From 021f1ed4c32e097f0f38e0229d211bbdab2bcb78 Mon Sep 17 00:00:00 2001
From: Francesco Sabatini <francesco.sabatini@idiv.de>
Date: Sat, 14 Nov 2020 13:41:11 +0100
Subject: [PATCH] Aligned Figure numbers - Output for all combinations
---
02_Mesobromion_ExamineOutput.R | 206 ++++++++++++++++++++--
03_Figures_Simulations.R | 310 +++++++++++++++++++++++----------
04_Additional_Figs.R | 88 ++++++----
3 files changed, 462 insertions(+), 142 deletions(-)
diff --git a/02_Mesobromion_ExamineOutput.R b/02_Mesobromion_ExamineOutput.R
index 906ef04..66ee16a 100644
--- a/02_Mesobromion_ExamineOutput.R
+++ b/02_Mesobromion_ExamineOutput.R
@@ -348,7 +348,7 @@ mydata.best <- mydata %>%
write_csv(mydata.best %>%
dplyr::select(Trait.comb:sign_plus),
- path = "_output/S5_BestSolutionTiers.cov.csv")
+ path = "_output/S9_BestSolutionTiers.cov.csv")
### Graph of all the best combinations with text legend
(top.all <- ggplot(data=(mydata.best %>%
@@ -397,6 +397,188 @@ ggsave(filename = "_pics/Fig5_Best_AllCombinations_CI_cov.png", dpi=400,
width=6, height=2, topall.leg)
+
+
+###### ________R1___________ ######
+###### R1.2. Import output from Cluster ####
+##### R1.2.0. Trait labs for significant traits
+traits.sign.cov <- read_delim(file="_data/Mesobromion/traits.v2.10perc.cov.sign.txt", delim="\t")
+
+traits.sign.cov <- traits.sign.cov %>%
+ as.data.frame() %>%
+ mutate_if(~is.character(.), .funs=~as.factor(.)) %>%
+ column_to_rownames("species0")
+
+
+## adapt trait labs to sign traits only
+ trait.labs.sign.cov <- trait.labs %>%
+ filter(trait.name %in% colnames(traits.sign.cov)) %>%
+ arrange(match(trait.name, colnames(traits.sign.cov))) %>%
+ rename(Trait.comb.new=Trait.comb) %>%
+ mutate(Trait.comb=1:n()) %>%
+ dplyr::select(Trait.comb, everything(), -Trait.comb.new)
+
+
+
+##### R1.2.1 Cover values ######
+### sequential trait combo
+myfilelist1 <- list.files(path="_derived/Mesobromion/Cover/R1_all/", pattern="HIDDENcov-nona2_[0-9]+_.RData", full.names = T)
+dataFiles1 = purrr::map(myfilelist1, function(x){get(load(x))})
+#load("_derived/Mesobromion/PresAbs/HIDDEN_round_11.RData")
+corXY.all = bind_rows(dataFiles1) %>%
+ as_tibble() %>%
+ distinct()
+corXY.all.ci <- get.ci(corXY.all)
+corXY.all.ci <- corXY.all.ci %>%
+ mutate(Trait.comb2=Trait.comb) %>%
+ separate(Trait.comb2, into=paste0("trait", 1:7)) %>%
+ mutate_at(.vars=vars(trait1:trait7),
+ .funs=~factor(.,
+ levels=trait.labs.sign.cov$Trait.comb,
+ labels=trait.labs.sign.cov$trait.name)) %>%
+ arrange(ntraits, desc(Coef.obs)) %>%
+ #filter(ntraits>1) %>%
+ dplyr::select(Trait.comb, Test, n, ntraits, everything()) %>%
+ mutate(run="seq")
+
+rm( dataFiles1) #dataFiles0,
+
+### merge together
+corXY.ci <- corXY.all.ci # %>%
+
+mydata <- corXY.ci
+
+######## R1.2.1.4 Best - Graph of r(XY) using best combination of traits at each level of interaction N ########
+### extract best combinations of traits
+top.one.by.one <- get.best(mydata, N=1, labs=trait.labs.sign.cov)
+
+## Routine to extract the best combination at each level of interaction (up to max traits)
+maxtraits <- 7
+for(nn in 1:maxtraits){
+ if(nn==1) {
+ best.at.1 <- get.best(mydata, N=nn, labs=trait.labs.sign.cov)
+ newdata <- mydata %>%
+ filter_at(.vars=vars(trait1:trait7),
+ .vars_predicate = any_vars(. %in% best.at.1$trait.name | is.na(.)))
