Notebook.ipynb

{
   "cells": [
      {
         "cell_type": "markdown",
         "metadata": {},
         "source": [
            "# Anxiety in Computer-Gamers: differences, similiratires and learnings\n",
            "\n",
            "# Overview \n",
            "\n",
            "In this project we decided to analyze anxiety in Gamers. We picked the dataset from kaggle because it intersected our personal interests. The data and survey can be found [here](https://www.kaggle.com/datasets/divyansh22/online-gaming-anxiety-data)\n",
            "\n",
            "The data was acquired by a survey published and shared online. This way everyone could participate. For us that also means taking into account that the distribution and answers can be scewed. \n",
            "\n",
            "## Motivation - "
         ]
      },
      {
         "cell_type": "code",
         "execution_count": 1,
         "metadata": {},
         "outputs": [
            {
               "name": "stdout",
               "output_type": "stream",
               "text": [
                  "<src.Dataset.Dataset object at 0x0000017A2C32DCF0>\n",
                  "<class 'src.Dataset.Dataset'>\n"
               ]
            }
         ],
         "source": [
            "import matplotlib.pyplot as plt\n",
            "from src.Dataset import Dataset\n",
            "from src.Plotter import Plotter\n",
            "import yaml\n",
            "\n",
            "\n",
            "# Load Sphinx configuration options.\n",
            "config_path = \"config.yaml\"\n",
            "with open(config_path, \"r\") as f:\n",
            "    parameters = yaml.safe_load(f)\n",
            "\n",
            "dataset = Dataset(\"data\\GamingStudy_data.csv\")\n",
            "dataframe = dataset.get_dataframe()\n",
            "print(dataset)\n",
            "plotter = Plotter(dataset)"
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {},
         "source": [
            "# Data Exploration\n",
            "\n",
            "Because the data was accumulated in a semi-professional way for a pre-study we had to clean it up and make some changes. \n",
            "\n",
            "Some columns could be answered with an open text field. Naturally the answeres in those columns are very diversified and hard to analyze. \n",
            "\n",
            "#### Affected Columns\n",
            "+ Whyplay\n",
            "+ Earnings \n",
            "+ League\n",
            "\n",
            "In the following we will explain if and how we used these columns. \n",
            "\n",
            "Stuff like deleted columns, general overview of the distribution (men women, games, platform) and problems with it \n",
            "\n"
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {},
         "source": [
            "## Distribution of Participants \n",
            "### Gender\n",
            "\n",
            "'\"\"\"\n",
            "Put in reference to another survey\n",
            "\n",
            "\n",
            "\"\"\"'"
         ]
      },
      {
         "cell_type": "code",
         "execution_count": 2,
         "metadata": {},
         "outputs": [
            {
               "name": "stdout",
               "output_type": "stream",
               "text": [
                  "Gender\n",
                  "Female      688\n",
                  "Male      12108\n",
                  "Other        42\n",
                  "dtype: int64 Index(['Female', 'Male', 'Other'], dtype='object', name='Gender') [  688 12108    42]\n"
               ]
            },
            {
               "data": {
                  "image/png": "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",
                  "text/plain": [
                     "<Figure size 640x480 with 1 Axes>"
                  ]
               },
               "metadata": {},
               "output_type": "display_data"
            }
         ],
         "source": [
            "plotter.distribution_plot(\"Gender\", styling_params={\"title\": \"Gender Distributution\"})"
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {},
         "source": [
            "## Where is the data from ?  \n",
            "### Where did the participants found the survey ?"
