def compute_scores(data, demographics):
# Get rid of all records that don't have YR2
data.dropna(axis=1, subset=['youth_report_2_complete'])
data = data[data['youth_report_2_complete'] > 0]
data = data[~(data['youthreport2_missing'] > 0)]
# If no records to score, return empty DF
if len(data) == 0:
return pandas.DataFrame()
# Replace all column labels with the original LimeSurvey names
data.columns = Rwrapper.map_labels(data.columns, rc2lime)
# Call the scoring function for all table rows
scores = data.apply(Rwrapper.runscript,
axis=1,
Rscript='casq/CASQ.R',
scores_key='CASQ.ary')
# Replace all score columns with REDCap field names
scores.columns = Rwrapper.map_labels(scores.columns, R2rc)
# Simply copy completion status from the input surveys
scores['casq_complete'] = data['youth_report_2_complete'].map(int)
# Make a proper multi-index for the scores table
scores.index = pandas.MultiIndex.from_tuples(scores.index)
scores.index.names = ['study_id', 'redcap_event_name']
# Return the computed scores - this is what will be imported back into REDCap
outfield_list = ['casq_complete'] + R2rc.values()
return scores[outfield_list]
def compute_scores(data, demographics):
# Get rid of all records that don't have YR2
data.dropna(axis=1, subset=["youth_report_2_complete"])
data = data[data["youth_report_2_complete"] > 0]
data = data[~(data["youthreport2_missing"] > 0)]
# If no records to score, return empty DF
if len(data) == 0:
return pandas.DataFrame()
# Replace all column labels with the original LimeSurvey names
data.columns = Rwrapper.map_labels(data.columns, rc2lime)
# Call the scoring function for all table rows
scores = data.apply(Rwrapper.runscript, axis=1, Rscript="casq/CASQ.R", scores_key="CASQ.ary")
# Replace all score columns with REDCap field names
scores.columns = Rwrapper.map_labels(scores.columns, R2rc)
# Simply copy completion status from the input surveys
scores["casq_complete"] = data["youth_report_2_complete"].map(int)
# Make a proper multi-index for the scores table
scores.index = pandas.MultiIndex.from_tuples(scores.index)
scores.index.names = ["study_id", "redcap_event_name"]
# Return the computed scores - this is what will be imported back into REDCap
outfield_list = ["casq_complete"] + R2rc.values()
return scores[outfield_list]
"casq_set2 [casq7]",
"casq_set2 [casq8]",
"casq_set2 [casq9]",
"casq_set2 [casq10]",
"casq_set2 [casq11]",
"casq_set2 [casq12]",
"casq_set3 [casq13]",
"casq_set3 [casq14]",
"casq_set3 [casq15]",
"casq_set3 [casq16]",
]
# Dictionary to recover LimeSurvey field names from REDCap names
rc2lime = dict()
for field in lime_fields:
rc2lime[Rwrapper.label_to_sri("youthreport2", field)] = field
# REDCap fields names
input_fields = {"mrireport": ["youth_report_2_complete", "youthreport2_missing"] + rc2lime.keys()}
#
# This determines the name of the form in REDCap where the results are posted.
#
output_form = "clinical"
#
# CES-D field names mapping from R to REDCap
#
R2rc = {"Cleveland Adolescent Sleepiness Score": "casq_score"}
#
#
# LimeSurvey field names
lime_fields = [
"casq_set1 [casq1]", "casq_set1 [casq2]", "casq_set1 [casq3]",
"casq_set1 [casq4]", "casq_set1 [casq5]", "casq_set1 [casq6]",
"casq_set2 [casq7]", "casq_set2 [casq8]", "casq_set2 [casq9]",
"casq_set2 [casq10]", "casq_set2 [casq11]", "casq_set2 [casq12]",
"casq_set3 [casq13]", "casq_set3 [casq14]", "casq_set3 [casq15]",
"casq_set3 [casq16]"
]
# Dictionary to recover LimeSurvey field names from REDCap names
rc2lime = dict()
for field in lime_fields:
rc2lime[Rwrapper.label_to_sri('youthreport2', field)] = field
# REDCap fields names
input_fields = {
'mrireport':
['youth_report_2_complete', 'youthreport2_missing'] + rc2lime.keys()
