1. Introduction

In this assignment, I look at employment and unemployment in Victoria using data from the Australian Bureau of Statistics (ABS). I wanted to see how these figures have changed over time and whether employment and the unemployment rate appear to be related.

The main question for this report is:

How have employment and unemployment changed in Victoria over time, and what is the relationship between employment and the unemployment rate?

To answer this question, I created four visualisations. Three charts show changes in employment and unemployment over time. The fourth chart compares employment with the unemployment rate. I included static and interactive versions so the overall trends can be seen and individual values can be explored.

2. Load packages and check the data

The packages below are used to read the Excel file, prepare the data, create the charts, and generate this report.

library(readxl)
library(dplyr)
library(ggplot2)
library(plotly)
library(knitr)

theme_set(theme_minimal(base_size = 12))

file_path <- "data/62020003.xlsx"

if (!file.exists(file_path)) {
  stop(
    "Excel file not found. Please check that ",
    "62020003.xlsx is inside the data folder."
  )
}

cat("Excel file found successfully.")
## Excel file found successfully.

3. Inspect the Excel workbook

First, I checked which sheets were available in the workbook. I used the Data1 sheet for the analysis.

available_sheets <- excel_sheets(file_path)

kable(
  data.frame(Sheet = available_sheets),
  caption = "Sheets available in the Excel workbook"
)
Sheets available in the Excel workbook
Sheet
Index
Data1
Enquiries 
if (!"Data1" %in% available_sheets) {
  stop("The Data1 sheet was not found.")
}

I then imported the data. The first 10 rows contain metadata, so I skipped those rows when reading the observations.

raw_data <- read_excel(
  path = file_path,
  sheet = "Data1",
  skip = 10,
  .name_repair = "unique"
)

