Code
tibble(LAST_DOWNLOAD = as.Date(file.info("~/iCloud/website/data/eurostat/namq_10_fcs.RData")$mtime)) %>%
print_table_conditional()| LAST_DOWNLOAD |
|---|
| 2025-05-18 |
Final consumption aggregates - namq_10_fcs
Data - Eurostat
Info
LAST_DOWNLOAD
LAST_COMPILE
| LAST_COMPILE |
|---|
| 2025-05-18 |
Last
Code
namq_10_fcs %>%
group_by(time) %>%
summarise(Nobs = n()) %>%
arrange(desc(time)) %>%
head(1) %>%
print_table_conditional()| time | Nobs |
|---|---|
| 2025Q1 | 864 |
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 | 191316 |
| P311_S14 | Final consumption expenditure of households, durable goods | 183174 |
| P312N_S14 | Final consumption expenditure of households, semi-durable goods, non-durable goods and services | 183174 |
| P312_S14 | Final consumption expenditure of households, semi-durable goods | 165130 |
| P313_S14 | Final consumption expenditure of households, non-durable goods | 165130 |
| P314_S14 | Final consumption expenditure of households, services | 165130 |
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) | 471542 |
| SCA | Seasonally and calendar adjusted data | 454063 |
| CA | Calendar adjusted data, not seasonally adjusted data | 80600 |
| SA | Seasonally adjusted data, not calendar adjusted data | 46849 |
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 |
|---|---|---|
| CLV15_MEUR | Chain linked volumes (2015), million euro | 61080 |
| CLV15_MNAC | Chain linked volumes (2015), million units of national currency | 61080 |
| CLV_I15 | Chain linked volumes, index 2015=100 | 61080 |
| CP_MEUR | Current prices, million euro | 60804 |
| CP_MNAC | Current prices, million units of national currency | 60804 |
| CLV10_MEUR | Chain linked volumes (2010), million euro | 59916 |
| CLV10_MNAC | Chain linked volumes (2010), million units of national currency | 59916 |
| CLV_I10 | Chain linked volumes, index 2010=100 | 59916 |
| PC_GDP | Percentage of gross domestic product (GDP) | 59238 |
| CLV05_MEUR | Chain linked volumes (2005), million euro | 59004 |
| CLV05_MNAC | Chain linked volumes (2005), million units of national currency | 59004 |
| CLV_I05 | Chain linked volumes, index 2005=100 | 59004 |
| CLV_PCH_SM | Chain linked volumes, percentage change compared to same period in previous year | 57944 |
| CLV20_MEUR | NA | 57918 |
| CLV20_MNAC | NA | 57918 |
| CLV_I20 | NA | 57918 |
| PYP_MEUR | Previous year prices, million euro | 36042 |
| PYP_MNAC | Previous year prices, million units of national currency | 36042 |
| CLV_PCH_PRE | Chain linked volumes, percentage change on previous period | 28426 |
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 | 122.761 | 146.780 | 114.200 | 120.613 | 112.638 | 126.335 |
| CLV_I10 | Chain linked volumes, index 2010=100 | 114.214 | 122.342 | 112.159 | 113.536 | 108.120 | 116.783 |
| CLV_I15 | Chain linked volumes, index 2015=100 | 110.081 | 119.923 | 110.764 | 109.237 | 107.315 | 109.940 |
| CLV_I20 | NA | 112.031 | 113.084 | 115.844 | 111.938 | 108.794 | 113.111 |
| CLV_PCH_SM | Chain linked volumes, percentage change compared to same period in previous year | 7.300 | -1.200 | 8.300 | 8.100 | 0.200 | 13.000 |
| CLV05_MEUR | Chain linked volumes (2005), million euro | 287954.900 | 33307.500 | 28009.200 | 255548.400 | 75436.600 | 152076.400 |
| CLV05_MNAC | Chain linked volumes (2005), million units of national currency | 287954.900 | 33307.500 | 28009.200 | 255548.400 | 75436.600 | 152076.400 |
| CLV10_MEUR | Chain linked volumes (2010), million euro | 306557.900 | 29482.900 | 28531.500 | 277376.500 | 83239.100 | 165693.600 |
| CLV10_MNAC | Chain linked volumes (2010), million units of national currency | 306557.900 | 29482.900 | 28531.500 | 277376.500 | 83239.100 | 165693.600 |
| CLV15_MEUR | Chain linked volumes (2015), million euro | 319142.300 | 28016.100 | 29189.300 | 291174.700 | 89681.900 | 172201.200 |
| CLV15_MNAC | Chain linked volumes (2015), million units of national currency | 319142.300 | 28016.100 | 29189.300 | 291174.700 | 89681.900 | 172201.200 |
| CLV20_MEUR | NA | 333559.900 | 27107.800 | 28846.300 | 306452.100 | 98022.000 | 179583.800 |
| CLV20_MNAC | NA | 333559.900 | 27107.800 | 28846.300 | 306452.100 | 98022.000 | 179583.800 |
| CP_MEUR | Current prices, million euro | 341579.000 | 28061.300 | 29888.000 | 313517.700 | 101809.400 | 181820.300 |
| CP_MNAC | Current prices, million units of national currency | 341579.000 | 28061.300 | 29888.000 | 313517.700 | 101809.400 | 181820.300 |
| PC_GDP | Percentage of gross domestic product (GDP) | 51.400 | 4.200 | 4.500 | 47.200 | 15.300 | 27.400 |
| PYP_MEUR | Previous year prices, million euro | 333559.900 | 27107.800 | 28846.300 | 306452.100 | 98022.000 | 179583.800 |
| PYP_MNAC | Previous year prices, million units of national currency | 333559.900 | 27107.800 | 28846.300 | 306452.100 | 98022.000 | 179583.800 |
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))