1. Visualizing data
Welcome to this course on data visualization with Julia! My name is Gustavo, and I will be your instructor for this course. This starts your journey to becoming a plotting expert in Julia, a language that allows you to create fast, easy-to-use, and versatile visualizations. We'll explore techniques and tools to help you unlock Julia's power and create stunning visualizations that communicate insights effectively. So, let's get started on this exciting adventure together!
2. Why should I plot?
So why does data plotting matter? Here are some compelling reasons why it can make a difference.
Data visualization helps identify patterns and trends that may be difficult to see in tabular data.
Visualizing our data can also help spot errors and outliers quickly.
3. Why should I plot?
In addition, data visualization can convey the level of uncertainty in the data to provide a more complete picture of the underlying patterns and trends.
It can also help explore relationships between variables and identify correlations, often leading to new insights into our data.
4. Table versus graph
While displaying data on a table works well with a few data points, it becomes challenging when dealing with many points. For instance, consider the table below that shows the price of shares for a fund over time. While it is possible to see that the price was higher in November 2021 compared to January 2020, extracting further insights from the table is difficult.
This is where plotting becomes essential to understand the data effectively. The line plot shows that the share prices dipped in March 2020, coinciding with the declaration of COVID-19 as a pandemic. This visualization helps us to identify trends, patterns, and insights that may not be apparent from a table of numbers alone.
5. Table versus graph
Let's take another example to understand the limitations of tabular data. The table below displays the price and trading volume of four different funds. While we can discern that SPY and VOO have higher prices and SPY and QQQ have higher trading volumes, it's challenging to extract more information.
However, when we plot the data in a scatter plot, we can see how these funds are positioned in the space of prices and traded volumes much more effectively.
6. A preview of our plotting journey
As we progress through this course, we'll gain the skills to produce advanced visualizations using Julia. We'll explore scatter plots that uncover relationships between the traded volumes of ETF funds and plots that track the average prices of tomatoes in India throughout the year.
7. A preview of our plotting journey
We'll also create box plots that reveal the effects of music on mental health and correlation matrix plots that shed light on insurance patient data. And there's much more to come!
8. Understanding our dataset
In this chapter, we will utilize a dataset comprising daily prices for ETF funds, a type of investment fund representing a diverse portfolio of assets such as stocks, bonds, or commodities.
The price_date column comprises the dates and the open, close, high, and low columns, include the respective prices. The dataset also includes the daily traded volumes in the volume column.
9. Let's practice!
Visualizing data is crucial for gaining insights and communicating findings effectively. Before we delve into plotting, let's look at some exercises that illustrate the importance of data visualization.