GDP and main components (output, expenditure and income)

Data - Eurostat

Author

François Geerolf

Info

source dataset .html .RData
eurostat nama_10_gdp 2024-12-21 2024-12-22
eurostat namq_10_gdp 2024-12-22 2024-12-22

Data on macro

source dataset .html .RData
eurostat nama_10_a10 2024-11-23 2024-10-08
eurostat nama_10_a10_e 2024-11-23 2024-12-22
eurostat nama_10_gdp 2024-12-21 2024-12-22
eurostat nama_10_lp_ulc 2024-11-23 2024-10-08
eurostat namq_10_a10 2024-12-22 2024-12-22
eurostat namq_10_a10_e 2024-12-22 2024-12-22
eurostat namq_10_gdp 2024-12-22 2024-12-22
eurostat namq_10_lp_ulc 2024-11-23 2024-11-04
eurostat namq_10_pc 2024-11-23 2024-11-21
eurostat nasa_10_nf_tr 2024-12-14 2024-12-14
eurostat nasq_10_nf_tr 2024-11-23 2024-10-09
fred gdp 2024-12-22 2024-12-22
oecd QNA 2024-06-06 2024-12-22
oecd SNA_TABLE1 2024-12-22 2024-12-22
oecd SNA_TABLE14A 2024-09-15 2024-06-30
oecd SNA_TABLE2 2024-07-01 2024-04-11
oecd SNA_TABLE6A 2024-07-01 2024-06-30
wdi NE.RSB.GNFS.ZS 2024-09-18 2024-09-18
wdi NY.GDP.MKTP.CD 2024-09-18 2024-09-26
wdi NY.GDP.MKTP.PP.CD 2024-09-18 2024-09-18
wdi NY.GDP.PCAP.CD 2024-12-16 2024-12-22
wdi NY.GDP.PCAP.KD 2024-09-18 2024-09-18
wdi NY.GDP.PCAP.PP.CD 2024-12-22 2024-12-22
wdi NY.GDP.PCAP.PP.KD 2024-12-22 2024-12-22

Last

Code 
nama_10_gdp %>%
  group_by(time) %>%
  summarise(Nobs = n()) %>%
  arrange(desc(time)) %>%
  head(2) %>%
  print_table_conditional()
time Nobs
2023 33071
2022 33075

na_item

Code 
nama_10_gdp %>%
  left_join(na_item, by = "na_item") %>%
  group_by(na_item, Na_item) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

geo

Code 
nama_10_gdp %>%
  left_join(geo, by = "geo") %>%
  group_by(geo, Geo) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(Geo)),
         Flag = paste0('<img src="../../bib/flags/vsmall/', Flag, '.png" alt="Flag">')) %>%
  select(Flag, everything()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

unit

Code 
nama_10_gdp %>%
  left_join(unit, by = "unit") %>%
  group_by(unit, Unit) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional

time

Code 
nama_10_gdp %>%
  group_by(time) %>%
  summarise(Nobs = n()) %>%
  arrange(desc(time)) %>%
  print_table_conditional

Latest GDP Numbers

2019, 2020 Values

Code 
nama_10_gdp %>%
  filter(unit == "CP_MEUR",
         na_item == "B1GQ",
         time %in% c("2019", "2020")) %>%
  left_join(geo, by = "geo") %>%
  select(time, geo, Geo, values) %>%
  spread(time, values) %>%
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(Geo)),
         Flag = paste0('<img src="../../bib/flags/vsmall/', Flag, '.png" alt="Flag">')) %>%
  select(Flag, everything()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

2019, 2020 % increase

Code 
nama_10_gdp %>%
  filter(unit == "CLV_PCH_PRE",
         na_item == "B1GQ",
         time %in% c("2019", "2020")) %>%
  left_join(geo, by = "geo") %>%
  select(time, geo, Geo, values) %>%
  spread(time, values) %>%
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(Geo)),
         Flag = paste0('<img src="../../bib/flags/vsmall/', Flag, '.png" alt="Flag">')) %>%
  select(Flag, everything()) %>%
  arrange(`2020`) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

2019, 2020 All Units

Code 
nama_10_gdp %>%
  filter(na_item == "B1GQ",
         time %in% c("2020")) %>%
  left_join(geo, by = "geo") %>%
  select(unit, geo, Geo, values) %>%
  spread(unit, values) %>%
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(Geo)),
         Flag = paste0('<img src="../../bib/flags/vsmall/', Flag, '.png" alt="Flag">')) %>%
  select(Flag, everything()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

