inst/doc/working-with-rppi.R

In realestatebr: Import Brazilian Real Estate Data into R

## ----knitr-setup, include = FALSE---------------------------------------------
NOT_CRAN <- identical(tolower(Sys.getenv("NOT_CRAN")), "true")
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  eval = NOT_CRAN,
  fig.width = 7,
  fig.asp = 0.618,
  fig.align = "center",
  message = FALSE,
  warning = FALSE
)

## ----load-packages------------------------------------------------------------
# library(realestatebr)
# library(dplyr)

## ----setup, message = FALSE---------------------------------------------------
# library(ggplot2)
# 
# color_palette <- c(
#   "#1E3A5F",
#   "#DD6B20",
#   "#2C7A7B",
#   "#D69E2E",
#   "#805AD5",
#   "#C53030"
# )
# 
# theme_series <- function() {
#   theme_minimal(
#     # swap for other font if needed
#     base_family = "Avenir",
#     base_size = 10
#   ) +
#     theme(
#       plot.title = element_text(size = 16),
#       panel.grid.minor = element_blank(),
#       panel.grid.major.x = element_blank(),
#       axis.line.x = element_line(color = "gray10", linewidth = 0.5),
#       axis.ticks.x = element_line(color = "gray10", linewidth = 0.5),
#       axis.title.x = element_blank(),
#       legend.position = "bottom",
#       palette.color.discrete = color_palette
#     )
# }

## ----load-datasets-overview---------------------------------------------------
# igmi <- get_dataset("rppi", table = "igmi")
# ivar <- get_dataset("rppi", table = "ivar")
# fipezap <- get_dataset("rppi", table = "fipezap")

## ----load-stacked-datasets----------------------------------------------------
# sale <- get_dataset("rppi", table = "sale")
# rent <- get_dataset("rppi", table = "rent")

## ----ivgr-load----------------------------------------------------------------
# ivgr <- get_dataset("rppi", "ivgr")
# 
# glimpse(ivgr)

## ----ivgr-plot----------------------------------------------------------------
# ggplot(ivgr, aes(date, index)) +
#   geom_line(color = color_palette[1], linewidth = 0.7) +
#   labs(
#     title = "IVG-R — National Sale Index",
#     x = NULL,
#     y = "Index"
#   ) +
#   theme_series()

## ----igmi-load----------------------------------------------------------------
# igmi <- get_dataset("rppi", "igmi")
# 
# glimpse(igmi)

## ----igmi-plot----------------------------------------------------------------
# main_cities <- c("São Paulo", "Rio De Janeiro", "Belo Horizonte", "Brasília")
# 
# subigmi <- igmi |>
#   filter(name_muni %in% main_cities)
# 
# ggplot(subigmi, aes(date, index, color = name_muni)) +
#   geom_line(linewidth = 0.8) +
#   labs(
#     title = "IGMI-R — Sale Index by City",
#     x = NULL,
#     y = "Index",
#     color = NULL
#   ) +
#   theme_series()

## ----fipezap-sale-load--------------------------------------------------------
# fz <- get_dataset("rppi", table = "fipezap")
# 
# glimpse(fz)

## ----fipezap-sale-filter------------------------------------------------------
# subzap <- fz |>
#   filter(
#     market == "residential",
#     rent_sale == "sale",
#     rooms == "total",
#     variable == "index",
#     name_muni %in% main_cities
#   )

## ----fipezap-sale-plot--------------------------------------------------------
# ggplot(subzap, aes(date, value, color = name_muni)) +
#   geom_line(linewidth = 0.8) +
#   labs(
#     title = "FipeZap — Residential Sale Index",
#     x = NULL,
#     y = "Index",
#     color = NULL
#   ) +
#   theme_series()

## ----sale-stacked-load--------------------------------------------------------
# sale_indices <- get_dataset("rppi", "sale")
# 
# glimpse(sale_indices)

## ----sale-stacked-filter------------------------------------------------------
# comp_index <- sale_indices |>
#   filter(name_muni == "Brazil", date >= as.Date("2015-01-01"))

## ----sale-stacked-plot--------------------------------------------------------
# ggplot(comp_index, aes(date, acum12m * 100, color = source)) +
#   geom_line(linewidth = 0.7) +
#   labs(
#     title = "Comparing Sale Indices in Brazil",
#     subtitle = "12-month accumulated change (%)",
#     y = "%",
#     color = NULL
#   ) +
#   theme_series()

## ----ivar-load----------------------------------------------------------------
# ivar <- get_dataset("rppi", table = "ivar")
# 
# glimpse(ivar)

## ----ivar-trend---------------------------------------------------------------
# library(trendseries)
# 
# ivar_trend <- ivar |>
#   filter(name_muni != "Brazil") |>
#   augment_trends(
#     value_col = "index",
#     group_cols = "name_muni",
#     method = "ma",
#     window = 5
#   )

## ----ivar-plot----------------------------------------------------------------
# ggplot(ivar_trend, aes(date, color = name_muni)) +
#   geom_line(aes(y = index), lwd = 0.5, alpha = 0.5) +
#   geom_line(aes(y = trend_ma), lwd = 0.7) +
#   geom_hline(yintercept = 100) +
#   scale_x_date(date_breaks = "1 year", date_labels = "%Y") +
#   labs(
#     title = "IVAR — City Rent Indices",
#     subtitle = "Smoothed moving average (5-month window)",
#     x = NULL,
#     y = "Index"
#   ) +
#   theme_series()

## ----iqa-iqaiw-load-----------------------------------------------------------
# iqa <- get_dataset("rppi", "iqa")
# iqaiw <- get_dataset("rppi", "iqaiw")
# 
# glimpse(iqaiw)

## ----iqa-plot-----------------------------------------------------------------
# ggplot(iqa, aes(date, index, color = name_muni)) +
#   geom_line(linewidth = 0.7) +
#   geom_hline(yintercept = 100) +
#   scale_x_date(date_breaks = "1 year", date_labels = "%Y") +
#   labs(
#     title = "IQA — Rent Index",
#     subtitle = "Index (2019/06 = 100)",
#     y = "Index",
#     color = NULL
#   ) +
#   theme_series()

## ----iqaiw-plot---------------------------------------------------------------
# ggplot(
#   subset(iqaiw, rooms == "total" & !is.na(acum12m)),
#   aes(date, acum12m * 100, color = name_muni)
# ) +
#   geom_line(linewidth = 0.7) +
#   scale_x_date(date_breaks = "1 year", date_labels = "%Y") +
#   labs(
#     title = "IQAIW — Rent Index",
#     subtitle = "Accumulated 12-month change (%)",
#     y = "%",
#     color = NULL
#   ) +
#   theme_series()

## ----quintoandar-combine------------------------------------------------------
# quintoandar <- bind_rows(
#   list("IQA" = iqa, "IQAIW" = iqaiw),
#   .id = "source"
# )
# 
# quintoandar_spo <- quintoandar |>
#   filter(name_muni == "São Paulo", !rooms %in% c("1", "2", "3"))

## ----quintoandar-compare------------------------------------------------------
# ggplot(quintoandar_spo, aes(date, index, color = source)) +
#   geom_line(linewidth = 0.7) +
#   geom_hline(yintercept = 100) +
#   scale_x_date(date_breaks = "1 year", date_labels = "%Y") +
#   labs(
#     title = "QuintoAndar Rent Index — São Paulo",
#     subtitle = "IQA (pre-2023) and IQAIW (post-2023) use different methodologies",
#     x = NULL,
#     y = "Index",
#     color = NULL
#   ) +
#   theme_series()

## ----fipezap-rent-load--------------------------------------------------------
# fz <- get_dataset("rppi", table = "fipezap")
# 
# glimpse(fz)

## ----fipezap-rent-filter------------------------------------------------------
# fz_rent <- fz |>
#   filter(
#     market == "residential",
#     rent_sale == "rent",
#     rooms == "total",
#     variable == "acum12m",
#     date >= as.Date("2019-01-01")
#   )
# 
# sel_cities <- fz_rent |>
#   filter(date == "2019-01-01", !is.na(value)) |>
#   pull(name_muni)

## ----fipezap-rent-plot--------------------------------------------------------
# ggplot(subset(fz_rent, name_muni %in% sel_cities), aes(date, value * 100)) +
#   geom_line(linewidth = 0.7, color = color_palette[1]) +
#   geom_hline(yintercept = 0) +
#   facet_wrap(vars(name_muni)) +
#   labs(
#     title = "FipeZap — 12-month Rent Change by City",
#     x = NULL,
#     y = "Accumulated 12-month change (%)"
#   ) +
#   theme_series()

## ----secovi-load--------------------------------------------------------------
# secovi <- get_dataset("rppi", "secovi_sp")
# 
# glimpse(secovi)

## ----secovi-plot--------------------------------------------------------------
# ggplot(secovi, aes(date, acum12m * 100)) +
#   geom_line(color = color_palette[1], linewidth = 0.7) +
#   geom_hline(yintercept = 0) +
#   scale_x_date(date_breaks = "2 years", date_labels = "%Y") +
#   labs(
#     title = "SECOVI-SP — Residential Rent Index",
#     subtitle = "12-month accumulated change (%)",
#     x = NULL,
#     y = "%"
#   ) +
#   theme_series()

## ----rent-stacked-load--------------------------------------------------------
# rent_indices <- get_dataset("rppi", "rent")

## ----rent-stacked-filter------------------------------------------------------
# rent_indices_comp <- rent_indices |>
#   filter(
#     name_muni %in% c("São Paulo", "Rio de Janeiro"),
#     date >= as.Date("2019-01-01")
#   )

## ----rent-stacked-plot--------------------------------------------------------
# ggplot(rent_indices_comp, aes(date, acum12m, color = source)) +
#   geom_line(linewidth = 0.7) +
#   geom_hline(yintercept = 0) +
#   facet_wrap(vars(name_muni)) +
#   scale_x_date(date_breaks = "1 year", date_labels = "%Y") +
#   labs(
#     title = "Rent Indices — São Paulo and Rio de Janeiro",
#     subtitle = "12-month accumulated change",
#     y = "Accumulated change",
#     color = NULL
#   ) +
#   theme_series()

## ----sale-rebased-load--------------------------------------------------------
# sales <- get_dataset("rppi", "sale")
# 
# national <- sales |>
#   filter(
#     name_muni == "Brazil",
#     date >= as.Date("2018-01-01"),
#     date <= as.Date("2023-12-01")
#   )
# 
# national_rebased <- national |>
#   mutate(
#     index_rebased = index / first(index) * 100,
#     .by = source
#   )
# 
# total_growth <- national_rebased |>
#   summarise(
#     growth = last(index_rebased) - first(index_rebased),
#     date = last(date),
#     index_rebased = last(index_rebased),
#     .by = source
#   ) |>
#   mutate(label = sprintf("%s:\n+%.1f%%", source, growth))

## ----sale-rebased-plot--------------------------------------------------------
# ggplot(national_rebased, aes(date, index_rebased, color = source)) +
#   geom_line(linewidth = 0.8) +
#   geom_hline(yintercept = 100) +
#   geom_label(
#     data = total_growth,
#     aes(label = label),
#     hjust = 0,
#     nudge_x = 30,
#     nudge_y = c(-5, 10, -5),
#     show.legend = FALSE,
#     size = 3
#   ) +
#   scale_x_date(
#     date_breaks = "1 year",
#     date_labels = "%Y",
#     expand = expansion(mult = c(0, 0.125))
#   ) +
#   labs(
#     title = "Brazil National Sale Indices — Rebased to Jan 2018",
#     x = NULL,
#     y = "Index (Jan 2018 = 100)",
#     color = NULL
#   ) +
#   theme_series()

## ----bis-load-----------------------------------------------------------------
# bis <- get_dataset("rppi_bis")
# 
# bis_sub <- bis |>
#   filter(
#     ref_area_name %in% c("Brazil", "United States", "Germany", "Japan"),
#     is_nominal == 0,
#     unit == "index",
#     date >= as.Date("2000-01-01")
#   )

## ----bis-plot-----------------------------------------------------------------
# ggplot(bis_sub, aes(date, value, color = ref_area_name)) +
#   geom_line(linewidth = 0.8) +
#   geom_hline(yintercept = 100, linetype = "dashed", alpha = 0.4) +
#   labs(
#     title = "Real Residential Property Prices",
#     subtitle = "BIS, index 2010 = 100",
#     x = NULL,
#     y = "Index",
#     color = NULL
#   ) +
#   theme_series()