National accounts aggregates by industry (up to NACE A*64)

Data - Eurostat

Info

source dataset .html .RData
eurostat nama_10_a64 2024-11-05 2024-11-16
eurostat nama_10_a64_e 2024-11-16 2024-11-16

Data on macro

source dataset .html .RData
eurostat nama_10_a10 2024-11-08 2024-10-08
eurostat nama_10_a10_e 2024-11-08 2024-11-09
eurostat nama_10_gdp 2024-11-08 2024-10-08
eurostat nama_10_lp_ulc 2024-11-08 2024-10-08
eurostat namq_10_a10 2024-11-16 2024-11-16
eurostat namq_10_a10_e 2024-11-16 2024-11-16
eurostat namq_10_gdp 2024-11-05 2024-10-08
eurostat namq_10_lp_ulc 2024-11-05 2024-11-04
eurostat namq_10_pc 2024-11-05 2024-11-08
eurostat nasa_10_nf_tr 2024-11-05 2024-10-08
eurostat nasq_10_nf_tr 2024-11-05 2024-10-09
fred gdp 2024-11-09 2024-11-09
oecd QNA 2024-06-06 2024-11-16
oecd SNA_TABLE1 2024-09-15 2024-06-30
oecd SNA_TABLE14A 2024-09-15 2024-06-30
oecd SNA_TABLE2 2024-07-01 2024-04-11
oecd SNA_TABLE6A 2024-07-01 2024-06-30
wdi NE.RSB.GNFS.ZS 2024-09-18 2024-09-18
wdi NY.GDP.MKTP.CD 2024-09-18 2024-09-26
wdi NY.GDP.MKTP.PP.CD 2024-09-18 2024-09-18
wdi NY.GDP.PCAP.CD 2024-11-15 2024-11-16
wdi NY.GDP.PCAP.KD 2024-09-18 2024-09-18
wdi NY.GDP.PCAP.PP.CD 2024-11-16 2024-11-16
wdi NY.GDP.PCAP.PP.KD 2024-11-16 2024-11-16

Data on industry

Code
load_data("industry.RData")
industry %>%
  arrange(-(dataset == "nama_10_a64")) %>%
  source_dataset_file_updates()
source dataset .html .RData
ec INDUSTRY 2024-09-15 2023-10-01
eurostat ei_isin_m 2024-11-08 2024-10-09
eurostat htec_trd_group4 2024-11-08 2024-10-08
eurostat nama_10_a64 2024-11-05 2024-11-16
eurostat nama_10_a64_e 2024-11-16 2024-11-16
eurostat namq_10_a10_e 2024-11-16 2024-11-16
eurostat road_eqr_carmot 2024-11-05 2024-10-08
eurostat sts_inpp_m 2024-06-24 2024-10-08
eurostat sts_inppd_m 2024-11-05 2024-10-08
eurostat sts_inpr_m 2024-11-05 2024-10-08
eurostat sts_intvnd_m 2024-11-16 2024-11-16
fred industry 2024-11-09 2024-11-09
oecd ALFS_EMP 2024-04-16 2024-05-12
oecd BERD_MA_SOF 2024-04-16 2023-09-09
oecd GBARD_NABS2007 2024-04-16 2023-11-22
oecd MEI_REAL 2024-05-12 2024-05-03
oecd MSTI_PUB 2024-09-15 2023-10-04
oecd SNA_TABLE4 2024-09-15 2024-11-15
wdi NV.IND.EMPL.KD 2024-01-06 2024-09-18
wdi NV.IND.MANF.CD 2024-09-18 2024-09-18
wdi NV.IND.MANF.ZS 2024-01-06 2024-09-18
wdi NV.IND.TOTL.KD 2024-01-06 2024-09-18
wdi NV.IND.TOTL.ZS 2024-01-06 2024-09-18
wdi SL.IND.EMPL.ZS 2024-01-06 2024-09-18
wdi TX.VAL.MRCH.CD.WT 2024-01-06 2024-09-18

LAST_COMPILE

LAST_COMPILE
2024-11-16

Last

Code
nama_10_a64 %>%
  group_by(time) %>%
  summarise(Nobs = n()) %>%
  arrange(desc(time)) %>%
  head(2) %>%
  print_table_conditional()
time Nobs
2023 177313
2022 253770

na_item

Code
nama_10_a64 %>%
  left_join(na_item, by = "na_item") %>%
  group_by(na_item, Na_item) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional
na_item Na_item Nobs
B1G Value added, gross 2446781
P51C Consumption of fixed capital 1977288
P1 Output 1313413
P2 Intermediate consumption 1272660
D1 Compensation of employees 386643
D11 Wages and salaries 385191
D29X39 Other taxes less other subsidies on production 382308
B2A3N Operating surplus and mixed income, net 336110

nace_r2

All

Code
nama_10_a64 %>%
  left_join(nace_r2, by = "nace_r2") %>%
  group_by(nace_r2, Nace_r2) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional

Manufacturing

  • NACE Codes, Eurostat. html

  • NACE Codes, IL. pdf

  • Food. html

Code
nama_10_a64 %>%
  filter(grepl("C", nace_r2)) %>%
  left_join(nace_r2, by = "nace_r2") %>%
  group_by(nace_r2, Nace_r2) %>%
  summarise(Nobs = n()) %>%
  arrange(nace_r2) %>%
  print_table_conditional
nace_r2 Nace_r2 Nobs
C Manufacturing 95782
C10-C12 Manufacture of food products; beverages and tobacco products 91762
C13-C15 Manufacture of textiles, wearing apparel, leather and related products 91760
C16 Manufacture of wood and of products of wood and cork, except furniture; manufacture of articles of straw and plaiting materials 89716
C16-C18 Manufacture of wood, paper, printing and reproduction 91840
C17 Manufacture of paper and paper products 89726
C18 Printing and reproduction of recorded media 89737
C19 Manufacture of coke and refined petroleum products 86813
C20 Manufacture of chemicals and chemical products 89983
C21 Manufacture of basic pharmaceutical products and pharmaceutical preparations 91072
C22 Manufacture of rubber and plastic products 89744
C22_C23 Manufacture of rubber and plastic products and other non-metallic mineral products 91840
C23 Manufacture of other non-metallic mineral products 89744
C24 Manufacture of basic metals 89739
C24_C25 Manufacture of basic metals and fabricated metal products, except machinery and equipment 91840
C25 Manufacture of fabricated metal products, except machinery and equipment 89744
C26 Manufacture of computer, electronic and optical products 91759
C27 Manufacture of electrical equipment 91699
C28 Manufacture of machinery and equipment n.e.c. 91759
C29 Manufacture of motor vehicles, trailers and semi-trailers 89744
C29_C30 Manufacture of motor vehicles, trailers, semi-trailers and of other transport equipment 91840
C30 Manufacture of other transport equipment 89375
C31-C33 Manufacture of furniture; jewellery, musical instruments, toys; repair and installation of machinery and equipment 91843
C31_C32 Manufacture of furniture; other manufacturing 89744
C33 Repair and installation of machinery and equipment 89720
  • Sublists, non overlapping
Code
nama_10_a64 %>%
  filter(grepl("C", nace_r2),
         !(nace_r2 %in% c("C16", "C17", "C18", "C22", "C23", "C24", "C25",
                          "C29", "C30", "C33"))) %>%
  left_join(nace_r2, by = "nace_r2") %>%
  group_by(nace_r2, Nace_r2) %>%
  summarise(Nobs = n()) %>%
  arrange(nace_r2) %>%
  print_table_conditional
nace_r2 Nace_r2 Nobs
C Manufacturing 95782
C10-C12 Manufacture of food products; beverages and tobacco products 91762
C13-C15 Manufacture of textiles, wearing apparel, leather and related products 91760
C16-C18 Manufacture of wood, paper, printing and reproduction 91840
C19 Manufacture of coke and refined petroleum products 86813
C20 Manufacture of chemicals and chemical products 89983
C21 Manufacture of basic pharmaceutical products and pharmaceutical preparations 91072
C22_C23 Manufacture of rubber and plastic products and other non-metallic mineral products 91840
C24_C25 Manufacture of basic metals and fabricated metal products, except machinery and equipment 91840
C26 Manufacture of computer, electronic and optical products 91759
C27 Manufacture of electrical equipment 91699
C28 Manufacture of machinery and equipment n.e.c. 91759
C29_C30 Manufacture of motor vehicles, trailers, semi-trailers and of other transport equipment 91840
C31-C33 Manufacture of furniture; jewellery, musical instruments, toys; repair and installation of machinery and equipment 91843
C31_C32 Manufacture of furniture; other manufacturing 89744

unit

Code
nama_10_a64 %>%
  left_join(unit, by = "unit") %>%
  group_by(unit, Unit) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional
unit Unit Nobs
CP_MEUR Current prices, million euro 842592
CP_MNAC Current prices, million units of national currency 842592
PC_TOT Percentage of total 822507
PYP_MNAC Previous year prices, million units of national currency 323120
PYP_MEUR Previous year prices, million euro 320063
CLV10_MNAC Chain linked volumes (2010), million units of national currency 310838
CLV05_MNAC Chain linked volumes (2005), million units of national currency 309430
CLV10_MEUR Chain linked volumes (2010), million euro 308404
CLV05_MEUR Chain linked volumes (2005), million euro 306996
CLV_I10 Chain linked volumes, index 2010=100 303691
CLV15_MNAC Chain linked volumes (2015), million units of national currency 303012
PD10_EUR Price index (implicit deflator), 2010=100, euro 302184
PD10_NAC Price index (implicit deflator), 2010=100, national currency 302184
CLV15_MEUR Chain linked volumes (2015), million euro 300578
CLV_I15 Chain linked volumes, index 2015=100 296038
PD15_NAC Price index (implicit deflator), 2015=100, national currency 295084
CLV_PCH_PRE Chain linked volumes, percentage change on previous period 293434
PD_PCH_PRE_NAC Price index (implicit deflator), percentage change on previous period, national currency 292620
PD_PCH_PRE_EUR Price index (implicit deflator), percentage change on previous period, euro 292445
CLV20_MNAC NA 287208
CLV20_MEUR NA 284772
CLV_I20 NA 280638
PD20_NAC NA 279964

time

Code
nama_10_a64 %>%
  group_by(time) %>%
  summarise(Nobs = n()) %>%
  arrange(desc(time)) %>%
  print_table_conditional

geo

Code
nama_10_a64 %>%
  left_join(geo, by = "geo") %>%
  group_by(geo, Geo) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(Geo)),
         Flag = paste0('<img src="../../bib/flags/vsmall/', Flag, '.png" alt="Flag">')) %>%
  select(Flag, everything()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

Profit share by sector, with many countries

C - Manufacturing

Code
nama_10_a64 %>%
  filter(na_item %in% c("B1G","B2A3N"),
         geo %in% c("FR", "ES", "DE", "IT"),
         nace_r2 %in% c("C"),
         unit == "CP_MEUR") %>%
  year_to_date %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(geo, by = "geo") %>%
  spread(na_item, values) %>%
  mutate(values = B2A3N/B1G) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot + geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + theme_minimal()  + add_4flags +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2020, 5), "-01-01")),
               labels = date_format("%Y")) +
  xlab("") + ylab("Profit Share, Manufacturing (% of GDP)") +
  scale_y_continuous(breaks = 0.01*seq(-30, 50, 2),
                labels = percent_format(a = 1)) + 
  geom_hline(yintercept = 0, linetype = "dashed",  color = "black")

L - Real Estate Activities

Code
nama_10_a64 %>%
  filter(na_item %in% c("B1G","B2A3N"),
         geo %in% c("FR", "ES", "DE", "IT"),
         nace_r2 %in% c("L"),
         unit == "CP_MEUR") %>%
  year_to_date %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(geo, by = "geo") %>%
  spread(na_item, values) %>%
  mutate(values = B2A3N/B1G) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot + geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + theme_minimal()  + add_4flags +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2020, 5), "-01-01")),
               labels = date_format("%Y")) +
  xlab("") + ylab("Profit Share, Real Estate Activities (% of GDP)") +
  scale_y_continuous(breaks = 0.01*seq(-30, 100, 2),
                labels = percent_format(a = 1))

