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Unlocking Data Communication: Unleashing the Power of R with David Keyes
570 Plays12 days ago
The PolicyViz Podcast wraps up 2024 with David Keyes, author of the new book, R for the Rest of Us: A Statistics-Free Introduction! We not only talk about how you can get started in R using David’s book, but also building data and data visualization workflows with R, RMarkdown, and Quarto. We also talk about how to create consistent visualizations through themes and functions in R to help new R users leverage its features without being intimidated by complex statistics.
I hope you enjoy this episode and have a great holiday season! See you in 2025!!
Keywords: data, data visualization, PolicyVizPodcast, JonSchwabish, DavidKeyes, RForTheRestOfUs, DataCommunication, DataVisualization, Quarto, RMarkdown, DataPresentation, BrandedVisualizations, Excel, SelfTaughtR, QuantitativeEvaluation, ChatGPT, QualitativeDataAnalysis, TablesInR, EfficiencyInR, SPSS, SAS, Stata, ggplot, ReproducibleResearch, BeginnerFriendlyR, QuartoVsRMarkdown, SurveyDataAutomation, Netlify, DataManagementWorkflow, LearningR, mathematics, Al, machine learning
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Check out David’s website and podcast, and grab his book R for the Rest of Us on Amazon
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Email: jon@policyviz.com
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Transcript
Introduction to the Final Episode
00:00:12
Speaker
Welcome back to the PolicyBiz Podcast. I'm your host, John Travish. I hope you're having an enjoyable start to your holiday season. I am enjoying sharing with you the last episode of the podcast for 2024, but don't worry. I've got lots of great episodes coming your way in 2025, all about helping you do a better job visualizing your data, communicating your data,
00:00:34
Speaker
Presenting your data, we're gonna talk about lots of different tools like Tableau, Atlas dot.ai, and lots of other things. So make sure you stay tuned to the show. Make sure you get it in your feed on iTunes, on Spotify, or on YouTube if you're watching the show there.
Meet David Pies and His Book
00:00:50
Speaker
So on this week's episode of the show, I'm excited to be joined by author and our expert, our enthusiast, David Pies. Author of the new book are for the rest of us. This is a really interesting book because it doesn't focus on how do you clean data? How do you run regressions? How do you do estimates? It's all about using R to communicate data. So he starts with data visualization. He gets into data workflow issues like quarto and R Markdown. It's a really fascinating book and we focus our attention in our discussion on these various elements of communicating data through R. So we spent a lot of time talking about Cordo and R Markdown and the difference between the two if you don't know. We talked a lot about tables because tables are such an interesting part of visualizing data and there are some great ways to do that in R. And then we spent a bunch of time talking about themes and functions that can help you create more consistent branded visualizations either
00:01:44
Speaker
within your organization or just for yourself if you wanna have a consistent look and feel to your graphs and your charts and your maps and whatever else you're creating in R. So if you are relatively new to R, I think this will be a great episode for you because David's gonna walk you through how to use some of these other features that you may be a little hesitant to try out, but as you're going to hear, anyone can do this in R. The features, the
David's Journey from Anthropology to R
00:02:06
Speaker
packages, the libraries are there for you to help you create better graphs and charts and all other visual elements in R.
00:02:14
Speaker
So no more talking for me. Let's get right to the episode. So here's my conversation with David Kyes. Author of the new book are for the rest of us, a statistics free introduction.
00:02:28
Speaker
Hi, David. Welcome to the show. I don't think we've ever met so good to meet you like virtually at least. Yeah, great to meet you as well, John. i've I've followed your work for a while and I'm excited to chat with you. Yeah, terrific. Thank you. So new book are for the rest of us. I do like the subtitle statistics-free introduction, so it doesn't scare. Scary enough to learn how to code, a little less scary. We don't have to learn like statistics while you code. So I thought we would start simply with you know introductions and then um how you sort of got to writing a book about R. Sure. Um, yes, I'm David. I run a website, a business called also R for the rest of us. I've been doing this for about five, almost six years now. I have an unusual path to where I am. I actually did a PhD in anthropology and my dissertation was entirely qualitative. Um, but after I graduated, I realized I actually liked doing quantitative work as well.
