Non-financial transactions

Data - Eurostat

Info

source	dataset	.html	.RData
eurostat	nasq_10_nf_tr	2024-09-02	2024-09-02

Data on europe

source	dataset	.html	.RData
eurostat	bop_gdp6_q	2024-08-21	2024-08-21
eurostat	nama_10_a10	2024-08-21	2024-08-21
eurostat	nama_10_a10_e	2024-08-21	2024-08-25
eurostat	nama_10_gdp	2024-08-21	2024-08-21
eurostat	nama_10_lp_ulc	2024-08-21	2024-08-21
eurostat	namq_10_a10	2024-08-21	2024-08-25
eurostat	namq_10_a10_e	2024-08-21	2024-08-21
eurostat	namq_10_gdp	2024-08-21	2024-08-21
eurostat	namq_10_lp_ulc	2024-08-21	2024-08-21
eurostat	namq_10_pc	2024-08-21	2024-08-21
eurostat	nasa_10_nf_tr	2024-08-21	2024-08-21
eurostat	nasq_10_nf_tr	2024-09-02	2024-09-02
eurostat	tipsii40	2024-08-25	2024-08-25

Info

• Sector Accounts Dedicated Webpage. html

• Sector accounts. html

Exemples

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

Last

nasq_10_nf_tr %>%
  group_by(time) %>%
  summarise(Nobs = n()) %>%
  arrange(desc(time)) %>%
  head(2) %>%
  print_table_conditional()

time	Nobs
2024Q1	26636
2023Q4	26676

unit

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	1689689
CP_MEUR	Current prices, million euro	1678236

sector

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	739402
S13	General government	679678
S2	Rest of the world	582752
S14_S15	Households; non-profit institutions serving households	527085
S11	Non-financial corporations	410462
S12	Financial corporations	383442
S1N	Not Sectorised	43488
S14	Households	808
S15	Non-profit institutions serving households	808

direct

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	1808072
RECV	Received	1559853

na_item

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

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)	3005019
SCA	Seasonally and calendar adjusted data	362906

geo

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

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

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

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
BE	Belgium	0.4286057	0.4502323	0.4346782	-0.0155541	0.0060725
FR	France	0.3019399	0.3215238	0.3041303	-0.0173936	0.0021904
SE	Sweden	0.3726992	0.3998808	0.3728423	-0.0270384	0.0001431
ES	Spain	0.4159737	0.3975044	0.4147054	0.0172010	-0.0012683
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

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-

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

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

PAID

% of GDP

nasq_10_nf_tr %>%
  filter(geo %in% c("FR", "DE", "IT", "ES", "EA20"),
         na_item == "B2A3G",
         direct == "PAID",
         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))

% of Gross Value Added (GVA)

All

nasq_10_nf_tr %>%
  filter(geo %in% c("FR", "DE", "IT", "ES", "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_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-

nasq_10_nf_tr %>%
  filter(geo %in% c("FR", "DE", "IT", "ES", "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("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))

2014-

load_data("eurostat/geo_fr.Rdata")
geo_fr <- geo %>%
  setNames(c("geo", "Geo_fr"))
load_data("eurostat/geo.Rdata")
nasq_10_nf_tr %>%
  filter(geo %in% c("FR", "DE", "IT", "ES", "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("2014-01-01")) %>%
  left_join(geo, by = "geo") %>%
  left_join(geo_fr, 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, linetype = Geo_fr)) +
  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)) +
  theme(legend.position = c(0.55, 0.50),
        legend.title = element_blank())

2017-

load_data("eurostat/geo_fr.Rdata")
geo_fr <- geo %>%
  setNames(c("geo", "Geo_fr"))
load_data("eurostat/geo.Rdata")
nasq_10_nf_tr %>%
  filter(geo %in% c("FR", "DE", "IT", "ES", "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("2017-01-01")) %>%
  left_join(geo, by = "geo") %>%
  left_join(geo_fr, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA20", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  na.omit %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("B2A3G/B1G (% 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)) +
  theme(legend.position = c(0.55, 0.50),
        legend.title = element_blank()) +
  geom_text_repel(data = . %>%
                    group_by(geo) %>%
                    filter(date %in% c(as.Date("2017-01-01"),
                                       as.Date("2020-01-01"),
                                       max(date))),
            aes(x = date, y = values, color = color, label = percent(values, acc = 0.01)))

2017-

load_data("eurostat/geo_fr.Rdata")
geo_fr <- geo %>%
  setNames(c("geo", "Geo_fr")) %>%
  mutate(Geo_fr = ifelse(geo == "DE", "Allemagne", Geo_fr),
         Geo_fr = ifelse(geo == "EA20", "Zone Euro", Geo_fr))
load_data("eurostat/geo.Rdata")
load_data("eurostat/na_item_fr.Rdata")
nasq_10_nf_tr %>%
  filter(geo %in% c("FR", "DE", "IT", "ES", "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("2017-01-01")) %>%
  left_join(geo, by = "geo") %>%
  left_join(geo_fr, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA20", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  na.omit %>%
  ggplot + theme_minimal() + xlab("") + ylab("Excédent d'exploitation et revenu mixte (% du PIB)") +
  geom_line(aes(x = date, y = values, color = color, linetype = Geo_fr)) +
  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)) +
  theme(legend.position = c(0.1, 0.4),
        legend.title = element_blank())

RECV

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-

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

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

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

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

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

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

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

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

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-

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

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

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

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

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

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

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

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

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

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

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

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

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-

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

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

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-

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

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

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

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

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

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

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-

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-

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-

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-

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-

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

PAID

nasq_10_nf_tr %>%
  filter(geo %in% c("FR"),
         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",
         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()

RECV

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

Paid

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 == "PAID",
         # 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, 30000, 1000))

Received

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

Paid

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 == "PAID",
         # 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, 30000, 1000))

Received

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

Paid

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 == "PAID",
         # 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, 30000, 50))

Received

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))