tonyfischetti

(define (add6 n)
  (+ 6 n))
(define (return5)
  5)

;I want to create a macro (or series of macros) that will expand this:
(>>
  (return5)
  (add6)
  (display))

tonyfischetti

#!/usr/bin/Rscript --vanilla


rm(list=ls())

options(stringsAsFactors=FALSE)

# libraries
library(dplyr)
library(magrittr)

tonyfischetti

#!/usr/bin/Rscript --vanilla

###########################################################
##                                                       ##
##   elastic.R                                           ##
##                                                       ##
##                Author: Tony Fischetti                 ##
##                        [email protected]       ##
##                                                       ##
###########################################################

tonyfischetti

#!/usr/bin/env bash


THE_USER="janeway"
THE_HOME="/home/$THE_USER"

apt-get update
apt-get -y upgrade
apt-get install -y --force-yes rlwrap git vim zsh build-essential silversearcher-ag automake pkg-config moreutils libevent-dev libtinfo5 libncurses5-dev apt-transport-https python-pip python-webpy ipython htop sbcl

tonyfischetti

<small>*Version 2.0 of my data set validation package `assertr` hit CRAN just this weekend. It has some pretty great improvements over version 1. For those new to the package, what follows is a short and new introduction. For those who are already using `assertr`, the text below will point out the improvements.*</small>

I can (and have) go on and on about the treachery of messy/bad datasets. Though its substantially less exciting than… pretty much everything else, I believe (proportional to the heartache and stress it causes) we don’t spend enough time talking about it or building solutions around it. No matter how new and fancy your ML algorithm is, it’s success is predicated upon a properly sanitized dataset. If you are using bad data, your approach will fail—either flagrantly (best case), or unnoticeably (considerably more probable and considerably more pernicious). 

`assertr` is a R package to help you identify common dataset errors. More specifically, it helps you easily spell out your assumptions abou

tonyfischetti

Keybase proof

I hereby claim:

• I am tonyfischetti on github.
• I am tonyfischetti (https://keybase.io/tonyfischetti) on keybase.
• I have a public key ASB5SjGUR6lOGMRSTT_YDrLJgSE7TyjJIx9EBD4TLmm-4Qo

To claim this, I am signing this object: