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EDA.html

@@ -443,7 +443,17 @@ <h1 class="title"><span id="sec-exploratory-data-analysis" class="quarto-section
   </div>
 
 
-</header><section id="introduction" class="level2" data-number="10.1"><h2 data-number="10.1" class="anchored" data-anchor-id="introduction">
+</header><div class="callout callout-style-simple callout-warning">
+<div class="callout-body d-flex">
+<div class="callout-icon-container">
+<i class="callout-icon"></i>
+</div>
+<div class="callout-body-container">
+<p>Olá! Este capítulo do livro ainda <strong>não está traduzido</strong> para a versão Português-BR. <br> Caso você queira contribuir com o projeto de tradução, leia as instruções em: <a href="https://github.com/cienciadedatos/pt-r4ds/wiki" class="uri">https://github.com/cienciadedatos/pt-r4ds/wiki</a> <br> A versão original (em inglês) do livro R for Data Science está disponível em: <a href="https://r4ds.hadley.nz/" class="uri">https://r4ds.hadley.nz/</a></p>
+</div>
+</div>
+</div>
+<section id="introduction" class="level2" data-number="10.1"><h2 data-number="10.1" class="anchored" data-anchor-id="introduction">
 <span class="header-section-number">10.1</span> Introduction</h2>
 <p>This chapter will show you how to use visualization and transformation to explore your data in a systematic way, a task that statisticians call exploratory data analysis, or EDA for short. EDA is an iterative cycle. You:</p>
 <ol type="1">

base-R.html

@@ -440,7 +440,17 @@ <h1 class="title"><span id="sec-base-r" class="quarto-section-identifier"><span
   </div>
 
 
-</header><section id="introduction" class="level2" data-number="27.1"><h2 data-number="27.1" class="anchored" data-anchor-id="introduction">
+</header><div class="callout callout-style-simple callout-warning">
+<div class="callout-body d-flex">
+<div class="callout-icon-container">
+<i class="callout-icon"></i>
+</div>
+<div class="callout-body-container">
+<p>Olá! Este capítulo do livro ainda <strong>não está traduzido</strong> para a versão Português-BR. <br> Caso você queira contribuir com o projeto de tradução, leia as instruções em: <a href="https://github.com/cienciadedatos/pt-r4ds/wiki" class="uri">https://github.com/cienciadedatos/pt-r4ds/wiki</a> <br> A versão original (em inglês) do livro R for Data Science está disponível em: <a href="https://r4ds.hadley.nz/" class="uri">https://r4ds.hadley.nz/</a></p>
+</div>
+</div>
+</div>
+<section id="introduction" class="level2" data-number="27.1"><h2 data-number="27.1" class="anchored" data-anchor-id="introduction">
 <span class="header-section-number">27.1</span> Introduction</h2>
 <p>To finish off the programming section, we’re going to give you a quick tour of the most important base R functions that we don’t otherwise discuss in the book. These tools are particularly useful as you do more programming and will help you read code you’ll encounter in the wild.</p>
 <p>This is a good place to remind you that the tidyverse is not the only way to solve data science problems. We teach the tidyverse in this book because tidyverse packages share a common design philosophy, increasing the consistency across functions, and making each new function or package a little easier to learn and use. It’s not possible to use the tidyverse without using base R, so we’ve actually already taught you a <strong>lot</strong> of base R functions: from <code><a href="https://rdrr.io/r/base/library.html">library()</a></code> to load packages, to <code><a href="https://rdrr.io/r/base/sum.html">sum()</a></code> and <code><a href="https://rdrr.io/r/base/mean.html">mean()</a></code> for numeric summaries, to the factor, date, and POSIXct data types, and of course all the basic operators like <code>+</code>, <code>-</code>, <code>/</code>, <code>*</code>, <code>|</code>, <code>&amp;</code>, and <code>!</code>. What we haven’t focused on so far is base R workflows, so we will highlight a few of those in this chapter.</p>

communication.html

@@ -454,7 +454,17 @@ <h1 class="title"><span id="sec-communication" class="quarto-section-identifier"
   </div>
 
