multitrait_allweight_figures

Load all weight analyses for each trait

df_all <- list()
trait_names <- data.frame(trait_id=as.character(),
                  trait_name=as.character(),
                  trait_abbr=as.character())

####################

trait_id <- "ebi-a-GCST004131"
trait_name <- "Inflammatory Bowel Disease"
trait_abbr <- "IBD"
trait_dir <- paste0("/project2/mstephens/wcrouse/UKB_analysis_allweights_corrected/", trait_id)

load(paste(trait_dir, "results_df_nolnc.RData", sep="/"))
trait_names <- rbind(trait_names, data.frame(trait_id, trait_name, trait_abbr))
df_all[[trait_id]] <- df

####################

trait_id <- "scz-2018"
trait_name <- "Schizophrenia"
trait_abbr <- "SCZ"
trait_dir <- paste0("/project2/mstephens/wcrouse/UKB_analysis_allweights_scz/", trait_id)

load(paste(trait_dir, "results_df_nolnc.RData", sep="/"))
trait_names <- rbind(trait_names, data.frame(trait_id, trait_name, trait_abbr))
df_all[[trait_id]] <- df

####################

trait_id <- "ukb-a-360"
trait_name <- "Systolic Blood Pressure"
trait_abbr <- "SBP"
trait_dir <- paste0("/project2/mstephens/wcrouse/UKB_analysis_allweights_simpleharmonization/", trait_id)

load(paste(trait_dir, "results_df_nolnc.RData", sep="/"))
trait_names <- rbind(trait_names, data.frame(trait_id, trait_name, trait_abbr))
df_all[[trait_id]] <- df

Number of genes imputed for each trait and weight

library(ggplot2)

for (i in 1:length(df_all)){
  n_genes <- sapply(df_all[[i]], function(x){nrow(x$gene_pips)})
  weight <- names(n_genes)
  weight <- sapply(weight, function(x){paste(unlist(strsplit(x, "_")), collapse=" ")})
  df_plot_trait <- data.frame(n_genes=n_genes, weight=weight, trait=trait_names$trait_abbr[i])
  rownames(df_plot_trait) <- NULL
  
  if (i==1){
    df_plot <- df_plot_trait
  } else {
    df_plot <- rbind(df_plot, df_plot_trait)
  }
}

p <- ggplot(df_plot, aes(fill=weight, y=n_genes, x=trait)) + geom_bar(position="dodge", stat="identity") + xlab("Trait") + ylab("Number of Imputed Genes")
p <- p + theme_bw() + theme(legend.position="none")

p

Past versions of unnamed-chunk-2-1.png

####################

#minimum number of genes
df_plot[which.min(df_plot$n_genes),]

   n_genes        weight trait
79    6591 Kidney Cortex   SCZ

#maximum number of genes
df_plot[which.max(df_plot$n_genes),]

    n_genes weight trait
143   11985 Testis   SBP

####################


df_plot <- df_plot[rev(1:nrow(df_plot)),]

pdf(file = "output/ALL_number_imputed_genes.pdf", width = 7, height = 10)

par(mar=c(4.1, 9.6, 0.6, 1.6))

barplot(df_plot$n_genes, names.arg=df_plot$weight, las=2, xlab="Number of Imputed Genes", main="",
        cex.lab=0.8,
        cex.axis=0.8,
        cex.names=0.3,
        space=c(c(0, rep(0,48)), rep(c(3, rep(0,48)), 2)),
        col=rep(c("darkblue", "grey50"),3),
        axis.lty=1, 
        horiz=T,
        las=1,
        xlim=c(0,12000))

grid(nx = NULL,
     ny = NA,
     lty = 2, col = "grey", lwd = 1)

dev.off()

png 
  2 

####################
#average over tissues

df_plot <- aggregate(n_genes~weight, df_plot, mean)

pdf(file = "output/ALL_number_imputed_genes_mean.pdf", width = 7, height = 8)

par(mar=c(4.1, 9.6, 0.6, 1.6))

