dplyr-methods: Arrange rows by column values
In tidySingleCellExperiment: Brings SingleCellExperiment to the Tidyverse
Description
Usage
Arguments
Details
Value
Methods
Useful filter functions
Grouped tibbles
Useful functions
Backend variations
Useful mutate functions
Scoped selection and renaming
See Also
Examples
Description
'arrange()' order the rows of a data frame rows by the values of selected
columns.
Unlike other dplyr verbs, 'arrange()' largely ignores grouping; you
need to explicit mention grouping variables (or use 'by_group=TRUE')
in order to group by them, and functions of variables are evaluated
once per data frame, not once per group.
'filter()' retains the rows where the conditions you provide a 'TRUE'. Note
that, unlike base subsetting with '[', rows where the condition evaluates
to 'NA' are dropped.
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.
'summarise()' creates a new data frame. It will have one (or more) rows 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.
'mutate()' adds new variables and preserves existing ones;
'transmute()' adds new variables and drops existing ones.
New variables overwrite existing variables of the same name.
Variables can be removed by setting their value to 'NULL'.
Rename individual variables using 'new_name=old_name' syntax.
See [this repository](https://github.com/jennybc/row-oriented-workflows)
for alternative ways to perform row-wise operations.
'slice()' lets you index rows by their (integer) locations. It allows you
to select, remove, and duplicate rows. It is accompanied by a number of
helpers for common use cases:
* 'slice_head()' and 'slice_tail()' select the first or last rows.
* 'slice_sample()' randomly selects rows.
* 'slice_min()' and 'slice_max()' select rows with highest or lowest values
of a variable.
If '.data' is a [grouped_df], the operation will be performed on each group,
so that (e.g.) 'slice_head(df, n=5)' will select the first five rows in
each group.
Select (and optionally rename) variables in a data frame, using a concise
mini-language that makes it easy to refer to variables based on their name
(e.g. 'a:f' selects all columns from 'a' on the left to 'f' on the
right). You can also use predicate functions like [is.numeric] to select
variables based on their properties.
## Overview of selection features
“'r, child="man/rmd/overview.Rmd"
“'
\Sexpr[results=rd, stage=render]{lifecycle::badge("superseded")}
'sample_n()' and 'sample_frac()' have been superseded in favour of
[slice_sample()]. While they will not be deprecated in the near future,
retirement means that we will only perform critical bug fixes, so we recommend
moving to the newer alternative.
These functions were superseded because we realised it was more convenient to
have two mutually exclusive arguments to one function, rather than two
separate functions. This also made it to clean up a few other smaller
design issues with 'sample_n()'/'sample_frac':
* The connection to 'slice()' was not obvious.
* The name of the first argument, 'tbl', is inconsistent with other
single table verbs which use '.data'.
* The 'size' argument uses tidy evaluation, which is surprising and
undocumented.
* It was easier to remove the deprecated '.env' argument.
* '...' was in a suboptimal position.
‘pull()' is similar to '$'. It’s mostly useful because it looks a little
nicer in pipes, it also works with remote data frames, and it can optionally
name the output.
Usage
1
2
3
Arguments
...
For use by methods.
.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 SingleCellExperiment 3.0 A character vector of
length(x=c(x, y)). Appends the corresponding values to the start of each
objects' cell names.
.by_group
If TRUE, will sort first by grouping variable. Applies to
grouped data frames only.
.keep_all
If TRUE, keep all variables in .data. If a combination
of ... is not distinct, this keeps the first row of values. (See dplyr)
.preserve
when 'FALSE' (the default), the grouping structure
is recalculated based on the resulting data, otherwise it is kept as is.
.add
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
When '.drop=TRUE', empty groups are dropped. See
[group_by_drop_default()] for what the default value is for this argument.
.data
Input data frame.
x
tbls to join. (See dplyr)
y
tbls to join. (See dplyr)
by
A character vector of variables to join by. (See dplyr)
copy
If x and y are not from the same data source, and copy is TRUE,
then y will be copied into the same src as x. (See dplyr)
suffix
If there are non-joined duplicate variables in x and y, these
suffixes will be added to the output to disambiguate them. Should be a
character vector of length 2. (See dplyr)
tbl
A data.frame.
size
<['tidy-select'][dplyr_tidy_select]>
For 'sample_n()', the number of rows to select.
