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Zrobiono rozdział 8 - finito :D
2020-04-17 20:10:04 +02:00
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<meta name="generator" content="pandoc" />
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<meta name="author" content="altarin" />
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<title>Kurs_R.R</title>
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<h1 class="title toc-ignore">Kurs_R.R</h1>
<h4 class="author">altarin</h4>
<h4 class="date">2020-04-11</h4>
</div>
<pre class="r"><code>library(tidyverse)</code></pre>
<pre><code>## ── Attaching packages ───────────────────────────────────────────────────────────────────────────────────────────────── tidyverse 1.3.0 ──</code></pre>
<pre><code>## ✓ ggplot2 3.3.0 ✓ purrr 0.3.3
## ✓ tibble 2.1.3 ✓ dplyr 0.8.5
## ✓ tidyr 1.0.2 ✓ stringr 1.4.0
## ✓ readr 1.3.1 ✓ forcats 0.5.0</code></pre>
<pre><code>## ── Conflicts ──────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()</code></pre>
<div id="section" class="section level1">
<h1>2.3.1</h1>
<div id="zadanie-1" class="section level2">
<h2>Zadanie 1</h2>
<pre class="r"><code>airquality %&gt;%
select(Ozone, Solar.R, Wind, Temp) %&gt;%
filter(Ozone&gt;80)</code></pre>
<pre><code>## Ozone Solar.R Wind Temp
## 1 115 223 5.7 79
## 2 135 269 4.1 84
## 3 97 267 6.3 92
## 4 97 272 5.7 92
## 5 85 175 7.4 89
## 6 108 223 8.0 85
## 7 82 213 7.4 88
## 8 122 255 4.0 89
## 9 89 229 10.3 90
## 10 110 207 8.0 90
## 11 168 238 3.4 81
## 12 118 225 2.3 94
## 13 84 237 6.3 96
## 14 85 188 6.3 94
## 15 96 167 6.9 91
## 16 91 189 4.6 93</code></pre>
</div>
<div id="zadanie-2" class="section level2">
<h2>Zadanie 2</h2>
<pre class="r"><code>#install.packages('weathermetrics')
library(weathermetrics)
airquality %&gt;%
mutate(TempC=fahrenheit.to.celsius(Temp))</code></pre>
<pre><code>## Ozone Solar.R Wind Temp Month Day TempC
## 1 41 190 7.4 67 5 1 19.44
## 2 36 118 8.0 72 5 2 22.22
## 3 12 149 12.6 74 5 3 23.33
## 4 18 313 11.5 62 5 4 16.67
## 5 NA NA 14.3 56 5 5 13.33
## 6 28 NA 14.9 66 5 6 18.89
## 7 23 299 8.6 65 5 7 18.33
## 8 19 99 13.8 59 5 8 15.00
## 9 8 19 20.1 61 5 9 16.11
## 10 NA 194 8.6 69 5 10 20.56
## 11 7 NA 6.9 74 5 11 23.33
## 12 16 256 9.7 69 5 12 20.56
## 13 11 290 9.2 66 5 13 18.89
## 14 14 274 10.9 68 5 14 20.00
## 15 18 65 13.2 58 5 15 14.44
## 16 14 334 11.5 64 5 16 17.78
## 17 34 307 12.0 66 5 17 18.89
## 18 6 78 18.4 57 5 18 13.89
## 19 30 322 11.5 68 5 19 20.00
## 20 11 44 9.7 62 5 20 16.67
## 21 1 8 9.7 59 5 21 15.00
## 22 11 320 16.6 73 5 22 22.78
## 23 4 25 9.7 61 5 23 16.11
## 24 32 92 12.0 61 5 24 16.11
## 25 NA 66 16.6 57 5 25 13.89
## 26 NA 266 14.9 58 5 26 14.44
## 27 NA NA 8.0 57 5 27 13.89
## 28 23 13 12.0 67 5 28 19.44
## 29 45 252 14.9 81 5 29 27.22
## 30 115 223 5.7 79 5 30 26.11
## 31 37 279 7.4 76 5 31 24.44
## 32 NA 286 8.6 78 6 1 25.56
## 33 NA 287 9.7 74 6 2 23.33
## 34 NA 242 16.1 67 6 3 19.44
## 35 NA 186 9.2 84 6 4 28.89
## 36 NA 220 8.6 85 6 5 29.44
## 37 NA 264 14.3 79 6 6 26.11
## 38 29 127 9.7 82 6 7 27.78
## 39 NA 273 6.9 87 6 8 30.56
## 40 71 291 13.8 90 6 9 32.22
## 41 39 323 11.5 87 6 10 30.56
## 42 NA 259 10.9 93 6 11 33.89
## 43 NA 250 9.2 92 6 12 33.33
## 44 23 148 8.0 82 6 13 27.78
## 45 NA 332 13.8 80 6 14 26.67
## 46 NA 322 11.5 79 6 15 26.11
## 47 21 191 14.9 77 6 16 25.00
## 48 37 284 20.7 72 6 17 22.22
## 49 20 37 9.2 65 6 18 18.33
## 50 12 120 11.5 73 6 19 22.78
## 51 13 137 10.3 76 6 20 24.44
## 52 NA 150 6.3 77 6 21 25.00
## 53 NA 59 1.7 76 6 22 24.44
## 54 NA 91 4.6 76 6 23 24.44
## 55 NA 250 6.3 76 6 24 24.44
## 56 NA 135 8.0 75 6 25 23.89
## 57 NA 127 8.0 78 6 26 25.56
## 58 NA 47 10.3 73 6 27 22.78
## 59 NA 98 11.5 80 6 28 26.67
## 60 NA 31 14.9 77 6 29 25.00
## 61 NA 138 8.0 83 6 30 28.33
## 62 135 269 4.1 84 7 1 28.89
## 63 49 248 9.2 85 7 2 29.44
## 64 32 236 9.2 81 7 3 27.22
## 65 NA 101 10.9 84 7 4 28.89
## 66 64 175 4.6 83 7 5 28.33
## 67 40 314 10.9 83 7 6 28.33
## 68 77 276 5.1 88 7 7 31.11
## 69 97 267 6.3 92 7 8 33.33
## 70 97 272 5.7 92 7 9 33.33
## 71 85 175 7.4 89 7 10 31.67
## 72 NA 139 8.6 82 7 11 27.78
## 73 10 264 14.3 73 7 12 22.78
## 74 27 175 14.9 81 7 13 27.22
## 75 NA 291 14.9 91 7 14 32.78
## 76 7 48 14.3 80 7 15 26.67
## 77 48 260 6.9 81 7 16 27.22
## 78 35 274 10.3 82 7 17 27.78
## 79 61 285 6.3 84 7 18 28.89
## 80 79 187 5.1 87 7 19 30.56
## 81 63 220 11.5 85 7 20 29.44
## 82 16 7 6.9 74 7 21 23.33
## 83 NA 258 9.7 81 7 22 27.22
## 84 NA 295 11.5 82 7 23 27.78
## 85 80 294 8.6 86 7 24 30.00
## 86 108 223 8.0 85 7 25 29.44
## 87 20 81 8.6 82 7 26 27.78
## 88 52 82 12.0 86 7 27 30.00
## 89 82 213 7.4 88 7 28 31.11
## 90 50 275 7.4 86 7 29 30.00
## 91 64 253 7.4 83 7 30 28.33
## 92 59 254 9.2 81 7 31 27.22
## 93 39 83 6.9 81 8 1 27.22
## 94 9 24 13.8 81 8 2 27.22
## 95 16 77 7.4 82 8 3 27.78
## 96 78 NA 6.9 86 8 4 30.00
## 97 35 NA 7.4 85 8 5 29.44
## 98 66 NA 4.6 87 8 6 30.56
## 99 122 255 4.0 89 8 7 31.67
## 100 89 229 10.3 90 8 8 32.22
## 101 110 207 8.0 90 8 9 32.22
## 102 NA 222 8.6 92 8 10 33.33
## 103 NA 137 11.5 86 8 11 30.00
## 104 44 192 11.5 86 8 12 30.00
## 105 28 273 11.5 82 8 13 27.78
## 106 65 157 9.7 80 8 14 26.67
## 107 NA 64 11.5 