# Data Transfers

The strollur package stores the data associated with your Amplicon Sequence Analysis. This tutorial will explain how to save, load, copy, export, and import your `strollur` object. If you haven’t reviewed the Getting Started tuturial, we recommend you start there.

Let’s use the `miseq_sop_example()` function to create a strollur object from the Miseq SOP Example.

    miseq <- miseq_sop_example()
    #> Added 2425 sequences.
    #> Assigned 2425 sequence abundances.
    #> Assigned 2425 sequence taxonomies.
    #> Assigned 531 otu bins.
    #> Assigned 2425 asv bins.
    #> Assigned 63 phylotype bins.
    #> Assigned 19 samples to treatments.
    #> Assigned 531 otu bin taxonomies.
    #> Assigned 531 otu bin representative sequences.
    #> Added metadata.
    #> Added 2 resource references.
    #> Added a contigs_report.
    miseq
    #> miseq_sop:
    #>
    #>             starts ends nbases ambigs polymers numns   numseqs
    #> Minimum:         1  375    249      0        3     0      1.00
    #> 2.5%-tile:       1  375    252      0        4     0   2849.08
    #> 25%-tile:        1  375    252      0        4     0  28490.75
    #> Median:          1  375    253      0        4     0  56981.50
    #> 75%-tile:        1  375    253      0        5     0  85472.25
    #> 97.5%-tile:      1  375    254      0        6     0 111113.93
    #> Maximum:         1  375    256      0        6     0 113963.00
    #> Mean:            1  375    252      0        4     0  56981.64
    #>
    #> Number of unique seqs: 2425
    #> Total number of seqs: 113963
    #>
    #> Total number of samples: 19
    #> Total number of treatments: 2
    #> Total number of otus: 531
    #> Total number of otu bin classifications: 531
    #> Total number of asvs: 2425
    #> Total number of asv bin classifications: 2425
    #> Total number of phylotypes: 63
    #> Total number of phylotype bin classifications: 63
    #> Total number of sequence classifications: 2425
    #> Total number of resource references: 2
    #> Total number of custom reports: 1
    #> Your dataset includes metadata

## Saving and Loading

The strollur package has a function to save a dataset object as an .rds file, `save_dataset()`, and a function to create a dataset from an .rds file, `load_dataset()`. Let’s use the miseq data object to learn how to do that.

    file_name <- file.path(tempdir(), "miseq_sop.rds")
    save_dataset(miseq, file = file_name)
    #> [1] "/var/folders/pr/t7tfrwfx03l9hcpls7__t31r0000gp/T//Rtmpc9ydB2/miseq_sop.rds"

    miseq_from_rds <- load_dataset(file = file_name)
    miseq_from_rds
    #> miseq_sop:
    #>
    #>             starts ends nbases ambigs polymers numns   numseqs
    #> Minimum:         1  375    249      0        3     0      1.00
    #> 2.5%-tile:       1  375    252      0        4     0   2849.08
    #> 25%-tile:        1  375    252      0        4     0  28490.75
    #> Median:          1  375    253      0        4     0  56981.50
    #> 75%-tile:        1  375    253      0        5     0  85472.25
    #> 97.5%-tile:      1  375    254      0        6     0 111113.93
    #> Maximum:         1  375    256      0        6     0 113963.00
    #> Mean:            1  375    252      0        4     0  56981.64
    #>
    #> Number of unique seqs: 2425
    #> Total number of seqs: 113963
    #>
    #> Total number of samples: 19
    #> Total number of treatments: 2
    #> Total number of otus: 531
    #> Total number of otu bin classifications: 531
    #> Total number of asvs: 2425
    #> Total number of asv bin classifications: 2425
    #> Total number of phylotypes: 63
    #> Total number of phylotype bin classifications: 63
    #> Total number of sequence classifications: 2425
    #> Total number of resource references: 2
    #> Total number of custom reports: 1
    #> Your dataset includes metadata
    unlink(file_name)

