Audio_Features_and_Lyric_Spread_Metrics.ipynb

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  "metadata": {
    "colab": {
      "name": "Audio_Features_and_Lyric_Spread_Metrics",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyPCXa/k9PfiL+q+B+9PFr3u",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "ir",
      "display_name": "R"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/stelmanj/MusicAndLanguage/blob/master/Audio_Features_and_Lyric_Spread_Metrics.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1yux1RZU1H5i",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "install.packages(\"factoextra\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q-oxdfGIfmEa",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "install.packages(\"ggridges\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SRUiowWMfhCp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "library(tidyverse)\n",
        "library(ggridges)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F0OgRc331GJG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "library(cluster)\n",
        "library(factoextra)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nuDv34iAWmct",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "library(parallel)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VgfRX-c9UNPG",
        "colab_type": "text"
      },
      "source": [
        "# Introduction\n",
        "\n",
        "This is kind of a spin-off of the investigation done in \"A Brief Analysis of Lyric Metrics.\" We take the same 100-song sample used in that study. The question we examine here is how do the songs from widely spoken languages vary in comparison to songs from non-widely spoken languages? \n",
        "\n",
        "In this chapter, I'll look at the spreads of a few audio and lyric features from 100 songs in 5 different languages (20 songs written in each language). I want to know if languages spoken across a wider geographical area produce a wider range of patterns in audio features and song lyrics. In other words, do the songs written by, say, Spanish-speaking artists vary more than songs written by, say, Indonesian-speaking artists? less? \n",
        "\n",
        "The following two languages make up the *widely spoken* group:\n",
        "\n",
        "* English (en)\n",
        "\n",
        "* Spanish (es)\n",
        "\n",
        "The following three languages make up the *not widely spoken* group:\n",
        "\n",
        "* Indonesian (id)\n",
        "\n",
        "* Dutch (nl)\n",
        "\n",
        "* Swahili (sw)\n",
        "\n",
        "The distinction between the first and second groups is decided by whether a language is an official national language on at least three continents. \n",
        "\n",
        "I've used the Spotify Web API and the natural language processing package $\\verb|quanteda|$ to calculate the following audio/ lyric features for audio and first few lines of lyrics in each of the 100 sampled songs. (20 songs were randomly sampled from each of the 5 languages.)\n",
        "\n",
        "* Danceability (number between 0 and 1)\n",
        "\n",
        "* Energy (number between 0 and 1)\n",
        "\n",
        "* Key (category represented as a number between 0 and 11)*\n",
        "\n",
        "* Loudness (number between -25 and 0)\n",
        "\n",
        "* Speechiness (number between 0 and 1)\n",
        "\n",
        "* Acousticness (number between 0 and 1)\n",
        "\n",
        "* Valence (number between 0 and 1)**\n",
        "\n",
        "* Tempo (number between 50 and 250)\n",
        "\n",
        "* Type/Token Ratio, or TTR --> (# distinct words / # total words) in the first few lines of lyrics\n",
        "\n",
        "*Each of the 12 numbers corresponds to one of the 12 keys on the chromatic scale, (e.g.  C, C\\#, D, ..., A, A\\#, B).\n",
        "\n",
        "**I'm not entirely sure how Spotify measures valence, but I have the understanding that is an overall measure of how \"happy\" a song decidedly is based on other features.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dqLw5e3mTya0",
        "colab_type": "text"
      },
      "source": [
        "Let's load in the data frames we made in DataPreparation."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bjJPTMX_UQnr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "esinw_df <- read.csv(\n",
        "  \"https://github.com/stelmanj/MusicAndLanguage/blob/master/AFS_esinw.csv?raw=true\",\n",
        "  row.names = 1, stringsAsFactors = FALSE) "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AV_XxU_oUnud",
        "colab_type": "text"
      },
      "source": [
        "Let's make sure it looks right."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kTS-dRhyUdfC",
        "colab_type": "code",
        "outputId": "fa0d9fdc-708a-48ac-a4e7-cadd3cdc6221",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "head(esinw_df)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  sid                    danceability energy key loudness speechiness\n",
              "1 1AvAl5fj5mhoLHVo0to1xr 0.697        0.298   0  -16.929  0.0319     \n",
              "2 5pz1Q9QFHWsUBZiJ73Jx3j 0.579        0.815  10   -7.603  0.0332     \n",
              "3 6eVj2ARRKQiy2Gb9Za7jnf 0.701        0.866   9   -4.632  0.0298     \n",
              "4 5uswrfRMA6MpRICWXEE1Uq 0.631        0.921   9   -3.248  0.0447     \n",
              "5 2ef2rQVgykMmHX47tjecDp 0.542        0.704   1   -7.953  0.0364     \n",
              "6 23lfdALTPtgNbJrzhRnfks 0.469        0.603  10   -8.358  0.0333     \n",
              "  acousticness valence tempo   lang Types Tokens TTR      \n",
              "1 0.0411       0.233   113.571 nl   64     90    0.7111111\n",
              "2 0.1000       0.556   155.577 nl   95    188    0.5053191\n",
              "3 0.0535       0.933   119.949 nl   63    140    0.4500000\n",
              "4 0.1380       0.370   108.055 nl   66    114    0.5789474\n",
              "5 0.0215       0.253   119.942 nl   45     87    0.5172414\n",
              "6 0.0893       0.231   131.739 nl   58    133    0.4360902"
            ],
            "text/latex": "A data.frame: 6 × 13\n\\begin{tabular}{r|lllllllllllll}\n  & sid & danceability & energy & key & loudness & speechiness & acousticness & valence & tempo & lang & Types & Tokens & TTR\\\\\n  & <chr> & <dbl> & <dbl> & <int> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <chr> & <int> & <int> & <dbl>\\\\\n\\hline\n\t1 & 1AvAl5fj5mhoLHVo0to1xr & 0.697 & 0.298 &  0 & -16.929 & 0.0319 & 0.0411 & 0.233 & 113.571 & nl & 64 &  90 & 0.7111111\\\\\n\t2 & 5pz1Q9QFHWsUBZiJ73Jx3j & 0.579 & 0.815 & 10 &  -7.603 & 0.0332 & 0.1000 & 0.556 & 155.577 & nl & 95 & 188 & 0.5053191\\\\\n\t3 & 6eVj2ARRKQiy2Gb9Za7jnf & 0.701 & 0.866 &  9 &  -4.632 & 0.0298 & 0.0535 & 0.933 & 119.949 & nl & 63 & 140 & 0.4500000\\\\\n\t4 & 5uswrfRMA6MpRICWXEE1Uq & 0.631 & 0.921 &  9 &  -3.248 & 0.0447 & 0.1380 & 0.370 & 108.055 & nl & 66 & 114 & 0.5789474\\\\\n\t5 & 2ef2rQVgykMmHX47tjecDp & 0.542 & 0.704 &  1 &  -7.953 & 0.0364 & 0.0215 & 0.253 & 119.942 & nl & 45 &  87 & 0.5172414\\\\\n\t6 & 23lfdALTPtgNbJrzhRnfks & 0.469 & 0.603 & 10 &  -8.358 & 0.0333 & 0.0893 & 0.231 & 131.739 & nl & 58 & 133 & 0.4360902\\\\\n\\end{tabular}\n",
            "text/markdown": "\nA data.frame: 6 × 13\n\n| <!--/--> | sid &lt;chr&gt; | danceability &lt;dbl&gt; | energy &lt;dbl&gt; | key &lt;int&gt; | loudness &lt;dbl&gt; | speechiness &lt;dbl&gt; | acousticness &lt;dbl&gt; | valence &lt;dbl&gt; | tempo &lt;dbl&gt; | lang &lt;chr&gt; | Types &lt;int&gt; | Tokens &lt;int&gt; | TTR &lt;dbl&gt; |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 1AvAl5fj5mhoLHVo0to1xr | 0.697 | 0.298 |  0 | -16.929 | 0.0319 | 0.0411 | 0.233 | 113.571 | nl | 64 |  90 | 0.7111111 |\n| 2 | 5pz1Q9QFHWsUBZiJ73Jx3j | 0.579 | 0.815 | 10 |  -7.603 | 0.0332 | 0.1000 | 0.556 | 155.577 | nl | 95 | 188 | 0.5053191 |\n| 3 | 6eVj2ARRKQiy2Gb9Za7jnf | 0.701 | 0.866 |  9 |  -4.632 | 0.0298 | 0.0535 | 0.933 | 119.949 | nl | 63 | 140 | 0.4500000 |\n| 4 | 5uswrfRMA6MpRICWXEE1Uq | 0.631 | 0.921 |  9 |  -3.248 | 0.0447 | 0.1380 | 0.370 | 108.055 | nl | 66 | 114 | 0.5789474 |\n| 5 | 2ef2rQVgykMmHX47tjecDp | 0.542 | 0.704 |  1 |  -7.953 | 0.0364 | 0.0215 | 0.253 | 119.942 | nl | 45 |  87 | 0.5172414 |\n| 6 | 23lfdALTPtgNbJrzhRnfks | 0.469 | 0.603 | 10 |  -8.358 | 0.0333 | 0.0893 | 0.231 | 131.739 | nl | 58 | 133 | 0.4360902 |\n\n",
            "text/html": [
              "<table>\n",
              "<caption>A data.frame: 6 × 13</caption>\n",
              "<thead>\n",
              "\t<tr><th></th><th scope=col>sid</th><th scope=col>danceability</th><th scope=col>energy</th><th scope=col>key</th><th scope=col>loudness</th><th scope=col>speechiness</th><th scope=col>acousticness</th><th scope=col>valence</th><th scope=col>tempo</th><th scope=col>lang</th><th scope=col>Types</th><th scope=col>Tokens</th><th scope=col>TTR</th></tr>\n",
              "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><th scope=row>1</th><td>1AvAl5fj5mhoLHVo0to1xr</td><td>0.697</td><td>0.298</td><td> 0</td><td>-16.929</td><td>0.0319</td><td>0.0411</td><td>0.233</td><td>113.571</td><td>nl</td><td>64</td><td> 90</td><td>0.7111111</td></tr>\n",
              "\t<tr><th scope=row>2</th><td>5pz1Q9QFHWsUBZiJ73Jx3j</td><td>0.579</td><td>0.815</td><td>10</td><td> -7.603</td><td>0.0332</td><td>0.1000</td><td>0.556</td><td>155.577</td><td>nl</td><td>95</td><td>188</td><td>0.5053191</td></tr>\n",
              "\t<tr><th scope=row>3</th><td>6eVj2ARRKQiy2Gb9Za7jnf</td><td>0.701</td><td>0.866</td><td> 9</td><td> -4.632</td><td>0.0298</td><td>0.0535</td><td>0.933</td><td>119.949</td><td>nl</td><td>63</td><td>140</td><td>0.4500000</td></tr>\n",
              "\t<tr><th scope=row>4</th><td>5uswrfRMA6MpRICWXEE1Uq</td><td>0.631</td><td>0.921</td><td> 9</td><td> -3.248</td><td>0.0447</td><td>0.1380</td><td>0.370</td><td>108.055</td><td>nl</td><td>66</td><td>114</td><td>0.5789474</td></tr>\n",
              "\t<tr><th scope=row>5</th><td>2ef2rQVgykMmHX47tjecDp</td><td>0.542</td><td>0.704</td><td> 1</td><td> -7.953</td><td>0.0364</td><td>0.0215</td><td>0.253</td><td>119.942</td><td>nl</td><td>45</td><td> 87</td><td>0.5172414</td></tr>\n",
              "\t<tr><th scope=row>6</th><td>23lfdALTPtgNbJrzhRnfks</td><td>0.469</td><td>0.603</td><td>10</td><td> -8.358</td><td>0.0333</td><td>0.0893</td><td>0.231</td><td>131.739</td><td>nl</td><td>58</td><td>133</td><td>0.4360902</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eTGx6nwwUsVO",
        "colab_type": "text"
      },
      "source": [
        "# EDA\n",
        "\n",
        "### Density plots\n",
        "\n",
        "The plots below show the density curves, with each color-coded by language. For every measure besides Key, the density curves show us something about center, shape, and spread of their distributions among the songs of each language. \n",
        "\n",
        "Because key is actually a categorical variable, and not a quantitative variable, I used a different type of density plot for Key. Each key on the chromatic scale is associated with a certain value, 0-11, featured on the x-axis. The darker the shading at a value of x, the more songs written in the key associated with that x-value turned up in the sample. The idea is not to look for similarities in the locations of dark and light shading, but rather to compare and contrast the distribution of pigmentation across whole strips. This helps us get a better look at spread, which is all we are interested in, as shape and center don't have the same kind of meaning when dealing with a categorical variable as with quantitative variables.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9veO7J-ngAcf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# we don't need cols 1, 11 & 12 (sid, Types & Tokens)\n",
        "esinw_df2 <- lapply(c(2:9,13), function(x){  \n",
        "  cbind(\n",
        "    # each has three columns: the measure's value, the language, and the measure's name\n",
        "    esinw_df[,c(x,10)], measure = names(esinw_df[x])) %>%\n",
        "    # and rename the measure's value column as \"value\" so we can rbind all of the dfs later\n",
        "    dplyr::rename(value = names(esinw_df[x]))\n",
        "})\n",
        "\n",
        "# now rbind them\n",
        "esinw_df3 = esinw_df2[[1]]\n",
        "for (i in c(2:9)){\n",
        "  esinw_df3 <- rbind(esinw_df3,esinw_df2[[i]])\n",
        "}\n",
        "\n",
        "# and take out the trash\n",
        "rm(esinw_df2)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_JaB8YdhfsIK",
        "colab_type": "code",
        "outputId": "61de6495-91ba-464d-d40e-0800cf4e081a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 840
        }
      },
      "source": [
        "# GG Ridge Plot\n",
        "\n",
        "# make a ggplot and facet it by language\n",
        "f1 <- ggplot(esinw_df3, aes(x = value, y = lang, fill = lang)) + \n",
        "  facet_wrap(measure~. , ncol = 3, scales = 'free')\n",
        "# add some density plots to it to it for teh numical metric\n",
        "f2 <- f1 + geom_density_ridges_gradient(data = subset(esinw_df3,measure!='key'),\n",
        "                               scale = 1.8, rel_min_height = 0.01)\n",
        "# add some gradient for the key (categorical) metric\n",
        "f3 <- f2 +   stat_density(data = subset(esinw_df3,measure=='key'), \n",
        "                          aes(alpha = stat(count)),\n",
        "               geom = \"raster\", position = \"identity\", bw = 1/2)\n",
        "f3"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Picking joint bandwidth of 0.0675\n",
            "\n",
            "Picking joint bandwidth of 0.0852\n",
            "\n",
            "Picking joint bandwidth of NaN\n",
            "\n",
            "Warning message in min(data$x, na.rm = TRUE):\n",
            "“no non-missing arguments to min; returning Inf”\n",
            "Warning message in max(data$x, na.rm = TRUE):\n",
            "“no non-missing arguments to max; returning -Inf”\n",
            "Picking joint bandwidth of 1.34\n",
            "\n",
            "Picking joint bandwidth of 0.017\n",
            "\n",
            "Picking joint bandwidth of 0.128\n",
            "\n",
