Production in industry - monthly data

Data - Eurostat

Author

François Geerolf

Info

source dataset Title .html .rData
eurostat sts_inpr_m Production in industry - monthly data 2025-10-10 2025-10-11

Data on industry

source dataset Title .html .rData
eurostat sts_inpr_m Production in industry - monthly data 2025-10-10 2025-10-11
ec INDUSTRY Industry (sector data) 2025-08-28 2023-10-01
eurostat ei_isin_m Industry - monthly data - index (2015 = 100) (NACE Rev. 2) - ei_isin_m 2025-10-11 2025-10-11
eurostat htec_trd_group4 High-tech trade by high-tech group of products in million euro (from 2007, SITC Rev. 4) 2025-10-11 2025-10-11
eurostat nama_10_a64 National accounts aggregates by industry (up to NACE A*64) 2025-10-11 2025-10-11
eurostat nama_10_a64_e National accounts employment data by industry (up to NACE A*64) 2025-10-11 2025-10-11
eurostat namq_10_a10_e Employment A*10 industry breakdowns 2025-05-24 2025-10-11
eurostat road_eqr_carmot New registrations of passenger cars by type of motor energy and engine size - road_eqr_carmot 2025-10-10 2025-10-11
eurostat sts_inpp_m Producer prices in industry, total - monthly data 2025-10-10 2025-10-11
eurostat sts_inppd_m Producer prices in industry, domestic market - monthly data 2025-10-10 2025-10-11
eurostat sts_intvnd_m Turnover in industry, non domestic market - monthly data - sts_intvnd_m 2025-10-10 2025-10-11
fred industry Manufacturing, Industry 2025-10-09 2025-10-09
oecd ALFS_EMP Employment by activities and status (ALFS) 2024-04-16 2025-05-24
oecd BERD_MA_SOF Business enterprise R&D expenditure by main activity (focussed) and source of funds 2024-04-16 2023-09-09
oecd GBARD_NABS2007 Government budget allocations for R and D 2024-04-16 2023-11-22
oecd MEI_REAL Production and Sales (MEI) 2024-05-12 2025-05-24
oecd MSTI_PUB Main Science and Technology Indicators 2024-09-15 2025-05-24
oecd SNA_TABLE4 PPPs and exchange rates 2024-09-15 2025-05-24
wdi NV.IND.EMPL.KD Industry, value added per worker (constant 2010 USD) 2024-01-06 2025-09-27
wdi NV.IND.MANF.CD Manufacturing, value added (current USD) 2025-10-10 2025-09-27
wdi NV.IND.MANF.ZS Manufacturing, value added (% of GDP) 2025-05-24 2025-09-27
wdi NV.IND.TOTL.KD Industry (including construction), value added (constant 2015 USD) - NV.IND.TOTL.KD 2024-01-06 2025-09-27
wdi NV.IND.TOTL.ZS Industry, value added (including construction) (% of GDP) 2025-05-24 2025-09-27
wdi SL.IND.EMPL.ZS Employment in industry (% of total employment) 2025-10-10 2025-09-27
wdi TX.VAL.MRCH.CD.WT Merchandise exports (current USD) 2024-01-06 2025-09-27

Last

Code 
sts_inpr_m %>%
  group_by(time) %>%
  summarise(Nobs = n()) %>%
  arrange(desc(time)) %>%
  head(2) %>%
  print_table_conditional()
time Nobs
2025M08 17187
2025M07 20354

nace_r2

Code 
sts_inpr_m %>%
  left_join(nace_r2, by = "nace_r2") %>%
  group_by(nace_r2, Nace_r2) %>%
  summarise(Nobs = n()) %>%
  print_table_conditional()

s_adj

Code 
sts_inpr_m %>%
  left_join(s_adj, by = "s_adj") %>%
  group_by(s_adj, S_adj) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()
s_adj S_adj Nobs
CA Calendar adjusted data, not seasonally adjusted data 5750495
SCA Seasonally and calendar adjusted data 5613299
NSA Unadjusted data (i.e. neither seasonally adjusted nor calendar adjusted data) 3137286

unit

Code 
sts_inpr_m %>%
  left_join(unit, by = "unit") %>%
  group_by(unit, Unit) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()
unit Unit Nobs
I15 Index, 2015=100 4316426
I21 Index, 2021=100 3915022
I10 Index, 2010=100 2920855
PCH_PRE Percentage change on previous period 1684834
PCH_SM Percentage change compared to same period in previous year 1663943

geo

Code 
sts_inpr_m %>%
  left_join(geo, by = "geo") %>%
  group_by(geo, Geo) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(Geo)),
         Flag = paste0('<img src="../../bib/flags/vsmall/', Flag, '.png" alt="Flag">')) %>%
  select(Flag, everything()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

time

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

Last

Code 
sts_inpr_m %>%
  filter(time == max(time), !is.na(values)) %>%
  print_table_conditional()

