| source | dataset | Title | .html | .rData |
|---|---|---|---|---|
| eurostat | nama_10_a64 | National accounts aggregates by industry (up to NACE A*64) | 2025-10-10 | 2025-10-09 |
| eurostat | nama_10_a64_e | National accounts employment data by industry (up to NACE A*64) | 2025-10-10 | 2025-10-09 |
National accounts employment data by industry (up to NACE A*64)
Data - Eurostat
Info
Data on employment
| source | dataset | Title | .html | .rData |
|---|---|---|---|---|
| eurostat | nama_10_a64_e | National accounts employment data by industry (up to NACE A*64) | 2025-10-10 | 2025-10-09 |
| bls | jt | NA | NA | NA |
| bls | la | NA | NA | NA |
| bls | ln | NA | NA | NA |
| eurostat | nama_10_a10_e | Employment by A*10 industry breakdowns | 2025-10-10 | 2025-10-09 |
| eurostat | namq_10_a10_e | Employment A*10 industry breakdowns | 2025-05-24 | 2025-10-10 |
| eurostat | une_rt_m | Unemployment by sex and age – monthly data | 2025-10-10 | 2025-10-10 |
| oecd | ALFS_EMP | Employment by activities and status (ALFS) | 2024-04-16 | 2025-05-24 |
| oecd | EPL_T | Strictness of employment protection – temporary contracts | 2025-09-29 | 2023-12-10 |
| oecd | LFS_SEXAGE_I_R | LFS by sex and age - indicators | 2025-09-29 | 2024-04-15 |
| oecd | STLABOUR | Short-Term Labour Market Statistics | 2025-09-29 | 2025-01-17 |
Data on macro
| source | dataset | Title | .html | .rData |
|---|---|---|---|---|
| eurostat | nama_10_a10 | Gross value added and income by A*10 industry breakdowns | 2025-10-10 | 2025-10-10 |
| eurostat | nama_10_a10_e | Employment by A*10 industry breakdowns | 2025-10-10 | 2025-10-09 |
| eurostat | nama_10_gdp | GDP and main components (output, expenditure and income) | 2025-10-10 | 2025-10-09 |
| eurostat | nama_10_lp_ulc | Labour productivity and unit labour costs | 2025-10-10 | 2025-10-10 |
| eurostat | namq_10_a10 | Gross value added and income A*10 industry breakdowns | 2025-10-10 | 2025-10-09 |
| eurostat | namq_10_a10_e | Employment A*10 industry breakdowns | 2025-05-24 | 2025-10-10 |
| eurostat | namq_10_gdp | GDP and main components (output, expenditure and income) | 2025-10-10 | 2025-10-10 |
| eurostat | namq_10_lp_ulc | Labour productivity and unit labour costs | 2025-10-10 | 2025-09-26 |
| eurostat | namq_10_pc | Main GDP aggregates per capita | 2025-10-10 | 2025-10-09 |
| eurostat | nasa_10_nf_tr | Non-financial transactions | 2025-10-10 | 2025-10-10 |
| eurostat | nasq_10_nf_tr | Non-financial transactions | 2025-10-10 | 2025-09-26 |
| fred | gdp | Gross Domestic Product | 2025-10-09 | 2025-10-09 |
| oecd | QNA | Quarterly National Accounts | 2024-06-06 | 2025-05-24 |
| oecd | SNA_TABLE1 | Gross domestic product (GDP) | 2025-09-29 | 2025-05-24 |
| oecd | SNA_TABLE14A | Non-financial accounts by sectors | 2025-09-29 | 2024-06-30 |
| oecd | SNA_TABLE2 | Disposable income and net lending - net borrowing | 2024-07-01 | 2024-04-11 |
| oecd | SNA_TABLE6A | Value added and its components by activity, ISIC rev4 | 2024-07-01 | 2024-06-30 |
| wdi | NE.RSB.GNFS.ZS | External balance on goods and services (% of GDP) | 2025-10-10 | 2025-09-27 |
| wdi | NY.GDP.MKTP.CD | GDP (current USD) | 2025-10-10 | 2025-09-27 |
| wdi | NY.GDP.MKTP.PP.CD | GDP, PPP (current international D) | 2025-10-10 | 2025-09-27 |
| wdi | NY.GDP.PCAP.CD | GDP per capita (current USD) | 2025-10-10 | 2025-09-27 |
| wdi | NY.GDP.PCAP.KD | GDP per capita (constant 2015 USD) | 2025-10-10 | 2025-09-27 |
| wdi | NY.GDP.PCAP.PP.CD | GDP per capita, PPP (current international D) | 2025-10-10 | 2025-09-27 |
| wdi | NY.GDP.PCAP.PP.KD | GDP per capita, PPP (constant 2011 international D) | 2025-10-10 | 2025-09-27 |
LAST_COMPILE
| LAST_COMPILE |
|---|
| 2025-10-11 |
Last
Code
nama_10_a64_e %>%
group_by(time) %>%
summarise(Nobs = n()) %>%
arrange(desc(time)) %>%
head(1) %>%
print_table_conditional()| time | Nobs |
|---|---|
| 2024 | 21754 |
unit
Code
nama_10_a64_e %>%
left_join(unit, by = "unit") %>%
group_by(unit, Unit) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
print_table_conditional()| unit | Unit | Nobs |
|---|---|---|
| THS_PER | Thousand persons | 328395 |
| PCH_PRE_PER | Percentage change on previous period (based on persons) | 298877 |
| THS_HW | Thousand hours worked | 273223 |
| PCH_PRE_HW | Percentage change on previous period (based on hours worked) | 250352 |
| THS_JOB | Thousand jobs | 68290 |
| PCH_PRE_JOB | Percentage change on previous period (based on jobs) | 60175 |
nace_r2
Code
nama_10_a64_e %>%
left_join(nace_r2, by = "nace_r2") %>%
group_by(nace_r2, Nace_r2) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
print_table_conditional()na_item
Code
nama_10_a64_e %>%
left_join(na_item, by = "na_item") %>%
group_by(na_item, Na_item) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
print_table_conditional()| na_item | Na_item | Nobs |
|---|---|---|
| EMP_DC | Total employment domestic concept | 444274 |
| SAL_DC | Employees domestic concept | 429670 |
| SELF_DC | Self-employed domestic concept | 405368 |
time
Code
nama_10_a64_e %>%
group_by(time) %>%
summarise(Nobs = n()) %>%
arrange(desc(time)) %>%
print_table_conditionalgeo
Code
nama_10_a64_e %>%
left_join(geo, by = "geo") %>%
group_by(geo, Geo) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
mutate(Flag = gsub(" ", "-", str_to_lower(Geo)),
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 .}Number of employees
Code
nama_10_a64_e %>%
left_join(geo, by = "geo") %>%
filter(geo %in% c("FR", "DE", "IT"),
nace_r2 == "C",
unit == "THS_HW",
na_item == "EMP_DC") %>%
year_to_date() %>%
arrange(date) %>%
ggplot(.) + geom_line(aes(x = date, y = values/1000, color = Geo)) +
theme_minimal() + xlab("") + ylab("Number of Hours Worked") +
geom_image(data = . %>%
filter(date == as.Date("2010-01-01")) %>%
mutate(image = paste0("../../icon/flag/round/", str_to_lower(Geo), ".png")),
aes(x = date, y = values/1000, image = image), asp = 1.5) +
scale_x_date(breaks = seq(1960, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = seq(0, 1000000, 1000),
labels = dollar_format(accuracy = 1, prefix = "", suffix = "M")) +
scale_color_manual(values = c("#0055a4", "#000000", "#008c45")) +
theme(legend.position = c(0.2, 0.80),
legend.title = element_blank())
Number of hours worked
Code
nama_10_a64_e %>%
left_join(geo, by = "geo") %>%
filter(geo %in% c("FR", "DE", "IT"),
nace_r2 == "TOTAL",
unit == "THS_PER",
na_item == "EMP_DC") %>%
year_to_date() %>%
arrange(date) %>%
mutate(values = values/1000) %>%
ggplot(.) + geom_line(aes(x = date, y = values, color = Geo)) +
theme_minimal() + xlab("") + ylab("Number of Hours Worked") +
add_3flags +
scale_x_date(breaks = seq(1960, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_color_manual(values = c("#0055a4", "#000000", "#008c45")) +
theme(legend.position = c(0.2, 0.80),
legend.title = element_blank())
France VS Europe
Manufacture, Industrie
% de l’emploi
Code
nama_10_a64_e %>%
filter(na_item == "EMP_DC",
nace_r2 %in% c("C", "TOTAL", "B-E"),
geo %in% c("FR", "EA19"),
