Last updated: 2019-09-06

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Knit directory: apaQTL/analysis/

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Unstaged changes:
    Modified:   analysis/Readdistagainstfeatures.Rmd
    Modified:   analysis/overlapapaqtlsandeqtls.Rmd
    Modified:   analysis/version15bpfilter.Rmd
    Modified:   code/BothFracDTPlotGeneRegions.sh
    Modified:   code/Snakefile
    Modified:   code/SnakefilefiltPAS
    Modified:   code/apaQTLCorrectPvalMakeQQ.R
    Modified:   code/apaQTL_Nominal.sh
    Modified:   code/apaQTL_permuted.sh
    Modified:   code/apaQTLsnake.err
    Modified:   code/bam2bw.sh
    Modified:   code/bed2saf.py
    Modified:   code/cluster.json
    Modified:   code/clusterfiltPAS.json
    Modified:   code/config.yaml
    Modified:   code/environment.yaml
    Modified:   code/makePheno.py
    Modified:   code/mergeAllBam.sh
    Modified:   code/mergeByFracBam.sh
    Modified:   code/mergePeaks.sh
    Modified:   code/peakFC.sh
    Modified:   code/snakemake.batch
    Modified:   code/snakemakePAS.batch
    Modified:   code/snakemakefiltPAS.batch
    Modified:   code/submit-snakemake.sh
    Modified:   code/submit-snakemakePAS.sh
    Modified:   code/submit-snakemakefiltPAS.sh
    Deleted:    code/test.txt
    Modified:   data/MetaDataSequencing.txt
    Deleted:    reads_graphs.Rmd

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html 272b0b4 brimittleman 2019-07-08 Build site.
Rmd b1e6dd1 brimittleman 2019-07-08 update ptt analysis
html 429432a brimittleman 2019-07-02 Build site.
Rmd fe7b5dc brimittleman 2019-07-02 add eQTL overlap
html dad7bd8 brimittleman 2019-07-02 Build site.
Rmd fe41a93 brimittleman 2019-07-02 add prop of tested genes
html 2a63cde brimittleman 2019-07-01 Build site.
Rmd 8d36f9b brimittleman 2019-07-01 add res
html 5ba28ec brimittleman 2019-07-01 Build site.
Rmd 6db6003 brimittleman 2019-07-01 add qtl code
html a4a34bf brimittleman 2019-07-01 Build site.
Rmd 75b84f4 brimittleman 2019-07-01 add code premature term

library(reshape2)
library(workflowr)
This is workflowr version 1.4.0
Run ?workflowr for help getting started
library(tidyverse)
── Attaching packages ────────────────────────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
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── Conflicts ───────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()

Many papers have started to talk about premature termination. Premature terminated isoforms may be truncated protein or may be degraded. I am going to create a measure for this and test for genetic variation associated with it in my data. The measure will be sum of the reads in intronic PAS and the sum of the UTR reads. I will use leafcutter to put the ratios onto a normal distribution. I will then test for QTLs these ratios.

mkdir ../data/PreTerm_pheno

Prepare phenotype

Total

gene start and end

genes=read.table("/project2/gilad/briana/genome_anotation_data/RefSeq_annotations/FullTranscriptByName.bed", col.names = c("chr", "Gene_start", "Gene_end", "gene", "score", "strand"),stringsAsFactors = F) %>% select(chr,Gene_start, Gene_end, gene)
totalPAS=read.table("../data/phenotype_5perc/APApeak_Phenotype_GeneLocAnno.Total.5perc.fc.gz",stringsAsFactors = F,header = T) 


totalPASPheno=totalPAS %>% melt(id.vars="chrom", variable.name="Ind", value.name = "ratio") %>% separate(ratio, into=c("count", "geneCount"), sep="/") %>% separate(chrom, into=c("chr", "start", "end", "geneID"), sep=":") %>% separate(geneID, into=c("gene", "loc","strand", "PAS"), sep="_") %>% filter(loc=="utr3" | loc=="intron") %>% inner_join(genes,by=c("chr", "gene"))%>% mutate(gene=paste(chr,Gene_start, Gene_end, gene,sep=":")) %>% group_by(Ind,gene,loc) %>% summarise(SumCount=sum(as.integer(count))) %>% ungroup() %>% group_by(Ind,gene) %>% mutate(nType=n()) %>% filter(nType==2) %>% spread(loc, SumCount) %>% mutate(total=intron+utr3,PreTermInt=paste(intron,total, sep="/"),PreTermUTR=paste(utr3,total, sep="/")) %>% select(-nType, -intron,-utr3,-total)


