gleison_fig.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Attaching package: ‘dplyr’\n",
      "\n",
      "The following objects are masked from ‘package:stats’:\n",
      "\n",
      "    filter, lag\n",
      "\n",
      "The following objects are masked from ‘package:base’:\n",
      "\n",
      "    intersect, setdiff, setequal, union\n",
      "\n"
     ]
    }
   ],
   "source": [
    "library(ggplot2)\n",
    "library(dplyr)\n",
    "library(RColorBrewer)\n",
    "library(tidyr)\n",
    "library(readr)\n",
    "library(stringr)\n",
    "library(ggbeeswarm)\n",
    "options(scipen = 999)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read the csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = read.csv('~/Downloads/time_with_ratio.csv')\n",
    "df2 = read.csv('~/Downloads/time_with_ratio.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### filter\n",
    "Gleison asked me to remove every CETUS entry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df %>% filter(!grepl('CTS', Benchmark))\n",
    "df2 = df2 %>% filter(!grepl('CTS', Benchmark))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split the name\n",
    "Split the benchmark name into two different columns (t1, t2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df %>% mutate(t = str_split(Benchmark, \"_\")) %>% mutate(t1 = sapply(t, '[', 1)) %>% mutate(t2 = sapply(t, '[', 2))\n",
    "df2 = df2 %>% mutate(t = str_split(Benchmark, \"_\")) %>% mutate(t1 = sapply(t, '[', 1)) %>% mutate(t2 = sapply(t, '[', 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 6 × 9</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>Benchmark</th><th scope=col>Time_1</th><th scope=col>Time_2</th><th scope=col>Time_3</th><th scope=col>Time_4</th><th scope=col>Time_5</th><th scope=col>t</th><th scope=col>t1</th><th scope=col>t2</th></tr>\n",
       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>BFS_PWT</td><td>0.998054</td><td>1.019455</td><td>1.007782</td><td>0.983463</td><td>1.050583</td><td>BFS, PWT</td><td>BFS</td><td>PWT</td></tr>\n",
       "\t<tr><td>BFS_WO3</td><td>0.926070</td><td>0.929961</td><td>0.921206</td><td>0.922178</td><td>0.948443</td><td>BFS, WO3</td><td>BFS</td><td>WO3</td></tr>\n",
       "\t<tr><td>BFS_PNT</td><td>1.010700</td><td>1.007782</td><td>1.022373</td><td>0.984435</td><td>1.001945</td><td>BFS, PNT</td><td>BFS</td><td>PNT</td></tr>\n",
       "\t<tr><td>BFS_ATP</td><td>1.035019</td><td>1.042801</td><td>1.061284</td><td>1.035992</td><td>1.037937</td><td>BFS, ATP</td><td>BFS</td><td>ATP</td></tr>\n",
       "\t<tr><td>BFS_MAN</td><td>0.955252</td><td>0.942607</td><td>0.941634</td><td>0.944552</td><td>0.928988</td><td>BFS, MAN</td><td>BFS</td><td>MAN</td></tr>\n",
       "\t<tr><td>BPT_PWT</td><td>1.416176</td><td>1.357297</td><td>1.423923</td><td>1.389835</td><td>1.389835</td><td>BPT, PWT</td><td>BPT</td><td>PWT</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 9\n",
       "\\begin{tabular}{r|lllllllll}\n",
       " Benchmark & Time\\_1 & Time\\_2 & Time\\_3 & Time\\_4 & Time\\_5 & t & t1 & t2\\\\\n",
