Trying something different. Again. A map of Japan's shinkansen network as of 2015. If you know an updated source, please let me know. Like the Interstate 5 map, the camera angle and hovering lines may give the impression that they are off; they are not. 😅
Helped someone debug some tidyverse data processing issues. It turns out "NA" was a legitimate code used in their data and readr by default interprets it as NA, not a string. Careful folks! #rstats
Edit: for anyone who doesn't know, read_csv() has an na parameter. The default is na = c("", "NA"). Setting it to na = "" fixed the issue.
@odr_k4tana@sharoz
in some ways the raise of the #tidyverse sect has funnelled as side effect a general less knowledge of #rstats When I see people call readr::read_rds instead of readRDS I just despair
@sharoz@datamaps and that's exactly why I hate pnc apps. They restrict more than they enable in the grand scheme of things. SPSS was the same. Think about stepwise regression...I'm pretty sure SPSS kept enabling this nightmare by making it easy to do.
Since it was super tough to understand how R's protection mechanism can work with Rust, I wrote a blog post before I get burned out completely🔥 #rstats
I'd totally forgotten about this quirk of #rstats functions: arguments are not evaulated until they are used, so if argument b defaults to the value of argument a, you need to use argument b in the code before you make any changes to a (or of course don't change a)
((I spent half an hour debugging something due to this today))
x <- function(a, b = a) {
a = 1
return(b) # first use of b sets the value b = a = 1
}
x(2) # returns 1
y <- function(a, b = a) {
b # sets the value of b = a = 2
a = 1
return(b)
}
#rstats package {crul} 1.4 is on CRAN. It comes with many new features, I'm really excited about retry on Async requests. Thanks again @sckottie for the all the work on this package.
A colleague has been sending benchmarks after he essentially added arrow::to_duckdb() to our {dplyr} pipelines and my mind is being blown. Exciting times! #rstats
@milesmcbain for sure! Arrow has done a great job with this too, and the R community standardizing around the same API (and ability to dispatch like that) is killer.
The first one talks about non-compartmental analysis and has some very simple R code, the second one dives a little deeper and talks about compartmental models - which often require you to use an ODE solver in the model - in Stan and R.
I'm still learning pharmacokinetics so they're a little rough around the edges
@djnavarro Brings back fond memories of Apollo-era photographs of snow-capped Lunar peaks. It's a pity the ⬛⬛⬛⬛⬛⬛ later redacted them from the timeline.
I asked Bard how to read in a .mseed file into #rstats
If the seismology package existed and worked in this way I would be interested. But in terms of getting up and running with this specialist binary file, I would have been better (had I not known my options before hand) to use a more traditional source that restricted itself to indexing websites rather than making up convincingly communicated answers.