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####################################
# nuts rules
# fadn nuts2 heatmap############################
# fadn nuts2 heatmap
multiplot <- function(..., plotlist=NULL, cols) {
require(grid)
# Make a list from the ... arguments and plotlist
plots <- c(list(...), plotlist)
numPlots = length(plots)
# Make the panel
plotCols = cols # Number of columns of plots
plotRows = ceiling(numPlots/plotCols) # Number of rows needed, calculated from # of cols
# Set up the page
grid.newpage()
pushViewport(viewport(layout = grid.layout(plotRows, plotCols)))
vplayout <- function(x, y)
viewport(layout.pos.row = x, layout.pos.col = y)
# Make each plot, in the correct location
for (i in 1:numPlots) {
curRow = ceiling(i/plotCols)
curCol = (i-1) %% plotCols + 1
print(plots[[i]], vp = vplayout(curRow, curCol ))
}
}
countries <- unique(str_data$info$COUNTRY)
# heatmap function
heatmap.group <- function(group.by) {
for (country in countries){
heatmap_data <- str_data$info %>%
filter(COUNTRY == country) %>%
count({{group.by}},YEAR) %>%
arrange(YEAR) %>%
# pivot_wider(names_from = YEAR,values_from=n) %>%
mutate(across(3:last_col(),function(x)ifelse(is.na(x),0,1)))
# pivot_longer(c(`2004`:`2018` ), names_to = "YEAR", values_to = "n")
heatmap_data <- data.frame(lapply(heatmap_data,as.character))
if (NROW(heatmap_data$NUTS3 %>% unique()) >100 ) {
text.size = 3
} else{text.size = 11}
p <- heatmap_data %>% ggplot(aes(YEAR, {{group.by}}, fill= n)) + geom_tile() +
theme(legend.position="none") +
ggtitle(country)+ theme(axis.text.y = element_text(size = text.size))
#
# p_name <- country
#
# assign(p_name, heatmap_data %>% ggplot ( aes(YEAR,NUTS2, fill= n)) + geom_tile() +
# theme(legend.position="none") +
# ggtitle(country))
# ggsave(plot = p ,
# filename = paste0(CurrentProjectDirectory,"/plot/","fadn_",deparse(substitute(group.by)),"_rules/",country,".png"),
# width = 18, height = 8)
ggsave(plot = p ,
filename = paste0(CurrentProjectDirectory,"/plot/","fadn_",deparse(substitute(group.by)),"_plots/",country,".png"),
width = 18, height = 8)
}
}
heatmap.group(REGION)
heatmap.group(NUTS1)
heatmap.group(NUTS2)
heatmap.group(NUTS3)
# multiple plots
plots <- list(NED, BEL, BGR, CYP, CZE,DAN,DEU,ELL,ESP,EST,FRA,HUN,IRE,ITA,LTU,LUX,LVA,MLT,OST,POL,POR,ROU,SUO,SVE,SVK,SVN,UKI,HRV)
mulp <- multiplot(NED, BEL, BGR, CYP, CZE,DAN,DEU,ELL,ESP,EST,FRA,HUN,IRE,ITA,LTU,LUX,LVA,MLT,OST,POL,POR,ROU,SUO,SVE,SVK,SVN,UKI,HRV, cols=5)
ggsave(plot = mulp ,
filename = paste0(CurrentProjectDirectory,"/plots/animal/num_farms/nuts2/nuts2_rules/","all.png"),
width = 18, height = 8)
#check nuts function###########################################
check.nuts <- function (data, country, year, group.by, export.EXCEL = FALSE){
nuts2Excel <- paste0(CurrentProjectDirectory,"/plots/","fadn_",deparse(substitute(group.by)),"_rules/excel/")
export.name = paste0(country,".",deparse(substitute(group.by)),".",year[1])
assign(export.name, data %>%
filter(YEAR %in% year & COUNTRY == country) %>%
select({{group.by}}, COUNTRY) %>% distinct() %>% mutate(value = 1) )
# envir = parent.frame()
# )
# print(get(export.name))
if (deparse(substitute(group.by)) == 'REGION') export.EXCEL= FALSE
if(export.EXCEL){
write.xlsx(as.data.frame(get(export.name)),
paste0(nuts2Excel, deparse(substitute(group.by)),"_", country,year[1],".xlsx"),
row.names = FALSE)}
print(paste0(nuts2Excel, deparse(substitute(group.by)),"_", country,year[1],".xlsx"))
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return (get(export.name))
}
# nuts2 ##################################################
# DEU #######################################################################
deu.version07 <- str_data$info %>%
filter(YEAR == 2007 & COUNTRY == "DEU") %>%
select(NUTS2, COUNTRY) %>% distinct()%>%
rename_with(.fn = ~paste0(., "_version07"), .cols = NUTS2)
write.xlsx(as.data.frame(deu.version07), paste0(nuts2File,"deu.version07.xlsx"), sheetName = "NUTS2", row.names = FALSE)
deu.version08_09 <- str_data$info %>%
filter(YEAR %in% c(2008:2009) & COUNTRY == "DEU") %>%
select(NUTS2, COUNTRY) %>% distinct()%>%
rename_with(.fn = ~paste0(., "_version08_09"), .cols = NUTS2)
write.xlsx(as.data.frame(deu.version08_09), paste0(nuts2File,"deu.version08_09.xlsx"), sheetName = "NUTS2", row.names = FALSE)
# version 2010-2018
deu.version10_18 <- str_data$info %>%
filter(YEAR %in% c(2010:2018) & COUNTRY == "DEU") %>%
select(NUTS2, COUNTRY) %>% distinct()%>%
rename_with(.fn = ~paste0(., "_version10_18"), .cols = NUTS2)
write.xlsx(as.data.frame(deu.version10_18), paste0(nuts2File,"deu.version10_18.xlsx"), sheetName = "NUTS2", row.names = FALSE)
# version: 2007
#..................................
