ggrothendieck

Gas production from the North Sea is low because they imposed onerous windfall surtaxes on it so now it is unattractive to produce.

In R `nnls` (nonnegative least squares) does not guarantee integrality but in this case does give one solution and it happens to be integral: `library(nnls); nnls(A, b)`

The poorman package (on CRAN) implements a lot of tidyverse with no dependencies. Other packages that provide alternate implementations are datawizard and tidytable.

We used to call stuff like this "the warm body clause".

The HN news title seems inaccurate. This is not necessarily a drop in demand nor is it applicable to all shipping as also suggested by the title.

The article suggests it is an oversupply of container ships with very few ships scrapped as opposed to demand: "The industry invested heavily in new container ships during and after the pandemic to meet strong demand and benefit from record freight rates. A large number of new ships entered the market since the summer with no signs of idling or scrapping, said Clerc."

Furthermore just because container ships were over ordered does not mean that other types of ships were so it is misleading to just refer to shipping in the title. It should be "container shipping".

Of course it is possible to screw up but if you don't update your packages and record the date that does not seem to be R's fault.

The packages that were used in statistics were SAS, SPSS and Stata. perl is not a statistical package and has nowhere near the depth of statistical capabilities of R.

Don't forget that I also mentioned the checkpoint package in my post. You only need to know the date for that, not the version of each of the packages.

In your last paragraph I think you are referring more to software development practices than what is available through R. Simply using R or any language doesn't guarantee this.

Is the subset R interpreter you wrote available on the net?

1. Before R commercial statistical packages were mainly used. You can, in principle, just use assembler too and develop everything yourself but it isn't practical. Regarding C/C++ and Fortran, many R packages are, in fact, wrappers around code in those or other languages making it easier to access them. From that point of view R can be regarded as a glue language. 2. Regarding keeping versions straight, all past versions of packages in the CRAN repository are kept on CRAN. Microsoft MRAN repository also maintains histories of packages that can be accessed via the checkpoint package which will install packages as they existed on a given date. Furthermore, install_version in the remotes and devtools packages can install specific versions. 3. Regarding tidyverse dependencies you can reduce the number of packages you load by not using library(tidyverse) and instead load the specific packages you need. This will result in fewer packages being loaded.

What needs to be added is that before R the reproducibility problem in science was compounded by the fact that analyses were done with proprietary software limiting communication and replication of those analyses. This was and continues to be a major problem, particular in some fields, but at least now there is a common widely used language that can be used to overcome this. I wouldn't focus on idiosyncrasies but rather on the major problem it addresses. Any large system will grow over time and have some inconsistencies but after a while you learn the workarounds so they are less important than the big picture.

Furthermore a test just twice as large would be sufficient to determine significance at the 1% level even with a two sided test if the same death rate continued to hold.

  ivm <- c(3, 247-3)
  con <- c(10, 249-10)
  m <- rbind(ivm, con)
  fisher.test(2*m)$p.value  # 2* so that it is twice as large
  ## [1] 0.008490957

The Mech Vent and ICU also used two sided tests.

Also they say they used the Fisher exact test and got a p value for mortality of 0.09 so it seems they were doing a two-sided test which is the default for fisher.test in R.

  ivm <- c(3, 247-3)
  con <- c(10, 249-10)
  m <- rbind(ivm, con)

  fisher.test(m)$p.value
  ## [1] 0.08809225

However, I think a one sided test could be justified and in that case it is significant at the 5% level.

  fisher.test(m, alternative = "less")$p.value
  ##        ivm 
  ## 0.04541928