Manufacturing

Table by manuf. Value

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C", "TOTAL"),
         unit == "CP_MEUR",
         time == "2019") %>%
  select_if(~ n_distinct(.) > 1) %>%
  left_join(geo, by = "geo") %>%
  spread(nace_r2, values) %>%
  arrange(-TOTAL) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(Geo)),
         Flag = paste0('<img src="../../bib/flags/vsmall/', Flag, '.png" alt="Flag">')) %>%
  select(Flag, everything()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

Table by manuf. share (% of GDP)

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C", "TOTAL"),
         unit == "CP_MEUR",
         time == "2021") %>%
  select_if(~ n_distinct(.) > 1) %>%
  left_join(geo, by = "geo") %>%
  spread(nace_r2, values) %>%
  mutate(`Part manufacturier` = 100*C/TOTAL) %>%
  arrange(`Part manufacturier`) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(Geo)),
         Flag = paste0('<img src="../../bib/flags/vsmall/', Flag, '.png" alt="Flag">')) %>%
  select(Flag, everything()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

2019 Table - All Manufacturing (€)

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         grepl("C", nace_r2) | nace_r2 == "TOTAL",
         unit == "CP_MEUR",
         time == "2019") %>%
  select_if(~ n_distinct(.) > 1) %>%
  left_join(geo, by = "geo") %>%
  spread(nace_r2, values) %>%
  arrange(-TOTAL) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(Geo)),
         Flag = paste0('<img src="../../bib/flags/vsmall/', Flag, '.png" alt="Flag">')) %>%
  select(Flag, everything()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

Some Manufacturing

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C10-C12", "C13-C15", "C16-C18", "C22_C23", "C29_C30", "TOTAL"),
         unit == "CP_MEUR",
         time == "2019") %>%
  select_if(~ n_distinct(.) > 1) %>%
  left_join(geo, by = "geo") %>%
  spread(nace_r2, values) %>%
  arrange(-TOTAL) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(Geo)),
         Flag = paste0('<img src="../../bib/flags/vsmall/', Flag, '.png" alt="Flag">')) %>%
  select(Flag, everything()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

Greece, Germany, Spain, France, Italy

2019 Table (% du PIB)

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         geo %in% c("EL", "DE", "ES", "FR", "IT"),
         unit == "CP_MNAC",
         time == "2019") %>%
  select_if(~ n_distinct(.) > 1) %>%
  left_join(geo, by = "geo") %>%
  left_join(nace_r2, by = "nace_r2") %>%
  select(-geo) %>%
  group_by(Geo) %>%
  mutate(values = round(100* values/ values[nace_r2 == "TOTAL"], 2)) %>%
  mutate(Geo = gsub(" ", "-", str_to_lower(Geo)),
         Geo = paste0('<img src="../../bib/flags/vsmall/', Geo, '.png" alt="Flag">')) %>%
  spread(Geo, values) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

2019 Table (€)

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         geo %in% c("EL", "DE", "ES", "FR", "IT"),
         unit == "CP_MNAC",
         time == "2019") %>%
  select_if(~ n_distinct(.) > 1) %>%
  left_join(geo, by = "geo") %>%
  left_join(nace_r2, by = "nace_r2") %>%
  select(-geo) %>%
  mutate(Geo = gsub(" ", "-", str_to_lower(Geo)),
         Geo = paste0('<img src="../../bib/flags/vsmall/', Geo, '.png" alt="Flag">')) %>%
  spread(Geo, values) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

Manufacturing Value Added (% of GDP)

2019 France, Germany, Italy

Code
nama_10_a64 %>%
  filter(geo %in% c("FR", "DE", "IT"),
         unit == "CP_MNAC",
         na_item == "B1G",
         time == "2019") %>%
  left_join(nace_r2, by = "nace_r2") %>%
  select(geo, nace_r2, Nace_r2, values) %>%
  group_by(geo) %>%
  mutate(values = round(100*values /values[nace_r2 =="TOTAL"], 1)) %>%
  spread(geo, values) %>%
  filter(nace_r2 != "TOTAL") %>%
  arrange(-FR) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

France: 2019, 1999, 1979

Code
nama_10_a64 %>%
  filter(geo %in% c("FR"),
         unit == "CP_MNAC",
         na_item == "B1G",
         time %in% c("2019",  "1999", 1979)) %>%
  left_join(nace_r2, by = "nace_r2") %>%
  select(time, nace_r2, Nace_r2, values) %>%
  group_by(time) %>%
  mutate(values = round(100*values /values[nace_r2 =="TOTAL"], 1)) %>%
  spread(time, values) %>%
  filter(nace_r2 != "TOTAL") %>%
  arrange(-`2019`) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

France, Germany, United Kingdom

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C", "TOTAL"),
         geo %in% c("FR", "DE", "UK"),
         unit == "CP_MNAC") %>%
  year_to_date() %>%
  left_join(geo, by = "geo") %>%
  select(geo,  Geo, nace_r2, date, values) %>%
  spread(nace_r2, values) %>%
  mutate(values = C/TOTAL) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y =values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Manufacturing Value added (% of GDP)") +
  scale_color_identity() +
  scale_x_date(breaks = seq(1960, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  add_3flags +
  theme(legend.position = "none") +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 1),
                     labels = percent_format(accuracy = 1))

France, Germany, Greece, Italy, Portugal, Spain

All

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C", "TOTAL"),
         geo %in% c("FR", "DE", "EL", "ES", "IT", "PT"),
         unit == "CP_MNAC") %>%
  year_to_date() %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(geo, by = "geo") %>%
  select(geo,  Geo, nace_r2, date, values) %>%
  spread(nace_r2, values) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = C/TOTAL) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Manufacturing Value added (% of GDP)") +
  scale_color_identity() +
  scale_x_date(breaks = seq(1960, 2022, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  add_6flags +
  theme(legend.position = "none") +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 1),
                     labels = percent_format(accuracy = 1))

1995-

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C", "TOTAL"),
         geo %in% c("FR", "DE", "EL", "ES", "IT", "PT"),
         unit == "CP_MNAC") %>%
  year_to_date() %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(geo, by = "geo") %>%
  select(geo,  Geo, nace_r2, date, values) %>%
  spread(nace_r2, values) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = C/TOTAL) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Manufacturing Value added (% of GDP)") +
  scale_color_identity() +
  scale_x_date(breaks = seq(1960, 2022, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  add_6flags +
  theme(legend.position = "none") +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 1),
                     labels = percent_format(accuracy = 1))

France, Luxembourg, Cyprus

1995-

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C", "TOTAL"),
         geo %in% c("FR", "ME", "LU", "CY", "MT", "EL"),
         unit == "CP_MNAC") %>%
  year_to_date() %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(geo, by = "geo") %>%
  select(geo,  Geo, nace_r2, date, values) %>%
  spread(nace_r2, values) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = C/TOTAL) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Manufacturing Value added (% of GDP)") +
  scale_color_identity() +
  scale_x_date(breaks = seq(1960, 2022, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  add_6flags +
  theme(legend.position = "none") +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 1),
                     labels = percent_format(accuracy = 1))

Greece, Portugal, Spain

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C", "TOTAL"),
         geo %in% c("EL", "PT", "ES"),
         unit == "CP_MNAC") %>%
  year_to_date() %>%
  left_join(geo, by = "geo") %>%
  select(Geo, nace_r2, date, values) %>%
  spread(nace_r2, values) %>%
  ggplot(.) + geom_line(aes(x = date, y = C/TOTAL, color = Geo)) + 
  theme_minimal() + xlab("") + ylab("Manufacturing Value added (% of GDP)") +
  scale_color_manual(values = c("#0D5EAF", "#006600", "#C60B1E")) +
  scale_x_date(breaks = seq(1960, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  geom_image(data = . %>%
               filter(date == as.Date("2016-01-01")) %>%
               mutate(date = as.Date("2016-01-01"),
                      image = paste0("../../icon/flag/round/", str_to_lower(Geo), ".png")),
             aes(x = date, y = C/TOTAL, image = image), asp = 1.5) +
  theme(legend.position = "none") +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 1),
                     labels = percent_format(accuracy = 1))

1995-2018

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C", "TOTAL"),
         geo %in% c("EA", "FR", "DE", "IT", "ES", "NL", "AT", "FI"),
         unit == "CP_MNAC") %>%
  year_to_date() %>%
  filter(date >= as.Date("1995-01-01"),
         date <= as.Date("2019-01-01")) %>%
  left_join(geo, by = "geo") %>%
  select(geo, Geo, nace_r2, date, values) %>%
  spread(nace_r2, values) %>%
  mutate(values = C/TOTAL) %>%
  group_by(date) %>%
  mutate(values = values /values[geo == "EA"]) %>%
  filter(geo != "EA") %>%
  group_by(geo) %>%
  mutate(values = 100*values / values[date == as.Date("1995-01-01")]) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(color = ifelse(geo == "FR", color2, color)) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Valeur ajoutée manuf. par rapport à la Zone €") +
  scale_color_identity() + add_7flags +
  theme(legend.position = "none") +
  scale_x_date(breaks = seq(1995, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = seq(0, 200, 5)) +
  theme(legend.position = "none") +
  geom_hline(yintercept = 100, linetype = "dashed")

1995-

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C", "TOTAL"),
         geo %in% c("EA", "FR", "DE", "IT", "ES", "NL", "AT", "PL", "CZ"),
         unit == "CP_MNAC") %>%
  year_to_date() %>%
  filter(date >= as.Date("1995-01-01"),
         date <= as.Date("2019-01-01")) %>%
  left_join(geo, by = "geo") %>%
  select(geo, Geo, nace_r2, date, values) %>%
  spread(nace_r2, values) %>%
  mutate(values = C/TOTAL) %>%
  group_by(date) %>%
  mutate(values = values /values[geo == "EA"]) %>%
  filter(geo != "EA") %>%
  group_by(geo) %>%
  mutate(values = 100*values / values[date == as.Date("1995-01-01")]) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(color = ifelse(geo == "FR", color2, color)) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Valeur ajoutée manuf. par rapport à la Zone €") +
  scale_color_identity() +
  add_8flags +
  theme(legend.position = "none") +
  scale_x_date(breaks = seq(1995, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(0, 200, 5)) +
  theme(legend.position = "none") +
  geom_hline(yintercept = 100, linetype = "dashed")

2000-2018

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C", "TOTAL"),
         geo %in% c("EA", "FR", "DE", "IT", "ES", "NL", "AT", "FI"),
         unit == "CP_MNAC") %>%
  year_to_date() %>%
  filter(date >= as.Date("2000-01-01"),
         date <= as.Date("2019-01-01")) %>%
  left_join(geo, by = "geo") %>%
  select(geo, Geo, nace_r2, date, values) %>%
  spread(nace_r2, values) %>%
  mutate(values = C/TOTAL) %>%
  group_by(date) %>%
  mutate(values = values /values[geo == "EA"]) %>%
  filter(geo != "EA") %>%
  group_by(geo) %>%
  mutate(values = 100*values / values[date == as.Date("2000-01-01")]) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(color = ifelse(geo == "FR", color2, color)) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Valeur ajoutée manuf. par rapport à la Zone €") +
  scale_color_identity() + add_7flags +
  scale_x_date(breaks = seq(1960, 2020, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = seq(0, 200, 5)) +
  theme(legend.position = "none") +
  geom_hline(yintercept = 100, linetype = "dashed")

Comparing Deflators

Germany, France, Italy, Spain

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         geo %in% c("FR", "DE", "IT", "ES"),
         unit == "PD10_NAC",
         time %in% c("1995", "2019")) %>%
  select_if(~ n_distinct(.) > 1) %>%
  spread(time, values) %>%
  left_join(geo, by = "geo") %>%
  left_join(nace_r2, by = "nace_r2") %>%
  select(-geo) %>%
  mutate(values = round(100*((`2019`/`1995`)^(1/24)-1),2)) %>%
  select(-`1995`, -`2019`) %>%
  spread(Geo, values) %>%
  print_table_conditional

C - Manufacturing

Table - PD10_NAC

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         unit == "PD10_NAC",
         nace_r2 %in% c("C", "TOTAL"),
         time %in% c("1995", "2019")) %>%
  select_if(~ n_distinct(.) > 1) %>%
  spread(time, values) %>%
  left_join(geo, by = "geo") %>%
  mutate(values = round(100*((`2019`/`1995`)^(1/24)-1),2)) %>%
  select(-`1995`, -`2019`) %>%
  spread(nace_r2, values) %>%
  arrange(`C`) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(Geo)),
         Flag = paste0('<img src="../../bib/flags/vsmall/', Flag, '.png" alt="Flag">')) %>%
  select(Flag, everything()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

