We dig in right away, starting with a timeline of temperature over time. First, we need to get our bearings.

Making Your First Chart

First things first, we learn how to load our dataset and look at the structure.

Loading the weather dataset

We learn how to set up our chart - accessor functions are crucial for easy changes, documentation, and framing.

Setting up our line chart

To set up our chart environment, we learn why we need two containers: wrapper and bounds.

Drawing our chart

We create our wrapper and learn about d3 selection objects.

Creating our workspace

We learn how to create new elements inside of a d3 selection object.

Adding an SVG element

We create our bounding box, and shift it to respect our margins.

Creating our bounding box

We learn about scales and create our first ones: our y and x scales.

Creating our scales

We learn about path SVG elements and how to construct a string for their d attribute.

Drawing the line

We learn how to create axes and transform them into position.

Drawing the axes

Let's consolidate what we just learned with an exercise to play with this week.

Week 1: Exercise

Datasets

We dive into making a slightly more complex chart: a scatter plot. In this lesson, we talk about why you would create a scatter plot and its basic anatomy.

Making a Scatterplot

We solidify our foundation by splitting our chart-creating code into seven general steps.

Steps in drawing any chart

We talk through our first step: accessing our data.

Step 1: Access data

The second step: creating chart dimensions. This time, we learn how to create a square chart that fits within any browser window.

Step 2: Create chart dimensions

The third step: drawing our canvas. We create our wrapper & bounds, then shift them to respect our margins.

Step 3: Draw canvas

The fourth step: creating our scales. We talk about scales in more depth, and learn about d3.extent() & .nice().

Step 4: Create scales

The fifth step: drawing our data. This is an important lesson! We talk about data joins, which are one of the trickiest parts of d3, and necessary for updating our charts & binding our visualization to data.

Step 5: Draw data

Our sixth step: drawing peripherals. We draw our axes, learn about the text SVG element, and how to add (and rotate) labels.

Step 6: Draw peripherals

Our hard work completed, we sit back and talk about what insights our chart can teach us.

Looking at our chart

We learn how to create a custom color scale, then we color our circles based on another metric: cloud cover.

Extra credit: adding a color scale

Week 2: Exercise

Next, we embark on a mission to create a slightly more complex chart: a bar chart. We talk about what we can learn from a bar chart, and what a histogram is.

Making a Bar Chart

Next, we grab our data and create our accessor function (only one this time!)

Access data

A hopefully familiar step by now: creating our chart dimensions.

Create dimensions

Creating our wrapper and bounds elements.

Draw canvas

We create our scales, but first, we learn how to split our data into equally-sized bins.

Create scales

We finally get to draw our bars! We draw them in groups, so we can position them as well as labels.

Draw data

We draw a label over each bar, showing the number of points within that bin.

Adding Labels

We draw a line depicting the mean of our distribution, as well as our axes.

Draw peripherals

We talk about how to generalize our code, and change it so that we draw multiple histograms for different metrics.

Extra credit

This is a great lesson, and one that's hard to find good content on. We talk about the ways to make our charts accessible to screen readers, and walk through changing our histogram.

Accessibility

Week 3: Exercise

We talk about the different ways we can animate changes in our charts, and rule out SVG <animate>.

Animations and Transitions

We learn about CSS transitions and the different CSS transition properties, then run through a concrete example and look at how to debug them.

CSS transitions

We add CSS transitions to our histogram, animating changes as the bars change.

CSS transitions with a chart

We learn about d3.transition, and talk about when we would use it instead of CSS transitions. We then update our histogram to animate using d3.transition.

d3.transition

We talk through a more complex example: animating our line chart when it gets new data. This is trickier than it might seem at first!

Lines

Week 4: Exercise

Let's start talking about adding interactions to our charts!

Interactions

We learn how to add interaction event listeners using our d3 selection objects, then run through a concrete example.

d3 events

We can't let our event listeners hang around forever - we learn how to clean up after ourselves and cancel old ones.

Destroying d3 event listeners

We add a tooltip to our histogram. This involves creating a tooltip, updating its contents to show information about the hovered bar, and moving above the hovered bar.

Bar chart

We add a tooltip to our scatter plot. Along the way, we learn about d3.timeFormat and learn a great trick for making our tooltips feel smoother, using voronoi.

Scatter plot

Lastly, we add a tooltip to our timeline. We talk about how to add a listener rectangle, d3.leastIndex(), and how to add a dot to mark our place.

Line chart

Week 5: Exercise

It's really important that we understand the fundamentals of data visualization design, and what kind of chart to create.

Data Visualization Basics

We talk about the different types of data: the main buckets of qualitative and quantitative metrics, and their sub-categorizations.

Types of data

We talk about the different ways to visualize a single metric, and how to combine that with the different data types.

Ways to visualize a metric

We talk through a few tips & tricks that I use when designing charts.

Chart design

We run through a full redesign of a chart, learning about the motivation and implementation of each change.

Example redesign

Choosing colors is one of the hardest parts of designing a data visualization! We talk about the three main types of color scales, and related functions that are built into d3.

Color scales

We learn how to create our own custom color scales and specify their color space.

Custom color scales

We learn about three color spaces (rgb, hsl, and hcl) and the pros and cons of each.

Color spaces

We talk about a few tips & tricks for choosing effective, accessible colors for our data visualizations.

Color tips

Week 6: Exercise

Let's dive into a complicated data visualization: a circular chart showing overall weather for the whole year

Radar Weather Chart

We're going to breeze through the first four steps: accessing our data, creating our dimensions, drawing our canvas, and creating our scales.

Getting set up

We're going to skip step 5 and use step 6 to get our feet wet with angular math. We'll draw angular grid lines for each month, circular grid lines for temperature, and label both axes.