            "Picking joint bandwidth of 0.115\n",
            "\n",
            "Picking joint bandwidth of 12.2\n",
            "\n",
            "Picking joint bandwidth of 0.042\n",
            "\n",
            "Warning message in FUN(X[[i]], ...):\n",
            "“no non-missing arguments to max; returning -Inf”\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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1BwIAIBAdXR9BE7SugHhZ048qTT6dRqNaWUUJI+REcoyVjYEYFkNmJHBQoCIoBI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RkZHff/99t27d2rZtO2rUqNu3bxv37927\nt1+/fgEBAV27dl24cKHODP9GcBxnIjyxM5+bN2/6+TWcPv17Ozt5+Ai072CCIrB7/sTDJ1IX\nt7K1Cjqm64WUkJ+beDcrWfzKdWn+LMEM69sVK4Zx45AvHxYs+LFr18Do6Ogs75KzDHFxcV27\ndvrqq6/0+rhuQfjfWBT+6Ljb+w4exJnzkmchpzGeNdJyfOEcdjvbtypga7PqD3b0SMZjNiFX\nV1SoiFOnzu/YsUPpWN43ZcoUAAsWLFi5cmW5cuUmTJig0+mePn06b9683r17r127dvr06dev\nX+cVAjkuG+GTJ8xBr9cvWLBgxozpOp2henUEdYdJ1mWIjsaWDTSnRpzi5ZmBl4uULm/m22bj\n1r0XHy6YI/QfJGX1CvkFHDFhIubOwe7de7296y5ZsqJcuXJZ2yWntAcPHnTu3P7q1VuFC6P/\nADim5wvI6yisWSHYqOivfj4CSesIXIlcObcENGv4x8bFP+tz5WKuFvAR69ARly9h7NiR3t7e\nGo1G6XAS3b59+8aNG2PGjLGzswPQqVOn7du3nzx50snJiTFmZ2dHKS1QoMCsWbMo/XcI4Nq1\naydPnkx66Ovra5vSpfTkLwFACKHvCIIgCIKYSCWKKlFUC4JaoGoqaChRATRxhRvGQBhjMqUM\nTFKr1bJMZEYBickSYxKDzJicrBdq7IqAglBCKCEUjIBQAmJsUxBEAKIoAszYBYOc1BFjMmMS\nmJzYMpMZWOKRxh5AElsGBaGCIBBCNZr3ewGSPq4MjAEwNkjAGBhjEmNSYvvv3oUgUACCINCU\nPuofTAgihBCAECIQEEJFQkRCKKUqApFSlSAIAhUYo4BAqECIQIhAkp/YpEhkQWYCIMsyZTJl\nTJJlwkBl2WAMz/ijFEVREIR3b5AQIhBCjb0j8WwLxHjyiQBCCIwblBAK0MQACBUEQa0mjBmP\npAzEGBUBtbKy+thnlaT5rz8HntiZwZEjR4YPH3Tz5r0cOfB1b9RM641wnxa2ibyNk6d4Vctr\n/dG/D6nTisK6Vv4tN4QdCn80b5bQf6CU4q3rJmSXA6PG4I+V+HP3P40a+YwaNbpv335C5ocu\nOYt05MiRr77qEhX1tk5dBH2V8sSIVKxaQaNjpekNPIrZp28B4or58q5u5ddiXdj82WTsRNmp\nYPr6NTlnZzTwxp4/n8yZM2vEiFEKR/PO48ePAXTr1i35zmfPntWuXdvX13fIkCElS5Z0d3f3\n8vJycnJKOuDvv/+eP39+0kMvL6/8+fOn3hF5J3lWp1Kp1Gq1KKpVKo0oakRRm5TYGTMDIDHr\nYkxmzMBkIghElgWZGRgzpD2xS9z+b8oliqIoCkldJOsofYndu/wm5V4AvJfYGRtMMbEDGGOy\nSqUyHv/hSfzwpKaY2FGiolRFqJC4QcRPJXYGmRkYk2RZYLJg3JCZXpaF5OGp1WoAKSZ27xK4\njyZ277I6wXiiNBpV0nlD4qkjBNTGxuZjHyGDwZD6Z4xLjid2WejWrVuTJo3buXMPIahTF4Gd\nPnGTeLo8f4a9f8E5h903VSpmph1rlbi5TfM2G7ftv/Bw5lTh26GStbWpYkyZIKBLV7i6YvGv\nhokTJ23atG7mzHmVKlXK2l45s1u6dOmoUSMYk7t2Q8NG6X751as4elyulD9vxj7hDYo4z29U\nv8+uvXO+F8ZOlhRfuzigLU6dwty5c5s3b1m2bNlPvyDrGX9Vr1+/Xv1Bxm28we7MmTOnT59e\nu3bt4MGDk5bi8/LyKlq0aNKRtra2r1+//rDx5N/WGGOMMVmWJUkyGAwMegIdYwmyHC+K1GBQ\nCwIVRUKJngqG90bsGGSACQII5ISEOMYM7yV2/x3MIqkkdsbjRFGl0Wh0Op1erzO+4GOJHcA+\nltghcZyJqtVaQqjBIL3XS/LEzriVYmJHjNuQGZNFURBFQafTSSkmMakmdiBC8sSOEFFrZSNL\nkiwnvJfYsX+Dez+xY8zAZL1xQ2b6pBE7lVoUBBofH88Ye/cGU0jsAJpiYvfu4MTEztrKRqdL\nkGV8mNgZDCl8kJI+TikODHMp4oldlrh58+bcubPWr98oSXLx4ujSFS4lTdzFpnVUL8nja9fQ\nipkd7rJWiZvaNOsctnPb9btTxguDRkjvLQybFapUhUtJ/L4Mp05ebdy4UevWLUeOHFOkSJEs\n75jLenq9fvTokUuX/m5ri+AByMDFUFnGqmUCgTTHxyvtF2HfE1TB9WZk5MyT5xbMFoaNzvI7\nDVJnbY2gIMydI/Xp02PPngMf5lLmZxyHu3v3bunSpY17nj59WqBAAUmS3r59my9fPj8/Pz8/\nv0WLFu3YsSMpscufP3/yIbrIyEi9PoVbdNl/h53YB2RZNqZ6hEiABBgESmVmoIR8eCkWIJQw\nSZJk2SAzw6cuxTICBsIIYYD83lgapQIAY9fvApWTMsj3RuyMV07fS+wYZEKosQtZlgmBwWBI\n44jdh5digcS+KCWA8G9g7/loYofE/xFCCJNlQgmhlMiyLEmQZQKAUEaMZ4OR5CN2IEhK7ABZ\nlg1MTtwwpnrGxE5k/56xZCN2IIQl9g6WbMTO2LIxsWPvLsUmBkAINbYjSSx5YscAAiqKZqll\n/gXgkydMSZKkPXv2dOwYULu255o16/Plk7/pjwmTTJ/VPXiAYyfkcnkcAstlbq2Ud7SisLql\nX4+K5R88liaNFW5cN0mrn5AzJ4IHYPhIODqy9es31ahRvVevHufOnTNH31yWefXqVZs2zZcu\n/b1gQUyYlJGsDsDBA7j7UGrvWrpmOmcFvWdyXQ9/l2JXrku/L1H+37qq1VC7Dq5cuTlu3Bil\nYwEAZ2fnChUqLF68+MWLF5Ik7dy5Mzg4OCIiYv/+/YMGDbp16xZjLDIy8sGDB8kvxXIcZ+HM\n8R02MjLy119/vXTpUmxsrIuLS8+ePUuUKAFg7969GzZseP78ubW1da1atXr06GEJ32IzICEh\n4eDBgzt3bg8L2/TiRRSAYsXg54/q1UGz5rfJxjWCzKSJdWuleJttxoiU/tC4fqnc9qMPHJ06\nhXToxBo1NlXbqXFzQ+hUHDuKrVvlTZvCNm0KK1WqaEBAoK+vr4VcseLS7vTp0z16dH3y5KW7\nO/p+g4/fDJ2a+HhsWidYiSTEyyOT8VBClvg3qr9y/f4Drwo5k0a+Chel6BaEO7exePHSChXc\nAwOVL7I3ZMiQX375JTg4mDFWpEiRCRMm5M6d29vb+8WLF6GhoVFRUXZ2dlWqVOnevbvSkXIc\nl1bmSOymTJmSP3/+BQsWaDSatWvXTpgwYfHixREREfPmzZs0aZKbm9vz58+nTp0aFhYWEBBg\nhngyLy4uzjih7NKlS+fOnzh7Jjw+Xg/A2hpe9VCvnumH6JK7cQNnw6UaTgX8XYqZvPFvq7m7\n5nHotm338t/jb1+nX/WStVqTd/I+SlG7Djxr40I49u/DhQv3QkNDQ0ND8+a1r1mzVpUqNVxd\nXV1cXJydnbM8FC6jZFn+4YcFoaFTJElu1RqtWn9w7SjNtoWRyDfSyFrVCtmZ4K4aO7V6fWv/\n2svX/rEy3qkglC0mq9Xi20GYOB5DhgzKmzdvw4YNlYwGyJUr1/Dhw9/bSQjp2LFjx44dFQmJ\n47hMyvLELmMz6k+fPv3w4UPjtkaj8fLySnqKEKLNdK5hnKKVsXbevn3r5ub6+vXbpD1OTnAt\nh0qVUK4cRFUmQ/u0jasFQJpcN7ODGR/TsFjhE906BG7Zeezkswf3hX4DJfMkVISgkjsquePt\nW5w9gwsXcPVK1NatO7du3Wk8YPjwYZMmTTbJB0ClUiX9NwnNovHVL8Ddu3eDg/ucPHkuRw70\n6Qu3ChlvKjISu3eSfDZWQ2pUNlV4RXPmWNWiid+azQvn03GTpU9N4sxaTk74dhBmTJeDgrr+\n+utvTZo0UTIajuM+O1me2GVsRv2WLVt27dpl3M6VK1fTpk2TniKEmGp2TMbasbKy8m9WNSry\nTpEi9iVK2JcunStHTvNdQT5x4unl64d9ihauWzgLl3AonMNuX2CbEfuPLDz396Rx4pDhlRs3\nNuu0hnKuAMAYHj6IvnEj8s7d148eRdavX9e4BpipLtlrNJrki4qleA84l7rY2Nj58+fOmzdP\npzNUckfPnshpn6kGN62j8Tp5uld1O5PemFHHueBsH6/+f+6fO0MYN1kywzh0KlxdMXAQ5s4x\nBAV1GzdufL9+/fgyXRzHmUqWJ3YZm1HfoUOHevXqJbWQVKXA1taWMRYTE5P5wGxsbDLczsIf\nE9dhN2aZkiTFZrrkKqVUq9Wm3o4sy8G9PQgwsW6tTHb3SWpBmO3j5VnIqe+ufVNCTkW8dA/9\nbmrGFpwTBMHa2lqn0yUkJKT3te7/XQUlPj6eEJL5AkcqlUqr1cbHxydP5lQq1XtjeFwqYmJi\nVqxYMXfujBcvonLmRPce8Kyd2TYfPsTBw6xU7lzdK5p+WeGelcpfeP7il/BLixYIA4ZIyqZS\nFSpixCjMnc0mTJiwf/+emTPn8inhHMeZRJYndhmbUV++fPny5csnNfLy5UvjhjGxy0B+8B5C\niLW1tUnasbW1lWU5800JgqBWq1NvZ8OGDReuXGld2qVKgXyZ7C6NAsqULJ/Xoe2m7Qt/WnTj\n6rVfly7NwDCnMVsyGAwmOeGUUpO082FIfJ3ktGCMnT17dv36tWvXro6OjlOr4d8MzZrDJMsf\nrl0pyLIU4uUhZs1l8Vk+XldfRRw5/3jTetK6rcITKUqVQkgoFi3EwYNHPTxqdu7cuXfvvsWL\nF1c2Ko7jsrssT+ySZtQPGzYsd+7cf/7552+//bZo0aJz58798ccfo0ePLlGiRFRUFJ9R/0k6\nne67yZNVlE7K+uG65Mo45D7cpV3g5p17Dx5s0dRv9foNeUBsC+8AACAASURBVPPmNWcAFuWz\nn+KdooSEhOvXr4eHh584cfTAgX3Gqd92OdCiJRo3hl0O0/Ry8W+EX5RqOzs1L5lVyY2K0lUt\n/Dx+X7N5S3ThoqhaLYv6SatcuTBiFI4cwYZ1ht9+W7pkyVJ39/JNmrTw9PR0c3PL/L2kHMd9\ngcwxK5bPqDeJX3/55f6jR73d3VxyZe4mpvSz12i2tG3eZ+felZevNmvSZMOWLQULKl2kSSEZ\nmOIty/KZM2eSWnB0dMyRI4extpKprvyKogjAtA1SSlUq1ZMnT/z8Gt6//0iWE8e3bGxQywPV\na6BSJZhwyV9JwurlIoFhar1MX9BNVV5rq7Wtmnr/sf7nhXIBR7lQoSzt7dMIQZ068PDA8WM4\nsB/nz186d+4SAEqpt3fddes2491Ysgl/vhzHfcbMkdjxGfWZFxERMWvGjJwa9VjPGooEoKL0\n16YNc1tp558Jb+nvv3nbti8wt8vYFG+9Xt+vX7+kh0FBQf3798+K8KysrFKpop0BarX67du3\nghDr4sKcCqJIEZRwQZEiWbI6496/8OCxoVO5MlUds3zOqnv+vD829g7atnv+TGFciPTxApXm\nIwioXQe16yAqCpcv4eZN3L8nW9tIOXP+WyQ3lUqaHMdxSXhJsewhNCTk9du3ofU881ib8jd3\nuhDg+wZ11AKdefJcmxbNw3bszJfPTLf6WYiMTfEWRTE4ODjpoZubW0xMjFqtlmXZVJWtRVF8\nV/7SNBN7RVEUBCEhIcHe3j48/EEmWzPe0ipJUnx8fIoHvHjxIrhPBTt1QuZXJE6jDq6lwp89\nn3P6/Ka13us2brKxsaGUmmRWlpFWq9XpdLIsf/rQVBlDUqvVKpUqPj4+5WJTH8ETQY77MvHE\nLhsIDw9fsXKlSy77/hmqhm5aU7w8DTKbe/p8u9attmzfkXxE4bOXsSnegiC8lwu+fPmSUmqS\n2SRGxnVbdDrdxzKn9FKr1Wq1Oi4uziStUUqNid3HGhw5bFhUdPR39Twdbc2Xi4R4eVx4/nL3\nX3snjB8/bfp0Sqmp3i8AlUqVkJBgqsSdEKJSqXQ6Xbrmg/PEjuO+THxFVksnSdKwQYMkWZ7l\nU1dtGdM2p9avHVTB9fL1G507djBVapItJE3xTtrz9OlTAJIkvX792jjFe/z48b6+vjt27FAs\nyuzm4MGDazdsKJ/XoX/VSp8+2nRESpc3a1w4h93sOXPCwsLM2TXHcVzW4Ymdpfvl55/DL11q\nU9qlUTFLWeaKAD80btC8ZPETp8/0+bpn5q83ZRe8aLrJvX37dvCAARRY6NtAZfbKH3msrVa1\n9NMItHu3btevXzdz7xzHcVmBJ3YW7c6dO6FTpuTSamb5eH36aDMSCFnWrHGtgo7bdu4a97//\nKR2O+QwZMiRPnjzBwcGBgYH79+9PmuLdsGHD0NDQNm3aDBw4MH/+/HyKdxqNHDbswePH31Zz\nr+ZYQJEAqhTIN7dhvddv37Zp0fzt27effgHHcZxl4/fYWS6DwfBN795xCQk/NG2Y38YUy7+a\nlJUobmjt77Vy3aJffnEuXLh3nz5KR2QOfIq3Ca1atWrN+vUV8+UdX6emgmF0c3M98+TZL+GX\nvv3mm1+XLuXVvTiOy9b4iJ3lmjZ16pnw8FalSgSWK6N0LCnLbaUNC2ie19pq3Lhx27ZtUzoc\nLjs5ffr08KFDc6jVK5r7apS+eXSmd93qTgXCduyYP2+espFwHMdlEk/sLNSePXvmzZvnnMPu\nB98GSseSmmL2OTe2aaYVhb69e588eVLpcLjs4dq1a10CO+r1+qXNGpfMbe4Ftz+kFoTVLZvk\nt7EODQ09cOCA0uFwHMdlHL8Ua4lu3brVt1cvFaV/tPDNbfFlhao55l/ezLfdpu2dO3YM2769\nbNmySkfEWbTw8PDAdu0iol7PbVjPr0RRpcNJ5GRr+0eLJr6rN/Xu2WPPvv2FCxdWOqJsr0Lh\nwaW0neLi4kAIQEAAQkAICAUhDCxxJwAGAkBmkGRIEpEkyBIMBqLXE70eBr3ImGAw6GMl6AxE\nr4deR2SZSQYiMxjboRSUMpFAEJhKBZWaqTVMFKFSM7UaajUTBKhUEFUqorUmOeKEhHhBTygB\noQyA8fq7MTwjxsBY0jYBIMuMMcgyYQyyBMYgSVqDCJnp4uKYZHj3lAwmgzEw/NtC4hv/zx/G\nkHhOGAOgUWs0kiouNtag10OSiCxDMhDGmMFAJAkGPZFlGPSQJBgMhDFIUmKPxjMIPQBQHQMB\nIaIqWmaQGQMljDHjOQcx/hQoqPEnQkApCGX03c+FCiAigxqUJp0TtUar1qjj4uMlmSHpjFGa\neAwh7L/vy3gzAwMSuwBAEv8PAhudVXw8kaSkKXcMxLg6oyw5m/wz+IXiI3YW5+XLl4Ht2r5+\n+3Zeo3pK3VGeXk1div3QuMHr6Oi2rVsnXw2E45JjjP3+++/N/Ju+ioqa6VO3l7ub0hH9h2ch\npxnedSNev+ka2DE2NlbpcDiO4zKCj9hZljdv3nQICLj78J8hNSp3c3NVOpx0CKrgGhEfP/rA\n0VbNmm3Zvr1IEUtZnIUzrStXruzcufPM8eN379x5FRUFwNrKysmxQHGXkqVdXd3c3CpWrJg7\nd+73XiVJ0o4dO76bPOnUufM51OplLZu0KFlCifA/obe727mnz5ddvPJNn96/LfudT6TgOC7b\n4YmdBXn9+nWHgIALly8HlisT4uWpdDjpNrh65Vi9IeToyRZNm67fvNnFxUXpiDiTYYzt2LFj\n7swZ5y9eMu6x12pyabUA4mNjzv998cyFv7Fhg/Gp/A4OJV1cCjo7a6ytmSQ9evDg/IULkW/e\nAGjqUmyWd90iOXMo9UY+aV6jetcjIrft3DV92rQRI0cqHQ7HcVz68MTOUrx8+bJj27ZnL1xo\nU9rl5ybe2XSg4H+e1QVKJh4+4d/Ed8Wq1VWrVlU6Is4Erl+/PmzQwOOnzxCgUbEiHV1L1ytS\nKHn5L50k3Y56ffH5ywvPX4Q/e3HpxasjJ08i2WSagna2rSuW/6qCa1XH/Eq8g3TQCMLaln6e\ny9fOnDXLpWTJNm3aKB0Rx3FcOvDEziLcu3evY9uAW/fudyxX+pcmPqLZl+A3oVG1qtlrNEP2\nHmrVosWMWbPat2+vdERcpixevHjCuHHxOp1v8aJT6nmUy+Pw4TFqQSjrkLusQ+52ZUsZ98To\n9Y+i377V6dWC4Ghr7WBlZd6oMyWfjfWG1v7ef2wYOGBAoUKFatSooXREHMdxaZWNE4jPxtGj\nR30b+ty6d79/lYqL/Rpm66zOqG/lCutaNVUB/fv3HxgczBf0z6YSEhL69uo1cuRIa0qXN/fd\nHNAsxawuRTYqVancuSoXyFc+r0P2yuqMKuTL83uzxgaDoUtg4I0bN5QOh+M4Lq2yfQ6RrTHG\nfvzhh4A2bV6/iZ7j4zXDuy79XG7WbupS7EiXduXzOqxcvbpmtapbtmxROiIufSIiIlo3b75+\n06bKBfKdCOrQtkxJpSMytyYlis7x8Yp886Z9mzaPHz9WOhyO47g04YmdYl69etUlMHD8hAm5\nNOptbVv0qVxB6YhMrLRDriNd2g+pUfnps+ctW7b09fE5ceKE0kFxafL48WNvL69T5861KlVi\nb2CbwjnslI5IGT0rlR/jUf2fp0/btmr16tUrpcPhOI77NJ7YKWPnzp11PT13//VXbWenk0Ed\n6hUppHREWUIrClO8PI93a9+giPPh48ebNWvm16jhxo0bdTqd0qFxH/XgwQNfH59rt271dndb\n2aKJlfhF34k7tnaNPpUr3Lhzp22rlpGRkUqHw3Ec9wk8sTO3Z8+e9erevWvXrlFRURPr1trd\nobWTra3SQWUtt7x5drRv+VfH1j5FC585H967d28317KjRow4c+aM0qFx77t7925zP7/7jx6N\nqFl1bsN6n829AZkx28erq1vZi1evBbRsERERoXQ4HMdxqfmiv4ubWXx8/KJFi+bMmvU2NrZK\ngXwLfb0r5MujdFDmU9u54DbngtdeRfwafmnVlRu//vbbr7/9VqxQodbt2wcEBPBF7yzB7du3\nWzVv9uT5i3G1a472qKZ0OJaCAD/5essMKy5dbenvv3bjxgIFskdJGI7jvkB8xM4c3r59+9NP\nP1V1dw8JCVHJ0iyfuoe6tPuisrokZRxyz/Cue7ffV2tbNW1d2uXJ0yczZ86sVauWr7f30qVL\n37x5o3SAX66bN2+2bOb/5PmLSXVr8azuPZSQn5t496xU/urNm/6+vrdu3VI6Io7juJTxEbss\nJEnSiRMnNq5fv2njxujYWCtRHFjNfXitqrm1WqVDU5haEJqXLN68ZPE3Ot2WG7dXXr526OLF\ns8OGjR87tnWrVt2//trNzbKqiH72rl271qZly+evXoV4eQytUUXpcCwRJWR+o/q5tdrpJ840\n9fVdunx5rVq1lA7q/+zdd1QTWRcA8DsJKYSOCEhTiiICIoq9gYIiFuwN116wN+x1FbGsFbti\nYXXFLhYURRd7AymigogF6b1DSJvvj7h8iJQAaeD9nd1zksnMezfxZXJ58+Y9hBCqCBM7sWGz\n2dnZ2ampqYmJiTExMZFv3rx6/TqvsBAAdJRY87t1nNPeRovV8Cb0kihVOv0PK4s/rCwS8gt8\noz6cevvhrJ/fWT+/bh07zpo3z9nZmdLwZ/WTfxEREWNHjczOzdvu0GNhR1tZhyO/CIBNvboa\nqCgvefB45PDhnl5eU6ZMkXVQCCH0E0zsaoHD4URHR8fExHz58iUp4Xt6Skp2VnZBYUFefkFB\nURGHx6uwv76K8rC2bVxbmjoaG9EwQamWoarK2u6dV3XrFBD39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lOmTFmyZIm9vb2I84fPmDHD1tbW09NzzZo1EnpfOMYOIYQQQr8jFxcX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\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AAAcAIKOwAAAAAAnIDCDgAAAAAAJ6Cw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\nUNgBAAAAAODE/wOfuSrA92aO+wAAAABJRU5ErkJggg==",
            "text/plain": [
              "plot without title"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 420,
              "height": 420
            },
            "text/plain": {
              "width": 420,
              "height": 420
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ugHiETRSjGUi",
        "colab_type": "text"
      },
      "source": [
        "In the Key plot (top right), the pink strip seems to be the splotchiest-looking. This means that there appears to be less variation of song key among songs written in English than among songs written in other languages. \n",
        "\n",
        "It would seem that the distributions of many features of English (pink) and Spanish (olive) music have similar shapes (danceability, loudness, speechiness, acousticness, tempo, and TTR). For all of these but loudness, these distributions seem to have commonalities not just of shape, but also of center and spread (ignoring abnormal tail behavior). \n",
        "\n",
        "Swahili's distribution (purple) always tends to march to the beat of its own drum (terrible pun intended). Truthfully though, if you look at all nine of the plots, there doesn't seem to be one in which the purple curve (or strip) seems to mimic the distribution of another color. \n",
        "\n",
        "What's interesting about Indonesian music (teal) is that acousticness is basically its only bimodally distributed feature. All its other features are distributed pretty unimodally, which is unusual. Meanwhile, every other language's music has at least two features that are distributed bimodally. \n",
        "\n",
        "All in all, there doesn't seem to be a super clear unifying difference between the more widely-spoken and less widely-spoken languages."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IkUmbzUvjHhi",
        "colab_type": "text"
      },
      "source": [
        "### Box plots\n",
        "\n",
        "The boxplots below show the five-number-summaries and outliers for all of the same data that was used in the density curves above. Key, the categorical feature, is portrayed in a violin plot instead of a boxplot so that it is easier to see the concentrations at individual values of key."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GFwi9QSWjKe1",
        "colab_type": "code",
        "outputId": "7821c99a-dfbf-4fc7-9e94-3623489c4dd8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 470
        }
      },
      "source": [
        "# boxplots\n",
        "# make the setup\n",
        "f1 <- ggplot(esinw_df3, aes(y = value, x = lang, fill = lang))+ \n",
        "  facet_wrap(measure~. , ncol = 3, scales = 'free')\n",
        "# boxplots for the true numeric variables\n",
        "f2 <- f1+geom_boxplot(data = subset(esinw_df3, measure!='key')) + \n",
        "  coord_flip()\n",
        "# violin plot for key (the categorical variable in disguise)\n",
        "f3 <- f2+geom_violin(adjust = 1/5, data = subset(esinw_df3,measure=='key')) +\n",
        "  coord_flip()\n",
        "f3"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Coordinate system already present. Adding new coordinate system, which will replace the existing one.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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oPNuZdUKo2WRYvFYmagg81mM5lMWq1WpVK1tjc3qOsGPRE2xo4UxCSJJieLUhNE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\nLBamz35vzizNda7E83333Xffffc576pUqj7jedgf1wNjmfVs838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N8+fPZ7P7/SayWq2XL18+ffp0QUEBQkg1JaBxVaJV1vey1zYJty0j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ACwGFHQAAAACAhYDCDgAAAADAQkBhBwAAAABgIVd6G2EAACAASURBVKCwAwAAAACw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",
