Quarterly GDP and components - expenditure approach

Data - OECD

Info

source dataset .html .RData

oecd

QNA_EXPENDITURE_CAPITA

2024-10-29 2024-10-29

oecd

QNA_EXPENDITURE_NATIO_CURR

2024-10-30 2024-10-30

Last

obsTime Nobs
2024-Q1 3426

PRICE_BASE

Code
QNA_EXPENDITURE_NATIO_CURR %>%
  left_join(PRICE_BASE, by = "PRICE_BASE") %>%
  group_by(PRICE_BASE, Price_base) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()
PRICE_BASE Price_base Nobs
V Current prices 330580
L Chain linked volume 254260
Q Constant prices 19194

REF_AREA

Code
QNA_EXPENDITURE_NATIO_CURR %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  group_by(REF_AREA, Ref_area) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()

TRANSACTION

Code
QNA_EXPENDITURE_NATIO_CURR %>%
  left_join(TRANSACTION, by = "TRANSACTION") %>%
  group_by(TRANSACTION, Transaction) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()
TRANSACTION Transaction Nobs
P3 Final consumption expenditure 138018
P51G Gross fixed capital formation 38348
B1GQ Gross domestic product 31696
P6 Exports of goods and services 31456
P7 Imports of goods and services 31456
B11 External balance of goods and services 30535
P5 Gross capital formation 28977
P61 Exports of goods 27830
P62 Exports of services 27830
P71 Imports of goods 27830
P72 Imports of services 27830
P3T5 Domestic demand 22141
P31 Individual consumption expenditure 20418
P32 Collective consumption expenditure 20418
P41 Actual individual consumption 19647
P5M Changes in inventories and acquisitions less disposals of valuables 19626
P52 Changes in inventories 19027
YA0 Statistical discrepancy (expenditure approach) 12423
PPP_B1GQ Purchasing Power Parities for GDP 12283
P53 Acquisitions less disposals of valuables 11026
P3_P51G Final domestic demand excluding inventories 3466
P3T6 Total demand 1753

ACTIVITY

Code
QNA_EXPENDITURE_NATIO_CURR %>%
  left_join(ACTIVITY, by = "ACTIVITY") %>%
  group_by(ACTIVITY, Activity) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()
ACTIVITY Activity Nobs
_Z Not applicable 487030
_T Total - all activities 117004

U.S. VS Europe

Table

Code
QNA_EXPENDITURE_NATIO_CURR %>%
  filter(REF_AREA %in% c("USA", "EA20"),
         FREQ == "Q",
         # L = Chain linked volume
         PRICE_BASE == "L",
         ADJUSTMENT == "Y",
         SECTOR == "S1",
         obsTime %in% c("1999-Q1", "2023-Q4")) %>%
  left_join(TRANSACTION, by = "TRANSACTION") %>%
  left_join(QNA_POP_EMPNC_extract, by = c("obsTime", "REF_AREA")) %>%
  mutate(obsValue = 4*obsValue/population) %>%
  select_if(~ n_distinct(.) > 1) %>%
  select(REF_AREA, TRANSACTION, Transaction, obsTime, obsValue) %>%
  spread(obsTime, obsValue) %>%
  mutate(change = `2023-Q4`-`1999-Q1`) %>%
  select(REF_AREA, TRANSACTION, Transaction, change) %>%
  spread(REF_AREA, change) %>%
  print_table_conditional()
TRANSACTION Transaction EA20 USA
B11 External balance of goods and services 1.287156 -1.7291755
B1GQ Gross domestic product 7.041177 19.6331632
P3 Final consumption expenditure 4.479080 16.6880636
P3_P51G Final domestic demand excluding inventories NA 21.3604201
P3T5 Domestic demand NA 21.3806857
P5 Gross capital formation 1.220627 4.8037966
P51G Gross fixed capital formation 1.483995 4.7447538
P52 Changes in inventories NA -0.2439831
P5M Changes in inventories and acquisitions less disposals of valuables NA -0.2439831
P6 Exports of goods and services 9.951200 3.2639284
P61 Exports of goods 6.669007 2.2676906
P62 Exports of services 3.273337 1.0222977
P7 Imports of goods and services 8.664046 4.9931070
P71 Imports of goods 5.610402 4.2424669
P72 Imports of services 3.135997 0.7664190
YA0 Statistical discrepancy (expenditure approach) NA -0.2058566

