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6obs2.R
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################################################################################
############################## Observaciones 2 #################################
################################################################################
source("1Lib_DFs.R")
selec <-data_tortugas_tfg.xlsxselec <-function(ord,lista_tokens,var) {
paste(lista_tokens[-ord],collapse="|")
if(!is.na(ord)) return(grepl(lista_tokens[ord],tolower(var)) & !grepl(paste(lista_tokens[-ord],collapse="|"),tolower(var)))
else return(grepl(paste(lista_tokens,collapse="|"),tolower(var)))
}
# Lista de las lesiones de la tortuga
lista_lesiones<-c("amput|amputa|amputada","corte","enmalla|enreda|enmallamiento|
rafia|red|nylon|malla","anzuelo|anz","fractura","parás|paras|gusan",
"ahoga","petrol|hidrocarburo","herida|golpe|heridas|embarcaci","Apn|apn|APN")
# "herida","enredada","inflama"
# "golpe","infección","mordida","petrol","parásitos",
# "gangrenada","hinchada","necrosado","ahoga","lesiones")
colnames(df_tortugas_marinas)
# Lista del estado en el que se encuentra la tortuga
lista_estado<-c("muer|aplas|morib","déb|deb","necrosada|gangrena","flaca|delgada",
"infec|inflam|hincha|inflamada","putre|podri|descompuesta|descomposici","Apn|apn|APN")
# Lista de las partes del cuerpo afectada por la tortuga
lista_cuerpo<-c("aleta|delantera|izda|derecha|traseras|dcha","caparaz|caparazón|caparazon|cuerpo",
"ojo|ojos","boca|pico","cabeza","cuello","Apn|apn|APN")
df_tortugas_obs <- df_tortugas_marinas %>% mutate(
lesion= case_when(selec(1,lista_lesiones,observa)~"Amputación" ,
selec(2,lista_lesiones,observa)~"Cortes",
selec(3,lista_lesiones,observa)~"Enredadas",
selec(4,lista_lesiones,observa)~"Anzuelos",
selec(5,lista_lesiones,observa)~"Fracturas",
selec(6,lista_lesiones,observa)~"Parásitos",
selec(7,lista_lesiones,observa)~"Ahogadas",
selec(8,lista_lesiones,observa)~"Petroleadas",
selec(9,lista_lesiones,observa)~"Herida",
selec(10,lista_lesiones,observa)~"Nada",
selec(NA,lista_lesiones,observa) ~"Varias lesiones"),
estado= case_when(selec(1,lista_estado,observa) ~"Muerta" ,
selec(2,lista_estado,observa)~"Débil",
selec(3,lista_estado,observa)~"Necrosada",
selec(4,lista_estado,observa)~"Flaca",
selec(5,lista_estado,observa)~"Infección",
selec(6,lista_estado,observa)~"Putrefacta",
selec(7,lista_estado,observa)~"Nada"
),
cuerpo= case_when(selec(1,lista_cuerpo,observa) ~"Aleta" ,
selec(2,lista_cuerpo,observa)~"Caparazón",
selec(3,lista_cuerpo,observa)~"Ojos",
selec(4,lista_cuerpo,observa)~"Boca",
selec(5,lista_cuerpo,observa)~"Cabeza",
selec(6,lista_cuerpo,observa)~"Cuello",
selec(7,lista_cuerpo,observa)~"Nada",
selec(NA,lista_cuerpo,observa) ~"Varias")
) %>% as.data.frame()
###############################################################################
#### Variables de Lesión-Cuerpo-Estado de las tortugas llegadas al CRFS ######
###############################################################################
lesion <- expand.grid(lesion=as.character(unique(as.character(df_tortugas_obs$lesion)))) %>% left_join(
