GDP per capita (constant 2015 USD)

Data - WDI

Info

source	dataset	.html	.RData
wdi	NY.GDP.PCAP.KD	2024-09-15	2024-09-18

Data on macro

source	dataset	.html	.RData
eurostat	nama_10_a10	2024-09-15	2024-09-15
eurostat	nama_10_a10_e	2024-09-15	2024-09-18
eurostat	nama_10_gdp	2024-09-15	2024-09-15
eurostat	nama_10_lp_ulc	2024-09-15	2024-09-15
eurostat	namq_10_a10	2024-09-15	2024-09-18
eurostat	namq_10_a10_e	2024-09-15	2024-09-15
eurostat	namq_10_gdp	2024-09-04	2024-09-15
eurostat	namq_10_lp_ulc	2024-09-15	2024-09-15
eurostat	namq_10_pc	2024-08-21	2024-09-15
eurostat	nasa_10_nf_tr	2024-09-15	2024-09-15
eurostat	nasq_10_nf_tr	2024-09-02	2024-09-02
fred	gdp	2024-08-29	2024-09-18
oecd	QNA	2024-06-06	2024-06-30
oecd	SNA_TABLE1	2024-09-15	2024-06-30
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	2024-09-18	2024-09-18
wdi	NY.GDP.MKTP.CD	2024-09-18	2024-09-18
wdi	NY.GDP.MKTP.PP.CD	2024-09-18	2024-09-18
wdi	NY.GDP.PCAP.CD	2024-09-18	2024-09-18
wdi	NY.GDP.PCAP.KD	2024-09-15	2024-09-18
wdi	NY.GDP.PCAP.PP.CD	2024-09-15	2024-09-18
wdi	NY.GDP.PCAP.PP.KD	2024-09-15	2024-09-18

LAST_COMPILE

LAST_COMPILE
2024-09-18

Last

NY.GDP.PCAP.KD %>%
  group_by(year) %>%
  summarise(Nobs = n()) %>%
  arrange(desc(year)) %>%
  head(1) %>%
  print_table_conditional()

year	Nobs
2023	235

Nobs - Javascript

NY.GDP.PCAP.KD %>%
  left_join(iso2c, by = "iso2c") %>%
  group_by(iso2c, Iso2c) %>%
  mutate(value = round(value)) %>%
  summarise(Nobs = n(),
            `Year 1` = first(year),
            `GDP per capita (constant 2010 USD) 1` = first(value) %>% paste0("$ ", .),
            `Year 2` = last(year),
            `GDP per capita (constant 2010 USD) 2` = last(value) %>% paste0("$ ", .)) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

1990, 2019, growth 1990-2019

NY.GDP.PCAP.KD %>%
  filter(year %in% c(1990, 2019)) %>%
  left_join(iso2c, by = "iso2c") %>%
  spread(year, value) %>%
  mutate(growth = round(100*(`2019`/`1990`-1),1)) %>%
  mutate(`1990` = round(`1990`),
         `2019` = round(`2019`)) %>%
  arrange(-growth) %>%
  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 .}

Output - Countries

Png

include_graphics3b("bib/wdi/NY.GDP.PCAP.KD_ex1.png")

United States

NY.GDP.PCAP.KD %>%
  filter(iso2c %in% c("US")) %>%
  left_join(iso2c, by = "iso2c") %>%
  year_to_date %>%
  filter(date >= as.Date("1971-01-01")) %>%
  ggplot(.) + geom_line(aes(x = date, y = value)) +
  xlab("") + ylab("GDP per capita (constant 2010 USD)") + theme_minimal() +
  theme(legend.title = element_blank(),
        legend.position = c(0.2, 0.2)) +
  scale_x_date(breaks = seq(1950, 2020, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = c(23670, 30000, 40000, 53748),
                     labels = dollar_format(a = 1))

