National accounts aggregates by industry (up to NACE A*64)
Data - Eurostat
Info
Last observation: Annual: 2025 (N = 39,960)
First observation: Annual: 1975 (N = 15,411)
Last data update: 14 aoû 2026, 01:50. Last compile: 14 aoû 2026, 03:50
Structure
Profit share by sector, with many countries
C - Manufacturing
Code
nama_10_a64 |>
filter(na_item %in% c("B1G","B2A3N"),
geo %in% c("FR", "ES", "DE", "IT"),
nace_r2 %in% c("C"),
unit == "CP_MEUR") |>
year_to_date() |>
filter(date >= as.Date("1995-01-01")) |>
spread(na_item, values) |>
mutate(values = B2A3N/B1G) |>
left_join(colors, by = c("Geo" = "country")) |>
ggplot() + geom_line(aes(x = date, y = values, color = color)) +
scale_color_identity() + theme_minimal() + add_flags +
scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 5), "-01-01")),
labels = date_format("%Y")) +
xlab("") + ylab("Profit Share, Manufacturing (% of GDP)") +
scale_y_continuous(breaks = 0.01*seq(-30, 50, 2),
labels = percent_format(a = 1)) +
geom_hline(yintercept = 0, linetype = "dashed", color = "black")
L - Real Estate Activities
Code
nama_10_a64 |>
filter(na_item %in% c("B1G","B2A3N"),
geo %in% c("FR", "ES", "DE", "IT"),
nace_r2 %in% c("L"),
unit == "CP_MEUR") |>
year_to_date() |>
filter(date >= as.Date("1995-01-01")) |>
spread(na_item, values) |>
mutate(values = B2A3N/B1G) |>
left_join(colors, by = c("Geo" = "country")) |>
ggplot() + geom_line(aes(x = date, y = values, color = color)) +
scale_color_identity() + theme_minimal() + add_flags +
scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 5), "-01-01")),
labels = date_format("%Y")) +
xlab("") + ylab("Profit Share, Real Estate Activities (% of GDP)") +
scale_y_continuous(breaks = 0.01*seq(-30, 100, 2),
labels = percent_format(a = 1))
Manufacturing
Table by manuf. Value
Code
nama_10_a64 |>
filter(na_item == "B1G",
nace_r2 %in% c("C", "TOTAL"),
unit == "CP_MEUR",
time == "2019") %>%
select_if(~ n_distinct(.) > 1) |>
spread(nace_r2, values) |>
arrange(-TOTAL) |>
mutate(Flag = gsub(" ", "-", str_to_lower(Geo)),
Flag = paste0('<img src="../../bib/flags/vsmall/', Flag, '.png" alt="Flag">')) |>
select(Flag, everything()) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}2019 Table - All Manufacturing (€)
Code
nama_10_a64 |>
filter(na_item == "B1G",
grepl("C", nace_r2) | nace_r2 == "TOTAL",
unit == "CP_MEUR",
time == "2019") %>%
select_if(~ n_distinct(.) > 1) |>
spread(nace_r2, values) |>
arrange(-TOTAL) |>
mutate(Flag = gsub(" ", "-", str_to_lower(Geo)),
Flag = paste0('<img src="../../bib/flags/vsmall/', Flag, '.png" alt="Flag">')) |>
select(Flag, everything()) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}Some Manufacturing
Code
nama_10_a64 |>
filter(na_item == "B1G",
nace_r2 %in% c("C10-C12", "C13-C15", "C16-C18", "C22_C23", "C29_C30", "TOTAL"),
unit == "CP_MEUR",
time == "2019") %>%
select_if(~ n_distinct(.) > 1) |>
spread(nace_r2, values) |>
arrange(-TOTAL) |>
mutate(Flag = gsub(" ", "-", str_to_lower(Geo)),
Flag = paste0('<img src="../../bib/flags/vsmall/', Flag, '.png" alt="Flag">')) |>
select(Flag, everything()) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}Greece, Germany, Spain, France, Italy
2019 Table (% du PIB)
Code
nama_10_a64 |>
filter(na_item == "B1G",
geo %in% c("EL", "DE", "ES", "FR", "IT"),
unit == "CP_MNAC",
time == "2019") %>%
select_if(~ n_distinct(.) > 1) |>
select(-geo) |>
group_by(Geo) |>
mutate(values = round(100* values/ values[nace_r2 == "TOTAL"], 2)) |>
mutate(Geo = gsub(" ", "-", str_to_lower(Geo)),
Geo = paste0('<img src="../../bib/flags/vsmall/', Geo, '.png" alt="Flag">')) |>
spread(Geo, values) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}2019 Table (€)
Code
nama_10_a64 |>
filter(na_item == "B1G",
geo %in% c("EL", "DE", "ES", "FR", "IT"),
unit == "CP_MNAC",
time == "2019") %>%
select_if(~ n_distinct(.) > 1) |>
select(-geo) |>
mutate(Geo = gsub(" ", "-", str_to_lower(Geo)),
Geo = paste0('<img src="../../bib/flags/vsmall/', Geo, '.png" alt="Flag">')) |>
spread(Geo, values) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}Manufacturing Value Added (% of GDP)
2019 France, Germany, Italy
Code
nama_10_a64 |>
filter(geo %in% c("FR", "DE", "IT"),
unit == "CP_MNAC",
na_item == "B1G",
time == "2019") |>
select(geo, nace_r2, Nace_r2, values) |>
group_by(geo) |>
mutate(values = round(100*values /values[nace_r2 =="TOTAL"], 1)) |>
spread(geo, values) |>
filter(nace_r2 != "TOTAL") |>
arrange(-FR) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}France: 2019, 1999, 1979
Code
nama_10_a64 |>
filter(geo %in% c("FR"),
unit == "CP_MNAC",
na_item == "B1G",
time %in% c("2019", "1999", 1979)) |>
select(time, nace_r2, Nace_r2, values) |>
group_by(time) |>
mutate(values = round(100*values /values[nace_r2 =="TOTAL"], 1)) |>
spread(time, values) |>
filter(nace_r2 != "TOTAL") |>
arrange(-`2019`) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}France, Germany, United Kingdom
Code
nama_10_a64 |>
filter(na_item == "B1G",
nace_r2 %in% c("C", "TOTAL"),
geo %in% c("FR", "DE", "UK"),
unit == "CP_MNAC") |>
year_to_date() |>
select(geo, Geo, nace_r2, date, values) |>
spread(nace_r2, values) |>
mutate(values = C/TOTAL) |>
left_join(colors, by = c("Geo" = "country")) |>
ggplot() + geom_line(aes(x = date, y =values, color = color)) +
theme_minimal() + xlab("") + ylab("Manufacturing Value added (% of GDP)") +
scale_color_identity() +
scale_x_date(breaks = seq(1960, 2100, 5) |> paste0("-01-01") |> as.Date(),
labels = date_format("%Y")) +
add_flags +
theme(legend.position = "none") +
scale_y_continuous(breaks = 0.01*seq(-500, 200, 1),
labels = percent_format(accuracy = 1))
France, Germany, Greece, Italy, Portugal, Spain
All
Code
nama_10_a64 |>
filter(na_item == "B1G",
nace_r2 %in% c("C", "TOTAL"),
geo %in% c("FR", "DE", "EL", "ES", "IT", "PT"),
unit == "CP_MNAC") |>
year_to_date() |>
filter(date >= as.Date("1995-01-01")) |>
select(geo, Geo, nace_r2, date, values) |>
spread(nace_r2, values) |>
left_join(colors, by = c("Geo" = "country")) |>
mutate(values = C/TOTAL) |>
ggplot() + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + xlab("") + ylab("Manufacturing Value added (% of GDP)") +
scale_color_identity() +
scale_x_date(breaks = seq(1960, 2100, 2) |> paste0("-01-01") |> as.Date(),
labels = date_format("%Y")) +
add_flags +
theme(legend.position = "none") +
scale_y_continuous(breaks = 0.01*seq(-500, 200, 1),
labels = percent_format(accuracy = 1))
1995-
Code
nama_10_a64 |>
filter(na_item == "B1G",
nace_r2 %in% c("C", "TOTAL"),
geo %in% c("FR", "DE", "EL", "ES", "IT", "PT"),
unit == "CP_MNAC") |>
year_to_date() |>
filter(date >= as.Date("1995-01-01")) |>
select(geo, Geo, nace_r2, date, values) |>
spread(nace_r2, values) |>
left_join(colors, by = c("Geo" = "country")) |>
mutate(values = C/TOTAL) |>
ggplot() + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + xlab("") + ylab("Manufacturing Value added (% of GDP)") +
scale_color_identity() +
scale_x_date(breaks = seq(1960, 2100, 5) |> paste0("-01-01") |> as.Date(),
labels = date_format("%Y")) +
add_flags +
theme(legend.position = "none") +
scale_y_continuous(breaks = 0.01*seq(-500, 200, 1),
labels = percent_format(accuracy = 1))
All
Code
nama_10_a64 |>
filter(na_item == "B1G",
nace_r2 %in% c("C", "TOTAL"),
unit == "CP_MNAC") |>
year_to_date() |>
filter(date >= as.Date("1995-01-01")) |>
arrange(date) |>
select(geo, Geo, nace_r2, date, values) |>
spread(nace_r2, values) |>
left_join(colors, by = c("Geo" = "country")) |>
mutate(values = C/TOTAL) |>
ggplot() + geom_line(aes(x = date, y = values, color = color, group = geo)) +
theme_minimal() + xlab("") + ylab("Manufacturing Value added (% of GDP)") +
scale_color_identity() +
scale_x_date(breaks = seq(1960, 2100, 5) |> paste0("-01-01") |> as.Date(),
labels = date_format("%Y")) +
add_flags +
theme(legend.position = "none") +
scale_y_continuous(breaks = 0.01*seq(-500, 200, 1),
labels = percent_format(accuracy = 1)) +
geom_label_repel(data = . %>% group_by(geo)
%>% filter(date %in% c(max(date), min(date))),
aes(x = date, y = values, label = geo, color = color)) +
geom_line(data = . %>% filter(geo == "FR"),
aes(x = date, y = values, color = color), size = 2) +
geom_line(data = . %>% filter(geo == "EL"),
aes(x = date, y = values, color = color), size = 2)
France, Luxembourg, Cyprus
1995-
Code
nama_10_a64 |>
filter(na_item == "B1G",
nace_r2 %in% c("C", "TOTAL"),
geo %in% c("FR", "ME", "LU", "CY", "MT", "EL"),
unit == "CP_MNAC") |>
year_to_date() |>
filter(date >= as.Date("1995-01-01")) |>
select(geo, Geo, nace_r2, date, values) |>
spread(nace_r2, values) |>
left_join(colors, by = c("Geo" = "country")) |>
mutate(values = C/TOTAL) |>
ggplot() + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + xlab("") + ylab("Manufacturing Value added (% of GDP)") +
scale_color_identity() +
scale_x_date(breaks = seq(1960, 2100, 5) |> paste0("-01-01") |> as.Date(),
labels = date_format("%Y")) +
add_flags +
theme(legend.position = "none") +
scale_y_continuous(breaks = 0.01*seq(-500, 200, 1),
labels = percent_format(accuracy = 1))
Greece, Portugal, Spain
Code
nama_10_a64 |>
filter(na_item == "B1G",
nace_r2 %in% c("C", "TOTAL"),
geo %in% c("EL", "PT", "ES"),
unit == "CP_MNAC") |>
year_to_date() |>
select(Geo, nace_r2, date, values) |>
spread(nace_r2, values) |>
ggplot() + geom_line(aes(x = date, y = C/TOTAL, color = Geo)) +
theme_minimal() + xlab("") + ylab("Manufacturing Value added (% of GDP)") +
scale_color_manual(values = c("#0D5EAF", "#006600", "#C60B1E")) +
scale_x_date(breaks = seq(1960, 2100, 5) |> paste0("-01-01") |> as.Date(),
labels = date_format("%Y")) +
geom_image(data = . %>%
filter(date == as.Date("2016-01-01")) %>%
mutate(date = as.Date("2016-01-01"),
image = paste0("../../icon/flag/round/", str_to_lower(Geo), ".png")),
aes(x = date, y = C/TOTAL, image = image), asp = 1.5) +
theme(legend.position = "none") +
scale_y_continuous(breaks = 0.01*seq(-500, 200, 1),
labels = percent_format(accuracy = 1))
1995-2018
Code
nama_10_a64 |>
filter(na_item == "B1G",
nace_r2 %in% c("C", "TOTAL"),
geo %in% c("EA20", "FR", "DE", "IT", "ES", "NL", "AT", "FI"),
unit == "CP_MNAC") |>
year_to_date() |>
filter(date >= as.Date("1995-01-01"),
date <= as.Date("2019-01-01")) |>
select(geo, Geo, nace_r2, date, values) |>
spread(nace_r2, values) |>
mutate(values = C/TOTAL) |>
group_by(date) |>
mutate(values = values /values[geo == "EA20"]) |>
filter(geo != "EA20") |>
group_by(geo) |>
mutate(values = 100*values / values[1]) |>
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 +
theme(legend.position = "none") +
scale_x_date(breaks = seq(1995, 2100, 5) |> paste0("-01-01") |> as.Date(),
labels = date_format("%Y")) +
scale_y_continuous(breaks = seq(0, 200, 5)) +
theme(legend.position = "none") +
geom_hline(yintercept = 100, linetype = "dashed")
1995-
Code
nama_10_a64 |>
filter(na_item == "B1G",
nace_r2 %in% c("C", "TOTAL"),
