hyeshik

{
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  "signature": "sha256:f99925a6f73a1e36bc91415d84266705e3bdf72304d8dba7bcfb6c94ca7b270a"
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 "nbformat": 3,
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ZeccaLehn

Shows function Internals

library(BayesianFirstAid)
debugonce(bayes.t.test) # turns off debugging for function after first run
bayes.t.test(co2)

Returns hidden and unhidden environments

dgrtwo

---
title: "Effect of compression type and file complexity on saveRDS size and speed"
author: "David Robinson"
date: "April 20, 2015"
output: html_document
---

```{r echo = FALSE}
knitr::opts_chunk$set(cache = TRUE, message = FALSE)
```

gluc

This gist shows how to convert a nested JSON file to an R data.frame. To do this, it uses jsonlite and data.tree.

The gist contains two examples: one is a bit simpler, the second one a bit more advanced.

Example 1

In the first example, we download all the repos from Hadley Wickham's Github account from https://api.github.com/users/hadley/repos . This JSON contains a nested owner object. The code shows how to convert that in a flat data.frame in three statements:

1. line 5: download
2. line 8: convert to data.tree

mick001

# Set a seed
set.seed(500)

library(MASS)
data <- Boston

# Check that no data is missing
apply(data,2,function(x) sum(is.na(x)))

# Train-test random splitting for linear model

gcr

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": [
     "worksheet-0"
    ]
   },
   "source": [

gerarldlee

Installing ChromeOS on VirtualBox

1. Download the NeverWare's ChromeOS build from http://www.neverware.com/freedownload

2. Extract the *.bin.zip

3. Convert it to VDI. vboxmanage convertdd chromiumos_image.bin chromiumos_image.vdi

4. mv chromiumis_image.vdi C:\t\v\chromeos\

max-mapper

{"help": "https://catalog.data.gov/api/3/action/help_show?name=package_search", "success": true, "result": {"count": 48, "sort": "views_recent desc", "facets": {}, "results": [{"license_title": "License not specified", "maintainer": "New Media", "relationships_as_object": [], "private": false, "maintainer_email": "[email protected]", "num_tags": 5, "id": "59694770-b6b6-4ae0-a4b9-4ae69c0be2f6", "metadata_created": "2016-07-02T10:06:26.199575", "metadata_modified": "2016-07-02T10:06:26.199575", "author": null, "author_email": null, "state": "active", "version": null, "creator_user_id": "47303a9e-1187-4290-85a3-1fc02dc49e4a", "type": "dataset", "resources": [{"cache_last_updated": null, "package_id": "59694770-b6b6-4ae0-a4b9-4ae69c0be2f6", "webstore_last_updated": null, "id": "3a8a0ad1-19e7-4153-bb2f-d70cf88aaaf8", "size": null, "state": "active", "hash": "", "description": "", "format": "CSV", "tracking_summary": {"total": 32, "recent": 1}, "last_modified": null, "url_type": null, "no_real_name": "True", 

Eunsol-Lee

solve <- function(n) {
  result <- largeSubtract(largeAdd(sumMultiple(n, 3), sumMultiple(n, 5)), sumMultiple(n, 15))
}

right <- function (string, char){
  substr(string,nchar(string)-(char-1),nchar(string))
}

left <- function (string,char){
  substr(string,1,char)

swyoon

import numpy as np
import tensorflow as tf

__author__ = "Sangwoong Yoon"

def np_to_tfrecords(X, Y, file_path_prefix, verbose=True):
    """
    Converts a Numpy array (or two Numpy arrays) into a tfrecord file.
    For supervised learning, feed training inputs to X and training labels to Y.
    For unsupervised learning, only feed training inputs to X, and feed None to Y.