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

Data - Eurostat

Author

Info

source	dataset	.html	.RData
eurostat	nama_10_a64_p5	2024-06-20	2024-06-23
eurostat	nama_10_gdp	2024-06-20	2024-06-18

Data on inflation

source	dataset	.html	.RData
bis	CPI	2024-06-19	2022-01-20
ecb	CES	2024-06-19	2024-01-12
eurostat	nama_10_co3_p3	2024-06-20	2024-06-08
eurostat	prc_hicp_cow	2024-06-20	2024-06-08
eurostat	prc_hicp_ctrb	2024-06-20	2024-06-08
eurostat	prc_hicp_inw	2024-06-20	2024-06-23
eurostat	prc_hicp_manr	2024-06-20	2024-06-08
eurostat	prc_hicp_midx	2024-06-20	2024-06-23
eurostat	prc_hicp_mmor	2024-06-20	2024-06-18
eurostat	prc_ppp_ind	2024-06-20	2024-06-08
eurostat	sts_inpp_m	2024-06-20	2024-06-18
eurostat	sts_inppd_m	2024-06-20	2024-06-08
eurostat	sts_inppnd_m	2024-06-20	2024-06-08
fred	cpi	2024-06-20	2024-06-07
fred	inflation	2024-06-18	2024-06-07
imf	CPI	2024-06-20	2020-03-13
oecd	MEI_PRICES_PPI	2024-06-20	2024-04-15
oecd	PPP2017	2024-04-16	2023-07-25
oecd	PRICES_CPI	2024-04-16	2024-04-15
wdi	FP.CPI.TOTL.ZG	2023-01-15	2024-04-14
wdi	NY.GDP.DEFL.KD.ZG	2024-04-14	2024-04-14

LAST_COMPILE

LAST_COMPILE
2024-06-24

Last

Code

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

time	Nobs
2023	35358

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	7732498
P5G	Gross capital formation	373755
P52	Changes in inventories	187741
P52_P53	Changes in inventories and acquisitions less disposals of valuables	114337
P53	Acquisitions less disposals of valuables	87736

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)	1027147
N11MG	Machinery and equipment and weapons systems (gross)	972686
N112G	Other buildings and structures (gross)	961683
N1131G	Transport equipment (gross)	957157
N117G	Intellectual property products (gross)	828239
N11OG	Other machinery and equipment and weapons systems (gross)	717220
N1132G	ICT equipment (gross)	663380
N111G	Dwellings (gross)	623382
N115G	Cultivated biological resources (gross)	597765
N11KG	Total Construction (gross)	383839
N1G	Produced non-financial assets (gross)	373755
N12G	Inventories (gross)	187741
N1MG	Inventories and acquisitions less disposals of valuables (gross)	114337
N13G	Valuables (gross)	87736

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))