Counting and mining with the shell


Teaching: 60 min
Exercises: 30 min
  • How can I count data?

  • How can I find data within files?

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

  • Demonstrate counting lines, words, and characters with the shell command wc and appropriate flags

  • Use strings to mine files and extract matched lines with the shell

  • Create complex single line commands by combining shell commands and regular expressions to mine files

  • Redirect a command’s output to a file.

  • Process a file instead of keyboard input using redirection.

  • Construct command pipelines with two or more stages.

  • Explain Unix’s ‘small pieces, loosely joined’ philosophy.

Counting and mining data

Now that you know how to navigate the shell, we will move onto learning how to count and mine data using a few of the standard shell commands. While these commands are unlikely to revolutionise your work by themselves, they’re very versatile and will add to your foundation for working in the shell and for learning to code. The commands also replicate the sorts of uses library users might make of library data.

Counting and sorting

We will begin by counting the contents of files using the Unix shell. We can use the Unix shell to quickly generate counts from across files, something that is tricky to achieve using the graphical user interfaces of standard office suites.

Let’s start by navigating to the directory that contains our data using the cd command:

$ cd shell-lesson

Remember, if at any time you are not sure where you are in your directory structure, use the pwd command to find out:

$ pwd

And let’s just check what files are in the directory and how large they are with ls -lhS:

$ ls -lhS
total 139M
-rw-rw-r-- 1 riley staff 126M Jun 10  2015 2014-01_JA.tsv
-rw-r--r-- 1 riley staff 7.4M Jan 31 18:47 2014-01-31_JA-america.tsv
-rw-r--r-- 1 riley staff 3.6M Jan 31 18:47 2014-01-31_JA-africa.tsv
-rw-r--r-- 1 riley staff 1.4M Jan 31 18:47 2014-02-02_JA-britain.tsv
-rw-r--r-- 1 riley staff 598K Jan 31 18:47 gulliver.txt
-rw-r--r-- 1 riley staff 583K Feb  1 22:53 33504-0.txt
drwxr-xr-x 2 riley staff   68 Feb  2 00:58 backup

In this episode we’ll focus on the dataset 2014-01_JA.tsv, that contains journal article metadata, and the three .tsv files derived from the original dataset. Each of these three .tsv files includes all data where a keyword such as africa or america appears in the ‘Title’ field of 2014-01_JA.tsv.

CSV and TSV Files

CSV (Comma-separated values) is a common plain text format for storing tabular data, where each record occupies one line and the values are separated by commas. TSV (Tab-separated values) is just the same except that values are separated by tabs rather than commas. Confusingly, CSV is sometimes used to refer to both CSV, TSV and variations of them. The simplicity of the formats make them great for exchange and archival. They are not bound to a specific program (unlike Excel files, say, there is no CSV program, just lots and lots of programs that support the format, including Excel by the way.), and you wouldn’t have any problems opening a 40 year old file today if you came across one.

First, let’s have a look at the largest data file, using the tools we learned in Reading files:

$ cat 2014-01_JA.tsv

Like 829-0.txt before, the whole dataset cascades by and can’t really make any sense of that amount of text. To cancel this on-going concatenation, or indeed any process in the Unix shell, press Ctrl+C.

In most data files a quick glimpse of the first few lines already tells us a lot about the structure of the dataset, for example the table/column headers:

$ head -n 3 2014-01_JA.tsv
File    Creator    Issue    Volume    Journal    ISSN    ID    Citation    Title    Place    Labe    Language    Publisher    Date
History_1a-rdf.tsv  Doolittle, W. E.  1 59  KIVA -ARIZONA-  0023-1940 (Uk)RN001571862 KIVA -ARIZONA- 59(1), 7-26. (1993)  A Method for Distinguishing between Prehistoric and Recent Water and Soil Control Features  xxu eng ARIZONA ARCHAEOLOGICAL AND HISTORICAL SOCIETY 1993
History_1a-rdf.tsv  Nelson, M. C. 1 59  KIVA -ARIZONA-  0023-1940 (Uk)RN001571874 KIVA -ARIZONA- 59(1), 27-48. (1993) Classic Mimbres Land Use in the Eastern Mimbres Region, Southwestern New Mexico xxu eng ARIZONA ARCHAEOLOGICAL AND HISTORICAL SOCIETY 1993

In the header, we can see the common metadata fields of academic papers: Creator, Issue, Citation, etc.