 
-</header><section id="introduction" class="level2" data-number="11.1"><h2 data-number="11.1" class="anchored" data-anchor-id="introduction">
+</header><div class="callout callout-style-simple callout-warning">
+<div class="callout-body d-flex">
+<div class="callout-icon-container">
+<i class="callout-icon"></i>
+</div>
+<div class="callout-body-container">
+<p>Olá! Este capítulo do livro ainda <strong>não está traduzido</strong> para a versão Português-BR. <br> Caso você queira contribuir com o projeto de tradução, leia as instruções em: <a href="https://github.com/cienciadedatos/pt-r4ds/wiki" class="uri">https://github.com/cienciadedatos/pt-r4ds/wiki</a> <br> A versão original (em inglês) do livro R for Data Science está disponível em: <a href="https://r4ds.hadley.nz/" class="uri">https://r4ds.hadley.nz/</a></p>
+</div>
+</div>
+</div>
+<section id="introduction" class="level2" data-number="11.1"><h2 data-number="11.1" class="anchored" data-anchor-id="introduction">
 <span class="header-section-number">11.1</span> Introduction</h2>
 <p>In <a href="EDA.html"><span>Capítulo&nbsp;10</span></a>, you learned how to use plots as tools for <em>exploration</em>. When you make exploratory plots, you know—even before looking—which variables the plot will display. You made each plot for a purpose, could quickly look at it, and then move on to the next plot. In the course of most analyses, you’ll produce tens or hundreds of plots, most of which are immediately thrown away.</p>
 <p>Now that you understand your data, you need to <em>communicate</em> your understanding to others. Your audience will likely not share your background knowledge and will not be deeply invested in the data. To help others quickly build up a good mental model of the data, you will need to invest considerable effort in making your plots as self-explanatory as possible. In this chapter, you’ll learn some of the tools that ggplot2 provides to do so.</p>

data-import.html

@@ -439,7 +439,17 @@ <h1 class="title"><span id="sec-data-import" class="quarto-section-identifier"><
   </div>
 
 
-</header><section id="introduction" class="level2" data-number="7.1"><h2 data-number="7.1" class="anchored" data-anchor-id="introduction">
+</header><div class="callout callout-style-simple callout-warning">
+<div class="callout-body d-flex">
+<div class="callout-icon-container">
+<i class="callout-icon"></i>
+</div>
+<div class="callout-body-container">
+<p>Olá! Este capítulo do livro ainda <strong>não está traduzido</strong> para a versão Português-BR. <br> Caso você queira contribuir com o projeto de tradução, leia as instruções em: <a href="https://github.com/cienciadedatos/pt-r4ds/wiki" class="uri">https://github.com/cienciadedatos/pt-r4ds/wiki</a> <br> A versão original (em inglês) do livro R for Data Science está disponível em: <a href="https://r4ds.hadley.nz/" class="uri">https://r4ds.hadley.nz/</a></p>
+</div>
+</div>
+</div>
+<section id="introduction" class="level2" data-number="7.1"><h2 data-number="7.1" class="anchored" data-anchor-id="introduction">
 <span class="header-section-number">7.1</span> Introduction</h2>
 <p>Working with data provided by R packages is a great way to learn data science tools, but you want to apply what you’ve learned to your own data at some point. In this chapter, you’ll learn the basics of reading data files into R.</p>
 <p>Specifically, this chapter will focus on reading plain-text rectangular files. We’ll start with practical advice for handling features like column names, types, and missing data. You will then learn about reading data from multiple files at once and writing data from R to a file. Finally, you’ll learn how to handcraft data frames in R.</p>
@@ -825,7 +835,7 @@ <h1 class="title"><span id="sec-data-import" class="quarto-section-identifier"><
 <span><span class="co">#&gt; # A tibble: 1 × 5</span></span>
 <span><span class="co">#&gt;     row   col expected actual file                            </span></span>
 <span><span class="co">#&gt;   &lt;int&gt; &lt;int&gt; &lt;chr&gt;    &lt;chr&gt;  &lt;chr&gt;                           </span></span>
-<span><span class="co">#&gt; 1     3     1 a double .      /tmp/Rtmp7ye2gf/file228416ab4e78</span></span></code><button title="Copiar para a área de transferência" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<span><span class="co">#&gt; 1     3     1 a double .      /tmp/RtmprQ7oUD/file1e2549f0663d</span></span></code><button title="Copiar para a área de transferência" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>This tells us that there was a problem in row 3, col 1 where readr expected a double but got a <code>.</code>. That suggests this dataset uses <code>.</code> for missing values. So then we set <code>na = "."</code>, the automatic guessing succeeds, giving us the numeric column that we want:</p>
 <div class="cell">

data-tidy.html

@@ -440,7 +440,17 @@ <h1 class="title"><span id="sec-data-tidy" class="quarto-section-identifier"><sp
   </div>
 