# DEE1, DEE2, DEE3 merged into DEEO
# version: 2008-2009
#..........................
# DEE2 merged into DEE0
# version 2008-2009 -> 2010-2018
#............................
# DE41, DE41 merged into DE40
#............................
# Leipzig reccalculation by NSI
# DED5 new region
# DED3 boundary shift
#........................
# chemnitz reccalculation by NSI
# DED1 boundary shift
# DED4 new region
#..........................
# summary NUT2 level change .......................
str_data$info %>%
filter(COUNTRY == "DEU") %>%
mutate(NUTS2=case_when(
NUTS2 %in% c("DEE1","DEE2","DEE3") ~ "DEE0",
NUTS2 %in% c("DE41","DE42") ~ "DE40",
NUTS2 %in% c("DED1") ~ "DED4",
NUTS2 %in% c("DED3") ~ "DED5",
TRUE ~ NUTS2)) %>% count(NUTS2,YEAR) %>%
arrange(YEAR) %>%
mutate(across(3:last_col(),function(x)ifelse(is.na(x),0,1))) %>% ggplot ( aes(YEAR,NUTS2, fill= n)) + geom_tile() +
theme(legend.position="none")
# CZE ##################################################
# no chnange............................
cze.version07_13 <- str_data$info %>%
filter(YEAR %in% c(2007:2013) & COUNTRY == "CZE") %>%
select(NUTS2, COUNTRY) %>% distinct()%>%
rename_with(.fn = ~paste0(., "_version07_13"), .cols = NUTS2)
write.xlsx(as.data.frame(cze.version07_13), paste0(nuts2File,"cze.version07_13.xlsx"), sheetName = "NUTS2", row.names = FALSE)
cze.version14_18 <- str_data$info %>%
filter(YEAR %in% c(2014:2018) & COUNTRY == "CZE") %>%
select(NUTS2, COUNTRY) %>% distinct()%>%
rename_with(.fn = ~paste0(., "_version14_18"), .cols = NUTS2)
write.xlsx(as.data.frame(cze.version14_18), paste0(nuts2File,"cze.version14_18.xlsx"), sheetName = "NUTS2", row.names = FALSE)
# ELL ##################################################
ell.version07_09 <- str_data$info %>%
filter(YEAR %in% c(2007:2009) & COUNTRY == "ELL") %>%
select(NUTS2, COUNTRY) %>% distinct()
write.xlsx(as.data.frame(ell.version07_09), paste0(nuts2File,"ell.version07_09.xlsx"), sheetName = "NUTS2", row.names = FALSE)
ell.version10_18 <- str_data$info %>%
filter(YEAR %in% c(2010:2018) & COUNTRY == "ELL") %>%
select(NUTS2, COUNTRY) %>% distinct()
#.......................
# version 2003
# new region: GRZZ
# fadn has no GRZZ!!!!!
#.......................
# version 07-09 -> version 2010
#........................
# since version 2010
# code change
# GR11 changed into EL11
# GR12 changed into EL12
# GR13 changed into EL13
# GR14 changed into EL14
# GR21 changed into EL21
# GR22 changed into EL22
# GR23 changed into EL23
# GR24 changed into EL24
# GR25 changed into EL25
# GR30 changed into EL30
# GR41 changed into EL41
# GR42 changed into EL42
# GR43 changed into EL43
# GRZZ changed into ELZZ
#.................