       " <fct> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <list> & <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\t BFS\\_PWT & 0.998054 & 1.019455 & 1.007782 & 0.983463 & 1.050583 & BFS, PWT & BFS & PWT\\\\\n",
       "\t BFS\\_WO3 & 0.926070 & 0.929961 & 0.921206 & 0.922178 & 0.948443 & BFS, WO3 & BFS & WO3\\\\\n",
       "\t BFS\\_PNT & 1.010700 & 1.007782 & 1.022373 & 0.984435 & 1.001945 & BFS, PNT & BFS & PNT\\\\\n",
       "\t BFS\\_ATP & 1.035019 & 1.042801 & 1.061284 & 1.035992 & 1.037937 & BFS, ATP & BFS & ATP\\\\\n",
       "\t BFS\\_MAN & 0.955252 & 0.942607 & 0.941634 & 0.944552 & 0.928988 & BFS, MAN & BFS & MAN\\\\\n",
       "\t BPT\\_PWT & 1.416176 & 1.357297 & 1.423923 & 1.389835 & 1.389835 & BPT, PWT & BPT & PWT\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 9\n",
       "\n",
       "| Benchmark &lt;fct&gt; | Time_1 &lt;dbl&gt; | Time_2 &lt;dbl&gt; | Time_3 &lt;dbl&gt; | Time_4 &lt;dbl&gt; | Time_5 &lt;dbl&gt; | t &lt;list&gt; | t1 &lt;chr&gt; | t2 &lt;chr&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|\n",
       "| BFS_PWT | 0.998054 | 1.019455 | 1.007782 | 0.983463 | 1.050583 | BFS, PWT | BFS | PWT |\n",
       "| BFS_WO3 | 0.926070 | 0.929961 | 0.921206 | 0.922178 | 0.948443 | BFS, WO3 | BFS | WO3 |\n",
       "| BFS_PNT | 1.010700 | 1.007782 | 1.022373 | 0.984435 | 1.001945 | BFS, PNT | BFS | PNT |\n",
       "| BFS_ATP | 1.035019 | 1.042801 | 1.061284 | 1.035992 | 1.037937 | BFS, ATP | BFS | ATP |\n",
       "| BFS_MAN | 0.955252 | 0.942607 | 0.941634 | 0.944552 | 0.928988 | BFS, MAN | BFS | MAN |\n",
       "| BPT_PWT | 1.416176 | 1.357297 | 1.423923 | 1.389835 | 1.389835 | BPT, PWT | BPT | PWT |\n",
       "\n"
      ],
      "text/plain": [
       "  Benchmark Time_1   Time_2   Time_3   Time_4   Time_5   t        t1  t2 \n",
       "1 BFS_PWT   0.998054 1.019455 1.007782 0.983463 1.050583 BFS, PWT BFS PWT\n",
       "2 BFS_WO3   0.926070 0.929961 0.921206 0.922178 0.948443 BFS, WO3 BFS WO3\n",
       "3 BFS_PNT   1.010700 1.007782 1.022373 0.984435 1.001945 BFS, PNT BFS PNT\n",
       "4 BFS_ATP   1.035019 1.042801 1.061284 1.035992 1.037937 BFS, ATP BFS ATP\n",
       "5 BFS_MAN   0.955252 0.942607 0.941634 0.944552 0.928988 BFS, MAN BFS MAN\n",
       "6 BPT_PWT   1.416176 1.357297 1.423923 1.389835 1.389835 BPT, PWT BPT PWT"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df %>% head"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gather\n",
    "Gather will reshape the table\n",
    "\n",
    "https://uc-r.github.io/tidyr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df %>% gather(\"type\", \"value\", 2:6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 6 × 6</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>Benchmark</th><th scope=col>t</th><th scope=col>t1</th><th scope=col>t2</th><th scope=col>type</th><th scope=col>value</th></tr>\n",
       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>BFS_PWT</td><td>BFS, PWT</td><td>BFS</td><td>PWT</td><td>Time_1</td><td>0.998054</td></tr>\n",
       "\t<tr><td>BFS_WO3</td><td>BFS, WO3</td><td>BFS</td><td>WO3</td><td>Time_1</td><td>0.926070</td></tr>\n",
       "\t<tr><td>BFS_PNT</td><td>BFS, PNT</td><td>BFS</td><td>PNT</td><td>Time_1</td><td>1.010700</td></tr>\n",
       "\t<tr><td>BFS_ATP</td><td>BFS, ATP</td><td>BFS</td><td>ATP</td><td>Time_1</td><td>1.035019</td></tr>\n",
       "\t<tr><td>BFS_MAN</td><td>BFS, MAN</td><td>BFS</td><td>MAN</td><td>Time_1</td><td>0.955252</td></tr>\n",
       "\t<tr><td>BPT_PWT</td><td>BPT, PWT</td><td>BPT</td><td>PWT</td><td>Time_1</td><td>1.416176</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 6\n",
       "\\begin{tabular}{r|llllll}\n",
       " Benchmark & t & t1 & t2 & type & value\\\\\n",
       " <fct> & <list> & <chr> & <chr> & <chr> & <dbl>\\\\\n",
       "\\hline\n",
       "\t BFS\\_PWT & BFS, PWT & BFS & PWT & Time\\_1 & 0.998054\\\\\n",
       "\t BFS\\_WO3 & BFS, WO3 & BFS & WO3 & Time\\_1 & 0.926070\\\\\n",
       "\t BFS\\_PNT & BFS, PNT & BFS & PNT & Time\\_1 & 1.010700\\\\\n",
       "\t BFS\\_ATP & BFS, ATP & BFS & ATP & Time\\_1 & 1.035019\\\\\n",
       "\t BFS\\_MAN & BFS, MAN & BFS & MAN & Time\\_1 & 0.955252\\\\\n",
       "\t BPT\\_PWT & BPT, PWT & BPT & PWT & Time\\_1 & 1.416176\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 6\n",
       "\n",
       "| Benchmark &lt;fct&gt; | t &lt;list&gt; | t1 &lt;chr&gt; | t2 &lt;chr&gt; | type &lt;chr&gt; | value &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|\n",
       "| BFS_PWT | BFS, PWT | BFS | PWT | Time_1 | 0.998054 |\n",
       "| BFS_WO3 | BFS, WO3 | BFS | WO3 | Time_1 | 0.926070 |\n",
       "| BFS_PNT | BFS, PNT | BFS | PNT | Time_1 | 1.010700 |\n",
       "| BFS_ATP | BFS, ATP | BFS | ATP | Time_1 | 1.035019 |\n",
       "| BFS_MAN | BFS, MAN | BFS | MAN | Time_1 | 0.955252 |\n",
       "| BPT_PWT | BPT, PWT | BPT | PWT | Time_1 | 1.416176 |\n",
       "\n"
      ],
      "text/plain": [
       "  Benchmark t        t1  t2  type   value   \n",
       "1 BFS_PWT   BFS, PWT BFS PWT Time_1 0.998054\n",
       "2 BFS_WO3   BFS, WO3 BFS WO3 Time_1 0.926070\n",
       "3 BFS_PNT   BFS, PNT BFS PNT Time_1 1.010700\n",
       "4 BFS_ATP   BFS, ATP BFS ATP Time_1 1.035019\n",
       "5 BFS_MAN   BFS, MAN BFS MAN Time_1 0.955252\n",
       "6 BPT_PWT   BPT, PWT BPT PWT Time_1 1.416176"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df %>% head"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ggplot\n",
    "We create the basic layout for the graphics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "p = ggplot () +\n",
    "    theme_light() + \n",
    "    theme(axis.text.x = element_text(angle = 90, hjust = 1),axis.title.x = element_blank(), axis.title.y = element_text(colour = \"gray30\", size = 9)) +\n",
    "    theme(legend.direction = \"horizontal\", legend.position = \"top\", legend.title = element_blank(), legend.text = element_text(colour = \"gray30\", size = 9))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BoxPlot\n",
    "Create the Boxplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+zAzKr9sN9OS3MKlr1642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\nBBBAAIEUCBAgpaCTqCI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M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\nE090rKQUBwEEEEAAAQSyLpC55yBlHZzyI4DApwJ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p70a0mKiTEQrW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\nAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p + geom_boxplot(data = df, aes(x = t2, y = value)) + facet_wrap(~t1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 6 × 9</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>Benchmark</th><th scope=col>Time_1</th><th scope=col>Time_2</th><th scope=col>Time_3</th><th scope=col>Time_4</th><th scope=col>Time_5</th><th scope=col>t</th><th scope=col>t1</th><th scope=col>t2</th></tr>\n",
       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>BFS_PWT</td><td>0.998054</td><td>1.019455</td><td>1.007782</td><td>0.983463</td><td>1.050583</td><td>BFS, PWT</td><td>BFS</td><td>PWT</td></tr>\n",
       "\t<tr><td>BFS_WO3</td><td>0.926070</td><td>0.929961</td><td>0.921206</td><td>0.922178</td><td>0.948443</td><td>BFS, WO3</td><td>BFS</td><td>WO3</td></tr>\n",
       "\t<tr><td>BFS_PNT</td><td>1.010700</td><td>1.007782</td><td>1.022373</td><td>0.984435</td><td>1.001945</td><td>BFS, PNT</td><td>BFS</td><td>PNT</td></tr>\n",
       "\t<tr><td>BFS_ATP</td><td>1.035019</td><td>1.042801</td><td>1.061284</td><td>1.035992</td><td>1.037937</td><td>BFS, ATP</td><td>BFS</td><td>ATP</td></tr>\n",
       "\t<tr><td>BFS_MAN</td><td>0.955252</td><td>0.942607</td><td>0.941634</td><td>0.944552</td><td>0.928988</td><td>BFS, MAN</td><td>BFS</td><td>MAN</td></tr>\n",
       "\t<tr><td>BPT_PWT</td><td>1.416176</td><td>1.357297</td><td>1.423923</td><td>1.389835</td><td>1.389835</td><td>BPT, PWT</td><td>BPT</td><td>PWT</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 9\n",
       "\\begin{tabular}{r|lllllllll}\n",
       " Benchmark & Time\\_1 & Time\\_2 & Time\\_3 & Time\\_4 & Time\\_5 & t & t1 & t2\\\\\n",
       " <fct> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <list> & <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\t BFS\\_PWT & 0.998054 & 1.019455 & 1.007782 & 0.983463 & 1.050583 & BFS, PWT & BFS & PWT\\\\\n",
       "\t BFS\\_WO3 & 0.926070 & 0.929961 & 0.921206 & 0.922178 & 0.948443 & BFS, WO3 & BFS & WO3\\\\\n",
       "\t BFS\\_PNT & 1.010700 & 1.007782 & 1.022373 & 0.984435 & 1.001945 & BFS, PNT & BFS & PNT\\\\\n",
       "\t BFS\\_ATP & 1.035019 & 1.042801 & 1.061284 & 1.035992 & 1.037937 & BFS, ATP & BFS & ATP\\\\\n",
       "\t BFS\\_MAN & 0.955252 & 0.942607 & 0.941634 & 0.944552 & 0.928988 & BFS, MAN & BFS & MAN\\\\\n",
       "\t BPT\\_PWT & 1.416176 & 1.357297 & 1.423923 & 1.389835 & 1.389835 & BPT, PWT & BPT & PWT\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 9\n",
       "\n",
       "| Benchmark &lt;fct&gt; | Time_1 &lt;dbl&gt; | Time_2 &lt;dbl&gt; | Time_3 &lt;dbl&gt; | Time_4 &lt;dbl&gt; | Time_5 &lt;dbl&gt; | t &lt;list&gt; | t1 &lt;chr&gt; | t2 &lt;chr&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|\n",
       "| BFS_PWT | 0.998054 | 1.019455 | 1.007782 | 0.983463 | 1.050583 | BFS, PWT | BFS | PWT |\n",
       "| BFS_WO3 | 0.926070 | 0.929961 | 0.921206 | 0.922178 | 0.948443 | BFS, WO3 | BFS | WO3 |\n",
