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rmysql_tutorial.html
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<!DOCTYPE html>
<html xmlns="http://www.w3.org/1999/xhtml">
<head>
<meta charset="utf-8">
<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
<meta name="generator" content="pandoc" />
<meta name="viewport" content="width=device-width, initial-scale=1">
<link 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body {
box-sizing: border-box;
min-width: 200px;
max-width: 980px;
margin: 0 auto;
padding: 45px;
padding-top: 0px;
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</style>
</head>
<body>
<h1 id="rmysql-tutorial">RMySQL Tutorial</h1>
<p>Here are the steps I used to combine R and MySQL together happily.</p>
<ol>
<li><p>Get MySQL and the R package RMySQL.</p></li>
<li><p>Create a database in MySQL. Notice that while the syntax for is the same as Oracle, the data types are different. In particular, MySQL uses , , etc. for integers.</p></li>
</ol>
<h2 id="github-documents">GitHub Documents</h2>
<p>This is an R Markdown format used for publishing markdown documents to GitHub. When you click the <strong>Knit</strong> button all R code chunks are run and a markdown file (.md) suitable for publishing to GitHub is generated.</p>
<h2 id="including-code">Including Code</h2>
<p>You can include R code in the document as follows:</p>
<pre class="sourceCode r"><code class="sourceCode r"><span class="kw">summary</span>(cars)</code></pre>
<pre><code>## speed dist
## Min. : 4.0 Min. : 2.00
## 1st Qu.:12.0 1st Qu.: 26.00
## Median :15.0 Median : 36.00
## Mean :15.4 Mean : 42.98
## 3rd Qu.:19.0 3rd Qu.: 56.00
## Max. :25.0 Max. :120.00</code></pre>
<h2 id="including-plots">Including Plots</h2>
<p>You can also embed plots, for example:</p>
<p><img src="data:image/png;base64,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" /></p>
<p>Note that the <code>echo = FALSE</code> parameter was added to the code chunk to prevent printing of the R code that generated the plot.</p>
<p>\begin{verbatim}<br />$ mysql -u root -p -h localhost</p>
<p>mysql> CREATE DATABASE shop;</p>
<p>mysql> CREATE TABLE inventory<br /> (id SMALLINT PRIMARY KEY,<br /> name VARCHAR(50),<br /> quantity SMALLINT);</p>
<p>mysql> INSERT INTO inventory VALUES(1, 'Tomato', '10');<br />mysql> INSERT INTO inventory VALUES(2, 'Potato', '20');<br />mysql> INSERT INTO inventory VALUES(3, 'Rhubarb', '0');<br />mysql> INSERT INTO inventory VALUES(4, 'Eggplant', '2');<br />mysql> INSERT INTO inventory VALUES(5, 'Brussels Spouts', '15');<br />mysql> INSERT INTO inventory VALUES(6, 'Onion', '10');</p>
<p>mysql> SELECT * from inventory;</p>
<p>mysql> DESCRIBE inventory; -- This is a lot like the schema tab in Oracle SQL live<br />\end{verbatim}<br />\item<br />Next, we need to create a user with permission to access the database. To do that, I created a new user in MySQL.</p>
<p>\begin{verbatim}<br />$ mysql -u root -p -h localhost</p>
<p>mysql> CREATE USER <a href="mailto:'dangle'@'localhost">'dangle'@'localhost</a>' IDENTIFIED BY 'dongle';<br />mysql> -- username is dangle. password is dongle.<br />mysql> GRANT ALL PRIVILEGES ON shop.* TO <a href="mailto:'dangle'@'localhost'">'dangle'@'localhost'</a>;<br />mysql> quit;<br />\end{verbatim}<br />We can now access the database directly through R. But if you want to log in to MySQL as dangle, you would use the following command before entering the password:</p>