geo %in% c("EA20", "FR", "DE", "IT", "ES", "NL", "AT", "PL", "CZ"),
unit == "CP_MNAC") |>
year_to_date() |>
filter(date >= as.Date("1995-01-01"),
date <= as.Date("2019-01-01")) |>
select(geo, Geo, nace_r2, date, values) |>
spread(nace_r2, values) |>
mutate(values = C/TOTAL) |>
group_by(date) |>
mutate(values = values /values[geo == "EA20"]) |>
filter(geo != "EA20") |>
group_by(geo) |>
mutate(values = 100*values / values[1]) |>
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 +
theme(legend.position = "none") +
scale_x_date(breaks = seq(1995, 2100, 5) |> paste0("-01-01") |> as.Date(),
labels = date_format("%Y")) +
scale_y_log10(breaks = seq(0, 200, 5)) +
theme(legend.position = "none") +
geom_hline(yintercept = 100, linetype = "dashed")
2000-2018
Code
nama_10_a64 |>
filter(na_item == "B1G",
nace_r2 %in% c("C", "TOTAL"),
geo %in% c("EA20", "FR", "DE", "IT", "ES", "NL", "AT", "FI"),
unit == "CP_MNAC") |>
year_to_date() |>
filter(date >= as.Date("2000-01-01"),
date <= as.Date("2019-01-01")) |>
select(geo, Geo, nace_r2, date, values) |>
spread(nace_r2, values) |>
mutate(values = C/TOTAL) |>
group_by(date) |>
mutate(values = values /values[geo == "EA20"]) |>
filter(geo != "EA20") |>
group_by(geo) |>
mutate(values = 100*values / values[1]) |>
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, 1) |> paste0("-01-01") |> as.Date(),
labels = date_format("%Y")) +
scale_y_continuous(breaks = seq(0, 200, 5)) +
theme(legend.position = "none") +
geom_hline(yintercept = 100, linetype = "dashed")
Comparing Deflators
Germany, France, Italy, Spain
Code
nama_10_a64 |>
filter(na_item == "B1G",
geo %in% c("FR", "DE", "IT", "ES"),
unit == "PD10_NAC",
time %in% c("1995", "2019")) %>%
select_if(~ n_distinct(.) > 1) |>
spread(time, values) |>
select(-geo) |>
mutate(values = round(100*((`2019`/`1995`)^(1/24)-1),2)) |>
select(-`1995`, -`2019`) |>
spread(Geo, values) |>
print_table_conditional()C - Manufacturing
Table - PD10_NAC
Code
nama_10_a64 |>
filter(na_item == "B1G",
unit == "PD10_NAC",
nace_r2 %in% c("C", "TOTAL"),
time %in% c("1995", "2019")) %>%
select_if(~ n_distinct(.) > 1) |>
spread(time, values) |>
mutate(values = round(100*((`2019`/`1995`)^(1/24)-1),2)) |>
select(-`1995`, -`2019`) |>
spread(nace_r2, values) |>
arrange(`C`) |>
mutate(Flag = gsub(" ", "-", str_to_lower(Geo)),
Flag = paste0('<img src="../../bib/flags/vsmall/', Flag, '.png" alt="Flag">')) |>
select(Flag, everything()) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}Table - PD10_EUR
Code
nama_10_a64 |>
filter(na_item == "B1G",
unit == "PD10_EUR",
nace_r2 %in% c("C", "TOTAL"),
time %in% c("1995", "2019")) %>%
select_if(~ n_distinct(.) > 1) |>
spread(time, values) |>
mutate(values = round(100*((`2019`/`1995`)^(1/24)-1),2)) |>
select(-`1995`, -`2019`) |>
spread(nace_r2, values) |>
arrange(`C`) |>
mutate(Flag = gsub(" ", "-", str_to_lower(Geo)),
Flag = paste0('<img src="../../bib/flags/vsmall/', Flag, '.png" alt="Flag">')) |>
select(Flag, everything()) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}C10-C12 - Food products
Code
nama_10_a64 |>
filter(na_item == "B1G",
unit == "PD10_EUR",
nace_r2 %in% c("C10-C12", "TOTAL"),
time %in% c("1995", "2020")) %>%
select_if(~ n_distinct(.) > 1) |>
spread(time, values) |>
mutate(values = round(100*((`2020`/`1995`)^(1/24)-1),2)) |>
select(-`1995`, -`2020`) |>
spread(nace_r2, values) |>
arrange(`C10-C12`) |>
mutate(Flag = gsub(" ", "-", str_to_lower(Geo)),
Flag = paste0('<img src="../../bib/flags/vsmall/', Flag, '.png" alt="Flag">')) |>
select(Flag, everything()) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}C13-C15 - Textiles
Code
nama_10_a64 |>
filter(na_item == "B1G",
unit == "PD10_NAC",
nace_r2 %in% c("C13-C15", "TOTAL"),
time %in% c("1995", "2020")) %>%
select_if(~ n_distinct(.) > 1) |>
spread(time, values) |>
mutate(values = round(100*((`2020`/`1995`)^(1/24)-1),2)) |>
select(-`1995`, -`2020`) |>
spread(nace_r2, values) |>
arrange(`C13-C15`) |>
mutate(Flag = gsub(" ", "-", str_to_lower(Geo)),
Flag = paste0('<img src="../../bib/flags/vsmall/', Flag, '.png" alt="Flag">')) |>
select(Flag, everything()) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}C27 - Manufacture of electrical equipment
Table - PD10_NAC
Code
nama_10_a64 |>
filter(na_item == "B1G",
unit == "PD10_NAC",
nace_r2 %in% c("C27", "TOTAL"),
time %in% c("1995", "2020")) %>%
select_if(~ n_distinct(.) > 1) |>
spread(time, values) |>
mutate(values = round(100*((`2020`/`1995`)^(1/24)-1),2)) |>
select(-`1995`, -`2020`) |>
spread(nace_r2, values) |>
arrange(`C27`) |>
mutate(Flag = gsub(" ", "-", str_to_lower(Geo)),
Flag = paste0('<img src="../../bib/flags/vsmall/', Flag, '.png" alt="Flag">')) |>
select(Flag, everything()) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}Table - PD10_EUR
Code
nama_10_a64 |>
filter(na_item == "B1G",
unit == "PD10_EUR",
nace_r2 %in% c("C26", "TOTAL"),
time %in% c("1995", "2019")) %>%
select_if(~ n_distinct(.) > 1) |>
spread(time, values) |>
mutate(values = round(100*((`2019`/`1995`)^(1/24)-1),2)) |>
select(-`1995`, -`2019`) |>
spread(nace_r2, values) |>
arrange(`C26`) |>
mutate(Flag = gsub(" ", "-", str_to_lower(Geo)),
Flag = paste0('<img src="../../bib/flags/vsmall/', Flag, '.png" alt="Flag">')) |>
select(Flag, everything()) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}Graph
Code
nama_10_a64 |>
filter(na_item == "B1G",
nace_r2 %in% c("C27", "TOTAL"),
geo %in% c("FR", "DE", "IT", "ES"),
unit == "PD10_NAC") |>
year_to_date() |>
filter(date >= as.Date("1995-01-01")) |>
select(geo, Geo, nace_r2, date, values) |>
spread(nace_r2, values) |>
mutate(values = C27/TOTAL) |>
left_join(colors, by = c("Geo" = "country")) |>
group_by(Geo) |>
mutate(values = values/ values[1]) |>
ggplot() + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + xlab("") + ylab("Price Deflator (C27)") +
scale_color_identity() + add_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, 10))
C26 - Computer, electronics
Table - PD10_NAC
Code
nama_10_a64 |>
filter(na_item == "B1G",
unit == "PD10_NAC",
nace_r2 %in% c("C26", "TOTAL"),
time %in% c("1995", "2019")) %>%
select_if(~ n_distinct(.) > 1) |>
spread(time, values) |>
mutate(values = round(100*((`2019`/`1995`)^(1/24)-1),2)) |>
select(-`1995`, -`2019`) |>
spread(nace_r2, values) |>
arrange(`C26`) |>
mutate(Flag = gsub(" ", "-", str_to_lower(Geo)),
Flag = paste0('<img src="../../bib/flags/vsmall/', Flag, '.png" alt="Flag">')) |>
select(Flag, everything()) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}Table - PD10_EUR
Code
nama_10_a64 |>
filter(na_item == "B1G",
unit == "PD10_EUR",
nace_r2 %in% c("C26", "TOTAL"),
time %in% c("1995", "2019")) %>%
select_if(~ n_distinct(.) > 1) |>
spread(time, values) |>
mutate(values = round(100*((`2019`/`1995`)^(1/24)-1),2)) |>
select(-`1995`, -`2019`) |>
spread(nace_r2, values) |>
arrange(`C26`) |>
mutate(Flag = gsub(" ", "-", str_to_lower(Geo)),
Flag = paste0('<img src="../../bib/flags/vsmall/', Flag, '.png" alt="Flag">')) |>
select(Flag, everything()) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}Graph
Code
nama_10_a64 |>
filter(na_item == "B1G",
nace_r2 %in% c("C26", "TOTAL"),
geo %in% c("FR", "DE", "IT", "ES"),
unit == "PD10_NAC") |>
year_to_date() |>
filter(date >= as.Date("1995-01-01")) |>
select(geo, Geo, nace_r2, date, values) |>
spread(nace_r2, values) |>
mutate(values = C26/TOTAL) |>
left_join(colors, by = c("Geo" = "country")) |>
group_by(Geo) |>
mutate(values = values/ values[1]) |>
ggplot() + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + xlab("") + ylab("Price Deflator (C26)") +
scale_color_identity() + add_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, 10))
C33 - Repair and installation of machinery and equipment
Table - PD10_NAC
Code
nama_10_a64 |>
filter(na_item == "B1G",
unit == "PD10_NAC",
nace_r2 %in% c("C33", "TOTAL"),
time %in% c("1995", "2019")) %>%
select_if(~ n_distinct(.) > 1) |>
spread(time, values) |>
mutate(values = round(100*((`2019`/`1995`)^(1/24)-1),2)) |>
select(-`1995`, -`2019`) |>
spread(nace_r2, values) |>
arrange(`C33`) |>
mutate(Flag = gsub(" ", "-", str_to_lower(Geo)),
Flag = paste0('<img src="../../bib/flags/vsmall/', Flag, '.png" alt="Flag">')) |>
select(Flag, everything()) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}Table - PD10_EUR
Code
nama_10_a64 |>
filter(na_item == "B1G",
unit == "PD10_EUR",
nace_r2 %in% c("C33", "TOTAL"),
time %in% c("1995", "2019")) %>%
select_if(~ n_distinct(.) > 1) |>
spread(time, values) |>
mutate(values = round(100*((`2019`/`1995`)^(1/24)-1),2)) |>
select(-`1995`, -`2019`) |>
spread(nace_r2, values) |>
arrange(`C33`) |>
mutate(Flag = gsub(" ", "-", str_to_lower(Geo)),
Flag = paste0('<img src="../../bib/flags/vsmall/', Flag, '.png" alt="Flag">')) |>
select(Flag, everything()) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}Graph
Code
nama_10_a64 |>
filter(na_item == "B1G",
nace_r2 %in% c("C33", "TOTAL"),
geo %in% c("FR", "DE", "IT", "ES"),
unit == "PD10_NAC") |>
year_to_date() |>
filter(date >= as.Date("1995-01-01")) |>
select(geo, Geo, nace_r2, date, values) |>
spread(nace_r2, values) |>
mutate(values = C33/TOTAL) |>
left_join(colors, by = c("Geo" = "country")) |>
group_by(Geo) |>
mutate(values = values/ values[1]) |>
ggplot() + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + xlab("") + ylab("Price Deflator (C33)") +
scale_color_identity() + add_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, 10))
Germany, Italy, France, Spain, Netherlands
Table
Code
nace_r2 <- read_parquet("nace_r2_fr.parquet")
nama_10_a64 |>
filter(na_item == "B1G",
geo %in% c("FR", "DE", "IT", "ES"),
unit == "CP_MNAC",
time %in% c("2018")) |>
filter(!grepl("C", nace_r2) | nace_r2 == "TOTAL") |>
select(-na_item, -unit, -time) |>
mutate(Flag = gsub(" ", "-", str_to_lower(Geo)),
Flag = paste0('<img src="../../bib/flags/vsmall/', Flag, '.png" alt="Flag">')) |>
select(nace_r2, Nace_r2, Flag, values) |>
group_by(Flag) |>
mutate(values = round(100*values/ values[nace_r2 == "TOTAL"], 1)) |>
filter(nace_r2 != "TOTAL") |>
spread(Flag, values) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}Table - Manufacturing
Code
nace_r2 <- read_parquet("nace_r2_fr.parquet")
nama_10_a64 |>
filter(na_item == "B1G",
geo %in% c("FR", "DE", "IT", "ES"),
unit == "CP_MNAC",
time %in% c("2018")) |>
filter(grepl("C", nace_r2) | nace_r2 == "TOTAL") |>
select(-na_item, -unit, -time) |>
mutate(Flag = gsub(" ", "-", str_to_lower(Geo)),
Flag = paste0('<img src="../../bib/flags/vsmall/', Flag, '.png" alt="Flag">')) |>
select(nace_r2, Nace_r2, Flag, values) |>
group_by(Flag) |>
mutate(values = round(100*values/ values[nace_r2 == "TOTAL"], 1)) |>
filter(nace_r2 != "TOTAL") |>
spread(Flag, values) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}B - Mining and quarrying
Code
nama_10_a64 |>
filter(na_item == "B1G",
nace_r2 %in% c("B", "TOTAL"),
geo %in% c("FR", "DE", "IT", "ES", "EL"),
