D45Hub
3 months ago
commit
a852defa17
5 changed files with 347 additions and 0 deletions
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100analysis_helper.py
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60gen_completion_time_boxplot.py
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66gen_merged_sensor_data.py
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96gen_merged_sensor_data_and_distance_plot.py
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25text_merger.py
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import pandas as pd |
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import statsmodels.api as sm |
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from statsmodels.formula.api import ols |
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import scipy.stats as stats |
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from scipy.stats import f_oneway, friedmanchisquare, mannwhitneyu, ranksums |
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import numpy as np |
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import pingouin as pg |
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|
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# SUS |
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df = pd.read_csv('QuestionnaireDataSUS.csv') |
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df['TotalIMIScore'] = ((df['Q1'] - 1) + (5 - df['Q2']) + (df['Q3'] - 1) + (5 - df['Q4']) + (df['Q5'] - 1) + (5 - df['Q6']) + (df['Q7'] - 1) + (5 - df['Q8']) + (df['Q9'] - 1) + (5 - df['Q10'])) * 2.5 |
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|
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# IMI |
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#df = pd.read_csv('QuestionnaireDataIMI.csv') |
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#df['TotalIMIScore'] = (df['Q1'] + df['Q2'] + (8 - df['Q3']) + (8 - df['Q4']) + df['Q5'] + df['Q6'] + df['Q7']) / 7.0 |
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grouped = df.groupby('WebpageID').agg( |
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mean_IMIScore=('TotalIMIScore', 'mean'), |
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std_IMIScore=('TotalIMIScore', 'std'), |
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count=('TotalIMIScore', 'count') |
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) |
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grouped['variance_IMIScore'] = grouped['std_IMIScore'] ** 2 |
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|
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anova_data = [group['TotalIMIScore'].values for name, group in df.groupby('WebpageID')] |
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anova_result = f_oneway(*anova_data) |
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print(f"ANOVA Result: F-statistic = {anova_result.statistic}, p-value = {anova_result.pvalue}") |
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friedman_data = df.pivot(index='ParticipantID', columns='WebpageID', values='TotalIMIScore').dropna() |
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spher, W, chisq, dof, pval = pg.sphericity(data=df, within='WebpageID', dv='TotalIMIScore', subject='ParticipantID') |
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gg = pg.epsilon(data=df, within='WebpageID', dv='TotalIMIScore', subject='ParticipantID', correction='gg') |
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print(gg) |
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|
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friedman_result = friedmanchisquare(*[friedman_data[col] for col in friedman_data]) |
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print(f"Friedman Test Result: Chi-square statistic = {friedman_result.statistic}, p-value = {friedman_result.pvalue}") |
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model = ols('TotalIMIScore ~ C(WebpageID)', data=df).fit() |
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anova_table = sm.stats.anova_lm(model, typ=2) |
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print(anova_table) |
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print(stats.shapiro(anova_data[0])) |
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print(stats.shapiro(anova_data[1])) |
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print(stats.shapiro(anova_data[2])) |
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print(stats.shapiro(anova_data[3])) |
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print(stats.shapiro(anova_data[4])) |
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print(stats.shapiro(anova_data[5])) |
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print(stats.levene(*anova_data)) |
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print(spher, round(W, 5), round(chisq, 3), dof, round(pval, 3)) |
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group_1 = df[df['WebpageID'] == 1]['TotalIMIScore'] |
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group_2 = df[df['WebpageID'] == 2]['TotalIMIScore'] |
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group_3 = df[df['WebpageID'] == 3]['TotalIMIScore'] |
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group_4 = df[df['WebpageID'] == 4]['TotalIMIScore'] |
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group_5 = df[df['WebpageID'] == 5]['TotalIMIScore'] |
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group_6 = df[df['WebpageID'] == 6]['TotalIMIScore'] |
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def mann_whitney_test(group_a, group_b): |
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u_statistic, p_value = mannwhitneyu(group_a, group_b) |
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n1 = len(group_a) |
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n2 = len(group_b) |
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mu_u = n1 * n2 / 2 |
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sigma_u = np.sqrt(n1 * n2 * (n1 + n2 + 1) / 12) |
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z_value = (u_statistic - mu_u) / sigma_u |
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effect_size_r = z_value / np.sqrt(n1 + n2) |
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return u_statistic, p_value, z_value, effect_size_r |
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def wilcoxon_rank_sum_test(group_a, group_b): |
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rank_sum_statistic, p_value = ranksums(group_a, group_b) |
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effect_size_r = rank_sum_statistic / np.sqrt(len(group_a) + len(group_b)) |
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return rank_sum_statistic, p_value, effect_size_r |
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comparisons = [ |
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("2 vs 5", group_2, group_5), |
