Info

LAST_DOWNLOAD

tibble(dataset = c("TX.VAL.MRCH.CD.WT", "NY.GDP.MKTP.KD.ZG")) %>%
  mutate(LAST_DOWNLOAD = as.Date(file.info(paste0("~/Dropbox/website/data/wdi/", dataset, ".RData"))$mtime)) %>%
  mutate(html = paste0("[html](https://fgeerolf.com/data/wdi/", dataset, '.html)')) %>%
  print_table_conditional()

dataset	LAST_DOWNLOAD	html
TX.VAL.MRCH.CD.WT	2023-04-12	html
NY.GDP.MKTP.KD.ZG	2023-04-12	html

LAST_COMPILE

LAST_COMPILE
2024-01-06

Last

TX.VAL.MRCH.CD.WT %>%
  group_by(year) %>%
  summarise(Nobs = n()) %>%
  arrange(desc(year)) %>%
  head(1) %>%
  print_table_conditional()

year	Nobs
2021	266

Nobs - Javascript

TX.VAL.MRCH.CD.WT %>%
  left_join(iso2c, by = "iso2c") %>%
  group_by(iso2c, Iso2c) %>%
  rename(value = `TX.VAL.MRCH.CD.WT`) %>%
  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

TX.VAL.MRCH.CD.WT %>%
  right_join(iso2c %>%
              filter((region != "Aggregates") & (region != "NA")), 
             by = "iso2c") %>%
  group_by(iso2c, Iso2c) %>%
  rename(value = `TX.VAL.MRCH.CD.WT`) %>%
  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

TX.VAL.MRCH.CD.WT %>%
  right_join(iso2c, by = "iso2c") %>%
  group_by(iso2c, Iso2c) %>%
  rename(value = `TX.VAL.MRCH.CD.WT`) %>%
  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

Netherlands, Belgium, Switzerland, Turkey, Poland, Sweden

TX.VAL.MRCH.CD.WT %>%
  right_join(iso2c, by = "iso2c") %>%
  filter(iso2c %in% c("1W", "CH", "BE", "NL", "PO", "SE", "TR")) %>%
  year_to_date %>%
  group_by(date) %>%
  mutate(value = TX.VAL.MRCH.CD.WT/TX.VAL.MRCH.CD.WT[iso2c == "1W"]) %>%
  filter(!(iso2c == "1W")) %>%
  left_join(colors, by = c("Iso2c" = "country")) %>%
  ggplot(.) + theme_minimal() + scale_color_identity() + xlab("") + ylab("% of Merchandise exports") +
  geom_line(aes(x = date, y = value, color = color)) +
  geom_image(data = . %>%
               filter(date == as.Date("1967-01-01")) %>%
               mutate(image = paste0("../../icon/flag/", str_to_lower(gsub(" ", "-", Iso2c)), ".png")),
             aes(x = date, y = value, image = image), asp = 1.5) +
  scale_x_date(breaks = seq(1950, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(0, 70, 0.2),
                     labels = scales::percent_format(accuracy = .1),
                     limits = 0.01*c(0, 2))

Germany, France, Italy, United Kingdom, Spain

TX.VAL.MRCH.CD.WT %>%
  right_join(iso2c, by = "iso2c") %>%
  filter(iso2c %in% c("1W", "DE", "FR", "IT", "ES", "GB")) %>%
  year_to_date %>%
  group_by(date) %>%
  mutate(value = TX.VAL.MRCH.CD.WT/TX.VAL.MRCH.CD.WT[iso2c == "1W"]) %>%
  filter(!(iso2c == "1W")) %>%
  left_join(colors, by = c("Iso2c" = "country")) %>%
  ggplot(.) + theme_minimal() + scale_color_identity() + xlab("") + ylab("% of Merchandise exports") +
  geom_line(aes(x = date, y = value, color = color)) +
  add_5flags +
  scale_x_date(breaks = seq(1950, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(0, 70, 2),
                     labels = scales::percent_format(accuracy = 1))

Brazil, Mexico, Argentina

TX.VAL.MRCH.CD.WT %>%
  right_join(iso2c, by = "iso2c") %>%
  filter(iso2c %in% c("1W", "BR", "MX", "AR")) %>%
  year_to_date %>%
  group_by(date) %>%
  mutate(value = TX.VAL.MRCH.CD.WT/TX.VAL.MRCH.CD.WT[iso2c == "1W"]) %>%
  filter(!(iso2c == "1W")) %>%
  left_join(colors, by = c("Iso2c" = "country")) %>%
  mutate(color = ifelse(iso2c == "MX", color2, color)) %>%
  ggplot(.) + theme_minimal() + scale_color_identity() + xlab("") + ylab("% of Merchandise exports") +
  geom_line(aes(x = date, y = value, color = color)) +
  add_3flags +
  scale_x_date(breaks = seq(1950, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(0, 70, 0.5),
                     labels = scales::percent_format(accuracy = .1))

China, E.U., U.S.

