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Masiliunas, Dainius
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7a423bb9
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7a423bb9
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6 years ago
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Dainius
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Add example of how to use a custom function to summarise columns
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summarising_columns_per_group.ipynb
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7a423bb9
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th scope=col>Plot</th><th scope=col>Type</th><th scope=col>Trial</th><th scope=col>D1</th><th scope=col>D2</th><th scope=col>D3</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><td>FF1</td><td>FF </td><td>1 </td><td>1 </td><td>10 </td><td>30 </td></tr>\n",
"\t<tr><td>FF1</td><td>FF </td><td>2 </td><td>2 </td><td>15 </td><td>31 </td></tr>\n",
"\t<tr><td>DB1</td><td>DB </td><td>1 </td><td>3 </td><td>20 </td><td>32 </td></tr>\n",
"\t<tr><td>DB1</td><td>DB </td><td>2 </td><td>4 </td><td>25 </td><td>33 </td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"\\begin{tabular}{r|llllll}\n",
" Plot & Type & Trial & D1 & D2 & D3\\\\\n",
"\\hline\n",
"\t FF1 & FF & 1 & 1 & 10 & 30 \\\\\n",
"\t FF1 & FF & 2 & 2 & 15 & 31 \\\\\n",
"\t DB1 & DB & 1 & 3 & 20 & 32 \\\\\n",
"\t DB1 & DB & 2 & 4 & 25 & 33 \\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"| Plot | Type | Trial | D1 | D2 | D3 |\n",
"|---|---|---|---|---|---|\n",
"| FF1 | FF | 1 | 1 | 10 | 30 |\n",
"| FF1 | FF | 2 | 2 | 15 | 31 |\n",
"| DB1 | DB | 1 | 3 | 20 | 32 |\n",
"| DB1 | DB | 2 | 4 | 25 | 33 |\n",
"\n"
],
"text/plain": [
" Plot Type Trial D1 D2 D3\n",
"1 FF1 FF 1 1 10 30\n",
"2 FF1 FF 2 2 15 31\n",
"3 DB1 DB 1 3 20 32\n",
"4 DB1 DB 2 4 25 33"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Define sameple data (you'd import via read.delim() instead)\n",
"(my.data = data.frame(Plot=c(\"FF1\", \"FF1\", \"DB1\", \"DB1\"), Type=c(\"FF\", \"FF\", \"DB\", \"DB\"), Trial=c(\"1\",\"2\",\"1\",\"2\"), D1=1:4, D2=seq(10, by=5, length.out=4), D3=30:33))"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th></th><th scope=col>Plot</th><th scope=col>Type</th><th scope=col>Trial</th><th scope=col>D1</th><th scope=col>D2</th><th scope=col>D3</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><th scope=row>3</th><td>DB1 </td><td>DB </td><td>Mean</td><td>3.5 </td><td>22.5</td><td>32.5</td></tr>\n",
"\t<tr><th scope=row>1</th><td>FF1 </td><td>FF </td><td>Mean</td><td>1.5 </td><td>12.5</td><td>30.5</td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"\\begin{tabular}{r|llllll}\n",
" & Plot & Type & Trial & D1 & D2 & D3\\\\\n",
"\\hline\n",
"\t3 & DB1 & DB & Mean & 3.5 & 22.5 & 32.5\\\\\n",
"\t1 & FF1 & FF & Mean & 1.5 & 12.5 & 30.5\\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"| <!--/--> | Plot | Type | Trial | D1 | D2 | D3 |\n",
"|---|---|---|---|---|---|---|\n",
"| 3 | DB1 | DB | Mean | 3.5 | 22.5 | 32.5 |\n",
"| 1 | FF1 | FF | Mean | 1.5 | 12.5 | 30.5 |\n",
"\n"
],
"text/plain": [
" Plot Type Trial D1 D2 D3 \n",
"3 DB1 DB Mean 3.5 22.5 32.5\n",
"1 FF1 FF Mean 1.5 12.5 30.5"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Our custom col means; the input is a chunk of your data.frame with a particular Plot value (so same columns but fewer rows)\n",
"CustomColMeans = function(df)\n",
"{\n",
" # Run colMeans over that part of our data, but only on the columns from D1 to D3 (others are not numeric)\n",
" Means = colMeans(df[, which(names(df)==\"D1\"):which(names(df)==\"D3\")])\n",
" # For the rest of the variables, take the first occurrence\n",
" OtherCols = df[1, c(\"Plot\", \"Type\")]\n",
" # Merge the means with the other columns so we don't lose them.\n",
" # Also we need to transpose Means with t(), so that it's treated as rows and not a column.\n",
" # And add something to Trial indicate that the trial is actually the mean of the trials.\n",
" Result = cbind(OtherCols, Trial=\"Mean\", t(Means))\n",
"}\n",
"\n",
"# Run the custom function, and turn the result into a data.frame using Reduce(rbind, ...)\n",
"(my.data.means = Reduce(rbind, by(my.data, my.data$Plot, CustomColMeans)))"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th></th><th scope=col>Plot</th><th scope=col>Type</th><th scope=col>Trial</th><th scope=col>D1</th><th scope=col>D2</th><th scope=col>D3</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><th scope=row>1</th><td>FF1 </td><td>FF </td><td>1 </td><td>1.0 </td><td>10.0</td><td>30.0</td></tr>\n",
"\t<tr><th scope=row>2</th><td>FF1 </td><td>FF </td><td>2 </td><td>2.0 </td><td>15.0</td><td>31.0</td></tr>\n",
"\t<tr><th scope=row>3</th><td>DB1 </td><td>DB </td><td>1 </td><td>3.0 </td><td>20.0</td><td>32.0</td></tr>\n",
"\t<tr><th scope=row>4</th><td>DB1 </td><td>DB </td><td>2 </td><td>4.0 </td><td>25.0</td><td>33.0</td></tr>\n",
"\t<tr><th scope=row>31</th><td>DB1 </td><td>DB </td><td>Mean</td><td>3.5 </td><td>22.5</td><td>32.5</td></tr>\n",
"\t<tr><th scope=row>11</th><td>FF1 </td><td>FF </td><td>Mean</td><td>1.5 </td><td>12.5</td><td>30.5</td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"\\begin{tabular}{r|llllll}\n",
" & Plot & Type & Trial & D1 & D2 & D3\\\\\n",
"\\hline\n",
"\t1 & FF1 & FF & 1 & 1.0 & 10.0 & 30.0\\\\\n",
"\t2 & FF1 & FF & 2 & 2.0 & 15.0 & 31.0\\\\\n",
"\t3 & DB1 & DB & 1 & 3.0 & 20.0 & 32.0\\\\\n",
"\t4 & DB1 & DB & 2 & 4.0 & 25.0 & 33.0\\\\\n",
"\t31 & DB1 & DB & Mean & 3.5 & 22.5 & 32.5\\\\\n",
"\t11 & FF1 & FF & Mean & 1.5 & 12.5 & 30.5\\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"| <!--/--> | Plot | Type | Trial | D1 | D2 | D3 |\n",
"|---|---|---|---|---|---|---|\n",
"| 1 | FF1 | FF | 1 | 1.0 | 10.0 | 30.0 |\n",
"| 2 | FF1 | FF | 2 | 2.0 | 15.0 | 31.0 |\n",
"| 3 | DB1 | DB | 1 | 3.0 | 20.0 | 32.0 |\n",
"| 4 | DB1 | DB | 2 | 4.0 | 25.0 | 33.0 |\n",
"| 31 | DB1 | DB | Mean | 3.5 | 22.5 | 32.5 |\n",
"| 11 | FF1 | FF | Mean | 1.5 | 12.5 | 30.5 |\n",
"\n"
],
"text/plain": [
" Plot Type Trial D1 D2 D3 \n",
"1 FF1 FF 1 1.0 10.0 30.0\n",
"2 FF1 FF 2 2.0 15.0 31.0\n",
"3 DB1 DB 1 3.0 20.0 32.0\n",
"4 DB1 DB 2 4.0 25.0 33.0\n",
"31 DB1 DB Mean 3.5 22.5 32.5\n",
"11 FF1 FF Mean 1.5 12.5 30.5"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Merge our means with the original data\n",
