Last updated: 2019-10-15

Checks: 7 0

Knit directory: Comparative_APA/analysis/

This reproducible R Markdown analysis was created with workflowr (version 1.4.0). The Checks 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(20190902)

The command set.seed(20190902) 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.

File paths: relative

Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.

Repository version: 25a8b1e

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:    code/chimp_log/
    Ignored:    code/human_log/
    Ignored:    data/metadata_HCpanel.txt.sb-a3d92a2d-b9cYoF/
    Ignored:    data/metadata_HCpanel.txt.sb-f4823d1e-qihGek/

Untracked files:
    Untracked:  ._.DS_Store
    Untracked:  Chimp/
    Untracked:  Human/
    Untracked:  analysis/assessReadQual.Rmd
    Untracked:  code/._Config_chimp.yaml
    Untracked:  code/._Config_human.yaml
    Untracked:  code/._LiftOrthoPAS2chimp.sh
    Untracked:  code/._Snakefile
    Untracked:  code/._SnakefilePAS
    Untracked:  code/._SnakefilePASfilt
    Untracked:  code/._bed215upbed.py
    Untracked:  code/._bed2SAF_gen.py
    Untracked:  code/._buildStarIndex.sh
    Untracked:  code/._cleanbed2saf.py
    Untracked:  code/._cluster.json
    Untracked:  code/._extraSnakefiltpas
    Untracked:  code/._filter5percPAS.py
    Untracked:  code/._filterPASforMP.py
    Untracked:  code/._filterPostLift.py
    Untracked:  code/._fixUTRexonanno.py
    Untracked:  code/._formathg38Anno.py
    Untracked:  code/._formatpantro6Anno.py
    Untracked:  code/._intersectLiftedPAS.sh
    Untracked:  code/._liftPAS19to38.sh
    Untracked:  code/._makeSamplyGroupsHuman_TvN.py
    Untracked:  code/._maphg19.sh
    Untracked:  code/._maphg19_subjunc.sh
    Untracked:  code/._overlapapaQTLPAS.sh
    Untracked:  code/._prepareCleanLiftedFC_5perc4LC.py
    Untracked:  code/._preparePAS4lift.py
    Untracked:  code/._primaryLift.sh
    Untracked:  code/._recLiftchim2human.sh
    Untracked:  code/._revLiftPAShg38to19.sh
    Untracked:  code/._reverseLift.sh
    Untracked:  code/._runChimpDiffIso.sh
    Untracked:  code/._runHumanDiffIso.sh
    Untracked:  code/._runNuclearDifffIso.sh
    Untracked:  code/._run_verifyBam.sh
    Untracked:  code/._snakemake.batch
    Untracked:  code/._snakemakePAS.batch
    Untracked:  code/._snakemakePASchimp.batch
    Untracked:  code/._snakemakePAShuman.batch
    Untracked:  code/._snakemake_chimp.batch
    Untracked:  code/._snakemake_human.batch
    Untracked:  code/._snakemakefiltPAS.batch
    Untracked:  code/._snakemakefiltPAS_chimp
    Untracked:  code/._snakemakefiltPAS_chimp.sh
    Untracked:  code/._snakemakefiltPAS_human.sh
    Untracked:  code/._submit-snakemake-chimp.sh
    Untracked:  code/._submit-snakemake-human.sh
    Untracked:  code/._submit-snakemakePAS-chimp.sh
    Untracked:  code/._submit-snakemakePAS-human.sh
    Untracked:  code/._submit-snakemakefiltPAS-chimp.sh
    Untracked:  code/._submit-snakemakefiltPAS-human.sh
    Untracked:  code/._subset_diffisopheno_Nuclear_HvC.py
    Untracked:  code/._verifyBam4973.sh
    Untracked:  code/._wrap_verifyBam.sh
    Untracked:  code/.snakemake/
    Untracked:  code/Config_chimp.yaml
    Untracked:  code/Config_human.yaml