For 'sample_frac()', the fraction of rows to select.
If 'tbl' is grouped, 'size' applies to each group.
replace
Sample with or without replacement?
weight
<['tidy-select'][dplyr_tidy_select]> Sampling weights.
This must evaluate to a vector of non-negative numbers the same length as
the input. Weights are automatically standardised to sum to 1.
.env
DEPRECATED.
name
An optional parameter that specifies the column to be used
as names for a named vector. Specified in a similar manner as var.
Details
## Locales
The sort order for character vectors will depend on the collating sequence
of the locale in use: see [locales()].
## Missing values
Unlike base sorting with 'sort()', 'NA' are:
* always sorted to the end for local data, even when wrapped with 'desc()'.
* treated differently for remote data, depending on the backend.
dplyr is not yet smart enough to optimise filtering optimisation
on grouped datasets that don't need grouped calculations. For this reason,
filtering is often considerably faster on [ungroup()]ed data.
'rowwise()' is used for the results of [do()] when you
create list-variables. It is also useful to support arbitrary
complex operations that need to be applied to each row.
Currently, rowwise grouping only works with data frames. Its
main impact is to allow you to work with list-variables in
[summarise()] and [mutate()] without having to
use [[1]]. This makes 'summarise()' on a rowwise tbl
effectively equivalent to [plyr::ldply()].
Slice does not work with relational databases because they have no
intrinsic notion of row order. If you want to perform the equivalent
operation, use [filter()] and [row_number()].
Value
An object of the same type as '.data'.
* All rows appear in the output, but (usually) in a different place.
* Columns are not modified.
* Groups are not modified.
* Data frame attributes are preserved.
A tidySingleCellExperiment object
An object of the same type as '.data'.
* Rows are a subset of the input, but appear in the same order.
* Columns are not modified.
* The number of groups may be reduced (if '.preserve' is not 'TRUE').
* Data frame attributes are preserved.
A [grouped data frame][grouped_df()], unless the combination of
'...' and 'add' yields a non empty set of grouping columns, a
regular (ungrouped) data frame otherwise.
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.
* If 'x' is grouped by more than one variable, the output will be another
[grouped_df] with the right-most group removed.
* If 'x' is grouped by one variable, or is not grouped, the output will
be a [tibble].
* Data frame attributes are **not** preserved, because 'summarise()'
fundamentally creates a new data frame.
An object of the same type as '.data'.
For 'mutate()':
* Rows are not affected.
* Existing columns will be preserved unless explicitly modified.
* New columns will be added to the right of existing columns.
* Columns given value 'NULL' will be removed
* Groups will be recomputed if a grouping variable is mutated.
* Data frame attributes are preserved.
For 'transmute()':
* Rows are not affected.
* Apart from grouping variables, existing columns will be remove unless
explicitly kept.
* Column order matches order of expressions.
* Groups will be recomputed if a grouping variable is mutated.
* Data frame attributes are preserved.
An object of the same type as '.data'.
* Rows are not affected.
* Column names are changed; column order is preserved
* Data frame attributes are preserved.
* Groups are updated to reflect new names.
A 'tbl'
A 'tbl'
A tidySingleCellExperiment object
A tidySingleCellExperiment object
A tidySingleCellExperiment object
A tidySingleCellExperiment object
An object of the same type as '.data'. The output has the following
properties:
* Each row may appear 0, 1, or many times in the output.
* Columns are not modified.
* Groups are not modified.
* Data frame attributes are preserved.
An object of the same type as '.data'. The output has the following
properties:
* Rows are not affected.
* Output columns are a subset of input columns, potentially with a different
order. Columns will be renamed if 'new_name=old_name' form is used.
* Data frame attributes are preserved.
* Groups are maintained; you can't select off grouping variables.
A tidySingleCellExperiment object
A vector the same size as '.data'.
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:
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:
These function are **generic**s, 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:
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:
These function are **generic**s, 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:
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:
These function are **generic**s, 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:
* 'slice()': \Sexpr[stage=render,results=rd]{dplyr:::methods_rd("slice")}.
* 'slice_head()': \Sexpr[stage=render,results=rd]{dplyr:::methods_rd("slice_head")}.
