pivot_wider() "widens" data, increasing the number of columns and
decreasing the number of rows. The inverse transformation is
pivot_longer().
Learn more in vignette("pivot").
# S3 method for class 'SummarizedExperiment'
pivot_wider(
data,
...,
id_cols = NULL,
id_expand = FALSE,
names_from = name,
names_prefix = "",
names_sep = "_",
names_glue = NULL,
names_sort = FALSE,
names_vary = "fastest",
names_expand = FALSE,
names_repair = "check_unique",
values_from = value,
values_fill = NULL,
values_fn = NULL,
unused_fn = NULL
)A data frame to pivot.
Additional arguments passed on to methods.
<tidy-select> A set of columns that
uniquely identify each observation. Typically used when you have
redundant variables, i.e. variables whose values are perfectly correlated
with existing variables.
Defaults to all columns in data except for the columns specified through
names_from and values_from. If a tidyselect expression is supplied, it
will be evaluated on data after removing the columns specified through
names_from and values_from.
Should the values in the id_cols columns be expanded by
expand() before pivoting? This results in more rows, the output will
contain a complete expansion of all possible values in id_cols. Implicit
factor levels that aren't represented in the data will become explicit.
Additionally, the row values corresponding to the expanded id_cols will
be sorted.
<tidy-select> A pair of
arguments describing which column (or columns) to get the name of the
output column (names_from), and which column (or columns) to get the
cell values from (values_from).
If values_from contains multiple values, the value will be added to the
front of the output column.
String added to the start of every variable name. This is
particularly useful if names_from is a numeric vector and you want to
create syntactic variable names.
If names_from or values_from contains multiple
variables, this will be used to join their values together into a single
string to use as a column name.
Instead of names_sep and names_prefix, you can supply
a glue specification that uses the names_from columns (and special
.value) to create custom column names.
Should the column names be sorted? If FALSE, the default,
column names are ordered by first appearance.
When names_from identifies a column (or columns) with
multiple unique values, and multiple values_from columns are provided,
in what order should the resulting column names be combined?
"fastest" varies names_from values fastest, resulting in a column
naming scheme of the form: value1_name1, value1_name2, value2_name1, value2_name2. This is the default.
"slowest" varies names_from values slowest, resulting in a column
naming scheme of the form: value1_name1, value2_name1, value1_name2, value2_name2.
Should the values in the names_from columns be expanded
by expand() before pivoting? This results in more columns, the output
will contain column names corresponding to a complete expansion of all
possible values in names_from. Implicit factor levels that aren't
represented in the data will become explicit. Additionally, the column
names will be sorted, identical to what names_sort would produce.
What happens if the output has invalid column names?
The default, "check_unique" is to error if the columns are duplicated.
Use "minimal" to allow duplicates in the output, or "unique" to
de-duplicated by adding numeric suffixes. See vctrs::vec_as_names()
for more options.
Optionally, a (scalar) value that specifies what each
value should be filled in with when missing.
This can be a named list if you want to apply different fill values to different value columns.
Optionally, a function applied to the value in each cell
in the output. You will typically use this when the combination of
id_cols and names_from columns does not uniquely identify an
observation.
This can be a named list if you want to apply different aggregations
to different values_from columns.
Optionally, a function applied to summarize the values from
the unused columns (i.e. columns not identified by id_cols,
names_from, or values_from).
The default drops all unused columns from the result.
This can be a named list if you want to apply different aggregations to different unused columns.
id_cols must be supplied for unused_fn to be useful, since otherwise
all unspecified columns will be considered id_cols.
This is similar to grouping by the id_cols then summarizing the
unused columns using unused_fn.
tidySummarizedExperiment
pivot_wider() is an updated approach to spread(), designed to be both
simpler to use and to handle more use cases. We recommend you use
pivot_wider() for new code; spread() isn't going away but is no longer
under active development.
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., Vaughan, D. (2023). tidyr: Tidy Messy Data. R package version 2.0.0, https://CRAN.R-project.org/package=tidyr
pivot_wider_spec() to pivot "by hand" with a data frame that
defines a pivoting specification.
# See vignette("pivot") for examples and explanation
library(dplyr)
tidySummarizedExperiment::pasilla %>%
pivot_wider(names_from=feature, values_from=counts)
#> tidySummarizedExperiment says: A data frame is returned for independent data analysis.
#> Warning: tidySummarizedExperiment says: from version 1.3.1, the special columns including sample/feature id (colnames(se), rownames(se)) has changed to ".sample" and ".feature". This dataset is returned with the old-style vocabulary (feature and sample), however we suggest to update your workflow to reflect the new vocabulary (.feature, .sample)
#> # A tibble: 7 × 14,602
#> sample condition type FBgn0000003 FBgn0000008 FBgn0000014 FBgn0000015
#> <chr> <chr> <chr> <int> <int> <int> <int>
#> 1 untrt1 untreated single_end 0 92 5 0
#> 2 untrt2 untreated single_end 0 161 1 2
#> 3 untrt3 untreated paired_end 0 76 0 1
#> 4 untrt4 untreated paired_end 0 70 0 2
#> 5 trt1 treated single_end 0 140 4 1
#> 6 trt2 treated paired_end 0 88 0 0
#> 7 trt3 treated paired_end 1 70 0 0
#> # ℹ 14,595 more variables: FBgn0000017 <int>, FBgn0000018 <int>,
#> # FBgn0000022 <int>, FBgn0000024 <int>, FBgn0000028 <int>, FBgn0000032 <int>,
#> # FBgn0000036 <int>, FBgn0000037 <int>, FBgn0000038 <int>, FBgn0000039 <int>,
#> # FBgn0000042 <int>, FBgn0000043 <int>, FBgn0000044 <int>, FBgn0000045 <int>,
#> # FBgn0000046 <int>, FBgn0000047 <int>, FBgn0000052 <int>, FBgn0000053 <int>,
#> # FBgn0000054 <int>, FBgn0000055 <int>, FBgn0000056 <int>, FBgn0000057 <int>,
#> # FBgn0000061 <int>, FBgn0000063 <int>, FBgn0000064 <int>, …