NIRS Manuscript Summary Statistics and Figures
Jenna Hershberger
2021-02-08
Last updated: 2021-04-30
Checks: 7 0
Knit directory: CassavaNIRS/
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Summary statistics and figures
Load packages
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(reshape2)
Attaching package: 'reshape2'The following object is masked from 'package:tidyr':
smithslibrary(wesanderson)
library(readxl)
library(agricolae)
library(waves)
library(ggpubr)
iwanthue <- c("#c84d4c","#c77c3f","#d19f32","#647e3a","#61b858","#4db5a4","#6585cc","#975fc7","#c575a1","#cf4391")
namekey <- read.csv("data/TrialNameKey.csv") %>% rename(Trial = Abbreviated.Trial.Name) plots.aggregated <- read.csv("output/full_filtered_plots.csv", stringsAsFactors = F) %>%
left_join(namekey) %>%
dplyr::select(-studyName) %>%
rename(studyName = Trial) %>%
distinct()Joining, by = "studyName"#Figures and tables ##DMC summary table
dmc.summary <- plots.aggregated %>%
drop_na(dry.matter.content.percentage.CO_334.0000092) %>%
mutate(plot.id = paste(studyName, plotNumber, sep = "_")) %>%
drop_na(dry.matter.content.percentage.CO_334.0000092, X740) %>%
group_by(programName, studyName, studyDesign) %>%
summarize(`# Accessions` = n_distinct(germplasmName),
`# Plots` = n_distinct(observationUnitName),
`Mean DMC` = mean(dry.matter.content.percentage.CO_334.0000092),
`Maximum DMC` = max(dry.matter.content.percentage.CO_334.0000092),
`Minimum DMC` = min(dry.matter.content.percentage.CO_334.0000092),
`DMC Standard Deviation` = sd(dry.matter.content.percentage.CO_334.0000092)) %>%
rename(`Program Name` = programName,
`Trial Name` = studyName,
`Trial Design` = studyDesign)`summarise()` has grouped output by 'programName', 'studyName'. You can override using the `.groups` argument.plots.aggregated %>%
group_by(studyName) %>%
dplyr::select(studyName, germplasmName) %>%
distinct() %>%
summarize(n())# A tibble: 10 x 2
studyName `n()`
<fct> <int>
1 A-17IB 48
2 B-17IB 65
3 C-18IB 51
4 D-18IB 35
5 E-18IB 36
6 F-19IB 30
7 G-19IB 35
8 H-19IB 36
9 I-19IK 50
10 J-19IK 180write.csv(dmc.summary, "output/Table2_DMC_statistics.csv", row.names = F)Genotype overlap between trials
germ.by.trial <- plots.aggregated %>%
drop_na(dry.matter.content.percentage.CO_334.0000092, X740) %>%
dplyr::select(studyName, germplasmName) %>%
distinct() %>%
mutate(present = 1) %>%
pivot_wider(names_from = studyName, values_from = present) %>%
mutate_at(vars(unique(plots.aggregated$studyName)), ~ifelse(is.na(.), 0, 1)) %>%
rowwise() %>%
mutate(sum_representation = sum(c(`A-17IB`, `B-17IB`, `C-18IB`, `D-18IB`, `E-18IB`,
`F-19IB`, `G-19IB`, `H-19IB`, `I-19IK`, `J-19IK`))) %>%
dplyr::select(germplasmName, sum_representation, everything())
counts.v3 <- plots.aggregated %>%
dplyr::select(studyName, germplasmName) %>%
distinct() %>%
dplyr::count(., studyName, germplasmName) %>%
spread(studyName, n, fill = 0) %>%
select(-germplasmName) %>%
as.matrix() %>%
crossprod()
#write.csv(germ.by.trial, "output/germplasm_by_trial_inclusion_nicknames.csv", row.names = F)
write.csv(counts.v3, "output/S1_overlapping_accession_counts.csv", row.names = T)germplasm.order <- plots.aggregated %>% dplyr::select(studyName, germplasmName) %>%
arrange(studyName, germplasmName) %>% dplyr::select(-studyName) %>% distinct() %>% rownames_to_column()
cv.base.plot <- plots.aggregated %>%
full_join(germplasm.order) %>%
dplyr::select(studyName, germplasmName, rowname) %>%
distinct() %>%
arrange(studyName, rowname) %>%
mutate(germplasmName = factor(germplasmName, levels = germplasm.order$germplasmName)) %>%
ggplot(., aes(x = germplasmName, y = reorder(studyName, desc(studyName)))) + geom_tile() +
labs(x = "Clone", y = "Trial") +
theme_bw() +
theme(axis.text.x = element_blank(), axis.ticks.x = element_blank(),
panel.grid.major.x = element_blank(),
panel.grid.major.y = element_blank(),)Joining, by = "germplasmName"cv.base.plot