* 'slice_tail()': \Sexpr[stage=render,results=rd]{dplyr:::methods_rd("slice_tail")}.
* 'slice_min()': \Sexpr[stage=render,results=rd]{dplyr:::methods_rd("slice_min")}.
* 'slice_max()': \Sexpr[stage=render,results=rd]{dplyr:::methods_rd("slice_max")}.
* 'slice_sample()': \Sexpr[stage=render,results=rd]{dplyr:::methods_rd("slice_sample")}.
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("select")}.
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("pull")}.
Useful filter functions
* ['=='], ['>'], ['>='] etc
* ['&'], ['|'], ['!'], [xor()]
* [is.na()]
* [between()], [near()]
Grouped tibbles
Because filtering expressions are computed within groups, they may
yield different results on grouped tibbles. This will be the case
as soon as an aggregating, lagging, or ranking function is
involved. Compare this ungrouped filtering:
The former keeps rows with 'mass' greater than the global average
whereas the latter keeps rows with 'mass' greater than the gender
average.
Because mutating expressions are computed within groups, they may
yield different results on grouped tibbles. This will be the case
as soon as an aggregating, lagging, or ranking function is
involved. Compare this ungrouped mutate:
With the grouped equivalent:
The former normalises 'mass' by the global average whereas the
latter normalises by the averages within gender levels.
Useful functions
* Center: [mean()], [median()]
* Spread: [sd()], [IQR()], [mad()]
* Range: [min()], [max()], [quantile()]
* 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.
Useful mutate functions
* ['+'], ['-'], [log()], etc., for their usual mathematical meanings
* [lead()], [lag()]
* [dense_rank()], [min_rank()], [percent_rank()], [row_number()],
[cume_dist()], [ntile()]
* [cumsum()], [cummean()], [cummin()], [cummax()], [cumany()], [cumall()]
* [na_if()], [coalesce()]
* [if_else()], [recode()], [case_when()]
Scoped selection and renaming
Use the three scoped variants ([rename_all()], [rename_if()], [rename_at()])
to renaming a set of variables with a function.
See Also
[filter_all()], [filter_if()] and [filter_at()].
Examples
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`%>%` <- magrittr::`%>%`
pbmc_small %>%
tidy() %>%
arrange(nFeature_RNA)
`%>%` <- magrittr::`%>%`
pbmc_small %>%
tidy() %>%
distinct(groups)
`%>%` <- magrittr::`%>%`
pbmc_small %>%
tidy() %>%
filter(groups == "g1")
# Learn more in ?dplyr_tidy_eval
`%>%` <- magrittr::`%>%`
pbmc_small %>%
tidy() %>%
group_by(groups)
`%>%` <- magrittr::`%>%`
pbmc_small %>%
tidy() %>%
summarise(mean(nCount_RNA))
`%>%` <- magrittr::`%>%`
pbmc_small %>%
tidy() %>%
mutate(nFeature_RNA=1)
`%>%` <- magrittr::`%>%`
pbmc_small %>%
tidy() %>%
rename(s_score=nFeature_RNA)
`%>%` <- magrittr::`%>%`
`%>%` <- magrittr::`%>%`
tt <- pbmc_small %>% tidy()
tt %>% left_join(tt %>% distinct(groups) %>% mutate(new_column=1:2))
`%>%` <- magrittr::`%>%`
tt <- pbmc_small %>% tidy()
tt %>% inner_join(tt %>% distinct(groups) %>% mutate(new_column=1:2) %>% slice(1))
`%>%` <- magrittr::`%>%`
tt <- pbmc_small %>% tidy()
tt %>% right_join(tt %>% distinct(groups) %>% mutate(new_column=1:2) %>% slice(1))
`%>%` <- magrittr::`%>%`
tt <- pbmc_small %>% tidy()
tt %>% full_join(tibble::tibble(groups="g1", other=1:4))
`%>%` <- magrittr::`%>%`
pbmc_small %>%
tidy() %>%
slice(1)
`%>%` <- magrittr::`%>%`
pbmc_small %>%
tidy() %>%
select(cell, orig.ident)
`%>%` <- magrittr::`%>%`
pbmc_small %>%
tidy() %>%
sample_n(50)
pbmc_small %>%
tidy() %>%
sample_frac(0.1)
`%>%` <- magrittr::`%>%`
pbmc_small %>%
tidy() %>%
pull(groups)
tidySingleCellExperiment documentation built on Nov. 8, 2020, 6:54 p.m.