cat("Number of observations:", nrow(raw_data), "\n")
## Number of observations: 583
cat("Number of columns:", ncol(raw_data), "\n")
## Number of columns: 85
kable(
  head(raw_data, 6),
  caption = "First six rows of the imported data"
)
First six rows of the imported data
28522 1638.0600718999999 1631.0226347 1629.0236225000001 1053.6723942000001 1051.5126757999999 1052.5112253 584.38767770000004 579.50995890000002 576.51239720000001 1410.3916320999999 1387.3039778 1397.3242858999999 1006.3870084 997.14676529999997 1002.5367946 404.00462379999999 390.15721250000001 394.7874913 231.69933660000001 49.974430699999999 181.72490590000001 57.836441100000002 57.600762600000003 57.530166000000001 75.831147099999995 75.701001700000006 75.772889800000002 40.504246299999998 40.172190499999999 39.964396600000001 99.402363199999996 103.40971949999999 114.6719207 48.529360599999997 53.8988947 59.3131536 50.873002700000001 49.510824800000002 55.358767100000001 94.8530734 54.0180364 40.835037 19.818847300000002 5.2951172 14.5237301 5.7211230000000004 5.9621649999999997 6.5763729 4.4029470999999996 4.8759119000000002 5.3347591999999997 8.0082090000000008 7.8710952000000001 8.7610846000000002 6.3566890000000003 5.1126581 9.3739498000000001 7.8796876999999999 9.5805328999999997 7.4006781999999998 1737.4624351 1734.4323542 1743.6955432 1102.2017547999999 1105.4115704999999 1111.8243789000001 635.26068039999996 629.02078370000004 631.87116430000003 61.3461286 61.252752899999997 61.579889199999997 79.323729 79.581316599999994 80.042990599999996 44.030283300000001 43.604328799999998 43.801919900000001 1087.9034846 277.20965200000001 810.69383259999995 2831.5990277999999 1389.0340309000001 1442.5649969000001
1978-03-01 1642.711 1639.856 1652.718 1054.490 1052.995 1058.051 588.2202 586.8612 594.6663 1409.835 1406.691 1414.751 1004.2767 1001.8293 1006.5070 405.5581 404.8615 408.2441 237.9667 51.54445 186.4222 57.95256 57.82951 58.28307 75.83545 75.70381 76.06732 40.73173 40.62074 41.16098 98.95742 99.54597 104.43953 47.98991 48.68300 49.94429 50.96751 50.86296 54.49523 85.40861 44.69705 40.71156 19.03092 5.247245 13.78367 5.681762 5.722998 5.943664 4.352905 4.418986 4.507625 7.973794 7.975700 8.394711 5.693301 4.251986 9.068059 7.405095 9.239458 6.884747 1741.668 1739.402 1757.157 1102.480 1101.678 1107.996 639.1877 637.7241 649.1615 61.44364 61.34000 61.96613 79.28672 79.20381 79.65800 44.26102 44.14131 44.93298 1078.517 282.9452 795.5714 2835.674 1390.941 1444.733
1978-04-01 1646.521 1659.240 1664.759 1054.877 1067.419 1068.876 591.6444 591.8208 595.8823 1409.283 1421.406 1421.872 1002.2181 1014.1737 1013.2413 407.0646 407.2320 408.6306 242.8866 55.63499 187.2516 58.03881 58.48041 58.67493 75.80821 76.70333 76.80806 40.93009 40.93840 41.21935 98.15713 96.46936 96.85274 47.44235 46.32102 43.22224 50.71479 50.14834 53.63050 76.78601 39.24409 37.54192 20.06673 3.978147 16.08858 5.626088 5.494609 5.497963 4.303867 4.159052 3.886548 7.895082 7.811643 8.257036 5.123652 3.728707 8.414215 7.631289 6.673273 7.912150 1744.678 1755.709 1761.611 1102.319 1113.740 1112.099 642.3592 641.9691 649.5128 61.49879 61.88051 62.08853 79.21764 80.03190 79.91395 44.43855 44.40735 44.92917 1075.646 279.5215 796.1244 2837.257 1391.620 1445.637
1978-05-01 1649.450 1654.286 1656.605 1054.962 1055.798 1057.436 594.4882 598.4877 599.1695 1408.132 1419.953 1422.378 999.8165 1004.3375 1005.2343 408.3157 415.6151 417.1435 234.2273 52.20131 182.0260 58.09412 58.26895 58.35065 75.76032 75.81999 75.93760 41.08819 41.37358 41.42071 97.22232 98.87843 100.26568 46.97516 46.44344 45.85505 50.24716 52.43499 54.41062 79.08612 42.35415 36.73197 21.17956 3.500907 17.67865 5.566145 5.639999 5.707060 4.262963 4.213545 4.156208 7.793455 8.055487 8.325012 5.267267 4.043014 8.092962 8.292480 6.285041 8.852400 1746.672 1753.164 1756.871 1101.937 1102.241 1103.291 644.7353 650.9227 653.5801 61.51832 61.75175 61.88231 79.13376 79.15523 79.23058 44.56104 44.99842 45.18213 1082.181 289.2154 792.9655 2839.052 1392.506 1446.546
1978-06-01 1650.767 1654.249 1653.590 1054.511 1056.050 1055.331 596.2555 598.1994 598.2585 1406.133 1407.625 1405.100 997.0041 997.5436 995.5750 409.1292 410.0816 409.5254 248.4894 59.75626 188.7331 58.09255 58.22199 58.19878 75.67407 75.80568 75.75411 41.17164 41.30704 41.31112 95.94795 96.75687 94.65199 46.57187 47.64216 45.73253 49.37608 49.11470 48.91946 76.79696 43.23870 33.55826 17.85503 2.493827 15.36120 5.493053 5.525787 5.414125 4.229642 4.316618 4.153486 7.647717 7.587460 7.558889 5.182340 4.162315 7.573798 6.703738 4.006143 7.526522 1746.715 1751.006 1748.242 1101.083 1103.692 1101.064 645.6316 647.3141 647.1779 61.46908 61.62739 61.53010 79.01617 79.22555 79.03690 44.58108 44.69852 44.68912 1093.037 292.0372 801.0000 2841.279 1393.101 1448.178
1978-07-01 1649.833 1652.498 1655.016 1053.350 1055.095 1054.813 596.4822 597.4027 600.2032 1403.145 1403.856 1405.443 993.9291 992.6724 994.4647 409.2164 411.1838 410.9780 249.5733 60.34808 189.2252 58.01086 58.11086 58.19941 75.53559 75.67584 75.65558 41.14815 41.21528 41.40849 94.34353 94.76424 90.06338 46.38147 45.82354 43.45173 47.96206 48.94070 46.61166 73.42012 41.23383 32.18629 16.64327 2.217902 14.42537 5.409060 5.423585 5.160990 4.217526 4.162300 3.956399 7.442390 7.571935 7.206336 4.964633 3.981257 7.262834 6.251778 3.544901 7.083390 1744.176 1747.262 1745.079 1099.732 1100.919 1098.265 644.4443 646.3434 646.8149 61.32813 61.44329 61.36653 78.86159 78.96249 78.77212 44.45680 44.59173 44.62427 1098.620 295.9655 802.6541 2843.699 1394.230 1449.469
1978-08-01 1646.870 1644.359 1638.671 1051.357 1049.494 1045.062 595.5128 594.8647 593.6095 1399.532 1392.919 1376.933 990.9194 987.7245 982.2486 408.6129 405.1949 394.6842 261.7387 62.81344 198.9253 57.85591 57.76700 57.56721 75.33472 75.20181 74.88424 41.04102 40.99784 40.91133 93.02880 106.10172 100.76081 46.67685 52.89259 51.70427 46.35196 53.20913 49.05654 81.71392 47.62720 34.08671 19.04689 4.077067 14.96983 5.346795 6.061361 5.792741 4.250948 4.798008 4.714247 7.221451 8.210350 7.633287 5.602036 4.624558 7.949867 6.783429 6.095136 6.998675 1739.899 1750.460 1739.432 1098.034 1102.386 1096.766 641.8648 648.0738 642.6660 61.12409 61.49440 61.10698 78.67934 78.99184 78.58912 44.23546 44.66499 44.29229 1107.104 298.8038 808.3000 2846.536 1395.570 1450.966

