Welcome to the gist. I’m at recap. If you haven’t heard of just 9 minute before, we provide you with a weekly data science problem and challenge you to solve it with a 9 workflow. In the recap, I’ll give some of my thoughts on the challenge and highlight some of the solutions from the community on the 9 form.
Now, last week’s challenge was on CO2 emissions and creating a dashboard on some analysis that had already been done. This is really cool because it gives us the opportunity to talk a little bit about the new reporting framework. So if you’ve been using time for a while, you might remember doing some PDF reporting with Bert, which isn’t the most user friendly thing to use in the world. Now that we have this new reporting framework, you can take the components that you generate these dashboards, and you can output them as pdfs for your email reports or whatever else you choose to do with them.
Now let’s see what some of the community did in challenge No. 2. You’re a climate scientist studying CO2 emissions to make your research insights more accessible to your colleagues and then write a paper about it. You decide to build a report enabled component in 9 that allows users to check how the emissions vary from regions and sources. What are the most alarming insights illustrated in such a report? So the first solution that I want to highlight is from a form user named SRU. Now. So you actually enabled the PDF output for his component. So we can actually see what that looks like here. We have this output block here that connects to a PDF writer. You can add this with an email. If you’ve got multiple different dashboards you wanna combine, you can use the report combiner and have these different components generate different pages of your report. You really have a lot of flexibility with this tool. But let’s see what his actual report looks like too.
Now, in this one, we have a lot of line plots. So we can see the growth of the top five countries CO2 emissions over the years. We can see in this first one really clearly that China has taken over. The US has stayed pretty constant, actually, but China’s kind of grown up on us here a lot going down. We have the different sources as well as annual change in CO2 emissions, so we can see these growth rates. My favorite one is down here though, where we use the geospatial extension to actually generate these pie charts on top of these locations. So we can see how these CO2 sources differ around the world.
Now the second solution that I want to highlight is from a username R. Now, our Figles dashboard is really cool because he chooses to use this certified, verified component, sorry, where it enables you to create these very nice graphics at the top of your dashboards very easily. So this one here is for the title. It’s a data app title. And there’s another one that lets you kind of show your process map. That’s also really nice to use, but it generates a really pretty PDF as well. So if you want something that looks really nice on the page, this is a really great way to add that to it. I also like that we have some of interactivity here in this dashboard. We can select which country we want to visualize and we can see how there CO2 emissions change over time and how the sources change over time.
Now the final solution I want to highlight today is from a username Qatari. So his workflow itself is really just this dashboard here. So let’s just jump right into it and see what it looks like. So Qatari used another verified component, this one for the animated bar chart, which is actually really cool here since it allows us to kind of see it animated over time. Now we see if we replay it here, that the United States starts out on top, but China quickly catches up here over the years and tell it becomes No. 1. We’ve also got the line plots that you’d expect in the other dashboard. Now this one won’t output as a PDF as easily, but it is really cool if your dashboard is gonna be in the browser. Follow us on all of the usual socials and use the hashtag just 9 it to share your solutions with us. Come back tomorrow for the challenge that’ll have something to do with real estate analytics. It could be geospatial, it could be forecasting, maybe a little bit of both. Let me know what you think.