doc/NEFSC-Fishery.R
In EmilyMarkowitz-NOAA/FishEconProdOutput: Price Method Fisheries Economics Total Factor Productivity Outputs
## ---- include = FALSE---------------------------------------------------------
# https://r-pkgs.org/vignettes.html
# %\VignetteIndexEntry{FEUS-tables}
# %\VignetteEngine{knitr::rmarkdown}
# %\VignetteEncoding{UTF-8}
knitr::opts_chunk$set(message = FALSE, echo = TRUE, warning = FALSE,
collapse = TRUE,
comment = "#>"
)
TF<-FALSE
## ----setup--------------------------------------------------------------------
PKG <- c(# devtools::install_github("emilymarkowitz-NOAA/FishEconProdOutput", force = TRUE)
"FishEconProdOutput",
#Seperating species by taxonomic group
"taxize", # install.packages("remotes"); remotes::install_github("ropensci/taxize")
# Data Managment
"tidyverse",
"filesstrings",
"data.table",
"plyr",
"dplyr",
"rlist",
# #RMarkdown
"rmarkdown",
"ggpubr",
"kableExtra",
#Excel File Management
"xlsx",
"readxl"
)
for (p in PKG) {
if(!require(p,character.only = TRUE)) {
install.packages(p, repos = "http://cran.us.r-project.org")
require(p,character.only = TRUE)}
}
## -----------------------------------------------------------------------------
# Define what regions we are interested in
reg_order = c("Northeast")
reg_order_abbrv = c("NorE")
# Define Category
category0 = "category"
# Define Years
maxyr <- 2018
minyr <- 2007 #of data going into the analysis
baseyr <- minyr
## -----------------------------------------------------------------------------
# Folder name for output
folder<-"Northeast"
titleadd = paste0(minyr, "To", maxyr, "_NorE")
counter<-0
dir_in<-getwd()
if (TF) {
#Local Directories
dir_outputtables<-paste0(dir_in, "/output/")
dir.create(dir_outputtables)
# Define Directories
dir_analyses = paste0(dir_outputtables, folder)
dir.create(dir_analyses)
} else {
dir_analyses<-dir_outputtables<-dir_in
}
## ---- results='asis'----------------------------------------------------------
counter<-0
landings_data<-FishEconProdOutput::land
landings_data<-landings_data[landings_data$Region %in% c("New England", "Mid-Atlantic"),]
landings_data$Region<-"Northeast"
# so you can see what this data looks like
knitr::kable(head(landings_data), booktabs = T) %>%
kable_styling(latex_options = "striped")
## ---- results='asis', echo=FALSE----------------------------------------------
ne_spp<-data.frame(
stringsAsFactors = FALSE,
x = c("1 23 BF Bluefish",
"2 352 DF Spiny Dogfish",
"3 81 GFISH Atlantic Cod",
"4 120 GFISH Winter Flounder",
"5 122 GFISH Witch Flounder",
"6 123 GFISH Yellowtail Flounder",
"7 124 GFISH American Plaice Flounder",
"8 125 GFISH Sand Dab Flounder",
"9 147 GFISH Atlantic Haddock",
"10 153 GFISH White Hake",
"11 159 GFISH Atlantic Halibut",
"12 240 GFISH Redfish",
"13 250 GFISH Ocean Pout",
"14 269 GFISH Pollock",
"15 512 GFISH Wolffish",
"16 168 HER Atlantic Herring",
"17 12 MONK Monkfish",
"18 710 RCRAB Red Crab",
"19 800 SCAL Sea Scallop",
"20 754 SCOQ Ocean Quahog",
"21 769 SCOQ Surf clam",
"22 121 SF Summer Flounder",
"23 329 SF Scup",
"24 335 SF Black Sea Bass",
"25 364 SKATE Rosette Skate",
"26 365 SKATE Skates",
"27 366 SKATE Little Skate",
"28 367 SKATE Winter Skate",
"29 368 SKATE Barndoor Skate",
"30 369 SKATE Smooth Skate",
"31 370 SKATE Thorny Skate",
"32 372 SKATE Clearnose Skate",
"33 51 SMB Butterfish",
"34 212 SMB Atlantic Mackerel",
"35 801 SMB Loligo Squid",
"36 802 SMB Illex Squid",
"37 152 SMESH Red Hake",
"38 508 SMESH Offshore Hake",
"39 509 SMESH Silver Hake",
"40 444 TF Blueline Tilefish",
"41 446 TF Golden Tilefish")
)
ne_spp <- separate(data = ne_spp,
col = x,
into = c(NA, "code", "plan", "name", "name2", "name3", "name4"),