+ new.best.row <- newdata %>%
+ filter(Trait.comb==best.at.1$Trait.comb)
+ upper <- new.best.row$q975
+ lower <- new.best.row$q025
+ print(paste("new best at nn", nn, best.at.1$trait.name))
+ best.progr <- best.at.1$Trait.comb
+ }
+ if(nn>1){
+ better <- list()
+ better$Trait.comb <- newdata %>%
+ filter(ntraits==nn) %>%
+ filter(q025>upper) %>%
+ arrange(desc(Coef.obs)) %>%
+ slice(1) %>%
+ pull(Trait.comb)
+
+ if(length(better$Trait.comb>0)){
+ better$trait.name <- trait.labs.sign.cov %>%
+ filter(Trait.comb %in% strsplit(better$Trait.comb, split = "_")[[1]]) %>%
+ pull(trait.name)
+
+ newdata <- newdata %>%
+ rowwise() %>%
+ mutate(nmatching= sum(unlist(strsplit(Trait.comb, "_")) %in%
+ unlist(strsplit(better$Trait.comb, "_")),
+ na.rm=T)) %>%
+ ungroup() %>%
+ filter(nmatching==nn)
+
+ new.best.row <- newdata %>%
+ filter(Trait.comb==better$Trait.comb)
+ upper <- new.best.row$q975
+ lower <- new.best.row$q025
+ print(paste("new best at nn", nn, paste(better$trait.name, collapse=" ")))
+ best <- better
+ best.progr <- c(best.progr, better$Trait.comb)
+ }
+ }
+}
+
+best.traits.cov <- corXY.ci %>%
+ filter(as.character(Trait.comb)==best.progr[length(best.progr)]) %>%
+ dplyr::select(trait1:trait7) %>%
+ mutate_all(~as.character(.)) %>%
+ dplyr::select(colnames(.)[which(colSums(is.na(.))==0)])
+
+best.traits.cov <- as.character(best.traits.cov[1,])
+#"Leaf_Scleroph" "FP_Dur" "VP_Fragm" "Height" "SLA"
+
+### Create dataset with best combinations + all the one-way combinations
+mydata.best <- mydata %>%
+ #filter_at(.vars=vars(trait1:trait7),
+ # # .vars_predicate = all_vars(. %in% best$trait.name | is.na(.))) %>%
+ # .vars_predicate = all_vars(. %in% traits.sign.alone.cov | is.na(.))) %>%
+ filter(ntraits>1) %>%
+ filter(sign_plus==T) %>%
+ arrange(ntraits, Coef.obs) %>%
+ group_by(ntraits) %>%
+ slice(n()) %>%
+ ungroup() %>%
+ bind_rows(corXY.ci %>%
+ filter(run=="seq") %>%
+ filter(ntraits==1)) %>%
+ #filter(trait1 %in% traits.sign.alone.cov)) %>%
+ arrange(ntraits, Coef.obs) %>%
+ mutate(seq=1:n()) %>%
+ mutate(sign_plus=factor(Trait.comb %in% best.progr))
+
+write_csv(mydata.best %>%
+ dplyr::select(Trait.comb:sign_plus),
+ path = "_output/R1.S5_BestSolutionTiers.cov_allcombos.csv")
+
+### Graph of all the best combinations with text legend
+(top.all <- ggplot(data=(mydata.best %>%
+ mutate(size0=.6+(as.numeric(sign_plus)-1)*.6))) +
+ geom_segment(aes(x=q025, xend=q975, y=seq, yend=seq, col="a",
+ lwd=size0)) +
+ geom_point(aes(x=Coef.obs, y=seq), pch=15) +
+ scale_y_continuous(breaks=mydata.best$seq,
+ labels=mydata.best$Trait.comb, name=NULL) +
+ scale_x_continuous(name="RD correlation") +
+ scale_size_identity() +
+ theme_bw() +
+ theme(panel.grid.minor = element_blank(),
+ axis.text = element_text(size=7),
+ legend.position = "none"))
+# create legend of names
+tt2 <- ttheme_minimal(
+ core = list(fg_params=list(cex = .7),
+ padding=unit(c(1, 1), "mm")),
+ colhead = list(fg_params=list(cex = .7)),
+ rowhead = list(fg_params=list(cex = .7)))
+
+ttlabs <- trait.labs.sign.cov %>%
+ mutate(Code=1:n())
+
+tobold <- which(ttlabs$trait.name %in% best.traits.cov)
+tg <- tableGrob(ttlabs %>%
+ dplyr::select(Code, Trait=Long_English_name) %>%
+ mutate(Trait=replace(x = Trait,
+ list = Trait=="Vegetative Propagation - Fragmentation",
+ values = "Veg. Propag. - Fragmentation")),
+ theme=tt2, rows = NULL)
+## Make significant traits bold
+for (i in (11 + tobold)) {
+ tg$grobs[[i]] <- editGrob(tg$grobs[[i]], gp=gpar(fontface="bold"))
+}
+#arrange into a panel
+(topall.leg <- cowplot::plot_grid(top.all, tg,
+ nrow=1, rel_widths=c(0.60, 0.4)))
+ggsave(filename = "_pics/R1/Fig5_R1_Best_AllCombinations_CI_cov.png", dpi=400,
+ width=6, height=2, topall.leg)
+
+
+
+
+
+
+
break()
###### ___________________ ######
@@ -755,7 +937,7 @@ PCA.fuzz1_3 <- basemap0 +
ylab(paste("PC3 (", varexpl[3], "%)", sep=""))
PC_fuzzy <- cowplot::plot_grid(PCA.fuzz1_2,PCA.fuzz1_3, nrow=1)
-ggsave("_pics/S11_PC_Fuzzy_1-3.png", width=10, height=5, dpi=300, last_plot())
+ggsave("_pics/S13_PC_Fuzzy_1-3.png", width=10, height=5, dpi=300, last_plot())
#### 4.0.1 Alternative showing species scores ####
tmp <- as.data.frame(pca.fuzz$CA$v[,1:3]*7) %>%
@@ -812,8 +994,8 @@ PCAfuzz1_3.sp <- basemap0 %+% tmp +
ylab(paste("PC3 (", varexpl[3], "%)", sep=""))
-ggsave("_pics/S11a_PCA_Fuzzy_1-2_wSpecies.png", width=8, height=8, dpi=300, PCAfuzz1_2.sp)
-ggsave("_pics/S11b_PCA_Fuzzy_1-3_wSpecies.png", width=8, height=8, dpi=300, PCAfuzz1_3.sp)
+ggsave("_pics/S13a_PCA_Fuzzy_1-2_wSpecies.png", width=8, height=8, dpi=300, PCAfuzz1_2.sp)
+ggsave("_pics/S13b_PCA_Fuzzy_1-3_wSpecies.png", width=8, height=8, dpi=300, PCAfuzz1_3.sp)
