def __init__(self, info):

self.name = ''

self.survey = ['---place holder---', info[9], info[10], info[11], info[12], info[13], info[14], info[15], info[16], info[17], info[18], info[19]]

self.ID = info[0]

def makeName(self, first, last):

first = first.lower()

last = last.lower()

name = (first + last).replace(" ", "")

ids = ['3856740724', '3860877379', '3862687708']

if self.ID in ids:

name = ''

self.name = name

return self.name

def answers(self, index):

i = 1

while (i<7):

option = 0

for item in index[i]:

if (self.survey[i] == item):

self.numbs[i] = option

option+=1

i+1

def __init__(self, survey, runner):

self.ID = survey.ID

self.name = survey.name

self.survey = survey.survey

self.runner = runner

self.runs = runner.runs

self.count = runner.count

self.races = runner.races

self.num = runner.num

self.total = runner.total

self.avg = runner.avg

self.dur = runner.dur

self.mpd = runner.mpd

self.rpd = runner.rpd

self.median = runner.median

#from the survey:

self.age = 0 #age

self.sex = None

self.starter = 0

def makeAge(self):

input = self.survey[10]

input = input.replace(" ", "")

if (input!= ""):

age = int(input)

self.age = age

def makeGender(self):

input = self.survey[11]

ll = input.lower().replace(" ", "")

if (len(ll)==1):

if 'f' in ll:

sex = 'female'

if 'm' in ll:

sex = 'male'

else:

if 'female' in ll:

sex = 'female'

if 'male' in ll:

sex = 'male'

else:

sex = None

self.sex = sex

def addRuns(self, runs):

self.runs = sorted(runs)

return runs

def getRuns(self):

return self.runs

def getSurvey(self):

return self.survey

def get_start(self):

if (self.survey[5] == 'Yes'):

self.starter = 1

elif (self.survey[5] == 'No'):

self.starter = 0

else:

self.starter = 2

def __repr__(self):

string = self.name

'''

responses contains the list of respondent objects

index contains the list of questions

'''

def __init__(self, index, respondents, names):

self.INDEX = index

self.QA = []

self.responses = respondents

self.names = names

self.solo = []

self.mid = []

self.social = []

self.no = []

self.yes = []

self.plan = []

self.nr = []

self.oa = []

self.one = []

self.small = []

self.mid = []

self.large = []

def makeDictionary(self):

QA = []

for question in self.INDEX:

print "printing question: ", question

cat = {}

for answer in question:

#each question has a dictionary w/ range of options

cat[answer] = []

QA.append(cat)

self.QA = QA

return self.QA

def groupSocial(self):

mylist = self.INDEX

print mylist

solo = []

mid = []

social = []

q1 = mylist[1]

q2 = mylist[2]

for response in self.responses:

a1 = response.survey[1]

a2 = response.survey[2]

if ((a1 == q1[0]) | (a2 == q2[0])):

solo.append(response)

elif ((a1 == q1[1]) | (a2 == q2[1])):

mid.append(response)

else:

social.append(response)

self.solo = solo

self.mid = mid

self.social = social

def groupQ2(self):

mylist = self.INDEX

q1 = mylist[2]

one = []

small = []

mid = []

large = []

for response in self.responses:

a1 = response.survey[2]

if (a1 == q1[0]):

one.append(response)

if (a1 == q1[1]):

small.append(response)

if (a1 == q1[2]):

mid.append(response)

if (a1 == q1[3]):

large.append(response)

self.small = small

self.mid = mid

self.large = large

def groupQ1(self):

mylist = self.INDEX

q1 = mylist[1]

nr = []

oa = []

for response in self.responses:

a1 = response.survey[1]

if (a1 == q1[0]):

nr.append(response)

if (a1 == q1[1]):

nr.append(response)

if (a1 == q1[2]):

oa.append(response)

if (a1 == q1[3]):

oa.append(response)

self.nr = nr

self.oa = oa

def groupStarter(self):

mylist = self.INDEX

no = []

yes = []

plan = []

for response in self.responses:

if (response.starter == 0):

no.append(response)

elif (response.starter == 1):

yes.append(response)

else:

plan.append(response)

self.no = no

self.yes = yes

'''non responders'''

all_avgs = []

all_durs = []

all_totals = []

all_counts = []

mpd_n = []

all_runfreq = []

all_names = []

