R/tidyr_methods.R

In tidySummarizedExperiment: Brings SummarizedExperiment to the Tidyverse

#' unnest
#'
#' @importFrom tidyr unnest
#'
#' @param .data A tbl. (See tidyr)
#' @param cols <[`tidy-select`][tidyr_tidy_select]> Columns to unnest.
#'   If you `unnest()` multiple columns, parallel entries must be of
#'   compatible sizes, i.e. they're either equal or length 1 (following the
#'   standard tidyverse recycling rules).
#' @param ... <[`tidy-select`][tidyr_tidy_select]> Columns to nest, specified
#'   using name-variable pairs of the form `new_col=c(col1, col2, col3)`.
#'   The right hand side can be any valid tidy select expression.
#'
#'   \Sexpr[results=rd, stage=render]{lifecycle::badge("deprecated")}:
#'   previously you could write `df %>% nest(x, y, z)` and `df %>%
#'   unnest(x, y, z)`. Convert to `df %>% nest(data=c(x, y, z))`.
#'   and `df %>% unnest(c(x, y, z))`.
#'
#'   If you previously created new variable in `unnest()` you'll now need to
#'   do it explicitly with `mutate()`. Convert `df %>% unnest(y=fun(x, y, z))`
#'   to `df %>% mutate(y=fun(x, y, z)) %>% unnest(y)`.
#' @param names_sep If `NULL`, the default, the names will be left
#'   as is. In `nest()`, inner names will come from the former outer names;
#'   in `unnest()`, the new outer names will come from the inner names.
#'
#'   If a string, the inner and outer names will be used together. In `nest()`,
#'   the names of the new outer columns will be formed by pasting together the
#'   outer and the inner column names, separated by `names_sep`. In `unnest()`,
#'   the new inner names will have the outer names (+ `names_sep`) automatically
#'   stripped. This makes `names_sep` roughly symmetric between nesting and unnesting.
#' @param keep_empty See tidyr::unnest
#' @param names_repair See tidyr::unnest
#' @param ptype See tidyr::unnest
#' @param .drop See tidyr::unnest
#' @param .id tidyr::unnest
#' @param sep tidyr::unnest
#' @param .preserve See tidyr::unnest
#'
#'
#' @return A tidySummarizedExperiment objector a tibble depending on input
#'
#' @examples
#'
#' tidySummarizedExperiment::pasilla %>%
#'     tidy() %>%
#'     nest(data=-condition) %>%
#'     unnest(data)
#'
#' @rdname tidyr-methods
#' @name unnest
#'
#' @export
NULL

#' @importFrom rlang quo_name
#' @importFrom purrr imap
#'
#' @export
unnest.tidySummarizedExperiment_nested <-
    function(data, cols, ..., keep_empty=FALSE, ptype=NULL, names_sep=NULL, names_repair="check_unique", .drop, .id, .sep, .preserve) {


    # Need this otherwise crashes map
    .data_ <- data

    cols <- enquo(cols)

    .data_ %>%
        when(

            # If my only column to unnest is tidySummarizedExperiment
            pull(., !!cols) %>%
                .[[1]] %>%
                class() %>%
                as.character() %>%
                eq("tidySummarizedExperiment") %>%
                any() ~

            # Do my trick to unnest
            mutate(., !!cols := imap(
                !!cols, ~ .x %>%
                    bind_cols_(

                        # Attach back the columns used for nesting
                        .data_ %>%
                            select(-!!cols) %>%
                            slice(rep(.y, ncol(.x) * nrow(.x)))
                    )
            )) %>%
                pull(!!cols) %>%
                reduce(bind_rows),

            # Else do normal stuff
            ~ (.) %>%
                drop_class("tidySummarizedExperiment_nested") %>%
                tidyr::unnest(!!cols, ..., keep_empty=keep_empty, ptype=ptype, names_sep=names_sep, names_repair=names_repair) %>%
                add_class("tidySummarizedExperiment_nested")
        )
}

#' nest
#'
#' @importFrom tidyr nest
#'
#' @param .data A tbl. (See tidyr)
#' @param ... Name-variable pairs of the form new_col=c(col1, col2, col3) (See tidyr)
#' @param .names_sep See ?tidyr::nest
#'
#' @return A tidySummarizedExperiment objector a tibble depending on input
#'
#' @examples
#'
#' tidySummarizedExperiment::pasilla %>%
#'     tidy() %>%
#'     nest(data=-condition)
#'
#' @rdname tidyr-methods
#' @name nest
#'
#' @export
NULL

