World Economic Outlook - WEO

Data - IMF

Author

François Geerolf

SUBJECT / CONCEPT

xls

Code 
WEO %>%
  left_join(CL_SUBJECT_WEO, by = "SUBJECT") %>%
  group_by(SUBJECT, Subject) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

Country

SDMX - REF_AREA

Code 
WEO_PUB_OCT2020 %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  group_by(REF_AREA, Ref_area) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Ref_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 .}

Output gap (% of Potential Output)

Italy, France, Greece, Spain, Germany

Viridis

Code 
WEO %>%
  left_join(CL_AREA_WEO, by = "iso2c") %>%
  filter(SUBJECT == "NGAP_NPGDP",
         Iso2c %in% c("Italy", "France", "Greece", "Spain", "Germany"),
         date >= as.Date("2000-01-01")) %>%
  ggplot() + 
  geom_line(aes(x = date, y = value/100, color = Iso2c)) +
  
  theme_minimal() +
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.15, 0.2),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-90, 90, 2),
                     labels = percent_format(accuracy = 1)) +
  ylab("Output Gap (% of Potential Output)") + xlab("")

Flag colors

Code 
WEO_PUB_OCT2020 %>%
  time_to_date %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  filter(CONCEPT == "NGAP_NPGDP",
         Ref_area %in% c("Italy", "France", "Greece", "Spain", "Germany"),
         date >= as.Date("2000-01-01")) %>%
  ggplot() + 
  geom_line(aes(x = date, y = OBS_VALUE/100, color = Ref_area)) +
  scale_color_manual(values = c("#ED2939", "#000000", "#0D5EAF", "#009246", "#FFC400")) +
  theme_minimal() +
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.15, 0.2),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-90, 90, 2),
                     labels = percent_format(accuracy = 1)) +
  ylab("Output Gap (% of Potential Output)") + xlab("")

Flags

Code 
WEO_PUB_OCT2020 %>%
  time_to_date %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  filter(CONCEPT == "NGAP_NPGDP",
         Ref_area %in% c("Italy", "France", "Greece", "Spain", "Germany"),
         date >= as.Date("2000-01-01")) %>%
  ggplot() + 
  geom_line(aes(x = date, y = OBS_VALUE/100, color = Ref_area)) +
  scale_color_manual(values = c("#ED2939", "#000000", "#0D5EAF", "#009246", "#FFC400")) +
  theme_minimal() +
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  geom_image(data = . %>%
               filter(date == as.Date("2014-01-01")) %>%
               mutate(image = paste0("../../icon/flag/", str_to_lower(gsub(" ", "-", Ref_area)), ".png")),
             aes(x = date, y = OBS_VALUE/100, image = image), asp = 1.5) +
  theme(legend.position = c(0.15, 0.2),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-90, 90, 2),
                     labels = percent_format(accuracy = 1)) +
  ylab("Output Gap (% of Potential Output)") + xlab("")

Flags / No Legends

Code 
WEO_PUB_OCT2020 %>%
  time_to_date %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  filter(CONCEPT == "NGAP_NPGDP",
         Ref_area %in% c("Italy", "France", "Greece", "Spain", "Germany"),
         date >= as.Date("2000-01-01")) %>%
  ggplot() + 
  geom_line(aes(x = date, y = OBS_VALUE/100, color = Ref_area)) +
  scale_color_manual(values = c("#ED2939", "#000000", "#0D5EAF", "#009246", "#FFC400")) +
  theme_minimal() +
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  geom_image(data = . %>%
               filter(date == as.Date("2014-01-01")) %>%
               mutate(image = paste0("../../icon/flag/", str_to_lower(gsub(" ", "-", Ref_area)), ".png")),
             aes(x = date, y = OBS_VALUE/100, image = image), asp = 1.5) +
  theme(legend.position = "none") +
  scale_y_continuous(breaks = 0.01*seq(-90, 90, 2),
                     labels = percent_format(accuracy = 1)) +
  ylab("Output Gap (% of Potential Output)") + xlab("")

