eda-report-mental-health.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Workplace resources and willingness to speak out:\n",
    "\n",
    "*a study of Mental Health in Tech by the Real Ricardo*\n",
    "\n",
    "The data used in this notebook was downloaded from [Kaggle](https://www.kaggle.com/osmi/mental-health-in-tech-survey).\n",
    "The source of the data was a survey done in 2014 by [OSMI](https://osmihelp.org/), a non-profit organization dedicated to raising awareness, educating, and providing resources to support mental wellness in the tech and open source communities.\n",
    "\n",
    "\n",
    "**Below is a description of the questions (columns) asked in the survey:**\n",
    "\n",
    "| Column  | Description  |\n",
    "|---|---|\n",
    "| Timestamp  | Date survey was filled  |\n",
    "| Gender  | Gender identity of surveyed person  |\n",
    "| Country  | Country of residence  |\n",
    "| State  | US residents only  |\n",
    "| self_employed  | Are you self-employed?   |\n",
    "| family_history  | Do you have a family history of mental illness?  |\n",
    "| treatment  | Have you sought treatment for a mental health condition?  |\n",
    "| work_interfere  | If you have a mental health condition, do you feel that it interferes with your work?  |\n",
    "| no_employees  | How many employees does your company or organization have?  |\n",
    "| remote_work  | Do you work remotely (outside of an office) at least 50% of the time?  |\n",
    "| tech_company  | Is your employer primarily a tech company/organization?  |\n",
    "| benefits  | Does your employer provide mental health benefits?  |\n",
    "| care_options  | Do you know the options for mental health care your employer provides?  |\n",
    "| wellness_program  | Has your employer ever discussed mental health as part of an employee wellness program?  |\n",
    "| seek_help  | Does your employer provide resources to learn more about mental health issues and how to seek help?  |\n",
    "| anonymity  | Is your anonymity protected if you choose to take advantage of mental health or substance abuse treatment resources?  |\n",
    "| leave  | How easy is it for you to take medical leave for a mental health condition?  |\n",
    "| mental_health_consequence  | Do you think that discussing a mental health issue with your employer would have negative consequences?  |\n",
    "| phys_health_consequence  | Do you think that discussing a physical health issue with your employer would have negative consequences?  |\n",
    "| coworkers  | Would you be willing to discuss a mental health issue with your coworkers?  |\n",
    "| supervisors  | Would you be willing to discuss a mental health issue with your direct supervisor(s)?  |\n",
    "| mental_health_interview  | Would you bring up a mental health issue with a potential employer in an interview?  |\n",
    "| mental_vs_physical  | Do you feel that your employer takes mental health as seriously as physical health?  |\n",
    "| obs_consequence  | Have you heard of or observed negative consequences for coworkers with mental health conditions in your workplace?  |\n",
    "| comments  | Any additional notes or comments?  |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# classic importing of libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import stats\n",
    "import seaborn as sns\n",
    "\n",
    "# choosing color palette\n",
    "flatui = [\"#9b59b6\", \"#3498db\", \"#95a5a6\", \"#e74c3c\", \"#34495e\", \"#2ecc71\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Timestamp</th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Country</th>\n",
       "      <th>state</th>\n",
       "      <th>self_employed</th>\n",
       "      <th>family_history</th>\n",
       "      <th>treatment</th>\n",
       "      <th>work_interfere</th>\n",
       "      <th>no_employees</th>\n",
       "      <th>...</th>\n",
       "      <th>leave</th>\n",
       "      <th>mental_health_consequence</th>\n",
       "      <th>phys_health_consequence</th>\n",
       "      <th>coworkers</th>\n",
       "      <th>supervisor</th>\n",
       "      <th>mental_health_interview</th>\n",
       "      <th>phys_health_interview</th>\n",
       "      <th>mental_vs_physical</th>\n",
       "      <th>obs_consequence</th>\n",
       "      <th>comments</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2014-08-27 11:29:31</td>\n",
       "      <td>37</td>\n",
       "      <td>Female</td>\n",
       "      <td>United States</td>\n",
       "      <td>IL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Often</td>\n",
       "      <td>6-25</td>\n",
       "      <td>...</td>\n",
       "      <td>Somewhat easy</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Some of them</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2014-08-27 11:29:37</td>\n",
       "      <td>44</td>\n",
       "      <td>M</td>\n",
       "      <td>United States</td>\n",
       "      <td>IN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Rarely</td>\n",
       "      <td>More than 1000</td>\n",
       "      <td>...</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2014-08-27 11:29:44</td>\n",
       "      <td>32</td>\n",
       "      <td>Male</td>\n",
