source | dataset | .html | .RData |
---|---|---|---|
eurostat | namq_10_a10_e | 2024-11-01 | 2024-10-08 |
Employment A*10 industry breakdowns
Data - Eurostat
Info
Data on employment
source | dataset | .html | .RData |
---|---|---|---|
bls | jt | 2024-05-01 | NA |
bls | la | 2024-06-19 | NA |
bls | ln | 2024-06-19 | NA |
eurostat | nama_10_a10_e | 2024-11-05 | 2024-11-05 |
eurostat | nama_10_a64_e | 2024-11-05 | 2024-10-08 |
eurostat | namq_10_a10_e | 2024-11-01 | 2024-10-08 |
eurostat | une_rt_m | 2024-10-24 | 2024-10-24 |
oecd | ALFS_EMP | 2024-04-16 | 2024-05-12 |
oecd | EPL_T | 2024-04-16 | 2023-12-10 |
oecd | LFS_SEXAGE_I_R | 2024-09-15 | 2024-04-15 |
oecd | STLABOUR | 2024-09-15 | 2024-06-30 |
Data on industry
source | dataset | .html | .RData |
---|---|---|---|
ec | INDUSTRY | 2024-09-15 | 2023-10-01 |
eurostat | ei_isin_m | 2024-11-05 | 2024-10-09 |
eurostat | htec_trd_group4 | 2024-11-05 | 2024-10-08 |
eurostat | nama_10_a64 | 2024-11-05 | 2024-10-08 |
eurostat | nama_10_a64_e | 2024-11-05 | 2024-10-08 |
eurostat | namq_10_a10_e | 2024-11-01 | 2024-10-08 |
eurostat | road_eqr_carmot | 2024-11-01 | 2024-10-08 |
eurostat | sts_inpp_m | 2024-06-24 | 2024-10-08 |
eurostat | sts_inppd_m | 2024-10-09 | 2024-10-08 |
eurostat | sts_inpr_m | 2024-10-09 | 2024-10-08 |
eurostat | sts_intvnd_m | 2024-10-31 | 2024-10-31 |
fred | industry | 2024-11-01 | 2024-11-01 |
oecd | ALFS_EMP | 2024-04-16 | 2024-05-12 |
oecd | BERD_MA_SOF | 2024-04-16 | 2023-09-09 |
oecd | GBARD_NABS2007 | 2024-04-16 | 2023-11-22 |
oecd | MEI_REAL | 2024-05-12 | 2024-05-03 |
oecd | MSTI_PUB | 2024-09-15 | 2023-10-04 |
oecd | SNA_TABLE4 | 2024-09-15 | 2024-04-30 |
wdi | NV.IND.EMPL.KD | 2024-01-06 | 2024-09-18 |
wdi | NV.IND.MANF.CD | 2024-09-18 | 2024-09-18 |
wdi | NV.IND.MANF.ZS | 2024-01-06 | 2024-09-18 |
wdi | NV.IND.TOTL.KD | 2024-01-06 | 2024-09-18 |
wdi | NV.IND.TOTL.ZS | 2024-01-06 | 2024-09-18 |
wdi | SL.IND.EMPL.ZS | 2024-01-06 | 2024-09-18 |
wdi | TX.VAL.MRCH.CD.WT | 2024-01-06 | 2024-09-18 |
Last
Code
%>%
namq_10_a10_e group_by(time) %>%
summarise(Nobs = n()) %>%
arrange(desc(time)) %>%
head(2) %>%
print_table_conditional()
time | Nobs |
---|---|
2024Q2 | 25830 |
2024Q1 | 26555 |
na_item
Code
%>%
namq_10_a10_e left_join(na_item, by = "na_item") %>%
group_by(na_item, Na_item) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
if (is_html_output()) print_table(.) else .} {
na_item | Na_item | Nobs |
---|---|---|
EMP_DC | Total employment domestic concept | 1144545 |
SAL_DC | Employees domestic concept | 1134225 |
SELF_DC | Self-employed domestic concept | 1112391 |
unit
namq_10_a10_e
Code
%>%
namq_10_a10_e left_join(unit, by = "unit") %>%
group_by(unit, Unit) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
if (is_html_output()) datatable(., filter = 'top', rownames = F) else .} {
unit
Code
%>%
namq_10_a10_e left_join(unit, by = "unit") %>%
group_by(unit, Unit) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
if (is_html_output()) datatable(., filter = 'top', rownames = F) else .} {
nace_r2
Code
%>%
namq_10_a10_e left_join(nace_r2, by = "nace_r2") %>%
group_by(nace_r2, Nace_r2) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
