Gross value added and income A*10 industry breakdowns

Data - Eurostat

Author

François Geerolf

Info

source dataset .html .RData

eurostat

namq_10_a10

 2024-06-20 2024-06-23

Data on main macro

source dataset .html .RData

eurostat

nama_10_a10

 2024-06-23 2024-06-08

eurostat

nama_10_a10_e

 2024-06-23 2024-06-23

eurostat

nama_10_gdp

 2024-06-24 2024-06-18

eurostat

nama_10_lp_ulc

 2024-06-24 2024-06-08

eurostat

namq_10_a10

 2024-06-20 2024-06-23

eurostat

namq_10_a10_e

 2024-06-24 2024-06-08

eurostat

namq_10_gdp

 2024-06-20 2024-06-08

eurostat

namq_10_lp_ulc

 2024-06-20 2024-06-08

eurostat

namq_10_pc

 2024-06-20 2024-06-18

eurostat

nasa_10_nf_tr

 2024-06-21 2024-06-08

eurostat

nasq_10_nf_tr

 2024-06-20 2024-06-08

fred

gdp

 2024-06-20 2024-06-07

oecd

QNA

 2024-06-06 2024-06-05

oecd

SNA_TABLE1

 2024-06-20 2024-06-01

oecd

SNA_TABLE14A

 2024-06-20 2024-04-15

oecd

SNA_TABLE2

 2024-06-20 2024-04-11

oecd

SNA_TABLE6A

 2024-06-20 2024-04-15

wdi

NE.RSB.GNFS.ZS

 2024-06-20 2024-04-14

wdi

NY.GDP.MKTP.CD

 2024-06-20 2024-05-06

wdi

NY.GDP.MKTP.PP.CD

 2024-06-20 2024-04-14

wdi

NY.GDP.PCAP.CD

 2024-06-20 2024-04-22

wdi

NY.GDP.PCAP.KD

 2024-06-20 2024-05-06

wdi

NY.GDP.PCAP.PP.CD

 2024-06-20 2024-04-22

wdi

NY.GDP.PCAP.PP.KD

 2024-06-20 2024-05-06

LAST_COMPILE

LAST_COMPILE
2024-06-24

Last

Code 
namq_10_a10 %>%
  group_by(time) %>%
  summarise(Nobs = n()) %>%
  arrange(desc(time)) %>%
  head(1) %>%
  print_table_conditional()
time Nobs
2024Q1 36535

na_item

Code 
namq_10_a10 %>%
  left_join(na_item, by = "na_item") %>%
  group_by(na_item, Na_item) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) print_table(.) else .}
na_item Na_item Nobs
B1G Value added, gross 3524163
D1 Compensation of employees 488276
D11 Wages and salaries 472204
D12 Employers' social contributions 472204

nace_r2

Code 
namq_10_a10 %>%
  left_join(nace_r2, by = "nace_r2") %>%
  group_by(nace_r2, Nace_r2) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) print_table(.) else .}
nace_r2 Nace_r2 Nobs
TOTAL Total - all NACE activities 422269
B-E Industry (except construction) 413230
C Manufacturing 413230
A Agriculture, forestry and fishing 413022
F Construction 413022
K Financial and insurance activities 413022
O-Q Public administration, defence, education, human health and social work activities 413022
R-U Arts, entertainment and recreation; other service activities; activities of household and extra-territorial organizations and bodies 413022
L Real estate activities 411102
M_N Professional, scientific and technical activities; administrative and support service activities 411102
G-I Wholesale and retail trade, transport, accommodation and food service activities 410414
J Information and communication 410390

s_adj

Code 
namq_10_a10 %>%
  left_join(s_adj, by = "s_adj") %>%
  group_by(s_adj, S_adj) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) print_table(.) else .}
s_adj S_adj Nobs
SCA Seasonally and calendar adjusted data 2160465
NSA Unadjusted data (i.e. neither seasonally adjusted nor calendar adjusted data) 2133032
CA Calendar adjusted data, not seasonally adjusted data 397210
SA Seasonally adjusted data, not calendar adjusted data 266140

unit

Code 
namq_10_a10 %>%
  left_join(unit, by = "unit") %>%
  group_by(unit, Unit) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

geo

Code 
namq_10_a10 %>%
  left_join(geo, by = "geo") %>%
  group_by(geo, Geo) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
  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 .}

time

Code 
namq_10_a10 %>%
  group_by(time) %>%
  summarise(Nobs = n()) %>%
  arrange(desc(time)) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