            "text/plain": [
              "plot without title"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 420,
              "height": 420
            },
            "text/plain": {
              "width": 420,
              "height": 420
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TbCOgD28jPjE",
        "colab_type": "text"
      },
      "source": [
        "There are lots of outliers in speechiness, which, while prominent in the density curves, is even more prominent here, especially for Indonesian. \n",
        "\n",
        "There don't seem to be any consistent patterns. As far as what these plots portray, there is no clear relationship between song feature distribution characteristics and widespokenness. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PII21zvajWOO",
        "colab_type": "text"
      },
      "source": [
        "# Methods\n",
        "\n",
        "We want to know whether or not there is a difference between extent of variation within the music of more widely spoken languages vs music of less widely spoken languages. We're going to approach exploring this in two ways. \n",
        "\n",
        "* Running a statistical hypothesis test\n",
        "\n",
        "* Performing K-means clustering\n",
        "\n",
        "### Rank Sum tests\n",
        "\n",
        "Rank Sum tests are usually not used in the way I am using them here. The Wilcoxon Rank Sum test is designed to be run on a data set whose observations are all in the same units. In order to deal with this restriction, I have to take some intermediate steps. \n",
        "\n",
        "I'll first calculate the spread metric (standard deviation or IQR) for each of the nine features for each of the 5 languages (5 * 9 = 45 calculations). Second, within each of the nine features separately, I'll perform ranking on the spread metric. Third, I'll pool together those *ranks* (each will be a value 1-5) to form the set of \"observations\" on which the Wilcoxon Rank Sum Test will be performed. I'll be giving each one of the 45 \"observations\" a label specifying which of the two contributing \"populations\" (widely spoken/ not widely spoken) it pertains to. During the Rank Sum test, each one will get a new overall rank based on where it falls relative to the rest of the \"observations\". \n",
        "\n",
        "Those \"observations\" originating from data of English or Spanish music pertain to the **widely spoken** \"population\", and those \"observations\" originating from data of Indonesian, Dutch, or Swahili music pertain to the **not widely spoken** \"population.\"\n",
        "\n",
        "### Kmeans Clustering\n",
        "\n",
        "Unlike the Wilcoxon Rank Sum test, Kmeans, a clustering method, is built to handle feature-rich data. However, it is not built for computing how much evidence exists in favor of the clustering formation hypothesized. It only determines which of all the possible clustering formations has the most evidence in favor of it. Therefore I will take some additional steps to interpret the results of Kmeans as I would the outcome of a statistical test."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y3RdO_eajkyu",
        "colab_type": "text"
      },
      "source": [
        "# Results"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "we4HxqLvjwNQ",
        "colab_type": "text"
      },
      "source": [
        "### Rank Sum tests\n",
        "\n",
        "We'll perform two Wilcoxon Rank Sum tests. To start, we will use standard deviation as a metric of spread. Following that, we will repeat the same process but with IQR as our spread metric."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j7oXHmy7j5GQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# before anything else, we'll have to take some special measures to treat the key variable as categorical instead of numeric.\n",
        "\n",
        "# turn the 0-11 key variable into 12 bernoulli variables with 20 observations each\n",
        "dummies <- esinw_df[order(esinw_df$lang),] %>% select(key) \n",
        "\n",
        "# and its levels should be saved as separate lists\n",
        "dum.levels <- levels(factor(unlist(dummies)))\n",
        "key <- lapply(dummies, \n",
        "              function(x) table(sequence(nrow(dummies)), factor(x, levels = dum.levels)))$key"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QsTVqCYokx6-",
        "colab_type": "code",
        "outputId": "99f07891-2f79-4862-df19-ea9d89620ce8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 229
        }
      },
      "source": [
        "# Now, instead of this\n",
        "head(dummies)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "   key\n",
              "22 11 \n",
              "42 10 \n",
              "45  8 \n",
              "56  7 \n",
              "57  2 \n",
              "58  9 "
            ],
            "text/latex": "A data.frame: 6 × 1\n\\begin{tabular}{r|l}\n  & key\\\\\n  & <int>\\\\\n\\hline\n\t22 & 11\\\\\n\t42 & 10\\\\\n\t45 &  8\\\\\n\t56 &  7\\\\\n\t57 &  2\\\\\n\t58 &  9\\\\\n\\end{tabular}\n",
            "text/markdown": "\nA data.frame: 6 × 1\n\n| <!--/--> | key &lt;int&gt; |\n|---|---|\n| 22 | 11 |\n| 42 | 10 |\n| 45 |  8 |\n| 56 |  7 |\n| 57 |  2 |\n| 58 |  9 |\n\n",
            "text/html": [
              "<table>\n",
              "<caption>A data.frame: 6 × 1</caption>\n",
              "<thead>\n",
              "\t<tr><th></th><th scope=col>key</th></tr>\n",
              "\t<tr><th></th><th scope=col>&lt;int&gt;</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><th scope=row>22</th><td>11</td></tr>\n",
              "\t<tr><th scope=row>42</th><td>10</td></tr>\n",
              "\t<tr><th scope=row>45</th><td> 8</td></tr>\n",
              "\t<tr><th scope=row>56</th><td> 7</td></tr>\n",
              "\t<tr><th scope=row>57</th><td> 2</td></tr>\n",
              "\t<tr><th scope=row>58</th><td> 9</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "St4uOOGXkpdj",
        "colab_type": "code",
        "outputId": "29e69116-6f6e-49a8-d4e0-037e0b148c59",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 151
        }
      },
      "source": [
        "# we have this \n",
        "head(key)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "   \n",
              "    0 1 2 3 4 5 6 7 8 9 10 11\n",
              "  1 0 0 0 0 0 0 0 0 0 0  0  1\n",
              "  2 0 0 0 0 0 0 0 0 0 0  1  0\n",
              "  3 0 0 0 0 0 0 0 0 1 0  0  0\n",
              "  4 0 0 0 0 0 0 0 1 0 0  0  0\n",
              "  5 0 0 1 0 0 0 0 0 0 0  0  0\n",
              "  6 0 0 0 0 0 0 0 0 0 1  0  0"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dph5hO4ojzp9",
        "colab_type": "text"
      },
      "source": [
        "#### Standard Deviation Rank Sum test\n",
        "\n",
        "The first of the following tables contains the standard deviation of each feature for each individual language. The second table replaces the standard deviations with their ranks. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WDWUTjUqkPU2",
        "colab_type": "text"
      },
      "source": [
        "Step 1: Calculate the standard deviation for each of the nine features for each of the five individual languages.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t6ckVbfskAOu",
        "colab_type": "code",
        "outputId": "ddc6d128-cb7a-4c7c-9a95-40ac51621c5a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        }
      },
      "source": [
        "# Rank sum test of standard deviations\n",
        "# 1. make the matrix of \"data\"\n",
        "\n",
        "# add a column for widespokenness to the \"data\" matrix of standard deviations\n",
        "sds <- esinw_df %>% select(-sid, -Tokens, -Types) %>%\n",
        "  group_by(lang) %>% summarise_all(sd) %>% ungroup() %>%\n",
        "  mutate(is_widely_spoken = ifelse(lang %in% c('en','es'), T, F))\n",
        "\n",
        "# use a different method for key, as it's a not actually a numeric variable.\n",
        "# for each language, we made a binary vector for each of the 12 possible values, \n",
        "## with 1s in all places where key = key[i], and 0s elsewhere.\n",
        "## Take the sd of each of those 5 vectors, and then take the mean of those 5 sds\n",
        "key_sds <- sapply(0:4,function(c){\n",
        "  stdevskeys = c()\n",
        "  for (i in 1:12){\n",
        "    stdevskeys = append(stdevskeys,\n",
        "                        sd(key[(c*(20) + (1:20)),i]))}\n",
        "  mean(stdevskeys)}) \n",
        "\n",
        "# store that in the key column of the standard devation data frame\n",
        "sds$key <- key_sds\n",
        "\n",
        "# show the standard deviations\n",
        "sds %>% \n",
        "  mutate(`widely spoken?` = ifelse(is_widely_spoken, \"yes\", \"no\")) %>%\n",
        "  select(-is_widely_spoken)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  lang danceability energy    key       loudness speechiness acousticness\n",
              "1 en   0.1607261    0.1869324 0.2275846 2.302330 0.06248847  0.2843769   \n",
              "2 es   0.1144002    0.1642343 0.2249290 3.106435 0.07818603  0.2361951   \n",
              "3 id   0.1279928    0.1893246 0.2488772 2.234725 0.01420918  0.3069545   \n",
              "4 nl   0.1131354    0.2469996 0.2317776 3.621842 0.05255047  0.2937826   \n",
              "5 sw   0.2002997    0.1653345 0.2330659 2.684877 0.10075234  0.2423239   \n",
              "  valence   tempo    TTR        widely spoken?\n",
              "1 0.1994177 29.55932 0.09128328 yes           \n",
              "2 0.2600962 31.87556 0.08620658 yes           \n",
              "3 0.2251435 27.92545 0.07668810 no            \n",
              "4 0.2515147 27.10288 0.09430071 no            \n",
              "5 0.2232064 26.67659 0.11343660 no            "
            ],
            "text/latex": "A tibble: 5 × 11\n\\begin{tabular}{lllllllllll}\n lang & danceability & energy & key & loudness & speechiness & acousticness & valence & tempo & TTR & widely spoken?\\\\\n <chr> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <chr>\\\\\n\\hline\n\t en & 0.1607261 & 0.1869324 & 0.2275846 & 2.302330 & 0.06248847 & 0.2843769 & 0.1994177 & 29.55932 & 0.09128328 & yes\\\\\n\t es & 0.1144002 & 0.1642343 & 0.2249290 & 3.106435 & 0.07818603 & 0.2361951 & 0.2600962 & 31.87556 & 0.08620658 & yes\\\\\n\t id & 0.1279928 & 0.1893246 & 0.2488772 & 2.234725 & 0.01420918 & 0.3069545 & 0.2251435 & 27.92545 & 0.07668810 & no \\\\\n\t nl & 0.1131354 & 0.2469996 & 0.2317776 & 3.621842 & 0.05255047 & 0.2937826 & 0.2515147 & 27.10288 & 0.09430071 & no \\\\\n\t sw & 0.2002997 & 0.1653345 & 0.2330659 & 2.684877 & 0.10075234 & 0.2423239 & 0.2232064 & 26.67659 & 0.11343660 & no \\\\\n\\end{tabular}\n",
            "text/markdown": "\nA tibble: 5 × 11\n\n| lang &lt;chr&gt; | danceability &lt;dbl&gt; | energy &lt;dbl&gt; | key &lt;dbl&gt; | loudness &lt;dbl&gt; | speechiness &lt;dbl&gt; | acousticness &lt;dbl&gt; | valence &lt;dbl&gt; | tempo &lt;dbl&gt; | TTR &lt;dbl&gt; | widely spoken? &lt;chr&gt; |\n|---|---|---|---|---|---|---|---|---|---|---|\n| en | 0.1607261 | 0.1869324 | 0.2275846 | 2.302330 | 0.06248847 | 0.2843769 | 0.1994177 | 29.55932 | 0.09128328 | yes |\n| es | 0.1144002 | 0.1642343 | 0.2249290 | 3.106435 | 0.07818603 | 0.2361951 | 0.2600962 | 31.87556 | 0.08620658 | yes |\n| id | 0.1279928 | 0.1893246 | 0.2488772 | 2.234725 | 0.01420918 | 0.3069545 | 0.2251435 | 27.92545 | 0.07668810 | no  |\n| nl | 0.1131354 | 0.2469996 | 0.2317776 | 3.621842 | 0.05255047 | 0.2937826 | 0.2515147 | 27.10288 | 0.09430071 | no  |\n| sw | 0.2002997 | 0.1653345 | 0.2330659 | 2.684877 | 0.10075234 | 0.2423239 | 0.2232064 | 26.67659 | 0.11343660 | no  |\n\n",
            "text/html": [
              "<table>\n",
              "<caption>A tibble: 5 × 11</caption>\n",
              "<thead>\n",
              "\t<tr><th scope=col>lang</th><th scope=col>danceability</th><th scope=col>energy</th><th scope=col>key</th><th scope=col>loudness</th><th scope=col>speechiness</th><th scope=col>acousticness</th><th scope=col>valence</th><th scope=col>tempo</th><th scope=col>TTR</th><th scope=col>widely spoken?</th></tr>\n",
              "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><td>en</td><td>0.1607261</td><td>0.1869324</td><td>0.2275846</td><td>2.302330</td><td>0.06248847</td><td>0.2843769</td><td>0.1994177</td><td>29.55932</td><td>0.09128328</td><td>yes</td></tr>\n",
              "\t<tr><td>es</td><td>0.1144002</td><td>0.1642343</td><td>0.2249290</td><td>3.106435</td><td>0.07818603</td><td>0.2361951</td><td>0.2600962</td><td>31.87556</td><td>0.08620658</td><td>yes</td></tr>\n",
              "\t<tr><td>id</td><td>0.1279928</td><td>0.1893246</td><td>0.2488772</td><td>2.234725</td><td>0.01420918</td><td>0.3069545</td><td>0.2251435</td><td>27.92545</td><td>0.07668810</td><td>no </td></tr>\n",
              "\t<tr><td>nl</td><td>0.1131354</td><td>0.2469996</td><td>0.2317776</td><td>3.621842</td><td>0.05255047</td><td>0.2937826</td><td>0.2515147</td><td>27.10288</td><td>0.09430071</td><td>no </td></tr>\n",
              "\t<tr><td>sw</td><td>0.2002997</td><td>0.1653345</td><td>0.2330659</td><td>2.684877</td><td>0.10075234</td><td>0.2423239</td><td>0.2232064</td><td>26.67659</td><td>0.11343660</td><td>no </td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ofqqjIzBm6h_",
        "colab_type": "text"
      },
      "source": [
        "Step 2: Rank the five languages, within each feature separately, by standard deviation. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SWClqKSAm7bF",
        "colab_type": "code",
        "outputId": "b25a8162-b6fa-4feb-a568-095f207942c1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        }
      },
      "source": [
        "# take their ranks\n",
        "sd_ranks = sds\n",
        "for (i in c(2:10)){\n",
        "  sd_ranks[,i] <- sapply(sds[,i],identity) %>% rank\n",
        "}\n",
        "\n",