'''responders'''

res_avgs = []

res_durs = []

total_miles_list= []

run_count_list = []

duration_list = []

mpd_s = []

'''all'''

durations = []

totals = []

mpds = []

nn = 0

print 'non responders \n'

for runner in nR:

#remove far outliers for total and duration

if (runner.dur<185 and runner.total < 653 and runner.mpd<6.13):

all_avgs.append(runner.avg)

all_durs.append(runner.dur)

all_totals.append(runner.total)

all_counts.append(runner.count)

mpd_n.append(runner.mpd)

all_runfreq.append(runner.rpd)

all_names.append(runner.name)

else:

print "Error on: " , runner.name , "duration: ", runner.dur, "total: ", runner.total, "run count: ", runner.count

nn += 1

ns = 0

print 'responders \n'

for response in sR:

#remove outliers for duration and far outliers for distance

if (response.dur<264 and response.total < 838 and response.mpd<4.82):

res_avgs.append(response.avg)

res_durs.append(response.dur)

total_miles_list.append(response.total)

run_count_list.append(response.count)

duration_list.append(response.dur)

mpd_s.append(response.mpd)

ns += 1

if (response.total > 2000):

print 'name: ', response.name, 'total: ', response.total

print 'all \n'

for response in aR:

durations.append(response.dur)

totals.append(response.total)

mpds.append(response.mpd)

if (response.total > 2000):

print 'name: ', response.name, 'total: ', response.total

print "non responders: " , nn

print "responders: " , ns

t_statistic, p_value = stats.ttest_ind(mpd_n, mpd_s,equal_var=0)

print "t-statistic: " , t_statistic

print "p-value: " , p_value

print 'NON-RESPONDENTS: '

print 'count:' , len(mpd_n)

print 'mean: ', np.mean(mpd_n)

print 'std: ', np.std(mpd_n)

#print 'lower: ', l , ' upper: ', u , ' IQR: ', iqr

print 'SURVEY RESPONDENTS: '

print 'count: ', len(mpd_s)

print 'mean: ', np.mean(mpd_s)

print 'std: ', np.std(mpd_s)

fig = g.histogramT(all_durs, res_durs, durations, 'Non-responders', 'Responders', 'All')

#plot_url = py.plot(fig, filename='Distribution of Duration of Use')

#fig = histogramT(all_totals, total_miles_list, totals, 'Non-responders', 'Responders', 'All')

#plot_url = py.plot(fig, filename='Distribution of Total Mileage')

fig = g.histogramT(mpd_n, mpd_s, mpds, 'Non-responders', 'Responders', 'All')

#fig = histogram(durations)

plot_url = py.plot(fig, filename='Overall Mileage per Day (without outliers')

####graph the average run length vs. the duration of RWM use

title = 'average run length vs. duration of rwm use'

#fig = scatterPlot(all_avgs, all_durs, 'average miles', 'duration on RWM', title, all_names)

#plot_url = py.plot(fig, filename=title)

####graph the frequency of runs (#runs/duration) vs. the duration of RWM use

title = 'frequency vs. duration'

#fig = scatterPlot(all_runfreq, all_durs, 'frequency', 'duration on RWM', title, all_names)

real_races = []

num_known_list = []

gender_list = []

run_avg_list = []

run_count_list = []

total_miles_list = []

duration_list = []

num_rwmRace_list = []

total_frequencies = []

run_frequency = []

r_d = []

names = []

q1q3_durs = []

q1q3_races = []

q1q3_names = []

q1q3_run_frequency = []

q1q3_tf = []

q1q3_totals = []

for response in surveyResponders:

#from survey:

r = response.survey[3]

r = int(r.strip('+'))

real_races.append(r)

num_known = response.survey[6]

num_known = int(num_known.strip('+'))

num_known_list.append(num_known)

rd = response.survey[4]

r_d.append(rd)

gender = response.sex

if (gender == None):

gender = 'other/no response'

gender_list.append(gender)

#from the submissions data:

run_avg_list.append(response.avg)

total_miles_list.append(response.total)

run_count_list.append(response.count)

duration_list.append(response.dur)

total_frequencies.append(response.mpd)

run_frequency.append(response.rpd)

num_rwmRace_list.append(response.num)

names.append(response.name)

if (response.dur<264 and response.total < 838):

q1q3_durs.append(response.dur)

q1q3_run_frequency.append(response.rpd)

q1q3_tf.append(response.mpd)

q1q3_races.append(r)

q1q3_names.append(response.name)

q1q3_totals.append(response.total)

