Best Practices in Data Organisation Using Spreadsheets

Introduction

Overview

Teaching: 15 min
Exercises: 0 min
Questions

  • What are the basic principles for using spreadsheets for good data organisation?

Objectives

  • Describe best practices for organising data so that computers can make the best use of it for data analysis.

Spreadsheet programs

Good data organisation is the foundation of any research project that relies on data analysis - it is of paramount importance to get this first step right in order to avoid time-consuming “data wrangling” and “data cleaning” further down the line. Most researchers have data in spreadsheets or use spreadsheets for data entry. Spreadsheet programs are good for data entry and provide very useful graphical interfaces for handling basic data quality control functions. Spreadsheets can provide a lot of functionality that researchers need:

Many spreadsheet programs are available. Most researchers utilise Excel as their primary spreadsheet program (this lesson will make use of Excel examples), but free spreadsheet programs exist, including LibreOffice, Gnumeric, OpenOffice.org or Google Spreadsheets. Commands may differ a bit between programs, but the general idea is the same.

What’s wrong with spreadsheets?

The intricacies of spreadsheets make it hard to reproduce analysis and very difficult to spot and correct errors. Sometimes this is due to human error (you will learn how to avoid some of them); at other times it is due to the spreadsheet program itself: how many times have you accidentally done something in a spreadsheet that caused a problem (or just made you frustrated)? You are not alone! For example, 20% of genetics papers contain errors due to Excel converting gene names to calendar dates.

Using spreadsheets for data organisation, entry and cleaning

During this lesson, you will:

Key Points

  • Good data organisation is the foundation of any research project.

Organising data in spreadsheets

Overview

Teaching: 20 min
Exercises: 0 min
Questions

  • How do we organise and format data in spreadsheets for effective data use?

Objectives

  • Describe best practices for data entry and formatting in spreadsheets.

  • Apply best practices to arrange variables and observations in a spreadsheet.

A common mistake is use a spreadsheet like a lab notebook. In other words, to convey information not just with the data, but with notes in the margin and the spatial layout of the data. We humans can (usually) interpret these things, but computers cannot. They are incredibly literal, so unless we explain every single nuance of meaning that we intended, the computer will misinterpret our data - and that causes problems. This is why it is extremely important to start with well-formatted tables from the outset - before you even start entering data from your first preliminary experiment.

Data organisation is the foundation of your research project. It can make it easier or more difficult to work with your data (for yourself and others) throughout your analysis. You should start thinking about data organisation even before you start collecting data. There’s a lot of flexibility, but some of the choices you make now will limit your ability to work with the data in the future.

Best data formats may differ

The best layout for data entry might change dependent on the specific use case. Do not stick to a format just because you have used it previously. Choose the best format on a case-by-case basis. (And if you need to convert between formats, ideally you should automate the conversion with a script in, say, Python or R.

Structuring data in spreadsheets

Here are some cardinal rules when using spreadsheet programs for data organisation:

  1. Put each of your variables (i.e. the things you are measuring or observing, like ‘weight’ or ‘temperature’) in its own column. Put a single observation of these variables in its own row. Keep your data in a nice and tidy table structure.
  2. Include metadata about your data with your data (but not in your data).
  3. Leave the raw data raw - do not change it! This means anything you do to your data (even sorting it) - do it on a copy or you will change the original data irreversibly and will not be able to go back.
  4. Keep track of your analyses.
  5. Export the cleaned data to a text-based format like CSV (comma-separated values). This ensures that anyone can use the data, and is required by most data repositories and publishers. We will cover this later in the lesson.

Let’s have a look at each of these rules in action.

Tidy data principle

Do not try to combine multiple variables in one cell and do not put multiple observations of the same variable in the same row. Let’s look at the following data from a survey of small mammals in a desert ecosystem. Different scientists have gone to the field and entered data into a spreadsheet. They kept track of variables like species, plot, weight, sex and date when data was collected. Here’s an example of poor data organisation:

multiple-info example

There are problems like the species and sex variables being in the same field. This data format would make it difficult to easily look at all of one species, or look at different weight distributions by sex. If, instead, we put sex and species in different columns, it would be much easier to perform such analyses.

The same data could be better organised as:

single-info example

Columns for variables and rows for observations

The rule of thumb, when setting up data in a table is: columns = variables, rows = observations, cells = data values.

Including metadata with the data

“Metadata” is the data you record about your data (such as the date the experiment was conducted, who conducted it, etc). It is essential to understanding the circumstances under which your data was collected. You may be on intimate terms with your dataset while you are collecting and analysing it, but this will change over time. After six months, you are unlikely to remember the exact algorithm you used to transform a variable, or that “sglmemgp” means “single member of group”. You don’t need a photographic memory if you collect good metadata.

Your data is important, which means there will be many people who will want to examine it. They will need good metadata if they are to understand your findings, review your submitted publication, replicate your results, design a similar study, or even just want to archive your data. While digital data by definition are machine-readable, understanding their meaning is a job for human beings - and they need the help that metadata provides. The importance of documenting your data during the collection and analysis phase of your research cannot be overstated - it is fundamental.

Metadata should not be contained in the data file itself, because it can disrupt how programs interpret your data file. Rather, metadata should be stored as a separate file in the same directory as your data file, preferably in plain text format (i.e. .txt) with a name that clearly associates it with your data file. Because metadata files are free text format, they allow you to encode comments, units, information about how null values are encoded and related information. An alternative is to create a separate README tab in your spreadsheet with the metadata (but make sure you also export it when you export your cleaned data in e.g. CSV file format).

Additionally, file or database level metadata describes how files that make up the dataset relate to each other; what format they are in; and whether they supersede or are superseded by previous files. A folder-level README.txt file is the classic way of accounting for all the files and folders in a project.

Credit: MANTRA

The above text on metadata was adapted from the online course Research Data MANTRA by EDINA and Data Library, University of Edinburgh. MANTRA is licensed under a Creative Commons Attribution 4.0 International License.

