GDP and main components (output, expenditure and income) - namq_10_gdp

Data - Eurostat

Info

Last observation: 2026Q2 (N = 6616)

First observation: 1978Q1 (N = 1017)

Last data update: 14 aoû 2026, 20:18. Last compile: 18 aoû 2026, 02:54

Structure

freq

unit

s_adj

na_item

geo

Live

Last GDP Numbers

Instantaneous

namq_10_gdp |>
  filter(time %in% c("2021Q2", "2021Q1", "2019Q4", "2017Q2"),
         na_item == "B1GQ",
         # SCA: Seasonally and calendar adjusted data
         s_adj == "SCA",
         # CLV10_MEUR: Chain linked volumes (2010), million euro
         unit == "CLV10_MEUR") |>
  
  select(geo, Geo, time, values) |>
  spread(time, values) |>
  mutate(`2019Q4-2021Q2` = round(100*(`2021Q2`/`2019Q4`-1), 2)) |>
  mutate(Flag = gsub(" ", "-", str_to_lower(Geo)),
         Flag = paste0('<img src="../../icon/flag/vsmall/', Flag, '.png" alt="Flag">')) |>
  select(Flag, everything()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

Average

namq_10_gdp |>
  filter(na_item == "B1GQ",
         # SCA: Seasonally and calendar adjusted data
         s_adj == "SCA",
         # CLV10_MEUR: Chain linked volumes (2010), million euro
         unit == "CLV10_MEUR") |>
  quarter_to_date() |>
  filter(date >= as.Date("2019-10-01")) |>
  group_by(geo) |>
  arrange(date) |>
  mutate(values = 100*values/values[date == as.Date("2019-10-01")],
         values = cumsum(values) / seq_along(values)) |>
  group_by(geo) %>%
  do(tail(., 1)) |>
  
  select(geo, Geo, date, values) |>
  arrange(values) |>
  mutate(Flag = gsub(" ", "-", str_to_lower(Geo)),
         Flag = paste0('<img src="../../icon/flag/vsmall/', Flag, '.png" alt="Flag">')) |>
  select(Flag, everything()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

Germany, Italy, France, Spain

Table

namq_10_gdp |>
  filter(na_item == "B1GQ",
         s_adj == "SCA",
         unit == "CLV10_MEUR",
         geo %in% c("DE", "IT", "ES", "FR")) |>
  quarter_to_date() |>
  arrange(date) |>
  filter(date == as.Date("2019-10-01") | date == last(date)) |>
  group_by(geo) |>
  mutate(values = 100*values/values[1]) |>
  
  select(date, geo, Geo, values) |>
  print_table_conditional()

date	geo	Geo	values
2019-10-01	DE	Germany	100.0000
2019-10-01	ES	Spain	100.0000
2019-10-01	FR	France	100.0000
2019-10-01	IT	Italy	100.0000
2026-04-01	DE	Germany	101.7521
2026-04-01	ES	Spain	112.1175
2026-04-01	FR	France	106.4589
2026-04-01	IT	Italy	107.6752

Graph

data <- namq_10_gdp |>
  filter(na_item == "B1GQ",
         # SCA: Seasonally and calendar adjusted data
         s_adj == "SCA",
         # CLV10_MEUR: Chain linked volumes (2010), million euro
         unit == "CLV10_MEUR",
         geo %in% c("DE", "IT", "ES", "FR")) |>
  quarter_to_date() |>
  filter(date >= as.Date("2019-10-01")) |>
  group_by(geo) |>
  arrange(date) |>
  mutate(values = 100*values/values[1]) |>
  
  left_join(colors, by = c("Geo" = "country")) |>
  mutate(date = zoo::as.yearqtr(paste0(year(date), " Q", quarter(date))))
data |>
  ggplot() + geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + theme_minimal() + xlab("") + ylab("") + add_flags +
  zoo::scale_x_yearqtr(labels = date_format("%Y Q%q"),
                       breaks = seq(min(data$date), max(data$date), by = 0.25)) +
  theme(legend.position = c(0.35, 0.85),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  scale_y_log10(breaks = seq(10, 300, 5))

Germany, France, Europe

2017T2-

namq_10_gdp |>
  filter(na_item == "B1GQ",
         # SCA: Seasonally and calendar adjusted data
         s_adj == "SCA",
         # CLV10_MEUR: Chain linked volumes (2010), million euro
         unit == "CLV10_MEUR",
         geo %in% c("DE", "EA", "FR")) |>
  quarter_to_date() |>
  filter(date >= as.Date("2017-04-01")) |>
  group_by(geo) |>
  mutate(values = 100*values/values[date == as.Date("2017-04-01")]) |>
  
  mutate(Geo = ifelse(geo == "EA", "Europe", Geo)) |>
  left_join(colors, by = c("Geo" = "country")) |>
  mutate(date = zoo::as.yearqtr(paste0(year(date), " Q", quarter(date)))) |>
  ggplot() + geom_line(aes(x = date, y = values, color = color)) + add_flags +
  scale_color_identity() + theme_minimal() + xlab("") + ylab("PIB réel (Base 100 = 2019T4)") +
  zoo::scale_x_yearqtr(labels = date_format("%Y Q%q"),
                       breaks = seq(zoo::as.yearqtr("2017 Q2"), zoo::as.yearqtr("2100 Q1"), by = 0.25)) +
  theme(legend.position = c(0.35, 0.85),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  scale_y_log10(breaks = seq(10, 300, 2))

2019-

namq_10_gdp |>
  filter(na_item == "B1GQ",
         # SCA: Seasonally and calendar adjusted data
         s_adj == "SCA",
         # CLV10_MEUR: Chain linked volumes (2010), million euro
         unit == "CLV10_MEUR",
         geo %in% c("DE", "EA", "FR")) |>
  quarter_to_date() |>
  filter(date >= as.Date("2019-10-01")) |>
  group_by(geo) |>
  mutate(values = 100*values/values[date == as.Date("2019-10-01")]) |>
  
  mutate(Geo = ifelse(geo == "EA", "Europe", Geo)) |>
  left_join(colors, by = c("Geo" = "country")) |>
  mutate(date = zoo::as.yearqtr(paste0(year(date), " Q", quarter(date)))) |>
  ggplot() + geom_line(aes(x = date, y = values, color = color)) + add_flags +
  scale_color_identity() + theme_minimal() + xlab("") + ylab("PIB réel (Base 100 = 2019T4)") +
  zoo::scale_x_yearqtr(labels = date_format("%Y Q%q"),
                       breaks = seq(zoo::as.yearqtr("2019 Q4"), zoo::as.yearqtr("2100 Q1"), by = 0.25)) +
  theme(legend.position = c(0.35, 0.85),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  scale_y_log10(breaks = seq(10, 300, 2))

2022-

namq_10_gdp |>
  filter(na_item == "B1GQ",
         # SCA: Seasonally and calendar adjusted data
         s_adj == "SCA",
         # CLV10_MEUR: Chain linked volumes (2010), million euro
         unit == "CLV10_MEUR",
         geo %in% c("DE", "EA", "FR")) |>
  quarter_to_date() |>
  filter(date >= as.Date("2021-10-01")) |>
  group_by(geo) |>
  mutate(values = 100*values/values[date == as.Date("2021-10-01")]) |>
  
  mutate(Geo = ifelse(geo == "EA", "Europe", Geo)) |>
  left_join(colors, by = c("Geo" = "country")) |>
  mutate(date = zoo::as.yearqtr(paste0(year(date), " Q", quarter(date)))) |>
  ggplot() + geom_line(aes(x = date, y = values, color = color)) + add_flags +
  scale_color_identity() + theme_minimal() + xlab("") + ylab("PIB réel (Base 100 = 2019T4)") +
  zoo::scale_x_yearqtr(labels = date_format("%Y Q%q"),
                       breaks = seq(zoo::as.yearqtr("2019 Q4"), zoo::as.yearqtr("2100 Q1"), by = 0.25)) +
  theme(legend.position = c(0.35, 0.85),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  scale_y_log10(breaks = seq(10, 300, 1))

Average

namq_10_gdp |>
  filter(na_item == "B1GQ",
         # SCA: Seasonally and calendar adjusted data
         s_adj == "SCA",
         # CLV10_MEUR: Chain linked volumes (2010), million euro
         unit == "CLV10_MEUR",
         geo %in% c("DE", "EA", "FR")) |>
  quarter_to_date() |>
  filter(date >= as.Date("2019-10-01")) |>
  group_by(geo) |>
  arrange(date) |>
  mutate(values = 100*values/values[date == as.Date("2019-10-01")],
         values = cumsum(values) / seq_along(values)) |>
  
  mutate(Geo = ifelse(geo == "EA", "Europe", Geo)) |>
  left_join(colors, by = c("Geo" = "country")) |>
  mutate(date = zoo::as.yearqtr(paste0(year(date), " Q", quarter(date)))) |>
  ggplot() + geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + theme_minimal() + xlab("") + ylab("PIB réel moyen depuis le début du Covid-19") +
  zoo::scale_x_yearqtr(labels = date_format("%Y Q%q"),
                       breaks = seq(zoo::as.yearqtr("2019 Q4"), zoo::as.yearqtr("2100 Q1"), by = 0.25)) +
  add_flags +
  theme(legend.position = c(0.35, 0.85),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  scale_y_log10(breaks = seq(10, 300, 2))

