# Main Notebook is here. 警告メッセージ:
1: options(opt) で: 'PCRE_study' has no effect with PCRE2
2: options(opt) で:
'options(stringsAsFactors = TRUE)' is deprecated and will be disabled# https://dsx5wcobth.github.io/2012_to_2022/
# Supplementary Notebook is here.
# https://dsx5wcobth.github.io/2012_to_2022/RI_article_supplementary_20220715.nb.html
# All sorce code is here.
# https://github.com/DSx5WcObth/2012_to_2022
# The recommended execution environment to run the code is as follows.
# OS version : Ubuntu20.04 or macOS12
# R version : 4.0.34.0.3
# RStudio version : v1.4.1717
# tidyverse version : 1.3.1
options(warn=-1)
# Initialized.
rm(list=ls())
# Library loaded.
library(tidyr)
library(dplyr)
library(ggplot2)
library(lubridate)
library(stringr)
library(readr)
library(openxlsx)
library(ggrepel)
library(furrr)
options(warn=-1)
# CSV data loaded.
fdata <- c(
"./merge_csv/2012_rep.csv",
"./merge_csv/2013_rep.csv",
"./merge_csv/2014_rep.csv",
"./merge_csv/2015_rep.csv",
"./merge_csv/2016_rep.csv",
"./merge_csv/2017_rep.csv",
"./merge_csv/2018_rep.csv",
"./merge_csv/2019_rep.csv",
"./merge_csv/2020_1_rep.csv"
)
# plan(multisession, workers = 8)
data_2020_all_1 <- furrr::future_map_dfr(fdata, ~ readr::read_csv(.x, col_types=cols(.default = "c")))New names:
• `` -> `...1`New names:
• `` -> `...1`New names:
• `` -> `...1`New names:
• `` -> `...1`New names:
• `` -> `...1`New names:
• `` -> `...1`New names:
• `` -> `...1`New names:
• `` -> `...1`New names:
• `` -> `...1`rm(fdata)
# data_2020_all_2 <- readr::read_csv("./merge_csv/csv_archive/2020_2_archive.csv", col_types=cols(.default = "c"))
fdata <- c(
"./merge_csv/2020_2_rep.csv",
"./merge_csv/2021_rep.csv",
"./merge_csv/2022_rep.csv"
)
data_2020_all_2 <- furrr::future_map_dfr(fdata, ~ readr::read_csv(.x, col_types=cols(.default = "c")))New names:
• `` -> `...1`New names:
• `` -> `...1`New names:
• `` -> `...1`# "No" column in the data was converted to integer
data_2020_all_1[c("No")] <- data_2020_all_1[c("No")] %>% furrr::future_map( ~ as.integer(.x))
data_2020_all_2[c("No")] <- data_2020_all_2[c("No")] %>% furrr::future_map( ~ as.integer(.x))
options(warn=-1)
#### Information on Prefecture was not used due to as it is at low data quality.
data_all_1 <- data_2020_all_1 %>%
select(
"No", "都道府県", "市町村", "area", "Market",
"食品カテゴリ", "Category", "Category_2",
"品目名", "Item", "その他", "Others",
"食品分類", "Food_classfication",
"Inspection_instrument",
"Sampling_Date", "Sampling_Date_fix",
"Results_Obtained_Date", "Result_Date_fix",
"Press_Release_Date", "Press_Release_Date_fix",
"Sampling_Year", "Result_Year", "Press_Release_Year", "File_Year",
"Cesium_134", "Cs_134_fix", "Cs_134_ND",
"Cesium_137", "Cs_137_fix", "Cs_137_ND",
"Cesium_total", "Cs_total_fix", "Cs_total_ND", "exceed_action_levels")
data_all_2 <- data_2020_all_2 %>%
select(
"No", "都道府県", "市町村", "area", "Market",
"食品カテゴリ", "Category", "Category_2",
"品目名", "Item", "養殖_天然", "Farmed_Wild",
"食品分類", "Food_classfication",
"Inspection_instrument",
"Sampling_Date", "Sampling_Date_fix",
"Results_Obtained_Date", "Result_Date_fix",
"Press_Release_Date", "Press_Release_Date_fix",
"Sampling_Year", "Result_Year", "Press_Release_Year", "File_Year",
"Cesium_134", "Cs_134_fix", "Cs_134_ND",
"Cesium_137", "Cs_137_fix", "Cs_137_ND",
"Cesium_total", "Cs_total_fix", "Cs_total_ND", "exceed_action_levels")
colnames(data_all_2) <- c("No", "都道府県", "市町村", "area", "Market",
"食品カテゴリ", "Category", "Category_2",
"品目名", "Item", "その他", "Others",
"食品分類", "Food_classfication",
"Inspection_instrument",
"Sampling_Date", "Sampling_Date_fix",
"Results_Obtained_Date", "Result_Date_fix",
"Press_Release_Date", "Press_Release_Date_fix",
"Sampling_Year", "Result_Year", "Press_Release_Year", "File_Year",
"Cesium_134", "Cs_134_fix", "Cs_134_ND",
"Cesium_137", "Cs_137_fix", "Cs_137_ND",
"Cesium_total", "Cs_total_fix", "Cs_total_ND", "exceed_action_levels")
data_all_ <- rbind(data_all_1, data_all_2)
rm(data_2020_all_1, data_2020_all_2)
rm(data_all_1, data_all_2)
options(warn=-1)
data_2012_2020_fix_ <- data_all_
data_2012_2020_fix_ <- data_2012_2020_fix_ %>% drop_na(No)
# Market column data cleaned.
txt_pattern <- c("-|―")
txt_replace <- c("Not applicable")
data_2012_2020_fix_$Market <- purrr::reduce2(txt_pattern, txt_replace, .init=data_2012_2020_fix_$Market, str_replace)
# Undefined data in food category was changed to “others” and recategorized as "not applicable."
txt_pattern <- c("Others")
txt_replace <- c("Not applicable")
data_2012_2020_fix_$Category <- purrr::reduce2(txt_pattern, txt_replace, .init=data_2012_2020_fix_$Category, str_replace)
data_2012_2020_fix_$Category <- data_2012_2020_fix_$Category %>% str_trim()
# "Sampling_Date_fix", "Result_Date_fix" and "Press_Release_Date_fix" columns were created using the data in "Sampling_Date", "Result_Date" and "Press_Release_Date" columns, and the date format were fixed.
txt_pattern <- c("年|月")
txt_replace <- c("-")
column_fix <- c("Sampling_Date_fix", "Result_Date_fix", "Press_Release_Date_fix")
data_2012_2020_fix_[, column_fix] <- data_2012_2020_fix_[, column_fix] %>%
sapply(function(x) {x %>% str_replace(txt_pattern, txt_replace)})
txt_pattern <- c("-$")
txt_replace <- c("-01")
data_2012_2020_fix_[, column_fix] <- data_2012_2020_fix_[, column_fix] %>%
sapply(function(x) {x %>% str_replace(txt_pattern, txt_replace)})
txt_pattern <- c("^-01")
txt_replace <- c("-")
data_2012_2020_fix_[, column_fix] <- data_2012_2020_fix_[, column_fix] %>%
sapply(function(x) {x %>% str_replace(txt_pattern, txt_replace)})
column_fix <- c("Sampling_Date_fix", "Result_Date_fix", "Press_Release_Date_fix")
data_2012_2020_fix_[, column_fix] <- data_2012_2020_fix_[, column_fix] %>%
sapply(function(x) {x %>% str_sub(start=1, end=10)})
# Sampling_Year data were cleaned.
txt_pattern <- c("-|－|―| | |-|―|−|─|－|nan|不明")
txt_replace <- c("-")
column_fix <- c("Sampling_Year")
data_2012_2020_fix_[, column_fix] <- data_2012_2020_fix_[, column_fix] %>%
sapply(function(x) {x %>% str_replace(txt_pattern, txt_replace)})
# Sampling_Year data were cleaned.
txt_pattern <- c("-|－|―| | |-|―|−|─|－|nan|不明")
txt_replace <- c("-")
column_fix <- c("Sampling_Year")
data_2012_2020_fix_[, column_fix] <- data_2012_2020_fix_[, column_fix] %>%
sapply(function(x) {x %>% str_replace(txt_pattern, txt_replace)})
# Inspection_instrument data were cleaned.
column_fix <- c("Inspection_instrument")
data_2012_2020_fix_[[column_fix]] <- data_2012_2020_fix_[[column_fix]] %>%
sapply(function(x) {x %>% str_trim() %>% str_trunc(2, "right", ellipsis="")})
txt_pattern_1 <- c("^N.*")
txt_pattern_2 <- c("^Ｎ.*")
txt_replace <- c("NaI")
data_2012_2020_fix_[, column_fix] <- data_2012_2020_fix_[, column_fix] %>%
sapply(function(x) {x %>% str_replace(txt_pattern_1 , txt_replace)})
data_2012_2020_fix_[, column_fix] <- data_2012_2020_fix_[, column_fix] %>%
sapply(function(x) {x %>% str_replace(txt_pattern_2 , txt_replace)})
txt_pattern_1 <- "^G.*"
txt_pattern_2 <- "^Ｇ.*"
txt_replace <- "Ge"
data_2012_2020_fix_[, column_fix] <- data_2012_2020_fix_[, column_fix] %>%
sapply(function(x) {x %>% str_replace(txt_pattern_1 , txt_replace)})
data_2012_2020_fix_[, column_fix] <- data_2012_2020_fix_[, column_fix] %>%
sapply(function(x) {x %>% str_replace(txt_pattern_2 , txt_replace)})
txt_pattern_1 <- c("^Ｃ.*")
txt_pattern_2 <- c("^C.*")
txt_replace <- "CsI"
data_2012_2020_fix_[, column_fix] <- data_2012_2020_fix_[, column_fix] %>%
sapply(function(x) {x %>% str_replace(txt_pattern_1 , txt_replace)})
data_2012_2020_fix_[, column_fix] <- data_2012_2020_fix_[, column_fix] %>%
sapply(function(x) {x %>% str_replace(txt_pattern_2 , txt_replace)})
# Data in “Cesium_total” were cleaned, and the fixed data were saved in “Cs_total_fix data” column.
data_2012_2020_fix_ <- data_2012_2020_fix_ %>%
mutate(
Cs_fix=case_when(
Cesium_134=="<.0598" ~ "0598",
TRUE ~ Cs_134_fix
)
)
data_2012_2020_fix_["Cs_134_fix"] <- data_2012_2020_fix_["Cs_fix"]
data_2012_2020_fix_ <- data_2012_2020_fix_ %>%
mutate(
Cs_fix=case_when(
Cesium_137=="<.881" ~ "0.881",
Cesium_137=="<.12" ~ "0.12",
Cesium_137=="<\\.0441" ~ "0.0441",
TRUE ~ Cs_137_fix
)
)
data_2012_2020_fix_["Cs_137_fix"] <- data_2012_2020_fix_["Cs_fix"]
data_2012_2020_fix_ <- data_2012_2020_fix_ %>%
mutate(
Cs_fix=case_when(
Cesium_total=="<164" ~ "16.4",
Cesium_total=="^0$" ~ "25",
TRUE ~ Cs_total_fix
)
)
data_2012_2020_fix_["Cs_total_fix"] <- data_2012_2020_fix_["Cs_fix"]
txt_pattern <- c("^0.0$")
txt_replace <- c("NotDetected")
column_fix <- c("Cs_total_ND")
data_2012_2020_fix_[, column_fix] <- data_2012_2020_fix_[, column_fix] %>%
sapply(function(x) {x %>% str_replace(txt_pattern, txt_replace)})
txt_pattern <- c("<\\.|<\\.|^\\.|\\'|\\n|\\*|\\.$|N\\.D\\.|\\(|\\)|（|）|^\\.|\\.$")
data_2012_2020_fix_[, column_fix] <- data_2012_2020_fix_[, column_fix] %>%
sapply(function(x) {x %>% str_remove(txt_pattern)})
txt_pattern <- c("±")
txt_replace <- c(" ")
column_fix <- c("Cs_134_fix", "Cs_137_fix", "Cs_total_fix")
data_2012_2020_fix_[, column_fix] <- data_2012_2020_fix_[, column_fix] %>%
sapply(function(x) {x %>%
str_replace(txt_pattern, txt_replace) %>% str_sub(start=1, end=6)})
txt_pattern <- c("\\.\\.|,|．|\\.,|,\\.")
txt_replace <- c(".")
column_fix <- c("Cs_134_fix", "Cs_137_fix", "Cs_total_fix")
data_2012_2020_fix_[, column_fix] <- data_2012_2020_fix_[, column_fix] %>%
sapply(function(x) {x %>% str_replace(txt_pattern, txt_replace)})
# Cs values were converted to numeric data.
column_fix <- c("Cs_134_fix", "Cs_137_fix", "Cs_total_fix")
data_2012_2020_fix_[, column_fix] <- data_2012_2020_fix_[, column_fix] %>%
sapply(function(x) {x %>% str_trim() %>% as.numeric()})
# Over JML was determined from Cs values.
data_2012_2020_fix_ <- data_2012_2020_fix_ %>%
mutate(
Exceed=case_when(
Category_2 %in% "General foods" & Cs_total_ND=="Detected" & Cs_total_fix>100 ~ "Ex",
Category_2 %in% "Milk, infant foods" & Cs_total_ND=="Detected" & Cs_total_fix>50 ~ "Ex",
Category_2 %in% "Drinking water" & Cs_total_ND=="Detected" & Cs_total_fix>10 ~ "Ex"
)
)
# Cs detection was determined from Cs values.
data_2012_2020_fix_ <- data_2012_2020_fix_ %>%
mutate(
Gene_food_ND=case_when(
Category_2 %in% "General foods" & Cs_total_fix>25 ~ "Detected",
Category_2 %in% "General foods" & !Cs_total_fix>25 ~ "NotDetected",
Category_2 %in% "Milk, infant foods" & Cs_total_fix>25 ~ "Detected",
Category_2 %in% "Milk, infant foods" & !Cs_total_fix>25 ~ "NotDetected",
Category_2 %in% "Drinking water" & Cs_total_fix>10 ~ "Detected",
Category_2 %in% "Drinking water" & !Cs_total_fix>10 ~ "NotDetected",
is.na(Cs_total_fix) ~ "NotDetected"
)
)
data_2012_2020_fix_ <- data_2012_2020_fix_ %>%
mutate(
Cs_condition=case_when(
Cs_total_fix>100 ~ "Warning",
Cs_total_fix>50 ~ "Caution",
Cs_total_fix>25 ~ "Notice",
Cs_total_fix<=25 ~ "Info",
is.na(Cs_total_fix) ~ "NoData"
)
)
# The data in “exceed_action_levels” column were changed to “TRUE” when over JML.
txt_pattern <- c("1")
txt_replace <- c("TRUE")
column_fix <- c("exceed_action_levels")
data_2012_2020_fix_[, column_fix] <- data_2012_2020_fix_[, column_fix] %>%
sapply(function(x) {x %>% str_replace(txt_pattern, txt_replace)})
# Data in "character" type were converted into "factor" type.
column_fix <- c(
# "Prefecture", "Market", "Inspection_instrument",
"Market", "Inspection_instrument",
"Category", "Category_2", "Food_classfication",
"Cs_134_ND", "Cs_137_ND", "Cs_total_ND", "Cs_condition",
"exceed_action_levels", "Exceed",
"Sampling_Year", "Result_Year", "Press_Release_Year",
"File_Year"
)
data_2012_2020_fix_[, column_fix] <- data_2012_2020_fix_[, column_fix] %>%
lapply(as.factor) %>% data.frame()
column_fix <- c("Sampling_Date_fix",
"Result_Date_fix",
"Press_Release_Date_fix")
data_2012_2020_fix_[, column_fix] <- data_2012_2020_fix_[, column_fix] %>%
lapply(function(x) {x %>% as.Date()})
# Prefecture translate english.
data_2012_2020_fix_ <- data_2012_2020_fix_ %>% merge(readr::read_csv("./csv/Prefecture_english.csv"), by="都道府県", all.x = T)Rows: 54 Columns: 2
── Column specification ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr (2): 都道府県, Prefecture
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.# Data from "Sampling_Year" were basically used. Otherwise, "Result_Year," "Press_Release_Year," or year recorded to save the data file,
data_2012_2020_total_ <- data_2012_2020_fix_ %>%
mutate(Integration_Year=case_when(
Sampling_Year=="2012" | Sampling_Year=="2013" | Sampling_Year=="2014" |
Sampling_Year=="2015" | Sampling_Year=="2016" | Sampling_Year=="2017" |
Sampling_Year=="2018" | Sampling_Year=="2019" | Sampling_Year=="2020" |
Sampling_Year=="2021" | Sampling_Year=="2022" ~ Sampling_Year,
Sampling_Year!="2012" & Sampling_Year!="2013" & Sampling_Year!="2014" &
Sampling_Year!="2015" & Sampling_Year!="2016" & Sampling_Year!="2017" &
Sampling_Year!="2018" & Sampling_Year!="2019" & Sampling_Year!="2020" &
Sampling_Year!="2021" & Sampling_Year!="2022" & !is.na(Result_Year) ~ Result_Year,
TRUE ~ File_Year
)
)
# Translation mistakes from Japanese to English were corrected.
data_Fishery_ <- data_2012_2020_total_ %>%
filter(Category=="Fishery products")
data_Fishery_$Item <- data_Fishery_$Item %>%
str_replace(pattern="Japanese persimmon", replacement="Oyster")
data_Fishery_$Food_classfication <- data_Fishery_$Food_classfication %>%
str_replace(pattern="Fruits_including_nuts", replacement="Marine_products(invertebrate)")
data_Fishery_$食品分類 <- data_Fishery_$食品分類 %>%
str_replace(pattern="果実類（種実類含む）", replacement="水産物(無脊椎)")
data_NotFishery_ <- data_2012_2020_total_ %>% filter(Category!="Fishery products")
data_2012_2020_total_ <- data_Fishery_ %>% rbind(data_NotFishery_)
rm(data_Fishery_, data_NotFishery_)
# Food classfication to “No_Data” and “Confirming” was corrected and grouped into "Other".
data_Others_ <- data_2012_2020_total_ %>%
filter(is.na(Food_classfication) | Food_classfication=="No_Data" | Food_classfication=="Confirming")
data_Others_$Food_classfication <- "Other"
data_NotOthers_ <- data_2012_2020_total_ %>%
filter(!is.na(Food_classfication) & Food_classfication!="No_Data" & Food_classfication!="Confirming")
data_2012_2020_total_ <- data_Others_ %>% rbind(data_NotOthers_)
rm(data_Others_, data_NotOthers_)
data_2012_2020_total_$食品分類 <- data_2012_2020_total_$食品分類 %>%
str_replace(pattern="該当なし", replacement="その他")
data_Fiscal_Year_ <- data_2012_2020_total_ %>%
mutate(Year_Month=str_sub(Sampling_Date, start = 1, end = 8)) %>%
merge(
readr::read_csv("./csv/Sampling_Year_Month.csv" )[c("Year_Month", "Fiscal_Year", "Month")],
by="Year_Month", all.x=T)Rows: 3902 Columns: 4
── Column specification ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr (4): Year_Month, Day, Month, Fiscal_Year
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Year_Sampling <- data_Fiscal_Year_ %>%
filter(Fiscal_Year!="-" & !is.na(Fiscal_Year))
Year_Result <- data_Fiscal_Year_ %>%
filter(Fiscal_Year=="-" | is.na(Fiscal_Year)) %>%
select(-c(Year_Month, Fiscal_Year, Month)) %>%
mutate(Year_Month=str_sub(Results_Obtained_Date, start = 1, end = 8)) %>%
merge(
readr::read_csv("./csv/Results_Year_Month.csv" )[c("Year_Month", "Fiscal_Year", "Month")],
by="Year_Month", all.x=T)Rows: 2880 Columns: 4
── Column specification ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr (4): Year_Month, Day, Month, Fiscal_Year
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Year_Press <- Year_Result %>% filter(Fiscal_Year=="-" | is.na(Fiscal_Year))
Year_Result <- Year_Result %>% filter(Fiscal_Year!="-" & !is.na(Fiscal_Year))
Year_Press <- Year_Press %>%
filter(Fiscal_Year=="-" | is.na(Fiscal_Year)) %>%
select(-c(Year_Month, Fiscal_Year, Month)) %>%
mutate(Year_Month=str_sub(Press_Release_Date, start = 1, end = 8)) %>%
merge(
readr::read_csv("./csv/Press_Year_Month.csv" )[c("Year_Month", "Fiscal_Year", "Month")],
by="Year_Month", all.x=T)Rows: 600 Columns: 4
── Column specification ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr (4): Year_Month, Day, Month, Fiscal_Year
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.data_Fiscal_Year_ <- rbind(Year_Sampling %>% select(-c(Year_Month, Cs_fix)),
Year_Result %>% select(-c(Year_Month, Cs_fix)),
Year_Press %>% select(-c(Year_Month, Cs_fix)))
data_2012_2020_total_ <- data_Fiscal_Year_
rm(Year_Sampling, Year_Result, Year_Press)
rm(data_Fiscal_Year_, data_all_, data_2012_2020_fix_, data_Fishery_)
# Data analysis on the sample data and the year reported.
# Data using Cs detection instruments reported, “Ge”, “CsI”, “NaI” and “-,” were used for analysis.
# The sample data and the year reported were read.
data_0 <- data_2012_2020_total_ %>%
count(Fiscal_Year)
# Data analysis on the marketed/non-marketed sample and the year reported.
data_1 <- data_2012_2020_total_ %>%
count(Fiscal_Year, Market) %>%
spread(Market, n)
data_1 <- data_0 %>%
merge(data_1[c("Fiscal_Year",
"Market products", "Produce for sales",
"Non market products", "Produce not for sales",
"Not applicable")],
"Fiscal_Year")
# Rate calculated out of all data
data_2 <- data_0["Fiscal_Year"] %>%
mutate(Market_products_Rate=round(data_1$`Market products`/count(data_2012_2020_total_)$n, 4)*100) %>%
mutate(Produce_for_sales_Rate=round(data_1$`Produce for sales`/count(data_2012_2020_total_)$n, 4)*100) %>%
mutate(Non_market_products_Rate=round(data_1$`Non market products`/count(data_2012_2020_total_)$n, 4)*100) %>%
mutate(Produce_not_for_sales_Rate=round(data_1$`Produce not for sales`/count(data_2012_2020_total_)$n, 4)*100) %>%
mutate(Not_applicable_Rate=round(data_1$`Not applicable`/count(data_2012_2020_total_)$n, 6)*100)
Table1_1 <- data_1 %>%
merge(data_2, by="Fiscal_Year") %>%
select("Fiscal_Year", "n",
"Market products", "Market_products_Rate",
"Non market products", "Non_market_products_Rate",
"Produce for sales", "Produce_for_sales_Rate",
"Produce not for sales", "Produce_not_for_sales_Rate",
"Not applicable", "Not_applicable_Rate"
) %>% print()