@@ -833,7 +1015,7 @@ env.cor <- cor(env %>%
scores.pca, use = "pairwise.complete.obs") #double check
fuzz.cor <- cor(pca.fuzz$CA$u[,1:3], scores.pca)
-sink("_output/S9_EnvFit_CWMs_env.txt")
+sink("_output/S14_EnvFit_CWMs_env.txt")
cwms.cor
env.cor
fuzz.cor
@@ -953,8 +1135,8 @@ PCA3_4.sp <- basemap0 %+% tmp +
ylab(paste("PC4 (", varexpl[4], "%)", sep=""))
-ggsave("_pics/S10a_PCA_Beals_1-2_wSpecies.png", width=8, height=8, dpi=300, PCA1_2.sp)
-ggsave("_pics/S10b_PCA_Beals_3-4_wSpecies.png", width=8, height=8, dpi=300, PCA3_4.sp)
+ggsave("_pics/S14a_PCA_Beals_1-2_wSpecies.png", width=8, height=8, dpi=300, PCA1_2.sp)
+ggsave("_pics/S14b_PCA_Beals_3-4_wSpecies.png", width=8, height=8, dpi=300, PCA3_4.sp)
###### _ ######
@@ -1155,7 +1337,7 @@ PCA.t2 <- baseplot +
PC_traits <- cowplot::plot_grid(PCA.t1, PCA.t2, nrow=1)
-ggsave("_pics/S6_PCA_Traits_1-4_only7.png", width=10, height=5, dpi=300, PC_traits)
+ggsave("_pics/S10c_PCA_Traits_1-4_only7.png", width=10, height=5, dpi=300, PC_traits)
##### 4.3b Alternative version of figS6, showing the species ####
tmp <- as.data.frame(pca.scores[,1:4]*.2) %>%
@@ -1200,8 +1382,8 @@ PCA.t2.sp <- baseplot %+% tmp +
xlab(paste("PC3 (", varexpl[3], "%)", sep="")) +
ylab(paste("PC4 (", varexpl[4], "%)", sep=""))
-ggsave("_pics/S6a_PCA_Traits_1-2_wSpecies.png", width=8, height=8, dpi=300, PCA.t1.sp)
-ggsave("_pics/S6b_PCA_Traits_3-4_wSpecies.png", width=8, height=8, dpi=300, PCA.t2.sp)
+ggsave("_pics/S10_PCA_Traits_1-2_wSpecies.png", width=8, height=8, dpi=300, PCA.t1.sp)
+ggsave("_pics/S10b_PCA_Traits_3-4_wSpecies.png", width=8, height=8, dpi=300, PCA.t2.sp)
#traits.dummy %>% filter(species0 %in% (tmp %>% filter(labels %in% c("Fes_pal", "Ses_alb", "Car_hum")) %>% pull(species))) %>% dplyr::select(any_of(starts_with(as.character(traits.sign.alone.cov))))
@@ -1304,7 +1486,7 @@ traits7 <- traits %>%
# relocate(all_of(starts_with(as.character(best.traits.cov))), everything())
res <- cor(traits7, use = "pairwise.complete.obs")
-png(file="_pics/S7_Correlations_Trait.png", width=8, height=6.5, units = "in", res=300)
+png(file="_pics/S11_Correlations_Trait.png", width=8, height=6.5, units = "in", res=300)
corrplot(res, type = "upper",
tl.col = "black", tl.srt = 45, number.cex=0.6, addCoef.col = "black", diag=F)
dev.off()
@@ -1315,7 +1497,7 @@ res2 <- cor(CWM.wide %>%
dplyr::select(any_of(traits.sign.alone.cov)) %>% ## caution selecting only numerical variables
dplyr::select(sort(tidyselect::peek_vars())) %>%
relocate(any_of(best.traits.cov), everything()))
-png(file="_pics/S8_Correlations_CWMs.png", width=8, height=6.5, units = "in", res=300)
+png(file="_pics/S12_Correlations_CWMs.png", width=8, height=6.5, units = "in", res=300)
corrplot(res2, type = "upper",
tl.col = "black", tl.srt = 45, number.cex=0.6, addCoef.col = "black", diag=F)
dev.off()
diff --git a/03_Figures_Simulations.R b/03_Figures_Simulations.R
index fa2bdf1..aa01b8f 100644
--- a/03_Figures_Simulations.R
+++ b/03_Figures_Simulations.R
@@ -78,37 +78,38 @@ outp.summary <- FormatData(myfiles)
mypalette <- palette(c("#e41a1c", #1 - red)
"#ff7f00", #12 - orange
"#984ea3", #13 - violet
- "##ffed6f", #2 - yellow
+ "#ffed6f", #2 - yellow
"#4daf4a", #23 - green
"#377eb8")) #3 - blue
+outp.summary2 <- outp.summary %>%
+ mutate(main=main/100) %>%
+ mutate(corr=factor(corr/10, levels=c(0, 0.4, 0.8), labels=paste0("Correlation = ", c(0, 0.4, 0.8)))) %>%
+ mutate(inter=factor(inter/10, levels=c(0, 0.3, 0.5), labels=paste0("Interaction = ", c(0, 0.3, 0.5)))) %>%
+ ungroup() %>%
+ dplyr::filter(stat.type=="XY") %>%
+ dplyr::filter(trait %in% c("1", "2", "1 2", "3", "1 3", "2 3")) %>%
+ mutate(trait=factor(trait, levels=c("1", "2", "3", "1 2", "1 3", "2 3"),
+ labels=c("t1", "t2", "tn", "t1 t2", "t1 tn", "t2 tn"))) %>%
+ mutate(mylinetype=as.factor(ifelse(grepl(pattern="tn", x = trait), 2, 1)))
-ggplot(data=outp.summary %>%
- mutate(main=main/100) %>%
- mutate(corr=factor(corr/10, levels=c(0, 0.4, 0.8), labels=paste0("Correlation = ", c(0, 0.4, 0.8)))) %>%
- mutate(inter=factor(inter/10, levels=c(0, 0.3, 0.5), labels=paste0("Interaction = ", c(0, 0.3, 0.5)))) %>%
- ungroup() %>%
- dplyr::filter(stat.type=="XY") %>%
- dplyr::filter(trait %in% c("1", "2", "1 2", "3", "1 3", "2 3")) %>%
- mutate(trait=factor(trait, levels=c("1", "2", "3", "1 2", "1 3", "2 3"),
- labels=c("t1", "t2", "tn", "t1 t2", "t1 tn", "t2 tn"))) %>%
- mutate(linetype=as.factor(ifelse(grepl(pattern="tn", x = trait), 2, 1)))) +
- geom_line(aes(x=main, y=power, group=trait, col=trait, lty=linetype)) +
- guides(linetype = FALSE) +
- scale_color_manual("Trait\ncomb.",