unit== "THS_PER") %>%
year_to_date() %>%
left_join(geo, by = "geo") %>%
mutate(Geo = ifelse(geo == "EA19", "Europe", Geo)) %>%
left_join(nace_r2, by = "nace_r2") %>%
group_by(date) %>%
mutate(values = values/ values[nace_r2 == "TOTAL"]) %>%
filter(nace_r2 != "TOTAL",
date >= as.Date("1995-01-01")) %>%
mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
left_join(colors, by = c("Geo" = "country")) %>%
ggplot(.) + geom_line(aes(x = date, y = values, color = color, linetype = Nace_r2)) +
scale_color_identity() +
scale_linetype_manual(values = c("solid", "dashed")) +
theme_minimal() +
scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.8, 0.9),
legend.title = element_blank()) +
add_4flags +
scale_y_continuous(breaks = 0.01*seq(-60, 60, 2),
labels = scales::percent_format(accuracy = 1)) +
ylab("Emploi Manufacturier, Industriel (% de l'Emploi)") + xlab("")
THS_PER, Nombre d’emplois
Code
nama_10_a64_e %>%
filter(na_item == "EMP_DC",
nace_r2 %in% c("C", "B-E"),
geo %in% c("FR", "EA19", "DE"),
unit== "THS_PER") %>%
year_to_date() %>%
left_join(geo, by = "geo") %>%
mutate(Geo = ifelse(geo == "EA19", "Europe", Geo)) %>%
left_join(nace_r2, by = "nace_r2") %>%
#filter(date >= as.Date("1995-01-01")) %>%
left_join(colors, by = c("Geo" = "country")) %>%
ggplot(.) + geom_line(aes(x = date, y = values, color = color, linetype = Nace_r2)) +
scale_color_identity() +
scale_linetype_manual(values = c("solid", "dashed")) +
theme_minimal() +
scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.2, 0.9),
legend.title = element_blank()) +
add_6flags +
scale_y_log10(breaks = 100*seq(0, 1000, 10)) +
ylab("Emploi Manufacturier, Industriel (Milliers d'emplois)") + xlab("")
France VS Germany
Table
Code
load_data("eurostat/nace_r2_fr.RData")
nama_10_a64_e %>%
filter(geo %in% c("FR", "DE"),
unit == "THS_PER",
na_item == "EMP_DC",
time %in% c("2019")) %>%
filter(!grepl("C", nace_r2) | nace_r2 == "TOTAL") %>%
select(-na_item, -unit, -time) %>%
left_join(nace_r2, by = "nace_r2") %>%
left_join(geo, by = "geo") %>%
select(nace_r2, Nace_r2, Geo, values) %>%
group_by(Geo) %>%
mutate(values = round(100*values/ values[nace_r2 == "TOTAL"], 1)) %>%
filter(nace_r2 != "TOTAL") %>%
spread(Geo, values) %>%
mutate(difference = `France` - `Germany`) %>%
arrange(-difference) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}Table 1995, 2019
Code
load_data("eurostat/nace_r2_fr.RData")
nama_10_a64_e %>%
filter(geo %in% c("FR", "DE"),
unit == "THS_PER",
na_item == "EMP_DC",
time %in% c("1995", "2019")) %>%
left_join(nace_r2, by = "nace_r2") %>%
left_join(geo, by = "geo") %>%
select(-na_item, -unit) %>%
transmute(nace_r2, Nace_r2, variable = paste(Geo, time), values) %>%
group_by(variable) %>%
mutate(values = round(100*values/ values[nace_r2 == "TOTAL"], 1)) %>%
filter(nace_r2 != "TOTAL") %>%
spread(variable, values) %>%
mutate(difference = `France 2019` - `France 1995` - (`Germany 2019` - `Germany 1995`)) %>%
arrange(-abs(difference)) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}Manufacture, Industrie
% de l’emploi
Code
nama_10_a64_e %>%
filter(na_item == "EMP_DC",
nace_r2 %in% c("C", "TOTAL", "B-E"),
geo %in% c("FR", "DE"),
unit== "THS_PER") %>%
year_to_date() %>%
left_join(geo, by = "geo") %>%
left_join(nace_r2, by = "nace_r2") %>%
group_by(date) %>%
mutate(values = values/ values[nace_r2 == "TOTAL"]) %>%
filter(nace_r2 != "TOTAL",
date >= as.Date("1995-01-01")) %>%
mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
left_join(colors, by = c("Geo" = "country")) %>%
ggplot(.) + geom_line(aes(x = date, y = values, color = color, linetype = Nace_r2)) +
scale_color_identity() +
scale_linetype_manual(values = c("solid", "dashed")) +
theme_minimal() +
scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.8, 0.9),
legend.title = element_blank()) +
add_4flags +
scale_y_continuous(breaks = 0.01*seq(-60, 60, 2),
labels = scales::percent_format(accuracy = 1)) +
ylab("Emploi Manufacturier, Industriel (% de l'Emploi)") + xlab("")
% des heures (THS_HW)
Code
nama_10_a64_e %>%
filter(na_item == "EMP_DC",
nace_r2 %in% c("C", "TOTAL", "B-E"),
geo %in% c("FR", "DE"),
unit== "THS_HW") %>%
year_to_date() %>%
left_join(geo, by = "geo") %>%
left_join(nace_r2, by = "nace_r2") %>%
group_by(date) %>%
mutate(values = values/ values[nace_r2 == "TOTAL"]) %>%
filter(nace_r2 != "TOTAL",
date >= as.Date("1995-01-01")) %>%
mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
left_join(colors, by = c("Geo" = "country")) %>%
ggplot(.) + geom_line(aes(x = date, y = values, color = color, linetype = Nace_r2)) +
scale_color_identity() +
scale_linetype_manual(values = c("solid", "dashed")) +
theme_minimal() +
scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.8, 0.9),
legend.title = element_blank()) +
add_4flags +
scale_y_continuous(breaks = 0.01*seq(-60, 60, 2),
labels = scales::percent_format(accuracy = 1)) +
ylab("Emploi Manufacturier, Industriel (% des heures)") + xlab("")
THS_PER, Nombre d’emplois
Code
nama_10_a64_e %>%
filter(na_item == "EMP_DC",
nace_r2 %in% c("C", "B-E"),
geo %in% c("FR", "DE"),
unit== "THS_PER") %>%
year_to_date() %>%
left_join(geo, by = "geo") %>%
left_join(nace_r2, by = "nace_r2") %>%
#filter(date >= as.Date("1995-01-01")) %>%
mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
left_join(colors, by = c("Geo" = "country")) %>%
ggplot(.) + geom_line(aes(x = date, y = values, color = color, linetype = Nace_r2)) +
scale_color_identity() +
scale_linetype_manual(values = c("solid", "dashed")) +
theme_minimal() +
scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.2, 0.9),
legend.title = element_blank()) +
add_4flags +
scale_y_continuous(breaks = 100*seq(0, 1000, 10)) +
ylab("Emploi Manufacturier, Industriel (Milliers d'emplois)") + xlab("")
Manufacture, Administration
Code
nama_10_a64_e %>%
filter(na_item == "EMP_DC",
nace_r2 %in% c("O-Q", "TOTAL", "B-E"),
geo %in% c("FR", "DE"),
unit== "THS_HW") %>%
year_to_date() %>%
left_join(geo, by = "geo") %>%
group_by(date) %>%
mutate(values = values/ values[nace_r2 == "TOTAL"]) %>%
filter(nace_r2 != "TOTAL",
date >= as.Date("1995-01-01")) %>%
mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
ggplot(.) + geom_line(aes(x = date, y = values, color = Geo, linetype = nace_r2)) +
scale_color_manual(values = c("#0055a4", "#000000")) +
scale_linetype_manual(values = c("solid", "dashed")) +
theme_minimal() +
scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = "none") +
add_4flags +
scale_y_continuous(breaks = 0.01*seq(-60, 60, 2),
labels = scales::percent_format(accuracy = 1)) +
ylab("Emploi Manufacturier, Industriel (% de l'Emploi)") + xlab("")
G, C, F
Code
nama_10_a64_e %>%
filter(na_item == "EMP_DC",
nace_r2 %in% c("G", "C", "F","TOTAL"),
geo %in% c("FR", "DE"),
unit== "THS_HW") %>%
year_to_date() %>%
left_join(geo, by = "geo") %>%
left_join(nace_r2, by = "nace_r2") %>%
group_by(date) %>%
mutate(values = values/ values[nace_r2 == "TOTAL"]) %>%
filter(nace_r2 != "TOTAL",
date >= as.Date("1995-01-01")) %>%
