Species Barretts Relationships
Last updated: 2021-04-15
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Knit directory: esoph-micro-cancer-workflow/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | b11a4b5 | noah-padgett | 2021-03-25 | updated picrust analyses and results figures |
| Rmd | 56180cd | noah-padgett | 2021-03-14 | updated analyses and violin plots |
| html | 56180cd | noah-padgett | 2021-03-14 | updated analyses and violin plots |
| Rmd | 092bf33 | noah-padgett | 2021-02-25 | fixed group error in violin plots |
| html | 092bf33 | noah-padgett | 2021-02-25 | fixed group error in violin plots |
| Rmd | f91e666 | noah-padgett | 2021-02-25 | violin updates |
| html | f91e666 | noah-padgett | 2021-02-25 | violin updates |
| Rmd | 13f1528 | alisonjung | 2021-02-18 | Barretts violin plot updates |
| Rmd | fe971b9 | noah-padgett | 2021-02-13 | violin plot scale fixed |
| Rmd | 285a2fb | noah-padgett | 2021-02-10 | updated violion plots |
| html | 285a2fb | noah-padgett | 2021-02-10 | updated violion plots |
Violin Plot Fuso
# merge datasets by subsetting to specific variables then merging
analysis.dat <- dat.16s.s %>%
dplyr::mutate(ID = as.factor(accession.number)) %>%
dplyr::select(OTU, sample_type, Abundance, ID, source)
dat <- dat.rna.s %>%
dplyr::select(OTU, sample_type, Abundance, ID, source)
analysis.dat <- full_join(analysis.dat, dat)
dat <- dat.wgs.s %>%
dplyr::select(OTU, sample_type, Abundance, ID, source)
analysis.dat <- full_join(analysis.dat, dat) %>%
mutate(pres = ifelse(Abundance > 0, 1, 0)) %>%
filter(OTU=="Fusobacterium nucleatum")# create a presence/absences variable
tb <- analysis.dat %>%
filter(is.na(sample_type)==F)%>%
group_by(sample_type, OTU) %>%
summarise(
N=n(),
p = sum(pres, na.rm=T),
percent = p/N*100
)
kable(tb, format="html")%>%
kable_styling(full_width = T)| sample_type | OTU | N | p | percent |
|---|---|---|---|---|
| 16SrRNA Barrett’s (BO) | Fusobacterium nucleatum | 5 | 3 | 60.00000 |
| 16SrRNA Non-tumor (w/ Barrett’s history) | Fusobacterium nucleatum | 43 | 19 | 44.18605 |
| 16SrRNA Tumor (w/ Barrett’s history) | Fusobacterium nucleatum | 35 | 16 | 45.71429 |
| RNA-seq Non-tumor (w/ Barrett’s history) | Fusobacterium nucleatum | 3 | 2 | 66.66667 |
| RNA-seq Tumor (w/ Barrett’s history) | Fusobacterium nucleatum | 26 | 5 | 19.23077 |
| WGS Non-tumor (w/ Barrett’s history) | Fusobacterium nucleatum | 6 | 2 | 33.33333 |
| WGS Tumor (w/ Barrett’s history) | Fusobacterium nucleatum | 4 | 2 | 50.00000 |
analysis.dat <- analysis.dat %>%
filter(is.na(sample_type)==F)%>%
mutate(
Abund = Abundance*100
)
#root function
root<-function(x){