unit == "CP_MNAC") |>
year_to_date() |>
filter(date >= as.Date("1995-01-01")) |>
select(geo, Geo, nace_r2, date, values) |>
spread(nace_r2, values) |>
mutate(values = `B`/TOTAL) |>
left_join(colors, by = c("Geo" = "country")) |>
ggplot() + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + xlab("") + ylab("Mining and quarrying (% of GDP)") +
scale_color_identity() + add_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))
D - Electricity, gas, steam and air conditioning supply
Value
Code
nama_10_a64 |>
filter(na_item == "B1G",
nace_r2 %in% c("D", "TOTAL"),
geo %in% c("FR", "DE", "IT", "ES", "EL"),
unit == "CP_MNAC") |>
year_to_date() |>
filter(date >= as.Date("1995-01-01")) |>
select(geo, Geo, nace_r2, date, values) |>
spread(nace_r2, values) |>
mutate(values = `D`/TOTAL) |>
left_join(colors, by = c("Geo" = "country")) |>
ggplot() + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + xlab("") + ylab("Electricity, gas, steam and air conditioning supply (% of GDP)") +
scale_color_identity() + add_flags +
scale_x_date(breaks = seq(1961, 2100, 2) |> paste0("-01-01") |> as.Date(),
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-500, 200, .5),
labels = percent_format(accuracy = .1))
Volume
Code
nama_10_a64 |>
filter(na_item == "B1G",
nace_r2 %in% c("D", "TOTAL"),
geo %in% c("FR", "DE", "IT", "ES", "EL"),
unit == "CLV10_MEUR") |>
year_to_date() |>
filter(date >= as.Date("1995-01-01")) |>
select(geo, Geo, nace_r2, date, values) |>
spread(nace_r2, values) |>
mutate(values = `D`/TOTAL) |>
left_join(colors, by = c("Geo" = "country")) |>
ggplot() + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + xlab("") + ylab("Electricity, gas, steam - Volume (% of GDP)") +
scale_color_identity() + add_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, .2),
labels = percent_format(accuracy = .1))
1995-
Code
nama_10_a64 |>
filter(na_item == "B1G",
nace_r2 %in% c("D", "TOTAL"),
geo %in% c("FR", "DE", "IT", "ES", "EL"),
unit == "PD10_NAC") |>
year_to_date() |>
select(geo, Geo, nace_r2, date, values) |>
spread(nace_r2, values) |>
mutate(values = D/TOTAL) |>
filter(date >= as.Date("1995-01-01")) |>
left_join(colors, by = c("Geo" = "country")) |>
ggplot() + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + xlab("") + ylab("Price Deflator") +
scale_color_identity() + add_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, 10))
C - Manufacturing
Value
All
Code
nama_10_a64 |>
filter(na_item == "B1G",
nace_r2 %in% c("C", "TOTAL"),
geo %in% c("FR", "DE", "IT", "ES", "NL", "EL"),
unit == "CP_MNAC") |>
year_to_date() |>
select(geo, Geo, nace_r2, date, values) |>
spread(nace_r2, values) |>
mutate(values = `C`/TOTAL) |>
left_join(colors, by = c("Geo" = "country")) |>
ggplot() + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + xlab("") + ylab("Manufacturing (% of GDP)") +
scale_color_identity() + add_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
nama_10_a64 |>
filter(na_item == "B1G",
nace_r2 %in% c("C", "TOTAL"),
geo %in% c("FR", "DE", "IT", "ES", "NL", "EL"),
unit == "CP_MNAC") |>
year_to_date() |>
filter(date >= as.Date("1995-01-01")) |>
select(geo, Geo, nace_r2, date, values) |>
spread(nace_r2, values) |>
mutate(values = `C`/TOTAL) |>
left_join(colors, by = c("Geo" = "country")) |>
ggplot() + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + xlab("") + ylab("Manufacturing (% of GDP)") +
scale_color_identity() + add_flags +
scale_x_date(breaks = seq(1995, 2100, 2) |> paste0("-01-01") |> as.Date(),
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-500, 200, 1),
labels = percent_format(accuracy = 1))
Volume
Code
nama_10_a64 |>
filter(na_item == "B1G",
nace_r2 %in% c("C", "TOTAL"),
geo %in% c("FR", "DE", "IT", "ES"),
unit == "CLV10_MEUR") |>
year_to_date() |>
select(geo, Geo, nace_r2, date, values) |>
spread(nace_r2, values) |>
mutate(values = `C`/TOTAL) |>
left_join(colors, by = c("Geo" = "country")) |>
ggplot() + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + xlab("") + ylab("Manufacturing (% of GDP)") +
scale_color_identity() + add_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))
Price Deflator
All
Code
nama_10_a64 |>
filter(na_item == "B1G",
nace_r2 %in% c("C", "TOTAL"),
geo %in% c("FR", "DE", "IT", "ES"),
unit == "PD10_NAC") |>
year_to_date() |>
select(geo, Geo, nace_r2, date, values) |>
spread(nace_r2, values) |>
mutate(values = C/TOTAL) |>
left_join(colors, by = c("Geo" = "country")) |>
ggplot() + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + xlab("") + ylab("Price Deflator") +
scale_color_identity() + add_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, 10))
1995-
Code
nama_10_a64 |>
filter(na_item == "B1G",
nace_r2 %in% c("C", "TOTAL"),
geo %in% c("FR", "DE", "IT", "ES"),
unit == "PD10_NAC") |>
year_to_date() |>
select(geo, Geo, nace_r2, date, values) |>
spread(nace_r2, values) |>
mutate(values = C/TOTAL) |>
filter(date >= as.Date("1995-01-01")) |>
left_join(colors, by = c("Geo" = "country")) |>
ggplot() + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + xlab("") + ylab("Price Deflator") +
scale_color_identity() + add_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, 10))
C10-C12 - Food products
Value
All
Code
nama_10_a64 |>
filter(na_item == "B1G",
nace_r2 %in% c("C10-C12", "TOTAL"),
geo %in% c("FR", "DE", "IT", "ES"),
unit == "CP_MNAC") |>
year_to_date() |>
select(geo, Geo, nace_r2, date, values) |>
spread(nace_r2, values) |>
mutate(values = `C10-C12`/TOTAL) |>
left_join(colors, by = c("Geo" = "country")) |>
ggplot() + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + xlab("") + ylab("Food products (% of GDP)") +
scale_color_identity() + add_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, 0.1),
labels = percent_format(accuracy = .1))
1995-
Code
nama_10_a64 |>
filter(na_item == "B1G",
nace_r2 %in% c("C10-C12", "TOTAL"),
geo %in% c("FR", "DE", "IT", "ES", "NL"),
unit == "CP_MNAC") |>
year_to_date() |>
filter(date >= as.Date("1995-01-01")) |>
select(geo, Geo, nace_r2, date, values) |>
spread(nace_r2, values) |>
left_join(colors, by = c("Geo" = "country")) |>
mutate(color = ifelse(geo == "NL", color2, color)) |>
mutate(values = `C10-C12`/TOTAL) |>
ggplot() + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + xlab("") + ylab("Food products (% of GDP)") +
scale_color_identity() + add_flags +
scale_x_date(breaks = seq(1960, 2100, 2) |> paste0("-01-01") |> as.Date(),
labels = date_format("%Y")) +
theme(legend.position = "none") +
scale_y_continuous(breaks = 0.01*seq(-500, 200, 0.1),
labels = percent_format(accuracy = .1))
Volume
Code
nama_10_a64 |>
filter(na_item == "B1G",
nace_r2 %in% c("C10-C12", "TOTAL"),
geo %in% c("FR", "DE", "IT", "ES"),
unit == "CLV10_MEUR") |>
year_to_date() |>
select(geo, Geo, nace_r2, date, values) |>
spread(nace_r2, values) |>
mutate(values = `C10-C12`/TOTAL) |>
left_join(colors, by = c("Geo" = "country")) |>
ggplot() + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + xlab("") + ylab("Food products (% of GDP)") +
scale_color_identity() + add_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, 0.1),
labels = percent_format(accuracy = .1))
Price Deflator
Code
nama_10_a64 |>
filter(na_item == "B1G",
nace_r2 %in% c("C10-C12", "TOTAL"),
geo %in% c("FR", "DE", "IT", "ES"),
unit == "PD10_NAC") |>
year_to_date() |>
select(geo, Geo, nace_r2, date, values) |>
spread(nace_r2, values) |>
mutate(values = `C10-C12`/TOTAL) |>
left_join(colors, by = c("Geo" = "country")) |>
ggplot() + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + xlab("") + ylab("Price Deflator") +
scale_color_identity() + add_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, 10))
C13-C15 - Textiles
Value
All
Code
nama_10_a64 |>
filter(na_item == "B1G",
nace_r2 %in% c("C13-C15", "TOTAL"),
geo %in% c("FR", "DE", "IT", "ES"),
unit == "CP_MNAC") |>
year_to_date() |>
select(geo, Geo, nace_r2, date, values) |>
spread(nace_r2, values) |>
mutate(values = `C13-C15`/TOTAL) |>
left_join(colors, by = c("Geo" = "country")) |>
ggplot() + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + xlab("") + ylab("Textiles (% of GDP)") +
scale_color_identity() + add_flags +
scale_x_date(breaks = seq(1960, 2100, 5) |> paste0("-01-01") |> as.Date(),
labels = date_format("%Y")) +
theme(legend.position = "none") +
scale_y_continuous(breaks = 0.01*seq(-500, 200, 0.1),
labels = percent_format(accuracy = .1))
1995-
Code
nama_10_a64 |>
filter(na_item == "B1G",
nace_r2 %in% c("C13-C15", "TOTAL"),
geo %in% c("FR", "DE", "IT", "ES", "NL"),
unit == "CP_MNAC") |>
year_to_date() |>
filter(date >= as.Date("1995-01-01")) |>
select(geo, Geo, nace_r2, date, values) |>
spread(nace_r2, values) |>
mutate(values = `C13-C15`/TOTAL) |>
left_join(colors, by = c("Geo" = "country")) |>
mutate(color = ifelse(geo == "NL", color2, color)) |>
ggplot() + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + xlab("") + ylab("Textiles (% of GDP)") +
scale_color_identity() + add_flags +
scale_x_date(breaks = seq(1960, 2100, 5) |> paste0("-01-01") |> as.Date(),
labels = date_format("%Y")) +
theme(legend.position = "none") +
scale_y_continuous(breaks = 0.01*seq(-500, 200, 0.1),
labels = percent_format(accuracy = .1))
Volume
Code
nama_10_a64 |>
filter(na_item == "B1G",
nace_r2 %in% c("C13-C15", "TOTAL"),
geo %in% c("FR", "DE", "IT", "ES"),
unit == "CLV10_MEUR") |>
year_to_date() |>
select(geo, Geo, nace_r2, date, values) |>
spread(nace_r2, values) |>
mutate(values = `C13-C15`/TOTAL) |>
left_join(colors, by = c("Geo" = "country")) |>
ggplot() + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + xlab("") + ylab("Textiles (% of GDP)") +
scale_color_identity() + add_flags +
scale_x_date(breaks = seq(1960, 2100, 5) |> paste0("-01-01") |> as.Date(),
labels = date_format("%Y")) +
theme(legend.position = "none") +
scale_y_continuous(breaks = 0.01*seq(-500, 200, 0.1),
labels = percent_format(accuracy = .1))
Price Deflator
All
Code
nama_10_a64 |>
filter(na_item == "B1G",
nace_r2 %in% c("C13-C15", "TOTAL"),
geo %in% c("FR", "DE", "IT", "ES"),
unit == "PD10_NAC") |>
year_to_date() |>
select(geo, Geo, nace_r2, date, values) |>
spread(nace_r2, values) |>
mutate(values = `C13-C15`/TOTAL) |>
left_join(colors, by = c("Geo" = "country")) |>
ggplot() + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + xlab("") + ylab("Price Deflator") +
scale_color_identity() + add_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, 10))
1995-
Code
nama_10_a64 |>
filter(na_item == "B1G",
nace_r2 %in% c("C13-C15", "TOTAL"),
geo %in% c("FR", "DE", "IT", "ES"),
unit == "PD10_NAC") |>
year_to_date() |>
filter(date >= as.Date("1995-01-01")) |>
select(geo, Geo, nace_r2, date, values) |>
spread(nace_r2, values) |>
mutate(values = `C13-C15`/TOTAL) |>
left_join(colors, by = c("Geo" = "country")) |>
group_by(Geo) |>
mutate(values = values/ values[1]) |>
ggplot() + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + xlab("") + ylab("Price Deflator") +
scale_color_identity() + add_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, 10))
C16 - Manufacture of paper and paper products
Value
All
Code
nama_10_a64 |>
filter(na_item == "B1G",
nace_r2 %in% c("C16", "TOTAL"),
geo %in% c("FR", "DE", "IT", "ES"),
unit == "CP_MNAC") |>
year_to_date() |>
select(geo, Geo, nace_r2, date, values) |>
spread(nace_r2, values) |>
mutate(values = `C16`/TOTAL) |>
left_join(colors, by = c("Geo" = "country")) |>
ggplot() + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + xlab("") + ylab("Textiles (% of GDP)") +
scale_color_identity() + add_flags +
scale_x_date(breaks = seq(1960, 2100, 5) |> paste0("-01-01") |> as.Date(),
labels = date_format("%Y")) +
theme(legend.position = "none") +
scale_y_continuous(breaks = 0.01*seq(-500, 200, 0.1),
labels = percent_format(accuracy = .1))
1995-
Code
nama_10_a64 |>
filter(na_item == "B1G",
nace_r2 %in% c("C16", "TOTAL"),
geo %in% c("FR", "DE", "IT", "ES", "NL"),
unit == "CP_MNAC") |>
year_to_date() |>
filter(date >= as.Date("1995-01-01")) |>
select(geo, Geo, nace_r2, date, values) |>
spread(nace_r2, values) |>
mutate(values = `C16`/TOTAL) |>
left_join(colors, by = c("Geo" = "country")) |>
mutate(color = ifelse(geo == "NL", color2, color)) |>
ggplot() + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + xlab("") + ylab("Textiles (% of GDP)") +
scale_color_identity() + add_flags +
scale_x_date(breaks = seq(1960, 2100, 5) |> paste0("-01-01") |> as.Date(),
labels = date_format("%Y")) +
theme(legend.position = "none") +
scale_y_continuous(breaks = 0.01*seq(-500, 200, 0.1),
labels = percent_format(accuracy = .1))
Volume
Code
nama_10_a64 |>
filter(na_item == "B1G",
nace_r2 %in% c("C16", "TOTAL"),
geo %in% c("FR", "DE", "IT", "ES"),
unit == "CLV10_MEUR") |>
year_to_date() |>
select(geo, Geo, nace_r2, date, values) |>
spread(nace_r2, values) |>
mutate(values = `C16`/TOTAL) |>
left_join(colors, by = c("Geo" = "country")) |>
ggplot() + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + xlab("") + ylab("Textiles (% of GDP)") +
scale_color_identity() + add_flags +
scale_x_date(breaks = seq(1960, 2100, 5) |> paste0("-01-01") |> as.Date(),
labels = date_format("%Y")) +
theme(legend.position = "none") +
scale_y_continuous(breaks = 0.01*seq(-500, 200, 0.1),
labels = percent_format(accuracy = .1))
Price Deflator
All
Code
nama_10_a64 |>
filter(na_item == "B1G",
nace_r2 %in% c("C16", "TOTAL"),
geo %in% c("FR", "DE", "IT", "ES"),
unit == "PD10_NAC") |>
year_to_date() |>
select(geo, Geo, nace_r2, date, values) |>
spread(nace_r2, values) |>
mutate(values = `C16`/TOTAL) |>
left_join(colors, by = c("Geo" = "country")) |>
ggplot() + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + xlab("") + ylab("Price Deflator") +
scale_color_identity() + add_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, 10))
1995-
Code
nama_10_a64 |>
filter(na_item == "B1G",
nace_r2 %in% c("C16", "TOTAL"),
geo %in% c("FR", "DE", "IT", "ES"),
unit == "PD10_NAC") |>
year_to_date() |>
filter(date >= as.Date("1995-01-01")) |>
select(geo, Geo, nace_r2, date, values) |>
spread(nace_r2, values) |>
mutate(values = `C16`/TOTAL) |>
left_join(colors, by = c("Geo" = "country")) |>
group_by(Geo) |>
mutate(values = values/ values[1]) |>
ggplot() + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + xlab("") + ylab("Price Deflator") +
scale_color_identity() + add_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, 10))
C16-C18 - Wood, Paper, Printing
All
Code
nama_10_a64 |>
filter(na_item == "B1G",
nace_r2 %in% c("C16-C18", "TOTAL"),
geo %in% c("FR", "DE", "IT", "ES"),
unit == "CP_MNAC") |>
year_to_date() |>
select(geo, Geo, nace_r2, date, values) |>
spread(nace_r2, values) |>
ggplot() + geom_line(aes(x = date, y = `C16-C18`/TOTAL, color = Geo)) +
theme_minimal() + xlab("") + ylab("Wood, Paper, Printing (% of GDP)") +
scale_color_manual(values = c("#002395", "#000000", "#009246", "#C60B1E")) +
scale_x_date(breaks = seq(1960, 2100, 5) |> paste0("-01-01") |> as.Date(),
labels = date_format("%Y")) +
geom_image(data = . %>%
filter(date == as.Date("2017-01-01")) %>%
mutate(image = paste0("../../icon/flag/round/", str_to_lower(Geo), ".png")),
aes(x = date, y = `C16-C18`/TOTAL, image = image), asp = 1.5) +
theme(legend.position = "none") +
scale_y_continuous(breaks = 0.01*seq(-500, 200, 0.1),
labels = percent_format(accuracy = .1))
1995-
Code
nama_10_a64 |>
filter(na_item == "B1G",
nace_r2 %in% c("C16-C18", "TOTAL"),
geo %in% c("FR", "DE", "IT", "ES"),
unit == "CP_MNAC") |>
year_to_date() |>
filter(date >= as.Date("1995-01-01")) |>
select(geo, Geo, nace_r2, date, values) |>
spread(nace_r2, values) |>
ggplot() + geom_line(aes(x = date, y = `C16-C18`/TOTAL, color = Geo)) +
theme_minimal() + xlab("") + ylab("Wood, Paper, Printing (% of GDP)") +
scale_color_manual(values = c("#002395", "#000000", "#009246", "#C60B1E")) +
scale_x_date(breaks = seq(1960, 2100, 2) |> paste0("-01-01") |> as.Date(),
labels = date_format("%Y")) +
geom_image(data = . %>%
filter(date == as.Date("2017-01-01")) %>%
mutate(image = paste0("../../icon/flag/round/", str_to_lower(Geo), ".png")),
aes(x = date, y = `C16-C18`/TOTAL, image = image), asp = 1.5) +
theme(legend.position = "none") +
scale_y_continuous(breaks = 0.01*seq(-500, 200, 0.1),
labels = percent_format(accuracy = .1))
C17 - Textiles
Value
All
Code
nama_10_a64 |>
filter(na_item == "B1G",
nace_r2 %in% c("C17", "TOTAL"),
geo %in% c("FR", "DE", "IT", "ES"),
unit == "CP_MNAC") |>
year_to_date() |>
select(geo, Geo, nace_r2, date, values) |>
spread(nace_r2, values) |>
mutate(values = `C17`/TOTAL) |>
left_join(colors, by = c("Geo" = "country")) |>
ggplot() + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + xlab("") + ylab("Textiles (% of GDP)") +
scale_color_identity() + add_flags +
scale_x_date(breaks = seq(1960, 2100, 5) |> paste0("-01-01") |> as.Date(),
labels = date_format("%Y")) +
theme(legend.position = "none") +
scale_y_continuous(breaks = 0.01*seq(-500, 200, 0.1),
labels = percent_format(accuracy = .1))
1995-
Code
nama_10_a64 |>
filter(na_item == "B1G",
nace_r2 %in% c("C17", "TOTAL"),
geo %in% c("FR", "DE", "IT", "ES", "NL"),
unit == "CP_MNAC") |>
year_to_date() |>
filter(date >= as.Date("1995-01-01")) |>
select(geo, Geo, nace_r2, date, values) |>
spread(nace_r2, values) |>
mutate(values = `C17`/TOTAL) |>
left_join(colors, by = c("Geo" = "country")) |>
mutate(color = ifelse(geo == "NL", color2, color)) |>
ggplot() + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + xlab("") + ylab("Textiles (% of GDP)") +
scale_color_identity() + add_flags +
scale_x_date(breaks = seq(1960, 2100, 5) |> paste0("-01-01") |> as.Date(),
labels = date_format("%Y")) +
theme(legend.position = "none") +
scale_y_continuous(breaks = 0.01*seq(-500, 200, 0.1),
labels = percent_format(accuracy = .1))
Volume
Code
nama_10_a64 |>
filter(na_item == "B1G",
nace_r2 %in% c("C17", "TOTAL"),
geo %in% c("FR", "DE", "IT", "ES"),
unit == "CLV10_MEUR") |>
year_to_date() |>
select(geo, Geo, nace_r2, date, values) |>
spread(nace_r2, values) |>
mutate(values = `C17`/TOTAL) |>
left_join(colors, by = c("Geo" = "country")) |>
ggplot() + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + xlab("") + ylab("Textiles (% of GDP)") +
scale_color_identity() + add_flags +
scale_x_date(breaks = seq(1960, 2100, 5) |> paste0("-01-01") |> as.Date(),
labels = date_format("%Y")) +
theme(legend.position = "none") +
scale_y_continuous(breaks = 0.01*seq(-500, 200, 0.1),
labels = percent_format(accuracy = .1))
Price Deflator
All
Code
nama_10_a64 |>
filter(na_item == "B1G",
nace_r2 %in% c("C17", "TOTAL"),
geo %in% c("FR", "DE", "IT", "ES"),
unit == "PD10_NAC") |>
year_to_date() |>
select(geo, Geo, nace_r2, date, values) |>
spread(nace_r2, values) |>
mutate(values = `C17`/TOTAL) |>
left_join(colors, by = c("Geo" = "country")) |>
ggplot() + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + xlab("") + ylab("Price Deflator") +
scale_color_identity() + add_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, 10))
1995-
Code
nama_10_a64 |>
filter(na_item == "B1G",
nace_r2 %in% c("C17", "TOTAL"),
geo %in% c("FR", "DE", "IT", "ES"),
unit == "PD10_NAC") |>
year_to_date() |>
filter(date >= as.Date("1995-01-01")) |>
select(geo, Geo, nace_r2, date, values) |>
spread(nace_r2, values) |>
mutate(values = `C17`/TOTAL) |>
left_join(colors, by = c("Geo" = "country")) |>
group_by(Geo) |>
mutate(values = values/ values[1]) |>
ggplot() + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + xlab("") + ylab("Price Deflator") +
scale_color_identity() + add_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, 10))
C18 - Printing and reproduction of recorded media
Value
All
Code
nama_10_a64 |>
filter(na_item == "B1G",
nace_r2 %in% c("C18", "TOTAL"),
geo %in% c("FR", "DE", "IT", "ES"),
unit == "CP_MNAC") |>
year_to_date() |>
select(geo, Geo, nace_r2, date, values) |>
spread(nace_r2, values) |>
mutate(values = `C18`/TOTAL) |>
left_join(colors, by = c("Geo" = "country")) |>
ggplot() + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + xlab("") + ylab("Textiles (% of GDP)") +
scale_color_identity() + add_flags +
scale_x_date(breaks = seq(1960, 2100, 5) |> paste0("-01-01") |> as.Date(),
labels = date_format("%Y")) +
theme(legend.position = "none") +
scale_y_continuous(breaks = 0.01*seq(-500, 200, 0.1),
labels = percent_format(accuracy = .1))
1995-
Code
nama_10_a64 |>
filter(na_item == "B1G",
nace_r2 %in% c("C18", "TOTAL"),
geo %in% c("FR", "DE", "IT", "ES", "NL"),
unit == "CP_MNAC") |>
year_to_date() |>
filter(date >= as.Date("1995-01-01")) |>
select(geo, Geo, nace_r2, date, values) |>
spread(nace_r2, values) |>
mutate(values = `C18`/TOTAL) |>
left_join(colors, by = c("Geo" = "country")) |>
mutate(color = ifelse(geo == "NL", color2, color)) |>
ggplot() + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + xlab("") + ylab("Textiles (% of GDP)") +
scale_color_identity() + add_flags +
scale_x_date(breaks = seq(1960, 2100, 5) |> paste0("-01-01") |> as.Date(),
labels = date_format("%Y")) +
theme(legend.position = "none") +
scale_y_continuous(breaks = 0.01*seq(-500, 200, 0.1),
labels = percent_format(accuracy = .1))
Volume
Code
nama_10_a64 |>
filter(na_item == "B1G",
nace_r2 %in% c("C18", "TOTAL"),
geo %in% c("FR", "DE", "IT", "ES"),
unit == "CLV10_MEUR") |>
year_to_date() |>
select(geo, Geo, nace_r2, date, values) |>
spread(nace_r2, values) |>
mutate(values = `C18`/TOTAL) |>
left_join(colors, by = c("Geo" = "country")) |>
ggplot() + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + xlab("") + ylab("Textiles (% of GDP)") +
scale_color_identity() + add_flags +
scale_x_date(breaks = seq(1960, 2100, 5) |> paste0("-01-01") |> as.Date(),
labels = date_format("%Y")) +
theme(legend.position = "none") +
scale_y_continuous(breaks = 0.01*seq(-500, 200, 0.1),
labels = percent_format(accuracy = .1))
Price Deflator
All
Code