All

TX.VAL.MRCH.CD.WT %>%
  right_join(iso2c, by = "iso2c") %>%
  filter(iso2c %in% c("1W", "US", "CN", "EU")) %>%
  group_by(year) %>%
  mutate(value = TX.VAL.MRCH.CD.WT/TX.VAL.MRCH.CD.WT[iso2c == "1W"]) %>%
  year_to_date %>%
  filter(!(iso2c == "1W")) %>%
  mutate(Iso2c = ifelse(iso2c == "EU", "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_3flags +
  scale_x_date(breaks = seq(1950, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(0, 70, 5),
                     labels = scales::percent_format(accuracy = 1)) + 
  xlab("") + ylab("% des exportations mondiales")

1990-

TX.VAL.MRCH.CD.WT %>%
  right_join(iso2c, by = "iso2c") %>%
  filter(iso2c %in% c("1W", "US", "CN", "EU")) %>%
  group_by(year) %>%
  mutate(value = TX.VAL.MRCH.CD.WT/TX.VAL.MRCH.CD.WT[iso2c == "1W"]) %>%
  year_to_date %>%
  filter(!(iso2c == "1W")) %>%
  mutate(Iso2c = ifelse(iso2c == "EU", "Europe", Iso2c)) %>%
  filter(date >= as.Date("1990-01-01")) %>%
  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_3flags +
  theme(legend.title = element_blank(),
        legend.position = c(0.85, 0.85)) +
  scale_x_date(breaks = seq(1950, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(0, 70, 2),
                     labels = scales::percent_format(accuracy = 1)) + 
  xlab("") + ylab("% of Merchandise exports")

1995-

TX.VAL.MRCH.CD.WT %>%
  right_join(iso2c, by = "iso2c") %>%
  filter(iso2c %in% c("1W", "US", "CN", "EU")) %>%
  group_by(year) %>%
  mutate(value = TX.VAL.MRCH.CD.WT/TX.VAL.MRCH.CD.WT[iso2c == "1W"]) %>%
  year_to_date %>%
  filter(!(iso2c == "1W")) %>%
  mutate(Iso2c = ifelse(iso2c == "EU", "Europe", Iso2c)) %>%
  filter(date >= as.Date("1995-01-01")) %>%
  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_3flags +
  theme(legend.title = element_blank(),
        legend.position = c(0.85, 0.85)) +
  scale_x_date(breaks = seq(1950, 2025, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(0, 70, 2),
                     labels = scales::percent_format(accuracy = 1)) + 
  xlab("") + ylab("% du PIB mondial")

1996-

TX.VAL.MRCH.CD.WT %>%
  right_join(iso2c, by = "iso2c") %>%
  filter(iso2c %in% c("1W", "US", "CN", "EU")) %>%
  group_by(year) %>%
  mutate(value = TX.VAL.MRCH.CD.WT/TX.VAL.MRCH.CD.WT[iso2c == "1W"]) %>%
  year_to_date %>%
  filter(!(iso2c == "1W")) %>%
  mutate(Iso2c = ifelse(iso2c == "EU", "Europe", Iso2c)) %>%
  filter(date >= as.Date("1996-01-01")) %>%
  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_3flags +
  theme(legend.title = element_blank(),
        legend.position = c(0.85, 0.85)) +
  scale_x_date(breaks = seq(1996, 2025, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(0, 70, 2),
                     labels = scales::percent_format(accuracy = 1),
                     limits = 0.01*c(0, 35)) + 
  xlab("") + ylab("% du PIB mondial")

2005-

TX.VAL.MRCH.CD.WT %>%
  right_join(iso2c, by = "iso2c") %>%
  filter(iso2c %in% c("1W", "US", "CN", "EU")) %>%
  group_by(year) %>%
  mutate(value = TX.VAL.MRCH.CD.WT/TX.VAL.MRCH.CD.WT[iso2c == "1W"]) %>%
  year_to_date %>%
  filter(!(iso2c == "1W")) %>%
  mutate(Iso2c = ifelse(iso2c == "EU", "Europe", Iso2c)) %>%
  filter(date >= as.Date("2005-01-01")) %>%
  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_3flags +
  theme(legend.title = element_blank(),
        legend.position = c(0.85, 0.85)) +
  scale_x_date(breaks = seq(1950, 2022, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(0, 70, 5),
                     labels = scales::percent_format(accuracy = 1),
                     limits = 0.01*c(0, 30)) + 
  xlab("") + ylab("% of Merchandise exports")

South Korea, Russia, Indonesia

TX.VAL.MRCH.CD.WT %>%
  right_join(iso2c, by = "iso2c") %>%
  filter(iso2c %in% c("1W", "ID", "KR", "RU")) %>%
  group_by(year) %>%
  mutate(value = TX.VAL.MRCH.CD.WT/TX.VAL.MRCH.CD.WT[iso2c == "1W"]) %>%
  year_to_date %>%
  filter(!(iso2c == "1W")) %>%
  mutate(Iso2c = ifelse(iso2c == "KR", "South Korea", Iso2c)) %>%
  mutate(Iso2c = ifelse(iso2c == "RU", "Russia", Iso2c)) %>%
  left_join(colors, by = c("Iso2c" = "country")) %>%
  ggplot(.) + theme_minimal() + scale_color_identity() + xlab("") + ylab("% of Merchandise exports") +
  geom_line(aes(x = date, y = value, color = color)) +
  add_3flags +
  scale_x_date(breaks = seq(1950, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(0, 70, 1),
                     labels = scales::percent_format(accuracy = 1),
                     limits = 0.01*c(0, 4))

Japan, India, United Kingdom

TX.VAL.MRCH.CD.WT %>%
  right_join(iso2c, by = "iso2c") %>%
  filter(iso2c %in% c("1W", "IN", "JP", "GB")) %>%
  group_by(year) %>%
  mutate(value = TX.VAL.MRCH.CD.WT/TX.VAL.MRCH.CD.WT[iso2c == "1W"]) %>%
  year_to_date %>%
  filter(!(iso2c == "1W")) %>%
  left_join(colors, by = c("Iso2c" = "country")) %>%
  ggplot(.) + theme_minimal() + scale_color_identity() +
  geom_line(aes(x = date, y = value, color = color)) +
  add_3flags +
  theme(legend.title = element_blank(),
        legend.position = c(0.85, 0.85)) +
  scale_x_date(breaks = seq(1950, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(0, 70, 2),
                     labels = scales::percent_format(accuracy = 1),
                     limits = 0.01*c(0, 20)) + 
  xlab("") + ylab("% of Merchandise exports")

Germany, France, Italy

TX.VAL.MRCH.CD.WT %>%
  right_join(iso2c, by = "iso2c") %>%
  filter(iso2c %in% c("1W", "DE", "FR", "IT")) %>%
  group_by(year) %>%
  mutate(value = TX.VAL.MRCH.CD.WT/TX.VAL.MRCH.CD.WT[iso2c == "1W"]) %>%
  year_to_date %>%
  filter(!(iso2c == "1W")) %>%
  left_join(colors, by = c("Iso2c" = "country")) %>%
  ggplot(.) + theme_minimal() + scale_color_identity() +
  geom_line(aes(x = date, y = value, color = color)) +
  add_3flags +
  theme(legend.title = element_blank(),
        legend.position = c(0.85, 0.85)) +
  scale_x_date(breaks = seq(1950, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(0, 70, 2),
                     labels = scales::percent_format(accuracy = 1),
                     limits = 0.01*c(0, 10)) + 
  xlab("") + ylab("% of Merchandise exports")

Eurozone, United States

All

TX.VAL.MRCH.CD.WT %>%
  right_join(iso2c, by = "iso2c") %>%
  filter(iso2c %in% c("1W", "XC", "US")) %>%
  group_by(year) %>%
  mutate(value = TX.VAL.MRCH.CD.WT/TX.VAL.MRCH.CD.WT[iso2c == "1W"]) %>%
  year_to_date %>%
  filter(!(iso2c == "1W")) %>%
  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 +
  theme(legend.title = element_blank(),
        legend.position = c(0.85, 0.85)) +
  scale_x_date(breaks = seq(1950, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(0, 70, 5),
                     labels = scales::percent_format(accuracy = 1),
                     limits = 0.01*c(0, 50)) + 
  xlab("") + ylab("% of Merchandise exports")

1990-

TX.VAL.MRCH.CD.WT %>%
  right_join(iso2c, by = "iso2c") %>%
  filter(iso2c %in% c("1W", "XC", "US")) %>%
  group_by(year) %>%
  mutate(value = TX.VAL.MRCH.CD.WT/TX.VAL.MRCH.CD.WT[iso2c == "1W"]) %>%
  year_to_date %>%
  filter(!(iso2c == "1W"),
         date >= as.Date("1990-01-01")) %>%
  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 +
  theme(legend.title = element_blank(),
        legend.position = c(0.85, 0.85)) +
  scale_x_date(breaks = seq(1950, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(0, 70, 5),
                     labels = scales::percent_format(accuracy = 1),
                     limits = 0.01*c(0, 35)) + 
  xlab("") + ylab("% of Merchandise exports")

2005-

TX.VAL.MRCH.CD.WT %>%
  right_join(iso2c, by = "iso2c") %>%
  filter(iso2c %in% c("1W", "XC", "US")) %>%
  group_by(year) %>%
  mutate(value = TX.VAL.MRCH.CD.WT/TX.VAL.MRCH.CD.WT[iso2c == "1W"]) %>%
  year_to_date %>%
  filter(!(iso2c == "1W"),
         date >= as.Date("2005-01-01")) %>%
  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() + add_2flags +
  geom_line(aes(x = date, y = value, color = color)) +
  scale_x_date(breaks = seq(1950, 2020, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(0, 70, 5),
                     labels = scales::percent_format(accuracy = 1),
                     limits = 0.01*c(0, 35)) + 
  xlab("") + ylab("% of Merchandise exports")

Germany, Japan, United Kingdom

TX.VAL.MRCH.CD.WT %>%
  right_join(iso2c, by = "iso2c") %>%
  filter(iso2c %in% c("1W", "JP", "DE", "GB")) %>%
  group_by(year) %>%
  mutate(value = TX.VAL.MRCH.CD.WT/TX.VAL.MRCH.CD.WT[iso2c == "1W"]) %>%
  year_to_date %>%
  filter(!(iso2c == "1W")) %>%
  left_join(colors, by = c("Iso2c" = "country")) %>%
  ggplot(.) + theme_minimal() + scale_color_identity() +
  geom_line(aes(x = date, y = value, color = color)) +
  add_3flags +
  theme(legend.title = element_blank(),
        legend.position = c(0.85, 0.85)) +
  scale_x_date(breaks = seq(1950, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(0, 70, 3),
                     labels = scales::percent_format(accuracy = 1),
                     limits = 0.01*c(0, 20)) + 
  xlab("") + ylab("% of Merchandise exports")

United Kingdom, France, Italy

TX.VAL.MRCH.CD.WT %>%
  right_join(iso2c, by = "iso2c") %>%
  filter(iso2c %in% c("1W", "GB", "FR", "IT")) %>%
  group_by(year) %>%
  mutate(value = TX.VAL.MRCH.CD.WT/TX.VAL.MRCH.CD.WT[iso2c == "1W"]) %>%
  year_to_date %>%
  filter(!(iso2c == "1W")) %>%
  left_join(colors, by = c("Iso2c" = "country")) %>%
  ggplot(.) + theme_minimal() + scale_color_identity() +
  geom_line(aes(x = date, y = value, color = color)) +
  add_3flags +
  theme(legend.title = element_blank(),
        legend.position = c(0.85, 0.85)) +
  scale_x_date(breaks = seq(1950, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(0, 70, 1),
                     labels = scales::percent_format(accuracy = 1),
                     limits = 0.01*c(0, 8)) + 
  xlab("") + ylab("% of Merchandise exports")

Brazil, Russia, India

TX.VAL.MRCH.CD.WT %>%
  right_join(iso2c, by = "iso2c") %>%
  filter(iso2c %in% c("1W", "BR", "RU", "IN")) %>%
  group_by(year) %>%
  mutate(value = TX.VAL.MRCH.CD.WT/TX.VAL.MRCH.CD.WT[iso2c == "1W"]) %>%
  year_to_date %>%
  filter(!(iso2c == "1W")) %>%
  mutate(Iso2c = ifelse(iso2c == "RU", "Russia", Iso2c)) %>%
  left_join(colors, by = c("Iso2c" = "country")) %>%
  ggplot(.) + theme_minimal() + scale_color_identity() +
  geom_line(aes(x = date, y = value, color = color)) +
  add_3flags +
  theme(legend.title = element_blank(),
        legend.position = c(0.3, 0.85)) +
  scale_x_date(breaks = seq(1950, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(0, 70, 1),
                     labels = scales::percent_format(accuracy = 1),
                     limits = 0.01*c(0, 5)) + 
  xlab("") + ylab("% of Merchandise exports")

Merchandise exports (current USD)

Data - WDI

François Geerolf

Info

LAST_DOWNLOAD

LAST_COMPILE

Last

Nobs - Javascript

2018 GDP by Country

2018 GDP by Country and Aggregates

Illustration

Netherlands, Belgium, Switzerland, Turkey, Poland, Sweden

Germany, France, Italy, United Kingdom, Spain

Brazil, Mexico, Argentina

China, E.U., U.S.

All

1990-

1995-

1996-

2005-

South Korea, Russia, Indonesia

Japan, India, United Kingdom

Germany, France, Italy

Eurozone, United States

All

1990-

2005-

Germany, Japan, United Kingdom

United Kingdom, France, Italy

Brazil, Russia, India