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flexanova_plots_traits_soils.R
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flexanova_plots_traits_soils.R
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#################################################################
# L. Giraldo-Kalil Modificaddo 16 noviembre 2020
#
# Script diseñado para graficar porcentaje de variacion explicado
# segun flexanova. Se basa en los resutlados de este análisis generado con el script
# flexanova_traits_soils.R que se basa en la funcion "flexanova
# publicada por Leps et al. 2011 (Ecography 34: 856-863)
#
#################################################################
library(here)
library(xlsx)
library(ggplot2)
library(tidyr)
library(dplyr)
library("tidyverse")
library(forcats)
library(lemon)
datos<-read.xlsx(here("Datos", "Finales","weigthed_trait_intra_intersp_var.xlsx"),1)
pvalue<-read.xlsx(here("Datos", "Finales","weigthed_trait_intra_intersp_var.xlsx"),3)
#View(datos)
# arreglar datos en formato largo
longform<-gather(datos, key="relative_contribution",
value= "Values",7:10)
#Expresar en porcentage
#View(longform)
longform$relative_contribution<-as.factor(longform$relative_contribution)
longform$Percentage<-(longform$Values)*100
longform<-unite(longform, key,c(7:8), sep="_",remove=F)
#agregar clave completa
longform$key_comp<-paste(longform$key,"_",longform$variance_descriptors)
#View(longform)
#Extraer signos de covariacion
longform$sign<- ifelse(longform$Values<0,"-","+")
longform$csign<-ifelse(longform$relative_contribution!="Covariation"," ",longform$sign)
#View(longform)
#head(longform)
plong<-gather(pvalue, key="relative_contribution",
value="pvalues", 7:9)
plong<-unite(plong, key, c("key_tmp","relative_contribution"), sep="_", remove=F)
#head(plong)
#agregar clave completa
plong$key_comp<-paste(plong$key,"_",plong$variance_descriptors)
#View(plong)
#juntar las bases
todo<-left_join(longform, plong[,10:11], by="key_comp")
#View(todo)
#head(todo)
#Aqu?, excluyo los residuales y el total de los factores de variacion
rasgos<-subset(todo,todo$variance_descriptors!="Residuals")
rasgos<-subset(rasgos,rasgos$variance_descriptors!="Total")
#Verifico
rasgos$variance_descriptors %>% levels()
View(rasgos)
# agrego simbologia a la significancia
rasgos$significance<-ifelse(rasgos$pvalues<0.05,"*","")
nrow(rasgos)
head(rasgos)
#View(rasgos)
# borro valores feos en la significancia
rasgos$significance<-ifelse(rasgos$significance=="NANANA",
NA,rasgos$significance)
#View(rasgos)
#ajustamos para trampear usando el total y la covariacion como nuevas columnas
#Hacemos un subset solo con total y covariacion
Tot<-subset(rasgos,rasgos$relative_contribution=="Total")
cov_<-subset(rasgos,rasgos$relative_contribution=="Covariation")
TotW<-pivot_wider(Tot,names_from= relative_contribution,
values_from=Percentage)
cov_<-pivot_wider(cov_,names_from= relative_contribution,
values_from=Percentage)
#View(TotW)
#View(cov_)
#Hacemos aparte otro subset con las variables que nos interesan
#intra and turnover variation
new<-subset(rasgos,rasgos$relative_contribution=="Intraspec."|
rasgos$relative_contribution=="Turnover")
all<-left_join(new, TotW[,c(8,11,13,15)], by="key_tmp",
suffix=c("_var","_T"))
#View(all)
all<-all[,-c(14)]
all<-left_join(all,cov_[,c(8,12,15)], by="key_tmp")
