Last updated: 2019-04-08
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Knit directory: threeprimeseq/analysis/
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Unstaged changes:
Modified: analysis/28ind.peak.explore.Rmd
Modified: analysis/CompareLianoglouData.Rmd
Modified: analysis/ExampleQTLPlot2.Rmd
Modified: analysis/HistoneModandPAS.Rmd
Modified: analysis/NewPeakPostMP.Rmd
Modified: analysis/NuclearSpecQTL.Rmd
Modified: analysis/PeakToXper.Rmd
Modified: analysis/RNAdecayAndAPA.Rmd
Modified: analysis/apaQTLoverlapGWAS.Rmd
Modified: analysis/characterize_apaQTLs.Rmd
Modified: analysis/cleanupdtseq.internalpriming.Rmd
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Modified: analysis/dif.iso.usage.leafcutter.Rmd
Modified: analysis/diffIsoAnalysisNewMapping.Rmd
Modified: analysis/diff_iso_pipeline.Rmd
Modified: analysis/explainpQTLs.Rmd
Modified: analysis/explore.filters.Rmd
Modified: analysis/fixBWChromNames.Rmd
Modified: analysis/flash2mash.Rmd
Modified: analysis/initialPacBioQuant.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: analysis/unexplainedeQTL_analysis.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
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File | Version | Author | Date | Message |
---|---|---|---|---|
Rmd | 4568d1f | Briana Mittleman | 2019-04-08 | add boxplot and genometrack |
html | 0ff28d4 | Briana Mittleman | 2019-04-03 | Build site. |
Rmd | a79791b | Briana Mittleman | 2019-04-03 | start tot nuc example plots |
library(tidyverse)
── Attaching packages ──────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
✔ ggplot2 3.1.0 ✔ purrr 0.3.1
✔ tibble 2.0.1 ✔ dplyr 0.8.0.1
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✔ readr 1.3.1 ✔ forcats 0.4.0
Warning: package 'tibble' was built under R version 3.5.2
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── Conflicts ─────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
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library(reshape2)
Attaching package: 'reshape2'
The following object is masked from 'package:tidyr':
smiths
I want to make an example heatmap for the total vs nuclear difference similar to the ones I did for the qtls.
I will take a similar approach where I make one then create a script to make it for all examples
chr21:43762910:43762982:TFF2
Count data is in:
/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov_processed_GeneLocAnno_bothFrac/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_sm_quant_processed_fixed.fc
grep TFF2 /project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov_processed_GeneLocAnno_bothFrac/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_sm_quant_processed_fixed.fc > /project2/gilad/briana/threeprimeseq/data/TNcompExamp/TNcomp_TFF2.txt
I will need to divide by the mapped read counts for the library:
metadata=read.table("../data/threePrimeSeqMetaData55Ind_noDup_WASPMAP.txt",header = T) %>% select(Sample_ID,Mapped_noMP )
metadata_melt=melt(metadata, id.vars = c("Sample_ID"), value.name = "MappedRead") %>% mutate(MappPM=MappedRead/10^6)
Header file:
TNhead=read.table("../data/TNcompExamp/TNCountheader.txt", header = T,stringsAsFactors = F)
read in data and melt it:
TN_TFF2=read.table("../data/TNcompExamp/TNcomp_TFF2.txt", col.names =colnames(TNhead),stringsAsFactors = F) %>% select(-Chr,-Geneid,-Strand, -Length)
TN_TFF2$Start=as.character(TN_TFF2$Start)
TN_TFF2$End=as.character(TN_TFF2$End)
TN_TFF2= TN_TFF2 %>% mutate(PeakLoc= paste(Start,End,sep="_")) %>% select(-Start, -End)
TN_TFF2_melt=melt(TN_TFF2, id.vars =c("PeakLoc"), variable.name = "ID", value.name = "PeakCount" ) %>% mutate(Sample_ID=substr(ID, 2, length(ID)))
Join:
TN_TFF2_withMeta=TN_TFF2_melt %>% inner_join(metadata_melt, by=c("Sample_ID")) %>% mutate(Fraction=ifelse(grepl("T",Sample_ID), "Total","Nuclear")) %>% mutate(NormCount=PeakCount/MappPM) %>% group_by(PeakLoc,Fraction) %>% summarise(meanCPM=mean(NormCount))
Warning: Column `Sample_ID` joining character vector and factor, coercing
into character vector
my_palette <- colorRampPalette(c("white", "khaki1", "orange", "red", "darkred", "black"))