Keeping your raw data - raw

Never modify your original (raw) data - create a new file or a new tab with a copy of your data before you start transforming and cleaning your raw data. This includes even sorting your data as sometimes you can (accidentally or intentionally) sort a single column without expanding the sort to other columns and forget about it - if you save your data after this step this will change it irreversibly.

Keeping track of your analysis

When you are working with data in spreadsheets, the spreadsheet you end up with will often look very different to the one you started with. With each change, you are losing information about the history of the data. How many times have you needed to roll back an analysis, only to become completely lost when retracing your steps? There are ways of mitigating this problem:

In the following example, the spreadsheet has ‘raw’ tabs in which to store the original, untouched data and ‘clean’ tabs in which the processed data is stored. It is accompanied with a text file that describes the steps that have been taken.

spreadsheet setup

Version controlling your data

Although out of scope for this lesson, you can learn about version control in a separate course, which can be used to record the transformation of your data over time, and provides tools to roll back to any previous version of the data.

Fixing mistakes in data organisation

We will now look at some common mistakes and learn some best practices in data organisation that will help us keep our data clean and tidy.

Key Points

  • Organise your data according to the tidy data principles.

  • Never modify your raw data. Always make a copy before making any changes.

  • Keep track of all of the steps you take to clean your data in a plain text file.

  • Record metadata in a separate plain text file (such as README.txt) in your project root folder or folder with data.

Common spreadsheet errors

Overview

Teaching: 30 min
Exercises: 15 min
Questions

  • What are some common challenges with formatting data in spreadsheets and how can we avoid them?

Objectives

  • Recognise and resolve common spreadsheet formatting problems.

There are a few potential errors to be on the lookout for in spreadsheet data. If you are aware of the errors, and the complications they can cause on downstream data analysis and result interpretation, it helps motivate you and your project members to try and avoid them. It’s far more efficient to prevent problems by making small changes in the way you collect data, than spending weeks or months cleaning messy data. Ultimately, good organisation of your data in improves your research efficiency and reliability. Here are some common spreadsheet errors, which we will cover in detail below:

Using multiple tables

A common mistake is creating multiple data tables within a single spreadsheet, as shown below. This will confuse a data analysis program (and many humans too).

multiple tabs

There are a number of problems with multiple tables on one page. The main problem is that - although clearly separate to a human observer - a computer will rigidly interpret anything in the same row as belonging to the same observation.

In the example above, the computer will read row 4 and assume that all columns A-AF refer to the same sample. This row actually represents four distinct samples (sample 1 for each of four different collection dates - May 29th, June 12th, June 19th, and June 26th), as well as some calculated summary statistics (an average (avr) and standard error of measurement (SEM)) for two of those samples.

There is no reason why observations from different sites or different years should not go into a single table. You just need to keep track of them by using new columns (in this examples, one for site and one for year). Keeping data from the same experiment in a single table will help you stick to a consistent data structure, and avoid errors when looking up data from different tables.

Using multiple tables on one page also makes it difficult for humans to keep track of the data - especially if one of the tables is hidden off the edge of the spreadsheet. Multiple tables also increases the risk of using the same column name in multiple places, which will make it significantly harder to clean your data.

Using multiple tabs

But what about workbook tabs? Are they the easy solution to organising data? In a word, no!

There may be some situations when using an additional tab may useful (e.g. including a separate README tab with metadata). In other situations, think hard if you need to enter your data into multiple tabs - e.g. consider if adding another column (or two) to your spreadsheet might solve your problem.

Note on bigger data and column headers

Your datasheet might get very long over the course of the experiment. This makes it harder to enter data if you cannot see your headers at the top of the spreadsheet. But do not be tempted to repeat your header row in the middle of your data - these headers will get mixed into the data leading to problems down the road. Instead, you can freeze the column headers so that they remain visible even when you have a spreadsheet with many rows. if you are not sure how to do this, see the documentation on how to freeze column headers in Excel.

Starting data in the middle of the spreadsheet

When preparing data for further analysis, there is no reason to start your data from the middle of the spreadsheet and leave empty rows at the top and empty columns to the left of the data. Libraries for importing data into R or Python will expect data to start from the top left corner of your spreadsheet. While such libraries can now be configured to load data starting from any cell in the spreadsheet - that’s error prone and inviting trouble as you have to remember for each data sheet which cell you started entering the data from.The solution is to always start from cell A1.

Not filling in zeros

Sometimes the thing you are measuring throws out the odd zero - sometimes the observations are almost all zeros. Is it really necessary to keep typing in zeros? Wouldn’t it be more efficient to leave the column blank unless there’s a non-zero?

To a computer, there is a big difference between a zero and a blank cell. A zero is data. A blank cell means that there was no measurement, and the computer will interpret it as an unknown value (otherwise known as a null value).

Spreadsheets are likely to misinterpret blank cells that you intend to be zeros, and statistical analysis programs are very likely to interpret blank cells. By not entering the value of your observation, you are telling your computer to represent that data as unknown or missing (null). This can cause problems with later analysis. To take a simple example: the average of a set of numbers which includes a single null value is always null (because the computer cannot guess the value of the missing observations).

It is very important to record zeros as zeros and truly missing data as nulls.

Using problematic null values

Null values are also problematic! Different people take different approaches to recording the lack of data (see below), but not all approaches are useful.

White et al.

Sometimes different null values are used to describe the different reasons why the observation could not be made. “NULL”, “missing data” and “malfunction”, all convey important information, but you are in effect using a single column to capture three different types of information. This is messy, as described in the previous episode, and the solution is to include new columns - one for each type of information you need to capture.

Sometimes unacceptable null values are automatically recorded by the device you use to measure the observation (older devices are especially guilty of not following best practice). If the erroneous null values stem from the measuring device, you’re left with little choice but to clean the data and replace them with a better null value. A tool like OpenRefine, which will be introduced in a later lesson, is perfect for this kind of cleaning.

