InsightDemo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Insight Analysis Demo\n",
    "\n",
    "### The task at hand:\n",
    "\n",
    "#### Why? (Goal)\n",
    "\n",
    "The organization wants to know where to invest resources in future educational programs.\n",
    "\n",
    "(Use survey data to make decisions about future projects.)\n",
    "- Survey data (n=1000) collected over 1 month\n",
    "\n",
    "\n",
    "#### How?\n",
    "Compare (self-assessed) **eating disorder knowledge** accross different **occupational categories**.\n",
    "\n",
    "- 'Eating disorder knowledge'\n",
    "- 'Occupational category'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "also installing the dependencies ‘ps’, ‘processx’, ‘callr’, ‘desc’, ‘pkgbuild’, ‘ellipsis’, ‘pkgload’, ‘praise’, ‘rlang’, ‘testthat’, ‘isoband’\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  There is a binary version available but the source version is later:\n",
      "      binary source needs_compilation\n",
      "callr  3.4.2  3.4.3             FALSE\n",
      "\n",
      "\n",
      "The downloaded binary packages are in\n",
      "\t/var/folders/hh/xymmft6114g4g3zb_0vxkfrm0000gn/T//RtmpssTC8T/downloaded_packages\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "installing the source package ‘callr’\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The downloaded binary packages are in\n",
      "\t/var/folders/hh/xymmft6114g4g3zb_0vxkfrm0000gn/T//RtmpssTC8T/downloaded_packages\n",
      "\n",
      "The downloaded binary packages are in\n",
      "\t/var/folders/hh/xymmft6114g4g3zb_0vxkfrm0000gn/T//RtmpssTC8T/downloaded_packages\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "also installing the dependencies ‘vctrs’, ‘progress’, ‘fs’, ‘lifecycle’, ‘broom’, ‘dbplyr’, ‘forcats’, ‘haven’, ‘hms’, ‘httr’, ‘jsonlite’, ‘modelr’, ‘pillar’, ‘purrr’, ‘readr’, ‘readxl’, ‘reprex’, ‘rstudioapi’, ‘rvest’, ‘stringr’, ‘tibble’, ‘tidyr’, ‘xml2’\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  There are binary versions available but the source versions are later:\n",
      "       binary source needs_compilation\n",
      "fs      1.3.2  1.4.0              TRUE\n",
      "tibble  2.1.3  3.0.0              TRUE\n",
      "xml2    1.2.5  1.3.0              TRUE\n",
      "\n",
      "\n",
      "The downloaded binary packages are in\n",
      "\t/var/folders/hh/xymmft6114g4g3zb_0vxkfrm0000gn/T//RtmpssTC8T/downloaded_packages\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "installing the source packages ‘fs’, ‘tibble’, ‘xml2’\n",
      "\n",
      "Warning message in install.packages(\"tidyverse\"):\n",
      "“installation of package ‘xml2’ had non-zero exit status”"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The downloaded binary packages are in\n",
      "\t/var/folders/hh/xymmft6114g4g3zb_0vxkfrm0000gn/T//RtmpssTC8T/downloaded_packages\n",
      "\n",
      "The downloaded binary packages are in\n",
      "\t/var/folders/hh/xymmft6114g4g3zb_0vxkfrm0000gn/T//RtmpssTC8T/downloaded_packages\n",
      "\n",
      "The downloaded binary packages are in\n",
      "\t/var/folders/hh/xymmft6114g4g3zb_0vxkfrm0000gn/T//RtmpssTC8T/downloaded_packages\n",
      "\n",
      "The downloaded binary packages are in\n",
      "\t/var/folders/hh/xymmft6114g4g3zb_0vxkfrm0000gn/T//RtmpssTC8T/downloaded_packages\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in get(method, envir = home):\n",
      "“internal error -3 in R_decompress1”"
     ]
    },
    {
     "ename": "ERROR",
     "evalue": "Error: package or namespace load failed for ‘ggplot2’ in get(method, envir = home):\n lazy-load database '/Library/Frameworks/R.framework/Versions/3.5/Resources/library/ggplot2/R/ggplot2.rdb' is corrupt\n",
     "output_type": "error",
     "traceback": [
      "Error: package or namespace load failed for ‘ggplot2’ in get(method, envir = home):\n lazy-load database '/Library/Frameworks/R.framework/Versions/3.5/Resources/library/ggplot2/R/ggplot2.rdb' is corrupt\nTraceback:\n",
      "1. library(ggplot2)",
      "2. tryCatch({\n .     attr(package, \"LibPath\") <- which.lib.loc\n .     ns <- loadNamespace(package, lib.loc)\n .     env <- attachNamespace(ns, pos = pos, deps)\n . }, error = function(e) {\n .     P <- if (!is.null(cc <- conditionCall(e))) \n .         paste(\" in\", deparse(cc)[1L])\n .     else \"\"\n .     msg <- gettextf(\"package or namespace load failed for %s%s:\\n %s\", \n .         sQuote(package), P, conditionMessage(e))\n .     if (logical.return) \n .         message(paste(\"Error:\", msg), domain = NA)\n .     else stop(msg, call. = FALSE, domain = NA)\n . })",
      "3. tryCatchList(expr, classes, parentenv, handlers)",
      "4. tryCatchOne(expr, names, parentenv, handlers[[1L]])",
      "5. value[[3L]](cond)",
      "6. stop(msg, call. = FALSE, domain = NA)"
     ]
    }
   ],
   "source": [
    "# Set-up\n",
    "\n",
    "install.packages(\"ggplot2\")\n",
    "install.packages(\"dplyr\")\n",
    "install.packages(\"plyr\")\n",
    "install.packages(\"tidyverse\")\n",
    "install.packages(\"ggpubr\")\n",
    "install.packages(\"ggridges\")\n",
    "install.packages(\"multcomp\")\n",
    "install.packages(\"RAM\")\n",
    "\n",
    "\n",
    "# library(ggplot2)\n",
    "# library(dplyr)\n",
    "# library(plyr)\n",
    "# library(tidyverse)\n",
    "# library(ggpubr)\n",
    "\n",
    "library(ggplot2)\n",
    "library(ggpubr)\n",
    "theme_set(theme_pubr())\n",
    "library(dplyr)\n",
    "library(ggridges)\n",
    "library(multcomp)\n",
    "library(tidyverse)\n",
    "\n",
    "# Read in the csv file:\n",
    "\n",
    "CEDA_w_NA=read.csv(\"CEDAClean.csv\", na.strings=c(\"\",\"NA\"))\n",
    "str(CEDA_w_NA)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Constructing two variables\n",
    "\n",
    "### RSR Score (Dependent Variable)\n",
    "(Preparedness to recognize, support, and provide resources to individuals who may be experiencing EDs)\n",
    "\n",
    "RSR Score: Aggregates respondents' responses regarding their self-assessed preparedness to recognize, support, and provide resources individuals who may be experiencing EDs. RSR Score incorporates answers for three survey questions:\n",
    "- *Identify_Recognize*: ‘I believe I can recognize the signs of an eating disorder in others’\n",
    "- *Identify_Support*: ‘I feel prepared to support someone who is experience an eating disorder’\n",
    "- *Identify_Resources*: 'I know about the resources available to support people experiencing eating disorders’\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