       "| BFS_PNT | 1.010700 | 1.007782 | 1.022373 | 0.984435 | 1.001945 | BFS, PNT | BFS | PNT |\n",
       "| BFS_ATP | 1.035019 | 1.042801 | 1.061284 | 1.035992 | 1.037937 | BFS, ATP | BFS | ATP |\n",
       "| BFS_MAN | 0.955252 | 0.942607 | 0.941634 | 0.944552 | 0.928988 | BFS, MAN | BFS | MAN |\n",
       "| BPT_PWT | 1.416176 | 1.357297 | 1.423923 | 1.389835 | 1.389835 | BPT, PWT | BPT | PWT |\n",
       "\n"
      ],
      "text/plain": [
       "  Benchmark Time_1   Time_2   Time_3   Time_4   Time_5   t        t1  t2 \n",
       "1 BFS_PWT   0.998054 1.019455 1.007782 0.983463 1.050583 BFS, PWT BFS PWT\n",
       "2 BFS_WO3   0.926070 0.929961 0.921206 0.922178 0.948443 BFS, WO3 BFS WO3\n",
       "3 BFS_PNT   1.010700 1.007782 1.022373 0.984435 1.001945 BFS, PNT BFS PNT\n",
       "4 BFS_ATP   1.035019 1.042801 1.061284 1.035992 1.037937 BFS, ATP BFS ATP\n",
       "5 BFS_MAN   0.955252 0.942607 0.941634 0.944552 0.928988 BFS, MAN BFS MAN\n",
       "6 BPT_PWT   1.416176 1.357297 1.423923 1.389835 1.389835 BPT, PWT BPT PWT"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2 %>% head"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = df2 %>% mutate(mean = rowMeans(select(.,starts_with(\"Time\")), na.rm = TRUE))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 6 × 10</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>Benchmark</th><th scope=col>Time_1</th><th scope=col>Time_2</th><th scope=col>Time_3</th><th scope=col>Time_4</th><th scope=col>Time_5</th><th scope=col>t</th><th scope=col>t1</th><th scope=col>t2</th><th scope=col>mean</th></tr>\n",
       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>BFS_PWT</td><td>0.998054</td><td>1.019455</td><td>1.007782</td><td>0.983463</td><td>1.050583</td><td>BFS, PWT</td><td>BFS</td><td>PWT</td><td>1.0118674</td></tr>\n",
       "\t<tr><td>BFS_WO3</td><td>0.926070</td><td>0.929961</td><td>0.921206</td><td>0.922178</td><td>0.948443</td><td>BFS, WO3</td><td>BFS</td><td>WO3</td><td>0.9295716</td></tr>\n",
       "\t<tr><td>BFS_PNT</td><td>1.010700</td><td>1.007782</td><td>1.022373</td><td>0.984435</td><td>1.001945</td><td>BFS, PNT</td><td>BFS</td><td>PNT</td><td>1.0054470</td></tr>\n",
       "\t<tr><td>BFS_ATP</td><td>1.035019</td><td>1.042801</td><td>1.061284</td><td>1.035992</td><td>1.037937</td><td>BFS, ATP</td><td>BFS</td><td>ATP</td><td>1.0426066</td></tr>\n",
       "\t<tr><td>BFS_MAN</td><td>0.955252</td><td>0.942607</td><td>0.941634</td><td>0.944552</td><td>0.928988</td><td>BFS, MAN</td><td>BFS</td><td>MAN</td><td>0.9426066</td></tr>\n",
       "\t<tr><td>BPT_PWT</td><td>1.416176</td><td>1.357297</td><td>1.423923</td><td>1.389835</td><td>1.389835</td><td>BPT, PWT</td><td>BPT</td><td>PWT</td><td>1.3954132</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 10\n",
       "\\begin{tabular}{r|llllllllll}\n",
       " Benchmark & Time\\_1 & Time\\_2 & Time\\_3 & Time\\_4 & Time\\_5 & t & t1 & t2 & mean\\\\\n",
       " <fct> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <list> & <chr> & <chr> & <dbl>\\\\\n",
       "\\hline\n",
       "\t BFS\\_PWT & 0.998054 & 1.019455 & 1.007782 & 0.983463 & 1.050583 & BFS, PWT & BFS & PWT & 1.0118674\\\\\n",
