Here we will explore the asplenia data.
The goal is to make a UMAP chart of the data. It will be compared to PCA and t-SNE projections.
Source code: https://github.com/markziemann/asplenia
library("readxl")
library("tsne")
library("umap")
library("plotly")Loading required package: ggplot2
Attaching package: 'plotly'The following object is masked from 'package:ggplot2':
last_plotThe following object is masked from 'package:stats':
filterThe following object is masked from 'package:graphics':
layoutlibrary("gplots")
Attaching package: 'gplots'The following object is masked from 'package:stats':
lowessUsing ReadXL to read from Excel spreadsheet.
There are 3095 rows and 823 columns. That’s a lot of data.
x <- read_xlsx("Khoury_Clean_Asplenia_COVID study_18102023.xlsx")Warning: Expecting logical in HI1996 / R1996C217: got 'Yes'Warning: Expecting logical in HJ1996 / R1996C218: got 'Yes'Warning: Expecting logical in HK1996 / R1996C219: got 'No'Warning: Expecting logical in HL2267 / R2267C220: got 'No'Warning: Expecting logical in HQ2267 / R2267C225: got 'Yes'Warning: Expecting logical in IZ2267 / R2267C260: got 'No'Warning: Expecting logical in JO2267 / R2267C275: got 'No'New names:
• `` -> `...7`
• `` -> `...10`
• `` -> `...88`
• `Other side-effects` -> `Other side-effects...97`
• `` -> `...147`
• `Other side-effects` -> `Other side-effects...156`
• `` -> `...206`
• `Other side-effects` -> `Other side-effects...215`
• `` -> `...265`
• `Other side-effects` -> `Other side-effects...274`
• `` -> `...324`
• `Other side-effects` -> `Other side-effects...333`
• `` -> `...805`
• `The other vaccine that I received:` -> `The other vaccine that I
received:...808`
• `The other vaccine that I received:` -> `The other vaccine that I
received:...810`
• `The other vaccine that I received:` -> `The other vaccine that I
received:...812`
• `The other vaccine that I received:` -> `The other vaccine that I
received:...814`
• `The other vaccine that I received:` -> `The other vaccine that I
received:...816`
• `` -> `...817`dim(x)[1] 3095 823Here we will do a simple analysis of the gender composition of this group.
par(mar=c(3,15,2,1))
vals <- table(x$`What is your gender?`)
vals <- vals[order(vals)]
barplot(vals,xlim=c(0,2000),main="gender breakdown",xlab="no. participants",cex.names=0.8,las=1,horiz = TRUE)
text(vals+150,((1:length(vals)-0.4)*1.2),labels = vals)
Now we will look at the spleen status in these gender groups.
par(mar=c(3,15,25,1))
woman <- subset(x,`What is your gender?`=="Woman")
man <- subset(x,`What is your gender?`=="Man")
womanvals <- table(woman$`Have you ever been diagnosed with a spleen that doesn't work OR had it surgically removed?`)
womanvals <- womanvals[order(womanvals)]
names(womanvals) <- paste("(W)",names(womanvals))
manvals <- table(man$`Have you ever been diagnosed with a spleen that doesn't work OR had it surgically removed?`)
manvals <- manvals[order(manvals)]
names(manvals) <- paste("(M)",names(manvals))
catnames <- names(table(x$`Have you ever been diagnosed with a spleen that doesn't work OR had it surgically removed?`))
vals <- cbind("men"=manvals,"women"=womanvals)
par(mar=c(5,5,5,1))
barplot(vals,horiz = TRUE,las=1,main="spleen status by gender",xlab="no. participants",col=c("white","lightgray","darkgray"))
legend("right",legend=catnames,fill = c("white","lightgray","darkgray"),title="spleen status")
text(c(15,70,700,40,160,800),1.2*(c(0.9,1.1,1,1.9,2.1,2)-0.4),labels = vals,col="black",cex=1)
pc <- cbind("man"=manvals/sum(manvals)*100,"woman"=womanvals/sum(womanvals)*100)
barplot(pc,horiz = TRUE,las=1,main="spleen status by gender",xlab="proportion participants (%)",col = c("white","lightgray","darkgray","white","lightgray","darkgray"))
legend("right",legend=catnames,fill = c("white","lightgray","darkgray"),title="spleen status")
pcr <- signif(pc,digits = 2)
text(c(1.5,6,45,2.5,10,45),1.2*(c(0.9,1.1,1,1.9,2.1,2)-0.4),labels = pcr,col="black",cex=1)
#plot(0,type='n',axes=FALSE,ann=FALSE)
legend("right",legend=catnames,fill = c("white","lightgray","darkgray"),title="spleen status")
Need to check for NA values. Then convert responses to factors and then to numerical values.
