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<h1 class="title toc-ignore">Characterize Nuclear ApaQTLs</h1>
<h4 class="author"><em>Briana Mittleman</em></h4>
<h4 class="date"><em>10/24/2018</em></h4>
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<p><strong>Last updated:</strong> 2019-01-15</p>
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<p><details> <summary> <strong style="color:blue;">✔</strong> <strong>Repository version:</strong> <a href="https://github.com/brimittleman/threeprimeseq/tree/57fbf88f14ff2df2cdbc771144a25b88f733d8f7" target="_blank">57fbf88</a> </summary></p>
Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility. The version displayed above was the version of the Git repository at the time these results were generated. <br><br> Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use <code>wflow_publish</code> or <code>wflow_git_commit</code>). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:
<pre><code>
Ignored files:
Ignored: .DS_Store
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Unstaged changes:
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Modified: analysis/CompareLianoglouData.Rmd
Modified: analysis/NewPeakPostMP.Rmd
Modified: analysis/apaQTLoverlapGWAS.Rmd
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Modified: analysis/swarmPlots_QTLs.Rmd
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Modified: analysis/understandPeaks.Rmd
Modified: code/Snakefile
</code></pre>
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</table>
</ul>
<p></details></p>
<hr />
<p>This analysis is similar to the <a href="characterizeTotalApaQtls.html">Characterize Total APAqtl analysis</a></p>
<p>I would like to study:</p>
<ul>
<li>Distance metrics:
<ul>
<li>distance from snp to TSS of gene<br />
</li>
<li>Distance from snp to peak</li>
</ul></li>
<li>Expression metrics:
<ul>
<li>expression of genes with significant QTLs vs other genes (by rna seq)</li>
<li>expression of genes with significant QTLs vs other genes (peak coverage)</li>
</ul></li>
<li>Chrom HMM metrics:
<ul>
<li>look at the chrom HMM interval for the significant QTLs</li>
</ul></li>
</ul>
<div id="upload-libraries-and-data" class="section level2">
<h2>Upload Libraries and Data:</h2>
<p>Library</p>
<pre class="r"><code>library(workflowr)</code></pre>
<pre><code>This is workflowr version 1.1.1
Run ?workflowr for help getting started</code></pre>
<pre class="r"><code>library(reshape2)
library(tidyverse)</code></pre>
<pre><code>── Attaching packages ───────────────────────────────────────────────────────────── tidyverse 1.2.1 ──</code></pre>
<pre><code>✔ ggplot2 3.0.0 ✔ purrr 0.2.5
✔ tibble 1.4.2 ✔ dplyr 0.7.6
✔ tidyr 0.8.1 ✔ stringr 1.3.1
✔ readr 1.1.1 ✔ forcats 0.3.0</code></pre>
<pre><code>── Conflicts ──────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()</code></pre>
<pre class="r"><code>library(VennDiagram)</code></pre>
<pre><code>Loading required package: grid</code></pre>
<pre><code>Loading required package: futile.logger</code></pre>
<pre class="r"><code>library(data.table)</code></pre>
<pre><code>
Attaching package: 'data.table'</code></pre>
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<pre class="r"><code>library(ggpubr)</code></pre>
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<pre class="r"><code>library(cowplot)</code></pre>
<pre><code>
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<p>Permuted Results from APA:</p>
<p>I will add a column to this dataframe that will tell me if the association is significant at 10% FDR. This will help me plot based on significance later in the analysis. I am also going to seperate the PID into relevant pieces.</p>
<pre class="r"><code>NuclearAPA=read.table("../data/perm_QTL_trans/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Nuclear_transcript_permResBH.txt", stringsAsFactors = F, header=T) %>% mutate(sig=ifelse(-log10(bh)>=1, 1,0 )) %>% separate(pid, sep = ":", into=c("chr", "start", "end", "id")) %>% separate(id, sep = "_", into=c("gene", "strand", "peak"))
NuclearAPA$sig=as.factor(NuclearAPA$sig)
print(names(NuclearAPA))</code></pre>
<pre><code> [1] "chr" "start" "end" "gene" "strand" "peak" "nvar"
[8] "shape1" "shape2" "dummy" "sid" "dist" "npval" "slope"
[15] "ppval" "bpval" "bh" "sig" </code></pre>
</div>
<div id="distance-metrics" class="section level2">
<h2>Distance Metrics</h2>
<div id="distance-from-snp-to-tss" class="section level3">