}
#
# This determines the name of the form in REDCap where the results are posted.
#
output_form = 'clinical'
#
# CES-D field names mapping from R to REDCap
#
# LimeSurvey field names
lime_fields = [
"cesd_sec1 [cesd1]", "cesd_sec1 [cesd2]", "cesd_sec1 [cesd3]",
"cesd_sec1 [cesd4]", "cesd_sec1 [cesd5]", "cesd_sec1 [cesd6]",
"cesd_sec2 [cesd7]", "cesd_sec2 [cesd8]", "cesd_sec2 [cesd9]",
"cesd_sec2 [ces10]", "cesd_sec2 [ces11]", "cesd_sec2 [ces12]",
"cesd_sec3 [ces13]", "cesd_sec3 [ces14]", "cesd_sec3 [ces15]",
"cesd_sec3 [ces16]", "cesd_sec3 [ces17]", "cesd_sec3 [ces18]",
"cesd_sec3 [ces19]", "cesd_sec3 [ces20]"
]
# Dictionary to recover LimeSurvey field names from REDCap names
rc2lime = dict()
for field in lime_fields:
rc2lime[Rwrapper.label_to_sri('mrireport', field)] = field
# REDCap fields names
input_fields = {
'mrireport': ['mri_report_complete', 'mrireport_missing'] + rc2lime.keys()
}
#
# This determines the name of the form in REDCap where the results are posted.
#
output_form = 'clinical'
#
# CES-D field names mapping from R to REDCap
#
R2rc = {'CES Symptomatology Score': 'cesd_score'}
import Rwrapper
#
# Variables from surveys needed for CTQ
#
# LimeSurvey field names
lime_fields = [ "ctq_set1 [ctq1]", "ctq_set1 [ctq2]", "ctq_set1 [ctq3]", "ctq_set1 [ctq4]", "ctq_set1 [ctq5]", "ctq_set1 [ctq6]", "ctq_set1 [ctq7]", "ctq_set2 [ctq8]", "ctq_set2 [ctq9]", "ctq_set2 [ct10]", "ctq_set2 [ct11]",
"ctq_set2 [ct12]", "ctq_set2 [ct13]", "ctq_set2 [ct14]", "ctq_set3 [ctq15]", "ctq_set3 [ctq16]", "ctq_set3 [ctq17]", "ctq_set3 [ctq18]", "ctq_set3 [ctq19]", "ctq_set3 [ctq20]", "ctq_set3 [ctq21]",
"ctq_set4 [ctq22]", "ctq_set4 [ctq23]", "ctq_set4 [ctq24]", "ctq_set4 [ctq25]", "ctq_set4 [ctq26]", "ctq_set4 [ctq27]", "ctq_set4 [ctq28]" ]
# Dictionary to recover LimeSurvey field names from REDCap names
rc2lime = dict()
for field in lime_fields:
rc2lime[Rwrapper.label_to_sri( 'youthreport2', field )] = field
# REDCap fields names
input_fields = { 'mrireport' : [ 'youth_report_2_complete', 'youthreport2_missing' ] + rc2lime.keys() }
#
# This determines the name of the form in REDCap where the results are posted.
#
output_form = 'clinical'
#
# CTQ field names mapping from R to REDCap
#
R2rc = { 'Emotional Abuse Scale Total Score' : 'ctq_ea',
'Physical Abuse Scale Total Score' : 'ctq_pa',
'Sexual Abuse Scale Total Score' : 'ctq_sa',
import pandas
import Rwrapper
#
# Variables from surveys needed for CES-D
#
# LimeSurvey field names
lime_fields = [ "cesd_sec1 [cesd1]", "cesd_sec1 [cesd2]", "cesd_sec1 [cesd3]", "cesd_sec1 [cesd4]", "cesd_sec1 [cesd5]", "cesd_sec1 [cesd6]", "cesd_sec2 [cesd7]", "cesd_sec2 [cesd8]", "cesd_sec2 [cesd9]", "cesd_sec2 [ces10]",
"cesd_sec2 [ces11]", "cesd_sec2 [ces12]", "cesd_sec3 [ces13]", "cesd_sec3 [ces14]", "cesd_sec3 [ces15]", "cesd_sec3 [ces16]", "cesd_sec3 [ces17]", "cesd_sec3 [ces18]", "cesd_sec3 [ces19]", "cesd_sec3 [ces20]" ]
# Dictionary to recover LimeSurvey field names from REDCap names
rc2lime = dict()
for field in lime_fields:
rc2lime[Rwrapper.label_to_sri( 'mrireport', field )] = field
# REDCap fields names
input_fields = { 'mrireport' : [ 'mri_report_complete', 'mrireport_missing' ] + rc2lime.keys() }
#
# This determines the name of the form in REDCap where the results are posted.
#
output_form = 'clinical'
#
# CES-D field names mapping from R to REDCap
#
R2rc = { 'CES Symptomatology Score' : 'cesd_score' }
#