79 8 15 26.11
## 108 22 71 10.3 77 8 16 25.00
## 109 59 51 6.3 79 8 17 26.11
## 110 23 115 7.4 76 8 18 24.44
## 111 31 244 10.9 78 8 19 25.56
## 112 44 190 10.3 78 8 20 25.56
## 113 21 259 15.5 77 8 21 25.00
## 114 9 36 14.3 72 8 22 22.22
## 115 NA 255 12.6 75 8 23 23.89
## 116 45 212 9.7 79 8 24 26.11
## 117 168 238 3.4 81 8 25 27.22
## 118 73 215 8.0 86 8 26 30.00
## 119 NA 153 5.7 88 8 27 31.11
## 120 76 203 9.7 97 8 28 36.11
## 121 118 225 2.3 94 8 29 34.44
## 122 84 237 6.3 96 8 30 35.56
## 123 85 188 6.3 94 8 31 34.44
## 124 96 167 6.9 91 9 1 32.78
## 125 78 197 5.1 92 9 2 33.33
## 126 73 183 2.8 93 9 3 33.89
## 127 91 189 4.6 93 9 4 33.89
## 128 47 95 7.4 87 9 5 30.56
## 129 32 92 15.5 84 9 6 28.89
## 130 20 252 10.9 80 9 7 26.67
## 131 23 220 10.3 78 9 8 25.56
## 132 21 230 10.9 75 9 9 23.89
## 133 24 259 9.7 73 9 10 22.78
## 134 44 236 14.9 81 9 11 27.22
## 135 21 259 15.5 76 9 12 24.44
## 136 28 238 6.3 77 9 13 25.00
## 137 9 24 10.9 71 9 14 21.67
## 138 13 112 11.5 71 9 15 21.67
## 139 46 237 6.9 78 9 16 25.56
## 140 18 224 13.8 67 9 17 19.44
## 141 13 27 10.3 76 9 18 24.44
## 142 24 238 10.3 68 9 19 20.00
## 143 16 201 8.0 82 9 20 27.78
## 144 13 238 12.6 64 9 21 17.78
## 145 23 14 9.2 71 9 22 21.67
## 146 36 139 10.3 81 9 23 27.22
## 147 7 49 10.3 69 9 24 20.56
## 148 14 20 16.6 63 9 25 17.22
## 149 30 193 6.9 70 9 26 21.11
## 150 NA 145 13.2 77 9 27 25.00
## 151 14 191 14.3 75 9 28 23.89
## 152 18 131 8.0 76 9 29 24.44
## 153 20 223 11.5 68 9 30 20.00</code></pre>
</div>
</div>
<div id="section-1" class="section level1">
<h1>2.4.1</h1>
<div id="zadanie-1-1" class="section level2">
<h2>Zadanie 1</h2>
<pre class="r"><code>as_tibble(airquality)</code></pre>
<pre><code>## # A tibble: 153 x 6
## Ozone Solar.R Wind Temp Month Day
## &lt;int&gt; &lt;int&gt; &lt;dbl&gt; &lt;int&gt; &lt;int&gt; &lt;int&gt;
## 1 41 190 7.4 67 5 1
## 2 36 118 8 72 5 2
## 3 12 149 12.6 74 5 3
## 4 18 313 11.5 62 5 4
## 5 NA NA 14.3 56 5 5
## 6 28 NA 14.9 66 5 6
## 7 23 299 8.6 65 5 7
## 8 19 99 13.8 59 5 8
## 9 8 19 20.1 61 5 9
## 10 NA 194 8.6 69 5 10
## # … with 143 more rows</code></pre>
</div>
<div id="zadanie-2-1" class="section level2">
<h2>Zadanie 2</h2>
<pre class="r"><code>tibble(litery=letters[6:11],
miesiace=month.name[1:6])</code></pre>
<pre><code>## # A tibble: 6 x 2
## litery miesiace
## &lt;chr&gt; &lt;chr&gt;
## 1 f January
## 2 g February
## 3 h March
## 4 i April
## 5 j May
## 6 k June</code></pre>
</div>
</div>
<div id="section-2" class="section level1">
<h1>3.2.4</h1>
<div id="zadanie-1-2" class="section level2">
<h2>Zadanie 1</h2>
<pre class="r"><code>ggplot(data=mpg)</code></pre>
<p><img src="data:image/png;base64,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
<p>Co widzisz? Szare. puste pole ## Zadanie 2</p>
<pre class="r"><code>as_tibble(mtcars)</code></pre>
<pre><code>## # A tibble: 32 x 11
## mpg cyl disp hp drat wt qsec vs am gear carb
## &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt;
## 1 21 6 160 110 3.9 2.62 16.5 0 1 4 4
## 2 21 6 160 110 3.9 2.88 17.0 0 1 4 4
## 3 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1
## 4 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1
## 5 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2
## 6 18.1 6 225 105 2.76 3.46 20.2 1 0 3 1
## 7 14.3 8 360 245 3.21 3.57 15.8 0 0 3 4
## 8 24.4 4 147. 62 3.69 3.19 20 1 0 4 2
## 9 22.8 4 141. 95 3.92 3.15 22.9 1 0 4 2
## 10 19.2 6 168. 123 3.92 3.44 18.3 1 0 4 4
## # … with 22 more rows</code></pre>
<p>liczba wierszy: 32 ## Zadanie 3</p>
<pre class="r"><code>?mpg</code></pre>
<p>drv f = front-wheel drive, r = rear wheel drive, 4 = 4wd ## Zadanie 4</p>
<pre class="r"><code>ggplot(aes(x=hwy, y=cyl), data=as_tibble(mpg)) + geom_point()</code></pre>
<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAABUAAAAPACAMAAADDuCPrAAADAFBMVEUAAAABAQECAgIDAwMEBAQFBQUGBgYHBwcICAgJCQkKCgoLCwsMDAwNDQ0ODg4PDw8QEBARERESEhITExMUFBQVFRUWFhYXFxcYGBgZGRkaGhobGxscHBwdHR0eHh4fHx8gICAhISEiIiIjIyMkJCQlJSUmJiYnJycoKCgpKSkqKiorKyssLCwtLS0uLi4vLy8wMDAxMTEyMjIzMzM0NDQ1NTU2NjY3Nzc4ODg5OTk6Ojo7Ozs8PDw9PT0+Pj4/Pz9AQEBBQUFCQkJDQ0NERERFRUVGRkZHR0dISEhJSUlKSkpLS0tMTExNTU1OTk5PT09QUFBRUVFSUlJTU1NUVFRVVVVWVlZXV1dYWFhZWVlaWlpbW1tcXFxdXV1eXl5fX19gYGBhYWFiYmJjY2NkZGRlZWVmZmZnZ2doaGhpaWlqampra2tsbGxtbW1ubm5vb29wcHBxcXFycnJzc3N0dHR1dXV2dnZ3d3d4eHh5eXl6enp7e3t8fHx9fX1+fn5/f3+AgICBgYGCgoKDg4OEhISFhYWGhoaHh4eIiIiJiYmKioqLi4uMjIyNjY2Ojo6Pj4+QkJCRkZGSkpKTk5OUlJSVlZWWlpaXl5eYmJiZmZmampqbm5ucnJydnZ2enp6fn5+goKChoaGioqKjo6OkpKSlpaWmpqanp6eoqKipqamqqqqrq6usrKytra2urq6vr6+wsLCxsbGysrKzs7O0tLS1tbW2tra3t7e4uLi5ubm6urq7u7u8vLy9vb2+vr6/v7/AwMDBwcHCwsLDw8PExMTFxcXGxsbHx8fIyMjJycnKysrLy8vMzMzNzc3Ozs7Pz8/Q0NDR0dHS0tLT09PU1NTV1dXW1tbX19fY2NjZ2dna2trb29vc3Nzd3d3e3t7f39/g4ODh4eHi4uLj4+Pk5OTl5eXm5ubn5+fo6Ojp6enq6urr6+vs7Ozt7e3u7u7v7+/w8PDx8fHy8vLz8/P09PT19fX29vb39/f4+Pj5+fn6+vr7+/v8/Pz9/f3+/v7////isF19AAAACXBIWXMAAB2HAAAdhwGP5fFlAAAgAElEQVR4nO3dd4BkVZmw8duTmMAACp+CwLqgYFzTKLKuCROy6x3igAQJIrAiooIgguKAIrooSVGRFVHSIqDCoiILIkgakggi4BAlDk2OE3rmftXVoS7De6q7a/rp2zX9/P6wq6tfT52qqXq6YpMVkqSWZFVvQJLalQGVpBYZUElqkQGVpBYZUElqkQGVpBYZUElqkQGVpBYZUElqkQGVpBYZUElqkQGVpBYZUElqkQGVpBYNb0Af6Rz1imeq3sEQLWqLi7Xs8eLxqrcwNI8UxaKq9zBEzxRV72CIniiK56vewxDNXxgda0Cr3sEQGVCcAeUZUANaDQOKM6A8A2pAq2FAcQaUZ0ANaDUMKM6A8gyoAa2GAcUZUJ4BNaDVMKA4A8ozoAa0GgYUZ0B5BtSAVsOA4gwoz4Aa0GoYUJwB5RlQA1oNA4ozoDwDakCrYUBxBpRnQA1oNQwozoDyDKgBrYYBxRlQngE1oNUwoDgDyjOgBrQaBhRnQHkG1IBWw4DiDCjPgBrQahhQnAHlGVADWg0DijOgPANqQKthQHEGlGdADWg1DCjOgPIMqAGthgHFGVCeATWg1TCgOAPKM6AGtBoGFGdAeQbUgFbDgOIMKM+AGtBqGFCcAeUZUANaDQOKM6A8A2pAq2FAcQaUZ0ANaDUMKM6A8gyoAa2GAcUZUJ4BfYFFF/zgkB9dvMSA8gwozoDyDGjZ3Xvk3T639GptcEs3oDgDyjOgPC6g8z+df/XGB6/bJ//SUvdBW7uln7/eiiu+9sJlOq/d7jr43z+y55zmM/n4jo6X3NJ05I7XTBw/7dPNl3lfR81RzWfeNGHchI2aj5y/1gorvOKs5jM/fM9r3v7l5iMP/Oqwr/33rc1nLvnUhz72rfubjjz0hTe9+t0/bb7M7T+d/Y0z7ms+8/OtN9nm1OYjcw/YZOO9b2g+M4IMKM+AlszJ96jdLyqe3i6/Y9kDes+0rG7FB5bt7O48rr7MW+9IjxzUc0rZ1CbLrN078930yFG9Ix1NlpnRO7NZeuT+FXpGJt6WnvlFz8y4LzU5qZ+s2T0yabd70yM3vrq+zIT9miyzZ0d9Zspv0yMP7DO1e2S1JpdN5xkr1ZdZudnvhc3rJ9XxriY7HlEGlGdAS07Pj6l//Wr+f8sc0Ac6elOTjVumc/u+vmVWuSs18oW+kWx8cpmV+me+mRo5vn8kXdB1+2fem5zpP+NZ8p7hT/tHdkku842+kXcme3TjlL6ZbZPLbNZ/pn6VGnnwo30zn00uc0Lfueo4KTnzpr5lVm9+l3jEGFCeAS35df6V+tdP55ctc0DX7G9Ets4ynNnjGsu8LzXTGMnyxMj+pZnUMo3wJUN8UWmZVNZe0RhZOXVSExsz1yRG/jShf+TzqWVe31jm3IF3PDm1zOGNmVRk712hsUyqjl9tLLNF6qRGlgHlGdCSR7fNf7ek6Dot/+T8ZQ5oKTXJZA3CKxurjEvccvcaxEmNL40knnm8chDLTCmNrJWYGcQy3yiNvCMxs1NjZKXEGf9raZm3JpZ5XWkm9Txo6TLeODHypdIyByVmVm2MTEyMjDADyjOgZXN3y3fcd/v8c/f3ff/pHo8vHKpLyh25Ycj/937l8v1PPDKxfFKJZcojK8UjU4a4zLh45I7yzM/jmTeXRqYlTmq90syf4pFDSyNTE8usUJp5dzxye2lkxcQy7yjNvDMemV8+41cl1hlhRbGk6i0MUVdR9Q6GaFFRLK56D0O0OLxSDEtA7/x8/W1MX3mo9/vrZ/R4YsgrHVi+OX2zpc3UlZc5LB7pKM8MYpmJg1jm2oGXSZzUt8sj28Qza5VGJiR2vHJp5pfxyO6lkfGJZcaVZtaPR+aUd/xUPLNuaeRV8cg/ysv8PLEdqT20FNC5W2/60/vm331MvuODPUe0HtCTyzen81rZTI9y1n4cj5TvpA4moFPikYlDXCYx88fyyMHxzGtKI5MTJ1WO7EXxSPlx9QqJZcrnakY88rfSyISueOZfSjNvjkeeL5/x3ye2I7WHlgL6xfy0+tdj82/3HNH1ZI9HhuzB8s1p6P/3fuU7YjfFI68ZxEmVRzaMR3YdxDLlVk8ZxEndHY9sWxr5p8QyH2mMjL8tHjm3tMzaiWVeVprZOx55YHpj5E2JZbYpLbNdYmZyY6TjH4mZkfVoUSyqeg9D9GxR9Q6G6Mnar86q9zBE8xdGxw5DQJ/L80frB27Ll3rs2cKLSJMaN6fky7+DsENjmdSrNuWXbVZJjJRfSxnEMqn3MZUjm3pJpvHqefINXHNLyxyXmDmxMfLR1I4b781KvrIzu3SmUi+f79iYSb3Ha07jkUDHNYmZjzaWeX1qxyPLF5F4vojU8HQ+c0H9wP35rBd+FKmFgJ7TuDkty4eRHuy/Czrud6mZlw8cx1IdX5caKb3x6uLUTOMpxeRvhfMHbmzn5v0j/5QaefjDfSMvuTo1c2J/1tZOjXSu1n9Su6ZG/tb/zqsZyQ89zOpfJvmW07v6X4abcFVyOyPKgPIMaMmu+fX1rxfkB7zwB618Eqn/bYHJd64Pyg29eZx4YnpmWt9JXZIcubFv5BXpZfofg26Snukr6KT0yMF9y+yVnvlQX/geSo7cs2nPyCub/P75Vu9zCusmP2TQeX/fg/jN08tc1XsHfaMmn52aOfAyV6zS+7vl7PTMiDKgPANacmq+2321L3O3z5d6EaClz8Lfs27tDlLHevcs27nt7Dx0/RWnrrnD3c1GvtPR/G5Ytw07BshwZ+fxPR9FbLpM/ZM94z7TbOT++n3Zl6Wj1tn9dwImjZuw+veantR5u2zw1vzopp9Qv2WL1adOf0PzZQ5/2fhxk16X/t1S8+DxW7ztHdv/ouky57/rJVNf+u6mjybmHbDuitPW3n20fJLTgI4AA1rStV+++VePOWBmfvhSP2iDPxvkX2PC+deYeAaUB76R
</div>
<div id="zadanie-5" class="section level2">
<h2>Zadanie 5</h2>
<pre class="r"><code>ggplot(aes(x=class, y=drv), data=as_tibble(mpg)) + geom_point()</code></pre>
<p><img src="data:image/png;base64,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
<p>Dlaczego wykres jest bezuzyteczny? Wykres nie pokazuje żadnych liczbowych informacji na temat danych Zostały wykorzystane dwie cechy typu categorical Jedyną informacją jest to, czy dana kombinacja drv i class istanieje. # 3.3.1 ## Zadanie 1 ustalenie parametru color wewnątrz funckcji aes powinno być nazwą kolumny z categoriami, po których punkty zostaną pogrupowane, a nie istanieje kolumna “blue” w zbriorze danych</p>
<p>parametr color powinien zostać ustalony wewnątrz funkcji geom_point. Poprawiony kod poniżej.</p>
<pre class="r"><code>ggplot(data = mpg) +
geom_point(mapping = aes(x = displ, y = hwy), color = &quot;blue&quot;)</code></pre>
<p><img src="data:image/png;base64,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
</div>
<div id="zadanie-2-2" class="section level2">
<h2>Zadanie 2</h2>
<pre class="r"><code>mpg</code></pre>
<pre><code>## # A tibble: 234 x 11
## manufacturer model displ year cyl trans drv cty hwy fl class
## &lt;chr&gt; &lt;chr&gt; &lt;dbl&gt; &lt;int&gt; &lt;int&gt; &lt;chr&gt; &lt;chr&gt; &lt;int&gt; &lt;int&gt; &lt;chr&gt; &lt;chr&gt;
## 1 audi a4 1.8 1999 4 auto(l… f 18 29 p comp…
## 2 audi a4 1.8 1999 4 manual… f 21 29 p comp…
## 3 audi a4 2 2008 4 manual… f 20 31 p comp…
## 4 audi a4 2 2008 4 auto(a… f 21 30 p comp…
## 5 audi a4 2.8 1999 6 auto(l… f 16 26 p comp…
## 6 audi a4 2.8 1999 6 manual… f 18 26 p comp…
## 7 audi a4 3.1 2008 6 auto(a… f 18 27 p comp…
## 8 audi a4 quat… 1.8 1999 4 manual… 4 18 26 p comp…
## 9 audi a4 quat… 1.8 1999 4 auto(l… 4 16 25 p comp…
## 10 audi a4 quat… 2 2008 4 manual… 4 20 28 p comp…
## # … with 224 more rows</code></pre>
<p>kolumny z danymi kategorialnymi to: manufacturer, model, trans, frv, fl, class można zwrócić uwagę na typ danych w kolumnie jeśli typ to <chr>, to najprawdopodobniej jest to dana kategorialna ## Zadanie 3</p>
<pre class="r"><code>ggplot(data = mpg) +
geom_point(mapping = aes(x=displ, y=hwy, color=displ, size=displ))</code></pre>
