Good data organization is the foundation of any research project. Most researchers have data in spreadsheets, so it’s the place where many research projects start.

Typically we organize data in spreadsheets in ways that we want to work with the data. However, computers require data to be organized in particular ways. In order to use tools that make computation more efficient, such as programming languages like R or Python, we need to structure our data the way that computers need the data. Since this is where most research projects start, this is where we want to start, too.

In this lesson, you will learn:

1. Good data entry practices, including formatting data tables in spreadsheets
2. How to avoid common formatting mistakes
3. Approaches for handling dates in spreadsheets
4. Basic quality control and data manipulation in spreadsheets
5. Exporting data from spreadsheets

In this lesson, however, you will NOT learn about data analysis with spreadsheets. Much of your time as a researcher will be spent in the initial “data wrangling” stage, where you need to organize the data to perform a proper analysis later. In this lesson, you will learn how to think about data organization and some practices for more effective data wrangling. With this approach, you can better format current data and plan new data collection so less data wrangling is needed.

This lesson assumes no prior knowledge of the skills or tools.

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Before the session, please complete the lesson setup steps:

1. All participants: Download the three data files linked above, placing them in a location you can easily find and access on your computer (such as a folder on your desktop).
2. MacOS users who use the Apple Numbers program: Plan to use Microsoft Excel or another program for this session, as Numbers does not contain some of the features we will use.
3. Participants who do not have a spreadsheet application on their computer: Feel free to install the free open-source LibreOffice program linked above.