This lesson is in the early stages of development (Alpha version)

Redirection

Overview

Teaching: 30 min
Exercises: 15 min
Questions
  • How can I search within files?

  • How can I combine existing commands to do new things?

Objectives
  • Employ the grep command to search for information within files.

  • Print the results of a command to a file.

  • Construct command pipelines with two or more stages.

Searching files

We discussed in a previous episode how to search within a file using less. We can also search within files without even opening them, using grep. grep is a command-line utility for searching plain-text files for lines matching a specific sequence of characters (sometimes called a string) or a particular pattern (which can be specified using something called regular expressions). We’re not going to work with regular expressions in this lesson, and are instead going to specify the strings we are searching for. Let’s give it a try!

Indicator codes in WDI

The World Bank names its economic and social indicators according to a systematic coding convention. For example, “GDP per capita, PPP (constant 2011 international $)” is called NY.GDP.PCAP.PP.KD. The codes stand for “national accounts, income”, “GDP”, “per capita”, “at purchasing power parity”, “in constant dollars.”

We’ll search for indicators inside of our CSV files. Let’s first make sure we are in the correct directory:

$ cd ~/Downloads/shell-economics/data/raw/worldbank/

Suppose we want to see how many reads in our file have really bad segments containing 10 consecutive unknown nucleotides (Ns).

Let’s search for the string NY.GDP.PCAP.PP.KD in WDIData.csv:

$ grep NY.GDP.PCAP.PP.KD WDIData.csv

This command returns a lot of output to the terminal. Every single line in the WDIData.csv file that contains information about GDP per capita (as defined above) is printed to the terminal, regardless of how long or short the file is.

Exercise

Search for the indicator SP.POP.TOTL (corresponding to total population) in the WDIData.csv file.

Solution

grep SP.POP.TOTL WDIData.csv

Redirecting output

grep allowed us to identify lines in our CSV files that match a particular pattern. All of these lines were printed to our terminal screen, but in order to work with and perform other operations on them, we will need to capture that output in some way.

We can do this with something called “redirection”. The idea is that we are taking what would ordinarily be printed to the terminal screen and redirecting it to another location. In our case, we want to print this information to a file so that we can look at it later and use other commands to analyze this data.

The command for redirecting output to a file is >.

Let’s try out this command and copy all the records in WDI that contain NY.GDP.PCAP.PP.KD to another file called gdp_per_capita.csv.

$ grep NY.GDP.PCAP.PP.KD WDIData.csv > gdp_per_capita.csv

The prompt should sit there a little bit, and then it should look like nothing happened. But type ls. You should see a new file called gdp_per_capita.csv.

We can check the number of lines in our new file using a command called wc. wc stands for word count. This command counts the number of words, lines, and characters in a file.

$ wc gdp_per_capita.csv
     264    2345  175056 gdp_per_capita.csv

This will tell us the number of lines, words and characters in the file. If we want only the number of lines, we can use the -l flag for lines.

$ wc -l gdp_per_capita.csv
264 gdp_per_capita.csv

Exercise

How many rows in WDIData.csv contain GDP per capita data in current national currency (NY.GDP.PCAP.CN)?

Solution

$ grep NY.GDP.PCAP.CN WDIData.csv > gdp_per_capita.csv
$ wc -l gdp_per_capita.csv
264

Note that we did not check whether these rows actually contain data.

We might want to search multiple indicators in multiple files. However, we need to be careful, because each time we use the > command to redirect output to a file, the new output will replace the output that was already present in the file. You want to avoid overwriting your data files.

$ grep NY.GDP.PCAP.PP.KD WDISeries.csv  > gdp_per_capita.csv 
$ wc -l gdp_per_capita.csv
1 gdp_per_capita.csv
$ grep NY.GDP.PCAP.PP.KD WDISeries-Time.csv  > gdp_per_capita.csv
$ wc -l gdp_per_capita.csv
0 gdp_per_capita.csv

Here, the output of our second call to wc shows that we no longer have any lines in our gdp_per_capita.csv file. This is because the second file we searched (WDISeries-Time.csv) does not contain any lines that match our search pattern. So our file was overwritten and is now empty.

We can avoid overwriting our files by using the command >>. >> is known as the “append redirect” and will append new output to the end of a file, rather than overwriting it.

$ grep NY.GDP.PCAP.PP.KD WDISeries.csv >> gdp_per_capita.csv
$ wc -l gdp_per_capita.csv
1 gdp_per_capita.csv
$ grep NY.GDP.PCAP.PP.KD WDISeries-Time.csv >> gdp_per_capita.csv
$ wc -l gdp_per_capita.csv
1 gdp_per_capita.csv

The output of our second call to wc shows that we have not overwritten our original data.

We can also do this with a single line of code by using a wildcard:

$ grep NY.GDP.PCAP.PP.KD WDI*.csv > gdp_per_capita.csv
$ wc -l gdp_per_capita.csv
286 gdp_per_capita.csv

Since we might have multiple different criteria we want to search for, creating a new output file each time has the potential to clutter up our workspace. We also thus far haven’t been interested in the actual contents of those files, only in the number of reads that we’ve found. We created the files to store the reads and then counted the lines in the file to see how many reads matched our criteria. There’s a way to do this, however, that doesn’t require us to create these intermediate files - the pipe command (|).

This is probably not a key on your keyboard you use very much, so let’s all take a minute to find that key. What | does is take the output that is scrolling by on the terminal and use that output as input to another command. When our output was scrolling by, we might have wished we could slow it down and look at it, like we can with less. Well, it turns out that we can! We can redirect our output from our grep call through the less command.