Table - PD10_EUR

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         unit == "PD10_EUR",
         nace_r2 %in% c("C", "TOTAL"),
         time %in% c("1995", "2019")) %>%
  select_if(~ n_distinct(.) > 1) %>%
  spread(time, values) %>%
  left_join(geo, by = "geo") %>%
  mutate(values = round(100*((`2019`/`1995`)^(1/24)-1),2)) %>%
  select(-`1995`, -`2019`) %>%
  spread(nace_r2, values) %>%
  arrange(`C`) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(Geo)),
         Flag = paste0('<img src="../../bib/flags/vsmall/', Flag, '.png" alt="Flag">')) %>%
  select(Flag, everything()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

C10-C12 - Food products

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         unit == "PD10_EUR",
         nace_r2 %in% c("C10-C12", "TOTAL"),
         time %in% c("1995", "2020")) %>%
  select_if(~ n_distinct(.) > 1) %>%
  spread(time, values) %>%
  left_join(geo, by = "geo") %>%
  mutate(values = round(100*((`2020`/`1995`)^(1/24)-1),2)) %>%
  select(-`1995`, -`2020`) %>%
  spread(nace_r2, values) %>%
  arrange(`C10-C12`) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(Geo)),
         Flag = paste0('<img src="../../bib/flags/vsmall/', Flag, '.png" alt="Flag">')) %>%
  select(Flag, everything()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

C13-C15 - Textiles

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         unit == "PD10_NAC",
         nace_r2 %in% c("C13-C15", "TOTAL"),
         time %in% c("1995", "2020")) %>%
  select_if(~ n_distinct(.) > 1) %>%
  spread(time, values) %>%
  left_join(geo, by = "geo") %>%
  mutate(values = round(100*((`2020`/`1995`)^(1/24)-1),2)) %>%
  select(-`1995`, -`2020`) %>%
  spread(nace_r2, values) %>%
  arrange(`C13-C15`) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(Geo)),
         Flag = paste0('<img src="../../bib/flags/vsmall/', Flag, '.png" alt="Flag">')) %>%
  select(Flag, everything()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

C27 - Manufacture of electrical equipment

Table - PD10_NAC

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         unit == "PD10_NAC",
         nace_r2 %in% c("C27", "TOTAL"),
         time %in% c("1995", "2020")) %>%
  select_if(~ n_distinct(.) > 1) %>%
  spread(time, values) %>%
  left_join(geo, by = "geo") %>%
  mutate(values = round(100*((`2020`/`1995`)^(1/24)-1),2)) %>%
  select(-`1995`, -`2020`) %>%
  spread(nace_r2, values) %>%
  arrange(`C27`) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(Geo)),
         Flag = paste0('<img src="../../bib/flags/vsmall/', Flag, '.png" alt="Flag">')) %>%
  select(Flag, everything()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

Table - PD10_EUR

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         unit == "PD10_EUR",
         nace_r2 %in% c("C26", "TOTAL"),
         time %in% c("1995", "2019")) %>%
  select_if(~ n_distinct(.) > 1) %>%
  spread(time, values) %>%
  left_join(geo, by = "geo") %>%
  mutate(values = round(100*((`2019`/`1995`)^(1/24)-1),2)) %>%
  select(-`1995`, -`2019`) %>%
  spread(nace_r2, values) %>%
  arrange(`C26`) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(Geo)),
         Flag = paste0('<img src="../../bib/flags/vsmall/', Flag, '.png" alt="Flag">')) %>%
  select(Flag, everything()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

Graph

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C27", "TOTAL"),
         geo %in% c("FR", "DE", "IT", "ES"),
         unit == "PD10_NAC") %>%
  year_to_date() %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(geo, by = "geo") %>%
  select(geo,  Geo, nace_r2, date, values) %>%
  spread(nace_r2, values) %>%
  mutate(values = C27/TOTAL)  %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  group_by(Geo) %>%
  mutate(values = values/ values[1]) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Price Deflator (C27)") +
  scale_color_identity() + add_4flags +
  scale_x_date(breaks = seq(1960, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 10))

C26 - Computer, electronics

Table - PD10_NAC

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         unit == "PD10_NAC",
         nace_r2 %in% c("C26", "TOTAL"),
         time %in% c("1995", "2019")) %>%
  select_if(~ n_distinct(.) > 1) %>%
  spread(time, values) %>%
  left_join(geo, by = "geo") %>%
  mutate(values = round(100*((`2019`/`1995`)^(1/24)-1),2)) %>%
  select(-`1995`, -`2019`) %>%
  spread(nace_r2, values) %>%
  arrange(`C26`) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(Geo)),
         Flag = paste0('<img src="../../bib/flags/vsmall/', Flag, '.png" alt="Flag">')) %>%
  select(Flag, everything()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

Table - PD10_EUR

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         unit == "PD10_EUR",
         nace_r2 %in% c("C26", "TOTAL"),
         time %in% c("1995", "2019")) %>%
  select_if(~ n_distinct(.) > 1) %>%
  spread(time, values) %>%
  left_join(geo, by = "geo") %>%
  mutate(values = round(100*((`2019`/`1995`)^(1/24)-1),2)) %>%
  select(-`1995`, -`2019`) %>%
  spread(nace_r2, values) %>%
  arrange(`C26`) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(Geo)),
         Flag = paste0('<img src="../../bib/flags/vsmall/', Flag, '.png" alt="Flag">')) %>%
  select(Flag, everything()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

Graph

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C26", "TOTAL"),
         geo %in% c("FR", "DE", "IT", "ES"),
         unit == "PD10_NAC") %>%
  year_to_date() %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(geo, by = "geo") %>%
  select(geo,  Geo, nace_r2, date, values) %>%
  spread(nace_r2, values) %>%
  mutate(values = C26/TOTAL)  %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  group_by(Geo) %>%
  mutate(values = values/ values[1]) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Price Deflator (C26)") +
  scale_color_identity() + add_4flags +
  scale_x_date(breaks = seq(1960, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 10))

C33 - Repair and installation of machinery and equipment

Table - PD10_NAC

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         unit == "PD10_NAC",
         nace_r2 %in% c("C33", "TOTAL"),
         time %in% c("1995", "2019")) %>%
  select_if(~ n_distinct(.) > 1) %>%
  spread(time, values) %>%
  left_join(geo, by = "geo") %>%
  mutate(values = round(100*((`2019`/`1995`)^(1/24)-1),2)) %>%
  select(-`1995`, -`2019`) %>%
  spread(nace_r2, values) %>%
  arrange(`C33`) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(Geo)),
         Flag = paste0('<img src="../../bib/flags/vsmall/', Flag, '.png" alt="Flag">')) %>%
  select(Flag, everything()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

Table - PD10_EUR

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         unit == "PD10_EUR",
         nace_r2 %in% c("C33", "TOTAL"),
         time %in% c("1995", "2019")) %>%
  select_if(~ n_distinct(.) > 1) %>%
  spread(time, values) %>%
  left_join(geo, by = "geo") %>%
  mutate(values = round(100*((`2019`/`1995`)^(1/24)-1),2)) %>%
  select(-`1995`, -`2019`) %>%
  spread(nace_r2, values) %>%
  arrange(`C33`) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(Geo)),
         Flag = paste0('<img src="../../bib/flags/vsmall/', Flag, '.png" alt="Flag">')) %>%
  select(Flag, everything()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

Graph

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C33", "TOTAL"),
         geo %in% c("FR", "DE", "IT", "ES"),
         unit == "PD10_NAC") %>%
  year_to_date() %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(geo, by = "geo") %>%
  select(geo,  Geo, nace_r2, date, values) %>%
  spread(nace_r2, values) %>%
  mutate(values = C33/TOTAL)  %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  group_by(Geo) %>%
  mutate(values = values/ values[1]) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Price Deflator (C33)") +
  scale_color_identity() + add_4flags +
  scale_x_date(breaks = seq(1960, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 10))

Germany, Italy, France, Spain, Netherlands

Table

Code
load_data("eurostat/nace_r2_fr.RData")
nama_10_a64 %>%
  filter(na_item == "B1G",
         geo %in% c("FR", "DE", "IT", "ES"),
         unit == "CP_MNAC",
         time %in% c("2018")) %>%
  filter(!grepl("C", nace_r2) | nace_r2 == "TOTAL") %>%
  select(-na_item, -unit, -time) %>%
  left_join(nace_r2, by = "nace_r2") %>%
  left_join(geo, by = "geo") %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(Geo)),
         Flag = paste0('<img src="../../bib/flags/vsmall/', Flag, '.png" alt="Flag">')) %>%
  select(nace_r2, Nace_r2, Flag, values) %>%
  group_by(Flag) %>%
  mutate(values = round(100*values/ values[nace_r2 == "TOTAL"], 1)) %>%
  filter(nace_r2 != "TOTAL")  %>%
  spread(Flag, values) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

Table - Manufacturing

Code
load_data("eurostat/nace_r2_fr.RData")
nama_10_a64 %>%
  filter(na_item == "B1G",
         geo %in% c("FR", "DE", "IT", "ES"),
         unit == "CP_MNAC",
         time %in% c("2018")) %>%
  filter(grepl("C", nace_r2) | nace_r2 == "TOTAL") %>%
  select(-na_item, -unit, -time) %>%
  left_join(nace_r2, by = "nace_r2") %>%
  left_join(geo, by = "geo") %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(Geo)),
         Flag = paste0('<img src="../../bib/flags/vsmall/', Flag, '.png" alt="Flag">')) %>%
  select(nace_r2, Nace_r2, Flag, values) %>%
  group_by(Flag) %>%
  mutate(values = round(100*values/ values[nace_r2 == "TOTAL"], 1)) %>%
  filter(nace_r2 != "TOTAL")  %>%
  spread(Flag, values) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

B - Mining and quarrying

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("B", "TOTAL"),
         geo %in% c("FR", "DE", "IT", "ES", "EL"),
         unit == "CP_MNAC") %>%
  year_to_date() %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(geo, by = "geo") %>%
  select(geo,  Geo, nace_r2, date, values) %>%
  spread(nace_r2, values) %>%
  mutate(values = `B`/TOTAL) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Mining and quarrying (% of GDP)") +
  scale_color_identity() + add_5flags +
  scale_x_date(breaks = seq(1960, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, .1),
                     labels = percent_format(accuracy = .1))

D - Electricity, gas, steam and air conditioning supply

Value

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("D", "TOTAL"),
         geo %in% c("FR", "DE", "IT", "ES", "EL"),
         unit == "CP_MNAC") %>%
  year_to_date() %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(geo, by = "geo") %>%
  select(geo,  Geo, nace_r2, date, values) %>%
  spread(nace_r2, values) %>%
  mutate(values = `D`/TOTAL) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Electricity, gas, steam and air conditioning supply (% of GDP)") +
  scale_color_identity() + add_5flags +
  scale_x_date(breaks = seq(1961, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, .5),
                     labels = percent_format(accuracy = .1))

Volume

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("D", "TOTAL"),
         geo %in% c("FR", "DE", "IT", "ES", "EL"),
         unit == "CLV10_MEUR") %>%
  year_to_date() %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(geo, by = "geo") %>%
  select(geo,  Geo, nace_r2, date, values) %>%
  spread(nace_r2, values) %>%
  mutate(values = `D`/TOTAL) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Electricity, gas, steam - Volume (% of GDP)") +
  scale_color_identity() + add_5flags +
  scale_x_date(breaks = seq(1960, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, .2),
                     labels = percent_format(accuracy = .1))

1995-

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("D", "TOTAL"),
         geo %in% c("FR", "DE", "IT", "ES", "EL"),
         unit == "PD10_NAC") %>%
  year_to_date() %>%
  left_join(geo, by = "geo") %>%
  select(geo,  Geo, nace_r2, date, values) %>%
  spread(nace_r2, values) %>%
  mutate(values = D/TOTAL)  %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Price Deflator") +
  scale_color_identity() + add_5flags +
  scale_x_date(breaks = seq(1960, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 10))

C - Manufacturing

Value

All

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C", "TOTAL"),
         geo %in% c("FR", "DE", "IT", "ES", "NL", "EL"),
         unit == "CP_MNAC") %>%
  year_to_date() %>%
  left_join(geo, by = "geo") %>%
  select(geo,  Geo, nace_r2, date, values) %>%
  spread(nace_r2, values) %>%
  mutate(values = `C`/TOTAL) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Manufacturing (% of GDP)") +
  scale_color_identity() + add_6flags +
  scale_x_date(breaks = seq(1960, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 1),
                     labels = percent_format(accuracy = 1))

1995-

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C", "TOTAL"),
         geo %in% c("FR", "DE", "IT", "ES", "NL", "EL"),
         unit == "CP_MNAC") %>%
  year_to_date() %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(geo, by = "geo") %>%
  select(geo,  Geo, nace_r2, date, values) %>%
  spread(nace_r2, values) %>%
  mutate(values = `C`/TOTAL) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Manufacturing (% of GDP)") +
  scale_color_identity() + add_6flags +
  scale_x_date(breaks = seq(1995, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 1),
                     labels = percent_format(accuracy = 1))

Volume

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C", "TOTAL"),
         geo %in% c("FR", "DE", "IT", "ES"),
         unit == "CLV10_MEUR") %>%
  year_to_date() %>%
  left_join(geo, by = "geo") %>%
  select(geo,  Geo, nace_r2, date, values) %>%
  spread(nace_r2, values) %>%
  mutate(values = `C`/TOTAL) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Manufacturing (% of GDP)") +
  scale_color_identity() + add_4flags +
  scale_x_date(breaks = seq(1960, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 1),
                     labels = percent_format(accuracy = 1))

Price Deflator

All

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C", "TOTAL"),
         geo %in% c("FR", "DE", "IT", "ES"),
         unit == "PD10_NAC") %>%
  year_to_date() %>%
  left_join(geo, by = "geo") %>%
  select(geo,  Geo, nace_r2, date, values) %>%
  spread(nace_r2, values) %>%
  mutate(values = C/TOTAL)  %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Price Deflator") +
  scale_color_identity() + add_4flags +
  scale_x_date(breaks = seq(1960, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 10))

1995-

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C", "TOTAL"),
         geo %in% c("FR", "DE", "IT", "ES"),
         unit == "PD10_NAC") %>%
  year_to_date() %>%
  left_join(geo, by = "geo") %>%
  select(geo,  Geo, nace_r2, date, values) %>%
  spread(nace_r2, values) %>%
  mutate(values = C/TOTAL)  %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Price Deflator") +
  scale_color_identity() + add_4flags +
  scale_x_date(breaks = seq(1960, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 10))

C10-C12 - Food products

Value

All

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C10-C12", "TOTAL"),
         geo %in% c("FR", "DE", "IT", "ES"),
         unit == "CP_MNAC") %>%
  year_to_date() %>%
  left_join(geo, by = "geo") %>%
  select(geo,  Geo, nace_r2, date, values) %>%
  spread(nace_r2, values) %>%
  mutate(values = `C10-C12`/TOTAL) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Food products (% of GDP)") +
  scale_color_identity() + add_4flags +
  scale_x_date(breaks = seq(1960, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 0.1),
                     labels = percent_format(accuracy = .1))

1995-

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C10-C12", "TOTAL"),
         geo %in% c("FR", "DE", "IT", "ES", "NL"),
         unit == "CP_MNAC") %>%
  year_to_date() %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(geo, by = "geo") %>%
  select(geo,  Geo, nace_r2, date, values) %>%
  spread(nace_r2, values) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(color = ifelse(geo == "NL", color2, color)) %>%
  mutate(values = `C10-C12`/TOTAL) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Food products (% of GDP)") +
  scale_color_identity() + add_5flags +
  scale_x_date(breaks = seq(1960, 2026, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = "none") +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 0.1),
                     labels = percent_format(accuracy = .1))

Volume

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C10-C12", "TOTAL"),
         geo %in% c("FR", "DE", "IT", "ES"),
         unit == "CLV10_MEUR") %>%
  year_to_date() %>%
  left_join(geo, by = "geo") %>%
  select(geo,  Geo, nace_r2, date, values) %>%
  spread(nace_r2, values) %>%
  mutate(values = `C10-C12`/TOTAL) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Food products (% of GDP)") +
  scale_color_identity() + add_4flags +
  scale_x_date(breaks = seq(1960, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 0.1),
                     labels = percent_format(accuracy = .1))

Price Deflator

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C10-C12", "TOTAL"),
         geo %in% c("FR", "DE", "IT", "ES"),
         unit == "PD10_NAC") %>%
  year_to_date() %>%
  left_join(geo, by = "geo") %>%
  select(geo,  Geo, nace_r2, date, values) %>%
  spread(nace_r2, values) %>%
  mutate(values = `C10-C12`/TOTAL)  %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Price Deflator") +
  scale_color_identity() + add_4flags +
  scale_x_date(breaks = seq(1960, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 10))

C13-C15 - Textiles

Value

All

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C13-C15", "TOTAL"),
         geo %in% c("FR", "DE", "IT", "ES"),
         unit == "CP_MNAC") %>%
  year_to_date() %>%
  left_join(geo, by = "geo") %>%
  select(geo,  Geo, nace_r2, date, values) %>%
  spread(nace_r2, values) %>%
  mutate(values = `C13-C15`/TOTAL) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Textiles (% of GDP)") +
  scale_color_identity() + add_4flags +
  scale_x_date(breaks = seq(1960, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = "none") +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 0.1),
                     labels = percent_format(accuracy = .1))

1995-

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C13-C15", "TOTAL"),
         geo %in% c("FR", "DE", "IT", "ES", "NL"),
         unit == "CP_MNAC") %>%
  year_to_date() %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(geo, by = "geo") %>%
  select(geo,  Geo, nace_r2, date, values) %>%
  spread(nace_r2, values) %>%
  mutate(values = `C13-C15`/TOTAL) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(color = ifelse(geo == "NL", color2, color)) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Textiles (% of GDP)") +
  scale_color_identity() + add_5flags +
  scale_x_date(breaks = seq(1960, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = "none") +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 0.1),
                     labels = percent_format(accuracy = .1))

Volume

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C13-C15", "TOTAL"),
         geo %in% c("FR", "DE", "IT", "ES"),
         unit == "CLV10_MEUR") %>%
  year_to_date() %>%
  left_join(geo, by = "geo") %>%
  select(geo,  Geo, nace_r2, date, values) %>%
  spread(nace_r2, values) %>%
  mutate(values = `C13-C15`/TOTAL) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Textiles (% of GDP)") +
  scale_color_identity() + add_4flags +
  scale_x_date(breaks = seq(1960, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = "none") +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 0.1),
                     labels = percent_format(accuracy = .1))

Price Deflator

All

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C13-C15", "TOTAL"),
         geo %in% c("FR", "DE", "IT", "ES"),
         unit == "PD10_NAC") %>%
  year_to_date() %>%
  left_join(geo, by = "geo") %>%
  select(geo,  Geo, nace_r2, date, values) %>%
  spread(nace_r2, values) %>%
  mutate(values = `C13-C15`/TOTAL)  %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Price Deflator") +
  scale_color_identity() + add_4flags +
  scale_x_date(breaks = seq(1960, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 10))

1995-

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C13-C15", "TOTAL"),
         geo %in% c("FR", "DE", "IT", "ES"),
         unit == "PD10_NAC") %>%
  year_to_date() %>%
  left_join(geo, by = "geo") %>%
  filter(date >= as.Date("1995-01-01")) %>%
  select(geo,  Geo, nace_r2, date, values) %>%
  spread(nace_r2, values) %>%
  mutate(values = `C13-C15`/TOTAL)  %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  group_by(Geo) %>%
  mutate(values = values/ values[1]) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Price Deflator") +
  scale_color_identity() + add_4flags +
  scale_x_date(breaks = seq(1960, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 10))

C16 - Manufacture of paper and paper products

Value

All

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C16", "TOTAL"),
         geo %in% c("FR", "DE", "IT", "ES"),
         unit == "CP_MNAC") %>%
  year_to_date() %>%
  left_join(geo, by = "geo") %>%
  select(geo,  Geo, nace_r2, date, values) %>%
  spread(nace_r2, values) %>%
  mutate(values = `C16`/TOTAL) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Textiles (% of GDP)") +
  scale_color_identity() + add_4flags +
  scale_x_date(breaks = seq(1960, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = "none") +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 0.1),
                     labels = percent_format(accuracy = .1))

1995-

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C16", "TOTAL"),
         geo %in% c("FR", "DE", "IT", "ES", "NL"),
         unit == "CP_MNAC") %>%
  year_to_date() %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(geo, by = "geo") %>%
  select(geo,  Geo, nace_r2, date, values) %>%
  spread(nace_r2, values) %>%
  mutate(values = `C16`/TOTAL) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(color = ifelse(geo == "NL", color2, color)) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Textiles (% of GDP)") +
  scale_color_identity() + add_5flags +
  scale_x_date(breaks = seq(1960, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = "none") +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 0.1),
                     labels = percent_format(accuracy = .1))

Volume

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C16", "TOTAL"),
         geo %in% c("FR", "DE", "IT", "ES"),
         unit == "CLV10_MEUR") %>%
  year_to_date() %>%
  left_join(geo, by = "geo") %>%
  select(geo,  Geo, nace_r2, date, values) %>%
  spread(nace_r2, values) %>%
  mutate(values = `C16`/TOTAL) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Textiles (% of GDP)") +
  scale_color_identity() + add_4flags +
  scale_x_date(breaks = seq(1960, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = "none") +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 0.1),
                     labels = percent_format(accuracy = .1))

Price Deflator

All

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C16", "TOTAL"),
         geo %in% c("FR", "DE", "IT", "ES"),
         unit == "PD10_NAC") %>%
  year_to_date() %>%
  left_join(geo, by = "geo") %>%
  select(geo,  Geo, nace_r2, date, values) %>%
  spread(nace_r2, values) %>%
  mutate(values = `C16`/TOTAL)  %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Price Deflator") +
  scale_color_identity() + add_4flags +
  scale_x_date(breaks = seq(1960, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 10))

1995-

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C16", "TOTAL"),
         geo %in% c("FR", "DE", "IT", "ES"),
         unit == "PD10_NAC") %>%
  year_to_date() %>%
  left_join(geo, by = "geo") %>%
  filter(date >= as.Date("1995-01-01")) %>%
  select(geo,  Geo, nace_r2, date, values) %>%
  spread(nace_r2, values) %>%
  mutate(values = `C16`/TOTAL)  %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  group_by(Geo) %>%
  mutate(values = values/ values[1]) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Price Deflator") +
  scale_color_identity() + add_4flags +
  scale_x_date(breaks = seq(1960, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 10))

C16-C18 - Wood, Paper, Printing

All

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C16-C18", "TOTAL"),
         geo %in% c("FR", "DE", "IT", "ES"),
         unit == "CP_MNAC") %>%
  year_to_date() %>%
  left_join(geo, by = "geo") %>%
  select(geo,  Geo, nace_r2, date, values) %>%
  spread(nace_r2, values) %>%
  ggplot(.) + geom_line(aes(x = date, y = `C16-C18`/TOTAL, color = Geo)) + 
  theme_minimal() + xlab("") + ylab("Wood, Paper, Printing (% of GDP)") +
  scale_color_manual(values = c("#002395", "#000000", "#009246", "#C60B1E")) +
  scale_x_date(breaks = seq(1960, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  geom_image(data = . %>%
               filter(date == as.Date("2017-01-01")) %>%
               mutate(image = paste0("../../icon/flag/round/", str_to_lower(Geo), ".png")),
             aes(x = date, y = `C16-C18`/TOTAL, image = image), asp = 1.5) +
  theme(legend.position = "none") +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 0.1),
                     labels = percent_format(accuracy = .1))