Tables

B-E, C, TOTAL

Code 
namq_10_a10 %>%
  filter(nace_r2 %in% c("C", "TOTAL"),
         na_item == "B1G",
         # SCA: Seasonally and calendar adjusted data
         s_adj == "SCA",
         # CLV10_MEUR: Chain linked volumes (2010), million euro
         unit == "CP_MNAC",
         time %in% c("2021Q4", "2016Q4", "2011Q4")) %>%
  left_join(nace_r2, by = "nace_r2") %>%
  left_join(geo, by = "geo") %>%
  select(nace_r2, geo, Geo, values, time) %>%
  spread(nace_r2, values) %>%
  mutate(C_TOTAL = 100*C/TOTAL) %>%
  select(-C, -TOTAL) %>%
  spread(time, C_TOTAL) %>%
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(Geo)),
         Flag = paste0('<img src="../../bib/flags/vsmall/', Flag, '.png" alt="Flag">')) %>%
  select(Flag, everything()) %>%
  arrange(`2021Q4`) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

B-E, TOTAL

Code 
namq_10_a10 %>%
  filter(nace_r2 %in% c("B-E", "TOTAL"),
         na_item == "B1G",
         # SCA: Seasonally and calendar adjusted data
         s_adj == "SCA",
         # CLV10_MEUR: Chain linked volumes (2010), million euro
         unit == "CP_MNAC",
         time %in% c("2021Q4", "2001Q4", "2011Q4")) %>%
  left_join(nace_r2, by = "nace_r2") %>%
  left_join(geo, by = "geo") %>%
  select(nace_r2, geo, Geo, values, time) %>%
  spread(nace_r2, values) %>%
  mutate(C_TOTAL = 100*`B-E`/TOTAL) %>%
  select(-`B-E`, -TOTAL) %>%
  spread(time, C_TOTAL) %>%
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(Geo)),
         Flag = paste0('<img src="../../bib/flags/vsmall/', Flag, '.png" alt="Flag">')) %>%
  select(Flag, everything()) %>%
  arrange(`2021Q4`) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

B-E, C, TOTAL

Code 
load_data("eurostat/geo_fr.RData")
geo_fr <- geo
load_data("eurostat/geo.RData")
namq_10_a10 %>%
  filter(nace_r2 %in% c("B-E", "TOTAL", "C"),
         na_item == "B1G",
         # SCA: Seasonally and calendar adjusted data
         s_adj == "SCA",
         # CLV10_MEUR: Chain linked volumes (2010), million euro
         unit == "CP_MNAC",
         time %in% c("2021Q4")) %>%
  left_join(geo, by = "geo") %>%
  select(nace_r2, geo, Geo, values) %>%
  spread(nace_r2, values) %>%
  mutate(`Industrie Manufacturière` = round(100*`C`/TOTAL, 1),
         `Industrie Manufacturière + Energie` = round(100*`B-E`/TOTAL, 1)) %>%
  select(-`B-E`, -TOTAL, -`C`) %>%
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(Geo)),
         Flag = paste0('<img src="../../bib/flags/vsmall/', Flag, '.png" alt="Flag">')) %>%
  select(Flag, everything()) %>%
  select(-Geo) %>%
  left_join(geo_fr, by = "geo") %>%
  select(-geo) %>%
  select(Flag, Geo, everything()) %>%
  arrange(`Industrie Manufacturière + Energie`) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

PD_PCH_SM_EUR - Price index

France, Germany, Italy

TOTAL

1995-

Code 
namq_10_a10 %>%
  filter(geo %in% c("FR", "DE", "IT"),
         nace_r2 == "TOTAL",
         s_adj == "SCA",
         unit == "PD_PCH_SM_EUR") %>%
  quarter_to_date %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot + geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + theme_minimal() + xlab("") + ylab("") + add_3flags +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2020, 5), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.35, 0.85),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-10, 1000, 1),
                     labels = percent_format(accuracy = 1))

2015-

Code 
namq_10_a10 %>%
  filter(geo %in% c("FR", "DE", "IT"),
         nace_r2 == "TOTAL",
         s_adj == "SCA",
         unit == "PD_PCH_SM_EUR") %>%
  quarter_to_date %>%
  filter(date >= as.Date("2015-01-01")) %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot + geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + theme_minimal() + xlab("") + ylab("") + add_3flags +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2025, 1), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.35, 0.85),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-10, 1000, 1),
                     labels = percent_format(accuracy = 1))

A

1995-

Code 
namq_10_a10 %>%
  filter(geo %in% c("FR", "DE", "IT"),
         nace_r2 == "A",
         s_adj == "SCA",
         unit == "PD_PCH_SM_EUR") %>%
  quarter_to_date %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot + geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + theme_minimal() + xlab("") + ylab("") + add_3flags +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2020, 5), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.35, 0.85),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-100, 1000, 10),
                     labels = percent_format(accuracy = 1))

2015-

Code 
namq_10_a10 %>%
  filter(geo %in% c("FR", "DE", "IT"),
         nace_r2 == "A",
         s_adj == "SCA",
         unit == "PD_PCH_SM_EUR") %>%
  quarter_to_date %>%
  filter(date >= as.Date("2015-01-01")) %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot + geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + theme_minimal() + xlab("") + ylab("") + add_3flags +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2025, 1), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.35, 0.85),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-100, 1000, 5),
                     labels = percent_format(accuracy = 1))

F

1995-

Code 
namq_10_a10 %>%
  filter(geo %in% c("FR", "DE", "IT"),
         nace_r2 == "F",
         s_adj == "SCA",
         unit == "PD_PCH_SM_EUR") %>%
  quarter_to_date %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot + geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + theme_minimal() + xlab("") + ylab("") + add_3flags +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2020, 5), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.35, 0.85),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-10, 1000, 1),
                     labels = percent_format(accuracy = 1))

2015-

Code 
namq_10_a10 %>%
  filter(geo %in% c("FR", "DE", "IT"),
         nace_r2 == "F",
         s_adj == "SCA",
         unit == "PD_PCH_SM_EUR") %>%
  quarter_to_date %>%
  filter(date >= as.Date("2015-01-01")) %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot + geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + theme_minimal() + xlab("") + ylab("") + add_3flags +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2025, 1), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.35, 0.85),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-10, 1000, 1),
                     labels = percent_format(accuracy = 1))

C

1995-

Code 
namq_10_a10 %>%
  filter(geo %in% c("FR", "DE", "IT"),
         nace_r2 == "C",
         s_adj == "SCA",
         unit == "PD_PCH_SM_EUR") %>%
  quarter_to_date %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot + geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + theme_minimal() + xlab("") + ylab("") + add_3flags +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2020, 5), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.35, 0.85),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-10, 1000, 1),
                     labels = percent_format(accuracy = 1))

2015-

Code 
namq_10_a10 %>%
  filter(geo %in% c("FR", "DE", "IT"),
         nace_r2 == "C",
         s_adj == "SCA",
         unit == "PD_PCH_SM_EUR") %>%
  quarter_to_date %>%
  filter(date >= as.Date("2015-01-01")) %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot + geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + theme_minimal() + xlab("") + ylab("") + add_3flags +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2025, 1), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.35, 0.85),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-10, 1000, 1),
                     labels = percent_format(accuracy = 1))

B-E

1995-

Code 
namq_10_a10 %>%
  filter(geo %in% c("FR", "DE", "IT"),
         nace_r2 == "B-E",
         s_adj == "SCA",
         unit == "PD_PCH_SM_EUR") %>%
  quarter_to_date %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot + geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + theme_minimal() + xlab("") + ylab("") + add_3flags +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2020, 5), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.35, 0.85),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-10, 1000, 1),
                     labels = percent_format(accuracy = 1))

2015-

Code 
namq_10_a10 %>%
  filter(geo %in% c("FR", "DE", "IT"),
         nace_r2 == "B-E",
         s_adj == "SCA",
         unit == "PD_PCH_SM_EUR") %>%
  quarter_to_date %>%
  filter(date >= as.Date("2015-01-01")) %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/100) %>%
  ggplot + geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + theme_minimal() + xlab("") + ylab("") + add_3flags +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2025, 1), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.35, 0.85),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-10, 1000, 1),
                     labels = percent_format(accuracy = 1))

Manufacturing Value (Bn €)

Italy, France, Germany

Value

Code 
namq_10_a10 %>%
  filter(geo %in% c("FR", "DE", "IT"),
         nace_r2 == "C",
         # B1GQ: Gross domestic product at market prices
         na_item == "B1G",
         # SCA: Seasonally and calendar adjusted data
         s_adj == "SCA",
         # CLV10_MEUR: Chain linked volumes (2010), million euro
         unit == "CP_MNAC") %>%
  quarter_to_date %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/1000) %>%
  ggplot + geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + theme_minimal() + xlab("") + ylab("Production Manufacturière Trimestrielle") + add_3flags +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2020, 5), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.35, 0.85),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(0, 1000, 10),
                labels = dollar_format(suffix = " Mds€", prefix = "", accuracy = 1))

Indice

1990-

Code 
namq_10_a10 %>%
  filter(geo %in% c("FR", "DE", "IT"),
         nace_r2 == "C",
         # B1GQ: Gross domestic product at market prices
         na_item == "B1G",
         # SCA: Seasonally and calendar adjusted data
         s_adj == "SCA",
         # CLV10_MEUR: Chain linked volumes (2010), million euro
         unit == "CP_MNAC") %>%
  quarter_to_date %>%
  filter(date >= as.Date("1990-01-01")) %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  group_by(Geo) %>%
  arrange(date) %>%
  mutate(values = 100*values/values[1]) %>%
  ggplot + geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + theme_minimal() + xlab("") + ylab("Production Manufacturière Trimestrielle") + add_3flags +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2020, 5), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.35, 0.85),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(0, 1000, 5))

1995-

Code 
namq_10_a10 %>%
  filter(geo %in% c("FR", "DE", "IT"),
         nace_r2 == "C",
         # B1GQ: Gross domestic product at market prices
         na_item == "B1G",
         # SCA: Seasonally and calendar adjusted data
         s_adj == "SCA",
         # CLV10_MEUR: Chain linked volumes (2010), million euro
         unit == "CP_MNAC") %>%
  quarter_to_date %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  group_by(Geo) %>%
  arrange(date) %>%
  mutate(values = 100*values/values[1]) %>%
  ggplot + geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + theme_minimal() + xlab("") + ylab("Production Manufacturière Trimestrielle") + add_3flags +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2020, 5), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.35, 0.85),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(0, 1000, 5))

Code 
namq_10_a10 %>%
  filter(geo %in% c("FR", "DE", "IT"),
         nace_r2 == "C",
         # B1GQ: Gross domestic product at market prices
         na_item == "B1G",
         # SCA: Seasonally and calendar adjusted data
         s_adj == "SCA",
         # CLV10_MEUR: Chain linked volumes (2010), million euro
         unit == "CLV10_MEUR") %>%
  quarter_to_date %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/1000) %>%
  ggplot + geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + theme_minimal() + xlab("") + ylab("") + add_3flags +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2020, 5), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.35, 0.85),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(0, 1000, 10),
                labels = dollar_format(suffix = " Bn€", prefix = "", accuracy = 1))

Netherlands, Germany, Spain, France, Italy

Table (% du PIB)

Code 
namq_10_a10 %>%
  filter(na_item == "B1G",
         geo %in% c("NL", "DE", "ES", "FR", "IT"),
         unit == "CP_MNAC",
         s_adj == "SCA",
         time == "2021Q3") %>%
  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 .}

Table (€)

Code 
namq_10_a10 %>%
  filter(na_item == "B1G",
         geo %in% c("NL", "DE", "ES", "FR", "IT"),
         unit == "CP_MNAC",
         s_adj == "SCA",
         time == "2021Q3") %>%
  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 .}

A - Agriculture, forestry and fishing

Code 
namq_10_a10 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("A", "TOTAL"),
         geo %in% c("NL", "DE", "ES", "FR", "IT"),
         unit == "CP_MNAC",
         s_adj == "SCA") %>%
  quarter_to_date() %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(geo, by = "geo") %>%
  group_by(date) %>%
  mutate(values = values/ values[nace_r2 == "TOTAL"]) %>%
  filter(nace_r2 != "TOTAL") %>%
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
  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("% of GDP") +
  scale_color_identity() + add_5flags +
  scale_x_date(breaks = seq(1960, 2026, 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))

B-E - Industry (except construction)

Code 
namq_10_a10 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("B-E", "TOTAL"),
         geo %in% c("NL", "DE", "ES", "FR", "IT"),
         unit == "CP_MNAC",
         s_adj == "SCA") %>%
  quarter_to_date() %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(geo, by = "geo") %>%
  group_by(date) %>%
  mutate(values = values/ values[nace_r2 == "TOTAL"]) %>%
  filter(nace_r2 != "TOTAL") %>%
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("% of GDP") +
  scale_color_identity() + add_5flags +
  scale_x_date(breaks = seq(1960, 2026, 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))

C - Manufacturing Share

Code 
namq_10_a10 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C", "TOTAL"),
         geo %in% c("NL", "DE", "ES", "FR", "IT"),
         unit == "CP_MNAC",
         s_adj == "SCA") %>%
  quarter_to_date() %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(geo, by = "geo") %>%
  group_by(date) %>%
  mutate(values = values/ values[nace_r2 == "TOTAL"]) %>%
  filter(nace_r2 != "TOTAL") %>%
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Industrie Manufacturière (% du PIB)") +
  scale_color_identity() + add_5flags +
  scale_x_date(breaks = seq(1960, 2026, 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))

F - Construction

Code 
namq_10_a10 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("F", "TOTAL"),
         geo %in% c("NL", "DE", "ES", "FR", "IT"),
         unit == "CP_MNAC",
         s_adj == "SCA") %>%
  quarter_to_date() %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(geo, by = "geo") %>%
  group_by(date) %>%
  mutate(values = values/ values[nace_r2 == "TOTAL"]) %>%
  filter(nace_r2 != "TOTAL") %>%
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
  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("% of GDP") +
  scale_color_identity() + add_5flags +
  scale_x_date(breaks = seq(1960, 2026, 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))

G-I - Wholesale and retail trade, transport, accommodation and food service activities

Code 
namq_10_a10 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("G-I", "TOTAL"),
         geo %in% c("NL", "DE", "ES", "FR", "IT"),
         unit == "CP_MNAC",
         s_adj == "SCA") %>%
  quarter_to_date() %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(geo, by = "geo") %>%
  group_by(date) %>%
  mutate(values = values/ values[nace_r2 == "TOTAL"]) %>%
  filter(nace_r2 != "TOTAL") %>%
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
  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("% of GDP") +
  scale_color_identity() + add_5flags +
  scale_x_date(breaks = seq(1960, 2026, 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))

J - Information and communication

Code 
namq_10_a10 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("J", "TOTAL"),
         geo %in% c("NL", "DE", "ES", "FR", "IT"),
         unit == "CP_MNAC",
         s_adj == "SCA") %>%
  quarter_to_date() %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(geo, by = "geo") %>%
  group_by(date) %>%
  mutate(values = values/ values[nace_r2 == "TOTAL"]) %>%
  filter(nace_r2 != "TOTAL") %>%
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
  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("% of GDP") +
  scale_color_identity() + add_5flags +
  scale_x_date(breaks = seq(1960, 2026, 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))

K - Financial and insurance activities

Code 
namq_10_a10 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("K", "TOTAL"),
         geo %in% c("NL", "DE", "ES", "FR", "IT"),
         unit == "CP_MNAC",
         s_adj == "SCA") %>%
  quarter_to_date() %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(geo, by = "geo") %>%
  group_by(date) %>%
  mutate(values = values/ values[nace_r2 == "TOTAL"]) %>%
  filter(nace_r2 != "TOTAL") %>%
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
  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("% of GDP") +
  scale_color_identity() + add_5flags +
  scale_x_date(breaks = seq(1960, 2026, 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))

L - Real estate activities

Code 
namq_10_a10 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("L", "TOTAL"),
         geo %in% c("NL", "DE", "ES", "FR", "IT"),
         unit == "CP_MNAC",
         s_adj == "SCA") %>%
  quarter_to_date() %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(geo, by = "geo") %>%
  group_by(date) %>%
  mutate(values = values/ values[nace_r2 == "TOTAL"]) %>%
  filter(nace_r2 != "TOTAL") %>%
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
  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("% of GDP") +
  scale_color_identity() + add_5flags +
  scale_x_date(breaks = seq(1960, 2026, 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))

M_N - Professional, scientific and technical activities; administrative and support service activities

Code 
namq_10_a10 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("M_N", "TOTAL"),
         geo %in% c("NL", "DE", "ES", "FR", "IT"),
         unit == "CP_MNAC",
         s_adj == "SCA") %>%
  quarter_to_date() %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(geo, by = "geo") %>%
  group_by(date) %>%
  mutate(values = values/ values[nace_r2 == "TOTAL"]) %>%
  filter(nace_r2 != "TOTAL") %>%
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
  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("% of GDP") +
  scale_color_identity() + add_5flags +
  scale_x_date(breaks = seq(1960, 2026, 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))

O-Q - Public administration, defence, education, human health and social work activities

Code 
namq_10_a10 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("O-Q", "TOTAL"),
         geo %in% c("NL", "DE", "ES", "FR", "IT"),
         unit == "CP_MNAC",
         s_adj == "SCA") %>%
  quarter_to_date() %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(geo, by = "geo") %>%
  group_by(date) %>%
  mutate(values = values/ values[nace_r2 == "TOTAL"]) %>%
  filter(nace_r2 != "TOTAL") %>%
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
  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("% of GDP") +
  scale_color_identity() + add_5flags +
  scale_x_date(breaks = seq(1960, 2026, 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))

R-U - Arts, entertainment and recreation; other service activities; activities of household and extra-territorial organizations and bodies

Code 
namq_10_a10 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("R-U", "TOTAL"),
         geo %in% c("NL", "DE", "ES", "FR", "IT"),
         unit == "CP_MNAC",
         s_adj == "SCA") %>%
  quarter_to_date() %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(geo, by = "geo") %>%
  group_by(date) %>%
  mutate(values = values/ values[nace_r2 == "TOTAL"]) %>%
  filter(nace_r2 != "TOTAL") %>%
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
  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("% of GDP") +
  scale_color_identity() + add_5flags +
  scale_x_date(breaks = seq(1960, 2026, 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))

Industry and Manufacturing Share

France, Germany, Italy, Spain, Netherlands, Euro area

1995-

Code 
load_data("eurostat/nace_r2_fr.RData")
namq_10_a10 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C", "TOTAL", "B-E"),
         geo %in% c("FR", "DE", "IT", "ES", "EA"),
         unit == "CP_MNAC",
         s_adj == "SCA") %>%
  quarter_to_date() %>%
  left_join(geo, by = "geo") %>%
  left_join(nace_r2, by = "nace_r2") %>%
  group_by(date) %>%
  mutate(values = values/ values[nace_r2 == "TOTAL"]) %>%
  filter(nace_r2 != "TOTAL",
         date >= as.Date("1995-01-01")) %>%
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
  mutate(Geo = ifelse(geo == "EA", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color, linetype = Nace_r2)) +
  scale_color_identity() +
  scale_linetype_manual(values = c("dashed","solid")) +
  theme_minimal() +
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.2, 0.1),
        legend.title = element_blank()) +
  add_flags(10) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
                     labels = scales::percent_format(accuracy = 1)) +
  ylab("VA Manufacturière, Industrielle (% du PIB)") + xlab("")

Manufacturing Share (% of GDP)

Table: All Countries

Code 
namq_10_a10 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C", "TOTAL"),
         time %in% c("2019Q4", "2014Q4", "2009Q4", "2004Q4", "1999Q4", "1994Q4"),
         unit == "CP_MNAC",
         s_adj == "SCA") %>%
  left_join(geo, by = "geo") %>%
  group_by(time, geo) %>%
  mutate(values = round(100*values/ values[nace_r2 == "TOTAL"], 1) %>% paste0("%")) %>%
  filter(nace_r2 != "TOTAL") %>%
  select(geo, Geo, time, values) %>%
  spread(time, values) %>%
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
  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 .}

France, Germany, Italy

All

Code 
namq_10_a10 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C", "TOTAL"),
         geo %in% c("FR", "DE", "IT"),
         unit == "CP_MNAC",
         s_adj == "SCA") %>%
  quarter_to_date() %>%
  left_join(geo, by = "geo") %>%
  group_by(date) %>%
  mutate(values = values/ values[nace_r2 == "TOTAL"]) %>%
  filter(nace_r2 != "TOTAL") %>%
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("% of GDP") +
  scale_color_identity() + add_3flags +
  scale_x_date(breaks = seq(1960, 2020, 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 
namq_10_a10 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C", "B-E", "TOTAL"),
         geo %in% c("FR", "DE", "IT"),
         unit == "CP_MNAC",
         s_adj == "SCA") %>%
  quarter_to_date() %>%
  left_join(geo, by = "geo") %>%
  group_by(date) %>%
  mutate(values = values / values[nace_r2 == "TOTAL"]) %>%
  filter(nace_r2 != "TOTAL",
         date >= as.Date("1995-01-01")) %>%
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color, linetype = nace_r2)) + 
  theme_minimal() + xlab("") + ylab("% of GDP") +
  scale_color_identity() + add_3flags +
  scale_x_date(breaks = seq(1960, 2020, 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))

Poland, Spain, Austria

Code 
namq_10_a10 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C", "TOTAL"),
         geo %in% c("PL", "ES", "AT"),
         unit == "CP_MNAC",
         s_adj == "SCA") %>%
  quarter_to_date() %>%
  left_join(geo, by = "geo") %>%
  group_by(date) %>%
  mutate(values = values/ values[nace_r2 == "TOTAL"]) %>%
  filter(nace_r2 != "TOTAL") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("% of GDP") +
  scale_color_identity() + add_3flags +
  scale_x_date(breaks = seq(1960, 2020, 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))

Greece, Spain, Austria

Code 
namq_10_a10 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C", "TOTAL"),
         geo %in% c("EL", "RS", "RO"),
         unit == "CP_MNAC",
         s_adj == "SCA") %>%
  quarter_to_date() %>%
  left_join(geo, by = "geo") %>%
  group_by(date) %>%
  mutate(values = values/ values[nace_r2 == "TOTAL"]) %>%
  filter(nace_r2 != "TOTAL") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("% of GDP") +
  scale_color_identity() + add_3flags +
  scale_x_date(breaks = seq(1960, 2020, 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))

Italy, Germany, Spain, France, Netherlands

1990-

Code 
namq_10_a10 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C", "TOTAL"),
         geo %in% c("NL", "DE", "ES", "FR", "IT"),
         unit == "CP_MNAC",
         s_adj == "SCA") %>%
  quarter_to_date() %>%
  filter(date >= as.Date("1990-01-01")) %>%
  left_join(geo, by = "geo") %>%
  group_by(date) %>%
  mutate(values = values/ values[nace_r2 == "TOTAL"]) %>%
  filter(nace_r2 != "TOTAL") %>%
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
  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("% du PIB") +
  scale_color_identity() + add_5flags +
  scale_x_date(breaks = seq(1960, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(0, 30, 1),
                     labels = percent_format(accuracy = 1))

1995-

Code 
namq_10_a10 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C", "TOTAL"),
         geo %in% c("NL", "DE", "ES", "FR", "IT"),
         unit == "CP_MNAC",
         s_adj == "SCA") %>%
  quarter_to_date() %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(geo, by = "geo") %>%
  group_by(date) %>%
  mutate(values = values/ values[nace_r2 == "TOTAL"]) %>%
  filter(nace_r2 != "TOTAL") %>%
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
  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("% of GDP") +
  scale_color_identity() + add_5flags +
  scale_x_date(breaks = seq(1960, 2026, 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))

Denmark, Sweden, Switzerland

Code 
namq_10_a10 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C", "TOTAL"),
         geo %in% c("DK", "SE", "CH"),
         unit == "CP_MNAC",
         s_adj == "SCA") %>%
  quarter_to_date() %>%
  filter(date >= as.Date("1990-01-01")) %>%
  left_join(geo, by = "geo") %>%
  group_by(date) %>%
  mutate(values = values/ values[nace_r2 == "TOTAL"]) %>%
  filter(nace_r2 != "TOTAL") %>%
  mutate(Geo = ifelse(geo == "DE", "Allemagne", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("% of GDP") +
  scale_color_identity() + add_3flags +
  scale_x_date(breaks = seq(1960, 2020, 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))

Euro Area, Europe

Code 
load_data("eurostat/geo.RData")
namq_10_a10 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C", "TOTAL"),
         geo %in% c("EA19", "EU15", "EU28", "EU27_2020", "EA", "EA12"),
         unit == "CP_MNAC",
         s_adj == "SCA") %>%
  quarter_to_date() %>%
  left_join(geo, by = "geo") %>%
  group_by(date) %>%
  mutate(values = values/ values[nace_r2 == "TOTAL"]) %>%
  filter(nace_r2 != "TOTAL") %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = Geo)) + 
  theme_minimal() + xlab("") + ylab("% of GDP") +
  scale_color_manual(values = viridis(7)[1:6]) +
  scale_x_date(breaks = seq(1960, 2020, 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),
                     limits = c(0.14, 0.21)) +
  theme(legend.position = c(0.6, 0.75),
        legend.title = element_blank())

Industry and Manufacturing Share

France, Germany, Italy

1995- (Flags)

Code 
namq_10_a10 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C", "B-E", "TOTAL"),
         geo %in% c("FR", "DE", "IT"),
         unit == "CP_MNAC",
         s_adj == "SCA") %>%
  quarter_to_date() %>%
  left_join(geo, by = "geo") %>%
  group_by(date) %>%
  mutate(values = values/ values[nace_r2 == "TOTAL"]) %>%
  filter(nace_r2 != "TOTAL",
         date >= as.Date("1995-01-01")) %>%
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color, linetype = nace_r2)) +
  scale_color_identity() +
  scale_linetype_manual(values = c("solid", "dashed")) +
  theme_minimal() +
  scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = "none") +
  add_6flags +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 2),
                     labels = scales::percent_format(accuracy = 1)) +
  ylab("VA Manufacturière, Industrielle (% du PIB)") + xlab("")

% of GDP

France

Code 
namq_10_a10 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C", "TOTAL", "L", "F"),
         geo %in% c("FR"),
         unit == "CP_MNAC",
         s_adj == "SCA") %>%
  quarter_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(4)[1:3]) +
  scale_x_date(breaks = seq(1960, 2020, 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())

France (1995-)

Code 
namq_10_a10 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C", "TOTAL", "L", "Q", "F"),
         geo %in% c("FR"),
         unit == "CP_MNAC",
         s_adj == "SCA") %>%
  quarter_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(4)[1:3]) +
  scale_x_date(breaks = seq(1960, 2020, 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

Code 
namq_10_a10 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C", "TOTAL", "L", "Q", "F"),
         geo %in% c("DE"),
         unit == "CP_MNAC",
         s_adj == "SCA") %>%
  quarter_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(4)[1:3]) +
  scale_x_date(breaks = seq(1960, 2020, 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 
namq_10_a10 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C", "TOTAL", "L", "Q", "F"),
         geo %in% c("IT"),
         unit == "CP_MNAC",
         s_adj == "SCA") %>%
  quarter_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(4)[1:3]) +
  scale_x_date(breaks = seq(1960, 2020, 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 
namq_10_a10 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C", "TOTAL", "L", "Q", "F"),
         geo %in% c("ES"),
         unit == "CP_MNAC",
         s_adj == "SCA") %>%
  quarter_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(4)[1:3]) +
  scale_x_date(breaks = seq(1960, 2020, 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())

Greece

Code 
namq_10_a10 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C", "TOTAL", "L", "Q", "F"),
         geo %in% c("EL"),
         unit == "CP_MNAC",
         s_adj == "SCA") %>%
  quarter_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(4)[1:3]) +
  scale_x_date(breaks = seq(1960, 2020, 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(0.25, 0.85),
        legend.title = element_blank())

Netherlands

Code 
namq_10_a10 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C", "TOTAL", "L", "Q", "F"),
         geo %in% c("NL"),
         unit == "CP_MNAC",
         s_adj == "SCA") %>%
  quarter_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(4)[1:3]) +
  scale_x_date(breaks = seq(1960, 2020, 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 
namq_10_a10 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C", "TOTAL", "L", "Q", "F"),
         geo %in% c("DK"),
         unit == "CP_MNAC",
         s_adj == "SCA") %>%
  quarter_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(4)[1:3]) +
  scale_x_date(breaks = seq(1960, 2020, 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 
namq_10_a10 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C", "TOTAL", "L", "Q", "F"),
         geo %in% c("BE"),
         unit == "CP_MNAC",
         s_adj == "SCA") %>%
  quarter_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(4)[1:3]) +
  scale_x_date(breaks = seq(1960, 2020, 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 
namq_10_a10 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C", "TOTAL", "L", "Q", "F"),
         geo %in% c("FI"),
         unit == "CP_MNAC",
         s_adj == "SCA") %>%
  quarter_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(4)[1:3]) +
  scale_x_date(breaks = seq(1960, 2020, 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 
namq_10_a10 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C", "TOTAL", "L", "Q", "F"),
         geo %in% c("PT"),
         unit == "CP_MNAC",
         s_adj == "SCA") %>%
  quarter_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(4)[1:3]) +
  scale_x_date(breaks = seq(1960, 2020, 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 
namq_10_a10 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C", "TOTAL", "L", "Q", "F"),
         geo %in% c("AT"),
         unit == "CP_MNAC",
         s_adj == "SCA") %>%
  quarter_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(4)[1:3]) +
  scale_x_date(breaks = seq(1960, 2020, 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 
namq_10_a10 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C", "TOTAL", "L", "Q", "F"),
         geo %in% c("SE"),
         unit == "CP_MNAC",
         s_adj == "SCA") %>%
  quarter_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(4)[1:3]) +
  scale_x_date(breaks = seq(1960, 2020, 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 
namq_10_a10 %>%
  filter(na_item == "B1G",
         nace_r2 %in% c("C", "TOTAL", "L", "Q", "F"),
         geo %in% c("UK"),
         unit == "CP_MNAC",
         s_adj == "SCA") %>%
  quarter_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(4)[1:3]) +
  scale_x_date(breaks = seq(1960, 2020, 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-

NSA

Code 
namq_10_a10 %>%
  filter(na_item == "B1G",
         nace_r2 == "C",
         geo %in% c("EA", "FR", "DE", "IT", "ES", "NL", "AT", "FI"),
         unit == "CP_MNAC",
         s_adj == "NSA") %>%
  quarter_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")

SCA

Code 
namq_10_a10 %>%
  filter(na_item == "B1G",
         nace_r2 == "C",
         geo %in% c("EA", "FR", "DE", "IT", "ES", "NL", "AT", "FI"),
         unit == "CP_MNAC",
         s_adj == "SCA") %>%
  quarter_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")

SCA

Code 
namq_10_a10 %>%
  filter(na_item == "B1G",
         nace_r2 == "C",
         geo %in% c("EA", "FR", "DE", "IT", "ES", "NL"),
         unit == "CP_MNAC",
         s_adj == "SCA") %>%
  quarter_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() == 6) %>%
  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_5flags +
  scale_x_date(breaks = seq(1960, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = seq(0, 200, 5)) +
  theme(legend.position = "none")

2000-

Code 
namq_10_a10 %>%
  filter(na_item == "B1G",
         nace_r2 == "C",
         geo %in% c("EA", "FR", "DE", "IT", "ES", "NL", "AT", "FI"),
         unit == "CP_MNAC",
         s_adj == "SCA") %>%
  quarter_to_date() %>%
  filter(date >= as.Date("2000-01-01")) %>%
  #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("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, 2026, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = seq(0, 200, 5)) +
  theme(legend.position = "none")