<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAABUAAAAPACAIAAAB7BESOAAAACXBIWXMAAB2HAAAdhwGP5fFlAAAgAElEQVR4nOzdZ3gUVd/H8bM1m91NDyb0FpqACAoYQQVFVCwgCmIBkaKAFREVRaSJN6CIImKhSrFxK6iABRVRkY406YEk1JBCet2d58X65I5p7C67U7Lfz7UvwsyZOf/MtST57TlzRidJkgAAAAAAAOqmV7oAAAAAAABwcQR4AAAAAAA0gAAPAAAAAIAGEOABAAAAANAAAjwAAAAAABpAgAcAAAAAQAMI8AAAAAAAaAABHgAAAAAADSDAAwAAAACgAQR4AAAAAAA0gAAPAAAAAIAGEOABAAAAANAAAjwAAAAAABpAgAcAAAAAQAOMShegLmlpaZIk+enkUVFReXl5+fn5fjq/FgUHB9tsNiFETk5OQUGB0uWoiM1mM5vNGRkZSheiIjqdLioqSghRXFycmZmpdDkqYjAYIiIiMjMzi4uLla5FRUJDQ81msxAiIyPD4XAoXY6KhIWFOZ3O7OxspQtREbPZHBoaKoTIz8/Pzc1VuhwVsVgsdrvdr38daVFkZKRer5ckKS0tTela1CU6Ojo3N5e/dcuyWq1Wq1UIkZ2dXVhYqHQ5/xIdHa10CfAGI/AAAAAAAGgAAR4AAAAAAA0gwAMAAAAAoAEEeAAAAAAANIAADwAAAACABhDgAQAAAADQAAI8AAAAAAAaQIAHAAAAAEADCPAAAAAAAGgAAR4AAAAAAA0gwAMAAAAAoAEEeAAAAAAANMCodAHqYrFY/HdynU5nMpn8d34tKr0gJpNJp9MpW4yqGI1GnU4XHBysdCEqUvoO0ev1XJmy9Hq9ECIoKMho5Ef6/7guixDCYrE4nU5li1EVvV7Pj5dyDAaD6wuj0ciVKcv1U8Wvfx1pUenvI94tFfG3bjmlF8RsNpf+YgIuhU6SJKVrUBFJkoiRAAAAAAAVYrjmX9LT0/33iUZUVFReXl5+fr6fzq9FwcHBNptNCJGTk1NQUKB0OSpis9nMZnNGRobShaiITqeLiooSQhQXF2dmZipdjooYDIaIiIjMzMzi4mKla1GR0NBQs9kshMjIyHA4HEqXoyJhYWFOpzM7O1vpQlTEbDaHhoYKIfLz83Nzc5UuR0UsFovdbk9LS2O8p6zIyEi9Xi9JUlpamtK1qEt0dHRubi5/65ZltVqtVqsQIjs7u7CwUOly/iU6OlrpEuANJnIAAAAAAKABBHgAAAAAADSAAA8AAAAAgAYQ4AEAAAAA0AACPAAAAAAAGkCABwAAAABAAwjwAAAAAABoAAEeAAAAAAANIMADAAAAAKABBHgAAAAAADSAAA8AAAAAgAYQ4AEAAAAA0AACPAAAAAAAGkCABwAAAABAA4xKFwDNczqd32/ctvvAsZzc/Jhakbd16xTXsK7SRQEAAABATUOAh/fy8wtffHP+z3/sdDqdpRvfX/F1aIjt2SH9+vW6QcHaAAAAAKCGYQo9vHQ08dQNDzyz/rftZdO7EEISIis7d+Lbi4e+OKPcLgAAAACA1wjw8EZqelb/Jybl5hVU3KX7/y827zrw6Lg35awKAAAAAGowAjy8MeTFGYVFxRdt9udff6/64TcZ6gEAAACAGo8AD4/t2nf0WOIpNxvP/OhzvxYDAAAAAAGCAA+Pzf/iW/cbX8jKSTx1zn/FAAAAAECAIMDDY4eOJXvU/rtft/ipEgAAAAAIHAR4eCynsrXrqnEqJc1PlQAAAABA4CDAw2NBQSaP2oeH2P1UCQAAAAAEDgI8PBYbHeFR+w6XN/dTJQAAAAAQOAjw8Fjvm7u639hkNHSLb+e/YgAAAAAgQBDg4bEH7rrJZrW42bhX92v8WgwAAAAABAgCPLwxZfQQIXQXbRZit018ZrD/ywEAAACAmo8AD2/ccn3HkQPvqj7DB1uCPp/zitlolK0qAAAAAKjBCPDw0hMP9Xnrlcft1uBK9ulE62aNN3wyq0GdGNnrAgAAAICaidFReK9n16t6dr3qi7W/rv7h91MpqUXFJXZbcOtmjYfdd/vlcQ2Urg4AAAAAahQCPC5Vv1439Ot1g9JVAAAAAEANxxR6AAAAAAA0gAAPAAAAAIAGEOABAAAAANAAAjwAAAAAABpAgAcAAAAAQAMI8AAAAAAAaAABHgAAAAAADSDAAwAAAACgAQR4AAAAAAA0gAAPAAAAAIAGEOABAAAAANAAAjwAAAAAABpAgAcAAAAAQAMI8AAAAAAAaAABHgAAAAAADSDAAwAAAACgAQR4AAAAAAA0gAAPAAAAAIAGEOABAAAAANAAAjwAAAAAABpAgAcAAAAAQAMI8AAAAAAAaAABHgAAAAAADSDAAwAAAACgAQR4AAAAAAA0gAAPAAAAAIAGEOABAAAAANAAAjwAAAAAABpAgAcAAAAAQAMI8AAAAAAAaAABHgAAAAAADSDAAwAAAACgAQR4AAAAAAA0gAAPAAAAAIAGEOABAAAAANAAAjwAAAAAABpAgAcAAAAAQAMI8AAAAAAAaAABHgAAAAAADSDAAwAAAACgAQR4AAAAAAA0gAAPAAAAAIAGEOABAAAAANAAAjwAAAAAABpAgAcAAAAAQAMI8AAAAAAAaAABHgAAAAAADSDAAwAAAACgAQR4AAAAAAA0gAAPAAAAAIAGEOABAAAAANAAAjwAAAAAABpAgAcAAAAAQAMI8AAAAAAAaAABHgAAAAAADTAqXQBqAofTmZaRlZtfEBUeGmq3Kl0OAAAAANRABHhckr+PJi5aue63rXuzc/NcWxrXi+3V/ZqBd98cYiPJAwAAAIDPEODhpRKHY+aHny1fvV6SpLLbj588O3fpqhVf/zTzxcfiO7RWqjwAAAAAqGEI8PCGJEkvTP/wu1+3VtUgIzN7xPi33nn1yRs6t5OzMAAAAAA1xsyZM/Py8q6//vru3bsLIfLz82fMmCGEuOuuu9q3by9DAZIkTZ48WQjRs2fP+Ph4GXqsHgEe3lj61Y/VpHeXEofj+f98sOrDqbVrRcpTFQAAAICaZMaMGampqePHj3cF+Ly8vIkTJwoh6tWrJ0+Adzqdrh7tdrsaAjyr0MNjWTl576/42p2WOXn57y1d5e96AAAAACAQEODhsZ//3JWZnetm43W/bi0sKvZrPQAAAAACgcViGTFixIgRI1q2bKl0LcpgCj08tnX3Afcb5xcU7jl4rOMVAfofDAAAAICv2Gy2efPmKV2FkhiBh8fOpWZ41P7s+XQ/VQIAAAAAgYMReHjMaDB41N5k5G0GAAAAoDrbtm3bsWPHhQsXateu3aNHj7p161ZsU1xcPG3aNFHFKvRZWVk7d+48evRoRkZGo0aNWrZs2bZt24q9rF279rLLLhs5cmR+fv6qVat27NhhtVpjYmLatm3btWtXvV7Vg9wkK3isTkyUX9sDAAAACByJiYn9+vXbtm1b6Ra9Xj948OC33367XMuioqJKV6EvKiqaPn36rFmzLly4ULZ9jx49Zs+e3bp169ItW7dunThxYps2bXr06NG7d+8DB/51d3CTJk3mzZvXs2dP331zPqbqTxegTl2uauNOM0kIIUR4qL1188Z+rQcAAACARv36668dOnQoTe+NGze22WxOp3PhwoV33313SUmJOycZMWLEhAkTLly4YDKZWrRo0b59+7CwMCHE+vXre/XqlZ5e/pbe7OzsO+64w5Xeg4ODmzZtqtPphBAJCQm9evVauXKlL79DnyLAw2M3dGrnzqC6TgghRP/buxnUPQsFAAAAgCKKi4uHDBniCthPPvlkSkpKQkLChQsXvvvuu+jo6PXr15cbUa/Uzz//vGjRIiHEvffee/LkyYMHD+7cufPcuXPjxo0TQiQlJb311lvlDklMTDx8+HBYWNhXX32VmZl59OjRtLS0CRMm6HQ6h8Px+OOP5+Xl+eHb9QGSFTxmMhlfeOx+12dU1asTEzW0fy8ZSgIAAACgOe+//35CQoIQYvz48e+8806tWrWEEEaj8ZZbbvn999+DgoLcOcnvv/8uhDAYDAsWLLjssstcG4OCgqZN
<p>zmienna ciągła jest interpolowania między dwoma kolorami tworząc gradient tak jak samo z rozmiarem, rozmiar jest skalowany w przypadku shape powoduje to błąd. Gdyby podać dane kategorialne podział byłby dyskretny, na różne kolory, wielkości, kształty ## Zadanie 4 Przykład w punkcie wyżej Ta sama zmienna bedzie przedstawiona różnymi metodami ## Zadanie 5 stroke wpływa na grubuść konturu, obrysu, mozna stosować z punktami i liniami</p>
<pre class="r"><code>?geom_point</code></pre>
</div>
<div id="zadanie-6" class="section level2">
<h2>Zadanie 6</h2>
<pre class="r"><code>ggplot(data = mpg) +
geom_point(mapping = aes(x=displ, y=hwy, color=displ &lt; 5))</code></pre>
<p><img src="data:image/png;base64,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
<pre class="r"><code>mpg %&gt;%
filter(displ &lt; 5) %&gt;%
mutate(is_less_than_5=displ &lt; 5)</code></pre>
<pre><code>## # A tibble: 196 x 12
## manufacturer model displ year cyl trans drv cty hwy fl class
## &lt;chr&gt; &lt;chr&gt; &lt;dbl&gt; &lt;int&gt; &lt;int&gt; &lt;chr&gt; &lt;chr&gt; &lt;int&gt; &lt;int&gt; &lt;chr&gt; &lt;chr&gt;
## 1 audi a4 1.8 1999 4 auto… f 18 29 p comp…
## 2 audi a4 1.8 1999 4 manu… f 21 29 p comp…
## 3 audi a4 2 2008 4 manu… f 20 31 p comp…
## 4 audi a4 2 2008 4 auto… f 21 30 p comp…
## 5 audi a4 2.8 1999 6 auto… f 16 26 p comp…
## 6 audi a4 2.8 1999 6 manu… f 18 26 p comp…
## 7 audi a4 3.1 2008 6 auto… f 18 27 p comp…
## 8 audi a4 q… 1.8 1999 4 manu… 4 18 26 p comp…
## 9 audi a4 q… 1.8 1999 4 auto… 4 16 25 p comp…
## 10 audi a4 q… 2 2008 4 manu… 4 20 28 p comp…
## # … with 186 more rows, and 1 more variable: is_less_than_5 &lt;lgl&gt;</code></pre>
<p>Przypisanie displ &lt; 5 podzieliło data set na dwie grupy TRUE oraz FALSE, w zalezności od tego czy warunek był spełniony czy nie. # 3.5.1 ## Zadanie 1</p>