       "      <td>Canada</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Rarely</td>\n",
       "      <td>6-25</td>\n",
       "      <td>...</td>\n",
       "      <td>Somewhat difficult</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2014-08-27 11:29:46</td>\n",
       "      <td>31</td>\n",
       "      <td>Male</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Often</td>\n",
       "      <td>26-100</td>\n",
       "      <td>...</td>\n",
       "      <td>Somewhat difficult</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Some of them</td>\n",
       "      <td>No</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2014-08-27 11:30:22</td>\n",
       "      <td>31</td>\n",
       "      <td>Male</td>\n",
       "      <td>United States</td>\n",
       "      <td>TX</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Never</td>\n",
       "      <td>100-500</td>\n",
       "      <td>...</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Some of them</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Don't know</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Timestamp  Age  Gender         Country state self_employed  \\\n",
       "0  2014-08-27 11:29:31   37  Female   United States    IL           NaN   \n",
       "1  2014-08-27 11:29:37   44       M   United States    IN           NaN   \n",
       "2  2014-08-27 11:29:44   32    Male          Canada   NaN           NaN   \n",
       "3  2014-08-27 11:29:46   31    Male  United Kingdom   NaN           NaN   \n",
       "4  2014-08-27 11:30:22   31    Male   United States    TX           NaN   \n",
       "\n",
       "  family_history treatment work_interfere    no_employees  ...  \\\n",
       "0             No       Yes          Often            6-25  ...   \n",
       "1             No        No         Rarely  More than 1000  ...   \n",
       "2             No        No         Rarely            6-25  ...   \n",
       "3            Yes       Yes          Often          26-100  ...   \n",
       "4             No        No          Never         100-500  ...   \n",
       "\n",
       "                leave mental_health_consequence phys_health_consequence  \\\n",
       "0       Somewhat easy                        No                      No   \n",
       "1          Don't know                     Maybe                      No   \n",
       "2  Somewhat difficult                        No                      No   \n",
       "3  Somewhat difficult                       Yes                     Yes   \n",
       "4          Don't know                        No                      No   \n",
       "\n",
       "      coworkers supervisor mental_health_interview phys_health_interview  \\\n",
       "0  Some of them        Yes                      No                 Maybe   \n",
       "1            No         No                      No                    No   \n",
       "2           Yes        Yes                     Yes                   Yes   \n",
       "3  Some of them         No                   Maybe                 Maybe   \n",
       "4  Some of them        Yes                     Yes                   Yes   \n",
       "\n",
       "  mental_vs_physical obs_consequence comments  \n",
       "0                Yes              No      NaN  \n",
       "1         Don't know              No      NaN  \n",
       "2                 No              No      NaN  \n",
       "3                 No             Yes      NaN  \n",
       "4         Don't know              No      NaN  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# loading data into pandas dataframe and exploring columns and first rows\n",
    "survey = pd.read_csv('survey.csv')\n",
    "survey.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "timestamp                    object\n",
      "age                           int64\n",
      "gender                       object\n",
      "country                      object\n",
      "state                        object\n",
      "self_employed                object\n",
      "family_history               object\n",
      "treatment                    object\n",
      "work_interfere               object\n",
      "no_employees                 object\n",
      "remote_work                  object\n",
      "tech_company                 object\n",
      "benefits                     object\n",
      "care_options                 object\n",
      "wellness_program             object\n",
      "seek_help                    object\n",
      "anonymity                    object\n",
      "leave                        object\n",
      "mental_health_consequence    object\n",
      "phys_health_consequence      object\n",
      "coworkers                    object\n",
      "supervisor                   object\n",
      "mental_health_interview      object\n",
      "phys_health_interview        object\n",
      "mental_vs_physical           object\n",
      "obs_consequence              object\n",
      "comments                     object\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "# renaming columns to have all lowercase\n",
    "survey.columns = [col.lower() for col in survey.columns]\n",
    "\n",
    "# looking at dtypes, we can see all of them are pandas objects except for age that is int.\n",