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IAAAgiEC3hqUE7v3r3NKW8NFv/85z+b\ni0xvvPFG+c1vfhMa8ayjnwYNGiRDhgwxNzjXRy527txZHnjggfB28xoBBBBAAAEEEEAgQwKu\nHJSTqG16iltvv6G3kIge0W0tq6e8rXmcRu9Z8/IXgWwLMCgn28KsHwEEEEAgnwKeOkJpQekg\nHLv7iVn5+leHvtvday18Hl4jgAACCCCAAAIIpC/gyYAy/WazBgQQ8KIAR3q92GvUGQEECkHA\nU4NyCqFDaCMCCCCAAAIIIOA1AQJKr/UY9UUAAQQQQAABBFwmQEDpsg6hOggggAACCCCAgNcE\nCCi91mPUFwEEEEAAAQQQcJkAAaXLOoTqIIAAAggggAACXhMgoPRaj1FfBBBAAAEEEEDAZQIE\nlC7rEKqDAAIIIIAAAgh4TYCA0ms9Rn0RQAABBBBAAAGXCRBQuqxDqA4CCCCAAAIIIOA1AQJK\nr/UY9UUAAQQQQAABBFwmQEDpsg6hOggggAACCCCAgNcECCi91mPUFwEEEEAAAQQQcJkAAaXL\nOoTqIIAAAggggAACXhMgoPRaj1FfBBBAAAEEEEDAZQIElC7rEKqDAAIIIIAAAgh4TYCA0ms9\nRn0RQAABBBBAAAGXCRBQuqxDqA4CCCCAAAIIIOA1AQJKr/UY9UUAAQQQQAABBFwmQEDpsg6h\nOggggAACCCCAgNcECCi91mPUFwEEEEAAAQQQcJkAAaXLOoTqIIAAAggggAACXhMgoPRaj1Ff\nBBBAAAEEEEDAZQIElC7rEKqDAAIIIIAAAgh4TYCA0ms9Rn0RQAABBBBAAAGXCRBQuqxDqA4C\nCCCAAAIIIOA1AQJKr/UY9UUAAQQQQAABBFwmQEDpsg6hOggggAACCCCAgNcECCi91mPUFwEE\nEEAAAQQQcJkAAaXLOoTqIIAAAggggAACXhMgoPRaj1FfBBBAAAEEEEDAZQIElC7rEKqDAAII\nIIAAAgh4TYCA0ms9Rn0RQAABBBBAAAGXCRBQuqxDqA4CCCCAAAIIIOA1AQJKr/UY9UUAAQQQ\nQAABBFwmQEDpsg6hOggggAACCCCAgNcECCi91mPUFwEEEEAAAQQQcJkAAaXLOoTqIIAAAggg\ngAACXhMgoPRaj1FfBBBAAAEEEEDAZQIElC7rEKqDAAIIIIAAAgh4TYCA0ms9Rn0RQAABBBBA\nAAGXCRBQuqxDqA4CCCCAAAIIIOA1AQJKr/UY9UUAAQQQQAABBFwmQEDpsg6hOggggAACCCCA\ngNcECCi91mPUFwEEEEAAAQQQcJkAAaXLOoTqIIAAAggggAACXhMgoPRaj1FfBBBAAAEEEEDA\nZQIElC7rEKqDAAIIIIAAAgh4TYCA0ms9Rn0RQAABBBBAAAGXCRBQuqxDqA4CCCCAAAIIIOA1\nAQJKr/UY9UUAAQQQQAABBFwmQEDpsg6hOggggAACCCCAgNcECCi91mPUFwEEEEAAAQQQcJkA\nAaXLOoTqIIAAAggggAACXhMo67UKU18EEMiOQJU+U7Oz4jTWWjy2expLsygCCCCAQK4EOEKZ\nK2nKQQABBBBAAAEEfCpAQOnTjqVZCCCAAAIIIIBArgQIKHMlTTkIIIAAAggggIBPBQgofdqx\nNAsBBBBAAAEEEMiVAAFlrqQpBwEEEEAAAQQQ8KkAAaVPO5ZmIYAAAggggAACuRIgoMyVNOUg\ngAACCCCAAAI+FSCg9GnH0iwEEEAAAQQQQCBXAgSUuZKmHAQQQAABBBBAwKcCBJQ+7ViahQAC\nCCCAAAII5EqAgDJX0pSDAAIIIIAAAgj4VICA0qcdS7MQQAABBBBAAIFcCRBQ5kqachBAAAEE\nEEAAAZ8KuDagnDx5suzcudOW/cCBA/LBBx/IvHnz5Pjx4ynPY7sgExFAAAEEEEAAAQRKJFC2\nRHPnaOb33ntPRo0aJc8//7w0bNgwotQxY8bIO++8I61btzYBZ6lSpWTEiBER8yUzT8RKeYMA\nAggggAACCCCQsoCrjlCePn1aXnzxRRk9erRtg9atWycTJkyQYcOGyciRI+WNN96QOnXqyPDh\nw0PzJzNPaGZeIIAAAggggAACCKQt4KojlAMGDJD9+/fL448/bhtUTpo0Sa666ipp27ataXiF\nChWkV69e8utf/1r27Nkj9evXl2TmSVuNFSCAAAIeFqjSZ6rral88trvr6kSFEEAgeQFXHaHs\n0qWLOerYqVMn2xZo0NiyZcuIvBYtWpj327dvN3+TmSd8BefPn5ezZ89G/Fy4cEH0JxAIhM8a\nem3lhyaEvdBlkslPtO5E+WFFRrx0c9laUa2fXbLcstnudMtOpW5Wu+zanO9pVt2c2pXv+mn5\nbq6bk08y+2C622I2ynZaZ66mq4lTf1umifLt6qrLWMvHy0+07kT58dYdr7+d1qvrs+rtNI+V\nb1d2+PJ2+bpOXT7RuhPl26072bLjuSRTt3yWnYqLZZ5uu/NRtpN1+HRXBZS9e/eWGjVqhNcv\n4vXevXulevXqEdOqVq1q3hcVFZm/ycwTvoKnnnrKXI+p12RaP+PGjZMpU6bIwYMHw2cNvZ4+\nfbrJt9soiouLTd7ChQtD84e/0MBX171+/frwyaHXq1atMvn79u0LTQt/MWvWLJP/7bffhk82\nr0+ePGnyPv3005g8nbBr1y6Tv2bNGtv8v//97yZfg3K7NHfuXJOvlyZEpzNnzpi82bNnR2eZ\n95aVlmGX1q5da5bfsWOHXbbMnz/f5J84cSIm/9y5cyZvxowZMXk6QQdxqfmKFSts8zds2GDy\nt27dapu/aNEik+80AEzX/cknn9guq9ul5rsxbdmyxdRt06ZNbqyeqZPTfuDWClv7wZw5c2yr\nqJ9Puj2sXr3aNt/aD5wGJCazH8ycOdN23Xr2R8teuXKlbX6+J2rddJu0S0uWLDF1P3z4sF22\nfPzxx/LRRx/Z5h05csQs+9lnn9nmJ9oPli1bZpZ3+j6YNm2aybf7ktfPDG2X0/fBtm3bZPPm\nzbb10onaV7q8036gfa358b4PdPCqXUr0faDbqK47ne8DL+4H1veB9o1divd9oNuAmuk2YZcO\nHTpk8nWbsku6LejyifYD3abtku4H+mOXrP1A9yW7ZO0H+hmWanLVKe9EjVCQihUrRsymp701\naTClKZl5zIz/+FWlShW5+OKLwyeJrrN8+fJSurR9vK15TkkHCWl+uXLlbGcpU6aMyde/dqls\n2bIJy9ajqnbJKtupftoezdMy7FIyZTutO1HZmq8pUdlOLlqu/ljrCa9/MmXrsk59YrU7lbK1\nHvHWbdUteKwtvMqueG2126lP3FBJp33QDXWzq4PV37pN2KVk90GnbVG3Yaf9QMvTvFTLtqtv\nLqdpvRO122l70M9su4BO62+ZO7lomZrntB9Y5vHKtvtcCi/b6bNHy3Zqsy6fqGytt/6zbld+\nutuiVbZT/ax8u7K17lo3J3OrT5zMrc+mfJStZWq90ynbqb+tdjvlZ6JstbdL1vaQqGyn/rRb\nZ/S0UsGd0HXfdPqfdI8ePcwo7w4dOoTqrNO6du0q/fv3D03TI4LdunWTwYMHS8eOHc1yieYJ\nLcwLBHIk4IVr1qhjahuDF6/980Jfp9YbLIUAAvkSsD9Ula/aJCi3Vq1aEn3Kwzr0W69ePbN0\nMvMkKIZsBBBAIGUBgrWU6VgQAQQ8LGB/TtelDdJrHJcuXRpxakOvB9DD0w0aNDC1TmYelzaP\naiGAAAIIIIAAAp4U8FRA2bNnT9GLWvXG5adOnTIDW8aPHy99+/aVypUrmw5IZh5P9hSVRgAB\nBBBAAAEEXCrgqVPedevWlUGDBsmQIUPMDc51hHfnzp3lgQceCPEmM09oZl4ggAACCCCAAAII\npC3gyoBSn36zYMEC28bddNNN5vYQevsNnc9ulFgy89iunIkIIIAAAggggAACJRZI6ZT3sWPH\nJNG96/Q+avpM7mwkHdauT8WxCyat8pKZx5qXvwgggAACCCCAAAKpC6QUUE6ePFmuvvrquKXq\nM7n1eka9BRAJAQQQQAABBBBAwL8CSZ/yfvvtt0Xv+ahp8eLF5u///M//mL/Rv/RpJvpMbT2C\naD3JJnoe3iOAAAIIIIAAAgj4QyDpgFIfiff0009HtLpfv34R76Pf3H///XLRRRdFT+Y9Aggg\ngAACCCCAgI8Ekg4on3jiCXPdoj5YR59Lqrfu0WdeRye9dlEf7aMDZnRwDAkBBBBAAAEEEEDA\n3wJJB5QaJD744INGo3HjxqIPve/du7e/dWgdAggggAACCCCAQEKBpAPK8DXdfPPNoj8kBBBA\nAAEEEEAAAQRSCiiVraioSF5++WXzKES9jdD58+dtNefOnWs7nYkIIIAAAggggAAC/hBIKaA8\nc+aMdOnSRVatWuUPBVqBAAIIIIAAAgggkLJASvehnDhxogkm77rrLlm3bp3obYLOnTtn+5Ny\nzVgQAQQQQAABBBBAwBMCKR2h/OKLL0zjXn31ValXr54nGkolEUAAAQQQQAABBLIjkNIRyrp1\n65qblleoUCE7tWKtCCCAAAIIIIAAAp4RSCmgvOWWW8wgHH16DgkBBBBAAAEEEECgsAVSOuXd\nrl07GTZsmPzud78TvZF5hw4dzKnv0qVj49NLLrmksIVpPQIIIIAAAggg4HOBlALKd9991wSU\nBw8elAEDBsQl0ifrkBBAAAEEEEAAAQT8K5BSQFm7dm1p3769f1VoGQIIIIAAAggggEDSAikF\nlHoNpf6QEEAAAQQQQAABBBCIvegREwQQQAABBBBAAAEESiCQ0hFKfZziqFGjkipm8uTJSc3H\nTAgggAACCCCAAALeFEgpoNy5c6d88MEHcVtctWpVqVGjRtx5yEQAAQQQQAABBBDwvkBKAWWv\nXr3knnvui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IFIwAAWXBdDUNRQABBBBA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\nyJvA/wJd/VhShU5K6AAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# A function that aggregates respondents' responses regarding their preparedness to recognize, support, and provide resources \n",
    "# individuals who may be experiencing EDs. # We check answers for three survey questions:\n",
    "# Identify_Recognize: ‘I believe I can recognize the signs of an eating disorder in others’\n",
    "# Identify_Support: ‘I feel prepared to support someone who is experience an eating disorder’\n",
    "# Identify_Resources: 'I know about the resources available to support people experiencing eating disorders’\n",
    "\n",
    "\n",
    "RSR_scoring <- function(df){\n",
    "\n",
    "Identify_Recognize_num <- ConvertCategoriesNum(df$Identify_Recognize)  \n",
    "Identify_Support <- ConvertCategoriesNum(df$Identify_Support)  \n",
    "Identify_Resources <- ConvertCategoriesNum(df$Identify_Resources)\n",
    "\n",
    "RSR_score <- Identify_Recognize_num + Identify_Support + Identify_Resources\n",
    "RSR_score\n",
    "}\n",
    "\n",
    "\n",
    "RSR_score <- RSR_scoring(CEDA_w_NA)\n",
    "\n",
    "# str(RSR_score)\n",
    "# table(RSR_score)\n",
    "\n",
    "# omit NAs\n",
    "CEDA_w_NA$RSR_Score <- RSR_score\n",
    "# summary(CEDA$RSR_Score) # - Has one NA, remove:\n",
    "CEDA<- data.frame(\"RSR_Score\" = na.omit(CEDA$RSR_Score), )\n",
    "# summary(CEDA_narm)\n",
    "\n",
    "options(repr.plot.width = 5.5, repr.plot.height = 3)\n",
    "\n",
    "rsr_base <- ggplot(CEDA_narm, aes(RSR_Score))\n",
    "# Number of cars in each class:\n",
    "rsr_base + \n",
    "  geom_bar(fill = \"#0073C2FF\") +\n",
    "  theme_pubclean()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Occupational Categories (Independent Variable)\n",
    "We compare the differences _between_ categories – first we must define the categories!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A tibble: 7 × 2</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>Occupation</th><th scope=col>counts</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>Other          </td><td>693</td></tr>\n",
       "\t<tr><td>Education      </td><td> 92</td></tr>\n",
       "\t<tr><td>Support        </td><td> 60</td></tr>\n",
       "\t<tr><td>Health         </td><td> 44</td></tr>\n",
       "\t<tr><td>Allied health  </td><td> 39</td></tr>\n",
       "\t<tr><td>First responder</td><td> 21</td></tr>\n",
       "\t<tr><td>Fitness        </td><td> 13</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 7 × 2\n",
       "\\begin{tabular}{r|ll}\n",
       " Occupation & counts\\\\\n",
       " <chr> & <int>\\\\\n",
       "\\hline\n",
       "\t Other           & 693\\\\\n",
       "\t Education       &  92\\\\\n",
       "\t Support         &  60\\\\\n",
       "\t Health          &  44\\\\\n",
       "\t Allied health   &  39\\\\\n",
       "\t First responder &  21\\\\\n",
       "\t Fitness         &  13\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 7 × 2\n",