ggplot(TN_TFF2_withMeta, aes(x=PeakLoc, y=Fraction)) + geom_tile(aes(fill = meanCPM))+ scale_fill_gradientn(colors =my_palette(100)) + theme(axis.text.x = element_text(angle = 90, hjust = 1)) + labs(x="PAS", title="TFF2")
Version | Author | Date |
---|---|---|
0ff28d4 | Briana Mittleman | 2019-04-03 |
Super low expression of this gene. Better example when it is
TvNHeatmap.R
library(tidyverse)
library(reshape2)
library(optparse)
library(cowplot)
option_list = list(
make_option(c("-P", "--pheno"), action="store", default=NA, type='character',
help="input pheno file"),
make_option(c("-g", "--gene"), action="store", default=NA, type='character',
help="gene"),
make_option(c("-o", "--output"), action="store", default=NA, type='character',
help="output file for plot")
)
opt_parser <- OptionParser(option_list=option_list)
opt <- parse_args(opt_parser)
metadata=read.table("/project2/gilad/briana/threeprimeseq/data/TNcompExamp/threePrimeSeqMetaData55Ind_noDup_WASPMAP.txt",header=T, stringsAsFactors = F) %>% select(Sample_ID,Mapped_noMP )
metadata_melt=melt(metadata, id.vars = c("Sample_ID"), value.name = "MappedRead") %>% mutate(MappPM=MappedRead/10^6)
TNhead=read.table("/project2/gilad/briana/threeprimeseq/data/TNcompExamp/TNCountheader.txt", header = T,stringsAsFactors = F)
TN=read.table(opt$pheno, col.names =colnames(TNhead),stringsAsFactors = F) %>% select(-Chr,-Geneid,-Strand, -Length)
TN$Start=as.character(TN$Start)
TN$End=as.character(TN$End)
TN= TN%>% mutate(PeakLoc= paste(Start,End,sep="-")) %>% select(-Start, -End)
TN_melt=melt(TN, id.vars =c("PeakLoc"), variable.name = "ID", value.name = "PeakCount" ) %>% mutate(Sample_ID=substr(ID, 2, length(ID)))
TN_withMeta=TN_melt %>% inner_join(metadata_melt, by=c("Sample_ID")) %>% mutate(Fraction=ifelse(grepl("T",Sample_ID), "Total","Nuclear")) %>% mutate(NormCount=PeakCount/MappPM) %>% group_by(PeakLoc,Fraction) %>% summarise(meanCPM=mean(NormCount))
my_palette <- colorRampPalette(c("white", "khaki1", "orange", "red", "darkred", "black"))
outplot=ggplot(TN_withMeta, aes(x=PeakLoc, y=Fraction)) + geom_tile(aes(fill = meanCPM))+ scale_fill_gradientn(colors =my_palette(100)) + theme(axis.text.x = element_text(angle = 90, hjust = 1)) + labs(x="PAS", title=opt$gene)
ggsave(plot=outplot, filename=opt$output, height=10, width=10)
Script to make phenotype file and run R script:
TvNMakeHeatmap.sh
#!/bin/bash
#SBATCH --job-name=TvNMakeHeatmap
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=TvNMakeHeatmap.out
#SBATCH --error=TvNMakeHeatmap.err
#SBATCH --partition=broadwl
#SBATCH --mem=18G
#SBATCH --mail-type=END
module load Anaconda3
source activate three-prime-env
gene=$1
grep ${gene} /project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov_processed_GeneLocAnno_bothFrac/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_sm_quant_processed_fixed.fc > /project2/gilad/briana/threeprimeseq/data/TNcompExamp/TNcomp_${gene}.txt
Rscript TvNHeatmap.R -P /project2/gilad/briana/threeprimeseq/data/TNcompExamp/TNcomp_${gene}.txt -g ${gene} -o /project2/gilad/briana/threeprimeseq/data/TNcompExamp/TNcomp_${gene}_heatmap.png
diffIso=read.table(file="../data/diff_iso_GeneLocAnno/SigPeaksHigherInNuc.txt", header = T) %>% arrange(deltapsi)
head(diffIso)
intron logef Nuclear Total
1 chr1:151134497:151134579:TNFAIP8L2 -1.531127 0.7805416 0.1426529
2 chr21:43762910:43762982:TFF2 -1.292723 0.7517177 0.1857824
3 chr3:23306502:23306675:UBE2E2 -1.576854 0.6895186 0.1527728
4 chr14:67029307:67029417:GPHN -1.178720 0.7952505 0.2688294
5 chr6:84007319:84007404:ME1 -1.941535 0.6378959 0.1158490
6 chr7:73885912:73885994:GTF2IRD1 -1.094156 0.8030045 0.3136450
deltapsi
1 -0.6378887
2 -0.5659353
3 -0.5367458
4 -0.5264211
5 -0.5220469
6 -0.4893595
sbatch TvNMakeHeatmap.sh GPHN
sbatch TvNMakeHeatmap.sh ME1
sbatch TvNMakeHeatmap.sh GTF2IRD1
sbatch TvNMakeHeatmap.sh DOCK9
sbatch TvNMakeHeatmap.sh UNQ6494
I want to make normalized BW to plot on tracks. This will be easier to show for this. I can use bamCoverage in deeptools and normalize to cpm.
