treemaps.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Visualization: Implementation of treemaps in R and Python\n",
    "#### Author: Alexander Ullmann"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Outline\n",
    "1. Introduction to treemaps\n",
    "2. Quality of Life Categorical Data\n",
    "3. Implementation in R\n",
    "4. (Implementation in SAS)\n",
    "5. Implementation in Python\n",
    "6. Comparison of different implementation methods\n",
    "7. Exercises"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Introduction to treemaps\n",
    "In information visualization and computing, treemapping is a method for displaying hierarchical data using nested figures, usually rectangles. The rectangles represent areas proportional to specified dimensions of the data. Often the leaf nodes are colored to show a separate dimension of the data. [source: wikipedia/Treemapping](https://en.wikipedia.org/wiki/Treemapping). \n",
    "<br>\n",
    "<br>\n",
    "In this tutorial we want to show you how to build treemaps in `R` and `Python`. We will be using data from a questionnaire regarding the satisfaction with quality of life. Similar kind of data is often found in clinical research where focus on life quality and satisfaction after a medical procedure or medication regime is of interest.\n",
    "<br>\n",
    "***\n",
    "The basic idea of our approach is to give an efficient visual overview of the data. The aim is to provide a first comprehensive look at the distribution of an outcome variable in this case a pseudo-continuous (on Likert-Scale) over different subgroup variables which are categorical in nature. From there a more sophisticated analysis could be made for example with regression models and alike.\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Quality of Life Categorical Data\n",
    "\n",
    "The data which we will use is provided by GESIS - Leibniz-Institut für Sozialwissenschaften (2016): German General Social Survey - ALLBUS 2014. GESIS Datenarchiv, Köln. ZA5242 Datenfile Version 1.0.0, http://dx.doi.org/10.4232/1.12437. This data set was put online by the PSI Organisation within the [Wonderful Wednesdays Workshops](https://www.psiweb.org/sigs-special-interest-groups/visualisation/welcome-to-wonderful-wednesdays). This data is publicly available and made free for usage in their [github repository](https://github.com/VIS-SIG/Wonderful-Wednesdays/tree/master/data/2020/2020-05-13) under the CC0 1.0 Universal license. \n",
    "<br>\n",
    "<br>\n",
    "\n",
    "The primary end point was overall satisfaction with life.\n",
    "<br>\n",
    "<br>\n",
    "\n",
    "### Question to participants was:\n",
    "* How would you rate your satisfaction with your life overall on a scale from 0-10? (10=completely satisfied)\n",
    "<br>\n",
    "<br>\n",
    "\n",
    "### Categorical variables that were also collected:\n",
    "* Age\n",
    "* BMI\n",
    "* Working hours\n",
    "* Doctor visits per year\n",
    "* Net income\n",
    "* Smoker\n",
    "* Gender\n",
    "* Employment status\n",
    "* Graduation\n",
    "* Graduation of father\n",
    "* Graduation of mother\n",
    "* Highest educational grade (f=father/m=mother)\n",
    "<br>\n",
    "<br>\n",
    "\n",
    "### Aim: What are the most relevant factors for quality of life/satisfaction in life? \n",
    "We want to provide a first graphical overview for the data that might facilitate a further analysis to answer this question. A deep dive into statistical modeling of the data is not intended to be part of this tutorial. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Implementation in R\n",
    "There are several possibilities to make treemap-plots in R. Here is a list of packages that might allow you to easily implement these plots (not necessarily a complete list of all the packages):\n",
    "\n",
    "* library(treemap)\n",
    "* library(d3treeR) -> adds mouse over effects/interactivity\n",
    "* library(plotly) -> interactive treemaps\n",
    "* library(highcharter)\n",
    "* *library(ggplot2)*\n",
    "* *library(treemapify)*\n",
    "* base R (basicly your own implementation from scratch)\n",
    "<br>\n",
    "<br>\n",
    "\n",
    "\n",
    "***\n",
    "We will focus on a `ggplot2` implementation plus the additional `treemapify` package as ggplot is widely used to produce graphics for R nowadays. Feel free to try out the other packages and see what fits your needs best. For the R-code to work you need to set up your jupyter notebook with an R kernel and activate it. An alternative is to paste the code in a R-IDE like R-Studio.\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load the packages\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading required package: ggplot2\n",
      "\n",
      "Warning message:\n",
      "\"package 'ggplot2' was built under R version 3.6.2\"\n",
      "Loading required package: treemapify\n",
      "\n",
      "Warning message:\n",
      "\"package 'treemapify' was built under R version 3.6.3\"\n",
      "Loading required package: dplyr\n",
      "\n",
      "Warning message:\n",
      "\"package 'dplyr' was built under R version 3.6.3\"\n",
      "\n",
      "Attaching package: 'dplyr'\n",
      "\n",
      "\n",
      "The following objects are masked from 'package:stats':\n",
      "\n",
      "    filter, lag\n",
      "\n",
      "\n",
      "The following objects are masked from 'package:base':\n",
      "\n",
      "    intersect, setdiff, setequal, union\n",
      "\n",
      "\n",
      "Loading required package: RColorBrewer\n",
      "\n",
      "Loading required package: repr\n",
      "\n",
      "Warning message:\n",
      "\"package 'repr' was built under R version 3.6.3\"\n",
      "Loading required package: IRdisplay\n",
      "\n",
      "Warning message:\n",
      "\"package 'IRdisplay' was built under R version 3.6.3\"\n"
     ]
    }
   ],
   "source": [
    "if (!require('ggplot2')) install.packages('treemapify');\n",
    "if (!require('treemapify')) install.packages('treemapify');\n",
    "if (!require('dplyr')) install.packages('dplyr');\n",
    "if (!require('RColorBrewer')) install.packages('RColorBrewer');\n",
    "if (!require('repr')) install.packages('repr');\n",
    "if (!require('IRdisplay')) install.packages('IRdisplay');\n",
    "# used for graphics\n",
    "library(ggplot2)\n",
    "# used to add treemaps to ggplot graphics\n",
    "library(treemapify)\n",
    "# used for data manipulation\n",
    "library(dplyr)\n",
    "# used for colors\n",
    "library(RColorBrewer)\n",
    "# managing output devices\n",
    "library(repr)\n",
    "# manage image outout to jupyter notebook\n",
    "library(IRdisplay)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read the data + Data wrangling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 10 × 17</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>X</th><th scope=col>ID</th><th scope=col>age</th><th scope=col>bmi</th><th scope=col>w_hours</th><th scope=col>todoctor</th><th scope=col>income</th><th scope=col>smoker</th><th scope=col>gender</th><th scope=col>employed</th><th scope=col>graduat</th><th scope=col>graduat_f</th><th scope=col>graduat_m</th><th scope=col>high_grad</th><th scope=col>high_grad_f</th><th scope=col>high_grad_m</th><th scope=col>satisfaction</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td> 1</td><td>1359</td><td><span style=white-space:pre-wrap>&gt;40  </span></td><td>&gt;30 </td><td><span style=white-space:pre-wrap>NA    </span></td><td>&lt;6 visits</td><td><span style=white-space:pre-wrap>&lt;1000      </span></td><td>Y</td><td>female</td><td><span style=white-space:pre-wrap>non-working      </span></td><td><span style=white-space:pre-wrap>secondary school           </span></td><td><span style=white-space:pre-wrap>secondary school   </span></td><td>university entrance</td><td><span style=white-space:pre-wrap>apprenticeship     </span></td><td>industrial/agric. teaching</td><td><span style=white-space:pre-wrap>master craftman           </span></td><td>10</td></tr>\n",