       "\n",
       "| Occupation &lt;chr&gt; | counts &lt;int&gt; |\n",
       "|---|---|\n",
       "| Other           | 693 |\n",
       "| Education       |  92 |\n",
       "| Support         |  60 |\n",
       "| Health          |  44 |\n",
       "| Allied health   |  39 |\n",
       "| First responder |  21 |\n",
       "| Fitness         |  13 |\n",
       "\n"
      ],
      "text/plain": [
       "  Occupation      counts\n",
       "1 Other           693   \n",
       "2 Education        92   \n",
       "3 Support          60   \n",
       "4 Health           44   \n",
       "5 Allied health    39   \n",
       "6 First responder  21   \n",
       "7 Fitness          13   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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MbWwR3zZsWp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\nsj0wf6JUAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# This section creates respondent categories based on their occupation type. \n",
    "# (Variable name: Occupation_Cat_Lab)\n",
    "\n",
    "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # \n",
    "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #\n",
    "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # \n",
    "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #\n",
    "\n",
    "# This section sets up boolean variables to make it easy to access subsets of the data by occupation\n",
    "\n",
    "# Occupations_Sum is a pre-processed list of respondents occupations, broadly grouped by sector and by \n",
    "# their level of interaction with other people who they may provide services to in a professional capacity.\n",
    "#\n",
    "# This section processes of respondents' reported occupation and classifies it into the categories:\n",
    "# Allied health, health, other care (non-medical including counselling, psychologists, etc) and \n",
    "# support-related professions, first responders, education, fitness, and other\n",
    "\n",
    "# We classify respondent's occupations by creating boolean variables for each of these occupations:\n",
    "# Boolean_occ_alliedhealth\n",
    "# Boolean_occ_health\n",
    "# Boolean_occ_support\n",
    "# Boolean_occ_firstresp\n",
    "# Boolean_occ_edu\n",
    "# Boolean_occ_fitness\n",
    "# Boolean_occu_other\n",
    "\n",
    "# Below are the detailed break-downs of the occupations included in each category.\n",
    "\n",
    "# Boolean_occ_alliedhealth: Health and allied health services for this analysis include:\n",
    "# Note: Technicians in laboratories are excluded, because in survey respondent data it was not always \n",
    "# possible to separate non-medical lab technicians from medical lab technicians based on open-ended occupation \n",
    "# responses.\n",
    "# Medical, Nurse practitioner, Nurse, Occupational therapist, Psychologist, Psychiatrist, Psychotherapist, \n",
    "# Physical therapist, General practitioner (family doctor), Paramedic, Social worker, Nutritionist or dietitian, \n",
    "\n",
    "# Boolean_occ_health: Interaction with health service users: \n",
    "# Medical, Nurse practitioner, Nurse, General practitioner (family doctor)\n",
    "\n",
    "# Boolean_occ_support: Counselling, therapy, guidance, support, and social service work, social worker\n",
    "# Counselling (other), Occupational therapist, psychologist, social service worker, Psychiatrist, \n",
    "# Psychotherapist, Social worker, Therapy (other)\n",
    "\n",
    "# Boolean_occ_firstresp: First responders include:\n",
    "# First responder (other), Firefighter, Paramedic, Police officer, Police (other)\n",
    "\n",
    "# Boolean_occ_edu: Educator responders include:\n",
    "# College/University course instructor/professor, School teacher, Education (other), School guidance counsellor\n",
    "\n",
    "# Boolean_occ_fitness: Fitness professional include:\n",
    "# Athletic team coach, Sports or fitness instructor, Personal fitness trainer\n",
    "\n",
    "# Boolean_occu_other: Work environments not related to health, fitness, education.\n",
    "# Admin, HR, Service/Retail/Sales, Management\n",
    "\n",
    "\n",
    "# Analysis set-up:\n",
    "# Boolean occupational category columns for occupation groups:\n",
    "\n",
    "# Boolean_occ_alliedhealth\n",
    "# Boolean_occ_health\n",
    "# Boolean_occ_support\n",
    "# Boolean_occ_firstresp\n",
    "# Boolean_occ_edu\n",
    "# Boolean_occ_fitness\n",
    "# Boolean_occu_other\n",
    "\n",
    "# Boolean_occ_alliedhealth: Health and allied health services for this analysis include:\n",
    "# Note: Technicians in laboratories are excluded, because in survey respondent data it was not always \n",
    "# possible to separate non-medical lab technicians from medical lab technicians based on open-ended \n",
    "# occupation responses.\n",
    "# Medical, Nutritionist or dietitian, \n",
    "\n",
    "# The boolean variables are created below:\n",
    "\n",
    "Boolean_occ_alliedhealth <- c(CEDA$Occupations_Sum == \"Medical\"|\n",
    "                          CEDA$Occupations_Sum == \"Nutritionist or dietitian\"\n",
    ")\n",
    "\n",
    "# Boolean_occ_health: Interaction with health service users: \n",
    "# Medical, Nurse practitioner, Nurse, General practitioner (family doctor)\n",
    "\n",
    "\n",
    "Boolean_occ_health <- c(CEDA$Occupations_Sum == \"Nurse practitioner\"|\n",
    "                          CEDA$Occupations_Sum == \"Nurse\"|\n",
    "                          CEDA$Occupations_Sum == \"General practitioner (family doctor)\"\n",
    ")\n",
    "\n",
    "# Boolean_occ_support: Counselling, therapy, guidance, support, and social service work, social worker\n",
    "# Counselling (other), School guidance counsellor, Occupational therapist, Psychologist, Social service worker, \n",
    "# Psychiatrist, Psychotherapist, Social worker, Therapy (other)\n",
    "\n",
    "Boolean_occ_support <- c(CEDA$Occupations_Sum == \"Counselling (other)\"|\n",
    "                           CEDA$Occupations_Sum == \"Occupational therapist\"|\n",
    "                           CEDA$Occupations_Sum == \"Psychologist\"|\n",
    "                           CEDA$Occupations_Sum == \"Social service worker\"|\n",
    "                           CEDA$Occupations_Sum == \"Psychiatrist\"|\n",
    "                           CEDA$Occupations_Sum == \"Psychotherapist\"|\n",
    "                           CEDA$Occupations_Sum == \"Social worker\"|\n",
    "                           CEDA$Occupations_Sum == \"Therapy (other)\"|\n",
    "                           CEDA$Occupations_Sum == \"School guidance counsellor\"\n",
    ") \n",
    "\n",
    "# Boolean_occ_firstresp: First responders include:\n",
    "# First responder (other), Firefighter, Paramedic, Police officer, Police (other)\n",
    "\n",
    "Boolean_occ_firstresp <- c(CEDA$Occupations_Sum == \"First responder (other)\"|\n",
    "                             CEDA$Occupations_Sum == \"Firefighter\"|\n",
    "                             CEDA$Occupations_Sum == \"Paramedic\"|\n",
    "                             CEDA$Occupations_Sum == \"Police officer\"|\n",
    "                             CEDA$Occupations_Sum == \"Police (other)\"\n",
    ") \n",
    "\n",
    "# Boolean_occ_edu: Educator responders include:\n",