#View(all)
all$significanceT<-ifelse(all$pvalues_T<0.05,"*","")
#quito signos y asteriscos duplicados
all$sign_T<-ifelse(all$relative_contribution=="Turnover",
all$sign_T,NA)
all$significanceT<-ifelse(all$relative_contribution=="Turnover",
all$significanceT,NA)
all$csign<-ifelse(all$relative_contribution=="Turnover",
all$csign,NA)
# Reordenar niveles de variables ambientales
all$group <- factor(all$environmental_variable, # Reordering group factor levels
levels = c("Clay",
"pH",
"SOC",
"STN",
"NO3",
"NH4",
"CNratio",
"STP","SAP"
))
View(all)
all$Functional_trait<-ifelse(all$Functional_trait=="LeafNP","Leaf N:P",
all$Functional_trait)
#View(all)
#Escribir tablas modificadas en formato excel
write.xlsx(all, here("Resultados","flexanova",
file="flexanova_var_P.xlsx"))
write.xlsx(longform, here("Resultados",
file="flexanova_var_longformat.xlsx"))
#Acotamos a lo que m?s nos interesa
#red<-subset(all,subset = (all$environmental_variable=="Clay"|
# all$environmental_variable=="NO3"|
# all$environmental_variable=="NH4"|
# all$environmental_variable=="SAP"))
#graficamos
all$ord<-ifelse(all$Functional_trait=="SLA",1,
ifelse(all$Functional_trait=="LDMC",2,
ifelse(all$Functional_trait=="LNmass",3,
ifelse(all$Functional_trait=="LPmass",4,5))))
# Reorganizacion de factores (rasgos)
all <- all %>%
mutate(trait = fct_reorder(Functional_trait, ord))
levels(all$trait)
#anotacion letras texto
dat_text <- data.frame(
label = c("A","B","C","D","E","G","H","I"),
group = c("Clay","pH","STN","NH4","NO3","CNratio","STP","SAP")
)
#Graficar usando ggplot2
#gestion de leyendas y ejes: con el paquete lemon
wplot<-ggplot(subset(all, type %in% c("weighted")),
aes(x=trait,
y=Percentage,
fill=relative_contribution))+
geom_bar(position="stack", stat="identity")+
geom_text(aes(label = significance),
position = position_stack(vjust = 0.05),
size=7, color="black")+
facet_wrap(~group,
labeller = as_labeller(c(Clay="Clay",
pH="pH",
SOC="SOC",
STN="STN",
NH4 = "NH[4]",
NO3 = "NO[3]",
CNratio="C:N",
STP="STP",
SAP = "SAP"),
default = label_parsed))+
facet_rep_wrap(~group, repeat.tick.labels = T)+
xlab("Leaf traits")+ ylab("% Explained variability")+
theme_classic()+
theme(legend.position="bottom",
aspect.ratio = 1,
legend.key.size = unit(0.8,"line"),
axis.text = element_text(size = 8),
axis.title= element_text(size= 9),
axis.text.x = element_text(angle=45,hjust = 1),
legend.text= element_text (size=8),
strip.text.x = element_text(hjust = 1, face="bold",
margin=margin(0,0,0,0)),
strip.background = element_rect(colour=NA),
strip.placement = "inside",
strip.text= element_text(size=8),
panel.border = element_rect(colour = "black", fill=NA))+
labs(fill = "relative contribution")+
stat_summary(fun = mean, geom = "errorbar",
aes(x=Functional_trait,
y=Total,
ymax = ..y.., ymin = ..y..,
width = 1, linetype = "solid"),show.legend = F)+
geom_text(aes(label=csign),position= position_stack(vjust = 1.4),
color="blue")+
scale_fill_manual(labels = c("Intraspecific","Interspecific"),
values=c("azure4","lightgrey"))+
labs(fill = "relative contribution")+
geom_text(aes(label = significanceT),
position = position_stack(vjust = 1.5),fontface="bold",
color="darkgreen", size=7)
wplot
ggsave("Appendix_S9.pdf",
plot= wplot,
path = "Figuras",
device= "pdf",
height=9,
width=7.25,
units= "in", dpi=600)