normbam2BW.sh
#!/bin/bash
#SBATCH --job-name=normbam2BW
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=normbam2BW.out
#SBATCH --error=normbam2BW.err
#SBATCH --partition=broadwl
#SBATCH --mem=18G
#SBATCH --mail-type=END
module load Anaconda3
source activate three-prime-env
i=$1
bamCoverage -b /project2/gilad/briana/threeprimeseq/data/sort/YL-SP-$i-sort.bam -o /project2/gilad/briana/threeprimeseq/data/normalizedBW/YL-SP-$i-norm.bw \
--normalizeUsingRPKM
Run on all file is /project2/gilad/briana/threeprimeseq/data/sort/
(need to move these)
run_normbam2BW.sh
#!/bin/bash
#SBATCH --job-name=run_normbam2BW
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=run_normbam2BW.out
#SBATCH --error=run_normbam2BW.err
#SBATCH --partition=broadwl
#SBATCH --mem=10G
#SBATCH --mail-type=END
module load Anaconda3
source activate three-prime-env
for i in $(ls /project2/gilad/briana/threeprimeseq/data/sort/*.bam)
do
describer=$(echo ${i} | sed -e 's/.*YL-SP-//' | sed -e "s/-sort.bam//")
sbatch normbam2BW.sh $describer
done
Make a merged version.
/project2/gilad/briana/threeprimeseq/data/normalizedBW_merged
mergeNormBW.sh
#!/bin/bash
#SBATCH --job-name=mergeNormBW
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=mergeNormBW.out
#SBATCH --error=mergeNormBW.err
#SBATCH --partition=broadwl
#SBATCH --mem=10G
#SBATCH --mail-type=END
module load Anaconda3
source activate three-prime-env
ls -d -1 /project2/gilad/briana/threeprimeseq/data/normalizedBW/*T* | tail -n +2 > /project2/gilad/briana/threeprimeseq/data/list_bw/list_of_TotNorombigwig.txt
ls -d -1 /project2/gilad/briana/threeprimeseq/data/normalizedBW/*N* | tail -n +2 > /project2/gilad/briana/threeprimeseq/data/list_bw/list_of_NucNorombigwig.txt
bigWigMerge -inList /project2/gilad/briana/threeprimeseq/data/list_bw/list_of_TotNorombigwig.txt /project2/gilad/briana/threeprimeseq/data/normalizedBW_merged/Total_NormalizedMerged.bg
sort -k1,1 -k2,2n /project2/gilad/briana/threeprimeseq/data/normalizedBW_merged/Total_NormalizedMerged.bg > /project2/gilad/briana/threeprimeseq/data/normalizedBW_merged/Total_NormalizedMerged.sort.bg
bedGraphToBigWig /project2/gilad/briana/threeprimeseq/data/normalizedBW_merged/Total_NormalizedMerged.sort.bg /project2/gilad/briana/genome_anotation_data/chrom.length.txt /project2/gilad/briana/threeprimeseq/data/normalizedBW_merged/Total_NormalizedMerged.bw
bigWigMerge -inList /project2/gilad/briana/threeprimeseq/data/list_bw/list_of_NucNorombigwig.txt /project2/gilad/briana/threeprimeseq/data/normalizedBW_merged/Nuclear_NormalizedMerged.bg
sort -k1,1 -k2,2n /project2/gilad/briana/threeprimeseq/data/normalizedBW_merged/Nuclear_NormalizedMerged.bg > /project2/gilad/briana/threeprimeseq/data/normalizedBW_merged/Nuclear_NormalizedMerged.sort.bg
bedGraphToBigWig /project2/gilad/briana/threeprimeseq/data/normalizedBW_merged/Nuclear_NormalizedMerged.sort.bg /project2/gilad/briana/genome_anotation_data/chrom.length.txt /project2/gilad/briana/threeprimeseq/data/normalizedBW_merged/Nuclear_NormalizedMerged.bw
Use pygenome tracks:
makeTNcompINIfile.sh
#!/bin/bash
#SBATCH --job-name=makeTNcompINIfile
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=makeTNcompINIfile.out
#SBATCH --error=makeTNcompINIfile.err
