jboner

Latency Comparison Numbers (~2012)
----------------------------------
L1 cache reference                           0.5 ns
Branch mispredict                            5   ns
L2 cache reference                           7   ns                      14x L1 cache
Mutex lock/unlock                           25   ns
Main memory reference                      100   ns                      20x L2 cache, 200x L1 cache
Compress 1K bytes with Zippy             3,000   ns        3 us
Send 1K bytes over 1 Gbps network       10,000   ns       10 us
Read 4K randomly from SSD*             150,000   ns      150 us          ~1GB/sec SSD

dsparks

# A simple approach to visually-weighted regression plots

doInstall <- TRUE  # Change to FALSE if you don't want packages installed.
toInstall <- c("ggplot2", "reshape2", "MASS")
if(doInstall){install.packages(toInstall, repos = "http://cran.us.r-project.org")}
lapply(toInstall, library, character.only = TRUE)

# Generate some data:
nn <- 1000
myData <- data.frame(X = rnorm(nn),

stephenturner

## See http://gettinggeneticsdone.blogspot.com/2013/06/customize-rprofile.html

## Load packages
library(BiocInstaller)

## Don't show those silly significanct stars
options(show.signif.stars=FALSE)

## Do you want to automatically convert strings to factor variables in a data.frame?
## WARNING!!! This makes your code less portable/reproducible.

killercup

/*
 * I add this to html files generated with pandoc.
 */

html {
  font-size: 100%;
  overflow-y: scroll;
  -webkit-text-size-adjust: 100%;
  -ms-text-size-adjust: 100%;
}

hadley

next_letter <- local({
  i <- 0 
  function() {
    i <<- i + 1
    paste0("x_", c(letters, LETTERS))[i]
  }
})

f <- function() {
  new_arg <- alist(x = )

johnmyleswhite

a <- 1
b <- 2

ftw <- function()
{
	vars <- ls(envir = .GlobalEnv)
	rm(list = vars, envir = .GlobalEnv)
}

ftw()

leeper

#' The goals here are to:
#' (1) play around with themes to make base graphics less ugly
#' (2) atomically specify multiple plot aspects (data, axes, grid, title) in a single call
#' (3) Recycle last plot theme to easily duplicate its appearance in the next plot
#' The idea is to create a set of customizable functions like `theme()`, which return a list of graphics arguments
#' These can then be passed to `splot`, which will cleanly handle them by passing some to `par` and some to `plot`
#' Different `theme()` functions can then be used to modify color palettes, axes, etc.

merge.list <-
function (x, y) {

daattali

# rasmusab/beepr package is great for telling you when your R code finishes running.
# But if the code throws an error, the beep will never come, and you won't know that
# it finished running until you visually check.
# Solution: change the error handler to a failure beep.

# Example
foo <- function(success = TRUE) {
  if (!success) {
    stop("Error!")
  }

gaborcsardi

library(jsonlite)

## Download
pkgs <- fromJSON("http://crandb.r-pkg.org/-/events")

## Filter
na_pkgs <- unique(pkgs$name[ is.na(pkgs$date) ])
events <- pkgs[ ! pkgs$name %in% na_pkgs, c("date", "name", "event")]

halhen

# data from http://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/population-distribution-demography/geostat
# Originally seen at http://spatial.ly/2014/08/population-lines/

# So, this blew up on both Reddit and Twitter. Two bugs fixed (southern Spain was a mess,
# and some countries where missing -- measure twice, submit once, damnit), and two silly superflous lines removed after
# @hadleywickham pointed that out. Also, switched from geom_segment to geom_line.

# The result of the code below can be seen at http://imgur.com/ob8c8ph

library(tidyverse)