1 + 1[1] 2Tallas Quarto
Tallas
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#setwd("C:/Users/luis.lacruz/Music/Tesis abordo")
dir() [1] "~$DACCIÓN de Tesis_Luis La Cruz_y_German Chacón VER.10JUL2023.docx"
[2] "~$DACCIÓN de Tesis_Luis La Cruz_y_German Chacón VER.DIC2023 - copia_parte 22222222222.docx"
[3] "~$DACCIÓN de Tesis_Luis La Cruz_y_German Chacón VER.DIC2023.docx"
[4] "000018.png"
[5] "1.-Datos-y-limpieza_Objetivo_2_files"
[6] "1. Datos y limpieza.qmd"
[7] "1. Datos y limpieza_Objetivo_1.qmd"
[8] "1. Datos y limpieza_Objetivo_2.html"
[9] "1. Datos y limpieza_Objetivo_2.qmd"
[10] "1. Datos y limpieza_Objetivo_2_files"
[11] "2.-Prueba-de-normalidad_Objetivo__1_files"
[12] "2.-Prueba-de-normalidad_Objetivo_1.rmarkdown"
[13] "2.-Prueba-de-normalidad_Objetivo_2_files"
[14] "2. Prueba de normalidad.qmd"
[15] "2. Prueba de normalidad_Objetivo__1.html"
[16] "2. Prueba de normalidad_Objetivo__1.qmd"
[17] "2. Prueba de normalidad_Objetivo__1_files"
[18] "2. Prueba de normalidad_Objetivo_2.html"
[19] "2. Prueba de normalidad_Objetivo_2.qmd"
[20] "2. Prueba de normalidad_Objetivo_2_files"
[21] "3.-Estadística-descriptiva_Objetivo_2_files"
[22] "3.-Estadística-descriptiva_Objetivo01_puedeSER.rmarkdown"
[23] "3.-Estadística-descriptiva_Objetivo01_puedeSER_files"
[24] "3. Estadística descriptiva.qmd"
[25] "3. Estadística descriptiva_Objetivo_2.html"
[26] "3. Estadística descriptiva_Objetivo_2.qmd"
[27] "3. Estadística descriptiva_Objetivo_2_files"
[28] "3. Estadística descriptiva_Objetivo01_puedeSER.html"
[29] "3. Estadística descriptiva_Objetivo01_puedeSER.qmd"
[30] "3. Estadística descriptiva_Objetivo01_puedeSER.rmarkdown"
[31] "3. Estadística descriptiva_Objetivo01_puedeSER_files"
[32] "3_Estadística descriptiva_Objetivo_1.qmd"
[33] "4.-Ancova-lineal_objetivo_1_files"
[34] "4.-Ancova-lineal_Objetivo_2_files"
[35] "4. Ancova lineal.qmd"
[36] "4. Ancova lineal_objetivo_1.html"
[37] "4. Ancova lineal_objetivo_1.qmd"
[38] "4. Ancova lineal_objetivo_1_files"
[39] "4. Ancova lineal_Objetivo_2.html"
[40] "4. Ancova lineal_Objetivo_2.qmd"
[41] "4. Ancova lineal_Objetivo_2_files"
[42] "4. Ancova.qmd"
[43] "5.-Segmentación-objetivo1.rmarkdown"
[44] "5.-Segmentación-objetivo1_files"
[45] "5.-Segmentación_Objetivo_2_files"
[46] "5. Segmentación objetivo1.html"
[47] "5. Segmentación objetivo1.qmd"
[48] "5. Segmentación objetivo1.rmarkdown"
[49] "5. Segmentación objetivo1_files"
[50] "5. Segmentación.qmd"
[51] "5. Segmentación_Objetivo_2.html"
[52] "5. Segmentación_Objetivo_2.qmd"
[53] "5. Segmentación_Objetivo_2_files"
[54] "Analisis-FM-Especies_files"
[55] "Analisis-FM_Tesis_Enero_2024.html"
[56] "Analisis-FM_Tesis_Enero_2024.Rmd"
[57] "Analisis-FM_Tesis_Junio_2023.html"
[58] "Analisis FM Especies.html"
[59] "Analisis FM Especies.qmd"
[60] "Analisis FM Especies_files"
[61] "Analisis_FM_Tesis_Junio_2023_files"
[62] "Anchoverso-modal_files"
[63] "Anchoverso modal.qmd"
[64] "Anchoverso_modal_Marzo_2024.html"
[65] "Anchoverso_modal_Marzo_2024.qmd"
[66] "Anchoverso_modal_Marzo_2024_files"
[67] "Barras_Lances_FM.tiff"
[68] "Base_generada_para_RF_respaldo.csv"
[69] "Base_generada_para_RF_respaldo_01ENERO2024.csv"
[70] "box plot especies.tiff"
[71] "box plot especies_sv.tiff"
[72] "dat.csv"
[73] "dat_clean_modified_zscore_anchoveta.csv"
[74] "dat_clean_modified_zscore_especies.csv"
[75] "dat_clean_std_anchoveta.csv"
[76] "dat_clean_sv_anchoveta.csv"
[77] "dat2.csv"
[78] "Datos original TOTAL_clasificacion RF_probabilidad de clasificacion.csv"
[79] "Datos_FM_Tesis.xlsx"
[80] "Datos_FM_Tesis_moda - copia.xlsx"
[81] "Datos_FM_Tesis_moda.xlsx"
[82] "descriptiva_clean_Sv.csv"
[83] "descriptiva_clean_Sv_lineal.csv"
[84] "descriptiva_clean_Sv_por banda.csv"
[85] "Exploración FM-20231127T002638Z-001"
[86] "Figura 1_Sv_std.tiff"
[87] "Figura 2_Sv_depth.tiff"
[88] "Figura 3_Sv_std.tiff"
[89] "Figuras"
[90] "firmas especies_std_lm.tiff"
[91] "firmas especies_sv_gam.tiff"
[92] "firmas especies_sv_lm.tiff"
[93] "FR.qmd"
[94] "Lances_FM.tiff"
[95] "last"
[96] "mc.tiff"
[97] "Metadata_FM_Tesis.xlsx"
[98] "Prueba K-S entre grupos de tallas.csv"
[99] "prueba.csv"
[100] "prueba2.csv"
[101] "prueba2.xlsx"
[102] "REDACCIÓN de Tesis_Luis La Cruz_y_German Chacón VER.DIC2023 - copia.docx"
[103] "REDACCIÓN de Tesis_Luis La Cruz_y_German Chacón VER.DIC2023 - copia_parte 22222222222.docx"
[104] "REDACCIÓN de Tesis_Luis La Cruz_y_German Chacón VER.DIC2023.docx"
[105] "resumen descriptivo.csv"
[106] "RF-Clases-anchoveta_files"
[107] "RF-Clases_files"
[108] "RF Clases anchoveta basado solo en frecuencias.qmd"
[109] "RF Clases anchoveta.html"
[110] "RF Clases anchoveta_files"
[111] "RF Clases.html"
[112] "RF Clases_files"
[113] "rsconnect"
[114] "salida.csv"
[115] "Std_anc_vin.tiff"
[116] "subset_FM.csv"
[117] "Sv_species.tiff"
[118] "Tallas.html"
[119] "Tallas.png"
[120] "Tallas.qmd"
[121] "Tallas.rmarkdown"
[122] "Tallas.tiff"
[123] "Tallas_files"
[124] "Tallas2222.png"
[125] "Tesis.docx"
[126] "Tesis.pdf"
[127] "Tesis.qmd" library(tidyverse)── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr 1.1.4 ✔ readr 2.1.5
✔ forcats 1.0.0 ✔ stringr 1.5.1
✔ ggplot2 3.5.1 ✔ tibble 3.2.1
✔ lubridate 1.9.3 ✔ tidyr 1.3.1
✔ purrr 1.0.2
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(gridExtra)
Adjuntando el paquete: 'gridExtra'
The following object is masked from 'package:dplyr':
combinelibrary(ggplot2)
library(readxl)
library(tidyverse)datos_talla=read_excel("Metadata_FM_Tesis.xlsx", sheet="Tallas")
datos_talla # A tibble: 14 × 40
N_Catch_Year `1` `1.5` `2` `2.5` `3` `3.5` `4` `4.5` `5` `5.5`
<chr> <lgl> <lgl> <lgl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 L18_A18 NA NA NA NA NA NA NA NA NA NA
2 L12_A18 NA NA NA NA NA NA NA NA NA NA
3 L19_A18 NA NA NA NA NA NA NA NA NA NA
4 L22_A18 NA NA NA NA NA NA NA NA NA NA
5 L33_A18 NA NA NA NA NA NA NA NA NA NA
6 L74_A18 NA NA NA 2 32 124 11 NA NA NA
7 L78_A18 NA NA NA NA 93 118 17 NA NA NA
8 L12_A21 NA NA NA NA NA NA NA NA NA NA
9 L19_A21 NA NA NA NA NA NA NA NA NA NA
10 L28_A21 NA NA NA NA NA NA NA NA NA NA
11 L36_A21 NA NA NA NA NA NA NA NA NA NA
12 L56_A21 NA NA NA NA NA NA NA NA NA NA
13 L76_A21 NA NA NA NA NA NA NA NA NA NA
14 L106_A21 NA NA NA NA NA 1 7 7 7 1
# ℹ 29 more variables: `6` <dbl>, `6.5` <dbl>, `7` <lgl>, `7.5` <lgl>,
# `8` <lgl>, `8.5` <dbl>, `9` <dbl>, `9.5` <dbl>, `10` <dbl>, `10.5` <dbl>,
# `11` <dbl>, `11.5` <dbl>, `12` <dbl>, `12.5` <dbl>, `13` <dbl>,
# `13.5` <dbl>, `14` <dbl>, `14.5` <dbl>, `15` <dbl>, `15.5` <dbl>,
# `16` <dbl>, `16.5` <dbl>, `17` <lgl>, `17.5` <lgl>, `18` <lgl>,
# `18.5` <lgl>, `19` <lgl>, `19.5` <lgl>, `20` <lgl>datos_talla_longer= datos_talla %>%
pivot_longer(!N_Catch_Year, names_to = "Length", values_to = "count",
values_drop_na = TRUE)
datos_talla_longer# A tibble: 130 × 3
N_Catch_Year Length count
<chr> <chr> <dbl>
1 L18_A18 8.5 8
2 L18_A18 9 52
3 L18_A18 9.5 84
4 L18_A18 10 85
5 L18_A18 10.5 126
6 L18_A18 11 87
7 L18_A18 11.5 68
8 L18_A18 12 39
9 L18_A18 12.5 15
10 L18_A18 13 13
# ℹ 120 more rowslibrary(dplyr)
datos_talla_longer_freq= datos_talla_longer %>%
group_by(
N_Catch_Year) %>%
mutate(freq = count / sum(count))
datos_talla_longer_freq# A tibble: 130 × 4
# Groups: N_Catch_Year [14]
N_Catch_Year Length count freq
<chr> <chr> <dbl> <dbl>
1 L18_A18 8.5 8 0.0136
2 L18_A18 9 52 0.0883
3 L18_A18 9.5 84 0.143
4 L18_A18 10 85 0.144
5 L18_A18 10.5 126 0.214
6 L18_A18 11 87 0.148
7 L18_A18 11.5 68 0.115
8 L18_A18 12 39 0.0662
9 L18_A18 12.5 15 0.0255
10 L18_A18 13 13 0.0221
# ℹ 120 more rowsdatos_talla_longer_freq$Composicion_tallas=cut(as.numeric(datos_talla_longer_freq$Length), breaks=c(0,3.5,5,11.5,16),
include.lowest=F, right=T,labels=c("2-3.5","4-5","5.5-11.5","12-16.5"))
# datos_talla_longer_freq$Composicion_tallas=cut(as.numeric(datos_talla_longer_freq$Length), breaks=c(0,4.5,8,11.5,14.5,20),
# include.lowest=F, right=T,labels=c("2-4.5","5-8","8.5-11.5","12-14.5","15-20"))
tabla_composicion_tallas=table(datos_talla_longer_freq$Composicion_tallas,datos_talla_longer_freq$N_Catch_Year)
tabla.prop_tabla_composicion_tallas=as.data.frame(prop.table(x=tabla_composicion_tallas, margin=2))
names(tabla.prop_tabla_composicion_tallas)=c("Longitud","Lance","Composicion")
tabla.prop_tabla_composicion_tallas$Composicion_porcentaje=tabla.prop_tabla_composicion_tallas$Composicion*100
tabla.prop_tabla_composicion_tallas Longitud Lance Composicion Composicion_porcentaje
1 2-3.5 L106_A21 0.1428571 14.28571
2 4-5 L106_A21 0.4285714 42.85714
3 5.5-11.5 L106_A21 0.4285714 42.85714
4 12-16.5 L106_A21 0.0000000 0.00000
5 2-3.5 L12_A18 0.0000000 0.00000
6 4-5 L12_A18 0.0000000 0.00000
7 5.5-11.5 L12_A18 0.0000000 0.00000
8 12-16.5 L12_A18 1.0000000 100.00000
9 2-3.5 L12_A21 0.0000000 0.00000
10 4-5 L12_A21 0.0000000 0.00000
11 5.5-11.5 L12_A21 0.3076923 30.76923
12 12-16.5 L12_A21 0.6923077 69.23077
13 2-3.5 L18_A18 0.0000000 0.00000
14 4-5 L18_A18 0.0000000 0.00000
15 5.5-11.5 L18_A18 0.5000000 50.00000
16 12-16.5 L18_A18 0.5000000 50.00000
17 2-3.5 L19_A18 0.0000000 0.00000
18 4-5 L19_A18 0.0000000 0.00000
19 5.5-11.5 L19_A18 0.3076923 30.76923
20 12-16.5 L19_A18 0.6923077 69.23077
21 2-3.5 L19_A21 0.0000000 0.00000
22 4-5 L19_A21 0.0000000 0.00000
23 5.5-11.5 L19_A21 0.4166667 41.66667
24 12-16.5 L19_A21 0.5833333 58.33333
25 2-3.5 L22_A18 0.0000000 0.00000
26 4-5 L22_A18 0.0000000 0.00000
27 5.5-11.5 L22_A18 0.3571429 35.71429
28 12-16.5 L22_A18 0.6428571 64.28571
29 2-3.5 L28_A21 0.0000000 0.00000
30 4-5 L28_A21 0.0000000 0.00000
31 5.5-11.5 L28_A21 0.0000000 0.00000
32 12-16.5 L28_A21 1.0000000 100.00000
33 2-3.5 L33_A18 0.0000000 0.00000
34 4-5 L33_A18 0.0000000 0.00000
35 5.5-11.5 L33_A18 0.1111111 11.11111
36 12-16.5 L33_A18 0.8888889 88.88889
37 2-3.5 L36_A21 0.0000000 0.00000
38 4-5 L36_A21 0.0000000 0.00000
39 5.5-11.5 L36_A21 0.3846154 38.46154
40 12-16.5 L36_A21 0.6153846 61.53846
41 2-3.5 L56_A21 0.0000000 0.00000
42 4-5 L56_A21 0.0000000 0.00000
43 5.5-11.5 L56_A21 0.5454545 54.54545
44 12-16.5 L56_A21 0.4545455 45.45455
45 2-3.5 L74_A18 0.7500000 75.00000
46 4-5 L74_A18 0.2500000 25.00000
47 5.5-11.5 L74_A18 0.0000000 0.00000
48 12-16.5 L74_A18 0.0000000 0.00000
49 2-3.5 L76_A21 0.0000000 0.00000
50 4-5 L76_A21 0.0000000 0.00000
51 5.5-11.5 L76_A21 0.8000000 80.00000
52 12-16.5 L76_A21 0.2000000 20.00000
53 2-3.5 L78_A18 0.6666667 66.66667
54 4-5 L78_A18 0.3333333 33.33333
55 5.5-11.5 L78_A18 0.0000000 0.00000
56 12-16.5 L78_A18 0.0000000 0.00000datos_talla_longer_freq# A tibble: 130 × 5
# Groups: N_Catch_Year [14]
N_Catch_Year Length count freq Composicion_tallas
<chr> <chr> <dbl> <dbl> <fct>
1 L18_A18 8.5 8 0.0136 5.5-11.5
2 L18_A18 9 52 0.0883 5.5-11.5
3 L18_A18 9.5 84 0.143 5.5-11.5
4 L18_A18 10 85 0.144 5.5-11.5
5 L18_A18 10.5 126 0.214 5.5-11.5
6 L18_A18 11 87 0.148 5.5-11.5
7 L18_A18 11.5 68 0.115 5.5-11.5
8 L18_A18 12 39 0.0662 12-16.5
9 L18_A18 12.5 15 0.0255 12-16.5
10 L18_A18 13 13 0.0221 12-16.5
# ℹ 120 more rowsLances
library(egg)
library(envalysis)
library(dplyr)p=ggplot(datos_talla_longer_freq)+
geom_col(aes(as.numeric(Length),freq*100,fill=Composicion_tallas),alpha=0.5)+
scale_x_continuous(name = "Longitud (cm)",breaks=c(2:20),labels = c("2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17","18","19","20"))+
scale_y_continuous(name="Frecuencia (%)")+ #limits = c(0,50)
#scale_fill_viridis_d(option = "G")+
#scale_fill_brewer(palette = "RdYlBu",name="Longitud(cm)")+
#scale_color_manual(name="Longitud (cm)",values =c("royalblue1","orange","green3","darkorchid4"))+
scale_fill_manual(name="Longitud (cm)",values =c("royalblue1","orange","green3","darkorchid4"))+
#scale_fill_manual(values=c("red", "orange", "yellow", "blue", "navy"),name="Clase (cm)")+
geom_vline(xintercept=12, color='black', size=0.5)+ #linetype='dashed'
theme_presentation()+
theme(legend.position = "none")Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
ℹ Please use `linewidth` instead.p
