2022-04-02-mtg-art-color-distribution.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "80755ec6",
   "metadata": {},
   "source": [
    "## Import libraries and datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "b3af1d55",
   "metadata": {},
   "outputs": [],
   "source": [
    "library(tidyverse)\n",
    "library(readr) # Read in datasets.\n",
    "options(scipen = 9999)\n",
    "library(reshape2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "7bc63df0",
   "metadata": {},
   "outputs": [],
   "source": [
    "lea_color_data <- read_csv(file = \"C:/Users/chris/Documents/Datasets/MTG Art Colors/lea_color_data.csv\",\n",
    "                          show_col_types = FALSE)\n",
    "CSS3_NAMES_TO_HEX <- read_csv(file = \"C:/Users/chris/Documents/Datasets/MTG Art Colors/CSS3_NAMES_TO_HEX.csv\",\n",
    "                          show_col_types = FALSE)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e1f5704",
   "metadata": {},
   "source": [
    "## Omit ubiquitous gray colors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "618bf90d",
   "metadata": {},
   "outputs": [],
   "source": [
    "lea_color_data <- lea_color_data[,-which(names(lea_color_data) %in% c(\"darkslategray\",\n",
    "                                                                      \"dimgray\",\n",
    "                                                                      \"gray\",\n",
    "                                                                      \"darkgray\",\n",
    "                                                                      \"silver\",\n",
    "                                                                      \"lightgray\",\n",
    "                                                                      \"slategray\",\n",
    "                                                                      \"lightslategray\"))]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "255525ca",
   "metadata": {},
   "source": [
    "## Filter by color identity and sum each detected color."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "ad20bb1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "stacked <- lea_color_data %>%\n",
    "  filter(colorIdentity %in% c(\"W\", \"U\", \"B\", \"R\", \"G\", \"C\")) %>%\n",
    "  group_by(colorIdentity) %>%\n",
    "  summarize(lightpink = sum(lightpink),darkslateblue = sum(darkslateblue),black = sum(black),\n",
    "            peru = sum(peru),seagreen = sum(seagreen),olivedrab = sum(olivedrab),\n",
