Last updated: 2018-08-13
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File | Version | Author | Date | Message |
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Rmd | e2ded68 | brimittleman | 2018-08-13 | refseq gene |
html | 2e06a70 | brimittleman | 2018-08-13 | Build site. |
Rmd | 3901f7e | brimittleman | 2018-08-13 | pre and post clean peaks per gene |
html | 38bfbaf | brimittleman | 2018-08-09 | Build site. |
Rmd | 03761cb | brimittleman | 2018-08-09 | start peak explore analysis for 54 libraries |
I know have 28 individuals sequences on 2 lanes. I have combined these and used the peak coverage pipeline to call and clean peaks. I will use this analysis to explore the library sizes and coverage at these peaks.
library(tidyverse)
── Attaching packages ────────────────────────────────── tidyverse 1.2.1 ──
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── Conflicts ───────────────────────────────────── tidyverse_conflicts() ──
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library(workflowr)
This is workflowr version 1.1.1
Run ?workflowr for help getting started
library(cowplot)
Attaching package: 'cowplot'
The following object is masked from 'package:ggplot2':
ggsave
library(reshape2)
Attaching package: 'reshape2'
The following object is masked from 'package:tidyr':
smiths
library(devtools)
map_stats=read.csv("../data/comb_map_stats.csv", header=T)
map_stats$line=as.factor(map_stats$line)
map_stats$batch=as.factor(map_stats$batch)
The number of reads for each library and the number of mapped reads.
read_plot=ggplot(map_stats, aes(x=line, y=comb_reads, fill=fraction))+ geom_bar(stat="identity", position="dodge") +labs(y="Reads", title="Reads by line and fraction")
map_plot=ggplot(map_stats, aes(x=line, y=comb_mapped, fill=fraction))+ geom_bar(stat="identity", position="dodge") +labs(y="Mapped Reads", title="Mapped reads by line and fraction") + geom_hline(yintercept=10000000) + annotate("text",label="10 million mapped reads", y=9000000, x=10)
plot_grid(read_plot, map_plot)
Version | Author | Date |
---|---|---|
38bfbaf | brimittleman | 2018-08-09 |
The percent of reads that map per line are pretty uniform accross libraries. The mean is 72%.
ggplot(map_stats, aes(x=line, y=comb_prop_mapped, fill=fraction))+ geom_bar(stat="identity", position="dodge") +labs(y="Mapped Percent", title="Percent of reads mapping by line and fraction")
Version | Author | Date |
---|---|---|
38bfbaf | brimittleman | 2018-08-09 |
mean(map_stats$comb_prop_mapped)
[1] 0.7230478
peak_quant=read.table(file = "../data/clean_peaks/APAquant.fc.cleanpeaks.fc", header=T)
Fix the names
file_names=colnames(peak_quant)[7:62]
file_names_split=lapply(file_names, function(x)strsplit(x,".", fixed=T))
libraries=c()
for (i in file_names_split){
unlist_i=unlist(i)
libraries=c(libraries, paste(unlist_i[10], unlist_i[11], sep="-"))
}
colnames(peak_quant)=c(colnames(peak_quant)[1:6], libraries)
Explore the peaks before quantifications:
#length of peaks
plot(sort(peak_quant$Length,decreasing = T), main="Peak Lengths", ylab="Peak Length", xlab="Peak index")
Version | Author | Date |
---|---|---|
38bfbaf | brimittleman | 2018-08-09 |
#mean cov of peaks
peak_cov=peak_quant %>% select(contains("-"))
peak_mean=apply(peak_cov,1,mean)
peak_var=apply(peak_cov, 1, var)
plot(log10(sort(peak_mean,decreasing = T)))
Version | Author | Date |
