Last updated: 2020-12-03
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Follow outlined GenomicPredictionChecklist and previous pipeline to process cassavabase data for ultimate genomic prediction.
Below we will clean and format training data.
Downloaded all IITA field trials with studyYear 2018, 2019, 2020.
DatabaseDownload_2020Sep15/ uploaded to Cassavabase FTP server.2018 trials: probably redundant to those previously downloaded in July 2019 for the genomic prediction of GS C4. In case some trials weren’t harvested as of July 2019, use the 2018 trials downloaded here instead of the ones from 2019.
2019 trials: All trials harvested as of now (Sep. 15, 2020) are to be added to refresh the genomic predictions.
2020 trials: If any current trials already have e.g. disease data, will use it.
library(tidyverse)
library(magrittr)
source(here::here("code", "gsFunctions.R"))Read DB data directly from the Cassavabase FTP server.
# dbdata19<-readDBdata(phenotypeFile =
# 'ftp://ftp.cassavabase.org/marnin_datasets/NGC_BigData/DatabaseDownload_72419/2019-07-24T144915phenotype_download.csv',
# metadataFile =
# 'ftp://ftp.cassavabase.org/marnin_datasets/NGC_BigData/DatabaseDownload_72419/2019-07-24T144144metadata_download.csv')
# dbdata20<-readDBdata(phenotypeFile =
# 'ftp://ftp.cassavabase.org/marnin_datasets/NGC_BigData/DatabaseDownload_2020Sep15/2020-09-15T175322phenotype_download.csv',
# metadataFile =
# 'ftp://ftp.cassavabase.org/marnin_datasets/NGC_BigData/DatabaseDownload_2020Sep15/2020-09-15T175517metadata_download.csv')
nrow(dbdata19) # [1] 463841 plots
nrow(dbdata20) # [1] 176787 plotsCheck for overlapping trials between the two flatfiles.
table(unique(dbdata20$studyName) %in% unique(dbdata19$studyName))
# FALSE TRUE 174 197A quick visual inspection revealed that phenotypes were definitely added to trials after download last year.
More exciting, I see that e.g. Chromometer data have trait-ontology terms now. They didn’t last year! Furthermore, based on the cassavabase website right now, many IITA trials at least back till 2014 have had their chromometer data go “live”. So…. I think this justifies download an entirely fresh flatfile of ALL IITA trials. Make sure to capture all traits.
Downloaded all IITA field trials.
DatabaseDownload_2020Sep15/ uploaded to Cassavabase FTP server.Possible database bug? The entire >500Mb phenotype dataset for IITA downloaded without a problem. However, I’m getting an “server error” message trying to download the corresponding meta-data in one chunk.
Solution: combine meta-data downloaded for “all” trials in July 2019, with meta-data download for the 2018-2020 period done Sep. 15, 2020. Feed joined file to readDBdata().
metadata19 <- read.csv("ftp://ftp.cassavabase.org/marnin_datasets/NGC_BigData/DatabaseDownload_72419/2019-07-24T144144metadata_download.csv",
na.strings = c("#VALUE!", NA, ".", "", " ", "-", "\""), stringsAsFactors = F)
metadata20 <- read.csv("ftp://ftp.cassavabase.org/marnin_datasets/NGC_BigData/DatabaseDownload_2020Sep15/2020-09-15T175517metadata_download.csv",
na.strings = c("#VALUE!", NA, ".", "", " ", "-", "\""), stringsAsFactors = F)
metadata19 %>% # remove lines for trials in the 2020 download
filter(studyName %in% metadata20$studyName) %>% bind_rows(metadata20) %>% # ensure no duplicate lines
distinct %>% # write to disk
write.csv(., here::here("output", "all_iita_metadata.csv"), row.names = F)rm(list = ls())
library(tidyverse)
library(magrittr)
source(here::here("code", "gsFunctions.R"))Read DB data directly from the Cassavabase FTP server.
dbdata <- readDBdata(phenotypeFile = "ftp://ftp.cassavabase.org/marnin_datasets/NGC_BigData/DatabaseDownload_2020Sep15/2020-09-15T185453phenotype_download.csv",
metadataFile = here::here("output", "all_iita_metadata.csv"))Make TrialType Variable
dbdata <- makeTrialTypeVar(dbdata)
dbdata %>% count(TrialType) TrialType n
1 AYT 51641
2 CET 70402
3 Conservation 997
4 CrossingBlock 1546
5 ExpCET 1865
6 GeneticGain 51078
7 NCRP 3764
8 PYT 59101
9 SN 155596
10 UYT 73284
11 <NA> 77684Looking at the studyName’s of trials getting NA for TrialType, which can’t be classified at present.