nama_10_a64 |>
filter(na_item == "B1G",
nace_r2 %in% c("C18", "TOTAL"),
geo %in% c("FR", "DE", "IT", "ES"),
unit == "PD10_NAC") |>
year_to_date() |>
select(geo, Geo, nace_r2, date, values) |>
spread(nace_r2, values) |>
mutate(values = `C18`/TOTAL) |>
left_join(colors, by = c("Geo" = "country")) |>
ggplot() + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + xlab("") + ylab("Price Deflator") +
scale_color_identity() + add_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, 10))
1995-
Code
nama_10_a64 |>
filter(na_item == "B1G",
nace_r2 %in% c("C18", "TOTAL"),
geo %in% c("FR", "DE", "IT", "ES"),
unit == "PD10_NAC") |>
year_to_date() |>
filter(date >= as.Date("1995-01-01")) |>
select(geo, Geo, nace_r2, date, values) |>
spread(nace_r2, values) |>
mutate(values = `C18`/TOTAL) |>
left_join(colors, by = c("Geo" = "country")) |>
group_by(Geo) |>
mutate(values = values/ values[1]) |>
ggplot() + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + xlab("") + ylab("Price Deflator") +
scale_color_identity() + add_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, 10))
C19 - Manufacture of coke and refined petroleum products
Value
All
Code
nama_10_a64 |>
filter(na_item == "B1G",
nace_r2 %in% c("C19", "TOTAL"),
geo %in% c("FR", "DE", "IT", "ES"),
unit == "CP_MNAC") |>
year_to_date() |>
select(geo, Geo, nace_r2, date, values) |>
spread(nace_r2, values) |>
mutate(values = `C19`/TOTAL) |>
left_join(colors, by = c("Geo" = "country")) |>
ggplot() + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + xlab("") + ylab("Textiles (% of GDP)") +
scale_color_identity() + add_flags +
scale_x_date(breaks = seq(1960, 2100, 5) |> paste0("-01-01") |> as.Date(),
labels = date_format("%Y")) +
theme(legend.position = "none") +
scale_y_continuous(breaks = 0.01*seq(-500, 200, 0.1),
labels = percent_format(accuracy = .1))
1995-
Code
nama_10_a64 |>
filter(na_item == "B1G",
nace_r2 %in% c("C19", "TOTAL"),
geo %in% c("FR", "DE", "IT", "ES", "NL"),
unit == "CP_MNAC") |>
year_to_date() |>
filter(date >= as.Date("1995-01-01")) |>
select(geo, Geo, nace_r2, date, values) |>
spread(nace_r2, values) |>
mutate(values = `C19`/TOTAL) |>
left_join(colors, by = c("Geo" = "country")) |>
mutate(color = ifelse(geo == "NL", color2, color)) |>
ggplot() + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + xlab("") + ylab("Textiles (% of GDP)") +
scale_color_identity() + add_flags +
scale_x_date(breaks = seq(1960, 2100, 5) |> paste0("-01-01") |> as.Date(),
labels = date_format("%Y")) +
theme(legend.position = "none") +
scale_y_continuous(breaks = 0.01*seq(-500, 200, 0.1),
labels = percent_format(accuracy = .1))
Volume
Code
nama_10_a64 |>
filter(na_item == "B1G",
nace_r2 %in% c("C19", "TOTAL"),
geo %in% c("FR", "DE", "IT", "ES"),
unit == "CLV10_MEUR") |>
year_to_date() |>
select(geo, Geo, nace_r2, date, values) |>
spread(nace_r2, values) |>
mutate(values = `C19`/TOTAL) |>
left_join(colors, by = c("Geo" = "country")) |>
ggplot() + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + xlab("") + ylab("Textiles (% of GDP)") +
scale_color_identity() + add_flags +
scale_x_date(breaks = seq(1960, 2100, 5) |> paste0("-01-01") |> as.Date(),
labels = date_format("%Y")) +
theme(legend.position = "none") +
scale_y_continuous(breaks = 0.01*seq(-500, 200, 0.1),
labels = percent_format(accuracy = .1))
Price Deflator
All
Code
nama_10_a64 |>
filter(na_item == "B1G",
nace_r2 %in% c("C19", "TOTAL"),
geo %in% c("FR", "DE", "IT", "ES"),
unit == "PD10_NAC") |>
year_to_date() |>
select(geo, Geo, nace_r2, date, values) |>
spread(nace_r2, values) |>
mutate(values = `C19`/TOTAL) |>
left_join(colors, by = c("Geo" = "country")) |>
ggplot() + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + xlab("") + ylab("Price Deflator") +
scale_color_identity() + add_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, 10))
1995-
Code
nama_10_a64 |>
filter(na_item == "B1G",
nace_r2 %in% c("C19", "TOTAL"),
geo %in% c("FR", "DE", "IT", "ES"),
unit == "PD10_NAC") |>
year_to_date() |>
filter(date >= as.Date("1995-01-01")) |>
select(geo, Geo, nace_r2, date, values) |>
spread(nace_r2, values) |>
mutate(values = `C19`/TOTAL) |>
left_join(colors, by = c("Geo" = "country")) |>
group_by(Geo) |>
mutate(values = values/ values[1]) |>
ggplot() + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + xlab("") + ylab("Price Deflator") +
scale_color_identity() + add_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, 10))
C20 - Manufacture of chemicals and chemical products
Value
All
Code
nama_10_a64 |>
filter(na_item == "B1G",
nace_r2 %in% c("C20", "TOTAL"),
geo %in% c("FR", "DE", "IT", "ES"),
unit == "CP_MNAC") |>
year_to_date() |>
select(geo, Geo, nace_r2, date, values) |>
spread(nace_r2, values) |>
mutate(values = `C20`/TOTAL) |>
left_join(colors, by = c("Geo" = "country")) |>
ggplot() + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + xlab("") + ylab("Textiles (% of GDP)") +
scale_color_identity() + add_flags +
scale_x_date(breaks = seq(1960, 2100, 5) |> paste0("-01-01") |> as.Date(),
labels = date_format("%Y")) +
theme(legend.position = "none") +
scale_y_continuous(breaks = 0.01*seq(-500, 200, 0.1),
labels = percent_format(accuracy = .1))
1995-
Code
nama_10_a64 |>
filter(na_item == "B1G",
nace_r2 %in% c("C20", "TOTAL"),
geo %in% c("FR", "DE", "IT", "ES", "NL"),
unit == "CP_MNAC") |>
year_to_date() |>
filter(date >= as.Date("1995-01-01")) |>
select(geo, Geo, nace_r2, date, values) |>
spread(nace_r2, values) |>
mutate(values = `C20`/TOTAL) |>
left_join(colors, by = c("Geo" = "country")) |>
mutate(color = ifelse(geo == "NL", color2, color)) |>
ggplot() + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + xlab("") + ylab("Textiles (% of GDP)") +
scale_color_identity() + add_flags +
scale_x_date(breaks = seq(1960, 2100, 5) |> paste0("-01-01") |> as.Date(),
labels = date_format("%Y")) +
theme(legend.position = "none") +
scale_y_continuous(breaks = 0.01*seq(-500, 200, 0.1),
labels = percent_format(accuracy = .1))
Volume
Code
nama_10_a64 |>
filter(na_item == "B1G",
nace_r2 %in% c("C20", "TOTAL"),
geo %in% c("FR", "DE", "IT", "ES"),
unit == "CLV10_MEUR") |>
year_to_date() |>
select(geo, Geo, nace_r2, date, values) |>
spread(nace_r2, values) |>
mutate(values = `C20`/TOTAL) |>
left_join(colors, by = c("Geo" = "country")) |>
ggplot() + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + xlab("") + ylab("Textiles (% of GDP)") +
scale_color_identity() + add_flags +
scale_x_date(breaks = seq(1960, 2100, 5) |> paste0("-01-01") |> as.Date(),
labels = date_format("%Y")) +
theme(legend.position = "none") +
scale_y_continuous(breaks = 0.01*seq(-500, 200, 0.1),
labels = percent_format(accuracy = .1))
Price Deflator
All
Code
nama_10_a64 |>
filter(na_item == "B1G",
nace_r2 %in% c("C20", "TOTAL"),
geo %in% c("FR", "DE", "IT", "ES"),
unit == "PD10_NAC") |>
year_to_date() |>
select(geo, Geo, nace_r2, date, values) |>
spread(nace_r2, values) |>
mutate(values = `C20`/TOTAL) |>
left_join(colors, by = c("Geo" = "country")) |>
ggplot() + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + xlab("") + ylab("Price Deflator") +
scale_color_identity() + add_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, 10))
1995-
Code
nama_10_a64 |>
filter(na_item == "B1G",
nace_r2 %in% c("C20", "TOTAL"),
geo %in% c("FR", "DE", "IT", "ES"),
unit == "PD10_NAC") |>
year_to_date() |>
filter(date >= as.Date("1995-01-01")) |>
select(geo, Geo, nace_r2, date, values) |>
spread(nace_r2, values) |>
mutate(values = `C20`/TOTAL) |>
left_join(colors, by = c("Geo" = "country")) |>
group_by(Geo) |>
mutate(values = values/ values[1]) |>
ggplot() + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + xlab("") + ylab("Price Deflator") +
scale_color_identity() + add_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, 10))
C21 - Manufacture of basic pharmaceutical products and pharmaceutical preparations
Value
All
Code
nama_10_a64 |>
filter(na_item == "B1G",
nace_r2 %in% c("C21", "TOTAL"),
geo %in% c("FR", "DE", "IT", "ES"),
unit == "CP_MNAC") |>
year_to_date() |>
select(geo, Geo, nace_r2, date, values) |>
spread(nace_r2, values) |>
mutate(values = `C21`/TOTAL) |>
left_join(colors, by = c("Geo" = "country")) |>
ggplot() + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + xlab("") + ylab("Manufacture of basic pharmaceutical products (% of GDP)") +
scale_color_identity() + add_flags +
scale_x_date(breaks = seq(1960, 2100, 5) |> paste0("-01-01") |> as.Date(),
labels = date_format("%Y")) +
theme(legend.position = "none") +
scale_y_continuous(breaks = 0.01*seq(-500, 200, 0.1),
labels = percent_format(accuracy = .1))
1995-
Code
nama_10_a64 |>
filter(na_item == "B1G",
nace_r2 %in% c("C21", "TOTAL"),
geo %in% c("FR", "DE", "IT", "ES", "NL"),
unit == "CP_MNAC") |>
year_to_date() |>
filter(date >= as.Date("1995-01-01")) |>
select(geo, Geo, nace_r2, date, values) |>
spread(nace_r2, values) |>
mutate(values = `C21`/TOTAL) |>
left_join(colors, by = c("Geo" = "country")) |>
mutate(color = ifelse(geo == "NL", color2, color)) |>
ggplot() + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + xlab("") + ylab("Manufacture of basic pharmaceutical products (% of GDP)") +
scale_color_identity() + add_flags +
scale_x_date(breaks = seq(1960, 2100, 5) |> paste0("-01-01") |> as.Date(),
labels = date_format("%Y")) +
theme(legend.position = "none") +
scale_y_continuous(breaks = 0.01*seq(-500, 200, 0.1),
labels = percent_format(accuracy = .1))
Volume
All
Code
nama_10_a64 |>
filter(na_item == "B1G",
nace_r2 %in% c("C21", "TOTAL"),
geo %in% c("FR", "DE", "IT", "ES"),
unit == "CLV10_MEUR") |>
year_to_date() |>
select(geo, Geo, nace_r2, date, values) |>
spread(nace_r2, values) |>
mutate(values = `C21`/TOTAL) |>
left_join(colors, by = c("Geo" = "country")) |>
ggplot() + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + xlab("") + ylab("Manufacture of basic pharmaceutical products (% of GDP)") +
scale_color_identity() + add_flags +
scale_x_date(breaks = seq(1960, 2100, 5) |> paste0("-01-01") |> as.Date(),
labels = date_format("%Y")) +
theme(legend.position = "none") +
scale_y_continuous(breaks = 0.01*seq(-500, 200, 0.1),
labels = percent_format(accuracy = .1))
1995-
Code
nama_10_a64 |>
filter(na_item == "B1G",
nace_r2 %in% c("C21", "TOTAL"),
geo %in% c("FR", "DE", "IT", "ES", "NL"),
unit == "CLV10_MEUR") |>
year_to_date() |>
filter(date >= as.Date("1995-01-01")) |>
select(geo, Geo, nace_r2, date, values) |>
spread(nace_r2, values) |>
mutate(values = `C21`/TOTAL) |>
left_join(colors, by = c("Geo" = "country")) |>
ggplot() + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + xlab("") + ylab("Manufacture of basic pharmaceutical products (% of GDP)") +
scale_color_identity() + add_flags +
scale_x_date(breaks = seq(1960, 2100, 5) |> paste0("-01-01") |> as.Date(),
labels = date_format("%Y")) +
theme(legend.position = "none") +
scale_y_continuous(breaks = 0.01*seq(-500, 200, 0.1),
labels = percent_format(accuracy = .1))
Price Deflator
All
Code
nama_10_a64 |>
filter(na_item == "B1G",
nace_r2 %in% c("C21", "TOTAL"),
geo %in% c("FR", "DE", "IT", "ES"),
unit == "PD10_NAC") |>
year_to_date() |>
select(geo, Geo, nace_r2, date, values) |>
spread(nace_r2, values) |>
mutate(values = `C21`/TOTAL) |>
left_join(colors, by = c("Geo" = "country")) |>
ggplot() + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + xlab("") + ylab("Price Deflator") +
scale_color_identity() + add_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, 10))
1995-
Code
nama_10_a64 |>
filter(na_item == "B1G",
nace_r2 %in% c("C21", "TOTAL"),
geo %in% c("FR", "DE", "IT", "ES", "NL"),
unit == "PD10_NAC") |>
year_to_date() |>
filter(date >= as.Date("1995-01-01")) |>
select(geo, Geo, nace_r2, date, values) |>
spread(nace_r2, values) |>
mutate(values = `C21`/TOTAL) |>
left_join(colors, by = c("Geo" = "country")) |>
group_by(Geo) |>
mutate(values = values/ values[1],
color = ifelse(geo== "NL", color2, color)) |>
ggplot() + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + xlab("") + ylab("Price Deflator") +
scale_color_identity() + add_flags +
scale_x_date(breaks = seq(1960, 2100, 5) |> paste0("-01-01") |> as.Date(),
labels = date_format("%Y")) +
scale_y_log10(breaks = 0.01*seq(-500, 200, 10))
C29 - Motor vehicles
All
Code
nama_10_a64 |>
filter(na_item == "B1G",
nace_r2 %in% c("C29", "TOTAL"),
geo %in% c("FR", "DE", "IT", "ES", "EA20"),
unit == "CP_MNAC") |>
year_to_date() |>
mutate(Geo = ifelse(geo == "EA20", "Europe", Geo)) |>
select(geo, Geo, nace_r2, date, values) |>
spread(nace_r2, values) |>
mutate(values = `C29`/TOTAL) |>
filter(!is.na(values)) |>
left_join(colors, by = c("Geo" = "country")) |>
ggplot() + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + xlab("") + ylab("Motor vehicles (% of GDP)") +
scale_color_identity() +
scale_x_date(breaks = seq(1960, 2100, 5) |> paste0("-01-01") |> as.Date(),
labels = date_format("%Y")) + add_flags +
theme(legend.position = "none") +
scale_y_continuous(breaks = 0.01*seq(-500, 200, 0.5),
labels = percent_format(accuracy = .1))
1995-
Code
nama_10_a64 |>
filter(na_item == "B1G",
nace_r2 %in% c("C29", "TOTAL"),
geo %in% c("FR", "DE", "IT", "ES", "EA20"),
unit == "CP_MNAC") |>
year_to_date() |>
filter(date >= as.Date("1995-01-01")) |>
mutate(Geo = ifelse(geo == "EA20", "Europe", Geo)) |>
select(geo, Geo, nace_r2, date, values) |>
spread(nace_r2, values) |>
mutate(values = `C29`/TOTAL) |>
filter(!is.na(values)) |>
left_join(colors, by = c("Geo" = "country")) |>
ggplot() + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + xlab("") + ylab("Motor vehicles (% of GDP)") +
scale_color_identity() +
scale_x_date(breaks = seq(1960, 2100, 5) |> paste0("-01-01") |> as.Date(),
labels = date_format("%Y")) + add_flags +
theme(legend.position = "none") +
scale_y_continuous(breaks = 0.01*seq(-500, 200, 0.5),
labels = percent_format(accuracy = .1))
2000-
Code
nama_10_a64 |>
filter(na_item == "B1G",
nace_r2 %in% c("C29", "TOTAL"),
geo %in% c("FR", "DE", "IT", "ES"),
unit == "CP_MNAC") |>
year_to_date() |>
filter(date >= as.Date("2000-01-01")) |>
select(geo, Geo, nace_r2, date, values) |>
spread(nace_r2, values) |>
mutate(values = `C29`/TOTAL) |>
left_join(colors, by = c("Geo" = "country")) |>
mutate(color = ifelse(geo == "NL", color2, color)) |>
ggplot() + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + xlab("") + ylab("Industrie automobile (% du PIB)") +
scale_color_identity() + add_flags +
scale_x_date(breaks = seq(1995, 2100, 5) |> paste0("-01-01") |> as.Date(),
labels = date_format("%Y")) +
theme(legend.position = "none") +
scale_y_continuous(breaks = 0.01*seq(-500, 200, 0.5),
labels = percent_format(accuracy = .1))
France, Europe
B1G
Code
# nace_r2 <- read_parquet("nace_r2_fr.parquet")
nama_10_a64 |>
filter(na_item == "B1G",
nace_r2 %in% c("C29", "TOTAL"),
geo %in% c("FR", "EA20"),
unit == "CP_MNAC") |>
year_to_date() |>
#filter(date >= as.Date("1995-01-01")) %>%
mutate(Geo = ifelse(geo == "EA20", "Europe", Geo)) |>
select(geo, Geo, nace_r2, date, values) |>
spread(nace_r2, values) |>
mutate(values = `C29`/TOTAL) |>
filter(!is.na(values)) |>
left_join(colors, by = c("Geo" = "country")) |>
ggplot() + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + xlab("") + ylab("Industrie automobile (% du PIB)") +
scale_color_identity() +
scale_x_date(breaks = seq(1960, 2100, 5) |> paste0("-01-01") |> as.Date(),
labels = date_format("%Y")) + add_flags +
theme(legend.position = "none") +
scale_y_continuous(breaks = 0.01*seq(-500, 200, 0.1),
labels = percent_format(accuracy = .1))
P1
Code
# nace_r2 <- read_parquet("nace_r2_fr.parquet")
nama_10_a64 |>
filter(na_item == "P1",
nace_r2 %in% c("C29", "TOTAL"),
geo %in% c("FR", "EA20"),
unit == "CP_MNAC") |>
year_to_date() |>
#filter(date >= as.Date("1995-01-01")) %>%
mutate(Geo = ifelse(geo == "EA20", "Europe", Geo)) |>
select(geo, Geo, nace_r2, date, values) |>
spread(nace_r2, values) |>
mutate(values = `C29`/TOTAL) |>
filter(!is.na(values)) |>
left_join(colors, by = c("Geo" = "country")) |>
ggplot() + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + xlab("") + ylab("Production, industrie automobile (% de la production)") +
scale_color_identity() +
scale_x_date(breaks = seq(1960, 2100, 5) |> paste0("-01-01") |> as.Date(),
labels = date_format("%Y")) + add_flags +
theme(legend.position = "none") +
scale_y_continuous(breaks = 0.01*seq(-500, 200, 0.1),
labels = percent_format(accuracy = .1))
C29_C30 - Motor vehicles and other transport equipment
All
Code
nama_10_a64 |>
filter(na_item == "B1G",
nace_r2 %in% c("C29_C30", "TOTAL"),
geo %in% c("FR", "DE", "IT", "ES"),
unit == "CP_MNAC") |>
year_to_date() |>
select(geo, Geo, nace_r2, date, values) |>
spread(nace_r2, values) |>
mutate(values = `C29_C30`/TOTAL) |>
ggplot() + geom_line(aes(x = date, y = values, color = Geo)) +
theme_minimal() + xlab("") + ylab("Motor vehicles and other transport equipment (% of GDP)") +
scale_color_manual(values = c("#002395", "#000000", "#009246", "#C60B1E")) +
scale_x_date(breaks = seq(1960, 2100, 5) |> paste0("-01-01") |> as.Date(),
labels = date_format("%Y")) + add_flags +
theme(legend.position = "none") +
scale_y_continuous(breaks = 0.01*seq(-500, 200, 0.5),
labels = percent_format(accuracy = .1))
1995-
Code
nama_10_a64 |>
filter(na_item == "B1G",
nace_r2 %in% c("C29_C30", "TOTAL"),
geo %in% c("FR", "DE", "IT", "ES"),
unit == "CP_MNAC") |>
year_to_date() |>
filter(date >= as.Date("1995-01-01")) |>
select(geo, Geo, nace_r2, date, values) |>
spread(nace_r2, values) |>
mutate(values = `C29_C30`/TOTAL) |>
ggplot() + geom_line(aes(x = date, y = values, color = Geo)) +
theme_minimal() + xlab("") + ylab("Motor vehicles and other transport equipment (% of GDP)") +
scale_color_manual(values = c("#002395", "#000000", "#009246", "#C60B1E")) +
scale_x_date(breaks = seq(1960, 2100, 2) |> paste0("-01-01") |> as.Date(),
labels = date_format("%Y")) +
add_flags +
theme(legend.position = "none") +
scale_y_continuous(breaks = 0.01*seq(-500, 200, 0.5),
labels = percent_format(accuracy = .1))
C28 - Machinery and equipment
All
Code
nama_10_a64 |>
filter(na_item == "B1G",
nace_r2 %in% c("C28", "TOTAL"),
geo %in% c("FR", "DE", "IT", "ES"),
unit == "CP_MNAC") |>
year_to_date() |>
select(geo, Geo, nace_r2, date, values) |>
spread(nace_r2, values) |>
mutate(values = `C28`/TOTAL) |>
left_join(colors, by = c("Geo" = "country")) |>
ggplot() + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + xlab("") + ylab("Machinery and equipment (% of GDP)") +
scale_color_identity() + add_flags +
scale_x_date(breaks = seq(1960, 2100, 5) |> paste0("-01-01") |> as.Date(),
labels = date_format("%Y")) +
theme(legend.position = "none") +
scale_y_continuous(breaks = 0.01*seq(-500, 200, 0.1),
labels = percent_format(accuracy = .1))
1995-
Code
nama_10_a64 |>
filter(na_item == "B1G",
nace_r2 %in% c("C28", "TOTAL"),
geo %in% c("FR", "DE", "IT", "ES"),
unit == "CP_MNAC") |>
year_to_date() |>
filter(date >= as.Date("1995-01-01")) |>
select(geo, Geo, nace_r2, date, values) |>
spread(nace_r2, values) |>
mutate(values = `C28`/TOTAL) |>
left_join(colors, by = c("Geo" = "country")) |>
ggplot() + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + xlab("") + ylab("Machinery and equipment (% of GDP)") +
scale_color_identity() + add_flags +
scale_x_date(breaks = seq(1960, 2100, 2) |> paste0("-01-01") |> as.Date(),
labels = date_format("%Y")) +
theme(legend.position = "none") +
scale_y_continuous(breaks = 0.01*seq(-500, 200, 0.1),
labels = percent_format(accuracy = .1))
L - Real Estate
Value
All
Code
nama_10_a64 |>
filter(na_item == "B1G",
nace_r2 %in% c("L", "TOTAL"),
geo %in% c("FR", "DE", "IT", "ES", "NL"),
unit == "CP_MNAC") |>
year_to_date() |>
select(geo, Geo, nace_r2, date, values) |>
spread(nace_r2, values) |>
mutate(values = `L`/TOTAL) |>
left_join(colors, by = c("Geo" = "country")) |>
ggplot() + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + xlab("") + ylab("Real Estate (% of GDP)") +
scale_color_identity() + add_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
nama_10_a64 |>
filter(na_item == "B1G",
nace_r2 %in% c("L", "TOTAL"),
geo %in% c("FR", "DE", "IT", "ES", "NL"),
unit == "CP_MNAC") |>
year_to_date() |>
filter(date >= as.Date("1995-01-01")) |>
select(geo, Geo, nace_r2, date, values) |>
spread(nace_r2, values) |>
mutate(values = `L`/TOTAL) |>
left_join(colors, by = c("Geo" = "country")) |>
ggplot() + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + xlab("") + ylab("Real Estate (% of GDP)") +
scale_color_identity() + add_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))
Volume
All
Code
nama_10_a64 |>
filter(na_item == "B1G",
nace_r2 %in% c("L", "TOTAL"),
geo %in% c("FR", "DE", "IT", "ES"),
unit == "CLV10_MEUR") |>
year_to_date() |>
select(geo, Geo, nace_r2, date, values) |>
spread(nace_r2, values) |>
mutate(values = `L`/TOTAL) |>
left_join(colors, by = c("Geo" = "country")) |>
ggplot() + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + xlab("") + ylab("Real Estate (% of GDP)") +
scale_color_identity() + add_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
nama_10_a64 |>
filter(na_item == "B1G",
nace_r2 %in% c("L", "TOTAL"),
geo %in% c("FR", "DE", "IT", "ES", "NL"),
unit == "CLV10_MEUR") |>
year_to_date() |>
filter(date >= as.Date("1995-01-01")) |>
select(geo, Geo, nace_r2, date, values) |>
spread(nace_r2, values) |>
mutate(values = `L`/TOTAL) |>
left_join(colors, by = c("Geo" = "country")) |>
ggplot() + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + xlab("") + ylab("Real Estate (% of GDP)") +
scale_color_identity() + add_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))
Price Deflator
Code
nama_10_a64 |>
filter(na_item == "B1G",
nace_r2 %in% c("L", "TOTAL"),
geo %in% c("FR", "DE", "IT", "ES", "NL"),
unit == "PD10_NAC") |>
year_to_date() |>