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("2 vs 3", group_2, group_3), |
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("3 vs 5", group_3, group_5), |
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("1 vs 6", group_1, group_6), |
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("4 vs 6", group_4, group_6), |
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("1 vs 4", group_1, group_4), |
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] |
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results = [] |
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res = pg.rm_anova(data=df, within='WebpageID', dv='TotalIMIScore', subject='ParticipantID', detailed=True) |
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print(res) |
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for label, group_a, group_b in comparisons: |
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mw_u_statistic, mw_p_value, mw_z_value, mw_effect_size_r = mann_whitney_test(group_a, group_b) |
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ws_rank_sum_statistic, ws_p_value, ws_effect_size_r = wilcoxon_rank_sum_test(group_a, group_b) |
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results.append({ |
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'Comparison': label, |
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'Mann-Whitney U Statistic': mw_u_statistic, |
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'Mann-Whitney p-value': mw_p_value, |
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'Mann-Whitney Z-value': mw_z_value, |
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'Mann-Whitney Effect Size (r)': mw_effect_size_r, |
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'Wilcoxon Rank-Sum Statistic': ws_rank_sum_statistic, |
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'Wilcoxon p-value': ws_p_value, |
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'Wilcoxon Effect Size (r)': ws_effect_size_r |
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}) |
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results_df = pd.DataFrame(results) |
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print(results_df) |
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@ -0,0 +1,60 @@ |
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import os |
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import json |
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import pandas as pd |
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import matplotlib.pyplot as plt |
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data_dir = './' |
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all_data = [] |
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all_tap_logs = [] |
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for filename in os.listdir(data_dir): |
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if filename.endswith('.json'): |
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file_path = os.path.join(data_dir, filename) |
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with open(file_path, encoding='utf-8') as file: |
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data = json.load(file) |
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tap_logs = data.get('sensorLog', {}).get('tapLog', []) |
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for entry in tap_logs: |
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entry['participant_id'] = filename |
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all_tap_logs.append(entry) |
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df = pd.DataFrame(all_tap_logs) |
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df['timestamp'] = pd.to_datetime(df['timestamp']) |
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df['path'] = df['url'].apply(lambda x: x.split('://')[-1].split('/', 1)[-1]) |
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# Mapping with tour_operators being mergeable to study page 2. :) |
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path_to_label = { |
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"study-page-1": "Study Page 1", |
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"study-page-2": "Study Page 2", |
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"study-page-3": "Study Page 3", |
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"study-page-4": "Study Page 4", |
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"study-page-5": "Study Page 5", |
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"study-page-6": "Study Page 6", |
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"tour_operators": "Study Page 2" |
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} |
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df['label'] = df['path'].map(path_to_label) |
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completion_times = df.groupby(['participant_id', 'label'])['timestamp'].agg(['min', 'max']).reset_index() |
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completion_times['completion_time'] = (completion_times['max'] - completion_times['min']).dt.total_seconds() |
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# Filter out technical outliers |
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comp_query = 'completion_time < 500' |
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average_completion_times = completion_times.query(comp_query).groupby('label')['completion_time'].mean().reset_index() |
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c_times_by_page = completion_times.query(comp_query).groupby('label', group_keys=True)[['completion_time']].apply(lambda x: x) |
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c_times_list = [c_times_by_page.groupby('label').get_group('Study Page 1')['completion_time'], c_times_by_page.groupby('label').get_group('Study Page 2')['completion_time'], c_times_by_page.groupby('label').get_group('Study Page 3')['completion_time'], c_times_by_page.groupby('label').get_group('Study Page 4')['completion_time'], c_times_by_page.groupby('label').get_group('Study Page 5')['completion_time'], c_times_by_page.groupby('label').get_group('Study Page 6')['completion_time']] |
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# Draw plots |
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plt.figure(figsize=(10, 6)) |
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#plt.bar(average_completion_times['label'], average_completion_times['completion_time'], color='skyblue') |
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plt.boxplot(c_times_list) |
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plt.xlabel('Page') |
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plt.xticks([1,2,3,4,5,6], ["1 - BudgetBird", "2 - Hotel", "3 - UVV", "4 - Iceland", "5 - Rental", "6 - QuickDeliver"]) |
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plt.ylabel('Average Task Completion Time (s)') |
