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7biometria.R
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################################################################################
############################# Biometría ###############################
################################################################################
biometria <- read_excel("biometria_tfg.xlsx",
range = "A3:J891", col_types = c("text",
"text", "text", "text", "text", "numeric",
"numeric", "numeric", "numeric",
"numeric")) %>%
select(fecha=Fecha, especie=Especie, lrc = `L. Recto`, lcc = `L. Curvo`,
arc = `A. Recto`, acc = `A. Curvo`, peso = Peso)
biometria$fecha <- strptime(as.character(biometria$fecha), "%d/%m/%Y")
nueva_fecha <- format(biometria$fecha, "%Y-%m-%d")
biometria <- biometria %>%
mutate(anio=year(fecha),
mes=month(fecha, label = T),
estacion=case_when(mes == "dic" | mes == "ene" | mes == "feb"~"Winter",
mes == "mar" | mes == "abr" | mes == "may"~"Spring",
mes == "jun" | mes == "jul" | mes == "ago"~"Summer",
mes == "sep" | mes == "oct" | mes == "nov"~"Otoño")) %>%
filter(lrc > 0,lcc > 0,
acc > 0,arc > 0, arc < 2000,
peso > 0,peso < 250) %>%
mutate(estadio=case_when(lcc<20 ~ "Hatchling",
lcc>=20 & lcc<40~ "Small.Juv",
lcc>=40 & lcc<60~"Big.Juv",
lcc>=60 & lcc<80 ~"Subadult",
lcc>=80 ~"Adult"))
# biometria %>%
# group_by(especie,estadio) %>%
# summarise(n=n())
# summarise(n=n(),
# media_lrc=mean(lrc),
# media_lcc=mean(lcc),
# media_arc=mean(arc),
# media_acc=mean(acc),
# media_peso=mean(peso))
#------------------------------------------------------------------------------#
# Caretta caretta #
#------------------------------------------------------------------------------#
biomet.cc <- biometria %>% filter(especie %in% "Caretta caretta") # n = 671
# #------------------------------------------------------------------------------#
# # Estudio de la longitud del recto del caparazón #
# #------------------------------------------------------------------------------#
estudio_l_c <- data.frame(
valores=c(27,380,227,33,4),
tipo=c("Hatchling", "Small juvenile",
"Big juvenile","subadult",
"Adult")
) %>% mutate(tipo=factor(tipo,
levels = c("Hatchling", "Small juvenile",
"Big juvenile","subadult",
"Adult")))
cc <- estudio_l_c %>%
ggplot(aes(tipo, valores)) +
geom_bar(stat = "identity", fill="orange",
col="black", width = .65) +
geom_text(data = tibble(x=c(1,2,3,4,5), y=estudio_l_c$valores+45),
aes(x=x,y=y), label=c("27","380","227","33","4"),
inherit.aes = F, size=4) +
scale_y_continuous(expand = expansion(0),
limits = c(0,550))+
scale_x_discrete(breaks=c("Hatchling", "Small juvenile",
"Big juvenile","subadult",
"Adult"),
labels=c("(<20cm)\nHatchling","(20-40cm)\nSmall\njuvenile",
"(40-60cm)\nBig\njuvenile","(60-80cm)\nSubadult",
"(>80cm)\nAdult")) +
labs(title = "Curved caparace length, *C.caretta*",
subtitle="n = (671)",
x="CCL (cm)",
y="Num.turtles") +
theme_pander() +
theme(
# plot.background = element_rect(fill = "lightblue2", color = "lightblue2"),
panel.background = element_blank(),
panel.grid = element_line(color = "lightblue4"),
plot.title = element_markdown(hjust = .5),
plot.subtitle = element_markdown(hjust = .5),
axis.line.x = element_line(),
axis.title.x = element_text(face = "bold",
margin = margin(t=10)),
axis.title.y = element_text(face = "bold",
margin = margin(r=10)));cc
#
# #------------------------------------------------------------------------------#
# # Chelonia mydas #
# #------------------------------------------------------------------------------#
#
biometcm <- biometria %>% filter(especie %in% "Chelonia mydas")
# n = 18
# #------------------------------------------------------------------------------#
# # Estudio de la longitud del recto del caparazón #
# #------------------------------------------------------------------------------#
estudio_l_cm <- data.frame(
valores=c(0,5,6,7,0),
tipo=c("Hatchling", "Small juvenile",
"Big juvenile","subadult",
"Adult")
) %>% mutate(tipo=factor(tipo,
levels = c("Hatchling", "Small juvenile",
"Big juvenile","subadult",
"Adult")))
