summarise | R Documentation |
summarise()
creates a new data frame. It returns one row for each
combination of grouping variables; if there are no grouping variables, the
output will have a single row summarising all observations in the input. It
will contain one column for each grouping variable and one column for each of
the summary statistics that you have specified.
summarise()
and summarize()
are synonyms.
## S3 method for class 'Seurat'
summarise(.data, ...)
## S3 method for class 'Seurat'
summarize(.data, ...)
An object usually of the same type as .data
.
The rows come from the underlying group_keys()
.
The columns are a combination of the grouping keys and the summary expressions that you provide.
The grouping structure is controlled by the .groups=
argument, the
output may be another grouped_df, a tibble or a rowwise data frame.
Data frame attributes are not preserved, because summarise()
fundamentally creates a new data frame.
Center: mean()
, median()
Spread: sd()
, IQR()
, mad()
Range: min()
, max()
,
Position: first()
, last()
, nth()
,
Count: n()
, n_distinct()
Logical: any()
, all()
The data frame backend supports creating a variable and using it in the
same summary. This means that previously created summary variables can be
further transformed or combined within the summary, as in mutate()
.
However, it also means that summary variables with the same names as previous
variables overwrite them, making those variables unavailable to later summary
variables.
This behaviour may not be supported in other backends. To avoid unexpected results, consider using new names for your summary variables, especially when creating multiple summaries.
This function is a generic, which means that packages can provide implementations (methods) for other classes. See the documentation of individual methods for extra arguments and differences in behaviour.
The following methods are currently available in loaded packages: \Sexpr[stage=render,results=rd]{dplyr:::methods_rd("summarise")}.
Other single table verbs:
arrange()
,
mutate()
,
rename()
,
slice()
data(pbmc_small)
pbmc_small |> summarise(mean(nCount_RNA))
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