Next, let’s learn about a basic data analysis tool: wc is the “word count” command: it counts the number of lines, words, and bytes. Since we love the wildcard operator, let’s run the command wc *.tsv to get counts for all the .tsv files in the current directory (it takes a little time to complete):

$ wc *.tsv
    13712    511261   3773660 2014-01-31_JA-africa.tsv
    27392   1049601   7731914 2014-01-31_JA-america.tsv
   507732  17606310 131122144 2014-01_JA.tsv
     5375    196999   1453418 2014-02-02_JA-britain.tsv
   554211  19364171 144081136 total

The first three columns contains the number of lines, words and bytes.

If we only have a handful of files to compare, it might be faster or more convenient to just check with Microsoft Excel, OpenRefine or your favourite text editor, but when we have tens, hundreds or thousands of documents, the Unix shell has a clear speed advantage. The real power of the shell comes from being able to combine commands and automate tasks, though. We will touch upon this slightly.

For now, we’ll see how we can build a simple pipeline to find the shortest file in terms of number of lines. We start by adding the -l flag to get only the number of lines, not the number of words and bytes:

$ wc -l *.tsv
    13712 2014-01-31_JA-africa.tsv
    27392 2014-01-31_JA-america.tsv
   507732 2014-01_JA.tsv
     5375 2014-02-02_JA-britain.tsv
   554211 total

The wc command itself doesn’t have a flag to sort the output, but as we’ll see, we can combine three different shell commands to get what we want.

First, we have the wc -l *.tsv command. We will save the output from this command in a new file. To do that, we redirect the output from the command to a file using the ‘greater than’ sign (>), like so:

$ wc -l *.tsv > lengths.txt

There’s no output now since the output went into the file lengths.txt, but we can check that the output indeed ended up in the file using cat or less (or Notepad or any text editor).

$ cat lengths.txt
    13712 2014-01-31_JA-africa.tsv
    27392 2014-01-31_JA-america.tsv
   507732 2014-01_JA.tsv
     5375 2014-02-02_JA-britain.tsv
   554211 total

Next, there is the sort command. We’ll use the -n flag to specify that we want numerical sorting, not lexical sorting, we output the results into yet another file, and we use cat to check the results:

$ sort -n lengths.txt > sorted-lengths.txt
$ cat sorted-lengths.txt
     5375 2014-02-02_JA-britain.tsv
    13712 2014-01-31_JA-africa.tsv
    27392 2014-01-31_JA-america.tsv
   507732 2014-01_JA.tsv
   554211 total

Finally we have our old friend head, that we can use to get the first line of the sorted-lengths.txt:

$ head -n 1 sorted-lengths.txt
     5375 2014-02-02_JA-britain.tsv

But we’re really just interested in the end result, not the intermediate results now stored in lengths.txt and sorted-lengths.txt. What if we could send the results from the first command (wc -l *.tsv) directly to the next command (sort -n) and then the output from that command to head -n 1? Luckily we can, using a concept called pipes. On the command line, you make a pipe with the vertical bar character |. Let’s try with one pipe first:

$ wc -l *.tsv | sort -n
     5375 2014-02-02_JA-britain.tsv
    13712 2014-01-31_JA-africa.tsv
    27392 2014-01-31_JA-america.tsv
   507732 2014-01_JA.tsv
   554211 total

Notice that this is exactly the same output that ended up in our sorted-lengths.txt earlier. Let’s add another pipe:

$ wc -l *.tsv | sort -n | head -n 1
     5375 2014-02-02_JA-britain.tsv

It can take some time to fully grasp pipes and use them efficiently, but it’s a very powerful concept that you will find not only in the shell, but also in most programming languages.

Redirects and Pipes

Pipes and Filters

This simple idea is why Unix has been so successful. Instead of creating enormous programs that try to do many different things, Unix programmers focus on creating lots of simple tools that each do one job well, and that work well with each other. This programming model is called “pipes and filters”. We’ve already seen pipes; a filter is a program like wc or sort that transforms a stream of input into a stream of output. Almost all of the standard Unix tools can work this way: unless told to do otherwise, they read from standard input, do something with what they’ve read, and write to standard output.

The key is that any program that reads lines of text from standard input and writes lines of text to standard output can be combined with every other program that behaves this way as well. You can and should write your programs this way so that you and other people can put those programs into pipes to multiply their power.

Adding another pipe

We have our wc -l *.tsv | sort -n | head -n 1 pipeline. What would happen if you piped this into cat? Try it!


