01 Starter Demos

01_Starter_Demos.ipynb

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    "# 01 Starter Demos\n",
    "\n",
    "-  Using R Interactively (CLI)\n",
    "-  Using R Studio\n",
    "-  Using R Programmatically\n",
    "-  Scripting with R\n",
    "-  Accessing Documentation\n",
    "\n",
    "[Sample Interactive Session](https://cran.r-project.org/doc/manuals/r-release/R-intro.html#A-sample-session)  \n",
    "[Invoking R from the command line](https://cran.r-project.org/doc/manuals/r-release/R-intro.html#Invoking-R-from-the-command-line)  \n",
    "[Invoking R under Windows](https://cran.r-project.org/doc/manuals/r-release/R-intro.html#Invoking-R-under-Windows)  \n",
    "[Invoking R under macOS](https://cran.r-project.org/doc/manuals/r-release/R-intro.html#Invoking-R-under-macOS)  \n",
    "[Scripting with R](https://cran.r-project.org/doc/manuals/r-release/R-intro.html#Scripting-with-R)  "
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    "## Using R Interactively (CLI)\n",
    "\n",
    "Try out the following commands in a Windows command prompt, or a Mac or Linux termnal:\n",
    "\n",
    "\n",
    "```\n",
    "$ mkdir work\n",
    "$ cd work\n",
    "$ R\n",
    "> help(solve)\n",
    "> ?solve\n",
    "> help(\"[[\")\n",
    "> help.start() # needs web server\n",
    "> x <- rnorm(50)\n",
    "> y <- rnorm(x)\n",
    "> plot(x, y) # Linux needs GUI or devie X11cairo running (e.g. VcXsrv)\n",
    "> ls()\n",
    "> q()\n",
    "```\n",
    "\n",
    "Try out many more R commands described here:\n",
    "\n",
    "[Appendix A: A sample session](https://cran.r-project.org/doc/manuals/r-release/R-intro.html#A-sample-session)  "
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    "## Using R Studio\n",
    "\n",
    "Try running the following commands in R Studio and see where the output goes. You can run them one at a time, or you\n",
    "can run them all at once using the following keyboard short cut:\n",
    "\n",
    "Run current document:\n",
    "-  Windows: **Ctrl+Alt+R**\n",
    "-  Mac: **Command+Option+R**\n",
    "\n",
    "```\n",
    "mtcars\n",
    "?sum\n",
    "hist(mtcars$mpg)\n",
    "avg_mpg <- mean(mtcars$mpg)\n",
    "random_numbers <- runif(10)\n",
    "random_numbers\n",
    "history()\n",
    "```\n",
    "\n",
    "![UsingRStudio](images/UsingRStudio.png)  \n"
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    "## Using R Programmatically"
   ]
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    "TRUE                     # TRUE\n",
    "FALSE                    # FALSE\n",
    "T                        # TRUE\n",
    "F                        # FALSE\n",
    "F + 0                    # 0\n",
    "T + 0                    # 1\n",
    "x <- c(T,F,T,F,F,F,T)\n",
    "x                        # TRUE FALSE TRUE FALSE FALSE FALSE TRUE\n",
    "as.integer(x)            # 1 0 1 0 0 0 1\n",
    "sum(x)                   # 1+0+1+0+0+0+1 =  3\n",
    "y <- c(1,2,3,4,5,6,7)\n",
    "y                        # 1 2 3 4 5 6 7\n",
    "sum(y)                   # 1+2+3+4+5+6+7 = 28\n",
    "sum(y[x])                # 1+  3+      7 = 11\n",
    "y %% 2                   # 1 0 1 0 1 0 1\n",
    "sum(y %% 2)              # 1+0+1+0+1+0+1 =  4\n",
    "y %% 2 == 0              # FALSE TRUE FALSE TRUE FALSE TRUE FALSE\n",
    "as.integer(y %% 2 == 0)  # 0 1 0 1 0 1 0\n",
    "sum(y %% 2 == 0)         # 0+1+0+1+0+1+0 =  3\n",
    "as.integer(x %% 2 == 0)  # 0 1 0 1 1 1 0\n",
    "y[x %% 2 == 0]           # 2 4 5 6\n",
    "sum(y[x %% 2 == 0])      # 0+2+0+4+5+6+0 = 17 "
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   "source": [
    "# get path for current working directory\n",
    "wdsave <- getwd()\n",
    "wdsave\n",
    "\n",
    "# set working directory to specified path\n",
    "setwd(\"/\")\n",
    "\n",
    "getwd()\n",
    "\n",
    "setwd(wdsave)\n",
    "\n",
    "getwd()"
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    "# list all objects in environment\n",
    "ls()            # display all objects in working environment\n",
    "x <- 42         # create object x\n",
    "ls()            # display all objects in working environment\n",
    "y <- 3.141592   # create object y\n",
    "ls()            # display all objects in working environment\n",
    "exists(\"x\")     # TRUE\n",
    "rm(x)           # remove object x\n",
    "exists(\"x\")     # FALSE\n",
    "rm(list = ls()) # remove all objects from working environment\n",
    "ls()            # nothing displayed here!"
