source | dataset | .html | .RData |
---|---|---|---|
2024-09-11 | 2024-06-30 |
Non-financial accounts by sectors
Data - OECD
Info
Data on macro
source | dataset | .html | .RData |
---|---|---|---|
2024-09-14 | 2024-09-14 | ||
2024-09-14 | 2024-09-14 | ||
2024-09-14 | 2024-09-14 | ||
2024-09-14 | 2024-09-14 | ||
2024-09-14 | 2024-09-14 | ||
2024-09-14 | 2024-09-14 | ||
2024-09-04 | 2024-09-14 | ||
2024-09-14 | 2024-09-14 | ||
2024-08-21 | 2024-09-14 | ||
2024-09-14 | 2024-09-14 | ||
2024-09-02 | 2024-09-02 | ||
2024-08-29 | 2024-09-14 | ||
2024-06-06 | 2024-06-30 | ||
2024-09-15 | 2024-06-30 | ||
2024-09-11 | 2024-06-30 | ||
2024-07-01 | 2024-04-11 | ||
2024-07-01 | 2024-06-30 | ||
2024-09-15 | 2024-09-15 | ||
2024-09-15 | 2024-09-15 | ||
2024-09-15 | 2024-09-15 | ||
2024-09-15 | 2024-09-15 | ||
2024-09-15 | 2024-09-15 | ||
2024-09-15 | 2024-09-15 | ||
2024-09-15 | 2024-09-15 |
LAST_COMPILE
LAST_COMPILE |
---|
2024-09-15 |
Last
obsTime | Nobs |
---|---|
2023 | 3 |
Sources
It presents the whole set of non financial accounts, from the production account to the acquisitions of non-financial assets accounts. For general government sector, property income, other current transfers and capital transfers are consolidated. It has been prepared from statistics reported to the OECD by Member countries in their answers to the new version of the annual national accounts questionnaire.
Layout - By Location
- OECD Website. html
United States
Code
ig_b("oecd", "SNA_TABLE14A")
France
Code
ig_b("oecd", "SNA_TABLE14A_FRA")
Layout - By sector
SS1 - All sectors
Code
ig_b("oecd", "SNA_TABLE14A_SS1")
SS11 - Non-financial corporations
Code
ig_b("oecd", "SNA_TABLE14A_SS11")
SS12 - Financial corporations
Code
ig_b("oecd", "SNA_TABLE14A_SS12")
SS13 - General government
Code
ig_b("oecd", "SNA_TABLE14A_SS13")
SS14_S15 - Households and Non-profits
Code
ig_b("oecd", "SNA_TABLE14A_SS14_S15")
TRANSACT
Code
%>%
SNA_TABLE14A left_join(SNA_TABLE14A_var$TRANSACT, by = "TRANSACT") %>%
group_by(TRANSACT, Transact) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
if (is_html_output()) datatable(., filter = 'top', rownames = F) else .} {
LOCATION
Code
%>%
SNA_TABLE14A left_join(SNA_TABLE14A_var$LOCATION, by = "LOCATION") %>%
group_by(LOCATION, Location) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Location))),
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 .} {
SECTOR
Code
$SECTOR %>%
SNA_TABLE14A_varif (is_html_output()) print_table(.) else .} {
SECTOR | Sector |
---|---|
NFAS | 14A- NFAS : NON FINANCIAL ACCOUNTS BY SECTORS |
S1_S2 | Total economy and rest of the world |
S1 | Total economy |
S11 | Non-financial corporations |
S11001 | of which: Public non-financial corporations |
S12 | Financial corporations |
S12001 | of which: Public financial corporations |
S13 | General government |
S14_S15 | Households and non-profit institutions serving households |
S14 | Households |
S15 | Non-profit institutions serving households |
SN | Not sectorized |
S2 | Rest of the world |
Saving Rate (%): NFB8GR (Gross Saving) / NFB6GR (Disposable Income, Gross)
All Sectors
Code
%>%
SNA_TABLE14A # NFK1R: Consumption of fixed capital
# NFP5P: Gross capital formation
filter(TRANSACT %in% c("NFB8GR", "NFB6GR"),
== "S1",
SECTOR %in% c("FRA", "USA", "DEU")) %>%
LOCATION select(LOCATION, TRANSACT, obsTime, obsValue) %>%
spread(TRANSACT, obsValue) %>%
left_join(SNA_TABLE14A_var$LOCATION, by = "LOCATION") %>%
%>%
na.omit %>%
year_to_date mutate(obsValue = NFB8GR/NFB6GR) %>%
filter(date >= as.Date("1995-01-01")) %>%
left_join(colors, by = c("Location" = "country")) %>%
mutate(color = ifelse(LOCATION == "USA", color2, color)) %>%
ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
scale_color_identity() + theme_minimal() + add_3flags +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-60, 60, 2),
labels = scales::percent_format(accuracy = 1)) +
ylab("Gross saving rate (% of disposable income)") + xlab("")
Households
Code
%>%
SNA_TABLE14A # NFK1R: Consumption of fixed capital
# NFP5P: Gross capital formation
filter(TRANSACT %in% c("NFB8GR", "NFB6GR"),
== "S14_S15",
SECTOR %in% c("FRA", "USA", "DEU")) %>%
LOCATION select(LOCATION, TRANSACT, obsTime, obsValue) %>%
spread(TRANSACT, obsValue) %>%
left_join(SNA_TABLE14A_var$LOCATION, by = "LOCATION") %>%
%>%
na.omit %>%
year_to_date mutate(obsValue = NFB8GR/NFB6GR) %>%
filter(date >= as.Date("1995-01-01")) %>%
left_join(colors, by = c("Location" = "country")) %>%
ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
scale_color_identity() + theme_minimal() + add_3flags +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-60, 60, 2),
labels = scales::percent_format(accuracy = 1)) +
ylab("Gross saving rate (% of disposable income)") + xlab("")
Rents (% of GDP)
France, United States, Germany
Code
%>%
SNA_TABLE14A # NFK1R: Consumption of fixed capital
# NFP5P: Gross capital formation
filter(TRANSACT %in% c("NFD45R"),
== "S13",
SECTOR %in% c("FRA", "USA", "DEU")) %>%
LOCATION select(LOCATION, TRANSACT, obsTime, obsValue) %>%
spread(TRANSACT, obsValue) %>%
left_join(SNA_TABLE1 %>%
filter(TRANSACT == "B1_GE",
== "C") %>%
MEASURE select(obsTime, LOCATION, B1_GE = obsValue),
by = c("LOCATION", "obsTime")) %>%
left_join(SNA_TABLE14A_var$LOCATION, by = "LOCATION") %>%
%>%
na.omit %>%
year_to_date mutate(obsValue = (NFD45R) / B1_GE) %>%
filter(date >= as.Date("1995-01-01")) %>%
left_join(colors, by = c("Location" = "country")) %>%
mutate(color = ifelse(LOCATION == "USA", color2, color)) %>%
ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
scale_color_identity() + theme_minimal() + add_3flags +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-60, 60, 0.1),
labels = scales::percent_format(accuracy = .1)) +
ylab("Net Capital Formation (% of GDP) - Government") + xlab("") +
geom_hline(yintercept = 0, linetype = "dashed")
Operating surplus, Non-Financial corporations
% of Value Added
France, United States, Germany, Italy, Spain
Code
%>%
SNA_TABLE14A # NFK1R: Consumption of fixed capital
# NFB2GP: Operating surplus, gross
# NFB1GP: Gross domestic product / Gross value added
filter(TRANSACT %in% c("NFB2GP", "NFB1GP"),
== "S11",
SECTOR %in% c("FRA", "USA", "DEU", "ITA", "ESP")) %>%
LOCATION select(LOCATION, TRANSACT, obsTime, obsValue) %>%
spread(TRANSACT, obsValue) %>%
left_join(SNA_TABLE14A_var$LOCATION, by = "LOCATION") %>%
%>%
na.omit %>%
year_to_date mutate(obsValue = (NFB2GP) / NFB1GP) %>%
left_join(colors, by = c("Location" = "country")) %>%
ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
scale_color_identity() + theme_minimal() + add_5flags +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-60, 60, 2),
