Last updated: 2021-02-19

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Knit directory: Human_Development_RNAseq_bulk/

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Rmd cd389d4 evangelynsim 2021-02-19 wflow_publish(c(“analysis/01.Generate_reference_genome.Rmd”,

Introduction

In the GEO submission 3 processed files were uploaded.

  1. hrna_dev_mf_fulllen_se_strrev_q30.mx.all.fix_filt.csv
  2. hrna_dev_mf_fulllen_se_strrev_q30.mx.all.MvsF.fix_filt.csv
  3. hrna_dev_mf_fulllen_se_strrev_q30.mx.all_unfiltered.csv

They have been uploaded in the /output folder and will be used below to generate different figures.

Used libraries and functions

library(edgeR)
Loading required package: limma
library(limma)
library(dplyr)

Attaching package: 'dplyr'
The following objects are masked from 'package:stats':

    filter, lag
The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union

Read files

PRIOR = 20
FDR = 0.05

rm1 <- read.csv("/group/card2/Evangelyn_Sim/Transcriptome_chromatin_human/Sequencing_ATAC_RNA/GITHUB/Human_Development_RNAseq_bulk/output/hrna_dev_mf_fulllen_se_strrev_q30.mx.all.fix_filt.csv", row.names = 1)

info = read.delim("/group/card2/Evangelyn_Sim/Transcriptome_chromatin_human/Sequencing_ATAC_RNA/GITHUB/Human_Development_RNAseq_bulk/output/RNAseq_samplesheet.txt", header = TRUE, sep = "\t", stringsAsFactors = F)
info$Dev = c(3,1,2)[factor(info$Group)]

m = match(info$ID,names(rm1))
rm2 = rm1[,m]

rm1 = rm2

sampleinfo = info
levels(factor(sampleinfo$Group))
[1] "Adult" "Fetal" "Young"
sampleinfo$colour = c("violetred4","darkgoldenrod2","tomato")[factor(sampleinfo$Group)]
table(colnames(rm2)==sampleinfo$ID)

TRUE 
  21 
matrix = rm2
pheno = info

dir.create("../output/edgeR")
Warning in dir.create("../output/edgeR"): cannot create dir '../output/edgeR',
reason 'No such file or directory'
#write.table(pheno, file="../output/edgeR/pheno.matrix_all.txt", sep="\t", quote = F, row.names = F)
#pheno = read.delim(file="../output/edgeR/pheno.matrix_all.txt")

Differential Gene Expresison Analysis Comparing 2 Groups

attach(pheno)
design = model.matrix(as.formula("~ 0  + Group + Sex + Batch"))
detach(pheno)
design
   GroupAdult GroupFetal GroupYoung SexM Batch
1           0          1          0    0     1
2           0          1          0    0     1
3           0          1          0    0     1
4           0          1          0    1     2
5           0          1          0    1     2
6           0          1          0    0     2
7           0          0          1    1     1
8           0          0          1    0     1
9           0          0          1    1     1
10          0          0          1    1     1
11          1          0          0    1     2
12          1          0          0    1     1
13          1          0          0    0     2
14          1          0          0    1     2
15          1          0          0    0     2
16          1          0          0    0     2
17          1          0          0    0     2
18          1          0          0    1     2
19          1          0          0    1     1
20          1          0          0    0     2
21          1          0          0    0     2
attr(,"assign")
[1] 1 1 1 2 3
attr(,"contrasts")
attr(,"contrasts")$Group
[1] "contr.treatment"

attr(,"contrasts")$Sex
[1] "contr.treatment"
D = DGEList(counts=matrix)
D = calcNormFactors(D)
D = estimateGLMCommonDisp(D, design)
D = estimateGLMTagwiseDisp(D, design, prior.df = PRIOR)
fit = glmFit(D, design, prior.count = PRIOR)

Contrast = makeContrasts(FetalvsYoung = GroupYoung - GroupFetal,
                         YoungvsAdult = GroupAdult - GroupYoung,
                         FetalvsAdult = GroupAdult - GroupFetal,
                         levels=design)

res = list()
contrast.name = colnames(Contrast)

for(i in 1:length(contrast.name)){
  lrt = glmLRT(fit, contrast = Contrast[,i])   
  
  results = lrt$table
  disp = lrt$dispersion
  fitted.vals = lrt$fitted.values
  coefficients = lrt$coefficients
  
  results$adj.p.value = p.adjust(p = results$PValue, method = "fdr" )
  table(row.names(results) == row.names(fitted.vals))
  
