mick001

# Set a seed
set.seed(500)

library(MASS)
data <- Boston

# Check that no data is missing
apply(data,2,function(x) sum(is.na(x)))

# Train-test random splitting for linear model

noamross

### Title: Back to basics: High quality plots using base R graphics
###	An interactive tutorial for the Davis R Users Group meeting on April 24, 2015
###
### Date created: 20150418
### Last updated: 20150423
###
### Author: Michael Koontz
### Email: mikoontz@gmail.com
###	Twitter: @michaeljkoontz
###

rich-iannone

# Install the latest version of DiagrammeR from GitHub
devtools::install_github("rich-iannone/DiagrammeR")

# Ensure that the package is loaded
library("DiagrammeR")

# So, here's a script that essentially takes an empty graph series, and
# creates a new graph on each new day it is triggered. It will create
# random nodes each time it's triggered and add those nodes to the graph
# belonging to the current day. Throughout the script, '_SELF_' refers

benmarwick

# Here are a few methods for getting text from PDF files. Do read through 
# the instructions carefully! NOte that this code is written for Windows 7,
# slight adjustments may be needed for other OSs

# Tell R what folder contains your 1000s of PDFs
dest <- "G:/somehere/with/many/PDFs"

# make a vector of PDF file names
myfiles <- list.files(path = dest, pattern = "pdf",  full.names = TRUE)

ddd1600

getCIK = function(ticker) {
  stopifnot(is.character(ticker))
  uri = "http://www.sec.gov/cgi-bin/browse-edgar"

  response = getForm(uri,CIK=ticker,action="getcompany")
  html = htmlParse(response)
  CIKNode = getNodeSet(html, "//acronym[@title=\"Central Index Key\"][text() = \"CIK\"]")
  CIKNodeText = sapply(CIKNode, function(x) xmlValue(getSibling(getSibling(x))))
  CIK = sub(" .*","",CIKNodeText)
  CIK = sub("^0*","",CIK)

timelyportfolio

#devtools::install("jeroenooms/V8")
library("V8")
library(pipeR)

ct = new_context("window")
# min.js gives me a call stack size error but non-min works fine
ct$source(
  "https://raw.githubusercontent.com/morganherlocker/turf/master/turf.js"
)
# one of the examples from turf API docs

andrie

## Analyze R packages for popularity, using pagerank algorithm

# Inspired by Antonio Piccolboni, http://piccolboni.info/2012/05/essential-r-packages.html

library(miniCRAN)
library(igraph)
library(magrittr)


# Download matrix of available packages at specific date ------------------

hrbrmstr

library(rgdal)
library(rgeos)
library(ggplot2)
library(readr)
library(tidyr)
library(dplyr)
library(grid)

us <- readOGR("us_states_hexgrid.geojson", "OGRGeoJSON")

jeroen

# Proof of concept of using V8 to parse HTML in R
# Example taken from rvest readme
# Jeroen Ooms, 2015

library(V8)
stopifnot(packageVersion("V8") >= "0.4")

# Get Document
html <- paste(readLines("http://www.imdb.com/title/tt1490017/"), collapse="\n")

djnavarro

library(tidyverse)
library(e1071)
library(gganimate)

# parameters for the simulation
ntimes <- 100
nseries <- 20

# construct tibble storing simulation
tbl <- tibble(