readrba

R build status Lifecycle: maturing Codecov test coverage CRAN status

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

library(ggplot2)
library(dplyr)
library(readrba)

Quick examples

With just 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 frequency series_type units source pub_date  
#>   <date>     <chr>           <dbl> <chr>     <chr>       <chr> <chr>  <date>    
#> 1 1922-06-30 Consumer price…   2.8 Quarterly Original    Inde… ABS /… 2024-02-01
#> 2 1922-09-30 Consumer price…   2.8 Quarterly Original    Inde… ABS /… 2024-02-01
#> 3 1922-12-31 Consumer price…   2.7 Quarterly Original    Inde… ABS /… 2024-02-01
#> 4 1923-03-31 Consumer price…   2.7 Quarterly Original    Inde… ABS /… 2024-02-01
#> 5 1923-06-30 Consumer price…   2.8 Quarterly Original    Inde… ABS /… 2024-02-01
#> 6 1923-09-30 Consumer price…   2.9 Quarterly Original    Inde… ABS /… 2024-02-01
#> # ℹ 3 more variables: series_id <chr>, description <chr>, table_title <chr>

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 frequency series_type units source pub_date  
#>   <date>     <chr>           <dbl> <chr>     <chr>       <chr> <chr>  <date>    
#> 1 2013-07-03 Australian dol… 37899 Weekly    Original    $ mi… RBA    2024-02-02
#> 2 2013-07-10 Australian dol… 35106 Weekly    Original    $ mi… RBA    2024-02-02
#> 3 2013-07-17 Australian dol… 32090 Weekly    Original    $ mi… RBA    2024-02-02
#> 4 2013-07-24 Australian dol… 39592 Weekly    Original    $ mi… RBA    2024-02-02
#> 5 2013-07-31 Australian dol… 41286 Weekly    Original    $ mi… RBA    2024-02-02
#> 6 2013-08-07 Australian dol… 37974 Weekly    Original    $ mi… RBA    2024-02-02
#> # ℹ 3 more variables: series_id <chr>, description <chr>, table_title <chr>

unique(rba_data$table_title)
#> [1] "A1 Reserve Bank Of Australia - Balance Sheet"
#> [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 frequency series_type units source pub_date  
#>   <date>     <chr>           <dbl> <chr>     <chr>       <chr> <chr>  <date>    
#> 1 1922-06-30 Consumer price…   2.8 Quarterly Original    Inde… ABS /… 2024-02-01
#> 2 1922-09-30 Consumer price…   2.8 Quarterly Original    Inde… ABS /… 2024-02-01
#> 3 1922-12-31 Consumer price…   2.7 Quarterly Original    Inde… ABS /… 2024-02-01
#> 4 1923-03-31 Consumer price…   2.7 Quarterly Original    Inde… ABS /… 2024-02-01
#> 5 1923-06-30 Consumer price…   2.8 Quarterly Original    Inde… ABS /… 2024-02-01
#> 6 1923-09-30 Consumer price…   2.9 Quarterly Original    Inde… ABS /… 2024-02-01
#> # ℹ 3 more variables: series_id <chr>, description <chr>, table_title <chr>
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 frequency series_type units source pub_date  
#>   <date>     <chr>           <dbl> <chr>     <chr>       <chr> <chr>  <date>    
#> 1 1994-06-01 Australian dol… 13680 Weekly    Original    $ mi… RBA    2023-05-05
#> 2 1994-06-08 Australian dol… 13055 Weekly    Original    $ mi… RBA    2023-05-05
#> 3 1994-06-15 Australian dol… 13086 Weekly    Original    $ mi… RBA    2023-05-05
#> 4 1994-06-22 Australian dol… 12802 Weekly    Original    $ mi… RBA    2023-05-05
#> 5 1994-06-29 Australian dol… 13563 Weekly    Original    $ mi… RBA    2023-05-05
#> 6 1994-07-06 Australian dol… 12179 Weekly    Original    $ mi… RBA    2023-05-05
#> # ℹ 3 more variables: series_id <chr>, description <chr>, table_title <chr>