        "# show the ranks\n",
        "sd_ranks %>%\n",
        "  mutate(`widely spoken?` = ifelse(is_widely_spoken, \"yes\", \"no\")) %>%\n",
        "  select(-is_widely_spoken)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  lang danceability energy key loudness speechiness acousticness valence tempo\n",
              "1 en   4            3      2   2        3           3            1       4    \n",
              "2 es   2            1      1   4        4           1            5       5    \n",
              "3 id   3            4      5   1        1           5            3       3    \n",
              "4 nl   1            5      3   5        2           4            4       2    \n",
              "5 sw   5            2      4   3        5           2            2       1    \n",
              "  TTR widely spoken?\n",
              "1 3   yes           \n",
              "2 2   yes           \n",
              "3 1   no            \n",
              "4 4   no            \n",
              "5 5   no            "
            ],
            "text/latex": "A tibble: 5 × 11\n\\begin{tabular}{lllllllllll}\n lang & danceability & energy & key & loudness & speechiness & acousticness & valence & tempo & TTR & widely spoken?\\\\\n <chr> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <chr>\\\\\n\\hline\n\t en & 4 & 3 & 2 & 2 & 3 & 3 & 1 & 4 & 3 & yes\\\\\n\t es & 2 & 1 & 1 & 4 & 4 & 1 & 5 & 5 & 2 & yes\\\\\n\t id & 3 & 4 & 5 & 1 & 1 & 5 & 3 & 3 & 1 & no \\\\\n\t nl & 1 & 5 & 3 & 5 & 2 & 4 & 4 & 2 & 4 & no \\\\\n\t sw & 5 & 2 & 4 & 3 & 5 & 2 & 2 & 1 & 5 & no \\\\\n\\end{tabular}\n",
            "text/markdown": "\nA tibble: 5 × 11\n\n| lang &lt;chr&gt; | danceability &lt;dbl&gt; | energy &lt;dbl&gt; | key &lt;dbl&gt; | loudness &lt;dbl&gt; | speechiness &lt;dbl&gt; | acousticness &lt;dbl&gt; | valence &lt;dbl&gt; | tempo &lt;dbl&gt; | TTR &lt;dbl&gt; | widely spoken? &lt;chr&gt; |\n|---|---|---|---|---|---|---|---|---|---|---|\n| en | 4 | 3 | 2 | 2 | 3 | 3 | 1 | 4 | 3 | yes |\n| es | 2 | 1 | 1 | 4 | 4 | 1 | 5 | 5 | 2 | yes |\n| id | 3 | 4 | 5 | 1 | 1 | 5 | 3 | 3 | 1 | no  |\n| nl | 1 | 5 | 3 | 5 | 2 | 4 | 4 | 2 | 4 | no  |\n| sw | 5 | 2 | 4 | 3 | 5 | 2 | 2 | 1 | 5 | no  |\n\n",
            "text/html": [
              "<table>\n",
              "<caption>A tibble: 5 × 11</caption>\n",
              "<thead>\n",
              "\t<tr><th scope=col>lang</th><th scope=col>danceability</th><th scope=col>energy</th><th scope=col>key</th><th scope=col>loudness</th><th scope=col>speechiness</th><th scope=col>acousticness</th><th scope=col>valence</th><th scope=col>tempo</th><th scope=col>TTR</th><th scope=col>widely spoken?</th></tr>\n",
              "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><td>en</td><td>4</td><td>3</td><td>2</td><td>2</td><td>3</td><td>3</td><td>1</td><td>4</td><td>3</td><td>yes</td></tr>\n",
              "\t<tr><td>es</td><td>2</td><td>1</td><td>1</td><td>4</td><td>4</td><td>1</td><td>5</td><td>5</td><td>2</td><td>yes</td></tr>\n",
              "\t<tr><td>id</td><td>3</td><td>4</td><td>5</td><td>1</td><td>1</td><td>5</td><td>3</td><td>3</td><td>1</td><td>no </td></tr>\n",
              "\t<tr><td>nl</td><td>1</td><td>5</td><td>3</td><td>5</td><td>2</td><td>4</td><td>4</td><td>2</td><td>4</td><td>no </td></tr>\n",
              "\t<tr><td>sw</td><td>5</td><td>2</td><td>4</td><td>3</td><td>5</td><td>2</td><td>2</td><td>1</td><td>5</td><td>no </td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RJsuB3drnNYk",
        "colab_type": "code",
        "outputId": "2fe3aeac-9b1d-4c22-d08f-3a0a513a1234",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "#Let's run a statistical test to evaluate the variances associated with each langauge. \n",
        "\n",
        "# we need to make a dataframe that has two colums, a value column and a label column\n",
        "# the value column will actualy be a rank column (remember, we are takeing ranks of ranks)\n",
        "# the label column will contain 40 TRUEs (indicating songs written in widely spoken languages)\n",
        "## and 60 FALSEs (indicating songs written in not widely spoken languages)\n",
        "wilcox_df <-  sd_ranks[sd_ranks$is_widely_spoken,1:10] %>% stack() %>% .[,1] %>%\n",
        "  cbind(\"widely_spoken\") %>% \n",
        "  rbind(\n",
        "    sd_ranks[!sd_ranks$is_widely_spoken,1:10] %>% stack() %>% .[,1] %>%\n",
        "      cbind(\"not_widely_spoken\")\n",
        "  ) %>% data.frame() \n",
        "names(wilcox_df) <- c(\"rank_sd\",\"cat\")\n",
        "wilcox_df <- wilcox_df %>% mutate(rank_sd = as.character(rank_sd)) %>%\n",
        "  mutate(rank_sd  = as.numeric(rank_sd))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Warning message:\n",
            "“NAs introduced by coercion”\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EKrj44SgngDz",
        "colab_type": "text"
      },
      "source": [
        "Step 3: Take the values from Table 2 to be the set of \"observations\" on which the Wilcoxon Rank Sum Test is to be performed. Determine each one's individual *overall* rank. Keep track of which \"observations\" belong to which \"population.\""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rKutKA8vndJY",
        "colab_type": "code",
        "outputId": "36f591c7-744d-43a1-d7f8-b2fa2f6ac079",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 155
        }
      },
      "source": [
        "# create a new table to show my work\n",
        "wilcox_table = wilcox_df\n",
        "\n",
        "# change the categorical values to make the table more readable and smaller\n",
        "wilcox_table$widely <- wilcox_table$cat %>% sapply(\n",
        "  function(x) ifelse(x == \"widely_spoken\",\"`yes`\",\"\"))\n",
        "# do this separately for the two language types\n",
        "wilcox_table$` spoken?` <- wilcox_table$cat %>% sapply(\n",
        "  function(x) ifelse(x == \"not_widely_spoken\",\"`no`\",\"\"))\n",
        "\n",
        "# get rid of the old categorical variable column\n",
        "wilcox_table$cat = NULL\n",
        "\n",
        "# get the ranks like the wilcox test does, and save in obs.rank\n",
        "obs.rank = wilcox_df$rank_sd %>% rank\n",
        "# make these more readable by getting rid of \".0\"s and put in \"``\"s for separation\n",
        "wilcox_table$obs_rank = obs.rank %>% as.character() %>%\n",
        "  sapply(function(x) paste0(\"`\",x,\"`\"))\n",
        "\n",
        "# make the values more readable by getting rid of \".0\"s\n",
        "wilcox_table$obs_value = wilcox_df$rank_sd \n",
        "\n",
        "# put it all together\n",
        "wilcox_table <- wilcox_table %>% \n",
        "  select(obs_value, obs_rank, widely, ` spoken?`) %>%\n",
        "  mutate(obs_value = as.character(obs_value))\n",
        "\n",
        "# show the first third of the table\n",
        "wilcox_table[order(wilcox_table$obs_rank)[1:15],] %>%\n",
        "  t() %>% data.frame()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "          X4    X7    X9    X20   X29  X37  X41  X44  X46  X5    X11   X13  \n",
              "obs_value 2     2     2     2     2    2    2    2    2    3     3     3    \n",
              "obs_rank  `14`  `14`  `14`  `14`  `14` `14` `14` `14` `14` `23`  `23`  `23` \n",
              "widely    `yes` `yes` `yes` `yes`                          `yes` `yes` `yes`\n",
              " spoken?                          `no` `no` `no` `no` `no`                  \n",
              "          X19   X24  X31 \n",
              "obs_value 3     3    3   \n",
              "obs_rank  `23`  `23` `23`\n",
              "widely    `yes`          \n",
              " spoken?        `no` `no`"
            ],
            "text/latex": "A data.frame: 4 × 15\n\\begin{tabular}{r|lllllllllllllll}\n  & X4 & X7 & X9 & X20 & X29 & X37 & X41 & X44 & X46 & X5 & X11 & X13 & X19 & X24 & X31\\\\\n  & <fct> & <fct> & <fct> & <fct> & <fct> & <fct> & <fct> & <fct> & <fct> & <fct> & <fct> & <fct> & <fct> & <fct> & <fct>\\\\\n\\hline\n\tobs\\_value & 2     & 2     & 2     & 2     & 2    & 2    & 2    & 2    & 2    & 3     & 3     & 3     & 3     & 3    & 3   \\\\\n\tobs\\_rank & `14`  & `14`  & `14`  & `14`  & `14` & `14` & `14` & `14` & `14` & `23`  & `23`  & `23`  & `23`  & `23` & `23`\\\\\n\twidely & `yes` & `yes` & `yes` & `yes` &      &      &      &      &      & `yes` & `yes` & `yes` & `yes` &      &     \\\\\n\t spoken? &       &       &       &       & `no` & `no` & `no` & `no` & `no` &       &       &       &       & `no` & `no`\\\\\n\\end{tabular}\n",
            "text/markdown": "\nA data.frame: 4 × 15\n\n| <!--/--> | X4 &lt;fct&gt; | X7 &lt;fct&gt; | X9 &lt;fct&gt; | X20 &lt;fct&gt; | X29 &lt;fct&gt; | X37 &lt;fct&gt; | X41 &lt;fct&gt; | X44 &lt;fct&gt; | X46 &lt;fct&gt; | X5 &lt;fct&gt; | X11 &lt;fct&gt; | X13 &lt;fct&gt; | X19 &lt;fct&gt; | X24 &lt;fct&gt; | X31 &lt;fct&gt; |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| obs_value | 2     | 2     | 2     | 2     | 2    | 2    | 2    | 2    | 2    | 3     | 3     | 3     | 3     | 3    | 3    |\n| obs_rank | `14`  | `14`  | `14`  | `14`  | `14` | `14` | `14` | `14` | `14` | `23`  | `23`  | `23`  | `23`  | `23` | `23` |\n| widely | `yes` | `yes` | `yes` | `yes` | <!----> | <!----> | <!----> | <!----> | <!----> | `yes` | `yes` | `yes` | `yes` | <!----> | <!----> |\n|  spoken? | <!----> | <!----> | <!----> | <!----> | `no` | `no` | `no` | `no` | `no` | <!----> | <!----> | <!----> | <!----> | `no` | `no` |\n\n",
            "text/html": [
              "<table>\n",
              "<caption>A data.frame: 4 × 15</caption>\n",
              "<thead>\n",
              "\t<tr><th></th><th scope=col>X4</th><th scope=col>X7</th><th scope=col>X9</th><th scope=col>X20</th><th scope=col>X29</th><th scope=col>X37</th><th scope=col>X41</th><th scope=col>X44</th><th scope=col>X46</th><th scope=col>X5</th><th scope=col>X11</th><th scope=col>X13</th><th scope=col>X19</th><th scope=col>X24</th><th scope=col>X31</th></tr>\n",
              "\t<tr><th></th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><th scope=row>obs_value</th><td>2    </td><td>2    </td><td>2    </td><td>2    </td><td>2   </td><td>2   </td><td>2   </td><td>2   </td><td>2   </td><td>3    </td><td>3    </td><td>3    </td><td>3    </td><td>3   </td><td>3   </td></tr>\n",
              "\t<tr><th scope=row>obs_rank</th><td>`14` </td><td>`14` </td><td>`14` </td><td>`14` </td><td>`14`</td><td>`14`</td><td>`14`</td><td>`14`</td><td>`14`</td><td>`23` </td><td>`23` </td><td>`23` </td><td>`23` </td><td>`23`</td><td>`23`</td></tr>\n",
              "\t<tr><th scope=row>widely</th><td>`yes`</td><td>`yes`</td><td>`yes`</td><td>`yes`</td><td>    </td><td>    </td><td>    </td><td>    </td><td>    </td><td>`yes`</td><td>`yes`</td><td>`yes`</td><td>`yes`</td><td>    </td><td>    </td></tr>\n",
              "\t<tr><th scope=row> spoken?</th><td>     </td><td>     </td><td>     </td><td>     </td><td>`no`</td><td>`no`</td><td>`no`</td><td>`no`</td><td>`no`</td><td>     </td><td>     </td><td>     </td><td>     </td><td>`no`</td><td>`no`</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3qa7Rhh2n0pT",
        "colab_type": "code",
        "outputId": "3ca210a9-58bf-4eea-9957-72b6d697da16",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 155
        }
      },
      "source": [
        "# show the second third of the table\n",
        "wilcox_table[order(wilcox_table$obs_rank)[16:30],] %>%\n",
        "  t() %>% data.frame()\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "          X35  X42  X45  X3    X10   X12   X17   X27  X32  X40  X43  X49  X16  \n",
              "obs_value 3    3    3    4     4     4     4     4    4    4    4    4    5    \n",
              "obs_rank  `23` `23` `23` `32`  `32`  `32`  `32`  `32` `32` `32` `32` `32` `41` \n",
              "widely                   `yes` `yes` `yes` `yes`                          `yes`\n",
              " spoken?  `no` `no` `no`                         `no` `no` `no` `no` `no`      \n",
              "          X18   X26 \n",
              "obs_value 5     5   \n",
              "obs_rank  `41`  `41`\n",
              "widely    `yes`     \n",
              " spoken?        `no`"
            ],
            "text/latex": "A data.frame: 4 × 15\n\\begin{tabular}{r|lllllllllllllll}\n  & X35 & X42 & X45 & X3 & X10 & X12 & X17 & X27 & X32 & X40 & X43 & X49 & X16 & X18 & X26\\\\\n  & <fct> & <fct> & <fct> & <fct> & <fct> & <fct> & <fct> & <fct> & <fct> & <fct> & <fct> & <fct> & <fct> & <fct> & <fct>\\\\\n\\hline\n\tobs\\_value & 3    & 3    & 3    & 4     & 4     & 4     & 4     & 4    & 4    & 4    & 4    & 4    & 5     & 5     & 5   \\\\\n\tobs\\_rank & `23` & `23` & `23` & `32`  & `32`  & `32`  & `32`  & `32` & `32` & `32` & `32` & `32` & `41`  & `41`  & `41`\\\\\n\twidely &      &      &      & `yes` & `yes` & `yes` & `yes` &      &      &      &      &      & `yes` & `yes` &     \\\\\n\t spoken? & `no` & `no` & `no` &       &       &       &       & `no` & `no` & `no` & `no` & `no` &       &       & `no`\\\\\n\\end{tabular}\n",
            "text/markdown": "\nA data.frame: 4 × 15\n\n| <!--/--> | X35 &lt;fct&gt; | X42 &lt;fct&gt; | X45 &lt;fct&gt; | X3 &lt;fct&gt; | X10 &lt;fct&gt; | X12 &lt;fct&gt; | X17 &lt;fct&gt; | X27 &lt;fct&gt; | X32 &lt;fct&gt; | X40 &lt;fct&gt; | X43 &lt;fct&gt; | X49 &lt;fct&gt; | X16 &lt;fct&gt; | X18 &lt;fct&gt; | X26 &lt;fct&gt; |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| obs_value | 3    | 3    | 3    | 4     | 4     | 4     | 4     | 4    | 4    | 4    | 4    | 4    | 5     | 5     | 5    |\n| obs_rank | `23` | `23` | `23` | `32`  | `32`  | `32`  | `32`  | `32` | `32` | `32` | `32` | `32` | `41`  | `41`  | `41` |\n| widely | <!----> | <!----> | <!----> | `yes` | `yes` | `yes` | `yes` | <!----> | <!----> | <!----> | <!----> | <!----> | `yes` | `yes` | <!----> |\n|  spoken? | `no` | `no` | `no` | <!----> | <!----> | <!----> | <!----> | `no` | `no` | `no` | `no` | `no` | <!----> | <!----> | `no` |\n\n",
            "text/html": [
              "<table>\n",
              "<caption>A data.frame: 4 × 15</caption>\n",
              "<thead>\n",
