{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scatter Plot in R"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " In this post, we will learn how make scatter plots using R and the package ggplot2. This is the notebook for the scatter plot in R tutorial: https://www.marsja.se/how-to-make-a-scatter-plot-in-r-with-ggplot2/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Table of Contents"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- <a href=\"#Required_r-packages\" title=\"Required r-packages\">Required r-packages</a><ul><li><a href=\"#How_to_Install_R-packages\" title=\"How to Install R-packages\">How to Install R-packages</a></li></ul>\n",
    "- <a href=\"#How_to_Install_R-packages\" title=\"How to Install R-packages\">How to Install R-packages</a>\n",
    "- <a href=\"#How_to_Make_a_Scatter_Plot_in_R\" title=\"How to Make a Scatter Plot in R\">How to Make a Scatter Plot in R</a>\n",
    "- <a href=\"#How_to_use_Ggplot2_to_Produce_Scatter_Plots_in_R\" title=\"How to use Ggplot2 to Produce Scatter Plots in R\">How to use Ggplot2 to Produce Scatter Plots in R</a><ul><li><a href=\"#How_to_Make_a_Scatter_Plot_in_R-2\" title=\"How to Make a Scatter Plot in R\">How to Make a Scatter Plot in R</a></li><li><a href=\"#How_to_Change_the_Size_of_the_Dots_in_a_Scatter_Plot\" title=\"How to Change the Size of the Dots in a Scatter Plot\">How to Change the Size of the Dots in a Scatter Plot</a></li><li><a href=\"#How_to_Add_a_Trend_Line_to_a_Scatter_Plot_in_R\" title=\"How to Add a Trend Line to a Scatter Plot in R\">How to Add a Trend Line to a Scatter Plot in R</a></li><li><a href=\"#How_to_Add_Text_to_Scatter_Plot_in_R\" title=\"How to Add Text to Scatter Plot in R\">How to Add Text to Scatter Plot in R</a></li><li><a href=\"#How_to_Rotate_the_Axis_using_Ggplot2\" title=\"How to Rotate the Axis using Ggplot2\">How to Rotate the Axis using Ggplot2</a></li><li><a href=\"#How_to_Style_a_Scatter_plot_in_R\" title=\"How to Style a Scatter plot in R\">How to Style a Scatter plot in R</a></li><li><a href=\"#Saving_a_High_Resolution_Plot_in_R\" title=\"Saving a High Resolution Plot in R\">Saving a High Resolution Plot in R</a></li><li><a href=\"#How_to_Save_a_Scatter_Plot_to_PDF_in_R\" title=\"How to Save a Scatter Plot to PDF in R\">How to Save a Scatter Plot to PDF in R</a></li><li><a href=\"#How_to_Save_a_Scatter_Plot_to_TIFF_in_R\" title=\"How to Save a Scatter Plot to TIFF in R\">How to Save a Scatter Plot to TIFF in R</a></li></ul>\n",
    "- <a href=\"#How_to_Make_a_Scatter_Plot_in_R-2\" title=\"How to Make a Scatter Plot in R\">How to Make a Scatter Plot in R</a>\n",
    "- <a href=\"#How_to_Change_the_Size_of_the_Dots_in_a_Scatter_Plot\" title=\"How to Change the Size of the Dots in a Scatter Plot\">How to Change the Size of the Dots in a Scatter Plot</a>\n",
    "- <a href=\"#How_to_Add_a_Trend_Line_to_a_Scatter_Plot_in_R\" title=\"How to Add a Trend Line to a Scatter Plot in R\">How to Add a Trend Line to a Scatter Plot in R</a>\n",
    "- <a href=\"#How_to_Add_Text_to_Scatter_Plot_in_R\" title=\"How to Add Text to Scatter Plot in R\">How to Add Text to Scatter Plot in R</a>\n",
    "- <a href=\"#How_to_Rotate_the_Axis_using_Ggplot2\" title=\"How to Rotate the Axis using Ggplot2\">How to Rotate the Axis using Ggplot2</a>\n",
    "- <a href=\"#How_to_Style_a_Scatter_plot_in_R\" title=\"How to Style a Scatter plot in R\">How to Style a Scatter plot in R</a>\n",
    "- <a href=\"#Saving_a_High_Resolution_Plot_in_R\" title=\"Saving a High Resolution Plot in R\">Saving a High Resolution Plot in R</a>\n",
    "- <a href=\"#How_to_Save_a_Scatter_Plot_to_PDF_in_R\" title=\"How to Save a Scatter Plot to PDF in R\">How to Save a Scatter Plot to PDF in R</a>\n",
    "- <a href=\"#How_to_Save_a_Scatter_Plot_to_TIFF_in_R\" title=\"How to Save a Scatter Plot to TIFF in R\">How to Save a Scatter Plot to TIFF in R</a>\n",
    "- <a href=\"#Reproducible_Data_Visualization\" title=\"Reproducible Data Visualization\">Reproducible Data Visualization</a>\n",
    "- <a href=\"#Conclusion\" title=\"Conclusion\">Conclusion</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <span class=\"ez-toc-section\" id=\"Required_r-packages\">Required r-packages</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You need to install the packages used in this tutorial packages before continuing. \n",
    "- The easiest method to get all of the packages is to install the <a aria-label=\"tidyverse packages (opens in a new tab)\" href=\"https://www.tidyverse.org/\" rel=\"noreferrer noopener\" target=\"_blank\">tidyverse packages</a>. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <span class=\"ez-toc-section\" id=\"How_to_Install_R-packages\">How to Install R-packages</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You install packages with the <code>install.packages()</code> function. Make sure to uncomment (remove the '#') if you actually need to install the packages!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# install.packages(c(\"tidyverse\", \"GGally\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's the single packages used in the tutorial, if you only want those installed;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# to.install <- c(\"magittr\", \"purrr\", \n",
    "#  \"ggplot2\", \"dplyr\", \"broom\", \"GGally\")\n",
