Examples of how Data Scientists are using Observable

Allison Horst

Observable

Wrangling data in JavaScript with Arquero: a primer for R users

Jupyter-ObservableHQ Bridge

Job van der Zwan

Variations on Symmetric Nearest Neighbour smoothing

Kaydee Hartmann

Introduction to Linear Regression

Bryan Gin-ge Chen

Union-find data structure

Harry Stevens

Introducing d3-regression

Jeffrey Heer

UW Interactive Data Lab

Tidy Data in JavaScript

Thomas Ballinger

Visualize a data frame with Observable, in Jupyter

Maria Ciampa

Using "import" is a fast and intuitive way to access helpful code and content from across the vast network of Observable notebooks.

3 ways using 'Import' expedites data science workflows

Maya Gans

When I use Observable in my Data Science Workflow:

Paul Buffa

Data wrangling essentials: comparisons in JavaScript, Python, SQL, R, and Excel

Matt Dzugan

Classifying Penguin Species 🐧 🐧 🐧

Descriptive statistics: comparisons in JavaScript, Python, SQL, R, and Excel

Christoph Pahmeyer

Hierarchical Clustering

Principal Component Analysis (PCA)

Mario Delgado 

Estimating Logistic Regression Parameters

Tom MacWright

Quick sentiment analysis with TensorFlow

Linear models in Observable notebooks

Robert Lesser

Wordle, 15 Million Tweets Later

Andy Fleming

Western Power

Power and Energy Model

Rene Cutura

Hierarchical Clustering with Druid.js 🍃

Kerry Rodden

Electoral College Decision Tree

"Ta" Virot C.

Machine Learning in JS with TensorFlow.js (Part I)

Ian Johnson

Working with SQL

WinkJS

How to visualize key sentences in a document?

Collections related to specific areas of interest, curated by the Observable team. 

Topics

Data Science 

Collection of notebooks related to data science in Observable

Data Science in Observable

Data Analysis

Workflow Resources

Rather than hardcoding the column names, how can one specify column names through a variable? Let's say from a checkbox input for example.

Roy Francis

Sure! You could do something like have one cell like this:

columns = ['a', 'b', 'c', 'd']

And then the map would be:

myData.map(d => ({...columns.map(e => d[e])}) )

You could use checkbox input for the "columns" cell so its interactive. I can make a quick example if this doesnt make sense :)

Thanks! This was helpful. I would love to see a full realistic analysis workflow. For example; Read in a csv file, capture the column names as a variable, filter columns to keep, then select columns to filter by rows (categorical and numeric filtering) with input widgets and all that through variable without any hard coded values. ie; it should work with any random csv file.

You got it! This should accomplish what you're looking for https://observablehq.com/d/55dd0fe0f276ccfd

The function `d3.rollup` returns a Map, which does not allow for further reference to the elements, by their names. How to deal with that if I want to do some more grouping or other wrangling afterwards?

JuliaR

Good question! You can use d3.flatRollup or d3.rollups instead to return an array. Combined with map (as shown here: https://observablehq.com/@d3/d3-flatgroup#cell-54), this will get your grouped values back into a 2D array that you can continue with.