Gain insights into creating interactive web data visualizations with D3.js. Learn about SVGs, selections, advanced scatter plots, scales, animations, and interactivity to transform data into meaningful insights.

In this course, you will learn to create your own interactive data visualizations for the web with D3.js, a very popular JavaScript library. 

Starting with the fundamentals, you'll learn about SVG, selections, classes, and styles, as well as displaying data. This will give you the base to start working with more advanced concepts. 

Once you’ve got the basics out of the way, you will start practicing drawing various scatter plots, where you’ll learn all the best practices for creating stunning visualizations.

Lastly, you will dive into scales and one of the more fun parts: adding animation and interactivity.

By the time you finish this course, you will have a powerful new library to add to your resume, and you’ll be able to transform data into meaningful insights.

Master D3.js for Data Visualization

We are going to add a bonus feature to our histogram. The reader may want to know what the mean is in our dataset. The mean is the average value in our dataset. Averages give readers a good idea of what to expect from our data. 

We will add the average value by drawing a line in our histogram. The line will be positioned where the average is. In addition, the line will animate into a different location whenever the metric changes. Let’s get started.

# Creating the line

In the script section, we are going to create a selection for the line. There will only be one line in our image. It would not be a bad idea to draw it before we draw the histogram. We are going to draw the line right before the `histogram()` function.

Create a variable called `meanLine`. Its value will be the `ctr.append()` function. 

const meanLine = ctr.append("line")
  .classed('mean-line', true)

The element we will insert into the container is called `line`. The line shape is an SVG shape that draws a line. When we need to update the line, we are going to reference the `meanLine` selection.

The `mean-line` class is defined in the stylesheet that is provided in the CSS section. It will change the color of the line to red and make it look a little thicker than the default. The browser will draw the line, but it will be invisible because we need to set the coordinates. We are not going to set the coordinates because we do not know where to draw the line yet. 
# Calculating the mean

We are going to set the coordinate after the bars and labels have been drawn. We will manipulate the line after drawing the labels in our function. The first thing we need to figure out is the mean. The mean is the average value in our dataset. 

D3 comes with a function for calculating the mean for us. We will create a variable called `mean`. Its value will be the `d3.mean()` function.

```javascript
const mean = d3.mean(dataset, xAccessor)
```

The `mean()` function will calculate the mean based on the data we provide it. It has two arguments. The first argument is the data. We are passing in the `dataset` variable.

We are providing the original dataset, not the formatted dataset. The `mean()` function does not require the data to be formatted. So, we will just provide it with the original dataset. The second argument is an accessor function. We need to tell it which metric it should calculate the mean for. We are passing in the `xAccessor` function.

The value returned by this function will be the mean. We can begin to move the line. 

# Moving the line

After calculating the mean, we will chain the `transition()` function on the `meanLine` selection.

The mean shows the average within a dataset.

Overview

SVG Basics

D3 Fundamentals

Drawing a Scatter Plot

Scales

Animations & Interactivity

Even More Charts!

Conclusion

Adding the Mean

Creating the line