# Welcome to Wrattler! This document gives a brief overview of the different sample notebooks that are available as part of the Wrattler demo. If you are new to Wrattler, then it's best to follow them in order. That way, you will learn how to use Wrattler before moving to more interesting examples. If you already know Wrattler, you can jump to the mroe interesting ones. You can also open `news.wrattler`, which is a mirror of our [progress page](https://github.com/wrattler/wrattler/wiki/Wrattler-project-progress) and documents the recent work on Wrattler. ### Evaluation and dependencies - `welcome.wrattler` - This notebook introduces Wrattler. It shows how Wrattler uses dependency tracking to make sure your results are never out-of-date and how to create various kinds of cells, pass data between them and create JavaScript visualizations. - `dependencies.wrattler` - In this notebook, we look at the dependency graph in more detail. We create a sample notebook with dependencies between R, Python and JavaScript and we use Wrattler's dependency graph visualizer to see what the dependency graph looks like. ### Visualization and JavaScript - `charts.wrattler` - This example gives an overview of data visualization tools accessible from Wrattler. Those include matplotlib in Python, ggplot in R and a variety of JavaScript libraries - here we use a popular simple library Plotly. - `jstools.wrattler` - This notebook shows the flexibility of Wrattler. It loads a simple tool (available in `fullscreen.js` in the `resources` folder), which lets us create full-screen data visualization. - `maps.wrattler` - In this example, we import the Leaflet.js libary for creating maps and we create a simple map of London with The Alan Turing Institute. This also works nicely with the above tool, letting us create large nice maps. ### Loading sample data - `rdata.wrattler` - If you want to play with Wrattler, you will need some sample data. One of the easiest ways of loading data is to use sample datasets from R, all of which are easily accessible not just in R, but in any language. - `pydata.wrattler` - For more interesting datasets, you can look at CSV files available online. The easiest way to get them into Wrattler is to use the Pandas library, which can load data from a URL. ### AI assistants - `aiassistants.wrattler` - Data wrangling constitute 80\% of typical data engineering work. This notebook demonstrates AI assistants, semi-automatic interactive tools to streamline data wrangling tasks such as merging data, removing outliers and inferring data types. ### Organizing code - `imports.wrattler` - Having large blocks of code in a notebook is inconvenient and a bad software engineering practice. Wrattler lets you organize code using files. This example shows how to put helper functions into separate files and load them. ### Sample data analyses - `scenic-analysis.wrattler` - The Broken Window hypothesis proposes that visible signs of crime and civil disorder creates an environment that encourages further crime and disorder. In this notebook, we study the relationship between crime rates and scenicness in areas across London, as well as accounting for other indices of deprivation.