Flags / No Legends

Code 
WEO_PUB_OCT2020 %>%
  time_to_date %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  filter(CONCEPT == "NGAP_NPGDP",
         Ref_area %in% c("Italy", "France", "Greece", "Spain", "Germany"),
         date >= as.Date("2000-01-01"),
         date <= as.Date("2019-01-01")) %>%
  ggplot() + 
  geom_line(aes(x = date, y = OBS_VALUE/100, color = Ref_area)) +
  scale_color_manual(values = c("#ED2939", "#000000", "#0D5EAF", "#009246", "#FFC400")) +
  theme_minimal() +
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  geom_image(data = . %>%
               filter(date == as.Date("2014-01-01")) %>%
               mutate(image = paste0("../../icon/flag/", str_to_lower(gsub(" ", "-", Ref_area)), ".png")),
             aes(x = date, y = OBS_VALUE/100, image = image), asp = 1.5) +
  theme(legend.position = "none") +
  scale_y_continuous(breaks = 0.01*seq(-90, 90, 2),
                     labels = percent_format(accuracy = 1)) +
  ylab("Output Gap (% of Potential Output)") + xlab("")

Flags / No Legends (français)

Code 
WEO_PUB_OCT2020 %>%
  time_to_date %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  filter(CONCEPT == "NGAP_NPGDP",
         Ref_area %in% c("Italy", "France", "Greece", "Spain", "Germany"),
         date >= as.Date("2000-01-01")) %>%
  ggplot() + 
  geom_line(aes(x = date, y = OBS_VALUE/100, color = Ref_area)) +
  scale_color_manual(values = c("#ED2939", "#000000", "#0D5EAF", "#009246", "#FFC400")) +
  theme_minimal() +
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  geom_image(data = . %>%
               filter(date == as.Date("2014-01-01")) %>%
               mutate(image = paste0("../../icon/flag/", str_to_lower(gsub(" ", "-", Ref_area)), ".png")),
             aes(x = date, y = OBS_VALUE/100, image = image), asp = 1.5) +
  theme(legend.position = "none") +
  scale_y_continuous(breaks = 0.01*seq(-90, 90, 2),
                     labels = percent_format(accuracy = 1)) +
  ylab("Ecarts de Production (% de PIB Potentiel)") + xlab("")

DEU, FRA, ITA, USA 2018 - All variables

Code 
WEO %>%
  filter(date == "2018-01-01",
         iso3c %in% c("USA", "FRA", "DEU", "ITA")) %>%
  left_join(CL_SUBJECT_WEO, by = "SUBJECT") %>%
  select(SUBJECT, Subject, iso3c, value) %>%
  spread(iso3c, value) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

Date

xls

Code 
WEO %>%
  group_by(date) %>%
  summarise(Nobs = n()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

SDMX

Code 
WEO_PUB_OCT2020 %>%
  group_by(TIME_PERIOD) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

Real GDP Growth

Real Growth Rates (1990-)

xls

Code 
WEO %>%
  left_join(CL_AREA_WEO, by = "iso2c") %>%
  filter(SUBJECT == "NGDP_RPCH",
         Iso2c %in% c("Italy", "France", "Greece", "Spain", "Germany"),
         date >= as.Date("1990-01-01"),
         date <= as.Date("2019-01-01")) %>%
  ggplot() + 
  geom_line(aes(x = date, y = value/100, color = Iso2c)) +
  
  theme_minimal() +
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.15, 0.2),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-90, 90, 2),
                     labels = percent_format(accuracy = 1)) +
  ylab("Real Growth Rate") + xlab("")

SDMX

Code 
WEO_PUB_OCT2020 %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  time_to_date %>%
  filter(CONCEPT == "NGDP_RPCH",
         Ref_area %in% c("Italy", "France", "Greece", "Spain", "Germany"),
         date >= as.Date("1990-01-01")) %>%
  ggplot() + 
  geom_line(aes(x = date, y = OBS_VALUE/100, color = Ref_area, linetype = Ref_area)) +
  
  theme_minimal() +
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.15, 0.2),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-90, 90, 2),
                     labels = percent_format(accuracy = 1)) +
  ylab("Real Growth Rate") + xlab("")

South America (1990-2019)

Code 
WEO %>%
  left_join(CL_AREA_WEO, by = "iso2c") %>%
  filter(SUBJECT == "NGDP_RPCH",
         Iso2c %in% c("Argentina", "Chile", "Brazil"),
         date >= as.Date("1990-01-01"),
         date <= as.Date("2019-01-01")) %>%
  ggplot() + 
  geom_line(aes(x = date, y = value/100, color = Iso2c)) +
  scale_color_manual(values = viridis(4)[1:3]) +
  theme_minimal() +
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.15, 0.2),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-90, 90, 2),
                     labels = percent_format(accuracy = 1)) +
  ylab("Real Growth Rate (%)") + xlab("")

Gross Domestic Product in $

Inflation

Venezuela

For Ex, inflation variable == "PCPIPCH" in Venezuela country == "VE" can be obtained using the following command:

Code 
WEO %>%
  filter(SUBJECT == "PCPIPCH", 
         iso2c == "VE") %>%
  select(date, value) %>%
  filter(year(date) >= 1990) %>%
  {if (is_html_output()) print_table(.) else .}
date value
1990-01-01 40.656
1991-01-01 34.205
1992-01-01 31.423
1993-01-01 38.122
1994-01-01 60.817
1995-01-01 59.923
1996-01-01 99.876
1997-01-01 50.040
1998-01-01 35.782
1999-01-01 23.570
2000-01-01 16.206
2001-01-01 12.531
2002-01-01 22.434
2003-01-01 31.091
2004-01-01 21.747
2005-01-01 15.955
2006-01-01 13.663
2007-01-01 18.699
2008-01-01 31.441
2009-01-01 26.041
2010-01-01 28.187
2011-01-01 26.090
2012-01-01 21.069
2013-01-01 40.639
2014-01-01 62.169
2015-01-01 121.738
2016-01-01 254.949
2017-01-01 438.117
2018-01-01 65374.082
2019-01-01 200000.000
2020-01-01 500000.000
2021-01-01 500000.000

Argentina

Code 
WEO %>%
  filter(SUBJECT == "PCPIPCH", 
         iso2c == "AR") %>%
  select(date, value) %>%
  filter(year(date) >= 1990) %>%
  {if (is_html_output()) print_table(.) else .}
date value
1998-01-01 0.925
1999-01-01 -1.167
2000-01-01 -0.939
2001-01-01 -1.065
2002-01-01 25.869
2003-01-01 13.443
2004-01-01 4.416
2005-01-01 9.642
2006-01-01 10.898
2007-01-01 8.830
2008-01-01 8.585
2009-01-01 6.270
2010-01-01 10.461
2011-01-01 9.775
2012-01-01 10.043
2013-01-01 10.619
2017-01-01 25.675
2018-01-01 34.277
2019-01-01 54.440
2020-01-01 50.997
2021-01-01 32.260
2022-01-01 26.869
2023-01-01 22.213
2024-01-01 16.977

Unemployment Data

Unemployment - Argentina, Chile, Venezuela (%)

Code 
WEO %>%
  left_join(CL_AREA_WEO, by = "iso2c") %>%
  filter(SUBJECT == "LUR",
         Iso2c %in% c("Argentina", "Chile", "Venezuela")) %>%
  ggplot() + 
  geom_line(aes(x = date, y = value/100, color = Iso2c)) +
  scale_color_manual(values = viridis(4)[1:3]) +
  theme_minimal() +
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.15, 0.9),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-90, 90, 2),
                     labels = percent_format(accuracy = 1)) +
  ylab("Unemployment (%)") + xlab("")

Current Account Balance

Positive

Code 
WEO %>%
  filter(SUBJECT == "BCA", 
         date == as.Date("2018-01-01"), 
         value > 0) %>%
  left_join(CL_AREA_WEO, by = "iso2c") %>%
  arrange(-value) %>%
  mutate(value = round(value)) %>%
  select(Iso2c, `Current Account Balance (Billions)` = value) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Iso2c))),
         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 .}

2018 Current Accounts

Euro Area (% of GDP)

Code 
WEO %>%
  filter(SUBJECT == "BCA_NGDPD", 
         date == as.Date("2018-01-01")) %>%
  left_join(CL_AREA_WEO, by = "iso2c") %>%
  right_join(eurozone_squeezed, by = "iso2c") %>%
  ggplot(., aes(x = long, y = lat, group = group, fill = value/100)) +
  geom_polygon() + coord_map() +
  scale_fill_viridis_c(na.value = "white",
                       labels = scales::percent_format(accuracy = 1, suffix = "% of GDP"),
                       breaks = c(-0.06,-0.02, 0, 0.02, 0.04, 0.06, 0.08),
                       values = c(0, 0, 0.2, 0.4, 0.6, 0.8, 1)) +
  theme_void() + theme(legend.position = c(0.25, 0.85)) + 
  labs(fill = "2018 Current Account")

World (% of GDP)

Code 
WEO %>%
  filter(SUBJECT == "BCA_NGDPD", 
         date == as.Date("2018-01-01")) %>%
  right_join(world, by = "iso2c") %>%
  ggplot() + theme_void() +
  geom_polygon(aes(long, lat, group = group, fill = value/100), 
               colour = alpha("black", 1/2), size = 0.1)  +
  scale_fill_viridis_c(name = "Current Account (%)",
                       labels = scales::percent_format(accuracy = 1),
                       breaks = c(-0.16,-0.12,-0.08,-0.04, 0, 0.04, 0.08, 0.12, 0.16),
                       values = c(0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 1)) +
  theme(legend.position = c(0.1, 0.4),
        legend.title = element_text(size = 10))

2008 Current Accounts in Europe (% of GDP)

Code 
WEO %>%
  filter(SUBJECT == "BCA_NGDPD", 
         date == as.Date("2008-01-01")) %>%
  right_join(eurozone_squeezed, by = "iso2c") %>%
  ggplot(., aes(x = long, y = lat, group = group, fill = value/100)) +
  geom_polygon() + coord_map() +
  scale_fill_viridis_c(na.value = "white",
                       labels = scales::percent_format(accuracy = 1, suffix = "% of GDP"),
                       breaks = c(-0.16,-0.12,-0.08,-0.04, 0, 0.04, 0.08, 0.12, 0.16),
                       values = c(0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 1)) +
  theme_void() + theme(legend.position = c(0.25, 0.85)) + 
  labs(fill = "2008 Current Account")

Current Account Balance (% of GDP)

IT, FR, GR, ES, DE

Code 
WEO %>%
  left_join(CL_AREA_WEO, by = "iso2c") %>%
  filter(SUBJECT == "BCA_NGDPD",
         Iso2c %in% c("Italy", "France", "Greece", "Spain", "Germany"),
         date >= as.Date("2000-01-01")) %>%
  ggplot() + 
  geom_line(aes(x = date, y = value/100, color = Iso2c)) +
  
  theme_minimal() +
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.75, 0.2),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-90, 90, 2),
                     labels = percent_format(accuracy = 1)) +
  ylab("Current Account Balance (% of GDP)") + xlab("")

AR, VE, CH

Code 
WEO %>%
  left_join(CL_AREA_WEO, by = "iso2c") %>%
  filter(SUBJECT == "BCA_NGDPD",
         Iso2c %in% c("Argentina", "Venezuela", "Chile")) %>%
  ggplot() + 
  geom_line(aes(x = date, y = value/100, color = Iso2c)) +
  scale_color_manual(values = viridis(4)[1:3]) +
  theme_minimal() +
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.75, 0.2),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-90, 90, 2),
                     labels = percent_format(accuracy = 1)) +
  ylab("Current Account Balance (% of GDP)") + xlab("")

France, Germany, Italy

1985-2019

Code 
WEO %>%
  left_join(CL_AREA_WEO, by = "iso2c") %>%
  filter(SUBJECT == "BCA_NGDPD",
         Iso2c %in% c("Italy", "France", "Germany"),
         date >= as.Date("1985-01-01"),
         date <= as.Date("2019-01-01")) %>%
  ggplot() + 
  geom_line(aes(x = date, y = value/100, color = Iso2c)) +
  scale_color_manual(values = c("#002395", "#000000", "#009246")) +
  theme_minimal() +
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.3, 0.9),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-90, 90, 2),
                     labels = percent_format(accuracy = 1)) +
  ylab("Current Account Balance (% of GDP)") + xlab("")

1980-2019

Code 
WEO %>%
  left_join(CL_AREA_WEO, by = "iso2c") %>%
  filter(SUBJECT == "BCA_NGDPD",
         Iso2c %in% c("France", "Germany", "Italy"),
         date <= as.Date("2019-01-01")) %>%
  ggplot() + 
  geom_line(aes(x = date, y = value/100, color = Iso2c)) +
  scale_color_manual(values = c("#002395", "#000000", "#009246")) +
  theme_minimal() +
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.3, 0.9),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-90, 90, 2),
                     labels = percent_format(accuracy = 1)) +
  ylab("Current Account Balance (% of GDP)") + xlab("")

Spain, UK, US - 1980-2019

Code 
WEO %>%
  left_join(CL_AREA_WEO, by = "iso2c") %>%
  filter(SUBJECT == "BCA_NGDPD",
         Iso2c %in% c("United States", "United Kingdom", "Spain"),
         date <= as.Date("2019-01-01")) %>%
  ggplot() + 
  geom_line(aes(x = date, y = value/100, color = Iso2c)) +
  
  theme_minimal() +
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.3, 0.3),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-90, 90, 2),
                     labels = percent_format(accuracy = 1)) +
  ylab("Current Account Balance (% of GDP)") + xlab("")