Adding gridlines

To ease into our drawing data step, let's start by adding a circle that shows what temperatures are below freezing.

Adding freezing

We create a radial area shape, bounded by the min and max temperatures for each day. Then we create a gradient and use it to color our area.

Adding the temperature area

We add marks to show which days are above a certain UV index threshold.

Adding the UV index marks

Next, we'll add our ring of circles around the outside to represent the amount of cloud cover for each day. We'll also learn something really important about visualizing data using circles.

Adding the cloud cover bubbles

Next, we learn about ordinal scales and create the inner ring of circles to show the precipitation probability and type for each day.

Adding the precipitation bubbles

We'll add annotations to our chart to tell our viewer what the different parts of the chart are showing. To do this, we'll create a generic function, since there are so many things to label.

Adding annotations

Onto our last step: adding interactions! The tooltip we'll build will be unlike any you've seen before - it will follow the mouse all the way around our chart, highlight the relevant data elements, and describe the weather for that hovered date.

Adding the tooltip

A quick debrief! Congrats on making it through!

Wrapping up

Week 7: Exercise

Advanced example

We'll talk about the best way to incorporate d3 when using a javascript framework like React, Svelte, or Angular.

D3 + Javascript Frameworks

We are introduced to React.js and learn a basic principle for combining d3 + React.

React.js

We walk through the structure of our React application, then dive into the first step of our chart drawing checklist: accessing our data

Digging in

Creating dimensions in React can be really easy! We use a custom hook for watching the size of our wrapper and automatically calculating the dimensions of our bounds.

Creating dimensions in React

We create our wrapper and bounds within our Chart component to prevent from having to repeat ourselves for every chart we create.

Drawing our canvas in React

Next, we need to create our scales. We also create scaled accessor functions to pass to the children of our Timeline, so they don't need to know about our scales.

Creating our scales in React

Next, we fill out our Line component for a flexible way to draw our data.

Drawing our data in React

We take a naive approach to creating an Axis component

Drawing our peripherals in React

We learn an alternate, less hacky solution for creating an Axis component that lets React do all of the rendering.

Drawing our peripherals in React, take two

We wrap up by talking about how to add interactions and look at the completed code for a Timeline, Scatter plot, and Histogram.

Setting up interactions in React, and wrapping up

We take a look at how to combine d3 + Angular, with a complete codebase.

Using d3 with Angular.js

We take a look at how to combine d3 + Svelte.js, with a complete codebase.

Using d3 with Svelte.js

Coming soon! Interviews with amazing experts in the data visualization field. You'll learn about how different people get into data visualization, what most excites them, and their tips & tricks.

Interviews

Shirley and I talk about her job title, how she got into data visualization, how working in industry is different than working as a freelancer, and her new book!

Shirley Wu

Ian and I talk about his long history of building up the d3.js community, his day-to-day working at Observable, and the value of breaking projects down into small pieces.

Ian Johnson

Russell Goldenberg talks to us about what it's like working at the Pudding, his journey from Comp Sci to creative computation, and drops some great tips for working on data viz projects.

Russell Goldenberg

Will Chase talks us through how he got into data viz, the difference between work and freelance projects, and walks us through his most recent, wonderful visualization of hurricane names.

Will Chase

Charts communicate ideas with simplicity, clarity, and detail.

Once you're comfortable with D3 you can quickly:

* understand key metrics at a glance
* catch anomalies (before they turn into problems)
* give key insights to your customers
* and impress your boss and co-workers

Today, I will show you the exact methods I use to create beautiful, understandable data visualizations using D3.

The Fullstack D3 masterclass is the complete guide to D3. With dozens of code examples showing each step, you will gain new insights into your data by creating visualizations in this self-paced online course.

Fullstack D3 Masterclass

How to create any visualization you can imagine

Given a dataset, the first task is to determine the structure of the available metrics. Let's look at the first item of our weather dataset.

There are many different values here, but two basic types: 

. These two types can roughly be split (respectively) into two basic types of data: 

 data (our strings) does not have a numerical value, but it can be put into categories. For example, 

 can either have a value of "rain" or "snow".

 data (our numbers) is numerical and can be measured objectively. For example, 

 has values ranging from 10°F to 100°F.

Both of these types of data can be broken down even further.

 data can be placed into only two categories.

---
title: "Types of data"
description: "We talk about the different types of data: the main buckets of qualitative and quantitative metrics, and their sub-categorizations."
useMdx: true
privateVideoUrl: https://fullstack.wistia.com/medias/el1xo4pfe2
---

# Types of data

Given a dataset, the first task is to determine the structure of the available metrics. Let's look at the first item of our weather dataset.

![Our dataset](https://d2uusema5elisf.cloudfront.net/courses/fullstack-d3-masterclass/module_05/lesson_05.01/public/images/1-making-your-first-chart/dataset.png)

There are many different values here, but two basic types: **strings** and **numbers**. These two types can roughly be split (respectively) into two basic types of data: **qualitative** and **quantitative**.

**Qualitative** data (our strings) does not have a numerical value, but it can be put into categories. For example, `precipType` can either have a value of "rain" or "snow".

![data type - qualitative](https://d2uusema5elisf.cloudfront.net/courses/fullstack-d3-masterclass/module_05/lesson_05.01/public/images/7-data-visualization-design/qualitative.png)

**Quantitative** data (our numbers) is numerical and can be measured objectively. For example, `temperatureMax` has values ranging from 10°F to 100°F.

![data type - quantitative](https://d2uusema5elisf.cloudfront.net/courses/fullstack-d3-masterclass/module_05/lesson_05.01/public/images/7-data-visualization-design/quantitative.png)

Both of these types of data can be broken down even further.