Last updated: 2021-04-30
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Knit directory: CassavaNIRS/
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| Rmd | 759b463 | Jenna Hershberger | 2021-04-22 | Update curation code |
library(tidyverse)── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──✓ ggplot2 3.3.3 ✓ purrr 0.3.4
✓ tibble 3.1.1 ✓ dplyr 1.0.5
✓ tidyr 1.1.2 ✓ stringr 1.4.0
✓ readr 1.4.0 ✓ forcats 0.5.0── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
x dplyr::filter() masks stats::filter()
x dplyr::lag() masks stats::lag()library(magrittr)
Attaching package: 'magrittr'The following object is masked from 'package:purrr':
set_namesThe following object is masked from 'package:tidyr':
extractlibrary(waves)
pheno <- read.csv("data/raw_pheno.csv")
scans_all <- read.csv("data/raw_scans.csv")colnames_to_keep <- c("studyYear", "programName", "studyName", "studyDesign", "plotWidth",
"plotLength", "plantingDate", "harvestDate", "MAP", "locationName",
"germplasmName", "observationLevel", "observationUnitName",
"replicate", "blockNumber", "plotNumber", "rowNumber", "colNumber",
"entryType", "sample.prep",
"dry.matter.content.percentage.CO_334.0000092")
joined_colnames <- c(colnames_to_keep, "rootNumber", "subsample", "scanTimestamp",
"device_id", "comments", paste0("X", 740:1070))
joined_colnames_no_subsample <- c(colnames_to_keep, "scanTimestamp",
"device_id", "comments", paste0("X", 740:1070))
scan_colnames_plots <- c("studyName", "plotNumber", "rootNumber", "subsample",
"scanTimestamp", "device_id", "comments")Filter scans based on Mahalanobis distance
scans_all$X740 <- as.numeric(scans_all$X740)
scans_filtered <- scans_all %>%
# have to remove columns with missing values before FilterSpectra()
# because of waves requirement
dplyr::select(rowname, starts_with("X")) %>%
FilterSpectra(filter = TRUE,
return.distances = FALSE,
num.col.before.spectra = 1,
window.size = 10) %>%
left_join(x = ., y = scans_all[,1:8], by = "rowname") %>%
mutate(subsample = ifelse(is.na(subsample), rootNumber, subsample)) %>%
dplyr::select(all_of(scan_colnames_plots), starts_with("X")) %>%
distinct()
Removed 8 rows.scans_removed_df <- scans_all %>%
# have to remove columns with missing values before FilterSpectra()
# because of waves requirement
dplyr::select(rowname, starts_with("X")) %>%
FilterSpectra(filter = FALSE,
return.distances = TRUE,
num.col.before.spectra = 1,
window.size = 10) %>%
left_join(x = ., y = scans_all[,1:8], by = "rowname") %>%
mutate(subsample = ifelse(is.na(subsample), rootNumber, subsample)) %>%
dplyr::select(all_of(scan_colnames_plots), h.distances, starts_with("X"), -comments) %>%
filter(h.distances > 300) %>%
rename(Mahalanobis.distance = h.distances) %>%
arrange(-Mahalanobis.distance) %>%
distinct()
write.csv(scans_removed_df, "output/S3_removed_scans.csv", row.names = F)scans_filtered_subsample <- scans_filtered %>%
# AggregateSpectra() requires a column named "reference"
mutate(reference = 1) %>%
drop_na(subsample) %>%
# have to remove columns with missing values before AggregateSpectra()
# because of waves requirement
dplyr::select(studyName, plotNumber, subsample, reference, starts_with("X")) %>%
AggregateSpectra(grouping.colnames = c("studyName", "plotNumber", "subsample"),
reference.value.colname = "reference",
agg.function = "mean") %>%
dplyr::select(-reference) %>%
left_join(x = ., y = scans_filtered[1:7], by = c("studyName",
"plotNumber",
"subsample")) %>%
dplyr::select(all_of(scan_colnames_plots), starts_with("X")) %>%
distinct()scans_filtered_plots <- scans_filtered %>%
# AggregateSpectra() requires a column named "reference"
mutate(reference = 1) %>%