       "\t<tr><th scope=row>2</th><td> 2</td><td>2455</td><td><span style=white-space:pre-wrap>&gt;40  </span></td><td>&lt;=30</td><td><span style=white-space:pre-wrap>&lt;35h  </span></td><td>&lt;6 visits</td><td><span style=white-space:pre-wrap>&lt;1000      </span></td><td>N</td><td>female</td><td>regular half-time</td><td><span style=white-space:pre-wrap>secondary school           </span></td><td><span style=white-space:pre-wrap>elementary school  </span></td><td><span style=white-space:pre-wrap>elementary school  </span></td><td><span style=white-space:pre-wrap>apprenticeship     </span></td><td><span style=white-space:pre-wrap>master craftman           </span></td><td><span style=white-space:pre-wrap>commercial teaching       </span></td><td> 9</td></tr>\n",
       "\t<tr><th scope=row>3</th><td> 3</td><td> 200</td><td>30-40</td><td>&gt;30 </td><td><span style=white-space:pre-wrap>NA    </span></td><td>&lt;6 visits</td><td><span style=white-space:pre-wrap>NA         </span></td><td>N</td><td><span style=white-space:pre-wrap>male  </span></td><td><span style=white-space:pre-wrap>non-working      </span></td><td><span style=white-space:pre-wrap>university entrance        </span></td><td>university entrance</td><td>university entrance</td><td><span style=white-space:pre-wrap>university degree  </span></td><td><span style=white-space:pre-wrap>university degree         </span></td><td><span style=white-space:pre-wrap>university degree         </span></td><td> 8</td></tr>\n",
       "\t<tr><th scope=row>4</th><td> 4</td><td>1280</td><td><span style=white-space:pre-wrap>&gt;40  </span></td><td>&lt;=30</td><td><span style=white-space:pre-wrap>&gt;45h  </span></td><td>&lt;6 visits</td><td><span style=white-space:pre-wrap>NA         </span></td><td>N</td><td><span style=white-space:pre-wrap>male  </span></td><td>regular full-time</td><td>tech.college entrance qual.</td><td><span style=white-space:pre-wrap>secondary school   </span></td><td><span style=white-space:pre-wrap>secondary school   </span></td><td>tech.college degree</td><td><span style=white-space:pre-wrap>commercial teaching       </span></td><td><span style=white-space:pre-wrap>commercial teaching       </span></td><td> 8</td></tr>\n",
       "\t<tr><th scope=row>5</th><td> 5</td><td>2384</td><td><span style=white-space:pre-wrap>&lt;30  </span></td><td>&lt;=30</td><td><span style=white-space:pre-wrap>NA    </span></td><td>&lt;6 visits</td><td><span style=white-space:pre-wrap>NA         </span></td><td>N</td><td>female</td><td><span style=white-space:pre-wrap>non-working      </span></td><td><span style=white-space:pre-wrap>NA                         </span></td><td><span style=white-space:pre-wrap>secondary school   </span></td><td><span style=white-space:pre-wrap>elementary school  </span></td><td><span style=white-space:pre-wrap>NA                 </span></td><td>industrial/agric. teaching</td><td>industrial/agric. teaching</td><td> 7</td></tr>\n",
       "\t<tr><th scope=row>6</th><td> 6</td><td>2258</td><td><span style=white-space:pre-wrap>&lt;30  </span></td><td>&lt;=30</td><td><span style=white-space:pre-wrap>&lt;35h  </span></td><td>&lt;6 visits</td><td><span style=white-space:pre-wrap>NA         </span></td><td>N</td><td><span style=white-space:pre-wrap>male  </span></td><td>regular half-time</td><td>tech.college entrance qual.</td><td><span style=white-space:pre-wrap>NA                 </span></td><td><span style=white-space:pre-wrap>NA                 </span></td><td><span style=white-space:pre-wrap>no degree          </span></td><td><span style=white-space:pre-wrap>NA                        </span></td><td><span style=white-space:pre-wrap>NA                        </span></td><td> 9</td></tr>\n",
       "\t<tr><th scope=row>7</th><td> 7</td><td> 837</td><td><span style=white-space:pre-wrap>&gt;40  </span></td><td>&lt;=30</td><td><span style=white-space:pre-wrap>&gt;45h  </span></td><td>&lt;6 visits</td><td><span style=white-space:pre-wrap>&gt;=3000     </span></td><td>N</td><td><span style=white-space:pre-wrap>male  </span></td><td>regular full-time</td><td><span style=white-space:pre-wrap>NA                         </span></td><td><span style=white-space:pre-wrap>elementary school  </span></td><td><span style=white-space:pre-wrap>elementary school  </span></td><td><span style=white-space:pre-wrap>master craftman    </span></td><td>industrial/agric. teaching</td><td><span style=white-space:pre-wrap>no degree                 </span></td><td> 8</td></tr>\n",
       "\t<tr><th scope=row>8</th><td> 8</td><td>1343</td><td><span style=white-space:pre-wrap>&lt;30  </span></td><td>&lt;=30</td><td><span style=white-space:pre-wrap>NA    </span></td><td>&lt;6 visits</td><td><span style=white-space:pre-wrap>&lt;1000      </span></td><td>N</td><td><span style=white-space:pre-wrap>male  </span></td><td><span style=white-space:pre-wrap>part-time job    </span></td><td><span style=white-space:pre-wrap>elementary school          </span></td><td><span style=white-space:pre-wrap>no degree          </span></td><td><span style=white-space:pre-wrap>no degree          </span></td><td><span style=white-space:pre-wrap>no degree          </span></td><td><span style=white-space:pre-wrap>no degree                 </span></td><td><span style=white-space:pre-wrap>commercial teaching       </span></td><td> 9</td></tr>\n",
       "\t<tr><th scope=row>9</th><td> 9</td><td>3129</td><td><span style=white-space:pre-wrap>&gt;40  </span></td><td>&lt;=30</td><td>35-45h</td><td>&lt;6 visits</td><td>1000- &lt;2000</td><td>N</td><td><span style=white-space:pre-wrap>male  </span></td><td>regular full-time</td><td><span style=white-space:pre-wrap>elementary school          </span></td><td><span style=white-space:pre-wrap>elementary school  </span></td><td><span style=white-space:pre-wrap>elementary school  </span></td><td><span style=white-space:pre-wrap>apprenticeship     </span></td><td>industrial/agric. teaching</td><td><span style=white-space:pre-wrap>commercial teaching       </span></td><td> 8</td></tr>\n",
       "\t<tr><th scope=row>10</th><td>10</td><td>2961</td><td><span style=white-space:pre-wrap>&gt;40  </span></td><td>&gt;30 </td><td><span style=white-space:pre-wrap>&lt;35h  </span></td><td>&lt;6 visits</td><td>2000- &lt;3000</td><td>N</td><td>female</td><td>regular half-time</td><td>tech.college entrance qual.</td><td><span style=white-space:pre-wrap>elementary school  </span></td><td><span style=white-space:pre-wrap>elementary school  </span></td><td><span style=white-space:pre-wrap>university degree  </span></td><td>industrial/agric. teaching</td><td><span style=white-space:pre-wrap>commercial teaching       </span></td><td> 8</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 10 × 17\n",
       "\\begin{tabular}{r|lllllllllllllllll}\n",
       "  & X & ID & age & bmi & w\\_hours & todoctor & income & smoker & gender & employed & graduat & graduat\\_f & graduat\\_m & high\\_grad & high\\_grad\\_f & high\\_grad\\_m & satisfaction\\\\\n",
       "  & <int> & <int> & <fct> & <fct> & <fct> & <fct> & <fct> & <fct> & <fct> & <fct> & <fct> & <fct> & <fct> & <fct> & <fct> & <fct> & <int>\\\\\n",
       "\\hline\n",
       "\t1 &  1 & 1359 & >40   & >30  & NA     & <6 visits & <1000       & Y & female & non-working       & secondary school            & secondary school    & university entrance & apprenticeship      & industrial/agric. teaching & master craftman            & 10\\\\\n",
       "\t2 &  2 & 2455 & >40   & <=30 & <35h   & <6 visits & <1000       & N & female & regular half-time & secondary school            & elementary school   & elementary school   & apprenticeship      & master craftman            & commercial teaching        &  9\\\\\n",
       "\t3 &  3 &  200 & 30-40 & >30  & NA     & <6 visits & NA          & N & male   & non-working       & university entrance         & university entrance & university entrance & university degree   & university degree          & university degree          &  8\\\\\n",
       "\t4 &  4 & 1280 & >40   & <=30 & >45h   & <6 visits & NA          & N & male   & regular full-time & tech.college entrance qual. & secondary school    & secondary school    & tech.college degree & commercial teaching        & commercial teaching        &  8\\\\\n",
       "\t5 &  5 & 2384 & <30   & <=30 & NA     & <6 visits & NA          & N & female & non-working       & NA                          & secondary school    & elementary school   & NA                  & industrial/agric. teaching & industrial/agric. teaching &  7\\\\\n",
