Code
tibble(LAST_DOWNLOAD = as.Date(file.info("~/iCloud/website/data/eurostat/namq_10_fcs.RData")$mtime)) %>%
print_table_conditional()| LAST_DOWNLOAD |
|---|
| 2025-10-11 |
Final consumption aggregates - namq_10_fcs
Data - Eurostat
Info
LAST_DOWNLOAD
LAST_COMPILE
| LAST_COMPILE |
|---|
| 2025-10-11 |
Last
Code
namq_10_fcs %>%
group_by(time) %>%
summarise(Nobs = n()) %>%
arrange(desc(time)) %>%
head(1) %>%
print_table_conditional()| time | Nobs |
|---|---|
| 2025Q2 | 7994 |
na_item
Code
namq_10_fcs %>%
left_join(na_item, by = "na_item") %>%
group_by(na_item, Na_item) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
print_table_conditional()| na_item | Na_item | Nobs |
|---|---|---|
| P31_S14 | Final consumption expenditure of households | 194519 |
| P311_S14 | Final consumption expenditure of households, durable goods | 186241 |
| P312N_S14 | Final consumption expenditure of households, semi-durable goods, non-durable goods and services | 186241 |
| P312_S14 | Final consumption expenditure of households, semi-durable goods | 167917 |
| P313_S14 | Final consumption expenditure of households, non-durable goods | 167917 |
| P314_S14 | Final consumption expenditure of households, services | 167917 |
s_adj
Code
namq_10_fcs %>%
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 |
|---|---|---|
| NSA | Unadjusted data (i.e. neither seasonally adjusted nor calendar adjusted data) | 476852 |
| SCA | Seasonally and calendar adjusted data | 468641 |
| CA | Calendar adjusted data, not seasonally adjusted data | 81856 |
| SA | Seasonally adjusted data, not calendar adjusted data | 43403 |
unit
Code
namq_10_fcs %>%
left_join(unit, by = "unit") %>%
group_by(unit, Unit) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
print_table_conditional()| unit | Unit | Nobs |
|---|---|---|
| CP_MEUR | Current prices, million euro | 61988 |
| CP_MNAC | Current prices, million units of national currency | 61988 |
| PC_GDP | Percentage of gross domestic product (GDP) | 61130 |
| CLV10_MEUR | Chain linked volumes (2010), million euro | 61094 |
| CLV10_MNAC | Chain linked volumes (2010), million units of national currency | 61094 |
| CLV15_MEUR | Chain linked volumes (2015), million euro | 61094 |
| CLV15_MNAC | Chain linked volumes (2015), million units of national currency | 61094 |
| CLV_I10 | Chain linked volumes, index 2010=100 | 61094 |
| CLV_I15 | Chain linked volumes, index 2015=100 | 61094 |
| CLV05_MEUR | Chain linked volumes (2005), million euro | 60158 |
| CLV05_MNAC | Chain linked volumes (2005), million units of national currency | 60158 |
| CLV_I05 | Chain linked volumes, index 2005=100 | 60158 |
| CLV_PCH_SM | Chain linked volumes, percentage change compared to same period in previous year | 59146 |
| CLV20_MEUR | NA | 59096 |
| CLV20_MNAC | NA | 59096 |
| CLV_I20 | NA | 59096 |
| PYP_MEUR | Previous year prices, million euro | 36470 |
| PYP_MNAC | Previous year prices, million units of national currency | 36470 |
| CLV_PCH_PRE | Chain linked volumes, percentage change on previous period | 29234 |
geo
Code
namq_10_fcs %>%
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
namq_10_fcs %>%
group_by(time) %>%
summarise(Nobs = n()) %>%
arrange(desc(time)) %>%
print_table_conditional()France
Table
Code
namq_10_fcs %>%
filter(geo == "FR",
time == "2021Q4",
s_adj == "NSA") %>%
select_if(~ n_distinct(.) > 1) %>%
left_join(unit, by = "unit") %>%
left_join(na_item, by = "na_item") %>%
mutate(Na_item = paste0(na_item, " - ", Na_item)) %>%
select(-na_item) %>%
spread(Na_item, values) %>%
print_table_conditional()| unit | Unit | P31_S14 - Final consumption expenditure of households | P311_S14 - Final consumption expenditure of households, durable goods | P312_S14 - Final consumption expenditure of households, semi-durable goods | P312N_S14 - Final consumption expenditure of households, semi-durable goods, non-durable goods and services | P313_S14 - Final consumption expenditure of households, non-durable goods | P314_S14 - Final consumption expenditure of households, services |
|---|---|---|---|---|---|---|---|
| CLV_I05 | Chain linked volumes, index 2005=100 | 122.810 | 148.083 | 114.518 | 120.571 | 112.178 | 126.486 |
| CLV_I10 | Chain linked volumes, index 2010=100 | 114.260 | 123.428 | 112.471 | 113.496 | 107.678 | 116.922 |
| CLV_I15 | Chain linked volumes, index 2015=100 | 110.125 | 120.987 | 111.072 | 109.199 | 106.877 | 110.070 |
| CLV_I20 | NA | 112.076 | 114.088 | 116.166 | 111.899 | 108.349 | 113.245 |
| CLV_PCH_SM | Chain linked volumes, percentage change compared to same period in previous year | 7.400 | -1.200 | 8.300 | 8.300 | 0.500 | 13.000 |
| CLV05_MEUR | Chain linked volumes (2005), million euro | 288070.700 | 33603.100 | 28087.100 | 255459.500 | 75128.300 | 152257.400 |
| CLV05_MNAC | Chain linked volumes (2005), million units of national currency | 288070.700 | 33603.100 | 28087.100 | 255459.500 | 75128.300 | 152257.400 |
| CLV10_MEUR | Chain linked volumes (2010), million euro | 306681.100 | 29744.500 | 28610.900 | 277280.100 | 82899.000 | 165890.800 |
| CLV10_MNAC | Chain linked volumes (2010), million units of national currency | 306681.100 | 29744.500 | 28610.900 | 277280.100 | 82899.000 | 165890.800 |
| CLV15_MEUR | Chain linked volumes (2015), million euro | 319270.600 | 28264.800 | 29270.600 | 291073.500 | 89315.600 | 172406.100 |
| CLV15_MNAC | Chain linked volumes (2015), million units of national currency | 319270.600 | 28264.800 | 29270.600 | 291073.500 | 89315.600 | 172406.100 |
| CLV20_MEUR | NA | 333694.000 | 27348.400 | 28926.600 | 306345.600 | 97621.500 | 179797.500 |
| CLV20_MNAC | NA | 333694.000 | 27348.400 | 28926.600 | 306345.600 | 97621.500 | 179797.500 |
| CP_MEUR | Current prices, million euro | 341838.900 | 28447.000 | 29976.000 | 313391.900 | 101410.400 | 182005.500 |
| CP_MNAC | Current prices, million units of national currency | 341838.900 | 28447.000 | 29976.000 | 313391.900 | 101410.400 | 182005.500 |
| PC_GDP | Percentage of gross domestic product (GDP) | 51.600 | 4.300 | 4.500 | 47.300 | 15.300 | 27.500 |
| PYP_MEUR | Previous year prices, million euro | 333694.000 | 27348.400 | 28926.600 | 306345.600 | 97621.500 | 179797.500 |
| PYP_MNAC | Previous year prices, million units of national currency | 333694.000 | 27348.400 | 28926.600 | 306345.600 | 97621.500 | 179797.500 |
France, Germany, Spain, Italy, Netherlands
All
Consumption
Code
namq_10_fcs %>%
filter(geo %in% c("FR", "EA", "ES", "IT", "DE"),
unit == "PC_GDP",
s_adj == "NSA",
na_item == "P31_S14") %>%
quarter_to_date %>%
mutate(values = values / 100) %>%
left_join(geo, by = "geo") %>%
mutate(Geo = ifelse(geo == "EA", "Europe", Geo)) %>%
left_join(colors, by = c("Geo" = "country")) %>%
mutate(color = ifelse(geo == "EA", color2, color)) %>%
ggplot + geom_line(aes(x = date, y = values, color = color)) +
scale_color_identity() + theme_minimal() + add_4flags + xlab("") +
ylab("Final consumption expenditure of households (% of GDP)") +
scale_x_date(breaks = as.Date(paste0(seq(1960, 2022, 5), "-01-01")),
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-30, 100, 2),
labels = percent_format(a = 1))
1995-
Consumption
Code
namq_10_fcs %>%
filter(geo %in% c("FR", "NL", "ES", "IT", "DE"),
unit == "PC_GDP",
s_adj == "NSA",
na_item == "P31_S14") %>%
quarter_to_date %>%
filter(date >= as.Date("1995-01-01")) %>%
mutate(values = values / 100) %>%
left_join(geo, by = "geo") %>%
left_join(colors, by = c("Geo" = "country")) %>%
mutate(color = ifelse(geo == "NL", color2, color)) %>%
ggplot + geom_line(aes(x = date, y = values, color = color)) +
scale_color_identity() + theme_minimal() + add_4flags + xlab("") +
ylab("Final consumption expenditure of households (% of GDP)") +
scale_x_date(breaks = as.Date(paste0(seq(1960, 2022, 5), "-01-01")),
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-30, 100, 2),
labels = percent_format(a = 1))
Durable goods
Code
namq_10_fcs %>%
filter(geo %in% c("FR", "NL", "ES", "IT", "DE"),
unit == "PC_GDP",
s_adj == "NSA",
na_item == "P311_S14") %>%
quarter_to_date %>%
filter(date >= as.Date("1995-01-01")) %>%
mutate(values = values / 100) %>%
left_join(geo, by = "geo") %>%
left_join(colors, by = c("Geo" = "country")) %>%
mutate(color = ifelse(geo == "NL", color2, color)) %>%
ggplot + geom_line(aes(x = date, y = values, color = color)) +
scale_color_identity() + theme_minimal() + add_4flags + xlab("") +
ylab("Durable goods (% of GDP)") +
scale_x_date(breaks = as.Date(paste0(seq(1960, 2022, 5), "-01-01")),
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-30, 100, 1),
labels = percent_format(a = 1))
Non-durable goods
Code
namq_10_fcs %>%
filter(geo %in% c("FR", "NL", "ES", "IT", "DE"),
unit == "PC_GDP",
s_adj == "NSA",
na_item == "P313_S14") %>%
quarter_to_date %>%
filter(date >= as.Date("1995-01-01")) %>%
mutate(values = values / 100) %>%
left_join(geo, by = "geo") %>%
left_join(colors, by = c("Geo" = "country")) %>%
mutate(color = ifelse(geo == "NL", color2, color)) %>%
ggplot + geom_line(aes(x = date, y = values, color = color)) +
scale_color_identity() + theme_minimal() + add_4flags + xlab("") +
ylab("Non-durable goods (% of GDP)") +
scale_x_date(breaks = as.Date(paste0(seq(1960, 2022, 5), "-01-01")),
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-30, 100, 1),
labels = percent_format(a = 1))
Services
Code
namq_10_fcs %>%
filter(geo %in% c("FR", "NL", "ES", "IT", "DE"),
unit == "PC_GDP",
s_adj == "NSA",
na_item == "P314_S14") %>%
quarter_to_date %>%
filter(date >= as.Date("1995-01-01")) %>%
mutate(values = values / 100) %>%
left_join(geo, by = "geo") %>%
left_join(colors, by = c("Geo" = "country")) %>%
mutate(color = ifelse(geo == "NL", color2, color)) %>%
ggplot + geom_line(aes(x = date, y = values, color = color)) +
scale_color_identity() + theme_minimal() + add_4flags + xlab("") +
ylab("Services (% of GDP)") +
scale_x_date(breaks = as.Date(paste0(seq(1960, 2022, 5), "-01-01")),
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-30, 100, 1),
labels = percent_format(a = 1))