if (is_html_output()) print_table(.) else .} {
nace_r2 | Nace_r2 | Nobs |
---|---|---|
TOTAL | Total - all NACE activities | 284833 |
A | Agriculture, forestry and fishing | 282901 |
G-I | Wholesale and retail trade, transport, accommodation and food service activities | 282901 |
C | Manufacturing | 282833 |
B-E | Industry (except construction) | 282825 |
F | Construction | 282825 |
M_N | Professional, scientific and technical activities; administrative and support service activities | 282825 |
R-U | Arts, entertainment and recreation; other service activities; activities of household and extra-territorial organizations and bodies | 282807 |
J | Information and communication | 282473 |
O-Q | Public administration, defence, education, human health and social work activities | 282459 |
L | Real estate activities | 282083 |
K | Financial and insurance activities | 279396 |
s_adj
Code
%>%
namq_10_a10_e left_join(s_adj, by = "s_adj") %>%
group_by(s_adj, S_adj) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
if (is_html_output()) print_table(.) else .} {
s_adj | S_adj | Nobs |
---|---|---|
SCA | Seasonally and calendar adjusted data | 1627964 |
NSA | Unadjusted data (i.e. neither seasonally adjusted nor calendar adjusted data) | 1406954 |
SA | Seasonally adjusted data, not calendar adjusted data | 196590 |
CA | Calendar adjusted data, not seasonally adjusted data | 159653 |
geo
Code
%>%
namq_10_a10_e left_join(geo, by = "geo") %>%
group_by(geo, Geo) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
mutate(Flag = gsub(" ", "-", str_to_lower(Geo)),
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 .} {
% of Total Employment
France
Code
load_data("eurostat/nace_r2.RData")
%>%
namq_10_a10_e filter(na_item == "EMP_DC",
%in% c("C", "TOTAL", "L", "F"),
nace_r2 %in% c("FR"),
geo == "SCA",
s_adj == "THS_HW") %>%
unitquarter_to_date() %>%
left_join(nace_r2, by = "nace_r2") %>%
select(nace_r2, Nace_r2, date, values) %>%
group_by(date) %>%
mutate(values = values/ values[nace_r2 == "TOTAL"]) %>%
filter(nace_r2 != "TOTAL") %>%
ggplot(.) + geom_line(aes(x = date, y = values, color = Nace_r2)) +
theme_minimal() + xlab("") + ylab("% of Employment") +
scale_color_manual(values = viridis(4)[1:3]) +
scale_x_date(breaks = seq(1960, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-500, 200, 1),
labels = percent_format(accuracy = 1)) +
theme(legend.position = c(0.75, 0.85),
legend.title = element_blank())
Germany
Code
%>%
namq_10_a10_e filter(na_item == "EMP_DC",
%in% c("C", "TOTAL", "L", "F"),
nace_r2 %in% c("DE"),
geo == "SCA",
s_adj == "THS_HW") %>%
unitquarter_to_date() %>%
left_join(nace_r2, by = "nace_r2") %>%
select(nace_r2, Nace_r2, date, values) %>%
group_by(date) %>%
mutate(values = values/ values[nace_r2 == "TOTAL"]) %>%
filter(nace_r2 != "TOTAL") %>%
ggplot(.) + geom_line(aes(x = date, y = values, color = Nace_r2)) +
theme_minimal() + xlab("") + ylab("% of Employment") +
scale_color_manual(values = viridis(4)[1:3]) +
scale_x_date(breaks = seq(1960, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-500, 200, 1),
labels = percent_format(accuracy = 1)) +
