| source | dataset | .html | .RData |
|---|---|---|---|
| 2024-09-15 | 2024-09-18 |
GDP (current USD)
Data - WDI
Info
Data on macro
| source | dataset | .html | .RData |
|---|---|---|---|
| 2024-09-15 | 2024-09-15 | ||
| 2024-09-15 | 2024-09-18 | ||
| 2024-09-15 | 2024-09-15 | ||
| 2024-09-15 | 2024-09-15 | ||
| 2024-09-15 | 2024-09-18 | ||
| 2024-09-15 | 2024-09-15 | ||
| 2024-09-04 | 2024-09-15 | ||
| 2024-09-15 | 2024-09-15 | ||
| 2024-08-21 | 2024-09-15 | ||
| 2024-09-15 | 2024-09-15 | ||
| 2024-09-02 | 2024-09-02 | ||
| 2024-08-29 | 2024-09-18 | ||
| 2024-06-06 | 2024-06-30 | ||
| 2024-09-15 | 2024-06-30 | ||
| 2024-09-15 | 2024-06-30 | ||
| 2024-07-01 | 2024-04-11 | ||
| 2024-07-01 | 2024-06-30 | ||
| 2024-09-18 | 2024-09-18 | ||
| 2024-09-15 | 2024-09-18 | ||
| 2024-09-15 | 2024-09-18 | ||
| 2024-09-15 | 2024-09-18 | ||
| 2024-09-15 | 2024-09-18 | ||
| 2024-09-15 | 2024-09-18 | ||
| 2024-09-15 | 2024-09-18 |
LAST_COMPILE
| LAST_COMPILE |
|---|
| 2024-09-18 |
Last
Code
NY.GDP.MKTP.CD %>%
group_by(year) %>%
summarise(Nobs = n()) %>%
arrange(desc(year)) %>%
head(1) %>%
print_table_conditional()| year | Nobs |
|---|---|
| 2023 | 234 |
Nobs - Javascript
Code
NY.GDP.MKTP.CD %>%
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.CD %>%
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.CD %>%
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
Netherlands, Belgium, Switzerland, Turkey, Poland, Sweden
Code
NY.GDP.MKTP.CD %>%
right_join(iso2c, by = "iso2c") %>%
filter(iso2c %in% c("1W", "CH", "BE", "NL", "PO", "SE", "TR")) %>%
year_to_date %>%
group_by(date) %>%
mutate(value = value/value[iso2c == "1W"]) %>%
filter(!(iso2c == "1W")) %>%
left_join(colors, by = c("Iso2c" = "country")) %>%
ggplot(.) + theme_minimal() + scale_color_identity() + xlab("") + ylab("% of World GDP") +
geom_line(aes(x = date, y = value, color = color)) + add_5flags +
scale_x_date(breaks = seq(1950, 2100, 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
Code
NY.GDP.MKTP.CD %>%
right_join(iso2c, by = "iso2c") %>%
filter(iso2c %in% c("1W", "DE", "FR", "IT", "ES", "GB")) %>%
year_to_date %>%
group_by(date) %>%
mutate(value = value/value[iso2c == "1W"]) %>%
filter(!(iso2c == "1W")) %>%
left_join(colors, by = c("Iso2c" = "country")) %>%
ggplot(.) + theme_minimal() + scale_color_identity() + xlab("") + ylab("% of World GDP") +
geom_line(aes(x = date, y = value, color = color)) + add_5flags +
scale_x_date(breaks = seq(1950, 2100, 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, 9))
Brazil, Mexico, Argentina
Code
NY.GDP.MKTP.CD %>%
right_join(iso2c, by = "iso2c") %>%
filter(iso2c %in% c("1W", "BR", "MX", "AR")) %>%
year_to_date %>%
group_by(date) %>%
mutate(value = value/value[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 World GDP") +
geom_line(aes(x = date, y = value, color = color)) + add_3flags +
scale_x_date(breaks = seq(1950, 2100, 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),
limits = 0.01*c(0, 3.8))
China, E.U., U.S.
All
Code
NY.GDP.MKTP.CD %>%
right_join(iso2c, by = "iso2c") %>%
filter(iso2c %in% c("1W", "US", "CN", "EU", "JP")) %>%
group_by(year) %>%
mutate(value = value/value[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_4flags +
scale_x_date(breaks = seq(1950, 2100, 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, 40)) +
xlab("") + ylab("% du PIB mondial")
China, E.U., U.S.
All
English
Code
NY.GDP.MKTP.CD %>%
right_join(iso2c, by = "iso2c") %>%
filter(iso2c %in% c("1W", "US", "CN", "EU")) %>%
group_by(year) %>%
mutate(value = value/value[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, 2100, 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, 40)) +
xlab("") + ylab("% of World GDP")
French
Code
NY.GDP.MKTP.CD %>%
right_join(iso2c, by = "iso2c") %>%
filter(iso2c %in% c("1W", "US", "CN", "EU")) %>%
group_by(year) %>%
mutate(value = value/value[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, 2100, 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, 40)) +
xlab("") + ylab("% du PIB mondial")
1990-
Code
NY.GDP.MKTP.CD %>%
right_join(iso2c, by = "iso2c") %>%
filter(iso2c %in% c("1W", "US", "CN", "EU")) %>%
group_by(year) %>%
mutate(value = value/value[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, 2100, 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("% of World GDP")
1995-
Code
NY.GDP.MKTP.CD %>%
right_join(iso2c, by = "iso2c") %>%
filter(iso2c %in% c("1W", "US", "CN", "EU")) %>%
group_by(year) %>%
mutate(value = value/value[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, 2100, 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")
1996-
Code
NY.GDP.MKTP.CD %>%
right_join(iso2c, by = "iso2c") %>%
filter(iso2c %in% c("1W", "US", "CN", "EU")) %>%
group_by(year) %>%
mutate(value = value/value[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, 2100, 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-
Code
NY.GDP.MKTP.CD %>%
right_join(iso2c, by = "iso2c") %>%
filter(iso2c %in% c("1W", "US", "CN", "EU")) %>%
group_by(year) %>%
mutate(value = value/value[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 World GDP")
Russia, Germany, France
Code
NY.GDP.MKTP.CD %>%
right_join(iso2c, by = "iso2c") %>%
filter(iso2c %in% c("1W", "FR", "DE", "RU")) %>%
group_by(year) %>%
mutate(value = value/value[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")) %>%
mutate(color = ifelse(iso2c == "FR", color2, color)) %>%
ggplot(.) + theme_minimal() + scale_color_identity() + xlab("") + ylab("% of World GDP") +
geom_line(aes(x = date, y = value, color = color)) +
add_3flags +
scale_x_date(breaks = seq(1950, 2100, 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))
South Korea, Russia, Indonesia
Code
NY.GDP.MKTP.CD %>%
right_join(iso2c, by = "iso2c") %>%
filter(iso2c %in% c("1W", "ID", "KR", "RU")) %>%
group_by(year) %>%
mutate(value = value/value[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 World GDP") +
geom_line(aes(x = date, y = value, color = color)) +
add_3flags +
scale_x_date(breaks = seq(1950, 2100, 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
Code
NY.GDP.MKTP.CD %>%
right_join(iso2c, by = "iso2c") %>%
filter(iso2c %in% c("1W", "IN", "JP", "GB")) %>%
group_by(year) %>%
mutate(value = value/value[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, 2100, 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 World GDP")
Germany, France, Italy
Code
NY.GDP.MKTP.CD %>%
right_join(iso2c, by = "iso2c") %>%
filter(iso2c %in% c("1W", "DE", "FR", "IT")) %>%
group_by(year) %>%
mutate(value = value/value[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, 2100, 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 World GDP")
United States
All
Code
NY.GDP.MKTP.CD %>%
right_join(iso2c, by = "iso2c") %>%
filter(iso2c %in% c("1W", "XC", "US")) %>%
group_by(year) %>%
mutate(value = value/value[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, 2100, 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("% of World GDP") +
geom_vline(xintercept = as.Date("1980-01-01"))
Eurozone, United States
All
Code
NY.GDP.MKTP.CD %>%
right_join(iso2c, by = "iso2c") %>%
filter(iso2c %in% c("1W", "XC", "US")) %>%
group_by(year) %>%
mutate(value = value/value[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, 2100, 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("% of World GDP")
1990-
Code
NY.GDP.MKTP.CD %>%
right_join(iso2c, by = "iso2c") %>%
filter(iso2c %in% c("1W", "XC", "US")) %>%
group_by(year) %>%
mutate(value = value/value[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, 2100, 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 World GDP")
2005-
Code
NY.GDP.MKTP.CD %>%
right_join(iso2c, by = "iso2c") %>%
filter(iso2c %in% c("1W", "XC", "US")) %>%
group_by(year) %>%
mutate(value = value/value[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, 2100, 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 World GDP")
Germany, Japan, United Kingdom
Code
NY.GDP.MKTP.CD %>%
right_join(iso2c, by = "iso2c") %>%
filter(iso2c %in% c("1W", "JP", "DE", "GB")) %>%
group_by(year) %>%
mutate(value = value/value[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, 2100, 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 World GDP")
United Kingdom, France, Italy
Code
NY.GDP.MKTP.CD %>%
right_join(iso2c, by = "iso2c") %>%
filter(iso2c %in% c("1W", "GB", "FR", "IT")) %>%
group_by(year) %>%
mutate(value = value/value[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, 2100, 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 World GDP")
Brazil, Russia, India
Code
NY.GDP.MKTP.CD %>%
right_join(iso2c, by = "iso2c") %>%
filter(iso2c %in% c("1W", "BR", "RU", "IN")) %>%
group_by(year) %>%
mutate(value = value/value[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, 2100, 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 World GDP")
Euro Area vs. US
Base 100
Code
NY.GDP.MKTP.CD %>%
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.CD %>%
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")))
France vs. US
Avec dollars
Code
NY.GDP.MKTP.CD %>%
left_join(iso2c, by = "iso2c") %>%
year_to_date %>%
filter(iso2c %in% c("FR", "US"),
date >= as.Date("2008-01-01")) %>%
group_by(iso2c) %>%
arrange(date) %>%
mutate(Iso2c = ifelse(iso2c == "FR", "France", 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[1]) %>%
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 = 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")))