"(my.data.combined = rbind(my.data, my.data.means))"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using Plot, Type, Trial as id variables\n"
]
},
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th scope=col>Plot</th><th scope=col>Type</th><th scope=col>Trial</th><th scope=col>Depth</th><th scope=col>Resistance</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><td>FF1 </td><td>FF </td><td>1 </td><td>1 </td><td> 1.0</td></tr>\n",
"\t<tr><td>FF1 </td><td>FF </td><td>2 </td><td>1 </td><td> 2.0</td></tr>\n",
"\t<tr><td>DB1 </td><td>DB </td><td>1 </td><td>1 </td><td> 3.0</td></tr>\n",
"\t<tr><td>DB1 </td><td>DB </td><td>2 </td><td>1 </td><td> 4.0</td></tr>\n",
"\t<tr><td>DB1 </td><td>DB </td><td>Mean</td><td>1 </td><td> 3.5</td></tr>\n",
"\t<tr><td>FF1 </td><td>FF </td><td>Mean</td><td>1 </td><td> 1.5</td></tr>\n",
"\t<tr><td>FF1 </td><td>FF </td><td>1 </td><td>2 </td><td>10.0</td></tr>\n",
"\t<tr><td>FF1 </td><td>FF </td><td>2 </td><td>2 </td><td>15.0</td></tr>\n",
"\t<tr><td>DB1 </td><td>DB </td><td>1 </td><td>2 </td><td>20.0</td></tr>\n",
"\t<tr><td>DB1 </td><td>DB </td><td>2 </td><td>2 </td><td>25.0</td></tr>\n",
"\t<tr><td>DB1 </td><td>DB </td><td>Mean</td><td>2 </td><td>22.5</td></tr>\n",
"\t<tr><td>FF1 </td><td>FF </td><td>Mean</td><td>2 </td><td>12.5</td></tr>\n",
"\t<tr><td>FF1 </td><td>FF </td><td>1 </td><td>3 </td><td>30.0</td></tr>\n",
"\t<tr><td>FF1 </td><td>FF </td><td>2 </td><td>3 </td><td>31.0</td></tr>\n",
"\t<tr><td>DB1 </td><td>DB </td><td>1 </td><td>3 </td><td>32.0</td></tr>\n",
"\t<tr><td>DB1 </td><td>DB </td><td>2 </td><td>3 </td><td>33.0</td></tr>\n",
"\t<tr><td>DB1 </td><td>DB </td><td>Mean</td><td>3 </td><td>32.5</td></tr>\n",
"\t<tr><td>FF1 </td><td>FF </td><td>Mean</td><td>3 </td><td>30.5</td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"\\begin{tabular}{r|lllll}\n",
" Plot & Type & Trial & Depth & Resistance\\\\\n",
"\\hline\n",
"\t FF1 & FF & 1 & 1 & 1.0\\\\\n",
"\t FF1 & FF & 2 & 1 & 2.0\\\\\n",
"\t DB1 & DB & 1 & 1 & 3.0\\\\\n",
"\t DB1 & DB & 2 & 1 & 4.0\\\\\n",
"\t DB1 & DB & Mean & 1 & 3.5\\\\\n",
"\t FF1 & FF & Mean & 1 & 1.5\\\\\n",
"\t FF1 & FF & 1 & 2 & 10.0\\\\\n",
"\t FF1 & FF & 2 & 2 & 15.0\\\\\n",
"\t DB1 & DB & 1 & 2 & 20.0\\\\\n",
"\t DB1 & DB & 2 & 2 & 25.0\\\\\n",
"\t DB1 & DB & Mean & 2 & 22.5\\\\\n",
"\t FF1 & FF & Mean & 2 & 12.5\\\\\n",
"\t FF1 & FF & 1 & 3 & 30.0\\\\\n",
"\t FF1 & FF & 2 & 3 & 31.0\\\\\n",
"\t DB1 & DB & 1 & 3 & 32.0\\\\\n",
"\t DB1 & DB & 2 & 3 & 33.0\\\\\n",
"\t DB1 & DB & Mean & 3 & 32.5\\\\\n",
"\t FF1 & FF & Mean & 3 & 30.5\\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"| Plot | Type | Trial | Depth | Resistance |\n",
"|---|---|---|---|---|\n",
"| FF1 | FF | 1 | 1 | 1.0 |\n",
"| FF1 | FF | 2 | 1 | 2.0 |\n",
"| DB1 | DB | 1 | 1 | 3.0 |\n",
"| DB1 | DB | 2 | 1 | 4.0 |\n",
"| DB1 | DB | Mean | 1 | 3.5 |\n",
"| FF1 | FF | Mean | 1 | 1.5 |\n",
"| FF1 | FF | 1 | 2 | 10.0 |\n",
"| FF1 | FF | 2 | 2 | 15.0 |\n",
"| DB1 | DB | 1 | 2 | 20.0 |\n",
"| DB1 | DB | 2 | 2 | 25.0 |\n",
"| DB1 | DB | Mean | 2 | 22.5 |\n",
"| FF1 | FF | Mean | 2 | 12.5 |\n",
"| FF1 | FF | 1 | 3 | 30.0 |\n",
"| FF1 | FF | 2 | 3 | 31.0 |\n",
"| DB1 | DB | 1 | 3 | 32.0 |\n",
"| DB1 | DB | 2 | 3 | 33.0 |\n",
"| DB1 | DB | Mean | 3 | 32.5 |\n",
"| FF1 | FF | Mean | 3 | 30.5 |\n",
"\n"
],
"text/plain": [
" Plot Type Trial Depth Resistance\n",
"1 FF1 FF 1 1 1.0 \n",
"2 FF1 FF 2 1 2.0 \n",
"3 DB1 DB 1 1 3.0 \n",
"4 DB1 DB 2 1 4.0 \n",
"5 DB1 DB Mean 1 3.5 \n",
"6 FF1 FF Mean 1 1.5 \n",
"7 FF1 FF 1 2 10.0 \n",
"8 FF1 FF 2 2 15.0 \n",
"9 DB1 DB 1 2 20.0 \n",
"10 DB1 DB 2 2 25.0 \n",
"11 DB1 DB Mean 2 22.5 \n",
"12 FF1 FF Mean 2 12.5 \n",
"13 FF1 FF 1 3 30.0 \n",
"14 FF1 FF 2 3 31.0 \n",
"15 DB1 DB 1 3 32.0 \n",
"16 DB1 DB 2 3 33.0 \n",
"17 DB1 DB Mean 3 32.5 \n",
"18 FF1 FF Mean 3 30.5 "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Make it into a long format\n",
"my.data.long = reshape2::melt(my.data.combined, variable.name=\"Depth\", value.name=\"Resistance\")\n",
"# Make depth numeric\n",
"my.data.long$Depth = substr(my.data.long$Depth, 2, 3)\n",
"# Check it\n",
"my.data.long"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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LhpN8U7+brcTkbUYnb8W+1fFSb9Kuur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\nhxFEniz62Enk18Lu6yR404YDAC4KJ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CfUKgQ8OKW5+8Gmmac+xkove0euT59uXr\nDxTqQryUyb1ZahFbVfuc3FeqlYbXwu6DxuegzeBGK1+QpwaFqJ0Yq3DvyTEQgkayFiUuc9d8\nWbk+WPT7U6cJbAkAANoFTOxAh6bbsYWrrZb7DVCiYxnCS+qFtVkmg0jmD7KK9EW83ES5O2/R\nCUfB/QGT/y9gIkMCALARjx9tThsxu4wPFOq89FqfMBfbCG+ULnMS+cngWbCjFICrC0zsQMfF\n5+eKhw9ofp3kkTcxhNtl/NV+i6Lhmf2sfkaWzeP/OPvJLuuRm7wSXwr7P4ZwAJgJx44gjnN3\nS2AL31dgkFU8NNohMP2Nz3WVfF+zNVoXMst/DFsCAID2AnXsQAeFnU7DxrUIY8e4KUQQacMJ\nQssPW2rt/Og4R88Qlk5K/y5f/lXVL90Mkf+NepKH4iagDfGV5XxlhRLThe0MuKqh1Dy9yJOB\nrEWJ/1m61E2Up0PmihiuEaBD0+v1hDC2VLkozFROq0OBNy3ooHRb1uOGetcNI7VQlmasG7L4\nY6VCbCf3LfEsW+uS6/YuKl0aJPp9H/OCF29iGAEAZsKxTISQksDYsO7QWV29gxsa4zRJLBe8\nbGf+z3Up3fWRU32GsSUAQJsRReqP/dc8mNiBjkg4dUI8flQNCpEHs1xaTpSidZmCRafNTrQy\nVIrItOc8VPCOhISvoxeGSQEMCQDArqmNmE6ndIljG2BPrgFjdANrUeKXi5doRHsubD5U4QYd\nn8vFuIv0Uq6BRqkwsQMdDm60GjYlE553jpuCeJ423OriPtmOEEZzB1gteuo2SmXumnl5rzqJ\n/EXU0/1NjFdWAJgJBbnYanX36suwAwEhdLpSLKkXeobIAWaWfaVptuytDRkDTd1v9oIz4OAq\nIMssO20uQ5Kkq/1uLHwgAx0MIfqNa5HDLo8cowVQF3pQNfR1mrnegab1U6L9qct3NaqOO3Ne\nLJGrnguZP8FnCG04AM3XdB/WzXoetplFif9ZshQh9EzoPLZwAEC7g4kd6FjEIxlC7hk1vLPc\nfyBD+LpjprxqoW9ndFN36uUKlWgPFrx9zJE323/MX4OmM7w6AM2E3W7h9Eni5a2Gs7QRq2zk\nT1ZI4T4sn2oQQlsaDqQ0Zt3klTjUzFgVGQDQ7mBiBzoQrr5Ot3ML0ekd46ci+sXwzBLdnlxD\nJ5N630jEsJL+7NlPf6lPu8Hc883wv9BHA9AC+JPZ2C3LPXox/P4jhHbnGAhhXK4jiCwq+QYj\n/EwILNcBcBWDiR3oMAjRr/8Jy7Jz9FjiTd2PtbKRX3HILHBk3sBGg0T94t9Ub/ysKrmrPvyr\n6IUSB8esQPuQsjMRQkr3ngyxdhkfLNJ567XeoSzbyX+q3Z3lyJ3sM7S3kaUYOACgg4CJHego\npLQU/myh0iVe6Uld5UFW8TfpXk4F39bXFuZDfRN2u/XQU0Uf+wleS2Oeg7aYoL1gq5UvKlCD\nQzWmNmKpeQZZxUNjHDz933WVaG+Wfcdj7qmQ2QwvDQDoOGBiBzoErqpC2ruLGIzOsSydu1Yf\nMZc18IOjnIkR1BVZTzoL7817ncPcF9FPx+hCGV4dgBYhZmciTWMrX6dqeG++XuLJwEiWosTf\nVm867Tw7y29MnD6CIRwA0HHAxA60P6yq+uRVWFVct0wgRupqwHvz9BlFuhAvZVIv6lrEFUrt\nrJyXrKr9nYi/woZx0L6E7CzEce5uPRhiD53VWZ3cwEinkb4osYu4/1X+ow6LTwTfyfDSAFxX\nQkJCMManT58+/0FCSHh4OMb45MmT7ZXYOTCxA+1P2rODr6xw9+rrjutOG1tUK6w9