 
-</header><section id="introduction" class="level2" data-number="5.1"><h2 data-number="5.1" class="anchored" data-anchor-id="introduction">
+</header><div class="callout callout-style-simple callout-warning">
+<div class="callout-body d-flex">
+<div class="callout-icon-container">
+<i class="callout-icon"></i>
+</div>
+<div class="callout-body-container">
+<p>Olá! Este capítulo do livro ainda <strong>não está traduzido</strong> para a versão Português-BR. <br> Caso você queira contribuir com o projeto de tradução, leia as instruções em: <a href="https://github.com/cienciadedatos/pt-r4ds/wiki" class="uri">https://github.com/cienciadedatos/pt-r4ds/wiki</a> <br> A versão original (em inglês) do livro R for Data Science está disponível em: <a href="https://r4ds.hadley.nz/" class="uri">https://r4ds.hadley.nz/</a></p>
+</div>
+</div>
+</div>
+<section id="introduction" class="level2" data-number="5.1"><h2 data-number="5.1" class="anchored" data-anchor-id="introduction">
 <span class="header-section-number">5.1</span> Introduction</h2>
 <blockquote class="blockquote">
 <p>“Happy families are all alike; every unhappy family is unhappy in its own way.”<br>

data-transform.html

@@ -455,7 +455,17 @@ <h1 class="title"><span id="sec-data-transform" class="quarto-section-identifier
   </div>
 
 
-</header><section id="introduction" class="level2" data-number="3.1"><h2 data-number="3.1" class="anchored" data-anchor-id="introduction">
+</header><div class="callout callout-style-simple callout-warning">
+<div class="callout-body d-flex">
+<div class="callout-icon-container">
+<i class="callout-icon"></i>
+</div>
+<div class="callout-body-container">
+<p>Olá! Este capítulo do livro ainda <strong>não está traduzido</strong> para a versão Português-BR. <br> Caso você queira contribuir com o projeto de tradução, leia as instruções em: <a href="https://github.com/cienciadedatos/pt-r4ds/wiki" class="uri">https://github.com/cienciadedatos/pt-r4ds/wiki</a> <br> A versão original (em inglês) do livro R for Data Science está disponível em: <a href="https://r4ds.hadley.nz/" class="uri">https://r4ds.hadley.nz/</a></p>
+</div>
+</div>
+</div>
+<section id="introduction" class="level2" data-number="3.1"><h2 data-number="3.1" class="anchored" data-anchor-id="introduction">
 <span class="header-section-number">3.1</span> Introduction</h2>
 <p>Visualization is an important tool for generating insight, but it’s rare that you get the data in exactly the right form you need to make the graph you want. Often you’ll need to create some new variables or summaries to answer your questions with your data, or maybe you just want to rename the variables or reorder the observations to make the data a little easier to work with. You’ll learn how to do all that (and more!) in this chapter, which will introduce you to data transformation using the <strong>dplyr</strong> package and a new dataset on flights that departed from New York City in 2013.</p>
 <p>The goal of this chapter is to give you an overview of all the key tools for transforming a data frame. We’ll start with functions that operate on rows and then columns of a data frame, then circle back to talk more about the pipe, an important tool that you use to combine verbs. We will then introduce the ability to work with groups. We will end the chapter with a case study that showcases these functions in action and we’ll come back to the functions in more detail in later chapters, as we start to dig into specific types of data (e.g., numbers, strings, dates).</p>

databases.html

@@ -445,7 +445,17 @@ <h1 class="title"><span id="sec-import-databases" class="quarto-section-identifi
   </div>
 
 
-</header><section id="introduction" class="level2" data-number="21.1"><h2 data-number="21.1" class="anchored" data-anchor-id="introduction">
+</header><div class="callout callout-style-simple callout-warning">
+<div class="callout-body d-flex">
+<div class="callout-icon-container">
+<i class="callout-icon"></i>
+</div>
+<div class="callout-body-container">
+<p>Olá! Este capítulo do livro ainda <strong>não está traduzido</strong> para a versão Português-BR. <br> Caso você queira contribuir com o projeto de tradução, leia as instruções em: <a href="https://github.com/cienciadedatos/pt-r4ds/wiki" class="uri">https://github.com/cienciadedatos/pt-r4ds/wiki</a> <br> A versão original (em inglês) do livro R for Data Science está disponível em: <a href="https://r4ds.hadley.nz/" class="uri">https://r4ds.hadley.nz/</a></p>
+</div>
+</div>
+</div>
+<section id="introduction" class="level2" data-number="21.1"><h2 data-number="21.1" class="anchored" data-anchor-id="introduction">
 <span class="header-section-number">21.1</span> Introduction</h2>
 <p>A huge amount of data lives in databases, so it’s essential that you know how to access it. Sometimes you can ask someone to download a snapshot into a <code>.csv</code> for you, but this gets painful quickly: every time you need to make a change you’ll have to communicate with another human. You want to be able to reach into the database directly to get the data you need, when you need it.</p>
 <p>In this chapter, you’ll first learn the basics of the DBI package: how to use it to connect to a database and then retrieve data with a SQL<a href="#fn1" class="footnote-ref" id="fnref1" role="doc-noteref"><sup>1</sup></a> query. <strong>SQL</strong>, short for <strong>s</strong>tructured <strong>q</strong>uery <strong>l</strong>anguage, is the lingua franca of databases, and is an important language for all data scientists to learn. That said, we’re not going to start with SQL, but instead we’ll teach you dbplyr, which can translate your dplyr code to the SQL. We’ll use that as a way to teach you some of the most important features of SQL. You won’t become a SQL master by the end of the chapter, but you will be able to identify the most important components and understand what they do.</p>
@@ -563,7 +573,7 @@ <h1 class="title"><span id="sec-import-databases" class="quarto-section-identifi
 <div class="sourceCode" id="cb8"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="va">diamonds_db</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://dplyr.tidyverse.org/reference/tbl.html">tbl</a></span><span class="op">(</span><span class="va">con</span>, <span class="st">"diamonds"</span><span class="op">)</span></span>
 <span><span class="va">diamonds_db</span></span>
 <span><span class="co">#&gt; # Source:   table&lt;diamonds&gt; [?? x 10]</span></span>
-<span><span class="co">#&gt; # Database: DuckDB v0.9.1 [unknown@Linux 6.2.0-1015-azure:R 4.3.2/:memory:]</span></span>
+<span><span class="co">#&gt; # Database: DuckDB v0.9.1 [unknown@Linux 6.2.0-1016-azure:R 4.3.2/:memory:]</span></span>
 <span><span class="co">#&gt;   carat cut       color clarity depth table price     x     y     z</span></span>
 <span><span class="co">#&gt;   &lt;dbl&gt; &lt;fct&gt;     &lt;fct&gt; &lt;fct&gt;   &lt;dbl&gt; &lt;dbl&gt; &lt;int&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt;</span></span>
 <span><span class="co">#&gt; 1  0.23 Ideal     E     SI2      61.5    55   326  3.95  3.98  2.43</span></span>
@@ -600,7 +610,7 @@ <h1 class="title"><span id="sec-import-databases" class="quarto-section-identifi
 <span></span>
 <span><span class="va">big_diamonds_db</span></span>
 <span><span class="co">#&gt; # Source:   SQL [?? x 5]</span></span>
-<span><span class="co">#&gt; # Database: DuckDB v0.9.1 [unknown@Linux 6.2.0-1015-azure:R 4.3.2/:memory:]</span></span>
+<span><span class="co">#&gt; # Database: DuckDB v0.9.1 [unknown@Linux 6.2.0-1016-azure:R 4.3.2/:memory:]</span></span>
 <span><span class="co">#&gt;   carat cut       color clarity price</span></span>
 <span><span class="co">#&gt;   &lt;dbl&gt; &lt;fct&gt;     &lt;fct&gt; &lt;fct&gt;   &lt;int&gt;</span></span>
 <span><span class="co">#&gt; 1  1.54 Premium   E     VS2     15002</span></span>
@@ -828,7 +838,7 @@ <h1 class="title"><span id="sec-import-databases" class="quarto-section-identifi
 <span><span class="co">#&gt; Use `na.rm = TRUE` to silence this warning</span></span>
 <span><span class="co">#&gt; This warning is displayed once every 8 hours.</span></span>
 <span><span class="co">#&gt; # Source:   SQL [?? x 2]</span></span>
-<span><span class="co">#&gt; # Database: DuckDB v0.9.1 [unknown@Linux 6.2.0-1015-azure:R 4.3.2/:memory:]</span></span>
+<span><span class="co">#&gt; # Database: DuckDB v0.9.1 [unknown@Linux 6.2.0-1016-azure:R 4.3.2/:memory:]</span></span>
 <span><span class="co">#&gt;   dest  delay</span></span>
 <span><span class="co">#&gt;   &lt;chr&gt; &lt;dbl&gt;</span></span>
 <span><span class="co">#&gt; 1 SFO    2.67</span></span>