$ grep NY.GDP.PCAP.PP.KD WDIData.csv | less

We can now see the output from our grep call within the less interface. We can use the up and down arrows to scroll through the output and use q to exit less.

If we don’t want to create a file before counting lines of output from our grep search, we could directly pipe the output of the grep search to the command wc -l. This can be helpful for investigating your output if you are not sure you would like to save it to a file.

$ grep NY.GDP.PCAP.PP.KD WDIData.csv | wc -l 

Redirecting output is often not intuitive, and can take some time to get used to. Once you’re comfortable with redirection, however, you’ll be able to combine any number of commands to do all sorts of exciting things with your data!

None of the command line programs we’ve been learning do anything all that impressive on their own, but when you start chaining them together, you can do some really powerful things very efficiently.

Writing for loops

Loops are key to productivity improvements through automation as they allow us to execute commands repeatedly. Similar to wildcards and tab completion, using loops also reduces the amount of typing (and typing mistakes). Loops are helpful when performing operations on groups of sequencing files, such as unzipping or trimming multiple files. We will use loops for these purposes in subsequent analyses, but will cover the basics of them for now.

When the shell sees the keyword for, it knows to repeat a command (or group of commands) once for each item in a list. Each time the loop runs (called an iteration), an item in the list is assigned in sequence to the variable, and the commands inside the loop are executed, before moving on to the next item in the list. Inside the loop, we call for the variable’s value by putting $ in front of it. The $ tells the shell interpreter to treat the variable as a variable name and substitute its value in its place, rather than treat it as text or an external command. In shell programming, this is usually called “expanding” the variable.

Sometimes, we want to expand a variable without any whitespace to its right. Suppose we have a variable named foo that contains the text abc, and would like to expand foo to create the text abcEFG.

$ foo=abc
$ echo foo is $foo
foo is abc
$ echo foo is $fooEFG      # doesn't work
foo is

The interpreter is trying to expand a variable named fooEFG, which (probably) doesn’t exist. We can avoid this problem by enclosing the variable name in braces ({ and }, sometimes called “squiggle braces”).

$ foo=abc
$ echo foo is $foo
foo is abc
$ echo foo is ${foo}EFG      # now it works!
foo is abcEFG

Let’s write a for loop to show us the first two lines of the CSV files in the worldbank folder. You will notice the shell prompt changes from $ to > and back again as we were typing in our loop. The second prompt, >, is different to remind us that we haven’t finished typing a complete command yet. A semicolon, ;, can be used to separate two commands written on a single line.

$ for filename in *.csv
> do
> head -n 2 ${filename}
> done

The for loop begins with the formula for <variable> in <group to iterate over>. In this case, the word filename is designated as the variable to be used over each iteration. In our case WDICountry-Series.csv, WDICountry.csv, WDIData.csv, WDIFootNote.csv, WDISeries-Time.csv, WDISeries.csv, and gdp_per_capita.csv will be substituted for filename because they fit the pattern of ending with .csv in the directory we’ve specified. The next line of the for loop is do. The next line is the code that we want to execute. We are telling the loop to print the first two lines of each variable we iterate over. Finally, the word done ends the loop.

After executing the loop, you should see the first two lines of both fastq files printed to the terminal. Let’s create a loop that will save this information to a file.

$ for filename in *.csv
> do
> head -n 2 ${filename} >> wdi_headers.txt
> done

Note that we are using >> to append the text to our wdi_headers.txt file. If we used >, the wdi_headers.txt file would be rewritten every time the loop iterates, so it would only have text from the last variable used. Instead, >> adds to the end of the file.

Using Basename in for loops

Basename is a function in UNIX that is helpful for removing a uniform part of a name from a list of files. In this case, we will use basename to remove the .csv extension from the files that we’ve been working with.

$ basename WDICountry.csv .csv

We see that this returns

WDICountry

If we try the same thing but use .xls as the file extension instead, nothing happens. This is because basename only works when it exactly matches a string in the file.

$ basename WDICountry.csv .xls
WDICountry.csv

Basename is really powerful when used in a for loop. It allows to access just the file prefix, which you can use to name things. Let’s try this.

Inside our for loop, we create a new name variable. We call the basename function inside the parenthesis, then give our variable name from the for loop, in this case ${filename}, and finally state that .csv should be removed from the file name. It’s important to note that we’re not changing the actual files, we’re creating a new variable called name. The line > echo $name will print to the terminal the variable name each time the for loop runs. Because we are iterating over two files, we expect to see two lines of output.

$ for filename in *.csv
> do
> name=$(basename ${filename} .csv)
> echo ${name}
> done

Exercise

Print the file prefix of all of the .md (markdown) files in ~/Downloads/shell-economics.

Solution

$ cd ~/Downloads/shell-economics
$ for filename in *.md
> do
> name=$(basename ${filename} .md)
> echo ${name}
> done

One way this is really useful is to move files. Let’s rename all of our .md files using mv so that they have the years on them, which will document when we created them.

$ for filename in *.md
> do
> name=$(basename ${filename} .md)
> mv ${filename}  ${name}-2019.md
> done

Exercise

Remove -2019 from all of the .md files.

Solution

$ for filename in *-2019.md
> do
> name=$(basename ${filename} -2019.md)
> mv ${filename} ${name}.md
> done

Key Points

  • grep is a powerful search tool with many options for customization.

  • >, >>, and | are different ways of redirecting output.

  • command > file redirects a command’s output to a file.

  • command >> file redirects a command’s output to a file without overwriting the existing contents of the file.

  • command_1 | command_2 redirects the output of the first command as input to the second command.

  • for loops are used for iteration.

  • basename gets rid of repetitive parts of names.