Data Manipulation Olympics

Author(s) orcid logoSaskia Hiltemann avatar Saskia Hiltemann Yongbin Li avatar Yongbin Li
Editor(s) orcid logoHelena Rasche avatar Helena Rasche
Reviewers Saskia Hiltemann avatar Yongbin Li avatar Helena Rasche avatar Donny Vrins avatar
Overview
Creative Commons License: CC-BY Questions:

  • How can I do basic data manipulation in Galaxy?

  • Which tools are available to convert, reformat, filter, sort etc my text-based data?

Objectives:

  • Familiarize yourself with data manipulation tools in Galaxy

  • Perform basic text manipulation tasks in Galaxy

  • Become comfortable converting text-based files in a variety of ways.

  • Reason about the expected outcome of tools

Time estimation: 1 hour
Level: Introductory
Supporting Materials:
Published: Jul 20, 2022
Last modification: Aug 7, 2024
License: Tutorial Content is licensed under Creative Commons Attribution 4.0 International License. The GTN Framework is licensed under MIT
PURL: https://gxy.io/GTN:T00184
Rating: 4.8 (8 recent ratings, 18 all time)
Revision: 10

Scientific analyses often consist of a number of tools that run one after the other, in order to go from the raw data to scientific insight. Between these specialized tools, simple data manipulation steps are often needed as a kind of “glue” between tools. For example, the output of tool A may produce a file that contains all the information needed as input for tool B, but tool B expects the columns in a different order. Or in genomic data analysis, some tools expect chromosome X to be listed as chrX, while others simply expect X. In these situations, extra data manipulation steps are needed to prepare files for input to analysis tools.

Galaxy has a large collection of tools to perform such basic data manipulation tasks, and becoming familiar with these operations will allow to perform your analysis more easily in Galaxy (and outside).

Agenda

In this tutorial, we will cover:

  1. Cheatsheet
  2. Background
  3. Upload Data
    1. About this dataset
  4. Choose your adventure!
  5. File Format Conversion
    1. Converting vs Changing the datatype
  6. Sorting
    1. Sort on multiple columns at once
    2. Exercises
  7. Filtering
    1. Advanced Filtering with Python Syntax
    2. Exercises
  8. Counting
    1. Exercises
  9. Grouping
    1. Grouping on multiple columns
    2. Exercises
  10. Computing
    1. Exercises
  11. Find and Replace
    1. Exercises
  12. Removing Columns
    1. Exercises
  13. Unique
  14. Joining Files
  15. Concatenating
  16. Splitting Files
    1. Exercises
  17. Conclusion
  18. Exercises: Putting it all together!

Cheatsheet

Here is an overview table of the different data manipulations in this tutorial, with links to the tools in Galaxy.

If you’ve opened this tutorial via the icon in Galaxy (top menu bar), you can click on the tool names in the last column to quickly open them in Galaxy and start using them on your own!

Operation Description Galaxy Tool
Convert format Change the file format Edit attributes
Word count Count the number of lines, words and characters in a file Line/Word/Character count
Sort on a column Change the order of the rows based on values in one or more columns Sort ( Galaxy version 1.1.1)
Filter Remove rows based on values in one or more columns Filter
Counting Count occurrences of values in a column Count , Datamash ( Galaxy version 1.1.0+galaxy2)
Group on a column And perform simple operations (count, mean, min, max etc) Group , Datamash ( Galaxy version 1.1.0+galaxy2)
Compute on rows to derive new column values from existing ones Compute ( Galaxy version 2.0)
Find and Replace in a specific column Column Regex Find and Replace ( Galaxy version 1.0.1)
Find and Replace on every line Regex Find and Replace ( Galaxy version 1.0.1)
Join two Datasets side by side on a specified field Join two Datasets
Concatenate datasets one after the other Concatenate datasets
Remove Beginning Good for removing header lines Remove beginning of a file
Select First lines Good for finding top 10s or saving header lines Select first lines
Cut Columns By header name Remove columns by heading ( Galaxy version 0.0.1)
Cut Columns By column number Cut columns from a table, Advanced Cut columns from a table ( Galaxy version 1.1.0)
Paste Two files side by side Paste
Split file Based on values of a column Split ( Galaxy version 0.6)
Unique Remove duplicate rows Unique ( Galaxy version 1.1.0)

TIP: Adding tools to your Favourites: If you find yourself frequently using the same tool often but struggle to find it back in the long list of tools, you can star your favourite tools in Galaxy!

Galaxy servers can have a lot of tools available, which can make it challenging to find the tool you are looking for. To help find your favourite tools, you can:

  • Keep a list of your favourite tools to find them back easily later.
    • Adding tools to your favourites
      • Open a tool
      • Click on the star icon next to the tool name to add it to your favourites
    • Viewing your favourite tools
      • Click on the star icon at the top of the Galaxy tool panel (above the tool search bar)
      • This will filter the toolbox to show all your starred tools
  • Change the tool panel view
    • Click on the icon at the top of the Galaxy tool panel (above the tool search bar)
    • Here you can view the tools by EDAM ontology terms
      • EDAM Topics (e.g. biology, ecology)
      • EDAM Operations (e.g. quality control, variant analysis)
      • You can always get back to the default view by choosing “Full Tool Panel”

In this tutorial, these tools are explained in more detail, and we provide some exercises for you to practice.

Background

In this tutorial, we will use as our dataset a table with results from the Olympics, from the games in Athens in 1896 until Tokyo in 2020. The objective is to familiarize you with a large number of the most important data manipulation tools in Galaxy. Much like the Olympics, there are many different disciplines (types of operations), and for each operation there are often multiple techniques (tools) available to athletes (data analysts, you) that are great for achieving the goal.

image of olympic rings, logo and two athletes around the words "Data Analysis Olympics".

We will show you many of these commonly needed data manipulation operations, and some examples of how to perform them in Galaxy. We also provide many exercises so that you can train your skills and become a data manipulation Olympian!

Upload Data

Before we can do any manipulation, we will need some data. Let’s upload our table with Olympics results now.

Hands-on: Get data

  1. Import the file olympics.tsv via link

    https://zenodo.org/record/6803028/files/olympics.tsv
    • Copy the link location

    • Click Upload Data at the top of the tool panel

    • Select Paste/Fetch Data

    • Paste the link(s) into the text field

    • Press Start

    • Close the window

  2. Expand on the item in your history to see some metadata and a short preview of the contents.

  3. View the dataset by clicking on the eye icon.

    Question
    1. What is the format of the file?
    2. What does each row represent?
    3. How many lines are in the file? (Hint: Line/Word/Character count)
    4. How many columns?
    1. When you expand the olympics.tsv dataset in your history (see also screenshot below), you will see format: tabular, this is another term for a tab-separated (tsv) file.
    2. Each row represents an athlete’s participation in an event. If an athlete competes in multiple events, there is a line for each event.
    3. 234,523. Look at the expanded view in your history, this tells us there are ~250,000 rows in the dataset. We can get the exact number using the Line/Word/Character count tool.
    4. There are 17 columns in this file. There are multiple ways to find this answer:
      • Count the columns manually (only doable for small files)
      • In the expanded view, scroll sideways on the dataset preview, at the top the columns are numbered
      • Click on the i icon on the dataset, here you will find more detailed information about the file and the job that created it. At the bottom is also a preview (peek) of the dataset, and numbered columns

    a screenshot of the expanded view of the dataset in the history, it shows the datatype, number of lines in the file, and a preview> > > of the dataset with numbered columns.

About this dataset

The data was obtained from Olympedia. The file olympics.tsv contains 234,522 rows and 17 columns. Each row corresponds to an individual athlete competing in an individual Olympic event. The columns are:

  • athlete_id - Unique number for each athlete
  • name - Athlete’s name
  • sex - M or F
  • birth_year - 4-digit number
  • birth_day - e.g. 24 July
  • birth_place - town and/or country
  • height - In centimeters (or NA if data not known)
  • weight - In kilograms (or NA if data not known)
  • team - Team name
  • noc - National Olympic Committee 3-letter code
  • games - Year and season
  • year - Integer
  • season - Summer or Winter
  • city - Host city
  • sport - Sport
  • event - Event
  • medal - Gold, Silver, Bronze (or NA if no medal was won)

We will use this dataset to practice our data manipulation skills in Galaxy.

Choose your adventure!

This tutorial is structured a bit differently than most. You do not have to do the steps in the order they are presented below. Every section in this tutorial uses the dataset you just uploaded (the olympics.tsv file) as input, so you can jump to any section in this tutorial right now if you have a particular data manipulation operation in mind you want to learn more about.

File Format Conversion

The file we uploaded is a .tsv file. This stands for tab-separated values. This means that this is a file containing rows and columns, where a TAB character is used to signify a column ends and a new one begins. Galaxy is great at understanding tab-separated files files, and most of the data manipulation tools are designed to work with such files.

A similar format you may come across a lot in data science, is the .csv file, or comma-separated values file. This is the same as .tsv, but uses comma (,) characters to indicate new columns, instead of TAB (\t) characters.

Galaxy can convert these two formats into each other.

Hands-on: Convert Tabular to CSV

  1. Convert the olympics.tsv file into a CSV file

    • Click on the pencil icon for the dataset to edit its attributes
    • In the central panel, click on the Convert tab on the top
    • In the upper part Convert, select csv (using 'Convert tabular to CSV')
    • Click the Create dataset button to start the conversion.

  2. View the converted dataset

    Question
    1. What do you notice?
    2. Why are some values in quotes?
    1. Galaxy does not display the table as nicely as before. This is because Galaxy is optimized to work with tsv files. For most rows you now see commas separating the different columns.
    2. If the data in a column contains a comma (e.g. in this file we have events such as swimming 5,000 meters), we put the value in quotes to signifiy that that comma is part of the data, not a column delimiter.

Converting vs Changing the datatype

The file format conversion step changed the dataset itself (by changing TABs to commas), and therefore created another dataset in your history. It is also possible to change the datatype; this does not change the underlying file/data, but just tells Galaxy what type of file it is. Galaxy uses this information in a variety of ways, such as:

  • How to display the data when you click the View button
  • Which visualization options to offer
  • Filtering the list of possible inputs for tools (to only allow those of the correct datatype and protect you from mistakes)
  • Which file format conversions to make available
  • etc..

When you upload a file to Galaxy, by default it will attempt to auto-detect the file format. This is very often correct, but some times you may need to manually set the datatype to the correct value.

Hands-on: Change datatype from Tabular to TXT

  1. Change the datatype of the olympics.tsv file to txt.

    • Click on the pencil icon for the dataset to edit its attributes
    • In the central panel, click Datatypes tab on the top
    • In the Assign Datatype, select txt from “New type” dropdown
      • Tip: you can start typing the datatype into the field to filter the dropdown menu
    • Click the Save button

  2. View the dataset again

    Question
    1. What do you see?
    2. Why didn’t this step create a new item in your history?

    1. Since Galaxy now no longer know that this is a tabular file with rows and columns, it displays the data as-is, no longer arranging the view into neat columns.

    2. The file itself did not change, only the metadata. We simply told Galaxy to treat this file as a plain text file, instead of a tabular file.

  3. Change the file back from txt to tabular

    • Click on the pencil icon for the dataset to edit its attributes
    • In the central panel, click Datatypes tab on the top
    • In the Assign Datatype, select tabular from “New type” dropdown
      • Tip: you can start typing the datatype into the field to filter the dropdown menu
    • Click the Save button

Sorting

We have a lot of data in this file, but it is ordered by the athlete ID number, which is a somewhat arbitrary and meaningless number. But we can sort the rows in this file to something more convenient, for example alphabetically by name of the athlete, or chronologically by year of the Olympics.

Hands-on: Sort table based on a column

  1. We will sort the file in chronological order based on the year of the Olympic games

    Question
    1. Which column contains the year?
    2. Do we want ascending or descending order if we want the oldest games at the top?
    3. What should we do with the very first row (the one with the header names?)
    1. Column 12
    2. The file should be sorted in ascending (increasing) order
    3. The header line should always stay on top, so we want to ignore it when we sort the file.
  2. Open the Sort - data in ascending or descending order ( Galaxy version 1.1.1) tool
    • Read the help text at the bottom of the tool form
    • Which settings do you think we need to use?
  3. Sort - data in ascending or descending order ( Galaxy version 1.1.1) with the following parameters:
    • “Sort Query”: olympics.tsv
    • “Number of header lines”: 1`
    • Column selections
      • “on column”: Column 12
      • “in”: Ascending order
      • “Flavor”: Fast numeric sort

  4. View the sorted file.

    Question

    Which athlete is listed at the top of the file now?

    1. J. Defert. Who competed in a Tennis event 1896 Summer Olympics in Athens.
  5. Rename the resulting file to somthing meaningful (e.g. Olympics in chronological order)
    • It can be easy to lose track of what steps you have performed and what each output contains, especially if you are doing many data manipulation steps. Do future-you a favour and give datasets good names!

This is great, but maybe it would make more sense to sort alphabetically by athlete name within each year.

Sort on multiple columns at once

So we want to sort twice, first by year, an then within each year, we sort again alphabetically by name. The sort tool can do this!

Hands-on: Sort table based on a column

  1. We will sort the file in chronological order based on the year of the Olympic games

    Question
    1. Which column contains athlete names?
    2. Do we want ascending or descending order if we want to sort alphabetically with A first?
    1. Column 2
    2. The file should be sorted in ascending (increasing) order
  2. Rerun the sort tool with the following parameters:
    • All parameter from the first step should already be set for you, and should remain the same
    • Insert Column selections
      • “on column”: Column 2
      • “in”: Ascending order
      • “Flavor”: Alphabetical sort

  3. View the sorted file.

    Question

    Which athlete is listed at the top now? Which discipline (sport) did they compete in?

    1. A. Grigoriadis. He competed in the 500 meters freestyle swimming event.
  4. Rename the resulting file to somthing meaningful

Exercises

Ok, time to train! Let’s see if you can use the sort tool to answer the following questions:

Exercise: Reverse the sort

Which athlete comes last by alphabet, in the most recent Olympics?

Žolt Peto who competed in table tennis at the 2020 Summer Olympics in Tokyo.
We do this by repeating the previous sort (on year and then name), but changing the order to descending for both, to get the answer to the top of the file.

Exercise: sort by height
  1. What is the height of the tallest competing athlete? Which athlete(s) are of this height?
  2. What is the shortest?
  3. Who was the tallest athlete from the most recent Olympics? How tall were they?
  1. Height is listed in column 7. Since this is a simple number (no scientific notation), we can use fast numerical sort, and because we want the tallest on top, we will need to sort in descending (decreasing) order.
  2. Rerun the tool for step 1, but change the order to ascending
  3. First sort by year (descending), then by height (descending)
  1. Adam Sandurski from Poland is the tallest athlete in the file, at 214 cm tall.
  2. Here we don’t get a clear answer. That is because not all athletes have height data available, and blank fields (NA) are being sorted to the top. We can filter out rows with missing data to get our answer (see Filter section to learn how to do this). For now we cannot answer this question with just the sort tool. Some times multiple tools are required to perform such tasks. The exercise section at the end of this tutorial has many exercises that require a combination of different tools.
  3. Gennaro Di Mauro, 210 cm. (2020 Summer Olympics in Tokyo)
  1. Sort - data in ascending or descending order ( Galaxy version 1.1.1) with the following parameters:
    • “Sort Query”: olympics.tsv
    • “Number of header lines”: 1`
    • Column selections
    • “on column”: Column 7
    • “in”: Descending order
    • “Flavor”: Fast numeric sort
  2. Sort - data in ascending or descending order ( Galaxy version 1.1.1) with the following parameters:
    • “Sort Query”: olympics.tsv
    • “Number of header lines”: 1`
    • Column selections
    • “on column”: Column 7
    • “in”: Ascending order
    • “Flavor”: Fast numeric sort
  3. Sort - data in ascending or descending order ( Galaxy version 1.1.1) with the following parameters:
    • “Sort Query”: olympics.tsv
    • “Number of header lines”: 1`
    • Column selections
    • “on column”: Column 12 (year column)
    • “in”: Descending order (most recent on top)
    • “Flavor”: Fast numeric sort - Insert Column selections
    • “on column”: Column 7 (height column)
    • “in”: Descending order (tallest first)
    • “Flavor”: Fast numeric sort

Filtering

This file contains a lot of data, but we may only be interested in a subset of this data. For example, we may only want to look at one particular Olympics, or one particular sport. In such cases we can filter the dataset. This will create a new dataset, removing any rows that are not of interest to us (i.e. that don’t meet the criteria we provide).

Hands-on: Filter table based on a column

We will filter the file to show only winter Olympics

  1. Look at the olympics.tsv file and answer the following questions

    Question
    1. Which column contains this information?
    2. Which values can this column have? (make sure to notice capitalisation, ‘Winter’ is not the same as ‘winter’ to these tools)
    1. Column 13, the column with the season header
    2. The values can be Summer or Winter
  2. Open the tool Filter data on any column using simple expressions
    • read the help text at the bottom of the tool carefully
    • get familiar with how to indicate columns and write expressions for filtering
    Question
    1. How would you write the expressions for the following conditions:
      1. column 6 must be ‘Yes’
      2. column 13 must be smaller than 75
      3. column 7 cannot be ‘NA’
      4. column 2 cannot be empty
    2. It is also possible to combine multiple conditions, using and, or, not and parentheses How would you write expressions for the following filtering conditions:
      1. column 5 is larger than 2 or smaller than -2
      2. column 5 is larger than 2 and smaller than 10
      3. the sum of columns 4 and 6 is greater than or equal to 25,000
    1. The answers are:
      1. c6=='Yes'
      2. c13<75
      3. c7!='NA'
      4. c2!=''
    2. The answers are:
      1. c5>2 or c5<-2
      2. c5>2 and c5<10
      3. c4+c6 >= 25000

    Ok, great, now that you’ve got the hang of writing expressions for this tool, let’s create a file with only Winter Olympics:

  3. Filter data on any column using simple expressions with the following parameters:
    • “Filter”: olympics.tsv
    • “With the following condition”: c13=='Winter'
    • “Number of header lines to skip”: 1

  4. View the filtered file.

    Question

    How many lines are in this file? (Hint: expand the dataset in your history or use Line/Word/Character count )

    44,681 (this is including the header line)

  5. Repeat the step for the Summer Olympics

    Question
    1. How many lines do you expect in the this file?
    2. How many lines are in this file? Were you right?
    1. Use the Line/Word/Character count to find the number of lines in the olympics.tsv file and subtract the number of rows in the Winter Olympics file
    2. Be careful to consider whether these counts include the header line of the file or not
    1. The original file has 234,523 lines, and the Winter Olympics had 44,681 lines. So we would expect 234,523 - 44,681 = 189,842 rows of data. Since we have subtracted the header line in this equation as well, we expect the Summer Olympics file to have 1 more line that this, so 189,843 total lines.
    2. 189,843. If you were off by one or two lines, it may have been that you counted the header lines double
      It is always useful to take a moment to think about the expected outcome, this makes it easier to spot mistakes and will save you time in the long run.
  6. Rename both outputs to something descriptive (e.g. Filter: Winter Olympics only etc)
    • This is primarily to get you into the habit. If this is a file you might want to view or use again later, it will be hard to find back again unless you name it well.

Advanced Filtering with Python Syntax

In addition to basic filtering, the Galaxy filtering tool allows for more advanced filtering using Python syntax. This can be particularly useful when you need to apply more complex conditions or manipulate the data in specific ways.

Comment: Understanding Python Syntax for Filtering

Before we dive into specific examples, let’s briefly discuss some basic Python concepts that will help you understand how to write these filtering expressions:

  • Methods: In Python, methods are functions that are associated with an object. For example, in the expression c2.startswith('Liu'), startswith is a method that checks if the value in column 2 (c2) starts with the substring ‘Liu’.

  • Boolean Expressions: These are expressions that evaluate to either True or False. For example, c2.startswith('Liu') will return True if the value in column 2 starts with ‘Liu’, and False otherwise.

  • Comparison Operators: These include == (equal to), != (not equal to), < (less than), > (greater than), <= (less than or equal to), and >= (greater than or equal to). These operators are used to compare values and are commonly used in filtering.