labels = scales::percent_format(accuracy = 1)) +
ylab("Net operating surplus, gross (% of GDP)") + xlab("")
France, United States, Germany
Code
%>%
SNA_TABLE14A # NFK1R: Consumption of fixed capital
# NFB2GP: Operating surplus, gross
# NFB1GP: Gross domestic product / Gross value added
filter(TRANSACT %in% c("NFB2GP", "NFB1GP"),
== "S11",
SECTOR %in% c("FRA", "USA", "DEU")) %>%
LOCATION select(LOCATION, TRANSACT, obsTime, obsValue) %>%
spread(TRANSACT, obsValue) %>%
left_join(SNA_TABLE14A_var$LOCATION, by = "LOCATION") %>%
%>%
na.omit %>%
year_to_date mutate(obsValue = (NFB2GP) / NFB1GP) %>%
left_join(colors, by = c("Location" = "country")) %>%
ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
scale_color_identity() + theme_minimal() + add_3flags +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-60, 60, 2),
labels = scales::percent_format(accuracy = 1)) +
ylab("Net operating surplus, gross (% of GDP)") + xlab("")
Italy, Spain, Portugal
Code
%>%
SNA_TABLE14A # NFK1R: Consumption of fixed capital
# NFB2GP: Operating surplus, gross
filter(TRANSACT %in% c("NFB2GP", "NFB1GP"),
== "S11",
SECTOR %in% c("ITA", "ESP", "PRT")) %>%
LOCATION select(LOCATION, TRANSACT, obsTime, obsValue) %>%
spread(TRANSACT, obsValue) %>%
left_join(SNA_TABLE14A_var$LOCATION, by = "LOCATION") %>%
%>%
na.omit %>%
year_to_date mutate(obsValue = (NFB2GP) / NFB1GP) %>%
left_join(colors, by = c("Location" = "country")) %>%
mutate(color = ifelse(LOCATION == "PRT", color2, color)) %>%
ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
scale_color_identity() + theme_minimal() + add_3flags +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-60, 60, 2),
labels = scales::percent_format(accuracy = 1)) +
ylab("Net operating surplus, gross (% of GDP)") + xlab("")
% of GDP
France, United States, Germany
Code
%>%
SNA_TABLE14A # NFK1R: Consumption of fixed capital
# NFB2GP: Operating surplus, gross
# NFB1GP: Gross domestic product / Gross value added
filter(TRANSACT %in% c("NFK1R", "NFB1GP", "NFB2GP"),
== "S11",
SECTOR %in% c("FRA", "USA", "DEU")) %>%
LOCATION select(LOCATION, TRANSACT, obsTime, obsValue) %>%
spread(TRANSACT, obsValue) %>%
left_join(SNA_TABLE1 %>%
filter(TRANSACT == "B1_GE",
== "C") %>%
MEASURE select(obsTime, LOCATION, B1_GE = obsValue),
by = c("LOCATION", "obsTime")) %>%
left_join(SNA_TABLE14A_var$LOCATION, by = "LOCATION") %>%
%>%
na.omit %>%
year_to_date mutate(obsValue = (NFB2GP) / B1_GE) %>%
left_join(colors, by = c("Location" = "country")) %>%
ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
scale_color_identity() + theme_minimal() + add_3flags +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-60, 60, 2),
labels = scales::percent_format(accuracy = 1)) +
ylab("Net operating surplus, gross (% of GDP)") + xlab("")
Italy, Spain, Portugal
Code
%>%
SNA_TABLE14A # NFK1R: Consumption of fixed capital
# NFB2GP: Operating surplus, gross
filter(TRANSACT %in% c("NFK1R", "NFB2GP"),
== "S1",
SECTOR %in% c("ITA", "ESP", "PRT")) %>%
LOCATION select(LOCATION, TRANSACT, obsTime, obsValue) %>%
spread(TRANSACT, obsValue) %>%
left_join(SNA_TABLE1 %>%
filter(TRANSACT == "B1_GE",
== "C") %>%
MEASURE select(obsTime, LOCATION, B1_GE = obsValue),
by = c("LOCATION", "obsTime")) %>%
left_join(SNA_TABLE14A_var$LOCATION, by = "LOCATION") %>%
%>%
na.omit %>%
year_to_date mutate(obsValue = (NFB2GP) / B1_GE) %>%
left_join(colors, by = c("Location" = "country")) %>%
mutate(color = ifelse(LOCATION == "PRT", color2, color)) %>%
ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
scale_color_identity() + theme_minimal() + add_3flags +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-60, 60, 2),
labels = scales::percent_format(accuracy = 1)) +
ylab("Net operating surplus, gross (% of GDP)") + xlab("")
Operating surplus, gross (% of GDP) - All Economy
France, United States, Germany
Code
%>%
SNA_TABLE14A # NFK1R: Consumption of fixed capital
# NFB2GP: Operating surplus, gross
filter(TRANSACT %in% c("NFK1R", "NFB2GP"),
== "S1",
SECTOR %in% c("FRA", "USA", "DEU")) %>%
LOCATION select(LOCATION, TRANSACT, obsTime, obsValue) %>%
spread(TRANSACT, obsValue) %>%
left_join(SNA_TABLE1 %>%
filter(TRANSACT == "B1_GE",
== "C") %>%
MEASURE select(obsTime, LOCATION, B1_GE = obsValue),
by = c("LOCATION", "obsTime")) %>%
left_join(SNA_TABLE14A_var$LOCATION, by = "LOCATION") %>%
%>%
na.omit %>%
year_to_date mutate(obsValue = (NFB2GP) / B1_GE) %>%
left_join(colors, by = c("Location" = "country")) %>%
mutate(color = ifelse(LOCATION == "USA", color2, color)) %>%
ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
scale_color_identity() + theme_minimal() + add_3flags +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-60, 60, 2),
labels = scales::percent_format(accuracy = 1)) +
ylab("Net operating surplus, gross (% of GDP)") + xlab("")
Italy, Spain, Portugal
Code
%>%
SNA_TABLE14A # NFK1R: Consumption of fixed capital
# NFB2GP: Operating surplus, gross
filter(TRANSACT %in% c("NFK1R", "NFB2GP"),
== "S1",
SECTOR %in% c("ITA", "ESP", "PRT")) %>%
LOCATION select(LOCATION, TRANSACT, obsTime, obsValue) %>%
spread(TRANSACT, obsValue) %>%
left_join(SNA_TABLE1 %>%
filter(TRANSACT == "B1_GE",
== "C") %>%
MEASURE select(obsTime, LOCATION, B1_GE = obsValue),
by = c("LOCATION", "obsTime")) %>%
left_join(SNA_TABLE14A_var$LOCATION, by = "LOCATION") %>%
%>%
na.omit %>%
year_to_date mutate(obsValue = (NFB2GP) / B1_GE) %>%
left_join(colors, by = c("Location" = "country")) %>%
mutate(color = ifelse(LOCATION == "PRT", color2, color)) %>%
ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
scale_color_identity() + theme_minimal() + add_3flags +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-60, 60, 2),
labels = scales::percent_format(accuracy = 1)) +
ylab("Net operating surplus, gross (% of GDP)") + xlab("")
Gross saving - investment (% of GDP) - NFB8GP-NFP5P
Germany
Code
%>%
SNA_TABLE14A # NFK1R: Consumption of fixed capital
# NFP5P: Gross capital formation
filter(TRANSACT %in% c("NFB8GP", "NFP5P"),
%in% c("S1", "S11", "S13", "S14_S15"),
SECTOR %in% c("DEU")) %>%
LOCATION select(SECTOR, LOCATION, TRANSACT, obsTime, obsValue) %>%
spread(TRANSACT, obsValue) %>%
left_join(SNA_TABLE1 %>%
filter(TRANSACT == "B1_GE",
== "C") %>%
MEASURE select(obsTime, LOCATION, B1_GE = obsValue),
by = c("LOCATION", "obsTime")) %>%
left_join(SNA_TABLE14A_var$SECTOR, by = "SECTOR") %>%
%>%
na.omit %>%
year_to_date mutate(obsValue = (NFB8GP - NFP5P) / B1_GE) %>%
ggplot() + geom_line(aes(x = date, y = obsValue, color = Sector)) +
theme_minimal() + ylab("Gross Saving - Investment (% of GDP)") + xlab("") +