       "\t BFS\\_WO3 & 0.926070 & 0.929961 & 0.921206 & 0.922178 & 0.948443 & BFS, WO3 & BFS & WO3 & 0.9295716\\\\\n",
       "\t BFS\\_PNT & 1.010700 & 1.007782 & 1.022373 & 0.984435 & 1.001945 & BFS, PNT & BFS & PNT & 1.0054470\\\\\n",
       "\t BFS\\_ATP & 1.035019 & 1.042801 & 1.061284 & 1.035992 & 1.037937 & BFS, ATP & BFS & ATP & 1.0426066\\\\\n",
       "\t BFS\\_MAN & 0.955252 & 0.942607 & 0.941634 & 0.944552 & 0.928988 & BFS, MAN & BFS & MAN & 0.9426066\\\\\n",
       "\t BPT\\_PWT & 1.416176 & 1.357297 & 1.423923 & 1.389835 & 1.389835 & BPT, PWT & BPT & PWT & 1.3954132\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 10\n",
       "\n",
       "| Benchmark &lt;fct&gt; | Time_1 &lt;dbl&gt; | Time_2 &lt;dbl&gt; | Time_3 &lt;dbl&gt; | Time_4 &lt;dbl&gt; | Time_5 &lt;dbl&gt; | t &lt;list&gt; | t1 &lt;chr&gt; | t2 &lt;chr&gt; | mean &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|\n",
       "| BFS_PWT | 0.998054 | 1.019455 | 1.007782 | 0.983463 | 1.050583 | BFS, PWT | BFS | PWT | 1.0118674 |\n",
       "| BFS_WO3 | 0.926070 | 0.929961 | 0.921206 | 0.922178 | 0.948443 | BFS, WO3 | BFS | WO3 | 0.9295716 |\n",
       "| BFS_PNT | 1.010700 | 1.007782 | 1.022373 | 0.984435 | 1.001945 | BFS, PNT | BFS | PNT | 1.0054470 |\n",
       "| BFS_ATP | 1.035019 | 1.042801 | 1.061284 | 1.035992 | 1.037937 | BFS, ATP | BFS | ATP | 1.0426066 |\n",
       "| BFS_MAN | 0.955252 | 0.942607 | 0.941634 | 0.944552 | 0.928988 | BFS, MAN | BFS | MAN | 0.9426066 |\n",
       "| BPT_PWT | 1.416176 | 1.357297 | 1.423923 | 1.389835 | 1.389835 | BPT, PWT | BPT | PWT | 1.3954132 |\n",
       "\n"
      ],
      "text/plain": [
       "  Benchmark Time_1   Time_2   Time_3   Time_4   Time_5   t        t1  t2 \n",
       "1 BFS_PWT   0.998054 1.019455 1.007782 0.983463 1.050583 BFS, PWT BFS PWT\n",
       "2 BFS_WO3   0.926070 0.929961 0.921206 0.922178 0.948443 BFS, WO3 BFS WO3\n",
       "3 BFS_PNT   1.010700 1.007782 1.022373 0.984435 1.001945 BFS, PNT BFS PNT\n",
       "4 BFS_ATP   1.035019 1.042801 1.061284 1.035992 1.037937 BFS, ATP BFS ATP\n",
       "5 BFS_MAN   0.955252 0.942607 0.941634 0.944552 0.928988 BFS, MAN BFS MAN\n",
       "6 BPT_PWT   1.416176 1.357297 1.423923 1.389835 1.389835 BPT, PWT BPT PWT\n",
       "  mean     \n",
       "1 1.0118674\n",
       "2 0.9295716\n",
       "3 1.0054470\n",
       "4 1.0426066\n",
       "5 0.9426066\n",
       "6 1.3954132"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2 %>% head"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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7SnIJnM0wGNdLptGlIQS6fx3XzzzfGZ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IIIAAAmEVIEAKa82QLwRCLvDxI/LtqOpr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\n/wKz73LUhsq0AAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p + \n",
    "    geom_point(data = df2, aes(x = t1, y = mean, colour = t2, shape = t2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "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": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}