####graph the number of real world races vs. the count

title = '# of Real Races vs. # of RWM Runs'

#fig = scatterPlot(real_races, run_count_list, '# of Races (in past year)', '# of RWM Runs', title, names)

#plot_url = py.plot(fig, filename=title)

####graph the number of real world races vs. the duration of use

title = '# of Real Races vs. Time on RunWithMe'

#fig = scatterPlot(real_races, duration_list, '# of Races (in past year)', 'Time on RWM (in days)', title, names)

#plot_url = py.plot(fig, filename=title)

####graph the number of real world races vs. the duration of use (limiting duration to q1-q3)

title = '# of Real Races vs. Time on RunWithMe (based on Q1-Q3)'

#fig = scatterPlot(q1q3_races, q1q3_durs, '# of Races (in past year)', 'Time on RWM (in days)', title, q1q3_names)

#plot_url = py.plot(fig, filename=title)

####graph the number of real world races vs. the duration of use (IQR)

title = '# of Real Races vs. Run Frequency (inner quartile of duraton)'

#fig = scatterPlot(q1q3_races, q1q3_run_frequency, '# of Races (in past year)', 'Run Frequency', title, q1q3_names)

#plot_url = py.plot(fig, filename=title)

####graph the number of real world races vs. the duration of use

title = '# of Real Races vs. Overall Miles/Day (excluding outliers)'

fig = g.scatterPlot(q1q3_races, q1q3_tf, '# of Races (in past year)', 'Overall Miles/Day', title, q1q3_names)

#plot_url = py.plot(fig, filename=title)

title = 'Total Distance vs. Days on RWM (without outliers for time and far outliers for distance)'

fig = g.scatterPlot(q1q3_durs, q1q3_totals, 'Days', 'Total Distance (in miles)', title, q1q3_names)

linear_reg(q1q3_durs, q1q3_totals)

#plot_url = py.plot(fig, filename=title)

title = 'Total Distance vs. Days on RWM (full data)'

fig = g.scatterPlot(duration_list, total_miles_list, 'Days', 'Total Distance (in miles)', title, names)

#plot_url = py.plot(fig, filename=title)

####graph the number of RWM users someone knows, and the number of races they are in

#title = 'number known vs. number of races'

#fig = scatterPlot(num_known_list, num_rwmRace_list, 'number known', 'number of races', title, names)

#plot_url = py.plot(fig, filename=title)

####graph the average run length vs. the duration of RWM use

#title = 'average run length vs. duration of rwm use'

#fig = fig = scatterPlot(run_avg_list, duration_list, 'average miles', 'duration on RWM', title, names)

#plot_url = py.plot(fig, filename=title)

####graph the frequency of runs (#runs/duration) vs. the duration of RWM use

#title = 'frequency'

#fig = fig = scatterPlot(run_frequency, duration_list, 'frequency', 'duration on RWM', title, names)

slope, intercept, r_value, p_value, std_err = stats.linregress(x,y)

print 'slope: ', slope

print 'intercept: ', intercept

print 'r value: ', r_value

print 'p_value: ', p_value

print 'standard deviation: ', std_err

line = slope*x+intercept

starters = SD.yes

nonstart = SD.no

maybe = SD.plan

plan_mpd = []

no_mpd = []

yes_mpd = []

plan_durs = []

no_durs = []

yes_durs = []

plan_median = []

no_median = []

yes_median = []

plan_rpd = []

no_rpd = []

yes_rpd = []

plan_counts = []

no_counts = []

yes_counts = []

plan_total = []

no_total = []

yes_total = []

no_real = []

plan_real = []

yes_real = []

no_known = []

plan_known = []

yes_known = []

for response in nonstart:

r = response.survey[3]

r = int(r.strip('+'))

no_real.append(r)

num_known = response.survey[6]

num_known = int(num_known.strip('+'))

no_known.append(num_known)

no_mpd.append(response.mpd)

no_durs.append(response.dur)

no_median.append(response.median)

no_rpd.append(response.rpd)

no_counts.append(response.count)

no_total.append(response.total)

for response in maybe:

r = response.survey[3]

r = int(r.strip('+'))

plan_real.append(r)

num_known = response.survey[6]

num_known = int(num_known.strip('+'))

plan_known.append(num_known)

plan_mpd.append(response.mpd)

