Genotype Frequencies - ICI vs PBS

workflowr

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Last updated: 2022-04-06

Checks: 5 2

Knit directory: Serreze-T1D_Workflow/

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File paths: absolute

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/Users/corneb/Documents/MyJax/CS/Projects/Serreze/qc/workflowr/Serreze-T1D_Workflow	.

Repository version: 2d6f369

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    Untracked:  data/ici.vs.pbs_marker.freq_low.geno.freq.removed_sample.outliers.removed_geno.ratiov_5.batches.csv
    Untracked:  data/ici.vs.pbs_marker.freq_low.geno.freq.removed_sample.outliers.removed_geno.ratiov_5.batches_mis.csv
    Untracked:  data/ici.vs.pbs_marker.freq_low.probs.freq.removed_geno.ratio.csv
    Untracked:  data/ici.vs.pbs_marker.freq_low.probs.freq.removed_geno.ratio_5.batches.csv
    Untracked:  data/ici.vs.pbs_marker.freq_low.probs.freq.removed_geno.ratio_5.batches_mis.csv
    Untracked:  data/ici.vs.pbs_marker.freq_low.probs.freq.removed_sample.outliers.removed_geno.ratio.csv
    Untracked:  data/ici.vs.pbs_marker.freq_low.probs.freq.removed_sample.outliers.removed_geno.ratio_5.batches.csv
    Untracked:  data/ici.vs.pbs_marker.freq_low.probs.freq.removed_sample.outliers.removed_geno.ratio_5.batches_mis.csv
    Untracked:  data/ici.vs.pbs_sample.genos_marker.freq_low.geno.freq.removed.csv
    Untracked:  data/ici.vs.pbs_sample.genos_marker.freq_low.geno.freq.removed_sample.outliers.removed.csv
    Untracked:  data/ici.vs.pbs_sample.genos_marker.freq_low.probs.freq.removed.csv
    Untracked:  data/ici.vs.pbs_sample.genos_marker.freq_low.probs.freq.removed_sample.outliers.removed.csv
    Untracked:  data/percent_missing_id_3.batches.RData
    Untracked:  data/percent_missing_id_4.batches.RData
    Untracked:  data/percent_missing_id_4.batches_bc.RData
    Untracked:  data/percent_missing_id_5.batches.RData
    Untracked:  data/percent_missing_marker_4.batches.RData
    Untracked:  data/percent_missing_marker_5.batches.RData
    Untracked:  data/pheno.csv
    Untracked:  data/pheno_BC312.csv
    Untracked:  data/physical_map.csv
    Untracked:  data/physical_map_BC312.csv
    Untracked:  data/qc_info_bad_sample_3.batches.RData
    Untracked:  data/qc_info_bad_sample_4.batches.RData
    Untracked:  data/qc_info_bad_sample_4.batches_bc.RData
    Untracked:  data/qc_info_bad_sample_5.batches.RData
    Untracked:  data/remaining.markers_geno.freq.xlsx
    Untracked:  data/sample_geno.csv
    Untracked:  data/sample_geno_AHB_BC312.csv
    Untracked:  data/sample_geno_bc.csv
    Untracked:  data/sample_geno_bc_BC312.csv
    Untracked:  data/serreze_probs.rds
    Untracked:  data/serreze_probs_BC312.rds
    Untracked:  data/serreze_probs_allqc.rds
    Untracked:  data/serreze_probs_allqc_5.batches.rds
    Untracked:  data/serreze_probs_allqc_5.batches_mis.rds
    Untracked:  data/summary.cg_3.batches.RData
    Untracked:  data/summary.cg_4.batches.RData
    Untracked:  data/summary.cg_4.batches_bc.RData
    Untracked:  data/summary.cg_5.batches.RData
    Untracked:  output/Percent_missing_genotype_data_5.batches.pdf
    Untracked:  output/Percent_missing_genotype_data_per_marker_5.batches.pdf
    Untracked:  output/Proportion_matching_genotypes_before_removal_of_bad_samples_5.batches.pdf
    Untracked:  output/genotype_error_marker_5.batches.pdf
    Untracked:  output/genotype_frequency_marker_5.batches.pdf

Unstaged changes:
    Modified:   analysis/4.1.1_qtl.analysis_binary_ici.vs.eoi_snpsqc_dis_no-x_updated.Rmd
    Modified:   analysis/4.1.1_qtl.analysis_binary_ici.vs.pbs_snpsqc_dis_no-x_updated.Rmd

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.