scale_x_date(breaks = seq(1920, 2100, 5) %>% 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(-60, 60, 2),
labels = scales::percent_format(accuracy = 1))
France
Code
%>%
SNA_TABLE14A # NFK1R: Consumption of fixed capital
# NFP5P: Gross capital formation
filter(TRANSACT %in% c("NFB8GP", "NFP5P"),
%in% c("S1", "S11", "S13", "S14_S15"),
SECTOR %in% c("FRA")) %>%
LOCATION select(SECTOR, LOCATION, TRANSACT, obsTime, obsValue) %>%
spread(TRANSACT, obsValue) %>%
left_join(SNA_TABLE1 %>%
filter(TRANSACT == "B1_GE",
== "C") %>%
MEASURE select(obsTime, LOCATION, B1_GE = obsValue),
by = c("LOCATION", "obsTime")) %>%
left_join(SNA_TABLE14A_var$SECTOR, by = "SECTOR") %>%
%>%
na.omit %>%
year_to_date mutate(obsValue = (NFB8GP - NFP5P) / B1_GE) %>%
ggplot() + geom_line(aes(x = date, y = obsValue, color = Sector)) +
theme_minimal() + ylab("Gross Saving - Investment (% of GDP)") + xlab("") +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.3, 0.9),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(-60, 60, 2),
labels = scales::percent_format(accuracy = 1))
United States
Code
%>%
SNA_TABLE14A # NFK1R: Consumption of fixed capital
# NFP5P: Gross capital formation
filter(TRANSACT %in% c("NFB8GP", "NFP5P"),
%in% c("S1", "S11", "S13", "S14_S15"),
SECTOR %in% c("USA")) %>%
LOCATION select(SECTOR, LOCATION, TRANSACT, obsTime, obsValue) %>%
spread(TRANSACT, obsValue) %>%
left_join(SNA_TABLE1 %>%
filter(TRANSACT == "B1_GE",
== "C") %>%
MEASURE select(obsTime, LOCATION, B1_GE = obsValue),
by = c("LOCATION", "obsTime")) %>%
left_join(SNA_TABLE14A_var$SECTOR, by = "SECTOR") %>%
%>%
na.omit %>%
year_to_date mutate(obsValue = (NFB8GP - NFP5P) / B1_GE) %>%
ggplot() + geom_line(aes(x = date, y = obsValue, color = Sector)) +
theme_minimal() + ylab("Gross Saving - Investment (% of GDP)") + xlab("") +
scale_x_date(breaks = seq(1920, 2100, 5) %>% 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(-60, 60, 2),
labels = scales::percent_format(accuracy = 1))
Italy
Code
%>%
SNA_TABLE14A # NFK1R: Consumption of fixed capital
# NFP5P: Gross capital formation
filter(TRANSACT %in% c("NFB8GP", "NFP5P"),
%in% c("S1", "S11", "S13", "S14_S15"),
SECTOR %in% c("ITA")) %>%
LOCATION select(SECTOR, LOCATION, TRANSACT, obsTime, obsValue) %>%
spread(TRANSACT, obsValue) %>%
left_join(SNA_TABLE1 %>%
filter(TRANSACT == "B1_GE",
== "C") %>%
MEASURE select(obsTime, LOCATION, B1_GE = obsValue),
by = c("LOCATION", "obsTime")) %>%
left_join(SNA_TABLE14A_var$SECTOR, by = "SECTOR") %>%
%>%
na.omit %>%
year_to_date mutate(obsValue = (NFB8GP - NFP5P) / B1_GE) %>%
ggplot() + geom_line(aes(x = date, y = obsValue, color = Sector)) +
theme_minimal() + ylab("Gross Saving - Investment (% of GDP)") + xlab("") +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.3, 0.9),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(-60, 60, 2),
labels = scales::percent_format(accuracy = 1))
France, United States, Germany
All
Code
%>%
SNA_TABLE14A # NFK1R: Consumption of fixed capital
# NFP5P: Gross capital formation
filter(TRANSACT %in% c("NFB8GP", "NFP5P"),
== "S1",
SECTOR %in% c("FRA", "USA", "DEU")) %>%
LOCATION select(LOCATION, TRANSACT, obsTime, obsValue) %>%
spread(TRANSACT, obsValue) %>%
left_join(SNA_TABLE1 %>%
filter(TRANSACT == "B1_GE",
== "C") %>%
MEASURE select(obsTime, LOCATION, B1_GE = obsValue),
by = c("LOCATION", "obsTime")) %>%
left_join(SNA_TABLE14A_var$LOCATION, by = "LOCATION") %>%
%>%
na.omit %>%
year_to_date mutate(obsValue = (NFB8GP - NFP5P) / B1_GE) %>%
filter(date >= as.Date("1995-01-01")) %>%
left_join(colors, by = c("Location" = "country")) %>%
mutate(color = ifelse(LOCATION == "USA", color2, color)) %>%
ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
scale_color_identity() + theme_minimal() + add_3flags +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-60, 60, 2),
labels = scales::percent_format(accuracy = 1)) +
ylab("Net Capital Formation (% of GDP) - Government") + xlab("") +
geom_hline(yintercept = 0, linetype = "dashed")
S13 - Government
Code
%>%
SNA_TABLE14A # NFK1R: Consumption of fixed capital
# NFP5P: Gross capital formation
filter(TRANSACT %in% c("NFB8GP", "NFP5P"),
== "S13",
SECTOR %in% c("FRA", "USA", "DEU")) %>%
LOCATION select(LOCATION, TRANSACT, obsTime, obsValue) %>%
spread(TRANSACT, obsValue) %>%
left_join(SNA_TABLE1 %>%
filter(TRANSACT == "B1_GE",
== "C") %>%
MEASURE select(obsTime, LOCATION, B1_GE = obsValue),
by = c("LOCATION", "obsTime")) %>%
left_join(SNA_TABLE14A_var$LOCATION, by = "LOCATION") %>%
%>%
na.omit %>%
year_to_date mutate(obsValue = (NFB8GP - NFP5P) / B1_GE) %>%
filter(date >= as.Date("1995-01-01")) %>%
left_join(colors, by = c("Location" = "country")) %>%
mutate(color = ifelse(LOCATION == "USA", color2, color)) %>%
ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
scale_color_identity() + theme_minimal() + add_3flags +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-60, 60, 2),
labels = scales::percent_format(accuracy = 1)) +
ylab("Net Capital Formation (% of GDP) - Government") + xlab("") +
geom_hline(yintercept = 0, linetype = "dashed")
S11
Code
%>%
SNA_TABLE14A # NFK1R: Consumption of fixed capital
# NFP5P: Gross capital formation
filter(TRANSACT %in% c("NFB8GP", "NFP5P"),
== "S11",
SECTOR %in% c("FRA", "USA", "DEU")) %>%
LOCATION select(LOCATION, TRANSACT, obsTime, obsValue) %>%
spread(TRANSACT, obsValue) %>%
left_join(SNA_TABLE1 %>%
filter(TRANSACT == "B1_GE",
== "C") %>%
MEASURE select(obsTime, LOCATION, B1_GE = obsValue),
by = c("LOCATION", "obsTime")) %>%
left_join(SNA_TABLE14A_var$LOCATION, by = "LOCATION") %>%
%>%
na.omit %>%
year_to_date mutate(obsValue = (NFB8GP - NFP5P) / B1_GE) %>%
filter(date >= as.Date("1995-01-01")) %>%
left_join(colors, by = c("Location" = "country")) %>%
mutate(color = ifelse(LOCATION == "USA", color2, color)) %>%
ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
scale_color_identity() + theme_minimal() + add_3flags +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-60, 60, 2),
labels = scales::percent_format(accuracy = 1)) +
ylab("Net Capital Formation (% of GDP) - Government") + xlab("") +
geom_hline(yintercept = 0, linetype = "dashed")
S12
Code
%>%
SNA_TABLE14A # NFK1R: Consumption of fixed capital
# NFP5P: Gross capital formation
filter(TRANSACT %in% c("NFB8GP", "NFP5P"),
== "S12",
SECTOR %in% c("FRA", "USA", "DEU")) %>%
LOCATION select(LOCATION, TRANSACT, obsTime, obsValue) %>%
spread(TRANSACT, obsValue) %>%
left_join(SNA_TABLE1 %>%
filter(TRANSACT == "B1_GE",
== "C") %>%
MEASURE select(obsTime, LOCATION, B1_GE = obsValue),
by = c("LOCATION", "obsTime")) %>%
