stephenturner

Summary

Produced with llama3.3

This appears to be a compilation of social media posts from various individuals in the data science and R programming communities. Here are some observations and insights:

Common themes:

1. R programming: Many posts mention R, indicating its widespread use in data analysis and visualization.
2. Data visualization: Several posts share visualizations created using R libraries like ggplot2, reactable, and others.

stephenturner

# Get top Bluesky posts for #Rstats ---------------------------------------

library(dplyr)
library(glue)
library(atrrr)
library(ellmer)

# authenticate to bluesky using vars in ~/.Rprofile
# or do this interactively with just `auth()`
auth(user = Sys.getenv("BSKY_USER"),

stephenturner

Summary

Produced with mistral

1. Nicola Peltz has a new post about using regex in R, breaking down the process with song lyrics as examples to help readers understand pattern matching and quantifiers. (Source: Data Elixir) #rstats
2. Andrew Ba Tran, data journalist at Washington Post, is planning to analyze the boarding school data recently released by their organization in a live stream with exploratory data visualizations. (Source: Andrew Ba Tran) #rstats
3. Kasia Banas, who is new to regex, completed a crash course and used the skills to solve the Day 3 Advent of Code challenge in R. (Source: Kasia Banas) #adventofcode
4. John MacKinton shares an example of data in a challenging format with various inconsistencies, but notes that one single function in R can handle it all. (Source: John MacKintosh) [#rstats](https://twitter.com/hashtag/rstats?src=ha

stephenturner

Summary

Produced with gemma2

This looks like a collection of posts from data scientists and R enthusiasts on the social platform "Bsky". Let's break down what we see:

Key Themes:

• R Programming: The most prevalent theme is R programming itself. Users are discussing tips, tools, problems, and solutions within the R ecosystem.
• Data Visualization & Analysis: Many posts showcase projects or techniques using R for data visualization and analysis.

stephenturner

################################################################################
# GCP/AWS setup script for general bioinformatics
# Base image: Ubuntu 22.04 LTS x86/64
################################################################################

# Make a directory in which to build stuff
sudo mkdir -p ${HOME}/build && sudo chmod 777 ${HOME}/build && cd ${HOME}/build

# Install system libs
sudo apt-get update

stephenturner

library(tidyverse)
library(rvest)
library(tidytext)

## Function to get full text given a DOI
get_full_text <- function(doi) {
  paste0("https://www.biorxiv.org/content/", doi, ".full") |>
    rvest::read_html() |>
    rvest::html_nodes("div.section") |>
    rvest::html_text() |>

stephenturner

library(tidyverse)
library(httr2)

# Set URL path variables
url <- "https://api.biorxiv.org/"
what <- "details"
server <- "biorxiv"
date1 <- "2014-01-01"
date2 <- "2023-12-31"

stephenturner

# Get / clean data ----
library(tidyverse)
library(httr2)

# Set URL path variables
url <- "https://api.biorxiv.org/"
what <- "details"
server <- "biorxiv"
date1 <- "2014-01-01"
date2 <- "2023-12-31"

stephenturner

// Place your key bindings in this file to overwrite the defaults
[
    // RStudio: Copy lines up/down with alt+cmd+up/down
    {
        "key": "alt+cmd+down",
        "command": "editor.action.copyLinesDownAction",
        "when": "editorTextFocus && !editorReadonly"
    },
    {
        "key": "shift+alt+down",

stephenturner

# R session 1: model training and deployment ------------------------------

library(tidymodels)
library(vetiver)
library(plumber)

# Not the way you'd actually split data - for demo only
cars_train <- mtcars[1:24,]
cars_test <- mtcars[25:32,]