Average 2

namq_10_gdp |>
  filter(na_item == "B1GQ",
         # SCA: Seasonally and calendar adjusted data
         s_adj == "SCA",
         # CLV10_MEUR: Chain linked volumes (2010), million euro
         unit == "CLV10_MEUR",
         geo %in% c("DE", "EA", "FR", "EL", "BE", "CH", "IT", "ES")) |>
  quarter_to_date() |>
  filter(date >= as.Date("2019-10-01")) |>
  group_by(geo) |>
  arrange(date) |>
  mutate(values = 100*values/values[date == as.Date("2019-10-01")],
         values = cumsum(values) / seq_along(values)) |>
  
  mutate(Geo = ifelse(geo == "EA", "Europe", Geo)) |>
  left_join(colors, by = c("Geo" = "country")) |>
  mutate(date = zoo::as.yearqtr(paste0(year(date), " Q", quarter(date)))) |>
  ggplot() + geom_line(aes(x = date, y = values, color = color)) + add_flags + 
  scale_color_identity() + theme_minimal() + xlab("") + ylab("PIB réel moyen depuis le début du Covid-19") +
  zoo::scale_x_yearqtr(labels = date_format("%Y Q%q"),
                       breaks = seq(zoo::as.yearqtr("2019 Q4"), zoo::as.yearqtr("2100 Q1"), by = 0.25)) +
  theme(legend.position = c(0.35, 0.85),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  scale_y_log10(breaks = seq(10, 300, 2))

Deflators

Shall we compare deflators to inflation ? (do a stacked graph with both)

GDP

Table, PD_PCH_SM_EUR

namq_10_gdp |>
  filter(na_item == "B1GQ",
         s_adj == "NSA",
         unit == "PD_PCH_SM_EUR",
         time %in% c("2022Q1", "2022Q2", "2022Q3","2022Q4",  max(time))) |>
  select(time, na_item, geo, Geo, values) |>
  
  spread(time, values) |>
  arrange(-`2022Q4`) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

Table, PD15_EUR

namq_10_gdp |>
  filter(na_item == "B1GQ",
         s_adj == "NSA",
         unit == "PD15_EUR",
         time %in% c("2022Q1", "2022Q2", "2022Q3","2022Q4",  max(time))) |>
  select(time, na_item, geo, Geo, values) |>
  
  spread(time, values) |>
  arrange(-`2022Q4`) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

Germany, France

GDP

All

namq_10_gdp |>
  filter(na_item == "B1GQ",
         s_adj == "NSA",
         unit == "PD15_EUR",
         geo %in% c("FR", "DE")) |>
  quarter_to_date() |>
  filter(date >= as.Date("1996-01-01")) |>
  
  mutate(Geo= ifelse(geo == "EA", "Europe", Geo)) |>
  left_join(colors, by = c("Geo" = "country")) |>
  group_by(Geo) |>
  mutate(values = 100*values/values[1]) |>
  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, 2), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = "none",
        legend.title = element_blank()) +
  xlab("") + ylab("GDP Deflator") + 
  scale_y_log10(breaks = seq(90, 200, 5)) +
  geom_label_repel(data = . %>% filter(date == max(date)),
                   aes(x = date, y = values, label = round(values, 1), color = color))

1999-

namq_10_gdp |>
  filter(na_item == "B1GQ",
         s_adj == "NSA",
         unit == "PD15_EUR",
         geo %in% c("FR", "DE")) |>
  quarter_to_date() |>
  filter(date >= as.Date("1999-01-01")) |>
  
  mutate(Geo= ifelse(geo == "EA", "Europe", Geo)) |>
  left_join(colors, by = c("Geo" = "country")) |>
  group_by(Geo) |>
  mutate(values = 100*values/values[date == as.Date("1999-01-01")]) |>
  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, 2), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = "none",
        legend.title = element_blank()) +
  xlab("") + ylab("GDP Deflator") + 
  scale_y_log10(breaks = seq(90, 200, 5)) +
  geom_label_repel(data = . %>% filter(date == max(date)),
                   aes(x = date, y = values, label = round(values, 1), color = color))

2018-

namq_10_gdp |>
  filter(na_item == "B1GQ",
         s_adj == "NSA",
         unit == "PD15_EUR",
         geo %in% c("FR", "DE")) |>
  quarter_to_date() |>
  filter(date >= as.Date("2018-01-01")) |>
  
  mutate(Geo= ifelse(geo == "EA", "Europe", Geo)) |>
  left_join(colors, by = c("Geo" = "country")) |>
  group_by(Geo) |>
  mutate(values = 100*values/values[date == as.Date("2018-01-01")]) |>
  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, 1), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = "none",
        legend.title = element_blank()) +
  xlab("") + ylab("GDP Deflator") + 
  scale_y_log10(breaks = seq(90, 200, 5)) +
  geom_label(data = . %>% filter(date == max(date)),
                   aes(x = date, y = values, label = round(values, 1), color = color))

Consumption

All

namq_10_gdp |>
  filter(na_item == "P3",
         s_adj == "NSA",
         unit == "PD15_EUR",
         geo %in% c("FR", "DE")) |>
  quarter_to_date() |>
  filter(date >= as.Date("1996-01-01")) |>
  
  mutate(Geo= ifelse(geo == "EA", "Europe", Geo)) |>
  left_join(colors, by = c("Geo" = "country")) |>
  group_by(Geo) |>
  mutate(values = 100*values/values[1]) |>
  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, 2), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = "none",
        legend.title = element_blank()) +
  xlab("") + ylab("Consumption Deflator") + 
  scale_y_log10(breaks = seq(90, 200, 5)) +
  geom_label_repel(data = . %>% filter(date == max(date)),
                   aes(x = date, y = values, label = round(values, 1), color = color))

1999-

namq_10_gdp |>
  filter(na_item == "P3",
         s_adj == "NSA",
         unit == "PD15_EUR",
         geo %in% c("FR", "DE")) |>
  quarter_to_date() |>
  filter(date >= as.Date("1999-01-01")) |>
  
  mutate(Geo= ifelse(geo == "EA", "Europe", Geo)) |>
  left_join(colors, by = c("Geo" = "country")) |>
  group_by(Geo) |>
  mutate(values = 100*values/values[date == as.Date("1999-01-01")]) |>
  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, 2), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = "none",
        legend.title = element_blank()) +
  xlab("") + ylab("Consumption Deflator") + 
  scale_y_log10(breaks = seq(90, 200, 5)) +
  geom_label_repel(data = . %>% filter(date == max(date)),
                   aes(x = date, y = values, label = round(values, 1), color = color))

GDP, Consumption

namq_10_gdp |>
  filter(na_item %in% c("P3", "B1GQ"),
         s_adj == "NSA",
         unit == "PD15_EUR",
         geo %in% c("FR", "DE")) |>
  quarter_to_date() |>
  filter(date >= as.Date("1996-01-01")) |>
  
  
  mutate(Geo= ifelse(geo == "EA", "Europe", Geo)) |>
  left_join(colors, by = c("Geo" = "country")) |>
  group_by(Geo) |>
  mutate(values = 100*values/values[1]) |>
  ggplot() + geom_line(aes(x = date, y = values, color = color, linetype = Na_item)) +
  scale_color_identity() + theme_minimal()  + add_flags +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 2), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = "none",
        legend.title = element_blank()) +
  xlab("") + ylab("GDP, Consumption Deflator") + 
  scale_y_log10(breaks = seq(90, 200, 5)) +
  geom_label_repel(data = . %>% filter(date == max(date)),
                   aes(x = date, y = values, label = round(values, 1), color = color, linetype = Na_item))

Greece, Portugal, France, Germany

2021-

namq_10_gdp |>
  filter(na_item == "B1GQ",
         s_adj == "NSA",
         unit == "PD15_EUR",
         geo %in% c("FR", "DE", "EL", "PT")) |>
  quarter_to_date() |>
  filter(date >= as.Date("2021-01-01")) |>
  
  mutate(Geo= ifelse(geo == "EA", "Europe", Geo)) |>
  left_join(colors, by = c("Geo" = "country")) |>
  group_by(Geo) |>
  mutate(values = 100*values/values[date == as.Date("2021-01-01")]) |>
  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, 1), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = "none",
        legend.title = element_blank()) +
  xlab("") + ylab("GDP Deflator") + 
  scale_y_log10(breaks = seq(90, 200, 2)) +
  geom_label_repel(data = . %>% filter(date == max(date)),
                   aes(x = date, y = values, label = round(values, 1), color = color))

Germany, France, Euro Area, Italy, Spain

PD_PCH_SM_EUR

1996-

namq_10_gdp |>
  filter(na_item == "B1GQ",
         s_adj == "NSA",
         unit == "PD_PCH_SM_EUR",
         geo %in% c("FR", "DE", "EA", "IT")) |>
  transmute(time, na_item, geo, Geo, values = values/100) |>
  quarter_to_date() |>
  filter(date >= as.Date("1996-01-01")) |>
  
  mutate(Geo= ifelse(geo == "EA", "Europe", Geo)) |>
  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, 2), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = "none",
        legend.title = element_blank()) +
  xlab("") + ylab("Net Exports (% of GDP)") +
  scale_y_continuous(breaks = 0.01*seq(-30, 30, 1),
                labels = percent_format(a = 1)) + 
  geom_hline(yintercept = 0, linetype = "dashed",  color = "black")

