R for the Rest of Us Podcast Episode 20: Christine Parker

In this episode, I talk with Christine Parker, the Senior GIS Analyst on the Community Broadband Networks team at the Institute for Local Self Reliance. Christine shares how she used R to clean, combine, and summarize data for a dashboard tracking enrollment in the Affordable Connectivity Program (ACP), a COVID-era initiative to help people access affordable internet. The dashboard gained wide attention. It was shared in advocacy circles, referenced in Congress, and discussed with the White House. Christine highlights R's value for performing repeatable data tasks, particularly with regularly updated datasets, and its advantages compared to manual Excel processes.

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You can also watch the conversation on YouTube.

Important resources mentioned:

• The ACP dashboard 

• Code that Christine used to create the dashboard

Connect with Christine on X (@birdwrangler) and LinkedIn (Christine Parker, PhD)

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Transcript

[00:00:00] David Keyes: I'm delighted to be joined today by Christine Parker. Christine is the solo data wrangler and map crafter for the community broadband networks team at the Institute for Local Self Reliance. Her work guides and supports the team's education and advocacy work related to internet access and digital equity across the United States. Christine uses R regularly for data wrangling tasks and creating geographic datasets that she can visualize in maps. Christine, welcome and thanks for joining me.

[00:00:56] Christine Parker: Hi, nice to be here.

[00:00:58] David Keyes: So you and I met because we were in speaker training for the posit::conf(2024 Um, and your talk was particularly interesting to me because it was about how you used R to develop, um, or as part of the development process for a map that showed access to, um, the affordable connectivity program. And we'll talk more about that.

In a minute, I'll mention thatit was a pandemic era it was a program to help people get access to affordable internet, you know, at a time when obviously everybody was working and going to school from home. Um, so before we get into that program and kind of how it the R work that you did fit into it, I'm curious if you can just give me kind of a brief sketch of your background and specifically kind of how you got into R.

[00:01:56] Christine Parker: Sure. Um, so I went to grad school, um, at the University of Illinois at Urbana-Champaign And there I wound up getting both my master's and PhD in natural resource and environmental science. Um, and I specifically focused on bird behavior. Uh, so I've had a major context change since then. Um, during my PhD, I really got into using R in more advanced ways, but initially, um, during my, kind of my master's years, um, any kind of stats courses that were available were only teaching SAS, but there were other students that were using R and it became like a, a sort of, um, peer pressure, like, these are the cool kids using R, kind of situation.

Like, I could hear them commiserating about their struggles with learning and using R, and I was like, well, I want to be a part of that club. It also seemed to make more sense, because it's an open source, and it's free to use, and looking forward into, you know, career stuff, it made sense to, you know, to work with something that I could use on my own and practice outside of academia.

[00:03:09] David Keyes: Yeah.

[00:03:10] Christine Parker: and then, yeah, in my PhD work, I was studying wild turkey behavior, and we put little, um, GPS backpacks on wild turkeys, and they collected, it collected,

like, Fitbit data, essentially.

[00:03:23] David Keyes: research. I'm just imagining little turkeys walking around with backpacks on.

Sorry, can you

[00:03:28] Christine Parker: I know.

[00:03:29] David Keyes: I'll let you continue, but can you just, is that actually what happened?

[00:03:35] Christine Parker: Uh, kind of, it's not like what you would consider a standard backpack, but it's like a little black sensor that's, um, I don't know, like, Not bigger than a standard cell phone at this point in

time. Um, and it has, uh, like this elastic cording that goes, it straps over their wings like a backpack would on your arms.

Um, and it holds it on their back. And so that is communicating with satellites and we're able, it collects, um, location data every two hours. And then it's also continuously collecting, um, movement data. Uh, information. And so it generates huge datasets and, um, yeah, so it became really useful for me to understand, get into the like spatial data side of, of working with R.

Yeah.

[00:04:25] David Keyes: Interesting. Okay, so sorry, I interrupted you.

[00:04:28] Christine Parker: Sorry, that was a big

[00:04:29] David Keyes: by turkey backpacks, so please continue. So

[00:04:33] Christine Parker: Um, so yeah, uh, you know, I was influenced by these other students using R and because there were no courses on it, it was largely self taught and like meeting with other students about like, when I found out, you know, this person was using this package. So they're the quasi expert. And then I also found out about the swirl package, um, which I found immensely helpful.

