Purchasing power parities (PPPs), price level indices and real expenditures for ESA 2010 aggregates

Data - Eurostat

Author

François Geerolf

Info

Data on inflation

source dataset .html .RData

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 2024-06-19 2022-01-20

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CES

 2024-06-19 2024-01-12

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nama_10_co3_p3

 2024-06-20 2024-06-08

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 2024-06-24 2024-06-08

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prc_hicp_ctrb

 2024-06-24 2024-06-08

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prc_hicp_inw

 2024-06-20 2024-06-23

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prc_hicp_manr

 2024-06-24 2024-06-08

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prc_hicp_midx

 2024-06-24 2024-06-23

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prc_hicp_mmor

 2024-06-24 2024-06-18

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prc_ppp_ind

 2024-06-20 2024-06-08

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sts_inpp_m

 2024-06-20 2024-06-18

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 2024-06-20 2024-06-08

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sts_inppnd_m

 2024-06-20 2024-06-08

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cpi

 2024-06-20 2024-06-07

fred

inflation

 2024-06-18 2024-06-07

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CPI

 2024-06-20 2020-03-13

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 2024-06-20 2024-04-15

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PPP2017

 2024-04-16 2023-07-25

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PRICES_CPI

 2024-04-16 2024-04-15

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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 
prc_ppp_ind %>%
  group_by(time) %>%
  summarise(Nobs = n()) %>%
  arrange(desc(time)) %>%
  head(1) %>%
  print_table_conditional()
time Nobs
2023 296

What’s PPP ?

PPPs are nothing more than price relatives that show the ratio of the prices in national currencies of the same good or service in different countries. For example, if the price of a litre of Coca Cola is 2.30 euros in France and 2.00 dollars in the United States, then the PPP for Coca Cola between France and the United States is the ratio 2.30 euros to 2.00 dollars or 1.15 euros to the dollar. This means that for every dollar spent on Coca Cola in the United States, 1.15 euros would have to be spent in France to obtain the same quantity and quality – or, in other words, the same volume - of Coca Cola.

Eurostat Website

Actual individual consumption per capita

Code 
include_graphics("https://ec.europa.eu/eurostat/documents/4187653/11581511/Map+AIC+per+capita+2020.jpg/fbd93f3e-ebe8-29bc-8cd3-8d790990ca30?t=1624002507913")

Volume indices of AIC and GDP per capita

Code 
include_graphics("https://ec.europa.eu/eurostat/documents/4187653/11581511/AIC+GDP+per+capita+2020.jpg/c1f554b3-807e-d0b7-1337-333e3eef5d17?t=1624002507551")

na_item

Code 
prc_ppp_ind %>%
  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
PPP_EU15 Purchasing power parities (EU15=1) 72538
PLI_EU15 Price level indices (EU15=100) 72419
PPP_EU27_2007 Purchasing power parities (EU27_2007=1) 67666
PLI_EU27_2007 Price level indices (EU27_2007=100) 67610
PPP_EU27_2020 Purchasing power parities (EU27_2020=1) 63652
PLI_EU27_2020 Price level indices (EU27_2020=100) 63621
EXP_NAC Nominal expenditure in national currency 59598
EXP_EUR_HAB Nominal expenditure per inhabitant (in euro) 59547
EXP_EUR Nominal expenditure (in euro) 59546
EXP_NAC_PC_GDP Nominal expenditure as a percentage of GDP (GDP=100) 59509
EXP_PPS_EU15_HAB Real expenditure per capita (in PPS_EU15) 55259
EXP_PPS_EU15 Real expenditure (in PPS_EU15) 55133
VI_PPS_EU15_HAB Volume indices of real expenditure per capita (in PPS_EU15=100) 55133
PPP_EU28 Purchasing power parities (EU28=1) 54622
PLI_EU28 Price level indices (EU28=100) 54608
EXP_PPS_EU27_2007_HAB Real expenditure per capita (in PPS_EU27_2007) 51648
EXP_PPS_EU27_2007 Real expenditure (in PPS_EU27_2007) 51521
VI_PPS_EU27_2007_HAB Volume indices of real expenditure per capita (in PPS_EU27_2007=100) 51521
EXP_PPS_EU27_2020 Real expenditure (in PPS_EU27_2020) 48437
EXP_PPS_EU27_2020_HAB Real expenditure per capita (in PPS_EU27_2020) 48437
VI_PPS_EU27_2020_HAB Volume indices of real expenditure per capita (in PPS_EU27_2020=100) 48437
EXP_PPS_EU28 Real expenditure (in PPS_EU28) 41721
EXP_PPS_EU28_HAB Real expenditure per capita (in PPS_EU28) 41721
VI_PPS_EU28_HAB Volume indices of real expenditure per capita (in PPS_EU28=100) 41721

ppp_cat

Code 
prc_ppp_ind %>%
  left_join(ppp_cat, by = "ppp_cat") %>%
  group_by(ppp_cat, Ppp_cat) %>%
  summarise(Nobs = n()) %>%
  print_table_conditional()

geo

Code 
prc_ppp_ind %>%
  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 
prc_ppp_ind %>%
  group_by(time) %>%
  summarise(Nobs = n()) %>%
  print_table_conditional()
time Nobs
1995 12204
1996 12183
1997 12318
1998 12327
1999 31969
2000 32109
2001 32361
2002 32109
2003 58733
2004 58733
2005 63244
2006 63382
2007 63382
2008 63382
2009 63382
2010 63792
2011 63792
2012 63792
2013 64135
2014 64197
2015 64197
2016 64197
2017 64197
2018 64250
2019 64196
2020 50588
2021 21089
2022 21089
2023 296

Germany, France, Italy

GDP - Gross domestic product

Code 
prc_ppp_ind %>%
  filter(ppp_cat == "GDP",
         geo %in% c("FR", "DE", "IT"),
         na_item == "PPP_EU27_2020") %>%
  year_to_date %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot + geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + theme_minimal()  + add_3flags +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2028, 2), "-01-01")),
               labels = date_format("%Y")) +
  xlab("") + ylab("") +
  scale_y_continuous(breaks = seq(0, 4000, .05))

E01 - Final consumption expenditure

Code 
prc_ppp_ind %>%
  filter(ppp_cat == "E01",
         geo %in% c("FR", "DE", "IT"),
         na_item == "PPP_EU27_2020") %>%
  year_to_date %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot + geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + theme_minimal()  + add_3flags +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2028, 2), "-01-01")),
               labels = date_format("%Y")) +
  xlab("") + ylab("") +
  scale_y_continuous(breaks = seq(0, 4000, .05))

E011 - Household final consumption expenditure

Code 
prc_ppp_ind %>%
  filter(ppp_cat == "E011",
         geo %in% c("FR", "DE", "IT"),
         na_item == "PPP_EU27_2020") %>%
  year_to_date %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot + geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + theme_minimal()  + add_3flags +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2028, 2), "-01-01")),
               labels = date_format("%Y")) +
  xlab("") + ylab("") +
  scale_y_continuous(breaks = seq(0, 4000, .05))

A01 - Actual individual consumption

Code 
prc_ppp_ind %>%
  filter(ppp_cat == "A01",
         geo %in% c("FR", "DE", "IT"),
         na_item == "PPP_EU27_2020") %>%
  year_to_date %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot + geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + theme_minimal()  + add_3flags +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2028, 2), "-01-01")),
               labels = date_format("%Y")) +
  xlab("") + ylab("") +
  scale_y_continuous(breaks = seq(0, 4000, .05))

Housing, water, electricity, gas and other fuels - A0104

France

Code 
prc_ppp_ind %>%
  filter(ppp_cat == "A0104",
         geo %in% c("FR")) %>%
  left_join(na_item, by = "na_item") %>%
  group_by(na_item, Na_item) %>%
  summarise(Nobs = n(),
            first = first(time),
            last = last(time)) %>%
  print_table_conditional()
na_item Na_item Nobs first last
EXP_EUR Nominal expenditure (in euro) 28 1995 2022
EXP_EUR_HAB Nominal expenditure per inhabitant (in euro) 28 1995 2022
EXP_NAC Nominal expenditure in national currency 28 1995 2022
EXP_NAC_PC_GDP Nominal expenditure as a percentage of GDP (GDP=100) 28 1995 2022
EXP_PPS_EU15 Real expenditure (in PPS_EU15) 26 1995 2020
EXP_PPS_EU15_HAB Real expenditure per capita (in PPS_EU15) 26 1995 2020
EXP_PPS_EU27_2007 Real expenditure (in PPS_EU27_2007) 22 1999 2020
EXP_PPS_EU27_2007_HAB Real expenditure per capita (in PPS_EU27_2007) 22 1999 2020
EXP_PPS_EU27_2020 Real expenditure (in PPS_EU27_2020) 20 2003 2022
EXP_PPS_EU27_2020_HAB Real expenditure per capita (in PPS_EU27_2020) 20 2003 2022
EXP_PPS_EU28 Real expenditure (in PPS_EU28) 17 2003 2019
EXP_PPS_EU28_HAB Real expenditure per capita (in PPS_EU28) 17 2003 2019
PLI_EU15 Price level indices (EU15=100) 26 1995 2020
PLI_EU27_2007 Price level indices (EU27_2007=100) 22 1999 2020
PLI_EU27_2020 Price level indices (EU27_2020=100) 20 2003 2022
PLI_EU28 Price level indices (EU28=100) 17 2003 2019
PPP_EU15 Purchasing power parities (EU15=1) 26 1995 2020
PPP_EU27_2007 Purchasing power parities (EU27_2007=1) 22 1999 2020
PPP_EU27_2020 Purchasing power parities (EU27_2020=1) 20 2003 2022
PPP_EU28 Purchasing power parities (EU28=1) 17 2003 2019
VI_PPS_EU15_HAB Volume indices of real expenditure per capita (in PPS_EU15=100) 26 1995 2020
VI_PPS_EU27_2007_HAB Volume indices of real expenditure per capita (in PPS_EU27_2007=100) 22 1999 2020
VI_PPS_EU27_2020_HAB Volume indices of real expenditure per capita (in PPS_EU27_2020=100) 20 2003 2022
VI_PPS_EU28_HAB Volume indices of real expenditure per capita (in PPS_EU28=100) 17 2003 2019

