| source | dataset | .html | .RData |
|---|---|---|---|
| eurostat | nama_10_gdp | 2025-02-01 | 2025-01-31 |
| eurostat | namq_10_gdp | 2025-02-01 | 2025-01-31 |
GDP and main components (output, expenditure and income)
Data - Eurostat
Info
Data on macro
| Title | source | dataset | .html | .RData |
|---|---|---|---|---|
| Gross value added and income by A*10 industry breakdowns | eurostat | nama_10_a10 | 2025-02-01 | 2024-10-08 |
| Employment by A*10 industry breakdowns | eurostat | nama_10_a10_e | 2025-02-01 | 2025-02-01 |
| GDP and main components (output, expenditure and income) | eurostat | nama_10_gdp | 2025-02-01 | 2025-01-31 |
| Labour productivity and unit labour costs | eurostat | nama_10_lp_ulc | 2025-02-01 | 2024-10-08 |
| Gross value added and income A*10 industry breakdowns | eurostat | namq_10_a10 | 2025-02-01 | 2025-02-01 |
| Employment A*10 industry breakdowns | eurostat | namq_10_a10_e | 2025-02-01 | 2025-01-31 |
| GDP and main components (output, expenditure and income) | eurostat | namq_10_gdp | 2025-02-01 | 2025-01-31 |
| Labour productivity and unit labour costs | eurostat | namq_10_lp_ulc | 2025-02-01 | 2024-11-04 |
| Main GDP aggregates per capita | eurostat | namq_10_pc | 2025-02-01 | 2024-12-29 |
| Non-financial transactions | eurostat | nasa_10_nf_tr | 2025-02-01 | 2024-12-14 |
| Non-financial transactions | eurostat | nasq_10_nf_tr | 2025-02-01 | 2024-10-09 |
| Gross Domestic Product | fred | gdp | 2025-01-26 | 2025-01-26 |
| Quarterly National Accounts | oecd | QNA | 2024-06-06 | 2025-01-31 |
| Gross domestic product (GDP) | oecd | SNA_TABLE1 | 2025-01-31 | 2025-01-31 |
| Non-financial accounts by sectors | oecd | SNA_TABLE14A | 2024-09-15 | 2024-06-30 |
| Disposable income and net lending - net borrowing | oecd | SNA_TABLE2 | 2024-07-01 | 2024-04-11 |
| Value added and its components by activity, ISIC rev4 | oecd | SNA_TABLE6A | 2024-07-01 | 2024-06-30 |
| External balance on goods and services (% of GDP) | wdi | NE.RSB.GNFS.ZS | 2025-01-24 | 2025-01-24 |
| GDP (current USD) | wdi | NY.GDP.MKTP.CD | 2024-09-18 | 2024-09-26 |
| GDP, PPP (current international D) | wdi | NY.GDP.MKTP.PP.CD | 2024-09-18 | 2024-09-18 |
| GDP per capita (current USD) | wdi | NY.GDP.PCAP.CD | 2025-01-31 | 2025-01-31 |
| GDP per capita (constant 2015 USD) | wdi | NY.GDP.PCAP.KD | 2024-09-18 | 2024-09-18 |
| GDP per capita, PPP (current international D) | wdi | NY.GDP.PCAP.PP.CD | 2025-01-31 | 2025-01-31 |
| GDP per capita, PPP (constant 2011 international D) | wdi | NY.GDP.PCAP.PP.KD | 2025-01-31 | 2025-01-31 |
Last
- 2023Q1. Flash
Code
namq_10_gdp %>%
group_by(time) %>%
summarise(Nobs = n()) %>%
arrange(desc(time)) %>%
head(3) %>%
print_table_conditional()| time | Nobs |
|---|---|
| 2024Q4 | 3526 |
| 2024Q3 | 61050 |
| 2024Q2 | 62563 |
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 | 3476595 |
| NSA | Unadjusted data (i.e. neither seasonally adjusted nor calendar adjusted data) | 3373649 |
| CA | Calendar adjusted data, not seasonally adjusted data | 470777 |
| SA | Seasonally adjusted data, not calendar adjusted data | 411345 |
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 |
| 2024-10-01 | DE | Germany | 99.8733 |
| 2024-10-01 | ES | Spain | 107.5620 |
| 2024-10-01 | FR | France | 103.9803 |
| 2024-10-01 | IT | Italy | 105.5960 |
