FinalProjectGroup096-Fa21.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# COGS 108 Meta Project - An Analysis of Previous COGS 108 Projects "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Permissions\n",
    "\n",
    "Place an `X` in the appropriate bracket below to specify if you would like your group's project to be made available to the public. (Note that student names will be included (but PIDs will be scraped from any groups who include their PIDs).\n",
    "\n",
    "* [X] YES - make available\n",
    "* [  ] NO - keep private"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Overview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This project looks over 364 previous COGS 108 projects over previous quarters and explores the relationship between topics, word count, table count, graph count, projects marked by graders as special, as well as how all these variables change throughout the quarters. For example, one may expect the popularity of health related topics to increase due to COVID-19 becoming such a hot topic recently. We aimed to find out if such intuitions actually fit the data, and perhaps discover unexpected trends in the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Names\n",
    "\n",
    "- Colin Lintereur\n",
    "- James Chung \n",
    "- Joonsung Park\n",
    "- Madison Hambly\n",
    "- Richard Gross"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='research_question'></a>\n",
    "# Research Question"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How do the most popular topics from previous COGS 108 final projects vary per quarter from 2019 to 2021? Also what other trends can we find in past submissions, like changes in word count, and number of graphs used?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='background'></a>\n",
    "\n",
    "## Background & Prior Work"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While looking through the previous COGS 108 final projects for our project review, we noticed that the main COGS108 repository contains a lot of final project submission from previous quarters [1]. During the process for looking through the previous submissions, we thought that there might have been an increase of projects related to certain topics, like COVID. Because of this, we thought it would be interesting to use those submissions as our dataset and to quantify the popularity of the topics of those previous projects and how popular topics for projects changed over time. In order to be able to use the previous submissions, we looked into how we could parse the notebook files so that we could easily extract whatever we needed from them since the structure of notebook files is strange [2]. Understanding this methodology would be essential to deciding the approach that we will take to acquire and eventually wrangle the topic data that we seek to analyze. Looking at other meta-analyses of research topics (Information Systems topics in this case), [3] understanding how ordinal data can help order data for analysis (specifically Mann-Whitney U Test) is valuable for finding significant trends.\n",
    "\n",
    "References (include links):\n",
    "- 1) https://github.com/orgs/COGS108/repositories?page=1\n",
    "- 2) https://towardsdatascience.com/notebook-meta-analysis-jupyter-as-a-zero-infrastructure-alternative-to-experiment-trackers-69e7343d1343?gi=b945c70dda05\n",
    "- 3) https://libres.uncg.edu/ir/uncg/f/P_Palvia_Methodological_2015.pdf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hypothesis\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We predict that projects related to public health and politics will be more popular and we think that popular topics do not vary too much per quarter. Due to most of the submissions between 2019 and 2021 being submitted during COVID and periods of social unrest, we suspect that it would have been very popular to run data analysis on topics related to these events. We also expect that whatever topics were popular in one quarter, were still popular in subsequent quarters since quarters are a relatively short amount of time and it would be unlikely that popular project topics would change drastically during that time."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dataset(s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Dataset Name: Past COGS108 final projects\n",
    "- Links to the datasets: \n",
    "    - [https://github.com/COGS108/FinalProjects-Sp21](https://github.com/COGS108/FinalProjects-Sp21)\n",
    "    - [https://github.com/COGS108/FinalProjects-Wi21](https://github.com/COGS108/FinalProjects-Wi21)\n",
    "    - [https://github.com/COGS108/FinalProjects-Fa20](https://github.com/COGS108/FinalProjects-Fa20)\n",
    "    - [https://github.com/COGS108/FinalProjects-Sp19](https://github.com/COGS108/FinalProjects-Sp19)\n",
    "    - [https://github.com/COGS108/FinalProjects-Sp20](https://github.com/COGS108/FinalProjects-Sp20)\n",
    "    - [https://github.com/COGS108/FinalProjects-Wi20](https://github.com/COGS108/FinalProjects-Wi20)\n",
    "- Number of observations: 364\n",
    "\n",
    "We looked through the COGS108 github repositories and downloaded the zip file for each quarter of submissions we wanted to include in our analysis. From there, we process each jupyter notebook in the zip file and collect relevant data, parsing it to find the research question of the group. We then generate a csv file for each quarter, and once we have all the csv files of each quarter, we combine them into one final dataset we want to study. There are more quarters and submissions that we decided to not include due to factors like strange project formatting, or some quarters not having their own repository."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Observations: 364\n"
     ]
    }
   ],
   "source": [
    "# importing libraries\n",
    "import numpy as np\n",
    "import json\n",
    "import re\n",
    "import requests\n",
    "import pandas as pd\n",
    "import glob\n",
    "import seaborn as sns\n",
    "import zipfile\n",
    "sns.set(font_scale=1, style=\"white\")\n",
    "\n",
    "# import matplotlib\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.style as style\n",
    "from matplotlib.cbook import boxplot_stats\n",
    "\n",
    "plt.rcParams['figure.figsize'] = (20, 10)\n",
    "\n",
    "# Statmodels & patsy\n",
    "import patsy\n",
    "import statsmodels.api as sm\n",
    "\n",
    "# Scipy\n",
    "import scipy\n",
    "\n",
    "# using glob to get all csvs\n",
    "files = glob.glob('./csvs/*.csv')\n",
    "df = pd.DataFrame()\n",
    "entries = 0\n",
    "\n",
    "# read all csvs to dataframes and combine into one df\n",
    "for f_name in files:\n",
    "    csv = pd.read_csv(f_name)\n",
    "    df = df.append(csv)\n",
    "    entries = entries + len(csv.index)\n",
    "\n",
    "assert(len(df.index) == entries)\n",
    "print('Observations: ' + str(len(df.index)))\n",
    "df.head()\n",
    "\n",
    "# copy/paste API key for uClassify\n",
    "API_KEY = 'pUllRXrepgLH'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now search through the zip file of a quarter we want to study. We can get the quarter/group/year information from the file names. We get the research question by looking for the \"Research Question\" (or some similar) header and getting the text after it. This assumption we made about the research question was the most important factor, in terms of project formatting, for our project. Other than that assumption, every project had very similar formatting which we could take advantage of to collect data like word count, graph count, and table counts: \n",