       "\t6 &  6 & 2258 & <30   & <=30 & <35h   & <6 visits & NA          & N & male   & regular half-time & tech.college entrance qual. & NA                  & NA                  & no degree           & NA                         & NA                         &  9\\\\\n",
       "\t7 &  7 &  837 & >40   & <=30 & >45h   & <6 visits & >=3000      & N & male   & regular full-time & NA                          & elementary school   & elementary school   & master craftman     & industrial/agric. teaching & no degree                  &  8\\\\\n",
       "\t8 &  8 & 1343 & <30   & <=30 & NA     & <6 visits & <1000       & N & male   & part-time job     & elementary school           & no degree           & no degree           & no degree           & no degree                  & commercial teaching        &  9\\\\\n",
       "\t9 &  9 & 3129 & >40   & <=30 & 35-45h & <6 visits & 1000- <2000 & N & male   & regular full-time & elementary school           & elementary school   & elementary school   & apprenticeship      & industrial/agric. teaching & commercial teaching        &  8\\\\\n",
       "\t10 & 10 & 2961 & >40   & >30  & <35h   & <6 visits & 2000- <3000 & N & female & regular half-time & tech.college entrance qual. & elementary school   & elementary school   & university degree   & industrial/agric. teaching & commercial teaching        &  8\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 10 × 17\n",
       "\n",
       "| <!--/--> | X &lt;int&gt; | ID &lt;int&gt; | age &lt;fct&gt; | bmi &lt;fct&gt; | w_hours &lt;fct&gt; | todoctor &lt;fct&gt; | income &lt;fct&gt; | smoker &lt;fct&gt; | gender &lt;fct&gt; | employed &lt;fct&gt; | graduat &lt;fct&gt; | graduat_f &lt;fct&gt; | graduat_m &lt;fct&gt; | high_grad &lt;fct&gt; | high_grad_f &lt;fct&gt; | high_grad_m &lt;fct&gt; | satisfaction &lt;int&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 1 |  1 | 1359 | &gt;40   | &gt;30  | NA     | &lt;6 visits | &lt;1000       | Y | female | non-working       | secondary school            | secondary school    | university entrance | apprenticeship      | industrial/agric. teaching | master craftman            | 10 |\n",
       "| 2 |  2 | 2455 | &gt;40   | &lt;=30 | &lt;35h   | &lt;6 visits | &lt;1000       | N | female | regular half-time | secondary school            | elementary school   | elementary school   | apprenticeship      | master craftman            | commercial teaching        |  9 |\n",
       "| 3 |  3 |  200 | 30-40 | &gt;30  | NA     | &lt;6 visits | NA          | N | male   | non-working       | university entrance         | university entrance | university entrance | university degree   | university degree          | university degree          |  8 |\n",
       "| 4 |  4 | 1280 | &gt;40   | &lt;=30 | &gt;45h   | &lt;6 visits | NA          | N | male   | regular full-time | tech.college entrance qual. | secondary school    | secondary school    | tech.college degree | commercial teaching        | commercial teaching        |  8 |\n",
       "| 5 |  5 | 2384 | &lt;30   | &lt;=30 | NA     | &lt;6 visits | NA          | N | female | non-working       | NA                          | secondary school    | elementary school   | NA                  | industrial/agric. teaching | industrial/agric. teaching |  7 |\n",
       "| 6 |  6 | 2258 | &lt;30   | &lt;=30 | &lt;35h   | &lt;6 visits | NA          | N | male   | regular half-time | tech.college entrance qual. | NA                  | NA                  | no degree           | NA                         | NA                         |  9 |\n",
       "| 7 |  7 |  837 | &gt;40   | &lt;=30 | &gt;45h   | &lt;6 visits | &gt;=3000      | N | male   | regular full-time | NA                          | elementary school   | elementary school   | master craftman     | industrial/agric. teaching | no degree                  |  8 |\n",
       "| 8 |  8 | 1343 | &lt;30   | &lt;=30 | NA     | &lt;6 visits | &lt;1000       | N | male   | part-time job     | elementary school           | no degree           | no degree           | no degree           | no degree                  | commercial teaching        |  9 |\n",
       "| 9 |  9 | 3129 | &gt;40   | &lt;=30 | 35-45h | &lt;6 visits | 1000- &lt;2000 | N | male   | regular full-time | elementary school           | elementary school   | elementary school   | apprenticeship      | industrial/agric. teaching | commercial teaching        |  8 |\n",
       "| 10 | 10 | 2961 | &gt;40   | &gt;30  | &lt;35h   | &lt;6 visits | 2000- &lt;3000 | N | female | regular half-time | tech.college entrance qual. | elementary school   | elementary school   | university degree   | industrial/agric. teaching | commercial teaching        |  8 |\n",
       "\n"
      ],
      "text/plain": [
       "   X  ID   age   bmi  w_hours todoctor  income      smoker gender\n",
       "1   1 1359 >40   >30  NA      <6 visits <1000       Y      female\n",
       "2   2 2455 >40   <=30 <35h    <6 visits <1000       N      female\n",
       "3   3  200 30-40 >30  NA      <6 visits NA          N      male  \n",
       "4   4 1280 >40   <=30 >45h    <6 visits NA          N      male  \n",
       "5   5 2384 <30   <=30 NA      <6 visits NA          N      female\n",
       "6   6 2258 <30   <=30 <35h    <6 visits NA          N      male  \n",
       "7   7  837 >40   <=30 >45h    <6 visits >=3000      N      male  \n",
       "8   8 1343 <30   <=30 NA      <6 visits <1000       N      male  \n",
       "9   9 3129 >40   <=30 35-45h  <6 visits 1000- <2000 N      male  \n",
       "10 10 2961 >40   >30  <35h    <6 visits 2000- <3000 N      female\n",
       "   employed          graduat                     graduat_f          \n",
       "1  non-working       secondary school            secondary school   \n",
       "2  regular half-time secondary school            elementary school  \n",
       "3  non-working       university entrance         university entrance\n",
       "4  regular full-time tech.college entrance qual. secondary school   \n",
       "5  non-working       NA                          secondary school   \n",
       "6  regular half-time tech.college entrance qual. NA                 \n",
       "7  regular full-time NA                          elementary school  \n",
       "8  part-time job     elementary school           no degree          \n",
       "9  regular full-time elementary school           elementary school  \n",
       "10 regular half-time tech.college entrance qual. elementary school  \n",
       "   graduat_m           high_grad           high_grad_f               \n",
       "1  university entrance apprenticeship      industrial/agric. teaching\n",
       "2  elementary school   apprenticeship      master craftman           \n",
       "3  university entrance university degree   university degree         \n",
       "4  secondary school    tech.college degree commercial teaching       \n",
       "5  elementary school   NA                  industrial/agric. teaching\n",
       "6  NA                  no degree           NA                        \n",
       "7  elementary school   master craftman     industrial/agric. teaching\n",
       "8  no degree           no degree           no degree                 \n",
       "9  elementary school   apprenticeship      industrial/agric. teaching\n",
       "10 elementary school   university degree   industrial/agric. teaching\n",
       "   high_grad_m                satisfaction\n",
       "1  master craftman            10          \n",
       "2  commercial teaching         9          \n",
       "3  university degree           8          \n",
       "4  commercial teaching         8          \n",
       "5  industrial/agric. teaching  7          \n",
       "6  NA                          9          \n",
       "7  no degree                   8          \n",
       "8  commercial teaching         9          \n",
       "9  commercial teaching         8          \n",
       "10 commercial teaching         8          "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'data.frame':\t3466 obs. of  17 variables:\n",
      " $ X           : int  1 2 3 4 5 6 7 8 9 10 ...\n",