plan_durs.append(response.dur)

plan_median.append(response.median)

plan_rpd.append(response.rpd)

plan_counts.append(response.count)

plan_total.append(response.total)

for response in starters:

r = response.survey[3]

r = int(r.strip('+'))

yes_real.append(r)

num_known = response.survey[6]

num_known = int(num_known.strip('+'))

yes_known.append(num_known)

yes_mpd.append(response.mpd)

yes_durs.append(response.dur)

yes_median.append(response.median)

yes_rpd.append(response.rpd)

yes_counts.append(response.count)

yes_total.append(response.total)

print 'mpd for no : ', np.mean(no_mpd)

print 'mpd for maybe: ' , np.mean(plan_mpd)

print 'mpd for yes: ' , np.mean(yes_mpd)

print 'No vs. Planning to (mpd)'

t_statistic, p_value = stats.ttest_ind(no_mpd, plan_mpd, equal_var=0)

print 't-statistic: ' , t_statistic

print 'p-value: ', p_value

print 'dur for no : ', np.mean(no_durs)

print 'dur for maybe: ' , np.mean(plan_durs)

print 'dur for yes: ' , np.mean(yes_durs)

print 'No vs. Planning to (duration)'

t_statistic, p_value = stats.ttest_ind(no_durs, plan_durs, equal_var=0)

print 't-statistic: ' , t_statistic

print 'p-value: ', p_value

print 'median for no : ', np.mean(no_median)

print 'median for maybe: ' , np.mean(plan_median)

print 'median for yes: ' , np.mean(yes_median)

print 'No vs. Planning to (median)'

t_statistic, p_value = stats.ttest_ind(no_median, plan_median, equal_var=0)

print 't-statistic: ' , t_statistic

print 'p-value: ', p_value

print 'rpd for no : ', np.mean(no_rpd)

print 'rpd for maybe: ' , np.mean(plan_rpd)

print 'rpd for yes: ' , np.mean(yes_rpd)

print 'No vs. Planning to (rpd)'

t_statistic, p_value = stats.ttest_ind(no_rpd, plan_rpd, equal_var=0)

print 't-statistic: ' , t_statistic

print 'p-value: ', p_value

print 'count for no : ', np.mean(no_counts)

print 'count for maybe: ' , np.mean(plan_counts)

print 'count for yes: ' , np.mean(yes_counts)

print 'No vs. Planning to (countation)'

t_statistic, p_value = stats.ttest_ind(no_counts, plan_counts, equal_var=0)

print 't-statistic: ' , t_statistic

print 'p-value: ', p_value

print 'total for no : ', np.mean(no_total)

print 'total for maybe: ' , np.mean(plan_total)

print 'total for yes: ' , np.mean(yes_total)

print 'No vs. Planning to (total)'

t_statistic, p_value = stats.ttest_ind(no_total, plan_total, equal_var=0)

print 't-statistic: ' , t_statistic

print 'p-value: ', p_value

print 'races for no : ', np.mean(no_real)

print 'races for maybe: ' , np.mean(plan_real)

print 'races for yes: ' , np.mean(yes_real)

print 'No vs. Planning to (countation)'

t_statistic, p_value = stats.ttest_ind(no_real, plan_real, equal_var=0)

print 't-statistic: ' , t_statistic

print 'p-value: ', p_value

print 'known for no : ', np.mean(no_known)

print 'known for maybe: ' , np.mean(plan_known)

print 'known for yes: ' , np.mean(yes_known)

print 'No vs. Planning to (known)'

t_statistic, p_value = stats.ttest_ind(no_known, plan_known, equal_var=0)

print 't-statistic: ' , t_statistic

solo = SD.solo

mid = SD.mid

high = SD.social

social = mid + high

soloStarters = []

socialStarters = []

socialmpd = []

solompd = []

socialnames = []

solonames = []

solo_median_miles = []

social_median_miles = []

for response in solo:

soloStarters.append(response.starter)

if (response.dur<264 and response.total < 838):

solompd.append(response.mpd)

solonames.append(response.mpd)

solo_median_miles.append(response.median)

for response in social:

socialStarters.append(response.starter)

if (response.dur<264 and response.total < 838):

socialmpd.append(response.mpd)

socialnames.append(response.mpd)

social_median_miles.append(response.median)