P3 - Final consumption expenditure / capita

Code
metadata_load_fr("REF_AREA", "CL_AREA", data = QNA_EXPENDITURE_NATIO_CURR_var)
plot <- QNA_EXPENDITURE_NATIO_CURR %>%
  filter(REF_AREA %in% c("USA", "EA20"),
         FREQ == "Q",
         TRANSACTION == "P3",
         # L = Chain linked volume
         PRICE_BASE == "L",
         ADJUSTMENT == "Y",
         SECTOR == "S1") %>%
  left_join(QNA_POP_EMPNC_extract, by = c("obsTime", "REF_AREA")) %>%
  mutate(obsValue = obsValue/population) %>%
  quarter_to_date %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  filter(date >= as.Date("1999-01-01")) %>%
  mutate(Ref_area = ifelse(REF_AREA == "EA20", "Zone euro", Ref_area)) %>%
  group_by(Ref_area) %>%
  arrange(date) %>%
  mutate(obsValue = 100 * obsValue / obsValue[1]) %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("Consommation par habitant (1999T1 = 100)") +
  geom_line(aes(x = date, y = obsValue, color = Ref_area)) +
  scale_color_manual(values = c("#B22234", "#003399")) +
  scale_x_date(breaks = c(seq(1999, 2100, 5), seq(1997, 2100, 5)) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.26, 0.8),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(50, 200, 5)) +
  geom_rect(data = nber_recessions %>%
              filter(Peak > as.Date("1999-01-01")), 
            aes(xmin = Peak, xmax = Trough, ymin = 0, ymax = +Inf),
            fill = '#B22234', alpha = 0.1)  +
  geom_rect(data = cepr_recessions %>%
              filter(Peak > as.Date("1999-01-01")), 
            aes(xmin = Peak, xmax = Trough, ymin = 0, ymax = +Inf), 
            fill = '#003399', alpha = 0.1) +
  labs(caption = "Source: OCDE, Comptes Trimestriels, Volumes chaînés")

plot

Code
save(plot, file = "QNA_EXPENDITURE_NATIO_CURR_files/figure-html/USA-EA20-1999-percapita-1.RData")

U.S., Europe, France, Germany

B1GQ - GDP

1995-

Code
metadata_load("REF_AREA", "CL_AREA", data = QNA_EXPENDITURE_NATIO_CURR_var)
QNA_EXPENDITURE_NATIO_CURR %>%
  filter(REF_AREA %in% c("USA", "EA20", "FRA", "DEU"),
         FREQ == "Q",
         TRANSACTION == "B1GQ",
         PRICE_BASE == "L",
         ADJUSTMENT == "Y",
         SECTOR == "S1") %>%
  quarter_to_date %>%
  arrange(desc(date)) %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  filter(date >= as.Date("1995-01-01")) %>%
  mutate(Ref_area = ifelse(REF_AREA == "EA20", "Europe", Ref_area)) %>%
  group_by(Ref_area) %>%
  arrange(date) %>%
  mutate(obsValue = 100 * obsValue / obsValue[1]) %>%
  left_join(colors, by = c("Ref_area" = "country")) %>%
  mutate(color = ifelse(Ref_area != "DEU", color2, color)) %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("") +
  geom_line(aes(x = date, y = obsValue, color = color)) + add_4flags +
  scale_color_identity() +
  scale_x_date(breaks = c(seq(1995, 2100, 5)) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = "none") +
  scale_y_log10(breaks = seq(50, 200, 5)) + 
  geom_label_repel(data = . %>% filter(date == max(date)),
             aes(x = date, y = obsValue, label = round(obsValue, 1), color = color))

1999-

Total

Code
QNA_EXPENDITURE_NATIO_CURR %>%
  filter(REF_AREA %in% c("USA", "EA20", "FRA", "DEU"),
         FREQ == "Q",
         TRANSACTION == "B1GQ",
         PRICE_BASE == "L",
         ADJUSTMENT == "Y",
         SECTOR == "S1") %>%
  quarter_to_date %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  filter(date >= as.Date("1999-01-01")) %>%
  mutate(Ref_area = ifelse(REF_AREA == "EA20", "Europe", Ref_area)) %>%
  group_by(Ref_area) %>%
  arrange(date) %>%
  mutate(obsValue = 100 * obsValue / obsValue[1]) %>%
  left_join(colors, by = c("Ref_area" = "country")) %>%
  mutate(color = ifelse(Ref_area != "DEU", color2, color)) %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("") +
  geom_line(aes(x = date, y = obsValue, color = color)) + add_4flags +
  scale_color_identity() +
  scale_x_date(breaks = c(seq(1999, 2100, 5), seq(1997, 2100, 5)) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = "none") +
  scale_y_log10(breaks = seq(50, 200, 5)) + 
  geom_label_repel(data = . %>% filter(date == max(date)),
             aes(x = date, y = obsValue, label = round(obsValue, 1), color = color))

Per Capita

Code
QNA_EXPENDITURE_NATIO_CURR %>%
  filter(REF_AREA %in% c("USA", "EA20", "FRA", "DEU"),
         FREQ == "Q",
         TRANSACTION == "B1GQ",
         # L = Chain linked volume
         PRICE_BASE == "L",
         ADJUSTMENT == "Y",
         SECTOR == "S1") %>%
  left_join(QNA_POP_EMPNC_extract, by = c("obsTime", "REF_AREA")) %>%
  mutate(obsValue = obsValue/population) %>%
  quarter_to_date %>%
  
  left_join(REF_AREA, by = "REF_AREA") %>%
  filter(date >= as.Date("1999-01-01")) %>%
  mutate(Ref_area = ifelse(REF_AREA == "EA20", "Europe", Ref_area)) %>%
  group_by(Ref_area) %>%
  arrange(date) %>%
  mutate(obsValue = 100 * obsValue / obsValue[1]) %>%
  left_join(colors, by = c("Ref_area" = "country")) %>%
  mutate(color = ifelse(Ref_area != "DEU", color2, color)) %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("") +
  geom_line(aes(x = date, y = obsValue, color = color)) + add_4flags +
  scale_color_identity() +
  scale_x_date(breaks = c(seq(1999, 2100, 5), seq(1997, 2100, 5)) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = "none") +
  scale_y_log10(breaks = seq(50, 200, 5)) + 
  geom_label_repel(data = . %>% filter(date == max(date)),
             aes(x = date, y = obsValue, label = round(obsValue, 1), color = color))

P51G - Gross fixed capital formation

1995-

Code
QNA_EXPENDITURE_NATIO_CURR %>%
  filter(REF_AREA %in% c("USA", "EA20", "FRA", "DEU"),
         FREQ == "Q",
         TRANSACTION == "P51G",
         PRICE_BASE == "L",
         ADJUSTMENT == "Y",
         SECTOR == "S1") %>%
  quarter_to_date %>%
  arrange(desc(date)) %>%
  
  left_join(REF_AREA, by = "REF_AREA") %>%
  filter(date >= as.Date("1995-01-01")) %>%
  mutate(Ref_area = ifelse(REF_AREA == "EA20", "Europe", Ref_area)) %>%
  group_by(Ref_area) %>%
  arrange(date) %>%
  mutate(obsValue = 100 * obsValue / obsValue[1]) %>%
  left_join(colors, by = c("Ref_area" = "country")) %>%
  mutate(color = ifelse(Ref_area != "DEU", color2, color)) %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("") +
  geom_line(aes(x = date, y = obsValue, color = color)) + add_4flags +
  scale_color_identity() +
  scale_x_date(breaks = c(seq(1995, 2100, 5)) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = "none") +
  scale_y_log10(breaks = seq(50, 500, 5)) + 
  geom_label_repel(data = . %>% filter(date == max(date)),
             aes(x = date, y = obsValue, label = round(obsValue, 1), color = color))

1999-

Total

Code
QNA_EXPENDITURE_NATIO_CURR %>%
  filter(REF_AREA %in% c("USA", "EA20", "FRA", "DEU"),
         FREQ == "Q",
         TRANSACTION == "P51G",
         PRICE_BASE == "L",
         ADJUSTMENT == "Y",
         SECTOR == "S1") %>%
  quarter_to_date %>%
  
  left_join(REF_AREA, by = "REF_AREA") %>%
  filter(date >= as.Date("1999-01-01")) %>%
  mutate(Ref_area = ifelse(REF_AREA == "EA20", "Europe", Ref_area)) %>%
  group_by(Ref_area) %>%
  arrange(date) %>%
  mutate(obsValue = 100 * obsValue / obsValue[1]) %>%
  left_join(colors, by = c("Ref_area" = "country")) %>%
  mutate(color = ifelse(Ref_area != "DEU", color2, color)) %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("") +
  geom_line(aes(x = date, y = obsValue, color = color)) + add_4flags +
  scale_color_identity() +
  scale_x_date(breaks = c(seq(1999, 2100, 5), seq(1997, 2100, 5)) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = "none") +
  scale_y_log10(breaks = seq(50, 200, 5)) + 
  geom_label_repel(data = . %>% filter(date == max(date)),
             aes(x = date, y = obsValue, label = round(obsValue, 1), color = color))

Per Capita

Code
QNA_EXPENDITURE_NATIO_CURR %>%
  filter(REF_AREA %in% c("USA", "EA20", "FRA", "DEU"),
         FREQ == "Q",
         TRANSACTION == "P51G",
         # L = Chain linked volume
         PRICE_BASE == "L",
         ADJUSTMENT == "Y",
         SECTOR == "S1") %>%
  left_join(QNA_POP_EMPNC_extract, by = c("obsTime", "REF_AREA")) %>%
  mutate(obsValue = obsValue/population) %>%
  quarter_to_date %>%
  