1995-

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C16-C18", "TOTAL"),
         geo %in% c("FR", "DE", "IT", "ES"),
         unit == "CP_MNAC") %>%
  year_to_date() %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(geo, by = "geo") %>%
  select(geo,  Geo, nace_r2, date, values) %>%
  spread(nace_r2, values) %>%
  ggplot(.) + geom_line(aes(x = date, y = `C16-C18`/TOTAL, color = Geo)) + 
  theme_minimal() + xlab("") + ylab("Wood, Paper, Printing (% of GDP)") +
  scale_color_manual(values = c("#002395", "#000000", "#009246", "#C60B1E")) +
  scale_x_date(breaks = seq(1960, 2026, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  geom_image(data = . %>%
               filter(date == as.Date("2017-01-01")) %>%
               mutate(image = paste0("../../icon/flag/round/", str_to_lower(Geo), ".png")),
             aes(x = date, y = `C16-C18`/TOTAL, image = image), asp = 1.5) +
  theme(legend.position = "none") +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 0.1),
                     labels = percent_format(accuracy = .1))

C17 - Textiles

Value

All

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C17", "TOTAL"),
         geo %in% c("FR", "DE", "IT", "ES"),
         unit == "CP_MNAC") %>%
  year_to_date() %>%
  left_join(geo, by = "geo") %>%
  select(geo,  Geo, nace_r2, date, values) %>%
  spread(nace_r2, values) %>%
  mutate(values = `C17`/TOTAL) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Textiles (% of GDP)") +
  scale_color_identity() + add_4flags +
  scale_x_date(breaks = seq(1960, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = "none") +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 0.1),
                     labels = percent_format(accuracy = .1))

1995-

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C17", "TOTAL"),
         geo %in% c("FR", "DE", "IT", "ES", "NL"),
         unit == "CP_MNAC") %>%
  year_to_date() %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(geo, by = "geo") %>%
  select(geo,  Geo, nace_r2, date, values) %>%
  spread(nace_r2, values) %>%
  mutate(values = `C17`/TOTAL) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(color = ifelse(geo == "NL", color2, color)) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Textiles (% of GDP)") +
  scale_color_identity() + add_5flags +
  scale_x_date(breaks = seq(1960, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = "none") +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 0.1),
                     labels = percent_format(accuracy = .1))

Volume

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C17", "TOTAL"),
         geo %in% c("FR", "DE", "IT", "ES"),
         unit == "CLV10_MEUR") %>%
  year_to_date() %>%
  left_join(geo, by = "geo") %>%
  select(geo,  Geo, nace_r2, date, values) %>%
  spread(nace_r2, values) %>%
  mutate(values = `C17`/TOTAL) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Textiles (% of GDP)") +
  scale_color_identity() + add_4flags +
  scale_x_date(breaks = seq(1960, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = "none") +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 0.1),
                     labels = percent_format(accuracy = .1))

Price Deflator

All

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C17", "TOTAL"),
         geo %in% c("FR", "DE", "IT", "ES"),
         unit == "PD10_NAC") %>%
  year_to_date() %>%
  left_join(geo, by = "geo") %>%
  select(geo,  Geo, nace_r2, date, values) %>%
  spread(nace_r2, values) %>%
  mutate(values = `C17`/TOTAL)  %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Price Deflator") +
  scale_color_identity() + add_4flags +
  scale_x_date(breaks = seq(1960, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 10))

1995-

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C17", "TOTAL"),
         geo %in% c("FR", "DE", "IT", "ES"),
         unit == "PD10_NAC") %>%
  year_to_date() %>%
  left_join(geo, by = "geo") %>%
  filter(date >= as.Date("1995-01-01")) %>%
  select(geo,  Geo, nace_r2, date, values) %>%
  spread(nace_r2, values) %>%
  mutate(values = `C17`/TOTAL)  %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  group_by(Geo) %>%
  mutate(values = values/ values[1]) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Price Deflator") +
  scale_color_identity() + add_4flags +
  scale_x_date(breaks = seq(1960, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 10))

C18 - Printing and reproduction of recorded media

Value

All

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C18", "TOTAL"),
         geo %in% c("FR", "DE", "IT", "ES"),
         unit == "CP_MNAC") %>%
  year_to_date() %>%
  left_join(geo, by = "geo") %>%
  select(geo,  Geo, nace_r2, date, values) %>%
  spread(nace_r2, values) %>%
  mutate(values = `C18`/TOTAL) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Textiles (% of GDP)") +
  scale_color_identity() + add_4flags +
  scale_x_date(breaks = seq(1960, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = "none") +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 0.1),
                     labels = percent_format(accuracy = .1))

1995-

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C18", "TOTAL"),
         geo %in% c("FR", "DE", "IT", "ES", "NL"),
         unit == "CP_MNAC") %>%
  year_to_date() %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(geo, by = "geo") %>%
  select(geo,  Geo, nace_r2, date, values) %>%
  spread(nace_r2, values) %>%
  mutate(values = `C18`/TOTAL) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(color = ifelse(geo == "NL", color2, color)) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Textiles (% of GDP)") +
  scale_color_identity() + add_5flags +
  scale_x_date(breaks = seq(1960, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = "none") +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 0.1),
                     labels = percent_format(accuracy = .1))

Volume

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C18", "TOTAL"),
         geo %in% c("FR", "DE", "IT", "ES"),
         unit == "CLV10_MEUR") %>%
  year_to_date() %>%
  left_join(geo, by = "geo") %>%
  select(geo,  Geo, nace_r2, date, values) %>%
  spread(nace_r2, values) %>%
  mutate(values = `C18`/TOTAL) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Textiles (% of GDP)") +
  scale_color_identity() + add_4flags +
  scale_x_date(breaks = seq(1960, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = "none") +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 0.1),
                     labels = percent_format(accuracy = .1))

Price Deflator

All

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C18", "TOTAL"),
         geo %in% c("FR", "DE", "IT", "ES"),
         unit == "PD10_NAC") %>%
  year_to_date() %>%
  left_join(geo, by = "geo") %>%
  select(geo,  Geo, nace_r2, date, values) %>%
  spread(nace_r2, values) %>%
  mutate(values = `C18`/TOTAL)  %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Price Deflator") +
  scale_color_identity() + add_4flags +
  scale_x_date(breaks = seq(1960, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 10))

1995-

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C18", "TOTAL"),
         geo %in% c("FR", "DE", "IT", "ES"),
         unit == "PD10_NAC") %>%
  year_to_date() %>%
  left_join(geo, by = "geo") %>%
  filter(date >= as.Date("1995-01-01")) %>%
  select(geo,  Geo, nace_r2, date, values) %>%
  spread(nace_r2, values) %>%
  mutate(values = `C18`/TOTAL)  %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  group_by(Geo) %>%
  mutate(values = values/ values[1]) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Price Deflator") +
  scale_color_identity() + add_4flags +
  scale_x_date(breaks = seq(1960, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 10))

C19 - Manufacture of coke and refined petroleum products

Value

All

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C19", "TOTAL"),
         geo %in% c("FR", "DE", "IT", "ES"),
         unit == "CP_MNAC") %>%
  year_to_date() %>%
  left_join(geo, by = "geo") %>%
  select(geo,  Geo, nace_r2, date, values) %>%
  spread(nace_r2, values) %>%
  mutate(values = `C19`/TOTAL) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Textiles (% of GDP)") +
  scale_color_identity() + add_4flags +
  scale_x_date(breaks = seq(1960, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = "none") +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 0.1),
                     labels = percent_format(accuracy = .1))

1995-

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C19", "TOTAL"),
         geo %in% c("FR", "DE", "IT", "ES", "NL"),
         unit == "CP_MNAC") %>%
  year_to_date() %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(geo, by = "geo") %>%
  select(geo,  Geo, nace_r2, date, values) %>%
  spread(nace_r2, values) %>%
  mutate(values = `C19`/TOTAL) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(color = ifelse(geo == "NL", color2, color)) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Textiles (% of GDP)") +
  scale_color_identity() + add_5flags +
  scale_x_date(breaks = seq(1960, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = "none") +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 0.1),
                     labels = percent_format(accuracy = .1))

Volume

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C19", "TOTAL"),
         geo %in% c("FR", "DE", "IT", "ES"),
         unit == "CLV10_MEUR") %>%
  year_to_date() %>%
  left_join(geo, by = "geo") %>%
  select(geo,  Geo, nace_r2, date, values) %>%
  spread(nace_r2, values) %>%
  mutate(values = `C19`/TOTAL) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Textiles (% of GDP)") +
  scale_color_identity() + add_4flags +
  scale_x_date(breaks = seq(1960, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = "none") +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 0.1),
                     labels = percent_format(accuracy = .1))

Price Deflator

All

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C19", "TOTAL"),
         geo %in% c("FR", "DE", "IT", "ES"),
         unit == "PD10_NAC") %>%
  year_to_date() %>%
  left_join(geo, by = "geo") %>%
  select(geo,  Geo, nace_r2, date, values) %>%
  spread(nace_r2, values) %>%
  mutate(values = `C19`/TOTAL)  %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Price Deflator") +
  scale_color_identity() + add_4flags +
  scale_x_date(breaks = seq(1960, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 10))

1995-

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C19", "TOTAL"),
         geo %in% c("FR", "DE", "IT", "ES"),
         unit == "PD10_NAC") %>%
  year_to_date() %>%
  left_join(geo, by = "geo") %>%
  filter(date >= as.Date("1995-01-01")) %>%
  select(geo,  Geo, nace_r2, date, values) %>%
  spread(nace_r2, values) %>%
  mutate(values = `C19`/TOTAL)  %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  group_by(Geo) %>%
  mutate(values = values/ values[1]) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Price Deflator") +
  scale_color_identity() + add_4flags +
  scale_x_date(breaks = seq(1960, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 10))

C20 - Manufacture of chemicals and chemical products

Value

All

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C20", "TOTAL"),
         geo %in% c("FR", "DE", "IT", "ES"),
         unit == "CP_MNAC") %>%
  year_to_date() %>%
  left_join(geo, by = "geo") %>%
  select(geo,  Geo, nace_r2, date, values) %>%
  spread(nace_r2, values) %>%
  mutate(values = `C20`/TOTAL) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Textiles (% of GDP)") +
  scale_color_identity() + add_4flags +
  scale_x_date(breaks = seq(1960, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = "none") +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 0.1),
                     labels = percent_format(accuracy = .1))

1995-

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C20", "TOTAL"),
         geo %in% c("FR", "DE", "IT", "ES", "NL"),
         unit == "CP_MNAC") %>%
  year_to_date() %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(geo, by = "geo") %>%
  select(geo,  Geo, nace_r2, date, values) %>%
  spread(nace_r2, values) %>%
  mutate(values = `C20`/TOTAL) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(color = ifelse(geo == "NL", color2, color)) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Textiles (% of GDP)") +
  scale_color_identity() + add_5flags +
  scale_x_date(breaks = seq(1960, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = "none") +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 0.1),
                     labels = percent_format(accuracy = .1))

Volume

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C20", "TOTAL"),
         geo %in% c("FR", "DE", "IT", "ES"),
         unit == "CLV10_MEUR") %>%
  year_to_date() %>%
  left_join(geo, by = "geo") %>%
  select(geo,  Geo, nace_r2, date, values) %>%
  spread(nace_r2, values) %>%
  mutate(values = `C20`/TOTAL) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Textiles (% of GDP)") +
  scale_color_identity() + add_4flags +
  scale_x_date(breaks = seq(1960, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = "none") +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 0.1),
                     labels = percent_format(accuracy = .1))

Price Deflator

All

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C20", "TOTAL"),
         geo %in% c("FR", "DE", "IT", "ES"),
         unit == "PD10_NAC") %>%
  year_to_date() %>%
  left_join(geo, by = "geo") %>%
  select(geo,  Geo, nace_r2, date, values) %>%
  spread(nace_r2, values) %>%
  mutate(values = `C20`/TOTAL)  %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Price Deflator") +
  scale_color_identity() + add_4flags +
  scale_x_date(breaks = seq(1960, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 10))

1995-

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C20", "TOTAL"),
         geo %in% c("FR", "DE", "IT", "ES"),
         unit == "PD10_NAC") %>%
  year_to_date() %>%
  left_join(geo, by = "geo") %>%
  filter(date >= as.Date("1995-01-01")) %>%
  select(geo,  Geo, nace_r2, date, values) %>%
  spread(nace_r2, values) %>%
  mutate(values = `C20`/TOTAL)  %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  group_by(Geo) %>%
  mutate(values = values/ values[1]) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Price Deflator") +
  scale_color_identity() + add_4flags +
  scale_x_date(breaks = seq(1960, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 10))

C21 - Manufacture of basic pharmaceutical products and pharmaceutical preparations

Value

All

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C21", "TOTAL"),
         geo %in% c("FR", "DE", "IT", "ES"),
         unit == "CP_MNAC") %>%
  year_to_date() %>%
  left_join(geo, by = "geo") %>%
  select(geo,  Geo, nace_r2, date, values) %>%
  spread(nace_r2, values) %>%
  mutate(values = `C21`/TOTAL) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Manufacture of basic pharmaceutical products (% of GDP)") +
  scale_color_identity() + add_4flags +
  scale_x_date(breaks = seq(1960, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = "none") +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 0.1),
                     labels = percent_format(accuracy = .1))

1995-

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C21", "TOTAL"),
         geo %in% c("FR", "DE", "IT", "ES", "NL"),
         unit == "CP_MNAC") %>%
  year_to_date() %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(geo, by = "geo") %>%
  select(geo,  Geo, nace_r2, date, values) %>%
  spread(nace_r2, values) %>%
  mutate(values = `C21`/TOTAL) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(color = ifelse(geo == "NL", color2, color)) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Manufacture of basic pharmaceutical products (% of GDP)") +
  scale_color_identity() + add_5flags +
  scale_x_date(breaks = seq(1960, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = "none") +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 0.1),
                     labels = percent_format(accuracy = .1))

Volume

All

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C21", "TOTAL"),
         geo %in% c("FR", "DE", "IT", "ES"),
         unit == "CLV10_MEUR") %>%
  year_to_date() %>%
  left_join(geo, by = "geo") %>%
  select(geo,  Geo, nace_r2, date, values) %>%
  spread(nace_r2, values) %>%
  mutate(values = `C21`/TOTAL) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Manufacture of basic pharmaceutical products (% of GDP)") +
  scale_color_identity() + add_4flags +
  scale_x_date(breaks = seq(1960, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = "none") +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 0.1),
                     labels = percent_format(accuracy = .1))

1995-

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C21", "TOTAL"),
         geo %in% c("FR", "DE", "IT", "ES", "NL"),
         unit == "CLV10_MEUR") %>%
  year_to_date() %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(geo, by = "geo") %>%
  select(geo,  Geo, nace_r2, date, values) %>%
  spread(nace_r2, values) %>%
  mutate(values = `C21`/TOTAL) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Manufacture of basic pharmaceutical products (% of GDP)") +
  scale_color_identity() + add_5flags +
  scale_x_date(breaks = seq(1960, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = "none") +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 0.1),
                     labels = percent_format(accuracy = .1))

Price Deflator

All

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C21", "TOTAL"),
         geo %in% c("FR", "DE", "IT", "ES"),
         unit == "PD10_NAC") %>%
  year_to_date() %>%
  left_join(geo, by = "geo") %>%
  select(geo,  Geo, nace_r2, date, values) %>%
  spread(nace_r2, values) %>%
  mutate(values = `C21`/TOTAL)  %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Price Deflator") +
  scale_color_identity() + add_4flags +
  scale_x_date(breaks = seq(1960, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 10))

1995-

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C21", "TOTAL"),
         geo %in% c("FR", "DE", "IT", "ES", "NL"),
         unit == "PD10_NAC") %>%
  year_to_date() %>%
  left_join(geo, by = "geo") %>%
  filter(date >= as.Date("1995-01-01")) %>%
  select(geo,  Geo, nace_r2, date, values) %>%
  spread(nace_r2, values) %>%
  mutate(values = `C21`/TOTAL)  %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  group_by(Geo) %>%
  mutate(values = values/ values[1],
         color = ifelse(geo== "NL", color2, color)) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Price Deflator") +
  scale_color_identity() + add_5flags +
  scale_x_date(breaks = seq(1960, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = 0.01*seq(-500, 200, 10))

C29 - Motor vehicles

All

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C29", "TOTAL"),
         geo %in% c("FR", "DE", "IT", "ES", "EA"),
         unit == "CP_MNAC") %>%
  year_to_date() %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA", "Europe", Geo)) %>%
  select(geo,  Geo, nace_r2, date, values) %>%
  spread(nace_r2, values) %>%
  mutate(values = `C29`/TOTAL) %>%
  filter(!is.na(values)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Motor vehicles (% of GDP)") +
  scale_color_identity() +
  scale_x_date(breaks = seq(1960, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) + add_5flags +
  theme(legend.position = "none") +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 0.5),
                     labels = percent_format(accuracy = .1))

1995-

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C29", "TOTAL"),
         geo %in% c("FR", "DE", "IT", "ES", "EA"),
         unit == "CP_MNAC") %>%
  year_to_date() %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA", "Europe", Geo)) %>%
  select(geo,  Geo, nace_r2, date, values) %>%
  spread(nace_r2, values) %>%
  mutate(values = `C29`/TOTAL) %>%
  filter(!is.na(values)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Motor vehicles (% of GDP)") +
  scale_color_identity() +
  scale_x_date(breaks = seq(1960, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) + add_5flags +
  theme(legend.position = "none") +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 0.5),
                     labels = percent_format(accuracy = .1))

2000-

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C29", "TOTAL"),
         geo %in% c("FR", "DE", "IT", "ES"),
         unit == "CP_MNAC") %>%
  year_to_date() %>%
  filter(date >= as.Date("2000-01-01")) %>%
  left_join(geo, by = "geo") %>%
  select(geo,  Geo, nace_r2, date, values) %>%
  spread(nace_r2, values) %>%
  mutate(values = `C29`/TOTAL) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(color = ifelse(geo == "NL", color2, color)) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Industrie automobile (% du PIB)") +
  scale_color_identity() + add_4flags +
  scale_x_date(breaks = seq(1995, 2026, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = "none") +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 0.5),
                     labels = percent_format(accuracy = .1))

France, Europe

B1G

Code
# load_data("eurostat/nace_r2_fr.RData")
nama_10_a64 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C29", "TOTAL"),
         geo %in% c("FR", "EA20"),
         unit == "CP_MNAC") %>%
  year_to_date() %>%
  #filter(date >= as.Date("1995-01-01")) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA20", "Europe", Geo)) %>%
  select(geo,  Geo, nace_r2, date, values) %>%
  spread(nace_r2, values) %>%
  mutate(values = `C29`/TOTAL) %>%
  filter(!is.na(values)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Industrie automobile (% du PIB)") +
  scale_color_identity() +
  scale_x_date(breaks = seq(1960, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) + add_2flags +
  theme(legend.position = "none") +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 0.1),
                     labels = percent_format(accuracy = .1))

P1

Code
# load_data("eurostat/nace_r2_fr.RData")
nama_10_a64 %>%
  filter(na_item == "P1",
         nace_r2 %in% c("C29", "TOTAL"),
         geo %in% c("FR", "EA20"),
         unit == "CP_MNAC") %>%
  year_to_date() %>%
  #filter(date >= as.Date("1995-01-01")) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA20", "Europe", Geo)) %>%
  select(geo,  Geo, nace_r2, date, values) %>%
  spread(nace_r2, values) %>%
  mutate(values = `C29`/TOTAL) %>%
  filter(!is.na(values)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Production, industrie automobile (% de la production)") +
  scale_color_identity() +
  scale_x_date(breaks = seq(1960, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) + add_2flags +
  theme(legend.position = "none") +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 0.1),
                     labels = percent_format(accuracy = .1))

C29_C30 - Motor vehicles and other transport equipment

All

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C29_C30", "TOTAL"),
         geo %in% c("FR", "DE", "IT", "ES"),
         unit == "CP_MNAC") %>%
  year_to_date() %>%
  left_join(geo, by = "geo") %>%
  select(geo,  Geo, nace_r2, date, values) %>%
  spread(nace_r2, values) %>%
  mutate(values = `C29_C30`/TOTAL) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = Geo)) + 
  theme_minimal() + xlab("") + ylab("Motor vehicles and other transport equipment (% of GDP)") +
  scale_color_manual(values = c("#002395", "#000000", "#009246", "#C60B1E")) +
  scale_x_date(breaks = seq(1960, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) + add_4flags +
  theme(legend.position = "none") +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 0.5),
                     labels = percent_format(accuracy = .1))

1995-

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C29_C30", "TOTAL"),
         geo %in% c("FR", "DE", "IT", "ES"),
         unit == "CP_MNAC") %>%
  year_to_date() %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(geo, by = "geo") %>%
  select(geo,  Geo, nace_r2, date, values) %>%
  spread(nace_r2, values) %>%
  mutate(values = `C29_C30`/TOTAL) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = Geo)) + 
  theme_minimal() + xlab("") + ylab("Motor vehicles and other transport equipment (% of GDP)") +
  scale_color_manual(values = c("#002395", "#000000", "#009246", "#C60B1E")) +
  scale_x_date(breaks = seq(1960, 2026, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  add_4flags +
  theme(legend.position = "none") +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 0.5),
                     labels = percent_format(accuracy = .1))

C28 - Machinery and equipment

All

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C28", "TOTAL"),
         geo %in% c("FR", "DE", "IT", "ES"),
         unit == "CP_MNAC") %>%
  year_to_date() %>%
  left_join(geo, by = "geo") %>%
  select(geo,  Geo, nace_r2, date, values) %>%
  spread(nace_r2, values) %>%
  mutate(values = `C28`/TOTAL) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Machinery and equipment (% of GDP)") +
  scale_color_identity() + add_4flags +
  scale_x_date(breaks = seq(1960, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = "none") +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 0.1),
                     labels = percent_format(accuracy = .1))

1995-

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C28", "TOTAL"),
         geo %in% c("FR", "DE", "IT", "ES"),
         unit == "CP_MNAC") %>%
  year_to_date() %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(geo, by = "geo") %>%
  select(geo,  Geo, nace_r2, date, values) %>%
  spread(nace_r2, values) %>%
  mutate(values = `C28`/TOTAL) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Machinery and equipment (% of GDP)") +
  scale_color_identity() + add_4flags +
  scale_x_date(breaks = seq(1960, 2026, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = "none") +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 0.1),
                     labels = percent_format(accuracy = .1))

L - Real Estate

Value

All

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("L", "TOTAL"),
         geo %in% c("FR", "DE", "IT", "ES", "NL"),
         unit == "CP_MNAC") %>%
  year_to_date() %>%
  left_join(geo, by = "geo") %>%
  select(geo,  Geo, nace_r2, date, values) %>%
  spread(nace_r2, values) %>%
  mutate(values = `L`/TOTAL) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Real Estate (% of GDP)") +
  scale_color_identity() + add_5flags +
  scale_x_date(breaks = seq(1960, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 1),
                     labels = percent_format(accuracy = 1))

1995-

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("L", "TOTAL"),
         geo %in% c("FR", "DE", "IT", "ES", "NL"),
         unit == "CP_MNAC") %>%
  year_to_date() %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(geo, by = "geo") %>%
  select(geo,  Geo, nace_r2, date, values) %>%
  spread(nace_r2, values) %>%
  mutate(values = `L`/TOTAL) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Real Estate (% of GDP)") +
  scale_color_identity() + add_5flags +
  scale_x_date(breaks = seq(1960, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 1),
                     labels = percent_format(accuracy = 1))

Volume

All

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("L", "TOTAL"),
         geo %in% c("FR", "DE", "IT", "ES"),
         unit == "CLV10_MEUR") %>%
  year_to_date() %>%
  left_join(geo, by = "geo") %>%
  select(geo,  Geo, nace_r2, date, values) %>%
  spread(nace_r2, values) %>%
  mutate(values = `L`/TOTAL) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Real Estate (% of GDP)") +
  scale_color_identity() + add_4flags +
  scale_x_date(breaks = seq(1960, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 1),
                     labels = percent_format(accuracy = 1))

1995-

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("L", "TOTAL"),
         geo %in% c("FR", "DE", "IT", "ES", "NL"),
         unit == "CLV10_MEUR") %>%
  year_to_date() %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(geo, by = "geo") %>%
  select(geo,  Geo, nace_r2, date, values) %>%
  spread(nace_r2, values) %>%
  mutate(values = `L`/TOTAL) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Real Estate (% of GDP)") +
  scale_color_identity() + add_5flags +
  scale_x_date(breaks = seq(1960, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 1),
                     labels = percent_format(accuracy = 1))