theme(legend.position = c(0.75, 0.85),
legend.title = element_blank())
Italy
Code
%>%
namq_10_a10_e filter(na_item == "EMP_DC",
%in% c("C", "TOTAL", "L", "F"),
nace_r2 %in% c("IT"),
geo == "SCA",
s_adj == "THS_HW") %>%
unitquarter_to_date() %>%
left_join(nace_r2, by = "nace_r2") %>%
select(nace_r2, Nace_r2, date, values) %>%
group_by(date) %>%
mutate(values = values/ values[nace_r2 == "TOTAL"]) %>%
filter(nace_r2 != "TOTAL") %>%
ggplot(.) + geom_line(aes(x = date, y = values, color = Nace_r2)) +
theme_minimal() + xlab("") + ylab("% of Employment") +
scale_color_manual(values = viridis(4)[1:3]) +
scale_x_date(breaks = seq(1960, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-500, 200, 1),
labels = percent_format(accuracy = 1)) +
theme(legend.position = c(0.75, 0.85),
legend.title = element_blank())
Spain
Code
%>%
namq_10_a10_e filter(na_item == "EMP_DC",
%in% c("C", "TOTAL", "L", "F"),
nace_r2 %in% c("ES"),
geo == "SCA",
s_adj == "THS_HW") %>%
unitquarter_to_date() %>%
left_join(nace_r2, by = "nace_r2") %>%
select(nace_r2, Nace_r2, date, values) %>%
group_by(date) %>%
mutate(values = values/ values[nace_r2 == "TOTAL"]) %>%
filter(nace_r2 != "TOTAL") %>%
ggplot(.) + geom_line(aes(x = date, y = values, color = Nace_r2)) +
theme_minimal() + xlab("") + ylab("% of Employment") +
scale_color_manual(values = viridis(4)[1:3]) +
scale_x_date(breaks = seq(1960, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-500, 200, 1),
labels = percent_format(accuracy = 1)) +
theme(legend.position = c(0.75, 0.85),
legend.title = element_blank())
Greece
Code
%>%
namq_10_a10_e filter(na_item == "EMP_DC",
%in% c("C", "TOTAL", "L", "F"),
nace_r2 %in% c("EL"),
geo == "SCA",
s_adj == "THS_HW") %>%
unitquarter_to_date() %>%
left_join(nace_r2, by = "nace_r2") %>%
select(nace_r2, Nace_r2, date, values) %>%
group_by(date) %>%
mutate(values = values/ values[nace_r2 == "TOTAL"]) %>%
filter(nace_r2 != "TOTAL") %>%
ggplot(.) + geom_line(aes(x = date, y = values, color = Nace_r2)) +
theme_minimal() + xlab("") + ylab("% of Employment") +
scale_color_manual(values = viridis(4)[1:3]) +
scale_x_date(breaks = seq(1960, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-500, 200, 1),
labels = percent_format(accuracy = 1)) +
theme(legend.position = c(0.8, 0.9),
legend.title = element_blank())
Netherlands
Code
%>%
namq_10_a10_e filter(na_item == "EMP_DC",
%in% c("C", "TOTAL", "L", "F"),
nace_r2 %in% c("NL"),
geo == "SCA",
s_adj == "THS_HW") %>%
unitquarter_to_date() %>%
left_join(nace_r2, by = "nace_r2") %>%
select(nace_r2, Nace_r2, date, values) %>%
group_by(date) %>%
mutate(values = values/ values[nace_r2 == "TOTAL"]) %>%
filter(nace_r2 != "TOTAL") %>%
ggplot(.) + geom_line(aes(x = date, y = values, color = Nace_r2)) +
theme_minimal() + xlab("") + ylab("% of Employment") +
scale_color_manual(values = viridis(4)[1:3]) +
scale_x_date(breaks = seq(1960, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-500, 200, 1),
labels = percent_format(accuracy = 1)) +
theme(legend.position = c(0.8, 0.9),
legend.title = element_blank())
Denmark
Code
%>%
namq_10_a10_e filter(na_item == "EMP_DC",
%in% c("C", "TOTAL", "L", "F"),