---

There are no past versions. Publish this analysis with wflow_publish() to start tracking its development.

---

with sample outliers

load("data/gm_allqc_5.batches_mis.RData")

#gm_allqc
gm=gm_allqc
gm

Object of class cross2 (crosstype "bc")

Total individuals               308
No. genotyped individuals       308
No. phenotyped individuals      308
No. with both geno & pheno      308

No. phenotypes                    1
No. covariates                    6
No. phenotype covariates          0

No. chromosomes                  20
Total markers                131356

No. markers by chr:
   1    2    3    4    5    6    7    8    9   10   11   12   13   14   15   16 
9956 9987 7848 7586 7609 7736 7399 6458 6713 6385 7143 6110 6082 5966 5346 5015 
  17   18   19    X 
5080 4605 3562 4770 

pr <- readRDS("data/serreze_probs_allqc_5.batches_mis.rds")
#pr <- readRDS("data/serreze_probs.rds")

geno <- read.csv("/Users/corneb/Documents/MyJax/CS/Projects/Serreze/haplotype.reconstruction/output_5.batches/sample_geno_bc.csv", as.is=T)
names(geno) <- gsub("\\.","-",names(geno))
rownames(geno) <- geno$marker

## extracting animals with ici and pbs group status
miceinfo <- gm$covar[gm$covar$group == "PBS" | gm$covar$group == "ICI",]
table(miceinfo$group)

ICI PBS 
104  34 

mice.ids <- rownames(miceinfo)

gm <- gm[mice.ids]
gm

Object of class cross2 (crosstype "bc")

Total individuals               138
No. genotyped individuals       138
No. phenotyped individuals      138
No. with both geno & pheno      138

No. phenotypes                    1
No. covariates                    6
No. phenotype covariates          0

No. chromosomes                  20
Total markers                131356

No. markers by chr:
   1    2    3    4    5    6    7    8    9   10   11   12   13   14   15   16 
9956 9987 7848 7586 7609 7736 7399 6458 6713 6385 7143 6110 6082 5966 5346 5015 
  17   18   19    X 
5080 4605 3562 4770 

table(gm$covar$group)

ICI PBS 
104  34 

covars <- read_csv("data/covar_corrected_ici.vs.pbs_5.batches_mis.csv")
# removing any missing info
#covars <- subset(covars, covars$age.of.onset!='')
nrow(covars)

[1] 138

table(covars$group)

ICI PBS 
104  34 

# keeping only informative mice
gm <- gm[covars$Mouse.ID]
gm

Object of class cross2 (crosstype "bc")

Total individuals               138
No. genotyped individuals       138
No. phenotyped individuals      138
No. with both geno & pheno      138

No. phenotypes                    1
No. covariates                    6
No. phenotype covariates          0

No. chromosomes                  20
Total markers                131356

No. markers by chr:
   1    2    3    4    5    6    7    8    9   10   11   12   13   14   15   16 
9956 9987 7848 7586 7609 7736 7399 6458 6713 6385 7143 6110 6082 5966 5346 5015 
  17   18   19    X 
5080 4605 3562 4770 

table(gm$covar$group)

ICI PBS 
104  34 

pr.qc.ids <- pr
for (i in 1:20){pr.qc.ids[[i]] = pr.qc.ids[[i]][covars$Mouse.ID,,]}

geno <- geno[,covars$Mouse.ID]
geno <- geno[marker_names(gm),]
dim(geno)