2019

Code 
prc_ppp_ind %>%
  filter(ppp_cat == "A0104",
         time == "2019",
         geo %in% c("FR", "DE", "IT")) %>%
  select(na_item, geo, values) %>%
  left_join(na_item, by = "na_item") %>%
  spread(geo, values) %>%
  print_table_conditional()
na_item Na_item DE FR IT
EXP_EUR Nominal expenditure (in euro) 4.12979e+05 3.49680e+05 2.44421e+05
EXP_EUR_HAB Nominal expenditure per inhabitant (in euro) 4.97000e+03 5.16100e+03 4.09200e+03
EXP_NAC Nominal expenditure in national currency 4.12979e+05 3.49680e+05 2.44421e+05
EXP_NAC_PC_GDP Nominal expenditure as a percentage of GDP (GDP=100) 1.19000e+01 1.43000e+01 1.36000e+01
EXP_PPS_EU15 Real expenditure (in PPS_EU15) 4.33824e+05 3.49749e+05 3.00959e+05
EXP_PPS_EU15_HAB Real expenditure per capita (in PPS_EU15) 5.20000e+03 5.20000e+03 5.00000e+03
EXP_PPS_EU27_2007 Real expenditure (in PPS_EU27_2007) 3.88724e+05 3.13390e+05 2.69672e+05
EXP_PPS_EU27_2007_HAB Real expenditure per capita (in PPS_EU27_2007) 4.70000e+03 4.60000e+03 4.50000e+03
EXP_PPS_EU27_2020 Real expenditure (in PPS_EU27_2020) 3.57522e+05 2.88235e+05 2.48026e+05
EXP_PPS_EU27_2020_HAB Real expenditure per capita (in PPS_EU27_2020) 4.30000e+03 4.30000e+03 4.20000e+03
EXP_PPS_EU28 Real expenditure (in PPS_EU28) 3.87042e+05 3.12034e+05 2.68505e+05
EXP_PPS_EU28_HAB Real expenditure per capita (in PPS_EU28) 4.70000e+03 4.60000e+03 4.50000e+03
PLI_EU15 Price level indices (EU15=100) 9.52000e+01 1.00000e+02 8.12000e+01
PLI_EU27_2007 Price level indices (EU27_2007=100) 1.06200e+02 1.11600e+02 9.06000e+01
PLI_EU27_2020 Price level indices (EU27_2020=100) 1.15500e+02 1.21300e+02 9.85000e+01
PLI_EU28 Price level indices (EU28=100) 1.06700e+02 1.12100e+02 9.10000e+01
PPP_EU15 Purchasing power parities (EU15=1) 9.51950e-01 9.99803e-01 8.12139e-01
PPP_EU27_2007 Purchasing power parities (EU27_2007=1) 1.06240e+00 1.11580e+00 9.06363e-01
PPP_EU27_2020 Purchasing power parities (EU27_2020=1) 1.15511e+00 1.21318e+00 9.85464e-01
PPP_EU28 Purchasing power parities (EU28=1) 1.06701e+00 1.12065e+00 9.10302e-01
VI_PPS_EU15_HAB Volume indices of real expenditure per capita (in PPS_EU15=100) 1.09000e+02 1.07000e+02 1.05000e+02
VI_PPS_EU27_2007_HAB Volume indices of real expenditure per capita (in PPS_EU27_2007=100) 1.12000e+02 1.10000e+02 1.08000e+02
VI_PPS_EU27_2020_HAB Volume indices of real expenditure per capita (in PPS_EU27_2020=100) 1.10000e+02 1.09000e+02 1.07000e+02
VI_PPS_EU28_HAB Volume indices of real expenditure per capita (in PPS_EU28=100) 1.12000e+02 1.10000e+02 1.08000e+02

Germany, France, Italy

PPP_EU27_2020

Code 
prc_ppp_ind %>%
  filter(ppp_cat == "A0104",
         geo %in% c("FR", "DE", "IT"),
         na_item == "PPP_EU27_2020") %>%
  year_to_date %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot + geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + theme_minimal()  + add_3flags +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2028, 2), "-01-01")),
               labels = date_format("%Y")) +
  xlab("") + ylab("") +
  scale_y_continuous(breaks = seq(0, 4000, .05))

