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George Mount | R for Excel Users - First Steps | RStudio Meetup

Abstract: Excel's built-in programming language has served as an entry point to coding for many. If you’re a data analyst steeped in Excel, chances are you could also benefit from learning R for projects of increased scope and complexity. This presentation serves as a hands-on introduction to R for Excel users: How R differs from Excel as an open source software tool How to translate common Excel concepts such as cells, ranges, and tables to R equivalents Example use cases that you can take and apply to your own work How to enhance Excel and Power BI with R By the end of this presentation, you will have a clear path forward for building repeatable processes, compelling visualizations, and robust data analyses in R. Speaker Bio: George Mount is the founder of Stringfest Analytics, a consulting firm specializing in analytics education and upskilling. He has worked with leading bootcamps, learning platforms and practice organizations to help individuals excel at analytics. George regularly blogs and speaks on data analysis, data education and workforce development. He is the author of Advancing into Analytics: From Excel to Python and R (O’Reilly). Link to George's white paper “Five things Excel users should know about R”https://stringfestanalytics.com/five-things-r-excel/ Working group sign-up for those interested! Within many organizations Microsoft Excel is a preferred tool for working with data for non data analytics users. In order to build a data driven organization, source data and analytical models must be accessible to all data users (technical and non-technical) within their preferred tool. Let’s rally the R community to welcome Excel users into our data driven culture by building an Excel add-on to access data and models available within RStudio. If you're interested in continuing this conversation and joining a working group, let us know! rstd.io/excel-r-community Links shared at the meetup! George's GitHub/ Presentation Resources: https://github.com/stringfestdata/rstudio-mar-2022 Packages? Where to find them & recommendations: CRAN Task Views: https://cran.r-project.org/web/views/ Mark shared: for folks who primarily use excel to present formatted tables, the `gt` package is a great way to start doing this programmatically in R: https://gt.rstudio.com/ Ivan shared: In addition to regular Google, I'd recommend https://rseek.org/, given that the character 'R' is sometimes not search friendly :) Jeff shared: Fpp2 is great for forecasting and time series analysis - https://otexts.com/fpp2/ Floris shared: https://otexts.com/fpp3/ Ivan shared: If you're into tidyverse, there's an equivalent for time-series: https://tidyverts.org/ George shared: https://dplyr.tidyverse.org/ Ryan shared: This can be a helpful package for dynamically editing tables, like in excel https://github.com/DillonHammill/DataEditR Ryan shared: This is a great package for making and learning ggplot visualizations: https://cran.r-project.org/web/packages/esquisse/vignettes/get-started.html Other resources: Monaly shared: There is a R help group: r-help@r-project.org George shared: Helpful book/site on statistics: https://moderndive.com/ Ryan shared:Harvard has a good online source (free options) that has a number of classes, the following for stats: https://www.edx.org/professional-certificate/harvardx-data-science George shared: R for Data Science free book: https://r4ds.had.co.nz/ Fernando shared: big book of R https://www.bigbookofr.com/index.html Floris shared: Advanced R Book: https://adv-r.hadley.nz/ Pedro shared: The R for Data Science Slack channel is a great learning resource! r4ds.io/join (we just made a channel there called #chat-excel_to_r Ivan shared: For teams who are deeply entrenched in Excel (like my old team), this tool may be useful - https://bert-toolkit.com/. It allows running R code in .xls, so you can learn R while doing .xls :) Re: Glossary of terms: Ivan shared: inner_join() is like VLOOKUP in .xls. Dan shared: Here's one cheat sheet (glossary of Excel to R) that I just found; https://paulvanderlaken.com/2018/07/31/transitioning-from-excel-to-r-dictionary-of-common-functions/ Extra Meetup Links Feedback: rstd.io/meetup-feedback Talk submission: rstd.io/meetup-speaker-form If you'd like to find out about upcoming events you can also add this calendar: rstd.io/community-events RStudio conference/submit a talk: https://www.rstudio.com/conference/ Recordings of all meetups: https://www.youtube.com/playlist?list=PL9HYL-VRX0oRKK9ByULWulAOO5jN70eXv

Mar 15, 2022
59 min

image: thumbnail.jpg

Transcript#

This transcript was generated automatically and may contain errors.