# Data analysis on the food category data and the year reported.
data_3 <- data_2012_2020_total_ %>%
count(Fiscal_Year, Category) %>%
spread(Category, n)
data_3 <- data_3[c("Fiscal_Year", "Fishery products",
"Livestock products", "Agricultural products",
"Wild animal meat", "Milk, infant formula",
"Drinking water", "Not applicable")]
data_4 <- data_0[1] %>%
mutate(Fishery_products_Rate=round(data_3$`Fishery products`/count(data_2012_2020_total_)$n, 4)*100) %>%
mutate(Livestock_products_Rate=round(data_3$`Livestock products`/count(data_2012_2020_total_)$n, 4)*100) %>%
mutate(Agricultural_products_Rate=round(data_3$`Agricultural products`/count(data_2012_2020_total_)$n, 4)*100) %>%
mutate(Wild_animal_meat_Rate=round(data_3$`Wild animal meat`/count(data_2012_2020_total_)$n, 4)*100) %>%
mutate(Milk_infant_formula_Rate=round(data_3$`Milk, infant formula`/count(data_2012_2020_total_)$n, 4)*100) %>%
mutate(Drinking_water_Rate=round(data_3$`Drinking water`/count(data_2012_2020_total_)$n, 4)*100) %>%
mutate(Not_applicable_Rate=round(data_3$`Not applicable`/count(data_2012_2020_total_)$n, 4)*100)
Table1_2 <- data_3 %>%
merge(data_4, by="Fiscal_Year") %>%
select("Fiscal_Year",
"Fishery products", "Fishery_products_Rate",
"Livestock products", "Livestock_products_Rate",
"Agricultural products", "Agricultural_products_Rate",
"Wild animal meat", "Wild_animal_meat_Rate",
"Milk, infant formula", "Milk_infant_formula_Rate",
"Drinking water", "Drinking_water_Rate",
"Not applicable", "Not_applicable_Rate"
) %>% print()
# Data analysis on the samples with Cs detected/Cs non-detected and the year reported.
data_5 <- data_2012_2020_total_ %>%
count(Fiscal_Year, Cs_total_ND) %>%
spread(Cs_total_ND, n)
data_6 <- data_0[1] %>%
mutate(Detected_Rate=round(data_5$Detected/count(data_2012_2020_total_)$n*100, 4)) %>%
mutate(NotDetected_Rate=round(data_5$NotDetected/count(data_2012_2020_total_)$n*100, 4))
Table1_3 <- data_5 %>%
merge(data_6, by="Fiscal_Year") %>%
select("Fiscal_Year",
"Detected", "Detected_Rate",
"NotDetected", "NotDetected_Rate"
) %>% print()
# Data analysis on the Cs concentration data and the year reported.
data_7 <- data_2012_2020_total_ %>%
count(Fiscal_Year, Cs_condition) %>%
spread(Cs_condition, n)
data_7_Plus <- data_7[c("Fiscal_Year", "Warning", "Caution", "Notice", "Info")]
data_7_Plus <- data_7_Plus %>%
mutate(Info=replace_na(data_7$Info,0) +
replace_na(data_7$NoData, 0) #+
# replace_na(data_7$NotDetected, 0)
)
data_8 <- data_0["Fiscal_Year"] %>%
mutate(Warning_Rate=round(data_7_Plus$Warning/count(data_2012_2020_total_)$n*100, 4)) %>%
mutate(Caution_Rate=round(data_7_Plus$Caution/count(data_2012_2020_total_)$n*100, 4)) %>%
mutate(Notice_Rate=round(data_7_Plus$Notice/count(data_2012_2020_total_)$n*100, 4)) %>%
mutate(Info_Rate=round(data_7_Plus$Info/count(data_2012_2020_total_)$n*100, 4))
Table1_4 <- data_7_Plus %>%
filter(Fiscal_Year!="-" & Fiscal_Year!="2011") %>%
merge(data_8, by="Fiscal_Year") %>%
mutate() %>%
select("Fiscal_Year",
"Warning", "Warning_Rate",
"Caution", "Caution_Rate",
"Notice", "Notice_Rate",
"Info", "Info_Rate"
) %>% print()
rm(data_0, data_1, data_2, data_3, data_4, data_5, data_6, data_7, data_8, data_7_Plus)
# Data using Cs detection instruments reported, “Ge”, “CsI”, “NaI” and “-,” were used for analysis.
data_2012_2020_fish_ <- data_2012_2020_total_ %>%
filter(Category=="Fishery products" & !is.na(Fiscal_Year)) %>%
mutate(
Marine_Water = case_when(Food_classfication=="Marine_products(freshwater)" ~ "Freshwater",
Food_classfication!="Marine_products(freshwater)" ~ "Marine")
) %>%
mutate(Period=case_when(
Fiscal_Year==2012 | Fiscal_Year==2013 | Fiscal_Year==2014 | Fiscal_Year==2015 ~ "Early",
Fiscal_Year==2016 | Fiscal_Year==2017 | Fiscal_Year==2018 | Fiscal_Year==2019 ~ "Middle",
Fiscal_Year==2020 | Fiscal_Year==2021 ~ "Later",
TRUE ~ "Others"
)
) %>%
mutate(Farmed_Wild=case_when(
str_detect(品目名, "養殖") & !str_detect(その他, "養殖ではない") ~ "Farmed",
str_detect(その他, "養殖") & !str_detect(その他, "養殖ではない") ~ "Farmed",
!str_detect(その他, "養殖") | str_detect(その他, "養殖ではない") ~ "Wild",
!str_detect(その他, "養殖") | str_detect(その他, "養殖ではない") ~ "Wild",
TRUE ~ "-"
)
) %>%
mutate(Fresh_Farmed=case_when(
Food_classfication=="Marine_products(freshwater)" & Farmed_Wild=="Wild" ~ "Freshwater, wild",
Food_classfication!="Marine_products(freshwater)" & Farmed_Wild=="Wild" ~ "Marine, wild",