We can see that the summaries of miseq and miseq_from_rds are identical. Let’s modify miseq_from_rds to verify they are not referring to the same object. We will add clusters created by mothur using vsearch’s distance-based greedy clustering (dgc) algorithm.

    dgc_data <- read_mothur_list(list = strollur_example("final.dgc.list.gz"))

    assign(miseq_from_rds, table = dgc_data, bin_type = "dgc")
    #> Assigned 361 dgc bins.
    miseq_from_rds
    #> miseq_sop:
    #>
    #>             starts ends nbases ambigs polymers numns   numseqs
    #> Minimum:         1  375    249      0        3     0      1.00
    #> 2.5%-tile:       1  375    252      0        4     0   2849.08
    #> 25%-tile:        1  375    252      0        4     0  28490.75
    #> Median:          1  375    253      0        4     0  56981.50
    #> 75%-tile:        1  375    253      0        5     0  85472.25
    #> 97.5%-tile:      1  375    254      0        6     0 111113.93
    #> Maximum:         1  375    256      0        6     0 113963.00
    #> Mean:            1  375    252      0        4     0  56981.64
    #>
    #> Number of unique seqs: 2425
    #> Total number of seqs: 113963
    #>
    #> Total number of samples: 19
    #> Total number of treatments: 2
    #> Total number of otus: 531
    #> Total number of otu bin classifications: 531
    #> Total number of asvs: 2425
    #> Total number of asv bin classifications: 2425
    #> Total number of phylotypes: 63
    #> Total number of phylotype bin classifications: 63
    #> Total number of dgcs: 361
    #> Total number of dgc bin classifications: 361
    #> Total number of sequence classifications: 2425
    #> Total number of resource references: 2
    #> Total number of custom reports: 1
    #> Your dataset includes metadata
    miseq
    #> miseq_sop:
    #>
    #>             starts ends nbases ambigs polymers numns   numseqs
    #> Minimum:         1  375    249      0        3     0      1.00
    #> 2.5%-tile:       1  375    252      0        4     0   2849.08
    #> 25%-tile:        1  375    252      0        4     0  28490.75
    #> Median:          1  375    253      0        4     0  56981.50
    #> 75%-tile:        1  375    253      0        5     0  85472.25
    #> 97.5%-tile:      1  375    254      0        6     0 111113.93
    #> Maximum:         1  375    256      0        6     0 113963.00
    #> Mean:            1  375    252      0        4     0  56981.64
    #>
    #> Number of unique seqs: 2425
    #> Total number of seqs: 113963
    #>
    #> Total number of samples: 19
    #> Total number of treatments: 2
    #> Total number of otus: 531
    #> Total number of otu bin classifications: 531
    #> Total number of asvs: 2425
    #> Total number of asv bin classifications: 2425
    #> Total number of phylotypes: 63
    #> Total number of phylotype bin classifications: 63
    #> Total number of sequence classifications: 2425
    #> Total number of resource references: 2
    #> Total number of custom reports: 1
    #> Your dataset includes metadata

We can see from the summary that 361 ‘dgc’ bins were added to miseq_from_rds and not to miseq.

## Export and Import

The .rds file is in binary format and is not human readable. You can use the `export_dataset()` to see a human readable form of the raw data stored in the dataset. Let’s export miseq and look at the table created.

