I recently gave a talk on housing markets, beginning with some observations and asking a few pressing questions. U.S. house prices are increasing at about a 20 percentage point annual rate in recent months, the highest rate of growth ever recorded. The level of real (inflation-adjusted) house prices is the highest in 131 years of house price data stretching back to 1890. What does this mean for the housing market?
Yesterday I gave a virtual talk at JSM 2021 on Visualizing Economic and Financial Market Trends in Volatile Times. Here is a summary of my remarks. The COVID-19 pandemic and associated economic slowdown has led to unprecedented volatility in many economic and financial time series. With swings well outside of historical ranges, many forecasting models break down. Data visualization techniques are a powerful tool to help the analyst understand evolving economic and financial market trends.
I have been recently messing around with the new ggfx package. using #rstats ggfx::with_bloom and ggridges::geom_density left with ggfx, right without pic.twitter.com/L8yknjAJVw — 📈 Len Kiefer 📊 (@lenkiefer) March 4, 2021 Most of my applications (see below for a gallery) have maybe not been applying good dataviz guidelines. But I think I have found a good example. We can use the ggfx::with_blend function to layer a recession indicator with a time series and color code the lines.
Yesterday I gave a virtual lecture on data visualization at GMU. Here I’m posting the slides I used for that talk and including my discussion notes for the portion of the talk where I discussed guidelines for data visualization. At the beginning of the talk I spoke a bit about data visualization guidelines. I framed this part of my talk around Jon Schwabish’s five guidelines from his new book Better Data Visualizations see (on Amazon) and here for a blog summary.
Today I got to talk #dataviz and shared a bunch of my charts, which always sort of feels like sharing my vacation photos. But it was fun to talk about why I made some of these charts, what they mean, and how different data visualization techniques can bring out new insights in familiar data. The pdf version is below or here. The pptx version has animations, get it here.
For several months now, I’ve been working on a new research paper with Sumit Agarwal, Souphala Chomsisengphet, Hua Kiefer, and Paolina Medina studying refinance activity this year. When we started the project back in the spring we were not sure that the pandemic would allow households to refinance at rates similar to prior periods. It seemed possible that the pandemic would disrupt the mortgage market and make it difficult for households to take advantage of historically low rates.
Recently I’ve been putting my y-axis labels on the right for some time series. I think this idea has been rattling around in my head since it was suggested on Twitter by Maarten Lambrechts: A y-axis on the left is almost always the default, while the most recent and usually most relevant data are on the right. Then shouldn't a y-axis on the right be the default (when there are no data labels), to improve legibility?
Yesterday I shared with you observations on the economy, which form the core of many of my recent economic outlook talks. In that article I used some charts with alternative formatting. No not spooky, but a blue theme kind of like those alternative road uniforms some sportsball teams wear. Here, I will share with you the R code for these delicious plots. Setup First we’ll need to set up our chart theme, tweak some ggplot2 defaults and load some libraries.
Yesterday I completed the elusive presentation quadfecta. I did a talk on Zoom, Teams, WebEx and Skype. These communication apps are great, but after a few hours of maintaining “resting Zoom face” (you want to look interested as the camera is always rolling), I felt a bit exhausted. But it was totally worth it. The highlight for me was being able to join Jeffrey Shaffer, Steve Wexler, Amanda Makulec, and Andy Cotgreave for Chart Chat.
Yesterday I gave a virtual talk at Gonzaga University hosted by Ryan Herzog. Below are the slides I used, here is a link (pdf).
This could have been a tweet. Sometimes it is a tweet. After I have taken a break from social media, blogging, etc and I try to get back in the swing I find it incredibly difficult to get restarted. I feel enormous pressure to say something insightful, something funny, something grand. An effective strategy is not to shoot for the stars. Rather than writing something great, I set my sights lower.
I’m hearing that folks have been invited to speak at the upcoming Rstudio conference. Congratulations to the folks who got accepted this year. I am not sure if I’m going to go to the conference this year, but I recommend you consider it if you love R. I spoke there last year, giving an E-Poster. It was a lot of fun. The best part was getting a chance to meet other R enthusiasts.
