hrbrmstr

# slopegraph.py
#
# Author: Bob Rudis (@hrbrmstr)
#
# Basic Python skeleton to do simple two value slopegraphs
# with output to PDF (most useful form for me...Cairo has tons of options)
#
# Find out more about & download Cairo here:
# http://cairographics.org/
#

hrbrmstr

# need "chron" package for is.weekend()
require(chron)

# generate a sequence of dates
m.days <- seq.Date(from=as.Date("2013-08-01"), to=as.Date("2013-08-31"), by="1 day")

# if a day in the sequence is a weekend, return it
wknds <- ldply(m.days, function(m.day) {
  if (is.weekend(m.day)) m.day
})

hrbrmstr

# "dig" needs to be on your system and in the execution path for this to work
resolved = sapply(ip.list, function(x) system(sprintf("dig -x %s +short",x), intern=TRUE))

hrbrmstr

notify <- function(msg="Operation complete") {

  in.osx <- (Sys.info()['sysname'] == "Darwin")
  in.rstudio <- (Sys.getenv("RSTUDIO") == "1")
  in.rgui <- (Sys.getenv("R_GUI_APP_REVISION") != "")
  
  if (in.rstudio) { # hack to see if running in RStudio
    title <- "RStudio"
    sender <- activate <- "org.rstudio.RStudio"
  }

hrbrmstr

library(RCurl)
library(RJSONIO)
library(ggplot2)

# get data formats from: http://www.bls.gov/help/hlpforma.htm
# this one is "Average Hourly Earnings of All Employees: Private Service-Providing (CEU0800000003)"

bls.content <- getURLContent("http://api.bls.gov/publicAPI/v1/timeseries/data/CEU0800000003")
bls.json <- fromJSON(bls.content, simplify=TRUE)
bls.df <- data.frame(year=sapply(bls.json$Results[[1]]$series[[1]]$data,"[[","year"),

hrbrmstr

#include <string.h>
#include <arpa/inet.h>
#include <stdio.h>
#include <stdlib.h>

//
// Read in a file with one IP address per line
// and output (to stdout) the same list with
// the longint version of it.
//

hrbrmstr

set.seed(200109) # founding of The Pirate Bay

walk<-function(n) {
  m <- matrix(6.62606896e-34, ncol = 2, nrow = n) 
  i <- cbind(seq(n), sample(c(1, 2), n, TRUE)) 
  m[i] <- sample(c(-1, 1), n, TRUE) 
  m[,1] <- cumsum(m[, 1]) 
  m[,2] <- cumsum(m[, 2]) 
  m 
} 

hrbrmstr

library(maps)
library(hexbin)
library(maptools)
library(ggplot2)
library(sp)
library(mapproj)

# piRate the data from the militaRy
download.file("http://msi.nga.mil/MSISiteContent/StaticFiles/Files/ASAM_shp.zip", destfile="ASAM_shp.zip")
unzip("ASAM_shp.zip")

hrbrmstr

#!/usr/bin/python

import urllib2
from urlparse import urljoin
import sys
from bs4 import BeautifulSoup

if len(sys.argv) < 2:
   print "Usage:\n   hrefs url [url] ..."
   sys.exit(1)

hrbrmstr

library(ggplot)

nosql.df <- read.csv("nosql.csv", header=TRUE)
nosql.df$Database <- factor(nosql.df$Database,
                            levels=c("MongoDB","Cassandra","Redis","HBase","CouchDB",
                                     "Neo4j","Riak","MarkLogic","Couchbase","DynamoDB"))

gg <- ggplot(data=nosql.df, aes(x=Quarter, y=Index))
gg <- gg + geom_point(aes(color=Quarter), size=3)
gg <- gg + facet_grid(Database~.)