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38bfbaf | brimittleman | 2018-08-09 |
plot(peak_var)
Version | Author | Date |
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38bfbaf | brimittleman | 2018-08-09 |
plot(log10(peak_var)~log10(peak_mean))
Version | Author | Date |
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38bfbaf | brimittleman | 2018-08-09 |
Plot the coverage vs the length:
plot(peak_mean~peak_quant$Length)
Version | Author | Date |
---|---|---|
38bfbaf | brimittleman | 2018-08-09 |
CLustering:
pca_peak= prcomp(peak_cov,center = TRUE,scale. = TRUE)
summary(pca_peak)
Importance of components:
PC1 PC2 PC3 PC4 PC5 PC6 PC7
Standard deviation 6.5936 2.7291 1.66983 0.71519 0.6392 0.5651 0.52807
Proportion of Variance 0.7763 0.1330 0.04979 0.00913 0.0073 0.0057 0.00498
Cumulative Proportion 0.7763 0.9093 0.95915 0.96828 0.9756 0.9813 0.98626
PC8 PC9 PC10 PC11 PC12 PC13
Standard deviation 0.35808 0.29502 0.25231 0.23732 0.22343 0.19515
Proportion of Variance 0.00229 0.00155 0.00114 0.00101 0.00089 0.00068
Cumulative Proportion 0.98855 0.99010 0.99124 0.99224 0.99314 0.99382
PC14 PC15 PC16 PC17 PC18 PC19
Standard deviation 0.1832 0.17407 0.16032 0.14346 0.14122 0.13574
Proportion of Variance 0.0006 0.00054 0.00046 0.00037 0.00036 0.00033
Cumulative Proportion 0.9944 0.99496 0.99542 0.99578 0.99614 0.99647
PC20 PC21 PC22 PC23 PC24 PC25
Standard deviation 0.1300 0.12760 0.11966 0.11231 0.10982 0.10799
Proportion of Variance 0.0003 0.00029 0.00026 0.00023 0.00022 0.00021
Cumulative Proportion 0.9968 0.99706 0.99732 0.99754 0.99776 0.99796
PC26 PC27 PC28 PC29 PC30 PC31
Standard deviation 0.10167 0.09704 0.09243 0.08607 0.08012 0.07887
Proportion of Variance 0.00018 0.00017 0.00015 0.00013 0.00011 0.00011
Cumulative Proportion 0.99815 0.99832 0.99847 0.99860 0.99872 0.99883
PC32 PC33 PC34 PC35 PC36 PC37
Standard deviation 0.07689 0.07601 0.07153 0.06993 0.06656 0.06334
Proportion of Variance 0.00011 0.00010 0.00009 0.00009 0.00008 0.00007
Cumulative Proportion 0.99893 0.99904 0.99913 0.99922 0.99929 0.99937
PC38 PC39 PC40 PC41 PC42 PC43
Standard deviation 0.06152 0.05822 0.05643 0.05371 0.05236 0.04715
Proportion of Variance 0.00007 0.00006 0.00006 0.00005 0.00005 0.00004
Cumulative Proportion 0.99943 0.99949 0.99955 0.99960 0.99965 0.99969
PC44 PC45 PC46 PC47 PC48 PC49
Standard deviation 0.04656 0.04584 0.04252 0.04214 0.03873 0.03696
Proportion of Variance 0.00004 0.00004 0.00003 0.00003 0.00003 0.00002
Cumulative Proportion 0.99973 0.99977 0.99980 0.99983 0.99986 0.99988
PC50 PC51 PC52 PC53 PC54 PC55
Standard deviation 0.03557 0.03351 0.03191 0.03020 0.02938 0.02733
Proportion of Variance 0.00002 0.00002 0.00002 0.00002 0.00002 0.00001
Cumulative Proportion 0.99991 0.99993 0.99994 0.99996 0.99998 0.99999
PC56
Standard deviation 0.02525
Proportion of Variance 0.00001
Cumulative Proportion 1.00000
pc_df=as.data.frame(pca_peak$rotation) %>% rownames_to_column(var="lib") %>% mutate(fraction=ifelse(grepl("T", lib), "total", "nuclear"))
ggplot(pc_df, aes(x=PC1, y=PC2, col=fraction)) + geom_point() + labs(x="PC1: Prop explained 0.7763", y="PC2: Prop explained 0.1330", title="Raw PAS qunatification data")
Version | Author | Date |
---|---|---|
2e06a70 | brimittleman | 2018-08-13 |
38bfbaf | brimittleman | 2018-08-09 |
I now want to explore what the first PC is representing. Some ideas are:
batch
sequencing depth
mapped reads
All of this info is in the map stats.