Thank you, Rachel. Thanks, everybody, for coming. As Rachel mentioned, lines are open at any time. We want to get your questions answered and they can be, as Rachel mentioned, anonymous or not.

So let me kind of switch gears here and get to my slides. I will share some links to get my resources and everything in a moment as well. But if you want to let me know at the beginning, again, we want to make this as conversational as possible. So let me know, what brought you here today? What have you tried? What's worked? What's not worked? I don't have all the answers on this, but we're all helping each other.

It's not the easiest thing to get a team who's used to using Excel to kind of think the art of the possible and using R and see how possible it is. I hope the presentation shows you how easy it is.

R vs Excel: is R really harder?

So just for a little cold open, I want to really get it out of your head that R is necessarily harder than Excel. I think that's the word on the street. You do have to code it, but you have to code a lot of things in Excel as well. So I'm not sure that that's the best thing to think about it as being necessarily harder. I've got a few other case studies like this, and then we're going to move into a short demo and then we'll go for questions.

I've also got a couple of books to give away. So make sure that you stick around and you may get a copy of my book, Advancing in Analytics. If you don't want a copy, you get a consolation prize. You can read it for free. I will give you a link to do that. So everybody's a winner with the book.

Okay. Let's say you want to visualize a bunch of variables. This is how I see you would do it. Now, again, I could be wrong with these examples where I'm showing Excel and R and how you do something. Let's say, you know, I have a bunch of variables in my data set and I want to see the relationships between all of them. Okay. This would be a lot of code in Excel, right? That's a lot.

This is literally three lines in R. Okay. And this is a pretty nice thing to have. You have data, you want to visualize it. That's not a bad idea. A lot easier to do in R.

So we're going to do a few more examples of that. But again, just to give you a sense of what we're going to do, really understanding how Excel and R fit together in the analytics stack. I'm not going to tell you to get rid of Excel. I'm not going to tell you that Excel sucks. I'm not going to spread any lies about Excel that often happen when people try to push other tools.

We're going to introduce R for you, kind of see how all these tools fit together. Hopefully you'll have fun. I hope you're telling that I'm having fun.

Okay. The first link is going to be here. Now, if you want to follow along, and I'll put this in the chat as well. This is going to have all the resources and everything. Now I'm aware that a lot of people may be coming to this for the first time in R, they don't have it installed, they can't get it installed, because they're at work, etc.

Just click this link, you can follow along and that will run, it'll take a few minutes, but you'll be able to run everything that we're doing on the cloud. And then when you read the book, you'll learn how to download it. And then we'll help you make the case to get it installed at work, hopefully.

Where R helps Excel users

So where's R going to help you? Now, these are some things and if you've used R before, if you're an Excel user, I just kind of boiled this down into a few categories of tasks that I find really helpful if you're an Excel user, where R can help. Now, the first thing I'm going to mention is Power Query. It's kind of the elephant in the room. Power Query is a data preparation, data transformation tool in Excel. And it can do a lot of the things that R can do, to be honest. It can build a reproducible workflow. It can work with big data.

But there's still things that are helpful with Excel. So like data visualizations we saw. You have a bunch of variables, you want to visualize them. You want to iterate on that. R is going to help you there. Anything I found that where you're working with groups, where you have some data and you want to go through group by group and do some kind of complex transformation, think about R for that. Again, window functions kind of related to that. Cumulative averages, lagging variables, leading variables.

And then anything having to do with randomization. Excel, I recently learned that Excel's random number generator is proprietary intentionally. They have not shared how the random numbers are actually randomly generated for a reason. And that has proprietary benefits. But if you're trying to make your workflow reproducible, that might not be a good thing if you want to seed your random data.

So again, just to make this really concrete, I want to create an index column by group. Now, again, if you have another way to do this in Excel, let me know. This is the way that I found. It's a lot of code. It can't even fit on the slide. R for four lines of code. And it's pretty readable too, right? We're grouping by something. We're going to take an index number that looks like it's based on the row. Then I'm going to print the head of it.

