Choosing the Right Scatterplot: Categorical vs. Numerical Variables
When it comes to data visualization, scatterplots are an invaluable tool for uncovering relationships and patterns within your data. However, one critical consideration often overlooked is whether to use a scatterplot for categorical or numerical variables. In this guide, we’ll explore the nuances of choosing the right scatterplot based on the nature of your variables. If you want to learn different types of data science courses in Canada, please read our previous article.
Understanding Scatterplots
Before diving into the specifics of scatterplots for different types of variables, let’s establish a solid understanding of what scatterplots are and why they matter.
A scatterplot is a graphical representation of data points on a two-dimensional plane. It consists of points or markers, each representing an individual data observation.
The position of each point is determined by two variables: one on the horizontal axis (x-axis — years of experience) and the other on the vertical axis (y-axis — Salary) as shown in the below graph. Scatterplots are widely used in various fields, including statistics, data science, and research, for their ability to visually convey relationships between variables.
Now, let’s explore when and how to use scatterplots effectively for categorical and numerical variables.
Scatterplots for Numerical Variables
Numerical variables represent quantities and can take on a wide range of values. Examples include variables such as age, income, temperature, and height. Scatterplots are particularly well-suited for visualizing relationships between two numerical variables. Here’s why:
1. Visualizing Relationships: When you want to understand the relationship between two numerical variables, a scatterplot graph is your choice. For instance, if you’re exploring the connection between a person’s salary and their work experience, you can create a scatterplot with salary on the x-axis and years of experience on the y-axis. Each data point on the plot represents an individual, allowing you to quickly identify if there’s a correlation between salary and years of experience.
2. Identifying Outliers: Outliers are data points that significantly differ from the majority of your data. They can skew your analysis and conclusions. Scatterplots make it easy to spot outliers, helping you make informed decisions about whether to include or exclude them from your analysis. Look at the below image:
3. Talk about Distribution: Scatterplots also offer a means to gain an understanding of your data’s distribution. They enable you to assess whether data points are closely concentrated around a central line or if there is a notable dispersion, visually conveying valuable insights into the distribution of your numerical variables.
Scatterplots for Categorical Variables
Conversely, categorical variables represent well-defined categories or groups and lack inherent order. Illustrative examples encompass gender, colour, product type, and similar distinctions. Here’s how scatterplots can be effectively applied to categorical variables:
1. Creating Grouped Scatterplots: When working with categorical variables, an approach involves crafting grouped scatterplots to compare various categories. In this case, rather than employing a continuous scale on the x-axis, discrete categories are utilized. For instance, you could generate a scatterplot to assess the relationship between the heights and weights of individuals, with the x-axis categorically representing groups such as “Male” and “Female.”
2. Avoid Overplotting: Categorical scatterplots can become overcrowded if you have many categories. In such cases, it’s often more effective to use alternative types of plots, such as bar charts or box plots, to visualize your data without overcrowding the plot.
3. Combining with Numeric Data: Scatterplots can still be useful when combined with categorical and numerical data. In this scenario, you might create a scatterplot with a categorical variable on one axis and a numerical variable on the other. For example, you could visualize the total revenue of different stores over time.
When to choose Scatter Plot?
The decision to use a scatterplot with categorical or numerical variables ultimately hinges on your research question and the nature of your data. Here are some guidelines to help you make the right choice:
1. Numerical-Numeral Scatterplots: Use scatterplots when both variables are numerical, and you want to visualize their relationship, identify outliers, or assess distribution.
2. Categorical-Categorical Scatterplots: Consider grouped scatterplots when you want to compare categories within two categorical variables.
3. Categorical-Numerical Scatterplots: If you have a mix of categorical and numerical data, scatterplots can still be useful for exploring relationships.
4. Large Categorical Data: Be cautious about overcrowding if you have numerous categories for a categorical variable, and explore alternative vis methods when necessary.
Conclusion
Scatterplots serve as versatile tools for visualizing relationships between variables, and their suitability hinges on your data’s characteristics. Discerning when to employ scatterplots for categorical and numerical variables is pivotal for proficient data visualization and analysis. Hence, when embarking on your data exploration journey, exercise prudence in selecting the appropriate scatterplot.
In essence, the art of selecting the correct scatterplot holds paramount significance within the realm of data visualization. It can be the differentiating factor between unearthing profound insights and grappling with obscure visual representations. By adhering to the delineated principles provided herein, you’ll be better prepared to make an informed choice when it comes to the ideal scatterplot for your distinct data analysis requisites.
Keep in mind that data visualization extends beyond crafting aesthetically pleasing graphs; it is a means of crafting compelling narratives and extracting invaluable insights from your dataset. Hence, deliberate judiciously when making your scatterplot selection, allowing your data’s essence to radiate brightly.
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