Dpananos

library(tidyverse)

# Genuine confounding example.  Sex confounds relationship between drug and death

set.seed(0)
n <- 1000000
is_male <- rbinom(n, 1, 0.5)
drug <- rbinom(n, 1, 0.6 + 0.3*is_male)
y <- rbinom(n, 1, 0.4 - 0.1*drug + 0.4*is_male)
d <- tibble(drug, is_male, y)

friendly

#' ---
#' title: Animate transition from PCA <--> tsne
#' ---
# idea from: https://jef.works/genomic-data-visualization-2024/blog/2024/03/06/akwok1/

#' ## Load packages and data
library(ggplot2) 
library(gganimate) 
library(Rtsne) 
library(patchwork)

alexpghayes

library(tidyverse)
library(mgcv)
#> Loading required package: nlme
#> 
#> Attaching package: 'nlme'
#> The following object is masked from 'package:dplyr':
#> 
#>     collapse
#> This is mgcv 1.8-41. For overview type 'help("mgcv-package")'.

mitchellh

I get asked pretty regularly what my opinion is on merge commits vs rebasing vs squashing. I've typed up this response so many times that I've decided to just put it in a gist so I can reference it whenever it comes up again.

I use merge, squash, rebase all situationally. I believe they all have their merits but their usage depends on the context. I think anyone who says any particular strategy is the right answer 100% of the time is wrong, but I think there is considerable acceptable leeway in when you use each. What follows is my personal and professional opinion:

cpursley

PostgreSQL is Enough

See the new site: https://postgresisenough.dev

---

• Simplify: move code into database functions
• Just Use Postgres for Everything
• Just use Postgres
• PostgreSQL is the worlds’ best database

veekaybee

Anti-hype LLM reading list

Goals: Add links that are reasonable and good explanations of how stuff works. No hype and no vendor content if possible. Practical first-hand accounts of models in prod eagerly sought.

Foundational Concepts

Pre-Transformer Models

AlbertRapp

```{r}
library(tidyverse)


#   https://www.produce.co.nz/seasonality-chart/

fruits <- tibble::tribble(
  ~ fruit,       ~start,      ~ end,
  "Blueberry",   "October",    "April",

sharadhr

$HOME, Not So Sweet $HOME

• Preface
• 1 What is $HOME to you?
• 2 Setup
  • 2.1 Linux
    • 2.1.1 $HOME
    • 2.1.2 Dot-files and dot-directories
    • 2.1.3 XDG Base Directories
• 2.1.4 xdg-user-dirs

AlbertRapp

library(dplyr)
library(ggplot2)
library(patchwork)
library(ggiraph)

dat <- gapminder::gapminder |> 
  janitor::clean_names() |> 
  mutate(
    # ID that is shared for boxplots (this one uses factors, i.e. numbers, as ID instead of continents)

tonyelhabr

Scraping a specific stat for this match. Inspiration here. You can get other player stats in a similar fashion.

library(httr)
library(tibble)
library(tidyr)
library(dplyr)
library(purrr)
library(janitor)