rasmusab

library(tidyverse)
library(purrr)
library(rstan)

### Defining the data ###
#########################
bumblebees <- c(1, 0,  0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
                1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 
                0, 0, 1, 0, 0, 0, 0, 0, 0)
toddler_steps <- c(26, 16, 37, 101, 12, 122, 90, 55, 56, 39, 55, 15, 45, 8)

hadley

If you were to give recommendations to your "little brother/sister" on things that they need to do to become a data scientist, what would those things be?

I think the "Data Science Venn Diagram" (http://drewconway.com/zia/2013/3/26/the-data-science-venn-diagram) is a great place to start. You need three things to be a good data scientist:

• Statistical knowledge
• Programming/hacking skills
• Domain expertise

Statistical knowledge

araastat

#’ Create a Kaplan-Meier plot using ggplot2
#’
#’ @param sfit a \code{\link[survival]{survfit}} object
#’ @param returns logical: if \code{TRUE}, return an ggplot object
#’ @param xlabs x-axis label
#’ @param ylabs y-axis label
#’ @param ystratalabs The strata labels. \code{Default = levels(summary(sfit)$strata)}
#’ @param ystrataname The legend name. Default = “Strata”
#’ @param timeby numeric: control the granularity along the time-axis
#’ @param main plot title

sckott

The function modified

multiplot <- function(..., plotlist=NULL, file, cols=1, layout=NULL, 
                      labs=list(), labpos=list(c(0.5,0.03), c(0.03,0.5))) {
  require(grid)
  
  # Make a list from the ... arguments and plotlist
  plots <- c(list(...), plotlist)
  

cjbayesian

###################################################
##
## Functions for calculating AUC and plotting ROC
## Corey Chivers, 2013
## corey.chivers@mail.mcgill.ca
##
###################################################


## Descrete integration for AUC calc

akloster

# -*- coding: utf-8 -*-
# <nbformat>3.0</nbformat>

# <codecell>

from numpy import *
from pandas import *


from scipy.signal import argrelextrema

schaunwheeler

# The MIT License (MIT)
#
# Copyright (c) 2012 Schaun Jacob Wheeler
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:

davidliwei

#!/usr/bin/env python
'''
Automatically estimate insert size of the paired-end reads for a given SAM/BAM file.
Usage: getinsertsize.py <SAM file> or samtools view <BAM file> | getinsertsize.py -
Author: Wei Li

Copyright (c) <2015> <Wei Li>

malcook

library(IRanges)       # for: coverage, psetdiff, etc
library(GenomicRanges) # for: readGappedAlignments, 
library(Rsamtools)     # for: reading bam files scanBam, countBam etc
library(rtracklayer)   # for: track file IO (bed, wig, etc)
library(sqldf)         # for: querying dataframes using SQL\

bamTabulateGaps <- function(bamPath,
                            bedPath=paste(gsub('.bam$','',bamPath),'.junctions.bed',sep=''),
                            trackName=gsub('\\..*','',basename(bamPath)),
                            trackLine=sprintf("track name=%s graphType=junctions",trackName),