The preprocessing and transformation of input data, also known as “data wrangling,” is an essential step in the data analysis pipeline. A variety of challenges must be overcome when preparing data for analysis, including handling missing data, integrating diverse data types from multiple sources, and ensuring that both individual data fields as well as the larger organization of data are in the correct format for downstream analysis.

On this page, we provide an overview of resources for learning how to wrangle data, software for data wrangling, and tools developed at Fred Hutch. While this is not an exhaustive list, we have highlighted many of the most commonly used and readily accessible resources for data scientists.

Tidy Data

Beyond wrangling/cleaning your data, the practice of “data tidying” ensures that your datasets have a consistent structure and and are easy to manipulate, model, and visualize. Tidy datasets list individual observations as rows and variables as columns. We highly recommend you include tidying your data as a key step in your data wrangling process!

Code-based data wrangling

Data Wrangling in R

Although base R offers some basic functions for data wrangling, there are a variety of fast, intuitive packages available in the R ecosystem for cleaning, transforming, and reshaping data.

Packages for data wrangling

  • The tidyverse is a comprehensive collection of R packages for data science - two relevant packages for data wrangling are dplyr for data manipulation and tidyr for data tidying.
  • Bioconductor is a specialized set of R packages designed for the analysis of genomic data and other high-throughput biological data.
    • A list of Bioconductor packages

Packages for handling large data

  • duckdb is a fast in-process SQL database for querying and wrangling of very large datasets in a wide variety of formats.
  • data.table is a high-performance version of base R’s data.frame for efficient data manipulation from large datasets. It works by loading data in memory for fast manipulations

Data Wrangling in Python

Python is also widely used for data wrangling, particularly for handling complex and large-scale biomedical datasets. Several libraries in Python simplify the process of cleaning, transforming, and analyzing data.

Core Libraries for Data Wrangling

  • pandas: powerful, easy to use open-source data analysis and manipulation tool
  • numpy: fast and versatile package for handling numerical and vectorized data, including N-dimensional arrays (i.e.ndarray’s)

Specialized Libraries for Biomedical Data

  • biopython: Python-based freely available tools for biological computation, including data wrangling / analysis for genomic and proteomic data
  • scikit-bio: library for working with bioinformatics / biological data, including genomics, microbiomics, ecology, evolutionary biology, and more

Handling Large Datasets

  • duckdb - in addition to R, duckdb can be applied in Python.
  • dask is a flexible open-source Python library for parallel computing on large datasets

Community Resources

The FH-Data Slack is always available as a space for researchers to ask questions and share resources about data wrangling.

  • You can join the #question-answer channel on the FH-Data Slack to ask questions, share resources, and discuss strategies for managing complex biomedical data.
  • The Data Science Lab offers a variety of learning communities, providing spaces for biomedical data professionals to learn together, ask for help, and share best practices for wrangling large and complex datasets.
  • The Fred Hutch Bioinformatics Core provides services to support the wrangling and analysis of high-throughput biological data such as genomic and proteomic datasets.

Learning Resources

Books and online tutorials can provide in-depth coverage of data wrangling techniques, offering a solid foundation for both novice and advanced biomedical data scientists.

Books for R

Books for Python

Other resources

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