  Name = row.names(results)
  res0 = cbind(Name, results, disp, fitted.vals, coefficients)
  res[[i]] = res0[order(res0$adj.p.value),]
  
  # write.table(res[[i]], file= paste0("../output/edgeR/edgeR_RNA_all_", contrast.name[i] ,".xls"), quote=F, sep="\t", col.names = T, row.names = F)
  
  
  res[[i]]= mutate(res[[i]], cs= ifelse(res[[i]]$adj.p.value <= 0.05 & res[[i]]$logFC <= 0, "blue",
                               ifelse(res[[i]]$adj.p.value <= 0.05 & res[[i]]$logFC >= 0, "red", "grey")))
  
  mxFDR = res[[i]][res[[i]]$adj.p.value <= FDR,]
  mxFDR_Up = mxFDR[mxFDR$logFC>0,]
  mxFDR_Dn = mxFDR[mxFDR$logFC<0,]
  
  res[[i]]= mutate(res[[i]], FDR= nrow(mxFDR))
  res[[i]]= mutate(res[[i]], FDRup= nrow(mxFDR_Up))
  res[[i]]= mutate(res[[i]], FDRdn= nrow(mxFDR_Dn))


}


for(i in 1:length(contrast.name)){
  print(contrast.name[i])
  print(table(res[[i]]$adj.p.value < 0.05))
}
[1] "FetalvsYoung"

FALSE  TRUE 
14855  3511 
[1] "YoungvsAdult"

FALSE  TRUE 
18084   282 
[1] "FetalvsAdult"

FALSE  TRUE 
12199  6167 
par(mfrow=c(1,3))

for(i in 1:length(contrast.name)){

  plot(res[[i]]$logCPM, res[[i]]$logFC, pch=20, cex=1, col=res[[i]]$cs, 
        xlab = "logCPM", ylab = "logFC",
        main = paste0(contrast.name[i], 
                      "\nFDR=0.05, N=", res[[i]][1,ncol(res[[i]])-2], 
                      "\nUp=",res[[i]][1,ncol(res[[i]])-1],", Dn=",res[[i]][1,ncol(res[[i]])]))
}

Differential Gene Expresison Analysis Comparing Fetal to Young to Adult Group

########################################################################################################Dev

attach(pheno)
design_dev = model.matrix(as.formula("~ 0  + Dev + Sex + Batch"))
detach(pheno)
design_dev
   Dev SexF SexM Batch
1    1    1    0     1
2    1    1    0     1
3    1    1    0     1
4    1    0    1     2
5    1    0    1     2
6    1    1    0     2
7    2    0    1     1
8    2    1    0     1
9    2    0    1     1
10   2    0    1     1
11   3    0    1     2
12   3    0    1     1
13   3    1    0     2
14   3    0    1     2
15   3    1    0     2
16   3    1    0     2
17   3    1    0     2
18   3    0    1     2
19   3    0    1     1
20   3    1    0     2
21   3    1    0     2
attr(,"assign")
[1] 1 2 2 3
attr(,"contrasts")
attr(,"contrasts")$Sex
[1] "contr.treatment"
D_dev = DGEList(counts=matrix)
D_dev = calcNormFactors(D_dev)
D_dev = estimateGLMCommonDisp(D_dev, design_dev)
D_dev = estimateGLMTagwiseDisp(D_dev, design_dev, prior.df = PRIOR)
fit_dev = glmFit(D_dev, design_dev, prior.count = PRIOR)

Contrast_dev = makeContrasts(Development = Dev,
                             levels=design_dev)

res_dev = list()
contrast.name_dev = colnames(Contrast_dev)

for(i in 1:length(contrast.name_dev)){
  lrt_dev = glmLRT(fit_dev, contrast = Contrast_dev[,i])   
  results_dev = lrt_dev$table
  disp_dev = lrt_dev$dispersion
  fitted.vals_dev = lrt_dev$fitted.values
  coefficients_dev = lrt_dev$coefficients
  
  results_dev$adj.p.value = p.adjust(p = results_dev$PValue, method = "fdr" )
  table(row.names(results_dev) == row.names(fitted.vals_dev))
  