    "# College/University course instructor/professor, School teacher, Education (other), School guidance counsellor\n",
    "\n",
    "Boolean_occ_edu <- c(CEDA$Occupations_Sum == \"College/University course instructor/professor\"|\n",
    "                       CEDA$Occupations_Sum == \"School teacher\"|\n",
    "                       CEDA$Occupations_Sum == \"Education (other)\"\n",
    ") \n",
    "\n",
    "# Boolean_occ_fitness: Fitness professional include:\n",
    "# Athletic team coach, Sports or fitness instructor, Personal fitness trainer\n",
    "\n",
    "Boolean_occ_fitness <- c(CEDA$Occupations_Sum == \"Athletic team coach\"|\n",
    "                           CEDA$Occupations_Sum == \"Sports or fitness instructor\"|\n",
    "                           CEDA$Occupations_Sum == \"Personal fitness trainer\"\n",
    ") \n",
    "\n",
    "# Boolean_occu_other: Work environments not related to health, fitness, education.\n",
    "# Admin, HR, Service/Retail/Sales, Management\n",
    "\n",
    "Boolean_occ_other <- c(CEDA$Occupations_Sum == \"Admin\"|\n",
    "                         CEDA$Occupations_Sum == \"HR\"|\n",
    "                         CEDA$Occupations_Sum == \"Service/Retail/Sales\"|\n",
    "                         CEDA$Occupations_Sum == \"Management\"\n",
    ") \n",
    "\n",
    "\n",
    "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # \n",
    "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # \n",
    "# Brief summaries of each occupational category :\n",
    "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # \n",
    "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # \n",
    "\n",
    "# summary(Boolean_occ_alliedhealth)\n",
    "# summary(Boolean_occ_health)\n",
    "# summary(Boolean_occ_support)\n",
    "# summary(Boolean_occ_firstresp)\n",
    "# summary(Boolean_occ_edu)\n",
    "# summary(Boolean_occ_fitness)\n",
    "# summary(Boolean_occ_other)\n",
    "\n",
    "\n",
    "\n",
    "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # \n",
    "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # \n",
    "#\n",
    "# To work more easily with each occupational we write functions to calculate 'grade' the stigma and \n",
    "# literacy scores of a group of survey respondents.\n",
    "# We create a subgroup for each occupational category, and then create a function that allows us\n",
    "# to grade how a subgroup scored on their attitudes and knowledge of EDs (a stigma and a literacy score)\n",
    "# We also create a function that applies the scoring function to all subgroups, to automate the\n",
    "# process of scoring all of the occupational categories.\n",
    "#\n",
    "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # \n",
    "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # \n",
    "\n",
    "# First create a subset for each occupational category, with the help of the boolean variables set up above. \n",
    "# We can use these subsets to calculate literacy and stigma scores, as well as descriptive statistics \n",
    "# (e.g. mean, sd, etc) for each ocupational category.\n",
    "\n",
    "Group_alliedhealth <- subset(CEDA,Boolean_occ_alliedhealth==TRUE)\n",
    "Group_health <- subset(CEDA,Boolean_occ_health==TRUE)  \n",
    "Group_support <- subset(CEDA,Boolean_occ_support==TRUE) \n",
    "Group_firstresp <- subset(CEDA,Boolean_occ_firstresp==TRUE)   \n",
    "Group_edu <- subset(CEDA,Boolean_occ_edu==TRUE)\n",
    "Group_fitness <- subset(CEDA,Boolean_occ_fitness==TRUE)  \n",
    "Group_other <- subset(CEDA,Boolean_occ_other==TRUE)\n",
    "\n",
    "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # \n",
    "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # \n",
    "\n",
    "# And create a column of zeros/ones for each class\n",
    "\n",
    "CEDA$Num_occ_alliedhealth  <- as.numeric(Boolean_occ_alliedhealth)\n",
    "CEDA$Num_occ_health  <- as.numeric(Boolean_occ_health)\n",
    "CEDA$Num_occ_support  <- as.numeric(Boolean_occ_support)\n",
    "CEDA$Num_occ_firstresp  <- as.numeric(Boolean_occ_firstresp)\n",
    "CEDA$Num_occ_edu  <- as.numeric(Boolean_occ_edu)\n",
    "CEDA$Num_occ_fitness  <- as.numeric(Boolean_occ_fitness)\n",
    "\n",
    "\n",
    "# We assign eaach category a numerical in the vector 'Occupation_Cat':\n",
    "\n",
    "CEDA$Occupation_Cat <- CEDA$Num_occ_alliedhealth + 2*CEDA$Num_occ_health + 3*CEDA$Num_occ_support + \n",
    "    4*CEDA$Num_occ_firstresp + 5*CEDA$Num_occ_edu + 6*CEDA$Num_occ_fitness \n",
    "# table(CEDA$Occupation_Cat)\n",
    "\n",
    "# Allied health = Category 1\n",
    "# Health = Category 2\n",
    "# Support = Category 3\n",
    "# First responders = Category 4\n",
    "# Education = Category 5\n",
    "# Fitness = Category 6\n",
    "# Other = Category 0\n",
    "\n",
    "# Check for no repeating categories (to satisfy analysis conditions that categories are independent):\n",
    "\n",
    "CEDA$Occupation_Conditions <- CEDA$Num_occ_alliedhealth + CEDA$Num_occ_health + CEDA$Num_occ_support + \n",
    "    CEDA$Num_occ_firstresp + CEDA$Num_occ_edu + CEDA$Num_occ_fitness \n",
    "\n",
    "# The test bellow checks that every respondend is assigned to exactly one category (i.e. has exactly one value of 1 \n",
    "# among all boolean categorical variables)\n",
    "# max(CEDA$Occupation_Conditions)\n",
    "\n",
    "\n",
    "# We create a new column, Occupation_Cat_Lab, for the occupational categorical variable\n",
    "CEDA$Occupation_Cat_Lab <- ifelse(CEDA$Occupation_Cat == 1, \"Allied health\",\"Other\")       # 'Other' category is labelled here\n",
    "CEDA$Occupation_Cat_Lab <- ifelse(CEDA$Occupation_Cat == 2, \"Health\",CEDA$Occupation_Cat_Lab)\n",
    "CEDA$Occupation_Cat_Lab <- ifelse(CEDA$Occupation_Cat == 3, \"Support\",CEDA$Occupation_Cat_Lab)\n",
    "CEDA$Occupation_Cat_Lab <- ifelse(CEDA$Occupation_Cat == 4, \"First responder\",CEDA$Occupation_Cat_Lab)\n",
    "CEDA$Occupation_Cat_Lab <- ifelse(CEDA$Occupation_Cat == 5, \"Education\",CEDA$Occupation_Cat_Lab)\n",
    "CEDA$Occupation_Cat_Lab <- ifelse(CEDA$Occupation_Cat == 6, \"Fitness\",CEDA$Occupation_Cat_Lab)\n",
    "\n",
    "# table(CEDA$Occupation_Cat_Lab)\n",
    "\n",
    "# A quick plot to look at counts of respondents per category:\n",
    "\n",
    "# cat_counts_1 <- ggplot(CEDA, aes(Occupation_Cat_Lab)) +\n",
    "#    geom_bar(fill = \"#0073C2FF\") +\n",
    "#    theme_pubclean() + \n",
    "#    coord_flip() +\n",
    "#    ylab(\"Respondents per occupation\") + xlab(\"Occupation categories\")\n",
    "\n",
    "# Same as above but bars reordered by frequency of response, and resized\n",
    "\n",
    "\n",
    "df_cat_counts <- CEDA %>%\n",
    "  group_by(Occupation = Occupation_Cat_Lab) %>%\n",
    "  summarise(counts = n()) %>%\n",
    "  arrange(desc(counts))\n",
    "\n",
    "\n",
    "df_cat_counts\n",
    "\n",
    "options(repr.plot.width = 7, repr.plot.height = 3.5)\n",
    "\n",
    "ggplot(df_cat_counts, aes(x = Occupation, y = counts)) +\n",
    "  geom_bar(fill = \"#0073C2FF\", stat = \"identity\") +\n",
    "  geom_text(aes(label = counts), vjust = -0.3) + \n",
    "  theme_pubclean() +\n",
    "  ylab(\"Respondents per occupation\") + xlab(\"Occupation categories\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# WE DEFINE A FEW A FEW SET-UP FUNCTIONS:\n",
