GDP and main components (output, expenditure and income)

Data - Eurostat

Author

François Geerolf

Info

source dataset .html .RData

eurostat

nama_10_gdp

 2024-06-24 2024-06-18

eurostat

namq_10_gdp

 2024-06-20 2024-06-08

Data on macro

Title source dataset .html .RData
Gross value added and income by A*10 industry breakdowns

eurostat

nama_10_a10

 2024-06-23 2024-06-08
Employment by A*10 industry breakdowns

eurostat

nama_10_a10_e

 2024-06-23 2024-06-23
GDP and main components (output, expenditure and income)

eurostat

nama_10_gdp

 2024-06-24 2024-06-18
Labour productivity and unit labour costs

eurostat

nama_10_lp_ulc

 2024-06-24 2024-06-08
Gross value added and income A*10 industry breakdowns

eurostat

namq_10_a10

 2024-06-24 2024-06-23
Employment A*10 industry breakdowns

eurostat

namq_10_a10_e

 2024-06-24 2024-06-08
GDP and main components (output, expenditure and income)

eurostat

namq_10_gdp

 2024-06-20 2024-06-08
Labour productivity and unit labour costs

eurostat

namq_10_lp_ulc

 2024-06-20 2024-06-08
Main GDP aggregates per capita

eurostat

namq_10_pc

 2024-06-20 2024-06-18
Non-financial transactions

eurostat

nasa_10_nf_tr

 2024-06-21 2024-06-08
Non-financial transactions

eurostat

nasq_10_nf_tr

 2024-06-20 2024-06-08
Gross Domestic Product

fred

gdp

 2024-06-20 2024-06-07
Quarterly National Accounts, Per Capita

oecd

QNA

 2024-06-06 2024-06-05
Gross domestic product (GDP)

oecd

SNA_TABLE1

 2024-06-20 2024-06-01
Non-financial accounts by sectors

oecd

SNA_TABLE14A

 2024-06-20 2024-04-15
Disposable income and net lending - net borrowing

oecd

SNA_TABLE2

 2024-06-20 2024-04-11
Value added and its components by activity, ISIC rev4

oecd

SNA_TABLE6A

 2024-06-20 2024-04-15
External balance on goods and services (% of GDP)

wdi

NE.RSB.GNFS.ZS

 2024-06-20 2024-04-14
GDP (current USD)

wdi

NY.GDP.MKTP.CD

 2024-06-20 2024-05-06
GDP, PPP (current international D)

wdi

NY.GDP.MKTP.PP.CD

 2024-06-20 2024-04-14
GDP per capita (current USD)

wdi

NY.GDP.PCAP.CD

 2024-06-20 2024-04-22
GDP per capita (constant 2015 USD)

wdi

NY.GDP.PCAP.KD

 2024-06-20 2024-05-06
GDP per capita, PPP (current international D)

wdi

NY.GDP.PCAP.PP.CD

 2024-06-20 2024-04-22
GDP per capita, PPP (constant 2011 international D)

wdi

NY.GDP.PCAP.PP.KD

 2024-06-20 2024-05-06

Last

Code 
namq_10_gdp %>%
  group_by(time) %>%
  summarise(Nobs = n()) %>%
  arrange(desc(time)) %>%
  head(3) %>%
  print_table_conditional()
time Nobs
2024Q1 47788
2023Q4 51026
2023Q3 51298

na_item

Code 
namq_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 .}

s_adj

Code 
namq_10_gdp %>%
  left_join(s_adj, by = "s_adj") %>%
  group_by(s_adj, S_adj) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) print_table(.) else .}
s_adj S_adj Nobs
SCA Seasonally and calendar adjusted data 2949202
NSA Unadjusted data (i.e. neither seasonally adjusted nor calendar adjusted data) 2836160
CA Calendar adjusted data, not seasonally adjusted data 355803
SA Seasonally adjusted data, not calendar adjusted data 326548

geo

Code 
namq_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="../../icon/flag/vsmall/', Flag, '.png" alt="Flag">')) %>%
  select(Flag, everything()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

unit

Code 
namq_10_gdp %>%
  left_join(unit, by = "unit") %>%
  group_by(unit, Unit) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

time

Code 
namq_10_gdp %>%
  group_by(time) %>%
  summarise(Nobs = n()) %>%
  arrange(desc(time)) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

Live

Last GDP Numbers

Instantaneous

Code 
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") %>%
  left_join(geo, by = "geo") %>%
  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

Code 
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)) %>%
  left_join(geo, by = "geo") %>%
  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

