Non-financial transactions

Data - Eurostat

Info

source dataset .html .RData
eurostat nasq_10_nf_tr 2024-11-22 2024-10-09

Data on europe

source dataset .html .RData
eurostat bop_gdp6_q 2024-11-23 2024-10-09
eurostat nama_10_a10 2024-11-23 2024-10-08
eurostat nama_10_a10_e 2024-11-23 2024-11-23
eurostat nama_10_gdp 2024-11-23 2024-11-22
eurostat nama_10_lp_ulc 2024-11-23 2024-10-08
eurostat namq_10_a10 2024-11-23 2024-11-23
eurostat namq_10_a10_e 2024-11-23 2024-11-22
eurostat namq_10_gdp 2024-11-23 2024-11-22
eurostat namq_10_lp_ulc 2024-11-23 2024-11-04
eurostat namq_10_pc 2024-11-23 2024-11-21
eurostat nasa_10_nf_tr 2024-11-23 2024-10-08
eurostat nasq_10_nf_tr 2024-11-22 2024-10-09
eurostat tipsii40 2024-11-22 2024-11-23

Info

  • Sector Accounts Dedicated Webpage. html

  • Sector accounts. html

Exemples

Code
ig_b("eurostat", "nasq_10_nf_tr", "650px-MS_S1M_B6G_20Q4_F")

Last

Code
nasq_10_nf_tr %>%
  group_by(time) %>%
  summarise(Nobs = n()) %>%
  arrange(desc(time)) %>%
  head(2) %>%
  print_table_conditional()
time Nobs
2024Q2 4188
2024Q1 25121

unit

Code
nasq_10_nf_tr %>%
  left_join(unit, by = "unit") %>%
  group_by(unit, Unit) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) print_table(.) else .}
unit Unit Nobs
CP_MNAC Current prices, million units of national currency 1631014
CP_MEUR Current prices, million euro 1619900

sector

Code
nasq_10_nf_tr %>%
  left_join(sector, by = "sector") %>%
  group_by(sector, Sector) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) print_table(.) else .}
sector Sector Nobs
S1 Total economy 730794
S2 Rest of the world 586234
S13 General government 568352
S14_S15 Households; non-profit institutions serving households 532196
S11 Non-financial corporations 409822
S12 Financial corporations 379378
S1N Not Sectorised 42522
S14 Households 808
S15 Non-profit institutions serving households 808

direct

Code
nasq_10_nf_tr %>%
  left_join(direct, by = "direct") %>%
  group_by(direct, Direct) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) print_table(.) else .}
direct Direct Nobs
PAID Paid 1745066
RECV Received 1505848

na_item

Code
nasq_10_nf_tr %>%
  left_join(na_item, by = "na_item") %>%
  group_by(na_item, Na_item) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

s_adj

Code
nasq_10_nf_tr %>%
  left_join(s_adj, by = "s_adj") %>%
  group_by(s_adj, S_adj) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) print_table(.) else .}
s_adj S_adj Nobs
NSA Unadjusted data (i.e. neither seasonally adjusted nor calendar adjusted data) 2878676
SCA Seasonally and calendar adjusted data 372238

geo

Code
nasq_10_nf_tr %>%
  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/vsmasll/', Flag, '.png" alt="Flag">')) %>%
  select(Flag, everything()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

Belgium, Luxembourg

All

Code
nasq_10_nf_tr %>%
  filter(geo %in% c("BE", "LU", "FR", "DE", "EA20"),
         na_item %in% c("B2A3G", "B1G"),
         direct == "PAID",
         unit == "CP_MNAC",
         s_adj == "SCA",
         sector == "S11") %>%
  select(geo, time, values, na_item) %>%
  spread(na_item, values) %>%
  mutate(values = B2A3G/B1G) %>%
  quarter_to_date %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA20", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  na.omit %>%
  ggplot + theme_minimal() + xlab("") + ylab("% of Gross Value Added") +
  geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + add_4flags +
  scale_x_date(breaks = as.Date(paste0(seq(1940, 2100, 2), "-01-01")),
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(0, 100, 1),
                labels = percent_format(a = 1))

2010

Code
nasq_10_nf_tr %>%
  filter(geo %in% c("BE", "LU", "FR", "DE", "EU"),
         na_item %in% c("B2A3G", "B1G"),
         direct == "PAID",
         unit == "CP_MNAC",
         s_adj == "SCA",
         sector == "S11") %>%
  select(geo, time, values, na_item) %>%
  spread(na_item, values) %>%
  mutate(values = B2A3G/B1G) %>%
  quarter_to_date %>%
  filter(date >= as.Date("2010-01-01")) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EU27_2020", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  na.omit %>%
  ggplot + theme_minimal() + xlab("") + ylab("% of Gross Value Added") +
  geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + add_3flags +
  scale_x_date(breaks = as.Date(paste0(seq(1940, 2100, 1), "-01-01")),
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(0, 100, 1),
                labels = percent_format(a = 1))

France, Germany, Italy, Spain, Europe

Operating surplus and mixed income, gross - B2A3G, S11

Table

Code
load_data("eurostat/geo.Rdata")
nasq_10_nf_tr %>%
  filter(na_item %in% c("B2A3G", "B1G"),
         direct == "PAID",
         unit == "CP_MNAC",
         s_adj == "SCA",
         sector == "S11",
         time %in% c("2022Q4", "2021Q4", "2019Q4")) %>%
  left_join(geo, by = "geo") %>%
  select(geo, Geo, time, values, na_item) %>%
  spread(na_item, values) %>%
  mutate(values = B2A3G/B1G) %>%
  select(-B2A3G, -B1G) %>%
  spread(time, values) %>%
  mutate(`2021Q4-2022Q4` = `2022Q4` - `2021Q4`,
         `2019Q4-2022Q4` = `2022Q4` - `2019Q4`) %>%
  arrange(-`2019Q4-2022Q4`) %>%
  print_table_conditional
geo Geo 2019Q4 2021Q4 2022Q4 2021Q4-2022Q4 2019Q4-2022Q4
NO Norway 0.4849330 0.6270687 0.6506859 0.0236173 0.1657529
EL Greece 0.3724988 0.4784970 0.4920762 0.0135792 0.1195774
DK Denmark 0.4165516 0.4700756 0.4648564 -0.0052192 0.0483048
IE Ireland 0.7405391 0.7683709 0.7848684 0.0164975 0.0443293
NL Netherlands 0.3996424 0.4461210 0.4431846 -0.0029364 0.0435423
IT Italy 0.4293277 0.4336255 0.4641125 0.0304870 0.0347848
PL Poland 0.4634694 0.4576226 0.4907693 0.0331467 0.0272999
DE Germany 0.3741968 0.4080551 0.3941170 -0.0139381 0.0199202
EU27_2020 European Union - 27 countries (from 2020) 0.4023438 0.4148981 0.4181921 0.0032940 0.0158483
CZ Czechia 0.4492632 0.4336417 0.4649583 0.0313167 0.0156952
EA20 Euro area – 20 countries (from 2023) 0.3969854 0.4103196 0.4121270 0.0018074 0.0151416
HU Hungary 0.4584003 0.4732205 0.4691625 -0.0040580 0.0107622
FI Finland 0.4344735 0.4488574 0.4449644 -0.0038930 0.0104909
EE Estonia 0.4403670 0.4579710 0.4495842 -0.0083868 0.0092172
FR France 0.3019122 0.3176104 0.3088310 -0.0087793 0.0069189
BE Belgium 0.4286057 0.4502323 0.4346782 -0.0155541 0.0060725
SE Sweden 0.3725062 0.3996176 0.3725036 -0.0271140 -0.0000026
ES Spain 0.4116541 0.3847911 0.4086185 0.0238274 -0.0030357
AT Austria 0.4119117 0.4031868 0.4073947 0.0042079 -0.0045171
PT Portugal 0.3888253 0.3431072 0.3814885 0.0383813 -0.0073368
EU28 European Union - 28 countries (2013-2020) 0.3912343 NA NA NA NA
RO Romania 0.5392315 NA NA NA NA
UK United Kingdom 0.3694683 NA NA NA NA

All

Code
nasq_10_nf_tr %>%
  filter(geo %in% c("FR", "DE", "IT", "ES", "EA20"),
         na_item == "B2A3G",
         direct == "PAID",
         unit == "CP_MNAC",
         s_adj == "NSA",
         sector == "S11") %>%
  select(geo, time, values, sector) %>%
  left_join(gdp, by = c("geo", "time")) %>%
  mutate(values = values/gdp) %>%
  quarter_to_date %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA20", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  na.omit %>%
  ggplot + theme_minimal() + xlab("") + ylab("") +
  geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + add_5flags +
  scale_x_date(breaks = as.Date(paste0(seq(1940, 2100, 10), "-01-01")),
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(0, 100, 1),
                labels = percent_format(a = 1))

1998-

Code
nasq_10_nf_tr %>%
  filter(geo %in% c("FR", "DE", "IT", "ES", "EA20"),
         na_item == "B2A3G",
         direct == "PAID",
         unit == "CP_MNAC",
         s_adj == "NSA",
         sector == "S11") %>%
  select(geo, time, values, sector) %>%
  left_join(gdp, by = c("geo", "time")) %>%
  mutate(values = values/gdp) %>%
  quarter_to_date %>%
  filter(date >= as.Date("1998-01-01")) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA20", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  na.omit %>%
  ggplot + theme_minimal() + xlab("") + ylab("") +
  geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + add_5flags +
  scale_x_date(breaks = as.Date(paste0(seq(1940, 2100, 2), "-01-01")),
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(0, 100, 1),
                labels = percent_format(a = 1))

2010-

NSA

Code
nasq_10_nf_tr %>%
  filter(geo %in% c("FR", "DE", "IT", "ES", "EA20"),
         na_item == "B2A3G",
         direct == "PAID",
         unit == "CP_MNAC",
         s_adj == "NSA",
         sector == "S11") %>%
  select(geo, time, values, sector) %>%
  left_join(gdp, by = c("geo", "time")) %>%
  mutate(values = values/gdp) %>%
  quarter_to_date %>%
  filter(date >= as.Date("2010-01-01")) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA20", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  na.omit %>%
  ggplot + theme_minimal() + xlab("") + ylab("") +
  geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + add_5flags +
  scale_x_date(breaks = as.Date(paste0(seq(1940, 2100, 1), "-01-01")),
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(0, 100, 1),
                labels = percent_format(a = 1))

SCA

RECV
Code
nasq_10_nf_tr %>%
  filter(geo %in% c("FR", "DE", "IT", "ES", "EA20"),
         na_item == "B2A3G",
         direct == "RECV",
         unit == "CP_MNAC",
         s_adj == "SCA",
         sector == "S11") %>%
  select(geo, time, values, sector) %>%
  left_join(gdp, by = c("geo", "time")) %>%
  mutate(values = values/gdp) %>%
  quarter_to_date %>%
  filter(date >= as.Date("2010-01-01")) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA20", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  na.omit %>%
  ggplot + theme_minimal() + xlab("") + ylab("% of GDP") +
  geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + add_5flags +
  scale_x_date(breaks = as.Date(paste0(seq(1940, 2100, 1), "-01-01")),
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(0, 100, 1),
                labels = percent_format(a = 1))

2017-

Code
nasq_10_nf_tr %>%
  filter(geo %in% c("FR", "DE", "IT", "ES", "EA20"),
         na_item == "B2A3G",
         direct == "PAID",
         unit == "CP_MNAC",
         s_adj == "NSA",
         sector == "S11") %>%
  select(geo, time, values, sector) %>%
  left_join(gdp, by = c("geo", "time")) %>%
  mutate(values = values/gdp) %>%
  quarter_to_date %>%
  filter(date >= as.Date("2017-01-01")) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA20", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  na.omit %>%
  ggplot + theme_minimal() + xlab("") + ylab("") +
  geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + add_5flags +
  scale_x_date(breaks = as.Date(paste0(seq(1940, 2100, 1), "-01-01")),
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(0, 100, 1),
                labels = percent_format(a = 1))

Net lending / Borrowing: financial saving rate

France, Germany, Italy, Spain

B9

B9

Code
nasq_10_nf_tr %>%
  filter(geo %in% c("FR"),
         na_item == "B9",
         s_adj == "NSA",
         #direct == "PAID",
         unit == "CP_MNAC") %>%
  left_join(sector, by = "sector") %>%
  left_join(gdp_adj, by = c("geo", "time", "s_adj")) %>%
  mutate(values = values/gdp) %>%
  quarter_to_date %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA20", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  filter(date >= as.Date("1999-01-01")) %>%
  ggplot + theme_minimal() + xlab("") + ylab("% of GDP") +
  geom_line(aes(x = date, y = values, color = Sector)) +
  theme(legend.position = c(0.3, 0.8),
        legend.title = element_blank()) +
  scale_x_date(breaks = as.Date(paste0(seq(1940, 2100, 2), "-01-01")),
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-100, 100, 1),
                labels = percent_format(a = 1))

1999-

% of GDP

Code
nasq_10_nf_tr %>%
  filter(geo %in% c("FR", "DE", "IT", "ES", "EA20"),
         na_item == "B9",
         #s_adj == "SCA",
         #direct == "PAID",
         unit == "CP_MNAC",
         sector %in% c("S14_S15")) %>%
  select(geo, time, values, sector) %>%
  left_join(namq_10_gdp_B1GQ_NSA_CPMNAC, by = c("geo", "time")) %>%
  mutate(values = values/B1GQ_NSA_CPMNAC) %>%
  quarter_to_date %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA20", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  filter(date >= as.Date("1999-01-01")) %>%
  ggplot + theme_minimal() + xlab("") + ylab("% of GDP") +
  geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + add_5flags +
  scale_x_date(breaks = as.Date(paste0(seq(1940, 2100, 2), "-01-01")),
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-100, 100, 1),
                labels = percent_format(a = 1))

1999-

% of GDP

Code
nasq_10_nf_tr %>%
  filter(geo %in% c("FR", "DE", "IT", "ES", "EA20"),
         na_item == "B9",
         s_adj == "SCA",
         #direct == "PAID",
         unit == "CP_MNAC",
         sector %in% c("S13")) %>%
  select(geo, time, values, sector) %>%
  left_join(namq_10_gdp_B1GQ_NSA_CPMNAC, by = c("geo", "time")) %>%
  mutate(values = values/B1GQ_NSA_CPMNAC) %>%
  quarter_to_date %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA20", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  filter(date >= as.Date("1999-01-01")) %>%
  ggplot + theme_minimal() + xlab("") + ylab("% of GDP") +
  geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + add_5flags +
  scale_x_date(breaks = as.Date(paste0(seq(1940, 2100, 2), "-01-01")),
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-100, 100, 1),
                labels = percent_format(a = 1))

Saving Rate (B8G)

France, Germany, Italy, Spain

All

Code
nasq_10_nf_tr %>%
  filter(geo %in% c("FR", "DE", "IT", "ES"),
         na_item == "B8G",
         s_adj == "SCA",
         direct == "PAID",
         unit == "CP_MNAC",
         sector == "S14_S15") %>%
  select(geo, time, values, sector) %>%
  left_join(namq_10_gdp_B1GQ_NSA_CPMNAC, by = c("geo", "time")) %>%
  mutate(values = values/B1GQ_NSA_CPMNAC) %>%
  quarter_to_date %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot + theme_minimal() + xlab("") + ylab("% of GDP") +
  geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + add_4flags +
  scale_x_date(breaks = as.Date(paste0(seq(1940, 2100, 10), "-01-01")),
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(0, 100, 1),
                labels = percent_format(a = 1))

1999-

% of GDP

Code
nasq_10_nf_tr %>%
  filter(geo %in% c("FR", "DE", "IT", "ES", "EA20"),
         na_item == "B8G",
         s_adj == "SCA",
         direct == "PAID",
         unit == "CP_MNAC",
         sector == "S14_S15") %>%
  select(geo, time, values, sector) %>%
  left_join(namq_10_gdp_B1GQ_NSA_CPMNAC, by = c("geo", "time")) %>%
  mutate(values = values/B1GQ_NSA_CPMNAC) %>%
  quarter_to_date %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA20", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  filter(date >= as.Date("1999-01-01")) %>%
  ggplot + theme_minimal() + xlab("") + ylab("% of GDP") +
  geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + add_5flags +
  scale_x_date(breaks = as.Date(paste0(seq(1940, 2100, 2), "-01-01")),
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(0, 100, 1),
                labels = percent_format(a = 1))

% of Disposable income

Code
nasq_10_nf_tr %>%
  filter(geo %in% c("FR", "DE", "IT", "ES", "EA20"),
         na_item %in% c("B8G", "B6G"),
         s_adj == "SCA",
         direct == "PAID",
         unit == "CP_MNAC",
         sector == "S14_S15") %>%
  select(geo, time, values, sector, na_item) %>%
  spread(na_item, values) %>%
  mutate(values = B8G/B6G) %>%
  quarter_to_date %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA20", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  filter(date >= as.Date("1999-01-01")) %>%
  ggplot + theme_minimal() + xlab("") + ylab("B8G/B6G (% of Disposable income)") +
  geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + add_5flags +
  scale_x_date(breaks = as.Date(paste0(seq(1940, 2100, 2), "-01-01")),
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(0, 100, 2),
                labels = percent_format(a = 1))

2000-

% of GDP

Code
nasq_10_nf_tr %>%
  filter(geo %in% c("FR", "DE", "IT", "ES", "EA20"),
         na_item == "B8G",
         s_adj == "SCA",
         direct == "PAID",
         unit == "CP_MNAC",
         sector == "S14_S15") %>%
  select(geo, time, values, sector) %>%
  left_join(namq_10_gdp_B1GQ_NSA_CPMNAC, by = c("geo", "time")) %>%
  mutate(values = values/B1GQ_NSA_CPMNAC) %>%
  quarter_to_date %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA20", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  filter(date >= as.Date("2000-01-01")) %>%
  ggplot + theme_minimal() + xlab("") + ylab("% of GDP") +
  geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + add_5flags +
  scale_x_date(breaks = as.Date(paste0(seq(1940, 2100, 2), "-01-01")),
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(0, 100, 1),
                labels = percent_format(a = 1))

% of Disposable income

Code
nasq_10_nf_tr %>%
  filter(geo %in% c("FR", "DE", "IT", "ES", "EA20"),
         na_item %in% c("B8G", "B6G"),
         s_adj == "SCA",
         direct == "PAID",
         unit == "CP_MNAC",
         sector == "S14_S15") %>%
  select(geo, time, values, sector, na_item) %>%
  spread(na_item, values) %>%
  mutate(values = B8G/B6G) %>%
  quarter_to_date %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA20", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  filter(date >= as.Date("2000-01-01")) %>%
  ggplot + theme_minimal() + xlab("") + ylab("B8G/B6G (% of Disposable income)") +
  geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + add_5flags +
  scale_x_date(breaks = as.Date(paste0(seq(1940, 2100, 2), "-01-01")),
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(0, 100, 2),
                labels = percent_format(a = 1))

2015-

Code
nasq_10_nf_tr %>%
  filter(geo %in% c("FR", "DE", "IT", "ES", "EA20"),
         na_item == "B8G",
         s_adj == "SCA",
         direct == "PAID",
         unit == "CP_MNAC",
         sector == "S14_S15") %>%
  select(geo, time, values, sector) %>%
  left_join(namq_10_gdp_B1GQ_NSA_CPMNAC, by = c("geo", "time")) %>%
  mutate(values = values/B1GQ_NSA_CPMNAC) %>%
  quarter_to_date %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA20", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  filter(date >= as.Date("2015-01-01")) %>%
  ggplot + theme_minimal() + xlab("") + ylab("% of GDP") +
  geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + add_5flags +
  scale_x_date(breaks = as.Date(paste0(seq(1940, 2100, 1), "-01-01")),
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(0, 100, 1),
                labels = percent_format(a = 1))

Operating surplus and mixed income, gross

Code
nasq_10_nf_tr %>%
  filter(geo %in% c("FR", "DE", "IT"),
         # B2A3G: Operating surplus and mixed income, gross
         na_item == "B2A3G",
         # SCA: Seasonally and calendar adjusted data
         s_adj == "SCA",
         # PAID: Paid
         direct == "PAID",
         # CP_MNAC: Current prices, million units of national currency
         unit == "CP_MNAC",
         # S1: Total economy
         sector == "S1") %>%
  quarter_to_date %>%
  left_join(geo, by = "geo") %>%
  ggplot + geom_line(aes(x = date, y = values/1000, color = Geo, linetype = Geo)) +
  scale_color_manual(values = viridis(4)[1:3]) +
  theme_minimal()  +
  scale_x_date(breaks = as.Date(paste0(seq(1940, 2100, 5), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.3, 0.85),
        legend.title = element_blank()) +
  xlab("") + ylab("") +
  scale_y_log10(breaks = c(c(1, 2, 3, 5, 8, 10), 10*c(1, 2, 3, 5, 8, 10), 100*c(1, 2, 3, 5, 8, 10)),
                labels = dollar_format(suffix = " Bn€", prefix = "", accuracy = 1))

Tables

France

Code
nasq_10_nf_tr %>%
  filter(geo == "FR",
         time == "2019Q1",
         s_adj == "NSA",
         direct == "PAID",
         unit == "CP_MNAC") %>%
  left_join(namq_10_gdp_B1GQ_NSA_CPMNAC, by = c("geo", "time")) %>%
  mutate(values = round(100*values/B1GQ_NSA_CPMNAC, 1) %>% paste0("%")) %>%
  left_join(na_item, by = "na_item") %>%
  select(na_item, Na_item, sector, values) %>%
  spread(sector, values) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

Germany

Code
nasq_10_nf_tr %>%
  filter(geo == "DE",
         time == "2019Q1",
         s_adj == "NSA",
         direct == "PAID",
         unit == "CP_MNAC") %>%
  left_join(namq_10_gdp_B1GQ_NSA_CPMNAC, by = c("geo", "time")) %>%
  mutate(values = round(100*values/B1GQ_NSA_CPMNAC, 1) %>% paste0("%")) %>%
  left_join(na_item, by = "na_item") %>%
  select(na_item, Na_item, sector, values) %>%
  spread(sector, values) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

Italy

Code
nasq_10_nf_tr %>%
  filter(geo == "IT",
         time == "2019Q1",
         s_adj == "NSA",
         direct == "PAID",
         unit == "CP_MNAC") %>%
  left_join(namq_10_gdp_B1GQ_NSA_CPMNAC, by = c("geo", "time")) %>%
  mutate(values = round(100*values/B1GQ_NSA_CPMNAC, 1) %>% paste0("%")) %>%
  left_join(na_item, by = "na_item") %>%
  select(na_item, Na_item, sector, values) %>%
  spread(sector, values) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

B8G

France

Code
nasq_10_nf_tr %>%
  filter(geo == "FR",
         time == "2019Q1",
         s_adj == "NSA",
         direct == "PAID",
         unit == "CP_MNAC") %>%
  left_join(namq_10_gdp_B1GQ_NSA_CPMNAC, by = c("geo", "time")) %>%
  mutate(values = round(100*values/B1GQ_NSA_CPMNAC, 1) %>% paste0("%")) %>%
  left_join(na_item, by = "na_item") %>%
  select(na_item, Na_item, sector, values) %>%
  spread(sector, values) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

France

Code
nasq_10_nf_tr %>%
  filter(geo == "FR",
         na_item == "B8G",
         s_adj == "SCA",
         #direct == "PAID",
         unit == "CP_MNAC") %>%
  select(geo, time, values, sector, direct) %>%
  left_join(namq_10_gdp_B1GQ_NSA_CPMNAC, by = c("geo", "time")) %>%
  mutate(values = values/B1GQ_NSA_CPMNAC) %>%
  quarter_to_date %>%
  left_join(sector, by = "sector") %>%
  ggplot + theme_minimal() + xlab("") + ylab("") +
  geom_line(aes(x = date, y = values, color = Sector, linetype = direct)) +
  scale_x_date(breaks = as.Date(paste0(seq(1940, 2100, 5), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.4, 0.7),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(0, 100, 1),
                labels = percent_format(a = 1))

Germany

Code
nasq_10_nf_tr %>%
  filter(geo == "DE",
         na_item == "B8G",
         s_adj == "SCA",
         direct == "PAID",
         unit == "CP_MNAC") %>%
  select(geo, time, values, sector) %>%
  left_join(namq_10_gdp_B1GQ_NSA_CPMNAC, by = c("geo", "time")) %>%
  mutate(values = values/B1GQ_NSA_CPMNAC) %>%
  quarter_to_date %>%
  left_join(sector, by = "sector") %>%
  ggplot + theme_minimal() + xlab("") + ylab("") +
  geom_line(aes(x = date, y = values, color = Sector)) +
  scale_color_manual(values = viridis(4)[1:3]) +
  scale_x_date(breaks = as.Date(paste0(seq(1940, 2100, 5), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.3, 0.85),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(0, 100, 1),
                labels = percent_format(a = 1))

France, Germany, Italy

B8G - Saving, Gross

Code
nasq_10_nf_tr %>%
  filter(geo %in% c("FR", "DE", "IT", "ES", "NL"),
         # B2A3G: Operating surplus and mixed income, gross
         na_item == "B8G",
         # SCA: Seasonally and calendar adjusted data
         s_adj == "SCA",
         # PAID: Paid
         direct == "PAID",
         # CP_MNAC: Current prices, million units of national currency
         unit == "CP_MNAC",
         # S1: Total economy
         sector == "S1") %>%
  quarter_to_date %>%
  left_join(geo, by = "geo") %>%
  mutate(values = values/1000) %>%
  left_join(colors, by = c( "Geo" = "country")) %>%
  ggplot + geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + theme_minimal()  + xlab("") + ylab("") + add_4flags +
  scale_x_date(breaks = as.Date(paste0(seq(1940, 2100, 5), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.3, 0.85),
        legend.title = element_blank()) +
  scale_y_log10(breaks = c(c(1, 2, 3, 5, 8, 10),
                           10*c(1, 2, 3, 5, 8, 10),
                           100*c(1, 2, 3, 5, 8, 10)),
                labels = dollar_format(suffix = " Bn€", prefix = "", accuracy = 1))

B6G_R_HAB - GDI of households in real terms per capita

1999-

Value

Code
nasq_10_nf_tr %>%
  filter(geo %in% c("FR", "DE", "IT", "ES", "NL"),
         # B2A3G: Operating surplus and mixed income, gross
         na_item == "B6G_R_HAB",
         # SCA: Seasonally and calendar adjusted data
         s_adj == "SCA",
         # PAID: Paid
         direct == "PAID",
         # CP_MNAC: Current prices, million units of national currency
         unit == "CP_MNAC") %>%
  quarter_to_date %>%
  filter(date >= as.Date("1999-01-01")) %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c( "Geo" = "country")) %>%
  ggplot + geom_line(aes(x = date, y = values, color = color, linetype = direct)) +
  scale_color_identity() + theme_minimal()  + xlab("") + ylab("") + add_5flags +
  scale_x_date(breaks = as.Date(paste0(seq(1940, 2100, 2), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.3, 0.85),
        legend.title = element_blank())

Index = 1999

These
Code
nasq_10_nf_tr %>%
  filter(geo %in% c("FR", "DE", "IT", "ES", "EA"),
         # B2A3G: Operating surplus and mixed income, gross
         na_item == "B6G_R_HAB",
         # SCA: Seasonally and calendar adjusted data
         s_adj == "SCA",
         # PAID: Paid
         direct == "PAID",
         # CP_MNAC: Current prices, million units of national currency
         unit == "CP_MNAC") %>%
  quarter_to_date %>%
  filter(date >= as.Date("1999-01-01")) %>%
  left_join(geo, by = "geo") %>%
  group_by(geo) %>%
  arrange(date) %>%
  mutate(values = 100*values/values[1]) %>%
  left_join(colors, by = c( "Geo" = "country")) %>%
  ggplot + geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + theme_minimal()  + xlab("") + ylab("GDI of households in real terms per capita") + add_5flags +
  scale_x_date(breaks = as.Date(paste0(seq(1999, 2100, 2), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.3, 0.85),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(10, 200, 2)) +
  geom_label_repel(data = . %>% filter(date == max(date)), aes(x = date, y = values, label = round(values, 1), color = color))

Index = 1999

These
Code
nasq_10_nf_tr %>%
  filter(geo %in% c("FR", "DE", "IT", "ES", "NL"),
         # B2A3G: Operating surplus and mixed income, gross
         na_item == "B6G_R_HAB",
         # SCA: Seasonally and calendar adjusted data
         s_adj == "SCA",
         # PAID: Paid
         direct == "PAID",
         # CP_MNAC: Current prices, million units of national currency
         unit == "CP_MNAC") %>%
  quarter_to_date %>%
  filter(date >= as.Date("1999-01-01")) %>%
  left_join(geo, by = "geo") %>%
  group_by(geo) %>%
  arrange(date) %>%
  mutate(values = 100*values/values[1]) %>%
  left_join(colors, by = c( "Geo" = "country")) %>%
  ggplot + geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + theme_minimal()  + xlab("") + ylab("GDI of households in real terms per capita") + add_5flags +
  scale_x_date(breaks = as.Date(paste0(seq(1999, 2100, 2), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.3, 0.85),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(10, 200, 2)) +
  geom_label_repel(data = . %>% filter(date == max(date)), aes(x = date, y = values, label = round(values, 1), color = color))

All
Code
nasq_10_nf_tr %>%
  filter(na_item == "B6G_R_HAB",
         # SCA: Seasonally and calendar adjusted data
         s_adj == "SCA",
         # PAID: Paid
         direct == "PAID",
         # CP_MNAC: Current prices, million units of national currency
         unit == "CP_MNAC") %>%
  quarter_to_date %>%
  filter(date >= as.Date("1999-01-01")) %>%
  left_join(geo, by = "geo") %>%
  group_by(geo) %>%
  arrange(date) %>%
  mutate(values = 100*values/values[1]) %>%
  left_join(colors, by = c( "Geo" = "country")) %>%
  ggplot + geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + theme_minimal()  + xlab("") + ylab("GDI of households in real terms per capita") +
  scale_x_date(breaks = as.Date(paste0(seq(1999, 2100, 2), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.3, 0.85),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(10, 200, 2)) +
  geom_label_repel(data = . %>% filter(date == max(date)), aes(x = date, y = values, label = paste0(Geo, ": ", round(values, 1)), color = color))

2017-

Code
nasq_10_nf_tr %>%
  filter(geo %in% c("FR", "DE", "IT", "ES", "NL"),
         # B2A3G: Operating surplus and mixed income, gross
         na_item == "B6G_R_HAB",
         # SCA: Seasonally and calendar adjusted data
         s_adj == "SCA",
         # PAID: Paid
         direct == "PAID",
         # CP_MNAC: Current prices, million units of national currency
         unit == "CP_MNAC") %>%
  quarter_to_date %>%
  filter(date >= as.Date("2017-01-01")) %>%
  left_join(geo, by = "geo") %>%
  group_by(geo) %>%
  arrange(date) %>%
  mutate(values = 100*values/values[1]) %>%
  left_join(colors, by = c( "Geo" = "country")) %>%
  ggplot + geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + theme_minimal()  + xlab("") + ylab("B6G - Disposable income, Gross") + add_5flags +
  scale_x_date(breaks = as.Date(paste0(seq(1940, 2100, 1), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.3, 0.85),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(10, 200, 2))

Implicit price index in it

B6G/POP/B6G_R_HAB

Code
nasq_10_nf_tr %>%
  filter(geo %in% c("FR", "DE", "IT", "ES", "NL"),
         # B2A3G: Operating surplus and mixed income, gross
         na_item %in% c("B6G_R_HAB", "B6G"),
         sector == "S14_S15",
         # SCA: Seasonally and calendar adjusted data
         s_adj == "SCA",
         # PAID: Paid
         direct == "PAID",
         # CP_MNAC: Current prices, million units of national currency
         unit == "CP_MNAC") %>%
  select(time, geo, na_item, values) %>%
  spread(na_item, values) %>%
  left_join(POP, by = c("geo", "time")) %>%
  transmute(time, geo, values = B6G/NSA/B6G_R_HAB) %>%
  quarter_to_date %>%
  filter(date >= as.Date("1999-01-01")) %>%
  left_join(geo, by = "geo") %>%
  group_by(geo) %>%
  arrange(date) %>%
  mutate(values = 100*values/values[1]) %>%
  left_join(colors, by = c( "Geo" = "country")) %>%
  ggplot + geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + theme_minimal()  + xlab("") + ylab("Implicit price index") + add_5flags +
  scale_x_date(breaks = as.Date(paste0(seq(1999, 2100, 2), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.3, 0.85),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(10, 200, 2)) +
  geom_label_repel(data = . %>% filter(date == max(date)), aes(x = date, y = values, label = paste0(date, " : ", round(values, 1)), color = color))

B6G - Disposable income, Gross

All

Code
nasq_10_nf_tr %>%
  filter(geo %in% c("FR", "DE", "IT", "ES", "NL"),
         # B2A3G: Operating surplus and mixed income, gross
         na_item == "B6G",
         # SCA: Seasonally and calendar adjusted data
         s_adj == "SCA",
         # PAID: Paid
         direct == "PAID",
         # CP_MNAC: Current prices, million units of national currency
         unit == "CP_MNAC",
         # S1: Total economy
         sector == "S1") %>%
  quarter_to_date %>%
  left_join(geo, by = "geo") %>%
  mutate(values = values/1000) %>%
  left_join(colors, by = c( "Geo" = "country")) %>%
  ggplot + geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + theme_minimal()  + xlab("") + ylab("") + add_4flags +
  scale_x_date(breaks = as.Date(paste0(seq(1940, 2100, 5), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.3, 0.85),
        legend.title = element_blank()) +
  scale_y_log10(breaks = c(c(1, 2, 3, 5, 8, 10),
                           10*c(1, 2, 3, 5, 8, 10),
                           100*c(1, 2, 3, 5, 8, 10)),
                labels = dollar_format(suffix = " Bn€", prefix = "", accuracy = 1))

1996-

Code
nasq_10_nf_tr %>%
  filter(geo %in% c("FR", "DE", "IT", "ES", "NL"),
         # B2A3G: Operating surplus and mixed income, gross
         na_item == "B6G",
         # SCA: Seasonally and calendar adjusted data
         s_adj == "SCA",
         # PAID: Paid
         direct == "PAID",
         # CP_MNAC: Current prices, million units of national currency
         unit == "CP_MNAC",
         # S1: Total economy
         sector == "S1") %>%
  quarter_to_date %>%
  filter(date >= as.Date("1996-01-01")) %>%
  left_join(geo, by = "geo") %>%
  group_by(geo) %>%
  arrange(date) %>%
  mutate(values = 100*values/values[1]) %>%
  left_join(colors, by = c( "Geo" = "country")) %>%
  ggplot + geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + theme_minimal()  + xlab("") + ylab("B6G - Disposable income, Gross") + add_4flags +
  scale_x_date(breaks = as.Date(paste0(seq(1995, 2100, 2), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.3, 0.85),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(10, 1000, 5)) +
  geom_label_repel(data = . %>% filter(date == max(date)), aes(x = date, y = values, label = round(values, 1), color = color))

1999-

These

Code
nasq_10_nf_tr %>%
  filter(geo %in% c("FR", "DE", "IT", "ES", "NL"),
         # B2A3G: Operating surplus and mixed income, gross
         na_item == "B6G",
         # SCA: Seasonally and calendar adjusted data
         s_adj == "SCA",
         # PAID: Paid
         direct == "PAID",
         # CP_MNAC: Current prices, million units of national currency
         unit == "CP_MNAC",
         # S1: Total economy
         sector == "S1") %>%
  quarter_to_date %>%
  filter(date >= as.Date("1999-01-01")) %>%
  left_join(geo, by = "geo") %>%
  group_by(geo) %>%
  arrange(date) %>%
  mutate(values = 100*values/values[1]) %>%
  left_join(colors, by = c( "Geo" = "country")) %>%
  ggplot + geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + theme_minimal()  + xlab("") + ylab("B6G - Disposable income, Gross") + add_4flags +
  scale_x_date(breaks = as.Date(paste0(seq(1999, 2100, 2), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.3, 0.85),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(10, 1000, 5)) +
  geom_label_repel(data = . %>% filter(date == max(date)), aes(x = date, y = values, label = paste0(Geo, ": ", round(values, 1)), color = color))

Population

Code
nasq_10_nf_tr %>%
  filter(geo %in% c("FR", "DE", "IT", "ES", "NL"),
         # B2A3G: Operating surplus and mixed income, gross
         na_item == "B6G",
         # SCA: Seasonally and calendar adjusted data
         s_adj == "NSA",
         # PAID: Paid
         direct == "PAID",
         # CP_MNAC: Current prices, million units of national currency
         unit == "CP_MNAC",
         # S1: Total economy
         sector == "S1") %>%
  left_join(POP, by = c("geo", "time")) %>%
  quarter_to_date %>%
  filter(date >= as.Date("1999-01-01")) %>%
  left_join(geo, by = "geo") %>%
  group_by(geo) %>%
  arrange(date) %>%
  mutate(values = values/NSA) %>%
  mutate(values = 100*values/values[1]) %>%
  left_join(colors, by = c( "Geo" = "country")) %>%
  ggplot + geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + theme_minimal()  + xlab("") + ylab("B6G - Disposable income, Gross/Population") + add_4flags +
  scale_x_date(breaks = as.Date(paste0(seq(1999, 2100, 2), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.3, 0.85),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(10, 1000, 5)) +
  geom_label_repel(data = . %>% filter(date == max(date)), aes(x = date, y = values, label = paste0(Geo, ": ", round(values, 1)), color = color))

All

Code
nasq_10_nf_tr %>%
  filter(na_item == "B6G",
         geo != "RO",
         # SCA: Seasonally and calendar adjusted data
         s_adj == "SCA",
         # PAID: Paid
         direct == "PAID",
         # CP_MNAC: Current prices, million units of national currency
         unit == "CP_MNAC",
         # S1: Total economy
         sector == "S1") %>%
  quarter_to_date %>%
  filter(date >= as.Date("1999-01-01")) %>%
  left_join(geo, by = "geo") %>%
  group_by(geo) %>%
  arrange(date) %>%
  mutate(values = 100*values/values[1]) %>%
  left_join(colors, by = c( "Geo" = "country")) %>%
  ggplot + geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + theme_minimal()  + xlab("") + ylab("B6G - Disposable income, Gross") + add_4flags +
  scale_x_date(breaks = as.Date(paste0(seq(1999, 2100, 2), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.3, 0.85),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(10, 1000, 5)) +
  geom_label_repel(data = . %>% filter(date == max(date)), aes(x = date, y = values, label = paste0(Geo, ": ", round(values, 1)), color = color))

2017-

Nominal

Code
nasq_10_nf_tr %>%
  filter(geo %in% c("FR", "DE", "IT", "ES", "NL"),
         # B2A3G: Operating surplus and mixed income, gross
         na_item == "B6G",
         # SCA: Seasonally and calendar adjusted data
         s_adj == "SCA",
         # PAID: Paid
         direct == "PAID",
         # CP_MNAC: Current prices, million units of national currency
         unit == "CP_MNAC",
         # S1: Total economy
         sector == "S1") %>%
  quarter_to_date %>%
  filter(date >= as.Date("2017-01-01")) %>%
  left_join(geo, by = "geo") %>%
  group_by(geo) %>%
  arrange(date) %>%
  mutate(values = 100*values/values[1]) %>%
  left_join(colors, by = c( "Geo" = "country")) %>%
  ggplot + geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + theme_minimal()  + xlab("") + ylab("B6G - Disposable income, Gross") + add_4flags +
  scale_x_date(breaks = as.Date(paste0(seq(1940, 2100, 1), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.3, 0.85),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(10, 200, 5))

Households

Code
nasq_10_nf_tr %>%
  filter(geo %in% c("FR", "DE", "IT", "ES", "NL"),
         # B2A3G: Operating surplus and mixed income, gross
         na_item == "B6G",
         # SCA: Seasonally and calendar adjusted data
         s_adj == "SCA",
         # PAID: Paid
         direct == "PAID",
         # CP_MNAC: Current prices, million units of national currency
         unit == "CP_MNAC",
         # S1: Total economy
         sector == "S14_S15") %>%
  quarter_to_date %>%
  left_join(geo, by = "geo") %>%
  mutate(values = values/1000) %>%
  left_join(colors, by = c( "Geo" = "country")) %>%
  ggplot + geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + theme_minimal()  + xlab("") + ylab("") + add_5flags +
  scale_x_date(breaks = as.Date(paste0(seq(1940, 2100, 5), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.3, 0.85),
        legend.title = element_blank()) +
  scale_y_log10(breaks = c(c(1, 2, 3, 5, 8, 10),
                           10*c(1, 2, 3, 5, 8, 10),
                           100*c(1, 2, 3, 5, 8, 10)),
                labels = dollar_format(suffix = " Bn€", prefix = "", accuracy = 1))

B2A3G - Operating surplus and mixed income, gross

All

Code
nasq_10_nf_tr %>%
  filter(geo %in% c("FR", "DE", "IT", "ES", "NL"),
         na_item == "B2A3G",
         s_adj == "SCA",
         direct == "PAID",
         unit == "CP_MNAC",
         sector == "S1") %>%
  quarter_to_date %>%
  left_join(geo, by = "geo") %>%
  mutate(values = values/1000) %>%
  left_join(colors, by = c( "Geo" = "country")) %>%
  ggplot + geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + theme_minimal()  + xlab("") + ylab("") + add_5flags +
  scale_x_date(breaks = as.Date(paste0(seq(1940, 2100, 5), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.3, 0.85),
        legend.title = element_blank()) +
  scale_y_log10(breaks = c(c(1, 2, 3, 5, 8, 10),
                           10*c(1, 2, 3, 5, 8, 10),
                           100*c(1, 2, 3, 5, 8, 10)),
                labels = dollar_format(suffix = " Bn€", prefix = "", accuracy = 1))

2000-

Code
nasq_10_nf_tr %>%
  filter(geo %in% c("FR", "DE", "IT", "ES", "NL"),
         na_item == "B2A3G",
         s_adj == "SCA",
         direct == "PAID",
         unit == "CP_MNAC",
         sector == "S1") %>%
  quarter_to_date %>%
  left_join(geo, by = "geo") %>%
  mutate(values = values/1000) %>%
  left_join(colors, by = c( "Geo" = "country")) %>%
  filter(date >= as.Date("2000-01-01")) %>%
  ggplot + geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + theme_minimal()  + xlab("") + ylab("") + add_5flags +
  scale_x_date(breaks = as.Date(paste0(seq(1940, 2100, 5), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.3, 0.85),
        legend.title = element_blank()) +
  scale_y_log10(breaks = c(c(1, 2, 3, 5, 8, 10),
                           10*c(1, 2, 3, 5, 8, 10),
                           100*c(1, 2, 3, 5, 8, 10)),
                labels = dollar_format(suffix = " Bn€", prefix = "", accuracy = 1))

D4 - Property income

1999-

Code
nasq_10_nf_tr %>%
  filter(geo %in% c("FR", "DE", "IT", "ES", "NL"),
         # B2A3G: Operating surplus and mixed income, gross
         na_item == "D4",
         # SCA: Seasonally and calendar adjusted data
         s_adj == "SCA",
         # PAID: Paid
         direct == "PAID",
         # CP_MNAC: Current prices, million units of national currency
         unit == "CP_MNAC",
         # S1: Total economy
         sector == "S14_S15") %>%
  quarter_to_date %>%
  filter(date >= as.Date("1999-01-01")) %>%
  left_join(geo, by = "geo") %>%
  group_by(geo) %>%
  arrange(date) %>%
  mutate(values = 100*values/values[1]) %>%
  left_join(colors, by = c( "Geo" = "country")) %>%
  ggplot + geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + theme_minimal()  + xlab("") + ylab("Households, D4 - Property Income") + add_5flags +
  scale_x_date(breaks = as.Date(paste0(seq(1999, 2100, 5), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.3, 0.85),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(20, 1000, 20))

2017-

Code
nasq_10_nf_tr %>%
  filter(geo %in% c("FR", "DE", "IT", "ES", "NL"),
         # B2A3G: Operating surplus and mixed income, gross
         na_item == "D4",
         # SCA: Seasonally and calendar adjusted data
         s_adj == "SCA",
         # PAID: Paid
         direct == "PAID",
         # CP_MNAC: Current prices, million units of national currency
         unit == "CP_MNAC",
         # S1: Total economy
         sector == "S14_S15") %>%
  quarter_to_date %>%
  filter(date >= as.Date("2017-01-01")) %>%
  left_join(geo, by = "geo") %>%
  group_by(geo) %>%
  arrange(date) %>%
  mutate(values = 100*values/values[1]) %>%
  left_join(colors, by = c( "Geo" = "country")) %>%
  ggplot + geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + theme_minimal()  + xlab("") + ylab("Households, D4 - Property Income") + add_5flags +
  scale_x_date(breaks = as.Date(paste0(seq(1940, 2100, 1), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.3, 0.85),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(20, 1000, 20))

D41

1999-

Code
nasq_10_nf_tr %>%
  filter(geo %in% c("FR", "DE", "IT", "ES", "NL"),
         # B2A3G: Operating surplus and mixed income, gross
         na_item == "D41",
         # SCA: Seasonally and calendar adjusted data
         s_adj == "NSA",
         # PAID: Paid
         direct == "PAID",
         # CP_MNAC: Current prices, million units of national currency
         unit == "CP_MNAC",
         # S1: Total economy
         sector == "S14_S15") %>%
  quarter_to_date %>%
  filter(date >= as.Date("1999-01-01")) %>%
  left_join(geo, by = "geo") %>%
  group_by(geo) %>%
  arrange(date) %>%
  mutate(values = 100*values/values[1]) %>%
  left_join(colors, by = c( "Geo" = "country")) %>%
  ggplot + geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + theme_minimal()  + xlab("") + ylab("D41") + add_5flags +
  scale_x_date(breaks = as.Date(paste0(seq(1999, 2100, 5), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.3, 0.85),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(20, 1000, 20))

2017-

Code
nasq_10_nf_tr %>%
  filter(geo %in% c("FR", "DE", "IT", "ES", "NL"),
         # B2A3G: Operating surplus and mixed income, gross
         na_item == "D4",
         # SCA: Seasonally and calendar adjusted data
         s_adj == "NSA",
         # PAID: Paid
         direct == "PAID",
         # CP_MNAC: Current prices, million units of national currency
         unit == "CP_MNAC",
         # S1: Total economy
         sector == "S14_S15") %>%
  quarter_to_date %>%
  filter(date >= as.Date("2017-01-01")) %>%
  left_join(geo, by = "geo") %>%
  group_by(geo) %>%
  arrange(date) %>%
  mutate(values = 100*values/values[1]) %>%
  left_join(colors, by = c( "Geo" = "country")) %>%
  ggplot + geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + theme_minimal()  + xlab("") + ylab("Households, D4 - Property Income") + add_5flags +
  scale_x_date(breaks = as.Date(paste0(seq(1940, 2100, 1), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.3, 0.85),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(20, 1000, 20))

France

2017-

Table

RECV

Code
nasq_10_nf_tr %>%
  filter(geo %in% c("FR"),
         s_adj == "NSA",
         # PAID: Paid
         direct == "RECV",
         # CP_MNAC: Current prices, million units of national currency
         unit == "CP_MNAC",
         # S1: Total economy
         sector == "S14_S15",
         time %in% c("2017Q1", "2023Q3")) %>%
  left_join(na_item, by = "na_item") %>%
  select_if(~ n_distinct(.) > 1) %>%
  spread(time, values) %>%
  mutate(growth = 100*(.[[4]]/.[[3]]-1)) %>%
  #arrange(-growth) %>%
  print_table_conditional()

Compensation of employees

Received

Code
nasq_10_nf_tr %>%
  filter(geo %in% c("FR"),
         # B2A3G: Operating surplus and mixed income, gross
         na_item %in% c("D1"),
         # SCA: Seasonally and calendar adjusted data
         s_adj == "NSA",
         # PAID: Paid
         direct == "RECV",
         # CP_MNAC: Current prices, million units of national currency
         unit == "CP_MNAC",
         # S1: Total economy
         sector == "S14_S15") %>%
  quarter_to_date %>%
  left_join(na_item, by = "na_item") %>%
  filter(date >= as.Date("2017-01-01")) %>%
  ggplot + geom_line(aes(x = date, y = values, color = Na_item)) +
  theme_minimal()  + xlab("") + ylab("") +
  scale_x_date(breaks = as.Date(paste0(seq(1940, 2100, 1), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.3, 0.85),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = seq(0000, 1000000, 10000))

Interest

Received

Code
nasq_10_nf_tr %>%
  filter(geo %in% c("FR"),
         # B2A3G: Operating surplus and mixed income, gross
         na_item %in% c("D4", "D41", "D41G"),
         # SCA: Seasonally and calendar adjusted data
         s_adj == "NSA",
         # PAID: Paid
         direct == "RECV",
         # CP_MNAC: Current prices, million units of national currency
         unit == "CP_MNAC",
         # S1: Total economy
         sector == "S14_S15") %>%
  quarter_to_date %>%
  left_join(na_item, by = "na_item") %>%
  filter(date >= as.Date("2017-01-01")) %>%
  ggplot + geom_line(aes(x = date, y = values, color = Na_item)) +
  theme_minimal()  + xlab("") + ylab("") +
  scale_x_date(breaks = as.Date(paste0(seq(1940, 2100, 1), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.3, 0.85),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = seq(0000, 100000, 5000))

Rents

Received

Code
nasq_10_nf_tr %>%
  filter(geo %in% c("FR"),
         # B2A3G: Operating surplus and mixed income, gross
         na_item %in% c("D42", "D43", "D44", "D45", "D42_TO_D45"),
         # SCA: Seasonally and calendar adjusted data
         s_adj == "NSA",
         # PAID: Paid
         direct == "RECV",
         # CP_MNAC: Current prices, million units of national currency
         unit == "CP_MNAC",
         # S1: Total economy
         sector == "S14_S15") %>%
  quarter_to_date %>%
  left_join(na_item, by = "na_item") %>%
  filter(date >= as.Date("2017-01-01")) %>%
  arrange(date) %>%
  ggplot + geom_line(aes(x = date, y = values, color = Na_item)) +
  theme_minimal()  + xlab("") + ylab("") +
  scale_x_date(breaks = as.Date(paste0(seq(1940, 2100, 1), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.3, 0.85),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = seq(0000, 100000, 10000))