Last updated: 2021-01-21
Checks: 7 0
Knit directory: TARI_2020GS/
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Unstaged changes:
Modified: output/TARI_trials_NOT_identifiable.csv
Modified: output/maxNOHAV_byStudy.csv
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.
These are the previous versions of the repository in which changes were made to the R Markdown (analysis/01-cleanTPdata.Rmd) and HTML (docs/01-cleanTPdata.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.
| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | ac1cf61 | wolfemd | 2021-01-21 | Kibaha samples added. Cross-validation and predictions redone. |
| html | 5549754 | wolfemd | 2021-01-21 | Build site. |
| Rmd | bd635a2 | wolfemd | 2021-01-21 | Customized and updated matching germplasmName to FullSampleName (GBS / |
| html | abaf52a | wolfemd | 2020-12-23 | Build site. |
| Rmd | fae176a | wolfemd | 2020-12-23 | Publish the first set of analyses and files for TARI 2020 GS. |
Follow outlined GenomicPredictionChecklist and previous pipeline to process cassavabase data for ultimate genomic prediction.
Below we will clean and format training data.
Downloaded all TARI field trials.
data/DatabaseDownload_2021Jan20/.rm(list=ls())
library(tidyverse); library(magrittr);
source(here::here("code","gsFunctions.R"))But first…. TARI seems to have a’lot of plant-basis data which I am not usually including.
indata<-read.csv(here::here("data/DatabaseDownload_2021Jan20","2021-01-20T203949phenotype_download.csv"),
na.strings = c("#VALUE!",NA,".",""," ","-","\""),
stringsAsFactors = F)
indata %>% count(observationLevel) observationLevel n
1 plant 191079
2 plot 22978Over 191K “plants” between 2018-2020.
The following printed studyNames have plant-basis data. They WILL NOT be included in subsequent analyses.
indata %>% filter(observationLevel=="plant") %$% unique(studyName) [1] "18CROSSING_BLOCK_TRIAL_MRK" "19_ayt_iita_material_bunda"
[3] "19_ayt_iita_material_ukerewe" "19_ayt_iita_material_Ukiriguru"
[5] "2019_CBSD_IMMUNE_BUN" "2019_CBSD_IMMUNE_UKE"
[7] "2019_CBSD_IMMUNE_UKGR" "2019_NPT_UKE_TZ"
[9] "2019_NPT_UKG_5CP" "2019_NPT_UKG_TZ"
[11] "2019_UYT_BUN" "2019_UYT_BWA"
[13] "2019_UYT_UKE" "2019_UYT_UKG"
[15] "2020_AYT2_BUN" "2020AYT2BWANGA"
[17] "2020_AYT2_UKE" "2020_AYT2_UKG"
[19] "2020_AYT3_BUN" "2020AYT3BWANGA"
[21] "2020_AYT3_UKE" "2020_AYT3_UKG"
[23] "2020_AYT_IITA_BUN" "2020_AYT_IITA_CHATO"
[25] "2020_AYT_IITA_UKE" "2020_AYT_IITA_UKG"
[27] "2020_AYT_TP_BUN" "2020_AYT_TP_BW"
[29] "2020_AYT_TP_KAS" "2020_AYT_TP_UKE"
[31] "2020_AYT_TP_UKG" "2020_CBSD_IMMUNE_BUN"
[33] "2020_CBSD_IMMUNE_UKE" "2020_CBSD_IMMUNE_UKG"
[35] "2020_GWAS_BUNDA" "2020_GWAS_CHAMBEZI_TRIAL"
[37] "2020_GWAS_Ukerewe" "2020_GWAS_UKIRIGURU"
[39] "2020_GxE_ILO" "2020_GxE_KBH"
[41] "2020_GxE_UKG" "2020_PYT_NMKS1_BUN"
[43] "2020_PYT_NMKS1_KAS" "2020_PYT_NMKS1_KIS"
[45] "2020_PYT_NMKS1_UKG" "2020_PYT_NMKxAR37-80_BUN"
[47] "2020_PYT_NMKxAR37-80_KAS" "2020_PYT_NMKxAR37-80_UKG"
[49] "2020_uyt1A_GAIRO" "2020_UYT_BUN"
[51] "2020_UYT_BW" "2020_UYT_UKE"
[53] "2020_UYT_UKG" "GXE KIBAHA"
[55] "MULTILOCATIONAL_EZ_TP2" "NGTZ18KBH_AYT1"
[57] "NGTZ18KBH_AYT3" "NGTZ18KBH_AYT4"
[59] "NGTZ18KBH_AYT5" "NGTZ18KBH_AYT6"
[61] "pyt_2018" Read DB data directly from the Cassavabase FTP server.
