Análisis exploratorio de datos de Biodiversidad del Ictioplancton para varios cruceros ERFEN
Universidad del Valle
Centro de indestigaciones Oceanográficas e Hidrográficas del
Pacífico
Centro de indestigaciones Oceanográficas e Hidrográficas del Pacífico
Beatriz Beltrán
Christian Bermúdez Rivas
Christian Bermúdez Rivas
2025-03-03
1 Análisis exploratorio de diversidad
En este apartado se harán los análisis exploratorio de biodiversidad del ictioplancton de los años 1993, 2004, 2005 y 2019.
1.1 Carga de librerías necesarias para los análisis
Se cargan las librerías necesarias para el análisis de EDA de biodiversidad
require(dplyr)## Loading required package: dplyr##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionrequire(ggplot2)## Loading required package: ggplot2require(reactable)## Loading required package: reactablerequire(iNEXT)## Loading required package: iNEXTrequire(readxl)## Loading required package: readxl1.2 Carga de los datos
Se cargan los datos necesarios para los análisis exploratorios.
data_quantity<-readxl::read_excel("../data/raw/biodiversity/Listado_Especies_v3 revisada_BB.xlsx", sheet="organismQuantity")
reactable(data_quantity, defaultPageSize = 50)Histograma de la ddensidad
#densidad_ordenado$rango<-1:261densidad_total<-as.data.frame(colSums(data_quantity[, 12:272]))
colnames(densidad_total)<-c("Total_Density")
# Ordenar por la columna A de manera descendente
densidad_ordenado <- densidad_total %>%
arrange(desc(Total_Density))
reactable(densidad_ordenado, defaultPageSize = 50)Histograma de la ddensidad
#densidad_ordenado$rango<-1:261summary(densidad_total$Total_Density)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.0 12.0 38.0 464.2 133.0 48603.0boxplot(densidad_total$Total_Density)
Histograma de la ddensidad
hist(densidad_total$Total_Density,
freq = TRUE)
Histograma de la ddensidad
options(scipen=9999)ggplot()+
geom_point(aes(y=densidad_ordenado[["Total_Density"]], x=seq(1:261)))+
scale_y_continuous(trans='log10', limits = function(x){c(min(x), ceiling(100000))})+
labs(colour = "", subtitle = "Rango - Densidad", title="Abundancia Total", x ="Rango", y =expression(paste("log(abundancia) [ind 10 ", m^-2, "]")),tag="A.")+
theme_bw()+
theme(legend.position="bottom",
axis.text.y = element_text(size = 8),
axis.title = element_text(size = 8),
plot.title = element_text(size = 8),
plot.subtitle = element_text(size = 8),
plot.caption= element_text(size = 8),
plot.tag = element_text(size=8))
Total_Rank_data<- vegan::rad.lognormal(densidad_ordenado$Total_Density)
Total_Rank_radfit<- vegan::radfit(densidad_ordenado$Total_Density)
ggplot()+
geom_point(aes(y=Total_Rank_radfit[["y"]], x=seq(1:261)))+
geom_line(aes(y= Total_Rank_radfit[["models"]][["Null"]][["fitted.values"]],x=seq(1:261),color="Null" ))+
geom_line(aes(y= Total_Rank_radfit[["models"]][["Preemption"]][["fitted.values"]],x=seq(1:261),color="Preemption"))+
geom_line(aes(y= Total_Rank_radfit[["models"]][["Lognormal"]][["fitted.values"]],x=seq(1:261),color="Lognormal" ))+
geom_line(aes(y= Total_Rank_radfit[["models"]][["Zipf"]][["fitted.values"]],x=seq(1:261),color="Zipf"))+
geom_line(aes(y= Total_Rank_radfit[["models"]][["Mandelbrot"]][["fitted.values"]],x=seq(1:261),color="Mandelbrot" ))+
scale_y_continuous(trans='log10')+
scale_color_manual(values = c( "#d7191c","#fdae61", "#a6dba0", "#008837", "#2b83ba"),
#breaks=c("Jet Chocó", "Null", "Preemption", "Lognormal", "Zipf", "Mandelbrot"),
name = " ",
labels=c( "Mandelbrot", "Zipf","Lognormal","Preemption", "Null")
)+
labs(colour = "", subtitle = "Rango - Densidad", title="Densidad total", x ="Rango", y =expression(paste("log(Densidad) [ind 10 ", m^-2, "]")),tag="B.")+
theme_bw()+
theme(legend.position="bottom",
axis.text = element_text(size = 8),
axis.title = element_text(size = 8),
plot.title = element_text(size = 8),
plot.subtitle = element_text(size = 8),
plot.caption= element_text(size = 8),
plot.tag = element_text(size=8))
#Diversidad####
D0_total<-hilldiv::hill_div(densidad_ordenado, qvalue = 0)## Registered S3 methods overwritten by 'FSA':
## method from
## confint.boot car
## hist.boot carD1_total<-hilldiv::hill_div(densidad_ordenado, qvalue = 1)
D2_total<-hilldiv::hill_div(densidad_ordenado, qvalue = 2)
Div_hill_Total<-base::list(D0_total,D1_total,D2_total)
Div_hill_Total_df<-base::as.data.frame(Div_hill_Total)
base::colnames(Div_hill_Total_df)<-c("q0", "q1","q2")
Div_hill_Total_df_trans<-as.data.frame(t(Div_hill_Total_df))
Div_hill_Total_df_trans$Orden<-c(0,1,2)
Rango_DivPlot <- ggplot2::ggplot() +
geom_line(data = Div_hill_Total_df_trans, aes(x = Orden, y = Total_Density ), color = "black", lwd = 0.5, linetype = 1) +
geom_point(data = Div_hill_Total_df_trans, aes(x = Orden, y = Total_Density ), color = "black", size =3) +
labs(
title = "Perfiles de diversidad",
x = "Orden",
y = "Diversidad",
color = "Variable") +
theme_minimal()
Rango_DivPlot
2 Analisis relacionados con el ENOS
2.1 Gráficas Indice ONI
datos_indice_Climaticos <- readxl::read_excel("../data/processed/Indices_Total.xlsx", sheet = "indices")
#datos_indice_Climaticos <- readxl::read_excel("../data/processed/Indices_Total.xlsx", sheet = "crucero_analizados")
ONI <- datos_indice_Climaticos%>%
subset(index_name == "ONI")
ONI$date <- as.Date(ONI$date)
cruceros<-as.data.frame(as.Date(c("1993-04-01",
"1993-10-01",
"2004-09-01",
"2005-09-01",
"2006-03-01",
"2006-09-01",
"2019-03-01",
"2019-09-01")))
ceros<-as.data.frame(rep(0, times=8))
cruceros<-cbind(ceros, cruceros)
colnames(cruceros) <-c("value.y", "date")
group.colors <- c(Neutro = "#808080", Niño = "#d7191c", Niña ="#2c7bb6")
ggplot() +
geom_col(data = ONI, aes(x = date, y = value, fill = event)) +
scale_fill_manual(values = group.colors) +
scale_x_date(limits = c(as.Date("1993-01-01"), as.Date("2019-12-31")),
date_labels = "%Y", date_breaks = "2 years") +
geom_hline(yintercept = 0.5, color = "#d7191c", size = 1) + # Línea roja en 0.5
geom_hline(yintercept = -0.5, color = "#2c7bb6", size = 1) + # Línea azul en -0.5
labs(
x = "Date",
y = "ONI",
fill = "Evento",
title = "Índice Oceánico El Niño (ONI)",
subtitle = "3-month rolling mean of ERSST.v5 SST anomalies in the Niño 3.4 region"
) +
theme_bw(base_size = 12) +
geom_point(data = cruceros, aes(x=date, y =value.y ), size = 2)+
theme(legend.position = "bottom")
2.2 Tabla con características de El Niño Oscilación Sur durante los cruceros analizados
datos_indice_Climaticos_cruceros <- readxl::read_excel("../data/processed/Indices_Total.xlsx", sheet = "crucero_analizados")
datos_indice_Climaticos_cruceros<-datos_indice_Climaticos_cruceros%>%
select(!index_description)
reactable(datos_indice_Climaticos_cruceros)nino<-data_quantity%>%
filter(ONI == "Niño")
nina<-data_quantity%>%
filter(ONI == "Niña")
neutro<-data_quantity%>%
filter(ONI == "Neutro")
densidad_nino<-as.data.frame(colSums(nino[, 12:272]))
colnames(densidad_nino)<-c("Niño")
# Ordenar por la columna A de manera descendente
densidad_nino_ord <- densidad_nino %>%
arrange(desc(Niño))%>%
filter(Niño != 0)
densidad_nina<-as.data.frame(colSums(nina[, 12:272]))
colnames(densidad_nina)<-c("Niña")
# Ordenar por la columna A de manera descendente
densidad_nina_ord <- densidad_nina %>%
arrange(desc(Niña))%>%
filter(Niña != 0)
densidad_neutro<-as.data.frame(colSums(neutro[, 12:272]))
colnames(densidad_neutro)<-c("Neutro")
# Ordenar por la columna A de manera descendente
densidad_neutro_ord <- densidad_neutro %>%
arrange(desc(Neutro))%>%
filter(Neutro != 0)
Diversidad_Total_Enos<-cbind(densidad_nino, densidad_nina, densidad_neutro)