df_tortugas_obs %>% group_by(lesion) %>% summarize(n=n()),
by="lesion"
) %>% mutate(n=ifelse(is.na(n),0,n))
# cuerpo %>% arrange(n) %>%
# mutate(x=c(1,2,3,4,5,6,7,8,9,10,11),
# y=n+100,
# n_character=as.character(n)) %>%
# filter(!(n_character %in% "1045")) %>%
# summarise(suma=sum(n))
# Etiquetas de lesiones
etiquetas_lesiones <- lesion %>% arrange(n) %>%
mutate(x=c(1,2,3,4,5,6,7,8,9,10,11,12),
y=n+100,
n_character=as.character(n));etiquetas_lesiones
graf_lesion <- lesion %>%
#filter(lesion != "NA") %>%
ggplot(aes(reorder(lesion,n),n, fill = lesion)) +
geom_bar(stat = "identity", show.legend = F,
width = .75, col = "black") +
geom_text(data = etiquetas_lesiones,
aes(x=x,y=y,label=n_character),inherit.aes = F) +
scale_x_discrete(breaks = c(NA,"Varias lesiones","Enredadas","Cortes","Amputación","Anzuelos",
"Herida","Nada","Fracturas","Petroleadas","Parásitos","Ahogadas"),
labels = c("Unclassified","Many injuries","Entangled","Cuts","Amputation","Hooks",
"Wound","Nothing","Fractures","Oiled","Parasites","Drowned")) +
scale_fill_manual(breaks = c(NA,"Varias lesiones","Enredadas","Cortes","Amputación","Anzuelos",
"Herida","Nada","Fracturas","Petroleadas","Parásitos","Ahogadas"),
values = c("lightslategray","navajowhite1","salmon4","cadetblue2","grey28","goldenrod2",
"gray93","olivedrab3","brown2","pink4","grey20","gray60","white","blue")) +
coord_flip() +
scale_y_continuous(expand = expansion(0),
limits = c(0,1300),
breaks = seq(0,1100,200)) +
labs(x = "Injury",
y = "Num.turtles",
title = "List of turtle injuries") +
theme(
panel.background = element_rect(fill = "white", color = "white"),
plot.background = element_rect(fill = "white", color = "white"),
# panel.grid = element_blank(),
axis.line.x = element_line(),
title = element_markdown(size = 10, face = "bold"),
#axis.ticks.x = element_blank(),
axis.text = element_text(size=10),
axis.text.x = element_markdown(),
axis.text.y = element_markdown(hjust = 0.5),
axis.title.x = element_text(margin = margin(t = 10),size = 12),
axis.title.y = element_text(margin = margin(r = 10),size = 12),
plot.caption = element_markdown(hjust = 0, face = "italic"));graf_lesion
cuerpo <- expand.grid(cuerpo=as.character(unique(as.character(df_tortugas_obs$cuerpo)))) %>% left_join(
df_tortugas_obs %>% group_by(cuerpo) %>% summarize(n=n()),
by="cuerpo"
) %>% mutate(n=ifelse(is.na(n),0,n))
# Etiquetas del cuerpo
etiquetas_cuerpo <- cuerpo %>% arrange(n) %>%
mutate(x=c(1,2,3,4,5,6,7,8,9),
y=n+90,
n_character=as.character(n));etiquetas_cuerpo
graf_cuerpo <- cuerpo %>%
#filter(cuerpo != "NA") %>%
ggplot(aes(reorder(cuerpo,n),n, fill = cuerpo)) +
geom_bar(stat = "identity", show.legend = F,
col = "black", width = .75) +
geom_text(data = etiquetas_cuerpo,
aes(x=x,y=y,label=n_character),inherit.aes = F) +
scale_x_discrete(breaks=c(NA,"Aleta","Varias","Caparazón",
"Boca","Cuello","Ojos","Nada","Cabeza"),
labels=c("Unclassified ","Fin","Many","Carapace",
"Mouth","Neck","Esyes","Nothing","Head")) +
scale_fill_manual(breaks = c("Aleta","Varias","Caparazón","Boca",
"cuello","Ojos","Cabeza"),
values = c("lightslategray","navajowhite1","salmon4","cadetblue2","grey28","goldenrod2",