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plt.title('Average Task Completion Time by Page') |
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plt.xticks(rotation=45, ha='right') |
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plt.tight_layout() |
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plt.show() |
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import os |
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import json |
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import pandas as pd |
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import matplotlib.pyplot as plt |
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import seaborn as sns |
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data_dir = './' |
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all_data = [] |
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all_tap_logs = [] |
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for filename in os.listdir(data_dir): |
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if filename.endswith('.json'): |
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file_path = os.path.join(data_dir, filename) |
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with open(file_path, encoding='utf-8') as file: |
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data = json.load(file) |
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tap_logs = data.get('sensorLog', {}).get('tapLog', []) |
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for entry in tap_logs: |
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entry['participant_id'] = filename |
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all_tap_logs.append(entry) |
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df = pd.DataFrame(all_tap_logs) |
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df['timestamp'] = pd.to_datetime(df['timestamp']) |
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df['path'] = df['url'].apply(lambda x: x.split('://')[-1].split('/', 1)[-1]) |
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# Mapping with tour_operators being mergeable to study page 2. :) |
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path_to_label = { |
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"study-page-1": "Study Page 1", |
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"study-page-2": "Study Page 2", |
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"study-page-3": "Study Page 3", |
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"study-page-4": "Study Page 4", |
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"study-page-5": "Study Page 5", |
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"study-page-6": "Study Page 6", |
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#"tour_operators": "Study Page 2" |
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} |
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df['label'] = df['path'].map(path_to_label) |
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grouped = df.groupby('label') |
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# JSON list structure |
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def generate_heatmap_data(group): |
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heatmap_data = group[['x', 'y']].copy() |
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heatmap_data['radius'] = 40 |
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heatmap_data['value'] = 5 |
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heatmap_data['x'] = heatmap_data['x'].astype(str) |
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heatmap_data['y'] = heatmap_data['y'].astype(str) |
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heatmap_data_list = heatmap_data.to_dict(orient='records') |
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min_value = 1 |
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max_value = 9999 |
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return { |
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"min": min_value, |
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"max": max_value, |
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"data": heatmap_data_list |
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} |
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for label, group in grouped: |
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heatmap_data = generate_heatmap_data(group.apply(lambda x: round(x))) |
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json_filename = f"{label.replace(' ', '_').lower()}.json" |
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with open(json_filename, 'w', encoding='utf-8') as json_file: |
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json.dump(heatmap_data, json_file, indent=4) |
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print(f"Generated {json_filename} with {len(heatmap_data)} records.") |
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@ -0,0 +1,96 @@ |
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import os |
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import json |
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import pandas as pd |
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import numpy as np |
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import matplotlib.pyplot as plt |
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import seaborn as sns |
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data_dir = './' |
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all_data = [] |
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all_tap_logs = [] |
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for filename in os.listdir(data_dir): |
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if filename.endswith('.json'): |
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file_path = os.path.join(data_dir, filename) |
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with open(file_path, encoding='utf-8') as file: |
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data = json.load(file) |
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tap_logs = data.get('sensorLog', {}).get('tapLog', []) |
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for entry in tap_logs: |
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entry['participant_id'] = filename |
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all_tap_logs.append(entry) |
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df = pd.DataFrame(all_tap_logs) |
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df['timestamp'] = pd.to_datetime(df['timestamp']) |
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df['path'] = df['url'].apply(lambda x: x.split('://')[-1].split('/', 1)[-1]) |
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# Mapping with tour_operators being mergeable to study page 2. :) |