France, Germany, Italy, UK

Billions

Code 
nama_10_gdp %>%
  filter(unit == "CP_MEUR",
         time == "2019",
         geo %in% c("FR", "DE", "IT", "UK")) %>%
  select(na_item, geo, values) %>%
  left_join(na_item, by = "na_item") %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
  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 .}

% of GDP

Code 
nama_10_gdp %>%
  filter(unit == "PC_GDP",
         time == "2019",
         geo %in% c("FR", "DE", "IT", "UK")) %>%
  select(na_item, geo, values) %>%
  left_join(na_item, by = "na_item") %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
  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 .}

Operating Surplus and Mixed Income / GDP (B1G)

Table

Code 
nama_10_gdp %>%
  filter(na_item %in% c("B2A3G"),
         unit == "PC_GDP",
         time %in% c("1989", "1999", "2009", "2019")) %>%
  select(time, geo, values) %>%
  left_join(geo, by = "geo") %>%
  spread(time, values) %>%
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(Geo)),
         Flag = paste0('<img src="../../bib/flags/vsmall/', Flag, '.png" alt="Flag">')) %>%
  select(Flag, everything()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

Net Exports

Table

Code 
nama_10_gdp %>%
  filter(na_item %in% c("P6", "P7"),
         unit == "PC_GDP",
         time %in% c("1989", "1999", "2009", "2019")) %>%
  select(time, na_item, geo, values) %>%
  left_join(geo, by = "geo") %>%
  spread(na_item, values) %>%
  mutate(NX = P6 - P7) %>%
  select(-P6, -P7) %>%
  mutate(NX = round(as.numeric(NX), 1)) %>%
  spread(time, NX) %>%
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(Geo)),
         Flag = paste0('<img src="../../bib/flags/vsmall/', Flag, '.png" alt="Flag">')) %>%
  select(Flag, everything()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

Greece, Portugal, Spain

Code 
nama_10_gdp %>%
  filter(na_item %in% c("P6", "P7"),
         unit == "PC_GDP",
         geo %in% c("EL", "ES", "PT")) %>%
  select(time, na_item, geo, values) %>%
  year_to_date %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo= ifelse(geo == "EA20", "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_3flags +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 5), "-01-01")),
               labels = date_format("%Y")) +
  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

Code 
nama_10_gdp %>%
  filter(na_item %in% c("P6", "P7"),
         unit == "PC_GDP",
         geo %in% c("FR", "DE", "EA20")) %>%
  select(time, na_item, geo, values) %>%
  year_to_date %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo= ifelse(geo == "EA20", "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_3flags +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 5), "-01-01")),
               labels = date_format("%Y")) +
  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, Italy, Eurozone

Code 
nama_10_gdp %>%
  filter(na_item %in% c("P6", "P7"),
         unit == "PC_GDP",
         geo %in% c("FR", "DE", "IT", "EA20")) %>%
  select(time, na_item, geo, values) %>%
  year_to_date %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo= ifelse(geo == "EA20", "Europe", Geo)) %>%
  spread(na_item, values) %>%
  mutate(values = (P6 - P7)/100) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  filter(date >= as.Date("1995-01-01")) %>%
  ggplot + geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + theme_minimal()  + add_4flags +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 2), "-01-01")),
               labels = date_format("%Y")) +
  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, Netherlands

All

Code 
nama_10_gdp %>%
  filter(na_item %in% c("P6", "P7"),
         unit == "PC_GDP",
         geo %in% c("FR", "DE", "EA20", "NL")) %>%
  select(time, na_item, geo, values) %>%
  year_to_date %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo= ifelse(geo == "EA20", "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_4flags +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 5), "-01-01")),
               labels = date_format("%Y")) +
  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-

Code 
nama_10_gdp %>%
  filter(na_item %in% c("P6", "P7"),
         unit == "PC_GDP",
         geo %in% c("FR", "DE", "EA20", "NL")) %>%
  select(time, na_item, geo, values) %>%
  year_to_date %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo= ifelse(geo == "EA20", "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_4flags +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 5), "-01-01")),
               labels = date_format("%Y")) +
  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, Italy

Code 
nama_10_gdp %>%
  filter(na_item %in% c("P6", "P7"),
         unit == "PC_GDP",
         geo %in% c("FR", "DE", "IT")) %>%
  select(time, na_item, geo, values) %>%
  year_to_date %>%
  left_join(geo, by = "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_3flags +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 5), "-01-01")),
               labels = date_format("%Y")) +
  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")