Here is the list of trials I am not including.
dbdata %>% filter(is.na(TrialType)) %$% unique(studyName) %>% write.csv(., file = here::here("output",
"iita_trials_NOT_identifiable.csv"), row.names = F)Wrote to disk a CSV in the output/ sub-directory.
Should any of these trials have been included?
Especially the following new trials (post 2018)?
dbdata %>% filter(is.na(TrialType), as.numeric(studyYear) > 2018) %$% unique(studyName) [1] "18Hawaii_Parents" "19CB1IB"
[3] "19CVS12Chitala" "19CVS12Mkondezi"
[5] "19flowexpPGR22UB" "19flowexpRedLight22UB"
[7] "19flowLightIntensityUB" "19flowPGRFeminizationIB"
[9] "19flowPGRFreqIB" "19flowPGRRatioIB"
[11] "19flowPGRRtflwrIB" "19GhanaGermplasmUB"
[13] "19GRCgermplasmUB" "19.GS.C1.C2.C3.SelGain.AB"
[15] "19HarvTimeKabangwe" "19LocalGermplasmUB"
[17] "19SN5968Chitala" "2019GXEBUKEMBA"
[19] "2019GXEMUHANGA" "2019GXENGOMA"
[21] "2019GXENYAGATARE" "2019GXERUBIRIZI"
[23] "2019GXERUBONA" "20CSV12Chitala"
[25] "20CSV12Mkondezi" "20GRCgermplasmIB"
[27] "20LocalGermplasmIB" "20PTY49Kabangwe"
[29] "Hawaii_IITA_seed_2019" "Hawaii_seed_Asia_2019"
[31] "Hawaii_seed_CIAT_2019" dbdata %<>% filter(!is.na(TrialType))
dbdata %>% group_by(programName) %>% summarize(N = n())# A tibble: 1 x 2
programName N
<chr> <int>
1 IITA 469274# 469274 plots (~155K are seedling nurseries which will be excluded from most
# analyses)Making a table of abbreviations for renaming. Since July 2019 version: added chromometer traits (L, a, b) and added branching levels count (BRLVLS) at IYR’s request.
traitabbrevs <- tribble(~TraitAbbrev, ~TraitName, "CMD1S", "cassava.mosaic.disease.severity.1.month.evaluation.CO_334.0000191",
"CMD3S", "cassava.mosaic.disease.severity.3.month.evaluation.CO_334.0000192",
"CMD6S", "cassava.mosaic.disease.severity.6.month.evaluation.CO_334.0000194",
"CMD9S", "cassava.mosaic.disease.severity.9.month.evaluation.CO_334.0000193",
"CGM", "Cassava.green.mite.severity.CO_334.0000033", "CGMS1", "cassava.green.mite.severity.first.evaluation.CO_334.0000189",
"CGMS2", "cassava.green.mite.severity.second.evaluation.CO_334.0000190", "DM",
"dry.matter.content.percentage.CO_334.0000092", "PLTHT", "plant.height.measurement.in.cm.CO_334.0000018",
"BRNHT1", "first.apical.branch.height.measurement.in.cm.CO_334.0000106", "BRLVLS",
"branching.level.counting.CO_334.0000079", "SHTWT", "fresh.shoot.weight.measurement.in.kg.per.plot.CO_334.0000016",
"RTWT", "fresh.storage.root.weight.per.plot.CO_334.0000012", "RTNO", "root.number.counting.CO_334.0000011",