Whatever the reason, it is a problem if unknown or missing data is recorded as -999, 999, or 0. Statistical programs do not know that these are intended to represent missing (null) values and, because they are valid numbers, they will be included in calculations which will lead to incorrect results. How these values are interpreted will depend on the software you use to analyse your data.

It is essential to use a clearly defined and consistent null indicator. Although open to ambiguity, blanks are still used by a number of applications, but options like and ‘NA’ (for R) and ‘NaN’ (for Python) are good choices. For more information, see White et al. Nine simple ways to make it easier to (re)use your data.

Using formatting to convey information

A common examples of using formatting to convey information is to highlight cells in a specific colour that you want dealt with differently to others. For example, highlighting cells that should be excluded from the analysis (see below). Another example is to leave a blank row to indicate a separation in the data. Both of these highlighting approaches will cause problems with later analysis because they are undetectable to computers.

formatting

The solution - as is so often the case with spreadsheets - is to create a new column to encode the data that should be excluded.

good formatting

Using formatting to make the spreadsheet look pretty

It’s very difficult not to tweak your tables to make them look prettier, e.g. merging cells (especially in headers) or using borders to separate different data. If you are not careful, formatting a worksheet to be more aesthetically pleasing can compromise your computer’s ability to see associations in the data. For example, merged cells will confuse statistics software, which will read the merged cell as a single data value and cause misalignment with data in the following rows.

Your primary goal with structuring data is to accurately capture the data and make the connections in the data transparent to yourself and any program you use for analysis. You will want pretty tables for publications and reports, but those should be completely separate from the tables you use to record the data.

Placing units in cells

Do not include units in cells! They cause a headache in later analysis when you have to separate out the unit from its associated value. Ideally, all the measurements you place in one column should be in the same unit, but if for some reason they are not, create a new column to specify the units.

Including units in data cells

Placing comments in cells

Sometimes you need to make a note or a comment on an observation. For example, you may want to identify observations that were collected by a summer student who you later found out was misidentifying some of your species. These data you will want to identify as suspect.

The problem is the same as that with formatting data to convey information: most analysis software cannot see Excel or LibreOffice comments, so they would be ignored. The solution is the same as described for formatting, create another column if you need to add notes to cells.

Including comments in data cells

Entering more than one piece of information in a cell

As described in the previous episode, the rule is “one cell, one observation of one variable”. For example, if you are counting species and you find one male and one female of the same species, you could enter this as ‘1M, 1F.’ By this stage in the lesson, you will probably find yourself instinctually repelled by this practice! If you record two pieces of data in the same cell, you will confuse data analysis software and this risks mistakes in analysis.

The solution is to include one column for the number of individuals and a separate column for the sex.

Using problematic column names

The key here is to choose descriptive column names to allow for easy data identification.

There is a delicate balance in choosing the correct length of column name. Longer names allow you to adequately describe the data, but they can become unwieldy. It is better to err on the side of a longer, descriptive column name, than a shorter, ambiguous one. You can use abbreviations to reduce the length of column names, but these can quickly become obscure (especially if you don’t return to the data for 6 months or longer). If you use unconventional abbreviations, make sure to keep a record of their full description in the text file you keep with the spreadsheet data.

You must be careful with the characters you use. Do not use spaces, numbers, or special characters of any kind. All of these can cause problems when conducting later analysis. Spaces can be misinterpreted as delimiters (i.e. they can be used by some programs to show where a column begins and ends), some programs do not like field names that are text strings that start with numbers, and some characters (e.g. “/”) can be misinterpreted by data analysis programs.

Instead of spaces, the best advice is to use underscores (_) to separate words. Some people use CamelCase (where uppercase letters are used to delimit words, e.g. ExampleFileName) but they are more difficult to read and, if you ever want to return the whitespaces later in your analysis, it is easier to do this with underscore-separated words than CamelCase.

Where all the observations share the same unit, it can be useful to include the unit in the field name to avoid later confusion. Alternatively, as described above, include the unit in a separate column.

The table below gives some examples of best practice in naming:





Good Name Good Alternative Avoid Reason to avoid
Max_temp_C MaxTempC Maximum Temp (°C) Uses a special character (°)
Precipitation_mm Precipitation precmm Not very readable
Mean_year_growth MeanYearGrowth Mean growth/year Uses a special character (/)
sex sex M/F Uses special character (/) and not very readable
weight weight w. Uses a special character (.) and not very readable
cell_type CellType Cell Type Uses a blank character
Observation_01 first_observation 1st Obs Uses a blank character and starts with a number

Using special characters in data

Do not use your spreadsheet as a word processor! If you copy text directly from a Microsoft Word (or similar applications), you are likely to include lots of formatting information (e.g. tabs, line breaks, etc.) and fancy non-standard characters (left- and right-aligned quotation marks, em-dashes, etc.) that will confuse data analysis software.

Best practice is to avoid adding anything other than text and spaces into a cell.

Inclusion of metadata within data

We have already learned that metadata is the data you record about your data - how the data was collected and organised. This is just to reiterate that metadata should not be contained within the data itself. So, you should explain comments, units, information about how null values are encoded and all other information relevant to your data in a separate file in the same folder as your data or a separate tab within your data spreadsheet.

Fixing mistakes in data organisation

Let’s put this theory into practice! If you have not already done so, download the messy survey data (as outlined in the Setup section). We will now clean this data and learn some best practices in the process.

Take the messy version of the survey data and open it in a spreadsheet program. There are four tabs:

Exercise

The goal of this exercise is to create a cleaned data set in a new tab by combining the data from tabs ‘2013’ and ‘2014’. You will need to identify what is wrong with the way the data is arranged in these two tabs, and then make steps to clean the data before combining it.

Whilst completing this exercise, you might like to contemplate how much time could be have been saved if the researchers had agreed on a data format before the collection took place, and how long it would take to clean real-world research data that could contain many millions of records.

Do not forget to create a new file or a new tab for the cleaned data; never modify your original (raw) data.

Solution

When you finish, compare your clean spreadsheet with the structure suggested below or with surveys.csv which is the example cleaned data file you downloaded during setup. For example, you could chose to record your data using the following columns:

  • date_collected
  • plot_id
  • species_id
  • sex
  • hindfoot_length_cm
  • weight_grams
  • scale_calibrated
  • comments

Your cleaned spreadsheet might not be identical to either of the above, but it should have a similar overall structure. We will address the reasoning behind separating date components in 3 separate columns in surveys.csv in the next episode on formatting dates in spreadsheets.

Full & clean dataset

If you want to have a look at the full, clean dataset - have a look at some of the other files you downloaded. surveys.csv combines data from all the surveys and combined.csv includes the cleaned data from surveys.csv, plots.csv and species.csv combined into one clean file.

Key Points

  • Avoid using multiple tables within one spreadsheet.

  • Avoid spreading data across multiple tabs.

  • Record zeros as zeros.

  • Use an appropriate null value to record missing data.

  • Do not use formatting to convey information or to make your spreadsheet look pretty.

  • Place comments in a separate column.

  • Record units in column headers.

  • Include only one piece of information in a cell.

  • Avoid spaces, numbers and special characters in column headers.

  • Avoid special characters in your data.

Dates as data

Overview

Teaching: 15 min
Exercises: 10 min
Questions

  • What are good approaches for handling dates in spreadsheets?

Objectives

  • Describe how dates are stored and formatted in spreadsheets.

  • Describe the advantages of alternative date formatting in spreadsheets.

  • Demonstrate best practices for entering dates in spreadsheets.

Dates are the scourge of spreadsheets! They are the cause of a huge number of problems with misinterpreted data. There are several reasons for this and we will cover some of them - however there is no reason why you should not be able to store dates in spreadsheets and do it correctly if you follow a few best practices.

One of the issues with dates is that they are not stored as we type them - all spreadsheet programs have their internal ways of storing and handling dates and ways to display dates in many different formats. Nothing wrong with that, per se, however ambiguity with data can arise when spreadsheet programs attempt to “help” you, for example by automatically converting data to dates by filling in what it believes is the missing year in the date you expected to only include a day and a month. So, ambiguity can creep into your dates when you (intentionally or by mistake) omit a part of a date. In addition, ambiguity can occur with regional date variations (e.g. UK vs. US date formats) or if different software products (e.g., LibreOffice, Microsoft Excel, Gnumeric) are used to handle data (each using its own way to resolve ambiguities).

Additionally, Excel can turn things that are not dates into dates, for example names or identifiers like MAR1, DEC1, OCT4. Recently, scientists had to rename human genes to stop Microsoft Excel from misreading them as dates, as it was estimated that 20% genetics research papers contained errors!

The image below demonstrates some of the many date formatting options available in Excel.

Many formats, many ambiguities

With such a bewildering array of date formats, combined with the fact that many real-world data sets are built up over time by different people, using different programs, all of whom may be making different assumptions, it becomes difficult to believe that the dates in any sizeable dataset can be accurate! Fortunately, there is a solution. Before we get to this solution, let’s first investigate some of the major issues with how dates are handled in spreadsheets.

Dates stored as integers

We have seen that Excel displays dates in many different formats, but it stores dates for its own purposes in only one format: a number. This number represents how many days have elapsed since 31 December 1899 (the reason for this is disconcertingly long-winded and quirky). It means 1 January 1900 is stored as number 1, 2 January 1900 is stored as number 2, and so on. In the example above, 2 July 2014 is stored as the number 41822.

Storing dates as a number has some advantages: it’s easy to add days to a given date using simple arithmetic. If you had a plan to conduct interviews every ninety days, you could type:

=B2+90

And Excel would return:

30-Sep

In most cases, it retains the format of the cell that is being operated upon. Month and year rollovers are internally tracked and applied.

However, there is also a significant problem with this system, which is that Excel refuses to believe that dates existed before 31 December 1899. Open a spreadsheet and add the following to the A1 cell:

 1 January 1900

Now move to a new cell and enter:

=A1+2

Excel will provide the answer 3 January 1900 (in some format) because the result of this arithmetic returns a date 2 days after 1 January 1900. Now try removing 2 days. Move to another cell and enter:

=A1-2

Excel returns an error, because it is incapable of understanding that dates existed prior to 1 January 1900. This poses a huge problem for storing historical data!

Note on adding and subtracting dates and times

Adding months and years to a date is slightly trickier than adding days because we need to make sure that we are adding the amount to the correct entity (i.e. month or year). To do so,

  • first we need to extract single entities (day, month or year) from the date (how to do that is explained below),
  • then, we can add values to each entity (e.g. month or year),
  • finally, we need to reconstruct the date from the updated day, month, year entities using the DATE() function.

Times are handled in a similar way; seconds can be directly added, but to add hours and minutes we need to make sure that we are adding the quantities to the correct entities. Extracting seconds, minutes and hours from a time is explained in one of the exercises below.

Issues with historical data

As far as Excel is concerned, time began on 31 December 1899 and any date before this time is not real. If you are mixing historic data from before and after this date, Excel will translate only the post-1900 dates into its internal format, resulting in mixed data. If you are working with historical data, be extremely careful with your dates!

Regional date formatting issues

Different countries write dates differently. If you are in the UK, you will interpret 7/12/88 as the 7th of December 1988. A researcher in the US will interpret the same entry as the 12th of July 1988. This regional variation is handled automatically by your spreadsheet program through a setting in the preferences. Errors are easily added to your spreadsheet if your preferences are set incorrectly or, more likely, if you share a spreadsheet with a person from a country that uses a different date format. It is especially devastating if only some of the date formats are changed, which leaves you with a spreadsheet full of ambiguous dates and no way to repair them.

Exercise

Look at the tab ‘dates’ in the messy data spreadsheet you used in previous exercises. It contains a copy of the ‘plot 3’ table from the ‘2014’ tab (this is the table that contains the problematic dates). You will notice that there are years missing from the “Date collected” column. Just by looking at the entered dates, can you figure out the date format that was used?

Solution

The date format is ‘MM/DD/YYYY’ because we have entries like ‘2/19’ and ‘5/18’, which means that the person who entered these dates most probably used the US version of Excel where month comes before day. If we did not have such entries, we would not be able to tell what date was entered! For example, is ‘7/11’ ‘7th November’ or ‘11th July’?

Issues with missing bits of dates

If you miss out some part of a date - spreadsheet programmes such as Excel will do their best to guess and form a valid date (so they can store it properly internally) from your partial information. This will happen automatically and often without you even noticing, but can cause you some headache down the line.

Dates with no year

If no year is specified, the spreadsheet program will assume you mean the current year and will insert that value. This may be incorrect if you are working with historical data so be very cautious when working with data that does not have a year specified within its date variable.

Exercise

Look at the tab ‘dates’ in the messy data spreadsheet. It contains a copy of the ‘plot 3’ table from the ‘2014’ tab (this is the table that contains the problematic dates). Select one of the cells in “Date collected” column, for example cell ‘A3’. What is the date Excel thinks is entered? What is the year of the date?

Solution

Select any cell in “Date collected” column. In the field for the cell formula at the top, Excel will display the value that is stored internally for this cell. drag_dates From the cell that is selected in the figure above, we can see that Excel has stored the date “08/01/2015” (i.e. 8 January 2015 using the DD/MM/YYYY format). This data was meant to relate to 2014 but Excel has added the year 2015 instead.

The cause of this change is that the field assistant who collected the data initially forgot to add their data for ‘plot 3’ in the dataset. They came back in 2015 to add the missing data into the dataset and entered the dates for ‘plot 3’ as they had been recorded - without the year. Excel automatically interpreted the year as 2015 the year in which the data was entered into the spreadsheet. This exemplifies how easy it is to introduce errors in dates.

Dates with 2-digit year

When you type a date using a 2-digit year number (e.g. “88”), spreadsheet programmes use specific rules to determine which century to use for the date. For example, Excel determines the century by using a cutoff year of “29”, which means that 2-digit years 00-29 will be treated to be in the 21st century (i.e. after 2000), and 2-digit year 30-99 will be treated to be in the 20st century (i.e. after 1900).

Exercise

Type the following dates in an Excel spreadsheet:

  • 1/1/00
  • 1/1/01
  • 1/1/29
  • 1/1/30
  • 1/1/31
  • 1/1/99

What year does Excel assume for each of these dates?

Solution

Excel is using the years 29/30 as the cutoff for the century by default - dates before and including the year “29” will be treated as in the 2000s and year “30” and above in 1900s:

  • 1/1/00 = 2000-Jan-1
  • 1/1/01 = 2001-Jan-1
  • 1/1/29 = 2029-Jan-1
  • 1/1/30 = 1930-Jan-1
  • 1/1/31 = 1931-Jan-1
  • 1/1/99 = 1999-Jan-1

Note on missing bits of dates

Make sure you remove any ambiguity when working with dates (and data in general), so as to avoid situations where spreadsheet programmes are making guesses on what your data should be.

Issues when exporting data containing dates

Exercise

What happens to the dates in the dates tab of the messy spreadsheet if we export this sheet to .csv (Comma–Separated Value format) and then open the file in a plain text editor (like TextEdit or Notepad)? What happens to the dates if we then open the .csv file back in Excel?

Solution

  1. Click to the dates tab of the messy data spreadsheet.
  2. Select File -> Save As in Excel and in the drop down menu for file format select CSV UTF-8 (Comma delimited) (.csv) (or Text CSV (.csv) in LibreOffice). Enter a file name, e.g. dates-export.csv and click Save.
  3. You will see a pop-up that says “This workbook cannot be saved in the selected file format because it contains multiple sheets.” Choose Save Active Sheet or OK depending on your Excel version (In LibreOffice you will see a Confirm File Format dialogue: select Use Text CSV format. You will then be presented with a Export Text File dialogue: select OK. You will then be presented with a Warning dialogue: select OK.)
  4. Close your current messy data spreadsheet (you can reopen it later).
  5. Open the dates-export.csv file (or whatever you named it). Right click and select Open With. Choose a plain text editor (like TextEdit or Notepad) and view the file. Notice that the dates display as month/day without any year information.
  6. Now right click on the file again and open with Excel (or LibreOffice). In Excel, notice that the dates display with the current year, not 2015 as previously assumed by Excel. In LibreOffice, no year is added, but the date is no longer treated as a date. As you can see, exporting data from a spreadsheet program and then importing it back again can fundamentally change the data!

Note on exporting

Some versions of Excel, when exporting into a text-based format (such as CSV), will export its internal date integer representation of the date instead of the date’s value. This can potentially lead to problems if you use other software to manipulate the data as they may not understand Excel’s date encodings.

Preferred ways of storing dates

Entering a date in a single cell is quick and easy, but it is open to so many potential problems that a new solution is needed if we are to remove ambiguity from spreadsheet dates. Let’s have a look at some good options for storing dates.

Storing dates in separate YEAR, MONTH, DAY columns

One good option for storing dates is to separate them into their component parts and store them separately YEAR, MONTH, DAY columns. For instance, the following is a spreadsheet that represents insect counts that were taken every few days over the summer:

So, so ambiguous, it is even confusing Excel

According to Excel, this person had been collecting bugs over a number of years, including some dates in the future (in year 2026)!

Exercise

Challenge: pulling month, day and year out of dates.

  1. Look at the tab ‘dates’ in our messy data spreadsheet. Extract month, day and year from the dates in the “Date collected” column into three new columns called ‘month’, ‘day’, ‘year’. To do so, you can use the following built-in Excel functions: YEAR(), MONTH(), and DAY().
  2. Apply the formulas on each of the row of table ‘plot 3’ in the ‘dates’ tab. To do so, drag the formula for the first row down to the last row.
  3. Make sure the new columns are formatted as a number and not as a date.