    "- Any word in a markdown cell was counted towards the word count\n",
    "- Any png image in an output cell was counted towards the graph count since this is the image type that graphs are stored in\n",
    "    - This does have the chance to include any non-graph images generated in an output cell but this is rare\n",
    "- Any output cell marked as containing 'text/html' was counted towards the table count since python stores table outputs as html tables\n",
    "\n",
    "Finally, we can determine if a project is marked as 'special' if there is a '\\_S' suffix in the file name.\n",
    "\n",
    "We manually ran this code for each project repo/zip. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# initializing lists\n",
    "research_questions = {}\n",
    "qtr = []\n",
    "year = []\n",
    "group = []\n",
    "# special projects are marked with '_S' at the end of its name\n",
    "special = []\n",
    "word_counts = []\n",
    "graph_counts = []\n",
    "table_counts = []\n",
    "index = 0\n",
    "\n",
    "# a magic number to guess if this string is the research question\n",
    "# (minimum character length)\n",
    "MIN_RQ_LEN = 30\n",
    "\n",
    "# reading zip file for a certain quarter\n",
    "with zipfile.ZipFile(\"zips/FinalProjects-Wi21-main.zip\", \"r\") as f:\n",
    "    for i, name in enumerate(f.namelist()):\n",
    "        # skip files that are not jupyter notebooks\n",
    "        if '.ipynb' not in name:\n",
    "            continue\n",
    "        rf = json.loads(f.read(name))\n",
    "        word_count = 0\n",
    "        graph_count = 0\n",
    "        table_count = 0\n",
    "        rq_found = False\n",
    "        rq_in_header = False\n",
    "        \n",
    "        # loop through every cell\n",
    "        for j, cell in enumerate(rf['cells']):\n",
    "            if cell['cell_type'] == 'code':\n",
    "                if ('outputs' in cell) and (cell['outputs'] != []):# and ('data' in cell['outputs'][0]):# and ('image/png' in cell['outputs'][0]['data'])\n",
    "                    for k,_ in enumerate(cell['outputs']):\n",
    "                        if 'data' in cell['outputs'][k]:\n",
    "                            if 'image/png' in cell['outputs'][k]['data']:\n",
    "                                graph_count += 1\n",
    "                            if 'text/html' in cell['outputs'][k]['data']:\n",
    "                                table_count += 1\n",
    "            elif cell['cell_type'] == 'markdown':\n",
    "                # for the cell with the rq header, source_len > 1\n",
    "                # means the question is also in this cell\n",
    "                source_len = len(cell['source'])\n",
    "                for c in cell['source']:\n",
    "                    # if cell contains the research question header, update lists and extract the research question from the next cell\n",
    "                    if (rq_found == False) and (rq_in_header == True or re.search(r\"(^#.*research question)\", c.lower()) != None):\n",
    "                        # some other string in the cell's source list should be the question\n",
    "                        if source_len > 1 and rq_in_header == False:\n",
    "                            rq_in_header = True\n",
    "                            continue\n",
    "\n",
    "                        # If there is more text after the header, question may be in THIS cell\n",
    "                        if rq_in_header == True:\n",
    "                            # ignore '\\n' string or any string that is probably not the rq\n",
    "                            if len(c.lstrip()) < MIN_RQ_LEN:\n",
    "                                continue\n",
    "                            rq = str(c)\n",
    "                            rq_in_header = False\n",
    "                        # get the research question from the cell AFTER the cell that contains the header 'Research Question'\n",
    "                        else:\n",
    "                            rq = ' '.join(rf['cells'][j+1]['source'])\n",
    "                        # remove pesky html tags for API to handle correctly\n",
    "                        rq = re.sub('<[^<]+?>', '', rq)\n",
    "                        \n",
    "                        # extract quarter info from repo name\n",
    "                        date = re.search(r\"-(.*)-\", name).group(1)\n",
    "                        qtr.append(date[:2].upper())\n",
    "                        year.append(int(date[2:]))\n",
    "\n",
    "                        # extract group number\n",
    "                        fname = re.search(r\"/.*\", name).group(0)\n",
    "                        group.append(int(re.search(r\"[$0-9^]{2,3}\", fname)[0]))\n",
    "\n",
    "                        special.append(False if re.search(r\"_S\\.ipynb\", name) == None else True)\n",
    "                        \n",
    "                        research_questions[str(group[index])+\"_\"+qtr[index]+\"_\"+str(year[index])] = rq          \n",
    "                        rq_found = True\n",
    "                        index += 1\n",
    "                    word_count += len(c.replace('#', '').lstrip().split(' '))\n",
    "        if rq_found:\n",
    "            word_counts.append(word_count)\n",
    "            graph_counts.append(graph_count)\n",
    "            table_counts.append(table_count)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After collecting all the data we need from the submissions, we can use a third-party text classifier to find the general topic of the submission. We are currently using an API made by uClassify to do this classification for us. See [here](https://www.uclassify.com/browse/uclassify/iab-taxonomy-v2?input=Text) for details on the API we are using. This classifier is able to generate very specific topics and sub-topics for any text but we only use the most general topic that it returns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EXAMPLE:\n",
      " *What is the relative effect of unemployment, poverty, and homelessness on adult obesity rates (BMI >= 30) across states in the United States?*  ::  business and finance\n"
     ]
    }
   ],
   "source": [
    "# initialize topics list\n",
    "topic = []\n",
    "\n",
    "# loop through research questions and find the most confident topic class from topic classifier\n",
    "for i, (g, q, y) in enumerate(zip(group, qtr, year)):\n",
    "    text = research_questions[str(group[i])+\"_\"+qtr[i]+\"_\"+str(year[i])]\n",
    "\n",
    "    # conform to latin-1 encoding for API\n",
    "    text = text.encode('latin-1', 'replace').decode('latin-1')\n",
    "    # don't allow literal quotes and remove unknown symbols\n",
    "    text = text.replace('\\\"', '').replace('?', '')\n",
    "    \n",
    "    # get the Response object\n",
    "    response = requests.post('https://api.uclassify.com/v1/uclassify/iab-taxonomy-v2/classify',\n",
    "        data = \"{\\\"texts\\\":[\\\"\" + text + \"\\\"]}\",\n",
    "        headers = {'Authorization': 'Token ' + API_KEY})\n",
    "\n",
    "    closest_topic = ''\n",
