Direction of Trade Statistics - China - DOT_CN

Data - IMF

Author

François Geerolf

Info

DOWNLOAD_TIME

Code 
tibble(DOWNLOAD_TIME = as.Date(file.info("~/iCloud/website/data/imf/DOT_CN.RData")$mtime)) %>%
  print_table_conditional()
DOWNLOAD_TIME
2025-01-05

Last

Code 
DOT_CN %>%
  group_by(TIME_PERIOD) %>%
  summarise(Nobs = n()) %>%
  arrange(desc(TIME_PERIOD)) %>%
  head(1) %>%
  print_table_conditional()
TIME_PERIOD Nobs
2024-Q3 694

FREQ

Code 
DOT_CN %>%
  left_join(FREQ, by = "FREQ") %>%
  group_by(FREQ, Freq) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) print_table(.) else .}
FREQ Freq Nobs
M Monthly 297625
Q Quarterly 102870
A Annual 26976

INDICATOR

Code 
DOT_CN %>%
  left_join(INDICATOR, by = "INDICATOR") %>%
  group_by(INDICATOR, Indicator) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) print_table(.) else .}
INDICATOR Indicator Nobs
TBG_USD Goods, Value of Trade Balance, US Dollars 151495
TXG_FOB_USD Goods, Value of Exports, Free on board (FOB), US Dollars 149810
TMG_CIF_USD Goods, Value of Imports, Cost, Insurance, Freight (CIF), US Dollars 126166

COUNTERPART_AREA

Code 
DOT_CN %>%
  left_join(COUNTERPART_AREA, by = "COUNTERPART_AREA") %>%
  group_by(COUNTERPART_AREA, Counterpart_area) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Counterpart_area))),
         Flag = paste0('<img src="../../icon/flag/vsmall/', Flag, '.png" alt="Flag">')) %>%
  select(Flag, everything()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

TIME_PERIOD

Code 
DOT_CN %>%
  group_by(TIME_PERIOD) %>%
  summarise(Nobs = n()) %>%
  arrange(desc(TIME_PERIOD)) %>%
  slice(1:10, (n()-10):n()) %>%
  print_table_conditional()
TIME_PERIOD Nobs
2024-Q3 694
2024-Q2 691
2024-Q1 694
2024-09 687
2024-08 690
2024-07 686
2024-06 688
2024-05 683
2024-04 687
2024-03 684
1961-10 24
1961-09 24
1961-08 24
1961-07 24
1961-06 24
1961-05 24
1961-04 24
1961-03 24
1961-02 24
1961-01 24
1961 184

Bilateral Trade with China

All

Code 
DOT_CN %>%
  filter(COUNTERPART_AREA %in% c("DE", "FR", "U2", "US", "E1", "B0"),
         FREQ == "Q",
         INDICATOR == "TBG_USD") %>%
  mutate(OBS_VALUE = OBS_VALUE*10^(UNIT_MULT)) %>%
  left_join(INDICATOR, by = "INDICATOR") %>%
  quarter_to_date2 %>%
  ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = COUNTERPART_AREA))

France, Europe, Germany

Code 
DOT_CN %>%
  filter(COUNTERPART_AREA %in% c("DE", "FR", "U2", "US"),
         FREQ == "Q",
         INDICATOR == "TBG_USD") %>%
  mutate(OBS_VALUE = OBS_VALUE*10^(UNIT_MULT)) %>%
  left_join(INDICATOR, by = "INDICATOR") %>%
  quarter_to_date2 %>%
  ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = COUNTERPART_AREA))

China - Germany Surplus

Annual

Code 
DOT_CN %>%
  filter(COUNTERPART_AREA == "DE",
         FREQ == "A") %>%
  mutate(OBS_VALUE = OBS_VALUE*10^(UNIT_MULT)) %>%
  left_join(INDICATOR, by = "INDICATOR") %>%
  select(TIME_PERIOD, Indicator, OBS_VALUE) %>%
  spread(Indicator, OBS_VALUE) %>%
  arrange(desc(TIME_PERIOD)) %>%
  print_table_conditional()