totalPASPheno_melt= totalPASPheno %>% melt(id.vars=c("Ind", "gene"), variable.name="Type", value.name = "Value") %>% mutate(chrom=paste(gene, Type, sep="_")) %>% spread(Ind, Value) %>% select(-gene, -Type)


write.table(totalPASPheno_melt,"../data/PreTerm_pheno/Total_preterminationPheno.txt",quote=F, row.names=F,col.names=T, sep=" ")
#python2
gzip ../data/PreTerm_pheno/Total_preterminationPheno.txt
python prepare_phenotype_table.py ../data/PreTerm_pheno/Total_preterminationPheno.txt.gz

#activate env  

sh ../data/PreTerm_pheno/Total_preterminationPheno.txt.gz_prepare.sh

#top 2 pcs
head -n 3  ../data/PreTerm_pheno/Total_preterminationPheno.txt.gz.PCs > ../data/PreTerm_pheno/Total_preterminationPheno.txt.gz.2PCs 

Nuclear

nuclearPAS=read.table("../data/phenotype_5perc/APApeak_Phenotype_GeneLocAnno.Nuclear.5perc.fc.gz",stringsAsFactors = F,header = T) 


nuclearPASPheno=nuclearPAS %>% melt(id.vars="chrom", variable.name="Ind", value.name = "ratio") %>% separate(ratio, into=c("count", "geneCount"), sep="/") %>% separate(chrom, into=c("chr", "start", "end", "geneID"), sep=":") %>% separate(geneID, into=c("gene", "loc","strand", "PAS"), sep="_") %>% filter(loc=="utr3" | loc=="intron") %>% inner_join(genes,by=c("chr", "gene"))%>% mutate(gene=paste(chr,Gene_start, Gene_end, gene,sep=":")) %>% group_by(Ind,gene,loc) %>% summarise(SumCount=sum(as.integer(count))) %>% ungroup() %>% group_by(Ind,gene) %>% mutate(nType=n()) %>% filter(nType==2) %>% spread(loc, SumCount) %>% mutate(total=intron+utr3,PreTermInt=paste(intron,total, sep="/"),PreTermUTR=paste(utr3,total, sep="/")) %>% select(-nType, -intron,-utr3,-total)


nuclearPASPheno_melt= nuclearPASPheno %>% melt(id.vars=c("Ind", "gene"), variable.name="Type", value.name = "Value") %>% mutate(chrom=paste(gene, Type, sep="_")) %>% spread(Ind, Value) %>% select(-gene, -Type)


write.table(nuclearPASPheno_melt,"../data/PreTerm_pheno/Nuclear_preterminationPheno.txt",quote=F, row.names=F,col.names=T, sep=" ")
#python2
gzip ../data/PreTerm_pheno/Nuclear_preterminationPheno.txt
python prepare_phenotype_table.py ../data/PreTerm_pheno/Nuclear_preterminationPheno.txt.gz
#env
sh ../data/PreTerm_pheno/Nuclear_preterminationPheno.txt.gz_prepare.sh

#top 2 pcs
head -n 3  ../data/PreTerm_pheno/Nuclear_preterminationPheno.txt.gz.PCs > ../data/PreTerm_pheno/Nuclear_preterminationPheno.txt.gz.2PCs 

Call QTLs

Sample list from previous work

mkdir ../data/PrematureQTLNominal
mkdir ../data/PrematureQTLPermuted
sbatch PrematureQTLNominal.sh
sbatch PrematureQTLPermuted.sh

May want to only test one number per gene but do this for now because I want to take advantage of the leafcutter normalization software.