barplot(df_plot$n_genes, names.arg=df_plot$weight, las=2, xlab="Number of Imputed Genes", main="",
        cex.lab=0.8,
        cex.axis=0.8,
        cex.names=0.6,
        col=c("darkblue", "grey50"),
        axis.lty=1, 
        horiz=T,
        las=1,
        xlim=c(0,12000))

grid(nx = NULL,
     ny = NA,
     lty = 2, col = "grey", lwd = 1)

dev.off()

png 
  2 

Number of genes in PredictDB weights

weight_dir <- "/project2/mstephens/wcrouse/predictdb_nolnc/"
weight_files <- list.files(weight_dir)
weight_files <- weight_files[grep(".db", weight_files)]

df_plot <- data.frame(weight=as.character(), n_genes=as.numeric())

for (i in 1:length(weight_files)){
  weight_file <- weight_files[i]
  
  weight <- unlist(strsplit(weight_file, "mashr_"))[2]
  weight <- unlist(strsplit(weight, "_nolnc.db"))
  weight <- paste(unlist(strsplit(weight, "_")), collapse=" ")
  
  sqlite <- RSQLite::dbDriver("SQLite")
  db = RSQLite::dbConnect(sqlite, paste0(weight_dir, weight_file))
  query <- function(...) RSQLite::dbGetQuery(db, ...)
  gene_info <- query("select gene, genename, gene_type from extra")
  RSQLite::dbDisconnect(db)
  
  df_plot_weight <- data.frame(weight=as.character(weight), n_genes=as.numeric(nrow(gene_info)))
  
  if (i==1){
    df_plot <- df_plot_weight
  } else {
    df_plot <- rbind(df_plot, df_plot_weight)
  }
}

p <- ggplot(df_plot, aes(y=n_genes, x=reorder(weight, dplyr::desc(weight)))) + geom_bar(position="dodge", stat="identity") + xlab("Tissue") + ylab("Number of Protein-Coding Genes")
p <- p + theme_bw() + ylim(0,15100)
p <- p + coord_flip()

p

Past versions of unnamed-chunk-3-1.png

####################

#minimum number of genes
df_plot[which.min(df_plot$n_genes),]

          weight n_genes
30 Kidney Cortex    9898

#maximum number of genes
df_plot[which.max(df_plot$n_genes),]

   weight n_genes
45 Testis   14324

Estimated parameters for each trait and weight

#prior inclusion

for (i in 1:length(df_all)){
  prior <- sapply(df_all[[i]], function(x){x$prior})
  prior <- prior[1,]
  weight <- names(prior)
  weight <- sapply(weight, function(x){paste(unlist(strsplit(x, "_")), collapse=" ")})
  df_plot_trait <- data.frame(prior=prior, weight=weight, trait=trait_names$trait_abbr[i])
  rownames(df_plot_trait) <- NULL
  
  if (i==1){
    df_plot <- df_plot_trait
  } else {
    df_plot <- rbind(df_plot, df_plot_trait)
  }
}

p <- ggplot(df_plot, aes(x=trait, y=prior)) + geom_boxplot(fill='#A4A4A4', color="black", outlier.shape=NA) + expand_limits(y=0)
p <- p + geom_jitter(shape=16, position=position_jitter(0.2))
p <- p + theme_bw()
p <- p + xlab("Trait") + ylab("Prior Inclusion Probability")

p

#minimum prior inclusion
df_plot[which.min(df_plot$prior),]

         prior weight trait
48 0.001760182 Vagina   IBD

#maximum prior inclusion
df_plot[which.max(df_plot$prior),]

         prior       weight trait
102 0.02159532 Artery Aorta   SBP

####################

p_pi <- p + ylab(bquote(pi)) + ggtitle("Proportion Causal")