x <- ifelse(x < 0, 0, x)
x**(0.2)
}
#inverse root function
invroot<-function(x){
x**(5)
}
# colors
cols <- c("16SrRNA Tumor (w/ Barrett's history)" = "#C0392B",
"16SrRNA Non-tumor (w/ Barrett's history)"= "#336699",
"16SrRNA Barrett's (BO)"= "#229667",
"RNA-seq Tumor (w/ Barrett's history)"= "#C0392B",
"RNA-seq Non-tumor (w/ Barrett's history)"= "#336699",
"WGS Non-tumor (w/ Barrett's history)"= "#336699",
"WGS Tumor (w/ Barrett's history) " = "#C0392B")
# "#2C3E50", "#2980B9", "#8E44AD", "#C0392B", "#D35400", "#F39C12", "#F1C40F", "#27AE60", "#196f3e"
# mycolors = c()
# mycolors$Tissue["T"] = "#C0392B"
# mycolors$Tissue["N"] = "#336699"
# mycolors$Tissue["BO"] = "#229667"
p <- ggplot(analysis.dat, aes(sample_type, Abund, color=sample_type)) +
geom_violin(scale="width", adjust=0.5)+
geom_point(alpha=0.75)+
scale_y_continuous(
trans=scales::trans_new("root", root, invroot),
breaks=c(0, 0.001,0.01, 0.1, 1,10,50, 100),
labels = c(0, 0.001,0.01, 0.1, 1,10,50, 100),
limits = c(0, 110)
)+
scale_color_manual(values=cols)+
annotate(
"text", x=c(1:7), y=c(rep(110, 7)),
label=c(paste0(round(tb[1,5], 0),"% (",tb[1,4],"/",tb[1,3],")"),
paste0(round(tb[2,5], 0),"% (",tb[2,4],"/",tb[2,3],")"),
paste0(round(tb[3,5], 0),"% (",tb[3,4],"/",tb[3,3],")"),
paste0(round(tb[4,5], 0),"% (",tb[4,4],"/",tb[4,3],")"),
paste0(round(tb[5,5], 0),"% (",tb[5,4],"/",tb[5,3],")"),
paste0(round(tb[6,5], 0),"% (",tb[6,4],"/",tb[6,3],")"),
paste0(round(tb[7,5], 0),"% (",tb[7,4],"/",tb[7,3],")"))
)+
labs(x=NULL, y="% Abundance")+
theme(
axis.text.x = element_text(angle=30, hjust=0.95, vjust=0.95),
legend.position = "none"
)
p
ggsave("output/Barretts_violin-fuso.pdf", p, units = "in", width = 10, height = 6)Violin Plot Strepto
# merge datasets by subsetting to specific variables then merging
analysis.dat <- dat.16s.s %>%
dplyr::mutate(ID = as.factor(accession.number)) %>%
dplyr::select(OTU, sample_type, Abundance, ID, source)
dat <- dat.rna.s %>%
dplyr::select(OTU, sample_type, Abundance, ID, source)
analysis.dat <- full_join(analysis.dat, dat)
dat <- dat.wgs.s %>%
dplyr::select(OTU, sample_type, Abundance, ID, source)
analysis.dat <- full_join(analysis.dat, dat) %>%
mutate(pres = ifelse(Abundance > 0, 1, 0)) %>%
filter(OTU%like%"Streptococcus")# create a presence/absences variable
tb <- analysis.dat %>%
filter(is.na(sample_type)==F)%>%
group_by(sample_type, OTU) %>%
summarise(
N=n(),
p = sum(pres, na.rm=T),
percent = p/N*100
)
kable(tb, format="html")%>%
kable_styling(full_width = T)| sample_type | OTU | N | p | percent |
|---|---|---|---|---|
| 16SrRNA Barrett’s (BO) | Streptococcus spp. (not uniquely identified) | 5 | 5 | 100.00000 |
