Ian
LightNews LightNews
ianmoran11.bsky.social
Ian
@ianmoran11.bsky.social
130 followers 1.9K following 0 posts
Posts Replies Media Videos
Reposted by Ian
coolbutuseless.fosstodon.org.ap.brid.gy
{insitu} #rstats Dev Dairy

A first attempt at writing the in-situ operations as idiomatic R.

This shows a 3x speedup over regular R's copy-on-modify.

Use `with(insitu, ...)` to wrap a block of code to use in-situ operations.

User has to remember the first […]

[Original post on fosstodon.org]
Benchmarking of R code with (a) regular base R's copy-on-modify (b) using in-situ "by reference" operations to reduce memory allocation Results of benchmark showing: * 3x speedup * Memory allocation dropped from 2.3MB to ZERO!
November 28, 2024 at 3:37 AM
Reposted by Ian
posit.co
We are excited to announce reactable-py: interactive tables in #Python! 🐍

This tool is a port of the R package reactable. It offers quick options for sorting, searching, and paginating data, working with pandas and polars, and customization.

Check it out today! machow.github.io/reactable-py...
Quickly scrolling through a few interactive Python table examples made with reactable-py
November 25, 2024 at 3:28 PM
Reposted by Ian
andrew.heiss.phd
From the archives: Have you (like me!) wondered what the ATT means and how it's different from average treatment effects? I use #rstats to explore why we care about (and how to calculate) the ATE, ATT, and ATU #polisky #episky #econsky www.andrewheiss.com/blog/2024/03...
Mirrored histogram showing “weird” parts of the population: treated people who were unlikely to be treated, and untreated people who were likely to be treated Mirrored histogram showing pseudo-populations of treated and untreated people that have been reweighted to be more comparable and unconfounded Table showing potential and realized outcomes for 9 simulated people Before we calculate these different treatment effects with the realized outcomes instead of the hypothetical potential outcomes, let's look really quick at the practical difference between the true ATE, AT 1, and ATU. All three estimands are useful for policymaking! The ATE is -15, implying that mosquito nets cause a 15 point reduction in malaria risk for every person in the country. This includes people who live at high elevations where mosquitoes don't live, people who live near mosquito-infested swamps, people who are rich enough to buy Bill Gates's mosquito laser, and people who can't afford a net but would really like to use one. If we worked in the Ministry of Health and wanted to know if we should make a new national program that gave everyone a free bed net, the overall reduction in risk is -15, which is probably pretty good! The ATT is -16.29, which is bigger than the ATE. The effect of net usage is bigger for people who are already using the nets. This is because of underlying systematic reasons, or selection bias. Those using nets want to use them because they need them more or can access them more easily-they might live in areas more prone to mosquitoes, or they can afford to buy their own nets, or something else. They know themselves and understand some notion of their personal individual causal effect and seek out nets. If we removed access to their nets, it would have a strong effect. The ATU is -13.63, which is smaller than the ATE. The effect of net usage is smaller for people who aren't using the nets. Again, this is because of selection bias. Those not using nets are likely not using them for systematic reasons-they live far away from mosquitoes, they've received a future malaria vaccine, they have some other form of mosquito abatement, or something else. Because they can read their own minds, they know that mosquito net use won't do much for them personally, so they don't seek out nets. If we expanded access to nets to them, they wouldn't benefit
November 22, 2024 at 2:51 PM