#prior variance

for (i in 1:length(df_all)){
  prior_var <- sapply(df_all[[i]], function(x){x$prior_var})
  prior_var <- prior_var[1,]
  weight <- names(prior_var)
  weight <- sapply(weight, function(x){paste(unlist(strsplit(x, "_")), collapse=" ")})
  df_plot_trait <- data.frame(prior_var=prior_var, weight=weight, trait=trait_names$trait_abbr[i])
  rownames(df_plot_trait) <- NULL
  
  if (i==1){
    df_plot <- df_plot_trait
  } else {
    df_plot <- rbind(df_plot, df_plot_trait)
  }
}

p <- ggplot(df_plot, aes(x=trait, y=prior_var)) + geom_boxplot(fill='#A4A4A4', color="black", outlier.shape=NA) + expand_limits(y=0)
p <- p + geom_jitter(shape=16, position=position_jitter(0.2))
p <- p + theme_bw()
p <- p + xlab("Trait") + ylab("Prior Variance")

p

#minimum prior variance
df_plot[which.min(df_plot$prior_var),]

   prior_var weight trait
36  8.028828  Ovary   IBD

#maximum prior variance
df_plot[which.max(df_plot$prior_var),]

   prior_var weight trait
48  32.02479 Vagina   IBD

####################

p_sigma2 <- p + ylab(bquote(sigma^2)) + ggtitle("Effect Size")

for (i in 1:length(df_all)){
  prior <- sapply(df_all[[i]], function(x){x$prior})
  enrich <- prior[1,]/prior[2,]
  weight <- names(enrich)
  weight <- sapply(weight, function(x){paste(unlist(strsplit(x, "_")), collapse=" ")})
  df_plot_trait <- data.frame(enrich=enrich, weight=weight, trait=trait_names$trait_abbr[i])
  rownames(df_plot_trait) <- NULL
  
  if (i==1){
    df_plot <- df_plot_trait
  } else {
    df_plot <- rbind(df_plot, df_plot_trait)
  }
}

p <- ggplot(df_plot, aes(x=trait, y=enrich)) + geom_boxplot(fill='#A4A4A4', color="black", outlier.shape=NA) + expand_limits(y=0)
p <- p + geom_jitter(shape=16, position=position_jitter(0.2))
p <- p + theme_bw()
p <- p + xlab("Trait") + ylab("Enrichment")

p

####################

p_enrich <- p + ylab(bquote(pi[G]/pi[S])) + ggtitle("Enrichment")

#prior variance explained

for (i in 1:length(df_all)){
  pve <- sapply(df_all[[i]], function(x){x$pve})
  pve <- pve[1,]
  weight <- names(pve)
  weight <- sapply(weight, function(x){paste(unlist(strsplit(x, "_")), collapse=" ")})
  df_plot_trait <- data.frame(pve=pve, weight=weight, trait=trait_names$trait_abbr[i])
  rownames(df_plot_trait) <- NULL
  
  if (i==1){
    df_plot <- df_plot_trait
  } else {
    df_plot <- rbind(df_plot, df_plot_trait)
  }
}

p <- ggplot(df_plot, aes(x=trait, y=pve)) + geom_boxplot(fill='#A4A4A4', color="black", outlier.shape=NA) + expand_limits(y=0)
p <- p + geom_jitter(shape=16, position=position_jitter(0.2))
p <- p + theme_bw()
p <- p + xlab("Trait") + ylab("Proportion of Variance Explained")

p

#minimum PVE
df_plot[which.min(df_plot$pve),]

            pve                         weight trait
140 0.002115861 Small Intestine Terminal Ileum   SBP

#maximum PVE
df_plot[which.max(df_plot$pve),]

          pve      weight trait
49 0.03920475 Whole Blood   IBD

####################

p_pve <- p + ylab(bquote(h^2[G])) + ggtitle("PVE")

#mediated heritability

for (i in 1:length(df_all)){
  h2_med <- sapply(df_all[[i]], function(x){x$pve})
  h2_med <- apply(h2_med, 2, function(x){x[1]/(sum(x))})
  weight <- names(h2_med)
  weight <- sapply(weight, function(x){paste(unlist(strsplit(x, "_")), collapse=" ")})
  df_plot_trait <- data.frame(h2_med=h2_med, weight=weight, trait=trait_names$trait_abbr[i])
  rownames(df_plot_trait) <- NULL
  
  if (i==1){
    df_plot <- df_plot_trait
  } else {
    df_plot <- rbind(df_plot, df_plot_trait)
  }
}

p <- ggplot(df_plot, aes(x=trait, y=h2_med)) + geom_boxplot(fill='#A4A4A4', color="black", outlier.shape=NA) + expand_limits(y=0)
p <- p + geom_jitter(shape=16, position=position_jitter(0.2))
p <- p + theme_bw()
p <- p + xlab("Trait") + ylab("Proportion of Mediated Heritability")