rm(indata);
dbdata<-readDBdata(phenotypeFile = here::here("data/DatabaseDownload_2021Jan20","2021-01-20T203949phenotype_download.csv"),
metadataFile = here::here("data/DatabaseDownload_2021Jan20","2021-01-20T174234metadata_download.csv"))Before proceeding, the 2019 seedling nursery….
dbdata %>%
filter(studyName=="19_C1_GS_Seedling_Nursery_Chambezi",
germplasmName=="TZMRK180069") %>%
distinct(germplasmName,observationUnitName,plantNumber,plotNumber,observationUnitName) %>%
rmarkdown::paged_table()snps5629<-readRDS(here::here("output","DosageMatrix_DCas20_5629_EA_REFimputedAndFiltered.rds"))
rownames(snps5629) %>% grep("TZMRK180069",., value = T, ignore.case = T) [1] "TARI0050_A04...TZMRK180069" "TARI0050_A05...TZMRK180069"
[3] "TARI0050_B04...TZMRK180069" "TARI0050_B05...TZMRK180069"
[5] "TARI0050_C04...TZMRK180069_15.5217" "TARI0050_C05...TZMRK180069"
[7] "TARI0050_D04...TZMRK180069" "TARI0050_D05...TZMRK180069_5.5207"
[9] "TARI0050_E03...TZMRK180069" "TARI0050_E04...TZMRK180069"
[11] "TARI0050_E05...TZMRK180069" "TARI0050_F03...TZMRK180069"
[13] "TARI0050_F04...TZMRK180069" "TARI0050_F05...TZMRK180069"
[15] "TARI0050_G03...TZMRK180069_20.5222" "TARI0050_G04...TZMRK180069"
[17] "TARI0050_G05...TZMRK180069_1.5203" "TARI0050_H03...TZMRK180069"
[19] "TARI0050_H04...TZMRK180069_10.5212"Unfortunately, these don’t currently match. As of Jan 21, wrote to TARI team about this. Will proceed with prediction, but CBSD phenos for the GS C1 seedlings won’t be included at this time.
rm(snps5629); gc() used (Mb) gc trigger (Mb) limit (Mb) max used (Mb)
Ncells 1147732 61.3 2888658 154.3 NA 2072271 110.7
Vcells 4007928 30.6 61635754 470.3 102400 62660784 478.1dbdata %<>%
mutate(locationName=ifelse(locationName=="bwanga","Bwanga",locationName),
locationName=ifelse(locationName=="kasulu","Kasulu",locationName))Make TrialType Variable
dbdata<-makeTrialTypeVar(dbdata)
dbdata %>%
count(TrialType) %>% rmarkdown::paged_table()Looking 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","TARI_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?
dbdata %>%
filter(is.na(TrialType)) %$% unique(studyName) [1] "17uytbwanga" "18_CBSD_IMMUNE"
[3] "18_NAMIKONGA_S1" "18_NMKxAR37-80"
[5] "19_C1_GS_Seedling_Nursery_Chambezi" "2020_AYT_TP_BW"
[7] "2020_CBSD_IMMUNE_BUN" "2020_CBSD_IMMUNE_UKE"
[9] "2020_CBSD_IMMUNE_UKG" "2020_GWAS_BUNDA"
[11] "2020_GWAS_Ukerewe" "2020_GWAS_UKIRIGURU"
[13] "2020_GxE_UKG" "2020_UYT1C_GAIRO"
[15] "95_iita_tz_materials" "ACCESSION FOR GENOTYPING"
[17] "bunda 2018" "CET_1_2016"
[19] "GXE KIBAHA" "IITA GENOTYPING PLATE"
[21] "ilonga_trial" "KIBAHA GERMPLASM"
[23] "local_germplam_Southern" "Local_germplasm_eastern"
[25] "Local_germplasm_islands" "local_germplasm_northern"
[27] "Local_germplasm_Nothern" "Local_germplasm_SMS"
[29] "LOCAL VARIETIES" "MULTILOCATIONAL_EZ_TP2"
[31] "NDL_OP_UK" "NEW_LOCAL_GERMPLASM_UKIRIGURU"
[33] "NGTZ18KBH_AYT5" "NGTZ18KBH_SEEDLING"
[35] "NGTZ_CBSDIMMUNE_16VAR_CHAMBZ" "NGTZKBH-2018-19-UYT2"
[37] "Old_local_germplasm_ukiriguru" "QC_CET_1"
[39] "QC_CET_2" "QC_PYT"
[41] "Seedlings_Kibaha" "TARI KIBAHA GERMPLASM" Include (by request) the “19_C1_GS_Seedling_Nursery_Chambezi”.
