After submitting our paper, the NCHS released the 2016 multiple cause of death files — so a couple months ago we were curious to see how (or if) our results would change when adding the 2016 data.
[NOTE (2/25/2019): Since this post, the 2017 data have also been released. I did a similar analysis comparing our original paper with the additional two years of data. See this post here or our Github repo.]
While the New York Times may have made the original, most data viz people will recognize the plot above from Tufte’s classic, Visual Display of Quantitative Information. It presents a ton of data in a clear, concise, and appealing way. The background bars show the record high and low daily temperature, the mid-ground bars show the “normal” (though as far as I can tell, normal is never clearly defined) high and low temperature, and the foreground shows the high and low for that year. In addition, we have annotations for days that met or made the record. The original plot even had a subplot for daily precipitation.
Here is a similar plot for Boston:
regex to do relatively complex find and replace functions. For example, The Lancet requires that all decimal points be middle dots (i.e., interpuncts). This is pretty trivial in LaTeX or Rmd and turns out it’s equally easy in Word.Just use ([0-9]{1})(\.)([0-9]{1}) as your search query and \1·\3 as your replacement with the “Use wildcards” option.
We (as a field) should still be moving over to doing our drafting in Rmd or LaTeX though. The bloat on MS Word makes working with moderate sized manuscripts with figures painful.
One such journal does did not. New England Journal of Medicine (Figure 1).
Despite well known drawbacks,1 plotting parameters onto maps provides a convenient way of seeing context, patterns, and outliers. However, one of the many problems with choropleths is that the area of the regions tend to distort our perception of the value of the region. For example, in the United States, huge (in terms of land mass) counties will tend to have a greater visual impact than small counties (despite often having similar or even smaller population sizes).One way to address this is to use a histogram as a legend on your map. The histogram then provides you with a way of showing raw counts of equal weights while the map allows you to provide the spatial context of the values.
csv files, sometimes tens of thousands of them, in order to combine them into a single analytical dataset I can use. When it’s only a few dozen, using fread(), read_csv, or the like can be fine, but nothing is quite as fast as using awk or cat.Here’s a snippet of code that allows one to use bash in R to concatenate csv files in a directory. People in the lab have found it helpful so maybe others will as well.
I recently came across this paper by Michal Brzezinski about (the lack of) power laws in citation distributions. It made me a little curious about the citations of my own articles so I threw together a little script using James Keirstead’s Scholar package for R. In the plot above, every line represents a single article with time on the x-axis and (cumulative) number of citations on the y-axis.It’s not super informative, so we can break it down a few ways to graphically explore the data.
Until then, I’m just going to throw up some random code snippets that resulted from the summer.
Way back when I was first learning R, I ran across an old listserv post that talked about how the colon (:) operator was the fastest way to generate a sequence. I never really thought about it, but I got in the habit of always using it whenever I needed a sequence.