pc_df=as.data.frame(pca_peak$rotation) %>% rownames_to_column(var="lib") %>% mutate(fraction=ifelse(grepl("T", lib), "total", "nuclear")) %>% mutate(reads=map_stats$comb_reads) %>% mutate(batch=map_stats$batch) %>% mutate(mapped=map_stats$comb_mapped)
batch_gg= ggplot(pc_df, aes(x=PC1, y=PC2, col=batch)) + geom_point() + labs(x="PC1: Prop explained 0.7763", y="PC2: Prop explained 0.1330", title="Batch")
frac_gg= ggplot(pc_df, aes(x=PC1, y=PC2, col=fraction)) + geom_point() + labs(x="PC1: Prop explained 0.7763", y="PC2: Prop explained 0.1330", title="Fraction")
reads_gg= ggplot(pc_df, aes(x=PC1, y=PC2, col=reads)) + geom_point() + labs(x="PC1: Prop explained 0.7763", y="PC2: Prop explained 0.1330", title="Reads")
mapped_gg= ggplot(pc_df, aes(x=PC1, y=PC2, col=mapped)) + geom_point() + labs(x="PC1: Prop explained 0.7763", y="PC2: Prop explained 0.1330", title="Mapped Reads")
plot_grid(frac_gg,batch_gg,reads_gg,mapped_gg)
Version | Author | Date |
---|---|---|
2e06a70 | brimittleman | 2018-08-13 |
Proportion of reads mapping to peaks. This may be in the feature counts summary.
map_summary=read.table("../data/clean_peaks/APAquant.fc.cleanpeaks.fc.summary", header=T)
colnames(map_summary)=c(colnames(map_summary)[1], libraries)
map_summary_m=map_summary[,2:57] %>% t
colnames(map_summary_m)=map_summary$Status
map_summary_m_df= as.data.frame(map_summary_m) %>% mutate(perc_feature=(Assigned/(Assigned+Unassigned_NoFeatures)))
pc_df_mapsum=as.data.frame(pca_peak$rotation) %>% rownames_to_column(var="lib") %>% mutate(fraction=ifelse(grepl("T", lib), "total", "nuclear")) %>% mutate(reads=map_stats$comb_reads) %>% mutate(batch=map_stats$batch) %>% mutate(mapped=map_stats$comb_mapped) %>% mutate(PercFeature=map_summary_m_df$perc_feature)
propfrac_gg= ggplot(pc_df_mapsum, aes(x=PC1, y=PC2, col=PercFeature)) + geom_point() + labs(x="PC1: Prop explained 0.7763", y="PC2: Prop explained 0.1330", title="Percent Reads mapping to a feature")
propfrac_gg
I want to use the bedtools closest command to find the clostest protein coding gene to each peak.
-s (require same strandedness)
-d add a column with distance to closest (strand spec) report distance wrt the genes
A is the peak
B is protein coding genes
#!/bin/bash
#SBATCH --job-name=mapgene2peak
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=mapgene2peak.out
#SBATCH --error=mapgene2peak.err
#SBATCH --partition=broadwl
#SBATCH --mem=12G
#SBATCH --mail-type=END
module load Anaconda3
source activate three-prime-env
bedtools closest -s -d -a /project2/gilad/briana/threeprimeseq/data/clean.peaks_comb/APApeaks_combined_clean_fixed.bed -b /project2/gilad/briana/genome_anotation_data/gencode.v19.annotation.proteincodinggene.bed > /project2/gilad/briana/threeprimeseq/data/clean.peaks_comb/APApeaks_comb_clean_dist2gene.txt
clean_peaks=read.table("../data/clean_peaks/APApeaks_combined_clean.bed", header=F)
This scirpt is called fix_cleanpeakbed.py
from misc_helper import *
fout = file("/project2/gilad/briana/threeprimeseq/data/clean.peaks_comb/APApeaks_combined_clean_fixed.bed",'w')
for ln in open("/project2/gilad/briana/threeprimeseq/data/clean.peaks_comb/APApeaks_combined_clean.bed"):
chrom, start, end, name, cov, strand, score2 = ln.split()
chrom_nochr= int(chrom[3:])
start_i = int(start)
end_i = int(end)
cov_i=float(cov)
fout.write("%d\t%d\t%d\t%s\t%f\t%s\n"%(chrom_nochr, start_i, end_i, name, cov_i, strand))
fout.close()
Second option is to use bedtools map. This will tell me directly how many peaks overlap each gene. I will use the -c and -o commands. I want it to tell me the number of peaks rather than the mean of the coverage. I can give it the name column as the -c and -0 count_distinct
#!/bin/bash
#SBATCH --job-name=mapgene2peak2
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=mapgene2peak2.out
#SBATCH --error=mapgene2peak2.err
#SBATCH --partition=broadwl
#SBATCH --mem=12G
#SBATCH --mail-type=END
module load Anaconda3
source activate three-prime-env
bedtools map -c 4 -o count_distinct -a /project2/gilad/briana/genome_anotation_data/gencode.v19.annotation.proteincodinggene.bed -b /project2/gilad/briana/threeprimeseq/data/clean.peaks_comb/APApeaks_combined_clean_fixed.bed > /project2/gilad/briana/threeprimeseq/data/clean.peaks_comb/APApeaks_combined_clean_countdistgenes.txt
First look at the count for peaks in genes. this will not give us information for peaks outside of the annotaiton.
