| source | dataset | Title | .html | .rData |
|---|---|---|---|---|
| eurostat | nama_10_a64_p5 | Gross capital formation by industry (up to NACE A*64) | 2026-02-23 | 2026-02-22 |
| eurostat | nama_10_gdp | GDP and main components (output, expenditure and income) | 2026-02-23 | 2026-02-22 |
Gross capital formation by industry (up to NACE A*64)
Data - Eurostat
Info
Data on inflation
| source | dataset | Title | .html | .rData |
|---|---|---|---|---|
| bis | CPI | Consumer Price Index | 2026-02-22 | 2026-02-22 |
| 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) | 2026-02-23 | 2026-02-22 |
| eurostat | prc_hicp_cow | HICP - country weights | 2026-02-23 | 2026-02-22 |
| eurostat | prc_hicp_ctrb | Contributions to euro area annual inflation (in percentage points) | 2026-02-23 | 2026-02-22 |
| eurostat | prc_hicp_inw | HICP - item weights | 2026-02-23 | 2026-02-22 |
| eurostat | prc_hicp_manr | HICP (2015 = 100) - monthly data (annual rate of change) | 2026-02-23 | 2026-02-22 |
| eurostat | prc_hicp_midx | HICP (2015 = 100) - monthly data (index) | 2026-02-23 | 2026-02-22 |
| eurostat | prc_hicp_mmor | HICP (2015 = 100) - monthly data (monthly rate of change) | 2026-02-23 | 2026-02-22 |
| eurostat | prc_ppp_ind | Purchasing power parities (PPPs), price level indices and real expenditures for ESA 2010 aggregates | 2026-02-23 | 2026-02-22 |
| eurostat | sts_inpp_m | Producer prices in industry, total - monthly data | 2026-02-23 | 2026-02-22 |
| eurostat | sts_inppd_m | Producer prices in industry, domestic market - monthly data | 2026-02-23 | 2026-02-22 |
| eurostat | sts_inppnd_m | Producer prices in industry, non domestic market - monthly data | 2024-06-24 | 2026-02-22 |
| fred | cpi | Consumer Price Index | 2026-02-22 | 2026-02-22 |
| fred | inflation | Inflation | 2026-02-22 | 2026-02-22 |
| imf | CPI | Consumer Price Index - CPI | 2026-02-22 | 2020-03-13 |
| oecd | MEI_PRICES_PPI | Producer Prices - MEI_PRICES_PPI | 2026-02-22 | 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 %) | 2026-02-22 | 2026-02-22 |
| wdi | NY.GDP.DEFL.KD.ZG | Inflation, GDP deflator (annual %) | 2026-02-22 | 2026-02-22 |
LAST_COMPILE
| LAST_COMPILE |
|---|
| 2026-02-24 |
Last
Code
nama_10_a64_p5 %>%
group_by(time) %>%
summarise(Nobs = n()) %>%
arrange(desc(time)) %>%
head(1) %>%
print_table_conditional()| time | Nobs |
|---|---|
| 2024 | 407654 |
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 | 17614922 |
| P5G | Gross capital formation | 521228 |
| P52 | Changes in inventories | 173347 |
| P52_P53 | Changes in inventories and acquisitions less disposals of valuables | 88948 |
| P53 | Acquisitions less disposals of valuables | 64667 |
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) | 1639661 |
| N11MG | Machinery and equipment and weapons systems (gross) | 1595940 |
| N112G | Other buildings and structures (gross) | 1573722 |
| N117G | Intellectual property products (gross) | 1568201 |
| N1131G | Transport equipment (gross) | 1562708 |
| N11KG | Total Construction (gross) | 1487062 |
| N11OG | Other machinery and equipment and weapons systems (gross) | 1314660 |
| N1132G | ICT equipment (gross) | 1307121 |
| N1173G | Computer software and databases (gross) | 1132485 |
| N11321G | Computer hardware (gross) | 976065 |
| N1171G | Research and development (gross) | 960265 |
| N111G | Dwellings (gross) | 890014 |
| N11322G | Telecommunications equipment (gross) | 863071 |
| N115G | Cultivated biological resources (gross) | 743947 |
| N1G | Produced non-financial assets (gross) | 521228 |
| N12G | Inventories (gross) | 173347 |
| N1MG | Inventories and acquisitions less disposals of valuables (gross) | 88948 |
| N13G | Valuables (gross) | 64667 |
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))