00:03:27
Speaker
and was working in what's called evaluation. So um I tell people it's kind of like management consulting for like nonprofit or government. And I was an Excel user, you know did that, and then realized I wanted to learn something else, taught myself R, and then started a business to kind of help other people like me who who wanted to learn R, but maybe didn't come from that you know quantitative background like so many R users do.
00:03:56
Speaker
um So yeah, that's, that's kind of how I got into it. And the book came out, worked on the book for a couple of years and then it ah finally came out um a few
Why Write a Book About R?
00:04:06
Speaker
months ago. So it's been exciting to see it out there. And were you, I guess this sort of bleeds into my first main question, which is, you know, writing a book about a coding language can be kind of tricky, right? Cause especially a tool like R where it's constantly changing people, creating new libraries and new packages and new techniques.
00:04:25
Speaker
How did you make that decision that a book would be the best way for folks to learn rather than, you know, using Google or chat GBT and, you know, all the various sites people can sort of go to. Yeah. I mean, I had before I wrote the book and and I still do make video courses. Um, so that's the main way I had been teaching, but.
00:04:48
Speaker
Just through talking to people, I know that there are certain people who really like learning through online courses, and there are certain people who really like books. um And so I don't
Focus on Data Visualization in R
00:05:00
Speaker
necessarily think that books are the best way, nor nor are video courses necessarily the best way. I think they're just different ways.
00:05:07
Speaker
um And so, yeah, I wanted to provide you know an opportunity for folks who like that. I know that people also like books, um you even in an era of chat GPT, a lot of people like having even that the physical book to be able to mark it up, to you know have as a reference. So I think, again, just giving opportunity, you know various options for people who want to to learn in different ways was was a big part of the motivation to write the book. right It's interesting to me, I mean, your book, similar, I think, to Hadley Wickham's book, but different from a lot of other books, starts with data visualization. And the book, I think, primarily focuses on the communication possibilities with our exporting, the collaboration, the visualizations.
00:05:59
Speaker
Did you ever have a part of yourself that was like, I really need to show folks how to do like regression analysis and data cleaning and all those pieces? Kind of like, I think in the workflow, you kind of lots of people think about the data communication piece towards the end, which may or may not be right.
00:06:16
Speaker
but right You know, there's like, how do you clean the data? You know, all, all of those pieces, but, but I mean, just so folks in case folks haven't, haven't sort of looked into it very quickly, like five chapters in part one, that is visualizations. There are five chapters in part two that are reports, presentations and websites. And then the last section is an automation collaboration. So none of that is like data cleaning, regression analysis, right? So like, what were you thinking of the guide and like your, your target reader?
00:06:44
Speaker
Yeah, well, so I mean, I was for me never going to make a ah section on anything statistical because given my background and my kind of approach with R, a lot of what I've wanted to do is show people that even if you never do complex statistics, R can be still incredibly useful because that's exactly how I use it. um I do R for the rest of us, we also do some consulting work and the reports that we make for clients, you know the the most complex statistics we're doing are like means and medians, you know that type of thing. And again, we get a huge amount of value out of out of it. And so I wanted the book to to be a way to show people that you don't have to use R in that way. Because I think everybody already knows, you know if you're doing statistics, R obviously is a fantastic tool for that.
00:07:39
Speaker
But really, I also wanted to show people that it could do more than that. And actually, the original title of the book was R Without Statistics. that's That was the working title for a long time. um And eventually, my publisher decided, working together, we decided to to shift it and make that more like the the subtitle, as you mentioned. um But hopefully, that that shows you kind of the motivation behind the book. um You asked also about the kind of like intended audience I think it's two groups of people. Number one is people who have never used R. There's a chapter early on that's just like a a crash course in how R works. um And that comes from the years of experience I have teaching folks who have never used R. But then number two is more experienced R users who have maybe gotten in their head that R is only useful for certain things. And oftentimes, that's you know more statistically oriented tasks.
00:08:36
Speaker
And so showing people that, hey, you know R can help you with like incredible data viz or improving your reporting workflow. um I think that is something that even many experienced users don't appreciate um the the value of R.