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Description Usage Arguments Details Value Methods Useful filter functions Grouped tibbles Useful functions Backend variations Useful mutate functions Scoped selection and renaming See Also Examples
Description
'arrange()' order the rows of a data frame rows by the values of selected columns.
Unlike other dplyr verbs, 'arrange()' largely ignores grouping; you need to explicit mention grouping variables (or use 'by_group=TRUE') in order to group by them, and functions of variables are evaluated once per data frame, not once per group.
'filter()' retains the rows where the conditions you provide a 'TRUE'. Note that, unlike base subsetting with '[', rows where the condition evaluates to 'NA' are dropped.
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.
'summarise()' creates a new data frame. It will have one (or more) rows 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.
'mutate()' adds new variables and preserves existing ones; 'transmute()' adds new variables and drops existing ones. New variables overwrite existing variables of the same name. Variables can be removed by setting their value to 'NULL'.
Rename individual variables using 'new_name=old_name' syntax.
See [this repository](https://github.com/jennybc/row-oriented-workflows) for alternative ways to perform row-wise operations.
'slice()' lets you index rows by their (integer) locations. It allows you to select, remove, and duplicate rows. It is accompanied by a number of helpers for common use cases:
* 'slice_head()' and 'slice_tail()' select the first or last rows. * 'slice_sample()' randomly selects rows. * 'slice_min()' and 'slice_max()' select rows with highest or lowest values of a variable.
If '.data' is a [grouped_df], the operation will be performed on each group, so that (e.g.) 'slice_head(df, n=5)' will select the first five rows in each group.
Select (and optionally rename) variables in a data frame, using a concise mini-language that makes it easy to refer to variables based on their name (e.g. 'a:f' selects all columns from 'a' on the left to 'f' on the right). You can also use predicate functions like [is.numeric] to select variables based on their properties.
## Overview of selection features
“'r, child="man/rmd/overview.Rmd" “'
\Sexpr[results=rd, stage=render]{lifecycle::badge("superseded")}'sample_n()' and 'sample_frac()' have been superseded in favour of [slice_sample()]. While they will not be deprecated in the near future, retirement means that we will only perform critical bug fixes, so we recommend moving to the newer alternative.
These functions were superseded because we realised it was more convenient to have two mutually exclusive arguments to one function, rather than two separate functions. This also made it to clean up a few other smaller design issues with 'sample_n()'/'sample_frac':
* The connection to 'slice()' was not obvious. * The name of the first argument, 'tbl', is inconsistent with other single table verbs which use '.data'. * The 'size' argument uses tidy evaluation, which is surprising and undocumented. * It was easier to remove the deprecated '.env' argument. * '...' was in a suboptimal position.
‘pull()' is similar to '$'. It’s mostly useful because it looks a little nicer in pipes, it also works with remote data frames, and it can optionally name the output.
Usage
1 2 3 |
Arguments
... |
For use by methods. |
.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 SingleCellExperiment 3.0 A character vector of length(x=c(x, y)). Appends the corresponding values to the start of each objects' cell names. |
.by_group |
If TRUE, will sort first by grouping variable. Applies to grouped data frames only. |
.keep_all |
If TRUE, keep all variables in .data. If a combination of ... is not distinct, this keeps the first row of values. (See dplyr) |
.preserve |
when 'FALSE' (the default), the grouping structure is recalculated based on the resulting data, otherwise it is kept as is. |
.add |
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 |
When '.drop=TRUE', empty groups are dropped. See [group_by_drop_default()] for what the default value is for this argument. |
.data |
Input data frame. |
x |
tbls to join. (See dplyr) |
y |
tbls to join. (See dplyr) |
by |
A character vector of variables to join by. (See dplyr) |
copy |
If x and y are not from the same data source, and copy is TRUE, then y will be copied into the same src as x. (See dplyr) |
suffix |
If there are non-joined duplicate variables in x and y, these suffixes will be added to the output to disambiguate them. Should be a character vector of length 2. (See dplyr) |
tbl |
A data.frame. |
size |
<['tidy-select'][dplyr_tidy_select]> For 'sample_n()', the number of rows to select. For 'sample_frac()', the fraction of rows to select. If 'tbl' is grouped, 'size' applies to each group. |
replace |
Sample with or without replacement? |
weight |
<['tidy-select'][dplyr_tidy_select]> Sampling weights. This must evaluate to a vector of non-negative numbers the same length as the input. Weights are automatically standardised to sum to 1. |
.env |
DEPRECATED. |
name |
An optional parameter that specifies the column to be used
as names for a named vector. Specified in a similar manner as |
Details
## Locales The sort order for character vectors will depend on the collating sequence of the locale in use: see [locales()].