    table <- export_dataset(miseq)
    str(table)
    #> List of 15
    #>  $ sequence_data                    :'data.frame':   2425 obs. of  5 variables:
    #>   ..$ sequence_id     : int [1:2425] 0 1 2 3 4 5 6 7 8 9 ...
    #>   ..$ sequence_name   : chr [1:2425] "M00967_43_000000000-A3JHG_1_1101_10133_8460" "M00967_43_000000000-A3JHG_1_1101_10331_23332" "M00967_43_000000000-A3JHG_1_1101_10382_22128" "M00967_43_000000000-A3JHG_1_1101_11035_15765" ...
    #>   ..$ sequence        : chr [1:2425] "TAC--GT-AG-GGG--GCA-A-G-C-G-T-T--AT-C-CGG-AT--TT-A-C-T--GG-GT--GT-A-AA-GG-GA-GC-G-TA-GGC-G-G-C-CA-T-G-C-AA-G-T-"| __truncated__ "TAC--GG-AG-GAT--GCG-A-G-C-G-T-T--AT-C-CGG-AT--TT-A-T-T--GG-GT--TT-A-AA-GG-GA-GC-G-CA-GGC-G-G-C-AT-G-G-C-AA-G-T-"| __truncated__ "TAC--GT-AG-GTA--GCA-A-G-C-G-T-T--GT-C-CGG-AT--TT-A-C-T--GG-GT--GT-A-AA-GG-GC-GT-G-TA-GCC-G-G-G-CT-T-A-C-AA-G-T-"| __truncated__ "TAC--GT-AG-GGG--GCA-A-G-C-G-T-T--AT-C-CGG-AT--TT-A-C-T--GG-GT--GT-A-AA-GG-GG-GC-G-TA-GAC-G-G-C-AG-T-G-C-AA-G-T-"| __truncated__ ...
    #>   ..$ taxonomy        : chr [1:2425] "Bacteria(100);Firmicutes(100);Clostridia(100);Clostridiales(100);Lachnospiraceae(85);Lachnospiraceae_unclassified(85);" "Bacteria(100);Firmicutes(99);Clostridia(98);Clostridiales(98);Lachnospiraceae(95);Lachnospiraceae_unclassified(95);" "Bacteria(100);Firmicutes(100);Clostridia(100);Clostridiales(100);Ruminococcaceae(98);Ruminococcaceae_unclassified(98);" "Bacteria(100);Firmicutes(100);Clostridia(100);Clostridiales(100);Lachnospiraceae(100);Lachnospiraceae_unclassified(100);" ...
    #>   ..$ include_sequence: logi [1:2425] TRUE TRUE TRUE TRUE TRUE TRUE ...
    #>  $ sequence_report                  :'data.frame':   2425 obs. of  7 variables:
    #>   ..$ sequence_id        : int [1:2425] 0 1 2 3 4 5 6 7 8 9 ...
    #>   ..$ start              : int [1:2425] 1 1 1 1 1 1 1 1 1 1 ...
    #>   ..$ end                : int [1:2425] 375 375 375 375 375 375 375 375 375 375 ...
    #>   ..$ length             : int [1:2425] 253 253 253 252 253 252 253 253 252 252 ...
    #>   ..$ ambig              : int [1:2425] 0 0 0 0 0 0 0 0 0 0 ...
    #>   ..$ longest_homopolymer: int [1:2425] 5 4 5 5 4 5 5 5 4 4 ...
    #>   ..$ num_n              : int [1:2425] 0 0 0 0 0 0 0 0 0 0 ...
    #>  $ sequence_abundance_table         :'data.frame':   5539 obs. of  4 variables:
    #>   ..$ sequence_id: int [1:5539] 0 0 0 0 0 0 0 0 0 0 ...
    #>   ..$ abundance  : num [1:5539] 32 127 1 1 1 222 5 13 20 17 ...
    #>   ..$ sample     : chr [1:5539] "F3D0" "F3D1" "F3D146" "F3D149" ...
    #>   ..$ treatment  : chr [1:5539] "Early" "Early" "Late" "Late" ...
    #>  $ otu_bin_data                     :'data.frame':   531 obs. of  5 variables:
    #>   ..$ bin_id     : int [1:531] 0 1 2 3 4 5 6 7 8 9 ...
    #>   ..$ bin_name   : chr [1:531] "Otu001" "Otu002" "Otu003" "Otu004" ...
    #>   ..$ abundance  : num [1:531] 12288 8892 7794 7476 7450 ...
    #>   ..$ taxonomy   : chr [1:531] "Bacteria(100);\"Bacteroidetes\"(100);\"Bacteroidia\"(100);\"Bacteroidales\"(100);\"Porphyromonadaceae\"(100);\""| __truncated__ "Bacteria(100);\"Bacteroidetes\"(100);\"Bacteroidia\"(100);\"Bacteroidales\"(100);\"Porphyromonadaceae\"(100);\""| __truncated__ "Bacteria(100);\"Bacteroidetes\"(100);\"Bacteroidia\"(100);\"Bacteroidales\"(100);\"Porphyromonadaceae\"(100);\""| __truncated__ "Bacteria(100);\"Bacteroidetes\"(100);\"Bacteroidia\"(100);\"Bacteroidales\"(100);\"Porphyromonadaceae\"(100);Barnesiella(100);" ...