00:08:54
Speaker
Yeah. What is your
Benefits and Challenges of R Tables
00:08:56
Speaker
experience on the qualitative, you start, you said you started like with anthro degree and I'm curious about, uh, the qualitative part of working with R. I can't think of a time I've really like yeah gone in there, but like there are new NLP packages coming out. I mean, I know there's a word tree package, but you know, but like, you know, how do you, have you, have you really dug into the qualitative side of things in R?
00:09:22
Speaker
Not really. um I mean, people ask me that. um And I'm always looking for an answer because I get asked that so much. But no, at this point, I don't really do very much with qualitative data in R.
00:09:36
Speaker
um So yeah, I don't know that I actually have a ton to offer there. It's more just and that that's my background. Right. Right. I mean, there are other tools, you know, in vivo and deduce and right and so maybe folks are just living in those tools and say, okay, like I've seen people.
00:09:57
Speaker
try to combine the tools. And there've also been like packages that people have made to attempt to do the kinds of things that like in vivo or deduce or those things do, where you are doing that more kind of manual tagging of qualitative data and looking for themes and that type of thing. Actually, the one thing I will say I have done, I connected R to using chat GPT.
00:10:25
Speaker
and said, you know, go in, like, here's my data. Here's my survey. I mean, I was using fake data. I know there are privacy reasons why you have to be careful how you how you do this, but I just wanted to see would this be possible. And so, yeah, I hooked up R to chat GPT, gave it some fake survey data and said, you know, pull out the top three themes in this data. um So, yeah, it could it could be used in that way.
00:10:50
Speaker
right But I don't generally use it for a lot of qualitative analysis. Well, it'll be interesting to see what happens in that area over the next coming months, years, I guess. um Okay. So there are, there are three parts of the book that I found especially unique and innovative that I want to talk about. um You have a whole section
Templates and Reproducible Reporting in R
00:11:08
Speaker
on tables, you have a whole section on templates, and then you have a whole chapter on our Markdown and Cordo.
00:11:15
Speaker
I want to start on tables. From your perspective, what are some of the advantages and disadvantages of using R for creating tables? Sure. um Well, first of all, I will actually say, I think this is in the book. um Hopefully you picked this up. The tables chapter was actually inspired by you because you have um an article about ah effective tables. Yeah. And then Tom Mock, who works at Posit, took that and did showed how you would implement those principles in R using a package called GT. um And so then I interviewed the the way the book is structured. Each chapter is framed around an interview with someone who's using R in daily life um to do something. So I interviewed Tom for that chapter and talked about um how you implement those principles.
00:12:05
Speaker
yeah So ask your questions about advantages and disadvantages of using R to create tables. If you're already doing your reporting in R, if you're using a tool like R Markdown or Cuarto, which I know we'll talk about shortly.
00:12:18
Speaker
um I think doing tables in R is absolutely the best way to go. The reason why I think it is that is because if you think about a more typical workflow or a typical workflow for someone who's not using R, say they're using a tool like SBSS or SAS or Stata and say, you know, you get your data, you do your data cleaning and and your analysis in that tool.
00:12:43
Speaker
And then you kind of have to spit out you know that you have to export your data um and then you copy it into Word and you make your tables in Word. Now, if you just do that once, sure, that's fine. But the reality is, for most people, you're you're not typically doing that once. you know like Maybe you edit your Word document, you have to go back and like create your table again. Or you, say, if you're doing surveys, like you know you get 10 additional surveys, well, then you have to run your analysis in SPSS or SAS or Stata or whatever, spit out your data, copy it to Word.
00:13:17
Speaker
And every time you are copying from one tool to another is a chance of you know human error. um And I mean, I think anybody who works with data knows that it's just going to happen. There's no way around it. And so with R, what you can do is you can work with your data, do all of your analysis within R,
00:13:40
Speaker
and then do what's called piping it directly into the GT package or any other package to make tables. And so, I mean, there's always the possibility of human error. You can make an error in your code, of course, but you're avoiding that copy paste error.