    Untracked:  code/LiftOrthoPAS2chimp.sh
    Untracked:  code/LiftorthoPAS.err
    Untracked:  code/LiftorthoPASt.out
    Untracked:  code/Log.out
    Untracked:  code/Rev_liftoverPAShg19to38.err
    Untracked:  code/Rev_liftoverPAShg19to38.out
    Untracked:  code/SAF215upbed_gen.py
    Untracked:  code/Snakefile
    Untracked:  code/SnakefilePAS
    Untracked:  code/SnakefilePASfilt
    Untracked:  code/Upstream10Bases_general.py
    Untracked:  code/apaQTLsnake.err
    Untracked:  code/apaQTLsnake.out
    Untracked:  code/apaQTLsnakePAS.err
    Untracked:  code/apaQTLsnakePAS.out
    Untracked:  code/apaQTLsnakePAShuman.err
    Untracked:  code/bed215upbed.py
    Untracked:  code/bed2SAF_gen.py
    Untracked:  code/bed2saf.py
    Untracked:  code/bg_to_cov.py
    Untracked:  code/buildStarIndex.sh
    Untracked:  code/callPeaksYL.py
    Untracked:  code/chooseAnno2Bed.py
    Untracked:  code/chooseAnno2SAF.py
    Untracked:  code/cleanbed2saf.py
    Untracked:  code/cluster.json
    Untracked:  code/clusterPAS.json
    Untracked:  code/clusterfiltPAS.json
    Untracked:  code/convertNumeric.py
    Untracked:  code/extraSnakefiltpas
    Untracked:  code/filter5perc.R
    Untracked:  code/filter5percPAS.py
    Untracked:  code/filter5percPheno.py
    Untracked:  code/filterBamforMP.pysam2_gen.py
    Untracked:  code/filterMissprimingInNuc10_gen.py
    Untracked:  code/filterPASforMP.py
    Untracked:  code/filterPostLift.py
    Untracked:  code/filterSAFforMP_gen.py
    Untracked:  code/filterSortBedbyCleanedBed_gen.R
    Untracked:  code/filterpeaks.py
    Untracked:  code/fixFChead.py
    Untracked:  code/fixFChead_bothfrac.py
    Untracked:  code/fixUTRexonanno.py
    Untracked:  code/formathg38Anno.py
    Untracked:  code/generateStarIndex.err
    Untracked:  code/generateStarIndex.out
    Untracked:  code/intersectAnno.err
    Untracked:  code/intersectAnno.out
    Untracked:  code/intersectLiftedPAS.sh
    Untracked:  code/liftPAS19to38.sh
    Untracked:  code/liftoverPAShg19to38.err
    Untracked:  code/liftoverPAShg19to38.out
    Untracked:  code/log/
    Untracked:  code/make5percPeakbed.py
    Untracked:  code/makeFileID.py
    Untracked:  code/makePheno.py
    Untracked:  code/makeSamplyGroupsChimp_TvN.py
    Untracked:  code/makeSamplyGroupsHuman_TvN.py
    Untracked:  code/maphg19.err
    Untracked:  code/maphg19.out
    Untracked:  code/maphg19.sh
    Untracked:  code/maphg19_sub.err
    Untracked:  code/maphg19_sub.out
    Untracked:  code/maphg19_subjunc.sh
    Untracked:  code/namePeaks.py
    Untracked:  code/overlapPAS.err
    Untracked:  code/overlapPAS.out
    Untracked:  code/overlapapaQTLPAS.sh
    Untracked:  code/peak2PAS.py
    Untracked:  code/pheno2countonly.R
    Untracked:  code/prepareCleanLiftedFC_5perc4LC.py
    Untracked:  code/preparePAS4lift.py
    Untracked:  code/prepare_phenotype_table.py
    Untracked:  code/primaryLift.err
    Untracked:  code/primaryLift.out
    Untracked:  code/primaryLift.sh
    Untracked:  code/quantLiftedPAS.err
    Untracked:  code/quantLiftedPAS.out
    Untracked:  code/quantLiftedPAS.sh
    Untracked:  code/recChimpback2Human.err
    Untracked:  code/recChimpback2Human.out