left_join(SNA_TABLE14A_var$LOCATION, by = "LOCATION") %>%
%>%
na.omit %>%
year_to_date mutate(obsValue = (NFB8GP - NFP5P) / B1_GE) %>%
filter(date >= as.Date("1995-01-01")) %>%
left_join(colors, by = c("Location" = "country")) %>%
mutate(color = ifelse(LOCATION == "USA", color2, color)) %>%
ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
scale_color_identity() + theme_minimal() + add_3flags +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
labels = scales::percent_format(accuracy = 1)) +
ylab("Net Capital Formation (% of GDP) - Government") + xlab("") +
geom_hline(yintercept = 0, linetype = "dashed")
S14_S15
Code
%>%
SNA_TABLE14A # NFK1R: Consumption of fixed capital
# NFP5P: Gross capital formation
filter(TRANSACT %in% c("NFB8GP", "NFP5P"),
== "S14_S15",
SECTOR %in% c("FRA", "USA", "DEU")) %>%
LOCATION select(LOCATION, TRANSACT, obsTime, obsValue) %>%
spread(TRANSACT, obsValue) %>%
left_join(SNA_TABLE1 %>%
filter(TRANSACT == "B1_GE",
== "C") %>%
MEASURE select(obsTime, LOCATION, B1_GE = obsValue),
by = c("LOCATION", "obsTime")) %>%
left_join(SNA_TABLE14A_var$LOCATION, by = "LOCATION") %>%
%>%
na.omit %>%
year_to_date mutate(obsValue = (NFB8GP - NFP5P) / B1_GE) %>%
filter(date >= as.Date("1995-01-01")) %>%
left_join(colors, by = c("Location" = "country")) %>%
mutate(color = ifelse(LOCATION == "USA", color2, color)) %>%
ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
scale_color_identity() + theme_minimal() + add_3flags +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
labels = scales::percent_format(accuracy = 1)) +
ylab("Net Capital Formation (% of GDP) - Government") + xlab("") +
geom_hline(yintercept = 0, linetype = "dashed")
Gross saving (% of GDP)
Germany
Code
%>%
SNA_TABLE14A # NFK1R: Consumption of fixed capital
# NFP5P: Gross capital formation
filter(TRANSACT %in% c("NFB8GP"),
%in% c("S1", "S11", "S13", "S14_S15"),
SECTOR %in% c("DEU")) %>%
LOCATION left_join(SNA_TABLE1 %>%
filter(TRANSACT == "B1_GE",
== "C") %>%
MEASURE select(obsTime, LOCATION, B1_GE = obsValue),
by = c("LOCATION", "obsTime")) %>%
left_join(SNA_TABLE14A_var$SECTOR, by = "SECTOR") %>%
mutate(obsValue = obsValue / B1_GE) %>%
%>%
year_to_date ggplot() + geom_line(aes(x = date, y = obsValue, color = Sector)) +
theme_minimal() + ylab("Gross Saving (% of GDP)") + xlab("") +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.3, 0.9),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(-60, 60, 5),
labels = scales::percent_format(accuracy = 1))
France
Code
%>%
SNA_TABLE14A # NFK1R: Consumption of fixed capital
# NFP5P: Gross capital formation
filter(TRANSACT %in% c("NFB8GP"),
%in% c("S1", "S11", "S13", "S14_S15"),
SECTOR %in% c("FRA")) %>%
LOCATION left_join(SNA_TABLE1 %>%
filter(TRANSACT == "B1_GE",
== "C") %>%
MEASURE select(obsTime, LOCATION, B1_GE = obsValue),
by = c("LOCATION", "obsTime")) %>%
left_join(SNA_TABLE14A_var$SECTOR, by = "SECTOR") %>%
mutate(obsValue = obsValue / B1_GE) %>%
%>%
year_to_date select(date, obsValue, Sector) %>%
%>%
na.omit ggplot() + geom_line(aes(x = date, y = obsValue, color = Sector)) +
theme_minimal() + ylab("Gross Saving (% of GDP)") + xlab("") +
scale_x_date(breaks = seq(1920, 2100, 5) %>% 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(-60, 60, 5),
labels = scales::percent_format(accuracy = 1))
Gross capital formation (% of GDP)
Germany
Code
%>%
SNA_TABLE14A # NFK1R: Consumption of fixed capital
# NFP5P: Gross capital formation
filter(TRANSACT %in% c("NFP5P"),
%in% c("S1", "S11", "S13", "S14_S15"),
SECTOR %in% c("DEU")) %>%
LOCATION left_join(SNA_TABLE1 %>%
filter(TRANSACT == "B1_GE",
== "C") %>%
MEASURE select(obsTime, LOCATION, B1_GE = obsValue),
by = c("LOCATION", "obsTime")) %>%
left_join(SNA_TABLE14A_var$SECTOR, by = "SECTOR") %>%
mutate(obsValue = obsValue / B1_GE) %>%
%>%
year_to_date ggplot() + geom_line(aes(x = date, y = obsValue, color = Sector)) +
theme_minimal() + ylab("Gross Capital Formation (% of GDP)") + xlab("") +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.45, 0.9),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(-60, 60, 5),
labels = scales::percent_format(accuracy = 1),
limits = c(0, 0.35))
France
Code
%>%
SNA_TABLE14A # NFK1R: Consumption of fixed capital
# NFP5P: Gross capital formation
filter(TRANSACT %in% c("NFP5P"),
%in% c("S1", "S11", "S13", "S14_S15"),
SECTOR %in% c("FRA")) %>%
LOCATION left_join(SNA_TABLE1 %>%
filter(TRANSACT == "B1_GE",
== "C") %>%
MEASURE select(obsTime, LOCATION, B1_GE = obsValue),
by = c("LOCATION", "obsTime")) %>%
left_join(SNA_TABLE14A_var$SECTOR, by = "SECTOR") %>%
mutate(obsValue = obsValue / B1_GE) %>%
%>%
year_to_date select(date, obsValue, Sector) %>%
%>%
na.omit ggplot() + geom_line(aes(x = date, y = obsValue, color = Sector)) +
theme_minimal() + ylab("Gross Capital Formation (% of GDP)") + xlab("") +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.65, 0.9),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(-60, 60, 5),
labels = scales::percent_format(accuracy = 1),
limits = c(0, 0.35))
Italy
Code
%>%
SNA_TABLE14A # NFK1R: Consumption of fixed capital
# NFP5P: Gross capital formation
filter(TRANSACT %in% c("NFP5P"),
%in% c("S1", "S11", "S13", "S14_S15"),
SECTOR %in% c("ITA")) %>%
LOCATION left_join(SNA_TABLE1 %>%
filter(TRANSACT == "B1_GE",
== "C") %>%
MEASURE select(obsTime, LOCATION, B1_GE = obsValue),
by = c("LOCATION", "obsTime")) %>%
left_join(SNA_TABLE14A_var$SECTOR, by = "SECTOR") %>%
mutate(obsValue = obsValue / B1_GE) %>%
%>%
year_to_date select(date, obsValue, Sector) %>%
%>%
na.omit ggplot() + geom_line(aes(x = date, y = obsValue, color = Sector)) +
theme_minimal() + ylab("Gross Capital Formation (% of GDP)") + xlab("") +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.65, 0.9),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(-60, 60, 5),
labels = scales::percent_format(accuracy = 1),
limits = c(0, 0.35))
Number of observations
Gross capital formation (% of GDP)
Code
%>%
SNA_TABLE14A # NFK1R: Consumption of fixed capital
filter(TRANSACT %in% c("NFP5P", "B1_GE"),
== "S1") %>%
SECTOR left_join(SNA_TABLE14A_var$LOCATION, by = "LOCATION") %>%
select_if(~ n_distinct(.) > 1) %>%
select(LOCATION, Location, TRANSACT, obsTime, obsValue) %>%
spread(TRANSACT, obsValue) %>%
%>%
na.omit mutate(NFP5P_B1_GE = (100*NFP5P / B1_GE) %>% round(1) %>% paste("%")) %>%
select(Location, obsTime, NFP5P_B1_GE) %>%
group_by(Location) %>%
summarise(year_first = first(obsTime),
value_first = first(NFP5P_B1_GE),
year_last = last(obsTime),
value_last = last(NFP5P_B1_GE)) %>%
if (is_html_output()) datatable(., filter = 'top', rownames = F) else .} {