  Name = row.names(results_dev)
  res0_dev = cbind(Name, results_dev, disp_dev, fitted.vals_dev, coefficients_dev)
  res_dev[[i]] = res0_dev[order(res0_dev$adj.p.value),]
  #write.table(res_dev[[i]], file= paste0("../output/edgeR/edgeR_RNA_all_", contrast.name_dev[i] ,".xls"), quote=F, sep="\t", col.names = T, row.names = F)
  
  res_dev[[i]]= mutate(res_dev[[i]], cs= ifelse(res_dev[[i]]$adj.p.value <= 0.05 & res_dev[[i]]$logFC <= 0, "blue",
                               ifelse(res_dev[[i]]$adj.p.value <= 0.05 & res_dev[[i]]$logFC >= 0, "red", "grey")))
  
  mxFDR = res_dev[[i]][res_dev[[i]]$adj.p.value <= FDR,]
  mxFDR_Up = mxFDR[mxFDR$logFC>0,]
  mxFDR_Dn = mxFDR[mxFDR$logFC<0,]
  
  res_dev[[i]]= mutate(res_dev[[i]], FDR= nrow(mxFDR))
  res_dev[[i]]= mutate(res_dev[[i]], FDRup= nrow(mxFDR_Up))
  res_dev[[i]]= mutate(res_dev[[i]], FDRdn= nrow(mxFDR_Dn))


}


for(i in 1:length(contrast.name_dev)){
  print(contrast.name_dev[i])
  print(table(res_dev[[i]]$adj.p.value < 0.05))
}
[1] "Development"

FALSE  TRUE 
12260  6106 
par(mfrow=c(1,1))

for(i in 1:length(contrast.name_dev)){

  plot(res_dev[[i]]$logCPM, res_dev[[i]]$logFC, pch=20, cex=1, col=res_dev[[i]]$cs, 
        xlab = "logCPM", ylab = "logFC",
        main = paste0(contrast.name_dev[i], 
                      "\nFDR=0.05, N=", res_dev[[i]][1,ncol(res_dev[[i]])-2], 
                      "\nUp=",res_dev[[i]][1,ncol(res_dev[[i]])-1],", Dn=",res_dev[[i]][1,ncol(res_dev[[i]])]))
}

Remove Chr X & Y genes

rm3 = read.delim("/group/card2/Evangelyn_Sim/Transcriptome_chromatin_human/Sequencing_ATAC_RNA/GITHUB/Human_Development_RNAseq_bulk/output/hrna_dev_mf_fulllen_se_strrev_q30.mx.chr", header = TRUE, row.names = 1)
rm3 = merge(rm3, rm2, by = "row.names")
rm3 = rm3[!grepl("Y",rm3$Chr),]
rm3 = rm3[!grepl("X",rm3$Chr),]
row.names(rm3)= rm3$Row.names
rm3 = rm3[,c(7:ncol(rm3))]

matrix = rm3

Differential Gene Expresison Analysis Comparing 2 Groups (removed Chr X & Y genes)

attach(pheno)
design = model.matrix(as.formula("~ 0  + Group + Sex + Batch"))
detach(pheno)
design
   GroupAdult GroupFetal GroupYoung SexM Batch
1           0          1          0    0     1
2           0          1          0    0     1
3           0          1          0    0     1
4           0          1          0    1     2
5           0          1          0    1     2
6           0          1          0    0     2
7           0          0          1    1     1
8           0          0          1    0     1
9           0          0          1    1     1
10          0          0          1    1     1
11          1          0          0    1     2
12          1          0          0    1     1
13          1          0          0    0     2
14          1          0          0    1     2
15          1          0          0    0     2
16          1          0          0    0     2
17          1          0          0    0     2
18          1          0          0    1     2
19          1          0          0    1     1
20          1          0          0    0     2
21          1          0          0    0     2
attr(,"assign")
[1] 1 1 1 2 3
attr(,"contrasts")
attr(,"contrasts")$Group
[1] "contr.treatment"

attr(,"contrasts")$Sex
[1] "contr.treatment"
D = DGEList(counts=matrix)
D = calcNormFactors(D)
D = estimateGLMCommonDisp(D, design)
D = estimateGLMTagwiseDisp(D, design, prior.df = PRIOR)
fit = glmFit(D, design, prior.count = PRIOR)