00:14:00
Speaker
Um, so I think that's like kind of the major advantage. Plus, as I talk about in the chapter, the GT package, um, especially is set up with some really nice defaults that make it so that you're, you basically have to work to mess up your tables. Um, as an example, it will automatically write align numeric data so that it aligns and it's easier to read and compare. Um,
00:14:26
Speaker
And as I show in the chapter, like you can change that, but that's the default, which is really helpful. um ah When it comes to disadvantages, um and I don't think this is specific to tables, I think this is specific to R, there's a learning curve. um And R, I mean, I know you did an an episode of this podcast where you worked with Aaron Williams and you talked about your self-learning R, and I think that's a good example of, you know, it's challenging. And so,
00:14:55
Speaker
I think you have to see it as a long-term investment to learn R because that workflow that I talked about where you're working in R and making your tables automatically will save you time in the long term. But the first couple of times you do it, it's definitely not going to save you time. So I think that's, that's the major disadvantage. Yeah. It's also, especially with tables that it's there just feels like there's infinitely more variations in a standard table than in say, like a bar chart, right? Like a bar chart, you're going to have access, access rectangles. Right. Do you want, you know, the infrastructure is kind of all set. Whereas tables, like, are you going to have one that spans multiple columns? Are you going to break the grid lines? Like it's just,
00:15:38
Speaker
And yeah, like you said, like you got to kind of iterate through it to figure out how do you want to make those tables look good each time. um So that segues very nicely into the next thing I want to ask about was was templates because you have a whole section on templates, which I'm a big fan of.
00:15:54
Speaker
um so So I want to ask you about building templates and then particularly, I guess, as we think or just go back kind of the tables, like, how do you think about building? ah This is something I've always struggled with because I'm always asked like, can you build a table template for us in you know Excel or PowerPoint or whatever? And I've always sort of pushed back on that because there's so many variations on ah on a table. So maybe we could just start with like the table section in general and and why you felt that was, you know,
00:16:22
Speaker
key to someone learning R. And then we could talk about that. It's kind of what I see as like this table template challenge.
Enhancing Workflow with R
00:16:30
Speaker
Yeah. I mean, the reason I think learning to make effective tables is useful is because it's something that everybody is going to do um no matter what else you're doing in R. Everybody at some point is going to make a table. And I found that out because when I first started learning R, because I'm an anthropologist, because I'm not doing statistically complicated things, I actually felt really um kind of uncertain, like, oh, am I a real R user? And then I started talking to people, and everyone was like, yeah. I mean, sure, you know some people are doing more complex statistics, but the the tips and tricks that get shared online that resonate with everybody are things like how to make good tables, how to make good data vis. And so I think for that reason, you know it's really
00:17:19
Speaker
it's really useful to to think about making table, like everybody will want to to learn how to make tables. yeah um I actually forgot your other questions. Well, just generally on on templates of, yeah, I guess ah maybe maybe I'll put it this way, like walk us, for folks who haven't picked up the book yet, and obviously they should, and they will after this after this episode, but like walk us through the template section and and I guess why,
00:17:48
Speaker
I'm not really sure how to ask this question. i guess I guess it is an extension of what you mentioned earlier, like the advantages of R and then using taking all those advantages and sort of like packaging it together in a template.
00:17:59
Speaker
Yeah. So, and I assume now we're talking about, um, kind of our markdown or, or Cuarto those types of. time Yeah. Or even, you know, the the, I know you talked to, um, Aaron over at, at urban and a couple others, like even in the, in the GG plot themes, like why you see that as, as valuable. Yeah. Okay. So I think I'll actually start with the idea of, um, tables and then I'll, I'll move on to the GG plot and our markdown. So with, with, with R because it's a coding language.
00:18:29
Speaker
You can, if you've written code to make a table and say, you know, you have a style guide for your organization where the top row is always bold, 12 point, aerial font, you know, whatever whatever it is. You code that once, then you can turn it into what's called a function, which is just a kind of reusable piece of code.