Code 
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]) %>%
  left_join(geo, by = "geo") %>%
  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
2024-01-01 DE Germany 100.3394
2024-01-01 ES Spain 103.7016
2024-01-01 FR France 103.3268
2024-01-01 IT Italy 104.6287

Graph

Code 
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(geo, by = "geo") %>%
  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_4flags +
  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

2019-

Code 
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", "EA12", "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")]) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA12", "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_3flags +
  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-

Code 
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", "EA12", "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")]) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA12", "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_3flags +
  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

Code 
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", "EA12", "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)) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA12", "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_3flags +
  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

Code 
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", "EA12", "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)) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA12", "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_8flags + 
  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

Code 
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, values) %>%
  left_join(geo, by = "geo") %>%
  spread(time, values) %>%
  arrange(-`2022Q4`) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

Table, PD15_EUR

Code 
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, values) %>%
  left_join(geo, by = "geo") %>%
  spread(time, values) %>%
  arrange(-`2022Q4`) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

Germany, France

GDP

All
Code 
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")) %>%
  left_join(geo, by = "geo") %>%
  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("1996-01-01")]) %>%
  ggplot + geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + theme_minimal()  + add_2flags +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2026, 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-
Code 
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")) %>%
  left_join(geo, by = "geo") %>%
  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_2flags +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2026, 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))

Consumption

All
Code 
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")) %>%
  left_join(geo, by = "geo") %>%
  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("1996-01-01")]) %>%
  ggplot + geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + theme_minimal()  + add_2flags +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2026, 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-
Code 
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")) %>%
  left_join(geo, by = "geo") %>%
  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_2flags +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2026, 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

Code 
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")) %>%
  left_join(geo, by = "geo") %>%
  left_join(na_item, by = "na_item") %>%
  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("1996-01-01")]) %>%
  ggplot + geom_line(aes(x = date, y = values, color = color, linetype = Na_item)) +
  scale_color_identity() + theme_minimal()  + add_4flags +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2026, 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))

Germany, France, Euro Area, Italy, Spain

PD_PCH_SM_EUR

1996-
Code 
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, values = values/100) %>%
  quarter_to_date %>%
  filter(date >= as.Date("1996-01-01")) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo= ifelse(geo == "EA", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot + geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + theme_minimal()  + add_4flags +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2026, 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-
Code 
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, values = values/100) %>%
  quarter_to_date %>%
  filter(date >= as.Date("2010-01-01")) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo= ifelse(geo == "EA", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot + geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + theme_minimal()  + add_4flags +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2026, 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
Code 
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, values = values/100) %>%
  quarter_to_date %>%
  filter(date >= as.Date("2018-01-01")) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo= ifelse(geo == "EA", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot + geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + theme_minimal()  + add_5flags +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2026, 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
Code 
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, values = values/100) %>%
  quarter_to_date %>%
  filter(date >= as.Date("2018-01-01")) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo= ifelse(geo == "EA", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot + geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + theme_minimal()  + add_5flags +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2026, 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
Code 
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, values = values/100) %>%
  quarter_to_date %>%
  filter(date >= as.Date("2018-01-01")) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo= ifelse(geo == "EA", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot + geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + theme_minimal()  + add_4flags +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2026, 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
Code 
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, values = values/100) %>%
  quarter_to_date %>%
  filter(date >= as.Date("2018-01-01")) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo= ifelse(geo == "EA", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot + geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + theme_minimal()  + add_4flags +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2026, 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

Code 
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, values) %>%
  mutate(values = round(values, 1)) %>%
  left_join(geo, by = "geo") %>%
  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-

Code 
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, values) %>%
  quarter_to_date %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(geo, by = "geo") %>%
  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_3flags +
  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

Code 
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, values) %>%
  quarter_to_date %>%
  left_join(geo, by = "geo") %>%
  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_4flags +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2026, 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-

Code 
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, values) %>%
  quarter_to_date %>%
  filter(date >= as.Date("1990-01-01")) %>%
  left_join(geo, by = "geo") %>%
  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_4flags +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2026, 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

Code 
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, values) %>%
  quarter_to_date %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(geo, by = "geo") %>%
  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_4flags +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2030, 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

Code 
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, values) %>%
  quarter_to_date %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(geo, by = "geo") %>%
  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_4flags +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2030, 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-

Code 
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, values) %>%
  quarter_to_date %>%
  filter(date >= as.Date("1996-01-01")) %>%
  left_join(geo, by = "geo") %>%
  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_4flags +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2026, 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")