France VS EU

EU 2027

All

Code 
sts_inpr_m %>%
  filter(nace_r2 == "C",
         unit == "I21",
         geo %in% c("FR", "EU27_2020"),
         s_adj == "SCA") %>%
  select(geo, time, values) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
  mutate(Geo = ifelse(geo == "EU27_2020", "Europe", Geo)) %>%
  month_to_date %>%
  group_by(geo) %>%
  arrange(date) %>%
  mutate(values = 100*values/values[1]) %>%
  #filter(date >= as.Date("2000-01-01")) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot() + ylab("Industrial Production") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() +
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  add_2flags +
  scale_y_log10(breaks = seq(-60, 300, 10))

1992-

Code 
sts_inpr_m %>%
  filter(nace_r2 == "C",
         unit == "I21",
         geo %in% c("FR", "EU27_2020"),
         s_adj == "SCA") %>%
  select(geo, time, values) %>%
  group_by(geo) %>%
  mutate(values = 100*values/values[time == "1992M01"]) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
  mutate(Geo = ifelse(geo == "EU27_2020", "Europe", Geo)) %>%
  month_to_date %>%
  filter(date >= as.Date("1992-01-01")) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot() + ylab("Industrial Production") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() +
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  add_2flags +
  scale_y_log10(breaks = seq(-60, 300, 10))

2000-

Code 
sts_inpr_m %>%
  filter(nace_r2 == "C",
         unit == "I21",
         geo %in% c("FR", "EU27_2020"),
         s_adj == "SCA") %>%
  select(geo, time, values) %>%
  group_by(geo) %>%
  mutate(values = 100*values/values[time == "2000M01"]) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
  mutate(Geo = ifelse(geo == "EU27_2020", "Europe", Geo)) %>%
  month_to_date %>%
  filter(date >= as.Date("2000-01-01")) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot() + ylab("Industrial Production") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() +
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  add_2flags +
  scale_y_log10(breaks = seq(-60, 300, 10))

Eurozone

2000-

Code 
sts_inpr_m %>%
  filter(nace_r2 == "C",
         unit == "I21",
         geo %in% c("FR", "EA20"),
         s_adj == "SCA") %>%
  select(geo, time, values) %>%
  group_by(geo) %>%
  mutate(values = 100*values/values[time == "2020M02"]) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
  mutate(Geo = ifelse(geo == "EA20", "Europe", Geo)) %>%
  month_to_date %>%
  filter(date >= as.Date("2000-01-01")) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot() + ylab("Industrial Production") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() +
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  add_2flags +
  scale_y_log10(breaks = seq(-60, 300, 10))

Germany

Index, Change

Code 
sts_inpr_m %>%
  filter(geo == "DE",
         s_adj == "SCA",
        
         time %in% c("2022M08", "2022M07", "2022M03")) %>%
  select_if(~ n_distinct(.) > 1) %>%
  spread(time, values) %>%
  left_join(nace_r2, by = "nace_r2") %>%
  select(nace_r2, Nace_r2, everything()) %>%
  print_table_conditional()

Index

Code 
sts_inpr_m %>%
  filter(geo == "DE",
         s_adj == "SCA",
         unit == "I21",
         time %in% c("2022M08", "2022M07", "2022M03")) %>%
  select_if(~ n_distinct(.) > 1) %>%
  spread(time, values) %>%
  left_join(nace_r2, by = "nace_r2") %>%
  select(nace_r2, Nace_r2, everything()) %>%
  print_table_conditional()