Food_classfication=="Marine_products(freshwater)" & Farmed_Wild=="Farmed" ~ "Freshwater, aquaculture",
Food_classfication!="Marine_products(freshwater)" & Farmed_Wild=="Farmed" ~ "Marine, aquaculture",
TRUE ~ "-"
)
) %>%
mutate(Ex_Detect=case_when(
Cs_total_fix>100 ~ "Ex",
Cs_total_fix>25 ~ "Detect",
!Cs_total_fix>100 ~ "ND"
))
# Farmed, Freshwater fishery foodstuffs
Table2_1 <- data_2012_2020_fish_ %>%
filter(Farmed_Wild=="Farmed") %>%
filter(Food_classfication=="Marine_products(freshwater)") %>%
count(Fiscal_Year, Exceed) %>%
spread(Exceed, n) %>%
replace_na(list(Ex=0)) %>%
mutate(Examined=Ex+`<NA>`) %>%
mutate(Ratio=Ex/(Examined)*100) %>%
select("Fiscal_Year", "Examined", "Ex", "Ratio") %>%
print()
# Farmed, Marine fishery foodstuffs
Table2_2 <- data_2012_2020_fish_ %>%
filter(Farmed_Wild=="Farmed") %>%
filter(Food_classfication!="Marine_products(freshwater)") %>%
count(Fiscal_Year, Exceed) %>%
spread(Exceed, n) %>%
# replace_na(list(Ex=0)) %>%
mutate(Ex=0) %>%
mutate(Examined=Ex+`<NA>`) %>%
mutate(Ratio=Ex/(Examined)*100) %>%
select("Fiscal_Year", "Examined", "Ex", "Ratio") %>%
print()
# Wild, Freshwater fishery foodstuffs
Table2_3 <- data_2012_2020_fish_ %>%
filter(Farmed_Wild!="Farmed") %>%
filter(Food_classfication=="Marine_products(freshwater)") %>%
count(Fiscal_Year, Exceed) %>%
spread(Exceed, n) %>%
replace_na(list(Ex=0)) %>%
mutate(Examined=Ex+`<NA>`) %>%
mutate(Ratio=Ex/(Examined)*100) %>%
select("Fiscal_Year", "Examined", "Ex", "Ratio") %>%
print()
# Wild, Marine fishery foodstuffs
Table2_4 <- data_2012_2020_fish_ %>%
filter(Farmed_Wild!="Farmed") %>%
filter(Food_classfication!="Marine_products(freshwater)") %>%
count(Fiscal_Year, Exceed) %>%
spread(Exceed, n) %>%
replace_na(list(Ex=0)) %>%
mutate(Examined=Ex+`<NA>`) %>%
mutate(Ratio=Ex/(Examined)*100) %>%
select("Fiscal_Year", "Examined", "Ex", "Ratio") %>%
print()
rm(data_2012_2020_fish_)
# Data using Cs detection instruments reported, “Ge”, “CsI”, “NaI” and “-,” were used for analysis.
Fig1 <- data_2012_2020_total_ %>%
filter(Item!="Cattle meat" & !str_detect(その他,"全頭検査")) %>%
filter(Item!="Cattle meat" & !str_detect(その他,"全島検査")) %>%
filter(Fiscal_Year!="-") %>%
count(Fiscal_Year, Exceed) %>%
spread(Exceed, n) %>%
mutate(Total=Ex+`<NA>`) %>%
mutate(Year_Rate=Total/sum(Total)*100) %>%
mutate(Ex_Rate=Ex/Total*100) %>%
print()NA# Food categories analyzed for samples having >100 Bq/kg.
# Pie graphs showing the rate of the food categories reported in year (A) 2012, (B) 2014, (C) 2016 and (D) 2019.
# Freshwater products were shaded in line. Marine products were black-painted
# Data using Cs detection instruments reported, “Ge”, “CsI”, “NaI” and “-,” were used for analysis.
# "Marine products(freshwater)" are the freshwater fishery products.
data_Ex_Item_0 <- data_2012_2020_total_ %>%
count(Food_classfication, Exceed) %>%
spread(Exceed, n) %>%
# filter(Ex>0) %>%
replace_na(list(Ex=0, `<NA>`=0)) %>%
mutate(Inspect=Ex+`<NA>`) %>%
mutate(Rate=round((Ex/Inspect)*100,2)) %>%
select("Food_classfication", "Ex", "Inspect", "Rate") %>%
arrange(desc(Ex))
data_Ex_Item_ <- data_Ex_Item_0
for (Year_ in c(2012:2021)) {
data_Ex_Item_Year_ <-
data_2012_2020_total_ %>%
filter(Fiscal_Year==Year_) %>%
count(Food_classfication, Exceed) %>%
spread(Exceed, n) %>%
replace_na(list(Ex=0 ,`<NA>`=0)) %>%
mutate(Inspect=Ex+`<NA>`) %>%
mutate(Rate=round((Ex/Inspect)*100,2)) %>%
select("Food_classfication", "Ex", "Inspect", "Rate")
colnames(data_Ex_Item_Year_) <- c("Food_classfication", paste0("Ex_", Year_), paste0("Inspect_", Year_), paste0("Rate_", Year_))
data_Ex_Item_Year_
data_Ex_Item_ <- data_Ex_Item_ %>%
merge(
data_Ex_Item_Year_,
by="Food_classfication", all.x=T
)
}
data_Ex_Item_ %>%
arrange(desc(Ex_2012)) %>%
# View()
print()
Fig2_2012 <- data_Ex_Item_ %>% filter(Ex_2012>0) %>% arrange(desc(Ex_2012))
Fig2_2014 <- data_Ex_Item_ %>% filter(Ex_2014>0) %>% arrange(desc(Ex_2014))
Fig2_2016 <- data_Ex_Item_ %>% filter(Ex_2016>0) %>% arrange(desc(Ex_2016))
# Fig2_2019 <- data_Ex_Item_ %>% filter(Ex_2019>0) %>% arrange(desc(Ex_2019))
Fig2_2021 <- data_Ex_Item_ %>% filter(Ex_2021>0) %>% arrange(desc(Ex_2021))
Fig2_2012 %>% print()Fig2_2014 %>% print()Fig2_2016 %>% print()# Fig2_2019 %>% print()
Fig2_2021 %>% print()
rm(data_Ex_Item_, data_Ex_Item_0, data_Ex_Item_Year_)