    "            darksalmon = sum(darksalmon),lightcoral = sum(lightcoral),sandybrown = sum(sandybrown),\n",
    "            khaki = sum(khaki),midnightblue = sum(midnightblue),sienna = sum(sienna),\n",
    "            linen = sum(linen),darkkhaki = sum(darkkhaki),cadetblue = sum(cadetblue),\n",
    "            rosybrown = sum(rosybrown),gainsboro = sum(gainsboro),steelblue = sum(steelblue),\n",
    "            burlywood = sum(burlywood),wheat = sum(wheat),saddlebrown = sum(saddlebrown),\n",
    "            palevioletred = sum(palevioletred),darkgoldenrod = sum(darkgoldenrod),darkseagreen = sum(darkseagreen),\n",
    "            coral = sum(coral),tan = sum(tan),palegoldenrod = sum(palegoldenrod),\n",
    "            goldenrod = sum(goldenrod),lightsteelblue = sum(lightsteelblue),yellowgreen = sum(yellowgreen),\n",
    "            brown = sum(brown),powderblue = sum(powderblue),teal = sum(teal),\n",
    "            indianred = sum(indianred),beige = sum(beige),darkolivegreen = sum(darkolivegreen),\n",
    "           )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c0eaeea",
   "metadata": {},
   "source": [
    "## Melt dataframe so we have columns for \"color identity\" and \"color\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "e92b6da8",
   "metadata": {},
   "outputs": [],
   "source": [
    "stacked <- melt(stacked, id = \"colorIdentity\")\n",
    "write.csv(stacked,\"C:/Users/chris/Documents/Datasets/MTG Art Colors/stacked.csv\", row.names = FALSE)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86726f19",
   "metadata": {},
   "source": [
    "## Create stacked bar chart showing distribution of detected colors between color identities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "1c897ffa",
   "metadata": {},
   "outputs": [
    {
     "data": {
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kO462+Fhx8oUcigVgAJShQyqBVAghKF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\nRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(stacked, aes(fill=colorIdentity, y = value, x = reorder(variable, -value))) + \n",
    "  geom_bar(position=\"stack\", stat=\"identity\", color=\"black\") + \n",
    "  scale_fill_manual(values = c(\"black\", \"gray\", \"yellowgreen\", \"indianred\", \"skyblue\", \"beige\")) +\n",
    "  theme(axis.text.x = element_text(face=\"bold\", vjust=0.5, hjust=1, size=10, angle=90),\n",
    "        axis.text.y = element_blank(),\n",
    "        axis.ticks = element_blank(),\n",
    "        axis.title = element_text(face=\"bold\", size = 20),\n",
