Description

Download and Tidy Time Series Data from the Australian Bureau of Statistics.

Description

Downloads, imports, and tidies time series data from the Australian Bureau of Statistics <https://www.abs.gov.au/>.

README.md

readabs

Overview

{readabs} helps you easily download, import, and tidy data from the Australian Bureau of Statistics within R. This saves you time manually downloading and tediously tidying data and allows you to spend more time on your analysis.

Installing {readabs}

Install the latest CRAN version of {readabs} with:

install.packages("readabs")

You can install the development version of {readabs} from GitHub with:

# if you don't have devtools installed, first run:
# install.packages("devtools")
devtools::install_github("mattcowgill/readabs")

Using {readabs}

The ABS releases data in many different formats, through many different dissemination channels.

The {readabs} contains functions for working with three different types of ABS data:

read_abs() and related functions downloads, imports, and tidies ABS time series data.
download_abs_data_cube() and related functions find and download ABS data cubes, which are spreadsheets on the ABS website that are not in the standard time series format.
read_api() and related functions find, filter, and import data from the ABS.Stat API.

ABS time series data

A key function in {readabs} is read_abs(), which downloads, imports, and tidies time series data from the ABS website. Note that read_abs() only works with spreadsheets in the standard ABS time series format.

First we’ll load {readabs} and the {tidyverse}:

library(readabs)
library(tidyverse)

Now we’ll create one data frame that contains all the time series data from the Wage Price Index, catalogue number 6345.0:

all_wpi <- read_abs("6345.0")
#> Finding URLs for tables corresponding to ABS catalogue 6345.0
#> Attempting to download files from catalogue 6345.0, Wage Price Index, Australia
#> Extracting data from downloaded spreadsheets
#> Tidying data from imported ABS spreadsheets

This is what it looks like:

str(all_wpi)
#> tibble [68,137 × 12] (S3: tbl_df/tbl/data.frame)
#>  $ table_no        : chr [1:68137] "634501" "634501" "634501" "634501" ...
#>  $ sheet_no        : chr [1:68137] "Data1" "Data1" "Data1" "Data1" ...
#>  $ table_title     : chr [1:68137] "Table 1. Total Hourly Rates of Pay Excluding Bonuses: Sector, Original, Seasonally Adjusted and Trend" "Table 1. Total Hourly Rates of Pay Excluding Bonuses: Sector, Original, Seasonally Adjusted and Trend" "Table 1. Total Hourly Rates of Pay Excluding Bonuses: Sector, Original, Seasonally Adjusted and Trend" "Table 1. Total Hourly Rates of Pay Excluding Bonuses: Sector, Original, Seasonally Adjusted and Trend" ...
#>  $ date            : Date[1:68137], format: "1997-09-01" "1997-09-01" ...
#>  $ series          : chr [1:68137] "Quarterly Index ;  Total hourly rates of pay excluding bonuses ;  Australia ;  Private ;  All industries ;" "Quarterly Index ;  Total hourly rates of pay excluding bonuses ;  Australia ;  Public ;  All industries ;" "Quarterly Index ;  Total hourly rates of pay excluding bonuses ;  Australia ;  Private and Public ;  All industries ;" "Quarterly Index ;  Total hourly rates of pay excluding bonuses ;  Australia ;  Private ;  All industries ;" ...
#>  $ value           : num [1:68137] 67.4 64.7 66.7 67.3 64.8 66.6 67.3 64.8 66.7 NA ...
#>  $ series_type     : chr [1:68137] "Original" "Original" "Original" "Seasonally Adjusted" ...
#>  $ data_type       : chr [1:68137] "INDEX" "INDEX" "INDEX" "INDEX" ...
#>  $ collection_month: chr [1:68137] "3" "3" "3" "3" ...
#>  $ frequency       : chr [1:68137] "Quarter" "Quarter" "Quarter" "Quarter" ...
#>  $ series_id       : chr [1:68137] "A2603039T" "A2603989W" "A2603609J" "A2713846W" ...
#>  $ unit            : chr [1:68137] "Index Numbers" "Index Numbers" "Index Numbers" "Index Numbers" ...

