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

Data - Eurostat

Author

Info

source	dataset	.html	.RData
eurostat	nama_10_a64	2024-12-22	2024-12-22
eurostat	nama_10_a64_e	2024-12-22	2024-12-22

Data on macro

source	dataset	.html	.RData
eurostat	nama_10_a10	2024-11-23	2024-10-08
eurostat	nama_10_a10_e	2024-11-23	2024-12-22
eurostat	nama_10_gdp	2024-12-22	2024-12-22
eurostat	nama_10_lp_ulc	2024-11-23	2024-10-08
eurostat	namq_10_a10	2024-12-22	2024-12-22
eurostat	namq_10_a10_e	2024-12-22	2024-12-22
eurostat	namq_10_gdp	2024-12-22	2024-12-22
eurostat	namq_10_lp_ulc	2024-11-23	2024-11-04
eurostat	namq_10_pc	2024-11-23	2024-11-21
eurostat	nasa_10_nf_tr	2024-12-14	2024-12-14
eurostat	nasq_10_nf_tr	2024-11-23	2024-10-09
fred	gdp	2024-12-22	2024-12-22
oecd	QNA	2024-06-06	2024-12-22
oecd	SNA_TABLE1	2024-12-22	2024-12-22
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	2024-12-16	2024-12-22
wdi	NY.GDP.PCAP.KD	2024-09-18	2024-09-18
wdi	NY.GDP.PCAP.PP.CD	2024-12-22	2024-12-22
wdi	NY.GDP.PCAP.PP.KD	2024-12-22	2024-12-22

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	2024-11-23	2024-10-09
eurostat	htec_trd_group4	2024-11-23	2024-10-08
eurostat	nama_10_a64	2024-12-22	2024-12-22
eurostat	nama_10_a64_e	2024-12-22	2024-12-22
eurostat	namq_10_a10_e	2024-12-22	2024-12-22
eurostat	road_eqr_carmot	2024-11-22	2024-10-08
eurostat	sts_inpp_m	2024-06-24	2024-12-21
eurostat	sts_inppd_m	2024-12-21	2024-12-21
eurostat	sts_inpr_m	2024-11-22	2024-10-08
eurostat	sts_intvnd_m	2024-12-22	2024-12-22
fred	industry	2024-12-22	2024-12-22
oecd	ALFS_EMP	2024-04-16	2024-12-22
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	2024-12-22
oecd	MSTI_PUB	2024-09-15	2024-12-22
oecd	SNA_TABLE4	2024-09-15	2024-12-16
wdi	NV.IND.EMPL.KD	2024-01-06	2024-09-18
wdi	NV.IND.MANF.CD	2024-12-22	2024-12-22
wdi	NV.IND.MANF.ZS	2024-12-21	2024-12-21
wdi	NV.IND.TOTL.KD	2024-01-06	2024-09-18
wdi	NV.IND.TOTL.ZS	2024-12-21	2024-12-21
wdi	SL.IND.EMPL.ZS	2024-12-21	2024-12-21
wdi	TX.VAL.MRCH.CD.WT	2024-01-06	2024-09-18

LAST_COMPILE

LAST_COMPILE
2024-12-22

Last

Code

nama_10_a64 %>%
  group_by(time) %>%
  summarise(Nobs = n()) %>%
  arrange(desc(time)) %>%
  head(2) %>%
  print_table_conditional()

time	Nobs
2023	183627
2022	267915

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	2469199
P51C	Consumption of fixed capital	1988156
P1	Output	1316473
P2	Intermediate consumption	1275462
D1	Compensation of employees	389498
D11	Wages and salaries	388046
D29X39	Other taxes less other subsidies on production	385182
B2A3N	Operating surplus and mixed income, net	347756

nace_r2

Liste. pdf

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	96909
C10-C12	Manufacture of food products; beverages and tobacco products	91952
C13-C15	Manufacture of textiles, wearing apparel, leather and related products	91950
C16	Manufacture of wood and of products of wood and cork, except furniture; manufacture of articles of straw and plaiting materials	89906
C16-C18	Manufacture of wood, paper, printing and reproduction	92030
C17	Manufacture of paper and paper products	89916
C18	Printing and reproduction of recorded media	89927
C19	Manufacture of coke and refined petroleum products	86765
C20	Manufacture of chemicals and chemical products	90173
C21	Manufacture of basic pharmaceutical products and pharmaceutical preparations	91262
C22	Manufacture of rubber and plastic products	89934
C22_C23	Manufacture of rubber and plastic products and other non-metallic mineral products	92030
C23	Manufacture of other non-metallic mineral products	89934
C24	Manufacture of basic metals	89929
C24_C25	Manufacture of basic metals and fabricated metal products, except machinery and equipment	92030
C25	Manufacture of fabricated metal products, except machinery and equipment	89934
C26	Manufacture of computer, electronic and optical products	91949
C27	Manufacture of electrical equipment	91889
C28	Manufacture of machinery and equipment n.e.c.	91949
C29	Manufacture of motor vehicles, trailers and semi-trailers	89934
C29_C30	Manufacture of motor vehicles, trailers, semi-trailers and of other transport equipment	92030
C30	Manufacture of other transport equipment	89565
C31-C33	Manufacture of furniture; jewellery, musical instruments, toys; repair and installation of machinery and equipment	92033
C31_C32	Manufacture of furniture; other manufacturing	89934
C33	Repair and installation of machinery and equipment	89910

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	96909
C10-C12	Manufacture of food products; beverages and tobacco products	91952
C13-C15	Manufacture of textiles, wearing apparel, leather and related products	91950
C16-C18	Manufacture of wood, paper, printing and reproduction	92030
C19	Manufacture of coke and refined petroleum products	86765
C20	Manufacture of chemicals and chemical products	90173
C21	Manufacture of basic pharmaceutical products and pharmaceutical preparations	91262
C22_C23	Manufacture of rubber and plastic products and other non-metallic mineral products	92030
C24_C25	Manufacture of basic metals and fabricated metal products, except machinery and equipment	92030
C26	Manufacture of computer, electronic and optical products	91949
C27	Manufacture of electrical equipment	91889
C28	Manufacture of machinery and equipment n.e.c.	91949
C29_C30	Manufacture of motor vehicles, trailers, semi-trailers and of other transport equipment	92030
C31-C33	Manufacture of furniture; jewellery, musical instruments, toys; repair and installation of machinery and equipment	92033
C31_C32	Manufacture of furniture; other manufacturing	89934

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	856190
CP_MNAC	Current prices, million units of national currency	856190
PC_TOT	Percentage of total	836459
PYP_MNAC	Previous year prices, million units of national currency	324251
PYP_MEUR	Previous year prices, million euro	320924
CLV10_MNAC	Chain linked volumes (2010), million units of national currency	311701
CLV05_MNAC	Chain linked volumes (2005), million units of national currency	310317
CLV10_MEUR	Chain linked volumes (2010), million euro	309267
CLV05_MEUR	Chain linked volumes (2005), million euro	307883
CLV_I10	Chain linked volumes, index 2010=100	304560
CLV15_MNAC	Chain linked volumes (2015), million units of national currency	303823
PD10_EUR	Price index (implicit deflator), 2010=100, euro	303071
PD10_NAC	Price index (implicit deflator), 2010=100, national currency	303071
CLV15_MEUR	Chain linked volumes (2015), million euro	301389
CLV_I15	Chain linked volumes, index 2015=100	296907
PD15_NAC	Price index (implicit deflator), 2015=100, national currency	295971
CLV_PCH_PRE	Chain linked volumes, percentage change on previous period	294305
PD_PCH_PRE_NAC	Price index (implicit deflator), percentage change on previous period, national currency	293492
PD_PCH_PRE_EUR	Price index (implicit deflator), percentage change on previous period, euro	293317
CLV20_MNAC	NA	288361
CLV20_MEUR	NA	285925
CLV_I20	NA	281536
PD20_NAC	NA	280862

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")