Diversidad_Total_Enos$Niño<-as.integer(Diversidad_Total_Enos$Niño)
Diversidad_Total_Enos$Niña<-as.integer(Diversidad_Total_Enos$Niña)
Diversidad_Total_Enos$Neutro<-as.integer(Diversidad_Total_Enos$Neutro)
reactable(Diversidad_Total_Enos, defaultPageSize = 50)#Diversidad ENOS####
#Niño
D0_nino<-hilldiv::hill_div(densidad_nino_ord, qvalue = 0)
D1_nino<-hilldiv::hill_div(densidad_nino_ord, qvalue = 1)
D2_nino<-hilldiv::hill_div(densidad_nino_ord, qvalue = 2)
Div_hill_nino<-base::list(D0_nino,D1_nino,D2_nino)
Div_hill_nino_df<-base::as.data.frame(Div_hill_nino)
base::colnames(Div_hill_nino_df)<-c("q0", "q1","q2")
Div_hill_nino_df_trans<-as.data.frame(t(Div_hill_nino_df))
Div_hill_nino_df_trans$Orden<-c(0,1,2)
#Niña
D0_nina<-hilldiv::hill_div(densidad_nina_ord, qvalue = 0)
D1_nina<-hilldiv::hill_div(densidad_nina_ord, qvalue = 1)
D2_nina<-hilldiv::hill_div(densidad_nina_ord, qvalue = 2)
Div_hill_nina<-base::list(D0_nina,D1_nina,D2_nina)
Div_hill_nina_df<-base::as.data.frame(Div_hill_nina)
base::colnames(Div_hill_nina_df)<-c("q0", "q1","q2")
Div_hill_nina_df_trans<-as.data.frame(t(Div_hill_nina_df))
Div_hill_nina_df_trans$Orden<-c(0,1,2)
#Neutro
D0_neutro<-hilldiv::hill_div(densidad_neutro_ord, qvalue = 0)
D1_neutro<-hilldiv::hill_div(densidad_neutro_ord, qvalue = 1)
D2_neutro<-hilldiv::hill_div(densidad_neutro_ord, qvalue = 2)
Div_hill_neutro<-base::list(D0_neutro,D1_neutro,D2_neutro)
Div_hill_neutro_df<-base::as.data.frame(Div_hill_neutro)
base::colnames(Div_hill_neutro_df)<-c("q0", "q1","q2")
Div_hill_neutro_df_trans<-as.data.frame(t(Div_hill_neutro_df))
Div_hill_neutro_df_trans$Orden<-c(0,1,2)ggplot()+
geom_point(aes(y=densidad_nino_ord[["Niño"]], x=seq(1:145)), color ="#d7191c")+
geom_point(aes(y=densidad_nina_ord[["Niña"]], x=seq(1:125)), color ="#2c7bb6")+
geom_point(aes(y=densidad_neutro_ord[["Neutro"]], x=seq(1:202)), color ="#808080")+
scale_y_continuous(trans='log10', limits = function(x){c(min(x), ceiling(100000))})+
labs(colour = "", subtitle = "Rango - Abundancia", title="Abundancia Total", x ="Rango", y =expression(paste("log(abundancia) [ind 10 ", m^-2, "]")),tag="A.")+
geom_label(data = Div_hill_nino_df_trans[nrow(Div_hill_nino_df_trans), ],aes(x = 20, y = 10000, label = "Niño"), color = "#d7191c", fill = "white")+
geom_label(data = Div_hill_nino_df_trans[nrow(Div_hill_nino_df_trans), ], aes(x = 30, y = 2500, label = "Neutro"), color = "#808080", fill = "white")+
geom_label(data = Div_hill_nino_df_trans[nrow(Div_hill_nino_df_trans), ], aes(x = 10, y = 35000, label = "Niña"), color = "#2c7bb6", fill = "white")+
theme_bw()+
theme(legend.position="bottom",
axis.text.y = element_text(size = 8),
axis.title = element_text(size = 8),
plot.title = element_text(size = 8),
plot.subtitle = element_text(size = 8),
plot.caption= element_text(size = 8),
plot.tag = element_text(size=8))
valor = 60000
Calculo_Inext_ENOS<-iNEXT(Diversidad_Total_Enos,q=c(0,1,2),datatype = "abundance",endpoint = valor)data_quantity_size<-Calculo_Inext_ENOS[["iNextEst"]][["size_based"]]
subset_observed = data_quantity_size%>%
subset(Method == "Observed")
subset_Extrapolation = data_quantity_size%>%
subset(Method == "Extrapolation")
subset_Rarefaction = data_quantity_size%>%
subset(Method == "Rarefaction")
subset_extrapolation_max = subset_Extrapolation%>%
subset(m==valor)grafica_extrapolation <- ggplot() +
# Línea sólida para Rarefacción
geom_line(data = subset_Rarefaction, aes(x = m, y = qD, color = Assemblage, linetype = "Rarefaction")) +
# Puntos para valores observados
geom_point(data = subset_observed, aes(x = m, y = qD, color = Assemblage), size = 2) +
# Línea discontinua para Extrapolación
geom_line(data = subset_Extrapolation, aes(x = m, y = qD, color = Assemblage, linetype = "Extrapolation")) +