ajJKZP4g\nq8jRXdKcRJ6fu6hIrngi+M7b/UbTvjQALYgvL+WrKpToLgyfbRBCu3P0HEZDY1iW6z6vTC6W\nK/8UMAHKcQPgCYzx8uXLz38kLS2ttLS0vfK5AEzsQDvji4ukA6mal7dr1FjaWLuMlx6waBqe\n0dfqZ6TbWkcQeaTg3wdsJyb7DH0yZBbtSwPQsv7XRoylfN2pCrG0QegR7PI3UW8wtWnO98tX\nmjj934JmMLw0ANehoUOHrlix4vxHVq5cOXTo0PbK5wIwsQPtCbvd+vVrECHO8VMIZSMXQtB3\nGZZaOz8qzt4zhLr4+KLSb1bV7upn7PpB5GOYpToKAC1H08QTx4hOp8SyNDtpKko8ogvLct0H\n5SurlPq/BE7rJHgzhANwHZo8efLRo0fPnDlz7pGVK1fedttt5/5ZX1//wAMPREZGent7T548\n+dwzT548eeutt/r6+np5ed14442ZmZlNj4uiuG/fvttvvz0mJqZLly4XzBppwcQOtCfdto1c\nXY2cNFiNiKKN3XrKeLJCiu3kvqWbnTb2+5qt75Ytj5ACl8W+YOCu+s6A4Gon5OdiW6MSn0AE\n6jaPlY38qQop3EeJ8qMuSlyjNnxS+bMf7/Vg4FTaWACuW35+fjfddNO56dehQ4cqKyvHjv3t\nptPUqVNPnDjx9ddfb9682WQyjRgxoq6uDiE0Z84cl8u1YsWKNWvWEELuu+++cyFPP/304sWL\nT58+PWfOnHnz5jmdLJ/TmkCvWNBuhPwcMeuQ5t9JHjaKNvZMpbjlpNGi02YnWjnK5bZ9jcce\nL/zAwhu/jXkeVilARyAcO4JY24jtPGMgCI3swlKU+J2yH62q/eWw/7PwRoZwADqIhWvMGvWp\noQu9ONGmEzwdZcaMGR9//PHTTz+NEFq5cuXEiRMNBkPTt9LS0lJSUsrLy319fRFCS5cujYqK\n2r1798SJE2+//fYZM2bExMQghEpKSh599NFzAzYt1yGE7r333pdffrm4uDg2lrGiJEzsQPvA\nDod+/RqEsXP8VNpVinoH9+0BC8Jo7gCrRa9RxebLZfNzFxGEvoh6uruBpWsTAC0Lu1zCmVPE\n20cNoy41YpO5Q0U6b4PWM4S6KHGpu3pJ5YZg0e+egPG0sQB0KAaRNH9iR7VEMHXq1AceeCA3\nNzcmJmblypWvvvrquW8dP37c7XYHBv5WjVJRlDNnzmCMH3vssX379q1fvz4jI2P9+vXnD9i9\n+69nB43G5n7KgokdaB+6zeuwrVEePloNpisdp2ro2wMWm8xN7mWjbYhZq1jvPPNijdrwRsSD\nN3r1o4oFoJUIp45jxe3q0ZuhjdjeXL1bw8Nj7AxFiV8v/dZJ5KdCZusxfasWADqSZ8dT17pq\nJn9//1GjRq1YsWLixIkFBQXjxo2rrKxs+pa3t7efn191dfUFIXa7fcyYMZWVlVOnTp02bdrw\n4cP//ve/n/uuJLXY2xAmdqAdCNmZ4slsNSjENWAIbWzyUVN+jZgQIg+Nobv35CbKn/IX57iK\nHwqcfk8nWKIAHYXYdB62B3UZRVXD+/L1OoGlKHGOq/iHmm0xutA7/W6ijQUAIIRmzJjx2Wef\nuVyu8ePHn7/MlpCQUFNTc/To0Z49eyKEqqqq7r333kWLFuXl5WVmZlZXV+t0OoTQZ5991kqJ\nweEJ0Naw1arfupEIonPSdMTzVLGZJbqUPEMnk3pHPyvt4sZjhe/vsWbe7DXgudD5lKEAtBbc\nUM+fLdBCwzU/f9rYjCKd1cUNjHTqReq7UItKvlGI+o+QuSKGj/cAsJg2bdrBgwf/+9//zpjx\nu1JBcXFx06dPnzNnzvbt23fv3j1v3rzjx4/HxcVZLBabzbZy5crc3NzPPvvspZdeslqt5w7G\ntiCY2IG2RYh+48/Y6XDdOEbzpbuSVTbyKw6ZRY7MHWClvZL9q+yHH2q29TLEfBb9FI/h1x50\nFFJ2FiJEpl+uQwjtyWUsSnzMkZdcn9pDHzXZp6NU3gLgqhMQEDBixIjKysoJEyZc8K1vvvlm\n6NChd91116RJk3Q63S+//CIIwogRI1544YUFCxYMHDhw48aNO3bsGDdu3MKFC/84ssFg4Dj2\n6xR8VgNtSjp0QMjLUaNi3H2TqAJlFX+T7uVU8O39rKHeClXs2rqU18uWBYt+S2OeM3J6qlgA\nWpVw/CjieaVbT9rA7FK+rEHoHeqiLc2NEHq5eIlGtOfD7ubgQw4AlM7vMLFt27ZzX0dGRhLy\n64qD0Wj86KOPPvroowtiX3zxxRdffPHcP1evXt30hdv9235xf39/u526htf54F0N2g5XVyvt\n2kr0esfYSbT7xFcfMZc18EOinUmd6U7/HbaffqjgHR0Wv455NlTqRBULQKviy0q4qgolpiv5\nX6EEz207ISCEhsdSVzlJs2Vvsx4cZOpxk1cibSwAoOODiR1oK5pmWLcKu2XXmHHEi656XEqu\nPqNIF+qtTOxJd/SpSK6YnfOyi7g/iXqin7ErVSwAra2pjZib/j5scS06UcpF+rkj/ehWrxFC\ni0q+QQg9EzqPNhAAcFWAiR1oI7p9e7iSYiWum7s73WWsqFZIPmYySmT+QKvIUWyta1Qdc3Ne\nqVTqXgr90zjvwZT5AtDKmtqI6Q0qfRuxjUcRQWhELPXuuk316Xsbj47xSrrBTH3zFwBwVYCJ\nHWgLfEWZtG83MZkdt0ykCrTLeGm6RdPwzL5WX5q9RCrR7i94M9uZP8f/5gcCp1DmC0CrE/LO\nYLtN6ZZAKM+GW504LQf5m0hCMN22BI1o/yz9BiMMy3UAXMNgYgdaHVZV/fqfkKo6b52EDBQ1\ntQlB32VYah386Hh7QohM9aKP57y3qT59lFf/tyIeoswXgLbw631Y+jZie3J1bhWN6qbQHptb\nXbf7qCNvqu/wXoYY2hcFAFwtYGIHWp20aytXWeHuk6jE0O1y23LSeLJC6hLgvjme7ojQOwXf\n/6fkpzh9xKdRTwmYbjkEgDaAXS4h55Tm5a2GhFEFKhpOzZUMIhoSQ3cY1k2U10u/5TH3ZPAs\nqkAAwNUFJnagdfFnC6WMNM3bx3XjzVSBpyvFraeM3gZtTstdYssAACAASURBVJKVozlBu6X+\nwJOn3/cXvJfGPOfNm+jSBaBNCCePYUVx9+xLezz8QKHO6sQjuyHaUo7LqrfkuUrn+N/SVR9O\nFQgAuLpAHTvQirDLZVj/E0LIOX4KoWmEV+fgvj1gwRjNSbKaJM3zwBOOgvvy3hCxsKrXP6NR\nCHXGALSJ/7URozvBQBDak2vgOHRTD7qXcxH3v8p/0GFxQdDtdJEAdGxN7blaEKZv2dzRwMQO\ntCLdtl9wfZ08aJgaHul5lKqhZQcsdpmb0ssW5ee+csD/lLtr7sx9yarav+n5wiBLD6vVSp8y\nAK0O19fxxUVqaDht85WT5VKFle8f4fY3iw6aAnafVqwtkav+EjgtTAqgyxWAjk2iWTK4TsCt\nWNBahDMnxaNHtE6B8tARVIFrj5nza8Teoa6hMRTXLieR78p7rViu/Efo3DkhYymTBaDtSNmZ\niBCGYxO7cwwIoRFd6Q7DNqi29ytWmjj9w0G30b4iAOCqAxM70Cqww67fmEx43jlxOuEpFoaP\nFOv25uoDzOqMfo2eR2lEeyD/rYO2U1N9hy8IuYM+XwDajpCdhXjeHU93P7XMKpypFKP83JF+\ndMcmPqxYXaM0/DXotk4CXWFwAMDVCCZ2oFXoNq3Ddps87EY1INDzqMpGfuVhs8STeQMa9ALF\n3vCXS75aV5c6yNTjw8gFGF31OyTANYwrKeZqqpXYOKrSPwihnacNBKERXeh6iNWoDf+t/NmP\n97o/YDJVIADgKgUTO9DyxKOHxVPH1dBwOWmI51Gygr/e7+VU8LQ+jcFeFGsSy6q3fFixqrMU\n9FXMQgnDtlHQoUnZLG3EGpzckWKdr1HtEUxX0PHt0h8aVceCkDssPN08EgBwlYKJHWhh2Nqg\n276JiKJz/FREU0F1daa53MrfEO1MjKDYQrS38eiTZz/yFSw/xL7kL3jR5wtAG1JV4cRRYjCo\nlDUd9+bqFQ2N6OKkKv1zVq5cUrU+RPSf3+lWujwBAFctmNiBFkWIYf1P2Ol0jR6r+fp5HpeS\nZ8go0kX4KpN62jyPOu08Oz/vNULIZ1F/76KnK/QKQNsT8s5gh0Pp1pOqjZhbxWkFBoNIBkTQ\nNYd9o3SZTJS/h8zRYzg5CMD1AiZ2oCVJGWl8Yb4aHevu1c/zqMJaYd1Rk1Eic5OsPOfp1roa\npWFO7st1SuM/w+8fYenDlC8AbaqpjZhMeR82vVBnk/HgKKdEs/E0x1W8vHZ7rC7sDr/RdFkC\nAK5mMLEDLQZXVUq7tyGD0TFuiuf19O0y/jbdohI0K9Hqa/R0a52suf+UtzjPVfpI0Ey4zQSu\nCtjpEHJOaT5+Gk0bMYLQ3jwDz6Eh0XTHJl4t/loh6jOh86CrHgDXFZjYgRaiaeLalVhRnDeP\nJyazh0GEoGUZlloHPybOHh/o6a5wgshjRR+kNGZN9LnhmZC5rBkD0KaEk9lYVZWevanaiB0v\nkyqsfO9Ql4+BogXLEfuZdfWpCYboid4UB5gAANcAmNiBFrJjM1darPToTVWda9NJ46kKqWuA\n+6Z4u+dRb5Z+92PNtj7GLh9GPsZh+B0GVwfxWCbC2N2Dri5xU1HiYTTFuhFCr5V+QxB5Iewe\neIMAcL2B9zxoAXx5Kdq9nVgsztEULR9OV4rbTxl9DNrsJKvnZ/3W1O15q+z7ENH/m5hnjZye\nJV0A2hzXUM+XnFXDIjRvH8+jiuuFnCoxxt8d4at4HrWv8dj2hoODTD1GWSi2ugIArg0wsQPN\nhRW3PnkV0jT3+KnEYPAwqs7BfXvAgjGanWQ1SZ7eY9pvO/5Q/r9MvP672BdCRLo+mwC0I/Ho\nYYY2Yk3LdcNj6ZbrXi5ZghB6JnQeVRQA4NoAEzvQXLqdW7maajTwBi02zsMQVUPfHrDYZW5i\nQmOUn9vDqCK5Yn7uawpSP458PMEQzZovAO1AyD5KeEGJo9io0ODkMot1nUx0RYl/qU9Lt524\nxXvADeae9GkCAK56MLEDzcLn54qH0jUfX3TzOM+jfj5qLqgR+4S5hsZ4WpfLqtrn5L5cpdS/\nGn7frd6DmJIFoH3wJWe52mqlSxzRU2weSMkzKBoaFuvw/KyFRrTFpd9ymPtHCCzXAXCdgokd\nYIddTsPGtQhjx4TpSNJ5GHW4WJeapw8wq7f1bfQwxE2UP+UvPu4omOc/9t5OE1nzBaB9iMcy\nEUIKzX1Yt4r35+uNEknqTNGIZWXtzmOOvGk+w3vCkjYA1yuY2AF2ui0bcEO9a9AwLdTTulyV\njfzKw2aJJ/MGNOg9rra68OynOxoOjbb0fyPiQdZkAWgnqiqezCYGoxIV63nQ/kK9TcaDIx0S\n7+nbxE2UN0qXiVj4e8gcpkQBANcCmNgBRsKpE2J2lhoYLA8Z7mGIrOCv93u5FDytT2Owl6e1\niD8oX/Vl1fp4fedPo5+CUqvgqiPknEIOu7t7AvK4jRghaE+OnufQkGiKHmJLqzfly2Vz/G+O\n1oUwZQoAuBbAxA6wwHabfvM6wvOuCVM9v1ytyjSXW/mhMc7ECE/vLm1uSH+19KtAwfe72Be8\neBNrvgC0GzE7CyGkJFB0vTtWJlXb+L5hLm+PixI7ifxu+XIdFh8Lup0lSwDAtQImdoCFfsPP\n2G6TR9ykdgr0MGRPruFgkS7CV5mYYPMwJMuRe2/eGyISvop5JkLy9IUA6Diww8HnntZ8/dXg\nUM+jGKqc/Lfi5xK56r7ASaFSJ+osAQDXEJjYAWrikYNC7mk1LEJO9PR0amGtsP6YySiRuQOs\nPOfRnqEyd83c3FccmuvfkY8kmbo1I18A2o1w4ihWVaUnxXLd2Tohr1rsEuAO9fa0KHGDavug\nYpUXb3o48DamNAEA1w6Y2AE6XH2dbscmotM5JkzzsOWlXcbfHvBSCZqVaPU1eLS1zqG57sp9\ntUSuWhg6b7rviOalDEC7kbKzEMbu7hQl5XY1LdfR9BB7v3xlrWJ9KHCan+BFnSIA4NoCEztA\ngxD9hjVYll2jbyWedUYiBC3LsNTauTHx9vhAj+qsakS7P/+tQ/bTd/rd9EjQzOZlDEC74epq\nuNJiNTzS8zZi9U4