    #>   ..$ include_bin: logi [1:531] TRUE TRUE TRUE TRUE TRUE TRUE ...
    #>  $ otu_sequence_bin_assignment      :'data.frame':   2425 obs. of  2 variables:
    #>   ..$ bin_id     : int [1:2425] 28 274 34 32 41 3 123 333 220 0 ...
    #>   ..$ sequence_id: int [1:2425] 0 1 2 3 4 5 6 7 8 9 ...
    #>  $ otu_bin_representative_sequence  :'data.frame':   531 obs. of  2 variables:
    #>   ..$ bin_id     : int [1:531] 0 1 2 3 4 5 6 7 8 9 ...
    #>   ..$ sequence_id: int [1:531] 592 439 21 373 1501 419 666 859 2027 87 ...
    #>  $ asv_bin_data                     :'data.frame':   2425 obs. of  5 variables:
    #>   ..$ bin_id     : int [1:2425] 0 1 2 3 4 5 6 7 8 9 ...
    #>   ..$ bin_name   : chr [1:2425] "Asv0001" "Asv0002" "Asv0003" "Asv0004" ...
    #>   ..$ abundance  : num [1:2425] 12196 8829 7698 7436 7307 ...
    #>   ..$ taxonomy   : chr [1:2425] "Bacteria(100);\"Bacteroidetes\"(100);\"Bacteroidia\"(100);\"Bacteroidales\"(100);\"Porphyromonadaceae\"(100);\""| __truncated__ "Bacteria(100);\"Bacteroidetes\"(100);\"Bacteroidia\"(100);\"Bacteroidales\"(100);\"Porphyromonadaceae\"(100);\""| __truncated__ "Bacteria(100);\"Bacteroidetes\"(100);\"Bacteroidia\"(100);\"Bacteroidales\"(100);\"Porphyromonadaceae\"(100);\""| __truncated__ "Bacteria(100);\"Bacteroidetes\"(100);\"Bacteroidia\"(100);\"Bacteroidales\"(100);\"Porphyromonadaceae\"(100);Barnesiella(100);" ...
    #>   ..$ include_bin: logi [1:2425] TRUE TRUE TRUE TRUE TRUE TRUE ...
    #>  $ asv_sequence_bin_assignment      :'data.frame':   2425 obs. of  2 variables:
    #>   ..$ bin_id     : int [1:2425] 27 2419 2404 2401 518 2382 2377 2372 2363 283 ...
    #>   ..$ sequence_id: int [1:2425] 0 1 2 3 4 5 6 7 8 9 ...
    #>  $ phylotype_bin_data               :'data.frame':   63 obs. of  5 variables:
    #>   ..$ bin_id     : int [1:63] 0 1 2 3 4 5 6 7 8 9 ...
    #>   ..$ bin_name   : chr [1:63] "Phylo01" "Phylo02" "Phylo03" "Phylo04" ...
    #>   ..$ abundance  : num [1:63] 21639 53147 2805 1773 5337 ...
    #>   ..$ taxonomy   : chr [1:63] "Bacteria(100);Firmicutes(100);Clostridia(100);Clostridiales(100);Lachnospiraceae(100);Lachnospiraceae_unclassified(100);" "Bacteria(100);\"Bacteroidetes\"(100);\"Bacteroidia\"(100);\"Bacteroidales\"(100);\"Porphyromonadaceae\"(100);\""| __truncated__ "Bacteria(100);Firmicutes(100);Clostridia(100);Clostridiales(100);Ruminococcaceae(100);Ruminococcaceae_unclassified(100);" "Bacteria(100);Firmicutes(100);Clostridia(100);Clostridiales(100);Clostridiales_unclassified(100);Clostridiales_"| __truncated__ ...
    #>   ..$ include_bin: logi [1:63] TRUE TRUE TRUE TRUE TRUE TRUE ...
    #>  $ phylotype_sequence_bin_assignment:'data.frame':   2425 obs. of  2 variables:
    #>   ..$ bin_id     : int [1:2425] 0 0 2 0 0 1 3 0 24 1 ...
    #>   ..$ sequence_id: int [1:2425] 0 1 2 3 4 5 6 7 8 9 ...
    #>  $ resource_reference               :'data.frame':   2 obs. of  10 variables:
    #>   ..$ vendor           : chr [1:2] "Schloss Lab - University of Michigan" "SILVA"