- values=c("#e41a1c", #1 - red)
- "#e6ab02", #2 - yellow
- "#377eb8", #3 - blue
- "#d95f02", #12 - orange
- "#984ea3", #13 - violet
- "#4daf4a" #23 - green
- )
- ) +
+ggplot(data=outp.summary2) +
+ geom_line(aes(x=main, y=power, col=trait, lty=trait)) +
facet_grid(corr~inter) +
theme_bw() +
scale_x_continuous(name="Effect of factor e1 -> trait t1") +
scale_y_continuous(name="Prop. of significant r(XY)") +
- theme(panel.grid = element_blank())
-
+ theme(panel.grid = element_blank()) +
+ scale_color_manual(name="Trait\ncomb.",
+ values = c("#e41a1c",#1 - red)
+ "#e6ab02",#2 - yellow
+ "#377eb8",#3 - blue
+ "#d95f02",#12 - orange
+ "#984ea3",#13 - violet
+ "#4daf4a" #23 - green
+ )) +
+ scale_linetype_manual(name="Trait\ncomb.",
+ values = c(1,1,2,1,2,2))
+
ggsave(filename="_pics/R1/Fig2_CorrInte_02March.png", width=6, height=5, device="png", dpi = 300, last_plot())
@@ -170,23 +171,28 @@ hugepalette <- data.frame(trait=c('t1', 't2','t3', 't4', 'tn',
't1 t2 t3 t4', 't1 t2 t3 tn','t1 t2 t4 tn','t1 t3 t4 tn','t2 t3 t4 tn',
't1 t2 t3 t4 tn'),
trait.col=factor(hugepalette0, levels=hugepalette0))
+hugelinetype <- c(1, 1, 1, 1, 2,
+ 1*1, 1*1, 1*1, 1*2, 1*1, 1*1, 1*2, 1*1, 1*2, 1*2,
+ 1*1*1, 1*1*1, 1*1*2, 1*1*1, 1*1*2, 1*1*2, 1*1*1, 1*1*2, 1*1*2, 1*1*2,
+ 1*1*1*1, 1*1*1*2, 1*1*1*2, 1*1*1*2, 1*1*1*2,
+ 1*1*1*1*2)
outp.summary2 <- outp.summary2 %>%
left_join(hugepalette, by="trait")
fig3 <- ggplot(data=outp.summary2) +
- geom_line(aes(x=main, y=power, group=trait, col=trait.col, lty=linetype)) +
- guides(linetype = FALSE) +
+ geom_line(aes(x=main, y=power, col=trait, lty=trait)) + #group=trait,
scale_x_continuous(name="Effect of factor e1 -> trait t1", n.breaks = 4) +
scale_y_continuous(name="Prop. of significant r(XY)") +
facet_grid(sel.ntraits.lab~ntraits.lab) +
- scale_color_identity(guide = "legend",
- labels= hugepalette$trait) +
+ scale_color_manual(name="trait", #guide = "legend",
+ values=hugepalette0) +
+ #labels= hugepalette$trait) +
+ scale_linetype_manual(name="trait", #guide="legend",
+ values=hugelinetype) +
theme_bw() +
theme(panel.grid = element_blank())
-
-
ncols <- c(2,4,4,3,1)
@@ -196,10 +202,11 @@ for(tier in 1:5){
outp.summary.tier <- outp.summary2 %>% filter(sel.ntraits==tier)
ncombinations <- length(levels(factor(outp.summary.tier$trait)))
leg.list[[tier+1]] <- cowplot::get_legend(fig3 %+% outp.summary.tier +
- guides(col=guide_legend(ncol=ncols[tier], byrow=TRUE))+
- scale_color_identity(name=ifelse(tier==1, "Trait combination - tier 1",paste0("tier - ", tier)),
- guide = "legend",
- labels= hugepalette$trait[col.used:(col.used+ncombinations-1)])
+ guides(col=guide_legend(ncol=ncols[tier], byrow=TRUE))+
+ scale_color_manual(name=ifelse(tier==1, "Trait combination - tier 1",paste0("tier - ", tier)),
+ values= hugepalette0[col.used:(col.used+ncombinations-1)]) +
+ scale_linetype_manual(name=ifelse(tier==1, "Trait combination - tier 1",paste0("tier - ", tier)),
+ values=hugelinetype[col.used:(col.used+ncombinations-1)])
)
col.used <- col.used + ncombinations
}
@@ -232,19 +239,17 @@ ggplot(data=outp.summary %>%
mutate(trait=factor(trait, levels=c("1", "2", "3", "4"),
labels=c("t1", "t2", "t3","tn"))) %>%
mutate(linetype=as.factor(ifelse(grepl(pattern="tn", x = trait), 2, 1)))) +
- geom_line(aes(x=main, y=power, group=trait, col=trait, lty=linetype)) +
- guides(linetype = FALSE) +
- #scale_colour_brewer(palette = "Dark2") +
- #scale_color_manual("Trait\ncomb.",
+ geom_line(aes(x=main, y=power, col=trait, lty=trait)) +
scale_color_manual("Trait",
values=c("#e41a1c", #1 - red)
"#e6ab02", #2 - yellow
"#4daf4a", #23 - green
- "#377eb8", #3 - blue
- "#d95f02", #12 - orange
- "#984ea3" #13 - violet
- )
- ) +
+ "#377eb8" #3 - blue
+ #"#d95f02", #12 - orange
+ #"#984ea3" #13 - violet
+ )) +
+ scale_linetype_manual(name="Trait",
+ values = c(1,1,1,2)) +
facet_grid(corr~inter) +
theme_bw() +
scale_x_continuous(name="Effect of factor e1 -> trait t1") +
@@ -255,53 +260,119 @@ ggsave(filename="_pics/R1/FigS4_Extra_CorrInte_08Jul20.png", width=6, height=5,
-### Additional Figures for JVS R1 ####
-#### FIGURE SXXX - XW ####
-mypath <- "_data/Experiment_30Oct2020_FactorInteraction&TraitCorr_XW"
+
+### Additional Figures for JVS R1 ####
+
+#### FIGURE R1.S5 - effect of adding neutral traits ####
+#### import function
+mypath <- "_data/Experiment_01Nov2020_NeutralTraitNumber/"
myfiles <- list.files(path=mypath, pattern = "Summary.txt", recursive = T, full=T)
outp.summary <- FormatData(myfiles)
+outp.summary <- outp.summary %>%
+ rename(ntraits = inter)
-mypalette <- palette(c("#e41a1c", #1 - red)
- "#ff7f00", #12 - orange
- "#984ea3", #13 - violet
- "##ffed6f", #2 - yellow
- "#4daf4a", #23 - green
- "#377eb8")) #3 - blue
+## plotting power for XY with corr
+add.t.label <- function(x) {
+ x <- gsub(pattern=" ", replacement=" t", x=x, perl=T)
+ x <- paste0("t", x)
+ return(x)
+}
-ggplot(data=outp.summary %>%
- mutate(main=main/100) %>%
- mutate(corr=factor(corr/10, levels=c(0, 0.4, 0.8), labels=paste0("Correlation = ", c(0, 0.4, 0.8)))) %>%
- mutate(inter=factor(inter/10, levels=c(0, 0.3, 0.5), labels=paste0("Interaction = ", c(0, 0.3, 0.5)))) %>%
- ungroup() %>%
- dplyr::filter(stat.type=="XY") %>%
- dplyr::filter(trait %in% c("1", "2", "1 2", "3", "1 3", "2 3")) %>%
- mutate(trait=factor(trait, levels=c("1", "2", "3", "1 2", "1 3", "2 3"),
- labels=c("t1", "t2", "tn", "t1 t2", "t1 tn", "t2 tn"))) %>%
- mutate(linetype=as.factor(ifelse(grepl(pattern="tn", x = trait), 2, 1)))) +
- geom_line(aes(x=main, y=power, group=trait, col=trait, lty=linetype)) +
- guides(linetype = FALSE) +
- scale_color_manual("Trait\ncomb.",
- values=c("#e41a1c", #1 - red)
- "#e6ab02", #2 - yellow
- "#377eb8", #3 - blue
- "#d95f02", #12 - orange
- "#984ea3", #13 - violet
- "#4daf4a" #23 - green
- )) +
- facet_grid(corr~inter) +
+outp.summary2 <- outp.summary %>%
+ ungroup() %>%
+ mutate(main=main/10) %>%
+ mutate(ntraits.lab=factor(ntraits, levels=3:5, labels=paste0("n. neutral traits = ", 1:3))) %>%
+ rowwise() %>%
+ mutate(sel.ntraits=factor(get.ntraits(trait))) %>%
+ mutate(sel.ntraits.lab=factor(sel.ntraits, levels=levels(sel.ntraits), labels=paste0("Comb. tier = ", levels(sel.ntraits)))) %>%
+ ungroup() %>%
+ dplyr::filter(stat.type=="XY") %>%
+ mutate(trait=factor(trait)) %>%
+ mutate(trait=factor(trait, levels=levels(trait),
+ labels=add.t.label(levels(trait))))
+
+### rename null trait to "tn"
+outp.summary2 <- outp.summary2 %>%
+ #mutate(tn.name=paste0("t", ntraits)) %>%
+ mutate(trait=str_replace(trait, pattern="t3", replacement="tn1")) %>%
+ mutate(trait=str_replace(trait, pattern="t4", replacement="tn2")) %>%
+ mutate(trait=str_replace(trait, pattern="t5", replacement="tn3")) %>%
+ mutate(linetype=as.factor(ifelse(grepl(pattern="tn", x = trait), 2, 1)))
+
+#reorder factors
+outp.summary2$trait <- factor(outp.summary2$trait, levels= c('t1', 't2','tn1', 'tn2', 'tn3',
+ 't1 t2', 't1 tn1','t1 tn2','t1 tn3','t2 tn1','t2 tn2','t2 tn3','tn1 tn2','tn1 tn3', 'tn2 tn3',
+ 't1 t2 tn1', 't1 t2 tn2','t1 t2 tn3','t1 tn1 tn2', 't1 tn1 tn3','t1 tn2 tn3','t2 tn1 tn2','t2 tn1 tn3', 't2 tn2 tn3','tn1 tn2 tn3',
+ 't1 t2 tn1 tn2', 't1 t2 tn1 tn3','t1 t2 tn2 tn3','t1 tn1 tn2 tn3','t2 tn1 tn2 tn3',
+ 't1 t2 tn1 tn2 tn3'))
+
+hugepalette0 <- c(RColorBrewer::brewer.pal(4, "Dark2"),
+ gray(0.2),
+ RColorBrewer::brewer.pal(10, "Paired"),
+ RColorBrewer::brewer.pal(10, "Set3"),
+ RColorBrewer::brewer.pal(5, "Pastel1"), "brown")
+#change tone of yellow of t1-t2-tn2
+hugepalette0[17] <- "#ccebc5"
+hugepalette <- data.frame(trait=c('t1', 't2','tn1', 'tn2', 'tn3',
+ 't1 t2', 't1 tn1','t1 tn2','t1 tn3','t2 tn1','t2 tn2','t2 tn3','tn1 tn2','tn1 tn3', 'tn2 tn3',
+ 't1 t2 tn1', 't1 t2 tn2','t1 t2 tn3','t1 tn1 tn2', 't1 tn1 tn3','t1 tn2 tn3','t2 tn1 tn2','t2 tn1 tn3', 't2 tn2 tn3','tn1 tn2 tn3',
+ 't1 t2 tn1 tn2', 't1 t2 tn1 tn3','t1 t2 tn2 tn3','t1 tn1 tn2 tn3','t2 tn1 tn2 tn3',
+ 't1 t2 tn1 tn2 tn3'),
+ trait.col=factor(hugepalette0, levels=hugepalette0))
+
+hugelinetype <- c(1, 1, 1, 1, 2,
+ 1*1, 1*1, 1*1, 1*2, 1*1, 1*1, 1*2, 1*1, 1*2, 1*2,
+ 1*1*1, 1*1*1, 1*1*2, 1*1*1, 1*1*2, 1*1*2, 1*1*1, 1*1*2, 1*1*2, 1*1*2,
+ 1*1*1*1, 1*1*1*2, 1*1*1*2, 1*1*1*2, 1*1*1*2,
+ 1*1*1*1*2)
+
+outp.summary2 <- outp.summary2 %>%
+ left_join(hugepalette, by="trait")
+
+figS5 <- ggplot(data=outp.summary2) +
+ geom_line(aes(x=main, y=power, col=trait, lty=trait)) + #group=trait,
+ scale_x_continuous(name="Effect of factor e1 -> trait t1", n.breaks = 4) +
+ scale_y_continuous(name="Prop. of significant r(XY)") +
+ facet_grid(sel.ntraits.lab~ntraits.lab) +
+ scale_color_manual(name="trait", #guide = "legend",
+ values=hugepalette0) +
+ scale_linetype_manual(name="trait", #guide="legend",
+ values=hugelinetype) +
theme_bw() +
- scale_x_continuous(name="Effect of factor e1 -> trait t1") +
- scale_y_continuous(name="Prop. of significant r(XW)") +
theme(panel.grid = element_blank())
-ggsave(filename="_pics/R1/FigSXXX_CorrInte_30November_XW.png", width=6, height=5, device="png", dpi = 300, last_plot())
+ncols <- c(2,4,4,3,1)
+
+
+leg.list <- list()
+col.used <- 1
+for(tier in 1:5){
+ outp.summary.tier <- outp.summary2 %>% filter(sel.ntraits==tier)
+ ncombinations <- length(levels(factor(outp.summary.tier$trait)))