It only takes you a few lines of code to make a graph from your data:

all_wpi %>%
  filter(
    series == "Percentage Change From Corresponding Quarter of Previous Year ;  Australia ;  Total hourly rates of pay excluding bonuses ;  Private and Public ;  All industries ;",
    !is.na(value)
  ) %>%
  ggplot(aes(x = date, y = value, col = series_type)) +
  geom_line() +
  theme_minimal() +
  labs(y = "Annual wage growth (per cent)")

In the example above we downloaded all the time series from a catalogue number. This will often be overkill. If you know the data you need is in a particular table, you can just get that table like this:

wpi_t1 <- read_abs("6345.0", tables = 1)
#> Warning in read_abs("6345.0", tables = 1): `tables` was providedyet
#> `check_local = TRUE` and fst files are available so `tables` will be ignored.

If you want multiple tables, but not the whole catalogue, that’s easy too:

wpi_t1_t5 <- read_abs("6345.0", tables = c("1", "5a"))
#> Warning in read_abs("6345.0", tables = c("1", "5a")): `tables` was providedyet
#> `check_local = TRUE` and fst files are available so `tables` will be ignored.

For more examples, please see the vignette on working with time series data (run browseVignettes("readabs")).

Some other functions that may come in handy when working with ABS time series data:

read_abs_local() imports and tidies time series data from ABS spreadsheets stored on a local drive. Thanks to Hugh Parsonage for contributing to this functionality.
separate_series() splits the series column of a tidied ABS time series spreadsheet into multiple columns, reducing the manual wrangling that’s needed to work with the data. Thanks to David Diviny for writing this function.

Convenience functions for loading time series data

There are several functions that load specific ABS time series data:

read_cpi() imports the Consumer Price Index numbers as a two-column tibble: date and cpi. This is useful for joining to other series to adjust data for changes in consumer prices.
read_awe() returns a long time series of Average Weekly Earnings data.
read_job_mobility() downloads, imports and tidies tables from the ABS Job Mobility dataset.

ABS data cubes

The ABS (generally) releases time series data in a standard format, which allows read_abs() to download, import and tidy it (see above). But not all ABS data is time series data - the ABS also releases data as ‘data cubes’. These are all formatted in their own, unique way.

Unfortunately, because data cubes are all formatted in their own way, there is no one function that can import tidy data cubes for you in the same way that read_abs() works with all time series. But {readabs} still has functions that can help. Thanks to David Diviny for writing these functions.

The download_abs_data_cube() function can download an ABS data cube for you. It works with any data cube on the ABS website. To use this function, we need two things: a catalogue_string (the short name of the release) and cube, a (unique fragment of) the filename within the catalogue you wish to download.

For example, let’s say you wanted to download table 4 from Weekly Payroll Jobs and Wages in Australia. We can find the catalogue name like this:

search_catalogues("payroll")
#> # A tibble: 2 × 4
#>   heading sub_heading                                         catalogue    url  
#>   <chr>   <chr>                                               <chr>        <chr>
#> 1 Jobs    Weekly Payroll Jobs                                 weekly-payr… http…
#> 2 Jobs    Weekly Payroll Jobs and Wages in Australia, Interim weekly-payr… http…

Now we know that the string "weekly-payroll-jobs" is the catalogue_string for this release. We can now see what files are available to download from this catalogue:

show_available_files("weekly-payroll-jobs")
#> # A tibble: 7 × 3
#>   label                                                              file  url  
#>   <chr>                                                              <chr> <chr>
#> 1 Table 20: Payroll jobs - characteristics distributionsContains se… 6160… http…
#> 2 Table 4: Payroll jobs indexes                                      6160… http…
#> 3 Table 6: Industry subdivision - Payroll jobs indexes               6160… http…
#> 4 Table 7: Employer characteristics - Payroll jobs index             6160… http…
#> 5 Table 8: Jobholder characteristics - Payroll jobs index            6160… http…
#> 6 Table 9: Sector - Payroll jobs index                               6160… http…
#> 7 All data cubes                                                     6160… http…

We want Table 4, which has the filename 6160055001_DO004.xlsx.