## Missing values Unlike base sorting with 'sort()', 'NA' are: * always sorted to the end for local data, even when wrapped with 'desc()'. * treated differently for remote data, depending on the backend.
dplyr is not yet smart enough to optimise filtering optimisation on grouped datasets that don't need grouped calculations. For this reason, filtering is often considerably faster on [ungroup()]ed data.
'rowwise()' is used for the results of [do()] when you create list-variables. It is also useful to support arbitrary complex operations that need to be applied to each row.
Currently, rowwise grouping only works with data frames. Its
main impact is to allow you to work with list-variables in
[summarise()] and [mutate()] without having to
use [[1]]. This makes 'summarise()' on a rowwise tbl
effectively equivalent to [plyr::ldply()].
Slice does not work with relational databases because they have no intrinsic notion of row order. If you want to perform the equivalent operation, use [filter()] and [row_number()].
Value
An object of the same type as '.data'.
* All rows appear in the output, but (usually) in a different place. * Columns are not modified. * Groups are not modified. * Data frame attributes are preserved.
A tidySingleCellExperiment object
An object of the same type as '.data'.
* Rows are a subset of the input, but appear in the same order. * Columns are not modified. * The number of groups may be reduced (if '.preserve' is not 'TRUE'). * Data frame attributes are preserved.
A [grouped data frame][grouped_df()], unless the combination of '...' and 'add' yields a non empty set of grouping columns, a regular (ungrouped) data frame otherwise.
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. * If 'x' is grouped by more than one variable, the output will be another [grouped_df] with the right-most group removed. * If 'x' is grouped by one variable, or is not grouped, the output will be a [tibble]. * Data frame attributes are **not** preserved, because 'summarise()' fundamentally creates a new data frame.
An object of the same type as '.data'.
For 'mutate()':
* Rows are not affected. * Existing columns will be preserved unless explicitly modified. * New columns will be added to the right of existing columns. * Columns given value 'NULL' will be removed * Groups will be recomputed if a grouping variable is mutated. * Data frame attributes are preserved.
For 'transmute()':
* Rows are not affected. * Apart from grouping variables, existing columns will be remove unless explicitly kept. * Column order matches order of expressions. * Groups will be recomputed if a grouping variable is mutated. * Data frame attributes are preserved.
An object of the same type as '.data'. * Rows are not affected. * Column names are changed; column order is preserved * Data frame attributes are preserved. * Groups are updated to reflect new names.
A 'tbl'
A 'tbl'
A tidySingleCellExperiment object
A tidySingleCellExperiment object
A tidySingleCellExperiment object
A tidySingleCellExperiment object
An object of the same type as '.data'. The output has the following properties:
* Each row may appear 0, 1, or many times in the output. * Columns are not modified. * Groups are not modified. * Data frame attributes are preserved.
An object of the same type as '.data'. The output has the following properties:
* Rows are not affected. * Output columns are a subset of input columns, potentially with a different order. Columns will be renamed if 'new_name=old_name' form is used. * Data frame attributes are preserved. * Groups are maintained; you can't select off grouping variables.
A tidySingleCellExperiment object
A vector the same size as '.data'.
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:
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:
These function are **generic**s, 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:
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:
These function are **generic**s, 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:
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:
These function are **generic**s, 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:
* 'slice()': \Sexpr[stage=render,results=rd]{dplyr:::methods_rd("slice")}. * 'slice_head()': \Sexpr[stage=render,results=rd]{dplyr:::methods_rd("slice_head")}. * 'slice_tail()': \Sexpr[stage=render,results=rd]{dplyr:::methods_rd("slice_tail")}. * 'slice_min()': \Sexpr[stage=render,results=rd]{dplyr:::methods_rd("slice_min")}. * 'slice_max()': \Sexpr[stage=render,results=rd]{dplyr:::methods_rd("slice_max")}. * 'slice_sample()': \Sexpr[stage=render,results=rd]{dplyr:::methods_rd("slice_sample")}.
