Package 'dsTidyverse'

Title: 'DataSHIELD' 'Tidyverse' Serverside Package
Description: Implementation of selected 'Tidyverse' functions within 'DataSHIELD', an open-source federated analysis solution in R. Currently, DataSHIELD contains very limited tools for data manipulation, so the aim of this package is to improve the researcher experience by implementing essential functions for data manipulation, including subsetting, filtering, grouping, and renaming variables. This is the serverside package which should be installed on the server holding the data, and is used in conjuncture with the clientside package 'dsTidyverseClient' which is installed in the local R environment of the analyst. For more information, see <https://www.tidyverse.org/> and <https://datashield.org/>.
Authors: Tim Cadman [aut, cre] , Mariska Slofstra [aut] , Stuart Wheater [aut], Demetris Avraam [aut]
Maintainer: Tim Cadman <[email protected]>
License: LGPL (>= 2.1)
Version: 1.0.1
Built: 2024-10-28 10:20:47 UTC
Source: https://github.com/molgenis/ds-tidyverse

Help Index

Order the rows of a data frame by the values of selected columns

Description

DataSHIELD implentation of dplyr::arrange.

Usage

arrangeDS(tidy_expr, df.name, .by_group)

Arguments

tidy_expr

Variables, or functions of variables. Use desc() to sort a variable in descending order.
df.name

A data frame, data frame extension (e.g. a tibble), or a lazy data frame (e.g. from dbplyr or dtplyr).
.by_group

If TRUE, will sort first by grouping variable. Applies to grouped data frames only.

Value

An object of the same type as df.name, typically a data frame or tibble.

Coerce a data frame or matrix to a tibble

Description

DataSHIELD implementation of tibble::as_tibble. Currently only implemented for data frames and matrices.

Usage

asTibbleDS(tidy_expr, x, .rows, .name_repair, rownames)

Arguments

tidy_expr

Unused in present function.
x

A data frame or matrix.
.rows

The number of rows, useful to create a 0-column tibble or just as an additional check.
.name_repair

Treatment of problematic column names:

  • "minimal": No name repair or checks, beyond basic existence.

  • "unique": Make sure names are unique and not empty.

  • "check_unique": (default value), no name repair, but check they are unique.

  • "universal": Make the names unique and syntactic.

rownames

How to treat existing row names of a data frame or matrix:

  • 'NULL': remove row names. This is the default.

  • 'NA': keep row names.

  • A string: the name of a new column. Existing rownames are transferred into this column and the row.names attribute is deleted. No name repair is applied to the new column name, even if x already contains a column of that name.

Value

A tibble.

Bind multiple data frames by column

Description

DataSHIELD implementation of dplyr::bind_cols.

Usage

bindColsDS(to_combine = NULL, .name_repair = NULL)

Arguments

to_combine

Data frames to combine. Each argument can either be a data frame, a list that could be a data frame, or a list of data frames. Columns are matched by name, and any missing columns will be filled with NA.
.name_repair

One of "unique", "universal", or "check_unique". See vctrs::vec_as_names() for the meaning of these options.

Value

A data frame the same type as the first element of to_combine

Bind multiple data frames by row.

Description

DataSHIELD implementation of dplyr::bind_rows.

Usage

bindRowsDS(to_combine = NULL, .id = NULL)

Arguments

to_combine

Data frames to combine. Each argument can either be a data frame, a list that could be a data frame, or a list of data frames. Columns are matched by name, and any missing columns will be filled with NA.
.id

he name of an optional identifier column. Provide a string to create an output column that identifies each input. The column will use names if available, otherwise it will use positions.

Value

A data frame the same type as the first element of to_combine

Performs dplyr case_when

Description

DataSHIELD implentation of dplyr::case_when.