We can download the file as follows:

payrolls_t4_path <- download_abs_data_cube("weekly-payroll-jobs", "004")
#> File downloaded in /tmp/Rtmpgdh0CC/6160055001_DO004.xlsx

payrolls_t4_path
#> [1] "/tmp/Rtmpgdh0CC/6160055001_DO004.xlsx"

The download_abs_data_cube() function downloads the file and returns the full file path to the saved file. You can then pipe that in to another function:

payrolls_t4_path %>%
  readxl::read_excel(
    sheet = "Payroll jobs index",
    skip = 5
  )

Convenience functions for data cubes

As it happens, if you want the ABS Weekly Payrolls data, you don’t need to use download_abs_data_cube() directly. Instead, there is a convenience function available that downloads, imports, and tidies the data for you:

read_payrolls()

There is also a convenience function available for data cube GM1 from the monthly Labour Force data, which contains labour force gross flows:

read_lfs_grossflows()

Finding and loading data from the ABS.Stat API

The ABS has created a new site to access its data, called the ABS Data Explorer, also known as ABS.Stat. As at early 2023, this site is in Beta mode. The site provides an API.

The {readabs} package includes functions to query the ABS.Stat API. Thank you to Kinto Behr for writing these functions. The functions are:

read_api_dataflows() lists available dataflows (roughly equivalent to ‘tables’)
read_api_datastructure() lists variables within a particular dataflow and the levels of those variables, which you can use to filter the data server-side in an API query
read_api() downloads data from the ABS.Stat API.

Let’s list available dataflows:

flows <- read_api_dataflows()

Say from this I am interested in the first dataflow, the projected population of Aboriginal and Torres Strait Islander Australians. The id for this dataflow is "ABORIGINAL_POP_PROJ", which I can use to download the data.

In this case, I could download the entire dataflow with:

read_api("ABORIGINAL_POP_PROJ")
#> # A tibble: 72,144 × 11
#>    measure         sex_abs age       asgs_2011 proj_series frequency time_period
#>    <chr+lbl>       <dbl+l> <chr+lbl> <dbl+lbl> <dbl+lbl>   <chr+lbl>       <int>
#>  1 POP_PROJ [Proj… 3 [Per… A04 [0 -… 1 [New S… 3 [Series … A [Annua…        2016
#>  2 POP_PROJ [Proj… 3 [Per… A04 [0 -… 1 [New S… 3 [Series … A [Annua…        2017
#>  3 POP_PROJ [Proj… 3 [Per… A04 [0 -… 1 [New S… 3 [Series … A [Annua…        2018
#>  4 POP_PROJ [Proj… 3 [Per… A04 [0 -… 1 [New S… 3 [Series … A [Annua…        2019
#>  5 POP_PROJ [Proj… 3 [Per… A04 [0 -… 1 [New S… 3 [Series … A [Annua…        2020
#>  6 POP_PROJ [Proj… 3 [Per… A04 [0 -… 1 [New S… 3 [Series … A [Annua…        2021
#>  7 POP_PROJ [Proj… 3 [Per… A04 [0 -… 1 [New S… 3 [Series … A [Annua…        2022
#>  8 POP_PROJ [Proj… 3 [Per… A04 [0 -… 1 [New S… 3 [Series … A [Annua…        2023
#>  9 POP_PROJ [Proj… 3 [Per… A04 [0 -… 1 [New S… 3 [Series … A [Annua…        2024
#> 10 POP_PROJ [Proj… 3 [Per… A04 [0 -… 1 [New S… 3 [Series … A [Annua…        2025
#> # ℹ 72,134 more rows
#> # ℹ 4 more variables: obs_value <int>, unit_measure <chr+lbl>,
#> #   obs_status <chr+lbl>, obs_comment <lgl>

Let’s say I’m only interested in the population projections for males, not females or all persons. In that case, I can filter the data on the ABS server before downloading my query. I can use read_api_datastructure() to help with this.

read_api_datastructure("ABORIGINAL_POP_PROJ")
#> # A tibble: 332 × 6
#>    role      var     position desc    code     label               
#>    <chr>     <chr>   <chr>    <chr>   <chr>    <chr>               
#>  1 dimension measure 1        Measure POP_PROJ Projected population
#>  2 dimension sex_abs 2        Sex     1        Males               
#>  3 dimension sex_abs 2        Sex     2        Females             
#>  4 dimension sex_abs 2        Sex     3        Persons             
#>  5 dimension age     3        Age     0        0                   
#>  6 dimension age     3        Age     0014     0 - 14              
#>  7 dimension age     3        Age     0015     0 - 15              
#>  8 dimension age     3        Age     0104     1 - 4               
#>  9 dimension age     3        Age     0514     5 - 14              
#> 10 dimension age     3        Age     1        1                   
#> # ℹ 322 more rows

From this, I can see that there’s a variable (var) called sex_abs, which can take the value 1, 2, or 3, corresponding to Males, Females and Persons. If I only want to data for Males, I can obtain this by supplying a datakey:

read_api("ABORIGINAL_POP_PROJ", datakey = list(sex_abs = 1))
#> # A tibble: 24,048 × 11
#>    measure         sex_abs age       asgs_2011 proj_series frequency time_period
#>    <chr+lbl>       <dbl+l> <chr+lbl> <dbl+lbl> <dbl+lbl>   <chr+lbl>       <int>
#>  1 POP_PROJ [Proj… 1 [Mal… A04 [0 -… 1 [New S… 7 [Series … A [Annua…        2016
#>  2 POP_PROJ [Proj… 1 [Mal… A04 [0 -… 1 [New S… 7 [Series … A [Annua…        2017
#>  3 POP_PROJ [Proj… 1 [Mal… A04 [0 -… 1 [New S… 7 [Series … A [Annua…        2018
#>  4 POP_PROJ [Proj… 1 [Mal… A04 [0 -… 1 [New S… 7 [Series … A [Annua…        2019
#>  5 POP_PROJ [Proj… 1 [Mal… A04 [0 -… 1 [New S… 7 [Series … A [Annua…        2020
#>  6 POP_PROJ [Proj… 1 [Mal… A04 [0 -… 1 [New S… 7 [Series … A [Annua…        2021
#>  7 POP_PROJ [Proj… 1 [Mal… A04 [0 -… 1 [New S… 7 [Series … A [Annua…        2022
#>  8 POP_PROJ [Proj… 1 [Mal… A04 [0 -… 1 [New S… 7 [Series … A [Annua…        2023
#>  9 POP_PROJ [Proj… 1 [Mal… A04 [0 -… 1 [New S… 7 [Series … A [Annua…        2024
#> 10 POP_PROJ [Proj… 1 [Mal… A04 [0 -… 1 [New S… 7 [Series … A [Annua…        2025
#> # ℹ 24,038 more rows
#> # ℹ 4 more variables: obs_value <int>, unit_measure <chr+lbl>,
#> #   obs_status <chr+lbl>, obs_comment <lgl>

Note that in some cases, querying the API without filtering the data will return an error, as the table will be too big. In this case, you will need to supply a datakey that reduces the size of the data.

Resolving network issues by manually setting the download method

Certain corporate networks restrict your ability to download files in an R session. On some of these networks, the "wininet" method must be used when downloading files. Users can now specify the method that will be used to download files by setting the "R_READABS_DL_METHOD" environment variable.

For example, the following code sets the environment variable for your current session:

Sys.setenv("R_READABS_DL_METHOD" = "wininet")

You can add "R_READABS_DL_METHOD" to your .Rprofile to have this persist across sessions.

If you have other issues using {readabs} in your corporate environment, I would appreciate you opening an issue on GitHub.

Bug reports and feedback

GitHub issues containing error reports or feature requests are welcome. Please try to make a reprex (a minimal, reproducible example) if possible.

Alternatively you can email the package maintainer at mattcowgill at gmail dot com.

Disclaimer

The {readabs} package is not associated with the Australian Bureau of Statistics. All data is provided subject to any restrictions and licensing arrangements noted on the ABS website.

Awesome Official Statistics Software

We’re pleased to be included in a list of software that can be used to work with official statistics.

r-readabs

readabs

Overview

Installing {readabs}

Using {readabs}

ABS time series data

Convenience functions for loading time series data

ABS data cubes

Convenience functions for data cubes

Finding and loading data from the ABS.Stat API

Resolving network issues by manually setting the download method

Bug reports and feedback

Disclaimer

Awesome Official Statistics Software

Version

License

Status

Source

Homepage

Platforms (75)

readabs

Overview

Installing {readabs}

Using {readabs}

ABS time series data

Convenience functions for loading time series data

ABS data cubes

Convenience functions for data cubes

Finding and loading data from the ABS.Stat API

Resolving network issues by manually setting the download method

Bug reports and feedback

Disclaimer

Awesome Official Statistics Software

Version

License

Status

Source

Homepage

Platforms75 (75)

Platforms (75)