GDP (current LCU)

Data - WDI

Author

François Geerolf

Info

source dataset .html .RData
wdi NY.GDP.MKTP.CN 2024-09-18 2025-03-09

Data on macro

source dataset .html .RData
eurostat nama_10_a10 2025-02-01 2024-10-08
eurostat nama_10_a10_e 2025-02-01 2025-03-09
eurostat nama_10_gdp 2025-02-01 2025-01-31
eurostat nama_10_lp_ulc 2025-02-01 2024-10-08
eurostat namq_10_a10 2025-03-04 2025-03-09
eurostat namq_10_a10_e 2025-03-04 2025-03-04
eurostat namq_10_gdp 2025-02-04 2025-01-31
eurostat namq_10_lp_ulc 2025-02-01 2024-11-04
eurostat namq_10_pc 2025-02-01 2024-12-29
eurostat nasa_10_nf_tr 2025-02-01 2024-12-14
eurostat nasq_10_nf_tr 2025-02-12 2025-02-12
fred gdp 2025-01-26 2025-01-26
oecd QNA 2024-06-06 2025-03-04
oecd SNA_TABLE1 2025-03-04 2025-03-04
oecd SNA_TABLE14A 2024-09-15 2024-06-30
oecd SNA_TABLE2 2024-07-01 2024-04-11
oecd SNA_TABLE6A 2024-07-01 2024-06-30
wdi NE.RSB.GNFS.ZS 2025-03-09 2025-03-09
wdi NY.GDP.MKTP.CD 2025-03-09 2025-03-09
wdi NY.GDP.MKTP.PP.CD 2024-09-18 2025-03-09
wdi NY.GDP.PCAP.CD 2025-01-31 2025-03-09
wdi NY.GDP.PCAP.KD 2024-09-18 2025-03-09
wdi NY.GDP.PCAP.PP.CD 2025-01-31 2025-03-09
wdi NY.GDP.PCAP.PP.KD 2025-01-31 2025-03-09

LAST_COMPILE

LAST_COMPILE
2025-03-09

Last

Code 
NY.GDP.MKTP.CN %>%
  group_by(year) %>%
  summarise(Nobs = n()) %>%
  arrange(desc(year)) %>%
  head(1) %>%
  print_table_conditional()
year Nobs
2023 194

Nobs - Javascript

Code 
NY.GDP.MKTP.CN %>%
  left_join(iso2c, by = "iso2c") %>%
  group_by(iso2c, Iso2c) %>%
  
  mutate(value = round(value/(10^9))) %>%
  summarise(Nobs = n(),
            `Year 1` = first(year),
            `GDP 1 (Bn)` = first(value) %>% paste0("$ ", .),
            `Year 2` = last(year),
            `GDP 2 (Bn)` = last(value) %>% paste0("$ ", .)) %>%
  arrange(-Nobs) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(Iso2c)),
         Flag = paste0('<img src="../../bib/flags/vsmall/', Flag, '.png" alt="Flag">')) %>%
  select(Flag, everything()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

2018 GDP by Country

Code 
NY.GDP.MKTP.CN %>%
  right_join(iso2c %>%
              filter((region != "Aggregates") & (region != "NA")), 
             by = "iso2c") %>%
  group_by(iso2c, Iso2c) %>%
  
  mutate(value = round(value/(10^9))) %>%
  summarise(`GDP (Bn)` = last(value)) %>%
  arrange(-`GDP (Bn)`) %>%
  mutate(`GDP (Bn)` = `GDP (Bn)` %>% paste0("$ ", ., " Bn")) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(Iso2c)),
         Flag = paste0('<img src="../../bib/flags/vsmall/', Flag, '.png" alt="Flag">')) %>%
  select(Flag, everything()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

2018 GDP by Country and Aggregates

Code 
NY.GDP.MKTP.CN %>%
  right_join(iso2c, by = "iso2c") %>%
  group_by(iso2c, Iso2c) %>%
  
  mutate(value = round(value/(10^9))) %>%
  summarise(`GDP (Bn)` = last(value)) %>%
  arrange(-`GDP (Bn)`) %>%
  mutate(`GDP (Bn)` = `GDP (Bn)` %>% paste0("$ ", ., " Bn")) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

Illustration

Euro Area vs. US

Base 100

Code 
NY.GDP.MKTP.CN %>%
  left_join(iso2c, by = "iso2c") %>%
  year_to_date %>%
  filter(iso2c %in% c("XC", "US"),
         date >= as.Date("2008-01-01")) %>%
  group_by(iso2c) %>%
  arrange(date) %>%
  mutate(value = 100*value/value[1]) %>%
  mutate(Iso2c = ifelse(iso2c == "XC", "Europe", Iso2c)) %>%
  left_join(colors, by = c("Iso2c" = "country")) %>%
  mutate(color = ifelse(iso2c == "US", color2, color)) %>%
  ggplot(.) + theme_minimal() + scale_color_identity() +
  geom_line(aes(x = date, y = value, color = color)) +
  add_2flags +
  scale_x_date(breaks = seq(1950, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(70, 200, 5)) + 
  xlab("") + ylab("PIB en $ (100 = 2008)")

Avec dollars

Code 
NY.GDP.MKTP.CN %>%
  left_join(iso2c, by = "iso2c") %>%
  year_to_date %>%
  filter(iso2c %in% c("XC", "US"),
         date >= as.Date("2008-01-01")) %>%
  group_by(iso2c) %>%
  arrange(date) %>%
  mutate(Iso2c = ifelse(iso2c == "XC", "Europe", Iso2c)) %>%
  left_join(colors, by = c("Iso2c" = "country")) %>%
  mutate(color = ifelse(iso2c == "US", color2, color)) %>%
  ungroup %>%
  mutate(dollar = value/10^9,
         value = 100*value/value[2]) %>%
  ggplot(.) + theme_minimal() + scale_color_identity() +
  geom_line(aes(x = date, y = value, color = color)) +
  add_2flags +
  scale_x_date(breaks = seq(1950, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(10, 200, 5)) + 
  xlab("") + ylab("PIB en $ (100 = Zone Euro, 2008)") + 
  geom_text_repel(data = . %>% filter(year(date) %in% seq(2008, 2022, 2)),
                                      aes(x = date, y = value, label = paste0("$", round(dollar, digits = -2), " Md")))