sep = "[[:blank:]]+")
ne_spp$name2[is.na(ne_spp$name2)]<-ne_spp$name3[is.na(ne_spp$name3)]<-ne_spp$name4[is.na(ne_spp$name4)]<-""
ne_spp$name<-trimws(paste(ne_spp$name, ne_spp$name2, ne_spp$name3, ne_spp$name4))
ne_spp$name2<-ne_spp$name3<-ne_spp$name4<-NULL
knitr::kable((ne_spp), booktabs = T) %>%
kable_styling(latex_options = "striped")
## -----------------------------------------------------------------------------
# Find TSN values for species of interest
spcat.list<-list('Bluefish' = 168559, # Species Pomatomus saltatrix (Linnaeus, 1766) – bluefish, anjova
'Spiny dogfish' = 160617, #Species Squalus acanthias Linnaeus, 1758 – cazón espinoso común, piked dogfish, spiny dogfish, galludo espinoso, aiguillat commun, dogfish, grayfish, spurdog
'Atlantic Cod' = 164712, # Species Gadus morhua Linnaeus, 1758 – morue de l'Atlantique, bacalao del Atlántico, cod, rock cod, morue franche, Atlantic cod
'Summer flounder' = 172735, #Species Paralichthys dentatus (Linnaeus, 1766) – summer flounder, fluke, cardeau d'été, Summer Flounder
'Winter Flounder' = 172905, # Species Pseudopleuronectes americanus (Walbaum, 1792) – plie rouge, blackback, Georges Bank flounder, lemon sole, rough flounder, winter flounder, Winter Flounder
'Witch Flounder' = 172873, #Species Glyptocephalus cynoglossus (Linnaeus, 1758) – witch flounder, gray sole, plie grise, Witch Flounder
'Yellowtail Flounder' = 172909, #Species Limanda ferruginea (Storer, 1839) – limande à queue jaune, rusty flounder, yellowtail flounder, Yellowtail Flounder
'American Plaice Flounder' = 172877, # Species Hippoglossoides platessoides (Fabricius, 1780) – American plaice, plie canadienne, American dab, Canadian plaice, dab, American Plaice
'Sand Dab Flounder' = 172746, #Species Scophthalmus aquosus (Mitchill, 1815) – windowpane, brill, sand dab, spotted flounder, turbot de sable, Windowpane
'Atlantic haddock' = 164744, # Species Melanogrammus aeglefinus (Linnaeus, 1758) – haddock, aiglefin
'White hake' = 164732, # Species Urophycis tenuis (Mitchill, 1814) – white hake, mud hake, merluche blanche
'Red hake' = c(164730 # Species Urophycis chuss (Walbaum, 1792) – red hake, squirrel hake, merluche-écureuil
),#164729), # Genus Urophycis Gill, 1863 – codlings # Toledo, includes other hake
'Silver hake' = c(164791 #Species Merluccius bilinearis (Mitchill, 1814) – silver hake, merlu argenté
), # 164790), # Genus Merluccius Rafinesque, 1810 – hakes # Toledo, includes other hake
'Skates' = 160845, # Family Rajidae Blainville, 1816 – rayas, rays, skates, raies
"Rosette skate" = 564136,# Species Leucoraja garmani (Whitley, 1939) – rosette skate
'Little Skate' = 564130,#Species Leucoraja erinacea (Mitchill, 1825) – raie-hérisson, common skate, little skate, summer skate
'Winter Skate' = 564145,#Species Leucoraja ocellata (Mitchill, 1815) – raie tachetée, big skate, eyed skate, winter skate
'Barndoor Skate' = 564139,#Species Dipturus laevis (Mitchill, 1818) – grande raie, barndoor skate
'Smooth Skate' = 564151,# Species Malacoraja senta (Garman, 1885) – raie à queue de velours, smooth skate
'Thorny Skate' = 564149,#Species Amblyraja radiata (Donovan, 1808) – raie épineuse, starry skate, thorny skate
'Clearnose Skate' = 160855,#Species Raja eglanteria Bosc in Lacepède, 1800 – raya naricita, clearnose skate
'Loligo squid' = 82370, # Genus Loligo Lamarck, 1798
'Illex Squid' = 82520, # Genus Illex Steenstrup, 1880
'Ocean pout' = 630979, # Species Zoarces americanus (Bloch and Schneider, 1801) – ocean pout, loquette d'Amérique
'Atlantic mackerel' = 172414, #Species Scomber scombrus Linnaeus, 1758 – caballa del Atlántico, maquereau commun, maquereau bleu, Atlantic mackerel
'Atlantic pollock' = 164727, #Species Pollachius virens (Linnaeus, 1758) – pollock, coalfish, carbonero, lieu noir, saithe, goberge
'Atlantic Wolffish' = 171336, # Genus Anarhichas Linnaeus, 1758 – Atlantic wolffishes
'Black sea bass' = 167687, # Species Centropristis striata (Linnaeus, 1758) – black sea bass
'Scups' = 169181, # Genus Stenotomus Gill, 1865
'Butterfish' = 172567, # Species Peprilus triacanthus (Peck, 1804) – palometa estrecha, butterfish, stromatée à fossettes
'Blueline Tilefish' = 168543, # Species Caulolatilus microps Goode and Bean, 1878 – blueline tilefish, blanquillo lucio
'Golden Tilefish' = 168546, # Species Lopholatilus chamaeleonticeps Goode and Bean, 1879 – blue tilefish, tilefish, conejo amarillo
'Monkfish' = 164499 , #Species Lophius americanus Valenciennes in Cuvier and Valenciennes, 1837 – goosefish, monkfish, baudroie d'Amérique
'Acadian Redfish' = 166774, # Species Sebastes fasciatus Storer, 1854 – Acadian redfish, Labrador redfish, Acadian rockfish, sébaste acadien
'Atlantic Herring' = 161722, # Species Clupea harengus Linnaeus, 1758 – Baltic herring, herring, hareng atlantique, Atlantic herring
'Atlantic surf clam' = 80944, #Species Spisula solidissima (Dillwyn, 1817) – Atlantic surfclam
'Offshore Hake' = 164793, # Species Merluccius albidus (Mitchill, 1818) – offshore hake, offshore whiting
'Ocean quahog clam' = 81343, # Species Arctica islandica (Linnaeus, 1767) – ocean quahog
'Red Crab' = 620992, # Species Chaceon quinquedens (S. I. Smith, 1879) – red deepsea crab
'Sea scallop' = 79718, #Species Placopecten magellanicus (Gmelin, 1791) – sea scallop
'Atlantic halibut' = 172933 # Species Hippoglossus hippoglossus (Linnaeus, 1758) – Atlantic halibut, flétan atlantique, Atlantic Halibut
)
## -----------------------------------------------------------------------------
# a bit hacky, but I am linking all of the TSN numbers to their respective species here in the northeast fisheries list
spcat<-c()
for (i in 1:nrow(ne_spp)) {
spcat<-c(spcat,
ifelse(sum(grepl(pattern = ne_spp$name[i], x = names(spcat.list) , ignore.case = T)) == 0,
NA, grep(pattern = ne_spp$name[i], x = names(spcat.list), ignore.case = T) ))
}
ne_spp$TSN<-as.numeric(unlist(spcat.list[spcat]))
# Now convert the Northeast Fisheires list into a list for the itis_reclassify function
categories<-list()
for (i in 1:length(unique(ne_spp$plan))) {
categories[i]<-list(ne_spp$TSN[ne_spp$plan %in% unique(ne_spp$plan)[i]])
names(categories)[i]<-unique(ne_spp$plan)[i]
}
## -----------------------------------------------------------------------------
# Use the itis_reclassify function to sort each species listed in the FOSS data into each classification group
temp<-itis_reclassify(tsn = unique(landings_data$Tsn),
categories = categories,
uncategorized_name="Uncategorized")
tsn_id<-temp$df_out
# Remove anything that wasn't classified (we don't them for what we are doing here)
if (sum(tsn_id$category %in% c("Other", "Uncategorized"))>0) {
tsn_id<-tsn_id[!(tsn_id$category %in% c("Other", "Uncategorized")),
c("TSN", "category")]
}
# renaming columns for joining other datasets to this dataset
landings_data<-dplyr::rename(landings_data,
TSN = Tsn)
tsn_id$TSN<-as.numeric(tsn_id$TSN)
# Join the FOSS landings data to their respctive categories
landings_data<-dplyr::left_join(x = landings_data,
y = tsn_id,
by = "TSN")
# Rename columns so they match what the funciton uses
landings_data<-dplyr::rename(landings_data,
Tsn = TSN,
category = category.y)
# Minor data editing.
landings_data<-landings_data[landings_data$Year>=minyr & landings_data$Year<=maxyr, # Only include years that you intend to assess.
c("Year","Pounds","Dollars","category","Tsn", "State", "Region", "abbvreg")] # These are all of the columns you need
# Sometimes negative numbers can be in these columns. They're flags and not real values, so we'll want to remove those here
landings_data<-landings_data[(landings_data$Pounds>=0), ]
landings_data<-landings_data[(landings_data$Dollars>=0), ]
# Print out the resultant table so we can see our hard work!
knitr::kable(head(landings_data), booktabs = T) %>%
kable_styling(latex_options = "striped")
## -----------------------------------------------------------------------------
out <- OutputAnalysis(landings_data = landings_data,
category0 = "category", # the name of the column you are categorizing by
baseyr = baseyr,
titleadd = titleadd,
dir_analyses = dir_analyses,
skipplots = TRUE,
reg_order = "Northeast", # The region(s) you want to assess
reg_order_abbrv = "NorE", # The region(s) you want to assess
save_outputs_to_file = TF) # Here I use the variable TF so I can change it once at the begining of my code, depending on my reporting purposes
names(out)
# make an object of everything that comes out of this function
for (jjj in 1:length(out)) {
assign(names(out)[jjj], out[[jjj]])
}
## ---- results='asis'----------------------------------------------------------
result <- lapply(index_list, "[", , c("Year", "cat", "PI_CB", "Q_CB", "v"))
a<-result$Northeast
a<-a[a$Year %in% minyr:maxyr, ]
a<-dplyr::rename(a,
PI = PI_CB,
Q = Q_CB,
V = v)
# Create table of raw values
a.pi<-spread(a[!(names(a) %in% c("V", "Q"))], cat, PI)
names(a.pi)[-1]<-paste0(names(a.pi)[-1], "_PI")
a.q<-spread(a[!(names(a) %in% c("PI", "V"))], cat, Q)
names(a.q)[-1]<-paste0(names(a.q)[-1], "_Q")
a.v<-spread(a[!(names(a) %in% c("PI", "Q"))], cat, V)
names(a.v)[-1]<-paste0(names(a.v)[-1], "_V")
b<-left_join(a.pi, a.q, by = c("Year"))
b<-left_join(b, a.v, by = c("Year"))
b<-b[,match(x = c("Year",
names(b)[grep(pattern = "_V", x = names(b), ignore.case = T)],
names(b)[grep(pattern = "_PI", x = names(b), ignore.case = T)],
names(b)[grep(pattern = "_Q", x = names(b), ignore.case = T)]),
names(b))]
b<-b[,match(x = c("Year",
names(b)[grep(pattern = "fin", x = names(b), ignore.case = T)],
names(b)[grep(pattern = "Shell", x = names(b), ignore.case = T)],
names(b)[grep(pattern = "Total", x = names(b))]),
names(b))]
b<-b[b$Year %in% minyr:maxyr, ]
temp_code<-b
temp_code$Footnotes<-NA
# Create a nice-looking formatted table of values with PrettyNum
b<-a
b$PI<-round(x = b$PI, digits = 2)
b$Q<-prettyNum(x = round(x = b$Q/1e6), digits = 2, big.mark = ",")
b$V<-prettyNum(x = round(x = b$V/1e6), digits = 2, big.mark = ",")
b.pi<-spread(b[!(names(b) %in% c("V", "Q"))], cat, PI)
names(b.pi)[-1]<-paste0(names(b.pi)[-1], "_PI")
b.q<-spread(b[!(names(b) %in% c("PI", "V"))], cat, Q)
names(b.q)[-1]<-paste0(names(b.q)[-1], "_Q")
b.v<-spread(b[!(names(b) %in% c("PI", "Q"))], cat, V)
names(b.v)[-1]<-paste0(names(b.v)[-1], "_V")
b<-left_join(b.pi, b.q, by = c("Year"))
b<-left_join(b, b.v, by = c("Year"))
b<-b[,match(x = c("Year",
names(b)[grep(pattern = "_V", x = names(b), ignore.case = T)],
names(b)[grep(pattern = "_PI", x = names(b), ignore.case = T)],
names(b)[grep(pattern = "_Q", x = names(b), ignore.case = T)]),
names(b))]
b<-b[,match(x = c("Year",
names(b)[grep(pattern = "fin", x = names(b), ignore.case = T)],
names(b)[grep(pattern = "Shell", x = names(b), ignore.case = T)],
names(b)[grep(pattern = "Total", x = names(b))]),
names(b))]
b<-b[b$Year %in% minyr:maxyr, ]
temp_print<-b
temp_print$Footnotes<-NA
# Save Outputs.
# Here I use the variable TF so I can change it once at the begining of my code, depending on my reporting purposes
ProdOutputUS_Raw<-temp_code
if (TF) {
write_csv(x = ProdOutputUS_Raw, file = paste0(dir_analyses, "/ProdOutputNorE_Raw.csv"))
}
ProdOutputUS_Print<-temp_print
if (TF) {
write_csv(x = ProdOutputUS_Print, file = paste0(dir_analyses, "/ProdOutputNorE_Print.csv"))
}
# Print out the resultant table so we can see our hard work!
ProdOutputUS_Print$Footnotes<-NULL
knitr::kable(ProdOutputUS_Print, booktabs = T) %>%
kable_styling(latex_options = "striped")
## ---- fig.width=8-------------------------------------------------------------
figures_list$Northeast__PI_CB_PI
figures_list$Northeast__Q_CB_CatTot_QCatTot
EmilyMarkowitz-NOAA/FishEconProdOutput documentation built on Aug. 30, 2021, 6:49 p.m.
R Package Documentation
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
## ---- include = FALSE---------------------------------------------------------
# https://r-pkgs.org/vignettes.html
# %\VignetteIndexEntry{FEUS-tables}
# %\VignetteEngine{knitr::rmarkdown}
# %\VignetteEncoding{UTF-8}
knitr::opts_chunk$set(message = FALSE, echo = TRUE, warning = FALSE,
collapse = TRUE,
comment = "#>"
)
TF<-FALSE
## ----setup--------------------------------------------------------------------
PKG <- c(# devtools::install_github("emilymarkowitz-NOAA/FishEconProdOutput", force = TRUE)
"FishEconProdOutput",
#Seperating species by taxonomic group
"taxize", # install.packages("remotes"); remotes::install_github("ropensci/taxize")
# Data Managment
"tidyverse",
"filesstrings",
"data.table",
"plyr",
"dplyr",
"rlist",
# #RMarkdown
"rmarkdown",
"ggpubr",
"kableExtra",
#Excel File Management
"xlsx",
"readxl"
)
for (p in PKG) {
if(!require(p,character.only = TRUE)) {
install.packages(p, repos = "http://cran.us.r-project.org")
require(p,character.only = TRUE)}
}
## -----------------------------------------------------------------------------
# Define what regions we are interested in
reg_order = c("Northeast")
reg_order_abbrv = c("NorE")
# Define Category
category0 = "category"
# Define Years
maxyr <- 2018
minyr <- 2007 #of data going into the analysis
baseyr <- minyr
## -----------------------------------------------------------------------------
# Folder name for output
folder<-"Northeast"
titleadd = paste0(minyr, "To", maxyr, "_NorE")
counter<-0
dir_in<-getwd()
if (TF) {
#Local Directories
dir_outputtables<-paste0(dir_in, "/output/")
dir.create(dir_outputtables)
# Define Directories
dir_analyses = paste0(dir_outputtables, folder)
dir.create(dir_analyses)
} else {
dir_analyses<-dir_outputtables<-dir_in
}
## ---- results='asis'----------------------------------------------------------
counter<-0
landings_data<-FishEconProdOutput::land
landings_data<-landings_data[landings_data$Region %in% c("New England", "Mid-Atlantic"),]
landings_data$Region<-"Northeast"
# so you can see what this data looks like
knitr::kable(head(landings_data), booktabs = T) %>%
kable_styling(latex_options = "striped")
## ---- results='asis', echo=FALSE----------------------------------------------
ne_spp<-data.frame(
stringsAsFactors = FALSE,
x = c("1 23 BF Bluefish",
"2 352 DF Spiny Dogfish",
"3 81 GFISH Atlantic Cod",
"4 120 GFISH Winter Flounder",
"5 122 GFISH Witch Flounder",
"6 123 GFISH Yellowtail Flounder",
"7 124 GFISH American Plaice Flounder",
"8 125 GFISH Sand Dab Flounder",
"9 147 GFISH Atlantic Haddock",
"10 153 GFISH White Hake",
"11 159 GFISH Atlantic Halibut",
"12 240 GFISH Redfish",
"13 250 GFISH Ocean Pout",
"14 269 GFISH Pollock",
"15 512 GFISH Wolffish",
"16 168 HER Atlantic Herring",
"17 12 MONK Monkfish",
"18 710 RCRAB Red Crab",
"19 800 SCAL Sea Scallop",
"20 754 SCOQ Ocean Quahog",
"21 769 SCOQ Surf clam",
"22 121 SF Summer Flounder",
"23 329 SF Scup",
"24 335 SF Black Sea Bass",
"25 364 SKATE Rosette Skate",
"26 365 SKATE Skates",
"27 366 SKATE Little Skate",
"28 367 SKATE Winter Skate",
"29 368 SKATE Barndoor Skate",
"30 369 SKATE Smooth Skate",
"31 370 SKATE Thorny Skate",
"32 372 SKATE Clearnose Skate",
"33 51 SMB Butterfish",
"34 212 SMB Atlantic Mackerel",
"35 801 SMB Loligo Squid",
"36 802 SMB Illex Squid",
"37 152 SMESH Red Hake",
"38 508 SMESH Offshore Hake",
"39 509 SMESH Silver Hake",
"40 444 TF Blueline Tilefish",
"41 446 TF Golden Tilefish")
)
ne_spp <- separate(data = ne_spp,
col = x,
into = c(NA, "code", "plan", "name", "name2", "name3", "name4"),
sep = "[[:blank:]]+")
ne_spp$name2[is.na(ne_spp$name2)]<-ne_spp$name3[is.na(ne_spp$name3)]<-ne_spp$name4[is.na(ne_spp$name4)]<-""
ne_spp$name<-trimws(paste(ne_spp$name, ne_spp$name2, ne_spp$name3, ne_spp$name4))
ne_spp$name2<-ne_spp$name3<-ne_spp$name4<-NULL
knitr::kable((ne_spp), booktabs = T) %>%
kable_styling(latex_options = "striped")
## -----------------------------------------------------------------------------
# Find TSN values for species of interest
spcat.list<-list('Bluefish' = 168559, # Species Pomatomus saltatrix (Linnaeus, 1766) – bluefish, anjova
'Spiny dogfish' = 160617, #Species Squalus acanthias Linnaeus, 1758 – cazón espinoso común, piked dogfish, spiny dogfish, galludo espinoso, aiguillat commun, dogfish, grayfish, spurdog
'Atlantic Cod' = 164712, # Species Gadus morhua Linnaeus, 1758 – morue de l'Atlantique, bacalao del Atlántico, cod, rock cod, morue franche, Atlantic cod
'Summer flounder' = 172735, #Species Paralichthys dentatus (Linnaeus, 1766) – summer flounder, fluke, cardeau d'été, Summer Flounder
'Winter Flounder' = 172905, # Species Pseudopleuronectes americanus (Walbaum, 1792) – plie rouge, blackback, Georges Bank flounder, lemon sole, rough flounder, winter flounder, Winter Flounder
'Witch Flounder' = 172873, #Species Glyptocephalus cynoglossus (Linnaeus, 1758) – witch flounder, gray sole, plie grise, Witch Flounder
'Yellowtail Flounder' = 172909, #Species Limanda ferruginea (Storer, 1839) – limande à queue jaune, rusty flounder, yellowtail flounder, Yellowtail Flounder
'American Plaice Flounder' = 172877, # Species Hippoglossoides platessoides (Fabricius, 1780) – American plaice, plie canadienne, American dab, Canadian plaice, dab, American Plaice
'Sand Dab Flounder' = 172746, #Species Scophthalmus aquosus (Mitchill, 1815) – windowpane, brill, sand dab, spotted flounder, turbot de sable, Windowpane
'Atlantic haddock' = 164744, # Species Melanogrammus aeglefinus (Linnaeus, 1758) – haddock, aiglefin
'White hake' = 164732, # Species Urophycis tenuis (Mitchill, 1814) – white hake, mud hake, merluche blanche
'Red hake' = c(164730 # Species Urophycis chuss (Walbaum, 1792) – red hake, squirrel hake, merluche-écureuil
),#164729), # Genus Urophycis Gill, 1863 – codlings # Toledo, includes other hake
'Silver hake' = c(164791 #Species Merluccius bilinearis (Mitchill, 1814) – silver hake, merlu argenté
), # 164790), # Genus Merluccius Rafinesque, 1810 – hakes # Toledo, includes other hake
'Skates' = 160845, # Family Rajidae Blainville, 1816 – rayas, rays, skates, raies
"Rosette skate" = 564136,# Species Leucoraja garmani (Whitley, 1939) – rosette skate
'Little Skate' = 564130,#Species Leucoraja erinacea (Mitchill, 1825) – raie-hérisson, common skate, little skate, summer skate
'Winter Skate' = 564145,#Species Leucoraja ocellata (Mitchill, 1815) – raie tachetée, big skate, eyed skate, winter skate
'Barndoor Skate' = 564139,#Species Dipturus laevis (Mitchill, 1818) – grande raie, barndoor skate
'Smooth Skate' = 564151,# Species Malacoraja senta (Garman, 1885) – raie à queue de velours, smooth skate
'Thorny Skate' = 564149,#Species Amblyraja radiata (Donovan, 1808) – raie épineuse, starry skate, thorny skate
'Clearnose Skate' = 160855,#Species Raja eglanteria Bosc in Lacepède, 1800 – raya naricita, clearnose skate
'Loligo squid' = 82370, # Genus Loligo Lamarck, 1798
'Illex Squid' = 82520, # Genus Illex Steenstrup, 1880
'Ocean pout' = 630979, # Species Zoarces americanus (Bloch and Schneider, 1801) – ocean pout, loquette d'Amérique
'Atlantic mackerel' = 172414, #Species Scomber scombrus Linnaeus, 1758 – caballa del Atlántico, maquereau commun, maquereau bleu, Atlantic mackerel
'Atlantic pollock' = 164727, #Species Pollachius virens (Linnaeus, 1758) – pollock, coalfish, carbonero, lieu noir, saithe, goberge
'Atlantic Wolffish' = 171336, # Genus Anarhichas Linnaeus, 1758 – Atlantic wolffishes
'Black sea bass' = 167687, # Species Centropristis striata (Linnaeus, 1758) – black sea bass
'Scups' = 169181, # Genus Stenotomus Gill, 1865
'Butterfish' = 172567, # Species Peprilus triacanthus (Peck, 1804) – palometa estrecha, butterfish, stromatée à fossettes
'Blueline Tilefish' = 168543, # Species Caulolatilus microps Goode and Bean, 1878 – blueline tilefish, blanquillo lucio
'Golden Tilefish' = 168546, # Species Lopholatilus chamaeleonticeps Goode and Bean, 1879 – blue tilefish, tilefish, conejo amarillo
'Monkfish' = 164499 , #Species Lophius americanus Valenciennes in Cuvier and Valenciennes, 1837 – goosefish, monkfish, baudroie d'Amérique
'Acadian Redfish' = 166774, # Species Sebastes fasciatus Storer, 1854 – Acadian redfish, Labrador redfish, Acadian rockfish, sébaste acadien
'Atlantic Herring' = 161722, # Species Clupea harengus Linnaeus, 1758 – Baltic herring, herring, hareng atlantique, Atlantic herring
'Atlantic surf clam' = 80944, #Species Spisula solidissima (Dillwyn, 1817) – Atlantic surfclam
'Offshore Hake' = 164793, # Species Merluccius albidus (Mitchill, 1818) – offshore hake, offshore whiting
'Ocean quahog clam' = 81343, # Species Arctica islandica (Linnaeus, 1767) – ocean quahog
'Red Crab' = 620992, # Species Chaceon quinquedens (S. I. Smith, 1879) – red deepsea crab
'Sea scallop' = 79718, #Species Placopecten magellanicus (Gmelin, 1791) – sea scallop
'Atlantic halibut' = 172933 # Species Hippoglossus hippoglossus (Linnaeus, 1758) – Atlantic halibut, flétan atlantique, Atlantic Halibut
)
## -----------------------------------------------------------------------------
# a bit hacky, but I am linking all of the TSN numbers to their respective species here in the northeast fisheries list
spcat<-c()
for (i in 1:nrow(ne_spp)) {
spcat<-c(spcat,
ifelse(sum(grepl(pattern = ne_spp$name[i], x = names(spcat.list) , ignore.case = T)) == 0,
NA, grep(pattern = ne_spp$name[i], x = names(spcat.list), ignore.case = T) ))
}
ne_spp$TSN<-as.numeric(unlist(spcat.list[spcat]))
# Now convert the Northeast Fisheires list into a list for the itis_reclassify function
categories<-list()
for (i in 1:length(unique(ne_spp$plan))) {
categories[i]<-list(ne_spp$TSN[ne_spp$plan %in% unique(ne_spp$plan)[i]])
names(categories)[i]<-unique(ne_spp$plan)[i]
}
## -----------------------------------------------------------------------------
# Use the itis_reclassify function to sort each species listed in the FOSS data into each classification group
temp<-itis_reclassify(tsn = unique(landings_data$Tsn),
categories = categories,
uncategorized_name="Uncategorized")
tsn_id<-temp$df_out
# Remove anything that wasn't classified (we don't them for what we are doing here)
if (sum(tsn_id$category %in% c("Other", "Uncategorized"))>0) {
tsn_id<-tsn_id[!(tsn_id$category %in% c("Other", "Uncategorized")),
c("TSN", "category")]
}
# renaming columns for joining other datasets to this dataset
landings_data<-dplyr::rename(landings_data,
TSN = Tsn)
tsn_id$TSN<-as.numeric(tsn_id$TSN)
# Join the FOSS landings data to their respctive categories
landings_data<-dplyr::left_join(x = landings_data,
y = tsn_id,
by = "TSN")
# Rename columns so they match what the funciton uses
landings_data<-dplyr::rename(landings_data,
Tsn = TSN,
category = category.y)
# Minor data editing.
landings_data<-landings_data[landings_data$Year>=minyr & landings_data$Year<=maxyr, # Only include years that you intend to assess.
c("Year","Pounds","Dollars","category","Tsn", "State", "Region", "abbvreg")] # These are all of the columns you need
# Sometimes negative numbers can be in these columns. They're flags and not real values, so we'll want to remove those here
landings_data<-landings_data[(landings_data$Pounds>=0), ]
landings_data<-landings_data[(landings_data$Dollars>=0), ]
# Print out the resultant table so we can see our hard work!
knitr::kable(head(landings_data), booktabs = T) %>%
kable_styling(latex_options = "striped")
## -----------------------------------------------------------------------------
out <- OutputAnalysis(landings_data = landings_data,
category0 = "category", # the name of the column you are categorizing by
baseyr = baseyr,
titleadd = titleadd,
dir_analyses = dir_analyses,
skipplots = TRUE,
reg_order = "Northeast", # The region(s) you want to assess
reg_order_abbrv = "NorE", # The region(s) you want to assess
save_outputs_to_file = TF) # Here I use the variable TF so I can change it once at the begining of my code, depending on my reporting purposes
names(out)
# make an object of everything that comes out of this function
for (jjj in 1:length(out)) {
assign(names(out)[jjj], out[[jjj]])
}
## ---- results='asis'----------------------------------------------------------
result <- lapply(index_list, "[", , c("Year", "cat", "PI_CB", "Q_CB", "v"))
a<-result$Northeast
a<-a[a$Year %in% minyr:maxyr, ]
a<-dplyr::rename(a,
PI = PI_CB,
Q = Q_CB,
V = v)
# Create table of raw values
a.pi<-spread(a[!(names(a) %in% c("V", "Q"))], cat, PI)
names(a.pi)[-1]<-paste0(names(a.pi)[-1], "_PI")
a.q<-spread(a[!(names(a) %in% c("PI", "V"))], cat, Q)
names(a.q)[-1]<-paste0(names(a.q)[-1], "_Q")
a.v<-spread(a[!(names(a) %in% c("PI", "Q"))], cat, V)
names(a.v)[-1]<-paste0(names(a.v)[-1], "_V")
b<-left_join(a.pi, a.q, by = c("Year"))
b<-left_join(b, a.v, by = c("Year"))
b<-b[,match(x = c("Year",
names(b)[grep(pattern = "_V", x = names(b), ignore.case = T)],
names(b)[grep(pattern = "_PI", x = names(b), ignore.case = T)],
names(b)[grep(pattern = "_Q", x = names(b), ignore.case = T)]),
names(b))]
b<-b[,match(x = c("Year",
names(b)[grep(pattern = "fin", x = names(b), ignore.case = T)],
names(b)[grep(pattern = "Shell", x = names(b), ignore.case = T)],
names(b)[grep(pattern = "Total", x = names(b))]),
names(b))]
b<-b[b$Year %in% minyr:maxyr, ]
temp_code<-b
temp_code$Footnotes<-NA
# Create a nice-looking formatted table of values with PrettyNum
b<-a
b$PI<-round(x = b$PI, digits = 2)
b$Q<-prettyNum(x = round(x = b$Q/1e6), digits = 2, big.mark = ",")
b$V<-prettyNum(x = round(x = b$V/1e6), digits = 2, big.mark = ",")
b.pi<-spread(b[!(names(b) %in% c("V", "Q"))], cat, PI)
names(b.pi)[-1]<-paste0(names(b.pi)[-1], "_PI")
b.q<-spread(b[!(names(b) %in% c("PI", "V"))], cat, Q)
names(b.q)[-1]<-paste0(names(b.q)[-1], "_Q")
b.v<-spread(b[!(names(b) %in% c("PI", "Q"))], cat, V)
names(b.v)[-1]<-paste0(names(b.v)[-1], "_V")
b<-left_join(b.pi, b.q, by = c("Year"))
b<-left_join(b, b.v, by = c("Year"))
b<-b[,match(x = c("Year",
names(b)[grep(pattern = "_V", x = names(b), ignore.case = T)],
names(b)[grep(pattern = "_PI", x = names(b), ignore.case = T)],
names(b)[grep(pattern = "_Q", x = names(b), ignore.case = T)]),
names(b))]
b<-b[,match(x = c("Year",
names(b)[grep(pattern = "fin", x = names(b), ignore.case = T)],
names(b)[grep(pattern = "Shell", x = names(b), ignore.case = T)],
names(b)[grep(pattern = "Total", x = names(b))]),
names(b))]
b<-b[b$Year %in% minyr:maxyr, ]
temp_print<-b
temp_print$Footnotes<-NA
# Save Outputs.
# Here I use the variable TF so I can change it once at the begining of my code, depending on my reporting purposes
ProdOutputUS_Raw<-temp_code
if (TF) {
write_csv(x = ProdOutputUS_Raw, file = paste0(dir_analyses, "/ProdOutputNorE_Raw.csv"))
}
ProdOutputUS_Print<-temp_print
if (TF) {
write_csv(x = ProdOutputUS_Print, file = paste0(dir_analyses, "/ProdOutputNorE_Print.csv"))
}
# Print out the resultant table so we can see our hard work!
ProdOutputUS_Print$Footnotes<-NULL
knitr::kable(ProdOutputUS_Print, booktabs = T) %>%
kable_styling(latex_options = "striped")
## ---- fig.width=8-------------------------------------------------------------
figures_list$Northeast__PI_CB_PI
figures_list$Northeast__Q_CB_CatTot_QCatTot
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