#SBATCH --partition=broadwl
#SBATCH --mem=10G
#SBATCH --mail-type=END
module load Anaconda3
source activate three-prime-env
make_tracks_file --trackFiles /project2/gilad/briana/threeprimeseq/data/normalizedBW_merged/Nuclear_NormalizedMerged.bw /project2/gilad/briana/threeprimeseq/data/normalizedBW_merged/Total_NormalizedMerged.bw /project2/gilad/briana/threeprimeseq/data/normalizedBW/YL-SP-18499-N-combined-norm.bw /project2/gilad/briana/threeprimeseq/data/normalizedBW/YL-SP-18499-T-combined-norm.bw /project2/gilad/briana/threeprimeseq/data/normalizedBW/YL-SP-19128-N-combined-norm.bw /project2/gilad/briana/threeprimeseq/data/normalizedBW/YL-SP-19128-T-combined-norm.bw /project2/gilad/briana/threeprimeseq/data/normalizedBW/YL-SP-18516-N-combined-norm.bw /project2/gilad/briana/threeprimeseq/data/normalizedBW/YL-SP-18516-T-combined-norm.bw /project2/gilad/briana/threeprimeseq/data/normalizedBW/YL-SP-18912-N-combined-norm.bw /project2/gilad/briana/threeprimeseq/data/normalizedBW/YL-SP-18912-T-combined-norm.bw /project2/gilad/briana/genome_anotation_data/NCBI_refseq_forPyGenTrack_sort.bed -o /project2/gilad/briana/threeprimeseq/data/TNcompExamp/TotalvNuc_exmple.ini
#/project2/gilad/briana/threeprimeseq/data/peaks4DT/APAPeaks_5percCov_fixedStrand.bed
Can add
/project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript_noMP_GeneLocAnno/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno.NoMP_sm_quant.Total_fixed.pheno.5percPeaks.txt
/project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript_noMP_GeneLocAnno/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno.NoMP_sm_quant.Nuclear_fixed.pheno.5percPeaks.txt
Would need to convert to bed
try on a region:
DOCK9
get region from iGV for now
pyGenomeTracks --tracks TotalvNuc_exmple.ini --region 13:99,439,464-99,736,383 -out testDock9.png
pyGenomeTracks --tracks TotalvNuc_exmple.ini --region 14:66,968,548-67,658,985 -out testGPHN.png
pyGenomeTracks --tracks TotalvNuc_exmple.ini --region 21:43,766,162-43,772,074 -out testTFF2.png
pyGenomeTracks --tracks TotalvNuc_exmple.ini --region 9:92,252,698-92,336,674 -out testUNQ6495.png
chr14:66,968,548-67,658,985
TN_TFF2_withMetaBox=TN_TFF2_melt %>% inner_join(metadata_melt, by=c("Sample_ID")) %>% mutate(Fraction=ifelse(grepl("T",Sample_ID), "Total","Nuclear"))
Warning: Column `Sample_ID` joining character vector and factor, coercing
into character vector
ggplot(TN_TFF2_withMetaBox, aes(x=Fraction, y=MappPM, fill=Fraction)) + geom_boxplot(width=.45) + geom_jitter() + scale_fill_brewer(palette = "YlOrRd") + labs(y="CPM", title="TFF2")
R script:
TvNBoxplot.R
library(tidyverse)
library(reshape2)
library(optparse)
library(cowplot)
option_list = list(
make_option(c("-P", "--pheno"), action="store", default=NA, type='character',
help="input pheno file"),
make_option(c("-g", "--gene"), action="store", default=NA, type='character',
help="gene"),
make_option(c("-o", "--output"), action="store", default=NA, type='character',
help="output file for plot")
)
opt_parser <- OptionParser(option_list=option_list)
opt <- parse_args(opt_parser)
metadata=read.table("/project2/gilad/briana/threeprimeseq/data/TNcompExamp/threePrimeSeqMetaData55Ind_noDup_WASPMAP.txt",header=T, stringsAsFactors = F) %>% select(Sample_ID,Mapped_noMP )