"TCHART", "total.carotenoid.by.chart.1.8.CO_334.0000161", "LCHROMO", "L.chromometer.value.CO_334.0002065",
"ACHROMO", "a.chromometer.value.CO_334.0002066", "BCHROMO", "b.chromometer.value.CO_334.0002064",
"NOHAV", "plant.stands.harvested.counting.CO_334.0000010")
traitabbrevs# A tibble: 19 x 2
TraitAbbrev TraitName
<chr> <chr>
1 CMD1S cassava.mosaic.disease.severity.1.month.evaluation.CO_334.0000191
2 CMD3S cassava.mosaic.disease.severity.3.month.evaluation.CO_334.0000192
3 CMD6S cassava.mosaic.disease.severity.6.month.evaluation.CO_334.0000194
4 CMD9S cassava.mosaic.disease.severity.9.month.evaluation.CO_334.0000193
5 CGM Cassava.green.mite.severity.CO_334.0000033
6 CGMS1 cassava.green.mite.severity.first.evaluation.CO_334.0000189
7 CGMS2 cassava.green.mite.severity.second.evaluation.CO_334.0000190
8 DM dry.matter.content.percentage.CO_334.0000092
9 PLTHT plant.height.measurement.in.cm.CO_334.0000018
10 BRNHT1 first.apical.branch.height.measurement.in.cm.CO_334.0000106
11 BRLVLS branching.level.counting.CO_334.0000079
12 SHTWT fresh.shoot.weight.measurement.in.kg.per.plot.CO_334.0000016
13 RTWT fresh.storage.root.weight.per.plot.CO_334.0000012
14 RTNO root.number.counting.CO_334.0000011
15 TCHART total.carotenoid.by.chart.1.8.CO_334.0000161
16 LCHROMO L.chromometer.value.CO_334.0002065
17 ACHROMO a.chromometer.value.CO_334.0002066
18 BCHROMO b.chromometer.value.CO_334.0002064
19 NOHAV plant.stands.harvested.counting.CO_334.0000010 Run function renameAndSelectCols() to rename columns and remove everything unecessary
dbdata <- renameAndSelectCols(traitabbrevs, indata = dbdata, customColsToKeep = "TrialType")Standard code, recycled… should be a function?
dbdata <- dbdata %>% mutate(CMD1S = ifelse(CMD1S < 1 | CMD1S > 5, NA, CMD1S), CMD3S = ifelse(CMD3S <
1 | CMD3S > 5, NA, CMD3S), CMD6S = ifelse(CMD6S < 1 | CMD1S > 5, NA, CMD6S),
CMD9S = ifelse(CMD9S < 1 | CMD1S > 5, NA, CMD9S), CGM = ifelse(CGM < 1 | CGM >
5, NA, CGM), CGMS1 = ifelse(CGMS1 < 1 | CGMS1 > 5, NA, CGMS1), CGMS2 = ifelse(CGMS2 <
1 | CGMS2 > 5, NA, CGMS2), DM = ifelse(DM > 100 | DM <= 0, NA, DM), RTWT = ifelse(RTWT ==
0 | NOHAV == 0 | is.na(NOHAV), NA, RTWT), SHTWT = ifelse(SHTWT == 0 | NOHAV ==
0 | is.na(NOHAV), NA, SHTWT), RTNO = ifelse(RTNO == 0 | NOHAV == 0 | is.na(NOHAV),
NA, RTNO), NOHAV = ifelse(NOHAV == 0, NA, NOHAV), NOHAV = ifelse(NOHAV >
42, NA, NOHAV), RTNO = ifelse(!RTNO %in% 1:10000, NA, RTNO))dbdata <- dbdata %>% mutate(HI = RTWT/(RTWT + SHTWT))I anticipate this will not be necessary as it will be computed before or during data upload.
