Last updated: 2022-01-07
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Knit directory: CassavaArchitectureGP/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | ee108a9 | LucianoRogerio | 2021-10-15 | Update of the trials of a Breeding program per year |
| html | ee108a9 | LucianoRogerio | 2021-10-15 | Update of the trials of a Breeding program per year |
| Rmd | 5d56695 | LucianoRogerio | 2021-10-11 | Add of the First Branch height measure at 9 MAP and new table for the number of trials for each Institute |
| html | 5d56695 | LucianoRogerio | 2021-10-11 | Add of the First Branch height measure at 9 MAP and new table for the number of trials for each Institute |
| Rmd | 0d2341c | LucianoRogerio | 2021-09-09 | Small changes at writing |
| html | 0d2341c | LucianoRogerio | 2021-09-09 | Small changes at writing |
| Rmd | 1b63c8c | LucianoRogerio | 2021-07-28 | Add Home button to the PhenData html Page |
| html | 1b63c8c | LucianoRogerio | 2021-07-28 | Add Home button to the PhenData html Page |
| html | 2b49915 | LucianoRogerio | 2021-07-22 | Build site. |
| Rmd | 7f7b622 | LucianoRogerio | 2021-07-22 | Update PhenDataRmd |
| html | 7f7b622 | LucianoRogerio | 2021-07-22 | Update PhenDataRmd |
| Rmd | 6d25535 | LucianoRogerio | 2021-07-22 | Update PhenDataRmd |
| html | 6d25535 | LucianoRogerio | 2021-07-22 | Update PhenDataRmd |
| Rmd | 3e1bc0d | LucianoRogerio | 2021-07-22 | PhenD update |
| html | 3e1bc0d | LucianoRogerio | 2021-07-22 | PhenD update |
| html | 7a63d40 | LucianoRogerio | 2021-07-22 | Build site. |
| html | d25ce40 | LucianoRogerio | 2021-07-22 | Build site. |
| Rmd | 73f4e1f | LucianoRogerio | 2021-07-22 | wflow_publish("analysis/PhenData.Rmd") |
| html | ce3ca50 | LucianoRogerio | 2021-07-21 | Build site. |
| Rmd | e7db3e0 | LucianoRogerio | 2021-07-21 | Publish the update 1 Phenotypic data CassavaArchitectureGP |
Wrong section? get back to home.
Initialy we are going to download phenotypic data from Cassavabase. In this case we are going to download the data from traits related to plant Architecture, as the following table:
| Trait | Abbreviation | Cassavabase code | Description |
|---|---|---|---|
| plant architecture visual rating 1-5 | PlantArchitecture | CO_334:0000099 | Plant architecture on a 1-5 scale with 1 = excellent, 2 good, 3 = fair, 4 = bad, and 5 = very bad |
| plant architecture visual rating 1-5 at month 8 | PlantArchitecture8m | COMP:0000119 | Plant architecture on a 1-5 scale with 1 = excellent, 2 good, 3 = fair, 4 = bad, and 5 = very bad |
| flowering ability visual assessment 0-3 | FlowerVisRate | CO_334:0000233 | Presence of flowers (0=none; 1=little; 2=intermediate; 3=many). |
| flower visual rating 0 & 1 | FlowerPresence | CO_334:0000111 | Visual rating of flowers (50%) in plant with 0 = absent and 1 = present. |
| initial plant vigor assessment 1-5 | VigorCIAT | CO_334:0000220 | Visual assessment of plant vigor during establishment (1=very little vigor, and 5 = very vigorous). as being evaluated by CIAT. |
| initial vigor assessment 1-7 | Vigor | CO_334:0000009 | Visual assessment of plant vigor during establishment scored one month after planting. 3 = Not vigorous, 5 = Medium vigor, 7 = highly vigorous. |
| number of forks counting | NFC | CO_334:0000146 | Number of branches (2 forks/branches (dichotomous), 3 forks/branches (trichotomous), or 4 forks/branches (tetrachotomous)) at every branching level. |
| number of forks on branching level 1 counting | NFC1 | CO_334:0000522 | Number of forks (2 forks/branches (dichotomous), 3 forks/branches (trichotomous), or 4 forks/branches (tetrachotomous)) on the first branching level. |
| number of forks on branching level 2 counting | NFC2 | CO_334:0000523 | Number of forks (2 forks/branches (dichotomous), 3 forks/branches (trichotomous), or 4 forks/branches (tetrachotomous)) on the second branching level. |
| number of forks on branching level 3 counting | NFC3 | CO_334:0000524 | Number of forks (2 forks/branches (dichotomous), 3 forks/branches (trichotomous), or 4 forks/branches (tetrachotomous)) on the third branching level. |
| number of nodes at branching level 1 counting | NNC1 | CO_334:0000352 | Number of nodes at the first branching level. |
| number of nodes at branching level 2 counting | NNC2 | CO_334:0000363 | Number of nodes at the second branching level. |
| number of nodes at branching level 3 counting | NNC3 | CO_334:0000368 | Number of nodes at the third branching level. |
| first apical branch height measurement in cm | FirstBranchHeight | CO_334:0000106 | Height of first apical branch (ground level to point of first Apical branch, 9 months after planting) in cm |
