| source | dataset | .html | .RData |
|---|---|---|---|
| wdi | NV.IND.MANF.CD | 2024-12-21 | 2024-12-22 |
Manufacturing, value added (current USD)
Data - WDI
Info
Data on macro
| source | dataset | .html | .RData |
|---|---|---|---|
| eurostat | nama_10_a10 | 2024-11-23 | 2024-10-08 |
| eurostat | nama_10_a10_e | 2024-11-23 | 2024-12-22 |
| eurostat | nama_10_gdp | 2024-12-21 | 2024-12-21 |
| eurostat | nama_10_lp_ulc | 2024-11-23 | 2024-10-08 |
| eurostat | namq_10_a10 | 2024-12-22 | 2024-12-22 |
| eurostat | namq_10_a10_e | 2024-12-22 | 2024-12-22 |
| eurostat | namq_10_gdp | 2024-12-21 | 2024-12-21 |
| eurostat | namq_10_lp_ulc | 2024-11-23 | 2024-11-04 |
| eurostat | namq_10_pc | 2024-11-23 | 2024-11-21 |
| eurostat | nasa_10_nf_tr | 2024-12-14 | 2024-12-14 |
| eurostat | nasq_10_nf_tr | 2024-11-23 | 2024-10-09 |
| fred | gdp | 2024-12-22 | 2024-12-22 |
| oecd | QNA | 2024-06-06 | 2024-12-22 |
| oecd | SNA_TABLE1 | 2024-12-22 | 2024-12-22 |
| 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 | 2024-09-26 |
| wdi | NY.GDP.MKTP.PP.CD | 2024-09-18 | 2024-09-18 |
| wdi | NY.GDP.PCAP.CD | 2024-12-16 | 2024-12-22 |
| wdi | NY.GDP.PCAP.KD | 2024-09-18 | 2024-09-18 |
| wdi | NY.GDP.PCAP.PP.CD | 2024-12-22 | 2024-12-22 |
| wdi | NY.GDP.PCAP.PP.KD | 2024-12-22 | 2024-12-22 |
LAST_COMPILE
| LAST_COMPILE |
|---|
| 2024-12-22 |
Last
Code
NV.IND.MANF.CD %>%
group_by(year) %>%
summarise(Nobs = n()) %>%
arrange(desc(year)) %>%
head(1) %>%
print_table_conditional()| year | Nobs |
|---|---|
| 2023 | 202 |
Nobs - Javascript
Code
NV.IND.MANF.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
NV.IND.MANF.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
NV.IND.MANF.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 .}Netherlands, Belgium, Switzerland, Turkey, Poland, Sweden
Code
NV.IND.MANF.CD %>%
right_join(iso2c, by = "iso2c") %>%
filter(iso2c %in% c("1W", "CH", "BE", "NL", "PO", "SE", "TR")) %>%
year_to_date %>%
filter(date >= as.Date("1997-01-01")) %>%
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 Manufacturing value added") +
geom_line(aes(x = date, y = value, color = color)) + add_5flags +
scale_x_date(breaks = seq(1950, 2100, 2) %>% 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))
Germany, France, Italy, United Kingdom, Spain
Code
NV.IND.MANF.CD %>%
right_join(iso2c, by = "iso2c") %>%
filter(iso2c %in% c("1W", "DE", "FR", "IT", "ES", "GB")) %>%
year_to_date %>%
filter(date >= as.Date("1997-01-01")) %>%
group_by(date) %>%
mutate(value = value/value[iso2c == "1W"]) %>%
filter(!(iso2c == "1W")) %>%
left_join(colors, by = c("Iso2c" = "country")) %>%
mutate(color = ifelse(iso2c == "FR", color2, color)) %>%
ggplot(.) + theme_minimal() + scale_color_identity() + xlab("") +
xlab("") + ylab("% de la VA manufacturière mondiale") +
geom_line(aes(x = date, y = value, color = color)) + add_5flags +
scale_x_date(breaks = seq(1950, 2100, 2) %>% 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))
Brazil, Mexico, Argentina
Code
NV.IND.MANF.CD %>%
right_join(iso2c, by = "iso2c") %>%
filter(iso2c %in% c("1W", "BR", "MX", "AR")) %>%
year_to_date %>%
filter(date >= as.Date("1997-01-01")) %>%
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 Manufacturing value added") +
geom_line(aes(x = date, y = value, color = color)) + add_3flags +
scale_x_date(breaks = seq(1950, 2100, 2) %>% 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))
China, E.U., U.S.
All
Code
data <- NV.IND.MANF.CD %>%
right_join(iso2c, by = "iso2c") %>%
filter(iso2c %in% c("1W", "US", "CN", "EU")) %>%
year_to_date %>%
filter(date >= as.Date("1997-01-01")) %>%
group_by(date) %>%
mutate(value = value/value[iso2c == "1W"]) %>%
filter(!(iso2c == "1W")) %>%
mutate(Iso2c = ifelse(iso2c == "EU", "Europe", Iso2c)) %>%
left_join(colors, by = c("Iso2c" = "country")) %>%
mutate(color = ifelse(iso2c == "US", color2, color)) %>%
select(iso2c, Iso2c, date, value, color)
data %>%
ggplot(.) + theme_minimal() + scale_color_identity() +
geom_line(aes(x = date, y = value, color = color)) +
add_3flags +
scale_x_date(breaks = seq(1950, 2100, 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)) +
xlab("") + ylab("% de la VA manufacturière mondiale")
2005-
Code
NV.IND.MANF.CD %>%
right_join(iso2c, by = "iso2c") %>%
filter(iso2c %in% c("1W", "US", "CN", "EU")) %>%
year_to_date %>%
filter(date >= as.Date("1997-01-01")) %>%
group_by(date) %>%
mutate(value = value/value[iso2c == "1W"]) %>%
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, 2),
labels = scales::percent_format(accuracy = 1)) +
xlab("") + ylab("% de la VA manufacturière mondiale")