              "\t<tr><th></th><th scope=col>X35</th><th scope=col>X42</th><th scope=col>X45</th><th scope=col>X3</th><th scope=col>X10</th><th scope=col>X12</th><th scope=col>X17</th><th scope=col>X27</th><th scope=col>X32</th><th scope=col>X40</th><th scope=col>X43</th><th scope=col>X49</th><th scope=col>X16</th><th scope=col>X18</th><th scope=col>X26</th></tr>\n",
              "\t<tr><th></th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><th scope=row>obs_value</th><td>3   </td><td>3   </td><td>3   </td><td>4    </td><td>4    </td><td>4    </td><td>4    </td><td>4   </td><td>4   </td><td>4   </td><td>4   </td><td>4   </td><td>5    </td><td>5    </td><td>5   </td></tr>\n",
              "\t<tr><th scope=row>obs_rank</th><td>`23`</td><td>`23`</td><td>`23`</td><td>`32` </td><td>`32` </td><td>`32` </td><td>`32` </td><td>`32`</td><td>`32`</td><td>`32`</td><td>`32`</td><td>`32`</td><td>`41` </td><td>`41` </td><td>`41`</td></tr>\n",
              "\t<tr><th scope=row>widely</th><td>    </td><td>    </td><td>    </td><td>`yes`</td><td>`yes`</td><td>`yes`</td><td>`yes`</td><td>    </td><td>    </td><td>    </td><td>    </td><td>    </td><td>`yes`</td><td>`yes`</td><td>    </td></tr>\n",
              "\t<tr><th scope=row> spoken?</th><td>`no`</td><td>`no`</td><td>`no`</td><td>     </td><td>     </td><td>     </td><td>     </td><td>`no`</td><td>`no`</td><td>`no`</td><td>`no`</td><td>`no`</td><td>     </td><td>     </td><td>`no`</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mNATPcOyoTQ0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# show the last third of the table\n",
        "wilcox_table[order(wilcox_table$obs_rank)[31:45],] %>%\n",
        "  t() %>% data.frame()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PuIP8aBxoJIN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# delete the table, we don't need it anymore\n",
        "rm(wilcox_table)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MIrcxR4Qomct",
        "colab_type": "text"
      },
      "source": [
        "Above are the values used as observations, their overall ranks, and whether or not they each belong to the widely spoken language group. They're ordered by overall rank. \n",
        "\n",
        "Now let's run the test to see if the variation behavior of songs' audio/ lyric features (as can be detected at the standard deviation level) is associated with how widely spoken the language of their lyrics:\n",
        "\n",
        "Alternative Hypothesis: The features of music written in more widely spoken languages will vary to either a greater or a lesser extent than the features of music written in less widely spoken languages."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VyBECQ1qonw8",
        "colab_type": "code",
        "outputId": "87ae5110-c0a7-4db3-dac5-bd44cc5fad62",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 151
        }
      },
      "source": [
        "# now the test\n",
        "wilcox.test(rank_sd ~ cat, data = wilcox_df, alternative = \"two.sided\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Warning message in wilcox.test.default(x = c(3, 1, 5, 4, 5, 2, 5, 3, 4, 1, 5, 3, :\n",
            "“cannot compute exact p-value with ties”\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\n",
              "\tWilcoxon rank sum test with continuity correction\n",
              "\n",
              "data:  rank_sd by cat\n",
              "W = 279, p-value = 0.4013\n",
              "alternative hypothesis: true location shift is not equal to 0\n"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wkRzno69ouPQ",
        "colab_type": "text"
      },
      "source": [
        "As 0.401 is a very large p-value, it seems there is not evidence of a unidirectional relationship between varied-ness within audio/ lyric features and how widely spoken a language is. Or at least there is not evidence of such a relationship that is detectable on the standard deviation level. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mTVoFFWdovzG",
        "colab_type": "text"
      },
      "source": [
        "#### IQR Rank Sum test\n",
        "\n",
        "The first of the following tables contains the IQR of each feature for each individual language. The second table replaces the IQRs with their ranks. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Nw0ox3HgpEDi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Rank sum test of iqrs\n",
        "iqrs <- esinw_df %>% select(-sid, -Tokens, -Types) %>%\n",
        "  group_by(lang) %>% summarise_all(IQR) %>% ungroup() %>%\n",
        "  mutate(is_widely_spoken = ifelse(lang %in% c('en','es'), T, F))\n",
        "\n",
        "# use a different method for key, (same drill as for sds)\n",
        "key_iqrs <- sapply(0:4,function(c){\n",
        "  iqrkeys = c()\n",
        "  for (i in 1:12){\n",
        "    iqrkeys = append(iqrkeys,\n",
        "                        IQR(key[(c*(20) + (1:20)),i]))\n",
        "  }\n",
        "  mean(iqrkeys)\n",
        "  }) \n",
        "\n",
        "iqrs$key <- key_iqrs"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fhuq5Br6pRP6",
        "colab_type": "text"
      },
      "source": [
        "Step 1: Calculate the IQR for each of the nine features for each of the five individual languages."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vf92H9YSpU8G",
        "colab_type": "code",
        "outputId": "d1df8102-96a7-4b40-94a5-3ce6ce66b29d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        }
      },
      "source": [
        "# show the standard deviations\n",
        "iqrs %>%\n",
        "  mutate(`widely spoken?` = ifelse(is_widely_spoken, \"yes\", \"no\")) %>%\n",
        "  select(-is_widely_spoken)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  lang danceability energy  key        loudness speechiness acousticness\n",
              "1 en   0.19150      0.20275 0.02083333 3.80000  0.071275    0.464075    \n",
              "2 es   0.14950      0.23650 0.08333333 4.76075  0.031775    0.349625    \n",
              "3 id   0.18025      0.26900 0.00000000 2.40550  0.005200    0.516000    \n",
              "4 nl   0.13375      0.36300 0.00000000 4.94500  0.013475    0.296925    \n",
              "5 sw   0.32975      0.14675 0.02083333 4.46025  0.108750    0.447300    \n",
              "  valence tempo    TTR        widely spoken?\n",
              "1 0.32800 37.51150 0.14966286 yes           \n",
              "2 0.42675 35.80675 0.13557898 yes           \n",
              "3 0.30625 43.10125 0.06176096 no            \n",
              "4 0.37800 28.96450 0.13508692 no            \n",
              "5 0.38325 25.14725 0.14297928 no            "
            ],
            "text/latex": "A tibble: 5 × 11\n\\begin{tabular}{lllllllllll}\n lang & danceability & energy & key & loudness & speechiness & acousticness & valence & tempo & TTR & widely spoken?\\\\\n <chr> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <chr>\\\\\n\\hline\n\t en & 0.19150 & 0.20275 & 0.02083333 & 3.80000 & 0.071275 & 0.464075 & 0.32800 & 37.51150 & 0.14966286 & yes\\\\\n\t es & 0.14950 & 0.23650 & 0.08333333 & 4.76075 & 0.031775 & 0.349625 & 0.42675 & 35.80675 & 0.13557898 & yes\\\\\n\t id & 0.18025 & 0.26900 & 0.00000000 & 2.40550 & 0.005200 & 0.516000 & 0.30625 & 43.10125 & 0.06176096 & no \\\\\n\t nl & 0.13375 & 0.36300 & 0.00000000 & 4.94500 & 0.013475 & 0.296925 & 0.37800 & 28.96450 & 0.13508692 & no \\\\\n\t sw & 0.32975 & 0.14675 & 0.02083333 & 4.46025 & 0.108750 & 0.447300 & 0.38325 & 25.14725 & 0.14297928 & no \\\\\n\\end{tabular}\n",
            "text/markdown": "\nA tibble: 5 × 11\n\n| lang &lt;chr&gt; | danceability &lt;dbl&gt; | energy &lt;dbl&gt; | key &lt;dbl&gt; | loudness &lt;dbl&gt; | speechiness &lt;dbl&gt; | acousticness &lt;dbl&gt; | valence &lt;dbl&gt; | tempo &lt;dbl&gt; | TTR &lt;dbl&gt; | widely spoken? &lt;chr&gt; |\n|---|---|---|---|---|---|---|---|---|---|---|\n| en | 0.19150 | 0.20275 | 0.02083333 | 3.80000 | 0.071275 | 0.464075 | 0.32800 | 37.51150 | 0.14966286 | yes |\n| es | 0.14950 | 0.23650 | 0.08333333 | 4.76075 | 0.031775 | 0.349625 | 0.42675 | 35.80675 | 0.13557898 | yes |\n| id | 0.18025 | 0.26900 | 0.00000000 | 2.40550 | 0.005200 | 0.516000 | 0.30625 | 43.10125 | 0.06176096 | no  |\n| nl | 0.13375 | 0.36300 | 0.00000000 | 4.94500 | 0.013475 | 0.296925 | 0.37800 | 28.96450 | 0.13508692 | no  |\n| sw | 0.32975 | 0.14675 | 0.02083333 | 4.46025 | 0.108750 | 0.447300 | 0.38325 | 25.14725 | 0.14297928 | no  |\n\n",
            "text/html": [
              "<table>\n",
              "<caption>A tibble: 5 × 11</caption>\n",
              "<thead>\n",
              "\t<tr><th scope=col>lang</th><th scope=col>danceability</th><th scope=col>energy</th><th scope=col>key</th><th scope=col>loudness</th><th scope=col>speechiness</th><th scope=col>acousticness</th><th scope=col>valence</th><th scope=col>tempo</th><th scope=col>TTR</th><th scope=col>widely spoken?</th></tr>\n",
              "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><td>en</td><td>0.19150</td><td>0.20275</td><td>0.02083333</td><td>3.80000</td><td>0.071275</td><td>0.464075</td><td>0.32800</td><td>37.51150</td><td>0.14966286</td><td>yes</td></tr>\n",
              "\t<tr><td>es</td><td>0.14950</td><td>0.23650</td><td>0.08333333</td><td>4.76075</td><td>0.031775</td><td>0.349625</td><td>0.42675</td><td>35.80675</td><td>0.13557898</td><td>yes</td></tr>\n",
              "\t<tr><td>id</td><td>0.18025</td><td>0.26900</td><td>0.00000000</td><td>2.40550</td><td>0.005200</td><td>0.516000</td><td>0.30625</td><td>43.10125</td><td>0.06176096</td><td>no </td></tr>\n",
              "\t<tr><td>nl</td><td>0.13375</td><td>0.36300</td><td>0.00000000</td><td>4.94500</td><td>0.013475</td><td>0.296925</td><td>0.37800</td><td>28.96450</td><td>0.13508692</td><td>no </td></tr>\n",
              "\t<tr><td>sw</td><td>0.32975</td><td>0.14675</td><td>0.02083333</td><td>4.46025</td><td>0.108750</td><td>0.447300</td><td>0.38325</td><td>25.14725</td><td>0.14297928</td><td>no </td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O4TExlOupgao",
        "colab_type": "text"
      },
      "source": [
        "Step 2: Rank the five languages, within each feature separately, by IQR. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gu4QMrRLphxW",
        "colab_type": "code",
        "outputId": "ee9536c7-4b88-44d7-e6e8-13ab187df68d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        }
      },
      "source": [
        "iqr_ranks = iqrs\n",
        "for (i in c(2:10)){\n",
        "  iqr_ranks[,i] <- sapply(iqrs[,i],identity) %>% rank\n",
        "}\n",
        "\n",
        "# show the ranks\n",
        "iqr_ranks %>%\n",
        "  mutate(`widely spoken?` = ifelse(is_widely_spoken, \"yes\", \"no\")) %>%\n",
        "  select(-is_widely_spoken)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  lang danceability energy key loudness speechiness acousticness valence tempo\n",
              "1 en   4            2      3.5 2        4           4            2       4    \n",
              "2 es   2            3      5.0 4        3           2            5       3    \n",
              "3 id   3            4      1.5 1        1           5            1       5    \n",
              "4 nl   1            5      1.5 5        2           1            3       2    \n",
              "5 sw   5            1      3.5 3        5           3            4       1    \n",
              "  TTR widely spoken?\n",
              "1 5   yes           \n",
              "2 3   yes           \n",
              "3 1   no            \n",
              "4 2   no            \n",
              "5 4   no            "
            ],
            "text/latex": "A tibble: 5 × 11\n\\begin{tabular}{lllllllllll}\n lang & danceability & energy & key & loudness & speechiness & acousticness & valence & tempo & TTR & widely spoken?\\\\\n <chr> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <chr>\\\\\n\\hline\n\t en & 4 & 2 & 3.5 & 2 & 4 & 4 & 2 & 4 & 5 & yes\\\\\n\t es & 2 & 3 & 5.0 & 4 & 3 & 2 & 5 & 3 & 3 & yes\\\\\n\t id & 3 & 4 & 1.5 & 1 & 1 & 5 & 1 & 5 & 1 & no \\\\\n\t nl & 1 & 5 & 1.5 & 5 & 2 & 1 & 3 & 2 & 2 & no \\\\\n\t sw & 5 & 1 & 3.5 & 3 & 5 & 3 & 4 & 1 & 4 & no \\\\\n\\end{tabular}\n",
            "text/markdown": "\nA tibble: 5 × 11\n\n| lang &lt;chr&gt; | danceability &lt;dbl&gt; | energy &lt;dbl&gt; | key &lt;dbl&gt; | loudness &lt;dbl&gt; | speechiness &lt;dbl&gt; | acousticness &lt;dbl&gt; | valence &lt;dbl&gt; | tempo &lt;dbl&gt; | TTR &lt;dbl&gt; | widely spoken? &lt;chr&gt; |\n|---|---|---|---|---|---|---|---|---|---|---|\n| en | 4 | 2 | 3.5 | 2 | 4 | 4 | 2 | 4 | 5 | yes |\n| es | 2 | 3 | 5.0 | 4 | 3 | 2 | 5 | 3 | 3 | yes |\n| id | 3 | 4 | 1.5 | 1 | 1 | 5 | 1 | 5 | 1 | no  |\n| nl | 1 | 5 | 1.5 | 5 | 2 | 1 | 3 | 2 | 2 | no  |\n| sw | 5 | 1 | 3.5 | 3 | 5 | 3 | 4 | 1 | 4 | no  |\n\n",
            "text/html": [
              "<table>\n",
              "<caption>A tibble: 5 × 11</caption>\n",
              "<thead>\n",
              "\t<tr><th scope=col>lang</th><th scope=col>danceability</th><th scope=col>energy</th><th scope=col>key</th><th scope=col>loudness</th><th scope=col>speechiness</th><th scope=col>acousticness</th><th scope=col>valence</th><th scope=col>tempo</th><th scope=col>TTR</th><th scope=col>widely spoken?</th></tr>\n",
              "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><td>en</td><td>4</td><td>2</td><td>3.5</td><td>2</td><td>4</td><td>4</td><td>2</td><td>4</td><td>5</td><td>yes</td></tr>\n",
              "\t<tr><td>es</td><td>2</td><td>3</td><td>5.0</td><td>4</td><td>3</td><td>2</td><td>5</td><td>3</td><td>3</td><td>yes</td></tr>\n",
              "\t<tr><td>id</td><td>3</td><td>4</td><td>1.5</td><td>1</td><td>1</td><td>5</td><td>1</td><td>5</td><td>1</td><td>no </td></tr>\n",
              "\t<tr><td>nl</td><td>1</td><td>5</td><td>1.5</td><td>5</td><td>2</td><td>1</td><td>3</td><td>2</td><td>2</td><td>no </td></tr>\n",
              "\t<tr><td>sw</td><td>5</td><td>1</td><td>3.5</td><td>3</td><td>5</td><td>3</td><td>4</td><td>1</td><td>4</td><td>no </td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YS8tbUXPptRi",
        "colab_type": "text"
      },
      "source": [
        "Now let's run a Wilcoxon Rank Sum test on the Inter-quartile ranges:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nPkqk_uKpxOk",
        "colab_type": "code",
        "outputId": "d487c669-501b-4dd9-f543-f6da6fec75d5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "# we need to make a dataframe that has two colums, a value column and a label column\n",
        "# the value column will actualy be a rank column (remember, we are taking ranks of ranks)\n",
        "# the label column will contain 40 TRUEs (indicating songs written in widely spoken languages)\n",
        "## and 60 FALSEs (indicating songs written in not widely spoken languages)\n",
        "wilcox_df2 <-  iqr_ranks[iqr_ranks$is_widely_spoken,1:10] %>% \n",
        "  stack() %>% .[,1] %>%\n",
        "  cbind(\"widely_spoken\") %>% \n",
        "  rbind(\n",
        "    iqr_ranks[!iqr_ranks$is_widely_spoken,1:10] %>% stack() %>% .[,1] %>%\n",
        "      cbind(\"not_widely_spoken\")\n",
        "  ) %>% data.frame() \n",
        "names(wilcox_df2) <- c(\"rank_iqr\",\"cat\")\n",
        "wilcox_df2 <- wilcox_df2 %>% mutate(rank_iqr = as.character(rank_iqr)) %>%\n",
        "  mutate(rank_iqr = as.numeric(rank_iqr))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Warning message:\n",
            "“NAs introduced by coercion”\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j8-LY3exp4b_",
        "colab_type": "text"
      },
      "source": [
        "Step 3: Take the values from Table 2 to be the set of \"observations\" on which the Wilcoxon Rank Sum Test is to be performed. Determine each one's individual *overall* rank. Keep track of which \"observations\" belong to which \"population.\""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "esbHC1SXp5bX",
        "colab_type": "code",
        "outputId": "0b8a09eb-e79e-4116-d63e-b48fdb110043",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 155
        }
      },
      "source": [
        "# create a new table to show my work\n",
        "wilcox_table = wilcox_df2\n",
        "\n",
        "# change the categorical values to make the table more readable and smaller\n",
        "wilcox_table$widely <- wilcox_table$cat %>% sapply(\n",
        "  function(x) ifelse(x == \"widely_spoken\",\"`yes`\",\"\"))\n",
        "# do this separately for the two language types\n",
        "wilcox_table$` spoken?` <- wilcox_table$cat %>% sapply(\n",
        "  function(x) ifelse(x == \"not_widely_spoken\",\"`no`\",\"\"))\n",
        "\n",
        "# get rid of the old categorical variable column\n",
        "wilcox_table$cat = NULL\n",
        "\n",
        "# get the ranks like the wilcox test does, and save in obs.rank\n",
        "obs.rank = wilcox_df2$rank_iqr %>% rank\n",
        "# make these more readable by getting rid of \".0\"s and put in \"``\"s for separation\n",
        "wilcox_table$obs_rank = obs.rank %>% as.character() %>%\n",
        "  sapply(function(x) paste0(\"`\",x,\"`\"))\n",
        "\n",
        "# make the values more readable by getting rid of \".0\"s\n",
        "wilcox_table$obs_value = wilcox_df2$rank_iqr \n",
        "\n",
        "# put it all together\n",
        "wilcox_table <- wilcox_table %>% \n",
        "  select(obs_value, obs_rank, widely, ` spoken?`) %>%\n",
        "  mutate(obs_value = as.character(obs_value))\n",
        "\n",
        "# show the first third of the table\n",
        "wilcox_table[order(obs.rank)[1:15],] %>%\n",
        "  t() %>% data.frame()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "          X25   X29   X33   X36   X40   X42   X47   X48   X30   X31   X4    \n",
              "obs_value 1     1     1     1     1     1     1     1     1.5   1.5   2     \n",
              "obs_rank  `4.5` `4.5` `4.5` `4.5` `4.5` `4.5` `4.5` `4.5` `9.5` `9.5` `14.5`\n",
              "widely                                                                `yes` \n",
              " spoken?  `no`  `no`  `no`  `no`  `no`  `no`  `no`  `no`  `no`  `no`        \n",
              "          X5     X9     X14    X15   \n",
              "obs_value 2      2      2      2     \n",
              "obs_rank  `14.5` `14.5` `14.5` `14.5`\n",
              "widely    `yes`  `yes`  `yes`  `yes` \n",
              " spoken?                             "
            ],
            "text/latex": "A data.frame: 4 × 15\n\\begin{tabular}{r|lllllllllllllll}\n  & X25 & X29 & X33 & X36 & X40 & X42 & X47 & X48 & X30 & X31 & X4 & X5 & X9 & X14 & X15\\\\\n  & <fct> & <fct> & <fct> & <fct> & <fct> & <fct> & <fct> & <fct> & <fct> & <fct> & <fct> & <fct> & <fct> & <fct> & <fct>\\\\\n\\hline\n\tobs\\_value & 1     & 1     & 1     & 1     & 1     & 1     & 1     & 1     & 1.5   & 1.5   & 2      & 2      & 2      & 2      & 2     \\\\\n\tobs\\_rank & `4.5` & `4.5` & `4.5` & `4.5` & `4.5` & `4.5` & `4.5` & `4.5` & `9.5` & `9.5` & `14.5` & `14.5` & `14.5` & `14.5` & `14.5`\\\\\n\twidely &       &       &       &       &       &       &       &       &       &       & `yes`  & `yes`  & `yes`  & `yes`  & `yes` \\\\\n\t spoken? & `no`  & `no`  & `no`  & `no`  & `no`  & `no`  & `no`  & `no`  & `no`  & `no`  &        &        &        &        &       \\\\\n\\end{tabular}\n",
            "text/markdown": "\nA data.frame: 4 × 15\n\n| <!--/--> | X25 &lt;fct&gt; | X29 &lt;fct&gt; | X33 &lt;fct&gt; | X36 &lt;fct&gt; | X40 &lt;fct&gt; | X42 &lt;fct&gt; | X47 &lt;fct&gt; | X48 &lt;fct&gt; | X30 &lt;fct&gt; | X31 &lt;fct&gt; | X4 &lt;fct&gt; | X5 &lt;fct&gt; | X9 &lt;fct&gt; | X14 &lt;fct&gt; | X15 &lt;fct&gt; |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| obs_value | 1     | 1     | 1     | 1     | 1     | 1     | 1     | 1     | 1.5   | 1.5   | 2      | 2      | 2      | 2      | 2      |\n| obs_rank | `4.5` | `4.5` | `4.5` | `4.5` | `4.5` | `4.5` | `4.5` | `4.5` | `9.5` | `9.5` | `14.5` | `14.5` | `14.5` | `14.5` | `14.5` |\n| widely | <!----> | <!----> | <!----> | <!----> | <!----> | <!----> | <!----> | <!----> | <!----> | <!----> | `yes`  | `yes`  | `yes`  | `yes`  | `yes`  |\n|  spoken? | `no`  | `no`  | `no`  | `no`  | `no`  | `no`  | `no`  | `no`  | `no`  | `no`  | <!----> | <!----> | <!----> | <!----> | <!----> |\n\n",
            "text/html": [
              "<table>\n",
              "<caption>A data.frame: 4 × 15</caption>\n",
              "<thead>\n",
              "\t<tr><th></th><th scope=col>X25</th><th scope=col>X29</th><th scope=col>X33</th><th scope=col>X36</th><th scope=col>X40</th><th scope=col>X42</th><th scope=col>X47</th><th scope=col>X48</th><th scope=col>X30</th><th scope=col>X31</th><th scope=col>X4</th><th scope=col>X5</th><th scope=col>X9</th><th scope=col>X14</th><th scope=col>X15</th></tr>\n",
              "\t<tr><th></th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><th scope=row>obs_value</th><td>1    </td><td>1    </td><td>1    </td><td>1    </td><td>1    </td><td>1    </td><td>1    </td><td>1    </td><td>1.5  </td><td>1.5  </td><td>2     </td><td>2     </td><td>2     </td><td>2     </td><td>2     </td></tr>\n",
              "\t<tr><th scope=row>obs_rank</th><td>`4.5`</td><td>`4.5`</td><td>`4.5`</td><td>`4.5`</td><td>`4.5`</td><td>`4.5`</td><td>`4.5`</td><td>`4.5`</td><td>`9.5`</td><td>`9.5`</td><td>`14.5`</td><td>`14.5`</td><td>`14.5`</td><td>`14.5`</td><td>`14.5`</td></tr>\n",
              "\t<tr><th scope=row>widely</th><td>     </td><td>     </td><td>     </td><td>     </td><td>     </td><td>     </td><td>     </td><td>     </td><td>     </td><td>     </td><td>`yes` </td><td>`yes` </td><td>`yes` </td><td>`yes` </td><td>`yes` </td></tr>\n",
              "\t<tr><th scope=row> spoken?</th><td>`no` </td><td>`no` </td><td>`no` </td><td>`no` </td><td>`no` </td><td>`no` </td><td>`no` </td><td>`no` </td><td>`no` </td><td>`no` </td><td>      </td><td>      </td><td>      </td><td>      </td><td>      </td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mMPOTxNEqAkj",
        "colab_type": "code",
        "outputId": "c9d57a0a-694a-4a65-e0a6-43d7ac0d771f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 155
        }
      },
      "source": [
        "#show the second third of the table\n",
        "wilcox_table[order(obs.rank)[16:30],] %>%\n",
        "  t() %>% data.frame() "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "          X37    X46    X49    X6     X12    X18    X20    X24    X35    X41   \n",
              "obs_value 2      2      2      3      3      3      3      3      3      3     \n",
              "obs_rank  `14.5` `14.5` `14.5` `22.5` `22.5` `22.5` `22.5` `22.5` `22.5` `22.5`\n",
              "widely                         `yes`  `yes`  `yes`  `yes`                      \n",
              " spoken?  `no`   `no`   `no`                               `no`   `no`   `no`  \n",
              "          X43    X7     X32    X3     X10   \n",
              "obs_value 3      3.5    3.5    4      4     \n",
              "obs_rank  `22.5` `27.5` `27.5` `32.5` `32.5`\n",
              "widely           `yes`         `yes`  `yes` \n",
              " spoken?  `no`          `no`                "
            ],
            "text/latex": "A data.frame: 4 × 15\n\\begin{tabular}{r|lllllllllllllll}\n  & X37 & X46 & X49 & X6 & X12 & X18 & X20 & X24 & X35 & X41 & X43 & X7 & X32 & X3 & X10\\\\\n  & <fct> & <fct> & <fct> & <fct> & <fct> & <fct> & <fct> & <fct> & <fct> & <fct> & <fct> & <fct> & <fct> & <fct> & <fct>\\\\\n\\hline\n\tobs\\_value & 2      & 2      & 2      & 3      & 3      & 3      & 3      & 3      & 3      & 3      & 3      & 3.5    & 3.5    & 4      & 4     \\\\\n\tobs\\_rank & `14.5` & `14.5` & `14.5` & `22.5` & `22.5` & `22.5` & `22.5` & `22.5` & `22.5` & `22.5` & `22.5` & `27.5` & `27.5` & `32.5` & `32.5`\\\\\n\twidely &        &        &        & `yes`  & `yes`  & `yes`  & `yes`  &        &        &        &        & `yes`  &        & `yes`  & `yes` \\\\\n\t spoken? & `no`   & `no`   & `no`   &        &        &        &        & `no`   & `no`   & `no`   & `no`   &        & `no`   &        &       \\\\\n\\end{tabular}\n",
            "text/markdown": "\nA data.frame: 4 × 15\n\n| <!--/--> | X37 &lt;fct&gt; | X46 &lt;fct&gt; | X49 &lt;fct&gt; | X6 &lt;fct&gt; | X12 &lt;fct&gt; | X18 &lt;fct&gt; | X20 &lt;fct&gt; | X24 &lt;fct&gt; | X35 &lt;fct&gt; | X41 &lt;fct&gt; | X43 &lt;fct&gt; | X7 &lt;fct&gt; | X32 &lt;fct&gt; | X3 &lt;fct&gt; | X10 &lt;fct&gt; |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| obs_value | 2      | 2      | 2      | 3      | 3      | 3      | 3      | 3      | 3      | 3      | 3      | 3.5    | 3.5    | 4      | 4      |\n| obs_rank | `14.5` | `14.5` | `14.5` | `22.5` | `22.5` | `22.5` | `22.5` | `22.5` | `22.5` | `22.5` | `22.5` | `27.5` | `27.5` | `32.5` | `32.5` |\n| widely | <!----> | <!----> | <!----> | `yes`  | `yes`  | `yes`  | `yes`  | <!----> | <!----> | <!----> | <!----> | `yes`  | <!----> | `yes`  | `yes`  |\n|  spoken? | `no`   | `no`   | `no`   | <!----> | <!----> | <!----> | <!----> | `no`   | `no`   | `no`   | `no`   | <!----> | `no`   | <!----> | <!----> |\n\n",
            "text/html": [
              "<table>\n",
              "<caption>A data.frame: 4 × 15</caption>\n",
              "<thead>\n",
              "\t<tr><th></th><th scope=col>X37</th><th scope=col>X46</th><th scope=col>X49</th><th scope=col>X6</th><th scope=col>X12</th><th scope=col>X18</th><th scope=col>X20</th><th scope=col>X24</th><th scope=col>X35</th><th scope=col>X41</th><th scope=col>X43</th><th scope=col>X7</th><th scope=col>X32</th><th scope=col>X3</th><th scope=col>X10</th></tr>\n",
              "\t<tr><th></th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><th scope=row>obs_value</th><td>2     </td><td>2     </td><td>2     </td><td>3     </td><td>3     </td><td>3     </td><td>3     </td><td>3     </td><td>3     </td><td>3     </td><td>3     </td><td>3.5   </td><td>3.5   </td><td>4     </td><td>4     </td></tr>\n",
              "\t<tr><th scope=row>obs_rank</th><td>`14.5`</td><td>`14.5`</td><td>`14.5`</td><td>`22.5`</td><td>`22.5`</td><td>`22.5`</td><td>`22.5`</td><td>`22.5`</td><td>`22.5`</td><td>`22.5`</td><td>`22.5`</td><td>`27.5`</td><td>`27.5`</td><td>`32.5`</td><td>`32.5`</td></tr>\n",
              "\t<tr><th scope=row>widely</th><td>      </td><td>      </td><td>      </td><td>`yes` </td><td>`yes` </td><td>`yes` </td><td>`yes` </td><td>      </td><td>      </td><td>      </td><td>      </td><td>`yes` </td><td>      </td><td>`yes` </td><td>`yes` </td></tr>\n",
              "\t<tr><th scope=row> spoken?</th><td>`no`  </td><td>`no`  </td><td>`no`  </td><td>      </td><td>      </td><td>      </td><td>      </td><td>`no`  </td><td>`no`  </td><td>`no`  </td><td>`no`  </td><td>      </td><td>`no`  </td><td>      </td><td>      </td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gUT-S3B_qF_u",
        "colab_type": "code",
        "outputId": "657ba5ae-a301-4464-934c-a1ca2b190e03",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 155
        }
      },
      "source": [
        "#show the last third of the table\n",
        "wilcox_table[order(obs.rank)[31:45],] %>%\n",
        "  t() %>% data.frame() "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "          X11    X13    X17    X27    X44    X50    X8    X16   X19   X26  X28 \n",
              "obs_value 4      4      4      4      4      4      5     5     5     5    5   \n",
              "obs_rank  `32.5` `32.5` `32.5` `32.5` `32.5` `32.5` `41`  `41`  `41`  `41` `41`\n",
              "widely    `yes`  `yes`  `yes`                       `yes` `yes` `yes`          \n",
              " spoken?                       `no`   `no`   `no`                     `no` `no`\n",
              "          X34  X38  X39  X45 \n",
              "obs_value 5    5    5    5   \n",
              "obs_rank  `41` `41` `41` `41`\n",
              "widely                       \n",
              " spoken?  `no` `no` `no` `no`"
            ],
            "text/latex": "A data.frame: 4 × 15\n\\begin{tabular}{r|lllllllllllllll}\n  & X11 & X13 & X17 & X27 & X44 & X50 & X8 & X16 & X19 & X26 & X28 & X34 & X38 & X39 & X45\\\\\n  & <fct> & <fct> & <fct> & <fct> & <fct> & <fct> & <fct> & <fct> & <fct> & <fct> & <fct> & <fct> & <fct> & <fct> & <fct>\\\\\n\\hline\n\tobs\\_value & 4      & 4      & 4      & 4      & 4      & 4      & 5     & 5     & 5     & 5    & 5    & 5    & 5    & 5    & 5   \\\\\n\tobs\\_rank & `32.5` & `32.5` & `32.5` & `32.5` & `32.5` & `32.5` & `41`  & `41`  & `41`  & `41` & `41` & `41` & `41` & `41` & `41`\\\\\n\twidely & `yes`  & `yes`  & `yes`  &        &        &        & `yes` & `yes` & `yes` &      &      &      &      &      &     \\\\\n\t spoken? &        &        &        & `no`   & `no`   & `no`   &       &       &       & `no` & `no` & `no` & `no` & `no` & `no`\\\\\n\\end{tabular}\n",