      " $ ID          : int  1359 2455 200 1280 2384 2258 837 1343 3129 2961 ...\n",
      " $ age         : Factor w/ 3 levels \"<30\",\">40\",\"30-40\": 2 2 3 2 1 1 2 1 2 2 ...\n",
      " $ bmi         : Factor w/ 2 levels \"<=30\",\">30\": 2 1 2 1 1 1 1 1 1 2 ...\n",
      " $ w_hours     : Factor w/ 3 levels \"<35h\",\">45h\",..: NA 1 NA 2 NA 1 2 NA 3 1 ...\n",
      " $ todoctor    : Factor w/ 3 levels \"<6 visits\",\">10 visits\",..: 1 1 1 1 1 1 1 1 1 1 ...\n",
      " $ income      : Factor w/ 4 levels \"<1000\",\">=3000\",..: 1 1 NA NA NA NA 2 1 3 4 ...\n",
      " $ smoker      : Factor w/ 2 levels \"N\",\"Y\": 2 1 1 1 1 1 1 1 1 1 ...\n",
      " $ gender      : Factor w/ 2 levels \"female\",\"male\": 1 1 2 2 1 2 2 2 2 1 ...\n",
      " $ employed    : Factor w/ 4 levels \"non-working\",..: 1 4 1 3 1 4 3 2 3 4 ...\n",
      " $ graduat     : Factor w/ 5 levels \"elementary school\",..: 3 3 5 4 NA 4 NA 1 1 4 ...\n",
      " $ graduat_f   : Factor w/ 5 levels \"elementary school\",..: 3 1 5 3 3 NA 1 2 1 1 ...\n",
      " $ graduat_m   : Factor w/ 5 levels \"elementary school\",..: 5 1 5 3 1 NA 1 2 1 1 ...\n",
      " $ high_grad   : Factor w/ 6 levels \"apprenticeship\",..: 1 1 6 5 NA 3 2 3 1 6 ...\n",
      " $ high_grad_f : Factor w/ 7 levels \"commercial teaching\",..: 2 3 7 1 2 NA 2 4 2 2 ...\n",
      " $ high_grad_m : Factor w/ 7 levels \"commercial teaching\",..: 3 1 7 1 2 NA 4 1 1 1 ...\n",
      " $ satisfaction: int  10 9 8 8 7 9 8 9 8 8 ...\n"
     ]
    }
   ],
   "source": [
    "# read data directly from the github\n",
    "data <- read.csv(url('https://raw.githubusercontent.com/VIS-SIG/Wonderful-Wednesdays/master/data/2020/2020-05-13/Satisfaction_wW2005.csv'), na.strings = c(\".\", \"\"), header = TRUE)\n",
    "# show the first 10 observations\n",
    "head(data, 10)\n",
    "# show the structure of the data\n",
    "str(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the following we do some data manipulation steps to summarize\n",
    "the categorical variables and calculate the mean of the \n",
    "outcome variable `satisfaction` for the different subgroups.\n",
    "\n",
    "As an example we show how to summarize the information for the categorical variable: `age`. \n",
    "Then we write a function which performs the same task for all the subgroups.\n",
    "We use the pipe notation from the `dplyr` package."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "\"Factor `age` contains implicit NA, consider using `forcats::fct_explicit_na`\"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[90m# A tibble: 4 x 5\u001b[39m\n",
      "  Variable     n mean_satisfaction percent group\n",
      "  \u001b[3m\u001b[90m<fct>\u001b[39m\u001b[23m    \u001b[3m\u001b[90m<int>\u001b[39m\u001b[23m             \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m   \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m\n",
      "\u001b[90m1\u001b[39m <30        401              7.61 11.6    Age  \n",
      "\u001b[90m2\u001b[39m >40       \u001b[4m2\u001b[24m458              7.51 70.9    Age  \n",
      "\u001b[90m3\u001b[39m 30-40      604              7.70 17.4    Age  \n",
      "\u001b[90m4\u001b[39m \u001b[31mNA\u001b[39m           3              7     0.086\u001b[4m6\u001b[24m Age  \n"
     ]
    }
   ],
   "source": [
    "d_age<- data %>%\n",
    "        filter(!is.na(satisfaction)) %>% # remove missings from outcome variable\n",
    "        group_by(age) %>% # group by categories within the variable age\n",
    "        summarize(n = n(), \n",
    "                  mean_satisfaction = mean(satisfaction)) %>% # get N' and mean of satisfaction for each category in age\n",
    "        mutate(percent = n / sum(n)*100) %>% # calculate relative frequency\n",
    "        mutate(group = \"Age\") %>% # give the category a label\n",
    "        rename(Variable = names(.)[1]) # rename the first column as \"Variable\"\n",
    "print(d_age)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the warning \"Factor `age` contains implicit NA, consider using `forcats::fct_explicit_na` \n",
    "is not of a big issue as it only makes us aware that the variable age has missings.\n",
    "\n",
    "Printing the results data set will show you that we calculated \n",
    "the mean of satisfaction over the distinct categorical values of `age` as well for `NA`'s (missings) which are present in `age`. That means that participants who didn't answer the question regarding age\n",
    "still answered the main question."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Now let's fit the previous data manipulation steps in a function so we can apply it to all subgroups."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "foo_summarize<- function(dat = data, my_var = NULL, group_label = \"NA\"){\n",
    "   suppressWarnings(\n",
    "     ans<- dat %>%\n",
    "        filter(!is.na(satisfaction)) %>%\n",
    "        group_by({{my_var}}) %>%\n",
    "        summarize(n = n(), \n",
    "                  mean_satisfaction = mean(satisfaction)) %>%\n",
    "        mutate(percent = n / sum(n)*100) %>%\n",
    "        mutate(group = group_label) %>%\n",
    "        rename(Variable = names(.)[1])\n",
    "   )\n",
    "  \n",
    "   return(ans)\n",
    "}\n",
    "# Note that we suppress the warning about the missings as we want them in our results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[90m# A tibble: 71 x 5\u001b[39m\n",
      "   Variable     n mean_satisfaction percent group        \n",
      "   \u001b[3m\u001b[90m<fct>\u001b[39m\u001b[23m    \u001b[3m\u001b[90m<int>\u001b[39m\u001b[23m             \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m   \u001b[3m\u001b[90m<dbl>\u001b[39m\u001b[23m \u001b[3m\u001b[90m<chr>\u001b[39m\u001b[23m        \n",
      "\u001b[90m 1\u001b[39m <30        401              7.61 11.6    Age          \n",
      "\u001b[90m 2\u001b[39m >40       \u001b[4m2\u001b[24m458              7.51 70.9    Age          \n",
      "\u001b[90m 3\u001b[39m 30-40      604              7.70 17.4    Age          \n",
      "\u001b[90m 4\u001b[39m \u001b[31mNA\u001b[39m           3              7     0.086\u001b[4m6\u001b[24m Age          \n",
      "\u001b[90m 5\u001b[39m <=30      \u001b[4m2\u001b[24m784              7.56 80.3    BMI          \n",
      "\u001b[90m 6\u001b[39m >30        635              7.54 18.3    BMI          \n",
      "\u001b[90m 7\u001b[39m \u001b[31mNA\u001b[39m          47              7.40  1.36   BMI          \n",
      "\u001b[90m 8\u001b[39m <35h       387              7.70 11.2    Working hours\n",
      "\u001b[90m 9\u001b[39m >45h       398              7.58 11.5    Working hours\n",
      "\u001b[90m10\u001b[39m 35-45h    \u001b[4m1\u001b[24m133              7.71 32.7    Working hours\n",
      "\u001b[90m# ... with 61 more rows\u001b[39m\n"
     ]
    }
   ],
   "source": [
    "# calculate statistics for all the subgroups\n",
    "d_age<- foo_summarize(dat=data, my_var=age, group_label = \"Age\")\n",
    "d_bmi<- foo_summarize(dat=data, my_var=bmi, group_label = \"BMI\")\n",
    "d_w_hours<- foo_summarize(dat=data, my_var=w_hours, group_label = \"Working hours\")\n",
    "d_todoctor<- foo_summarize(dat=data, my_var=todoctor, group_label = \"Doctor visits per year\")\n",
    "d_income<- foo_summarize(dat=data, my_var=income, group_label = \"Net income\")\n",
    "d_smoker<- foo_summarize(dat=data, my_var=smoker, group_label = \"Smoker\")\n",
    "d_gender<- foo_summarize(dat=data, my_var=gender, group_label = \"Gender\")\n",
    "d_employed<- foo_summarize(dat=data, my_var=employed, group_label = \"Employment status\")\n",
    "d_graduat<- foo_summarize(dat=data, my_var=graduat, group_label = \"Graduation\")\n",
    "d_graduat_f<- foo_summarize(dat=data, my_var=graduat_f, group_label = \"Graduation of father\")\n",
    "d_graduat_m<- foo_summarize(dat=data, my_var=graduat_m, group_label = \"Graduation of mother\")\n",
    "d_high_grad<- foo_summarize(dat=data, my_var=high_grad, group_label = \"Highest educational grade\")\n",
    "d_high_grad_f<- foo_summarize(dat=data, my_var=high_grad_f, group_label = \"Highest educational grade (father)\")\n",
    "d_high_grad_m<- foo_summarize(dat=data, my_var=high_grad_m, group_label = \"Highest educational grade (mother)\")\n",