Quarterly

Graph

Code 
DOT_CN %>%
  filter(COUNTERPART_AREA == "DE",
         FREQ == "Q",
         INDICATOR == "TBG_USD") %>%
  mutate(OBS_VALUE = OBS_VALUE*10^(UNIT_MULT)) %>%
  left_join(INDICATOR, by = "INDICATOR") %>%
  select(TIME_PERIOD, Indicator, OBS_VALUE) %>%
  quarter_to_date2 %>%
  ggplot + geom_line(aes(x = date, y = OBS_VALUE))

Table

Code 
DOT_CN %>%
  filter(COUNTERPART_AREA == "DE",
         FREQ == "Q") %>%
  mutate(OBS_VALUE = OBS_VALUE*10^(UNIT_MULT)) %>%
  left_join(INDICATOR, by = "INDICATOR") %>%
  select(TIME_PERIOD, Indicator, OBS_VALUE) %>%
  spread(Indicator, OBS_VALUE) %>%
  arrange(desc(TIME_PERIOD)) %>%
  print_table_conditional()

Graph

Code 
DOT_CN %>%
  filter(COUNTERPART_AREA == "DE",
         FREQ == "M",
         INDICATOR == "TBG_USD") %>%
  mutate(OBS_VALUE = OBS_VALUE*10^(UNIT_MULT)) %>%
  left_join(INDICATOR, by = "INDICATOR") %>%
  select(TIME_PERIOD, Indicator, OBS_VALUE) %>%
  month_to_date2 %>%
  ggplot + geom_line(aes(x = date, y = OBS_VALUE))

Monthly

Code 
DOT_CN %>%
  filter(COUNTERPART_AREA == "DE",
         FREQ == "M") %>%
  mutate(OBS_VALUE = OBS_VALUE*10^(UNIT_MULT)) %>%
  left_join(INDICATOR, by = "INDICATOR") %>%
  select(TIME_PERIOD, Indicator, OBS_VALUE) %>%
  spread(Indicator, OBS_VALUE) %>%
  arrange(desc(TIME_PERIOD)) %>%
  print_table_conditional()

Table - 2019, 2013, 2010, 2007, 2004

All

Code 
DOT_CN %>%
  left_join(NGDP_USD %>%
              select(FREQ, REF_AREA, TIME_PERIOD, NGDP_USD = OBS_VALUE),
            by = c("TIME_PERIOD", "REF_AREA", "FREQ")) %>%
  filter(TIME_PERIOD %in% c("2019", "2010", "2000", "1990"),
         INDICATOR == "TBG_USD") %>%
  mutate(OBS_VALUE = OBS_VALUE*10^(UNIT_MULT)/NGDP_USD) %>%
  left_join(COUNTERPART_AREA, by = "COUNTERPART_AREA") %>%
  select(COUNTERPART_AREA, Counterpart_area, TIME_PERIOD, OBS_VALUE) %>%
  spread(TIME_PERIOD, OBS_VALUE) %>%
  arrange(-`2019`) %>%
  mutate_at(vars(-1, -2), funs(round(100*., 2))) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Counterpart_area))),
         Flag = paste0('<img src="../../icon/flag/vsmall/', Flag, '.png" alt="Flag">')) %>%
  select(Flag, everything()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