PLI_EU28

Code 
prc_ppp_ind %>%
  filter(ppp_cat == "A0104",
         geo %in% c("FR", "DE", "IT"),
         na_item == "PLI_EU28") %>%
  year_to_date %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot + geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + theme_minimal()  + add_3flags +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2028, 2), "-01-01")),
               labels = date_format("%Y")) +
  xlab("") + ylab("") +
  scale_y_continuous(breaks = seq(0, 4000, 5))

Table

Household final consumption expenditure - E011

Code 
prc_ppp_ind %>%
  filter(ppp_cat == "E011",
         time == "2019",
         geo %in% c("FR", "DE", "IT")) %>%
  select(na_item, geo, values) %>%
  left_join(na_item, by = "na_item") %>%
  spread(geo, values) %>%
  print_table_conditional()
na_item Na_item DE FR IT
EXP_EUR Nominal expenditure (in euro) 1.752890e+06 1.256724e+06 1.064898e+06
EXP_EUR_HAB Nominal expenditure per inhabitant (in euro) 2.109600e+04 1.854800e+04 1.782900e+04
EXP_NAC Nominal expenditure in national currency 1.752890e+06 1.256724e+06 1.064898e+06
EXP_NAC_PC_GDP Nominal expenditure as a percentage of GDP (GDP=100) 5.050000e+01 5.160000e+01 5.930000e+01
EXP_PPS_EU15 Real expenditure (in PPS_EU15) 1.802577e+06 1.219516e+06 1.153432e+06
EXP_PPS_EU15_HAB Real expenditure per capita (in PPS_EU15) 2.170000e+04 1.800000e+04 1.930000e+04
EXP_PPS_EU27_2007 Real expenditure (in PPS_EU27_2007) 1.691843e+06 1.144599e+06 1.082576e+06
EXP_PPS_EU27_2007_HAB Real expenditure per capita (in PPS_EU27_2007) 2.040000e+04 1.690000e+04 1.810000e+04
EXP_PPS_EU27_2020 Real expenditure (in PPS_EU27_2020) 1.637041e+06 1.107524e+06 1.047509e+06
EXP_PPS_EU27_2020_HAB Real expenditure per capita (in PPS_EU27_2020) 1.970000e+04 1.630000e+04 1.750000e+04
EXP_PPS_EU28 Real expenditure (in PPS_EU28) 1.689175e+06 1.142794e+06 1.080869e+06
EXP_PPS_EU28_HAB Real expenditure per capita (in PPS_EU28) 2.030000e+04 1.690000e+04 1.810000e+04
PLI_EU15 Price level indices (EU15=100) 9.720000e+01 1.031000e+02 9.230000e+01
PLI_EU27_2007 Price level indices (EU27_2007=100) 1.036000e+02 1.098000e+02 9.840000e+01
PLI_EU27_2020 Price level indices (EU27_2020=100) 1.071000e+02 1.135000e+02 1.017000e+02
PLI_EU28 Price level indices (EU28=100) 1.038000e+02 1.100000e+02 9.850000e+01
PPP_EU15 Purchasing power parities (EU15=1) 9.724360e-01 1.030510e+00 9.232420e-01
PPP_EU27_2007 Purchasing power parities (EU27_2007=1) 1.036080e+00 1.097960e+00 9.836700e-01
PPP_EU27_2020 Purchasing power parities (EU27_2020=1) 1.070770e+00 1.134720e+00 1.016600e+00
PPP_EU28 Purchasing power parities (EU28=1) 1.037720e+00 1.099690e+00 9.852240e-01
VI_PPS_EU15_HAB Volume indices of real expenditure per capita (in PPS_EU15=100) 1.120000e+02 9.300000e+01 9.900000e+01
VI_PPS_EU27_2007_HAB Volume indices of real expenditure per capita (in PPS_EU27_2007=100) 1.180000e+02 9.800000e+01 1.050000e+02
VI_PPS_EU27_2020_HAB Volume indices of real expenditure per capita (in PPS_EU27_2020=100) 1.220000e+02 1.010000e+02 1.080000e+02
VI_PPS_EU28_HAB Volume indices of real expenditure per capita (in PPS_EU28=100) 1.190000e+02 9.800000e+01 1.060000e+02

Gross domestic product - GDP

Code 
prc_ppp_ind %>%
  filter(ppp_cat == "GDP",
         time == "2019",
         geo %in% c("FR", "DE", "IT")) %>%
  select(na_item, geo, values) %>%
  left_join(na_item, by = "na_item") %>%
  spread(geo, values) %>%
  print_table_conditional()
na_item Na_item DE FR IT
EXP_EUR Nominal expenditure (in euro) 3.474110e+06 2.437635e+06 1.796648e+06
EXP_EUR_HAB Nominal expenditure per inhabitant (in euro) 4.181000e+04 3.597700e+04 3.008000e+04
EXP_NAC Nominal expenditure in national currency 3.474110e+06 2.437635e+06 1.796648e+06
EXP_NAC_PC_GDP Nominal expenditure as a percentage of GDP (GDP=100) 1.000000e+02 1.000000e+02 1.000000e+02
EXP_PPS_EU15 Real expenditure (in PPS_EU15) 3.431946e+06 2.448269e+06 1.968004e+06
EXP_PPS_EU15_HAB Real expenditure per capita (in PPS_EU15) 4.130000e+04 3.610000e+04 3.290000e+04
EXP_PPS_EU27_2007 Real expenditure (in PPS_EU27_2007) 3.226618e+06 2.301793e+06 1.850261e+06
EXP_PPS_EU27_2007_HAB Real expenditure per capita (in PPS_EU27_2007) 3.880000e+04 3.400000e+04 3.100000e+04
EXP_PPS_EU27_2020 Real expenditure (in PPS_EU27_2020) 3.147472e+06 2.245332e+06 1.804876e+06
EXP_PPS_EU27_2020_HAB Real expenditure per capita (in PPS_EU27_2020) 3.790000e+04 3.310000e+04 3.020000e+04
EXP_PPS_EU28 Real expenditure (in PPS_EU28) 3.220536e+06 2.297454e+06 1.846774e+06
EXP_PPS_EU28_HAB Real expenditure per capita (in PPS_EU28) 3.880000e+04 3.390000e+04 3.090000e+04
PLI_EU15 Price level indices (EU15=100) 1.012000e+02 9.960000e+01 9.130000e+01
PLI_EU27_2007 Price level indices (EU27_2007=100) 1.077000e+02 1.059000e+02 9.710000e+01
PLI_EU27_2020 Price level indices (EU27_2020=100) 1.104000e+02 1.086000e+02 9.950000e+01
PLI_EU28 Price level indices (EU28=100) 1.079000e+02 1.061000e+02 9.730000e+01
PPP_EU15 Purchasing power parities (EU15=1) 1.012290e+00 9.956560e-01 9.129290e-01
PPP_EU27_2007 Purchasing power parities (EU27_2007=1) 1.076700e+00 1.059020e+00 9.710240e-01
PPP_EU27_2020 Purchasing power parities (EU27_2020=1) 1.103780e+00 1.085650e+00 9.954410e-01
PPP_EU28 Purchasing power parities (EU28=1) 1.078740e+00 1.061020e+00 9.728580e-01
VI_PPS_EU15_HAB Volume indices of real expenditure per capita (in PPS_EU15=100) 1.130000e+02 9.900000e+01 9.000000e+01
VI_PPS_EU27_2007_HAB Volume indices of real expenditure per capita (in PPS_EU27_2007=100) 1.200000e+02 1.050000e+02 9.600000e+01
VI_PPS_EU27_2020_HAB Volume indices of real expenditure per capita (in PPS_EU27_2020=100) 1.210000e+02 1.060000e+02 9.700000e+01
VI_PPS_EU28_HAB Volume indices of real expenditure per capita (in PPS_EU28=100) 1.200000e+02 1.050000e+02 9.600000e+01

Actual individual consumption - A01

Code 
prc_ppp_ind %>%
  filter(ppp_cat == "E011",
         time == "2019",
         geo %in% c("FR", "DE", "IT")) %>%
  select(na_item, geo, values) %>%
  left_join(na_item, by = "na_item") %>%
  spread(geo, values) %>%
  print_table_conditional()
na_item Na_item DE FR IT
EXP_EUR Nominal expenditure (in euro) 1.752890e+06 1.256724e+06 1.064898e+06
EXP_EUR_HAB Nominal expenditure per inhabitant (in euro) 2.109600e+04 1.854800e+04 1.782900e+04
EXP_NAC Nominal expenditure in national currency 1.752890e+06 1.256724e+06 1.064898e+06
EXP_NAC_PC_GDP Nominal expenditure as a percentage of GDP (GDP=100) 5.050000e+01 5.160000e+01 5.930000e+01
EXP_PPS_EU15 Real expenditure (in PPS_EU15) 1.802577e+06 1.219516e+06 1.153432e+06
EXP_PPS_EU15_HAB Real expenditure per capita (in PPS_EU15) 2.170000e+04 1.800000e+04 1.930000e+04
EXP_PPS_EU27_2007 Real expenditure (in PPS_EU27_2007) 1.691843e+06 1.144599e+06 1.082576e+06
EXP_PPS_EU27_2007_HAB Real expenditure per capita (in PPS_EU27_2007) 2.040000e+04 1.690000e+04 1.810000e+04
EXP_PPS_EU27_2020 Real expenditure (in PPS_EU27_2020) 1.637041e+06 1.107524e+06 1.047509e+06
EXP_PPS_EU27_2020_HAB Real expenditure per capita (in PPS_EU27_2020) 1.970000e+04 1.630000e+04 1.750000e+04
EXP_PPS_EU28 Real expenditure (in PPS_EU28) 1.689175e+06 1.142794e+06 1.080869e+06
EXP_PPS_EU28_HAB Real expenditure per capita (in PPS_EU28) 2.030000e+04 1.690000e+04 1.810000e+04
PLI_EU15 Price level indices (EU15=100) 9.720000e+01 1.031000e+02 9.230000e+01
PLI_EU27_2007 Price level indices (EU27_2007=100) 1.036000e+02 1.098000e+02 9.840000e+01
PLI_EU27_2020 Price level indices (EU27_2020=100) 1.071000e+02 1.135000e+02 1.017000e+02
PLI_EU28 Price level indices (EU28=100) 1.038000e+02 1.100000e+02 9.850000e+01
PPP_EU15 Purchasing power parities (EU15=1) 9.724360e-01 1.030510e+00 9.232420e-01
PPP_EU27_2007 Purchasing power parities (EU27_2007=1) 1.036080e+00 1.097960e+00 9.836700e-01
PPP_EU27_2020 Purchasing power parities (EU27_2020=1) 1.070770e+00 1.134720e+00 1.016600e+00
PPP_EU28 Purchasing power parities (EU28=1) 1.037720e+00 1.099690e+00 9.852240e-01
VI_PPS_EU15_HAB Volume indices of real expenditure per capita (in PPS_EU15=100) 1.120000e+02 9.300000e+01 9.900000e+01
VI_PPS_EU27_2007_HAB Volume indices of real expenditure per capita (in PPS_EU27_2007=100) 1.180000e+02 9.800000e+01 1.050000e+02
VI_PPS_EU27_2020_HAB Volume indices of real expenditure per capita (in PPS_EU27_2020=100) 1.220000e+02 1.010000e+02 1.080000e+02
VI_PPS_EU28_HAB Volume indices of real expenditure per capita (in PPS_EU28=100) 1.190000e+02 9.800000e+01 1.060000e+02

Actual Individual Consumption

Table - 2019 - Countries

Code 
prc_ppp_ind %>%
  filter(ppp_cat == "A01",
         na_item == "VI_PPS_EU28_HAB",
         time == "2019") %>%
  left_join(geo, by = "geo") %>%
  select(-ppp_cat, -time, -na_item) %>%
  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 .}

Maps

Text

Code 
europe_NUTS0_new <- prc_ppp_ind %>%
  filter(ppp_cat == "A01",
         na_item == "VI_PPS_EU28_HAB",
         time == "2019") %>%
  select(geo, values) %>%
  right_join(europe_NUTS0 %>%
               filter(long >= -10,
                      lat >= 20), by = "geo") %>%
  group_by(geo, values) %>%
  summarise(long = mean(long), lat = mean(lat))

prc_ppp_ind %>%
  filter(ppp_cat == "A01",
         na_item == "VI_PPS_EU28_HAB",
         time == "2019") %>%
  select(geo, values) %>%
  right_join(europe_NUTS0 %>%
               filter(long >= -10,
                      lat >= 20), by = "geo") %>%
  ggplot(aes(x = long, y = lat)) +
  geom_polygon(aes(group = group, fill = values)) + coord_map() +
  scale_fill_viridis_c(na.value = "white",
                       labels = dollar_format(a = 1, p = "", su = ""),
                       breaks = seq(20, 140, 20)) +
  geom_text(aes(label = values), data = europe_NUTS0_new,  size = 3, hjust = 0.5) +
  theme_void() + theme(legend.position = c(0.25, 0.85)) + 
  labs(fill = "Actual Individual Consumption")

No Text

Code 
europe_NUTS0_new <- prc_ppp_ind %>%
  filter(ppp_cat == "A01",
         na_item == "VI_PPS_EU28_HAB",
         time == "2019") %>%
  select(geo, values) %>%
  right_join(europe_NUTS0 %>%
               filter(long >= -10,
                      lat >= 20), by = "geo") %>%
  group_by(geo, values) %>%
  summarise(long = mean(long), lat = mean(lat))

prc_ppp_ind %>%
  filter(ppp_cat == "A01",
         na_item == "VI_PPS_EU28_HAB",
         time == "2019") %>%
  select(geo, values) %>%
  right_join(europe_NUTS0 %>%
               filter(long >= -10,
                      lat >= 20), by = "geo") %>%
  ggplot(aes(x = long, y = lat)) +
  geom_polygon(aes(group = group, fill = values)) + coord_map() +
  scale_fill_viridis_c(na.value = "white",
                       labels = dollar_format(a = 1, p = "", su = ""),
                       breaks = seq(20, 140, 20)) +
  theme_void() + theme(legend.position = c(0.25, 0.85)) + 
  labs(fill = "Actual Individual Consumption")

France - 1995, 2005, 2015, 2019

Code 
prc_ppp_ind %>%
  filter(ppp_cat == "A01",
         geo == "FR",
         time %in% c("1995", "2005", "2015", "2019")) %>%
  left_join(na_item, by = "na_item") %>%
  select(-ppp_cat, -geo) %>%
  spread(time, values) %>%
  print_table_conditional()
freq na_item Na_item 1995 2005 2015 2019
A EXP_EUR Nominal expenditure (in euro) 8.44481e+05 1.221803e+06 1.530261e+06 1.671646e+06
A EXP_EUR_HAB Nominal expenditure per inhabitant (in euro) 1.41860e+04 1.934200e+04 2.298300e+04 2.467200e+04
A EXP_NAC Nominal expenditure in national currency 8.40038e+05 1.221803e+06 1.530261e+06 1.671646e+06
A EXP_NAC_PC_GDP Nominal expenditure as a percentage of GDP (GDP=100) 6.90000e+01 6.920000e+01 6.960000e+01 6.860000e+01
A EXP_PPS_EU15 Real expenditure (in PPS_EU15) 7.83596e+05 1.230618e+06 1.584858e+06 1.698150e+06
A EXP_PPS_EU15_HAB Real expenditure per capita (in PPS_EU15) 1.32000e+04 1.950000e+04 2.380000e+04 2.510000e+04
A EXP_PPS_EU27_2007 Real expenditure (in PPS_EU27_2007) 7.33387e+05 1.161561e+06 1.474939e+06 1.582929e+06
A EXP_PPS_EU27_2007_HAB Real expenditure per capita (in PPS_EU27_2007) 1.23000e+04 1.840000e+04 2.220000e+04 2.340000e+04
A EXP_PPS_EU27_2020 Real expenditure (in PPS_EU27_2020) 7.23445e+05 1.106516e+06 1.383803e+06 1.524484e+06
A EXP_PPS_EU27_2020_HAB Real expenditure per capita (in PPS_EU27_2020) 1.22000e+04 1.750000e+04 2.080000e+04 2.250000e+04
A EXP_PPS_EU28 Real expenditure (in PPS_EU28) 7.31946e+05 1.159578e+06 1.471976e+06 1.579992e+06
A EXP_PPS_EU28_HAB Real expenditure per capita (in PPS_EU28) 1.23000e+04 1.840000e+04 2.210000e+04 2.330000e+04
A PLI_EU15 Price level indices (EU15=100) 1.07800e+02 9.930000e+01 9.660000e+01 9.840000e+01
A PLI_EU27_2007 Price level indices (EU27_2007=100) 1.15100e+02 1.052000e+02 1.038000e+02 1.056000e+02
A PLI_EU27_2020 Price level indices (EU27_2020=100) 1.16700e+02 1.104000e+02 1.106000e+02 1.097000e+02
A PLI_EU28 Price level indices (EU28=100) 1.15400e+02 1.054000e+02 1.040000e+02 1.058000e+02
A PPP_EU15 Purchasing power parities (EU15=1) 1.07203e+00 9.928370e-01 9.655510e-01 9.843920e-01
A PPP_EU27_2007 Purchasing power parities (EU27_2007=1) 1.14542e+00 1.051860e+00 1.037510e+00 1.056050e+00
A PPP_EU27_2020 Purchasing power parities (EU27_2020=1) 1.16116e+00 1.104190e+00 1.105840e+00 1.096530e+00
A PPP_EU28 Purchasing power parities (EU28=1) 1.14768e+00 1.053660e+00 1.039600e+00 1.058010e+00
A VI_PPS_EU15_HAB Volume indices of real expenditure per capita (in PPS_EU15=100) 1.01000e+02 1.000000e+02 1.020000e+02 1.010000e+02
A VI_PPS_EU27_2007_HAB Volume indices of real expenditure per capita (in PPS_EU27_2007=100) 1.17000e+02 1.120000e+02 1.100000e+02 1.070000e+02
A VI_PPS_EU27_2020_HAB Volume indices of real expenditure per capita (in PPS_EU27_2020=100) 1.20000e+02 1.170000e+02 1.130000e+02 1.090000e+02
A VI_PPS_EU28_HAB Volume indices of real expenditure per capita (in PPS_EU28=100) 1.18000e+02 1.130000e+02 1.100000e+02 1.070000e+02

