readjsa

{readjsa} streamlines the process of getting data from Jobs and Skills Australia into R.

Note that this package is marked experimental. The code is stable, but brittle. Any changes by JSA to its data and how it’s distributed are likely to break the functionality of the package, at least temporarily.

Installation

You can install the development version of readjsa from GitHub with :

# install.packages("devtools")
devtools::install_github("MattCowgill/readjsa")

Usage

At the moment, {readjsa} can be used to get data from the Recruitment Experience & Outlook Survey (REOS), the Internet Vacancy Index (IVI), and Small Area Labour Markets (SALM). The functions for doing so are read_reos, read_ivi, and read_salm respectively - see below for more.

Getting REOS data - read_reos()

It’s straightforward to get data from the JSA REOS:

library(readjsa)
library(ggplot2)
library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union

reos <- read_reos(tables = "all")

reos |>
  filter(
    series == "Recruitment rate",
    frequency == "Monthly"
  ) |>
  ggplot(aes(x = date, y = value, col = disaggregation)) +
  geom_line()

Getting data from the JSA IVI - read_ivi()

There are several different tables in the IVI data, at different levels of aggregation. See ?read_ivi(). In the example below, we’re visualising total job vacancies at the state level.

ivi_states <- read_ivi("2dig_states")

ivi_states |>
  filter(
    level == 1,
    state != "AUST"
  ) |>
  ggplot(aes(x = date, y = value, col = series_type)) +
  geom_line() +
  facet_wrap(~state,
    scales = "free_y",
    nrow = 2
  ) +
  theme(
    legend.position = "bottom",
    legend.direction = "horizontal"
  )

Getting SAML data - read_salm()

There are three SALM tables (total labour force in persons, total unemployment in persons, and unemployment rate) available by either Statistical Area Level 2 (SA2) and Local Government Area (LGA) levels. See ?read_salm() for more information, and the JSA website for methodology and usage recommendations.

In this example, we visualise smoothed estimates of unemployment rates across five LGAs in Perth since 2010.

salm_LGA <- read_salm("unemp_rate")
perth_LGAs <- c("Stirling", "Joondalup", "Nedlands", "Swan", "Rockingham")

salm_LGA |>
  rename(LGA = local_government_area_lga_2023_asgs) |>
  filter(
    LGA %in% perth_LGAs
  ) |>
  ggplot(aes(
    x = quarter,
    y = value,
    col = LGA
  )) + 
  geom_line()