README.md

readrba

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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 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 1922-06-01 Consumer price…   2.8 Quarterly Original    Inde… ABS /… 2022-04-28
#> 2 1922-09-01 Consumer price…   2.8 Quarterly Original    Inde… ABS /… 2022-04-28
#> 3 1922-12-01 Consumer price…   2.7 Quarterly Original    Inde… ABS /… 2022-04-28
#> 4 1923-03-01 Consumer price…   2.7 Quarterly Original    Inde… ABS /… 2022-04-28
#> 5 1923-06-01 Consumer price…   2.8 Quarterly Original    Inde… ABS /… 2022-04-28
#> 6 1923-09-01 Consumer price…   2.9 Quarterly Original    Inde… ABS /… 2022-04-28
#> # … with 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 1994-06-01 Australian dol… 13680 Weekly    Original    $ mi… RBA    2022-06-10
#> 2 1994-06-08 Australian dol… 13055 Weekly    Original    $ mi… RBA    2022-06-10
#> 3 1994-06-15 Australian dol… 13086 Weekly    Original    $ mi… RBA    2022-06-10
#> 4 1994-06-22 Australian dol… 12802 Weekly    Original    $ mi… RBA    2022-06-10
#> 5 1994-06-29 Australian dol… 13563 Weekly    Original    $ mi… RBA    2022-06-10
#> 6 1994-07-06 Australian dol… 12179 Weekly    Original    $ mi… RBA    2022-06-10
#> # … with 3 more variables: series_id <chr>, description <chr>,
#> #   table_title <chr>

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 frequency series_type units source pub_date  
#>   <date>     <chr>           <dbl> <chr>     <chr>       <chr> <chr>  <date>    
#> 1 1922-06-01 Consumer price…   2.8 Quarterly Original    Inde… ABS /… 2022-04-28
#> 2 1922-09-01 Consumer price…   2.8 Quarterly Original    Inde… ABS /… 2022-04-28
#> 3 1922-12-01 Consumer price…   2.7 Quarterly Original    Inde… ABS /… 2022-04-28
#> 4 1923-03-01 Consumer price…   2.7 Quarterly Original    Inde… ABS /… 2022-04-28
#> 5 1923-06-01 Consumer price…   2.8 Quarterly Original    Inde… ABS /… 2022-04-28
#> 6 1923-09-01 Consumer price…   2.9 Quarterly Original    Inde… ABS /… 2022-04-28
#> # … with 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 1977-07-31 Australian Gov…   654 Monthly   Original; … $ mi… RBA    2015-06-26
#> 2 1977-08-31 Australian Gov…   665 Monthly   Original; … $ mi… RBA    2015-06-26
#> 3 1977-09-30 Australian Gov…   695 Monthly   Original; … $ mi… RBA    2015-06-26
#> 4 1977-10-31 Australian Gov…   609 Monthly   Original; … $ mi… RBA    2015-06-26
#> 5 1977-11-30 Australian Gov…   560 Monthly   Original; … $ mi… RBA    2015-06-26
#> 6 1977-12-31 Australian Gov…   614 Monthly   Original; … $ mi… RBA    2015-06-26
#> # … with 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: 123 × 5
#>    title                                   no    url   current_or_hist… readable
#>    <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 Secu… A3.1  http… current          TRUE    
#>  6 Securities Lending Repurchase and Swit… A3.2  http… current          TRUE    
#>  7 Foreign Exchange Transactions and Hold… A4    http… current          TRUE    
#>  8 Daily Foreign Exchange Market Interven… A5    http… current          TRUE    
#>  9 Banknotes on Issue by Denomination      A6    http… current          TRUE    
#> 10 Detected Australian Counterfeits by De… A7    http… current          TRUE    
#> # … with 113 more rows
browse_rba_series()
#> # A tibble: 4,412 × 8
#>    table_no series        series_id series_type table_title cur_hist description
#>    <chr>    <chr>         <chr>     <chr>       <chr>       <chr>    <chr>      
#>  1 A1       Australian d… ARBAAASTW Original    A1 Reserve… current  Australian…
#>  2 A1       Capital and … ARBALCRFW Original    A1 Reserve… current  Capital an…
#>  3 A1       Deposits (ex… ARBALDEPW Original    A1 Reserve… current  Deposits (…
#>  4 A1       Exchange set… ARBALESBW Original    A1 Reserve… current  Exchange s…
#>  5 A1       Gold and for… ARBAAGFXW Original    A1 Reserve… current  Gold and f…
#>  6 A1       Notes on iss… ARBALNOIW Original    A1 Reserve… current  Notes on i…
#>  7 A1       Other assets… ARBAAOAW  Original    A1 Reserve… current  Other asse…
#>  8 A1       Other liabil… ARBALOLW  Original    A1 Reserve… current  Other liab…
#>  9 A1       Total assets  ARBAATAW  Original    A1 Reserve… current  Total RBA …
#> 10 A1       Total liabil… ARBALTLW  Original    A1 Reserve… current  Total RBA …
#> # … with 4,402 more rows, and 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_hist… readable
#>   <chr>                                    <chr> <chr> <chr>            <lgl>   
#> 1 Consumer Price Inflation                 G1    http… current          TRUE    
#> 2 Consumer Price Inflation – Expenditure … 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: 7,039 × 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 rat… 1990-03-01    <NA>  JEFG     5.9 1989-12-01    1989. unemp…
#>  8 Unemployment rat… 1990-03-01    <NA>  JEFG     6.3 1990-03-01    1990. unemp…
#>  9 Unemployment rat… 1990-03-01    <NA>  JEFG     6.5 1990-06-01    1990. unemp…
#> 10 Unemployment rat… 1990-03-01    <NA>  JEFG     6.7 1990-09-01    1990. unemp…
#> # … with 7,029 more rows

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 series_desc source notes year_qtr
#>    <date>        <date>     <chr>        <dbl> <chr>       <chr>  <chr>    <dbl>
#>  1 2022-05-01    2021-12-01 aena_change    3.3 Nominal (n… SMP    (a) …    2021.
#>  2 2022-05-01    2022-06-01 aena_change    6   Nominal (n… SMP    (a) …    2022.
#>  3 2022-05-01    2022-12-01 aena_change    4.4 Nominal (n… SMP    (a) …    2022.
#>  4 2022-05-01    2023-06-01 aena_change    4.8 Nominal (n… SMP    (a) …    2023.
#>  5 2022-05-01    2023-12-01 aena_change    4.9 Nominal (n… SMP    (a) …    2023.
#>  6 2022-05-01    2024-06-01 aena_change    4.9 Nominal (n… SMP    (a) …    2024.
#>  7 2022-05-01    2021-12-01 business_in…   5.4 Business i… SMP    (a) …    2021.
#>  8 2022-05-01    2022-06-01 business_in…  -0.2 Business i… SMP    (a) …    2022.
#>  9 2022-05-01    2022-12-01 business_in…   5   Business i… SMP    (a) …    2022.
#> 10 2022-05-01    2023-06-01 business_in…   8.3 Business i… SMP    (a) …    2023.
#> # … with 92 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 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.



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readrba documentation built on June 17, 2022, 9:07 a.m.