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# Original blog post
# https://fronkonstin.com/2019/01/10/rcpp-camaron-de-la-isla-and-the-beauty-of-maths/

# For this example to work, you must have a conda environment with the datashader library
# See http://datashader.org/getting_started/index.html#installation for installation instructions

library(Rcpp)
library(reticulate)
library(dplyr)
use_condaenv("datashader", required = TRUE)

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# load the package ----------------------------------------------------

# install.packages("secret")
library(secret)
library(magrittr)


# set up local user ---------------------------------------------------

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library(segmented)
library(Ecdat)
library(ggplot2)
data("CRANpackages")
str(CRANpackages)

CRANpackages$Version <- as.character(CRANpackages$Version)
CRANpackages <- rbind(CRANpackages, 
                      data.frame(Version = "3.2", 

andrie

# Instructions for using miniCRAN to create a package repository for installing packages on SQL Server 2016
#
# 1. Create a local repository on a machine connected to the Internet
# 2. Copy the miniCRAN repository to the target machine
# 3. Install the packages on the target machine

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# Scrapes CRAN archives to determine the number of packages per release

# Create a list of pages to scrape, including both archive and current
extract_url <- function(){
  url <- list(
    archive = "https://cran-archive.r-project.org/bin/windows/contrib/",
    active  = "https://cran.r-project.org/bin/windows/contrib/"
  )
  
  get_urls <- function(url){

andrie

library(ggplot2)
dat <- read.csv("world records.csv", stringsAsFactors = FALSE)

# Clean and transform the data --------------------------------------------

track <- within(dat, {
  Time <- as.numeric(Time.in.hours)
  Date <- as.Date(Date, format = "%d-%b-%y")
  Speed <- Distance / Time
  logDistance <- log10(Distance)

andrie

# Install from github ----------------

# install.packages(c("chron", "ggplot2", "dplyr", "stringi"))
# devtools::install_github("trinker/wakefield")

# Create a sample data frame ---------
library(wakefield)
r_data_frame(
  n = 500,
  id, 

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library(magrittr)
library(dplyr)
library(tidyr)
library(ggplot2)
set.seed(1)
dat <- r_data_frame(12,
                    name,
                    r_series(grade, 100, relate = "+1_6")
) 

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library(parallel)
set.seed(1)
m <- 10000
n <- 2000
A <- matrix(runif (m*n),m,n)


setMKLthreads(1)
system.time(S <- svd (A,nu=0,nv=0))
# user  system elapsed 

andrie

library(doSNOW)
library(tcltk)

cl <- makeSOCKcluster(2)
registerDoSNOW(cl)

pb <- txtProgressBar(max=100, style=3)
progress <- function(n) setTxtProgressBar(pb, n)
opts <- list(progress=progress)
r <- foreach(i=1:100, .options.snow=opts) %dopar% {