Consumption of fixed capital (% of GDP)
Code
%>%
SNA_TABLE14A # NFK1R: Consumption of fixed capital
filter(TRANSACT %in% c("NFK1R", "B1_GE"),
== "S1") %>%
SECTOR left_join(SNA_TABLE14A_var$LOCATION, by = "LOCATION") %>%
select(Location, TRANSACT, obsTime, obsValue) %>%
spread(TRANSACT, obsValue) %>%
%>%
na.omit mutate(NFK1R_B1_GE = (100*NFK1R / B1_GE) %>% round(1) %>% paste("%")) %>%
select(Location, obsTime, NFK1R_B1_GE) %>%
group_by(Location) %>%
summarise(year_first = first(obsTime),
value_first = first(NFK1R_B1_GE),
year_last = last(obsTime),
value_last = last(NFK1R_B1_GE)) %>%
if (is_html_output()) datatable(., filter = 'top', rownames = F) else .} {
Net capital formation (% of GDP)
Code
%>%
SNA_TABLE14A # NFK1R: Consumption of fixed capital
filter(TRANSACT %in% c("NFK1R", "NFP5P", "B1_GE"),
== "S1") %>%
SECTOR left_join(SNA_TABLE14A_var$LOCATION, by = "LOCATION") %>%
select(Location, TRANSACT, obsTime, obsValue) %>%
spread(TRANSACT, obsValue) %>%
%>%
na.omit mutate(net_inv_B1_GE = (100*(NFP5P - NFK1R) / B1_GE) %>% round(1) %>% paste("%")) %>%
select(Location, obsTime, net_inv_B1_GE) %>%
group_by(Location) %>%
summarise(year_first = first(obsTime),
value_first = first(net_inv_B1_GE),
year_last = last(obsTime),
value_last = last(net_inv_B1_GE)) %>%
if (is_html_output()) datatable(., filter = 'top', rownames = F) else .} {
Adjustment for the change in net equity of households in pension funds (% of GDP)
Code
%>%
SNA_TABLE14A # NFD8P: Adjustment for the change in net equity of households in pension funds
filter(TRANSACT %in% c("NFD8P", "B1_GE"),
== "S1") %>%
SECTOR left_join(SNA_TABLE14A_var$LOCATION, by = "LOCATION") %>%
select(Location, TRANSACT, obsTime, obsValue) %>%
spread(TRANSACT, obsValue) %>%
%>%
na.omit mutate(NFD8P_B1_GE = (100*NFD8P / B1_GE) %>% round(2) %>% paste("%")) %>%
select(Location, obsTime, NFD8P_B1_GE) %>%
group_by(Location) %>%
summarise(year_first = first(obsTime),
value_first = first(NFD8P_B1_GE),
year_last = last(obsTime),
value_last = last(NFD8P_B1_GE)) %>%
if (is_html_output()) datatable(., filter = 'top', rownames = F) else .} {
Time Series
Gross capital formation (% of GDP)
All
S1
Code
%>%
SNA_TABLE14A # NFK1R: Consumption of fixed capital
# NFP5P: Gross capital formation
filter(TRANSACT %in% c("NFK1R", "NFP5P", "B1_GE"),
== "S1",
SECTOR %in% c("FRA", "USA", "DEU", "JPN")) %>%
LOCATION left_join(SNA_TABLE14A_var$LOCATION, by = "LOCATION") %>%
select(Location, TRANSACT, obsTime, obsValue) %>%
spread(TRANSACT, obsValue) %>%
%>%
na.omit %>%
year_to_date mutate(obsValue = (NFP5P) / B1_GE) %>%
left_join(colors, by = c("Location" = "country")) %>%
ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
scale_color_identity() + theme_minimal() + add_4flags +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
labels = scales::percent_format(accuracy = 1)) +
ylab("Gross Capital Formation (% of GDP)") + xlab("")
S11
Code
%>%
SNA_TABLE14A # NFK1R: Consumption of fixed capital
# NFP5P: Gross capital formation
filter(TRANSACT %in% c("NFP5P"),
== "S11",
SECTOR %in% c("FRA", "USA", "DEU", "JPN")) %>%
LOCATION left_join(SNA_TABLE14A_var$LOCATION, by = "LOCATION") %>%
left_join(SNA_TABLE1 %>%
filter(TRANSACT == "B1_GE",
== "C") %>%
MEASURE select(obsTime, LOCATION, B1_GE = obsValue),
by = c("LOCATION", "obsTime")) %>%
mutate(obsValue = obsValue / B1_GE) %>%
%>%
year_to_date left_join(colors, by = c("Location" = "country")) %>%
ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
scale_color_identity() + theme_minimal() + add_4flags +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
labels = scales::percent_format(accuracy = 1)) +
ylab("Gross Capital Formation (% of GDP)") + xlab("")
S13
5
Code
%>%
SNA_TABLE14A # NFK1R: Consumption of fixed capital
# NFP5P: Gross capital formation
filter(TRANSACT %in% c("NFP5P"),
== "S13",
SECTOR %in% c("FRA", "USA", "DEU", "JPN")) %>%
LOCATION left_join(SNA_TABLE14A_var$LOCATION, by = "LOCATION") %>%
left_join(SNA_TABLE1 %>%
filter(TRANSACT == "B1_GE",
== "C") %>%
MEASURE select(obsTime, LOCATION, B1_GE = obsValue),
by = c("LOCATION", "obsTime")) %>%
mutate(obsValue = obsValue / B1_GE) %>%
%>%
year_to_date left_join(colors, by = c("Location" = "country")) %>%
ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
scale_color_identity() + theme_minimal() + add_4flags +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
labels = scales::percent_format(accuracy = 1)) +
ylab("Gross Capital Formation (% of GDP) - Government") + xlab("")
France, Unnited STates, Germany
Code
%>%
SNA_TABLE14A # NFK1R: Consumption of fixed capital
# NFP5P: Gross capital formation
filter(TRANSACT %in% c("NFP5P"),
%in% c("S13"),
SECTOR %in% c("FRA", "USA", "DEU")) %>%
LOCATION left_join(SNA_TABLE14A_var$LOCATION, by = "LOCATION") %>%
left_join(SNA_TABLE1 %>%
filter(TRANSACT == "B1_GE",
== "C") %>%
MEASURE select(obsTime, LOCATION, B1_GE = obsValue),
by = c("LOCATION", "obsTime")) %>%
mutate(obsValue = obsValue / B1_GE) %>%
%>%
year_to_date filter(date >= as.Date("1995-01-01")) %>%
left_join(colors, by = c("Location" = "country")) %>%
mutate(color = ifelse(LOCATION == "USA", color2, color)) %>%
ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
scale_color_identity() + theme_minimal() + add_3flags +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-60, 60, 0.5),
labels = scales::percent_format(accuracy = .1)) +
ylab("Gross Capital Formation (% of GDP) - Government") + xlab("")
More countries
Code
%>%
SNA_TABLE14A # NFK1R: Consumption of fixed capital
# NFP5P: Gross capital formation
filter(TRANSACT %in% c("NFP5P"),
%in% c("S13"),
SECTOR %in% c("PRT", "USA", "DEU", "ITA", "ESP", "GRC")) %>%
LOCATION left_join(SNA_TABLE14A_var$LOCATION, by = "LOCATION") %>%
left_join(SNA_TABLE1 %>%
filter(TRANSACT == "B1_GE",
== "C") %>%
MEASURE select(obsTime, LOCATION, B1_GE = obsValue),
by = c("LOCATION", "obsTime")) %>%
mutate(obsValue = obsValue / B1_GE) %>%
%>%
year_to_date filter(date >= as.Date("1995-01-01")) %>%
left_join(colors, by = c("Location" = "country")) %>%
mutate(color = ifelse(LOCATION == "USA", color2, color)) %>%
ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
scale_color_identity() + theme_minimal() + add_6flags +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-60, 60, 0.5),
labels = scales::percent_format(accuracy = .1)) +
ylab("Gross Capital Formation (% of GDP) - Government") + xlab("")
Code
%>%
SNA_TABLE14A # NFK1R: Consumption of fixed capital
# NFP5P: Gross capital formation
filter(TRANSACT %in% c("NFP5P"),
%in% c("S13"),
SECTOR %in% c("PRT", "USA", "DEU", "ITA", "ESP", "GRC")) %>%
