reportabs

The reportabs package is designed to make reporting on ABS data easier. It is designed to work with (most of!) the data included in the aitidata package. reportabs contains functions to help with both visual and textual reporting of data.

Installation

reportabs can be installed from github with:

# install.packages("remotes")
remotes::install_github("aiti-flinders/reportabs")

Examples of how to use this package use the data included in the aitidata package. It can be installed, however it is quite big. If you don’t want to install it, you can access data from within reportabs.

# library(aitidata)
remotes::install_github("aiti-flinders/aitidata")

labour_force <- read_absdata("labour_force)

It is also recommended that the tidyverse is installed and loaded.

# library(tidyverse)
install.packages("tidyverse")

What can this package do?

This package is designed to:

• report changes in labour market (or other data) indicators and;
• plot labour market (or other data) indicators over time

Reporting

The key to reporting data is filter_with(). Each function in this package takes a list of parameters which must be specified to generate the correct data. filter_with() requires you to specify at least an indicator by list(indicator = " "). The available indicators for each dataset can be viewed with distinct() from the dplyr package for example distinct(labour_force, indicator). filter_with() will also accept a gender, a state/territory (including Australia), an age group, and a series type. If they are not specified it will default to:

list(indicator = "",
gender = "Persons",
state = "Australia",
series_type = "Seasonally Adjusted"
)

The following functions can assist with reporting ABS labour market indicators:

• average_over(): Calculate the average value of a labour market indicator over a period.

average_over(data = labour_force, filter_with = list(indicator = "Employed total"), between = c(2010, 2020))
#> [1] 11878156

• change(): Calculate the absolute and relative change in a labour market indicator over any period of time, and report the result nicely, with correct grammar.

change(data = labour_force, filter_with = list(indicator = "Employed total"))
#> Returning data for 2022 May
#> Returning data for 2021 May
#> [1] "increased by 386,141 (2.9%) to 13.51 million"

• current(): Report the current value of a labour market indicator.
• growth(): Report the growth of a labour market indicator.
• last_value(): Report the value of a labour market indicator for the previous year or month.
• value_at(): Report the value of a labour market indicator in a specific year and month.

Numbers can be formatted nicely for inclusion in documents using as_comma(), as_percent() and as_percentage_point().

Plotting

abs_plot() will do most of the heavy lifting for you, if you know the indicator you want to plot. If not, typing plot_ and pressing tab will show the included plots. abs_plot(labour_force, indicator = "Employed total") is identical to plot_employed_total("Australia").

abs_plot(labour_force, indicator = "Employed total", states = "Australia")
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