# Bandas de confianza
geom_ribbon(data = subset_Rarefaction, aes(x = m, ymin = qD.LCL, ymax = qD.UCL, fill = Assemblage), alpha = 0.2) +
geom_ribbon(data = subset_Extrapolation, aes(x = m, ymin = qD.LCL, ymax = qD.UCL, fill = Assemblage), alpha = 0.2) +
# Definir colores de línea
scale_color_manual(values = c("Neutro" = "#808080", "Niño" = "#d7191c", "Niña" = "#2c7bb6")) +
# Definir colores de bandas de confianza
scale_fill_manual(values = c("Neutro" = "#808080", "Niño" = "#d7191c", "Niña" = "#2c7bb6")) +
# Definir tipos de línea según método
scale_linetype_manual(values = c("Rarefaction" = "solid", "Extrapolation" = "dashed")) +
# Separar por orden de diversidad
facet_wrap(~ Order.q) +
# Estilo general
theme_bw(base_size = 6) +
labs(
x = "Número de Individuos",
y = "Diversidad de especies"
) +
theme(legend.position = "bottom",
legend.title = element_blank(),
legend.text = element_text(size = 9),
axis.text = element_text(size = 9),
axis.title = element_text(size = 9),
strip.text = element_text(size = 9),
plot.title = element_text(size = 9),
plot.subtitle = element_text(size = 9),
plot.caption = element_text(size = 9))
# Mostrar la gráfica
print(grafica_extrapolation)
data_coverage<-Calculo_Inext_ENOS[["iNextEst"]][["coverage_based"]]
subset_observed_coverage = data_coverage%>%
subset(Method == "Observed")
subset_Extrapolation_coverage = data_coverage%>%
subset(Method == "Extrapolation")
subset_Rarefaction_coverage = data_coverage%>%
subset(Method == "Rarefaction")
subset_extrapolation_max_coverage = subset_Extrapolation_coverage%>%
subset(m==valor)grafica_extrapolation <- ggplot() +
# Línea sólida para Rarefacción
geom_line(data = subset_Rarefaction_coverage, aes(x = SC, y = qD, color = Assemblage, linetype = "Rarefaction")) +
# Puntos para valores observados
geom_point(data = subset_observed_coverage, aes(x = SC, y = qD, color = Assemblage), size = 2) +
# Línea discontinua para Extrapolación
geom_line(data = subset_Extrapolation_coverage, aes(x = SC, y = qD, color = Assemblage, linetype = "Extrapolation")) +
# Bandas de confianza
geom_ribbon(data = subset_Rarefaction_coverage, aes(x = SC, ymin = qD.LCL, ymax = qD.UCL, fill = Assemblage), alpha = 0.2) +
geom_ribbon(data = subset_Extrapolation_coverage, aes(x = SC, ymin = qD.LCL, ymax = qD.UCL, fill = Assemblage), alpha = 0.2) +
# Definir colores de línea
scale_color_manual(values = c("Neutro" = "#808080", "Niño" = "#d7191c", "Niña" = "#2c7bb6")) +
# Definir colores de bandas de confianza
scale_fill_manual(values = c("Neutro" = "#808080", "Niño" = "#d7191c", "Niña" = "#2c7bb6")) +
# Definir tipos de línea según método
scale_linetype_manual(values = c("Rarefaction" = "solid", "Extrapolation" = "dashed")) +
# Separar por orden de diversidad
facet_wrap(~ Order.q) +
# Estilo general
theme_bw(base_size = 6) +
labs(
x = "Cobertura del muestreo",
y = "Diversidad de especies"
) +
theme(legend.position = "bottom",
legend.title = element_blank(),
legend.text = element_text(size = 9),
axis.text = element_text(size = 9),
axis.title = element_text(size = 9),
strip.text = element_text(size = 9),
plot.title = element_text(size = 9),
plot.subtitle = element_text(size = 9),
plot.caption = element_text(size = 9))
# Mostrar la gráfica
print(grafica_extrapolation)
ggplot2::ggplot() +
geom_line(data = subset_observed, aes(x = Order.q, y = qD , color = Assemblage ), lwd = 0.5, linetype = 1) +
geom_point(data = subset_observed, aes(x = Order.q, y = qD , color = Assemblage ), size =3) +
scale_color_manual(values = c("Neutro" = "#808080", "Niño" = "#d7191c", "Niña" = "#2c7bb6")) +
geom_label(data = Div_hill_nino_df_trans[nrow(Div_hill_nino_df_trans), ],
aes(x = 0.1, y = 160, label = "Niño"), color = "#d7191c", fill = "white")+