| plant height measurement in cm | PlantHeight | CO_334:0000018 | Vertical height of plants from the ground to top of the canopy measured in centimeter (cm). |
| plant height measurement in cm at month 12 | PlantHeight12m | COMP:0000181 | Vertical height of plants from the ground to top of the canopy measured in centimeter (cm). |
| plant height with leaf in cm | PlantHeightLeaf | CO_334:0000123 | Portion of the stem with leaves measured as the distance in centimeter from the point of attachment of the oldest leaf to the youngest leaf (apical leaf portion). |
| plant height without leaf | PlantHeightNLeaf | CO_334:0000125 | Portion of stem with no leaf measured in centimeter (cm) by deducting plant height with leaf from plant height. |
| plant height without leaf at month 12 | PlantHeightNLeaf12m | COMP:0000182 | Portion of stem with no leaf measured in centimeter (cm) by deducting plant height with leaf from plant height. |
| stalk length evaluation | StalkLength | CO_334:0000227 | Visual assessment of the average length of the stalks (1=short; 2=intermediate; 3=long) |
| stem diameter measurement in cm | StemDiam | CO_334:0000257 | Measurement of stem diameter taken on the middle of the plant in centimeter (cm) using the vernier caliper. |
| stem diameter measurement in cm at month 5 | StemDiam5m | COMP:0000129 | Measurement of stem diameter taken on the middle of the plant in centimeter (cm) using the vernier caliper. |
| stem diameter measurement in cm at month 6 | StemDiam6m | COMP:0000130 | Measurement of stem diameter taken on the middle of the plant in centimeter (cm) using the vernier caliper. |
Use the following filters:
TraitsLucianoGSTrial Types:
Years from 2010 to 2021.trials available at cassavabase after the filter.library(tidyverse); library(data.table); library(here)
RawPhenoData <- read.csv(file = here::here("data", "phenodata.csv"), header = T, na.strings = "")
RawPhenoData %>% mutate("FlowerPresence" = flower.visual.rating.0.1.CO_334.0000111,
"FlowerVisRate" = flowering.ability.visual.assessment.0.3.CO_334.0000233,
"VigorCIAT" = initial.plant.vigor.assessment.1.5.CO_334.0000220,
"Vigor" = initial.vigor.assessment.1.7.CO_334.0000009,
"PlantArchitecture" = plant.architecture.visual.rating.1.5.CO_334.0000099,
"PlantHeight" = plant.height.measurement.in.cm.CO_334.0000018,
"PlantHeight12m" = plant.height.measurement.in.cm.month.12.COMP.0000181,
"PlantHeightLeaf" = plant.height.with.leaf.in.cm.CO_334.0000123,
"PlantHeightNLeaf" = plant.height.without.leaf.CO_334.0000125,
"PlantHeightNLeaf12m" = plant.height.without.leaf.month.12.COMP.0000182,
"StalkLength" = stalk.length.evaluation.CO_334.0000227,
"StemDiam" = stem.diameter.measurement.in.cm.CO_334.0000257,
"FirstBranchHeight" = first.apical.branch.height.measurement.in.cm.CO_334.0000106,
.keep = "unused") %>% melt(data = .,
id.vars = c("studyYear", "programDbId", "programName", "programDescription",
"studyDbId", "studyName", "studyDesign", "plotWidth",
"plotLength", "fieldSize", "fieldTrialIsPlannedToBeGenotyped",
"fieldTrialIsPlannedToCross", "plantingDate", "harvestDate",
"locationDbId", "locationName", "germplasmDbId", "germplasmName",
"germplasmSynonyms", "observationLevel", "observationUnitDbId",
"observationUnitName", "replicate", "blockNumber", "plotNumber",
"rowNumber", "colNumber", "entryType", "plantNumber"),
variable.name = "Trait",
value.name = "Value") -> PhenoData
PhenoData2 <- PhenoData[!is.na(PhenoData$Value),]
saveRDS(object = PhenoData2, file = here::here("data", "phenotypePAGP.RDS"))
rm(list = ls())suppressMessages(library(tidyverse)); library(reactable); library(here)
PhenoData <- readRDS(here("data","phenotypePAGP.RDS"))
PhenoData %>% dplyr::select(programName, studyYear, Trait, studyName) %>% unique() -> PhenoData2
Table2 <- table(PhenoData2$programName, PhenoData2$studyYear, PhenoData2$Trait) %>% as.data.frame()
colnames(Table2) <- c("programName", "Year", "Trait", "N˚Trials")
Table2 <- Table2[Table2$`N˚Trials`!=0,]library(plyr)
source(here::here("code", "MixedModelsFunctions.R"))
PhenoData$check <- ifelse(PhenoData$entryType!="test", PhenoData$germplasmName, "999")
PhenoData$new <- ifelse(PhenoData$entryType == "test", 1, 0)
PhenoData$blockNumber <- as.character(PhenoData$blockNumber)
#PhenoData2 <- PhenoData[!PhenoData$studyName %in% BadTrialsList,]
#fmfit <- PhenoData2 %>% filter(Trait == "PlantArchitecture") %>% dlply(.variables = c("Trait", "studyName"),
# .fun = analyzeTrial.sommer)The heritability and Reliability were estimated for each trial. We will proceed with the trails with reliability and heritability bigger than 0.2 and 0.1, respectively.