Japan

NY.GDP.PCAP.KD %>%
  filter(iso2c %in% c("JP")) %>%
  left_join(iso2c, by = "iso2c") %>%
  year_to_date %>%
  ggplot(.) + geom_line(aes(x = date, y = value)) +
  xlab("") + ylab("GDP per capita (constant 2010 USD)") + theme_minimal() +
  theme(legend.title = element_blank(),
        legend.position = c(0.2, 0.2)) +
  scale_x_date(breaks = seq(1950, 2020, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = seq(0, 200000, 10000),
                     labels = dollar_format())

Iceland (1990-2020)

NY.GDP.PCAP.KD %>%
  year_to_date %>%
  filter(iso2c %in% c("IS"),
         date >= as.Date("1990-01-01")) %>%
  left_join(iso2c, by = "iso2c") %>%
  ggplot(.) + geom_line(aes(x = date, y = value)) +
  xlab("") + ylab("GDP per capita (constant 2010 USD)") + theme_minimal() +
  theme(legend.title = element_blank(),
        legend.position = c(0.2, 0.2)) +
  scale_x_date(breaks = seq(1950, 2020, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = seq(0, 200000, 2000),
                     labels = dollar_format())

Euro Area vs. US

Base 100

NY.GDP.PCAP.KD %>%
  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/habitant en $ (100 = 2008)")

Avec dollars

NY.GDP.PCAP.KD %>%
  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,
         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/habitant 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), " /hab")))

Euro area vs. US vs. France

Linear

load_data("wdi/Iso2c2.RData")
NY.GDP.PCAP.KD %>%
  # XC: Euro area
  filter(iso2c %in% c("US", "XC", "FR")) %>%
  left_join(iso2c, by = "iso2c") %>%
  mutate(Iso2c = ifelse(iso2c == "XC", "Europe", Iso2c)) %>%
  year_to_date %>%
  left_join(colors, by = c("Iso2c" = "country")) %>%
  mutate(color = ifelse(iso2c == "XC", color2, color)) %>%
  ggplot(.) + xlab("") + ylab("GDP per capita") +
  geom_line(aes(x = date, y = value, color = color)) + 
  theme_minimal() + scale_color_identity() + add_3flags +
  scale_x_date(breaks = seq(1950, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = seq(0, 80000, 5000),
                labels = dollar_format(acc = 1))

Base 100 = 1960

NY.GDP.PCAP.KD %>%
  filter(iso2c %in% c("US", "XC", "FR")) %>%
  left_join(iso2c, by = "iso2c") %>%
  mutate(Iso2c = ifelse(iso2c == "XC", "Europe", Iso2c)) %>%
  year_to_date %>%
  left_join(colors, by = c("Iso2c" = "country")) %>%
  mutate(color = ifelse(iso2c == "XC", color2, color)) %>%
  group_by(iso2c, Iso2c) %>%
  arrange(date) %>%
  mutate(value = 100*value/value[1]) %>%
  ggplot(.) + xlab("") + ylab("GDP per capita, 100 = 1960") +
  geom_line(aes(x = date, y = value, color = color)) + 
  theme_minimal() + scale_color_identity() + add_3flags +
  scale_x_date(breaks = seq(1950, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks =c(100, 200, 400, 800, 1000, 2000, 4000, 8000, 10000, 20000))

Base 100 = 1990

NY.GDP.PCAP.KD %>%
  filter(iso2c %in% c("US", "XC", "FR")) %>%
  left_join(iso2c, by = "iso2c") %>%
  mutate(Iso2c = ifelse(iso2c == "XC", "Europe", Iso2c)) %>%
  year_to_date %>%
  left_join(colors, by = c("Iso2c" = "country")) %>%
  mutate(color = ifelse(iso2c == "XC", color2, color)) %>%
  group_by(iso2c, Iso2c) %>%
  arrange(date) %>%
  filter(date >= as.Date("1990-01-01")) %>%
  mutate(value = 100*value/value[1]) %>%
  ggplot(.) + xlab("") + ylab("GDP per capita, 100 = 1990") +
  geom_line(aes(x = date, y = value, color = color)) + 
  theme_minimal() + scale_color_identity() + add_3flags +
  scale_x_date(breaks = seq(1950, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(100, 400, 10))