geom_label(data = Div_hill_nino_df_trans[nrow(Div_hill_nino_df_trans), ],
aes(x = 0.15, y = 217, label = "Neutro"), color = "#808080", fill = "white")+
geom_label(data = Div_hill_nino_df_trans[nrow(Div_hill_nino_df_trans), ],
aes(x = 0.1, y = 95, label = "Niña"), color = "#2c7bb6", fill = "white")+
labs(
title = "Perfiles de diversidad observada",
x = "Orden",
y = "Diversidad",
color = "Variable") +
theme_minimal()+
theme(legend.position = "none") # Elimina la leyenda
ggplot2::ggplot() +
geom_line(data = subset_extrapolation_max, aes(x = Order.q, y = qD , color = Assemblage ), lwd = 0.5, linetype = 1) +
geom_point(data = subset_extrapolation_max, aes(x = Order.q, y = qD , color = Assemblage ), size =3) +
geom_ribbon(data = subset_extrapolation_max, aes(x = Order.q, ymin = qD.LCL, ymax = qD.UCL, fill = Assemblage), alpha = 0.2) +
geom_ribbon(data = subset_extrapolation_max, aes(x = Order.q, ymin = qD.LCL, ymax = qD.UCL, fill = Assemblage), alpha = 0.2) +
scale_fill_manual(values = c("Neutro" = "#808080", "Niño" = "#d7191c", "Niña" = "#2c7bb6")) +
scale_color_manual(values = c("Neutro" = "#808080", "Niño" = "#d7191c", "Niña" = "#2c7bb6")) +
geom_label(data = Div_hill_nino_df_trans[nrow(Div_hill_nino_df_trans), ],
aes(x = 0.1, y = 160, label = "Niño"), color = "#d7191c", fill = "white")+
geom_label(data = Div_hill_nino_df_trans[nrow(Div_hill_nino_df_trans), ],
aes(x = 0.15, y = 217, label = "Neutro"), color = "#808080", fill = "white")+
geom_label(data = Div_hill_nino_df_trans[nrow(Div_hill_nino_df_trans), ],
aes(x = 0.1, y = 95, label = "Niña"), color = "#2c7bb6", fill = "white")+
labs(
title = "Perfiles de diversidad estimada ",
x = "Orden",
y = "Diversidad",
color = "Variable") +
theme_minimal()+
theme(legend.position = "none") # Elimina la leyenda
3 Análisis por Cruceros
datos_anos<-data_quantity[,c(2,12:272)]
datos_anos_trans<-as.data.frame(t(datos_anos %>%
group_by(crucero) %>%
summarise(across(everything(), sum, na.rm = TRUE))))
colnames(datos_anos_trans) <- datos_anos_trans[1, ] # Usa la primera fila como nombres de columna
datos_anos_trans <- datos_anos_trans[-1, ] # Elimina la primera fila repetida
datos_anos_trans <- datos_anos_trans %>%
mutate(across(everything(), as.integer))
reactable(datos_anos_trans)C1993.04<-datos_anos%>%
filter(crucero == "1993-04")
C1993.10<-datos_anos%>%
filter(crucero == "1993-10")
C2004.09<-datos_anos%>%
filter(crucero == "2004-09")
C2005.09<-datos_anos%>%
filter(crucero == "2005-09")
C2006.03<-datos_anos%>%
filter(crucero == "2006-03")
C2006.09<-datos_anos%>%
filter(crucero == "2006-09")
C2019.03<-datos_anos%>%
filter(crucero == "2019-03")
C2019.09<-datos_anos%>%
filter(crucero == "2019-09")
C1993.04_total<-as.data.frame(colSums(C1993.04[,2:262]))
C1993.10_total<-as.data.frame(colSums(C1993.10[,2:262]))
C2004.09_total<-as.data.frame(colSums(C2004.09[,2:262]))
C2005.09_total<-as.data.frame(colSums(C2005.09[,2:262]))
C2006.03_total<-as.data.frame(colSums(C2006.03[,2:262]))
C2006.09_total<-as.data.frame(colSums(C2006.09[,2:262]))
C2019.03_total<-as.data.frame(colSums(C2019.03[,2:262]))
C2019.09_total<-as.data.frame(colSums(C2019.09[,2:262]))
colnames(C1993.04_total)<-c("C1993.04")
colnames(C1993.10_total)<-c("C1993.10")
colnames(C2004.09_total)<-c("C2004.09")
colnames(C2005.09_total)<-c("C2005.09")
colnames(C2006.03_total)<-c("C2006.03")
colnames(C2006.09_total)<-c("C2006.09")
colnames(C2019.03_total)<-c("C2019.03")
colnames(C2019.09_total)<-c("C2019.09")
# Ordenar por la columna A de manera descendente
C1993.04_ord <- C1993.04_total %>% arrange(desc(C1993.04))%>% filter(C1993.04 != 0)
C1993.10_ord <- C1993.10_total %>% arrange(desc(C1993.10))%>% filter(C1993.10 != 0)
C2004.09_ord <- C2004.09_total %>% arrange(desc(C2004.09))%>% filter(C2004.09 != 0)