    "\n",
    "\n",
    "# Many variables have answers ranging from 'Strongly agree' to 'Strongly disagree'\n",
    "# We create a function to convert these variables to numbers:\n",
    "   \n",
    "# A function that convers categorical data to numerical (Strongly agree = 5, ..., Strongly disagree = 0)\n",
    "   \n",
    "ConvertCategoriesNum <- function(column){\n",
    "  \n",
    "  numerical <- \n",
    "    ifelse(column == \"Strongly agree\", 5,\n",
    "           ifelse(column == \"Somewhat agree\",4,\n",
    "                  ifelse(column == \"Neither disagree nor agree\"|column == \"Neither disagree or agree\",3,\n",
    "                         ifelse(column == \"Disagree\"|column == \"Somewhat disagree\",2,\n",
    "                                ifelse(column == \"Strongly disagree\",1,0\n",
    "                                )))))\n",
    "  \n",
    "  numerical\n",
    "}\n",
    "\n",
    "# A function that convers categorical data to numerical (Strongly DISAGREE = 5, ..., Strongly AGREE = 0)\n",
    "# (This is the 'reverse function from the one above)\n",
    "\n",
    "ConvertCategoriesNum_Rev <- function(column){\n",
    "  \n",
    "  numerical <- \n",
    "    ifelse(column == \"Strongly agree\", 1,\n",
    "           ifelse(column == \"Somewhat agree\",2,\n",
    "                  ifelse(column == \"Neither disagree nor agree\"|column == \"Neither disagree or agree\",3,\n",
    "                         ifelse(column == \"Disagree\"|column == \"Somewhat disagree\",4,\n",
    "                                ifelse(column == \"Strongly disagree\",5,0\n",
    "                                )))))\n",
    "  \n",
    "  numerical\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Picking joint bandwidth of 1.07\n",
      "Warning message:\n",
      "“Removed 1 rows containing non-finite values (stat_density_ridges).”"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A tibble: 7 × 5</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>Occupation</th><th scope=col>mean</th><th scope=col>max</th><th scope=col>min</th><th scope=col>sd</th></tr>\n",
       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>Support        </td><td>9.850000</td><td>15</td><td>3</td><td>2.748343</td></tr>\n",
       "\t<tr><td>Allied health  </td><td>9.552632</td><td>14</td><td>3</td><td>2.983716</td></tr>\n",
       "\t<tr><td>First responder</td><td>8.952381</td><td>14</td><td>5</td><td>2.801360</td></tr>\n",
       "\t<tr><td>Health         </td><td>8.636364</td><td>15</td><td>4</td><td>2.796101</td></tr>\n",
       "\t<tr><td>Fitness        </td><td>8.307692</td><td>12</td><td>5</td><td>2.097006</td></tr>\n",
       "\t<tr><td>Education      </td><td>7.891304</td><td>15</td><td>3</td><td>2.959271</td></tr>\n",
       "\t<tr><td>Other          </td><td>7.776335</td><td>15</td><td>3</td><td>2.622802</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 7 × 5\n",
       "\\begin{tabular}{r|lllll}\n",
       " Occupation & mean & max & min & sd\\\\\n",
       " <fct> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t Support         & 9.850000 & 15 & 3 & 2.748343\\\\\n",
       "\t Allied health   & 9.552632 & 14 & 3 & 2.983716\\\\\n",
       "\t First responder & 8.952381 & 14 & 5 & 2.801360\\\\\n",
       "\t Health          & 8.636364 & 15 & 4 & 2.796101\\\\\n",
       "\t Fitness         & 8.307692 & 12 & 5 & 2.097006\\\\\n",
       "\t Education       & 7.891304 & 15 & 3 & 2.959271\\\\\n",
       "\t Other           & 7.776335 & 15 & 3 & 2.622802\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 7 × 5\n",
       "\n",
       "| Occupation &lt;fct&gt; | mean &lt;dbl&gt; | max &lt;dbl&gt; | min &lt;dbl&gt; | sd &lt;dbl&gt; |\n",
       "|---|---|---|---|---|\n",
       "| Support         | 9.850000 | 15 | 3 | 2.748343 |\n",
       "| Allied health   | 9.552632 | 14 | 3 | 2.983716 |\n",
       "| First responder | 8.952381 | 14 | 5 | 2.801360 |\n",
       "| Health          | 8.636364 | 15 | 4 | 2.796101 |\n",
       "| Fitness         | 8.307692 | 12 | 5 | 2.097006 |\n",
       "| Education       | 7.891304 | 15 | 3 | 2.959271 |\n",
       "| Other           | 7.776335 | 15 | 3 | 2.622802 |\n",
       "\n"
      ],
      "text/plain": [
       "  Occupation      mean     max min sd      \n",
       "1 Support         9.850000 15  3   2.748343\n",
       "2 Allied health   9.552632 14  3   2.983716\n",
       "3 First responder 8.952381 14  5   2.801360\n",
       "4 Health          8.636364 15  4   2.796101\n",
       "5 Fitness         8.307692 12  5   2.097006\n",
       "6 Education       7.891304 15  3   2.959271\n",
       "7 Other           7.776335 15  3   2.622802"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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fpy7r777mgx/hhhq3Hjxv56dCUsrJgg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CbouAKAxuO3TScCIgCoM8B45EICUlBR9+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\ndPr0aa3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FUI1CIXHzuehSeePYElKxL1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\nrNMXrAkJkAAJkAAJkAAJkAAJWI7A/wHYIx5Lx2mwNQAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a dataframe (CEDA_analysis) that will be used for analyses regarding questions 4, 8, \n",
    "# and respondents' RSR scores\n",
    "\n",
    "CEDA_analysis <- data.frame(\"Occupation\" = CEDA$Occupation_Cat_Lab,\n",
    "                              \"RSR_Score\" = RSR_score\n",
    "                              )\n",
    "\n",
    "# Graph of distributions of RSR Score by Occupational Category:\n",
    "\n",
    "cbPalette <- c(\"#999999\", \"#E69F00\", \"#56B4E9\", \"#009E73\", \"#F0E442\", \"#0072B2\", \"#D55E00\")\n",
    "\n",
    "\n",
    "options(repr.plot.width = 6.5, repr.plot.height = 4.9)\n",
    "ggplot(\n",
    "  CEDA_analysis, \n",
    "    aes(x = RSR_Score, y = Occupation)) +\n",
    "    geom_density_ridges(aes(fill = Occupation)) +\n",
    "    scale_fill_manual(values = cbPalette)+\n",
    "    labs(title = \"RSR Score by Occupation\") \n",
    "\n",
    "\n",
    "means_cat <- CEDA_analysis %>%\n",
    "  group_by(Occupation) %>%\n",
    "  summarise(mean = mean(RSR_Score,na.rm=TRUE), \n",
    "            max= max(RSR_Score,na.rm=TRUE), \n",
    "            min = min(RSR_Score,na.rm=TRUE), \n",
    "            sd = sd(RSR_Score,na.rm=TRUE)) %>%\n",
    "  arrange(desc(mean))\n",
    "\n",
    "means_cat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysis\n",
    "\n",
    "## ANOVA\n",
    "Is there an effect?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "             Df Sum Sq Mean Sq F value   Pr(>F)    \n",
       "Occupation    6    367   61.24   8.494 5.27e-09 ***\n",
       "Residuals   954   6878    7.21                     \n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "1 observation deleted due to missingness"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ANOVA:\n",
    "# Ho: The mean RSR score is the same accross occupational categories\n",
    "# Ho: The mean RSR score is the same accross occupational categories\n",
    "\n",
    "# tapply(CEDA_analysis$RSR_Score,CEDA_analysis$Occupation,mean, na.rm=TRUE)\n",
    "\n",
    "anova_RSR <- aov(RSR_Score ~ Occupation, data=CEDA_analysis)\n",
    "\n",
    "summary(anova_RSR)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tukey's HSD\n",
    "What is the effect?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "  Tukey multiple comparisons of means\n",
       "    95% family-wise confidence level\n",
       "\n",
       "Fit: aov(formula = RSR_Score ~ Occupation, data = CEDA_analysis)\n",
       "\n",
       "$Occupation\n",
       "                                    diff        lwr        upr     p adj\n",
       "Education-Allied health       -1.6613272 -3.1911936 -0.1314609 0.0232606\n",
       "First responder-Allied health -0.6002506 -2.7574590  1.5569577 0.9827011\n",
       "Fitness-Allied health         -1.2449393 -3.7940526  1.3041741 0.7782106\n",
       "Health-Allied health          -0.9162679 -2.6732055  0.8406696 0.7199310\n",
       "Other-Allied health           -1.7762968 -3.0981031 -0.4544905 0.0014955\n",
       "Support-Allied health          0.2973684 -1.3474318  1.9421686 0.9983382\n",
       "First responder-Education      1.0610766 -0.8576063  2.9797595 0.6602360\n",
       "Fitness-Education              0.4163880 -1.9343087  2.7670847 0.9985194\n",
       "Health-Education               0.7450593 -0.7091159  2.1992345 0.7365636\n",
       "Other-Education               -0.1149696 -0.9952921  0.7653530 0.9997424\n",
       "Support-Education              1.9586957  0.6421989  3.2751924 0.0002470\n",
       "Fitness-First responder       -0.6446886 -3.4444800  2.1551027 0.9936579\n",
       "Health-First responder        -0.3160173 -2.4202232  1.7881886 0.9994224\n",
       "Other-First responder         -1.1760462 -2.9333238  0.5812314 0.4298121\n",
       "Support-First responder        0.8976190 -1.1139029  2.9091410 0.8433671\n",
       "Health-Fitness                 0.3286713 -2.1757474  2.8330901 0.9997351\n",
       "Other-Fitness                 -0.5313575 -2.7522708  1.6895557 0.9922015\n",
       "Support-Fitness                1.5423077 -0.8847587  3.9693741 0.4958031\n",
       "Other-Health                  -0.8600289 -2.0934413  0.3733836 0.3775023\n",
       "Support-Health                 1.2136364 -0.3610071  2.7882799 0.2561202\n",
       "Support-Other                  2.0736652  1.0060303  3.1413002 0.0000003\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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A3YgA3YgA3YgA3YgA3YgA3YgA3YgA3YgA3YgA3YgA3YgA3YgA3YgA3YgA3YgA3Y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Cyw601IFqu/TbUr+8vx2wA3bADuThgGbf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\nMKNgL/6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nRoly+//HJeJY/BfW/cAQeUjfvjrXagMQdWZeOHKQU0CzgIvgTd+HwO\nhDfTu0n/A6SHoT+sDLtH6Y9YXg8KIjcFSX8LeQYs1UqKlie/JratC+kQUA4nfSW8CNJfQO0p\noDwenoHF8B9Qn+MBhG6wrv5rHOEm76pXQaak2dNL61OFPexM8aML2GWlddddd1H79u1/UcA+\nFVV0m222qf9HpqVLl/4PwZOOR5sKr7tBb/qyVWt1pKamZjnq1weURkU/unTq1IlFdo9/owZU\n+Mb1119/40mTJj1f4cNw9+2AHbADreLA1tQ6PaXm35Afgqx7SB8albuY5XlRWosLQGU3BwVu\nmqGM8zPWFZi+BkFXkFBgJ/4EXUGBZi+I6wRW/hZl6HudCgLjdY+Jtg1l+ThoxlGBpQLKjWA7\neBGkg2B0fapdu21Yvh6ltdgCVH9rS22Uop3WHkdj9a/ARh3LAY0VKuE2ffA5o5XbuzbP+vVV\njOfyLOti5edALV0SVkYd8AxlRg+sh1USBxRsdYeBoEAsSLOEe0E8cAzbtMw1yzib/EWwNigt\nLQcKAhVQalvQ5SRuj1Z0OTofzaLQ+VAbFdZMmGbAVgQFigpc9wEFnLps3h4ak4Jfq/gOzKHK\n3eCl4lfdrBp1KTrf51izGihgp5sp+3QB5V3UDtiBEjoQvzxWwmbdlB3IhAO6BHwMXAcbRCNS\noPZH0En4rijvC5bLR+m0xTQ2vA2HRwVWZjkFtozW4wvNDj4REZ8pjJdJpjUDeRgoSJXUZ12y\nVjt6H1BfP4U9YVnoCHFpDGHfeL7TxXfgYapUsF8O0iXK1v4u54g8B6qvgugrGpYdsANl6IBn\nKMvwoLhLFeVALb3tBneCZvs6gYI9/fhF33uUtH4pqFyaNON3KNwCR4LWLwOd0DVD2VJdRAU3\nwlR4DxS8asZSgeIN8BzokvdboJP2ehCfmZrE+mbwNJwAlh2wA3bADtgBO2AH7EArOKDL3zUp\n9XYhv0PKtmR2TzKauuSc3Cffdc2U5pppVJ5mJhuTxta1sQKtvK0avkPZyha6ejvQZg7U0rKw\nMuqAZygzemA9rDZxYFYjrc5rZFtyky7ttZb0/chc0nc1m5JmTfWDHcsO2AE7YAfswNccSJtN\n+Vohr9gBO2AH7IAdsAN2wA7YgTQHHFCmOeN8O2AH7IAdsAN2wA7YgbwccECZl00uZAfsgB2w\nA63kwNatVK+rtQN2oIQOOKAsodluqqIc0C+22/IHKBVlljtrB1rgwDEt2Ne72gE7UCYOOKAs\nkwPhbpSNA9vSE93bcSroxzHPQrjHJMk2Uy9aTjvxKvBdCioTl27v87d4RoHp7Sj/YrRPvH39\nU86rBdbl4nbADtgBO5BhBxxQZvjgemgFO6B7SN4Pp4JuAdQjWg9/Ochqm0l/j7hbm7Xerl1b\nt9+GQ3fTdsAO2AE70JQDDiibcsjbq8kBvR70X8ofgP6pZB5cCJoZ1C22tgQFnEH67te90crJ\nLH8NukG4ZjfPgSD9S43+h1j52r4LBO1A4m7QLYdU16ogKagdBfoHnavhtzAEdHPy5kj3tTwd\npsO7cBqEe11uRHos6J9y3gH1Na7VWUm2Lz/+F9RvzeKuD5YdCA7o70jTZtRDmXJbbkqHTim3\nTjXRn3XYfnYTZbzZDpTEAd+HsiQ2u5EKcSAEkOPo72PwAPwNFBBKurSsN/AgrfeNVhQI6gR6\nAEwBBYmvwK2wHhwIQ2AruAl08tLNztXGSXAiXALXw26g+obDT2EGTIB94GeQJgW0n8c2qq1w\nf8mDSQ8H/YOP/lbxGhgDqlf9uRP2hF3hFrgBgt4noZNWaF/BYz/QP+t8Cy4CBd57QyHqTOHe\nBeyg96vFBZR30bZzYChN7w8P5dGF5SnTN49yrV1kdxo4Eu5o7YaKWP/3qOs4OLOIdboqO9As\nB/QGbdkBO/BfB84iqQDyUDgD/gDHw5+gKT1BgQejQgrKFIApoJT+AvpbQ6GZxyGgYGoyaAZS\nUv5r0FMraDTcVZ9q125tlgtAs4hp0om5JrZRAWsIKIeTvhJeBEn90QlUAaXG9wwoWPsPfAmh\nDyTr/wZyDst4+6pXQaZ0HVxanyrs4VyKjyxklwEDBsy/8sor1Z5Vxg4ceeSRQ1588cW+dPHt\nPLu5X57lSlEs3z6Xoi9NttG+ffsl48ePL/vXxPHHH7/dhAkTnmpyQC5QsQ44oKzYQ+eOt5ID\nmr17MuIolppl0WzdI5BU+0RG/M3yObbFT5JPx8pq23rQE+L5r7OuWb81QJrWsPjG4xXkHBLl\n3szypCh9CkvNJgadQGLHaKUvy3PgzGhdC12qljrBw7AFvAl6X4gHpqx+QzNiObrsrdnaQqWA\nPZ9APdQ7dtKkSYsHDRr0PyHDy7J14Jf0TK+dfGatNcutDzJtrQPpwJEwrK07UkD7uyxduvSC\nCnlN1BYwLhetQAccUFbgQXOXW82BfahZ34X8TtTCUpa3gwK2TeAjUPAVtAqJeFC5TtjAciOY\nGlvXNgWM0oYwDhS06btmQauS6A7vRBmaMcyly8lUv6R4ANmQk/tRQd/5UBtt1uVm1b8ijIaf\ngcb/GeiyeXxcrH5DS76RU3jGfHapK2C3RQWUddG2dWAszeu5VJdHN/Ipk0c1LS6ir5/oeV3X\n4ppKV8GjNLV66ZpzS3Yg3YGmZiHS9/QWO5A9B3QS3AwUXIXAcSjpjeHfoICyD2hmUdL3EePS\nbKACwmVhDxgHQQrW9HrrB2pjPIyBwdAXJM06aoZwtlYS+oL15aI8BaZPRIQgNdqUunicLYdB\nqOM60mfDyqB+3QW6nL4nqP8dIa54+/F8p+1ALgeeIfNPuTaUcd5L9O3iMu5frq69TeaZuTY4\nzw6U2gEHlKV23O2VswOaxRsGR8EnoMBOgde+8CEo2LsbXo2YyTKuD1j5D2j7dLgMghRIav8n\n4Zgo/TTLC0Ensjfgf0CBaC5NIlOBqPZpji5iJ80YTQW1twJoxrIOboDn4ClQ4DsB1oO4Wtp+\nvC6n7YAdsAN2wA7YATtQFQ50ZZQ9Ukaqy8SdE9s0s3EuaGZTwVpcdazoe2IrQQdISrOBqyQz\nc6zrA6D61RItz85hljJej/KWjWfkSBej/RzV5p2loFtYdsAOVJ4DtXRZWBl1wN+hzOiB9bBa\n7IB+6SxySZeG07SADSKXNOuZSwvJ/DjXhkSevt+V1qdE0dTVz1O2zE3Jj2cXo/14fU7bATtg\nB+xARhxwQJmRA+lhtLkDd9KDtB/RnMK26W3eQ3fADtgBO2AH7EArOaBLWJYdsAMtd+BZqng+\npZo7yM/1Q5uU4s62A1XlwLVVNVoP1g5k1AEHlBk9sB6WHbADdsAO2AE7YAdK5YADylI57Xbs\ngB2wA3bADtgBO5BRBxxQZvTAelit4oDuP1mJrxn9slz3x7TsgB2wA3bADrSKA5V4cmwVI1yp\nHWjEAf2NnH6hPRl0L8eroQu0tYbRAd14PU2rsUE3LJ8DuvfkK6CbILeHoFEkdKsjqQ50eyPL\nDtgBO2AH7EBBDjigLMguF65CB3ZhzEfCANAM5bqwPpwBba2f0YE+KZ1QwPsv0D/pbAKrw26w\nKyggljRzeS74fUBuWNXigO7F+t02GuzWtNurDdrWHV30XmbZATtgB+xAGzmwP+0qKIvf9HtN\n1jeP+nMay6OjtBaa8ftptP44y5/DVHgOwhu6ZgFvAf0Lz0dwH4S/c1Rwp/r0i/F3QYFrCPjG\nkj4V3offg2YeZ8AhkJTaHZfMZH1l0O2NdFLTr8+Xgv7ZRzdxr4Oz4G1Qn0dAkP4i8gX4BO6G\ncCN2Bdoax4OgepaD1pJvbN5azrZtvfHXTyl6si+N6DXUFtK/Uek1XGptQ4N6rXcudcOx9mpJ\nCyujDvg+lBk9sB5W0Rx4iJpOgjq4H7T+d5gG0moQfx1pXTcAl9aDA2EIbAU3waag2cODQEHh\nRnAhXAV7wVGgmcfDoSPolioKLLVUfTuDyii400yp+nMXJLUZGf9OZrKu2xdNAfVHJ/L9YEcI\nN1YfRlpsD2rzZlgBRsMJoCD5AvgljISuoID2IvgrzIV8pfrUh3y11kYbbbSwtrZWflnZcqBk\nx/Scc84Z+NBDD/VcaaWVZpbaws8++6xbx44dN+/atateOyXTokWLlpkzZ067xx577Lxu3brp\nA2XJdeSRR245adIkfVC2MupA/ESY0SF6WHagRQ58zt5DYB9QEKiZRZ2Ivg//gab0Fwq8FTGK\n5ZAorTf1s0HfybwYXoZO8GO4GsaDVAs/AQV30qXwYH2q4R955pCeF63HFwpsVWcuKUDtA5pt\nlBRMavZC0oldM5R1oLbWgcGgujR2SbOyj8BIraD5cAaEOpSXjx6jUK6+p+171Ntvvz1n6dKl\n6pdlB5rlwMSJE/fnObQVgV3JgtjQUdo9FibT9tiQV4rlwoUL16Sd4ydPnvyHgQMH6vVacr3+\n+ut9S96oGyypAw4oS2q3G6tAB/QDlgWg2UWxPCigqYWtoCk9HSvwHOn1QAGmAjQFk5IuqX8J\na0NfiO/zFOuHQVCYGQ3rYTmDRLjcrEt6L8EaYWNiqWAyLRhWsCkpOFTAqRlI9UtjTc7ohPq1\nj8oXqsnsIPLVd+bNm9du0KBBH+S7g8vZgRwOaJbs/z788MPf5tjW2lnfmj9//sO0fWtrN5So\nf13WdzrhhBP0Wm2TGUraLeTDY6L7Xq0EB