It's like an interactive teaching package built in that you can get for R. Um, and it, it walks you through like kind of the basics. Really basic stuff, but they have kind of more complicated advanced packages now and they're really helpful. Um, so I always recommend that for new folks, um, like when I've interns um, but yeah, and then over time I just kind of progressed and I learned new things and, um, here I am today.

[00:05:25] David Keyes: how did you move then from, um, studying wild turkeys to working at the Institute for Local Self Reliance, doing, you know, internet access work?

[00:05:39] Christine Parker: So, um, I mentioned that the turkeys had these GPS units on it and it was, uh, a massive, like, spatial geographic data set. And working with those kind of data and creating maps, um, was really interesting to me and, um, a lot of fun. And when I started, when I finished my PhD and was, like, looking at the job market, I started to consider that focusing on that side of my skill set.

Um, would be probably a better path forward career wise, um, than being kind of fixed in my, like, ecology

context. And so I wasn't, like, closed off to ecology, but, um, I really wanted to kind of stay on this, like, map and GIS spatial data wrangling track.

[00:06:27] David Keyes: And So you moved to the Institute for Local Self Reliance. Can you talk to me about, generally speaking, what your job there entails and how R fits into it?

[00:06:39] Christine Parker: Sure. Um, so at the Institute, uh, like I mentioned, I'm in the Community Broadband Networks Initiative. And so we focus on advocating for locally owned and accountable internet networks, which also, and also, um, like advocating for affordable and reliable internet access. And so, um, initially, uh, when I started there, I was.

most frequently working with this massive data set that's managed by the FCC or the Federal Communications Commission. And this data set is now known as the Broadband Data Collection. At the time when I started, it was, um, the data were aggregated at a census block level, which is a really small geography that the Census Bureau uses.

And R could handle that. Um, I could pull in the whole data set for the whole country. By the way, rewind, um, this data set is, uh, represents, uh, claims of where any internet service provider could, um, offer a particular kind of internet service. And so there's all kinds of information about, like, the type of service and what technology is used, um, but, They have to update this twice a year.

And so it's at the time it was like a 20 gig size data set and R could handle the whole thing. It would really bog

down. Um, but I was able to do everything I needed to. And then, um, a couple of years ago they made this wonderful change, um, and converted, uh, The data set into a location level data set, which is great because it's much more granular and you can really get some more nuanced insights about connectivity across the country, but it's massive now.

And R is like,

no, thank you. Uh, and, uh, Uh, so I have, I've been using R a little bit less lately, only because, um, I do a lot of the bigger wrangling. I have a Postgresql database that I kind of keep everything in and do some overarching, like, wrangling there and then, you know. Bring it into R. Um, and so I, I use R as like kind of the fine tuning and creating like, uh, tables and plots and things like that.

Um, but I'm, you know, after being at the Posit conference, I learned about DuckDB and I'm really interested to, um, use, in using the wrapper that they have for R. Unfortunately, I guess they don't have a Windows binary yet, so it, It's not working for me yet, but, uh, I look forward to that day.

[00:09:21] David Keyes: Yeah. And so one thing that you've worked on in your time at the Institute for Local Self Reliance is this Affordable Connectivity Program. Can you describe its origins, and kind of how your work at the Institute for Local Self Reliance, like, how that began, how you began to, to do work on that program.

[00:09:45] Christine Parker: Yeah, so when the program came about, um, it was, it had a bank account of like 14 billion dollars, um, but it had no timeline, no one knew when it was meant to end, um, there were no like checkpoints along the way, um, we just knew there was this funding and there was like eligibility, uh, Uh, characteristics, um, so a household could be eligible based on their income and a bunch of other ways.

Um, but there, there was no good information other than that, really. And I started, you know, started to hear a lot of questions from my team, and then we started to get questions externally about the program. And because it's a federal program, there is, uh, Uh, a website, um, where you can get access to all of the, um, enrollment and claims data.

