Typos

Author: zcrysler

06-ExploringData.Rmd

@@ -41,7 +41,7 @@ df.alltags.sub <- readRDS("./data/dfAlltagsSub.rds") # change dir to local direc
 
 ```
 
-Or, if you've applied a custom filter to your .motus file, you can load the previously downloaded sample .motus data (see Chapter \@ref(accessingData) and clean it now. Currently the main benefit of using the custom filter is that you apply the filter to the .motus file, which allows you more flexibility in applying dplyr functions to manage and filter the data (e.g., you can select different variables to include in the data than we included in the RDS file in Chapter \@ref(dataCleaning). This approach also allows you to more readily integrate new data added to your database with the tagme function. Because we are selecting the same variables and filtering the same records, the following gives you the same dataset as the readRDS statement above:
+Or, if you've applied a custom filter to your .motus file, you can load the previously downloaded sample .motus data (see Chapter \@ref(accessingData)) and clean it now. Currently the main benefit of using the custom filter is that you apply the filter to the .motus file, which allows you more flexibility in applying dplyr functions to manage and filter the data (e.g., you can select different variables to include in the data than we included in the RDS file in Chapter \@ref(dataCleaning)). This approach also allows you to more readily integrate new data added to your database with the tagme function. Because we are selecting the same variables and filtering the same records, the following gives you the same dataset as the readRDS statement above:
 
 ```{r importData, eval = FALSE}
 

MotusRBook.log

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+This is pdfTeX, Version 3.14159265-2.6-1.40.17 (TeX Live 2016) (preloaded format=pdflatex 2017.2.20)  5 AUG 2018 01:38
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-Output written on MotusRBook.pdf (121 pages, 15249540 bytes).
+Output written on MotusRBook.pdf (121 pages, 15249562 bytes).
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_book/MotusRBook.pdf

@@ -334,7 +334,7 @@ <h2><span class="header-section-number">6.2</span> Load data</h2>
 <p>If you followed along with the the previous Chapter (Chapter <a href="dataCleaning.html#dataCleaning">5</a>) and are working with the cleaned ‘df.alltags.sub’ file, you can skip this step and move to section <a href="exploreData.html#dataSummaries">6.3</a>.</p>
 <p>Otherwise, if you saved your data as an RDS file, you can load it using:</p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">df.alltags.sub &lt;-<span class="st"> </span><span class="kw">readRDS</span>(<span class="st">&quot;./data/dfAlltagsSub.rds&quot;</span>)  <span class="co"># change dir to local directory</span></code></pre></div>
-<p>Or, if you’ve applied a custom filter to your .motus file, you can load the previously downloaded sample .motus data (see Chapter <a href="accessingData.html#accessingData">3</a> and clean it now. Currently the main benefit of using the custom filter is that you apply the filter to the .motus file, which allows you more flexibility in applying dplyr functions to manage and filter the data (e.g., you can select different variables to include in the data than we included in the RDS file in Chapter <a href="dataCleaning.html#dataCleaning">5</a>. This approach also allows you to more readily integrate new data added to your database with the tagme function. Because we are selecting the same variables and filtering the same records, the following gives you the same dataset as the readRDS statement above:</p>
+<p>Or, if you’ve applied a custom filter to your .motus file, you can load the previously downloaded sample .motus data (see Chapter <a href="accessingData.html#accessingData">3</a>) and clean it now. Currently the main benefit of using the custom filter is that you apply the filter to the .motus file, which allows you more flexibility in applying dplyr functions to manage and filter the data (e.g., you can select different variables to include in the data than we included in the RDS file in Chapter <a href="dataCleaning.html#dataCleaning">5</a>). This approach also allows you to more readily integrate new data added to your database with the tagme function. Because we are selecting the same variables and filtering the same records, the following gives you the same dataset as the readRDS statement above:</p>
 <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="co"># load the .motus file</span>
 proj.num =<span class="st"> </span><span class="dv">176</span>
 sql.motus &lt;-<span class="st"> </span><span class="kw">tagme</span>(proj.num, <span class="dt">update =</span> <span class="ot">TRUE</span>, <span class="dt">dir =</span> <span class="st">&quot;./data/&quot;</span>)