Net Exports

Table

Code 
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, values) %>%
  mutate(values = round(values, 1)) %>%
  left_join(geo, by = "geo") %>%
  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-

Code 
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, values) %>%
  quarter_to_date %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(geo, by = "geo") %>%
  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_3flags +
  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

Code 
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, values) %>%
  quarter_to_date %>%
  left_join(geo, by = "geo") %>%
  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_4flags +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2026, 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-

Code 
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, values) %>%
  quarter_to_date %>%
  filter(date >= as.Date("1990-01-01")) %>%
  left_join(geo, by = "geo") %>%
  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_4flags +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2026, 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

Code 
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, values) %>%
  quarter_to_date %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(geo, by = "geo") %>%
  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_4flags +
  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

Code 
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, values) %>%
  quarter_to_date %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(geo, by = "geo") %>%
  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_4flags +
  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-

Code 
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, values) %>%
  quarter_to_date %>%
  filter(date >= as.Date("1996-01-01")) %>%
  left_join(geo, by = "geo") %>%
  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_4flags +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2026, 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

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

SCA

Code 
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, values) %>%
  quarter_to_date %>%
  left_join(geo, by = "geo") %>%
  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_4flags +
  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)

Code 
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, values) %>%
  quarter_to_date %>%
  left_join(geo, by = "geo") %>%
  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_3flags +
  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

Code 
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, values) %>%
  quarter_to_date %>%
  left_join(geo, by = "geo") %>%
  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_3flags +
  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-

Code 
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, values) %>%
  quarter_to_date %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(geo, by = "geo") %>%
  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_3flags +
  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-

Code 
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, values) %>%
  quarter_to_date %>%
  filter(date >= as.Date("1996-01-01")) %>%
  left_join(geo, by = "geo") %>%
  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_3flags +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2026, 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-

Code 
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, values) %>%
  quarter_to_date %>%
  filter(date >= as.Date("2000-01-01")) %>%
  left_join(geo, by = "geo") %>%
  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_3flags +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2026, 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-

Code 
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, values) %>%
  quarter_to_date %>%
  filter(date >= as.Date("2010-01-01")) %>%
  left_join(geo, by = "geo") %>%
  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_3flags +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2026, 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

Code 
load_data("eurostat/geo.RData")
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, values) %>%
  quarter_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()  +
  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

Code 
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, values) %>%
  quarter_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")) +
  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

Code 
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, values) %>%
  quarter_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")) +
  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

Code 
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, values) %>%
  quarter_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")) +
  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

Code 
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, values) %>%
  quarter_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")) +
  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

Code 
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, values) %>%
  quarter_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")) +
  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

Code 
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, values) %>%
  quarter_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")) +
  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

Code 
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, values) %>%
  quarter_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")) +
  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

Code 
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, values) %>%
  quarter_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()  +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 5), "-01-01")),
               labels = date_format("%Y")) + add_3flags +
  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

Code 
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, values) %>%
  quarter_to_date %>%
  left_join(geo, by = "geo") %>%
  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_5flags +
  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

Code 
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, values) %>%
  quarter_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()  +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 5), "-01-01")),
               labels = date_format("%Y")) + add_3flags +
  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

Code 
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, values) %>%
  quarter_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()  +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 5), "-01-01")),
               labels = date_format("%Y")) + add_6flags +
  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

Code 
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")) %>%
  left_join(na_item, by = "na_item") %>%
  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

Code 
namq_10_gdp %>%
  filter(s_adj == "SCA",
         unit == "PC_GDP",
         time %in% c("2019Q1"),
         geo %in% c("FR", "IT", "DE", "ES")) %>%
  left_join(na_item, by = "na_item") %>%
  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

Code 
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")) %>%
  left_join(na_item, by = "na_item") %>%
  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

Code 
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")) %>%
  left_join(na_item, by = "na_item") %>%
  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-

Code 
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")) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA19", "Europe", Geo)) %>%
  group_by(geo) %>%
  mutate(values = 100*values / values[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() + theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2026, 5), "-01-01")),
               labels = date_format("%Y")) +
  add_4flags +
  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-

Code 
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")) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA19", "Europe", Geo)) %>%
  group_by(geo) %>%
  mutate(values = 100*values / values[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() + theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2026, 5), "-01-01")),
               labels = date_format("%Y")) +
  add_4flags +
  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-

Code 
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")) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA19", "Europe", Geo)) %>%
  group_by(geo) %>%
  mutate(values = 100*values / values[date == as.Date("1996-01-01")]) %>%
  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, 2026, 2), "-01-01")),
               labels = date_format("%Y")) +
  add_4flags +
  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-

Code 
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")) %>%
  left_join(geo, by = "geo") %>%
  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, 2026, 2), "-01-01")),
               labels = date_format("%Y")) +
  add_4flags +
  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-

Code 
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")) %>%
  left_join(geo, by = "geo") %>%
  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_4flags +
  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-

Code 
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") %>%
  left_join(geo, by = "geo") %>%
  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 813.6452 814.6665 1.0213
EA20 Euro area – 20 countries (from 2023) 2862.5519 2951.9531 89.4012
FR France 586.8223 605.3973 18.5750
IE Ireland 87.1085 104.8348 17.7263
IT Italy 430.5326 448.9197 18.3871

Allemagne, France, Italie

All

Code 
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(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/1000) %>%
  ggplot + geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + theme_minimal() + xlab("") + ylab("") + add_3flags +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 5), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.35, 0.85),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(0, 1000, 100),
                labels = dollar_format(suffix = " Bn€", prefix = "", accuracy = 1))

1995-

Code 
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(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/1000) %>%
  ggplot + geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + theme_minimal() + xlab("") + ylab("") + add_3flags +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 5), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.35, 0.85),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(0, 1000, 100),
                labels = dollar_format(suffix = " Bn€", prefix = "", accuracy = 1))

2019Q4 to 2020Q1

Code 
load_data("eurostat/geo.RData")
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")) %>%
  left_join(geo, by = "geo") %>%
  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

Code 
load_data("eurostat/geo.RData")
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("EA12", "EA19"))) %>%
  left_join(geo, by = "geo") %>%
  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 .}

Maps: 2008 Current Accounts in Europe (% of GDP)

Code 
load_data("eurostat/geo.RData")
namq_10_gdp %>%
  filter(na_item %in% c("P6", "P7"),
         s_adj == "NSA",
         unit == "PC_GDP",
         time == "2019Q4") %>%
  select(na_item, geo, values) %>%
  spread(na_item, values) %>%
  mutate(values = (P6 - P7)/100) %>%
  left_join(geo, by = "geo") %>%
  rename(region = Geo) %>%
  right_join(eurozone %>%
              mutate(region = case_when(region == "UK" ~ "United Kingdom",
                                        region == "Slovakia" ~ "Slovak Republic",
                                        TRUE ~ region)), 
            by = "region") %>%
  ggplot(., aes(x = long, y = lat, group = group, fill = values)) +
  geom_polygon() + coord_map() +
  scale_fill_viridis_c(na.value = "white",
                       labels = scales::percent_format(accuracy = 1, suffix = "% of GDP"),
                       breaks = c(-0.16,-0.12,-0.08,-0.04, 0, 0.04, 0.08, 0.12, 0.16),
                       values = c(0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 1)) +
  theme_void() +
  theme(legend.position = c(0.05, 0.55)) + 
  labs(fill = "2019Q4 Current Account")

Consumption

Germany, France, Europe, Italy

All

Code 
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 %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA19", "Europe", Geo)) %>%
  group_by(geo) %>%
  mutate(values = 100*values / values[date == as.Date("1996-01-01")]) %>%
  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, 2026, 2), "-01-01")),
               labels = date_format("%Y")) +
  add_4flags +
  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

Code 
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")) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA19", "Europe", Geo)) %>%
  group_by(geo) %>%
  mutate(values = 100*values / values[date == as.Date("1996-01-01")]) %>%
  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, 2026, 2), "-01-01")),
               labels = date_format("%Y")) +
  add_4flags +
  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

Code 
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")) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA19", "Europe", Geo)) %>%
  group_by(geo) %>%
  mutate(values = 100*values / values[date == as.Date("1996-01-01")]) %>%
  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, 2026, 2), "-01-01")),
               labels = date_format("%Y")) +
  add_4flags +
  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-

Code 
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")) %>%
  left_join(geo, by = "geo") %>%
  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_4flags +
  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-

Code 
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")) %>%
  left_join(geo, by = "geo") %>%
  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, 2026, 1), "-01-01")),
               labels = date_format("%Y")) +
  add_4flags +
  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-

Code 
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")) %>%
  left_join(geo, by = "geo") %>%
  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, 2026, 1), "-01-01")),
               labels = date_format("%Y")) +
  add_4flags +
  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

Code 
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`) %>%
  left_join(geo, by = "geo") %>%
  select(geo, Geo, everything()) %>%
  print_table_conditional

Last observation, Eurozone

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