Summarise each group down to one row

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 'SingleCellExperiment'
summarise(.data, ...)

# S3 method for class 'SingleCellExperiment'
summarize(.data, ...)

Arguments

.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.

...

<data-masking> Name-value pairs of summary functions. The name will be the name of the variable in the result.

The value can be:

• A vector of length 1, e.g. min(x), n(), or sum(is.na(y)).

• A data frame, to add multiple columns from a single expression.

Returning values with size 0 or >1 was deprecated as of 1.1.0. Please use reframe() for this instead.

Value

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.

Useful functions

• Center: mean(), median()

• Spread: sd(), IQR(), mad()

• Range: min(), max(),

• Position: first(), last(), nth(),

• Count: n(), n_distinct()

• Logical: any(), all()

Backend variations

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.

Methods

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: dbplyr (tbl_lazy), dplyr (data.frame, grouped_df, rowwise_df), plotly (plotly), tidySingleCellExperiment (SingleCellExperiment) .

See also

Other single table verbs: arrange(), mutate(), rename(), slice()

Examples

data(pbmc_small)
pbmc_small |> summarise(mean(nCount_RNA))
#> tidySingleCellExperiment says: A data frame is returned for independent data analysis.
#> # A tibble: 1 × 1
#>   `mean(nCount_RNA)`
#>                <dbl>
#> 1               245.