Load data and packages

library(tidyverse)
library(patchwork)
dental_inv <- readr::read_csv("https://raw.githubusercontent.com/bbartholdy/dental-inv-plot/main/data/dental-inv.csv")

The variables are:

Convert data to long format

First step is to convert the data to long format with the pivot_longer function. This will take all of the tooth columns and combine them to two columns; one with the tooth name, and one with the tooth status.

dental_inv_long <- dental_inv %>%
  pivot_longer(-id, names_to = "tooth", values_to = "status")

dental_inv_long %>%
  slice_head(n = 10) # show first 10 entries

# A tibble: 10 × 3
   id    tooth status
   <chr> <chr> <chr> 
 1 id08  t11   p     
 2 id08  t12   p     
 3 id08  t13   m     
 4 id08  t14   p     
 5 id08  t15   p     
 6 id08  t16   p     
 7 id08  t17   p     
 8 id08  t18   m     
 9 id08  t21   p     
10 id08  t22   m     

It would also be useful to have a column that indicates to which region of the mouth the tooth belongs.

maxilla <- c(paste0("t", 11:18), paste0("t", 21:28))
left <- c(paste0("t", 21:28), paste0("t", 31:38))

dental_inv_long <- dental_inv %>%
  pivot_longer(
    t11:t48, names_to = "tooth",
    values_to = "status"
  ) %>%
   mutate(
    region = if_else(tooth %in% maxilla, "maxilla", "mandible"), 
    side = if_else(tooth %in% left, "left", "right"), .before = status
  )

Prepare separate plots

Indicate order of the teeth in each region as in the FDI diagram shown above. starting from the upper right third molar. We will use this to reorder the teeth shown in the plot.

upper_order <- c(paste0("t", 18:11), paste0("t", 21:28))
lower_order <- c(paste0("t", 48:41), paste0("t", 31:38))
upper_order

 [1] "t18" "t17" "t16" "t15" "t14" "t13" "t12" "t11" "t21" "t22" "t23" "t24"
[13] "t25" "t26" "t27" "t28"

lower_order

 [1] "t48" "t47" "t46" "t45" "t44" "t43" "t42" "t41" "t31" "t32" "t33" "t34"
[13] "t35" "t36" "t37" "t38"

The plots for the maxilla and mandible are prepared separately, then combined with the patchwork package.

First, the plot of the maxillary dentition:

# Plot for the maxillary dentition
maxilla_pl <- dental_inv_long %>%
  filter(region == "maxilla") %>% # we only want maxillary teeth
  mutate(tooth = factor(tooth, levels = upper_order)) %>% # make sure the order on the plot is according to the order the teeth are positioned in the mouth
  ggplot(aes(x = tooth, fill = status)) +
    geom_bar(position = "stack") + # stacked bar plot
    scale_x_discrete(position = "top") + # move x-axis text to top
    scale_fill_viridis_d() # colour-blind-friendly pallette

maxilla_pl

Then, the plot of the mandibular dentition:

# Plot for the mandibular dentition
mandible_pl <- dental_inv_long %>%
  filter(region == "mandible") %>%
  mutate(tooth = factor(tooth, levels = lower_order)) %>%
  ggplot(aes(x = tooth, fill = status)) +
    geom_bar(position = "stack") +
    scale_fill_viridis_d()

mandible_pl

Combine plots

UPDATE: The two dental plots are combined using the patchwork package. Thanks @jfy133 for bringing it to my attention! (and of course @thomasp85, the package developer).

We will combine them using / instead of +, because we want one on top of the other to mimic the maxilla’s and mandible’s position in the mouth:

maxilla_pl / mandible_pl

The plot_layout() function allows us to easily combine the shared legends and axes.

maxilla_pl / mandible_pl + plot_layout(guides = "collect", axis_titles = "collect")

We can also add shared theme elements

maxilla_pl / mandible_pl + 
  plot_layout(guides = "collect", axis_titles = "collect") & 
  theme_minimal() +
  theme(
    panel.grid = element_blank(),
    axis.title.x.top = element_blank() # remove the extra x-axis title
  )

And done! This gives (in my opinion) a nice, intuitive overview of the dental inventory in a sample, and could also be used to look at dental diseases (caries, calculus, periodontitis, etc).

Why should you care about FAIR?

It’s good for science and accumulating knowledge. Not convincing enough? It’s also good for you.

By making your research data findable to other researchers, you are also increasing the findability of your work, and potentially increasing your impact.

By ensuring that others can easily access and work on your data, you are also ensuring that your future self will be easily(ish) able to access and work on your data.

By increasing interoperability, you are ensuring that others, especially those from related fields, can readily integrate your work into their workflow and encourage collaboration.

By making it easier for other researchers to reuse your data, you are also increasing the likelihood that they will actually reuse your data, and not give up because they have to jump through hoops to do this. Just remember to provide a clear license and citation so that you get credit for your work!

Definition of the FAIR Principles

Findable means you can actually locate the (meta)data. This requires the data to be:

• assigned a persistent identifier (PID)
• accompanied by metadata
• registered in a searchable resource.

Accessible means there should be a clear path to accessing the (meta)data. Preferably accessible via:

• an open, standardised protocol.
• restricted access (authentication), if necessary.

