Preprocess R data with python

(for archival purposes only, probably don’t use this.)

This script can be easily modified to code, recode, or modify csv files prior to loading in R.

#  transform a raw data file into a happy proto data frame
#

import csv

f = open( 'transposed.csv' )
hnps = csv.reader( f, delimiter = ',' )
header = hnps.next()

verbs = [
     'mentioned',
     'indicated',
     'proposed',
     'suggested',
     'recommended',
     'confessed',
     'stated',
     'announced',
     'muttered',
     'explained',
     ]

out_name = 'reshaped.csv'
out = open( out_name, 'w' )
data = csv.writer( out, delimiter = ',' )
data.writerow( [ 'subject', 'score', 'shift', 'length', 'verb' ])

for line in hnps:
    shift_length = line[header.index('V1')]

    try:
    if shift_length[0] == "N":
        shift = 'N'
        length = shift_length[1:]
    elif shift_length[0] == "S":
        shift = 'S'
        length = shift_length[1:]
    except IndexError:
    continue

    sentence = line[header.index('ResponseID')]
    for v in verbs:
    # we're in trouble if one of the sentences contains more than one verb
    # fortunately, they don't.
    if v in sentence:
        verb = v
        break

    for s in range( 1, 193 ):
    try:
        line[header.index( 'ID.' + str(s))]
    except IndexError:
        # that cell is empty for this subject
        continue
    else:
        # if this subject responded to this item add data point to file
        score = line[header.index( 'ID.' + str(s))]
        if score.isdigit():
        data.writerow( [ s, score, shift, length, verb ])

f.close()
out.close()