Poland, France, Italy

Code 
nama_10_gdp %>%
  filter(na_item %in% c("P6", "P7"),
         unit == "PC_GDP",
         geo %in% c("PL", "DE", "IT")) %>%
  select(time, na_item, geo, values) %>%
  year_to_date %>%
  left_join(geo, by = "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_3flags +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 5), "-01-01")),
               labels = date_format("%Y")) +
  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")

Pologne, Hongrie, Slovénie

Code 
nama_10_gdp %>%
  filter(na_item %in% c("P6", "P7"),
         unit == "PC_GDP",
         geo %in% c("PL", "HU", "SI")) %>%
  select(time, na_item, geo, values) %>%
  year_to_date %>%
  left_join(geo, by = "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_3flags +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 5), "-01-01")),
               labels = date_format("%Y")) +
  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")

Poland, Austria, Germany, Tchequia, Hungary, Slovakia

All

Code 
nama_10_gdp %>%
  filter(na_item %in% c("P6", "P7"),
         unit == "PC_GDP",
         geo %in% c("PL", "DE", "SK", "CZ", "HU", "AT")) %>%
  select(time, na_item, geo, values) %>%
  year_to_date %>%
  left_join(geo, by = "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_6flags +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 5), "-01-01")),
               labels = date_format("%Y")) +
  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-

Code 
nama_10_gdp %>%
  filter(na_item %in% c("P6", "P7"),
         unit == "PC_GDP",
         geo %in% c("PL", "DE", "SK", "CZ", "HU", "AT")) %>%
  select(time, na_item, geo, values) %>%
  year_to_date %>%
  filter(date >= as.Date("2000-01-01")) %>%
  left_join(geo, by = "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_6flags +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 5), "-01-01")),
               labels = date_format("%Y")) +
  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")

United Kingdom, Portugal, Finland, Romania, France, Greece, Sweden

Code 
nama_10_gdp %>%
  filter(na_item %in% c("P6", "P7"),
         unit == "PC_GDP",
         geo %in% c("UK", "PT", "FI", "RO", "FR", "EL", "SE")) %>%
  select(time, na_item, geo, values) %>%
  year_to_date %>%
  filter(date >= as.Date("2000-01-01")) %>%
  left_join(geo, by = "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_7flags +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 5), "-01-01")),
               labels = date_format("%Y")) +
  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")

Gross Domestic Product

Table

Code 
nama_10_gdp %>%
  filter(time %in% c("2020", "2019", "2000"),
         # B1GQ: Gross domestic product at market prices
         na_item == "B1GQ",
         unit == "CLV10_MEUR") %>%
  left_join(geo, by = "geo") %>%
  select(geo, Geo, time, values) %>%
  spread(time, values) %>%
  print_table_conditional()

France, Germany, Italy

All

Code 
nama_10_gdp %>%
  filter(geo %in% c("FR", "DE", "IT"),
         # B1GQ: Gross domestic product at market prices
         na_item == "B1GQ",
         unit == "CLV10_MEUR") %>%
  left_join(geo, by = "geo") %>%
  year_to_date %>%
  mutate(values = values/1000) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot + geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + add_3flags + theme_minimal()  +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 5), "-01-01")),
               labels = date_format("%Y")) +
  xlab("") + ylab("") +
  scale_y_log10(breaks = seq(0, 3000, 100),
                labels = dollar_format(suffix = " Bn€", prefix = "", accuracy = 1))

1995-

Code 
nama_10_gdp %>%
  filter(geo %in% c("FR", "DE", "IT"),
         # B1GQ: Gross domestic product at market prices
         na_item == "B1GQ",
         unit == "CLV10_MEUR") %>%
  left_join(geo, by = "geo") %>%
  year_to_date %>%
  mutate(values = values/1000) %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot + geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + add_3flags + theme_minimal()  +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 5), "-01-01")),
               labels = date_format("%Y")) +
  xlab("") + ylab("") +
  scale_y_log10(breaks = seq(0, 3000, 100),
                labels = dollar_format(suffix = " Bn€", prefix = "", accuracy = 1))