    "# install.packages(to.install)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <span class=\"ez-toc-section\" id=\"How_to_Make_a_Scatter_Plot_in_R\">How to Make a Scatter Plot in R</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Time to learn how to produce a scatter plot using R statistical programming environment and we start by using the mtcars dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading required package: ggplot2\n",
      "Warning message:\n",
      "\"package 'ggplot2' was built under R version 3.6.1\""
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>mpg</th><th scope=col>cyl</th><th scope=col>disp</th><th scope=col>hp</th><th scope=col>drat</th><th scope=col>wt</th><th scope=col>qsec</th><th scope=col>vs</th><th scope=col>am</th><th scope=col>gear</th><th scope=col>carb</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>Mazda RX4</th><td>21.0 </td><td>6    </td><td>160  </td><td>110  </td><td>3.90 </td><td>2.620</td><td>16.46</td><td>0    </td><td>1    </td><td>4    </td><td>4    </td></tr>\n",
       "\t<tr><th scope=row>Mazda RX4 Wag</th><td>21.0 </td><td>6    </td><td>160  </td><td>110  </td><td>3.90 </td><td>2.875</td><td>17.02</td><td>0    </td><td>1    </td><td>4    </td><td>4    </td></tr>\n",
       "\t<tr><th scope=row>Datsun 710</th><td>22.8 </td><td>4    </td><td>108  </td><td> 93  </td><td>3.85 </td><td>2.320</td><td>18.61</td><td>1    </td><td>1    </td><td>4    </td><td>1    </td></tr>\n",
       "\t<tr><th scope=row>Hornet 4 Drive</th><td>21.4 </td><td>6    </td><td>258  </td><td>110  </td><td>3.08 </td><td>3.215</td><td>19.44</td><td>1    </td><td>0    </td><td>3    </td><td>1    </td></tr>\n",
       "\t<tr><th scope=row>Hornet Sportabout</th><td>18.7 </td><td>8    </td><td>360  </td><td>175  </td><td>3.15 </td><td>3.440</td><td>17.02</td><td>0    </td><td>0    </td><td>3    </td><td>2    </td></tr>\n",
       "\t<tr><th scope=row>Valiant</th><td>18.1 </td><td>6    </td><td>225  </td><td>105  </td><td>2.76 </td><td>3.460</td><td>20.22</td><td>1    </td><td>0    </td><td>3    </td><td>1    </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllllllllll}\n",
       "  & mpg & cyl & disp & hp & drat & wt & qsec & vs & am & gear & carb\\\\\n",
       "\\hline\n",
       "\tMazda RX4 & 21.0  & 6     & 160   & 110   & 3.90  & 2.620 & 16.46 & 0     & 1     & 4     & 4    \\\\\n",
       "\tMazda RX4 Wag & 21.0  & 6     & 160   & 110   & 3.90  & 2.875 & 17.02 & 0     & 1     & 4     & 4    \\\\\n",
       "\tDatsun 710 & 22.8  & 4     & 108   &  93   & 3.85  & 2.320 & 18.61 & 1     & 1     & 4     & 1    \\\\\n",
       "\tHornet 4 Drive & 21.4  & 6     & 258   & 110   & 3.08  & 3.215 & 19.44 & 1     & 0     & 3     & 1    \\\\\n",
       "\tHornet Sportabout & 18.7  & 8     & 360   & 175   & 3.15  & 3.440 & 17.02 & 0     & 0     & 3     & 2    \\\\\n",
       "\tValiant & 18.1  & 6     & 225   & 105   & 2.76  & 3.460 & 20.22 & 1     & 0     & 3     & 1    \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | mpg | cyl | disp | hp | drat | wt | qsec | vs | am | gear | carb |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| Mazda RX4 | 21.0  | 6     | 160   | 110   | 3.90  | 2.620 | 16.46 | 0     | 1     | 4     | 4     |\n",
       "| Mazda RX4 Wag | 21.0  | 6     | 160   | 110   | 3.90  | 2.875 | 17.02 | 0     | 1     | 4     | 4     |\n",
       "| Datsun 710 | 22.8  | 4     | 108   |  93   | 3.85  | 2.320 | 18.61 | 1     | 1     | 4     | 1     |\n",
       "| Hornet 4 Drive | 21.4  | 6     | 258   | 110   | 3.08  | 3.215 | 19.44 | 1     | 0     | 3     | 1     |\n",
       "| Hornet Sportabout | 18.7  | 8     | 360   | 175   | 3.15  | 3.440 | 17.02 | 0     | 0     | 3     | 2     |\n",
       "| Valiant | 18.1  | 6     | 225   | 105   | 2.76  | 3.460 | 20.22 | 1     | 0     | 3     | 1     |\n",
       "\n"
      ],
      "text/plain": [
       "                  mpg  cyl disp hp  drat wt    qsec  vs am gear carb\n",
       "Mazda RX4         21.0 6   160  110 3.90 2.620 16.46 0  1  4    4   \n",
       "Mazda RX4 Wag     21.0 6   160  110 3.90 2.875 17.02 0  1  4    4   \n",
       "Datsun 710        22.8 4   108   93 3.85 2.320 18.61 1  1  4    1   \n",
       "Hornet 4 Drive    21.4 6   258  110 3.08 3.215 19.44 1  0  3    1   \n",
       "Hornet Sportabout 18.7 8   360  175 3.15 3.440 17.02 0  0  3    2   \n",
       "Valiant           18.1 6   225  105 2.76 3.460 20.22 1  0  3    1   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "require(ggplot2)\n",
    "\n",
    "head(mtcars)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data can, also, be stored in Excel files:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- <a href=\"https://www.marsja.se/how-to-read-and-write-json-files-using-python-and-pandas/\" rel=\"noreferrer noopener\" target=\"_blank\">How to Read and Write Excel Files in R</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <span class=\"ez-toc-section\" id=\"How_to_use_Ggplot2_to_Produce_Scatter_Plots_in_R\">How to use Ggplot2 to Produce Scatter Plots in R</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section we will learn how to make scattergraphs in R using ggplot2."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <span class=\"ez-toc-section\" id=\"How_to_Make_a_Scatter_Plot_in_R-2\">How to Make a Scatter Plot in R</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will start by visualizing the variables <strong>wt </strong>(x-axis) and <strong>mpg </strong>(y-axis). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before going on and creating the first scatter plot in R we will briefly cover ggplot2 and the plot functions we are going to use. First, we start by using <em>ggplot</em> to create a plot object."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Inside of the <code>ggplot()</code> function, we’re calling the <code>aes()</code> function that describe how variables in our data are mapped to visual properties . In this simple scatter plot in R example, we only use the x- and y-axis arguments and ggplot2 to put our variable wt on the x-axis, and put mpg on the y-axis. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "require(ggplot2)\n",
