Gross capital formation by industry (up to NACE A*64)

Data - Eurostat

Author

Info

source	dataset	Title	.html	.rData
eurostat	nama_10_a64_p5	Gross capital formation by industry (up to NACE A*64)	2026-01-29	2026-01-29
eurostat	nama_10_gdp	GDP and main components (output, expenditure and income)	2026-01-29	2026-01-29

Data on inflation

source	dataset	Title	.html	.rData
bis	CPI	Consumer Price Index	2026-01-29	2026-01-29
ecb	CES	Consumer Expectations Survey	2025-08-28	2025-05-24
eurostat	nama_10_co3_p3	Final consumption expenditure of households by consumption purpose (COICOP 3 digit)	2026-01-29	2026-01-29
eurostat	prc_hicp_cow	HICP - country weights	2026-01-29	2026-01-29
eurostat	prc_hicp_ctrb	Contributions to euro area annual inflation (in percentage points)	2026-01-29	2026-01-29
eurostat	prc_hicp_inw	HICP - item weights	2026-01-29	2026-01-29
eurostat	prc_hicp_manr	HICP (2015 = 100) - monthly data (annual rate of change)	2026-01-29	2026-01-29
eurostat	prc_hicp_midx	HICP (2015 = 100) - monthly data (index)	2025-12-22	2026-01-29
eurostat	prc_hicp_mmor	HICP (2015 = 100) - monthly data (monthly rate of change)	2026-01-29	2026-01-29
eurostat	prc_ppp_ind	Purchasing power parities (PPPs), price level indices and real expenditures for ESA 2010 aggregates	2026-01-29	2026-01-29
eurostat	sts_inpp_m	Producer prices in industry, total - monthly data	2026-01-29	2026-01-29
eurostat	sts_inppd_m	Producer prices in industry, domestic market - monthly data	2026-01-29	2026-01-29
eurostat	sts_inppnd_m	Producer prices in industry, non domestic market - monthly data	2024-06-24	2026-01-29
fred	cpi	Consumer Price Index	2026-01-29	2026-01-29
fred	inflation	Inflation	2026-01-29	2026-01-29
imf	CPI	Consumer Price Index - CPI	2026-01-29	2020-03-13
oecd	MEI_PRICES_PPI	Producer Prices - MEI_PRICES_PPI	2026-01-16	2024-04-15
oecd	PPP2017	2017 PPP Benchmark results	2024-04-16	2023-07-25
oecd	PRICES_CPI	Consumer price indices (CPIs)	2024-04-16	2024-04-15
wdi	FP.CPI.TOTL.ZG	Inflation, consumer prices (annual %)	2026-01-29	2026-01-29
wdi	NY.GDP.DEFL.KD.ZG	Inflation, GDP deflator (annual %)	2026-01-29	2026-01-29

LAST_COMPILE

LAST_COMPILE
2026-01-31

Last

Code

nama_10_a64_p5 %>%
  group_by(time) %>%
  summarise(Nobs = n()) %>%
  arrange(desc(time)) %>%
  head(1) %>%
  print_table_conditional()

time	Nobs
2024	404753

nace_r2

Code

nama_10_a64_p5 %>%
  left_join(nace_r2, by = "nace_r2") %>%
  group_by(nace_r2, Nace_r2) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

na_item

Code

nama_10_a64_p5 %>%
  left_join(na_item, by = "na_item") %>%
  group_by(na_item, Na_item) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) print_table(.) else .}

na_item	Na_item	Nobs
P51G	Gross fixed capital formation	17587640
P5G	Gross capital formation	521204
P52	Changes in inventories	173299
P52_P53	Changes in inventories and acquisitions less disposals of valuables	88944
P53	Acquisitions less disposals of valuables	64663

asset10

Code

nama_10_a64_p5 %>%
  left_join(asset10, by = "asset10") %>%
  group_by(asset10, Asset10) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) print_table(.) else .}