nace_r2 %in% c("DK"),
geo == "SCA",
s_adj == "THS_HW") %>%
unitquarter_to_date() %>%
left_join(nace_r2, by = "nace_r2") %>%
select(nace_r2, Nace_r2, date, values) %>%
group_by(date) %>%
mutate(values = values/ values[nace_r2 == "TOTAL"]) %>%
filter(nace_r2 != "TOTAL") %>%
ggplot(.) + geom_line(aes(x = date, y = values, color = Nace_r2)) +
theme_minimal() + xlab("") + ylab("% of Employment") +
scale_color_manual(values = viridis(4)[1:3]) +
scale_x_date(breaks = seq(1960, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-500, 200, 1),
labels = percent_format(accuracy = 1)) +
theme(legend.position = c(0.8, 0.9),
legend.title = element_blank())
Finland
Code
%>%
namq_10_a10_e filter(na_item == "EMP_DC",
%in% c("C", "TOTAL", "L", "F"),
nace_r2 %in% c("FI"),
geo == "SCA",
s_adj == "THS_HW") %>%
unitquarter_to_date() %>%
left_join(nace_r2, by = "nace_r2") %>%
select(nace_r2, Nace_r2, date, values) %>%
group_by(date) %>%
mutate(values = values/ values[nace_r2 == "TOTAL"]) %>%
filter(nace_r2 != "TOTAL") %>%
ggplot(.) + geom_line(aes(x = date, y = values, color = Nace_r2)) +
theme_minimal() + xlab("") + ylab("% of Employment") +
scale_color_manual(values = viridis(4)[1:3]) +
scale_x_date(breaks = seq(1960, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-500, 200, 1),
labels = percent_format(accuracy = 1)) +
theme(legend.position = c(0.8, 0.9),
legend.title = element_blank())
Poland
Code
%>%
namq_10_a10_e filter(na_item == "EMP_DC",
%in% c("C", "TOTAL", "L", "F"),
nace_r2 %in% c("PL"),
geo == "SCA",
s_adj == "THS_HW") %>%
unitquarter_to_date() %>%
left_join(nace_r2, by = "nace_r2") %>%
select(nace_r2, Nace_r2, date, values) %>%
group_by(date) %>%
mutate(values = values/ values[nace_r2 == "TOTAL"]) %>%
filter(nace_r2 != "TOTAL") %>%
ggplot(.) + geom_line(aes(x = date, y = values, color = Nace_r2)) +
theme_minimal() + xlab("") + ylab("% of Employment") +
scale_color_manual(values = viridis(4)[1:3]) +
scale_x_date(breaks = seq(1960, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-500, 200, 1),
labels = percent_format(accuracy = 1)) +
theme(legend.position = c(0.8, 0.9),
legend.title = element_blank())
Hungary
Code
%>%
namq_10_a10_e filter(na_item == "EMP_DC",
%in% c("C", "TOTAL", "L", "F"),
nace_r2 %in% c("HU"),
geo == "SCA",
s_adj == "THS_HW") %>%
unitquarter_to_date() %>%
left_join(nace_r2, by = "nace_r2") %>%
select(nace_r2, Nace_r2, date, values) %>%
group_by(date) %>%
mutate(values = values/ values[nace_r2 == "TOTAL"]) %>%
filter(nace_r2 != "TOTAL") %>%
ggplot(.) + geom_line(aes(x = date, y = values, color = Nace_r2)) +
theme_minimal() + xlab("") + ylab("% of Employment") +
scale_color_manual(values = viridis(4)[1:3]) +
scale_x_date(breaks = seq(1960, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-500, 200, 1),
labels = percent_format(accuracy = 1)) +
theme(legend.position = c(0.8, 0.9),
legend.title = element_blank())
Portugal
Code
%>%
namq_10_a10_e filter(na_item == "EMP_DC",
%in% c("C", "TOTAL", "L", "F"),
nace_r2 %in% c("PT"),
geo == "SCA",
s_adj == "THS_HW") %>%
unitquarter_to_date() %>%
left_join(nace_r2, by = "nace_r2") %>%
select(nace_r2, Nace_r2, date, values) %>%
group_by(date) %>%