Large Surpluses

Code 
DOT_CN %>%
  left_join(NGDP_USD %>%
              select(FREQ, REF_AREA, TIME_PERIOD, NGDP_USD = OBS_VALUE),
            by = c("TIME_PERIOD", "REF_AREA", "FREQ")) %>%
  filter(TIME_PERIOD %in% c("2019", "2010", "2000", "1990"),
         INDICATOR == "TBG_USD") %>%
  mutate(OBS_VALUE = OBS_VALUE*10^(UNIT_MULT)/NGDP_USD) %>%
  left_join(COUNTERPART_AREA, by = "COUNTERPART_AREA") %>%
  select(COUNTERPART_AREA, Counterpart_area, TIME_PERIOD, OBS_VALUE) %>%
  spread(TIME_PERIOD, OBS_VALUE) %>%
  arrange(-`2019`) %>%
  mutate_at(vars(-1, -2), funs(round(100*., 2))) %>%
  filter(!is.na(`2019`),
         `2019` > 0.04) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Counterpart_area))),
         Flag = paste0('<img src="../../icon/flag/vsmall/', Flag, '.png" alt="Flag">')) %>%
  select(Flag, everything()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

Large Deficits

Code 
DOT_CN %>%
  left_join(NGDP_USD %>%
              select(FREQ, REF_AREA, TIME_PERIOD, NGDP_USD = OBS_VALUE),
            by = c("TIME_PERIOD", "REF_AREA", "FREQ")) %>%
  filter(TIME_PERIOD %in% c("2019", "2010", "2000", "1990"),
         INDICATOR == "TBG_USD") %>%
  mutate(OBS_VALUE = OBS_VALUE*10^(UNIT_MULT)/NGDP_USD) %>%
  left_join(COUNTERPART_AREA, by = "COUNTERPART_AREA") %>%
  select(COUNTERPART_AREA, Counterpart_area, TIME_PERIOD, OBS_VALUE) %>%
  spread(TIME_PERIOD, OBS_VALUE) %>%
  arrange(`2019`) %>%
  mutate_at(vars(-1, -2), funs(round(100*., 2))) %>%
  filter(!is.na(`2019`),
         `2019` < -0.11) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Counterpart_area))),
         Flag = paste0('<img src="../../icon/flag/vsmall/', Flag, '.png" alt="Flag">')) %>%
  select(Flag, everything()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

Large Surpluses in 2020

US, UK, Singapore

Code 
DOT_CN %>%
  filter(COUNTERPART_AREA  %in% c("GB", "US", "SG"),
         INDICATOR %in% c("TBG_USD"),
         FREQ == "A") %>%
  inner_join(NGDP_USD %>%
              filter(REF_AREA == "FR",
                     FREQ == "A") %>%
              mutate(OBS_VALUE = OBS_VALUE*10^(UNIT_MULT)) %>%
              select(TIME_PERIOD, NGDP_USD = OBS_VALUE),
            by = "TIME_PERIOD") %>%
  year_to_date2 %>%
  mutate(OBS_VALUE = OBS_VALUE*10^(UNIT_MULT)/NGDP_USD) %>%
  left_join(COUNTERPART_AREA, by = "COUNTERPART_AREA") %>%
  mutate(Counterpart_area = ifelse(COUNTERPART_AREA == "U2", "Europe", Counterpart_area),
         Counterpart_area = ifelse(COUNTERPART_AREA == "W00", "World", Counterpart_area)) %>%
  left_join(colors, by = c("Counterpart_area" = "country")) %>%
  mutate(color = ifelse(COUNTERPART_AREA == "DE", color2, color)) %>%
  ggplot(.) + theme_minimal() + scale_color_identity() +
  geom_line(aes(x = date, y = OBS_VALUE, color = color)) + 
  theme(legend.position = "none") + add_3flags +
  scale_x_date(breaks = seq(1950, 2100, 10) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-500, 500, .1),
                     labels = percent_format(accuracy = .1)) + 
  xlab("") + ylab("China Bilateral Trade Surplus (% of GDP)") + 
  geom_hline(yintercept = 0, linetype = "dashed", color = "black")