         ]
      },
      {
         "cell_type": "code",
         "execution_count": 12,
         "metadata": {},
         "outputs": [
            {
               "name": "stdout",
               "output_type": "stream",
               "text": [
                  "Reference\n",
                  "CrowdFlower           2\n",
                  "Other                57\n",
                  "Reddit            12715\n",
                  "TeamLiquid.net       51\n",
                  "dtype: int64 Index(['CrowdFlower', 'Other', 'Reddit', 'TeamLiquid.net'], dtype='object', name='Reference') [    2    57 12715    51]\n"
               ]
            },
            {
               "data": {
                  "image/png": "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",
                  "text/plain": [
                     "<Figure size 640x480 with 1 Axes>"
                  ]
               },
               "metadata": {},
               "output_type": "display_data"
            }
         ],
         "source": [
            "plotter.distribution_plot(\"Reference\", styling_params={\"title\": \"Reference Distributution\"})"
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {},
         "source": [
            "## Explanation of technical terms \n",
            "\n",
            "### SPIN \n",
            "SPIN stands for Social Phobia Inventory \n",
            "The SPIN is a standardized set of 17 question. After answering the questionnaire a “SPIN” value is calculated which is effective for screening for and measuring the severity of social anxiety disorder\n",
            "1. I am afraid of people in authority.\n",
            "2. I am bothered by blushing in front of people.\n",
            "3. Parties and social events scare me.\n",
            "4. I avoid talking to people I don’t know.\n",
            "5. Being criticized scares me a lot.\n",
            "6. I avoid doing things or speaking to people for fear of embarrassment.\n",
            "7. Sweating in front of people causes me distress.\n",
            "8. I avoid going to parties.\n",
            "9. I avoid activities in which I am the center of attention.\n",
            "10. Talking to strangers scares me.\n",
            "11. I avoid having to give speeches.\n",
            "12. I would do anything to avoid being criticized.\n",
            "13. Heart palpitations bother me when I am around people.\n",
            "14. I am afraid of doing things when people might be watching.\n",
            "15. Being embarrassed or looking stupid are among my worst fears.\n",
            "16. I avoid speaking to anyone in authority.\n",
            "17. Trembling or shaking in front of others is distressing to me. \n",
            "### GAD \n",
            "is a mental and behavioral, disorder, specifally an anxiety disorder characterized by excessive, uncontrollable and often irrational worry about events or activities. There are specific questionaires you can use to evaluate the disorder. In the questionnaire the minimum is 0 and maximum is 21 \n",
            "#### Worries of concern\n",
            "- Health\n",
            "- Finances\n",
            "- Death\n",
            "- Family\n",
            "- Relationships\n",
            "- Work\n",
            "#### Symptoms \n",
            "- Excessive worry\n",
            "- Restlessness,\n",
            "- Low Concentration\n",
            "- Trouble sleeping\n",
            "- Exhaustion / Fatigablity\n",
            "- Irritability\n",
            "- Sweating\n",
            "- Trembling (Muscle contraction)\n",
            "In the questionnaire the question target these symptoms and worries and summarize them into a score between 0 and 21. \n",
            "### SWL\n",
            "#### Explanation\n",
            "The survey has 5 questions. You fill it in yourself (not a psychiatrist).\n",
            "For each question, you choose any integer between 1 (highly disagree) to 7 (highly agree).\n",
            "In general, lower numbers mean you are less satisfied with life in a certain way.\n",
            "This means you can score between 5 (least satisfied) to 35 (most satisfied).\n",
            "#### Interpretation\n",
            "The (total) SWL score can be interpreted as:\n",
            "\n",
            "- 31 - 35 Extremely satisfied\n",
            "- 26 - 30 Satisfied\n",
            "- 21 - 25 Slightly satisfied\n",
            "- 20 Neutral\n",
            "- 15 - 19 Slightly dissatisfied\n",
            "- 10 - 14 Dissatisfied\n",
            "- 5 - 9 Extremely dissatisfied\n",
            "\n",
            "A more detailed interpretation can be found [here](http://labs.psychology.illinois.edu/~ediener/Documents/Understanding%20SWLS%20Scores.pdf).\n",
            "\n",
