Last updated: 2026-02-17
Checks: 7 0
Knit directory: jovial-shockley/
This reproducible R Markdown analysis was created with workflowr (version 1.7.1). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.
Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.
Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.
The command set.seed(20260127) was run prior to running
the code in the R Markdown file. Setting a seed ensures that any results
that rely on randomness, e.g. subsampling or permutations, are
reproducible.
Great job! Recording the operating system, R version, and package versions is critical for reproducibility.
Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.
Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.
Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.
The results in this page were generated with repository version c592bd3. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.
Note that you need to be careful to ensure that all relevant files for
the analysis have been committed to Git prior to generating the results
(you can use wflow_publish or
wflow_git_commit). workflowr only checks the R Markdown
file, but you know if there are other scripts or data files that it
depends on. Below is the status of the Git repository when the results
were generated:
Ignored files:
Ignored: .claude/
Ignored: renv/library/
Ignored: renv/staging/
Untracked files:
Untracked: Data/laconia_messinia_cups_skulls.csv
Untracked: Data/period_dictionary.csv
Untracked: Data/tomb_type_dictionary.csv
Unstaged changes:
Deleted: data/laconia_messinia_cups_skulls - Sheet2.csv
Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.
These are the previous versions of the repository in which changes were
made to the R Markdown (analysis/data_dictionary.Rmd) and
HTML (docs/data_dictionary.html) files. If you’ve
configured a remote Git repository (see ?wflow_git_remote),
click on the hyperlinks in the table below to view the files as they
were in that past version.
| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | c592bd3 | tinalasisi | 2026-02-17 | Add data dictionary worksheet and chamber space analysis |
This worksheet helps you define two key ordinal variables for analysis:
The dictionaries you complete here will be imported automatically into your analysis files.
data/period_dictionary.csv)Three columns need your input:
| Column | What to Enter | Example |
|---|---|---|
chronological_order |
A number ranking this period chronologically (1 = earliest, higher = latest) | 1, 2, 3, 4… |
earliest_component |
If this is a range period, the earliest single period it includes | For “LHIIA-LHIIIB”, enter “LHIIA” |
latest_component |
If this is a range period, the latest single period it includes | For “LHIIA-LHIIIB”, enter “LHIIIB” |
Single Periods — entry columns are the same:
period_code,period_name,chronological_order,earliest_component,latest_component
LHIIA,Late Helladic IIA,5,LHIIA,LHIIA
LHIIIB,Late Helladic IIIB,11,LHIIIB,LHIIIBRange Periods — earliest and latest differ:
period_code,period_name,chronological_order,earliest_component,latest_component
LHIIA-LHIIIB,Late Helladic IIA to IIIB,8,LHIIA,LHIIIB
MH-LHIII,Middle Helladic to LHIII,15,MH,LHIIIWhat this means: - The range “LHIIA-LHIIIB” spans from period 5 (LHIIA) to period 11 (LHIIIB) - R will later extract both the start and end period for analysis - This lets you analyze: “When did this grave context start?”, “When did it end?”, or “What’s the span?”
Determine chronological order: Arrange all periods from earliest (1) to latest (highest number)
For single periods: Copy the period code to both
earliest_component and
latest_component
For range periods: Identify the start and end, then copy them to the respective columns
data/tomb_type_dictionary.csv)One column needs your input:
| Column | What to Enter | Example |
|---|---|---|
tomb_type_order |
A number ranking this tomb type (1 = first, higher = last) | You decide the ordering criterion |
Choose one ranking system and apply it consistently:
Option A: Architectural Complexity (simplest → most complex) - Simple grave (1) - Pit (2) - Cist (3) - Chamber tomb (4) - Tholos (5)
Option B: Labor/Resource Investment (least → most) - Natural crevices (1) - Simple grave (2) - Pit grave (3) - Cist grave (4) - Built chamber tomb (5) - Tholos (6)
Option C: Frequency in Dataset (rarest → most common) - Count which tomb types appear most often - Rank accordingly
Here are the actual tomb types in your dataset. Choose one ranking system and assign each a number:
| Tomb Type | Option A (Complexity) | Option B (Labor) | Option C (Frequency) | Your Choice |
|---|---|---|---|---|
| natural crevices | 1 (simplest) | 1 (least labor) | ? (count in data) | |
| simple grave | 2 | 2 | ? | |
| grave | 2-3 | 2-3 | ? | |
| circle | ? | ? | ? | |
| pithoi/pithos/Pithos | 2-3 | 2-3 | ? | |
| pit/pits | 3 | 3 | ? | |
| cist | 4 | 4 | ? | |
| dromos of tholos | varies | 4-5 | ? | |
| shaft grave | 3-4 | 4 | ? | |
| chamber tomb | 5 | 5 | ? | |
| build/built chamber tomb | 5 | 5-6 | ? | |
| tholos | 5-6 (most complex) | 6 (most labor) | ? | |
| mound | ? | ? | ? | |
| Grave peribolos | ? | ? | ? | |
| graves in settlement context | 2-3 | 2-3 | ? |
Notes: - Some types are unclear from the dataset — use archaeological knowledge to rank them - “grave” and “simple grave” are probably similar (assign similar ranks) - “build chamber tomb” and “built chamber tomb” are likely the same (assign same rank) - “pithoi/pithos/Pithos” (burial in large storage jars) is relatively simple - If you choose Option C (Frequency), count how many graves of each type appear in the dataset and rank from rarest to most common
Which ranking system are you using? ___________________
Why did you choose this?