p

####################
#mediated heritability - top tissues only

n_top_tissues <- 5


for (i in 1:length(df_all)){
  h2_med <- sapply(df_all[[i]], function(x){x$pve})
  h2_med <- apply(h2_med, 2, function(x){x[1]/(sum(x))})
  
  h2_med <- (-sort(-h2_med))[1:n_top_tissues]

  weight <- names(h2_med)
  weight <- sapply(weight, function(x){paste(unlist(strsplit(x, "_")), collapse=" ")})
  df_plot_trait <- data.frame(h2_med=h2_med, weight=weight, trait=trait_names$trait_abbr[i])
  rownames(df_plot_trait) <- NULL
  
  if (i==1){
    df_plot <- df_plot_trait
  } else {
    df_plot <- rbind(df_plot, df_plot_trait)
  }
}

p <- ggplot(df_plot, aes(fill=weight, y=h2_med, x=trait)) + geom_bar(position="dodge", stat="identity") + xlab("Trait") + ylab("Proportion of Mediated Heritability")
p <- p + theme_bw()

p

####################
#mediated heritability - top tissues only

n_top_tissues <- 2


for (i in 1:length(df_all)){
  h2_med <- sapply(df_all[[i]], function(x){x$pve})
  h2_med <- apply(h2_med, 2, function(x){x[1]/(sum(x))})
  
  h2_med <- (-sort(-h2_med))[1:n_top_tissues]

  weight <- names(h2_med)
  weight <- sapply(weight, function(x){paste(unlist(strsplit(x, "_")), collapse=" ")})
  df_plot_trait <- data.frame(h2_med=h2_med, weight=weight, trait=trait_names$trait_abbr[i])
  rownames(df_plot_trait) <- NULL
  
  if (i==1){
    df_plot <- df_plot_trait
  } else {
    df_plot <- rbind(df_plot, df_plot_trait)
  }
}

p <- ggplot(df_plot, aes(fill=weight, y=h2_med, x=trait)) + geom_bar(position="dodge", stat="identity") + xlab("Trait") + ylab("Proportion of Mediated Heritability")
p <- p + theme_bw()

p

####################

#maximum mediated heritability by trait
aggregate(h2_med ~ trait, data = df_plot, max)

  trait    h2_med
1   IBD 0.1415441
2   SCZ 0.1081910
3   SBP 0.1552622

Plot of all estimated parameters for each trait and weight

library(cowplot)

title_size <- 12

p_pi <- p_pi + theme(plot.title=element_text(size=title_size))
p_sigma2 <- p_sigma2 + theme(plot.title=element_text(size=title_size))
p_enrich <- p_enrich + theme(plot.title=element_text(size=title_size))
p_pve <- p_pve + theme(plot.title=element_text(size=title_size))

pdf(file = "output/ALL_parameters.pdf", width = 6, height = 4)

plot_grid(p_pi, p_sigma2, p_enrich, p_pve)

dev.off()

png 
  2 

Table of all estimated parameters for each trait and weight

for (i in 1:length(df_all)){
  prior <- sapply(df_all[[i]], function(x){x$prior})
  rownames(prior) <- c("prior_g", "prior_s")
  
  enrich <- prior[1,]/prior[2,]
  
  prior_var <- sapply(df_all[[i]], function(x){x$prior_var})
  rownames(prior_var) <- c("prior_var_g", "prior_var_s")
  
  pve <- sapply(df_all[[i]], function(x){x$pve})
  rownames(pve) <- c("pve_g", "pve_s")
  
  h2 <- colSums(pve)
  
  prop_h2_g <- apply(pve, 2, function(x){x[1]/sum(x)})
  
  weight <- colnames(prior)
  trait <- trait_names$trait_abbr[i]
  
  parameter_table_current <- as.data.frame(t(rbind(prior, prior_var, enrich, pve, h2, prop_h2_g)))
  
  parameter_table_current <- cbind(trait, weight, parameter_table_current)
  rownames(parameter_table_current) <- NULL
    
  if (i==1){
    parameter_table <- parameter_table_current
  } else {
    parameter_table <- rbind(parameter_table, parameter_table_current)
  }
}

write.csv(parameter_table, file="output/ALL_parameters.csv" ,row.names=F)