For calculating fresh root yield:
dbdata <- dbdata %>% mutate(PlotSpacing = ifelse(programName != "IITA", 1, ifelse(studyYear <
2013, 1, ifelse(TrialType %in% c("CET", "GeneticGain", "ExpCET"), 1, 0.8))))
maxNOHAV_byStudy <- dbdata %>% group_by(programName, locationName, studyYear, studyName,
studyDesign) %>% summarize(MaxNOHAV = max(NOHAV, na.rm = T)) %>% ungroup() %>%
mutate(MaxNOHAV = ifelse(MaxNOHAV == "-Inf", NA, MaxNOHAV))
write.csv(maxNOHAV_byStudy %>% arrange(studyYear), file = here::here("output", "maxNOHAV_byStudy.csv"),
row.names = F)# I log transform yield traits to satisfy homoskedastic residuals assumption of
# linear mixed models
dbdata <- left_join(dbdata, maxNOHAV_byStudy) %>% mutate(RTWT = ifelse(NOHAV > MaxNOHAV,
NA, RTWT), SHTWT = ifelse(NOHAV > MaxNOHAV, NA, SHTWT), RTNO = ifelse(NOHAV >
MaxNOHAV, NA, RTNO), HI = ifelse(NOHAV > MaxNOHAV, NA, HI), logFYLD = log(RTWT/(MaxNOHAV *
PlotSpacing) * 10), logTOPYLD = log(SHTWT/(MaxNOHAV * PlotSpacing) * 10), logRTNO = log(RTNO),
PropNOHAV = NOHAV/MaxNOHAV)
# remove non transformed / per-plot (instead of per area) traits
dbdata %<>% select(-RTWT, -SHTWT, -RTNO)dbdata <- dbdata %>% mutate(MCMDS = rowMeans(.[, c("CMD1S", "CMD3S", "CMD6S", "CMD9S")],
na.rm = T)) %>% select(-CMD1S, -CMD3S, -CMD6S, -CMD9S)This step is mostly copy-pasted from previous processing of IITA-specific data.
Uses 3 flat files, which are available e.g. here. Specifically, IITA_GBStoPhenoMaster_33018.csv, GBSdataMasterList_31818.csv and NRCRI_GBStoPhenoMaster_40318.csv. I copy them to the data/ sub-directory for the current analysis.
In addition, DArT-only samples are now expected to also have phenotypes. Therefore, checking for matches in new flatfiles, deposited in the data/ (see code below).
library(tidyverse); library(magrittr)
gbs2phenoMaster<-dbdata %>%
select(germplasmName) %>%
distinct %>%
left_join(read.csv(here::here("data","NRCRI_GBStoPhenoMaster_40318.csv"),
stringsAsFactors = F)) %>%
mutate(FullSampleName=ifelse(grepl("C2a",germplasmName,ignore.case = T) &
is.na(FullSampleName),germplasmName,FullSampleName)) %>%
filter(!is.na(FullSampleName)) %>%
select(germplasmName,FullSampleName) %>%
bind_rows(dbdata %>%
select(germplasmName) %>%
distinct %>%
left_join(read.csv(here::here("data","IITA_GBStoPhenoMaster_33018.csv"),
stringsAsFactors = F)) %>%
filter(!is.na(FullSampleName)) %>%
select(germplasmName,FullSampleName)) %>%
bind_rows(dbdata %>%
select(germplasmName) %>%
distinct %>%
left_join(read.csv(here::here("data","GBSdataMasterList_31818.csv"),
stringsAsFactors = F) %>%
select(DNASample,FullSampleName) %>%
rename(germplasmName=DNASample)) %>%
filter(!is.na(FullSampleName)) %>%
select(germplasmName,FullSampleName)) %>%
bind_rows(dbdata %>%
select(germplasmName) %>%
distinct %>%
mutate(germplasmSynonyms=ifelse(grepl("^UG",germplasmName,ignore.case = T),
gsub("UG","Ug",germplasmName),germplasmName)) %>%
left_join(read.csv(here::here("data","GBSdataMasterList_31818.csv"),
stringsAsFactors = F) %>%
select(DNASample,FullSampleName) %>%
rename(germplasmSynonyms=DNASample)) %>%
filter(!is.na(FullSampleName)) %>%
select(germplasmName,FullSampleName)) %>%
bind_rows(dbdata %>%
select(germplasmName) %>%
distinct %>%
mutate(germplasmSynonyms=ifelse(grepl("^TZ",germplasmName,