mutate(values = values/ values[nace_r2 == "TOTAL"]) %>%
filter(nace_r2 != "TOTAL") %>%
ggplot(.) + geom_line(aes(x = date, y = values, color = Nace_r2)) +
theme_minimal() + xlab("") + ylab("% of Employment") +
scale_color_manual(values = viridis(4)[1:3]) +
scale_x_date(breaks = seq(1960, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-500, 200, 1),
labels = percent_format(accuracy = 1)) +
theme(legend.position = c(0.8, 0.9),
legend.title = element_blank())
Austria
Code
%>%
namq_10_a10_e filter(na_item == "EMP_DC",
%in% c("C", "TOTAL", "L", "F"),
nace_r2 %in% c("AT"),
geo == "SCA",
s_adj == "THS_HW") %>%
unitquarter_to_date() %>%
left_join(nace_r2, by = "nace_r2") %>%
select(nace_r2, Nace_r2, date, values) %>%
group_by(date) %>%
mutate(values = values/ values[nace_r2 == "TOTAL"]) %>%
filter(nace_r2 != "TOTAL") %>%
ggplot(.) + geom_line(aes(x = date, y = values, color = Nace_r2)) +
theme_minimal() + xlab("") + ylab("% of Employment") +
scale_color_manual(values = viridis(4)[1:3]) +
scale_x_date(breaks = seq(1960, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-500, 200, 1),
labels = percent_format(accuracy = 1)) +
theme(legend.position = c(0.8, 0.9),
legend.title = element_blank())
Sweden
Code
%>%
namq_10_a10_e filter(na_item == "EMP_DC",
%in% c("C", "TOTAL", "L", "F"),
nace_r2 %in% c("SE"),
geo == "SCA",
s_adj == "THS_HW") %>%
unitquarter_to_date() %>%
left_join(nace_r2, by = "nace_r2") %>%
select(nace_r2, Nace_r2, date, values) %>%
group_by(date) %>%
mutate(values = values/ values[nace_r2 == "TOTAL"]) %>%
filter(nace_r2 != "TOTAL") %>%
ggplot(.) + geom_line(aes(x = date, y = values, color = Nace_r2)) +
theme_minimal() + xlab("") + ylab("% of Employment") +
scale_color_manual(values = viridis(4)[1:3]) +
scale_x_date(breaks = seq(1960, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-500, 200, 1),
labels = percent_format(accuracy = 1)) +
theme(legend.position = c(0.8, 0.9),
legend.title = element_blank())
United Kingdom
Code
%>%
namq_10_a10_e filter(na_item == "EMP_DC",
%in% c("C", "TOTAL", "L", "F"),
nace_r2 %in% c("UK"),
geo == "SCA",
s_adj == "THS_HW") %>%
unitquarter_to_date() %>%
left_join(nace_r2, by = "nace_r2") %>%
select(nace_r2, Nace_r2, date, values) %>%
group_by(date) %>%
mutate(values = values/ values[nace_r2 == "TOTAL"]) %>%
filter(nace_r2 != "TOTAL") %>%
ggplot(.) + geom_line(aes(x = date, y = values, color = Nace_r2)) +
theme_minimal() + xlab("") + ylab("% of Employment") +
scale_color_manual(values = viridis(4)[1:3]) +
scale_x_date(breaks = seq(1960, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-500, 200, 1),
labels = percent_format(accuracy = 1)) +
theme(legend.position = c(0.8, 0.9),
legend.title = element_blank())
France, Germany, Italy
Number of hours worked
All
Code
%>%
namq_10_a10_e left_join(geo, by = "geo") %>%
filter(geo %in% c("FR", "DE", "IT"),
== "C",
nace_r2 == "SCA",
s_adj == "THS_HW",
unit == "EMP_DC") %>%
na_item quarter_to_date() %>%
arrange(date) %>%
mutate(values = values/1000) %>%
left_join(colors, by = c("Geo" = "country")) %>%
ggplot(.) + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + xlab("") + ylab("Number of Hours Worked, Millions") +