# Data using Cs detection instrument reported, “Ge,” were used for analysis.
# Other fishery foodstuffs except freshwater fishery foodstuffs were defined as marine fishery foodstuffs.
# Samples marketed or non-marketed foodstuffs were summarized.
# "Marine products(freshwater)" are the freshwater fishery products.
# The foodstuffs that were marketed and produced for sales were counted.
Fig3_A <- data_2012_2020_total_ %>%
filter(Inspection_instrument=="Ge" & Category=="Fishery products") %>%
filter(Market=="Market products") %>%
filter(Food_classfication!="Marine_products(freshwater)") %>%
count(Fiscal_Year, Exceed) %>%
spread(Exceed, n) %>%
replace_na(list(Ex=0, `<NA>`=0)) %>%
mutate(Inspect=Ex+`<NA>`) %>%
rename(c(Market_Ex=Ex, Market_NA=`<NA>`, Market_Inspect=Inspect)) %>%
mutate(Market_Rate=Market_Ex/Market_Inspect*100)
Fig3_A%>%
select("Fiscal_Year", "Market_Ex", "Market_Inspect", "Market_Rate") %>% print()NA# Data using Cs detection instrument reported, “Ge,” were used for analysis.
# Other fishery foodstuffs except freshwater fishery foodstuffs were defined as marine fishery foodstuffs.
# "Marine products(freshwater)" are the freshwater fishery products.
# Wild or aquaculture fishery foodstuffs were counted.
Non_Market_0 <- data_2012_2020_total_ %>%
filter(Inspection_instrument=="Ge" & Category=="Fishery products") %>%
filter(Market!="Market products") %>%
filter(Food_classfication!="Marine_products(freshwater)") %>%
count(Fiscal_Year) %>%
rename(Total=n)
Non_Market <- data_2012_2020_total_ %>%
filter(Inspection_instrument=="Ge" & Category=="Fishery products") %>%
filter(Market=="Non market products") %>%
filter(Food_classfication!="Marine_products(freshwater)") %>%
count(Fiscal_Year, Exceed) %>%
spread(Exceed, n) %>%
rename(c(Non_Market_Ex=Ex, Non_Market_NA=`<NA>`))
For_Sales <- data_2012_2020_total_ %>%
filter(Inspection_instrument=="Ge" & Category=="Fishery products") %>%
filter(Market=="Produce for sales") %>%
filter(Food_classfication!="Marine_products(freshwater)") %>%
count(Fiscal_Year, Exceed) %>%
spread(Exceed, n) %>%
mutate(Ex=0) %>%
rename(c(For_Sale_Ex=Ex, For_Sale_NA=`<NA>`))
Not_For_Sales <- data_2012_2020_total_ %>%
filter(Inspection_instrument=="Ge" & Category=="Fishery products") %>%
filter(Market=="Produce not for sales") %>%
filter(Food_classfication!="Marine_products(freshwater)") %>%
count(Fiscal_Year, Exceed) %>%
spread(Exceed, n) %>%
rename(c(Not_For_Sale_Ex=Ex, Not_For_Sale_NA=`<NA>`))
Fig3_B <- Non_Market_0 %>%
merge(Non_Market, by="Fiscal_Year", all.x=T) %>%
merge(For_Sales, by="Fiscal_Year", all.x=T) %>%
merge(Not_For_Sales, by="Fiscal_Year", all.x=T) %>%
replace_na(
list(Non_Market_Ex=0, Non_Market_NA=0,
For_Sale_Ex=0, For_Sale_NA=0,
Not_For_Sale_Ex=0, Not_For_Sale_NA=0)
) %>%
mutate(Non_Market_Inspect=Non_Market_Ex+Non_Market_NA,
For_Sale_Inspect=For_Sale_Ex+For_Sale_NA,
Not_For_Sale_Inspect=Not_For_Sale_Ex+Not_For_Sale_NA) %>%
mutate(Rate=(Non_Market_Ex + For_Sale_Ex + Not_For_Sale_Ex)/Total*100)
Fig3_B %>%
select("Fiscal_Year", "Non_Market_Ex", "Non_Market_Inspect",
"For_Sale_Ex", "For_Sale_Inspect",
"Not_For_Sale_Ex", "Not_For_Sale_Inspect",
"Rate","Total") %>% print()
rm(Non_Market_0, Non_Market, For_Sales, Not_For_Sales)
# Data using Cs detection instrument reported, “Ge,” were used for analysis.
# Freshwater fishery foodstuffs were summarized.
# Samples marketed or non-marketed foodstuffs were summarized.
# "Marine products(freshwater)" are the freshwater fishery products.
# The foodstuffs that were marketed and produced for sales were counted.
Fig3_C <- data_2012_2020_total_ %>%
filter(Inspection_instrument=="Ge" & Category=="Fishery products") %>%
filter(Market=="Market products") %>%
filter(Food_classfication=="Marine_products(freshwater)") %>%
count(Fiscal_Year, Exceed) %>%
spread(Exceed, n) %>%
replace_na(list(Ex=0, `<NA>`=0)) %>%
mutate(Inspect=Ex+`<NA>`) %>%
rename(c(Market_Ex=Ex, Market_NA=`<NA>`, Market_Inspect=Inspect)) %>%
mutate(Market_Rate=Market_Ex/Market_Inspect*100)
Fig3_C %>%
select("Fiscal_Year", "Market_Ex", "Market_Inspect", "Market_Rate") %>% print()NA
NA# Data using Cs detection instrument reported, “Ge,” were used for analysis.
# Freshwater fishery foodstuffs were summarized.
# "Marine products(freshwater)" are the freshwater fishery products.
# Wild or aquaculture fishery foodstuffs were counted.
Non_Market_0 <- data_2012_2020_total_ %>%
filter(Inspection_instrument=="Ge" & Category=="Fishery products") %>%
filter(Market!="Market products") %>%
filter(Food_classfication=="Marine_products(freshwater)") %>%
count(Fiscal_Year) %>%
rename(Total=n)
Non_Market <- data_2012_2020_total_ %>%
filter(Inspection_instrument=="Ge" & Category=="Fishery products") %>%
filter(Market=="Non market products") %>%
filter(Food_classfication=="Marine_products(freshwater)") %>%
count(Fiscal_Year, Exceed) %>%
spread(Exceed, n) %>%
rename(c(Non_Market_Ex=Ex, Non_Market_NA=`<NA>`))
For_Sales <- data_2012_2020_total_ %>%
filter(Inspection_instrument=="Ge" & Category=="Fishery products") %>%
filter(Market=="Produce for sales") %>%
filter(Food_classfication=="Marine_products(freshwater)") %>%
count(Fiscal_Year, Exceed) %>%
spread(Exceed, n) %>%
mutate(Ex=0) %>%
rename(c(For_Sale_Ex=Ex, For_Sale_NA=`<NA>`))
Not_For_Sales <- data_2012_2020_total_ %>%
filter(Inspection_instrument=="Ge" & Category=="Fishery products") %>%
filter(Market=="Produce not for sales") %>%
filter(Food_classfication=="Marine_products(freshwater)") %>%
count(Fiscal_Year, Exceed) %>%
spread(Exceed, n) %>%
rename(c(Not_For_Sale_Ex=Ex, Not_For_Sale_NA=`<NA>`))
Fig3_D <- Non_Market_0 %>%
merge(Non_Market, by="Fiscal_Year", all.x=T) %>%
merge(For_Sales, by="Fiscal_Year", all.x=T) %>%
merge(Not_For_Sales, by="Fiscal_Year", all.x=T) %>%
replace_na(
list(Non_Market_Ex=0, Non_Market_NA=0,
For_Sale_Ex=0, For_Sale_NA=0,
Not_For_Sale_Ex=0, Not_For_Sale_NA=0)
) %>%
mutate(Non_Market_Inspect=Non_Market_Ex+Non_Market_NA,
For_Sale_Inspect=For_Sale_Ex+For_Sale_NA,
Not_For_Sale_Inspect=Not_For_Sale_Ex+Not_For_Sale_NA) %>%
mutate(Rate=(Non_Market_Ex + For_Sale_Ex + Not_For_Sale_Ex)/Total*100)
Fig3_D %>%
select("Fiscal_Year", "Non_Market_Ex", "Non_Market_Inspect",
"For_Sale_Ex", "For_Sale_Inspect",
"Not_For_Sale_Ex", "Not_For_Sale_Inspect",
"Rate","Total") %>% print()
rm(Non_Market_0, Non_Market, For_Sales, Not_For_Sales)
# Data using Cs detection instrument reported, “Ge,” were used for analysis.
# "Marine products(freshwater)" are the freshwater fishery products.
data_2012_2020_fish_ <- data_2012_2020_total_ %>%
filter(Inspection_instrument=="Ge") %>%
filter(Category=="Fishery products" & !is.na(Fiscal_Year)) %>%
mutate(
Marine_Water = case_when(Food_classfication=="Marine_products(freshwater)" ~ "Freshwater",
Food_classfication!="Marine_products(freshwater)" ~ "Marine")
) %>%
mutate(Period=case_when(
Fiscal_Year==2012 | Fiscal_Year==2013 | Fiscal_Year==2014 | Fiscal_Year==2015 ~ "Early",
Fiscal_Year==2016 | Fiscal_Year==2017 | Fiscal_Year==2018 | Fiscal_Year==2019 ~ "Middle",
Fiscal_Year==2020 | Fiscal_Year==2021 ~ "Later",
TRUE ~ "Others"
)
) %>%
mutate(Farmed_Wild=case_when(
str_detect(品目名, "養殖") & !str_detect(その他, "養殖ではない") ~ "Farmed",
str_detect(その他, "養殖") & !str_detect(その他, "養殖ではない") ~ "Farmed",
!str_detect(その他, "養殖") | str_detect(その他, "養殖ではない") ~ "Wild",
!str_detect(その他, "養殖") | str_detect(その他, "養殖ではない") ~ "Wild",
TRUE ~ "Wild"
)
) %>%
mutate(Fresh_Farmed=case_when(
Food_classfication=="Marine_products(freshwater)" & Farmed_Wild=="Wild" ~ "Freshwater, wild",
Food_classfication!="Marine_products(freshwater)" & Farmed_Wild=="Wild" ~ "Marine, wild",
Food_classfication=="Marine_products(freshwater)" & Farmed_Wild=="Farmed" ~ "Freshwater, aquaculture",
Food_classfication!="Marine_products(freshwater)" & Farmed_Wild=="Farmed" ~ "Marine, aquaculture",
TRUE ~ "-"
)
) %>%
mutate(Ex_Detect=case_when(
Cs_total_fix>100 ~ "Ex",
Cs_total_fix>25 ~ "Detect",
!Cs_total_fix>100 ~ "ND"
))
data_Fishery_ <- data_2012_2020_fish_ %>% filter(Period!="Early")
data_Fishery_ %>%
count(Fresh_Farmed) %>%
rename(Examined=n) %>%
merge(
data_Fishery_ %>%
filter(Cs_total_fix>25) %>%
count(Fresh_Farmed) %>%
rename(Detected=n),
by="Fresh_Farmed", all.x = T
) %>%
merge(
data_Fishery_ %>%
filter(Cs_total_fix>100) %>%
count(Fresh_Farmed) %>%
rename(Exceed=n),
by="Fresh_Farmed", all.x = T
) %>%
replace_na(list(Fresh_Farmed="Others", Examined=0, Detected=0, Exceed=0)) %>%
mutate(Detect_Rate=Detected/Examined*100) %>%
mutate(Ex_Rate=Exceed/Examined*100) %>%
print()
rm(data_Fishery_, data_2012_2020_fish_)