Justin Marciszewski justinjm

Keybase proof

I hereby claim:

I am justinjm on github.
I am hianalytics (https://keybase.io/hianalytics) on keybase.
I have a public key ASArdGwJEbzw7JyWuRaLCyd-RW0s00eecjmU_p1jERz7OQo

To claim this, I am signing this object:

There are packages for this now!

2017-08-03: Since I wrote this in 2014, the universe, specifically Kirill Müller (https://github.com/krlmlr), has provided better solutions to this problem. I now recommend that you use one of these two packages:

rprojroot: This is the main package with functions to help you express paths in a way that will "just work" when developing interactively in an RStudio Project and when you render your file.
here: A lightweight wrapper around rprojroot that anticipates the most likely scenario: you want to write paths relative to the top-level directory, defined as an RStudio project or Git repo. TRY THIS FIRST.

I love these packages so much I wrote an ode to here.

I use these packages now instead of what I describe below. I'll leave this gist up for historical interest. 😆

	"""
	The most atomic way to train and run inference for a GPT in pure, dependency-free Python.
	This file is the complete algorithm.
	Everything else is just efficiency.

	@karpathy
	"""

	import os # os.path.exists
	import math # math.log, math.exp

	FROM gcr.io/deeplearning-platform-release/r-cpu

	# install dependencies
	RUN apt-get update && \
	apt-get install -y \
	autoconf \
	automake \
	g++ \
	gcc \
	cmake \

	# Takes an ordered vector of numeric values and returns a small bar chart made
	# out of Unicode block elements. Works well inside dplyr mutate() or summarise()
	# calls on grouped data frames.

	sparkbar <- function(values) {
	span <- max(values) - min(values)
	if(span > 0 & !is.na(span)) {
	steps <- round(values / (span / 7))
	blocks <- c('▁', '▂', '▃', '▄', '▅', '▆', '▇', '█')
	paste(sapply(steps - (min(steps) - 1), function(i) blocks[i]), collapse = '')

	# config ------------------------------------------------------------------
	## load packages
	library(googleAuthR)
	library(bigQueryR)
	library(dplyr)
	library(prophet)
	library(ggplot2)
	library(plotly)
	library(scales)

	library(googleAuthR)
	library(googleAnalyticsR)
	library(dplyr)

	#' Get Google Analytics Data for Dashboards
	#'
	#' Extract and join session and hit level data from GA, save as
	#' rds files for dashboards
	#'
	#' @param dates date range list of pairs, from and to dates

	import mimetypes
	import sys
	from collections import OrderedDict

	filename = sys.argv[1]

	def file_type(filename):
	type = mimetypes.guess_type(filename)
	return type

	library(googleAuthR)
	library(shiny)
	library(shinyjs)