Price Deflator

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("L", "TOTAL"),
         geo %in% c("FR", "DE", "IT", "ES", "NL"),
         unit == "PD10_NAC") %>%
  year_to_date() %>%
  left_join(geo, by = "geo") %>%
  select(geo,  Geo, nace_r2, date, values) %>%
  spread(nace_r2, values) %>%
  mutate(values = L/TOTAL)  %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Price Deflator") +
  scale_color_identity() + add_5flags +
  scale_x_date(breaks = seq(1960, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = 0.01*seq(-500, 200, 10))

Individual Countries

France

Table

All

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         geo %in% c("FR"),
         unit == "CP_MNAC",
         time %in% c("1978", "1998",  "2008", "2018")) %>%
  left_join(nace_r2, by = "nace_r2") %>%
  select(nace_r2, Nace_r2, time, values) %>%
  group_by(time) %>%
  mutate(values = round(100*values/ values[nace_r2 == "TOTAL"], 1)) %>%
  filter(nace_r2 != "TOTAL")  %>%
  spread(time, values) %>%
  print_table_conditional

Manufacturing

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         geo %in% c("FR"),
         unit == "CP_MNAC",
         time %in% c("1978", "1998",  "2008", "2018")) %>%
  filter(grepl("C", nace_r2) | nace_r2 == "TOTAL") %>%
  left_join(nace_r2, by = "nace_r2") %>%
  select(nace_r2, Nace_r2, time, values) %>%
  group_by(time) %>%
  mutate(values = round(100*values/ values[nace_r2 == "TOTAL"], 1)) %>%
  filter(nace_r2 != "TOTAL")  %>%
  spread(time, values) %>%
  arrange(-`2018`) %>%
  print_table_conditional
nace_r2 Nace_r2 1978 1998 2008 2018
C Industrie manufacturière 21.6 16.3 12.3 11.2
C10-C12 Industries alimentaires; fabrication de boissons et de produits à base de tabac 3.0 2.5 1.9 1.9
C29_C30 Industrie automobile et construction navale 2.3 1.9 1.5 1.6
C31-C33 Fabrication de meubles, bijouterie, instruments de musique, jouets, réparation et installation de machines et équipements 2.8 1.9 1.6 1.6
C24_C25 Métallurgie et fabrication de produits métalliques, à l'exception des machines et des équipements 2.2 2.1 1.7 1.4
C33 Réparation et installation de machines et d'équipements NA 1.4 1.1 1.3
C25 Fabrication de produits métalliques, à l'exception des machines et des équipements NA 1.5 1.2 1.1
C30 Fabrication d'autres matériels de transport NA 0.6 0.7 1.0
C22_C23 Fabrication de produits en caoutchouc et en plastique et autres produits minéraux non métalliques 2.1 1.5 1.1 0.9
C20 Industrie chimique 1.5 1.0 0.7 0.8
C16-C18 Travail du bois et du papier, imprimerie et reproduction 1.3 1.1 0.7 0.6
C21 Industrie pharmaceutique 0.6 0.7 0.7 0.6
C28 Fabrication de machines et équipements n.c.a. 1.6 1.0 0.9 0.6
C29 Industrie automobile NA 1.3 0.8 0.6
C22 Fabrication de produits en caoutchouc et en plastique NA 0.9 0.6 0.5
C26 Fabrication de produits informatiques, électroniques et optiques 1.3 0.9 0.6 0.5
C23 Fabrication d'autres produits minéraux non métalliques NA 0.6 0.5 0.4
C27 Fabrication d'équipements électriques 1.0 0.7 0.5 0.4
C31_C32 Fabrication de meubles; autres industries manufacturières NA 0.6 0.4 0.4
C13-C15 Fabrication de textiles, industrie de l'habillement, du cuir et de la chaussure 1.8 0.8 0.4 0.3
C17 Industrie du papier et du carton NA 0.5 0.3 0.3
C24 Métallurgie NA 0.6 0.5 0.3
C16 Travail du bois et fabrication d'articles en bois et en liège, à l'exception des meubles; fabrication d'articles en vannerie et sparterie NA 0.3 0.2 0.2
C18 Imprimerie et reproduction d'enregistrements NA 0.4 0.2 0.2
C19 Cokéfaction et raffinage 0.2 0.1 0.1 0.1

Construction, Human health, Manufacturing, Real estate

All

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C", "TOTAL", "L", "Q", "F"),
         geo %in% c("FR"),
         unit == "CP_MNAC") %>%
  year_to_date() %>%
  left_join(nace_r2, by = "nace_r2") %>%
  select(nace_r2, Nace_r2, date, values) %>%
  group_by(date) %>%
  mutate(values = values/ values[nace_r2 == "TOTAL"]) %>%
  filter(nace_r2 != "TOTAL")  %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = Nace_r2)) + 
  theme_minimal() + xlab("") + ylab("% of GDP") +
  scale_color_manual(values = viridis(5)[1:4]) +
  scale_x_date(breaks = seq(1960, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 1),
                     labels = percent_format(accuracy = 1)) +
  theme(legend.position = c(0.75, 0.85),
        legend.title = element_blank())

1995-

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C", "TOTAL", "L", "Q", "F"),
         geo %in% c("FR"),
         unit == "CP_MNAC") %>%
  year_to_date() %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(nace_r2, by = "nace_r2") %>%
  select(nace_r2, Nace_r2, date, values) %>%
  group_by(date) %>%
  mutate(values = values/ values[nace_r2 == "TOTAL"]) %>%
  filter(nace_r2 != "TOTAL")  %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = Nace_r2)) + 
  theme_minimal() + xlab("") + ylab("% of GDP") +
  scale_color_manual(values = viridis(5)[1:4]) +
  scale_x_date(breaks = seq(1960, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(0, 30, 2),
                     labels = percent_format(accuracy = 1),
                     limits = c(0, 0.3)) +
  theme(legend.position = c(0.75, 0.85),
        legend.title = element_blank())

Germany

Table

All

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         geo %in% c("DE"),
         unit == "CP_MNAC",
         time %in% c("1978", "1998",  "2008", "2018")) %>%
  left_join(nace_r2, by = "nace_r2") %>%
  select(nace_r2, Nace_r2, time, values) %>%
  group_by(time) %>%
  mutate(values = round(100*values/ values[nace_r2 == "TOTAL"], 1)) %>%
  filter(nace_r2 != "TOTAL")  %>%
  spread(time, values) %>%
  print_table_conditional

Manufacturing

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         geo %in% c("DE"),
         unit == "CP_MNAC",
         time %in% c("1978", "1998",  "2008", "2018")) %>%
  filter(grepl("C", nace_r2) | nace_r2 == "TOTAL") %>%
  left_join(nace_r2, by = "nace_r2") %>%
  select(nace_r2, Nace_r2, time, values) %>%
  group_by(time) %>%
  mutate(values = round(100*values/ values[nace_r2 == "TOTAL"], 1)) %>%
  filter(nace_r2 != "TOTAL")  %>%
  spread(time, values) %>%
  arrange(-`2018`) %>%
  print_table_conditional
nace_r2 Nace_r2 1998 2008 2018
C Industrie manufacturière 22.2 22.1 22.2
C29_C30 Industrie automobile et construction navale 3.4 3.6 4.9
C29 Industrie automobile 3.1 3.2 4.4
C28 Fabrication de machines et équipements n.c.a. 3.2 3.7 3.6
C24_C25 Métallurgie et fabrication de produits métalliques, à l'exception des machines et des équipements 2.8 3.1 2.7
C25 Fabrication de produits métalliques, à l'exception des machines et des équipements 1.9 2.0 1.9
C22_C23 Fabrication de produits en caoutchouc et en plastique et autres produits minéraux non métalliques 2.0 1.7 1.6
C10-C12 Industries alimentaires; fabrication de boissons et de produits à base de tabac 1.9 1.6 1.5
C20 Industrie chimique 1.8 1.6 1.5
C26 Fabrication de produits informatiques, électroniques et optiques 1.3 1.4 1.5
C27 Fabrication d'équipements électriques 1.7 1.6 1.5
C31-C33 Fabrication de meubles, bijouterie, instruments de musique, jouets, réparation et installation de machines et équipements 1.4 1.4 1.3
C22 Fabrication de produits en caoutchouc et en plastique 1.1 1.0 1.0
C16-C18 Travail du bois et du papier, imprimerie et reproduction 1.5 1.1 0.8
C21 Industrie pharmaceutique 0.6 0.9 0.8
C24 Métallurgie 0.9 1.1 0.8
C31_C32 Fabrication de meubles; autres industries manufacturières 0.9 0.8 0.8
C23 Fabrication d'autres produits minéraux non métalliques 0.9 0.6 0.6
C30 Fabrication d'autres matériels de transport 0.3 0.4 0.5
C33 Réparation et installation de machines et d'équipements 0.5 0.6 0.5
C17 Industrie du papier et du carton 0.5 0.4 0.4
C19 Cokéfaction et raffinage 0.2 0.1 0.3
C13-C15 Fabrication de textiles, industrie de l'habillement, du cuir et de la chaussure 0.5 0.3 0.2
C16 Travail du bois et fabrication d'articles en bois et en liège, à l'exception des meubles; fabrication d'articles en vannerie et sparterie 0.4 0.3 0.2
C18 Imprimerie et reproduction d'enregistrements 0.6 0.4 0.2

Construction, Human health, Manufacturing, Real estate

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C", "TOTAL", "L", "Q", "F"),
         geo %in% c("DE"),
         unit == "CP_MNAC") %>%
  year_to_date() %>%
  left_join(nace_r2, by = "nace_r2") %>%
  select(nace_r2, Nace_r2, date, values) %>%
  group_by(date) %>%
  mutate(values = values/ values[nace_r2 == "TOTAL"]) %>%
  filter(nace_r2 != "TOTAL")  %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = Nace_r2)) + 
  theme_minimal() + xlab("") + ylab("% of GDP") +
  scale_color_manual(values = viridis(5)[1:4]) +
  scale_x_date(breaks = seq(1960, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(0, 30, 2),
                     labels = percent_format(accuracy = 1),
                     limits = c(0, 0.3)) +
  theme(legend.position = c(0.75, 0.85),
        legend.title = element_blank())

Italy

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C", "TOTAL", "L", "Q", "F"),
         geo %in% c("IT"),
         unit == "CP_MNAC") %>%
  year_to_date() %>%
  left_join(nace_r2, by = "nace_r2") %>%
  select(nace_r2, Nace_r2, date, values) %>%
  group_by(date) %>%
  mutate(values = values/ values[nace_r2 == "TOTAL"]) %>%
  filter(nace_r2 != "TOTAL")  %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = Nace_r2)) + 
  theme_minimal() + xlab("") + ylab("% of GDP") +
  scale_color_manual(values = viridis(5)[1:4]) +
  scale_x_date(breaks = seq(1960, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 1),
                     labels = percent_format(accuracy = 1)) +
  theme(legend.position = c(0.75, 0.85),
        legend.title = element_blank())

Spain

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C", "TOTAL", "L", "Q", "F"),
         geo %in% c("ES"),
         unit == "CP_MNAC") %>%
  year_to_date() %>%
  left_join(nace_r2, by = "nace_r2") %>%
  select(nace_r2, Nace_r2, date, values) %>%
  group_by(date) %>%
  mutate(values = values/ values[nace_r2 == "TOTAL"]) %>%
  filter(nace_r2 != "TOTAL")  %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = Nace_r2)) + 
  theme_minimal() + xlab("") + ylab("% of GDP") +
  scale_color_manual(values = viridis(5)[1:4]) +
  scale_x_date(breaks = seq(1960, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 1),
                     labels = percent_format(accuracy = 1)) +
  theme(legend.position = c(0.75, 0.85),
        legend.title = element_blank())

Netherlands

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C", "TOTAL", "L", "Q", "F"),
         geo %in% c("NL"),
         unit == "CP_MNAC") %>%
  year_to_date() %>%
  left_join(nace_r2, by = "nace_r2") %>%
  select(nace_r2, Nace_r2, date, values) %>%
  group_by(date) %>%
  mutate(values = values/ values[nace_r2 == "TOTAL"]) %>%
  filter(nace_r2 != "TOTAL")  %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = Nace_r2)) + 
  theme_minimal() + xlab("") + ylab("% of GDP") +
  scale_color_manual(values = viridis(5)[1:4]) +
  scale_x_date(breaks = seq(1960, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 1),
                     labels = percent_format(accuracy = 1)) +
  theme(legend.position = c(0.75, 0.85),
        legend.title = element_blank())