Select the trials with unexpected number of blocks, controls for their experimental design
PhenoData %>% dplyr::group_by(programName, studyYear, studyDesign, studyName) %>%
dplyr::summarise(n_blocks = length(unique(blockNumber)),
n_reps = length(unique(replicate)),
controls = length(unique(check))) -> TrialsInfo`summarise()` has grouped output by 'programName', 'studyYear', 'studyDesign'. You can override using the `.groups` argument.TrialsInfo %>% dplyr::filter((studyDesign %in% c("Augmented", "Alpha", "RCBD") & n_blocks == 1) |
(studyDesign == "Augmented" & controls == 1)) -> BadTrialsInfo
BadTrialsList <- BadTrialsInfo$studyName
sessionInfo()R version 4.1.1 (2021-08-10)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS Big Sur 11.6.1
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.1-arm64/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.1-arm64/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] lme4_1.1-27.1 sommer_4.1.5 crayon_1.4.2 lattice_0.20-45
[5] MASS_7.3-54 Matrix_1.4-0 plyr_1.8.6 here_1.0.1
[9] reactable_0.2.3 forcats_0.5.1 stringr_1.4.0 dplyr_1.0.7
[13] purrr_0.3.4 readr_2.1.1 tidyr_1.1.4 tibble_3.1.6
[17] ggplot2_3.3.5 tidyverse_1.3.1
loaded via a namespace (and not attached):
[1] httr_1.4.2 sass_0.4.0 jsonlite_1.7.2 splines_4.1.1
[5] modelr_0.1.8 bslib_0.3.1 assertthat_0.2.1 cellranger_1.1.0
[9] yaml_2.2.1 pillar_1.6.4 backports_1.4.1 glue_1.6.0
[13] digest_0.6.29 promises_1.2.0.1 minqa_1.2.4 rvest_1.0.2
[17] colorspace_2.0-2 htmltools_0.5.2 httpuv_1.6.5 reactR_0.4.4
[21] pkgconfig_2.0.3 broom_0.7.11 haven_2.4.3 scales_1.1.1
[25] whisker_0.4 later_1.3.0 tzdb_0.2.0 git2r_0.29.0
[29] generics_0.1.1 ellipsis_0.3.2 withr_2.4.3 cli_3.1.0
[33] magrittr_2.0.1 readxl_1.3.1 evaluate_0.14 fs_1.5.2
[37] fansi_0.5.0 nlme_3.1-153 xml2_1.3.3 tools_4.1.1
[41] hms_1.1.1 lifecycle_1.0.1 munsell_0.5.0 reprex_2.0.1
[45] compiler_4.1.1 jquerylib_0.1.4 rlang_0.4.12 nloptr_1.2.2.3
[49] grid_4.1.1 rstudioapi_0.13 htmlwidgets_1.5.4 crosstalk_1.2.0
[53] rmarkdown_2.11 boot_1.3-28 gtable_0.3.0 DBI_1.1.2
[57] R6_2.5.1 lubridate_1.8.0 knitr_1.37 fastmap_1.1.0
[61] utf8_1.2.2 workflowr_1.7.0 rprojroot_2.0.2 stringi_1.7.6
[65] Rcpp_1.0.7 vctrs_0.3.8 dbplyr_2.1.1 tidyselect_1.1.1
[69] xfun_0.29