ggplot(.) + geom_line(aes(x = date, y = values, color = Geo, linetype = Nace_r2)) +
scale_color_manual(values = c("#0055a4", "#000000")) +
scale_linetype_manual(values = c("solid", "dashed", "longdash")) +
theme_minimal() +
scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.4, 0.5)) +
add_6flags +
scale_y_continuous(breaks = 0.01*seq(-60, 60, 2),
labels = scales::percent_format(accuracy = 1)) +
ylab("") + xlab("")
P, M, K
Code
nama_10_a64_e %>%
filter(na_item == "EMP_DC",
nace_r2 %in% c("P", "M", "K","TOTAL"),
geo %in% c("FR", "DE"),
unit== "THS_HW") %>%
year_to_date() %>%
left_join(geo, by = "geo") %>%
left_join(nace_r2, by = "nace_r2") %>%
group_by(date) %>%
mutate(values = values/ values[nace_r2 == "TOTAL"]) %>%
filter(nace_r2 != "TOTAL",
date >= as.Date("1995-01-01")) %>%
ggplot(.) + geom_line(aes(x = date, y = values, color = Geo, linetype = Nace_r2)) +
scale_color_manual(values = c("#0055a4", "#000000")) +
scale_linetype_manual(values = c("solid", "dashed", "longdash")) +
theme_minimal() +
scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.3, 0.7)) +
add_6flags +
scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
labels = scales::percent_format(accuracy = 1)) +
ylab("Emploi Manufacturier, Industriel (% de l'Emploi)") + xlab("")
Germany, Italy, France, Spain
Table - Flags
Code
load_data("eurostat/nace_r2_fr.RData")
nama_10_a64_e %>%
filter(geo %in% c("FR", "DE", "IT", "ES"),
unit == "THS_PER",
na_item == "EMP_DC",
time %in% c("2018")) %>%
filter(!grepl("C", nace_r2) | nace_r2 == "TOTAL") %>%
select(-na_item, -unit, -time) %>%
left_join(nace_r2, by = "nace_r2") %>%
left_join(geo, by = "geo") %>%
mutate(Flag = gsub(" ", "-", str_to_lower(Geo)),
Flag = paste0('<img src="../../bib/flags/vsmall/', Flag, '.png" alt="Flag">')) %>%
select(nace_r2, Nace_r2, Flag, values) %>%
group_by(Flag) %>%
mutate(values = round(100*values/ values[nace_r2 == "TOTAL"], 1)) %>%
filter(nace_r2 != "TOTAL") %>%
spread(Flag, values) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}Table - Manufacturing
Code
load_data("eurostat/nace_r2_fr.RData")
nama_10_a64_e %>%
filter(geo %in% c("FR", "DE", "IT", "ES"),
unit == "THS_PER",
na_item == "EMP_DC",
time %in% c("2018")) %>%
filter(grepl("C", nace_r2) | nace_r2 == "TOTAL") %>%
select(-na_item, -unit, -time) %>%
left_join(nace_r2, by = "nace_r2") %>%
left_join(geo, by = "geo") %>%
mutate(Flag = gsub(" ", "-", str_to_lower(Geo)),
Flag = paste0('<img src="../../bib/flags/vsmall/', Flag, '.png" alt="Flag">')) %>%
select(nace_r2, Nace_r2, Flag, values) %>%
group_by(Flag) %>%
mutate(values = round(100*values/ values[nace_r2 == "TOTAL"], 1)) %>%
filter(nace_r2 != "TOTAL") %>%
spread(Flag, values) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}C - Manufacturing
Value
Code
nama_10_a64_e %>%
filter(unit == "THS_PER",
na_item == "EMP_DC",
nace_r2 %in% c("C", "TOTAL"),
geo %in% c("FR", "DE", "IT", "ES")) %>%
year_to_date() %>%
left_join(geo, by = "geo") %>%
select(geo, Geo, nace_r2, date, values) %>%
spread(nace_r2, values) %>%
mutate(values = `C`/TOTAL) %>%
left_join(colors, by = c("Geo" = "country")) %>%
ggplot(.) + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + xlab("") + ylab("Manufacturing (% of GDP)") +
scale_color_identity() + add_4flags +
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))
Productivity
Normal
Code
nama_10_a64 %>%
filter(na_item == "B1G",
nace_r2 %in% c("C"),
geo %in% c("FR", "DE", "IT", "ES", "NL"),
unit %in% c("CP_MNAC", "CLV10_MEUR")) %>%
select_if(~ n_distinct(.) > 1) %>%
rename(B1G = values) %>%
left_join(nama_10_a64_e %>%
filter(nace_r2 %in% c("C"),
geo %in% c("FR", "DE", "IT", "ES", "NL"),
unit == "THS_PER",
na_item == "EMP_DC") %>%
select_if(~ n_distinct(.) > 1) %>%
rename(EMP_DC = values), by = c("geo", "time")) %>%
mutate(values = B1G/EMP_DC) %>%
year_to_date() %>%
filter(date >= as.Date("1995-01-01")) %>%
left_join(geo, by = "geo") %>%
left_join(unit, by = "unit") %>%
left_join(colors, by = c("Geo" = "country")) %>%
mutate(color = ifelse(geo == "NL", color2, color)) %>%
ggplot(.) + geom_line(aes(x = date, y = values, color = color, linetype = Unit)) +
theme_minimal() + xlab("") + ylab("C - Manufacturing\n Labour Productivity = B1G / EMP_DC") +
scale_color_identity() + add_flags +
scale_x_date(breaks = seq(1960, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.3, 0.8)) +
scale_y_continuous(breaks = seq(0, 2000, 10),
labels = dollar_format(pre = "", su = "k€", acc = 1))
Log
Code
nama_10_a64 %>%
filter(na_item == "B1G",
nace_r2 %in% c("C"),
geo %in% c("FR", "DE", "IT", "ES", "NL"),
unit %in% c("CP_MNAC", "CLV10_MEUR")) %>%
select_if(~ n_distinct(.) > 1) %>%
rename(B1G = values) %>%
left_join(nama_10_a64_e %>%
filter(nace_r2 %in% c("C"),
geo %in% c("FR", "DE", "IT", "ES", "NL"),
unit == "THS_PER",
na_item == "EMP_DC") %>%
select_if(~ n_distinct(.) > 1) %>%
rename(EMP_DC = values), by = c("geo", "time")) %>%
mutate(values = B1G/EMP_DC) %>%
year_to_date() %>%
filter(date >= as.Date("1995-01-01")) %>%
left_join(geo, by = "geo") %>%
left_join(unit, by = "unit") %>%
left_join(colors, by = c("Geo" = "country")) %>%
mutate(color = ifelse(geo == "NL", color2, color)) %>%
ggplot(.) + geom_line(aes(x = date, y = values, color = color, linetype = Unit)) +
theme_minimal() + xlab("") + ylab("C - Manufacturing\n Labour Productivity = B1G / EMP_DC") +
scale_color_identity() + add_flags +
scale_x_date(breaks = seq(1960, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.3, 0.8)) +
scale_y_log10(breaks = seq(0, 2000, 50),
labels = dollar_format(pre = "", su = "k€", acc = 1))
Base 100
Code
nama_10_a64 %>%
filter(na_item == "B1G",
nace_r2 %in% c("C"),
geo %in% c("FR", "DE", "IT", "ES", "NL"),
unit %in% c("CP_MNAC", "CLV10_MEUR")) %>%
select_if(~ n_distinct(.) > 1) %>%
rename(B1G = values) %>%
left_join(nama_10_a64_e %>%
filter(nace_r2 %in% c("C"),
geo %in% c("FR", "DE", "IT", "ES", "NL"),
unit == "THS_PER",
na_item == "EMP_DC") %>%
select_if(~ n_distinct(.) > 1) %>%
rename(EMP_DC = values), by = c("geo", "time")) %>%
mutate(values = B1G/EMP_DC) %>%
year_to_date() %>%
filter(date >= as.Date("1995-01-01")) %>%
left_join(geo, by = "geo") %>%
left_join(unit, by = "unit") %>%
left_join(colors, by = c("Geo" = "country")) %>%
mutate(color = ifelse(geo == "NL", color2, color)) %>%
group_by(Geo, Unit) %>%
arrange(date) %>%
mutate(values = 100*values/values[1]) %>%
ggplot(.) + geom_line(aes(x = date, y = values, color = color, linetype = Unit)) +
theme_minimal() + xlab("") + ylab("C - Manufacturing\n Labour Productivity = B1G / EMP_DC") +
scale_color_identity() + add_flags +
scale_x_date(breaks = seq(1960, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.3, 0.8)) +
scale_y_log10(breaks = seq(0, 2000, 20))
C10-C12 - Food products
Value
All
Code
nama_10_a64_e %>%
filter(nace_r2 %in% c("C10-C12", "TOTAL"),
geo %in% c("FR", "DE", "IT", "ES"),
unit == "THS_PER",
na_item == "EMP_DC") %>%
year_to_date() %>%
left_join(geo, by = "geo") %>%
select(geo, Geo, nace_r2, date, values) %>%
spread(nace_r2, values) %>%