    "\n",
    "gp <- ggplot(aes(x = wt, y = mpg), \n",
    "             data = mtcars)\n",
    "\n",
    "gp + geom_point()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <span class=\"ez-toc-section\" id=\"How_to_Change_the_Size_of_the_Dots_in_a_Scatter_Plot\">How to Change the Size of the Dots in a Scatter Plot</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we'' change the size of the markers size using the <em>size</em> argument."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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p/EF32v9c1bcy55nPeihTR+GSFd9t1W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\ngg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gp + geom_point(size = 4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note, we used <code>aes()</code> but added the size argument to the <code>geom_point()</code> function. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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sVASC6ftJwCIVmF2zsSldTtCSp90nIKhJQmJCSbT1pOgZDShEtDmtfaI53gXZ+0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\n7ePs3wqIUQAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gp + geom_point(aes(size = wt))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### How to Change the Number of ´ticks using ggplot2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To change the x-axis we use the function <code>scale_x_continuous</code> and to change the y-axis we use the function <code>scale_y_continuous</code>. Furthermore, we use the arguments limits, which take a vector, and we can set the limits to change the ticks. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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qQC9Cb17lfAtzN1DVRIBegt6v2/J7uZ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\nITk3wwnJuRlOSM7NcEJybob7P+ZLquqytTAUAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gp <- ggplot(aes(x = wt, y = mpg), \n",
    "             data = mtcars) + \n",
    "    geom_point()\n",
    "\n",
    "gp + scale_y_continuous(limits=c(1, 40)) +\n",
    "   scale_x_continuous(limits=c(0, 6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we also change the number of ticks by adding the breaks argument to the above functions. Furthermore, we add the <code>seq</code> function to create a numeric vector."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAMAAADKOT/pAAAANlBMVEUAAAAzMzNNTU1oaGh8\nfHyMjIyampqnp6eysrK9vb3Hx8fQ0NDZ2dnh4eHp6enr6+vw8PD////agy6EAAAACXBIWXMA\nABJ0AAASdAHeZh94AAAcSklEQVR4nO3d22ITZ7oG4UbsEpIh6P5vdmyCbdlBcnerJf1Vqu+A\nkAw8eZedWrKEMdO+67qzb7r1gK4zXCF13QZXSF23wRVS121whdR1G1whdd0GV0hdt8EVUtdt\ncGtD+uf9m/Nj1lzuheHc2W4hCVzcYKFbSAIXN1joFpLAxQ0WuoUkcHGDhW4hCVzcYKFbSAIX\nN1joFpLAxQ0WuoUkcHGDhW4hCVzcYKFbSAIXN1joFpLAxQ0WuoUkcHGDhW4hCVzcYKF7fkhd\n1x1cj0hYFzdY6BaSwMUNFrqFJHBxg4VuIQlc3GChW0gCFzdY6BaSwMUNFrqFJHBxg4VuIQlc\n3GChW0gCFzdY6BaSwMUNFrqFJHBxg4VuIQlc3GChW0gCFzdY6BaSwMUNFrqFJHBxg4VuIQlc\n3GChW0gCFzdY6BaSwMUNFrqFJHBxg4VuIQlc3GChW0gCFzdY6BaSwMUNFrqFJHBxg4VuIQlc\n3GChW0gCFzdY6BaSwMUNFrqFJHBxg4VuIQlc3GChW0gCFzdY6BaSwMUNFrqFJHBxg4VuIQlc\n3GChW0gCFzdY6BaSwMUNFrqFJHBxg4VuIQlc3GChW0gCFzdY6BaSwMUNFrqFJHBxg4VuIQlc\n3GChO3hI0zRdxD19NBc3WOiOHdI0nSxJ+P4YC86d7Q4d0jSdLkn4/hgLzp3tFpLAxQ0WuoUk\ncHGDhe7QIfUc6cZw7mx37JB61e62cO5sd/CQcm8K5852C0ng4gYL3UISuLjBQreQBC5usNA9\nP6Su6w6uRySsixssdAtJ4OIGC91CEri4wUK3kAQubrDQLSSBixssdAtJ4OIGC91CEri4wUK3\nkAQubrDQLSSBixssdAtJ4OIGC91CEri4wUK3kAQubrDQLSSBixssdAtJ4OIGC91CEri4wUK3\nkAQubrDQLSSBixssdAtJ4OIGC91CEri4wUJXHtLpLzC53l13heR13SG98yWPV7srr5C8rjqk\n974I/1p37RWS1y2kFe7aKySvW0gr3LVXSF5XHVLPkXKv5bpD6lW73Cu58pDuw8UNFrqFJHBx\ng4VuIQlc3GChW0gCFzdY6BaSwMUNFrqFJHBxg4VuIQlc3GChW0gCFzdY6BaSwMUNFrqFJHBx\ng4VuIQlc3GChW0gCFzdY6BaSwMUNFrqFJHBxg4VuIQlc3GChW0gCFzdY6BaSwMUNFrqFJHBx\ng4VuIQlc3GChW0gCFzdY6BaSwMUNFrpLQto93K/vFdJILm6w0F0Q0u7lm0IaysUNFrprQtr1\niDSWixssdBeE9FJTIY3l4gYL3bNC+vB4s35a193LzQipFxvGdHGDhe6ikPZ9aDekixssdAtJ\n4OIGC90FIb28/F1IY7m4wUK3kAQubrDQXRBSn9kwqosbLHSXhPT722bHqsu9MJw72y0kgYsb\nLHSvE9J//wTKdX8m5Vv3fELh4gYL3auE9N8/E3nln5L8dvzZgsPFDRa61whpmt52899/suqE\n74+x4NzZbiEJXNxgoVtIAhc3WOheI6SeI13YxQ0WulcJqVftLuviBgvd64R09v8luTeBc2e7\nhSRwcYOFbiEJXNxgoVtIAhc3WOgWksDFDRa6hSRwcYOFbiEJXNxgoVtIAhc3WOgWksDFDRa6\nhSRwcYOFbiEJXNxgoVtIAhc3WOgWksDFDRa6hSRwcYOFbiEJXNxgoVtIAhc3WOgWksDFDRa6\nhSRwcYOFbiEJXNxgoVtIAhc3WOgWksDFDRa6hSRwcYOFbiEJXNxgoVtIAhc3WOgWksDFDRa6\nhSRwcYOFbiEJXNxgoVtIAhc3WOgWksDFDRa6hSRwcYOFbiEJXNxgoVtIAhc3WOgWksDFDRa6\nhSRwcYOFbiEJXNxgoVtIAhc3WOieH1LXdQfXIxLWxQ0WuoUkcHGDhW4hCVzcYKFbSAIXN1jo\nFpLAxQ0WuoUkcHGDhW4hCVzcYKFbSAIXN1joFpLAxQ0WuoUkcHGDhW4hCVzcYKFbSAIXN1jo\nFpLAxQ0WuoUkcHGDhW4hCVzcYKFbSAIXN1joFpLAxQ0WuoUkcHGDhW4hCVzcYKFbSAIXN1jo\nFpLAxQ0WuriQpmm6iHt4NBc3WOjSQpqmg5KE74+x4NzZLiykaTosSfj+GAvOne0W0m+O5uIG\nC91C+s3RXNxgoQsLqedIV4VzZ7u0kHrV7ppw7mwXF1LuFeHc2W4hCVzcYKFbSAIXN1joFpLA\nxQ0WuoUkcHGDhW4hCVzcYKFbSAIXN1joFpLAxQ0WuoUkcHGDhW4hCVzcYKFbSAIXN1joFpLA\nxQ0WuoUkcHGDhW4hCVzcYKFbSAIXN