    "    max_p = 0\n",
    "    # find the highest predicted topic\n",
    "    for t in response.json()[0]['classification']:\n",
    "        if t['p'] > max_p:\n",
    "            closest_topic = t['className']\n",
    "            max_p = t['p']\n",
    "    topic.append(closest_topic.split('_')[0])\n",
    "    if i == 0:\n",
    "        print(\"EXAMPLE:\\n\", research_questions[str(group[i])+\"_\"+qtr[i]+\"_\"+str(year[i])], \" :: \", topic[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have stored the topics of the submissions, we can run a sanity check to make sure that all of our list are the same length and then export the data to a csv file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "65 65 65 65 65 65 65 65\n"
     ]
    }
   ],
   "source": [
    "# double check that all the lists are the same length\n",
    "print(len(group), len(qtr), len(year), len(topic), len(special), len(word_counts), len(graph_counts), len(table_counts))\n",
    "\n",
    "# generate data frame and output it to a csv file\n",
    "d = {'Group':group, 'Quarter':qtr, 'Year':year, 'Topic':topic, 'Special':special, 'Word_Count':word_counts, 'Graph_Count':graph_counts, 'Table_Count':table_counts}\n",
    "df = pd.DataFrame(data = d)\n",
    "output = df.to_csv(path_or_buf=\"./csvs/\"+qtr[0] + \"20\" + str(year[0])+\".csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Data Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we are collecting the data ourselves, there is not much work to be done cleaning the data after collecting all of the csv files for each quarter and merging them together into one large dataset. One operation we will perform, however, is generating a \"Quarter_Index\" so that we can analyze our data in chronological order based on the quarter. We generate a dictionary called `qtr_index_dict` which will generate an index for each quarter from Spring 19 to Spring 21. \n",
    "\n",
    "*NOTE, we do not have any data for Fall 2019 so this quarter is skipped when indexing.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Observations: 364\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Group</th>\n",
       "      <th>Quarter</th>\n",
       "      <th>Year</th>\n",
       "      <th>Topic</th>\n",
       "      <th>Special</th>\n",
       "      <th>Word_Count</th>\n",
       "      <th>Graph_Count</th>\n",
       "      <th>Table_Count</th>\n",
       "      <th>Quarter_Index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>FA</td>\n",
       "      <td>20</td>\n",
       "      <td>music and audio</td>\n",
       "      <td>False</td>\n",
       "      <td>6999</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>FA</td>\n",
       "      <td>20</td>\n",
       "      <td>automotive</td>\n",
       "      <td>False</td>\n",
       "      <td>8686</td>\n",
       "      <td>18</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>FA</td>\n",
       "      <td>20</td>\n",
       "      <td>video gaming</td>\n",
       "      <td>False</td>\n",
       "      <td>1864</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>FA</td>\n",
       "      <td>20</td>\n",
       "      <td>sports</td>\n",
       "      <td>False</td>\n",
       "      <td>5483</td>\n",
       "      <td>32</td>\n",
       "      <td>18</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>FA</td>\n",
       "      <td>20</td>\n",
       "      <td>pets</td>\n",
       "      <td>False</td>\n",
       "      <td>4595</td>\n",
       "      <td>19</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Group Quarter  Year            Topic  Special  Word_Count  Graph_Count  \\\n",
       "0      1      FA    20  music and audio    False        6999           15   \n",
       "1      2      FA    20       automotive    False        8686           18   \n",
       "2      4      FA    20     video gaming    False        1864           13   \n",
       "3      5      FA    20           sports    False        5483           32   \n",
       "4      6      FA    20             pets    False        4595           19   \n",
       "\n",
       "   Table_Count  Quarter_Index  \n",
       "0            2              3  \n",
       "1           16              3  \n",
       "2            5              3  \n",
       "3           18              3  \n",
       "4            7              3  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# using glob to get all csvs\n",
    "files = glob.glob('./csvs/*.csv')\n",
    "df = pd.DataFrame()\n",
    "entries = 0\n",
    "\n",
    "# read all csvs to dataframes and combine into one df\n",
    "for f_name in files:\n",
    "    csv = pd.read_csv(f_name)\n",
    "    df = df.append(csv)\n",
    "    entries = entries + len(csv.index)\n",
    "\n",
    "assert(len(df.index) == entries)\n",
    "print('Observations: ' + str(len(df.index)))\n",
    "\n",
    "#Add chronological order to rows\n",
    "chronological = []\n",
    "#starting from spring 19 as 0\n",
    "#NOTE: We do not have any data for Fall 201\n",
    "9\n",
    "qtr_index_dict = {\"SP19\": 0, \"WI20\": 1, \"SP20\": 2, \"FA20\": 3, \"WI21\": 4, \"SP21\": 5}\n",
    "qtr_index = []\n",
    "\n",
    "for index, row in df.iterrows():\n",
    "#     print(row['Quarter'], row['Year'])\n",
    "    qtr = row['Quarter'] + str(row['Year'])\n",
    "    qtr_index.append(qtr_index_dict[qtr])\n",
    "df['Quarter_Index'] = qtr_index\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Analysis & Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we see how much data we have from each quarter to see if there are any quarters with a lot of projects which may bias our analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Quarter_Index', ylabel='count'>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='Quarter_Index',data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that our first quarter, Spring 2019, has a much larger number of projects in our datasets than the other quarters. This means that any topics in vogue during that quarter will be disproportionately represented in our data, which will need to be taken into account during analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we counted the total amount of each topic we classified. The overarching topic of each project was found through a topic determining API reading the initial research proposal at the start of each project."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Total amount of each topic\n",
    "topic_count = sns.countplot(y='Topic',data=df, order=df['Topic'].value_counts().index)\n",
    "topic_count.set(title='Project Count per Topic') # title barplot\n",
    "# label each bar in barplot\n",
    "for p in topic_count.patches:\n",
    "    height = p.get_height() # height of each horizontal bar is the same\n",
    "    width = p.get_width() # width (average number of passengers)\n",
    "    #adding text to each bar\n",
    "    topic_count.text(x = width+1, # x-coordinate position of data label, padded 1 to right of bar\n",
    "    y = p.get_y()+(height/2), # # y-coordinate position of data label, padded to be in the middle of the bar\n",
    "    s = '{:.0f}'.format(width), # data label, formatted to ignore decimals\n",
