Counting and extracting with the shell


Teaching: 20 min
Exercises: 20 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 6.6M
384K -rw-r--r-- 1 your.username 1049089 384K Mar 12 16:25 000003160_01_text.json
584K -rw-r--r-- 1 your.username 583K Mar 12 16:25 33504-0.txt
600K -rw-r--r-- 1 your.username 598K Mar 12 16:25 829-0.txt
   0 drwxr-xr-x 1 your.username 1049089    0 Mar 18 13:23 airphotodata/
4.0K drwxr-xr-x 1 your.username 1049089    0 Mar 18 13:23 desktrackers/
 20K -rw-r--r-- 1 your.username 1049089  19K Mar 12 16:25 diary.html
1.1M -rw-r--r-- 1 your.username 1049089 1.1M Mar 12 16:25 pg514.txt
4.0M -rw-r--r-- 1 your.username 1049089 4.0M Mar 16 15:40
   0 drwxr-xr-x 1 your.username 1049089    0 Mar 18 13:35 waitz/

In this episode we’ll focus on the datasets in the desktracker
directory, that contains four .csv Desktracker files.

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 the 2016 file, using the tools we learned in Reading files:

$ cat Desk_Tracker.csv

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 Desk_Tracker.csv
Type,Contact Type (text),Question Type,Question Type 
(text),Directional Informational Type,Directional 
Informational Type (text),Time Spent in 
minutes,Comment,Reference   Research Type,Reference   
Research Type (text),Number of Participants,Instructor,Type 
of Event,Type of Event (text),Date & Time,Unit Area Name,Unit 
Area Name (text),How many patrons were in the Art & 
Architecture Collection,Ref Question Type,Ref Question Type 
(text),Directional   Informational Type,Directional   
Informational Type (text),Comments,Reference  Research 
Type,Reference  Research Type (text),Directional   
Informational Type 2,Directional   Informational Type 2 
(text),Directional   Informational Type 3,Directional   
Informational Type 3 (text),Directional   Informational Type 
4,Directional   Informational Type 4 (text),Directional   
Informational Type 5,Directional   Informational Type 5 
(text),Directional   Informational Type 6,Directional   
Informational Type 6 (text),Directional   Informational Type 
7,Directional   Informational Type 7 (text),Directional   
Informational Type 8,Directional   Informational Type 8 
(text),Directional   Informational Type 9,Directional   
Informational Type 9 (text),Directional   Informational Type 
10,Directional   Informational Type 10 (text),Directional   
Informational Type 11,Directional   Informational Type 11 
(text),Directional   Informational Type 12,Directional   
Informational Type 12 (text),Directional   Informational Type 
13,Directional   Informational Type 13 (text),Reference  
Research Type 2,Reference  Research Type 2 (text),Reference  
Research Type 3,Reference  Research Type 3 (text),Reference  
Research Type 4,Reference  Research Type 4 (text),Question 
Type 2,Question Type 2 (text),Reference   Research Type 
2,Reference   Research Type 2 (text),Reference   Research 
Type 3,Reference   Research Type 3 (text),Type of 
Question,Type of Question (text),Time Spent on Question,Time 
Spent on Question (text),Brief Description of 
Question,Reference   Research Type 4,Reference   Research 
Type 4 (text),Date   Time,Gate Count,Reference Research 
Type,Reference Research Type (text),Question Type 
required,Question Type required (text)
"9898","","2016-01-04 08:00:00","Desk 
Statistics","RefBuddy","Reference Services","Information 
Desk","University of California, Santa Barbara","In 
Person","","Directional / 
Informational","","Directions/Locations (where to 
"9899","","2016-01-04 08:00:00","Desk 
Statistics","RefBuddy","Reference Services","Information 
Desk","University of California, Santa Barbara","In 
Person","","Directional / 
Informational","","Directions/Locations (where to 

In the header, we can see the common metadata fields of Desktracker: user, Contact Type, `library’.

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 *.csv to get counts for all the .csv files in the Desktrackers directory. (it takes a little time to complete):

$ wc *.csv  
   122995   2171893  54797588 Desk_Tracker.csv
    43842    757941  18654589 Desk_Tracker_2016.csv
    31456    565013  12271344 Desk_Tracker_2017.csv
    27274    491149   9833562 Desk_Tracker_2018.csv
    20426    358164   6947675 Desk_Tracker_2019.csv
   245993   4344160 102504758 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 (this looks confusing in the shell, but this is a lower-case letter L, not the number one) to get only the number of lines, not the number of words and bytes:

$ wc -l *.csv
   122995 Desk_Tracker.csv
    43842 Desk_Tracker_2016.csv
    31456 Desk_Tracker_2017.csv
    27274 Desk_Tracker_2018.csv
    20426 Desk_Tracker_2019.csv
   245993 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 *.csv 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 *.csv > 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
   122995 Desk_Tracker.csv
    43842 Desk_Tracker_2016.csv
    31456 Desk_Tracker_2017.csv
    27274 Desk_Tracker_2018.csv
    20426 Desk_Tracker_2019.csv
   245993 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
    20426 Desk_Tracker_2019.csv
    27274 Desk_Tracker_2018.csv
    31456 Desk_Tracker_2017.csv
    43842 Desk_Tracker_2016.csv
   122995 Desk_Tracker.csv