Base 100 = 2006

NY.GDP.PCAP.KD %>%
  filter(iso2c %in% c("US", "XC", "FR")) %>%
  left_join(iso2c, by = "iso2c") %>%
  mutate(Iso2c = ifelse(iso2c == "XC", "Europe", Iso2c)) %>%
  year_to_date %>%
  left_join(colors, by = c("Iso2c" = "country")) %>%
  mutate(color = ifelse(iso2c == "XC", color2, color)) %>%
  group_by(iso2c, Iso2c) %>%
  arrange(date) %>%
  filter(date >= as.Date("2006-01-01")) %>%
  mutate(value = 100*value/value[1]) %>%
  ggplot(.) + xlab("") + ylab("GDP per capita, 100 = 2006") +
  geom_line(aes(x = date, y = value, color = color)) + 
  theme_minimal() + scale_color_identity() + add_3flags +
  scale_x_date(breaks = seq(1950, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(10, 400, 5))

Base 100 = 2007

NY.GDP.PCAP.KD %>%
  filter(iso2c %in% c("US", "XC", "FR")) %>%
  left_join(iso2c, by = "iso2c") %>%
  mutate(Iso2c = ifelse(iso2c == "XC", "Europe", Iso2c)) %>%
  year_to_date %>%
  left_join(colors, by = c("Iso2c" = "country")) %>%
  mutate(color = ifelse(iso2c == "XC", color2, color)) %>%
  group_by(iso2c, Iso2c) %>%
  arrange(date) %>%
  filter(date >= as.Date("2007-01-01")) %>%
  mutate(value = 100*value/value[1]) %>%
  ggplot(.) + xlab("") + ylab("GDP per capita, 100 = 2007") +
  geom_line(aes(x = date, y = value, color = color)) + 
  theme_minimal() + scale_color_identity() + add_3flags +
  scale_x_date(breaks = seq(1950, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(10, 400, 5))

Base 100 = 2008

NY.GDP.PCAP.KD %>%
  filter(iso2c %in% c("US", "XC", "FR")) %>%
  left_join(iso2c, by = "iso2c") %>%
  mutate(Iso2c = ifelse(iso2c == "XC", "Europe", Iso2c)) %>%
  year_to_date %>%
  left_join(colors, by = c("Iso2c" = "country")) %>%
  mutate(color = ifelse(iso2c == "XC", color2, color)) %>%
  group_by(iso2c, Iso2c) %>%
  arrange(date) %>%
  filter(date >= as.Date("2008-01-01")) %>%
  mutate(value = 100*value/value[1]) %>%
  ggplot(.) + xlab("") + ylab("GDP per capita, 100 = 2008") +
  geom_line(aes(x = date, y = value, color = color)) + 
  theme_minimal() + scale_color_identity() + add_3flags +
  scale_x_date(breaks = seq(1950, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(10, 400, 5))

Switzerland, France, Germany

Linear

NY.GDP.PCAP.KD %>%
  filter(iso2c %in% c("CH", "FR", "DE")) %>%
  left_join(iso2c, by = "iso2c") %>%
  year_to_date %>%
  left_join(colors, by = c("Iso2c" = "country")) %>%
  ggplot(.) + xlab("") + ylab("GDP per capita") +
  geom_line(aes(x = date, y = value, color = color)) + 
  theme_minimal() + scale_color_identity() + add_3flags +
  scale_x_date(breaks = seq(1950, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = seq(0, 80000, 5000),
                labels = dollar_format(acc = 1))

