I know I’m not the first person to notice this. But it’s fun anyway. Also: I don’t have a problem with either Harry Potter or adverbs.

Someone (who shall remain nameless) has been blasting her way through the Harry Potter series as a way of dealing with the pandemic, and was unfortunate enough to notice Rowling’s, umm, creative ways with adverbs. After her umpteenth lazily, I couldn’t stand by idly (cough) any longer, and had to get involved. With grep of course! Except actually with ripgrep because it’s super fast and has pretty colours.

I converted all seven ebooks to plaintext, dropped them in a directory together and got to work. (In all the following, you can replace rg with grep and the output should be much the same.) The first issue was that she recalled one particular offender, but only remembered that it was an adverb that started with a p… No problem:

rg " p[[:lower:]]{3,}ly"
# match a space
# then 'p'
# then 3+ letters
# then 'ly'

Just within Harry Potter and the Order of the Phoenix (the tome in question), there were 202 matches! This prints out a massive wall of text (as the full context for each match is printed too) but this makes the sleuthing more enjoyable. Nonetheless, in a few moments the culprit is found:

The class stared perplexedly at her and then at each other. Harry, however, thought he knew what was the matter.

Let’s dig into some more analysis (and chuckles)

To make this a bit more amenable to analysis (and chuckles), let’s drop the context, and count the number of uses of each unique adverb.

rg " p[[:lower:]]{3,}ly" -o \
    | sed 's/^.*: //' \
    | sort \
    | uniq -c
# -o leaves out the context
# sed to get rid of the line number
# sort alphabetically
# unique values with counts

Which gives us this:

      2 perplexedly
     13 personally
      1 piercingly
      1 pimply
      1 pitilessly
      1 pityingly
      3 placidly
     30 plainly
      1 playfully
      1 pleadingly
      9 pleasantly
      1 pleasurably
      5 pointedly
      2 pointlessly
      1 poisonously
      9 politely
      2 pompously

Perplexedly strikes twice, as does pompously and a horde of others: 61 different -ly words starting with a p! Let’s go one step further and look for phrases that match the pattern verb-person-adverb, e.g. “said Snape snappishly”:

rg " [[:lower:]]{4,} \
[[:upper:]][[:alpha:]]+ \
[[:lower:]]{4,}ly" -o \
    | sed 's/^.*://' \
    | sort \
    | uniq -c
# match a space
# then a word with 4+ letters
# then a space
# then an upper-case word
# then a space
# then a -ly word

This has a lot of false positives, such as “whole Weasley family”, but also unearth such joys as:

whispered Hermione desperately (she also whispers gleefully)

thought Harry scathingly (he also thinks bitterly and miserably)

squeaked Dobby confidentially (he also squeaks angrily, anxiously, and happily)

said Umbridge sleekly (she does this twice, along with breathlessly, curtly, eagerly, furiously, harshly, impatiently, loudly, quietly, shrilly, silkily, smugly, softly, 4x sweetly, swiftly, and triumphantly)

said Ron weakly (seven times! his favourite way of saying behind suddenly, slowly, sharply, quietly, quickly, loudly, irritably, impatiently, grumpily, furiously, excitedly, eagerly, darkly, bitterly, and angrily)

said Percy pompously (twice!)

said Hermione sharply (her preferred method, at 18 times)

said Harry incredulously (four times, also irritably, exasperatedly, dispiritedly, and a host of other ways)

All in all, there are 1431 unique version of this throughout the books!

One last thing, let’s get a quick adverb count per 10,000 words. The following snippet goes through all files in subdirectories, gets the number of adverbs, then the total word count, then divides them.

for f in **/*.txt; do
    m=$(rg " [[:lower:]]{4,}ly" \
        -c --no-filename -g "$f")
    w=$(cat $f | wc -w)
    r=$(echo "10000*$m/$w" | bc)
    echo $f $r
done

And we have the following counts. Note that this is counting any words that match the pattern, so we have false positives (like ‘family’) and probably some false negatives as well (such as ‘fast’). In general this is probably over-predicting on average.

Philsophers Stone        107*
Chamber of Secrets       153
Prisoner of Azkaban      154
Goblet of Fire           156
Order of the Phoenix     179
Half-Blood Prince        164
Deathly Hallows          112

* adverbs per 10,000 words

So she starts off slowly, builds up to an astonishing 179 in book five (the one that started this investigation, unsurprisingly) and then settles down again (in response to critics?).

Let’s have a quick look at some other authors. This was much less fun, as although there are loads of -ly words throughout, there are relatively few verb-person-adverb constructs to laugh at.

Unsurprisingly, The Road doesn’t contain a single “said Snape snappishly”, and Huckleberry Finn gets by with very few. The Lord of the Rings has plenty, and “said Strider suddenly” manages to sneak in twice. Tolstoy is quite generous with adverbs, and “perplexedly” even appeared in the Pevear and Volokhonsky translation. Unsurprisingly, the nearest competitor to Potter’s dominance is another YA series: Sabriel by Garth Nix, one of my absolute favourite books then and now. But even in this case no expression is repeated more than twice, and it’s a drab (but telling) “replied Sabriel distantly”.

At this point I’d normally start building some charts out of this and seeing how we can analyse this in more interesting ways. For example, which character uses which adverb most often, some more interesting comparisons between authors… But instead, to show the value of a bit of bash knowledge for this kind of analysis, I’m going to show the amount of Python code necessary just to count the number of times each adverb is used within some files (what we did further up with one line in bash):

#!/usr/bin/env python

import re
from pathlib import Path
from collections import defaultdict

p = re.compile(r" \w*ly")

counts = defaultdict(int)
for fi in Path().glob("**/*.txt"):
    with open(fi) as f:
        for l in f.readlines():
            m = p.search(l)
            if m:
                counts[m.group()] += 1

for k in sorted(counts):
    print(f"{k:<20}{counts[k]}")

Note: I was going for minimum line count here, but would normally use intermediate data arrays to have more flexibility for adding/modifying subsequent analysis.

And to do the whole list of books above, the Python script takes 0.6 seconds, to ripgrep’s 0.2 seconds. The big advantages of the Python approach, are that (a) I’ll understand how it works in a year’s time, and (b) if I wanted to now do something more interesting with that data (clustering, networks, NLP…) I would be able to without changing tools.