dbdata %<>%
mutate(TrialType=ifelse(studyName=="19_C1_GS_Seedling_Nursery_Chambezi","SeedlingNursery",TrialType))dbdata %<>%
filter(!is.na(TrialType))
dbdata %>%
group_by(programName) %>%
summarize(N=n()) %>% rmarkdown::paged_table()# 18591 (now including a ~5K plot seedling nursery) plotsMaking a table of abbreviations for renaming
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",
"CBSD3S","cassava.brown.streak.disease.leaf.severity.3.month.evaluation.CO_334.0000204",
"CBSD6S","cassava.brown.streak.disease.leaf.severity.6.month.evaluation.CO_334.0000205",
"CBSD9S","cassava.brown.streak.disease.leaf.severity.9.month.evaluation.CO_334.0000206",
"CBSDRS","cassava.brown.streak.disease.root.severity.12.month.evaluation.CO_334.0000201",
#"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.by.specific.gravity.method.CO_334.0000160",
# "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",
"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",
"NOHAV","plant.stands.harvested.counting.CO_334.0000010")
traitabbrevs %>% rmarkdown::paged_table()# dbdata %>% colnames(.) %>% grep("fresh.root",.,value=T)
# dbdata$cassava.green.mite.severity.first.evaluation.CO_334.0000189 %>% summaryRun function renameAndSelectCols() to rename columns and remove everything unecessary
dbdata<-renameAndSelectCols(traitabbrevs,indata=dbdata,customColsToKeep = c("TrialType","observationUnitName"))dbdata<-dbdata %>%
mutate(#CMD1S=ifelse(CMD1S<1 | CMD1S>5,NA,CMD1S),
CMD3S=ifelse(CMD3S<1 | CMD3S>5,NA,CMD3S),
CMD6S=ifelse(CMD6S<1 | CMD6S>5,NA,CMD6S),
CMD9S=ifelse(CMD9S<1 | CMD9S>5,NA,CMD9S),
CBSD3S=ifelse(CBSD3S<1 | CBSD3S>5,NA,CBSD3S),
CBSD6S=ifelse(CBSD6S<1 | CBSD6S>5,NA,CBSD6S),
CBSD9S=ifelse(CBSD9S<1 | CBSD9S>5,NA,CMD9S),
CBSDRS=ifelse(CBSDRS<1 | CBSDRS>5,NA,CBSDRS),
#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),
FYLD=RTWT/(MaxNOHAV*PlotSpacing)*10,
DYLD=FYLD*(DM/100),
logFYLD=log(FYLD),
logDYLD=log(DYLD),
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,-FYLD,-DYLD)dbdata<-dbdata %>%
mutate(MCMDS=rowMeans(.[,c("CMD3S","CMD6S","CMD9S")], na.rm = T),
MCBSDS=rowMeans(.[,c("CBSD3S","CBSD6S","CBSD9S")], na.rm = T)) %>%
select(-CMD3S,-CMD6S,-CMD9S,-CBSD3S,-CBSD6S,-CBSD9S)I customized this step for TARI.
Match “germplasmName” from TARI phenotyping trials to “FullSampleName” from TARI GBS and DArT genotyping data.
Uses 2 flat files, which are available e.g. here. Specifically, IITA_GBStoPhenoMaster_33018.csv, GBSdataMasterList_31818.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)
# Distinct "germplasmName" identifying clones in TARI phenotyping plots
tzgermnames<-dbdata %>%
distinct(germplasmName)
# 1) Match TARI samples where germplasmName is prefixed with TZ, but FullSampleName
phenos2genos<-tzgermnames %>%
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)) %>%
# 2) Match additional samples based on genotyping done by IITA and NaCRRI:
## IITA
bind_rows(tzgermnames %>%
left_join(read.csv(here::here("data","IITA_GBStoPhenoMaster_33018.csv"),
stringsAsFactors = F))) %>%
## NaCRRI
bind_rows(tzgermnames %>%
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)))
phenos2genos %>% filter(!is.na(FullSampleName)) %>% distinct(germplasmName) %>% nrow(.) # [1] 435[1] 435# Only about half the germplasmName we expectOnly about half the ~834 germplasmName we expect to correspond to the clones from Ukiriguru and Kibaha.