PCH_PRE - Change

Code 
sts_inpr_m %>%
  filter(geo == "DE",
         #s_adj == "SCA",
         unit == "PCH_SM",
         time %in% c("2022M12", "2022M11", "2022M08", "2022M05","2022M03")) %>%
  select_if(~ n_distinct(.) > 1) %>%
  spread(time, values) %>%
  left_join(nace_r2, by = "nace_r2") %>%
  select(nace_r2, Nace_r2, everything()) %>%
  arrange(`2022M11`) %>%
  print_table_conditional()

Fertilizers, Chemical industry

All

Code 
sts_inpr_m %>%
  filter(geo == "DE",
         s_adj == "SCA",
         unit == "I21",
         nace_r2 %in% c("C2015", "C20_C21", "C20", "C2442")) %>%
  month_to_date %>%
  left_join(nace_r2, by = "nace_r2") %>%
  mutate(Nace_r2 = gsub("Manufacture", "Manuf.", Nace_r2)) %>%
  ggplot + geom_line(aes(x = date, y = values, color = Nace_r2)) +
  xlab("") + ylab("") + theme_minimal() +
  theme(legend.position = c(0.5, 0.25),
        legend.title = element_blank()) + 
  scale_x_date(breaks = "5 years",
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(60, 120, 10))

1995-

Code 
sts_inpr_m %>%
  filter(geo == "DE",
         s_adj == "SCA",
         unit == "I21",
         nace_r2 %in% c("C2015", "C20_C21", "C20", "C2442")) %>%
  month_to_date %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(nace_r2, by = "nace_r2") %>%
  mutate(Nace_r2 = gsub("Manufacture", "Manuf.", Nace_r2)) %>%
  ggplot + geom_line(aes(x = date, y = values, color = Nace_r2)) +
  xlab("") + ylab("") + theme_minimal() +
  theme(legend.position = c(0.5, 0.25),
        legend.title = element_blank()) + 
  scale_x_date(breaks = "5 years",
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(60, 120, 10))

2010-

Code 
sts_inpr_m %>%
  filter(geo == "DE",
         s_adj == "SCA",
         unit == "I21",
         nace_r2 %in% c("C2015", "C20_C21", "C20", "C2442")) %>%
  month_to_date %>%
  filter(date >= as.Date("2010-01-01")) %>%
  left_join(nace_r2, by = "nace_r2") %>%
  mutate(Nace_r2 = gsub("Manufacture", "Manuf.", Nace_r2)) %>%
  ggplot + geom_line(aes(x = date, y = values, color = Nace_r2)) +
  xlab("") + ylab("") + theme_minimal() +
  theme(legend.position = c(0.5, 0.25),
        legend.title = element_blank()) + 
  scale_x_date(breaks = "1 year",
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(60, 120, 10))

2017-

Code 
sts_inpr_m %>%
  filter(geo == "DE",
         s_adj == "SCA",
         unit == "I21",
         nace_r2 %in% c("C2015", "C20_C21", "C20", "C2442")) %>%
  month_to_date %>%
  filter(date >= as.Date("2017-01-01")) %>%
  left_join(nace_r2, by = "nace_r2") %>%
  mutate(Nace_r2 = gsub("Manufacture", "Manuf.", Nace_r2)) %>%
  ggplot + geom_line(aes(x = date, y = values, color = Nace_r2)) +
  xlab("") + ylab("") + theme_minimal() +
  theme(legend.position = c(0.5, 0.25),
        legend.title = element_blank()) + 
  scale_x_date(breaks = "1 year",
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(40, 120, 10))

2020-

Code 
sts_inpr_m %>%
  filter(geo == "DE",
         s_adj == "SCA",
         unit == "I21",
         nace_r2 %in% c("C2015", "C20_C21", "C20", "C2442")) %>%
  month_to_date %>%
  filter(date >= as.Date("2020-01-01")) %>%
  left_join(nace_r2, by = "nace_r2") %>%
  mutate(Nace_r2 = gsub("Manufacture", "Manuf.", Nace_r2)) %>%
  ggplot + geom_line(aes(x = date, y = values, color = Nace_r2)) +
  xlab("") + ylab("") + theme_minimal() +
  theme(legend.position = c(0.5, 0.25),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1),
        legend.title = element_blank()) + 
  scale_x_date(breaks = "3 months",
               labels = date_format("%b %y")) +
  scale_y_log10(breaks = seq(40, 120, 5))

France, Germany, Italy, Greece, Europe

1995-

Code 
sts_inpr_m %>%
  filter(nace_r2 == "C",
         unit == "I21",
         geo %in% c("FR", "DE", "IT", "EA20", "EL"),
         s_adj == "SCA") %>%
  select(geo, time, values) %>%
  group_by(geo) %>%
  month_to_date %>%
  filter(date >= as.Date("1995-01-01")) %>%
  arrange(date) %>%
  mutate(values = 100*values/values[1]) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
  mutate(Geo = ifelse(geo == "EA20", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot() + ylab("Industrial Production") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() +
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  add_5flags +
  scale_y_log10(breaks = seq(-60, 300, 10))

2000-

Code 
sts_inpr_m %>%
  filter(nace_r2 == "C",
         unit == "I21",
         geo %in% c("FR", "DE", "IT", "EA20", "EL"),
         s_adj == "SCA") %>%
  select(geo, time, values) %>%
  group_by(geo) %>%
  month_to_date %>%
  filter(date >= as.Date("2000-01-01")) %>%
  arrange(date) %>%
  mutate(values = 100*values/values[1]) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
  mutate(Geo = ifelse(geo == "EA20", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot() + ylab("Industrial Production") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() +
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  add_5flags +
  scale_y_log10(breaks = seq(-60, 300, 10))

2008-

Code 
sts_inpr_m %>%
  filter(nace_r2 == "C",
         unit == "I21",
         geo %in% c("FR", "DE", "IT", "EA20", "EL"),
         s_adj == "SCA") %>%
  select(geo, time, values) %>%
  group_by(geo) %>%
  month_to_date %>%
  filter(date >= as.Date("2008-01-01")) %>%
  arrange(date) %>%
  mutate(values = 100*values/values[1]) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
  mutate(Geo = ifelse(geo == "EA20", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot() + ylab("Industrial Production") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() +
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  add_5flags +
  scale_y_log10(breaks = seq(-60, 300, 10))

France, Germany, Italy

C20 - Manufacture of chemicals and chemical products

All

Code 
sts_inpr_m %>%
  filter(nace_r2 == "C20",
         unit == "I21",
         geo %in% c("FR", "DE", "IT", "EA20"),
         s_adj == "SCA") %>%
  select(geo, time, values) %>%
  group_by(geo) %>%
  mutate(values = 100*values/values[time == "2010M01"]) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
  mutate(Geo = ifelse(geo == "EA20", "Europe", Geo)) %>%
  month_to_date %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot() + ylab("Industrial Production") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + add_4flags +
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = "none") +
  scale_y_log10(breaks = seq(-60, 300, 10))

1992-

Code 
sts_inpr_m %>%
  filter(nace_r2 == "C20",
         unit == "I21",
         geo %in% c("FR", "DE", "IT", "EA20"),
         s_adj == "SCA") %>%
  select(geo, time, values) %>%
  group_by(geo) %>%
  mutate(values = 100*values/values[time == "2001M01"]) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
  mutate(Geo = ifelse(geo == "EA20", "Europe", Geo)) %>%
  month_to_date %>%
  filter(date >= as.Date("1992-01-01")) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot() + ylab("Industrial Production") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + add_4flags +
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = "none") +
  scale_y_log10(breaks = seq(-60, 300, 10))

2000-

Code 
sts_inpr_m %>%
  filter(nace_r2 == "C20",
         unit == "I21",
         geo %in% c("FR", "DE", "IT", "EA20"),
         s_adj == "SCA") %>%
  select(geo, time, values) %>%
  group_by(geo) %>%
  mutate(values = 100*values/values[time == "2001M01"]) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
  mutate(Geo = ifelse(geo == "EA20", "Europe", Geo)) %>%
  month_to_date %>%
  filter(date >= as.Date("2000-01-01")) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot() + ylab("Industrial Production\nManufacture of fertilisers and nitrogen compounds") + xlab("") +
  geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + add_4flags + theme_minimal() +
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(-60, 300, 10))