metadata_melt=melt(metadata, id.vars = c("Sample_ID"), value.name = "MappedRead") %>% mutate(MappPM=MappedRead/10^6)
TNhead=read.table("/project2/gilad/briana/threeprimeseq/data/TNcompExamp/TNCountheader.txt", header = T,stringsAsFactors = F)
TN=read.table(opt$pheno, col.names =colnames(TNhead),stringsAsFactors = F) %>% select(-Chr,-Geneid,-Strand, -Length)
TN$Start=as.character(TN$Start)
TN$End=as.character(TN$End)
TN= TN%>% mutate(PeakLoc= paste(Start,End,sep="-")) %>% select(-Start, -End)
TN_melt=melt(TN, id.vars =c("PeakLoc"), variable.name = "ID", value.name = "PeakCount" ) %>% mutate(Sample_ID=substr(ID, 2, length(ID)))
TN_withMetaBox=TN_melt %>% inner_join(metadata_melt, by=c("Sample_ID")) %>% inner_join(metadata_melt, by=c("Sample_ID")) %>% mutate(Fraction=ifelse(grepl("T",Sample_ID), "Total","Nuclear"))
outplot=ggplot(TN_TFF2_withMetaBox, aes(x=Fraction, y=MappPM, fill=Fraction)) + geom_boxplot(width=.45) + geom_jitter() + scale_fill_brewer(palette = "YlOrRd") + labs(y="CPM", title=opt$gene)
ggsave(plot=outplot, filename=opt$output, height=10, width=10)
TvNMakeHeatmapandBoxplot.sh
#!/bin/bash
#SBATCH --job-name=TvNMakeHeatmapandBoxplot
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=TvNMakeHeatmapandBoxplot.out
#SBATCH --error=TvNMakeHeatmapandBoxplot.err
#SBATCH --partition=broadwl
#SBATCH --mem=18G
#SBATCH --mail-type=END
module load Anaconda3
source activate three-prime-env
gene=$1
grep ${gene} /project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov_processed_GeneLocAnno_bothFrac/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_sm_quant_processed_fixed.fc > /project2/gilad/briana/threeprimeseq/data/TNcompExamp/TNcomp_${gene}.txt
Rscript TvNHeatmap.R -P /project2/gilad/briana/threeprimeseq/data/TNcompExamp/TNcomp_${gene}.txt -g ${gene} -o /project2/gilad/briana/threeprimeseq/data/TNcompExamp/TNcomp_${gene}_heatmap.png
Rscript TvNBoxplot.R -P /project2/gilad/briana/threeprimeseq/data/TNcompExamp/TNcomp_${gene}.txt -g ${gene} -o /project2/gilad/briana/threeprimeseq/data/TNcompExamp/TNcomp_${gene}_boxplot.png
sbatch TvNMakeHeatmapandBoxplot.sh UNQ6494
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] reshape2_1.4.3 forcats_0.4.0 stringr_1.4.0 dplyr_0.8.0.1
[5] purrr_0.3.1 readr_1.3.1 tidyr_0.8.3 tibble_2.0.1
[9] ggplot2_3.1.0 tidyverse_1.2.1
loaded via a namespace (and not attached):
[1] Rcpp_1.0.0 RColorBrewer_1.1-2 cellranger_1.1.0
[4] plyr_1.8.4 pillar_1.3.1 compiler_3.5.1
[7] git2r_0.24.0 workflowr_1.2.0 tools_3.5.1
[10] digest_0.6.18 lubridate_1.7.4 jsonlite_1.6
[13] evaluate_0.13 nlme_3.1-137 gtable_0.2.0
[16] lattice_0.20-38 pkgconfig_2.0.2 rlang_0.3.1
[19] cli_1.0.1 rstudioapi_0.9.0 yaml_2.2.0
[22] haven_2.1.0 xfun_0.5 withr_2.1.2
[25] xml2_1.2.0 httr_1.4.0 knitr_1.21
[28] hms_0.4.2 generics_0.0.2 fs_1.2.6
[31] rprojroot_1.3-2 grid_3.5.1 tidyselect_0.2.5
[34] glue_1.3.0 R6_2.4.0 readxl_1.3.0
[37] rmarkdown_1.11 modelr_0.1.4 magrittr_1.5
[40] whisker_0.3-2 backports_1.1.3 scales_1.0.0
[43] htmltools_0.3.6 rvest_0.3.2 assertthat_0.2.0
[46] colorspace_1.4-0 labeling_0.3 stringi_1.3.1
[49] lazyeval_0.2.1 munsell_0.5.0 broom_0.5.1
[52] crayon_1.3.4