<h3>Distance from snp to TSS</h3>
<p>I ran the QTL analysis based on the starting position of the gene.</p>
<pre class="r"><code>ggplot(NuclearAPA, aes(x=dist, fill=sig, by=sig)) + geom_density(alpha=.5) + labs(title="Distance from snp to TSS", x="Base Pairs") + scale_fill_discrete(guide = guide_legend(title = "Significant QTL")) + scale_fill_brewer(palette="Paired")</code></pre>
<pre><code>Scale for 'fill' is already present. Adding another scale for 'fill',
which will replace the existing scale.</code></pre>
<p><img src="figure/characterizeNuclearApaQtls.Rmd/unnamed-chunk-3-1.png" width="672" style="display: block; margin: auto;" /></p>
<details> <summary><em>Expand here to see past versions of unnamed-chunk-3-1.png:</em></summary>
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<a href="https://github.com/brimittleman/threeprimeseq/blob/de860f0e5f84639460e9157c55f0024274bfb7d4/docs/figure/characterizeNuclearApaQtls.Rmd/unnamed-chunk-3-1.png" target="_blank">de860f0</a>
</td>
<td style="text-align:left;">
Briana Mittleman
</td>
<td style="text-align:left;">
2018-10-24
</td>
</tr>
</tbody>
</table>
<p></details></p>
<p>Zoom in to 100,000.</p>
<pre class="r"><code>ggplot(NuclearAPA, aes(x=dist, fill=sig, by=sig)) + geom_density(alpha=.5)+coord_cartesian(xlim = c(-150000, 150000)) + scale_fill_brewer(palette="Paired")</code></pre>
<p><img src="figure/characterizeNuclearApaQtls.Rmd/unnamed-chunk-4-1.png" width="672" style="display: block; margin: auto;" /></p>
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<a href="https://github.com/brimittleman/threeprimeseq/blob/de860f0e5f84639460e9157c55f0024274bfb7d4/docs/figure/characterizeNuclearApaQtls.Rmd/unnamed-chunk-4-1.png" target="_blank">de860f0</a>
</td>
<td style="text-align:left;">
Briana Mittleman
</td>
<td style="text-align:left;">
2018-10-24
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</table>
<p></details></p>
</div>
<div id="distance-from-snp-to-peak" class="section level3">
<h3>Distance from snp to peak</h3>
<p>To perform this analysis I need to recover the peak positions.</p>
<p>The peak file I used for the QTL analysis is: /project2/gilad/briana/threeprimeseq/data/mergedPeaks_comb/filtered_APApeaks_merged_allchrom_refseqTrans.noties_sm.fixed.bed</p>
<pre class="r"><code>peaks=read.table("../data/PeaksUsed/filtered_APApeaks_merged_allchrom_refseqTrans.noties_sm.fixed.bed", col.names = c("chr", "peakStart", "peakEnd", "PeakNum", "PeakScore", "Strand", "Gene")) %>% mutate(peak=paste("peak", PeakNum,sep="")) %>% mutate(PeakCenter=peakStart+ (peakEnd- peakStart))</code></pre>
<p>I want to join the peak start to the NuclearAPA file but the peak column. I will then create a column that is snppos-peakcenter.</p>
<pre class="r"><code>NuclearAPA_peakdist= NuclearAPA %>% inner_join(peaks, by="peak") %>% separate(sid, into=c("snpCHR", "snpLOC"), by=":")
NuclearAPA_peakdist$snpLOC= as.numeric(NuclearAPA_peakdist$snpLOC)
NuclearAPA_peakdist= NuclearAPA_peakdist %>% mutate(DisttoPeak= snpLOC-PeakCenter)</code></pre>
<p>Plot this by significance.</p>
<pre class="r"><code>ggplot(NuclearAPA_peakdist, aes(x=DisttoPeak, fill=sig, by=sig)) + geom_density(alpha=.5) + labs(title="Distance from snp peak", x="log10 absolute value Distance to Peak") + scale_fill_discrete(guide = guide_legend(title = "Significant QTL")) + scale_fill_brewer(palette="Paired")</code></pre>
<pre><code>Scale for 'fill' is already present. Adding another scale for 'fill',
which will replace the existing scale.</code></pre>
<p><img src="figure/characterizeNuclearApaQtls.Rmd/unnamed-chunk-7-1.png" width="672" style="display: block; margin: auto;" /></p>
<details> <summary><em>Expand here to see past versions of unnamed-chunk-7-1.png:</em></summary>
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<a href="https://github.com/brimittleman/threeprimeseq/blob/de860f0e5f84639460e9157c55f0024274bfb7d4/docs/figure/characterizeNuclearApaQtls.Rmd/unnamed-chunk-7-1.png" target="_blank">de860f0</a>
</td>
<td style="text-align:left;">
Briana Mittleman
</td>
<td style="text-align:left;">
2018-10-24
</td>
</tr>
</tbody>
</table>
<p></details></p>
<p>Look at the summarys based on significance:</p>
<pre class="r"><code>NuclearAPA_peakdist_sig=NuclearAPA_peakdist %>% filter(sig==1)
NuclearAPA_peakdist_notsig=NuclearAPA_peakdist %>% filter(sig==0)
summary(NuclearAPA_peakdist_sig$DisttoPeak)</code></pre>
<pre><code> Min. 1st Qu. Median Mean 3rd Qu. Max.
-1003786 -17579 -91 -8818 6588 891734 </code></pre>
<pre class="r"><code>summary(NuclearAPA_peakdist_notsig$DisttoPeak)</code></pre>
<pre><code> Min. 1st Qu. Median Mean 3rd Qu. Max.
-70147526 -265059 -2067 7263 255169 125172864 </code></pre>
<pre class="r"><code>ggplot(NuclearAPA_peakdist, aes(y=DisttoPeak,x=sig, fill=sig, by=sig)) + geom_boxplot() + scale_fill_discrete(guide = guide_legend(title = "Significant QTL")) + scale_fill_brewer(palette="Paired")</code></pre>
<pre><code>Scale for 'fill' is already present. Adding another scale for 'fill',
which will replace the existing scale.</code></pre>
<p><img src="figure/characterizeNuclearApaQtls.Rmd/unnamed-chunk-9-1.png" width="672" style="display: block; margin: auto;" /></p>
<details> <summary><em>Expand here to see past versions of unnamed-chunk-9-1.png:</em></summary>
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<a href="https://github.com/brimittleman/threeprimeseq/blob/de860f0e5f84639460e9157c55f0024274bfb7d4/docs/figure/characterizeNuclearApaQtls.Rmd/unnamed-chunk-9-1.png" target="_blank">de860f0</a>
</td>
<td style="text-align:left;">
Briana Mittleman
</td>
<td style="text-align:left;">
2018-10-24
</td>
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</tbody>
</table>
<p></details></p>
<p>Look like there are some outliers that are really far. I will remove variants greater than 1*10^6th away</p>
<pre class="r"><code>NuclearAPA_peakdist_filt=NuclearAPA_peakdist %>% filter(abs(DisttoPeak) <= 1*(10^6))
ggplot(NuclearAPA_peakdist_filt, aes(y=DisttoPeak,x=sig, fill=sig, by=sig)) + geom_boxplot() + scale_fill_discrete(guide = guide_legend(title = "Significant QTL")) + facet_grid(.~strand) + scale_fill_brewer(palette="Paired")</code></pre>
<pre><code>Scale for 'fill' is already present. Adding another scale for 'fill',
which will replace the existing scale.</code></pre>
<p><img src="figure/characterizeNuclearApaQtls.Rmd/unnamed-chunk-10-1.png" width="672" style="display: block; margin: auto;" /></p>
<details> <summary><em>Expand here to see past versions of unnamed-chunk-10-1.png:</em></summary>
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<a href="https://github.com/brimittleman/threeprimeseq/blob/de860f0e5f84639460e9157c55f0024274bfb7d4/docs/figure/characterizeNuclearApaQtls.Rmd/unnamed-chunk-10-1.png" target="_blank">de860f0</a>
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<td style="text-align:left;">
Briana Mittleman
</td>
<td style="text-align:left;">
2018-10-24
</td>
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</tbody>
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<p></details></p>
<pre class="r"><code>ggplot(NuclearAPA_peakdist_filt, aes(x=DisttoPeak, fill=sig, by=sig)) + geom_density() + scale_fill_discrete(guide = guide_legend(title = "Significant QTL")) + facet_grid(.~strand)+ scale_fill_brewer(palette="Paired")</code></pre>
<pre><code>Scale for 'fill' is already present. Adding another scale for 'fill',
which will replace the existing scale.</code></pre>
<p><img src="figure/characterizeNuclearApaQtls.Rmd/unnamed-chunk-10-2.png" width="672" style="display: block; margin: auto;" /></p>
<details> <summary><em>Expand here to see past versions of unnamed-chunk-10-2.png:</em></summary>
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<a href="https://github.com/brimittleman/threeprimeseq/blob/de860f0e5f84639460e9157c55f0024274bfb7d4/docs/figure/characterizeNuclearApaQtls.Rmd/unnamed-chunk-10-2.png" target="_blank">de860f0</a>
</td>
<td style="text-align:left;">
Briana Mittleman
</td>
<td style="text-align:left;">
2018-10-24
</td>
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<p></details></p>
<p>I am going to plot a violin plot for just the significant ones.</p>
<pre class="r"><code>ggplot(NuclearAPA_peakdist_sig, aes(x=log10(abs(DisttoPeak)+1))) + geom_density(fill="deepskyblue3")+ labs(title="Nuclear: Distance from QTL to PAS Peak", x="Distance from SNP to PAS") </code></pre>
<p><img src="figure/characterizeNuclearApaQtls.Rmd/unnamed-chunk-11-1.png" width="672" style="display: block; margin: auto;" /></p>
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<a href="https://github.com/brimittleman/threeprimeseq/blob/a5b4cf677cb4da813fbf7aee2733afe7dccfb8d6/docs/figure/characterizeNuclearApaQtls.Rmd/unnamed-chunk-11-1.png" target="_blank">a5b4cf6</a>
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<td style="text-align:left;">
Briana Mittleman
</td>
<td style="text-align:left;">
2018-10-29
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<a href="https://github.com/brimittleman/threeprimeseq/blob/de860f0e5f84639460e9157c55f0024274bfb7d4/docs/figure/characterizeNuclearApaQtls.Rmd/unnamed-chunk-11-1.png" target="_blank">de860f0</a>
</td>
<td style="text-align:left;">
Briana Mittleman
</td>
<td style="text-align:left;">
2018-10-24
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<p></details></p>
<p>Within 1000 bases of the peak center.</p>
<pre class="r"><code>NuclearAPA_peakdist_sig %>% filter(abs(DisttoPeak) < 1000) %>% nrow()</code></pre>
<pre><code>[1] 192</code></pre>
<pre class="r"><code>NuclearAPA_peakdist_sig %>% filter(abs(DisttoPeak) < 10000) %>% nrow()</code></pre>
<pre><code>[1] 420</code></pre>
<pre class="r"><code>NuclearAPA_peakdist_sig %>% filter(abs(DisttoPeak) < 100000) %>% nrow()</code></pre>
<pre><code>[1] 726</code></pre>
<p>192 QTLs are within 1000 bp, 420 are within 10000, and 726 are within 100,000bp</p>
</div>
</div>
<div id="expression-metrics" class="section level2">
<h2>Expression metrics</h2>
<p>Next I want to pull in the expression values and compare the expression of genes with a nuclear APA qtl in comparison to genes without one. I will also need to pull in the gene names file to add in the gene names from the ensg ID.</p>
<p>Remove the # from the file.</p>
<pre class="r"><code>expression=read.table("../data/mol_pheno/fastqtl_qqnorm_RNAseq_phase2.fixed.noChr.txt", header = T,stringsAsFactors = F)
expression_mean=apply(expression[,5:73],1,mean,na.rm=TRUE)
expression_var=apply(expression[,5:73],1,var,na.rm=TRUE)
expression$exp.mean= expression_mean
expression$exp.var=expression_var
expression= expression %>% separate(ID, into=c("Gene.stable.ID", "ver"), sep ="[.]")</code></pre>
<p>Now I can pull in the names and join the dataframes.</p>
<pre class="r"><code>geneNames=read.table("../data/ensemble_to_genename.txt", sep="\t", header=T,stringsAsFactors = F)
expression=expression %>% inner_join(geneNames,by="Gene.stable.ID")
expression=expression %>% select(Chr, start, end, Gene.name, exp.mean,exp.var) %>% rename("gene"=Gene.name)</code></pre>
<p>Now I can join this with the qtls.</p>
<pre class="r"><code>NuclearAPA_wExp=NuclearAPA %>% inner_join(expression, by="gene") </code></pre>
<pre class="r"><code>gene_wQTL_N= NuclearAPA_wExp %>% group_by(gene) %>% summarise(sig_gene=sum(as.numeric(as.character(sig)))) %>% ungroup() %>% inner_join(expression, by="gene") %>% mutate(sigGeneFactor=ifelse(sig_gene>=1, 1,0))
gene_wQTL_N$sigGeneFactor= as.factor(as.character(gene_wQTL_N$sigGeneFactor))
summary(gene_wQTL_N$sigGeneFactor)</code></pre>
<pre><code> 0 1
4589 607 </code></pre>
<p>There are 607 genes with a QTL</p>
<pre class="r"><code>ggplot(gene_wQTL_N, aes(x=exp.mean, by=sigGeneFactor, fill=sigGeneFactor)) + geom_density(alpha=.3) +labs(title="Mean in RNA expression by genes with significant QTL", x="Mean in normalized expression") + scale_fill_discrete(guide = guide_legend(title = "Significant QTL"))+ scale_fill_brewer(palette="Paired")</code></pre>
<pre><code>Scale for 'fill' is already present. Adding another scale for 'fill',
which will replace the existing scale.</code></pre>
<p><img src="figure/characterizeNuclearApaQtls.Rmd/unnamed-chunk-17-1.png" width="672" style="display: block; margin: auto;" /></p>
<details> <summary><em>Expand here to see past versions of unnamed-chunk-17-1.png:</em></summary>
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<a href="https://github.com/brimittleman/threeprimeseq/blob/de860f0e5f84639460e9157c55f0024274bfb7d4/docs/figure/characterizeNuclearApaQtls.Rmd/unnamed-chunk-17-1.png" target="_blank">de860f0</a>
</td>
<td style="text-align:left;">
Briana Mittleman
</td>
<td style="text-align:left;">
2018-10-24
</td>
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<p></details></p>
<p>I can do a similar analysis but test the variance in the gene expression.</p>
<pre class="r"><code>ggplot(gene_wQTL_N, aes(x=exp.var, by=sigGeneFactor, fill=sigGeneFactor)) + geom_density(alpha=.3) + labs(title="Varriance in RNA expression by genes with significant QTL", x="Variance in normalized expression") + scale_fill_discrete(guide = guide_legend(title = "Significant QTL"))+ scale_fill_brewer(palette="Paired")</code></pre>
<pre><code>Scale for 'fill' is already present. Adding another scale for 'fill',
which will replace the existing scale.</code></pre>
<p><img src="figure/characterizeNuclearApaQtls.Rmd/unnamed-chunk-18-1.png" width="672" style="display: block; margin: auto;" /></p>
<details> <summary><em>Expand here to see past versions of unnamed-chunk-18-1.png:</em></summary>
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<a href="https://github.com/brimittleman/threeprimeseq/blob/de860f0e5f84639460e9157c55f0024274bfb7d4/docs/figure/characterizeNuclearApaQtls.Rmd/unnamed-chunk-18-1.png" target="_blank">de860f0</a>
</td>
<td style="text-align:left;">
Briana Mittleman
</td>
<td style="text-align:left;">
2018-10-24
</td>
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<p></details></p>
<div id="peak-coverage-for-qtls" class="section level3">
<h3>Peak coverage for QTLs</h3>
<p>I can also look at peak coverage for peaks with QLTs and those without. I will first look at this on peak level then mvoe to gene level. The peak scores come from the coverage in the peaks.</p>
<p>The NuclearAPA_peakdist data frame has the information I need for this.</p>
<pre class="r"><code>ggplot(NuclearAPA_peakdist, aes(x=PeakScore,fill=sig,by=sig)) + geom_density(alpha=.5)+ scale_x_log10() + labs(title="Peak score by significance") + scale_fill_brewer(palette="Paired")</code></pre>
<p><img src="figure/characterizeNuclearApaQtls.Rmd/unnamed-chunk-19-1.png" width="672" style="display: block; margin: auto;" /></p>
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<a href="https://github.com/brimittleman/threeprimeseq/blob/de860f0e5f84639460e9157c55f0024274bfb7d4/docs/figure/characterizeNuclearApaQtls.Rmd/unnamed-chunk-19-1.png" target="_blank">de860f0</a>
</td>
<td style="text-align:left;">
Briana Mittleman
</td>
<td style="text-align:left;">
2018-10-24
</td>
</tr>
</tbody>
</table>
<p></details></p>
<p>This is expected. It makes sense that we have more power to detect qtls in higher expressed peaks. This leads me to believe that filtering out low peaks may add power but will not mitigate the effect. ##Where are the SNPs</p>
<p>I created the significant SNP files in the <a href="characterizeTotalApaQtls.html">Characterize Total APAqtl analysis</a> analysis.</p>
<pre class="r"><code>chromHmm=read.table("../data/ChromHmmOverlap/chromHMM_regions.txt", col.names = c("number", "name"), stringsAsFactors = F)
NuclearOverlapHMM=read.table("../data/ChromHmmOverlap/Nuc_overlapHMM.bed", col.names=c("chrom", "start", "end", "sid", "significance", "strand", "number"))
NuclearOverlapHMM$number=as.integer(NuclearOverlapHMM$number)
NuclearOverlapHMM_names=NuclearOverlapHMM %>% left_join(chromHmm, by="number")</code></pre>
<pre class="r"><code>ggplot(NuclearOverlapHMM_names, aes(x=name)) + geom_bar() + labs(title="ChromHMM labels for Nuclear APAQtls" , y="Number of SNPs", x="Region")+theme(axis.text.x = element_text(angle = 90, hjust = 1))</code></pre>
<p><img src="figure/characterizeNuclearApaQtls.Rmd/unnamed-chunk-21-1.png" width="672" style="display: block; margin: auto;" /></p>
<details> <summary><em>Expand here to see past versions of unnamed-chunk-21-1.png:</em></summary>
<table style="border-collapse:separate; border-spacing:5px;">
<thead>
<tr>
<th style="text-align:left;">
Version
</th>
<th style="text-align:left;">
Author
</th>
<th style="text-align:left;">
Date
</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;">
<a href="https://github.com/brimittleman/threeprimeseq/blob/de860f0e5f84639460e9157c55f0024274bfb7d4/docs/figure/characterizeNuclearApaQtls.Rmd/unnamed-chunk-21-1.png" target="_blank">de860f0</a>
</td>
<td style="text-align:left;">
Briana Mittleman
</td>
<td style="text-align:left;">
2018-10-24
</td>
</tr>
</tbody>
</table>
<p></details></p>
</div>
</div>
<div id="session-information" class="section level2">
<h2>Session information</h2>
<pre class="r"><code>sessionInfo()</code></pre>
<pre><code>R version 3.5.1 (2018-07-02)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS 10.14.1
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] grid stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] bindrcpp_0.2.2 cowplot_0.9.3 ggpubr_0.1.8
[4] magrittr_1.5 data.table_1.11.8 VennDiagram_1.6.20
[7] futile.logger_1.4.3 forcats_0.3.0 stringr_1.3.1
[10] dplyr_0.7.6 purrr_0.2.5 readr_1.1.1
[13] tidyr_0.8.1 tibble_1.4.2 ggplot2_3.0.0
[16] tidyverse_1.2.1 reshape2_1.4.3 workflowr_1.1.1
loaded via a namespace (and not attached):
[1] tidyselect_0.2.4 haven_1.1.2 lattice_0.20-35
[4] colorspace_1.3-2 htmltools_0.3.6 yaml_2.2.0
[7] rlang_0.2.2 R.oo_1.22.0 pillar_1.3.0
[10] glue_1.3.0 withr_2.1.2 R.utils_2.7.0
[13] RColorBrewer_1.1-2 lambda.r_1.2.3 modelr_0.1.2
[16] readxl_1.1.0 bindr_0.1.1 plyr_1.8.4
[19] munsell_0.5.0 gtable_0.2.0 cellranger_1.1.0
[22] rvest_0.3.2 R.methodsS3_1.7.1 evaluate_0.11
[25] labeling_0.3 knitr_1.20 broom_0.5.0
[28] Rcpp_0.12.19 formatR_1.5 backports_1.1.2
[31] scales_1.0.0 jsonlite_1.5 hms_0.4.2
[34] digest_0.6.17 stringi_1.2.4 rprojroot_1.3-2
[37] cli_1.0.1 tools_3.5.1 lazyeval_0.2.1
[40] futile.options_1.0.1 crayon_1.3.4 whisker_0.3-2
[43] pkgconfig_2.0.2 xml2_1.2.0 lubridate_1.7.4
[46] assertthat_0.2.0 rmarkdown_1.10 httr_1.3.1
[49] rstudioapi_0.8 R6_2.3.0 nlme_3.1-137
[52] git2r_0.23.0 compiler_3.5.1 </code></pre>
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