x2 <- as.data.frame(x)
rownames(x2) <- x2$`Participant ID`
x2 <- x2[,-c(1,2)]
x2[1:4,1:5] I am willing to participate in this project
1 Yes
2 Yes
3 Yes
4 Yes
Have you ever been diagnosed with a spleen that doesn't work OR had it surgically removed?
1 Yes, mine has been removed
2 Yes, mine has been removed
3 Yes, mine has been removed
4 Yes, mine has been removed
Please select your age range What is your gender? ...7
1 41-50 Woman <NA>
2 41-50 Man <NA>
3 51-60 Woman <NA>
4 61-70 Woman <NA>hist(apply(x2,1,function(y) { length(which(is.na(y))) } ),main="NAs per row",xlab="NA count")
hist(apply(x2,2,function(y) { length(which(is.na(y))) } ),main="NAs per column",xlab="NA count")
x2f <- x2[,which (apply(x2,2,function(y) { length(which(is.na(y))) } ) < 50 )]
dim(x2)[1] 3095 821dim(x2f)[1] 3095 673x2fn <- apply(x2f,2, function(j) { as.numeric(factor(j))} )
rownames(x2fn) <- rownames(x2)mds <- cmdscale(dist(x2fn))
plot(mds, xlab="Coordinate 1", ylab="Coordinate 2", type = "n",main="PCA")
text(mds, labels=rownames(x2fn) ) #mytsne <- tsne(mtcars)
#plot(mytsne,pch=19,col="gray",cex=2)
#text(mytsne,labels = rownames(mtcars))
mytsne <- tsne(x2fn)
plot(mytsne,pch=1,cex=0.5,xlab="t-SNE1",ylab="t-SNE2",main="t-SNE")
grid()
#text(mytsne,labels = rownames(x2fn))First do a UMAP, then label groups
#set.seed(100) ; x2fn.umap <- umap(x2fn,preserve.seed = 100)
#saveRDS(x2fn.umap,"umap.rds")
x2fn.umap <- readRDS("umap.rds")
str(x2fn.umap)List of 4
$ layout: num [1:3095, 1:2] 2.74 5.97 1.99 1.68 -15.09 ...
..- attr(*, "dimnames")=List of 2
.. ..$ : chr [1:3095] "1" "2" "3" "4" ...
.. ..$ : NULL
$ data : num [1:3095, 1:673] 1 2 2 1 2 1 2 2 2 2 ...
..- attr(*, "dimnames")=List of 2
.. ..$ : chr [1:3095] "1" "2" "3" "4" ...
.. ..$ : chr [1:673] "PRIOR TO THE PANDEMIC, did you have any of the below medical conditions?Please tick the one(s) that apply (choice=Allergies)" "PRIOR TO THE PANDEMIC, did you have any of the below medical conditions?Please tick the one(s) that apply (choice=Anxiety)" "PRIOR TO THE PANDEMIC, did you have any of the below medical conditions?Please tick the one(s) that apply (choice=Arthritis)" "PRIOR TO THE PANDEMIC, did you have any of the below medical conditions?Please tick the one(s) that apply (choice=Asthma)" ...
$ knn :List of 2
..$ indexes : num [1:3095, 1:15] 1 2 3 4 5 6 7 8 9 10 ...
.. ..- attr(*, "dimnames")=List of 2
.. .. ..$ : chr [1:3095] "1" "2" "3" "4" ...
.. .. ..$ : NULL
..$ distances: num [1:3095, 1:15] 0 0 0 0 0 0 0 0 0 0 ...
.. ..- attr(*, "dimnames")=List of 2
.. .. ..$ : chr [1:3095] "1" "2" "3" "4" ...
.. .. ..$ : NULL
..- attr(*, "class")= chr "umap.knn"
$ config:List of 24
..$ n_neighbors : int 15
..$ n_components : int 2
..$ metric : chr "euclidean"
..$ n_epochs : int 200
..$ input : chr "data"
..$ init : chr "spectral"
..$ min_dist : num 0.1
..$ set_op_mix_ratio : num 1
..$ local_connectivity : num 1
..$ bandwidth : num 1
..$ alpha : num 1
..$ gamma : num 1
..$ negative_sample_rate: int 5
..$ a : num 1.58
..$ b : num 0.895
..$ spread : num 1
..$ random_state : int 330461345
..$ transform_state : int NA
..$ knn : logi NA
..$ knn_repeats : num 1
..$ verbose : logi FALSE
..$ umap_learn_args : logi NA
..$ method : chr "naive"
..$ metric.function :function (m, origin, targets)
..- attr(*, "class")= chr "umap.config"
- attr(*, "class")= chr "umap"par(mar = c(5.1, 4.1, 4.1, 2.1) )
plot(x2fn.umap$layout,cex=0.5,xlab="UMAP1",ylab="UMAP2",main="UMAP") ;grid()
xco <- c(-14.5, 14, 6.5, 3.5,-3.5,-2.5,1)
yco <- c( 5.5, 18,-6,-11,-8, -4.5, 5)
labs <- c("A","B","C","D","E","F","G")
text(xco,yco,labels=labs,col="red")
rect(-16,2,-13,6.5,border = "red") #A
rect(13,16,16.5,20,border = "red") #B
rect(5.5,-10,8,-5,border = "red") #C
rect(2.5,-15,5,-10,border = "red") #D
rect(-4,-10.5,1,-7,border = "red") #E
rect(-3,-6,2.5,-2.5,border = "red") #F
rect(-1.5,-2,4,6,border = "red") #G
Understand UMAP clustering.
head(x2fn.umap$layout) [,1] [,2]
1 2.73868076 1.9709873
2 5.97274339 -7.1771966
3 1.98603866 -0.5288591
4 1.67703032 0.2138904
5 -15.08640343 3.8437682
8 -0.05341738 -4.3158785A <- rownames(x2fn.umap$layout[which(x2fn.umap$layout[,1] < -10),])
B <- rownames(x2fn.umap$layout[which(x2fn.umap$layout[,2] > 10),])
C <- rownames(x2fn.umap$layout[which(x2fn.umap$layout[,1] > 5 & x2fn.umap$layout[,2] < -5),])
D <- rownames(x2fn.umap$layout[which(x2fn.umap$layout[,2] < -11 ),])
E <- rownames(x2fn.umap$layout[which(x2fn.umap$layout[,1] < 1 & x2fn.umap$layout[,2] < -7),])
F <- rownames(x2fn.umap$layout[which(x2fn.umap$layout[,1] < 2.5 & x2fn.umap$layout[,2] > -6 & x2fn.umap$layout[,2] < -2.5),])
G <- rownames(x2fn.umap$layout[which(x2fn.umap$layout[,1] > -1.5 & x2fn.umap$layout[,1] < 4 & x2fn.umap$layout[,2] > -2),])
groups <- list(A,B,C,D,E,F,G)
len <- sapply(groups,length)
names(len) <- c("A","B","C","D","E","F","G")
len A B C D E F G
507 242 151 189 317 326 1358 Distinguishing features of groups.
dfa <- x2fn[A,]
dfb <- x2fn[B,]
dfc <- x2fn[C,]
dfd <- x2fn[D,]
dfe <- x2fn[E,]
dff <- x2fn[F,]
dfg <- x2fn[G,]
ma <- colMeans(dfa)
mb <- colMeans(dfb)
mc <- colMeans(dfc)
md <- colMeans(dfd)
me <- colMeans(dfe)
mf <- colMeans(dff)
mg <- colMeans(dfg)
j <- data.frame(ma,mb,mc,md,me,mf,mg)
head(j) ma
PRIOR TO THE PANDEMIC, did you have any of the below medical conditions?Please tick the one(s) that apply (choice=Allergies) 1.998028
PRIOR TO THE PANDEMIC, did you have any of the below medical conditions?Please tick the one(s) that apply (choice=Anxiety) 2.000000
PRIOR TO THE PANDEMIC, did you have any of the below medical conditions?Please tick the one(s) that apply (choice=Arthritis) 2.000000
PRIOR TO THE PANDEMIC, did you have any of the below medical conditions?Please tick the one(s) that apply (choice=Asthma) 1.996055
PRIOR TO THE PANDEMIC, did you have any of the below medical conditions?Please tick the one(s) that apply (choice=Autoimmune diseases (e.g. Rheumatoid arthritis, Lupus)) 2.000000
PRIOR TO THE PANDEMIC, did you have any of the below medical conditions?Please tick the one(s) that apply (choice=Bronchiectasis (damage to the airways in the lung)) 1.998028
mb
PRIOR TO THE PANDEMIC, did you have any of the below medical conditions?Please tick the one(s) that apply (choice=Allergies) 1.991736
PRIOR TO THE PANDEMIC, did you have any of the below medical conditions?Please tick the one(s) that apply (choice=Anxiety) 2.000000
PRIOR TO THE PANDEMIC, did you have any of the below medical conditions?Please tick the one(s) that apply (choice=Arthritis) 1.995868
PRIOR TO THE PANDEMIC, did you have any of the below medical conditions?Please tick the one(s) that apply (choice=Asthma) 1.995868
PRIOR TO THE PANDEMIC, did you have any of the below medical conditions?Please tick the one(s) that apply (choice=Autoimmune diseases (e.g. Rheumatoid arthritis, Lupus)) 2.000000
PRIOR TO THE PANDEMIC, did you have any of the below medical conditions?Please tick the one(s) that apply (choice=Bronchiectasis (damage to the airways in the lung)) 2.000000
mc
PRIOR TO THE PANDEMIC, did you have any of the below medical conditions?Please tick the one(s) that apply (choice=Allergies) 1.728477
PRIOR TO THE PANDEMIC, did you have any of the below medical conditions?Please tick the one(s) that apply (choice=Anxiety) 1.000000
PRIOR TO THE PANDEMIC, did you have any of the below medical conditions?Please tick the one(s) that apply (choice=Arthritis) 1.695364
PRIOR TO THE PANDEMIC, did you have any of the below medical conditions?Please tick the one(s) that apply (choice=Asthma) 1.781457
PRIOR TO THE PANDEMIC, did you have any of the below medical conditions?Please tick the one(s) that apply (choice=Autoimmune diseases (e.g. Rheumatoid arthritis, Lupus)) 1.841060
PRIOR TO THE PANDEMIC, did you have any of the below medical conditions?Please tick the one(s) that apply (choice=Bronchiectasis (damage to the airways in the lung)) 1.933775
md
PRIOR TO THE PANDEMIC, did you have any of the below medical conditions?Please tick the one(s) that apply (choice=Allergies) 1.936508
PRIOR TO THE PANDEMIC, did you have any of the below medical conditions?Please tick the one(s) that apply (choice=Anxiety) 1.984127
PRIOR TO THE PANDEMIC, did you have any of the below medical conditions?Please tick the one(s) that apply (choice=Arthritis) 1.878307
PRIOR TO THE PANDEMIC, did you have any of the below medical conditions?Please tick the one(s) that apply (choice=Asthma) 1.962963
PRIOR TO THE PANDEMIC, did you have any of the below medical conditions?Please tick the one(s) that apply (choice=Autoimmune diseases (e.g. Rheumatoid arthritis, Lupus)) 1.936508
PRIOR TO THE PANDEMIC, did you have any of the below medical conditions?Please tick the one(s) that apply (choice=Bronchiectasis (damage to the airways in the lung)) 1.984127
me
PRIOR TO THE PANDEMIC, did you have any of the below medical conditions?Please tick the one(s) that apply (choice=Allergies) 1.848580
PRIOR TO THE PANDEMIC, did you have any of the below medical conditions?Please tick the one(s) that apply (choice=Anxiety) 1.940063
PRIOR TO THE PANDEMIC, did you have any of the below medical conditions?Please tick the one(s) that apply (choice=Arthritis) 1.700315
PRIOR TO THE PANDEMIC, did you have any of the below medical conditions?Please tick the one(s) that apply (choice=Asthma) 1.899054
PRIOR TO THE PANDEMIC, did you have any of the below medical conditions?Please tick the one(s) that apply (choice=Autoimmune diseases (e.g. Rheumatoid arthritis, Lupus)) 1.946372
PRIOR TO THE PANDEMIC, did you have any of the below medical conditions?Please tick the one(s) that apply (choice=Bronchiectasis (damage to the airways in the lung)) 1.971609
mf
PRIOR TO THE PANDEMIC, did you have any of the below medical conditions?Please tick the one(s) that apply (choice=Allergies) 1.536810
PRIOR TO THE PANDEMIC, did you have any of the below medical conditions?Please tick the one(s) that apply (choice=Anxiety) 1.779141
PRIOR TO THE PANDEMIC, did you have any of the below medical conditions?Please tick the one(s) that apply (choice=Arthritis) 1.631902
PRIOR TO THE PANDEMIC, did you have any of the below medical conditions?Please tick the one(s) that apply (choice=Asthma) 1.723926
PRIOR TO THE PANDEMIC, did you have any of the below medical conditions?Please tick the one(s) that apply (choice=Autoimmune diseases (e.g. Rheumatoid arthritis, Lupus)) 1.748466
PRIOR TO THE PANDEMIC, did you have any of the below medical conditions?Please tick the one(s) that apply (choice=Bronchiectasis (damage to the airways in the lung)) 1.975460
mg
PRIOR TO THE PANDEMIC, did you have any of the below medical conditions?Please tick the one(s) that apply (choice=Allergies) 1.827688
PRIOR TO THE PANDEMIC, did you have any of the below medical conditions?Please tick the one(s) that apply (choice=Anxiety) 1.848306
PRIOR TO THE PANDEMIC, did you have any of the below medical conditions?Please tick the one(s) that apply (choice=Arthritis) 1.864507
PRIOR TO THE PANDEMIC, did you have any of the below medical conditions?Please tick the one(s) that apply (choice=Asthma) 1.871870
PRIOR TO THE PANDEMIC, did you have any of the below medical conditions?Please tick the one(s) that apply (choice=Autoimmune diseases (e.g. Rheumatoid arthritis, Lupus)) 1.891016
PRIOR TO THE PANDEMIC, did you have any of the below medical conditions?Please tick the one(s) that apply (choice=Bronchiectasis (damage to the airways in the lung)) 1.988218Selecting rows with some coefficient of variation.
cv <- function(x) { sd(x)/mean(x) }
#hist(apply(j,1,cv))
j2 <- j[which(apply(j,1,cv)>0),]
dim(j2)[1] 392 7#hist(apply(j2,1,cv))
j3 <- j[which(apply(j,1,cv)>0.03),]
dim(j3)[1] 34 7#hist(apply(j3,1,cv))Plot the results.
rn <- gsub("TO THE PANDEMIC, did you have any of the below medical conditions\\?Please tick the one\\(s\\) that apply","symptoms:",rownames(j3))
rn <- gsub("Which test did confirm your diagnosis\\?Please tick the one\\(s\\) that apply ","test:",rn)
rn <- gsub("Which test did confirm your diagnosis\\? Pleas tick the one\\(s\\) that apply","test:",rn)
rn <- gsub("During the FIRST TWO WEEKS of your confirmed COVID diagnosis, did you experience any of the below COVID related-symptoms Please tick the one\\(s\\) that apply","symptoms:",rn)
rn <- gsub("If YES, which treatment\\(s\\) did you receive\\? Please tick the one\\(s\\) that apply","teatment:",rn)
rn <- gsub("Did you experience any side effects from the treatment\\(s\\)\\? Please tick the one\\(s\\) that apply","side effects:",rn)
rn [1] "PRIOR symptoms: (choice=Allergies)"
[2] "PRIOR symptoms: (choice=Anxiety)"
[3] "PRIOR symptoms: (choice=Arthritis)"
[4] "PRIOR symptoms: (choice=Asthma)"
[5] "PRIOR symptoms: (choice=Autoimmune diseases (e.g. Rheumatoid arthritis, Lupus))"
[6] "PRIOR symptoms: (choice=Cancer)"
[7] "PRIOR symptoms: (choice=Depression)"
[8] "PRIOR symptoms: (choice=High blood pressure)"
[9] "PRIOR symptoms: (choice=Overweight/obesity)"
[10] "PRIOR symptoms: (choice=Sleep disorders)"
[11] "PRIOR symptoms: (choice=Type 2 diabetes)"
[12] "PRIOR symptoms: (choice=I didnt have any medical conditions before COVID)"
[13] "[Infection 1] test:(choice=RAT)"
[14] "[Infection 1] test:(choice=PCR)"
[15] "[Infection 1] symptoms: (choice=Altered or loss of sense of smell)"
[16] "[Infection 1] symptoms: (choice=Altered or loss of sense of taste)"
[17] "[Infection 1] symptoms: (choice=Brain fog)"
[18] "[Infection 1] symptoms: (choice=Chills)"
[19] "[Infection 1] symptoms: (choice=Congestion and/or runny nose)"
[20] "[Infection 1] symptoms: (choice=Cough)"
[21] "[Infection 1] symptoms: (choice=Exhaustion/Extreme fatigue)"
[22] "[Infection 1] symptoms: (choice=Fever)"
[23] "[Infection 1] symptoms: (choice=Headache)"
[24] "[Infection 1] symptoms: (choice=Joint pain)"
[25] "[Infection 1] symptoms: (choice=Loss of appetite)"
[26] "[Infection 1] symptoms: (choice=Muscle aches)"
[27] "[Infection 1] symptoms: (choice=Poor concentration)"
[28] "[Infection 1] symptoms: (choice=Shortness of breath)"
[29] "[Infection 1] symptoms: (choice=Sleep changes/disturbance)"
[30] "[Infection 1] symptoms: (choice=Sneezing)"
[31] "[Infection 1] symptoms: (choice=Sore throat)"
[32] "[Infection 1] teatment: (choice=Antivirals [tablets])"
[33] "[Infection 1] side effects: (choice=No side effects)"
[34] "[Infection 2] test: (choice=RAT)" rownames(j3) <- rn
colnames(j3) <- c("A","B","C","D","E","F","G")
heatmap.2(as.matrix(j3),scale="none", margins = c(3,32), trace="none", cexRow = 1, cexCol = 1.2,
colsep=0:ncol(j3), rowsep=0:nrow(j3), sepwidth=c(0.02, 0.02), sepcolor='black',srtCol=0)
sessionInfo()R version 4.3.2 (2023-10-31)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 22.04.3 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
locale:
[1] LC_CTYPE=en_AU.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_AU.UTF-8 LC_COLLATE=en_AU.UTF-8
[5] LC_MONETARY=en_AU.UTF-8 LC_MESSAGES=en_AU.UTF-8
[7] LC_PAPER=en_AU.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_AU.UTF-8 LC_IDENTIFICATION=C
time zone: Australia/Melbourne
tzcode source: system (glibc)
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] gplots_3.1.3 plotly_4.10.2 ggplot2_3.4.3 umap_0.2.10.0 tsne_0.1-3.1
[6] readxl_1.4.3
loaded via a namespace (and not attached):
[1] utf8_1.2.3 generics_0.1.3 tidyr_1.3.0 bitops_1.0-7
[5] KernSmooth_2.23-22 gtools_3.9.4 lattice_0.22-5 caTools_1.18.2
[9] digest_0.6.33 magrittr_2.0.3 evaluate_0.21 grid_4.3.2
[13] fastmap_1.1.1 cellranger_1.1.0 jsonlite_1.8.7 Matrix_1.6-1
[17] RSpectra_0.16-1 httr_1.4.7 purrr_1.0.2 fansi_1.0.4
[21] viridisLite_0.4.2 scales_1.2.1 lazyeval_0.2.2 cli_3.6.1
[25] rlang_1.1.1 munsell_0.5.0 withr_2.5.0 yaml_2.3.7
[29] tools_4.3.2 dplyr_1.1.3 colorspace_2.1-0 reticulate_1.32.0
[33] vctrs_0.6.3 R6_2.5.1 png_0.1-8 lifecycle_1.0.3
[37] htmlwidgets_1.6.2 pkgconfig_2.0.3 pillar_1.9.0 gtable_0.3.4
[41] glue_1.6.2 data.table_1.14.8 Rcpp_1.0.11 xfun_0.40
[45] tibble_3.2.1 tidyselect_1.2.0 rstudioapi_0.15.0 knitr_1.44
[49] htmltools_0.5.6 rmarkdown_2.24 compiler_4.3.2 askpass_1.2.0
[53] openssl_2.1.0