File size: 18,491 Bytes
8a79f2e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 |
import sys
import os
import pandas as pd
import random
import re
import string
from functools import reduce
import numpy as np
import math
from datetime import datetime, timedelta
from lib.data_helpers import data_utils
from lib.experiment_specs import study_config
from lib.utilities import codebook
from lib.utilities import serialize
from lib.data_helpers.confidential import Confidential
from lib.data_helpers.manual_changes import ManualChanges
from lib.data_helpers.builder_utils import BuilderUtils
random.seed(23555)
"""
Purpose: Cleans Qualtrics surveys based on their configs in study_config
Input:
- Survey Name only input survey names found in data/{study}/configurations/study_config
- input_dir: directory of raw file
- output_dir: directory of clean_master file
- remove_embedded: if True, removes the embedded data from a raw qualtrics survey
- test: bool - if True, keeps all banned email responses
- codebook: bool - if True, the qualtrics cleaner will update the master codebook. for check_survey.py, this is set to False, and for the main pipeline, this is set to true
- helper_paths : the dictionary specifies the absolute path locations of 'Manual Changes' an excel spreadsheet where specify
changes to the survey answers, and 'Testers' where we specify information for the PhoneAddictionTeam
{'ManualChanges':manual_changes_path, 'Testers':testers_path}
MainFunction: clean_survey
Output: Clean survey file
Note: DON'T MAKE ANY NEW VARS -- THEY WILL BE DELETED BECAUSE WE CROP ALL DATA BEFORE THE FIRST VAR AND ALL DATA AFTER THE LAST VAR
Note: all variables are converted to strings, and NaN will be coded as "nan"
"""
class CleanSurvey():
default_helper_paths = {"Intermediate": os.path.join("data", "external", "intermediate"),
"Confidential": os.path.join("data", "external", "dropbox_confidential")}
def __init__(self, survey_name: str, input_dir: str, output_dir: str,
remove_embedded: bool = True, test: bool = False, codebook_bool: bool = True,
helper_paths: dict = default_helper_paths):
self.survey_name = survey_name
self.open_date_time = study_config.surveys[survey_name]["Start"]
self.close_date_time = study_config.surveys[survey_name]["End"]
self.code = study_config.surveys[survey_name]["Code"]
self.raw_file_path = os.path.join(input_dir, survey_name + ".csv")
self.clean_file_path = os.path.join(output_dir, survey_name + ".csv")
self.intermediate_dir = helper_paths["Intermediate"]
self.confidential_dir = helper_paths["Confidential"]
self.testers = pd.read_excel(os.path.join(self.confidential_dir,"Testers.xlsm"), sheet_name="Testers")
self.timezones_path = os.path.join(self.intermediate_dir,"Timezones")
self.manual_changes_path = os.path.join(self.confidential_dir,"ManualChanges.xlsx")
self.pii_path = Confidential.id_file
self.remove_embedded = remove_embedded
self.test = test
self.codebook_bool = codebook_bool
def clean_survey(self):
raw_data_file = os.path.join(self.raw_file_path)
print(f"\n Processing {self.survey_name}")
df = pd.read_csv(raw_data_file, dtype=str)
if len(df)==0:
print(f"No Data in{self.survey_name}")
return pd.DataFrame()
print(f"obs before processing {len(df)-2}")
df = self._process_col_names(df)
df = self._process_col_values(df)
""" we know the qualtricts datetimes are in eastern, so we
adjust the qualtrics times to local using the timezones found in the PD data"""
df = self._process_datetimes(df)
df = self._filter_timeframe(df)
df = self._filter_emails(df)
df = self._validate_completes(df)
if self.survey_name in study_config.text_surveys:
df = self._reshape_text_survey(df)
df = ManualChanges.manual_clean(df, self.survey_name, self.manual_changes_path)
df.to_csv(self.clean_file_path, index=False)
return df
df = self._filter_duplicates(df)
df = ManualChanges.manual_clean(df, self.survey_name, self.manual_changes_path)
df = df.reset_index(drop=True)
Confidential.build_id_map(df,self.survey_name, self.pii_path)
Confidential.anonymize_cols(df)
if self.code is not None:
df = data_utils.add_survey_code(df, self.code)
#if self.test:
# self.clean_file_path = self.clean_file_path.replace(".csv", "TEST.csv")
df.to_csv(self.clean_file_path, index=False)
"""in prep for merge, remove embedded data. keep in csv exported above just for comparison"""
if self.remove_embedded:
