Animal Audiogram Database usage examples.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook contains some usage examples for the [Animal Audiograms Database](https://animalaudiograms.museumfuernaturkunde.berlin/).\n",
    "\n",
    "If you have any questions, please send us an email: audiograms@mfn.berlin."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Downloading data\n",
    "To download audiogram data, open an audiogram and click on the \"Download original data\" button (figure 1). \n",
    "\n",
    "You will be given the choice to save the data or to open it in your spreadsheet program (e.g. Excel or equivalent). \n",
    "Choose \"Save file\". The downloaded file will be called \"Audiogram_XX.csv\", where XX is the Id of the audiogram in the database. The data file is in .csv format (\"comma-separated values\").\n",
    "\n",
    "Once the file is on your local computer, you can open and process the data using R or Python. The following examples are in R.\n",
    "\n",
    "![](images/download.png)\n",
    "\n",
    "*Fig. 1: Downloading data*\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting an audiogram\n",
    "If you would like to plot the audiogram of the downloaded data using your own parameters for the plot, proceed as follows. An example file is provided in ./data/Audiogram_30.csv, change this to the path of your downloaded file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# load graphics libraries\n",
    "library(ggplot2)\n",
    "\n",
    "# Read the downloaded data\n",
    "data_file <- \"./data/Audiogram_30.csv\" # change this to your file\n",
    "audiogram <- read.table(data_file, header=TRUE, sep=\",\")\n",
    "\n",
    "# prepare the labels for the plot axes\n",
    "label_x <- \"Frequency (kHz)\"\n",
    "label_y <- paste0(\"Threshold (dB \", audiogram$SPL.reference[1], \")\")\n",
    "\n",
    "# add some sensible limits\n",
    "limits_x <- c(0.1, 200)  # Plot frequencies from 0.1 to 200 kHz\n",
    "limits_y <- c(-50, 160)  # Plot SPL from -50 to 160 dB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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2omaX6BVNEzY43qsoBEq45DqhwmRtMH8gukJ8Qo5WUBiVYdhyRLeoqnyAP5BNKe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\ngQAJIQcCJIQcCJAQcqD/AyPL/IwnlJ6rAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      },
      "text/plain": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# a basic plot of SPL vs. frequency, frequency is on a logarithmic scale\n",
    "basic_plot <- ggplot(audiogram, aes(Frequency.in.kHz, SPL)) +\n",
    "    labs(x = label_x, y = label_y) +\n",
    "    geom_line() + \n",
    "    geom_point() +\n",
    "    scale_x_log10(limits = limits_x) +\n",
    "    scale_y_continuous(limits = limits_y) +\n",
    "    theme_bw()\n",