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     "output_type": "stream",
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      "[1] \"Hello World!\"\n"
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       "[1] \"Hello World!\""
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    "# console output\n",
    "print(\"Hello World!\")\n",
    "\"Hello World!\""
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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot a graph\n",
    "x <- 1:5\n",
    "y <- x^2\n",
    "plot(x,y, type = \"o\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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pZxMQ4X45igbkAdLeNiHC7GMUHdgDpaxsU4XIxjgroBATkiJEAAIQECCAkQQEiA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\nAAAASUVORK5CYII=",
      "text/plain": [
       "Plot with title \"Normal Distribution (100000 observations)\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Normal distribution (100000 observations)\n",
    "x <- rnorm(100000)\n",
    "hist(x, col='red', breaks=50, main=\"Normal Distribution (100000 observations)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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eAQPVj22Teq7osNu7Kf+hDNsz1qklft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\nQEgQQEgQQEgQQEgQQEgQQEgQ4H+yxnNUDeiI9AAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# scatter plot: linear + noise \n",
    "x <- 1:10\n",
    "y_intercept <- 2            # y_intercept\n",
    "slope       <- 3            # slope\n",
    "noise <- 2*rnorm(10)\n",
    "y <- slope*x + y_intercept + noise\n",
    "plot(x,y)\n",
    "abline(y_intercept, slope)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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UnLBRbuQcLH5iUtF1i4Bwkfm5e0XGDh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",
      "text/plain": [
       "Plot with title \"Uniform Distribution (100000 observations)\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Uniform distribution (100000 observations)\"\n",
    "x <- runif(100000)\n",
    "hist(x, col='red', breaks=50, main=\"Uniform Distribution (100000 observations)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Scripting with R\n",
    "\n",
    "-  Run script file at command prompt\n",
    "-  Run script file within code\n",
    "\n",
    "Given the following script file:\n",
    "```\n",
    "# test.R\n",
    "myString <- \"Hello, World!\"\n",
    "print(myString)\n",
    "```\n",
    "\n",
    "Run at command prompt and see output file test.Rout that is written:  \n",
    "**R CMD BATCH test.R**\n",
    "\n",
    "or\n",
    "\n",
    "Run within code and see output returned:  \n",
    "**source(\"test.R\")**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Hello, World!\"\n"
     ]
    }
   ],
   "source": [
    "# run within code\n",
    "source(\"scripts/test.R\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Accessing documentation\n",
    "\n",
    "-  R Help: **help()** and **?**\n",
    "-  Vignettes and Demos: **browseVignettes()**, **vignette()** and **demo()**\n",
    "-  Find objects by partial name: **apropos()**\n",
    "-  Search Help System: **help.search()**\n",
    "-  Search for keywords/phrases in docs: **RSiteSearch()**\n",
    "-  Hypertext documentation: **help.start()**\n",
    "-  Demonstrations of R Functionality: **demo()**\n",
    "-  [CRAN Task Views](https://cran.r-project.org/web/views)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "help(sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "sum> ## Pass a vector to sum, and it will add the elements together.\n",
      "sum> sum(1:5)\n",