select(geo, Geo, nace_r2, date, values) |>
spread(nace_r2, values) |>
mutate(values = L/TOTAL) |>
left_join(colors, by = c("Geo" = "country")) |>
ggplot() + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + xlab("") + ylab("Price Deflator") +
scale_color_identity() + add_flags +
scale_x_date(breaks = seq(1960, 2100, 5) |> paste0("-01-01") |> as.Date(),
labels = date_format("%Y")) +
scale_y_log10(breaks = 0.01*seq(-500, 200, 10))
Individual Countries
France
Table
All
Code
nama_10_a64 |>
filter(na_item == "B1G",
geo %in% c("FR"),
unit == "CP_MNAC",
time %in% c("1978", "1998", "2008", "2018")) |>
select(nace_r2, Nace_r2, time, values) |>
group_by(time) |>
mutate(values = round(100*values/ values[nace_r2 == "TOTAL"], 1)) |>
filter(nace_r2 != "TOTAL") |>
spread(time, values) |>
print_table_conditional()Manufacturing
Code
nama_10_a64 |>
filter(na_item == "B1G",
geo %in% c("FR"),
unit == "CP_MNAC",
time %in% c("1978", "1998", "2008", "2018")) |>
filter(grepl("C", nace_r2) | nace_r2 == "TOTAL") |>
select(nace_r2, Nace_r2, time, values) |>
group_by(time) |>
mutate(values = round(100*values/ values[nace_r2 == "TOTAL"], 1)) |>
filter(nace_r2 != "TOTAL") |>
spread(time, values) |>
arrange(-`2018`) |>
print_table_conditional()| nace_r2 | Nace_r2 | 1978 | 1998 | 2008 | 2018 |
|---|---|---|---|---|---|
| C | Manufacturing | 21.6 | 16.3 | 12.3 | 11.2 |
| C10-C12 | Manufacture of food products; beverages and tobacco products | 3.0 | 2.5 | 1.9 | 1.9 |
| C29_C30 | Manufacture of motor vehicles, trailers, semi-trailers and of other transport equipment | 2.3 | 1.9 | 1.5 | 1.6 |
| C31-C33 | Manufacture of furniture; jewellery, musical instruments, toys; repair and installation of machinery and equipment | 2.8 | 1.9 | 1.6 | 1.6 |
| C24_C25 | Manufacture of basic metals and fabricated metal products, except machinery and equipment | 2.2 | 2.1 | 1.7 | 1.4 |
| C33 | Repair and installation of machinery and equipment | NA | 1.4 | 1.1 | 1.3 |
| C25 | Manufacture of fabricated metal products, except machinery and equipment | NA | 1.5 | 1.2 | 1.1 |
| C30 | Manufacture of other transport equipment | NA | 0.6 | 0.7 | 1.0 |
| C22_C23 | Manufacture of rubber and plastic products and other non-metallic mineral products | 2.1 | 1.5 | 1.1 | 0.9 |
| C20 | Manufacture of chemicals and chemical products | 1.5 | 1.0 | 0.7 | 0.8 |
| C16-C18 | Manufacture of wood, paper, printing and reproduction | 1.3 | 1.1 | 0.7 | 0.6 |
| C21 | Manufacture of basic pharmaceutical products and pharmaceutical preparations | 0.6 | 0.7 | 0.7 | 0.6 |
| C28 | Manufacture of machinery and equipment n.e.c. | 1.6 | 1.0 | 0.9 | 0.6 |
| C29 | Manufacture of motor vehicles, trailers and semi-trailers | NA | 1.3 | 0.8 | 0.6 |
| C22 | Manufacture of rubber and plastic products | NA | 0.9 | 0.6 | 0.5 |
| C26 | Manufacture of computer, electronic and optical products | 1.3 | 0.9 | 0.6 | 0.5 |
| C23 | Manufacture of other non-metallic mineral products | NA | 0.6 | 0.5 | 0.4 |
| C27 | Manufacture of electrical equipment | 1.0 | 0.7 | 0.5 | 0.4 |
| C31_C32 | Manufacture of furniture; other manufacturing | NA | 0.6 | 0.4 | 0.4 |
| C13-C15 | Manufacture of textiles, wearing apparel, leather and related products | 1.8 | 0.8 | 0.4 | 0.3 |
| C17 | Manufacture of paper and paper products | NA | 0.5 | 0.3 | 0.3 |
| C24 | Manufacture of basic metals | NA | 0.6 | 0.5 | 0.3 |
| C16 | Manufacture of wood and of products of wood and cork, except furniture; manufacture of articles of straw and plaiting materials | NA | 0.3 | 0.2 | 0.2 |
| C18 | Printing and reproduction of recorded media | NA | 0.4 | 0.2 | 0.2 |
| C19 | Manufacture of coke and refined petroleum products | 0.2 | 0.1 | 0.1 | 0.1 |
Construction, Human health, Manufacturing, Real estate
All
Code
nama_10_a64 |>
filter(na_item == "B1G",
nace_r2 %in% c("C", "TOTAL", "L", "Q", "F"),
geo %in% c("FR"),
unit == "CP_MNAC") |>
year_to_date() |>
select(nace_r2, Nace_r2, date, values) |>
group_by(date) |>
mutate(values = values/ values[nace_r2 == "TOTAL"]) |>
filter(nace_r2 != "TOTAL") |>
ggplot() + geom_line(aes(x = date, y = values, color = Nace_r2)) +
theme_minimal() + xlab("") + ylab("% of GDP") +
scale_color_manual(values = viridis(5)[1:4]) +
scale_x_date(breaks = seq(1960, 2100, 5) |> paste0("-01-01") |> as.Date(),
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-500, 200, 1),
labels = percent_format(accuracy = 1)) +
theme(legend.position = c(0.75, 0.85),
legend.title = element_blank())
1995-
Code
nama_10_a64 |>
filter(na_item == "B1G",
nace_r2 %in% c("C", "TOTAL", "L", "Q", "F"),
geo %in% c("FR"),
unit == "CP_MNAC") |>
year_to_date() |>
filter(date >= as.Date("1995-01-01")) |>
select(nace_r2, Nace_r2, date, values) |>
group_by(date) |>
mutate(values = values/ values[nace_r2 == "TOTAL"]) |>
filter(nace_r2 != "TOTAL") |>
ggplot() + geom_line(aes(x = date, y = values, color = Nace_r2)) +
theme_minimal() + xlab("") + ylab("% of GDP") +
scale_color_manual(values = viridis(5)[1:4]) +
scale_x_date(breaks = seq(1960, 2100, 5) |> paste0("-01-01") |> as.Date(),
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(0, 30, 2),
labels = percent_format(accuracy = 1),
limits = c(0, 0.3)) +
theme(legend.position = c(0.75, 0.85),
legend.title = element_blank())
Germany
Table
All
Code
nama_10_a64 |>
filter(na_item == "B1G",
geo %in% c("DE"),
unit == "CP_MNAC",
time %in% c("1978", "1998", "2008", "2018")) |>
select(nace_r2, Nace_r2, time, values) |>
group_by(time) |>
mutate(values = round(100*values/ values[nace_r2 == "TOTAL"], 1)) |>
filter(nace_r2 != "TOTAL") |>
spread(time, values) |>
print_table_conditional()Manufacturing
Code
nama_10_a64 |>
filter(na_item == "B1G",
geo %in% c("DE"),
unit == "CP_MNAC",
time %in% c("1978", "1998", "2008", "2018")) |>
filter(grepl("C", nace_r2) | nace_r2 == "TOTAL") |>
select(nace_r2, Nace_r2, time, values) |>
group_by(time) |>
mutate(values = round(100*values/ values[nace_r2 == "TOTAL"], 1)) |>
filter(nace_r2 != "TOTAL") |>
spread(time, values) |>
arrange(-`2018`) |>
print_table_conditional()| nace_r2 | Nace_r2 | 1998 | 2008 | 2018 |
|---|---|---|---|---|
| C | Manufacturing | 22.2 | 22.1 | 22.2 |
| C29_C30 | Manufacture of motor vehicles, trailers, semi-trailers and of other transport equipment | 3.4 | 3.6 | 4.9 |
| C29 | Manufacture of motor vehicles, trailers and semi-trailers | 3.1 | 3.2 | 4.4 |
| C28 | Manufacture of machinery and equipment n.e.c. | 3.2 | 3.7 | 3.5 |
| C24_C25 | Manufacture of basic metals and fabricated metal products, except machinery and equipment | 2.8 | 3.1 | 2.7 |
| C25 | Manufacture of fabricated metal products, except machinery and equipment | 1.9 | 2.0 | 1.9 |
| C22_C23 | Manufacture of rubber and plastic products and other non-metallic mineral products | 2.0 | 1.7 | 1.6 |
| C10-C12 | Manufacture of food products; beverages and tobacco products | 1.9 | 1.6 | 1.5 |
| C20 | Manufacture of chemicals and chemical products | 1.8 | 1.6 | 1.5 |
| C26 | Manufacture of computer, electronic and optical products | 1.3 | 1.4 | 1.5 |
| C27 | Manufacture of electrical equipment | 1.7 | 1.6 | 1.5 |
| C31-C33 | Manufacture of furniture; jewellery, musical instruments, toys; repair and installation of machinery and equipment | 1.4 | 1.4 | 1.3 |
| C22 | Manufacture of rubber and plastic products | 1.1 | 1.0 | 1.0 |
| C16-C18 | Manufacture of wood, paper, printing and reproduction | 1.5 | 1.1 | 0.8 |
| C24 | Manufacture of basic metals | 0.9 | 1.1 | 0.8 |
| C31_C32 | Manufacture of furniture; other manufacturing | 0.9 | 0.8 | 0.8 |
| C21 | Manufacture of basic pharmaceutical products and pharmaceutical preparations | 0.6 | 0.9 | 0.7 |
| C23 | Manufacture of other non-metallic mineral products | 0.9 | 0.6 | 0.6 |
| C30 | Manufacture of other transport equipment | 0.3 | 0.4 | 0.5 |
| C33 | Repair and installation of machinery and equipment | 0.5 | 0.6 | 0.5 |
| C17 | Manufacture of paper and paper products | 0.5 | 0.4 | 0.4 |
| C19 | Manufacture of coke and refined petroleum products | 0.2 | 0.1 | 0.4 |
| C13-C15 | Manufacture of textiles, wearing apparel, leather and related products | 0.5 | 0.3 | 0.3 |
| C16 | Manufacture of wood and of products of wood and cork, except furniture; manufacture of articles of straw and plaiting materials | 0.4 | 0.3 | 0.2 |
| C18 | Printing and reproduction of recorded media | 0.6 | 0.4 | 0.2 |
Construction, Human health, Manufacturing, Real estate
Code
nama_10_a64 |>
filter(na_item == "B1G",
nace_r2 %in% c("C", "TOTAL", "L", "Q", "F"),
geo %in% c("DE"),
unit == "CP_MNAC") |>
year_to_date() |>
select(nace_r2, Nace_r2, date, values) |>
group_by(date) |>
mutate(values = values/ values[nace_r2 == "TOTAL"]) |>
filter(nace_r2 != "TOTAL") |>
ggplot() + geom_line(aes(x = date, y = values, color = Nace_r2)) +
theme_minimal() + xlab("") + ylab("% of GDP") +
scale_color_manual(values = viridis(5)[1:4]) +
scale_x_date(breaks = seq(1960, 2100, 5) |> paste0("-01-01") |> as.Date(),
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(0, 30, 2),
labels = percent_format(accuracy = 1),
limits = c(0, 0.3)) +
theme(legend.position = c(0.75, 0.85),
legend.title = element_blank())
Italy
Code
nama_10_a64 |>
filter(na_item == "B1G",
nace_r2 %in% c("C", "TOTAL", "L", "Q", "F"),
geo %in% c("IT"),
unit == "CP_MNAC") |>
year_to_date() |>
select(nace_r2, Nace_r2, date, values) |>
group_by(date) |>
mutate(values = values/ values[nace_r2 == "TOTAL"]) |>
filter(nace_r2 != "TOTAL") |>
ggplot() + geom_line(aes(x = date, y = values, color = Nace_r2)) +
theme_minimal() + xlab("") + ylab("% of GDP") +
scale_color_manual(values = viridis(5)[1:4]) +
scale_x_date(breaks = seq(1960, 2100, 5) |> paste0("-01-01") |> as.Date(),
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-500, 200, 1),
labels = percent_format(accuracy = 1)) +
theme(legend.position = c(0.75, 0.85),
legend.title = element_blank())
Spain
Code
nama_10_a64 |>
filter(na_item == "B1G",
nace_r2 %in% c("C", "TOTAL", "L", "Q", "F"),
geo %in% c("ES"),
unit == "CP_MNAC") |>
year_to_date() |>
select(nace_r2, Nace_r2, date, values) |>
group_by(date) |>
mutate(values = values/ values[nace_r2 == "TOTAL"]) |>
filter(nace_r2 != "TOTAL") |>
ggplot() + geom_line(aes(x = date, y = values, color = Nace_r2)) +
theme_minimal() + xlab("") + ylab("% of GDP") +
scale_color_manual(values = viridis(5)[1:4]) +
scale_x_date(breaks = seq(1960, 2100, 5) |> paste0("-01-01") |> as.Date(),
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-500, 200, 1),
labels = percent_format(accuracy = 1)) +
theme(legend.position = c(0.75, 0.85),
legend.title = element_blank())
Netherlands
Code
nama_10_a64 |>
filter(na_item == "B1G",
nace_r2 %in% c("C", "TOTAL", "L", "Q", "F"),
geo %in% c("NL"),
unit == "CP_MNAC") |>
year_to_date() |>
select(nace_r2, Nace_r2, date, values) |>
group_by(date) |>
mutate(values = values/ values[nace_r2 == "TOTAL"]) |>
filter(nace_r2 != "TOTAL") |>
