Supervisory Banking Statistics

Data - ECB

Author

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Info

source	dataset	.html	.RData
ecb	SUP	2024-12-29	2025-05-18

Data on monetary policy

source	dataset	Title	Download	Compile
ecb	SUP	Supervisory Banking Statistics	2025-05-18	[2024-12-29]
bdf	FM	Marché financier, taux	2025-03-27	[2025-03-27]
bdf	MIR	Taux d'intérêt - Zone euro	2025-01-22	[2025-03-09]
bdf	MIR1	Taux d'intérêt - France	2025-01-22	[2025-03-09]
bis	CBPOL	Policy Rates, Daily	2024-12-29	[2024-12-29]
ecb	BSI	Balance Sheet Items	2024-11-19	[2025-05-18]
ecb	BSI_PUB	Balance Sheet Items - Published series	2025-05-18	[2025-05-18]
ecb	FM	Financial market data	2025-05-18	[2025-05-18]
ecb	ILM	Internal Liquidity Management	2025-05-18	[2025-05-18]
ecb	ILM_PUB	Internal Liquidity Management - Published series	2024-09-10	[2025-05-18]
ecb	MIR	MFI Interest Rate Statistics	2025-05-18	[2024-06-19]
ecb	RAI	Risk Assessment Indicators	2025-05-18	[2025-05-18]
ecb	YC	Financial market data - yield curve	2024-11-19	[2024-12-29]
ecb	YC_PUB	Financial market data - yield curve - Published series	2025-05-18	[2024-12-29]
ecb	liq_daily	Daily Liquidity	2024-09-11	[2025-05-18]
eurostat	ei_mfir_m	Interest rates - monthly data	2025-04-28	[2025-04-28]
eurostat	irt_st_m	Money market interest rates - monthly data	2025-05-18	[2025-02-07]
fred	r	Interest Rates	2025-05-18	[2025-05-18]
oecd	MEI	Main Economic Indicators	2025-02-25	[2024-04-16]
oecd	MEI_FIN	Monthly Monetary and Financial Statistics (MEI)	2025-02-25	[2024-09-15]

LAST_COMPILE

LAST_COMPILE
2025-05-18

Last

Code

SUP %>%
  group_by(TIME_PERIOD, FREQ) %>%
  summarise(Nobs = n()) %>%
  ungroup %>%
  group_by(FREQ) %>%
  arrange(desc(TIME_PERIOD)) %>%
  filter(row_number() == 1) %>%
  print_table_conditional()

TIME_PERIOD	FREQ	Nobs
2024-S2	H	1768
2024-Q4	Q	18136

Info

Data Structure Definition (DSD). html

TITLE

Code

SUP %>%
  group_by(TITLE) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

BS_SUFFIX

Code

SUP %>%
  left_join(BS_SUFFIX,  by = "BS_SUFFIX") %>%
  group_by(BS_SUFFIX, Bs_suffix) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()

BS_SUFFIX	Bs_suffix	Nobs
E	Euro	325007
PCT	Percentage	107239
LAF	NA	29583
Z	Not applicable	14652

CB_EXP_TYPE

Code

SUP %>%
  left_join(CB_EXP_TYPE,  by = "CB_EXP_TYPE") %>%
  group_by(CB_EXP_TYPE, Cb_exp_type) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()

CB_EXP_TYPE	Cb_exp_type	Nobs
ALL	All exposures	322659
_Z	Not applicable	110554
N_	Non-performing exposures	17429
ST2	Assets with significant increase in credit risk since initial recognition but not credit-impaired (Stage 2)	9128
P_	Performing exposures	4386
NFM	Non-performing exposures with forbearance measures	3948
PFM	Performing exposures with forbearance measures	3941
ST1	Assets without significant increase in credit risk since initial recognition (Stage 1)	1588
ST3	Credit-impaired assets (Stage 3)	1588
PCI	Purchased or originated credit-impaired financial assets	1260

CB_ITEM

Code

SUP %>%
  left_join(CB_ITEM,  by = "CB_ITEM") %>%
  group_by(CB_ITEM, Cb_item) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()

SBS_DI_1

Code

SUP %>%
  left_join(SBS_DI_1,  by = "SBS_DI_1") %>%
  group_by(SBS_DI_1, Sbs_di_1) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()

SBS_DI_1	Sbs_di_1	Nobs
SII	Significant institutions	359408
LSI	Less significant institutions	117073

SBS_BREAKDOWN

Code

SUP %>%
  left_join(SBS_BREAKDOWN,  by = "SBS_BREAKDOWN") %>%
  group_by(SBS_BREAKDOWN, Sbs_breakdown) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()

SBS_BREAKDOWN	Sbs_breakdown	Nobs
_T	Total	277695
AMC	Classification by business model - asset manager & custodian	11899
CWH	Classification by business model - corporate/wholesale lenders	11899
DEV	Classification by business model - development/promotional lenders	11899
DIV	Classification by business model - diversified lenders	11899
NC	Classification by business model - others/ not classified	11899
RCCL	Classification by business model - retail lenders and consumer credit lenders	11899
UNI	Classification by business model - universal and investment banks	11899
GSIB	Classification by size/business model - G-SIBs	8526
SL30	Classification by size - banks with total assets less than 30 billion of EUR	8526
SM20	Classification by size - banks with total assets more than 200 billion of EUR	8526
ST10	Classification by size - banks with total assets between 30 billion and 100 billion of EUR	8526
ST20	Classification by size - banks with total assets between 100 billion and 200 billion of EUR	8526
LORI	Classification by risk - banks with low risk	8205
MHRI	Classification by risk - banks with medium, high risk and non-rated	8205
SML	Classification by business model - small market lenders	8205
DOM	Classification by geographical diversification - banks with significant domestic exposures	8172
EEA	Classification by geographical diversification - banks with largest non-domestic exposures in non-SSM EEA	8172
NEEA	Classification by geographical diversification - banks with largest non-domestic exposures in non-EEA Europe	8172
ROW	Classification by geographical diversification - banks with largest non-domestic exposures in RoW	8172
SSM	Classification by geographical diversification - banks with largest non-domestic exposures in the SSM	8172
CSCB	Classification by business model-central savings and cooperative banks	3694
EML	Classification by business model-emerging markets lenders	3694

COUNT_AREA

Code

SUP %>%
  left_join(COUNT_AREA,  by = "COUNT_AREA") %>%
  group_by(COUNT_AREA, Count_area) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()

FREQ

Code

SUP %>%
  left_join(FREQ,  by = "FREQ") %>%
  group_by(FREQ, Freq) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()

FREQ	Freq	Nobs
Q	Quarterly	455673
H	Half-yearly	20808

REF_AREA

Code

SUP %>%
  left_join(REF_AREA,  by = "REF_AREA") %>%
  group_by(REF_AREA, Ref_area) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()

REF_AREA	Ref_area	Nobs
B01	EU countries participating in the Single Supervisory Mechanism (SSM) (changing composition)	225575
AT	Austria	12334
BE	Belgium	12334
CY	Cyprus	12334
DE	Germany	12334
EE	Estonia	12334
ES	Spain	12334
FI	Finland	12334
FR	France	12334
GR	Greece	12334
IE	Ireland	12334
IT	Italy	12334
LT	Lithuania	12334
LU	Luxembourg	12334
LV	Latvia	12334
MT	Malta	12334
NL	Netherlands	12334
PT	Portugal	12334
SI	Slovenia	12334
SK	Slovakia	12334
BG	Bulgaria	8280
HR	Croatia	8280

COUNTERPART_SECTOR

Code

SUP %>%
  left_join(COUNTERPART_SECTOR,  by = "COUNTERPART_SECTOR") %>%
  group_by(COUNTERPART_SECTOR, Counterpart_sector) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()

COUNTERPART_SECTOR	Counterpart_sector	Nobs
_Z	Not applicable	372750
S13	General government	27985
S11	Non financial corporations	22566
S14	Households	21058
S12R	Other financial corporations	11276
S122Z	Deposit-taking corporations except the central bank and excluding electronic money institutions principally engaged in financial intermediation	7170
S121	Central bank	7139
S1V	Non-financial corporations, households and NPISH	6537

TIME_FORMAT

Code

SUP %>%
  group_by(TIME_FORMAT) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()