mutate(values = `C10-C12`/TOTAL) %>%
left_join(colors, by = c("Geo" = "country")) %>%
ggplot(.) + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + xlab("") + ylab("Food products (% of GDP)") +
scale_color_identity() + add_4flags +
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, 0.1),
labels = percent_format(accuracy = .1))
1995-
Code
nama_10_a64_e %>%
filter(nace_r2 %in% c("C10-C12", "TOTAL"),
geo %in% c("FR", "DE", "IT", "ES"),
unit == "THS_PER",
na_item == "EMP_DC") %>%
year_to_date() %>%
filter(date >= as.Date("1995-01-01")) %>%
left_join(geo, by = "geo") %>%
select(geo, Geo, nace_r2, date, values) %>%
spread(nace_r2, values) %>%
ggplot(.) + geom_line(aes(x = date, y = `C10-C12`/TOTAL, color = Geo)) +
theme_minimal() + xlab("") + ylab("Food products (% of GDP)") +
scale_color_manual(values = c("#002395", "#000000", "#009246", "#C60B1E")) +
scale_x_date(breaks = seq(1960, 2020, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
geom_image(data = . %>%
filter(date == as.Date("2015-01-01")) %>%
mutate(image = paste0("../../icon/flag/round/", str_to_lower(Geo), ".png")),
aes(x = date, y = `C10-C12`/TOTAL, image = image), asp = 1.5) +
theme(legend.position = "none") +
scale_y_continuous(breaks = 0.01*seq(-500, 200, 0.1),
labels = percent_format(accuracy = .1))
Productivity
Normal
Code
nama_10_a64 %>%
filter(na_item == "B1G",
nace_r2 %in% c("C10-C12"),
geo %in% c("FR", "DE", "IT", "ES", "NL"),
unit %in% c("CP_MNAC", "CLV10_MEUR")) %>%
select_if(~ n_distinct(.) > 1) %>%
rename(B1G = values) %>%
left_join(nama_10_a64_e %>%
filter(nace_r2 %in% c("C10-C12"),
geo %in% c("FR", "DE", "IT", "ES", "NL"),
unit == "THS_PER",
na_item == "EMP_DC") %>%
select_if(~ n_distinct(.) > 1) %>%
rename(EMP_DC = values), by = c("geo", "time")) %>%
mutate(values = B1G/EMP_DC) %>%
year_to_date() %>%
filter(date >= as.Date("1995-01-01")) %>%
left_join(geo, by = "geo") %>%
left_join(unit, by = "unit") %>%
left_join(colors, by = c("Geo" = "country")) %>%
mutate(color = ifelse(geo == "NL", color2, color)) %>%
ggplot(.) + geom_line(aes(x = date, y = values, color = color, linetype = Unit)) +
theme_minimal() + xlab("") + ylab("Labour Productivity = B1G / EMP_DC") +
scale_color_identity() + add_flags +
scale_x_date(breaks = seq(1960, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.3, 0.8)) +
scale_y_continuous(breaks = seq(0, 2000, 50),
labels = dollar_format(pre = "", su = "k€", acc = 1))
Log
Code
nama_10_a64 %>%
filter(na_item == "B1G",
nace_r2 %in% c("C10-C12"),
geo %in% c("FR", "DE", "IT", "ES", "NL"),
unit %in% c("CP_MNAC", "CLV10_MEUR")) %>%
select_if(~ n_distinct(.) > 1) %>%
rename(B1G = values) %>%
left_join(nama_10_a64_e %>%
filter(nace_r2 %in% c("C10-C12"),
geo %in% c("FR", "DE", "IT", "ES", "NL"),
unit == "THS_PER",
na_item == "EMP_DC") %>%
select_if(~ n_distinct(.) > 1) %>%
rename(EMP_DC = values), by = c("geo", "time")) %>%
mutate(values = B1G/EMP_DC) %>%
year_to_date() %>%
filter(date >= as.Date("1995-01-01")) %>%
left_join(geo, by = "geo") %>%
left_join(unit, by = "unit") %>%
left_join(colors, by = c("Geo" = "country")) %>%
mutate(color = ifelse(geo == "NL", color2, color)) %>%
ggplot(.) + geom_line(aes(x = date, y = values, color = color, linetype = Unit)) +
theme_minimal() + xlab("") + ylab("Labour Productivity = B1G / EMP_DC") +
scale_color_identity() + add_flags +
scale_x_date(breaks = seq(1960, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.3, 0.8)) +
scale_y_log10(breaks = seq(0, 2000, 50),
labels = dollar_format(pre = "", su = "k€", acc = 1))
Base 100
Code
nama_10_a64 %>%
filter(na_item == "B1G",
nace_r2 %in% c("C10-C12"),
geo %in% c("FR", "DE", "IT", "ES", "NL"),
unit %in% c("CP_MNAC", "CLV10_MEUR")) %>%
select_if(~ n_distinct(.) > 1) %>%
rename(B1G = values) %>%
left_join(nama_10_a64_e %>%
filter(nace_r2 %in% c("C10-C12"),
geo %in% c("FR", "DE", "IT", "ES", "NL"),
unit == "THS_PER",
na_item == "EMP_DC") %>%
select_if(~ n_distinct(.) > 1) %>%
rename(EMP_DC = values), by = c("geo", "time")) %>%
mutate(values = B1G/EMP_DC) %>%
year_to_date() %>%
filter(date >= as.Date("1995-01-01")) %>%
left_join(geo, by = "geo") %>%
left_join(unit, by = "unit") %>%
left_join(colors, by = c("Geo" = "country")) %>%
mutate(color = ifelse(geo == "NL", color2, color)) %>%
group_by(Geo, Unit) %>%
arrange(date) %>%
mutate(values = 100*values/values[1]) %>%
ggplot(.) + geom_line(aes(x = date, y = values, color = color, linetype = Unit)) +
theme_minimal() + xlab("") + ylab("Labour Productivity = B1G / EMP_DC") +
scale_color_identity() + add_flags +
scale_x_date(breaks = seq(1960, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.3, 0.8)) +
scale_y_log10(breaks = seq(0, 2000, 20))
C13-C15 - Textiles
Value
All
Code
nama_10_a64_e %>%
filter(nace_r2 %in% c("C13-C15", "TOTAL"),
geo %in% c("FR", "DE", "IT", "ES"),
unit == "THS_PER",
na_item == "EMP_DC") %>%
year_to_date() %>%
left_join(geo, by = "geo") %>%
select(geo, Geo, nace_r2, date, values) %>%
spread(nace_r2, values) %>%
mutate(values = `C13-C15`/TOTAL) %>%
left_join(colors, by = c("Geo" = "country")) %>%
ggplot(.) + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + xlab("") + ylab("Textiles (% of GDP)") +
scale_color_identity() + add_4flags +
scale_x_date(breaks = seq(1960, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = "none") +
scale_y_continuous(breaks = 0.01*seq(-500, 200, 0.1),
labels = percent_format(accuracy = .1))
1995-
Code
nama_10_a64_e %>%
filter(nace_r2 %in% c("C13-C15", "TOTAL"),
geo %in% c("FR", "DE", "IT", "ES"),
unit == "THS_PER",
na_item == "EMP_DC") %>%
year_to_date() %>%
filter(date >= as.Date("1995-01-01")) %>%
left_join(geo, by = "geo") %>%
select(geo, Geo, nace_r2, date, values) %>%
spread(nace_r2, values) %>%
mutate(values = `C13-C15`/TOTAL) %>%
left_join(colors, by = c("Geo" = "country")) %>%
ggplot(.) + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + xlab("") + ylab("Textiles (% of GDP)") +
scale_color_identity() + add_4flags +
scale_x_date(breaks = seq(1960, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = "none") +
scale_y_continuous(breaks = 0.01*seq(-500, 200, 0.1),
labels = percent_format(accuracy = .1))
C21 - Manufacture of basic pharmaceutical products and pharmaceutical preparations
Value
All
Code
nama_10_a64_e %>%
filter(nace_r2 %in% c("C21", "TOTAL"),
geo %in% c("FR", "DE", "IT", "ES"),
unit == "THS_PER",
na_item == "EMP_DC") %>%
year_to_date() %>%
left_join(geo, by = "geo") %>%
select(geo, Geo, nace_r2, date, values) %>%
spread(nace_r2, values) %>%
mutate(values = `C21`/TOTAL) %>%
left_join(colors, by = c("Geo" = "country")) %>%
ggplot(.) + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + xlab("") + ylab("Manufacture of basic pharmaceutical products (% of GDP)") +
scale_color_identity() + add_4flags +
scale_x_date(breaks = seq(1960, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = "none") +
scale_y_continuous(breaks = 0.01*seq(-500, 200, 0.02),
labels = percent_format(accuracy = .01))
1995-
% of employment
Code
nama_10_a64_e %>%