00:18:51
Speaker
And you can then, you know, if I have like a table function called like table decay, anytime I want to make a table, I don't have to remember the 20 lines of code that I use to make the table. I just type table decay and it will format it in that way. And I know you talked about, you know, with Excel or PowerPoint, like the challenges making a template because, well, it might, you know, it might vary in different ways,
00:19:19
Speaker
With R, one cool thing you can do is when you make a function, you can add what are called arguments so that you can say, I don't know, say sometimes you want to ah use aerial for your header font, but other times you want to use some other font. Well, you can set the argument for you know header font equals And then you can you can change it. And so you can make a kind of basic template, but you also give a little bit of flexibility for those who want to change it. And it's the exact same thing with ggplot, which is the main ah way that folks make dataviz in R. There is called a theme. So there's a chapter in the book where I interview folks at the BBC who made a theme for dataviz.
00:20:07
Speaker
And their theme has you know arguments that you can use. So you know say sometimes you want to include ah you know your x and y grid lines. Sometimes you don't want them. You can add arguments to to give you that flexibility. And when it comes to kind of our markdown or quarto, which are tools for what's typically referred to in a kind of wonky way, it's reproducible research ah or reproducible reporting. What that means is just, it allows you to combine your text and code all in one document. And you use that then to kind of render your document to some usable format like Word or PDF or HTML. And I know I talked before about, you know, the typical workflow of going from like SPSS to Word.
00:21:01
Speaker
The way I typically talk about R Markdown or Quarto, which are really very similar tools, is with a typical workflow, say you'd work in SPSS, do your data analysis, then you spit out your kind of you clean data to Excel, you use that to make your charts, you copy your charts into Word, then you write your your report in Word, add your tables there as well. With R Markdown or Quarto, you're doing that entirely within R. And so where you want to make data vis, for example, you add the code that makes the the charts. Same thing with the tables. But then you also have that narrative text alongside it. And it's only when you hit, there's a ah button, a render button that allows you then to export to something. And that facilitates, um you know if you need to make a report every month, well, no problem. You just write your code.
00:21:57
Speaker
Every time you want to render it with your new data, you just hit that render button and it spits out a new report. Or in the case of of what the folks at Urban were doing, they're using a technique called parameterized reporting where, you know, say you want to make one report for each state in the US. Well, doing that manually is a ton of work, incredibly error-prone.
00:22:21
Speaker
they have created R Markdown um templates that they then use and write some additional code and iterate to say, okay, first make a state make a report for Alaska, then for Alabama, then Arizona all the way down through all the states. Yeah. So I want to come back to the Cordo R Markdown um language in a second. But for folks who are listening to this, who may be um earlier on in their R learning Yeah. Experience education. I would suspect that many of them sort of when they hear the words functions and building themes, that that's a little intimidating. Like should they feel intimidated about writing functions and writing themes or is it just a simple extension of the existing R language that that is something that really anybody can do in their learning of the tool?
00:23:10
Speaker
Yeah, I mean, it I would not recommend starting out when you're learning R by trying to create your own GG plot theme, definitely not. Yeah, yeah, right, yeah. But the cool thing is, because R is open source, so many other people have created themes. um And so ah you can rely on, you can you know use other people's themes. like ah I don't know why I would want to do this, but if I wanted to make plots in the Urban Institute ah style, I could use the theme package that you all have made to you know make plots in that style. I can make BBC style themes. So um I think for most people starting out for a long time, when you're learning R, you're relying on other people's you know code that they have written.
00:23:57
Speaker
And that's great because it makes it a lot easier. But eventually you get to a point where you're like, oh, I want to tweak this a little bit. And then you realize, wait, if I can you know write my own themes or my own functions, I can do this in a way that ah you know I can customize it to my exact needs.
00:24:14
Speaker
Yeah,
Comparing R Markdown and Quarto
00:24:15
Speaker
but it's also advantageous like take the urban theme or the BBC theme right like you could get that and then you could go under the hood and say you to your point you don't want to use the ladle font you like the urban color you like everything about urban except for the font right you can just change that one argument and then you have a whole new thing.