Qatar, Hong Kong, Egypt

Code 
DOT_CN %>%
  filter(COUNTERPART_AREA  %in% c("QA", "HK", "EG"),
         INDICATOR %in% c("TBG_USD"),
         FREQ == "A") %>%
  inner_join(NGDP_USD %>%
              filter(REF_AREA == "FR",
                     FREQ == "A") %>%
              mutate(OBS_VALUE = OBS_VALUE*10^(UNIT_MULT)) %>%
              select(TIME_PERIOD, NGDP_USD = OBS_VALUE),
            by = "TIME_PERIOD") %>%
  year_to_date2 %>%
  mutate(OBS_VALUE = OBS_VALUE*10^(UNIT_MULT)/NGDP_USD) %>%
  left_join(COUNTERPART_AREA, by = "COUNTERPART_AREA") %>%
  mutate(Counterpart_area = ifelse(COUNTERPART_AREA == "HK", "Hong Kong", Counterpart_area),
         Counterpart_area = ifelse(COUNTERPART_AREA == "W00", "World", Counterpart_area)) %>%
  left_join(colors, by = c("Counterpart_area" = "country")) %>%
  mutate(color = ifelse(COUNTERPART_AREA == "DE", color2, color)) %>%
  ggplot(.) + theme_minimal() + scale_color_identity() +
  geom_line(aes(x = date, y = OBS_VALUE, color = color)) + 
  theme(legend.position = "none") + add_3flags +
  scale_x_date(breaks = seq(1950, 2100, 10) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-500, 500, .1),
                     labels = percent_format(accuracy = .1)) + 
  xlab("") + ylab("China Bilateral Trade Surplus (% of GDP)") + 
  geom_hline(yintercept = 0, linetype = "dashed", color = "black")

Large Deficits in 2020

Germany, Nertherlands, Belgium

Code 
DOT_CN %>%
  filter(COUNTERPART_AREA  %in% c("DE", "NL", "BE"),
         INDICATOR %in% c("TBG_USD"),
         FREQ == "A") %>%
  inner_join(NGDP_USD %>%
              filter(REF_AREA == "FR",
                     FREQ == "A") %>%
              mutate(OBS_VALUE = OBS_VALUE*10^(UNIT_MULT)) %>%
              select(TIME_PERIOD, NGDP_USD = OBS_VALUE),
            by = "TIME_PERIOD") %>%
  year_to_date2 %>%
  mutate(OBS_VALUE = OBS_VALUE*10^(UNIT_MULT)/NGDP_USD) %>%
  left_join(COUNTERPART_AREA, by = "COUNTERPART_AREA") %>%
  mutate(Counterpart_area = ifelse(COUNTERPART_AREA == "U2", "Europe", Counterpart_area),
         Counterpart_area = ifelse(COUNTERPART_AREA == "W00", "World", Counterpart_area)) %>%
  left_join(colors, by = c("Counterpart_area" = "country")) %>%
  mutate(color = ifelse(COUNTERPART_AREA == "DE", color2, color)) %>%
  ggplot(.) + theme_minimal() + scale_color_identity() +
  geom_line(aes(x = date, y = OBS_VALUE, color = color)) + 
  theme(legend.position = "none") + add_3flags +
  scale_x_date(breaks = seq(1950, 2100, 10) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-500, 500, .1),
                     labels = percent_format(accuracy = .1)) + 
  xlab("") + ylab("China Bilateral Trade Surplus (% of GDP)") + 
  geom_hline(yintercept = 0, linetype = "dashed", color = "black")