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: 126 × 5
#>    title                              no    url   current_or_historical readable
#>    <chr>                              <chr> <chr> <chr>                 <lgl>   
#>  1 RBA Balance Sheet                  A1    http… current               TRUE    
#>  2 Monetary Policy Changes            A2    http… current               TRUE    
#>  3 Monetary Policy Operations – Curr… A3    http… current               TRUE    
#>  4 Holdings of Australian Government… A3.1  http… current               TRUE    
#>  5 Securities Lending Repurchase and… A3.2  http… current               TRUE    
#>  6 Foreign Exchange Transactions and… A4    http… current               TRUE    
#>  7 Daily Foreign Exchange Market Int… A5    http… current               TRUE    
#>  8 Banknotes on Issue by Denomination A6    http… current               TRUE    
#>  9 Detected Australian Counterfeits … A7    http… current               TRUE    
#> 10 Assets of Financial Institutions   B1    http… current               TRUE    
#> # ℹ 116 more rows
browse_rba_series()
#> # A tibble: 4,313 × 8
#>    table_no series        series_id series_type table_title cur_hist description
#>    <chr>    <chr>         <chr>     <chr>       <chr>       <chr>    <chr>      
#>  1 A1       Australian G… ARBALDOG… Original    A1 Reserve… current  Australian…
#>  2 A1       Australian d… ARBAAASTW Original    A1 Reserve… current  Australian…
#>  3 A1       Australian d… ARBAAASTW Original    A1 Reserve… histori… Australian…
#>  4 A1       Capital and … ARBALCRFW Original    A1 Reserve… current  Capital an…
#>  5 A1       Capital and … ARBALCRFW Original    A1 Reserve… histori… Capital an…
#>  6 A1       Deposits (ex… ARBALDEPW Original    A1 Reserve… histori… Deposits (…
#>  7 A1       Deposits of … ARBALDOO… Original    A1 Reserve… current  Deposits o…
#>  8 A1       Exchange set… ARBALESBW Original    A1 Reserve… current  Exchange s…
#>  9 A1       Exchange set… ARBALESBW Original    A1 Reserve… histori… Exchange s…
#> 10 A1       Gold and for… ARBAAGFXW Original    A1 Reserve… current  Gold and f…
#> # ℹ 4,303 more rows
#> # ℹ 1 more variable: frequency <chr>

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   current_or_historical readable
#>   <chr>                               <chr> <chr> <chr>                 <lgl>   
#> 1 Consumer Price Inflation            G1    http… current               TRUE    
#> 2 Consumer Price Inflation – Expendi… G2    http… current               TRUE    
#> 3 Inflation Expectations              G3    http… current               TRUE

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: 6,977 × 8
#>    series_desc       forecast_date notes source value date       year_qtr series
#>    <chr>             <date>        <chr> <chr>  <dbl> <date>        <dbl> <chr> 
#>  1 CPI - 4 quarter … 1990-03-01    <NA>  JEFG     8.6 1990-03-01    1990. cpi_a…
#>  2 CPI - 4 quarter … 1990-03-01    <NA>  JEFG     7.6 1990-06-01    1990. cpi_a…
#>  3 CPI - 4 quarter … 1990-03-01    <NA>  JEFG     6.5 1990-09-01    1990. cpi_a…
#>  4 CPI - 4 quarter … 1990-03-01    <NA>  JEFG     6   1990-12-01    1990. cpi_a…
#>  5 CPI - 4 quarter … 1990-03-01    <NA>  JEFG     5.9 1991-03-01    1991. cpi_a…
#>  6 CPI - 4 quarter … 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…
#> # ℹ 6,967 more rows

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

rba_forecasts(all_or_latest = "latest")
#> # A tibble: 156 × 8
#>    forecast_date date       series       value series_desc source notes year_qtr
#>    <date>        <date>     <chr>        <dbl> <chr>       <chr>  <chr>    <dbl>
#>  1 2024-02-01    2023-12-01 aena_change    5.5 Nominal (n… SMP    (a) …    2023.
#>  2 2024-02-01    2024-06-01 aena_change    7   Nominal (n… SMP    (a) …    2024.
#>  3 2024-02-01    2024-12-01 aena_change    4.3 Nominal (n… SMP    (a) …    2024.
#>  4 2024-02-01    2025-06-01 aena_change    3.9 Nominal (n… SMP    (a) …    2025.
#>  5 2024-02-01    2025-12-01 aena_change    3.8 Nominal (n… SMP    (a) …    2025.
#>  6 2024-02-01    2026-06-01 aena_change    3.7 Nominal (n… SMP    (a) …    2026.
#>  7 2024-02-01    2023-12-01 business_in…   7.6 Business i… SMP    (a) …    2023.
#>  8 2024-02-01    2024-06-01 business_in…   1.2 Business i… SMP    (a) …    2024.
#>  9 2024-02-01    2024-12-01 business_in…   1.2 Business i… SMP    (a) …    2024.
#> 10 2024-02-01    2025-06-01 business_in…   1.6 Business i… SMP    (a) …    2025.
#> # ℹ 146 more rows

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 2022 ex_daily_1822
Exchange Rates – Daily – 2023 to Current ex_daily_23cur
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
Indicative Mid Rates of Australian Government Securities – 1992 to 2008 F16 historical
Indicative Mid Rates of Australian Government Securities – 2009 to 2013 F16 historical
Zero-coupon Interest Rates – Analytical Series – 1992 to 2008 F17 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.