2010-

namq_10_gdp |>
  filter(na_item == "B1GQ",
         s_adj == "NSA",
         unit == "PD_PCH_SM_EUR",
         geo %in% c("FR", "DE", "EA", "IT")) |>
  transmute(time, na_item, geo, Geo, values = values/100) |>
  quarter_to_date() |>
  filter(date >= as.Date("2010-01-01")) |>
  
  mutate(Geo= ifelse(geo == "EA", "Europe", Geo)) |>
  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, 2), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = "none",
        legend.title = element_blank()) +
  xlab("") + ylab("GDP Deflator, Annual % change") +
  scale_y_continuous(breaks = 0.01*seq(-30, 30, 1),
                labels = percent_format(a = 1)) + 
  geom_hline(yintercept = 0, linetype = "dashed",  color = "black")

2018-

GDP

namq_10_gdp |>
  filter(na_item == "B1GQ",
         s_adj == "NSA",
         unit == "PD_PCH_SM_EUR",
         geo %in% c("FR", "DE", "EA", "IT", "ES")) |>
  transmute(time, na_item, geo, Geo, values = values/100) |>
  quarter_to_date() |>
  filter(date >= as.Date("2018-01-01")) |>
  
  mutate(Geo= ifelse(geo == "EA", "Europe", Geo)) |>
  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, 1), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = "none",
        legend.title = element_blank()) +
  xlab("") + ylab("GDP Deflator, Annual % change") +
  scale_y_continuous(breaks = 0.01*seq(-30, 30, 1),
                labels = percent_format(a = 1)) + 
  geom_hline(yintercept = 0, linetype = "dashed",  color = "black")

P3 Consumption Deflator

namq_10_gdp |>
  filter(na_item == "P3",
         s_adj == "NSA",
         unit == "PD_PCH_SM_EUR",
         geo %in% c("FR", "DE", "EA", "IT", "ES")) |>
  transmute(time, na_item, geo, Geo, values = values/100) |>
  quarter_to_date() |>
  filter(date >= as.Date("2018-01-01")) |>
  
  mutate(Geo= ifelse(geo == "EA", "Europe", Geo)) |>
  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, 1), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = "none",
        legend.title = element_blank()) +
  xlab("") + ylab("Consumption Deflator, Annual % change") +
  scale_y_continuous(breaks = 0.01*seq(-30, 30, 1),
                labels = percent_format(a = 1)) + 
  geom_hline(yintercept = 0, linetype = "dashed",  color = "black")

P31_S14 Final consumption expenditure of households

namq_10_gdp |>
  filter(na_item == "P31_S14",
         s_adj == "NSA",
         unit == "PD_PCH_SM_EUR",
         geo %in% c("FR", "DE", "EA", "IT", "ES")) |>
  transmute(time, na_item, geo, Geo, values = values/100) |>
  quarter_to_date() |>
  filter(date >= as.Date("2018-01-01")) |>
  
  mutate(Geo= ifelse(geo == "EA", "Europe", Geo)) |>
  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, 1), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = "none",
        legend.title = element_blank()) +
  xlab("") + ylab("Final consumption expenditure of households, Annual % change") +
  scale_y_continuous(breaks = 0.01*seq(-30, 30, 1),
                labels = percent_format(a = 1)) + 
  geom_hline(yintercept = 0, linetype = "dashed",  color = "black")

P41 Actual Individual Consumption

namq_10_gdp |>
  filter(na_item == "P41",
         s_adj == "NSA",
         unit == "PD_PCH_SM_EUR",
         geo %in% c("FR", "DE", "EA", "IT", "ES")) |>
  transmute(time, na_item, geo, Geo, values = values/100) |>
  quarter_to_date() |>
  filter(date >= as.Date("2018-01-01")) |>
  
  mutate(Geo= ifelse(geo == "EA", "Europe", Geo)) |>
  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, 1), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = "none",
        legend.title = element_blank()) +
  xlab("") + ylab("Actual Individual Consumption, Annual % change") +
  scale_y_continuous(breaks = 0.01*seq(-30, 30, 1),
                labels = percent_format(a = 1)) + 
  geom_hline(yintercept = 0, linetype = "dashed",  color = "black")

Net Exports of Goods

Table

namq_10_gdp |>
  filter(na_item %in% c("P61", "P71"),
         s_adj == "NSA",
         unit == "PC_GDP",
         time %in% c("1989Q4", "1999Q4", "2009Q4", "2019Q4", "2022Q2", "2022Q3")) |>
  select(time, na_item, geo, Geo, values) |>
  mutate(values = round(values, 1)) |>
  
  spread(na_item, values) |>
  mutate(NX = round(P61 - P71, 1)) |>
  select(-P61, -P71) |>
  spread(time, NX) |>
  arrange(`2022Q3`) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

Germany, France, Netherlands

1995-

namq_10_gdp |>
  filter(na_item %in% c("P61", "P71"),
         s_adj == "SCA",
         unit == "PC_GDP",
         geo %in% c("FR", "DE", "NL")) |>
  select(time, na_item, geo, Geo, values) |>
  quarter_to_date() |>
  filter(date >= as.Date("1995-01-01")) |>
  
  mutate(Geo= ifelse(geo == "EA", "Europe", Geo)) |>
  spread(na_item, values) |>
  mutate(values = (P61 - P71)/100) |>
  left_join(colors, by = c("Geo" = "country")) |>
  mutate(color = ifelse(geo == "NL", color2, color)) |>
  ggplot() + geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + theme_minimal()  +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 5), "-01-01")),
               labels = date_format("%Y")) +
  add_flags +
  theme(legend.position = "none",
        legend.title = element_blank()) +
  xlab("") + ylab("Net Exports (% of GDP)") +
  scale_y_continuous(breaks = 0.01*seq(-30, 30, 1),
                labels = percent_format(a = 1)) + 
  geom_hline(yintercept = 0, linetype = "dashed",  color = "black")

Germany, France, Eurozone, Italy

All

namq_10_gdp |>
  filter(na_item %in% c("P61", "P71"),
         s_adj == "NSA",
         unit == "PC_GDP",
         geo %in% c("FR", "DE", "EA", "IT")) |>
  select(time, na_item, geo, Geo, values) |>
  quarter_to_date() |>
  
  mutate(Geo= ifelse(geo == "EA", "Europe", Geo)) |>
  spread(na_item, values) |>
  mutate(values = (P61 - P71)/100) |>
  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, 2), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = "none",
        legend.title = element_blank()) +
  xlab("") + ylab("Net Exports (% of GDP)") +
  scale_y_continuous(breaks = 0.01*seq(-30, 30, 1),
                labels = percent_format(a = 1)) + 
  geom_hline(yintercept = 0, linetype = "dashed",  color = "black")

1990-

namq_10_gdp |>
  filter(na_item %in% c("P61", "P71"),
         s_adj == "NSA",
         unit == "PC_GDP",
         geo %in% c("FR", "DE", "EA", "IT")) |>
  select(time, na_item, geo, Geo, values) |>
  quarter_to_date() |>
  filter(date >= as.Date("1990-01-01")) |>
  
  mutate(Geo= ifelse(geo == "EA", "Europe", Geo)) |>
  spread(na_item, values) |>
  mutate(values = (P61 - P71)/100) |>
  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, 2), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = "none",
        legend.title = element_blank()) +
  xlab("") + ylab("Net Exports (% of GDP)") +
  scale_y_continuous(breaks = 0.01*seq(-30, 30, 1),
                labels = percent_format(a = 1)) + 
  geom_hline(yintercept = 0, linetype = "dashed",  color = "black")

1995-

English

namq_10_gdp |>
  filter(na_item %in% c("P61", "P71"),
         s_adj == "SCA",
         unit == "PC_GDP",
         geo %in% c("FR", "DE", "EA", "IT")) |>
  select(time, na_item, geo, Geo, values) |>
  quarter_to_date() |>
  filter(date >= as.Date("1995-01-01")) |>
  
  mutate(Geo= ifelse(geo == "EA", "Europe", Geo)) |>
  spread(na_item, values) |>
  mutate(values = (P61 - P71)/100) |>
  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, 2), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = "none",
        legend.title = element_blank()) +
  xlab("") + ylab("Net Exports (% of GDP)") +
  scale_y_continuous(breaks = 0.01*seq(-30, 30, 1),
                labels = percent_format(a = 1)) + 
  geom_hline(yintercept = 0, linetype = "dashed",  color = "black")

Français

namq_10_gdp |>
  filter(na_item %in% c("P61", "P71"),
         s_adj == "SCA",
         unit == "PC_GDP",
         geo %in% c("FR", "DE", "EA", "IT")) |>
  select(time, na_item, geo, Geo, values) |>
  quarter_to_date() |>
  filter(date >= as.Date("1995-01-01")) |>
  
  mutate(Geo= ifelse(geo == "EA", "Europe", Geo)) |>
  spread(na_item, values) |>
  mutate(values = (P61 - P71)/100) |>
  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, 2), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = "none",
        legend.title = element_blank()) +
  xlab("") + ylab("Balance commerciale (% du PIB)") +
  scale_y_continuous(breaks = 0.01*seq(-30, 30, 1),
                labels = percent_format(a = 1)) + 
  geom_hline(yintercept = 0, linetype = "dashed",  color = "black")