+ leg.list[[tier+1]] <- cowplot::get_legend(figS5 %+% outp.summary.tier +
+ guides(col=guide_legend(ncol=ncols[tier], byrow=TRUE))+
+ scale_color_manual(name=ifelse(tier==1, "Trait combination - tier 1",paste0("tier - ", tier)),
+ values= hugepalette0[col.used:(col.used+ncombinations-1)]) +
+ scale_linetype_manual(name=ifelse(tier==1, "Trait combination - tier 1",paste0("tier - ", tier)),
+ values=hugelinetype[col.used:(col.used+ncombinations-1)])
+ )
+ col.used <- col.used + ncombinations
+}
+leg.list[[tier+2]] <- NULL
+
+figS5.panel <- cowplot::plot_grid(figS5 + theme(legend.position = "none"),
+ cowplot::plot_grid(plotlist = leg.list, nrow = 7,
+ rel_heights = c(0.05, .2,.2,.2,.2,.2, 0.15), align="hv"),
+ nrow=1, rel_widths = c(0.55,.45))
+ggsave(filename="_pics/R1/FigS5_Neutral_TraitNumber.png",
+ width=9, height=7, device="png", dpi = 300, figS5.panel)
-### FIGURE SXXY - SBM ####
+#### FIGURE R1.S6a - XW ####
mypath <- "_data/Experiment_30Oct2020_FactorInteraction&TraitCorr_XW"
myfiles <- list.files(path=mypath, pattern = "Summary.txt", recursive = T, full=T)
outp.summary <- FormatData(myfiles)
@@ -318,14 +389,13 @@ ggplot(data=outp.summary %>%
mutate(corr=factor(corr/10, levels=c(0, 0.4, 0.8), labels=paste0("Correlation = ", c(0, 0.4, 0.8)))) %>%
mutate(inter=factor(inter/10, levels=c(0, 0.3, 0.5), labels=paste0("Interaction = ", c(0, 0.3, 0.5)))) %>%
ungroup() %>%
- dplyr::filter(stat.type=="SbM") %>%
- dplyr::filter(envir==1) %>%
+ dplyr::filter(stat.type=="XY") %>%
dplyr::filter(trait %in% c("1", "2", "1 2", "3", "1 3", "2 3")) %>%
mutate(trait=factor(trait, levels=c("1", "2", "3", "1 2", "1 3", "2 3"),
- labels=c("t1", "t2", "tn", "t1 t2", "t1 tn", "t2 tn"))) %>%
- mutate(linetype=as.factor(ifelse(grepl(pattern="tn", x = trait), 2, 1)))) +
- geom_line(aes(x=main, y=power, group=trait, col=trait, lty=linetype)) +
- guides(linetype = FALSE) +
+ labels=c("t1", "t2", "tn", "t1 t2", "t1 tn", "t2 tn")))# %>%
+ #mutate(linetype=as.factor(ifelse(grepl(pattern="tn", x = trait), 2, 1)))
+ ) +
+ geom_line(aes(x=main, y=power, col=trait, lty=trait)) + #group=trait,
scale_color_manual("Trait\ncomb.",
values=c("#e41a1c", #1 - red)
"#e6ab02", #2 - yellow
@@ -334,16 +404,19 @@ ggplot(data=outp.summary %>%
"#984ea3", #13 - violet
"#4daf4a" #23 - green
)) +
+ scale_linetype_manual(name="Trait\ncomb.",
+ values = c(1,1,2,1,2,2)) +
facet_grid(corr~inter) +
theme_bw() +
scale_x_continuous(name="Effect of factor e1 -> trait t1") +
- scale_y_continuous(name="Prop. of significant tests") +
+ scale_y_continuous(name="Prop. of significant r(XW)") +
theme(panel.grid = element_blank())
-ggsave(filename="_pics/R1/FigSXXY_CorrInte_30November_SbM.png", width=6, height=5, device="png", dpi = 300, last_plot())
+ggsave(filename="_pics/R1/FigS6a_CorrInte_30November_XW.png", width=6, height=5, device="png", dpi = 300, last_plot())
+
-#### FIGURE SXXO - XE ####
+#### FIGURE R1.S6b - XE ####
mypath <- "_data/Experiment_02Mar2020_FactorInteraction&TraitCorr"
myfiles <- list.files(path=mypath, pattern = "Summary.txt", recursive = T, full=T)
outp.summary <- FormatData(myfiles)
@@ -366,8 +439,7 @@ ggplot(data=outp.summary %>%
mutate(trait=factor(trait, levels=c("1", "2", "3", "1 2", "1 3", "2 3"),
labels=c("t1", "t2", "tn", "t1 t2", "t1 tn", "t2 tn"))) %>%
mutate(linetype=as.factor(ifelse(grepl(pattern="tn", x = trait), 2, 1)))) +
- geom_line(aes(x=main, y=power, group=trait, col=trait, lty=linetype)) +
- guides(linetype = FALSE) +
+ geom_line(aes(x=main, y=power, col=trait, lty=trait)) + #group=trait,
scale_color_manual("Trait\ncomb.",
values=c("#e41a1c", #1 - red)
"#e6ab02", #2 - yellow
@@ -375,18 +447,22 @@ ggplot(data=outp.summary %>%
"#d95f02", #12 - orange
"#984ea3", #13 - violet
"#4daf4a" #23 - green
- )
- ) +
+ )) +
+ scale_linetype_manual(name="Trait\ncomb.",
+ values = c(1,1,2,1,2,2)) +
facet_grid(corr~inter) +
theme_bw() +
scale_x_continuous(name="Effect of factor e1 -> trait t1") +
scale_y_continuous(name="Prop. of significant r(XE)") +
theme(panel.grid = element_blank())
-ggsave(filename="_pics/R1/FigSXXO_CorrInte_02March_XE.png", width=6, height=5, device="png", dpi = 300, last_plot())
+ggsave(filename="_pics/R1/FigS6b_CorrInte_02March_XE.png", width=6, height=5, device="png", dpi = 300, last_plot())
+
+
-#### FIGURE SXXP rXY & rXW vs Sample Size ####
+
+#### FIGURE R1.S7 rXY & rXW vs Sample Size ####
mypath <- "_data/Experiment_30Oct2020_FactorInteraction&TraitCorr_XY_SampleSize_Main=040_Inter=00_Corr=00_v21169/"
myfiles <- list.files(path=mypath, pattern = "Summary.txt", recursive = T, full=T)
myfiles <- myfiles[!grepl("_new", x=myfiles)] ## exclude 'new' directories --> correct?