    Untracked:  code/recLiftchim2human.sh
    Untracked:  code/revLift.err
    Untracked:  code/revLift.out
    Untracked:  code/revLiftPAShg38to19.sh
    Untracked:  code/reverseLift.sh
    Untracked:  code/runChimpDiffIso.sh
    Untracked:  code/runHumanDiffIso.sh
    Untracked:  code/runNuclearDifffIso.sh
    Untracked:  code/run_Chimpleafcutter_ds.err
    Untracked:  code/run_Chimpleafcutter_ds.out
    Untracked:  code/run_Humanleafcutter_ds.err
    Untracked:  code/run_Humanleafcutter_ds.out
    Untracked:  code/run_Nuclearleafcutter_ds.err
    Untracked:  code/run_Nuclearleafcutter_ds.out
    Untracked:  code/run_verifyBam.sh
    Untracked:  code/run_verifybam.err
    Untracked:  code/run_verifybam.out
    Untracked:  code/slurm-62824013.out
    Untracked:  code/slurm-62825841.out
    Untracked:  code/slurm-62826116.out
    Untracked:  code/snakePASChimp.err
    Untracked:  code/snakePASChimp.out
    Untracked:  code/snakePAShuman.out
    Untracked:  code/snakemake.batch
    Untracked:  code/snakemakePAS.batch
    Untracked:  code/snakemakePASFiltChimp.err
    Untracked:  code/snakemakePASFiltChimp.out
    Untracked:  code/snakemakePASFiltHuman.err
    Untracked:  code/snakemakePASFiltHuman.out
    Untracked:  code/snakemakePASchimp.batch
    Untracked:  code/snakemakePAShuman.batch
    Untracked:  code/snakemake_chimp.batch
    Untracked:  code/snakemake_human.batch
    Untracked:  code/snakemakefiltPAS.batch
    Untracked:  code/snakemakefiltPAS_chimp.sh
    Untracked:  code/snakemakefiltPAS_human.sh
    Untracked:  code/submit-snakemake-chimp.sh
    Untracked:  code/submit-snakemake-human.sh
    Untracked:  code/submit-snakemakePAS-chimp.sh
    Untracked:  code/submit-snakemakePAS-human.sh
    Untracked:  code/submit-snakemakefiltPAS-chimp.sh
    Untracked:  code/submit-snakemakefiltPAS-human.sh
    Untracked:  code/subset_diffisopheno.py
    Untracked:  code/subset_diffisopheno_Chimp_tvN.py
    Untracked:  code/subset_diffisopheno_Huma_tvN.py
    Untracked:  code/subset_diffisopheno_Nuclear_HvC.py
    Untracked:  code/verifyBam4973.sh
    Untracked:  code/verifybam4973.err
    Untracked:  code/verifybam4973.out
    Untracked:  code/wrap_verifyBam.sh
    Untracked:  code/wrap_verifybam.err
    Untracked:  code/wrap_verifybam.out
    Untracked:  data/._metadata_HCpanel.txt
    Untracked:  data/._metadata_HCpanel.txt.sb-a3d92a2d-b9cYoF
    Untracked:  data/._metadata_HCpanel.txt.sb-f4823d1e-qihGek
    Untracked:  data/._metadata_HCpanel.xlsx
    Untracked:  data/._~$metadata_HCpanel.xlsx
    Untracked:  data/CompapaQTLpas/
    Untracked:  data/DiffIso_Nuclear/
    Untracked:  data/MapStats/
    Untracked:  data/NuclearHvC/
    Untracked:  data/Peaks_5perc/
    Untracked:  data/Pheno_5perc/
    Untracked:  data/Pheno_5perc_nuclear/
    Untracked:  data/Pheno_5perc_total/
    Untracked:  data/chainFiles/
    Untracked:  data/cleanPeaks_anno/
    Untracked:  data/cleanPeaks_byspecies/
    Untracked:  data/cleanPeaks_lifted/
    Untracked:  data/liftover_files/
    Untracked:  data/metadata_HCpanel.txt
    Untracked:  data/metadata_HCpanel.xlsx
    Untracked:  data/primaryLift/
    Untracked:  data/reverseLift/
    Untracked:  data/~$metadata_HCpanel.xlsx

