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<p>
<center>
<h3>vcfR documentation</h3>
by
<br>
Brian J. Knaus and Niklaus J. Grünwald
</center>
</p>
<div id="header">
<h1 class="title toc-ignore">tidy vcfR</h1>
</div>
<p>Much of the functionality of vcfR was built around R and it’s base
graphics system. More recently, the concept of ‘tidy data’ has been
described in the <a
href="http://vita.had.co.nz/papers/tidy-data.html">tidy data</a> article
and implemented in the CRAN package <a
href="https://CRAN.R-project.org/package=tidyr">tidyr</a> (and an
increasing number of other packages). In order to accomodate the
audience that is interested in working with vcfR in a ‘tidy’ framework,
several functions have been added. (Thank you Eric!)</p>
<p>For our example we will load an example data set provided in
vcfR.</p>
<pre class="r"><code>library(vcfR)
data("vcfR_test")
vcfR_test</code></pre>
<pre><code>## ***** Object of Class vcfR *****
## 3 samples
## 1 CHROMs
## 5 variants
## Object size: 0 Mb
## 0 percent missing data
## ***** ***** *****</code></pre>
<p>The function <code>vcfR2tidy()</code> will convert all of the data in
the vcfR object to a ‘tibble.’ A ‘tibble’ is a trimmed down version of a
data.frame. (See <code>?tibble::tibble</code> for more information.)
This could result in the creation of a large data structure. We can also
specify what parts of the VCF data we want to have converted into our
tibble. The funciton <code>vcf_field_names()</code> can remind us of
what data are contained in our vcfR object.</p>
<pre class="r"><code>vcf_field_names(vcfR_test, tag = "FORMAT")</code></pre>
<pre><code>## # A tibble: 4 × 5
## Tag ID Number Type Description
## <chr> <chr> <chr> <chr> <chr>
## 1 FORMAT GT 1 String Genotype
## 2 FORMAT GQ 1 Integer Genotype Quality
## 3 FORMAT DP 1 Integer Read Depth
## 4 FORMAT HQ 2 Integer Haplotype Quality</code></pre>
<pre class="r"><code>Z <- vcfR2tidy(vcfR_test, format_fields = c("GT", "DP"))</code></pre>
<pre><code>## Extracting gt element GT</code></pre>
<pre><code>## Extracting gt element DP</code></pre>
<pre class="r"><code>names(Z)</code></pre>
<pre><code>## [1] "fix" "gt" "meta"</code></pre>
<p>The result is a list containing three elements named ‘fix, ’gt’ and
‘meta.’ This is analogous to the three slots in the vcfR object. Each
element of the list is a tibble that we can examine as we would any
other list element.</p>
<pre class="r"><code>Z$meta</code></pre>
<pre><code>## # A tibble: 8 × 5
## Tag ID Number Type Description
## <chr> <chr> <chr> <chr> <chr>
## 1 INFO NS 1 Integer Number of Samples With Data
## 2 INFO DP 1 Integer Total Depth
## 3 INFO AF A Float Allele Frequency
## 4 INFO AA 1 String Ancestral Allele
## 5 INFO DB 0 Flag dbSNP membership, build 129
## 6 INFO H2 0 Flag HapMap2 membership
## 7 FORMAT gt_GT 1 String Genotype
## 8 FORMAT gt_DP 1 Integer Read Depth</code></pre>
<pre class="r"><code>Z$fix</code></pre>
<pre><code>## # A tibble: 5 × 14
## ChromKey CHROM POS ID REF ALT QUAL FILTER NS DP AF AA
## <int> <chr> <int> <chr> <chr> <chr> <dbl> <chr> <int> <int> <chr> <chr>
## 1 1 20 14370 rs605… G A 29 PASS 3 14 0.5 <NA>
## 2 1 20 17330 <NA> T A 3 q10 3 11 0.017 <NA>
## 3 1 20 1110696 rs604… A G,T 67 PASS 2 10 0.33… T
## 4 1 20 1230237 <NA> T <NA> 47 PASS 3 13 <NA> T
## 5 1 20 1234567 micro… GTC G,GT… 50 PASS 3 9 <NA> G
## # … with 2 more variables: DB <lgl>, H2 <lgl></code></pre>
<pre class="r"><code>Z$gt</code></pre>
<pre><code>## # A tibble: 15 × 6
## ChromKey POS Indiv gt_GT gt_DP gt_GT_alleles
## <int> <int> <chr> <chr> <int> <chr>
## 1 1 14370 NA00001 0|0 1 G|G
## 2 1 17330 NA00001 0|0 3 T|T
## 3 1 1110696 NA00001 1|2 6 G|T
## 4 1 1230237 NA00001 0|0 7 T|T
## 5 1 1234567 NA00001 0/1 4 GTC/G
## 6 1 14370 NA00002 1|0 8 A|G
## 7 1 17330 NA00002 0|1 5 T|A
## 8 1 1110696 NA00002 2|1 0 T|G
## 9 1 1230237 NA00002 0|0 4 T|T
## 10 1 1234567 NA00002 0/2 2 GTC/GTCT
## 11 1 14370 NA00003 1/1 5 A/A
## 12 1 17330 NA00003 0/0 3 T/T
## 13 1 1110696 NA00003 2/2 4 T/T
## 14 1 1230237 NA00003 0/0 2 T/T
## 15 1 1234567 NA00003 1/1 3 G/G</code></pre>
<p>Note that the fix and gt elements have a ‘ChromKey’ to help
coordinate the variants in both structures. Also, the information from
the meta region has been used to assign a type to each column (e.g.,
integer, character, etc.). These data structures should now be in a
format that other packages in the ‘tidyverse’ can work with. More
information about <code>vcfR2tidy()</code> can be found in its manual
page (<code>?vcfR2tidy</code>).</p>
<center>
<hr class="style1">
<p>Copyright © 2017, 2018 Brian J. Knaus. All rights reserved.</p>
<p>USDA Agricultural Research Service, Horticultural Crops Research Lab.</p>
</center>
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