#' @importFrom rlang enquos
#' @importFrom rlang :=
#'
#' @export
nest.tidySummarizedExperiment <- function(.data, ..., .names_sep = NULL) {
    my_data__ <- .data
    cols <- enquos(...)
    col_name_data <- names(cols)

    my_data__ %>%

        # This is needed otherwise nest goes into loop and fails
        as_tibble() %>%
        tidyr::nest(...) %>%
        mutate(
            !!as.symbol(col_name_data) := map(
                !!as.symbol(col_name_data),
                ~ my_data__ %>%

                    # Subset cells
                    filter(sample %in% .x$sample & transcript %in% .x$transcript) %>%

                    # Subset columns
                    select(colnames(.x))
            )
        ) %>%

        # Coerce to tidySummarizedExperiment_nested for unnesting
        add_class("tidySummarizedExperiment_nested")
}

#' Extract a character column into multiple columns using regular
#' expression groups
#'
#' Given a regular expression with capturing groups, `extract()` turns
#' each group into a new column. If the groups don't match, or the input
#' is NA, the output will be NA.
#'
#' @importFrom tidyr extract
#'
#' @param data A tidySummarizedExperiment object
#' @param col Column name or position. This is passed to
#'   [tidyselect::vars_pull()].
#'
#'   This argument is passed by expression and supports
#'   [quasiquotation][rlang::quasiquotation] (you can unquote column
#'   names or column positions).
#' @param into Names of new variables to create as character vector.
#'    Use `NA` to omit the variable in the output.
#' @param regex a regular expression used to extract the desired values.
#'   There should be one group (defined by `()`) for each element of `into`.
#' @param remove If `TRUE`, remove input column from output data frame.
#' @param convert If `TRUE`, will run [type.convert()] with
#'   `as.is=TRUE` on new columns. This is useful if the component
#'   columns are integer, numeric or logical.
#'
#'   NB: this will cause string `"NA"`s to be converted to `NA`s.
#' @param ... Additional arguments passed on to methods.
#' @seealso [separate()] to split up by a separator.
#'
#' @rdname tidyr-methods
#' @name extract
#'
#' @export
#' @examples
#'
#' tidySummarizedExperiment::pasilla %>%
#'     tidy() %>%
#'     extract(type, into="sequencing", regex="([a-z]*)_end", convert=TRUE)
#' @return A tidySummarizedExperiment objector a tibble depending on input
#'
#' @importFrom tidyr extract
#'
#' @export
NULL

#' @importFrom rlang enquo
#' @export
extract.tidySummarizedExperiment <- function(data, col, into, regex="([[:alnum:]]+)", remove=TRUE,
    convert=FALSE, ...) {
    col <- enquo(col)

    tst =
        intersect(
            into %>% quo_names(),
            get_special_columns(data) %>% c(get_needed_columns())
        ) %>%
        length() %>%
        gt(0) &
        remove


    if (tst) {
        columns =
            get_special_columns(data) %>%
            c(get_needed_columns()) %>%
            paste(collapse=", ")
        stop(
            "tidySummarizedExperiment says: you are trying to rename a column that is view only",
            columns,
            "(it is not present in the colData). If you want to mutate a view-only column, make a copy and mutate that one."
        )
    }

    data %>%
        as_tibble() %>%
        tidyr::extract(col=!!col, into=into, regex=regex, remove=remove, convert=convert, ...) %>%
        update_SE_from_tibble(data)
}

