Godiva_1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] 0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Expecting 0 so no duplicate distance\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] 0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Expecting 0 so no duplicate distance\n",
      "Parsed with column specification:\n",
      "cols(\n",
      "  Name = col_character(),\n",
      "  Sex = col_character(),\n",
      "  Age = col_integer(),\n",
      "  Time = col_character(),\n",
      "  ct_evt = col_integer(),\n",
      "  Event = col_character(),\n",
      "  mins = col_integer(),\n",
      "  secs = col_character(),\n",
      "  track_time = col_double(),\n",
      "  temp_dist = col_character(),\n",
      "  dist_m = col_integer(),\n",
      "  Place = col_integer(),\n",
      "  Max_Hamlyn_pts = col_integer(),\n",
      "  Date_Meet = col_date(format = \"\"),\n",
      "  agegrp = col_character()\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "# World Masters Athletics (WMA) Age-graded track tables can be found on\n",
    "# Howard Grubb's website:\n",
    "# http://www.howardgrubb.co.uk/athletics/data/wavacalc06.xls\n",
    "\n",
    "library(rvest)\n",
    "library(readr)\n",
    "library(stringr)\n",
    "library(dplyr)\n",
    "library(tidyr)\n",
    "library(readxl)\n",
    "library(lubridate)\n",
    "library(patchwork)\n",
    "library(ggplot2)\n",
    "\n",
    "setwd('C:/Users/Packr1/Documents/Personal/Track')\n",
    "WMA_Women <- read_xls('wavacalc06.xls', sheet = 1) %>%\n",
    "    select(2:ncol(.)) %>%\n",
    "    mutate(dist_m = 1000 * `dist(km)`) %>%\n",
    "    mutate(Sex = 'F') %>%\n",
    "              dplyr::filter(dist_m > 0 & \n",
    "                            !grepl('Road', Event))\n",
    "              print(nrow(WMA_Women) - nrow(WMA_Women %>% distinct(dist_m)))\n",
    "              message(\"Expecting 0 so no duplicate distance\")\n",
    "WMA_Men   <- read_xls('wavacalc06.xls', sheet = 2) %>%\n",
    "              select(2:ncol(.)) %>%\n",
    "              mutate(dist_m = 1000 * `dist(km)`) %>%\n",
    "              mutate(Sex = 'M') %>%\n",
    "              dplyr::filter(dist_m > 0 & \n",
    "                            !grepl('Road', Event))\n",
    "              print(nrow(WMA_Men) - nrow(WMA_Men %>% distinct(dist_m)))\n",
    "              message(\"Expecting 0 so no duplicate distance\")\n",
    "\n",
    "track_file <- \"C:/Users/Packr1/Documents/Personal/Track/track_res.csv\"\n",
    "track_res  <- read_csv(track_file)\n",
    "pics_dir   <- 'C:/Users/Packr1/Documents/Personal/Track/Godiva/Jupyter_Pics/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>Name</th><th scope=col>Sex</th><th scope=col>Event</th><th scope=col>Date_Meet</th><th scope=col>ct</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>Aimee Bazin      </td><td>F                </td><td>100m Dash        </td><td>2014-07-23       </td><td>2                </td></tr>\n",
       "\t<tr><td>Aimee Bazin      </td><td>F                </td><td>200m             </td><td>2013-07-10       </td><td>2                </td></tr>\n",
       "\t<tr><td>Alex Andre       </td><td>M                </td><td>200m             </td><td>2013-07-10       </td><td>2                </td></tr>\n",
       "\t<tr><td>Alex Emmons      </td><td>M                </td><td>Mile Run         </td><td>2016-07-13       </td><td>2                </td></tr>\n",
       "\t<tr><td>Alex Taylor      </td><td>M                </td><td>200m             </td><td>2009-07-15       </td><td>2                </td></tr>\n",
       "\t<tr><td>Aline Lloyd      </td><td>F                </td><td>200m             </td><td>2013-07-10       </td><td>2                </td></tr>\n",
       "\t<tr><td>Barbara Hindenach</td><td>F                </td><td>100m Dash        </td><td>2014-07-23       </td><td>2                </td></tr>\n",
       "\t<tr><td>Barbara Hindenach</td><td>F                </td><td>200m             </td><td>2009-07-15       </td><td>2                </td></tr>\n",
       "\t<tr><td>Barbara Hindenach</td><td>F                </td><td>200m             </td><td>2013-07-10       </td><td>2                </td></tr>\n",
       "\t<tr><td>Barbara Hindenach</td><td>F                </td><td>800 Meter Run    </td><td>2016-06-29       </td><td>2                </td></tr>\n",
       "\t<tr><td>Barbara Hindenach</td><td>F                </td><td>800m             </td><td>2013-05-29       </td><td>2                </td></tr>\n",
       "\t<tr><td>Ben Barker       </td><td>M                </td><td>100m Dash        </td><td>2014-07-23       </td><td>2                </td></tr>\n",
       "\t<tr><td>Bill Hansley     </td><td>M                </td><td>200m             </td><td>2009-07-15       </td><td>2                </td></tr>\n",
       "\t<tr><td>Brendan Murray   </td><td>M                </td><td>5K Run           </td><td>2018-05-30       </td><td>2                </td></tr>\n",
       "\t<tr><td>Brian Tajlili    </td><td>M                </td><td>800 Meter Run    </td><td>2016-06-29       </td><td>2                </td></tr>\n",
       "\t<tr><td>Brianna Honea    </td><td>F                </td><td>100m Dash        </td><td>2014-07-23       </td><td>2                </td></tr>\n",
       "\t<tr><td>Carolyn Huettel  </td><td>F                </td><td>800m             </td><td>2013-05-29       </td><td>2                </td></tr>\n",
       "\t<tr><td>Chris Gould      </td><td>M                </td><td>200m             </td><td>2009-07-15       </td><td>2                </td></tr>\n",
       "\t<tr><td>Chris Gould      </td><td>M                </td><td>800 Meter Run    </td><td>2016-06-29       </td><td>2                </td></tr>\n",
       "\t<tr><td>Chris Gould      </td><td>M                </td><td>800m             </td><td>2013-05-29       </td><td>2                </td></tr>\n",
       "\t<tr><td>Dan Woodlief     </td><td>M                </td><td>800m             </td><td>2013-05-29       </td><td>2                </td></tr>\n",
       "\t<tr><td>Ed Davis         </td><td>M                </td><td>800 Meter Run    </td><td>2016-06-29       </td><td>2                </td></tr>\n",
       "\t<tr><td>Ed Davis         </td><td>M                </td><td>800m             </td><td>2013-05-29       </td><td>2                </td></tr>\n",
       "\t<tr><td>Edwin Harris     </td><td>M                </td><td>800 Meter Run    </td><td>2016-06-29       </td><td>2                </td></tr>\n",
       "\t<tr><td>Fabrienne Swepson</td><td>F                </td><td>200m             </td><td>2009-07-15       </td><td>2                </td></tr>\n",
       "\t<tr><td>Gordon Keeler    </td><td>M                </td><td>800 Meter Run    </td><td>2016-06-29       </td><td>2                </td></tr>\n",
       "\t<tr><td>Harold Hill      </td><td>M                </td><td>200m             </td><td>2009-07-15       </td><td>2                </td></tr>\n",
       "\t<tr><td>Jacob Andre      </td><td>M                </td><td>200m             </td><td>2013-07-10       </td><td>2                </td></tr>\n",
       "\t<tr><td>Jacob Sternfeld  </td><td>M                </td><td>800m             </td><td>2013-05-29       </td><td>2                </td></tr>\n",
       "\t<tr><td>Jason Figge      </td><td>M                </td><td>100m Dash        </td><td>2014-07-23       </td><td>2                </td></tr>\n",
       "\t<tr><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr>\n",
       "\t<tr><td>Martin Warters    </td><td>M                 </td><td>100m Dash         </td><td>2014-07-23        </td><td>2                 </td></tr>\n",
       "\t<tr><td>Mary Szymkowski   </td><td>F                 </td><td>200m              </td><td>2013-07-10        </td><td>2                 </td></tr>\n",
       "\t<tr><td>Matthew Swepson   </td><td>M                 </td><td>200m              </td><td>2009-07-15        </td><td>2                 </td></tr>\n",
       "\t<tr><td>Michelle Hickerson</td><td>F                 </td><td>100m Dash         </td><td>2014-07-23        </td><td>2                 </td></tr>\n",
       "\t<tr><td>Mike Nobles       </td><td>M                 </td><td>800 Meter Run     </td><td>2016-06-29        </td><td>2                 </td></tr>\n",
       "\t<tr><td>Nan Lujan         </td><td>F                 </td><td>100m Dash         </td><td>2014-07-23        </td><td>2                 </td></tr>\n",
       "\t<tr><td>Owen Astrachan    </td><td>M                 </td><td>800m              </td><td>2013-05-29        </td><td>2                 </td></tr>\n",
       "\t<tr><td>Ray Dooley        </td><td>M                 </td><td>100m Dash         </td><td>2014-07-23        </td><td>2                 </td></tr>\n",
       "\t<tr><td>Ray Dooley        </td><td>M                 </td><td>200m              </td><td>2009-07-15        </td><td>2                 </td></tr>\n",
       "\t<tr><td>Ray Dooley        </td><td>M                 </td><td>800 Meter Run     </td><td>2016-06-29        </td><td>2                 </td></tr>\n",
       "\t<tr><td>Ray Dooley        </td><td>M                 </td><td>800m              </td><td>2013-05-29        </td><td>2                 </td></tr>\n",
       "\t<tr><td>Richard Kohrman   </td><td>M                 </td><td>800m              </td><td>2013-05-29        </td><td>2                 </td></tr>\n",
       "\t<tr><td>Rob McCauley      </td><td>M                 </td><td>800m              </td><td>2013-05-29        </td><td>2                 </td></tr>\n",
       "\t<tr><td>Roxanne Springer  </td><td>F                 </td><td>100m Dash         </td><td>2014-07-23        </td><td>2                 </td></tr>\n",
       "\t<tr><td>Roxanne Springer  </td><td>F                 </td><td>800m              </td><td>2013-05-29        </td><td>2                 </td></tr>\n",
       "\t<tr><td>Sarah Roberts     </td><td>F                 </td><td>800m              </td><td>2013-05-29        </td><td>2                 </td></tr>\n",
       "\t<tr><td>Stanton Sarvis    </td><td>M                 </td><td>200m              </td><td>2013-07-10        </td><td>2                 </td></tr>\n",
       "\t<tr><td>Sydney Barker     </td><td>F                 </td><td>100m Dash         </td><td>2014-07-23        </td><td>2                 </td></tr>\n",
       "\t<tr><td>Sydney Barker     </td><td>F                 </td><td>200m              </td><td>2013-07-10        </td><td>2                 </td></tr>\n",
       "\t<tr><td>Tim O'Brien       </td><td>M                 </td><td>800 Meter Run     </td><td>2016-06-29        </td><td>2                 </td></tr>\n",
       "\t<tr><td>Tom Hoerger       </td><td>M                 </td><td>100m Dash         </td><td>2014-07-23        </td><td>2                 </td></tr>\n",
       "\t<tr><td>Tom Kirby         </td><td>M                 </td><td>200m              </td><td>2013-07-10        </td><td>2                 </td></tr>\n",
       "\t<tr><td>Tom Kirby         </td><td>M                 </td><td>800 Meter Run     </td><td>2016-06-29        </td><td>2                 </td></tr>\n",
       "\t<tr><td>Tom Kirby         </td><td>M                 </td><td>800m              </td><td>2013-05-29        </td><td>2                 </td></tr>\n",
       "\t<tr><td>Tyle Stelzig      </td><td>M                 </td><td>800m              </td><td>2013-05-29        </td><td>2                 </td></tr>\n",
       "\t<tr><td>Victoria Swepson  </td><td>F                 </td><td>200m              </td><td>2009-07-15        </td><td>2                 </td></tr>\n",
       "\t<tr><td>W. Patrick Gale   </td><td>M                 </td><td>800 Meter Run     </td><td>2016-06-29        </td><td>2                 </td></tr>\n",
       "\t<tr><td>William Schmitz   </td><td>M                 </td><td>100m Dash         </td><td>2014-07-23        </td><td>2                 </td></tr>\n",
       "\t<tr><td>William Schmitz   </td><td>M                 </td><td>800 Meter Run     </td><td>2016-06-29        </td><td>2                 </td></tr>\n",
       "\t<tr><td>William Schmitz   </td><td>M                 </td><td>800m              </td><td>2013-05-29        </td><td>2                 </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllll}\n",
       " Name & Sex & Event & Date\\_Meet & ct\\\\\n",
       "\\hline\n",
       "\t Aimee Bazin       & F                 & 100m Dash         & 2014-07-23        & 2                \\\\\n",
       "\t Aimee Bazin       & F                 & 200m              & 2013-07-10        & 2                \\\\\n",
       "\t Alex Andre        & M                 & 200m              & 2013-07-10        & 2                \\\\\n",
       "\t Alex Emmons       & M                 & Mile Run          & 2016-07-13        & 2                \\\\\n",
       "\t Alex Taylor       & M                 & 200m              & 2009-07-15        & 2                \\\\\n",
       "\t Aline Lloyd       & F                 & 200m              & 2013-07-10        & 2                \\\\\n",
       "\t Barbara Hindenach & F                 & 100m Dash         & 2014-07-23        & 2                \\\\\n",
       "\t Barbara Hindenach & F                 & 200m              & 2009-07-15        & 2                \\\\\n",
       "\t Barbara Hindenach & F                 & 200m              & 2013-07-10        & 2                \\\\\n",
       "\t Barbara Hindenach & F                 & 800 Meter Run     & 2016-06-29        & 2                \\\\\n",
       "\t Barbara Hindenach & F                 & 800m              & 2013-05-29        & 2                \\\\\n",
       "\t Ben Barker        & M                 & 100m Dash         & 2014-07-23        & 2                \\\\\n",
       "\t Bill Hansley      & M                 & 200m              & 2009-07-15        & 2                \\\\\n",
       "\t Brendan Murray    & M                 & 5K Run            & 2018-05-30        & 2                \\\\\n",
       "\t Brian Tajlili     & M                 & 800 Meter Run     & 2016-06-29        & 2                \\\\\n",
       "\t Brianna Honea     & F                 & 100m Dash         & 2014-07-23        & 2                \\\\\n",
       "\t Carolyn Huettel   & F                 & 800m              & 2013-05-29        & 2                \\\\\n",
       "\t Chris Gould       & M                 & 200m              & 2009-07-15        & 2                \\\\\n",
       "\t Chris Gould       & M                 & 800 Meter Run     & 2016-06-29        & 2                \\\\\n",
       "\t Chris Gould       & M                 & 800m              & 2013-05-29        & 2                \\\\\n",
       "\t Dan Woodlief      & M                 & 800m              & 2013-05-29        & 2                \\\\\n",
       "\t Ed Davis          & M                 & 800 Meter Run     & 2016-06-29        & 2                \\\\\n",
       "\t Ed Davis          & M                 & 800m              & 2013-05-29        & 2                \\\\\n",
       "\t Edwin Harris      & M                 & 800 Meter Run     & 2016-06-29        & 2                \\\\\n",
       "\t Fabrienne Swepson & F                 & 200m              & 2009-07-15        & 2                \\\\\n",
       "\t Gordon Keeler     & M                 & 800 Meter Run     & 2016-06-29        & 2                \\\\\n",
       "\t Harold Hill       & M                 & 200m              & 2009-07-15        & 2                \\\\\n",
       "\t Jacob Andre       & M                 & 200m              & 2013-07-10        & 2                \\\\\n",
       "\t Jacob Sternfeld   & M                 & 800m              & 2013-05-29        & 2                \\\\\n",
       "\t Jason Figge       & M                 & 100m Dash         & 2014-07-23        & 2                \\\\\n",
       "\t ... & ... & ... & ... & ...\\\\\n",
       "\t Martin Warters     & M                  & 100m Dash          & 2014-07-23         & 2                 \\\\\n",
       "\t Mary Szymkowski    & F                  & 200m               & 2013-07-10         & 2                 \\\\\n",
       "\t Matthew Swepson    & M                  & 200m               & 2009-07-15         & 2                 \\\\\n",
       "\t Michelle Hickerson & F                  & 100m Dash          & 2014-07-23         & 2                 \\\\\n",
       "\t Mike Nobles        & M                  & 800 Meter Run      & 2016-06-29         & 2                 \\\\\n",
       "\t Nan Lujan          & F                  & 100m Dash          & 2014-07-23         & 2                 \\\\\n",
       "\t Owen Astrachan     & M                  & 800m               & 2013-05-29         & 2                 \\\\\n",
       "\t Ray Dooley         & M                  & 100m Dash          & 2014-07-23         & 2                 \\\\\n",
       "\t Ray Dooley         & M                  & 200m               & 2009-07-15         & 2                 \\\\\n",
       "\t Ray Dooley         & M                  & 800 Meter Run      & 2016-06-29         & 2                 \\\\\n",
       "\t Ray Dooley         & M                  & 800m               & 2013-05-29         & 2                 \\\\\n",
       "\t Richard Kohrman    & M                  & 800m               & 2013-05-29         & 2                 \\\\\n",
       "\t Rob McCauley       & M                  & 800m               & 2013-05-29         & 2                 \\\\\n",
       "\t Roxanne Springer   & F                  & 100m Dash          & 2014-07-23         & 2                 \\\\\n",
       "\t Roxanne Springer   & F                  & 800m               & 2013-05-29         & 2                 \\\\\n",
       "\t Sarah Roberts      & F                  & 800m               & 2013-05-29         & 2                 \\\\\n",
       "\t Stanton Sarvis     & M                  & 200m               & 2013-07-10         & 2                 \\\\\n",
       "\t Sydney Barker      & F                  & 100m Dash          & 2014-07-23         & 2                 \\\\\n",
       "\t Sydney Barker      & F                  & 200m               & 2013-07-10         & 2                 \\\\\n",
       "\t Tim O'Brien        & M                  & 800 Meter Run      & 2016-06-29         & 2                 \\\\\n",
       "\t Tom Hoerger        & M                  & 100m Dash          & 2014-07-23         & 2                 \\\\\n",
       "\t Tom Kirby          & M                  & 200m               & 2013-07-10         & 2                 \\\\\n",
       "\t Tom Kirby          & M                  & 800 Meter Run      & 2016-06-29         & 2                 \\\\\n",
       "\t Tom Kirby          & M                  & 800m               & 2013-05-29         & 2                 \\\\\n",
       "\t Tyle Stelzig       & M                  & 800m               & 2013-05-29         & 2                 \\\\\n",
       "\t Victoria Swepson   & F                  & 200m               & 2009-07-15         & 2                 \\\\\n",
       "\t W. Patrick Gale    & M                  & 800 Meter Run      & 2016-06-29         & 2                 \\\\\n",
       "\t William Schmitz    & M                  & 100m Dash          & 2014-07-23         & 2                 \\\\\n",
       "\t William Schmitz    & M                  & 800 Meter Run      & 2016-06-29         & 2                 \\\\\n",
       "\t William Schmitz    & M                  & 800m               & 2013-05-29         & 2                 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "Name | Sex | Event | Date_Meet | ct | \n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| Aimee Bazin       | F                 | 100m Dash         | 2014-07-23        | 2                 | \n",
       "| Aimee Bazin       | F                 | 200m              | 2013-07-10        | 2                 | \n",
       "| Alex Andre        | M                 | 200m              | 2013-07-10        | 2                 | \n",
       "| Alex Emmons       | M                 | Mile Run          | 2016-07-13        | 2                 | \n",
       "| Alex Taylor       | M                 | 200m              | 2009-07-15        | 2                 | \n",
       "| Aline Lloyd       | F                 | 200m              | 2013-07-10        | 2                 | \n",
       "| Barbara Hindenach | F                 | 100m Dash         | 2014-07-23        | 2                 | \n",
       "| Barbara Hindenach | F                 | 200m              | 2009-07-15        | 2                 | \n",
       "| Barbara Hindenach | F                 | 200m              | 2013-07-10        | 2                 | \n",
       "| Barbara Hindenach | F                 | 800 Meter Run     | 2016-06-29        | 2                 | \n",
       "| Barbara Hindenach | F                 | 800m              | 2013-05-29        | 2                 | \n",
       "| Ben Barker        | M                 | 100m Dash         | 2014-07-23        | 2                 | \n",
       "| Bill Hansley      | M                 | 200m              | 2009-07-15        | 2                 | \n",
       "| Brendan Murray    | M                 | 5K Run            | 2018-05-30        | 2                 | \n",
       "| Brian Tajlili     | M                 | 800 Meter Run     | 2016-06-29        | 2                 | \n",
       "| Brianna Honea     | F                 | 100m Dash         | 2014-07-23        | 2                 | \n",
       "| Carolyn Huettel   | F                 | 800m              | 2013-05-29        | 2                 | \n",
       "| Chris Gould       | M                 | 200m              | 2009-07-15        | 2                 | \n",
       "| Chris Gould       | M                 | 800 Meter Run     | 2016-06-29        | 2                 | \n",
       "| Chris Gould       | M                 | 800m              | 2013-05-29        | 2                 | \n",
       "| Dan Woodlief      | M                 | 800m              | 2013-05-29        | 2                 | \n",
       "| Ed Davis          | M                 | 800 Meter Run     | 2016-06-29        | 2                 | \n",
       "| Ed Davis          | M                 | 800m              | 2013-05-29        | 2                 | \n",
       "| Edwin Harris      | M                 | 800 Meter Run     | 2016-06-29        | 2                 | \n",
       "| Fabrienne Swepson | F                 | 200m              | 2009-07-15        | 2                 | \n",
       "| Gordon Keeler     | M                 | 800 Meter Run     | 2016-06-29        | 2                 | \n",
       "| Harold Hill       | M                 | 200m              | 2009-07-15        | 2                 | \n",
       "| Jacob Andre       | M                 | 200m              | 2013-07-10        | 2                 | \n",
       "| Jacob Sternfeld   | M                 | 800m              | 2013-05-29        | 2                 | \n",
       "| Jason Figge       | M                 | 100m Dash         | 2014-07-23        | 2                 | \n",
       "| ... | ... | ... | ... | ... | \n",
       "| Martin Warters     | M                  | 100m Dash          | 2014-07-23         | 2                  | \n",
       "| Mary Szymkowski    | F                  | 200m               | 2013-07-10         | 2                  | \n",
       "| Matthew Swepson    | M                  | 200m               | 2009-07-15         | 2                  | \n",
       "| Michelle Hickerson | F                  | 100m Dash          | 2014-07-23         | 2                  | \n",
       "| Mike Nobles        | M                  | 800 Meter Run      | 2016-06-29         | 2                  | \n",
       "| Nan Lujan          | F                  | 100m Dash          | 2014-07-23         | 2                  | \n",
       "| Owen Astrachan     | M                  | 800m               | 2013-05-29         | 2                  | \n",
       "| Ray Dooley         | M                  | 100m Dash          | 2014-07-23         | 2                  | \n",
       "| Ray Dooley         | M                  | 200m               | 2009-07-15         | 2                  | \n",
       "| Ray Dooley         | M                  | 800 Meter Run      | 2016-06-29         | 2                  | \n",
       "| Ray Dooley         | M                  | 800m               | 2013-05-29         | 2                  | \n",
       "| Richard Kohrman    | M                  | 800m               | 2013-05-29         | 2                  | \n",
       "| Rob McCauley       | M                  | 800m               | 2013-05-29         | 2                  | \n",
       "| Roxanne Springer   | F                  | 100m Dash          | 2014-07-23         | 2                  | \n",
       "| Roxanne Springer   | F                  | 800m               | 2013-05-29         | 2                  | \n",
       "| Sarah Roberts      | F                  | 800m               | 2013-05-29         | 2                  | \n",
       "| Stanton Sarvis     | M                  | 200m               | 2013-07-10         | 2                  | \n",
       "| Sydney Barker      | F                  | 100m Dash          | 2014-07-23         | 2                  | \n",
       "| Sydney Barker      | F                  | 200m               | 2013-07-10         | 2                  | \n",
       "| Tim O'Brien        | M                  | 800 Meter Run      | 2016-06-29         | 2                  | \n",
       "| Tom Hoerger        | M                  | 100m Dash          | 2014-07-23         | 2                  | \n",
       "| Tom Kirby          | M                  | 200m               | 2013-07-10         | 2                  | \n",
       "| Tom Kirby          | M                  | 800 Meter Run      | 2016-06-29         | 2                  | \n",
       "| Tom Kirby          | M                  | 800m               | 2013-05-29         | 2                  | \n",
       "| Tyle Stelzig       | M                  | 800m               | 2013-05-29         | 2                  | \n",
       "| Victoria Swepson   | F                  | 200m               | 2009-07-15         | 2                  | \n",
       "| W. Patrick Gale    | M                  | 800 Meter Run      | 2016-06-29         | 2                  | \n",
       "| William Schmitz    | M                  | 100m Dash          | 2014-07-23         | 2                  | \n",
       "| William Schmitz    | M                  | 800 Meter Run      | 2016-06-29         | 2                  | \n",
       "| William Schmitz    | M                  | 800m               | 2013-05-29         | 2                  | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "    Name               Sex Event         Date_Meet  ct \n",
       "1   Aimee Bazin        F   100m Dash     2014-07-23 2  \n",
       "2   Aimee Bazin        F   200m          2013-07-10 2  \n",
       "3   Alex Andre         M   200m          2013-07-10 2  \n",
       "4   Alex Emmons        M   Mile Run      2016-07-13 2  \n",
       "5   Alex Taylor        M   200m          2009-07-15 2  \n",
       "6   Aline Lloyd        F   200m          2013-07-10 2  \n",
       "7   Barbara Hindenach  F   100m Dash     2014-07-23 2  \n",
       "8   Barbara Hindenach  F   200m          2009-07-15 2  \n",
       "9   Barbara Hindenach  F   200m          2013-07-10 2  \n",
       "10  Barbara Hindenach  F   800 Meter Run 2016-06-29 2  \n",
       "11  Barbara Hindenach  F   800m          2013-05-29 2  \n",
       "12  Ben Barker         M   100m Dash     2014-07-23 2  \n",
       "13  Bill Hansley       M   200m          2009-07-15 2  \n",
       "14  Brendan Murray     M   5K Run        2018-05-30 2  \n",
       "15  Brian Tajlili      M   800 Meter Run 2016-06-29 2  \n",
       "16  Brianna Honea      F   100m Dash     2014-07-23 2  \n",
       "17  Carolyn Huettel    F   800m          2013-05-29 2  \n",
       "18  Chris Gould        M   200m          2009-07-15 2  \n",
       "19  Chris Gould        M   800 Meter Run 2016-06-29 2  \n",
       "20  Chris Gould        M   800m          2013-05-29 2  \n",
       "21  Dan Woodlief       M   800m          2013-05-29 2  \n",
       "22  Ed Davis           M   800 Meter Run 2016-06-29 2  \n",
       "23  Ed Davis           M   800m          2013-05-29 2  \n",
       "24  Edwin Harris       M   800 Meter Run 2016-06-29 2  \n",
       "25  Fabrienne Swepson  F   200m          2009-07-15 2  \n",
       "26  Gordon Keeler      M   800 Meter Run 2016-06-29 2  \n",
       "27  Harold Hill        M   200m          2009-07-15 2  \n",
       "28  Jacob Andre        M   200m          2013-07-10 2  \n",
       "29  Jacob Sternfeld    M   800m          2013-05-29 2  \n",
       "30  Jason Figge        M   100m Dash     2014-07-23 2  \n",
       "... ...                ... ...           ...        ...\n",
       "50  Martin Warters     M   100m Dash     2014-07-23 2  \n",
       "51  Mary Szymkowski    F   200m          2013-07-10 2  \n",
       "52  Matthew Swepson    M   200m          2009-07-15 2  \n",
       "53  Michelle Hickerson F   100m Dash     2014-07-23 2  \n",
       "54  Mike Nobles        M   800 Meter Run 2016-06-29 2  \n",
       "55  Nan Lujan          F   100m Dash     2014-07-23 2  \n",
       "56  Owen Astrachan     M   800m          2013-05-29 2  \n",
       "57  Ray Dooley         M   100m Dash     2014-07-23 2  \n",
       "58  Ray Dooley         M   200m          2009-07-15 2  \n",
       "59  Ray Dooley         M   800 Meter Run 2016-06-29 2  \n",
       "60  Ray Dooley         M   800m          2013-05-29 2  \n",
       "61  Richard Kohrman    M   800m          2013-05-29 2  \n",
       "62  Rob McCauley       M   800m          2013-05-29 2  \n",
       "63  Roxanne Springer   F   100m Dash     2014-07-23 2  \n",
       "64  Roxanne Springer   F   800m          2013-05-29 2  \n",
       "65  Sarah Roberts      F   800m          2013-05-29 2  \n",
       "66  Stanton Sarvis     M   200m          2013-07-10 2  \n",
       "67  Sydney Barker      F   100m Dash     2014-07-23 2  \n",
       "68  Sydney Barker      F   200m          2013-07-10 2  \n",
       "69  Tim O'Brien        M   800 Meter Run 2016-06-29 2  \n",
       "70  Tom Hoerger        M   100m Dash     2014-07-23 2  \n",
       "71  Tom Kirby          M   200m          2013-07-10 2  \n",
       "72  Tom Kirby          M   800 Meter Run 2016-06-29 2  \n",
       "73  Tom Kirby          M   800m          2013-05-29 2  \n",
       "74  Tyle Stelzig       M   800m          2013-05-29 2  \n",
       "75  Victoria Swepson   F   200m          2009-07-15 2  \n",
       "76  W. Patrick Gale    M   800 Meter Run 2016-06-29 2  \n",
       "77  William Schmitz    M   100m Dash     2014-07-23 2  \n",
       "78  William Schmitz    M   800 Meter Run 2016-06-29 2  \n",
       "79  William Schmitz    M   800m          2013-05-29 2  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "track_res %>% group_by(Name, Sex, Event, Date_Meet) %>% \n",
    "         summarise(ct = n()) %>% dplyr::filter(ct > 1) %>% ungroup()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>Name</th><th scope=col>Sex</th><th scope=col>Age</th><th scope=col>Time</th><th scope=col>ct_evt</th><th scope=col>Event</th><th scope=col>mins</th><th scope=col>secs</th><th scope=col>track_time</th><th scope=col>temp_dist</th><th scope=col>dist_m</th><th scope=col>Place</th><th scope=col>Max_Hamlyn_pts</th><th scope=col>Date_Meet</th><th scope=col>agegrp</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>Aimee Bazin</td><td>F          </td><td>31         </td><td>18.1       </td><td>2000       </td><td>100m Dash  </td><td> 0         </td><td>18.1       </td><td> 18.1      </td><td>100m       </td><td>NA         </td><td> 6         </td><td>0          </td><td>2014-07-23 </td><td>Age 30 - 34</td></tr>\n",
       "\t<tr><td>Aimee Bazin</td><td>F          </td><td>31         </td><td>11:14      </td><td>2000       </td><td>100m Dash  </td><td>11         </td><td>14         </td><td>674.0      </td><td>100m       </td><td>NA         </td><td>21         </td><td>0          </td><td>2014-07-23 </td><td>Age 30 - 34</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllllllllllllll}\n",
       " Name & Sex & Age & Time & ct\\_evt & Event & mins & secs & track\\_time & temp\\_dist & dist\\_m & Place & Max\\_Hamlyn\\_pts & Date\\_Meet & agegrp\\\\\n",
       "\\hline\n",
       "\t Aimee Bazin & F           & 31          & 18.1        & 2000        & 100m Dash   &  0          & 18.1        &  18.1       & 100m        & NA          &  6          & 0           & 2014-07-23  & Age 30 - 34\\\\\n",
       "\t Aimee Bazin & F           & 31          & 11:14       & 2000        & 100m Dash   & 11          & 14          & 674.0       & 100m        & NA          & 21          & 0           & 2014-07-23  & Age 30 - 34\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "Name | Sex | Age | Time | ct_evt | Event | mins | secs | track_time | temp_dist | dist_m | Place | Max_Hamlyn_pts | Date_Meet | agegrp | \n",
       "|---|---|\n",
       "| Aimee Bazin | F           | 31          | 18.1        | 2000        | 100m Dash   |  0          | 18.1        |  18.1       | 100m        | NA          |  6          | 0           | 2014-07-23  | Age 30 - 34 | \n",
       "| Aimee Bazin | F           | 31          | 11:14       | 2000        | 100m Dash   | 11          | 14          | 674.0       | 100m        | NA          | 21          | 0           | 2014-07-23  | Age 30 - 34 | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "  Name        Sex Age Time  ct_evt Event     mins secs track_time temp_dist\n",
       "1 Aimee Bazin F   31  18.1  2000   100m Dash  0   18.1  18.1      100m     \n",
       "2 Aimee Bazin F   31  11:14 2000   100m Dash 11   14   674.0      100m     \n",
       "  dist_m Place Max_Hamlyn_pts Date_Meet  agegrp     \n",
       "1 NA      6    0              2014-07-23 Age 30 - 34\n",
       "2 NA     21    0              2014-07-23 Age 30 - 34"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "track_res %>% dplyr::filter(str_trim(Name) == 'Aimee Bazin' & \n",
    "                            str_trim(toupper(Event)) == '100M DASH' &\n",
    "                            Date_Meet == '2014-07-23')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use of :: means object on the left is a package\n",
    "png(paste0(pics_dir, \"dupe_name_no_strong_tag.PNG\"))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>Name</th><th scope=col>Sex</th><th scope=col>Event</th><th scope=col>Date_Meet</th><th scope=col>ct</th></tr></thead>\n",
       "<tbody>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllll}\n",
       " Name & Sex & Event & Date\\_Meet & ct\\\\\n",
       "\\hline\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "Name | Sex | Event | Date_Meet | ct | \n",
       "||\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "     Name Sex Event Date_Meet ct"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Select the fastest time whenever there are duplicates per name, event, date\n",
    "track_res <- track_res %>% \n",
    "               group_by(Name, Event, Date_Meet) %>%\n",
    "               slice(which.min(track_time)) %>% ungroup()\n",
    "track_res %>% group_by(Name, Sex, Event, Date_Meet) %>% \n",
    "         summarise(ct = n()) %>% dplyr::filter(ct > 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>Name</th><th scope=col>Sex</th><th scope=col>Age</th><th scope=col>Time</th><th scope=col>ct_evt</th><th scope=col>Event</th><th scope=col>mins</th><th scope=col>secs</th><th scope=col>track_time</th><th scope=col>temp_dist</th><th scope=col>dist_m</th><th scope=col>Place</th><th scope=col>Max_Hamlyn_pts</th><th scope=col>Date_Meet</th><th scope=col>agegrp</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>Aimee Bazin</td><td>F          </td><td>31         </td><td>18.1       </td><td>2000       </td><td>100m Dash  </td><td>0          </td><td>18.1       </td><td>18.1       </td><td>100m       </td><td>NA         </td><td>6          </td><td>0          </td><td>2014-07-23 </td><td>Age 30 - 34</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllllllllllllll}\n",
       " Name & Sex & Age & Time & ct\\_evt & Event & mins & secs & track\\_time & temp\\_dist & dist\\_m & Place & Max\\_Hamlyn\\_pts & Date\\_Meet & agegrp\\\\\n",
       "\\hline\n",
       "\t Aimee Bazin & F           & 31          & 18.1        & 2000        & 100m Dash   & 0           & 18.1        & 18.1        & 100m        & NA          & 6           & 0           & 2014-07-23  & Age 30 - 34\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "Name | Sex | Age | Time | ct_evt | Event | mins | secs | track_time | temp_dist | dist_m | Place | Max_Hamlyn_pts | Date_Meet | agegrp | \n",
       "|---|\n",
       "| Aimee Bazin | F           | 31          | 18.1        | 2000        | 100m Dash   | 0           | 18.1        | 18.1        | 100m        | NA          | 6           | 0           | 2014-07-23  | Age 30 - 34 | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "  Name        Sex Age Time ct_evt Event     mins secs track_time temp_dist\n",
       "1 Aimee Bazin F   31  18.1 2000   100m Dash 0    18.1 18.1       100m     \n",
       "  dist_m Place Max_Hamlyn_pts Date_Meet  agegrp     \n",
       "1 NA     6     0              2014-07-23 Age 30 - 34"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "track_res %>% dplyr::filter(str_trim(Name) == 'Aimee Bazin' & \n",
    "                            str_trim(toupper(Event)) == '100M DASH' &\n",
    "                            Date_Meet == '2014-07-23')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "track_res <- track_res %>% dplyr::filter(!is.na(Age)) %>%\n",
    "    distinct(Name, Sex, Event, Date_Meet, .keep_all = TRUE)\n",
    "track_res <- track_res %>% dplyr::filter(!is.na(agegrp)) %>%\n",
    "              # Age-graded track tables start at age 4\n",
    "              dplyr::filter(Age > 4) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "m_ages_l <- length(unique(track_res %>% dplyr::filter(Sex=='M') %>% pull(Age)))\n",
    "f_ages_l <- length(unique(track_res %>% dplyr::filter(Sex=='F') %>% pull(Age)))\n",
    "m_ages <- unique(track_res %>% dplyr::filter(Sex=='M') %>% pull(Age))\n",
    "f_ages <- unique(track_res %>% dplyr::filter(Sex=='F') %>% pull(Age))\n",
    "dat_m1 <- data.frame()\n",
    "dat_f1 <- data.frame()\n",
    "\n",
    "for(m_age in 1:m_ages_l){\n",
    "    m_age_in   <- m_ages[m_age]\n",
    "    # Notice need sym instead of enquo and as.character because sym \n",
    "    # requires string\n",
    "    m_age_nam  <- sym(as.character(m_age_in))\n",
    "    WMA_Men_in <- WMA_Men %>% mutate(agefactor := !!m_age_nam) %>%\n",
    "                    select(agefactor, dist_m, Sex)\n",
    "      dat_m1_0 <- track_res %>% dplyr::filter(Age == m_age_in) %>%\n",
    "                     inner_join(., WMA_Men_in, by = c(\"Sex\", \"dist_m\")) %>%\n",
    "                     mutate(age_grade_track_time = track_time * agefactor) %>%\n",
    "                     select(Name, Sex, Date_Meet, dist_m, age_grade_track_time, agefactor)\n",
    "      dat_m1   <- bind_rows(dat_m1, dat_m1_0)\n",
    "}\n",
    "\n",
    "for(f_age in 1:f_ages_l){\n",
    "    f_age_in   <- f_ages[f_age]\n",
    "    # Notice need sym instead of enquo and as.character because sym \n",
    "    # requires string\n",
    "    f_age_nam  <- sym(as.character(f_age_in))\n",
    "    WMA_Women_in <- WMA_Women %>% mutate(agefactor := !!f_age_nam) %>%\n",
    "                     select(agefactor, dist_m, Sex)\n",
    "      dat_f1_0 <- track_res %>% dplyr::filter(Age == f_age_in) %>%\n",
    "                     inner_join(., WMA_Women_in, by = c(\"Sex\", \"dist_m\")) %>%\n",
    "                     mutate(age_grade_track_time = track_time * agefactor) %>%\n",
    "                     select(Name, Sex, Date_Meet, dist_m, age_grade_track_time, agefactor)\n",
    "      dat_f1   <- bind_rows(dat_f1, dat_f1_0)\n",
    "}\n",
    "\n",
    "dat_all <- bind_rows(dat_m1, dat_f1)\n",
    "track_res_all <- left_join(track_res, dat_all, by = c(\"Name\", \"Sex\", \"Date_Meet\", \"dist_m\"))\n",
    "\n",
    "track_res_all <- track_res_all %>% dplyr::filter(!is.na(agegrp)) %>%\n",
    "                    arrange(track_time) \n",
    "\n",
    "track_res_age <- track_res_all %>%\n",
    "    group_by(dist_m, agegrp) %>%\n",
    "    summarise(median_age = median(track_time)) %>%\n",
    "    ungroup()\n",
    "\n",
    "track_res_ind <- track_res_all %>%\n",
    "    group_by(dist_m, Name) %>%\n",
    "    summarise(median_ind = median(track_time)) %>%\n",
    "    ungroup()\n",
    "\n",
    "track_res_age_age <- track_res_all %>%\n",
    "    group_by(dist_m, agegrp) %>%\n",
    "    summarise(median_age = median(age_grade_track_time)) %>%\n",
    "    ungroup()\n",
    "\n",
    "track_res_ind_age <- track_res_all %>%\n",
    "    group_by(dist_m, Name) %>%\n",
    "    summarise(median_ind = median(age_grade_track_time)) %>%\n",
    "    ungroup()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>dist_m</th><th scope=col>n</th><th scope=col>percent</th><th scope=col>valid_percent</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>  NA      </td><td>13        </td><td>0.325     </td><td>        NA</td></tr>\n",
       "\t<tr><td>   3      </td><td>12        </td><td>0.300     </td><td>0.44444444</td></tr>\n",
       "\t<tr><td>   5      </td><td>10        </td><td>0.250     </td><td>0.37037037</td></tr>\n",
       "\t<tr><td>1609      </td><td> 3        </td><td>0.075     </td><td>0.11111111</td></tr>\n",
       "\t<tr><td> 200      </td><td> 1        </td><td>0.025     </td><td>0.03703704</td></tr>\n",
       "\t<tr><td> 800      </td><td> 1        </td><td>0.025     </td><td>0.03703704</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llll}\n",
       " dist\\_m & n & percent & valid\\_percent\\\\\n",
       "\\hline\n",
       "\t   NA       & 13         & 0.325      &         NA\\\\\n",
       "\t    3       & 12         & 0.300      & 0.44444444\\\\\n",
       "\t    5       & 10         & 0.250      & 0.37037037\\\\\n",
       "\t 1609       &  3         & 0.075      & 0.11111111\\\\\n",
       "\t  200       &  1         & 0.025      & 0.03703704\\\\\n",
       "\t  800       &  1         & 0.025      & 0.03703704\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "dist_m | n | percent | valid_percent | \n",
       "|---|---|---|---|---|---|\n",
       "|   NA       | 13         | 0.325      |         NA | \n",
       "|    3       | 12         | 0.300      | 0.44444444 | \n",
       "|    5       | 10         | 0.250      | 0.37037037 | \n",
       "| 1609       |  3         | 0.075      | 0.11111111 | \n",
       "|  200       |  1         | 0.025      | 0.03703704 | \n",
       "|  800       |  1         | 0.025      | 0.03703704 | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "  dist_m n  percent valid_percent\n",
       "1   NA   13 0.325           NA   \n",
       "2    3   12 0.300   0.44444444   \n",
       "3    5   10 0.250   0.37037037   \n",
       "4 1609    3 0.075   0.11111111   \n",
       "5  200    1 0.025   0.03703704   \n",
       "6  800    1 0.025   0.03703704   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "track_res_age_age %>% dplyr::filter(is.na(median_age)) %>% janitor::tabyl(dist_m) %>% arrange(desc(n))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "plotfunc <- function(dist=100, subtitl_txt=\"\", gender=c(\"M\", \"F\"), sz = 16, yr = 2018){\n",
    "    \n",
    "    track_res_all_top3age <-\n",
    "        track_res_all %>%\n",
    "        dplyr::filter(Sex %in% gender & dist_m == dist) %>%\n",
    "        dplyr::filter(year(Date_Meet)==yr) %>%\n",
    "        group_by(Name) %>%\n",
    "        slice(which.min(track_time)) %>%\n",
    "        group_by(agegrp) %>%\n",
    "        top_n(-3, track_time) %>%\n",
    "        mutate(Name = paste0(Name, \" (\", Age, \")\")) %>%\n",
    "        ungroup() %>%\n",
    "        arrange(track_time)\n",
    "        \n",
    "    ggplot(track_res_all_top3age, aes(x = reorder(Name, desc(track_time)), y = track_time)) +\n",
    "               geom_point(stat='identity', aes(col=agegrp), size = 6) +\n",
    "               scale_color_viridis_d(name='Age Group') +\n",
    "               xlab(\"Runner's Name (Age)\") +\n",
    "               ylab(\"Run time (seconds)\") +\n",
    "               ggtitle(paste0(\"Carolina Godiva Track Club Summer Track Series (\", yr, \")\"),\n",
    "                       subtitle = subtitl_txt) +\n",
    "        theme(plot.title = element_text(hjust = 0.5, color = '#EEEEEE',\n",
    "                                        size = sz),\n",
    "              plot.subtitle = element_text(hjust = 0.5, color = '#EEEEEE',\n",
    "                                           size = sz * 20/28),\n",
    "              text = element_text(size = 14, color = '#EEEEEE'),\n",
    "              axis.text = element_text(size = 10, color = '#EEEEEE'),\n",
    "              legend.text = element_text(size = 12, color = '#EEEEEE'),\n",
    "              legend.background = element_rect(fill = '#333333'),\n",
    "              panel.background = element_rect(fill = '#333333'),\n",
    "              strip.background = element_rect(fill = '#333333'),\n",
    "              plot.background = element_rect(fill = '#333333')) +\n",
    "               coord_flip()\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "plotfunc_grade <- function(dist=100, subtitl_txt=\"\", gender=c(\"M\", \"F\"), sz = 16, yr = 2018){\n",
    "    \n",
    "    track_res_all_top3age_grade <-\n",
    "        track_res_all %>%\n",
    "        dplyr::filter(Sex %in% gender & dist_m == dist) %>%\n",
    "        dplyr::filter(year(Date_Meet)==yr) %>%\n",
    "        group_by(Name) %>%\n",
    "        slice(which.min(track_time)) %>%\n",
    "        group_by(agegrp) %>%\n",
    "        top_n(-3, age_grade_track_time) %>%\n",
    "        mutate(Name = paste0(Name, \" (\", Age, \")\")) %>%\n",
    "        ungroup() %>%\n",
    "        arrange(age_grade_track_time)\n",
    "    \n",
    "    ggplot(track_res_all_top3age_grade, aes(x = reorder(Name, desc(age_grade_track_time)), y = age_grade_track_time)) +\n",
    "        geom_point(stat='identity', aes(col=agegrp), size = 6) +\n",
    "        scale_color_viridis_d(name='Age Group') +\n",
    "        xlab(\"Runner's Name (Age)\") +\n",
    "        ylab(\"Age-Graded run time (seconds)\") +\n",
    "        ggtitle(paste0(\"Carolina Godiva Track Club Summer Track Series (\", yr, \")\"),\n",
    "                subtitle = subtitl_txt) +\n",
    "        theme(plot.title = element_text(hjust = 0.5, color = '#EEEEEE',\n",
    "                                        size = sz),\n",
    "              plot.subtitle = element_text(hjust = 0.5, color = '#EEEEEE',\n",
    "                                           size = sz * 20/28),\n",
    "              text = element_text(size = 14, color = '#EEEEEE'),\n",
    "              axis.text = element_text(size = 10, color = '#EEEEEE'),\n",
    "              legend.text = element_text(size = 12, color = '#EEEEEE'),\n",
    "              legend.background = element_rect(fill = '#333333'),\n",
    "              panel.background = element_rect(fill = '#333333'),\n",
    "              strip.background = element_rect(fill = '#333333'),\n",
    "              plot.background = element_rect(fill = '#333333')) +\n",
    "        coord_flip()\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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VO7oiwjytrAtjldn+Vv593UrYjIQLLFhuIsZR2eYBxSC5/7LyJxl4mbEZGBZCAV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\nVwYSFrnx8a8kkCqchzVnaoc2MiaDZM+RSrHkrmSOWpJYPdlJ9OP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JcUhe/jaQVrfkrpKfI6Um0e81D0ib593UAmveKSAll5Qt++C9cJD6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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotfunc(dist=100, \"100 Meter: dot plot [top 3 per age group]\", c(\"M\", \"F\"), yr = 2018)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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OzNs31xak69a/Oxv9ucRu7+6dIg06uG5n2r9C5x6Y3mdap8kC6mjnMeuXcTQGIX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\nvRIAae7kUqpcRU6vDJBGJdH/aZJ1By/w+oIXc99VB/YbkghIb9y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1tuKp5grStt0kGuDmV6u1g9Qm+Hou1f0+VgGS2NWRN3oQIwWpiJtE\n8zGCKK0TpGYe7bGeFjXTd1+WV+yNHuRIQSrhJtEYjixJ6wTpWU+5uNaTOdgZtRIVf6PjGClI\nRdwkGoORRWmdIHVXdtd+cQMaeqO7MScj3dPq/qhdrptEkwnSrs2d/B0ggVGQI9zT6v6wXa6b\nROM5akkiVeUn0T/c8BQLLLD1+o595Xrh2Eu7U/PVl8clRp8voehnisblZ7un1f1pu1w3iRbg\nqCHJryo/if6pHdsa6KYGW9vZgdwtD9PZcK3uNUK7x0zpG4or8pmSmWJZ7ml1f94u102i5YCU\nn0T/9TpEZlu4rcdu6jqTrY6A1M6mPdYt30YaJAVJnptEC3FUk+RXlZ1Ev80puQtcEIKt7UT2\nH647mz6Q3T/Msy7Oz6iVqPBnymTTyHBPq3sBu1w3iZYBUn4S/ZuJZaADW+9NiiL2y12EDhHK\nUvAIGY4oSQpSATeJlgFSfhL9p6kur+BIHrAV5MkjezgKpJTcKGhr2MCM6E7aAxe1WwoHUZDE\nuUm0dJBGJNE35h4ZyQO2poJUF7+nDGeIDLX2ywwZxnWxe9gYugoo9JmyHBGSFKQCbhItHaQR\nSfRNzdsuNJIHbM0CKWVibmTyDykzYJgDJG+Sky8FSZybREsHaUQSfdPcBe0DI3nA1vkv7dCC\nnVfUZaGLpKvDM4vsdCOc6w5PiLVBUTIVuhcK0obcJFoySGOS6Pt8eOea2/rKAOmacWmH566C\n2xQy8dUw5a3Jz3WHQDJMFAcdZdYd42D6pwBIMyWXUuUqcsKlP0cak0T/HgUJbG177YZzf6No\nMcEz2pAF99LvSTB+Ac8Y2wYZ8ZogvpCVtkji3CRaMkhjkuhfmm37wINTsPXSxWNufz4KEjjz\nDW0+JoNEd6FSkLbhJtFSx9qNSqL/hm5Xdyfw3/YKtuaMbLhGH051QiD1jIDrLdPd+WAD02QN\ng+QTWHmLCtIW3SRa6ujvcUn0L94zJiyw9RksyHxj3+scHAbbi7ZIFTmRmXR1o1skFJgujwRJ\nnyOt1k2ipYI0Mon+6RH4snJv664Z/c3uIb20AwpFNuBv7B7J6yaYdGlHQRq4/nPHGDoMBWmt\nbhJt0gzZIkn0c0DCZzlYaV8axoA24i46vMoYPxxTPw6ZB5KOtVurm0Qbl7Nh9Un0MWfePcyI\n50igfPdEydhHS+HnSF4kVBE6xvCBDHOkIJVwk2jjsghtIIn+FAUbwoQiCtIm3CTa3xFFqiqa\nRH93edT/3x9MH9+n5ffOxQumboh+pgMYKUhF3CTaSJCWEwXp9Oq6r5e4svQVuzHDBTNWx9/o\nAY4UpBJuEm3skIjFREDa1zdo9dl8ZJ87SdTQGx3DSEEq4ibR5LVIt3ZceVU/0WKGFEmU4PNJ\n7q5/PUivvge6ShstJEDDb3QsSaSCVMBNoskDyXwfSPq1Lqtzk2iAm3+0WjtIzaDYBqH9qrPo\nZ2jgjdYvGlufm0TzMYIorROkq71HWuSB8BKKvtGDX+2iIBVwk2gMR5akdYK0e71JeoN0vDfp\nVrcgBUmcm0RjMLIorROkav/qhuO8htO+ylDsjR7++ksFqYCbRBMIUlWd6/RDjwtpj7L7HgaG\nvk6RNxo2MvY7+pkyHPkkKUgF3CQaz1FLEqkqLYk+zpx/jpymOMX+kT/JMsba5YM00pccGU6z\nD9elIIlzk2gBjhqSyNgck5JEH2fOP0T+3+MU+zs+HfISIH2AJIsNXaAKHyHLkUeSglTATaLl\ngJSWRB9lzm+noycUbBJLsnuYC1J/qdZdsxkylcHQOQ0GGDxj/6Iv5pXCUd0RKEjbdpNoIY5q\nkvyq0pLow8z5Z/MIg4RT7P8E7lUoSPUFIT+xj8yzM2DyndtCTndsQEYDVqKtpD7jB+ScoAg8\nRu7AGylIK3WTaBkgJSbRh5nzbRlOKMX+PoQc963mEZAC5y23pYLne8BoQuZweC9wBKSETGoB\nkGbJ0qbKFXd+9p9lBkiJSfRh5vxDbBgPSrH/MPtEkG7mEUojlA+SN63VzAsSaQxhEXiMgePR\nFmm1bhItHaTUJPpe5vxIXgVQ8NJNjWD2kI61C6bjMhANe6vjb/FbpL716LdBIwGJ3EnhqCyF\nqBoFaRNuEi0dpPQk+ihzfgwkW7DJmpcMEher/2381XQLubQz3iIoT0HiSrF+C5lxC1719hiZ\nI2qlIK3UTaKlg5SaRN/LnB8DyRZ8vnahkgSkBzcwKHyFFb+0c78ngRQswfCbBZI+R1qpm0RL\nBik5ib6XOR/iESp4axq4RJCO7FBVcjtiEeC3OBcqDwCiILFbcVQF6UvcJFryc6TkJPpe5vww\nSK5gqBeu2UOas8EwmVadGTzfaU9wfwsFyd38GGiEv/ytzHMkFDu4wP230LF24twkWjJIyUn0\nvcz54Us7VzALJAM1fIQJRT6h8AUts05Hf4tzk2ipY+3Sk+h7mfPDJ7ufYj+5s0FBSncPSkGa\nRcmjv9OT6HuZ8yMnu5dif/oQIU6FQArVyx9jPFQUIwWpiJtESwUpI4k+zpwfazVwiv3PgCRB\nQ0cY5UhBKuEm0SbNkC2SRH+DGj7CMEYKUhE3iTYuZ0PRJPrhFkcUyAAAH+VJREFUQatSlfKZ\n/m410j2t7o/Z5bpJtHFZhEom0Y8MWpWq4c/0t9MI97S6P2iX6ybR/o4oUlXBJPqRQatSNfiZ\n/v4dIUlBKuAm0UaCtJxyBq1K1cAb/dtXlnta3Z+1y3WTaGPnXyymcd9qLksKkjg3iSavRWIH\nrcpW/I0mHHkkKUgF3CSaPJD4QauiFX2jGY4wSQpSATeJBrj5V6u1g8QPWo3Idu/x14RDV4oj\nOwi9wd52iCtXVkES5ybRfIwgSusEKXOsHZzcGtk+5M+V5civPnesHcsRIklBKuAm0RiOLEmb\nAAm0DAVA6r/43K1TkDbhJtEYjCxK6wQpU2CKnemzLLTr4YI3tQgyCufo2QiBKUnOpiBt202i\nbR4kf7KqndGKFvBkV3TGQxJRmIAPmgxkiMzsS0oBFQBphuRSqlxFzrL/8Ry1JBFjdu7vgQsw\nkPu7Hk3OpkOePmjV7gC6UUlZ6O19WwbWgytFsqKiING2CkpbJHFuEi3AUUMS7S7LzP19iIME\ncn+3k5ou3B5O7WzoPNVokFwUHG8YJMMsKUibcJNoOSBl5/4+xL923BU8N8nxbtzgn1lAquyJ\nbn0MP2QT+osbF3Cv5PlcUe9XuNtOQRLnJtFCHNUk+VVl5/4+s20MU/DRpPrac49ag5d2+3NK\n8+j32nl9CJSoaggkstXzuUXDopkJkj5HWqWbRMsAKT/399mcq7BAwf42npkoGLlHOjf4DYgF\nacqlXQAk9h6pX1KQtuYm0TJAys/9fTenG5xMjgUKernw0B6GQdolzdD1blLszRK5a6roGvgX\nrYcp7jxf0DTuHknH2q3STaKlgzQi93fLWOh0BwXbXF/HTJASB4Lbm6nQcyT7m66pKBM0xR2T\nMI9c76GuQ186+lucm0RLB2lE7u92Rvo1cIEHCl7MfVcd2C92iYAU+zbAQU2wTgqvIG3CTaKl\ngzQy93cN2bPiBAs2X1TOfmdfGKQ3eAl9iCEJAklnyK7PTaIlgzQ29zdcDBd8N0yvS9o9Euz9\nzhoF7oUZb50Sn109/JlGOFKQSrhJtOTnSGNzf1dBkEjBA9d0RUC6bWXOecpnGsJIQSriJtGS\nQRqT+7vtmdgHHsuSgmeuoOa1W7Nb8K5/YtAqz9H03N/Xun9idw08OEUF33c7x6dNAw73UEFq\n9avVSPe0uj9ll+sm0VJHf4/K/b1r+hCCU8NJQa7lioK0kUnnCZ/pL6cR7ml1f84u102ipYI0\nLvf37l3sGR7d4Arub2+MUkZ/X58u4E/CgCURGv5Mf/0Kk6QgFXCTaJNmyC6f+/vhnvDWj52+\nBKRfvrLc0+r+qF2um0Qbl7OhVO7vc3dvdmqvC7eSclVBEucm0cZlESqV+/vR9Rbed+/m6JUy\n+FuEBj5TwhEmSUEq4CbR/o4oUlWh3N/ttdzu3RbxswBzhMbQeWNOmWKxgXRBn+2RcQtc2fhn\nynCESFKQCrhJtJEgLScGpPq0fE6+qgOPibu/0WKZIJlQwfwhQgrSCt0kmlCQZmgMU0FiphEt\nChLLESRJQSrgJtHGZk1ZTDxIMwR2hKBLOzDTwivmNuApGGAyhccLB5yCtAk3iSa0RZohMA+S\nqSBVlb2BwjP4jL+i8tBxaBm0ULEkKUji3CTaN4PUn/+GIQIh4crRBW8lcHkE0hmyacnUAiBN\nz9KmylXkbIIg/bvVN4METvg5QPKW6c1WJ22RxLlJNB8jiNIaQfI0ITAhyFTgOgxfibHMdCtR\nF3cFXf6ygrQdN4nGcGRJ2jxI3X1SDxLqdIDFwI1UoEXCpRWkzbtJNAYji9L6QJpTFKSqwpiA\nv0Mg+QAZ/NuQlUj6HEmcm0T7bpAQPKhrwL9Iwx11ft+E36EAOxZwj4OCtA03icZz1JJEqkpK\noo8uu65vyzXwna/+9dmRvVBbGKQOFIOL2ZJ5z5FAJLfA7IqOtRPnJtECHDUk+VWlJdHv8aiZ\na2c98EmEUMGqmdzH7qHMGbLBu7cRIOno7/W5SbQckBKT6Dc61cm7jt2sh1hm4VO/lU3GpSDV\nUpBW5ybRQhzVJPlVpSXRb7R71dPG2/npP7GB2m3BptSmQAqRxB/jYLQwRgpSETeJlgFSYhL9\nRm3+1XuTeSj65S5twSaZ0LZAylHCEYY5UpBKuEm0DJASk+jXOrTDs4cHIhz6cdwPs1eQogpg\npCAVcZNo6SClJtGv1bUzwyD1DdLl3dIpSFF1H8tI97S6P2WX6ybR0kFKTaJf1e1M2ycxCFJf\nsLn6U5AiAh/MCPe0uj9nl+sm0dJBSk2iX7kkeIMg9QWf9RfJKkhhoU8m2z2t7g/a5bpJtGSQ\nkpPoV5Xt23t5IIUK3hqeFKSQyGeT5Z5W90ftct0kWvJzpOQk+qCbru21c7m/fZD6grFBqAqS\ngrRCN4mWDFJyEn3w1bGXrlgow0JfUEGKivl0MtzT6v6sXa6bREsda5eeRL9mq0vxMzSy4W5g\nLiC9tOPF/p9Ldk+r+8N2uW4SLXX0d0YS/XfjtbNL9BlTxRWstQxIXNPnD/XmBiDkToAy2GP/\ncmUVJHFuEi0VpJwk+u502zWjv4O14/NyIZDQH+9FhJPI4O1I8X4Jjy73FT1CliNIkoJUwE2i\nTZohu3wS/enyp+Ch5WGQUknCIAGGFKRNuEm0cTkbSiXRn0ED84vQFVjavKT+NyzFsqkgbcdN\noo3LIlQqif4MgpdofWNh6ALcDH14UmxHldtO2y0fJLAiLQdUAKTPJp5ScYqcVsKS6M8g0NFu\nT2x+gUWD94VKMxxlTzXXFmmNbhJtJEjL6YMtUh5IoAeOFMcR2QUFaVNuEu1bQQrz40BCHd6Q\niH5DFkiR3goFSZybRBt7TbiYCoLE+ipCiQOJSRAZbaN66XMkcW4STVukvHukquKKd38jzQ/b\n+9BLQRLnJtG+FaTRvXbIh7bEQOLaMycdayfOTaIBbv7TausgwTufEc+RsA9t8VkDf/yeCiwd\n/S3OTaL5GEGUNgnSB8WAFC/US0ES5ybRGI4sSas4g+WAZIIvhjYMH2EYIwWpiJtEYzCyKK3i\nDJYCkj/kNkASuzrhCMMcKUgl3CSagrQCJR1hACMFqYibROM5akkiVWUn0Y9O4kHTYutpGWxe\ncQVpzW7Bu/4BkBiOGpL8qrKT6B+SQHLZ9rnUxgqS1T9ajXRPq/szdrluEi0HpOwk+tFkxbDg\nuckyeUMzz/s9VJBa/cNphHta3Z+yy3WTaCGOapL8qrKT6J9j6fNhwUeT1mHPzW9SkFr94x8h\nkhSkAm4SLQOk/CT6Np1QWG3BPr0XM+NWQar1D19Z7ml1f9Au102iZYCUn0T/bk63WM6GlGz7\nClItBWllbhItHaQRSfRbxmKZHbqCbdK8o4IUEOEIkqQgFXCTaOkgjUqiX6d2uIYv8PqCF3Pf\nVQf2G5KmgBSZAkRXkWHhYXNktbfd+DMrRj2QZTgCJClIBdwkWjpII5Lot9qFvkQWFHzVEdgv\nv5wBpLQOktC3JY8DyWHjXbaOGSKkIK3OTaIlgzQmiX6nwST6Vd31/brMfo8UaQS4wnzxCSAZ\nuDgeJJYjR5KCVMBNoiU/RxqTRL9TECSv4IFruiaDBCqv3LndLsPdASChyzLYtoDZFwYVMd3/\nFL5qeL2oIG3CTaIlgzQmiX7bM7EPPpb1Cp65glNBMngZgYRBc2e/gebAC7jGoDUuqKH3SCxJ\nCpI4N4mWOtZuVBL9a90/sbsGxxWBgreqOj77h1RoD6eBhG9NDAIKX8oBMgxjIO2Vv+BTWbFs\nQZBS06kFQJqapU2Vq8ipljz6e1QS/V3ThxDOx+oX5FquaSAZjEs2SJUHSiZIsAJtkbbkJtFS\nQRqfRP8ZHt3gCu5vb4xmH/0Nrt0s+FGQui0eSKFsdiimgvRVbhJt0gzZtSfRByeyASvJxRoq\nTEDirvXAQqUgfaGbRBuXs0FIEn1wChuwlAESaG7KXdrpc6T1uUm0cVmEhCTRh6ewR04IpApd\nqhm/uHthuLUkGF2lIG3CTaL9HVGkKhlJ9PHtj4lwhf92GzKeI1UeSBUo6a1h9lPH2olzk2gj\nQVpOQgetcrwEN+job3FuEm1sv/liUpBqKUgrc5No2iJ9SAGS2NUpRxjCSEEq4ibRFKQVKOkI\nQxwpSCXcJJqCtAIlHiGLkYJUxE2iAW7+20pBWlzpR6iXditxk2g+RhClVZzBCpKTdjasxk2i\nMRxZklZxBitIVvocaT1uEo3ByKK0ijNYQeoU7AFXkAq4STQFaQVSkMS5STSeo5YkUlVSEv1m\n7sTr2s40AovRguEk4QpSq/AoIQWpgJtEC3DUkORXlZZEf/9yM/3aSX+BJEKgYCTbvoLUiOGo\nJ0lBKuAm0XJASkuif+vy39WTx7sZ6cehgpFs+/OBFP5ejGbr4Ba2xMBQIDTONVheQRLnJtFC\nHNUk+VWlJdEHGYba+ek/gVT6oGAk2/5sIHnz+9jNsS18gZANjhnvF4KGEWPtAEkKUgE3iZYB\nUmIS/Y61+nbq3mQeCjU3oGAk2/5cIEVxiG2IABCx4RkY3nwMXwqSODeJlgFSYhL9S3fFhtPf\nMQIFI9n2ZwIJ7wLOR8fnqcMTiQx2elORcDhigrugIG3CTaKlg5ScRP9cdyI0bVQcJFAwkm3/\nEyDZi63ABRhJeEeX/QmyIJzzDuS1y0gEFQBpUnIpVa4i51cGSMlJ9C99PtYhkGDBYLb9D4CE\nr7hCORjQlFkTclIbLAhujQwFyUpbJHFuEi0dpNQk+ucmK+SthiIOEijYis22PydIMHtWDkjx\nohGQvDjoJZCCJM5NoiWDlJxE/9k1UU/XnQBuMPiCdjuzhzO3SCNAQjdDxoCj7porBCiO6VVd\nKUjbcJNoyc+RkpPog21tr53L/Y1Bou3VSkFyv8kND2qIPgiSPkdamZtESwYpOYl+2ww1XROX\nrhifbggUjGTb/0D39yiQAs60SzuWKycFSZybREsda5eeRP9q6uFzTf78+MgGUDCSbf8TD2RN\nBblwPW54PXdvY/wFavM9KHo1FiQda7cuN4mWOvo7I4n+w3VlP4O92rhgJNv+R4YIwYdA3UtD\n1nsgoRK4qBcOLJMhQhxHOvpbnptESwUpJ4l+M6i7Wdq5RU64IJ9tX+igVY6X4AYFSZybRJs0\nQ3btSfQLan6QdIbsmtwk2ricDUKS6JdUgCR2deoRshwpSCXcJNq4LEJCkuhLUfoRUowUpCJu\nEu3viCJVyUiiL0WCzye5u74OkJaTggT1q9VI97S6P2CX6ybRxo52XUwKktMvpxHuaXV/xC7X\nLU8KktWvXyxJClIBtzwpSJ1++cpyT6v7U3a5bnlSkDopSGtyy5OC1IpwZElSkAq45WlZkMjs\nKoO3eaWr8BCGfhBePw5vYjouhqOeJAWpgFueFgYpvIKMS42CxExFoiNbeylI4tzytBaQuMHb\nySBxcwOBho+Q5agjSUEq4JangiDZ2RX+BpfFywBG0KUfhobOVEJSkMS55akcSO5yrAq0RGSa\nn98E2TukqVmEFKSVueWpTGdDFZrdV8EtQ7NqHWTsDNmMjGoBkCZlaVPlau6zbVEVa5EYMrh0\nXgaY+GbH4FjaIm3BLU9rAqlbQPm3jMuGZJjilYK0Sbc8rREkWMQBxRavFKRNuuVpJSDBOxxy\nj+SzQsiZDJI+R1qZW55W0muHeuW4XrvuJgrHod16CtI23PJUcIgQfI5UoedEpIBBT5UqR81s\n6biCHClIJdzyJHTQKsdLcIOO/hbnlicFqZOCtCa3PAkFKUQSuzrxCFmMFKQibnmSClKOUo+Q\n5UhBKuGWJwUJimKkIBVxy5OC5Ol3q5HuaXXPbZfrlicFCem30wj3tLrnt8t1y5OCBPX7NyVJ\nQSrglicFyem3ryz3tLo/YpfrlicFyUlBWo1bnhQkK8JRS5KCVMAtTwpSL4ajhiQFqYBbnj4P\nUmg2OT82wct7FyztjwOfmNeuUpBW5ZanMiBxr9Fa468zbLEK8BMmKekIWY5qkhSkAm55WilI\nQ+2X5c2bxqQgbcMtTwuDZFw+VNCW+PP7QGk3E4nmtVOQNuuWp2VB4ua+4gbHL22nnBt/q9sW\nmCGblwsqANJnEk+pOM151i2uBUBys2JJNgZ6b+NPIke5G4IgTU0QWWmLtC63PC3aIvlo+Ndl\nFem1M7EsQoZZUpA24ZanZUHiMtYxiMBmJgyS9yvcbacgiXPLU4EWqXJ3PbG2ZvDSjrlkrMaD\npM+R1uSWp6KXdl53gfubBBJTslKQtuGWp5X22rkCKSDNcY+kY+3W5JanNT9H6lPZ4e9JYhcm\n57WrxXKkIJVwy5PQQascL8ENCpI4tzwpSFAUIwWpiFuehIIUIoldnXGElCMFqYRbnqSClKOs\nI/QwUpCKuOVJQVqzW/Cub/+0wlKQeP2n1Uj3tLpns8t1y5OCxOk/TgpSCbc8KUiM/gOlIBVw\ny5OCRPQfXwvWPa9drlueFCQiBam8W54UJF+Eo0kkKUhfooVAQoPp0ChVtpjB65jC/Vg8m9Wh\nHW039YEsy9EUkhSkL9FSIBlvYSJIdJRreBaFgiTPLU/rAomjYVmQWI4mkKQgfYkWu7Qz7m94\nEgWeomev1uAMDFiOWVCQNuGWp6IgkWl9ZIKsQTdVFB+6avzEvkYK0irc8rRcZ4NrXcg8WQ8M\nPH3ceRhaIguZee06BUCaOYObitOsZ9zSKggS5sUVBFCYKSBZaYskzi1Pi4IECLLJuLxvnwCt\nFKIIl1OQNu6WpwWfIxkEEup0QCVRhwNukWC4oQUnBUmcW56KgVRVABDUHxcByW+RYr0PTvoc\nSZxbnpYc2QDhCfTauRfdhV+8105B2qhbnoqC1F3k8ZdtNhmXhcqQQklXdjrWTp5bnoQOWuV4\nCW7Q0d/i3PKkIBEpSOXd8iQUpBBJ7GqdISvOLU9SQcpR/hHC5khBKuCWJwWJl7uqU5AKuOVJ\nQRrSv1sVqVswCts/rbAUpLj+7bR43ZPtct3ypCBF9e9/TyRJQfoSKUgR/dvXgnXPYZfrlicF\nKSIFqZRbnhSksAhHI0hSkL5EClJQDEf5JClIX6KPgkQGaQdLjAoNJvv1I1rnGdnQSkEq55an\nJUCK0DIWJG/aBB5P7mvcEbIcZZOkIH2JFgApBss8IIF6FKRNuOXp8yCB+UXtS5ijAU4x6tPY\n2SzEYZs3/0hB2ppbnj4OkvGWDaYLlW2znMBXyTbvd62JyaECIM2SeErFaZ6TrpA+DRJOURxM\nvhDfnmCrKEhW2iKJc8vTh0Ey5CwPENG8MArSrHa5bnn6LEjgMs1+BUtl11XwtAdp7PryCTYF\naZtueVqq14499cOXduTV8iDpc6SCbnla6jlS5j0SKc3b/BuwSkHahlueFnsgC7vgQiB513Bm\n0Ab/GrwKSMfaiXPL03JDhAzswbZb3JdWujR2/YYqaCPUGBPmSEd/y3PLk9BBqxwvwQ0Kkji3\nPClIUU3ESEH6GgkFKUQSu3rKEU7kSEH6FkkFKUfTjnAKRgrS10hBWrNb8K5v/7TCUpCS3NrZ\nsKxbnhSkBLc+kF3aLU8K0rB7StedgvQlUpCGNO1hkoL0JVKQhqQgFXDLk4I0oIkD7hSkL9GH\nQTLGjaYbKgdfdivbX5wZTVZy/k8+kB1FkoL0JVqgRRqCqC/ilwuTgSYE9r+CsygUJHlueVoF\nSPzI7XSQuAmEQHONtRtDkoL0JVoQJJhYC0x8wCd/u9XNq7CvYum4wKKCtAm3PC0Hkp2i537h\nAu2yw8EQF+PwQZpzYl+lIJVyy9NiIOFJ4wEs6FVaysRatOjWzJFqLQDSHKFVvqaeaEW1GpBo\ntqCpIFlpiyTOLU+rAQluHQMS17x1UpDEueVpKyCxPRedFCRxbnlaA0h+d0Q6SJGWCUifI4lz\ny1OBXruq8kEi63JBordKUAqSOLc8lXiOVBGQQPatipahIHk9C3CsELMDOtZOnFuehA5a5XgJ\nbtDR3+Lc8qQgDUlBKuCWJ6EghUhiV+sMWXFueZIKUo4mnxHjOVKQvkUKUpJ7JEYK0tdIQRrp\n/lerz9YtGIXtn1ZYCtIo97+cPlm3YBS2f1phKUhj3P/6VypJCtKXSEHKd//L18fqFozC9k8r\nLAUp360gfdwtTwpStptwFCNJQfoSKUi5boajCEkK0pdoEZCSstuBkdzRSRFwA/iiWYPSQCAp\nSOLc8rRYixSFCBQw3MpA4QYdzM/8Y+08N8tRmCQF6Uu0NpAMtzJQGNAUNShI4tzytDhI8ex2\nkI02pR2aZ8QVrhA9CtIm3PK0NEh2oh65LDNeoZaV2AxZRxm4/3IkfSZnVACkz1T2XZr/pFtQ\nC4MUydxgengoOSYAEp29PvtUc22RirjlaU0gIV78mefoVohMS2deOilI4tzytCKQaKIGBUku\nCgrSh5RyaVfFQfLT7hcBSZ8jLeGWp7WB5LU+iC6/k5u9gVKQtuCWp2K9dlWVBBJkygcJB1qo\ns0HH2i3hlqdyz5EqHqS+hfF6uCuMSE+NIQtM1Tr6W5xbngQNWmVAihfqpSCJc8uTHJBM8MXQ\nhvnPiFSMFKSvkRSQ/JHjAZLY1R84I1I5UpC+RVJAmqKPnBFJGClIXyMFac1uwbu+/dMKS0Fa\ns1vwrsfc/201Kf7apCCt2S1418Pu/zpNqmFdUpDW7Ba860H3f/+7SZIUpDW7Be96wP1fX5Mq\nWZEUpDW7Be+6grQ9rfB8klD5J9yEo82QNBdIoa9xDQ5BgFuZ4XaoBJPHqx/T6o21W+qB7EJu\nwbvOuhmOtkLSbCChP2R93OWN7/YH1TEx8IQ/Mu8Ca3Xnk4zKFaQczQwSOZE/CRI3H0lBWrGb\n5WgjJH0AJHTV1cyDsGtMv6YrEAHJXScaVx5XpyAJcytIw/JBsvPz8KWXsTc8biMqXvl+fwWs\nzZsEiHYEaG3nk5DKFaQcfeAeCZ3akYXKI4OwA+f44fL2L80e6UAqnaZN5SsAUr95+llYUB/q\ntfPaDti3NiNI2iLJcmuLNCyvZWBA6iniz3p3f0Rvfzo3Sacab+Sc1nY+CalcQcrRvJd2kRZp\n4KwfBClUm4Ikx60gDYsFKf0eKREkbZFku7fL0ac6G/ibncrdI3XXayQAd/sTaufYjQrSit0K\n0qAABx07foea6R4c2ZIGZqrzQapIzwV9jlQVSse1nFvwrutYOxHieAluWN/5JKJyHf2dIwVp\nSArSnG4FaWUKkMSuXuH5JKHyD7k3iZFckHK0yvNp/ZV/yr1JjhSkVbsF73rMvT2MFKR1uwXv\n+vZPKywFac1uwbu+/dMK6xtAUqk+LgVJpZpBCpJKNYMUJJVqBilIKtUMUpBUqhmkIKlUM0hB\nUqlmkIKkUs0gBUmlmkEKUlgwR2yJ6u1eLF4zmXisGpKCFBSfLmK56sEs/YVr7msvc+AypSCF\nxOYoWrJ6L+fFgjV3v8scuFApSHF9LUhl6pYrBSmuUiDZBGbLV264lOqqASlIUdHclMtVXAwk\nPimnKioFKapS5xPq6NB7JAFSkGIqei4rSJKkIEVkyO+lKrZfvqEgCZGCFBbKglzgQVKhyhWk\nMVKQgkLdwAVOp7IPZAvVLVYKUkjwOwh1iJBqQAqSSjWDFCSVagYpSCrVDFKQVKoZpCCpVDNI\nQVKpZpCCpFLNIAVJpZpBCpJKNYMUpLnVDzg1j3NK8cOzsvkZ0nS8j9sxRqTe+3G22F8mBWlu\nGadHUvEqE6TXYeSeBSqHOrxmi/1lUpDmlj05Ty9zzSiequt8DRJT+T1hl1WMFKS55U7Og3nm\nFE/Tzpxy9yin8pPZzRf+m6QgzS1wcqLLtmaanqmOD2NuB1eiGWHeF9w/zPPNyc/TPH+a7bvr\ny7xuexD+2oXbXZ/GPK8HWuxwe5lnd3+2rzdc9139oO734uvS1osiVSmtqIpKQZpbfouEQTq1\n6BxsYQjS8dVs+2lW1rf9+1dbADRC5tb86bfQYidwf9Ytv06VV3e7eKvrRZGq6qYTJ0ZJQZpb\nPTi79z3SufJBMtdddXh0NNji3a+6NbiZu7nUf2oS3hHeV1pn87LXWyfTtjWPOlC1OzeswmI7\nYy5NFe9yu5e57ardrdkA696Z17vF+2mmG6FIdZQZLx2/SArS3PJ77TBIDQYH9vLPNHi9Qbi3\nf+qTuqXmp0ar1bVrzODdDSp2aSFtWsOLaTsmGjRh3RfbeJH7pINe242SgjS3LEb3U/e6sn+N\n2aN1FQapu5c59OvvfTHXkX7vIjzf4Xd2HSj2MK53/NFdrh3rDbDuR+d4GRzprb2ZsVfwi6Qg\nza0Wkqt57OBr29mA1lUYJLituxJ0+YSws7nJeV6P7TpQDDYwdtmvu1+ugYKRvH1TpUtBmlvd\nifjsW5HPgNT0wNUE7KeA1PwFkbx9U6VLQZpb3Yn4vhe5wNfjQApFr7U71f3WN69YFkhdF52N\n5AdQJUtBmlv9iXgBNztVfe+RD1J/j8NE77QnxSL3SC7Cw+4jjsTUoEqTgjS37In46nvtmjP7\nPAKkvtftyHU27Phil7bXbWdepNfOVdj3/T0NjlRpZ8NYKUhzy0LyvouvhyfczONQP6YJgrQP\ngrR7mfu+GbRnm5wegbN5vNftbs1TIVhs13Rzd4+L8HMkV/eueUZ7fLZ97C5Spd3fY6UgzS0H\nyaNuFeqbpWbUQQCkBxwiVHl/urEI4NzuH8hWj27Qws4v1r/Yg+VT5dXdbrg0yzCSPpAdKwVp\nbjlIDu2pvb+Z+p9+AKT9841bCKRmEJ1Bk4Re/ZXX+Q3As3tQi4od3vV14+u6sXY7WGf79+DG\n2qFI1V0nUoySgiRMVzNcZor0ym6cFCRhmnUaBZVOoxgpBUmarinzbkfroQ3SOClI4jTjVHMi\nnWo+VgqSOJ0++KDnrl12I6UgqVQzSEFSqWaQgqRSzSAFSaWaQQqSSjWDFCSVaga9QVKpVJP1\n/3v9ok/Z1vZ4AAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotfunc(dist=100, \"100 Meter: dot plot [top 3 Males per age group]\", c(\"M\"), yr = 2018)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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dtW57ou2p8rc4tM85YAklVqz/g9aPkzanX+hFalqYrKlFEZns29N9xVZo3WHUCy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\nQQkEkCAogQASBCUQQIKgBHqCBEHQ2/r/OHRcVtqwwU8AAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotfunc(dist=100, \"100 Meter: dot plot [top 3 Females per age group]\", c(\"F\"), yr = 2018)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "ename": "ERROR",
     "evalue": "Error in parse(text = x, srcfile = src): <text>:1:1: unexpected input\n1: %R X=c(1,4,5,7); sd(X); mean(X)\n    ^\n",
     "output_type": "error",
     "traceback": [
      "Error in parse(text = x, srcfile = src): <text>:1:1: unexpected input\n1: %R X=c(1,4,5,7); sd(X); mean(X)\n    ^\nTraceback:\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}