Usage

caseWhenDS(tidy_expr = NULL, .default = NULL, .ptype = NULL, .size = NULL)

Arguments

tidy_expr

A sequence of two-sided formulas. The left hand side (LHS) determines which values match this case. The right hand side (RHS) provides the replacement value. The LHS inputs must evaluate to logical vectors. The RHS inputs will be coerced to their common type. All inputs will be recycled to their common size. That said, we encourage all LHS inputs to be the same size. Recycling is mainly useful for RHS inputs, where you might supply a size 1 input that will be recycled to the size of the LHS inputs. NULL inputs are ignored.
.default

The value used when all of the LHS inputs return either FALSE or NA.
.ptype

An optional prototype declaring the desired output type. If supplied, this overrides the common type of true, false, and missing.
.size

An optional size declaring the desired output size. If supplied, this overrides the size of condition.

Value

A vector with the same size as the common size computed from the inputs in tidy_expr and the same type as the common type of the RHS inputs in tidy_expr.

checkPermissivePrivacyControlLevel

Description

This serverside function check that the server is running in "permissive" privacy control level.

Usage

checkPermissivePrivacyControlLevel(privacyControlLevels)

Arguments

privacyControlLevels

is a vector of strings which contains the privacy control level names which are permitted by the calling method.

Details

Tests whether the R option "datashield.privacyControlLevel" is set to "permissive", if it isn't will cause a call to stop() with the message "BLOCKED: The server is running in 'non-permissive' mode which has caused this method to be blocked".

Value

Returns an error if the method is not permitted; otherwise, no value is returned.

Author(s)

Wheater, Dr SM., DataSHIELD Team.

Keep distinct/unique rows

Description

DataSHIELD implentation of dplyr::distinct.

Usage

distinctDS(tidy_expr, df.name, .keep_all)

Arguments

tidy_expr

Optional variables to use when determining uniqueness. If there are multiple rows for a given combination of inputs, only the first row will be preserved. If omitted, will use all variables in the data frame.
df.name

A data frame, data frame extension (e.g. a tibble), or a lazy data frame (e.g. from dbplyr or dtplyr).
.keep_all

If TRUE, keep all variables in df.name If a combination of expr is not distinct, this keeps the first row of values.

Value

An object of the same type as df.name, typically a data frame or tibble.

Performs dplyr filter

Description

DataSHIELD implentation of dplyr::filter.

Usage

filterDS(tidy_expr, df.name, .by, .preserve)

Arguments

tidy_expr

Diffused expression that return a logical value, and are defined in terms of the variables in df.name.
df.name

A data frame, data frame extension (e.g. a tibble), or a lazy data frame (e.g. from dbplyr or dtplyr).
.by

Optionally, a selection of columns to group by for just this operation, functioning as an alternative to group_by.
.preserve

Relevant when the df.name input is grouped. If .preserve = FALSE (the default), the grouping structure is recalculated based on the resulting data, otherwise the grouping is kept as is.

Value

An object of the same type as df.name, typically a data frame or tibble.

Group by one or more variables

Description

DataSHIELD implentation of dplyr::group_by.

Usage

groupByDS(tidy_expr, df.name, .add, .drop)

Arguments

tidy_expr

Diffused grouping expression.
df.name

A data frame, data frame extension (e.g. a tibble), or a lazy data frame (e.g. from dbplyr or dtplyr).
.add

When FALSE, the default, group_by() will override existing groups. To add to the existing groups, use .add = TRUE.
.drop

Drop groups formed by factor levels that don't appear in the data? The default is TRUE except when df.name has been previously grouped with .drop = FALSE.

Value

A grouped data frame with class grouped_df, unless the combination of tidy_expr and .add yields a empty set of grouping columns, in which case a tibble will be returned.

Performs dplyr group_keys.

Description

DataSHIELD implentation of dplyr::group_keys

Usage

groupKeysDS(tidy_select, x)

Arguments

tidy_select

Unused in this function.
x

a grouped tibble.

Value

A data frame describing the groups.

Vectorised if-else

Description

DataSHIELD implentation of dply::if_else.