ignore.case = T),
gsub("TZ","",germplasmName),germplasmName)) %>%
left_join(read.csv(here::here("data","GBSdataMasterList_31818.csv"),
stringsAsFactors = F) %>%
select(DNASample,FullSampleName) %>%
rename(germplasmSynonyms=DNASample)) %>%
filter(!is.na(FullSampleName)) %>%
select(germplasmName,FullSampleName)) %>%
distinct %>%
left_join(read.csv(here::here("data","GBSdataMasterList_31818.csv"),
stringsAsFactors = F) %>%
select(FullSampleName,OrigKeyFile,Institute) %>%
rename(OriginOfSample=Institute)) %>%
mutate(OrigKeyFile=ifelse(grepl("C2a",germplasmName,ignore.case = T),
ifelse(is.na(OrigKeyFile),"LavalGBS",OrigKeyFile),
OrigKeyFile),
OriginOfSample=ifelse(grepl("C2a",germplasmName,ignore.case = T),
ifelse(is.na(OriginOfSample),"NRCRI",OriginOfSample),
OriginOfSample))
## NEW: check for germName-DArT name matches
germNamesWithoutGBSgenos<-dbdata %>%
select(programName,germplasmName) %>%
distinct %>%
left_join(gbs2phenoMaster) %>%
filter(is.na(FullSampleName)) %>%
select(-FullSampleName)
germNamesWithDArT<-germNamesWithoutGBSgenos %>%
inner_join(read.table(here::here("data","chr1_RefPanelAndGSprogeny_ReadyForGP_72719.fam"), header = F, stringsAsFactors = F)$V2 %>%
grep("TMS16|TMS17|TMS18|TMS19|TMS20",.,value = T, ignore.case = T) %>%
tibble(dartName=.) %>%
separate(dartName,c("germplasmName","dartID"),"_",extra = 'merge',remove = F)) %>%
group_by(germplasmName) %>%
slice(1) %>%
ungroup() %>%
rename(FullSampleName=dartName) %>%
mutate(OrigKeyFile="DArTseqLD", OriginOfSample="IITA") %>%
select(-dartID)
print(paste0(nrow(germNamesWithDArT)," germNames with DArT-only genos"))[1] "2401 germNames with DArT-only genos"# [1] "2401 germNames with DArT-only genos"
# first, filter to just program-DNAorigin matches
germNamesWithGenos<-dbdata %>%
select(programName,germplasmName) %>%
distinct %>%
left_join(gbs2phenoMaster) %>%
filter(!is.na(FullSampleName))
print(paste0(nrow(germNamesWithGenos)," germNames with GBS genos"))[1] "9323 germNames with GBS genos"# [1] "9323 germNames with GBS genos"
# program-germNames with locally sourced GBS samples
germNamesWithGenos_HasLocalSourcedGBS<-germNamesWithGenos %>%
filter(programName==OriginOfSample) %>%
select(programName,germplasmName) %>%
semi_join(germNamesWithGenos,.) %>%
group_by(programName,germplasmName) %>% # select one DNA per germplasmName per program
slice(1) %>% ungroup()
print(paste0(nrow(germNamesWithGenos_HasLocalSourcedGBS)," germNames with local GBS genos"))[1] "8257 germNames with local GBS genos"# [1] "8257 germNames with local GBS genos"
# the rest (program-germNames) with GBS but coming from a different breeding program
germNamesWithGenos_NoLocalSourcedGBS<-germNamesWithGenos %>%
filter(programName==OriginOfSample) %>%
select(programName,germplasmName) %>%
anti_join(germNamesWithGenos,.) %>%
# select one DNA per germplasmName per program
group_by(programName,germplasmName) %>%
slice(1) %>% ungroup()
print(paste0(nrow(germNamesWithGenos_NoLocalSourcedGBS)," germNames without local GBS genos"))[1] "167 germNames without local GBS genos"# [1] "167 germNames without local GBS genos"
genosForPhenos<-bind_rows(germNamesWithGenos_HasLocalSourcedGBS,
germNamesWithGenos_NoLocalSourcedGBS) %>%
bind_rows(germNamesWithDArT)