C2005.09_ord <- C2005.09_total %>% arrange(desc(C2005.09))%>% filter(C2005.09 != 0)
C2006.03_ord <- C2006.03_total %>% arrange(desc(C2006.03))%>% filter(C2006.03 != 0)
C2006.09_ord <- C2006.09_total %>% arrange(desc(C2006.09))%>% filter(C2006.09 != 0)
C2019.03_ord <- C2019.03_total %>% arrange(desc(C2019.03))%>% filter(C2019.03 != 0)
C2019.09_ord <- C2019.09_total %>% arrange(desc(C2019.09))%>% filter(C2019.09 != 0)
colnames(C1993.04_ord)<-c("1993-04")
colnames(C1993.10_ord)<-c("1993-10")
colnames(C2004.09_ord)<-c("2004-09")
colnames(C2005.09_ord)<-c("2005-09")
colnames(C2006.03_ord)<-c("2006-03")
colnames(C2006.09_ord)<-c("2006-09")
colnames(C2019.03_ord)<-c("2019-03")
colnames(C2019.09_ord)<-c("2019-09")ggplot() +
geom_point(aes(y = C1993.04_ord[["1993-04"]], x = seq_len(79), color = "1993-04")) +
geom_point(aes(y = C1993.10_ord[["1993-10"]], x = seq_len(70), color = "1993-10")) +
geom_point(aes(y = C2004.09_ord[["2004-09"]], x = seq_len(102), color = "2004-09")) +
geom_point(aes(y = C2005.09_ord[["2005-09"]], x = seq_len(146), color = "2005-09")) +
geom_point(aes(y = C2006.03_ord[["2006-03"]], x = seq_len(125), color = "2006-03")) +
geom_point(aes(y = C2006.09_ord[["2006-09"]], x = seq_len(91), color = "2006-09")) +
geom_point(aes(y = C2019.03_ord[["2019-03"]], x = seq_len(31), color = "2019-03")) +
geom_point(aes(y = C2019.09_ord[["2019-09"]], x = seq_len(28), color = "2019-09")) +
scale_y_continuous(trans = 'log10', limits = c(NA, 100000)) + # Límite corregido
labs(
colour = "",
subtitle = "Rango - Abundancia",
title = "Abundancia Total",
x = "Rango",
y = expression(paste("log(abundancia) [ind 10 ", m^-2, "]")),
tag = "A."
) +
scale_color_manual(values = c(
"1993-04" = "#e41a1c",
"1993-10" = "#377eb8",
"2004-09" = "#4daf4a",
"2005-09" = "#984ea3",
"2006-03" = "#ff7f00",
"2006-09" = "#dfc27d",
"2019-03" = "#a65628",
"2019-09" = "#f781bf"
)) +
theme_bw() +
theme(
legend.position = "bottom",
axis.text.y = element_text(size = 8),
axis.title = element_text(size = 8),
plot.title = element_text(size = 8),
plot.subtitle = element_text(size = 8),
plot.caption = element_text(size = 8),
plot.tag = element_text(size = 8)
)
valor = 50000
Calculo_Inext_cruceros<-iNEXT(datos_anos_trans,q=c(0,1,2),datatype = "abundance",endpoint = valor)rarefaccion_size_cruceros<-Calculo_Inext_cruceros[["iNextEst"]][["size_based"]]
cruceros_subset_observed = rarefaccion_size_cruceros%>%
subset(Method == "Observed")
cruceros_subset_Extrapolation = rarefaccion_size_cruceros%>%
subset(Method == "Extrapolation")
cruceros_subset_Rarefaction = rarefaccion_size_cruceros%>%
subset(Method == "Rarefaction")
cruceros_subset_extrapolation_max = cruceros_subset_Extrapolation%>%
subset(m==valor)ggplot() +
# Línea sólida para Rarefacción
geom_line(data = cruceros_subset_Rarefaction, aes(x = m, y = qD, color = Assemblage, linetype = "Rarefaction")) +
# Puntos para valores observados
geom_point(data = cruceros_subset_observed, aes(x = m, y = qD, color = Assemblage), size = 2) +
# Línea discontinua para Extrapolación
geom_line(data = cruceros_subset_Extrapolation, aes(x = m, y = qD, color = Assemblage, linetype = "Extrapolation")) +
# Bandas de confianza
geom_ribbon(data = cruceros_subset_Rarefaction, aes(x = m, ymin = qD.LCL, ymax = qD.UCL, fill = Assemblage), alpha = 0.2) +
geom_ribbon(data = cruceros_subset_Extrapolation, aes(x = m, ymin = qD.LCL, ymax = qD.UCL, fill = Assemblage), alpha = 0.2) +
# Definir colores de línea
scale_color_manual(values = c("1993-04" = "#e41a1c",
"1993-10" = "#377eb8",
"2004-09" = "#4daf4a",
"2005-09" = "#984ea3",
"2006-03" = "#ff7f00",
"2006-09" = "#dfc27d",
"2019-03" = "#a65628",
"2019-09" = "#f781bf")) +
scale_fill_manual(values = c("1993-04" = "#e41a1c",