Found in Translation On a hill in Kyoto, Japan there is a most delightful sign. Near the Kiyomizu Temple temple in the Higashiyama District there are several picturesque streets. The presevered historic district is a favorite place for tourist shopping, tasty snacks, photo opportunities with majestic temples in the background. Many folks like to rent Kimonos (mostly women, but also a few men) and snap photos in the street. On my way through the district I happened across this wonderful sign attached to a private residence:
On intrinsic irreducible uncertainty of economic outcomes (forecasting is hard) Here’s a picture of me and some bears. Don’t worry, I didn’t get hurt. Those bears couldn’t hurt anybody. First, because they are behind some glass. And second, because they are stuffed. There are a lot of stuffed bears out there. They’ve grown aggressive lately. Maybe because they haven’t eaten much. This current economic expansion is over 10 years old now.
Updated May 28, 2019 I’m giving a seminar about my new working paper “What Happens in Vegas Doesn’t Always Stay in Vegas”“. The slides for the talk are posted below. I made the slides with R and the xaringan package. You can easily print the html to pdf with Chrome. The pdf version is below and available at this link. Long seminar slides .html or [.pdf](../../../../img/charts_may_22_2019/what happens in vegas preso long.
Last week I gave a speech in Cincinnati, Ohio at the UC/PNC Economic Outlook program. My speech was titled “Forecasting in a Vulnerable Economy”. You can find slides and detailed notes over on LinkedIn: https://www.linkedin.com/pulse/forecasting-vulnerable-economy-leonard-kiefer/. In this post I want to share R code for the first three plots on the Vulnerable Economy. We’ll get the data via the St Louis Fed’s FRED. We’re going to grab the Fed Funds rate FEDFUNDS, the Unemployment Rate UNRATE the Congressional Budget Office’s estimate of the long-run natural rate of unemployment NROU and the spread between the 10-year and 2-year U.
I really like R, but I love the R community. Since I’ve started using R intensively in the past couple of years, I’ve constantly been awed and inspired by all the amazing things that people are doing with R. The spirit of the open source community and people’s willingness to share their thoughts and code is fantastic. Many times in this space we’ve remixed different data visualizations with R, often relying on awesome new packages that others have developed.
EARLIER THIS WEEK I TWEETED out a poll asking whether or not folks wanted to see a thread/tweetstorm with slides from an upcoming presentation on the economy and housing markets that I’m giving. Over 90 percent voted for a thread. So I shared it. In this post let me add a little more commentary on the individual slides. Here’s the thread I ended up posting: Thread (0/5). I'm giving an update on economy, #housing and #mortgage market trends.
I PUT TOGETHER SOME SLIDES SUMMARIZING our recent work on dynamic model averaging. See here and here for more blah blah blah. See below for some slides. Click here for a fullscreen version here. Making the Preso Let me also share with you the R code I used to generate these slides. The code below is the Rmarkdown I used to generate the slides (saved as .txt). The slides were put together using the xaringan package.
WE ARE ON OUR WAY TOWARDS BUILDING a tidy PowerPoint workflow. In this post I want to build on my earlier posts (see here for an introduction and here for a more sophisticated approach) for building a PowerPoint presentation with R and try to make it even purrrtier. I saw that somebody shared my posts on reddit and I thought I would take a look at the comments. Folks on the internet are known for kindness and offering helpful advice right?
I’ve BEEN MESSING AROUND MORE WITH R and OFFICER and having too much fun for a Monday. I’m going to dive into some details later, but I’ll just leave a couple files here. See the attached PowerPoint .pptx file for all the charts. Here’s a gif version I started with: Then after I created the PowerPoint I started messing around with the drawing tools and made increasingly ill-advised edits.
LOOK I DON’T HAVE ANYTHING BAD TO SAY about PowerPoint. Others have said it (see for example Tufte and Harvard Business Review). It’s a tool and a fact of life for many of us. I am interested in making better PowerPoints. In this post we’ll use some R tools to generate a PowerPoint deck. OfficeR The package officer allows you to access and manipulate ‘Microsoft Word’ and ‘Microsoft PowerPoint’ documents from R.
R statistics dataviz housing mortgage data
R statistics dataviz plotly housing mortgage data
R statistics dataviz plotly housing mortgage data
See data speak data data visualziation and presentation thoughts