    "    va = 'center') # sets vertical alignment (va) to center\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that medical health is very popular as research topics, which is not too surprising given these quarters were all impacted by COVID. However, even if we ignore the popularity of health related topics, we can see that certain topics are much more popular than others, such as finance and sports over pets and pop culture, as opposed to a uniform distribution of topics."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we measured the word count, graph count, and table count per quarter to see if we could notice any trends between quarters. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Quarter_Index', ylabel='Word_Count'>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='Quarter_Index', y='Word_Count', data=df)\n",
    "sns.lineplot(x='Quarter_Index', y='Word_Count', data=df, marker=\"o\" ,color ='black')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This graph compares word count per quarter. Interestingly, even though the first quarter on the left, Spring 2019 (0), had double the projects, it has the smallest word count. The word count mean also increases by quarter until Fall 2020 (3), and evens out at Winter 2021 (4)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Quarter_Index', ylabel='Graph_Count'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='Quarter_Index', y='Graph_Count', data=df)\n",
    "sns.lineplot(x='Quarter_Index', y='Graph_Count', data=df, marker=\"o\" ,color ='black')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This graph compares the amount of images/graphs per quarter. There appears to be no significant trend, with most projects being somewhere between 10 to 20 graphs. However, the single outlier with over 100 graphs in Winter 2021 (4) is impressive."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Quarter_Index', ylabel='Table_Count'>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='Quarter_Index', y='Table_Count', data=df)\n",
    "sns.lineplot(x='Quarter_Index', y='Table_Count', data=df, marker=\"o\" ,color ='black')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This graph compares the amount of data tables that appear in the project by quarter. Interestingly, there appears to be a clear upward trend when looking at the medians of the boxplots. This trend is also noticeable when looking at the line plot estimate over the quarters."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we measured the word count, graph count, and table count per topic to see if picking certain topics influences your final report."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Word_Count', ylabel='Topic'>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#wordcount boxplot per topic, ordered by topic popularity\n",
    "sns.boxplot(y='Topic', x='Word_Count', data=df, order=df['Topic'].value_counts().index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In terms of word count, it seems that the popularity of a topic does not have much of an impact. We can see that more popular topics, which are higher up on the boxplots, have longer whiskers but this is most likely caused by higher variability in topics that are more popular since there are more papers on those topics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Graph_Count', ylabel='Topic'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#graphcount boxplot per topic, ordered by topic popularity\n",
    "sns.boxplot(y='Topic', x='Graph_Count', data=df, order=df['Topic'].value_counts().index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In terms of graph count, there does not seem to be much impact based on the popularity of the topic."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Table_Count', ylabel='Topic'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#tablecount boxplot per topic, ordered by topic popularity\n",
    "sns.boxplot(y='Topic', x='Table_Count', data=df, order=df['Topic'].value_counts().index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With table count, we can see that less popular topics tend to have higher amounts of tables. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we graphed the counts of the top 10 most popular topics throughout each quarter using our quarter index, in order to determine if there were any trends of popular topics throughout the quarters. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Topic', ylabel='count'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='Topic',data=df, hue='Quarter_Index', order=df['Topic'].value_counts().index[:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most topic distributions seem fairly uniform but one notable outlier is 'medical health' in Winter 2021 (4) which had a significant number of papers on that topic. Next, we wanted to fit a linear model to this data to see what the real trend is for each topic."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fbb46687c88>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1339.85x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "topics = []\n",
    "indices = []\n",
    "counts = []\n",
    "\n",
    "for topic in df['Topic'].value_counts().index[:10]:\n",
    "    for i in range(6):\n",
    "        topics.append(topic)\n",
    "        indices.append(i)\n",
    "        counts.append(len(df[(df['Topic'] == topic) & (df['Quarter_Index'] == i)]) / len(df[df['Quarter_Index'] == i]))\n",
    "        \n",
    "tempDf = pd.DataFrame()\n",
    "tempDf['Topic'] = topics\n",
    "tempDf['Quarter_Index'] = indices\n",
    "tempDf['Count'] = counts\n",
    "sns.lmplot(x='Quarter_Index',data=tempDf, hue='Topic', y='Count', height=8, aspect=2, ci=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# time and the proportion of topics pertaining to medical health\n",
    "\n",
    "yr_qtr_index_groups = df.groupby('Quarter_Index')\n",
    "prop_topic_2 = yr_qtr_index_groups['Topic'].value_counts(normalize = True).rename('Proportion')\n",
    "prop_topic_2 = prop_topic_2.to_frame().reset_index()\n",
    "\n",
    "prop_topic_2 = prop_topic_2[prop_topic_2['Topic'] == 'medical health']\n",
    "\n",
    "regression_graph= sns.regplot(x='Quarter_Index', y='Proportion', data = prop_topic_2).set_title(\"Proportion of Medical Health Topics Per Quarter\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interestingly, among the top 10 most popular topics, 'medical health' is the only topic with a very positive trend while every other topic has either a flat or negative trend. The second graph shows the confidence interval for the 'medical health' trend and we can still see that the regression is fairly confident about the positive trend. While 'medical health' becoming more popular is not really a surprising finding, it is more surprising how every other topic does not become nearly as popular as 'medical health' over this time period. \n",
    "\n",
    "*Note: the counts for each quarter were normalized to the total amount of papers submitted in that quarter*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f21d2b599b0>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "w_counts = []\n",
    "g_counts = []\n",
    "t_counts = []\n",
    "indices = []\n",
    "\n",
    "for i in range(6):\n",
    "    indices.append(i)\n",
    "    w_counts.append(0)\n",
    "    g_counts.append(0)\n",
    "    t_counts.append(0)\n",
    "    w_counts[i] += sum(df[df['Quarter_Index'] == i]['Word_Count']) / len(df[df['Quarter_Index'] == i]['Word_Count'])\n",