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path_to_label = { |
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"study-page-1": "Study Page 1", |
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"study-page-2": "Study Page 2", |
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"study-page-3": "Study Page 3", |
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"study-page-4": "Study Page 4", |
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"study-page-5": "Study Page 5", |
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"study-page-6": "Study Page 6", |
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"tour_operators": "Study Page 2" |
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} |
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df['label'] = df['path'].map(path_to_label) |
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def calculate_distances(group): |
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group = group.sort_values(by='timestamp') |
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x_diff = group['x'].diff().fillna(0) |
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y_diff = group['y'].diff().fillna(0) |
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distances = np.sqrt(x_diff**2 + y_diff**2) |
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total_distance = distances.sum() |
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return total_distance |
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grouped = df.groupby(['participant_id', 'label']) |
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distance_data = grouped.apply(calculate_distances).reset_index() |
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distance_data.columns = ['participant_id', 'label', 'total_distance'] |
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def generate_heatmap_data(group): |
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heatmap_data = group[['x', 'y']].copy() |
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heatmap_data['radius'] = 40 |
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heatmap_data['value'] = 5 |
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heatmap_data['x'] = heatmap_data['x'].astype(str) |
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heatmap_data['y'] = heatmap_data['y'].astype(str) |
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heatmap_data_list = heatmap_data.to_dict(orient='records') |
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min_value = 1 |
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max_value = 999 |
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return { |
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"min": min_value, |
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"max": max_value, |
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"data": heatmap_data_list |
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} |
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for label, group in df.groupby('label'): |
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heatmap_data = generate_heatmap_data(group) |
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json_filename = f"{label.replace(' ', '_').lower()}.json" |
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with open(json_filename, 'w', encoding='utf-8') as json_file: |
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json.dump(heatmap_data, json_file, indent=4) |
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print(f"Generated {json_filename} with {len(heatmap_data['data'])} records.") |
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distance_data.to_csv('distance_data.csv', index=False) |
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print("Distance data saved to distance_data.csv") |
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# Filter out technical outliers... |
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comp_query = 'total_distance < 15000' |
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# Boxplot drawing |
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plt.figure(figsize=(12, 6)) |
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sns.boxplot(x='label', y='total_distance', data=distance_data.query(comp_query).apply(lambda x: x)) |
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plt.xticks(rotation=45) |
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plt.xlabel('Study Page') |
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plt.xticks([0,1,2,3,4,5], ["1 - BudgetBird", "2 - Hotel", "3 - UVV", "4 - Iceland", "5 - Rental", "6 - QuickDeliver"]) |
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plt.ylabel('Total Distance Traveled (pixels)') |
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plt.title('Total Distance Traveled per Study Page') |
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plt.tight_layout() |
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plt.savefig('distance_boxplot.png') |
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plt.show() |
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@ -0,0 +1,25 @@ |
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import os |
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main_directory = './' |
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participant_dirs = range(1, 34) |
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output_file = os.path.join(main_directory, 'merged_notes.txt') |
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with open(output_file, 'w') as outfile: |
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for participant in participant_dirs: |
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participant_dir = os.path.join(main_directory, str(participant)) |
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if os.path.exists(participant_dir): |
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for filename in os.listdir(participant_dir): |
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if filename.endswith('.txt'): |
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file_path = os.path.join(participant_dir, filename) |
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|
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outfile.write(f"\n\n--- Participant {participant} ---\n\n") |
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|
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with open(file_path, 'r') as infile: |
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outfile.write(infile.read()) |
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else: |
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print(f"Directory {participant_dir} does not exist.") |
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|
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print("Merging complete. Check the merged_notes.txt file in the main directory.") |
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