1joFpLAxQ0WuoUkcHGDhW4hCVzcYKFbSAIXN1joLglp\n93CHfy2kQVzcYKG7IKTdr292z38zakiHXyBlS/fcKySvawzp1Zfs2tA9+wrJ6y4I6amm3et/\nss2OVfd79/UXkdzOPf8KyeuuCunpOdKHx5v10655v0K69YzuTm/Wf3mPAf2saOAP7XpEyr2+\nuyakp+8MGlLPkXKv7y4Laff6m0FD6lW73Ku7i0Lavalp1JDuzcUNFrpLQtq9fVgqpDFc3GCh\nuyCk3e7XpzT0mQ2DubjBQnfJI9Lvb5sdqy73wnDubLeQBC5usNAtJIGLGyx0C0ng4gYL3UIS\nuLjBQreQBC5usNAtJIGLGyx0C0ng4gYL3UISuLjBQreQBC5usNAtJIGLGyx0C0ng4gYL3UIS\nuLjBQreQBC5usNAtJIGLGyx0C0ng4gYL3UISuLjBQreQBC5usNAtJIGLGyx0C0ng4gYL3UIS\nuLjBQreQBC5usNAtJIGLGyx0C2kkd+3XWta9IXhuIQ3krv7q/7Y3BNAtpHHc9X8ejewNQXQL\naRy3kMBuIY3jFhLYLaSB3J4jcd1CGsntVTusW0gCFzdY6BaSwMUNFrqFJHBxg4VuIQlc3GCh\nW0gCFzdY6BaSwMUNFrqFJHBxg4VuIQlc3GChW0gCFzdY6BaSwMUNFrqFJHBxg4VuIQlc3GCh\nW0gCFzdY6BaSwMUNFrqFJHBxg4Xu+SF1XXdwPSJhXdxgoVtIAhc3WOgWksDFDRa6hSRwcYOF\nbiEJXNxgoVtIAhc3WOgWksDFDRa6hSRwcYOFbiEJXNxgoVtIAhc3WOjeZUjvfWXg6+9d+7WK\n34XPu9zZ7j2G9O7Xqr/63tVfPf89+MzLne3eYUjv/+kp1967/s9zeQc+93Jnu4W0mfv+FZLX\nLaTN3PevkLzuHYbUc6Tc7d17DKlX7XI3d+8yJJuLGyx0C0ng4gYL3UISuLjBQreQBC5usNAt\nJIGLGyx0C0ng4gYL3UISuLjBQreQBC5usNAtJIGLGyx0C0ng4gYL3UISuLjBQreQBC5usNAt\nJIGLGyx0C0ng4gYL3UISuLjBQreQBC5usNAtJIGLGyx0C0ng4gYL3UISuLjBQreQBC5usNAt\npMP79TVIMHsvDefOdgvp4J6+KhZl78Xh3NluIb3c89dpXOzO/Gpas9w1X5kL8gY2u4X0cqtD\nmvv1Hee4q75WJOQNbHYL6eXWhjT7Kw7PcNd99WLIG9jsFtLBrXyOVEi5hfTq1r1qV0i5hbSF\n23Ok3ELawu1Vu7t3C0ng4gYL3SUh7R7u1/cKaSQXN1joLghp9/LNHYa06COuIz+4kLzumpB2\nd/iItOg1gGM/uJC87oKQXmq6v5AWvSp99AcXktc9K6QPjzfrp9HvVxsX+MGd7Wa94+/1xYYe\nkXLf+SGFNMvtOVLu6R+yKKTdm7/eT0i9apd7+ocsCWn3n+/cT0gju7jBQndJSLvffK+QBnBx\ng4XugpB2u+dPbSikoVzcYKG75BHp97fNjlWXe2E4d7ZbSAIXN1joFpLAxQ0WuoUkcHGDhW4h\nCVzcYKFbSAIXN1joFpLAxQ0WuoUkcHGDhW4hCVzcYKFbSAIXN1joFpLAxQ0WuoUkcHGDhW4h\nCVzcYKFbSAIXN1joFpLAxQ0WuoUkcHGDhW4hne/O/nIOheR1C+lsd/4XGCokr1tI57oLvuRd\nIXndQjrXLaTcfwrpfLeQcv8ppA3cniPlFtIWbq/a5RaSwcUNFrqFJHBxg4VuIQlc3GChW0gC\nFzdY6BYS1T18ieMM+OQrJYg3xBhuIUHdVy+6r4dPv3ZPeEMM4hYS0339y8Cr4Xd+NRnwhhjF\nLSSmW0iDuYXEdAtpMLeQoG7PkcZyC4nq9qrdUG4hCVzcYKFbSAIXN1joFpLAxQ0WuoVEdTd6\njnTycme7hQR1N3rV7vTlznaPhjQ93+7L90Iazd3o15HeudzZ7oyQHu5USdvsWHV37BbSYO7R\nkP6aPj3k8/3T9G3/dfpSSIO5hTSYezSkj9OPf//xx8dHp0Iaze050lju0ZCe4nn8ayEN6Paq\n3VDu0ZA+PX1o92n/9+OjUtd1c+5NSN93v16z+/7wgPTniZ+3TdCrLvfCcO5s92hI+x9/fJym\nj18fnilNX08FuM2OVZd7YTh3tns8pLm3zY5Vl3thOHe2W0gCFzdY6B4P6cfXhw/tPv1RSOO7\nuMFC92hIBy82FNKo7tNL4JjBXvdoSM8vf5/6rIZCuqn7/IuylMFi92hIv34R9sfJX4wtpFu6\nL58mBBlsdo+G9PnpU4Q+FdKgbiEN5B4Naf/l0/8eP7T71HOkUd1CGsg9GtKr30bR59oN6fYc\naRy3kKjuY0O9ajeMezSk2bfNjlV3z26/jWIst5CYbr+xbzC3kJhuIQ3mHg3px5cZz48K6VZu\nIQ3mHg3p85wXGgrpZm7PkcZyj4Y0Td/eKaiQbur2W82Hco+G9HHuc6Ztdqy63AvDubPdoyF9\n//mbYwuJ4OIGC92jIe2/9RyJ4uIGC92jIfViA8edC0+n/lCxM9zFJ3SPhtSLDRx3Jvzqhb4N\n3eUndE88Is3rqJBu786DX//S03buihO6R0Pafz79h1AU0jhuId3ePRrSrM/8LqQh3EK6vVtI\nArfnSLd3j4Y0+7bZsepyl8G9anc5t5AELm6w0C0kgYsbLHQLSeDiBgvdQhK4uMFCt5AELm6w\n0C0kgYsbLHQLSeDiBgvdQhK4uMFCt5AELm6w0C0kgYsbLHQLSeDiBgvdQhK4uMFCt5AE7jUG\nL/6E15nupldI6y73wvCLu/y3YMxzt71CWne5F4af3RW/KXCWu/EV0rrLvTBcSLPdQhK4hXR7\nt5AEbs+Rbu8WksDtVbvbu8tC2v377cMV0kAubrDQXRTSv/28fFNIY7i4wUJ3SUi7fSEN6eIG\nC91Fj0hvGyqkMVzcYKG7KqSn50gfHm/GT+u6+7n5Ie32fWi3vXvOy2KqNwTTXRPS8zeFtJ17\n1i/UmN4QULeQxnDP+9QB0RuC6hbSGG4hwd1CGsMtJLi7IqQ+s+ESbs+R2O6ykH532+xYdSq3\nV+3QbiEJ3Ad4088pPXAvc0K3kATuP/ttf5fDi7s9aXULSeBu/fvuno/2hiikdZf76wrp5m4h\nCdxCur1bSAK350i3dwsJ6r4KZ+mrdnN/NOENMYhbSEz39UPQQnj24xfgDTGKW0hI982TomXw\n/GdU478hhnELCekW0mhuISHd4yHNKKSQLuAWEtM99hxpViI9R9reLSSo++ZVu5d/OrOkWf8S\nwhtiELeQBO7ikBa7G5/QLSSBW0i3dwtJ4C59jrTC3faEbiEJ3IWv2q1yN70R3VNvuEK6Dxc3\neED35EN5Id2Hixs8nnv6yWUh3YeLGzyeW0i5F4Tvxy2k3AvCd+T2HCmXN3hEt1ftcnGDhW4h\nCVzcYKFbSAK3X5C9vVtIArdPEbq9W0gCt09avb1bSAK3kG7vFpLALaTbu4UkcHuOdHu3kARu\nr9rd3i0kgYsbLHQLSeDiBgvdQhK4uMFCt5AELm6w0C0kgYsbLHQLSeDiBgvdQhK4uMFCt5AE\nLm6w0C0kgYsbLHQLSeDiBgvdQhK4uMFCt5AELm6w0D0/pK7rDq5HJKx7Bnz6k8Vpbwj0I9I2\nO1Zd7rnwO799ifaGKKR1l3sm/N5vqKW9IQpp3eWeCRfSZm4hCdxCur1bSAK350i3dwtJ4Paq\n3e3dQhK4uMFCt5AELm6w0C0kgYsbLHQLSeDiBgvdQhK4uMFCt5AELm6w0C0kgYsbLHQLSeDi\nBgvdQhK4uMFCt5AELm6w0C0kgYsbLHQLSeDiBgvdQhK4uMFCt5AELm6w0C0kgYsbLHQLSeDi\nBgvdQhK4uMFCt5AELm6w0C0kgYsbLHQLSeDiBgvdQhK4uMFCt5AELm6w0C0kgYsbLHQLSeDi\nBgvdQhK4uMFCt5AELm6w0C0kgYsbLHQLSeDiBgvdQhK4uMFCt5AELm6w0C0kgYsbLHQLSeDi\nBgvdQhK4uMFCt5AELm6w0C0kgYsbLHQLSeDiBgvdQhK4uMFCt5AELm6w0C0kgYsbLHQLSeDi\nBgvdZSHt3vy1kIZwcYOF7qKQdrvXfy2kMVzcYKG7JKTdr0eiXY9IY7m4wUJ30SNSH9qN6eIG\nC92zQvrweDN+Wtfdz/WIhHVxg4VuIQlc3GChW0gCFzdY6BaSwMUNFrqFJHBxg4VuIQlc3GCh\nuyyk3902O1Zd7oXh3NluIQlc3GChW0gCFzdY6BaSwMUNHsCdpmn+/1xI9+HiBt/enaaTJb35\nnwvpPlzc4Ju703SypLf/cyHdh4sbfHO3kHKvCWvdQsq9Jux1e46Ue0VY7PaqXe714NzZbiEJ\nXNxgoVtIAhc3WOgWksDFDRa6hSRwcYOFbiEJXNxgoVtIAhc3WOgWksDFDRa6hSRwcYOFbiEJ\nXNxgoVtIAhc3WOgWksDFDRa6hSRwcYOFbiEJXNxgoVtIAhc3WOgWksDFDRa6hSRwcYOFbiEJ\nXNxgoVtIAhc3WOgWksDFDRa6hSRwcYOFbiEJXNxgoVtIAhc3WOgWksDFDRa6hSRwcYOFbiEJ\nXNxgoVtIAhc3WOgWksDFDRa6hSRwcYOFbiEJXNxgoVtIAhc3WOgWksDFDRa6hSRwcYOFbiEJ\nXNxgoVtIAhc3WOieH1LXdQfXIxLWxQ0WuoUkcHGDhW4hCVzcYKFbSAIXN1joFpLAxQ0WuoUk\ncHGDhW4hCVzcYKFbSAIXN1joFpLAxQ0WuoUkcHGDhW4hCVzcYKFbSAIXN1joFpLAxQ0WuoUk\ncHGDhW4hCVzcYKFbSAIXN1joFpLAxQ0WuoUkcHGDhW4hCVzcYKFbSAIXN1joFpLAxQ0WuoUk\ncHGDhW4hCVzcYKFbSAIXN1joFpLAxQ0WuoUkcHGDhW4hCVzcYKFbSAIXN1joFpLAxQ0WuoUk\ncHGDhW4hCVzcYKFbSAIXN1joFpLAxQ0WuoUkcHGDhW4hCVzcYKFbSAIXN1joFpLAxQ0WuoUk\ncHGDhW4hCVzcYKFbSAIXN1joFpLAxQ0WuoUkcHGDhW4hCVzcYKFbSAIXN1joFpLAxQ0WuoUk\ncHGDhW4hCVzcYKG7JqTdwxXSQC5usNBdEdLu+ZtCGsPFDRa6hSRwcYOF7tqQXm6bHasu98Jw\n7mx3VUhPz5E+PN7cn9Z1d3HzQ3qsqA/tBnJxg4XumpCevymkMVzcYKFbSAIXN1joFpLAxQ0W\nuoUkcHGDhe6KkPrMhtFc3GChuyak17fNjlWXe2E4d7ZbSAIXN1joFpLAxQ0WuoUkcHGDhW4h\nCVzcYKFbSAIXN1joFpLAxQ0WuoUkcHGDhW4hCVzcYKFbSAIXN1joFpLAxQ0WuoUkcHGDhW4h\nCVzcYKFbSAIXN1joFpLAxQ0WuoUkcHGDhW4hCVzcYKFbSAIXN1joFpLAxQ0WuoUkcHGDhW4h\nCVzcYKFbSAIXN1joFpLAxQ0WuoUkcHGDhW4hCVzcYKFbSAIXN1joFpLAxQ0WuoUkcHGDhW4h\nCVzcYKFbSAIXN1joFpLAxQ0WuoUkcHGDhW4hCVzcYKFbSAIXN1joFpLAxQ0WuoUkcHGDhW4h\nCVzcYKFbSAIXN1joFpLAxQ0WuoUkcHGDhW4hCVzcYKFbSAIXN1joFpLAxQ0WuoUkcHGDhW4h\nCVzcYKF7fkhd1x1cj0hYFzdY6BaSwMUNFrqFJHBxg4VuIQlc3GChW0gCFzdY6BaSwMUNFrqF\nJHBxg4VuIQlc3GChW0gCFzdY6BaSwMUNFrqFJHBxg4VuIQlc3GChW0gCFzdY6BaSwMUNFrqF\nJHBxg4VuIQlc3GChW0gCFzdY6BaSwMUNFrqFJHBxg4VuIQlc3GChW0gCFzdY6BaSwMUNFrqF\nJHBxg4VuIQlc3GChW0gCFzdY6BaSwMUNFrqFJHBxg4VuIQlc3GChW0gCFzdY6BaSwMUNFrqF\nJHBxg4VuIQlc3GChW0gCFzdY6BaSwMUNFrqFJHBxg4VuIQlc3GChW0gCFzdY6BaSwMUNFrqF\nJHBxg4VuIQlc3GChW0gCFzdY6BaSwMUNFrqFJHBxg4VuIQlc3GChW0gCFzdY6BaSwMUNFrqF\nJHBxg4XuupB2hTSSixssdFeFtCukoVzcYKG7JqRdj0hjubjBQnfVI1IhjeXiBgvds0L68HgL\nflrX+a9HJKyLGyx0C0ng4gYL3UISuLjBQreQBC5usNAtJIGLGyx0C0ng4gYL3XUhHd42O1Zd\n7oXh3NluIQlc3GChW0gCFzdY6BaSwMUNFrqFJHBxg4VuIQlc3GChW0gCFzdY6BaSwMUNFrqF\nJHBxg4VuIQlc3GChW0gCFzdY6BaSwMUNFrqFJHBxg4VuIQlc3GChW0gCFzdY6BaSwMUNFrqF\nJHBxg4VuIQlc3GChW0gCFzdY6BaSwMUNFrqFJHBxg4VuIQlc3GChW0gCFzdY6BaSwMUNFrqF\nJHBxg4VuIQlc3GChW0gCFzdY6BaSwMUNFrrnhzTjaH88Jm0vbrB6byE9H20vbrB6byE9H20v\nbrB6byE9H20vbrB67wVD6rr7uULqug2ukLpugyukrtvgCqnrNrhC6roN7iIh7V6+93iX+Fds\neIcTCXt5gw/+iyDsPdw4e+8lQjr4N4/+Ntv/mrg7/JvBDzf48L8Iwt7dke+fvAuEtNsX0iUP\nN/jwvwjC3lFC2rPebD9v9+ovgCMN3rG6X9XRpUMCfET8857+u6TsRQ0+DAmwd9VTpGs8Io3+\nhtu/+X+Y4+89eEc/fzPu7fasN/Dhxvl7L/yq3ewdt73d0b8Z9dZ9/HGL++9/i2Pv/fcWv30L\nac/bu0eFtHv78dHYe/+9wUIiPJK/+mWv13875q370OO214d266c8fWfwN9urgZy9B+/o4Qfv\nX4c0+t63z+lu/mLDzxGQF2me3mCAvS8jMYMP4ifsXfX27XPtum6DK6Su2+AKqes2uELqug2u\nkLpugyukrtvgCqnrNrhC6roNrpC6boMrJPj9OfwnCtzHFRL8pt6DQ1zvBvgV0hjXu4F2f01f\nHr79e/rr4dvP0+PdelG3LyTgTY/Pir5OXx+/W0ijXO8F3H2Z/rff7x5z+t/Dg1MdjXG9G3D3\n1/THw0d2X6e/938+fHxXSGNc7wbeTZ8ePrL78fCx3eepFxtGud4NvPsy/dh93n/e7afPhTTK\n9W7g3cPHdtO3/beHj+6+FdIo17sBeNM0fd//ePj2RyGNcr0bgPdl+vjw7ceH50qPIfU5QiNc\nIQHvr5+/iPT48d1+/2chDXGF1HUbXCF13QZXSF23wRVS121whdR1G1whdd0GV0hdt8EVUtdt\ncIXUdRtcIXXdBldIXbfBFVLXbXD/B5jZ0Mjqm+wPAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gp + scale_y_continuous(limits=c(1, 35),\n",
    "                                breaks=seq(1, 35, 5)) +\n",
    "   scale_x_continuous(limits=c(1.5, 5.5),\n",
    "                     breaks=seq(1.5, 5.5, 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Grouped Scatter Plot in R"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we group by using <em>color </em>argument and the <code>factor</code> function to change the variable vs to a factor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/1d3azXP1br+6d7gt8Wf9c/V4tvO7Me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\nAlEkYwJRJGMC+X/uM6jFCW3qDwAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gp <- ggplot(aes(x=wt, y=mpg, color=factor(vs)), \n",
    "             data=mtcars)\n",
    "gp + geom_point()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another option is using the <code>as.factor</code> function and change *vs* to a factor in the dataframe object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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iZ81nIEQqoUMjDhs5YjEFKlkIEJn7UcgZAqhY98/H0km89ajkBIlcIHH39D1uez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\nAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mtcars$vs <- as.factor(mtcars$vs)\n",
    "gp <-ggplot(aes(x=wt, y=mpg, color=vs), \n",
    "             data=mtcars)\n",
    "gp + geom_point()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Changing the Markers (the dots)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we are adding thea <code>aes()</code> function in the <code>geom_point()</code> function. In the <code>aes()</code> function we are adding the <em>color</em> and <em>shape</em> arguments and add the class column (the categorical variable)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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tksXZkDIb0q6t4sfTzrtO2/wqLr/Olv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\n54D9H5up5ysF30IyAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data(Burt, package = 'carData')\n",
    "Burt$class <- as.factor(Burt$class)\n",
    "\n",
    "gp <- ggplot(aes(x = IQbio, y = IQfoster), data = Burt)\n",
    "gp + geom_point(aes(color = class, \n",
    "             shape = class))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <span class=\"ez-toc-section\" id=\"How_to_Add_a_Trend_Line_to_a_Scatter_Plot_in_R\">How to Add a Trend Line to a Scatter Plot in R</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use the <code>geom_smooth()</code> function and the method “lm” to add a regression line."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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STAYEhVXL0UZLBxLsyJJMBgSDVcv6RqrHEuzYkkwGBIFRQuTRxqnItzIgkwGNJt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\nATAkEQBDEgEwJBEAQxIBMCQRAEMSAfg/KccUuJW2I0kAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gp <- ggplot(aes(x = IQbio, y = IQfoster), data = Burt)\n",
    "gp + geom_point(aes(color = class, \n",
    "             shape = class)) +\n",