"gray93","olivedrab3")) +
scale_y_continuous(expand = expansion(0),
limits = c(0,1150),
breaks = seq(0,900,150)) +
coord_flip() +
labs(y = "Num.turtles",
x = "Part of the body",
title = "Part of the body affected list") +
theme(
panel.background = element_rect(fill = "white", color = "white"),
plot.background = element_rect(fill = "white", color = "white"),
axis.line.x = element_line(),
title = element_markdown(size = 10, face = "bold"),
#axis.ticks.x = element_blank(),
axis.text.x = element_markdown(),
axis.text = element_text(size=10),
axis.text.y = element_markdown(hjust = .5),
axis.title.x = element_text(margin = margin(t = 10),size = 12),
axis.title.y = element_text(margin = margin(r = 10),size = 12),
plot.caption = element_markdown(hjust = 0, face = "italic"));graf_cuerpo
estado <- expand.grid(estado=as.character(unique(as.character(df_tortugas_obs$estado)))) %>% left_join(
df_tortugas_obs %>% group_by(estado) %>% summarize(n=n()),
by="estado"
) %>% mutate(n=ifelse(is.na(n),0,n))
# Etiquetas del estado
etiquetas_estado <- estado %>% arrange(n) %>%
mutate(x=c(1,2,3,4,5,6,7,8),
y=n+150,
n_character=as.character(n));etiquetas_estado
graf_estado <- estado %>%
#filter(estado != "NA") %>%
ggplot(aes(reorder(estado,n),n, fill = estado)) +
geom_bar(stat = "identity", show.legend = F, col = "black",
width = .75) +
geom_text(data = etiquetas_estado,
aes(x=x,y=y,label=n_character),inherit.aes = F) +
coord_flip() +
scale_y_continuous(expand = expansion(0),
limits = c(0,1900),
breaks = seq(0,1600,300)) +
scale_fill_manual(breaks = c(NA,"Necrosada","Infección","Flaca","Muerta","Nada",
"Débil","Putrefacta"),
values = c("lightslategray","navajowhite1","salmon4","cadetblue2","grey28","goldenrod2",
"gray93","olivedrab3","brown2","pink4","grey20","gray60","blue")) +
scale_x_discrete(breaks=c(NA,"Necrosada","Infección","Flaca","Muerta","Nada",
"Débil","Putrefacta"),
labels=c("Unclassified ","Necrotic","Infection","Skinny","Dead","Nothing",
"Weak","Putrefacted")) +
labs(x = "State",
y = "Num.Turtles",
title = "State of the turtle list") +
theme(
panel.background = element_rect(fill = "white", color = "white"),
plot.background = element_rect(fill = "white", color = "white"),
axis.line.x = element_line(),
title = element_markdown(size = 10, face = "bold"),
#axis.ticks.x = element_blank(),
axis.text = element_text(size=10),
axis.text.x = element_markdown(),
axis.title.x = element_text(margin = margin(t = 10),size = 12),
axis.title.y = element_text(margin = margin(r = 10),size = 12),
plot.caption = element_markdown(hjust = 0, face = "italic"));graf_estado
#---------------------------#
# Juntar todos las gráficos #
#---------------------------#
# plot_grid(graf_cuerpo,"",graf_estado,graf_lesion,
# rel_heights = 1,
# labels = c("A","","B","C"))
columna_1 <- plot_grid(graf_cuerpo,graf_estado,
ncol = 1, nrow = 2,
labels = c("A","B"))
columna_2 <- plot_grid(graf_lesion,"",
ncol = 1,nrow = 2,
rel_heights = c(1,.25),
labels = c("C",""))
plot_grid(columna_1,columna_2)
ggsave("obs1.png", path = "C:\\Users\\jcge9\\Desktop\\TFG\\Tortugas_La_Tahonilla\\graficas",
width = 7.5, height = 5)