4. Check the metadata

The workbook contains information about the different series, including their descriptions, units, and adjustment types.

metadata <- read_excel(
  path = file_path,
  sheet = "Data1",
  n_max = 10,
  col_names = FALSE
)
## New names:
## • `` -> `...1`
## • `` -> `...2`
## • `` -> `...3`
## • `` -> `...4`
## • `` -> `...5`
## • `` -> `...6`
## • `` -> `...7`
## • `` -> `...8`
## • `` -> `...9`
## • `` -> `...10`
## • `` -> `...11`
## • `` -> `...12`
## • `` -> `...13`
## • `` -> `...14`
## • `` -> `...15`
## • `` -> `...16`
## • `` -> `...17`
## • `` -> `...18`
## • `` -> `...19`
## • `` -> `...20`
## • `` -> `...21`
## • `` -> `...22`
## • `` -> `...23`
## • `` -> `...24`
## • `` -> `...25`
## • `` -> `...26`
## • `` -> `...27`
## • `` -> `...28`
## • `` -> `...29`
## • `` -> `...30`
## • `` -> `...31`
## • `` -> `...32`
## • `` -> `...33`
## • `` -> `...34`
## • `` -> `...35`
## • `` -> `...36`
## • `` -> `...37`
## • `` -> `...38`
## • `` -> `...39`
## • `` -> `...40`
## • `` -> `...41`
## • `` -> `...42`
## • `` -> `...43`
## • `` -> `...44`
## • `` -> `...45`
## • `` -> `...46`
## • `` -> `...47`
## • `` -> `...48`
## • `` -> `...49`
## • `` -> `...50`
## • `` -> `...51`
## • `` -> `...52`
## • `` -> `...53`
## • `` -> `...54`
## • `` -> `...55`
## • `` -> `...56`
## • `` -> `...57`
## • `` -> `...58`
## • `` -> `...59`
## • `` -> `...60`
## • `` -> `...61`
## • `` -> `...62`
## • `` -> `...63`
## • `` -> `...64`
## • `` -> `...65`
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## • `` -> `...69`
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## • `` -> `...83`
## • `` -> `...84`
## • `` -> `...85`
cat("Metadata has been loaded for checking.")
## Metadata has been loaded for checking.

For this report, I used three measures:

The selected series are in columns 3, 33, and 48 of the supplied workbook.