    #>   ..$ name             : chr [1:2] "R phylotypr package" "silva.bacteria.fasta"
    #>   ..$ version          : chr [1:2] "0.1.1" "1.38.1"
    #>   ..$ usage            : chr [1:2] "classification of sequences" "alignment of sequences"
    #>   ..$ note             : chr [1:2] "classification using Bayesian method" "alignment reference trimmed to V4 region"
    #>   ..$ method_url       : chr [1:2] "doi:10.1128/mra.01144-24" "NA"
    #>   ..$ documentation_url: chr [1:2] "https://mothur.org/phylotypr/" "https://mothur.org/wiki/silva_reference_files/"
    #>   ..$ parameter        : chr [1:2] "kmer_size=8,num_bootstraps=100,min_confidence=80" "NA"
    #>   ..$ citation         : chr [1:2] "@article{doi:10.1128/AEM.00062-07, author = {Qiong Wang and George M. Garrity and James M. Tiedje and James R. "| __truncated__ "NA"
    #>   ..$ creation_date    : chr [1:2] "2026-06-19" "2026-06-19"
    #>  $ metadata                         :'data.frame':   19 obs. of  2 variables:
    #>   ..$ sample        : chr [1:19] "F3D0" "F3D1" "F3D141" "F3D142" ...
    #>   ..$ days_post_wean: num [1:19] 0 1 141 142 143 144 145 146 147 148 ...
    #>  $ contigs_report                   :'data.frame':   2425 obs. of  8 variables:
    #>   ..$ Name           : chr [1:2425] "M00967_43_000000000-A3JHG_1_1101_10133_8460" "M00967_43_000000000-A3JHG_1_1101_10331_23332" "M00967_43_000000000-A3JHG_1_1101_10382_22128" "M00967_43_000000000-A3JHG_1_1101_11035_15765" ...
    #>   ..$ Length         : num [1:2425] 253 253 253 252 253 252 253 253 252 252 ...
    #>   ..$ Overlap_Length : num [1:2425] 249 249 249 250 249 249 248 249 250 248 ...
    #>   ..$ Overlap_Start  : num [1:2425] 2 2 2 1 2 2 3 2 1 3 ...
    #>   ..$ Overlap_End    : num [1:2425] 251 251 251 251 251 251 251 251 251 251 ...
    #>   ..$ MisMatches     : num [1:2425] 0 0 2 19 2 0 4 1 2 8 ...
    #>   ..$ Num_Ns         : num [1:2425] 0 0 0 0 0 0 0 0 0 0 ...
    #>   ..$ Expected_Errors: num [1:2425] 0.00207 0.00231 0.01135 0.08721 0.00674 ...
    #>   ..- attr(*, "sequence_name")= chr "Name"
    #>  $ sequence_tree                    :List of 4
    #>   ..$ edge       : int [1:4848, 1:2] 2426 2427 2427 2426 2428 2429 2429 2428 2430 2431 ...
    #>   ..$ edge.length: num [1:4848] NaN 0.00395 0.00395 0 0.00198 ...
    #>   ..$ Nnode      : int 2424
    #>   ..$ tip.label  : chr [1:2425] "M00967_43_000000000-A3JHG_1_1114_15727_25995" "M00967_43_000000000-A3JHG_1_2109_19976_22044" "M00967_43_000000000-A3JHG_1_1102_9244_9305" "M00967_43_000000000-A3JHG_1_2101_14159_9619" ...
    #>   ..- attr(*, "class")= chr "phylo"
    #>   ..- attr(*, "order")= chr "cladewise"
    #>  $ sample_tree                      :List of 5
    #>   ..$ edge       : int [1:36, 1:2] 20 21 22 23 24 25 26 27 27 26 ...
    #>   ..$ edge.length: num [1:36] 0.03913 0.01741 0.02565 0.00379 0.01732 ...
    #>   ..$ Nnode      : int 18
    #>   ..$ tip.label  : chr [1:19] "F3D9" "F3D8" "F3D6" "F3D5" ...
    #>   ..$ root.edge  : num 0.221
    #>   ..- attr(*, "class")= chr "phylo"
    #>   ..- attr(*, "order")= chr "cladewise"
    #>  - attr(*, "strollur_version")= chr "0.1.0"
    #>  - attr(*, "dataset_name")= chr "miseq_sop"

