Nothing
## ---- include = FALSE---------------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.width = 5.6,
fig.height = 4,
fig.align = "center",
fig.retina = 2,
out.width = "100%",
out.extra = 'style="border:0px;"'
)
## ----setup, results=FALSE, message=FALSE, warning=FALSE-----------------------
library(readrba)
library(tidyr)
library(dplyr)
library(ggplot2)
library(lubridate)
theme_set(theme_minimal())
## ----10yearnorun, eval=FALSE--------------------------------------------------
# browse_rba_series("Australian government 10 year")
## ----10year, echo=FALSE-------------------------------------------------------
browse_rba_series("Australian government 10 year") %>%
knitr::kable()
## ----bondyield, eval = F------------------------------------------------------
# bond_yield <- read_rba(series_id = "FCMYGBAG10")
## ---- include = F, eval = F---------------------------------------------------
# bond_yield <- read_rba(series_id = "FCMYGBAG10")
#
# save(bond_yield, file = file.path("vignettes", "FCMYGBAG10.rda"))
#
## ---- include = F-------------------------------------------------------------
load("FCMYGBAG10.rda")
## ----glimpsebondyield---------------------------------------------------------
glimpse(bond_yield)
## ----vizbondyield-------------------------------------------------------------
bond_yield %>%
ggplot(aes(x = date, y = value)) +
geom_line()
## ----yieldbrowse, eval=FALSE--------------------------------------------------
# browse_rba_tables("government bond")
## ----yieldbrowse2, echo=FALSE-------------------------------------------------
browse_rba_tables("government bond") %>%
knitr::kable()
## ----variousyields, eval = F--------------------------------------------------
# f2 <- read_rba("f2")
## ---- include = F-------------------------------------------------------------
# tempf2 <- tempfile(fileext = ".xls")
# get_rba_urls("f2") %>%
# download.file(destfile = tempf2)
# f2 <- read_rba_local(tempf2)
# save(f2, file = "vignettes/f2.rda", compress = TRUE, compression_level = 9)
load("f2.rda")
## ----uniquef2-----------------------------------------------------------------
unique(f2$series)
## ----filterf2-----------------------------------------------------------------
filtered_f2 <- f2 %>%
filter(grepl("Australian Government", series) &
!grepl("Indexed", series))
## ----vizf2--------------------------------------------------------------------
filtered_f2 %>%
ggplot(aes(x = date, y = value, col = series)) +
geom_line()
## -----------------------------------------------------------------------------
filtered_f2 %>%
select(date, series, value) %>%
spread(key = series, value = value) %>%
mutate(spread_10_2 = `Australian Government 10 year bond` - `Australian Government 2 year bond`) %>%
ggplot(aes(x = date, y = spread_10_2)) +
geom_line()
## ---- eval = F----------------------------------------------------------------
# forecasts <- rba_forecasts()
## ---- include = F-------------------------------------------------------------
forecasts <- rba_forecasts(refresh = FALSE)
## ---- echo = F----------------------------------------------------------------
dplyr::glimpse(forecasts)
## ----viz-unemp-forecasts------------------------------------------------------
forecasts %>%
filter(series == "unemp_rate") %>%
ggplot(aes(x = date,
y = value,
group = forecast_date,
colour = forecast_date)) +
geom_line()
## ----change-in-forecasts, fig.height = 6------------------------------------
# We've already created the `forecasts` object, like this:
# forecasts <- rba_forecasts
latest_two <- forecasts %>%
filter(forecast_date %in% c(max(forecast_date),
max(forecast_date) - months(3)))
latest_two %>%
mutate(forecast_date = format(forecast_date, "%b %Y")) %>%
ggplot(aes(x = date, y = value, col = forecast_date)) +
geom_line() +
guides(colour = guide_legend(title = "Forecast issued: ")) +
facet_wrap(~series_desc, scales = "free_y",
labeller = label_wrap_gen(17)) +
theme_minimal(base_size = 12) +
theme(legend.position = "top",
legend.direction = "horizontal",
legend.title = element_text(),
axis.title = element_blank(),
axis.text = element_text(size = 6),
plot.margin = margin(),
strip.text = element_text(size = 8)) +
labs(subtitle = paste0("RBA's forecasts issued in ",
unique(latest_two$forecast_date) %>% format("%B %Y") %>% paste(collapse = " and ")),
caption = "Source: RBA Statement on Monetary Policy")
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