      "[1] 15\n",
      "\n",
      "sum> ## Pass several numbers to sum, and it also adds the elements.\n",
      "sum> sum(1, 2, 3, 4, 5)\n",
      "[1] 15\n",
      "\n",
      "sum> ## In fact, you can pass vectors into several arguments, and everything gets added.\n",
      "sum> sum(1:2, 3:5)\n",
      "[1] 15\n",
      "\n",
      "sum> ## If there are missing values, the sum is unknown, i.e., also missing, ....\n",
      "sum> sum(1:5, NA)\n",
      "[1] NA\n",
      "\n",
      "sum> ## ... unless  we exclude missing values explicitly:\n",
      "sum> sum(1:5, NA, na.rm = TRUE)\n",
      "[1] 15\n"
     ]
    }
   ],
   "source": [
    "example(sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "help(min)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "min> require(stats); require(graphics)\n",
      "\n",
      "min>  min(5:1, pi) #-> one number\n",
      "[1] 1\n",
      "\n",
      "min> pmin(5:1, pi) #->  5  numbers\n",
      "[1] 3.141593 3.141593 3.000000 2.000000 1.000000\n",
      "\n",
      "min> x <- sort(rnorm(100));  cH <- 1.35\n",
      "\n",
      "min> pmin(cH, quantile(x)) # no names\n",
      "[1] -1.86465917 -0.68324599  0.01740251  0.76651463  1.35000000\n",
      "\n",
      "min> pmin(quantile(x), cH) # has names\n",
      "         0%         25%         50%         75%        100% \n",
      "-1.86465917 -0.68324599  0.01740251  0.76651463  1.35000000 \n",
      "\n",
      "min> plot(x, pmin(cH, pmax(-cH, x)), type = \"b\", main =  \"Huber's function\")\n",
      "\n",
      "min> cut01 <- function(x) pmax(pmin(x, 1), 0)\n",
      "\n",
      "min> curve(      x^2 - 1/4, -1.4, 1.5, col = 2)\n"
     ]
    },
    {
     "data": {
      "image/png": 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jRnBanvsQGbkI7m6brYS2GOijnqTwJi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\nCAkQICRAgJAAAUICBAgJECAkQICQAIF/O4+7KOVSZOsAAAAASUVORK5CYII=",
      "text/plain": [
       "Plot with title \"Huber's function\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "min> curve(cut01(x^2 - 1/4), col = \"blue\", add = TRUE, n = 500)\n",
      "\n",
      "min> ## pmax(), pmin() preserve attributes of *first* argument\n",
      "min> D <- diag(x = (3:1)/4) ; n0 <- numeric()\n",
      "\n",
      "min> stopifnot(identical(D,  cut01(D) ),\n",
      "min+           identical(n0, cut01(n0)),\n",
      "min+           identical(n0, cut01(NULL)),\n",
      "min+           identical(n0, pmax(3:1, n0, 2)),\n",
      "min+           identical(n0, pmax(n0, 4)))\n"
     ]
    },
    {
     "data": {
      "image/png": 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wyPhJCu4lmSNZYHQkjXWZ5blywPhJCu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\nEBIgQEiAACEBAoQECPwPgAetke+taWYAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "example(min)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\tdemo(graphics)\n",
      "\t---- ~~~~~~~~\n",
      "\n",
      "> #  Copyright (C) 1997-2009 The R Core Team\n",
      "> \n",
      "> require(datasets)\n",
      "\n",
      "> require(grDevices); require(graphics)\n",
      "\n",
      "> ## Here is some code which illustrates some of the differences between\n",
      "> ## R and S graphics capabilities.  Note that colors are generally specified\n",
      "> ## by a character string name (taken from the X11 rgb.txt file) and that line\n",
      "> ## textures are given similarly.  The parameter \"bg\" sets the background\n",
      "> ## parameter for the plot and there is also an \"fg\" parameter which sets\n",
      "> ## the foreground color.\n",
      "> \n",
      "> \n",
      "> x <- stats::rnorm(50)\n",
      "\n",
