Production in industry - monthly data

Data - Eurostat

Author

François Geerolf

Info

source dataset .html .RData
eurostat sts_inpr_m 2024-10-09 2024-10-08

Data on industry

source dataset .html .RData
ec INDUSTRY 2024-09-15 2023-10-01
eurostat ei_isin_m 2024-11-05 2024-10-09
eurostat htec_trd_group4 2024-11-05 2024-10-08
eurostat nama_10_a64 2024-11-05 2024-10-08
eurostat nama_10_a64_e 2024-11-05 2024-10-08
eurostat namq_10_a10_e 2024-11-05 2024-10-08
eurostat road_eqr_carmot 2024-11-05 2024-10-08
eurostat sts_inpp_m 2024-06-24 2024-10-08
eurostat sts_inppd_m 2024-11-05 2024-10-08
eurostat sts_inpr_m 2024-10-09 2024-10-08
eurostat sts_intvnd_m 2024-10-31 2024-10-31
fred industry 2024-11-01 2024-11-01
oecd ALFS_EMP 2024-04-16 2024-05-12
oecd BERD_MA_SOF 2024-04-16 2023-09-09
oecd GBARD_NABS2007 2024-04-16 2023-11-22
oecd MEI_REAL 2024-05-12 2024-05-03
oecd MSTI_PUB 2024-09-15 2023-10-04
oecd SNA_TABLE4 2024-09-15 2024-04-30
wdi NV.IND.EMPL.KD 2024-01-06 2024-09-18
wdi NV.IND.MANF.CD 2024-09-18 2024-09-18
wdi NV.IND.MANF.ZS 2024-01-06 2024-09-18
wdi NV.IND.TOTL.KD 2024-01-06 2024-09-18
wdi NV.IND.TOTL.ZS 2024-01-06 2024-09-18
wdi SL.IND.EMPL.ZS 2024-01-06 2024-09-18
wdi TX.VAL.MRCH.CD.WT 2024-01-06 2024-09-18

Last

Code 
sts_inpr_m %>%
  group_by(time) %>%
  summarise(Nobs = n()) %>%
  arrange(desc(time)) %>%
  head(2) %>%
  print_table_conditional()
time Nobs
2024M08 4075
2024M07 21524

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 5640877
SCA Seasonally and calendar adjusted data 5493770
NSA Unadjusted data (i.e. neither seasonally adjusted nor calendar adjusted data) 3083300

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 3745002
I10 Index, 2010=100 2920855
PCH_PRE Percentage change on previous period 1625872
PCH_SM Percentage change compared to same period in previous year 1609792

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))