            "Residents of developed nations (e.g. DE) usually score 20-24.\n",
            "#### Questions \n",
            "____ In most ways my life is close to my ideal.<br>\n",
            "____ The conditions of my life are excellent.<br>\n",
            "____ I am satisfied with my life.<br>\n",
            "____ So far I have gotten the important things I want in life.<br>\n",
            "____ If I could live my life over, I would change almost nothing.<br>\n",
            "\n",
            "---"
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {},
         "source": [
            "# Analysis\n",
            "\n",
            "## Preprocessing \n",
            "* Explained new columns and why we did that *\n",
            "\n",
            "Some columns gave the options to write individual responses. Naturally those are not useful in data analysis. In some cases we cleaned the columns and changes the unusual cases to \"Other\"/\"NA\"\n",
            "### Cleaned Columns\n",
            "+ \"Whyplay\" \n",
            "+ Accept \n",
            "## Normalizing the Data \n",
            "\n",
            "### Creating [\"Is_narcissist\"]\n",
            "### Creating [\"Anxiety_score\"]"
         ]
      },
      {
         "cell_type": "code",
         "execution_count": 13,
         "metadata": {},
         "outputs": [
            {
               "data": {
                  "text/plain": [
                     "0        0.202288\n",
                     "1        0.517320\n",
                     "2        0.497993\n",
                     "3        0.272969\n",
                     "4        0.533567\n",
                     "           ...   \n",
                     "13459    0.212092\n",
                     "13460    0.601914\n",
                     "13461    0.125210\n",
                     "13462    0.591783\n",
                     "13463    0.243231\n",
                     "Length: 12838, dtype: float64"
                  ]
               },
               "execution_count": 13,
               "metadata": {},
               "output_type": "execute_result"
            }
         ],
         "source": [
            "# Executing and showing new columns \n",
            "dataset.get_combined_anxiety_score(dataset.get_dataframe())"
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {},
         "source": [
            "### Creating [\"Is_competetive\"]"
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {},
         "source": [
            "## Q1 -  Which gamers are more anxiety prone ? \n",
            "\n",
            "Text .......\n",
            "\n",
            "We compare\n",
            "\n",
            "### Women vs Men \n",
            "\n",
            "Explanation \n",
            "![Example Plot](https://cdn.discordapp.com/attachments/806128836332879924/1127988009627832320/Unbenannt.png)"
         ]
      },
      {
         "cell_type": "code",
         "execution_count": 14,
         "metadata": {},
         "outputs": [
            {
               "data": {
                  "text/plain": [
                     "'# SIDE BY SIDE PLOTS \\n# LEFT = LINE Graph distribution of Anxiety Score Related to Group\\n# RIGHT = Stacked Bars comparing the GROUP with = \\n# 1.[Work] - 4 Bars\\n# 2.[Degree] - 5 Bars\\n# 3.[Whyplay ] - 4 Bars (Everything until \"All of them\")\\n'"
                  ]
               },
               "execution_count": 14,
               "metadata": {},
               "output_type": "execute_result"
            }
         ],
         "source": [
            "\n",
            "\"\"\"# SIDE BY SIDE PLOTS \n",
            "# LEFT = LINE Graph distribution of Anxiety Score Related to Group\n",
            "# RIGHT = Stacked Bars comparing the GROUP with = \n",
            "# 1.[Work] - 4 Bars\n",
            "# 2.[Degree] - 5 Bars\n",
            "# 3.[Whyplay ] - 4 Bars (Everything until \"All of them\")\n",
            "\"\"\"\n",
            "#"
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {},
         "source": [
            "### Competetive vs Easy Going Players \n",
            "Explanation "
         ]
      },
      {
         "cell_type": "code",
         "execution_count": 15,
         "metadata": {},
         "outputs": [
            {
               "data": {
                  "text/plain": [
                     "'# SIDE BY SIDE PLOTS \\n# LEFT = LINE Graph distribution of Anxiety Score Related to Group\\n# RIGHT = Stacked Bars comparing the GROUP with = \\n# 1.[Work] - 4 Bars\\n# 2.[Degree] - 5 Bars\\n# 3.[Whyplay ] - 4 Bars (Everything until \"All of them\")'"
                  ]
               },
               "execution_count": 15,
               "metadata": {},
               "output_type": "execute_result"
            }
         ],
         "source": [