filter(nace_r2 %in% c("C21", "TOTAL"),
geo %in% c("FR", "DE", "IT", "ES", "NL"),
unit == "THS_PER",
na_item == "EMP_DC") %>%
year_to_date() %>%
filter(date >= as.Date("1995-01-01")) %>%
left_join(geo, by = "geo") %>%
select(geo, Geo, nace_r2, date, values) %>%
spread(nace_r2, values) %>%
mutate(values = `C21`/TOTAL) %>%
left_join(colors, by = c("Geo" = "country")) %>%
ggplot(.) + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + xlab("") + ylab("Manufacture of basic pharmaceutical products\n % of Employment") +
scale_color_identity() + add_5flags +
scale_x_date(breaks = seq(1960, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = "none") +
scale_y_continuous(breaks = 0.01*seq(-500, 200, 0.02),
labels = percent_format(accuracy = .01))
Productivity
Normal
Code
nama_10_a64 %>%
filter(na_item == "B1G",
nace_r2 %in% c("C21"),
geo %in% c("FR", "DE", "IT", "ES", "NL"),
unit %in% c("CP_MNAC", "CLV10_MEUR")) %>%
select_if(~ n_distinct(.) > 1) %>%
rename(B1G = values) %>%
left_join(nama_10_a64_e %>%
filter(nace_r2 %in% c("C21"),
geo %in% c("FR", "DE", "IT", "ES", "NL"),
unit == "THS_PER",
na_item == "EMP_DC") %>%
select_if(~ n_distinct(.) > 1) %>%
rename(EMP_DC = values), by = c("geo", "time")) %>%
mutate(values = B1G/EMP_DC) %>%
year_to_date() %>%
filter(date >= as.Date("1995-01-01")) %>%
left_join(geo, by = "geo") %>%
left_join(unit, by = "unit") %>%
left_join(colors, by = c("Geo" = "country")) %>%
mutate(color = ifelse(geo == "NL", color2, color)) %>%
ggplot(.) + geom_line(aes(x = date, y = values, color = color, linetype = Unit)) +
theme_minimal() + xlab("") + ylab("Manufacture of basic pharmaceutical products\n Labour Productivity = B1G / EMP_DC") +
scale_color_identity() + add_flags +
scale_x_date(breaks = seq(1960, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.3, 0.8)) +
scale_y_continuous(breaks = seq(0, 2000, 50),
labels = dollar_format(pre = "", su = "k€", acc = 1))
Log
Code
nama_10_a64 %>%
filter(na_item == "B1G",
nace_r2 %in% c("C21"),
geo %in% c("FR", "DE", "IT", "ES", "NL"),
unit %in% c("CP_MNAC", "CLV10_MEUR")) %>%
select_if(~ n_distinct(.) > 1) %>%
rename(B1G = values) %>%
left_join(nama_10_a64_e %>%
filter(nace_r2 %in% c("C21"),
geo %in% c("FR", "DE", "IT", "ES", "NL"),
unit == "THS_PER",
na_item == "EMP_DC") %>%
select_if(~ n_distinct(.) > 1) %>%
rename(EMP_DC = values), by = c("geo", "time")) %>%
mutate(values = B1G/EMP_DC) %>%
year_to_date() %>%
filter(date >= as.Date("1995-01-01")) %>%
left_join(geo, by = "geo") %>%
left_join(unit, by = "unit") %>%
left_join(colors, by = c("Geo" = "country")) %>%
mutate(color = ifelse(geo == "NL", color2, color)) %>%
ggplot(.) + geom_line(aes(x = date, y = values, color = color, linetype = Unit)) +
theme_minimal() + xlab("") + ylab("Manufacture of basic pharmaceutical products\n Labour Productivity = B1G / EMP_DC") +
scale_color_identity() + add_flags +
scale_x_date(breaks = seq(1960, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.3, 0.8)) +
scale_y_log10(breaks = seq(0, 2000, 50),
labels = dollar_format(pre = "", su = "k€", acc = 1))
Base 100
Code
nama_10_a64 %>%
filter(na_item == "B1G",
nace_r2 %in% c("C21"),
geo %in% c("FR", "DE", "IT", "ES", "NL"),
unit %in% c("CP_MNAC", "CLV10_MEUR")) %>%
select_if(~ n_distinct(.) > 1) %>%
rename(B1G = values) %>%
left_join(nama_10_a64_e %>%
filter(nace_r2 %in% c("C21"),
geo %in% c("FR", "DE", "IT", "ES", "NL"),
unit == "THS_PER",
na_item == "EMP_DC") %>%
select_if(~ n_distinct(.) > 1) %>%
rename(EMP_DC = values), by = c("geo", "time")) %>%
mutate(values = B1G/EMP_DC) %>%
year_to_date() %>%
filter(date >= as.Date("1995-01-01")) %>%
left_join(geo, by = "geo") %>%
left_join(unit, by = "unit") %>%
left_join(colors, by = c("Geo" = "country")) %>%
mutate(color = ifelse(geo == "NL", color2, color)) %>%
group_by(Geo, Unit) %>%
arrange(date) %>%
mutate(values = 100*values/values[1]) %>%
ggplot(.) + geom_line(aes(x = date, y = values, color = color, linetype = Unit)) +
theme_minimal() + xlab("") + ylab("Manufacture of basic pharmaceutical products\n Labour Productivity = B1G / EMP_DC") +
scale_color_identity() + add_flags +
scale_x_date(breaks = seq(1960, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.3, 0.8)) +
scale_y_log10(breaks = seq(0, 2000, 20))
C16-C18 - Wood, Paper, Printing
All
Code
nama_10_a64_e %>%
filter(nace_r2 %in% c("C16-C18", "TOTAL"),
geo %in% c("FR", "DE", "IT", "ES"),
unit == "THS_PER",
na_item == "EMP_DC") %>%
year_to_date() %>%
left_join(geo, by = "geo") %>%
select(geo, Geo, nace_r2, date, values) %>%
spread(nace_r2, values) %>%
ggplot(.) + geom_line(aes(x = date, y = `C16-C18`/TOTAL, color = Geo)) +
theme_minimal() + xlab("") + ylab("Wood, Paper, Printing (% of GDP)") +
scale_color_manual(values = c("#002395", "#000000", "#009246", "#C60B1E")) +
scale_x_date(breaks = seq(1960, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
geom_image(data = . %>%
filter(date == as.Date("2017-01-01")) %>%
mutate(image = paste0("../../icon/flag/round/", str_to_lower(Geo), ".png")),
aes(x = date, y = `C16-C18`/TOTAL, image = image), asp = 1.5) +
theme(legend.position = "none") +
scale_y_continuous(breaks = 0.01*seq(-500, 200, 0.1),
labels = percent_format(accuracy = .1))
1995-
Code
nama_10_a64_e %>%
filter(nace_r2 %in% c("C16-C18", "TOTAL"),
geo %in% c("FR", "DE", "IT", "ES"),
unit == "THS_PER",
na_item == "EMP_DC") %>%
year_to_date() %>%
filter(date >= as.Date("1995-01-01")) %>%
left_join(geo, by = "geo") %>%
select(geo, Geo, nace_r2, date, values) %>%
spread(nace_r2, values) %>%
ggplot(.) + geom_line(aes(x = date, y = `C16-C18`/TOTAL, color = Geo)) +
theme_minimal() + xlab("") + ylab("Wood, Paper, Printing (% of GDP)") +
scale_color_manual(values = c("#002395", "#000000", "#009246", "#C60B1E")) +
scale_x_date(breaks = seq(1960, 2020, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
geom_image(data = . %>%
filter(date == as.Date("2017-01-01")) %>%
mutate(image = paste0("../../icon/flag/round/", str_to_lower(Geo), ".png")),
aes(x = date, y = `C16-C18`/TOTAL, image = image), asp = 1.5) +
theme(legend.position = "none") +
scale_y_continuous(breaks = 0.01*seq(-500, 200, 0.1),
labels = percent_format(accuracy = .1))
C29 - Motor vehicles
France, Europe
All
Code
# load_data("eurostat/nace_r2_fr.RData")
nama_10_a64_e %>%
filter(nace_r2 %in% c("C29", "TOTAL"),
geo %in% c("FR", "EA20"),
unit == "THS_PER",
na_item == "EMP_DC") %>%
year_to_date() %>%
#filter(date >= as.Date("1995-01-01")) %>%
left_join(geo, by = "geo") %>%
mutate(Geo = ifelse(geo == "EA20", "Europe", Geo)) %>%
select(geo, Geo, nace_r2, date, values) %>%
spread(nace_r2, values) %>%
mutate(values = `C29`/TOTAL) %>%
filter(!is.na(values)) %>%
left_join(colors, by = c("Geo" = "country")) %>%
ggplot(.) + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + xlab("") + ylab("Industrie automobile (% de l'emploi)") +
scale_color_identity() +
scale_x_date(breaks = seq(1960, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) + add_2flags +
theme(legend.position = "none") +
