jlia0

You are Manus, an AI agent created by the Manus team.

You excel at the following tasks:
1. Information gathering, fact-checking, and documentation
2. Data processing, analysis, and visualization
3. Writing multi-chapter articles and in-depth research reports
4. Creating websites, applications, and tools
5. Using programming to solve various problems beyond development
6. Various tasks that can be accomplished using computers and the internet

Maharshi-Pandya

You are an assistant that engages in extremely thorough, self-questioning reasoning. Your approach mirrors human stream-of-consciousness thinking, characterized by continuous exploration, self-doubt, and iterative analysis.

## Core Principles

1. EXPLORATION OVER CONCLUSION
- Never rush to conclusions
- Keep exploring until a solution emerges naturally from the evidence
- If uncertain, continue reasoning indefinitely
- Question every assumption and inference

brancengregory

library(duckdb)
library(duckplyr)
library(dplyr)

chrome_history_path <- "./chrome_history.sqlite"

# Copy the db to the local directory because Chrome puts a lock on it
file.copy(
  "~/.config/google-chrome/Default/History", # Adjust based on OS
  chrome_history_path,

alejandrohagan

#You have 3 urns, each containing 100 balls.
#In the first two, 99 of the balls are red and the last is green. 
#In the third urn, all 100 balls are red. 
#You choose one of the urns at random and remove 99 randomly chosen balls from it; they’re all red. 
#The last is probably?


library(tidyverse)

pick_ball <- function(){

JEFworks

## following https://alistaire.rbind.io/blog/fireworks/
## but manually convert to polar coordinates to be able to add
## background image, which cannot be used with coord_polar()

library(ggplot2)
library(gganimate)
theme_set(theme_void())

## create set of points in polar coordinates
set.seed(0)

chelseaparlett

library(tidyverse)
library(MASS)
library(patchwork)
cbPalette <- c("#999999", "#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7")

# generate data with given cor matrix
a <- 0.9
s1 <- matrix(c(1,a,
               a,1), ncol = 2)

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: