knitr::opts_chunk$set(message = FALSE,
warning = FALSE,
fig.path="Figuras/")Pesquería Erizo X y XI Regiones
Análisis Exploratorio Datos Tallas Seguimiento Bentónico 1996-2022
Set de trabajo y librerías utilizadas
options(bitmapType = "cairo")
#XQuartz is a mess, put this in your onload to default to cairo instead (https://github.com/tidyverse/ggplot2/issues/2655)
# Lo mapas se hacen mas rapido
# solo para IOslibrary(GGally)
library(knitr)
library(tidyverse)
library(patchwork)
library(marmap)
library(mapproj)
library(maps)
library(raster)
library(knitr)
library(ggrepel)
library(sf)
library(ggthemes) # nuevos fondos
library(readxl)
library(performance)
library(ggridges)Analisis exploratorio y preparación de estructuras de tallas para la evaluación de stockde erizo en las 3 macrozonas.
En este documento se presentan las rutas de análisis exploratorio de estructuras de tallas para cada una de las zonas de evaluación de stock de erizo, a saber; Macrozona X Norte, X Sur y XI, que lleva a cabo el Departamento de Evaluación de Recursos.
Exploración de datos totales
Los datos se actualizan año a año y se van estructurando las estructuras de tallas para cada zona de evaluación
ta9622 <- read.table("Tallas_96_22.csv",sep=",", header = T)
names(ta9622) [1] "X" "REGION" "PUERTO" "ANO_ARR" "MES_ARR"
[6] "DIA_ARR" "FUNCION" "PROCED" "CAPTURA" "DESTINO"
[11] "PESO_M" "LONGITUD" "FRECUENCIA"dim(ta9622)[1] 484547 13Una vez realizado el cbind, procedo a hacer los cruces con los poligonos
pro <- read.csv2("M_Procedencias.csv", header =T, sep=";")
proifop <- read.csv2("M_Procedencias_Poligonos.csv", header =T, sep=",")
names(pro) [1] "ACTIVA.INACTIVA" "X1.seg.y.2..otros" "USADA_EN"
[4] "CODIGO" "POLIGONO" "REGION"
[7] "NOMBRE_PROCEDENCIA" "OBSERVACIONES_1" "PUERTO_1"
[10] "PUERTO_2" "PUERTO_3" "PUERTO_4"
[13] "GRADO_LATITUD" "MIN_LATITUD" "SEGUNDO_LATITUD"
[16] "GRADO_LONGITUD" "MIN_LONGITUD" "SEGUNDO_LONGITUD"
[19] "LATITUD" "LONGITUD" names(proifop) [1] "ACTIVA.INACTIVA" "X1.seg.y.2..otros" "USADA_EN"
[4] "CODIGO" "POLIGONO" "REGION"
[7] "NOMBRE_PROCEDENCIA" "OBSERVACIONES_1" "PUERTO_1"
[10] "PUERTO_2" "PUERTO_3" "PUERTO_4"
[13] "GRADO_LATITUD" "MIN_LATITUD" "SEGUNDO_LATITUD"
[16] "GRADO_LONGITUD" "MIN_LONGITUD" "SEGUNDO_LONGITUD"
[19] "LATITUD" "LONGITUD" "POLIGONO_IFOP"
[22] "IN" policru<- subset(proifop, select=c(4,5, 21))
names(policru)[1] "CODIGO" "POLIGONO" "POLIGONO_IFOP"colnames(policru)<- c("PROCED", "POLIGONO", "POLIGONO_IFOP")
names(policru)[1] "PROCED" "POLIGONO" "POLIGONO_IFOP"tageo9622 <- merge(policru, ta9622, by="PROCED")Identifico los registros por año para comprobar lo del 2022
table(tageo9622$ANO_ARR)
1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008
7549 9369 8893 13727 9237 7567 11172 17352 27705 22306 22654 15944 17208
2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021
22783 23438 33490 25131 23569 22228 22225 24235 22607 21448 17761 13545 9327
2022
9337 Exploraciòn Datos Talla del 2020
Una vez he generado la base completa, comienzo a explorar los datos de tallas del 2020.