Number of genes in top tissue groups for each trait

weight_groups <- as.data.frame(matrix(c("Adipose_Subcutaneous", "Adipose",
                                        "Adipose_Visceral_Omentum", "Adipose",
                                        "Adrenal_Gland", "Endocrine",
                                        "Artery_Aorta", "Cardiovascular",                        
                                        "Artery_Coronary", "Cardiovascular",
                                        "Artery_Tibial", "Cardiovascular",
                                        "Brain_Amygdala", "CNS",
                                        "Brain_Anterior_cingulate_cortex_BA24", "CNS",
                                        "Brain_Caudate_basal_ganglia", "CNS",
                                        "Brain_Cerebellar_Hemisphere", "CNS",
                                        "Brain_Cerebellum", "CNS",
                                        "Brain_Cortex", "CNS",
                                        "Brain_Frontal_Cortex_BA9", "CNS",
                                        "Brain_Hippocampus", "CNS",
                                        "Brain_Hypothalamus", "CNS",
                                        "Brain_Nucleus_accumbens_basal_ganglia", "CNS",
                                        "Brain_Putamen_basal_ganglia", "CNS",
                                        "Brain_Spinal_cord_cervical_c-1", "CNS",
                                        "Brain_Substantia_nigra", "CNS",
                                        "Breast_Mammary_Tissue", "None",
                                        "Cells_Cultured_fibroblasts", "Skin",
                                        "Cells_EBV-transformed_lymphocytes", "Blood or Immune",
                                        "Colon_Sigmoid", "Digestive",
                                        "Colon_Transverse", "Digestive",
                                        "Esophagus_Gastroesophageal_Junction", "Digestive",
                                        "Esophagus_Mucosa", "Digestive",
                                        "Esophagus_Muscularis", "Digestive",
                                        "Heart_Atrial_Appendage", "Cardiovascular",
                                        "Heart_Left_Ventricle", "Cardiovascular",
                                        "Kidney_Cortex", "None",
                                        "Liver", "None",
                                        "Lung", "None",
                                        "Minor_Salivary_Gland", "None",
                                        "Muscle_Skeletal", "None",
                                        "Nerve_Tibial", "None",
                                        "Ovary", "None",
                                        "Pancreas", "None",
                                        "Pituitary", "Endocrine",
                                        "Prostate", "None",
                                        "Skin_Not_Sun_Exposed_Suprapubic", "Skin",
                                        "Skin_Sun_Exposed_Lower_leg", "Skin",
                                        "Small_Intestine_Terminal_Ileum", "Digestive",
                                        "Spleen", "Blood or Immune",
                                        "Stomach", "Digestive",
                                        "Testis", "Endocrine",
                                        "Thyroid", "Endocrine",
                                        "Uterus", "None",
                                        "Vagina", "None",
                                        "Whole_Blood", "Blood or Immune"),
                                      nrow=49, ncol=2, byrow=T), stringsAsFactors=F)
colnames(weight_groups) <- c("weight", "group")

n_top_tissue_groups <- 2

for (i in 1:length(df_all)){
  trait <- trait_names$trait_abbr[trait_names$trait_id==names(df_all)[i]]

  ctwas_genes_by_group <- list()
  ctwas_genes_by_tissue <- sapply(df_all[[i]], function(x){x$ctwas})
  
  for (j in 1:length(ctwas_genes_by_tissue)){
    weight <- names(ctwas_genes_by_tissue)[j]
    group <- weight_groups$group[weight_groups$weight==weight]
    ctwas_genes_by_group[[group]] <- c(ctwas_genes_by_group[[group]], ctwas_genes_by_tissue[j])
  }
  
  ctwas_genes_by_group$None <- NULL
  
  top_groups <- rev(sort(sapply(ctwas_genes_by_group, function(x){length(unique(unlist(x)))})))[1:n_top_tissue_groups]
  
  df_plot_trait <- data.frame(weight=as.character(names(top_groups)), trait=as.character(trait), n_ctwas=as.numeric(top_groups))
  rownames(df_plot_trait) <- NULL
  