### Median Run Length by Social Running Habits

title = 'Average Run Length by Social Running'

fig = g.histogramTwo(solo_median_miles, social_median_miles, 'Solitary Runners', 'Social Runners')

plot_url = py.plot(fig, filename='Comparing Social Running Habits and Median Run Distance')

print 'Social Running Habits and Median Run Distance'

print 'mean for solitary group: ', np.mean(solo_median_miles)

print 'mean for social group: ', np.mean(social_median_miles)

t_statistic, p_value = stats.ttest_ind(solo_median_miles, social_median_miles, equal_var=0)

print 't-statistic: ' , t_statistic

print 'p-value: ', p_value

### Race Initiators vs. Social Running Habits

#fig = histogramTwo(soloStarters, socialStarters)

#plot_url = py.plot(fig, filename='Comparing Social Running Habits and Race Initiators')

#t_statistic, p_value = stats.ttest_ind(solompd, socialmpd, equal_var=0)

#print 't-statistic: ' , t_statistic

#print 'p-value: ', p_value

#fig = g.histogramTwo(solompd, socialmpd, 'Solitary Runners', 'Social Runners')

never_rarely = SD.nr

often_always = SD.oa

nr_med = []

oa_med = []

nr_rpd = []

oa_rpd = []

nr_races = []

oa_races = []

nr_r4 = []

oa_r4 = []

for response in never_rarely:

nr_med.append(response.median)

nr_rpd.append((response.rpd*7))

r = response.survey[3]

r = int(r.strip('+'))

nr_races.append(r)

a = response.survey[4]

nr_r4.append(a)

for response in often_always:

oa_med.append(response.median)

oa_rpd.append((response.rpd*7))

r = response.survey[3]

r = int(r.strip('+'))

oa_races.append(r)

a = response.survey[4]

oa_r4.append(a)

### Median Run Length by Social Running Habits

title = 'Median Run Length by Q1'

fig = g.histogramTwo(nr_med, oa_med, 'Never/Rarely', 'Often/Always')

plot_url = py.plot(fig, filename=title)

print 'Median Run Length by Q1'

print 'mean for never/rarely group: ', np.mean(nr_med)

print 'mean for often/always group: ', np.mean(oa_med)

t_statistic, p_value = stats.ttest_ind(nr_med, oa_med, equal_var=0)

print 't-statistic: ' , t_statistic

print 'p-value: ', p_value

title = 'rpd Run Length by Q1'

fig = g.histogramTwo(nr_rpd, oa_rpd, 'Never/Rarely', 'Often/Always')

plot_url = py.plot(fig, filename=title)

print 'rpd by Q1'

print 'mean for never/rarely group: ', np.mean(nr_rpd)

print 'mean for often/always group: ', np.mean(oa_rpd)

t_statistic, p_value = stats.ttest_ind(nr_rpd, oa_rpd, equal_var=0)

print 't-statistic: ' , t_statistic

print 'p-value: ', p_value

title = 'Num. Races by Q1'

fig = g.histogramTwo(nr_races, oa_races, 'Never/Rarely', 'Often/Always')

plot_url = py.plot(fig, filename=title)

print 'Num. Races by Q1'

print 'mean for never/rarely group: ', np.mean(nr_races)

print 'mean for often/always group: ', np.mean(oa_races)

t_statistic, p_value = stats.ttest_ind(nr_races, oa_races, equal_var=0)

print 't-statistic: ' , t_statistic

print 'p-value: ', p_value

title = 'Num. r4 by Q1'

fig = g.histogramTwo(nr_r4, oa_r4, 'Never/Rarely', 'Often/Always')

plot_url = py.plot(fig, filename=title)

print 'Num. r4 by Q1'

print 'mean for never/rarely group: ', np.mean(nr_r4)

print 'mean for often/always group: ', np.mean(oa_r4)

t_statistic, p_value = stats.ttest_ind(nr_r4, oa_r4, equal_var=0)

print 't-statistic: ' , t_statistic

print 'p-value: ', p_value

### Race Initiators vs. Social Running Habits

#fig = histogramTwo(soloStarters, socialStarters)

#plot_url = py.plot(fig, filename='Comparing Social Running Habits and Race Initiators')

#t_statistic, p_value = stats.ttest_ind(solompd, socialmpd, equal_var=0)