Creating a lag variable, another one. You actually have to create your own custom function to do this, as far as I know, in Power Query. In R, somebody's already made that function for you. That's another thing. We have packages in R. They're like apps. Somebody's built a package, and you can use it, and it's bundled the code. And then that means that all I have to do is say, oh, I want a lagged variable.

And you're seeing that these are all kind of the same pattern, right? I mean, the whole idea with what we're doing here in R is that these are really supposed to be predictable. And when you're going to do something, it should follow kind of the same paradigm. Whereas you're seeing in Excel, we've been all over the place already, right? Everything, nothing's really kind of syntactically matching. And that's really a big thing of R, specifically with the tidyverse we talk about. Everything's kind of meant to be interchangeable parts.

Everything, nothing's really kind of syntactically matching. And that's really a big thing of R, specifically with the tidyverse we talk about. Everything's kind of meant to be interchangeable parts.

So data profiling. Let's say you have missing data. Now, Power Query has some ways to do that for us, but it's not necessarily things that you can code. Again, I want to be able to check my data, check my metadata, be able to see what values are missing, how many columns I have. It really worked programmatically, not just with the data, but with the data about the data. And that's one thing that I find that Excel and a lot of the BI tools aren't really so great at.

Random data again. I mean, what the heck's going on here? I don't know, because I can just do this in one line with R. And this is nice, right? You have a big data set, you want to spot check, right? And this, obviously, if you're familiar with machine learning, there's a lot of randomization that goes on there. You got to split your data, all that stuff. Not only is this really, really hard, but you can't see this randomly so that you know when your colleague does the same simulation, they're going to get the same results.

This slide, I think, is good to really understand that learning R is an investment of your time. And you have a couple ways when you do something. You can hack it together manually, and sometimes needs must, and, right, you need to get something done by the end of the day, you can hack it together in Excel. But in the long run, you're going to save time, and you're going to win, right? You spend all this time, you're goofing around, you're running scripts, and it doesn't work, and your boss, your colleagues are thinking, what the heck? Why can't they just get the report done? But lo and behold, you get to that tipping point, and everything's automated, you don't have to do things manually anymore.

Live demo: baseball data analysis

So, for the next 15 or so minutes, maybe less, we're going to do, and I don't know how much of this we'll get through, but I just want to show you a little example of using R to conduct some data analysis. Okay. So, I'm going to call this data baseball, and I've split this into a few steps here. The idea is, let's say, is Major League Baseball, it's almost baseball season. I want to know, is there a relationship between a team's payroll and other variables, right? Can we use the team payroll to understand how much they win, how many people attend, right?

A few steps. So, we're going to import the data. We're going to do some basic data profiling. So, how many rows, how many columns, how many tables, et cetera. Then we'll analyze the data. Visualization, I kind of call the shortstop here. And generally with R, and that's, I think, important to understand if you're an Excel user, your data generally isn't going to live in R. You're probably going to get it from some other place, and that could be a database, or an API, or an Excel file, or wherever, right? And then you'll do your work in R, and then you're going to send it out.

So, the first thing to understand is we're going to use a lot of packages. This, think of these as like the apps of R. You get a smartphone. It's pretty cool. And it's got some basic utilities. You can make calls and schedule appointments and stuff like that, take notes, et cetera. But really, a lot of the power comes in the apps that you download from the app store that let you do whatever special use case, or help you do things a little more productively. So, that's going to be the same thing with R, right?

So, there's a lot of cool data that you can find to practice with in R, which is alone a real blessing. The tidyverse, I mentioned a time or two. So, this is really a package of packages. We're going to use that a lot to create visualizations, to model our data, to do all those data analysis tasks.

I do have in the book a brief tour of RStudio. I will also say that RStudio has a lot of tremendous learning resources. And really the idea of RStudio is everything you need to do your job in R is here. So, there's a reason there are so many panes and windows. But you'll get more familiar with it. I would definitely encourage you early and often to check the help window up here. There are some really great cheat sheets. There's also a help tab over here. You can learn more about different functions, packages, things like that.