Similarly to `load_dataset()`, you can use the `import_dataset()` function to create a new dataset object from the exported table.

    miseq_import <- import_dataset(table = table)
    #> Added 2425 sequences.
    #> Assigned 2425 sequence taxonomies.
    #> Assigned 2425 sequence abundances.
    #> Assigned 531 otu bins.
    #> Assigned 531 otu bin representative sequences.
    #> Assigned 531 otu bin taxonomies.
    #> Assigned 2425 asv bins.
    #> Assigned 2425 asv bin taxonomies.
    #> Assigned 63 phylotype bins.
    #> Assigned 63 phylotype bin taxonomies.
    #> Added metadata.
    #> Added 2 resource references.
    #> Added a contigs_report.
    miseq_import
    #> miseq_sop:
    #>
    #>             starts ends nbases ambigs polymers numns   numseqs
    #> Minimum:         1  375    249      0        3     0      1.00
    #> 2.5%-tile:       1  375    252      0        4     0   2849.08
    #> 25%-tile:        1  375    252      0        4     0  28490.75
    #> Median:          1  375    253      0        4     0  56981.50
    #> 75%-tile:        1  375    253      0        5     0  85472.25
    #> 97.5%-tile:      1  375    254      0        6     0 111113.93
    #> Maximum:         1  375    256      0        6     0 113963.00
    #> Mean:            1  375    252      0        4     0  56981.64
    #>
    #> Number of unique seqs: 2425
    #> Total number of seqs: 113963
    #>
    #> Total number of samples: 19
    #> Total number of treatments: 2
    #> Total number of otus: 531
    #> Total number of otu bin classifications: 531
    #> Total number of asvs: 2425
    #> Total number of asv bin classifications: 2425
    #> Total number of phylotypes: 63
    #> Total number of phylotype bin classifications: 63
    #> Total number of sequence classifications: 2425
    #> Total number of resource references: 2
    #> Total number of custom reports: 1
    #> Your dataset includes metadata