  • String Methods: Strings in Python have several built-in methods that can be used for filtering. Some commonly used ones include:

    • startswith(substring): Checks if a string starts with a specified substring.
    • endswith(substring): Checks if a string ends with a specified substring.
    • find(substring): Returns the lowest index of the substring if it is found in the string; otherwise, it returns -1.
    • count(substring): Returns the number of non-overlapping occurrences of the substring in the string.

These concepts will be used in the filtering expressions we write in Galaxy. Let’s now explore some specific scenarios where you might use these expressions.

Hands-on: Filtering Based on Substring Occurrence

We want to filter rows where a particular column contains a specific substring using the cX.find() method, where X is the column number.

For example, while you can easily filter Summer or Winter Olympics using c13=="Summer" or c13=="Winter" if the data is stored in a dedicated column, sometimes the data may not be that straightforward. The relevant information might be embedded in another column, like in c11 with entries such as “1992 Summer Olympics”.

In such cases, you can use substring filtering:

Question
  1. How would you filter the rows where the 11th column contains “Summer”?
  2. How would you filter the rows where the 11th column does not contain “Summer”?
  1. c11.find('Summer') != -1
  2. c11.find('Summer') == -1

Ok, great, now that you’ve got the hang of writing expressions for this tool, let’s filter the file for rows where column 11 contains “Summer”:

  1. Filter data on any column using simple expressions with the following parameters:
    • “Filter”: olympics.tsv
    • “With the following condition”: c11.find('Summer') != -1
    • “Number of header lines to skip”: 1

  2. View the filtered file.

    Question
    1. How many rows contained the string “Summer” in column 11? (Hint: use Line/Word/Character count )
    1. 189843(this is including the header line)
  3. Rename both outputs to something descriptive.
Hands-on: Filtering Rows that Start or End with a Specific Value

Filter rows based on whether a column starts or ends with a particular value using cX.startswith() or cX.endswith().

For example, if we want to:

  • Find all athletes whose names start with “Liu”.
  • Find all teams whose names end with “China”.
Question
  1. How would you filter rows where the 2nd column starts with “Liu”?
  2. How would you filter rows where the 9th column ends with “China”?
  1. c2.startswith('Liu')
  2. c9.endswith('China')

Ok, great, now that you’ve got the hang of writing expressions for this tool, let’s filter the file to find all athletes whose names start with “Liu”:

  1. Filter data on any column using simple expressions with the following parameters:
    • “Filter”: olympics.tsv
    • “With the following condition”: c2.startswith('Liu')
    • “Number of header lines to skip”: 1

  2. View the filtered file.

    Question
    1. How many rows contained the string start with “Liu” in column 2? (Hint: expand the dataset in your history or use Line/Word/Character count )
    1. 324 (this is including the header line)
  3. Rename both outputs to something descriptive.
Hands-on: Filtering Based on the Count of Substring Occurrences

Sometimes, you may need to filter rows based on how many times a certain substring appears in a column. This can be done using the cX.count() method.

For example, if we want to:

  • Find all athletes born in December, assuming the birth date could be in either “day-month” or “month-day” format.
Question
  1. How would you filter rows where the 5th column contains “December”?
  1. c5.count('December') == 1

Ok, great, now that you’ve got the hang of writing expressions for this tool, let’s filter the file to find all athletes born in December:

  1. Filter data on any column using simple expressions with the following parameters:
    • “Filter”: olympics.tsv
    • “With the following condition”: c5.count('December') == 1
    • “Number of header lines to skip”: 1

  2. View the filtered file.

    Question
    1. How many rows contained the string “December” in column 5? (Hint: expand the dataset in your history or use Line/Word/Character count )
    1. 18009 (this is including the header line)
  3. Rename both outputs to something descriptive.

Exercises

Ok, time to train! let’s see if you can use the Filter tool to answer the following questions:

Exercise: Medal winners
  1. How many gold medals were handed out?
  2. How many total medals?
  3. How many medals were handed out during the 2018 Olympics?
  4. How many medals were won by individuals with a height between 170 and 180 cm?
  5. How many gold medals were won by individuals shorter than 160cm or taller than 190?
  • Column 17 contains information about medals
  • The possible values are Gold, Silver, Bronze, and `` (empty).
  • Expand the output or use the tool Line/Word/Character count to see the number of lines in the file
  • Don’t forget that the output (and line count) may include the header line
  • Do not use quotes on number columns (e.g. year)
  • You may need parentheses for complex conditions
  1. 8,110 (Expression: c17=='Gold')
  2. 24,633 (Expression: c17=='Gold' or c17=='Silver' or c17=='Bronze', or c17!='NA')
  3. 131 (Expression: c17=='Gold' and c12==2018 (note: do not use quotes around 2018, as it is a numerical value))
  4. 8,086 (Expression: c17!='NA' and c7>=170 and c7<=180)
  5. 812 (Expression: c17=='Gold' and (c7<160 or c7>190) (note: parentheses are important here))

Note: these numbers are found by determining the number of lines in the file after each filtering step, and subtracting 1 for the header line.

Counting

A common operation we might want to perform on tables of data, is simple counting. How many times does a certain value appear? For our dataset for instance, we might want to know how many countries participated in each Olympics, how many women, etc; any column that has categorical data that we can count. The tool Count occurrences of each record does exactly this.

Hands-on: Count occurrences of values in columns

Let’s start by simply counting how many different Olympic Games we have in our dataset, and how many times it appears (so how many participations there were each year)

  1. Count occurrences of each record with the following parameters
    • “from dataset”: olympics.tsv
    • “Count occurrences of values in column(s)”: Column 11

  2. View the results.

    Question
    1. How many different Olympic games are in our file?
    2. Which Olympic games had the most participations? (Tip: set the parameter “How should the results be sorted?” to most common values first)

    1. 52; there are 53 lines in the resulting file, with one line containing the value of the column header (games).

      The resulting file looks something like:

       615	1896 Summer Olympics
 2503	1900 Summer Olympics
 2643	1904 Summer Olympics
 3213	1908 Summer Olympics
 4610	1912 Summer Olympics
 3448	1920 Summer Olympics
 5242	1924 Summer Olympics
 358	1924 Winter Olympics
 4493	1928 Summer Olympics
 ...

    2. 1996 Summer Olympics. (10501 participations)

    You may have noticed that we could have selected multiple columns in this tool. This lets us count on combinations of columns. Let’s try counting the number of men and women in each olympic games.

  3. Count occurrences of each record with the following parameters
    • “from dataset”: olympics.tsv
    • “Count occurrences of values in column(s)”: Column 11, Column 3

  4. View the results.

    Question

    You see the resulting file has a line for every combination of the two columns (games and sex), providing the count for each.

    1. How many women were in the first Olympic games?

    2. Which Olympic games had the most women participants? (Tip: set the parameter “How should the results be sorted?” to most common values first)

    1. 2 women participated in the 1896 Olympics. (note that we cannot be sure if this is two different women, or 1 woman participating twice). The file looks something like this: 
      2	F	1896 Summer Olympics
43	F	1900 Summer Olympics
17	F	1904 Summer Olympics
55	F	1908 Summer Olympics
97	F	1912 Summer Olympics
132	F	1920 Summer Olympics
269	F	1924 Summer Olympics
    2. 2020 Summer Olympics (4652)

Let’s say we wanted to know how many different sports there were in each Olympics. If we used the counting tool above, we would get a results file for each combination of sport and olympics, with the number of lines (participations) of each. But we don’t really care about the number of lines that have this combination, just the total number of unique sports in each games.

To answer these types of questions we can use a slightly more advanced tool, called Datamash ( Galaxy version 1.1.0+galaxy2). This tool can do a lot more than counting, but here we will show you how to use it to count in this way.

Hands-on: Counting number of different sports per Olympic Games

We will now determine how many different sport there were in each of the different Olympics

  1. Datamash ( Galaxy version 1.1.0+galaxy2) with the following parameters:
    • “Input tabular dataset”: olympics.tsv
    • “Group by fields”: 11 (the games column)
    • “Input file has a header line”: Yes
    • “Sort Input”: Yes
    • “Operation to perform on each group”:
      • “Type”: Count Unique values
      • “On Column”: Column: 15 (the sport column)

  2. View the results.

    Question
    1. Why did we tell the tool to sort the file?
    2. How many sport were in the first Olympics? How many in the latest?
    3. Which Olympics had the most different sports?
    1. This is for technical reasons; the tool assumes that your file is sorted (on the column that you supplied to the “Group by fields” parameter). This assumption can greatly speed up the calculation, but will give incorrect results if the file was not actually sorted. If you know for sure that your data is sorted in the right way, you can skip this step. If you are unsure, enable this parameter to be safe, and the tool will perform the sorting step before its calculation.
    2. 10 and 38.
    3. The 2020 Summer Olympics had the most different sports (38)
  3. Rename the output to something descriptive

Note: Datamash can do a lot more than counting, and we will showcase some of these other operatins in the Grouping section.

Exercises

Ok, let’s practice!

Exercise: Number of participations per country
  1. Which country has had the most participations in the Olympics?
  2. How many countries participated in the first Olympics? How many in the last?
  1. Since we are counting participations (rows), we can use the simple Count tool
  2. Since we are counting a bit more complex question, we need the Datamash ( Galaxy version 1.1.0+galaxy2) tool
  1. The United States with 17,286 participations
  2. 15 and 250.
  1. Count with the following parameters:
    • “from dataset”: olympics.tsv
    • “Count occurrences of values in column(s)”: Column 9 (the team column)
    • “How should the results be sorted?”: With the most common values first

    This gives an output like:

    17286	United States
11700	France
10230	Great Britain
8898	Italy
7988	Canada
  2. Datamash ( Galaxy version 1.1.0+galaxy2) with the following parameters:
    • “Input tabular dataset”: olympics.tsv
    • “Group by fields”: 11 (the games column)
    • “Input file has a header line”: yes
    • “Sort Input”: yes
    • “Operation to perform on each group”:
    • “Type”: Count Unique values
    • “On Column”: Column: 9 (the team column)

This gives an output like:

  1896 Summer Olympics	15
  1900 Summer Olympics	29
  1904 Summer Olympics	13
  ...
  2016 Summer Olympics	280
  2018 Winter Olympics	108
  2020 Summer Olympics	250

Grouping

Often we may want to group rows based on a value in a column, and perform some operation on the resulting rows. For example we would like to group the olympics data by one value (e.g. year, country, sport), and determine some value for each group (e.g. number of medals won, average age of athletes, etc).

