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.

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

Initial cassavabase download

[User input] Cassavabase download

Downloaded all IITA field trials with studyYear 2018, 2019, 2020.

  1. Cassavabase search wizard:
  2. Selected all IITA trials with studyYear 2018, 2019, 2020. Make a list. Named it IITA_Trials_2018to2020_2020Sep15.
  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 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.

Read-in trial data

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 plots

Check for overlapping trials between the two flatfiles.

table(unique(dbdata20$studyName) %in% unique(dbdata19$studyName))
# FALSE TRUE 174 197

A 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.

[User input] Cassavabase download

Downloaded all IITA field trials.

  1. Cassavabase search wizard:
  2. Selected all IITA trials currently available. Make a list. Named it ALL_IITA_TRIALS_2020Sep15.
  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 DatabaseDownload_2020Sep15/ uploaded to Cassavabase FTP server.

Join metadata files

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)

Read-in trial data

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"))

Group and select trials to analyze

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>  77684

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", 
    "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"    

Remove unclassified trials

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)

Traits and TraitAbbreviations

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")

QC Trait values

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))

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), 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)

Season-wide mean CMDS

dbdata <- dbdata %>% mutate(MCMDS = rowMeans(.[, c("CMD1S", "CMD3S", "CMD6S", "CMD9S")], 
    na.rm = T)) %>% select(-CMD1S, -CMD3S, -CMD6S, -CMD9S)

[User input] Assign genos to phenos

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 TMS18

[User input] Choose locations

WARNING: 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] 427294

Output “cleaned” file

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

Next step

  1. Curate by trait-trial: Model each trait-trial separately, remove outliers, get BLUPs

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