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
Rmd 25a8b1e brimittleman 2019-10-15 fix name bug add number PAS analysis 
library(tidyverse)
── Attaching packages ───────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
✔ ggplot2 3.1.1       ✔ purrr   0.3.2  
✔ tibble  2.1.1       ✔ dplyr   0.8.0.1
✔ tidyr   0.8.3       ✔ stringr 1.3.1  
✔ readr   1.3.1       ✔ forcats 0.3.0  
── Conflicts ──────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
library(reshape2)

Attaching package: 'reshape2'
The following object is masked from 'package:tidyr':

    smiths
library(workflowr)
This is workflowr version 1.4.0
Run ?workflowr for help getting started

Both fraction

In this analysis I will look at thenumber of PAS per species at a gene level. I am only including PAS in chr1-22. These results use mean usage accross fraction.

PAS=read.table("../data/Peaks_5perc/Peaks_5perc_either_bothUsage_noUnchr.txt", stringsAsFactors = F, header = T)

I want to look at the number of PAS at 5% in each gene by human and chimp.

PAS_sm=PAS %>% select(gene, Chimp, Human) 
PAS_m= melt(PAS_sm, id.var="gene", variable.name="species", value.name="meanUsage") %>% filter(meanUsage >=0.05) %>% group_by(species, gene) %>% summarise(nPAS=n())

#pos = more human, neg = more chimp 
PAS_spread=PAS_m %>% spread(species, nPAS, fill=0) %>% mutate(DiffPAS=Human-Chimp)
summary(PAS_spread$DiffPAS)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
-7.0000  0.0000  0.0000  0.1267  0.0000  8.0000 
#more human
nrow(PAS_spread[PAS_spread$DiffPAS>0,])
[1] 3789
#more chimp
nrow(PAS_spread[PAS_spread$DiffPAS<0,])
[1] 2596
#same
nrow(PAS_spread[PAS_spread$DiffPAS==0,])
[1] 9326
#all
nrow(PAS_spread)
[1] 15711
prop.test(x=c(3759, 2596), n=c(15711,15711), alternative ="greater")

    2-sample test for equality of proportions with continuity
    correction

data:  c(3759, 2596) out of c(15711, 15711)
X-squared = 266.33, df = 1, p-value < 2.2e-16
alternative hypothesis: greater
95 percent confidence interval:
 0.06653819 1.00000000
sample estimates:
   prop 1    prop 2 
0.2392591 0.1652345 
ggplot(PAS_spread, aes(x=DiffPAS)) + geom_bar(stat="count") +geom_vline(xintercept = mean(PAS_spread$DiffPAS),col="red") + labs(title="Difference in number of PAS at 5% Human vs Chimp", y="Genes", x="N Human PAS - N Chimp PAS")

Plot distribution of N pas by species:

Wilcoxan test to see if there is a difference in this distribution.

ChimpNPAS=PAS_m %>% filter(species=="Chimp")
HumanNPAS=PAS_m %>% filter(species=="Human")


wilcox.test(HumanNPAS$nPAS,ChimpNPAS$nPAS ,alternative = "greater")

    Wilcoxon rank sum test with continuity correction

data:  HumanNPAS$nPAS and ChimpNPAS$nPAS
W = 123630000, p-value = 2.55e-06
alternative hypothesis: true location shift is greater than 0
ggplot(PAS_m,aes(x=nPAS, by=species, fill=species)) + geom_density(stat="count",alpha=.5) +  scale_fill_brewer(palette = "Dark2") + annotate(geom="text",x=7.5, y=4000, label="1 sided Wilcoxan Test p=2.55e-6 \n Human > Chimp") + labs(title="Distribution for number of PAS >= 5%", x="Number of PAS",y="Genes")

Total fraction

HumanAnno=read.table("../Human/phenotype/ALLPAS_postLift_LocParsed_Human_Pheno.txt", header = T, stringsAsFactors = F) %>% tidyr::separate(chrom, sep = ":", into = c("chr", "start", "end", "id")) %>% tidyr::separate(id, sep="_", into=c("gene", "strand", "peak"))  %>% separate(peak,into=c("loc", "disc","PAS"), sep="-")
IndH=colnames(HumanAnno)[9:ncol(HumanAnno)]

HumanUsage=read.table("../Human/phenotype/ALLPAS_postLift_LocParsed_Human_Pheno_countOnlyNumeric.txt", col.names = IndH) %>% select(contains("_T"))


HumanMeanTotal=as.data.frame(cbind(HumanAnno[,1:8], Human=rowMeans(HumanUsage))) 
ChimpAnno=read.table("../Chimp/phenotype/ALLPAS_postLift_LocParsed_Chimp_Pheno.txt", header = T, stringsAsFactors = F) %>% tidyr::separate(chrom, sep = ":", into = c("chr", "start", "end", "id")) %>% tidyr::separate(id, sep="_", into=c("gene", "strand", "peak"))  %>% separate(peak,into=c("loc", "disc","PAS"), sep="-")
IndC=colnames(ChimpAnno)[9:ncol(ChimpAnno)]

ChimpUsage=read.table("../Chimp/phenotype/ALLPAS_postLift_LocParsed_Chimp_Pheno_countOnlyNumeric.txt", col.names = IndC) %>% select(contains("_T"))

ChimpMeanTotal=as.data.frame(cbind(ChimpAnno[,1:8], Chimp=rowMeans(ChimpUsage)))

Filter 5% and group by gene

BothMean_total=HumanMeanTotal %>% inner_join(ChimpMeanTotal,by=c("chr", "start", "end", "strand","loc", "disc", "PAS", "gene")) %>% filter(Chimp >=.05 | Human >=0.05) 