genes_per_peak=read.table("../data/clean_peaks/APApeaks_combined_clean_countdistgenes.txt", header=F, stringsAsFactors = F, col.names = c("chr", "strart", "end", "gene", "score", "strand", "numPeaks"))
nrow(genes_per_peak)
[1] 20345
genes_with_peak= genes_per_peak %>% filter(numPeaks>0) %>% nrow()
genes_with_peak
[1] 12867
genes_with_2peak= genes_per_peak %>% filter(numPeaks>1) %>% nrow()
genes_with_2peak
[1] 10797
There are in total 20,345 genes in the protein coding gene set. There are 12,867 with at least 1 peak and 10,797 with at least 2 peaks.
Look at the distribution:
plot(sort(genes_per_peak$numPeaks, decreasing = T), ylab="Number of Peaks", main="Number of peaks mapping to each protein coding gene")
Version | Author | Date |
---|---|---|
2e06a70 | brimittleman | 2018-08-13 |
The next dataset has more information aboutthe distance to the gene. If it overlaps, the distance should be 0.
dist_to_peak=read.table("../data/clean_peaks/APApeaks_comb_clean_dist2gene.txt", header=F)
rnaseq_18486=read.table("../data/18486.genecov.txt", header=F,col.names = c("chr", "start", "end", "name", "score", "strand", "count"))
expressed_genes=rnaseq_18486 %>% filter(count>0) %>% nrow()
expressed_genes
[1] 16925
Get gene level coverage for 18486T 3’ seq data. I have this in /project2/gilad/briana/threeprimeseq/data/gene_cov.
threeprime_18486T=read.table("../data/YL-SP-18486-T-combined-genecov.txt", header=F, col.names = c("chr", "start", "end", "name", "score", "strand", "count"))
rnaseq_sm=rnaseq_18486 %>% select("name", "count")
threeprime_sm=threeprime_18486T %>% select("name", "count")
gene_cov=rnaseq_sm %>% left_join(threeprime_sm, by= "name")
names(gene_cov)= c("name", "rnaseq", "threeprime")
ggplot(gene_cov,aes(x=log10(rnaseq), y=log10(threeprime)))+ geom_point(na.rm=TRUE, size=.1) + geom_density2d(na.rm = TRUE, size = 1, colour = 'red') + labs(y='Log(three prime seq gene Count)', x='Log(RNA seq gene Count)', title="Correlation between three prime seq and rna seq read counts") + xlab('Log(RNA seq gene Count)') + geom_smooth(method="lm")
Warning: Removed 6778 rows containing non-finite values (stat_smooth).
Version | Author | Date |
---|---|---|
2e06a70 | brimittleman | 2018-08-13 |
Subset genes with coverage in RNA seq but not in three prime seq.
genecov_onlyRNA=gene_cov %>% filter(threeprime==0 & rnaseq !=0) %>% arrange(desc(rnaseq))
What I actually want to look at genes with no peaks but RNA reads.
colnames(rnaseq_sm)=c("gene", "RNAseqCount")
cov_rnavthreeprime= rnaseq_sm %>% left_join(genes_per_peak, by="gene") %>% select(gene, RNAseqCount, numPeaks)
Warning: Column `gene` joining factor and character vector, coercing into
character vector
ggplot(cov_rnavthreeprime,aes(x=log10(RNAseqCount), y=log10(numPeaks)))+ geom_point(na.rm=TRUE, size=.1) + geom_density2d(na.rm = TRUE, size = 1, colour = 'red') + geom_smooth(method="lm")
Warning: Removed 7575 rows containing non-finite values (stat_smooth).