5. Prepare and clean the data

I selected the date, employment, unemployment, and unemployment-rate columns. The date conversion below handles the date formats expected from the workbook.

required_columns <- c(1, 3, 33, 48)

if (ncol(raw_data) < max(required_columns)) {
  stop("The workbook does not contain all the expected columns.")
}

date_values <- raw_data[[1]]

if (inherits(date_values, "Date")) {

  dates <- as.Date(date_values)

} else if (inherits(date_values, "POSIXt")) {

  dates <- as.Date(date_values)

} else if (is.numeric(date_values)) {

  dates <- as.Date(
    date_values,
    origin = "1899-12-30"
  )

} else {

  date_text <- trimws(as.character(date_values))

  dates <- as.Date(
    date_text,
    tryFormats = c(
      "%Y-%m-%d",
      "%d/%m/%Y",
      "%m/%d/%Y",
      "%d-%m-%Y",
      "%Y/%m/%d",
      "%b %Y",
      "%B %Y"
    )
  )
}

analysis_data <- data.frame(
  date = dates,
  employment = suppressWarnings(
    as.numeric(raw_data[[3]])
  ),
  unemployment = suppressWarnings(
    as.numeric(raw_data[[33]])
  ),
  unemployment_rate = suppressWarnings(
    as.numeric(raw_data[[48]])
  )
) |>
  filter(!is.na(date)) |>
  arrange(date)

if (nrow(analysis_data) == 0) {
  stop(
    "No valid dates were found. Please check the first column ",
    "of the Data1 sheet."
  )
}

kable(
  head(analysis_data, 6),
  digits = 2,
  caption = "First six rows of the prepared data"
)
First six rows of the prepared data
date employment unemployment unemployment_rate
1978-03-01 1639.86 99.55 5.72
1978-04-01 1659.24 96.47 5.49
1978-05-01 1654.29 98.88 5.64
1978-06-01 1654.25 96.76 5.53
1978-07-01 1652.50 94.76 5.42
1978-08-01 1644.36 106.10 6.06

6. Check the data quality

Before making the charts, I checked the number of observations, the date range, missing values, and duplicate dates.

missing_values <- colSums(is.na(analysis_data))
duplicate_dates <- sum(duplicated(analysis_data$date))

quality_summary <- data.frame(
  Check = c(
    "Total observations",
    "First observation",
    "Last observation",
    "Duplicate dates"
  ),
  Result = c(
    as.character(nrow(analysis_data)),
    format(min(analysis_data$date), "%B %Y"),
    format(max(analysis_data$date), "%B %Y"),
    as.character(duplicate_dates)
  )
)

kable(
  quality_summary,
  caption = "Data quality summary"
)
Data quality summary
Check Result
Total observations 583
First observation March 1978
Last observation September 2026
Duplicate dates 0 
kable(
  data.frame(
    Variable = names(missing_values),
    Missing_values = as.integer(missing_values)
  ),
  caption = "Missing values in the selected variables"
)
Missing values in the selected variables
Variable Missing_values
date 0
employment 0
unemployment 0
unemployment_rate 0

The checks show whether the data contain missing values or repeated dates. This is useful because data-quality problems can affect the charts and the conclusions drawn from them.

7. Visualisation 1: Unemployment rate over time

I started by looking at the unemployment rate because it shows what percentage of the labour force was unemployed at different points in time.

Static chart

ggplot(
  analysis_data,
  aes(x = date, y = unemployment_rate)
) +
  geom_line(
    linewidth = 0.7,
    na.rm = TRUE
  ) +
  labs(
    title = "Unemployment Rate in Victoria Over Time",
    subtitle = "Seasonally adjusted data",
    x = "Year",
    y = "Unemployment rate (%)",
    caption = "Source: Australian Bureau of Statistics"
  ) +
  theme_minimal(base_size = 12)

Interactive chart

Move the pointer over the line to check the unemployment rate for a particular date.

plotly::plot_ly(
  data = analysis_data,
  x = ~date,
  y = ~unemployment_rate,
  type = "scatter",
  mode = "lines",
  hovertemplate = paste(
    "Date: %{x|%b %Y}",
    "<br>Unemployment rate: %{y:.2f}%",
    "<extra></extra>"
  )
) |>
  plotly::layout(
    title = "Interactive Unemployment Rate in Victoria",
    xaxis = list(title = "Year"),
    yaxis = list(title = "Unemployment rate (%)")
  )

What the chart shows

The unemployment rate goes up and down over the period. One of the clearest peaks is around the early 1990s. There is also a noticeable increase around 2020.