<p>\begin{verbatim}<br />$ mysql -u dangle -p -h localhost<br />\end{verbatim}<br />\item<br />We're now ready to use R. First, we need to create a database connection, which we'll name :</p>
<p>\begin{Schunk}<br />\begin{Sinput}</p>
<blockquote>
<p>library(RMySQL)<br />dbcon = dbConnect(MySQL(), user = 'dangle',</p>
</blockquote>
<ul>
<li><pre><code> password = 'dongle', </code></pre></li>
<li><pre><code> dbname = 'shop', </code></pre></li>
<li><pre><code> host = '127.0.0.1')</code></pre>
\end{Sinput}<br />\end{Schunk}<br />\item<br />Now we're ready. First, let's look at the tables in the database, and then the columns in the inventory.</li>
</ul>
<p>\begin{Schunk}<br />\begin{Sinput}</p>
<blockquote>
<p>dbListTables(dbcon)<br />\end{Sinput}<br />\begin{Soutput}<br />[1] "employee" "inventory"<br />\end{Soutput}<br />\begin{Sinput}<br />dbListFields(dbcon, 'inventory')<br />\end{Sinput}<br />\begin{Soutput}<br />[1] "id" "name" "quantity"<br />\end{Soutput}<br />\end{Schunk}<br />\vspace{25 pt}<br />Next, let's bring the table data into R.</p>
</blockquote>
<p>\begin{Schunk}<br />\begin{Sinput}</p>
<blockquote>
<p>inventory_table = dbReadTable(dbcon, "inventory")<br />inventory_table<br />\end{Sinput}<br />\begin{Soutput}<br /> id name quantity<br />1 1 Tomato 10<br />2 2 Potato 20<br />3 3 Rhubarb 0<br />4 4 Eggplant 2<br />5 5 Brussels Spouts 15<br />6 6 Onion 10<br />\end{Soutput}<br />\end{Schunk}<br />\vspace{25 pt}<br />We can go the other way, too. Let's create a new table in R, called and send that table to the database.</p>
</blockquote>
<p>\begin{Schunk}<br />\begin{Sinput}</p>
<blockquote>
<p>fname = c("Alice", "Bob", "Charlie", "Dave")<br />lname = c("Alvarez", "Brown", "Chaplin", "Dangle")<br />employee = data.frame(lname, fname)<br />employee<br />\end{Sinput}<br />\begin{Soutput}<br /> lname fname<br />1 Alvarez Alice<br />2 Brown Bob<br />3 Chaplin Charlie<br />4 Dangle Dave<br />\end{Soutput}<br />\begin{Sinput}<br />dbWriteTable(dbcon, "employee",</p>
</blockquote>
<ul>
<li><pre><code> employee, </code></pre></li>
<li><pre><code> overwrite = TRUE, </code></pre></li>
<li><pre><code> append = FALSE)</code></pre>
\end{Sinput}<br />\begin{Soutput}<br />[1] TRUE<br />\end{Soutput}<br />\end{Schunk}<br />\vspace{25 pt}<br />Finally, let's send queries to the database from within R. First we send a query to SQL. Then the command imports the table into R.</li>
</ul>
<p>\begin{Schunk}<br />\begin{Sinput}</p>
<blockquote>
<p>sql_query = dbSendQuery(dbcon,</p>
</blockquote>
<ul>
<li><pre><code> 'SELECT name, quantity </code></pre></li>
<li><pre><code> FROM inventory </code></pre></li>
<li><pre><code> WHERE quantity > 8')</code></pre>
<blockquote>
<p>sql_query<br />\end{Sinput}<br />\begin{Soutput}<br /><MySQLResult:8,0,8><br />\end{Soutput}<br />\begin{Sinput}<br />inventory_table = fetch(sql_query, n=-1)<br />inventory_table<br />\end{Sinput}<br />\begin{Soutput}<br /> name quantity<br />1 Tomato 10<br />2 Potato 20<br />3 Brussels Spouts 15<br />4 Onion 10<br />\end{Soutput}<br />\begin{Sinput}<br />on.exit(dbDisconnect(dbcon))<br />\end{Sinput}<br />\end{Schunk}<br />\end{enumerate}</p>
</blockquote></li>
</ul>
<p>\end{document}</p>
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