In the counting section of this tutorial we show how to get answers that require a count (e.g. number of medals won), but sometimes we want to do something more complex, like calculating the average height of athletes in a group, say per country or per sport. This section will show some example of these types of questions.

We can use the Datamash ( Galaxy version 1.1.0+galaxy2) tool for this purpose.

Hands-on: Tallest athlete per sport

We would like to answer the following question: How tall was the tallest athlete of each sport?

  1. Open the Datamash ( Galaxy version 1.1.0+galaxy2) tool and read the help section at the bottom

    Question

    Which settings do you think we need to provide to answer our question?

    • “Group by fields”: We want to group by sport (Column 15).
    • “Sort”: Yes. This may not be obvious, but because our file is currently not sorted by our chosen group (sport), we need to tell the tool to do this.
    • “Skip NA or NaN values”: since we do have NA values for athletes for whom height data is unknown, we should set this to Yes
    • Our file has a header line, so we should indicate this as well
    • “Operation”: we want to determine the Maximum height (Column 7)
  2. Datamash ( Galaxy version 1.1.0+galaxy2) with the following parameters:
    • “Input tabular dataset”: olympics.tsv
    • “Group by fields”: 15
    • “Sort input”: yes
    • “Input file has a header line”: yes
    • “Skip NA or NaN values”: yes
    • “Operation to perform on each group”:
      • “Type”: Maximum
      • “On Column”: Column: 7

  3. View the results.

    Question
    1. How tall was the tallest athlete in basketball? And what about karate?
    2. Why do some sports have a value of inf?
    1. Basketball’s tallest athlete was 192cm. For Karate it is 163.
    2. Our dataset had quite a number of NA (unknown) values in the height column, especially for the earlier Olympics. For sports that had only NA values, there is no maximum so the tool outputs inf instead.

Grouping on multiple columns

You may have noticed that we could also provide multiple columns to group on. If we do this, we can compute values for combinations of groups, such as sex and sport, to find e.g. the tallest woman in basketball or the shortest man per Olympics. There are also many more options for the computation we perform, so perhaps we are more interested not in the tallest athlete, but the average height. Let’s perform some of these slightly more advanced queries now.

Hands-on: Average height of men and women per sport

The question we would like to answer here, is what is the average height for men and women per sport?

  1. Open the Datamash ( Galaxy version 1.1.0+galaxy2) tool
    • Which parameters do you think we need?
    • Refer to the help section at the bottom of the tool page if you need more information
  2. Datamash ( Galaxy version 1.1.0+galaxy2) with the following parameters:
    • “Input tabular dataset”: olympics.tsv
    • “Group by fields”: 15,3 (The sports column, and the sex column)
    • “Sort input”: yes
    • “Input file has a header line”: yes
    • “Print header line”: yes
    • “Skip NA or NaN values”: yes
    • “Operation to perform on each group”:
      • “Type”: Mean
      • “On Column”: Column: 7
  3. View the results.
    • Notice the header line in this output that we requested with the “Print header line parameter”. Adding this line will help you remember which columns you grouped on and which computation you performed. In our case it was obvious, but if you have a dataset with multiple columns with similar values, this can be useful
    • See if you can answer the following questions based on the output file.
    Question
    1. What is the average height of women participating in archery?
    2. What is the average height of men participating in ballooning?
    3. Why do some values have nan instead of a height?
    4. Why do some sports not have a value for one of the sexes?
    5. Can you find a sport where women were taller than the men? (Hint: it starts with the letter A)
    1. 167.25677031093 cm
    2. 170 cm
    3. If none of the rows in the group had height data available, it will output nan (not a number) instead. This is most common for sports that were only featured a few times in the early years of the Olympics.
    4. Sports such as artistic swimming only exist for women, so no M appears in the data for that group, so there simply is no row for the mean height of men doing artistic swimming in our output.
    5. Art Competitions

    If all went well, your output file should look something like:

    GroupBy(sport)	     GroupBy(sex)  mean(height)
Aeronautics         M             nan
Alpine Skiing       F             167.38324708926
Alpine Skiing       M             178.18747142204
Alpinism            M             nan
Archery             F             167.25677031093
Archery             M             178.5865470852
Art Competitions    F             175.33333333333
Art Competitions    M             173.97260273973
Artistic Gymnastics F             156.15316901408

Exercises

Exercise: Grouping and computing
  1. How tall is the shortest woman Badminton player to win a gold medal?
  2. What is the average height and standard deviation of athletes from Denmark (DEN) in the 1964 Olympics?
  3. Can you determine how heavy the heaviest tennis player in the 2020 Olympics is? Why not?
  1. We need to group on 3 columns: medals, sport and sex (note: the order you provide the columns determines the order they are listed in in the output)
  2. We need to group on 2 columns: country (team) and year, then compute 2 things: the average (mean) and population standard deviation over column 7 (height). (explanation of sample vs population standard deviation)
  3. You should get an error message here, try to read it carefully to find out why it didn’t work, and how we might be able to fix it (Tip: troubleshooting errors HOWTO)

TIP: You can use CTRL+F in your browser to search for values in the file (e.g. “Badminton”)

  1. 161 cm.
  2. mean height: 175.91304347826, standard deviation: 7.0335410308672`
  3. We get an error message saying: datamash: invalid numeric value in line 2434 field 8: '63-67'. So from this we see that our weight column is not always a single number, but sometimes a range, such as 63-67 (kg), e.g. for sports with weight classes such as boxing. Datamash does not know how to handle such values, and will therefore fail. If we want to do this computation, we will have to clean up our data first (e.g. by replacing each range by its upper or lower value. We will do this in the final exercise section of this tutorial)
  1. Datamash ( Galaxy version 1.1.0+galaxy2) with the following parameters:
    • “Input tabular dataset”: olympics.tsv
    • “Group by fields”: 17,15,3 (The medal, sports, and the sex columns)
    • “Sort input”: yes
    • “Input file has a header line”: yes
    • “Print header line”: yes
    • “Skip NA or NaN values”: yes
    • “Operation to perform on each group”:
      • “Type”: Minimum
      • “On Column”: Column: 7

    This will give an output like below, scroll down to find the gold medalists, then badminton, then F (if you used a different order in the Group by fields parameter, this file may look a bit different, but will still provide the same information)

    GroupBy(medal)	GroupBy(sport)	GroupBy(sex)	min(height)
Bronze	Alpine Skiing	F	156
Bronze	Alpine Skiing	M	167
Bronze	Archery	F	155
Bronze	Archery	M	166
Bronze	Art Competitions	F	-inf
Bronze	Art Competitions	M	172
Bronze	Artistic Gymnastics	F	136
...
  2. Datamash ( Galaxy version 1.1.0+galaxy2) with the following parameters:
    • “Input tabular dataset”: olympics.tsv
    • “Group by fields”: 12,9 (year and team columns)
    • “Sort input”: yes
    • “Input file has a header line”: yes
    • “Print header line”: yes
    • “Skip NA or NaN values”: yes
    • “Operation to perform on each group”:
      • “Type”: Mean
      • “On Column”: Column: 7
    • “Insert Operation to perform on each group”:
      • “Type”: Population Standard deviation
      • “On Column”: Column: 7

    This will give an output like below:

    GroupBy(year)	GroupBy(team)	mean(height)	pstdev(height)
1896	Australia	nan	nan
1896	Austria	nan	nan
1896	Belgium	nan	nan
1896	Bulgaria	nan	nan
1896	Denmark	nan	nan
1896	France	167.62962962963	4.6836844324555
...

  3. Any calculations you run which try to compute anything over the weight column (Column 8) will fail. Please see the final exercise section for the solution to this question, in which we will first clean up the data in the weight column using the Find and Replace operation.

    This type of situation occurs quite frequently, where you data does not fit with your expectations or assumptions, and you may have to perform additional data manipulation steps to clean up your data. It is very useful to know how to read the error messages of tools. Depending on the tool, the error messages may or may not be very informative, but in many cases it can give you a clue as to why it failed, which sometimes can be fixed by you. If you think it is a problem with the tool itself, please submit a bug report, and the tool authors will be able to have a look at it. More information about troubleshooting and reporting errors can be found in this FAQ

Computing

Sometimes we want to use the data in our column to compute a new value, and add that to the table. For instance, for our dataset we could caluclate athtletes BMI (using height and weight columns), or their age at time of participation (from year of birth and year of the Olymics). By adding these computed values as a new colum to our datset, we can more easily query the dataset for these values. We can do these types of operations using the Compute on rows ( Galaxy version 2.0) tool.

As an example, let’s calculate the age of each athlete at the time of participation, and add this as a new column to our dataset.

Hands-on: Compute age of athletes
  1. Open the Compute on rows ( Galaxy version 2.0) tool.
    • read the help text at the bottom of the tool
    • what parameters do you think we need to use?
  2. Compute on rows ( Galaxy version 2.0) with the following parameters:.
    • “Input file”: olympics.tsv
    • “Input has a header line with column names?”: Yes
    • In “Expressions”:
      • “Add expression”: int(c12)-int(c4) (year column minus the year_of_birth column)
      • “Mode of the operation?”: Append
      • “The new column name”: age
    • Under “Error handling”:
      • “Autodetect column types”: No
      • “If an expression cannot be computed for a row”: Fill in a replacement value
      • “Replacement value”: NA (not available)

  3. View the results.

    Question

    1. Why did we set up the Error handling section of the tool like we did?

      The help at the bottom of the tool interface has a section dedicated to its handling of errors. Read it and try to relate it to the detailed contents of our input data. You can also try rerunning the tool with any of the Error handling settings changed and see what happens.