#print 't-statistic: ' , t_statistic

#print 'p-value: ', p_value

#fig = g.histogramTwo(solompd, socialmpd, 'Solitary Runners', 'Social Runners')

small = SD.small

mid = SD.mid

large = SD.large

small_med = []

mid_med = []

large_mid = []

for response in small:

small_med.append(response.median)

for response in mid:

mid_med.append(response.median)

for response in large:

large_mid.append(response.median)

print 'mean for small groups: ', np.mean(small_med)

print 'mean for medium groups: ', np.mean(mid_med)

outfile = open(fileName, 'wb')

fieldnames = ['name', 'duration', 'count', 'total miles','avg. miles', 'miles_days', 'runs_days']

writer = csv.DictWriter(outfile, fieldnames=fieldnames)

writer.writeheader()

for r in runners:

writer.writerow({'name': r.name , 'duration': r.dur, 'count': r.count, 'total miles': r.total, 'avg. miles': r.avg, 'miles_days': r.mpd, 'runs_days': r.rpd})

non_responders = []

x = len(run_data.runners)

y = len(SD.names)

for runner in run_data.runners:

if (runner.name not in SD.names):

non_responders.append(runner)

z = len(non_responders)

print 'total: ' , x

print 'responses: ', y

print 'non-responders: ', z

file = open(survey, 'rU')

#replace NULL values

infile = csv.reader(x.replace('\0', '') for x in file)

i = 0

respondents = [] #list of respondent objects

names = []

for row in infile:

if (i>1):

myR = Survey(row)

myR.makeName(row[6], row[7])

# if survey name doesn't match rwm name

if myR.name != '':

runner = d.Data.get_runner(run_data, myR.name)

respondent = Respondent(myR, runner)

respondent.makeAge()

respondent.get_start()

respondent.makeGender()

respondents.append(respondent)

names.append(myR.name)

i+=1

file.close()

SDATA = SurveyData(INDEX, respondents, names)

Q0 = []

Q1 = ['Never', 'Rarely', 'Often', 'Always']

Q2 = ['1 (I mostly run by myself)', '2-3 (I run with a buddy or two)', '3-6 (I\'m part of a group- it depends who shows up)', '>6 (The more the merrier)', 'It really varies.']

Q3 = [0,1,2,3,4,5,6,7,8,9]

Q4 = ['<20 miles', '21-50 miles', '51-100 miles', '101-200 miles', '201-300 miles', '301-500 miles', '501-700 miles', '701-1000 miles', '>1000 miles']

Q5 = ['Yes', 'No', 'Not yet, but I am planning to.']

Q6 = [0,1,2,3,4,5,6,7,8,9]

Q7 =['Dedicated URL for my profile', 'Message board for each race','Display my bio photo as my avatar on the map', 'Automatically push my daily mileage to Facebook']

Q8 = 'free format'

Q9 = 'free format'

Q10 = 'make integer'

Q11 = ['male', 'female']

Q = [Q0, Q1, Q2, Q3, Q4, Q5, Q6, Q7, Q8, Q9, Q10, Q11]

try:

credentials = tls.get_credentials_file()

except:

## except credentials error and print for them to enter something

credentials = {}

credentials['username'] = raw_input("Plotly Username: ") ## get username

credentials['api_key'] = raw_input("api key: ") ### get password

py.sign_in(credentials['username'], credentials['api_key'])

survey_file = "survey.csv"

run_data = d.main()

for runner in run_data.runners:

runner.make_data()

print runner.median

#print runner.num , runner.total , runner.count, runner.avg, runner.dur, runner.mpd, runner.rpd

INDEX = completeSurvey()

#SD is a SurveyData object, has all of the respondents

SD = read_survey(survey_file, run_data, INDEX)

mydict = SD.makeDictionary()

SD.groupSocial()

SD.groupStarter()

SD.groupQ1()

SD.groupQ2()

#list of runners that did not respond

nonResponders = sort(run_data, SD)

#list of runners that did respond

surveyResponders = SD.responses

plotQ1(SD)

#plotQ2(SD)

#starters(SD)

plotSocial(SD)

#df = pd.DataFrame(surveyResponders)

#df.to_csv('stest1.csv')

# graph = int(input('''Please select an option:

# 1. compare all vs. those that responded

# 2. graphs for respondents

# 3. compare between groups (of respondents)'''))

# if (graph == 1):

# compare_resp_all(surveyResponders, nonResponders, run_data.runners)

# if (graph == 2):

# graph_responses(surveyResponders)

# if (graph == 3):

# plotSocial(SD)

# else: