Get data from the Reserve Bank of Australia in a tidy tibble.

Installation

Install from CRAN using:

install.packages("readrba")

Or install the development version from GitHub:

remotes::install_github("mattcowgill/readrba")

Examples

Quick examples

With a few lines of code, you can get a data series from the RBA and visualise it. Here’s the unemployment rate:

unemp_rate <- read_rba(series_id = "GLFSURSA") 

unemp_rate %>%
  ggplot(aes(x = date, y = value)) +
  geom_line() +
  theme_minimal() +
  labs(title = "Unemployment rate (actual)")

And you can also easily get the RBA’s public forecasts - from 1990 to present - and visualise those. Here’s every public forecast of the unemployment rate the RBA has made over the past three decades:

unemp_forecasts <- rba_forecasts() %>%
  filter(series == "unemp_rate")


unemp_forecasts %>%
  ggplot(aes(x = date, 
             y = value, 
             group = forecast_date, 
             col = forecast_date)) +
  geom_line() +
  theme_minimal() +
  labs(title = "Unemployment rate (RBA forecasts)")

Reading RBA data

There primary function in {readrba} is read_rba().

Here’s how you fetch the current version of a single RBA statistical table: table G1, consumer price inflation using read_rba():

cpi_table <- read_rba(table_no = "g1")

The object returned by read_rba() is a tidy tibble (ie. in ‘long’ format):

head(cpi_table)
#> # A tibble: 6 × 11
#>   date       series        value frequ…¹ serie…² units source pub_date   serie…³
#>   <date>     <chr>         <dbl> <chr>   <chr>   <chr> <chr>  <date>     <chr>  
#> 1 1922-06-01 Consumer pri…   2.8 Quarte… Origin… Inde… ABS /… 2022-07-28 GCPIAG 
#> 2 1922-09-01 Consumer pri…   2.8 Quarte… Origin… Inde… ABS /… 2022-07-28 GCPIAG 
#> 3 1922-12-01 Consumer pri…   2.7 Quarte… Origin… Inde… ABS /… 2022-07-28 GCPIAG 
#> 4 1923-03-01 Consumer pri…   2.7 Quarte… Origin… Inde… ABS /… 2022-07-28 GCPIAG 
#> 5 1923-06-01 Consumer pri…   2.8 Quarte… Origin… Inde… ABS /… 2022-07-28 GCPIAG 
#> 6 1923-09-01 Consumer pri…   2.9 Quarte… Origin… Inde… ABS /… 2022-07-28 GCPIAG 
#> # … with 2 more variables: description <chr>, table_title <chr>, and
#> #   abbreviated variable names ¹​frequency, ²​series_type, ³​series_id

You can also request multiple tables. They’ll be returned together as one tidy tibble:

rba_data <- read_rba(table_no = c("a1", "g1"))

head(rba_data)
#> # A tibble: 6 × 11
#>   date       series        value frequ…¹ serie…² units source pub_date   serie…³
#>   <date>     <chr>         <dbl> <chr>   <chr>   <chr> <chr>  <date>     <chr>  
#> 1 1994-06-01 Australian d… 13680 Weekly  Origin… $ mi… RBA    2022-09-02 ARBAAA…
#> 2 1994-06-08 Australian d… 13055 Weekly  Origin… $ mi… RBA    2022-09-02 ARBAAA…
#> 3 1994-06-15 Australian d… 13086 Weekly  Origin… $ mi… RBA    2022-09-02 ARBAAA…
#> 4 1994-06-22 Australian d… 12802 Weekly  Origin… $ mi… RBA    2022-09-02 ARBAAA…
#> 5 1994-06-29 Australian d… 13563 Weekly  Origin… $ mi… RBA    2022-09-02 ARBAAA…
#> 6 1994-07-06 Australian d… 12179 Weekly  Origin… $ mi… RBA    2022-09-02 ARBAAA…
#> # … with 2 more variables: description <chr>, table_title <chr>, and
#> #   abbreviated variable names ¹​frequency, ²​series_type, ³​series_id

unique(rba_data$table_title)
#> [1] "A1 Reserve Bank Of Australia - Liabilities And Assets - Summary"
#> [2] "G1 Consumer Price Inflation"

You can also retrieve data based on the unique RBA time series identifier(s). For example, to getch the consumer price index series only:

cpi_series <- read_rba(series_id = "GCPIAG")
head(cpi_series)
#> # A tibble: 6 × 11
#>   date       series        value frequ…¹ serie…² units source pub_date   serie…³
#>   <date>     <chr>         <dbl> <chr>   <chr>   <chr> <chr>  <date>     <chr>  
#> 1 1922-06-01 Consumer pri…   2.8 Quarte… Origin… Inde… ABS /… 2022-07-28 GCPIAG 
#> 2 1922-09-01 Consumer pri…   2.8 Quarte… Origin… Inde… ABS /… 2022-07-28 GCPIAG 
#> 3 1922-12-01 Consumer pri…   2.7 Quarte… Origin… Inde… ABS /… 2022-07-28 GCPIAG 
#> 4 1923-03-01 Consumer pri…   2.7 Quarte… Origin… Inde… ABS /… 2022-07-28 GCPIAG 
#> 5 1923-06-01 Consumer pri…   2.8 Quarte… Origin… Inde… ABS /… 2022-07-28 GCPIAG 
#> 6 1923-09-01 Consumer pri…   2.9 Quarte… Origin… Inde… ABS /… 2022-07-28 GCPIAG 
#> # … with 2 more variables: description <chr>, table_title <chr>, and
#> #   abbreviated variable names ¹​frequency, ²​series_type, ³​series_id
unique(cpi_series$series_id)
#> [1] "GCPIAG"

The convenience function read_rba_seriesid() is a wrapper around read_rba(). This means read_rba_seriesid("GCPIAG") is equivalent to read_rba(series_id = "GCPIAG").

By default, read_rba() fetches the current version of whatever table you request. You can specify the historical version of a table, if it’s available, using the cur_hist argument:


hist_a11 <- read_rba(table_no = "a1.1", cur_hist = "historical")

head(hist_a11)
#> # A tibble: 6 × 11
#>   date       series        value frequ…¹ serie…² units source pub_date   serie…³
#>   <date>     <chr>         <dbl> <chr>   <chr>   <chr> <chr>  <date>     <chr>  
#> 1 1977-07-31 Australian G…   654 Monthly Origin… $ mi… RBA    2015-06-26 ARBALD…
#> 2 1977-08-31 Australian G…   665 Monthly Origin… $ mi… RBA    2015-06-26 ARBALD…
#> 3 1977-09-30 Australian G…   695 Monthly Origin… $ mi… RBA    2015-06-26 ARBALD…
#> 4 1977-10-31 Australian G…   609 Monthly Origin… $ mi… RBA    2015-06-26 ARBALD…
#> 5 1977-11-30 Australian G…   560 Monthly Origin… $ mi… RBA    2015-06-26 ARBALD…
#> 6 1977-12-31 Australian G…   614 Monthly Origin… $ mi… RBA    2015-06-26 ARBALD…
#> # … with 2 more variables: description <chr>, table_title <chr>, and
#> #   abbreviated variable names ¹​frequency, ²​series_type, ³​series_id

Browsing RBA data

Two functions are provided to help you find the table number or series ID you need. These are browse_rba_tables() and browse_rba_series(). Each returns a tibble with information about the available RBA data.

browse_rba_tables()
#> # A tibble: 123 × 5
#>    title                                             no    url   curre…¹ reada…²
#>    <chr>                                             <chr> <chr> <chr>   <lgl>  
#>  1 Liabilities and Assets – Summary                  A1    http… current TRUE   
#>  2 Liabilities and Assets – Detailed                 A1.1  http… current TRUE   
#>  3 Monetary Policy Changes                           A2    http… current TRUE   
#>  4 Monetary Policy Operations – Current              A3    http… current TRUE   
#>  5 Holdings of Australian Government Securities and… A3.1  http… current TRUE   
#>  6 Securities Lending Repurchase and Switch Transac… A3.2  http… current TRUE   
#>  7 Foreign Exchange Transactions and Holdings of Of… A4    http… current TRUE   
#>  8 Daily Foreign Exchange Market Intervention Trans… A5    http… current TRUE   
#>  9 Banknotes on Issue by Denomination                A6    http… current TRUE   
#> 10 Detected Australian Counterfeits by Denomination  A7    http… current TRUE   
#> # … with 113 more rows, and abbreviated variable names ¹​current_or_historical,
#> #   ²​readable
browse_rba_series()
#> # A tibble: 4,478 × 8
#>    table_no series               serie…¹ serie…² table…³ cur_h…⁴ descr…⁵ frequ…⁶
#>    <chr>    <chr>                <chr>   <chr>   <chr>   <chr>   <chr>   <chr>  
#>  1 A1       Australian dollar i… ARBAAA… Origin… A1 Res… current Austra… Weekly 
#>  2 A1       Capital and Reserve… ARBALC… Origin… A1 Res… current Capita… Weekly 
#>  3 A1       Deposits (excluding… ARBALD… Origin… A1 Res… current Deposi… Weekly 
#>  4 A1       Exchange settlement… ARBALE… Origin… A1 Res… current Exchan… Weekly 
#>  5 A1       Gold and foreign ex… ARBAAG… Origin… A1 Res… current Gold a… Weekly 
#>  6 A1       Notes on issue       ARBALN… Origin… A1 Res… current Notes … Weekly 
#>  7 A1       Other assets (inclu… ARBAAO… Origin… A1 Res… current Other … Weekly 
#>  8 A1       Other liabilities    ARBALO… Origin… A1 Res… current Other … Weekly 
#>  9 A1       Total assets         ARBAAT… Origin… A1 Res… current Total … Weekly 
#> 10 A1       Total liabilities    ARBALT… Origin… A1 Res… current Total … Weekly 
#> # … with 4,468 more rows, and abbreviated variable names ¹​series_id,
#> #   ²​series_type, ³​table_title, ⁴​cur_hist, ⁵​description, ⁶​frequency

You can specify a search string to filter the tables or series, as in:

browse_rba_tables("inflation")
#> # A tibble: 3 × 5
#>   title                                         no    url        curre…¹ reada…²
#>   <chr>                                         <chr> <chr>      <chr>   <lgl>  
#> 1 Consumer Price Inflation                      G1    https://w… current TRUE   
#> 2 Consumer Price Inflation – Expenditure Groups G2    https://w… current TRUE   
#> 3 Inflation Expectations                        G3    https://w… current TRUE   
#> # … with abbreviated variable names ¹​current_or_historical, ²​readable

RBA forecasts

The function rba_forecasts() provides easy access to all the RBA’s public forecasts of key economic variables since 1990. The function scrapes the RBA website to obtain the latest Statement on Monetary Policy forecasts.

rba_forecasts()
#> # A tibble: 5,485 × 8
#>    series_desc           forecast…¹ notes source value date       year_…² series
#>    <chr>                 <date>     <chr> <chr>  <dbl> <date>       <dbl> <chr> 
#>  1 CPI - 4 quarter chan… 1990-03-01 <NA>  JEFG     8.6 1990-03-01   1990. cpi_a…
#>  2 CPI - 4 quarter chan… 1990-03-01 <NA>  JEFG     7.6 1990-06-01   1990. cpi_a…
#>  3 CPI - 4 quarter chan… 1990-03-01 <NA>  JEFG     6.5 1990-09-01   1990. cpi_a…
#>  4 CPI - 4 quarter chan… 1990-03-01 <NA>  JEFG     6   1990-12-01   1990. cpi_a…
#>  5 CPI - 4 quarter chan… 1990-03-01 <NA>  JEFG     5.9 1991-03-01   1991. cpi_a…
#>  6 CPI - 4 quarter chan… 1990-03-01 <NA>  JEFG     6.2 1991-06-01   1991. cpi_a…
#>  7 Unemployment rate - … 1990-03-01 <NA>  JEFG     5.9 1989-12-01   1989. unemp…
#>  8 Unemployment rate - … 1990-03-01 <NA>  JEFG     6.3 1990-03-01   1990. unemp…
#>  9 Unemployment rate - … 1990-03-01 <NA>  JEFG     6.5 1990-06-01   1990. unemp…
#> 10 Unemployment rate - … 1990-03-01 <NA>  JEFG     6.7 1990-09-01   1990. unemp…
#> # … with 5,475 more rows, and abbreviated variable names ¹​forecast_date,
#> #   ²​year_qtr

If you just want the latest forecasts, you can request them:

rba_forecasts(all_or_latest = "latest")
#> # A tibble: 102 × 8
#>    forecast_date date       series            value serie…¹ source notes year_…²
#>    <date>        <date>     <chr>             <dbl> <chr>   <chr>  <chr>   <dbl>
#>  1 2022-08-01    2022-06-01 aena_change         5.2 Nomina… SMP    (a) …   2022.
#>  2 2022-08-01    2022-12-01 aena_change         3   Nomina… SMP    (a) …   2022.
#>  3 2022-08-01    2023-06-01 aena_change         4.1 Nomina… SMP    (a) …   2023.
#>  4 2022-08-01    2023-12-01 aena_change         5   Nomina… SMP    (a) …   2023.
#>  5 2022-08-01    2024-06-01 aena_change         5   Nomina… SMP    (a) …   2024.
#>  6 2022-08-01    2024-12-01 aena_change         4.9 Nomina… SMP    (a) …   2024.
#>  7 2022-08-01    2022-06-01 business_inv_cha…   1.6 Busine… SMP    (a) …   2022.
#>  8 2022-08-01    2022-12-01 business_inv_cha…   4.9 Busine… SMP    (a) …   2022.
#>  9 2022-08-01    2023-06-01 business_inv_cha…   5.9 Busine… SMP    (a) …   2023.
#> 10 2022-08-01    2023-12-01 business_inv_cha…   6.6 Busine… SMP    (a) …   2023.
#> # … with 92 more rows, and abbreviated variable names ¹​series_desc, ²​year_qtr

Data availability

The read_rba() function is able to import most tables on the Statistical Tables page of the RBA website. These are the tables that are downloaded when you use read_rba(cur_hist = "current"), the default.

read_rba() can also download many of the tables on the Historical Data page of the RBA website. To get these, specify cur_hist = "historical" in read_rba().

Historical exchange rate tables

The historical exchange rate tables do not have table numbers on the RBA website. They can still be downloaded, using the following table numbers:

Table title table_no
Exchange Rates – Daily – 1983 to 1986 ex_daily_8386
Exchange Rates – Daily – 1987 to 1990 ex_daily_8790
Exchange Rates – Daily – 1991 to 1994 ex_daily_9194
Exchange Rates – Daily – 1995 to 1998 ex_daily_9598
Exchange Rates – Daily – 1999 to 2002 ex_daily_9902
Exchange Rates – Daily – 2003 to 2006 ex_daily_0306
Exchange Rates – Daily – 2007 to 2009 ex_daily_0709
Exchange Rates – Daily – 2010 to 2013 ex_daily_1013
Exchange Rates – Daily – 2014 to 2017 ex_daily_1417
Exchange Rates – Daily – 2018 to Current ex_daily_18cur
Exchange Rates – Monthly – January 2010 to latest complete month of current year ex_monthly_10cur
Exchange Rates – Monthly – July 1969 to December 2009 ex_monthly_6909

Non-standard tables

read_rba() is currently only able to import RBA statistical tables that are formatted in a (more or less) standard way. Some are formatted in a non-standard way, either because they’re distributions rather than time series, or because they’re particularly old.

Tables that are not able to be downloaded are:

Table title table_no current_or_historical
Household Balance Sheets – Distribution E3 current
Household Gearing – Distribution E4 current
Household Financial Assets – Distribution E5 current
Household Non-Financial Assets – Distribution E6 current
Household Debt – Distribution E7 current
Open Market Operations – 2012 to 2013 A3 historical
Open Market Operations – 2009 to 2011 A3 historical
Open Market Operations – 2003 to 2008 A3 historical
Individual Banks’ Assets – 1991–1992 to 1997–1998 J1 historical
Individual Banks’ Liabilities – 1991–1992 to 1997–1998 J2 historical
Treasury Note Tenders - 1989–2006 E4 historical
Treasury Bond Tenders – 1982–2006 E5 historical
Treasury Bond Tenders – Amount Allotted, by Years to Maturity – 1982–2006 E5 historical
Treasury Bond Switch Tenders – 2008 E6 historical
Treasury Capital Indexed Bonds – 1985–2006 E7 historical

Issues and contributions

I welcome any feature requests or bug reports. The best way is to file a GitHub issue.

I would welcome contributions to the package. Please start by filing an issue, outlining the bug you intend to fix or functionality you intend to add or modify.

Disclaimer

This package is not affiliated with or endorsed by the Reserve Bank of Australia. All data is provided subject to any conditions and restrictions set out on the RBA website.