    2. What changed in our file?
    3. How old was Arnaud Boetsch during his Olympic tennis participation?
    1. Rationale behind Error handling settings
      • We disabled column type autodetection because some of the numerical columns in our input have “NA” values. The tool would figure out that the columns are meant to hold numbers, but would then fail to convert these “NA” values to a number. Without column type autodetection the tool will treat all column values as strings (words) and that is why we have to use int() in the expression to compute, which says: take the value in parentheses and convert it to an integer number.
      • We configured ‘NA’ as the replacement value to use in rows for which our expression cannot be computed. If you inspect the input data carefully, you will find that some rows do not specify a year of birth of the athlete (look at athlete IDs 13 and 14 for example). Since the tool would not be able to compute our expression on such lines we needed to specify what to do instead.
    2. A new age column was added to the end of our file, the value is the age of the athlete in years at time of the olympics.
    3. Arnaud Boetsch is listed on lines 6 and 7. He turned 27 the year of the Olympics in Atlanta.
  4. Rename the output to something descriptive (e.g. Compute: add age column)

This was a simple computation, but much more complex mathematical expressions can be computed with this tool. See the help section at the bottom of the tool for a list of all supported operations. In the exercise below, we will compute the BMI for each athlete as an example.

Exercises

BMI stands for Body Mass Index, is a metric to provide a very crude measure of how healthy your weight is. The formula to compute BMI is:

\[BMI = weight / (height^2)\]

(with weight in kilograms and height in meters).

Adolphe Quetelet, who invented BMI, was not a doctor of medicine, instead he was a Belgian astronomer, mathematician, statistician, and sociologist. His data consisted probably entirely of cisgender, white, european men and women Eknoyan 2007. In his defense, he did argue it should not be used at individual levels, however it came to be used as such due to simplicity. (See Wikipedia’s History section). BMI is a poor measure of health, especially for populations with high muscle content, and cannot be simply re-used as-is with anyone other than Anglo Saxons Caleyachetty et al. 2021, Wikipedia:Limitations.

However as it is an easy to calculate metric, we include the calculation here.

Let’s use the Compute on rows ( Galaxy version 2.0) tool to compute this data for all athletes and add it as a new column directly after the existing height and weight columns.

Exercise: Calculating BMI
  1. How would you express this calculation in the tool?

    • Remember that our height is in cm, and the formula expects height in meters

    • Try to apply the lessons learnt with regard to error handling from the previous age calculation, i.e. remember the problematic NA values in the weight and height columns.

  2. How do you configure the tool to insert the new BMI column directly to the right of the weight column?
  3. What is the BMI for Arnaud Boetsch?
  • division is / and multiplication is ` * ` .
  • The tool does not recognize the ^ operation as exponentiation. You can use height * height or pow(height,2), or replace ^ with **.
  • Parentheses may be required.

  • Use int(column) like before to tell the tool the columns contain numbers

    This is only needed because the “NA” values will require disabling column type autodetection again.

  • Append mode will add the newly computed column at the end of the row, but there are other options, too.
  1. int(c8)/(int(c7)*int(c7))*10000 (other variations are possible)
  2. You can choose Insert mode and specify column 9 as the insert position.
  3. 22.69
  1. Compute on rows ( Galaxy version 2.0) with the following parameters:
    • “Input file”: olympics.tsv
    • “Input has a header line with column names?”: Yes
    • In “Expressions”:
      • “Add expression”: int(c8)/(int(c7)*int(c7))*10000
      • “Mode of the operation?”: Insert
      • “Insert new column before existing column number”: 9
      • “The new column name”: BMI
    • Under “Error handling”:
      • “Autodetect column types”: No
      • “If an expression cannot be computed for a row”: Fill in a replacement value
      • “Replacement value”: NA (not available)

Find and Replace

Often you may need to change the contents of a file a bit to fit the expectations of an analysis tool. For instance, our file uses NA for missing values, but other conventions included leaving the cell empty, or using NaN (not a number) instead. Or, when working with chromosomal data, you may need to add or remove the chr prefix from a column before using it as input to a certain tool. In such situations, we can find all occurrences of a certain pattern in our file, and replace it with another value.

If we want to perform such a replacement on a single column in our data, we can use the Column Regex Find and Replace ( Galaxy version 1.0.1) tool, or if we want to do a replacement on a line by line basis (e.g. if our data isn’t tabular and therefore doesn’t have columns), we can use the Regex Find and Replace ( Galaxy version 1.0.1) tool. Both of these tools are similar, in that they use regular expressions (or regex) to define a pattern to search for. Regular expressions are a standardized way to describe patterns, and while they can seem quite complex at first, with just a few of the basics and a bit of practice, you can perform powerful operations on large datasets with ease.

A few of the basics of regular expression, plus some links to further resources are given in the box below:

Regular expressions are a standardized way of describing patterns in textual data. They can be extremely useful for tasks such as finding and replacing data. They can be a bit tricky to master, but learning even just a few of the basics can help you get the most out of Galaxy.

Finding

Below are just a few examples of basic expressions:

Regular expression Matches
abc an occurrence of abc within your data
(abc|def) abc or def
[abc] a single character which is either a, b, or c
[^abc] a character that is NOT a, b, nor c
[a-z] any lowercase letter
[a-zA-Z] any letter (upper or lower case)
[0-9] numbers 0-9
\d any digit (same as [0-9])
\D any non-digit character
\w any alphanumeric character
\W any non-alphanumeric character
\s any whitespace
\S any non-whitespace character
. any character
\.  
{x,y} between x and y repetitions
^ the beginning of the line
$ the end of the line

Note: you see that characters such as *, ?, ., + etc have a special meaning in a regular expression. If you want to match on those characters, you can escape them with a backslash. So \? matches the question mark character exactly.

Examples

Regular expression matches
\d{4} 4 digits (e.g. a year)
chr\d{1,2} chr followed by 1 or 2 digits
.*abc$ anything with abc at the end of the line
^$ empty line
^>.* Line starting with > (e.g. Fasta header)
^[^>].* Line not starting with > (e.g. Fasta sequence)

Replacing

Sometimes you need to capture the exact value you matched on, in order to use it in your replacement, we do this using capture groups (...), which we can refer to using \1, \2 etc for the first and second captured values. If you want to refer to the whole match, use &.

Regular expression Input Captures
chr(\d{1,2}) chr14 \1 = 14
(\d{2}) July (\d{4}) 24 July 1984 \1 = 24, \2 = 1984

An expression like s/find/replacement/g indicates a replacement expression, this will search (s) for any occurrence of find, and replace it with replacement. It will do this globally (g) which means it doesn’t stop after the first match.

Example: s/chr(\d{1,2})/CHR\1/g will replace chr14 with CHR14 etc.

You can also use replacement modifier such as convert to lower case \L or upper case \U. Example: s/.*/\U&/g will convert the whole text to upper case.

Note: In Galaxy, you are often asked to provide the find and replacement expressions separately, so you don’t have to use the s/../../g structure.

There is a lot more you can do with regular expressions, and there are a few different flavours in different tools/programming languages, but these are the most important basics that will already allow you to do many of the tasks you might need in your analysis.

Tip: RegexOne is a nice interactive tutorial to learn the basics of regular expressions.

Tip: Regex101.com is a great resource for interactively testing and constructing your regular expressions, it even provides an explanation of a regular expression if you provide one.

Tip: Cyrilex is a visual regular expression tester.

Let’s start with a simple example:

Hands-on: Find and Replace

Our file uses a mix of Athina and Athens to indicate the Capital City of Greece in the city column. Let’s standardize this by replacing occurrences of Athina with Athens.

  1. Open Column Regex Find and Replace ( Galaxy version 1.0.1)
    • Read the help text at the bottom, what settings do you think we need to use?

    • Read the Regular expressions 101 FAQ below

      Regular expressions are a standardized way of describing patterns in textual data. They can be extremely useful for tasks such as finding and replacing data. They can be a bit tricky to master, but learning even just a few of the basics can help you get the most out of Galaxy.

      Finding

      Below are just a few examples of basic expressions:

      Regular expression Matches
      abc an occurrence of abc within your data
      (abc|def) abc or def
      [abc] a single character which is either a, b, or c
      [^abc] a character that is NOT a, b, nor c
      [a-z] any lowercase letter
      [a-zA-Z] any letter (upper or lower case)
      [0-9] numbers 0-9
      \d any digit (same as [0-9])
      \D any non-digit character
      \w any alphanumeric character
      \W any non-alphanumeric character
      \s any whitespace
      \S any non-whitespace character
      . any character
      \.  
      {x,y} between x and y repetitions
      ^ the beginning of the line
      $ the end of the line

      Note: you see that characters such as *, ?, ., + etc have a special meaning in a regular expression. If you want to match on those characters, you can escape them with a backslash. So \? matches the question mark character exactly.

      Examples

      Regular expression matches
      \d{4} 4 digits (e.g. a year)
      chr\d{1,2} chr followed by 1 or 2 digits
      .*abc$ anything with abc at the end of the line
      ^$ empty line
      ^>.* Line starting with > (e.g. Fasta header)
      ^[^>].* Line not starting with > (e.g. Fasta sequence)

      Replacing

      Sometimes you need to capture the exact value you matched on, in order to use it in your replacement, we do this using capture groups (...), which we can refer to using \1, \2 etc for the first and second captured values. If you want to refer to the whole match, use &.

      Regular expression Input Captures
      chr(\d{1,2}) chr14 \1 = 14
      (\d{2}) July (\d{4}) 24 July 1984 \1 = 24, \2 = 1984

      An expression like s/find/replacement/g indicates a replacement expression, this will search (s) for any occurrence of find, and replace it with replacement. It will do this globally (g) which means it doesn’t stop after the first match.

      Example: s/chr(\d{1,2})/CHR\1/g will replace chr14 with CHR14 etc.

      You can also use replacement modifier such as convert to lower case \L or upper case \U. Example: s/.*/\U&/g will convert the whole text to upper case.

      Note: In Galaxy, you are often asked to provide the find and replacement expressions separately, so you don’t have to use the s/../../g structure.

      There is a lot more you can do with regular expressions, and there are a few different flavours in different tools/programming languages, but these are the most important basics that will already allow you to do many of the tasks you might need in your analysis.

      Tip: RegexOne is a nice interactive tutorial to learn the basics of regular expressions.