Usage

ifElseDS(
  condition = NULL,
  true = NULL,
  false = NULL,
  missing = NULL,
  ptype = NULL,
  size = NULL
)

Arguments

condition

A list, specifying a logical vector in tidyverse syntax, ie data and column names unquoted.
true

Vector to use for TRUE value of condition.
false

Vector to use for FALSE value of condition.
missing

If not NULL, will be used as the value for NA values of condition. Follows the same size and type rules as true and false.
ptype

An optional prototype declaring the desired output type. If supplied, this overrides the common type of true, false, and missing.
size

An optional size declaring the desired output size. If supplied, this overrides the size of condition.

Value

A vector with the same size as condition and the same type as the common type of true, false, and missing.

List of Permitted Tidyverse Functions

Description

This function returns a vector of function names that are permitted to be passed within the dsTidyverse functions, e.g. within the 'tidy_select' argument of 'ds.mutate.'

Usage

listPermittedTidyverseFunctionsDS()

Value

A character vector of function names, each representing a permitted function. Functions not included in this list will be blocked.

Create, modify, and delete columns

Description

DataSHIELD implentation of mutate.

Usage

mutateDS(tidy_expr, df.name, .keep = NULL, .before = NULL, .after = NULL)

Arguments

tidy_expr

Name-value pairs. The name gives the name of the column in the output.
df.name

A data frame, data frame extension (e.g. a tibble), or a lazy data frame (e.g. from dbplyr or dtplyr).
.keep

.keep Control which columns from df.name are retained in the output. Grouping columns and columns created by tidy_expr are always kept.

  • "all": Retains all columns from df.name. This is the default.

  • "used": Retains only the columns used in tidy_expr to create new columns.

  • "unused": Retains only the columns not used in tidy_expr to create new columns. This is useful if you generate new columns but no longer need the columns used to generate them.

  • "none": Doesn't retain any extra columns from df.name. Only the grouping variables and columns created by tidy_expr are kept.

Grouping columns and columns created by tidy_expr are always kept.
.before

<tidy-select> Optionally, control where new columns should appear (the default is to add to the right hand side). See relocate for more details.
.after

<tidy-select> Optionally, control where new columns should appear (the default is to add to the right hand side). See relocate for more details.

Value

An object of the same type as df.name, typically a data frame or tibble.

Rename columns

Description

DataSHIELD implentation ofdplyr::rename.

Usage

renameDS(tidy_expr, df.name)

Arguments

tidy_expr

list containing diffused expression.
df.name

A data frame or tibble.

Value

An object of the same type as df.name, typically a data frame or tibble.

Keep or drop columns using their names and types

Description

DataSHIELD implentation of dplyr::select.

Usage

selectDS(tidy_expr, df.name)

Arguments

tidy_expr

One or more unquoted expressions separated by commas.
df.name

A data frame or tibble.

Details

Performs dplyr select

Value

An object of the same type as df.name, typically a data frame or tibble.

Subset rows using their positions

Description

DataSHIELD implentation of dplyr::slice.

Usage

sliceDS(tidy_expr, df.name, .by, .preserve)

Arguments

tidy_expr

Provide either positive values to keep, or negative values to drop. The values provided must be either all positive or all negative. Indices beyond the number of rows in the input are silently ignored.
df.name

A data frame, data frame extension (e.g. a tibble), or a lazy data frame (e.g. from dbplyr or dtplyr).
.by

Optionally, a selection of columns to group by for just this operation, functioning as an alternative to group_by.
.preserve

Relevant when the df.name input is grouped. If .preserve = FALSE (the default), the grouping structure is recalculated based on the resulting data, otherwise the grouping is kept as is.

Value

An object of the same type as df.name, typically a data frame or tibble.

Remove grouping from a tibble or data frame

Description

DataSHIELD implentation of dplyr::ungroup.

Usage

ungroupDS(tidy_expr, x)

Arguments

tidy_expr

Unused in this function.
x

A tibble.

Value

An ungrouped data frame or tibble.