scale_y_continuous(breaks = 0.01*seq(-500, 200, 0.1),
labels = percent_format(accuracy = .1))
1995-
Code
# load_data("eurostat/nace_r2_fr.RData")
nama_10_a64_e %>%
filter(nace_r2 %in% c("C29", "TOTAL"),
geo %in% c("FR", "EA20"),
unit == "THS_PER",
na_item == "EMP_DC") %>%
year_to_date() %>%
filter(date >= as.Date("1995-01-01")) %>%
left_join(geo, by = "geo") %>%
mutate(Geo = ifelse(geo == "EA20", "Europe", Geo)) %>%
select(geo, Geo, nace_r2, date, values) %>%
spread(nace_r2, values) %>%
mutate(values = `C29`/TOTAL) %>%
filter(!is.na(values)) %>%
left_join(colors, by = c("Geo" = "country")) %>%
ggplot(.) + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + xlab("") + ylab("Industrie automobile (% de l'emploi)") +
scale_color_identity() +
scale_x_date(breaks = seq(1960, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) + add_2flags +
theme(legend.position = "none") +
scale_y_continuous(breaks = 0.01*seq(-500, 200, 0.1),
labels = percent_format(accuracy = .1))
All
Code
nama_10_a64_e %>%
filter(nace_r2 %in% c("C29", "TOTAL"),
geo %in% c("FR", "DE", "IT", "ES"),
unit == "THS_PER",
na_item == "EMP_DC") %>%
year_to_date() %>%
left_join(geo, by = "geo") %>%
select(geo, Geo, nace_r2, date, values) %>%
spread(nace_r2, values) %>%
ggplot(.) + geom_line(aes(x = date, y = `C29`/TOTAL, color = Geo)) +
theme_minimal() + xlab("") + ylab("Motor vehicles (% of GDP)") +
scale_color_manual(values = c("#002395", "#000000", "#009246", "#C60B1E")) +
scale_x_date(breaks = seq(1960, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
geom_image(data = . %>%
filter(date == as.Date("2017-01-01")) %>%
mutate(image = paste0("../../icon/flag/round/", str_to_lower(Geo), ".png")),
aes(x = date, y = `C29`/TOTAL, image = image), asp = 1.5) +
theme(legend.position = "none") +
scale_y_continuous(breaks = 0.01*seq(-500, 200, 0.5),
labels = percent_format(accuracy = .1))
1995-
Code
nama_10_a64_e %>%
filter(nace_r2 %in% c("C29", "TOTAL"),
geo %in% c("FR", "DE", "IT", "ES"),
unit == "THS_PER",
na_item == "EMP_DC") %>%
year_to_date() %>%
filter(date >= as.Date("1995-01-01")) %>%
left_join(geo, by = "geo") %>%
select(geo, Geo, nace_r2, date, values) %>%
spread(nace_r2, values) %>%
ggplot(.) + geom_line(aes(x = date, y = `C29`/TOTAL, color = Geo)) +
theme_minimal() + xlab("") + ylab("Motor vehicles (% of GDP)") +
scale_color_manual(values = c("#002395", "#000000", "#009246", "#C60B1E")) +
scale_x_date(breaks = seq(1960, 2020, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
geom_image(data = . %>%
filter(date == as.Date("2017-01-01")) %>%
mutate(image = paste0("../../icon/flag/round/", str_to_lower(Geo), ".png")),
aes(x = date, y = `C29`/TOTAL, image = image), asp = 1.5) +
theme(legend.position = "none") +
scale_y_continuous(breaks = 0.01*seq(-500, 200, 0.5),
labels = percent_format(accuracy = .1))
C29_C30 - Motor vehicles and other transport equipment
All
Code
nama_10_a64_e %>%
filter(nace_r2 %in% c("C29_C30", "TOTAL"),
geo %in% c("FR", "DE", "IT", "ES"),
unit == "THS_PER",
na_item == "EMP_DC") %>%
year_to_date() %>%
left_join(geo, by = "geo") %>%
select(geo, Geo, nace_r2, date, values) %>%
spread(nace_r2, values) %>%
mutate(values = `C29_C30`/TOTAL) %>%
ggplot(.) + geom_line(aes(x = date, y = values, color = Geo)) +
theme_minimal() + xlab("") + ylab("Motor vehicles and other transport equipment") +
scale_color_manual(values = c("#002395", "#000000", "#009246", "#C60B1E")) +
scale_x_date(breaks = seq(1960, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) + add_4flags +
theme(legend.position = "none") +
scale_y_continuous(breaks = 0.01*seq(-500, 200, 0.5),
labels = percent_format(accuracy = .1))
1995-
Code
nama_10_a64_e %>%
filter(nace_r2 %in% c("C29_C30", "TOTAL"),
geo %in% c("FR", "DE", "IT", "ES"),
unit == "THS_PER",
na_item == "EMP_DC") %>%
year_to_date() %>%
filter(date >= as.Date("1995-01-01")) %>%
left_join(geo, by = "geo") %>%
select(geo, Geo, nace_r2, date, values) %>%
spread(nace_r2, values) %>%
ggplot(.) + geom_line(aes(x = date, y = `C29_C30`/TOTAL, color = Geo)) +
theme_minimal() + xlab("") + ylab("Motor vehicles and other transport equipment (% of GDP)") +
scale_color_manual(values = c("#002395", "#000000", "#009246", "#C60B1E")) +
scale_x_date(breaks = seq(1960, 2020, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
geom_image(data = . %>%
filter(date == as.Date("2017-01-01")) %>%
mutate(image = paste0("../../icon/flag/round/", str_to_lower(Geo), ".png")),
aes(x = date, y = `C29_C30`/TOTAL, image = image), asp = 1.5) +
theme(legend.position = "none") +
scale_y_continuous(breaks = 0.01*seq(-500, 200, 0.5),
labels = percent_format(accuracy = .1))
C28 - Machinery and equipment
All
Code
nama_10_a64_e %>%
filter(nace_r2 %in% c("C28", "TOTAL"),
geo %in% c("FR", "DE", "IT", "ES"),
unit == "THS_PER",
na_item == "EMP_DC") %>%
year_to_date() %>%
left_join(geo, by = "geo") %>%
select(geo, Geo, nace_r2, date, values) %>%
spread(nace_r2, values) %>%
mutate(values = `C28`/TOTAL) %>%
ggplot(.) + geom_line(aes(x = date, y = values, color = Geo)) +
theme_minimal() + xlab("") + ylab("Machinery and equipment (% of GDP)") +
scale_color_manual(values = c("#002395", "#000000", "#009246", "#C60B1E")) +
scale_x_date(breaks = seq(1960, 2025, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) + add_4flags +
theme(legend.position = "none") +
scale_y_continuous(breaks = 0.01*seq(-500, 200, 0.1),
labels = percent_format(accuracy = .1))
1995-
Code
nama_10_a64_e %>%
filter(nace_r2 %in% c("C28", "TOTAL"),
geo %in% c("FR", "DE", "IT", "ES"),
unit == "THS_PER",
na_item == "EMP_DC") %>%
year_to_date() %>%
filter(date >= as.Date("1995-01-01")) %>%
left_join(geo, by = "geo") %>%
select(geo, Geo, nace_r2, date, values) %>%
spread(nace_r2, values) %>%
mutate(values = `C28`/TOTAL) %>%
ggplot(.) + geom_line(aes(x = date, y = values, color = Geo)) +
theme_minimal() + xlab("") + ylab("Machinery and equipment (% of GDP)") +
scale_color_manual(values = c("#002395", "#000000", "#009246", "#C60B1E")) +
scale_x_date(breaks = seq(1960, 2020, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) + add_4flags +
theme(legend.position = "none") +
scale_y_continuous(breaks = 0.01*seq(-500, 200, 0.1),
labels = percent_format(accuracy = .1))
L - Real Estate
Value
Code
nama_10_a64_e %>%
filter(nace_r2 %in% c("L", "TOTAL"),
geo %in% c("FR", "DE", "IT", "ES"),
unit == "THS_PER",
na_item == "EMP_DC") %>%
year_to_date() %>%
left_join(geo, by = "geo") %>%
select(geo, Geo, nace_r2, date, values) %>%
spread(nace_r2, values) %>%
mutate(values = `L`/TOTAL) %>%
left_join(colors, by = c("Geo" = "country")) %>%
ggplot(.) + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + xlab("") + ylab("Real Estate (% of GDP)") +
scale_color_identity() + add_4flags +
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))
Productivity
Normal
Code
nama_10_a64 %>%
filter(na_item == "B1G",
nace_r2 %in% c("L"),
geo %in% c("FR", "DE", "IT", "ES", "NL"),
unit %in% c("CP_MNAC", "CLV10_MEUR")) %>%
select_if(~ n_distinct(.) > 1) %>%