      Tip: Regex101.com is a great resource for interactively testing and constructing your regular expressions, it even provides an explanation of a regular expression if you provide one.

      Tip: Cyrilex is a visual regular expression tester.

  2. Column Regex Find and Replace ( Galaxy version 1.0.1) with the following parameters:
    • “Select cells from”: olympics.tsv
    • “using column”: Column 14
    • “Check”
      • “Find Regex”: Athina
      • “Replacement”: Athens
  3. View the results.
    • Look at the file before and after. Athlete 7 (Patrick Chila) near the top of the olympics.tsv file, had a value of Athina in the city column. Verify that it has been changed to Athens.
    Question

    Why did we use the column replace tool, and not the general replace tool?

    It is safer to use the column replace tool in our case. We know the city name only occurs in one of the columns. If we had used the line replace tool, it would have replaced all occurrences of Athina, which may have unforeseen consequences (e.g. maybe somebody was named Athina, in that case we don’t want to replace it)

This was rather simple example, so let’s try a few more examples with slightly more complex expressions.

Exercises

You may have noticed that our file has a lot of missing data. Especially for the earlier years, things like height, weight and birthday of athletes was not registered, or simply not known. In some columns you see these missing values have been replaced with an NA (not available) value. In other columns (for example birth place), the cells have simply been left empty.

Different tools may expect different ways of handling missing data. So you may have to change your missing data from empty to NA, NaN, or something else, between analysis steps

Hands-on: Fill empty cells

We will now replace empty cells in the birth_place column, to use NA instead

  1. Open Column Regex Find and Replace ( Galaxy version 1.0.1)
    • Read the help text at the bottom, what settings do you think we need to use?

    • Read the Regular expressions 101 FAQ below

      Regular expressions are a standardized way of describing patterns in textual data. They can be extremely useful for tasks such as finding and replacing data. They can be a bit tricky to master, but learning even just a few of the basics can help you get the most out of Galaxy.

      Finding

      Below are just a few examples of basic expressions:

      Regular expression Matches
      abc an occurrence of abc within your data
      (abc|def) abc or def
      [abc] a single character which is either a, b, or c
      [^abc] a character that is NOT a, b, nor c
      [a-z] any lowercase letter
      [a-zA-Z] any letter (upper or lower case)
      [0-9] numbers 0-9
      \d any digit (same as [0-9])
      \D any non-digit character
      \w any alphanumeric character
      \W any non-alphanumeric character
      \s any whitespace
      \S any non-whitespace character
      . any character
      \.  
      {x,y} between x and y repetitions
      ^ the beginning of the line
      $ the end of the line

      Note: you see that characters such as *, ?, ., + etc have a special meaning in a regular expression. If you want to match on those characters, you can escape them with a backslash. So \? matches the question mark character exactly.

      Examples

      Regular expression matches
      \d{4} 4 digits (e.g. a year)
      chr\d{1,2} chr followed by 1 or 2 digits
      .*abc$ anything with abc at the end of the line
      ^$ empty line
      ^>.* Line starting with > (e.g. Fasta header)
      ^[^>].* Line not starting with > (e.g. Fasta sequence)

      Replacing

      Sometimes you need to capture the exact value you matched on, in order to use it in your replacement, we do this using capture groups (...), which we can refer to using \1, \2 etc for the first and second captured values. If you want to refer to the whole match, use &.

      Regular expression Input Captures
      chr(\d{1,2}) chr14 \1 = 14
      (\d{2}) July (\d{4}) 24 July 1984 \1 = 24, \2 = 1984

      An expression like s/find/replacement/g indicates a replacement expression, this will search (s) for any occurrence of find, and replace it with replacement. It will do this globally (g) which means it doesn’t stop after the first match.

      Example: s/chr(\d{1,2})/CHR\1/g will replace chr14 with CHR14 etc.

      You can also use replacement modifier such as convert to lower case \L or upper case \U. Example: s/.*/\U&/g will convert the whole text to upper case.

      Note: In Galaxy, you are often asked to provide the find and replacement expressions separately, so you don’t have to use the s/../../g structure.

      There is a lot more you can do with regular expressions, and there are a few different flavours in different tools/programming languages, but these are the most important basics that will already allow you to do many of the tasks you might need in your analysis.

      Tip: RegexOne is a nice interactive tutorial to learn the basics of regular expressions.

      Tip: Regex101.com is a great resource for interactively testing and constructing your regular expressions, it even provides an explanation of a regular expression if you provide one.

      Tip: Cyrilex is a visual regular expression tester.

    Question
    1. Should we use the column or line replace tool?
    2. What is the expression for an empty line? (hint: see regular expressions 101 box above)
    1. Since we only want to replace in one column, we will use the column replace tool
    2. ^$ indicates an empty line (^ indicated the beginning, and $ the end). The value in the column is treated as the line, since we are looking only in 1 column.
  2. Column Regex Find and Replace ( Galaxy version 1.0.1) with the following parameters:
    • “Select cells from”: olympics.tsv
    • “using column”: Column 6
    • “Check”
      • “Find Regex”: ^$
      • “Replacement”: NA
  3. View the results.
    • Look at the file before replacement, find a line without a value in the birth_place column. Verify that it has been replaced in the resulting file.

Let’s do another example, this one using capture groups.

Look at the birth_day column. It has values in a format like 12 December. Suppose we have a tool that expects this data to be in the reverse format, December 12. The file is too big to change this manually in every column. But with regular expression tools we can make this replacement easily

Hands-on: Reverse Birthday Format

We will now change the format in birthday column from day month to month day

  1. Open Column Regex Find and Replace ( Galaxy version 1.0.1)
    • Read the help text at the bottom, what settings do you think we need to use?

    • Read the Regular expressions 101 FAQ below

      Regular expressions are a standardized way of describing patterns in textual data. They can be extremely useful for tasks such as finding and replacing data. They can be a bit tricky to master, but learning even just a few of the basics can help you get the most out of Galaxy.

      Finding

      Below are just a few examples of basic expressions:

      Regular expression Matches
      abc an occurrence of abc within your data
      (abc|def) abc or def
      [abc] a single character which is either a, b, or c
      [^abc] a character that is NOT a, b, nor c
      [a-z] any lowercase letter
      [a-zA-Z] any letter (upper or lower case)
      [0-9] numbers 0-9
      \d any digit (same as [0-9])
      \D any non-digit character
      \w any alphanumeric character
      \W any non-alphanumeric character
      \s any whitespace
      \S any non-whitespace character
      . any character
      \.  
      {x,y} between x and y repetitions
      ^ the beginning of the line
      $ the end of the line

      Note: you see that characters such as *, ?, ., + etc have a special meaning in a regular expression. If you want to match on those characters, you can escape them with a backslash. So \? matches the question mark character exactly.

      Examples

      Regular expression matches
      \d{4} 4 digits (e.g. a year)
      chr\d{1,2} chr followed by 1 or 2 digits
      .*abc$ anything with abc at the end of the line
      ^$ empty line
      ^>.* Line starting with > (e.g. Fasta header)
      ^[^>].* Line not starting with > (e.g. Fasta sequence)

      Replacing

      Sometimes you need to capture the exact value you matched on, in order to use it in your replacement, we do this using capture groups (...), which we can refer to using \1, \2 etc for the first and second captured values. If you want to refer to the whole match, use &.

      Regular expression Input Captures
      chr(\d{1,2}) chr14 \1 = 14
      (\d{2}) July (\d{4}) 24 July 1984 \1 = 24, \2 = 1984

      An expression like s/find/replacement/g indicates a replacement expression, this will search (s) for any occurrence of find, and replace it with replacement. It will do this globally (g) which means it doesn’t stop after the first match.

      Example: s/chr(\d{1,2})/CHR\1/g will replace chr14 with CHR14 etc.

      You can also use replacement modifier such as convert to lower case \L or upper case \U. Example: s/.*/\U&/g will convert the whole text to upper case.

      Note: In Galaxy, you are often asked to provide the find and replacement expressions separately, so you don’t have to use the s/../../g structure.

      There is a lot more you can do with regular expressions, and there are a few different flavours in different tools/programming languages, but these are the most important basics that will already allow you to do many of the tasks you might need in your analysis.

      Tip: RegexOne is a nice interactive tutorial to learn the basics of regular expressions.

      Tip: Regex101.com is a great resource for interactively testing and constructing your regular expressions, it even provides an explanation of a regular expression if you provide one.

      Tip: Cyrilex is a visual regular expression tester.

    Question
    1. How do we match on the birthday format? How strict/exact shoule we be here?
    2. How do we captures both the day and the month?
    3. How do we refer to the values we captured (for the replacement value)
    1. Birthday is one or more digits, followed by a space, followed by one or more letters.
    2. Remember that you can capture values using parentheses (..)
    3. \1 will be the variable containing the value in the first capture, \2 the second, etc
    1. There are multiple solutions here, depending on how strict you want to be
      • \d+ ([a-zA-Z]+) (not strict, would also match on 142 Septober
      • [0-9]{1,2} (January|February|March|April|May|June|July|August|September|October|November|December) (this is much more strict, only matches on 1 or 2 digits, followed by a space, followed by one of the months. But this would still match on 42 April, and may miss it if one month names didn’t start with a capital letter)
      • [123]?[0-9] [a-zA-Z]+ this will only allow dates below 40

      There are different ways to express this, and there is no one perfect solution. If you know your data is clean, and you do not have to worry about values like 42 Septober, then you can be less strict. If your data is less clean, you may be more worried about capturing things that aren’t valid birthdays and might want to be a bit stricter. It all depends on your data and use case.

    2. ([\d]{1,2}) ([a-zA-Z]+) captures both the day and the month
    3. We can use \1 to refer to the day we captured, and \2 for the month in our replacement.
  2. Column Regex Finda and Replace ( Galaxy version 1.0.1) with the following parameters:
    • “Select cells from”: olympics.tsv
    • “using column”: Column 5
    • “Check”
      • “Find Regex”: (\d{1,2}) ([a-zA-Z]+) (or your own variation)
      • “Replacement”: \2 \1 (first month, then space, then day)
  3. View the results.
    • Did it work? If not, no shame, it often takes some trial and error to get your expression right. Click the rerun button on the tool, and tweak your expression until it works.