df = self._remove_embedded_data(df)
print("Created {0} with {1} entries".format(self.survey_name, len(df.index)))
return df
def _process_col_names(self, df: pd.DataFrame):
df.columns = df.columns.str.replace(' ', '')
df.columns = df.columns.str.replace('_', '')
df = codebook.rename_vars(df)
if self.codebook_bool:
self._update_codebook(df)
df = df.iloc[2:, :]
df_dup = df.loc[:, df.columns.duplicated()]
if len(list(df_dup.columns)) > 0:
print(f" \t Duplicate Columns to delete: {df_dup.columns}")
df = df.loc[:, ~df.columns.duplicated()]
return df
def _process_col_values(self, df):
df = df.astype(str).applymap(lambda x: x.strip())
for p_col in ["PhoneNumber","PhoneNumberConfirm","FriendContact"]:
if p_col in df.columns.values:
df[p_col] = df[p_col].apply(lambda x: re.sub("[^0-9]", "", str(x)))
df[p_col] = df[p_col].apply(lambda x: "1"+x if len(x)==10 else x)
df[p_col] = df[p_col].apply(lambda x: x if len(x) == 11 else 'nan')
#silly bug
if self.survey_name == "Baseline":
print("\t dealing with dumb baseline bug to ensure appcode assertion passes")
print(f"\t Len before dropping sherry {len(df)}")
df = df.loc[df["MainEmail"]!="xy1087@nyu.edu"]
print(f"\t len after dropping sherry {len(df)}")
# ADD APPCODES to survey without appcode
if "AppCode" not in df.columns.values:
print("\t No AppCode in Survey")
# make the main email col in pii the raw email col in the survey w/o appcode
pii = serialize.open_pickle(self.pii_path).reset_index().rename(columns = {"index":"AppCode"})
email_col = study_config.surveys[self.survey_name]["RawEmailCol"]
pii = pii.loc[pii["MainEmail"] != "nan",["AppCode", "MainEmail"]].rename(columns = {"MainEmail":email_col})
df = df.merge(pii, on=email_col, how='left')
print("done merger")
for appcode_col in ["AppCode", "AppCodeConfirm"]:
if appcode_col in df.columns.values:
df = data_utils.add_A_to_appcode(df,appcode_col)
"If no appcode, assign 'UNASSIGNED_' + ResponseID as Appcode"
df.loc[df[appcode_col]=="nan", appcode_col] = 'UNASSIGNED_'+df["ResponseID"]
if "AppCode" not in df.columns:
print("AppCode not in df.columns. Anonymize with Response ID")
df["AppCode"] = 'UNASSIGNED_'+df["ResponseID"]
#Temptation Stratification for SurveyChecks
if self.survey_name =="Recruitment":
df["Age"] = df["Age"].astype(float)
df.loc[(df["Age"]>=18)&(df["Age"]<=34),"AgeStrat"] = "18-34"
df.loc[(df["Age"] >= 35) & (df["Age"] <= 50), "AgeStrat"] = "35-50"
df.loc[(df["Age"] > 50), "AgeStrat"] = "50+"
df["Age"] = df["Age"].fillna("nan").astype(str)
return df
def _process_datetimes(self,df):
df['SurveyStartEasternDatetime'] = pd.to_datetime(df['SurveyStartDatetime'], infer_datetime_format=True)
df['SurveyEndEasternDatetime'] = pd.to_datetime(df['SurveyEndDatetime'], infer_datetime_format=True)
df.loc[:, 'OpenEasternDateTime'] = self.open_date_time
df.loc[:, 'CloseEasternDateTime'] = self.close_date_time
# Create Local Datetime
try:
# Get the modal timezone for each user to adjust the easter survey times to local time of user
timezones = serialize.open_pickle(self.timezones_path)
df = df.merge(timezones, on = "AppCode", how = 'left')
for time_var in ['SurveyStartEasternDatetime','SurveyEndEasternDatetime','OpenEasternDateTime','CloseEasternDateTime']:
df[time_var.replace("Eastern","")]= df[time_var]+df["EastToLocal"]
#if we can't find timezone, let local timezone equal eastern timezone
df.loc[df[time_var.replace("Eastern","")].isnull(),time_var.replace("Eastern","")] = df[time_var]
df.loc[df["EastToLocal"].isnull(),"EastToLocal"] = timedelta(0)
except:
print("Could not merge status exporter likely because Appcode not in survey or because timezones file not in path")
df['SurveyStartDatetime'] = pd.to_datetime(df['SurveyStartDatetime'], infer_datetime_format=True)
df['SurveyEndDatetime'] = pd.to_datetime(df['SurveyEndDatetime'], infer_datetime_format=True)
return df
return df
"""for each possible email column, drop nan's, drop banned emails, keep last of duplicate"""
def _filter_emails(self, df):
print(f"obs before filtering tester emails {len(df)}")
other_email_cols = ['SchoolEmail', 'PreferEmail', 'RecipientEmail', "Email", "EmailConfirm", "Email.1"]
if not self.test:
for email_col in other_email_cols+["ParentEmail"]:
if email_col in list(df.columns.values):
df = df.loc[~df[email_col].isin(list(self.testers["Email"]))]
else:
print("Because self.test == True, all banned emails will be included!!")