      "> opar <- par(bg = \"white\")\n",
      "\n",
      "> plot(x, ann = FALSE, type = \"n\")\n",
      "\n",
      "> abline(h = 0, col = gray(.90))\n",
      "\n",
      "> lines(x, col = \"green4\", lty = \"dotted\")\n",
      "\n",
      "> points(x, bg = \"limegreen\", pch = 21)\n",
      "\n",
      "> title(main = \"Simple Use of Color In a Plot\",\n",
      "+       xlab = \"Just a Whisper of a Label\",\n",
      "+       col.main = \"blue\", col.lab = gray(.8),\n",
      "+       cex.main = 1.2, cex.lab = 1.0, font.main = 4, font.lab = 3)\n",
      "\n",
      "> ## A little color wheel.\t This code just plots equally spaced hues in\n",
      "> ## a pie chart.\tIf you have a cheap SVGA monitor (like me) you will\n",
      "> ## probably find that numerically equispaced does not mean visually\n",
      "> ## equispaced.  On my display at home, these colors tend to cluster at\n",
      "> ## the RGB primaries.  On the other hand on the SGI Indy at work the\n",
      "> ## effect is near perfect.\n",
      "> \n",
      "> par(bg = \"gray\")\n",
      "\n",
      "> pie(rep(1,24), col = rainbow(24), radius = 0.9)\n"
     ]
    },
    {
     "data": {
      "image/png": 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Umv9ulLznXTuBQ04SPM34JwcDURCGkeNssyAI9k7VUcZ9du+m1aBQhpNYNDeI23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\nEAAhAQiAkAAEQEgAAiAkAAEQEoAACAlAAIQEIABCAhAAIQEIoFR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      "text/plain": [
       "Plot with title \"Simple Use of Color In a Plot\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "> title(main = \"A Sample Color Wheel\", cex.main = 1.4, font.main = 3)\n",
      "\n",
      "> title(xlab = \"(Use this as a test of monitor linearity)\",\n",
      "+       cex.lab = 0.8, font.lab = 3)\n",
      "\n",
      "> ## We have already confessed to having these.  This is just showing off X11\n",
      "> ## color names (and the example (from the postscript manual) is pretty \"cute\".\n",
      "> \n",
      "> pie.sales <- c(0.12, 0.3, 0.26, 0.16, 0.04, 0.12)\n",
      "\n",
      "> names(pie.sales) <- c(\"Blueberry\", \"Cherry\",\n",
      "+ \t\t      \"Apple\", \"Boston Cream\", \"Other\", \"Vanilla Cream\")\n",
      "\n",
      "> pie(pie.sales,\n",
      "+     col = c(\"purple\",\"violetred1\",\"green3\",\"cornsilk\",\"cyan\",\"white\"))\n"
     ]
    },
    {
     "data": {
      "image/png": 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Jq9s7EcxVySRELqKR1rah1ra8nVrxG0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\nGAIkhBgCJIQYAiSEGAIkhBj6/5toayB8XG37AAAAAElFTkSuQmCC",
      "text/plain": [
       "Plot with title \"A Sample Color Wheel\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "> title(main = \"January Pie Sales\", cex.main = 1.8, font.main = 1)\n",
      "\n",
      "> title(xlab = \"(Don't try this at home kids)\", cex.lab = 0.8, font.lab = 3)\n",
      "\n",
      "> ## Boxplots:  I couldn't resist the capability for filling the \"box\".\n",
      "> ## The use of color seems like a useful addition, it focuses attention\n",
      "> ## on the central bulk of the data.\n",
      "> \n",
      "> par(bg=\"cornsilk\")\n",
      "\n",
      "> n <- 10\n",
      "\n",
      "> g <- gl(n, 100, n*100)\n",
      "\n",
      "> x <- rnorm(n*100) + sqrt(as.numeric(g))\n",
      "\n",
      "> boxplot(split(x,g), col=\"lavender\", notch=TRUE)\n"
     ]
    },
    {
     "data": {
      "image/png": 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ae0I1KaYv7Hve2WZGAKnoqOSMnbUhkR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\ngJAAAYQECCAkQMD/AwS/S+xIZ3kJAAAAAElFTkSuQmCC",
      "text/plain": [