  left_join(REF_AREA, by = "REF_AREA") %>%
  filter(date >= as.Date("1999-01-01")) %>%
  mutate(Ref_area = ifelse(REF_AREA == "EA20", "Europe", Ref_area)) %>%
  group_by(Ref_area) %>%
  arrange(date) %>%
  mutate(obsValue = 100 * obsValue / obsValue[1]) %>%
  left_join(colors, by = c("Ref_area" = "country")) %>%
  mutate(color = ifelse(Ref_area != "DEU", color2, color)) %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("") +
  geom_line(aes(x = date, y = obsValue, color = color)) + add_4flags +
  scale_color_identity() +
  scale_x_date(breaks = c(seq(1999, 2100, 5), seq(1997, 2100, 5)) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = "none") +
  scale_y_log10(breaks = seq(50, 200, 5)) + 
  geom_label_repel(data = . %>% filter(date == max(date)),
             aes(x = date, y = obsValue, label = round(obsValue, 1), color = color))

P3 - Final consumption expenditure

All

Code
QNA_EXPENDITURE_NATIO_CURR %>%
  filter(REF_AREA %in% c("USA", "EA20", "FRA", "DEU"),
         FREQ == "Q",
         TRANSACTION == "P3",
         PRICE_BASE == "L",
         ADJUSTMENT == "Y",
         SECTOR == "S1") %>%
  quarter_to_date %>%
  arrange(desc(date)) %>%
  
  left_join(REF_AREA, by = "REF_AREA") %>%
  mutate(Ref_area = ifelse(REF_AREA == "EA20", "Europe", Ref_area)) %>%
  group_by(Ref_area) %>%
  arrange(date) %>%
  mutate(obsValue = 100 * obsValue / obsValue[1]) %>%
  left_join(colors, by = c("Ref_area" = "country")) %>%
  mutate(color = ifelse(Ref_area != "DEU", color2, color)) %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("") +
  geom_line(aes(x = date, y = obsValue, color = color)) + add_4flags +
  scale_color_identity() +
  scale_x_date(breaks = c(seq(1900, 2100, 5)) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = "none") +
  scale_y_log10(breaks = seq(20, 600, 20))

1995-

Code
QNA_EXPENDITURE_NATIO_CURR %>%
  filter(REF_AREA %in% c("USA", "EA20", "FRA", "DEU"),
         FREQ == "Q",
         TRANSACTION == "P3",
         PRICE_BASE == "L",
         ADJUSTMENT == "Y",
         SECTOR == "S1") %>%
  quarter_to_date %>%
  arrange(desc(date)) %>%
  
  left_join(REF_AREA, by = "REF_AREA") %>%
  filter(date >= as.Date("1995-01-01")) %>%
  mutate(Ref_area = ifelse(REF_AREA == "EA20", "Europe", Ref_area)) %>%
  group_by(Ref_area) %>%
  arrange(date) %>%
  mutate(obsValue = 100 * obsValue / obsValue[1]) %>%
  left_join(colors, by = c("Ref_area" = "country")) %>%
  mutate(color = ifelse(Ref_area != "DEU", color2, color)) %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("") +
  geom_line(aes(x = date, y = obsValue, color = color)) + add_4flags +
  scale_color_identity() +
  scale_x_date(breaks = c(seq(1995, 2100, 5)) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = "none") +
  scale_y_log10(breaks = seq(50, 200, 5)) + 
  geom_label_repel(data = . %>% filter(date == max(date)),
             aes(x = date, y = obsValue, label = round(obsValue, 1), color = color))

1999-

Total

Code
QNA_EXPENDITURE_NATIO_CURR %>%
  filter(REF_AREA %in% c("USA", "EA20", "FRA", "DEU"),
         FREQ == "Q",
         TRANSACTION == "P3",
         # L = Chain linked volume
         PRICE_BASE == "L",
         ADJUSTMENT == "Y",
         SECTOR == "S1") %>%
  quarter_to_date %>%
  
  left_join(REF_AREA, by = "REF_AREA") %>%
  filter(date >= as.Date("1999-01-01")) %>%
  mutate(Ref_area = ifelse(REF_AREA == "EA20", "Europe", Ref_area)) %>%
  group_by(Ref_area) %>%
  arrange(date) %>%
  mutate(obsValue = 100 * obsValue / obsValue[1]) %>%
  left_join(colors, by = c("Ref_area" = "country")) %>%
  mutate(color = ifelse(Ref_area != "DEU", color2, color)) %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("") +
  geom_line(aes(x = date, y = obsValue, color = color)) + add_4flags +
  scale_color_identity() +
  scale_x_date(breaks = c(seq(1999, 2100, 5), seq(1997, 2100, 5)) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.26, 0.8),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(50, 200, 5)) + 
  geom_label_repel(data = . %>% filter(date == max(date)),
             aes(x = date, y = obsValue, label = round(obsValue, 1), color = color))