#' Pivot data from wide to long
#'
#' @description
#' \Sexpr[results=rd, stage=render]{lifecycle::badge("maturing")}
#'
#' `pivot_longer()` "lengthens" data, increasing the number of rows and
#' decreasing the number of columns. The inverse transformation is
#' [pivot_wider()]
#'
#' Learn more in `vignette("pivot")`.
#'
#' @importFrom ellipsis check_dots_used
#' @importFrom tidyr pivot_longer
#'
#' @details
#' `pivot_longer()` is an updated approach to [gather()], designed to be both
#' simpler to use and to handle more use cases. We recommend you use
#' `pivot_longer()` for new code; `gather()` isn't going away but is no longer
#' under active development.
#'
#' @param data A data frame to pivot.
#' @param cols <[`tidy-select`][tidyr_tidy_select]> Columns to pivot into
#'   longer format.
#' @param names_to A string specifying the name of the column to create
#'   from the data stored in the column names of `data`.
#'
#'   Can be a character vector, creating multiple columns, if `names_sep`
#'   or `names_pattern` is provided. In this case, there are two special
#'   values you can take advantage of:
#'
#'   * `NA` will discard that component of the name.
#'   * `.value` indicates that component of the name defines the name of the
#'     column containing the cell values, overriding `values_to`.
#' @param names_prefix A regular expression used to remove matching text
#'   from the start of each variable name.
#' @param names_sep,names_pattern If `names_to` contains multiple values,
#'   these arguments control how the column name is broken up.
#'
#'   `names_sep` takes the same specification as [separate()], and can either
#'   be a numeric vector (specifying positions to break on), or a single string
#'   (specifying a regular expression to split on).
#'
#'   `names_pattern` takes the same specification as [extract()], a regular
#'   expression containing matching groups (`()`).
#'
#'   If these arguments do not give you enough control, use
#'   `pivot_longer_spec()` to create a spec object and process manually as
#'   needed.
#' @param names_repair 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.
#' @param values_to A string specifying the name of the column to create
#'   from the data stored in cell values. If `names_to` is a character
#'   containing the special `.value` sentinel, this value will be ignored,
#'   and the name of the value column will be derived from part of the
#'   existing column names.
#' @param values_drop_na If `TRUE`, will drop rows that contain only `NA`s
#'   in the `value_to` column. This effectively converts explicit missing values
#'   to implicit missing values, and should generally be used only when missing
#'   values in `data` were created by its structure.
#' @param names_transform,values_transform A list of column name-function pairs.
#'   Use these arguments if you need to change the type of specific columns.
#'   For example, `names_transform=list(week=as.integer)` would convert
#'   a character week variable to an integer.
#' @param names_ptypes,values_ptypes A list of column name-prototype pairs.
#'   A prototype (or ptype for short) is a zero-length vector (like `integer()`
#'   or `numeric()`) that defines the type, class, and attributes of a vector.
#'   Use these arguments to confirm that the created columns are the types that
#'   you expect.
#'
#'   If not specified, the type of the columns generated from `names_to` will
#'   be character, and the type of the variables generated from `values_to`
#'   will be the common type of the input columns used to generate them.
#' @param ... Additional arguments passed on to methods.
#'
#' @return A tidySummarizedExperiment objector a tibble depending on input
#'
#' @rdname tidyr-methods
#' @name pivot_longer
#'
#' @export
#' @examples
#' # See vignette("pivot") for examples and explanation
#'
#' library(dplyr)
#' tidySummarizedExperiment::pasilla %>%
#'     tidy() %>%
#'     pivot_longer(c(condition, type), names_to="name", values_to="value")
NULL

#' @export
pivot_longer.tidySummarizedExperiment <- function(data,
    cols,
    names_to="name",
    names_prefix=NULL,
    names_sep=NULL,
    names_pattern=NULL,
    names_ptypes=list(),
    names_transform=list(),
    names_repair="check_unique",
    values_to="value",
    values_drop_na=FALSE,
    values_ptypes=list(),
    values_transform=list(),
    ...) {
    cols <- enquo(cols)

    message(data_frame_returned_message)

    data %>%
        as_tibble() %>%
        tidyr::pivot_longer(!!cols,
            names_to=names_to,
            names_prefix=names_prefix,
            names_sep=names_sep,
            names_pattern=names_pattern,
            names_ptypes=names_ptypes,
            names_transform=names_transform,
            names_repair=names_repair,
            values_to=values_to,
            values_drop_na=values_drop_na,
            values_ptypes=values_ptypes,
            values_transform=values_transform,
            ...
        )
}

#' Unite multiple columns into one by pasting strings together
#'
#' Convenience function to paste together multiple columns into one.
#'
#' @importFrom ellipsis check_dots_unnamed
#' @importFrom tidyr unite
#'
#' @param data A data frame.
#' @param col The name of the new column, as a string or symbol.
#'
#'   This argument is passed by expression and supports
#'   [quasiquotation][rlang::quasiquotation] (you can unquote strings
#'   and symbols). The name is captured from the expression with
#'   [rlang::ensym()] (note that this kind of interface where
#'   symbols do not represent actual objects is now discouraged in the
#'   tidyverse; we support it here for backward compatibility).
#' @param ... <[`tidy-select`][tidyr_tidy_select]> Columns to unite
#' @param sep Separator to use between values.
#' @param na.rm If `TRUE`, missing values will be remove prior to uniting
#'   each value.
#' @param remove If `TRUE`, remove input columns from output data frame.
#' @seealso [separate()], the complement.
#'
#' @return A tidySummarizedExperiment objector a tibble depending on input
#'
#' @rdname tidyr-methods
#' @name unite
#'
#' @export
#' @examples
#'
#' tidySummarizedExperiment::pasilla %>%
#'     tidy() %>%
#'     unite("group", c(condition, type))
NULL

#' @export
unite.tidySummarizedExperiment <- function(data, col, ..., sep="_", remove=TRUE, na.rm=FALSE) {

    # Check that we are not modifying a key column
    cols <- enquo(col)

    tst =
        intersect(
            cols %>% quo_names(),
            get_special_columns(data) %>% c(get_needed_columns())
        ) %>%
        length() %>%
        gt(0) &
        remove


    if (tst) {
        columns =
            get_special_columns(data) %>%
            c(get_needed_columns()) %>%
            paste(collapse=", ")
        stop(
            "tidySummarizedExperiment says: you are trying to rename a column that is view only",
            columns,
            "(it is not present in the colData). If you want to mutate a view-only column, make a copy and mutate that one."
        )
    }



    data %>%
        as_tibble() %>%
        tidyr::unite(!!cols, ..., sep=sep, remove=remove, na.rm=na.rm) %>%
        update_SE_from_tibble(data)
}

#' Separate a character column into multiple columns with a regular
#' expression or numeric locations
#'
#' Given either a regular expression or a vector of character positions,
#' `separate()` turns a single character column into multiple columns.
#'
#' @importFrom ellipsis check_dots_used
#' @importFrom tidyr separate
#'
#' @inheritParams extract
#' @param sep Separator between columns.
#'
#'   If character, `sep` is interpreted as a regular expression. The default
#'   value is a regular expression that matches any sequence of
#'   non-alphanumeric values.
#'
#'   If numeric, `sep` is interpreted as character positions to split at. Positive
#'   values start at 1 at the far-left of the string; negative value start at -1 at
#'   the far-right of the string. The length of `sep` should be one less than
#'   `into`.
#' @param extra If `sep` is a character vector, this controls what
#'   happens when there are too many pieces. There are three valid options:
#'
#'   * "warn" (the default): emit a warning and drop extra values.
#'   * "drop": drop any extra values without a warning.
#'   * "merge": only splits at most `length(into)` times
#' @param fill If `sep` is a character vector, this controls what
#'   happens when there are not enough pieces. There are three valid options:
#'
#'   * "warn" (the default): emit a warning and fill from the right
#'   * "right": fill with missing values on the right
#'   * "left": fill with missing values on the left
#' @seealso [unite()], the complement, [extract()] which uses regular
#'   expression capturing groups.
#'
#' @return A tidySummarizedExperiment objector a tibble depending on input
#'
#' @rdname tidyr-methods
#' @name separate
#'
#' @export
#' @examples
#'
#' un <- tidySummarizedExperiment::pasilla %>%
#'     tidy() %>%
#'     unite("group", c(condition, type))
#' un %>% separate(col=group, into=c("condition", "type"))
NULL

#' @export
separate.tidySummarizedExperiment <- function(data, col, into, sep="[^[:alnum:]]+", remove=TRUE,
    convert=FALSE, extra="warn", fill="warn", ...) {

    # Check that we are not modifying a key column
    cols <- enquo(col)

    tst =
        intersect(
            cols %>% quo_names(),
            get_special_columns(data) %>% c(get_needed_columns())
        ) %>%
        length() %>%
        gt(0) &
        remove


    if (tst) {
        columns =
            get_special_columns(data) %>%
            c(get_needed_columns()) %>%
            paste(collapse=", ")
        stop(
            "tidySummarizedExperiment says: you are trying to rename a column that is view only",
            columns,
            "(it is not present in the colData). If you want to mutate a view-only column, make a copy and mutate that one."
        )
    }


    data %>%
        as_tibble() %>%
        tidyr::separate(!!cols, into=into, sep=sep, remove=remove, convert=convert, extra=extra, fill=fill, ...) %>%
        update_SE_from_tibble(data)
}