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
2017T2-
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", "EA", "FR")) %>%
quarter_to_date %>%
filter(date >= as.Date("2017-04-01")) %>%
group_by(geo) %>%
mutate(values = 100*values/values[date == as.Date("2017-04-01")]) %>%
left_join(geo, by = "geo") %>%
mutate(Geo = ifelse(geo == "EA", "Europe", Geo)) %>%
left_join(colors, by = c("Geo" = "country")) %>%
mutate(date = zoo::as.yearqtr(paste0(year(date), " Q", quarter(date)))) %>%
ggplot + geom_line(aes(x = date, y = values, color = color)) + add_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("2017 Q2"), zoo::as.yearqtr("2100 Q1"), by = 0.25)) +
theme(legend.position = c(0.35, 0.85),
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
scale_y_log10(breaks = seq(10, 300, 2))
2019-
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", "EA", "FR")) %>%
quarter_to_date %>%
filter(date >= as.Date("2019-10-01")) %>%
group_by(geo) %>%
mutate(values = 100*values/values[date == as.Date("2019-10-01")]) %>%
left_join(geo, by = "geo") %>%
mutate(Geo = ifelse(geo == "EA", "Europe", Geo)) %>%
left_join(colors, by = c("Geo" = "country")) %>%
mutate(date = zoo::as.yearqtr(paste0(year(date), " Q", quarter(date)))) %>%
ggplot + geom_line(aes(x = date, y = values, color = color)) + add_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", "EA", "FR")) %>%
quarter_to_date %>%
filter(date >= as.Date("2021-10-01")) %>%
group_by(geo) %>%
mutate(values = 100*values/values[date == as.Date("2021-10-01")]) %>%
left_join(geo, by = "geo") %>%
mutate(Geo = ifelse(geo == "EA", "Europe", Geo)) %>%
left_join(colors, by = c("Geo" = "country")) %>%
mutate(date = zoo::as.yearqtr(paste0(year(date), " Q", quarter(date)))) %>%
ggplot + geom_line(aes(x = date, y = values, color = color)) + add_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", "EA", "FR")) %>%
quarter_to_date %>%
filter(date >= as.Date("2019-10-01")) %>%
group_by(geo) %>%
arrange(date) %>%
mutate(values = 100*values/values[date == as.Date("2019-10-01")],
values = cumsum(values) / seq_along(values)) %>%
left_join(geo, by = "geo") %>%
mutate(Geo = ifelse(geo == "EA", "Europe", Geo)) %>%
left_join(colors, by = c("Geo" = "country")) %>%
mutate(date = zoo::as.yearqtr(paste0(year(date), " Q", quarter(date)))) %>%
ggplot + geom_line(aes(x = date, y = values, color = color)) +
scale_color_identity() + theme_minimal() + xlab("") + ylab("PIB réel moyen depuis le début du Covid-19") +
zoo::scale_x_yearqtr(labels = date_format("%Y Q%q"),
breaks = seq(zoo::as.yearqtr("2019 Q4"), zoo::as.yearqtr("2100 Q1"), by = 0.25)) +
add_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", "EA", "FR", "EL", "BE", "CH", "IT", "ES")) %>%
quarter_to_date %>%
filter(date >= as.Date("2019-10-01")) %>%
group_by(geo) %>%
arrange(date) %>%
mutate(values = 100*values/values[date == as.Date("2019-10-01")],
values = cumsum(values) / seq_along(values)) %>%
left_join(geo, by = "geo") %>%
mutate(Geo = ifelse(geo == "EA", "Europe", Geo)) %>%
left_join(colors, by = c("Geo" = "country")) %>%
mutate(date = zoo::as.yearqtr(paste0(year(date), " Q", quarter(date)))) %>%
ggplot + geom_line(aes(x = date, y = values, color = color)) + add_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))
2018-
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("2018-01-01")) %>%