rename(B1G = values) %>%
left_join(nama_10_a64_e %>%
filter(nace_r2 %in% c("L"),
geo %in% c("FR", "DE", "IT", "ES", "NL"),
unit == "THS_PER",
na_item == "EMP_DC") %>%
select_if(~ n_distinct(.) > 1) %>%
rename(EMP_DC = values), by = c("geo", "time")) %>%
mutate(values = B1G/EMP_DC) %>%
year_to_date() %>%
filter(date >= as.Date("1995-01-01")) %>%
left_join(geo, by = "geo") %>%
left_join(unit, by = "unit") %>%
left_join(colors, by = c("Geo" = "country")) %>%
mutate(color = ifelse(geo == "NL", color2, color)) %>%
ggplot(.) + geom_line(aes(x = date, y = values, color = color, linetype = Unit)) +
theme_minimal() + xlab("") + ylab("Labour Productivity = B1G / EMP_DC") +
scale_color_identity() + add_flags +
scale_x_date(breaks = seq(1960, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.3, 0.8)) +
scale_y_continuous(breaks = seq(0, 2000, 50),
labels = dollar_format(pre = "", su = "k€", acc = 1))
Log
Code
nama_10_a64 %>%
filter(na_item == "B1G",
nace_r2 %in% c("L"),
geo %in% c("FR", "DE", "IT", "ES", "NL"),
unit %in% c("CP_MNAC", "CLV10_MEUR")) %>%
select_if(~ n_distinct(.) > 1) %>%
rename(B1G = values) %>%
left_join(nama_10_a64_e %>%
filter(nace_r2 %in% c("L"),
geo %in% c("FR", "DE", "IT", "ES", "NL"),
unit == "THS_PER",
na_item == "EMP_DC") %>%
select_if(~ n_distinct(.) > 1) %>%
rename(EMP_DC = values), by = c("geo", "time")) %>%
mutate(values = B1G/EMP_DC) %>%
year_to_date() %>%
filter(date >= as.Date("1995-01-01")) %>%
left_join(geo, by = "geo") %>%
left_join(unit, by = "unit") %>%
left_join(colors, by = c("Geo" = "country")) %>%
mutate(color = ifelse(geo == "NL", color2, color)) %>%
ggplot(.) + geom_line(aes(x = date, y = values, color = color, linetype = Unit)) +
theme_minimal() + xlab("") + ylab("Labour Productivity = B1G / EMP_DC") +
scale_color_identity() + add_flags +
scale_x_date(breaks = seq(1960, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.3, 0.8)) +
scale_y_log10(breaks = seq(0, 2000, 50),
labels = dollar_format(pre = "", su = "k€", acc = 1))
Base 100
Code
nama_10_a64 %>%
filter(na_item == "B1G",
nace_r2 %in% c("L"),
geo %in% c("FR", "DE", "IT", "ES", "NL"),
unit %in% c("CP_MNAC", "CLV10_MEUR")) %>%
select_if(~ n_distinct(.) > 1) %>%
rename(B1G = values) %>%
left_join(nama_10_a64_e %>%
filter(nace_r2 %in% c("L"),
geo %in% c("FR", "DE", "IT", "ES", "NL"),
unit == "THS_PER",
na_item == "EMP_DC") %>%
select_if(~ n_distinct(.) > 1) %>%
rename(EMP_DC = values), by = c("geo", "time")) %>%
mutate(values = B1G/EMP_DC) %>%
year_to_date() %>%
filter(date >= as.Date("1995-01-01")) %>%
left_join(geo, by = "geo") %>%
left_join(unit, by = "unit") %>%
left_join(colors, by = c("Geo" = "country")) %>%
mutate(color = ifelse(geo == "NL", color2, color)) %>%
group_by(Geo, Unit) %>%
arrange(date) %>%
mutate(values = 100*values/values[1]) %>%
ggplot(.) + geom_line(aes(x = date, y = values, color = color, linetype = Unit)) +
theme_minimal() + xlab("") + ylab("Labour Productivity = B1G / EMP_DC") +
scale_color_identity() + add_flags +
scale_x_date(breaks = seq(1960, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.3, 0.8)) +
scale_y_log10(breaks = seq(0, 2000, 20))
Germany, Italy, France, Spain
Table
Code
load_data("eurostat/nace_r2.RData")
nama_10_a64_e %>%
filter(geo %in% c("FR", "DE", "IT", "ES"),
unit == "THS_PER",
na_item == "EMP_DC",
time %in% c("2018")) %>%
filter(!grepl("C", nace_r2) | nace_r2 == "TOTAL") %>%
select(-na_item, -unit, -time) %>%
left_join(nace_r2, by = "nace_r2") %>%
left_join(geo, by = "geo") %>%
mutate(Flag = gsub(" ", "-", str_to_lower(Geo)),
Flag = paste0('<img src="../../bib/flags/vsmall/', Flag, '.png" alt="Flag">')) %>%
select(nace_r2, Nace_r2, Flag, values) %>%
group_by(Flag) %>%
mutate(values = round(100*values/ values[nace_r2 == "TOTAL"], 1)) %>%
filter(nace_r2 != "TOTAL") %>%
spread(Flag, values) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}Table - Manufacturing
Code
load_data("eurostat/nace_r2_fr.RData")
nama_10_a64_e %>%
filter(geo %in% c("FR", "DE", "IT", "ES"),
unit == "THS_PER",
na_item == "EMP_DC",
time %in% c("2018")) %>%
filter(grepl("C", nace_r2) | nace_r2 == "TOTAL") %>%
select(-na_item, -unit, -time) %>%
left_join(nace_r2, by = "nace_r2") %>%
left_join(geo, by = "geo") %>%
mutate(Flag = gsub(" ", "-", str_to_lower(Geo)),
Flag = paste0('<img src="../../bib/flags/vsmall/', Flag, '.png" alt="Flag">')) %>%
select(nace_r2, Nace_r2, Flag, values) %>%
group_by(Flag) %>%
mutate(values = round(100*values/ values[nace_r2 == "TOTAL"], 1)) %>%
filter(nace_r2 != "TOTAL") %>%
spread(Flag, values) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}C - Manufacturing
Persons
Code
nama_10_a64_e %>%
filter(nace_r2 %in% c("C", "TOTAL"),
geo %in% c("FR", "DE", "IT", "ES"),
unit == "THS_PER",
na_item == "EMP_DC") %>%
year_to_date() %>%
left_join(geo, by = "geo") %>%
select(geo, Geo, nace_r2, date, values) %>%
spread(nace_r2, values) %>%
mutate(values = `C`/TOTAL) %>%
left_join(colors, by = c("Geo" = "country")) %>%
ggplot(.) + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + xlab("") + ylab("Manufacturing (% of Employment)") +
scale_color_identity() + add_4flags +
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))
Hours worked
Code
nama_10_a64_e %>%
filter(nace_r2 %in% c("C", "TOTAL"),
geo %in% c("FR", "DE", "IT", "ES"),
unit == "THS_HW",
na_item == "EMP_DC") %>%
year_to_date() %>%
left_join(geo, by = "geo") %>%
select(geo, Geo, nace_r2, date, values) %>%
spread(nace_r2, values) %>%
mutate(values = `C`/TOTAL) %>%
left_join(colors, by = c("Geo" = "country")) %>%
ggplot(.) + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + xlab("") + ylab("Manufacturing (% of Hours worked)") +
scale_color_identity() + add_4flags +
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))
Q - Health
Persons
Code
nama_10_a64_e %>%
filter(nace_r2 %in% c("Q", "TOTAL"),
geo %in% c("FR", "DE", "IT", "ES"),
unit == "THS_PER",
na_item == "EMP_DC") %>%
year_to_date() %>%
left_join(geo, by = "geo") %>%
select(geo, Geo, nace_r2, date, values) %>%
spread(nace_r2, values) %>%
mutate(values = `Q`/TOTAL) %>%
left_join(colors, by = c("Geo" = "country")) %>%
ggplot(.) + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + xlab("") + ylab("Health (% of Employment)") +
scale_color_identity() + add_4flags +
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))
Hours worked
Code
nama_10_a64_e %>%
filter(nace_r2 %in% c("Q", "TOTAL"),
geo %in% c("FR", "DE", "IT", "ES"),
unit == "THS_HW",
na_item == "EMP_DC") %>%
year_to_date() %>%
left_join(geo, by = "geo") %>%
select(geo, Geo, nace_r2, date, values) %>%
spread(nace_r2, values) %>%
mutate(values = `Q`/TOTAL) %>%
left_join(colors, by = c("Geo" = "country")) %>%
ggplot(.) + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + xlab("") + ylab("Health (% of Hours worked)") +
scale_color_identity() + add_4flags +
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))
L - Real estate
Persons
Code
nama_10_a64_e %>%
filter(nace_r2 %in% c("L", "TOTAL"),
geo %in% c("FR", "DE", "IT", "ES"),