    "    g_counts[i] += sum(df[df['Quarter_Index'] == i]['Graph_Count']) / len(df[df['Quarter_Index'] == i]['Graph_Count'])\n",
    "    t_counts[i] += sum(df[df['Quarter_Index'] == i]['Table_Count']) / len(df[df['Quarter_Index'] == i]['Table_Count'])\n",
    "        \n",
    "tempDf = pd.DataFrame()\n",
    "tempDf['Quarter_Index'] = indices\n",
    "tempDf['Word_Count'] = w_counts\n",
    "tempDf['Graph_Count'] = g_counts\n",
    "tempDf['Table_Count'] = t_counts\n",
    "\n",
    "sns.lmplot(x='Quarter_Index',data=tempDf, y='Word_Count', height=8, aspect=2)#, ci=None)\n",
    "sns.lmplot(x='Quarter_Index',data=tempDf, y='Graph_Count', height=8, aspect=2)#, ci=None)\n",
    "sns.lmplot(x='Quarter_Index',data=tempDf, y='Table_Count', height=8, aspect=2)#, ci=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that, while the confidence intervals are very large, there seems to be some upward trend among every quarter for word count, graph count, and table count."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we wanted to compare the popularity of different project topics within the projects that the instructional staff flagged as being \"particularly strong and impressive\" (denoted as \"special\"). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Topics Among Projects Denoted as \"Special\"')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "special_df = df.loc[df.Special == True]\n",
    "\n",
    "special_df.head()\n",
    "\n",
    "top_special = special_df['Topic'].value_counts().index.tolist()\n",
    "\n",
    "top_special_df = special_df[special_df.Topic.isin(top_special)]\n",
    "\n",
    "top_special_df = top_special_df.reset_index(drop = True)\n",
    "\n",
    "sns.countplot(y=top_special_df['Topic'], order = top_special_df['Topic'].value_counts().index).set_title('Topics Among Projects Denoted as \"Special\"')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As seen in the bar plot above, the most popular topic category within these special projects was \"Sports\" by a large margin (five projects out of the total seventeen). The \"Food and Drink\" category was the second most popular category among the special projects (two out of the seventeen). All other categories listed only had one special project in each. Despite the massive amount of health related projects, only one was marked as impressive."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we took a look at the proportion of projects that were flagged as \"special\" by the COGS 108 instructional team in each quarter. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JEyc6LHv++eeNqVOnOizr3r27sX79esMwDOPzzz83hg8fbpw+fdo4ffq0MXjwYGPp0qUF9n/lz9/EiRONZs2aGbt37zZycnKMJ554whg3bpxhGIaxbds2o1evXsYff/xh5OXlGQcPHjRSU1Pt2135M3H5dfbKNrt37zYaN25s7Nixw7DZbMbx48ftz2FBr63L+5owYYIxcuRI48yZM8axY8eM4OBge/vium4ePXrUaNCggfHCCy8YO3fuNM6ePeuw/sr34evtq6DrXkG//wEovRjpBKDUadKkidq1ayeLxaKwsDD7f4337t1rH2rv4eGhmjVrql+/foqLi7tqX0uWLFFgYKCCg4OVmZnpcLPsXr166V//+pfc3Ny0Y8cO1apVSz179pSbm5tCQ0N19913a8uWLfb2YWFhqlOnjsqXL6/HH39c69evl81mU1xcnNq1a6dWrVrJ3d1dDz30kC5cuOAwgmPw4MGyWq0qW7bsdY8/JSVFP/zwg5566imVKVNGfn5+Cg8P18cff3zdc3SlL7744rrHdT1mnsNLVqxYoYcffli1a9eWm5ubRo4cqX379ikpKUlffPGFbr/9dg0bNkxlypRRxYoV1bBhw6vW5+rqql9//VUXLlyQl5dXkYbsjx49WmXLllW9evVUr169q57Hy8/Dpa+JEyfa1/Xt21cVK1aUh4eH/T/xZ86cUV5enlatWqVnn31W3t7eslgsaty4sTw8PIpUwx9//KG8vDxVr14937rq1asrIyOjUMfbvHlz1a1bV66urqpXr566d++ub7/91qHNmDFjVL58+au+Vq/13F3y8MMPq0qVKoV6vV/i6+urfv36yWKxqFevXjpx4oROnjwpSWrfvr3uvPNOubi4qFmzZmrVqpXDyJ/radq0qX744QcZhqFdu3YpMDBQjRo10u7du+3LmjVrZj++8ePHy8fHRx4eHho9erQ2bNjgMJXu8nNUmPNxpRYtWthfR5eP2rlcWFiYqlatKjc3Nw0bNkzZ2dk6fPiwJGndunV65JFHdNttt8nHx0dDhgyxb7d7927l5uZqyJAhcnd3V3BwsO699177+pUrVyoiIkINGza0n2t3d3f9+OOP2r17t3JycvTAAw/I3d1dXbp0cdj2SrVq1VKrVq3k4eEhT09PPfjgg/ruu+8kSRaLRdnZ2Tp06JBycnJUo0YN+8iXK7355psKDAzU/Pnz1alTJ4WFhWnPnj0ObUaNGqXy5curbt266t27t2JjY6/7fMXGxuq+++5TaGio3N3dVbVqVfn5+eXbf9WqVRUSEqJy5cqpYsWKevTRR+3HURj33HOPunTpInd3d/tUtN27d8vLy0uBgYFav369JGn79u2qWrWqGjRoUOi+patfIwrzWr3k9OnTBV4/LsnIyLC/3q50+TUmIyNDt99+u33d8uXLFRgYqICAAE2ZMkXSxdfulClTtGPHDg0ePFj33XefFi5cWKRjvuTcuXOqVKmSw7KKFSsqMzNTklS/fn3l5OTYR9tZLBZFRkYWuv+goCD5+/vLzc1N999/v/bt2ydJcnNzU2Zmpn777TcZhqHatWvLy8vrLx3Dhx9+qD59+qhVq1ZydXWVt7e3ateufd3tLv1+8eSTT6pixYqqUaOGHnzwQYfRvMVx3axZs6b+97//KTU1VePGjVOLFi00adIk+zkvyI1eowGUbvnfOQDgJnf5L7Rly5ZVVlaWcnNzlZSUpLS0NAUGBtrX22w2h8dXGjZsmMP9Hy5ntVrt36elpcnX19dhva+vr8NUncvb+/r6KicnRxkZGfm2dXV1ldVqveq215OWlqbbbrtNFStWdNjfTz/9ZH98tXN05R8MhTmu6zHzHF6SnJysF198UbNmzbIvMwxDqampSklJueofp1cqX7685syZoyVLlujZZ59V48aNNXHixEL9Qi85nsdy5crp3LlzV217tfNgs9k0Z84crV+/Xunp6XJ1vfj/noyMDGVnZysrK+uaN4UtTA2VK1eWq6urTpw4ke/YTpw4UeC0lILs3r1bs2fP1q+//qqcnBxlZ2fbpz5ccmnKzNVc67m74447JBXt9X7JledBkv1cbN26VfPmzdORI0eUl5enCxcuqE6dOoXuu1GjRsrMzNSBAwe0a9cuDRgwQBUqVJCPj4992eDBg+3HN2rUKPvzKF38mT516pT98eXnqDDn40pff/11gX/cX27x4sX68MMPlZaWJhcXF509e9b+h39aWprDOb68nrS0NHl7ezvcp+3ytsnJyVqzZo2WL19uX5aTk2Pfz5XbXvkzfbmTJ0/qhRde0K5du5SZmSnDMFS5cmVJFwOpZ555RtHR0Tp48KBat26tSZMmydvbO18/t912m5566ik99dRTSk9P1yuvvKJRo0Zp27ZtBR7DHXfcoQMHDtiP52rPV2GvJefPn9dLL72k7du3648//pAkZWZmymazFeo+Npef/0uhQlpamqSLwfz777+vfv366ZNPPlFYWNh1+7vS1a4R1zr2K89zlSpVdOLEiavuo2rVqsrIyCjwfeTEiROqWrVqgf0MGjRIgwYN0pw5cxyu9ffff7/uv/9+5eTkaNOmTZowYYL8/PzUpk2bIh17+fLl890YPDMzUxUqVJAkjRs3TnXr1tX8+fNlGIZmzZqlCRMm6M033yxU/1e+l146ty1bttTAgQM1Y8YMJSUlKTg4WBMnTnR4Xy6slJQU+3TRosjIyFBOTo7Dz+CV76nFdd1s1KiR/Rzu2bNH48eP14IFC/Tkk08W2P5Gr9EASjdGOgG4ZVitVtWoUUO7du2yf8XHxzvc+6IoLv/DysvLS8nJyQ7rU1JSHH5xT0lJcfj+0n/Or9zWMIx8217rRt1X8vLy0h9//OHwi/aV/RWlr+sd140o6jm8xGq1avr06Q7P5Z49e9S4cWNZrdarflx0QeexTZs2Wrp0qXbs2KG7775bzz333A0eVdGsXbtWmzdv1tKlS/X999/r888/l3TxdVC1alWVKVPmhj/+unz58mrUqJF9xMTl1q1bp+bNm0u6+EfH5feLuvKPzCeffFKdOnXS1q1b9f3336t///4yDMOhzeXnuKDzfa3n7lrbFWZdQbKzszV27FgNGzZMX375pXbt2qW2bdva6y5Mf2XKlNG9996rLVu2OAR3gYGB2rJli3755Rf7TcR9fHy0aNEih+Pbu3fvVX+eC3M+imrXrl16++239cYbb+i7777Trl27VKlSJfsxV69e3eGeQ5d/X716daWmpjo8r5dfu6xWq0aOHOlQ7+7duxUaGlrgtlf+TF/u9ddfl4uLi9auXasffvhBr776qsO2PXr00Pvvv68tW7bIxcVFs2fPvu6xe3p6atiwYUpLS3O4P9Hlx5CcnGwfdXKt58tqtdrv0XMtS5Ys0eHDh/XBBx/ohx9+0LvvvitJ+X42ruby85+Xl6fU1FR7fUFBQfrll1904MABffHFF+rRo0eh+iyMwrxWL7nvvvu0Y8eOqwbrAQEB8vDw0MaNGx2WZ2Zmatu2bfYbgbds2VKfffZZoWt0d3dX165dVadOHYd73RXWv/71L4fRn+fOndPRo0ftN6Xev3+/+vfvr/Lly6tChQoaMGCAQ1h5I4YMGaLVq1crLi5OR44c0dtvvy2p4GtOuXLldP78efvjy6+9hX0dXqlq1apyd3d3+Bks7Pv39a6bReHv76/g4GD783fl8Zu5LwClE6ETgFuGv7+/KlSooIULF+rChQuy2Ww6cOBAvmkYf0W7du105MgRrV27Vrm5uYqLi9PBgwfVvn17e5tPPvlEBw8e1Pnz5/Xmm28qJCREFotFXbt21datW7Vz507l5ORoyZIl8vDwUEBAwFX3d/vtt181iLBarQoICNDrr7+urKws7d+/Xx9++OFf+nS/whyXWYqyr/79+2vhwoX2X2LPnDmjdevWSbo4TP/EiRNatmyZsrOzdfbsWe3evVvSxRvWJiUl2W+kevLkSW3atEnnzp2Th4eHypcv7/Bff2fIzMyUh4eHqlatqvPnz+v111+3r3N1dVWfPn300ksvKTU1VTabTfHx8X/pxqlPPvmk1qxZo3feeUdnz57VH3/8oTlz5uj777/XI488IkmqV6+efv31V+3bt09ZWVn5PsUpMzNTt912m8qUKaM9e/bYpyhdjaenp1xdXR1eq9d67gqjoD6vJTs7W9nZ2fL09JSbm5u2bt2qL7/80r6+WrVqOn36tM6cOXPNfpo2bap33nnH4eeySZMmeuedd1S9enX7iJgBAwbYb54sXfz0rE2bNl213xs9HwXJzMyUxWKRp6encnNz9e9//9shhO7atav+85//6I8//lBqaqrDqKVGjRrJYrFo+fLlys3N1aZNmxw+aCA8PFwrVqywTy08d+6cvvjiC509e1aNGjWSm5ub3nnnHeXk5Gjjxo3X/JCCzMxMlS9fXpUqVVJqaqr9j3JJ+u2337Rz505lZ2fLw8NDZcqUuerP5quvvqoDBw4oNzdXZ8+e1fvvv69atWrZR9dI0vz583X+/Hn9+uuvWr16tbp16ybp2s9Xjx499NVXXykuLk65ubnKyMiwT5+68jjKlCmjypUr6/Tp0/r3v/99zefnSj///LM2btyo3Nxc/fe//5WHh4d9SnCZMmUUEhKiJ598Uvfee+81R45d632hIEV5rYaFhcnHx0djxozRoUOHlJeXp4yMDC1YsEBbt25VpUqVNGrUKM2cOVPbtm1TTk6OEhMTNW7cOPn4+NhHaI0ePVq7du2yX9Mu7fe3336z72v16tX211ReXp62bt2qgwcPyt/fv8DacnNzlZWVpby8PNlsNvvIXUnq3Lmzfv31V23YsEFZWVmaN2+e6tataw+OGzRooJiYGF24cEEXLlzQypUrVbdu3UKfw6vZs2ePfbppuXLl5OHhYX/9VqtWLd8nhtarV0+xsbGy2Wzatm2bw/TMvn37avXq1dq5c6c9lLx0I/RrPecWi0VdunTRnDlzdPbsWSU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      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "yr_qtr = (df.Quarter).str.cat(df.Year.astype(str))\n",
