For understanding patterns, it's incredibly useful to be able to easily and quickly aggregate metadata into hierarchies.
Suppose you're looking at texts you have classified at the level of their "county."
There are a lot of different things you might want to do with that county-level information. A county is also part of state and a census region; it has a winner in the 2012 presidential election, and you might class it as urban, rural, or suburban.
The most straightforward way to do this sort of classification is just to include data like this for every entry in your Bookworm. Want to include election winner? Just have a field called county_election_winner in every catalog entry.
There's a reason, though, librarians don't store things like this on their cards: they would quickly get out of control. Even in a system like Bookworm, it's both inefficient and confusing to think of the election winner as being a property of the individual text: the election winner "belongs" to the county.
This is especially true if you want to update a Bookworm after the fact with some new information. (There are a lot, lot, lot of cases where this is incredibly valuable to do: frequently it's not until you begin data exploration that you'll know what useful patterns might be.) Generally, the data you'll want to put in will be not about the original texts, but about one of the metadata fields you already have.
county
|
state
|
census region
|
urban, rural, or suburban
|
winner in 2012 presidential election
Bookworm allows you to add additional data into the database once it's been created.
To do this, run bookworm.py with the add_metadata argument.
bookworm add_metadata \
--file=yourfilename.txt \
--field_descriptions=separate_field_descriptions.json
file is the new metadata: A full list of the available options can be seen in the shell by typing:
bookworm add_metadata --help
These include 'key', which is the metadata field that links into your existing database.
In general, the metadata here is handled in exactly the same way as the original metadata you supplied to bookworm. The format for the field_descriptions file is the same as the field_descriptions.json file; the format for the file imported is identical to the format for the jsoncatalog.txt file.
An much easier but less powerful method is to import a TSV file.
bookworm add_metadata --format=tsv --file=filename.tsv
This is just a convenience wrapper; the code will convert the tsv to a bunch of json entries; if you do not pass in a key argument it assumes the first column is the anchor field.
This makes it easy to quickly add new fields in the course of looking at data.
For example, if you are looking at data that is categorized by a year field called date_year, you might find that the resolution is too high, and you want to view by decade instead.
To do that, you'd simply create a file at ~/misc/new_metadata.tsv (or whatever, the name and location doesn't matter), like this:
date_year date_decade
[...]
1887 1880
1888 1880
1889 1880
1890 1890
1891 1890
1892 1890
[...]
Then go to your bookworm folder, and type in
bookworm add_metadata \
--format=tsv \
--file=~/misc/new_metadata.tsv
Once this is run, the database will have a new public field called date_decade that can be used by any API calls the same as date_year, but will aggregate at a higher level.
This particular trick--rounding a numberic variable--can be so handy that I'll give you a little shell script for doing it by any number on any variable at all.
# Variables you might change
floor_to_nearest=10;
old_variable=year
new_variable=decade
database=SOTU
# Print headers for the tsv
echo -e "$old_variable\t$new_variable" > new_data.tsv
# Download all of the year variables through the API, and round them
# using awk.
curl -G localhost/cgi-bin/dbbindings.py --data-urlencode "query={\"database\":\"$database\",\"groups\":[\"$old_variable\"],\"counttype\":[\"WordCount\"],\"method\":\"return_tsv\"}" |
awk "BEGIN{OFS=\"\t\"} \
{if(NF==2) {\
print \$1,$floor_to_nearest*int(\$1/$floor_to_nearest)\
}}" >> new_data.tsv
bookworm add_metadata \
--format=tsv \
--file=new_data.tsv