RBasics5.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## R Basics 5: Graphics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'data.frame':\t150 obs. of  5 variables:\n",
      " $ Sepal.Length: num  5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...\n",
      " $ Sepal.Width : num  3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...\n",
      " $ Petal.Length: num  1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...\n",
      " $ Petal.Width : num  0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...\n",
      " $ Species     : Factor w/ 3 levels \"setosa\",\"versicolor\",..: 1 1 1 1 1 1 1 1 1 1 ...\n"
     ]
    }
   ],
   "source": [
    "# iris is an internal dataset in R, stored as dataframe with 4 features and one target (5 columns)\n",
    "str(iris)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# for categorical data (factors), plot displays histogram. In iris, each species has 50 samples \n",
    "# if two categorical variables are plotted, an area plot is generated \n",
    "plot(iris$Species)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AABggQIECRAgCABAkGCVBWmqNoQPTvyfOn1XCZXP341niaU/twes5I/YiGCtB/m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\nRK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# for numerical data, a scatter plot\n",
    "# iris$Sepal.Length is the first column\n",
    "plot(iris$Sepal.Length)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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N/xlkO2vct28bqytCiL3x7+fRvSj1vS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\nAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# can specify 2-d plots \n",
    "plot(iris$Sepal.Length, iris$Sepal.Width)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HRh8OGLJrNcQrow+PBFk1kuIV0YfPiiySyXkC4MPnzRZJZLSBcGH75oMsslpAuD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\nOCne1f4cqQAAAABJRU5ErkJggg==",
      "text/plain": [
       "Plot with title \"Histogram of setosa$Sepal.Length\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# histograms \n",
    "# let's plot setosa species \n",
    "setosa <- iris[iris$Species == \"setosa\",]\n",
    "hist(setosa$Sepal.Length)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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cvRota+pyr8iAQgBJEADEAkAAMQCcAARAIwAJEADEAkAAMQCcAARAIwAJEADEAk\nAAMQCcAARAIwAJEADEAkAAMQCcAARAIwAJEADEAkAAMQCcAARAIwAJEADEAkAAMQCcAARAIw\nAJEADEAkAAMQCcAARAIwAJEADEAkAAMQCcAARIoads9aYE8pmPaJqfd99Ql9ef04P2xC2E77\nZLvOp+nO+LTwy8z20IMNJ2Azaavfmk9qz6rHj49aDVk+YcWvRGrGhEjfwoYTMG26bsMhD/m2\n/qDifdjey0PUdtKnFn8lUkCkP8GGEzBtulatQpHXh6QQ6kNRPmlNRBLAhluYy7Y8t2nOiU6b\nkFVnOfWLtMcPHh/o/Wp12YXHR5dnpTzt7ulP8Laj6seH9oPOXyt+EKmz1n0XsmP9w0NWHuTK\nVo8xNR0eDetPBURallNzblNN4F39aPsSadv+oNPq2DyqhDiEzaXdPYewvz/7fHZU9nRsH3ZW\nHBepu1Z9xnVsh7DvirRrhwuzQKRlycKtKM5hUx4xwjYvylOeSzu9zyG7FbcsnLutQvX03DTY\nV5P82vRTzvjNoXnc6+jZRWfFUZF6a5UPT82YmvXD66VduwjmgUjLEkJ7qXtXn+bkYddO2l29\n6NIcoy5va9XfbodSpV3zo0ulVXZ576jpYtdbcVSk3lrXx6J2CB2Rrv0OYCJssmUpVdjdbtWj\n0PJ2Yl99e7UqivvluH3O5HDZvF5nXY9ZNc8HHT2+PVccFWk0vrN+d0yINB822cIcq/OR7P7/\nIr1aPU6cniKVx5DO66xb8+pvVKTXiogkgE22OJfDppn/z58MRXq22ofN6XLviNSd8G9Pel10\nVvwg0kg8IpnBJvsFr/OR59PXCUrvBKde1PjQXP6u30faPV7fPZ50OrrWXex7K344R3qL/3CO\n1F8LpsImW5ZNczFt87hIV5yas/3qRVznqt2rVaXGrTnV2Ydde2fDNYRTXn7bVkL1Omq6uPRW\nHBWpt1a7qHfV7l4g0vewyZbl3JyYVAeO5iSmOhHaNHfQvd5HerU6hOfDx/119b127Y+rxp2O\nyh+Ex4W9zoodm14nRr21irbB9rm8GRMifQubbGHqexaaN4BO5WSt31e9bho9Tlnvzoa61b56\n0Lzeux9ed3/f9qVW23PR76ic8bu2i9eK4yL11np+LSO21+phMyZE+hY2WdT8Y/cYzfjmQAd/\nAZGiZmGR6tsh8t2ke8rhf0GkNfNXkR436GU2o0kaRFozf35pd6rv4TMZS+IgEoABiARgACIB\nGIBIAAYgEoABiARgACIBGIBIAAYgEoABiARgACIBGIBIAAYgEoABiARgACIBGIBIAAYgEtsI\njsYAAAAtSURBVIABiARgACIBGIBIAAYgEoABiARgACIBGIBIAAYgEoABiARgACIBGPAf5IzC\nCnkBzbwAAAAASUVORK5CYII=",
      "text/plain": [