uq0QXYFa7BXlalLhaafisKtlf8PpzwGTWTAEA1w6Y2AEK0v69fFGB0iXe\n7fGtpU0njKcqpK4B7pvi7B6GPF/8+Yb6fYPNCW93/itrpgC0P+FoJm0bsT05BlVDw2mKEr9V\n+l2j6ng8+E4z72lDPwDANQwmdsBTXFWFlLKTGAzOsZ6WCD5RLm07bfQxaHOSrJxnF6pvqzd/\nUvlzlBT8VcwzEhbY0wWgfREiZmcSXlC6erpD1KXg/QV6o0T6e3xsvEiu+Lr6l3ApYH4nitYv\nAIBrGEzsgGdUVZ+8GquK65aJxOhR2ZFaO/f9QTOH0Zwkq1HyqGrD9oaDTxR96CtYvu/yoh8P\nu4XAVYwvLuLq65Su8Z63EUsv1DvceEgURVHiN8qWyUR5KmQ2fAoCADSBiR3wiC5lB19Z7u7Z\n1x3X3ZPnqxr+Jt3LLnOTejZG+rk9CTnlLLov/w2M8BdRT8fqPG1lAUDH9L82Yp5uWtAISsnV\n8xy5weOixGecxStqdsTqwmb6jmLMEgBwzYGJHbgyvuSslJ5KLF6uUTd7GLImy3S2Tugb5vLw\nKlWjNszJebletb3T+eFhFoo9SQB0QFhVhFPZxGhSIj3t2XqsVFdt4/uFuyx6T4sSv1r6lULU\nZ8Pugo4sAIBzYGIHrgC73fr1PyFCHOOnEr1Hu7MPn9Xty9cHmNXb+jZ68nwXcc/NeSVfLlsQ\nfMcdfqObly8A7U84cwo7ne7uPT3vy7IrR48QGh7r6XLdwcaT6+v29TF2meA9hDFLAMC1CCZ2\n4Ap02zdytTVy4iC1c5Qnzy9r4FccNksCuWtgg0648lYhgsijhe+l205M8hn69+DZzU0XgA5A\n+PU+rKdrz0W1QkGNGBfoDvHytCjx8/mfEUSeDbkLI48P0AIArgMwsQOXw+fliJmHNP9O8nCP\nFtJkBS894CWreHrvxiCLR7WIF5d+u6JmR19j1w8jH+Mw/EKCq5/DLuTnaJ0C1KAQDyN2nqHr\nIbar9vC2uozB5oQbvfoxJgkAuEbBdRRcEnY6DRvXIoyd46cSwaMzdz8eMldY+aExDg/rNayu\n3fVO2Y8RUuCy2OcNnK55+QLQIUjHjyFVdffwdLmu1s4dLdMFmNW4AE+LEj99+kOE0POhd7Nl\nCAC4hsHEDlySbvM6bG2QbxjhYf/ynad1mSW6zr7KhASbJ89Ps/0/e/cdGEW1tw/8TNuaTa8k\nkErvEZBeBGmCCDaaovfartd7rehVsF4blp/66ntfvYKNYo+iVDtVgVACKRDSIb1ns212Zs7v\nj8UYwmYTJMluJs/nL3bmuPkyMrtPZs58T+Y9ha8ZOd3GxCfC+Pa25gfwcXxG2kUtI7YvT68o\nZHJSe5sSb6399df69KuCx4/GGsoAcAG0PgL3+MwTwskMOSLKMWZCe8bnV7FbTggGDV022sy3\n4/eFIrF8Rd5zMlHeiV05UBd7qeUC+Aa2tporK5Fj46l/QHvGOyTmYJHOqFHaeZFbocoLxetZ\nhn0i9tZLqxQA1AlX7MANptGs+2kH5QX7vEXteayv0cH+dxcvU7L0MnOQvu2pdbWS+cbcJ6ul\nhueib58ZMLojSgbwCXx6GiFEbPd92AOFOruTGR9vF9h2NSX+vPaXdGve0siZQ42Jf75KAFAv\nBDu4AKW6Hd8yNptjynQlOKQdw8knR0x1Nmb2QHu/8LYnCTmpdFvBmhx78V3hC/4a1t7VyQC6\nAUqFrHTKC3L7+ngrlOzP03EsHdu+do9OKr1S+rHA8E8l3nZphQKAaiHYQUvC0UN8fo4cG+8c\n2a5raTuzDNkVwoBIZcbAdt1LevTsO7vNadP9L3uq118urVIA38KdLWTr66R+A6hG057x6aXa\nGiuXHOMwadvVlPijqh0FYtmK8DmJeizNAgDuIdjBedi6Wu3un6hWZ5t9NWnHXO6T5ZqfcwyB\neuW2SRLbjqnfr5d9/mHVjoH62HfjHubQ3ATURbjI9nV7cvWEkInta0psp+Ib5V/oGM0DUTf+\n6QoBQPXwzQrNKIp+61eMU7TPmNOeqd+1VvbTIyaWIctGmY3atmcIbanb/0LZhggh+OOEJ02c\noSMqBvAVjCzxp09Sg1Hq065lxApqhMIavn+42M6mxG+Xby51Vt8RfnUvTeilVQoAaoZgB3/Q\nHtjHlpyV+g6QBg1tc7CsMOsP+VtE5uohjbHBzjbHp1lz/l74mobwH8WvitaEdUS9AD6EP32K\nsdudg4cRtl2fq7tzXGuItaspcb1s+U/lV/6c8Z7wRZdUJQCoHYIdnMNVlGl+3U0NRvvMq9oz\n/uvjxrN1/IgYx7h2zPsudVbflPesnYpvxz2UbOx3ycUC+Bw+I420+z5srY3LLNNG+st9w9v+\npYgQ8lbFl7WS+R8R1wbxpkuqEgDUDsEOCCGEkWXt1q+JLNtnz6cGY5vjj57VHijUhfnJ1w5v\nbHNwo2xbkvt0qbP6iV4rrgrEguWgQozVwhfmK6HhclhEe8bvydEplExKtLWnJ3GVVL+2cksI\n739bKJ4iB4A2INgBIYRodv/IVVU4hydLiW1fTisz818c89PwdMWYBi3fxtQ6mSp3Fb6SYctf\nGjLj77iLBColZKUTWXa273KdXWJSi3R+WmVkTLseJH+l7ONG2fZQ1BI/Tn9pZQKA+iHYAeGK\nz2iOHFQCAh1TZ7Y52CExGw6ZnDJzw8jGcFPbvYhXF7+7s/7geL8hL8fc3RHFAvgiPuN4+5cR\nO1Cgs0vM+Hg7346mxGfEivVVO3trwm8OmX3JZQKA+iHY9XSMU9Rt+5pQap+zoM3mW5SQz476\nVZi5iQm2Yb3avtiwvnrn2sotfXUxH8av0rBCB5UM4FvY6iquvFSOTaAm/zYHKwrZn6/nWDo2\nrl1dTl4s3ShS6eHIpRoGK0ACQNsQ7Ho67Q872Lpaccx4uXfbC7buydGfKNHGBktzB1vaHPyT\n+cjDZ/4vmPffmPBEIO/XEcUC+CJX+zqxHc+SE0KOl2hrreyoPg6/djQlPmkr/LL2lyRd9HXB\nUy+xSADoIRDsejQ+55SQfkwJDRMnTGlzcGENvyPL6KdVlo9q4Nv6h3PKXnR7/kssw74X/694\nbVTHlAvggygVsk5QQZD7DWjP8L15eoaQiQnt6nLyQtkGmSqro1bwTNtLNgMAEAS7noyx2XTf\nbSUcZ5+3iHJt3OVpdLAbDvnLlCxONgfo27jSUCHVLs59yixbX+/9jwl+7bqMAdBNcWcKmIZ6\nud9AKrS9jFh+tVBUy/ePECPaMT/1qPX09roDIwx95waO7YhKAaBHQLDrubTfbWEsjY4JU9ps\n0EAp+eSIqd7Ozhpg7ddW2y07FVfkPX9WrFwZteT64GkdVy+AL7qo+7CuNcTa2ZT42ZIPKaGr\ne93MkPY0RQEAIATBrscS0tOE7Cwlurc4enybg3ecNGZXCAMixGl9rZ5HUkLvLXwj1XJyQeDE\nhyIXd1CxAD6KkST+9CnqZ5LbsYxYrZXLLNNEmqSksLabEu8yH9ttThvnN3iKaURHVAoAPQWC\nXU/EmBu0P39HBcE2Z0Gbyx9llWl+ydYHGZTFyY1MWxcOnitZn1K7e4xx4P/GPYDLDKB6/Oks\nxmF3DhranmXEdufqFUomJ7WrKfGLpRsJIY/3uuVSSwSAHgbBruehVL9tM2O3OabNVIKCPY+t\ntbKfHTWxLL1pdINB08bUuk1V379R/nkfTcSHCau0DJqbgPrxGccJIdKgtvsSW0XmUJHWT6uM\niBHbHLylbn+q5eTcwLGjje16IAMAoAmCXY+jOXyAK8qX4xKdw5I9j5QUZv0hf4vILBhqiQmU\nPA/eW3/8/oL/MXGGDQmPh/IBHVcvgI9yLSMmh0fIYeFtDj5QoBMlZmJC202JZaq8WLqRZdiH\nI5d2UKUA0IMg2PUsbE2VZs9PVKezzZ5P2rqx+vVx49k6fkSMo81OqgVi2Y0ZjyuUvh//6EB9\n2/3wAFRAyDxBFEUaPLzNkbJCfi3QC1y7mhJ/XvPzKXvR9UFTB+vbnrcHANACgl1Poii6bZsZ\nSXJceVWbLfKPntUeLNRF+svXjWj0PLJWMi/Oeapaqn8x9i5M9Iaeg89IIyzbnmXE0oq1dTZ2\nVB9Hm/MZRMX5StknAsOvjFrSQWUCQM+CYNeDaH7dzZUWOwcOdQ4Y7HlkmZn/4pifhqfLRzVo\nOE93jpxU+kvBi7mO4gd6L/5r+LwOrRfAd7FVFVxFuRQbT41tr6qyL1/PEDIhvu0uJx/V7CwU\ny24OnRWrieyIMgGgx0Gw6ym48lLtgX3Uz+SY3sZS4g6J2XDIJMnMjSMbwz22UaWE3l/05l7z\n8Sv9R/877o4OrRfAp7na10mD235sIrdKOFPLD4wUPZ9NhBCrYn+t9DMdo7k34vqOqRIAeh4E\nux6BkSXdts1EUeyz51O93sNISsinR/wqzNykRNvQXg7Pb/v/yj79tOanYYbEtfEPcwz+LUGP\nQamQlU4FjZTU9lOr7W9K/E7lNxVS7V0RC6KEkA4oEgB6JHwZ9wiaX75nqyqcI0ZJ8UmeR+7O\n0aeXamODpdmDLJ5Hflu3b03ppkgheH38agOr67BaAXweV5jHmBuk/gOp0EZbnyoLl1WuifSX\nEkLbaEpcL1v+U/FVAGe8O2xhx1UKAD0Ogp36cYX5mqOpSkCgY8p0zyMLa/gdWUY/rbJ8dAPv\n8Z/GMevpvxe+ZmC1Hyc+1UsT2pHlAvg8TeYJQoizHfdh9+bqKSVT29GU+H/Kv6iTGv8RcW0Q\nb+qIGgGgh2pj6Xfo7hiHXb/jG8IwtnmLPC9S3uhg1x/yVyhZnGwO0Hl6du+MWLE09xkHdX4Q\n/+gQdGSAHoZxOrnsk9RkkmPa6OxjFZnUIq2/ThkW3cashnJnzbuV34bzQbeHze+4SgGgJ8IV\nO5XT/bCDaah3XD5B6RXjYZhCyceHTQ12dtYAS79wT/eMGmXbstxnKqW6Z6L/OidgbEfXC+Dr\nuOwsxik6Bw1vcxmx3wr0osxMiLd5vv5NCHm17FOb4ngg8gbMagCAS4Rgp2b86ZN85nE5PEIc\nN9nzyB1ZxtOVwoAIcVpfT1O8ZarcWfhylr1wecjMO8Ou7tBiAbqHc/dh22pfJyvk1wKdwNEx\nbTUlPiNWbKz5vrcm/KbQNp5YBwBoE4KdajE2q+67rZTjHFctJBznYWRWmWbXaX2QQbkx2ex5\nNYrHiv/7Xf2haf7JL/e+u4PLBegOmEYzV5QvR0QpbS0jdvSstt7Gjol1GDVtrCH2fOl6UXH+\nK2qZhsHcGAC4VAh2qqXb/g1jtYiTrpBDPX0D1VrZT4+aWJbeNLrB8zfQ2xWb36vc2k/X+924\nh3nGU1IEUKvflxFrx2MTeXqGIePbakqcZStMqd09QB97bdDUjikRAHo2BDt1Eo4f4XOz5eje\n4mWXexgmKcxHB/2tInPNMEtMoORh5I8Nh58qeS+Y89+Y+EQAZ+zoegG6Bz7zOGHZNtduOV0p\nlNTzgyPFML82mhI/X7peocpjUcvRCRIAOgQ+SlSIra/T/vwdFTT2uQs8z+/+Ks1YXM+PjHFc\nHutpGlC6Lf+v+S/yhNuQ+HgcVjqCnootL+MqK6S4xDaXEWtnU+Ijluyd9QdHGvrODvD0CxgA\nQPsh2KkOpbrtmxlRdEyfrQQGexh45Iz2UJEu0iRdN6LRw7ByZ83yvH9bFccbsfeONrbdZx9A\nrfiMNNKOZcQqG7lTFZqYQCk+pI2mxM+WfkQJXd1rBUPa7HMHANAuCHZqozm0nztTKCX2cw4d\n4WFYaQP/ZZqflqfLR5sFrtWpdXYq3pz/XLFY+Wiv5dcGTemEegG6CUXhM09QrVZK6ud54J5c\nPaVtX677ueHIHnPaFNOIyabhHVclAPR0CHaqwlZVavbuonq9fdY8D8NsTuajgyZJZm4Y2ehh\nYXKFKncWvHzEkr0waPJ9WJUcejaak81YGqV+gyjvaRkxq8gcOaMN0CnDPC61TAldU7aJEPKv\nqGUdXCgA9GwIdioiy7rtmxlZcsyc52EOECXki2Omags3Kck21ON3zzMlH26r++1y46C3Yu/H\nrSLo4eixw6Qdy4j9mq8XZWZCgo3z+OG6pW7/YcupeYHjR2F6AwB0KAQ79dDu38WVlUhDhjv7\nDfQw7JfT+hMlmthgafZAi4dhm6p/+N+KlFhN5IcJq9BeC3o4xinSrHQaECjH9PEwTFaY/QU6\nDUfHeHwaSabKmtJNHMPich0AdDgEO5Vgi89oDu6nJn/7tJkehuVVCztPGv20yvLRDR6WOdrf\nmL7y7H+CeNMniU+F8P4dXy5At8KfyiKiwzloKPHYwvvoWa3Zzo6JtRs8toT8rPanU/ai64Om\n9dd5iokAAH8Cgp0aME6nfvs3hFL77PlUp29tWKOD3ZRqIpQsTjYH6JTWhp22n12R/xyldG3c\nI0m66M4pGaA7ETKOE0LkIW085bAnV8cyZEKCp8t1ouJ8pfQTDSs8FLW4I0sEACCEINipg/aX\n79naajF5jBSX2NoYRSHrD5ka7OysgZZ+4a12YaiRGpblPVMnNb4Qcyce1gMghDBmM3e2kMT0\nUYJDPQzLrhBKG/hBkY4Qo6emxB9Uby8Sy28OnhWLlpAA0AkQ7Lo9riBXSDusBIeKk6d7GLY9\ny5hfLQyMFKf2bbULg6g4/5L/Yr6j9L7I61dgPXIAQgghQmYaURR25CjPw1xNiScnebpcZ1Hs\nr5d9bmB190fd0JElAgD8DsGum7PZ9Du3EIaxz11A+VYfccgs0+zO0QcZlBtGmlubIkQJve/M\nm/saT8wLGP9o5PJOqheg2xGy0gnLEo/3YSsbuewKTUygFBfsqSnx2xVfV0p1d4ZdHc4HdXSZ\nAACEINh1d/TbL5mGenHcJDmq1clw1RbukyMmjqU3jW4wtj6n++XSjz+v+Xm4Iel/4+5nsWwl\nACGEEK68lK2skBL6Mn4mD8N25egpIVOSPDUlrpMa367cHMAZ7w5f2NFlAgCcg+/vbkw5dpic\nOCZHRDkun9jaGElhNhwy2Z3MwuGWmECptWGb6/a+UvZJlBCyPmG1gdV1Tr0A3Q+fcZy0tYyY\nRWSPntEGGZShUZ4aQ75R8Xmd1PjPiOsC+TaWmgUA+NMQ7LorptHs3Pw54QX73AWE41oblpJm\nLK7nR8Y4RvdpderPQUvW3QWvGjndx4lPRgkhnVMvQDekKMLJDKrVSQl9PYzan6dzKsyEeBvb\n+gdqubNmXeXWCCH49rD5HV8nAMDvEOy6J0p1O74lVisz6yolNLy1UQcLdalFukiTdN2IxtbG\nFInlK/Kek4nyduxDg/XxnVMuQLfEF+QylkZpwCAPE1hlhfmtQKfl22hK/HLZxzbF8WDkjXpW\n2wmVAgCcg2DXLQnHUrm802xSP3L5hNbGlDbwm48btTxdPtoscO6n1pll6/K8f1dJ9c9G3z4r\nYEyn1QvQLbnuwzoHe3ps4vAZrdnBjom164RWJ7AWieUf1/zYWxO+LPjKjq8SAKAZBLvuh62v\n0+76kWp1/PXLWuuDb3MyHx00SQpzY3JjuMl9Vy0nlW7NfyHLVnh72PzbwuZ1ZskA3Q/jcPA5\n2TQgUO4V42HY3ry2mxI/X7JeVJyPRd2kYYWOLhMA4DwIdt0NpbptXzNO0TFjDhPovmMCJeTz\no6ZqCzc5yTak9dncj5397y7zsStMyc9E/7XTygXorvjsTEZyioOHeVhG7FSFpqyBHxLlCDa0\n2pQ4y174Vd2egfrYRUGTO6dSAIA/INh1M9oD+7izRVJSfw+P6f2SrU8v1cQGS7MHWlob81Z5\nygdV2/vr+rwb/zDPtPrsBUCP5VpGTBo41MMYV1PiSYmeupw8W/KhQpXHom5CFyEA6AL4oOlO\nuIpyzf5dVG+wz2r1zmlulbDzlNFPq9w0uoFr5X/v3l3F6QAAIABJREFU9w2Hni39MJwP+iTx\nKX/O2FnlAnRbTEM9d7ZIie6tBLf6nHhZA3e6QogNdsYGt9pI6Igl+/v61GRjP8xhBYCugWDX\nbTCyrN32FZFl+8yrqMF9Gmt0sJsOmwglSy4z++sUt2OOW3Nvy39JIPyHCY/FaMI6s2SA7kqT\neYJQKg7ydLlud66eEjI50dPsun+XfkgJXR11M0NavZ8LANCBEOy6Dc2en7jKCuewkVK/gW4H\nKApZf8hktrOzB1r6hrlf16jMWXNT/rM2xfE/sfeOMg7ozHoBujE+4zjhOKn/4NYGNDrYY2e1\nQXp5cGSr01h/Mh/Zaz4+zT95ksnTc7UAAB0Iwa574IrPaA4fUAICHVNntjZmW6Yxv1oYFClO\n6et+xo9FsS/JfapErFrd6+aFmMcN0AqurIStqZIS+1K9vrUx+/J1ksJMSrK31pSYEvpcyUcM\nYR6JXNpZhQIAXADBrhtgnE7dts2EUvucBVTrvrtpZplmT64+SC/fMNLs9paPQpW/Fbyabstf\nHDz9nxHXdWrBAN0an5FGCHEObPX5JElhDhTodDwd1bvV+7Df1u0/bs2dFzj+MmP/TqkSAMAd\nBLtuQPvjDrauRhw9Tu4d63ZAtYX75IiJY+nNl5sNGvddUp8oXre9/rdxfoNf7XNPZxYL0M0p\ninAyk+r0cmKry4ilFmkbHezlca02JZapsqZ0I8ew/4pa1mmFAgC4gWDn6/jcbOHEUSU0TJw4\n1e0ASWHWHzLZnczC4ZboAPdP522o/u6dym/iNJEfJDymYVpdHAkA+PwcxmqRBgymrSzBTAnZ\nm6dnWTI+vtUuJ5/U/JhtP3Nj8BX9dL07rVIAADcQ7HwaY7Ppdm4hHGefs4By7gPZl8eMJfX8\n6D720X3c3xX62Xx05Zn/BPP+nyY9Hcz5d2a9AN2eaxkxD8/DnirXVJi5YVGOIIP7B88d1PlK\n2ccaVngwcnFnVQkA0AoEO5+m/X4rY2kUx0+RI3u5HbAnmz18RhfpL10zzH0v4lP2otvz1zCE\nWRf3SILW/ZsAgAvjsPO52UpgkNL6MmKupsQTW29K/H7ltrNi5S0hc/poIjqlSgCA1iHY+S4+\nPU04lalE93aMGe92wJka8nkqrxfoijFmgXMz16dGblie++8G2fpan39MNLU6ExwAXPhTmYwk\nSa0vI1Zm5nMqhfgQZ58g99MeLIr9jfLPjazuvsjrO7NSAAD3EOx8FGM2637+jvKCbc7VxF1D\nBavIvPUDccrkuhHmEKObdSod1Lk8998FYtkDkTfeGHxF55cM0O0JGccJwzgHtfpb0K7Teupx\nDbH/VHxVJdXfFX5NGB/YOTUCAHiCYOeTKNXt+Iax2xzTrlSC3KxoRAn5/KhfpZnMHCwP7SW6\nG0DvLXzjkOXk1YETHolCGy2AtjH1dVzxGblXjBIY5HZAg51NK9YGGeRBkW5OOkJIjdTwfxVf\nB/J+d4Ut6MxKAQBahWDnizRHDvIFuXJconP4ZW4H/HzakF6q6R9JFoxwc62OEPJCyYYva3eN\nMPR9K/Z+rGUE0B6ajOOEUufgVi/X7cvVSgqZnGRnWzml3ij/wixb7w2/PpD366wqAQA8QrDz\nOWxNlWb3j1Sns82a53aiT26V8F2WwU+r3DnN7U1a8lXt7tfLP++tCd+U+ISedd/QGABa4LNO\nEI5ztrJknyiR3wp0eoGObqUpcZmz5v2qbRFC8F/DrurMMgEAPEGw8zGKotu2mZEkx5VXUf+A\nC/ebHeymwybCkJvHNAYa3LzBAUvmPYWvGTndxsQnMMsHoJ3YkrNsTbUzsR/RuzuvCNmfy1pE\nZmycXcO7b0r8Uukmm+JYGbkEv00BgBch2PkWza97uNJiqd9A5wA3q48rCtlwyGS2s3MGWuJD\nnBcOKBTLVuQ9JxPlndiVA3Xul6kAgAtpMk8QQqRW7sNSQn7KYjiWjGulKXGeo+STmh/7aCKW\nBE/vxCoBANqCYOdDuPJS7YG91M9knznP7YCtmcb8amFQpDg5yc23S61kXpz7VLXU8Hz0HTMD\nRndysQAqIsv8yXSq10vxSW73Z5VpyuqZ4dFioN59U+IXSzc6qbSq180aVujMQgEA2oBg5ysY\nWdJt20wUxT5rPtXrLxyQUarZm6sPMcqLk80XzrxzUum2gjU59uK/hV/zF0zxAbgYfN5pxmaT\nBgwhrSwj5mpKPCnR/ey6LHvh5rq9A3Wx1wRO7MQqAQDaAcHOV2h2/chWVTiHXyYluLlmUGXh\nPj1q4li6fLTZ7brj/zr7zm5z2nT/y57sdWvnFwugKkLmCUKI2Er7uuJ6PrdK6BdBe7fSlPiZ\n4g8UqqzutYJl8IkKAF6GjyGfwBXla44cVAICHVNnXLjXqTAbDpnsTmbRcEt0gJuvltfLPv+o\nascQffy6+H9x+GoBuBiM3cblZitBwUpUKwv35eoJITMGub8Je8CS+UNDarKx35UBozqxSgCA\n9kEI8D7GYddv/4YQYp97DRU0Fw74Ks2vpJ6/PNY+qo+bO0Fb6ve/ULYhQgjekPC4kdV1erkA\n6sKfzGBkWRo83G13oQY7e7xYG2qUh/d2/zDsCyUbCCGPR61Aw0gA8AUIdt6n+3En01DvuHyC\nHNPnwr2/FehSi7RR/tLVwywX7j1iPvX3gtc0hP8oflW0JqzziwVQm9+XERvqdu++PL2kkImJ\nNreLx/7QkLqv8cQVpmSsxQwAPgLBzsv4nFN8RpocHiGOn3Lh3pJ6/tsTRr1AV1xuFtiWFwyK\nHZXXpj9mp+I7cSuTjf26pF4AVWHrarnSYjm6txLgpumjU2YOFuoMGjqqj+PCvZTQ50vWM4RZ\n1evmzq8UAKBdEOy8ibFZdTu3UI5zzF144eN4VpH58KBJUpjrR5qDDS2XDmuUbXOPPFAiVj3Z\n65a5gWO7qmQAVeHPLSM23O3eg0U6i8iMjbVpODf3YTfX7j1hy7s6cMIwQ2InlwkA0F4Idt6k\n+24rY7WIE6fJYeEtdlFCPj9mqrVy0/rZhkS1XHFcpsqdBS8fb8y5JWru3eELu6peALURstIp\nx0vulhGjlOzN1XEsGRfvZm6rTJWXyjZxDPtw1NLOLxMAoL0Q7LxGOHGUz86So3uLo9xcb/sp\n25BRqkkMdc7s72Zq3erid3fWH5wSNPLNpAc6v1IAdeKKz7C11VJSP6pz89RRRpmm2sKNiHYE\nuGtKvKnm+9P2s4uDp/fT9e78SgEA2gvBzjvYhnrtz99RQbDPXUDYlv8XcquE708aTFpl6WXm\nC3aSdyu/XVu5pa8u5qvha9DmHuBPEzKOk9aXEfu9KbGbVV4c1Plq2adaRngockmnVggAcLEQ\n7LyBUt22zYzD4bhithIY3GKn2cFuSjURhiwfbTbpWl4q+Ml85InidcG8/6bEJ4MEU1dVDKA6\nsixkZ1G9QYpzM0PubB2fXy0khTl7uescua5yS7FYeWvY3Bg8ig4APgbBzgs0qb9xZwqkuETn\n0BEtdskKWX/QZHawcwdZ4kOcLfaetBXenv8Sy7Dvxz8ar43qqnoBVEjIzSY2q3Og+2XEdrsu\n1yW4uVxnUexvln9pZHX3Rlzf6VUCAFwkBLuuxlZVavb+TPV6+9wFFzZE3ZphLKgRBkWKF94A\nqpBql+Q9bZatr/f+x3i/IV1VL4A68a3fh623sydKtGF+8oCIls8tEUL+tzylSqq/O3xhKB/Q\n6VUCAFwkBLuupSi67ZsZSXJceRU1+rXYmVGq2ZenDzHKi5PNLRKfnYor8p4/K1aujFpyffC0\nLqsXQJUYm43Lz1FCQuVIN8uI7c3VywqZ5K4pcY3c8Hbl5mDe/2/h13RFoQAAFwnBrktp9v3C\nlZVIg4c5+w9qsavKwn16xMSx9KbRZp1wXtMsSug/C99ItZxcEDjxocjFXVgvgDrxJ9MZWXYO\ncnO5ziGda0qc3NtNU+LXyj4zy9Z7I64zcYbOLxMA4KIh2HUdtviM9uB+ajLZr5jVYpcoMx8d\n9LdLzLUjLBdO1n625KOvanePMQ78T9yDWI8S4NJpMo8ThpHcLSN2qEhnczLj4t00JS51Vn9Q\nuT1SCL41dG6XlAkAcNEQ7LoIIzn1278hlNpnXU11+hZ7vz7uV9bAjY2zX9a7ZSvUj2t++J/y\nL/poIj5MWKVh+K6qF0C12NoatrRE7h2r+LecJKdQsi9Px7F0fJybpsQvlmywU/HhqKV6Vtsl\nlQIAXDQEuy6i/eUHtrZaHDlaim/ZW2F/vi61SBvlL80f2rIX8a+NGQ+d+Y+JM2xIeBwztQE6\nBJ+RRih1untsIqNUW23hRsY4Luw0lGM7+0n1jwnaXouDp3dJmQAAfwaCXVfgCnKFY6lKcKg4\nZUaLXWdq+W/TjQYNXXG5WWDPu/WTYy++Of9ZSun78Y8O1Md2Yb0A6kWpkHmC8rzUd8CFO3fn\n6gghkxLdXK57Iu9dicqPRi0XcOEcAHwYgl2nY+x2/c4thGFscxZQ/ryvBKvIbEg1KQpz3Qhz\nsEFuvqtGaliW90yd1Ph8zB1TTC3b3QHAn8MVF7H1dVLfAVTbchmxM7V8YY3QL9wZ5d9ynmum\nveDLyl8G6eOuDpzQVZUCAPwZCHadTvvDNqah3jF2ktIruvl2SsnHh021Vm5aP+uQqPPaZTmp\n9NeCNXmOkn9EXHtL6JyurRdAzTwsI7Yrp9U1xJ4++75ClSeib2EZfGYCgE/Dh1Tn4rOzhKx0\nOSJKHDuxxa4fsw2nKjSJoc6ZA6zNt1NC7yt6c6/5+JX+o1dF3dyFxQKoHCNLfHYWNRil2IQW\nu2qtbHqZNsxP7hfWsinxAUvmT+Yj4wOGXhkwuqsqBQD4kxDsOhHTaNZ/t5VynH3OghbLFuVU\nCj+cMpi0ytLLzOz5DUz+X9mnn9X8NMyQuDb+YQ6XBwA6Dp+TzdjtzkFDCdvyzNqXp1cUMjnJ\nTVPi50vWE0KeSbi9a4oEALgUyA2dhlLdzm+JzSpOmaGEhTffU29jN6aaCEOWjza3ePjum7p9\na0o3RQrB6+NXG9iWc4AA4FK0toyYQ2IOFumMGuXCpsTf1R/a35g+w3/UlMCRXVQlAMAlQLDr\nLELaYT4vR47pIyaPab5dVsjGVJNFZOcNtsSHOJvvOmY9fU/hawZW+3HiU700oV1bL4DKMTYr\nX5CrhIbJ4ZEtdh0o1NmdzPh4e4sn0ymhL5ZuYAjzWK+burBSAIA/D8GuU7D1ddpdP1Ctzjb3\nGnL+rZ0t6caCGmFwlDgh4bw52mfEiqW5zzio8//iHhyij+/aegHUT8hKJ7J8Yfs6hZL9eTqO\npWPjW3Y5+ap2zwlb3jVBk4bqW87JAwDwTQh2nYBS3bavGVG0XzGLBgQ233O8RLsvXx9qlG8c\naW4e98yydVnuM5VS3b+j/zonYGwX1wvQE/AZxwnDOAcMabE9vVRbY+WSYxwm7XnzIiQqv1y2\niWPYlZFLurBMAIBLgmDX8TQH9nFni6Sk/tKQ4c23VzZyXxz1E1i6fLRZJ/xxx0emyp0Fr2TZ\nC5eHzLwj7OourxdA/djaaq6sRI6NpxcsI7YnV08ImXhBU+KN1d/n2IuXBl/ZVxfTRVUCAFwy\nBLsOxlWWa/fvonqDfda85ttFmVl/yN8uMQuHN/YKOK/96WPF//2+4dA008iXe9/dtcUC9BRC\nehohRBw4tMX2ghqhsIbvHy62aErsoM7/V/6plhEejLyx66oEALhkCHYdiZFl7daviCw7Zl5F\nDcbmu75K8ytr4MbG2Uf1Oe+xu/+r+Pq9yq39dX3ejX+EZzgCAB2OUj7zBOUFud/AFnt257jW\nEGvZlPjdim9LxKq/hs2L1oR1UZEAAB0Bwa4jafb+wlVWOIeOcJ7//bE/T3f4jLZXgDR/qKX5\n9h8aUp8ueT+Y89+Q+HgAZyQA0Am4M4VsQ73cbyDVaJpvr7VxmWXaSH+5b/h5z6c3yrb/rUgx\nsrp/RFzbtZUCAFwqBLsOwxWf0aT+qvgHOKbNar79TC3/bYbRoKErxpibN1M4Ycu7LX8NT7gN\niY/HaVr2XwCAjuJaRkwc1PI+7J4cnULJpERbi57Eb1WkVEn1f49YFMq3nJAHAODjEOw6BuN0\n6rZtJpTa5y6gWm3TdqvIbEg1KQpz/QhzkEFu2l7urLkp71mr4ngj9t7RxgHeKBmgR2BkiT99\nkhr95NjzugjZJSa1SOenVUbGnDc7okZueKdyczDnf1fYgq6tFACgAyDYdQztTzvZuhpx1Fi5\nd1zTRkrJx4dNtVbuiv7WwVF/LEBpp+LN+c8Vi5WP9lp+bdAUL5QL0GPw2ScZh5tlxA4U6OwS\nMz7ezp/flPjV0k8bZdv9kTeYOEPXVgoA0AEQ7DoAX5ArnDiqhISKE6c13/7DKcOpCk1SmPPK\n/tamjQpV7ix4+Ygle3HI9PsjbujyYgF6lt+XETuv95CikP35eo6lY+PO63JyVqz8oGpblBBy\nS9icLq0SAKCDINhdKsZm023bTBjGPvcayvNN209XCj9mGwL0yrJRZrbZFJ6nSz7YVvfbWL/B\nr/a+xwvlAvQkjNXCF+YpoeHy+es1Hy/R1lrZUX0cfuc3JX65bJNIpUeilukYDQEA6IYQ7C6V\n9vutjKVRnDBVjuzVtLHOxm5MNTEMWTbKbNT88c2xsfr7/1R8FauJ/CD+MQ3Du3s/AOgwQuYJ\noijO81uFE0L25ukZQiaev6xfrqP4s5qfE7XRNwZf0YU1AgB0JAS7S8JnHhdOZcoRUY7R45o2\nygrZlGqyiuy8wZa44D/aKOxrPPHw2f8L4k2fJj0Vwvt7o16AnsXtMmL51UJRLd8/Qowwyc23\nP1eyXqLyo72Wo6MkAHRfCHZ/HmM2637cSXnBPn8R4f74Jvg2w6+gRhjWyzGh2fWA0/azK/Ke\no5SujXskURvtjXoBeha2qpKrKJPjEqjJ1Hy7aw2xFk2J06w5W+r2D9LFzQ8Y36VVAgB0KAS7\nP4tS3c5vGLvNMXWGEhTStDmtWLs/TxfmJ183srFpY43UsCzvmXrZ8mLMnZNNLe8KAUBnEDLd\ntK+rtXKZZZpIk5QUdl5T4udK11NCn4y5lWXwqQgA3Rg+wv4kzdFUPj9XjktwjhjVtLGykfvy\nmJ+GozeNbtDx53ooOKhzed6/8x2l90Vcf3PobC/VC9DDUCpkpVNBkPue1ydyd65eoWRy0nlN\niX9rzPi54cjlxkFXmJK7uEwAgI6FYPdnsHW1mt0/Up3ONms+Yc59QYgS89FBf7vELBzeGOl/\nbu4OJfT+ojcPWU7OCxj/aNRy75UM0LNwRQVMQ73cbxAV/ni+1Soyh4q0flplRIzYfPAzJR8Q\nQh7rdVMXFwkA0OEQ7C6eoui3pjBO0TFjLvX/Y8Whr477lZu58fH2y3r/0cj+pdJNn9f8PNyQ\n9J+4B3CLB6DLaNzdhz1QoBMlZmLCeU2Jd9YfPGQ5OTNg9Hi/IS3fBQCgu0HUuGj8vl1sSbHU\nb4Bz4B9fA/vy9YfPaHsHSfOHWJo2bq7b+2rZp1FCyPqE1XpW6+7NAKDjMZKTy86ifia5T1zT\nRlkhvxboBe68psQKVV4s3cAQ5tEoXK4DADVAsLs4TFkJt+8XavSzzZzXtPFMLb813WjQ0OWj\nzNzvVwIOWrLuLnjVyOk+SXoqSghp5f0AoONx2ScZUXQOGtZ8GbG0Ym2djR3Vx2Fo1loypXZ3\nui1/YdCkIfp4d+8EANDNINhdBEaWSconRFHss+cT/bl1JK0is+GQSaZkyWXmIMO5qXVFYvmK\nvOdkorwd+9AgXZzXKgbokYRzy4iddx92X76eIWRC/B9dTpxUWlO6kWe4lZFLu7pEAIDOgWB3\nETS7fiDlZfLI0VJCX9cWSsnHh021Nm56P2v/8HPTsc2ydXnev6uk+mejb58VMMZ79QL0RIyl\nkS/Kl8Mj5dA/lhHLrRLO1PIDI8XwZk2JN1R/VyCWLQu5MkmH1pIAoBIIdu3FnS3SHDlIgoLl\n6X+0LPn+lOFUhSYpzDmjv9W1xUmlW/NfyLIV3h42/7awea28GQB0FtcyYtLgYc03XtiU2EGd\nr5d/rmWEByJu7OoSAQA6DYJduzAOh37b14QQuvAGqjnXPeF0pfBTtiFQrywbZWZ/b4r12Nn/\n7jIfu8KU/Ez0X71VLUBPxmccJyzb/NmmKguXVa6J9JcSQv9oSvzfim9KxKrbw+f30oR6o0wA\ngE6BYNcuup92MPV14uUTSGyCa0udjd2YamIYsnSU2fj7XOw3y7/8oGr7AH3su/EPY7lJgK7H\nVlVwleVSbAI1+jVt3Jurp5RMbdaUuEG2vFWR4s8Z/xF+rVfqBADoJAh2beNzTvHpaUpouDh+\nsmuLrJCNqSaryM4b3BgXfO4awPcNh54r/SicD/o44Ul/zui9egF6LiE9jRDS/D6sVWRSi7T+\nOmVY9B8NJt8qT6mRGu4OvyaY9/dClQAAnQbBrg2MzarbuYVynH3eIsrxro3fpPsV1gjDox0T\nEs41xDpuzb0t/yWB8B8lrIrRhHmvXoAejFLhZAbVaqW+/Zu2/VagF2VmQryN//3TrlpqeLfq\n22DO/86wBd6pEwCg0yDYtUH73VbGahEnTpXDzj1hd+SM8Gu+LsxPvnZEo2tLmbPmpvxnbYrj\nf2LvvczYv/U3A4BOxBXkMeYGqe9AyguuLbJCfi3QCRwd06wp8SulHzfKtgejbvTj9F6qFACg\nsyDYeSKkHxOys+ReMeKoca4t5Q3Mp6k6DUdvGt2g4ykhxKLYF+c+VSJWre5188KgyV6tF6BH\n02SeIIQ4m92HPXpWW29jx8Q6jJpzncPPiBUfVe+I0YTdEjrXO1UCAHQmBLtWMeYG7c/fUUGw\nz73G1b9elJj/7tY4JGbh8MZIf5kQolDlbwWvZtjylwTP+GfEdd4uGaDnYpxO7vRJajLJvWOb\nNu7N0zMMGd+sKfFLpZtEKj0ctVTD8N4oEwCgcyHYtYJS/bavGbvdccUsJSjYtS3luF9pPTMp\nSbys97lZ2I8Xr9te/9s4v8Gv9L7be7UCAOFOZTJO0Tl4OGHOPfx6ulIoqecHR4phfueaEufY\ni7+o/SVRG3190DTvVQoA0IkQ7NzTHD7AFRXI8YnOoSNdW/bm6Y+c0caGKAuGnZuss6H6u/9W\nfpOki/4ofrWGFbxXLAAQTdYJQohzwB/t6y5sSvxc6UcSlVf1ugndiABArRDs3GCrqzR7fiJ6\ng23OAtdv/0W1/LYMo0FDb5vk5DlCCPnZfHTlmf8E8/4bE54I5P3aeEcA6ExMo5krKpAjeym/\nP+RU2cidqtDEBErxIecaEh2znt5a9+twQ9K8wPHeqxQAoHMh2F1AUXTbvmYkyX7lXFePU6vI\nbEz1lylZcpk5xEgJIafsRbfnr2EIsy7ukQRtL29XDNDTCZnHWywjtidXT+l5l+ueLfmIEroq\n6iaGMO7eAwBADRDsWtLs38WVlUiDhjn7DyKEUEo2HTbVWtkZ/a39w0VCSLVUvzz33w2y9fU+\n/5hoGtbW+wFAp+MzThCWdQ4Y7HppFZkjZ7QBOmVYr3PTYX9tzNhlPjbWb/A0/2TvlQkA0OkQ\n7M7DlZdqD+6nJpP9ilmuLd+dNGRXaPqGOaf3sxJC7Iq4+NSTBWLZg5E33hB8hVeLBQBCCOEq\nyriqCik+iRrOrfjya75elJmJiTbu90+4f5d8QAh5otct3ikRAKCrINj9gZGcui0pRFHss+ZT\nvZ4QcrJc89NpQ6BeWTbKzDKEEnpX9ksHG7OuDpzwcNRSb9cLAIQQwmccJ82WEZMVZn+BTsPR\n0X3OPee0vf63Q5aTswMuH20c4LUqAQC6BILdH7S//MDWVIsjR0nxSYSQWiv7yRE/liFLR5kN\nGoUQ8nzJ+k8rfxxp7PdW7P2YpgPgExRFyEqnWq2U2M+14chZrdnOjom1GzSUEKJQZU3pJpZh\n/xW1zKuFAgB0BQS7c7iCPOFYqhIYJE6eTgiRFWZDqr9VZOcPbowLdhJCPqn+8fXyz2N1kZ/1\nf0bPar1dLwAQQghfmMdYGqX+gyh/ruHw3lwdy5CmdZy/qP0lw5a/KGjyYH2898oEAOgiCHaE\nEMI47Pqd3xKGsV21iAoaQsjmE8YztfyIaMf4BDsh5LfGjAfPvGXiDF8Mei5cCPJ2vQBwjus+\nbNMyYtkVQmkDPyjSEWKUCSFOKr1c+rHA8I/gch0A9AwIdoQQov1+O9NQ7xg7UekVTQg5dlb7\nW4EuzE++dkQjIaRQLLsl/3mZKO/EPTTEmODtYgHgHMbh4E+fogGBcnQf1xZXU+LJSecu131U\ntaNALFseMjNOE+m1KgEAuhCCHeGzTwpZJ+TwSHHsJEJIWQP3xTE/DU9vHtOg5WmtZL4x56lq\nqeH56Duu9B/t7WIB4A/86SxGcoqDhroaiVc2ctkVmphAyTV9wk7FN8q/0DGa+yKu93alAABd\npKcHO8Zq0X2/lXKc46prCMeJErMh1V+UmUXDGiNMspNKfy1Yk+so/lv4NX8Ju8rbxQLAeQTX\n87CDhrpe7srRU0KmJJ1rSvxOxeZSZ/Ud4Vf30oR6rUQAgK7V04Odbvs3jNUiTp4uh4YTQj47\n6ldh5iYk2JJ7Owgh/zr7zh5z2gz/UU/2utXblQLAeRizmTtbpPSKVoJDCSEWkT16RhtkUIZG\nOQgh9bLlfyu+8ueM94Qv8nalAABdp0uDnSRJy5YtM5vNXflDPRDSDvN5p+Xo3uJllxNC9uTq\nj5do+wRJVw22EEJeL//8o6odQ/UJa+Mf4ZienoABfI2QkUYURRx07rGJ/Xk6p8JMiLexLCGE\nvFXxZa1kvidiURBv8maVAABdi++aHyOK4smTJ3fs2OE7qY6tr9P+8j3Vam1XLSQMU1jDb880\nGjR02Wgzz5It9ftfKN0QIQSvT1htZHXeLhYAWhJOphOOkwYMIYTICvNbgU7L0zGxdkJIlVS/\ntnJLCO9/e+h8b5cJANCluijYbdmyZcuWLU7guIdXAAATmElEQVSns2t+XNso1W3fzIiifc4C\nGhDY6GA3HPKXKVl6mTlIL6dZc+4u+H8awn8UvypaE+btWgGgJa6shK2skJL6uxaJOXxGa3aw\nkxJtOoESQl4p+7hRtq3qfbMfp/d2pQAAXaqLgt2iRYsWLVqUk5PzwAMPXLg3Ozv7iy++aHp5\n/fXX9+7du/kAlmX9/Pw6sqCD+5kzhWTAYO3YCVpCNh4W6u3svGFScoLGIsvLM551UOcnA5+e\nHNJyvXBBEFiW1Wg0HVnMJeB5voOPzJ/F87xer9dqvd+6mWEY4mNHxmAwKIri7ULOHRlBELx4\nZCilHvYajcamP7MsS1qvljl9khDCXzbGtXd/gZZlyMwhrJ+fX4lYtb76u1hd5N/6XKtlhY6q\nnGVZo9Houf6u4fnIdD3XkfF2FYT8fmS0Wi3Hcd6uhRBCGIbxkf9HrgOi1Wp5vou+9C8ky7K3\nfnRP47X/x80VFxenpKQ0vZwxY0bfvn2bD2BZVqfr0PuhYydK5gZuynRGpyOELBlHwjPJwlE8\nw/A6ontr4IPF9srro2e4/U995CPDpeOPzCXwqSPDcZzv1OM7vwkQbx8Zz8FIq9W60meT1qql\nV85RwiO4YSMJzxNC7p5OsstIdKiWEJKgi/ly2AuEkABDB8+u84XfW5rwPO/FL+kWfOdTiODI\ntE4QBEHosF91LpYP3bJTO6YrfwF1XbHbuHGjyXTeB67ZbD579mzTy5CQkOZfhIGBgZIkNTY2\ndlmdHhgMBqfT6Qv/QBmGCQgI8KkjI4qiJEneLoSwLOvv7+90Oi0Wi7drIYQQo9Fot9t94bdV\njuNMJpMoilar1YtlBAYGtrarvr6+6RPJVa3D4bDZbF1Vmicmk8lisfjClVfX1Wi73W63271d\nCyGE+Pv7m81mX7iWKQiC63TznSPT0NDg7SoIIUSj0RgMBpvN5nA4vFUDpTQoCOs2dQWf+LXG\nZDINHDiw6WV9ff2FyckXEgMhRFEUWZZ9oRjXhQ1FUXyhGEIIpdRHjozrdgyOTGt858hcSJKk\nFvmAUuoj1boq8YVg5zr3fe3I+EKwc537PnW6+UglrsvePnVkoPOgiwcAAACASiDYAQAAAKgE\ngh0AAACASnTpHLukpKRvvvmmK38iAAAAQM+BK3YAAAAAKoFgBwAAAKASCHYAAAAAKoFgBwAA\nAKASCHYAAAAAKoFgBwAAAKASCHYAAAAAKoFgBwAAAKASCHYAAAAAKoFgBwAAAKASCHYAAAAA\nKoFgBwAAAKASCHYAAAAAKoFgBwAAAKASCHYAAAAAKoFgBwAAAKASCHYAAAAAKoFgBwAAAKAS\nCHYAAAAAKoFgBwAAAKASCHYAAAAAKoFgBwAAAKASCHYAAAAAKoFgBwAAAKASCHYAAAAAKoFg\nBwAAAKASCHYAAAAAKoFgBwAAAKASCHYAAAAAKoFgBwAAAKASCHYAAAAAKoFgBwAAAKASCHYA\nAAAAKoFgBwAAAKASCHYAAAAAKoFgBwAAAKASCHYAAAAAKoFgBwAAAKASCHYAAAAAKoFgBwAA\nAKASCHYAAAAAKoFgBwAAAKASCHYAAAAAKoFgBwAAAKASCHYAAAAAKoFgBwAAAKASCHYAAAAA\nKoFgBwAAAKASCHYAAAAAKoFgBwAAAKASCHYAAAAAKoFgBwAAAKASCHYAAAAAKoFgBwAAAKAS\nCHYAAAAAKoFgBwAAAKASCHYAAAAAKoFgBwAAAKASCHYAAAAAKoFgBwAAAKASCHYAAAAAKoFg\nBwAAAKASCHYAAAAAKoFgBwAAAKASCHYAAAAAKoFgBwAAAKASCHYAAAAAKoFgBwAAAKASCHYA\nAAAAKoFgBwAAAKASCHYAAAAAKoFgBwAAAKASCHYAAAAAKoFgBwAAAKASCHYAAAAAKoFgBwAA\nAKASCHYAAAAAKoFgBwAAAKASCHYAAAAAKoFgBwAAAKASCHYAAAAAKoFgBwAAAKASCHYAAAAA\nKoFgBwAAAKASCHYAAAAAKoFgBwAAAKASCHYAAAAAKoFgBwAAAKASCHYAAAAAKoFgBwAAAKAS\nCHYAAAAAKoFgBwAAAKASCHYAAAAAKoFgBwAAAKASCHYAAAAAKoFgBwAAAKASCHYAAAAAKoFg\nBwAAAKASCHYAAAAAKoFgBwAAAKASCHYAAAAAKoFgBwAAAKASCHYAAAAAKoFgBwAAAKASCHYA\nAAAAKoFgBwAAAKASCHYAAAAAKoFgBwAAAKASCHYAAAAAKoFgBwAAAKASCHYAAAAAKoFgBwAA\nAKASCHYAAAAAKoFgBwAAAKASCHYAAAAAKoFgBwAAAKASCHYAAAAAKoFgBwAAAKASCHYAAAAA\nKoFgBwAAAKASCHYAAAAAKoFgBwAAAKASCHYAAAAAKoFgBwAAAKASCHYAAAAAKoFgBwAAAKAS\nCHYAAAAAKoFgBwAAAKASCHYAAAAAKoFgBwAAAKASCHYAAAAAKoFgBwAAAKASDKXU2zV4Qil9\n4YUXoqOjV6xY4e1afIvD4Xj11Vfj4+OXLFni7Vp8S0NDw1tvvTVgwIBFixZ5uxbfUlFRsXbt\n2hEjRsydO9fbtbStvLx83bp1ycnJs2fP9nYtvqWoqGjDhg3jxo2bNm2at2vxLdnZ2V988cXU\nqVPHjx/v7Vp8y4kTJ7799tvZs2cnJyd7uxbodN3gil1KSsquXbu8XYXPcTqdKSkp+/fv93Yh\nPsdqtaakpBw6dMjbhfichoaGlJSUo0ePeruQdqmrq0tJSTl27Ji3C/E5lZWVKSkpGRkZ3i7E\n55SWlqakpJw6dcrbhficoqKilJSU3NxcbxcCXaEbBDsAAAAAaA8EOwAAAACVQLADAAAAUAlf\nf3gCAAAAANoJV+wAAAAAVALBDgAAAEAlEOwAAAAAVALBDgAAAEAleG8X4Iksyx9++OH+/fsl\nSRozZsztt98uCIK3i/IySZJWrFjx9ttvm0wm1xYcpbq6uvfff//YsWOiKPbv3/+WW26Ji4sj\nODKEnD179r333jt58iTHcUOHDv3LX/4SGhpKusOR8f0Kux7O/Qvh3G9N9z334dJxTz31lLdr\naNW6dev27dt31113jRs37ttvv83Pzx83bpy3i/IaURQzMjLWr1+fk5Nz7bXXarVa13Ycpeee\ne668vPyee+6ZMWNGTk7Oxx9/fMUVV+j1+h5+ZJxO5yOPPBIWFvb3v/992LBhqampe/bsmTlz\nJukO/2Z8v8KuhHO/NTj33erW5z50AOqrrFbr9ddfv3fvXtfL1NTUhQsX1tXVebcqL/ryyy9v\nvfXW5cuXz58/v6GhwbURR6mqqmr+/PlZWVmul5IkLV26dMeOHTgyp06dmj9/vtlsdr1MS0ub\nP3++zWbz/SPj+xV2MZz7buHcb033PfehQ/juHLvCwkK73T5ixAjXy+HDh8uynJeX592qvGjR\nokXvvffek08+2XwjjpKiKEuWLElMTHS9lCRJFEVFUXBkkpKSPvvsMz8/P7vdnp+fv2/fvr59\n++p0Ot8/Mr5fYRfDue8Wzv3WdN9zHzqE786xq62t5XneaDS6XvI87+fnV1NT492qfA2OUlhY\n2JIlS1x/djgcr7/+uslkmjhxYnp6eg8/MizL6nQ6QshTTz2VmZnp5+e3Zs0a0h3+zfh+hb4A\nRwnnfmu677kPHcJ3gx2llGGYFhtlWfZKMT4LR8mFUvrzzz9v2LAhIiLitddeM5lMODJNVq1a\nZbPZvvvuu0cfffTdd9/1/SPj+xX6AhwlF5z7HnS7cx86hO8Gu+DgYKfTabPZ9Ho9IUSW5cbG\nRtdzPdAER4kQUl9fv2bNmvLy8hUrVkyePNn1yYUjU1hYWF1dnZycbDKZTCbTsmXLNm/efOLE\nCd8/Mr5foS/AUSI491vRfc996BC+O8euT58+Wq32xIkTrpeZmZksy8bHx3u3Kl+Do0Qpffrp\npw0Gw5tvvjllypSm30dxZPLz81977bWmX8etVqsoijzP+/6R8f0KfQGOEs791nTfcx86hO9e\nsTMYDDNmzHj//fdDQkIYhlm7du2UKVOCgoK8XZdvwVE6fvx4bm7uggULTp8+3bQxOjo6NDS0\nhx+Z5OTkd999980335w3b57T6fzkk0+ioqIGDx6s1Wp9/MjgX3V74Cjh3G9N9z33oUMwlFJv\n19AqWZbfe++9X3/9VVGUyy+//LbbbkMrxZycnAceeGDjxo3Nm5T25KP09ddfv/feey023nnn\nnVdddVUPPzKEkOzs7Pfffz8/P1+r1Q4ZMmTFihXh4eGkO/yb8f0Kux7O/RZw7nvQfc99uHQ+\nHewAAAAAoP18d44dAAAAAFwUBDsAAAAAlUCwAwAAAFAJBDsAAAAAlUCwAwAAAFAJBDsAAAAA\nlUCwAwAAAFAJBDsAAAAAlUCwAwAAAFAJBDuAnmvr1q3MBSIjI2fMmPHzzz97uzoAALhovLcL\nAAAvmzNnzmWXXeb6syRJubm5X3/99U8//bRr165JkyZdyjtHRUWVlZW1uW7hq6+++tBDD1VV\nVYWEhFzKjwMAAAQ7gJ5uwYIFd955Z/MtP/zww5VXXrlmzZpLDHZhYWGXVhoAAFwcBDsAaGnG\njBnBwcGZmZmX+D7Hjx/vkHoAAKCdMMcOANzT6XRNf87Pz7/xxhvj4uICAgKmTJmybdu2pl1m\ns/mxxx7r27evwWBITExcuXKlxWJx7ZozZ87o0aM9D5s2bdpDDz1ECAkNDb3ppptcgzdt2nT5\n5ZcHBQX5+/snJyevXbu26cfNmTNn4cKFZ8+enTVrlp+fX1RU1B133NHQ0NA0YP/+/bNmzQoJ\nCYmOjl66dGlhYWF7/hYAAOqAYAcALe3ataumpuaqq65yvUxLSxsxYsTevXsXL178wAMP1NTU\nzJs3b926da69N99888svvzx8+PBHH3104MCBr7zyyn333Xfhe7Y27PXXX//b3/5GCNm8efOq\nVasIISkpKcuWLWMY5uGHH77rrrskSbr99tu/+OKLpreqqKhYtmzZHXfckZ6e/sQTT6xdu/b+\n++937frmm2+mTJlSWlr6z3/+c/HixVu3bp0+fbrZbG7zbwEAoBIUAHqqLVu2EELmzZv31O9W\nr169fPlynU43e/Zsq9XqGjZlypQ+ffpUV1e7XoqiOHXqVJPJZDab6+vrGYa59957m97zhhtu\n6Nevn+vPs2fPHjVqFKXU87BXXnmFEFJVVeV6uXDhwpiYGIfD4Xppt9v9/f3vuOOOpvckhHz/\n/fdNbzV79uw+ffq4CktMTBw+fHhT5Tt27CCEvPfee57/Fh1zNAEAfADm2AH0dFu2bHElvCaC\nIMyfP1+v1xNCamtrd+3a9eyzzwYHBzftveeee6677roDBw6MGTOGELJnz57i4uLo6GhCyKef\nfnrhj2AYpj3DXN59912WZTUajeul2WyWZdlqtTYNCA4OnjFjRtPL6Ojo1NRUQsjRo0dzc3PX\nrVvnqpwQMnPmzJdeeqlPnz6e/xbTp0+/uEMGAOCrcCsWoKd7++23m/+2V1BQMHPmzL///e8/\n/PADIeTUqVOEkNWrVzfvdXfdddcRQiorK00m09NPP33s2LHY2NipU6euWrXqt99+u/BHtHOY\nS0hISHV19fr16x988MGpU6fGxMQ0Tdpz6dOnT/OXrtRICMnJySGEDBo0qPmulStXTp8+3fPf\n4k8eOAAA34NgBwDniY2Nfe211wghP/74IyHEdeXsX//61y8XmDp1KiHk8ccfP378+OrVq2VZ\nfvXVV8eNG3f11VfLstzibds5jBDy5ptvDho06L777