Removing Columns

We can remove columns from a table using either Remove columns by heading ( Galaxy version 0.0.1) if your table has a header line, or Cut columns from a table if it does not (in this case we just indicate columns by their number). These tools can also be used to change the order of columns in your file. There is also the tool Advanced Cut columns from a table ( Galaxy version 1.1.0) that offers a few more options.

To do the reverse, adding one or more columns, we can use the Paste tool. This assumes we have the same number of rows in both files, already in the correct order. It is a very “dumb” tool that simple combines the two files next to each other.

Hands-on: Remove columns

Suppose we want to simplify our file a bit. All we want is file with 4 columns: athlete name, sport, olympic games, and medals.

  1. Open the Remove columns by heading ( Galaxy version 0.0.1) tool and read the help text at the bottom.
    • Which settings do you think we need?
  2. Remove columns by heading ( Galaxy version 0.0.1) with the following parameters:
    • “Tabular File”: olympics.tsv
    • “Header name”: name
    • “Header name”: sport
    • “Header name”: games
    • “Header name”: medals
    • “Keep named columns”: Yes

  3. View the results.

    Question
    1. How many rows and columns do you expect the output to have?
    1. We expect the same number of rows as the original dataset, but now only the 4 columns we requested to keep.

Notice that during this step, we also changed the order of the columns. This tool can also be used to re-arrange columns, if you supply all column names but in a different order.

Exercises

Exercise: Removing Columns
  1. Create a file with 4 columns: name, sport, games, medal (same task as the previous hands-on), but use the Cut columns from a table tool instead.
  2. Create a file that is similar to olympics.tsv, but without the first column (athlete_id column)
  3. You want to keep all the columns of olympics.tsv, but change the order so that sport and event come right after the athlete’s name.
  1. We need to determine the column numbers and provide these to the tool, rather than the column headers
  2. Think about what the “Keep named colums” parameter does to simplify the settings here
  3. Which of the two tools would be easier to use? (this can be a personal preference, but think about whether you would rather provide column names or numbers)
  1. Cut columns from a table using the following parameters:
    • “Cut Columns”: c2,c15,c11,c17
    • “Delimited By”: TAB
    • “From”: olympics.tsv
  2. Remove columns by heading ( Galaxy version 0.0.1) with the following parameters:
    • “Tabular File”: olympics.tsv
    • “Header name”: athlete_id
    • “Keep named columns”: No

    Note: the “Keep named columns” parameter determines wheter we keep or remove the columns we specified. You could have obtained the same result by supplying all column names except the first one, and selecting “Keep named columns”: No, but that would have been a lot more work.

  3. Cut columns from a table using the following parameters:
    • “Cut Columns”: c1,c2,c15,c16,c3,c4,c5,c6,c7,c8,c9,c10,c11,c12,c13,c14,c17
    • “Delimited By”: TAB
    • “From”: olympics.tsv

    Note: you can also use the Remove columns by heading ( Galaxy version 0.0.1), it just requires a bit more typing, but on the other hand it is also a bit less error-prone (i.e. it is easier to mix up column numbers than column names).

Unique

Sometimes, in the course of our data manipulations, we may end up with a file that has duplicate rows. In order to filter these out, we can use the Unique occurrences of each record ( Galaxy version 1.1.0) tool.

Let’s say we would like to create a list of all unique athletes (id and name).

Hands-on: Obtaining list of unique athletes

First we will cut just the athlete_id and name columns from our dataset (please see the Removing Columns section for more info on this step)

  1. Cut columns from a table using the following parameters:
    • “Cut Columns”: c1,c2
    • “Delimited By”: TAB
    • “From”: olympics.tsv

  2. View the resulting dataset

    Question
    1. Do you see duplication? Why is that?
    1. Yes. For all athletes who participated more than once, the row will be identical.
  3. Unique - occurrences of each record ( Galaxy version 1.1.0) with the following parameters:
    • “File to scan for unique values”: output from the first step

  4. View the resulting dataset

    Question
    1. What happened to our header line? How could we fix that?
    2. How many unique athletes do we have?
    1. The unique tool has done some sorting internally, so our header line is not longer at the top. We could fix this in several ways:
      • sort the file again to get it back to the top (using natural sort in descending order will work).
      • or, we could have removed the header line before running Unique (with remove beginning tool), then add it back afterwards (with concatenate tool).
    2. 94,733; The resulting file has 94,734 lines (one of which is our header line)

Joining Files

This file contains a lot of information, but we may want to add more information. For example if we had a file with information about each country (population, capital city, etc), we could join that information with our Olympics data, to get a single file with all information in every row.

For example, if we would like to be able to group by continent, to e.g. count athletes, medals etc per continent, we will have to add a continent column to our file. To do this we would need a second file that maps each country to the corresponding continent. This is what we will do in the next hands-on section.

We obtained country information data from DataHub. More information about this file can be found in the description there. It has 56 columns with a wide variety of data about each country (from country codes, to capital city, languages spoken, etc)

Hands-on: Get data

  1. Import the file country-information.tsv via link

    https://zenodo.org/record/6803028/files/country-information.tsv
    • Copy the link location

    • Click Upload Data at the top of the tool panel

    • Select Paste/Fetch Data

    • Paste the link(s) into the text field

    • Press Start

    • Close the window

  2. View file.

    Question
    1. How many columns does this file have?
    2. Which column(s) in this file are the same as in the olympics.tsv file?
    1. The country information file has 56 columns (see DataHub for more details).
    2. Both files have a NOC column with the 3-letter country code (NOC stands for National Olympic Committee). We can use this column to join the appropriate country data to each row in our olympics.tsv file.

We would now like to take our Olympics dataset as the basis, and add columns to every row of this file with some information about the country. In order to join, we will need to have one column that is shared between the two files, on which we can match. The NOC column is perfect for this because it is a defined standard. Both files also contain a column with the country name in it, which is also a possible candidate to use for joining, but because it is less standardised, it is safer to use the NOC column. For example, if one file uses “Netherlands”, while the other uses “The Netherlands” to indicate the same country, the joining will fail for these rows. So always make sure the columns you join on are compatible!

Hands-on: Joining country information to the Olympics data.
  1. Open Join two Datasets side by side on a specified field, and read the help text at the bottom
    • Which settings do you think we need to use?
  2. Join two Datasets side by side on a specified field with the following parameters:
    • “Join”: olympics.tsv
    • “using column”: Column 10 (the noc column)
    • “with”: country-information.tsv
    • “and column”: Column 2 (the NOC column)
    • “Keep the header lines?”: Yes

  3. View the results.

    Question
    1. What do you expect the output to look like? Were you right?
    2. How many columns are in the resulting file? What about the NOC column?
    3. What is a possible downside to this approach?
    1. All the columns from the country information file are added to the end of each row of our olympics dataset
    2. Our olympics datset had 17 columns, the country information file has 56 columns. Therefore we have 17+56=73 columns columns in our resulting file. This also means the NOC column we joined on appears twice in our output.
    3. There is a lot of data duplication in this file now. The exact same country information is added to every line of every athlete from a certain country. This means much larger file size, and more usage of your quota. If you do not need all these columns, it could save you a lot of space to remove unneeded columns from the country-information.tsv file, before joining.

Concatenating

Concatenation of two files simple means putting the contents of the two files together, one after the other. Our dataset was created in 2021, but since then we’ve had another Olympic event, the 2022 Winter Olympics in Beijing. If we have the same data for this latest Olympics, we could simply add the rows from the 2022 games to our current file with data, in order to create a single file with all data from 1896 to 2022.

First, let’s get this data for the 2022 Olympics

Hands-on: Get 2022 Olympics data

  1. Import the file olympics_2022.tsv via link

    https://zenodo.org/record/6803028/files/olympics_2022.tsv
    • Copy the link location

    • Click Upload Data at the top of the tool panel

    • Select Paste/Fetch Data

    • Paste the link(s) into the text field

    • Press Start

    • Close the window

  2. View the new dataset, does it have the same structure as our original olympics.tsv file?

    Question
    1. Does the new file have the same structure?
    2. Can we simply add the lines of the new files to the end of our existing olympics dataset?
    1. Yes, this file has all the same columns, in the same order, so concatenation should be relatively straightforward.
    2. If we simply put the contents of this file after our existing dataset, we will have a second header line in the middle of our data rows. It is best to remove the header line from the second dataset after we have verified it is compatible. This way we will only add the real data rows to our dataset.

Since this new dataset has the exact same structure (number and order of columns), we can simple add the lines from this file to the end of our existing olympics.tsv file.

Hands-on: Adding 2022 Olympics to our dataset

First we need to remove the header line from the 2022 file (since these lines will be added to the end of our olympic dataset, we don’t want to have a second header line floating around in our file)

  1. Remove beginning of a file with the following parameters:
    • “Remove first”: 1
    • “from”: olympics_2022.tsv

    Now we can perform the concatenation:

  2. Concatenate datasets tail-to-head with the following parameters:
    • “Concatenate Datasets”: olympics.tsv (this file will be first)
    • “Insert Dataset”:
      • “Dataset”: output from step 1 (2022 data without the header)

  3. View the results.

    Question
    1. How many lines do you expect in the new file? Were you correct? (Hint: use Line/Word/Character count to count lines)
    2. Where are the lines of the 2022 Olympics?
    1. The olympics.tsv file had 234,523 lines, and the olympics_2022.tsv file had 4076 lines. Both of these numbers include a header line, which we removed for the second file, so we expect our concatenated file to contain 234,523 + 4076 - 1 = 238,598 lines (which it does).
    2. The new file has the entire contents of olympics.tsv at the beginning of the file, followed by the contents of the olympics_2022.tsv file at the end.

Now this only works so simply because our two datasets had the same structure; the same number of columns, in the same order. If your data comes from different sources, you may have to do some additional data manipulation before you can concatenate, e.g. to make sure the columns match, or how each file deals with missing data (empty cells, NA, NaN or something else).

Splitting Files

This dataset contains a lot of data, we might want to split the data into one file per Olympic games, or have one file for all the winter games, and another file for all the summer games. In these situtations, where we want to create new, smaller files, based on the values in a column, we can use the tool Split file according to the values of a column ( Galaxy version 0.6).