LOCATION left_join(SNA_TABLE14A_var$LOCATION, by = "LOCATION") %>%
left_join(SNA_TABLE1 %>%
filter(TRANSACT == "B1_GE",
== "C") %>%
MEASURE select(obsTime, LOCATION, B1_GE = obsValue),
by = c("LOCATION", "obsTime")) %>%
mutate(obsValue = obsValue / B1_GE) %>%
%>%
year_to_date filter(date >= as.Date("2012-01-01")) %>%
left_join(colors, by = c("Location" = "country")) %>%
mutate(color = ifelse(LOCATION == "USA", color2, color)) %>%
ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
scale_color_identity() + theme_minimal() + add_6flags +
scale_x_date(breaks = seq(1920, 2100, 1) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-60, 60, 0.5),
labels = scales::percent_format(accuracy = .1)) +
ylab("Gross Capital Formation (% of GDP) - Government") + xlab("")
1994-
Code
%>%
SNA_TABLE14A # NFK1R: Consumption of fixed capital
# NFP5P: Gross capital formation
filter(TRANSACT %in% c("NFK1R", "NFP5P", "B1_GE"),
== "S1",
SECTOR %in% c("FRA", "USA", "DEU", "JPN")) %>%
LOCATION left_join(SNA_TABLE14A_var$LOCATION, by = "LOCATION") %>%
select(Location, TRANSACT, obsTime, obsValue) %>%
spread(TRANSACT, obsValue) %>%
%>%
na.omit %>%
year_to_date filter(date >= as.Date("1994-01-01")) %>%
mutate(obsValue = (NFP5P) / B1_GE) %>%
left_join(colors, by = c("Location" = "country")) %>%
ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
scale_color_identity() + theme_minimal() + add_4flags +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
labels = scales::percent_format(accuracy = 1)) +
ylab("Gross Capital Formation (% of GDP)") + xlab("")
France
Disposable Income, GDP
All
Code
%>%
SNA_TABLE14A filter((SECTOR %in% c("S14_S15", "S1") & TRANSACT == "NFB6GP") |
== "S1" & TRANSACT == "B1_GE"),
(SECTOR %in% c("FRA")) %>%
LOCATION %>%
year_to_date left_join(SNA_TABLE14A_var$TRANSACT, by = "TRANSACT") %>%
left_join(SNA_TABLE14A_var$SECTOR, by = "SECTOR") %>%
group_by(TRANSACT, SECTOR) %>%
arrange(date) %>%
mutate(obsValue = 100*obsValue/obsValue[1]) %>%
ggplot() + geom_line(aes(x = date, y = obsValue, color = paste0(Transact, " - ", Sector))) + theme_minimal() +
scale_color_manual(values = viridis(4)[1:3]) +
theme(legend.position = c(0.4, 0.9),
legend.title = element_blank()) +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_log10(breaks = seq(1000, 50000, 1000)) +
ylab("Indice 1995 = 100") + xlab("")
1990-
Code
%>%
SNA_TABLE14A filter((SECTOR %in% c("S14_S15", "S1") & TRANSACT == "NFB6GP") |
== "S1" & TRANSACT == "B1_GE"),
(SECTOR %in% c("FRA")) %>%
LOCATION %>%
year_to_date filter(date >= as.Date("1990-01-01")) %>%
left_join(SNA_TABLE14A_var$TRANSACT, by = "TRANSACT") %>%
left_join(SNA_TABLE14A_var$SECTOR, by = "SECTOR") %>%
group_by(TRANSACT, SECTOR) %>%
mutate(obsValue = 100*obsValue/obsValue[1]) %>%
mutate(Variable = paste0(Transact, " - ", Sector)) %>%
ggplot() + geom_line(aes(x = date, y = obsValue, color = Variable)) +
theme_minimal() + ylab("Indice 1990 = 100") + xlab("") +
scale_color_manual(values = viridis(4)[1:3]) +
theme(legend.position = c(0.45, 0.9),
legend.title = element_blank()) +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_log10(breaks = seq(100, 300, 10))
1995-
Code
%>%
SNA_TABLE14A filter((SECTOR %in% c("S14_S15", "S1") & TRANSACT == "NFB6GP") |
== "S1" & TRANSACT == "B1_GE"),
(SECTOR %in% c("FRA")) %>%
LOCATION %>%
year_to_date filter(date >= as.Date("1995-01-01")) %>%
left_join(SNA_TABLE14A_var$TRANSACT, by = "TRANSACT") %>%
left_join(SNA_TABLE14A_var$SECTOR, by = "SECTOR") %>%
group_by(TRANSACT, SECTOR) %>%
mutate(obsValue = 100*obsValue/obsValue[1]) %>%
mutate(Variable = paste0(Transact, " - ", Sector)) %>%
ggplot() + geom_line(aes(x = date, y = obsValue, color = Variable)) +
theme_minimal() + ylab("Indice 1995 = 100") + xlab("") +
scale_color_manual(values = viridis(4)[1:3]) +
theme(legend.position = c(0.45, 0.9),
legend.title = element_blank()) +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_log10(breaks = seq(100, 300, 5))
1996-
Code
%>%
SNA_TABLE14A filter((SECTOR %in% c("S14_S15", "S1") & TRANSACT == "NFB6GP") |
== "S1" & TRANSACT == "B1_GE"),
(SECTOR %in% c("FRA")) %>%
LOCATION %>%
year_to_date filter(date >= as.Date("1996-01-01")) %>%
left_join(SNA_TABLE14A_var$TRANSACT, by = "TRANSACT") %>%
left_join(SNA_TABLE14A_var$SECTOR, by = "SECTOR") %>%
group_by(TRANSACT, SECTOR) %>%
mutate(obsValue = 100*obsValue/obsValue[1]) %>%
mutate(Variable = paste0(Transact, " - ", Sector)) %>%
ggplot() + geom_line(aes(x = date, y = obsValue, color = Variable)) +
theme_minimal() + ylab("Indice 1996 = 100") + xlab("") +
scale_color_manual(values = viridis(4)[1:3]) +
theme(legend.position = c(0.45, 0.9),
legend.title = element_blank()) +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_log10(breaks = seq(100, 300, 5))
2008-
Code
%>%
SNA_TABLE14A filter((SECTOR %in% c("S14_S15", "S1") & TRANSACT == "NFB6GP") |
== "S1" & TRANSACT == "B1_GE"),
(SECTOR %in% c("FRA")) %>%
LOCATION %>%
year_to_date filter(date >= as.Date("2008-01-01")) %>%
left_join(SNA_TABLE14A_var$TRANSACT, by = "TRANSACT") %>%
left_join(SNA_TABLE14A_var$SECTOR, by = "SECTOR") %>%
group_by(TRANSACT, SECTOR) %>%
mutate(obsValue = 100*obsValue/obsValue[1]) %>%
mutate(Variable = paste0(Transact, " - ", Sector)) %>%
ggplot() + geom_line(aes(x = date, y = obsValue, color = Variable)) +
theme_minimal() + ylab("Indice 2008 = 100") + xlab("") +
scale_color_manual(values = viridis(4)[1:3]) +
theme(legend.position = c(0.45, 0.9),
legend.title = element_blank()) +
scale_x_date(breaks = seq(1920, 2100, 1) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_log10(breaks = seq(100, 300, 5))
B6NS1 - Net National Disposable Income / GDP
B6GS1 - Gross National Disposable Income / GDP
Households (% of GDP)
France, Germany, Belgium, Netherlands, Italy
Code
%>%
SNA_TABLE14A filter((SECTOR == "S14_S15" & TRANSACT == "NFB6GP") |
== "S1" & TRANSACT == "B1_GE"),
(SECTOR %in% c("FRA", "DEU", "BEL", "ESP", "NLD", "ITA")) %>%
LOCATION %>%
year_to_date left_join(SNA_TABLE14A_var$LOCATION, by = "LOCATION") %>%
select(Location, date, TRANSACT, obsValue) %>%
spread(TRANSACT, obsValue) %>%
group_by(Location) %>%
mutate(obsValue = NFB6GP / B1_GE) %>%
select(Location, date, obsValue) %>%
%>%
na.omit left_join(colors, by = c("Location" = "country")) %>%
mutate(color = ifelse(Location == "Netherlands", color2, color)) %>%
filter(date >= as.Date("1995-01-01")) %>%
ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
scale_color_identity() + theme_minimal() + add_6flags +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-10, 120, 1),
labels = scales::percent_format(accuracy = 1)) +
ylab("Gross National Disposable Income (% of GDP)") + xlab("")