    "df_yr_qtr = df\n",
    "\n",
    "df_yr_qtr = df_yr_qtr.drop(columns = ['Quarter'])\n",
    "\n",
    "\n",
    "df_yr_qtr['Quarter'] = yr_qtr\n",
    "\n",
    "\n",
    "yr_qtr_groups = df_yr_qtr.groupby('Quarter')\n",
    "prop_special = yr_qtr_groups['Special'].value_counts(normalize = True).rename('Proportion')\n",
    "prop_df = prop_special.to_frame().reset_index()\n",
    "\n",
    "prop_special_df = prop_df.loc[prop_df['Special'] == True]\n",
    "\n",
    "\n",
    "prop_plot = sns.barplot(x = 'Special', y = 'Proportion', hue = 'Quarter', data = prop_special_df).set(xticklabels = [], title = ('The Proportion of Projects in Each Quarter that Were Flagged as Special by the COGS 108 Instuctional Staff'), xlabel = ('Special Projects'))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interestingly, none of the projects in the Spring 2019, Winter 2020 or Winter 2021 quarters were given the distinction of being \"special\". Approximately 16% of projects from Spring 2020 were flagged as special, the largest proportion among all of the quarters.\n",
    "\n",
    "Finally, we ran some linear regression models trying to predict word count, graph count, and table count based on if a project was marked as 'special'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>Word_Count</td>    <th>  R-squared:         </th> <td>   0.048</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.045</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   18.17</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 01 Dec 2021</td> <th>  Prob (F-statistic):</th> <td>2.58e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>13:07:49</td>     <th>  Log-Likelihood:    </th> <td> -3180.3</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   364</td>      <th>  AIC:               </th> <td>   6365.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   362</td>      <th>  BIC:               </th> <td>   6372.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "         <td></td>            <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>       <td> 3805.2190</td> <td>   81.148</td> <td>   46.892</td> <td> 0.000</td> <td> 3645.638</td> <td> 3964.800</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Special[T.True]</th> <td> 1600.6633</td> <td>  375.496</td> <td>    4.263</td> <td> 0.000</td> <td>  862.235</td> <td> 2339.092</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>60.523</td> <th>  Durbin-Watson:     </th> <td>   1.787</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td>  90.218</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 1.053</td> <th>  Prob(JB):          </th> <td>2.57e-20</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 4.231</td> <th>  Cond. No.          </th> <td>    4.75</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:             Word_Count   R-squared:                       0.048\n",
       "Model:                            OLS   Adj. R-squared:                  0.045\n",
       "Method:                 Least Squares   F-statistic:                     18.17\n",
       "Date:                Wed, 01 Dec 2021   Prob (F-statistic):           2.58e-05\n",
       "Time:                        13:07:49   Log-Likelihood:                -3180.3\n",
       "No. Observations:                 364   AIC:                             6365.\n",
       "Df Residuals:                     362   BIC:                             6372.\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "===================================================================================\n",
       "                      coef    std err          t      P>|t|      [0.025      0.975]\n",
       "-----------------------------------------------------------------------------------\n",
       "Intercept        3805.2190     81.148     46.892      0.000    3645.638    3964.800\n",
       "Special[T.True]  1600.6633    375.496      4.263      0.000     862.235    2339.092\n",
       "==============================================================================\n",
       "Omnibus:                       60.523   Durbin-Watson:                   1.787\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               90.218\n",
       "Skew:                           1.053   Prob(JB):                     2.57e-20\n",
       "Kurtosis:                       4.231   Cond. No.                         4.75\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outcome, predictors = patsy.dmatrices('Word_Count ~ Special', df)\n",
    "mod = sm.OLS(outcome, predictors)\n",
    "res = mod.fit()\n",
    "res.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>Table_Count</td>   <th>  R-squared:         </th> <td>   0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>  -0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>  0.9060</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 01 Dec 2021</td> <th>  Prob (F-statistic):</th>  <td> 0.342</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>12:37:06</td>     <th>  Log-Likelihood:    </th> <td> -1379.7</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   364</td>      <th>  AIC:               </th> <td>   2763.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   362</td>      <th>  BIC:               </th> <td>   2771.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "         <td></td>            <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>       <td>   10.8127</td> <td>    0.577</td> <td>   18.748</td> <td> 0.000</td> <td>    9.679</td> <td>   11.947</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Special[T.True]</th> <td>    2.5403</td> <td>    2.669</td> <td>    0.952</td> <td> 0.342</td> <td>   -2.708</td> <td>    7.788</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>201.754</td> <th>  Durbin-Watson:     </th> <td>   1.672</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>1273.343</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 2.335</td>  <th>  Prob(JB):          </th> <td>3.14e-277</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td>10.884</td>  <th>  Cond. No.          </th> <td>    4.75</td> \n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:            Table_Count   R-squared:                       0.002\n",
       "Model:                            OLS   Adj. R-squared:                 -0.000\n",
       "Method:                 Least Squares   F-statistic:                    0.9060\n",
       "Date:                Wed, 01 Dec 2021   Prob (F-statistic):              0.342\n",
       "Time:                        12:37:06   Log-Likelihood:                -1379.7\n",
       "No. Observations:                 364   AIC:                             2763.\n",
       "Df Residuals:                     362   BIC:                             2771.\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "===================================================================================\n",
       "                      coef    std err          t      P>|t|      [0.025      0.975]\n",
       "-----------------------------------------------------------------------------------\n",