    "\n",
    "#combine all the results in one data set\n",
    "d_all<- do.call(rbind, list(d_age, d_bmi, d_w_hours, d_todoctor, d_income, d_smoker, d_gender, d_employed, d_graduat,\n",
    "                            d_graduat_f, d_graduat_m, d_high_grad, d_high_grad_f, d_high_grad_m))\n",
    "# print data\n",
    "print(d_all)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We will rename the NA's as \"Missing\" because it looks better in the plot,\n",
    "# before that we make sure that we turn the factors to character,\n",
    "# else we can't make changes to the variable.\n",
    "d_all$Variable<- as.character(d_all$Variable)\n",
    "d_all$Variable[which(is.na(d_all$Variable))]<- \"Missing\"\n",
    "\n",
    "# create descrete response variable to display colors in the plot better\n",
    "d_all$satisfation_categories<- cut(d_all$mean_satisfaction, \n",
    "                                   breaks=seq(round(min(d_all$mean_satisfaction)), round(max(d_all$mean_satisfaction)), 0.5), \n",
    "                                   labels=c(\"(6,6.5]\", \"(6.5,7]\", \"(7,7.5]\", \"(7.5,8]\", \"(8,8-5]\", \"(8.5-9]\"), right = TRUE)\n",
    "# create response with label\n",
    "d_all$satisfaction_lab<- paste(d_all$Variable, \" (\", round(d_all$mean_satisfaction, 1), \")\", sep = \"\")\n",
    "# colors for plot\n",
    "palette<- brewer.pal(n = 6, name = 'RdYlBu')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Making the plot\n",
    "First we want to make a basic treemap plot and then finish it up by making it pretty"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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wFeToCHBHiOALciwH39bID5PB8wxbul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\nAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 840,
       "width": 960
      },
      "text/plain": {
       "height": 840,
       "width": 960
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# width and height of output window in jupyter\n",
    "options(repr.plot.width=16, repr.plot.height=14) \n",
    "g1<- ggplot(d_all, aes(area=percent, label = Variable, fill = satisfation_categories, subgroup = group)) + \n",
    "        geom_treemap() + # makes the treemap\n",
    "        facet_wrap( ~ group) # adds the subgroups\n",
    "g1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see the above 3 lines of code produce an image output. But there are several issues with this basic treeplot that need to be addressed:\n",
    "* the labels on the tiles are missing\n",
    "* the colors are not really appealing and suitable for this plot\n",
    "* the label on the legend has to be adjusted\n",
    "* title and context information is missing\n",
    "* the text describing the subgroups is too small"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ndImDrwce6XPr95+fG23DXKGo4k8ddwsG6d+cLyV/5Cz+oKnss3+x90J2dIZug2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bQVEHOK40h4PzWveJQdrbkjt01flEjFyeSFlJDKwwI06PKQ8i4bZWEWyezzNDza3A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LOtNjp1gQNzOD5QAI4WyGuoXN5wiOPRjS0K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\nB+EAAACA6Pw/QPqCEAGl4kEAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 840,
       "width": 1080
      },
      "text/plain": {
       "height": 840,
       "width": 1080
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=18, repr.plot.height=14) \n",
    "\n",
    "g2<- ggplot(d_all, aes(area=percent, label = Variable, fill = satisfation_categories, subgroup = group)) + \n",
    "        geom_treemap() + # makes the treemap \n",
    "        geom_treemap_subgroup_border(size = 1.5, color = \"black\") + # changing the border color\n",
    "        geom_treemap_text(grow = T, reflow = T, place = \"topleft\", layout = \"scol\", min.size = 1,\n",
    "                          color = \"Darkblue\", size = 8) + # putting text on the tiles, make the text adjust to the size of the tiles automatically\n",
    "        scale_fill_manual(values = palette) + # add the custom colors              \n",
    "        facet_wrap( ~ group) + # wrap display by subgroup\n",
    "        theme(strip.text.x = element_text(size = 18, colour = \"Darkblue\")) # make subgroup text bigger\n",
    "\n",
    "g2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WTfTzhaDc6bMZc2CFQ3OzJl8iBqfH7kd8G98IzJGD+d1oApY3o4rEJWgDYFymd0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\n/CkTjpVbhlP0f9a1ssSrB02bfdK2Wdmo6zZsUgM7WOPCaiQIKZY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/MZ90XgRqSqWlHEw4eoO03BUlbxCOd6oVPWsffjSb1sPvKuQz8cB4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\nHBG8umkFiJSkK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jnw/Ff45loMvlZIjW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\nAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# finally we add some titles and labels to make the plot more accessible and self explaining\n",
    "options(repr.plot.width=18, repr.plot.height=14) \n",
    "g3<- ggplot(d_all, aes(area=percent, label = Variable, fill = satisfation_categories, subgroup = group)) + \n",
    "            geom_treemap() + \n",
    "            geom_treemap_subgroup_border(size = 1.5, color = \"black\") +\n",
    "            geom_treemap_text(grow = T, reflow = T, place = \"topleft\", layout = \"scol\", min.size = 1,\n",
    "                              color = \"Darkblue\", size = 10) +\n",
    "            scale_fill_manual(values = palette) + \n",
    "            facet_wrap( ~ group) + \n",
    "            theme(legend.position = \"bottom\", \n",
    "                  legend.text = element_text(color=\"black\", size = 18), \n",
    "                  legend.title = element_text(size = 18),\n",
    "                  plot.title = element_text(size = 24),\n",
    "                  plot.subtitle = element_text(size = 20), \n",
    "                  plot.caption = element_text(size = 14),\n",
    "                  strip.text.x = element_text(size = 18, colour = \"Darkblue\"))+ # make subgroup text bigger\n",
    "            labs(title = 'How would you rate your satisfaction with your life overall on a scale from 0-10? (10=completely satisfied)',\n",
    "                 subtitle = 'Descriptive analysis by subgroup: tiles are proportional to rel. frequencies of the choices within a subgroup',\n",
    "                 caption =\"Created with ggplot2-treemapify\",\n",
    "                 fill = \"Mean Satisfaction\")\n",
    "\n",
    "g3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Explanation to the plot:\n",
    "So what does the plot tell us.\n",
    "* The tiles represent the different subgroups. The distribution of the values within a subgroup is proportional to the size of the tiles. For example there are more non-smokers as smokers. The distribution of males and females is even. Most of the participants are over the age of 40. And so on. You also see the proportion of missing data for each subgroup.\n",
    "* You can approximately deduct the mean of the satisfaction for the different sub-categories. For example participant who have more then 10 doctor visits per year are less satisfied. Non-smokers have a slightly higher satisfaction score then smokers. From a first overview you might also see that the mean variability of satisfaction over the categories is small and tends to be in the upper range.\n",
    "* Such an overview-plot may get you started with the process of understanding the data and give you ideas on how to progress on further analyses.\n",
    "<br>\n",
    "<br>\n",
    "Issues with such an plot could be names as: \n",
    "* The text on the tiles is not always quite readable because it gets too small. The text is not always fitting on the tiles. Here one could try out the other packages that produce treemaps and see if they handle these issues better. You could also dive deeper into the ggplot2 options and explore them.\n",
    "* Working in jupyter notebook and making the plot fit to the dimensions of the output window, makes most of the text unreadable. You could save the image to your hard disk drive with higher dimensions and look at it in fullscreen mode. Also producing that plot in RStudio and resizing the window to fullscreen would yield better visual representation. \n",
    "* You could go one step further and use this kind of plots in Rshiny to add mouse-over effects for the tiles displaying the actual percentages or satisfaction means. Also make sure to look at the `d3treeR+treemap` or `plotly` package if interactivity is desired.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. (Implementation in SAS)\n",
    "When it comes to SAS and treemaps there is no easy to use solution in SAS® Base. But there is the possibility to work with [SAS Visual Analytics](https://documentation.sas.com/?docsetId=vaobj&docsetTarget=p0cvtwmyn64desn1w721b6u8tzur.htm&docsetVersion=8.1&locale=en) and create treemaps with that. Note that this software comes with a price and may therefore be not easily accessible throughout the departments at your company and in your private work environment.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Implementation in Python\n",
    "For the following code to work you should switch your Jupyter Kernel from `R` to `Python`. We are using `Python 3`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can use Anaconda Navigator to install packages for your environment. Make sure that your kernel/python environment has the packages installed. Alternatively use the anaconda prompt to install packages like this: \n",
    "* activate the environment (for example if your environment for the Python Kernel is named \"my_python_env\"): conda activate my_python_env.\n",
    "* pip install pandas\n",
    "* pip install matplotlib\n",
    "* pip install plotly\n",
    "\n",
    "### Load the packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# load the packages\n",
    "from IPython.display import Image\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import plotly.express as px\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Make more space for your plots, so you don't have a box with a scroll bar."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "IPython.OutputArea.prototype._should_scroll = function(lines){\n",
       "    return false;\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%javascript\n",
    "IPython.OutputArea.prototype._should_scroll = function(lines){\n",
    "    return false;\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read the data + Data wrangling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>Unnamed: 0</th>\n",
       "      <th>ID</th>\n",
       "      <th>age</th>\n",
       "      <th>bmi</th>\n",
       "      <th>w_hours</th>\n",
       "      <th>todoctor</th>\n",
       "      <th>income</th>\n",
       "      <th>smoker</th>\n",
       "      <th>gender</th>\n",
       "      <th>employed</th>\n",
       "      <th>graduat</th>\n",
       "      <th>graduat_f</th>\n",
       "      <th>graduat_m</th>\n",
       "      <th>high_grad</th>\n",
       "      <th>high_grad_f</th>\n",
       "      <th>high_grad_m</th>\n",
       "      <th>satisfaction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1359</td>\n",
       "      <td>&gt;40</td>\n",
       "      <td>&gt;30</td>\n",
       "      <td>missing</td>\n",
       "      <td>&lt;6 visits</td>\n",
       "      <td>&lt;1000</td>\n",
       "      <td>Y</td>\n",
       "      <td>female</td>\n",
       "      <td>non-working</td>\n",
       "      <td>secondary school</td>\n",
       "      <td>secondary school</td>\n",
       "      <td>university entrance</td>\n",
       "      <td>apprenticeship</td>\n",
       "      <td>industrial/agric. teaching</td>\n",
       "      <td>master craftman</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2455</td>\n",
       "      <td>&gt;40</td>\n",
       "      <td>&lt;=30</td>\n",
       "      <td>&lt;35h</td>\n",
       "      <td>&lt;6 visits</td>\n",
       "      <td>&lt;1000</td>\n",
       "      <td>N</td>\n",
       "      <td>female</td>\n",
       "      <td>regular half-time</td>\n",
       "      <td>secondary school</td>\n",
       "      <td>elementary school</td>\n",
       "      <td>elementary school</td>\n",
       "      <td>apprenticeship</td>\n",
       "      <td>master craftman</td>\n",
       "      <td>commercial teaching</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>200</td>\n",
       "      <td>30-40</td>\n",
       "      <td>&gt;30</td>\n",
       "      <td>missing</td>\n",
       "      <td>&lt;6 visits</td>\n",
       "      <td>missing</td>\n",
       "      <td>N</td>\n",
       "      <td>male</td>\n",
       "      <td>non-working</td>\n",
       "      <td>university entrance</td>\n",
       "      <td>university entrance</td>\n",
       "      <td>university entrance</td>\n",
       "      <td>university degree</td>\n",
       "      <td>university degree</td>\n",
       "      <td>university degree</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1280</td>\n",
       "      <td>&gt;40</td>\n",
       "      <td>&lt;=30</td>\n",
       "      <td>&gt;45h</td>\n",
       "      <td>&lt;6 visits</td>\n",
       "      <td>missing</td>\n",
       "      <td>N</td>\n",
       "      <td>male</td>\n",
       "      <td>regular full-time</td>\n",
       "      <td>tech.college entrance qual.</td>\n",
       "      <td>secondary school</td>\n",
       "      <td>secondary school</td>\n",
       "      <td>tech.college degree</td>\n",
       "      <td>commercial teaching</td>\n",
       "      <td>commercial teaching</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2384</td>\n",
       "      <td>&lt;30</td>\n",
       "      <td>&lt;=30</td>\n",
       "      <td>missing</td>\n",
       "      <td>&lt;6 visits</td>\n",
       "      <td>missing</td>\n",
       "      <td>N</td>\n",
       "      <td>female</td>\n",
       "      <td>non-working</td>\n",
       "      <td>missing</td>\n",
       "      <td>secondary school</td>\n",
       "      <td>elementary school</td>\n",
       "      <td>missing</td>\n",
       "      <td>industrial/agric. teaching</td>\n",
       "      <td>industrial/agric. teaching</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0    ID    age   bmi  w_hours   todoctor   income smoker  gender  \\\n",
       "0           1  1359    >40   >30  missing  <6 visits    <1000      Y  female   \n",
       "1           2  2455    >40  <=30     <35h  <6 visits    <1000      N  female   \n",
       "2           3   200  30-40   >30  missing  <6 visits  missing      N    male   \n",
       "3           4  1280    >40  <=30     >45h  <6 visits  missing      N    male   \n",
       "4           5  2384    <30  <=30  missing  <6 visits  missing      N  female   \n",
       "\n",
       "            employed                      graduat            graduat_f  \\\n",
       "0        non-working             secondary school     secondary school   \n",
       "1  regular half-time             secondary school    elementary school   \n",
       "2        non-working          university entrance  university entrance   \n",
       "3  regular full-time  tech.college entrance qual.     secondary school   \n",
       "4        non-working                      missing     secondary school   \n",
       "\n",
       "             graduat_m            high_grad                 high_grad_f  \\\n",
       "0  university entrance       apprenticeship  industrial/agric. teaching   \n",
       "1    elementary school       apprenticeship             master craftman   \n",
       "2  university entrance    university degree           university degree   \n",
       "3     secondary school  tech.college degree         commercial teaching   \n",
       "4    elementary school              missing  industrial/agric. teaching   \n",
       "\n",
       "                  high_grad_m  satisfaction  \n",
       "0             master craftman            10  \n",
       "1         commercial teaching             9  \n",
       "2           university degree             8  \n",
       "3         commercial teaching             8  \n",
       "4  industrial/agric. teaching             7  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read the data\n",
    "my_data = pd.read_csv(\"https://raw.githubusercontent.com/VIS-SIG/Wonderful-Wednesdays/master/data/2020/2020-05-13/Satisfaction_wW2005.csv\")\n",
    "\n",
    "# handle missings\n",
    "my_data = my_data.fillna(\"missing\")\n",
    "my_data = my_data.replace(to_replace = \".\", value = \"missing\")\n",
    "my_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want to use a simple and basic approach to manipulate the data\n",
    "as close to the R - code above as possible.  \n",
    "The interested reader is encouraged to implement a more elegant way.\n",
    "\n",
    "Define a function to calculate the `n`, `percent` and `mean` of satisfaction for a subgroup."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def summarizing(data, variables, labels, response, stat = \"mean\"):\n",
    "    df_results = pd.DataFrame() # empty dataframe to store end results\n",
    "    \n",
    "    for i in range(len(variables)):\n",
    "        df_var = data.groupby([variables[i]]).size() # get counts\n",
    "        df_percent = df_var/df_var.sum() # calc percentages\n",
    "        group_values = df_percent.axes[0].tolist() # capture the group levels\n",
    "        df_mean = data.groupby([variables[i]]).agg({response: stat}) # calculate mean  \n",
    "        df_temp = pd.concat([df_var, df_percent, df_mean], axis = 1).reset_index(drop=True) # column bind the data and remove the header column\n",
    "        df_temp[\"labels\"] = labels[i] # assign labels \n",
    "        df_temp[\"group\"] = group_values # assign group levels\n",
    "        df_results = pd.concat([df_temp, df_results]) # row bind the data to get end results\n",
    "        \n",
    "    return df_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>N</th>\n",
       "      <th>Percentage</th>\n",
       "      <th>Mean of satisfaction</th>\n",
       "      <th>Labels</th>\n",
       "      <th>Group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2784</td>\n",
       "      <td>0.803231</td>\n",
       "      <td>7.562141</td>\n",
       "      <td>BMI</td>\n",
       "      <td>&lt;=30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>635</td>\n",
       "      <td>0.183208</td>\n",
       "      <td>7.538583</td>\n",
       "      <td>BMI</td>\n",
       "      <td>&gt;30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>47</td>\n",
       "      <td>0.013560</td>\n",
       "      <td>7.404255</td>\n",
       "      <td>BMI</td>\n",
       "      <td>missing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>604</td>\n",
       "      <td>0.174264</td>\n",
       "      <td>7.698675</td>\n",
       "      <td>Age</td>\n",
       "      <td>30-40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>401</td>\n",
       "      <td>0.115695</td>\n",
       "      <td>7.613466</td>\n",
       "      <td>Age</td>\n",
       "      <td>&lt;30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      N  Percentage  Mean of satisfaction Labels    Group\n",
       "0  2784    0.803231              7.562141    BMI     <=30\n",
       "1   635    0.183208              7.538583    BMI      >30\n",
       "2    47    0.013560              7.404255    BMI  missing\n",
       "0   604    0.174264              7.698675    Age    30-40\n",
       "1   401    0.115695              7.613466    Age      <30"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#apply the function to one subgroup to test it\n",
    "d_test = summarizing(data=my_data, \n",
    "                 variables=[\"age\", \"bmi\"],\n",
    "                 labels=[\"Age\", \"BMI\"],\n",
    "                 response=\"satisfaction\",\n",
    "                 stat=\"mean\")\n",
    "\n",
    "# rename columns\n",
    "d_test.columns = [\"N\", \"Percentage\", \"Mean of satisfaction\", \"Labels\", \"Group\"] \n",
    "d_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "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>N</th>\n",
       "      <th>Percentage</th>\n",
       "      <th>Mean of satisfaction</th>\n",
       "      <th>Labels</th>\n",
       "      <th>Group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>760</td>\n",
       "      <td>0.219</td>\n",
       "      <td>7.655</td>\n",
       "      <td>Highest educational grade (mother)</td>\n",
       "      <td>commercial teaching</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>832</td>\n",
       "      <td>0.240</td>\n",
       "      <td>7.540</td>\n",
       "      <td>Highest educational grade (mother)</td>\n",
       "      <td>industrial/agric. teaching</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>148</td>\n",
       "      <td>0.043</td>\n",
       "      <td>7.703</td>\n",
       "      <td>Highest educational grade (mother)</td>\n",
       "      <td>master craftman</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>264</td>\n",
       "      <td>0.076</td>\n",
       "      <td>7.583</td>\n",
       "      <td>Highest educational grade (mother)</td>\n",
       "      <td>missing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1109</td>\n",
       "      <td>0.320</td>\n",
       "      <td>7.429</td>\n",
       "      <td>Highest educational grade (mother)</td>\n",
       "      <td>no degree</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>86</td>\n",
       "      <td>0.025</td>\n",
       "      <td>7.535</td>\n",
       "      <td>Highest educational grade (mother)</td>\n",
       "      <td>other degree</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>106</td>\n",
       "      <td>0.031</td>\n",
       "      <td>7.425</td>\n",
       "      <td>Highest educational grade (mother)</td>\n",
       "      <td>tech.college degree</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>161</td>\n",
       "      <td>0.046</td>\n",
       "      <td>7.957</td>\n",
       "      <td>Highest educational grade (mother)</td>\n",
       "      <td>university degree</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>231</td>\n",
       "      <td>0.067</td>\n",
       "      <td>7.593</td>\n",
       "      <td>Highest edu. grade (father)</td>\n",
       "      <td>commercial teaching</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1491</td>\n",
       "      <td>0.430</td>\n",
       "      <td>7.436</td>\n",
       "      <td>Highest edu. grade (father)</td>\n",
       "      <td>industrial/agric. teaching</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      N  Percentage  Mean of satisfaction                              Labels  \\\n",
       "0   760       0.219                 7.655  Highest educational grade (mother)   \n",
       "1   832       0.240                 7.540  Highest educational grade (mother)   \n",
       "2   148       0.043                 7.703  Highest educational grade (mother)   \n",
       "3   264       0.076                 7.583  Highest educational grade (mother)   \n",
       "4  1109       0.320                 7.429  Highest educational grade (mother)   \n",
       "5    86       0.025                 7.535  Highest educational grade (mother)   \n",
       "6   106       0.031                 7.425  Highest educational grade (mother)   \n",
       "7   161       0.046                 7.957  Highest educational grade (mother)   \n",
       "0   231       0.067                 7.593         Highest edu. grade (father)   \n",
       "1  1491       0.430                 7.436         Highest edu. grade (father)   \n",
       "\n",
       "                        Group  \n",
       "0         commercial teaching  \n",
       "1  industrial/agric. teaching  \n",
       "2             master craftman  \n",
       "3                     missing  \n",
       "4                   no degree  \n",
       "5                other degree  \n",
       "6         tech.college degree  \n",
       "7           university degree  \n",
       "0         commercial teaching  \n",
       "1  industrial/agric. teaching  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create the data that we need to plotting, use all the variables that we have.\n",
    "d1 = summarizing(data=my_data, \n",
    "                 variables=[\"age\", \"bmi\", \"w_hours\", \"todoctor\", \"income\", \"smoker\", \"gender\", \"employed\",\n",
    "                           \"graduat\", \"graduat_f\", \"graduat_m\", \"high_grad\", \"high_grad_f\",\n",