2019-

Code 
sts_inpr_m %>%
  filter(nace_r2 == "C20",
         unit == "I21",
         geo %in% c("FR", "DE", "IT", "EA20"),
         s_adj == "SCA") %>%
  select(geo, time, values) %>%
  group_by(geo) %>%
  mutate(values = 100*values/values[time == "2019M01"]) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
  mutate(Geo = ifelse(geo == "EA20", "Europe", Geo)) %>%
  month_to_date %>%
  filter(date >= as.Date("2019-01-01")) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot() + ylab("Industrial Production\nManufacture of fertilisers and nitrogen compounds") + xlab("") +
  geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + add_4flags + theme_minimal() +
  scale_x_date(breaks = "3 months",
               labels = date_format("%b %y")) +
  add_4flags +
  theme(legend.position = "none",
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  scale_y_log10(breaks = seq(-60, 300, 5))

2020-

Code 
sts_inpr_m %>%
  filter(nace_r2 == "C20",
         unit == "I21",
         geo %in% c("FR", "DE", "IT", "EA20"),
         s_adj == "SCA") %>%
  select(geo, time, values) %>%
  group_by(geo) %>%
  mutate(values = 100*values/values[time == "2020M01"]) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
  mutate(Geo = ifelse(geo == "EA20", "Europe", Geo)) %>%
  month_to_date %>%
  filter(date >= as.Date("2020-01-01")) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot() + ylab("Industrial Production\nManufacture of fertilisers and nitrogen compounds") + xlab("") +
  geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + add_4flags + theme_minimal() +
  theme(legend.position = "none",
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  scale_x_date(breaks = "2 months",
               labels = date_format("%b %y")) +
  scale_y_log10(breaks = seq(-60, 300, 5))

2021-

Code 
sts_inpr_m %>%
  filter(nace_r2 == "C20",
         unit == "I21",
         geo %in% c("FR", "DE", "IT", "EA20"),
         s_adj == "SCA") %>%
  select(geo, time, values) %>%
  group_by(geo) %>%
  mutate(values = 100*values/values[time == "2021M01"]) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
  mutate(Geo = ifelse(geo == "EA20", "Europe", Geo)) %>%
  month_to_date %>%
  filter(date >= as.Date("2021-01-01")) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot() + ylab("Industrial Production\nManufacture of chemicals and chemical products") + xlab("") +
  geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + add_4flags + theme_minimal() +
  theme(legend.position = "none",
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  scale_x_date(breaks = "2 months",
               labels = date_format("%b %y")) +
  scale_y_log10(breaks = seq(-60, 300, 5))

C2015 - Manufacture of fertilisers and nitrogen compounds

All

Code 
sts_inpr_m %>%
  filter(nace_r2 == "C2015",
         unit == "I21",
         geo %in% c("FR", "DE", "IT", "EA20"),
         s_adj == "SCA") %>%
  select(geo, time, values) %>%
  group_by(geo) %>%
  mutate(values = 100*values/values[time == "2010M01"]) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
  mutate(Geo = ifelse(geo == "EA20", "Europe", Geo)) %>%
  month_to_date %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot() + ylab("Industrial Production") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + add_4flags +
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = "none") +
  scale_y_log10(breaks = seq(-60, 300, 10))

1992-

Code 
sts_inpr_m %>%
  filter(nace_r2 == "C2015",
         unit == "I21",
         geo %in% c("FR", "DE", "IT", "EA20"),
         s_adj == "SCA") %>%
  select(geo, time, values) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
  mutate(Geo = ifelse(geo == "EA20", "Europe", Geo)) %>%
  month_to_date %>%
  group_by(geo) %>%
  arrange(date) %>%
  mutate(values = 100*values/values[1]) %>%
  filter(date >= as.Date("1992-01-01")) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot() + ylab("Industrial Production") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + add_4flags +
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = "none") +
  scale_y_log10(breaks = seq(-60, 300, 10))

2000-

Code 
sts_inpr_m %>%
  filter(nace_r2 == "C2015",
         unit == "I21",
         geo %in% c("FR", "DE", "IT", "EA20"),
         s_adj == "SCA") %>%
  select(geo, time, values) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
  mutate(Geo = ifelse(geo == "EA20", "Europe", Geo)) %>%
  month_to_date %>%
  group_by(geo) %>%
  mutate(values = 100*values/values[1]) %>%
  filter(date >= as.Date("2000-01-01")) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot() + ylab("Industrial Production\nManufacture of fertilisers and nitrogen compounds") + xlab("") +
  geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + add_4flags + theme_minimal() +
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%y")) +
  scale_y_log10(breaks = seq(-60, 300, 10))

2019-

Code 
sts_inpr_m %>%
  filter(nace_r2 == "C2015",
         unit == "I21",
         geo %in% c("FR", "DE", "IT", "EA20"),
         s_adj == "SCA") %>%
  select(geo, time, values) %>%
  group_by(geo) %>%
  mutate(values = 100*values/values[time == "2019M01"]) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
  mutate(Geo = ifelse(geo == "EA20", "Europe", Geo)) %>%
  month_to_date %>%
  filter(date >= as.Date("2019-01-01")) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot() + ylab("Industrial Production\nManufacture of fertilisers and nitrogen compounds") + xlab("") +
  geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + add_4flags + theme_minimal() +
  scale_x_date(breaks = "3 months",
               labels = date_format("%b %y")) +
  add_4flags +
  theme(legend.position = "none",
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  scale_y_log10(breaks = seq(-60, 300, 10))

2020-

Code 
sts_inpr_m %>%
  filter(nace_r2 == "C2015",
         unit == "I21",
         geo %in% c("FR", "DE", "IT", "EA20"),
         s_adj == "SCA") %>%
  select(geo, time, values) %>%
  group_by(geo) %>%
  mutate(values = 100*values/values[time == "2020M01"]) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
  mutate(Geo = ifelse(geo == "EA20", "Europe", Geo)) %>%
  month_to_date %>%
  filter(date >= as.Date("2020-01-01")) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot() + ylab("Industrial Production\nManufacture of fertilisers and nitrogen compounds") + xlab("") +
  geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + add_4flags + theme_minimal() +
  theme(legend.position = "none",
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  scale_x_date(breaks = "2 months",
               labels = date_format("%b %y")) +
  scale_y_log10(breaks = seq(-60, 300, 10))

2021-

Code 
load_data("eurostat/nace_r2_fr.RData")
sts_inpr_m %>%
  filter(nace_r2 == "C2015",
         unit == "I21",
         geo %in% c("FR", "DE", "IT", "EA20"),
         s_adj == "SCA") %>%
  group_by(geo) %>%
  mutate(values = 100*values/values[time == "2021M01"]) %>%
  left_join(geo, by = "geo") %>%
  left_join(nace_r2, by = "nace_r2") %>%
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
  mutate(Geo = ifelse(geo == "EA20", "Europe", Geo)) %>%
  month_to_date %>%
  filter(date >= as.Date("2021-01-01")) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot() + ylab("Production industrielle\nFabrication de produits azotés et d'engrais") + xlab("") +
  geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + add_4flags + theme_minimal() +
  theme(legend.position = "none",
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  scale_x_date(breaks = "2 months",
               labels = date_format("%b %y")) +
  scale_y_log10(breaks = seq(-60, 300, 10))

Manufacturing

All

Code 
sts_inpr_m %>%
  filter(nace_r2 == "C",
         unit == "I21",
         geo %in% c("FR", "DE", "IT", "EA20"),
         s_adj == "SCA") %>%
  select(geo, time, values) %>%
  group_by(geo) %>%
  mutate(values = 100*values/values[time == "1997M01"]) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
  mutate(Geo = ifelse(geo == "EA20", "Europe", Geo)) %>%
  month_to_date %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot() + ylab("Industrial Production") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + add_4flags +
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = "none") +
  scale_y_log10(breaks = seq(-60, 300, 10))

1992-

Code 
sts_inpr_m %>%
  filter(nace_r2 == "C",
         unit == "I21",
         geo %in% c("FR", "DE", "IT", "EA20"),
         s_adj == "SCA") %>%
  select(geo, time, values) %>%
  group_by(geo) %>%
  month_to_date %>%
  arrange(date) %>%
  mutate(values = 100*values/values[1]) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
  mutate(Geo = ifelse(geo == "EA20", "Europe", Geo)) %>%
  filter(date >= as.Date("1992-01-01")) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot() + ylab("Industrial Production") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + add_4flags +
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = "none") +
  scale_y_log10(breaks = seq(-60, 300, 10))