Interoperable means the (meta)data should be in a format that allows them to be used in common applications and workflows, preferably with:

• formal, accessible, broadly applicable language
• vocabularies that follow FAIR principles
• references to other related (meta)data

Reusable means the data should be well-documented, including:

• domain-relevant guidance using community standards for researchers who wish to reuse the data
• provenance of their creation
• a license to indicate the terms of reuse

https://www.go-fair.org/

Putting principles into practice

Things to ask yourself:

• Can someone search for and find your data?
• Do the data have a unique persistent identifier that you can refer to when communicating your findings?
• Once someone finds your data, can they be downloaded or accessed in any way?
• can information about your data be found, even if the dataset no longer exists?
• Once the data have been obtained, can they easily be integrated into existing workflows?
• Is it clear how researchers should re-use the data, and how they are allowed to do this?
• How should they give you credit when re-using the data?

I have found that it helps to prepare FAIR data by working backwards through the FAIR principles.

It all starts by ensuring your data are reusable. Proper documentation early on is much easier to do when the data are fresh in your memory. The most basic form of documentation is a README. This can be a plain-text (README.txt) or markdown (README.md) file. Here, you should describe the project and the data, including the methodology of data collection and a description of the variables (data dictionary). It may also be useful to add how the data can be accessed and what software might be needed to work with the data. For more information, see https://www.makeareadme.com/.

Documenting the analytical workflow is also important for reuse. This can be done using using code notebooks such as Jupyter, R Markdown, or Quarto (my personal favourite).

Making sure your data can be easily integrated into workflows (interoperable) requires using tools and data vocabularies that are standard to your field. To ensure wider adoption of the data, you should also be using free and open source tools and formats (for example, export your data as a CSV file rather than an Excel spreadsheet).

Making the data accessible is as easy (or difficult) as uploading them to a trusted data repository. The best way to choose is by exploring what repositories others in your field use. There are also general purpose repositories like Zenodo. You can find useful information on (trusted) repositories on re3data.

Finally, to make your data findable, you should make sure to include the persistent identifier in your research article (Data Availability Statement). Often the data repository will allow you to reserve a DOI for your dataset. This means you can wait to publish your data until the manuscript has been accepted. Simply add the reserved DOI to your article, and then make the repository public when the manuscript has been accepted/published. Avoid using an email address for contact, or a personal/group website as the (only) data repository. These are more susceptible to change than, for example, an ORCID and a Digital Object Identifier (DOI) (though, these are also not foolproof).

What a repository can do for you

Repositories have the infrastructure to ensure long-term storage of your dataset. Zenodo, for example, is funded by CERN and OpenAIRE, among others. CERN has also committed to hosting the repository for the lifetime of CERN itself. Repositories also provide you with a persistent identifier and register your repository metadata in searchable resources. They implement access protocols for both humans (a nice big ‘Download’ button) and computers (Application Programming Interface).

Repositories will make it easy to adhere to general metadata standards (during the upload process you provide information about your data), and some repositories will enforce field-specific metadata standards. They will also track the (re-)usage (views, dowloads, citations) and display the license and recommended citation.

Common misconceptions

I still have questions!?

Where can you go for more help? Most institutions have dedicated (and passionate!) people working on promoting research data managment (including FAIR) and Open Science. These can be Data Stewards or (Digital) Librarians. Try to find them at your institute and reach out! Otherwise The Turing Way is also a good place to start.

Hello World

This is the inaugural post for The Bioarchaeologist’s Guide. The topic of this post will be The Bioarchaeologist’s Guide. How it came to be, what will be its purpose, and who is this Bjørn person anyway?

How it came to be

I have a bunch of notes on various topics that I wanted to share. Now I’m sharing them. That’s it.

What will be its purpose

The purpose of this site… blog… whatever it ends up being, is to provide a set of helpful resources on common topics in the field of bioarchaeology. Some may be interesting, others won’t. Some may be broadly applicable to many fields, others won’t. Topics will include dental diseases, statistics, the R programming language, and open science. It will not contain any entries on The Hitchhiker’s Guide to the Galaxy, but there will be quotes. There are, at the time of writing, two types of resources: Blog posts and Entries. Blog posts, like the one you are reading now, are long, often opinionated—but still grounded in facts— posts on a particular subject. If you don’t care about my opinions and find my writing to be unnecessarily filled with h2g2 references that you don’t understand and/or care about, you will often be able to find the essential parts of the blog posts in Entries. Basically the TL;DR.

I have done my best to make sure this site… blog… thing, will be as accessible as possible. The colours were generated using randoma11y and are chosen for their high contrast. The primary theme was styled after the BBC television adaptation of the Hitchhiker’s Guide, but there is also a secondary theme with a lower contrast and Atkinson Hyperlegible Font (themes can be accessed using the switch in the navigation bar). I will also make sure that all images have alt text.

Who is this Bjørn person anyway?

I am a human, and the errors will reflect that (and I’m genuinely happy to have them pointed out). I’m a PhD student doing bioarchaeology. I have also been known to fumble may way through topics like Open Science and statistics.

Well, that about wraps it up for this post.