So, uh, the providers would sit, submit claims to the FCC based on, like, how many customers they have using this program and, and then, uh, The FCC has a company that manages all this data and puts it on this website, and it is in many different formats. Much to my dismay. And that's ultimately what gave me the idea to create a dashboard, because there were many different geographies of data available, and there were different timelines for when these datasets were updated.

And it was just really confusing to try and explain to anyone, like, what actually any of this meant, without just summarizing it and creating some sort of a resource for folks to use that was more straightforward. Because pointing folks to this, like, oh, you could, you know, look up your zip code and this CSV file, maybe, um, is not really particularly helpful.

And, the advocacy folks that we work with, especially those that are, you know, out in communities and helping people get signed up and dedicating their time and money on that kind of a effort, um, don't have the time to go and track down, like, what enrollment looks

like in their city because, It it would just be a real hassle.

[00:12:04] David Keyes: And so is the, the work that you did, was that about showing what programs are available for people who wanted to, take advantage of them? Or was it showing how many people are taking advantage of the programs? Or possibly both,

[00:12:22] Christine Parker: Uh, yeah. So we wanted to highlight, um, where enrollment was occurring, um, and also like the actual rate, um, And the actual, like, eligibility, uh, was an important factor in all of this, and there ended up being kind of some, like, different variations of how you calculated that, depending on who you talk to.

Um, and, and it became really important to be able to demonstrate, like, you know, because you could look at enrollment across the country, but without knowing, like, how many folks were actually eligible, it's not a terribly helpful

[00:12:59] David Keyes: number true. And so this dashboard So it sounds like you Are you the one, or other folks at your organization had the idea to make this dashboard?

[00:13:09] Christine Parker: Yeah, it was kind of a collaborative effort to, to go forward with that, um, and the dashboard, I think it had been kind of a tentative suggestion at the time because we really hadn't done anything quite like that yet. So, um, it was exciting to see it actually,

[00:13:27] David Keyes: Yeah,

[00:13:28] Christine Parker: uh, blow up like this.

[00:13:30] David Keyes: yeah, and well, not to cut to the chase, but in the end it got, you know, a ton of views and you ended up, we'll come back to this, but you ended up speaking to folks at the White House about the,

you know, the work that you had done. Yeah, so can you talk just at a high level about the role that R played? Because I know the dashboard itself, the presentation layer was actually done with Tableau, but talk about the role that R played before getting to Tableau. Sure.

[00:14:03] Christine Parker: Yeah, so R was, um, really helpful in a couple of big ways. One was, um, taking all these different data sources and combining them, cleaning them, rearranging them, and, and then, you know, ultimately summarizing them into the different, uh, like, values or elements that were displayed in that dashboard. And the other way, um, Was pulling data from the Census Bureau because rather than like going to their website and then also navigating another federal website, um, there's a great package called tidy census that allows you to get access to those data in a really pretty straightforward and easy way.

So those are kind of the big, big lifts, um, that R helped me with on this project.

[00:14:51] David Keyes: Yeah, and it seems like, you know, having, you know, Looked at the code, which we'll have you show in just a moment. A big part of this was being able to combine multiple data sources, um, you know, like you said, bringing in data from the Census Bureau alongside other data and having it all in one place. Um, it seems like that was a major benefit of doing all the data wrangling in R. Um, well, do you want to put your screen up and just kind of, um, walk me through a bit of the code?

um, okay. So we're now looking at the code. You wrote this, um, I can tell that obviously this is in our markdown document. Just out of curiosity, before we dive into the code, what led you to do it in an R Markdown document versus like an R Script file or any other format?

[00:15:48] Christine Parker: Um, uh, that's a good question. I think originally, actually, um, it was in a script.

And then at some point along this journey, I learned about R Markdown documents. And so I, and, I ended up learning how to use that format, and it made a lot of sense to have the code chunked out. So that helped it be more organized, but it's a long stretch from being a perfect setup in any way. But yeah, that was kind of how it came about this way.

[00:16:25] David Keyes: Yeah, and I know different people have different approaches. Like some people I don't love doing like data wrangling in, you know, either R Markdown or quarto. Some people are very much like I only do it in R script files. Um, so I'm always just curious. I don't think there's a right or wrong way. I'm always just curious why people make the choices that they do.

Um, could I actually have you go to the very top of it before we get to this?