2oqoZPuox1oQwduo+0TYu1rxvJ82BgUbC6BThC0\nSkhES83wBW1IYmq0shbL8PpTYKZgUG/2mi1cH4I2IlEXVlguiqXjyf1Y+WGEAtcJsAd0hLg2\nZ6Uv6JJ5Lmk8Sc0mYwx0i7E6afVXSutTw1Y/2oHycuApujO0jbqkD4elDiY1VH2I3QTaKphU\nH6yMOxBOaBkfpodnB5rtgL6jeBJ8O6pBs/rHgGYRNSunWbstQNK9HgfXp/77sA9Jvc76wWYw\nHqSVYOf6VMP3LBUAfgEK3PYHzQwuD3vDM5BLKq8ykk6ST0QoALwXXoErQXVJ68L1cApoX51c\nNPsaZjZJ5pT6NAz6R1v1XU/1V15YdsAO2AE7YAd8QvBzwA404YACsFXhb9AFFEQpwDsEpBvh\nJzAFFKSNg7gUSL4JCuoUiCq9DXwAl8AfQd9p2h2kc+AWqAMFfONAebn0BJmXgmYOL0oU0Ezj\nQXAFvAYKftXONaB9gsaSeBsGhowcy4nknQ7Pg8apfh0BnpnEBMsO2AE7YAfsgB2wA4U40IPC\nCgxzqWeOzDryvgWajewAQQooX41WNKuZSwoS09qKl1eQG687vi2k25NIXooP27RsaoYylFU7\njdUTyrXWspaKhZUtB47O1nA8mhQHaskXVkYdqMnouDwsO9AaDujytn48k0sf5cqM8j5hqVm9\nXJqVK5O8zyCtrfgu+qJ7Wt2hnGYn9d3MNM1N25DIVzuN1ZMo7lU7kJcD+oBl2QE7UOEOOKCs\n8APo7pe1A/qu4vQcPdQl5l/lyHeWHbADdsAO2IGKdMBfqq/Iw+ZOV4gDd6T0UzOdd6dsc7Yd\nsAN2wA7YgYpzwDOUFXfI3GE7YAfsgB2wA3bADpSXAw4oy+t4uDd2wA7YATtgB+yAHag4B3zJ\nu+IOmTtsB6rage0ZfS1sDvplvW7VlFV1ZGD6Bb5+1JVV6Q4EfUD3b9WPx7Iq3elBP35bmNUB\nMi49V9+HF1PGqNfukynbnJ0BBxxQZuAgegh2oEocuCs2Tt0bVCewLAeUum3UipDlgFLnoM6g\nW1Jl+b6mOo66S0LWA0q9LtOkYDL+Gk4r53w7YAfsgB2wAyVzQDecv7ZkrbVNQyNo9q22abpk\nra5LS5qZXLNkLbZNQ1Np9tC2abpkrd5IS1eXrDU3VHYO+DuUZXdI3CE7YAfsgB2wA3bADlSW\nAw4oK+t4ubd2wA7YATtgB+yAHSg7BxxQlt0hcYfsgB2wA3bADtgBO1BZDjigrKzj5d7aATtg\nB+yAHbADdqDsHHBAWXaHxB2yA3bADtgBO2AH7EBlOeCAsrKOl3trB+yAHbADdsAO2IGyc8AB\nZdkdEnfIDtgBO2AH7IAdsAOV5YADyso6Xu6tHbADdsAO2AE7YAfKzgEHlGV3SNwhO2AH7IAd\nsAN2wA5UlgMOKCvreLm3dsAO2AE7YAfsgB0oOwf0/6mWHbADdqDSHND/Ik+GNyqt4wX0V/9T\nPhv+XcA+lVb0Szqs//IeDTqmWZXOtY/DrKwOkHHp+L0MWX5NZvjweWh2wA7YATtgB+yAHbAD\ndsAO2AE7YAfsgB2wA3bADtgBO2AH7IAdsAN2wA7YATtgB+yAHbADdsAO2AE7YAfsgB2wA3bA\nDtgBO2AH7IAdsAN2wA7YATtgB+yAHbADdsAO2AE7YAfsgB2wA3bADtgBO2AH7IAdsAN2wA7Y\nATtgB+yAHbADdsAO2AE7YAfsgB2wA3bADtgBO2AH7IAdsAN2wA7YATtgB+yAHbADdsAO2AE7\nYAfsgB2wA3bADtgBO2AH7IAdsAN2wA7YATtgB+yAHbADdsAO2AE7YAfsgB2wA3bADtgBO2AH\n7IAdsAN2wA7YATtgB+yAHbADdsAO2AE7YAfsgB2wA3bADtgBO2AH7IAdsAN2wA7YATtgB+yA\nHbADdsAO2AE7YAfsgB2wA3bADtgBO2AH7IAdsAN2wA7YATtgB+yAHbADdsAO2AE7YAfsgB2w\nA3bADtgBO2AH7IAdsAN2wA7YATtgB+yAHbADdsAO2AE7YAcKdaAbO7wDOxW6YwWUX4Y+XgQv\nRpzLsiNkRUMYyFPwHtwFy0PWtCEDuhnehH/APpBVZfm1mNVj5nHZATtgB+xA5MA1LD+FLAaU\nhzOuB0GBlngYlJcFrcIgZsB3oCv8Ga6ErOkRBnQstIf14UNYDbKoLL8Ws3i8ij6mmqLX6Art\ngB2wA6Vx4IdRM6+WprmSt6KZyRPg84iJLLeHLGhrBqHjNh6+hN/C/pAl6fz6F7gKlsLr8Bls\nCVlT1l+LWTteHo8dsAN2wA5EDmiGSwHWyvAMZHGGkmF9pWVJ1cF+X+VUduIIun9TbAgan4Ku\nTrG8rCWHMKDZoOdsllRtr8UsHbuijsUzlEW105XZATtQIgd0ifQ80Ak661qGASr40ozlnRkZ\nbA/GMTc2Fs1SSln8HqXGtS7ou5QnQNaes9X0WuTwWWkOOKBMc8b5dsAOlIsDO9MRfU9S6Ht3\n+mGDfuwwC4aCfgywOawBlSyNLYxTY5YUTN4GK0GWLgl/zHhWgCAFkvMha8GWxqdg8p/wO7gB\nsqSsvhazdIxKNha9WVl2wA7YgXJ24Dk6F76jtZi0voOmD8N/AGlt0A8fFJDdApUqXc7uEHX+\nPyw1xlroCd+HeZAVvctA1ooNRunpoMveWVIfBjMGroDfZ2lg0VhWZ5nF12IGD5WHZAfsgB2w\nA005kNXvUB7DwHWZe0VQoCl08s6CujCID2EQaGJDl001g5c1jWVAuvVTOH5a6hffWVVWX4tZ\nPV4elx2wA3bADsQcyOpJ7G3GqBm7OA/Gxl3pyYMYwGfwFugejfFL4KxWvPRL9vixC+nDKn5k\n6QPI6msxfcTeYgfsgB2wA3bADrS5A5qdzNqvntvcVHfADtgBO2AH7IAdsAN2wA7YATtgB+yA\nHbADdsAO2AE7YAfsgB2wA3bADtgBO2AH7IAdsAN2wA7YATtgB+yAHbADdsAO2AE7YAfsgB2w\nA3bADtgBO2AH7IAdsAN2wA7YATtgB+yAHbADdsAO2AE7YAfsgB2wA3bADtgBO2AH7IAdsAN2\nwA7YATtgB+yAHbADdsAO2AE7YAfsgB2wA3bADtgBO2AH7IAdsAN2wA7YATtgB+yAHbADdsAO\n2AE7YAfsgB2wA3bADtgBO2AH7IAdsAN2wA7YATtgB+yAHbADdsAO2AE7YAfsgB2wA3bADtgB\nO2AH7IAdsAN2wA7YATtgB+yAHbADdsAO2AE7YAfsgB2wA3bADtgBO2AH7IAdsAN2wA7YATtg\nB+yAHbADdsAO2AE7YAfsgB2wA3bADtgBO2AH7IAdsAN2wA7YATtgB+yAHbADdsAO2AE7YAfs\ngB2wA3bADtgBO2AH7IAdsAN2wA7YATtgB+yAHbADdsAO2AE7YAfsgB2wA3bADtgBO2AH7IAd\nsAN2wA7YATtgB+yAHbADdsAO2AE7YAfsgB2wA3bADtgBO2AH7IAdsAN2wA7YATtgB+yAHbAD\ndsAO2AE7YAfsgB2wA3bADtgBO2AH7IAdsAN2wA7YATtgB+yAHbADdsAO2AE7YAfsgB2wA3bA\nDtgBO2AH7IAdsAN2wA7YATtgB+yAHbADdsAOyIH2tsEO2IGKdaA/Pd800fvFrH8MM2EKLIKk\nViBjJ3gLJsU2diW9I3wb5sJ18CFsAMpfHSbA38DK34E0v/OvofpKrsyQh8Dr8DKUg7alE6uB\nnv96nZVaaa/PpvqxPAU2hwGwJkyHf8NzUO3Se90a8CAsqHYzPH47YAeq14GTGfrSRniTbYfD\nMhDXJqxovytimfpweV+UH+rszfogmB/Lv5K0VZgDufwurIbqK63nnZ6HF5XR0B+O+qTArtRK\ne3021Y99KPARhNd0fPkM+Ws1VUGGtuvD982J8dzJujzpmcj3ajMcSJ5omlGFd7EDdqCNHbiF\n9h+N+lDDUjMSmlVUMHk1rAenQdDnJPSJ/KWQwXJd2BNegYNAJ6H34GLoBL+E62EeWIU5kMvv\nwmpw6Wp3IO312Zgvl7PxePgEToInYSYMhd1AwabeN7YH5Wdd9zLALolBPs+6PiB4djJhjFft\ngB2oLgfCDOUJKcMeTP5s0OW57VLKhGyV1Sf1S0JGtPwHyyWgINWyA6VywDOUX3c67fX59VL/\nXRtCUq9nfWjsDbl0Jpkq8yzoaxlZ1xsMcFrWB9mW4/MMZVu677btQOs68ATVaxZiDPwBtgRp\nJdBs5Kugy14/BM1SSJoJOQzeh16g7xfppKN6JM1SBuk7nMNAs6F1MA5ehLi0Xd+HGw0jYBW4\nH+Kzo/nUM4R9dFlKl6g0jiGg77O9APfAfEhK/ddszDbwAagPkyGpfNrXPjox7wv9QN8tfRk0\n07sIGlPSb5UdAoWOR/vFNYSVHnAXaIw7gt7Tx8JTIOnY7Ar67txzcDvoeMalfX4Am0NnkKfy\n6ktISu2pHdWrcb0JmvmKH3cd7z1gPOh5tAuofzoGj0G8LKsFq6njNYQa1wY9zz6BuJZlZT9Q\nYKEPS0FN1RnKJZftyZC/CoD1oUuvqX/C65CvNqHgzqA+vw1PgI5BUK7X5yQ2xsuEslp2gD9G\nGceyfC9KJxdnk7EhHAg/h3MhrnxfP02V24lK9T5yE+jDbZCufOhqyFQYG2UOYanXtZ6nep4p\nkNaH4gdBAWEubUfm5rAezAJ5r2M/D6RusFe07MjyMAg+f5d0X1B7oTzJ+qsye7P8llaQ/H4A\nvtBKpJVZtubzPLTjpR2wA3ag1R04mRaWQtoMZeiA3ogXgt7AJZ3AtN8VWkETQesBzUjeBlrG\n87SuE6h0EiiIU55OzgqqdLI4H0IZkvWByTssdYILdT2mDZHyreceys+AE0FtajyhvudJd4e4\njmBF21VWJ1Sl1b8REFe+7eukqBOO6pkJGrvSz8Ia0JiSfqtsoePJVb/qkPdngPqiy3ZaiqNB\nJ1H1M55/K+txrcvKM6B9PgWNTWkF3ptBXPoQ8hGEssEP+XpKrKD2U5lz4OUoHfqg43YkNKVB\nFFAdFyUK5nO8jon2PTaxr1Z/HG2Lv2byqVP7Phzt21UrSMHJ30H91PP/gygtz38G+ej3FNK+\nep7q+a2l/FT+MiDlen2e3bAp5+MG5KpPT+Xc+vXMIayqrI5TXPm+fvIpl/QttKOATG3fGzJY\n3gN6Dl4K2vYazI7SV7KMa0VW7gCVk2/huRn2W508qT9ou/JD2fA6uDPK68kyaAsSb4DKfgZ6\nXSitvG9DUDGe56EuL+2AHbADbepAvgHlffRSb4ibRL3VUushoFT2sChvlFZieoK0TnhxacZE\n+/8Twpv2CqRvifIPYxk0moT210nh/8FBMBikQurRiUYnBc0MHgU6GfUCzRqoL6dBkOpVm2NA\nZaSN4RXQSUcnIqmQ9t+ivPZVPdLycD6o7QuhMeXyu5DxpNUd6phDgV2gI3wPdAKcD7PgOFgJ\n+sLroP5uBFJ7eBYUwBwSrbOony37mOVr0EkZqBvo5Kr8raEDLAc/ArWlmZvg62ak1Y6Ol074\n3wEFR3tC6NeypBvTIDaqjnhAme/x0ng1uzoeknqEDAW3PaIN+dap4snA6CfkqY8Xwwog6fmh\nwFDtqx+NaQQbtb/qXS0qqH79DZSvQDco7fUZtseXYUzXxTNT0jpmakvHKhxr7Z/v6yefcknf\nqL5eeg2r7XsbVusf9ZxW3sewfX1Ow3Pnqij/yChPi7NAZS+DcDz13L4zytfrMy4FhPoAFlco\nGwLKrmx8FT6HA6EG9DrZG/T8fxf0WpCK8TxvqMmPdsAO2IE2duBk2tcb6glN9CO8Ge8Tldsk\n2q+5AaXecNXuVlF9YaEAQ4GFTqh6E5ZGg8rmmrEppJ5wovmpKo1JJ3DVr+1BT5FQP3qFjGi5\nO0sFVQdE6/m234XyCrrGQRgXyfrLw6ex3E0rjSiX34WMJ63qUMexiQIPsC5Pzknkj4ry94ry\nD4zWdYyS0r6qI/g9iPTDcDgkFY7xptGGzVhq3zoIQQrJet3Bo7apTGNSeyoXDyhfjfLyed7d\nGpVdj2VQbxIKgO4OGSwLqVPjV58UdEgKWLQ+VCsxfY+0jkkIEmObvkrqeTQbFDgpqItLr6P3\nYA4oLRUSUIb3hV837Nrk4xRKaBxrRSXzff3kWy7pW+hQYwHl8aFQtOzMcjq8G8tXIPkoLBvL\nU1LPD43nLq3ElE9A+QvKa98zYvuF5InRtjOjjGI8z0PdmVjqU6NlB+xAth1YJRre3CIMU7Mu\nG4DenBdCMjB4lrzBsDrE3/yfYT2u5tbzdLwS0nXRerdoqRmFATAe3o/ywkKBlpAKbV8nz++C\n2ldQpJPkK3ABtERNjSefuv8vUeg/rP8AJiTy34vWV4iWg6LlP1gmj+PkaNvWLK+Ef8OuUZ4W\nHaEfbA4hcEqe2NWvBRBXXbQSjld8W2PpQo/XdVR2IPwYzo4qPpilZla1TSq0zoa9/vs4huTp\noBnFG+HvIC8fjWCRqrXZova136eJUnqd3gtHw4aQPL5kNapO0dYljZb670YdS2k+5Pv6ybdc\nfcXNeLglsY/6Jl//B1aFD+HnEFcPVuSXgm8p+XxsyG38Ue8d0s0Ni6893sTa70CvibiK+TyP\n11txaQeUFXfI3GE7ULADOvFLbzUsWvTYP9pby0mN1LQe296NbX87llayufW8n6hnXrSuE5yk\nsWpWZ5pWGlGh7e9LXbeBZqMUiP0eNKYb4DewAJqjpsaTT53vJAppNlX6pGHx1WPIDxnBA40l\nTTqOQUqfBENA6WVAQcsckNo3LL56TI5NG5LH66vCTSRCX7XM53n3OOX0HIgHlIeyrj49DFKh\ndTbs9d9HBY//DxRkKPgTCnzU9rmQ/BBF1lfaIEolj10oEPLlc6EBZXjuhzZCnbmWXcjsA3r+\nKkhTe/m8fvrlWY5iBUsB9cwce02P8r7FUsG8XvM6psNBeeGD82zSUvL52JDb+KM8WwrBw3hp\n+fMlyKO4ivk8j9dbcellKq7H7rAdsAOFOLAChfUGqBNdHbRUISB4hIouaaSylxLbkgFXc+tp\natZFs6ZSU7MThbavk8kwWB92g11hCJwJ34FdoDlqajz51Jn0Np99VCZ4oKDrg5Sd9L0xaUN4\nGvR80rG/EV6AZ+EsOAaSKsbYQp2hr/k+79T29TAKvg0KBDT79FtYBFKhdTbs9fXHa1i9GXYC\nPQf03PgB7Bytj2OZS+FqgYK3XJLPUuhjw1p+j2MppqBoEOgcH8ZL8hsaTI4CrydA++T7+sm3\nHFV+pWSAF8b4VYEo0ZGlyqo/cYVZ7RDA/ZGNR8NbcAfouagPGzPgPWiOdFzUdlcIYwz1dCLR\nBZLHpJjP89BWRS71ZLPsgB3IrgO/Ymg6af0Vmht4xN15kxW90fcAzRIkNZCMxRBmrZLbw3qx\n6gn1heVUEhqnZlCS6knGraCgRCejfMch/7aAj+A1eB0uB82I6AT2PVgddCKrJGkckoLG5LHU\n5ditIZy8jye9MgyH6yGu9aOVDvHMIqeb83yppQ96/u8HITi4jnRQc+oM+2q5HmjsD8EDESza\nnQoXwkEwDnLpjShz41wbyQv576Rsbyx7OhvVpx/AsaDnai7VkHlxtCEc03xfP79jv3xeZ5dQ\nLgRgCsa+iNrTItdrVPmdYG2og7g2ZEX7vwqrgoLJyaDnqT4wBG0XJZrzfNRx2RHk/7+jesJC\n7SvYbM4xCXVkeqknlGUH7ED2HNCb6U/hFzAfwomDZIukN+5HYSv4fqKmTVh/AjRro2CtMRWr\nnmQbmi34O2wJ4cQSyhxDQicLfZAupH0FDv+CmyCuj1nRyUUBdDhpxreXe1rf/dNxOh30fIlL\nAfdj8J0oc51o+Xa0DIstSIQymllqLRVyvEIfppDQ83EvUFA5ARSABDWnzrCvlgqWHoQfaCWm\n56O0gp80fcAGBSzfAwVEcenHTXuAvJ4U31BA+lDKvgznw3BISrN9N4NmbcfBLSDl+/rJt5zq\nDJevh2klpuFRWkFaUsnvR6qf8kqvQ73ewvNRPuo4BqkuBZpS8vm4kLzl6rekP9wXbTqNZbJf\nep1I9zYs/GgH7IAdyI4DJzMUBQT/Ab0RigdgIswGbVOgsxvEpcBP266IZerNXnmjYnlK6oS8\nKJG3Aet6Exdnws5wCmjGR2XjJ8jRrKtencCSKqSee9hZ9WhWMK4aVpT/j1hmf9Lqmzw4AXaF\nP8HnoCBjZZAKaV/1qx15fBjsDzeA8u6GxpTL70LGk1Z3Wh3nsYP6tX1ix+FR/k9i+ddGeU+y\nPAAUfF0f5d3PMmgkCdX5f3AQbAunwkegwFrbtK+0GWj9D1pJ6BzWtW1wIj+5Oigqd1FsQyHH\nK+x2WFSP2jwqZMaWhdT5cFRX12j/oSwV3EyH80HPMwUieh3odbcNNKat2LgAPoGTYEfQ81V+\nigEQNIyExjAqZOSx7EOZqaD9FIhdCMfDX2EaKF9Bdm+IK9/XT77ldqFytfUB/AqGg15H74Fm\nx5UOCs/pJWTo/UlB5NGg55n6HPq6LOkPQfXq+f4d0PP3XpgLev1PgrjGsaLy18EIkO4E5fXU\nSqTQB72X6jm9J4S8q0iHQHMz0tq3Jc9zdrfsgB2wA23vwMl0QW9ocfRGrDfaiaDvi60LSeUK\ncNJOWE+w86JkBaxvCNqmE2poXyfWwyCu0axoe7d4Ziydbz3hDX2V2L5K1oDq/4dWYtIYn4HQ\nNy0fhX4QV77tq13N4siLUKdOhn+EjtCYcvld6Hhy1Z9Wh06w6uP2iZ2GR/k/ieXLv5GgoCaM\nS8+hu6AXBHUg8WeIj/991o+ArUH7/gWkzUDrLTnRDorquIhlXPker7DPciR0nBRgrBQyE8t8\n63yY/VqDY80AAALmSURBVDSurrH9FcTUQdy7l1kfCPloCwo9B2F/zWo+DltCXMNYUZlR8cw8\n0utQ5mrQ+EMbWuqD1anQCXIp39dPvuWOoxEdB7Wt59fzoNfiVFAQGBSe0z8mQ88vlZ8Pj4Ge\nV3Ftz8obEMa1iLTeb/pGS703rQ5B3yXxAaj8S1HmndF6PKDU6/kc+DzapvJvwm8gBJMki/I8\nVz2WHbADdsAO4IBmCjaHtUFBR3NVrHqS7XcjQ/3rntyQWM+3/eXZTydRzc7ETy6J6ipydS16\nPQDkWZqCn/ETdVrZ1szP93gV0ofm1qmgvA8oCGzMu8b6ov02g7QAr7F989mmIEkB3DagWb58\nn7vheDf1+smnnHzaCHpAmu5hgwI4fYBTHzWDvAKkSXX2BXnXBfLRahTKp6zal2dr5lOpy9gB\nO2AH7IAdsAN2wA6UhwPxgLI8euRe5O2AonvLDtgBO2AH7IAdsAN2wA402wEHlM22zjvaATtg\nB+yAHbADRXRgIXUtAF32tuyAHbADdsAO2AE7YAfsgB2wA3bADtgBO2AH7IAdsAN2wA7YATtg\nB+yAHbADdsAO2AE7YAfsgB2wA3bADtgBO2AH7IAdsAN2wA7YATtgB+yAHbADdsAO2AE7YAfs\ngB2wA3bADtgBO2AH7IAdsAN2wA7YATtgB+yAHbADdsAO2AE7YAfsgB2wA3bADtgBO2AH7IAd\nsAN2wA7YATtgB+yAHbADdsAO2AE7YAfsgB2wA3bADtgBO2AH7IAdsAN2wA7YATtgB+yAHbAD\ndsAO2AE7YAf+fzt0LAAAAAAwyN96FPsKIQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMG\nDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMG\nDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYGAcCG3H\nhnz8OPn4AAAAAElFTkSuQmCC",
      "text/plain": [
       "Plot with title “95% family-wise confidence level\n",
       "”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We note that we have a few low p-vals: Education-Allied health, Other-Allied health, Support-Education, and Support-Other\n",
    "tuk <- TukeyHSD(anova_RSR)\n",
    "tuk\n",
    "options(repr.plot.width = 5.5, repr.plot.height = 4.75)\n",
    "with(par(mai=c(1,2.5,1,1)),{plot(tuk, las=1,cex.axis=0.6)})\n",
    "\n",
    "# plot(tuk) # Default plot without formatting\n",
    "\n",
    "# Examining the differences, we see that Support and Allied Health scored significantly higher, particularly relative to Education and Other\n",
    "# Other occupation groups scored somewhere in between, and where not statistically distinguishable\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compact Letter Display\n",
    "Interpreting the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A tibble: 7 × 2</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>categories</th><th scope=col>cld</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>Education      </td><td>a </td></tr>\n",
       "\t<tr><td>Other          </td><td>a </td></tr>\n",
       "\t<tr><td>First responder</td><td>ab</td></tr>\n",
       "\t<tr><td>Fitness        </td><td>ab</td></tr>\n",
       "\t<tr><td>Health         </td><td>ab</td></tr>\n",
       "\t<tr><td>Allied health  </td><td>b </td></tr>\n",
       "\t<tr><td>Support        </td><td>b </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 7 × 2\n",
       "\\begin{tabular}{r|ll}\n",
       " categories & cld\\\\\n",
       " <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\t Education       & a \\\\\n",
       "\t Other           & a \\\\\n",
       "\t First responder & ab\\\\\n",
       "\t Fitness         & ab\\\\\n",
       "\t Health          & ab\\\\\n",
       "\t Allied health   & b \\\\\n",
       "\t Support         & b \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 7 × 2\n",
       "\n",
       "| categories &lt;chr&gt; | cld &lt;chr&gt; |\n",
       "|---|---|\n",
       "| Education       | a  |\n",
       "| Other           | a  |\n",
       "| First responder | ab |\n",
       "| Fitness         | ab |\n",
       "| Health          | ab |\n",
       "| Allied health   | b  |\n",
       "| Support         | b  |\n",
       "\n"
      ],
      "text/plain": [
       "  categories      cld\n",
       "1 Education       a  \n",
       "2 Other           a  \n",
       "3 First responder ab \n",
       "4 Fitness         ab \n",
       "5 Health          ab \n",
       "6 Allied health   b  \n",
       "7 Support         b  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compact letter display:\n",
    "tuk_2 <- glht(anova_RSR, linfct = mcp(Occupation = \"Tukey\"))\n",
    "### extract information\n",
    "tuk.cld <- cld(tuk_2)\n",
    "#cld.letters <- print(tuk.cld)\n",
    "tibble.cld <- tibble(categories = names(cld.letters), cld = cld.letters)\n",
    "tibble.cld %>% arrange(cld)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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3HeeecdSdkYldoVA+pQVQ6VhTBVH8rK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\nID69unqgv4utGAuFMuHECDACjEBrI5AdJ60Ks7V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ztzE1uXQDBT09PeGHH34QJcixutPIgAZKN/9UbmKe8Gs6++clqCqrguTQVGE1OoPE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\nVa2+AAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Venn diagram of compact letter display\n",
    "library(RAM)\n",
    "\n",
    "options(repr.plot.width = 5, repr.plot.height = 3.4)\n",
    "High_RSR_Score <- c('Support','Allied Health','Health','Fitness','First responder')\n",
    "Low_RSR_Score <- c('Health','Fitness','First responder', 'Other', 'Education')\n",
    "group.venn(list('Low '=Low_RSR_Score, 'High  '=High_RSR_Score), label=TRUE, \n",
    "    fill = c(\"green\", \"blue\"),\n",
    "    cat.pos = c(0, 0),\n",
    "    lab.cex=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Drawing actionable conclusions\n",
    "\n",
    "\n",
    "- **Stigma Score**: Attitudes and beliefs which were seen as stigmatizing ED\n",
    "- **Literacy Score**: An ED literacy score, quiz-score based\n",
    "- **Openness to ED education**: ED-related education education, openness to and work towards receiving more ED-related education\n",
    "- **RSR score**: Preparedness to recognize, support, and provide resources to individuals who may be experiencing EDs"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "ir"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}