IronistM

library(RCurl)
library(XML)
library(plyr)

#' get the Qualys SSL Labs rating for a domain+cert
#' 
#' @param site domain to test SSL configuration of
#' @param ip address of \code{site} (will resolve it and take\cr
#'  first response if not specified, but that may not always work as you expect)
#' @param pause timeout between tries (default 5s)

IronistM

require(redshift)

conn <- redshift.connect("jdbc:postgresql://mycluster.redshift.amazonaws.com:5439/data", "user", "pass")

# we can retrieve a list of tables
tables <- redshift.tables(conn)

# and get some info about the columns in one of those tables
cols <- redshift.columns(conn, "weblog")

IronistM

function onOpen(){
  // example send for Sheets
  sendGAMP("UA-XXXX-1", SpreadsheetApp.getActiveSpreadsheet().getUrl());
  
  // example send for Documents
  //sendGAMP("UA-XXXX-1", DocumentApp.getActiveDocument().getUrl());
  
  // example send for Forms *NOTE* getUrl not implemented yet in New Sheets
  //sendGAMP("UA-XXXX-1", FormApp.getActiveForm().getUrl());
}

IronistM

// Includes functions for exporting active sheet or all sheets as JSON object (also Python object syntax compatible).
// Tweak the makePrettyJSON_ function to customize what kind of JSON to export.

var FORMAT_ONELINE   = 'One-line';
var FORMAT_MULTILINE = 'Multi-line';
var FORMAT_PRETTY    = 'Pretty';

var LANGUAGE_JS      = 'JavaScript';
var LANGUAGE_PYTHON  = 'Python';

IronistM

#########################################
## GLOBAL REQUIREMENTS AND DEFINITIONS ##
#########################################
require(RCurl)
require(XML)

loginURL        <- "https://accounts.google.com/ServiceLogin"
authenticateURL <- "https://accounts.google.com/accounts/ServiceLoginAuth"

IronistM

####################
# Create relogit predicted probabilities using Zelig and ggplot2
# Two Sword Lengths: Losers' Consent and Violence in National Legislatures (Working Paper 2012)
# Christopher Gandrud
# Updated 26 April 2012
###################

## Load required packages
library(RCurl)
library(Zelig)

IronistM

# Library Loading
library("RPostgreSQL");
library("car");

# Connect to Database
pgDrv <- dbDriver("PostgreSQL")
dbh <- dbConnect(pgDrv, host="localhost", dbname="dnsmonitor", user="dnsmon", password="tooEasy")

# Retrieve Statistics from DB
stats <- dbGetQuery(dbh, "select client.id, client.ip, sum(queries) as queries, sum(nx) as nx, sum(answers) as answers, sum(errors) as errors, count(distinct day) as days_active

IronistM

suppressMessages(library(forecast))

data<-read.csv( file('stdin') )

anomaly_detection<-function(data){
  
  seasonality<-48
  data_series<-ts(data$count,frequency=seasonality)
  
  train_start<-1 ## train on 1 month of data

IronistM

ANOVA<-function(fit1,fit2){
  temp <- anova(fit2,fit1 )
  fin.aov <- anova(fit1)   
  reg <- temp[2,2:6]
  rownames(reg) <- "Regression"
  reg[1,1:2] <- reg[1,2:3]
  reg[1,3] <- reg[1,2]/reg[1,1]
  colnames(reg) <- colnames(fin.aov)
  res <- fin.aov[tail(nrow(fin.aov),1),]
  tot <- cbind(reg[1,1:2]+res[1,1:2],NA,NA,NA)

IronistM

N <- 10
id  <-  1:10
x  <- 1 + rnorm(N) - 1*id
date  <- seq(as.Date("2013-07-01"), by = "year", along = x)
df  <- data.frame(x = x, date = date)
plot(df$date, df$x)
summary(lm(x ~ date, data = df))