Contrast = makeContrasts(FetalvsYoung = GroupYoung - GroupFetal,
                         YoungvsAdult = GroupAdult - GroupYoung,
                         FetalvsAdult = GroupAdult - GroupFetal,
                         levels=design)

res = list()
contrast.name = colnames(Contrast)

for(i in 1:length(contrast.name)){
  lrt = glmLRT(fit, contrast = Contrast[,i])   
  
  results = lrt$table
  disp = lrt$dispersion
  fitted.vals = lrt$fitted.values
  coefficients = lrt$coefficients
  
  results$adj.p.value = p.adjust(p = results$PValue, method = "fdr" )
  table(row.names(results) == row.names(fitted.vals))
  
  Name = row.names(results)
  res0 = cbind(Name, results, disp, fitted.vals, coefficients)
  res[[i]] = res0[order(res0$adj.p.value),]
  #write.table(res[[i]], file= paste0("../output/edgeR/edgeR_RNA_all_", contrast.name[i] ,"_noXY.xls"), quote=F, sep="\t", col.names = T, row.names = F)
  
  res[[i]]= mutate(res[[i]], cs= ifelse(res[[i]]$adj.p.value <= 0.05 & res[[i]]$logFC <= 0, "blue",
                               ifelse(res[[i]]$adj.p.value <= 0.05 & res[[i]]$logFC >= 0, "red", "grey")))
  
  mxFDR = res[[i]][res[[i]]$adj.p.value <= FDR,]
  mxFDR_Up = mxFDR[mxFDR$logFC>0,]
  mxFDR_Dn = mxFDR[mxFDR$logFC<0,]
  
  res[[i]]= mutate(res[[i]], FDR= nrow(mxFDR))
  res[[i]]= mutate(res[[i]], FDRup= nrow(mxFDR_Up))
  res[[i]]= mutate(res[[i]], FDRdn= nrow(mxFDR_Dn))


}


for(i in 1:length(contrast.name)){
  print(contrast.name[i])
  print(table(res[[i]]$adj.p.value < 0.05))
}
[1] "FetalvsYoung"

FALSE  TRUE 
14421  3317 
[1] "YoungvsAdult"

FALSE  TRUE 
17475   263 
[1] "FetalvsAdult"

FALSE  TRUE 
11849  5889 
par(mfrow=c(1,3))

for(i in 1:length(contrast.name)){

  plot(res[[i]]$logCPM, res[[i]]$logFC, pch=20, cex=1, col=res[[i]]$cs, 
        xlab = "logCPM", ylab = "logFC",
        main = paste0(contrast.name[i], 
                      "\nFDR=0.05, N=", res[[i]][1,ncol(res[[i]])-2], 
                      "\nUp=",res[[i]][1,ncol(res[[i]])-1],", Dn=",res[[i]][1,ncol(res[[i]])]))
}

Differential Gene Expresison Analysis Comparing Fetal to Young to Adult Group (removed Chr X & Y genes)

attach(pheno)
design_dev = model.matrix(as.formula("~ 0  + Dev + Sex + Batch"))
detach(pheno)
design_dev
   Dev SexF SexM Batch
1    1    1    0     1
2    1    1    0     1
3    1    1    0     1
4    1    0    1     2
5    1    0    1     2
6    1    1    0     2
7    2    0    1     1
8    2    1    0     1
9    2    0    1     1
10   2    0    1     1
11   3    0    1     2
12   3    0    1     1
13   3    1    0     2
14   3    0    1     2
15   3    1    0     2
16   3    1    0     2
17   3    1    0     2
18   3    0    1     2
19   3    0    1     1
20   3    1    0     2
21   3    1    0     2
attr(,"assign")
[1] 1 2 2 3
attr(,"contrasts")
attr(,"contrasts")$Sex
[1] "contr.treatment"
D_dev = DGEList(counts=matrix)
D_dev = calcNormFactors(D_dev)
D_dev = estimateGLMCommonDisp(D_dev, design_dev)
D_dev = estimateGLMTagwiseDisp(D_dev, design_dev, prior.df = PRIOR)
fit_dev = glmFit(D_dev, design_dev, prior.count = PRIOR)