# ensure the main email col is "MainEmail"
raw_email_col = study_config.surveys[self.survey_name]["RawEmailCol"]
if raw_email_col != "MainEmail":
# drop the current MainEmail Version, and create a new one using the raw email data
if "MainEmail" in df.columns:
df = df.drop(columns=["MainEmail"])
df = df.rename(columns={raw_email_col: "MainEmail"})
# drop other email cols
for col in other_email_cols:
if col in df.columns:
df = df.drop(columns=[col])
return df
# creates new variable "Complete" which indicates if the participant completed the survey, or the last column they filled in
def _validate_completes(self, df):
last_question = study_config.surveys[self.survey_name]["LastQuestion"]
# Assert the last question will not be filled by an anonymous code, regardless if the cell were empty
id_cols = list(study_config.id_cols.values())
assert last_question not in sum(id_cols, [])
question_index = list(df.columns).index(last_question)
survey_cols = list(df.columns)[:question_index+1:]
reverse_survey_cols = survey_cols[::-1]
complete_q = study_config.surveys[self.survey_name]["CompleteQuestion"]
df.loc[(df["Finished"] == 'True') & (df[complete_q] != 'nan'), "Complete"] = "Complete"
df.loc[df["Complete"] != 'Complete', "Complete"] = "UnfinishedOther"
# Find the last completed survey variable
df_dict = df.to_dict(orient = 'index')
for key, value in df_dict.copy().items():
if value["Complete"] == "Complete":
continue
else:
for col in reverse_survey_cols:
#exclude empty cols
if (value[col] != "nan"):
try:
#exclude cols that are automatically filled in
if "UNASSIGNED" not in str(value[col]):
df_dict[key]["Complete"] = col
break
except:
print("bug")
df = pd.DataFrame.from_dict(df_dict, orient = "index")
return df
def _filter_duplicates(self, df):
# filter out duplicates-- prioritize keeping complete obs, then the later obs
ranks = range(0,len(df.columns))
rank_of_cols = dict(zip(list(df.columns),ranks))
rank_of_cols["Complete"] = 9999999
df["CompleteRank"] = df["Complete"].apply(lambda x: rank_of_cols[x])
scratch_path = os.path.join(self.intermediate_dir, "Scratch")
for dup_col in ["MainEmail","AppCode"]:
if dup_col not in df.columns:
print(f"{self.survey_name} doesn't have an {dup_col} column")
else:
print(f"obs before dropping dups of {dup_col}: {len(df)}")
#sort obs by, appcode, how far they completed the survey, then time survey was started
df = df.sort_values(by = [dup_col,"CompleteRank","SurveyStartDatetime"])
#if dup_col == "MainEmail":
# dup = df[(df.duplicated(subset=["MainEmail"], keep=False)) & (df['MainEmail'] != "nan")]
# dup.to_csv(os.path.join(scratch_path, f"RecruitDups.csv"))
# mark all entries as duplicates, except for the last one
df = df.loc[(~df.duplicated(subset=[dup_col], keep='last')) | (df[dup_col] == "nan")]
#if dup_col == "MainEmail":
# df.to_csv(os.path.join(scratch_path, f"Keeps.csv"))
print(f"obs after dropping dups of {dup_col}: {len(df)}")
df = df.sort_values("SurveyStartDatetime")
return df
def _filter_timeframe(self, df):
print(f"obs before droppingpeople that began before survey start or ended after close {len(df)}")
if self.test == False:
df = df.loc[df['SurveyStartEasternDatetime'] >= df['OpenEasternDateTime']]
df = df.loc[df['SurveyEndEasternDatetime'] <= df['CloseEasternDateTime']]
