R: Fast Transform and Compute Columns on a Data Frame

ftransform {collapse} | R Documentation

## Fast Transform and Compute Columns on a Data Frame

### Description

`ftransform` is a much faster version of `transform` for data frames. It returns the data frame with new columns computed and/or existing columns modified or deleted. `settransform` does all of that by reference. `fcompute` computes and returns new columns. These functions evaluate all arguments simultaneously, allow list-input (nested pipelines) and natively disregard grouped data.

Catering to the tidyverse user, v1.7.0 introduced `fmutate`, providing familiar functionality i.e. arguments are evaluated sequentially, computation on grouped data is done by groups, and functions can be applied to multiple columns using `across`. See also the Details.

### Usage

    # Modify and return data frame
    ftransform(.data, ...)
    ftransformv(.data, vars, FUN, ..., apply = TRUE)
    tfm(.data, ...)               # Shortcut for ftransform
    tfmv(.data, vars, FUN, ..., apply = TRUE)

    # Modify data frame by reference
    settransform(.data, ...)
    settransformv(.data, vars, FUN, ..., apply = TRUE)
    settfm(.data, ...)            # Shortcut for settransform
    settfmv(.data, vars, FUN, ..., apply = TRUE)

    # Replace/add modified columns in/to a data frame
    ftransform(.data) <- value
    tfm(.data) <- value           # Shortcut for ftransform<-

    # Compute columns, returned as a new data frame
    fcompute(.data, ..., keep = NULL)
    fcomputev(.data, vars, FUN, ..., apply = TRUE, keep = NULL)

    # New: dplyr-style mutate (sequential evaluation + across() feature)
    fmutate(.data, ..., .keep = "all")
    mtt(.data, ..., .keep = "all") # Shortcut for fmutate

### Arguments

`.data` | a data frame or named list of columns.
`...` | further arguments of the form `column = value`. The `value` can be a combination of other columns, a scalar value, or `NULL`, which deletes `column`. Alternatively it is also possible to place a single list here, which will be treated like a list of `column = value` arguments. For `ftransformv` and `fcomputev`, `...` can be used to pass further arguments to `FUN`. Note: The ellipsis (`...`) is always evaluated within the data frame (`.data`) environment. See Examples. `fmutate` supports `across` statements, and evaluates tagged vector expressions sequentially.
`vars` | variables to be transformed by applying `FUN` to them: select using names, indices, a logical vector or a selector function (e.g. `is.numeric`). Since v1.7 `vars` is evaluated within the `.data` environment, permitting expressions on columns e.g. `c(col1, col3:coln)`.
`FUN` | a single function yielding a result of length `NROW(.data)` or 1. See also `apply`.
`apply` | logical. `TRUE` (default) will apply `FUN` to each column selected in `vars`; `FALSE` will apply `FUN` to the subsetted data frame i.e. `FUN(get_vars(.data, vars), ...)`. The latter is useful for collapse functions with data frame or grouped / panel data frame methods, yielding performance gains and enabling grouped transformations. See Examples.
`value` | a named list of replacements, it will be treated like an evaluated list of `column = value` arguments.
`keep` | select columns to preserve using column names, indices or a function (e.g. `is.numeric`). By default computed columns are added after the preserved ones, unless they are assigned the same name in which case the preserved columns will be replaced in order.
`.keep` | Either one of `"all", "used", "unused"` or `"none"` (see `mutate`), or columns names/indices/function as `keep`.

### Details

The `...` arguments to `ftransform` are tagged vector expressions, which are evaluated in the data frame `.data`. The tags are matched against `names(.data)`, and for those that match, the values replace the corresponding variable in `.data`, whereas the others are appended to `.data`. It is also possible to delete columns by assigning `NULL` to them, i.e. `ftransform(data, colk = NULL)` removes `colk` from the data. Note that `names(.data)` and the names of the `...` arguments are checked for uniqueness beforehand, yielding an error if this is not the case.

Since collapse v1.3.0, is is also possible to pass a single named list to `...`, i.e. `ftransform(data, newdata)`. This list will be treated like a list of tagged vector expressions. Note the different behavior: `ftransform(data, list(newcol = col1))` is the same as `ftransform(data, newcol = col1)`, whereas `ftransform(data, newcol = as.list(col1))` creates a list column. Something like `ftransform(data, as.list(col1))` gives an error because the list is not named. See Examples.