print(paste0(nrow(genosForPhenos)," total germNames with genos either GBS or DArT"))[1] "10825 total germNames with genos either GBS or DArT"# [1] "10825 total germNames with genos either GBS or DArT"
dbdata %<>%
left_join(genosForPhenos)
# Create a new identifier, GID
## Equals the value SNP data name (FullSampleName)
## else germplasmName if no SNP data
dbdata %<>%
mutate(GID=ifelse(is.na(FullSampleName),germplasmName,FullSampleName))# going to check against SNP data
# snps<-readRDS(file=url(paste0('ftp://ftp.cassavabase.org/marnin_datasets/NGC_BigData/',
# 'DosageMatrix_RefPanelAndGSprogeny_ReadyForGP_73019.rds')))
# rownames_snps<-rownames(snps); rm(snps); gc() # current matches to SNP data
# dbdata %>% distinct(GID,germplasmName,FullSampleName) %>%
# semi_join(tibble(GID=rownames_snps)) %>% nrow() # 10707 dbdata %>%
# distinct(GID,germplasmName,FullSampleName) %>%
# semi_join(tibble(GID=rownames_snps)) %>%
# filter(grepl('TMS13|2013_',GID,ignore.case = F)) %>% nrow() # 2424 TMS13 dbdata
# %>% distinct(GID,germplasmName,FullSampleName) %>%
# semi_join(tibble(GID=rownames_snps)) %>% filter(grepl('TMS14',GID,ignore.case =
# F)) %>% nrow() # 2236 TMS14 dbdata %>%
# distinct(GID,germplasmName,FullSampleName) %>%
# semi_join(tibble(GID=rownames_snps)) %>% filter(grepl('TMS15',GID,ignore.case =
# F)) %>% nrow() # 2287 TMS15 dbdata %>%
# distinct(GID,germplasmName,FullSampleName) %>%
# semi_join(tibble(GID=rownames_snps)) %>% filter(grepl('TMS18',GID,ignore.case =
# F)) %>% nrow() # 2401 TMS18WARNING: User input required! If I had preselected locations before downloading, this wouldn’t have been necessary.
Based on previous locations used for IITA analysis, but adding based on list of locations used in IYR’s trial list data/2019_GS_PhenoUpload.csv: “Ago-Owu” wasn’t used last year.
dbdata %<>% filter(locationName %in% c("Abuja", "Ago-Owu", "Ibadan", "Ikenne", "Ilorin",
"Jos", "Kano", "Malam Madori", "Mokwa", "Ubiaja", "Umudike", "Warri", "Zaria"))
nrow(dbdata) # [1] 427294[1] 427294saveRDS(dbdata, file = here::here("output", "IITA_CleanedTrialData.rds"))
sessionInfo()R version 4.0.2 (2020-06-22)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Catalina 10.15.7
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] magrittr_2.0.1 forcats_0.5.0 stringr_1.4.0 dplyr_1.0.2
[5] purrr_0.3.4 readr_1.4.0 tidyr_1.1.2 tibble_3.0.4
[9] ggplot2_3.3.2 tidyverse_1.3.0 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] tidyselect_1.1.0 xfun_0.19 haven_2.3.1 colorspace_2.0-0
[5] vctrs_0.3.5 generics_0.1.0 htmltools_0.5.0 yaml_2.2.1
[9] utf8_1.1.4 rlang_0.4.9 later_1.1.0.1 pillar_1.4.7
[13] withr_2.3.0 glue_1.4.2 DBI_1.1.0 dbplyr_2.0.0
[17] modelr_0.1.8 readxl_1.3.1 lifecycle_0.2.0 munsell_0.5.0
[21] gtable_0.3.0 cellranger_1.1.0 rvest_0.3.6 evaluate_0.14
[25] knitr_1.30 ps_1.4.0 httpuv_1.5.4 fansi_0.4.1
[29] broom_0.7.2 Rcpp_1.0.5 promises_1.1.1 backports_1.2.0
[33] scales_1.1.1 formatR_1.7 jsonlite_1.7.1 fs_1.5.0
[37] hms_0.5.3 digest_0.6.27 stringi_1.5.3 rprojroot_2.0.2
[41] grid_4.0.2 here_1.0.0 cli_2.2.0 tools_4.0.2
[45] crayon_1.3.4 whisker_0.4 pkgconfig_2.0.3 ellipsis_0.3.1
[49] xml2_1.3.2 reprex_0.3.0 lubridate_1.7.9.2 rstudioapi_0.13
[53] assertthat_0.2.1 rmarkdown_2.5 httr_1.4.2 R6_2.5.0
[57] git2r_0.27.1 compiler_4.0.2