<pre class="r"><code>ggplot(data = mpg) +
geom_point(mapping = aes(x = displ, y = hwy)) +
facet_grid(drv ~ displ)</code></pre>
<p><img src="data:image/png;base64,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
<p>ggplot potraktuje ją jako zmienną kategorialną, tworząc siatkę dla kazdej unikatowej wartości zmiennej ciagłęj ## Zadanie 2</p>
<pre class="r"><code>ggplot(data = mpg) +
geom_point(mapping = aes(x = drv, y = cyl))</code></pre>
<p><img src="data:image/png;base64,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
<pre class="r"><code>ggplot(data = mpg) +
geom_point(mapping = aes(x = drv, y = cyl)) +
facet_grid(cyl~drv)</code></pre>
<p><img src="data:image/png;base64,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
<p>puste komórki oznacznaczają brak danych ## Zadanie 3</p>
<pre class="r"><code>ggplot(data = mpg) +
geom_point(mapping = aes(x = displ, y = hwy)) +
facet_grid(drv ~ .)</code></pre>
<p><img src="data:image/png;base64,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
<pre class="r"><code>ggplot(data = mpg) +
geom_point(mapping = aes(x = displ, y = hwy)) +
facet_grid(. ~ cyl)</code></pre>
<p><img src="data:image/png;base64,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
<p>kropka traktowana jest jako puste pole, wtedy jedna z osi, nie bedzie kategoryzowana ## Zadanie 4</p>
<pre class="r"><code>ggplot(data = mpg) +
geom_point(mapping = aes(x = displ, y = hwy)) +
facet_wrap(~ class, nrow = 2)</code></pre>
<p><img src="data:image/png;base64,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
</div>
</div>
<div id="section-3" class="section level1">
<h1>3.6.1</h1>
<div id="zadanie-1-3" class="section level2">
<h2>Zadanie 1</h2>
<p>liniowy - geom_line() pudełkowy - geom_boxplot() histogram - geom_histogram() warstwowy - geom_area() ## Zadanie 2</p>
<pre class="r"><code>ggplot(data = mpg, mapping = aes(x = displ, y = hwy, color = drv)) +
geom_point() +
geom_smooth(se = FALSE)</code></pre>
<pre><code>## `geom_smooth()` using method = 'loess' and formula 'y ~ x'</code></pre>
<p><img src="data:image/png;base64,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
</div>
<div id="zadanie-3" class="section level2">
<h2>Zadanie 3</h2>
<p>show.legend = FALSE, sluży do ukrycia legendy ## Zadanie 4 parametr se w funcji geom_smooth() służy do pokazania, lub ukrycia przedziałów ufnośći na wykresie. ## Zadanie 5 dwa poniższe wyresy są takie same, różnią się tylko sposobem denifincji mapowania. mapowanie wewnątrz aes w ggplot, przenosi mapowania na wszystkie geometrie, gdy nie sa zdefiniowane inne.</p>
<pre class="r"><code>ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) +
geom_point() +
geom_smooth()</code></pre>
<pre><code>## `geom_smooth()` using method = 'loess' and formula 'y ~ x'</code></pre>
<p><img src="data:image/png;base64,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
<pre class="r"><code>ggplot() +
geom_point(data = mpg, mapping = aes(x = displ, y = hwy)) +
geom_smooth(data = mpg, mapping = aes(x = displ, y = hwy))</code></pre>
<pre><code>## `geom_smooth()` using method = 'loess' and formula 'y ~ x'</code></pre>
<p><img src="data:image/png;base64,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
</div>
<div id="zadanie-6-1" class="section level2">
<h2>Zadanie 6</h2>
<p>Wykres 1</p>
<pre class="r"><code>mpg %&gt;%
ggplot(aes(x=displ, y=hwy)) +
geom_point() +
geom_smooth(se=FALSE)</code></pre>
<pre><code>## `geom_smooth()` using method = 'loess' and formula 'y ~ x'</code></pre>
<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAABUAAAAPACAIAAAB7BESOAAAACXBIWXMAAB2HAAAdhwGP5fFlAAAgAElEQVR4nOzdeWBTVf738XOztk3StGmh7BUoICAioAgiKA7gBoKigiIODsKIMgiiKKKAIK4gLgOyK5vDOAMIbsMoiDgi6rApCFQERLYCbZPSJG2zPX9kfn1q2iZNaJKe9v36C+45uefb06TJJ/fecxWfzycAAAAAAEDNpop3AQAAAAAAIDQCPAAAAAAAEiDAAwAAAAAgAQI8AAAAAAASIMADAAAAACABAjwAAAAAABIgwAMAAAAAIAECPAAAAAAAEiDAAwAAAAAgAQI8AAAAAAASIMADAAAAACABAjwAAAAAABIgwAMAAAAAIAECPAAAAAAAEtDEu4CaJTc31+fzRXsUtVqdmppqs9lcLle0x6pNDAZDYmKiEKKgoKCkpCTe5cjEYDDo9fq8vLx4FyKZtLQ0RVG8Xi9TFy6LxVJcXGy32+NdiEx0Ol1ycrIQwul0MnVh0Wq1ZrM5Pz/f4/HEuxaZmEwmvV4vhLBarW63O97lyMRoNGo0GqvVGu9CJJOeni6E8Hg8+fn58a5FMunp6Xa73el0xrsQmej1epPJJIRwOBwOhyPe5YTgf3VUEUfgAQAAAACQAAEeAAAAAAAJEOABAAAAAJAAAR4AAAAAAAkQ4AEAAAAAkAABHgAAAAAACRDgAQAAAACQAAEeAAAAAAAJEOABAAAAAJAAAR4AAAAAAAkQ4AEAAAAAkAABHgAAAAAACWjiXUDNkpCQEINRVCqVEEKv12s0zH8YSqdLp9Op1er4FiMXjUajKEpiYmK8C5ESUxcBRVE0Gg3zFpbSP2tMXbj8U5eQkOD1euNdi0xKn3J6vV6r1ca3GLloNBqVSsXrNDK8q0aGF2m4SoND7XtXVXw+X7xrqEF8Pp+iKPGuAgAAAACAQBwB/p28vLwYfKOhVqtTU1NtNpvL5Yr2WLWJwWDwf39WUFBQUlIS73JkYjAY9Hp9Xl5evAuRTFpamqIoXq+XqQuXxWIpLi622+3xLkQmOp0uOTlZCOF0Opm6sGi1WrPZnJ+f7/F44l2LTEwmk16vF0JYrVa32x3vcmRiNBo1Go3Vao13IZJJT08XQng8nvz8/HjXIpn09HS73e50OuNdiEz0er3JZBJCOBwOh8MR73JC8L86qohr4AEAAAAAkAABHgAAAAAACRDgAQAAAACQAAEeAAAAAAAJEOABAAAAAJAAAR4AAAAAAAkQ4AEAAAAAkAABHgAAAAAACRDgAQAAAACQAAEeAAAAAAAJEOABAAAAAJAAAR4AAAAAAAkQ4AEAAAAAkAABHgAAAAAACRDgAQAAAACQAAEeAAAAAAAJaOJdAOqKs2fPLlmy5MCBA0KIdu3ajRo1Kj09Pd5FAQAAAIA0CPCIOrfbPXz48M2bN/t8Pv+Wf/3rX3Pnzu3Xr9+KFStUKk4DAQAAAIDQyE6ILq/X2717988//7w0vfv5fL5Nmzb16NEjXoUBAAAAgFwI8IiuKVOmHDt2rLLWw4cPT5s2LYblAAAAAICsCPCIrvfeey94hxUrVsSmEgAAAACQGgEeUZSTk+NwOIL3KSwstFqtsakHAAAAAORFgEcU/fzzz1XpdujQoWhXAgAAAACyI8AjiurVq1eVbhkZGdGuBAAAAABkR4BHFLVq1UqtVgfvo9FoLrnkkpiUAwAAAAASI8AjilQqVefOnYP36dq1a2yKAQAAAACpEeARXUuXLtVqtZW16nS6xYsXx7IeAAAAAJAUAR7R1bBhw40bNxoMhvJNRqPxk08+qV+/fuyrAgAAAADpEOARdVdeeeXhw4fHjh2bmZlpMBgMBsMll1wyfvz4n3/+uWPHjvGuDgAAAADkoIl3AagTNBrNtGnTpk2bFu9CAAAAAEBWHIEHAAAAAEACBHgAAAAAACRAgAcAAAAAQAIEeAAAAAAAJECABwAAAABAAgR4AAAAAAAkQIAHAAAAAEACBHgAAAAAACRAgAcAAAAAQAIEeAAAAAAAJECABwAAAABAAgR4AAAAAAAkQIAHAAAAAEACBHgAAAAAACRAgAcAAAAAQAIEeAAAAAAAJECABwAAAABAAgR4AAAAAAAkQIAHAAAAAEACBHgAAAAAACRAgAcAAAAAQAIEeAAAAAAAJECABwAAAABAAgR4AAAAAAAkQIAHAAAAAEACBHgAAAAAACRAgAcAAAAAQAIEeAAAAAAAJECABwAAAABAAgR4AAAAAAAkQIAHAAAAAEACBHgAAAAAACRAgAcAAAAAQAIEeAAAAAAAJECABwAAAABAAgR4AAAAAAAkQIAHAAAAAEACBHgAAAAAACRAgAcAAAAAQAIEeAAAAAAAJECABwAAAABAAgR4AAAAAAAkQIAHAAAAAEACBHgAAAAAACRAgAcAAAAAQAIEeAAAAAAAJECABwAAAABAAgR4AAAAAAAkQIAHAAAAAEACBHgAAAAAACRAgAcAAAAAQAIEeAAAAAAAJECABwAAAABAAgR4AAAAAAAkQIAHAAAAAEACBHgAAAAAACRAgAcAAAAAQAIEeAAAAAAAJECABwAAAABAApp4F4A6JC8v7+jRo4qitGjRIiUlJd7lAAAAAIBMCPCIhW+//faFF17YsWOH1+sVQqhUqh49ekyePPmqq66Kd2kAAAAAIAdOoUfUvfvuuwMHDty+fbs/vQshvF7vV199ddttt61evTq+tQEAAACALAjwiK7//ve/Tz75pMfjKd/kdrsnTpy4d+/e2FcFAAAAANIhwCO65syZU3rgvTyPxzNnzpxY1gMAAAAAkiLAI4pKSkq2bdsWvM+WLVtcLlds6gEAAAAAeRHgEUU5OTklJSXB+xQXF587dy429QAAAACAvAjwiCK1Wl2VbhoNd0MAAAAAgBAI8Iii+vXrm0ym4H1SUlLS09NjUw8AAAAAyIsAjyjSaDS33HJL8D633nqrSsXzEAAAAABCIDghup544gmj0VhZa3Jy8uOPPx7LegAAAABAUgR4RFdmZubKlStTU1PLN1ksltWrVzdp0iT2VQEAAACAdAjwiLprr71227ZtDz30UNOmTf1bmjVr9vDDD3/11VfdunWLb20AAAAAIAtW/0YsNGjQYObMmTNnzvTfVU6n08W7IgAAAACQDAEeMUV0BwAAAIDIcAo9AAAAAAASIMADAAAAACABAjwAAAAAABIgwAMAAAAAIAECPAAAAAAAEiDAAwAAAAAgAQI8AAAAAAASkC/AHzp06NixY5W1nj179vDhwyUlJTGsCAAAAACAqNPEu4DwfP311y+//HLHjh1nzpwZ0PTbb7/NmTPnyJEjQgi1Wt2+fftHH320Xr168SgTAAAAAIBqJtMR+Nzc3Pnz51fYtG/fvieeeOLIkSMNGjTo0aNHamrqDz/8MHHixF9//TXGRQIAAAAAEA3SBHifz/f6669fuHChfJPb7X7zzTcdDseAAQMWLVr05JNPLl269JprrrFarfPmzYt9qQAAAAAAVDtpAvyGDRv27t3bokWL8k07d+48c+ZMRkbGyJEj/VsURZkwYUJiYuLBgwd/+eWX2FYKAAAAAED1kyPAHz16dOXKla1atRo8eHD51u+++04I0bVrV5Xq//84er2+U6dOpa0AAAAAAEhNggBfUlIyZ84clUr12GOPqdXq8h38i9JfdtllAdv9W44fPx79GgEAAAAAiC4JVqF/9913jx8/PmbMmMaNG1e4KN3Zs2eFECkpKQHbzWazECInJ6f8Q0pKSs6dO1d+e0JCQoXfEVQv/5kCKpUqBmPVJqVnWDB14VIURQjBpEVGURSmLgLMW7hK/8QxdeEqfVeNdyGS8b81CN5Vw6coCq/Ti8HURYCnXLhq8btqTQ/wu3bt+vjjj7t06XLzzTdX1sfpdAohjEZjwHaTyVTaGmD//v2jRo0qv33z5s3+2B8D/vIQgfK/a1RFampqvEuQkqIoTF0EEhISEhIS4l2FlJi6yMTs7bv2SU5OjncJUuKtITJqtZqpi0BSUlJSUlK8q5BSYmJiYmJi
<p>Wykres 2</p>
<pre class="r"><code>mpg %&gt;%
ggplot(aes(x=displ, y=hwy, group=drv)) +
geom_point() +
geom_smooth(se=FALSE)</code></pre>
<pre><code>## `geom_smooth()` using method = 'loess' and formula 'y ~ x'</code></pre>
<p><img src="data:image/png;base64,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
<p>Wykres 3</p>
<pre class="r"><code>mpg %&gt;%
ggplot(aes(x=displ, y=hwy, color=drv)) +
geom_point() +
geom_smooth(se=FALSE)</code></pre>
<pre><code>## `geom_smooth()` using method = 'loess' and formula 'y ~ x'</code></pre>
<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAABUAAAAPACAIAAAB7BESOAAAACXBIWXMAAB2HAAAdhwGP5fFlAAAgAElEQVR4nOzdd2AUdf7/8c9sy5bUTUILHQIiKoqKYkE8FCuiIKJywv1UrIgNRASBk6KoiJxf0FOxoadiQRTbnQ1UrDQBaQktCSG9bDa72Ta/P9bLhd1kg2F3ZyZ5Pv4RPp/PzrwZN9l9zXzmM5IsywIAAAAAAKibTukCAAAAAABA8wjwAAAAAABoAAEeAAAAAAANIMADAAAAAKABBHgAAAAAADSAAA8AAAAAgAYQ4AEAAAAA0AACPAAAAAAAGkCABwAAAABAAwjwAAAAAABoAAEeAAAAAAANIMADAAAAAKABBHgAAAAAADSAAA8AAAAAgAYYlC5AXcrKymRZjtHG09PTa2trXS5XjLavRRaLxWazCSFqamrcbrfS5aiIzWYzmUwVFRVKF6IikiSlp6cLIbxeb1VVldLlqIher09LS6uqqvJ6vUrXoiLJyckmk0kIUVFR4ff7lS5HRVJSUgKBgMPhULoQFTGZTMnJyUIIl8vldDqVLkdFzGZzYmJiTL8daZHdbtfpdLIsl5WVKV2LumRkZDidTr7rNmS1Wq1WqxDC4XDU1dUpXc4RMjIylC4BLcEVeAAAAAAANIAADwAAAACABhDgAQAAAADQAAI8AAAAAAAaQIAHAAAAAEADCPAAAAAAAGgAAR4AAAAAAA0gwAMAAAAAoAEEeAAAAAAANIAADwAAAACABhDgAQAAAADQAAI8AAAAAAAaYFC6AHUxm82x27gkSUajMXbb16L6A2I0GiVJUrYYVTEYDJIkWSwWpQtRkfp3iE6n48g0pNPphBAJCQkGA7/S/yd4WIQQZrM5EAgoW4yq6HQ6fr2E0Ov1wT8YDAaOTEPB3yox/XakRfWfR7xbwvFdN0T9ATGZTPUfTMCxkGRZVroGFZFlmRgJAAAAAFAhLtccoby8PHZnNNLT02tra10uV4y2r0UWi8Vmswkhampq3G630uWoiM1mM5lMFRUVSheiIpIkpaenCyG8Xm9VVZXS5aiIXq9PS0urqqryer1K16IiycnJJpNJCFFRUeH3+5UuR0VSUlICgYDD4VC6EBUxmUzJyclCCJfL5XQ6lS5HRcxmc2JiYllZGdd7GrLb7TqdTpblsrIypWtRl4yMDKfTyXfdhqxWq9VqFUI4HI66ujqlyzlCRkaG0iWgJZjIAQAAAACABhDgAQAAAADQAAI8AAAAAAAaQIAHAAAAAEADCPAAAAAAAGgAAR4AAAAAAA0gwAMAAAAAoAEEeAAAAAAANIAADwAAAACABhDgAQAAAADQAAI8AAAAAAAaQIAHAAAAAEADCPAAAAAAAGgAAR4AAAAAAA0gwAMAAAAAoAEEeAAAAAAANMCgdAHQPF/A96+KL9dWb3YGXF1M7a61DzvV1lfpogAAAACgtSHA45j8s2T1IwWvemRvfcsrpZ92MbV7t/fcngmdFCwMAAAAAFoZptCj5eYcemlm/osN03tQnqf4nB137nEXKFIVAAAAALRKBHi00C533rKiD5rq9cq+sbkPx7MeAAAAAGjdCPBooVkFL8hCjjAgz1Pym2tv3OoBAAAAgNaNAI8W2lyb2+yYN8u+iEMlAAAAANAWEODRQrUBd7NjCjwlcagEAAAAANoCAjxayHgUjzBI0SfGoRIAAAAAaAsI8GihLgntmh0zNOnkOFQCAAAAAG0BAR4tdHu7q5rulIUQZsl0lf3cuNUDAAAAAK0bAR4tdK39L/0s3ZrolIQQ8zvfouMNBgAAAABRQr5Cy33