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

Finally we have our old friend head, that we can use to get the first line of the sorted-lengths.txt: (Earlier, we had the issue of is this a lower case L or a number one. Here, its the number 1 because the ‘-n’ is for sorting lines, and we want the number)

$ head -n 1 sorted-lengths.txt
   20426 Desk_Tracker_2019.csv

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 *.csv) 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 *.csv | sort -n
    20426 Desk_Tracker_2019.csv
    27274 Desk_Tracker_2018.csv
    31456 Desk_Tracker_2017.csv
    43842 Desk_Tracker_2016.csv
   122995 Desk_Tracker.csv
   245993 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 *.csv | sort -n | head -n 1
   20426 Desk_Tracker_2019.csv

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 *.csv | 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 *.csv | sort -n | head -n 1


$ wc -l *.csv | sort -n | head -n 1 | cat
20426 Desk_Tracker_2019.csv

Extracting data from our 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 response_set_id, date_time, Question Type, and Contact Type columns from our article data. With cut we’d:

cut -f1,3,10,12 -d "," Desk_Tracker_2017.csv | head
response_set_id,date_time,Contact Type (text),Question Type (text)
"51530", "2017-01-03 08:02:32", "Personal Email", "Reference / Research Assistance"
"51800", "2017-01-09 14:57:27", "In Person", "Reference / Research Assistance"
"51801", "2017-01-09 14:57:47", "In Person", "Directional / Informational"
"52089", "2017-01-11 14:17:19", "In Person", "Reference / Research Assistance"
"52209", "2017-01-12 15:23:41", "In Person", "Directional / Informational"
"52240", "2017-01-12 18:51:06". "In Person", "Reference / Research Assistance"
"52335", "2017-01-13 15:06:25", "Personal Email", "Reference / Research Assistance"
"52336", "2017-01-13 15:07:19", "In Person", "Reference / Research Assistance"
"52337", "2017-01-13 15:07:33", "Personal Email", "Reference / Research Assistance"

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 some other delimeter. 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 response_time_id, date_time, Contact Type, Question Type and direct the output into a new file using > as described in the previous episode. You can name it something like Desk_Tracker_2016_simp.csv.


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

head -n 1 Desk_Tracker_2016.csv
response_set_id, parent_response_set_id, date_time, page, user, branch, desk, library, Contact Type, Contact
Type (text), Question Type, Question Type (text)

Ok, now we know response_set_id is column 1, date_time 3, Contact Type 10, and Question Type 12. We use these positional column numbers to construct our cut command:

cut -f1,3,10,12 -d "," Desk_Tracker_2016.csv > Desk_Tracker_2016_simp.csv

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

head Desk_Tracker_2016_simp.csv
response_set_id,date_time,Contact Type (text),Question Type (text)
"43050", "2016-10-10 14:27:52", "In Person", "Reference / Research Assistance"
"43106", "2016-10-10 15:55:16", "In Person", "Reference / Research Assistance"
"43161", "2016-10-10 18:57:35", "Collab Email", "Reference / Research Assistance"
"43447", "2016-10-11 14:50:00", "In Person", "Reference / Research Assistance"
"43421", "2016-10-11 19:43:52", "In Person", "Directional / Informational"
"43580", "2016-10-12 15:20:49", "In Person", "Directional / Informational"
"43581", "2016-10-12 15:20:59", "In Person", "Directional / Informational"
"43590", "2016-10-12 16:06:24", "In Person", "Directional / Informational"
"43613", "2016-10-12 17:12:29", "In Person", "Reference / Research Assistance"

Count, sort and print

A ‘faded example’ is a partially solved problem. Let’s build on what we know and fill in the blanks.

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

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

Now let’s change the scenario. Let’s say we have a whole collection of files, and we want to know the 10 files that contain the most words. 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).

__ -w *.csv | sort __ | ____


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

wc -w *.csv | sort -n | tail -n 1

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, or something close to the answer.


You get close with

$ ls -l | wc -l

but the count will be one too high, since the “total” line from ls is included in the count. We’ll get back to a way to fix that later when we’ve learned about the grep command.

Counting the number of 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). Try it with the .csv 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 .csv files:

$ wc -w *.csv
 2171893 Desk_Tracker.csv
  757941 Desk_Tracker_2016.csv
  565013 Desk_Tracker_2017.csv
  491149 Desk_Tracker_2018.csv
  358164 Desk_Tracker_2019.csv
 4344160 total

And to sort the lines numerically:

$ wc -w *.csv | sort -n
  358164 Desk_Tracker_2019.csv
  491149 Desk_Tracker_2018.csv
  565013 Desk_Tracker_2017.csv
  757941 Desk_Tracker_2016.csv
 2171893 Desk_Tracker.csv
 4344160 total

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

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