Version | Author | Date |
---|---|---|
2e06a70 | brimittleman | 2018-08-13 |
Explore how many genes have coverage in the RNAseq set but not the
NotInThreePrime=cov_rnavthreeprime %>% filter(numPeaks==0 & RNAseqCount !=0) %>% arrange(desc(RNAseqCount))
par(mfrow=c(1,2))
plot(log10(sort(cov_rnavthreeprime$RNAseqCount, decreasing = T)))
plot(log10(NotInThreePrime$RNAseqCount), main="Post-clean up, coverage of RNA seq without a PAS", xlab="gene", ylab="log10 coverage RNA seq 18486")
Version | Author | Date |
---|---|---|
2e06a70 | brimittleman | 2018-08-13 |
Top RNA seq without peaks:
GAPDH - doesnt male sense. Visually see peak
EEF2
PFN1
PTMA
In looking at IGV the places have coverage and peaks are called in Yang protocol but they are all removed in the cleaning step. I want to look at how many genes have APA when we use the pre-cleaned list of peaks. This file is filtered_APApeaks_merged_allchrom.bed. I need to map these to genes like I did the other peaks.
This scirpt is called fix_dirtypeakbed.py
from misc_helper import *
fout = file("/project2/gilad/briana/threeprimeseq/data/mergedPeaks_comb/filtered_APApeaks_merged_allchrom.named.fixed.bed",'w')
for ln in open("/project2/gilad/briana/threeprimeseq/data/mergedPeaks_comb/filtered_APApeaks_merged_allchrom.named.bed"):
chrom, start, end, name, cov, strand, score2 = ln.split()
chrom_nochr= int(chrom[3:])
start_i = int(start)
end_i = int(end)
cov_i=float(cov)
fout.write("%d\t%d\t%d\t%s\t%f\t%s\n"%(chrom_nochr, start_i, end_i, name, cov_i, strand))
fout.close()
mapgene2peak_dirty.sh
#!/bin/bash
#SBATCH --job-name=mapgene2peak_dirty
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=mapgene2peak_dirty.out
#SBATCH --error=mapgene2peak_dirty.err
#SBATCH --partition=broadwl
#SBATCH --mem=12G
#SBATCH --mail-type=END
module load Anaconda3
source activate three-prime-env
bedtools closest -s -d -a /project2/gilad/briana/threeprimeseq/data/mergedPeaks_comb/filtered_APApeaks_merged_allchrom.named.fixed.bed -b /project2/gilad/briana/genome_anotation_data/gencode.v19.annotation.proteincodinggene.bed > /project2/gilad/briana/threeprimeseq/data/mergedPeaks_comb/filtered_APApeaks_merged_allchrom_dist2gene.txt
#!/bin/bash
#SBATCH --job-name=mapgene2peak2_dirty
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=mapgene2peak2_dirty.out
#SBATCH --error=mapgene2peak2_dirty.err
#SBATCH --partition=broadwl
#SBATCH --mem=12G
#SBATCH --mail-type=END
module load Anaconda3
source activate three-prime-env
bedtools map -c 4 -o count_distinct -a /project2/gilad/briana/genome_anotation_data/gencode.v19.annotation.proteincodinggene.bed -b /project2/gilad/briana/threeprimeseq/data/mergedPeaks_comb/filtered_APApeaks_merged_allchrom.named.fixed.bed > /project2/gilad/briana/threeprimeseq/data/mergedPeaks_comb/filtered_APApeaks_merged_allchrom_countdistgenes.txt
dirty_peak=read.table("../data/clean_peaks/filtered_APApeaks_merged_allchrom_countdistgenes.txt", col.names = c("chr", "start", "end", "gene", "score", "strand", "numPeaks"))
genes_with_peak_dirty= dirty_peak %>% filter(numPeaks>0) %>% nrow()
genes_with_peak_dirty
[1] 14678
genes_with_2peak_dirty= dirty_peak %>% filter(numPeaks>1) %>% nrow()
genes_with_2peak_dirty
[1] 12761
rnaseq_dirtypeaks=rnaseq_sm %>% left_join(dirty_peak, by="gene") %>% mutate(length=end-start) %>% select(gene, RNAseqCount, numPeaks, length, start, end, chr)
dirty_onlyinRNA= rnaseq_dirtypeaks %>% filter(numPeaks==0 & RNAseqCount !=0) %>% arrange(desc(RNAseqCount))
summary(rnaseq_dirtypeaks$length)
Min. 1st Qu. Median Mean 3rd Qu. Max.