Solution

  1. Create column ‘day’ for the first row of the table (note that this is not the first row of the spreadsheet) and insert formula =DAY(A3) in cell E3. This applies the function DAY() on the value in cell A3, which is the date of the first observation.
  2. Drag this formula to the last row of the table - you will see the formulas and calculations for the month appearing in the cells below. drag_dates
  3. To format the column ‘day’ as a whole number, right click on the column ‘E’ (for ‘day’) and select Format cells... option. format_dates Select ‘Number’ and set decimal places to 0. dates_as_number Repeat the process for month and year. You should end up with a table like the one below. dates, exersize 1 Again, as in the previous exercise but this time using the YEAR() function, we can see that Excel saved the year for data as 2015 (the year the data is entered) instead of 2014 (the year the data was collected). This was entirely the mistake of the field assistant but by being helpful Excel managed to “mask” this error.

As for dates, times are handled in a similar way and there are functions to extract hours, minutes and seconds.

(Optional) Exercise

Challenge: pulling hour, minute and second out of the current time.

Current time and date are best retrieved using the functions NOW(), which returns the current date and time, and TODAY(), which returns the current date. The results will be formatted according to your computer’s settings.

  1. Extract the year, month and day from the current date and time string returned by the NOW() function.
  2. Calculate the current time using NOW()-TODAY().
  3. Extract the hour, minute and second from the current time using functions HOUR(), MINUTE() and SECOND().

Solution

  1. To get the year, type =YEAR(NOW()) into any cell in your spreadsheet. To get the month, type =MONTH(NOW()). To get the day, type =DAY(NOW()).
  2. Typing =NOW()-TODAY() will result in a decimal value that is not easily human parsable to a clock-based time. You will need to use the strategies in the third part of this challenge to convert this decimal value to readable time.
  3. To extract the hour, type =HOUR(NOW()-TODAY()) and similarly for minute and second.

While we have added 2 more columns now to our data, we have removed any ambiguity pertaining to our dates. If you ever need to combine data back to a single date value, spreadsheet programs provide function DATE(year, month, day) for precisely this purpose.

Storing date as a single string

An alternative is to convert the date string into a single string using, e.g. the YYYYMMDDhhmmss format. For example the date March 24, 2015 17:25:35 would become 20150324172535, where:

Such strings will be correctly sorted in ascending or descending order and can be correctly parsed by the receiving data analysis software. Make sure your column containing such data is formatted as ‘text’ before you start entering your data.

Similarly, you can store dates using the international ISO 8601 date and time standard where date March 24, 2015 17:25:35 would be encoded as 2015-03-24T17:25:35 (or simply 2015-03-24 if the time part is not needed).

Key Points

  • Use extreme caution when working with date data.

  • Splitting dates into their component values can make them easier to handle.

Quality assurance and control

Overview

Teaching: 15 min
Exercises: 10 min
Questions

  • How can we carry out basic quality control and quality assurance in spreadsheets?

Objectives

  • Apply quality control techniques to identify errors in spreadsheets and limit incorrect data entry.

When you have a well-structured data table, you can use several simple techniques within your spreadsheet to ensure the data you enter is free of errors. These approaches include techniques that are implemented prior to entering data (quality assurance) and techniques that are used after entering data to check for errors (quality control). These techniques can make data entry easier as well as more robust.

Validating data on input

When we input data into a cell of a spreadsheet we are typically not constrained in the type of data we enter. In any one column, the spreadsheets software will not warn us if we start to enter a mix of text, numbers or dates in different rows. Even if we are not facing constraints from the software, as a researcher we often anticipate that all data in one column will be of a certain type (it should be). It is also possible that the nature of the data contained in the table allows us to place additional restrictions on the acceptable values for cells in a column. For example a column recording age in years should be numeric, greater than 0 and is unlikely to be greater than 120.

We will be working with a couple of examples of data validation rules but many others exist. For an overview of data validation rules available in Excel, check out the Excel support page on data validation. General steps to employ data validation are as follows.

  1. Open the tab ‘semi-cleaned-combined’ of the messy data spreadsheet. It contains tables from the messy data tabs ‘2013’ and ‘2014’ combined into a single table (you may note that data is not fully yet cleaned, although it is in a much better shape than it was in the tabs ‘2013’ and ‘2014’).

  2. Select the column ‘plot_id’.

  3. On the Data tab from the top level menu select Data Validation, or Data Tools and then Data Validation or Validation Tools (depending on your version of Excel) or Validity in LibreOffice.

    Image of Data Validation button on Data tab

  4. In the Allow box of tab Settings select ‘Whole numbers’ of data that should be in the column. Other options include decimals, lists of items, dates, and other values.

    Image of Data Validation window

  5. Now attempt to enter text into a cell in the ‘plot_id’ column. You should be prevented from doing so, as the column is now locked down to whole numbers only.

Excel allows us to specify a variety of data validations to be applied to cell contents. If the validation fails, an error is raised and the data we attempted to enter is rejected. In addition to providing validation when we enter data, Excel allows us to add validations to data that has already been entered. Note, however, that the validation is not applied retrospectively, so that incorrect data are not removed. Instead, if a particular cell with a value entered previously fails the validation check - depending on the Excel version - a triangle may be placed in the top left corner of the cell as a warning, or you may not get any warning at all (but you may be able to request invalid cells to be circled from the Data validation drop-down button). For this reason, it is best practice to set up validations before you start entering data.

Restricting data to a numeric range

Now let’s try this out by setting the ‘plot_id’ in our spreadsheet to only allow plot values that are integers between 1 and 24.

Exercise

Set the ‘plot_id’ column in tab ‘semi-cleaned-combined’ of the messy data spreadsheet to only allow values that are integers between 1 and 24.

