source | dataset | .html | .RData |
---|---|---|---|
2024-09-11 | 2024-04-16 |
Population and employment by main activity
Data - OECD
Info
Données sur l’industrie
source | dataset | .html | .RData |
---|---|---|---|
2024-09-14 | 2024-09-14 | ||
2024-09-14 | 2024-09-14 | ||
2024-06-07 | 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 | 2024-09-08 | ||
2024-09-14 | 2024-09-14 | ||
2024-09-14 | 2021-08-01 | ||
2024-09-14 | 2024-09-14 | ||
2024-04-16 | 2024-05-12 | ||
2024-09-11 | 2024-04-16 |
LAST_COMPILE
LAST_COMPILE |
---|
2024-09-15 |
Last
obsTime | Nobs |
---|---|
2023 | 188 |
Layout
- OECD Website. html
TRANSACT
Code
%>%
SNA_TABLE3 left_join(SNA_TABLE3_var$TRANSACT, by = "TRANSACT") %>%
group_by(TRANSACT, Transact) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
print_table_conditional()
MEASURE
Code
%>%
SNA_TABLE3 left_join(SNA_TABLE3_var$MEASURE, by = "MEASURE") %>%
group_by(MEASURE, Measure) %>%
summarise(Nobs = n()) %>%
print_table_conditional()
MEASURE | Measure | Nobs |
---|---|---|
FTE | Full-time equivalents | 5544 |
HRS | Hours | 47402 |
JOB | Jobs | 13379 |
PER | Persons | 62019 |
LOCATION
Code
%>%
SNA_TABLE3 left_join(SNA_TABLE3_var$LOCATION, by = "LOCATION") %>%
group_by(LOCATION, Location) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Location))),
Flag = paste0('<img src="../../icon/flag/vsmall/', Flag, '.png" alt="Flag">')) %>%
select(Flag, everything()) %>%
if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .} {
PER - Persons (Thousands)
France, Germany, United States
Code
%>%
SNA_TABLE3 filter(obsTime == "2018",
%in% c("FRA", "DEU", "USA"),
LOCATION == "PER") %>%
MEASURE left_join(SNA_TABLE3_var$TRANSACT, by = "TRANSACT") %>%
select(LOCATION, TRANSACT, Transact, obsValue) %>%
mutate(obsValue = round(obsValue)) %>%
spread(LOCATION, obsValue) %>%
if (is_html_output()) datatable(., filter = 'top', rownames = F) else .} {
Spain, Italy, United Kingdom
Code
%>%
SNA_TABLE3 filter(obsTime == "2018",
%in% c("ITA", "ESP", "GBR"),
LOCATION == "PER") %>%
MEASURE left_join(SNA_TABLE3_var$TRANSACT, by = "TRANSACT") %>%
select(LOCATION, TRANSACT, Transact, obsValue) %>%
mutate(obsValue = round(obsValue)) %>%
spread(LOCATION, obsValue) %>%
if (is_html_output()) datatable(., filter = 'top', rownames = F) else .} {
Population
France, Germany, United States, United Kingdom, Europe
1990-
Code
%>%
SNA_TABLE3 filter(TRANSACT %in% c("POPNC"),
== "PER",
MEASURE %in% c("FRA", "DEU", "GBR", "USA", "EA19")) %>%
LOCATION %>%
year_to_date left_join(SNA_TABLE3_var$LOCATION, by = "LOCATION") %>%
mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) %>%
select(Location, date, TRANSACT, obsValue) %>%
left_join(colors, by = c("Location" = "country")) %>%
group_by(Location) %>%
filter(date >= as.Date("1990-01-01")) %>%
mutate(obsValue = 100*obsValue/obsValue[1]) %>%
ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
scale_color_identity() + theme_minimal() + add_5flags +
scale_x_date(breaks = seq(1920, 2100, 10) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.15, 0.9),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(100, 300, 10)) +
ylab("Employment") + xlab("")
1995-
Code
%>%
SNA_TABLE3 filter(TRANSACT %in% c("POPNC"),
== "PER",
MEASURE %in% c("FRA", "DEU", "GBR", "USA", "EA19")) %>%
LOCATION %>%
year_to_date left_join(SNA_TABLE3_var$LOCATION, by = "LOCATION") %>%
mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) %>%
select(Location, date, TRANSACT, obsValue) %>%
left_join(colors, by = c("Location" = "country")) %>%
group_by(Location) %>%
filter(date >= as.Date("1995-01-01")) %>%
mutate(obsValue = 100*obsValue/obsValue[1]) %>%
ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
scale_color_identity() + theme_minimal() + add_5flags +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.15, 0.9),
legend.title = element_blank()) +
scale_y_log10(breaks = seq(100, 300, 5)) +
ylab("Employment") + xlab("")
2008-
Code
%>%
SNA_TABLE3 filter(TRANSACT %in% c("POPNC"),
== "PER",
MEASURE %in% c("FRA", "DEU", "GBR", "USA", "EA19")) %>%