1996-

namq_10_gdp |>
  filter(na_item %in% c("P61", "P71"),
         s_adj == "NSA",
         unit == "PC_GDP",
         geo %in% c("FR", "DE", "EA", "IT")) |>
  select(time, na_item, geo, Geo, values) |>
  quarter_to_date() |>
  filter(date >= as.Date("1996-01-01")) |>
  
  mutate(Geo= ifelse(geo == "EA", "Europe", Geo)) |>
  spread(na_item, values) |>
  mutate(values = (P61 - P71)/100) |>
  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, 2), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = "none",
        legend.title = element_blank()) +
  xlab("") + ylab("Net Exports (% of GDP)") +
  scale_y_continuous(breaks = 0.01*seq(-30, 30, 1),
                labels = percent_format(a = 1)) + 
  geom_hline(yintercept = 0, linetype = "dashed",  color = "black")

France vs. Gemrany

Français

namq_10_gdp |>
  filter(na_item %in% c("P61", "P71"),
         s_adj == "SCA",
         unit == "PC_GDP",
         geo %in% c("FR", "DE", "EA19")) |>
  select(time, na_item, geo, Geo, values) |>
  quarter_to_date() |>
  filter(date >= as.Date("1995-01-01")) |>
  
  mutate(Geo= ifelse(geo == "EA19", "Europe", Geo)) |>
  spread(na_item, values) |>
  mutate(values = (P61 - P71)/100) |>
  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, 2), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = "none",
        legend.title = element_blank()) +
  xlab("") + ylab("Balance commerciale (% du PIB)") +
  scale_y_continuous(breaks = 0.01*seq(-30, 30, 1),
                labels = percent_format(a = 1)) + 
  geom_hline(yintercept = 0, linetype = "dashed",  color = "black")

Government Spending

Final consumption expenditure of general government

namq_10_gdp |>
  filter(na_item %in% c("P3_S13"),
         s_adj == "SCA",
         unit == "PC_GDP",
         geo %in% c("FR", "DE", "NL", "IT", "ES")) |>
  quarter_to_date() |>
  filter(date >= as.Date("1995-01-01")) |>
  
  mutate(Geo= ifelse(geo == "EA", "Europe", Geo)) |>
  left_join(colors, by = c("Geo" = "country")) |>
  mutate(color = ifelse(geo == "NL", color2, color)) |>
  mutate(values = values/100) |>
  ggplot() + geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + theme_minimal()  +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 2), "-01-01")),
               labels = date_format("%Y")) +
  add_flags +
  theme(legend.position = "none",
        legend.title = element_blank()) +
  xlab("") + ylab("Government Consumption (% of GDP)") +
  scale_y_continuous(breaks = 0.01*seq(-30, 100, 1),
                labels = percent_format(a = 1))

Individual consumption expenditure of general government

namq_10_gdp |>
  filter(na_item %in% c("P31_S13"),
         s_adj == "SCA",
         unit == "PC_GDP",
         geo %in% c("FR", "DE", "NL", "IT", "ES")) |>
  quarter_to_date() |>
  filter(date >= as.Date("1995-01-01")) |>
  
  mutate(Geo= ifelse(geo == "EA", "Europe", Geo)) |>
  left_join(colors, by = c("Geo" = "country")) |>
  mutate(color = ifelse(geo == "NL", color2, color)) |>
  mutate(values = values/100) |>
  ggplot() + geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + theme_minimal()  +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 2), "-01-01")),
               labels = date_format("%Y")) +
  add_flags +
  theme(legend.position = "none",
        legend.title = element_blank()) +
  xlab("") + ylab("Government Consumption (% of GDP)") +
  scale_y_continuous(breaks = 0.01*seq(-30, 100, 1),
                labels = percent_format(a = 1))

Collective consumption expenditure of general government

namq_10_gdp |>
  filter(na_item %in% c("P32_S13"),
         s_adj == "SCA",
         unit == "PC_GDP",
         geo %in% c("FR", "DE", "NL", "IT", "ES")) |>
  quarter_to_date() |>
  filter(date >= as.Date("1995-01-01")) |>
  
  mutate(Geo= ifelse(geo == "EA", "Europe", Geo)) |>
  left_join(colors, by = c("Geo" = "country")) |>
  mutate(color = ifelse(geo == "NL", color2, color)) |>
  mutate(values = values/100) |>
  ggplot() + geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + theme_minimal()  +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 2), "-01-01")),
               labels = date_format("%Y")) +
  add_flags +
  theme(legend.position = "none",
        legend.title = element_blank()) +
  xlab("") + ylab("Government Consumption (% of GDP)") +
  scale_y_continuous(breaks = 0.01*seq(-30, 100, 1),
                labels = percent_format(a = 1))

Net Exports

Table

namq_10_gdp |>
  filter(na_item %in% c("P6", "P7"),
         s_adj == "NSA",
         unit == "PC_GDP",
         time %in% c("1989Q4", "1999Q4", "2009Q4", "2019Q4", "2022Q2", "2022Q3")) |>
  select(time, na_item, geo, Geo, values) |>
  mutate(values = round(values, 1)) |>
  
  spread(na_item, values) |>
  mutate(NX = round(P6 - P7, 1)) |>
  select(-P6, -P7) |>
  spread(time, NX) |>
  arrange(`2022Q3`) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

Germany, France, Netherlands

1995-

namq_10_gdp |>
  filter(na_item %in% c("P6", "P7"),
         s_adj == "SCA",
         unit == "PC_GDP",
         geo %in% c("FR", "DE", "NL")) |>
  select(time, na_item, geo, Geo, values) |>
  quarter_to_date() |>
  filter(date >= as.Date("1995-01-01")) |>
  
  mutate(Geo= ifelse(geo == "EA", "Europe", Geo)) |>
  spread(na_item, values) |>
  mutate(values = (P6 - P7)/100) |>
  left_join(colors, by = c("Geo" = "country")) |>
  mutate(color = ifelse(geo == "NL", color2, color)) |>
  ggplot() + geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + theme_minimal()  +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 2), "-01-01")),
               labels = date_format("%Y")) +
  add_flags +
  theme(legend.position = "none",
        legend.title = element_blank()) +
  xlab("") + ylab("Net Exports (% of GDP)") +
  scale_y_continuous(breaks = 0.01*seq(-30, 30, 1),
                labels = percent_format(a = 1)) + 
  geom_hline(yintercept = 0, linetype = "dashed",  color = "black")

Germany, France, Eurozone, Italy

All

namq_10_gdp |>
  filter(na_item %in% c("P6", "P7"),
         s_adj == "NSA",
         unit == "PC_GDP",
         geo %in% c("FR", "DE", "EA", "IT")) |>
  select(time, na_item, geo, Geo, values) |>
  quarter_to_date() |>
  
  mutate(Geo= ifelse(geo == "EA", "Europe", Geo)) |>
  spread(na_item, values) |>
  mutate(values = (P6 - P7)/100) |>
  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, 2), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = "none",
        legend.title = element_blank()) +
  xlab("") + ylab("Net Exports (% of GDP)") +
  scale_y_continuous(breaks = 0.01*seq(-30, 30, 1),
                labels = percent_format(a = 1)) + 
  geom_hline(yintercept = 0, linetype = "dashed",  color = "black")

1990-

namq_10_gdp |>
  filter(na_item %in% c("P6", "P7"),
         s_adj == "NSA",
         unit == "PC_GDP",
         geo %in% c("FR", "DE", "EA", "IT")) |>
  select(time, na_item, geo, Geo, values) |>
  quarter_to_date() |>
  filter(date >= as.Date("1990-01-01")) |>
  
  mutate(Geo= ifelse(geo == "EA", "Europe", Geo)) |>
  spread(na_item, values) |>
  mutate(values = (P6 - P7)/100) |>
  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, 2), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = "none",
        legend.title = element_blank()) +
  xlab("") + ylab("Net Exports (% of GDP)") +
  scale_y_continuous(breaks = 0.01*seq(-30, 30, 1),
                labels = percent_format(a = 1)) + 
  geom_hline(yintercept = 0, linetype = "dashed",  color = "black")

1995-

English

namq_10_gdp |>
  filter(na_item %in% c("P6", "P7"),
         s_adj == "SCA",
         unit == "PC_GDP",
         geo %in% c("FR", "DE", "EA", "IT")) |>
  select(time, na_item, geo, Geo, values) |>
  quarter_to_date() |>
  filter(date >= as.Date("1995-01-01")) |>
  
  mutate(Geo= ifelse(geo == "EA", "Europe", Geo)) |>
  spread(na_item, values) |>
  mutate(values = (P6 - P7)/100) |>
  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")) +
  theme(legend.position = "none",
        legend.title = element_blank()) +
  xlab("") + ylab("Net Exports (% of GDP)") +
  scale_y_continuous(breaks = 0.01*seq(-30, 30, 1),
                labels = percent_format(a = 1)) + 
  geom_hline(yintercept = 0, linetype = "dashed",  color = "black")