R5+n+5u7h7TpJmtFp/PiMi+JeEQAAAAC0Ws2vQwY0xaQzfNPvmY+qvn/68Dv76wq9sj9JZz098bhHs27taEpXujoAAAAAaFUI8DhWI1LOHpFyttJVAAAAAEArxxR6AAAAAAA0gAAPAAAAAIAGEOABAAAAANAAAjwAAAAAABpAgAcAAAAAQAMI8AAAAAAAaAABHgAAAAAADSDAAwAAAACgAQR4AAAAAAA0gAAPAAAAAIAGEOABAAAAANAAAjwAAAAAABpAgAcAAAAAQAMI8AAAAAAAaAABHgAAAAAADSDAAwAAAACgAQR4AAAAAAA0gAAPAAAAAIAGEOABAAAAANAAAjwAAAAAABpAgAcAAAAAQAMI8AAAAAAAaAABHgAAAAAADSDAAwAAAACgAQR4AAAAAAA0gAAPAAAAAIAGEOABAAAAANAAAjwAAAAAABpAgAcAAAAAQAMI8AAAAAAAaAABHgAAAAAADSDAAwAAAACgAQR4AAAAAAA0gAAPAAAAAIAGEOABAAAAANAAAjwAAAAAABpAgAcAAAAAQAMI8AAAAAAAaAABHgAAAAAADSDAAwAAAACgAQR4AAAAAAA0gAAPAAAAAIAGEOABAAAAANAAAjwAAAAAABpAgAcAAAAAQAMI8AAAAAAAaAABHgAAAAAADSDAAwAAAACgAQR4AAAAAAA0gAAPAAAAAIAGEOABAAAAANAAAjwAAAAAABpAgAcAAAAAQAMI8AAAAAAAaAABHgAAAAAADSDAAwAAAACgAQR4AAAAAAA0gAAPAAAAAIAGEOABAAAAANAAg9IFoDXwBLx76vJrA3XtjWldTe2VLgcAAAAAWiECPI5Jia9ywaEV71WsdQXqgi3dTR3ubD9qQsbFkpCUrQ0AAAAAWhMCPFpup+vAqJyZJb7Kho37PYen5i1b69j8QvcHDJJeqdoAAAAAoJXhHni0kEf23bB3Xkh6r7emcv2Th9+Kc0kAAAAA0IoR4NFC75R/vd9zOMKAZcWravyuuNUDAAAAAK0bAR4t9J/qXyIPcAXqvqv5LT7FAAAAAECrR4BHC+V5iqMyBgAAAABwNAjwaKGjWaCORewAAAAAIFoI8GihXglZURkDAAAAADgaBHi00OUpZ0UekGFIGZzYPz7FAAAAAECrR4BHC12SekbkfD476/8ZJUPc6gEAAACA1o0AjxaShPRKz4cG2vqEd+kk3YMdx11rHxb/qgAAAACgteICKVrOrk9ek71wRennb5V/+Zsr1y8HkvW285JOvqPdlafZjlO6OgAAAABoVQjwOCZGyXBj5mU3Zl4mC7k2UGfTmZWuCAAAAABaJ6bQIzokIZHeAQAAACB2CPAAAAAAAGgAAR4AAAAAAA0gwAMAAAAAoAEEeAAAAAAANIAADwAAAACABhDgAQAAAADQAAI8AAAAAAAaoL0Av2vXrv379zfVW1xcnJOT4/F44lgRAAAAAAAxZ1C6gD/n+++/X7hw4YABA+bOnRvSlZeXt2jRor179woh9Hp9//7977777szMTCXKBAAAAAAgyrR0Bb6srGzZsmWNdm3btm3q1Kl79+7t0KHD2WefnZaW9ttvv91///0HDhyIc5EAAAAAAMSCZgK8LMtPP/20w+EI7/L5fP/4xz9qa2tHjBjx/PPPT5s2bfny5WeddVZlZeXSpUvjXyoAAAAAAFGnmQC/evXqLVu29OzZM7xrw4YNhw8fbt++/U033RRskSTp3nvvtVgsO3fuzM3NjW+lAAAAAABEnzYC/L59+1asWJGdnT169Ojw3p9//lkIMWjQIJ3uf/+chISEU045pb4XAAAAAABN00CA93g8ixYt0ul09913n16vDx8QXJT+hBNOCGkPthw8eDD2NQIAAAAAEFsaWIX+lVdeOXjw4O23356VldXoonTFxcVCiNTU1JD2lJQUIURRUVH4SzweT0lJSXi72Wxu9BxBtEiSFNPta079pAmOTAhJkoQQHJOGgsdE8G4JE/w50ul0HJaG6t8wHJYQkiTxQxSCD6OmBI+MXq+XZVnpWtSId0s4fohC1P964WMa0aL2AL9x48aPP/741FNPveSSS5oa43K5hBCJiYkh7UlJSfW9IbZv3z5x4sTw9i+//DIY+2PEarVardbYbV+7bDabzWZTugrVSUtLU7oENTIYDByZcMHfeAiXnJysdAlqZDKZlC5Bjcxms9lsVroK1Qm/RgIhhCRJfBiF47tuU/iui2hR9RT66urq
<p>Wykres 4</p>
<pre class="r"><code>mpg %&gt;%
ggplot(aes(x=displ, y=hwy)) +
geom_point(aes(color=drv)) +
geom_smooth(se=FALSE)</code></pre>
<pre><code>## `geom_smooth()` using method = 'loess' and formula 'y ~ x'</code></pre>
<p><img src="data:image/png;base64,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
<p>Wykres 5</p>
<pre class="r"><code>mpg %&gt;%
ggplot(aes(x=displ, y=hwy)) +
geom_point(aes(color=drv)) +
geom_smooth(aes(linetype=drv), se=FALSE)</code></pre>
<pre><code>## `geom_smooth()` using method = 'loess' and formula 'y ~ x'</code></pre>
<p><img src="data:image/png;base64,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
<p>Wykres 6</p>
<pre class="r"><code>mpg %&gt;%
ggplot(aes(x=displ, y=hwy)) +
geom_point(aes(fill=drv), shape=21, stroke=2, color='white', size=2)</code></pre>
<p><img src="data:image/png;base64,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
</div>
</div>
<div id="section-4" class="section level1">
<h1>3.7.1</h1>
<pre class="r"><code># Zadanie 1
?stat_summary</code></pre>
<p>Funcja stat_summary jest związana w funcją geom_pointrange()</p>
<pre class="r"><code>diamonds %&gt;%
ggplot() +
geom_pointrange(aes(x = cut, y = depth), stat='summary')</code></pre>
<pre><code>## No summary function supplied, defaulting to `mean_se()`</code></pre>
<p><img src="data:image/png;base64,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
<div id="zadanie-2-3" class="section level2">
<h2>Zadanie 2</h2>
<p>funkcja geom_bar służy do wykresów słupkowych/kolumnowych i jej domyslna statystyka do ‘count’, funcja geom_col, ma domyślną statystykę ‘identity’ ## Zadanie 3</p>
<p>geom_bar : stat_count geom_col : identity geom_histogram: stat_bin geom_line : identity geom_path : identity geom_step : identity geom_segment : identity geom_curve : identity geom_spoke : identity geom_polygon : identity geom_ribbot : identity geom_area : identity geom_freqpoly : stat_bin</p>
</div>
<div id="zadanie-4" class="section level2">
<h2>Zadanie 4</h2>
<p>?geom_smmoth : stat_smooth y - predicted value ymin - lower pointwise confidence interval around the mean yman - upper pointwise confidence interval around the mean se = standard error</p>
</div>
<div id="zadanie-5-1" class="section level2">
<h2>Zadanie 5</h2>
<p>każdy “słupek” jest taki sam</p>
<pre class="r"><code>ggplot(data = diamonds) +
geom_bar(mapping = aes(x = cut, y = ..prop..))</code></pre>
<p><img src="data:image/png;base64,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
<pre class="r"><code>ggplot(data = diamonds) +
geom_bar(mapping = aes(x = cut, fill = color, y = ..prop..))</code></pre>
<p><img src="data:image/png;base64,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
<pre class="r"><code># uzyliśmy group=1, aby nadpisać domysle grupowanie funcji geom_bar, które grupuje po x, czyli po cut,
# dlatego każdy słupek wyglądał identycznie, ponieważ powinniśmy w tym przypadku zgrupować po całym zbiorze, usunąć grupy.
