diff --git a/hot-dog-survey-data/README.md b/hot-dog-survey-data/README.md deleted file mode 100644 index 13e8e5e5..00000000 --- a/hot-dog-survey-data/README.md +++ /dev/null @@ -1,13 +0,0 @@ -# Hot Dog Survey Data - -We collected data from students by having them filling out a survey. The raw data are uploaded in the file hot_dog_survey_spring25.xlsx, and the data dictionary describing the data is in the file data_dictionary_sp25. - -* Raw Data: - * Hotdog Form and YikYak - * https://myuva-my.sharepoint.com/:x:/g/personal/tbh7cm_virginia_edu/EU5yFl_KW5FGhbHH7qBTUhgBp9r1UPJ_iS4GKGTcA2PJ-A?e=HxAxGm -* Data Dictionary: - * Included within the HotdogSurvey.csv -* Raw Data: -* Established Data: Combined Yik Yak, Survey, and Srat/Frat Data - - diff --git a/hot-dog-survey-data/sp25/DS-4002- PO- Analysis Plan.pdf b/hot-dog-survey-data/sp25/DS-4002- PO- Analysis Plan.pdf new file mode 100644 index 00000000..1ebe8915 Binary files /dev/null and b/hot-dog-survey-data/sp25/DS-4002- PO- Analysis Plan.pdf differ diff --git a/hot-dog-survey-data/sp25/Hot Dog Presentation.pptx b/hot-dog-survey-data/sp25/Hot Dog Presentation.pptx new file mode 100644 index 00000000..96d7a4b4 Binary files /dev/null and b/hot-dog-survey-data/sp25/Hot Dog Presentation.pptx differ diff --git a/hot-dog-survey-data/sp25/Hotdog Survey (Responses).xlsx b/hot-dog-survey-data/sp25/Hotdog Survey (Responses).xlsx new file mode 100644 index 00000000..4d44fd84 Binary files /dev/null and b/hot-dog-survey-data/sp25/Hotdog Survey (Responses).xlsx differ diff --git a/hot-dog-survey-data/sp25/README.md b/hot-dog-survey-data/sp25/README.md new file mode 100644 index 00000000..99851c70 --- /dev/null +++ b/hot-dog-survey-data/sp25/README.md @@ -0,0 +1,13 @@ +# Hot Dog Survey Data + +We collected data from fulltime undergraduate students by having them filling out a Google FOrm. The raw data are uploaded in the file Hotdog Survey (Responses).xlsx, and the data dictionary describing the data is in the file spring_data_25_dictionary. + +* Raw Data: + * Hotdog Form + * https://docs.google.com/forms/d/e/1FAIpQLSezZpF928-VT6ni4q9eiV3ARgxC1ETJo7THdXbLGDpmauzfoQ/viewform?usp=dialog +* Data Dictionary: + * Included within the HotdogSurvey.csv +* Raw Data: Survey Results + + + diff --git a/hot-dog-survey-data/sp25/eda.ipynb b/hot-dog-survey-data/sp25/eda.ipynb new file mode 100644 index 00000000..23e2fb5d --- /dev/null +++ b/hot-dog-survey-data/sp25/eda.ipynb @@ -0,0 +1,175 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 4, + "id": "87636862", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total responses: 79\n", + "\n", + "First few rows:\n", + " Timestamp Are you a full-time undergraduate UVA student? \\\n", + "0 2026-01-14 14:54:43.655 Yes \n", + "1 2026-01-14 14:55:13.908 Yes \n", + "2 2026-01-14 14:56:48.883 Yes \n", + "3 2026-01-14 14:58:15.394 Yes \n", + "4 2026-01-14 14:58:24.763 Yes \n", + "\n", + " What year are you? Do you believe a hotdog is a sandwich? \n", + "0 3rd No \n", + "1 4th No \n", + "2 4th No \n", + "3 4th Yes \n", + "4 4th Yes \n", + "UVA students only: 76 responses\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "BIAS\n", + "\n", + "1. Year Representation:\n", + " 1st: 0 students (0.0%)\n", + " 2nd: 11 students (14.5%)\n", + " 3rd: 10 students (13.2%)\n", + " 4th: 55 students (72.4%)\n", + "\n", + "2. Opinion by Year:\n", + " 2nd: 9.1% say Yes\n", + " 3rd: 20.0% say Yes\n", + " 4th: 27.3% say Yes\n", + "\n", + "CONCLUSIONS\n", + "\n", + "Out of 76 UVA students:\n", + "18 say YES (23.7%) - hot dog IS a sandwich\n", + "58 say NO (76.3%) - hot dog IS NOT a sandwich\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "# Load the data\n", + "df = pd.read_excel('Hotdog Survey (Responses).xlsx')\n", + "\n", + "print(\"Total responses:\", len(df))\n", + "print(\"\\nFirst few rows:\")\n", + "print(df.head())\n", + "\n", + "# Filter for only UVA students\n", + "df_uva = df[df['Are you a full-time undergraduate UVA student?'] == 'Yes']\n", + "print(f\"UVA students only: {len(df_uva)} responses\")\n", + "\n", + "\n", + "fig, axes = plt.subplots(1, 3, figsize=(15, 4))\n", + "\n", + "#Is a hot dog a sandwich?