thomasp85

library(future)
trim_model <- function(model, predictor = predict, ..., ignore_warnings = TRUE) {
  # Cache the correct output
  true_pred <- predictor(model, ...)
  
  # Treat prediction warnings as errors?
  if (!ignore_warnings) {
    old_ops <- options(warn = 2)
    on.exit(options(old_ops))
  }

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

kdkorthauer

sudo sh -c 'echo "deb http://cran.rstudio.com/bin/linux/ubuntu trusty/" >> /etc/apt/sources.list'
gpg --keyserver keyserver.ubuntu.com --recv-key E084DAB9
gpg -a --export E084DAB9 | sudo apt-key add -
sudo apt-get update
sudo apt-get -y install r-base libapparmor1 libcurl4-gnutls-dev libxml2-dev libssl-dev gdebi-core
sudo apt-get install libcairo2-dev
sudo apt-get install libxt-dev
sudo apt-get install git-core

sudo /bin/dd if=/dev/zero of=/var/swap.1 bs=1M count=1024

lecy

Using the dataUSA.io API for Census Data in R

This gist contains some notes on constructing a query for census and economic data from the DataUSA.io site. This is a quick-start guide to their API; for in-depth documentation check out their API documentation.

A great way to learn how to structure a query is to visit a specific datausa.io page and click on the "Options" button on top of any graph, then select "API" to see the query syntax that created the graph.

Example Use

carlbfrederick

#Internal Functions
MYagsurv <- function(y, x, wt, risk, survtype=3, vartype=3) {
    nvar <- ncol(as.matrix(x))
    status <- y[, ncol(y)]
    dtime <- y[, ncol(y) - 1]
    death <- (status == 1)
    time <- sort(unique(dtime))
    nevent <- as.vector(rowsum(wt * death, dtime))
    ncens <- as.vector(rowsum(wt * (!death), dtime))
    wrisk <- c(wt * risk)  #Had to add c() to remove the dimnames so that the multiplication later would work

ryanwitt

confusion.glm <- function(data, model) {
  prediction <- ifelse(predict(model, data, type='response') > 0.5, TRUE, FALSE)
  confusion  <- table(prediction, as.logical(model$y))
  confusion  <- cbind(confusion, c(1 - confusion[1,1]/(confusion[1,1]+confusion[2,1]), 1 - confusion[2,2]/(confusion[2,2]+confusion[1,2])))
  confusion  <- as.data.frame(confusion)
  names(confusion) <- c('FALSE', 'TRUE', 'class.error')
  confusion
}