    "      geom_smooth(method = \"lm\", se = FALSE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the next scatter plot example, we are going to add a regression line to the plot for each factor (category) also. Remember, we just add the color and shape arguments to the <code>geom_point()</code> function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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BNJgEEh5fMZHVJSRy7GbsOJJMCgkLI5/YEU5UzryMPYjTiRBBgUUi7nAw0RzqWz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\nAIUkBMD/A6SqUP7VlpcIAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gp + geom_point(aes(color = class, \n",
    "             shape = class)) +\n",
    "      geom_smooth(aes(color = class), method = \"lm\", se = FALSE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Bivariate Distribution on a Scatter plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are adding a bivariate distribution on the scatter plot in R using the <code>geom_density2d()</code> function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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VBuDO16KD04h5KEI6pOR5B2CZL6Gel0dxlGEpIbkp28iX4sSRaQwIQEqTpIfZIQ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\nTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gp <- ggplot(aes(x=wt, y=mpg), \n",
    "             data=mtcars)\n",
    "gp + geom_point() + geom_density2d()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <span class=\"ez-toc-section\" id=\"How_to_Add_Text_to_Scatter_Plot_in_R\">How to Add Text to Scatter Plot in R</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's carry out correlation analysis using R, extract the <em>r</em>– and <em>p</em>-values, and later learn how to add this as text to our scatter plot. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading required package: dplyr\n",
      "\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",
      "Loading required package: broom\n",
      "Warning message:\n",
      "\"package 'broom' was built under R version 3.6.1\""
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>estimate</th><th scope=col>statistic</th><th scope=col>p.value</th><th scope=col>parameter</th><th scope=col>conf.low</th><th scope=col>conf.high</th><th scope=col>method</th><th scope=col>alternative</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>-0.8677                             </td><td>-9.559                              </td><td>0                                   </td><td>30                                  </td><td>-0.9338                             </td><td>-0.7441                             </td><td>Pearson's product-moment correlation</td><td>two.sided                           </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllll}\n",
       " estimate & statistic & p.value & parameter & conf.low & conf.high & method & alternative\\\\\n",
       "\\hline\n",
       "\t -0.8677                              & -9.559                               & 0                                    & 30                                   & -0.9338                              & -0.7441                              & Pearson's product-moment correlation & two.sided                           \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| estimate | statistic | p.value | parameter | conf.low | conf.high | method | alternative |\n",
       "|---|---|---|---|---|---|---|---|\n",
       "| -0.8677                              | -9.559                               | 0                                    | 30                                   | -0.9338                              | -0.7441                              | Pearson's product-moment correlation | two.sided                            |\n",
       "\n"
      ],
      "text/plain": [
       "  estimate statistic p.value parameter conf.low conf.high\n",
       "1 -0.8677  -9.559    0       30        -0.9338  -0.7441  \n",
       "  method                               alternative\n",
       "1 Pearson's product-moment correlation two.sided  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "require(dplyr)\n",
    "require(broom)\n",
    "\n",
    "corr <- mtcars  %$%\n",
    "             cor.test(mpg, wt) %>%\n",
    "             tidy %>%\n",
    "             mutate_if(is.numeric, round, 4)\n",
    "\n",
    "corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'r = -0.8677, p &lt; 0.05'"
      ],
      "text/latex": [
       "'r = -0.8677, p < 0.05'"
      ],
      "text/markdown": [
       "'r = -0.8677, p &lt; 0.05'"
      ],
      "text/plain": [
       "[1] \"r = -0.8677, p < 0.05\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "text = paste0('r = ', corr$estimate, ', ',\n",
    "             ifelse(corr$p.value <= 0,\n",
    "                           'p < 0.05',\n",
    "                   paste('p = ', corr$p.value))\n",
    "             )\n",
    "\n",
    "text"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Adding Text to a Plot in R"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We add text using the<code>annotate</code> function. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SSZ0kvJiNQkue5Fq68SV8l9oM8vIlKD5Lr/jWIkYZPMB/riRURqj1z1P/aNJ2qS\n90BfvohI7ZEFRIrug3sP9OWLiNQeWUKk2KLkPdCXLyJSg+TVz5F2iZhkPtAXLyJSi+SVd+0O\nCZjkPtDnFxEJcjlyUiXF0rPIiAS5JDllkmTpOWREglyUnDBJs/QMMiJBLkx+aJJq6clkRIJc\nmvxoUZItPZWMSJCLkx+YpFt6IhmRIJcn3zdJuPQ0MiJBrkG+p5J06SlkRIJchXzHJO3SE8iI\nBLkOedwk8dJxMiJBrkUeM0m+dJSMSJCrkUcWJf3SQTIiQa5Hfm2SQekYGZEgVyS/MsmhdIiM\nSJCrkm9U8igdICMS5Lrka5NMSqfJiAS5MvnKJJfSSTIiQa5OvjDJp3SCjEiQ65PPi5JR6cdk\nRIK8zfT7mCzKySTR4ZhORiTI32fdWWtZjiZpDscMMiJBnnevx6XZmyQ5HHPIiAR5HZH2i5Lk\ncMwhIxLklUSadxv0cBApHsi5yNXPkfYpaRIixQM5G7nyrt0xBU1CpHggu5MLmoRI8UB2J39/\nKWYSIsUD2Z28QZfackCkeCC7k7foQiYhUjyQ3ck7dBmTECkeyO7kA7qESYgUD2R38hFdYFFC\npHggu5NP6PwmIVI8kN3JZ3R2kxApHsju5Et0ZpUQKR7I7uQrdF6TECkeyO7ka3RWkxApHsju\n5Ft0RpMQKR7I7uRX6HyLEiLFA9md/BqdzSREigeyO3kEncskRIoHsjt5FJ1HJUSKB7I7eRyd\nxSREigeyO/kOOodJiBQPZHfyXfRykxApHsju5PvoxYsSIsUD2Z38AL3UJESKB7I7+RF6oUmI\nFA9kd/Jj9CKVECkeyO7kBHqJSYgUD2R3cgq9wCREigeyOzmNnm0SIsUD2Z0cQM9dlBApHsju\n5Ah6pkmIFA9kd3IIPc8kRIoHsjs5iJ6jEiLFA9mdHEXPMAmR4oHsTg6jp5uESPFAdidPQE81\nCZHigexOnoKeuCghUjyQ3cmT0NNMQqR4ILuTp6EnmYRI8UB2J09FT1AJkeKB7E6ejI6bhEjx\nQHYnT0eHTUKkeCC7k+eggyYhUjyQ3cmz0LFFCZHigexOnocOmYRI8UB2J89ER0xCpHggu5Nn\no9MqIVI8kN3J89FJkxApHsju5AXolEmIFA9kd/Ii9GOTECkeyO7kZeiHixIixQPZnbwQ/cgk\nRIoHsjt5KfqBSYgUD2R38nL0XZUQKR7I7uQM6HsmIVI8kN3JOdB3TEKkeCC7k/OgR01CpHgg\nu5MzoccWJUSKB7I7ORd6xCREigeyOzkb+rVJiBQPZHdyRvStSogUD2R3ck70jUmIFA9kd3JW\n9LVJiBQPZHdyZvSlSYgUD2R3cm70xaKESPFAdidnR59NQqR4ILuT86NPJiFSPFSzRrIAAAVX\nSURBVJDdySXQB5UQKR7I7uQi6L1JiBQPZHdyGfTOJESKB7I7uRR6xl3Q40EkyGrkYugZt0EP\nB5Egq5HLoSeZNAzDhN9GJMhq5ILoCSYNw0OTbn8bkSCrkYuWjqo0DA9NevXbiARZjVy2dNAk\nRILsTi5cOmYSIkF2JxcvHTcp/tuIBFmNXL50aFFi1w6yN7lC6fzvKCESZDVyjdLZTUIkyGrk\nOqUzq5RBpKf9x00QCbI2+pKc16TlIu39OX9AJMiy6CtyVpMWi/T0gkiQXdA35Iwm5Tq0QyTI\nBuhbcr5FKb9IP20T+GOErJ/9f/crH1YkyNXJVUvnWpM4tIOsRq5cOo9JiARZjVy7dJZFCZEg\nq5Grl85hEiJBViOvUHq5SVzZAFmNvEbpxSZxrR1kNfIqpZeahEiQ1cjrlF5oEiJBViOvVHrZ\nlgMiQVYjr1V6kUmIBFmNvFrpJSYhEmQ18oql55uESJDVyGuWnr0oIRJkNfKqpeeahEiQ1cjr\nlp5pEiJBViOvXXqWSogEWY28euk5JiESZDXy+qVnmIRIkNXICqUnm4RIkNXIEqWnLkqIBFmN\nrFF6okmIBFmNLFJ6mkmIBFmNLFN6ikqIBFmNrFN6gkmIBFmNLFQ6bhIiQVYjS5WOmoRIkNXI\nWqWDixIiQVYji5WOmYRIkNXIaqVDJiESZDWyXumASogEWY0sWDptEiJBViMrlk6ahEiQ1cia\npRMmIRJkNbJo6ceLEiJBViOrln5oEiJBViPLln5kEiJBViMLl76vEiJBViMrl75rEiJBViNL\nl75nEiJBViOLlx43CZEgq5HVS48uSogEWY0sX3rMJESCrEbWLz1iEiJBViM7lH5lEiJBViNb\nlGZFgqxO9iiNSJDFyZalEQmyGtmyNCJBViNblkYkyGpky9KIBFmNbFkakSCrkS1LIxJkNbJl\naUSCrEa2LI1IkNXIlqURCbIa2bI0IkFWI1uWRiTIamTL0ogEWY1sWRqRIKuRLUsjEmQ1smVp\nRIKsRrYsjUiQ1ciWpREJshrZsjQiQVYjW5ZGJMhqZMvSiARZjWxZGpEgq5EtSyMSZDWyZWlE\ngqxGtiyNSJDVyJalEQmyGtmyNCJBViNblkYkyGpky9KIBFmNbFkakSCrkS1LIxJkNbJlaUSC\nrEa2LI1IkNXIlqULibRiflq7wIzQuVJWK41IVULnSkGkeBwnmM6VgkjxOE4wnSsFkQhxDiIR\nkiGIREiGIBIhGYJIhGQIIhGSIXYiPW2ydoepcey8iV/np/VG2k2kp9MHnzh2ftl+V67dYHJW\nbIxI5ePYeVvYrjIiTYzfDPt1fvKrvGphRKoUt86WIq14Muookt0Ev/htNjy9GI7zmsfQiFQp\nXqU9T+t2QaRomN/yeXpa8zBpURApGL/Zdf3n3a4yh3YTYje7iFQta57YuYlkechhWHkbv87s\n2hHiHUQiJEMQiZAMQSRCMgSRCMkQRCIkQxCJkAxBJEIyBJEIyRBEMs/vftcfNBlEMs/ADEqE\naTAPImmEaXDLX8Pz5uP/hr82Hz8N26zdiLwgkmGG7VnR5+Hz9iEiqYRZsMvz8M/Ly9NWp382\nixMeaYRpsMtfw6+bI7vPw/9eft8c3yGSRpgGvwwfNkd2PzbHdp8GNhtUwjT45Xn48fTp5dPT\ny/AJkVTCNPhlc2w3/Pny5+bo7k9EUgnTYJhhGP59+bH5+AORVMI0GOZ5eLf5+G5zrrQViWuE\nFIJIhvlr9ybS9vju5eV3RJIIIhGSIYhESIYgEiEZgkiEZAgiEZIhiERIhiASIRmCSIRkCCIR\nkiGIREiGIBIhGYJIhGTI/wGgJro7ygtqhAAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gp <- ggplot(aes(x = wt, y = mpg), \n",
    "             data = mtcars)\n",
    "gp + geom_point() + geom_smooth(method = \"lm\", se = FALSE) +\n",
    "    annotate('text',  x = 4.5, y = 35, label=text)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### More Complex Correlation Test and Text Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading required package: tidyr\n",
      "Loading required package: purrr\n"
     ]
    },
    {
     "data": {
      "image/png": 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rcQeRx7MiS9sW81p0dHc87Qs4J8nEnkd7q2zFs2//LgfI50vcIqFtJ112739XD9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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "require(tidyr)\n",
    "require(purrr)\n",
    "\n",
    "data(Burt, package = 'carData')\n",
    "\n",
    "\n",
    "corr <- Burt %>% group_by(class) %>%\n",
    "  nest() %>% \n",
    "  mutate(Cor = map(data, ~ cor.test(.$IQbio, .$IQfoster)),\n",
    "         p   = map_dbl(Cor, 'p.value'),\n",
    "         est = map_dbl(Cor, 'estimate')\n",
    "             ) %>%\n",
    "  mutate_if(is.numeric, round, 4) %>%\n",
    "  select(class, p, est, Cor)\n",
    "\n",
    "text <- corr %>%\n",
    "  mutate(\n",
    "        text = paste0('r = ', est, ', ',\n",
    "             ifelse(p <= 0.01,\n",
    "                           'p < 0.05',\n",
    "                   paste('p = ', p))))\n",
    "\n",
    "\n",
    "Burt$class <- as.factor(Burt$class)\n",
    "\n",
    "gp <- ggplot(aes(x = IQbio, y = IQfoster), \n",
    "             data = Burt) \n",
    "\n",
    "\n",
    "corrp <- gp + geom_point(aes(color = class, \n",
    "             shape=class)) +\n",
    "      geom_smooth(aes(color = class), method = \"lm\", se = FALSE) +\n",
    "      geom_text(aes(x = 120, y = 137, color=\"high\", \n",
    "                    label=subset(text, class == \"high\")$text)) +\n",
    "      geom_text(aes(x = 118, y = 109, color=\"medium\", \n",
    "                    label=subset(text, class == \"medium\")$text)) +\n",
    "      geom_text(aes(x = 124, y = 103, color=\"low\", \n",
    "                    label=subset(text, class == \"low\")$text))\n",
    "\n",
    "corrp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <span class=\"ez-toc-section\" id=\"How_to_Rotate_the_Axis_using_Ggplot2\">How to Rotate the Axis using Ggplot2</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's how to rotate the axis labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "data(Salaries, package = \"carData\")\n",
    "Salaries$rank <- as.factor(Salaries$rank)\n",
    "\n",
    "gp <- ggplot(aes(x = salary, y = yrs.since.phd), \n",
    "             data = Salaries) + \n",