    "        plot.title = element_text(face=\"bold\", size = 20),\n",
    "        plot.subtitle = element_text(size = 16)) +\n",
    "  labs(title = \"Distribution of Colors in Card Art among Color Identities\",\n",
    "       subtitle = \"Limited Edition Alpha\",\n",
    "       x = \"Colors Found in Card Art\",\n",
    "       y = \"\") + \n",
    "  guides(fill=guide_legend(title=\"Color Identity\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "5e50fb96",
   "metadata": {},
   "outputs": [],
   "source": [
    "ggsave(\"mtg-art-color-stacked-bar.png\", width=15, height=10, dpi=\"retina\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91903b3c",
   "metadata": {},
   "source": [
    "## Filter for White cards. Drop color columns if sum = 0. Append color hex values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "ca8b6da3",
   "metadata": {},
   "outputs": [],
   "source": [
    "white <- lea_color_data %>%\n",
    "  filter(colorIdentity == \"W\")\n",
    "cols_to_dropW = c(rep(TRUE, 12), colSums(white[,13:ncol(white)]) > 0)\n",
    "white <- white[,cols_to_dropW]\n",
    "\n",
    "white_sums <- data.frame(sum = colSums(white[,13:ncol(white)]))\n",
    "white_sums <- cbind(color = rownames(white_sums), white_sums)\n",
    "rownames(white_sums) <- 1:nrow(white_sums)\n",
    "white_sums <- merge(x = white_sums, y = CSS3_NAMES_TO_HEX, by = \"color\", all.x = TRUE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "e33e5e8b-e885-4f8b-93a8-f360dd3163dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 4 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>color</th><th scope=col>sum</th><th scope=col>hex</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;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>18</th><td>rosybrown</td><td>227</td><td>#bc8f8f</td></tr>\n",
       "\t<tr><th scope=row>10</th><td>indianred</td><td>200</td><td>#cd5c5c</td></tr>\n",
       "\t<tr><th scope=row>8</th><td>gainsboro</td><td>192</td><td>#dcdcdc</td></tr>\n",
       "\t<tr><th scope=row>20</th><td>sienna   </td><td>131</td><td>#a0522d</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 4 × 3\n",
       "\\begin{tabular}{r|lll}\n",
       "  & color & sum & hex\\\\\n",
       "  & <chr> & <dbl> & <chr>\\\\\n",
       "\\hline\n",
       "\t18 & rosybrown & 227 & \\#bc8f8f\\\\\n",
       "\t10 & indianred & 200 & \\#cd5c5c\\\\\n",
       "\t8 & gainsboro & 192 & \\#dcdcdc\\\\\n",
       "\t20 & sienna    & 131 & \\#a0522d\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 4 × 3\n",
       "\n",