At this point, I realized the Kibaha samples are missing.
The solution is in the code below. It required some staring at names. Heneriko supplied a list from the 2016 predictions (see: data/TARI 2016_TP_CLONES.csv), which was helpful.
phenos2genos %<>%
bind_rows(tzgermnames %>%
mutate(germplasmSynonyms=gsub("^TZ","",germplasmName),
germplasmSynonyms=gsub("HS","_",germplasmSynonyms),
germplasmSynonyms=gsub("FS","_",germplasmSynonyms)) %>%
left_join(read.csv(here::here("data","GBSdataMasterList_31818.csv"),
stringsAsFactors = F) %>%
select(DNASample,FullSampleName) %>%
rename(germplasmSynonyms=DNASample) %>%
filter(grepl("^KBH",germplasmSynonyms)) %>%
mutate(germplasmSynonyms=gsub("KBH2012","KBH12",germplasmSynonyms),
germplasmSynonyms=gsub("KBH2013","KBH13",germplasmSynonyms),
germplasmSynonyms=gsub("KBH2014","KBH14",germplasmSynonyms),
germplasmSynonyms=gsub("KBH2015","KBH15",germplasmSynonyms),
germplasmSynonyms=gsub("KBH2016","KBH16",germplasmSynonyms),
germplasmSynonyms=gsub("KBH2017","KBH17",germplasmSynonyms),
germplasmSynonyms=gsub("KBH2018","KBH18",germplasmSynonyms),
germplasmSynonyms=gsub("KBH2019","KBH19",germplasmSynonyms))))
phenos2genos %<>%
filter(!is.na(FullSampleName)) %>%
distinct(germplasmName,FullSampleName)
phenos2genos %>% distinct(germplasmName) %>% nrow(.) # [1] 914[1] 914Now there are 914 germplasmName-FullSampleName matches.
For both the “germplasmName” and the “FullSampleName” lists, try matching by making everything upper case on both sides. There are many capitolization related issues I see. Examples:
But also, e.g.:
phenos2genos %<>%
bind_rows(tzgermnames %>%
anti_join(phenos2genos) %>%
mutate(germplasmSynonyms=toupper(germplasmName),
germplasmSynonyms=gsub("-","_",germplasmSynonyms)) %>%
left_join(read.csv(here::here("data","GBSdataMasterList_31818.csv"),
stringsAsFactors = F) %>%
select(DNASample,FullSampleName) %>%
rename(germplasmSynonyms=DNASample) %>%
mutate(germplasmSynonyms=toupper(germplasmSynonyms),
germplasmSynonyms=gsub("-","_",germplasmSynonyms)))) %>%
filter(!is.na(FullSampleName)) %>%
distinct(germplasmName,FullSampleName)
phenos2genos %>% distinct(germplasmName) %>% nrow(.) # [1] 921 .... not an awesome improvement[1] 921Next, and last but not least, need to check for matches with the new germplasm genotyped only by DArTseqLD (DCas20_5629). Based on the check I did above, this is not currently possible, so skip.
