Intermediate Plotting.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Contents**\n",
    "\n",
    "1. Continuous Data\n",
    "2. Discrete Data\n",
    "3. Miscellaneous Options\n",
    "\n",
    "\n",
    "We look at some more options for plotting, and we assume that you are familiar with the basic plotting commands (Basic Plots). A variety of different subjects ranging from plotting options to the formatting of plots is given.\n",
    "\n",
    "In many of the examples below we use some of R’s commands to generate random numbers according to various distributions. The section is divided into three sections. The focus of the first section is on graphing continuous data. The focus of the second section is on graphing discrete data. The third section offers some miscellaneous options that are useful in a variety of contexts."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Continuous Data\n",
    "\n",
    "**Contents**\n",
    "\n",
    "1. Multiple Data Sets on One Plot\n",
    "2. Error Bars\n",
    "3. Adding Noise (jitter)\n",
    "4. Multiple Graphs on One Image\n",
    "5. Density Plots\n",
    "6. Pairwise Relationships\n",
    "7. Shaded Regio\n",
    "8. Plotting a Surface"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "0.778074154829117"
      ],
      "text/latex": [
       "0.778074154829117"
      ],
      "text/markdown": [
       "0.778074154829117"
      ],
      "text/plain": [
       "[1] 0.7780742"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x <- rnorm(10,sd=5,mean=20)\n",
    "y <- 2.5*x - 1.0 + rnorm(10,sd=9,mean=0)\n",
    "cor(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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DCWSUL631CExMIICQKqbsj+9ac3IbEw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\nBAFCgoD/AOnBlU1pYWteAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "Plot with title \"Random Stuff\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(x,y,xlab=\"Independent\",ylab=\"Dependent\",main=\"Random Stuff\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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A4Rup+QICR4ZvZLu2GJJF1JIiR4Zu7G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",
      "text/plain": [
       "Plot with title \"Random Stuff\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(x,y,xlab=\"Independent\",ylab=\"Dependent\",main=\"Random Stuff\")\n",
    "points(x1,y1,col=2,pch=3)\n",
    "points(x2,y2,col=4,pch=5)\n",
    "legend(14,70,c(\"Original\",\"one\",\"two\"),col=c(1,2,4),pch=c(1,3,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAMAAADKOT/pAAAANlBMVEUAAAAAAP9NTU1oaGh8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\nAgQICRAgJECAkAABQgIECAkQ+A+9EwTYTOsYaAAAAABJRU5ErkJggg==",
      "text/plain": [
       "Plot with title \"Random Stuff\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(x,y,xlab=\"Independent\",ylab=\"Dependent\",main=\"Random Stuff\",xlim=c(0,30),ylim=c(0,100))\n",
    "points(x1,y1,col=2,pch=3)\n",
    "points(x2,y2,col=4,pch=5)\n",
    "legend(14,70,c(\"Original\",\"one\",\"two\"),col=c(1,2,4),pch=c(1,3,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "Plot with title \"Random Stuff\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(x,y,xlab=\"Independent\",ylab=\"Dependent\",main=\"Random Stuff\")\n",
    "xHigh <- x\n",
    "yHigh <- y + abs(rnorm(10,sd=3.5))\n",
    "xLow <- x\n",
    "yLow <- y - abs(rnorm(10,sd=3.1))\n",
    "arrows(xHigh,yHigh,xLow,yLow,col=2,angle=90,length=0.1,code=3)"
   ]
  }
 ],
 "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
}