  #df_plot_trait <- rbind(df_plot_trait, data.frame(weight="",trait="",n_ctwas=0))
  
  if (i==1){
    df_plot <- df_plot_trait
  } else {
    df_plot <- rbind(df_plot, df_plot_trait)
  }
}


ggplot(df_plot, aes(fill=weight, y=n_ctwas, x=trait)) + geom_bar(position="dodge", stat="identity") + xlab("Trait") + ylab("Number of cTWAS Genes")

Past versions of unnamed-chunk-12-1.png

Number of genes in top tissues for each trait

n_top_tissue_groups <- 2

for (i in 1:length(df_all)){
  trait <- trait_names$trait_abbr[trait_names$trait_id==names(df_all)[i]]
  ctwas_genes_by_tissue <- sapply(df_all[[i]], function(x){length(x$ctwas)})
  ctwas_genes_by_tissue <- rev(sort(ctwas_genes_by_tissue))[1:n_top_tissue_groups]
  
  df_plot_trait <- data.frame(weight=as.character(names(ctwas_genes_by_tissue)), trait=as.character(trait), n_ctwas=as.numeric(ctwas_genes_by_tissue))
  rownames(df_plot_trait) <- NULL

  if (i==1){
    df_plot <- df_plot_trait
  } else {
    df_plot <- rbind(df_plot, df_plot_trait)
  }

}

df_plot$weight <- sapply(as.character(df_plot$weight), function(x){paste(unlist(strsplit(x, "_")), collapse=" ")})

ggplot(df_plot, aes(fill=weight, y=n_ctwas, x=trait)) + geom_bar(position="dodge", stat="identity") + xlab("Trait") + ylab("Number of cTWAS Genes")

Past versions of unnamed-chunk-13-1.png

####################

df_plot <-  df_plot[order(as.character(df_plot$trait)),]

pdf(file = "output/ALL_number_ctwas_genes.pdf", width = 2.75, height = 3)

par(mar=c(6.6, 3.6, 1.6, 0.6))

barplot(df_plot$n_ctwas, names.arg=df_plot$weight, las=2, ylab="Number of cTWAS Genes", main="",
        cex.lab=0.7,
        cex.axis=0.7,
        cex.names=0.7,
        space=c(0.4, 0, 0.4, 0, 0.4, 0),
        col=rep(c("grey50", "grey"),3),
        axis.lty=1)

dev.off()

png 
  2 

Session information

sessionInfo()

R version 3.6.1 (2019-07-05)
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] cowplot_1.1.1 ggplot2_3.3.5

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.6       pillar_1.7.0     compiler_3.6.1   later_0.8.0     
 [5] git2r_0.26.1     workflowr_1.6.2  tools_3.6.1      bit_4.0.4       
 [9] digest_0.6.20    memoise_2.0.0    RSQLite_2.2.7    evaluate_0.14   
[13] lifecycle_1.0.1  tibble_3.1.7     gtable_0.3.0     pkgconfig_2.0.3 
[17] rlang_1.0.2      DBI_1.1.1        cli_3.3.0        yaml_2.2.0      
[21] xfun_0.8         fastmap_1.1.0    withr_2.4.1      stringr_1.4.0   
[25] dplyr_1.0.9      knitr_1.23       generics_0.0.2   fs_1.5.2        
[29] vctrs_0.4.1      bit64_4.0.5      tidyselect_1.1.2 rprojroot_2.0.2 
[33] grid_3.6.1       glue_1.6.2       R6_2.5.0         fansi_0.5.0     
[37] rmarkdown_1.13   blob_1.2.1       farver_2.1.0     purrr_0.3.4     
[41] magrittr_2.0.3   whisker_0.3-2    scales_1.2.0     promises_1.0.1  
[45] ellipsis_0.3.2   htmltools_0.5.2  colorspace_1.4-1 httpuv_1.5.1    
[49] labeling_0.3     utf8_1.2.1       stringi_1.4.3    munsell_0.5.0   
[53] cachem_1.0.5     crayon_1.4.1