germNamesWithoutGBSgenos<-tzgermnames %>%
anti_join(phenos2genos)
germNamesWithoutGBSgenos %>% nrow() # [1] 2938[1] 2938Select one genotype record (FullSampleName) per unique clone (germplasmName)
genosChosenForPhenos<-phenos2genos %>%
group_by(germplasmName) %>%
slice(1) %>% ungroup()
print(paste0(nrow(genosChosenForPhenos)," germNames with GBS geno. records"))[1] "921 germNames with GBS geno. records"dbdata %<>%
left_join(genosChosenForPhenos)
# Create a new identifier, GID
## Equals the value SNP data name (FullSampleName)
## else germplasmName if no SNP data
## [FOR TARI] if studyName=="19_C1_GS_Seedling_Nursery_Chambezi", GID should be the "observationUnitName"
dbdata %<>%
mutate(GID=ifelse(is.na(FullSampleName),
ifelse(studyName=="19_C1_GS_Seedling_Nursery_Chambezi",
observationUnitName,germplasmName),
FullSampleName))# snps_refpanel<-readRDS(here::here("output","DosageMatrix_ImputationReferencePanel_StageVI_91119.rds"))
# snps5629<-readRDS(here::here("output","DosageMatrix_DCas20_5629_EA_REFimputedAndFiltered.rds"))
# rownames(snps_refpanel) %>%
# write.csv(.,file = here::here("output","rownames_DosageMatrix_ImputationReferencePanel_StageVI_91119.csv"), row.names = F)
# rownames(snps5629) %>%
# write.csv(.,file = here::here("output","rownames_DosageMatrix_DCas20_5629_EA_REFimputedAndFiltered.csv"), row.names = F)
# rm(snps_refpanel,snps5629); gc()
write.csv(genosChosenForPhenos,
file = here::here("output","OnlyChosen_germplasmName_to_FullSampleName_matches_TARI_2021Jan21.csv"),
row.names = F)
write.csv(phenos2genos,
file = here::here("output","AllIdentified_germplasmName_to_FullSampleName_matches_TARI_2021Jan21.csv"),
row.names = F)saveRDS(dbdata,file=here::here("output","TARI_CleanedTrialData_2021Jan21.rds"))The next step is to check the experimental design of each trial. If you are absolutely certain of the usage of the design variables in your dataset, you might not need this step.
Examples of reasons to do the step below:
One reason it might be important to get this right is that the variance among complete blocks might not be the same among incomplete blocks. If we treat a mixture of complete and incomplete blocks as part of the same random-effect (replicated-within-trial), we assume they have the same variance.
Also error variances might be heterogeneous among different trial-types (blocking scheme available) and/or plot sizes (maxNOHAV).
Start with cleaned data from previous step.
rm(list=ls()); gc() used (Mb) gc trigger (Mb) limit (Mb) max used (Mb)
Ncells 1141828 61.0 2888658 154.3 NA 2888658 154.3
Vcells 2243037 17.2 49308604 376.2 102400 62660784 478.1library(tidyverse); library(magrittr);
source(here::here("code","gsFunctions.R"))
dbdata<-readRDS(here::here("output","TARI_CleanedTrialData_2021Jan21.rds"))dbdata %>% head %>% rmarkdown::paged_table()Detect designs
dbdata<-detectExptDesigns(dbdata)dbdata %>%
count(programName,CompleteBlocks,IncompleteBlocks) %>% rmarkdown::paged_table()saveRDS(dbdata,file=here::here("output","TARI_ExptDesignsDetected_2021Jan21.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_1.0.3
[5] purrr_0.3.4 readr_1.4.0 tidyr_1.1.2 tibble_3.0.5
[9] ggplot2_3.3.3 tidyverse_1.3.0 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] tidyselect_1.1.0 xfun_0.20 haven_2.3.1 colorspace_2.0-0
[5] vctrs_0.3.6 generics_0.1.0 htmltools_0.5.1 yaml_2.2.1
[9] rlang_0.4.10 later_1.1.0.1 pillar_1.4.7 withr_2.4.0
[13] glue_1.4.2 DBI_1.1.1 dbplyr_2.0.0 modelr_0.1.8
[17] readxl_1.3.1 lifecycle_0.2.0 cellranger_1.1.0 munsell_0.5.0
[21] gtable_0.3.0 rvest_0.3.6 evaluate_0.14 knitr_1.30
[25] httpuv_1.5.5 fansi_0.4.2 broom_0.7.3 Rcpp_1.0.6
[29] promises_1.1.1 backports_1.2.1 scales_1.1.1 jsonlite_1.7.2
[33] fs_1.5.0 hms_1.0.0 digest_0.6.27 stringi_1.5.3
[37] rprojroot_2.0.2 grid_4.0.2 here_1.0.1 cli_2.2.0
[41] tools_4.0.2 crayon_1.3.4 whisker_0.4 pkgconfig_2.0.3
[45] ellipsis_0.3.1 xml2_1.3.2 reprex_0.3.0 lubridate_1.7.9.2
[49] assertthat_0.2.1 rmarkdown_2.6 httr_1.4.2 rstudioapi_0.13
[53] R6_2.5.0 git2r_0.28.0 compiler_4.0.2