unit == "THS_PER",
na_item == "EMP_DC") %>%
year_to_date() %>%
left_join(geo, by = "geo") %>%
select(geo, Geo, nace_r2, date, values) %>%
spread(nace_r2, values) %>%
mutate(values = `L`/TOTAL) %>%
left_join(colors, by = c("Geo" = "country")) %>%
ggplot(.) + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + xlab("") + ylab("Real Estate (% of Employment)") +
scale_color_identity() + add_4flags +
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))
Hours worked
Code
nama_10_a64_e %>%
filter(nace_r2 %in% c("L", "TOTAL"),
geo %in% c("FR", "DE", "IT", "ES"),
unit == "THS_HW",
na_item == "EMP_DC") %>%
year_to_date() %>%
left_join(geo, by = "geo") %>%
select(geo, Geo, nace_r2, date, values) %>%
spread(nace_r2, values) %>%
mutate(values = `L`/TOTAL) %>%
left_join(colors, by = c("Geo" = "country")) %>%
ggplot(.) + geom_line(aes(x = date, y = values, color = color)) +
theme_minimal() + xlab("") + ylab("Real Estate (% of Hours worked)") +
scale_color_identity() + add_4flags +
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))
Individual Countries
France
Table
All
Code
nama_10_a64_e %>%
filter(geo %in% c("FR"),
unit == "THS_HW",
na_item == "EMP_DC",
time %in% c("1978", "1998", "2008", "2018")) %>%
left_join(nace_r2, by = "nace_r2") %>%
select(nace_r2, Nace_r2, time, values) %>%
group_by(time) %>%
mutate(values = round(100*values/ values[nace_r2 == "TOTAL"], 1)) %>%
filter(nace_r2 != "TOTAL") %>%
spread(time, values) %>%
print_table_conditionalManufacturing
Code
nama_10_a64_e %>%
filter(geo %in% c("FR"),
unit == "THS_HW",
na_item == "EMP_DC",
time %in% c("1978", "1998", "2008", "2018")) %>%
filter(grepl("C", nace_r2) | nace_r2 == "TOTAL") %>%
left_join(nace_r2, by = "nace_r2") %>%
select(nace_r2, Nace_r2, time, values) %>%
group_by(time) %>%
mutate(values = round(100*values/ values[nace_r2 == "TOTAL"], 1)) %>%
filter(nace_r2 != "TOTAL") %>%
spread(time, values) %>%
arrange(-`2018`) %>%
print_table_conditional| nace_r2 | Nace_r2 | 1978 | 1998 | 2008 | 2018 |
|---|---|---|---|---|---|
| C | Industrie manufacturière | 22.3 | 15.1 | 11.8 | 9.9 |
| C10-C12 | Industries alimentaires; fabrication de boissons et de produits à base de tabac | 2.8 | 2.7 | 2.3 | 2.3 |
| C31-C33 | Fabrication de meubles, bijouterie, instruments de musique, jouets, réparation et installation de machines et équipements | 3.6 | 2.6 | 2.1 | 1.9 |
| C24_C25 | Métallurgie et fabrication de produits métalliques, à l'exception des machines et des équipements | 3.0 | 1.9 | 1.6 | 1.3 |
| C22_C23 | Fabrication de produits en caoutchouc et en plastique et autres produits minéraux non métalliques | 1.8 | 1.2 | 1.1 | 0.8 |
| C29_C30 | Industrie automobile et construction navale | 2.2 | 1.2 | 1.0 | 0.8 |
| C16-C18 | Travail du bois et du papier, imprimerie et reproduction | 1.5 | 1.2 | 0.9 | 0.6 |
| C28 | Fabrication de machines et équipements n.c.a. | 1.3 | 0.9 | 0.8 | 0.5 |
| C13-C15 | Fabrication de textiles, industrie de l'habillement, du cuir et de la chaussure | 3.4 | 1.4 | 0.6 | 0.4 |
| C20 | Industrie chimique | 0.9 | 0.6 | 0.5 | 0.4 |
| C26 | Fabrication de produits informatiques, électroniques et optiques | 0.7 | 0.6 | 0.4 | 0.3 |
| C27 | Fabrication d'équipements électriques | 0.7 | 0.6 | 0.4 | 0.3 |
| C21 | Industrie pharmaceutique | 0.2 | 0.2 | 0.2 | 0.1 |
| C19 | Cokéfaction et raffinage | 0.1 | 0.0 | 0.0 | 0.0 |
Construction, Human health, Manufacturing, Real estate
All
Code
nama_10_a64_e %>%
filter(nace_r2 %in% c("C", "TOTAL", "L", "Q", "F"),
geo %in% c("FR"),
unit == "THS_HW",
na_item == "EMP_DC") %>%
year_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 GDP") +
scale_color_manual(values = viridis(5)[1:4]) +
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())
1995-
Code
nama_10_a64_e %>%
filter(nace_r2 %in% c("C", "TOTAL", "L", "Q", "F"),
geo %in% c("FR"),
unit == "THS_HW",
na_item == "EMP_DC") %>%
year_to_date() %>%
filter(date >= as.Date("1995-01-01")) %>%
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 GDP") +
scale_color_manual(values = viridis(5)[1:4]) +
scale_x_date(breaks = seq(1960, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(0, 30, 2),
labels = percent_format(accuracy = 1),
limits = c(0, 0.3)) +
theme(legend.position = c(0.75, 0.85),
legend.title = element_blank())
Germany
Table
All
Code
nama_10_a64_e %>%
filter(geo %in% c("DE"),
unit == "THS_HW",
na_item == "EMP_DC",
time %in% c("1978", "1998", "2008", "2018")) %>%
left_join(nace_r2, by = "nace_r2") %>%
select(nace_r2, Nace_r2, time, values) %>%
group_by(time) %>%
mutate(values = round(100*values/ values[nace_r2 == "TOTAL"], 1)) %>%
filter(nace_r2 != "TOTAL") %>%
spread(time, values) %>%
print_table_conditionalManufacturing
Code
nama_10_a64_e %>%
filter(geo %in% c("DE"),
unit == "THS_HW",
na_item == "EMP_DC",
time %in% c("1978", "1998", "2008", "2018")) %>%
filter(grepl("C", nace_r2) | nace_r2 == "TOTAL") %>%
left_join(nace_r2, by = "nace_r2") %>%
select(nace_r2, Nace_r2, time, values) %>%
group_by(time) %>%
mutate(values = round(100*values/ values[nace_r2 == "TOTAL"], 1)) %>%
filter(nace_r2 != "TOTAL") %>%
spread(time, values) %>%
arrange(-`2018`) %>%
print_table_conditional| nace_r2 | Nace_r2 | 1998 | 2008 | 2018 |
|---|---|---|---|---|
| C | Industrie manufacturière | 20.5 | 19.0 | 18.1 |
| C24_C25 | Métallurgie et fabrication de produits métalliques, à l'exception des machines et des équipements | 3.1 | 3.0 | 2.9 |
| C28 | Fabrication de machines et équipements n.c.a. | 2.8 | 2.8 | 2.8 |
| C29_C30 | Industrie automobile et construction navale | 2.5 | 2.4 | 2.4 |
| C10-C12 | Industries alimentaires; fabrication de boissons et de produits à base de tabac | 2.3 | 2.3 | 2.1 |
| C22_C23 | Fabrication de produits en caoutchouc et en plastique et autres produits minéraux non métalliques | 1.9 | 1.7 | 1.7 |
| C31-C33 | Fabrication de meubles, bijouterie, instruments de musique, jouets, réparation et installation de machines et équipements | 1.8 | 1.6 | 1.6 |
| C27 | Fabrication d'équipements électriques | 1.4 | 1.3 | 1.2 |
| C16-C18 | Travail du bois et du papier, imprimerie et reproduction | 1.7 | 1.3 | 1.0 |
| C20 | Industrie chimique | 1.0 | 0.9 | 0.9 |
| C26 | Fabrication de produits informatiques, électroniques et optiques | 0.9 | 0.9 | 0.9 |
| C13-C15 | Fabrication de textiles, industrie de l'habillement, du cuir et de la chaussure | 0.7 | 0.4 | 0.3 |
| C21 | Industrie pharmaceutique | 0.3 | 0.3 | 0.3 |
| C19 | Cokéfaction et raffinage | 0.1 | 0.0 | 0.0 |
Construction, Human health, Manufacturing, Real estate
Code
nama_10_a64_e %>%
filter(nace_r2 %in% c("C", "TOTAL", "L", "Q", "F"),
geo %in% c("DE"),
unit == "THS_HW",
na_item == "EMP_DC") %>%
year_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 GDP") +
scale_color_manual(values = viridis(5)[1:4]) +
scale_x_date(breaks = seq(1960, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(0, 30, 2),
labels = percent_format(accuracy = 1),
limits = c(0, 0.3)) +
theme(legend.position = c(0.75, 0.85),
legend.title = element_blank())