Using spreadsheet programs for data organization

Last updated on 2024-06-07 | Edit this page

Estimated time: 15 minutes



  • What are good data practices for using spreadsheets for organizing data?


  • Understanding some drawbacks and advantages of using spreadsheet programs
  • Distinguish machine readable tidy data from data that is easy to read for humans
  • Introduce that we’re teaching data organization, and that we’re using spreadsheets, because most people do data entry in spreadsheets or have data in spreadsheets.
  • Emphasize that we are teaching good practice in data organization and that this is the foundation of their research practice. Without organized and clean data, it will be difficult for them to apply the things we’re teaching in the rest of the workshop to their data.
  • Much of their lives as a researcher will be spent on this ‘data wrangling’ stage, but some of it can be prevented with good strategies for data collection up front.
  • Tell that we’re not teaching data analysis or plotting in spreadsheets, because it’s very manual and also not reproducible. That’s why we’re teaching SQL, R, Python!
  • Now let’s talk about spreadsheets, and when we say spreadsheets, we mean any program that does spreadsheets like Excel, LibreOffice, OpenOffice. Most learners are probably using Excel.
  • Ask the audience any things they’ve accidentally done in spreadsheets. Talk about an example of your own, like that you accidentally sorted only a single column and not the rest of the data in the spreadsheet. What are the pain points!?
  • As people answer highlight some of these issues with spreadsheets

Good data organization is the foundation of much of our day-to-day work in libraries. Most librarians have data or do data entry in spreadsheets. Spreadsheet programs are very useful graphical interfaces for designing data tables and handling very basic data quality control functions.

Spreadsheets encompass a lot of the things we need to be able to do as librarians. We can use them for:

  • Data entry
  • Organizing data
  • Subsetting and sorting data
  • Statistics
  • Plotting

Jargon busting (Optional, not included in timing)

The Jargon Busting exercise is a helpful way to begin to explore terms, phrases, and ideas related to code and software development.

This exercise can be useful when you teach Tidy Data as the introduction to a full LC workshop, especially if you want learners to have an opportunity to meet each other and interact. It can take anywhere from 10 to 45 minutes, depending on your approach.

Spreadsheet outline

In this lesson, we will look at:

  • Good data entry practices - formatting data tables in spreadsheets
  • How to avoid common formatting mistakes
  • Dates as data - beware!
  • Basic quality control and data manipulation in spreadsheets
  • Exporting data from spreadsheets

Much of your time when you’re producing a report will be spent in this ‘data wrangling’ stage. It’s not the most fun, but it’s necessary. We’ll teach you how to think about data organization and some practices for more effective data wrangling.

What this lesson will not teach you

  • How to do statistics in a spreadsheet
  • How to do plotting in a spreadsheet
  • How to write code in spreadsheet programs

If you’re looking to do this, a good reference is Head First Excel by O’Reilly Media.

Why aren’t we teaching data analysis in spreadsheets

  • Data analysis in spreadsheets usually requires a lot of manual work. If you want to change a parameter or run an analysis with a new dataset, you usually have to redo everything by hand. (We do know that you can create macros, but see the next point.)

  • It is also difficult to track or reproduce statistical or plotting analyses done in spreadsheet programs when you want to go back to your work or someone asks for details of your analysis.

Spreadsheet programs

There are a number of spreadsheet programs available for use on a desktop or web browser:

  • LibreOffice Calc
  • Microsoft Excel
  • Apple Numbers
  • Google Sheets
  • Gnumeric
  • Apache OpenOffice Calc

Commands may differ a bit between programs, but the general idea is the same. In this lesson, we will assume that you are most likely using Excel as your primary spreadsheet program. There are others with similar functionality, including Gnumeric, OpenOffice Calc, and Google Sheets, but Excel is the package you’re most likely to have available on your work computer.


  • How many people have used spreadsheets in their work?
  • What kind of operations do you do in spreadsheets?
  • Which ones do you think spreadsheets are good for?


  • Spreadsheets can be very useful, but they can also be frustrating and even sometimes give us incorrect results. What are some things that you’ve accidentally done in a spreadsheet, or have been frustrated that you can’t do easily?

Problems with Spreadsheets

Spreadsheets are good for data entry, but in reality we tend to use spreadsheet programs for much more than data entry. We use them to create data tables for publications, to generate summary statistics, and make figures.

Generating tables for reports in a spreadsheet is not optimal - often, when formatting a data table for publication, we’re reporting key summary statistics in a way that is not really meant to be read as data, and often involves special formatting (merging cells, creating borders, making it pretty). We advise you to do this sort of operation within your document editing software.

The latter two applications, generating statistics and figures, should be used with caution: because of the graphical, drag and drop nature of spreadsheet programs, it can be very difficult, if not impossible, to replicate your steps (much less retrace anyone else’s), particularly if your stats or figures require you to do more complex calculations. Furthermore, in doing calculations in a spreadsheet, it’s easy to accidentally apply a slightly different formula to multiple adjacent cells. When using a command-line based statistics program like R or SAS, it’s practically impossible to accidentally apply a calculation to one observation in your dataset but not another unless you’re doing it on purpose.

Using Spreadsheets for Data Entry and Cleaning

HOWEVER, there are circumstances where you might want to use a spreadsheet program to produce “quick and dirty” calculations or figures, and some of these features can be used in data cleaning, prior to importation into a statistical analysis program. We will show you how to use some features of spreadsheet programs to check your data quality along the way and produce preliminary summary statistics.

In this lesson, we’re going to talk about:

  1. Formatting data tables in spreadsheets
  2. Formatting problems
  3. Dates as data
  4. Basic quality control and data manipulation in spreadsheets
  5. Exporting data from spreadsheets
  6. Data export formats caveats

Key Points

  • We will discuss good practices for data entry and formatting
  • We will not discuss analysis or visualisation