Germany, Nertherlands, Belgium, World

All

Code 
DOT_CN %>%
  filter(COUNTERPART_AREA  %in% c("DE", "NL", "BE", "W00"),
         INDICATOR %in% c("TBG_USD"),
         FREQ == "A") %>%
  inner_join(NGDP_USD %>%
              filter(REF_AREA == "FR",
                     FREQ == "A") %>%
              mutate(OBS_VALUE = OBS_VALUE*10^(UNIT_MULT)) %>%
              select(TIME_PERIOD, NGDP_USD = OBS_VALUE),
            by = "TIME_PERIOD") %>%
  year_to_date2 %>%
  mutate(OBS_VALUE = OBS_VALUE*10^(UNIT_MULT)/NGDP_USD) %>%
  left_join(COUNTERPART_AREA, by = "COUNTERPART_AREA") %>%
  mutate(Counterpart_area = ifelse(COUNTERPART_AREA == "U2", "Europe", Counterpart_area),
         Counterpart_area = ifelse(COUNTERPART_AREA == "W00", "World", Counterpart_area)) %>%
  left_join(colors, by = c("Counterpart_area" = "country")) %>%
  mutate(color = ifelse(COUNTERPART_AREA == "DE", color2, color)) %>%
  ggplot(.) + theme_minimal() + scale_color_identity() +
  geom_line(aes(x = date, y = OBS_VALUE, color = color)) + 
  theme(legend.position = "none") + add_4flags +
  scale_x_date(breaks = seq(1950, 2100, 10) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-500, 500, .5),
                     labels = percent_format(accuracy = .1)) + 
  xlab("") + ylab("China Bilateral Trade Surplus (% of GDP)") + 
  geom_hline(yintercept = 0, linetype = "dashed", color = "black")

1990-

Code 
DOT_CN %>%
  filter(COUNTERPART_AREA  %in% c("DE", "NL", "BE", "W00"),
         INDICATOR %in% c("TBG_USD"),
         FREQ == "A") %>%
  inner_join(NGDP_USD %>%
              filter(REF_AREA == "FR",
                     FREQ == "A") %>%
              mutate(OBS_VALUE = OBS_VALUE*10^(UNIT_MULT)) %>%
              select(TIME_PERIOD, NGDP_USD = OBS_VALUE),
            by = "TIME_PERIOD") %>%
  year_to_date2 %>%
  filter(date >= as.Date("1990-01-01")) %>%
  mutate(OBS_VALUE = OBS_VALUE*10^(UNIT_MULT)/NGDP_USD) %>%
  left_join(COUNTERPART_AREA, by = "COUNTERPART_AREA") %>%
  mutate(Counterpart_area = ifelse(COUNTERPART_AREA == "U2", "Europe", Counterpart_area),
         Counterpart_area = ifelse(COUNTERPART_AREA == "W00", "World", Counterpart_area)) %>%
  left_join(colors, by = c("Counterpart_area" = "country")) %>%
  mutate(color = ifelse(COUNTERPART_AREA == "DE", color2, color)) %>%
  ggplot(.) + theme_minimal() + scale_color_identity() +
  geom_line(aes(x = date, y = OBS_VALUE, color = color)) + 
  theme(legend.position = "none") + 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(-500, 500, .5),
                     labels = percent_format(accuracy = .1)) + 
  xlab("") + ylab("China Bilateral Trade Surplus (% of GDP)") + 
  geom_hline(yintercept = 0, linetype = "dashed", color = "black")

China, Italy, Spain

Code 
DOT_CN %>%
  filter(COUNTERPART_AREA  %in% c("CN", "IT", "ES"),
         INDICATOR %in% c("TBG_USD"),
         FREQ == "A") %>%
  inner_join(NGDP_USD %>%
              filter(REF_AREA == "FR",
                     FREQ == "A") %>%
              mutate(OBS_VALUE = OBS_VALUE*10^(UNIT_MULT)) %>%
              select(TIME_PERIOD, NGDP_USD = OBS_VALUE),
            by = "TIME_PERIOD") %>%
  year_to_date2 %>%
  mutate(OBS_VALUE = OBS_VALUE*10^(UNIT_MULT)/NGDP_USD) %>%
  left_join(COUNTERPART_AREA, by = "COUNTERPART_AREA") %>%
  mutate(Counterpart_area = ifelse(COUNTERPART_AREA == "U2", "Europe", Counterpart_area),
         Counterpart_area = ifelse(COUNTERPART_AREA == "W00", "World", Counterpart_area)) %>%
  left_join(colors, by = c("Counterpart_area" = "country")) %>%
  mutate(color = ifelse(COUNTERPART_AREA == "ES", color2, color)) %>%
  ggplot(.) + theme_minimal() + scale_color_identity() +
  geom_line(aes(x = date, y = OBS_VALUE, color = color)) + 
  theme(legend.position = "none") + add_3flags +
  scale_x_date(breaks = seq(1950, 2100, 10) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-500, 500, .1),
                     labels = percent_format(accuracy = .1)) + 
  xlab("") + ylab("China Bilateral Trade Surplus (% of GDP)") + 
  geom_hline(yintercept = 0, linetype = "dashed", color = "black")

China, Germany, Europe

All

Code 
DOT_CN %>%
  filter(COUNTERPART_AREA  %in% c("CN", "DE", "U2"),
         INDICATOR %in% c("TBG_USD"),
         FREQ == "A") %>%
  inner_join(NGDP_USD %>%
              filter(REF_AREA == "FR",
                     FREQ == "A") %>%
              mutate(OBS_VALUE = OBS_VALUE*10^(UNIT_MULT)) %>%
              select(TIME_PERIOD, NGDP_USD = OBS_VALUE),
            by = "TIME_PERIOD") %>%
  year_to_date2 %>%
  mutate(OBS_VALUE = OBS_VALUE*10^(UNIT_MULT)/NGDP_USD) %>%
  left_join(COUNTERPART_AREA, by = "COUNTERPART_AREA") %>%
  mutate(Counterpart_area = ifelse(COUNTERPART_AREA == "U2", "Europe", Counterpart_area),
         Counterpart_area = ifelse(COUNTERPART_AREA == "W00", "World", Counterpart_area)) %>%
  left_join(colors, by = c("Counterpart_area" = "country")) %>%
  mutate(color = ifelse(COUNTERPART_AREA == "DE", color2, color)) %>%
  ggplot(.) + theme_minimal() + scale_color_identity() +
  geom_line(aes(x = date, y = OBS_VALUE, color = color)) + 
  theme(legend.position = "none") + add_3flags +
  scale_x_date(breaks = seq(1950, 2100, 10) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-500, 500, 1),
                     labels = percent_format(accuracy = .1)) + 
  xlab("") + ylab("China Bilateral Trade Surplus (% of GDP)") + 
  geom_hline(yintercept = 0, linetype = "dashed", color = "black")

1980-

Code 
DOT_CN %>%
  filter(COUNTERPART_AREA  %in% c("CN", "DE", "U2"),
         INDICATOR %in% c("TBG_USD"),
         FREQ == "A") %>%
  inner_join(NGDP_USD %>%
              filter(REF_AREA == "FR",
                     FREQ == "A") %>%
              mutate(OBS_VALUE = OBS_VALUE*10^(UNIT_MULT)) %>%
              select(TIME_PERIOD, NGDP_USD = OBS_VALUE),
            by = "TIME_PERIOD") %>%
  year_to_date2 %>%
  filter(date >= as.Date("1980-01-01")) %>%
  mutate(OBS_VALUE = OBS_VALUE*10^(UNIT_MULT)/NGDP_USD) %>%
  left_join(COUNTERPART_AREA, by = "COUNTERPART_AREA") %>%
  mutate(Counterpart_area = ifelse(COUNTERPART_AREA == "U2", "Europe", Counterpart_area),
         Counterpart_area = ifelse(COUNTERPART_AREA == "W00", "World", Counterpart_area)) %>%
  left_join(colors, by = c("Counterpart_area" = "country")) %>%
  mutate(color = ifelse(COUNTERPART_AREA == "DE", color2, color)) %>%
  ggplot(.) + theme_minimal() + scale_color_identity() +
  geom_line(aes(x = date, y = OBS_VALUE, color = color)) + 
  theme(legend.position = "none") + add_3flags +
  scale_x_date(breaks = seq(1950, 2100, 10) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-500, 500, 1),
                     labels = percent_format(accuracy = .1)) + 
  xlab("") + ylab("China Bilateral Trade Surplus (% of GDP)") + 
  geom_hline(yintercept = 0, linetype = "dashed", color = "black")