TIME_FORMAT	Nobs
P3M	455673
P6M	20808

Performance Indicators

https://www.bankingsupervision.europa.eu/banking/statistics/html/index.en.html

Return on equity

Graph

significant institutions (SIs) and less significant institutions (LSIs):

Code

SUP %>%
  filter(grepl("Return on equity", TITLE),
         REF_AREA %in% c("U2", "FR", "IT", "DE"),
         BS_SUFFIX == "PCT") %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  left_join(SBS_DI_1,  by = "SBS_DI_1") %>%
  quarter_to_date %>%
  select_if(~ n_distinct(.) > 1) %>%
  left_join(colors, by = c("Ref_area" = "country")) %>%
  mutate(OBS_VALUE = OBS_VALUE/100) %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("Return on equity") +
  geom_line(aes(x = date, y = OBS_VALUE, color = color, linetype = Sbs_di_1)) + 
  add_flags(7) + scale_color_identity() +
  scale_x_date(breaks = seq(1960, 2030, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-100, 100, 2),
                     labels = percent_format(accuracy = 1)) +
  theme(legend.position = c(0.25, 0.90),
        legend.title = element_blank())

Cost-to-income ratio

Code

SUP %>%
  filter(grepl("Cost-to-income ratio", TITLE),
         REF_AREA %in% c("U2", "FR", "IT", "DE"),
         BS_SUFFIX == "PCT") %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  left_join(SBS_DI_1,  by = "SBS_DI_1") %>%
  quarter_to_date %>%
  select_if(~ n_distinct(.) > 1) %>%
  left_join(colors, by = c("Ref_area" = "country")) %>%
  mutate(OBS_VALUE = OBS_VALUE/100) %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("Cost-to-income Ratio") +
  geom_line(aes(x = date, y = OBS_VALUE, color = color, linetype = Sbs_di_1)) + 
  add_flags(7) + scale_color_identity() +
  scale_x_date(breaks = seq(1960, 2030, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 100, 10),
                     labels = percent_format(accuracy = .1)) +
  theme(legend.position = c(0.25, 0.90),
        legend.title = element_blank())

Net interest income

FR, IT, DE

Code

SUP %>%
  filter(grepl("Net interest income", TITLE),
         REF_AREA %in% c("U2", "FR", "IT", "DE"),
         BS_SUFFIX == "PCT") %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  left_join(SBS_DI_1,  by = "SBS_DI_1") %>%
  quarter_to_date %>%
  select_if(~ n_distinct(.) > 1) %>%
  left_join(colors, by = c("Ref_area" = "country")) %>%
  mutate(OBS_VALUE = OBS_VALUE/100) %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("Net Interest Income/Total operating income") +
  geom_line(aes(x = date, y = OBS_VALUE, color = color, linetype = Sbs_di_1)) + 
  add_flags(7) + scale_color_identity() +
  scale_x_date(breaks = seq(1960, 2030, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 100, 10),
                     labels = percent_format(accuracy = .1)) +
  theme(legend.position = c(0.25, 0.90),
        legend.title = element_blank())

FR, IT, DE, ES, NL, SI

Net interest margin

FR, IT, DE

Code

SUP %>%
  filter(grepl("Net interest margin", TITLE),
         REF_AREA %in% c("U2", "FR", "IT", "DE")) %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  left_join(SBS_DI_1,  by = "SBS_DI_1") %>%
  quarter_to_date %>%
  select_if(~ n_distinct(.) > 1) %>%
  left_join(colors, by = c("Ref_area" = "country")) %>%
  mutate(OBS_VALUE = OBS_VALUE/100) %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("Net Interest Margin") +
  geom_line(aes(x = date, y = OBS_VALUE, color = color, linetype = Sbs_di_1)) + 
  add_flags(6) + scale_color_identity() +
  scale_x_date(breaks = seq(1960, 2030, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 50, 0.1),
                     labels = percent_format(accuracy = .1)) +
  theme(legend.position = c(0.25, 0.90),
        legend.title = element_blank())

FR, IT, DE, ES, NL, SI

Code

SUP %>%
  filter(grepl("Net interest margin", TITLE),
         REF_AREA %in% c("U2", "FR", "IT", "DE", "ES", "NL", "SI"),
         SBS_DI_1 == "SII") %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  quarter_to_date %>%
  arrange(desc(date)) %>%
  left_join(SBS_DI_1,  by = "SBS_DI_1") %>%
  select_if(~ n_distinct(.) > 1) %>%
  left_join(colors, by = c("Ref_area" = "country")) %>%
  mutate(OBS_VALUE = OBS_VALUE/100) %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("Net Interest Margin") +
  geom_line(aes(x = date, y = OBS_VALUE, color = color)) + 
  add_flags(6) + scale_color_identity() +
  scale_x_date(breaks = seq(1960, 2030, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 50, 0.1),
                     labels = percent_format(accuracy = .1)) +
  theme(legend.position = c(0.25, 0.90),
        legend.title = element_blank())

FR, IT, DE, ES, NL, BE, AT, PT

2015-

Code

SUP %>%
  filter(grepl("Net interest margin", TITLE),
         REF_AREA %in% c("U2", "FR", "IT", "DE", "ES", "NL", "BE", "AT", "PT"),
         SBS_DI_1 == "SII") %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  quarter_to_date %>%
  arrange(desc(date)) %>%
  left_join(SBS_DI_1,  by = "SBS_DI_1") %>%
  select_if(~ n_distinct(.) > 1) %>%
  left_join(colors, by = c("Ref_area" = "country")) %>%
  mutate(OBS_VALUE = OBS_VALUE/100) %>%
  mutate(color = ifelse(REF_AREA == "FR", color2, color)) %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("Net Interest Margin") +
  geom_line(aes(x = date, y = OBS_VALUE, color = color)) + 
  add_flags(8) + scale_color_identity() +
  scale_x_date(breaks = seq(1960, 2030, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 50, 0.1),
                     labels = percent_format(accuracy = .1)) +
  theme(legend.position = c(0.25, 0.90),
        legend.title = element_blank())

2018-

Code

SUP %>%
  filter(grepl("Net interest margin", TITLE),
         REF_AREA %in% c("U2", "FR", "IT", "DE", "ES", "NL", "BE", "AT", "PT"),
         SBS_DI_1 == "SII") %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  quarter_to_date %>%
  filter(date >= as.Date("2018-01-01")) %>%
  arrange(desc(date)) %>%
  left_join(SBS_DI_1,  by = "SBS_DI_1") %>%
  select_if(~ n_distinct(.) > 1) %>%
  left_join(colors, by = c("Ref_area" = "country")) %>%
  mutate(OBS_VALUE = OBS_VALUE/100) %>%
  mutate(color = ifelse(REF_AREA == "FR", color2, color)) %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("Net Interest Margin") +
  geom_line(aes(x = date, y = OBS_VALUE, color = color)) + 
  add_flags(8) + scale_color_identity() +
  scale_x_date(breaks = seq(1960, 2030, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 50, 0.1),
                     labels = percent_format(accuracy = .1)) +
  theme(legend.position = c(0.25, 0.90),
        legend.title = element_blank())

Liquidity

Liquidity coverage ratios (LCR)

The LCR is the percentage resulting from dividing the bank’s stock of high-quality assets by the estimated total net cash outflows over a 30 calendar day stress scenario.

Code

SUP %>%
  filter(CB_ITEM == "I3017",
         REF_AREA %in% c("U2", "FR", "IT", "DE"),
         !(SBS_DI_1 == "_Z")) %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  left_join(SBS_DI_1,  by = "SBS_DI_1") %>%
  quarter_to_date %>%
  select_if(~ n_distinct(.) > 1) %>%
  arrange(desc(date)) %>%
  left_join(colors, by = c("Ref_area" = "country")) %>%
  mutate(OBS_VALUE = OBS_VALUE/100) %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("Liquidity coverage ratio") +
  geom_line(aes(x = date, y = OBS_VALUE, color = color, linetype = Sbs_di_1)) + 
  add_flags(6) + scale_color_identity() +
  scale_x_date(breaks = seq(1960, 2030, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(0, 500, 50),
                     labels = percent_format(accuracy = 1),
                     limits = c(0, 3.5)) +
  theme(legend.position = c(0.2, 0.90),
        legend.title = element_blank())