cat ../data/PrematureQTLPermuted/Total_preterminationPheno.txt.gz.qqnorm_chr* > ../data/PrematureQTLPermuted/Total_preterminationPheno.txt.gz.qqnorm_AllChr.txt

cat ../data/PrematureQTLPermuted/Nuclear_preterminationPheno.txt.gz.qqnorm_chr* > ../data/PrematureQTLPermuted/Nuclear_preterminationPheno.txt.gz.qqnorm_AllChr.txt

Tot

totRes=read.table("../data/PrematureQTLPermuted/Total_preterminationPheno.txt.gz.qqnorm_AllChr.txt", stringsAsFactors = F,col.names = c("pid", "nvar", "shape1", "shape2", "dummy", "sid", "dist", "npval", "slope", "ppval", "bpval"))

totRes$bh=p.adjust(totRes$bpval, method="fdr")

totRes_sig=totRes %>% filter(-log10(bh)>1) 


totRes_sig_genes=totRes_sig %>% separate(pid, into=c("chr","start","end", "geneID"), sep=":") %>% separate(geneID, into=c("gene", "Frac"),sep="_") %>% select(gene) %>% unique()

write.table(totRes, file = "../data/PrematureQTLPermuted/Total_preterminationPheno.txt.gz.qqnorm_AllChrBH.txt", col.names = T, row.names = F, quote = F)
nrow(totRes_sig_genes)
[1] 24

Proportion of genes tested:

tottested_genes=totRes %>% separate(pid, into=c("chr","start","end", "geneID"), sep=":") %>% separate(geneID, into=c("gene", "Frac"),sep="_") %>% select(gene) %>% unique()
nrow(totRes_sig_genes)/nrow(tottested_genes)
[1] 0.007418856

qqplot:

qqplot(-log10(runif(nrow(totRes))), -log10(totRes$bpval),ylab="-log10 Total permuted pvalue", xlab="Uniform expectation", main="Total premature termination")
abline(0,1)

Version Author Date
272b0b4 brimittleman 2019-07-08
dad7bd8 brimittleman 2019-07-02
ggplot(totRes, aes(x=dist)) + geom_histogram(bins=100)
Warning: Removed 332 rows containing non-finite values (stat_bin).

Version Author Date
272b0b4 brimittleman 2019-07-08

Nuclear:

nucRes=read.table("../data/PrematureQTLPermuted/Nuclear_preterminationPheno.txt.gz.qqnorm_AllChr.txt", stringsAsFactors = F,col.names = c("pid", "nvar", "shape1", "shape2", "dummy", "sid", "dist", "npval", "slope", "ppval", "bpval"))

nucRes$bh=p.adjust(nucRes$bpval, method="fdr")

nucRes_sig=nucRes %>% filter(-log10(bh)>1)


nucRes_sig_genes=nucRes_sig %>% separate(pid, into=c("chr","start","end", "geneID"), sep=":") %>% separate(geneID, into=c("gene", "Frac"),sep="_") %>% select(gene) %>% unique()


write.table(nucRes, file = "../data/PrematureQTLPermuted/Nuclear_preterminationPheno.txt.gz.qqnorm_AllChrBH.txt", col.names = T, row.names = F, quote = F)
nrow(nucRes_sig_genes)
[1] 69

Proportion of genes tested:

nuctested_genes=nucRes %>% separate(pid, into=c("chr","start","end", "geneID"), sep=":") %>% separate(geneID, into=c("gene", "Frac"),sep="_") %>% select(gene) %>% unique()
nrow(nucRes_sig_genes)/nrow(nuctested_genes)
[1] 0.01431535

qqplot:

qqplot(-log10(runif(nrow(nucRes))), -log10(nucRes$bpval),ylab="-log10 Nuclear permuted pvalue", xlab="Uniform expectation", main="Nuclear premature termination")
abline(0,1)

Version Author Date
272b0b4 brimittleman 2019-07-08

More likely in nuclear:

prop.test(x=c(nrow(nucRes_sig_genes),nrow(totRes_sig_genes)), n=c(nrow(nuctested_genes),nrow(tottested_genes)),alternative = "greater")

    2-sample test for equality of proportions with continuity
    correction

data:  c(nrow(nucRes_sig_genes), nrow(totRes_sig_genes)) out of c(nrow(nuctested_genes), nrow(tottested_genes))
X-squared = 7.4745, df = 1, p-value = 0.003129
alternative hypothesis: greater
95 percent confidence interval:
 0.002886 1.000000
sample estimates:
     prop 1      prop 2 
0.014315353 0.007418856 

overlap with eGenes

I next want to look at the proportion of eGenes.

