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 27b3161. 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:    .DS_Store
    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 27b3161 tinalasisi 2026-02-17 Add comprehensive guiding comments for student customization
html 2f9f6dc tinalasisi 2026-02-17 Build site.
Rmd c592bd3 tinalasisi 2026-02-17 Add data dictionary worksheet and chamber space analysis

Overview

This worksheet helps you define two key ordinal variables for analysis:

  1. Archaeological Periods — arrange chronologically from earliest to latest
  2. Tomb Types — arrange by complexity, labor investment, or prevalence

The dictionaries you complete here will be imported automatically into your analysis files.

Important: Your decisions here will directly affect: - Which analyses are possible in your main analysis file - How temporal trends are interpreted - Whether tomb type complexity correlates with burial practices - Regional comparisons and statistical tests

You may have your own ideas about how to group periods or rank tomb types based on your archaeological knowledge. This is YOUR research — make the decisions that make sense for your thesis questions!


Period Dictionary (data/period_dictionary.csv)

What to Fill In

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”

Examples

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,LHIIIB

Range 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,LHIII

What 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?”

Filling It In

  1. Determine chronological order: Arrange all periods from earliest (1) to latest (highest number)

    • See standard Aegean chronologies (Rutter, Manning, etc.)
    • MH typically comes before LH
    • Within LH: LHI → LHII → LHIII
    • Subdivisions (IIA, IIB) fit sequentially
  2. For single periods: Copy the period code to both earliest_component and latest_component

  3. For range periods: Identify the start and end, then copy them to the respective columns

    • “LHIIA-LHIIIB” → earliest = LHIIA, latest = LHIIIB
    • “MH-LHIII” → earliest = MH, latest = LHIII

Tomb Type Dictionary (data/tomb_type_dictionary.csv)

What to Fill In

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

How to Rank Tomb Types

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

Your Tomb Types

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

Decision

Which ranking system are you using? ___________________

Why did you choose this?


Filling in Your CSV

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,2

Key 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


How These Will Be Used in Analysis

Once you’ve filled these in, the analysis code will:

  1. For periods: Automatically match range periods to their earliest/latest components
  2. Create new columns: period_start_order, period_end_order, and period_midpoint_order
  3. For tomb types: Use your ranking to create an ordinal factor for statistical tests

This lets you analyze relationships across time and tomb architecture!


Checklist Before Submitting

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


Questions?

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