| library(ggraph) | |
| library(gganimate) | |
| library(igraph) | |
| # Data from http://konect.uni-koblenz.de/networks/sociopatterns-infectious | |
| infect <- read.table('out.sociopatterns-infectious', skip = 2, sep = ' ', stringsAsFactors = FALSE) | |
| infect$V3 <- NULL | |
| names(infect) <- c('from', 'to', 'time') | |
| infect$timebins <- as.numeric(cut(infect$time, breaks = 100)) | |
| # We want that nice fading effect so we need to add extra data for the trailing |
| # Simple script to show the ISN value from a tshark output (extracting non relative ISN) | |
| # | |
| # tshark -n -r <yourcapturefile.cap" -T fields -e frame.time_epoch -T fields -e ip.src -T fields -e tcp.srcport -T fields -e ip.dst -T fields -e tcp.dstport -T fields -e tcp.seq -T fields -e tcp.flags -T fields -e ip.ttl -o tcp.relative_sequence_numbers:FALSE | awk -e '{print $1"\t"$6"\t"$5}' | python3 generate.py | |
| # | |
| # by Alexandre Dulaunoy - for analysis session given more info -> http://www.foo.be/cours/dess-20162017/ | |
| # | |
| # | |
| import numpy as np | |
| import fileinput |
| import org.apache.spark._ | |
| import org.apache.spark.SparkContext._ | |
| import org.apache.spark.rdd.RDD | |
| import scala.util.Random | |
| import java.io._ | |
| import java.util.Properties | |
| import org.apache.hadoop.fs._; | |
| import org.apache.hadoop.conf._; | |
| import org.apache.hadoop.io._; |
As compiled by Kevin Wright a.k.a @thecoda
(executive producer of the movie, and I didn't even know it... clever huh?)
please, please, please - If you know of any slides/code/whatever not on here, then ping me on twitter or comment this Gist!
This gist will be updated as and when I find new information. So it's probably best not to fork it, or you'll miss the updates!
Monday June 16th
| ## we need the libraries bookdown, knitr, inline, markdown, pryr | |
| system("git clone https://github.com/hadley/adv-r.git") | |
| setwd("adv-r") | |
| library(knitr) | |
| embed_png <- bookdown:::embed_png | |
| rmds <- list.files(pattern = "^.*rmd$") | |
| sapply(rmds, function(x) knit(x)) | |
| cat("---\ntitle: Advanced R\nauthor: Hadley Wickham\nlanguage: en-US\n...\n", | |
| file = "front_matter.txt") | |
| ## assuming we installed pandoc via cabal |
| This Shiny application is designed to help analysing trading strategies. It is an ongoing project that I improve when time allows. Feel free to get in touch should you have any suggestion. | |
| *How to use the App as it is? | |
| The App uses as input several csv files (one for each strategy). Each file has two columns: date and daily return. There is an example of such a file in the Github repository. The code is essentially made of 3 files. | |
| -ui.R: controls the layout and appearance of the app | |
| -server.R: contains the instructions needed to build the app. You can load as much strategies as you want as long as the corresponding csv file has the right format (see below). | |
| -shinyStrategyGeneral.R: loads the required packages and launches the app | |
| put ui.R and server.R file in a separate directory | |
| In the server.R file change the inputPath, inputFile and keepColumns parameters to match your setting. The first two are self explanatory the third one is a list of column names within the csv file. Keep only date and daily return |
| #include <stdlib.h> | |
| #include <sys/types.h> | |
| #include <unistd.h> | |
| int main () | |
| { | |
| pid_t child_pid; | |
| child_pid = fork (); | |
| if (child_pid > 0) { |
| { | |
| "metadata": { | |
| "name": "", | |
| "signature": "sha256:ca9fef18c8ba5d081020e90fd0b6e2aca0d619a72b0c58577939f5eeed6c8196" | |
| }, | |
| "nbformat": 3, | |
| "nbformat_minor": 0, | |
| "worksheets": [ | |
| { | |
| "cells": [ |
| nomad=fromJSON(paste(scan("http://nomadlist.io/api/v1", what="c",sep=""),collapse="")) | |
| nomad.has=function(){names(nomad[3]$cities[[1]])} | |
| nomad.get=function(y){unlist(lapply(nomad[3]$cities,function(x){x[[y]]}))} | |
| nomadCost = as.numeric(nomadvar("nomadCost")[names(nomadvar("nomadCost"))=="USD"]) | |
| nomadScore = as.numeric(nomadvar("nomadScore")) | |
| plot(nomadCost, nomadScore) | |
| cost.vs.score = lm(nomadScore ~ nomadCost) | |
| abline(cost.vs.score,col="red") |