       "Plot with title \"January Pie Sales\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "> title(main=\"Notched Boxplots\", xlab=\"Group\", font.main=4, font.lab=1)\n",
      "\n",
      "> ## An example showing how to fill between curves.\n",
      "> \n",
      "> par(bg=\"white\")\n",
      "\n",
      "> n <- 100\n",
      "\n",
      "> x <- c(0,cumsum(rnorm(n)))\n",
      "\n",
      "> y <- c(0,cumsum(rnorm(n)))\n",
      "\n",
      "> xx <- c(0:n, n:0)\n",
      "\n",
      "> yy <- c(x, rev(y))\n",
      "\n",
      "> plot(xx, yy, type=\"n\", xlab=\"Time\", ylab=\"Distance\")\n"
     ]
    },
    {
     "data": {
      "image/png": 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UWy3n47wookeoi+JDhrvZTo+db9TweRpgZnJfpAjN7/dKKKJPtFY4LgvEQfiNH7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\nD1sJ3CHVKaT9AAAAAElFTkSuQmCC",
      "text/plain": [
       "Plot with title \"Notched Boxplots\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "> polygon(xx, yy, col=\"gray\")\n",
      "\n",
      "> title(\"Distance Between Brownian Motions\")\n",
      "\n",
      "> ## Colored plot margins, axis labels and titles.\t You do need to be\n",
      "> ## careful with these kinds of effects.\tIt's easy to go completely\n",
      "> ## over the top and you can end up with your lunch all over the keyboard.\n",
      "> ## On the other hand, my market research clients love it.\n",
      "> \n",
      "> x <- c(0.00, 0.40, 0.86, 0.85, 0.69, 0.48, 0.54, 1.09, 1.11, 1.73, 2.05, 2.02)\n",
      "\n",
      "> par(bg=\"lightgray\")\n",
      "\n",
      "> plot(x, type=\"n\", axes=FALSE, ann=FALSE)\n"
     ]
    },
    {
     "data": {
      "image/png": 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WjzEMW/SALdGZE8g0gbnWjzEMWLR8mI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\nS1WnJ3WZJS0AAAAASUVORK5CYII=",
      "text/plain": [
       "Plot with title \"Distance Between Brownian Motions\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "> usr <- par(\"usr\")\n",
      "\n",
      "> rect(usr[1], usr[3], usr[2], usr[4], col=\"cornsilk\", border=\"black\")\n",
      "\n",
      "> lines(x, col=\"blue\")\n",
      "\n",
      "> points(x, pch=21, bg=\"lightcyan\", cex=1.25)\n",
      "\n",
      "> axis(2, col.axis=\"blue\", las=1)\n",
      "\n",
      "> axis(1, at=1:12, lab=month.abb, col.axis=\"blue\")\n",
      "\n",
      "> box()\n",
      "\n",
      "> title(main= \"The Level of Interest in R\", font.main=4, col.main=\"red\")\n",
      "\n",
      "> title(xlab= \"1996\", col.lab=\"red\")\n",
      "\n",
      "> ## A filled histogram, showing how to change the font used for the\n",
      "> ## main title without changing the other annotation.\n",
      "> \n",
      "> par(bg=\"cornsilk\")\n",
      "\n",
      "> x <- rnorm(1000)\n",
      "\n",
      "> hist(x, xlim=range(-4, 4, x), col=\"lavender\", main=\"\")\n"
     ]
    },
    {
     "data": {
      "image/png": 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UTybmAogoQAybuBwQgSAiTvBgYjSAiQ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\nIEiAAYIEGPg/IshRYCdRwUoAAAAASUVORK5CYII=",
      "text/plain": [
       "Plot with title \"The Level of Interest in R\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "> title(main=\"1000 Normal Random Variates\", font.main=3)\n",
      "\n",
      "> ## A scatterplot matrix\n",
      "> ## The good old Iris data (yet again)\n",
      "> \n",
      "> pairs(iris[1:4], main=\"Edgar Anderson's Iris Data\", font.main=4, pch=19)\n"
     ]
    },
    {
     "data": {
      "image/png": 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BkipPAIKUOEFB4hZYiQwiOkDBFSeISU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\nrkJggg==",
