Introduction
Here’s a collection of cool data visualization projects that I came across. Let me know if you have any other favorites!
Visualizing Data With D3.js
D3.js is a JavaScript library for visualizing data in web browsers. It’s a tool for creating interactive visualizations in HTML, SVG and CSS. D3 stands for Data-Driven Documents, which is fitting because it uses web standards to work in all modern browsers and falls back to VML for older ones.
D3 provides an intuitive way to create charts from scratch or by leveraging existing charting libraries like Highcharts or Google Charts (which are based on D3).
How to Make a Bar Chart in Excel
To make a bar chart, you’ll need to create a new worksheet in Excel and add the data that you want to visualize. Here’s how:
- Open up Excel and select File > New. This will open up the “Create A Document” window where you can choose what type of file you want to create (in our case we’re going with Workbook).
- Type in your column headers into cells A1 through C3 (or whatever range your data is in). In this example, I’ve typed in “Name”, “Age” and “Height”.
- Select all of these cells by clicking on cell A1 then holding down shift while pressing end or using Ctrl + Shift + End keys on Windows computers. Once all three columns have been selected click anywhere else on screen so that no cells are highlighted anymore; this means all three columns should now be selected without having any other rows highlighted at all! Now click on Insert tab from toolbar above menu bar at top left corner of screen; then click Bar Chart icon which looks like two bars stacked atop each other vertically with dots between them (it should appear highlighted blue when clicked).
How to Make a Line Graph in Excel
Line graphs are one of the most commonly used visualizations in data analysis. They’re simple, easy to understand and communicate a lot of information in a small space.
To create a line graph in Excel:
- Select the cells you want to plot on your graph (you can select multiple columns or rows if needed). You can also create a chart from scratch by going to Insert > Chart… or Charts > Recommended Charts> Line. If you don’t see these options, make sure that “Quick Analysis” is checked off under “Show Me” on the right side toolbar as shown below:
- In order for Excel not just draw lines between points but actually connect them with lines (which looks much nicer), click on Layout Options button at top-right corner of screen next to legend icon (or press CTRL+L):
How to Make a Pie Chart in Excel
- Make a pie chart in Excel.
- Learn how to make a pie chart in Excel.
- Examples of how to make pie charts in Excel.
Making Choropleth Maps with Shiny and R
Choropleth maps are one of the most common and popular ways to display data on a geographical scale. They’re also known as thematic maps or heat maps, and they can be used to show anything from population density to unemployment rates. Choropleth mapping is useful because it allows you to quickly see where specific areas fall in relation to each other (and often gives an idea of how they compare).
Choropleth maps are made using R and Shiny, two tools that allow you to create interactive visualizations with ease. In this tutorial we’ll walk through how you can make your own choropleth map using these two tools!
3D Choropleth Map With R And Meshlab
A 3D choropleth map is a great way to visualize data. In this tutorial, we will show you how to make one using R, Meshlab and Python.
In addition to these tools, Excel can also be used for making 3D maps if you want something simpler than other options.
Scatterplot Matrix With R And ggplot2
In this tutorial, we will learn how to create a scatterplot matrix with R and ggplot2. The data set that we will use is the iris dataset from thepackage. We will use the iris variables Sepal.Length and Sepal.Width as x- and y-axis respectively.
Clustering with k-Means and d3.js Code in Python
Cluster analysis is a technique used to identify groups of items in a dataset that share similar characteristics. It’s often used to segment customers, find trends within data sets or even determine the best locations for businesses.
In this tutorial, we’ll show you how to perform cluster analysis using Python’s scikit-learn package and d3js library. First let’s take a look at some code:
import numpy as np
from sklearn import datasets; from sklearn import cluster;
data = datasets[‘iris’].data[0:4] print(cluster(data).labels_)
A Dataset On The Boston Landmarks, courtesy of MIT OpenCourseWare (CC-BY)
The Boston Landmarks Dataset is a dataset on the Boston Landmarks, courtesy of MIT OpenCourseWare (CC-BY). The dataset includes information about buildings, such as their address and height, as well as their location in the city.
Conclusion
All in all, it’s a great time to be a data scientist. There are so many opportunities out there for people with this skill set, and you can use these tools to do anything from making charts in Excel to creating 3D maps of Boston landmarks!
