Recipes

Sometimes you’re not hungry for a background-heavy lunch but like to snack
on something fast: here are some recipes! This section contains small snippets that
will help you in the process of data wrangling. They might be small useful tips or
full-blown tutorials on tools or topics. Come in and find out!

How to find data
- Edited directories
Walkthrough: Downloading Data from the World Bank
Liberating HTML Data Tables
- Screenscraping HTML Tables Using Google Spreadsheets
Scraping websites using the Scraper extension for Chrome
- Walkthrough: Scraping a website with the Scraper extension
- Walkthrough: extended scraping with the Scraper extension
A short introduction to HTML
- Walkthrough: Exploring HTML with Google Chrome
- Other browsers
- HTML Elements
- Python code elements for scraping
Scraping – Beyond the Basics
- Extracting Data
Scraping multiple Pages using the Scraper Extension and Refine
- Walkthrough: Getting a list of URLs with scraper extension
- Walkthrough: Scraping multiple pages using Refine
Extracting Data from PDFs using Tabula
- Waltkthrough: Extracting data from PDF tables
Cleaning up Data Scraped from the Web
- Useful clean up steps
Sorting Data with Spreadsheets
- Walkthrough: Sorting a dataset.
Filtering Data
- Walkthrough: Filtering Data
Spreadsheet Formulae
- A quick introduction to common spreadsheet symbols
Geocoding Data in a Google Docs Spreadsheet
- App Script
Using a spreadsheet to clean up a dataset
- Introduction
  - What is this recipe and what do you get out of it?
  - Get set up: data and tools you will need to follow this recipe
- Problem 1: Showing the data plainly
  - What’s the problem?
  - What’s the solution?
- Problem 2: Whitespace and new lines – data that shouldn’t be there
- Problem 3: Blank cells – missing data that should be there
  - What’s the problem?
  - What’s the solution?
- Problem 4: Fixing numbers that aren’t numbers
- Problem 5: Structural problems – data in inconvenient places
  - What’s the problem?
  - What’s the solution?
- Problem 6: from “banabas” to “bananas” – dealing with inconsistencies in data
  - What’s the problem?
  - What’s the solution?
- Finishing touches
Cleaning Data with Refine
- What you’ll need:
- Step 1: Creating a new Project
- Step 2: Sorting and Facetting
- Walkthrough: Facetting rows based on a column
- Step 3 Dealing with Blank Cells
- Step 4: Fighting the Invisible Man
- Step 5. Reconciling categories
- Step 6: Splitting Columns
Creating Line Charts
Walkthrough: Scatterplot
Creating a Choropleth map
Walkthrough: Presenting our information as a webpage
Harvesting and Analyzing Tweets

Last updated on Dec 10, 2013.