that’s mostly my setup so not showing the code on proven on default. And then if people want to have a look, they can do it if the client knows a bit about R, but these are very simple to share. Yeah, that’s the thing.
JS: What is your sense? You may not know the answer to this, but what is your sense of people who are writing in R scripts versus people who are writing in Markdown? Do you know like what the split is? Maybe, maybe a better question is like the folks that you work with, like what’s the split of people using those two approaches?
CS: I think that’s a, that’s a bit hard to estimate for me. So I, I basically have two groups. So the scientists are now those that I know mostly using the notebooks.
CS: (inaudible 15:59) community on Twitter and so I see both but also mostly, mostly a Markdown which might be because I’m a tidyverse fan. I mean, I mean, you may know that there’s characters, people who use the tidyverse and people who don’t use it. And I think people who use the tidyverse are also more likely to, to use our studio and everything our studio provides. So I think even though non-tidyverse users might use to our studio, they might not use our Markdown just kind of subjective feeling maybe.
CS: So I, I kind of like, I like what our studio is doing. I like these ideas of having notebooks, so I definitely using it. And yeah, I’m also suggesting it to others. So when we have new PhD students studying, I mean, also the younger generations, they anyway learn that now. I think it’s more about the older ones who just know their own routines and they don’t.
JS: Yeah, absolutely. Yeah. I mean, it kind of makes sense. I mean, if it’s from a collaborative position, that makes sense that maybe scientists are more likely to use the notebooks because they are multi-person teams and maybe it’s a little bit easier to collaborate as opposed to like sharing a script back and forth even if you’re like doing a Git version control. Maybe the notebooks are a little bit easier to do that collaboration.
CS: Yeah, it’s also a collection of your output, why can I get an academic context. I’m creating also a new client context. I’m creating like, I don’t know, 20, 50, 100 plots, restorative things, different designs depending on the current stage. So it’s easy also to collect them and one document can just trawl through, and you don’t have to open it and find the file again. And also in terms of talking about ggplot2 or about visualization with R, the nice thing about these notebooks or about R Markdown is that you have kind of like settings, how high and wide these images should be. And then you see it the same way because if you are our studio user, you know that the, the plot window where, where the visualization shows up, it’s not what you then really see in the end when you save it. So that’s a way to fix the aspect ratio of the graphs to really see how they look when you save them.
JS: Right. So I want to shift gears a little bit and talk about your, your site, the tutorials on the sites and your book plans. So you’ve got a ton of R tutorials on your site. And what I’ve noticed you’re doing lately on Twitter is I think kind of ingenious is like you seem to like ask a question like, you ask a question like, what is your biggest challenge of like adding labels in an R plot and you sort of give people like four or five options and then you basically like write a blog post about like the best way to do it. So can you tell folks a little bit about the site and about the tutorials and maybe your kind of philosophy of how you figure out what you want to write about, what you want to talk about, and like the, the, the challenges you’re trying to solve for people?
CS: Oh, well, big question.
JS: You know, you know me. I try to go, I go for the big things. You know I’m not going to let you off the hook.
CS: Yes, for the one big tutorial I have on my, on my homepage, this is really, yeah, this was my learning. And at some point, I want to share it. So this was based on the tutorial by someone called Seth Ross and he’s also producing ggplot tutorials for free. And as mentioned, I found ggplots thanks to the faceting option and I just jumped in. And as many of us, we can just copy paste this code from some random pages, mostly Stack Overflow. And at some point, I was like, okay, why isn’t this working typical problems. And I was like, okay, just give myself one, two days, go go through the hopeful tutorial and kind of create my own version of it. And then I kind of updated it. And I mean, as, as mentioned, I’m a perfectionist so I wasn’t happy about the default look. So I was also polishing the plots, which (inaudible 19:43) already provided. At some point, I gave it a major update working on it every weekend for a few months. And yeah, now it contains, I don’t have the exact number but I think 120 different ggplots. Most of them are scatter plots nevertheless because it’s really about like okay how do I color the axis text on the exit and x axis but also about a bit about plot types. So there’s no mapping yet in there. So there are many, many more things to cover. So this is how we ended up where I ended up. We are thinking about writing a book and people approached me. So the original idea was really to this is kind of like a how to tutorial. So how do I do ABC. The idea was to turn that into a book. It now merged a bit with also having a broader context of how to do graphics design with teachable tool kind of like doing these more complex things with all in R without any post processing with any of the other tools, Illustrator Inkscape, Figma.
