ctoth

Working with Q — Coding Agent Protocol

What This Is

Applied rationality for a coding agent. Defensive epistemology: minimize false beliefs, catch errors early, avoid compounding mistakes.

This is correct for code, where:

• Reality has hard edges (the compiler doesn't care about your intent)
• Mistakes compound (a wrong assumption propagates through everything built on it)
• The cost of being wrong exceeds the cost of being slow

sj-io

Modern R Development Guide

This document captures current best practices for R development, emphasizing modern tidyverse patterns, performance, and style. Last updated: August 2025

Core Principles

1. Use modern tidyverse patterns - Prioritize dplyr 1.1+ features, native pipe, and current APIs
2. Profile before optimizing - Use profvis and bench to identify real bottlenecks
3. Write readable code first - Optimize only when necessary and after profiling
4. Follow tidyverse style guide - Consistent naming, spacing, and structure

sebkopf

Wolfram Alpha API from R

The attached code file provides an easy basic interface to the Wolfram Alpha API. Inspired by the wolframalpha module available for Python.

source("wa_lib.R")

Initialize client

rdinnager

Is there an easy way to convert a named list into a dataframe, preserving the elements of the list in a "list-column"?

library(dplyr)
library(magrittr)

## make a random matrix
rand_mat <- function() {
  Nrow <- sample(2:15,1)

Ncol <- sample(2:15,1)

aammd

Is there an easy way to convert a named list into a dataframe, preserving the elements of the list in a "list-column"?

    library(dplyr)
    library(magrittr)

    ## make a random matrix
    rand_mat <- function() {

aammd

Apparently, there is a simple problem called Fizz buzz which is sometimes used to identify competent programmers. A good opportunity to practice some dplyr and magrittr tricks.

library(dplyr)
library(magrittr)
library(knitr)

1:100 %>%
 data.frame %>%

aammd

Simple data manipulations in R

Many years ago, I was introduced to R by Cam Webb . At the time, his website contained a list of common data manipulations (original here). This list dated from Cam's early experience with R, and contained the R-help mailing list responses to a series of data manipulations. For a long time, I kept this file as a handy reference. I printed it out. I recommended it to friends.

Now I have been using R for years, and the state of the art has advanced considerably. Particulary, Hadley Wickham's reshape2 and dplyr packages have transformed the way most useRs manipulate their data. I decided that it would be interesting to revisit my favourite resource and try my hand at solving these problems with tools from these two packages.

library(reshape2)
library(dplyr)

woobe

The rBlocks Experiement

Having fun with Ramnath Vaidyanathan's new package rblocks!

Iris & AirPassengers

mhermans

# Requirements
#sudo apt-get install libcurl4-gnutls-dev # for RCurl on linux
#install.packages('RCurl')
#install.packages('RJSONIO')

library('RCurl')
library('RJSONIO')

query <- function(querystring) {
  h = basicTextGatherer()