deg_urb

Code

icw_sr_13 %>%
  left_join(deg_urb, by = "deg_urb") %>%
  group_by(deg_urb, Deg_urb) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) print_table(.) else .}

deg_urb	Deg_urb	Nobs
DEG1	Cities	87
DEG3	Rural areas	87
TOTAL	Total	87
DEG2	Towns and suburbs	86

unit

Code

icw_sr_13 %>%
  left_join(unit, by = "unit") %>%
  group_by(unit, Unit) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) print_table(.) else .}

unit	Unit	Nobs
PC_DI	Percentage of disposable income	347

geo

Code

icw_sr_13 %>%
  left_join(geo, by = "geo") %>%
  group_by(geo, Geo) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

time

Code

icw_sr_13 %>%
  group_by(time) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) print_table(.) else .}

time	Nobs
2015	124
2020	116
2010	107

2010

Code

icw_sr_13 %>%
  filter(time == "2010") %>%
  select(deg_urb, geo, values) %>%
  left_join(geo, by = "geo") %>%
  spread(deg_urb, values) %>%
  select(geo, Geo, everything()) %>%
  {if (is_html_output()) print_table(.) else .}

geo	Geo	DEG1	DEG2	DEG3	TOTAL
AT	Austria	14.6	21.4	19.7	18.0
BE	Belgium	7.2	11.5	6.6	8.9
BG	Bulgaria	43.4	40.1	31.5	38.0
CY	Cyprus	13.8	6.1	11.5	12.2
CZ	Czechia	23.1	25.1	23.9	23.9
DE	Germany	12.6	15.2	12.2	13.5
DK	Denmark	1.5	5.3	6.7	4.3
EE	Estonia	37.0	36.0	32.7	34.9
EL	Greece	-12.5	-9.2	-10.8	-11.2
ES	Spain	19.8	13.0	11.3	16.1
FI	Finland	21.6	21.7	23.1	22.2
FR	France	28.9	29.8	28.0	29.1
HR	Croatia	10.1	7.2	-3.7	4.0
HU	Hungary	17.2	15.3	16.1	16.3
IE	Ireland	21.4	16.8	16.7	18.4
IT	Italy	29.0	25.5	21.7	26.4
LT	Lithuania	31.2	NA	16.3	22.8
LU	Luxembourg	24.7	27.6	37.8	28.4
LV	Latvia	12.1	17.2	8.6	10.5
MT	Malta	13.6	14.0	NA	13.7
PL	Poland	26.4	25.4	22.4	24.7
PT	Portugal	14.8	9.1	15.1	13.1
RO	Romania	3.7	-0.9	-11.5	-5.1
SE	Sweden	14.6	23.1	18.3	18.0
SI	Slovenia	18.9	18.6	17.1	18.0
SK	Slovakia	28.2	22.2	23.7	24.4
UK	United Kingdom	22.3	23.7	23.8	22.9

2015

Code

icw_sr_13 %>%
  filter(time == "2015") %>%
  select(deg_urb, geo, values) %>%
  left_join(geo, by = "geo") %>%
  spread(deg_urb, values) %>%
  select(geo, Geo, everything()) %>%
  {if (is_html_output()) print_table(.) else .}

geo	Geo	DEG1	DEG2	DEG3	TOTAL
AT	Austria	16.8	21.6	24.5	21.1
BE	Belgium	16.5	20.1	20.1	19.0
BG	Bulgaria	19.7	16.1	14.2	17.4
CY	Cyprus	9.9	11.0	10.8	10.5
CZ	Czechia	25.0	25.2	26.2	25.5
DE	Germany	11.0	16.6	19.5	14.9
DK	Denmark	17.8	28.4	30.9	25.6
EA	Euro area (EA11-1999, EA12-2001, EA13-2007, EA15-2008, EA16-2009, EA17-2011, EA18-2014, EA19-2015, EA20-2023)	19.7	20.0	23.3	20.7
EA21	NA	19.7	19.7	22.6	20.5
EE	Estonia	37.3	31.8	33.2	34.9
EL	Greece	-4.8	-6.0	-8.8	-6.5
ES	Spain	21.3	18.9	15.7	19.2
EU	European Union (EU6-1958, EU9-1973, EU10-1981, EU12-1986, EU15-1995, EU25-2004, EU27-2007, EU28-2013, EU27-2020)	19.0	19.8	22.0	20.1
EU27_2020	European Union - 27 countries (from 2020)	20.4	20.5	22.2	21.0
FI	Finland	17.9	19.5	21.7	19.5
FR	France	33.3	34.9	36.3	34.6
HR	Croatia	10.6	5.5	-0.3	4.4
HU	Hungary	15.1	13.4	14.8	14.6
IE	Ireland	27.1	24.2	24.1	25.2
LT	Lithuania	37.7	23.4	26.0	31.6
LU	Luxembourg	25.3	23.7	30.4	27.0
LV	Latvia	25.7	23.5	23.7	24.6
MT	Malta	17.7	19.2	NA	17.8
NL	Netherlands	12.9	19.2	19.5	15.8
PL	Poland	30.9	30.1	27.4	29.5
PT	Portugal	19.7	18.5	17.8	18.9
RO	Romania	12.3	11.3	9.6	10.9
SE	Sweden	23.9	25.2	24.8	24.6
SI	Slovenia	23.4	23.7	22.2	22.8
SK	Slovakia	18.6	17.6	16.9	17.6
UK	United Kingdom	13.4	15.2	20.0	15.0

Germany, France, Sweden

Code

icw_sr_13 %>%
  filter(time == "2015",
         geo %in% c("SE", "FR", "DE")) %>%
  left_join(geo, by = "geo") %>%
  ggplot + geom_line(aes(x = deg_urb, y = values/100, color = Geo, group = Geo, linetype = Geo)) +
  scale_color_manual(values = viridis(4)[1:3]) + theme_minimal() +
  theme(legend.position = c(0.5, 0.7),
        legend.title = element_blank()) +
  xlab("by degree of urbanisation") + ylab("Median saving rate") +
  scale_y_continuous(breaks = 0.01*seq(-30, 50, 5),
                     labels = percent_format(accuracy = 1))