ZIWAUA0/KursR/Rodział 5/Kurs_R--Rozdział-5.html

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2020-04-16 20:34:53 +02:00
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<h1 class="title toc-ignore">Kurs_RRozdział-5.R</h1>
<h4 class="author">altarin</h4>
<h4 class="date">2020-04-16</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="ćwiczenia" class="section level1">
<h1>5.2.1 Ćwiczenia</h1>
<pre class="r"><code>ggplot(mpg, aes(displ, hwy)) +
geom_point(aes(colour = class)) +
geom_smooth(se = FALSE, method='lm') +
labs(
title = 'Wraz ze wzrostem objętości silnika, spada zasięg mil na galon',
subtitle = 'Wyjątkiem są pojazdy 2-osobowe',
x = &quot;Objętość skokowa silnika (l)&quot;,
y = &quot;Gospodarka paliwowa na autostradzie (mpg)&quot;,
colour = &quot;Rodzaj samochodu&quot;,
caption = 'Cezary Pukownik'
)</code></pre>
<pre><code>## `geom_smooth()` using formula 'y ~ x'</code></pre>
<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAABUAAAAPACAIAAAB7BESOAAAACXBIWXMAAB2HAAAdhwGP5fFlAAAgAElEQVR4nOzdd0AUR98H8N8B0osgCCjGgoCgRo1dNKJiiRVLfBJrEjWWGDFGY4wxVY1GY4zdRI2xG2OJigULoBRrsMSGqCCCoghK54Db9495nnk317jbuwMvfj9/3e3O7s7Ozu7db3d2RiYIAgEAAAAAAADAi82iqjMAAAAAAAAAABVDAA8AAAAAAABgBhDAAwAAAAAAAJgBBPAAAAAAAAAAZgABPAAAAAAAAIAZQAAPAAAAAAAAYAYQwAMAAAAAAACYAQTwAAAAAAAAAGYAATwAAAAAAACAGUAADwAAAAAAAGAGEMADAAAAAAAAmAEE8AAAAAAAAABmAAE8AAAAAAAAgBmwquoMvFiePHmyatUq9rlr166dOnXSlDI3N/fHH39kn4ODg0NDQzWlLC8vnzt3LvvcvHnzAQMGGC+/APAPeXl5S5YsISIvL6/x48fru/ixY8fi4+OJKCwsrFmzZsbPn4mdOnUqKiqKfe7bt2/Lli2NstqdO3fevHmTiEaNGlW/fn2jrFPfbR04cOCvv/4ioiFDhjRu3Nik25o3b15ZWZm1tfWsWbOMsiHQUUxMTHR0NBH17t27devWVZ2dF0Jlnn0vrMq8LLxUUIAA5koAkZKSEnt7e1Yyw4YN05Jyz549vAy7dOmiJSW7ODI//fSTsbMMAP/vwYMH7Fxr3ry5hMWnT5/OFt+4caPR82ZqT58+9fT0ZPkfNWpUeXm5sdYcFhbGVnv8+HFjrVPfbY0dO5ZN37Fjh6m3ZWtrS0SOjo7G2hDo6KuvvmJHZMWKFVWdlxdFZZ59L6zKvCy8VFCAAGYKT+D/wdraOjg4+NixY0QUFxenJSVLw8TFxRUUFDg4OKhNmZCQwD+HhIQYJ6MAKhQKxccff0xETk5O33zzTVVnB/6r0o5LeHh4ZmYmEY0ZM+bnn3+2sMAbUgAAAAD/NviHp4zH2Kmpqenp6ZqSiQN4uVzOWv2pxQN4Nze3pk2bGiWTAKoEQVi6dOnSpUt/+eWXqs4L/L/KOS4RERFbtmwhovHjx//yyy+I3gEAAAD+lfAnT1mXLl34Z/Y2rKqUlJTk5GTxlKNHj2paIV9J586dZTKZMfIIACYRHh5+4cKFCxcu9O3bt6rzoofc3NwJEyYQ0QcffLB69WqjX2cWL17MiqVNmzbGXfPLsy0AaVBLCYUAAPBPaEKvrHXr1o6Ojvn5+UQUHx//5ptvqqaJjIxkH1q2bHnx4kXSHMA/fvz47t277DPazwO84Hx8fHx8fKo6F3o7fvx4x44dmzRpMnv2bFOs39fX1xSrfam2BSANaimhEAAA/glP4JVZWVl17NiRfdb0GjxvPz979mwXFxciSkpKSklJUU2JF+ABwNQGDRq0fft2E0XvAAAAAPDiwBN4NUJCQo4cOUJEiYmJhYWFvF96RqFQnDx5koisrKy6devWrVs31iP90aNHVYetUvsC/OHDh8+dO0dEU6dOZfF/amrqiRMnrl+/3r9//9dff11pJbm5uXFxcampqTk5OW5ubj4+PvXq1dM04Id4JDztWrVq1adPH11SGmWdN27cuHDhwsOHD52dnX19fZs3b+7h4aHLOsU2b97MWjSEhIR07txZdRO///47+6x2GLCEhATWeqJGjRqTJ08mA3ZN34NYWloaGxubnJyclZXl4uLyyiuvdO7c2cnJqcLtFhcX37hx486dO/fu3XN1dW3QoEGjRo1q1aolThMVFXXq1CmFQsG+5ufnf/3110Rka2s7c+ZM1XXqdSz4yGqTJk3y8PBITU3dvXv37du33dzcvLy8GjZsGBoaWq1aNZ5eoVDExsZeu3YtNze3Xr16jRo1MnA8NslFx509e/bq1atPnjypUaNGo0aNgoODLS0tte+s9mHkbt68ef78eV6AzZo1q1mzpmoyvY6LSWuIWoWFhVFRUSkpKXl5ed7e3s2aNWvevLnalLt37/77779J6kBWeuVQ320p1c/i4uIjR47ExMRYW1vXqFHDz88vKCgoICDAiPt1+vRp9hNARMOHD2/YsKFSAr2u2EahewnzcRZfe+21fv36EVFCQsLhw4eLioqcnZ29vb2DgoLat2+vy1sYhuxmSkpKbGxsRkaGvb19vXr1unXrZmdnp8uCRixbw3/XJGfGFGeEjhclVVlZWcePH09LS3NwcGjUqFFISAjrQWP9+vUPHjywt7efMWOGLuthTPR7YeAlSHsm9bpoiJnuTJd8UEyRJX1/m4xVvACgTVV3g/8iOnv2LC+f6OhopbksbCOi4OBgQRDWrl3Lvg4aNEh1VTyQGzhwIJ/IQkciSklJEQRh/vz5NjY2bMrBgwfFi9+4cWPo0KFqg40WLVps3rxZdYvsR04XEyZM0LFADFznvn37AgMDlVJaW1u/8847N2/e1DEPDH/GOGDAANW5X3zxBV//tGnTVBOMGDGCzR05cqSBu6b7QczPz58zZ46zs7NqCYwcOfL+/fuadrawsPCHH35Q/RNmYWExcuTIO3fu8JSff/652jxXr15daZ0SjgUfWe3vv//+9ddfVUdb8PT0jIyMZIkjIiJeeeUVpQS9evUS51Z3EopOaRi56Ohof39/pcU9PDyWLVtWVlamZWc1DSP3559/BgUFqeZn9OjR169fV0qs43GphBqi5NmzZ1OmTFG6NUlEvr6+agcTeuutt1gCfQeykpBDTdvSNNyRuH7eunWrSZMmqgXet29ftdVb07a0DCMXExPDy+2jjz5Smivhim0gfUuYnyBjx47Nzs4eNGiQalZr167922+/admoIbuZmJioeu/V0dFxzpw5crlcyzByRi9bQ37XJGfGiGcEp9dFSSwnJ2fMmDHicJqIfHx8/vjjD0EQ2rVrR0Tu7u4VF6WIiX4v9L0s6J5JfS8ajOnOdMkHRVqWtBegtN8mw4sXACqEAF6NsrIyfsGaP3++0tx58+axWV999ZUgCLzlvIuLS2lpqThlaWkpf6ogHgFeHPvx/yuMOPY7evSoo6Oj6oVP7Pvvv1fK3gsVwJeXl7O+tTRxdHQ8fPiwjtkQBOH06dNswerVq6sOcy3ugLBly5aqi/PXm7dt22bgrul4ENPT09X+enGurq5xcXGqWS0sLGzVqpX2Bdm9A0EQFixY4OXlxccAt7Cw8PLy8vLy8vf3N/xY8B/jr7/+WtNzOSsrq/j4eC2Psxo3biyXy3U6xoYVnTiA3759u5bO2Pv161dcXKxpZ1UD+PLy8okTJ2rJj4ODg9K9G12OS+XUELG7d+9qf4Q1btw4hUIhXkRaAC8th5ID+BMnTnh7e2vaVs2aNR8+fKjjtjQF8PHx8fyaPHnyZKW50q7YhpBQwvwEGT16tPa3ut577z3Va6yBu/n7778rRSZi3bp1++yzz9hnpQDeFGUr+eIvOTPGPSMESRclLiMjQ7XxCDd79uy2bduSAQG8cX8vTBTAS7hoCKY80yUfFMlZ0lKAkn+bDCxeANAFAnj1eHu5Pn36KM3if3piY2PZFN4WiE9hzp8/zy9Vly9f5tN57Ldu3TqZTGZhYTFu3LhNmzadOnUqLy+PpXn+/LmXlxdL5u7u/umnn+7atSs6Onrv3r3z58/nDxVlMtnVq1fFGy0rK8vS7I8//mC/qRYWFocOHdKxNCSvk1/Hiahdu3YLFy48fPjwtm3bpk+fXr16dTbdysoqKipKx5yUlpay9upEdPHiRfEsuVwufqJoaWn5/PlzcYI7d+6wWRYWFllZWQbumi4HsaSkRNwa8I033li0aNHRo0dXr149atQoK6v/vsDi7OycmpqqtKcjR45kc21tbceOHbt9+/bo6Ojdu3dPnTqVP9No37690mFi0728vFSLTvKx4AuyomjatOmWLVuSkpLOnTs3ffp0fr/fw8ODJRg6dGhERMS9e/ciIyPFt1RUH6lpIbnoeHxSo0YNFoZ5enp+8MEHW7du/e233yZOnMjDaSKaMWOGpp1VDeDFTRbFBThjxgxXV1de61T/
</div>
<div id="ćwiczenia-1" class="section level1">
<h1>5.3.1 Ćwiczenia</h1>
<div id="zadanie-1" class="section level2">
<h2>Zadanie 1</h2>
<pre class="r"><code>label_11 &lt;- tibble(
displ = Inf,
hwy = Inf,
label = &quot;Większy rozmiar silnika \njest związany z gorszą gospodarką paliwową.&quot;
)
label_01 &lt;- tibble(
displ = -Inf,
hwy = Inf,
label = &quot;Większy rozmiar silnika \njest związany z gorszą gospodarką paliwową.&quot;
)
label_10 &lt;- tibble(
displ = Inf,
hwy = -Inf,
label = &quot;Większy rozmiar silnika \njest związany z gorszą gospodarką paliwową.&quot;
)
label_00 &lt;- tibble(
displ = -Inf,
hwy = -Inf,
label = &quot;Większy rozmiar silnika \njest związany z gorszą gospodarką paliwową.&quot;
)
ggplot(mpg, aes(displ, hwy)) +
geom_point() +
geom_text(aes(label = label),
data = label_11,
vjust = &quot;top&quot;,
hjust = &quot;right&quot;) +
geom_text(aes(label = label),
data = label_01,
vjust = &quot;top&quot;,
hjust = &quot;left&quot;) +
geom_text(aes(label = label),
data = label_10,
vjust = &quot;bottom&quot;,
hjust = &quot;right&quot;) +
geom_text(aes(label = label),
data = label_00,
vjust = &quot;bottom&quot;,
hjust = &quot;left&quot;)</code></pre>
<p><img src="data:image/png;base64,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
</div>
<div id="zadanie-2" class="section level2">
<h2>Zadanie 2</h2>
<pre class="r"><code>?annotate()</code></pre>