Liquidity buffer

Code

SUP %>%
  filter(CB_ITEM == "A6310",
         REF_AREA %in% c("U2", "FR", "IT", "DE")) %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  left_join(SBS_DI_1,  by = "SBS_DI_1") %>%
  quarter_to_date %>%
  select_if(~ n_distinct(.) > 1) %>%
  arrange(desc(date)) %>%
  left_join(colors, by = c("Ref_area" = "country")) %>%
  mutate(OBS_VALUE = OBS_VALUE/100) %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("Liquidity buffer") +
  geom_line(aes(x = date, y = OBS_VALUE, color = color, linetype = Sbs_di_1)) + 
  add_flags(6) + scale_color_identity() +
  scale_x_date(breaks = seq(1960, 2030, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = seq(0, 1000, 2)) +
  theme(legend.position = c(0.2, 0.90),
        legend.title = element_blank())

Net liquidity outflow

Code

SUP %>%
  filter(CB_ITEM == "A6320",
         REF_AREA %in% c("U2", "FR", "IT", "DE")) %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  left_join(SBS_DI_1,  by = "SBS_DI_1") %>%
  quarter_to_date %>%
  select_if(~ n_distinct(.) > 1) %>%
  arrange(desc(date)) %>%
  left_join(colors, by = c("Ref_area" = "country")) %>%
  mutate(OBS_VALUE = OBS_VALUE/100) %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("Net liquidity outflow") +
  geom_line(aes(x = date, y = OBS_VALUE, color = color, linetype = Sbs_di_1)) + 
  add_flags(6) + scale_color_identity() +
  scale_x_date(breaks = seq(1960, 2030, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = seq(0, 1000, 2)) +
  theme(legend.position = c(0.2, 0.90),
        legend.title = element_blank())

Net liquidity outflow

Code

SUP %>%
  filter(grepl("Net liquidity outflow", TITLE),
         REF_AREA %in% c("B01", "FR", "IT", "DE")) %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  left_join(SBS_DI_1,  by = "SBS_DI_1") %>%
  quarter_to_date %>%
  mutate(OBS_VALUE = OBS_VALUE/100,
         Ref_area = ifelse(REF_AREA == "B01", "Europe", Ref_area)) %>%
  select_if(~ n_distinct(.) > 1) %>%
  left_join(colors, by = c("Ref_area" = "country")) %>%
  na.omit %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("Net liquidity outflow") +
  geom_line(aes(x = date, y = OBS_VALUE, color = color, linetype = paste0(SBS_DI_1, SBS_BREAKDOWN))) + 
  add_flags(6) + scale_color_identity() +
  scale_x_date(breaks = seq(1960, 2030, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(0, 500, 50),
                     labels = percent_format(accuracy = 1),
                     limits = c(0, 3.5)) +
  theme(legend.position = c(0.55, 0.50),
        legend.title = element_blank())

Net Liquidity outflow - EU

B01 - EU countries participating in the Single Supervisory Mechanism (SSM)

Capital adequacy

Common equity Tier 1 ratio
Tier 1 ratio

https://www.bankingsupervision.europa.eu/press/pr/date/2023/html/ssm.pr230111_{4cb4953fd6.en.html#:}:text=The%20aggregate%20capital%20ratios%20of,capital%20ratio%20stood%20at%2018.68%25.

Code

SUP %>%
  filter(grepl("Common equity Tier 1 ratio", TITLE),
         REF_AREA %in% c("U2", "FR", "IT", "DE")) %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  left_join(SBS_DI_1,  by = "SBS_DI_1") %>%
  quarter_to_date %>%
  select_if(~ n_distinct(.) > 1) %>%
  left_join(colors, by = c("Ref_area" = "country")) %>%
  mutate(OBS_VALUE = OBS_VALUE/100) %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("Net Interest Margin") +
  geom_line(aes(x = date, y = OBS_VALUE, color = color, linetype = Sbs_di_1)) + 
  add_flags(6) + scale_color_identity() +
  scale_x_date(breaks = seq(1960, 2030, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 50, 1),
                     labels = percent_format(accuracy = .1)) +
  theme(legend.position = c(0.25, 0.90),
        legend.title = element_blank())