       "Intercept          10.8127      0.577     18.748      0.000       9.679      11.947\n",
       "Special[T.True]     2.5403      2.669      0.952      0.342      -2.708       7.788\n",
       "==============================================================================\n",
       "Omnibus:                      201.754   Durbin-Watson:                   1.672\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             1273.343\n",
       "Skew:                           2.335   Prob(JB):                    3.14e-277\n",
       "Kurtosis:                      10.884   Cond. No.                         4.75\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outcome, predictors = patsy.dmatrices('Table_Count ~ Special', df)\n",
    "mod = sm.OLS(outcome, predictors)\n",
    "res = mod.fit()\n",
    "res.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>Graph_Count</td>   <th>  R-squared:         </th> <td>   0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>  -0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>  0.4824</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 01 Dec 2021</td> <th>  Prob (F-statistic):</th>  <td> 0.488</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>12:37:13</td>     <th>  Log-Likelihood:    </th> <td> -1418.1</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   364</td>      <th>  AIC:               </th> <td>   2840.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   362</td>      <th>  BIC:               </th> <td>   2848.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "         <td></td>            <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>       <td>   15.1758</td> <td>    0.641</td> <td>   23.683</td> <td> 0.000</td> <td>   13.916</td> <td>   16.436</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Special[T.True]</th> <td>    2.0595</td> <td>    2.965</td> <td>    0.695</td> <td> 0.488</td> <td>   -3.772</td> <td>    7.891</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>260.694</td> <th>  Durbin-Watson:     </th> <td>   1.998</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>3704.822</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 2.871</td>  <th>  Prob(JB):          </th> <td>    0.00</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td>17.536</td>  <th>  Cond. No.          </th> <td>    4.75</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:            Graph_Count   R-squared:                       0.001\n",
       "Model:                            OLS   Adj. R-squared:                 -0.001\n",
       "Method:                 Least Squares   F-statistic:                    0.4824\n",
       "Date:                Wed, 01 Dec 2021   Prob (F-statistic):              0.488\n",
       "Time:                        12:37:13   Log-Likelihood:                -1418.1\n",
       "No. Observations:                 364   AIC:                             2840.\n",
       "Df Residuals:                     362   BIC:                             2848.\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "===================================================================================\n",
       "                      coef    std err          t      P>|t|      [0.025      0.975]\n",
       "-----------------------------------------------------------------------------------\n",
       "Intercept          15.1758      0.641     23.683      0.000      13.916      16.436\n",
       "Special[T.True]     2.0595      2.965      0.695      0.488      -3.772       7.891\n",
       "==============================================================================\n",
       "Omnibus:                      260.694   Durbin-Watson:                   1.998\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             3704.822\n",
       "Skew:                           2.871   Prob(JB):                         0.00\n",
       "Kurtosis:                      17.536   Cond. No.                         4.75\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outcome, predictors = patsy.dmatrices('Graph_Count ~ Special', df)\n",
    "mod = sm.OLS(outcome, predictors)\n",
    "res = mod.fit()\n",
    "res.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While table count and graph count do not seem to be well predicted by 'special' projects, word count has a p-value of 0 when predicting it using 'special'. This is likely due to how much word count can vary based on different projects but it also makes some sense as projects with more words are probably a good indicator of effort in the project."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Investigating Medical Health Trend\n",
    "Now let's try to do some data analysis to investigate the increase in interest in medical health topics. Our main hypothesis suggests that COVID has caused this increase in medical health projects, so let's test that hypothesis by splitting our data into pre-COVID (SP2019) and post-COVID. It turns out that we only have Spring 2019 data for pre-COVID data, so we need to be careful what data analysis method we apply."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Quarters SP2019 :: WI2020-SP2021\n",
    "dict1 = {}\n",
    "dict2 = {}\n",
    "pre_len = 0\n",
    "post_len = 0\n",
    "for i in range(len(df)):\n",
    "    entry = df.iloc[i]\n",
    "    dict1[entry['Topic']] = 0\n",
    "    dict2[entry['Topic']] = 0\n",
    "for i in range(len(df)):\n",
    "    entry = df.iloc[i]\n",
    "    # gather pre-COVID data\n",
    "    if entry['Quarter_Index'] == 0:\n",
    "        dict1[entry['Topic']] += 1\n",
    "        pre_len += 1\n",
    "    # gather post-COVID data\n",
    "    else:\n",
    "        dict2[entry['Topic']] += 1\n",
    "        post_len += 1\n",
    "for key, val in dict1.items():\n",
    "    dict1.update({key: val/pre_len})\n",
    "for key, val in dict2.items():\n",
    "    dict2.update({key: val/pre_len})\n",
    "dict3 = {'Topic': list(dict1.keys()), 'Percent': list(dict1.values())}\n",
    "df1 = pd.DataFrame.from_dict(dict3)\n",
    "dict4 = {'Topic': list(dict2.keys()), 'Percent': list(dict2.values())}\n",
    "df2 = pd.DataFrame.from_dict(dict4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will employ the Mann-Whitney U (MWU) test. In order to do so, the exact one-sided hypothesis we are testing is as follows: There is a positive shift in the distribution of popular topics (by percentage) from before and after Winter 2020? As we have seen from the exploratory data, the only noticeable increase in topic popularity is the medical health category. Testing for significance to answer this more general hypothesis will indirectly tell us that there is a statistically significant increase in popularity of medical health projects.\n",
    "Before carrying out the MWU test though, there are some assumptions we must verify:\n",
    "1) All observations from both groups are independent of each other: This is indeed true, there is no group between and within quarters that would have done more than one project.\n",
    "2) The responses are at least ordinal: Although in this course we have seen the MWU test done on strictly ordinal data, the data only needs to be ***at least*** ordinal. We are considering each topic by the proportion/percentage of total projects that are on the topic, which is quantitative, which is indeed at least ordinal/has some clear ordering.\n",