The cat command just outputs whatever it gets as input, so you get exactly the same output from

$ wc -l *.tsv | sort -n | head -n 1


$ wc -l *.tsv | sort -n | head -n 1 | cat

Count the number of words, sort and print (faded example)

To count the total lines in every tsv file, sort the results and then print the first line of the file we use the following:

wc -l *.tsv | sort -n | head -n 1

Now let’s change the scenario. We want to know the 10 files that contain the most words. Check the manual for the wc command (either using man wc or wc --help) to see if you can find out what flag to use to print out the number of words (but not the number of lines and bytes). Fill in the blanks below to count the words for each file, put them into order, and then make an output of the 10 files with the most words (Hint: The sort command sorts in ascending order by default).

wc __ *.tsv | sort __ | ____


Here we use the wc command with the -w (word) flag on all tsv files, sort them and then output the last 11 lines (10 files and the total) using the tail command.

wc -w *.tsv | sort -n | tail -n 11

Counting number of files

Let’s make a different pipeline. You want to find out how many files and directories there are in the current directory. Try to see if you can pipe the output from ls into wc to find the answer.


$ ls | wc -l

Writing to files

The date command outputs the current date and time. Can you write the current date and time to a new file called logfile.txt? Then check the contents of the file.


$ date > logfile.txt
$ cat logfile.txt

To check the contents, you could also use less or many other commands.

Beware that > will happily overwrite an existing file without warning you, so please be careful.

Appending to a file

While > writes to a file, >> appends something to a file. Try to append the current date and time to the file logfile.txt?


$ date >> logfile.txt
$ cat logfile.txt

Counting the number of words

We learned about the -w flag above, so now try using it with the .tsv files.

If you have time, you can also try to sort the results by piping it to sort. And/or explore the other flags of wc.


From man wc, you will see that there is a -w flag to print the number of words:

     -w      The number of words in each input file is written to the standard

So to print the word counts of the .tsv files:

$ wc -w *.tsv
  511261 2014-01-31_JA-africa.tsv
 1049601 2014-01-31_JA-america.tsv
17606310 2014-01_JA.tsv
  196999 2014-02-02_JA-britain.tsv
19364171 total

And to sort the lines numerically:

$ wc -w *.tsv | sort -n
  196999 2014-02-02_JA-britain.tsv
  511261 2014-01-31_JA-africa.tsv
 1049601 2014-01-31_JA-america.tsv
17606310 2014-01_JA.tsv
19364171 total

Mining or searching

Searching for something in one or more files is something we’ll often need to do, so let’s introduce a command for doing that: grep (short for global regular expression print). As the name suggests, it supports regular expressions and is therefore only limited by your imagination, the shape of your data, and - when working with thousands or millions of files - the processing power at your disposal.

To begin using grep, first navigate to the shell-lesson directory if not already there. Then create a new directory “results”:

$ mkdir results

Now let’s try our first search:

$ grep 1999 *.tsv

Remember that the shell will expand *.tsv to a list of all the .tsv files in the directory. grep will then search these for instances of the string “1999” and print the matching lines.


A string is a sequence of characters, or “a piece of text”.

Press the up arrow once in order to cycle back to your most recent action. Amend grep 1999 *.tsv to grep -c 1999 *.tsv and press enter.

$ grep -c 1999 *.tsv

The shell now prints the number of times the string 1999 appeared in each file. If you look at the output from the previous command, this tends to refer to the date field for each journal article.

We will try another search:

$ grep -c revolution *.tsv

We got back the counts of the instances of the string revolution within the files. Now, amend the above command to the below and observe how the output of each is different:

$ grep -ci revolution *.tsv

This repeats the query, but prints a case insensitive count (including instances of both revolution and Revolution and other variants). Note how the count has increased nearly 30 fold for those journal article titles that contain the keyword ‘america’. As before, cycling back and adding > results/, followed by a filename (ideally in .txt format), will save the results to a data file.

So far we have counted strings in files and printed to the shell or to file those counts. But the real power of grep comes in that you can also use it to create subsets of tabulated data (or indeed any data) from one or multiple files.

$ grep -i revolution *.tsv

This script looks in the defined files and prints any lines containing revolution (without regard to case) to the shell. We let the shell add today’s date to the filename:

$ grep -i revolution *.tsv > results/$(date "+%Y-%m-%d")_JAi-revolution.tsv

This saves the subsetted data to a new file.