       "| <!--/--> | color &lt;chr&gt; | sum &lt;dbl&gt; | hex &lt;chr&gt; |\n",
       "|---|---|---|---|\n",
       "| 18 | rosybrown | 227 | #bc8f8f |\n",
       "| 10 | indianred | 200 | #cd5c5c |\n",
       "| 8 | gainsboro | 192 | #dcdcdc |\n",
       "| 20 | sienna    | 131 | #a0522d |\n",
       "\n"
      ],
      "text/plain": [
       "   color     sum hex    \n",
       "18 rosybrown 227 #bc8f8f\n",
       "10 indianred 200 #cd5c5c\n",
       "8  gainsboro 192 #dcdcdc\n",
       "20 sienna    131 #a0522d"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "white_sums <- white_sums[order(-white_sums$sum),]\n",
    "white_sums <- head(white_sums, n=4)\n",
    "white_sums"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "776eb0b3",
   "metadata": {},
   "source": [
    "## Create bar chart showing breakdown of detected colors among White cards."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "cb8fee06",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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XjwK7PrRnF63dxdvsAOZSkoUrcOvg7f\nX/naHz05a85xOnHu9gjTmfnmgkj+ghGR3ha/vfY2Z3NeAzduX83w67vjnmrsq1/sfJ7vtt6e\nFruvY/xEZDhJ2g0/kN0fiU27oGVPyXKYw4+kMH5zsVeZZmh68EcNq1NQpHFT/OY8u2jwNL1/\ns29Xt+kdmH3qfaRTuGBEpPnywdPw/s3r4tWZYYU+r/z75evTl24+J1vHRRpOkppO5UHR5yEf\nr6dUOdJB3FfHInuVWXyhMYgpebHhFP7vvLZ4I7LdZG7HaT9cEMlfMBJwunQ2nARNPXq5Pk4/\nF/+4fP1ujrA1Wr91X5gb1OI0niQNu+Dh2O7B2ZxklePkLBljTmFcxqvM+Qhvt49eEYG1UYrk\nCTJ3c+HQzl8wEnDRxn3sdnZcPH+MJG+03j82s9Yhzj7RvaMhJZK/vRhookHcFILu+7/673zf\n7PLew4WS/LdEWv5u6Mvi9bRI/c8NPb9ZO4vuCl0z7By6Y7vpSt4fiBS72OCnYIg0/ADR4hdC\nQIVWJKMbmUjL3w3aLl5PivR8Xi8Pkc4m2hOi1+nobHPWanpvKS1StLcX90LjsX0vzE/BEunU\nHf+1o8MkMUqRtu621j5H8uJdeY5kneW473UOe5Cxr4Pd+tJVu66Re7VjSicl0ja263HuOeq7\ne4xdtTsNXURL+Lrlsp0apUg7926XabN96Q1Zf8FIwPCqXayz5XudT9OtB92/3lW7Zevp0Ysh\nUrtEM10wa20Nw18ux4Rzi1B/Od9PYXzsVWa+h9DKFNZCKdLb9PmFfXsOcRjvFGguiOQvGAkY\nvo8U62xxs+q44R/76vcChybSuhl2Bwfjql236i92AZvzo/Ed1pRIXjnmzBY3rR76t4j9FMZu\nvcpMar6xR1IjEak5rzHtKnw+ZW5/bu2w69f2/p39183FOxu8Bd1O+0fznQ2PTh/Lzl6d9bW/\niWfssbtDYD/d2bBsvetea7u23qdpf3hxua+bfwsuKpJRjol9e3NeG+l1N/0UrZvC1K1bmeNw\nj+N5HMsfowMBfyaSc7XpskiP43Wp6ZaxflM8XGN68kUKrme5C7qdDo3He+0enSwckXZ3y7s5\n94ujuHNC/dXlh1jr4cJzs98s3FtycBRbPoqKZJVj7mEzjX7YtZkpuJXZj82y7hiHgkhEaqe7\nP3Z6bd8OmW+jbm9d3r6eLorkLuh2Ojbedz8n6t+SsOzau4O1cZJvP5X6uI+33m36pPtdWuQA\nr3EOpTZzoKhIZjlm9o/9T8BOeyozBbeEh3a59kdVQQwnpS3XnZtTMwhgpWi5SqQjNYMAVoqW\nq0R6fri8DPzXQKSWq0TiVjYIQaQW3r+Ed8IaBFAARAIoACIBFACRAAqASAAFQCSAAiASQAEQ\nCaAAiARQgOtF+n9i5AGLQeZ6xJkjkgIy14NIdq7qgMUgcz2IZOeqDlgMMteDSHau6oDFIHM9\niGTnqg5YDDLXg0h2ruqAxSBzPYhk56oOWAwy14NIdq7qgMUgcz2IZOeqDlgMMteDSHau6oDF\nIHM9iGTnqg5YDDLXg0h2ruqAxSBzPYhk56oOWAwy14NIdq7qgMUgcz2IZOeqDlgMMteDSHau\n6oDFIHM9iGTnqg5YDDLXg0h2ruqAxSBzPYhk56oOWAwy14NIdq7qgMUgcz2IZOeqDlgMMteD\nSHau6oDFIHM9iGTnqg5YDDLXg0h2ruqAxSBzPYhk56oOWAwy14NIdq7qgMUgcz2IZOeqDlgM\nMteDSHau6oDFIHM9iGTnqg5YDDLX8y8R6e6joKmyJMoakHluuLVE+r8fA0RKQ+a54RBJUWVJ\nlDUg89xwiKSosiTKGpB5bjhEUlRZEmUNyDw3HCIpqiyJsgZknhvuHSKl+DAiFR01AHukNTdX\nkihrQOa54RBJUWVJlDUg89xwiKSosiTKGpB5bjhEUlRZEmUNyDw3HCIpqiyJsgZknhsOkRRV\nlkRZAzLPDYdIiipLoqwBmeeGQyRFlSVR1oDMc8MhkqLKkihrQOa54RBJUWVJlDUg89xwiKSo\nsiTKGpB5bjhEUlRZEmUNyDw3HCIpqiyJsgZknhsOkRRVlkRZAzLPDYdIiipLoqwBmeeGQyRF\nlSVR1oDMc8MhkqLKkihrQOa54RBJUWVJlDUg89xwiKSosiTKGpB5bjhEUlRZEmUNyDw3HCIp\nqiyJsgZknhsOkRRVlkRZAzLPDYdIiipLoqwBmeeGQyRFlSVR1oDMc8MhkqLKkihrQOa54RBJ\nUWVJlDUg89xwiKSosiTKGpB5bjhEUlRZEmUNyDw3HCIpqiyJsgZknhsOkRRVlkRZAzLPDYdI\niipLoqwBmeeGQyRFlSVR1oDMc8MhkqLKkihrQOa54RBJUWVJlDUg89xwiKSosiTKGpB5bjhE\nUlRZEmUNyDw3HCIpqiyJsgZknhsOkRRVlkRZAzLPDYdIiipLoqwBmeeGQyRFlSVR1oDMc8Mh\nkqLKkihrQOa54RBJUWVJlDUg89xwiKSosiTKGpB5bjhEUlRZEmUNyDw3HCIpqiyJsgZknhsO\nkRRVlkRZAzLPDYdIiipLoqwBmeeGQyRFlSVR1oDMc8MhkqLKkihrQOa54RBJUWVJlDUg89xw\niKSosiTKGpB5bjhEUlRZEmUNyDw3HCIpqiyJsgZknhsOkRRVlkRZAzLPDYdIiipLoqwBmeeG\nQyRFlSVR1oDMc8MhkqLKkihrQOa54RBJUWVJlDUg89xwiKSosiTKGpB5bjhEUlRZEmUNyDw3\nHCIpqiyJsgZknhsOkRRVlkRZAzLPDYdIiipLoqwBmeeGQyRFlSVR1oDMc8MhkqLKkihrQOa5\n4RBJUWVJlDUg89xwiKSosiTKGpB5brh3iJTiw4gUZv5RKDpfUAz2SEPm//MxYF+a5gPtkVL9\nItLaIFIaRJKCSGkQKTccIvmZ39qQTBApDSJJQaQ0iJQbDpH8zG9tSCaIlAaRpCBSGkTKDYdI\nfua3NiQTREqDSFIQKQ0i5YZDJD/zWxuSCSKlQSQpiJQGkXLDIZKf+a0NyQSR0iCSFERKg0i5\n4RDJz/zWhmSCSGkQSQoipUGk3HCI5Gd+a0MyQaQ0iCQFkdIgUm44RPIzv7UhmSBSGkSSgkhp\nECk3HCL5md/akEwQKQ0iSfk3iXTrb2PJptzqmwCRpPyrRPrfjwEiIVJFIFIaRJKCSHoQCZEq\nApHSIJIURNKDSIhUEYiUBpGkIJIeREKkikCkNIgkBZH0IBIiVQQipUEkKYikB5EQqSIQKQ0i\nSUEkPYiESBWBSGkQSQoi6UEkRKoIREqDSFIQSQ8iIVJFIFIaRJKCSHoQCZEqApHSIJIURNKD\nSIhUEYiUBpGkIJIeREKkikCkNIgkBZH0IBIiVQQipUEkKYikB5EQqSIQKQ0iSUEkPYiESBWB\nSGkQSQoi6UEkRKoIREqDSFIQSQ8iIVJFIFIaRJKCSHoQCZEqApHSIJIURNKDSIhUEYiUBpGk\nIJIeREKkikCkNIgkBZH0IBIiVQQipUEkKYikB5EQqSIQKQ0iSUEkPYh0BR9GpDDzWxuSSSTz\nWxuSSdk1rTrYIw2Z39qQTNgjpflAe6RUv4i0NoiUBpGkIJIeREKkikCkNIgkBZH0IBIiVQQi\npUEkKYikB5EQqSIQKQ0iSUEkPYiESBWBSGkQSQoi6UEkRKoIREqDSFIQSQ8iIVJFIFIaRJKC\nSHoQCZEqApHSIJIURNKDSIhUEYiUBpGkIJIeREKkikCkNIgkBZH0IBIiVQQipUEkKYikB5EQ\nqSIQKQ0iSUEkPYiESBWBSGkQSQoi6UEkRKoIREqDSFIQSQ8iIVJFIFIaRJKCSHoQCZEqApHS\nIJIURNKDSIhUEYiUBpGkIJIeREKkikCkNIgkBZH0IBIiVQQipUEkKYikB5EQqSIQKQ0iSUEk\nPYiESBWBSGkQSQoi6UEkRKoIREqDSFIQSQ8iIVJFIFIaRJKCSHoQCZEqApHSIJIURNKDSIhU\nEYiUBpGkIJIeREKkikCkNIgkBZH0IBIiVQQipUEkKYikB5EQqSIQKQ0iSUEkPYiESBWBSGkQ\nSQoi6UEkRKoIREqDSFIQSQ8iIVJFIFIaRJKCSHoQCZEqApHSIJIURNKDSIhUEYiUBpGkIJIe\nRLqCDyNSmPmtDckkkvmtDcmk7JpWHeyRhsxvbUgm7JHSfKA9UqpfRFobREqDSFIQSQ8iIVJF\nIFIaRJKCSHoQCZEqApHSIJIURNKDSIhUEYiUBpGkIJIeREKkikCkNIgkBZH0IBIiVQQipUEk\nKYikB5EQqSIQKQ0iSUEkPYiESBWBSGkQSQoi6UEkRKoIREqDSFIQSQ8iIVJFIFIaRJKCSHoQ\nCZEqApHSIJIURNKDSIhUEYiUBpGkIJIeREKkikCkNIgkBZH0IBIiVQQipUEkKYikB5EQqSIQ\nKQ0iSUEkPYiESBWBSGkQSQoi6UEkRKoIREqDSFIQSQ8iIVJFIFIaRJKCSHoQCZEqApHSIJIU\nRNKDSIhUEYiUBpGkIJIeREKkikCkNIgkBZH0IBIiVQQipUEkKYikB5EQqSIQKQ0iSUEkPYiE\nSBWBSGkQSQoi6UEkRKoIREqDSFIQSQ8iIVJF/JtEuvsgIJIJIumJZP5/PgSIZINIehAJkSoC\nkeQgkg0i6UEkRKoIRJKDSDaIpAeREKkiEEkOItkgkh5EQqSKQCQ5iGSDSHoQCZEqApHkIJIN\nIulBJESqCESSg0g2iKQHkRCpIhBJzloipfgwIoWZ39qQTCKZ39qQTCKZ31qRPPIUYY/EHkkD\neyREqghEkoNINoikB5EQqSIQSQ4i2SCSHkRCpIpAJDmIZINIehAJkSoCkeQgkg0i6UEkRKoI\nRJKDSDaIpAeREKkiEEkOItkgkh5EQqSKQCQ5iGSDSHoQCZEqApHkIJINIulBJESqCESSg0g2\niKQHkRCpIhBJDiLZIJIeREKkikAkOYhkg0h6EAmRKgKR5CCSDSLpQSREqghEkoNINoikB5EQ\nqSIQSQ4i2SCSHkRCpIpAJDmIZINIehAJkSoCkeQgkg0i6UEkRKoIRJKDSDaIpAeREKkiEEkO\nItkgkh5EQqSKQCQ5iGSDSHoQCZEqApHkIJINIulBJESqCESSg0g2iKQHkRCpIhBJDiLZIJIe\nREKkikAkOYhkg0h6EAmRKgKR5CCSDSLpQSREqghEkoNINoikB5EQqSIQSQ4i2SCSHkRCpIpA\nJDmIZINIehAJkSoCkeQgkg0i6UEkRKoIRJKDSDaIpAeREKkiEEkOItkgkh5EQqSKQCQ5iGSD\nSHoQCZEqApHkIJINIulBJESqCESSg0g2iKQHka7gw4gUZn5rQzKJZH5rQzKJZH5rRfLIU4Q9\nEnskDeyREKkiEEkOItkgkh5EQqSKQCQ5iGSDSHoQCZEqApHkIJINIulBJESqCESSg0g2iKQH\nkRCpIhBJDiLZIJIeREKkikAkOYhkg0h6EAmRKgKR5CCSDSLpQSREqghEkoNINoikB5EQqSIQ\nSQ4i2SCSHkRCpIpAJDmIZINIehAJkSoCkeQgkg0i6UEkRKoIRJKDSDaIpAeREKkiEEkOItkg\nkh5EQqSKQCQ5iGSDSHoQCZEqApHkIJINIulBJESqCESSg0g2iKQHkRCpIhBJDiLZIJIeREKk\nikAkOYhkg0h6EAmRKgKR5CCSDSLpQSREqghEkoNINoikB5EQqSIQSQ4i2SCSHkRCpIpAJDmI\nZINIehAJkSoCkeQgkg0i6UEkRKoIRJKDSDaIpAeREKkiEEkOItkgkh5EQqSKQCQ5iGSDSHoQ\nCZEqApHkIJINIulBJESqCESSg0g2iKQHkRCpIhBJDiLZIJIeREKkikAkOYhkg0h6EAmRKgKR\n5CCSDSLpQSREqghEkoNINoikB5EQqSIQSQ4i2SCSHkS6gg8jUpj5rQ3JJJL5rQ3JJJL5rRXJ\nI08R9kjskTSwR0KkikAkOYhkg0h6EAmRKgKR5CCSDSLpQSREqghEkoNINoikB5EQqSIQSQ4i\n2SCSHkRCpIpAJDmIZINIehAJkSoCkeQgkg0i6UEkRKoIRJKDSDaIpAeREKkiEEkOItkgkh5E\nQqSKQCQ5iGSDSHoQCZEqApHkIJINIulBJESqCESSg0g2iKQHkRCpIhBJDiLZIJIeREKkikAk\nOYhkg0h6EAmRKgKR5CCSDSLpQSREqghEkoNINoikB5EQqSIQSQ4i2SCSHkRCpIpAJDmIZINI\nehAJkSoCkeQgkg0i6UEkRKoIRJKDSDaIpAeREKkiEEkOItkgkh5EQqSKQCQ5iGSDSHoQCZEq\nAkwRkCsAAAvISURBVJHkIJINIulBJESqCESSg0g2iKQHkRCpIhBJDiLZIJIeREKkikAkOYhk\ng0h6EAmRKgKR5CCSDSLpQSREqghEkoNINoikB5EQqSIQSQ4i2SCSHkRCpIpAJDmIZINIehAJ\nkSoCkeQgkg0i6UEkRKoIRJKDSDaIpAeREKkiEEkOItkgkh5EQqSKQCQ5a4mU4sOIFGZ+a0My\niWR+a0MyiWR+a0XyyFOEPRJ7JA3skRCpIhBJDiLZIJIeREKkikAkOYhkg0h6EAmRKgKR5CCS\nDSLpQSREqghEkoNINoikB5EQqSIQSQ4i2SCSHkRCpIpAJDmIZINIehAJkSoCkeQgkg0i6UEk\nRKoIRJKDSDaIpAeREKkiEEkOItkgkh5EQqSKQCQ5iGSDSHoQCZEqApHkIJINIulBJESqCESS\ng0g2iKQHkRCpIhBJDiLZIJIeREKkikAkOYhkg0h6EAmRKgKR5CCSDSLpQSREqghEkoNINoik\nB5EQqSIQSQ4i2SCSHkRCpIpAJDmIZINIehAJkSoCkeQgkg0i6UEkRKoIRJKDSDaIpAeREKki\nEEkOItkgkh5EQqSKQCQ5iGSDSHoQCZEqApHkIJINIulBJESqCESSg0g2iKQHkRCpIhBJDiLZ\nIJIeREKkikAkOYhkg0h6EAmRKgKR5CCSDSLpQSREqghEkoNINoikB5EQqSIQSQ4i2SCSHkRC\npIpAJDmIZINIehAJkSoCkeQgkg0i6UEkRKoIRJKDSDaIpAeREKkiEEkOItkgkh5EuoIPI1KY\n+a0NySSS+a0NySSS+a0VySNPEfZI7JE0sEdCpIpAJDmIZINIehAJkSoCkeQgkg0i6UEkRKoI\nRJKDSDaIpAeREKkiEEkOItkgkh5EQqSKQCQ5iGSDSHoQCZEqApHkIJINIulBJESqCESSg0g2\niKQHkRCpIhBJDiLZIJIeREKkikAkOYhkg0h6EAmRKgKR5CCSDSLpQSREqghEkoNINoikB5EQ\nqSIQSQ4i2SCSHkRCpIpAJDmIZINIehAJkSoCkeQgkg0i6UEkRKoIRJKDSDaIpAeREKkiEEkO\nItkgkh5EQqSKQCQ5iGSDSHoQCZEqApHkIJINIulBJESqCESSg0g2iKQHkRCpIhBJDiLZIJIe\nREKkikAkOYhkg0h6EAmRKgKR5CCSDSLpQSREqghEkoNINoikB5EQqSIQSQ4i2SCSHkRCpIpA\nJDmIZINIehAJkSoCkeQgkg0i6UEkRKoIRJKDSDaIpAeREKkiEEkOItkgkh5EQqSKQCQ5iGSD\nSHoQCZEqApHkIJINIulBJESqCESSg0g2iKQHkRCpIhBJDiLZIJIeREKkikAkOYhkg0h6EOkK\n7j4KZK7nw2aet+pfLUtqj7QG8oDFIHM94swRSQGZ60EkO1d1wGKQuR5EsnNVBywGmetBJDtX\ndcBikLkeRLJzVQcsBpnrQSQ7V3XAYpC5HkSyc1UHLAaZ60EkO1d1wGKQuR5EsnNVBywGmetB\nJDtXdcBikLkeRLJzVQcsBpnrQSQ7V3XAYpC5HkSyc1UHLAaZ60EkO1d1wGKQuR5EsnNVBywG\nmetBJDtXdcBikLkeRLJzVQcsBpnrQSQ7V3XAYpC5HkSyc1UHLAaZ60EkO1d1wGKQuR5EsnNV\nBywGmetBJDtXdcBikLkeRLJzVQcsBpnrQSQ7V3XAYpC5HkSyc1UHLAaZ60EkO1d1wGKQuR5E\nsnNVBywGmetBJDtXdcBikLkeRLJzVQcsBpnr+UAiAUAAIgEUAJEACoBIAAVAJIACIBJAARAJ\noACIBFAARAIoACIBFACRAAqASAAFQCSAAlQrUtM0t05hJp3M8GqJjHWjzolU1Ry4VJcaIuWA\nSLVRXWqI9H4KplrXqOvKpm4Q6f0gEmhEaifkr+b+a/v3zy/3zf2Xf4ZXfpwfNZ++fO8Wuh+e\nvG/nr23z7dO50e9UF5+a5mf//6fulab9v132x+em+fKr5ABOYce/v943n775h3bf/zr/9WmR\n96LJchjfzk9//naKdVWIX1+a5q9/7MQiQ1pMSDAH3uTZs7oOQWodyypGir0cmzP+0qhEasfw\n+fzneWo7uvKfvg6Pmk+/2lc6J1obvnRt+lfvfyW6+Ltp/j7/9/v88Nf00GtaZgDDf8uOf933\nebgifR7Sm/I2hjEs9/kU6aoMQ6c/rcQi+S0nJCykO3nmrK5DkNppMajPY0ZesZcP3fGXRiVS\ny/dFNbr1/9v5v/P24ed5iH+dfnT+nLpZ+TG1aYYdldHFr76G38+P2q3S586nuemXcgNYJDF2\nfD8/nhc6j+nz7y7LL5Em8zDGae3XAa+rMgyd3luJhfk5ExLMgTt55qyuQ5ha+6xbxXix57Et\nx18alUjdZuT0Tzuo36ff7Rbsn+EYrn3609+/5kf9tA1z9L3/70IXn4dSdkd27bL3P7on7yPJ\n/OEATkHH3+cMFyJ96veNv+cNwDKXaRg/+ud/3Hf7Yb+rInidhomF+bkT4s2BV3lzStYhTO3k\nVzFS7MVDb/ylUYnUH9l+Xex1vvb73nk/+7Vf6nt/hND0+5h2Q/JXoouvXQm7zc7v9qDw737Z\nc/3avVWptXJe6xYd/zVnGLn8HW0yD6Pf7XarwtewqyIYnbrZuvm5E+LNgVd5c0rWIUzt5Fcx\nUuzIelCyxAtUIvVbqmGr0A3uUzfV7dlff2o0XClolxm2d91Z4e9piqNddO786I7Wf7SnSD8X\nTdcQadHxvZvhMtzPb1+jTZxhjF1/DrsqwtjpLyuxMD93Qrw58CpvTsk6hKmd/CpGiu2vB/P4\nSyO7auf+0f/1azyK77dj9+2EjJPhtbG66Ep43jD9breKn52jllVESqQ0Pv4+HrhHclm2mYiO\nrljS6cTc/LwJcXsIK29MySpEU3OraBU7Ov7S3FSk0+/xNPV+uOb2tT1O+BZpY3Zxluhs4Jfu\n/+HI4qYi/W4n/Mv3X/HJvKFI8cS8/NwJqUmkaGrXiOSNvzRakSIHAT+/fmrL8Ll/8n46IGmG\nRX9P1x7iXfzornF+H/7/uQy3skjxQ7svrs2WSPdObopDu3hiYa0WE+LOQeTQrm8hObQLUzv5\nVUyL5I2/NFqR4qelv8fj1k/duc70noB3scHootsc/e7WQW+lWFmk+MWG5GTOKX0Zz9Q7FBcb\n4plEazVOSHPpYkO/uORiQ5jaya9iuvbl1woHrUjehdJff38a34PuTm6+dTL8GNu0s7i4/B3t\nYr703f7/xQm3skg/mtjl76bfD3y/INKPYSX93l3N9bsqwlC6b00qMTc/b0LcOYhc/u4DSS5/\nh6md/CpeFGk5/tJoRZreAu+vU7e7n5/9Vqbb8i32Km2b4fTyU6qL+c3Y70sHvbCFBuB1/GnK\nxD20+xLYFYo0v5XY7XC9rsrgviEbT8zNz5sQbw68yttTsgZhau2zbhWTInnjL41YJPdmkt+f\nl2U4LfcqbZvv/XrwK9VFfwowvNM2O+iHLTMAr+PoLUK/5tX3Z9hkTunX6M7nSFdl+KcP8DOV\nmJufNyHeHIS3CI2RBLcIham1/7lVTIrkjb80apFO/7g3rf51Htf9Xz+Gh+20/ZzbfHNvQIx3\nMd6werbwLzfc2iJ1BzOf/ZtWf/XZ/UpuJFva0TXj0N2uCtHetHrejKcS84fkTIg3B17lE1Oy\nCkFqHcsqJkXyxl+auj5G8W2l+zf+0/xe+VoatFQl0j/361/2+e/Qn4Cf/vm80m2asKQikfpD\n2HUPD/5LzCfiFHV9ahNpvcs+/znGs2v28goqEunT+cz4++XFIJfff7d3et5/WeMiFXhUJBLA\nxwWRAAqASAAFQCSAAiASQAEQCaAAiARQAEQCKAAiARQAkQAKgEgABUAkgAIgEkABEAmgAIgE\nUABEAigAIgEUAJEACoBIAAVAJIACIBJAARAJoACIBFAARAIoACIBFACRAAqASAAFQCSAAiAS\nQAEQCaAAiARQAEQCKAAiARQAkQAKgEgABUAkgAIgEkABEAmgAIgEUABEAigAIgEUAJEACvD/\nAV4vPWsgmdHNAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(white_sums, aes(x = reorder(color, -sum), y = sum)) +\n",
    "  geom_bar(stat=\"identity\", fill = white_sums$hex, color = \"black\") +\n",
    "  theme(axis.text.x = element_text(face=\"bold\", vjust=0.5, size=15),\n",
    "        axis.text.y = element_blank(),\n",
    "        axis.ticks = element_blank(),\n",
    "        axis.title = element_text(face=\"bold\", size = 20),\n",
    "        plot.title = element_text(face=\"bold\", size = 20),\n",
    "        plot.subtitle = element_text(size = 16)) +\n",
    "  labs(title = \"Distribution of Most Prevalent Colors in Card Art\",\n",
    "       subtitle = \"Limited Edition Alpha, White Cards\",\n",
    "       x = \"\",\n",
    "       y = \"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "04ec5c8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "ggsave(\"mtg-art-color-bar-graph.png\", width=15, height=10, dpi=\"retina\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
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