How can you clean hierarchical or nested data for visualization?

1 What is hierarchical or nested data?

Hierarchical or nested data is a type of data that has a tree-like structure, where each element can contain other elements as sub-elements, and so on. For example, a JSON file can have an array of objects, each object can have properties and values, and some values can be arrays or objects themselves. Hierarchical or nested data is often used to store or exchange complex data, such as web pages, API responses, or configuration files. However, it can also make data analysis and visualization more difficult, as you need to access and manipulate different levels of data and deal with potential inconsistencies or errors.

2 How to flatten hierarchical or nested data?

One way to clean hierarchical or nested data for visualization is to flatten it, that is, to transform it into a tabular format, such as a spreadsheet or a data frame, where each row represents an observation and each column represents a variable. Flattening hierarchical or nested data can make it easier to explore, filter, aggregate, and visualize your data using common tools and libraries. To flatten hierarchical or nested data, you can use various methods and functions depending on the data format and the tool you are using. For example, you can use pandas.json_normalize() in Python to flatten JSON data into a data frame, or you can use tidyr::unnest() in R to flatten nested lists into a tibble.

3 How to handle missing values in hierarchical or nested data?

Another challenge of cleaning hierarchical or nested data for visualization is handling missing values, that is, values that are not present or not available in the data. Missing values can occur for various reasons, such as errors, omissions, or inconsistencies in the data collection or processing. Missing values can affect the quality and accuracy of your data analysis and visualization, as they can introduce bias, distort statistics, or cause errors. To handle missing values in hierarchical or nested data, you can use different strategies depending on the context and the goal of your visualization. For example, you can ignore, delete, impute, or flag missing values using various functions and parameters in your tool of choice.

4 How to standardize formats in hierarchical or nested data?

A third challenge of cleaning hierarchical or nested data for visualization is standardizing formats, that is, making sure that the data values are consistent and compatible with the type and scale of your visualization. Standardizing formats can involve converting data types, such as strings to numbers or dates, applying consistent units, such as meters or miles, or adjusting the level of detail, such as rounding or aggregating. Standardizing formats can help you avoid errors, improve readability, and enhance comparability of your data visualization. To standardize formats in hierarchical or nested data, you can use various functions and methods to parse, transform, or format your data values according to your needs and preferences.

5 How to validate hierarchical or nested data?

A final step of cleaning hierarchical or nested data for visualization is validating it, that is, checking that the data is accurate, complete, and reliable for your visualization purpose. Validating hierarchical or nested data can involve verifying the source, the structure, the content, and the quality of your data, as well as identifying and resolving any issues or anomalies. Validating hierarchical or nested data can help you ensure that your data visualization is based on trustworthy and relevant data, and that it conveys the intended message and meaning. To validate hierarchical or nested data, you can use various tools and techniques, such as schemas, rules, tests, or visual checks, to inspect and evaluate your data before or after cleaning it.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?