# version 2010 - 2013
# new old
# EL51 = EL11
# EL52 = EL12
# EL53 = EL13
# EL54 = EL21
# EL61 = EL14
# EL62 = EL22
# EL63 = EL23
# EL64 = EL24
# EL65 = EL25
#
# summary
str_data$info %>%
filter(COUNTRY == "ELL") %>%
mutate(NUTS2=case_when(
NUTS2 %in% c( "GR11","EL11") ~ "EL51" ,
NUTS2 %in% c("GR12", "EL12") ~ "EL52",
NUTS2 %in% c("GR13", "EL13") ~ "EL53",
NUTS2 %in% c("GR14", "EL14") ~ "EL54",
NUTS2 %in% c("GR21", "EL21") ~ "EL61",
NUTS2 %in% c("GR22", "EL22") ~"EL62",
NUTS2 %in% c("GR23", "EL23") ~ "EL63",
NUTS2 %in% c("GR24", "EL24") ~"EL64",
NUTS2 %in% c("GR25", "EL25") ~ "EL65",
NUTS2 == "GR30" ~ "EL30" ,
NUTS2 == "GR41" ~ "EL41" ,
NUTS2 == "GR42" ~ "EL42",
NUTS2 == "GR43" ~ "EL43" ,
TRUE ~ NUTS2)) %>% count(NUTS2,YEAR) %>%
arrange(YEAR) %>%
mutate(across(3:last_col(),function(x)ifelse(is.na(x),0,1))) %>% ggplot ( aes(YEAR,NUTS2, fill= n)) + geom_tile() +
theme(legend.position="none")
# FRA ###################################################
fra.version07_16 <- str_data$info %>%
filter(YEAR %in% c(2007:2016) & COUNTRY == "FRA") %>%
select(NUTS2, COUNTRY) %>% distinct()
write.xlsx(as.data.frame(fra.version07_16), paste0(nuts2File,"fra.version07_16.xlsx"), sheetName = "NUTS2", row.names = FALSE)
fra.version17_18 <- str_data$info %>%
filter(YEAR %in% c(2017:2018) & COUNTRY == "FRA") %>%
select(NUTS2, COUNTRY) %>% distinct()
write.xlsx(as.data.frame(fra.version17_18), paste0(nuts2File,"fra.version17_18.xlsx"), sheetName = "NUTS2", row.names = FALSE)
# version 2013 -> version 2016
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# FR24 FRB0
# FR26 FRC1
# FR43 FRC2
# FR25 FRD1
# FR23 FRD2
# FR30 FRE1
# FR22 FRE2
# FR42 FRF1
# FR21 FRF2
# FR41 FRF3
# FR51 FRG0
# FR52 FRH0
# FR61 FRI1
# FR63 FRI2
# FR53 FRI3
# FR81 FRJ1
# FR62 FRJ2
# FR72 FRK1
# FR71 FRK2
# FR82 FRL0
# FR83 FRM0
# FRA1 FRY1
# FRA2 FRY2
# FRA3 FRY3
# FRA4 FRY4
# FRA5 FRY5
# summary
str_data$info %>%
filter(COUNTRY == "FRA") %>%
mutate(NUTS2=case_when(
NUTS2 =="FR24"~ "FRB0",
NUTS2 =="FR26"~"FRC1",
NUTS2 =="FR43"~ "FRC2",
NUTS2 =="FR25"~ "FRD1",
NUTS2 =="FR23"~ "FRD2",
NUTS2 =="FR30"~ "FRE1",
NUTS2 =="FR22"~ "FRE2",
NUTS2 =="FR42"~ "FRF1",
NUTS2 =="FR21"~ "FRF2",
NUTS2 =="FR41"~ "FRF3",
NUTS2 =="FR51"~ "FRG0",
NUTS2 =="FR52"~ "FRH0",
NUTS2 =="FR61"~ "FRI1",
NUTS2 =="FR63"~ "FRI2",
NUTS2 =="FR53"~ "FRI3",
NUTS2 =="FR81"~ "FRJ1",
NUTS2 =="FR62"~ "FRJ2",
NUTS2 =="FR72"~ "FRK1",
NUTS2 =="FR71"~ "FRK2",
NUTS2 =="FR82"~ "FRL0",
NUTS2 =="FR83"~ "FRM0",
NUTS2 =="FRA1"~ "FRY1",
NUTS2 =="FRA2"~ "FRY2",
NUTS2 =="FRA3"~ "FRY3",
NUTS2 =="FRA4"~ "FRY4",
NUTS2 =="FRA5"~ "FRY5",
TRUE ~ NUTS2)) %>% count(NUTS2,YEAR) %>%
arrange(YEAR) %>%
mutate(across(3:last_col(),function(x)ifelse(is.na(x),0,1))) %>% ggplot ( aes(YEAR,NUTS2, fill= n)) + geom_tile() +
theme(legend.position="none")
# HUN #################################################
#
# since 2016
# HU10 discontinued; and split into new HU11 and HU12
# HU11 = HU101 (NUTS3)
# HU12 = HU102 (NUTS3)
# ????????????????
# fadn data: missing HU11?????
# nuts3 has no HU101 and HU102, but HU10
hun.version07_16 <- str_data$info %>%
filter(YEAR %in% c(2007:2016) & COUNTRY == "HUN") %>%
select(NUTS2, COUNTRY) %>% distinct() %>% mutate(value = 1)
write.xlsx(as.data.frame(hun.version07_16), paste0(nuts2File,"hun.version07_16.xlsx"), sheetName = "NUTS2", row.names = FALSE)
hun.version17_18 <- str_data$info %>%
filter(YEAR %in% c(2017:2018) & COUNTRY == "HUN") %>%
select(NUTS2, COUNTRY) %>% distinct()
write.xlsx(as.data.frame(hun.version17_18), paste0(nuts2File,"hun.version17_18.xlsx"), sheetName = "NUTS2", row.names = FALSE)
# summary ???????????????????????????????????
str_data$info %>%
filter(COUNTRY == "HUN") %>%
mutate(NUTS2=case_when(
NUTS2 =="HU10"~ "HU12",
TRUE ~ NUTS2)) %>% count(NUTS2,YEAR) %>%
arrange(YEAR) %>%
mutate(across(3:last_col(),function(x)ifelse(is.na(x),0,1))) %>% ggplot ( aes(YEAR,NUTS2, fill= n)) + geom_tile() +
theme(legend.position="none")
# IRE #################################################
ire.version07_16 <- str_data$info %>%
filter(YEAR %in% c(2007:2016) & COUNTRY == "IRE") %>%
select(NUTS2, COUNTRY) %>% distinct() %>% mutate(value = 1)
write.xlsx(as.data.frame(ire.version07_16), paste0(nuts2File,"ire.version07_16.xlsx"), sheetName = "NUTS2", row.names = FALSE)
ire.version17_18 <- str_data$info %>%
filter(YEAR %in% c(2017:2018) & COUNTRY == "IRE") %>%
select(NUTS2, COUNTRY) %>% distinct()
write.xlsx(as.data.frame(ire.version17_18), paste0(nuts2File,"ire.version17_18.xlsx"), sheetName = "NUTS2", row.names = FALSE)
# since 2016 version
#......................
# IE01 discontinued
# IE02 discontinued
# IE04 new region ?????????????????
# IE05 new region, made from ex-IE023, IE024 and IE025 IE05=IE023+IE024+IE025 (from NUTS3)
# IE06 new region ?????????????????
# summary ???????????????
# NUTS2 IE04 IRE 0.78
# NUTS2 IE05 IRE 0.81
# NUTS2 IE06 IRE 0.42
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# ITA ################################
ita.version07_09 <- str_data$info %>%
filter(YEAR %in% c(2007:2009) & COUNTRY == "ITA") %>%
select(NUTS2, COUNTRY) %>% distinct() %>% mutate(value = 1)
write.xlsx(as.data.frame(ita.version07_09), paste0(nuts2File,"ita.version07_09.xlsx"), sheetName = "NUTS2", row.names = FALSE)
# vrsion 2006 into version 2010
#.....................................
# recoded
# ITD1 ITH1 label change ITH1 = ITD1
# ITD2 ITH2 label change ITH2 = ITD2
# ITD3 ITH3 recoded ITH3 = ITD3
# ITD4 ITH4 recoded ITH4 = ITD4
# ITE4 ITI4 recoded ITI4 = ITE4
# ITE1 ITI1 recoded ITI1 = ITE1
# ITE2 ITI2 recoded ITI2 = ITE2
#....................................
# Boundary shift
# ITD5 Emilia-Romagna Boundary shift
# ITH5 Emilia-Romagna new region recalculation by NSI
# ITE3 Marche Boundary shift
# ITI3 Marche new region recalculation by NSI
str_data$info %>%
filter(COUNTRY == "ITA") %>%
mutate(NUTS2=case_when(
NUTS2 == "ITD1"~ "ITH1",
NUTS2 == "ITD2"~ "ITH2" ,
NUTS2 == "ITD3"~ "ITH3" ,
NUTS2 == "ITD4"~ "ITH4",
NUTS2 == "ITE4" ~"ITI4",
NUTS2 == "ITI1"~ "ITE1",
NUTS2 == "ITI2" ~"ITE2",
NUTS2 == "ITD5" ~ "ITH5",
NUTS2 =="ITE3" ~ "ITI3",
TRUE ~ NUTS2)) %>% count(NUTS2,YEAR) %>%
arrange(YEAR) %>%
mutate(across(3:last_col(),function(x)ifelse(is.na(x),0,1))) %>% ggplot ( aes(YEAR,NUTS2, fill= n)) + geom_tile() +
theme(legend.position="none")
# LTU ########################################
ltu.version07_16 <- str_data$info %>%
filter(YEAR %in% c(2007:2016) & COUNTRY == "LTU") %>%
select(NUTS2, COUNTRY) %>% distinct() %>% mutate(value = 1)
write.xlsx(as.data.frame(ltu.version07_16), paste0(nuts2File,"ltu.version07_16.xlsx"), sheetName = "NUTS2", row.names = FALSE)
ltu.version17_18 <- str_data$info %>%
filter(YEAR %in% c(2017:2018) & COUNTRY == "LTU") %>%
select(NUTS2, COUNTRY) %>% distinct()
write.xlsx(as.data.frame(ltu.version17_18), paste0(nuts2File,"ltu.version17_18.xlsx"), sheetName = "NUTS2", row.names = FALSE)
# version 2013 -> 2016
#..............................
# LT00 split into new LT01 LT02
# LT01=LT00A
# LT02=LT00-LT00A ??? how
# summary
str_data$info %>%
filter(COUNTRY == "LTU") %>%
mutate(NUTS2=case_when(
NUTS3 == "LT00A" ~ "LT01",
NUTS2 == "LT00" ~ "LT02",
TRUE ~ NUTS2)) %>% count(NUTS2,YEAR) %>%
arrange(YEAR) %>%
mutate(across(3:last_col(),function(x)ifelse(is.na(x),0,1))) %>% ggplot ( aes(YEAR,NUTS2, fill= n)) + geom_tile() +
theme(legend.position="none")
# LVA #######################################
lva.version07_08 <- str_data$info %>%
filter(YEAR %in% c(2007:2008) & COUNTRY == "LVA") %>%
select(NUTS2, COUNTRY) %>% distinct()
write.xlsx(as.data.frame(lva.version07_08), paste0(nuts2File,"lva.version07_08.xlsx"), sheetName = "NUTS2", row.names = FALSE)
lva.version09_18 <- str_data$info %>%
filter(YEAR %in% c(2009:2018) & COUNTRY == "LVA") %>%
select(NUTS2, COUNTRY) %>% distinct()
write.xlsx(as.data.frame(lva.version09_18), paste0(nuts2File,"lva.version09_18.xlsx"), sheetName = "NUTS2", row.names = FALSE)
# ????????????????
# empty nuts2
# OST ########################################
ost.version07 <- str_data$info %>%
filter(YEAR %in% c(2007) & COUNTRY == "OST") %>%
select(NUTS2, COUNTRY) %>% distinct() %>% mutate(value = 1)
write.xlsx(as.data.frame(ost.version07), paste0(nuts2File,"ost.version07.xlsx"), sheetName = "NUTS2", row.names = FALSE)
ost.version08_18 <- str_data$info %>%
filter(YEAR %in% c(2008:2018) & COUNTRY == "OST") %>%
select(NUTS2, COUNTRY) %>% distinct()
write.xlsx(as.data.frame(ost.version08_18), paste0(nuts2File,"ost.version08_18.xlsx"), sheetName = "NUTS2", row.names = FALSE)
# in 2007: "" item maybe AT13???
# POL ##########################################
pol.version07_16 <- str_data$info %>%
filter(YEAR %in% c(2007:2016) & COUNTRY == "POL") %>%
select(NUTS2, COUNTRY) %>% distinct() %>% mutate(value =1)
write.xlsx(as.data.frame(pol.version07_16), paste0(nuts2File,"pol.version07_16.xlsx"), sheetName = "NUTS2", row.names = FALSE)
pol.version17_18 <- str_data$info %>%
filter(YEAR %in% c(2017:2018) & COUNTRY == "POL") %>%
select(NUTS2, COUNTRY) %>% distinct()
write.xlsx(as.data.frame(pol.version17_18), paste0(nuts2File,"pol.version17_18.xlsx"), sheetName = "NUTS2", row.names = FALSE)
# version 2013 -> 2016
#.....................
# recoded
# PL11 PL71
# PL33 PL72
# PL31 PL81
# PL32 PL82
# PL34 PL84
#....................
# split
# PL12 split into new PL91, PL92
# PL91=PL127+PL129+PL12A-newPL926
# PL92=PL128+PL12B+PL12C+PL12D+PL12E+new PL926
# summary
# ??? before 2014 can't convert !!!!
str_data$info %>%
filter(COUNTRY == "POL") %>%
mutate(NUTS2=case_when(
NUTS2 == "PL11" ~ "PL71",
NUTS2 == "PL33" ~ "PL72",
NUTS2 == "PL31" ~ "PL81",
NUTS2 == "PL32" ~ "PL82",
NUTS2 == "PL34" ~ "PL84",
(NUTS2 == "PL12" & NUTS3 %in% c("PL127","PL129","PL12A", "PL926")) ~ "PL91",
(NUTS2 == "PL12" & NUTS3 %in% c("PL128","PL12B","PL12C","PL12D","PL12E", "PL926")) ~ "PL92",
TRUE ~ NUTS2)) %>% count(NUTS2,YEAR) %>%
arrange(YEAR) %>%
mutate(across(3:last_col(),function(x)ifelse(is.na(x),0,1))) %>% ggplot ( aes(YEAR,NUTS2, fill= n)) + geom_tile() +
theme(legend.position="none")
# SUO ############################################
suo.version07_09 <- str_data$info %>%
filter(YEAR %in% c(2007:2009) & COUNTRY == "SUO") %>%
select(NUTS2, COUNTRY) %>% distinct() %>% mutate(value = 1)
write.xlsx(as.data.frame(suo.version07_09), paste0(nuts2File,"suo.version07_09.xlsx"), sheetName = "NUTS2", row.names = FALSE)
suo.version10_18 <- str_data$info %>%
filter(YEAR %in% c(2010:2018) & COUNTRY == "SUO") %>%
select(NUTS2, COUNTRY) %>% distinct() %>% mutate(value = 1)
write.xlsx(as.data.frame(suo.version10_18), paste0(nuts2File,"suo.version10_18.xlsx"), sheetName = "NUTS2", row.names = FALSE)
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# new region FI1D
# FI1D = FI13 + FI1A
#...........................................
# FI18 split into new region FI1B and FI1C
# FI1B + FI1C = FI18
# FI1B + FI1C = FI18
str_data$info %>%
filter(COUNTRY == "SUO") %>%
mutate(NUTS2=case_when(
NUTS2 %in% c("FI13", "FI1A") ~ "FI1D",
# NUTS2 == "FI18" ~ "FI1B",
# NUTS2 == "FI18" ~ "FI1C",
TRUE ~ NUTS2)) %>% count(NUTS2,YEAR) %>%
arrange(YEAR) %>%
mutate(across(3:last_col(),function(x)ifelse(is.na(x),0,1))) %>% ggplot ( aes(YEAR,NUTS2, fill= n)) + geom_tile() +
theme(legend.position="none")
# SVE ########################################
sve.version07 <- str_data$info %>%
filter(YEAR %in% c(2007) & COUNTRY == "SVE") %>%
select(NUTS2, COUNTRY) %>% distinct() %>% mutate (value =1)
write.xlsx(as.data.frame(sve.version07), paste0(nuts2File,"sve.version07.xlsx"), sheetName = "NUTS2", row.names = FALSE)
sve.version08_18 <- str_data$info %>%
filter(YEAR %in% c(2008:2018) & COUNTRY == "SVE") %>%
select(NUTS2, COUNTRY) %>% distinct() %>% mutate (value =1)
write.xlsx(as.data.frame(sve.version08_18), paste0(nuts2File,"sve.version08_18.xlsx"), sheetName = "NUTS2", row.names = FALSE)
# version 2003 -> 2006
# code change...........
# old new
# SE01 SE11
# SE02 SE12
# SE09 SE21
# SE04 SE22
# SE0A SE23
# SE06 SE31
# SE07 SE32
# SE08 SE33
str_data$info %>%
filter(COUNTRY == "SVE") %>%
mutate(NUTS2=case_when(
NUTS2 == "SE01" ~ "SE11",
NUTS2 == "SE02" ~ "SE12",
NUTS2 == "SE09" ~ "SE21",
NUTS2 == "SE04" ~ "SE22",
NUTS2 == "SE0A" ~ "SE23",
NUTS2 == "SE06" ~ "SE31",
NUTS2 == "SE07" ~ "SE32",
NUTS2 == "SE08" ~ "SE33",
TRUE ~ NUTS2)) %>% count(NUTS2,YEAR) %>%
arrange(YEAR) %>%
mutate(across(3:last_col(),function(x)ifelse(is.na(x),0,1))) %>% ggplot ( aes(YEAR,NUTS2, fill= n)) + geom_tile() +
theme(legend.position="none")
# SVN ######################################
svn.version07_08 <- str_data$info %>%
filter(YEAR %in% c(2007:2008) & COUNTRY == "SVN") %>%
select(NUTS2, COUNTRY) %>% distinct() %>% mutate(value = 1)
write.xlsx(as.data.frame(svn.version07_08), paste0(nuts2File,"svn.version07_08.xlsx"), sheetName = "NUTS2", row.names = FALSE)
svn.version09_13 <- str_data$info %>%
filter(YEAR %in% c(2009:2013) & COUNTRY == "SVN") %>%
select(NUTS2, COUNTRY) %>% distinct() %>% mutate(value = 1)
write.xlsx(as.data.frame(svn.version09_13), paste0(nuts2File,"svn.version09_13.xlsx"), sheetName = "NUTS2", row.names = FALSE)
svn.version14_18 <- str_data$info %>%
filter(YEAR %in% c(2014:2018) & COUNTRY == "SVN") %>%
select(NUTS2, COUNTRY) %>% distinct() %>% mutate(value = 1)
write.xlsx(as.data.frame(svn.version14_18), paste0(nuts2File,"svn.version14_18.xlsx"), sheetName = "NUTS2", row.names = FALSE)
# version 2003 -> 2006
#............................
# SI00 split into SI01 and SI02
# version 2010 -> 2013
#.............................
# Boundary shift
# SI01 -> SI03 recalculation by NSI
# SI02 -> SI04 recalculation by NSI
#..................................
str_data$info %>%
filter(COUNTRY == "SVN") %>%
mutate(NUTS2=case_when(
NUTS2 == "SI01" ~ "SI03",
NUTS2 == "SI02" ~ "SI04",
TRUE ~ NUTS2)) %>% count(NUTS2,YEAR) %>%
arrange(YEAR) %>%
mutate(across(3:last_col(),function(x)ifelse(is.na(x),0,1))) %>% ggplot ( aes(YEAR,NUTS2, fill= n)) + geom_tile() +
theme(legend.position="none")
# UKI ##########################################
uki.version07_09 <- str_data$info %>%
filter(YEAR %in% c(2007:2009) & COUNTRY == "UKI") %>%
select(NUTS2, COUNTRY) %>% distinct() %>% mutate(value = 1)
write.xlsx(as.data.frame(uki.version07_09), paste0(nuts2File,"uki.version07_09.xlsx"), sheetName = "NUTS2", row.names = FALSE)
uki.version10_13 <- str_data$info %>%
filter(YEAR %in% c(2010:2013) & COUNTRY == "UKI") %>%
select(NUTS2, COUNTRY) %>% distinct()%>% mutate(value = 1)
uki.version14_18 <- str_data$info %>%
filter(YEAR %in% c(2014:2018) & COUNTRY == "UKI") %>%
select(NUTS2, COUNTRY) %>% distinct()%>% mutate(value = 1)
str_data$info %>%
filter(YEAR %in% c(2010:2013) & COUNTRY == "UKI") %>%
select(NUTS2, COUNTRY) %>% distinct() %>% filter (NUTS2 %in% c("UKI1","UKI3","UKI4","UKI2" ))
#......................
# version 2006 -> 2010
# Boundary shift
#.
# UKD2 -> UKD6
# UKD5 -> UKD7
#............................
# version 2010 -> 2013
# UKI1 split into UKI3 + UKI4
# UKI3 + UKI4 = UKI1
#...................................
# UKI2 split into UKI5 + UKI6 + UKI7
# UKI5 + UKI6 + UKI7 = UKI2
#..................................