After these peaks, the rate falls, although it changes again in later years. This shows that unemployment has not stayed at the same level throughout the period. The chart shows when the changes happened, but it does not explain their causes.

8. Visualisation 2: Employment over time

Next, I looked at the total number of employed people in Victoria to see how employment has changed over the years.

Static chart

ggplot(
  analysis_data,
  aes(x = date, y = employment)
) +
  geom_line(
    linewidth = 0.7,
    na.rm = TRUE
  ) +
  labs(
    title = "Total Employment in Victoria Over Time",
    subtitle = "Seasonally adjusted data",
    x = "Year",
    y = "Employment (thousands of people)",
    caption = "Source: Australian Bureau of Statistics"
  ) +
  theme_minimal(base_size = 12)

Interactive chart

Hover over the line to see the employment figure for different dates.

plotly::plot_ly(
  data = analysis_data,
  x = ~date,
  y = ~employment,
  type = "scatter",
  mode = "lines",
  hovertemplate = paste(
    "Date: %{x|%b %Y}",
    "<br>Employment: %{y:.2f} thousand",
    "<extra></extra>"
  )
) |>
  plotly::layout(
    title = "Interactive Total Employment in Victoria",
    xaxis = list(title = "Year"),
    yaxis = list(title = "Employment (thousands of people)")
  )

What the chart shows

Employment has increased overall during the period covered by the data. The number of employed people rises from around 1.6 million near the beginning of the series to more than 3.8 million near the end.

The increase is not completely steady. There are some periods when employment grows more slowly or falls for a time. A noticeable disruption can also be seen around 2020.

Overall, the chart shows long-term growth in employment. However, the chart alone cannot tell us exactly why employment has increased. Changes in population and economic conditions may also play a part.

9. Visualisation 3: Number of unemployed people over time

The unemployment rate is a percentage, while this chart shows the actual number of unemployed people. Looking at both measures helps give a clearer picture of unemployment.

Static chart

ggplot(
  analysis_data,
  aes(x = date, y = unemployment)
) +
  geom_line(
    linewidth = 0.7,
    na.rm = TRUE
  ) +
  labs(
    title = "Total Unemployment in Victoria Over Time",
    subtitle = "Seasonally adjusted data",
    x = "Year",
    y = "Unemployed people (thousands)",
    caption = "Source: Australian Bureau of Statistics"
  ) +
  theme_minimal(base_size = 12)

Interactive chart

Hover over the line to see how the number of unemployed people changes across the series.

plotly::plot_ly(
  data = analysis_data,
  x = ~date,
  y = ~unemployment,
  type = "scatter",
  mode = "lines",
  hovertemplate = paste(
    "Date: %{x|%b %Y}",
    "<br>Unemployed people: %{y:.2f} thousand",
    "<extra></extra>"
  )
) |>
  plotly::layout(
    title = "Interactive Number of Unemployed People in Victoria",
    xaxis = list(title = "Year"),
    yaxis = list(title = "Unemployed people (thousands)")
  )

What the chart shows

The number of unemployed people changes considerably over time. There is a clear peak around the early 1990s and another sharp increase around 2020.

The general pattern is similar to the unemployment-rate chart. However, the number of unemployed people and the unemployment rate are different measures. One shows the number of people, while the other shows unemployment as a percentage of the labour force.

Using both charts makes it easier to compare changes in the number of unemployed people with changes in the unemployment rate.

10. Visualisation 4: Employment and unemployment rate

For the final visualisation, I compared employment with the unemployment rate using a scatterplot. Each point represents one observation from the dataset.

Static chart

ggplot(
  analysis_data,
  aes(
    x = employment,
    y = unemployment_rate
  )
) +
  geom_point(
    alpha = 0.6,
    na.rm = TRUE
  ) +
  geom_smooth(
    method = "lm",
    se = TRUE,
    na.rm = TRUE
  ) +
  labs(
    title = "Employment and Unemployment Rate in Victoria",
    subtitle = "Seasonally adjusted observations",
    x = "Employment (thousands of people)",
    y = "Unemployment rate (%)",
    caption = "Source: Australian Bureau of Statistics"
  ) +
  theme_minimal(base_size = 12)
## `geom_smooth()` using formula = 'y ~ x'