1995-

Code 
sts_inpr_m %>%
  filter(nace_r2 == "C",
         unit == "I21",
         geo %in% c("FR", "DE", "IT", "EA20"),
         s_adj == "SCA") %>%
  select(geo, time, values) %>%
  group_by(geo) %>%
  month_to_date %>%
  filter(date >= as.Date("1995-01-01")) %>%
  arrange(date) %>%
  mutate(values = 100*values/values[1]) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
  mutate(Geo = ifelse(geo == "EA20", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot() + ylab("Industrial Production") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() +
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  add_4flags +
  scale_y_log10(breaks = seq(-60, 300, 10))

2000-

Code 
sts_inpr_m %>%
  filter(nace_r2 == "C",
         unit == "I21",
         geo %in% c("FR", "DE", "IT", "EA20"),
         s_adj == "SCA") %>%
  select(geo, time, values) %>%
  group_by(geo) %>%
  month_to_date %>%
  filter(date >= as.Date("2000-01-01")) %>%
  arrange(date) %>%
  mutate(values = 100*values/values[1]) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
  mutate(Geo = ifelse(geo == "EA20", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot() + ylab("Industrial Production") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() +
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  add_4flags +
  scale_y_log10(breaks = seq(-60, 300, 10))

2019-

Code 
sts_inpr_m %>%
  filter(nace_r2 == "C",
         unit == "I21",
         geo %in% c("FR", "DE", "IT", "EA20"),
         s_adj == "SCA") %>%
  select(geo, time, values) %>%
  group_by(geo) %>%
  mutate(values = 100*values/values[time == "2019M01"]) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
  mutate(Geo = ifelse(geo == "EA20", "Europe", Geo)) %>%
  month_to_date %>%
  filter(date >= as.Date("2019-01-01")) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot() + ylab("Industrial Production") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() +
  scale_x_date(breaks = "3 months",
               labels = date_format("%b %y")) +
  theme(legend.position = "none",
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  add_4flags +
  theme(legend.position = "none") +
  scale_y_log10(breaks = seq(-60, 300, 10))

2020-

Code 
sts_inpr_m %>%
  filter(nace_r2 == "C",
         unit == "I21",
         geo %in% c("FR", "DE", "IT", "EA20", "ES"),
         s_adj == "SCA") %>%
  select(geo, time, values) %>%
  group_by(geo) %>%
  mutate(values = 100*values/values[time == "2020M02"]) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
  mutate(Geo = ifelse(geo == "EA20", "Europe", Geo)) %>%
  month_to_date %>%
  filter(date >= as.Date("2020-02-01")) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot() + ylab("Industrial Production, Manufacturing") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + add_5flags +
  theme(legend.position = "none",
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  scale_x_date(breaks = "2 months",
               labels = date_format("%b %y")) +
  scale_y_log10(breaks = seq(-60, 300, 10))

Covid-19

Table

Code 
sts_inpr_m %>%
  filter(nace_r2 == "C",
         unit == "I21",
         s_adj == "SCA",
         time %in% c("2019M11", "2020M02", "2020M03", "2020M04", "2020M05", "2020M08", "2020M11"),
         !(geo %in% c("IE", "EU28"))) %>%
  select(geo, time, values) %>%
  group_by(geo) %>%
  mutate(values = 100*values/values[time == "2019M11"]) %>%
  left_join(geo, by = "geo") %>%
  spread(time, values) %>%
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(Geo)),
         Flag = paste0('<img src="../../bib/flags/vsmall/', Flag, '.png" alt="Flag">')) %>%
  select(Flag, everything()) %>%
  arrange(`2020M04`) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

2018

Code 
sts_inpr_m %>%
  filter(nace_r2 == "C",
         unit == "I21",
         geo %in% c("FR", "DE", "IT"),
         s_adj == "SCA") %>%
  select(geo, time, values) %>%
  group_by(geo) %>%
  mutate(values = 100*values/values[time == "2018M01"]) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
  month_to_date %>%
  filter(date >= as.Date("2018-01-01")) %>%
  ggplot() + ylab("Industrial Production") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = values, color = Geo)) +
  scale_color_manual(values = c("#0055a4", "#000000", "#008c45")) +
  scale_x_date(breaks = "3 months",
               labels = date_format("%b %y")) +
  geom_image(data = . %>%
               filter(date == as.Date("2021-01-01")) %>%
               mutate(image = paste0("../../icon/flag/", str_to_lower(Geo), ".png")),
             aes(x = date, y = values, image = image), asp = 1.5) +
  theme(legend.position = "none",
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  scale_y_log10(breaks = seq(-60, 300, 10))