Français

namq_10_gdp |>
  filter(na_item %in% c("P6", "P7"),
         s_adj == "SCA",
         unit == "PC_GDP",
         geo %in% c("FR", "DE", "EA", "IT")) |>
  select(time, na_item, geo, Geo, values) |>
  quarter_to_date() |>
  filter(date >= as.Date("1995-01-01")) |>
  
  mutate(Geo= ifelse(geo == "EA", "Europe", Geo)) |>
  spread(na_item, values) |>
  mutate(values = (P6 - P7)/100) |>
  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")) +
  theme(legend.position = "none",
        legend.title = element_blank()) +
  xlab("") + ylab("Balance commerciale (% du PIB)") +
  scale_y_continuous(breaks = 0.01*seq(-30, 30, 1),
                labels = percent_format(a = 1)) + 
  geom_hline(yintercept = 0, linetype = "dashed",  color = "black")

1996-

namq_10_gdp |>
  filter(na_item %in% c("P6", "P7"),
         s_adj == "NSA",
         unit == "PC_GDP",
         geo %in% c("FR", "DE", "EA", "IT")) |>
  select(time, na_item, geo, Geo, values) |>
  quarter_to_date() |>
  filter(date >= as.Date("1996-01-01")) |>
  
  mutate(Geo= ifelse(geo == "EA", "Europe", Geo)) |>
  spread(na_item, values) |>
  mutate(values = (P6 - P7)/100) |>
  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, 2), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = "none",
        legend.title = element_blank()) +
  xlab("") + ylab("Net Exports (% of GDP)") +
  scale_y_continuous(breaks = 0.01*seq(-30, 30, 1),
                labels = percent_format(a = 1)) + 
  geom_hline(yintercept = 0, linetype = "dashed",  color = "black")

Germany, France, Eurozone, Belgium

Table

namq_10_gdp |>
  filter(na_item %in% c("P6", "P7"),
         s_adj == "SCA",
         unit == "PC_GDP",
         time == "2023Q1") |>
  select(time, na_item, geo, Geo, values) |>
  quarter_to_date() |>
  
  mutate(Geo= ifelse(geo == "EA", "Europe", Geo)) |>
  spread(na_item, values) |>
  mutate(values = (P6 - P7)) |>
  arrange(values) |>
  print_table_conditional()

SCA

namq_10_gdp |>
  filter(na_item %in% c("P6", "P7"),
         s_adj == "SCA",
         unit == "PC_GDP",
         geo %in% c("FR", "DE", "EA", "BE")) |>
  select(time, na_item, geo, Geo, values) |>
  quarter_to_date() |>
  
  mutate(Geo= ifelse(geo == "EA", "Europe", Geo)) |>
  spread(na_item, values) |>
  mutate(values = (P6 - P7)/100) |>
  left_join(colors, by = c("Geo" = "country")) |>
  mutate(values = values) |>
  ggplot() + geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + theme_minimal()  +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 5), "-01-01")),
               labels = date_format("%Y")) +
  add_flags +
  theme(legend.position = "none",
        legend.title = element_blank()) +
  xlab("") + ylab("Net Exports (% of GDP)") +
  scale_y_continuous(breaks = 0.01*seq(-30, 30, 1),
                labels = percent_format(a = 1)) + 
  geom_hline(yintercept = 0, linetype = "dashed",  color = "black")

Germany, France, Eurozone

All

NSA - Unadjusted data (i.e. neither seasonally adjusted nor calendar adjusted data)

namq_10_gdp |>
  filter(na_item %in% c("P6", "P7"),
         s_adj == "NSA",
         unit == "PC_GDP",
         geo %in% c("FR", "DE", "EA")) |>
  select(time, na_item, geo, Geo, values) |>
  quarter_to_date() |>
  
  mutate(Geo= ifelse(geo == "EA", "Europe", Geo)) |>
  spread(na_item, values) |>
  mutate(values = (P6 - P7)/100) |>
  left_join(colors, by = c("Geo" = "country")) |>
  mutate(values = values) |>
  ggplot() + geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + theme_minimal()  +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 5), "-01-01")),
               labels = date_format("%Y")) +
  add_flags +
  theme(legend.position = "none",
        legend.title = element_blank()) +
  xlab("") + ylab("Net Exports (% of GDP)") +
  scale_y_continuous(breaks = 0.01*seq(-30, 30, 1),
                labels = percent_format(a = 1)) + 
  geom_hline(yintercept = 0, linetype = "dashed",  color = "black")

SCA

namq_10_gdp |>
  filter(na_item %in% c("P6", "P7"),
         s_adj == "SCA",
         unit == "PC_GDP",
         geo %in% c("FR", "DE", "EA")) |>
  select(time, na_item, geo, Geo, values) |>
  quarter_to_date() |>
  
  mutate(Geo= ifelse(geo == "EA", "Europe", Geo)) |>
  spread(na_item, values) |>
  mutate(values = (P6 - P7)/100) |>
  left_join(colors, by = c("Geo" = "country")) |>
  mutate(values = values) |>
  ggplot() + geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + theme_minimal()  +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 5), "-01-01")),
               labels = date_format("%Y")) +
  add_flags +
  theme(legend.position = "none",
        legend.title = element_blank()) +
  xlab("") + ylab("Net Exports (% of GDP)") +
  scale_y_continuous(breaks = 0.01*seq(-30, 30, 1),
                labels = percent_format(a = 1)) + 
  geom_hline(yintercept = 0, linetype = "dashed",  color = "black")

1995-

namq_10_gdp |>
  filter(na_item %in% c("P6", "P7"),
         s_adj == "SCA",
         unit == "PC_GDP",
         geo %in% c("FR", "DE", "EA")) |>
  select(time, na_item, geo, Geo, values) |>
  quarter_to_date() |>
  filter(date >= as.Date("1995-01-01")) |>
  
  mutate(Geo= ifelse(geo == "EA", "Europe", Geo)) |>
  spread(na_item, values) |>
  mutate(values = (P6 - P7)/100) |>
  left_join(colors, by = c("Geo" = "country")) |>
  mutate(color = ifelse(geo == "EA", color2, color)) |>
  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")) +
  theme(legend.position = "none",
        legend.title = element_blank()) +
  xlab("") + ylab("Exportations Nettes (% du PIB)") +
  scale_y_continuous(breaks = 0.01*seq(-30, 30, 1),
                labels = percent_format(a = 1)) + 
  geom_hline(yintercept = 0, linetype = "dashed",  color = "black")

1996-

namq_10_gdp |>
  filter(na_item %in% c("P6", "P7"),
         s_adj == "NSA",
         unit == "PC_GDP",
         geo %in% c("FR", "DE", "EA")) |>
  select(time, na_item, geo, Geo, values) |>
  quarter_to_date() |>
  filter(date >= as.Date("1996-01-01")) |>
  
  mutate(Geo= ifelse(geo == "EA", "Europe", Geo)) |>
  spread(na_item, values) |>
  mutate(values = (P6 - P7)/100) |>
  left_join(colors, by = c("Geo" = "country")) |>
  mutate(color = ifelse(geo == "EA", color2, color)) |>
  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, 2), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = "none",
        legend.title = element_blank()) +
  xlab("") + ylab("Net Exports (% of GDP)") +
  scale_y_continuous(breaks = 0.01*seq(-30, 30, 1),
                labels = percent_format(a = 1)) + 
  geom_hline(yintercept = 0, linetype = "dashed",  color = "black")

2000-

namq_10_gdp |>
  filter(na_item %in% c("P6", "P7"),
         s_adj == "NSA",
         unit == "PC_GDP",
         geo %in% c("FR", "DE", "EA")) |>
  select(time, na_item, geo, Geo, values) |>
  quarter_to_date() |>
  filter(date >= as.Date("2000-01-01")) |>
  
  mutate(Geo= ifelse(geo == "EA", "Europe", Geo)) |>
  spread(na_item, values) |>
  mutate(values = (P6 - P7)/100) |>
  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, 2), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = "none",
        legend.title = element_blank()) +
  xlab("") + ylab("Net Exports (% of GDP)") +
  scale_y_continuous(breaks = 0.01*seq(-30, 30, 1),
                labels = percent_format(a = 1)) + 
  geom_hline(yintercept = 0, linetype = "dashed",  color = "black")

2010-

namq_10_gdp |>
  filter(na_item %in% c("P6", "P7"),
         s_adj == "NSA",
         unit == "PC_GDP",
         geo %in% c("FR", "DE", "EA")) |>
  select(time, na_item, geo, Geo, values) |>
  quarter_to_date() |>
  filter(date >= as.Date("2010-01-01")) |>
  
  mutate(Geo= ifelse(geo == "EA", "Europe", Geo)) |>
  spread(na_item, values) |>
  mutate(values = (P6 - P7)/100) |>
  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, 1), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = "none",
        legend.title = element_blank()) +
  xlab("") + ylab("Net Exports (% of GDP)") +
  scale_y_continuous(breaks = 0.01*seq(-30, 30, 1),
                labels = percent_format(a = 1)) + 
  geom_hline(yintercept = 0, linetype = "dashed",  color = "black")