"1993-10" = "#377eb8",
"2004-09" = "#4daf4a",
"2005-09" = "#984ea3",
"2006-03" = "#ff7f00",
"2006-09" = "#dfc27d",
"2019-03" = "#a65628",
"2019-09" = "#f781bf")) +
# Definir tipos de línea según método
scale_linetype_manual(values = c("Rarefaction" = "solid", "Extrapolation" = "dashed")) +
# Separar por orden de diversidad
facet_wrap(~ Order.q) +
# Estilo general
theme_bw(base_size = 6) +
labs(
x = "Número de Individuos",
y = "Diversidad de especies"
) +
theme(legend.position = "bottom",
legend.title = element_blank(),
legend.text = element_text(size = 9),
axis.text = element_text(size = 9),
axis.title = element_text(size = 9),
strip.text = element_text(size = 9),
plot.title = element_text(size = 9),
plot.subtitle = element_text(size = 9),
plot.caption = element_text(size = 9))
cruceros_data_coverage<-Calculo_Inext_cruceros[["iNextEst"]][["coverage_based"]]
cruceros_subset_observed_coverage = cruceros_data_coverage%>%
subset(Method == "Observed")
cruceros_subset_Extrapolation_coverage = cruceros_data_coverage%>%
subset(Method == "Extrapolation")
cruceros_subset_Rarefaction_coverage = cruceros_data_coverage%>%
subset(Method == "Rarefaction")
cruceros_subset_extrapolation_max_coverage = subset_Extrapolation_coverage%>%
subset(m==valor)ggplot() +
# Línea sólida para Rarefacción
geom_line(data = cruceros_subset_Rarefaction_coverage, aes(x = SC, y = qD, color = Assemblage, linetype = "Rarefaction")) +
# Puntos para valores observados
geom_point(data = cruceros_subset_observed_coverage, aes(x = SC, y = qD, color = Assemblage), size = 2) +
# Línea discontinua para Extrapolación
geom_line(data = cruceros_subset_Extrapolation_coverage, aes(x = SC, y = qD, color = Assemblage, linetype = "Extrapolation")) +
# Bandas de confianza
geom_ribbon(data = cruceros_subset_Rarefaction_coverage, aes(x = SC, ymin = qD.LCL, ymax = qD.UCL, fill = Assemblage), alpha = 0.2) +
geom_ribbon(data = cruceros_subset_Extrapolation_coverage, aes(x = SC, ymin = qD.LCL, ymax = qD.UCL, fill = Assemblage), alpha = 0.2) +
# Definir colores de línea
scale_color_manual(values = c("1993-04" = "#e41a1c",
"1993-10" = "#377eb8",
"2004-09" = "#4daf4a",
"2005-09" = "#984ea3",
"2006-03" = "#ff7f00",
"2006-09" = "#dfc27d",
"2019-03" = "#a65628",
"2019-09" = "#f781bf")) +
scale_fill_manual(values = c("1993-04" = "#e41a1c",
"1993-10" = "#377eb8",
"2004-09" = "#4daf4a",
"2005-09" = "#984ea3",
"2006-03" = "#ff7f00",
"2006-09" = "#dfc27d",
"2019-03" = "#a65628",
"2019-09" = "#f781bf")) +
scale_linetype_manual(values = c("Rarefaction" = "solid", "Extrapolation" = "dashed")) +
# Separar por orden de diversidad
facet_wrap(~ Order.q) +
# Estilo general
theme_bw(base_size = 6) +
labs(
x = "Cobertura del muestreo",
y = "Diversidad de especies"
) +
theme(legend.position = "bottom",
legend.title = element_blank(),
legend.text = element_text(size = 9),
axis.text = element_text(size = 9),
axis.title = element_text(size = 9),
strip.text = element_text(size = 9),
plot.title = element_text(size = 9),
plot.subtitle = element_text(size = 9),
plot.caption = element_text(size = 9))
ggplot2::ggplot() +
geom_line(data = cruceros_subset_observed, aes(x = Order.q, y = qD , color = Assemblage ), lwd = 0.5, linetype = 1) +
geom_point(data = cruceros_subset_observed, aes(x = Order.q, y = qD , color = Assemblage ), size =3) +
scale_color_manual(values = c("1993-04" = "#e41a1c",
"1993-10" = "#377eb8",
"2004-09" = "#4daf4a",
"2005-09" = "#984ea3",
"2006-03" = "#ff7f00",
"2006-09" = "#dfc27d",
"2019-03" = "#a65628",
"2019-09" = "#f781bf")) +
labs(
title = "Perfiles de diversidad observada",
x = "Orden",
y = "Diversidad",
color = "Crucero") +
theme_minimal()+
theme(legend.position = "bottom") # Elimina la leyenda
ggplot2::ggplot() +