explainedEgenes=read.table("../data/Li_eQTLs/explainedEgenes.txt", col.names = c("gene"), stringsAsFactors = F)
unexplainedEgenes=read.table("../data/Li_eQTLs/UnexplainedEgenes.txt", col.names = c("gene"), stringsAsFactors = F)


allEgenes=bind_rows(explainedEgenes, unexplainedEgenes)

I want to test the proportion of overlap.

TotPre_uneGene=totRes_sig_genes %>% inner_join(unexplainedEgenes,by="gene")
NucPre_uneGene=nucRes_sig_genes %>% inner_join(unexplainedEgenes,by="gene")

TotPre_exeGene=totRes_sig_genes %>% inner_join(explainedEgenes,by="gene")
NucPre_exeGene=nucRes_sig_genes %>% inner_join(explainedEgenes,by="gene")

TotPre_alleGene=totRes_sig_genes %>% inner_join(allEgenes,by="gene")
NucPre_alleGene=nucRes_sig_genes %>% inner_join(allEgenes,by="gene")

Proportion of eGenes explaiend by this:

#total

nrow(TotPre_uneGene)/nrow(unexplainedEgenes)
[1] 0.006578947
nrow(TotPre_exeGene)/nrow(explainedEgenes)
[1] 0.004699248
nrow(TotPre_alleGene)/nrow(allEgenes)
[1] 0.005482456
#nuclear

nrow(NucPre_uneGene)/nrow(unexplainedEgenes)
[1] 0.007894737
nrow(NucPre_exeGene)/nrow(explainedEgenes)
[1] 0.01315789
nrow(NucPre_alleGene)/nrow(allEgenes)
[1] 0.01096491
prop.test(x=c(nrow(NucPre_uneGene),nrow(TotPre_uneGene)), n=c(nrow(unexplainedEgenes),nrow(unexplainedEgenes)))

    2-sample test for equality of proportions with continuity
    correction

data:  c(nrow(NucPre_uneGene), nrow(TotPre_uneGene)) out of c(nrow(unexplainedEgenes), nrow(unexplainedEgenes))
X-squared = 0, df = 1, p-value = 1
alternative hypothesis: two.sided
95 percent confidence interval:
 -0.008521981  0.011153560
sample estimates:
     prop 1      prop 2 
0.007894737 0.006578947 
prop.test(x=c(nrow(NucPre_exeGene),nrow(TotPre_exeGene)), n=c(nrow(explainedEgenes),nrow(explainedEgenes)))

    2-sample test for equality of proportions with continuity
    correction

data:  c(nrow(NucPre_exeGene), nrow(TotPre_exeGene)) out of c(nrow(explainedEgenes), nrow(explainedEgenes))
X-squared = 3.3988, df = 1, p-value = 0.06525
alternative hypothesis: two.sided
95 percent confidence interval:
 -0.0004665971  0.0173838903
sample estimates:
     prop 1      prop 2 
0.013157895 0.004699248 

Conclusion:

Total- 13 overlaps with all eGenes, 7 ex, 6 unexplained Nuclear- 24 overlaps with all eGenes, 13 ex, 11 unexpained

All eGenes=1824 Unexplained=760 Explained=1064

Are the total in the nuclear:

totInuc=totRes_sig_genes %>% anti_join(nucRes_sig_genes,by="gene") 
nrow(totRes_sig_genes)-nrow(totInuc)
[1] 14
#did we test all of the 
totInucTESTEDnuc=totInuc %>% anti_join(nuctested_genes, by="gene") 
nrow(totInucTESTEDnuc)
[1] 2
totInucTESTEDnuc
    gene
1 IPO5P1
2 ZNF718
#all
totInuc %>% inner_join(allEgenes,by="gene")
     gene
1  MTHFSD
2  IPO5P1
3 ANKRD44
4   ERAP2
#explained
totInuc %>% inner_join(explainedEgenes,by="gene")
    gene
1 IPO5P1
2  ERAP2
#unexplained
totInuc %>% inner_join(unexplainedEgenes,by="gene")
     gene
1  MTHFSD
2 ANKRD44