Albania, Austria, Bosnia

geo <- read_parquet("geo.parquet")
namq_10_gdp |>
  filter(na_item %in% c("P6", "P7"),
         s_adj == "NSA",
         unit == "PC_GDP",
         geo %in% c("AL", "AT", "BA")) |>
  select(time, na_item, geo, Geo, values) |>
  quarter_to_date() |>
  
  spread(na_item, values) |>
  mutate(values = (P6 - P7)/100) |>
  left_join(colors, by = c("Geo" = "country")) |>
  ggplot() + geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + theme_minimal()  +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 5), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.25, 0.25),
        legend.title = element_blank()) +
  xlab("") + ylab("") +
  scale_y_continuous(breaks = 0.01*seq(-30, 30, 5),
                labels = percent_format(a = 1)) + 
  geom_hline(yintercept = 0, linetype = "dashed",  color = "black")

Belgium, Bulgaria, Switzerland

namq_10_gdp |>
  filter(na_item %in% c("P6", "P7"),
         s_adj == "NSA",
         unit == "PC_GDP",
         geo %in% c("BE", "BG", "CH")) |>
  select(time, na_item, geo, Geo, values) |>
  quarter_to_date() |>
  
  spread(na_item, values) |>
  mutate(values = (P6 - P7)/100) |>
  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")) +
  theme(legend.position = c(0.25, 0.25),
        legend.title = element_blank()) +
  xlab("") + ylab("") +
  scale_y_continuous(breaks = 0.01*seq(-30, 30, 5),
                labels = percent_format(a = 1)) + 
  geom_hline(yintercept = 0, linetype = "dashed",  color = "black")

Cyprus, Czechia, Germany

namq_10_gdp |>
  filter(na_item %in% c("P6", "P7"),
         s_adj == "NSA",
         unit == "PC_GDP",
         geo %in% c("CY", "CZ", "DE")) |>
  select(time, na_item, geo, Geo, values) |>
  quarter_to_date() |>
  
  spread(na_item, values) |>
  mutate(values = (P6 - P7)/100) |>
  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")) +
  theme(legend.position = c(0.25, 0.25),
        legend.title = element_blank()) +
  xlab("") + ylab("") +
  scale_y_continuous(breaks = 0.01*seq(-30, 30, 5),
                labels = percent_format(a = 1)) + 
  geom_hline(yintercept = 0, linetype = "dashed",  color = "black")

Denmark, Greece, Spain

namq_10_gdp |>
  filter(na_item %in% c("P6", "P7"),
         s_adj == "NSA",
         unit == "PC_GDP",
         geo %in% c("DK", "EL", "ES")) |>
  select(time, na_item, geo, Geo, values) |>
  quarter_to_date() |>
  
  spread(na_item, values) |>
  mutate(values = (P6 - P7)/100) |>
  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")) +
  theme(legend.position = c(0.25, 0.95),
        legend.title = element_blank(),
        legend.direction = "horizontal") +
  xlab("") + ylab("") +
  scale_y_continuous(breaks = 0.01*seq(-30, 30, 5),
                labels = percent_format(a = 1)) + 
  geom_hline(yintercept = 0, linetype = "dashed",  color = "black")

Finland, France, Croatia

namq_10_gdp |>
  filter(na_item %in% c("P6", "P7"),
         s_adj == "NSA",
         unit == "PC_GDP",
         geo %in% c("FI", "FR", "HR")) |>
  select(time, na_item, geo, Geo, values) |>
  quarter_to_date() |>
  
  spread(na_item, values) |>
  mutate(values = (P6 - P7)/100) |>
  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")) +
  theme(legend.position = c(0.25, 0.95),
        legend.title = element_blank(),
        legend.direction = "horizontal") +
  xlab("") + ylab("") +
  scale_y_continuous(breaks = 0.01*seq(-30, 30, 5),
                labels = percent_format(a = 1)) + 
  geom_hline(yintercept = 0, linetype = "dashed",  color = "black")

Hungary, Ireland, Iceland

namq_10_gdp |>
  filter(na_item %in% c("P6", "P7"),
         s_adj == "NSA",
         unit == "PC_GDP",
         geo %in% c("HU", "IE", "IS")) |>
  select(time, na_item, geo, Geo, values) |>
  quarter_to_date() |>
  
  spread(na_item, values) |>
  mutate(values = (P6 - P7)/100) |>
  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")) +
  theme(legend.position = c(0.25, 0.95),
        legend.title = element_blank(),
        legend.direction = "horizontal") +
  xlab("") + ylab("") +
  scale_y_continuous(breaks = 0.01*seq(-30, 100, 5),
                labels = percent_format(a = 1)) + 
  geom_hline(yintercept = 0, linetype = "dashed",  color = "black")

Italy, Lithuania, Luxembourg

namq_10_gdp |>
  filter(na_item %in% c("P6", "P7"),
         s_adj == "SCA",
         unit == "PC_GDP",
         geo %in% c("IT", "LT", "LU")) |>
  select(time, na_item, geo, Geo, values) |>
  quarter_to_date() |>
  
  spread(na_item, values) |>
  mutate(values = (P6 - P7)/100) |>
  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")) +
  theme(legend.position = c(0.25, 0.95),
        legend.title = element_blank(),
        legend.direction = "horizontal") +
  xlab("") + ylab("") +
  scale_y_continuous(breaks = 0.01*seq(-30, 100, 5),
                labels = percent_format(a = 1)) + 
  geom_hline(yintercept = 0, linetype = "dashed",  color = "black")

Germany, France, Italy

NSA

namq_10_gdp |>
  filter(na_item %in% c("P6", "P7"),
         s_adj == "NSA",
         unit == "PC_GDP",
         geo %in% c("FR", "DE", "IT")) |>
  select(time, na_item, geo, Geo, values) |>
  quarter_to_date() |>
  
  spread(na_item, values) |>
  mutate(values = (P6 - P7)/100) |>
  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")) +
  theme(legend.position = c(0.35, 0.85),
        legend.title = element_blank()) +
  xlab("") + ylab("") +
  scale_y_continuous(breaks = 0.01*seq(-30, 30, 1),
                labels = percent_format(a = 1)) + 
  geom_hline(yintercept = 0, linetype = "dashed",  color = "black")

SCA

namq_10_gdp |>
  filter(na_item %in% c("P6", "P7"),
         s_adj == "SCA",
         unit == "PC_GDP",
         geo %in% c("FR", "DE", "IT")) |>
  select(time, na_item, geo, Geo, values) |>
  quarter_to_date() |>
  
  spread(na_item, values) |>
  mutate(values = (P6 - P7)/100) |>
  left_join(colors, by = c("Geo" = "country")) |>
  
  ggplot() + geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + theme_minimal()  +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 5), "-01-01")),
               labels = date_format("%Y")) + add_flags +
  theme(legend.position = c(0.35, 0.85),
        legend.title = element_blank()) +
  xlab("") + ylab("") +
  scale_y_continuous(breaks = 0.01*seq(-30, 30, 1),
                labels = percent_format(a = 1)) + 
  geom_hline(yintercept = 0, linetype = "dashed",  color = "black")

Germany, France, Italy, Spain, Eurozone

SCA

namq_10_gdp |>
  filter(na_item %in% c("P6", "P7"),
         s_adj == "SCA",
         unit == "PC_GDP",
         geo %in% c("FR", "DE", "IT", "EA", "ES")) |>
  select(time, na_item, geo, Geo, values) |>
  quarter_to_date() |>
  
  spread(na_item, values) |>
  mutate(values = (P6 - P7)/100) |>
  filter(date >= as.Date("1998-01-01")) |>
  mutate(Geo= ifelse(geo == "EA", "Europe", Geo)) |>
  left_join(colors, by = c("Geo" = "country")) |>
  
  ggplot() + geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + theme_minimal()  +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 2), "-01-01")),
               labels = date_format("%Y")) + add_flags +
  theme(legend.position = c(0.35, 0.85),
        legend.title = element_blank()) +
  xlab("") + ylab("") +
  scale_y_continuous(breaks = 0.01*seq(-30, 30, 1),
                labels = percent_format(a = 1)) + 
  geom_hline(yintercept = 0, linetype = "dashed",  color = "black")

Poland, France, Italy

namq_10_gdp |>
  filter(na_item %in% c("P6", "P7"),
         s_adj == "NSA",
         unit == "PC_GDP",
         geo %in% c("PL", "DE", "IT")) |>
  select(time, na_item, geo, Geo, values) |>
  quarter_to_date() |>
  
  spread(na_item, values) |>
  mutate(values = (P6 - P7)/100) |>
  left_join(colors, by = c("Geo" = "country")) |>
  
  ggplot() + geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + theme_minimal()  +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 5), "-01-01")),
               labels = date_format("%Y")) + add_flags +
  theme(legend.position = c(0.35, 0.85),
        legend.title = element_blank()) +
  xlab("") + ylab("") +
  scale_y_continuous(breaks = 0.01*seq(-30, 30, 1),
                labels = percent_format(a = 1)) + 
  geom_hline(yintercept = 0, linetype = "dashed",  color = "black")