2019M09

Code 
sts_inpr_m %>%
  filter(nace_r2 == "C",
         unit == "I21",
         geo %in% c("FR", "DE", "IT"),
         s_adj == "SCA") %>%
  select(geo, time, values) %>%
  group_by(geo) %>%
  mutate(values = 100*values/values[time == "2019M09"]) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
  month_to_date %>%
  filter(date >= as.Date("2019-09-01")) %>%
  ggplot() + ylab("Industrial Production") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = values, color = Geo)) +
  scale_color_manual(values = c("#0055a4", "#000000", "#008c45")) +
  scale_x_date(breaks = "2 months",
               labels = date_format("%b %y")) +
  geom_image(data = . %>%
               filter(date == as.Date("2021-05-01")) %>%
               mutate(image = paste0("../../icon/flag/", str_to_lower(Geo), ".png")),
             aes(x = date, y = values, image = image), asp = 1.5) +
  theme(legend.position = "none",
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  scale_y_log10(breaks = seq(-60, 300, 10))

2020M02

Code 
sts_inpr_m %>%
  filter(nace_r2 == "C",
         unit == "I21",
         geo %in% c("FR", "DE", "IT", "ES"),
         s_adj == "SCA") %>%
  select(geo, time, values) %>%
  group_by(geo) %>%
  mutate(values = 100*values/values[time == "2020M02"]) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
  month_to_date %>%
  filter(date >= as.Date("2020-02-01")) %>%
  ggplot() + ylab("Industrial Production") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = values, color = Geo)) +
  scale_color_manual(values = c("#0055a4", "#000000", "#008c45", "#C60B1E")) +
  scale_x_date(breaks = "3 months",
               labels = date_format("%b %y")) +
  geom_image(data = . %>%
               filter(date == as.Date("2020-08-01")) %>%
               mutate(image = paste0("../../icon/flag/", str_to_lower(Geo), ".png")),
             aes(x = date, y = values, image = image), asp = 1.5) +
  theme(legend.position = "none",
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  scale_y_log10(breaks = seq(-60, 300, 10))

Cumulative loss in production

Code 
sts_inpr_m %>%
  filter(nace_r2 == "C",
         unit == "I21",
         geo %in% c("FR", "DE", "IT", "ES", "UK"),
         s_adj == "SCA") %>%
  select(geo, time, values) %>%
  group_by(geo) %>%
  mutate(values = 100*values/values[time == "2020M02"]) %>%
  left_join(geo, by = "geo") %>%
  month_to_date %>%
  filter(date >= as.Date("2020-02-01")) %>%
  group_by(Geo) %>%
  arrange(date) %>%
  mutate(values = values-100,
         values = cumsum(values)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot() + ylab("Industrial Production (Cumulative loss)") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() +
  scale_x_date(breaks = "2 months",
               labels = date_format("%b %y")) +
  add_5flags +
  theme(legend.position = "none",
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  scale_y_continuous(breaks = seq(-300, 300, 10))

Cumulative loss in production

Code 
sts_inpr_m %>%
  filter(nace_r2 == "C",
         unit == "I21",
         geo %in% c("FR", "DE", "IT", "ES"),
         s_adj == "SCA") %>%
  select(geo, time, values) %>%
  group_by(geo) %>%
  mutate(values = 100*values/values[time == "2020M02"]) %>%
  left_join(geo, by = "geo") %>%
  month_to_date %>%
  filter(date >= as.Date("2020-02-01")) %>%
  group_by(Geo) %>%
  arrange(date) %>%
  mutate(values = values-100,
         values = cumsum(values)) %>%
  ggplot() + ylab("Industrial Production (Cumulative loss)") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = values, color = Geo)) +
  scale_color_manual(values = c("#0055a4", "#000000", "#008c45", "#C60B1E")) +
  scale_x_date(breaks = "2 months",
               labels = date_format("%b %y")) +
  add_4flags +
  theme(legend.position = "none",
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  scale_y_continuous(breaks = seq(-300, 300, 10))