[1] 131356    138

## calculating genotype frequencies
### from geno genotypes
g <- do.call("cbind", gm$geno)
gf_mar_geno <- t(apply(g, 2, function(a) table(factor(a, 1:2))/sum(a != 0)))
gn_mar_geno <- t(apply(g, 2, function(a) table(factor(a, 0:2))))
#gf_mar_raw<- gf_mar_raw[gf_mar_raw[,2] != "NaN",]
colnames(gf_mar_geno) <- c("freq_AA_geno_table","freq_AB_geno_table")
colnames(gn_mar_geno) <- c("count_missing_geno_table","count_AA_geno_table","count_AB_geno_table")
gfn_mar_geno <- merge(as.data.frame(gn_mar_geno), as.data.frame(gf_mar_geno), by="row.names")
rownames(gfn_mar_geno) <- gfn_mar_geno[,1]
gfn_mar_geno <- gfn_mar_geno[-1]

### from raw using table function in R
#genosl <- list()
#for(i in 1:nrow(geno)){
##for(i in 1:3){
#    genoi <- geno[i,]
#    freqf <- table(factor(geno[i,], c("-","AA","AB")))
#    genoi$count_AA_raw_rowSums <- rowSums(genoi == "AA")
#    genoi$count_AB_raw_rowSums <- rowSums(genoi == "AB")
#    genoi$count_missing_raw_rowSums <- rowSums(genoi == "-")
#    freqf <- t(table(factor(geno[i,], c("-","AA","AB"))))
#    freqf <- as.data.frame(t(freqf[1,]))
#    rownames(freqf) <- rownames(genoi)
#    colnames(freqf) <- c("count_missing_raw_table","count_AA_raw_table","count_AB_raw_table")
#    genoif <- cbind(freqf,genoi[c("count_AA_raw_rowSums","count_AB_raw_rowSums","count_missing_raw_rowSums")])
#    genosl[[i]] = genoif
#}
#gf_mar_raw <- do.call("rbind",genosl)
#gf_mar_raw <- gf_mar_raw[,c(1:3,6,4:5)]
#gf_mar_raw$index <- 1:nrow(gf_mar_raw)

### from probabilities
gf_mar_probs.1 <- calc_geno_freq(pr.qc.ids, by = "marker", omit_x = FALSE)
#gn_mar_probs <- calc_geno_freq(probs, by = "individual", omit_x = FALSE)
gf_mar_probs <- rbind(gf_mar_probs.1$A[,1:2], gf_mar_probs.1$X[,1:2])
colnames(gf_mar_probs) <- paste0("freq_",colnames(gf_mar_probs),"_probs")
gf_mar_probs <- as.data.frame(gf_mar_probs)
gf_mar_probs$index <- 1:nrow(gf_mar_probs)

### merging all genotype frequecies for all markers
#gf_mar.1 <- merge(as.data.frame(gf_mar_raw), as.data.frame(gfn_mar_geno), by="row.names")
#rownames(gf_mar.1) <- gf_mar.1[,1]
#gf_mar.1 <- gf_mar.1[-1]
#gf_mar <- merge(gf_mar.1,as.data.frame(gf_mar_probs), by="row.names")
gf_mar <- merge(as.data.frame(gfn_mar_geno),as.data.frame(gf_mar_probs), by="row.names")
rownames(gf_mar) <- gf_mar[,1]
gf_mar <- gf_mar[-1]
gf_mar <- gf_mar[order(gf_mar$index),]
dim(gf_mar)

[1] 131356      8

# Calculating ratio and flagging informative marker
gf_mar$ratio = as.numeric(gf_mar$freq_AA_geno_table)/as.numeric(gf_mar$freq_AB_geno_table)
gf_mar$Include = ifelse(gf_mar$ratio >= 0.90 & gf_mar$ratio <= 1.10, TRUE,FALSE)
table(gf_mar$Include)

 FALSE   TRUE 
123174   8182 

## filtering out <= 0.05
gf_mar$count.geno <- rowSums(gf_mar[c("freq_AA_geno_table","freq_AB_geno_table")] <=0.05)
filtered_gf_mar_geno <- gf_mar[gf_mar$count.geno != 1,]
filtered_gf_mar_geno <- filtered_gf_mar_geno[,-which(names(filtered_gf_mar_geno) %in% c("count.geno","index"))]
dim(filtered_gf_mar_geno)

[1] 32691     9

table(filtered_gf_mar_geno$Include)

FALSE  TRUE 
24509  8182 

gf_mar$count.probs <- rowSums(gf_mar[c("freq_AA_probs","freq_AB_probs")] <=0.05)
filtered_gf_mar_probs <- gf_mar[gf_mar$count.probs != 1,]
filtered_gf_mar_probs <- filtered_gf_mar_probs[,-which(names(filtered_gf_mar_probs) %in% c("count.geno","count.probs","index"))]
dim(filtered_gf_mar_probs)

[1] 33118     9

table(filtered_gf_mar_probs$Include)

FALSE  TRUE 
26680  6438 

## merging with sample_genos
#filtered_gf_mar_geno_sample <- merge(geno,filtered_gf_mar_geno, by="row.names", all.y=T)
#filtered_gf_mar_geno_sample <- filtered_gf_mar_geno_sample[order(filtered_gf_mar_geno_sample$index),]     
#filtered_gf_mar_geno_sample <- filtered_gf_mar_geno_sample[,-which(names(filtered_gf_mar_geno_sample) %in% c("count.geno","index"))]
#names(filtered_gf_mar_geno_sample)[1] <- c("marker")
#dim(filtered_gf_mar_geno_sample)

#filtered_gf_mar_probs_sample <- merge(geno,filtered_gf_mar_probs, by="row.names", all.y=T)
#filtered_gf_mar_probs_sample <- filtered_gf_mar_probs_sample[order(filtered_gf_mar_probs_sample$index),]
#filtered_gf_mar_probs_sample <- filtered_gf_mar_probs_sample[,-which(names(filtered_gf_mar_probs_sample) %in% c("count.geno","count.probs","index"))]
#names(filtered_gf_mar_probs_sample)[1] <- c("marker")
#dim(filtered_gf_mar_probs_sample)

## saving files
#write.csv(filtered_gf_mar_geno_sample, "data/ici.vs.pbs_sample.genos_marker.freq_low.geno.freq.removed.csv", quote=F)
#write.csv(filtered_gf_mar_probs_sample, "data/ici.vs.pbs_sample.genos_marker.freq_low.probs.freq.removed.csv", quote=F)

write.csv(filtered_gf_mar_geno, "data/ici.vs.pbs_marker.freq_low.geno.freq.removed_geno.ratio_5.batches_mis.csv", quote=F)
write.csv(filtered_gf_mar_probs, "data/ici.vs.pbs_marker.freq_low.probs.freq.removed_geno.ratio_5.batches_mis.csv", quote=F)

sample outliers removed

load("data/gm_allqc_5.batches_mis.RData")

#gm_allqc
gm=gm_allqc
gm

Object of class cross2 (crosstype "bc")

Total individuals               308
No. genotyped individuals       308
No. phenotyped individuals      308
No. with both geno & pheno      308

No. phenotypes                    1
No. covariates                    6
No. phenotype covariates          0

No. chromosomes                  20
Total markers                131356

No. markers by chr:
   1    2    3    4    5    6    7    8    9   10   11   12   13   14   15   16 
9956 9987 7848 7586 7609 7736 7399 6458 6713 6385 7143 6110 6082 5966 5346 5015 
  17   18   19    X 
5080 4605 3562 4770 

pr <- readRDS("data/serreze_probs_allqc_5.batches_mis.rds")
#pr <- readRDS("data/serreze_probs.rds")

geno <- read.csv("/Users/corneb/Documents/MyJax/CS/Projects/Serreze/haplotype.reconstruction/output_5.batches/sample_geno_bc.csv", as.is=T)
names(geno) <- gsub("\\.","-",names(geno))
rownames(geno) <- geno$marker

## extracting animals with ici and pbs group status
miceinfo <- gm$covar[gm$covar$group == "PBS" | gm$covar$group == "ICI",]
table(miceinfo$group)

ICI PBS 
104  34 

mice.ids <- rownames(miceinfo)

gm <- gm[mice.ids]
gm

Object of class cross2 (crosstype "bc")

Total individuals               138
No. genotyped individuals       138
No. phenotyped individuals      138
No. with both geno & pheno      138

No. phenotypes                    1
No. covariates                    6
No. phenotype covariates          0

No. chromosomes                  20
Total markers                131356

No. markers by chr:
   1    2    3    4    5    6    7    8    9   10   11   12   13   14   15   16 
9956 9987 7848 7586 7609 7736 7399 6458 6713 6385 7143 6110 6082 5966 5346 5015 
  17   18   19    X 
5080 4605 3562 4770 

table(gm$covar$group)

ICI PBS 
104  34 

covars <- read_csv("data/covar_corrected.cleaned_ici.vs.pbs_5.batches_mis.csv")
# removing any missing info
covars <- subset(covars, covars$age.of.onset!='')
nrow(covars)

[1] 138

table(covars$group)

ICI PBS 
104  34 

# keeping only informative mice
gm <- gm[covars$Mouse.ID]
gm

Object of class cross2 (crosstype "bc")

Total individuals               138
No. genotyped individuals       138
No. phenotyped individuals      138
No. with both geno & pheno      138

No. phenotypes                    1
No. covariates                    6
No. phenotype covariates          0

No. chromosomes                  20
Total markers                131356

No. markers by chr:
   1    2    3    4    5    6    7    8    9   10   11   12   13   14   15   16 
9956 9987 7848 7586 7609 7736 7399 6458 6713 6385 7143 6110 6082 5966 5346 5015 
  17   18   19    X 
5080 4605 3562 4770 

table(gm$covar$group)

ICI PBS 
104  34 

pr.qc.ids <- pr
for (i in 1:20){pr.qc.ids[[i]] = pr.qc.ids[[i]][covars$Mouse.ID,,]}

geno <- geno[,covars$Mouse.ID]
geno <- geno[marker_names(gm),]
dim(geno)

[1] 131356    138

## calculating genotype frequencies
### from geno genotypes
g <- do.call("cbind", gm$geno)
gf_mar_geno <- t(apply(g, 2, function(a) table(factor(a, 1:2))/sum(a != 0)))
gn_mar_geno <- t(apply(g, 2, function(a) table(factor(a, 0:2))))
#gf_mar_raw<- gf_mar_raw[gf_mar_raw[,2] != "NaN",]
colnames(gf_mar_geno) <- c("freq_AA_geno_table","freq_AB_geno_table")
colnames(gn_mar_geno) <- c("count_missing_geno_table","count_AA_geno_table","count_AB_geno_table")
gfn_mar_geno <- merge(as.data.frame(gn_mar_geno), as.data.frame(gf_mar_geno), by="row.names")
rownames(gfn_mar_geno) <- gfn_mar_geno[,1]
gfn_mar_geno <- gfn_mar_geno[-1]

### from raw using table function in R
#genosl <- list()
#for(i in 1:nrow(geno)){
##for(i in 1:3){
#    genoi <- geno[i,]
#    freqf <- table(factor(geno[i,], c("-","AA","AB")))
#    genoi$count_AA_raw_rowSums <- rowSums(genoi == "AA")
#    genoi$count_AB_raw_rowSums <- rowSums(genoi == "AB")
#    genoi$count_missing_raw_rowSums <- rowSums(genoi == "-")
#    freqf <- t(table(factor(geno[i,], c("-","AA","AB"))))
#    freqf <- as.data.frame(t(freqf[1,]))
#    rownames(freqf) <- rownames(genoi)
#    colnames(freqf) <- c("count_missing_raw_table","count_AA_raw_table","count_AB_raw_table")
#    genoif <- cbind(freqf,genoi[c("count_AA_raw_rowSums","count_AB_raw_rowSums","count_missing_raw_rowSums")])
#    genosl[[i]] = genoif
#}
#gf_mar_raw <- do.call("rbind",genosl)
#gf_mar_raw <- gf_mar_raw[,c(1:3,6,4:5)]
#gf_mar_raw$index <- 1:nrow(gf_mar_raw)

### from probabilities
gf_mar_probs.1 <- calc_geno_freq(pr.qc.ids, by = "marker", omit_x = FALSE)
#gn_mar_probs <- calc_geno_freq(probs, by = "individual", omit_x = FALSE)
gf_mar_probs <- rbind(gf_mar_probs.1$A[,1:2], gf_mar_probs.1$X[,1:2])
colnames(gf_mar_probs) <- paste0("freq_",colnames(gf_mar_probs),"_probs")
gf_mar_probs <- as.data.frame(gf_mar_probs)
gf_mar_probs$index <- 1:nrow(gf_mar_probs)

### merging all genotype frequecies for all markers
#gf_mar.1 <- merge(as.data.frame(gf_mar_raw), as.data.frame(gfn_mar_geno), by="row.names")
#rownames(gf_mar.1) <- gf_mar.1[,1]
#gf_mar.1 <- gf_mar.1[-1]
#gf_mar <- merge(gf_mar.1,as.data.frame(gf_mar_probs), by="row.names")
gf_mar <- merge(as.data.frame(gfn_mar_geno),as.data.frame(gf_mar_probs), by="row.names")
rownames(gf_mar) <- gf_mar[,1]
gf_mar <- gf_mar[-1]
gf_mar <- gf_mar[order(gf_mar$index),]
dim(gf_mar)

[1] 131356      8

# Calculating ratio and flagging informative marker
gf_mar$ratio = as.numeric(gf_mar$freq_AA_geno_table)/as.numeric(gf_mar$freq_AB_geno_table)
gf_mar$Include = ifelse(gf_mar$ratio >= 0.90 & gf_mar$ratio <= 1.10, TRUE,FALSE)
table(gf_mar$Include)

 FALSE   TRUE 
123174   8182 

## filtering out <= 0.05
gf_mar$count.geno <- rowSums(gf_mar[c("freq_AA_geno_table","freq_AB_geno_table")] <=0.05)
filtered_gf_mar_geno <- gf_mar[gf_mar$count.geno != 1,]
filtered_gf_mar_geno <- filtered_gf_mar_geno[,-which(names(filtered_gf_mar_geno) %in% c("count.geno","index"))]
dim(filtered_gf_mar_geno)

[1] 32691     9

table(filtered_gf_mar_geno$Include)

FALSE  TRUE 
24509  8182 

gf_mar$count.probs <- rowSums(gf_mar[c("freq_AA_probs","freq_AB_probs")] <=0.05)
filtered_gf_mar_probs <- gf_mar[gf_mar$count.probs != 1,]
filtered_gf_mar_probs <- filtered_gf_mar_probs[,-which(names(filtered_gf_mar_probs) %in% c("count.geno","count.probs","index"))]
dim(filtered_gf_mar_probs)

[1] 33118     9

table(filtered_gf_mar_probs$Include)

FALSE  TRUE 
26680  6438 

## merging with sample_genos
#filtered_gf_mar_geno_sample <- merge(geno,filtered_gf_mar_geno, by="row.names", all.y=T)
#filtered_gf_mar_geno_sample <- filtered_gf_mar_geno_sample[order(filtered_gf_mar_geno_sample$index),]     
#filtered_gf_mar_geno_sample <- filtered_gf_mar_geno_sample[,-which(names(filtered_gf_mar_geno_sample) %in% c("count.geno","index"))]
#names(filtered_gf_mar_geno_sample)[1] <- c("marker")
#dim(filtered_gf_mar_geno_sample)

#filtered_gf_mar_probs_sample <- merge(geno,filtered_gf_mar_probs, by="row.names", all.y=T)
#filtered_gf_mar_probs_sample <- filtered_gf_mar_probs_sample[order(filtered_gf_mar_probs_sample$index),]
#filtered_gf_mar_probs_sample <- filtered_gf_mar_probs_sample[,-which(names(filtered_gf_mar_probs_sample) %in% c("count.geno","count.probs","index"))]
#names(filtered_gf_mar_probs_sample)[1] <- c("marker")
#dim(filtered_gf_mar_probs_sample)

## saving files
#write.csv(filtered_gf_mar_geno_sample, "data/ici.vs.pbs_sample.genos_marker.freq_low.geno.freq.removed_sample.outliers.removed.csv", quote=F)
#write.csv(filtered_gf_mar_probs_sample, "data/ici.vs.pbs_sample.genos_marker.freq_low.probs.freq.removed_sample.outliers.removed.csv", quote=F)

write.csv(filtered_gf_mar_geno, "data/ici.vs.pbs_marker.freq_low.geno.freq.removed_sample.outliers.removed_geno.ratiov_5.batches_mis.csv", quote=F)
write.csv(filtered_gf_mar_probs, "data/ici.vs.pbs_marker.freq_low.probs.freq.removed_sample.outliers.removed_geno.ratio_5.batches_mis.csv", quote=F)

Session information

R version 3.6.2 (2019-12-12)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Catalina 10.15.7

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib

locale:
[1] en_AU.UTF-8/en_AU.UTF-8/en_AU.UTF-8/C/en_AU.UTF-8/en_AU.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] abind_1.4-5       qtl2_0.22         reshape2_1.4.4    ggplot2_3.3.5    
 [5] tibble_3.1.2      psych_2.0.7       readxl_1.3.1      cluster_2.1.0    
 [9] dplyr_1.0.8       optparse_1.6.6    rhdf5_2.28.1      mclust_5.4.6     
[13] tidyr_1.0.2       data.table_1.14.0 knitr_1.33        kableExtra_1.1.0 
[17] workflowr_1.6.2  

loaded via a namespace (and not attached):
 [1] httr_1.4.1        bit64_4.0.5       viridisLite_0.4.0 assertthat_0.2.1 
 [5] highr_0.9         blob_1.2.1        cellranger_1.1.0  yaml_2.2.1       
 [9] pillar_1.6.1      RSQLite_2.2.7     backports_1.2.1   lattice_0.20-38  
[13] glue_1.4.2        digest_0.6.27     promises_1.1.0    rvest_0.3.5      
[17] colorspace_2.0-2  htmltools_0.5.1.1 httpuv_1.5.2      plyr_1.8.6       
[21] pkgconfig_2.0.3   purrr_0.3.4       scales_1.1.1      webshot_0.5.2    
[25] getopt_1.20.3     later_1.0.0       git2r_0.26.1      generics_0.0.2   
[29] ellipsis_0.3.2    cachem_1.0.5      withr_2.4.2       cli_3.0.0        
[33] mnormt_1.5-7      magrittr_2.0.1    crayon_1.4.1      memoise_2.0.0    
[37] evaluate_0.14     fs_1.4.1          fansi_0.5.0       nlme_3.1-142     
[41] xml2_1.3.1        tools_3.6.2       hms_0.5.3         lifecycle_1.0.1  
[45] stringr_1.4.0     Rhdf5lib_1.6.3    munsell_0.5.0     compiler_3.6.2   
[49] rlang_1.0.2       grid_3.6.2        rstudioapi_0.13   rmarkdown_2.1    
[53] gtable_0.3.0      DBI_1.1.1         R6_2.5.0          fastmap_1.1.0    
[57] bit_4.0.4         utf8_1.2.1        rprojroot_1.3-2   readr_1.3.1      
[61] stringi_1.7.2     parallel_3.6.2    Rcpp_1.0.7        vctrs_0.3.8      
[65] tidyselect_1.1.2  xfun_0.24