    "basic_plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting the values of the data points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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I7/3QrbfT/ar5k18yJt9zbqHnBoETsB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\nP9jfpUdKHKQuC/XPz3cTXRMgqc1BSNWD2VCLm9e0Y+duiTP5ycG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5XIwASW2ARChxkI57ssaicQIktQESocRBeu+Udw7VWDhmgKQ2QCKU\nOEg9TmN1W2GCyHgBEqUjAVL3yzBBpECAROlIgGQ/QFIbIBECJLkAiRAgyYRTX5ICJEpHAiSc\n+lIoQKJ0JEDCqS+FAiRKRwIknPpSKECidCRAwqkvhQIkSkcCJJz6UihAolTrIeHUl6IBEqVa\nDwmnvhQNkCjVekhSAZLaAIlQgiCdu0hiTYCkNkAilCBI7GmJNQGS2gCJECDJBUiEAEkmQCIF\nSJRqP6QB00OJrgmQ1AZIhBIFyZzomgBJbYBEKFGQxn0aSnRNgKQ2QCKE35HkAiRCgCQTIJEC\nJEqAZBUgqQ2QCCUI0p8/lVgTIKkNkAjhvXZyARIhQJIJkEgBEiVAsgqQ1AZIhABJLkAiBEgy\nOQfpll/L4lTgLY23iEMVF7k0UKm3wKWRygoOuDRQsdetR68s362B1D/1ivo4BmlzeZz2e8vi\nLeJQxcUuDVTmLXBppPKCUpcGKvEWuTRSuXsPnvKnXrFjkLBrpzbs2hFKpV07QFIbIBECJLkA\niRAgyQRIpACJEiBZBUhqAyRCgCQXIBECJJkAiRQgUQIkqwBJbYBECJDkAiRCgCQTIJECJEqA\nZBUgqQ2QCAGSXIBECJBkAiRSgEQJkKwCJLUBEiFAkguQCAGSTIBECpAoAZJVgKQ2QCIESHIB\nEiFAkgmQSAESJUCyCpDUBkiEAEkuQCIESDIBEilAogRIVgGS2gCJECDJBUiEAEkmQCIFSJQA\nySpAUhsgEQIkuQCJECDJBEikAIkSIFkFSGoDJEKAJBcgEQIkmQCJFCBRAiSrAEltgEQIkOQC\nJEKAJBMgkQIkSoBkFSCpDZAIAZJcgEQIkGQCJFKARAmQrAIktQESIUCSC5AIAZJMgEQKkCgB\nklWApDZAIgRIcgESIUCSCZBIARIlQLIKkNQGSIQASS5AIgRIMgESKUCiBEhWAZLaAIkQIMkF\nSIQASSZAIgVIlADJKkBSGyARAiS5AIkQIMkESKQAiRIgWQVIagMkQoAkFyARAiSZAIkUIFEC\nJKsASW2ARAiQ5AIkQoAkEyCRAiRKgGQVIKkNkAgBklyARAiQZAIkUoBECZCsAiS1ARIhQJIL\nkAgBkkyARAqQKAGSVYCkNkAiBEhyARIhQJIJkEgBEiVAsgqQ1AZIhABJLkAiBEgyARIpQKIE\nSFYBktoAiRAgyQVIhABJJkAiBUiUAMkqQFIbIBECJLkAiRAgyQRIpACJEiBZBUhqAyRCgCQX\nIBECJJkIkEqeHD7kkX3Bq4CkNkAilNSQJk3YuuuJ24PfICCpDZAIJTMkb98tvp9K/b8PXAck\ntQESoWSGtHpAte/jHQsC1wFJbYBEKJkhLR2hfXxwju/Dpkd9Df2lJE753uJ4izhU4X6XBir2\n5rs0Ukm+aw+e161Hr8S9B0/5U6+gjzSkkUFIK3N83bDei9CR2u7espDW6rt2i3wfijf6Gr2t\nMk6F3op4izhU6QGXBjrk3e/SSJX7XXvwvK49egUuDeTCU69Metcuv+9m375nvw2B6/gdSW34\nHYlQMv+OxKeM37rz4burA1cBSW2ARCipIZVOH3bT5ILgVUBSGyARSmpIEQGS2gCJECDJBUiE\nAEkmQCIFSJQAySpAUhsgEQIkuQCJECDJBEikAIkSIFkFSGoDJEKAJBcgEQIkmQCJFCBRAiSr\nAEltgEQIkOQCJEKAJBMgkQIkSoBkFSCpDZAIAZJcgEQIkGQCJFKARAmQrAIktQESIUCSC5AI\nAZJMgEQKkCgBklWApDZAIgRIcgESIUCSCZBIARIlQLIKkNQGSIQASS5AIgRIMgESKUCiBEhW\nAZLaAIkQIMkFSIQASSZAIgVIlADJKkBSGyARAiS5AIkQIMkESKQAiRIgWQVIagMkQoAkFyAR\nAiSZAIkUIFECJKsASW2ARAiQ5AIkQoAkEyCRAiRKgGQVIKkNkAgBklyARAiQZAIkUoBECZCs\nAiS1ARIhQJILkAgBkkyARAqQKAGSVYCkNkAiBEhyARIhQJIJkEgBEiVAsgqQ1AZIhABJLkAi\nBEgyARIpQKIESFYBktoAiRAgyQVIhABJJkAiBUiUAMkqQFIbIBECJLkAiRAgyQRIpACJEiBZ\nBUhqAyRCgCQXIBECJJkAiRQgUQIkqwBJbYBECJDkAiRCgCQTIJECJEqAZBUgqQ2QCAGSXIBE\nCJBkAiRSgEQJkKwCJLUBEiFAkguQCAGSTIBECpAoAZJVgKQ2QCIESHIBEiFAkgmQSAESJUCy\nCpDUBkiEAEkuQCIESDIBEilAogRIVgGS2gCJECDJBUiEAEkmQCIFSJQAySpAUhsgEQIkuQCJ\nECDJBEikAIkSIFkFSGoDJEKpBGn0hv1xyvMWxFvEoQpcG8ib59JI+/Pc26Z8l0ba796D51U9\nwh+9nYJ067bDcSr0Hoq3iEOVHnBpoErvfpdGOry/wqWByryuPXoFLg3kwlOvHLt2lLBrRwm7\ndlYBktoAiRAgyQVIhABJJkAiBUiUAMkqQFIbIBECJLkAiRAgyQRIpACJEiBZBUhqAyRCgCQX\nIBECJJkAiRQgUQIkqwBJbYBECJDkAiRCgCQTIJECJEqAZBUgqQ2QCAGSXIBECJBkAiRSgEQJ\nkKwCJLUBEiFAkguQCAGSTIBECpAoAZJVgKQ2QCIESHIBEiFAkgmQSAESJUCyCpDUBkiEAEku\nQCIESDIBEilAogRIVgGS2gCJECDJBUiEAEkmQCIFSJQAySpAUhsgEQIkuQCJECDJBEikAIkS\nIFkFSGoDJEKAJBcgEQIkmQCJFCBRAiSrAEltgEQIkOQCJEKAJBMgkQIkSoBkFSCpDZAIAZJc\ngEQIkGQCJFKARAmQrAIktQESIUCSC5AIAZJMgEQKkCgBklWApDZAIgRIcgESIUCSCZBIARIl\nQLIKkNQGSIQASS5AIgRIMgESKUCiBEhWAZLaAIkQIMkFSIQASSZAIgVIlADJKkBSGyARAiS5\nAIkQIMkESKQAiRIgWQVIagMkQoAkFyARAiSZAIkUIFECJKsASW2ARAiQ5AIkQoAkEyCRAiRK\ngGQVIKkNkAgBklyARAiQZAIkUoBECZCsAiS1ARIhQJILkAgBkkyARAqQKAGSVYCkNkAiBEhy\nARIhQJIJkEgBEiVAsgqQ1AZIhABJLkAiBEgyARIpQKIESFYBktoAiRAgyQVIhABJJkAiBUiU\nAMkqQFIbIBECJLkAiRAgySQB6c4+vgZyXvLk8CGP7AveDEhqAyRCSQlp5Aderzef80kTtu56\n4vbgNwhIagMkQkkJ6fqv/Z+8fbf4fir1/z5wMyCpDZAIJSOkQ31m3jVq8k6+ekC179odCwK3\nA5LaAIlQMkIqHPrUpk0PDz2wdIR27cE5vg9r+voa/GNBnPK8+fEWcah81wby5rk0UoFrA9XG\nbcrzqh5hX28bkFb169dvo/9S2cDlS0cCUq180tXGbUoySKXbt28/qF+87fW1+q7dosCd2LVT\nG3btCCXjrt32pyt9NdFNgQAAB2pJREFUz9SBK/P7bvZ9g/02BG4HJLUBEqFkhFQ8ZPqenZNH\nHuRTxm/d+fDd1YHbAUltgEQoGSHxLRMH3Txpr29Hb/qwmyYXBG8GJLUBEqGkhBQlQFIbIBEC\nJLkAiRAgyQRIpACJEiBZBUhqAyRCgCQXIBECJJkAiRQgUQIkqwBJbYBECJDkAiRCgCQTIJEC\nJEqAZBUgqQ2QCAGSXIBECJBkAiRSgEQJkKwCJLUBEiFAkguQCAGSTIBECpAoAZJVgKQ2QCIE\nSHIBEiFAkgmQSAESJUCyCpDUBkiEAEkuQCIESDIBEilAogRIVgGS2gCJECDJBUiEAEkmQCIF\nSJQAySpAUhsgEQIkuQCJECDJBEikAIkSIFkFSGoDJEKAJBcgEQIkmRyE9I+X4zT72ZfiLeJQ\n8+a6NNBLz85yaaSXZ73o0kDznnXt0XvepYFceOq94Bikz9+O14RRr8ddxpkWLXJroFH3ujTS\n2wvecmmgmaOmuTTSWwtcGujtB0a9pnqIZU5Bit/4HNd+lLtVRc6tif4WHO+jnDcT/S043n05\nf7g3GCDZD5BSI0BK8gApNQKkJA+QUqPaBQmhIyFAQsiBAAkhBwIkhBxIGaSSJ4cPeWSfcWXn\nPf1UjeNqpu0I275Uzdge87ak8nZFbI6rm6UM0qQJW3c9cbv+Zqcvhk2vFZDM22HevlQtsD3m\nbUnh7YrcHFc3SxUkb98tvv8G+n/vv/KvP9bUCkim7QjbvlTN2B7ztqTydkVsjrubpQrS6gHV\nvo93LDCu1g5Ipu2I2L5Uzb895m1J7e0K2xx3N0sVpKUjtI8PzjGu1jpIEduXqvm3x7wtqb1d\nYZvj7mYpgzRS+1iLIYVvX6qmP/NM25La2xW2Oe5ulipIa/WfpYuMq7UOUsT2pWr+7TFvS2pv\nV9jmuLtZqiDl993MeVG/DcbVWgcpYvtSNf/2mLcltbcrbHPc3SxlL39PGb9158N3V/Pl73Ne\n4F3ez+stVzWUawW2Q9umwPalcoHtMf9bpfB2RW6Oq5ulDFLp9GE3TS7g/PGJnI/uo/WeqqFc\nK7Ad2jYFti+VC2yP+d8qhbcrcnNc3Sy8RQghBwIkhBwIkBByIEBCyIEACSEHAiSEHAiQEHIg\nQELIgQApkeUyoynOrzuv7Sje/aTgQGv0C/V6hpZ4sMk254c9UgOkRJbL7nna338cX3XVFV3L\n4kA6fGnOQcfHPVIDpEQWfH4736vsMx4HEt/omaZq+CMuQEpkJkjde3xw3Pmcf9Yrs8EZL/iu\nV+e2rtfl7dvSOe/WTbu/X1NuurfHhd9eltn8Rm02j+UXNW45cDPv3rRCW+ziZof8qzvc6SJu\nQHo97f4wSF8bu5M/cn5DiwMubm2tDpASmQnSZV1PfnYJ/yT9og+Wj2W+HxST2aBlC3JObWiG\nFLq3Z5uzV+x7K324z1HaFa+90P7YPS+wt3xL7fGM01f3OXuR65A+rzc6/CdS8QpfS5ofV8j5\nhyxFjylPvgApkZkg9WSLfR/P6Fjq+9g3s7z62A7VnP9ep5EZUvBe39Jfal/TivOz2lVy/lXd\nf5Q07uO75Wlm/Lb1ENvJ/ZA2Ht3vsDbQ4m3+6hq7diPrfeX7WFp3tItbW6sDpESWy97/3d9B\n3rOub59sH7ur3Ncstm4Hu11b4FwzpNC9vGdD7bbhHp7HbjPWNbKOb0evRxfj2jUttY/dT9p