Once you’ve decided on a ranking system, fill in the “Your
Choice” column above, then copy those numbers into
data/tomb_type_dictionary.csv in the
tomb_type_order column.
Example (using Option A: Complexity):
tomb_type,tomb_type_order
natural crevices,1
simple grave,2
grave,2
circle,3
pithoi,2
pit,3
cist,4
shaft grave,4
dromos of tholos,4
chamber tomb,5
build chamber tomb,5
built chamber tomb,5
tholos,6
mound,3
Grave peribolos,4
graves in settlement context,2Key points: - You can use the same number twice (e.g., “simple grave” and “grave” both = 2) - Numbers don’t have to count up perfectly (e.g., you can go 1, 2, 2, 3, 4, 4, 5, 6) - But they should be sequential overall (no gaps like 1, 2, 5, 6) - The actual numbers don’t matter — R only cares about the relative order
Once you’ve filled these in, the analysis code will:
period_start_order, period_end_order, and
period_midpoint_orderThis lets you analyze relationships across time and tomb architecture!
Period Dictionary: - ☐ All periods have a
chronological order number - ☐ Numbers count up (1, 2, 3… no gaps, no
duplicates unless periods occur simultaneously) - ☐ Single periods have
matching earliest_component and
latest_component - ☐ Range periods have different start and
end components - ☐ Start component chronologically precedes end
component
Tomb Type Dictionary: - ☐ All tomb types have a ranking order number - ☐ Ranking is consistent across all types (you can see your logic) - ☐ Numbers are sequential (1, 2, 3… or in logical order) - ☐ You documented your ranking system and rationale
If you’re unsure about: - Period dating: Check archaeological chronology tables (Rutter 2014, Manning 2010) - Period sequences: Look at the period names — Roman numerals usually help (I before II before III) - Tomb types: Consider what makes sense for your research question about drinking vessels and burial practices
sessionInfo()R version 4.5.2 (2025-10-31)
Platform: aarch64-apple-darwin20
Running under: macOS Tahoe 26.2
Matrix products: default
BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.1
locale:
[1] en_US/en_US/en_US/C/en_US/en_US
time zone: America/Detroit
tzcode source: internal
attached base packages:
[1] stats graphics grDevices datasets utils methods base
other attached packages:
[1] stringr_1.6.0
loaded via a namespace (and not attached):
[1] vctrs_0.7.1 cli_3.6.5 knitr_1.50 rlang_1.1.7
[5] xfun_0.54 stringi_1.8.7 renv_1.1.6 promises_1.3.3
[9] jsonlite_2.0.0 workflowr_1.7.1 glue_1.8.0 rprojroot_2.0.4
[13] git2r_0.36.2 htmltools_0.5.8.1 httpuv_1.6.16 sass_0.4.10
[17] rmarkdown_2.29 jquerylib_0.1.4 evaluate_1.0.3 tibble_3.3.1
[21] fastmap_1.2.0 yaml_2.3.10 lifecycle_1.0.5 whisker_0.4.1
[25] compiler_4.5.2 fs_1.6.6 Rcpp_1.1.1 pkgconfig_2.0.3
[29] later_1.4.2 digest_0.6.37 R6_2.6.1 pillar_1.11.1
[33] magrittr_2.0.4 bslib_0.9.0 tools_4.5.2 cachem_1.1.0