Alternative date commands

This way of writing dates is so common that on some platforms (not macOS X) you can get the same result by typing $(date -I) instead of $(date "+%Y-%m-%d").

However, if we look at this file, it contains every instance of the string ‘revolution’ including as a single word and as part of other words such as ‘revolutionary’. This perhaps isn’t as useful as we thought… Thankfully, the -w flag instructs grep to look for whole words only, giving us greater precision in our search.

$ grep -iw revolution *.tsv > results/$(date "+%Y-%m-%d")_JAiw-revolution.tsv

This script looks in both of the defined files and exports any lines containing the whole word revolution (without regard to case) to the specified .tsv file.

We can show the difference between the files we created.

$ wc -l results/*.tsv
   10585 2016-07-19_JAi-revolution.tsv
    7779 2016-07-19_JAiw-revolution.tsv
   18364 total

Automatically adding a date prefix

Notice how we didn’t type today’s date ourselves, but let the date command do that mindless task for us. Find out about the "+%Y-%m-%d" option and alternative options we could have used.


Using date --help will show you that the + option introduces a date format, where %Y, %m and %d are replaced by the year, month, and day respectively. There are many other percent-codes you could use.

You might also see that -I is short for –iso-8601, which essentially avoids the confusion between the European and American date formats DD.MM.YYYY and MM/DD/YYYY.

Finally, we’ll use the regular expression syntax covered earlier to search for similar words.

Basic, extended, and PERL-compatible regular expressions

There are, unfortunately, different ways of writing regular expressions. Across its various versions, grep supports “basic”, at least two types of “extended”, and “PERL-compatible” regular expressions. This is a common cause of confusion, since most tutorials, including ours, teach regular expressions compatible with the PERL programming language, but grep uses basic by default. Unless you want to remember the details, make your life easy by always using the most advanced regular expressions your version of grep supports (-E flag on macOS X, -P on most other platforms) or when doing something more complex than searching for a plain string.

The regular expression ‘fr[ae]nc[eh]’ will match “france”, “french”, but also “frence” and “franch”. It’s generally a good idea to enclose the expression in single quotation marks, since that ensures the shell sends it directly to grep without any processing (such as trying to expand the wildcard operator *).

$ grep -iwE 'fr[ae]nc[eh]' *.tsv

The shell will print out each matching line.

We include the -o flag to print only the matching part of the lines e.g. (handy for isolating/checking results):

$ grep -iwEo 'fr[ae]nc[eh]' *.tsv

Pair up with your neighbor and work on these exercises:

Search for all case sensitive instances of a whole word you choose in all four derived .tsv files in this directory. Print your results to the shell.


$ grep -w hero *.tsv

Case sensitive search in select files

Search for all case sensitive instances of a word you choose in the ‘America’ and ‘Africa’ .tsv files in this directory. Print your results to the shell.


$ grep hero *a.tsv

Count words (case sensitive)

Count all case sensitive instances of a word you choose in the ‘America’ and ‘Africa’ .tsv files in this directory. Print your results to the shell.


$ grep -c hero *a.tsv

Count words (case insensitive)

Count all case insensitive instances of that word in the ‘America’ and ‘Africa’ .tsv files in this directory. Print your results to the shell.


$ grep -ci hero *a.tsv

Case insensitive search in select files

Search for all case insensitive instances of that word in the ‘America’ and ‘Africa’ .tsv files in this directory. Print your results to a file results/hero.tsv.


$ grep -i hero *a.tsv > results/hero.tsv

Case insensitive search in select files (whole word)

Search for all case insensitive instances of that whole word in the ‘America’ and ‘Africa’ .tsv files in this directory. Print your results to a file results/hero-i.tsv.


$ grep -iw hero *a.tsv > results/hero-i.tsv

Searching with regular expressions

Use regular expressions to find all ISSN numbers (four digits followed by hyphen followed by four digits) in 2014-01_JA.tsv and print the results to a file results/issns.tsv. Note that you might have to use the -E flag (or -P with some versions of grep, e.g. with Git Bash on Windows).


$ grep -Eo '\d{4}-\d{4}' 2014-01_JA.tsv > results/issns.tsv


$ grep -Po '\d{4}-\d{4}' 2014-01_JA.tsv > results/issns.tsv

It is worth checking the file to make sure grep has interpreted the pattern correctly. You could use the less command for this.

The -o flag means that only the ISSN itself is printed out, instead of the whole line.