7\nwkXl3PTyINMhPcfm+j93Pd21ba3lAVIiCz6/P/X/dOHfBa4v/opN0ha4zgwpdC/v2Va7bTTj\nP7KHjXWtYk/yXWmPG9fO6ax97H58DluqD/TYO/4y/JBW1x2jL3bZce5saO0PkBJZLpv2gb88\nncZ3bNQaf961LFdboH84pMC9IUg/sf8LrKxTVz4jfbdx5eQLtI/dWed2JxTyyFft9rQ6t0K/\nel1j5dt4hARIicz8O1Jb34d8Nty4ukXfZTvdB+kM/+7auU1N94YgFTP//Djb/+B8CttwzlWB\nBYyfSMcWrU6/kUdAOtSj5U5jsctaO79RR2aAlMgiIfFzsrXTss1/sLKqWccqzn9J80G6rFm1\n77enBk1N94Yg8dOaF3P+s7aDtzt9CHsjsLrA70icP8JeioB0R53PA4vhdySnAqREVgPSZxld\n5y+bmDFCe9nt2rdmn6D9RJrBpuz99tLOTc33hiAt8Zz7+pxOLfb4rl7DssoCq3sw8KodP9y9\n8a9hkBawG7QXwFds4by03kj3NrZ2B0iJrAYkvuryzIxOj1f6nv8TjqnX9f0bfZAq7m5dr9sH\nt2ea7w1B4h+e17DFtb9qV99mtwTX/Kn2c0j/g+z27JwKM6S7jJcscjn/KPQjDNECpKRuUCMb\nC7/PvgperuxwicBX3NisxPa3hCwDpKTODqRDZ51nujaffRH3K372PB53GSQWICV14pD++95V\n6etM16t6nR5vRs6qnmek/qSdyRIgJXXikF5Ia/dh2A3etqPifMlDR2+V+Z6QVYCEkAMBEkIO\nBEgIORAgIeRAgISQAwESQg4ESAg5ECAh5ED/D63wSEF080UgAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      },
      "text/plain": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# basic plot, with data point values\n",
    "basic_plot +\n",
    "    geom_text(aes(label=paste(Frequency.in.kHz, SPL, sep=\",\")), check_overlap = TRUE,  hjust=0, nudge_x = 0.05)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting vector graphics\n",
    "To use the plot in a publication, you might need to save your plot in scalable vector graphics format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "svg(filename=\"./data/Audiogram_30.svg\") # change this to where you want to save the graphics file\n",
    "basic_plot\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Smoothing the curve\n",
    "You might want to fit a function to your audiogram instead of joining data points with straight lines (see Wright 2015 for an example of why this might be useful). In the following example, I applied locally estimated scatterplot smoothing (LOESS) to the data points."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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DsAlYwsBwfYtWzgsZj8tTv6YZVGsENVpGjprnVDz2ujrfdc4TQUL37i9Yov1xrH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\n/+abbwYOtmzZcvHixdO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1GtdrHuY/2x89rnhhfXivDNfCgiEEN3ldpFV26o136s\nz8v8b5l2RVHmz5+9ZUtJO8N1cPIo2K1Zs2by5MkLFy4s3Thu3Ljx48cvXryYYAcAcPHhhXdq\nlRiaaunczLZoa+iZrJKhuxMXNJP+L3zy8OL2je0+2YqoxqG7yu4Zs1U19/+F5RcUC6WCc9BX\nrmT7qC7Iw6Ph7PPnzz/00EPl24cMGfLdd9/5uiQAQHBLSor0YUiqF+dYMKboyVJPRL1RoJr5\ntmHDHr3Dp7Pz+iqP+nD9/3hPf+GKWq01CFUFx+uEhETnC4br4OJRsNNqtUVFReXbrVarRuOj\nm5QAAHLxYbxTq8WIHpZXxxS5pix2OMSGz3TT1xl+zi/3RK0quHw53x/xzrvVbjmg+/ybEOfr\nWg36lflUpVK98AJP9URZHgW7jh07/v3vf7dYLKUbTSbTm2++2blzZ/8UBgCQgQ/jXbMk+9/H\nFd3b4ub8bd9c0kxaGX78vI+HGHyY7bxOisfPa9Z9XDIdsUolVv3znZSUNq5PNRrN1KkznHdO\nMFyH0jy6xm7atGmDBg1q1qzZwIED69atqyhKZmbmBx98kJ2d/dFHH/m7RABAsPPVhXcRocqL\njxe/d0i3drfeZhdCiNwC1Yy3DK3q2wfeY+2eYvXtXHeiCrGpKj/vlVz1gs1hrhPNI3taUluJ\nXbsOHD9+ePfunXFxcUOGjIiJqeX1+iExj4LdwIEDt2zZMm3atBUrVrga27Ztu2rVqr59+/qt\nNgCAPHx1z6xKJYZ0taQ0sC94J/RKbsl5p+8yNd9lav65U9+/o/XBu63x0b6c7k5UMt5V8Wcs\nuWGiuOQUc5fmtsd7lTwTtn37u9u3v7v0wgzXoQxPJygeOnTo0KFDL1++nJWVpVKp6tevn5CQ\n4NfKAADy8VW8a5Zkf2N80ZJtoQdP3TyQ5RaoNu3Vbd6v69Lc9tA9lvZNqvW22cuX8xVFyc8v\njIqKqsq2lm7XX8guCax173JMHm5S+/IyQkiucg8QTkpKSkpK8lMpAIA7hE/infO07DcXNR98\nqTt4Smv/ZZDO4RAHT2kPntI2SbQ/0t3SLcWm8emExn617aDus69Lbpgw6JUXHy826G85Yx/D\ndSjPo85+/fr1UaNGJSQkaDQaVTn+LhEAICWf5JI2jex/e7T4n5MKnuhtjo38VQY6n615LT0s\nbWn4B1+GWGy3WkEN8t/DIWt23rxhYtJQU/14n07ogjuARyN2aWlp6enpqampAwYMCAkJ8XdN\nAIA7hK/OzMZGKk/0sjx2n+XQae0HX+i+uaRRfsl4V26o/+/D0I2f6Qd1sTx0jzUizGePrPCt\nLft1az/Wu8p+7D5L15bu0ijDdaiQR8Fux44dzz///IIFC/xdDQDgDuSreKdRi26tbN1a2S5c\nUW/Zr9t3MsR1fjavUPWfT/VbM3Sj+5n7dbTWtLNNb32if2evzvX2/vbWJ3qb3SxPqsOteHQq\nVlGUHj16+LsUAMCdzIcz3jVOcDw33LTiL4UPdbHoQ24O0RWaVEu3h858O8x1O23AKYpY9d9f\npboH77Y+O4QbJuAlj3p2t27dvv32W3+XAgCAM975JOElxDjG/8b8z0mFT/SyRJY6A3vsvPbP\nbxrS9+scgT4r63CIpdtDtx+6meoGdbE889BtUh3DdXDDo2C3fPnyjRs3bt26VVEC/SUAANwZ\nfBVfogzKE73N/5xUOKybRf3LQc9sVa3drZ++1vDTz74cusvNvfHGG/Oefvr3CxfOM5kqeBRn\naTa7eC09bNfRm1eu/+5+87jfmGvaaWIEF3fX2DVq1KhkIa3WZrMNGzYsNDS0/PR1Fy9e9E9t\nAIA7mq+uvRNChOqU0f3M3VNsS7aF/nCtJMx9c0kzcYXh9w9YHr7XUvVTn1u3bn722XE2m00I\n8eGH7y1duvCtt9J79OhV4cK5BaqF74a5HoamUomn+psHd7VUuHBpDNfBPXfBrmnTpm7eAgBQ\nDXz1ODIhRPO69jfGF278TL9lv855X4XZqvrnR/qM77SThpoSa3k/t4jRmDdp0nhnqnOyWCx/\n/OPjp079qNWWPdQe/l67eFtobmFJllSrxZ8Hmfp2tN52K6Q63Ja7YLd79+5qqwMAgFvxYbYL\n0Yg/PGDunmJdvDXswpWSobtvf9A8u8Iwur/5wU5e3jC7du0/rdayyay4uGjbtvRHHnnc1WK1\nibUf67cf0rmubNKoxXPDi3u0DoZ59hAMPL22oKio6KeffnK+Li4u/ve//71w4cLz58/7rTAA\nAG7y4T2zQogmiY6/jyv83f1m10Mpii2qN98Pnb0+7Hq+N8nup59+rLD9hx8u3nx9Tf3c6vD3\nDt5MdbVjHP87qsjDVMdwHTzhUbA7depU48aN165dK4Sw2Ww9e/YcPXr0888/36lTp6NHj/q5\nQgAASvgw3GjU4vGelkXjC5sk3nyk7JGz2gnLwv97uNJT8bdr17HC9nvvTXW++PREyPOrDRev\n3DzsdkuxLRpXlNLAZw+0BYSHwe6ll15KSEh49NFHhRAbN2786quv3nzzzbNnz7Zu3Xru3Ll+\nrhAAgJt8O3DVsLbjtbFFj3S/ecNskVn15vuhc/9fWPaNStww+9hjv7vrrrgyjQ0aNOzWrWfm\nNfWs/4S98W6oyVIyFhimUyYOMb3waLHnj8FguA4e8qjX7tu374UXXkhOThZCbNmypU2bNn/6\n05+Sk5MnTJhw6NAhP1cIAMCv+Pa0bIhGjOprnv/HojqxN2+eOHhK+8yy8JU79HmFHp2ZVavV\n7767s169+q6Wpk2b/2fj7v/boZ+4IvzI2ZtXtDdLsr8xvqhvh9vfKuFCqoPnPHqkWG5ubp06\ndYQQdrt9z549Tz/9tLM9Pj7+ypUrfqwOAIBb8OEdFUKIlvXt//hT4YY9+ncPlExcbLOL97/Q\n7T4W8tA91kfvsxj0txldS05uevDgN2fOnD5//kzHTl2P/Vh35ju/yoUqlRjUxTK6n1mrqURh\npDpUikfBLiEh4fz58/fff/8nn3xy48aNAQMGONszMzPvuusuf5YHAMAt+Tbb6bRiVF9zhya2\nVf+9OdedyaJK36/75HjIE73M/TpZNbc70VWnTlKeqs2sd/SXr/9q0eQ69qcHmLmiDv7mUbDr\n37//9OnTz549u2HDhuTk5J49ewohrl69unjx4u7du/u5QgAAbqn0JMZGY9727e9evvzj/ff3\n69z5Xu9W2L6JfUla4cfHQ9Z/qnfdIXujQPXmB6Hr9+hb1re3bmhvVd+eXMfuCnmKIjJz1N/+\noDl5SfP1xfCf8381IlcrQvlDH/MD7a1ezIHMcB0qy6NgN2fOnJMnT86fPz8uLm779u0ajUYI\nMXHixEuXLr311lt+rhAAgNtISopcsmTJc88955wieNGiBcnJzXbu3BsaavBibWq16NfR2qut\n9b2DuvT9ukJTSSLLLVQdPKU9eEorhNCHKM3rOprVtV++rv4uU1PhpXg6rRiSahnRwxKm8+aB\nnKQ6eMGjYFenTp2MjAyj0RgWFhYSUnIT+PPPP7948eLyTxgDAKCaHTt27K9//avDcfPuh3Pn\nvn/88SHbtu3yep06rRjRwzLgbmv6ft17h3TWX082Z7aqvr6o+friLS+Xu6e5bdwAc4K3T7Mg\n1cE7HgU7J51Od+zYsR9//PG+++6Li4vr0KFD+cekAABQ/WbMmFH4KtIrAAAfr0lEQVQ61Tkd\nPvyFyWQKDQ2typojwpRRfc0D77HuPqb99gftmR/VxZZbnlLVqEWTREu7xqJ7iq1pkveX05Hq\n4DVPk9nChQtnz56dn58vhMjIyIiLi5s5c+bly5dXrVpFvAMABFZmZmb5RkVRsrJ+TE72wYPO\n46MdT/SyCGFxOMSPOepvMzXfZWpOXtJczVXrQ5QmiY6UBvZWDeytG9jsFmNUVFTVtwh4x6NM\ntmrVqueff37w4MEDBw5MS0tzNrZo0WLBggUpKSlTpkzxZ4UAANxGgwYNjh8/XqZRpVLde29r\nnU7nwztn1WrRoLajQW3HgLutQojcAlWkQSl1F4WSb6nqJhiuQ1V4NEHx0qVL09LStm3bNmrU\nKFfjk08+OWXKlNWrV/utNgAAPPI///M/anXZI1r37t11Op34ZUJjfwSmmAjlthOgVAqpDlXk\nUX88c+bMI488Ur69d+/eFy5c8HVJAABUTrt27ZYsWeK6vU8IkZKSsmtX2TsnanhsquHlISh4\ndCo2KirKZDKVb8/LywsLC/N1SQAAVNqECRP+8Ic/pKenX7p06Te/+c2991Y8j13pee9qFFId\nfMKjYNeuXbvXX3+9T58+KtXNW4F+/vnnV155pWvXrn6rDQCASoiKiho9erQnS9a0eEeqg694\nFOxeeumlvn37tmvX7qGHHhJCrFq1asWKFe+++25xcfGKFSv8XCEAAH7h2yeSVaWMQJcAeXh0\njV3v3r0/+uijyMjIxYsXCyH+9a9/rV27tmXLlrt27eKRYgCA4OWnmyoqVUAAtw75eDoFXZ8+\nfY4cOXL16tXLly8LIRo2bFirVi1/FgYAQDUJyNAdkQ7+4NGIXbdu3T788EMhRO3atTt06NCh\nQwdSHQBAJtUcs0h18BOPgl1mZuapU6f8XQoAAAFUbadlSXXwH4+C3bJly1avXr1161ar1erv\nggAACCC/pq6AX9IH6Xl0jd3rr7+u1WqHDRum0+ni4uJKzwAphLh48aJfSgMAIBD8NBkKkQ7V\nwKNg53A44uPj+/Tp4+9qAACoIXwb70h1qB4eBbt9+/b5uw4AAGqgqt8wS6RDdfJ0uhMAAO5M\nXg/dEelQ/Ty6eeL69eujRo1KSEjQaDSqcvxdIgAAAVfZ+x5IdQgIj0bs0tLS0tPTU1NTBwwY\nUObOCQAA7hyuuFbhAF5SUmReniMmhkiHgPEo2O3YseP5559fsGCBv6sBACAoVDggpyhK9VcC\nlObRqVhFUXr06OHvUgAAAFAVnj5S7Ntvv/V3KQAAAKgKj4Ld8uXLN27cuHXrVgaZAQAAaix3\n19g1atSoZCGt1mazDRs2LDQ0NCEhocxiPHkCAACgJnAX7Jo2bermLQAAAGoUd8Fu9+7d1VYH\nAAAAqsjdNXZdu3bdvHlztZUCAACAqnAX7A4dOpSdnV1tpQAAAKAqPLorFgAAADUfwQ4AAEAS\nt3mk2J49e2w2m/tlJk2a5Lt6AAAA4KXbBLv09PT09HT3yxDsAAAAaoLbBLuJEycOGzasekoB\nAABAVdwm2DVr1qx3797VUgkAAACqhJsnAAAAJEGwAwAAkIS7YDd+/Pg2bdpUWykAAACoCnfX\n2K1YsaLa6gAAAEAVcSoWAABAEgQ7AAAASRDsAAAAJHGbeexqMofDYTKZiouL/boVu90uhDCZ\nTCqVyq8bkpLNZlMUJdBVBCXnfnM4HP7u4bJy/n5Qq/nbtdKcj5G0Wq2BLiRY8bX1GgdcT1it\nVvcH1iAOdkIIlUrl7/9+5/qrYUOyYtdVEXvPa/Q977h2GnvPO3S8KmIHunfbnRPEwU6tVuv1\n+tDQUL9uxWKxCCH0ej1/+ntHURR//x9JSVGUwsJCtVrN3vOO2WzW6/UajSbQhQQfs9lsMplC\nQkLoe15QFMVkMrHrvGOxWOx2Owdc9zQajftsx74DAACQBMEOAABAEgQ7AAAASRDsAAAAJEGw\nAwAAkATBDgAAQBIEOwAAAEkQ7AAAACRBsAMAAJAEwQ4AAEASBDsAAABJEOwAAAAkQbADAACQ\nBMEOAABAEgQ7AAAASRDsAAAAJEGwAwAAkATBDgAAQBIEOwAAAEkQ7AAAACRBsAMAAJAEwQ4A\nAEASBDsAAABJEOwAAAAkQbADAACQBMEOAABAEgQ7AAAASRDsAAAAJEGwAwAAkATBDgAAQBIE\nOwAAAEkQ7AAAACRBsAMAAJAEwQ4AAEASBDsAAABJEOwAAAAkQbADAACQBMEOAABAEgQ7AAAA\nSRDsAAAAJEGwAwAAkATBDgAAQBIEOwAAAEkQ7AAAACRBsAMAAJAEwQ4AAEASBDsAAABJEOwA\nAAAkQbADAACQBMEOAABAEgQ7AAAASRDsAAAAJEGwAwAAkATBDgAAQBIEOwAAAEkQ7AAAACRB\nsAMAAJAEwQ4AAEASBDsAAABJEOwAAAAkQbADAACQBMEOAABAEgQ7AAAASRDsAAAAJEGwAwAA\nkATBDgAAQBIEOwAAAEkQ7AAAACRBsAMAAJAEwQ4AAEASBDsAAABJEOwAAAAkQbADAACQBMEO\nAABAEgQ7AAAASRDsAAAAJEGwAwAAkIQ20AX8SkFBwcqVK0+cOGG1Wlu0aJGWlla7du1AFwUA\nABAcataI3aJFi65evTpz5szXXnvNYDC88sorDocj0EUBAAAEhxoU7HJycr788stx48Y1btw4\nKSkpLS0tKyvr66+/DnRdAAAAwaEGBbvvv/8+JCSkcePGzrcRERH16tU7ffp0YKsCAAAIFjXo\nGjuj0RgZGalSqVwt0dHReXl5rrdnzpzZvHmz621xcXFRUVFBQYFfq7LZbEKIwsLC0oXBQ3a7\nXVEUu90e6EKCj6IoQgi73e7vHi4ru91eVFTE19YLzi+s2Wzmm+sdh8PB19Y7HHA9YbVanQeI\nW6lBwU4I4f7/Misra8uWLa63ycnJZrPZZDL5vy5hNpurYSuycn5X4QWHw1E9PVxKfG2rwmaz\n8c31Gl/bquCb614wBbuYmBij0agoiive5eXl1apVy7VA586d33rrLdfbhQsXRkVFxcTE+LWq\nwsJCq9UaFRWlVteg09bBwmKxKIqi1+sDXUjwURQlLy9Pq9VGREQEupagVFBQYDAY+Np6wWKx\nFBUVhYWF8c31gqIoBQUFkZGRgS4kKHHA9YTVanW/f2pQsGvWrJnVaj137lzTpk2FEEajMTMz\ns1WrVq4FIiMjS7/VaDQajUar9e+P4EyZWq2WfuYFm82mKIq//4+k5PyDTKVSsfe8o1KpnL8i\nAl1I8HGegVWr1fQ9LzjHJth13uGA6wn3w3WiRt08ERsbm5qaumzZsgsXLmRlZb3xxhvJyckp\nKSmBrgsAACA41Ky/KiZOnLhy5cpZs2bZ7fbWrVtPnz6dKygBAAA8VLOCncFgmDRpUqCrAAAA\nCEo16FQsAAAAqoJgBwAAIAmCHQAAgCQIdgAAAJIg2AEAAEiCYAcAACAJgh0AAIAkCHYAAACS\nINgBAABIgmAHAAAgCYIdAACAJAh2AAAAkiDYAQAASIJgBwAAIAmCHQAAgCQIdgAAAJIg2AEA\nAEiCYAcAACAJgh0AAIAkCHYAAACSINgBAABIgmAHAAAgCYIdAACAJAh2AAAAkiDYAQAASIJg\nBwAAIAmCHQAAgCQIdgAAAJIg2AEAAEiCYAcAACAJgh0AAIAkCHYAAACSINgBAABIgmAHAAAg\nCYIdAACAJAh2AAAAkiDYAQAASIJgBwAAIAmCHQAAgCQIdgAAAJIg2AEAAEiCYAcAACAJgh0A\nAIAkCHYAAACSINgBAABIgmAHAAAgCYIdAACAJAh2AAAAkiDYAQAASIJgBwAAIAmCHQAAgCQI\ndgAAAJIg2AEAAEiCYAcAACAJgh0AAIAkCHYAAACSINgBAABIgmAHAAAgCYIdAACAJAh2AAAA\nkiDYAQAASIJgBwAAIAmCHQAAgCQIdgAAAJIg2AEAAEiCYAcAACAJgh0AAIAkCHYAAACSINgB\nAABIgmAHAAAgCYIdAACAJAh2AAAAkiDYAQAASIJgBwAAIAmCHQAAgCQIdgAAAJIg2AEAAEiC\nYAcAACAJbaAL8J7dbjcajbm5uf7eihAiLy9PpVL5dUNSUhRFCGE2mwNdSPBx7jqbzebvHi4r\n5+8HvrZecPa94uJivrnesdvtfG294zzgGo3GQBdSo1mtVofD4WaBIA52arU6PDw8MjLSr1sp\nKCiwWq0RERFqNaOblWY2mxVFCQ0NDXQhwUdRlNzcXI1G4+8eLqv8/HyDwaDRaAJdSPCxWCyF\nhYV6vZ5vrhcURcnPz+dr6x3nATc8PJwDrhtWq9X9/gniYKdSqTQajb9/cTv/4tdoNPQzL6jV\nakVROLh6wTlq4uzkga4lKFXP7wcpOX/XqdVq9p4XFEXha+s1DriecD9cJ7jGDgAAQBoEOwAA\nAEkQ7AAAACRBsAMAAJAEwQ4AAEASBDsAAABJEOwAAAAkQbADAACQBMEOAABAEgQ7AAAASRDs\nAAAAJEGwAwAAkATBDgAAQBIEOwAAAEkQ7AAAACRBsAMAAJAEwQ4AAEASBDsAAABJEOwAAAAk\nQbADAACQBMEOAABAEgQ7AAAASRDsAAAAJEGwAwAAkATBDgAAQBIEOwAAAEkQ7AAAACRBsAMA\nAJAEwQ4AAEASBDsAAABJEOwAAAAkQbADAACQBMEOAABAEgQ7AAAASRDsAAAAJEGwAwAAkATB\nDgAAQBIEOwAAAEkQ7AAAACRBsAMAAJAEwQ4AAEASBDsAAABJEOwAAAAkQbADAACQBMEOAABA\nEgQ7AAAASRDsAAAAJEGwAwAAkATBDgAAQBIEOwAAAEkQ7AAAACRBsAMAAJAEwQ4AAEASBDsA\nAABJEOwAAAAkQbADAACQBMEOAABAEgQ7AAAASRDsAAAAJEGwAwAAkATBDgAAQBIEOwAAAEkQ\n7AAAACRBsAMAAJAEwQ4AAEASBDsAAABJEOwAAAAkQbADAACQBMEOAABAEgQ7AAAASRDsAAAA\nJEGwAwAAkATBDgAAQBIEOwAAAEkQ7AAAACRBsAMAAJAEwQ4AAEAS2oBsdeLEiRcvXnS9DQ0N\n3bRpkxCioKBg5cqVJ06csFqtLVq0SEtLq127dkAqBAAACDqBCXYFBQXjxo3r2rWr861aXTJw\nuGjRooKCgpkzZ+r1+vXr17/yyitLlixxfQoAAAA3ApOZ8vPzExMT434RGxsrhMjJyfnyyy/H\njRvXuHHjpKSktLS0rKysr7/+OiAVAgAABJ0AjNhZrVaz2ZyRkfH222/n5+c3bdr0ySefrFu3\n7vfffx8SEtK4cWPnYhEREfXq1Tt9+nT79u2rv0gAAICgE4BgV1RUFBMTY7PZnnnmGSHEhg0b\npk2btnz5cqPRGBkZqVKpXEtGR0fn5eW53h48eHDevHmut+Hh4Xl5eREREX6t1uFwCCFyc3NL\nFwYPKYoihDCZTIEuJPg4d53NZrtx40agawlKDofDaDQGuoqg5Ox7RUVFfHO943A4+Np6x3nA\nLX3cR3lWq9W5o26lOoLdvn37Xn/9defrefPmtWrVat26da5Pp06dOmrUqAMHDgghCE8AAABe\nq45g16lTp8WLFztfJyYmlvk0LCwsPj4+JyenSZMmRqNRURRXvMvLy6tVq5Zrya5du27bts31\ndvz48dHR0aUX8Aej0WixWGJiYriHwwsmk0lRlLCwsEAXEnwURbl+/bpWq42Ojg50LUHJOZyv\n0WgCXUjwMZvN+fn5BoOBb64XFEXJy8uLiYkJdCFByXnAjY6O5oDrhtVqdb9/qmPfGQyGhr/Q\n6/WXLl1aunSpzWZzfmoyma5du5aYmNisWTOr1Xru3Dlnu9FozMzMbNWqVTVUCAAAIIEAXGMX\nGxubkZFhs9lGjhxpt9vXrVsXERHRrVs3vV6fmpq6bNmyiRMn6nS61atXJycnp6SkVH+FAAAA\nwSgAwS4yMnLOnDlr1qyZNGlSSEhIixYt5s2bp9frhRATJ05cuXLlrFmz7HZ769atp0+fzlV3\nAAAAHgrMBMVNmjSZM2dO+XaDwTBp0qTqrwcAAEA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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      },
      "text/plain": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(audiogram, aes(Frequency.in.kHz, SPL)) +\n",
    "    labs(x = label_x, y = label_y) +\n",
    "    geom_smooth(method='loess', formula = y ~ x, fill='navy', alpha=0.1) +\n",
    "    geom_point() +\n",
    "    scale_x_log10(limits = limits_x) +\n",
    "    scale_y_continuous(limits = limits_y) +\n",
    "    theme_bw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting data from the web service\n",
    "Instead of downloading data, you can plot data directly from the database backend. The backend is accessible through a web service, and each audiogram is identified by its database id. This id is displayed on each audiogram page (fig. 2).\n",
    "\n",
    "![](images/id.png)\n",
    "\n",
    "*Fig. 2: Getting the id of an audiogram*\n",
    "\n",
    "In this example, the audiogram's id is \"30\". You can then tell R to read the data from the web service as shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "# the backend URL of this audiogram\n",
    "url <- paste0(\"https://animalaudiograms.museumfuernaturkunde.berlin/api/v1/download?id=\", 30) # change the id to your id\n",
    "audiogram <- read.table(url, header=TRUE, sep=\",\")\n",
    "# proceed to plot the data as shown in the examples above ..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The documentation for the data web service can be found here: https://animalaudiograms.museumfuernaturkunde.berlin/apidoc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## References\n",
    "Wright, A.J., 2015. Sound science: maintaining numerical and statistical standards in the pursuit of noise exposure criteria for marine mammals. Frontiers in Marine Science, 2, p.99."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "ir"
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  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "3.6.1"
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 },
 "nbformat": 4,
 "nbformat_minor": 2
}