Interactive chart

Hover over individual points to see the date, employment level, and unemployment rate.

plotly::plot_ly(
  data = analysis_data,
  x = ~employment,
  y = ~unemployment_rate,
  type = "scatter",
  mode = "markers",
  text = ~format(date, "%b %Y"),
  hovertemplate = paste(
    "Date: %{text}",
    "<br>Employment: %{x:.2f} thousand",
    "<br>Unemployment rate: %{y:.2f}%",
    "<extra></extra>"
  )
) |>
  plotly::layout(
    title = "Interactive Relationship Between Employment and Unemployment Rate",
    xaxis = list(title = "Employment (thousands of people)"),
    yaxis = list(title = "Unemployment rate (%)")
  )

What the chart shows

The scatterplot shows a generally downward relationship between employment and the unemployment rate in this dataset. Higher employment levels are often associated with lower unemployment rates.

The fitted line helps show the overall direction of the relationship. However, the points do not all follow the line, which means the relationship is not perfectly linear.

The observations also cover many different years. Population and economic conditions may have changed during this time, so the pattern should be interpreted carefully. The chart shows an association between the two measures, but it does not prove that an increase in employment directly causes the unemployment rate to fall.

11. Overall findings

The four visualisations helped me look at different parts of Victoria’s labour market.

The employment chart shows an overall increase in the number of employed people across the period. In comparison, the unemployment rate and the number of unemployed people go up and down more noticeably. The early 1990s and around 2020 stand out as periods with higher unemployment.

The scatterplot shows that employment and the unemployment rate generally move in opposite directions across the observations. However, this relationship is not exact, and other factors may affect both measures.

Overall, using the four charts gives a better understanding of how employment and unemployment have changed in Victoria. The interactive charts are also useful because they allow individual dates and values to be checked rather than relying only on the overall trend.

12. Limitations

There are a few limitations to this analysis.

A future analysis could compare Victoria with other Australian states or look at different age groups and industries to explore these patterns in more detail.

13. References

Australian Bureau of Statistics. (2026). Labour Force, Australia, September 2026. https://www.abs.gov.au/statistics/labour/employment-and-unemployment/labour-force-australia/sep-2026

Plotly Technologies Inc. (n.d.). Plotly for R. https://plotly.com/r/

R Core Team. (2026). R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R-project.org/

Wickham, H. (2016). ggplot2: Elegant graphics for data analysis. Springer. https://ggplot2-book.org/

Wickham, H., François, R., Henry, L., Müller, K., Vaughan, D., & Posit Software, PBC. (2026). dplyr: A grammar of data manipulation. https://dplyr.tidyverse.org/

Wickham, H., & Bryan, J. (n.d.). readxl: Read Excel files. https://readxl.tidyverse.org/

Xie, Y., Allaire, J. J., & Grolemund, G. (2018). R Markdown: The definitive guide. Chapman and Hall/CRC. https://bookdown.org/yihui/rmarkdown/

Xie, Y. (n.d.). knitr: A general-purpose package for dynamic report generation in R. https://yihui.org/knitr/

14. Final checks

The final code below checks the number of observations, date range, missing values, and duplicate dates one more time.

cat("Total observations:", nrow(analysis_data), "\n")
## Total observations: 583
cat(
  "Date range:",
  format(min(analysis_data$date), "%B %Y"),
  "to",
  format(max(analysis_data$date), "%B %Y"),
  "\n"
)
## Date range: March 1978 to September 2026
cat("\nMissing values by variable:\n")
## 
## Missing values by variable:
print(colSums(is.na(analysis_data)))
##              date        employment      unemployment unemployment_rate 
##                 0                 0                 0                 0
cat("\nDuplicate dates:\n")
## 
## Duplicate dates:
print(sum(duplicated(analysis_data$date)))
## [1] 0
kable(
  head(analysis_data, 6),
  digits = 2,
  caption = "Final preview of the analysis data"
)
Final preview of the analysis data
date employment unemployment unemployment_rate
1978-03-01 1639.86 99.55 5.72
1978-04-01 1659.24 96.47 5.49
1978-05-01 1654.29 98.88 5.64
1978-06-01 1654.25 96.76 5.53
1978-07-01 1652.50 94.76 5.42
1978-08-01 1644.36 106.10 6.06