What’s New in overviewR 0.0.14?

A Quick Look at the Latest Update

If you work with panel data in R — data that tracks multiple units (like countries, companies, or people) over time — you’ve probably run into the challenge of simply understanding what your data set covers. Which units are present in which time periods? Where are the gaps? That’s exactly the problem overviewR tries to solve, and where the latest release brings some nice improvements.

Here’s a quick run-through of what you can expect from version 0.0.14.

Markdown Support: Beyond LaTeX

The biggest addition is a function called overview_markdown(). Previously, if you wanted to export your data summaries as nicely formatted tables, you could use overview_latex() to generate LaTeX output. A good aproach if you’re writing academic papers, but not so helpful if you’re writing a README, a blog post, or documentation in Markdown.

overview_markdown() converts the output of overview_tab() or overview_crosstab() directly into a clean Markdown table. You can customize headers, titles, and even export the result to a .md file.

toydata |> 
  overview_tab(id = ccode, time = year) |> 
  overview_markdown()

## Time and scope of the sample
| Sample | Time frame |
|--------|------------|
| AGO | 1990 - 1992 |
| BEN | 1995 - 1999 |
| FRA | 1993, 1996, 1999 |
| GBR | 1991, 1993, 1995, 1997, 1999 |
| RWA | 1990 - 1995 | 

Which, if put into a markdown file, gives you this beautiful table:

(The layout changes depending on the attached styles attached to your markdown rendering.)

What works for overview_tab should also work for overview_crosstab, right? And it does! Just set crosstab = TRUE.

overview_crosstab(
  dat = toydata,
  cond1 = gdp,
  cond2 = population,
  threshold1 = 25000,
  threshold2 = 27000,
  id = ccode,
  time = year
) |> 
overview_markdown(
  crosstab = TRUE
)

## Time and scope of the sample
| | **Condition 1**: Fulfilled | **Condition 1**: Not fulfilled |
|---|---|---|
| **Condition 2**: Fulfilled | AGO (1990, 1992), FRA (1993), GBR (1997) | BEN (1996, 1999), FRA (1999), GBR (1993), RWA (1992, 1994) |
| **Condition 2**: Not fulfilled | BEN (1997), RWA (1990) | AGO (1991), BEN (1995, 1998), FRA (1996), GBR (1991, 1995, 1999), RWA (1991, 1993, 1995) | 

And this gives you:

Smarter Venn Diagrams

The overview_overlap() function, which lets you visually compare two datasets, now supports area-proportional Euler diagrams. Previously, Venn diagrams in overviewR showed overlapping circles of equal size regardless of how much actual overlap existed. By setting proportional = TRUE when using plot_type = "venn", the circles are now drawn so that their sizes reflect the true proportions of overlap. Under the hood, this uses the eulerr package.

overview_overlap(
  dat1 = toydata,
  dat2 = toydata2,
  dat1_id = ccode,
  dat2_id = ccode,
  plot_type = "venn",
  proportional = TRUE
)

So the proportions of the circles reflect now the numbers.

More flexible plot colors

overview_plot() previously required mapping a column to a color aesthetic. Based on community feedback, you can now pass a simple color string (e.g., color = "steelblue") to style all lines and points uniformly:

overview_plot(
  dat = toydata, 
  id = ccode, 
  time = year,
  color = "steelblue"
)

If you want, you can also define the colors manually using ggplot2::scale_color_manual or pass color palettes using ggplot2::scale_color_brewer

overview_plot(
  dat = toydata, 
  id = ccode, 
  time = year,
  color = post_1995
) + ggplot2::scale_color_brewer(palette = "Set1")

Better Missing-Data Warnings

Working with time-series data means missing values can silently distort your results. Version 0.0.14 extends overview_na() to send warnings for NA values in month or day columns. This early heads-up can help you catch gaps in your time coverage before they cause surprises later on.

Getting Started

At the time when this blog post goes live, the newest version is also available on CRAN and you can install it directly from there:

install.packages("overviewR")

Alternatively, you can also get the development version from GitHub:

# install.packages("devtools")
devtools::install_github("cosimameyer/overviewR")

For more details, check out the full documentation.

Did you get a chance to give it a try? We’re very much looking forward to hearing your feedback!