Disposable income of private households by NUTS 2 regions

Data - Eurostat

Author

François Geerolf

Info

Data on inflation

Code 
load_data("inflation.RData")
inflation %>%
  arrange(-(dataset == "tgs00026")) %>%
  source_dataset_file_updates()
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 
tgs00026 %>%
  group_by(time) %>%
  summarise(Nobs = n()) %>%
  arrange(desc(time)) %>%
  head(2) %>%
  print_table_conditional()
time Nobs
2023 64
2022 248

unit

Code 
tgs00026 %>%
  left_join(unit, by = "unit") %>%
  group_by(unit, Unit) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()
unit Unit Nobs
MIO_PPS_EU27_2020 Million purchasing power standards (PPS, EU27 from 2020) 2822

time

Code 
tgs00026 %>%
  group_by(time) %>%
  summarise(Nobs = n()) %>%
  arrange(desc(time)) %>%
  print_table_conditional()
time Nobs
2023 64
2022 248
2021 249
2020 250
2019 251
2018 251
2017 251
2016 251
2015 251
2014 252
2013 252
2012 252

direct

Code 
tgs00026 %>%
  left_join(direct, by = "direct") %>%
  group_by(direct, Direct) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()
direct Direct Nobs
BAL Balance 2822

geo

Code 
tgs00026 %>%
  left_join(geo, by = "geo") %>%
  group_by(geo, Geo) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

Disposable household income

2015

Code 
tgs00026 %>% 
  filter(time == 2015, 
         nchar(geo) == 4) %>% 
  right_join(europe_NUTS2, by = "geo") %>%
  filter(long >= -15, lat >= 33) %>%
  ggplot(., aes(x = long, y = lat, group = group, fill = values/1000)) +
  geom_polygon() + coord_map() +
  scale_fill_viridis_c(na.value = "white",
                       labels = scales::dollar_format(accuracy = 1, prefix = "", suffix = " k€"),
                       breaks = c(seq(0, 300, 50)),
                       values = c(0, 0.1, 0.2, 0.3, 0.4, 0.5, 1)) +
  theme_void() + theme(legend.position = c(0.25, 0.85)) + 
  labs(fill = "Disposable Income")

2019

Code 
tgs00026 %>% 
  filter(time == 2019, 
         nchar(geo) == 4) %>% 
  right_join(europe_NUTS2, by = "geo") %>%
  filter(long >= -15, lat >= 33) %>%
  ggplot(., aes(x = long, y = lat, group = group, fill = values/1000)) +
  geom_polygon() + coord_map() +
  scale_fill_viridis_c(na.value = "white",
                       labels = scales::dollar_format(accuracy = 1, prefix = "", suffix = " k€"),
                       breaks = c(seq(0, 300, 50)),
                       values = c(0, 0.1, 0.2, 0.3, 0.4, 0.5, 1)) +
  theme_void() + theme(legend.position = c(0.25, 0.85)) + 
  labs(fill = "Disposable Income")