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("select")}.
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("pull")}.
Useful filter functions
* ['=='], ['>'], ['>='] etc * ['&'], ['|'], ['!'], [xor()] * [is.na()] * [between()], [near()]
Grouped tibbles
Because filtering expressions are computed within groups, they may yield different results on grouped tibbles. This will be the case as soon as an aggregating, lagging, or ranking function is involved. Compare this ungrouped filtering:
The former keeps rows with 'mass' greater than the global average whereas the latter keeps rows with 'mass' greater than the gender
average.
Because mutating expressions are computed within groups, they may yield different results on grouped tibbles. This will be the case as soon as an aggregating, lagging, or ranking function is involved. Compare this ungrouped mutate:
With the grouped equivalent:
The former normalises 'mass' by the global average whereas the latter normalises by the averages within gender levels.
Useful functions
* Center: [mean()], [median()] * Spread: [sd()], [IQR()], [mad()] * Range: [min()], [max()], [quantile()] * 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.
Useful mutate functions
* ['+'], ['-'], [log()], etc., for their usual mathematical meanings
* [lead()], [lag()]
* [dense_rank()], [min_rank()], [percent_rank()], [row_number()], [cume_dist()], [ntile()]
* [cumsum()], [cummean()], [cummin()], [cummax()], [cumany()], [cumall()]
* [na_if()], [coalesce()]
* [if_else()], [recode()], [case_when()]
Scoped selection and renaming
Use the three scoped variants ([rename_all()], [rename_if()], [rename_at()]) to renaming a set of variables with a function.
See Also
[filter_all()], [filter_if()] and [filter_at()].
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 | `%>%` <- magrittr::`%>%`
pbmc_small %>%
tidy() %>%
arrange(nFeature_RNA)
`%>%` <- magrittr::`%>%`
pbmc_small %>%
tidy() %>%
distinct(groups)
`%>%` <- magrittr::`%>%`
pbmc_small %>%
tidy() %>%
filter(groups == "g1")
# Learn more in ?dplyr_tidy_eval
`%>%` <- magrittr::`%>%`
pbmc_small %>%
tidy() %>%
group_by(groups)
`%>%` <- magrittr::`%>%`
pbmc_small %>%
tidy() %>%
summarise(mean(nCount_RNA))
`%>%` <- magrittr::`%>%`
pbmc_small %>%
tidy() %>%
mutate(nFeature_RNA=1)
`%>%` <- magrittr::`%>%`
pbmc_small %>%
tidy() %>%
rename(s_score=nFeature_RNA)
`%>%` <- magrittr::`%>%`
`%>%` <- magrittr::`%>%`
tt <- pbmc_small %>% tidy()
tt %>% left_join(tt %>% distinct(groups) %>% mutate(new_column=1:2))
`%>%` <- magrittr::`%>%`
tt <- pbmc_small %>% tidy()
tt %>% inner_join(tt %>% distinct(groups) %>% mutate(new_column=1:2) %>% slice(1))
`%>%` <- magrittr::`%>%`
tt <- pbmc_small %>% tidy()
tt %>% right_join(tt %>% distinct(groups) %>% mutate(new_column=1:2) %>% slice(1))
`%>%` <- magrittr::`%>%`
tt <- pbmc_small %>% tidy()
tt %>% full_join(tibble::tibble(groups="g1", other=1:4))
`%>%` <- magrittr::`%>%`
pbmc_small %>%
tidy() %>%
slice(1)
`%>%` <- magrittr::`%>%`
pbmc_small %>%
tidy() %>%
select(cell, orig.ident)
`%>%` <- magrittr::`%>%`
pbmc_small %>%
tidy() %>%
sample_n(50)
pbmc_small %>%
tidy() %>%
sample_frac(0.1)
`%>%` <- magrittr::`%>%`
pbmc_small %>%
tidy() %>%
pull(groups)
|
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