BothMean_total_M=melt(BothMean_total, id.vars = c("chr", "start", "end", "strand","loc", "disc", "PAS", "gene"), value.name = "Total_Usage", variable.name = "Species" )

BothMean_total_gene=BothMean_total_M %>% filter(Total_Usage>=0.05) %>% group_by(Species, gene) %>% summarise(nPASTotal=n()) 


#pos = more human, neg = more chimp 
PAS_Total_spread=BothMean_total_gene %>% spread(Species, nPASTotal, fill=0) %>% mutate(TotalDiffPAS=Human-Chimp)
ggplot(PAS_Total_spread, aes(x=TotalDiffPAS)) + geom_bar(stat="count") +geom_vline(xintercept = mean(PAS_Total_spread$TotalDiffPAS),col="red") + labs(title="Difference in number of PAS at 5% Human vs Chimp \n Total Fraction", y="Genes", x="N Human PAS - N Chimp PAS")

ggplot(BothMean_total_gene,aes(x=nPASTotal, by=Species, fill=Species)) + geom_density(stat="count",alpha=.5) +  scale_fill_brewer(palette = "Dark2")  + labs(title="Distribution for number of PAS >= 5%\n Total Fraction", x="Number of PAS",y="Genes")

###Nuclear fraction

HumanUsageNuclear=read.table("../Human/phenotype/ALLPAS_postLift_LocParsed_Human_Pheno_countOnlyNumeric.txt", col.names = IndH) %>% select(contains("_N"))


HumanMeanNuclear=as.data.frame(cbind(HumanAnno[,1:8], Human=rowMeans(HumanUsageNuclear))) 
ChimpUsageNuclear=read.table("../Chimp/phenotype/ALLPAS_postLift_LocParsed_Chimp_Pheno_countOnlyNumeric.txt", col.names = IndC) %>% select(contains("_N"))

ChimpMeanNuclear=as.data.frame(cbind(ChimpAnno[,1:8], Chimp=rowMeans(ChimpUsageNuclear)))

Filter 5% and group by gene

BothMean_nuclear=HumanMeanNuclear %>% inner_join(ChimpMeanNuclear,by=c("chr", "start", "end", "strand","loc", "disc", "PAS", "gene")) %>% filter(Chimp >=.05 | Human >=0.05) 

BothMean_nuclear_M=melt(BothMean_nuclear, id.vars = c("chr", "start", "end", "strand","loc", "disc", "PAS", "gene"), value.name = "Nuclear_Usage", variable.name = "Species" )



BothMean_nuclear_gene=BothMean_nuclear_M %>% filter(Nuclear_Usage>=0.05) %>% group_by(Species, gene) %>% summarise(nPASNuclear=n()) 


#pos = more human, neg = more chimp 
PAS_Nuclear_spread=BothMean_nuclear_gene %>% spread(Species, nPASNuclear, fill=0) %>% mutate(NuclearDiffPAS=Human-Chimp)
ggplot(PAS_Nuclear_spread, aes(x=NuclearDiffPAS)) + geom_bar(stat="count") +geom_vline(xintercept = mean(PAS_Nuclear_spread$NuclearDiffPAS),col="red") + labs(title="Difference in number of PAS at 5% Human vs Chimp \n Nuclear Fraction", y="Genes", x="N Human PAS - N Chimp PAS")

ggplot(BothMean_nuclear_gene,aes(x=nPASNuclear, by=Species, fill=Species)) + geom_density(stat="count",alpha=.5) +  scale_fill_brewer(palette = "Dark2")  + labs(title="Distribution for number of PAS >= 5%\n Nuclear Fraction", x="Number of PAS",y="Genes")

compare total and nuclear

Compare total and nuclear.

mean(PAS_Total_spread$TotalDiffPAS)
[1] 0.1640242
mean(PAS_Nuclear_spread$NuclearDiffPAS)
[1] 0.08575618

Is the skew different in total and nuclear:

t.test(PAS_Total_spread$TotalDiffPAS, PAS_Nuclear_spread$NuclearDiffPAS,alternative = "greater") 

    Welch Two Sample t-test

data:  PAS_Total_spread$TotalDiffPAS and PAS_Nuclear_spread$NuclearDiffPAS
t = 6.0396, df = 31288, p-value = 7.814e-10
alternative hypothesis: true difference in means is greater than 0
95 percent confidence interval:
 0.05695137        Inf
sample estimates:
 mean of x  mean of y 
0.16402420 0.08575618

This means skew toward more PAS in human that chimp is stronger in the total fraction.


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

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