# version 2013 -> 2016
# UKM7 boundary shift: lost exUKM24
# UKM7=UKM2-UKM24
#...............................................
# UKM3 discontinued; split into new UKM8 and UKM9
#.............................................
# new region
# UKM8=UKM31+UKM34+UKM35+UKM36
# UKM9=UKM24+UKM32+UKM33+UKM37+UKM38
str_data$info %>%
filter(COUNTRY == "UKI") %>%
mutate(NUTS2=case_when(
NUTS2 == "UKD2" ~ "UKD6",
NUTS2 == "UKD5" ~ "UKD7",
NUTS3 %in% c("UKM31","UKM34","UKM35","UKM36") ~ "UKM8",
NUTS3 %in% c("UKM24","UKM32","UKM33","UKM37", "UKM38") ~ "UKM9",
TRUE ~ NUTS2)) %>%
count(NUTS2,YEAR) %>%
arrange(YEAR) %>%
mutate(across(3:last_col(),function(x)ifelse(is.na(x),0,1))) %>% ggplot ( aes(YEAR,NUTS2, fill= n)) + geom_tile() +
theme(legend.position="none")
# nuts1 ##################################################
# ell ----
check.nuts(str_data$info, "ELL", c(2007:2009), NUTS1, FALSE)
check.nuts(str_data$info, "ELL", c(2010:2013), NUTS1, FALSE)
check.nuts(str_data$info, "ELL", c(2014:2018), NUTS1, FALSE)
# version 2006 -2010
#...................
# old new recoded
# GR1 EL1
# GR2 EL2
# GR3 EL3
# GR4 EL4
# version 2010-2013
#...........................
# boundary shift| new region
# EL1 | EL5
# EL2 | EL6
# fra ----
check.nuts(str_data$info, "FRA", c(2007:2011), NUTS1, export.EXCEL = TRUE)
check.nuts(str_data$info, "FRA", c(2012:2013), NUTS1, export.EXCEL = TRUE)
check.nuts(str_data$info, "FRA", c(2014:2016), NUTS1, export.EXCEL = TRUE)
check.nuts(str_data$info, "FRA", c(2017:2018), NUTS1, export.EXCEL = TRUE)
# version 2010 - 2013
#...................
# boundary shift
#..................
# new
# FR9 FRA
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# discontinued
# FR2
# FR3
# FR4
# FR5
# FR6
# FR8
#...............
# recoded
#...............
# old new
# FRA FRY
# FR7 FRK
#...............
# new region
#...............
# FRB=FR24
# FRC=FR26+FR43
# FRD=FR23+FR25
# FRE=FR22+FR30
# FRF=FR21+FR41+FR42
# FRG=FR51
# FRH=FR52
# FRI=FR53+FR61+FR63
# FRJ=FR62+FR81
# FRL=FR82
# FRM=FR83
# ita ---------
check.nuts(str_data$info, "ITA", c(2007:2009), NUTS1)
check.nuts(str_data$info, "ITA", c(2010:2018), NUTS1)
# version 2006 - 2010
#...............
# boundary shift
#................
# old new
# ITD ITH
# IDE ITI
# pol ---------
check.nuts(str_data$info, "POL", c(2007:2016), NUTS1)
check.nuts(str_data$info, "POL", c(2017:2018), NUTS1)
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# PL1, PL3 discontinued
# PL7=PL11+PL33
# PL8=PL3-PL33
# PL9=PL12
# sve -----------
check.nuts(str_data$info, "SVE", c(2007), NUTS1)
check.nuts(str_data$info, "SVE", c(2008:2018), NUTS1)
# version 2003 - 2006
# SE0 split into SE1 SE2 SE3
# NUTSConverter tool fehlt version 2003
# Nuts3 ####################################################
# BEL ####
check.nuts(str_data$info, "BEL", c(2007:2013), NUTS3, TRUE)
check.nuts(str_data$info, "BEL", c(2014:2018), NUTS3, TRUE)
# version 2003 - 2006
# split
# BE333 split into BE335 BE336
# CZE ####
check.nuts(str_data$info, "CZE", c(2007:2013), NUTS3, TRUE)
check.nuts(str_data$info, "CZE", c(2014:2018), NUTS3, TRUE)
# version 2003 - 2006
# CZ061 boundary shift Vysocina
# CZ063 new region Vysocina
#.................................
# CZ062 boundary shift Jihomoravsk? kraj
# CZ064 new region Jihomoravsk? kraj
# DEU ????####
deu.check <- check.nuts(str_data$info, "DEU", c(2007), NUTS3, TRUE)
deu.check.2008 <- check.nuts(str_data$info, "DEU", c(2008:2009), NUTS3, TRUE)
deu.check.2010 <- check.nuts(str_data$info, "DEU", c(2010:2013), NUTS3, TRUE)
deu.check.2014 <- check.nuts(str_data$info, "DEU", c(2014:2016), NUTS3, TRUE)
deu.check.2017 <- check.nuts(str_data$info, "DEU", c(2017:2018), NUTS3, TRUE)
