Last updated: 2020-12-23

Checks: 7 0

Knit directory: TARI_2020GS/

This reproducible R Markdown analysis was created with workflowr (version 1.6.2). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

R Markdown file: up-to-date

Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.

Environment: empty

Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.

Seed: set.seed(20201215)

The command set.seed(20201215) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.

Session information: recorded

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Cache: none

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File paths: relative

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Repository version: fae176a

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The results in this page were generated with repository version fae176a. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated: 


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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 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.

  • Inputs: “Raw” field trial data
  • Expected outputs: “Cleaned” field trial data

[User input] Cassavabase download

Downloaded all TARI field trials.

  1. Cassavabase search wizard:
  2. Selected all TARI trials currently available. Make a list. Named it ALL_TARI_TRIALS_2020Dec18.
  3. Go to Manage –> Download here. Download phenotypes (plot-basis only) and meta-data as CSV using the corresponding boxes / drop-downs.
  4. Store flatfiles, unaltered in directory data/DatabaseDownload_2020Dec18/.
  • TRIED TO DOWNLOAD META-DATA, BUT DB IS GIVING “SERVER ERROR”
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 = here::here("data/DatabaseDownload_2020Dec18", 
    "2020-12-18T183047phenotype_download.csv"), metadataFile = here::here("data/DatabaseDownload_2020Dec18", 
    "2020-12-18T174951metadata_download.csv"))
# meta<-read.csv(here::here('data/DatabaseDownload_2020Dec18','2020-12-18T174951metadata_download.csv'),stringsAsFactors
# = F)
dbdata %<>% mutate(locationName = ifelse(locationName == "bwanga", "Bwanga", locationName), 
    locationName = ifelse(locationName == "kasulu", "Kasulu", locationName))

Group and select trials to analyze

Make TrialType Variable

dbdata <- makeTrialTypeVar(dbdata)
dbdata %>% count(TrialType) %>% rmarkdown::paged_table()

Trials NOT included

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_Seedling_Nursery_Chambezi"      "2020_AYT_TP_BW"                   
 [7] "2020_CBSD_IMMUNE_BUN"              "2020_CBSD_IMMUNE_SEEDLING_NURSERY"
 [9] "2020_CBSD_IMMUNE_UKE"              "2020_CBSD_IMMUNE_UKG"             
[11] "2020_GWAS_BUNDA"                   "2020_GWAS_Ukerewe"                
[13] "2020_GWAS_UKIRIGURU"               "2020_GxE_UKG"                     
[15] "2020_UYT1C_GAIRO"                  "95_iita_tz_materials"             
[17] "ACCESSION FOR GENOTYPING"          "bunda 2018"                       
[19] "CET_1_2016"                        "GXE KIBAHA"                       
[21] "IITA GENOTYPING  PLATE"            "ilonga_trial"                     
[23] "KIBAHA GERMPLASM"                  "local_germplam_Southern"          
[25] "Local_germplasm_eastern"           "Local_germplasm_islands"          
[27] "local_germplasm_northern"          "Local_germplasm_Nothern"          
[29] "Local_germplasm_SMS"               "LOCAL VARIETIES"                  
[31] "MULTILOCATIONAL_EZ_TP2"            "NDL_OP_UK"                        
[33] "NEW_LOCAL_GERMPLASM_UKIRIGURU"     "NGTZ18KBH_AYT5"                   
[35] "NGTZ18KBH_SEEDLING"                "NGTZ_CBSDIMMUNE_16VAR_CHAMBZ"     
[37] "NGTZKBH-2018-19-UYT2"              "Old_local_germplasm_ukiriguru"    
[39] "QC_CET_1"                          "QC_CET_2"                         
[41] "QC_PYT"                            "Seedlings_Kibaha"                 
[43] "TARI KIBAHA GERMPLASM"

Remove unclassified trials

dbdata %<>% filter(!is.na(TrialType))
dbdata %>% group_by(programName) %>% summarize(N = n()) %>% rmarkdown::paged_table()
# 12718 plots

Making 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 %>% summary

Run function renameAndSelectCols() to rename columns and remove everything unecessary

dbdata <- renameAndSelectCols(traitabbrevs, indata = dbdata, customColsToKeep = "TrialType")

QC Trait values

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))

Post-QC traits

Harvest index

dbdata <- dbdata %>% mutate(HI = RTWT/(RTWT + SHTWT))

Unit area traits

I anticipate this will not be necessary as it will be computed before or during data upload.