ggplot() + geom_line(aes(x = date, y = values, color = Nace_r2)) +
theme_minimal() + xlab("") + ylab("% of GDP") +
scale_color_manual(values = viridis(5)[1:4]) +
scale_x_date(breaks = seq(1960, 2100, 5) |> paste0("-01-01") |> as.Date(),
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-500, 200, 1),
labels = percent_format(accuracy = 1)) +
theme(legend.position = c(0.75, 0.85),
legend.title = element_blank())
Danemark
Code
nama_10_a64 |>
filter(na_item == "B1G",
nace_r2 %in% c("C", "TOTAL", "L", "Q", "F"),
geo %in% c("DK"),
unit == "CP_MNAC") |>
year_to_date() |>
select(nace_r2, Nace_r2, date, values) |>
group_by(date) |>
mutate(values = values/ values[nace_r2 == "TOTAL"]) |>
filter(nace_r2 != "TOTAL") |>
ggplot() + geom_line(aes(x = date, y = values, color = Nace_r2)) +
theme_minimal() + xlab("") + ylab("% of GDP") +
scale_color_manual(values = viridis(5)[1:4]) +
scale_x_date(breaks = seq(1960, 2100, 5) |> paste0("-01-01") |> as.Date(),
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-500, 200, 1),
labels = percent_format(accuracy = 1)) +
theme(legend.position = c(0.75, 0.85),
legend.title = element_blank())
Belgium
Code
nama_10_a64 |>
filter(na_item == "B1G",
nace_r2 %in% c("C", "TOTAL", "L", "Q", "F"),
geo %in% c("BE"),
unit == "CP_MNAC") |>
year_to_date() |>
select(nace_r2, Nace_r2, date, values) |>
group_by(date) |>
mutate(values = values/ values[nace_r2 == "TOTAL"]) |>
filter(nace_r2 != "TOTAL") |>
ggplot() + geom_line(aes(x = date, y = values, color = Nace_r2)) +
theme_minimal() + xlab("") + ylab("% of GDP") +
scale_color_manual(values = viridis(5)[1:4]) +
scale_x_date(breaks = seq(1960, 2100, 5) |> paste0("-01-01") |> as.Date(),
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-500, 200, 1),
labels = percent_format(accuracy = 1)) +
theme(legend.position = c(0.75, 0.85),
legend.title = element_blank())
Finland
Code
nama_10_a64 |>
filter(na_item == "B1G",
nace_r2 %in% c("C", "TOTAL", "L", "Q", "F"),
geo %in% c("FI"),
unit == "CP_MNAC") |>
year_to_date() |>
select(nace_r2, Nace_r2, date, values) |>
group_by(date) |>
mutate(values = values/ values[nace_r2 == "TOTAL"]) |>
filter(nace_r2 != "TOTAL") |>
ggplot() + geom_line(aes(x = date, y = values, color = Nace_r2)) +
theme_minimal() + xlab("") + ylab("% of GDP") +
scale_color_manual(values = viridis(5)[1:4]) +
scale_x_date(breaks = seq(1960, 2100, 5) |> paste0("-01-01") |> as.Date(),
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-500, 200, 1),
labels = percent_format(accuracy = 1)) +
theme(legend.position = c(0.75, 0.85),
legend.title = element_blank())
Portugal
Code
nama_10_a64 |>
filter(na_item == "B1G",
nace_r2 %in% c("C", "TOTAL", "L", "Q", "F"),
geo %in% c("PT"),
unit == "CP_MNAC") |>
year_to_date() |>
select(nace_r2, Nace_r2, date, values) |>
group_by(date) |>
mutate(values = values/ values[nace_r2 == "TOTAL"]) |>
filter(nace_r2 != "TOTAL") |>
ggplot() + geom_line(aes(x = date, y = values, color = Nace_r2)) +
theme_minimal() + xlab("") + ylab("% of GDP") +
scale_color_manual(values = viridis(5)[1:4]) +
scale_x_date(breaks = seq(1960, 2100, 5) |> paste0("-01-01") |> as.Date(),
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-500, 200, 1),
labels = percent_format(accuracy = 1)) +
theme(legend.position = c(0.75, 0.85),
legend.title = element_blank())
Austria
Code
nama_10_a64 |>
filter(na_item == "B1G",
nace_r2 %in% c("C", "TOTAL", "L", "Q", "F"),
geo %in% c("AT"),
unit == "CP_MNAC") |>
year_to_date() |>
select(nace_r2, Nace_r2, date, values) |>
group_by(date) |>
mutate(values = values/ values[nace_r2 == "TOTAL"]) |>
filter(nace_r2 != "TOTAL") |>
ggplot() + geom_line(aes(x = date, y = values, color = Nace_r2)) +
theme_minimal() + xlab("") + ylab("% of GDP") +
scale_color_manual(values = viridis(5)[1:4]) +
scale_x_date(breaks = seq(1960, 2100, 5) |> paste0("-01-01") |> as.Date(),
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-500, 200, 1),
labels = percent_format(accuracy = 1)) +
theme(legend.position = c(0.75, 0.85),
legend.title = element_blank())
Sweden
Code
nama_10_a64 |>
filter(na_item == "B1G",
nace_r2 %in% c("C", "TOTAL", "L", "Q", "F"),
geo %in% c("SE"),
unit == "CP_MNAC") |>
year_to_date() |>
select(nace_r2, Nace_r2, date, values) |>
group_by(date) |>
mutate(values = values/ values[nace_r2 == "TOTAL"]) |>
filter(nace_r2 != "TOTAL") |>
ggplot() + geom_line(aes(x = date, y = values, color = Nace_r2)) +
theme_minimal() + xlab("") + ylab("% of GDP") +
scale_color_manual(values = viridis(5)[1:4]) +
scale_x_date(breaks = seq(1960, 2100, 5) |> paste0("-01-01") |> as.Date(),
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-500, 200, 1),
labels = percent_format(accuracy = 1)) +
theme(legend.position = c(0.75, 0.85),
legend.title = element_blank())
Iceland
Code
nama_10_a64 |>
filter(na_item == "B1G",
nace_r2 %in% c("C", "TOTAL", "L", "Q", "F"),
geo %in% c("IS"),
unit == "CP_MNAC") |>
year_to_date() |>
select(nace_r2, Nace_r2, date, values) |>
group_by(date) |>
mutate(values = values/ values[nace_r2 == "TOTAL"]) |>
filter(nace_r2 != "TOTAL") |>
ggplot() + geom_line(aes(x = date, y = values, color = Nace_r2)) +
theme_minimal() + xlab("") + ylab("% of GDP") +
scale_color_manual(values = viridis(5)[1:4]) +
scale_x_date(breaks = seq(1960, 2100, 5) |> paste0("-01-01") |> as.Date(),
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-500, 200, 1),
labels = percent_format(accuracy = 1)) +
theme(legend.position = c(0.75, 0.85),
legend.title = element_blank())
Relative to EA Manufacturing Value Added
1995-
Code
nama_10_a64 |>
filter(na_item == "B1G",
nace_r2 == "C",
geo %in% c("EA20", "FR", "DE", "IT", "ES", "NL", "AT", "FI"),
unit == "CP_MNAC") |>
year_to_date() |>
filter(date >= as.Date("1995-01-01")) |>
#filter(date <= as.Date("2019-01-01")) %>%
group_by(date) |>
filter(n() == 8) |>
mutate(values = values /values[geo == "EA20"]) |>
filter(geo != "EA20") |>
group_by(geo) |>
mutate(values = 100*values / values[1]) |>
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")
All-
Code
nama_10_a64 |>
filter(na_item == "B1G",
nace_r2 == "C",
unit == "CP_MNAC",
!(geo %in% c("BG", "RO", "RS"))) |>
year_to_date() |>
filter(date >= as.Date("1995-01-01"),
date <= as.Date("2023-01-01")) |>
group_by(geo) |>
filter(n() == 29) |>
group_by(date) |>
filter(!is.na(values)) |>
mutate(values = values /values[geo == "EA20"]) |>
filter(geo != "EA20") |>
group_by(geo) |>
mutate(values = 100*values / values[1]) |>
left_join(colors, by = c("Geo" = "country")) |>
ggplot() + geom_line(aes(x = date, y = values, color = color, group = geo)) +
theme_minimal() + xlab("") + ylab("Valeur ajoutée manuf. par rapport à la Zone €") +
scale_color_identity() +
scale_x_date(breaks = seq(1960, 2100, 5) |> paste0("-01-01") |> as.Date(),
labels = date_format("%Y")) +
scale_y_log10(breaks = c(seq(0, 1000, 100), seq(10, 100, 10))) +
theme(legend.position = "none") +
geom_label_repel(data = . %>% group_by(geo)
%>% filter(date %in% c(max(date), min(date))),
aes(x = date, y = values, label = geo, color = color)) +
geom_line(data = . %>% filter(geo == "FR"),
aes(x = date, y = values, color = color), size = 2) +
geom_line(data = . %>% filter(geo == "EL"),
aes(x = date, y = values, color = color), size = 2)
2000-2019
Code
nama_10_a64 |>
filter(na_item == "B1G",
nace_r2 == "C",
geo %in% c("EA20", "FR", "DE", "IT", "ES", "NL", "AT", "FI"),
unit == "CP_MNAC") |>
year_to_date() |>
filter(date >= as.Date("2000-01-01"),
date <= as.Date("2019-01-01")) |>
group_by(date) |>
mutate(values = values /values[geo == "EA20"]) |>
filter(geo != "EA20") |>
group_by(geo) |>
mutate(values = 100*values / values[1]) |>
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")
2000-2018 + Grèce
Code
nama_10_a64 |>
filter(na_item == "B1G",
nace_r2 == "C",
geo %in% c("EA20", "FR", "DE", "IT", "EL", "NL", "AT", "FI"),
unit == "CP_MNAC") |>
year_to_date() |>
filter(date >= as.Date("2000-01-01")) |>
filter(date <= as.Date("2018-01-01")) |>
group_by(date) |>
mutate(values = values /values[geo == "EA20"]) |>
filter(geo != "EA20") |>
group_by(geo) |>
mutate(values = 100*values / values[1]) |>
ggplot() + geom_line(aes(x = date, y = values, color = Geo)) +
theme_minimal() + xlab("") + ylab("Valeur ajoutée manuf. par rapport à la Zone €") +
scale_color_manual(values = c("#ED2939", "#003580", "#002395", "#000000",
"#0D5EAF", "#009246", "#AE1C28")) +
scale_x_date(breaks = seq(1960, 2100, 1) |> paste0("-01-01") |> as.Date(),
labels = date_format("%Y")) +
geom_image(data = . %>%
filter(date == as.Date("2012-01-01")) %>%
mutate(image = paste0("../../icon/flag/round/", str_to_lower(Geo), ".png")),
aes(x = date, y = values, image = image), asp = 1.5) +
scale_y_continuous(breaks = seq(0, 200, 5)) +
theme(legend.position = "none")
Industry
1995-2018
Code
nama_10_a64 |>
filter(na_item == "B1G",
nace_r2 == "B-E",
geo %in% c("EA20", "FR", "DE", "IT", "ES", "NL", "AT", "FI"),
unit == "CP_MNAC") |>
year_to_date() |>
filter(date >= as.Date("1995-01-01"),
date <= as.Date("2019-01-01")) |>
group_by(date) |>
mutate(values = values /values[geo == "EA20"]) |>
filter(geo != "EA20") |>
group_by(geo) |>
mutate(values = 100*values/values[1]) |>
ggplot() + geom_line(aes(x = date, y = values, color = Geo)) +
theme_minimal() + xlab("") + ylab("Valeur ajoutée manuf. par rapport à la Zone €") +
scale_color_manual(values = c("#ED2939", "#003580", "#002395", "#000000",
"#009246", "#AE1C28", "#FFC400")) +
scale_x_date(breaks = seq(1960, 2100, 5) |> paste0("-01-01") |> as.Date(),
labels = date_format("%Y")) +
geom_image(data = . %>%
filter(date == as.Date("2008-01-01")) %>%
mutate(image = paste0("../../icon/flag/round/", str_to_lower(Geo), ".png")),
aes(x = date, y = values, image = image), asp = 1.5) +
scale_y_continuous(breaks = seq(0, 200, 5)) +
theme(legend.position = "none")
2000-2018
Code
nama_10_a64 |>
filter(na_item == "B1G",
nace_r2 == "B-E",
geo %in% c("EA20", "FR", "DE", "IT", "ES", "NL", "AT", "FI"),
unit == "CP_MNAC") |>
year_to_date() |>
filter(date >= as.Date("2000-01-01")) |>
#filter(date <= as.Date("2018-01-01")) %>%
group_by(date) |>
filter(n() == 8) |>
mutate(values = values /values[geo == "EA20"]) |>
filter(geo != "EA20") |>
group_by(geo) |>
mutate(values = 100*values/values[1]) |>
ggplot() + geom_line(aes(x = date, y = values, color = Geo)) +
theme_minimal() + xlab("") + ylab("Valeur ajoutée manuf. par rapport à la Zone €") +
scale_color_manual(values = c("#ED2939", "#003580", "#002395", "#000000",
"#009246", "#AE1C28", "#FFC400")) +
geom_image(data = . %>%
filter(date == as.Date("2016-01-01")) %>%
mutate(image = paste0("../../icon/flag/round/", str_to_lower(Geo), ".png")),
aes(x = date, y = values, image = image), asp = 1.5) +
scale_x_date(breaks = seq(1960, 2100, 2) |> paste0("-01-01") |> as.Date(),
labels = date_format("%Y")) +
scale_y_continuous(breaks = seq(0, 200, 5)) +
theme(legend.position = "none")