@@ -419,7 +495,7 @@ ggplot(data= outp.summary %>%
theme(panel.grid = element_blank(),
legend.position = c(0.8, 0.2))
-ggsave(filename="_pics/R1/FigSXXP_CorrInte_02March_SampleSize.png",
+ggsave(filename="_pics/R1/FigS7_CorrInte_02March_SampleSize.png",
width=3, height=3, device="png", dpi = 300, last_plot())
@@ -432,3 +508,51 @@ ggsave(filename="_pics/R1/FigSXXP_CorrInte_02March_SampleSize.png",
+### FIGURE SXXY - SBM - only for review ####
+mypath <- "_data/Experiment_30Oct2020_FactorInteraction&TraitCorr_XW"
+myfiles <- list.files(path=mypath, pattern = "Summary.txt", recursive = T, full=T)
+outp.summary <- FormatData(myfiles)
+
+mypalette <- palette(c("#e41a1c", #1 - red)
+ "#ff7f00", #12 - orange
+ "#984ea3", #13 - violet
+ "##ffed6f", #2 - yellow
+ "#4daf4a", #23 - green
+ "#377eb8")) #3 - blue
+
+ggplot(data=outp.summary %>%
+ mutate(main=main/100) %>%
+ mutate(corr=factor(corr/10, levels=c(0, 0.4, 0.8), labels=paste0("Correlation = ", c(0, 0.4, 0.8)))) %>%
+ mutate(inter=factor(inter/10, levels=c(0, 0.3, 0.5), labels=paste0("Interaction = ", c(0, 0.3, 0.5)))) %>%
+ ungroup() %>%
+ dplyr::filter(stat.type=="SbM") %>%
+ dplyr::filter(envir==1) %>%
+ dplyr::filter(trait %in% c("1", "2", "1 2", "3", "1 3", "2 3")) %>%
+ mutate(trait=factor(trait, levels=c("1", "2", "3", "1 2", "1 3", "2 3"),
+ labels=c("t1", "t2", "tn", "t1 t2", "t1 tn", "t2 tn"))) %>%
+ mutate(linetype=as.factor(ifelse(grepl(pattern="tn", x = trait), 2, 1)))) +
+ geom_line(aes(x=main, y=power, col=trait, lty=trait)) + #group=trait,
+ scale_color_manual("Trait\ncomb.",
+ values=c("#e41a1c", #1 - red)
+ "#e6ab02", #2 - yellow
+ "#377eb8", #3 - blue
+ "#d95f02", #12 - orange
+ "#984ea3", #13 - violet
+ "#4daf4a" #23 - green
+ )) +
+ scale_linetype_manual(name="Trait\ncomb.",
+ values = c(1,1,2,1,2,2)) +
+ facet_grid(corr~inter) +
+ theme_bw() +
+ scale_x_continuous(name="Effect of factor e1 -> trait t1") +
+ scale_y_continuous(name="Prop. of significant tests") +
+ theme(panel.grid = element_blank())
+
+ggsave(filename="_pics/R1/FigSXXY_CorrInte_30November_SbM.png", width=6, height=5, device="png", dpi = 300, last_plot())
+
+
+
+
+
+
+
diff --git a/04_Additional_Figs.R b/04_Additional_Figs.R
index 95b52d4..1873263 100644
--- a/04_Additional_Figs.R
+++ b/04_Additional_Figs.R
@@ -122,6 +122,7 @@ do.the.parse <- function(toparse) {
}
+### Figure R1.S8 - Comparison beta vs cor ####
## Function to create Figure SXXV
create.panel <- function(x){
gg.betaW <- ggplot(data=x %>%
@@ -132,7 +133,6 @@ create.panel <- function(x){
geom_density(aes(value)) +
xlab("Proportional Beta Diversity (W)") +
xlim(c(0,1.1)) +
- ylim(c(0,60)) +
theme_classic()
gg.betaY <- gg.betaW %+%
@@ -149,7 +149,8 @@ create.panel <- function(x){
group_by(dataset, metric, matrix) %>%
summarize(value=max(abs(value))))+
xlab("Cor(WE)") +
- ylab(NULL)
+ ylab(NULL) +
+ ylim(c(0,13.5))
gg.corY <- gg.betaW %+%
(x %>%
@@ -159,56 +160,62 @@ create.panel <- function(x){
group_by(dataset, metric, matrix) %>%
summarize(value=max(abs(value)))) +
xlab("Cor(YE)") +
- ylab(NULL)
+ ylab(NULL)+
+ ylim(c(0,13.5))
gg.panel <- cowplot::plot_grid(gg.betaW, gg.corW,
gg.betaY, gg.corY,
- nrow=2, rel_widths = c(1,1.06))
+ nrow=2, rel_widths = c(1,1.06), align = "hv")
return(gg.panel)
}
-
-
-
-### Figure SXXV - comparison beta vs cor ####
+# Set path of files to import and parse
mypath <- "_data/Experiment_30Oct2020_FactorInteraction&TraitCorr_XY_SampleSize_Main=040_Inter=00_Corr=00_v21169"
myfiles <- list.files(path=mypath, pattern = "FinalSimulatedData.txt", recursive = T, full.names = T)
myfiles <- myfiles[grepl("_new", x=myfiles)]
+# Loop over files to import and parse. Create graphs
for(i in 1:length(myfiles)){
toparse <- myfiles[i]
sampleN <- regmatches(toparse, gregexpr("N=[[:digit:]]+", toparse))[[1]]
div.summary <- do.the.parse(toparse)
gg.out <- create.panel(div.summary)
- ggsave(filename = paste0("_pics/R1/FigSXXZ_Panel_BetaCor_", sampleN, ".png"),