            "\"\"\"# SIDE BY SIDE PLOTS \n",
            "# LEFT = LINE Graph distribution of Anxiety Score Related to Group\n",
            "# RIGHT = Stacked Bars comparing the GROUP with = \n",
            "# 1.[Work] - 4 Bars\n",
            "# 2.[Degree] - 5 Bars\n",
            "# 3.[Whyplay ] - 4 Bars (Everything until \"All of them\")\"\"\""
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {},
         "source": [
            "### Narcissist vs Non-Narcissist"
         ]
      },
      {
         "cell_type": "code",
         "execution_count": 16,
         "metadata": {},
         "outputs": [
            {
               "data": {
                  "text/plain": [
                     "'\"# SIDE BY SIDE PLOTS \\n# LEFT = LINE Graph distribution of Anxiety Score Related to Group\\n# RIGHT = Stacked Bars comparing the GROUP with = \\n# 1.[Work] - 4 Bars\\n# 2.[Degree] - 5 Bars\\n# 3.[Whyplay ] - 4 Bars (Everything until \"All of them\")'"
                  ]
               },
               "execution_count": 16,
               "metadata": {},
               "output_type": "execute_result"
            }
         ],
         "source": [
            "\"\"\"\"# SIDE BY SIDE PLOTS \n",
            "# LEFT = LINE Graph distribution of Anxiety Score Related to Group\n",
            "# RIGHT = Stacked Bars comparing the GROUP with = \n",
            "# 1.[Work] - 4 Bars\n",
            "# 2.[Degree] - 5 Bars\n",
            "# 3.[Whyplay ] - 4 Bars (Everything until \"All of them\")\"\"\""
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {},
         "source": [
            "## Q2 - Correlations between played hours and one's well being.\n",
            "**Maybe we can even add if hours watching Streams effect it**\n",
            "\n",
            "For research question two we wanted to know if there is a correlation \n",
            "between played hours and the player's well being. We went into the question\n",
            "with the expectation that players which play longer hours are more anxiety prone\n",
            "and less satisfied with life than those who play less. If that would be the \n",
            "case, a positive correlation of hours played and our combined anxiety score \n",
            "variable would be expected. We want to take a look at the data using a scatter-\n",
            "plot, showing the correlation of both variables of interest, using the\n",
            "plot_scatterplot() function of our Plotter class:\n",
            "code below: plotter.plot_scatterplot(\"Hours\", \"Anxiety_score\")"
         ]
      },
      {
         "cell_type": "code",
         "execution_count": 17,
         "metadata": {},
         "outputs": [
            {
               "data": {
                  "image/png": "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",
                  "text/plain": [
                     "<Figure size 640x480 with 1 Axes>"
                  ]
               },
               "metadata": {},
               "output_type": "display_data"
            }
         ],
         "source": [
            "plotter.plot_scatterplot(\"Hours\", \"Anxiety_score\", styling_params={\"title\": \"Beispiel\" }) \n",
            "\n",
            "#Still needs to be prettier"
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {},
         "source": [
            "## Q3 - Effect of the reason for playing on the satisfaction with life "
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {},
         "source": [
            "In this question, we visualise and discuss how a player's reason for playing and their satisfaction with life score (SWL) effect each other.\n",
            "\n",
            "Although a description of the columns are not given, we briefly describe them as follows:\n",
            "* \"improving\": players are competitive and derive satisfaction from outperforming themselves and others. \n",
            "* \"winning\": players are more competitive than those who wish to improve, and derive immense satisfaction from outperforming. Players who play to win experience games more intensely than those in other categories.\n",
            "* \"having fun\": players are not competitive. They are not particularly invested in improving or the outcome of the game, but instead play as a form of recreation. This does not imply the intensity or difficulty of a game is easy; a challenging game can still be fun as long as players derive satisfaction not from the outcome, but from the gameplay or environs (friends, etc).\n",
            "* \"relaxing\": players are playing to relax, and may play games to reduce their anxiety.\n",
            "* \"all of the above\": players in this category are generally competitive but also see the importance of enjoying the game itself."