talla2022 <- tageo9622 %>%
filter(ANO_ARR >= 2000)
#identifico la cantidad de registros
dim(talla2022)[1] 442269 15# Comandos para crear nueva columna de zona en funcion de los poligonos
talla2022$ZONA[talla2022$POLIGONO %in% c(1,2)] <-1
talla2022$ZONA[talla2022$POLIGONO %in% c(4,5,6,13)] <-2
talla2022$ZONA[talla2022$POLIGONO %in% c(7,8,9,10,11,12)] <-3
talla2022$ZONA<- as.factor(talla2022$ZONA)
class(talla2022$ZONA)[1] "factor"Lo primero es ver diferencias entre destinos
El data frame que corresponde usar es el de “talla2020”
p <- ggplot(talla2022, aes(x=LONGITUD, group=DESTINO, fill= DESTINO)) +
geom_density( alpha=0.4, show.legend = FALSE)+
facet_wrap(.~ANO_ARR, scales = "free_y", ncol=5) +
geom_vline(xintercept = 65, color = "red")+
xlim(40,150)+
xlab("Longitud (mm.)")+
theme_bw()
p 
p2 <- ggplot(talla2022, aes(x=LONGITUD, fill=DESTINO, group=DESTINO)) +
geom_histogram(position = "identity",alpha=0.3, show.legend = F) +
facet_wrap(.~ANO_ARR, ncol=5, scales = "free_y") +
#scale_fill_brewer(palette="Dark2")+
theme_bw() +
ylab("Frecuencia Absoluta")+
xlab("Longitud (mm.)")+
ggtitle("Tallas de erizo Destino Fresco e industria")+
geom_vline(xintercept = 65, color = "red")+
geom_vline(xintercept = 70, color = "blue")+
labs(fill="")
p2
Tallas destino Fresco
M <- ggplot(talla2022 %>%
filter(DESTINO==2), aes(LONGITUD)) +
geom_histogram(binwidth = 3, color="grey", alpha=0.4, fill="red", show.legend = FALSE) +
facet_wrap(.~ANO_ARR, ncol=5) +
xlim(40,150)+
scale_x_continuous(breaks = seq(from = 40, to = 150, by = 10))+
theme_bw()+
theme(axis.text.x = element_text(angle = 90, hjust = 1))+
ggtitle("Tallas de erizo Destino Fresco")+
geom_vline(xintercept = 65, color = "red")+
geom_vline(xintercept = 70, color = "blue")
M
Tallas destino Industria
M <- ggplot(talla2022 %>%
filter(DESTINO==1), aes(LONGITUD)) +
geom_histogram(binwidth = 3, color="grey", alpha=0.4, fill="#225ea8", show.legend = FALSE) +
facet_wrap(.~ANO_ARR, ncol=5) +
xlim(40,150)+
scale_x_continuous(breaks = seq(from = 40, to = 150, by = 10))+
theme_bw()+
theme(axis.text.x = element_text(angle = 90, hjust = 1))+
ggtitle("Tallas de erizo Destino Industria")+
geom_vline(xintercept = 65, color = "red")+
geom_vline(xintercept = 70, color = "blue")
M
Estructura de tallas por año y por Zona
jz3 <- ggplot(talla2022 %>%
drop_na(), aes(x=LONGITUD, y = as.factor(ANO_ARR),
fill = factor(stat(quantile))))+
stat_density_ridges(
geom = "density_ridges_gradient",
calc_ecdf = TRUE,
quantiles = c(0.25, 0.75)) +
scale_fill_manual(
name = "Probability", values = c("#FF0000A0", "#A0A0A0A0", "#0000FFA0"),
labels = c("[0, 0.25]", "[0.25, 0.75]", "[0.75, 1]"))+
facet_wrap(.~ZONA, ncol=3) +
geom_vline(xintercept = 65, color = "red")+
scale_x_continuous(breaks = seq(from = 1, to = 150, by = 10))+