       "Plot with title \"Histogram of setosa$Sepal.Length\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# breaks controls the number of bins\n",
    "hist(setosa$Sepal.Length, breaks = 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "Plot with title \"Sepal Width vs. Sepal Length\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot with options \n",
    "plot(setosa$Sepal.Length, setosa$Sepal.Width, \n",
    "    xlab = \"Sepal Length\",\n",
    "    ylab = \"Sepal Width\",\n",
    "    main = \"Sepal Width vs. Sepal Length\",\n",
    "    #type = \"o\",\n",
    "    col = \"green\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'blue'"
      ],
      "text/latex": [
       "'blue'"
      ],
      "text/markdown": [
       "'blue'"
      ],
      "text/plain": [
       "[1] \"blue\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# par() sets and displays plot parameters \n",
    "par(col = \"blue\")\n",
    "par()$col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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RDSGaujF3CekM5Y7T8KiQsI6Qwh0YeQ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\nAoQEAf8FZ3l4E9Gm3SYAAAAASUVORK5CYII=",
      "text/plain": [
       "Plot with title \"Y vs. X\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# control number of plots (number of rows, number of columns)\n",
    "par(mfrow = c(2,2))\n",
    "#par(mfcol = c(2,2))\n",
    "#par(mfrow = c(1,1)) # default\n",
    "# reset layout\n",
    "#layout(1)\n",
    "# define x and y as two vectors\n",
    "x <- c(2.2, 3, 3.8, 4.5, 7, 8.5, 9.7, 11.5)\n",
    "y <- c(4, 5.5, 6.5, 9, 11, 15.2, 18.3, 20.5)\n",
    "plot(x, y,\n",
    "xlab = \"X\",\n",
    "ylab = \"Y\",\n",
    "main = \"Y vs. X\",\n",
    "type = \"o\",\n",
    "col = \"orange\",\n",
    "col.main = \"darkblue\",\n",
    "cex.axis = 0.8,  # label size\n",
    "lty = 3,  # dotted\n",
    "pch = 0) # zero for square, defaul is 1 aka circle \n",
    "\n",
    "# all defaults, x^2 vs x\n",
    "plot(x, x^2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "Plot with title \"Y vs. X\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lm_line <- lm(y ~ x)\n",
    "plot(x, y,\n",
    "xlab = \"X\",\n",
    "ylab = \"Y\",\n",
    "main = \"Y vs. X\",\n",
    "type = \"o\",\n",
    "col = \"orange\",\n",
    "col.main = \"darkblue\",\n",
    "cex.axis = 0.8,  # label size\n",
    "lty = 3,  # dotted \n",
    "lwd = 4, # line width\n",
    "pch = 0) # pch is symbol type, 0 is square\n",
    "# add linear regression line\n",
    "abline(coef(lm_line), lwd = 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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V3Z2LPnQX35HOmKNrsI9HZ2C15gnuVc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\nCQKEBAFCggAhQYCQIOD/KWYDFsElCYIAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# you can find this and more at \n",
    "# http://www.sthda.com/english/wiki/r-plot-pch-symbols-the-different-point-shapes-available-in-r\n",
    "\n",
    "#++++++++++++++++++++++++++++++++++++++++++++\n",
    "#generate a plot of point shapes which R knows about.\n",
    "#++++++++++++++++++++++++++++++++++++++++++++\n",
    "generateRPointShapes <- function(){\n",
    "  oldPar <- par()\n",
    "  par(font=2, mar=c(0.5,0,0,0))\n",
    "  y=rev(c(rep(1,6),rep(2,5), rep(3,5), rep(4,5), rep(5,5)))\n",
    "  x=c(rep(1:5,5),6)\n",
    "  plot(x, y, pch = 0:25, cex=1.5, ylim=c(1,5.5), xlim=c(1,6.5), \n",
    "       axes=FALSE, xlab=\"\", ylab=\"\", bg=\"blue\")\n",
    "  text(x, y, labels=0:25, pos=3)\n",
    "  par(mar=oldPar$mar,font=oldPar$font )\n",
    "}\n",
    "generateRPointShapes()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# practice more and see other packages like gglot2 etc"
   ]
  }
 ],
 "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": "3.3.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}