ggplot2 is a great tool for complex data visualization. Let’s practice it a bit! Answers to these exercises are available here. For each exercise, please replicate the given graph. Some exercises require additional data wrangling. If you obtained a different (correct) answer than those listed on the solutions page, please feel free to post your […]

# data visualization

## GGplot Drilling: Exercises

Visualization is a key component to understanding and communicating your understanding to an audience. The more second nature turning your data into plots becomes, the more you can focus on the overall goals instead of being stuck on technical details. As a freelance data analyst, I know that often times between when a project […]

## Spatial Data Analysis: Introduction to Raster Processing (Part 2)

Background In the second part of this tutorial series on spatial data analysis using the raster package, we will explore new functionalities, namely: Raster algebra Cropping Reprojection and resampling We will also introduce a new type of object named RasterStack, which, in its essence, is a collection of RasterLayer objects with the same spatial extent, […]

## Graph Theory: Using iGraph Exercises (Part-2)

Following on from last time, this tutorial will focus on more advanced graph techniques and existing algorithms such as Dijkstra’s algorithm that can be used to draw real meaning from graphs. This is part 2 in the series of iGraph tutorials, for part 1, click here. When completing these tutorials be sure to read up […]

## Protected: Bonus: Sentiment Analysis using TidyText Exercises

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## Graph Theory: Using iGraph Exercises (Part-1)

This is part 1 of a series in analyzing and visualizing network data using iGraph. The rest of the series can be found here. Graph Theory, or network analysis as it is often called, is the mathematical portrayal of a series of edges and vertices. To contextually picture a network, think of each node being […]

## Visual Data Exploration Exercises

The first thing you should do when you start working with new data is to explore it to learn what’s in there. The easiest way to do this is by visualization. Distributions, point plots, etc. They are very helpful, but plotting all of them for each variable or pair of variables can be time-consuming. That’s […]

## Plotly basic charts – exercises

INTRODUCTION Plotly’s R graphing library makes interactive, publication-quality web graphs. More specifically it gives us the ability to make line plots, scatter plots, area charts, bar charts, error bars, box plots, histograms, heatmaps, subplots, multiple-axes, and 3D charts. In this tutorial we are going to make a first step in plotly’s world by learning to […]

## How to plot basic charts with plotly

INTRODUCTION Plotly’s R graphing library makes interactive, publication-quality web graphs. More specifically it gives us the ability to make line plots, scatter plots, area charts, bar charts, error bars, box plots, histograms, heatmaps, subplots, multiple-axes, and 3D charts. In this tutorial we are going to make a first step in plotly’s world by learning to […]

## ggvis Exercises (Part-2)

INTRODUCTION The ggvis package is used to make interactive data visualizations. The fact that it combines shiny’s reactive programming model and dplyr’s grammar of data transformation make it a useful tool for data scientists. This package may allows us to implement features like interactivity, but on the other hand every interactive ggvis plot must be […]