National accounts employment data by industry (up to NACE A*64)

Data - Eurostat

Info

source dataset .html .RData
eurostat nama_10_a64 2024-11-01 2024-10-08
eurostat nama_10_a64_e 2024-11-01 2024-10-08

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 2024-11-01 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 macro

source dataset .html .RData
eurostat nama_10_a10 2024-11-05 2024-10-08
eurostat nama_10_a10_e 2024-11-05 2024-11-05
eurostat nama_10_gdp 2024-11-01 2024-10-08
eurostat nama_10_lp_ulc 2024-11-01 2024-10-08
eurostat namq_10_a10 2024-11-01 2024-11-05
eurostat namq_10_a10_e 2024-11-01 2024-10-08
eurostat namq_10_gdp 2024-11-01 2024-10-08
eurostat namq_10_lp_ulc 2024-11-04 2024-11-04
eurostat namq_10_pc 2024-11-01 2024-10-08
eurostat nasa_10_nf_tr 2024-11-01 2024-10-08
eurostat nasq_10_nf_tr 2024-11-01 2024-10-09
fred gdp 2024-11-01 2024-11-01
oecd QNA 2024-06-06 2024-06-30
oecd SNA_TABLE1 2024-09-15 2024-06-30
oecd SNA_TABLE14A 2024-09-15 2024-06-30
oecd SNA_TABLE2 2024-07-01 2024-04-11
oecd SNA_TABLE6A 2024-07-01 2024-06-30
wdi NE.RSB.GNFS.ZS 2024-09-18 2024-09-18
wdi NY.GDP.MKTP.CD 2024-09-18 2024-09-26
wdi NY.GDP.MKTP.PP.CD 2024-09-18 2024-09-18
wdi NY.GDP.PCAP.CD 2024-10-15 2024-10-15
wdi NY.GDP.PCAP.KD 2024-09-18 2024-09-18
wdi NY.GDP.PCAP.PP.CD 2024-10-15 2024-10-15
wdi NY.GDP.PCAP.PP.KD 2024-09-18 2024-09-18

LAST_COMPILE

LAST_COMPILE
2024-11-05

Last

Code
nama_10_a64_e %>%
  group_by(time) %>%
  summarise(Nobs = n()) %>%
  arrange(desc(time)) %>%
  head(1) %>%
  print_table_conditional()
time Nobs
2023 12713

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 315427
PCH_PRE_PER Percentage change on previous period (based on persons) 273339
THS_HW Thousand hours worked 253513
PCH_PRE_HW Percentage change on previous period (based on hours worked) 222828
THS_JOB Thousand jobs 62247
PCH_PRE_JOB Percentage change on previous period (based on jobs) 52456

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 408869
SAL_DC Employees domestic concept 395435
SELF_DC Self-employed domestic concept 375506

time

Code
nama_10_a64_e %>%
  group_by(time) %>%
  summarise(Nobs = n()) %>%
  arrange(desc(time)) %>%
  print_table_conditional

geo

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(10) +
  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(10) +
  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(10) +
  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(10) +
  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(10) +
  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(10) +
  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(10) +
  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(10) +
  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(10) +
  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(10) +
  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(10) +
  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(10) +
  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_conditional

Manufacturing

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_conditional

Manufacturing

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 2.8
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 1.8 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())