CS: Maybe also a combination of both how to work with that, but more like not so much on the technical details but more about how to create creativity when you’re working with code as well because you also need to be creative to achieve with code what you want to having on your, yeah on your final plot. So (inaudible 20:55) creativity in terms of color and chart type and all these kinds of things and story, it’s also about the creativity. How can I force ggplot to do what I want?
JS: Right. Right. So no, I was just gonna say like, so how do you think about a book? I mean, my always concerned about writing like tools books, is like the tools change. So especially now they just changed so quickly and like packages are always being updated. And like today what you might have to sort of make a workaround for tomorrow, there’ll be a package where just be like, hey, load this package and, and you’re good to go. How do you think about that as you as you work through the book? Is it just, this is just where we are right now and, you know, follow along with like the website or this other thing and you know, you’ll see some updates, like how do you, how do you think about that?
CS: Yeah, that definitely is something we, we discussed a lot. So I’m relying on a lot of extension packages, so but only those I need. So let’s maybe first kind of like put out the philosophy of how, how I use extension packages. So for example, I’m not using a package which allows me to do a dumbbell chart or a lollipop chart because I can do the ggplot2, but for example if it’s kind of allowing me to create a fancy (inaudible 22:04) bump chart, there’s a package out there, I will definitely use it because I don’t want to call it on my own. So it really depends on what you mentioned also about MATLAB for example. So it’s kind of like doable for me and I now have quite some ggplot experience. So I know how to code many of these things without the other packages so I’m trying to replace them, but there are definitely some packages which are they are there to be used.
CS: Some of them are not even really officially hosted on this thing called C-RAN or the C-RAN, however you pronounce it. Yes.
JS: There’s another good question. We’ll see, we’ll see what the split is on (inaudible 22:35).
CS: Yeah, it’s officially C-RAN but.
JS: Okay. I’ve always got the C-RAN.
CS: And most people, most people call it C-RAN, so I’m obvious saying both ending up saying both which is kind of like playing stupid because when saying the correct name, no one understands me. When I’m saying the wrong name and then everyone gets it right.
JS: Alright. Yeah.
CS: (inaudible 22:55) so something we were definitely discussing like I kind of made a list which packages I definitely need.
CS: We will have an online version. It will be on hosts and GitHub so I will kind of like really try to keep it up to date. That’s another thing just besides the book also if you kind of working with ggplot or in any programming language, things change so fast, so you really need to collect, yeah, be up to date. And I feel like it’s super interesting that to see every few days something new and to find something new. At the same time, it can be also very stressing, so I’m getting older. So I kind of like thinking about what happens when I don’t want to scroll through Twitter every day writing packages.
JS: Right. No, but it’s, it’s a, it’s a very smart way to go about it right because then it doesn’t matter. It’s not just like, oh, I know how to make the reader, I know how to make a dot plot now because I follow (inaudible 23:55) like step by step. I don’t need any other packages. I can do it. But I’m sure just learning that helps me do a bunch of other things that I don’t also need packages for.
CS: Yeah, it’s more this idea like, like I mean, I get many questions I find very difficult to build a workshop on that but I often get questions like okay, how do we come up if this is a great idea. So from the design perspective, I mean you may be also get these questions and I mean of course your inspirations but also some things you just learned and you cannot even maybe say what exactly it is that you see things differently than others especially when it comes to colors and font choice sometimes as well but colors are super hot. I think maybe these are helps us now to kind of just say like the fire broke and you (inaudible 24:37) in general, yeah, but also with the coding. So I think as mentioned, I’m not a super coder so I’m not writing many new functions. So I’m really trying to trick the system just today I was super happy about some neat trick I did. I needed to have two colors but I already had the fill and the other color reserved for something else and used kind of like transparency as third level and these things make me just set me. Yeah. And this afternoon, I don’t know, after six years now into ggplot, I still kind of like every few days I find something new or a new trick or something. So it’s not a collection of kind of like these approaches, how can you approach these things and how could you maybe come up with something, yeah, which is not (inaudible 25:17) in this usual teaching books, things like, okay, the next step is adding a layer. The next thing is adding a coordinate system. It’s more thing out of the box maybe and going down. It’s also I mean, there are many ggplot books and also got these questions. I mean, I proposed the book and and also got feedback, something like yeah, but there’s already the cookbook for example and will not be like, okay, how do I build a boxplot with some jitter and label with the sample size. That will be more like okay, yep. The way I think ggplot, let’s put like this.
JS: Yeah, yeah. More of the philosophy.
JS: Maybe like a cookbook. Yeah. Well, that was great. I will look forward to it. We’ll see if I can.
CS: Yeah, give me some time.
JS: Yeah. I will give you some time. Yeah. So before I let you go, I wanted to back away out and ask you whether you think this is like a philosophical question. Whether because there’s always a conversation about this, but I’m curious whether you think everyone should learn to code?
CS: No, definitely not. I mean, everyone should, anyway should. I, I don’t like these kind of hot hot routes or hot like guess no decisions. Should anyways, everyone may benefit from coding, but I also don’t think everyone would. But let’s, let’s focus on our, on our kind of like bubbles. We have like the design bubbles on the analysts. And I think designers could really learn a lot or kind of use it a lot. And I mean, we see it with ggplot, now also with D3 and all these other libraries we see now emerging or some Python, there are some interesting ones. I think there’s a benefit. I mean, I heard about people who really kind of like, yeah, type in the numbers in their design tool and then they, if they need to update it, they just type it again. So I think of automating some of these things. And yeah, I’m coming from the complete coding perspective so I think one point I’m missing like what can I do outside of coding. So because I’m feel always challenged to do it in code with code at the same set. Yeah. I mean, you do what you feel comfortable with. But I think also many, many things can be more efficient. And I think Martin (inaudible 27:25) mentioned it to me in the very beginning like why are you doing everything with ggplot2. Yeah, for the tidy2 search challenges for example, this is kind of like the rule right. There are also people not doing everything in ggplot but for me it was kind of like, yeah, for sure, I caught everything in ggplot2, that’s, that’s the task. (inaudible 27:40) night just three hours to place my labels or based on coordinates, which is then at some point so I got really tired about that for example so I still do it. But if I see someone doing these crazy kind of like (inaudible 27:51) where you just need to pay lots of time to kind of like be perfectionist about it. Yeah. So I think a combination is good but I think coding, I mean, in a world full of kind of like being digital and full of computers and smartphones, I think it’s anyway a good idea to maybe deal with these, yeah, things that are around us anyway and then in terms of efficiency I think and in terms also of honesty. I mean, if I type in my numbers into some, some design tool, it’s pretty hard to check. It’s not only pretty hard to update, but it’s also pretty hard to check. I mean, yeah if it’s more really about reporting, I think this is important to be transparent, to be honest about what’s (inaudible 28:33). And yeah, we see it also that it becomes more and more standard I think not only in the scientific community, but also in the business and also maybe in the data design world. I think that that’s definitely a good thing. And also, I mean, I love to share. And I mean, I wouldn’t be here with all the people doing great stuff for the R community and also the database community helped me a lot so I love to share. I think I can share my code. It’s hard to share something like I clicked here, I clicked there, I need to write a blog post. But here I just share my code. People can pick it up, people can get inspired, reuse it. And I learned from them again and they learned from me and I think it’s also very neat exchange.
JS: Yeah, yeah. I think all that, all that’s spot on, although, although I fear there may be someone listening to this who is all excited about going to your site and learning R and then heard you just say you spent three hours when I tried to get the labels in the right spot. You’re like now.
CS: Yeah, those were many labels. And I mean, I’m not talking about labels on the axis just to be clear. I’m talking about invitations you know, right.
JS: Right. Right. Right. Right. Okay. So hopefully, hopefully, people aren’t too scared off and they’ll give a shot. They’ll give a check on your sites. And then, and then the book when it comes out and there’ll be a GitHub site and an online companion probably so that’s all. Cedric, thanks so much for coming on the show. Always good to challenge you. And yeah, take care.
CS: Thank you. Goodbye everyone.
JS: Thanks everyone for tuning into this week’s episode of the show. I hope you learned a lot about using R And I hope you’ll check out the episode notes of the show. I put in a lot of the links to Cedric’s work. His website is a treasure trove of free downloadable information in code that you can use to improve how you create visualizations in the R programming language. Okay, until next time, this has been the PolicyViz podcast. Thanks so much for listening.
In number of people help bring you the PolicyViz podcast, music is provided by the NRIs, audio editing is provided by Ken Skaggs, design and promotion is created with assistance from Sharon Stotsky Ramirez, and each episode is transcribed by Jenny Transcription Services. If you’d like to help support the podcast, please share it and review it on iTunes, Stitcher, Spotify, YouTube or wherever you get your podcasts. The PolicyViz podcast is ad-free and supported by listeners. If you’d like to help support the show financially, please visit our PayPal page or our Patreon page at patreon.com/policyviz.