      "text/plain": [
       "Plot with title \"1000 Normal Random Variates\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "> pairs(iris[1:4], main=\"Edgar Anderson's Iris Data\", pch=21,\n",
      "+       bg = c(\"red\", \"green3\", \"blue\")[unclass(iris$Species)])\n"
     ]
    },
    {
     "data": {
      "image/png": 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Nmpp61jbMwmd5SODNK68hgHO31JUnKS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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "> ## Contour plotting\n",
      "> ## This produces a topographic map of one of Auckland's many volcanic \"peaks\".\n",
      "> \n",
      "> x <- 10*1:nrow(volcano)\n",
      "\n",
      "> y <- 10*1:ncol(volcano)\n",
      "\n",
      "> lev <- pretty(range(volcano), 10)\n",
      "\n",
      "> par(bg = \"lightcyan\")\n",
      "\n",
      "> pin <- par(\"pin\")\n",
      "\n",
      "> xdelta <- diff(range(x))\n",
      "\n",
      "> ydelta <- diff(range(y))\n",
      "\n",
      "> xscale <- pin[1]/xdelta\n",
      "\n",
      "> yscale <- pin[2]/ydelta\n",
      "\n",
      "> scale <- min(xscale, yscale)\n",
      "\n",
      "> xadd <- 0.5*(pin[1]/scale - xdelta)\n",
      "\n",
      "> yadd <- 0.5*(pin[2]/scale - ydelta)\n",
      "\n",
      "> plot(numeric(0), numeric(0),\n",
      "+      xlim = range(x)+c(-1,1)*xadd, ylim = range(y)+c(-1,1)*yadd,\n",
      "+      type = \"n\", ann = FALSE)\n"
     ]
    },
    {
     "data": {
      "image/png": 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SMB1649ZBCATQ6CDRQQbGgPIQRgl9MOZySSOBzPyNIIcDyLydcYROoAaQQ4nsXk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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "> usr <- par(\"usr\")\n",
      "\n",
      "> rect(usr[1], usr[3], usr[2], usr[4], col=\"green3\")\n",
      "\n",
      "> contour(x, y, volcano, levels = lev, col=\"yellow\", lty=\"solid\", add=TRUE)\n",
      "\n",
      "> box()\n",
      "\n",
      "> title(\"A Topographic Map of Maunga Whau\", font= 4)\n",
      "\n",
      "> title(xlab = \"Meters North\", ylab = \"Meters West\", font= 3)\n",
      "\n",
      "> mtext(\"10 Meter Contour Spacing\", side=3, line=0.35, outer=FALSE,\n",
      "+       at = mean(par(\"usr\")[1:2]), cex=0.7, font=3)\n",
      "\n",
      "> ## Conditioning plots\n",
      "> \n",
      "> par(bg=\"cornsilk\")\n",
      "\n",
      "> coplot(lat ~ long | depth, data = quakes, pch = 21, bg = \"green3\")\n"
     ]
    },
    {
     "data": {
      "image/png": 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xDsOlOk1rbBEtX+aSyR5Ee2GJPUBrLSPili6A9WdKu5v1eJNLIzixTsUaQv4oQm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",
      "text/plain": [
       "Plot with title \"A Topographic Map of Maunga Whau\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "> par(opar)\n"
     ]
    },
    {
     "data": {
      "image/png": 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SIsaK8rXIyLNeYQEqe1fXZi3kmWgrVPpYVLcbHKHELitHYPIU077Q8hXbGGkKgV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\nhAQAAxASAAz8P/hGzdmUTXN4AAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "demo(graphics)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "© 2018 Peter Thorsteinson\n",
    "\n",
    "This work is licensed under the [Creative Commons Attribution-NonCommercial-NoDerivs 3.0](https://creativecommons.org/licenses/by-nc-nd/3.0/us/).\n",
    "\n",
    "Bitcoin donations: 1D4d7Bhxj5QWcgbQyZCowTMEi36CNYcmQf"
   ]
  }
 ],
 "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": "3.4.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}