| 16SrRNA Non-tumor (w/ Barrett’s history) | Streptococcus spp. (not uniquely identified) | 43 | 41 | 95.34884 |
| 16SrRNA Tumor (w/ Barrett’s history) | Streptococcus spp. (not uniquely identified) | 35 | 33 | 94.28571 |
| RNA-seq Non-tumor (w/ Barrett’s history) | Streptococcus sanguinis | 3 | 2 | 66.66667 |
| RNA-seq Tumor (w/ Barrett’s history) | Streptococcus sanguinis | 26 | 6 | 23.07692 |
| WGS Non-tumor (w/ Barrett’s history) | Streptococcus sanguinis | 6 | 2 | 33.33333 |
| WGS Tumor (w/ Barrett’s history) | Streptococcus sanguinis | 4 | 2 | 50.00000 |
analysis.dat <- analysis.dat %>%
filter(is.na(sample_type)==F)%>%
mutate(
Abund = Abundance*100
)
#root function
root<-function(x){
x <- ifelse(x < 0, 0, x)
x**(0.2)
}
#inverse root function
invroot<-function(x){
x**(5)
}
p <- ggplot(analysis.dat, aes(sample_type, Abund)) +
geom_violin(scale="width", adjust=2)+
geom_point(alpha=0.75)+
scale_y_continuous(
trans=scales::trans_new("root", root, invroot),
breaks=c(0, 0.001,0.01, 0.1, 1,10,50, 100),
labels = c(0, 0.001,0.01, 0.1, 1,10,50, 100),
limits = c(0, 110)
)+
annotate(
"text", x=c(1:7), y=c(rep(110, 7)),
label=c(paste0(round(tb[1,5], 0),"% (",tb[1,4],"/",tb[1,3],")"),
paste0(round(tb[2,5], 0),"% (",tb[2,4],"/",tb[2,3],")"),
paste0(round(tb[3,5], 0),"% (",tb[3,4],"/",tb[3,3],")"),
paste0(round(tb[4,5], 0),"% (",tb[4,4],"/",tb[4,3],")"),
paste0(round(tb[5,5], 0),"% (",tb[5,4],"/",tb[5,3],")"),
paste0(round(tb[6,5], 0),"% (",tb[6,4],"/",tb[6,3],")"),
paste0(round(tb[7,5], 0),"% (",tb[7,4],"/",tb[7,3],")"))
)+
labs(x=NULL, y="% Abundance")+
theme(
axis.text.x = element_text(angle=30, hjust=0.95, vjust=0.95)
)
p
ggsave("output/Barretts_violin-strepto.pdf", p, units = "in", width = 10, height = 6)Violin Plot Campy
# merge datasets by subsetting to specific variables then merging
analysis.dat <- dat.16s.s %>%
dplyr::mutate(ID = as.factor(accession.number)) %>%
dplyr::select(OTU, sample_type, Abundance, ID, source)
dat <- dat.rna.s %>%
dplyr::select(OTU, sample_type, Abundance, ID, source)
analysis.dat <- full_join(analysis.dat, dat)
dat <- dat.wgs.s %>%
dplyr::select(OTU, sample_type, Abundance, ID, source)
analysis.dat <- full_join(analysis.dat, dat) %>%
mutate(pres = ifelse(Abundance > 0, 1, 0)) %>%
filter(OTU %like% "Campylobacter")# create a presence/absences variable
tb <- analysis.dat %>%
filter(is.na(sample_type)==F)%>%
group_by(sample_type, OTU) %>%
summarise(
N=n(),
p = sum(pres, na.rm=T),
percent = p/N*100
)
kable(tb, format="html")%>%
kable_styling(full_width = T)| sample_type | OTU | N | p | percent |
|---|---|---|---|---|
| 16SrRNA Barrett’s (BO) | Campylobacter spp. (not uniquely identified) | 5 | 2 | 40.000000 |
| 16SrRNA Non-tumor (w/ Barrett’s history) | Campylobacter spp. (not uniquely identified) | 43 | 6 | 13.953488 |
| 16SrRNA Tumor (w/ Barrett’s history) | Campylobacter spp. (not uniquely identified) | 35 | 9 | 25.714286 |
| RNA-seq Non-tumor (w/ Barrett’s history) | Campylobacter concisus | 3 | 0 | 0.000000 |
| RNA-seq Tumor (w/ Barrett’s history) | Campylobacter concisus | 26 | 1 | 3.846154 |
| WGS Non-tumor (w/ Barrett’s history) | Campylobacter concisus | 6 | 1 | 16.666667 |
| WGS Tumor (w/ Barrett’s history) | Campylobacter concisus | 4 | 2 | 50.000000 |
analysis.dat <- analysis.dat %>%
filter(is.na(sample_type)==F)%>%
mutate(
Abund = Abundance*100
)
#root function
root<-function(x){
x <- ifelse(x < 0, 0, x)
x**(0.2)
}
#inverse root function
invroot<-function(x){
x**(5)
}
p <- ggplot(analysis.dat, aes(sample_type, Abund)) +
geom_violin(scale="width", adjust=2)+
geom_point(alpha=0.75)+
scale_y_continuous(
trans=scales::trans_new("root", root, invroot),
breaks=c(0, 0.001,0.01, 0.1, 1,10,50, 100),
labels = c(0, 0.001,0.01, 0.1, 1,10,50, 100),
limits = c(0, 110)
)+
annotate(
"text", x=c(1:7), y=c(rep(110, 7)),
label=c(paste0(round(tb[1,5], 0),"% (",tb[1,4],"/",tb[1,3],")"),
paste0(round(tb[2,5], 0),"% (",tb[2,4],"/",tb[2,3],")"),
paste0(round(tb[3,5], 0),"% (",tb[3,4],"/",tb[3,3],")"),
paste0(round(tb[4,5], 0),"% (",tb[4,4],"/",tb[4,3],")"),
paste0(round(tb[5,5], 0),"% (",tb[5,4],"/",tb[5,3],")"),
paste0(round(tb[6,5], 0),"% (",tb[6,4],"/",tb[6,3],")"),
paste0(round(tb[7,5], 0),"% (",tb[7,4],"/",tb[7,3],")"))
)+
labs(x=NULL, y="% Abundance")+
theme(
axis.text.x = element_text(angle=30, hjust=0.95, vjust=0.95)
)
p
ggsave("output/Barretts_violin-campy.pdf", p, units = "in", width = 10, height = 6)Violin Plot Prevo
# merge datasets by subsetting to specific variables then merging
analysis.dat <- dat.16s.s %>%
dplyr::mutate(ID = as.factor(accession.number)) %>%
dplyr::select(OTU, sample_type, Abundance, ID, source)
dat <- dat.rna.s %>%
dplyr::select(OTU, sample_type, Abundance, ID, source)
analysis.dat <- full_join(analysis.dat, dat)
dat <- dat.wgs.s %>%
dplyr::select(OTU, sample_type, Abundance, ID, source)
analysis.dat <- full_join(analysis.dat, dat) %>%
mutate(pres = ifelse(Abundance > 0, 1, 0)) %>%
filter(OTU %like% "Prevotella")# create a presence/absences variable
tb <- analysis.dat %>%
filter(is.na(sample_type)==F)%>%
group_by(sample_type, OTU) %>%
summarise(
N=n(),
p = sum(pres, na.rm=T),
percent = p/N*100
)
kable(tb, format="html")%>%
kable_styling(full_width = T)| sample_type | OTU | N | p | percent |
|---|---|---|---|---|
