A Gentle Introduction to Data Cleaning

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Introduction
In this section, learn about the “horror stories” of where data errors in spreadsheets have led to real consequences and how you can avoid them yourself.
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- 1.1
  Course Outline
Section 1: Nuts and chewing gum
In this section, you’ll gain practical knowledge of common formatting and layout features of spreadsheets and some ideas about how to present your raw data to others so they will love you!
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Section 2: The Invisible Man is in your spreadsheet, messing with your data
In this section, you’ll learn how even characters which you cannot see can cause havoc in data analysis. The section will teach you to ghostbust white space and other non-printable characters such as carriage returns, spaces and tabs. Once you have removed them, you can get on with your analysis in peace.
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Section 3: Your data is a witch’s brew
Three is not a number. Nor is a million. At least not when they are typed in as text in a cell in a spreadsheet. Your spreadsheet is pedantic and needs everything to be precise and consistent. Read on to learn more.
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Section 4: Did you bring the wrong suitcase (again)?
All our spreadsheet wants from us is each cell of data to be organised and neatly packed. In this section, you will analyse data to highlight problems in the way it has been structured so as to avoid them in your own work with data.
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Reading list

Watch this useful YouTube video from Excel is Fun about using CLEAN and TRIM.
Read Microsoft’s surprisingly helpful help page on removing extra spaces and non-printable characters, here. Much of this will work in LibreOffice and Google Spreadsheets, though there are differences. Cast your eye over the Function documentation: TRIM (Excel, LibreOffice, Google Docs) and CLEAN (Excel, LibreOffice, Google Docs).
Read the Feature documentation: Paste Special (Excel, LibreOffice, Google Docs), Autofilter (Excel, LibreOffice, Google Docs)
For advanced Invisible Man hunters you can read up about using regular expressions in LibreOffice, here. We also cover this in the longer task above, but it’s not straightforward.

Some work