Again, we can see that the summary of miseq_import is identical to the summary of miseq.

## Copy

Lastly, you can make a deep copy of your dataset using the `copy_dataset()` function. Note, if you use an assignment operator to copy it’s a shallow copy. The dataset object is an R6 object to keep the memory usage low. First let’s learn how to use the `copy_dataset()` function, then we will take a closer look at how deep and shallow copying differ.

    miseq_deep_copy <- copy_dataset(miseq)

    miseq_shallow_copy <- miseq

Let’s add the dgc_data to miseq_shallow_copy and then compare miseq, miseq_deep_copy, and mise_shallow_copy.

    assign(miseq_shallow_copy, table = dgc_data, bin_type = "dgc")
    #> Assigned 361 dgc bins.

    miseq
    #> miseq_sop:
    #>
    #>             starts ends nbases ambigs polymers numns   numseqs
    #> Minimum:         1  375    249      0        3     0      1.00
    #> 2.5%-tile:       1  375    252      0        4     0   2849.08
    #> 25%-tile:        1  375    252      0        4     0  28490.75
    #> Median:          1  375    253      0        4     0  56981.50
    #> 75%-tile:        1  375    253      0        5     0  85472.25
    #> 97.5%-tile:      1  375    254      0        6     0 111113.93
    #> Maximum:         1  375    256      0        6     0 113963.00
    #> Mean:            1  375    252      0        4     0  56981.64
    #>
    #> Number of unique seqs: 2425
    #> Total number of seqs: 113963
    #>
    #> Total number of samples: 19
    #> Total number of treatments: 2
    #> Total number of otus: 531
    #> Total number of otu bin classifications: 531
    #> Total number of asvs: 2425
    #> Total number of asv bin classifications: 2425
    #> Total number of phylotypes: 63
    #> Total number of phylotype bin classifications: 63
    #> Total number of dgcs: 361
    #> Total number of dgc bin classifications: 361
    #> Total number of sequence classifications: 2425
    #> Total number of resource references: 2
    #> Total number of custom reports: 1
    #> Your dataset includes metadata

    miseq_shallow_copy
    #> miseq_sop:
    #>
    #>             starts ends nbases ambigs polymers numns   numseqs
    #> Minimum:         1  375    249      0        3     0      1.00
    #> 2.5%-tile:       1  375    252      0        4     0   2849.08
    #> 25%-tile:        1  375    252      0        4     0  28490.75
    #> Median:          1  375    253      0        4     0  56981.50
    #> 75%-tile:        1  375    253      0        5     0  85472.25
    #> 97.5%-tile:      1  375    254      0        6     0 111113.93
    #> Maximum:         1  375    256      0        6     0 113963.00
    #> Mean:            1  375    252      0        4     0  56981.64
    #>
    #> Number of unique seqs: 2425
    #> Total number of seqs: 113963
    #>
    #> Total number of samples: 19
    #> Total number of treatments: 2
    #> Total number of otus: 531
    #> Total number of otu bin classifications: 531
    #> Total number of asvs: 2425
    #> Total number of asv bin classifications: 2425
    #> Total number of phylotypes: 63
    #> Total number of phylotype bin classifications: 63
    #> Total number of dgcs: 361
    #> Total number of dgc bin classifications: 361
    #> Total number of sequence classifications: 2425
    #> Total number of resource references: 2
    #> Total number of custom reports: 1
    #> Your dataset includes metadata

    miseq_deep_copy
    #> miseq_sop:
    #>
    #>             starts ends nbases ambigs polymers numns   numseqs
    #> Minimum:         1  375    249      0        3     0      1.00
    #> 2.5%-tile:       1  375    252      0        4     0   2849.08
    #> 25%-tile:        1  375    252      0        4     0  28490.75
    #> Median:          1  375    253      0        4     0  56981.50
    #> 75%-tile:        1  375    253      0        5     0  85472.25
    #> 97.5%-tile:      1  375    254      0        6     0 111113.93
    #> Maximum:         1  375    256      0        6     0 113963.00
    #> Mean:            1  375    252      0        4     0  56981.64
    #>
    #> Number of unique seqs: 2425
    #> Total number of seqs: 113963
    #>
    #> Total number of samples: 19
    #> Total number of treatments: 2
    #> Total number of otus: 531
    #> Total number of otu bin classifications: 531
    #> Total number of asvs: 2425
    #> Total number of asv bin classifications: 2425
    #> Total number of phylotypes: 63
    #> Total number of phylotype bin classifications: 63
    #> Total number of sequence classifications: 2425
    #> Total number of resource references: 2
    #> Total number of custom reports: 1
    #> Your dataset includes metadata

You can see from the summaries that the dgc_data was added to both miseq and miseq_shallow_copy because they actually reference the same object, but miseq_deep_copy was not modified.