quoqFiyZMmvv/7au3fv5gN43v2tBlEU\nW9vb5t8CAEAdcCsWAFqKj48nhFRVVRFCkpKSCCEsy06ZMqVpQGlpaXZ2dmBgYH19fVlZWXx8\nvGuKXl1d3cqVK9euXbt9+/Z58+Y1jW/nMEKIxWJZuXLl0qVL161bx3Gca6PD4WhP2a5Ss7Oz\nR40a1bTx5Zdf7t2799y5cz38LS76AAEA+CpcsQOAlliWJb/fo/T3958+ffp///vfpluWiqKs\nWLFi8eLFgiCkpqYOGDDgnXfece0KDAy8+uqrXWOav2F7hrn+nJ+f73A4Ro0a1ZTqdu7cWVFR\n0eIN3UpOTo6MjHzjjTdcl+4IIWlpaQ8//HB+fr7nv8WfOkgAAL4IV+wAoCWWZWNiYpo6wL38\n8suTJ08ePnz4rbfeynHc1q1bjxw5sn79eo7jxo4dGx8fv3r16rS0tMGDB586derrr7+Oj49v\ncX/T8zBXtHrttdfm/v/27lBVlSCOA7AnCRvUrIuI4CsIIoiCGE4VBIsYFgy+h8UnsPkC20xi\nF4y2bWv2CUzesCCXA9ebLnKH72vDziwzk35h/jPf391uN47jzWZzv9/b7fblcknTNI7j0+m0\n3++Xy+WbaUdRtN1uF4tFr9ebTqePx2O328VxXLyr8WYV/2QTAT7iU+W4wMcVxbA/iicKk8mk\nVCqlaVo0sywrbiGpVqv9fv9wOLx6Zlk2m83q9Xq5XG61WkmS3G634tPrupP33fI8H41GURSt\n1+vn83m9XsfjcaVSaTab8/k8z/Pz+TwYDJIk+fHPwmq16nQ6r+bxeBwOh7VardFoFMN/n+qf\nVgEQhq/n397nBgDgv+CMHQBAIAQ7AIBACHYAAIEQ7AAAAiHYAQAEQrADAAiEYAcAEAjBDgAg\nEIIdAEAgBDsAgEAIdgAAgRDsAAACIdgBAATiF/JdLlJqWmcLAAAAAElFTkSuQmCC",
"text/plain": [
"plot without title"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Plot\n",
"library(ggplot2)\n",
"# \"group\" is necessary for geom_line() as well, for some reason it doesn't take them from colour\n",
"ggplot(my.data.long, aes(Resistance, Depth, colour=Trial, group=Trial)) + geom_line() + facet_wrap(vars(Type))"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "R",
"language": "R",
"name": "ir"
},
"language_info": {
"codemirror_mode": "r",
"file_extension": ".r",
"mimetype": "text/x-r-source",
"name": "R",
"pygments_lexer": "r",
"version": "3.5.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
%% Cell type:code id: tags:
```
R
# Define sameple data (you'd import via read.delim() instead)
(
my.data
=
data.frame
(
Plot
=
c
(
"FF1"
,
"FF1"
,
"DB1"
,
"DB1"
),
Type
=
c
(
"FF"
,
"FF"
,
"DB"
,
"DB"
),
Trial
=
c
(
"1"
,
"2"
,
"1"
,
"2"
),
D1
=
1
:
4
,
D2
=
seq
(
10
,
by
=
5
,
length.out
=
4
),
D3
=
30
:
33
))
```
%% Output
| Plot | Type | Trial | D1 | D2 | D3 |
|---|---|---|---|---|---|
| FF1 | FF | 1 | 1 | 10 | 30 |
| FF1 | FF | 2 | 2 | 15 | 31 |
| DB1 | DB | 1 | 3 | 20 | 32 |
| DB1 | DB | 2 | 4 | 25 | 33 |
\begin{tabular}{r|llllll}
Plot & Type & Trial & D1 & D2 & D3\\
\hline
FF1 & FF & 1 & 1 & 10 & 30 \\
FF1 & FF & 2 & 2 & 15 & 31 \\
DB1 & DB & 1 & 3 & 20 & 32 \\
DB1 & DB & 2 & 4 & 25 & 33 \\
\end{tabular}
%% Cell type:code id: tags:
```
R
# Our custom col means; the input is a chunk of your data.frame with a particular Plot value (so same columns but fewer rows)
CustomColMeans
=
function
(
df
)
{
# Run colMeans over that part of our data, but only on the columns from D1 to D3 (others are not numeric)
Means
=
colMeans
(
df
[,
which
(
names
(
df
)
==
"D1"
)
:
which
(
names
(
df
)
==
"D3"
)])
# For the rest of the variables, take the first occurrence
OtherCols
=
df
[
1
,
c
(
"Plot"
,
"Type"
)]
# Merge the means with the other columns so we don't lose them.
# Also we need to transpose Means with t(), so that it's treated as rows and not a column.
# And add something to Trial indicate that the trial is actually the mean of the trials.
Result
=
cbind
(
OtherCols
,
Trial
=
"Mean"
,
t
(
Means
))
}
# Run the custom function, and turn the result into a data.frame using Reduce(rbind, ...)
(
my.data.means
=
Reduce
(
rbind
,
by
(
my.data
,
my.data
$
Plot
,
CustomColMeans
)))
```
%% Output
| <!--/--> | Plot | Type | Trial | D1 | D2 | D3 |
|---|---|---|---|---|---|---|
| 3 | DB1 | DB | Mean | 3.5 | 22.5 | 32.5 |
| 1 | FF1 | FF | Mean | 1.5 | 12.5 | 30.5 |
\begin{tabular}{r|llllll}
& Plot & Type & Trial & D1 & D2 & D3\\
\hline
3 & DB1 & DB & Mean & 3.5 & 22.5 & 32.5\\
1 & FF1 & FF & Mean & 1.5 & 12.5 & 30.5\\
\end{tabular}
%% Cell type:code id: tags:
```
R
# Merge our means with the original data
(
my.data.combined
=
rbind
(
my.data
,
my.data.means
))
```
%% Output
| <!--/--> | Plot | Type | Trial | D1 | D2 | D3 |
|---|---|---|---|---|---|---|
| 1 | FF1 | FF | 1 | 1.0 | 10.0 | 30.0 |
| 2 | FF1 | FF | 2 | 2.0 | 15.0 | 31.0 |
| 3 | DB1 | DB | 1 | 3.0 | 20.0 | 32.0 |
| 4 | DB1 | DB | 2 | 4.0 | 25.0 | 33.0 |
| 31 | DB1 | DB | Mean | 3.5 | 22.5 | 32.5 |
| 11 | FF1 | FF | Mean | 1.5 | 12.5 | 30.5 |
\begin{tabular}{r|llllll}
& Plot & Type & Trial & D1 & D2 & D3\\
\hline
1 & FF1 & FF & 1 & 1.0 & 10.0 & 30.0\\
2 & FF1 & FF & 2 & 2.0 & 15.0 & 31.0\\
3 & DB1 & DB & 1 & 3.0 & 20.0 & 32.0\\
4 & DB1 & DB & 2 & 4.0 & 25.0 & 33.0\\
31 & DB1 & DB & Mean & 3.5 & 22.5 & 32.5\\
11 & FF1 & FF & Mean & 1.5 & 12.5 & 30.5\\
\end{tabular}
%% Cell type:code id: tags:
```
R
# Make it into a long format
my.data.long
=
reshape2
::
melt
(
my.data.combined
,
variable.name
=
"Depth"
,
value.name
=
"Resistance"
)
# Make depth numeric
my.data.long
$
Depth
=
substr
(
my.data.long
$
Depth
,
2
,
3
)
# Check it
my.data.long
```
%% Output
Using Plot, Type, Trial as id variables
| Plot | Type | Trial | Depth | Resistance |
|---|---|---|---|---|
| FF1 | FF | 1 | 1 | 1.0 |
| FF1 | FF | 2 | 1 | 2.0 |
| DB1 | DB | 1 | 1 | 3.0 |
| DB1 | DB | 2 | 1 | 4.0 |
| DB1 | DB | Mean | 1 | 3.5 |
| FF1 | FF | Mean | 1 | 1.5 |
| FF1 | FF | 1 | 2 | 10.0 |
| FF1 | FF | 2 | 2 | 15.0 |
| DB1 | DB | 1 | 2 | 20.0 |
| DB1 | DB | 2 | 2 | 25.0 |
| DB1 | DB | Mean | 2 | 22.5 |
| FF1 | FF | Mean | 2 | 12.5 |
| FF1 | FF | 1 | 3 | 30.0 |
| FF1 | FF | 2 | 3 | 31.0 |
| DB1 | DB | 1 | 3 | 32.0 |
| DB1 | DB | 2 | 3 | 33.0 |
| DB1 | DB | Mean | 3 | 32.5 |
| FF1 | FF | Mean | 3 | 30.5 |
\begin{tabular}{r|lllll}
Plot & Type & Trial & Depth & Resistance\\
\hline
FF1 & FF & 1 & 1 & 1.0\\
FF1 & FF & 2 & 1 & 2.0\\
DB1 & DB & 1 & 1 & 3.0\\
DB1 & DB & 2 & 1 & 4.0\\
DB1 & DB & Mean & 1 & 3.5\\
FF1 & FF & Mean & 1 & 1.5\\
FF1 & FF & 1 & 2 & 10.0\\
FF1 & FF & 2 & 2 & 15.0\\
DB1 & DB & 1 & 2 & 20.0\\
DB1 & DB & 2 & 2 & 25.0\\
DB1 & DB & Mean & 2 & 22.5\\
FF1 & FF & Mean & 2 & 12.5\\
FF1 & FF & 1 & 3 & 30.0\\
FF1 & FF & 2 & 3 & 31.0\\
DB1 & DB & 1 & 3 & 32.0\\
DB1 & DB & 2 & 3 & 33.0\\
DB1 & DB & Mean & 3 & 32.5\\
FF1 & FF & Mean & 3 & 30.5\\
\end{tabular}
%% Cell type:code id: tags:
```
R
# Plot
library
(
ggplot2
)
# "group" is necessary for geom_line() as well, for some reason it doesn't take them from colour
ggplot
(
my.data.long
,
aes
(
Resistance
,
Depth
,
colour
=
Trial
,
group
=
Trial
))
+
geom_line
()
+
facet_wrap
(
vars
(
Type
))
```
%% Output
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