acoppock

rm(list = ls())

library(tidyverse)
library(estimatr)

N <- 1000

dat <-
  data.frame(
    Y = rnorm(N),

bgall

# The below demonstrates the madness of R's treatment of NA values.
# Some examples taken from https://stackoverflow.com/questions/25100974/na-matches-na-but-is-not-equal-to-na-why/25101796

# Logical examples

NA %in% NA
# [1] TRUE

NA == NA

leeper

# ttable: a grammar of tables
# https://gist.github.com/leeper/f9cfbe6bd185763762e126a4d8d7c286

# aggregate/summarize

# arrange

# annotation (metadata features)

# theme

artemklevtsov

variables:
  CODECOV_TOKEN: "CODECOV_TOKEN_STRING"
  _R_CHECK_CRAN_INCOMING_: "false"
  _R_CHECK_FORCE_SUGGESTS_: "true"
  APT_PKGS: "libcurl4-openssl-dev libssh2-1-dev libssl-dev libxml2-dev zlib1g-dev git"

before_script:
  - apt-get update
  - apt-get install -y --no-install-recommends ${APT_PKGS}
  - apt-get install -y --no-install-recommends qpdf pandoc pandoc-citeproc

jennybc

## http://polygraph.cool/films/
## https://github.com/matthewfdaniels/scripts

x <- "https://raw.githubusercontent.com/matthewfdaniels/scripts/master/data/character_list5.csv"
characters <- read.csv(x, na.strings = c("NULL", "?"),
                       fileEncoding = "ISO-8859-1", stringsAsFactors = FALSE)
## some ages are clearly (negative) birth years ... oops
characters$age[!is.na(characters$age) & characters$age < 0] <- NA
characters$age[!is.na(characters$age) & characters$age > 105] <- NA

hrbrmstr

library(inline)
library(stringi)

#' Replace a substring of a string
#' 
#' @param str string the source string that you want to replace characters in
#'            (a character vector of length 1)
#' @param repl another string (this will be copied into \code{str})
#' @param pos position of the first character to be replaced
#' @param len number of characters to replace (if the string is shorter,

hrbrmstr

#!/bin/bash
# AUTHOR: Bob Rudis <bob@rudis.net> (@hrbrmstr)
# LICENSE: MIT + file zLICENSE

TODAY=`date +%Y-%m-%d`
TODAY_MD=`date +%B\ %d,\ %Y`
YEAR=`date +%Y`

PACKAGENAME=$1

leeper

# The UNF algorithm provides a clearly defined specification for creating a data hash.
# The algorithm has many strengths, including the fact that it is file format-independent,
# controls for floating point and rounding errors, and is independent of the column-
# organization of a dataset (i.e., variable order is irrelevant).
#
# A weakness, however, is that UNF is row-order sensitive. This is useful in terms of 
# data subsetting (i.e., a subset of a dataset produces a different UNF than the full
# dataset), but it produces a weakness when two identical datasets are arranged in 
# different row orders. Here's a simple example:

hrbrmstr

ghelp <- function(topic, in_cran=TRUE) {
  
  require(htmltools) # for getting HTML to the viewer
  require(rvest)     # for scraping & munging HTML

  # github search URL base
  base_ext_url <- "https://github.com/search?utf8=%%E2%%9C%%93&q=%s+extension%%3AR"
  ext_url <- sprintf(base_ext_url, topic)

  # if searching with user:cran (the default) add that to the URL  

andrewheiss

library(httr)
library(jsonlite)
library(base64enc)

#----------------------------------------
# Functions to access RemoteControl API
#----------------------------------------
get_session_key <- function(username, password) {
  body.json = list(method = "get_session_key",
                   id = 1,