| 16SrRNA Barrett’s (BO) | Prevotella spp. | 5 | 4 | 80.00000 |
| 16SrRNA Non-tumor (w/ Barrett’s history) | Prevotella spp. | 43 | 28 | 65.11628 |
| 16SrRNA Tumor (w/ Barrett’s history) | Prevotella spp. | 35 | 27 | 77.14286 |
| RNA-seq Non-tumor (w/ Barrett’s history) | Prevotella denticola | 3 | 1 | 33.33333 |
| RNA-seq Non-tumor (w/ Barrett’s history) | Prevotella intermedia | 3 | 2 | 66.66667 |
| RNA-seq Non-tumor (w/ Barrett’s history) | Prevotella melaninogenica | 3 | 2 | 66.66667 |
| RNA-seq Non-tumor (w/ Barrett’s history) | Prevotella ruminicola | 3 | 1 | 33.33333 |
| RNA-seq Tumor (w/ Barrett’s history) | Prevotella denticola | 26 | 4 | 15.38462 |
| RNA-seq Tumor (w/ Barrett’s history) | Prevotella intermedia | 26 | 3 | 11.53846 |
| RNA-seq Tumor (w/ Barrett’s history) | Prevotella melaninogenica | 26 | 5 | 19.23077 |
| RNA-seq Tumor (w/ Barrett’s history) | Prevotella ruminicola | 26 | 0 | 0.00000 |
| WGS Non-tumor (w/ Barrett’s history) | Prevotella denticola | 6 | 1 | 16.66667 |
| WGS Non-tumor (w/ Barrett’s history) | Prevotella intermedia | 6 | 2 | 33.33333 |
| WGS Non-tumor (w/ Barrett’s history) | Prevotella melaninogenica | 6 | 1 | 16.66667 |
| WGS Non-tumor (w/ Barrett’s history) | Prevotella ruminicola | 6 | 0 | 0.00000 |
| WGS Tumor (w/ Barrett’s history) | Prevotella denticola | 4 | 1 | 25.00000 |
| WGS Tumor (w/ Barrett’s history) | Prevotella intermedia | 4 | 1 | 25.00000 |
| WGS Tumor (w/ Barrett’s history) | Prevotella melaninogenica | 4 | 3 | 75.00000 |
| WGS Tumor (w/ Barrett’s history) | Prevotella ruminicola | 4 | 0 | 0.00000 |
analysis.dat <- analysis.dat %>%
filter(is.na(sample_type)==F)%>%
mutate(
Abund = Abundance*100
)
#root function
root<-function(x){
x <- ifelse(x < 0, 0, x)
x**(0.2)
}
#inverse root function
invroot<-function(x){
x**(5)
}
p <- ggplot(analysis.dat, aes(sample_type, Abund)) +
geom_violin(scale="width", adjust=2)+
geom_point(alpha=0.75)+
scale_y_continuous(
trans=scales::trans_new("root", root, invroot),
breaks=c(0, 0.001,0.01, 0.1, 1,10,50, 100),
labels = c(0, 0.001,0.01, 0.1, 1,10,50, 100),
limits = c(0, 110)
)+
annotate(
"text", x=c(1:7), y=c(rep(110, 7)),
label=c(paste0(round(tb[1,5], 0),"% (",tb[1,4],"/",tb[1,3],")"),
paste0(round(tb[2,5], 0),"% (",tb[2,4],"/",tb[2,3],")"),
paste0(round(tb[3,5], 0),"% (",tb[3,4],"/",tb[3,3],")"),
paste0(round(tb[4,5], 0),"% (",tb[4,4],"/",tb[4,3],")"),
paste0(round(tb[5,5], 0),"% (",tb[5,4],"/",tb[5,3],")"),
paste0(round(tb[6,5], 0),"% (",tb[6,4],"/",tb[6,3],")"),
paste0(round(tb[7,5], 0),"% (",tb[7,4],"/",tb[7,3],")"))
)+
labs(x=NULL, y="% Abundance")+
theme(
axis.text.x = element_text(angle=30, hjust=0.95, vjust=0.95)