Solution

  1. Open the tab ‘semi-cleaned-combined’ of messy data spreadsheet and select the ‘plot_id’ column.
  2. On the Data tab the top level menu select Data Validation, or Data Tools and then Data Validation or Validation Tools (depending on your version of Excel) or Validity in LibreOffice.
  3. In the Allow box of the Settings tab select Whole number (and in LibreOffice select the Data field to be valid range.
  4. Set the minimum and maximum values to 1 and 24. Image of Data Validation window for validating numeric values Now let’s try entering a new value in the plot column that is not a valid plot id. The spreadsheet stops us from entering the wrong value and asks us if we would like to try again. Image of error when trying to enter invalid numeric data You can also customise the resulting message to be more informative by entering your own message in the Input Message tab (or, in LibreOffice, by selecting the Error alert tab and entering a message in the Error message field). Image of Input Message tab You can also set the invalid data to result in a warning rather than an error by modifying the Style option on the Error Alert tab (or the Action field in LibreOffice). Image of Error Alert tab
  5. In newer versions of Excel, you may also have an option to indicate all previous invalid entries once you have entered your restricted values. Image of invalid numeric entries circled You have now provided a restriction that will be validated each time you try and enter data into the selected cells. If you type a value which is not on the list you will get an error message. This not only prevents data input errors, but also makes it easier and faster to enter data.

Restricting data to entries from a list

If you use a list of options to restrict data entry, the spreadsheet will provide you with a drop-down list of the available items. Think about trying to remember how to spell “Dipodomys spectabilis” for species each time you need to enter that value, or whether or not you capitalised the words correctly. By restricting values in cells to a predefined list of values, you can select the right option from the list every time. Let’s apply this to our data.

Exercise

Restrict the values of the ‘species_id’ column in tab ‘semi-cleaned-combined’ of the messy data spreadsheet to allow only values from the following list: “DM, NL, DS, PP, PE, PF”.

Solution

  1. Open the tab ‘semi-cleaned-combined’ of messy_survey_data.xls and select the ‘species_id’ column.
  2. On the Data tab the top level menu select Data Validation, or Data Tools and then Data Validation or Validation Tools (depending on your version of Excel) or Validity in LibreOffice.
  3. In the Allow box of the Settings tab select List.
  4. Type a list of all the values that you want to be accepted in this column, separated by a comma (with no spaces). For example, type “DM,NL,DS,PP,PE,PF” (in LibreOffice, skip the commas and add each entry to a new line). Note this is only a small subset of allowed species ids - the whole set is much larger but for the purposes of this exercise we want to show you how to restrict a cell to a list of values and what happens when old data does not conform to this restriction. Image of Data Validation window for validating list values
  5. Create a meaningful input message and modify alert error type, as in the previous exercise, then click OK.
  6. Scroll to the bottom of your data and select a cell in the species_id column in a new row. When a cell in this column is selected, a drop-down arrow will appear. When you click the arrow you will be able to select a value from your list of allowed values.
  7. In newer versions of Excel, you may also have an option to indicate all previous invalid entries once you have entered your restricted values. Image of invalid list entries circled You have now provided a restriction that will be validated each time you try and enter data into the selected cells. If you type a value which is not on the list you will get an error message. This not only prevents data input errors, but also makes it easier and faster to enter data.

Typing a list of values where only a few possible values exist might be convenient, but if the list is longer (e.g. all allowed species) it makes sense to create it as a small table (in a separate tab of the workbook). We can give the table a name and then reference the table name as the source of acceptable inputs when the source box appears in the Data Validation pop-out. For example, the table of all species can be copied from the file species.csv and then referenced in your spreadsheet to provide the list of all valid species names and their abbreviations (ids). Using a table in this way makes the data entry process more flexible. If you add or remove contents from the table, then these are immediately reflected in any new cell entries based on this source. You can also have different cells refer to the same table of acceptable inputs.

Quality control - identifying outliers

Tip: keep your raw data - raw

Before doing any quality control operations, save your original file with the formulas and a name indicating it is the original data. Create a separate file with a new name, and ensure your data is stored as values and not as formulas. Because formulas refer to other cells, and you may be moving cells around, you may compromise the integrity of your data if you do not take this step!

Tip: keep track of your modifications

As you start manipulating your data files, create a README.txt text file to keep track of your files and document your manipulations so that they may be easily understood and replicated, either by your future self or by an independent researcher. Your README.txt file should document all of the files in your data set (including documentation), describe their content and format, and lay out the organising principles of folders and subfolders. For each of the separate files listed, it is a good idea to document the manipulations or analyses that were carried out on those data. Cornell University’s Research Data Management Service Group provides detailed guidelines for how to write a good README file, along with an adaptable template.

Sorting data

Bad values often sort to the bottom or top of the column. For example, if your data should be numeric, then alphabetical and null data will group at the ends of the sorted data. Sort your data by each field, one at a time. Scan through each column, but pay the most attention to the top and the bottom of a column. If your dataset is well-structured and does not contain formulas, sorting should never affect the integrity of your dataset.

Tip: expand the sort

Remember to expand your sort in order to prevent data corruption. Expanding your sort ensures that the all the data in one row move together instead of only sorting a single column in isolation. Sorting by only a single column will scramble your data - a single row will no longer represent an individual observation.

Exercise

Look at the ‘semi-cleaned-combined’ tab of the messy data spreadsheet. It contains tables from the messy data tabs ‘2013’ and ‘2014’ combined into a single table in a single tab (you will note that the data is not yet fully cleaned).

Sort the weight_grams column in your spreadsheet program from Largest to Smallest. What do you notice?