    "print(survey.dtypes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **after exploring some of the columns I have decided not to use the *timestamp, country, state or comments columns*.**\n",
    "- **i don't think there are is not enough data to do a geographical analysis.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "survey.drop(['timestamp', 'state', 'comments', 'country'], axis= 1, inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# after finding some weird responses in the age column ('such as 99999999 and -1729')\n",
    "# i decided to only include 'valid' numbers for age\n",
    "survey['age'] = survey['age'].apply(lambda x: x if 0 < x < 100 else np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "male      994\n",
       "female    247\n",
       "other      18\n",
       "Name: gender, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# gender is one big mess of data but I believe in myself.\n",
    "# first lower case and strip eveything to decrease options\n",
    "survey['gender'] = survey['gender'].apply(lambda x: x.lower().strip())\n",
    "\n",
    "# based on the replies made these lists manually, I hope to not offend anyone.\n",
    "male = ['male', 'm', 'make', 'cis male', 'man', 'cis man', 'msle', 'malr', 'mail', 'maile', 'something kinda male?', 'ostensibly male, unsure what that really means', 'male-ish', 'guy (-ish) ^_^', 'mal', 'male (cis)']\n",
    "female = ['female', 'f', 'woman', 'female (cis)', 'cis-female/femme', 'femake', 'cis female', 'femail']\n",
    "\n",
    "def regender(gender_input):\n",
    "    \n",
    "    \"\"\"\n",
    "    Input: a string about gender\n",
    "    Output: male, female or other depending on our dictionary\n",
    "    \n",
    "    \"\"\" \n",
    "    if gender_input in male:\n",
    "        return 'male'\n",
    "    elif gender_input in female:\n",
    "        return 'female'\n",
    "    else:\n",
    "        return 'other'\n",
    "    \n",
    "survey['gender'] = survey['gender'].apply(regender)\n",
    "\n",
    "# check our final results\n",
    "survey.gender.value_counts(dropna= False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "age (%): 0.003971405877680699\n",
      "self_employed (%): 0.014297061159650517\n",
      "work_interfere (%): 0.2096902303415409\n",
      "No            1092\n",
      "Yes            144\n",
      "Don't know      18\n",
      "Name: self_employed, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# checking for nans\n",
    "nan_cols = []\n",
    "for col in survey.columns:\n",
    "    if survey[col].isnull().sum():\n",
    "        print(col + ' (%): ' + str(sum(survey[col].isnull())/len(survey)))\n",
    "        nan_cols += [col]\n",
    "\n",
    "# nans are present in the self-employed column and in the work_interfere column.\n",
    "# at most there are 20% of values with nans so we'll replace with 'Don't know'\n",
    "for col in nan_cols:\n",
    "    survey[col].fillna(\"Don't know\", inplace= True)\n",
    "\n",
    "# i'm just gonna drop the rows without age.\n",
    "survey = survey[survey.age != \"Don't know\"]\n",
    "\n",
    "# in the context of our questions we need to drop everyone who is self-employed\n",
    "print(survey['self_employed'].value_counts())\n",
    "survey.loc[survey['self_employed'] == 'Yes'] = np.nan\n",
    "survey.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **it seems the rest of the columns are questions that might be yes or no, with few additional options**\n",
    "- **we can identify that by checking the possible answers in each question**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'gender': ['female', 'male', 'other'],\n",
       " 'self_employed': [\"Don't know\", 'No'],\n",
       " 'family_history': ['No', 'Yes'],\n",
       " 'treatment': ['Yes', 'No'],\n",
       " 'work_interfere': ['Often', 'Rarely', 'Never', 'Sometimes', \"Don't know\"],\n",
       " 'no_employees': ['6-25',\n",
       "  'More than 1000',\n",
       "  '26-100',\n",
       "  '100-500',\n",
       "  '1-5',\n",
       "  '500-1000'],\n",
       " 'remote_work': ['No', 'Yes'],\n",
       " 'tech_company': ['Yes', 'No'],\n",
       " 'benefits': ['Yes', \"Don't know\", 'No'],\n",
       " 'care_options': ['Not sure', 'No', 'Yes'],\n",
       " 'wellness_program': ['No', \"Don't know\", 'Yes'],\n",
       " 'seek_help': ['Yes', \"Don't know\", 'No'],\n",
       " 'anonymity': ['Yes', \"Don't know\", 'No'],\n",
       " 'leave': ['Somewhat easy',\n",
       "  \"Don't know\",\n",
       "  'Somewhat difficult',\n",
       "  'Very difficult',\n",
       "  'Very easy'],\n",
       " 'mental_health_consequence': ['No', 'Maybe', 'Yes'],\n",
       " 'phys_health_consequence': ['No', 'Yes', 'Maybe'],\n",
       " 'coworkers': ['Some of them', 'No', 'Yes'],\n",
       " 'supervisor': ['Yes', 'No', 'Some of them'],\n",
       " 'mental_health_interview': ['No', 'Yes', 'Maybe'],\n",
       " 'phys_health_interview': ['Maybe', 'No', 'Yes'],\n",
       " 'mental_vs_physical': ['Yes', \"Don't know\", 'No'],\n",
       " 'obs_consequence': ['No', 'Yes']}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# getting unique answers for every column\n",
    "exclude = ['age']\n",