Um, and again, if you just want to walk through it like super, super high level, like, you know, first we're like bringing in this data and we don't have to go through all of it because I know it's a very long script file, but just, um, yeah, some of the main highlights would be good.

[00:17:10] Christine Parker: Okay, uh, so in this first chunk, uh, loading my libraries, as one does, um, and some like option y settings, um, This chunk here, this one, I, I don't actually run every time I run the script.

Um, this is pulling in, uh, microdata from the census downloaded. Um, it's incredibly large and I, I think there is a way to do this through tidy census, but I haven't experimented with it yet. So, uh, admittedly, I, I went about it this way. Um, But that's

[00:17:48] David Keyes: a good example, like you're bringing in data, in this case that you have locally, which you're going to combine with data that you're going to get from the Census Bureau.

Yep,

[00:17:58] Christine Parker: yep, exactly. And so this is, you know, picking out specific variables from, uh, that data set that I was really interested in. So it's like household level data, person level response data, and just picking out these, like, specific variables here that I really, uh, was interested in because these were ultimately how we could define who was eligible, which households were eligible for this program based on these other data sources.

So that's generally what this, this section is, and then it's, uh, summarizing this at a state level to, uh, help us create an estimate at the state level about, um, well, this was like an adjustment factor thing, which was like a whole separate, uh, part of this class. Eligibility calculation that I don't remember enough of at this moment to go into.

 No problem.

Let's see. Uh, and so in this next section we're loading in, um, all the other data from like the a CP data tracker, which was the fccs, uh, website. Um, a

[00:19:13] David Keyes: CP is affordability Connectivity program.

[00:19:15] Christine Parker: Yes. Yep. Um, that is the, the short version of it, um, or the acronym. And I had this spreadsheet, uh. There was a another person I was collaborating with this on in terms of like collecting the data and so we started to put some of the like web table data into a Spreadsheet that we both maintained and so this is where I'd pull those things in Because they were they had web tables on a couple different pages, um, or pages on the site.

Um, what else? What else? Um, we had crosswalk. I don't know if you've ever heard of this before, um, but this was a new thing to me, um, where it allows you to convert one geography to another, um, and or convert geographies from one time period to another, um, so pages. Uh, we had to do some of that, um, at one point because they share the data.

Initially, they were only sharing it in zip code and county, um, but zip code doesn't really, um, Uh, play well with most other geographies. I mean, I

[00:20:28] David Keyes: know not everybody listening to this will be a super GIS nerd, but for those who are, I'm sure everybody right now is kind of rolling their eyes, knowing that everyone, non GIS people assume, oh yeah, you can easily map that.

You know, zip code to county, no problem, and it doesn't work like that.

[00:20:45] Christine Parker: Yeah, it's like very, you know, different data types trying to match them to be the same thing. They, they don't. Exactly.

Let's see, uh, yeah, pulling in the enrollment data for zip codes by counties, um, the claims, the different claims spreadsheets, uh, yeah, there was one for every year, um, and then, yeah, and then I ended up making, like, a, uh, compiled file that we shared on Git so other folks would not have to go through this, uh, annoying process.

Yeah. So up

[00:21:29] David Keyes: to this point, it seems like you've worked with, all the files you've worked with are files that you have locally. I mean, you obviously get them from other places, but then you bring them in locally, load them into R, and do, you know, some basic data wrangling on them.

[00:21:45] Christine Parker: Yeah, I found, um, Cause I had in, in some other previous projects, I had, uh, figured out how to like automate some downloads and stuff through R, but then, um, links would break, uh, during like the, the, their end of the updates and stuff.

So it just seemed a little more, uh, safe and less hassle for me to worry about that every time.

[00:22:10] David Keyes: Sure. That makes sense.

[00:22:12] Christine Parker: Um, and, Let's see, I know this section was, this was part of like a specific data request that I had, uh, because over time folks learned that we had all this data like put together and organized and and the eligibility estimates created.

So, um, I also started to get requests for those things, um, so people could do their own kind of work with it. Uh, and also putting These were some, like, little helper tables for myself that I pulled in that I'd created outside of R. I'm probably offending some very dedicated R users in the world by looking at all of this.

[00:23:03] David Keyes: Well, one thing I wanted to mention, so like on line 188, that's the first place I see, I know that function zcta is from Tidysensa, or no, sorry, that's from the Tigers package. Yep. So you're Bringing in, um, data on ZCTAs, which for normal people, basically zip codes, right? And then you, which I assume you're going to use later for some kind of mapping.

Well, I know you're not doing the mapping, some kind of analysis purposes. Is that, is that right?

[00:23:33] Christine Parker: Um, I actually did use it for mapping. Uh, I can use it for both, yeah. Um, so yeah, loading it in here, I can then join it with my finalized zip code analysis, um, later on, and then I can directly write it to my spatial database in ArcGIS.

Um, so it's a kind of a combination, yeah.

[00:23:54] David Keyes: Got it.

[00:23:56] Christine Parker: Um, and this was like, this little section here was creating some, um, taking the, like, all that data I just downloaded, basically, and creating some national level statistics, um, just, like, really basic, like, summary info about, um, enrollment and kind of, like, breakdown by tribal or non tribal lands, um, and, um, And it also added in a, like, month count, um, which was helpful.

Ultimately, I have a prediction model towards the very end of the script, which, um, wildly, kind of, to me, uh, worked. I used just a simple linear model to predict when the fund would run out because that was like the big overarching question everyone wanted to know. And we were actually right. We used, uh, just like the observed enrollment data and used that to predict when it would use up all 14 billion and it was the spring of this year.

And

[00:25:01] David Keyes: interesting.

[00:25:03] Christine Parker: I mean, I feel like that's a not the greatest thing to be like right about but sure I was like, yes It was right But

[00:25:11] David Keyes: the funding ran out

[00:25:14] Christine Parker: Yeah

[00:25:15] David Keyes: Cool. Well, I know you're almost to the chunk that we talked about before about having you actually kind of like show what it does So do you want to?

In the next chunk just kind of walk us through What that looks like?

[00:25:28] Christine Parker: Alright, so this one gets a little bit more, um, into the, like, nitty gritty of determining eligibility for a given household, and in this chunk, we're focused at the state level. Um, and so, uh, In this little bit here, I've split up the United States, um, into generally, like, three regions.

We have the lower 48, uh, which is these two, first two, uh, variables, and then Alaska is the next two, and then Hawaii. Uh, and the point of doing that is because the federal poverty level, which was a big factor in determining eligibility, varies greatly between those regions. Sure. Alaska and Hawaii have their own separate values, and I wanted our estimate to be able to vary based on which state we were focused on.

And so I pulled these values out of, uh, and I thankfully at some point pasted this link in here after losing it many times, uh, and that provided, uh, has like a pdf of the different, uh, poverty levels based on the number of people in the household. And so that This value for each of these, the P1, is the one person household value, and then the second, P2, is the value for each additional person added in a household.

Um, and so these next three sections here are all pretty similar. This is, um, getting into using tidy census. Um, I'm pulling data from the American Community Survey, their five year survey. Um, uh, At the state level and focused on the, this is the population table, um, from this, the time was most recent year they had, um, and we're downloading it without the geography, because for this part, we don't need the spatial attributes to come in.

Um, and so we're pulling out, um, average household income, um, levels, uh, number of households and household size, um, again, at the state level. And then, uh, down below here is where we start to combine all that to, um, get an understanding of how many households are actually eligible. Um, so, let's see, uh, we're joining this income, income levels, uh, data set with a table that was earlier.

These are actually, um, I can show this quick. This was like a little helper table I made for myself because the, the variables in tidy census are just, they're coming from the Census Bureau and they're just numeric alpha codes that are like, you know, not intuitive to understand. And so I just wanted to like, uh, set up this table because it establishes like what the maximum income level is within these, each of these ranges, because they're representing ranges ultimately.

Um, and I wanted to set that max threshold. And, uh, so going through here, let's see, because I don't want to take up too much time on not important stuff. Um, so here's where we get into, so we've done the join up here, and then, We're using that region, um, uh, definition, or returning to the region definition here is where we're actually setting it as a new variable, um, based on a geoid that is in this income table.