    "                            \"high_grad_m\"],\n",
    "                 labels=[\"Age\", \"BMI\", \"Working hours\", \"Doctor visits per year\",\"Net income\", \"Smoker\", \"Gender\", \"Employment status\",\n",
    "                        \"Graduation\", \"Graduation of father\", \"Graduation of mother\", \"Highest edu. grade\", \"Highest edu. grade (father)\",\n",
    "                         \"Highest educational grade (mother)\"],\n",
    "                 response=\"satisfaction\",\n",
    "                 stat=\"mean\")\n",
    "\n",
    "# assign proper labels\n",
    "d1.columns = [\"N\", \"Percentage\", \"Mean of satisfaction\", \"Labels\", \"Group\"]\n",
    "# round numbers\n",
    "d1 = d1.round(3) \n",
    "d1.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Making the plot\n",
    "We will use the plotly module in python to create an interactive plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
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{\"color\": \"#2a3f5f\"}, \"geo\": {\"bgcolor\": \"white\", \"lakecolor\": \"white\", \"landcolor\": \"#E5ECF6\", \"showlakes\": true, \"showland\": true, \"subunitcolor\": \"white\"}, \"hoverlabel\": {\"align\": \"left\"}, \"hovermode\": \"closest\", \"mapbox\": {\"style\": \"light\"}, \"paper_bgcolor\": \"white\", \"plot_bgcolor\": \"#E5ECF6\", \"polar\": {\"angularaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"radialaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"scene\": {\"xaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"yaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"zaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}}, \"shapedefaults\": {\"line\": {\"color\": \"#2a3f5f\"}}, \"ternary\": {\"aaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"baxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"How would you rate your satisfaction with your life overall on a scale from 0-10? (10=completely satisfied).<br>Descriptive analysis by subgroup: tiles are proportional to rel. frequencies of the choices within a subgroup\"}, \"width\": 1000},\n",
       "                        {\"responsive\": true}\n",
       "                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('8c20fccb-7d36-42f3-9972-2c1c47c02db4');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })\n",
       "                };\n",
       "                });\n",
       "            </script>\n",
       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = px.treemap(d1,\n",
    "                 path=['Labels', 'Group'], # nesting structure: First \"Labels\" then values within the Labels group as \"Group\"\n",
    "                 color='Mean of satisfaction', # Variable for coloring the tiles\n",
    "                 values='Percentage', # this is the variable that controls the partitioning of the tiles,\n",
    "                                      # we want the tiles to be propotional to \"Percentage\"\n",
    "                 color_continuous_scale='RdYlBu' # color palette             \n",
    "                 )\n",
    "\n",
    "# save title as string variable\n",
    "my_title = (\"How would you rate your satisfaction with your life overall on a scale from 0-10? (10=completely satisfied).\" +\n",
    "            \"<br>Descriptive analysis by subgroup: tiles are proportional to rel. frequencies of the choices within a subgroup\")\n",
    "            \n",
    "# update to show the title\n",
    "fig.update_layout(title=my_title, width=1000, height=800)\n",
    "# show the figure\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above plot is interactive. We have the ability to zoom into the subgroups. The zoom ability allows us to better see the smaller text. The plot also allows for mouse over effects. Here we get the numbers on the percentages for the categorical variables and the mean of the outcome variable for every subgroup. We observe that graduation grade, non-smoker, being healthy (few doctor's visits per year) correlate positively with satisfaction. The treemap approach to the data that we have demonstrated here is only one possible suggestion of many. Feel free to further explore the data set with other statistical methods. For example one could use regression modeling to look at the variables that contribute to the outcome `satisfaction` the most or use other visual tools like boxplots to capture variability."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Comparison of different implementation methods\n",
    "In this Jupyter tutorial we showed how to create efficient overview graphics for questionnaire data about quality of life. We used the `ggplot2` approach in `R` and the `plotly` module in `python`. The advantage of using `plotly` is that it easily incorporates mouse over effects for the tiles. Additionally one can zoom into the subgroups and have therefore a better view on the proportions and text labels of the tiles. Please note that the `plotly` package is also available for the `R` Programming Language. We intentionally showed two different approaches. An interactive graphic is also achievable with `R`. But we noticed that producing an interactive plot in `R` with `plotly` is a bit more advanced if you're not familiar with the `plotly` functionality, see [documentation](https://plotly.com/r/reference/). `Python` has the same hurdle when it comes to `plotly` but we were able to produce a decent output without diving too deep into it. Note that the [d3treeR package](https://github.com/d3treeR/d3treeR/) for interactive treemaps may not be fully supported for some R-versions. We had difficulties to reproduce the same output as in `Python` with it. In `SAS®` you would have to use `SAS® Visual Analytics` for treemaps. The `R` and `Python` approaches are open-source and free to use where as `SAS®` comes with a price."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. Exercises\n",
    "In this section we want to encourage you to get into the programming yourself and solve some fun exercises.\n",
    "<br>\n",
    "<br>\n",
    "***\n",
    "R-Exercises:\n",
    "\n",
    "1. The graphic that we produced with `ggplot` in Jupyter wasn't quite readable, because of the limited dimensions within\n",
    "   the notebook. One solution would be to save the graphic and look at the plot in fullscreen mode on you monitor. \n",
    "   Hint: use `ggsave()`. Get information through the help function `?ggsave` on how to use this command in `R`.\n",
    "   Make sure to try different values for \"width\", \"height\" and \"dpi\" parameters to make the plot more readable.\n",
    "       \n",
    "       \n",
    "2. Customization of colors. We used 'RdYlBu' that emphasized the middle values and the ends of the scale for the plot. \n",
    "   Change the color palette of the tiles to a sequential pattern and observe the difference.\n",
    "   Hint: ?display.brewer.all().\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Python-Exercises:\n",
    "***    \n",
    "1. Please save the interactive plot that we produced in `Python` so it can be shared among others.\n",
    "   Hint: [interactive-html-export documentation](https://plotly.com/python/interactive-html-export/).\n",
    "   \n",
    "   \n",
    "2. Change the range of the color scale. In our `Python` example we didn't specify the range of the color schema. Therefore \n",
    "   the color ranges default to the range of the input data. \n",
    "   The color legend start at about 6.4 and goes up to 9. In some cases\n",
    "   you want your color schema to fill the whole range. Adjust the range of the colors and see if that makes any sense for our plot.\n",
    "   Hint: use range_color = [ , ].\n",
    "   \n",
    "   \n",
    "3. Choosing different color scales. There are plenty built-in color schema in `Python`. Use the 'Inferno' and 'Sunset' color scale to see if that fits our data    better.\n",
    "   Hint: You can get the sequential color scale patterns with \n",
    "         fg_cols= px.colors.sequential.swatches()\n",
    "         fg_cols.show()\n",
    "   See also the complete [color scale documentation](https://plotly.com/python/builtin-colorscales/)\n",
    "***"
   ]
  }
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