scale_y_discrete(breaks = seq(from = 1996, to = 2022, by = 1))+
#scale_fill_viridis_d(name="SubArea")+
theme_bw()+
theme(axis.text.x = element_text(angle = 90, hjust = 1))+
xlim(20,150)+
xlab("Longitud (cm.)")+
ylab("")
jz3
Estructura de tallas por año y por Poligono de IFOP
jz3 <- ggplot(talla2022 %>%
drop_na(), aes(x=LONGITUD, y = as.factor(ANO_ARR),
fill = factor(stat(quantile))))+
stat_density_ridges(
geom = "density_ridges_gradient",
calc_ecdf = TRUE,
quantiles = 2,
scale = 2,
rel_min_height = 0.1,
alpha=0.3) +
scale_fill_viridis_d(option = "D",
name = "50%")+
facet_wrap(.~POLIGONO, ncol=4) +
geom_vline(xintercept = 65, color = "red")+
scale_x_continuous(breaks = seq(from = 1, to = 150, by = 10))+
scale_y_discrete(breaks = seq(from = 1996, to = 2022, by = 1))+
#scale_fill_viridis_d(name="SubArea")+
theme_bw()+
theme(axis.text.x = element_text(angle = 90, hjust = 1))+
xlim(20,150)+
xlab("Longitud (cm.)")+
ylab("")
jz3
jz <- ggplot(talla2022 %>%
drop_na(), aes(x=LONGITUD,
y = as.factor(ANO_ARR),
fill=POLIGONO))+
#geom_joy(alpha=0.9) +
geom_density_ridges(stat = "binline", bins = 30,
scale = 1, draw_baseline = FALSE)+
facet_wrap(.~POLIGONO, ncol=11) +
geom_vline(xintercept = 65, color = "red")+
scale_x_continuous(breaks = seq(from = 1, to = 10, by = 1))+
scale_y_discrete(breaks = seq(from = 2000, to = 2022, by = 1))+
scale_fill_viridis_c(name="SubArea")+
theme_bw()+
theme(axis.text.x = element_text(angle = 90, hjust = 1))+
xlim(0,160)+
xlab("Longitud (cm.)")+
ylab("")
jz
meant <-talla2022 %>%
dplyr::group_by(ANO_ARR,
ZONA) %>%
dplyr::summarise(avg=mean(LONGITUD))
glimpse(meant)Rows: 86
Columns: 3
Groups: ANO_ARR [23]
$ ANO_ARR <int> 2000, 2000, 2000, 2001, 2001, 2001, 2001, 2002, 2002, 2002, 20…
$ ZONA <fct> 1, 2, 3, 1, 2, 3, NA, 1, 2, 3, NA, 1, 2, 3, NA, 1, 2, 3, NA, 1…
$ avg <dbl> 88.36878, 75.49377, 75.79487, 88.31192, 80.68731, 76.67478, 90…pmea <- ggplot(meant %>%
drop_na(),
aes(ANO_ARR,avg))+
geom_point(shape=21, aes(fill=ZONA)) +
stat_smooth(method= "lm", colour='#253494')+
theme_bw()+
facet_wrap(.~ZONA)+
scale_x_continuous(breaks = seq(from = 2000, to = 2022, by = 2))+
#scale_y_discrete(breaks = seq(from = 1, to = 13, by = 1))+
theme(axis.text.x = element_text(angle = 90, hjust = 2))+
guides(fill = guide_legend(reverse=F))+
scale_fill_viridis_d(option="E")+
ylim(50,100)+
ylab("") +
xlab("") +
ggtitle("Lenght Mean sea urchin fishery")
pmea
Ahora se genera la expansion de tallas a la frecuecnia para obtener los vectores por año y por zona
names(ta9622) [1] "X" "REGION" "PUERTO" "ANO_ARR" "MES_ARR"
[6] "DIA_ARR" "FUNCION" "PROCED" "CAPTURA" "DESTINO"
[11] "PESO_M" "LONGITUD" "FRECUENCIA"dftalla3<-subset(ta9622,select=c(4,8, 12, 13))
names(dftalla3)[1] "ANO_ARR" "PROCED" "LONGITUD" "FRECUENCIA"dim(dftalla3)[1] 484547 4colnames(dftalla3) <- c( "ANO" , "PROC", "TALLA", "FRECUENCIA")