Danemark

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C", "TOTAL", "L", "Q", "F"),
         geo %in% c("DK"),
         unit == "CP_MNAC") %>%
  year_to_date() %>%
  left_join(nace_r2, by = "nace_r2") %>%
  select(nace_r2, Nace_r2, date, values) %>%
  group_by(date) %>%
  mutate(values = values/ values[nace_r2 == "TOTAL"]) %>%
  filter(nace_r2 != "TOTAL")  %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = Nace_r2)) + 
  theme_minimal() + xlab("") + ylab("% of GDP") +
  scale_color_manual(values = viridis(5)[1:4]) +
  scale_x_date(breaks = seq(1960, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 1),
                     labels = percent_format(accuracy = 1)) +
  theme(legend.position = c(0.75, 0.85),
        legend.title = element_blank())

Belgium

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C", "TOTAL", "L", "Q", "F"),
         geo %in% c("BE"),
         unit == "CP_MNAC") %>%
  year_to_date() %>%
  left_join(nace_r2, by = "nace_r2") %>%
  select(nace_r2, Nace_r2, date, values) %>%
  group_by(date) %>%
  mutate(values = values/ values[nace_r2 == "TOTAL"]) %>%
  filter(nace_r2 != "TOTAL")  %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = Nace_r2)) + 
  theme_minimal() + xlab("") + ylab("% of GDP") +
  scale_color_manual(values = viridis(5)[1:4]) +
  scale_x_date(breaks = seq(1960, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 1),
                     labels = percent_format(accuracy = 1)) +
  theme(legend.position = c(0.75, 0.85),
        legend.title = element_blank())

Finland

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C", "TOTAL", "L", "Q", "F"),
         geo %in% c("FI"),
         unit == "CP_MNAC") %>%
  year_to_date() %>%
  left_join(nace_r2, by = "nace_r2") %>%
  select(nace_r2, Nace_r2, date, values) %>%
  group_by(date) %>%
  mutate(values = values/ values[nace_r2 == "TOTAL"]) %>%
  filter(nace_r2 != "TOTAL")  %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = Nace_r2)) + 
  theme_minimal() + xlab("") + ylab("% of GDP") +
  scale_color_manual(values = viridis(5)[1:4]) +
  scale_x_date(breaks = seq(1960, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 1),
                     labels = percent_format(accuracy = 1)) +
  theme(legend.position = c(0.75, 0.85),
        legend.title = element_blank())

Portugal

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C", "TOTAL", "L", "Q", "F"),
         geo %in% c("PT"),
         unit == "CP_MNAC") %>%
  year_to_date() %>%
  left_join(nace_r2, by = "nace_r2") %>%
  select(nace_r2, Nace_r2, date, values) %>%
  group_by(date) %>%
  mutate(values = values/ values[nace_r2 == "TOTAL"]) %>%
  filter(nace_r2 != "TOTAL")  %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = Nace_r2)) + 
  theme_minimal() + xlab("") + ylab("% of GDP") +
  scale_color_manual(values = viridis(5)[1:4]) +
  scale_x_date(breaks = seq(1960, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 1),
                     labels = percent_format(accuracy = 1)) +
  theme(legend.position = c(0.75, 0.85),
        legend.title = element_blank())

Austria

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C", "TOTAL", "L", "Q", "F"),
         geo %in% c("AT"),
         unit == "CP_MNAC") %>%
  year_to_date() %>%
  left_join(nace_r2, by = "nace_r2") %>%
  select(nace_r2, Nace_r2, date, values) %>%
  group_by(date) %>%
  mutate(values = values/ values[nace_r2 == "TOTAL"]) %>%
  filter(nace_r2 != "TOTAL")  %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = Nace_r2)) + 
  theme_minimal() + xlab("") + ylab("% of GDP") +
  scale_color_manual(values = viridis(5)[1:4]) +
  scale_x_date(breaks = seq(1960, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 1),
                     labels = percent_format(accuracy = 1)) +
  theme(legend.position = c(0.75, 0.85),
        legend.title = element_blank())

Sweden

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C", "TOTAL", "L", "Q", "F"),
         geo %in% c("SE"),
         unit == "CP_MNAC") %>%
  year_to_date() %>%
  left_join(nace_r2, by = "nace_r2") %>%
  select(nace_r2, Nace_r2, date, values) %>%
  group_by(date) %>%
  mutate(values = values/ values[nace_r2 == "TOTAL"]) %>%
  filter(nace_r2 != "TOTAL")  %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = Nace_r2)) + 
  theme_minimal() + xlab("") + ylab("% of GDP") +
  scale_color_manual(values = viridis(5)[1:4]) +
  scale_x_date(breaks = seq(1960, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 1),
                     labels = percent_format(accuracy = 1)) +
  theme(legend.position = c(0.75, 0.85),
        legend.title = element_blank())

United Kingdom

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C", "TOTAL", "L", "Q", "F"),
         geo %in% c("UK"),
         unit == "CP_MNAC") %>%
  year_to_date() %>%
  left_join(nace_r2, by = "nace_r2") %>%
  select(nace_r2, Nace_r2, date, values) %>%
  group_by(date) %>%
  mutate(values = values/ values[nace_r2 == "TOTAL"]) %>%
  filter(nace_r2 != "TOTAL")  %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = Nace_r2)) + 
  theme_minimal() + xlab("") + ylab("% of GDP") +
  scale_color_manual(values = viridis(5)[1:4]) +
  scale_x_date(breaks = seq(1960, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 1),
                     labels = percent_format(accuracy = 1)) +
  theme(legend.position = c(0.75, 0.85),
        legend.title = element_blank())

Iceland

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C", "TOTAL", "L", "Q", "F"),
         geo %in% c("IS"),
         unit == "CP_MNAC") %>%
  year_to_date() %>%
  left_join(nace_r2, by = "nace_r2") %>%
  select(nace_r2, Nace_r2, date, values) %>%
  group_by(date) %>%
  mutate(values = values/ values[nace_r2 == "TOTAL"]) %>%
  filter(nace_r2 != "TOTAL")  %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = Nace_r2)) + 
  theme_minimal() + xlab("") + ylab("% of GDP") +
  scale_color_manual(values = viridis(5)[1:4]) +
  scale_x_date(breaks = seq(1960, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 1),
                     labels = percent_format(accuracy = 1)) +
  theme(legend.position = c(0.75, 0.85),
        legend.title = element_blank())

Relative to EA Manufacturing Value Added

1995-

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         nace_r2 == "C",
         geo %in% c("EA", "FR", "DE", "IT", "ES", "NL", "AT", "FI"),
         unit == "CP_MNAC") %>%
  year_to_date() %>%
  filter(date >= as.Date("1995-01-01")) %>%
  #filter(date <= as.Date("2019-01-01")) %>%
  left_join(geo, by = "geo") %>%
  group_by(date) %>%
  filter(n() == 8) %>%
  mutate(values = values /values[geo == "EA"]) %>%
  filter(geo != "EA") %>%
  group_by(geo) %>%
  mutate(values = 100*values / values[date == as.Date("1995-01-01")]) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(color = ifelse(geo == "FR", color2, color)) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Valeur ajoutée manuf. par rapport à la Zone €") +
  scale_color_identity() + add_7flags +
  scale_x_date(breaks = seq(1960, 2023, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = seq(0, 200, 5)) +
  theme(legend.position = "none")

2000-2019

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         nace_r2 == "C",
         geo %in% c("EA", "FR", "DE", "IT", "ES", "NL", "AT", "FI"),
         unit == "CP_MNAC") %>%
  year_to_date() %>%
  filter(date >= as.Date("2000-01-01"),
         date <= as.Date("2019-01-01")) %>%
  left_join(geo, by = "geo") %>%
  group_by(date) %>%
  mutate(values = values /values[geo == "EA"]) %>%
  filter(geo != "EA") %>%
  group_by(geo) %>%
  mutate(values = 100*values / values[date == as.Date("2000-01-01")]) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(color = ifelse(geo == "FR", color2, color)) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Valeur ajoutée manuf. par rapport à la Zone €") +
  scale_color_identity() + add_7flags +
  scale_x_date(breaks = seq(1960, 2023, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = seq(0, 200, 5)) +
  theme(legend.position = "none")

2000-2018 + Grèce

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         nace_r2 == "C",
         geo %in% c("EA", "FR", "DE", "IT", "EL", "NL", "AT", "FI"),
         unit == "CP_MNAC") %>%
  year_to_date() %>%
  filter(date >= as.Date("2000-01-01")) %>%
  filter(date <= as.Date("2018-01-01")) %>%
  left_join(geo, by = "geo") %>%
  group_by(date) %>%
  mutate(values = values /values[geo == "EA"]) %>%
  filter(geo != "EA") %>%
  group_by(geo) %>%
  mutate(values = 100*values / values[date == as.Date("2000-01-01")]) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = Geo)) + 
  theme_minimal() + xlab("") + ylab("Valeur ajoutée manuf. par rapport à la Zone €") +
  scale_color_manual(values = c("#ED2939", "#003580", "#002395", "#000000",
                                "#0D5EAF", "#009246", "#AE1C28")) +
  scale_x_date(breaks = seq(1960, 2020, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  geom_image(data = . %>%
               filter(date == as.Date("2012-01-01")) %>%
               mutate(image = paste0("../../icon/flag/round/", str_to_lower(Geo), ".png")),
             aes(x = date, y = values, image = image), asp = 1.5) +
  scale_y_continuous(breaks = seq(0, 200, 5)) +
  theme(legend.position = "none")

Industry

1995-2018

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         nace_r2 == "B-E",
         geo %in% c("EA", "FR", "DE", "IT", "ES", "NL", "AT", "FI"),
         unit == "CP_MNAC") %>%
  year_to_date() %>%
  filter(date >= as.Date("1995-01-01"),
         date <= as.Date("2019-01-01")) %>%
  left_join(geo, by = "geo") %>%
  group_by(date) %>%
  mutate(values = values /values[geo == "EA"]) %>%
  filter(geo != "EA") %>%
  group_by(geo) %>%
  mutate(values = 100*values/values[date == as.Date("1995-01-01")]) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = Geo)) + 
  theme_minimal() + xlab("") + ylab("Valeur ajoutée manuf. par rapport à la Zone €") +
  scale_color_manual(values = c("#ED2939", "#003580", "#002395", "#000000",
                                "#009246", "#AE1C28", "#FFC400")) +
  scale_x_date(breaks = seq(1960, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  geom_image(data = . %>%
               filter(date == as.Date("2008-01-01")) %>%
               mutate(image = paste0("../../icon/flag/round/", str_to_lower(Geo), ".png")),
             aes(x = date, y = values, image = image), asp = 1.5) +
  scale_y_continuous(breaks = seq(0, 200, 5)) +
  theme(legend.position = "none")

2000-2018

Code
nama_10_a64 %>%
  filter(na_item == "B1G",
         nace_r2 == "B-E",
         geo %in% c("EA", "FR", "DE", "IT", "ES", "NL", "AT", "FI"),
         unit == "CP_MNAC") %>%
  year_to_date() %>%
  filter(date >= as.Date("2000-01-01")) %>%
  #filter(date <= as.Date("2018-01-01")) %>%
  left_join(geo, by = "geo") %>%
  group_by(date) %>%
  filter(n() == 8) %>%
  mutate(values = values /values[geo == "EA"]) %>%
  filter(geo != "EA") %>%
  group_by(geo) %>%
  mutate(values = 100*values/values[date == as.Date("2000-01-01")]) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = Geo)) + 
  theme_minimal() + xlab("") + ylab("Valeur ajoutée manuf. par rapport à la Zone €") +
  scale_color_manual(values = c("#ED2939", "#003580", "#002395", "#000000",
                                "#009246", "#AE1C28", "#FFC400")) +
  geom_image(data = . %>%
               filter(date == as.Date("2016-01-01")) %>%
               mutate(image = paste0("../../icon/flag/round/", str_to_lower(Geo), ".png")),
             aes(x = date, y = values, image = image), asp = 1.5) +
  scale_x_date(breaks = seq(1960, 2030, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = seq(0, 200, 5)) +
  theme(legend.position = "none")