justinjm

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

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

justinjm

# 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 = '')

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:

justinjm

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

justinjm

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

justinjm

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. 😆

justinjm

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

justinjm

import mimetypes
import sys
from collections import OrderedDict

filename = sys.argv[1]

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