Here’s How to Choose Data Visualization Graphs

Many beginners in Data Science often get confused about how to choose data visualization graphs to visualize the data they are working on. Choosing the right data visualization graph depends on the nature of your data and the specific insights you’re looking to derive. So, if you want to learn how to choose data visualization graphs, this article is for you. In this article, I’ll take you through a guide on how to choose data visualization graphs.

Here’s How to Choose Data Visualization Graphs

Choosing the right data visualization graph is crucial in effectively communicating the insights from your data. Each graph serves a different purpose and can highlight various aspects of your data, from comparisons and compositions to trends, distributions, and relationships. Let’s go through all possible factors we can consider to decide how to choose data visualization graphs.

When You Want to Draw Comparison

Comparison in data visualization refers to assessing and highlighting the differences or similarities between different categories, groups, or data points. It helps understand how various elements in the dataset relate to each other in terms of magnitude, size, or value.

So, to compare values across different categories or groups, you can choose:

1. Bar Chart: Bar charts are great for comparing values across categories or groups. For instance, you can use a bar chart to compare sales figures across different regions or compare the performance of different products.
2. Pie Charts: Pie charts are effective for comparing parts of a whole. They work well when you have a limited number of categories, but they become less effective when you have many categories or small differences in data.

When You Want to Check Composition

Composition in data visualization involves illustrating the parts that make up a whole. It’s about showing the relative proportions or contributions of the components to a larger entity or dataset.

So, to understand how different parts make up the whole, you can choose:

1. Pie Charts or Donut Charts: These charts illustrate how different parts make up the whole. They are particularly effective when you have a small number of categories that form the entirety of a dataset.
2. Stacked Bar Charts: These charts are useful for showing the composition of different parts in relation to the whole. They display both the total and the proportion of each category’s contribution.

When You Want to Understand the Distribution

Distribution in data visualization refers to visualizing how data points are spread or distributed across different values or categories. It helps understand the shape and characteristics of data, such as whether it is skewed, symmetric, or bimodal.

So, to see the spread of data and understand its distribution, you can choose:

1. Histograms: Histograms are ideal for visualizing the distribution of a single variable. They show the frequency of data intervals, helping you understand the spread and shape of the data.
2. Box Plots: Box plots provide a concise summary of the distribution of data, including measures such as the median, quartiles, and potential outliers.
3. Violin Plots: Violin plots combine aspects of box plots with kernel density estimation, offering a way to visualize the distribution and probability density of data.

When You Want to Analyze Trends Over Time

Trends over time involve tracking how data points change or evolve across a continuous period, typically chronological. It’s about visualizing the patterns and fluctuations in data over a specific timeframe.

So, to observe how data points change over time, you can choose:

1. Line Chart: Line charts are the go-to choice for displaying trends over time. They are particularly useful for continuous data and time series analysis.
2. Area Chart: Area charts, similar to line charts, emphasize the magnitude of trends over time by filling the area beneath the line.

When You Want to Analyze Relationships

Relationships in data visualization focus on visualizing how variables or data points depend on each other. It’s about exploring the connections, associations, or dependencies between different variables.

So, to understand the relationship between two or more variables, you can choose:

1. Scatter Plot: Scatter plots are essential for visualizing relationships or correlations between two numerical variables. They help identify patterns and trends in data.
2. Heatmap: Heatmaps use colour coding to show the relationship between two variables. They are great for identifying patterns and clusters in large datasets.

Summary

So, the selection of graphs is crucial and varies based on the data’s nature and intended insights. Bar and pie charts are ideal for comparisons, illustrating differences across categories or parts of a whole, respectively. For understanding composition, pie or donut charts, and stacked bar charts effectively show how various parts contribute to a whole. Histograms, box plots, and violin plots are suitable for analyzing data distribution and revealing frequency, spread, and density. Line and area charts are preferred for trends over time, capturing continuous data changes. Lastly, scatter plots and heatmaps are excellent for examining relationships between variables and highlighting patterns and correlations in numerical and categorical data.

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