Tip: use this tool only if you want a separate file for all values in a column. For example, if you want a file for each Olympic games, use the split tool, but if you just want a file for one particular Olympics (say Tokyo 2020), you could simply use the Filter tools to extract the data from the full dataset, without making files for all the other Olympics as well.

Hands-on: Splitting the dataset by Olympics

We would like to create a separate file for each Olympic event.

  1. Open the tool Split file according to the values of a column ( Galaxy version 0.6), and read the help text at the bottom
    • which settings do you think we need to use?
  2. Split file according to the values of a column ( Galaxy version 0.6) with the following parameters:
    • “File to select”: olympics.tsv
    • “on column”: Column 11 (the games column)
    • “Include the header in all splitted files?”: Yes

  3. View the results.

    Question
    1. How many different files do we get?
    2. How many lines are in the file for the 1908 Summer Olympics?
    1. We get 1 collection with 52 files in it, one per Olympic games: screenshot of the output collection of the split tool.
    2. 3214 lines

Exercises

Let’s practice this a bit more, see if you can use the split file to answer the following questions:

Exercise: sort by height
  1. Create two files, one for Summer Olympics, one for Winter Olympics. Which has more lines?
  2. Split the file by sport, how many sports have there been at the Olympics?
  3. Split the file by medal, would you expect the output files to be equal sizes?
  1. Split on the season column (Column 13)
  2. Split on the sport column (Column 15)
  3. Split on the medal column (Column 17)
  1. The summer olympics file has more lines (~180,000) than the winter olympics file (~45,000) (the first winter Olympics wasn’t held until 1924)
  2. Each file in the resulting collection represents a sport, there are 91 datasets in the collection, representing 91 different sports in the history of the Olympics.
  3. The files for gold silver and bronze are roughly equal, but not exactly (think of situations like shared second place, then 2 silver medals are handed out and no bronze medal)

Conclusion

These tools and operations covered in the tutorial are just a few examples of some of the most common operations. There are many more tools available in Galaxy that perform other data manipulations. We encourage you to look around the General Text Tools section in the Galaxy toolbox (left panel) to find and explore more data manipulation tools. The more comfortable you are performing these kinds of steps, the more you can get out of Galaxy!

Exercises: Putting it all together!

This section provides a number of exercises that require you to combine two or more of the techniques you learned in this tutorial. This is a great way to practice your data manipulation skills. Full solutions are provided for every exercise (i.e. all tools and settings), but for many of these exercises there will be multiple solutions, so if you obtained the same results in a different way, that is correct too!

Exercise 1: Finding shortest/lightest athlete

If you have done exercises in the sorting section, you noticed that finding the shortest athlete ever to compete was not easy, because all the rows with missing height data (NA) in the column were sorted to the top. We need to filter out these values first, then perform our sort, so that our answer is on top.

  1. Find the shortest athlete ever to compete in the Olympics
  2. Find the shortest athlete of the Winter Olympics
  3. Find the lightest athlete of the most recent Summer Olympics
  1. You will need to filter out the columns with (NA) in the height column first
  2. You will need to filter by season as well
  3. You will need to filter out missing data in the weight column, filter out Summer Olympics, then sort (by 2 columns)
  4. Does the order in which you perform these steps matter?
  1. Lyton Mphande and Rosario Briones were both 127 cm tall, competing in boxing and gymnastics respectively
  2. Carolyn Krau was a 137 cm tall figure skater.
  3. Flávia Saraiva was the lightest athlete (31kg), she was a Artistic Gymnast from Brazil.

  1. First we filter out the NA values from the height column:

    Filter - data on any column using simple expressions with the following parameters:

    • “Filter”: olympics.tsv
    • “With following expression”: c7!='NA'
    • “Number of header lines to skip”: 1

    Then we can sort by height, in ascending order to get the shortest athletes on top:

    Sort - data in ascending or descending order ( Galaxy version 1.1.1) with the following parameters:

    • “Sort Query”: output from the previous step (filter)
    • “Number of header lines”: 1`
    • Column selections
      • “on column”: Column 7
      • “in”: Ascending order
      • “Flavor”: Fast numeric sort

  2. We can take the output from the first exercise, and filter for only Winter Olympics:

    Filter - data on any column using simple expressions with the following parameters:

    • “Filter”: output from previous exercise
    • “With following expression”: c13='Winter'
    • “Number of header lines to skip”: 1

  3. First we filter out the NA values from the weight column:

    Filter - data on any column using simple expressions with the following parameters:

    • “Filter”: olympics.tsv
    • “With following expression”: c8!='NA'
    • “Number of header lines to skip”: 1

    Then we can filter out only Summer Olympics:

    Filter - data on any column using simple expressions with the following parameters:

    • “Filter”: output from previous step
    • “With following expression”: c13='Summer'
    • “Number of header lines to skip”: 1

    Then we can sort by weigth, in ascending order to get the shortest athletes on top:

    Sort - data in ascending or descending order ( Galaxy version 1.1.1) with the following parameters:

    • “Sort Query”: output from the previous step
    • “Number of header lines”: 1`
    • Column selections
      • “on column”: Column 12 (year column)
      • “in”: Descending order (most recent on top)
      • “Flavor”: Fast numeric sort
    • Insert Column selections
      • “on column”: Column 8 (weight column)
      • “in”: Ascending order (lightest on top)
      • “Flavor”: Fast numeric sort

  4. In this case, changing the order of these operations will still have led to the same answer. However, sorting takes more time than filtering, so it is usually faster to filter first, then do the sorting on a smaller file. Especially noticeable if you have large dataset.

Ok, let’s try another exercise! If you have done the exercises in the Grouping section, you will have noticed that we were not able to perform computation on the weight column, because some of the values were weight classes, e.g. 63-78 (kg), and the Datamash ( Galaxy version 1.1.0+galaxy2) tool could not handle such values. In this exercise, we will first clean up this weight column to avoid ranges (by taking the lower number in the range for every row a weight range is used), and then answering the question a couple of questions around the weight of athletes.

Exercise 2: Data cleaning and computations of the weight column
  1. Get a list of all the values that occur in the weight column, take note of all the values that are not a single number or NA; anything else should be cleaned up
  2. Clean up the weight column (Colum 8) so that we only have single numbers; weight classes (e.g. 63-78) should be replace by the lower bound (63) of that class
  3. How heavy was the lightest woman competing in the Biathlon? And the heaviest?
  1. You can use the Count tool to get a list of all the different values that appear in Column 8
  2. You can use the Column Regex Find and Replace ( Galaxy version 1.0.1) tool to clean up the weight column. This tool is a bit complex, if you haven’t done so yet, we strongly recommend you work through the Find and Replace section first. To check if your cleaning worked, re-run the Count tool from step 1.
  3. Use the Datamash ( Galaxy version 1.1.0+galaxy2) tool to answer this question, make sure you use the cleaned up datset as input! Find the row showing Biathlon.

  1. We see values such as 100-105, but also 77,5, 100, 104 (notice the space!) and even 76, 77, 79 to indicate weight ranges or other unexpected weight values. All these variations must be converted to single numbers. For this example we will simply convert these ranges to their lower number, and remove the rest when we clean the column (taking the upper or median value would be equally valid, but for our solution we will remove everything after the first number.

  2. There are many possible regular expression that will clean this column. You can use a single check to catch all the exceptions, or use multiple, there is no one right answer. Check if your cleaning is successful using the Count tool again. Make sure you only have single number values in the weight column before proceeding to question 3 in this exercise. Also make sure the weights you have make sense (e.g. if you end up with columns having a weight of 0, something went wrong somewhere!)

  3. lightest woman weighed 45 kilograms, the heaviest 82.

  1. Count occurrences of each record with the following parameters
    • “from dataset”: olympics.tsv
    • “Count occurrences of values in column(s)”: Column 8

    This should output a file like:

    921	100
2	100, 104
2	100, 107
2	100-105
1	100-106
5	100-110
4	100-113
71	101
3	101-125
204	102
2	102, 103
4	102, 105
2	102-106
97	103
125	104
...

    We will want to change weight ranges to single numbers in the next step

  2. Column Regex Find and Replace ( Galaxy version 1.0.1) with the following parameters:
    • “Select cells from”: olympics.tsv
    • “using column”: Column 14
    • “Check”
    • “Find Regex”: (\d+)(,|-).* (one or more digits (captured), followed by a dash or comma, then zero or more of any other characters)
    • “Replacement”: \1 (only the first set of numbers we captured)

    NOTE: there will be a lot of valid answers here, you could do it all in one check like the solution above, or in multiple checks (e.g. one for each different kind of way to denote a weight range you found in step 1). As long as a re-run of the Count occurrences of each record tool shows only single numbers in the column after your find & replace step, then your answer was correct! Also make sure the weights make sense (e.g. if your resulting column has weights of 0 or other small numbers, this doesn’t make sense and you will have to tweak your expression. Don’t feel bad if this takes a bit of trial and error, that is expected!

  3. Datamash ( Galaxy version 1.1.0+galaxy2) with the following parameters:
    • “Input tabular dataset”: olympics-cleaned.tsv (output from your cleaning step)
    • “Group by fields”: 15,3
    • “Sort input”: yes
    • “Input file has a header line”: yes
    • “Print header line”: yes
    • “Skip NA or NaN values”: yes
    • “Operation to perform on each group”:
      • “Type”: Minimum
      • “On Column”: Column: 8
    • “Insert Operation to perform on each group”:
      • “Type”: Maximum
      • “On Column”: Column: 8

    Your resulting file should look something like:

    GroupBy(sport)	GroupBy(sex)	min(weight)	max(weight)
Aeronautics	M	-inf	inf
Alpine Skiing	F	45	90
Alpine Skiing	M	50	138
Alpinism	M	-inf	inf
Archery	F	42	95
Archery	M	46	130
Art Competitions	F	-inf	inf
Art Competitions	M	59	93
...

Congratulations! You have now mastered the basics of data manipulation! There are a lot more data manipulation operations available in Galaxy that you may need. Please explore the tools for yourself, and check back with this tutorial often, we plan to add more sections and exercises over time!