    "3) The two distributions have the same shape: To visualize this, let's construct a box-plot for pre-COVID and post-COVID data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "before = ['before'] * len(df1)\n",
    "df1['when'] = before\n",
    "after = ['after'] * len(df2)\n",
    "df2['when'] = after\n",
    "df3 = pd.concat([df1,df2])\n",
    "fig = sns.boxplot(x='when', y='Percent', data=df3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The two distributions do seem to have the same shape, so let's employ the MWU test now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MannwhitneyuResult(statistic=530.5, pvalue=0.011598699487086275)\n"
     ]
    }
   ],
   "source": [
    "results = scipy.stats.mannwhitneyu(df2['Percent'], df1['Percent'], alternative='greater')\n",
    "print(results)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With a significance level of $\\alpha=0.05$, we can reject the null hypothesis that the two populations are equal. So, there is a positive shift in the distribution of popular topics (by percentage) from pre-COVID to post-COVID quarters. As medical health is the most popular topic by percentage in post-COVID data, we can infer that there is a statistically significant increase in popularity of medical health. This can explain our exploratory analysis of topic trends over quarters."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ethics & Privacy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The projects we analyzed were publicly available on github and to our knowledge the projects are only public if the students explicitly agree for the projects to be public. If a student of a project dissents to its analysis due to perhaps not foreseeing the possibility that their project would be looked at, then they probably should not have agreed to make it public in the first place. As for the students' privacy, the names of the students were available on every project we analyzed. However, we only scraped the projects of its word count, topics, etc. and never touched or used the names for anything in our research. Our project not once mentions these names or calls attention to certain individuals or projects. So while there is room for privacy concerns regarding usage of names, we do not make the identities any less private than they already are. \n",
    "\n",
    "The biggest concern with bias in our analysis rests on the API we used for deciding topics after parsing the introductory paragraphs. It could classify up to 560 surprisingly specific topics, but there are obviously much more topics in existence. So we lose a lot of nuance with what the topic of the project is during analysis. We lose even more nuance when you consider that if for example a project topic is car racing, it could be read by the API to be about automobiles if the paragraph's contents is 60% cars and 40% sports, completely cutting out how the topic is 40% about sports and therefore treated equally as a topic that is about trucks. We originally accounted for this by switching from an initial API to the much better and more specific API we currently use, but beyond that there is not much more we can realistically do beyond checking the hundreds of projects by hand as API's are not at a human level of nuance yet. The best we can do is acknowledge that any analysis is not set in stone as some topic trends may be inflated by unique but somewhat similar topics. At the same time, analysis of trends in topics would be impossible if we actually managed to divide every single project into its own unique topic, as there would be no connection between projects, so we have to analyze by the large broad topics anyways."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion & Discussion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In our project, we examined trends in past COGS 108 final project submissions over the course of six quarters. Our research question considered how the most popular project topics varied from quarter to quarter from Spring 2019 to Spring 2021, as well as whether or not there would be any other tendencies in the data in regards to word count, graph count and more.\n",
    "\n",
    "As we predicted in our hypothesis, we found that 'medical health' has become an increasingly popular project topic. Notably, we also found that most other topics in the top ten most popular project topic category experienced downwards trends.\n",
    "\n",
    "We observed a slight upwards trend in the median number of data tables used by students in their final projects, perhaps suggesting that they are becoming a more popular way of communicating information. The data suggests that there is not a strong relationship between word count and a topic's popularity nor a strong relationship between graph count and a topic's popularity. Interestingly, some of the most unpopular topic categories utilized an unusually high number of tables in their projects. \n",
    "\n",
    "One noteworthy observation that we made is that even though Spring 2019 had approximately twice as many projects as the other quarters, it also had the smallest word count of all of the quarters. Furthermore, there also appears to be an upwards trend in the data across quarters in regards to the number of words, tables and graphs used in final project submissions. \n",
    "\n",
    "Additionally, we found that within the projects that were labelled as special, a large proportion fell into the 'sports' topic category. We also observed that the proportion of topics that were denoted as 'special' varied greatly from quarter to quarter, with half of the quarters containing no 'special' project distinctions at all.\n",
    "\n",
    "The most notable limitations that we faced were related to data collection. The only projects that we had access to in our analysis were those which had explicit consent provided by the project authors to be included in the COGS 108 project collection. While this is an excellent attribute of our dataset in the ethical sense, it is also an unideal one in that we have no way of knowing how this may have affected trends in the data nor can we know the number of project groups in each quarter that declined to have their projects included in the collection.\n",
    "\n",
    "Another limitation that we faced was that we were not able to include data from the Fall 2019 quarter in our analysis as there were only two projects published to the public collection for that quarter. This resulted in us instead choosing to examine the Spring 2019 quarter, thereby leading to an inconsistency in the temporal distance between quarters."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Team Contributions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Colin Lintereur\n",
    "    - Data retrieval\n",
    "    - Data cleaning\n",
    "    - Generated graphs\n",
    "- James Chung \n",
    "    - Data retrieval\n",
    "    - Data cleaning\n",
    "    - Generated graphs\n",
    "    - Data analysis\n",
    "- Joonsung Park\n",
    "    - Edited Written portion\n",
    "    - Generated graphs\n",
    "- Madison Hambly\n",
    "    - Written portions of project\n",
    "    - Generated graphs\n",
    "- Richard Gross\n",
    "    - Generated graphs\n",
    "    - Wrote and edited many stuff\n",
    "    - Many ideas\n",
    "    - Presentation"
   ]
  }
 ],
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