#################################################
#### Compbinación entre lesion-cuerpo-estado ####
#################################################
# Este análisis está pensado para, comparar las combinaciones de las afecciones de las
# tortugas, siempre y cuando, ambas tengas las dos varibles afectadas de alguna forma.
# Lesión-Cuerpo
lesion.cuerpo <- expand.grid(lesion=as.character(unique(as.character(df_tortugas_obs$lesion))),
cuerpo=as.character(unique(as.character(df_tortugas_obs$cuerpo)))) %>% left_join(
df_tortugas_obs %>% group_by(lesion,cuerpo) %>% summarize(n=n()),
by=c("lesion","cuerpo")
) %>% mutate(n=ifelse(is.na(n),0,n))
map_calor1 <- lesion.cuerpo %>%
filter(!(lesion %in% NA) & !(cuerpo %in% NA)) %>%
ggplot(aes(lesion, cuerpo, fill = n)) +
geom_tile(col = "black", alpha = .9) +
labs(x = "Injury",
y = "Body",
title = "Injury vs body",
caption = "") +
scale_x_discrete(breaks=c("Ahogadas","Amputación","Anzuelos","Cortes","Enredadas","Fracturas",
"Herida","Nada","Parásitos","Petroleadas","Varias lesiones"),
label=c("Drowned","Amputation","Hooks","Cuts","Entangled","Fractures",
"Wound","Nothing","Parasites","Oiled","Many")) +
scale_y_discrete(breaks=c("Varias","Ojos","Nada","Cuello",
"Caparazón","Cabeza","Boca","Aleta"),
label=c("Many","Eyes","Nothing","Neck",
"Carapace","Head","Mouth","Fin")) +
scale_fill_gradient(name="Nº tort.",
low = "skyblue", high = "red") +
theme(
panel.background = element_blank(),
# panel.background = element_rect(fill = "azure", color = "azure"),
# plot.background = element_rect(fill = "azure", color = "azure"),
# panel.grid = element_blank(),
#legend.background = element_rect(fill = "azure"),
axis.line.x = element_line(),
title = element_markdown(size = 11, face = "bold"),
axis.title.x = element_text(margin = margin(t = 10),size = 13),
axis.title.y = element_text(margin = margin(r = 10), size = 13),
axis.text = element_text(size=10),
axis.text.x = element_text(angle = 90,vjust = .16),
plot.caption = element_markdown(hjust = 0, face = "italic"),
legend.title = element_text(hjust = -1.5),
legend.key.size = unit(.45, "cm"),
legend.text = element_text(size = 7));map_calor1
# Lesion-estado
lesion.estado <- expand.grid(lesion=as.character(unique(as.character(df_tortugas_obs$lesion))),
estado=as.character(unique(as.character(df_tortugas_obs$estado)))) %>% left_join(
df_tortugas_obs %>% group_by(lesion,estado) %>% summarize(n=n()),
by=c("lesion","estado")
) %>% mutate(n=ifelse(is.na(n),0,n))
map_calor2 <- lesion.estado %>%
filter(!(lesion %in% NA) & !(estado %in% NA)) %>%
# group_by(lesion) %>%
# summarise(n=n())
ggplot(aes(lesion, estado, fill = n)) +
geom_tile(col = "black", alpha = .9) +
labs(x = "Injury",
y = "State",
title = "Injury vs State",
caption = "") +
scale_x_discrete(breaks=c("Ahogadas","Amputación","Anzuelos","Cortes","Enredadas","Fracturas",
"Herida","Nada","Parásitos","Petroleadas","Varias lesiones"),
label=c("Drowned","Amputation","Hooks","Cuts","Entangled","Fractures",
"Wounds","NOthing","Parasites","Oiled","Many")) +
scale_y_discrete(breaks = c("Putrefacta", "Necrosada", "Nada", "Muerta", "Infección",
"Flaca", "Débil"),
labels = c("Putrefacted","Necrotic","Nothing","Dead","Infection",
"Skinny","Weak")) +
scale_fill_gradient(name="Nº tort.",
low = "skyblue", high = "red") +
theme(
# panel.background = element_rect(fill = "azure", color = "azure"),
# plot.background = element_rect(fill = "azure", color = "azure"),
# panel.grid = element_blank(),
# legend.background = element_rect(fill = "azure"),
panel.background = element_blank(),
axis.line.x = element_line(),
title = element_markdown(size = 11, face = "bold"),
axis.title.x = element_text(margin = margin(t = 10),size = 13),
axis.title.y = element_text(margin = margin(r = 10), size = 13),
axis.text = element_text(size=10),
axis.text.x = element_text(angle = 90, vjust = 0.185),
plot.caption = element_markdown(hjust = 0, face = "italic"),
legend.title = element_text(hjust = -1.5),
legend.key.size = unit(.45, "cm"),
legend.text = element_text(size = 7));map_calor2
estado.cuerpo <- expand.grid(estado=as.character(unique(as.character(df_tortugas_obs$estado))),
cuerpo=as.character(unique(as.character(df_tortugas_obs$cuerpo)))) %>% left_join(
df_tortugas_obs %>% group_by(estado,cuerpo) %>% summarize(n=n()),