scale_x_date(breaks = seq(1960, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = seq(0, 1000000, 500),
labels = dollar_format(accuracy = 1, prefix = "", suffix = "M")) +
scale_color_identity() + add_3flags +
theme(legend.position = c(0.2, 0.80),
legend.title = element_blank())
2000-
Code
%>%
namq_10_a10_e left_join(geo, by = "geo") %>%
filter(geo %in% c("FR", "DE", "IT"),
== "C",
nace_r2 == "SCA",
s_adj == "THS_HW",
unit == "EMP_DC") %>%
na_item quarter_to_date() %>%
filter(date >= as.Date("2000-01-01")) %>%
arrange(date) %>%
mutate(values = values/1000) %>%
left_join(colors, by = c("Geo" = "country")) %>%
ggplot(.) + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + xlab("") + ylab("Number of Hours Worked, Millions") +
scale_x_date(breaks = seq(1960, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = seq(0, 1000000, 500),
labels = dollar_format(accuracy = 1, prefix = "", suffix = "M")) +
scale_color_identity() + add_3flags +
theme(legend.position = c(0.2, 0.80),
legend.title = element_blank())
Number of employees
All
Code
%>%
namq_10_a10_e left_join(geo, by = "geo") %>%
filter(geo %in% c("FR", "DE", "IT"),
== "TOTAL",
nace_r2 == "NSA",
s_adj == "THS_PER",
unit == "EMP_DC") %>%
na_item quarter_to_date() %>%
arrange(date) %>%
left_join(colors, by = c("Geo" = "country")) %>%
ggplot(.) + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + xlab("") + ylab("Number of Employees, '000") +
scale_x_date(breaks = seq(1960, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = seq(0, 1000000, 5000),
labels = dollar_format(accuracy = 1, prefix = "", suffix = "M")) +
scale_color_identity() + add_3flags +
theme(legend.position = c(0.2, 0.80),
legend.title = element_blank())
2000-
Code
%>%
namq_10_a10_e left_join(geo, by = "geo") %>%
filter(geo %in% c("FR", "DE", "IT"),
== "TOTAL",
nace_r2 == "NSA",
s_adj == "THS_PER",
unit == "EMP_DC") %>%
na_item quarter_to_date() %>%
filter(date >= as.Date("2000-01-01")) %>%
arrange(date) %>%
left_join(colors, by = c("Geo" = "country")) %>%
ggplot(.) + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + xlab("") + ylab("Number of Employees, '000") +
scale_x_date(breaks = seq(1960, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = seq(0, 1000000, 5000),
labels = dollar_format(accuracy = 1, prefix = "", suffix = "M")) +
scale_color_identity() + add_3flags +
theme(legend.position = c(0.2, 0.80),
legend.title = element_blank())
2010-
Code
%>%
namq_10_a10_e left_join(geo, by = "geo") %>%
filter(geo %in% c("FR", "DE", "IT"),
== "TOTAL",
nace_r2 == "NSA",
s_adj == "THS_PER",
unit == "EMP_DC") %>%
na_item quarter_to_date() %>%
filter(date >= as.Date("2010-01-01")) %>%
arrange(date) %>%
left_join(colors, by = c("Geo" = "country")) %>%
ggplot(.) + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + xlab("") + ylab("Number of Employees, '000") +
scale_x_date(breaks = seq(1960, 2026, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = seq(0, 1000000, 5000),
labels = dollar_format(accuracy = 1, prefix = "", suffix = "M")) +
scale_color_identity() + add_3flags +
theme(legend.position = c(0.2, 0.80),
legend.title = element_blank())