            "text/markdown": "\nA data.frame: 4 × 15\n\n| <!--/--> | X11 &lt;fct&gt; | X13 &lt;fct&gt; | X17 &lt;fct&gt; | X27 &lt;fct&gt; | X44 &lt;fct&gt; | X50 &lt;fct&gt; | X8 &lt;fct&gt; | X16 &lt;fct&gt; | X19 &lt;fct&gt; | X26 &lt;fct&gt; | X28 &lt;fct&gt; | X34 &lt;fct&gt; | X38 &lt;fct&gt; | X39 &lt;fct&gt; | X45 &lt;fct&gt; |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| obs_value | 4      | 4      | 4      | 4      | 4      | 4      | 5     | 5     | 5     | 5    | 5    | 5    | 5    | 5    | 5    |\n| obs_rank | `32.5` | `32.5` | `32.5` | `32.5` | `32.5` | `32.5` | `41`  | `41`  | `41`  | `41` | `41` | `41` | `41` | `41` | `41` |\n| widely | `yes`  | `yes`  | `yes`  | <!----> | <!----> | <!----> | `yes` | `yes` | `yes` | <!----> | <!----> | <!----> | <!----> | <!----> | <!----> |\n|  spoken? | <!----> | <!----> | <!----> | `no`   | `no`   | `no`   | <!----> | <!----> | <!----> | `no` | `no` | `no` | `no` | `no` | `no` |\n\n",
            "text/html": [
              "<table>\n",
              "<caption>A data.frame: 4 × 15</caption>\n",
              "<thead>\n",
              "\t<tr><th></th><th scope=col>X11</th><th scope=col>X13</th><th scope=col>X17</th><th scope=col>X27</th><th scope=col>X44</th><th scope=col>X50</th><th scope=col>X8</th><th scope=col>X16</th><th scope=col>X19</th><th scope=col>X26</th><th scope=col>X28</th><th scope=col>X34</th><th scope=col>X38</th><th scope=col>X39</th><th scope=col>X45</th></tr>\n",
              "\t<tr><th></th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><th scope=row>obs_value</th><td>4     </td><td>4     </td><td>4     </td><td>4     </td><td>4     </td><td>4     </td><td>5    </td><td>5    </td><td>5    </td><td>5   </td><td>5   </td><td>5   </td><td>5   </td><td>5   </td><td>5   </td></tr>\n",
              "\t<tr><th scope=row>obs_rank</th><td>`32.5`</td><td>`32.5`</td><td>`32.5`</td><td>`32.5`</td><td>`32.5`</td><td>`32.5`</td><td>`41` </td><td>`41` </td><td>`41` </td><td>`41`</td><td>`41`</td><td>`41`</td><td>`41`</td><td>`41`</td><td>`41`</td></tr>\n",
              "\t<tr><th scope=row>widely</th><td>`yes` </td><td>`yes` </td><td>`yes` </td><td>      </td><td>      </td><td>      </td><td>`yes`</td><td>`yes`</td><td>`yes`</td><td>    </td><td>    </td><td>    </td><td>    </td><td>    </td><td>    </td></tr>\n",
              "\t<tr><th scope=row> spoken?</th><td>      </td><td>      </td><td>      </td><td>`no`  </td><td>`no`  </td><td>`no`  </td><td>     </td><td>     </td><td>     </td><td>`no`</td><td>`no`</td><td>`no`</td><td>`no`</td><td>`no`</td><td>`no`</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LEOykn15qNMK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# delete the table, we don't need this anymore\n",
        "rm(wilcox_table)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b48HUO70qXFw",
        "colab_type": "text"
      },
      "source": [
        "Now let's run the test to see if the variation behavior of songs' audio/ lyric features (as can be detected at the IQR level) is associated with how widely spoken the language of their lyrics:\n",
        "\n",
        "Alternative Hypothesis: The features of music written in more widely spoken languages will vary to either a greater or a lesser extent than the features of music written in less widely spoken languages."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cReeazH0qcJa",
        "colab_type": "code",
        "outputId": "4438a7c4-f63b-46c9-aae6-a188017cafba",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 151
        }
      },
      "source": [
        "# now the test\n",
        "wilcox.test(rank_iqr ~ cat, data = wilcox_df2, alternative = \"two.sided\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Warning message in wilcox.test.default(x = c(3, 1, 5, 4, 5, 1, 1.5, 1.5, 3.5, 1, :\n",
            "“cannot compute exact p-value with ties”\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\n",
              "\tWilcoxon rank sum test with continuity correction\n",
              "\n",
              "data:  rank_iqr by cat\n",
              "W = 181.5, p-value = 0.1513\n",
              "alternative hypothesis: true location shift is not equal to 0\n"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bl0UAhgxyAKr",
        "colab_type": "text"
      },
      "source": [
        "It's a smaller p-value than we got from standard deviation. Still, 0.1513 is too large of a p-value to provide evidence supporting the existence of a one-way spectrum of variablity in audio/ lyric features associated with how widely spoken a language is. \n",
        "\n",
        "Based on these results, there is *no relationship* between varied-ness in audio/ lyric features and wide-spokenness of lyric language. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dbBx_dl50eVl",
        "colab_type": "text"
      },
      "source": [
        "### Kmeans Clustering"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fFHydLcY0sEW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# prep sd df for kmeans\n",
        "sds <- sds %>% as.data.frame()\n",
        "rownames(sds) <- sds$lang\n",
        "sds <- sds %>% select(-lang, -is_widely_spoken)\n",
        "\n",
        "# prep iqr df for kmeans\n",
        "iqrs <- iqrs %>% as.data.frame()\n",
        "rownames(iqrs) <- iqrs$lang\n",
        "iqrs <- iqrs %>% select(-lang, -is_widely_spoken)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-QpX2vh40z4E",
        "colab_type": "text"
      },
      "source": [
        "I used the $\\verb|cluster|$ and $\\verb|factoextra|$ packages to conduct Kmeans clustering analysis, first by Standard Deviation, and then by IQR. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OFHRhvka02HM",
        "colab_type": "text"
      },
      "source": [
        "#### Kmeans Clustering by Standard Deviations"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O1J9jiPu0u9F",
        "colab_type": "code",
        "outputId": "103181f5-139b-4448-d460-7b70aaed3743",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 806
        }
      },
      "source": [
        "# We want to see the data split into 2 clusters.\n",
        "# So we specify \"centers\" to be 2.\n",
        "km_sds <- kmeans(sds, centers = 2, nstart = 25)\n",
        "\n",
        "# lets take a look at the important information about our clusters.\n",
        "km_sds\n",
        "\n",
        "# And plot the result wit with fviz_cluster\n",
        "fviz_cluster(km_sds, data = sds)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "K-means clustering with 2 clusters of sizes 2, 3\n",
              "\n",
              "Cluster means:\n",
              "  danceability    energy       key loudness speechiness acousticness   valence\n",
              "1    0.1375631 0.1755834 0.2262568 2.704382  0.07033725    0.2602860 0.2297569\n",
              "2    0.1471426 0.2005529 0.2379069 2.847148  0.05583733    0.2810203 0.2332882\n",
              "     tempo        TTR\n",
              "1 30.71744 0.08874493\n",
              "2 27.23497 0.09480847\n",
              "\n",
              "Clustering vector:\n",
              "en es id nl sw \n",
              " 1  1  2  2  2 \n",
              "\n",
              "Within cluster sum of squares by cluster:\n",
              "[1] 3.010241 1.822857\n",
              " (between_SS / total_SS =  75.1 %)\n",
              "\n",
              "Available components:\n",
              "\n",
              "[1] \"cluster\"      \"centers\"      \"totss\"        \"withinss\"     \"tot.withinss\"\n",
              "[6] \"betweenss\"    \"size\"         \"iter\"         \"ifault\"      "
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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RdddNHQoUOLWQAAAHQrms2ySFjo2ZtQ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BD0F+xmzZr1i1/8ore3d9d3LlmyZOnSpfPmzfvFL35R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\nrkJggg==",
            "text/plain": [
              "plot without title"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 420,
              "height": 420
            },
            "text/plain": {
              "width": 420,
              "height": 420
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ITw9_E6d1ZPr",
        "colab_type": "text"
      },
      "source": [
        "Take a look at the numbers in the table below, which is a reformated version of the table shown in the R output chunk above. The row containing cluster means for the cluster which was formed around English and Spanish is labeled \"widely spoken.\" Seven times out of nine, the features of music from widely spoken languages varied *less*, on average, than features of music from \"not widely spoken\" languages, judged on the basis of standard deviation. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O0ZlwDsv1fMp",
        "colab_type": "code",
        "outputId": "64a5ed27-d9fa-4b6e-c2c0-1b7a7646eb89",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 115
        }
      },
      "source": [
        "# make a table for the infomation that matters with useful labels\n",
        "table5 <- km_sds$centers %>% round(3) %>% data.frame()\n",
        "\n",
        "# correct rownames\n",
        "rownames(table5) <- c(\"widely spoken\",\"not widely spoken\")\n",
        "\n",
        "# show table\n",
        "table5"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                  danceability energy key   loudness speechiness acousticness\n",
              "widely spoken     0.138        0.176  0.226 2.704    0.070       0.260       \n",
              "not widely spoken 0.147        0.201  0.238 2.847    0.056       0.281       \n",
              "                  valence tempo  TTR  \n",
              "widely spoken     0.230   30.717 0.089\n",
              "not widely spoken 0.233   27.235 0.095"
            ],
            "text/latex": "A data.frame: 2 × 9\n\\begin{tabular}{r|lllllllll}\n  & danceability & energy & key & loudness & speechiness & acousticness & valence & tempo & TTR\\\\\n  & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n\\hline\n\twidely spoken & 0.138 & 0.176 & 0.226 & 2.704 & 0.070 & 0.260 & 0.230 & 30.717 & 0.089\\\\\n\tnot widely spoken & 0.147 & 0.201 & 0.238 & 2.847 & 0.056 & 0.281 & 0.233 & 27.235 & 0.095\\\\\n\\end{tabular}\n",
            "text/markdown": "\nA data.frame: 2 × 9\n\n| <!--/--> | danceability &lt;dbl&gt; | energy &lt;dbl&gt; | key &lt;dbl&gt; | loudness &lt;dbl&gt; | speechiness &lt;dbl&gt; | acousticness &lt;dbl&gt; | valence &lt;dbl&gt; | tempo &lt;dbl&gt; | TTR &lt;dbl&gt; |\n|---|---|---|---|---|---|---|---|---|---|\n| widely spoken | 0.138 | 0.176 | 0.226 | 2.704 | 0.070 | 0.260 | 0.230 | 30.717 | 0.089 |\n| not widely spoken | 0.147 | 0.201 | 0.238 | 2.847 | 0.056 | 0.281 | 0.233 | 27.235 | 0.095 |\n\n",
            "text/html": [
              "<table>\n",
              "<caption>A data.frame: 2 × 9</caption>\n",
              "<thead>\n",
              "\t<tr><th></th><th scope=col>danceability</th><th scope=col>energy</th><th scope=col>key</th><th scope=col>loudness</th><th scope=col>speechiness</th><th scope=col>acousticness</th><th scope=col>valence</th><th scope=col>tempo</th><th scope=col>TTR</th></tr>\n",
              "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><th scope=row>widely spoken</th><td>0.138</td><td>0.176</td><td>0.226</td><td>2.704</td><td>0.070</td><td>0.260</td><td>0.230</td><td>30.717</td><td>0.089</td></tr>\n",
              "\t<tr><th scope=row>not widely spoken</th><td>0.147</td><td>0.201</td><td>0.238</td><td>2.847</td><td>0.056</td><td>0.281</td><td>0.233</td><td>27.235</td><td>0.095</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kbfxyRdyRvTy",
        "colab_type": "text"
      },
      "source": [
        "#### Kmeans Clustering by IQRs\n",
        "\n",
        "Let's try it again with IQR instead of standard deviation."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mioCvGvhRwbC",
        "colab_type": "code",
        "outputId": "9c548080-8254-4d1e-e07b-d93bdc8e6580",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 806
        }
      },
      "source": [
        "# once again, we'll start with 2 clusters\n",
        "km_iqrs <- kmeans(iqrs, centers = 2, nstart = 25)\n",
        "# information, please\n",
        "km_iqrs\n",
        "# Plotting the result...\n",
        "fviz_cluster(km_iqrs, data = iqrs)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "K-means clustering with 2 clusters of sizes 2, 3\n",
              "\n",
              "Cluster means:\n",
              "  danceability    energy        key loudness speechiness acousticness   valence\n",
              "1      0.23175 0.2548750 0.01041667 4.702625  0.06111250    0.3721125 0.3806250\n",
              "2      0.17375 0.2360833 0.03472222 3.655417  0.03608333    0.4432333 0.3536667\n",
              "     tempo       TTR\n",
              "1 27.05588 0.1390331\n",
              "2 38.80650 0.1156676\n",
              "\n",
              "Clustering vector:\n",
              "en es id nl sw \n",
              " 2  2  2  1  1 \n",
              "\n",
              "Within cluster sum of squares by cluster:\n",
              "[1]  7.461887 31.961671\n",
              " (between_SS / total_SS =  80.9 %)\n",
              "\n",
              "Available components:\n",
              "\n",
              "[1] \"cluster\"      \"centers\"      \"totss\"        \"withinss\"     \"tot.withinss\"\n",
              "[6] \"betweenss\"    \"size\"         \"iter\"         \"ifault\"      "
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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efKhnXa6LFCCCFJ2ojRyto1yvpvsuN2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\nwYKhQ4dOnTrV7Fqs4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            "text/plain": [
              "plot without title"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 420,
              "height": 420
            },
            "text/plain": {
              "width": 420,
              "height": 420