France, Germany, U.S., Denmark
Code
%>%
SNA_TABLE14A filter((SECTOR == "S14_S15" & TRANSACT == "NFB6GP") |
== "S1" & TRANSACT == "B1_GE"),
(SECTOR %in% c("FRA", "DEU", "USA", "DNK")) %>%
LOCATION %>%
year_to_date left_join(SNA_TABLE14A_var$LOCATION, by = "LOCATION") %>%
select(Location, date, TRANSACT, obsValue) %>%
spread(TRANSACT, obsValue) %>%
group_by(Location) %>%
mutate(obsValue = NFB6GP / B1_GE) %>%
select(Location, date, obsValue) %>%
%>%
na.omit left_join(colors, by = c("Location" = "country")) %>%
mutate(color = ifelse(Location == "Netherlands", color2, color)) %>%
filter(date >= as.Date("1995-01-01")) %>%
ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
scale_color_identity() + theme_minimal() + add_4flags +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-10, 120, 1),
labels = scales::percent_format(accuracy = 1)) +
ylab("Gross National Disposable Income (% of GDP)") + xlab("")
Corporations (% of GDP)
Code
%>%
SNA_TABLE14A filter((SECTOR == "S13" & TRANSACT == "NFB6GP") |
== "S1" & TRANSACT == "B1_GE"),
(SECTOR %in% c("FRA", "DEU", "BEL", "ESP", "NLD", "ITA")) %>%
LOCATION %>%
year_to_date left_join(SNA_TABLE14A_var$LOCATION, by = "LOCATION") %>%
select(Location, date, TRANSACT, obsValue) %>%
spread(TRANSACT, obsValue) %>%
group_by(Location) %>%
mutate(obsValue = NFB6GP / B1_GE) %>%
select(Location, date, obsValue) %>%
%>%
na.omit left_join(colors, by = c("Location" = "country")) %>%
mutate(color = ifelse(Location == "Netherlands", color2, color)) %>%
filter(date >= as.Date("1995-01-01")) %>%
ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
scale_color_identity() + theme_minimal() + add_6flags +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-10, 120, 1),
labels = scales::percent_format(accuracy = 1)) +
ylab("Gross National Disposable Income (% of GDP)") + xlab("")
Governments (% of GDP)
Code
%>%
SNA_TABLE14A filter((SECTOR == "S11" & TRANSACT == "NFB6GP") |
== "S1" & TRANSACT == "B1_GE"),
(SECTOR %in% c("FRA", "DEU", "BEL", "ESP", "NLD", "ITA")) %>%
LOCATION %>%
year_to_date left_join(SNA_TABLE14A_var$LOCATION, by = "LOCATION") %>%
select(Location, date, TRANSACT, obsValue) %>%
spread(TRANSACT, obsValue) %>%
group_by(Location) %>%
mutate(obsValue = NFB6GP / B1_GE) %>%
select(Location, date, obsValue) %>%
%>%
na.omit left_join(colors, by = c("Location" = "country")) %>%
mutate(color = ifelse(Location == "Netherlands", color2, color)) %>%
filter(date >= as.Date("1995-01-01")) %>%
ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
scale_color_identity() + theme_minimal() + add_6flags +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-10, 120, 1),
labels = scales::percent_format(accuracy = 1)) +
ylab("Gross National Disposable Income (% of GDP)") + xlab("")
All (% of GDP)
Code
%>%
SNA_TABLE14A filter((SECTOR == "S1" & TRANSACT == "NFB6GP") |
== "S1" & TRANSACT == "B1_GE"),
(SECTOR %in% c("FRA", "DEU", "BEL", "ESP", "NLD", "ITA")) %>%
LOCATION %>%
year_to_date left_join(SNA_TABLE14A_var$LOCATION, by = "LOCATION") %>%
select(Location, date, TRANSACT, obsValue) %>%
spread(TRANSACT, obsValue) %>%
group_by(Location) %>%
mutate(obsValue = NFB6GP / B1_GE) %>%
select(Location, date, obsValue) %>%
%>%
na.omit left_join(colors, by = c("Location" = "country")) %>%
mutate(color = ifelse(Location == "Netherlands", color2, color)) %>%
filter(date >= as.Date("1995-01-01")) %>%
ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
scale_color_identity() + theme_minimal() + add_6flags +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-10, 120, 1),
labels = scales::percent_format(accuracy = 1)) +
ylab("Gross National Disposable Income (% of GDP)") + xlab("")
Net capital formation (% of GDP)
All
Code
%>%
SNA_TABLE14A # NFK1R: Consumption of fixed capital
# NFP5P: Gross capital formation
filter(TRANSACT %in% c("NFK1R", "NFP5P", "B1_GE"),
== "S1",
SECTOR %in% c("FRA", "USA", "DEU", "JPN")) %>%
LOCATION left_join(SNA_TABLE14A_var$LOCATION, by = "LOCATION") %>%
select(Location, TRANSACT, obsTime, obsValue) %>%
spread(TRANSACT, obsValue) %>%
%>%
na.omit %>%
year_to_date mutate(obsValue = (NFP5P - NFK1R) / B1_GE) %>%
left_join(colors, by = c("Location" = "country")) %>%
ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
scale_color_identity() + theme_minimal() + add_4flags +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
labels = scales::percent_format(accuracy = 1)) +
ylab("Net Capital Formation (% of GDP)") + xlab("")
S13
France, United States, Germany
Code
%>%
SNA_TABLE14A # NFK1R: Consumption of fixed capital
# NFP5P: Gross capital formation
filter(TRANSACT %in% c("NFK1R", "NFP5P"),
== "S13",
SECTOR %in% c("FRA", "USA", "DEU")) %>%
LOCATION select(LOCATION, TRANSACT, obsTime, obsValue) %>%
spread(TRANSACT, obsValue) %>%
left_join(SNA_TABLE1 %>%
filter(TRANSACT == "B1_GE",
== "C") %>%
MEASURE select(obsTime, LOCATION, B1_GE = obsValue),
by = c("LOCATION", "obsTime")) %>%
left_join(SNA_TABLE14A_var$LOCATION, by = "LOCATION") %>%
%>%
na.omit %>%
year_to_date mutate(obsValue = (NFP5P - NFK1R) / B1_GE) %>%
filter(date >= as.Date("1995-01-01")) %>%
left_join(colors, by = c("Location" = "country")) %>%
mutate(color = ifelse(LOCATION == "USA", color2, color)) %>%
ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
scale_color_identity() + theme_minimal() + add_3flags +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-60, 60, 0.1),
labels = scales::percent_format(accuracy = .1)) +
ylab("Net Capital Formation (% of GDP) - Government") + xlab("") +
geom_hline(yintercept = 0, linetype = "dashed")
Germany, United States, Germany
Code
%>%
SNA_TABLE14A # NFK1R: Consumption of fixed capital
# NFP5P: Gross capital formation
filter(TRANSACT %in% c("NFK1R", "NFP5P"),
== "S13",
SECTOR %in% c("DEU", "LVA", "SVK", "CZE")) %>%
LOCATION select(LOCATION, TRANSACT, obsTime, obsValue) %>%
spread(TRANSACT, obsValue) %>%
left_join(SNA_TABLE1 %>%
filter(TRANSACT == "B1_GE",
== "C") %>%
MEASURE select(obsTime, LOCATION, B1_GE = obsValue),
by = c("LOCATION", "obsTime")) %>%
left_join(SNA_TABLE14A_var$LOCATION, by = "LOCATION") %>%
%>%
na.omit %>%
year_to_date mutate(obsValue = (NFP5P - NFK1R) / B1_GE) %>%
filter(date >= as.Date("1995-01-01")) %>%
left_join(colors, by = c("Location" = "country")) %>%
mutate(color = ifelse(LOCATION == "SVK", "#EE1C25", color)) %>%
ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
scale_color_identity() + theme_minimal() + add_4flags +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
labels = scales::percent_format(accuracy = 1)) +
ylab("Net Capital Formation (% of GDP) - Government") + xlab("") +
