Last observation: 2026Q2 (N = 3736)
First observation: 1978Q1 (N = 576)
Last data update: 14 aoû 2026, 20:16. Last compile: 18 aoû 2026, 02:49
Gross value added and income A*10 industry breakdowns - namq_10_a10
Data - Eurostat
Info
Structure
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")) |>
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")) |>
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
geo <- read_parquet("geo_fr.parquet")
geo_fr <- geo
geo <- read_parquet("geo.parquet")
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")) |>
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 .}PD20_EUR - Price index (implicit deflator), 2020=100, euro
C
1995-
Code
namq_10_a10 |>
filter(geo %in% c("FR", "DE", "IT", "ES", "EA21"),
nace_r2 == "C",
s_adj == "SCA",
unit == "PD20_EUR") |>
quarter_to_date() |>
filter(date >= as.Date("1995-01-01")) |>
mutate(Geo = ifelse(geo == "EA21", "Europe", Geo)) |>
left_join(colors, by = c("Geo" = "country")) |>
mutate(values = values/100) |>
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("C - Manufacturing \n PD20_EUR - Price index (implicit deflator), 1995T1=100") + add_flags +
scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 5), "-01-01")),
labels = date_format("%Y")) + add_flags +
scale_y_continuous(breaks = seq(10, 300, 10)) +
theme(legend.position = c(0.35, 0.85),
legend.title = element_blank()) +
geom_label(data = . %>% filter(date == max(date)), aes(x = date, y = values, label = round(values), color = color))
1995-
Code
namq_10_a10 |>
filter(nace_r2 == "C",
s_adj == "SCA",
unit == "PD20_EUR") |>
quarter_to_date() |>
filter(date >= as.Date("1995-01-01")) |>
mutate(Geo = ifelse(geo == "EA21", "Europe", Geo)) |>
left_join(colors, by = c("Geo" = "country")) |>
mutate(values = values/100) |>
group_by(Geo) |>
arrange(date) |>
mutate(values = 100*values/values[1]) |>
ggplot() + geom_line(aes(x = date, y = values, color = Geo)) +
theme_minimal() + xlab("") + ylab("") + add_flags +
scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 5), "-01-01")),
labels = date_format("%Y")) + add_flags +
scale_y_continuous(breaks = seq(10, 300, 10),
limits = c(90, 130)) +
theme(legend.position = c(0.35, 0.85),
legend.title = element_blank())
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(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_flags +
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(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_flags +
scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 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(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_flags +
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(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_flags +
scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 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(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_flags +
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(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_flags +
scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 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(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_flags +
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(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_flags +
scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 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(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_flags +
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(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_flags +
scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 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(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_flags +
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(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_flags +
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(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_flags +
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(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_flags +
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(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_flags +
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(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_flags +
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) |>
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) |>
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")) |>
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_flags +
scale_x_date(breaks = seq(1960, 2100, 2) |> paste0("-01-01") |> as.Date(),
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-500, 200, 1),
labels = percent_format(accuracy = 1))
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")) |>
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_flags +
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))
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")) |>
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_flags +
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))
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")) |>
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_flags +
scale_x_date(breaks = seq(1960, 2100, 2) |> paste0("-01-01") |> as.Date(),
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-500, 200, 1),
labels = percent_format(accuracy = 1))
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")) |>
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_flags +
scale_x_date(breaks = seq(1960, 2100, 2) |> paste0("-01-01") |> as.Date(),
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-500, 200, 1),
labels = percent_format(accuracy = 1))
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")) |>
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_flags +
scale_x_date(breaks = seq(1960, 2100, 2) |> paste0("-01-01") |> as.Date(),
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-500, 200, .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")) |>
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_flags +
scale_x_date(breaks = seq(1960, 2100, 2) |> paste0("-01-01") |> as.Date(),
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-500, 200, 1),
labels = percent_format(accuracy = 1))
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")) |>
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_flags +
scale_x_date(breaks = seq(1960, 2100, 2) |> paste0("-01-01") |> as.Date(),
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-500, 200, 1),
labels = percent_format(accuracy = 1))
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")) |>
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_flags +
scale_x_date(breaks = seq(1960, 2100, 2) |> paste0("-01-01") |> as.Date(),
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-500, 200, 1),
labels = percent_format(accuracy = 1))
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")) |>
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_flags +
scale_x_date(breaks = seq(1960, 2100, 2) |> paste0("-01-01") |> as.Date(),
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-500, 200, 1),
labels = percent_format(accuracy = 1))
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")) |>
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_flags +
scale_x_date(breaks = seq(1960, 2100, 2) |> paste0("-01-01") |> as.Date(),
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-500, 200, 1),
labels = percent_format(accuracy = 1))
Industry and Manufacturing Share
France, Germany, Italy, Spain, Netherlands, Euro area
1995-
Code
nace_r2 <- read_parquet("nace_r2_fr.parquet")
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() |>
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") |>
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() |>
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_flags +
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() |>
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_flags +
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() |>
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_flags +
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() |>
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_flags +
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")) |>
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_flags +
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")) |>
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_flags +
scale_x_date(breaks = seq(1960, 2100, 2) |> paste0("-01-01") |> as.Date(),
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-500, 200, 1),
labels = percent_format(accuracy = 1))
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")) |>
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_flags +
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
geo <- read_parquet("geo.parquet")
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() |>
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_flags +
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() |>
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, 2100, 2) |> paste0("-01-01") |> as.Date(),
labels = date_format("%Y")) +
theme(legend.position = "none") +
add_flags +
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() |>
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")) |>
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() |>
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() |>
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() |>
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() |>
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() |>
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() |>
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() |>
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() |>
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() |>
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() |>
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() |>
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")) %>%
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_flags +
scale_x_date(breaks = seq(1960, 2100, 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")) %>%
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_flags +
scale_x_date(breaks = seq(1960, 2100, 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")) %>%
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_flags +
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")) %>%
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_flags +
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")