# version 1999-2003 ----
# old new
# merge .....
# DE301 DE300 (part)
# DE302 DE300 (part)
# ..................
# recoded
# DE403 DE411
# DE405 DE412
# DE409 DE413
# DE40A DE414
# DE40C DE415
# DE40D DE416
# DE40F DE417
# DE40I DE418
# DE401 DE421
# DE402 DE422
# DE404 DE423
# DE406 DE424
# DE407 DE425
# DE408 DE426
# DE40B DE427
# DE40E DE428
# DE40G DE429
# DE40H DE42A
# DE6 DE600
# version 2003-2006 ----
# old new change
# DEE11 Terminated
# DEE12 Terminated
# DEE13 Terminated
# DEE14 Terminated
# DEE15 Terminated
# DEE16 Terminated
# DEE33 DEE07 Merged
# DEE36 DEE07 Merged
# DEE22 DEE08 Merged
# DEE27 DEE08 Merged
# DEE23 DEE0A Merged
# DEE26 DEE0A Merged
# DEE24 DEE0B Merged
# DEE25 DEE0B Merged
# DEE32 Terminated
# DEE34 Terminated
# DEE35 Terminated
# DEE38 Terminated
# DEE39 Terminated
# DEE3A Terminated
# DEE21 DEE02 recoded
# DEE31 DEE03 recoded
# DEE3B DEE04 recoded
# DEE37 DEE0D recoded
# version 2006-2010 ----
# DE411 DE403
# DE412 DE405
# DE413 DE409
# DE414 DE40A
# DE415 DE40C
# DE416 DE40D
# DE417 DE40F
# DE418 DE40I
# DE421 DE401
# DE422 DE402
# DE423 DE404
# DE424 DE406
# DE425 DE407
# DE426 DE408
# DE427 DE40B
# DE428 DE40E
# DE429 DE40G
# DE42A DE40H
# DED11 DED41
# DED31 DED51
# DEA21 DEA2D merge
# DEA25 DEA2D merge
# DEA2D DA2D = DEA21 + DEA25
# DED23 DED2C (part)
# DED24 DED2C (part)
# DED2B DED2C (part)
# DED2C DED2C = DED23 + DED24 + DED2B
# DED22 DED2D (part)
# DED26 DED2D (part)
# DED28 DED2D (part)
# DED2D DED2D = DED22 + DED26 + DED28
# DED25 DED2E (part)
# DED27 DED2E (part)
# DED2E DED2E = DED25 + DED27
# DED29 DED2F (part)
# DED2A DED2F (part)
# DED2F DED2F = DED29 + DED2A
# DED14 DED42 (part)
# DED18 DED42 (part)
# DED1A DED42 (part)
# DED1B DED42 (part)
# DED42 DED42 = DED14 + DED18 + DED1A + DED1B
# DED16 DED43 (part)
# DED19 DED43 (part)
# DED33 DED43 (part)
# DED43 DED43 = DED16 + DED19 + DED33
# DED12 DED44 (part)
# DED17 DED44 (part)
# DED44 DED44 = DED12 + DED17
# DED13 DED45 (part)
# DED15 DED45 (part)
# DED1C DED45 (part)
# DED45 DED45 = DED13 + DED15 + DED1C
# DED34 DED52 (part)
# DED35 DED52 (part)
# DED52 DED52 = DED34 + DED35
# DED32 DED53 (part)
# DED36 DED53 (part)
# DED53 DED53 = DED32 + DED36
# version 2010-2013
# ...........
# DE801 merge
# DE808 split
# DE80B merge
# DE80C merge
# DE80F merge
# DE80I merge
# DE802 merge
# DE80J DE80J=DE80C+DE80B+DE802+parts of DE808
# DE80N DE80N=DE801+DE80F+DE80I+parts of DE808
# DE807 DE80K (part)
# DE809 DE80K (part)
# DE80K DE80K = DE807 + DE809
# DE805 DE80L (part)
# DE80D DE80L (part)
# DE80H DE80L (part)
# DE80L DE80L = DE805 + DE80D + DE80H
# DE806 DE80M (part)
# DE80E DE80M (part)
# DE80M DE80M = DE806 + DE80E
# DE80A DE80O (part)
# DE80G DE80O (part)
# DE80O DE80O = DE80A + DE80G
# version 2013-2016
# DE915 discontinued; merged with ex-DE919
# DE919 discontinued; merged with ex-DE915
# DE91C DE91C=DE915+DE919
# DEB16 DEB1C boundary shift
# DEB19 DEB1D boundary shift
# version 2016-2021 no change ----
# no change
# checked ----
checked <- c("DE411","DE403","DEB1D", "DE600","DEE07")
string <- deu_nuts3 %>% filter(version == "2010To2013") %>% select(NUTS3) %>% c()
deu.check %>% filter(NUTS3 %in% string$NUTS3)
deu.check.2008 %>% filter(NUTS3 %in% string$NUTS3 )
deu.check.2010 %>% filter(NUTS3 %in% string$NUTS3 )
deu.check.2014 %>% filter(NUTS3 %in% string$NUTS3 )
deu.check.2017 %>% filter(NUTS3 %in% string$NUTS3 )
# deu_changed = read.table(text = "changed
# DEE11
# DEE12
# DEE13
# DEE14
# DEE15
# DEE16
# DEE33
# DEE36
# DEE22
# DEE27
# DEE23
# DEE26
# DEE24
# DEE25
# DEE32
# DEE34
# DEE35