1990-

Code 
DOT_CN %>%
  filter(COUNTERPART_AREA  %in% c("CN", "DE", "U2"),
         INDICATOR %in% c("TBG_USD"),
         FREQ == "A") %>%
  inner_join(NGDP_USD %>%
              filter(REF_AREA == "FR",
                     FREQ == "A") %>%
              mutate(OBS_VALUE = OBS_VALUE*10^(UNIT_MULT)) %>%
              select(TIME_PERIOD, NGDP_USD = OBS_VALUE),
            by = "TIME_PERIOD") %>%
  year_to_date2 %>%
  filter(date >= as.Date("1990-01-01")) %>%
  mutate(OBS_VALUE = OBS_VALUE*10^(UNIT_MULT)/NGDP_USD) %>%
  left_join(COUNTERPART_AREA, by = "COUNTERPART_AREA") %>%
  mutate(Counterpart_area = ifelse(COUNTERPART_AREA == "U2", "Europe", Counterpart_area),
         Counterpart_area = ifelse(COUNTERPART_AREA == "W00", "World", Counterpart_area)) %>%
  left_join(colors, by = c("Counterpart_area" = "country")) %>%
  mutate(color = ifelse(COUNTERPART_AREA == "DE", color2, color)) %>%
  ggplot(.) + theme_minimal() + scale_color_identity() +
  geom_line(aes(x = date, y = OBS_VALUE, color = color)) + 
  theme(legend.position = "none") + add_3flags +
  scale_x_date(breaks = seq(1950, 2100, 10) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-500, 500, 1),
                     labels = percent_format(accuracy = .1)) + 
  xlab("") + ylab("China Bilateral Trade Surplus (% of GDP)") + 
  geom_hline(yintercept = 0, linetype = "dashed", color = "black")

China, Germany, Europe, Russia

All

Code 
DOT_CN %>%
  filter(COUNTERPART_AREA  %in% c("CN", "DE", "U2", "RU"),
         INDICATOR %in% c("TBG_USD"),
         FREQ == "A") %>%
  inner_join(NGDP_USD %>%
              filter(REF_AREA == "FR",
                     FREQ == "A") %>%
              mutate(OBS_VALUE = OBS_VALUE*10^(UNIT_MULT)) %>%
              select(TIME_PERIOD, NGDP_USD = OBS_VALUE),
            by = "TIME_PERIOD") %>%
  year_to_date2 %>%
  mutate(OBS_VALUE = OBS_VALUE*10^(UNIT_MULT)/NGDP_USD) %>%
  left_join(COUNTERPART_AREA, by = "COUNTERPART_AREA") %>%
  mutate(Counterpart_area = ifelse(COUNTERPART_AREA == "U2", "Europe", Counterpart_area),
         Counterpart_area = ifelse(COUNTERPART_AREA == "W00", "World", Counterpart_area),
         Counterpart_area = ifelse(COUNTERPART_AREA == "RU", "Russia", Counterpart_area)) %>%
  left_join(colors, by = c("Counterpart_area" = "country")) %>%
  mutate(color = ifelse(COUNTERPART_AREA == "DE", color2, color)) %>%
  ggplot(.) + theme_minimal() + scale_color_identity() +
  geom_line(aes(x = date, y = OBS_VALUE, color = color)) + 
  theme(legend.position = "none") + add_4flags +
  scale_x_date(breaks = seq(1950, 2100, 10) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-500, 500, 1),
                     labels = percent_format(accuracy = .1)) + 
  xlab("") + ylab("China Bilateral Trade Surplus (% of GDP)") + 
  geom_hline(yintercept = 0, linetype = "dashed", color = "black")