)
p
ggsave("output/Barretts_violin-prevo.pdf", p, units = "in", width = 10, height = 6)
sessionInfo()R version 4.0.5 (2021-03-31)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19042)
Matrix products: default
locale:
[1] LC_COLLATE=English_United States.1252
[2] LC_CTYPE=English_United States.1252
[3] LC_MONETARY=English_United States.1252
[4] LC_NUMERIC=C
[5] LC_TIME=English_United States.1252
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] cowplot_1.1.1 dendextend_1.14.0 ggdendro_0.1.22 reshape2_1.4.4
[5] car_3.0-10 carData_3.0-4 gvlma_1.0.0.3 patchwork_1.1.1
[9] viridis_0.5.1 viridisLite_0.3.0 gridExtra_2.3 xtable_1.8-4
[13] kableExtra_1.3.4 MASS_7.3-53.1 data.table_1.14.0 readxl_1.3.1
[17] forcats_0.5.1 stringr_1.4.0 dplyr_1.0.5 purrr_0.3.4
[21] readr_1.4.0 tidyr_1.1.3 tibble_3.1.0 ggplot2_3.3.3
[25] tidyverse_1.3.0 lmerTest_3.1-3 lme4_1.1-26 Matrix_1.3-2
[29] vegan_2.5-7 lattice_0.20-41 permute_0.9-5 phyloseq_1.34.0
[33] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] minqa_1.2.4 colorspace_2.0-0 rio_0.5.26
[4] ellipsis_0.3.1 rprojroot_2.0.2 XVector_0.30.0
[7] fs_1.5.0 rstudioapi_0.13 farver_2.1.0
[10] fansi_0.4.2 lubridate_1.7.10 xml2_1.3.2
[13] codetools_0.2-18 splines_4.0.5 knitr_1.31
[16] ade4_1.7-16 jsonlite_1.7.2 nloptr_1.2.2.2
[19] broom_0.7.5 cluster_2.1.1 dbplyr_2.1.0
[22] BiocManager_1.30.10 compiler_4.0.5 httr_1.4.2
[25] backports_1.2.1 assertthat_0.2.1 cli_2.3.1
[28] later_1.1.0.1 htmltools_0.5.1.1 prettyunits_1.1.1
[31] tools_4.0.5 igraph_1.2.6 gtable_0.3.0
[34] glue_1.4.2 Rcpp_1.0.6 Biobase_2.50.0
[37] cellranger_1.1.0 jquerylib_0.1.3 vctrs_0.3.6
[40] Biostrings_2.58.0 rhdf5filters_1.2.0 multtest_2.46.0
[43] svglite_2.0.0 ape_5.4-1 nlme_3.1-152
[46] iterators_1.0.13 xfun_0.21 ps_1.6.0
[49] openxlsx_4.2.3 rvest_1.0.0 lifecycle_1.0.0
[52] statmod_1.4.35 zlibbioc_1.36.0 scales_1.1.1
[55] hms_1.0.0 promises_1.2.0.1 parallel_4.0.5
[58] biomformat_1.18.0 rhdf5_2.34.0 curl_4.3
[61] yaml_2.2.1 sass_0.3.1 stringi_1.5.3
[64] highr_0.8 S4Vectors_0.28.1 foreach_1.5.1
[67] BiocGenerics_0.36.0 zip_2.1.1 boot_1.3-27
[70] systemfonts_1.0.1 rlang_0.4.10 pkgconfig_2.0.3
[73] evaluate_0.14 Rhdf5lib_1.12.1 tidyselect_1.1.0
[76] plyr_1.8.6 magrittr_2.0.1 R6_2.5.0
[79] IRanges_2.24.1 generics_0.1.0 DBI_1.1.1
[82] foreign_0.8-81 pillar_1.5.1 haven_2.3.1
[85] whisker_0.4 withr_2.4.1 mgcv_1.8-34
[88] abind_1.4-5 survival_3.2-10 modelr_0.1.8
[91] crayon_1.4.1 utf8_1.1.4 rmarkdown_2.7
[94] progress_1.2.2 grid_4.0.5 git2r_0.28.0
[97] webshot_0.5.2 reprex_1.0.0 digest_0.6.27
[100] httpuv_1.5.5 numDeriv_2016.8-1.1 stats4_4.0.5
[103] munsell_0.5.0 bslib_0.2.4