France, Germany, Eurozone, Italy, Spain

Code 
nama_10_gdp %>%
  filter(geo %in% c("FR", "DE", "EA20", "IT", "ES"),
         # B1GQ: Gross domestic product at market prices
         na_item == "B1GQ",
         unit == "CLV10_MEUR") %>%
  left_join(geo, by = "geo") %>%
  year_to_date %>%
  mutate(values = values/1000) %>%
  filter(date >= as.Date("1995-01-01")) %>%
  mutate(Geo = ifelse(geo == "EA20", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot + geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + add_5flags + theme_minimal()  +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 5), "-01-01")),
               labels = date_format("%Y")) +
  xlab("") + ylab("") +
  scale_y_log10(breaks = seq(1000, 15000, 1000),
                labels = dollar_format(suffix = " Bn€", prefix = "", accuracy = 1))

Germany, France, Italy, Spain, Netherlands, Belgium

Code 
nama_10_gdp %>%
  filter(geo %in% c("FR", "DE", "IT", "ES", "NL", "BE"),
         # B1GQ: Gross domestic product at market prices
         na_item == "B1GQ",
         unit == "CLV10_MEUR") %>%
  left_join(geo, by = "geo") %>%
  year_to_date %>%
  mutate(values = values/1000) %>%
  filter(date >= as.Date("1995-01-01")) %>%
  mutate(Geo = ifelse(geo == "EA20", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot + geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + add_6flags + theme_minimal()  +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 5), "-01-01")),
               labels = date_format("%Y")) +
  xlab("") + ylab("") +
  scale_y_log10(breaks = c(seq(100, 1000, 100), 1500, seq(1000, 15000, 1000)),
                labels = dollar_format(suffix = " Bn€", prefix = "", accuracy = 1))

Investment Rates

France, Germany, Italy

Code 
nama_10_gdp %>%
  filter(geo %in% c("FR", "DE", "IT"),
         na_item %in% c("B1GQ", "P51G"),
         unit == "CLV10_MEUR") %>%
  select(-unit) %>%
  spread(na_item, values) %>%
  mutate(values = P51G / B1GQ) %>%
  left_join(geo, by = "geo") %>%
  year_to_date %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot + theme_minimal() + xlab("") + ylab("") +
  geom_line(aes(x = date, y = values, color = color)) +
  add_3flags + scale_color_identity() +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 5), "-01-01")),
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(0, 500, 1),
                labels = percent_format(accuracy = 1))

Deflator Index

France

Code 
nama_10_gdp %>%
  filter(geo == "FR",
         time %in% c("2019", "1995"),
         unit == "PD15_EUR") %>%
  left_join(na_item, by = "na_item") %>%
  select_if(~ n_distinct(.) > 1) %>%
  spread(time, values) %>%
  mutate(`Change` = round(100*((`2019`/`1995`)^(1/24)-1),2)) %>%
  arrange(Change) %>%
  print_table_conditional
na_item Na_item 1995 2019 Change
D31 Subsidies on products 176.158 111.952 -1.87
P71 Imports of goods 94.770 101.683 0.29
P61 Exports of goods 93.106 100.960 0.34
P7 Imports of goods and services 91.610 102.167 0.46
P6 Exports of goods and services 90.640 101.529 0.47
P62 Exports of services 84.247 102.786 0.83
P72 Imports of services 82.422 103.384 0.95
P3_P6 Final consumption expenditure, gross capital formation and exports of goods and services 80.227 103.156 1.05
P31_S14 Final consumption expenditure of households 80.411 103.728 1.07
P31_S14_S15 Household and NPISH final consumption expenditure 80.202 103.750 1.08
B1G Value added, gross 78.283 103.004 1.15
P41 Actual individual consumption 78.465 103.437 1.16
P3 Final consumption expenditure 77.903 103.418 1.19
B1GQ Gross domestic product at market prices 77.394 103.471 1.22
P3_P5 Final consumption expenditure and gross capital formation 77.519 103.680 1.22
P5G Gross capital formation 76.310 104.597 1.32
P51G Gross fixed capital formation 75.303 104.908 1.39
P31_S15 Final consumption expenditure of NPISH 74.629 104.263 1.40
P32_S13 Collective consumption expenditure of general government 73.677 103.265 1.42
P3_S13 Final consumption expenditure of general government 72.723 102.668 1.45
P31_S13 Individual consumption expenditure of general government 72.035 102.341 1.47
D21 Taxes on products 74.556 107.356 1.53
D21X31 Taxes less subsidies on products 70.168 107.162 1.78