00:24:33
Speaker
Yeah, and that's how a lot of people do it. they' They'll, when when they're, for example, making their own theme, they don't necessarily start from scratch. They'll look at what other people have done and then adapt it based on that. So yeah, that's a great way to go. So I want to get back to the, the corridor and our markdown. So, um, it is interesting because I know like, um, our folks at urban are very much into corridor, but when I talk to other people in other organizations,
00:25:00
Speaker
I don't know why, maybe it's just a take up situation. Like I still know a lot of people who haven't even heard of quarter and they're still using our markdown, but I was wondering if you could talk about kind of the difference between the two and then what are the advantages of, I mean, I guess kind of either cause they're, you know, they have their own advantages, but like why folks should start to get into the knitting and the rendering of a markdown language. Yeah. So. Well, I'll give a little bit of the backstory, which is.
00:25:26
Speaker
I started writing this book before Cuarto had come out. So most of the book covers are marked down with a chapter that I added. um that kind of compares the two and talks about the major differences. I will say overall, the differences between R Markdown and Cuarto are very minor. It's about kind of um like where you place arguments in code chunks. I'm trying not to get too overly technical for folks who might not be R users, but I've worked with many people who have been longtime R Markdown users,
00:26:05
Speaker
who have you know looked into Cuarto and said, Oh, I want to do it. I don't know if it's going to be too much work. And I tell them, just, just try it. And they do. And it's, it's really not that different. So what I'd say in the book is the difference between the workflow that I described before, the kind of SBSS to Excel to word workflow, the difference between that and like an R Markdown workflow is huge. Like that's incredibly yeah different compared to that switching between R Markdown and Cuarto is, is negligible.
00:26:36
Speaker
Um, so in terms of differences, um, I don't know that there's a huge amount. I mean, I will say posit the company who is behind, um, both our markdown and quarter has said our markdown is not going away.
00:26:56
Speaker
But when it comes to kind of new features, they'll be focusing those on Cuarto. And so as an example, this has actually just come out and I haven't actually fully explored it, but they're releasing this, I don't know if it's a package or kind of approach using a file.
00:27:15
Speaker
a kind of brand dot.yaml file, which allows you to, and this is just working within Cuarto, not within R Markdown, allows you to define kind of brand specifications. So you define colors in, you know, brand colors in one place. You define, you know, you set your brand logos, say if you have like multiple versions of your logo, you can define those. And then you can use that with, throughout,
00:27:44
Speaker
quarto documents, as well as I think within um the ideas that I'll eventually be able to apply, you'll be able to apply that pretty easily to things like tables and ggplot themes. So that's not covered in the book, because that literally has just come out. But that's the kind of thing that I think you're likely to see in quarto that is not necessarily going to exist in R Markdown. But overall, I'd say if you're already using R Markdown,
00:28:14
Speaker
And it's working for you. I don't necessarily think you need to go running to switch, but if you are starting out fresh, I'd probably go with Carto at this point. And for folks who've never worked in a Markdown language, aside from having to learn the syntax and and all that, and my experience has been, it's more of a structure of sort of build it, like it's still the same R code, it's just built kind of in a different structure. yeah But from your perspective, like what are the advantages, the big advantages of going to either the mark, you know, either R Markdown or Cordo, rather than just working in um sort of the base, the base, like an R script file, yeah like an R script, or, our you know, in our, I guess, yeah, like an R script, yeah.
00:28:57
Speaker
Yeah. I mean, the big advantage is, um, with the whole kind of reproducible reporting workflow that it allows you to do your entire, uh, report, you know, you can set up all the text and all the, um, code, and then you can automatically generate reports. So like my, my party trick when I, um, I occasionally do like webinars where I'll show people, you know, here's the value of learning arm.
00:29:26
Speaker
is I will set up a survey. I'll do it on usually on Google sheets or sorry, on Google forms. Um, and then at the beginning of the webinar, I'll tell people, okay, go fill out the survey. I mean, it's like a four question survey just to give me a little bit of data. They'll fill it out. And then I have set up my document, either an R Markdown or Cuarto such that it will go out, grab that data from um Google sheets, because I piped the data from the Google form into a Google sheet, it'll grab it, automatically summarize it. I have text, for example, at the top of my document that says, you know, X number of people filled out the survey today. And that
00:30:11
Speaker
X can be automatically updated, you know, based on the data. And so that type of workflow is possible using R Markdown or Cuarto. Whereas if you're using just an R script file, you still have to do that manual, you know, say you make a plot, for example, in an R script file. Well, then you'd still have to copy that into Word, for example. Right. That's both less efficient and you know potentially error prone as well.
