Distant Reader Study Carrel

About your study carrel

This page outlines the breadth & depth of your "study carrel" -- the results & analysis of your Distant Reader submission. Peruse the content of this page, and then consider learning how to dig deeper by reading the Distant Reader Study Carrel Cookbook. If you want "just the facts", then consider reading this text's synopsis.

Size & scope

First, the simple things. Your study carrel was created through the submission of a [SINGLE URL|FILE OF URLS|FILE FROM YOUR COMPUTER|ZIP FILE]. This ultimately resulted in a collection of 148 item(s). The original versions of these items have been saved in a cache, and each of them have been transformed & saved as a set of plain text files. All of the following analysis has been done against these plain text files.

Your study carrel is 8606226 words long. [0] Each item in your study carrel is, on average, 58150.0 words long. [1] If you dig deeper, then you might want to save yourself some time by reading a shorter item. On the other hand, if your desire is for more detail, then you might consider reading a longer item. The following histograms and box plots illustrate the overall size of your study carrel.


On a scale from 0 to 100, where 0 is very difficult and 100 is very easy, your documents have an average readability score of 85.0. [2] Consequently, if you want to read something more simplistic, then consider a document with a higher score. If you want something more specialized, then consider something with a lower score. The following histograms and box plots illustrate the overall readability of your study carrel.

Word frequencies

By merely counting & tabulating the frequency of individual words or phrases, you can begin to get an understanding of your carrel's "aboutness". Excluding "stop words", some of the more frequent words include: one, water, put, add, butter, salt, two, sugar, little, will, eggs, half, flour, milk, cut, boil, well, may, cream, sauce, take, cold, hot, serve, pepper. [3] Using the three most frequent words, the three files containing all of those words the most are ./txt/dh-crc-nd-edu-1225.txt, ./txt/dh-crc-nd-edu-9599.txt, and ./txt/dh-crc-nd-edu-7247.txt.

The most frequent two-word phrases (bigrams) include: cold water, boiling water, one cup, baking powder, ten minutes, add one, melted butter, bread crumbs, hot water, little salt, twenty minutes, two tablespoonfuls, lemon juice, salt pepper, moderate oven, one pound, five minutes, two ounces, one pint, two eggs, one quart, small pieces, chopped parsley, one tablespoonful, hot oven, and the three file that use all of the three most frequent phrases are ./txt/dh-crc-nd-edu-1225.txt ./txt/dh-crc-nd-edu-6642.txt, and ./txt/dh-crc-nd-edu-4360.txt.

While often deemed superficial or sophomoric, rudimentary frequencies and their associated "word clouds" can be quite insightful:




Sets of keywords -- statistically significant words -- can be enumerated by comparing the relative frequency of words with the number of times the words appear in an entire corpus. Some of the most statistically significant keywords in your study carrel include: add, little, cutting, boils, adds, egg, small, buttered, till, waters, half, cooks, cuts, sauce, sauces, cooked, creams, goodness, eggs, dish, serves, cakes, cups, creamed, foods. And now word clouds really begin to shine:


Through the use of a concordance -- a keyword-in-context tool, or a "poor man's search engine" -- you can see how words are used in relation to other words. Here is a random sample of concordance entries using the two most significant keyword as input:

r to each quart of the chopped vegetables add half a cupful of stock and one tablespoonful
ece of butter about the size of an egg little generous boil for half to threequarters of a
n something you see and ruth blushed a little we are thinking of building a house and we h
 water some salt and pepper and boil them add the liquor they were boiled in to the gravy 
uls of butter in a fryingpan and when hot add one tablespoonful of flour stir until smooth
r twice then put them in a large iron pot add a liberal quantity of cold water and simmer 
 and let it simmer for a few minutes then add two cutup mushrooms a tomato mashed up and a
nd garnish the dish with parsley so as to add to its appearance gravy for fried chickenif 
d be garnished in the same manner or a little melted apricot jam may be poured over the ap
add two or three wellbeaten eggs and then add slowly two cupfuls of milk and lastly a cupf
 formed turn it into a large earthen bowl add a tablespoonful of salt and two of white sug
e side then turn and brown the other then add half a tablespoonful of vinegar and a cup of
ctly still it is best to try it with a little bit of the cake to be fried if the heat is r
the other mixture and beat all very light add the melted butter and continue to beat pour 
 the ingredients together thoroughly then add cup of chopped raisins which have been seede
 chop up the onions and boil them in a little water until soft cut the potatoes in small d
 at firstthen fry a tiny cake test it and add whatever it seems to lack or need be rather 
 pounds of currans and let them boil a little then put in two pounds of raisins and two po
ith sugar and stir into the meat and suet add the apples and then the cider and other wett
arley soup take one quart of hot bouillon add a quarter pound barley which has been boiled
and prettylooking dish when approved a little mustard may be added to the sauce timeabout 
ter covering close while cooking add a little salt just before taking from the fire drain 
 raw apples the same thickness grate a little lemon rind over each layer cover and place i
en cut up the six hardboiled eggs into little pieces add sufficient butter sauce to moiste
ggs with two tablespoonfuls of cold water add to them half a pint of boiling soup and grad

Topic modeling

Topic modeling is another popular approach to connoting the aboutness of a corpus. If your study carrel could be summed up in a single word, then that word might be water, and ./txt/dh-crc-nd-edu-1634.txt is most about that word.