LOCATION %>%
year_to_date left_join(SNA_TABLE3_var$LOCATION, by = "LOCATION") %>%
mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) %>%
select(Location, date, TRANSACT, obsValue) %>%
left_join(colors, by = c("Location" = "country")) %>%
group_by(Location) %>%
filter(date >= as.Date("2008-01-01")) %>%
mutate(obsValue = 100*obsValue/obsValue[1]) %>%
ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
scale_color_identity() + theme_minimal() + add_5flags +
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_log10(breaks = seq(100, 300, 1)) +
ylab("Employment") + xlab("")
Employment / Population
Number of Persons: PER
All
Code
%>%
SNA_TABLE3 filter(TRANSACT %in% c("EEM", "POPNC"),
== "PER",
MEASURE %in% c("FRA", "DEU", "ITA")) %>%
LOCATION %>%
year_to_date left_join(SNA_TABLE3_var$LOCATION, by = "LOCATION") %>%
select(Location, date, TRANSACT, obsValue) %>%
spread(TRANSACT, obsValue) %>%
group_by(Location) %>%
mutate(POPNC_trend = log(POPNC) %>% hpfilter(1000000) %>% pluck("trend") %>% exp,
obsValue = EEM / POPNC_trend) %>%
left_join(colors, by = c("Location" = "country")) %>%
%>%
na.omit ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
scale_color_identity() + theme_minimal() + add_3flags +
scale_x_date(breaks = seq(1920, 2100, 10) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.15, 0.9),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(-10, 100, 5),
labels = scales::percent_format(accuracy = 1)) +
ylab("Employment / Population") + xlab("")
1995-
Code
%>%
SNA_TABLE3 filter(TRANSACT %in% c("EEM", "POPNC"),
== "PER",
MEASURE %in% c("FRA", "DEU", "ITA")) %>%
LOCATION %>%
year_to_date filter(date >= as.Date("1995-01-01")) %>%
left_join(SNA_TABLE3_var$LOCATION, by = "LOCATION") %>%
select(Location, date, TRANSACT, obsValue) %>%
spread(TRANSACT, obsValue) %>%
group_by(Location) %>%
mutate(POPNC_trend = log(POPNC) %>% hpfilter(1000000) %>% pluck("trend") %>% exp,
obsValue = EEM / POPNC_trend) %>%
left_join(colors, by = c("Location" = "country")) %>%
%>%
na.omit 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")) +
theme(legend.position = c(0.15, 0.9),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(-10, 100, 5),
labels = scales::percent_format(accuracy = 1)) +
ylab("Employment / Population") + xlab("")
Number of Hours: HRS
All
Code
%>%
SNA_TABLE3 filter((TRANSACT == "EEM" & MEASURE == "HRS") |
== "POPNC" & MEASURE == "PER"),
(TRANSACT %in% c("FRA", "DEU", "ITA")) %>%
LOCATION %>%
year_to_date left_join(SNA_TABLE3_var$LOCATION, by = "LOCATION") %>%
select(Location, date, TRANSACT, obsValue) %>%
spread(TRANSACT, obsValue) %>%
group_by(Location) %>%
mutate(POPNC_trend = log(POPNC) %>% hpfilter(1000000) %>% pluck("trend") %>% exp,
obsValue = EEM / POPNC_trend) %>%
left_join(colors, by = c("Location" = "country")) %>%
%>%
na.omit ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
scale_color_identity() + theme_minimal() + add_3flags +
scale_x_date(breaks = seq(1920, 2100, 10) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.15, 0.9),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(-10, 100, 5),
labels = scales::percent_format(accuracy = 1)) +
ylab("Employment / Population") + xlab("")
1995-
Code
%>%
SNA_TABLE3 filter((TRANSACT == "EEM" & MEASURE == "HRS") |
== "POPNC" & MEASURE == "PER"),
(TRANSACT %in% c("FRA", "DEU", "ITA")) %>%
LOCATION %>%
year_to_date filter(date >= as.Date("1995-01-01")) %>%
left_join(SNA_TABLE3_var$LOCATION, by = "LOCATION") %>%
select(Location, date, TRANSACT, obsValue) %>%
spread(TRANSACT, obsValue) %>%
group_by(Location) %>%
mutate(POPNC_trend = log(POPNC) %>% hpfilter(1000000) %>% pluck("trend") %>% exp,
obsValue = EEM / POPNC_trend) %>%
left_join(colors, by = c("Location" = "country")) %>%
%>%
na.omit 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")) +
theme(legend.position = c(0.15, 0.9),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(-10, 100, 5),
labels = scales::percent_format(accuracy = 1)) +
ylab("Employment / Population") + xlab("")