Some of you expressed an interest in being able to quickly count all the ads in a folder and determine how many were published in a given year, decade, or month (to detect seasonal patterns across the year).
Here is a script that can do that. It is designed to work on Mac or Linux systems.
To use it, you should first download our
adparsers repo by clicking on the "Download Zip" button on this page:
Unzip the downloaded file, and you should then have a directory that contains (among other things) the
You should now copy the file to the directory that contains the ads you want to count. You can do this the drag-and-drop way, or you can use your terminal and the
cp command. (If you forgot what that command does, revisit the Command Line bootcamp that was part of the MALLET homework. Once the script is in the directory, navigate to that directory in your terminal, and then run the command like this:
If you get an error message, you may need to follow the instructions in the comments at the start of the script (which you can read on GitHub) to change the permissions. But if all goes well, you’ll see a printed breakdown of chronological counts. For example, when I run the script in the directory containing all our Mississippi ads, the script returns this:
TOTAL 1632 DEC ADS 1830s 1118 1840s 178 1850s 133 1860s 4 YEAR ADS 1830 30 1831 54 1832 87 1833 68 1834 143 1835 157 1836 262 1837 226 1838 63 1839 28 1840 16 1841 16 1842 22 1843 33 1844 44 1845 25 1846 14 1847 1 1848 5 1849 2 1850 11 1851 17 1852 19 1853 15 1854 7 1855 9 1856 11 1857 23 1858 13 1859 8 1860 4 MONTH ADS 1 100 2 89 3 103 4 130 5 160 6 161 7 188 8 150 9 150 10 149 11 146 12 86
If you choose, you can also "redirect" this output to a file, like this:
./countads.sh > filename.txt
Now you should be able to open
filename.txt (which you can name whatever you want) in Microsoft Excel, and you’ll have a spreadsheet with all the numbers.
The script may seem to have limited value, but the key to its utility lies in first getting an interesting set of ads into a directory. That extends its usefulness. For example, if you wanted only to know the month distribution of ads in a particular year, you could first move all the ads from that year into a directory, and run the script from within it. You’d get lots of zeroes for all the years that you’re not interested in, but you would get the month breakdown that you are interested in. Depending on which ads you put in the directory that you are counting in, you can get a lot of useful data that can then be graphed or added into further calculations.