Direction of effect size:

nucRes_sig_dir= nucRes_sig %>% separate(pid, into=c("chr","start","end", "geneID"), sep=":") %>% separate(geneID, into=c("gene", "Frac"),sep="_")
ggplot(nucRes_sig_dir, aes(x=Frac, y=slope)) + geom_boxplot() + geom_jitter(aes(col=Frac)) + labs(title="Nuclear premature APA QTL effect sizes", y="Effect size",x="") + scale_x_discrete(labels=c( "Intronic Usage","3' UTR Usage"))+ scale_color_discrete(name ="", labels=c( "Intronic Usage","3' UTR Usage")) 

Version Author Date
272b0b4 brimittleman 2019-07-08
totRes_sig_dir= totRes_sig %>% separate(pid, into=c("chr","start","end", "geneID"), sep=":") %>% separate(geneID, into=c("gene", "Frac"),sep="_")
ggplot(totRes_sig_dir, aes(x=Frac, y=slope)) + geom_boxplot() + geom_jitter(aes(col=Frac))+ labs(title="Total premature APA QTL effect sizes", y="Effect size",x="") + scale_x_discrete(labels=c( "Intronic Usage","3' UTR Usage"))+ scale_color_discrete(name ="", labels=c( "Intronic Usage","3' UTR Usage")) 

Version Author Date
272b0b4 brimittleman 2019-07-08

The difference may just be due to the numbers but most of the variants are associated with decreased utr usage and increase intronic usage.

Code that will plot the non normalized intronic ratio:

mkdir ../data/pttQTLplots
TotPretermPhen=read.table("../data/PreTerm_pheno/Total_preterminationPheno.txt.gz", header = T,stringsAsFactors = F) %>% separate(chrom, into=c("chr","start","end","geneID"), sep=":") %>% separate(geneID, into = c("gene", "loc"),sep="_") %>% filter(loc=="PreTermInt") %>% select(-start,-chr,-end,-loc)

TotPretermPhen_melt=melt(TotPretermPhen, id.vars = "gene", variable.name = "Individual") %>% separate(value, into=c("num", "den"),sep="/") %>% mutate(ratio=as.integer(num)/as.integer(den)) %>% select(-num,-den)

write.table(TotPretermPhen_melt,file="../data/pttQTLplots/TotalPhenotype.txt",col.names = T, row.names = F, quote=F, sep="\t")
totpttQTL=read.table("../data/PrematureQTLPermuted/Total_preterminationPheno.txt.gz.qqnorm_AllChrBH.txt", stringsAsFactors = F, header = T) %>% filter(-log10(bh)>1) %>% select(pid,sid )
head(totpttQTL)
                                         pid        sid
1 10:124690418:124713919:C10orf88_PreTermInt  rs7904973
2 10:124690418:124713919:C10orf88_PreTermUTR  rs7904973
3    14:74181824:74253961:ELMSAN1_PreTermUTR rs73297476
4     14:104095524:104167888:KLC1_PreTermInt  rs4906340
5     14:104095524:104167888:KLC1_PreTermUTR  rs4906340
6     16:10854775:10912651:TVP23A_PreTermInt  rs2233541
less ../../li_genotypes/genotypesYRI.gen.proc.5MAF.vcf.gz | head -n 1 > ../data/pttQTLplots/genoHead.txt


less ../../li_genotypes/genotypesYRI.gen.proc.5MAF.chr10.vcf.gz | grep rs7091776 > ../data/pttQTLplots/rs7091776.txt

Remove #

geno_head=read.table("../data/pttQTLplots/genoHead.txt", header =T,stringsAsFactors = F)

rs7091776=read.table("../data/pttQTLplots/rs7091776.txt", col.names =colnames(geno_head),stringsAsFactors = F)%>% select(ID,contains("NA"))

lettersGeno=read.table("../data/pttQTLplots/rs7091776.txt", col.names =colnames(geno_head), colClasses = c("character")) %>% select(REF,ALT)

refAllele=as.character(lettersGeno$REF)
altAllele=as.character(lettersGeno$ALT)


genoMelt=melt(rs7091776, id.vars = "ID", value.name = "FullGeno", variable.name = "Individual" ) %>% select(Individual, FullGeno) %>% mutate(genotype=ifelse(round(as.integer(FullGeno))==0, paste(refAllele, refAllele, sep=""), ifelse(round(as.integer(FullGeno))==1, paste(refAllele,altAllele, sep=""), paste(altAllele,altAllele,sep=""))))



TotPretermPhen_melt_C10orf88= TotPretermPhen_melt %>% filter(gene=="C10orf88") %>% inner_join(genoMelt, by="Individual") 
Warning: Column `Individual` joining factors with different levels,
coercing to character vector
ggplot(TotPretermPhen_melt_C10orf88,aes(x=genotype, y=ratio, fill=genotype)) +  geom_boxplot(width=.5)+ geom_jitter(alpha=1) + labs(y="Intronic PAS usage Ratio") + scale_fill_brewer(palette = "Dark2")

NucPretermPhen=read.table("../data/PreTerm_pheno/Nuclear_preterminationPheno.txt.gz", header = T,stringsAsFactors = F) %>% separate(chrom, into=c("chr","start","end","geneID"), sep=":") %>% separate(geneID, into = c("gene", "loc"),sep="_") %>% filter(loc=="PreTermInt") %>% select(-start,-chr,-end,-loc)

NucPretermPhen_melt=melt(NucPretermPhen, id.vars = "gene", variable.name = "Individual") %>% separate(value, into=c("num", "den"),sep="/") %>% mutate(ratio=as.integer(num)/as.integer(den)) %>% select(-num,-den)

write.table(NucPretermPhen_melt,file="../data/pttQTLplots/NuclearPhenotype.txt",col.names = T, row.names = F, quote=F, sep="\t")

Code to run this for any example:

sbatch run_pttfacetboxplot.sh Total C10orf88 10 rs7091776

Not necessary to rerun: not using

This works. I want to write a script that will make all of them.

python writePTTexamplecode.py Total
python writePTTexamplecode.py Nuclear

sbatch Script4TotalPTTqtlexamples.sh
sbatch Script4NuclearPTTqtlexamples.sh

sessionInfo()
R version 3.5.1 (2018-07-02)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Scientific Linux 7.4 (Nitrogen)

Matrix products: default
BLAS/LAPACK: /software/openblas-0.2.19-el7-x86_64/lib/libopenblas_haswellp-r0.2.19.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] forcats_0.3.0   stringr_1.3.1   dplyr_0.8.0.1   purrr_0.3.2    
 [5] readr_1.3.1     tidyr_0.8.3     tibble_2.1.1    ggplot2_3.1.1  
 [9] tidyverse_1.2.1 workflowr_1.4.0 reshape2_1.4.3 

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.0         RColorBrewer_1.1-2 cellranger_1.1.0  
 [4] compiler_3.5.1     pillar_1.3.1       git2r_0.25.2      
 [7] plyr_1.8.4         highr_0.7          tools_3.5.1       
[10] digest_0.6.18      lubridate_1.7.4    jsonlite_1.6      
[13] evaluate_0.12      nlme_3.1-137       gtable_0.2.0      
[16] lattice_0.20-38    pkgconfig_2.0.2    rlang_0.4.0       
[19] cli_1.1.0          rstudioapi_0.10    yaml_2.2.0        
[22] haven_1.1.2        withr_2.1.2        xml2_1.2.0        
[25] httr_1.3.1         knitr_1.20         hms_0.4.2         
[28] generics_0.0.2     fs_1.3.1           rprojroot_1.3-2   
[31] grid_3.5.1         tidyselect_0.2.5   glue_1.3.0        
[34] R6_2.3.0           readxl_1.1.0       rmarkdown_1.10    
[37] modelr_0.1.2       magrittr_1.5       whisker_0.3-2     
[40] backports_1.1.2    scales_1.0.0       htmltools_0.3.6   
[43] rvest_0.3.2        assertthat_0.2.0   colorspace_1.3-2  
[46] labeling_0.3       stringi_1.2.4      lazyeval_0.2.1    
[49] munsell_0.5.0      broom_0.5.1        crayon_1.3.4