dftalla3 <- transform(dftalla3, TALLA = as.numeric(TALLA),
FRECUENCIA = as.numeric(FRECUENCIA))
dftalla3 <- na.omit(dftalla3)
dim(dftalla3)[1] 484544 4head(dftalla3) ANO PROC TALLA FRECUENCIA
1 2006 9185 61 6
2 2006 9185 59 4
3 2006 9185 58 1
4 2006 9185 67 10
5 2006 9185 88 1
6 2006 9185 62 8#ver tallas grandes
# hist y maxIdentifico las procedencias de la base
#table(dftalla4$PROC)
unique(dftalla3$PROC) [1] 9185 9148 9131 9122 9105 9038 9160 9152 9125 9278 8719 9130
[13] 8455 8765 9242 9259 9246 9146 9245 9225 9255 9264 9230 9209
[25] 9571 9572 9919 9254 9271 9679 9282 9300 9985 9217 9875 8904
[37] 9677 9995 9952 9220 9581 9628 9955 9864 9879 9525 9219 9629
[49] 9253 9988 9580 9263 9947 8970 9946 9643 9582 9610 9619 9218
[61] 9203 9232 9231 9270 9138 8757 9156 9164 9283 9175 9195 9202
[73] 9207 9192 9022 9071 9216 9562 9989 9267 9680 9666 9258 8954
[85] 8573 9616 8958 9877 9950 9561 9597 9944 9635 9945 9600 9574
[97] 9564 9603 9269 9212 9268 9595 9573 9986 9250 9243 8506 9276
[109] 9627 9565 9228 8254 9224 8256 8257 9172 9190 9178 9205 9210
[121] 9008 9715 9717 9287 9712 9678 9177 8759 9167 9157 8367 9176
[133] 8439 9248 9655 9664 9604 9173 0 9260 9636 9174 9346 9815
[145] 9284 9328 9460 9422 9182 9142 8615 8600 9711 8699 8620 9792
[157] 8602 9262 9092 9200 9238 9141 9158 9463 9431 9490 9470 8705
[169] 9429 9440 9430 9285 9710 8601 9709 8604 8623 8627 9713 8608
[181] 8534 9789 9351 9186 9714 8606 8716 9110 9281 8718 9072 9050
[193] 9030 9040 9055 9065 9000 9015 9052 9198 9208 8957 9464 9702
[205] 9188 9153 8594 8760 9134 8595 9159 9147 9653 9127 9223 9602
[217] 9392 9247 9876 9559 9594 9555 9222 9553 9658 9233 9612 9204
[229] 9644 9237 9626 9563 9227 8761 9786 9236 9126 9144 9637 9520
[241] 9575 9631 8368 9669 8002 9585 9887 9670 9672 9394 9266 9939
[253] 9918 9943 9991 9685 9671 9560 9406 9691 9633 9566 9652 9399
[265] 8571 8644 9661 8003 9591 9882 9659 8898 9992 9987 9920 9676
[277] 9663 9953 9240 9951 9915 9982 9244 9667 9883 11040 8227 9623
[289] 9641 9642 9650 9554 8007 8222 9940 9980 8211 8210 9956 9551
[301] 9656 9592 8212 9941 9936 9648 9577 8910 9398 9959 8955 9568
[313] 9234 9921 9235 9201 9618 9274 9070 9651 9395 9408 9397 9558
[325] 9692 9689 9624 9067 9931 9608 9681 9074 9087 9878 9257 9916
[337] 8609 9168 9161 8230 8370 8231 8219 9133 9751 8077 9135 9639
[349] 9611 9251 8750 9634 8076 8239 9145 9584 9668 8194 8193 8196
[361] 8195 9120 9475 9720 9476 9162 8258 9621 9942 9683 8906 8438
[373] 9583 9999 9884 8591 9783 9552 9169 8189 9132 9196 9784 9163
[385] 9937 9143 9279 9625 9199 9881 8390 8249 8080 9589 9640 9480
[397] 9599 9277 7777 8359 8179 9184 8364 9703 8365 8360 8200 9165
[409] 8235 8358 8181 8177 8071 8182 8197 8178 8255 8135 8136 8199