\n", + "sandwich_counts = df_uva['Do you believe a hotdog is a sandwich?'].value_counts()\n", + "axes[0].bar(sandwich_counts.index, sandwich_counts.values, color=['lightcoral','lightblue'])\n", + "axes[0].set_title('Is a Hot Dog a Sandwich?', fontsize=14, fontweight='bold')\n", + "axes[0].set_ylabel('Number of Students')\n", + "for i, v in enumerate(sandwich_counts.values):\n", + " axes[0].text(i, v + 1, str(v), ha='center', fontweight='bold')\n", + "\n", + "#Number of responses by year\n", + "year_counts = df_uva['What year are you?'].value_counts()\n", + "axes[1].bar(year_counts.index, year_counts.values, color='lightgreen')\n", + "axes[1].set_title('Responses by Year', fontsize=14, fontweight='bold')\n", + "axes[1].set_xlabel('Year at UVA')\n", + "axes[1].set_ylabel('Number of Students')\n", + "for i, (year, count) in enumerate(year_counts.items()):\n", + " axes[1].text(i, count + 1, str(count), ha='center', fontweight='bold')\n", + "\n", + "#Opinion by year \n", + "year_opinion = df_uva.groupby('What year are you?')['Do you believe a hotdog is a sandwich?'].apply(lambda x: (x == 'Yes').sum())\n", + "year_total = df_uva['What year are you?'].value_counts()\n", + "year_pct = (year_opinion / year_total * 100).sort_index()\n", + "axes[2].bar(year_pct.index, year_pct.values, color='lightyellow', edgecolor='orange', linewidth=2)\n", + "axes[2].set_title('% Who Say \"Yes\" by Year', fontsize=14, fontweight='bold')\n", + "axes[2].set_xlabel('Year at UVA')\n", + "axes[2].set_ylabel('Percentage (%)')\n", + "axes[2].set_ylim(0, 100)\n", + "for i, (year, pct) in enumerate(year_pct.items()):\n", + " axes[2].text(i, pct + 2, f'{pct:.0f}%', ha='center', fontweight='bold')\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "\n", + "\n", + "\n", + "print(\"\\nBIAS\")\n", + "\n", + "# Are all years equally represented?\n", + "print(\"\\n1. Year Representation:\")\n", + "for year in ['1st', '2nd', '3rd', '4th']:\n", + " count = (df_uva['What year are you?'] == year).sum()\n", + " pct = count / len(df_uva) * 100\n", + " print(f\" {year}: {count} students ({pct:.1f}%)\")\n", + "\n", + "#Do opinions differ by year?\n", + "print(\"\\n2. Opinion by Year:\")\n", + "for year in ['1st', '2nd', '3rd', '4th']:\n", + " year_data = df_uva[df_uva['What year are you?'] == year]\n", + " if len(year_data) > 0:\n", + " yes_pct = (year_data['Do you believe a hotdog is a sandwich?'] == 'Yes').sum() / len(year_data) * 100\n", + " print(f\" {year}: {yes_pct:.1f}% say Yes\")\n", + "\n", + "\n", + "\n", + "print(\"\\nCONCLUSIONS\")\n", + "\n", + "yes_count = (df_uva['Do you believe a hotdog is a sandwich?'] == 'Yes').sum()\n", + "no_count = (df_uva['Do you believe a hotdog is a sandwich?'] == 'No').sum()\n", + "yes_pct = yes_count / len(df_uva) * 100\n", + "no_pct = no_count / len(df_uva) * 100\n", + "\n", + "print(f\"\\nOut of {len(df_uva)} UVA students:\")\n", + "print(f\"{yes_count} say YES ({yes_pct:.1f}%) - hot dog IS a sandwich\")\n", + "print(f\"{no_count} say NO ({no_pct:.1f}%) - hot dog IS NOT a sandwich\")\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python (DS-2002)", + "language": "python", + "name": "ds-2002" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/hot-dog-survey-data/sp25/spring_data_25_dictionary.md b/hot-dog-survey-data/sp25/spring_data_25_dictionary.md new file mode 100644 index 00000000..2198b490 --- /dev/null +++ b/hot-dog-survey-data/sp25/spring_data_25_dictionary.md @@ -0,0 +1,10 @@ +### Data File +See file Hotdog Survey (Responses).xlsx + +### Data Dictionary +| Column| Description| Potential Reponses| +|-------|------------|-------------------| +| Timestamp | indicates the month/day/year and hour, minute, and second that the responder completed the survey |month/day/year, hour:minute:second| +| Enrolled at UVA | Answers if the responder is a fulltime undergraduate student at UVA | "Yes" indicates they are, "No" indicates they are not | +| Year at UVA | Answers year at UVA | First, Second, Third, Fourth | +| Sandwich | Answers the question: Is a hot dog a sandwich?| "Yes" indicates that the responder believes that a hot dog is a sandwich, "No" indicates that the responder believes that a hot dog is not a sandwich | \ No newline at end of file