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("2018-01-01")]) %>%
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, 1), "-01-01")),
labels = date_format("%Y")) +
theme(legend.position = "none",
legend.title = element_blank()) +
xlab("") + ylab("GDP Deflator") +
scale_y_log10(breaks = seq(90, 200, 5)) +
geom_label(data = . %>% filter(date == max(date)),
aes(x = date, y = values, label = round(values, 1), color = color))
Consumption
All
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))
Greece, Portugal, France, Germany
2021-
Code
namq_10_gdp %>%
filter(na_item == "B1GQ",
s_adj == "NSA",
unit == "PD15_EUR",
geo %in% c("FR", "DE", "EL", "PT")) %>%
quarter_to_date %>%
filter(date >= as.Date("2021-01-01")) %>%
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("2021-01-01")]) %>%
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, 2026, 1), "-01-01")),
labels = date_format("%Y")) +
theme(legend.position = "none",
legend.title = element_blank()) +
xlab("") + ylab("GDP Deflator") +
scale_y_log10(breaks = seq(90, 200, 2)) +
geom_label_repel(data = . %>% filter(date == max(date)),
aes(x = date, y = values, label = round(values, 1), color = color))
Germany, France, Euro Area, Italy, Spain
PD_PCH_SM_EUR
1996-
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")
France vs. Gemrany
Français
Code
namq_10_gdp %>%
filter(na_item %in% c("P61", "P71"),
s_adj == "SCA",
unit == "PC_GDP",
geo %in% c("FR", "DE", "EA19")) %>%
select(time, na_item, geo, values) %>%
quarter_to_date %>%
filter(date >= as.Date("1995-01-01")) %>%
left_join(geo, by = "geo") %>%
mutate(Geo= ifelse(geo == "EA19", "Europe", Geo)) %>%
spread(na_item, values) %>%
mutate(values = (P61 - P71)/100) %>%
left_join(colors, by = c("Geo" = "country")) %>%
ggplot + geom_line(aes(x = date, y = values, color = color)) +
scale_color_identity() + theme_minimal() + add_3flags +
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")
Government Spending
Final consumption expenditure of general government
Code
namq_10_gdp %>%
filter(na_item %in% c("P3_S13"),
s_adj == "SCA",
unit == "PC_GDP",
geo %in% c("FR", "DE", "NL", "IT", "ES")) %>%
quarter_to_date %>%
filter(date >= as.Date("1995-01-01")) %>%
left_join(geo, by = "geo") %>%
mutate(Geo= ifelse(geo == "EA", "Europe", Geo)) %>%
left_join(colors, by = c("Geo" = "country")) %>%
mutate(color = ifelse(geo == "NL", color2, color)) %>%
mutate(values = values/100) %>%
ggplot + geom_line(aes(x = date, y = values, color = color)) +
scale_color_identity() + theme_minimal() +
scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 2), "-01-01")),
labels = date_format("%Y")) +
add_5flags +
theme(legend.position = "none",
legend.title = element_blank()) +
xlab("") + ylab("Government Consumption (% of GDP)") +
scale_y_continuous(breaks = 0.01*seq(-30, 100, 1),
labels = percent_format(a = 1))
Individual consumption expenditure of general government
Code
namq_10_gdp %>%
filter(na_item %in% c("P31_S13"),
s_adj == "SCA",
unit == "PC_GDP",
geo %in% c("FR", "DE", "NL", "IT", "ES")) %>%
quarter_to_date %>%
filter(date >= as.Date("1995-01-01")) %>%
left_join(geo, by = "geo") %>%
mutate(Geo= ifelse(geo == "EA", "Europe", Geo)) %>%
left_join(colors, by = c("Geo" = "country")) %>%
mutate(color = ifelse(geo == "NL", color2, color)) %>%
mutate(values = values/100) %>%
ggplot + geom_line(aes(x = date, y = values, color = color)) +
scale_color_identity() + theme_minimal() +
scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 2), "-01-01")),
labels = date_format("%Y")) +
add_5flags +
theme(legend.position = "none",
legend.title = element_blank()) +
xlab("") + ylab("Government Consumption (% of GDP)") +
scale_y_continuous(breaks = 0.01*seq(-30, 100, 1),
labels = percent_format(a = 1))
Collective consumption expenditure of general government
Code
namq_10_gdp %>%
filter(na_item %in% c("P32_S13"),
s_adj == "SCA",
unit == "PC_GDP",
geo %in% c("FR", "DE", "NL", "IT", "ES")) %>%
quarter_to_date %>%
filter(date >= as.Date("1995-01-01")) %>%
left_join(geo, by = "geo") %>%
mutate(Geo= ifelse(geo == "EA", "Europe", Geo)) %>%
left_join(colors, by = c("Geo" = "country")) %>%
mutate(color = ifelse(geo == "NL", color2, color)) %>%
mutate(values = values/100) %>%
ggplot + geom_line(aes(x = date, y = values, color = color)) +
scale_color_identity() + theme_minimal() +
scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 2), "-01-01")),
labels = date_format("%Y")) +
add_5flags +
theme(legend.position = "none",
legend.title = element_blank()) +
xlab("") + ylab("Government Consumption (% of GDP)") +
scale_y_continuous(breaks = 0.01*seq(-30, 100, 1),
labels = percent_format(a = 1))
Net Exports
Table
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_conditionalSCA
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 | 830.0963 | 830.3344 | 0.2381 |
| EA20 | Euro area – 20 countries (from 2023) | 2886.6134 | 2993.4117 | 106.7983 |
| FR | France | 587.0123 | 606.3824 | 19.3701 |
| IE | Ireland | 89.4322 | 107.0898 | 17.6576 |
| IT | Italy | 431.4978 | 453.3909 | 21.8931 |
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("EA", "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_conditionalLast 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| geo | Geo | s_adj | CLV_I05 | CLV_I10 | CLV_I15 | CLV_I20 | CLV_PCH_ANN | CLV_PCH_PRE | CLV_PCH_SM | CLV05_MEUR | CLV05_MNAC | CLV10_MEUR | CLV10_MNAC | CLV15_MEUR | CLV15_MNAC | CLV20_MEUR | CLV20_MNAC | CON_PPCH_PRE | CON_PPCH_SM | CP_MEUR | CP_MNAC | PC_GDP | PD_PCH_PRE_EUR | PD_PCH_PRE_NAC | PD_PCH_SM_EUR | PD_PCH_SM_NAC | PD05_EUR | PD05_NAC | PD10_EUR | PD10_NAC | PD15_EUR | PD15_NAC | PD20_EUR | PD20_NAC | PYP_MEUR | PYP_MNAC |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| FR | France | CA | 126.458 | 120.697 | 114.144 | 115.617 | NA | NA | 0.9 | 556577.1 | 556577.1 | 601676.0 | 601676.0 | 628553.2 | 628553.2 | 669749.6 | 669749.6 | NA | 0.88 | 758735.1 | 758735.1 | 100 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 736879.0 | 736879.0 |
| LT | Lithuania | CA | 176.275 | 168.796 | 136.912 | 116.019 | NA | NA | 3.7 | 9246.6 | 9246.6 | 11670.2 | 11670.2 | 12816.1 | 12816.1 | 14575.6 | 14575.6 | NA | 3.68 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| SE | Sweden | CA | 147.820 | 136.369 | 122.542 | 114.965 | NA | NA | 1.1 | 116308.0 | 1079594.1 | 126710.4 | 1208475.0 | 138602.7 | 1296420.0 | 137517.8 | 1441846.8 | NA | 1.12 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| DE | Germany | NSA | 124.736 | 117.593 | 108.185 | 105.143 | NA | NA | -0.4 | 725249.6 | 725249.6 | 769076.6 | 769076.6 | 834555.9 | 834555.9 | 906732.6 | 906732.6 | NA | NA | 1101420.0 | 1101420.0 | 100 | NA | NA | 2.5 | 2.5 | 151.868 | 151.868 | 143.213 | 143.213 | 131.977 | 131.977 | 121.471 | 121.471 | 1049664.0 | 1049664.0 |
| EE | Estonia | NSA | 143.956 | 146.931 | 123.011 | 108.130 | NA | NA | -0.1 | 4083.1 | 4083.1 | 5415.1 | 5415.1 | 6461.4 | 6461.4 | 7531.1 | 7531.1 | NA | -0.08 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| ES | Spain | NSA | 128.430 | 122.836 | 122.219 | 124.126 | NA | NA | 3.3 | 297997.1 | 297997.1 | 330779.3 | 330779.3 | 332164.8 | 332164.8 | 350413.0 | 350413.0 | NA | 3.27 | 420897.0 | 420897.0 | 100 | NA | NA | 2.9 | 2.9 | 141.242 | 141.242 | 127.244 | 127.244 | 126.713 | 126.713 | 120.115 | 120.115 | 399767.0 | 399767.0 |
| FR | France | NSA | 126.174 | 120.415 | 114.057 | 115.403 | NA | NA | 0.9 | 555811.1 | 555811.1 | 600893.4 | 600893.4 | 627712.2 | 627712.2 | 668842.9 | 668842.9 | NA | 0.95 | 757694.4 | 757694.4 | 100 | NA | NA | 1.3 | 1.3 | 136.322 | 136.322 | 126.095 | 126.095 | 120.707 | 120.707 | 113.284 | 113.284 | 735881.5 | 735881.5 |
| IE | Ireland | NSA | 241.433 | 236.907 | 168.058 | 124.630 | NA | NA | 6.3 | 102794.3 | 102794.3 | 99172.8 | 99172.8 | 114508.1 | 114508.1 | 119086.6 | 119086.6 | NA | 6.27 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| LT | Lithuania | NSA | 176.692 | 168.797 | 136.981 | 116.075 | NA | NA | 3.8 | 9270.1 | 9270.1 | 11673.7 | 11673.7 | 12821.7 | 12821.7 | 14586.1 | 14586.1 | NA | 3.77 | 20245.9 | 20245.9 | 100 | NA | NA | 1.2 | 1.2 | 218.399 | 218.399 | 173.431 | 173.431 | 157.903 | 157.903 | 138.803 | 138.803 | 19564.3 | 19564.3 |
| SE | Sweden | NSA | 146.764 | 135.400 | 122.006 | 114.319 | NA | NA | 0.6 | 115705.4 | 1074000.8 | 126052.1 | 1202197.1 | 137996.5 | 1290750.0 | 136862.9 | 1434979.6 | NA | 0.60 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| FR | France | SA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 735357.6 | 735357.6 | 100 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| AT | Austria | SCA | 124.641 | 116.832 | 110.578 | 108.509 | -0.1 | 0.0 | -0.2 | 78621.7 | 78621.7 | 85786.2 | 85786.2 | 94503.5 | 94503.5 | 102931.8 | 102931.8 | -0.02 | -0.20 | 122035.7 | 122035.7 | 100 | 1.3 | 1.3 | 3.0 | 3.0 | 155.219 | 155.219 | 142.256 | 142.256 | 129.133 | 129.133 | 118.560 | 118.560 | NA | NA |
| BE | Belgium | SCA | NA | NA | NA | NA | NA | 0.2 | 1.1 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| CZ | Czechia | SCA | 144.891 | 128.399 | 118.370 | 108.801 | 1.9 | 0.5 | 1.6 | 39959.2 | 1190063.7 | 51153.1 | 1293354.7 | 50485.1 | 1377183.7 | 59942.0 | 1585766.0 | 0.48 | 1.61 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| DE | Germany | SCA | 124.087 | 117.061 | 107.567 | 104.662 | -0.8 | -0.2 | -0.2 | 720478.4 | 720478.4 | 764001.2 | 764001.2 | 829044.6 | 829044.6 | 900760.5 | 900760.5 | NA | NA | 1087972.0 | 1087972.0 | 100 | 1.1 | 1.1 | 2.4 | 2.4 | 151.007 | 151.007 | 142.404 | 142.404 | 131.232 | 131.232 | 120.784 | 120.784 | NA | NA |
| EA | Euro area (EA11-1999, EA12-2001, EA13-2007, EA15-2008, EA16-2009, EA17-2011, EA18-2014, EA19-2015, EA20-2023) | SCA | 123.095 | 118.197 | 113.383 | 111.728 | 0.1 | 0.0 | 0.9 | 2620997.3 | 2620997.3 | 2847692.5 | 2847692.5 | 3021187.5 | 3021187.5 | 3241711.6 | 3241711.6 | 0.03 | 0.93 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| EA12 | Euro area - 12 countries (2001-2006) | SCA | 122.310 | 117.586 | 112.949 | 111.599 | 0.1 | 0.0 | 0.9 | 2552087.6 | 2552087.6 | 2763275.8 | 2763275.8 | 2932065.3 | 2932065.3 | 3144260.2 | 3144260.2 | 0.02 | 0.90 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| EA19 | Euro area - 19 countries (2015-2022) | SCA | 123.025 | 118.124 | 113.287 | 111.645 | 0.1 | 0.0 | 0.9 | 2608176.9 | 2608176.9 | 2832356.9 | 2832356.9 | 3005757.1 | 3005757.1 | 3225158.3 | 3225158.3 | 0.03 | 0.92 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| EA20 | Euro area – 20 countries (from 2023) | SCA | 123.095 | 118.197 | 113.383 | 111.728 | 0.1 | 0.0 | 0.9 | 2620997.3 | 2620997.3 | 2847692.5 | 2847692.5 | 3021187.5 | 3021187.5 | 3241711.6 | 3241711.6 | 0.03 | 0.93 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| EE | Estonia | SCA | 137.595 | 139.768 | 117.123 | 103.027 | 0.2 | 0.1 | -0.1 | 3899.4 | 3899.4 | 5150.1 | 5150.1 | 6149.9 | 6149.9 | 7182.3 | 7182.3 | 0.06 | -0.08 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| ES | Spain | SCA | 125.707 | 120.231 | 119.627 | 121.494 | 3.1 | 0.8 | 3.5 | 291677.7 | 291677.7 | 323764.9 | 323764.9 | 325121.0 | 325121.0 | 342982.0 | 342982.0 | 0.75 | 3.49 | 407457.0 | 407457.0 | 100 | 1.1 | 1.1 | 2.3 | 2.3 | 139.694 | 139.694 | 125.850 | 125.850 | 125.325 | 125.325 | 118.798 | 118.798 | 391289.0 | 391289.0 |
| EU27_2020 | European Union - 27 countries (from 2020) | SCA | 127.114 | 121.082 | 115.114 | 111.933 | 0.5 | 0.1 | 1.1 | 3049697.2 | 3049697.2 | 3341343.8 | 3341343.8 | 3541091.1 | 3541091.1 | 3797491.6 | 3797491.6 | 0.13 | 1.05 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| FR | France | SCA | 122.801 | 117.207 | 110.843 | 112.274 | -0.3 | -0.1 | 0.7 | 540482.3 | 540482.3 | 584277.1 | 584277.1 | 610377.0 | 610377.0 | 650382.1 | 650382.1 | -0.08 | 0.66 | 736398.3 | 736398.3 | 100 | 0.4 | 0.4 | 1.6 | 1.6 | 136.248 | 136.248 | 126.036 | 126.036 | 120.646 | 120.646 | 113.225 | 113.225 | 715570.3 | 715570.3 |
| HU | Hungary | SCA | 139.032 | 140.068 | 126.160 | 111.443 | 2.1 | 0.5 | 0.2 | 31629.7 | 7845751.6 | 34863.8 | 9604287.4 | 35594.2 | 11034190.0 | 38713.9 | 13598253.0 | 0.53 | 0.19 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| IE | Ireland | SCA | 224.532 | 223.945 | 161.317 | 118.684 | -5.0 | -1.3 | 2.5 | 95565.3 | 95565.3 | 93632.1 | 93632.1 | 109817.2 | 109817.2 | 113617.2 | 113617.2 | -1.27 | 2.55 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| IT | Italy | SCA | 103.917 | 105.935 | 109.596 | 115.520 | -0.1 | 0.0 | 0.5 | 390014.3 | 390014.3 | 428258.7 | 428258.7 | 455644.4 | 455644.4 | 481963.9 | 481963.9 | -0.01 | 0.50 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| LT | Lithuania | SCA | 172.897 | 165.562 | 134.289 | 113.796 | 3.5 | 0.9 | 3.6 | 9069.4 | 9069.4 | 11446.6 | 11446.6 | 12570.5 | 12570.5 | 14296.3 | 14296.3 | 0.87 | 3.55 | 19761.6 | 19761.6 | 100 | -0.9 | -0.9 | 2.2 | 2.2 | 217.892 | 217.892 | 172.642 | 172.642 | 157.207 | 157.207 | 138.229 | 138.229 | NA | NA |
| PT | Portugal | SCA | NA | NA | NA | NA | NA | 1.5 | 2.7 | NA | NA | NA | NA | NA | NA | NA | NA | 1.48 | 2.70 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| SE | Sweden | SCA | 139.401 | 128.599 | 115.595 | 108.385 | 0.8 | 0.2 | 1.2 | 109664.2 | 1017924.6 | 119431.4 | 1139053.3 | 130690.1 | 1222410.2 | 129721.8 | 1360107.0 | 0.20 | 1.20 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |