library(MiscImport)

library(tidyverse)
credit_df = import_boi_credit_df(download_file = TRUE)
credit_df %>% 
  filter(lender == "banks") %>% 
  filter(!borrower %in% c("all_sectors","gov_sector")) %>%
  filter(!category == "total" | is.na(category)) %>%
  filter((instrument == "all_instruments") | is.na(instrument)) %>%
  filter(!is.na(value)) %>% 
  group_by(date) %>%
  mutate(value = value / sum(value)) %>%
  ungroup() %>% 
  filter(category == "residental") %>% 
  ggplot(aes(date, value)) + 
  geom_line() + 
  scale_y_continuous(labels = scales::percent_format(accuracy = 1)) + 
  xlab(NULL) + ylab(NULL) + 
  ggtitle("Share of residental credit out of total banking private credit")


MichaelGurkov/LearningMaterials documentation built on July 9, 2022, 5:17 p.m.