source | dataset | .html | .RData |
---|---|---|---|
2024-09-11 | 2023-12-20 |
Fixed assets by activity and by asset, ISIC rev4
Data - OECD
Info
LAST_DOWNLOAD
Code
tibble(LAST_DOWNLOAD = as.Date(file.info("~/Library/Mobile\ Documents/com~apple~CloudDocs/website/data/oecd/SNA_TABLE9A.RData")$mtime)) %>%
print_table_conditional()
LAST_DOWNLOAD |
---|
2023-12-20 |
LAST_COMPILE
LAST_COMPILE |
---|
2024-09-15 |
Last
TRANSACT
Code
%>%
SNA_TABLE9A left_join(SNA_TABLE9A_var$TRANSACT, by = "TRANSACT") %>%
group_by(TRANSACT, Transact) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
print_table_conditional()
LOCATION
Code
%>%
SNA_TABLE9A left_join(SNA_TABLE9A_var$LOCATION, by = "LOCATION") %>%
group_by(LOCATION, Location) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Location))),
Flag = paste0('<img src="../../icon/flag/vsmall/', Flag, '.png" alt="Flag">')) %>%
select(Flag, everything()) %>%
if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .} {
ACTIVITY
Code
%>%
SNA_TABLE9A left_join(SNA_TABLE9A_var$ACTIVITY, by = "ACTIVITY") %>%
group_by(ACTIVITY, Activity) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
print_table_conditional()
MEASURE
Code
%>%
SNA_TABLE9A left_join(SNA_TABLE9A_var$MEASURE, by = "MEASURE") %>%
group_by(MEASURE, Measure) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
print_table_conditional()
MEASURE | Measure | Nobs |
---|---|---|
C | Current prices | 996828 |
VP | Constant prices, previous year prices | 901237 |
V | Constant prices, national base year | 787733 |
DOB | Deflator | 757575 |
Equipment, Fixed Assets
United States
Code
%>%
SNA_TABLE9A rename(obsTime = Time, obsValue = ObsValue) %>%
mutate(obsValue = as.numeric(obsValue)) %>%
filter(TRANSACT %in% c("N1113NA", "N1113ONA", "N11131NA", "B1_GE"),
== "VTOT",
ACTIVITY == "C",
MEASURE == "USA") %>%
LOCATION left_join(SNA_TABLE9A_var$TRANSACT, by = "TRANSACT") %>%
%>%
year_to_date select(date, Transact, obsValue) %>%
spread(Transact, obsValue) %>%
rename(GDP = `Gross domestic product (expenditure approach)`) %>%
mutate_at(vars(-GDP, -date), funs(./ GDP)) %>%
select(-GDP) %>%
gather(Transact, value, -date) %>%
ggplot(.) + geom_line(aes(x = date, y = value, color = Transact)) +
theme_minimal() +
theme(legend.position = c(0.7, 0.3),
legend.title = element_blank(),
legend.text = element_text(size = 8),
legend.key.size = unit(0.9, 'lines')) +
scale_y_continuous(breaks = 0.01*seq(0, 500, 10),
labels = scales::percent_format(accuracy = 1),
limits = c(0, 0.6)) +
scale_x_date(breaks = seq(1700, 2020, 5) %>% paste0(., "-01-01") %>% as.Date,
limits = c(1970, 2010) %>% paste0(., "-01-01") %>% as.Date,
labels = date_format("%Y")) +
xlab("") + ylab("% of GDP")
Net Fixed Assets
png
Code
include_graphics3("bib/oecd/SNA_TABLE9A_ex1.png")
U.S. Net fixed Assets, Construction
Code
%>%
SNA_TABLE9A rename(obsTime = Time, obsValue = ObsValue) %>%
mutate(obsValue = as.numeric(obsValue)) %>%
filter(TRANSACT %in% c("N111XNA", "N11NA", "B1_GE"),
== "VTOT",
ACTIVITY == "C",
MEASURE == "USA") %>%
LOCATION left_join(SNA_TABLE9A_var$TRANSACT, by = "TRANSACT") %>%
%>%
year_to_date select(date, Transact, obsValue) %>%
spread(Transact, obsValue) %>%
rename(GDP = `Gross domestic product (expenditure approach)`) %>%
mutate_at(vars(-GDP, -date), funs(./ GDP)) %>%
select(-GDP) %>%
gather(Transact, value, -date) %>%
ggplot(.) + geom_line(aes(x = date, y = value, color = Transact)) +
theme_minimal() +
theme(legend.position = c(0.4, 0.9),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(0, 500, 10),
labels = scales::percent_format(accuracy = 1)) +
scale_x_date(breaks = seq(1700, 2020, 5) %>% paste0(., "-01-01") %>% as.Date,
limits = c(1978, 2018) %>% paste0(., "-01-01") %>% as.Date,
labels = date_format("%Y")) +
xlab("") + ylab("% of GDP")
France Net fixed Assets, Construction
Code
%>%
SNA_TABLE9A rename(obsTime = Time, obsValue = ObsValue) %>%
mutate(obsValue = as.numeric(obsValue)) %>%
filter(TRANSACT %in% c("N111XNA", "N11NA", "B1_GE"),
== "VTOT",
ACTIVITY == "C",
MEASURE == "FRA") %>%
LOCATION left_join(SNA_TABLE9A_var$TRANSACT, by = "TRANSACT") %>%
%>%
year_to_date select(date, Transact, obsValue) %>%
spread(Transact, obsValue) %>%
rename(GDP = `Gross domestic product (expenditure approach)`) %>%
mutate_at(vars(-GDP, -date), funs(./ GDP)) %>%
select(-GDP) %>%
gather(Transact, value, -date) %>%
ggplot(.) + geom_line(aes(x = date, y = value, color = Transact)) +
theme_minimal() +
theme(legend.position = c(0.4, 0.9),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(0, 500, 10),
labels = scales::percent_format(accuracy = 1)) +
scale_x_date(breaks = seq(1700, 2020, 5) %>% paste0(., "-01-01") %>% as.Date,
limits = c(1978, 2010) %>% paste0(., "-01-01") %>% as.Date,
labels = date_format("%Y")) +
xlab("") + ylab("% of GDP")