And if it is 2, then it set it as Alaska. If it's 15, then set the region as Hawaii. Otherwise, it's, I call this lower 48. And then we're bringing in the household size data, um, and for cases where we're saying, for cases where, um, there's an NA and no value, um, we're using an average, uh, size that we pulled.

Um. Um. For that point in time, and let's see, and then this is where we start to apply that like more state, nuanced state level, um, federal poverty level. So, in this calculation we're saying, um, this is that like one person household size, and then multiply this second person or additional person factor by the household size minus the first person.

So it ultimately calculates that for each state based on, um, which region they're, uh, grouped in. Then we create this, like, more generalized FPL field which takes all that information and, um, regardless of which region they're in. And we can then get rid of those specific region ones and then compare the FPL value that we've gotten to that max income field that we got from this table before.

So in cases where the FPL, that poverty level, is greater than or equal to this value, then we're saying, um, yes. This, this record includes households that could be eligible. Um, and then we do this for a few more steps. We're getting rid of this variable cause this was like an overarching, this is not one we wanted to include.

And just kind of like filtering down to, to get to the point where we just have eligible household records. Um, and then we finally summarize it and we should have in theory an estimate for each state. Of course it doesn't. I forgot I didn't run these ones before we got on.

Alright, so we're looking for income state 2. And there we go.

[00:31:49] David Keyes: And so that is the hhqualify variable represents what exactly? This

[00:31:56] Christine Parker: is telling us the number of households in a given state that are eligible. Uh, in this case, just based on income level.

[00:32:05] David Keyes: Got it. Okay. That makes sense. Cool. Yeah. I think that's actually a good place to stop because I think that what that shows is like how, you know, you're not doing anything mind blowing here.

I mean, no offense, but it's like nuts and bolts. You know, daily, I mean, the functions, you know, select, mutate, summarize, like, it's the kind of thing that, that all of us have in our code. And I think it's a good example of how, um, even I say, even with that, you know, more kind of like basic stuff, you can really have, um, a big impact.

So let's actually have you at this point, maybe stop sharing your

 screen Because I want to ask you some more sort of general questions. Um, so what's the value in doing this in R versus, you know, I imagine an Excel user could look at that and be like, well, I could do that same thing in Excel. Why, why do it in R? What, what did that get you?

[00:33:05] Christine Parker: Uh, so, and I have seen people work with these data in Excel and my justification for doing this in R, even so sometimes it would take me a little longer to like troubleshoot code and things like that is that it's repeatable. you know, I'm not altering the data necessarily in any way. I'm just bringing them in and then, you know, I edit the code and the script, but I'm not actually altering the, the numbers and the cells as I'm going along.

And I'm, I'm not a, Proficient Excel users. So

maybe there are like more nuanced ways to go about it these days. But, um, I, I find it ultimately it's much more, much easier to repeat running the script than having to go in and like manually type in. And edit cell level things,

[00:33:53] David Keyes: Because you also did tell me that you had to record. Rerun this regularly, right? Like it wasn't just a one time thing, correct?

[00:34:01] Christine Parker: right. Yeah. So they were updating, um, some datasets monthly. Um, the national data were updated each week. Um, so our kind of happy medium was to do it like twice a

month.

[00:34:13] David Keyes: Okay. Yeah. And so if you're having to do this every other week, you don't want to have to do that manually every time having your R

code allows you to just rerun that and have it, um, you know, give. the the updated results. Because in the end, like what you produced was data that was then used in the, the Tableau dashboard, right?

Like that was the kind of end result of, of that giant R Markdown document that you showed, is that right?

[00:34:47] Christine Parker: Yeah. Yeah. The end of the script, it puts together, you know, it like edits, uh, I think it was like a, there's a Google sheets package or something like that. Um, and so I would just update that, uh, every time I did this and that would be the workbook that we'd use to, uh, feed Tableau.

[00:35:06] David Keyes: Cool. And I think that's a really good example of how you don't have to use R for everything. I mean, I think sometimes people get really wrapped up on like, well, if I can't do, you know, every single step, then maybe I won't do an R. But you, you know, tackled one big problem. which was the data wrangling and, you know, for the reasons that you just mentioned found R to be really helpful, um, for, for that piece of it and then use Tableau for the actual presentation of the dashboard.

