marker_violin
Last updated: 2020-10-23
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Knit directory: DO_Opioid/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 0884b85 | xhyuo | 2020-10-23 | violin |
# loading libraries -----------------------------------------------------------------------
library("ggplot2")
library("gridExtra")
library("qtl2")
library("openxlsx")
options(stringsAsFactors = F)
rz.transform <- function(y) {
rankY=rank(y, ties.method="average", na.last="keep")
rzT=qnorm(rankY/(length(na.exclude(rankY))+1))
return(rzT)
}###first batch
#variation explained
#first batch
# Read phenotype data -----------------------------------------------------
do.pheno <- read.csv("data/pheno_qtl2_w_dob.csv")
do.pheno$DOB[do.pheno$DOB == "#N/A"] <- NA
#covar
GM_covar <- read.csv("data/GM_covar_07092018_morphine.csv",header = T)
rownames(GM_covar) <- gsub("morphine_DO_jbubier_","", GM_covar$id)
do.pheno <- merge(do.pheno, GM_covar, by.x = "X", by.y = "X", all.x = TRUE)
do.pheno$Survival.Time <- as.numeric(do.pheno$Survival.Time)
do.pheno$Recovery.Time <- as.numeric(do.pheno$Recovery.Time)
do.pheno$Min.depression <- as.numeric(do.pheno$Min.depression)
do.pheno$sex <- as.factor(do.pheno$sex)
rownames(do.pheno) <- gsub("morphine_DO_jbubier_","", do.pheno$X)
do.pheno$age <- as.Date(do.pheno$DOT) - as.Date(do.pheno$DOB, "%m/%d/%y")
# Read genotype data -----------------------------------------------------------
#load json file
do <- read_cross2(file="data/gm.json")
#ind_ids(do)
#replace id names
old_ids <- paste0(as.character(do.call(rbind.data.frame, strsplit(ind_ids(do), "_"))[,6]),"_",
as.character(do.call(rbind.data.frame, strsplit(ind_ids(do), "_"))[,7]))
new_ids <- setNames(old_ids,
ind_ids(do))
do <- replace_ids(do, new_ids)
#
#morphine pheno
morphine.pheno <- read.csv("data/pheno_qtl2_07092018_morphine.csv",header = T)
rownames(morphine.pheno) <- gsub("morphine_DO_jbubier_","", morphine.pheno$X)
morphine.pheno <- morphine.pheno[rownames(do.pheno),]
# #subset DO to DO morphine data
morphine.id <- rownames(morphine.pheno)
#subset
do.morphine <- do[morphine.id,]
#pmap and gmap
chr.names <- c("1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17","18","19", "X")
gmap <- list()
pmap <- list()
for (chr in chr.names){
gmap[[chr]] <- do.morphine$gmap[[chr]]
pmap[[chr]] <- do.morphine$pmap[[chr]]
}
attr(gmap, "is_x_chr") <- structure(c(rep(FALSE,19),TRUE), names=1:20)
attr(pmap, "is_x_chr") <- structure(c(rep(FALSE,19),TRUE), names=1:20)
#addcovar
do.pheno$DOT <- as.factor(do.pheno$DOT)
options(na.action='na.pass')
addcovar = model.matrix(~sex + weight + age + DOT, data = do.pheno)[,-1]
colnames(addcovar)[1] <- "sex"
pheno.name <- c("Survival.Time", "Recovery.Time", "Min.depression")
#variation by covariates
first_batch_var <- list()
for(i in pheno.name){
linearMod <- lm(do.pheno[,i] ~ sex + weight + age + DOT, data=do.pheno, na.action = na.omit) # build linear regression model on full data