+ ggsave(filename = paste0("_pics/R1/FigS8_Panel_BetaCor_", sampleN, ".png"),
width=6, height=5, device="png", dpi = 300, plot = gg.out)
}
-#### Figure SXXK - Comparison abg across combinatios Inter X Corr (Main=0.3) ####
+
+
+#### Figure R1.S1d - Comparison abg across combinatios Inter X Corr (Main=0.3) ####
create.panel2 <- function(xx, i, tit){
require(ggpubr)
- # alpha, min, mean, max
- gg.alpha <- ggplot(data=xx %>%
- filter(matrix=="W") %>%
- filter(metric %in% c("richness", "alpha")) %>%
- mutate(with=ifelse(!is.na(with), paste0("OTU Rich (", with, ")"), with)) %>%
- mutate(with=ifelse(is.na(with), "Eq. OTU (mean)", with))) +
- geom_density(aes(value, group=with, col=with), alpha=0.7, show.legend=FALSE)+
- stat_density(aes(x=value, colour=with),
- geom="line",position="identity") +
- scale_color_brewer(palette="Dark2", name=NULL) +
- theme_classic() +
- xlab("Alpha diversity (OTU Richness)") +
- theme(legend.position = c(0.65, 0.9)#,
- #legend.text = element_text(size=7)
- ) +
+ gg.rich <- ggplot(data=xx %>%
+ filter(matrix=="W") %>%
+ filter(metric %in% c("richness"))) +
+ geom_density(aes(value, group=with, col=with), alpha=0.7, show.legend=FALSE)+
+ stat_density(aes(x=value, colour=with),
+ geom="line",position="identity") +
+ scale_color_brewer(palette="Dark2", name=NULL) +
+ theme_classic() +
+ xlab("Species richness") +
+ theme(legend.position = c(0.75, 0.9)) +
xlim(c(-.1,100)) +
- ylim(c(0,0.25))
-
+ ylim(c(0,0.2))
+
+ # alpha, min, mean, max
+ gg.alpha <- gg.rich %+%
+ (xx %>%
+ filter(matrix == "W") %>%
+ filter(metric == "alpha")) +
+ geom_density(aes(value), show.legend = FALSE) +
+ xlab("Mean alpha diversity") +
+ ylim(c(0, 0.07)) +
+ ylab(NULL)
+
# beta
gg.beta <- ggplot(data=xx %>%
@@ -225,7 +232,7 @@ create.panel2 <- function(xx, i, tit){
(xx %>%
filter(matrix=="W") %>%
filter(metric %in% "propbeta")) +
- xlab("Proportional Beta Diversity") +
+ xlab("Proportional beta Diversity") +
xlim(c(0,0.7)) +
ylim(c(0,4))
#gamma
@@ -243,12 +250,14 @@ create.panel2 <- function(xx, i, tit){
))
if(i!=1){
- gg.alpha <- gg.alpha +
+ gg.rich <- gg.rich +
theme(legend.position="none")
- }
-
+ }
if(i!=3){
+ gg.rich <- gg.rich +
+ xlab(NULL) +
+ theme(axis.text.x = element_blank())
gg.alpha <- gg.alpha +
xlab(NULL) +
theme(axis.text.x = element_blank())
@@ -263,27 +272,32 @@ create.panel2 <- function(xx, i, tit){
theme(axis.text.x = element_blank())
}
- gg.panel <- cowplot::plot_grid(gg.title, gg.alpha, gg.beta, gg.propbeta, gg.gamma,
- nrow=1, rel_widths = c(0.08, 1,.94, .94, .94))
+ gg.panel <- cowplot::plot_grid(gg.title, gg.rich, gg.alpha, gg.beta, gg.propbeta, gg.gamma,
+ nrow=1, rel_widths = c(0.08, 1, .94, .94, .94, .94))
return(gg.panel)
}
-
+## set path of files to import and parse
mypath <- "_data/Experiment_30Oct2020_FactorInteraction&TraitCorr_XY_DataExamples/"
myfiles <- list.files(path=mypath, pattern = "FinalSimulatedData.txt", recursive = T, full.names = T)
+#loop of files, parse and create graphs
panel.list <- list()
for(i in 1:length(myfiles)){
toparse <- myfiles[i]
Inter <- regmatches(toparse, gregexpr("Inter=[[:digit:]]+", toparse))[[1]]
+ Inter <- gsub(pattern="=0", replacement=" = 0.", x = Inter)
+ Inter <- gsub(pattern="0.0", replacement="0", x = Inter)
Corr <- regmatches(toparse, gregexpr("Corr=[[:digit:]]+", toparse))[[1]]
+ Corr <- gsub(pattern="=0", replacement=" = 0.", x = Corr)
+ Corr <- gsub(pattern="0.0", replacement="0", x = Corr)
div.summary <- do.the.parse(toparse)
panel.list[[i]] <- create.panel2(div.summary, i=i,tit=paste(Inter, Corr))
}
-gg.SXXK <- cowplot::plot_grid(plotlist=panel.list, nrow=3)
-ggsave(filename = "_pics/R1/FigSXXK_Panel_abg.png",
- width=10, height=6, device="png", dpi = 300, plot = gg.SXXK)
+gg.S1d <- cowplot::plot_grid(plotlist=panel.list, nrow=3)
+ggsave(filename = "_pics/R1/FigS1d_Panel_abg.png",
+ width=10, height=6, device="png", dpi = 300, plot = gg.S1d)
--
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