<p>można zrobić np w ten sposób, podając bezpośrednio np text, oraz położenie wpisująć ręcznie, bez danych.</p>
<pre class="r"><code>p &lt;- ggplot(mtcars, aes(x = wt, y = mpg)) + geom_point()
p + annotate('label', x = 4, y = 25, label = &quot;Some text&quot;)</code></pre>
<p><img src="data:image/png;base64,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
<pre class="r"><code># Jak etykiety utworzone za pomocą funkcji geom_text() działają z panelami?
# Jak dodać etykietę do pojedynczego panelu? Jak umieścić różne etykiety na każdym panelu?
# (Wskazówka: pomyśl o danych, na podstawie których utworzyłeś wykres).</code></pre>
<p>Zadanie 3</p>
<pre class="r"><code>mpg %&gt;%
group_by(drv) %&gt;%
summarise(count=n()) %&gt;%
ggplot(aes(x=drv, y=count, label=count)) +
geom_bar(stat='identity') +
geom_text(size=10, nudge_y = 5)</code></pre>
<p><img src="data:image/png;base64,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
<pre class="r"><code>mpg %&gt;%
group_by(drv, class) %&gt;%
summarise(count=n()) %&gt;%
ggplot(aes(x=drv, y=count, label=count)) +
geom_bar(stat='sum') +
geom_text(size=10, nudge_y = 5) +
facet_wrap(~class)</code></pre>
<p><img src="data:image/png;base64,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
<p>na różnych panelach dane automatycznie się dostosowują do wykresu aby pokazać dane np ja jednym panelu można zrobić coś takiego:</p>
<pre class="r"><code>mpg %&gt;%
group_by(drv, class) %&gt;%
summarise(count=n()) %&gt;%
mutate(label = if_else(class=='compact', as.character(count), '')) %&gt;%
ggplot(aes(x=drv, y=count, label=label)) +
geom_bar(stat='sum') +
geom_text(size=10, nudge_y = 5) +
facet_wrap(~class) +
labs(title='Liczba samochodów klasy compakt ma znacznie więcej\nsamochodów napędzanych na przód niż na 4 koła.',
x = 'rodzaj napędu',
y = 'liczba samochodów')</code></pre>
<p><img src="data:image/png;base64,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
<p>teraz etykiety pokazywane są tylko na panelu gdzie clasa = compact Zadanie 4</p>
<p>label.padding = unit(0.25, “lines”), label.r = unit(0.15, “lines”), label.size = 0.25,<br />
Zadanie 5</p>
<pre class="r"><code># Jakie są cztery argumenty funkcji arrow()?