00:30:41
Speaker
Yeah. Yeah. It's, it is interesting to think about how the, these workflows can, can and maybe should change. Um, and also I find that the, the, the Cordo marked on pieces is easier to share a lot of things. Cause you just send an HTML file and you know, it's one thing you have everything sort of knitted together as opposed to like, as you said, like copying and pasting all these different places.
00:31:06
Speaker
Well, and I'll even do things where I'll create an HTML file and there are ways you can kind of set it up online um and you just give people the yeah URL and then anytime you have new data, you know, you just render it again, republish it and people just go to that same yeah URL to get the the yeah automatically up to date data.
00:31:25
Speaker
So on that point, so I'm sure people are thinking, OK, so where so where do you send it or where where are you? Are you putting it on your website as like an unpublished page or you know to your site and just sharing that that URL of folks? Yeah, so I use a tool called Netlify typically. um You have to. There are a number of ways to use it. um But i will I connect to GitHub. um ah So I connect Netlify to a GitHub repository and set it up. And I tell Netlify, hey, anytime there are changes in this GitHub repository, go and grab the latest version of the report and update it.
Conclusion and Holiday Wishes
00:32:05
Speaker
So then it's just Netlify creates a ah URL that I share with people. um So it's not it's like kind of like one-off URLs that I make. right And I can set passwords and that kind of thing as well, you know if you' obviously, if I don't want the data.
00:32:19
Speaker
available for anybody. Yeah, that's great. um you've You've talked about workflow a bunch in in our chat and I've heard you say, or maybe, I don't know if you wrote it or I heard in one of your talks, um but I've heard you say that R is a workflow tool that also happens to do some stats. And we've talked a lot about that today, but I was hoping maybe you could And I'll crystallize it for, for folks. Yeah. Well that's my, that's my perspective on our given that I'm, you know, an anthropologist who came to our who does not use our for much ah in the way of statistics.
00:32:54
Speaker
um And it was really, you know, like I mentioned before, I was really kind of insecure for a while about my R use feeling like, Oh, I'm not a real R user. Um, and it was only when I realized like how much it was improving my workflow that I was like, no, you know what, this is actually really valuable. And it comes back to things like, um, the reproducible reporting with R Markdown and quarto. parameterized reporting, especially um making multiple reports at one time. I mean, we we have one client who we work with who um he works for. a um it's It's called the sandio San Diego County Office of Education. They work with school districts throughout San Diego County in California.
00:33:41
Speaker
And he has to make reports for every single student. That's like 10,000 students. I mean, there's just no way you're going to do that, you know, in Excel, in Word. So R facilitates that. And I think it's a great tool to improve your workflow in so many ways I hadn't even really considered when I first started learning it.
00:34:04
Speaker
Right. OK, so the book is R for the Rest of Us. Folks should obviously get it. Wherever they get wherever you get your books, you should check it out. um If folks have questions for you, David, follow up. They want to learn more. Where can they find you? Sure. um So the website is just rfortherestofus.com. um If you want to learn more about the book, you can go to rfortherestofus.com slash book. And people should feel free to email me. Email me. I'm i'm i'm old school.
00:34:32
Speaker
Email's a ah good way to reach out to me. It's just david at r for the rest of us dot com. David, thanks so much for coming on the show. This was fun. I'm excited for R for the rest of us second edition. That'll go into all the Cordo stuff and all the other changes. So this is fun. Yeah, thanks so much for coming on the show. Thanks, John. I really appreciate you having me up.
00:35:02
Speaker
little break this holiday season. See if you can take a moment to rate or review the show on your favorite podcast provider. You can do the same thing on YouTube. You can subscribe to the show wherever you get it. And if you have a moment, rate, review my books on Amazon or Goodreads where you get them. Again, I'm just trying to share this information and sharing the learnings so that people can do a better job of using and communicating their data.
00:35:24
Speaker
So this wraps up the show for 2024. I hope you'll have a happy holiday season. And so until next time, until 2025, this is the policy of this podcast. Thanks so much for listening.