World Economic Outlook - WEO
Data - IMF
François Geerolf
SUBJECT / CONCEPT
Country
SDMX - REF_AREA
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
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)) +
scale_color_manual(values = viridis(6)[1:5]) +
theme_minimal() +
scale_x_date(breaks = seq(1920, 2025, 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
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, 2025, 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
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, 2025, 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
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, 2025, 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
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, 2025, 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)
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, 2025, 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
Date
xls
Real GDP Growth
1970s, 1980s, 1990s, 2000s, 2010s
WEO %>%
filter(SUBJECT == "NGDP_RPCH") %>%
mutate(period = case_when(year(date) >= 2010 & year(date) <= 2019 ~ "2010s",
year(date) >= 2000 & year(date) <= 2009 ~ "2000s",
year(date) >= 1990 & year(date) <= 1999 ~ "1990s",
year(date) >= 1980 & year(date) <= 1989 ~ "1980s",
year(date) >= 1970 & year(date) <= 1979 ~ "1970s",
T ~ "")) %>%
filter(period != "") %>%
left_join(CL_AREA_WEO, by = "iso2c") %>%
group_by(iso2c, Iso2c, period) %>%
summarise(value = round(mean(value), 1)) %>%
spread(period, value) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}Real Growth Rates (1990-)
xls
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)) +
scale_color_manual(values = viridis(6)[1:5]) +
theme_minimal() +
scale_x_date(breaks = seq(1920, 2025, 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
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)) +
scale_color_manual(values = viridis(6)[1:5]) +
theme_minimal() +
scale_x_date(breaks = seq(1920, 2025, 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)
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, 2025, 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:
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
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 |
Inflation Data
Unemployment Data
Unemployment - Argentina, Chile, Venezuela (%)
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, 2025, 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
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 .}Negative
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) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}2018 Current Accounts
Euro Area (% of GDP)
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)
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)
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
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)) +
scale_color_manual(values = viridis(6)[1:5]) +
theme_minimal() +
scale_x_date(breaks = seq(1920, 2025, 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
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, 2025, 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
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, 2025, 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
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, 2025, 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
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)) +
scale_color_manual(values = viridis(6)[1:5]) +
theme_minimal() +
scale_x_date(breaks = seq(1920, 2025, 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("")