So, I'm going to bring in my data. Now, what's really nice with R, and again, to be fair, someone mentioned Power Query 2, but there's never going to be a need to like randomly delete data or hide columns or any of that messy business that gets people in a lot of trouble. Everything's going to be programmatic. So, if I have a data set and I only need four columns, I'm not going to delete those other columns. That's not a good idea. You want to keep your source data intact, right?

So, let's see. I'm going to aggregate this data. Now, again, this is kind of like a pivot table. I have a salaries table. Then you see this funny little icon here. So, basically what I'm doing, think of this as a pipe where line by line by command, I'm cascading my output into the next input. So, I have salaries. I want to group that by your ID and team ID. So, imagine a pivot table and you're putting those in the rows sections, right? And then you need to aggregate it by something. So, these are kind of twin functions in a way.

And now what we can do is join them. So, again, if you're using Power Query, if you're using SQL, we have data from different tables based on those relationships. We want to put them into one place. Very easy to do here in R, specifically with the tidyverse. And I'm going to use an inner join, as you'll see. And I have a merged data set there.

Summary statistics, I'll also say R was really built for statistics. So, you even look at the official R, the R project for statistical computing. So, a lot of the things that you want to do related to statistics are going to be kind of built in. Now, often you can augment those things with, again, these packages, depending on what you're looking for.

I like this SCIM package. This is from our SCIM function. This is from a package called SCIM R. But again, you know, you could get some of these things in the data profiler in Power Query, but it's not necessarily going to be something that I can just keep in my script and run to it. There's going to be a lot of pointing and clicking. I can't necessarily easily share the output, right? So, in this case, I can see a lot of what I want to know about the data, and things like missing values, things like the distribution, everything.

I like this describe function. This is from a package called Cyc. This just gives you a little bit more meat to those descriptive statistics. So, if you want to know things like the range, the term to mean, all that, right? So, a lot of options. Usually, just like in Excel, there's almost always multiple ways to do something. It's probably going to be the case in R as well. So, you can get to know your favorite packages, share them, and help them. You can even support them because they're almost all going to be open source. They're taking contributors, so you can join that community, help with the documentation.

Data visualization in R

So, data visualization is a great one to know. I'm not going to get too much into the syntax here, but one thing that I really want you to see is that if you have like a histogram in Excel and you wanted to change it to, I don't know, a tree map or a bar chart or something, it would be kind of hard, I think. You have to change the chart type, and sometimes your axes get messed up. You want to switch the axes, and there's a lot of pointing and clicking. Generally, with what we're doing here in ggplot, and this is another tidyverse package, you just have to change a function, change an argument.

And that's another thing I try to really point out to Excel users. You've used functions a lot, right? You've used index. You've used VLOOKUP or XLOOKUP. You understand, okay, well, this argument is optional, and this one is required, and this has a default. That's the same kind of stuff. We're going to use a lot of functions in R. So, like I said in the description, you have a big head start.

What I wanted to show you is the output that you could get. Let's say you wanted to use R and do all the cool things you learned today, but you still have to put it into Excel because that's what people at work demand.

So we have a table. I wrote this from R. Now, if you needed to maybe, I don't know, make this formatted and add the currency and all that, you could do that in R. If you're a VBA person and you've worked with programmatically formatted columns and things like that, you could add these charts. Now, I put a chart from ggplot. You could build an Excel chart from R as well. Again, really anything you needed to do to build some Excel product, you could do it in R.

Q&A

If you want to summarize what you've learned today, this is going to be a white paper that I have. It's just a few pages just to learn about R as an Excel user. So, you can download that white paper from my website and we'll get that sent out to you quickly.

I see a few people have put questions into the chat, but just a reminder, you can use Slido as well to either ask anonymously or put your name. I think Gustavo asked a question.

One barrier I found was translating terms from Excel to R. For example, what's the equivalent of a data frame in Excel? It'd be nice to have a kind of like a dictionary or glossary of terms.