Log

NY.GDP.PCAP.KD %>%
  filter(iso2c %in% c("CH", "FR", "DE")) %>%
  left_join(iso2c, by = "iso2c") %>%
  year_to_date %>%
  left_join(colors, by = c("Iso2c" = "country")) %>%
  
  filter(date >= as.Date("1980-01-01")) %>%
  ggplot(.) + xlab("") + ylab("GDP per capita") +
  geom_line(aes(x = date, y = value, color = color)) + 
  theme_minimal() + scale_color_identity() + add_3flags +
  scale_x_date(breaks = seq(1950, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(10000, 90000, 10000),
                labels = dollar_format(acc = 1))

China, France, Germany

Linear

load_data("wdi/iso2c.RData")
NY.GDP.PCAP.KD %>%
  filter(iso2c %in% c("CN", "FR", "DE")) %>%
  left_join(iso2c, by = "iso2c") %>%
  year_to_date %>%
  left_join(colors, by = c("Iso2c" = "country")) %>%
  
  ggplot(.) + xlab("") + ylab("GDP per capita") +
  geom_line(aes(x = date, y = value, color = color)) + 
  theme_minimal() + scale_color_identity() + add_3flags +
  scale_x_date(breaks = seq(1950, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = seq(0, 80000, 5000),
                labels = dollar_format(acc = 1))

Log

NY.GDP.PCAP.KD %>%
  filter(iso2c %in% c("CN", "FR", "DE")) %>%
  left_join(iso2c, by = "iso2c") %>%
  year_to_date %>%
  left_join(colors, by = c("Iso2c" = "country")) %>%
  
  ggplot(.) + xlab("") + ylab("GDP per capita") +
  geom_line(aes(x = date, y = value, color = color)) + 
  theme_minimal() + scale_color_identity() + add_3flags +
  scale_x_date(breaks = seq(1950, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = c(100, 200, 500, 1000, 2000, 5000, 10000, 20000, 50000, 100000),
                labels = dollar_format(acc = 1))

Spain, Italy, France, Germany, United States

Log

All

NY.GDP.PCAP.KD %>%
  filter(iso2c %in% c("ES", "FR", "DE", "IT", "US")) %>%
  left_join(iso2c, by = "iso2c") %>%
  year_to_date %>%
  left_join(colors, by = c("Iso2c" = "country")) %>%
  
  ggplot(.) + xlab("") + ylab("GDP per capita") +
  geom_line(aes(x = date, y = value, color = color)) + 
  theme_minimal() + scale_color_identity() + add_5flags +
  scale_x_date(breaks = seq(1950, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(5000, 50000, 5000),
                labels = dollar_format(acc = 1))

1990-

NY.GDP.PCAP.KD %>%
  filter(iso2c %in% c("ES", "FR", "DE", "IT", "US")) %>%
  left_join(iso2c, by = "iso2c") %>%
  year_to_date %>%
  filter(date >= as.Date("1990-01-01")) %>%
  left_join(colors, by = c("Iso2c" = "country")) %>%
  
  ggplot(.) + xlab("") + ylab("GDP per capita") +
  geom_line(aes(x = date, y = value, color = color)) + 
  theme_minimal() + scale_color_identity() + add_5flags +
  scale_x_date(breaks = seq(1950, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(5000, 50000, 5000),
                labels = dollar_format(acc = 1))

Spain, Italy, France, Germany

Linear

load_data("wdi/iso2c.RData")
NY.GDP.PCAP.KD %>%
  filter(iso2c %in% c("ES", "FR", "DE", "IT")) %>%
  left_join(iso2c, by = "iso2c") %>%
  year_to_date %>%
  left_join(colors, by = c("Iso2c" = "country")) %>%
  
  ggplot(.) + xlab("") + ylab("GDP per capita") +
  geom_line(aes(x = date, y = value, color = color)) + 
  theme_minimal() + scale_color_identity() + add_4flags +
  scale_x_date(breaks = seq(1950, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = seq(0, 80000, 5000),
                labels = dollar_format(acc = 1))

