Instructor Notes

Instructor notes

Lesson motivation and learning objectives

The purpose of this lesson is not to teach how to do data analysis in spreadsheets, but to teach good data organization and how to do some data cleaning and quality control in a spreadsheet program.

Lesson design

Introduction

  • 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 or LibreOffice. 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

Formatting data

  • Introduce the dataset that will be used in this lesson, and in the other Social Sciences lessons, the Studying African Farmer-led Irrigation (SAFI) Dataset.
  • Go through the point about keeping track of your steps and keeping raw data raw
  • Go through the cardinal rule of spreadsheets about columns, rows and cells
  • Hand them a messy data file and have them pair up and work together to clean up the data. Give them 15 minutes to do this.
  • Learners who are using LibreOffice for the workshop will have problems with the dataset as the default for LibreOffice is to treat tabs, commas, and semicolons as delimiters. This can be fixed when opening LibreOffice by deselecting the “semicolons” and “tabs” checkboxes.
  • Ask for what people did to clean the data. As they bring up different points you can refer to them in the Common formatting problems file, or expand a bit on the point they brought up. All these mistakes are present in the messy dataset.
  • If you get a response where they’ve fixed the date, you can pause and go to the dates lesson. Or you can say you’ll come back to dates at the end. There’s an exercise in that file about how to change the date into three columns using Excel’s built in MONTH, DAY, YEAR functions. Have them run through that exercise.

Common formatting problems

  • Don’t go through this chapter except to refer to as responses to the exercise in the previous chapter.

Dates as data

  • Do the exercise and make the point about dates either in response to a learner bringing up date as an issue during the responses, or at the end of the response time.
  • If learners are using a non-English language version of Excel, the =MONTH(), =DAY(), and other date functions won’t work for them. They will need to type in their language’s equivalent of that word in the formula.
  • The spreadsheet for this episode has two tabs. The first tab is data stored as DD-MM-YYYY, the second is MM-DD-YYYY. If learners use the wrong tab for their location, they will get a #VALUE error.
  • When using Libre Office, it is helpful to first save the file in ods format. Then be sure to convert the date column to type date by right clicking on the cell, choose “Format Cells…” then choose Date and take a type of date that uses DD/MM/YYYY, such as English (Botswana). Once you click ok, you will find that the date has been pre-pended by an apostrophe. For example 21/11/2016 becomes ’21/11/2016. Edit the cell to remove the apostrophe. You will then find that the day(), month() and year() functions work.

Quality assurance

The challenge with this lesson is that the instructor’s version of the spreadsheet software is going to look different than about half the room’s. It makes it challenging to show where you can find menu options and navigate through.

Instead discuss the concepts of quality control, and how things like sorting can help you find outliers in your data.

Exporting data

  • Have the students export their cleaned data as CSV. Reiterate again the need for data in this format for the other tools we’ll be using.

Concluding points

  • Now your data is organized so that a computer can read and understand it. This let’s you use the full power of the computer for your analyses as we’ll see in the rest of the workshop.
  • While your data is now neatly organized, it still might have errors or missing data or other problems. It’s like you put all your data in the right drawers, but the drawers might still be messy. The next lesson is going to teach you OpenRefine which is great for data cleaning and for some of the quality control that we touched on in this lesson. It also has the advantage that it automatically keeps track of the steps you take.

Technical tips and tricks

Provide information on setting up your environment for learners to view your live coding (increasing text size, changing text color, etc), as well as general recommendations for working with coding tools to best suit the learning environment.

Common problems

Excel looks and acts different on different operating systems

The main challenge with this lesson is that Excel looks very different and how you do things is even different between Mac and PC, and between different versions of Excel. So, the presenter’s environment will only be the same as some of the learners.

We need better notes and screenshots of how things work on both Mac and PC. But we likely won’t be able to cover all the different versions of Excel.

If you have a helper who has experience with the other OS than you, it would be good to prep them to help with this lesson and tell how people to do things in the other OS.

Apple Numbers

Apple Numbers does not have data validation, which is needed for part of this lesson. A note is included in the setup instructions pointing Numbers users to either Microsoft Excel or LibreOffice.

People are not interactive or responsive on the Exercise

This lesson depends on people working on the exercise and responding with things that are fixed. If your audience is reluctant to participate, start out with some things on your own, or ask a helper for their answers. This generally gets even a reluctant audience started.

Common questions raised by participants

How do you extract date components from the interview_date field in SAFI_clean.csv?

The interview_date field in SAFI_clean.csv when saved to SAFI_clean.xlsx is difficult to manage because there isn’t a way to format the column as a date field, even using the custom field formats. The easiest solution to this question is to show the student how to extract the date information from the field. Make a new column and format it as a date. In the first cell of the new column type =LEFT(C2,10) and then apply this to the column. This function extracts the first 10 characters from the left side of the interview_date field and inserts them into a new column.

How would you automatically transform the items_owned field into a usable format?

If you are not following the course immediately with the OpenRefine lesson it is important to make it clear that in the current format SAFI_clean.csv is not ready for analysis. The items_owned column ideally needs to be split into separate yes / no / null columns. Example: set up a new column ‘bicycle’ and format it as a number. You then need to extract information from the items_owned column about whether the word ‘bicycle’ is in the column. One way of doing this is to use an IF statement: =IF(ISNUMBER(SEARCH(“bicycle”,K2))1,0). The IF statement can include a wild character e.g. “bicy*”.