Last updated: 2019-02-15

Checks: 6 0

Knit directory: threeprimeseq/analysis/

This reproducible R Markdown analysis was created with workflowr (version 1.2.0). The Report tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

R Markdown file: up-to-date

Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.

Environment: empty

Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.

Seed: set.seed(12345)

The command set.seed(12345) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.

Session information: recorded

Great job! Recording the operating system, R version, and package versions is critical for reproducibility.

Cache: none

Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.

Repository version: 606e562

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.

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 wflow_publish or wflow_git_commit). 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: 


Ignored files:
    Ignored:    .DS_Store
    Ignored:    .Rhistory
    Ignored:    .Rproj.user/
    Ignored:    data/.DS_Store
    Ignored:    data/perm_QTL_trans_noMP_5percov/
    Ignored:    output/.DS_Store

Untracked files:
    Untracked:  KalistoAbundance18486.txt
    Untracked:  analysis/4suDataIGV.Rmd
    Untracked:  analysis/DirectionapaQTL.Rmd
    Untracked:  analysis/EvaleQTLs.Rmd
    Untracked:  analysis/YL_QTL_test.Rmd
    Untracked:  analysis/ncbiRefSeq_sm.sort.mRNA.bed
    Untracked:  analysis/snake.config.notes.Rmd
    Untracked:  analysis/verifyBAM.Rmd
    Untracked:  analysis/verifybam_dubs.Rmd
    Untracked:  code/PeaksToCoverPerReads.py
    Untracked:  code/strober_pc_pve_heatmap_func.R
    Untracked:  data/18486.genecov.txt
    Untracked:  data/APApeaksYL.total.inbrain.bed
    Untracked:  data/ApaQTLs/
    Untracked:  data/ChromHmmOverlap/
    Untracked:  data/DistTXN2Peak_genelocAnno/
    Untracked:  data/GM12878.chromHMM.bed
    Untracked:  data/GM12878.chromHMM.txt
    Untracked:  data/LianoglouLCL/
    Untracked:  data/LocusZoom/
    Untracked:  data/NuclearApaQTLs.txt
    Untracked:  data/PeakCounts/
    Untracked:  data/PeakCounts_noMP_5perc/
    Untracked:  data/PeakCounts_noMP_genelocanno/
    Untracked:  data/PeakUsage/
    Untracked:  data/PeakUsage_noMP/
    Untracked:  data/PeakUsage_noMP_GeneLocAnno/
    Untracked:  data/PeaksUsed/
    Untracked:  data/PeaksUsed_noMP_5percCov/
    Untracked:  data/RNAkalisto/
    Untracked:  data/RefSeq_annotations/
    Untracked:  data/TotalApaQTLs.txt
    Untracked:  data/Totalpeaks_filtered_clean.bed
    Untracked:  data/UnderstandPeaksQC/
    Untracked:  data/WASP_STAT/
    Untracked:  data/YL-SP-18486-T-combined-genecov.txt
    Untracked:  data/YL-SP-18486-T_S9_R1_001-genecov.txt
    Untracked:  data/YL_QTL_test/
    Untracked:  data/apaExamp/
    Untracked:  data/apaQTL_examp_noMP/
    Untracked:  data/bedgraph_peaks/
    Untracked:  data/bin200.5.T.nuccov.bed
    Untracked:  data/bin200.Anuccov.bed
    Untracked:  data/bin200.nuccov.bed
    Untracked:  data/clean_peaks/
    Untracked:  data/comb_map_stats.csv
    Untracked:  data/comb_map_stats.xlsx
    Untracked:  data/comb_map_stats_39ind.csv
    Untracked:  data/combined_reads_mapped_three_prime_seq.csv
    Untracked:  data/diff_iso_GeneLocAnno/
    Untracked:  data/diff_iso_proc/
    Untracked:  data/diff_iso_trans/
    Untracked:  data/ensemble_to_genename.txt
    Untracked:  data/example_gene_peakQuant/
    Untracked:  data/explainProtVar/
    Untracked:  data/filtPeakOppstrand_cov_noMP_GeneLocAnno_5perc/
    Untracked:  data/filtered_APApeaks_merged_allchrom_refseqTrans.closest2End.bed
    Untracked:  data/filtered_APApeaks_merged_allchrom_refseqTrans.closest2End.noties.bed
    Untracked:  data/first50lines_closest.txt
    Untracked:  data/gencov.test.csv
    Untracked:  data/gencov.test.txt
    Untracked:  data/gencov_zero.test.csv
    Untracked:  data/gencov_zero.test.txt
    Untracked:  data/gene_cov/
    Untracked:  data/joined
    Untracked:  data/leafcutter/
    Untracked:  data/merged_combined_YL-SP-threeprimeseq.bg
    Untracked:  data/molPheno_noMP/
    Untracked:  data/mol_overlap/
    Untracked:  data/mol_pheno/
    Untracked:  data/nom_QTL/
    Untracked:  data/nom_QTL_opp/
    Untracked:  data/nom_QTL_trans/
    Untracked:  data/nuc6up/
    Untracked:  data/nuc_10up/
    Untracked:  data/other_qtls/
    Untracked:  data/pQTL_otherphen/
    Untracked:  data/peakPerRefSeqGene/
    Untracked:  data/perm_QTL/
    Untracked:  data/perm_QTL_GeneLocAnno_noMP_5percov/
    Untracked:  data/perm_QTL_GeneLocAnno_noMP_5percov_3UTR/
    Untracked:  data/perm_QTL_opp/
    Untracked:  data/perm_QTL_trans/
    Untracked:  data/perm_QTL_trans_filt/
    Untracked:  data/protAndAPAAndExplmRes.Rda
    Untracked:  data/protAndAPAlmRes.Rda
    Untracked:  data/protAndExpressionlmRes.Rda
    Untracked:  data/reads_mapped_three_prime_seq.csv
    Untracked:  data/smash.cov.results.bed
    Untracked:  data/smash.cov.results.csv
    Untracked:  data/smash.cov.results.txt
    Untracked:  data/smash_testregion/
    Untracked:  data/ssFC200.cov.bed
    Untracked:  data/temp.file1
    Untracked:  data/temp.file2
    Untracked:  data/temp.gencov.test.txt
    Untracked:  data/temp.gencov_zero.test.txt
    Untracked:  data/threePrimeSeqMetaData.csv
    Untracked:  data/threePrimeSeqMetaData55Ind.txt
    Untracked:  data/threePrimeSeqMetaData55Ind.xlsx
    Untracked:  data/threePrimeSeqMetaData55Ind_noDup.txt
    Untracked:  data/threePrimeSeqMetaData55Ind_noDup.xlsx
    Untracked:  data/threePrimeSeqMetaData55Ind_noDup_WASPMAP.txt
    Untracked:  data/threePrimeSeqMetaData55Ind_noDup_WASPMAP.xlsx
    Untracked:  output/picard/
    Untracked:  output/plots/
    Untracked:  output/qual.fig2.pdf