    "possible_answers = {col:[i for i in survey[col].unique()] for col in survey.columns if col not in exclude}\n",
    "possible_answers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **after looking at these answer possibilities want to normalize all 'half-answers' to 'Don't know', so changing 'Not sure' and 'Maybes'**\n",
    "- **this decision might be open for interpretation, but I made a judgement call for the sake of consistency**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def replace_uncertainty(survey_answer):\n",
    "    \"\"\"\n",
    "    Uniformizes all uncertain answers.\n",
    "    Input: half-answer (string)\n",
    "    Output: 'Don't know'\n",
    "    \n",
    "    \"\"\"\n",
    "    \n",
    "    uncertainty = ['Maybe', 'Some of them', 'Not sure']\n",
    "    if survey_answer in uncertainty:\n",
    "        return \"Don't know\"\n",
    "    else:\n",
    "        return survey_answer\n",
    "\n",
    "# apply function to all columns\n",
    "for col in survey.columns:\n",
    "    survey[col] = survey[col].apply(replace_uncertainty)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **the column 'care_options' asks the question: \"Do you know the options for mental health care your employer provides?\" and about 30% of the answers are 'Dont know'.** \n",
    "- **in my opinion: if the answer to a question 'Do you know?' is 'I don't know' - then the answer is No.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No     730\n",
       "Yes    380\n",
       "Name: care_options, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "survey['care_options'] = survey['care_options'].replace(to_replace=\"Don't know\", value= 'No')\n",
    "survey['care_options'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**I was trying to come up with a question to ask the data but I wasn't feeling very creative.**\n",
    "**So I'm gonna do an agnostic analysis and just make a correlation matrix bewteen all the variables.**\n",
    "\n",
    "1. The first thing I'll do is encode all the answers as numbers.\n",
    "- **Important notes:**\n",
    "- All the half answers go in the middle (0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a dictionary to map out all the answers in the survey\n",
    "encoding_dict = {'No': -1, \"Don't know\": 0, 'Yes': 1,\n",
    "                 'Never': -2, 'Rarely': -1, 'Sometimes': 1, 'Often': 2,\n",
    "                 'Very difficult': -2, 'Somewhat difficult': -1, 'Somewhat easy': 1, 'Very easy': 2,\n",
    "                 '1-5': 0, '6-25': 1, '26-100': 2, '100-500': 3, '500-1000': 4, 'More than 1000': 5,\n",
    "                 'male': -1, 'other': 0, 'female': 1}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# made a copy just to check values\n",
    "survey_coded = survey.copy()\n",
    "\n",
    "# for each column except the ones not excluded change the values in our dictionary\n",
    "for col in survey_coded.columns:\n",
    "    if col not in exclude:\n",
    "        survey_coded[col] = survey_coded[col].map(encoding_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>self_employed</th>\n",
       "      <th>family_history</th>\n",
       "      <th>treatment</th>\n",
       "      <th>work_interfere</th>\n",
       "      <th>no_employees</th>\n",
       "      <th>remote_work</th>\n",
       "      <th>tech_company</th>\n",
       "      <th>benefits</th>\n",
       "      <th>...</th>\n",
       "      <th>anonymity</th>\n",
       "      <th>leave</th>\n",
       "      <th>mental_health_consequence</th>\n",
       "      <th>phys_health_consequence</th>\n",
       "      <th>coworkers</th>\n",
       "      <th>supervisor</th>\n",
       "      <th>mental_health_interview</th>\n",
       "      <th>phys_health_interview</th>\n",
       "      <th>mental_vs_physical</th>\n",
       "      <th>obs_consequence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>44.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>5</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>32.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>31.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>...</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>31.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1254</td>\n",
       "      <td>26.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1255</td>\n",
       "      <td>32.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1256</td>\n",
       "      <td>34.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1257</td>\n",
       "      <td>46.0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1258</td>\n",
       "      <td>25.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1110 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age  gender  self_employed  family_history  treatment  work_interfere  \\\n",
       "0     37.0       1              0              -1          1               2   \n",
       "1     44.0      -1              0              -1         -1              -1   \n",
       "2     32.0      -1              0              -1         -1              -1   \n",
       "3     31.0      -1              0               1          1               2   \n",
       "4     31.0      -1              0              -1         -1              -2   \n",
       "...    ...     ...            ...             ...        ...             ...   \n",
       "1254  26.0      -1             -1              -1          1               0   \n",
       "1255  32.0      -1             -1               1          1               2   \n",
       "1256  34.0      -1             -1               1          1               1   \n",
       "1257  46.0       1             -1              -1         -1               0   \n",
       "1258  25.0      -1             -1               1          1               1   \n",
       "\n",
       "      no_employees  remote_work  tech_company  benefits  ...  anonymity  \\\n",
       "0                1           -1             1         1  ...          1   \n",
       "1                5           -1            -1         0  ...          0   \n",
       "2                1           -1             1        -1  ...          0   \n",
       "3                2           -1             1        -1  ...         -1   \n",
       "4                3            1             1         1  ...          0   \n",
       "...            ...          ...           ...       ...  ...        ...   \n",
       "1254             2           -1             1        -1  ...          0   \n",
       "1255             2            1             1         1  ...          1   \n",
       "1256             5           -1             1         1  ...          0   \n",
       "1257             3            1             1        -1  ...          0   \n",
       "1258             2           -1            -1         1  ...          1   \n",
       "\n",
       "      leave  mental_health_consequence  phys_health_consequence  coworkers  \\\n",
       "0         1                         -1                       -1          0   \n",
       "1         0                          0                       -1         -1   \n",
       "2        -1                         -1                       -1          1   \n",
       "3        -1                          1                        1          0   \n",
       "4         0                         -1                       -1          0   \n",
       "...     ...                        ...                      ...        ...   \n",
       "1254      1                         -1                       -1          0   \n",
       "1255     -1                         -1                       -1          0   \n",
       "1256     -1                          1                        1         -1   \n",
       "1257      0                          1                       -1         -1   \n",
       "1258      0                          0                       -1          0   \n",
       "\n",
       "      supervisor  mental_health_interview  phys_health_interview  \\\n",
       "0              1                       -1                      0   \n",
       "1             -1                       -1                     -1   \n",
       "2              1                        1                      1   \n",
       "3             -1                        0                      0   \n",
       "4              1                        1                      1   \n",
       "...          ...                      ...                    ...   \n",
       "1254           0                       -1                     -1   \n",
       "1255           1                       -1                     -1   \n",
       "1256          -1                       -1                     -1   \n",
       "1257          -1                       -1                     -1   \n",
       "1258          -1                       -1                     -1   \n",
       "\n",
       "      mental_vs_physical  obs_consequence  \n",
       "0                      1               -1  \n",
       "1                      0               -1  \n",
       "2                     -1               -1  \n",
       "3                     -1                1  \n",
       "4                      0               -1  \n",
       "...                  ...              ...  \n",
       "1254                   0               -1  \n",
       "1255                   1               -1  \n",
       "1256                  -1               -1  \n",
       "1257                  -1               -1  \n",
       "1258                   0               -1  \n",
       "\n",
       "[1110 rows x 23 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's take a look at our answer-encoded dataframe.\n",
    "survey_coded"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **because I'm still not feeling very creative, I'm gonna make a correlation matrix across all the variables in the study**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make the correlation matrix\n",
    "correlation = survey_coded.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17dd0c4d348>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# making a mask to only show half the table cause its duplicated.\n",
    "mask = np.triu(np.ones_like(correlation, dtype=np.bool))\n",
    "\n",
    "# choosing colors\n",
    "cmap = sns.diverging_palette(220, 10, as_cmap=True)\n",
    "\n",
    "# creating the heatmap\n",
    "plt.figure(figsize=(12, 8))\n",
    "sns.heatmap(correlation, cmap=cmap, mask=mask, annot= False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **This plot is very pretty but I've come to the realization that it is useless to me. there are way too many variables. It's hard to consider what is important or not and I don't have the know-how yet to disentangle all of this.**\n",
    "\n",
    "*Teach me please.*\n",
    "\n",
    "## 3. Hypothesis-driven analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**To create a score for the willingness to speak, we combined all the columns that ask 'If you're willing to speak to someone about mental health**\n",
    "\n",
    "**Following the same logic, I combined all the columns that ask about services provided by the company**\n",
    "\n",
    "We combined these columns by adding them together, it was important to make sure they all 'move in the same direction'\n",
    "The result of this are two columns:\n",
    "1. 'workplace_resources': with a possible value ranging from -6 to 6 and is the score of the company\n",
    "2. 'willingness': with a possible values ranging from -3 to 3 and is the metric of the employee"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define columns to combine\n",
    "workplace_resources = ['benefits', \n",
    "                      'wellness_program', \n",
    "                      'anonymity', \n",
    "                      'seek_help', \n",
    "                      'leave']\n",
    "\n",
    "willingness = ['coworkers',\n",
    "               'supervisor',\n",
    "               'mental_health_interview']\n",
    "\n",
    "# add columns together\n",
    "survey_coded['workplace_resources'] = survey_coded.benefits + survey_coded.wellness_program + survey_coded.seek_help + survey_coded.leave + survey_coded.anonymity\n",
    "survey_coded['willingness'] = survey_coded.coworkers + survey_coded.supervisor + survey_coded.mental_health_interview"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# gonna define a function to make a nice looking histogram\n",
    "\n",
    "def pretty_histogram(data, colname, label, save= False):\n",
    "    \n",
    "    \"\"\"\n",
    "    Input: data (pandas dataframe), column name (str), x label (str), save (bool)\n",
    "    Output: a pretty histogram\n",
    "    \"\"\"\n",
    "\n",
    "    hist = sns.distplot(data[colname], \n",
    "                        bins = data[colname].max() - data[colname].min() + 1,\n",
    "                        kde= False,\n",
    "                        color=flatui[-2],\n",
    "                        hist_kws= {'range': (data[colname].min(), data[colname].max()),\n",
    "                                   'rwidth': 0.95,\n",
    "                                   'alpha': 1})\n",
    "    \n",
    "    hist.set(xlabel= label, ylabel= 'Count')\n",
    "    sns.despine()\n",
    "    \n",
    "    if save:\n",
    "        figure = hist.get_figure()\n",
    "        figure.savefig('figures/' + colname + \"_hist.png\")\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# make a histogram for our newly created 'Willingness' variable.\n",
    "pretty_histogram(survey_coded, 'willingness', 'Willingness Score', save= False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**This looks like a right skewed distribution, meaning that the majority of people are unwilling to talk about mental health in their work environment.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# make a histogram for our newly created 'Workplace Resources' variable\n",
    "pretty_histogram(survey_coded, 'workplace_resources', 'Workplace Resources Score', save= True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**This one looks more normally distributed but still centered left of the zero, meaning that the majority of companies don't have many mental health resources**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Before moving forwards, one of the first questions we can ask is: does having more mental health services mean that your employees are aware these services exist?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>care_options</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>workplace_resources</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>-6</td>\n",
       "      <td>0.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>-5</td>\n",
       "      <td>0.441176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>-4</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>-3</td>\n",
       "      <td>0.215686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>-2</td>\n",
       "      <td>0.180000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>-1</td>\n",
       "      <td>0.176166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.252809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.377358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.432990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.519481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.754386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.829268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.869565</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     care_options\n",
       "workplace_resources              \n",
       "-6                       0.400000\n",
       "-5                       0.441176\n",
       "-4                       0.333333\n",
       "-3                       0.215686\n",
       "-2                       0.180000\n",
       "-1                       0.176166\n",
       " 0                       0.252809\n",
       " 1                       0.377358\n",
       " 2                       0.432990\n",
       " 3                       0.519481\n",
       " 4                       0.754386\n",
       " 5                       0.829268\n",
       " 6                       0.869565"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# interestingly to check the proportion of people aware of services I needed to recode the values in the\n",
    "# care options column from 'No' = -1 to 'No' = 0\n",
    "survey_coded['care_options'].replace(to_replace=-1, value=0, inplace= True)\n",
    "survey_coded.pivot_table(index=['workplace_resources'], values=['care_options'], aggfunc= lambda x: sum(x)/len(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot proportions of people that know about care options for each score of workplace resource.\n",
    "\n",
    "label = 'Workplace Resources Score'\n",
    "\n",
    "bars = sns.barplot(x='workplace_resources', y='care_options', data= survey_coded, \n",
    "                   ci= None, color= flatui[4])\n",
    "\n",
    "bars.set(xlabel= label, ylabel= 'Proportion Employee Awareness')\n",
    "sns.despine()\n",
    "figure = bars.get_figure()\n",
    "figure.savefig('figures/resources_and_awareness.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **This is a really interesting result, kind of U-shaped.**\n",
    "- **One possible interpretation is that the more services are available, more people know about them, and where there's none people ALSO know that.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **before moving on with our analysis, I would like to see what's the behavior of our willingness score according to some other variables, such as family history, treatment and work interfere**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# group by variable of interest, create a label and rename columns\n",
    "family_data = survey_coded.groupby('family_history').agg({'willingness':'mean'}).reset_index()\n",
    "family_data['label'] = ['Family History']*len(family_data)\n",
    "family_data.rename(columns= {'family_history':'Answer'}, inplace=True)\n",
    "\n",
    "treatment_data = survey_coded.groupby('treatment').agg({'willingness':'mean'}).reset_index()\n",
    "treatment_data['label'] = ['Treatment']*len(treatment_data)\n",
    "treatment_data.rename(columns= {'treatment':'Answer'}, inplace=True)\n",
    "\n",
    "willingness_variables = pd.concat([family_data, treatment_data], axis=0)\n",
    "willingness_variables.Answer.replace(to_replace=-1, value='No', inplace=True)\n",
    "willingness_variables.Answer.replace(to_replace=1, value='Yes', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# making a category bar plot\n",
    "fig, ax = plt.subplots(figsize=(5, 3))\n",
    "\n",
    "bars= sns.barplot(x=\"label\", y=\"willingness\", hue=\"Answer\", \n",
    "            palette= sns.color_palette(flatui[1:3]), \n",
    "            data=willingness_variables)\n",
    "\n",
    "def change_width(ax, new_value) :\n",
    "    for patch in ax.patches :\n",
    "        current_width = patch.get_width()\n",
    "        diff = current_width - new_value\n",
    "\n",
    "        # we change the bar width\n",
    "        patch.set_width(new_value)\n",
    "\n",
    "        # we recenter the bar\n",
    "        patch.set_x(patch.get_x() + diff * .5)\n",
    "\n",
    "\n",
    "change_width(ax, .37)\n",
    "sns.despine(bottom=True)\n",
    "bars.set(xlabel= '', ylabel= 'Willingness Score')\n",
    "bars.tick_params(axis='x', which='both', length=0)\n",
    "figure = bars.get_figure()\n",
    "figure.savefig('figures/history_treatment_willingness.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xlabel = 'Work Interfere'\n",
    "ylabel = 'Willingness Score'\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(5, 3))\n",
    "bars = sns.barplot(x= 'work_interfere', y= 'willingness', data= survey_coded, color= flatui[-1], errwidth=0.75)\n",
    "\n",
    "bars.set(xlabel= xlabel, ylabel= ylabel, xticklabels=['Never', 'Rarely', \"Don't know\", 'Sometimes', 'Often'])\n",
    "bars.set_xticklabels(['Never', 'Rarely', \"Don't know\", 'Sometimes', 'Often'], rotation=30)\n",
    "bars.set()\n",
    "sns.despine(bottom= True)\n",
    "change_width(ax, .95)\n",
    "bars.tick_params(axis='x', which='both', length=0)\n",
    "\n",
    "figure = bars.get_figure()\n",
    "figure.savefig('figures/work_interfere_willingness.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>willingness</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>-1</td>\n",
       "      <td>-0.659404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>-0.615385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>-1.053333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        willingness\n",
       "gender             \n",
       "-1        -0.659404\n",
       " 0        -0.615385\n",
       " 1        -1.053333"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# I also did this analysis for gender but I find it slightly less interesting.\n",