Contrast_dev = makeContrasts(Development = Dev,
                             levels=design_dev)

res_dev = list()
contrast.name_dev = colnames(Contrast_dev)

for(i in 1:length(contrast.name_dev)){
  lrt_dev = glmLRT(fit_dev, contrast = Contrast_dev[,i])   
  results_dev = lrt_dev$table
  disp_dev = lrt_dev$dispersion
  fitted.vals_dev = lrt_dev$fitted.values
  coefficients_dev = lrt_dev$coefficients
  
  results_dev$adj.p.value = p.adjust(p = results_dev$PValue, method = "fdr" )
  table(row.names(results_dev) == row.names(fitted.vals_dev))
  
  Name = row.names(results_dev)
  res0_dev = cbind(Name, results_dev, disp_dev, fitted.vals_dev, coefficients_dev)
  res_dev[[i]] = res0_dev[order(res0_dev$adj.p.value),]
  #write.table(res_dev[[i]], file= paste0("../output/edgeR/edgeR_RNA_all_", contrast.name_dev[i] ,"_noXY.xls"), quote=F, sep="\t", col.names = T, row.names = F)
  
  
  res_dev[[i]]= mutate(res_dev[[i]], cs= ifelse(res_dev[[i]]$adj.p.value <= 0.05 & res_dev[[i]]$logFC <= 0, "blue",
                               ifelse(res_dev[[i]]$adj.p.value <= 0.05 & res_dev[[i]]$logFC >= 0, "red", "grey")))
  
  mxFDR = res_dev[[i]][res_dev[[i]]$adj.p.value <= FDR,]
  mxFDR_Up = mxFDR[mxFDR$logFC>0,]
  mxFDR_Dn = mxFDR[mxFDR$logFC<0,]
  
  res_dev[[i]]= mutate(res_dev[[i]], FDR= nrow(mxFDR))
  res_dev[[i]]= mutate(res_dev[[i]], FDRup= nrow(mxFDR_Up))
  res_dev[[i]]= mutate(res_dev[[i]], FDRdn= nrow(mxFDR_Dn))


}


for(i in 1:length(contrast.name_dev)){
  print(contrast.name_dev[i])
  print(table(res_dev[[i]]$adj.p.value < 0.05))
}
[1] "Development"

FALSE  TRUE 
11925  5813 
par(mfrow=c(1,1))

for(i in 1:length(contrast.name_dev)){

  plot(res_dev[[i]]$logCPM, res_dev[[i]]$logFC, pch=20, cex=1, col=res_dev[[i]]$cs, 
        xlab = "logCPM", ylab = "logFC",
        main = paste0(contrast.name_dev[i], 
                      "\nFDR=0.05, N=", res_dev[[i]][1,ncol(res_dev[[i]])-2], 
                      "\nUp=",res_dev[[i]][1,ncol(res_dev[[i]])-1],", Dn=",res_dev[[i]][1,ncol(res_dev[[i]])]))
}


sessionInfo()
R version 3.6.1 (2019-07-05)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)

Matrix products: default
BLAS:   /hpc/software/installed/R/3.6.1/lib64/R/lib/libRblas.so
LAPACK: /hpc/software/installed/R/3.6.1/lib64/R/lib/libRlapack.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] dplyr_1.0.2     edgeR_3.26.8    limma_3.40.6    workflowr_1.6.2

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.5       pillar_1.4.6     compiler_3.6.1   later_1.1.0.1   
 [5] git2r_0.27.1     highr_0.8        tools_3.6.1      digest_0.6.27   
 [9] evaluate_0.14    lifecycle_0.2.0  tibble_3.0.3     lattice_0.20-41 
[13] pkgconfig_2.0.3  rlang_0.4.7      rstudioapi_0.11  yaml_2.2.1      
[17] xfun_0.18        stringr_1.4.0    knitr_1.30       generics_0.1.0  
[21] fs_1.5.0         vctrs_0.3.2      tidyselect_1.1.0 locfit_1.5-9.4  
[25] rprojroot_1.3-2  grid_3.6.1       glue_1.4.2       R6_2.5.0        
[29] rmarkdown_2.5    purrr_0.3.4      magrittr_1.5     whisker_0.4     
[33] backports_1.1.10 promises_1.1.1   ellipsis_0.3.1   htmltools_0.5.0 
[37] httpuv_1.5.4     stringi_1.5.3    crayon_1.3.4