Most data operations are done on groups defined by variables.
group_by() takes an existing tbl and converts it into a grouped tbl
where operations are performed "by group". ungroup() removes grouping.
# S3 method for class 'SummarizedExperiment'
group_by(.data, ..., .add = FALSE, .drop = group_by_drop_default(.data))A data frame, data frame extension (e.g. a tibble), or a lazy data frame (e.g. from dbplyr or dtplyr). See Methods, below, for more details.
In group_by(), variables or computations to group by.
Computations are always done on the ungrouped data frame.
To perform computations on the grouped data, you need to use
a separate mutate() step before the group_by().
Computations are not allowed in nest_by().
In ungroup(), variables to remove from the grouping.
When FALSE, the default, group_by() will
override existing groups. To add to the existing groups, use
.add = TRUE.
This argument was previously called add, but that prevented
creating a new grouping variable called add, and conflicts with
our naming conventions.
Drop groups formed by factor levels that don't appear in the
data? The default is TRUE except when .data has been previously
grouped with .drop = FALSE. See group_by_drop_default() for details.
A grouped data frame with class grouped_df,
unless the combination of ... and add yields a empty set of
grouping columns, in which case a tibble will be returned.
These function are generics, which means that packages can provide implementations (methods) for other classes. See the documentation of individual methods for extra arguments and differences in behaviour.
Methods available in currently loaded packages:
Currently, group_by() internally orders the groups in ascending order. This
results in ordered output from functions that aggregate groups, such as
summarise().
When used as grouping columns, character vectors are ordered in the C locale
for performance and reproducibility across R sessions. If the resulting
ordering of your grouped operation matters and is dependent on the locale,
you should follow up the grouped operation with an explicit call to
arrange() and set the .locale argument. For example:
This is often useful as a preliminary step before generating content intended for humans, such as an HTML table.
Prior to dplyr 1.1.0, character vector grouping columns were ordered in the
system locale. If you need to temporarily revert to this behavior, you can
set the global option dplyr.legacy_locale to TRUE, but this should be
used sparingly and you should expect this option to be removed in a future
version of dplyr. It is better to update existing code to explicitly call
arrange(.locale = ) instead. Note that setting dplyr.legacy_locale will
also force calls to arrange() to use the system locale.
Hutchison, W.J., Keyes, T.J., The tidyomics Consortium. et al. The tidyomics ecosystem: enhancing omic data analyses. Nat Methods 21, 1166–1170 (2024). https://doi.org/10.1038/s41592-024-02299-2
Wickham, H., François, R., Henry, L., Müller, K., Vaughan, D. (2023). dplyr: A Grammar of Data Manipulation. R package version 2.1.4, https://CRAN.R-project.org/package=dplyr
Other grouping functions:
group_map(),
group_nest(),
group_split(),
group_trim()
data(pasilla)
pasilla |> group_by(.sample)
#> tidySummarizedExperiment says: A data frame is returned for independent data analysis.
#> # A tibble: 102,193 × 5
#> # Groups: .sample [7]
#> .feature .sample counts condition type
#> <chr> <chr> <int> <chr> <chr>
#> 1 FBgn0000003 untrt1 0 untreated single_end
#> 2 FBgn0000008 untrt1 92 untreated single_end
#> 3 FBgn0000014 untrt1 5 untreated single_end
#> 4 FBgn0000015 untrt1 0 untreated single_end
#> 5 FBgn0000017 untrt1 4664 untreated single_end
#> 6 FBgn0000018 untrt1 583 untreated single_end
#> 7 FBgn0000022 untrt1 0 untreated single_end
#> 8 FBgn0000024 untrt1 10 untreated single_end
#> 9 FBgn0000028 untrt1 0 untreated single_end
#> 10 FBgn0000032 untrt1 1446 untreated single_end
#> # ℹ 102,183 more rows