This is an efficient implementation of the common pattern of `do.call(rbind, dfs)` or `do.call(cbind, dfs)` for binding many data frames into one.
This is an efficient implementation of the common pattern of `do.call(rbind, dfs)` or `do.call(cbind, dfs)` for binding many data frames into one.
Usage
# S3 method for Seurat
bind_rows(..., .id = NULL, add.cell.ids = NULL)
# S3 method for Seurat
bind_cols(..., .id = NULL)Arguments
- ...
Data frames to combine.
Each argument can either be a data frame, a list that could be a data frame, or a list of data frames.
When row-binding, columns are matched by name, and any missing columns will be filled with NA.
When column-binding, rows are matched by position, so all data frames must have the same number of rows. To match by value, not position, see mutate-joins.
- .id
Data frame identifier.
When `.id` is supplied, a new column of identifiers is created to link each row to its original data frame. The labels are taken from the named arguments to `bind_rows()`. When a list of data frames is supplied, the labels are taken from the names of the list. If no names are found a numeric sequence is used instead.
- add.cell.ids
from Seurat 3.0 A character vector of length(x = c(x, y)). Appends the corresponding values to the start of each objects' cell names.
Value
`bind_rows()` and `bind_cols()` return the same type as the first input, either a data frame, `tbl_df`, or `grouped_df`.
`bind_rows()` and `bind_cols()` return the same type as the first input, either a data frame, `tbl_df`, or `grouped_df`.
Details
The output of `bind_rows()` will contain a column if that column appears in any of the inputs.
The output of `bind_rows()` will contain a column if that column appears in any of the inputs.
Examples
data(pbmc_small)
tt <- pbmc_small
ttservice::bind_rows(tt, tt)
#> Warning: Some cell names are duplicated across objects provided. Renaming to enforce unique cell names.
#> # A Seurat-tibble abstraction: 160 × 8
#> # Features=230 | Cells=160 | Active assay=RNA | Assays=RNA
#> .cell orig.ident nCount_RNA nFeature_RNA RNA_snn_res.0.8 letter.idents groups
#> <chr> <chr> <dbl> <int> <chr> <chr> <chr>
#> 1 ATGC… SeuratPro… 70 47 0 A g2
#> 2 CATG… SeuratPro… 85 52 0 A g1
#> 3 GAAC… SeuratPro… 87 50 1 B g2
#> 4 TGAC… SeuratPro… 127 56 0 A g2
#> 5 AGTC… SeuratPro… 173 53 0 A g2
#> 6 TCTG… SeuratPro… 70 48 0 A g1
#> 7 TGGT… SeuratPro… 64 36 0 A g1
#> 8 GCAG… SeuratPro… 72 45 0 A g1
#> 9 GATA… SeuratPro… 52 36 0 A g1
#> 10 AATG… SeuratPro… 100 41 0 A g1
#> # ℹ 150 more rows
#> # ℹ 1 more variable: RNA_snn_res.1 <chr>
tt_bind <- tt |> select(nCount_RNA ,nFeature_RNA)
#> tidyseurat says: Key columns are missing. A data frame is returned for independent data analysis.
tt |> ttservice::bind_cols(tt_bind)
#> New names:
#> • `nCount_RNA` -> `nCount_RNA...2`
#> • `nFeature_RNA` -> `nFeature_RNA...3`
#> • `nCount_RNA` -> `nCount_RNA...8`
#> • `nFeature_RNA` -> `nFeature_RNA...9`
#> # A Seurat-tibble abstraction: 80 × 17
#> # Features=230 | Cells=80 | Active assay=RNA | Assays=RNA
#> .cell orig.ident nCount_RNA...2 nFeature_RNA...3 RNA_snn_res.0.8
#> <chr> <fct> <dbl> <int> <fct>
#> 1 ATGCCAGAACGACT SeuratProject 70 47 0
#> 2 CATGGCCTGTGCAT SeuratProject 85 52 0
#> 3 GAACCTGATGAACC SeuratProject 87 50 1
#> 4 TGACTGGATTCTCA SeuratProject 127 56 0
#> 5 AGTCAGACTGCACA SeuratProject 173 53 0
#> 6 TCTGATACACGTGT SeuratProject 70 48 0
#> 7 TGGTATCTAAACAG SeuratProject 64 36 0
#> 8 GCAGCTCTGTTTCT SeuratProject 72 45 0
#> 9 GATATAACACGCAT SeuratProject 52 36 0
#> 10 AATGTTGACAGTCA SeuratProject 100 41 0
#> # ℹ 70 more rows
#> # ℹ 12 more variables: letter.idents <fct>, groups <chr>, RNA_snn_res.1 <fct>,
#> # nCount_RNA...8 <dbl>, nFeature_RNA...9 <int>, PC_1 <dbl>, PC_2 <dbl>,
#> # PC_3 <dbl>, PC_4 <dbl>, PC_5 <dbl>, tSNE_1 <dbl>, tSNE_2 <dbl>