| Version | Author | Date |
|---|---|---|
| 88fee14 | Jenna Hershberger | 2021-04-30 |
cv.base.multi <- ggarrange(cv.base.plot, cv.base.plot, cv.base.plot, cv.base.plot,
labels = c("A", "B", "C", "D"),
nrow = 2, ncol = 2,
widths = c(1, 1))
cv.base.multi
| Version | Author | Date |
|---|---|---|
| 88fee14 | Jenna Hershberger | 2021-04-30 |
ggsave(plot = cv.base.multi, "output/cv_base.png", device = "png", units = "in", width = 10, height = 6)DMC violin and boxplots figure
# tukey https://stackoverflow.com/questions/48625620/adding-tukeys-significance-letters-to-boxplot
dmc.lm <- lm(dry.matter.content.percentage.CO_334.0000092~studyName, data = plots.aggregated)
dmc.aov <- aov(dmc.lm)
dmc.tuk <- TukeyHSD(dmc.aov)
dmc.tuk.agricolae <- HSD.test(dmc.aov, trt="studyName", unbalanced = TRUE)
tuk.means <- dmc.tuk.agricolae$means %>% rownames_to_column("studyName")
tuk.groups <- dmc.tuk.agricolae$groups %>% rownames_to_column("studyName") %>%
left_join(tuk.means, by = "studyName") %>%
dplyr::select(studyName, groups, Max)
dmc.violin.boxplot <- plots.aggregated %>% left_join(tuk.groups, by = "studyName") %>%
ggplot(aes(x = studyName, y = dry.matter.content.percentage.CO_334.0000092,
fill = studyName
)) + geom_violin()+
geom_boxplot(position = "identity", width = .2) +
theme_bw() +
geom_text(aes(label = groups, y = (.6 + Max)), vjust = 0) +
labs(x = "Trial", #title = "Plot mean dry matter content by trial",
y = "Dry matter content (%)") +
scale_fill_manual(values = iwanthue, name = "Trial") +
theme(legend.position = "none")
dmc.violin.boxplot
| Version | Author | Date |
|---|---|---|
| 88fee14 | Jenna Hershberger | 2021-04-30 |
ggsave(dmc.violin.boxplot, filename = "output/Figure2_DMC_distributions.png",
bg = "transparent", height = 5, width = 7)
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] ggpubr_0.4.0 waves_0.1.0 agricolae_1.3-3 readxl_1.3.1
[5] wesanderson_0.3.6 reshape2_1.4.4 forcats_0.5.0 stringr_1.4.0
[9] dplyr_1.0.5 purrr_0.3.4 readr_1.4.0 tidyr_1.1.2
[13] tibble_3.1.1 ggplot2_3.3.3 tidyverse_1.3.0 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] colorspace_2.0-0 ggsignif_0.6.0 prospectr_0.2.0
[4] rio_0.5.16 ellipsis_0.3.1 class_7.3-18
[7] rprojroot_2.0.2 pls_2.7-3 fs_1.5.0
[10] rstudioapi_0.13 farver_2.1.0 prodlim_2019.11.13
[13] fansi_0.4.2 lubridate_1.7.9.2 xml2_1.3.2
[16] codetools_0.2-18 splines_3.5.2 knitr_1.29
[19] jsonlite_1.7.2 pROC_1.17.0.1 caret_6.0-86
[22] broom_0.7.3 cluster_2.1.0 dbplyr_2.0.0
[25] shiny_1.6.0 compiler_3.5.2 httr_1.4.2
[28] spectacles_0.5-3 backports_1.2.1 assertthat_0.2.1
[31] Matrix_1.2-18 fastmap_1.1.0 cli_2.4.0
[34] later_1.1.0.1 htmltools_0.5.1 tools_3.5.2
[37] gtable_0.3.0 glue_1.4.2 Rcpp_1.0.6
[40] carData_3.0-4 limSolve_1.5.6 cellranger_1.1.0
[43] vctrs_0.3.7 baseline_1.3-1 nlme_3.1-151
[46] iterators_1.0.13 timeDate_3043.102 xfun_0.20
[49] gower_0.2.2 openxlsx_4.2.3 rvest_0.3.6
[52] lpSolve_5.6.15 mime_0.9 miniUI_0.1.1.1
[55] lifecycle_1.0.0 rstatix_0.6.0 MASS_7.3-53
[58] scales_1.1.1 ipred_0.9-9 hms_1.0.0
[61] promises_1.1.1 SparseM_1.78 curl_4.3
[64] yaml_2.2.1 pander_0.6.3 labelled_2.7.0
[67] rpart_4.1-15 stringi_1.5.3 highr_0.8
[70] klaR_0.6-15 AlgDesign_1.2.0 foreach_1.5.1
[73] randomForest_4.6-14 zip_2.1.1 lava_1.6.8.1
[76] epiR_2.0.19 rlang_0.4.10 pkgconfig_2.0.3
[79] evaluate_0.14 lattice_0.20-41 labeling_0.4.2
[82] recipes_0.1.15 cowplot_1.1.1 tidyselect_1.1.0
[85] plyr_1.8.6 magrittr_2.0.1 R6_2.5.0
[88] generics_0.1.0 combinat_0.0-8 DBI_1.1.1
[91] foreign_0.8-72 pillar_1.6.0 haven_2.3.1
[94] whisker_0.4 withr_2.4.2 abind_1.4-5
[97] survival_3.2-7 nnet_7.3-15 car_3.0-10
[100] modelr_0.1.8 crayon_1.4.1 questionr_0.7.4
[103] utf8_1.2.1 rmarkdown_2.6 grid_3.5.2
[106] data.table_1.13.6 git2r_0.28.0 ModelMetrics_1.2.2.2
[109] reprex_0.3.0 digest_0.6.27 xtable_1.8-4
[112] httpuv_1.5.5 signal_0.7-6 stats4_3.5.2
[115] munsell_0.5.0 BiasedUrn_1.07 quadprog_1.5-8