geom_hline(yintercept = 0, linetype = "dashed")
Table
Code
%>%
SNA_TABLE14A # NFK1R: Consumption of fixed capital
# NFP5P: Gross capital formation
filter(TRANSACT %in% c("NFK1R", "NFP5P"),
== "S13") %>%
SECTOR select(LOCATION, TRANSACT, obsTime, obsValue) %>%
spread(TRANSACT, obsValue) %>%
left_join(SNA_TABLE1 %>%
filter(TRANSACT == "B1_GE",
== "C") %>%
MEASURE select(obsTime, LOCATION, B1_GE = obsValue),
by = c("LOCATION", "obsTime")) %>%
left_join(SNA_TABLE14A_var$LOCATION, by = "LOCATION") %>%
%>%
na.omit %>%
year_to_date mutate(obsValue = (NFP5P - NFK1R) / B1_GE) %>%
filter(date >= as.Date("1995-01-01"),
<= as.Date("2019-01-01")) %>%
date group_by(LOCATION, Location) %>%
filter(n() == 25) %>%
summarise(`Avg Net Inv.` = round(100*mean(obsValue), 2)) %>%
arrange(`Avg Net Inv.`) %>%
mutate(`Avg Net Inv.` = paste0(`Avg Net Inv.`, " %")) %>%
mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Location))),
Flag = paste0('<img src="../../icon/flag/vsmall/', Flag, '.png" alt="Flag">')) %>%
select(Flag, everything()) %>%
if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .} {
Operating surplus and mixed income; gross (% of GDP)
Code
%>%
SNA_TABLE14A # NFB2G_B3GP: Operating surplus and mixed income
filter(TRANSACT %in% c("NFB2G_B3GP", "B1_GE"),
# S1: Total economy
== "S1",
SECTOR %in% c("FRA", "USA", "DEU", "JPN")) %>%
LOCATION left_join(SNA_TABLE14A_var$LOCATION, by = "LOCATION") %>%
select(Location, TRANSACT, obsTime, obsValue) %>%
spread(TRANSACT, obsValue) %>%
%>%
na.omit %>%
year_to_date mutate(obsValue = NFB2G_B3GP / B1_GE) %>%
left_join(colors, by = c("Location" = "country")) %>%
ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
scale_color_identity() + theme_minimal() + add_4flags +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
labels = scales::percent_format(accuracy = 1)) +
ylab("Operating surplus and mixed income (% of GDP)") + xlab("")
Adjustment for the change in net equity of households in pension funds -
Code
%>%
SNA_TABLE14A # NFD8P: Adjustment for the change in net equity of households in pension funds
filter(TRANSACT %in% c("NFD8P", "B1_GE"),
# S1: Total economy
== "S1",
SECTOR %in% c("DEU", "USA", "CAN", "JPN")) %>%
LOCATION left_join(SNA_TABLE14A_var$LOCATION, by = "LOCATION") %>%
select(Location, TRANSACT, obsTime, obsValue) %>%
spread(TRANSACT, obsValue) %>%
%>%
na.omit %>%
year_to_date mutate(obsValue = NFD8P / B1_GE) %>%
left_join(colors, by = c("Location" = "country")) %>%
ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
scale_color_identity() + theme_minimal() + add_3flags +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
labels = scales::percent_format(accuracy = 1)) +
ylab("Operating surplus and mixed income (% of GDP)") + xlab("")
2018
2017 - United States
Code
%>%
SNA_TABLE14A filter(LOCATION == "USA",
== "2017",
obsTime !(SECTOR %in% c("S2", "S14", "S15"))) %>%
left_join(SNA_TABLE14A_var$TRANSACT, by = "TRANSACT") %>%
left_join(SNA_TABLE1 %>%
filter(TRANSACT == "B1_GE",
== "C") %>%
MEASURE select(obsTime, LOCATION, B1_GE = obsValue),
by = c("LOCATION", "obsTime")) %>%
mutate(obsValue = round(100*obsValue / B1_GE, 1) %>% paste0(., "%")) %>%
select(SECTOR, TRANSACT, Transact, obsValue) %>%
mutate(SECTOR = paste0('<img src="../../icon/sector/vsmall/', SECTOR, '.png" alt="All">')) %>%
spread(SECTOR, obsValue) %>%
if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .} {
2018 - Germany
Code
%>%
SNA_TABLE14A filter(LOCATION == "DEU",
== "2018",
obsTime !(SECTOR %in% c("S2", "S14", "S15"))) %>%
left_join(SNA_TABLE14A_var$TRANSACT, by = "TRANSACT") %>%
left_join(SNA_TABLE1 %>%
filter(TRANSACT == "B1_GE",
== "C") %>%
MEASURE select(obsTime, LOCATION, B1_GE = obsValue),
by = c("LOCATION", "obsTime")) %>%
mutate(obsValue = round(100*obsValue / B1_GE, 1) %>% paste0(., "%")) %>%
select(SECTOR, TRANSACT, Transact, obsValue) %>%
mutate(SECTOR = paste0('<img src="../../icon/sector/vsmall/', SECTOR, '.png" alt="All">')) %>%
spread(SECTOR, obsValue) %>%
if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .} {
2018 - France
Code
%>%
SNA_TABLE14A filter(LOCATION == "FRA",
== "2018",
obsTime !(SECTOR %in% c("S2", "S14", "S15"))) %>%
left_join(SNA_TABLE14A_var$TRANSACT, by = "TRANSACT") %>%
left_join(SNA_TABLE1 %>%
filter(TRANSACT == "B1_GE",
== "C") %>%
MEASURE select(obsTime, LOCATION, B1_GE = obsValue),
by = c("LOCATION", "obsTime")) %>%
mutate(obsValue = round(100*obsValue / B1_GE, 1) %>% paste0(., "%")) %>%
select(SECTOR, TRANSACT, Transact, obsValue) %>%
mutate(SECTOR = paste0('<img src="../../icon/sector/vsmall/', SECTOR, '.png" alt="All">')) %>%
spread(SECTOR, obsValue) %>%
if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .} {
2017 - Japan
Code
%>%
SNA_TABLE14A filter(LOCATION == "JPN",
== "2017",
obsTime !(SECTOR %in% c("S2", "S14", "S15"))) %>%
left_join(SNA_TABLE14A_var$TRANSACT, by = "TRANSACT") %>%
left_join(SNA_TABLE1 %>%
filter(TRANSACT == "B1_GE",
== "C") %>%
MEASURE select(obsTime, LOCATION, B1_GE = obsValue),
by = c("LOCATION", "obsTime")) %>%
mutate(obsValue = round(100*obsValue / B1_GE, 1) %>% paste0(., "%")) %>%
select(SECTOR, TRANSACT, Transact, obsValue) %>%
mutate(SECTOR = paste0('<img src="../../icon/sector/vsmall/', SECTOR, '.png" alt="All">')) %>%
spread(SECTOR, obsValue) %>%
if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .} {
2018 - United Kingdom
Code
%>%
SNA_TABLE14A filter(LOCATION == "GBR",
== "2018",
obsTime !(SECTOR %in% c("S2", "S14", "S15"))) %>%
left_join(SNA_TABLE14A_var$TRANSACT, by = "TRANSACT") %>%
left_join(SNA_TABLE1 %>%
filter(TRANSACT == "B1_GE",
== "C") %>%
MEASURE select(obsTime, LOCATION, B1_GE = obsValue),
by = c("LOCATION", "obsTime")) %>%
mutate(obsValue = round(100*obsValue / B1_GE, 1) %>% paste0(., "%")) %>%
select(SECTOR, TRANSACT, Transact, obsValue) %>%
mutate(SECTOR = paste0('<img src="../../icon/sector/vsmall/', SECTOR, '.png" alt="All">')) %>%
spread(SECTOR, obsValue) %>%
if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .} {
2016 - China
Code
%>%
SNA_TABLE14A filter(LOCATION == "CHN",
== "2016",
obsTime !(SECTOR %in% c("S2", "S14", "S15"))) %>%
left_join(SNA_TABLE14A_var$TRANSACT, by = "TRANSACT") %>%
left_join(SNA_TABLE1 %>%
filter(TRANSACT == "B1_GE",
== "C") %>%
MEASURE select(obsTime, LOCATION, B1_GE = obsValue),
by = c("LOCATION", "obsTime")) %>%
mutate(obsValue = round(100*obsValue / B1_GE, 1) %>% paste0(., "%")) %>%
select(SECTOR, TRANSACT, Transact, obsValue) %>%
mutate(SECTOR = paste0('<img src="../../icon/sector/vsmall/', SECTOR, '.png" alt="All">')) %>%
spread(SECTOR, obsValue) %>%
if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .} {