Yeah, it would be good to have that all summarized. I do, I mean, that's really a foundation of the book is to translate those structures and functions from Excel into R. And that's the way we learn things is to associate what we already know with things that we're learning now. So, it's cool that you, Gustavo, are thinking in that way. That's going to make things a lot quicker. However, I will say that the book does talk about that, right? What would a vector look like in Excel or vice versa? What do you do with a pivot table in R and so forth?

I see Mark here. Mark, you mentioned for people who primarily use Excel to present tables, the gt package really helps. Do you want to add anything there?

Yeah, sure. I was just going to say I think that there's a lot of use cases for using R that go beyond just data analysis, you know, just speeding things up and making your life easier in other areas, and gt is a good example of that.

Would you want to jump in and ask that live? Sure. One of the questions I had just as a novice playing around with R is that there's an enormous number of packages. I am struggling with finding which ones I need and how to look for a function if it's not in a package I've already got. It seems overwhelming. Somebody pointed me in the chat to the CRAN webpage, and that's got... I can see where somebody would find that helpful, but it's still too abstract. There's 20,000 packages.

Yeah, it's hard. Yeah. So that's my thought for the day. Is there, Mickey, any particular area that you're looking to do, a domain or a use case or something like that, that might be the first place to start? Mostly looking at financial applications, George.

Okay, cool. So R has a pretty sizable number of time series related things. Going back to RStudio, the help menu here. Check out these cheat sheets and get familiar with some of these. dplyr, ggplot2, right? So just learning the fundamentals, because there's definitely going to be a power curve. And even in my script, most of these, probably 80% of these functions are coming from the same, I don't know, two or three or four packages. And then there's like, oh, hey, I really like this function, and I'm going to call on this package.

Get really good at probably a handful of them. Now, the CRAN task queues are good, but they are maybe overwhelming, because there's so many of them. But yeah, get familiar with these basic ones, like the tidyverse, right? And that's actually more than one package. And then from there, I would say you can start to specialize in, and I'm sure RStudio has some resources for finance.

I see a lot of great links being shared in the chat too, and I can collect those together. But I feel you on that. Sometimes there's also so many different ways to do the same thing as well. And I think something that has been most helpful for me is when I'm actually googling, how do I do this? And I see the packages that other people were using for that too.

Yeah, I just wanted to add, I feel your pain. Our journey started in 2018 when I decided for my first New Year's resolution in my life, I was going to stop using Excel or Google Sheets, because I saw them as the same, and only do stuff in R. Fast forward four or five years later, I think I would look at it differently. Rather than trying to solve, find the technology to solve the problem, what's the problem you're trying to do? In R, I think you could think of it as broken out into different kinds of steps.

So there's always, you're going to use packages to bring in data. So that would be like a step, right? Get to know ReadR. Again, this comes from tidyverse. So ReadR, R-E-A-D-R. And then there's also ReadXL for Excel stuff. There's other things if you're connecting to databases. Like George was saying, dplyr is going to be your best friend. That's your toolbox. It has your wrenches, your hammers for cleaning up stuff. And then TidyR would be another one for cleaning.

And then depending on the analysis, right? So that's step one. Step two is like connect, doing clean. In the data world, we call that wrangling or munging, like cleaning up kind of stuff, your broom and your dustpan. And then there's the analysis kind of point. And like George said, ours got a ton of financial stuff in there, like lots. And then you have your visualization and then you have your output. So if you break it into like four or five sections, connect, cleaning, wrangling, analysis, output, then it's a little bit less daunting to go through 20,000 packages on CRAN.

One is Excel is reactive. So for example, output is automatically updated when input changes, while R is generally not except with Shiny. How do you explain this to Excel users?

Yeah. When you have R and you're storing, the way I generally demonstrate it is that you in Excel, like was mentioned, you make one change and everything triggers. So I'll just open up a spreadsheet and make one reference point to another, point to another, and then change the first one and you see everything trickle down. And I think everybody has a horror story of that going wrong. Whereas in R, I'll generally just store a couple of items as objects and pretend that we're putting each of these into a box and we're naming this box, my object or my string or something like that. So this object is in your computer's memory now. It's tucked away. It's a box that nobody's going to open unless you tell your computer to, and they're all kind of separate entities now.