P71 - Imports of goods

Code 
nama_10_gdp %>%
  filter(na_item == "P71",
         unit == "PD15_EUR",
         geo %in% c("FR", "DE", "IT", "ES")) %>%
  year_to_date() %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  group_by(Geo) %>%
  arrange(date) %>%
  mutate(values = values/ values[1]) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Price Deflator (P71)") +
  scale_color_identity() + add_4flags +
  scale_x_date(breaks = seq(1960, 2025, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 10))

P61

Code 
nama_10_gdp %>%
  filter(na_item == "P61",
         unit == "PD15_EUR",
         geo %in% c("FR", "DE", "IT", "ES")) %>%
  year_to_date() %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  group_by(Geo) %>%
  arrange(date) %>%
  mutate(values = values/ values[1]) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Price Deflator (P61)") +
  scale_color_identity() + add_4flags +
  scale_x_date(breaks = seq(1960, 2025, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 10))

P5G

Code 
nama_10_gdp %>%
  filter(na_item == "P5G",
         unit == "PD15_EUR",
         geo %in% c("FR", "DE", "IT", "ES")) %>%
  year_to_date() %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  group_by(Geo) %>%
  arrange(date) %>%
  mutate(values = values/ values[1]) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Price Deflator (P5G)") +
  scale_color_identity() + add_4flags +
  scale_x_date(breaks = seq(1960, 2025, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 10))

P3

Code 
nama_10_gdp %>%
  filter(na_item == "P3",
         unit == "PD15_EUR",
         geo %in% c("FR", "DE", "IT", "ES")) %>%
  year_to_date() %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  group_by(Geo) %>%
  arrange(date) %>%
  mutate(values = values/ values[1]) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Price Deflator (P31_S14)") +
  scale_color_identity() + add_4flags +
  scale_x_date(breaks = seq(1960, 2025, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 10))

P31_S14 - Final consumption expenditure of households

Code 
nama_10_gdp %>%
  filter(na_item == "P31_S14",
         unit == "PD15_EUR",
         geo %in% c("FR", "DE", "IT", "ES")) %>%
  year_to_date() %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  group_by(Geo) %>%
  arrange(date) %>%
  mutate(values = values/ values[1]) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Price Deflator (P31_S14)") +
  scale_color_identity() + add_4flags +
  scale_x_date(breaks = seq(1960, 2025, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 10))

P31_S15

Code 
nama_10_gdp %>%
  filter(na_item == "P31_S15",
         unit == "PD15_EUR",
         geo %in% c("FR", "DE", "IT", "ES")) %>%
  year_to_date() %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  group_by(Geo) %>%
  arrange(date) %>%
  mutate(values = values/ values[1]) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Price Deflator (P31_S15)") +
  scale_color_identity() + add_4flags +
  scale_x_date(breaks = seq(1960, 2025, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 10))

P31_S14_S15

Code 
nama_10_gdp %>%
  filter(na_item == "P31_S14_S15",
         unit == "PD15_EUR",
         geo %in% c("FR", "DE", "IT", "ES")) %>%
  year_to_date() %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  group_by(Geo) %>%
  arrange(date) %>%
  mutate(values = values/ values[1]) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Price Deflator (P31_S14_S15)") +
  scale_color_identity() + add_4flags +
  scale_x_date(breaks = seq(1960, 2025, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 10))

B1G

Code 
nama_10_gdp %>%
  filter(na_item == "B1G",
         unit == "PD15_EUR",
         geo %in% c("FR", "DE", "IT", "ES")) %>%
  year_to_date() %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  group_by(Geo) %>%
  arrange(date) %>%
  mutate(values = values/ values[1]) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Price Deflator (B1G)") +
  scale_color_identity() + add_4flags +
  scale_x_date(breaks = seq(1960, 2025, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 10))

B1GQ

Code 
nama_10_gdp %>%
  filter(na_item == "B1GQ",
         unit == "PD15_EUR",
         geo %in% c("FR", "DE", "IT", "ES")) %>%
  year_to_date() %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  group_by(Geo) %>%
  arrange(date) %>%
  mutate(values = values/ values[1]) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Price Deflator (B1GQ)") +
  scale_color_identity() + add_4flags +
  scale_x_date(breaks = seq(1960, 2025, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 10))

P41

Code 
nama_10_gdp %>%
  filter(na_item == "P41",
         unit == "PD15_EUR",
         geo %in% c("FR", "DE", "IT", "ES")) %>%
  year_to_date() %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  group_by(Geo) %>%
  arrange(date) %>%
  mutate(values = values/ values[1]) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Price Deflator (P41)") +
  scale_color_identity() + add_4flags +
  scale_x_date(breaks = seq(1960, 2025, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 10))