geom_line(data = cruceros_subset_extrapolation_max, aes(x = Order.q, y = qD , color = Assemblage ), lwd = 0.5, linetype = 1) +
geom_point(data = cruceros_subset_extrapolation_max, aes(x = Order.q, y = qD , color = Assemblage ), size =3) +
geom_ribbon(data = cruceros_subset_extrapolation_max, aes(x = Order.q, ymin = qD.LCL, ymax = qD.UCL, fill = Assemblage), alpha = 0.2) +
geom_ribbon(data = cruceros_subset_extrapolation_max, aes(x = Order.q, ymin = qD.LCL, ymax = qD.UCL, fill = Assemblage), alpha = 0.2) +
scale_color_manual(values = c("1993-04" = "#e41a1c",
"1993-10" = "#377eb8",
"2004-09" = "#4daf4a",
"2005-09" = "#984ea3",
"2006-03" = "#ff7f00",
"2006-09" = "#dfc27d",
"2019-03" = "#a65628",
"2019-09" = "#f781bf")) +
scale_fill_manual(values = c("1993-04" = "#e41a1c",
"1993-10" = "#377eb8",
"2004-09" = "#4daf4a",
"2005-09" = "#984ea3",
"2006-03" = "#ff7f00",
"2006-09" = "#dfc27d",
"2019-03" = "#a65628",
"2019-09" = "#f781bf")) +
labs(
title = "Perfiles de diversidad estimada",
x = "Orden",
y = "Diversidad",
color = "Crucero",
fill = "Intervalo de Confianza") +
theme_minimal()+
theme(legend.position = "bottom") # Elimina la leyenda
ggplot2::ggplot() +
geom_line(data = cruceros_subset_extrapolation_max, aes(x = Order.q, y = qD , color = Assemblage ), lwd = 0.5, linetype = 1) +
geom_point(data = cruceros_subset_extrapolation_max, aes(x = Order.q, y = qD , color = Assemblage ), size =3) +
geom_ribbon(data = cruceros_subset_extrapolation_max, aes(x = Order.q, ymin = qD.LCL, ymax = qD.UCL, fill = Assemblage), alpha = 0.2) +
geom_ribbon(data = cruceros_subset_extrapolation_max, aes(x = Order.q, ymin = qD.LCL, ymax = qD.UCL, fill = Assemblage), alpha = 0.2) +
scale_color_manual(values = c("1993-04" = "#e41a1c",
"1993-10" = "#377eb8",
"2004-09" = "#4daf4a",
"2005-09" = "#984ea3",
"2006-03" = "#ff7f00",
"2006-09" = "#dfc27d",
"2019-03" = "#a65628",
"2019-09" = "#f781bf")) +
scale_fill_manual(values = c("1993-04" = "#e41a1c",
"1993-10" = "#377eb8",
"2004-09" = "#4daf4a",
"2005-09" = "#984ea3",
"2006-03" = "#ff7f00",
"2006-09" = "#dfc27d",
"2019-03" = "#a65628",
"2019-09" = "#f781bf")) +
labs(
title = "Perfiles de diversidad estimada",
x = "Orden",
y = "Diversidad",
color = "Crucero",
fill = "Intervalo de Confianza") +
theme_minimal()+
theme(legend.position = "bottom") # Elimina la leyenda
library(ggplot2)
# Crear dataframe de fechas de cruceros
cruceros <- data.frame(date = as.Date(c("1993-04-01", "1993-10-01",
"2004-09-01", "2005-09-01",
"2006-03-01", "2006-09-01",
"2019-03-01", "2019-09-01")))
# Filtrar datos por orden de diversidad
q0 <- cruceros_subset_observed %>% subset(Order.q == 0)
q1 <- cruceros_subset_observed %>% subset(Order.q == 1)
q2 <- cruceros_subset_observed %>% subset(Order.q == 2)
# Agregar datos de diversidad a 'cruceros'
cruceros$q0 <- q0$qD
cruceros$q1 <- q1$qD
cruceros$q2 <- q2$qD
# Convertir a formato largo para ggplot2
cruceros_long <- tidyr::pivot_longer(cruceros, cols = c(q0, q1, q2),
names_to = "Diversidad", values_to = "Valor")
# Asignar colores a cada índice de diversidad
colores_diversidad <- c("q0" = "#1b9e77", "q1" = "#d95f02", "q2" = "#7570b3")
# Gráfica con leyenda para las líneas
ggplot(cruceros_long, aes(x = date, y = Valor, color = Diversidad)) +
geom_line(size = 1) +
geom_point(size = 2) +
scale_x_date(limits = c(as.Date("1993-01-01"), as.Date("2019-12-31")),
date_labels = "%Y", date_breaks = "2 years") +
scale_color_manual(values = colores_diversidad,
labels = c("q0" = "Diversidad q0",
"q1" = "Diversidad q1",
"q2" = "Diversidad q2")) +
labs(
x = "Fecha",
y = "Índice de Diversidad",
color = "Número de especies", # Título de la leyenda
title = "Comportamiento de la diversidad en cada crucero"
) +
theme_bw(base_size = 12) +
theme(legend.position = "bottom")