If your study carrel could be summed up in three words ("topics") then those words might be: water, water, and little. And the respective files would be: ./txt/dh-crc-nd-edu-7834.txt, ./txt/dh-crc-nd-edu-7372.txt, and ./txt/dh-crc-nd-edu-60.txt.

If your study carrel could be summed up in five topics, and each topic were each denoted with three words, then those topics and their most significantly associated files would be:

  1. add salt water - ./txt/dh-crc-nd-edu-7834.txt
  2. water little boil - ./txt/dh-crc-nd-edu-6294.txt
  3. water food used - ./txt/dh-crc-nd-edu-4987.txt
  4. said little good - ./txt/dh-crc-nd-edu-60.txt
  5. oz lb ingredients - ./txt/dh-crc-nd-edu-7372.txt

Moreover, the totality of the study carrel's aboutness, can be visualized with the following pie chart:

Nouns & verbs

Through an analysis of your study carrel's parts-of-speech, you are able to answer question beyonds aboutness. For example, a list of the most frequent nouns (water, _, egg, butter, salt, sugar, cup, flour, milk, sauce, cream, pepper, minute, dish, pound, hour, bread, |, meat, pan, lemon, piece, half, pint, onion) helps you answer what questions; "What is discussed in this collection?" An enumeration of the lemmatized verbs (be, add, put, have, make, _, boil, serve, take, do, cut, use, cook, beat, let, pour, stir, mix, cover, bake, chop, keep, fry, remove, set) helps you learn what actions take place in a text or what the things in the text do. Very frequently, the most common lemmatized verbs are "be", "have", and "do"; the more interesting verbs usually occur further down the list of frequencies:



Proper nouns & pronouns

An extraction of proper nouns (|, _, Boil, Cream, Sauce, Fruit, Soup, Butter, Bread, Pudding, Baked, Coffee, Cake, Salad, Rice, Potatoes, Crisco, Mrs., Apple, Potato, MRS, Pie, Cheese, Tomato, SOUP) helps you determine the names of people and places in your study carrel. An analysis of personal pronouns (it, them, they, you, i, we, she, he, me, him, us, one, her, themselves, itself, herself, himself, myself, yourself, ''em, ourselves, ''s, mine, yours, thee) enables you to answer at least two questions: 1) "What, if any, is the overall gender of my study carrel?", and 2) "To what degree are the texts in my study carrel self-centered versus inclusive?" Below are words cloud of your study carrel's proper & personal pronouns.

proper nouns


Adjectives & adverbs

Learning about a corpus's adjectives (little, good, hot, cold, small, large, white, other, fine, teaspoonful, brown, same, few, thick, fresh, more, thin, sweet, dry, much, soft, grated, warm, many, ready) and adverbs (then, not, very, well, up, out, together, as, in, off, so, also, about, over, too, more, just, only, thoroughly, enough, quite, slowly, again, all, carefully) helps you answer how questions: "How are things described and how are things done?" An analysis of adjectives and adverbs also points to a corpus's overall sentiment. "In general, is my study carrel positive or negative?"




[0] Once upon a time, a corpus of a million words was deemed large.

[1] To put this into context, the typical scholarly journal article is about [NUMBER] words long, Shakespeare's Hamlet is [NUMBER] words long, and the Bible is [NUMBER] words long.

[2] In this case, a Flesch readability score is being calculated. It is based on things like the number of words in a document, the lengths of the words, the number of sentences, the lengths on the sentences, etc. In general children's stories are have lower Flesch scores while insurance documents and doctoral dissertations have higher scores.

[3] "Stop words" are sometimes called "function words", and they are words which carry little or no meaning. Every language has stop words, and in English they include but are not limited to "the", "a", "an", etc. A single set of stop words has been used through out the analysis of your collection.

[4] Concordances are one of the oldest forms of text mining, first developed in the 13th century to "read" religious documents.

[6] An unsupervised machine learning process, topic modeling is a very popular text mining operation. Assuming that a word is known by the company it keeps, topic modeling identifies sets of keywords denoted by their centrality in the text. Words which are both frequent as well as in close proximity to each other are considered significant.