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fTr6z-LpR3Th",
        "colab_type": "text"
      },
      "source": [
        "The algorithm did not split the data into the clusters we were hoping to see. Therefore, there is not evidence worth looking further into of relationship between varied-ness in audio/ lyric features and how widely spoken a language is at the IQR level.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PoEJ91nFUMfl",
        "colab_type": "text"
      },
      "source": [
        "#### So what?\n",
        "\n",
        "The clusters actually split the languages into widely and not widely spoken languages! \n",
        "\n",
        "But what does that mean? We'll need to do more investigating to see if anything can actually be concluded. \n",
        "\n",
        "There are ${5 \\choose 0} + {5 \\choose 1} + {5 \\choose 2} = 1 + 5 + 10 = 16$ possible ways that Kmeans could split 5 languages into exactly two groups, $5 + 10 = 15$ ways if we force each group to have at least one member. \n",
        "\n",
        "If we assume that, under a null hypothesis of *$H_0$: no relationship between varied-ness in audio/ lyric features and how widely spoken a language is*, there is an equal chance of each clustering outcome, there would be a $\\frac{1}{16}$ or $\\frac{1}{15}$ chance that, we would observe the outcome we got (we'll call this the target outcome). \n",
        "\n",
        "But we can do better than that. Rather than assuming equal chance and using fractions to determine the probability of seeing the outcome we observed (the target outcome), let's use simulations to come up with a more insightful estimate for our p-value. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wtm1maEjTQCI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# let's get rid of any extra baggage before we get too deep \n",
        "esinw_df2 <- dplyr::select(esinw_df,-sid,-Tokens,-Types)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3TNhNUQ9XqYx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# checks to see if the simulated data's 2-means clustering outcome was the same as the target\n",
        "# Inputs: df <data.frame> (a dataframe with the same structure as esinw_df2)\n",
        "  # spread_fxn <function> (a function for computing spread, for example sd() or iqr())\n",
        "  # target <chr vector> (the language labels making up the one cluster of the target outcome)\n",
        "# Output: <logical> (True if the simulated data's 2-means clustering outcome was the same as the target, FALSE otherwise)\n",
        "check_clusters <- function(df, spread_fxn, target){\n",
        "  dummies <- df[order(df$lang),] %>% select(key) \n",
        "  # and its levels\n",
        "  dum.levels <- levels(factor(unlist(dummies)))\n",
        "  key <- lapply(dummies, function(x) {\n",
        "    table(sequence(nrow(dummies)), factor(x, levels = dum.levels))\n",
        "  })$key\n",
        "  # now the spread metric\n",
        "  spreads <- df %>% group_by(lang) %>% summarise_all(spread_fxn) %>% ungroup() \n",
        "  # use a different method for key, as it's a not actually a numeric variable.\n",
        "  # for each language, make a binary vector for each of the 12 possible values, \n",
        "  ## with 1s in all places where key = key[i], and 0s elsewhere\n",
        "  ## run spread_fxn on each of those 5 vectors individually, \n",
        "  ## and then take the mean of those 5 values\n",
        "  key_spreads <- sapply(0:4,function(c){\n",
        "    spreadskeys = c()\n",
        "    for (i in 1:12){\n",
        "      spreadskeys = append(spreadskeys,\n",
        "                          spread_fxn(key[(c*(20) + (1:20)),i]))}\n",
        "    mean(spreadskeys)}) \n",
        "  # store that in the key column of the standard devation data frame\n",
        "  spreads$key <- key_spreads\n",
        "  # prep spread df for kmeans\n",
        "  spreads <- spreads %>% as.data.frame()\n",
        "  rownames(spreads) <- spreads$lang\n",
        "  spreads <- spreads %>% select(-lang)\n",
        "  # We want to see the data split into 2 clusters.\n",
        "  # So we specify \"centers\" to be 2.\n",
        "  km_spreads <- kmeans(spreads, centers = 2, nstart = 25)\n",
        "  # check if the clusters we got are the ones we wanted.\n",
        "  identical(target,\n",
        "            names(which(km_spreads$cluster==km_spreads$cluster[target[1]])))\n",
        "}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Emjswo3X27fm",
        "colab_type": "text"
      },
      "source": [
        "This will go very slowly, so we'll speed it up with help from R's $\\verb|parallel|$ package"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R00d6QF6Wiro",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# function to simulate the data n times and give the proportion of simulations whose 2-means clustering outcome matched the target\n",
        "# Inputs: n <integer> (the number of simulations)\n",
        "  # df <data.frame> (a dataframe with the same structure as esinw_df2)\n",
        "  # col2sim <chr> (the name of the label column)\n",
        "  # spread_fxn <function> (a function for computing spread, for example sd() or iqr())\n",
        "  # target <chr vector> (the language labels making up the one cluster of the target outcome)\n",
        "# Output: <logical> (True if the simulated data's 2-means clustering outcome was the same as the target, FALSE otherwise)\n",
        "ptarget_outcome <- function(n, df, col2sim, spread_fxn, target){\n",
        "  # this is for parallel computing, these clusters have nothing to do with the k-means clusters\n",
        "  cl <- makeCluster(detectCores())\n",
        "  # same with these clusters: \n",
        "  # now we need to tell the master node what variables to load up each worker node with so they can each do their job\n",
        "  clusterExport(cl, envir = environment(),\n",
        "                varlist = c(\"n\", \"df\", \"col2sim\", \"spread_fxn\", \"target\", \"check_clusters\", \"kmeans\",\n",
        "                            \"%>%\", \"select\", \"group_by\", \"ungroup\", \"summarise_all\"))\n",
        "  p <- mean(parSapply(\n",
        "      cl = cl, X = 1:n, FUN = function(x) {\n",
        "        # randomly scramble the label column (in order to randomly assign 20% of the songs to each language)\n",
        "        df[,col2sim] <- sample(df[,col2sim])\n",
        "        # see if the simulated data's 2-means clustering outcome was the same as the target\n",
        "        check_clusters(df = df, spread_fxn = spread_fxn, target = target)\n",
        "  })) # count the number of TRUEs and divide it by n\n",
        "  stopCluster(cl)\n",
        "  return(p)\n",
        "}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V33wdYkwjSr7",
        "colab_type": "text"
      },
      "source": [
        "Now the moment of truth..."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rweH6YeTjRaK",
        "colab_type": "code",
        "outputId": "a888614b-2a91-4f3a-ff55-ca49e7870ce2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "set.seed(74)\n",
        "prob_sds_n100000p <- ptarget_outcome(\n",
        "  n = 100000, df = esinw_df2, col2sim = \"lang\", spread_fxn = sd, target = c(\"en\",\"es\"))\n",
        "print(prob_sds_n100000p)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1] 0.06432\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K3mrq8pFWhy4",
        "colab_type": "text"
      },
      "source": [
        "Roughly speaking, when there is no relationship between varied-ness in audio/ lyric features and how widely spoken a language is, the target outcome has about a 0.06432 chance of occurring.\n",
        "\n",
        "We saw that in the standard deviation case, the target outcome,\n",
        "\n",
        "* English (en), Spanish (es)\n",
        "\n",
        "* Indonesian (id), Dutch (nl), Swahili (sw),\n",
        "\n",
        "actually did what we were hoping, and split the real languages into widely and not widely spoken languages.\n",
        "\n",
        "But what does that mean? We've needed to do more investigating to see if anything can actually be concluded. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ualLthYEkwTB",
        "colab_type": "text"
      },
      "source": [
        "#### **Statistical Hypothesis test:** \n",
        "\n",
        "$H_0$: There is *no relationship* between varied-ness in audio/ lyric features and how widely spoken a language is.\n",
        "\n",
        "$H_a$: There is a *relationship* between varied-ness in audio/ lyric features and how widely spoken a language is.\n",
        "\n",
        "* $\\alpha = .05$\n",
        "\n",
        "* $p.value = 0.06432$\n",
        "\n",
        "We discovered that, under the null hypothesis of no relationship, 0.06432 is the probability that the clustering procedure groups the languages in the following way:\n",
        "\n",
        "* English (en), Spanish (es)\n",
        "\n",
        "* Indonesian (id), Dutch (nl), Swahili (sw)\n",
        "\n",
        "Our p-value of 0.06432 is not small enough. We fail to reject the null hypothesis of no relationship between varied-ness in audio/ lyric features and how widely spoken a language is, on the grounds of standard deviation."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZhjuU73bzKFM",
        "colab_type": "text"
      },
      "source": [
        "# Discussion\n",
        "\n",
        "Standard deviation is not outlier-resistant, which is one way IQR differs from it. Standard Deviation is also more inflated by bimodal distribution behavior than IQR is, and as we saw in those density curves, there were quite a few cases of bimodality. I found it interesting that the outcome in the standard deviation version of the Kmeans test was closer to rejecting the null than the outcome in the IQR version. Yet, in the Rank Sum tests, though in neither version of the test was the null rejected, the standard deviation version resulted in a p-value that was, by far, the least rejectable (0.531).\n",
        "\n",
        "I have a theory for why this would be. Because ranks were used instead of the original values in the Rank Sum tests, magnitude of difference was not able to have any influence. However, magnitude of difference *was* able to influence the Kmeans test. This difference between Rank Sum test and Kmeans test didn't seem to change much in the case of IQR. However, it made a huge difference in the case of standard deviation. Standard deviation has more moving parts than IQR, making it more likely that distances between two values in the set of 45 standard deviations would vary more than distances between two values in the set of 45 IQRs. This might be one reason why the conclusions of the two hypothesis tests for standard deviation differed. \n",
        "\n",
        "Let's talk about what we observed in the standard-deviation based kmeans [table](https://colab.research.google.com/drive/1TUQto0h05axO4ak-X6CxlFMBNYuMqpWf#scrollTo=O0ZlwDsv1fMp&line=7&uniqifier=1). It was made apparent, for 78% of features, that there was less in-language variation in music from widely spoken languages than music from less widely spoken languages. \n",
        "\n",
        "This, I found quite interesting and unexpected. Initially, I predicited that, if anything, there would be *more* variation among songs from *more* widely spoken languages. The *more* places a language is spoken, the *more* speakers it has. The *more* speakers a language has, the *more* singers it has. The *more* singers that contribute their voices to music in a language, the *more* variation exists among music produced in it. \n",
        "\n",
        "However, there is another side to all of this. Even if the *list* of songs I began with for this study were truly a *simple random sample* of *all* the songs from each language, I'm still limited to only the songs with:\n",
        "\n",
        "* audio feature data on Spotify,\n",
        "\n",
        "* lyrics on Musixmatch,\n",
        "\n",
        "* lyrics encoded in a way that the software I'm using can process,\n",
        "\n",
        "* encoded lyrics decoded in a way that is recognized (as non-gibberish) by the language-detection function,\n",
        "\n",
        "* and decoded lyrics' languages correctly detected by the language-detection function.\n",
        "\n",
        "Probably, the more commercially successful the song, the more likely it is to have remained in the sampleable dataset by the end of all this. Therefore, we can't think just about the music that gets *made* by a lot of people. We have to think also about the music that gets *heard* by a lot of people. There are certain qualities associated with songs that get popular. One of them, undoubtedly, is a wide-spoken lyric language. \n",
        "\n",
        "I'm no expert, but I would guess that pop artists seeking to maximize their audiences don't opt to write their music in Swahili. On the other hand, those that *do* opt to write their music in Swahili probably aren't shooting for world-wide popularity above all else. Singers who write songs with pop music qualities have more incentive to write lyrics in more widely-spoken languages. Often, common goals result in common products. And if that common goal is commercial success, songs written in English or Spanish with pop music qualities are the common results. \n",
        "\n",
        "The music that remained in the dataset by the time I began sampling was probably a mix between pop music and non-pop music. However, it probably was not an unbiased mix. Music of the pop genre had probably been lost in the metadata collection process at a lower rate than music of other genres. This probably changed the genre diversity of the sampleable data across all five language groups, but had the highest pop-saturation effect on English and Spanish. \n",
        "\n",
        "# Acknowledgements\n",
        "\n",
        "Thank you to Spotify, Musixmatch, Everynoise.com, and lang-detect for the help I got from your libraries, websites, and APIs in the data collection and cleaning phase of this project, which I did in Python. \n"
      ]
    }
  ]
}