Unstaged changes:
    Modified:   analysis/28ind.peak.explore.Rmd
    Modified:   analysis/CompareLianoglouData.Rmd
    Modified:   analysis/NewPeakPostMP.Rmd
    Modified:   analysis/accountMapBias.Rmd
    Modified:   analysis/apaQTLoverlapGWAS.Rmd
    Modified:   analysis/cleanupdtseq.internalpriming.Rmd
    Modified:   analysis/coloc_apaQTLs_protQTLs.Rmd
    Modified:   analysis/dif.iso.usage.leafcutter.Rmd
    Modified:   analysis/diff_iso_pipeline.Rmd
    Modified:   analysis/explainpQTLs.Rmd
    Modified:   analysis/explore.filters.Rmd
    Modified:   analysis/flash2mash.Rmd
    Modified:   analysis/mispriming_approach.Rmd
    Modified:   analysis/overlapMolQTL.Rmd
    Modified:   analysis/overlapMolQTL.opposite.Rmd
    Modified:   analysis/overlap_qtls.Rmd
    Modified:   analysis/peakOverlap_oppstrand.Rmd
    Modified:   analysis/peakQCPPlots.Rmd
    Modified:   analysis/pheno.leaf.comb.Rmd
    Modified:   analysis/pipeline_55Ind.Rmd
    Modified:   analysis/swarmPlots_QTLs.Rmd
    Modified:   analysis/test.max2.Rmd
    Modified:   analysis/test.smash.Rmd
    Modified:   analysis/understandPeaks.Rmd
    Modified:   code/Snakefile

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

These are the previous versions of the R Markdown and HTML files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view them.

File Version Author Date Message
html b08b424 Briana Mittleman 2019-02-14 Build site.
Rmd d09198c Briana Mittleman 2019-02-14 expression and gene anno
html 450b389 Briana Mittleman 2019-02-06 Build site.
Rmd 6bac9f9 Briana Mittleman 2019-02-06 add distance plots for QC on APAqtls

I will use this to look at some metrics around the the QTLs from the pipeline for all 55 individuals. In this analysis I found 363 qtls in the total fraction and 623 in the nuclear.

library(workflowr)
This is workflowr version 1.2.0
Run ?workflowr for help getting started
library(tidyverse)
── Attaching packages ───────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
✔ ggplot2 3.0.0     ✔ purrr   0.2.5
✔ tibble  1.4.2     ✔ dplyr   0.7.6
✔ tidyr   0.8.1     ✔ stringr 1.4.0
✔ readr   1.1.1     ✔ forcats 0.3.0
Warning: package 'stringr' was built under R version 3.5.2
── Conflicts ──────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
totQTLs=read.table("../data/perm_QTL_GeneLocAnno_noMP_5percov/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno.NoMP_sm_quant.Total.fixed.pheno_5perc_permResBH.txt", stringsAsFactors = F, header=T)%>% filter(-log10(bh)>=1)

write.table(totQTLs,"../data/ApaQTLs/TotalapaQTLs.GeneLocAnno.noMP.5perc.10FDR.txt", row.names = F, col.names = F, quote = F)

nucQTLs=read.table("../data/perm_QTL_GeneLocAnno_noMP_5percov/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno.NoMP_sm_quant.Nuclear.fixed.pheno_5perc_permResBH.txt", stringsAsFactors = F, header=T)%>% filter(-log10(bh)>=1)

write.table(nucQTLs,"../data/ApaQTLs/NuclearapaQTLs.GeneLocAnno.noMP.5perc.10FDR.txt", row.names = F, col.names = F, quote = F)

Distance to end of PAS

I want to look at the distance between the QTL snp and the end of a peak. For a positive strand gene this is the end of the peak, for a - strand gene this is the start position of the peak. The peak strand here is opposite of the strand the gene is on.

I will make a python script that will take make the distance file for both the total and nucelar.

I copied these files to /project2/gilad/briana/threeprimeseq/data/ApaQTLs. I will put the QC files here as well.

getDistPeakEnd2QTL.py

#usage getDistPeakEnd2QTL.py "Total" or getDistPeakEnd2QTL.py "Nuclear"


def main(inFile, outFile):
   iFile=open(inFile, "r")
   oFile=open(outFile, "w")  
   oFile.write("PeakID\tPeakEnd\tGene\tGeneStrand\tSNP_chr\tSNP_loc\tEffectSize\tBH\tDistance\n")
   for ln in iFile:
      pid= ln.split()[0]
      peakStrand=pid.split(":")[3].split("_")[1]
      if peakStrand=="+":
          strand = "-"
          end = int(pid.split(":")[1])
      else: 
          strand = "+"
          end = int(pid.split(":")[2])
      gene=pid.split(":")[3].split("_")[0]
      peak=pid.split(":")[3].split("_")[2]
      SNP_Chr=ln.split()[5].split(":")[0]
      SNP_loc=int(ln.split()[5].split(":")[1])
      effectSize=ln.split()[8]
      BH=ln.split()[11]
      Dist= end - SNP_loc
      oFile.write("%s\t%d\t%s\t%s\t%s\t%d\t%s\t%s\t%d\n"%(peak, end, gene, strand, SNP_Chr, SNP_loc, effectSize, BH, Dist))
   oFile.close()  
   
   
if __name__ == "__main__":
    import sys
    fraction = sys.argv[1]
    inFile = "/project2/gilad/briana/threeprimeseq/data/ApaQTLs/%sapaQTLs.GeneLocAnno.noMP.5perc.10FDR.txt"%(fraction)
    outFile = "/project2/gilad/briana/threeprimeseq/data/ApaQTLs/Distance2EndPeak.%s.apaQTLs.txt"%(fraction)
    main(inFile, outFile)

Plot for total:

TotDist=read.table("../data/ApaQTLs/Distance2EndPeak.Total.apaQTLs.txt", header=T) %>% mutate(Fraction="Total") %>% select(Fraction, Distance)
NucDist=read.table("../data/ApaQTLs/Distance2EndPeak.Nuclear.apaQTLs.txt", header=T)%>% mutate(Fraction="Nuclear") %>% select(Fraction, Distance)

BothDist=data.frame(rbind(TotDist, NucDist))
ggplot(BothDist, aes(x=Distance, by=Fraction, fill=Fraction))+geom_histogram(bins=70, alpha=.5) + scale_fill_manual(values=c("deepskyblue3","darkviolet")) + labs(title="Distance From apaQTL to End of Peak" )

Version Author Date
450b389 Briana Mittleman 2019-02-06

Where are the SNP

I want to take all of the SNP locations see what region of the genome they are in. I can use the annotation in /project2/gilad/briana/genome_anotation_data/RefSeq_annotations/ncbiRefSeq_FormatedallAnnotation.sort.bed. I can do this with bedtools intersect if I make a bedfile for the QTLs.

Goal file: chr, loc -1, loc, peak:QTLgene, BH, geneStrand

I can get all of this information most easily from the distance file I made.

QTLfile2Bed.py

#usage QTLfile2Bed.py "Total" or QTLfile2Bed.py "Nuclear"


def main(inFile, outFile):
   iFile=open(inFile, "r")
   oFile=open(outFile, "w")  
   for num, ln in enumerate(iFile):
       if num > 0:
           peakID, peakend, gene, strand, chr, loc, effect, bh, dist = ln.split()
           start=int(loc) -1
           end= int(loc) 
           name= peakID + ":" + gene
           oFile.write("%s\t%d\t%d\t%s\t%s\t%s\n"%(chr, start, end, name, bh, strand))
   oFile.close()



if __name__ == "__main__":
    import sys
    fraction = sys.argv[1]
    inFile = "/project2/gilad/briana/threeprimeseq/data/ApaQTLs/Distance2EndPeak.%s.apaQTLs.txt"%(fraction)
    outFile = "/project2/gilad/briana/threeprimeseq/data/ApaQTLs/%s.apaQTLs.bed"%(fraction)
    main(inFile, outFile)

I will need to sort the output

sort -k1,1 -k2,2n /project2/gilad/briana/threeprimeseq/data/ApaQTLs/Total.apaQTLs.bed > /project2/gilad/briana/threeprimeseq/data/ApaQTLs/Total.apaQTLs.sort.bed

sort -k1,1 -k2,2n /project2/gilad/briana/threeprimeseq/data/ApaQTLs/Nuclear.apaQTLs.bed > /project2/gilad/briana/threeprimeseq/data/ApaQTLs/Nuclear.apaQTLs.sort.bed

Look at which regions these map to.

mapQTLs2GenomeLoc.sh

#!/bin/bash

#SBATCH --job-name=mapQTLs2GenomeLoc
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=mapQTLs2GenomeLoc.out
#SBATCH --error=mapQTLs2GenomeLoc.err
#SBATCH --partition=broadwl
#SBATCH --mem=12G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env

#annotation: /project2/gilad/briana/genome_anotation_data/RefSeq_annotations/ncbiRefSeq_FormatedallAnnotation.sort.bed

#QTL nucelar: /project2/gilad/briana/threeprimeseq/data/ApaQTLs/Nuclear.apaQTLs.sort.bed

#QTL total: /project2/gilad/briana/threeprimeseq/data/ApaQTLs/Total.apaQTLs.sort.bed


bedtools map -a /project2/gilad/briana/threeprimeseq/data/ApaQTLs/Nuclear.apaQTLs.sort.bed -b /project2/gilad/briana/genome_anotation_data/RefSeq_annotations/ncbiRefSeq_FormatedallAnnotation.sort.bed -c 4 -S -o distinct > /project2/gilad/briana/threeprimeseq/data/ApaQTLs/Nuclear.apaQTLs.sort_GeneAnno.bed


bedtools map -a /project2/gilad/briana/threeprimeseq/data/ApaQTLs/Total.apaQTLs.sort.bed -b /project2/gilad/briana/genome_anotation_data/RefSeq_annotations/ncbiRefSeq_FormatedallAnnotation.sort.bed -c 4 -S -o distinct > /project2/gilad/briana/threeprimeseq/data/ApaQTLs/Total.apaQTLs.sort_GeneAnno.bed

Most of the QTLs are not in any region.

QTLs by coverage

TotalCounts_AllInd=read.table("../data/filtPeakOppstrand_cov_noMP_GeneLocAnno_5perc/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Total.fixed.5perc.fc", header=T, stringsAsFactors = F) %>% separate(Geneid, into =c('peak', 'chr', 'start', 'end', 'strand', 'gene'), sep = ":") %>% select(-peak, -chr, -start, -end, -strand, -Chr, -Start, -End, -Strand, -Length, -gene) %>% rowMeans()

TotalCounts_AllInd_genes=read.table("../data/filtPeakOppstrand_cov_noMP_GeneLocAnno_5perc/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Total.fixed.5perc.fc", header=T, stringsAsFactors = F) %>% separate(Geneid, into =c('peak', 'chr', 'start', 'end', 'strand', 'gene'), sep = ":") %>% select(gene)

 
TotalCounts_AllInd_mean=data.frame(cbind(Gene=TotalCounts_AllInd_genes,Exp=TotalCounts_AllInd)) %>% filter(Exp>0)
#remove 0  



TotalCounts_no0_perc= TotalCounts_AllInd_mean %>% mutate(Percentile = percent_rank(Exp)) 

#Seperate by percentile
TotalCounts_no0_perc10= TotalCounts_no0_perc %>% filter(Percentile<.1) 

TotalCounts_no0_perc20= TotalCounts_no0_perc %>% filter(Percentile<.2& Percentile>.1)

TotalCounts_no0_perc30= TotalCounts_no0_perc %>% filter(Percentile<.3& Percentile>.2)

TotalCounts_no0_perc40= TotalCounts_no0_perc %>% filter(Percentile<.4& Percentile>.3)

TotalCounts_no0_perc50= TotalCounts_no0_perc %>% filter(Percentile<.5& Percentile>.4)
TotalCounts_no0_perc60= TotalCounts_no0_perc %>% filter(Percentile<.6& Percentile>.5)

TotalCounts_no0_perc70= TotalCounts_no0_perc %>% filter(Percentile<.7& Percentile>.6)

TotalCounts_no0_perc80= TotalCounts_no0_perc %>% filter(Percentile <.8 & Percentile>.7)
TotalCounts_no0_perc90= TotalCounts_no0_perc %>% filter(Percentile<.9 & Percentile>.8)

TotalCounts_no0_perc100= TotalCounts_no0_perc %>% filter( Percentile>.9)

Now I need to figure out the number of QTL gene in each percentile.

nucQTLGenes=nucQTLs %>% separate(pid, into=c('chr', 'start', 'end', 'id'), sep=":") %>% separate(id, sep="_", into=c("gene", 'strand', 'peak')) %>% select(gene) %>% unique()

totQTLGenes=totQTLs %>% separate(pid, into=c('chr', 'start', 'end', 'id'), sep=":") %>% separate(id, sep="_", into=c("gene", 'strand', 'peak')) %>% select(gene) %>% unique()

Per percent- use

totGene10= totQTLGenes %>% semi_join(TotalCounts_no0_perc10,by="gene") %>%  nrow()
totGene20= totQTLGenes %>% semi_join(TotalCounts_no0_perc20,by="gene")%>%  nrow()
totGene30= totQTLGenes %>% semi_join(TotalCounts_no0_perc30,by="gene")%>%  nrow()
totGene40= totQTLGenes %>% semi_join(TotalCounts_no0_perc40,by="gene")%>% nrow()
totGene50= totQTLGenes %>% semi_join(TotalCounts_no0_perc50,by="gene")%>% nrow()
totGene60= totQTLGenes %>% semi_join(TotalCounts_no0_perc60,by="gene")%>% nrow()
totGene70= totQTLGenes %>% semi_join(TotalCounts_no0_perc70,by="gene")%>%  nrow()
totGene80= totQTLGenes %>% semi_join(TotalCounts_no0_perc80,by="gene")%>%  nrow()
totGene90= totQTLGenes %>% semi_join(TotalCounts_no0_perc90,by="gene")%>%  nrow()
totGene100= totQTLGenes %>% semi_join(TotalCounts_no0_perc100,by="gene")%>%  nrow()


totGene_allPerc=c(totGene10,totGene20,totGene30,totGene40,totGene50,totGene60,totGene70,totGene80, totGene90,totGene100)

plot this:

plot(totGene_allPerc)

Version Author Date
b08b424 Briana Mittleman 2019-02-14
Why are more genes included


sessionInfo()
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] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] bindrcpp_0.2.2  forcats_0.3.0   stringr_1.4.0   dplyr_0.7.6    
 [5] purrr_0.2.5     readr_1.1.1     tidyr_0.8.1     tibble_1.4.2   
 [9] ggplot2_3.0.0   tidyverse_1.2.1 workflowr_1.2.0

loaded via a namespace (and not attached):
 [1] Rcpp_0.12.19     cellranger_1.1.0 plyr_1.8.4       compiler_3.5.1  
 [5] pillar_1.3.0     git2r_0.24.0     bindr_0.1.1      tools_3.5.1     
 [9] digest_0.6.17    lubridate_1.7.4  jsonlite_1.6     evaluate_0.13   
[13] nlme_3.1-137     gtable_0.2.0     lattice_0.20-35  pkgconfig_2.0.2 
[17] rlang_0.2.2      cli_1.0.1        rstudioapi_0.9.0 yaml_2.2.0      
[21] haven_1.1.2      withr_2.1.2      xml2_1.2.0       httr_1.3.1      
[25] knitr_1.20       hms_0.4.2        fs_1.2.6         rprojroot_1.3-2 
[29] grid_3.5.1       tidyselect_0.2.4 glue_1.3.0       R6_2.3.0        
[33] readxl_1.1.0     rmarkdown_1.11   modelr_0.1.2     magrittr_1.5    
[37] whisker_0.3-2    backports_1.1.2  scales_1.0.0     htmltools_0.3.6 
[41] rvest_0.3.2      assertthat_0.2.0 colorspace_1.3-2 labeling_0.3    
[45] stringi_1.2.4    lazyeval_0.2.1   munsell_0.5.0    broom_0.5.0     
[49] crayon_1.3.4