For calculating fresh root yield:

  1. PlotSpacing: Area in \(m^2\) per plant. plotWidth and plotLength metadata would hypothetically provide this info, but is missing for vast majority of trials. Therefore, use info from Fola.
  2. maxNOHAV: Instead of ExpectedNOHAV. Need to know the max number of plants in the area harvested. For some trials, only the inner (or “net”) plot is harvested, therefore the PlantsPerPlot meta-variable will not suffice. Besides, the PlantsPerPlot information is missing for the vast majority of trials. Instead, use observed max(NOHAV) for each trial. We use this plus the PlotSpacing to calc. the area over which the RTWT was measured. During analysis, variation in the actual number of plants harvested will be accounted for.
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)

Season-wide mean disease severity

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)

[User input] Assign genos to phenos

This step is mostly copy-pasted from previous processing of IITA- and NRCRI-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)
## 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] "0 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] "549 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] "421 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] "14 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] "435 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))

Write lists for matching genos-to-phenos

snps_refpanel <- readRDS(here::here("output", "DosageMatrix_ImputationReferencePanel_StageVI_91119.rds"))
snps5629 <- readRDS(here::here("output", "DosageMatrix_DCas20_5629_EA_REFimputedAndFiltered.rds"))

dbdata %>% distinct(germplasmName, FullSampleName) %>% write.csv(., file = here::here("output", 
    "germplasmName_to_DNAname_matches_TARI_2020Dec22.csv"), row.names = F)
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()
# dbdata %>% count(germplasmName,FullSampleName) %>%
# filter(is.na(FullSampleName)) %$% unique(germplasmName)
# going to check against SNP data
# DosageMatrix_ImputationReferencePanel_StageVI_91119.rds
# DosageMatrix_DCas20_5629_EA_REFimputedAndFiltered.rds
# 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() #1340 dbdata %>%
# distinct(GID,germplasmName,FullSampleName) %>%
# semi_join(tibble(GID=rownames_snps)) %>% filter(grepl('c1',GID,ignore.case =
# F)) # no C1 clones currently match dbdata %>%
# distinct(GID,germplasmName,FullSampleName) %>%
# semi_join(tibble(GID=rownames_snps)) %>% filter(grepl('c2',GID,ignore.case =
# F)) # no C2 clones either dbdata %>% distinct(GID,germplasmName,FullSampleName)
# %>% anti_join(tibble(GID=rownames_snps)) %>%
# filter(grepl('c1|c2',GID,ignore.case = T)) # definitely there are both C1 and
# C2 phenotypes # and there are C1 and C2 genotypes rownames_snps %>%
# grep('c1',.,value = T,ignore.case = T) %>% length # [1] 1762 rownames_snps %>%
# grep('c2',.,value = T,ignore.case = T) %>% length # [1] 4291

Output “cleaned” file

saveRDS(dbdata, file = here::here("output", "TARI_CleanedTrialData_2020Dec18.rds"))

Detect experimental designs

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:

  • Some trials appear to be complete blocked designs and the blockNumber is used instead of replicate, which is what most use.
  • Some complete block designs have nested, incomplete sub-blocks, others simply copy the “replicate” variable into the “blockNumber variable”
  • Some trials have only incomplete blocks but the incomplete block info might be in the replicate and/or the blockNumber column

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 1165914 62.3    2914854 155.7         NA  2914854 155.7
Vcells 2289334 17.5   26506618 202.3     102400 41413491 316.0
library(tidyverse)
library(magrittr)
source(here::here("code", "gsFunctions.R"))
dbdata <- readRDS(here::here("output", "TARI_CleanedTrialData_2020Dec18.rds"))
dbdata %>% head %>% rmarkdown::paged_table()

Detect designs

dbdata <- detectExptDesigns(dbdata)
dbdata %>% count(programName, CompleteBlocks, IncompleteBlocks) %>% rmarkdown::paged_table()

Output file

saveRDS(dbdata, file = here::here("output", "TARI_ExptDesignsDetected_2020Dec18.rds"))

Next step

  1. Get BLUPs combining all trial data: Combine data from all trait-trials to get BLUPs for downstream genomic prediction.
    • Fit mixed-model to multi-trial dataset and extract BLUPs, de-regressed BLUPs and weights. Include two rounds of outlier removal.

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] rlang_0.4.9       later_1.1.0.1     pillar_1.4.7      withr_2.3.0      
[13] glue_1.4.2        DBI_1.1.0         dbplyr_2.0.0      modelr_0.1.8     
[17] readxl_1.3.1      lifecycle_0.2.0   munsell_0.5.0     gtable_0.3.0     
[21] cellranger_1.1.0  rvest_0.3.6       evaluate_0.14     knitr_1.30       
[25] ps_1.5.0          httpuv_1.5.4      fansi_0.4.1       broom_0.7.2      
[29] Rcpp_1.0.5        promises_1.1.1    backports_1.2.1   scales_1.1.1     
[33] formatR_1.7       jsonlite_1.7.2    fs_1.5.0          hms_0.5.3        
[37] digest_0.6.27     stringi_1.5.3     rprojroot_2.0.2   grid_4.0.2       
[41] here_1.0.1        cli_2.2.0         tools_4.0.2       crayon_1.3.4     
[45] whisker_0.4       pkgconfig_2.0.3   ellipsis_0.3.1    xml2_1.3.2       
[49] reprex_0.3.0      lubridate_1.7.9.2 rstudioapi_0.13   assertthat_0.2.1 
[53] rmarkdown_2.6     httr_1.4.2        R6_2.5.0          git2r_0.27.1     
[57] compiler_4.0.2