Log

All

NY.GDP.PCAP.KD %>%
  filter(iso2c %in% c("ES", "FR", "DE", "IT")) %>%
  left_join(iso2c, by = "iso2c") %>%
  year_to_date %>%
  left_join(colors, by = c("Iso2c" = "country")) %>%
  
  ggplot(.) + xlab("") + ylab("GDP per capita") +
  geom_line(aes(x = date, y = value, color = color)) + 
  theme_minimal() + scale_color_identity() + add_4flags +
  scale_x_date(breaks = seq(1950, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(5000, 50000, 5000),
                labels = dollar_format(acc = 1))

1990-

NY.GDP.PCAP.KD %>%
  filter(iso2c %in% c("ES", "FR", "DE", "IT")) %>%
  left_join(iso2c, by = "iso2c") %>%
  year_to_date %>%
  filter(date >= as.Date("1990-01-01")) %>%
  left_join(colors, by = c("Iso2c" = "country")) %>%
  
  ggplot(.) + xlab("") + ylab("GDP per capita") +
  geom_line(aes(x = date, y = value, color = color)) + 
  theme_minimal() + scale_color_identity() + add_4flags +
  scale_x_date(breaks = seq(1950, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(5000, 50000, 5000),
                labels = dollar_format(acc = 1))

Poland, Portugal, Spain

Linear

NY.GDP.PCAP.KD %>%
  filter(iso2c %in% c("PL", "CZ", "FR")) %>%
  left_join(iso2c, by = "iso2c") %>%
  year_to_date %>%
  left_join(colors, by = c("Iso2c" = "country")) %>%
  
  ggplot(.) + xlab("") + ylab("GDP per capita") +
  geom_line(aes(x = date, y = value, color = color)) + 
  theme_minimal() + scale_color_identity() + add_3flags +
  scale_x_date(breaks = seq(1950, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = seq(0, 80000, 5000),
                labels = dollar_format(acc = 1))

Log

NY.GDP.PCAP.KD %>%
  filter(iso2c %in% c("PL", "CZ", "FR")) %>%
  left_join(iso2c, by = "iso2c") %>%
  year_to_date %>%
  left_join(colors, by = c("Iso2c" = "country")) %>%
  
  ggplot(.) + xlab("") + ylab("GDP per capita") +
  geom_line(aes(x = date, y = value, color = color)) + 
  theme_minimal() + scale_color_identity() + add_3flags +
  scale_x_date(breaks = seq(1950, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(0, 80000, 5000),
                labels = dollar_format(acc = 1))

Italy, Portugal, Spain

Linear

NY.GDP.PCAP.KD %>%
  filter(iso2c %in% c("ES", "IT", "PT")) %>%
  left_join(iso2c, by = "iso2c") %>%
  year_to_date %>%
  left_join(colors, by = c("Iso2c" = "country")) %>%
  
  ggplot(.) + xlab("") + ylab("GDP per capita") +
  geom_line(aes(x = date, y = value, color = color)) + 
  theme_minimal() + scale_color_identity() + add_3flags +
  scale_x_date(breaks = seq(1950, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = seq(0, 80000, 5000),
                labels = dollar_format(acc = 1))

Log

NY.GDP.PCAP.KD %>%
  filter(iso2c %in% c("ES", "IT", "PT")) %>%
  left_join(iso2c, by = "iso2c") %>%
  year_to_date %>%
  left_join(colors, by = c("Iso2c" = "country")) %>%
  
  ggplot(.) + xlab("") + ylab("GDP per capita") +
  geom_line(aes(x = date, y = value, color = color)) + 
  theme_minimal() + scale_color_identity() + add_3flags +
  scale_x_date(breaks = seq(1950, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(0, 80000, 5000),
                labels = dollar_format(acc = 1))

Spain, United Kingdom, United States

Linear

NY.GDP.PCAP.KD %>%
  filter(iso2c %in% c("US", "GB", "ES")) %>%
  left_join(iso2c, by = "iso2c") %>%
  year_to_date %>%
  left_join(colors, by = c("Iso2c" = "country")) %>%
  
  ggplot(.) + xlab("") + ylab("GDP per capita") +
  geom_line(aes(x = date, y = value, color = color)) + 
  theme_minimal() + scale_color_identity() + add_3flags +
  scale_x_date(breaks = seq(1950, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = seq(0, 80000, 5000),
                labels = dollar_format(acc = 1))

Log

NY.GDP.PCAP.KD %>%
  filter(iso2c %in% c("US", "GB", "ES")) %>%
  left_join(iso2c, by = "iso2c") %>%
  year_to_date %>%
  left_join(colors, by = c("Iso2c" = "country")) %>%
  
  ggplot(.) + xlab("") + ylab("GDP per capita") +
  geom_line(aes(x = date, y = value, color = color)) + 
  theme_minimal() + scale_color_identity() + add_3flags +
  scale_x_date(breaks = seq(1950, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(0, 80000, 5000),
                labels = dollar_format(acc = 1))

Argentina, Chile, Venezuela

Linear

NY.GDP.PCAP.KD %>%
  filter(iso2c %in% c("AR", "CL", "VE")) %>%
  left_join(iso2c, by = "iso2c") %>%
  mutate(Iso2c = ifelse(iso2c == "VE", "Venezuela", Iso2c)) %>%
  year_to_date %>%
  left_join(colors, by = c("Iso2c" = "country")) %>%
  
  ggplot(.) + xlab("") + ylab("GDP per capita") +
  geom_line(aes(x = date, y = value, color = color)) + 
  theme_minimal() + scale_color_identity() + add_3flags +
  scale_x_date(breaks = seq(1950, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = seq(0, 80000, 2000),
                labels = dollar_format(acc = 1))

Log

NY.GDP.PCAP.KD %>%
  filter(iso2c %in% c("AR", "CL", "VE")) %>%
  left_join(iso2c, by = "iso2c") %>%
  mutate(Iso2c = ifelse(iso2c == "VE", "Venezuela", Iso2c)) %>%
  year_to_date %>%
  left_join(colors, by = c("Iso2c" = "country")) %>%
  
  ggplot(.) + xlab("") + ylab("GDP per capita") +
  geom_line(aes(x = date, y = value, color = color)) + 
  theme_minimal() + scale_color_identity() + add_3flags +
  scale_x_date(breaks = seq(1950, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(0, 80000, 1000),
                labels = dollar_format(acc = 1))

Argentina

Log

NY.GDP.PCAP.KD %>%
  filter(iso2c %in% c("AR")) %>%
  left_join(iso2c, by = "iso2c") %>%
  mutate(Iso2c = ifelse(iso2c == "VE", "Venezuela", Iso2c)) %>%
  year_to_date %>%
  left_join(colors, by = c("Iso2c" = "country")) %>%
  
  ggplot(.) + xlab("") + ylab("GDP per capita") +
  geom_line(aes(x = date, y = value)) + 
  theme_minimal() + scale_color_identity() + add_3flags +
  scale_x_date(breaks = seq(1950, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(0, 80000, 1000),
                labels = dollar_format(acc = 1))

Argentina, Zimbabwe, Cuba

Log

NY.GDP.PCAP.KD %>%
  filter(iso2c %in% c("AR", "ZW", "CU")) %>%
  left_join(iso2c, by = "iso2c") %>%
  mutate(Iso2c = ifelse(iso2c == "VE", "Venezuela", Iso2c)) %>%
  year_to_date %>%
  left_join(colors, by = c("Iso2c" = "country")) %>%
  
  ggplot(.) + xlab("") + ylab("GDP per capita") +
  geom_line(aes(x = date, y = value, color = color)) + 
  theme_minimal() + scale_color_identity() + add_3flags +
  scale_x_date(breaks = seq(1950, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(0, 80000, 1000),
                labels = dollar_format(acc = 1))

