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* Make width narrower
* Convert deps to table
* Strip chapter status
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Hadley Wickham
2022-11-18 11:05:00 -06:00
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@@ -1,26 +1,5 @@
<section data-type="chapter" id="chp-databases">
<h1><span id="sec-import-databases" class="quarto-section-identifier d-none d-lg-block"><span class="chapter-title">Databases</span></span></h1><div data-type="note"><div class="callout-body d-flex">
<div class="callout-icon-container">
<i class="callout-icon"/>
</div>
</div>
<p>You are reading the work-in-progress second edition of R for Data Science. This chapter should be readable but is currently undergoing final polishing. You can find the complete first edition at <a href="https://r4ds.had.co.nz" class="uri">https://r4ds.had.co.nz</a>.</p>
<p>There are two other common ways to interact with a database. First, many corporate databases are very large so you need some hierarchy to keep all the tables organised. In that case you might need to supply a schema, or a catalog and a schema, in order to pick the table you’re interested in:</p><div class="cell">
<pre data-type="programlisting" data-code-language="downlit">diamonds_db &lt;- tbl(con, in_schema("sales", "diamonds"))
diamonds_db &lt;- tbl(con, in_catalog("north_america", "sales", "diamonds"))</pre>
</div><p>Other times you might want to use your own SQL query as a starting point:</p><div class="cell">
<pre data-type="programlisting" data-code-language="downlit">diamonds_db &lt;- tbl(con, sql("SELECT * FROM diamonds"))</pre>
</div>
<p>Note that while SQL is a standard, it is extremely complex and no database follows it exactly. While the main components that we’ll focus on in this book are very similar between DBMSs, there are many minor variations. Fortunately, dbplyr is designed to handle this problem and generates different translations for different databases. It’s not perfect, but it’s continually improving, and if you hit a problem you can file an issue <a href="https://github.com/tidyverse/dbplyr/issues/">on GitHub</a> to help us do better.</p>
<p>In the examples above note that <code>"year"</code> and <code>"type"</code> are wrapped in double quotes. That’s because these are <strong>reserved words</strong> in duckdb, so dbplyr quotes them to avoid any potential confusion between column/table names and SQL operators.</p><p>When working with other databases you’re likely to see every variable name quotes because only a handful of client packages, like duckdb, know what all the reserved words are, so they quote everything to be safe.</p><pre data-type="programlisting" data-code-language="sql">SELECT "tailnum", "type", "manufacturer", "model", "year"
FROM "planes"</pre><p>Some other database systems use backticks instead of quotes:</p><pre data-type="programlisting" data-code-language="sql">SELECT `tailnum`, `type`, `manufacturer`, `model`, `year`
FROM `planes`</pre></div>
<h1><span id="sec-import-databases" class="quarto-section-identifier d-none d-lg-block"><span class="chapter-title">Databases</span></span></h1><p>::: status callout-note You are reading the work-in-progress second edition of R for Data Science. This chapter should be readable but is currently undergoing final polishing. You can find the complete first edition at <a href="https://r4ds.had.co.nz" class="uri">https://r4ds.had.co.nz</a>. :::</p>
<section id="introduction" data-type="sect1">
<h1>
Introduction</h1>
@@ -203,8 +182,6 @@ diamonds_db
</div>
<p>You are reading the work-in-progress second edition of R for Data Science. This chapter should be readable but is currently undergoing final polishing. You can find the complete first edition at <a href="https://r4ds.had.co.nz" class="uri">https://r4ds.had.co.nz</a>.</p>
<p>There are two other common ways to interact with a database. First, many corporate databases are very large so you need some hierarchy to keep all the tables organised. In that case you might need to supply a schema, or a catalog and a schema, in order to pick the table you’re interested in:</p><div class="cell">
<pre data-type="programlisting" data-code-language="downlit">diamonds_db &lt;- tbl(con, in_schema("sales", "diamonds"))
diamonds_db &lt;- tbl(con, in_catalog("north_america", "sales", "diamonds"))</pre>
@@ -334,8 +311,6 @@ planes |&gt; show_query()
</div>
<p>You are reading the work-in-progress second edition of R for Data Science. This chapter should be readable but is currently undergoing final polishing. You can find the complete first edition at <a href="https://r4ds.had.co.nz" class="uri">https://r4ds.had.co.nz</a>.</p>
<p>There are two other common ways to interact with a database. First, many corporate databases are very large so you need some hierarchy to keep all the tables organised. In that case you might need to supply a schema, or a catalog and a schema, in order to pick the table you’re interested in:</p><div class="cell">
<pre data-type="programlisting" data-code-language="downlit">diamonds_db &lt;- tbl(con, in_schema("sales", "diamonds"))
diamonds_db &lt;- tbl(con, in_catalog("north_america", "sales", "diamonds"))</pre>
@@ -388,8 +363,6 @@ planes |&gt;
</div>
<p>You are reading the work-in-progress second edition of R for Data Science. This chapter should be readable but is currently undergoing final polishing. You can find the complete first edition at <a href="https://r4ds.had.co.nz" class="uri">https://r4ds.had.co.nz</a>.</p>
<p>There are two other common ways to interact with a database. First, many corporate databases are very large so you need some hierarchy to keep all the tables organised. In that case you might need to supply a schema, or a catalog and a schema, in order to pick the table you’re interested in:</p><div class="cell">
<pre data-type="programlisting" data-code-language="downlit">diamonds_db &lt;- tbl(con, in_schema("sales", "diamonds"))
diamonds_db &lt;- tbl(con, in_catalog("north_america", "sales", "diamonds"))</pre>
@@ -665,8 +638,8 @@ mutate_query &lt;- function(df, ...) {
mean = mean(arr_delay, na.rm = TRUE),
median = median(arr_delay, na.rm = TRUE)
)
#&gt; `summarise()` has grouped output by "year" and "month". You can override using
#&gt; the `.groups` argument.
#&gt; `summarise()` has grouped output by "year" and "month". You can override
#&gt; using the `.groups` argument.
#&gt; &lt;SQL&gt;
#&gt; SELECT
#&gt; "year",