[421] 8184 8138 8137 9137 9151 9166Expansión de tallas para todos los registros. Esto demora un poco
ahora una expansión de las tallas
#expandir long a fracuencias
#esto no lo pude hacer para el total dado q es mucha data
expanav<-dftalla3
#puerto, ano, long, Frec
head(expanav) ANO PROC TALLA FRECUENCIA
1 2006 9185 61 6
2 2006 9185 59 4
3 2006 9185 58 1
4 2006 9185 67 10
5 2006 9185 88 1
6 2006 9185 62 8filas<-nrow(expanav)
aux<-list();auxA<-list();auxB<-list()
for(i in 1:filas){
aux[[i]]<-rep(expanav[i,3],expanav[i,4])
auxA[[i]]<-rep(expanav[i,2],expanav[i,4])
auxB[[i]]<-rep(expanav[i,1],expanav[i,4])
}
length(aux)[1] 484544length(auxA)[1] 484544length(auxB)[1] 484544#aux<-as.numeric(aux)Long<-unlist(aux)
Proc<-unlist(auxA)
Year<-unlist(auxB)
#Puerto<-unlist(auxB);Puerto
tt<-cbind(Long,Proc, Year)
colnames(tt)<-c("Long","Proc", "Year")
dim(tt)[1] 2792662 3head(tt) Long Proc Year
[1,] 61 9185 2006
[2,] 61 9185 2006
[3,] 61 9185 2006
[4,] 61 9185 2006
[5,] 61 9185 2006
[6,] 61 9185 2006tt <-as.data.frame(tt)
tt$Long <- as.numeric(as.character(tt$Long))
tt$Year <- as.numeric(as.character(tt$Year))
glimpse(tt)Rows: 2,792,662
Columns: 3
$ Long <dbl> 61, 61, 61, 61, 61, 61, 59, 59, 59, 59, 58, 67, 67, 67, 67, 67, 6…
$ Proc <dbl> 9185, 9185, 9185, 9185, 9185, 9185, 9185, 9185, 9185, 9185, 9185,…
$ Year <dbl> 2006, 2006, 2006, 2006, 2006, 2006, 2006, 2006, 2006, 2006, 2006,…Ahora hago otro cruce para juntar poligonos nuevamente
names(pro) [1] "ACTIVA.INACTIVA" "X1.seg.y.2..otros" "USADA_EN"
[4] "CODIGO" "POLIGONO" "REGION"
[7] "NOMBRE_PROCEDENCIA" "OBSERVACIONES_1" "PUERTO_1"
[10] "PUERTO_2" "PUERTO_3" "PUERTO_4"
[13] "GRADO_LATITUD" "MIN_LATITUD" "SEGUNDO_LATITUD"
[16] "GRADO_LONGITUD" "MIN_LONGITUD" "SEGUNDO_LONGITUD"
[19] "LATITUD" "LONGITUD" policru<- subset(pro, select=c(4,5))
names(policru)[1] "CODIGO" "POLIGONO"colnames(policru)<- c("Proc", "POLIGONO")
names(policru)[1] "Proc" "POLIGONO"dftalla5 <- merge(policru, tt, by="Proc")
head(dftalla5) Proc POLIGONO Long Year
1 0 0 75 2010
2 0 0 75 2010
3 0 0 63 1997
4 0 0 64 1997
5 0 0 64 1997
6 0 0 98 2009Comparaciòn de serie historica de las series
Un boxplot total de tallas medias por años y por poligono
Boxplot por todos los años
n <-ggplot(data=dftalla5, aes(x=Year, y=Long, group=Year, fill=Year)) +
geom_boxplot( show.legend = FALSE, outlier.size = 0.05) +
scale_fill_viridis_c(alpha=0.6) +
#geom_jitter(size=0.4, alpha=0.2) +
facet_wrap(.~POLIGONO, ncol = 3)+
theme_bw() +
geom_hline(yintercept = 65, color = "red")+
scale_x_continuous(breaks = seq(from = 1996, to = 2022, by = 1))+
theme(axis.text.x = element_text(angle = 90, hjust = 2))+
ggtitle('Boxplot de tallas medias de Loco congl 2') +
ylab('Longitud (mm.)')+
xlab('Años')+
coord_flip()
n
Ahora voy a sacar tallas medias por año y por POLIGONO para graficar.
meanproc <-dftalla5 %>%
group_by(POLIGONO, Year) %>%
summarise(avg=mean(Long))
head(meanproc)# A tibble: 6 × 3
# Groups: POLIGONO [1]
POLIGONO Year avg
<int> <dbl> <dbl>
1 0 1996 70.9
2 0 1997 76.2
3 0 2001 87.6
4 0 2002 94.8
5 0 2003 98.1
6 0 2004 99.9dim(meanproc)[1] 283 3glimpse(meanproc)Rows: 283
Columns: 3
Groups: POLIGONO [13]
$ POLIGONO <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1…
$ Year <dbl> 1996, 1997, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2009, 2…
$ avg <dbl> 70.88372, 76.21957, 87.56881, 94.83152, 98.13441, 99.87218, 9…mn<-ggplot(meanproc, aes(Year, POLIGONO, size=avg, fill= avg))+
geom_point(alpha=0.5, shape=21, show.legend = T) +
scale_size(range = c(-4,10)) +
scale_x_continuous(breaks = seq(from = 1996, to = 2022, by = 1))+
scale_y_continuous(breaks = seq(from = 1, to = 13, by = 1))+
guides(fill = guide_legend(reverse=F))+
theme_bw()+
ylab("") +
xlab("") +
coord_flip()
mn
Ahora voy a sacar tallas medias por año y por POLIGONO IFOP para graficar.
meanpifop <-dftalla5 %>%
group_by(POLIGONO, Year) %>%
summarise(avg=mean(Long))
head(meanpifop)# A tibble: 6 × 3
# Groups: POLIGONO [1]
POLIGONO Year avg
<int> <dbl> <dbl>
1 0 1996 70.9
2 0 1997 76.2
3 0 2001 87.6
4 0 2002 94.8
5 0 2003 98.1
6 0 2004 99.9dim(meanpifop)[1] 283 3glimpse(meanpifop)Rows: 283
Columns: 3
Groups: POLIGONO [13]
$ POLIGONO <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1…
$ Year <dbl> 1996, 1997, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2009, 2…
$ avg <dbl> 70.88372, 76.21957, 87.56881, 94.83152, 98.13441, 99.87218, 9…mnif <- ggplot(meanpifop,
aes(Year, POLIGONO, size=avg, fill= avg))+
geom_point(alpha=0.5, shape=21, show.legend = T) +
scale_size(range = c(-4,8)) +
scale_x_discrete(breaks = seq(from = 1996,
to = 2022, by = 2))+
scale_y_discrete(breaks = seq(from = 1, to = 13,
by = 1))+
guides(fill = guide_legend(reverse=F))+
theme_bw()+
ylab("") +
xlab("") +
coord_flip()
mnif
Prepara los vectores para sumar a los .dat del año 2020
Un plot a traves de los años de las estructuras de tallas con la linea roja de TMEL (Talla Minima Extracción Legal) de 65 mm de diametro de testa.
Zona X Norte
tallaxnorte <- dftalla5 %>%
filter(POLIGONO==1 | POLIGONO ==2) %>%
filter(Year==2022)
tallaxnorte$cat_long <- as.numeric(as.character(cut(x = tallaxnorte$Long, breaks = seq(40,138,2),
labels = seq(40,136,2), right = FALSE)))
ttn <- table(tallaxnorte$Year, tallaxnorte$cat_long)
tail(ttn, 10)
# A su vez puedo generar el archivo por separado
write.csv(ttn, "tallanorte22.csv", sep = ",", row.names = TRUE)Zona X Sur
tallaxsur <- dftalla5 %>%
filter(POLIGONO == 4 | POLIGONO == 5 |
POLIGONO == 6 | POLIGONO == 13) %>%
filter(Year==2022)
tallaxsur$cat_long <- as.numeric(as.character(cut(x = tallaxsur$Long, breaks = seq(40,138,2),
labels = seq(40,136,2), right = FALSE)))
tts <- table(tallaxsur$Year, tallaxsur$cat_long)
tail(tts, 10)
# A su vez puedo generar el archivo por separado
write.csv(tts, "tallasur22.csv", sep = ",", row.names = TRUE)Zona XI
tallaxi <- dftalla5 %>%
filter(POLIGONO == 7 |
POLIGONO == 8 |
POLIGONO == 9|
POLIGONO == 10 |
POLIGONO == 11|
POLIGONO == 12) %>%
filter(Year==2022)
tallaxi$cat_long <- as.numeric(as.character(cut(x = tallaxi$Long, breaks = seq(40,138,2),
labels = seq(40,136,2), right = FALSE)))
ttxi <- table(tallaxi$Year, tallaxi$cat_long)
ttxi
# A su vez puedo generar el archivo por separado
write.csv(ttxi, "tallaxi22.csv", sep = ",", row.names = TRUE)Mapa de tallas medias
names(pro) [1] "ACTIVA.INACTIVA" "X1.seg.y.2..otros" "USADA_EN"
[4] "CODIGO" "POLIGONO" "REGION"
[7] "NOMBRE_PROCEDENCIA" "OBSERVACIONES_1" "PUERTO_1"
[10] "PUERTO_2" "PUERTO_3" "PUERTO_4"
[13] "GRADO_LATITUD" "MIN_LATITUD" "SEGUNDO_LATITUD"
[16] "GRADO_LONGITUD" "MIN_LONGITUD" "SEGUNDO_LONGITUD"
[19] "LATITUD" "LONGITUD" procru<- subset(pro, select=c(4,19, 20))
names(procru)[1] "CODIGO" "LATITUD" "LONGITUD"colnames(procru)<- c("PROCED", "LATITUD", "LONGITUDD")
names(procru)[1] "PROCED" "LATITUD" "LONGITUDD"tageo9622 <- merge(ta9622, procru, by="PROCED")Tallas engrilladas
ahora prceso los datos de tallas dentro de la grilla Grid2. debo sacar los datos NA
chi <- raster::getData("GADM", country = "CHL", level = 0)
chi@data GID_0 NAME_0
1 CHL Chile#
e <- extent(-77,-72,-46,-41)
#e2 <- extent(-70,-35,-68,-60)
rc <- crop(chi, e)
proj4string(rc) <- CRS("+init=epsg:4326")
rc3 <- st_as_sf(rc) # par dejarlo en formato geom_sf
# Saco bathymetria
bat <- getNOAA.bathy(-77,-72,-46,-41, res = 1)Grid2<- rc3 %>% #rc3 es el plot base original linea 487
sf::st_make_grid(cellsize = c(0.125,0.125)) %>% # para que quede cuadrada
sf::st_cast("MULTIPOLYGON") %>%
sf::st_sf() %>% # objeto en spatial feature
dplyr::mutate(cellid = row_number())
# la pruebo
ggplot() +
geom_contour(data = bat,
aes(x=x, y=y, z=z),
breaks=c(0, -10, -20, -50, -100, -200, -1000),
linewidth=0.2,
colour="grey")+
geom_sf(data = Grid2,
color="grey",
fill=NA,
size=0.3) +
geom_sf(data = rc3, fill=NA)+
theme_minimal()
#transformar los datos en un sf object. Aqui ojo por q no cambié el nombre de las tallas =LONGITUD
df3 <- st_as_sf(tageo9622, coords = c("LONGITUDD", "LATITUD"),
crs = 4326)
# join data to grid; make sure to join points to grid
# the first object drives the output geometry
result2 <- Grid2 %>%
st_join(df3) %>%
group_by(cellid, ANO_ARR) %>%
summarize(meanL = mean(LONGITUD)) %>%
filter(! is.na(meanL))ma <- ggplot() +
geom_sf(data=result2, aes(fill = cut(meanL,
breaks = seq(40, 120, by = 10))),
color=NA) +
geom_contour(data = bat,
aes(x=x, y=y, z=z),
breaks=c(0, -10, -20, -50, -100, -200, -1000),
linewidth=0.2,
colour="grey")+
geom_sf(data = rc3, fill=NA)+
scale_fill_brewer(type = "qual",
labels = seq(40, 100, by = 10), # if you must
palette = "Spectral",
name = "Tallas Medias") +
theme_bw()+
coord_sf(crs = 4326)+
labs(x = "Longitude", y = "Latitude")
ma