| source | dataset | Title | .html | .rData |
|---|---|---|---|---|
| eurostat | namq_10_a10 | Gross value added and income A*10 industry breakdowns | 2025-10-10 | 2025-10-09 |
Gross value added and income A*10 industry breakdowns
Data - Eurostat
Info
Data on macro
| source | dataset | Title | .html | .rData |
|---|---|---|---|---|
| eurostat | namq_10_a10 | Gross value added and income A*10 industry breakdowns | 2025-10-10 | 2025-10-09 |
| eurostat | nama_10_a10 | Gross value added and income by A*10 industry breakdowns | 2025-10-10 | 2025-10-10 |
| eurostat | nama_10_a10_e | Employment by A*10 industry breakdowns | 2025-10-10 | 2025-10-09 |
| eurostat | nama_10_gdp | GDP and main components (output, expenditure and income) | 2025-10-10 | 2025-10-09 |
| eurostat | nama_10_lp_ulc | Labour productivity and unit labour costs | 2025-10-10 | 2025-10-10 |
| eurostat | namq_10_a10_e | Employment A*10 industry breakdowns | 2025-05-24 | 2025-10-10 |
| eurostat | namq_10_gdp | GDP and main components (output, expenditure and income) | 2025-10-10 | 2025-10-10 |
| eurostat | namq_10_lp_ulc | Labour productivity and unit labour costs | 2025-10-10 | 2025-09-26 |
| eurostat | namq_10_pc | Main GDP aggregates per capita | 2025-10-10 | 2025-10-09 |
| eurostat | nasa_10_nf_tr | Non-financial transactions | 2025-10-10 | 2025-10-10 |
| eurostat | nasq_10_nf_tr | Non-financial transactions | 2025-10-10 | 2025-09-26 |
| fred | gdp | Gross Domestic Product | 2025-10-09 | 2025-10-09 |
| oecd | QNA | Quarterly National Accounts | 2024-06-06 | 2025-05-24 |
| oecd | SNA_TABLE1 | Gross domestic product (GDP) | 2025-09-29 | 2025-05-24 |
| oecd | SNA_TABLE14A | Non-financial accounts by sectors | 2025-09-29 | 2024-06-30 |
| oecd | SNA_TABLE2 | Disposable income and net lending - net borrowing | 2024-07-01 | 2024-04-11 |
| oecd | SNA_TABLE6A | Value added and its components by activity, ISIC rev4 | 2024-07-01 | 2024-06-30 |
| wdi | NE.RSB.GNFS.ZS | External balance on goods and services (% of GDP) | 2025-10-10 | 2025-09-27 |
| wdi | NY.GDP.MKTP.CD | GDP (current USD) | 2025-10-10 | 2025-09-27 |
| wdi | NY.GDP.MKTP.PP.CD | GDP, PPP (current international D) | 2025-10-10 | 2025-09-27 |
| wdi | NY.GDP.PCAP.CD | GDP per capita (current USD) | 2025-10-10 | 2025-09-27 |
| wdi | NY.GDP.PCAP.KD | GDP per capita (constant 2015 USD) | 2025-10-10 | 2025-09-27 |
| wdi | NY.GDP.PCAP.PP.CD | GDP per capita, PPP (current international D) | 2025-10-10 | 2025-09-27 |
| wdi | NY.GDP.PCAP.PP.KD | GDP per capita, PPP (constant 2011 international D) | 2025-10-10 | 2025-09-27 |
LAST_COMPILE
| LAST_COMPILE |
|---|
| 2025-10-11 |
Last
Code
namq_10_a10 %>%
group_by(time) %>%
summarise(Nobs = n()) %>%
arrange(desc(time)) %>%
head(1) %>%
print_table_conditional()| time | Nobs |
|---|---|
| 2025Q2 | 42064 |
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 | 4362056 |
| D1 | Compensation of employees | 510272 |
| D11 | Wages and salaries | 493244 |
| D12 | Employers' social contributions | 493240 |
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 | 496339 |
| B-E | Industry (except construction) | 488028 |
| C | Manufacturing | 488028 |
| A | Agriculture, forestry and fishing | 487401 |
| F | Construction | 487401 |
| K | Financial and insurance activities | 487401 |
| O-Q | Public administration, defence, education, human health and social work activities | 487401 |
| R-U | Arts, entertainment and recreation; other service activities; activities of household and extra-territorial organizations and bodies | 487401 |
| G-I | Wholesale and retail trade, transport, accommodation and food service activities | 487353 |
| J | Information and communication | 487353 |
| L | Real estate activities | 487353 |
| M_N | Professional, scientific and technical activities; administrative and support service activities | 487353 |
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 | 2542189 |
| NSA | Unadjusted data (i.e. neither seasonally adjusted nor calendar adjusted data) | 2461276 |
| CA | Calendar adjusted data, not seasonally adjusted data | 484299 |
| SA | Seasonally adjusted data, not calendar adjusted data | 371048 |
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, 2100, 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, 2100, 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, 2100, 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, 2100, 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, 2100, 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, 2100, 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))
Manufacturing Value (Bn €)
Italy, France, Germany
Value
Code
namq_10_a10 %>%
filter(geo %in% c("FR", "DE", "IT"),
nace_r2 == "F",
# 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("Construction en logements") + add_3flags +
scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 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))
Volume
Code
namq_10_a10 %>%
filter(geo %in% c("FR", "DE", "IT"),
nace_r2 == "F",
# 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 == "CLV15_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("Construction en logements") + add_3flags +
scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 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, 2100, 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, 2100, 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, 2100, 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 +
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, 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
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, 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))
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, 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))
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, 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))
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, 2100, 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, 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))
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, 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),
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, 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())
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, 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
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, 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
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, 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
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, 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())
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, 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(0.25, 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, 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
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, 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
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, 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
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, 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
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, 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
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, 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
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, 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
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, 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-
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")