cm <- estudio_l_cm %>%
ggplot(aes(tipo, valores)) +
geom_bar(stat = "identity", fill="forestgreen",
col="black", width = .65) +
geom_text(data = tibble(x=c(1,2,3,4,5), y=estudio_l_cm$valores+2),
aes(x=x,y=y), label=c("","5","6","7",""), inherit.aes = F, size=4) +
scale_y_continuous(expand = expansion(0),
limits = c(0,15))+
scale_x_discrete(breaks=c("Hatchling", "Small juvenile",
"Big juvenile","subadult",
"Adult"),
labels=c("(<20cm)\nHatchling","(20-40cm)\nSmall\njuvenile",
"(40-60cm)\nBig\njuvenile","(60-80cm)\nSubadult",
"(>80cm)\nAdult")) +
labs(title = "Curved caparace lenght, *C.mydas*",
subtitle="n = (18)",
x="CCL (cm)",
y="Num.turtles") +
theme_pander() +
theme(
# plot.background = element_rect(fill = "lightblue2", color = "lightblue2"),
panel.background = element_blank(),
panel.grid = element_line(color = "lightblue4"),
plot.title = element_markdown(hjust = .5),
plot.subtitle = element_markdown(hjust = .5),
axis.line.x = element_line(),
axis.title.x = element_text(face = "bold",
margin = margin(t=10)),
axis.title.y = element_text(face = "bold",
margin = margin(r=10))
);cm
plot_grid(cc,cm)
# ggsave("biometria1.png", path="C:\\Users\\jcge9\\Desktop\\cuarto_carrera\\Tortugas_La_Tahonilla\\graficas",
# width = 9,
# height = 4)
#------------------------------------------------------------------------------#
# PCA #
#------------------------------------------------------------------------------#
variables <- biomet.cc[,c("lrc","lcc","arc","acc","peso")]
pca <- prcomp(as.matrix(variables),center = T, scale = T)
sumatorio_pca<-summary(pca)
df_sumatorio <- sumatorio_pca$importance
var_pc1 <- round(df_sumatorio[2,1]*100, 2)
var_pc2 <- round(df_sumatorio[2,2]*100, 2)
plot(pca, main = "PCA",
xlab="Componentes")
biplot(pca, scale = 0)
plot(pca, main = "PCA",
xlab="Componentes")
biometria_es <- biomet.cc[,c("especie","estacion","estadio")]
pca_biomet <- data.frame(biometria_es,
pca$x[,c("PC1","PC2","PC3")]) %>% view()
#############################
# Correlación PC, variables #
#############################
cor_pca <- cor(biomet.cc[,c("lrc","lcc","arc","acc","peso")],
pca_biomet[,c("PC1","PC2")]);cor_pca
write.xlsx(cor_pca, 'pca_correlacion.xlsx')
#### PCA ESPECIES
colnames(pca_biomet)
pca_biomet %>%
filter(especie %in% "Caretta caretta") %>%
mutate(estadio=factor(estadio,
levels = c("Hatchling","Small.Juv",
"Big.Juv","Subadult",
"Adult"))) %>%
ggplot(aes(PC1,PC2,
col = estadio,
fill=estadio,
#alpha=especie
)) +
geom_point() +
theme_classic() +
labs(x=glue("PC1 ({var_pc1}% explained variance)"),
y=glue("PC2 ({var_pc2}% explained variance)"),
fill=NULL,
color=NULL,
title = glue("PCA Biometric variables,<br><span style='color: red'>cumulative variance explained: {var_pc1+var_pc2}%</span>")) +
stat_ellipse(geom = "polygon", alpha=.25) +
geom_vline(xintercept = 0, linetype = "dashed") +
geom_hline(yintercept = 0, linetype = "dashed") +
scale_color_manual(values = c("magenta", "gray","forestgreen",
"blue","orange")) +
scale_fill_manual(values = c("magenta", "gray","forestgreen",
"blue","orange")) +
theme(
# plot.background = element_rect(fill = "lightblue2", color = "lightblue2"),
panel.background = element_blank(),
panel.grid = element_line(color = "lightblue4"),
plot.title = element_markdown(face="bold", hjust = .5, size = 14),
axis.title = element_text(face = "bold", size = 12),
legend.position = c(.6,.3),
legend.background = element_rect(color = "black", fill = NA),
legend.margin = margin(t = -.1, r = .1, b = .1, l = .1, unit = "cm")
)
ggsave("biometria2.png", path = "C:\\Users\\jcge9\\Desktop\\cuarto_carrera\\Tortugas_La_Tahonilla\\graficas",
width = 6, height = 5)
##############
# Gráfico 3d #
##############
library(rgl)
biomet_3d <- as_tibble(pca_biomet) %>%
mutate(color=case_when(estadio=="Hatchling"~"red",
estadio=="Small.Juv"~"gray",
estadio=="Big.Juv"~"forestgreen",
estadio=="Subadult"~"blue",
estadio=="Adult"~"orange",
TRUE~NA_character_))
plot3d(x=pca_biomet$PC1,y=pca_biomet$PC2,z=pca_biomet$PC3,
col = biomet_3d$color, type = "s", size = 1,