asset10	Asset10	Nobs
N11G	Total fixed assets (gross)	1637495
N11MG	Machinery and equipment and weapons systems (gross)	1593786
N112G	Other buildings and structures (gross)	1571619
N117G	Intellectual property products (gross)	1566084
N1131G	Transport equipment (gross)	1560653
N11KG	Total Construction (gross)	1484949
N11OG	Other machinery and equipment and weapons systems (gross)	1312525
N1132G	ICT equipment (gross)	1304988
N1173G	Computer software and databases (gross)	1130372
N11321G	Computer hardware (gross)	973941
N1171G	Research and development (gross)	958631
N111G	Dwellings (gross)	888620
N11322G	Telecommunications equipment (gross)	861157
N115G	Cultivated biological resources (gross)	742820
N1G	Produced non-financial assets (gross)	521204
N12G	Inventories (gross)	173299
N1MG	Inventories and acquisitions less disposals of valuables (gross)	88944
N13G	Valuables (gross)	64663

unit

Code

nama_10_a64_p5 %>%
  left_join(unit, by = "unit") %>%
  group_by(unit, Unit) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

time

Code

nama_10_a64_p5 %>%
  group_by(time) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

geo

Code

nama_10_a64_p5 %>%
  left_join(geo, by = "geo") %>%
  group_by(geo, Geo) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

France, Italy, Germany

C - Manufacturing

Code

nama_10_a64_p5 %>%
  filter(unit == "CP_MEUR",
         geo %in% c("FR", "IT", "DE"),
         nace_r2 == "C",
         asset10 == "N11G") %>%
  left_join(nama_10_gdp %>%
              filter(na_item == "B1GQ",
                     unit == "CP_MEUR") %>%
              select(geo, time, gdp = values), 
            by = c("geo", "time")) %>%
  left_join(geo, by = "geo") %>%
  year_to_date %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/gdp) %>%
  ggplot + geom_line(aes(x = date, y = values, color = color)) +
  theme_minimal()  + add_3flags +
  scale_color_identity() + xlab("") + ylab("% of GDP") +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2020, 5), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.2, 0.85),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(0, 200, .1),
                     labels = scales::percent_format(accuracy = .1))

B-E - Energy

Code

nama_10_a64_p5 %>%
  filter(unit == "CP_MEUR",
         geo %in% c("FR", "IT", "DE"),
         nace_r2 == "B-E",
         asset10 == "N11G") %>%
  left_join(nama_10_gdp %>%
              filter(na_item == "B1GQ",
                     unit == "CP_MEUR") %>%
              select(geo, time, gdp = values), 
            by = c("geo", "time")) %>%
  left_join(geo, by = "geo") %>%
  year_to_date %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/gdp) %>%
  ggplot + geom_line(aes(x = date, y = values, color = color)) +
  theme_minimal()  + add_3flags +
  scale_color_identity() + xlab("") + ylab("% of GDP") +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2020, 5), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.2, 0.85),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(0, 200, .1),
                     labels = scales::percent_format(accuracy = .1))

France, Italy, United Kingdom, Spain, Germany

TOTAL investment

N11G - Total

Table

Code

nama_10_a64_p5 %>%
  filter(unit == "CP_MEUR",
         geo %in% c("FR", "NL", "IT", "ES", "DE"),
         asset10 == "N11G",
         time == "2021") %>%
  left_join(nama_10_gdp %>%
              filter(na_item == "B1GQ",
                     unit == "CP_MEUR") %>%
              select(geo, time, gdp = values), 
            by = c("geo", "time")) %>%
  left_join(geo, by = "geo") %>%
  left_join(nace_r2, by = "nace_r2") %>%
  mutate(values = 100*values/gdp) %>%
  select_if(~ n_distinct(.) > 1) %>%
  select(-geo, -gdp) %>%
  spread(Geo, values) %>%
  arrange(-France) %>%
  print_table_conditional()

TOTAL - All sectors

Code

nama_10_a64_p5 %>%
  filter(unit == "CP_MEUR",
         geo %in% c("FR", "NL", "IT", "ES", "DE"),
         nace_r2 == "TOTAL",
         asset10 == "N11G") %>%
  left_join(nama_10_gdp %>%
              filter(na_item == "B1GQ",
                     unit == "CP_MEUR") %>%
              select(geo, time, gdp = values), 
            by = c("geo", "time")) %>%
  left_join(geo, by = "geo") %>%
  year_to_date %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/gdp) %>%
  ggplot + geom_line(aes(x = date, y = values, color = color)) +
  theme_minimal()  + add_5flags +
  scale_color_identity() + xlab("") + ylab("% of GDP") +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2020, 5), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.2, 0.85),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(0, 200, 1),
                     labels = scales::percent_format(accuracy = 1))

J - Information - communication

Code

nama_10_a64_p5 %>%
  filter(unit == "CP_MEUR",
         geo %in% c("FR", "NL", "IT", "ES", "DE"),
         nace_r2 == "J",
         asset10 == "N11G") %>%
  left_join(nama_10_gdp %>%
              filter(na_item == "B1GQ",
                     unit == "CP_MEUR") %>%
              select(geo, time, gdp = values), 
            by = c("geo", "time")) %>%
  left_join(geo, by = "geo") %>%
  year_to_date %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/gdp) %>%
  ggplot + geom_line(aes(x = date, y = values, color = color)) +
  theme_minimal()  + add_5flags +
  scale_color_identity() + xlab("") + ylab("J - Information - communication\n% of GDP") +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2020, 5), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.2, 0.85),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(0, 200, .1),
                     labels = scales::percent_format(accuracy = .1))

C - Manufacturing

Code

nama_10_a64_p5 %>%
  filter(unit == "CP_MEUR",
         geo %in% c("FR", "NL", "IT", "ES", "DE"),
         nace_r2 == "C",
         asset10 == "N11G") %>%
  left_join(nama_10_gdp %>%
              filter(na_item == "B1GQ",
                     unit == "CP_MEUR") %>%
              select(geo, time, gdp = values), 
            by = c("geo", "time")) %>%
  left_join(geo, by = "geo") %>%
  year_to_date %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/gdp) %>%
  ggplot + geom_line(aes(x = date, y = values, color = color)) +
  theme_minimal()  + add_5flags +
  scale_color_identity() + xlab("") + ylab("% of GDP") +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2020, 5), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.2, 0.85),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(0, 200, .1),
                     labels = scales::percent_format(accuracy = .1))

M - Professional, scientific and technical activities

All

Code

nama_10_a64_p5 %>%
  filter(unit == "CP_MEUR",
         geo %in% c("FR", "NL", "IT", "ES", "DE"),
         nace_r2 == "M",
         asset10 == "N11G") %>%
  left_join(nama_10_gdp %>%
              filter(na_item == "B1GQ",
                     unit == "CP_MEUR") %>%
              select(geo, time, gdp = values), 
            by = c("geo", "time")) %>%
  left_join(geo, by = "geo") %>%
  year_to_date %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/gdp) %>%
  ggplot + geom_line(aes(x = date, y = values, color = color)) +
  theme_minimal()  + add_5flags +
  scale_color_identity() + xlab("") + ylab("M - Professional, scientific and technical activities\n% of GDP") +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2020, 5), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.2, 0.85),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(0, 200, .1),
                     labels = scales::percent_format(accuracy = .1))

N117G

Code

nama_10_a64_p5 %>%
  filter(unit == "CP_MEUR",
         geo %in% c("FR", "NL", "IT", "ES", "DE"),
         nace_r2 == "M",
         asset10 == "N117G") %>%
  left_join(nama_10_gdp %>%
              filter(na_item == "B1GQ",
                     unit == "CP_MEUR") %>%
              select(geo, time, gdp = values), 
            by = c("geo", "time")) %>%
  left_join(geo, by = "geo") %>%
  year_to_date %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/gdp) %>%
  ggplot + geom_line(aes(x = date, y = values, color = color)) +
  theme_minimal()  + add_5flags +
  scale_color_identity() + xlab("") + ylab("M - Professional, scientific and technical activities\n% of GDP") +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2020, 5), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.2, 0.85),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(0, 200, .1),
                     labels = scales::percent_format(accuracy = .1))

O - Public administration and defence; compulsory social security

Code

nama_10_a64_p5 %>%
  filter(unit == "CP_MEUR",
         geo %in% c("FR", "NL", "IT", "ES", "DE"),
         nace_r2 == "O",
         asset10 == "N11G") %>%
  left_join(nama_10_gdp %>%
              filter(na_item == "B1GQ",
                     unit == "CP_MEUR") %>%
              select(geo, time, gdp = values), 
            by = c("geo", "time")) %>%
  left_join(geo, by = "geo") %>%
  year_to_date %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/gdp) %>%
  ggplot + geom_line(aes(x = date, y = values, color = color)) +
  theme_minimal()  + add_5flags +
  scale_color_identity() + xlab("") + ylab("% of GDP") +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2020, 5), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.2, 0.85),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(0, 200, .1),
                     labels = scales::percent_format(accuracy = .1))

B-E - Industry

Code

nama_10_a64_p5 %>%
  filter(unit == "CP_MEUR",
         geo %in% c("FR", "NL", "IT", "ES", "DE"),
         nace_r2 == "B-E",
         asset10 == "N11G") %>%
  left_join(nama_10_gdp %>%
              filter(na_item == "B1GQ",
                     unit == "CP_MEUR") %>%
              select(geo, time, gdp = values), 
            by = c("geo", "time")) %>%
  left_join(geo, by = "geo") %>%
  year_to_date %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/gdp) %>%
  ggplot + geom_line(aes(x = date, y = values, color = color)) +
  theme_minimal()  + add_5flags +
  scale_color_identity() + xlab("") + ylab("% of GDP") +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2020, 5), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.2, 0.85),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(0, 200, .1),
                     labels = scales::percent_format(accuracy = .1))

F - Construction

Code

nama_10_a64_p5 %>%
  filter(unit == "CP_MEUR",
         geo %in% c("FR", "NL", "IT", "CH", "DE"),
         nace_r2 == "F",
         asset10 == "N11G") %>%
  left_join(nama_10_gdp %>%
              filter(na_item == "B1GQ",
                     unit == "CP_MEUR") %>%
              select(geo, time, gdp = values), 
            by = c("geo", "time")) %>%
  left_join(geo, by = "geo") %>%
  year_to_date %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/gdp) %>%
  ggplot + geom_line(aes(x = date, y = values, color = color)) +
  theme_minimal()  + add_4flags +
  scale_color_identity() + xlab("") + ylab("% of GDP") +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2020, 5), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.2, 0.85),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(0, 200, .1),
                     labels = scales::percent_format(accuracy = .1))

C20 - Chemicals

Code

nama_10_a64_p5 %>%
  filter(unit == "CP_MEUR",
         geo %in% c("FR", "NL", "IT", "ES", "DE"),
         nace_r2 == "C20",
         asset10 == "N11G") %>%
  left_join(nama_10_gdp %>%
              filter(na_item == "B1GQ",
                     unit == "CP_MEUR") %>%
              select(geo, time, gdp = values), 
            by = c("geo", "time")) %>%
  left_join(geo, by = "geo") %>%
  year_to_date %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/gdp) %>%
  ggplot + geom_line(aes(x = date, y = values, color = color)) +
  theme_minimal()  + add_3flags +
  scale_color_identity() + xlab("") + ylab("% of GDP") +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2020, 5), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.2, 0.85),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(0, 200, .1),
                     labels = scales::percent_format(accuracy = .1))

N117G - Intellectual property products (gross)

Code

nama_10_a64_p5 %>%
  filter(unit == "CP_MEUR",
         geo %in% c("FR", "NL", "IT", "ES", "DE"),
         nace_r2 == "TOTAL",
         asset10 == "N117G") %>%
  left_join(nama_10_gdp %>%
              filter(na_item == "B1GQ",
                     unit == "CP_MEUR") %>%
              select(geo, time, gdp = values), 
            by = c("geo", "time")) %>%
  left_join(geo, by = "geo") %>%
  year_to_date %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/gdp) %>%
  ggplot + geom_line(aes(x = date, y = values, color = color)) +
  theme_minimal()  + add_5flags +
  scale_color_identity() + xlab("") + ylab("% of GDP") +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2020, 5), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.2, 0.85),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(0, 200, 1),
                     labels = scales::percent_format(accuracy = 1))