Poland, Austria, Germany, Tchequia, Hungary, Slovakia

namq_10_gdp |>
  filter(na_item %in% c("P6", "P7"),
         s_adj == "NSA",
         unit == "PC_GDP",
         geo %in% c("PL", "DE", "SK", "CZ", "HU", "AT")) |>
  select(time, na_item, geo, Geo, values) |>
  quarter_to_date() |>
  
  spread(na_item, values) |>
  mutate(values = (P6 - P7)/100) |>
  left_join(colors, by = c("Geo" = "country")) |>
  
  ggplot() + geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + theme_minimal()  +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 5), "-01-01")),
               labels = date_format("%Y")) + add_flags +
  theme(legend.position = c(0.85, 0.25),
        legend.title = element_blank()) +
  xlab("") + ylab("") +
  scale_y_continuous(breaks = 0.01*seq(-30, 30, 5),
                labels = percent_format(a = 1)) + 
  geom_hline(yintercept = 0, linetype = "dashed",  color = "black")

Decompose GDP

Bn - 2019Q1 - France, Italy, Germany, Spain

namq_10_gdp |>
  filter(s_adj == "SCA",
         # CLV10_MEUR: Chain linked volumes (2010), million euro
         unit == "CLV10_MEUR",
         time %in% c("2019Q1"),
         geo %in% c("FR", "IT", "DE", "ES")) |>
  
  select(na_item, Na_item, geo, values) |>
  spread(geo, values) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

% of GDP - 2019Q1 - France, Italy, Germany, Spain

namq_10_gdp |>
  filter(s_adj == "SCA",
         unit == "PC_GDP",
         time %in% c("2019Q1"),
         geo %in% c("FR", "IT", "DE", "ES")) |>
  
  select(na_item, Na_item, geo, values) |>
  mutate(values = values |> paste0("%")) |>
  spread(geo, values) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

% - 2019Q1 - France, Italy, Germany, Spain

namq_10_gdp |>
  filter(s_adj == "SCA",
         # CLV10_MEUR: Chain linked volumes (2010), million euro
         unit == "CP_MEUR",
         time %in% c("2019Q1"),
         geo %in% c("FR", "IT", "DE", "ES")) |>
  
  select(na_item, Na_item, geo, values) |>
  group_by(geo) |>
  mutate(values = round(100*values/values[na_item == "B1GQ"], 1) |> paste0("%")) |>
  spread(geo, values) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

France - 1979, 1989, 1999, 2009, 2019

namq_10_gdp |>
  filter(s_adj == "SCA",
         # CLV10_MEUR: Chain linked volumes (2010), million euro
         unit == "CP_MEUR",
         time %in% c("2019Q1", "2009Q1", "1999Q1", "1989Q1", "1979Q1"),
         geo %in% c("FR")) |>
  
  select(na_item, Na_item, time, values) |>
  group_by(time) |>
  mutate(values = round(100*values/values[na_item == "B1GQ"], 1) |> paste0("%")) |>
  spread(time, values) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

REAL Gross Domestic Product

Allemagne, France, Europe, Italy

1990-

namq_10_gdp |>
  filter(geo %in% c("EA19", "DE", "IT", "FR"),
         # B1GQ: Gross domestic product at market prices
         na_item == "B1GQ",
         # SCA: Seasonally and calendar adjusted data
         s_adj == "SCA",
         # CLV10_MEUR: Chain linked volumes (2010), million euro
         unit == "CLV10_MEUR") |>
  quarter_to_date() |>
  filter(date >= as.Date("1990-01-01")) |>
  
  mutate(Geo = ifelse(geo == "EA19", "Europe", Geo)) |>
  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)) +
  scale_color_identity() + theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 5), "-01-01")),
               labels = date_format("%Y")) +
  add_flags +
  theme(legend.position = c(0.35, 0.85),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(80, 180, 5),
                labels = dollar_format(suffix = "", prefix = "", accuracy = 1))

1995-

namq_10_gdp |>
  filter(geo %in% c("EA19", "DE", "IT", "FR"),
         # B1GQ: Gross domestic product at market prices
         na_item == "B1GQ",
         # SCA: Seasonally and calendar adjusted data
         s_adj == "SCA",
         # CLV10_MEUR: Chain linked volumes (2010), million euro
         unit == "CLV10_MEUR") |>
  quarter_to_date() |>
  filter(date >= as.Date("1995-01-01")) |>
  
  mutate(Geo = ifelse(geo == "EA19", "Europe", Geo)) |>
  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)) +
  scale_color_identity() + theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 5), "-01-01")),
               labels = date_format("%Y")) +
  add_flags +
  theme(legend.position = c(0.35, 0.85),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(80, 180, 5),
                labels = dollar_format(suffix = "", prefix = "", accuracy = 1))

1996-

namq_10_gdp |>
  filter(geo %in% c("EA19", "DE", "IT", "FR"),
         # B1GQ: Gross domestic product at market prices
         na_item == "B1GQ",
         # SCA: Seasonally and calendar adjusted data
         s_adj == "SCA",
         # CLV10_MEUR: Chain linked volumes (2010), million euro
         unit == "CLV10_MEUR") |>
  quarter_to_date() |>
  filter(date >= as.Date("1996-01-01")) |>
  
  mutate(Geo = ifelse(geo == "EA19", "Europe", Geo)) |>
  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)) +
  scale_color_identity() + theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 2), "-01-01")),
               labels = date_format("%Y")) +
  add_flags +
  theme(legend.position = c(0.35, 0.85),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(80, 180, 5),
                labels = dollar_format(suffix = "", prefix = "", accuracy = 1))

2000-

namq_10_gdp |>
  filter(geo %in% c("EA19", "DE", "IT", "FR"),
         # B1GQ: Gross domestic product at market prices
         na_item == "B1GQ",
         # SCA: Seasonally and calendar adjusted data
         s_adj == "SCA",
         # CLV10_MEUR: Chain linked volumes (2010), million euro
         unit == "CLV10_MEUR") |>
  quarter_to_date() |>
  filter(date >= as.Date("2000-01-01")) |>
  
  mutate(Geo = ifelse(geo == "EA19", "Europe", Geo)) |>
  group_by(geo) |>
  mutate(values = 100*values / values[date == as.Date("2000-01-01")]) |>
  left_join(colors, by = c("Geo" = "country")) |>
  ggplot() + geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 2), "-01-01")),
               labels = date_format("%Y")) +
  add_flags +
  theme(legend.position = c(0.35, 0.85),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(80, 130, 5),
                labels = dollar_format(suffix = "", prefix = "", accuracy = 1))

2017-

namq_10_gdp |>
  filter(geo %in% c("EA19", "DE", "IT", "FR"),
         # B1GQ: Gross domestic product at market prices
         na_item == "B1GQ",
         # SCA: Seasonally and calendar adjusted data
         s_adj == "SCA",
         # CLV10_MEUR: Chain linked volumes (2010), million euro
         unit == "CLV10_MEUR") |>
  quarter_to_date() |>
  filter(date >= as.Date("2017-01-01")) |>
  
  mutate(Geo = ifelse(geo == "EA19", "Europe", Geo)) |>
  group_by(geo) |>
  mutate(values = 100*values / values[date == as.Date("2017-01-01")]) |>
  left_join(colors, by = c("Geo" = "country")) |>
  mutate(date = zoo::as.yearqtr(paste0(year(date), " Q", quarter(date)))) |>
  ggplot() + geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + theme_minimal() + xlab("") + ylab("") +
  zoo::scale_x_yearqtr(labels = date_format("%Y Q%q"),
                       breaks = seq(zoo::as.yearqtr("2017 Q1"), zoo::as.yearqtr("2100 Q1"), by = 0.25)) +
  add_flags +
  theme(legend.position = c(0.35, 0.85),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  scale_y_log10(breaks = seq(80, 130, 2),
                labels = dollar_format(suffix = "", prefix = "", accuracy = 1))

2019Q4-

namq_10_gdp |>
  filter(geo %in% c("EA20", "DE", "IT", "FR", "IE"),
         # B1GQ: Gross domestic product at market prices
         na_item == "B1GQ",
         time %in% c("2019Q4", "2023Q4"),
         # SCA: Seasonally and calendar adjusted data
         s_adj == "SCA",
         # CLV10_MEUR: Chain linked volumes (2010), million euro
         unit == "CLV15_MEUR") |>
  
  select(geo, Geo, values, time) |>
  mutate(values = values/1000) |>
  spread(time, values) |>
  mutate(Change = `2023Q4` - `2019Q4`) |>
  print_table_conditional()

geo	Geo	2019Q4	2023Q4	Change
DE	Germany	834.0268	836.9056	2.8788
EA20	Euro area – 20 countries (2023-2025)	2885.1528	3004.0807	118.9279
FR	France	587.0707	612.5223	25.4516
IE	Ireland	87.8725	108.6072	20.7347
IT	Italy	431.6323	456.5784	24.9461

Allemagne, France, Italie

All

namq_10_gdp |>
  filter(geo %in% c("FR", "DE", "IT"),
         # B1GQ: Gross domestic product at market prices
         na_item == "B1GQ",
         # SCA: Seasonally and calendar adjusted data
         s_adj == "SCA",
         # CLV10_MEUR: Chain linked volumes (2010), million euro
         unit == "CLV10_MEUR") |>
  quarter_to_date() |>
  
  left_join(colors, by = c("Geo" = "country")) |>
  mutate(values = values/1000) |>
  ggplot() + geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + theme_minimal() + xlab("") + ylab("") + add_flags +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 5), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.35, 0.85),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(0, 1000, 100),
                labels = dollar_format(suffix = " Bn€", prefix = "", accuracy = 1))