Base 100

NY.GDP.PCAP.KD %>%
  filter(iso2c %in% c("AR", "ZW", "CU")) %>%
  left_join(iso2c, by = "iso2c") %>%
  mutate(Iso2c = ifelse(iso2c == "VE", "Venezuela", Iso2c)) %>%
  year_to_date %>%
  left_join(colors, by = c("Iso2c" = "country")) %>%
  
  group_by(iso2c) %>%
  mutate(value = 100*value/value[date == as.Date("1970-01-01")]) %>%
  filter(date >= as.Date("1970-01-01")) %>%
  ggplot(.) + xlab("") + ylab("GDP per capita") +
  geom_line(aes(x = date, y = value, color = color)) + 
  theme_minimal() + scale_color_identity() + add_3flags +
  scale_x_date(breaks = seq(1950, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(40, 2000, 20))

Greece, Hong Kong, Mexico

Linear

NY.GDP.PCAP.KD %>%
  filter(iso2c %in% c("GR", "HK", "MX")) %>%
  left_join(iso2c, by = "iso2c") %>%
  mutate(Iso2c = ifelse(iso2c == "HK", "Hong Kong", Iso2c)) %>%
  year_to_date %>%
  left_join(colors, by = c("Iso2c" = "country")) %>%
  
  ggplot(.) + xlab("") + ylab("GDP per capita") +
  geom_line(aes(x = date, y = value, color = color)) + 
  theme_minimal() + scale_color_identity() + add_3flags +
  scale_x_date(breaks = seq(1950, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = seq(0, 80000, 5000),
                labels = dollar_format(acc = 1))

Log

NY.GDP.PCAP.KD %>%
  filter(iso2c %in% c("GR", "HK", "MX")) %>%
  left_join(iso2c, by = "iso2c") %>%
  mutate(Iso2c = ifelse(iso2c == "HK", "Hong Kong", Iso2c)) %>%
  year_to_date %>%
  left_join(colors, by = c("Iso2c" = "country")) %>%
  
  ggplot(.) + xlab("") + ylab("GDP per capita") +
  geom_line(aes(x = date, y = value, color = color)) + 
  theme_minimal() + scale_color_identity() + add_3flags +
  scale_x_date(breaks = seq(1950, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(0, 80000, 5000),
                labels = dollar_format(acc = 1))

Singapore, South Korea, Mexico

Linear

NY.GDP.PCAP.KD %>%
  filter(iso2c %in% c("KR", "SG", "CH")) %>%
  left_join(iso2c, by = "iso2c") %>%
  mutate(Iso2c = ifelse(iso2c == "KR", "Korea", Iso2c)) %>%
  year_to_date %>%
  left_join(colors, by = c("Iso2c" = "country")) %>%
  
  ggplot(.) + xlab("") + ylab("GDP per capita") +
  geom_line(aes(x = date, y = value, color = color)) + 
  theme_minimal() + scale_color_identity() + add_3flags +
  scale_x_date(breaks = seq(1950, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = seq(0, 80000, 5000),
                labels = dollar_format(acc = 1))

Log

NY.GDP.PCAP.KD %>%
  filter(iso2c %in% c("KR", "SG", "CH")) %>%
  left_join(iso2c, by = "iso2c") %>%
  mutate(Iso2c = ifelse(iso2c == "KR", "Korea", Iso2c)) %>%
  year_to_date %>%
  left_join(colors, by = c("Iso2c" = "country")) %>%
  
  ggplot(.) + xlab("") + ylab("GDP per capita") +
  geom_line(aes(x = date, y = value, color = color)) + 
  theme_minimal() + scale_color_identity() + add_3flags +
  scale_x_date(breaks = seq(1950, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = c(1000, 2000, 3000, 5000, 8000, seq(0, 80000, 10000)),
                labels = dollar_format(acc = 1))