by=c("estado","cuerpo")
) %>% mutate(n=ifelse(is.na(n),0,n))
map_calor3 <- estado.cuerpo %>%
filter(!(estado %in% NA) & !(cuerpo %in% NA)) %>%
ggplot(aes(estado, cuerpo, fill = n)) +
geom_tile(col = "black", alpha = .9) +
labs(x = "State",
y = "Body",
title = "State vs Body") +
scale_x_discrete(breaks = c("Débil", "Flaca", "Infección", "Muerta", "Nada", "Necrosada", "Putrefacta"),
labels = c("Weak", "Skinny", "Infection", "Dead", "Nothing","Necrotic", "Putrefacted")) +
scale_y_discrete(breaks = c("Varias", "Ojos", "Nada", "Cuello", "Caparazón", "Cabeza", "Boca", "Aleta"),
labels = c("Many", "Eyes", "Nothing", "Neck", "Carapace", "Head", "Mouth", "Fin")) +
scale_fill_gradient(name="Nº tort.",
low = "skyblue", high = "red") +
theme(
# panel.background = element_rect(fill = "azure", color = "azure"),
# plot.background = element_rect(fill = "azure", color = "azure"),
# panel.grid = element_blank(),
# legend.background = element_rect(fill = "azure"),
panel.background = element_blank(),
axis.line.x = element_line(),
title = element_markdown(size = 11, face = "bold"),
axis.title.x = element_text(margin = margin(t = 10),size = 13),
axis.title.y = element_text(margin = margin(r = 10), size = 13),
axis.text.x = element_text(angle = 90,vjust = 0.175),
axis.text = element_text(size=10),
plot.caption = element_markdown(hjust = 0, face = "italic"),
legend.title = element_text(hjust = -1.5),
legend.key.size = unit(.45, "cm"),
legend.text = element_text(size = 7));map_calor3
plot_grid(map_calor3,map_calor2,map_calor1,"",
labels = c("A","B","C","D"))
fila_1 <- plot_grid(map_calor3,map_calor2,
ncol = 2, nrow = 1,
labels = c("A","C"),
rel_widths = c(.5,.6));fila_1
fila_2 <- plot_grid(map_calor1,"C",
ncol = 2, nrow = 1,
labels = c("B",""),
rel_widths = c(1,.250));fila_2
plot_grid(fila_1,fila_2,
ncol = 1, nrow = 2)
# ggsave("obs2.png", path = "C:\\Users\\jcge9\\Desktop\\TFG\\Tortugas_La_Tahonilla\\graficas",
# width = 9, height = 6)
################################################################################
# Comparación de las causas observaciones #
################################################################################
causa.cuerpo <- expand.grid(causa_orig=as.character(unique(as.character(df_tortugas_marinas$causa_orig))),
lesion=as.character(unique(as.character(df_tortugas_marinas$lesion))),
anio=as.character(unique(as.character(df_tortugas_marinas$anio)))) %>% left_join(
df_tortugas_marinas %>% group_by(causa_orig,lesion, anio) %>% summarize(n=n()),
by=c("causa_orig","lesion", "anio")
) %>% mutate(n=ifelse(is.na(n),0,n))
sum(causa.cuerpo$n)
causa.cuerpo %>%
filter(causa_orig %in% "Indeterminado",
!(lesion %in% "NA"),
anio %in% c("2019","2020","2021")) %>%
ggplot(aes(causa_orig, reorder(lesion,n), fill = n)) +
geom_tile(col = "black", alpha = .9) +
labs(x = "Lesión",
y = "Parte",
title = "Lesión vs cuerpo",
caption = "") +
# scale_x_discrete(breaks=c("Ahogadas","Amputación","Anzuelos","Cortes","Enredadas","Fracturas",
# "Herida","Nada","Parásitos","Petroleadas","Varias lesiones"),
# label=c("Ahogadas","Amputación","Anzuelos","Cortes","Enredadas","Fracturas",
# "Herida","Nada","Parásitos","Petroleadas","Varias")) +
# scale_y_discrete(breaks=c("Varias","Ojos","Nada","Cuello",
# "Caparazón","Cabeza","Boca","Aleta"),
# label=c("Varias","Ojos","Nada","Cuello",
# "Caparazón","Cabeza","Boca","Aleta")) +
scale_fill_gradient(name="Nº tort.",
low = "skyblue", high = "red") +
theme(panel.background = element_blank(),
axis.line.x = element_line(),
title = element_markdown(size = 11, face = "bold"),
axis.title.x = element_text(margin = margin(t = 10),size = 13),
axis.title.y = element_text(margin = margin(r = 10), size = 13),
#axis.text.x = element_text(angle = 90,vjust = .16),
plot.caption = element_markdown(hjust = 0, face = "italic"),
legend.title = element_text(hjust = -1.5),
legend.key.size = unit(.45, "cm"),
legend.text = element_text(size = 7))
################################################################################
# Tortugas de tierra #
################################################################################
tor_tierra <- df_tortugas2 %>%
filter(
especie %in% c("Centrochelys sulcata",
"Chelonoidis carbonaria",
"Chelyda serpentina")
) %>%
select(observa) %>% view()