1995-

namq_10_gdp |>
  filter(geo %in% c("FR", "DE", "IT"),
         # B1GQ: Gross domestic product at market prices
         na_item == "B1GQ",
         # SCA: Seasonally and calendar adjusted data
         s_adj == "SCA",
         # CLV10_MEUR: Chain linked volumes (2010), million euro
         unit == "CLV10_MEUR") |>
  quarter_to_date() |>
  filter(date >= as.Date("1995-01-01")) |>
  
  left_join(colors, by = c("Geo" = "country")) |>
  mutate(values = values/1000) |>
  ggplot() + geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + theme_minimal() + xlab("") + ylab("") + add_flags +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 5), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.35, 0.85),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(0, 1000, 100),
                labels = dollar_format(suffix = " Bn€", prefix = "", accuracy = 1))

2019Q4 to 2020Q1

geo <- read_parquet("geo.parquet")
namq_10_gdp |>
  filter(na_item == "B1GQ",
         # SCA: Seasonally and calendar adjusted data
         s_adj == "SCA",
         # CLV10_MEUR: Chain linked volumes (2010), million euro
         unit == "CLV10_MEUR",
         time %in% c("2020Q1", "2019Q4")) |>
  
  select(geo, Geo, time, values) |>
  spread(time, values) |>
  transmute(geo, Geo, 
            `growth (%)` = round(100*(`2020Q1`/`2019Q4` - 1), 1)) |>
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) |>
  mutate(Flag = gsub(" ", "-", str_to_lower(Geo)),
         Flag = paste0('<img src="../../icon/flag/vsmall/', Flag, '.png" alt="Flag">')) |>
  select(Flag, everything()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

2019Q4 to 2020Q1 to 2021Q4

geo <- read_parquet("geo.parquet")
namq_10_gdp |>
  filter(na_item == "B1GQ",
         # SCA: Seasonally and calendar adjusted data
         s_adj == "SCA",
         # CLV10_MEUR: Chain linked volumes (2010), million euro
         unit == "CLV10_MEUR",
         time %in% c("2020Q1", "2019Q4", "2021Q4"),
         !(geo %in% c("EA", "EA19"))) |>
  
  mutate(Geo = ifelse(geo == "EA", "Eurozone", Geo),
         Geo = ifelse(geo == "EU27_2020", "Europe", Geo)) |>
  select(geo, Geo, time, values) |>
  spread(time, values) |>
  transmute(geo, Geo, 
            `2020-Q1 (%)` = round(100*(`2020Q1`/`2019Q4` - 1), 1), 
            `2021Q4 (%)` = round(100*(`2021Q4`/`2019Q4` - 1), 1)) |>
  filter(!is.na(`2021Q4 (%)`)) |>
  arrange(`2021Q4 (%)`) |>
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) |>
  mutate(Flag = gsub(" ", "-", str_to_lower(Geo)),
         Flag = paste0('<img src="../../icon/flag/vsmall/', Flag, '.png" alt="Flag">')) |>
  select(Flag, everything()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F, options = list(pageLength = 40)) else .}

Consumption

Germany, France, Europe, Italy

All

namq_10_gdp |>
  filter(geo %in% c("EA19", "DE", "IT", "FR"),
         # B1GQ: Gross domestic product at market prices
         na_item == "P3",
         # SCA: Seasonally and calendar adjusted data
         s_adj == "SCA",
         # CLV10_MEUR: Chain linked volumes (2010), million euro
         unit == "CP_MEUR") |>
  quarter_to_date() |>
  
  mutate(Geo = ifelse(geo == "EA19", "Europe", Geo)) |>
  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)) +
  scale_color_identity() + theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 2), "-01-01")),
               labels = date_format("%Y")) +
  add_flags +
  theme(legend.position = c(0.35, 0.85),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(100, 400, 20),
                labels = dollar_format(suffix = "", prefix = "", accuracy = 1))

1996-

Value

namq_10_gdp |>
  filter(geo %in% c("EA19", "DE", "IT", "FR"),
         # B1GQ: Gross domestic product at market prices
         na_item == "P3",
         # SCA: Seasonally and calendar adjusted data
         s_adj == "SCA",
         # CLV10_MEUR: Chain linked volumes (2010), million euro
         unit == "CP_MEUR") |>
  quarter_to_date() |>
  filter(date >= as.Date("1996-01-01")) |>
  
  mutate(Geo = ifelse(geo == "EA19", "Europe", Geo)) |>
  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)) +
  scale_color_identity() + theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 2), "-01-01")),
               labels = date_format("%Y")) +
  add_flags +
  theme(legend.position = c(0.35, 0.85),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(100, 400, 20),
                labels = dollar_format(suffix = "", prefix = "", accuracy = 1))

Volume

namq_10_gdp |>
  filter(geo %in% c("EA19", "DE", "IT", "FR"),
         # B1GQ: Gross domestic product at market prices
         na_item == "P3",
         # SCA: Seasonally and calendar adjusted data
         s_adj == "SCA",
         # CLV10_MEUR: Chain linked volumes (2010), million euro
         unit == "CLV10_MEUR") |>
  quarter_to_date() |>
  filter(date >= as.Date("1996-01-01")) |>
  
  mutate(Geo = ifelse(geo == "EA19", "Europe", Geo)) |>
  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)) +
  scale_color_identity() + theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 2), "-01-01")),
               labels = date_format("%Y")) +
  add_flags +
  theme(legend.position = c(0.35, 0.85),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(100, 400, 5),
                labels = dollar_format(suffix = "", prefix = "", accuracy = 1))

2000-

namq_10_gdp |>
  filter(geo %in% c("EA19", "DE", "IT", "FR"),
         # B1GQ: Gross domestic product at market prices
         na_item == "P3",
         # SCA: Seasonally and calendar adjusted data
         s_adj == "SCA",
         # CLV10_MEUR: Chain linked volumes (2010), million euro
         unit == "CP_MEUR") |>
  quarter_to_date() |>
  filter(date >= as.Date("2000-01-01")) |>
  
  mutate(Geo = ifelse(geo == "EA19", "Europe", Geo)) |>
  group_by(geo) |>
  mutate(values = 100*values / values[date == as.Date("2000-01-01")]) |>
  left_join(colors, by = c("Geo" = "country")) |>
  ggplot() + geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 5), "-01-01")),
               labels = date_format("%Y")) +
  add_flags +
  theme(legend.position = c(0.35, 0.85),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(100, 400, 20),
                labels = dollar_format(suffix = "", prefix = "", accuracy = 1))

2015-

namq_10_gdp |>
  filter(geo %in% c("EA19", "DE", "IT", "FR"),
         # B1GQ: Gross domestic product at market prices
         na_item == "P3",
         # SCA: Seasonally and calendar adjusted data
         s_adj == "SCA",
         # CLV10_MEUR: Chain linked volumes (2010), million euro
         unit == "CP_MEUR") |>
  quarter_to_date() |>
  filter(date >= as.Date("2015-01-01")) |>
  
  mutate(Geo = ifelse(geo == "EA19", "Europe", Geo)) |>
  group_by(geo) |>
  mutate(values = 100*values / values[date == as.Date("2015-01-01")]) |>
  left_join(colors, by = c("Geo" = "country")) |>
  ggplot() + geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 1), "-01-01")),
               labels = date_format("%Y")) +
  add_flags +
  theme(legend.position = c(0.35, 0.85),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(80, 400, 2),
                labels = dollar_format(suffix = "", prefix = "", accuracy = 1))

2017-

namq_10_gdp |>
  filter(geo %in% c("EA19", "DE", "IT", "FR"),
         # B1GQ: Gross domestic product at market prices
         na_item == "P3",
         # SCA: Seasonally and calendar adjusted data
         s_adj == "SCA",
         # CLV10_MEUR: Chain linked volumes (2010), million euro
         unit == "CP_MEUR") |>
  quarter_to_date() |>
  filter(date >= as.Date("2017-01-01")) |>
  
  mutate(Geo = ifelse(geo == "EA19", "Europe", Geo)) |>
  group_by(geo) |>
  mutate(values = 100*values / values[date == as.Date("2017-01-01")]) |>
  left_join(colors, by = c("Geo" = "country")) |>
  ggplot() + geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 1), "-01-01")),
               labels = date_format("%Y")) +
  add_flags +
  theme(legend.position = c(0.35, 0.85),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(80, 400, 5),
                labels = dollar_format(suffix = "", prefix = "", accuracy = 1))

Import Price index

Table

namq_10_gdp |>
  filter(na_item == "P7",
         unit == "PD15_EUR",
         time %in% c("2022Q1", "2021Q4")) |>
  spread(time, values) %>%
  select_if(~ n_distinct(.) > 1) |>
  mutate(`growth` = 100*( `2022Q1`/`2021Q4`-1 )) |>
  arrange(-`growth`) |>
  
  select(geo, Geo, everything()) |>
  print_table_conditional()

Last observation, Eurozone

namq_10_gdp |>
  filter(time == max(time),
         na_item == "B1GQ") |>
  spread(unit, values) %>%
  select_if(~ n_distinct(.) > 1) |>
  
  select(geo, Geo, everything()) |>
  print_table_conditional()