About a year ago I made a chart and Bob Rudis dubbed it a skyline chart. Here’s an updated version I made today. The chart shows a historgram over US weekly average 30-year mortgage rates by year broken into 1/8 percentage points buckets. We see that through this past week, 30-year mortgage rates had spend eight weeks around 2.875 percent. R code The R script below will generate it (using my darklyplot package’s theme_dark2 function).
Every week I track a lot of data. One useful source is the Mortgage Bankers Association Weekly Applications Survey. This survey provides a timely, high frequency (weekly) reading on the U.S. mortgage market. Most weeks I make a bunch of charts related to the release, often posting them on Twitter: Here's how I've been interpreting the hot recent housing market data. Still a lot of catch up from a lost spring.
I’ve been thinking about distributional forecasts. In particular I’ve been considering Quantile Autoregressions (QAR) as defined in KOENKER AND XIAO 2006. There are some handy lecture notes I’ll borrow from at this link (pdf) in the exercise here. This is all speculative, but I think this might be a useful way to think about the assymetry in likely outcomes given the uncertainty inherent in today’s economic forecasts. Setup Let’s define the QAR(1) model for quantile \(Q(\tau)\),
Been a while since I blogged here. Where does time go? On Twitter, I realized it’s just about time for spooky plots: U.S. existing home sales hit a seasonally adjusted annual rate of 6 million in August 2020, first time at 6 million since 2006 pic.twitter.com/4ZKrO2d0zN — 📈 Len Kiefer 📊 (@lenkiefer) September 22, 2020 Maybe too early? In this post, I want to share a simple R code pattern that’s been useful for me.
Yesterday I announced that I wrote a simple R package darklyplot. This is a vignette I have built to help explain ways you can use the package. The goal of darklyplot is to create simple time series plots with a dark background. The miniminum and maximum values are highlighted, and color coded along with the y axis and x axis labels. This vignette walks through basic usage and explores some of the package options.
Today I try my hand at building an R package called darklyplot. This package is a little extension of ggplot2 to create a dark themed time series plot. This packages lets you create simple dark-theme times series plots. It extends ggplot2 and relies on mdthemes to make color coded axis labels. The axis labels use ggthemes::geom_rangeframe to create Tufte-like axes. Through parameters, the user can alter the colors, include shading under the line, and also add a single reference line.
Over on Twitter Grant McDermott shares a neat ggplot2 trick: A shortcut I like to use is calling multiple geoms in an lapply() call, since this automatically generates a list. Works well for investigating plotting variations, e.g. ggplot(diamonds, aes(carat)) + lapply(c(50,200), function(b) geom_histogram(bins=b, alpha=0.3)) https://t.co/hf0vtvDkbk pic.twitter.com/jmmqlyJEKo — Grant McDermott (@grant_mcdermott) June 22, 2020 I applied this trick to create a gradient fill for a chart. Looks kind of like Kool-Aid.
Coronavirus Recession Over on LinkedIn I posted a summary of recent economic talks I have been giving: The Coronavirus Recession. Read the whole things for analysis and lots of charts, but I leave off with three key questions: Recession was here, but is it already gone? Housing market indicators have rebounded, but will the recovery be sustained? After effects of shutdown and possible second wave to the pandemic remain as risks to the outlook, how big are these risks?
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.
A couple years ago I posted R code for a remix of a remix of a US state unemployment rate chart. Post Working on a workout. Some of the images were lost in a blog transition. We’ll update below. Here’s an updated version: And another remix focusing just on April 2020 (latest data). R code ###################### ## Load Libraries ## ###################### library(data.table) library(quantmod) library(tidyverse) library(geofacet) # Download data big file ur.