N1132G - ICT equipment (gross)

Code

nama_10_a64_p5 %>%
  filter(unit == "CP_MEUR",
         geo %in% c("FR", "NL", "IT", "ES", "DE"),
         nace_r2 == "TOTAL",
         asset10 == "N1132G") %>%
  left_join(nama_10_gdp %>%
              filter(na_item == "B1GQ",
                     unit == "CP_MEUR") %>%
              select(geo, time, gdp = values), 
            by = c("geo", "time")) %>%
  left_join(geo, by = "geo") %>%
  year_to_date %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/gdp) %>%
  ggplot + geom_line(aes(x = date, y = values, color = color)) +
  theme_minimal()  + add_5flags +
  scale_color_identity() + xlab("") + ylab("% of GDP") +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2020, 5), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.2, 0.85),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(0, 200, .2),
                     labels = scales::percent_format(accuracy = .1))

Investissement en France

Long

Code

nama_10_a64_p5 %>%
  filter(geo == "FR",
         na_item == "P51G",
         unit == "CP_MEUR",
         time == "2018") %>%
  left_join(asset10, by = "asset10") %>%
  left_join(nace_r2, by = "nace_r2") %>%
  select(nace_r2, Nace_r2, asset10, Asset10, values) %>%
  arrange(-values) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

Large

Code

nama_10_a64_p5 %>%
  filter(geo == "FR",
         na_item == "P51G",
         unit == "CP_MEUR",
         time == "2018") %>%
  left_join(nace_r2, by = "nace_r2") %>%
  select(nace_r2, Nace_r2, asset10, values) %>%
  spread(asset10, values) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

France, Germany, Italy, Netherlands, Spain

N11G - All

Code

nama_10_a64_p5 %>%
  filter(unit == "PD15_EUR",
         geo %in% c("FR", "NL", "IT", "DE", "ES"),
         nace_r2 == "TOTAL",
         asset10 == "N11G") %>%
  left_join(geo, by = "geo") %>%
  year_to_date %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  group_by(Geo) %>%
  mutate(values = 100*values/values[date == as.Date("1995-01-01")]) %>%
  ggplot + geom_line(aes(x = date, y = values, color = color)) +
  theme_minimal()  + add_5flags +
  scale_color_identity() + xlab("") + ylab("") +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2022, 2), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.2, 0.85),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(10, 300, 10))

N1132G - ICT equipment (gross)

Code

nama_10_a64_p5 %>%
  filter(unit == "PD15_EUR",
         geo %in% c("FR", "NL", "IT", "DE", "ES"),
         nace_r2 == "TOTAL",
         asset10 == "N1132G") %>%
  left_join(geo, by = "geo") %>%
  year_to_date %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  group_by(Geo) %>%
  mutate(values = 100*values/values[date == as.Date("1995-01-01")]) %>%
  ggplot + geom_line(aes(x = date, y = values, color = color)) +
  theme_minimal()  + add_5flags +
  scale_color_identity() + xlab("") + ylab("") +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2022, 2), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.2, 0.85),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(10, 300, 10))

Information -communication

Code

nama_10_a64_p5 %>%
  filter(unit == "PD15_EUR",
         geo %in% c("FR", "NL", "IT", "DE", "ES"),
         nace_r2 == "J",
         asset10 == "N11G") %>%
  left_join(geo, by = "geo") %>%
  year_to_date %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  group_by(Geo) %>%
  mutate(values = 100*values/values[date == as.Date("1995-01-01")]) %>%
  ggplot + geom_line(aes(x = date, y = values, color = color)) +
  theme_minimal()  + add_5flags +
  scale_color_identity() + xlab("") + ylab("") +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2022, 2), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.2, 0.85),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(10, 300, 10))