#print(linearMod)
first_batch_var[[i]] <- summary(linearMod)$r.squared
}
#find peaks for every trait
load("output/qtl.morphine.out.weight_age.RData")
aprobs <- readRDS("output/do.morphine.probs_8state.rds")
g <- px <- first_batch_peak_var <- peaks <- p1 <- list()
pdf("output/DO_morphine_first_batch_peak_violin.pdf")
for(i in pheno.name){
peaks[[i]] <- qtl.morphine.peaks[[i]][which.max(qtl.morphine.peaks[[i]]$lod),]
marker <- find_marker(pmap, peaks[[i]]$chr, pos=peaks[[i]]$pos)
chr = peaks[[i]]$chr
peak_Mbp <- pmap[[chr]][marker]
g[[i]] <- maxmarg(aprobs, do.morphine$gmap, minprob = 0.4, chr=chr, pos=peaks[[i]]$pos, return_char = TRUE)
g[[i]] <- g[[i]][rownames(morphine.pheno)]
stopifnot(all.equal(names(g[[i]]), rownames(morphine.pheno)))
#variation
marker_apr <- pull_genoprobpos(aprobs, marker=marker)
linearMod <- lm(do.pheno[,i] ~ sex + weight + age + DOT + marker_apr, data=do.pheno, na.action = na.omit) # build linear regression model on full data
#print(linearMod)
first_batch_peak_var[[i]] <- summary(linearMod)$r.squared
px[[i]] <- cbind(g[[i]], morphine.pheno)
colnames(px[[i]])[1] <- "Strain"
#px[[i]] <- px[!is.na(px$Strain),]
px[[i]]$Strain <- factor(px[[i]]$Strain, levels = LETTERS[1:8])
#plot violin
p1[[i]] <- ggplot(data = px[[i]][!is.na(px[[i]]$Strain),], aes_string(x="Strain", y=i, group="Strain", fill="Strain")) +
geom_violin() +
geom_point() +
scale_fill_manual(drop=FALSE,labels = c("A/J", "C57BL/6J", "129S1/SvImJ", "NOD/ShiLtJ", "NZO/HlLtJ", "CAST/EiJ", "PWK/PhJ", "WSB/EiJ"),
values=as.vector(qtl2::CCcolors)) +
scale_x_discrete(drop=FALSE,labels=c("A" = "A/J",
"B" = "C57BL/6J",
"C" = "129S1/SvImJ",
"D" = "NOD/ShiLtJ",
"E" = "NZO/HlLtJ",
"F" = "CAST/EiJ",
"G" = "PWK/PhJ",
"H" = "WSB/EiJ")) +
xlab(paste0("marker:", marker, " chr", chr, ":", peak_Mbp, "Mbp")) +
theme(axis.text.x = element_text(angle = -45, hjust = 0.1, vjust = 0.1),
text = element_text(size=14))
print(p1[[i]])
}Warning: Removed 198 rows containing non-finite values (stat_ydensity).Warning: Removed 198 rows containing missing values (geom_point).Warning: Removed 121 rows containing non-finite values (stat_ydensity).Warning: Removed 121 rows containing missing values (geom_point).Warning: Removed 26 rows containing non-finite values (stat_ydensity).Warning: Removed 26 rows containing missing values (geom_point).dev.off()png
2 save(first_batch_var,
first_batch_peak_var,
g,
px,
peaks,
p1,
file = "output/first_batch_variation.RData")
for(i in pheno.name){
print(p1[[i]])
}Warning: Removed 198 rows containing non-finite values (stat_ydensity).Warning: Removed 198 rows containing missing values (geom_point).
Warning: Removed 121 rows containing non-finite values (stat_ydensity).Warning: Removed 121 rows containing missing values (geom_point).
Warning: Removed 26 rows containing non-finite values (stat_ydensity).Warning: Removed 26 rows containing missing values (geom_point).