drop_na(plotNumber) %>%
# have to remove columns with missing values before AggregateSpectra()
# because of waves requirement
dplyr::select(studyName, plotNumber, reference, starts_with("X")) %>%
AggregateSpectra(grouping.colnames = c("studyName", "plotNumber"),
reference.value.colname = "reference",
agg.function = "mean") %>%
dplyr::select(-reference) %>%
# join only with the relevant metadata from scans_filtered (studyName, plotNumber, scanTimestamp, device_id, comments)
left_join(x = ., y = scans_filtered[c(1,2,5,6,7)],
by = c("studyName", "plotNumber")) %>%
dplyr::select(all_of(scan_colnames_plots[c(1,2,5,6,7)]), starts_with("X")) %>%
distinct()full_filtered <- pheno %>%
full_join(scans_filtered, by = c("studyName", "plotNumber")) %>%
dplyr::select(all_of(joined_colnames)) %>%
drop_na(dry.matter.content.percentage.CO_334.0000092, X740) %>%
# comment out the line above to get counts of missingness
distinct()
full_subsamples <- pheno %>%
full_join(scans_filtered_subsample, by = c("studyName", "plotNumber")) %>%
dplyr::select(all_of(joined_colnames)) %>%
drop_na(dry.matter.content.percentage.CO_334.0000092, X740) %>%
distinct()
full_plots <- pheno %>% filter(programName == "IITA") %>%
full_join(scans_filtered_plots, by = c("studyName", "plotNumber")) %>%
dplyr::select(all_of(joined_colnames_no_subsample)) %>%
drop_na(dry.matter.content.percentage.CO_334.0000092, X740) %>%
distinct()
nrow(full_filtered)[1] 25514nrow(full_subsamples)[1] 6522nrow(full_plots)[1] 1250write.csv(full_filtered, "output/full_filtered_unaggregated.csv", row.names = F)
write.csv(full_subsamples, "output/full_filtered_subsamples.csv", row.names = F)
write.csv(full_plots, "output/full_filtered_plots.csv", row.names = F)
sessionInfo()R version 3.5.2 (2018-12-20)
Platform: x86_64-apple-darwin18.2.0 (64-bit)
Running under: macOS Mojave 10.14.6
Matrix products: default
BLAS/LAPACK: /usr/local/Cellar/openblas/0.3.6_1/lib/libopenblasp-r0.3.6.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] waves_0.1.0 magrittr_2.0.1 forcats_0.5.0 stringr_1.4.0
[5] dplyr_1.0.5 purrr_0.3.4 readr_1.4.0 tidyr_1.1.2
[9] tibble_3.1.1 ggplot2_3.3.3 tidyverse_1.3.0 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] nlme_3.1-151 fs_1.5.0 lubridate_1.7.9.2
[4] httr_1.4.2 rprojroot_2.0.2 tools_3.5.2
[7] backports_1.2.1 utf8_1.2.1 R6_2.5.0
[10] rpart_4.1-15 DBI_1.1.1 colorspace_2.0-0
[13] nnet_7.3-15 withr_2.4.2 tidyselect_1.1.0
[16] compiler_3.5.2 git2r_0.28.0 spectacles_0.5-3
[19] cli_2.4.0 rvest_0.3.6 BiasedUrn_1.07
[22] SparseM_1.78 xml2_1.3.2 scales_1.1.1
[25] quadprog_1.5-8 randomForest_4.6-14 digest_0.6.27
[28] rmarkdown_2.6 pkgconfig_2.0.3 htmltools_0.5.1
[31] dbplyr_2.0.0 rlang_0.4.10 readxl_1.3.1
[34] rstudioapi_0.13 generics_0.1.0 jsonlite_1.7.2
[37] wesanderson_0.3.6 ModelMetrics_1.2.2.2 Matrix_1.2-18
[40] Rcpp_1.0.6 munsell_0.5.0 fansi_0.4.2
[43] lifecycle_1.0.0 stringi_1.5.3 whisker_0.4
[46] pROC_1.17.0.1 yaml_2.2.1 MASS_7.3-53
[49] plyr_1.8.6 recipes_0.1.15 grid_3.5.2
[52] pls_2.7-3 promises_1.1.1 crayon_1.4.1
[55] lattice_0.20-41 haven_2.3.1 splines_3.5.2
[58] pander_0.6.3 hms_1.0.0 epiR_2.0.19
[61] knitr_1.29 pillar_1.6.0 lpSolve_5.6.15
[64] prospectr_0.2.0 stats4_3.5.2 reshape2_1.4.4
[67] codetools_0.2-18 reprex_0.3.0 glue_1.4.2
[70] evaluate_0.14 data.table_1.13.6 modelr_0.1.8
[73] vctrs_0.3.7 httpuv_1.5.5 foreach_1.5.1
[76] cellranger_1.1.0 gtable_0.3.0 assertthat_0.2.1
[79] xfun_0.20 gower_0.2.2 limSolve_1.5.6
[82] prodlim_2019.11.13 broom_0.7.3 baseline_1.3-1
[85] later_1.1.0.1 class_7.3-18 survival_3.2-7
[88] timeDate_3043.102 signal_0.7-6 iterators_1.0.13
[91] lava_1.6.8.1 ellipsis_0.3.1 caret_6.0-86
[94] ipred_0.9-9