tanho63

library(tidyverse)

scooby_data <- read.csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2021/2021-07-13/scoobydoo.csv")

x <- 
  scooby_data %>% 
  select(season, title, 
    starts_with("caught"),
    starts_with("captured"),
    starts_with("unmask"),

tanho63

# I struggled with uploading things to Microsoft Graph API/Sharepoint/OneDrive for business
# here's the code that finally worked for me.

library(AzureGraph)
library(httr)

# Creates authentication token via browser 
graph_token <- AzureGraph::create_graph_login()$token

# Send a PUT request to the Microsoft Graph API

chichacha

library(tidyverse)
library(deldir)
library(packcircles)

packing <- circleProgressiveLayout(x=runif(1000)) %>% mutate(id=row_number())

data_gg <- circleLayoutVertices(packing, npoints = 36) %>%
  inner_join(packing %>% select(id,radius), by=c("id"))

dxy1 <- deldir(packing$x, packing$y)

mattmc3

layout	author	title	revision	version	description
default	mattmc3	Modern SQL Style Guide	2019-01-17	1.0.1	A guide to writing clean, clear, and consistent SQL.

Modern SQL Style Guide

amrrs

library(shiny)

ui <- shinyUI(fluidPage(
  
  titlePanel("Testing File upload"),
  
  sidebarLayout(
    sidebarPanel(
      fileInput('file_input', 'upload file ( . pdf format only)', accept = c('.pdf'))
    ),

alexhanna

1. Code and Data for the Social Sciences: A Practitioner’s Guide, Gentzkow and Shapiro.
2. Good enough practices in scientific computing, Wilson et al.
3. Best Practices for Scientific Computing, Wilson et al.
4. Principled Data Processing, Patrick Ball.
5. The Plain Person’s Guide to Plain Text Social Science, Healy.
6. Avoiding technical debt in social science research, Toor.

• For background on Technical Debt: Wikipedia; D. Sculley et al., [Machine Learning: The High-Interest Credit Card of Technical Debt](https://ai.google/resear

davidgohel

library(ggiraph)
library(maps)


shinyServer(function(input, output, session) {

  selected_car <- reactive({
    if( is.null(input$plot_selected)){
      character(0)
    } else input$plot_selected

conormm

R to python data wrangling snippets

The dplyr package in R makes data wrangling significantly easier. The beauty of dplyr is that, by design, the options available are limited. Specifically, a set of key verbs form the core of the package. Using these verbs you can solve a wide range of data problems effectively in a shorter timeframe. Whilse transitioning to Python I have greatly missed the ease with which I can think through and solve problems using dplyr in R. The purpose of this document is to demonstrate how to execute the key dplyr verbs when manipulating data using Python (with the pandas package).

dplyr is organised around six key verbs:

kittenlane

//* http://gasolicious.com/remove-tabs-keep-product-description-woocommerce/
//  Location: add to functions.php
//  Output: removes woocommerce tabs

remove_action( 'woocommerce_after_single_product_summary', 'woocommerce_output_product_data_tabs', 10 );

maltefl

# Python port of Paul Bourke's http://local.wasp.uwa.edu.au/~pbourke/fractals/lyapunov/gen.c
# By Johan Bichel Lindegaard - http://johan.cc

import math
import random
from PIL import Image, ImageDraw
import argparse
import os

parser = argparse.ArgumentParser(description='Search for chaos.')