So that makes a ton of sense. Um, talk to me about how popular. This dashboard got and kind of how you ended up speaking to folks at the White House.

[00:35:52] Christine Parker: Uh, yeah, I, I was not expecting it to get as popular as it was. I was still really new in my role, um, when we were working on this initially. And so I had no idea, like, That that was possible, I guess, uh, but it started getting really widely shared in other, like in our advocacy community. So, you know, we were sharing it on our podcast, um, talking about it a lot.

We handed out like little, uh, postcards at a national conference that we went to. Um, and a lot of people were like, Oh, you guys made the dashboard when we were there. And I was like, Oh, wow, what's happening? I didn't recognize that this would be something to be known for, I guess. Um, but yeah, and people were like thanking us and it was just, it was really wild.

And it was really nice to like hear that it was so useful for people. Cause like I mentioned the folks that were, you know, trying to figure out where in their community they need to go focus their efforts to get people enrolled. They could use this to look at the zip code level and say, this area has really low enrollment relative to what it could be.

And so we should. You know, figure out what we can do there to get people enrolled, um, and that, you know, at the end of the day, it really felt good to be able to help with something like that. Um, and then, yeah, at the policy level, it started getting shared with, like, different, uh, off, different, like, offices in Congress, and then once folks finally started to push for the funding to, like, for the program to be refunded, um, there were, like, letters going amongst Congress.

uh, congressman. And this was getting cited in there and it was in a senate committee hearing. And so yeah it was, it was wild. Very exciting

to have something like this.

[00:37:41] David Keyes: know when they were working on trying to get additional funding for this program, folks at the White House actually contacted you to speak to them. Can you talk about what that was?

[00:37:54] Christine Parker: Yeah so uh when there was a need for like an actual number to pitch to congress to refund this program, uh, you know, numbers started floating around and, and the White House finally came out with a number of 6 billion. And we had predicted it would actually take around 7 billion at the current enrollment rate.

And I had a friend at ACLU who's like, well, you know, you know, I'm telling them this isn't enough, but they are going to better listen to someone who's actually working with these data. And so would you? Be willing to talk with them and explain your numbers to them. Um, and sure enough, that day I was on a call with them and, uh, they did not listen, but I mean, they listened, but you know, did they take my advice?

No,

[00:38:42] David Keyes: Wow, you can only, you can only do so much, right?

[00:38:45] Christine Parker: right. You know.

[00:38:46] David Keyes: Yes. Um, well, that's exciting. I mean, even if they didn't take your advice in the end, just, you know, seeing the impact that it had both in the advocacy community, as well as, you know, ultimately getting the attention of folks in the White House is, is impressive. So I'm curious, you know, now, When you reflect on this, the, what would you say you learned through the process of, of creating this, uh, affordability, affordable connectivity program dashboard?

[00:39:15] Christine Parker: Well you kind of already touched on this, but I'll just reiterate that it's okay not to do things perfectly. Like, I will jokingly like, call out a lot of things that are wrong with this script and how I use GitHub. Like, this is available on GitHub for folks that are interested, um, but you know, I, I don't know what to say.

You know, as someone who isn't using R in like the most perfect way, like, I don't, I'm like, I feel allergic to functions, um, and other things, like, I, I'm still learning and I want folks who are also still in that learning phase to feel comfortable to like, put their work out there, even if it's not perfect, because if you don't actually do it, it's never going to get done, whether or not it's perfect.

So, um, do it imperfectly. Do it scared you know, You might be really successful at it in the end. Um, and then, you know, just also like we've used a variety of tools because that's what we had available to us. It also allowed us to be, like, really creative. For me not to work alone on this, I had, you know, my team members that were able to help with certain aspects of this, like designing the Tableau interface.

I didn't do that part. Um, and so that allowed us to work together. Um, whereas if I had only done this in R, it would have been all on

me. And who knows if this would actually have gone anywhere, because I would have had to learn how to, how to make a dashboard in R, which was daunting.

[00:40:40] David Keyes: Yeah. Well, I think that's a really good place to leave it. Um, so Christine, thank you again, uh, for joining me and sharing about your work on this dashboard. It's really, it's really inspiring.

[00:40:53] Christine Parker: Happy to be here. Thank you for the invite.

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