59 8547 25654 65309 67508 2304638
summary(dirty_onlyinRNA$length)
Min. 1st Qu. Median Mean 3rd Qu. Max.
59 4500 16166 51003 53592 1117544
par(mfrow=c(1,2))
plot(log10(dirty_onlyinRNA$RNAseqCount), main="Pre-clean up, \n coverage of RNA seq without a PAS", xlab="gene", ylab="log10 coverage RNA seq 18486")
plot(log10(NotInThreePrime$RNAseqCount), main="Post-clean up, \n coverage of RNA seq without a PAS", xlab="gene", ylab="log10 coverage RNA seq 18486")
Version | Author | Date |
---|---|---|
2e06a70 | brimittleman | 2018-08-13 |
It looks like mapping to the refseq genes may be more effective here due to the better 3’ UTR annotatations
#!/bin/bash
#SBATCH --job-name=mapgene2peak2_dirty_refseq
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=mapgene2peak2_dirty_rs.out
#SBATCH --error=mapgene2peak2_dirty_rs.err
#SBATCH --partition=broadwl
#SBATCH --mem=12G
#SBATCH --mail-type=END
module load Anaconda3
source activate three-prime-env
bedtools map -c 4 -o count_distinct -a /project2/gilad/briana/genome_anotation_data/ncbiRefSeq_sm_noChr.sort.bed -b /project2/gilad/briana/threeprimeseq/data/mergedPeaks_comb/filtered_APApeaks_merged_allchrom.named.fixed.bed > /project2/gilad/briana/threeprimeseq/data/mergedPeaks_comb/filtered_APApeaks_merged_allchrom_refseq_countdistgenes.txt
Also do coverage for the RNA seq with this.
#!/bin/bash
#SBATCH --job-name=refseq-rnaseqcov18486
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=refseq-rnaseqcov18486.out
#SBATCH --error=refseq-rnaseqcov18486.err
#SBATCH --partition=broadwl
#SBATCH --mem=12G
#SBATCH --mail-type=END
module load Anaconda3
source activate three-prime-env
bedtools coverage -counts -sorted -a /project2/gilad/briana/genome_anotation_data/ncbiRefSeq_sm.sort.bed-b /project2/gilad/briana/threeprimeseq/data/rnaseq_bed/18486.bed > /project2/gilad/briana/threeprimeseq/data/rnaseq_cov/18486.refseq.gencov.txt
bedfile is currupt. i need to make one from teh original vcf.
sessionInfo()
R version 3.5.1 (2018-07-02)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Sierra 10.12.6
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 devtools_1.13.6 reshape2_1.4.3 cowplot_0.9.3
[5] workflowr_1.1.1 forcats_0.3.0 stringr_1.3.1 dplyr_0.7.6
[9] purrr_0.2.5 readr_1.1.1 tidyr_0.8.1 tibble_1.4.2
[13] ggplot2_3.0.0 tidyverse_1.2.1
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_2.1.19
[7] rlang_0.2.1 R.oo_1.22.0 pillar_1.3.0
[10] glue_1.3.0 withr_2.1.2 R.utils_2.6.0
[13] modelr_0.1.2 readxl_1.1.0 bindr_0.1.1
[16] plyr_1.8.4 munsell_0.5.0 gtable_0.2.0
[19] cellranger_1.1.0 rvest_0.3.2 R.methodsS3_1.7.1
[22] memoise_1.1.0 evaluate_0.11 labeling_0.3
[25] knitr_1.20 broom_0.5.0 Rcpp_0.12.18
[28] backports_1.1.2 scales_0.5.0 jsonlite_1.5
[31] hms_0.4.2 digest_0.6.15 stringi_1.2.4
[34] grid_3.5.1 rprojroot_1.3-2 cli_1.0.0
[37] tools_3.5.1 magrittr_1.5 lazyeval_0.2.1
[40] crayon_1.3.4 whisker_0.3-2 pkgconfig_2.0.1
[43] MASS_7.3-50 xml2_1.2.0 lubridate_1.7.4
[46] assertthat_0.2.0 rmarkdown_1.10 httr_1.3.1
[49] rstudioapi_0.7 R6_2.2.2 nlme_3.1-137
[52] git2r_0.23.0 compiler_3.5.1
This reproducible R Markdown analysis was created with workflowr 1.1.1