Per Capita

Code
metadata_load_fr("REF_AREA", "CL_AREA", data = QNA_EXPENDITURE_NATIO_CURR_var)
plot <- QNA_EXPENDITURE_NATIO_CURR %>%
  filter(REF_AREA %in% c("USA", "EA20", "FRA", "DEU"),
         FREQ == "Q",
         TRANSACTION == "P3",
         # L = Chain linked volume
         PRICE_BASE == "L",
         ADJUSTMENT == "Y",
         SECTOR == "S1") %>%
  left_join(QNA_POP_EMPNC_extract, by = c("obsTime", "REF_AREA")) %>%
  mutate(obsValue = obsValue/population) %>%
  quarter_to_date %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  filter(date >= as.Date("1999-01-01")) %>%
  mutate(Ref_area = ifelse(REF_AREA == "EA20", "Zone euro", Ref_area)) %>%
  group_by(Ref_area) %>%
  arrange(date) %>%
  mutate(obsValue = 100 * obsValue / obsValue[1]) %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("Consommation par habitant (1999T1 = 100)") +
  geom_line(aes(x = date, y = obsValue, color = Ref_area)) +
  scale_color_manual(values = c("#000000", "#B22234", "#808080", "#003399")) +
  scale_x_date(breaks = c(seq(1999, 2100, 5), seq(1997, 2100, 5)) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.26, 0.8),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(50, 200, 5))  +
  geom_rect(data = nber_recessions %>%
              filter(Peak > as.Date("1999-01-01")), 
            aes(xmin = Peak, xmax = Trough, ymin = 0, ymax = +Inf),
            fill = '#B22234', alpha = 0.1)  +
  geom_rect(data = cepr_recessions %>%
              filter(Peak > as.Date("1999-01-01")), 
            aes(xmin = Peak, xmax = Trough, ymin = 0, ymax = +Inf), 
            fill = '#003399', alpha = 0.1)  +
  labs(caption = "Source: OCDE, Comptes Trimestriels, Volumes chaînés")


save(plot, file = "QNA_EXPENDITURE_NATIO_CURR_files/figure-html/USA-EA20-FRA-DEU-1999-percapita-1.RData")

plot

P31 - Individual consumption expenditure

Code
metadata_load("REF_AREA", "CL_AREA", data = QNA_EXPENDITURE_NATIO_CURR_var)
QNA_EXPENDITURE_NATIO_CURR %>%
  filter(REF_AREA %in% c("USA", "EA20", "FRA", "DEU"),
         FREQ == "Q",
         TRANSACTION == "P31",
         # L = Chain linked volume
         PRICE_BASE == "L",
         ADJUSTMENT == "Y") %>%
  left_join(QNA_POP_EMPNC_extract, by = c("obsTime", "REF_AREA")) %>%
  mutate(obsValue = obsValue/population) %>%
  quarter_to_date %>%
  
  left_join(REF_AREA, by = "REF_AREA") %>%
  filter(date >= as.Date("1999-01-01")) %>%
  mutate(Ref_area = ifelse(REF_AREA == "EA20", "Europe", Ref_area)) %>%
  group_by(Ref_area) %>%
  arrange(date) %>%
  mutate(obsValue = 100 * obsValue / obsValue[1]) %>%
  left_join(colors, by = c("Ref_area" = "country")) %>%
  mutate(color = ifelse(Ref_area != "DEU", color2, color)) %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("") +
  geom_line(aes(x = date, y = obsValue, color = color)) + add_3flags +
  scale_color_identity() +
  scale_x_date(breaks = c(seq(1999, 2100, 5), seq(1997, 2100, 5)) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = "none") +
  scale_y_log10(breaks = seq(50, 200, 5)) + 
  geom_label_repel(data = . %>% filter(date == max(date)),
             aes(x = date, y = obsValue, label = round(obsValue, 1), color = color))

P32 - Collective consumption expenditure

Code
QNA_EXPENDITURE_NATIO_CURR %>%
  filter(REF_AREA %in% c("USA", "EA20", "FRA", "DEU"),
         FREQ == "Q",
         TRANSACTION == "P32",
         # L = Chain linked volume
         PRICE_BASE == "L",
         ADJUSTMENT == "Y") %>%
  left_join(QNA_POP_EMPNC_extract, by = c("obsTime", "REF_AREA")) %>%
  mutate(obsValue = obsValue/population) %>%
  quarter_to_date %>%
  
  left_join(REF_AREA, by = "REF_AREA") %>%
  filter(date >= as.Date("1999-01-01")) %>%
  mutate(Ref_area = ifelse(REF_AREA == "EA20", "Europe", Ref_area)) %>%
  group_by(Ref_area) %>%
  arrange(date) %>%
  mutate(obsValue = 100 * obsValue / obsValue[1]) %>%
  left_join(colors, by = c("Ref_area" = "country")) %>%
  mutate(color = ifelse(Ref_area != "DEU", color2, color)) %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("") +
  geom_line(aes(x = date, y = obsValue, color = color)) + add_3flags +
  scale_color_identity() +
  scale_x_date(breaks = c(seq(1999, 2100, 5), seq(1997, 2100, 5)) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = "none") +
  scale_y_log10(breaks = seq(50, 200, 5)) + 
  geom_label_repel(data = . %>% filter(date == max(date)),
             aes(x = date, y = obsValue, label = round(obsValue, 1), color = color))

P41 - Actual individual consumption

Code
QNA_EXPENDITURE_NATIO_CURR %>%
  filter(REF_AREA %in% c("USA", "EA20", "FRA", "DEU"),
         FREQ == "Q",
         TRANSACTION == "P41",
         # L = Chain linked volume
         PRICE_BASE == "L",
         ADJUSTMENT == "Y") %>%
  left_join(QNA_POP_EMPNC_extract, by = c("obsTime", "REF_AREA")) %>%
  mutate(obsValue = obsValue/population) %>%
  quarter_to_date %>%
  
  left_join(REF_AREA, by = "REF_AREA") %>%
  filter(date >= as.Date("1999-01-01")) %>%
  mutate(Ref_area = ifelse(REF_AREA == "EA20", "Europe", Ref_area)) %>%
  group_by(Ref_area) %>%
  arrange(date) %>%
  mutate(obsValue = 100 * obsValue / obsValue[1]) %>%
  left_join(colors, by = c("Ref_area" = "country")) %>%
  mutate(color = ifelse(Ref_area != "DEU", color2, color)) %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("") +
  geom_line(aes(x = date, y = obsValue, color = color)) + add_2flags +
  scale_color_identity() +
  scale_x_date(breaks = c(seq(1999, 2100, 5), seq(1997, 2100, 5)) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = "none") +
  scale_y_log10(breaks = seq(50, 200, 5)) + 
  geom_label_repel(data = . %>% filter(date == max(date)),
             aes(x = date, y = obsValue, label = round(obsValue, 1), color = color))

P3T5 - Domestic demand

Code
QNA_EXPENDITURE_NATIO_CURR %>%
  filter(REF_AREA %in% c("USA", "EA20", "FRA", "DEU"),
         FREQ == "Q",
         TRANSACTION == "P3T5",
         # L = Chain linked volume
         PRICE_BASE == "L",
         ADJUSTMENT == "Y") %>%
  left_join(QNA_POP_EMPNC_extract, by = c("obsTime", "REF_AREA")) %>%
  mutate(obsValue = obsValue/population) %>%
  quarter_to_date %>%
  
  left_join(REF_AREA, by = "REF_AREA") %>%
  filter(date >= as.Date("1999-01-01")) %>%
  mutate(Ref_area = ifelse(REF_AREA == "EA20", "Europe", Ref_area)) %>%
  group_by(Ref_area) %>%
  arrange(date) %>%
  mutate(obsValue = 100 * obsValue / obsValue[1]) %>%
  left_join(colors, by = c("Ref_area" = "country")) %>%
  mutate(color = ifelse(Ref_area != "DEU", color2, color)) %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("") +
  geom_line(aes(x = date, y = obsValue, color = color)) + add_3flags +
  scale_color_identity() +
  scale_x_date(breaks = c(seq(1999, 2100, 5), seq(1997, 2100, 5)) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = "none") +
  scale_y_log10(breaks = seq(50, 200, 5)) + 
  geom_label_repel(data = . %>% filter(date == max(date)),
             aes(x = date, y = obsValue, label = round(obsValue, 1), color = color))

Net exports

Eurozone, US, France, Germany

Tous

Code
QNA_EXPENDITURE_NATIO_CURR %>%
  filter(REF_AREA %in% c("EA20", "USA", "DEU", "FRA"),
         FREQ == "Q",
         TRANSACTION %in% c("B11", "B1GQ"),
         # L = Chain linked volume
         PRICE_BASE == "V",
         ADJUSTMENT == "Y") %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  mutate(Ref_area = ifelse(REF_AREA == "EA20", "Europe", Ref_area)) %>%
  group_by(Ref_area) %>%
  select_if(~ n_distinct(.) > 1) %>%
  select(-OBS_STATUS) %>%
  spread(TRANSACTION, obsValue) %>%
  quarter_to_date() %>%
  filter(date >= as.Date("1970-01-01")) %>%
  transmute(date, Ref_area, obsValue = B11/B1GQ) %>%
  left_join(colors, by = c("Ref_area" = "country")) %>%
  ggplot + geom_line(aes(x = date, y = obsValue, color = color)) +
  theme_minimal() + xlab("") + ylab("") + scale_color_identity() + add_4flags +
  scale_x_date(breaks = c(seq(1970, 2100, 5)) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-100, 100, 1),
                labels = percent_format(acc = 1)) +
  theme(legend.position = c(0.2, 0.8),
        legend.title = element_blank()) +
  geom_hline(yintercept = 0, linetype = "dashed")

1990-

Code
QNA_EXPENDITURE_NATIO_CURR %>%
  filter(REF_AREA %in% c("EA20", "USA", "DEU", "FRA"),
         FREQ == "Q",
         TRANSACTION %in% c("B11", "B1GQ"),
         # L = Chain linked volume
         PRICE_BASE == "V",
         ADJUSTMENT == "Y") %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  left_join(PRICE_BASE, by = "PRICE_BASE") %>%
  mutate(Ref_area = ifelse(REF_AREA == "EA20", "Europe", Ref_area)) %>%
  group_by(Ref_area) %>%
  select_if(~ n_distinct(.) > 1) %>%
  select(-OBS_STATUS) %>%
  spread(TRANSACTION, obsValue) %>%
  quarter_to_date() %>%
  filter(date >= as.Date("1990-01-01")) %>%
  transmute(date, Ref_area, obsValue = B11/B1GQ) %>%
  left_join(colors, by = c("Ref_area" = "country")) %>%
  ggplot + geom_line(aes(x = date, y = obsValue, color = color)) +
  theme_minimal() + xlab("") + ylab("") + scale_color_identity() + add_4flags +
  scale_x_date(breaks = c(seq(1970, 2100, 5)) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-100, 100, 1),
                labels = percent_format(acc = 1)) +
  theme(legend.position = c(0.2, 0.8),
        legend.title = element_blank()) +
  geom_hline(yintercept = 0, linetype = "dashed")

Eurozone, US

Tous

Code
QNA_EXPENDITURE_NATIO_CURR %>%
  filter(REF_AREA %in% c("EA20", "USA"),
         FREQ == "Q",
         TRANSACTION %in% c("B11", "B1GQ"),
         # L = Chain linked volume
         PRICE_BASE == "V",
         ADJUSTMENT == "Y") %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  mutate(Ref_area = ifelse(REF_AREA == "EA20", "Europe", Ref_area)) %>%
  group_by(Ref_area) %>%
  select_if(~ n_distinct(.) > 1) %>%
  spread(TRANSACTION, obsValue) %>%
  quarter_to_date() %>%
  transmute(date, REF_AREA, Ref_area, obsValue = B11/B1GQ) %>%
  left_join(colors, by = c("Ref_area" = "country")) %>%
  mutate(color = ifelse(REF_AREA == "USA", color2, color)) %>%
  ggplot + geom_line(aes(x = date, y = obsValue, color = color)) +
  theme_minimal() + xlab("") + ylab("") + scale_color_identity() + add_2flags +
  scale_x_date(breaks = c(seq(1920, 2100, 5)) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-100, 100, 1),
                labels = percent_format(acc = 1)) +
  theme(legend.position = c(0.2, 0.8),
        legend.title = element_blank()) +
  geom_hline(yintercept = 0, linetype = "dashed")

1970-

Code
QNA_EXPENDITURE_NATIO_CURR %>%
  filter(REF_AREA %in% c("EA20", "USA"),
         FREQ == "Q",
         TRANSACTION %in% c("B11", "B1GQ"),
         # L = Chain linked volume
         PRICE_BASE == "V",
         ADJUSTMENT == "Y") %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  mutate(Ref_area = ifelse(REF_AREA == "EA20", "Europe", Ref_area)) %>%
  group_by(Ref_area) %>%
  select_if(~ n_distinct(.) > 1) %>%
  spread(TRANSACTION, obsValue) %>%
  quarter_to_date() %>%
  filter(date >= as.Date("1970-01-01")) %>%
  transmute(date, REF_AREA, Ref_area, obsValue = B11/B1GQ) %>%
  left_join(colors, by = c("Ref_area" = "country")) %>%
  mutate(color = ifelse(REF_AREA == "USA", color2, color)) %>%
  ggplot + geom_line(aes(x = date, y = obsValue, color = color)) +
  theme_minimal() + xlab("") + ylab("") + scale_color_identity() + add_2flags +
  scale_x_date(breaks = c(seq(1970, 2100, 5)) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-100, 100, 1),
                labels = percent_format(acc = 1)) +
  theme(legend.position = c(0.2, 0.8),
        legend.title = element_blank()) +
  geom_hline(yintercept = 0, linetype = "dashed")

1990-

Code
QNA_EXPENDITURE_NATIO_CURR %>%
  filter(REF_AREA %in% c("EA20", "USA"),
         FREQ == "Q",
         TRANSACTION %in% c("B11", "B1GQ"),
         # L = Chain linked volume
         PRICE_BASE == "V",
         ADJUSTMENT == "Y") %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  mutate(Ref_area = ifelse(REF_AREA == "EA20", "Europe", Ref_area)) %>%
  group_by(Ref_area) %>%
  select_if(~ n_distinct(.) > 1) %>%
  spread(TRANSACTION, obsValue) %>%
  quarter_to_date() %>%
  filter(date >= as.Date("1995-01-01")) %>%
  transmute(date, REF_AREA, Ref_area, obsValue = B11/B1GQ) %>%
  left_join(colors, by = c("Ref_area" = "country")) %>%
  mutate(color = ifelse(REF_AREA == "USA", color2, color)) %>%
  ggplot + geom_line(aes(x = date, y = obsValue, color = color)) +
  theme_minimal() + xlab("") + ylab("") + scale_color_identity() + add_2flags +
  scale_x_date(breaks = c(seq(1970, 2100, 5)) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-100, 100, 1),
                labels = percent_format(acc = 1)) +
  theme(legend.position = c(0.2, 0.8),
        legend.title = element_blank()) +
  geom_hline(yintercept = 0, linetype = "dashed")

Exports and Imports as a % of GDP

EA 20

Code
QNA_EXPENDITURE_NATIO_CURR %>%
  filter(REF_AREA %in% c("EA20"),
         FREQ == "Q",
         TRANSACTION %in% c("P6", "P7", "B1GQ"),
         # L = Chain linked volume
         PRICE_BASE == "L",
         ADJUSTMENT == "Y") %>%
  select_if(~ n_distinct(.) > 1) %>%
  select(-OBS_STATUS) %>%
  spread(TRANSACTION, obsValue) %>%
  quarter_to_date() %>%
  transmute(date, `Exports (% of GDP)` = P6/B1GQ, `Imports (% of GDP)` = P7/B1GQ) %>%
  gather(variable, value, -date) %>%
  ggplot + geom_line(aes(x = date, y = value, color = variable)) +
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = c(seq(1999, 2100, 5), seq(1997, 2100, 5)) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-100, 100, 5),
                labels = percent_format(acc = 1)) +
  theme(legend.position = c(0.2, 0.8),
        legend.title = element_blank())

USA

Code
QNA_EXPENDITURE_NATIO_CURR %>%
  filter(REF_AREA %in% c("USA"),
         FREQ == "Q",
         TRANSACTION %in% c("P6", "P7", "B1GQ"),
         # L = Chain linked volume
         PRICE_BASE == "V",
         ADJUSTMENT == "Y") %>%
  select_if(~ n_distinct(.) > 1) %>%
  spread(TRANSACTION, obsValue) %>%
  quarter_to_date() %>%
  transmute(date, `Exports (% of GDP)` = P6/B1GQ, `Imports (% of GDP)` = P7/B1GQ) %>%
  gather(variable, value, -date) %>%
  ggplot + geom_line(aes(x = date, y = value, color = variable)) +
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = c(seq(1940, 2100, 5)) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-100, 100, 2),
                labels = percent_format(acc = 1)) +
  theme(legend.position = c(0.2, 0.8),
        legend.title = element_blank())

Germany

Code
QNA_EXPENDITURE_NATIO_CURR %>%
  filter(REF_AREA %in% c("DEU"),
         FREQ == "Q",
         TRANSACTION %in% c("P6", "P7", "B1GQ"),
         # L = Chain linked volume
         PRICE_BASE == "V",
         ADJUSTMENT == "Y") %>%
  select_if(~ n_distinct(.) > 1) %>%
  spread(TRANSACTION, obsValue) %>%
  quarter_to_date() %>%
  transmute(date, `Exports (% of GDP)` = P6/B1GQ, `Imports (% of GDP)` = P7/B1GQ) %>%
  gather(variable, value, -date) %>%
  ggplot + geom_line(aes(x = date, y = value, color = variable)) +
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = c(seq(1940, 2100, 5)) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-100, 100, 2),
                labels = percent_format(acc = 1)) +
  theme(legend.position = c(0.2, 0.8),
        legend.title = element_blank())

France

Code
QNA_EXPENDITURE_NATIO_CURR %>%
  filter(REF_AREA %in% c("FRA"),
         FREQ == "Q",
         TRANSACTION %in% c("P6", "P7", "B1GQ"),
         # L = Chain linked volume
         PRICE_BASE == "V",
         ADJUSTMENT == "Y") %>%
  select_if(~ n_distinct(.) > 1) %>%
  spread(TRANSACTION, obsValue) %>%
  quarter_to_date() %>%
  transmute(date, `Exports (% of GDP)` = P6/B1GQ, `Imports (% of GDP)` = P7/B1GQ) %>%
  gather(variable, value, -date) %>%
  ggplot + geom_line(aes(x = date, y = value, color = variable)) +
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = c(seq(1940, 2100, 5)) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-100, 100, 2),
                labels = percent_format(acc = 1)) +
  theme(legend.position = c(0.2, 0.8),
        legend.title = element_blank())