Germany - 1995, 2005, 2015, 2019

Code 
prc_ppp_ind %>%
  filter(ppp_cat == "A01",
         geo == "DE",
         time %in% c("1995", "2005", "2015", "2019")) %>%
  left_join(na_item, by = "na_item") %>%
  select(-ppp_cat, -geo) %>%
  spread(time, values) %>%
  print_table_conditional()
freq na_item Na_item 1995 2005 2015 2019
A EXP_EUR Nominal expenditure (in euro) 1.331342e+06 1.557206e+06 1.986392e+06 2.255723e+06
A EXP_EUR_HAB Nominal expenditure per inhabitant (in euro) 1.637400e+04 1.914500e+04 2.431700e+04 2.714700e+04
A EXP_NAC Nominal expenditure in national currency 1.275470e+06 1.557206e+06 1.986392e+06 2.255723e+06
A EXP_NAC_PC_GDP Nominal expenditure as a percentage of GDP (GDP=100) 6.730000e+01 6.810000e+01 6.560000e+01 6.490000e+01
A EXP_PPS_EU15 Real expenditure (in PPS_EU15) 1.213980e+06 1.626402e+06 2.129507e+06 2.333580e+06
A EXP_PPS_EU15_HAB Real expenditure per capita (in PPS_EU15) 1.490000e+04 2.000000e+04 2.610000e+04 2.810000e+04
A EXP_PPS_EU27_2007 Real expenditure (in PPS_EU27_2007) 1.136195e+06 1.535135e+06 1.981814e+06 2.175245e+06
A EXP_PPS_EU27_2007_HAB Real expenditure per capita (in PPS_EU27_2007) 1.400000e+04 1.890000e+04 2.430000e+04 2.620000e+04
A EXP_PPS_EU27_2020 Real expenditure (in PPS_EU27_2020) 1.120793e+06 1.462387e+06 1.859358e+06 2.094930e+06
A EXP_PPS_EU27_2020_HAB Real expenditure per capita (in PPS_EU27_2020) 1.380000e+04 1.800000e+04 2.280000e+04 2.520000e+04
A EXP_PPS_EU28 Real expenditure (in PPS_EU28) 1.133962e+06 1.532514e+06 1.977832e+06 2.171208e+06
A EXP_PPS_EU28_HAB Real expenditure per capita (in PPS_EU28) 1.390000e+04 1.880000e+04 2.420000e+04 2.610000e+04
A PLI_EU15 Price level indices (EU15=100) 1.097000e+02 9.570000e+01 9.330000e+01 9.670000e+01
A PLI_EU27_2007 Price level indices (EU27_2007=100) 1.172000e+02 1.014000e+02 1.002000e+02 1.037000e+02
A PLI_EU27_2020 Price level indices (EU27_2020=100) 1.188000e+02 1.065000e+02 1.068000e+02 1.077000e+02
A PLI_EU28 Price level indices (EU28=100) 1.174000e+02 1.016000e+02 1.004000e+02 1.039000e+02
A PPP_EU15 Purchasing power parities (EU15=1) 1.050650e+00 9.574540e-01 9.327940e-01 9.666360e-01
A PPP_EU27_2007 Purchasing power parities (EU27_2007=1) 1.122580e+00 1.014380e+00 1.002310e+00 1.037000e+00
A PPP_EU27_2020 Purchasing power parities (EU27_2020=1) 1.138010e+00 1.064840e+00 1.068320e+00 1.076750e+00
A PPP_EU28 Purchasing power parities (EU28=1) 1.124790e+00 1.016110e+00 1.004330e+00 1.038930e+00
A VI_PPS_EU15_HAB Volume indices of real expenditure per capita (in PPS_EU15=100) 1.150000e+02 1.020000e+02 1.120000e+02 1.130000e+02
A VI_PPS_EU27_2007_HAB Volume indices of real expenditure per capita (in PPS_EU27_2007=100) 1.330000e+02 1.150000e+02 1.200000e+02 1.200000e+02
A VI_PPS_EU27_2020_HAB Volume indices of real expenditure per capita (in PPS_EU27_2020=100) 1.360000e+02 1.200000e+02 1.230000e+02 1.220000e+02
A VI_PPS_EU28_HAB Volume indices of real expenditure per capita (in PPS_EU28=100) 1.330000e+02 1.160000e+02 1.210000e+02 1.200000e+02

Gross Domestic Product

Table - 2019 - Countries

Code 
prc_ppp_ind %>%
  filter(ppp_cat == "GDP",
         na_item == "VI_PPS_EU28_HAB",
         time == "2019") %>%
  left_join(geo, by = "geo") %>%
  select(-ppp_cat, -time, -na_item) %>%
  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 .}

Maps

Code 
europe_NUTS0_new <- prc_ppp_ind %>%
  filter(ppp_cat == "GDP",
         na_item == "VI_PPS_EU28_HAB",
         time == "2019") %>%
  select(geo, values) %>%
  right_join(europe_NUTS0 %>%
               filter(long >= -10,
                      lat >= 20), by = "geo") %>%
  group_by(geo, values) %>%
  summarise(long = mean(long), lat = mean(lat))

prc_ppp_ind %>%
  filter(ppp_cat == "GDP",
         na_item == "VI_PPS_EU28_HAB",
         time == "2019") %>%
  select(geo, values) %>%
  right_join(europe_NUTS0 %>%
               filter(long >= -10,
                      lat >= 20), by = "geo") %>%
  ggplot(aes(x = long, y = lat)) +
  geom_polygon(aes(group = group, fill = values)) + coord_map() +
  scale_fill_viridis_c(na.value = "white",
                       labels = dollar_format(a = 1, p = "", su = ""),
                       breaks = seq(20, 260, 20)) +
  geom_text(aes(label = values), data = europe_NUTS0_new,  size = 3, hjust = 0.5) +
  theme_void() + theme(legend.position = c(0.25, 0.85)) + 
  labs(fill = "Gross Domestic Product")