So that was a good question. I have some of those examples. I think I have some box illustrations in the book as well. But yeah, that is a key kind of mental model milestone for getting into this.

There's another anonymous question that says, I would like to ask a hypothetical question. Can a person upscale in both R and Python simultaneously?

Oh, yeah. So the big one. Okay. So the book talks about from Excel to Python and R. Although you will, if you read it, R is the second section right after Excel, because in my opinion, and some people think differently, but I think R is a little easier for Excel people to get into because, like I said, it was really built for statistics and data analysis. Everything in Excel is built around rows and columns, right? That you open it up and there are rows and columns that you see. And R is kind of built the same way with that data frame that's been talked about a couple of times. Python, not necessarily so much was built around the idea of rows and columns.

So I think that R is probably easier for Excel people. Now, as far as the question about learning both, I would, again, suggest depending on what you want, depending on your time, individual results may vary, as I always like to say. But this idea of the mental cross training, right? And if you're going to learn to do something in R, try to see if you can refactor it into Python or vice versa. Doing something in Excel, try to do it in Python or R. So I think that kind of interdisciplinary, if you will, approach is great.

Any recommendations on resources that teach stats using R as the tool?

Oh, okay. I like the Modern Dive book. It's a nice introduction that's pretty compatible with a lot of what we've covered today with R. I do a little bit of stats teaching in the book. But we start with Excel, just because people are familiar with that, and then reproduce those. So if anybody else has favorite resources for learning stats, you're welcome to let us know. But I think that Modern Dive is a pretty good start for that.

And I know a few people had asked me about the recording, if you joined a bit later, and yes, the recording will be shared to the RStudio YouTube, and I'll put it on the meetup page and LinkedIn and Twitter, or wherever you found the meetup too.

But just to go over to a few other questions from Slido. I see Gustavo, you had a question about building the logic behind the code. Would you want to jump in and add some context to that question?

Yeah, I mean, I think sometimes, you know, so I'm sort of at the beginning of my learning path with switching to R. And I don't know if this is my lack of, you know, having strong logic, even though I think I'm a logical person. But, you know, you have to kind of have, when you're building the code, you have to know what comes first and what comes after. I think we're, for better or worse, we're taught to, to kind of react. I think somebody said that earlier in Excel, you just kind of do things and you see and then you hide things and you delete things. But you're not building a sequence that you, you know, like you do in coding.

Okay. Yeah. So I think it was Ryan was talking about trying to break it into discrete steps is helpful. If you're an Excel user, then getting familiar with Power Query and seeing each of your steps, again, transformed into discrete things, right? That even the idea of extract, transform, and load, right? Three distinct steps. We're getting data from somewhere, we're transforming it, and then we're loading it somewhere else. There's always going to be iteration.

Community and next steps

I'm curious to hear from all of you where you are in your learning journey, or also if you're starting to maybe help teach other people across your team, what has worked best for you all too.

Good point, Ryan. I see working on a real work problem can be really helpful too. What I've found, like looking back on my own thoughts and then also like this past year where I've been teaching people in our organization is, no offense to anyone with what I'm about to say, most our journeys seem to be very linear. Start here, end here, and you'll be perfect. And that's just how it works. And everyone has 10 hours a day to learn that, like we did in high school and undergrad and stuff like that. Whereas in the workforce, you're trying to solve a little tiny problem and it seems ginormous.

So breaking them down into real problems. And it could be like, I just want to know how to open R, read in a piece of data, right? Breaking them down into small little things that you're trying to do and not trying to have a goal of, I'm going to be a superstar, or I'm going to wear a cape that has a big R on the back of it. There are some of those people in the world, and I think we know those names, but do we all profess to want to be that? I think it's being able to be really comfortable and efficient within your own pieces.

So in work, like what are you trying to do? Well, I'm trying to bring in this data and like change the name so that they're all capitals, or I want to calculate the average, the mean, the max, or I want to see our relationship, those little pieces and breaking them into modular little goals is really helpful. And for me, when I'm teaching, I try to not use any jargon, any actual names of stuff, because that can be a barrier until people's confidence comes up. And then with confidence, they get the capability and then they're okay to use terms.

And for me, when I'm teaching, I try to not use any jargon, any actual names of stuff, because that can be a barrier until people's confidence comes up. And then with confidence, they get the capability and then they're okay to use terms.

That's really helpful, Ryan. And also just thinking about, like, what is that first big win for you or like a project that you maybe can save a bunch of time once you actually get to doing it in R or automating something?

In most of my roles, typically I'd be doing all this ETL work in SQL and then pushing it down to Excel. So in that environment, as a self-taught person, you figure out how to do something. And it's important to build up a bunch of little chunks like that that you can remember, oh yes, one time I had to do this and it was in this job stream. And then I can go there, get and say, aha, I remember now. So building up a bunch of little chunks of stuff that you know work and you can remember where you put them to go back and refer to again will speed the process.

Definitely. That's a really good point. Like just being able to reuse your own code and being able to find it is very important too. I see someone else said in to the chat, any regular reporting, especially if the reporting requires copying paste.

I'm going through that right now with making, trying to make slides with R so that I'm not copying and pasting into Google Slides so that every time the data updates, I can just automatically—

Yeah, good call out. You can make a slideshow with R and the tools that we're using today. There's always going to be a trade-off. If you just have to do things once, maybe the sloppy way is okay. Right? It's okay to make PowerPoints depending on what you're doing. If you have to copy and paste, like somebody mentioned, then yeah, think about automating it. But, you know, sometimes I think we as tech people want to just automate everything. And that's a good instinct to have, but it can backfire at times.

Excel is the bane of my existence and everyone's existence. It's actually where I learned to code. Like you said, George, right? Like you learn functions and that kind of stuff. It got me in that path. I think the superhero in me wants to turn everyone into an R user. But I do know a little bit of the reality that not every single user in my organization, current or potentially future, will probably share the same sentiment that I do. So thinking through it and like in other places, what's the goal? And in our organization, in our hope, the goal is to turn people into data users and using data to make informed decision making.

So the likelihood of removing Excel is probably 20 plus years away, like realistically in my organization and maybe even more in others. So at another place at the City of Toronto, we took that on and was like, well, let's have people go where they're comfortable. I would love to make and have something that allows people to gain the power of R with inside of Excel. Realistically, what could that be? We've been playing with it and creating APIs and hosting them and connect so that people can pull data right into Excel.

So my thought was, is there a chance for desire or a thought that people would love to collaboratively work on building an add in that connects up where people connect to data sources for their organization and pull it in. Love to chat about it. These are some of the thoughts to really push R and help out that transition to a data organization and maybe another step for users to jump into the R world.

Definitely and curious to hear other people's thoughts too. Because I know also Ryan, when we talked about it, I mentioned, doesn't have to be someone who uses R every day. If you use Excel every day, we want to hear your perspectives to around what would be useful.

So I did just put a link into the chat right now. If you'd want to be part of that working group, you can just put your email and name in there. And we can schedule a smaller zoom meeting to just chat about it too. And see what what works best.

Love to see the people making connections, wanting to start a group, all that stuff. So that's great to see the community starting already within an hour. I did want to do, I picked another winner. So Michelle C., congratulations. You won a copy of Advancing into Analytics.

If you did win a copy, don't fear because you can still read the book. You can learn a lot more about the book, the chapters, where to get it and so forth at this link. Amazon, wherever you buy a book, you can read it from the publisher's website. You'll find a link to read it for free. So happy reading. And if you could, please leave a review, right? Because that lets people know of its quality. It's again, the community helping each other. So all I can ask for, for having attended today is if you choose to read the book, leave a review.

And I'm happy to answer any more questions. You can find me on LinkedIn if you'd like. I've got the website as well. You can check that out where the book is. And yeah, thanks. Thanks again, everybody. I really, really appreciate everybody's time and interest in the topic.

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