If you came up with something more advanced, perhaps including word boundaries, please share your result in the collaborative document and give yourself a pat on the shoulder.

Finding unique values

If you pipe something to the uniq command, it will filter out adjacent duplicate lines. In order for the ‘uniq’ command to only return unique values though, it needs to be used with the ‘sort’ command. Try piping the output from the command in the last exercise to sort and then piping these results to ‘uniq’ and then wc -l to count the number of unique ISSN values.


$ grep -Eo '\d{4}-\d{4}' 2014-01_JA.tsv | sort | uniq | wc -l


$ grep -Po '\d{4}-\d{4}' 2014-01_JA.tsv | sort | uniq | wc -l

Using a Loop to Count Words

We will now use a loop to automate the counting of certain words within a document. For this, we will be using the Little Women e-book from Project Gutenberg. The file is inside the shell-lesson folder and named pg514.txt. Let’s rename the file to littlewomen.txt.

$ mv pg514.txt littlewomen.txt

This renames the file to something easier to type.

Now let’s create our loop. In the loop, we will ask the computer to go through the text, looking for each girl’s name, and count the number of times it appears. The results will print to the screen.

$ for name in "Jo" "Meg" "Beth" "Amy"
> do
>    echo "$name"
>    grep -wo "$name" littlewomen.txt | wc -l
> done

What is happening in the loop?

Why are the variables double-quoted here?

a) In episode 4 we learned to use "$..." as a safeguard against white-space being misinterpreted. Why could we omit the "-quotes in the above example?

b) What happens if you add "Louisa May Alcott" to the first line of the loop and remove the " from $name in the loop’s code?


a) Because we are explicitly listing the names after in, and those contain no white-space. However, for consistency it’s better to use rather once too often than once too rarely.

b) Without "-quoting $name, the last loop will try to execute grep Louisa May Alcott littlewomen.txt. grep interprets only the first word as the search pattern, but May and Alcott as filenames. This produces two errors and a possibly untrustworthy count:

Louisa May Alcott
grep: May: No such file or directory
grep: Alcott: No such file or directory

Selecting columns from our article dataset

When you receive data it will often contain more columns or variables than you need for your work. If you want to select only the columns you need for your analysis, you can use the cut command to do so. cut is a tool for extracting sections from a file. For instance, say we want to retain only the Creator, Volume, Journal, and Citation columns from our article data. With cut we’d:

cut -f 2,4,5,8 2014-01_JA.tsv | head
Creator	Volume	Journal	Citation
Doolittle, W. E.  59  KIVA -ARIZONA-  KIVA -ARIZONA- 59(1), 7-26. (1993)
Nelson, M. C.	59	KIVA -ARIZONA-	KIVA -ARIZONA- 59(1), 27-48. (1993)
Deegan, A. C.	59	KIVA -ARIZONA-	KIVA -ARIZONA- 59(1), 49-64. (1993)
Stone, T.	59	KIVA -ARIZONA-	KIVA -ARIZONA- 59(1), 65-82. (1993)
Mohamed Ibrahim Khalil	1	NORTHEAST AFRICAN STUDIES	NORTHEAST AFRICAN STUDIES 1(2/3), 103-118. (1994)

Above we used cut and the -f flag to indicate which columns we want to retain. cut works on tab delimited files by default. We can use the flag -d to change this to a comma, or semicolon or another delimiter. If you are unsure of your column position and the file has headers on the first line, we can use head -n 1 <filename> to print those out.

Now your turn

Select the columns Issue, Volume, Language, Publisher and direct the output into a new file. You can name it something like 2014-01_JA_ivlp.tsv.


First, let’s see where our desired columns are:

head -n 1 2014-01_JA.tsv
File	Creator	Issue	Volume	Journal	ISSN	ID	Citation	Title	Place Labe	Language	Publisher	Date

Ok, now we know Issue is column 3, Volume 4, Language 11, and Publisher 12. We use these positional column numbers to construct our cut command:

cut -f 3,4,11,12 2014-01_JA.tsv > 2014-01_JA_ivlp.tsv

We can confirm this worked by running head on the file:

head 2014-01_JA_ivlp.tsv
Issue	Volume	Language	Publisher

Key Points

  • The shell can be used to count elements of documents

  • The shell can be used to search for patterns within files

  • Commands can be used to count and mine any number of files

  • Commands and flags can be combined to build complex queries specific to your work