    "    geom_point(aes(color = rank, \n",
    "             shape = rank)) +\n",
    "    geom_smooth(method = \"lm\") +\n",
    "   scale_y_continuous(limits = c(0, 60)) +\n",
    "   scale_x_continuous(limits = c(50000, 240000), \n",
    "                      breaks = seq(50000, 240000, by = 10000))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To rotate the axis do this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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TuwFGk/kTLHxs+O29fu26+819U+dP23wKWLFHjz5n2b3BLJWhRF2lqk4rHxs+O/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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gp + theme(axis.text.x = \n",
    "           element_text(angle = 90, hjust = 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <span class=\"ez-toc-section\" id=\"How_to_Style_a_Scatter_plot_in_R\">How to Style a Scatter plot in R</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we use the <code>theme_bw()</code> function to get a dark-light themed plot. Then, we are going to make the scatter plot in black and grey colors using the <code>scale_colour_grey()</code> function. Finally, we add a theme layer using the function <code>theme()</code>. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The function <code>element_blank()</code> will make draw “nothing” at that particular parameter. For instance, <code>plot.background = element_blank()</code> will give the plot a blank (white) background. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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v6OzW+ioU9E5Czs4L/910NOe39ovKNf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\nhAQIICRAACEBAv4fneIFJZ3Q/RwAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corrp + theme_bw() +  scale_colour_grey() +\n",
    "  theme(axis.line = element_line(colour = \"black\")\n",
    "        ,plot.background = element_blank()\n",
    "        ,panel.grid.major = element_blank()\n",
    "        ,panel.grid.minor = element_blank()\n",
    "        ,strip.background = element_blank()\n",
    "        ,panel.border = element_blank() \n",
    "        ,legend.title=element_blank()\n",
    "        ,legend.key = element_blank())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Pairplot in R: Scatterplot + Histogram"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's create the <em>pairplots</em> using the package GGally."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading required package: GGally\n",
      "Warning message:\n",
      "\"package 'GGally' was built under R version 3.6.1\"Registered S3 method overwritten by 'GGally':\n",
      "  method from   \n",
      "  +.gg   ggplot2\n",
      "\n",
      "Attaching package: 'GGally'\n",
      "\n",
      "The following object is masked from 'package:dplyr':\n",
      "\n",
      "    nasa\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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F3cay8AUhMZAVIzIH2TBJCylQSQqCQx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\ngg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "require(GGally)\n",
    "\n",
    "cols = c('mpg', 'wt', 'hp', 'qsec')\n",
    "ggpairs(mtcars, columns = cols)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <span class=\"ez-toc-section\" id=\"Saving_a_High_Resolution_Plot_in_R\">Saving a High Resolution Plot in R</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section, we are going to learn how to save ggplot2 plots as PDF and TIFF files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "data(Salaries, package = \"carData\")\n",
    "\n",
    "\n",
    "gp <- ggplot(aes(x=yrs.since.phd, y=salary), \n",
    "             data=Salaries) + \n",
    "    geom_point() +\n",
    "    geom_smooth(method = \"lm\", se = FALSE, colour=\"gray\") +\n",
    "    theme_bw() +\n",
    "    theme(axis.line = element_line(colour = \"black\")\n",
    "        ,plot.background = element_blank()\n",
    "        ,panel.grid.major = element_blank()\n",
    "        ,panel.grid.minor = element_blank()\n",
    "        ,strip.background = element_blank()\n",
    "        ,panel.border = element_blank() \n",
    "        ,legend.title=element_blank()\n",
    "        ,legend.key = element_blank())  +\n",
    "    xlab('Years since Ph.D.') +\n",
    "    ylab('Salary')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can use the <code>ggsave()</code> function to save the scatter plot."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <span class=\"ez-toc-section\" id=\"How_to_Save_a_Scatter_Plot_to_PDF_in_R\">How to Save a Scatter Plot to PDF in R</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's save a pdf!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "ggsave(\"salaries_by_year_scatterplot.pdf\", device = \"pdf\",\n",
    "       width = 12, height = 8,\n",
    "       units = \"cm\", dpi = 300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <span class=\"ez-toc-section\" id=\"How_to_Save_a_Scatter_Plot_to_TIFF_in_R\">How to Save a Scatter Plot to TIFF in R</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's save a TIFF!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "ggsave(\"salaries_by_year_scatterplot.tiff\", device = \"tiff\",\n",
    "       width = 12, height = 8,\n",
    "       units = \"cm\", dpi = 300)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "ir"
  },
  "language_info": {
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