ggsave(filename = "Tallas_total.png",
plot = p,
height = 10, # Specifies the height of the plot in inches
width = 25, # Specifies the width of the plot in inches
dpi = 1000, # Specifies the resolution in dots per inch
path = "F:/Tesis abordo/Tesis abordo/Figuras/Tallas/",device = "png") p2=ggplot(datos_talla_longer_freq)+
geom_col(aes(as.numeric(Length),freq*100,fill=Composicion_tallas),alpha=0.5)+
scale_x_continuous(name = "Longitud (cm)",breaks=c(2:20),labels = c("2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17","18","19","20"))+
scale_y_continuous(name="Frecuencia (%)")+ #limits = c(0,50)
#scale_fill_viridis_d(option = "G")+
#scale_fill_brewer(palette = "RdYlBu",name="Longitud(cm)")+
scale_fill_manual(name="Longitud (cm)",values =c("royalblue1","orange","green3","darkorchid4"))+
#scale_fill_manual(values=c("red", "orange", "yellow", "blue", "navy"),name="Clase (cm)")+
geom_vline(xintercept=12, color='black', size=0.5)+ #linetype='dashed'
facet_wrap(~N_Catch_Year,scales = "free_y",ncol = 2)+
theme_presentation(base_size = 15)+
theme(legend.position = "none")
p2
p2
ggsave(filename = "Tallas.png",
plot = p2,
height = 10, # Specifies the height of the plot in inches
width = 8, # Specifies the width of the plot in inches
dpi = 1000, # Specifies the resolution in dots per inch
path = "F:/Tesis abordo/Tesis abordo/Figuras/Tallas/",device = "png") Composición
p3=Figura_clases=ggplot(tabla.prop_tabla_composicion_tallas)+
geom_bar(aes(x=Lance, y=Composicion_porcentaje, fill=Longitud), stat="identity", width = 0.60, color="black",alpha=0.5)+
#scale_fill_manual(values=c("red", "orange", "yellow", "blue", "navy"))+
#scale_fill_brewer(palette = "RdYlBu",name="Longitud(cm)")+
scale_fill_manual(name="Longitud (cm)",values =c("royalblue1","orange","green3","darkorchid4"))+
labs(fill = "Longitud (cm)")+
scale_y_continuous(name="Composicion (%)",limits = c(0,100))+
scale_x_discrete(name="Lance")+
theme_presentation(base_size = 10)+
# theme(strip.text.y = element_blank() , strip.text.x = element_blank(),
# strip.background = element_blank(),
# plot.margin = unit( c(0,0,0,0) , units = "lines" ) )+
#
# theme(plot.title = element_text(hjust = 0.5),
# axis.text.x=element_text(size=9, angle = 0, vjust = 0.5, hjust=0.5),
# axis.text.y=element_text(size=12),
# strip.text = element_text(size = 120))+
#
# theme(axis.title.x = element_text(size = 12),
# axis.title.y = element_text(size = 12))+
#
theme(legend.position = "top")
#
# theme(legend.title = element_text(size=12),
# legend.text = element_text(size=12))+
#
# theme(axis.title.y.right = element_text(color = "black"))+
# theme(axis.title.y.left =element_text(color = "black"))
p3
p3
ggsave(filename = "Tallas_barras.png",
plot = p3,
height = 4, # Specifies the height of the plot in inches
width = 8, # Specifies the width of the plot in inches
dpi = 1000, # Specifies the resolution in dots per inch
path = "F:/Tesis abordo/Tesis abordo/Figuras/Tallas/",device = "png")