return df
def _update_codebook(self, df):
codebook_dic = {}
for col in df.columns:
codebook_dic[col] = {
"VariableLabel": str(df.loc[0,col]),
"DataType": df.dtypes[col],
"PrefixEncoding": "Survey"
}
#Remove Timing Data from Codebook
timing_vars = ["Timing-ClickCount",
"Timing-PageSubmit",
"Timing-FirstClick",
"Timing-LastClick",
"BrowserMetaInfo"]
for varname, chars in codebook_dic.copy().items():
if len([x for x in timing_vars if x in chars["VariableLabel"].replace(" ","")]) > 0:
del codebook_dic[varname]
codebook.add_vardic_to_codebook(codebook_dic)
def _remove_embedded_data(self, df_f):
#first remove all the qualtrics tracker vars
unimportant_vars = [y for y in df_f if
any(x in y for x in ["FirstClick", "LastClick", "PageSubmit", "ClickCount"])]
df_f = df_f.drop(columns=unimportant_vars)
#then remove the embedded data
non_survey_vars_to_keep = study_config.main_cols + [f"{self.code}_{x}" for x in study_config.kept_survey_data]
for question_type in ["FirstQuestion", "LastQuestion"]:
if question_type in study_config.surveys[self.survey_name]:
last_question = study_config.surveys[self.survey_name]["Code"] + "_" + \
study_config.surveys[self.survey_name][question_type]
question_index = list(df_f.columns).index(last_question)
if question_type == "FirstQuestion":
keep_cols = list(set(non_survey_vars_to_keep + list(df_f.columns)[question_index:]))
else:
keep_cols = list(set(non_survey_vars_to_keep + list(df_f.columns)[:question_index + 1]))
df = df_f[[x for x in df_f.columns if x in keep_cols]]
# re order columns (put main cols in front and maintain order of survey columns
var_order = [x for x in non_survey_vars_to_keep if x in df_f.columns] + [x for x in df_f.columns if x not in non_survey_vars_to_keep]
kept_var_order = [x for x in var_order if x in df.columns]
df = df[kept_var_order]
# remove hyphens from df, but if var is prefixed, drop IF the non prefix var is df
"""
for var in df.columns:
if f"{self.code}_{self.code}" in var:
non_pref_var = var.replace(f'{self.code}_', '')
if var.replace(f"{self.code}_", "") in df.columns:
# if df[var] == df[var.replace(f"{self.code}_","")]:
if df.equals(df[[var, non_pref_var]]):
df = df.drop(columns=[var])
print(f"Dropping {var} b/c {non_pref_var} and identical")
else:
test = df[[var, non_pref_var]]
print(f"Keeping {var} b/c {var} != {non_pref_var}")
else:
print(f"Keeping {var} b/c unprefixed var still in df")
"""
return df
def _reshape_text_survey(self,df):
df["SurveyStartDate"] = df["SurveyStartDatetime"].dt.date
df = BuilderUtils.add_phase_label(raw_df = df,raw_df_date="SurveyStartDate", start_buffer=0, end_buffer=-1)
# Replace Values of phase with the start survey code
codes = [study_config.phases[x]["StartSurvey"]["Code"] for x in list(study_config.phases.keys())]
rename_dic = dict(zip(list(study_config.phases.keys()), codes))
df["Phase"] = df["Phase"].apply(lambda x: rename_dic[x] if x in rename_dic else x)
keep_vars =[study_config.surveys[self.survey_name]["CompleteQuestion"],"SurveyStartDatetime","SurveyEndDatetime","Complete"]
df_p = df.pivot_table(index=["AppCode"],
values=keep_vars,
columns=["Phase"],
aggfunc='first')
df_p.columns = [f'_{self.code}'.join(col[::-1]).strip() for col in df_p.columns.values]
df_p = df_p.reset_index()
return df_p |