demel

## data input (number of reads mapped to each category)
total=100
rRNA=5 # mapped to nuclear rRNA regions
mtRNA=7  # mapped to mitochondria genome
# for the rest of above, then we divide into different category, like http://www.biomedcentral.com/1741-7007/8/149 did.
intergenic=48 
introns=12
exons=30
upstream=3
downstream=6

demel

#  ---------------------------------------------------------------------------
#
#  Description:  This file holds all my BASH configurations and aliases
#
#  Sections:
#  1.   Environment Configuration
#  2.   Make Terminal Better (remapping defaults and adding functionality)
#  3.   File and Folder Management
#  4.   Searching
#  5.   Process Management

demel

Movie Recommendations with k-Nearest Neighbors and Cosine Similarity

---

Introduction

The k-nearest neighbors (k-NN) algorithm is among the simplest algorithms in the data mining field. Distances / similarities are calculated between each element in the data set using some distance / similarity metric ^^[1]^ that the researcher chooses (there are many distance / similarity metrics), where the distance / similarity between any two elements is calculated based on the two elements' attributes. A data element’s k-NN are the k closest data elements according to this distance / similarity.

---

1. A distance metric measures distance; the higher the distance the further apart the neighbors. A similarity metric measures similarity; the higher the similarity the closer the neighbors.

demel

library(shiny)
library(datasets)
Logged = FALSE;
PASSWORD <- data.frame(Brukernavn = "withr", Passord = "25d55ad283aa400af464c76d713c07ad")
# Define server logic required to summarize and view the selected dataset
shinyServer(function(input, output) {
  source("www/Login.R",  local = TRUE)
  
  observe({
    if (USER$Logged == TRUE) {

demel

library(rredis)

shinyServer(function(input, output) {
    
    output$show <- renderText({
        redisConnect()
        res <- redisGet(input$key)
        redisClose()
        return(res)
    })

demel

## Reproducible package management for R:
install.packages("devtools")
library(devtools)

#devtools::install_github("rstudio/packrat")
# More info @ http://rstudio.github.io/packrat/

## great shiny apps
install.packages("radiant", repos = "http://vnijs.github.io/radiant_miniCRAN/")

demel

# An interface to data hosted online in Socrata data repositories
# This is the main file which uses other functions to download data from a Socrata repositories
#
# Author: Hugh J. Devlin, Ph.D et al. 2013-08-28
###############################################################################

# library("httr")       # for access to the HTTP header
# library("jsonlite")   # for parsing data types from Socrata
# library("mime")       # for guessing mime type
# library("geojsonio")  # for geospatial json

demel

basic httr idiom for splunk things tho it gets uglier with the saved searches

setup 3 variables in your .Renviron (the obvious ones from the code)

sample run:

> tmp <- search_now("host=vagrant sourcetype=syslog session closed")
> head(tmp)

X_serial X_time source sourcetype host index splunk_server

demel

Jenkins CI on OSX

Instructions on how to setup a secured Jenkins CI on a Mac.

Download & Install dependencies

All of these operations are done with your admin user.

Developer tools

Install the command line developer tools.

demel

#Install R and then following packages
#repr failed to create 
yum install R-*
install.packages("evaluate", dependencies = TRUE)
install.packages("base64enc", dependencies = TRUE)
install.packages("devtools", dependencies = TRUE)
install_github('IRkernel/repr')
install.packages("dplyr", dependencies = TRUE)
install.packages("caret", dependencies = TRUE)
install.packages("repr", dependencies = TRUE)