xlab = "PC1 (~94% variance explined)", ylab="PC2 (~4% variance explined)", zlab="PC3 (~1% variance explained)")
#################################
# Son significativos los grupo? #
#################################
library(nortest)
library(coin)
tapply(pca_biomet$PC1, pca_biomet$estadio, shapiro.test)
kruskal.test(PC1~as.factor(estadio), data = pca_biomet) # PC1 P<0.05
d_t.1 <- dunnTest(PC1~as.factor(estadio), data = pca_biomet,
method = "bonf");d_t.1$dtres
# Adulto p>0.05 -> Juv.grande y Subadulto; p<0.05 Cría, Juv.pequeño
# Cría p>0.05 -> ninguno ; p<0.05 Juv.grande, peq, subadulto
# Juv.grande p>0.05 ninguno; p<0.05 Juv.peq, subadulto
# Juv.peq p>0.05 ninguno; p<0.05 subadulto
write.xlsx(d_t.1$dtres, "tabla_pca1.xlsx")
tapply(pca_biomet$PC2, pca_biomet$estadio, shapiro.test)
kruskal.test(PC2~as.factor(estadio), data = pca_biomet) # PC2 P<0.05
d_t.2 <- dunnTest(PC2~as.factor(estadio), data = pca_biomet,
method = "bonf");d_t.2$dtres
# Adulto p>0.05 -> Cría, juv. peq, Subadulto; p<0.05 Juv.grande
# Cría p>0.05 subadulto; p<0.05 juv.peq y grande
# Juv.grande p>0.05 ninguno; p<0.05 Juv.peq;
##### PCA DE LAS ESTACIONES
pca_biomet %>%
filter(especie %in% "Caretta caretta") %>%
ggplot(aes(PC1,PC2, col = estacion, fill=estacion)) +
geom_point() +
theme_test() +
labs(title = "PCA parámetros biométricos-estación",
x="PC1 (94%)",
y="PC2 (0.4%)",
fill="Estación",
color="Estación") +
stat_ellipse(geom = "polygon", alpha=.25) +
geom_vline(xintercept = 0, linetype = "dashed") +
geom_hline(yintercept = 0, linetype = "dashed") +
scale_color_manual(values = c("cyan", "orange","yellowgreen","magenta")) +
scale_fill_manual(values = c("cyan", "orange","yellowgreen","magenta")) +
theme(
plot.title = element_text(face="bold", hjust = .5),
axis.title = element_text(face = "bold", size = 12),
legend.position = c(.5,.2)
)
###############################
# Gráfico 3D estaciones #
###############################
biomet_3d.2 <- pca_biomet %>%
mutate(color.2=case_when(estacion=="Primavera"~"yellowgreen",
estacion=="Verano"~"magenta",
estacion=="Otoño"~"orange",
estacion=="Invierno"~"cyan",
TRUE~NA_character_)) %>% view()
plot3d(x=pca_biomet$PC1,y=pca_biomet$PC2,z=pca_biomet$PC3,
col = biomet_3d.2$color, type = "s", size = 1,
xlab = "PC1 (94%)", ylab="PC2 (0.4%)", zlab="PC3 (0.01)")
#################################
# Son significativos los grupo? #
#################################
tapply(pca_biomet$PC1, pca_biomet$estacion, lillie.test)
kruskal.test(PC1~as.factor(estacion), data = pca_biomet)
d_te1 <- dunnTest(PC1~as.factor(estacion), data = pca_biomet,
method = "bonf");d_te1$dtres
tapply(pca_biomet$PC2, pca_biomet$estacion, lillie.test)
kruskal.test(PC2~as.factor(estacion), data = pca_biomet)
cor()
library(psych)
pairs.panels(biomet.cc[,c("lrc","lcc","arc","acc","peso")],
main = "Correlacion lineal tortuga boba")
rotados <- principal(variables, rotate = "varimax", nfactors = 2, scores = TRUE)
cr_biometria <- as_tibble(rotados$scores[,c(1,2)]) %>%
mutate(estadio = biomet.cc$estadio,
estadio=factor(estadio,
levels = c("Hatchling","Small.Juv",
"Big.Juv","Subadult",
"Adult")))
cr_biometria %>%
ggplot(aes(RC1, RC2, fill=estadio, color=estadio)) +
geom_point() +
theme_classic() +
labs(x=glue("PC1 ({var_pc1}% explained variance)"),
y=glue("PC2 ({var_pc2}% explained variance)"),
fill=NULL,
color=NULL,
title = "PCA Biometric variables") +
stat_ellipse(geom = "polygon", alpha=.25) +
geom_vline(xintercept = 0, linetype = "dashed") +
geom_hline(yintercept = 0, linetype = "dashed") +
scale_color_manual(values = c("red", "gray","forestgreen",
"blue","orange")) +
scale_fill_manual(values = c("red", "gray","forestgreen",
"blue","orange")) +
theme(
# plot.background = element_rect(fill = "lightblue2", color = "lightblue2"),
panel.background = element_blank(),
panel.grid = element_line(color = "lightblue4"),
plot.title = element_text(face="bold", hjust = .5, size = 14),
axis.title = element_text(face = "bold", size = 12),
legend.position = "top",
legend.background = element_rect(color = "black", fill = NA),
legend.margin = margin(t = -.1, r = .1, b = .1, l = .1, unit = "cm")
)