Solution

  1. Select weight_grams column.
  2. Click the Sort button on the Data tab from the top level menu in Excel (in LibreOffice select the Data menu and then select Sort. Image of sort button Note that in older versions of Excel you may get just the Sort button without the drop-down menu, followed by a pop up window to specify the column you want to sort and the sort order (shown in figure below). Image of sort pop up
  3. In the pop-up that appears make sure you select Expand the selection to make sure data in other columns is moved together with the data in the column you are sorting. Image of expanding sort pop up
  4. Note how the odd values sort to the top and bottom of the data. The cells containing no data values sort to the bottom of the tabular data, while the cells where the letter “g” was included can be found towards the top or bottom. This is a powerful way to check your data for outliers and odd values. Image of outliers sorted at the top

Conditional formatting

Conditional formatting allows you to format data based on some criteria, e.g. you can colour code your values by some criteria from lowest to highest. This can also make it easier to scan your data for outliers. Conditional formatting should be used with caution, but it can be a great way to flag inconsistent values or errors when entering data. Let’s apply some to our data.

Exercise

Look at the ‘semi-cleaned-combined’ tab of the messy data spreadsheet and apply any 2-Color Scale formatting rule to column weight_grams.

Solution

  1. Select weight_grams column.
  2. In the main Excel menu bar, click Home > Conditional Formatting... choose New rule (in LibreOffice from the Format menu, select Conditional and then Color scale). Image of Conditional Formatting - adding new rule
  3. In the New Formatting Rule window, apply any 2-Color Scale formatting rule. (In LibreOffice select Color scale (2 entries) from the top-middle drop-down list and click OK. Image of Conditional Formatting - 2-colour scale
  4. Now we can scan through the column and different colours will stand out. Do you notice any strange values?
  5. Cells that contain non-numerical values are not coloured making them easier to spot. This includes both the cells where the letter “g” was included and the empty cells. They show up at either at the top or at the bottom of the data but may not be as easy to spot if you have a very large dataset and have to scroll a lot. Image of Conditional Formatting - result

Scanning for outliers

It is nice to be able to do these scans for outliers in spreadsheets (such as using sorting or conditional formatting), but we can also do these checks in a programming languages like R, Python and SQL, or using OpenRefine - which may be even more suitable if you have larger datasets with, e.g., with 10K+ or 100K+ rows which are difficult to scroll down to the bottom of to spot outliers.

Key Points

  • Always copy your original spreadsheet file and work with a copy so you do not affect the raw data.

  • Use data validation to prevent accidentally entering invalid data.

  • Use sorting to check for invalid data.

  • Use conditional formatting (cautiously) to check for invalid data.

Exporting data

Overview

Teaching: 10 min
Exercises: 0 min
Questions

  • How can we export data from spreadsheets in a way that is useful for downstream applications?

Objectives

  • Store spreadsheet data in universal file formats.

  • Export data from a spreadsheet to a CSV file.

Storing the data you are going to work with for your analyses in Excel default file format (*.xls or *.xlsx - depending on the Excel version) is not a good idea. Here are some reasons why:

The above points also apply to other formats such as open data formats used by LibreOffice and Open Office. These formats are not static and do not get parsed the same way by different software packages.

As an example of inconsistencies in data storage, remember how we investigated how Excel stores dates. There are multiple defaults for different versions of the software, and you can switch between them all. So, say you are compiling Excel-stored data from multiple sources. There are dates in each file - Excel interprets them as their own internally consistent numbers. When you combine the data, Excel will take the serial number from the place you are importing it, and interpret it using the rule set for the version of Excel you are using. These rules may not align. Essentially, you could be adding errors to your data, and it would not necessarily be flagged by any data cleaning methods.

Storing data in a universal, open, and static format will help deal with this problem, e.g. formats such as tab-delimited (tab separated values or TSV) or comma-delimited (comma separated values or CSV). CSV files are plain text files where the columns are separated by commas, hence ‘comma separated values’ or CSV. The advantage of a CSV file over an Excel or other proprietary formats is that we can open and read a CSV file using just about any software, including plain text editors like TextEdit or NotePad. Hence these files are more robust and more likely to be accessible in the future.

Data in a CSV file can also be easily imported into other formats and environments, such as SQLite and R. We are not tied to a specific version of a specific (potentially expensive) program when we work with CSV files, so it is a good format to work with for maximum portability and endurance. Most spreadsheet programs can save to delimited text formats, although they may give you a warning during the file export.

To save a file you have opened in Excel in CSV format:

  1. From the top menu select File then Save as.
  2. You will be presented with different options for exporting to CSV file format, depending on your Excel version.
  3. In the ‘Format’ field, from the list select CSV UTF-8 (Comma-delimited)(*.csv) (or Text CSV (.csv) in LibreOffice). Saving an Excel file to CSV
  4. Double check the file name and the location where you want to save it and hit ‘Save’.

  5. If you are saving an Excel file with multiple tabs, Excel will save only the currently active sheet (there is no option for multiple tabs in CSV) and warn you that you cannot save multiple sheets to a CSV file at the same time. If you need to save multiple tabs, you must save each one as its own CSV file.

    Saving active sheet warning

Note on backwards compatibility

You can open CSV files in Excel (or LibreOffice)!

A note on Python/R and Excel’s .xls/.xlxs formats

There are Python and R packages that can read .xls/.xlxs files. It is even possible to access different worksheets in the Excel spreadsheet documents. However, because these packages parse data tables from proprietary and non-static software, there is no guarantee that they will continue to work on new versions of Excel. Exporting your data to CSV or TSV format is much safer and more reproducible.

Note: when data contains commas

In some datasets, the data values themselves may include commas (,). In that case, you need to make sure that the commas are properly escaped when saving the file. Otherwise, the software that you use (including Excel) will most likely incorrectly display the data in columns. This is because the commas which are a part of the data values will be interpreted as delimiters.

If you are working with data that contains commas, the fields should be enclosed with double quotes. This should occur automatically when you export from a spreadsheet program. However, it is always a good idea to double check your CSV after it is saved by opening it in a spreadsheet program and reviewing the columns to ensure that no new ones have been added. For more of a discussion on data formats and potential issues with commas within datasets see the discussion page.

Key Points

  • Data stored in common spreadsheet formats will often not be read correctly into data analysis software, introducing errors into your data.

  • Exporting data from spreadsheets to formats like CSV or TSV puts it in a format that can be used consistently by most programs.