###second batch
# Read phenotype data -----------------------------------------------------
second_set <- read.xlsx(xlsxFile = "data/MasterMorphine Second Set DO w DOB2.xlsx",
sheet = 1, detectDates = TRUE)
second_set <- second_set[,-11]
colnames(second_set)[2] <- "Sex"
colnames(second_set)[6] <- "Status24"
second_set$Date <- as.character(second_set$Date)
second_set$DOB <- as.character(second_set$DOB)
second_set$DOT <- as.character(second_set$DOT)
second_set[is.na(second_set$DOT),"DOT"] <- second_set[is.na(second_set$DOT),"Date"]
second_set$age <- as.Date(second_set$DOT) - as.Date(second_set$DOB)
#character
second_set[,c("ID", "Sex", "GROUP", "Status24")] <- lapply(second_set[, c("ID", "Sex", "GROUP", "Status24")], as.character)
second_set$Status_bin <- ifelse(second_set$Status24 == "ALIVE", 1, 0)
second_set <- second_set[-205,] #remove one duplicated "18189"
rownames(second_set) <- second_set$ID
#remove outlier
second_set <- second_set[second_set$ID != "empty",]
#remove negative survival time subjects
second_set <- second_set[!(!is.na(second_set$Survival.Time) & second_set$Survival.Time < 0),]
second_set$Survival.Time[!is.na(second_set$Survival.Time) & second_set$Survival.Time > 15] <- NA
second_set$Recovery.Time[!is.na(second_set$Recovery.Time) & second_set$Recovery.Time > 24] <- NA
second_set$Min.depression[!is.na(second_set$Min.depression) & second_set$Min.depression > 1] <- NA
#load json object
load("/projects/csna/csna_workflow/data/Jackson_Lab_11_batches/gm_DO2816_qc.RData")
#replace id names
old_ids <- paste0(as.character(do.call(rbind.data.frame, strsplit(ind_ids(gm_DO2816_qc), "_"))[,6]))
old_ids <- make.unique(as.character(old_ids), sep = "_")
new_ids <- setNames(old_ids,
ind_ids(gm_DO2816_qc))
second_set_gm <- replace_ids(gm_DO2816_qc, new_ids)[intersect(second_set$ID,old_ids), ]
#covar
second_set <- second_set[intersect(second_set$ID,old_ids), ]
second_set <- second_set[ind_ids(second_set_gm), ]
rownames(second_set) <- second_set$ID
all.equal(ind_ids(second_set_gm), second_set$ID)[1] TRUEsecond_set$Sex <- as.factor(second_set$Sex)
second_set$DOT <- as.factor(second_set$DOT)
second_set$DOB <- as.factor(second_set$DOB)
#addcovar
options(na.action='na.pass')
addcovar = model.matrix(~Sex + weight + age + DOT, data = second_set)[,-1]
colnames(addcovar)[1] <- "Sex"
pheno.name <- c("Survival.Time", "Recovery.Time", "Min.depression")
#variation by covariates
second_batch_var <- list()
for(i in pheno.name){
linearMod <- lm(second_set[,i] ~ Sex + weight + age + DOT, data=second_set, na.action = na.omit) # build linear regression model on full data
#print(linearMod)
second_batch_var[[i]] <- summary(linearMod)$r.squared
}
#find peaks for every trait
load("output/qtl.morphine.out.second_set.weight_age.RData")
aprobs <- readRDS("output/second_set_gm.probs_8state.rds")
g <- px <- second_batch_peak_var <- peaks <- p2 <- list()
pdf("output/DO_morphine_second_batch_peak_violin.pdf")
for(i in pheno.name){
peaks[[i]] <- qtl.morphine.peaks[[i]][which.max(qtl.morphine.peaks[[i]]$lod),]
marker <- find_marker(second_set_gm$pmap, peaks[[i]]$chr, pos=peaks[[i]]$pos)
chr = peaks[[i]]$chr
peak_Mbp <- second_set_gm$pmap[[chr]][marker]
g[[i]] <- maxmarg(aprobs, second_set_gm$gmap, minprob = 0.4, chr=chr, pos=peaks[[i]]$pos, return_char = TRUE)
g[[i]] <- g[[i]][rownames(second_set)]
stopifnot(all.equal(names(g[[i]]), rownames(second_set)))
#variation
marker_apr <- pull_genoprobpos(aprobs, marker=marker)
linearMod <- lm(second_set[,i] ~ Sex + weight + age + DOT + marker_apr, data=second_set, na.action = na.omit) # build linear regression model on full data
#print(linearMod)
second_batch_peak_var[[i]] <- summary(linearMod)$r.squared
px[[i]] <- cbind(g[[i]], second_set)
colnames(px[[i]])[1] <- "Strain"
#px[[i]] <- px[!is.na(px$Strain),]
#plot violin
p2[[i]] <- ggplot(data = px[[i]][!is.na(px[[i]]$Strain),], aes_string(x="Strain", y=i, group="Strain", fill="Strain")) +
geom_violin() +
geom_point() +
scale_fill_manual(drop=FALSE,labels = c("A/J", "C57BL/6J", "129S1/SvImJ", "NOD/ShiLtJ", "NZO/HlLtJ", "CAST/EiJ", "PWK/PhJ", "WSB/EiJ"),
values=as.vector(qtl2::CCcolors)) +
scale_x_discrete(drop=FALSE,labels=c("A" = "A/J",
"B" = "C57BL/6J",
"C" = "129S1/SvImJ",
"D" = "NOD/ShiLtJ",
"E" = "NZO/HlLtJ",
"F" = "CAST/EiJ",
"G" = "PWK/PhJ",
"H" = "WSB/EiJ")) +
xlab(paste0("second batch marker:", marker, " chr", chr, ":", peak_Mbp, "Mbp")) +
theme(axis.text.x = element_text(angle = -45, hjust = 0.1, vjust = 0.1),
text = element_text(size=14))
print(p2[[i]])
}Warning: Removed 191 rows containing non-finite values (stat_ydensity).Warning: Removed 191 rows containing missing values (geom_point).Warning: Removed 149 rows containing non-finite values (stat_ydensity).Warning: Removed 149 rows containing missing values (geom_point).Warning: Removed 16 rows containing non-finite values (stat_ydensity).Warning: Removed 16 rows containing missing values (geom_point).dev.off()png
2 save(second_batch_var,
second_batch_peak_var,
g,
px,
peaks,
p2,
file = "output/second_batch_variation.RData")
for(i in pheno.name){
print(p2[[i]])
}Warning: Removed 191 rows containing non-finite values (stat_ydensity).Warning: Removed 191 rows containing missing values (geom_point).
Warning: Removed 149 rows containing non-finite values (stat_ydensity).Warning: Removed 149 rows containing missing values (geom_point).
Warning: Removed 16 rows containing non-finite values (stat_ydensity).Warning: Removed 16 rows containing missing values (geom_point).
###first batch peak marker in second batch
#load first batch
load("output/first_batch_variation.RData")
pp <- peaks
g <- px <- second_batch_peak_var <- p3 <-list()
pdf("output/DO_morphine_first_batch_peak_in_second_batch_violin.pdf")
for(i in pheno.name){
peaks <- pp[[i]]
marker <- find_marker(second_set_gm$pmap, peaks$chr, pos=peaks$pos)
chr = peaks$chr
peak_Mbp <- second_set_gm$pmap[[chr]][marker]
g[[i]] <- maxmarg(aprobs, second_set_gm$gmap, minprob = 0.4, chr=chr, pos=peaks$pos, return_char = TRUE)
g[[i]] <- g[[i]][rownames(second_set)]
stopifnot(all.equal(names(g[[i]]), rownames(second_set)))
#variation
marker_apr <- pull_genoprobpos(aprobs, marker=marker)
linearMod <- lm(second_set[,i] ~ Sex + weight + age + DOT + marker_apr, data=second_set, na.action = na.omit) # build linear regression model on full data
#print(linearMod)
second_batch_peak_var[[i]] <- summary(linearMod)$r.squared
px[[i]] <- cbind(g[[i]], second_set)
colnames(px[[i]])[1] <- "Strain"
#px[[i]] <- px[!is.na(px$Strain),]
#plot violin
p3[[i]] <- ggplot(data = px[[i]][!is.na(px[[i]]$Strain),], aes_string(x="Strain", y=i, group="Strain", fill="Strain")) +
geom_violin() +
geom_point() +
scale_fill_manual(drop=FALSE,labels = c("A/J", "C57BL/6J", "129S1/SvImJ", "NOD/ShiLtJ", "NZO/HlLtJ", "CAST/EiJ", "PWK/PhJ", "WSB/EiJ"),
values=as.vector(qtl2::CCcolors)) +
scale_x_discrete(drop=FALSE,labels=c("A" = "A/J",
"B" = "C57BL/6J",
"C" = "129S1/SvImJ",
"D" = "NOD/ShiLtJ",
"E" = "NZO/HlLtJ",
"F" = "CAST/EiJ",
"G" = "PWK/PhJ",
"H" = "WSB/EiJ")) +
xlab(paste0("first batch marker in second batch:", marker, " chr", chr, ":", peak_Mbp, "Mbp")) +
theme(axis.text.x = element_text(angle = -45, hjust = 0.1, vjust = 0.1),
text = element_text(size=14))
print(p3[[i]])
}Warning: Removed 191 rows containing non-finite values (stat_ydensity).Warning: Removed 191 rows containing missing values (geom_point).Warning: Removed 151 rows containing non-finite values (stat_ydensity).Warning: Removed 151 rows containing missing values (geom_point).Warning: Removed 16 rows containing non-finite values (stat_ydensity).Warning: Removed 16 rows containing missing values (geom_point).dev.off()png
2 pdf("output/DO_morphine_first_batch_peak_in_second_batch_violin_sidebyside.pdf", width = 20)
grid.arrange(p1$Survival.Time, p3$Survival.Time, ncol=2)Warning: Removed 198 rows containing non-finite values (stat_ydensity).Warning: Removed 198 rows containing missing values (geom_point).Warning: Removed 191 rows containing non-finite values (stat_ydensity).Warning: Removed 191 rows containing missing values (geom_point). grid.arrange(p1$Recovery.Time, p3$Recovery.Time, ncol=2)Warning: Removed 121 rows containing non-finite values (stat_ydensity).Warning: Removed 121 rows containing missing values (geom_point).Warning: Removed 151 rows containing non-finite values (stat_ydensity).Warning: Removed 151 rows containing missing values (geom_point). grid.arrange(p1$Min.depression, p3$Min.depression, ncol=2)Warning: Removed 26 rows containing non-finite values (stat_ydensity).Warning: Removed 26 rows containing missing values (geom_point).Warning: Removed 16 rows containing non-finite values (stat_ydensity).Warning: Removed 16 rows containing missing values (geom_point).dev.off()png
2 grid.arrange(p1$Survival.Time, p3$Survival.Time, ncol=2)Warning: Removed 198 rows containing non-finite values (stat_ydensity).Warning: Removed 198 rows containing missing values (geom_point).Warning: Removed 191 rows containing non-finite values (stat_ydensity).Warning: Removed 191 rows containing missing values (geom_point).
grid.arrange(p1$Recovery.Time, p3$Recovery.Time, ncol=2)Warning: Removed 121 rows containing non-finite values (stat_ydensity).Warning: Removed 121 rows containing missing values (geom_point).Warning: Removed 151 rows containing non-finite values (stat_ydensity).Warning: Removed 151 rows containing missing values (geom_point).
grid.arrange(p1$Min.depression, p3$Min.depression, ncol=2)Warning: Removed 26 rows containing non-finite values (stat_ydensity).Warning: Removed 26 rows containing missing values (geom_point).Warning: Removed 16 rows containing non-finite values (stat_ydensity).Warning: Removed 16 rows containing missing values (geom_point).
###second batch peak marker in first batch
#load second batch
load("output/second_batch_variation.RData")
aprobs <- readRDS("output/do.morphine.probs_8state.rds")
pp <- peaks
g <- px <- first_batch_peak_var <- p4 <-list()
pdf("output/DO_morphine_second_batch_peak_in_first_batch_violin.pdf")
for(i in pheno.name){
peaks <- pp[[i]]
marker <- find_marker(pmap, peaks$chr, pos=peaks$pos)
chr = peaks$chr
peak_Mbp <- pmap[[chr]][marker]
g[[i]] <- maxmarg(aprobs, do.morphine$gmap, minprob = 0.4, chr=chr, pos=peaks$pos, return_char = TRUE)
g[[i]] <- g[[i]][rownames(morphine.pheno)]
stopifnot(all.equal(names(g[[i]]), rownames(morphine.pheno)))
#variation
marker_apr <- pull_genoprobpos(aprobs, marker=marker)
px[[i]] <- cbind(g[[i]], morphine.pheno)
colnames(px[[i]])[1] <- "Strain"
#px[[i]] <- px[[i]][!is.na(px[[i]]$Strain),]
px[[i]]$Strain <- factor(px[[i]]$Strain, levels = LETTERS[1:8])
#plot violin
p4[[i]] <- ggplot(data = px[[i]][!is.na(px[[i]]$Strain),], aes_string(x="Strain", y=i, group="Strain", fill="Strain")) +
geom_violin() +
geom_point() +
scale_fill_manual(drop = FALSE, labels = c("A/J", "C57BL/6J", "129S1/SvImJ", "NOD/ShiLtJ", "NZO/HlLtJ", "CAST/EiJ", "PWK/PhJ", "WSB/EiJ"),
values=as.vector(qtl2::CCcolors)) +
scale_x_discrete(drop=FALSE,labels=c("A" = "A/J",
"B" = "C57BL/6J",
"C" = "129S1/SvImJ",
"D" = "NOD/ShiLtJ",
"E" = "NZO/HlLtJ",
"F" = "CAST/EiJ",
"G" = "PWK/PhJ",
"H" = "WSB/EiJ")) +
xlab(paste0("second batch marker in the first batch:", marker, " chr", chr, ":", peak_Mbp, "Mbp")) +
theme(axis.text.x = element_text(angle = -45, hjust = 0.1, vjust = 0.1),
text = element_text(size=14))
print(p4[[i]])
}Warning: Removed 198 rows containing non-finite values (stat_ydensity).Warning: Removed 198 rows containing missing values (geom_point).Warning: Removed 121 rows containing non-finite values (stat_ydensity).Warning: Removed 121 rows containing missing values (geom_point).Warning: Removed 27 rows containing non-finite values (stat_ydensity).Warning: Removed 27 rows containing missing values (geom_point).dev.off()png
2 pdf("output/DO_morphine_second_batch_peak_in_first_batch_violin_sidebyside.pdf", width = 20)
grid.arrange(p2$Survival.Time, p4$Survival.Time, ncol=2)Warning: Removed 191 rows containing non-finite values (stat_ydensity).Warning: Removed 191 rows containing missing values (geom_point).Warning: Removed 198 rows containing non-finite values (stat_ydensity).Warning: Removed 198 rows containing missing values (geom_point). grid.arrange(p2$Recovery.Time, p4$Recovery.Time, ncol=2)Warning: Removed 149 rows containing non-finite values (stat_ydensity).Warning: Removed 149 rows containing missing values (geom_point).Warning: Removed 121 rows containing non-finite values (stat_ydensity).Warning: Removed 121 rows containing missing values (geom_point). grid.arrange(p2$Min.depression, p4$Min.depression, ncol=2)Warning: Removed 16 rows containing non-finite values (stat_ydensity).Warning: Removed 16 rows containing missing values (geom_point).Warning: Removed 27 rows containing non-finite values (stat_ydensity).Warning: Removed 27 rows containing missing values (geom_point).dev.off()png
2 grid.arrange(p2$Survival.Time, p4$Survival.Time, ncol=2)Warning: Removed 191 rows containing non-finite values (stat_ydensity).Warning: Removed 191 rows containing missing values (geom_point).Warning: Removed 198 rows containing non-finite values (stat_ydensity).Warning: Removed 198 rows containing missing values (geom_point).
grid.arrange(p2$Recovery.Time, p4$Recovery.Time, ncol=2)Warning: Removed 149 rows containing non-finite values (stat_ydensity).Warning: Removed 149 rows containing missing values (geom_point).Warning: Removed 121 rows containing non-finite values (stat_ydensity).Warning: Removed 121 rows containing missing values (geom_point).
grid.arrange(p2$Min.depression, p4$Min.depression, ncol=2)Warning: Removed 16 rows containing non-finite values (stat_ydensity).Warning: Removed 16 rows containing missing values (geom_point).Warning: Removed 27 rows containing non-finite values (stat_ydensity).Warning: Removed 27 rows containing missing values (geom_point).
sessionInfo()R version 4.0.0 (2020-04-24)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 18.04.4 LTS
Matrix products: default
BLAS/LAPACK: /usr/lib/x86_64-linux-gnu/libopenblasp-r0.2.20.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=C
[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] openxlsx_4.2.2 qtl2_0.22-8 gridExtra_2.3 ggplot2_3.3.2
[5] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] zip_2.1.1 Rcpp_1.0.4.6 compiler_4.0.0 pillar_1.4.4
[5] later_1.0.0 git2r_0.27.1 tools_4.0.0 bit_1.1-15.2
[9] digest_0.6.25 jsonlite_1.6.1 memoise_1.1.0 RSQLite_2.2.0
[13] evaluate_0.14 lifecycle_0.2.0 tibble_3.0.1 gtable_0.3.0
[17] pkgconfig_2.0.3 rlang_0.4.6 DBI_1.1.0 parallel_4.0.0
[21] yaml_2.2.1 xfun_0.13 withr_2.2.0 stringr_1.4.0
[25] dplyr_1.0.0 knitr_1.28 generics_0.0.2 fs_1.4.1
[29] vctrs_0.3.1 bit64_0.9-7 rprojroot_1.3-2 grid_4.0.0
[33] tidyselect_1.1.0 data.table_1.12.8 glue_1.4.0 R6_2.4.1
[37] rmarkdown_2.3 farver_2.0.3 blob_1.2.1 purrr_0.3.4
[41] magrittr_1.5 whisker_0.4 backports_1.1.6 scales_1.1.1
[45] promises_1.1.0 htmltools_0.4.0 ellipsis_0.3.0 colorspace_1.4-1
[49] httpuv_1.5.4 labeling_0.3 stringi_1.4.6 munsell_0.5.0
[53] crayon_1.3.4 This R Markdown site was created with workflowr