# równie dobrze będzie działać group=2, albo group='abc'
ggplot(data = diamonds) +
geom_bar(mapping = aes(x = cut, fill = color, y = ..prop.., group=1))</code></pre>
<p><img src="data:image/png;base64,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
</div>
</div>
<div id="section-5" class="section level1">
<h1>3.8.1</h1>
<div id="zadanie-1-4" class="section level2">
<h2>Zadanie 1</h2>
<pre class="r"><code>ggplot(data = mpg, mapping = aes(x = cty, y = hwy)) +
geom_point()</code></pre>
<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAABUAAAAPACAMAAADDuCPrAAADAFBMVEUAAAABAQECAgIDAwMEBAQFBQUGBgYHBwcICAgJCQkKCgoLCwsMDAwNDQ0ODg4PDw8QEBARERESEhITExMUFBQVFRUWFhYXFxcYGBgZGRkaGhobGxscHBwdHR0eHh4fHx8gICAhISEiIiIjIyMkJCQlJSUmJiYnJycoKCgpKSkqKiorKyssLCwtLS0uLi4vLy8wMDAxMTEyMjIzMzM0NDQ1NTU2NjY3Nzc4ODg5OTk6Ojo7Ozs8PDw9PT0+Pj4/Pz9AQEBBQUFCQkJDQ0NERERFRUVGRkZHR0dISEhJSUlKSkpLS0tMTExNTU1OTk5PT09QUFBRUVFSUlJTU1NUVFRVVVVWVlZXV1dYWFhZWVlaWlpbW1tcXFxdXV1eXl5fX19gYGBhYWFiYmJjY2NkZGRlZWVmZmZnZ2doaGhpaWlqampra2tsbGxtbW1ubm5vb29wcHBxcXFycnJzc3N0dHR1dXV2dnZ3d3d4eHh5eXl6enp7e3t8fHx9fX1+fn5/f3+AgICBgYGCgoKDg4OEhISFhYWGhoaHh4eIiIiJiYmKioqLi4uMjIyNjY2Ojo6Pj4+QkJCRkZGSkpKTk5OUlJSVlZWWlpaXl5eYmJiZmZmampqbm5ucnJydnZ2enp6fn5+goKChoaGioqKjo6OkpKSlpaWmpqanp6eoqKipqamqqqqrq6usrKytra2urq6vr6+wsLCxsbGysrKzs7O0tLS1tbW2tra3t7e4uLi5ubm6urq7u7u8vLy9vb2+vr6/v7/AwMDBwcHCwsLDw8PExMTFxcXGxsbHx8fIyMjJycnKysrLy8vMzMzNzc3Ozs7Pz8/Q0NDR0dHS0tLT09PU1NTV1dXW1tbX19fY2NjZ2dna2trb29vc3Nzd3d3e3t7f39/g4ODh4eHi4uLj4+Pk5OTl5eXm5ubn5+fo6Ojp6enq6urr6+vs7Ozt7e3u7u7v7+/w8PDx8fHy8vLz8/P09PT19fX29vb39/f4+Pj5+fn6+vr7+/v8/Pz9/f3+/v7////isF19AAAACXBIWXMAAB2HAAAdhwGP5fFlAAAgAElEQVR4nO3dd5xcZfX48bubSggBAkgHERERG+aLWACl/Shyk0AISE8IoNJ7r1ICKFICimJAUCAKShMEBaL0GkoCGEMJgSQkS+8lZH77TL07z2bunXPOzuyz+3n/EzZz9j6XF8ePW2eiHABAJGr2DQBAqAgoAAgRUAAQIqAAIERAAUCIgAKAEAEFACECCgBCBBQAhAgoAAgRUAAQIqAAIERAAUCIgAKAUBcG9LW2rvZxLvdGlx9S8tGnDTuqLZdr5GHvNeyod3K59xt22Nu51xt2VmM38ZOGHfUam1iNgAoRUD0CaoGA6hFQh4CaHBbC2goQUD0C6iGgQgRUj4BaIKB6BNQhoCaHhbC2AgRUj4B6CKgQAdUjoBYIqB4BdQioyWEhrK0AAdUjoB4CKkRA9QioBQKqR0AdAmpyWAhrK0BA9Qioh4AKEVA9AmqBgOoRUIeAmhwWwtoKEFA9AuohoEIEVI+AWiCgegTUIaAmh4WwtgIEVI+AehoX0Pe63IJc7oOuP6Xo088adtR7uVwjD/u4YUd9lGvgYR/m3m/YWY3dxAUNO4pN9DQuoAu77tIA0A3wKXxWfAqvx6fwFvgUXi+ET+EJqBhra4CA6hFQDwEVIqB6BNQCAdUjoA4BNTkshLUVIKB6BNRDQIUIqB4BtUBA9QioQ0BNDgthbQUIqB4B9RBQIQKqR0AtEFA9AuoQUJPDQlhbAQKqR0A9BFSIgOoRUAsEVI+AOgTU5LAQ1laAgOoRUA8BFSKgegTUAgHVI6AOATU5LIS1FSCgegTUQ0CFCKgeAbVAQPUIqENATQ4LYW0FCKgeAfUQUCECqkdALRBQPQLqEFCTw0JYWwECqkdAPQRUiIDqEVALBFSPgDoE1OSwENZWgIDqNT+g/zr7xAmPmp8067pf/XLSC6J3JaBCBFSPgFroRQH951ejdi1bTzU9Z/74oe6yg4+ZK3hnAipEQPUIqIXeE9Dr+kcFKz1uec7exatGw+fV/84EVIiA6hFQC70moC9+rlS6aBPDYyaVrxqdV/97E1AhAqpHQC30moD+qlK66B67Y35Yuera9b83ARUioHoE1EKvCej2iYCONztlXv/EZZ+p+90JqBAB1SOgFnpNQDdKlO5Qs1OmJ64aTa773QmoEAHVI6AWek1At06U7iSzU2a3JC5b/09IEVAhAqpHQC30moAemSjdtXbHrF256tD6f5CJgAoRUD0CaqHXBPTBfuXSrTrH7piTKgHdt/73JqBCBFSPgFroNQFtO7wUuj6TDI955auly672v/rfm4AKEVA9Amqh9wR0/mF98qEbcpnpOc98v9DPr0t+R5SAChFQPQJqofcEtP2z+IM2/MbmJ083Pmj+1bt+d4Odfv+q5H0JqBAB1SOgFnpTQLsKz8bkEFCTw0JYWwECqkdAPQRUiIDqEVALBFSPgDoE1OSwENZWgIDqEVAPARUioHoE1AIB1SOgDgE1OSyEtRUgoHoE1ENAhQioHgG1QED1CKhDQE0OC2FtBQioHgH1EFAhAqpHQC0QUD0C6hBQk8NCWFsBAqpHQD0EVIiA6hFQCwRUj4A6BNTksBDWVoCA6hFQDwEVIqB6BNQCAdUjoA4BNTkshLUVIKB6BNRDQIUIqB4BtUBA9QioQ0BNDgthbQUIqB4B9RBQIQKqR0AtEFA9AuoQUJPDQlhbAQKqR0A9BFSIgOoRUAsEVI+AOgTU5LAQ1laAgOoRUA8BFSKgegTUAgHVI6AOATU5LIS1FSCgegTUQ0CFCKgeAbVAQPUIqENATQ4LYW0FCKgeAfUQUCECqkdALRBQPQLqEFCTw0JYWwECqkdAPQRUiIDqEVALBFSPgDoE1OSwENZWgIDqEVAPARUioHoE1AIB1SOgDgE1OSyEtRUgoHoE1ENAhQioHgG1QED1CKhDQE0OC2FtBQioHgH1EFAhAqpHQC0QUD0C6hBQk8NCWFsBAqpHQD0EVIiA6hFQCwRUj4A6BNTksBDWVoCA6hFQDwEVIqB6BNQCAdUjoA4BNTkshLUVIKB6BNRDQIUIqB4BtUBA9QioQ0BNDgthbQUIqB4B9RBQIQKqR0AtEFA9AuoQUJPDQlhbAQKqR0A9BFSIgOoRUAsEVI+AOgTU5LAQ1laAgOoRUA8BFSKgegTUAgHVI6AOATU5LIS1FSCgegTUQ0CFCKgeAbVAQPUIqENATQ4LYW0FCKgeAfUQUCECqkdALRBQPQLqEFCTw0JYWwECqkdAPQRUiIDqEVALBFSPgDoE1OSwENZWgIDqEVAPARUioHoE1AIB1SOgDgE1OSyEtRUgoHoE1ENAhQioHgG1QED1CKhDQE0OC2FtBQioHgH1EFAhAqpHQC0QUD0C6hBQk8NCWFsBAqpHQD0EVIiA6hFQCwRUj4A6BNTksBDWVoCA6hFQDwEVIqB6BNSCNqDP3f6P6RlHCaiHgAoRUD0CakEX0H9t0ieKWr79t0zDBNRDQIUIqB4BtaAK6G/6RXmtp2WZJqAeAipEQPUIqAVNQO8p9rP9g9DrM4wTUA8BFSKgegTUgiago6Ky72UYJ6AeAipEQPUIqAVNQIdUAtrn+fRxAuohoEIEVI+AWlAE9Lko4e70eQLqIaBCBFSPgFpQBHRmMqD3p88TUA8BFSKgegTUguZT+BUq/Rzwcvo4AfUQUCECqkdALWgCuk8loNtmGCegHgIqRED1CKgFTUCfHlrq52IZPoMnoD4CKkRA9QioBdUP0t+2XKGfQ/6cZZqAegioEAHVI6AWdL/K+d+D1oiilfd+ItMwAfUQUCECqkdALaifjWn27KyTBNRDQIUIqB4BtcDT2ekRUIeAmhwWwtoKEFA9AuohoEIEVI+AWiCgegTUIaAmh4WwtgIEVI+AegioEAHVI6AWCKgeAXUIqMlhIaytAAHVI6AeAipEQPUIqAUCqkdAHQJqclgIaytAQPUI
</div>
<div id="zadanie-2-4" class="section level2">
<h2>Zadanie 2</h2>
<p>nakładają się na siebie punkty i nie widać dobrze zagęszczenia mozna poprawić przez dodanie posiiton=‘jitter’</p>
<pre class="r"><code> ggplot(data = mpg, mapping = aes(x = cty, y = hwy)) +
geom_point(position='jitter')</code></pre>
<p><img src="data:image/png;base64,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
</div>
<div id="zadanie-3-1" class="section level2">
<h2>Zadanie 3</h2>
<pre class="r"><code> ?geom_jitter</code></pre>
<p>paremtry width i height</p>
<pre class="r"><code> ggplot(data = mpg, mapping = aes(x = cty, y = hwy)) +
geom_jitter(position='jitter')</code></pre>
<p><img src="data:image/png;base64,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
<pre class="r"><code> ggplot(data = mpg, mapping = aes(x = cty, y = hwy)) +
geom_count(position='jitter')</code></pre>
<p><img src="data:image/png;base64,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
<pre class="r"><code> #' obie funcje pokazują zagęszczenie, geom jitter, rozsuwa je aby było widać kazdy punkt.
#' geom count pokazuje zagęszczenie przez parametr size.</code></pre>
<p>Zadanie 4</p>
<pre class="r"><code>?geom_boxplot</code></pre>
<p>domyślne dopasowanie dla geom_boxplot to ’dodge2</p>
<pre class="r"><code>mpg %&gt;%
ggplot(aes(x=cty, y=factor(cyl))) +
geom_boxplot()</code></pre>
<p><img src="data:image/png;base64,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