# Jak działają?
# Utwórz zestaw wykresów, który zaprezentuje najważniejsze opcje.
?arrow()</code></pre>
<pre><code>## Help on topic 'arrow' was found in the following packages:
##
## Package Library
## ggplot2 /home/altarin/R/x86_64-pc-linux-gnu-library/3.6
## grid /usr/lib/R/library
##
##
## Using the first match ...</code></pre>
<p>angle<br />
length<br />
ends<br />
type<br />
agnle - opisuje jak szeroni bedzie grot strzałki, im mniejsza wartość tym węższa bedzie strzałka lenght - opisuja jak długi bedzie grot sprzałki ends - jedno z “last”, “first”, “both” - wskazuje które końce lini, bedą zakończone grotem. type - jedno z “open”, “closed” - wskazuje, czy grot będzie otwrty, -&gt; jak strzła, czy zamknięty jak trojkąt -|&gt;</p>
<pre class="r"><code>data &lt;- data.frame('a'=c(1,2), 'b'=c(2,3))
ggplot(data, aes(x=a, y=b)) +
geom_line(arrow=arrow(angle=45, length = unit(5, &quot;cm&quot;), ends='both', type='closed')) +
annotate('text', x=1.25, y=2.75, label=&quot;ładna duża strzałka :)&quot;, size=9)</code></pre>
<p><img src="data:image/png;base64,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
<pre class="r"><code>ggplot(data, aes(x=a, y=b)) +
geom_line(arrow=arrow(angle=25, length = unit(2, &quot;cm&quot;), ends='last', type='open')) +
annotate('text', x=1.3, y=2.75, label=&quot;smukła strzałka w jedną stronę&quot;, size=7)</code></pre>
<p><img src="data:image/png;base64,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
</div>
<div id="ćwiczenia-2" class="section level2">
<h2>5.4.4 Ćwiczenia</h2>
<p>install.packages(hexbin)</p>
<pre class="r"><code>df &lt;- tibble(
x = rnorm(10000),
y = rnorm(10000)
)
ggplot(df, aes(x, y)) +
geom_hex() +
scale_color_gradient(low = &quot;white&quot;, high = &quot;red&quot;) +
coord_fixed()</code></pre>
<p><img src="data:image/png;base64,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
<p>ten kod przysłania domyslną skalę, ale nie tą którą chcemy. Poprawiony kod powinien wyglądać tak. Zmieniono <em>color</em> na <em>fill</em></p>
<pre class="r"><code>ggplot(df, aes(x, y)) +
geom_hex() +
scale_fill_gradient(low = &quot;white&quot;, high = &quot;red&quot;) +
coord_fixed()</code></pre>
<p><img src="data:image/png;base64,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
<pre class="r"><code>?scale_fill_gradient
# pierwrszy argument to colors.</code></pre>
</div>
</div>
<div id="zadanie-3" class="section level1">
<h1>Zadanie 3</h1>
<pre class="r"><code>years &lt;- lubridate::make_date(seq(lubridate::year(min(presidential$start)),
lubridate::year(max(presidential$end)),
by = 4), 1, 1)
library(glue)</code></pre>
<pre><code>##
## Attaching package: 'glue'</code></pre>
<pre><code>## The following object is masked from 'package:dplyr':
##
## collapse</code></pre>
<pre class="r"><code>presidential %&gt;%
mutate(id = 33 + row_number()) %&gt;%
mutate(x_label = glue('{name} ({id})')) -&gt; x_label
x_label = x_label$x_label
x_label = as.vector(x_label)
presidential %&gt;%
mutate(id = 33 + row_number()) %&gt;%
ggplot(aes(start, reorder(x_label, id), colour = party)) +
geom_point() +
geom_segment(aes(xend = end, yend = reorder(x_label, id))) +
scale_colour_manual(values = c(Republican = &quot;red&quot;, Democratic = &quot;blue&quot;)) +
scale_x_date(NULL, breaks = years, date_labels = &quot;'%y&quot;) +
scale_y_discrete(breaks = x_label)</code></pre>
<p><img src="data:image/png;base64,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
</div>
<div id="zadanie-4" class="section level1">
<h1>Zadanie 4</h1>
<pre class="r"><code>ggplot(diamonds, aes(carat, price)) +
geom_point(aes(colour = cut), alpha = 1/20) +
guides(colour = guide_legend(override.aes = list(alpha = 1, size=4)))</code></pre>
<p><img src="data:image/png;base64,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