         ]
      },
      {
         "cell_type": "code",
         "execution_count": 18,
         "metadata": {},
         "outputs": [
            {
               "name": "stdout",
               "output_type": "stream",
               "text": [
                  "Category distribution:\n",
                  "whyplay\n",
                  "having fun          5105\n",
                  "improving           4661\n",
                  "winning             1977\n",
                  "relaxing             623\n",
                  "other                424\n",
                  "all of the above      48\n",
                  "dtype: int64\n"
               ]
            },
            {
               "data": {
                  "image/png": "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",
                  "text/plain": [
                     "<Figure size 800x1000 with 1 Axes>"
                  ]
               },
               "metadata": {},
               "output_type": "display_data"
            }
         ],
         "source": [
            "print(\"Category distribution:\")\n",
            "print(dataframe.groupby(\"whyplay\").size().sort_values(ascending=False))\n",
            "\n",
            "fig, ax = plt.subplots(figsize=(parameters[\"plotter_fig_width\"], parameters[\"plotter_fig_height\"]))\n",
            "order = [\"relaxing\", \"having fun\", \"other\", \"improving\", \"winning\"]\n",
            "fig.suptitle(\"\")\n",
            "dataframe[dataframe[\"whyplay\"] != \"other\"].boxplot(column=[\"SWL_T\"], by=\"whyplay\", ax=ax)\n",
            "pass"
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {},
         "source": [
            "As seen in this plot, we can discover the following:\n",
            "* On average, those who play to have fun are more satisfied with life than any other group.\n",
            "    * We find this outcome reasonable. Those that are more satisfied with life generally do not rely so much on gaming as a means of fulfillment.\n",
            "* As expected, those who play to win are the least satisfied with their lives, as they disproportionately value being the best over enjoying the game.\n",
            "* Interestingly, those who play to relax are also less satisfied with their lives on average. This may be because this category of players are not satisfied with life and use gaming as a means to destress.\n",
            "* Those who selected \"all of the above\" have a much smaller range of SWL metrics. This is due to the small sample size."
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {},
         "source": [
            "### Effects of income level (`work`) and education level (`Degree`) on the reason to play"
         ]
      },
      {
         "cell_type": "code",
         "execution_count": 19,
         "metadata": {},
         "outputs": [
            {
               "data": {
                  "text/plain": [
                     "'#Overlaying Histogram \\n\\n# Histogram for the income level Y = %, X = low to high \\n# One in Green for the income \\n# One in Red for the Anxiety for those people '"
                  ]
               },
               "execution_count": 19,
               "metadata": {},
               "output_type": "execute_result"
            }
         ],
         "source": [
            "\n",
            "\"\"\"#Overlaying Histogram \n",
            "\n",
            "# Histogram for the income level Y = %, X = low to high \n",
            "# One in Green for the income \n",
            "# One in Red for the Anxiety for those people \"\"\""
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {},
         "source": [
            "## Q4 - Gamers from different countries "
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {},
         "source": [
            "1. Do they play different games ? \n",
            "    1. Are they reacting differently to those games \n",
            "2. Is the amount of educated players similar "
         ]
      },
      {
         "cell_type": "markdown",
         "metadata": {},
         "source": [
            "![Scatter](https://cdn.discordapp.com/attachments/1127973734884581386/1127973829344493679/image.png)"
         ]
      },
      {
         "cell_type": "code",
         "execution_count": 20,
         "metadata": {},
         "outputs": [
            {
               "data": {
                  "text/plain": [
                     "'#### Analyze the countries amounting to Top 7 or 90% of the survey. \\n\\n\\n#Q4.MAP PLOT = Most played game per country (Dont do it if its League everywhere. )\\n#Q4 MAP PLOT = Heat Map with redder areas for more Anxiety in the country. \\n#Q1.2 Grouped Bar Chart with the top game next to the \"Anxiety Score\"\\n\\n#2 Scatter PLot like in the example '"
                  ]
               },
               "execution_count": 20,
               "metadata": {},
               "output_type": "execute_result"
            }
         ],
         "source": [
            "\"\"\"#### Analyze the countries amounting to Top 7 or 90% of the survey. \n",
            "\n",
            "\n",
            "#Q4.MAP PLOT = Most played game per country (Dont do it if its League everywhere. )\n",
            "#Q4 MAP PLOT = Heat Map with redder areas for more Anxiety in the country. \n",
            "#Q1.2 Grouped Bar Chart with the top game next to the \"Anxiety Score\"\n",
            "\n",
            "#2 Scatter PLot like in the example \"\"\""
         ]
      }
   ],
   "metadata": {
      "kernelspec": {
         "display_name": "base",
         "language": "python",
         "name": "python3"
      },
      "language_info": {
         "codemirror_mode": {
            "name": "ipython",
            "version": 3
         },
         "file_extension": ".py",
         "mimetype": "text/x-python",
         "name": "python",
         "nbconvert_exporter": "python",
         "pygments_lexer": "ipython3",
         "version": "3.10.9"
      },
      "orig_nbformat": 4
   },
   "nbformat": 4,
   "nbformat_minor": 2
}