In emilyhmarkowitz/FisheriesEconomicProductivityIndex: What the Package Does (One Line, Title Case)

knitr::opts_chunk$set(echo = FALSE, error = FALSE, message = FALSE, warning = FALSE)

Fishery Productivity Output Price Index

maxyr.nextchunk<-as.numeric(paste0(ceiling(((maxyr)/10)), 
                        ifelse(substr(maxyr, 
                                      start = nchar((maxyr)), 
                                      stop = nchar((maxyr-29)))<5, 5, 0)))
baseyr.nextchunk<-as.numeric(paste0(floor(maxyr.nextchunk/10), 
                            ifelse(substr(maxyr.nextchunk, start = nchar(maxyr.nextchunk), 
                                          stop = nchar(maxyr.nextchunk))>=5, 5, 0)))
minyr.data.nextchunk<-as.numeric(paste0(floor((maxyr.nextchunk-24)/10), 
                            ifelse(substr(maxyr.nextchunk, start = nchar((maxyr.nextchunk-24)), 
                                          stop = nchar((maxyr.nextchunk-24)))>=5, 6, 1)))

Below are tables which show fishery value estimates at both national and regional levels based on commercial fishery landing data. The tables show inflation adjusted (i.e. real) landings value over a 20 year time period by region, along with the associated regional price indices used to derive the real values. In addition, a third table presents national nominal value, real value and the price index for shellfish, finfish and total landings. Price indices were calculated as chained Törnqvist indices over a 25 year period, which were then converted to a base year index with r baseyr as the base time period. The data window and base year will be adjusted every 5 years (such that in the r maxyr.nextchunk edition of the report, the base year will be r baseyr.nextchunk and the minimum year of the data used in the analysis will be r minyr.data.nextchunk to ensure consistency between editions of the Fisheries Economics of the US report.

dir.in<-dirname(getwd())
dir.data<-dir.rawdata<-paste0(dir.in, "/data/")
dir.output<-paste0(dir.in, "/output/")
dir.out<-paste0(dir.output, "ProductivityIndex_", Sys.Date(), "/")
dir.scripts<-paste0(dir.in, "/rscripts/")
dir.analyses<-paste0(dir.out, "/analyses/")
ProdI.Report<-paste0(dir.scripts, "ProductivityIndex_Report.rmd")

# aaa<-unlist(unique(lapply(fold0, `[[`, 1)))

# library(devtools)
# devtools::install_github("emilyhmarkowitz/FishEconProdOutput", dependencies = TRUE)
library(FishEconProdOutput)

### SOURCE FILES AND DATA #####
#Load data for this section
#Functions specific to this section
ProdI.Funct<-paste0(dir.scripts, "ProductivityIndex_Functions.R")
source(ProdI.Funct)

aa<-list.files(path = paste0(dir.analyses), 
                           pattern = paste0(minyr, "To", maxyr, "_FSFEUS"), 
                           full.names = TRUE)

bb<-list.files(path = paste0(aa, "/outputtables/"), full.names = TRUE, pattern = "000_All")

\newpage

Table [1]. Regional Landing Values, r minyr-r maxyr (Implicit Quantity in r baseyr $ Million)

a<-data.frame(Year = minyr:maxyr)
for (i in 2:length(reg.order)){
  temp<-read.xlsx(file = bb[grep(pattern = "_FinalOutput", x = bb)], sheetName = reg.order[i])
  temp<-temp[temp$Year %in% c(minyr:maxyr), ]
  a0<-data.frame(temp[,names(temp) %in% paste0("Q_", idx)])
  names(a0)<-reg.order[i]
  a<-cbind.data.frame(a, a0)
}
# names(a)<-c("Year", reg.order[2:length(reg.order)])
if (NortheastTFs == T){
  a$Northeast<-NULL
}
# names(a)<-gsub(pattern = " \\(Hawai`i\\)", replacement = "", x = names(a))

a[,2:ncol(a)]<-round(x = a[,2:ncol(a)]/1e6, digits = 2)

# a<-a[order(a$Year, decreasing = TRUE),]


if (designflowin %in% F){
  # print(a %>%
  #   knitr::kable(row.names = T, booktabs = T))


  a %>% 
  # tibble::rownames_to_column() %>% 
  flextable() %>%
  set_header_labels(rowname = "") %>% 
  # add_header_row(values = c("", "Group 1", "Group 2"), 
  #                colwidths = c(1, 2, 2)) %>% 
   theme_box()# %>% autofit()

}

write.csv(x = a, file = paste0(dir.temp, "T1_RegionalLandingValues.csv"))

\newpage

Table [2]. Regional Price Index, r minyr-r maxyr (r baseyr = 1)

a<-data.frame(Year = minyr:maxyr)
for (i in 2:length(reg.order)){
  temp<-read.xlsx(file = bb[grep(pattern = "_FinalOutput", x = bb)], sheetName = reg.order[i])
  temp<-temp[temp$Year %in% c(minyr:maxyr), ]
  a0<-data.frame(temp[,names(temp) %in% paste0("PI_", idx)])
  names(a0)<-reg.order[i]
  a<-cbind.data.frame(a, a0)
}
if (NortheastTFs == T){
  a$Northeast<-NULL
}
names(a)<-gsub(pattern = " \\(Hawai`i\\)", replacement = "", x = names(a))

a[,2:ncol(a)]<-round(x = a[,2:ncol(a)], digits = 2)

# a<-a[order(a$Year, decreasing = TRUE),]


if (designflowin %in% F){
  # print(a %>%
  #   knitr::kable(row.names = T, booktabs = T))

  a %>% 
  # tibble::rownames_to_column() %>% 
  flextable() %>%
  set_header_labels(rowname = "") %>% 
  # add_header_row(values = c("", "Group 1", "Group 2"), 
  #                colwidths = c(1, 2, 2)) %>% 
   theme_box()# %>% autofit()

}

write.csv(x = a, file = paste0(dir.temp, "T2_RegionalPriceIndex.csv"))

\newpage

Table [3]. National Nominal Value (in r baseyr $ Million), Price Index, (r baseyr = 1), and Landing Values (Implicit Quantity in r baseyr $ Million), r minyr-r maxyr

Date0 = Sys.Date()
yr = minyr
maxyr = maxyr
folderpattern = "FSFEUS"


  outputrun<-list.files(path = dir.output, pattern = as.character(Date0), full.names = TRUE)

  a<-list.files(path = (list.files(path = paste0(outputrun, "/analyses/"), full.names = TRUE, pattern = paste0(yr, "To", maxyr, "_", folderpattern))),
                     pattern = "outputtables", full.names = TRUE, ignore.case = TRUE)
  b<-a[(grepl(pattern =paste0(yr, "To"), x = a))]
  b<-list.files(path = b, pattern = "_US_", full.names = TRUE)
  b<-b[grep(pattern = "_AllData", x = b)]

  a<-read.csv(file = b)
  a<-a[a$Year %in% c(minyr:maxyr), ]

  a<-a[,c("Year", "cat", "v", paste0("PI_", idx),paste0("Q_", idx))]
  a<-dplyr::rename(a, 
                PI = paste0("PI_", idx), 
                Q = paste0("Q_", idx), 
                V = "v")

  a$PI<-round(x = a$PI, digits = 2)
  a$Q<-round(x = a$Q/1e6, digits = 2)
  a$V<-round(x = a$V/1e6, digits = 2)


  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")

  a<-left_join(a.pi, a.q, by = c("Year"))
  a<-left_join(a, a.v, by = c("Year"))
  # a<-a[order(a$Year, decreasing = TRUE),]
  # rownames(a)<-a$Year
  # a$Year<-NULL

    a<-a[,match(x = c("Year", 
                    names(a)[grep(pattern = "_V", x = names(a), ignore.case = T)], 
                    names(a)[grep(pattern = "_PI", x = names(a), ignore.case = T)], 
                    names(a)[grep(pattern = "_Q", x = names(a), ignore.case = T)]), 
              names(a))]

  a<-a[,match(x = c("Year", 
                    names(a)[grep(pattern = "Total", x = names(a))], 
                    names(a)[grep(pattern = "fin", x = names(a), ignore.case = T)], 
                    names(a)[grep(pattern = "Shell", x = names(a), ignore.case = T)]), 
              names(a))]

    typology<-data.frame(col_keys = names(a), 
                       colB = unlist(c("", lapply(X = strsplit(x = names(a)[-1], split = "_"), `[[`, 1) )), 
                       colA = unlist(c("Year", lapply(X = strsplit(x = names(a)[-1], split = "_"), `[[`, 2)))
                       )  

    typology$colA[typology$colA %in% "PI"]<-"Price Index"
    typology$colA[typology$colA %in% "Q"]<-"Real Value"
    typology$colA[typology$colA %in% "V"]<-"Nominal Value"

ft <- flextable(a, col_keys = names(a) ) 
ft<-set_header_df(ft, mapping = typology, key = "col_keys" )
ft<-merge_h(ft, part = "header")
ft<-merge_v(ft, part = "header") 

ft<-ft %>%
  theme_box() %>% 
  autofit() %>% 
  empty_blanks()


if (designflowin %in% F){
  ft
}

write.csv(x = a, file = paste0(dir.temp, "T3_NationalLandingValuesAndPriceIndex.csv"))

\newpage

Graph [1]. National Landing Values (Implicit Quantity in r baseyr $ Million), r minyr-r maxyr

# a<-data.frame(Year = minyr:maxyr)

temp<-read.xlsx(file = bb[grep(pattern = "_AllData", x = bb)], sheetName = reg.order[1])

a<-temp[, c("Year", "cat", paste0("Q_", idx))]
a<-a[a$Year %in% c(minyr:maxyr), ]

a[, paste0("Q_", idx)]<-round(x = a[, paste0("Q_", idx)]/1e6, digits = 2)

names(a)<-c("Year", "Category", "val")

# a<-a[,match(table = names(a),
#             x = c("Year","Total",
#                   "Finfish", "Shellfish"))]
# 
# 
# a0<-a
# a <- gather(a0, Category, val, names(a0)[-1], factor_key=TRUE)
# 
a$cat<-a$Category

temp0<-a


g<-plotnlines(dat = temp0, 
              titleyaxis = paste0("Implicit Quantity in [", minyr,"] $ Million"), 
              title0 = "")

if (designflowin %in% F){
  g
} 

# counter<-funct_counter(counter)
ggsave(g,
       filename = paste0(dir.temp, "G1_NationalLandingValues.pdf"),
       width = 4, height = 4)

\newpage

Graph [2]. Total Quantity Index for Each Region (r baseyr = 1), r minyr-r maxyr

a<-data.frame(Year = minyr:maxyr)
for (i in 2:length(reg.order)){
  temp<-read.xlsx(file = bb[grep(pattern = "_FinalOutput", x = bb)], sheetName = reg.order[i])
  temp<-temp[temp$Year %in% c(minyr:maxyr), ]
  a<-cbind.data.frame(a, temp[,names(temp) %in% paste0("QI_", idx)])
}
names(a)<-c("Year", reg.order[2:length(reg.order)])

a0<-a
a <- gather(a0, Category, val, names(a0)[-1], factor_key=TRUE)

a$cat<-a$Category

temp0<-a

g<-plotnlines(dat = temp0, titleyaxis = paste0("Quantity Index (", minyr," = 1)"), title0 = "")

if (designflowin %in% F){
  g
} 

ggsave(g,
       filename = paste0(dir.temp, "G2_TotalQuantityIndexForEachRegion.pdf"),
       width = 4, height = 4)

emilyhmarkowitz/FisheriesEconomicProductivityIndex documentation built on June 13, 2025, 10:31 a.m.