Column types and coercion

Scalars

str(samples[, c("Name", "Concentration", "Status", "Collected")])
#> tibble [3 × 4] (S3: tbl_df/tbl/data.frame)
#>  $ Name         : chr [1:3] "S-001" "S-002" "S-003"
#>  $ Concentration: num [1:3] 12.4 8.1 21
#>  $ Status       : chr [1:3] "draft" "ready" "shipped"
#>  $ Collected    : POSIXct[1:3], format: "2026-04-01 09:00:00" "2026-04-03 12:30:00" ...

• text / single-select come back as character. A single-select value is not checked against the column’s options when you write it: SeaTable adds an unknown name as a new option, so a typo becomes one.
• number becomes a double (caveat: SeaTable’s numeric column is 64-bit; large integers may round - if precision matters store them as text).
• checkbox becomes logical. A box that was never ticked reads as FALSE, as SeaTable shows it. When you write, harbouR sends the JSON true or false SeaTable needs: a number is ticked unless it is 0, and a string is read as as.logical() reads it, so "TRUE", "true" and "T" tick the box and "FALSE", "false" and "F" clear it. Any other non-empty string, NA and NaN are sent as empty, the string with a warning.
• date always becomes POSIXct, in UTC. In a date-only column - one whose format has no time - the value is midnight UTC on the date SeaTable shows, whatever the server’s time zone, so as.Date() gives that calendar date. A date-time column holds an instant: a server sends it with its UTC offset and harbouR converts it to UTC, while a .dtable stores it without one and harbouR reads it as UTC. A column that reports no format is read as a date-time. Written to a date-only column, a Date is sent as itself and a date-time as the date it shows in its own time zone, except one at exactly midnight UTC, which keeps its UTC date, so a cell read and written back keeps its day.

List-columns

These types always come back as list-columns, even when every row has one value or none. That is the type-stability rule: the same column has the same R type for every row.

subset(hb_column_types(), is_list)$seatable
#> [1] "multiple-select" "collaborator"    "image"           "file"           
#> [5] "geolocation"     "link"            "link-formula"    "button"         
#> [9] "digital-sign"

samples$Tags
#> [[1]]
#> [1] "urgent" "blood" 
#> 
#> [[2]]
#> character(0)
#> 
#> [[3]]
#> [1] "plasma"

Flatten with tidyr::unnest when you need a long form:

tidyr::unnest(samples[, c("Name", "Tags")], cols = "Tags")
#> # A tibble: 3 × 2
#>   Name  Tags  
#>   <chr> <chr> 
#> 1 S-001 urgent
#> 2 S-001 blood 
#> 3 S-003 plasma

File and image columns

samples$Reports[[1]]
#> [[1]]
#> [[1]]$name
#> [1] "S-001.pdf"
#> 
#> [[1]]$size
#> [1] 1234
#> 
#> [[1]]$type
#> [1] "application/pdf"
#> 
#> [[1]]$url
#> [1] "https://example.org/files/S-001.pdf"

Each entry is a list of file objects with name, size, type and url - ready to be downloaded via hb_download_file().

Read-only columns

harbouR leaves columns of these types out of every write, so you can round-trip a tibble through harbouR without writing back computed values by accident.

subset(hb_column_types(), read_only)$seatable
#>  [1] "link"          "link-formula"  "formula"       "auto-number"  
#>  [5] "button"        "digital-sign"  "creator"       "last-modifier"
#>  [9] "ctime"         "mtime"

Most of them are computed or maintained by SeaTable. Links are kept through SeaTable’s link endpoints rather than by writing the cell, and a button carries no data. A digital-sign cell is left out for another reason: a signature should come from the signer, not from a script.