    "gender_willingness = survey_coded.groupby('gender').agg({'willingness':'mean'})\n",
    "gender_willingness"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **contrary to expected, it seems that women are less likely to open up about mental health. However, upon further thinking, this might make sense if we consider that tech is a male-dominated industry where perhaps climbing up the ladder as a woman is significantly harder. in that case, they would be much more scared to bring up these issues and make their lives ever harder?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>willingness</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>workplace_resources</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>-6</td>\n",
       "      <td>-2.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>-5</td>\n",
       "      <td>-1.323529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>-4</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>-3</td>\n",
       "      <td>-1.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>-2</td>\n",
       "      <td>-0.820000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>-1</td>\n",
       "      <td>-0.782383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>-0.752809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>-0.641509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>-0.453608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>-0.441558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.070175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>-0.707317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.347826</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     willingness\n",
       "workplace_resources             \n",
       "-6                     -2.600000\n",
       "-5                     -1.323529\n",
       "-4                     -1.000000\n",
       "-3                     -1.333333\n",
       "-2                     -0.820000\n",
       "-1                     -0.782383\n",
       " 0                     -0.752809\n",
       " 1                     -0.641509\n",
       " 2                     -0.453608\n",
       " 3                     -0.441558\n",
       " 4                      0.070175\n",
       " 5                     -0.707317\n",
       " 6                      0.347826"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# to reach the answer to our main question\n",
    "willingness_treatment = survey_coded.groupby('workplace_resources').agg({'willingness':'mean'})\n",
    "willingness_treatment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create a bar plot with the average willingness score for each workplace resourses category\n",
    "xlabel = 'Workplace Resources Score'\n",
    "ylabel = 'Willingness Score'\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "bars = sns.barplot(x='workplace_resources', y='willingness', data= survey_coded, \n",
    "                   color= flatui[3], errwidth=0.75)\n",
    "\n",
    "bars.set(xlabel= xlabel, ylabel= ylabel)\n",
    "sns.despine()\n",
    "change_width(ax, .9)\n",
    "figure = bars.get_figure()\n",
    "figure.savefig('figures/resources_willingness.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **it looks like the average willingness score goes up the more resources are available in the company! which is pretty cool**\n",
    "- **to try and get some statistical power on this we took the raw data and tried to make a linear regression to see if we could predict the willing score of a person using workplace resources.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The slope is: 0.1253597181423454\n",
      "The intercept is: -0.7280097538526822\n",
      "The r_value is: 0.044725026501352225\n",
      "The std error is: 0.01740512006838772\n",
      "1.090037487724972e-12\n"
     ]
    }
   ],
   "source": [
    "# linear regression\n",
    "x = survey_coded['workplace_resources']\n",
    "y = survey_coded['willingness']\n",
    "\n",
    "slope, intercept, r_value, p_value, std_err = stats.linregress(x, y)\n",
    "\n",
    "print ('The slope is: ' + str(slope))\n",
    "print ('The intercept is: ' + str(intercept))\n",
    "print ('The r_value is: ' + str(r_value**2))\n",
    "print ('The std error is: ' + str(std_err))\n",
    "print(p_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xlabel = 'Workplace Resources Score'\n",
    "ylabel = 'Willingness Score'\n",
    "reg = sns.regplot(x, y, x_estimator=np.mean,\n",
    "                  scatter_kws={'color':flatui[3]}, \n",
    "                  line_kws = {'color':flatui[3],\n",
    "                              'ls':'dashed'})\n",
    "reg.set(xlabel= xlabel, ylabel= ylabel)\n",
    "sns.despine()\n",
    "figure = reg.get_figure()\n",
    "figure.savefig('figures/resources_willingness_regplot.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **there doesn't seem to be a strong relationship between these variables BUT!**\n",
    "- **Even though our model only explains a small amount of the variation, when you consider all the things that might affect behavior towards mental health, even a small effect should not be ignored.**"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}