The function `ftransformv` added in v1.3.2 provides a fast replacement for the functions `dplyr::mutate_at` and `dplyr::mutate_if` (without the grouping feature) facilitating mutations of groups of columns (`dplyr::mutate_all` is already accounted for by `dapply`). See Examples.

The function `settransform` does all of that by reference, but uses base-R's copy-on modify semantics, which is equivalent to replacing the data with `<-` (thus it is still memory efficient but the data will have a different memory address afterwards).

The function `fcompute(v)` works just like `ftransform(v)`, but returns only the changed / computed columns without modifying or appending the data in `.data`. See Examples.

The function `fmutate` added in v1.7.0, provides functionality familiar from dplyr 1.0.0 and higher. It evaluates tagged vector expressions sequentially and does operations by groups on a grouped frame (thus it is slower than `ftransform` if you have many tagged expressions or a grouped data frame). Note however that collapse does not depend on rlang, so fancy things like data masking or lambda expressions are not available. Note also that `fmutate` operates differently on grouped data whether you use `.FAST_FUN` or base R functions / functions from other packages. With `.FAST_FUN` (including `.OPERATOR_FUN`, excluding `fhdbetween` / `fhdwithin` / `HDW` / `HDB`), `fmutate` performs an efficient vectorized execution, i.e. the grouping object from the grouped data frame is passed to the `g` argument of these functions, and for `.FAST_STAT_FUN` also `TRA = "replace_fill"` is set (if not overwritten by the user), yielding internal grouped computation by these functions without the need for splitting the data by groups. For base R and other functions, `fmutate` performs classical split-apply combine computing i.e. the relevant columns of the data are selected and split into groups, the expression is evaluated for each group, and the result is recombined and suitably expanded to match the original data frame. Note that it is not possible to mix vectorized and standard execution in the same expression!! Vectorized execution is performed if any `.FAST_FUN` or `.OPERATOR_FUN` is part of the expression, thus a code like `mtcars |> gby(cyl) |> fmutate(new = fmin(mpg) / min(mpg))` will be expanded to something like `mtcars %>% gby(cyl) %>% ftransform(new = fmin(mpg, g = GRP(.), TRA = "replace_fill") / min(mpg))` and then executed, i.e. `fmin(mpg)` will be executed in a vectorized way, and `min(mpg)` will not be executed by groups at all.

### Value

The modified data frame `.data`, or, for `fcompute`, a new data frame with the columns computed on `.data`. All attributes of `.data` are preserved.

### Note

`ftransform` does not do anything per se with a grouped data frame. This is on purpose as it affords greater flexibility and performance in programming with the `.FAST_FUN`, through which collapse supports various kinds of fully vectorized grouped transformations (see `TRA` for a list of available transformations). In particular, you can run a nested pipeline inside `ftransform`, and decide which expressions should be grouped, and you can use the ad-hoc grouping functionality of the `.FAST_FUN`, allowing operations where different groupings are applied simultaneously in an expression. See Examples or the answer provided here.

`fmutate` on the other hand supports grouped operations just like `dplyr::mutate`, but works in two different ways depending on whether you use `.FAST_FUN` in an expression or other functions. See the Examples section of `fsummarise` for an illustration.

### See Also

`across`, `fsummarise`, Data Frame Manipulation, Collapse Overview

### Examples

    ## ftransform modifies and returns a data.frame
    head(ftransform(airquality, Ozone = -Ozone))
    head(ftransform(airquality, new = -Ozone, Temp = (Temp-32)/1.8))
    head(ftransform(airquality, new = -Ozone, new2 = 1, Temp = NULL))  # Deleting Temp
    head(ftransform(airquality, Ozone = NULL, Temp = NULL))            # Deleting columns

    # With collapse's grouped and weighted functions, complex operations are done on the fly
    head(ftransform(airquality, # Grouped operations by month:
                    Ozone_Month_median = fmedian(Ozone, Month, TRA = "replace_fill"),
                    Ozone_Month_sd = fsd(Ozone, Month, TRA = "replace"),
                    Ozone_Month_centered = fwithin(Ozone, Month)))

    # Grouping by month and above/below average temperature in each month
    head(ftransform(airquality, Ozone_Month_high_median =
                      fmedian(Ozone, list(Month, Temp > fbetween(Temp, Month)), TRA = "replace_fill")))

    ## ftransformv can be used to modify multiple columns using a function
    head(ftransformv(airquality, 1:3, log))
    head(`[<-`(airquality, 1:3, value = lapply(airquality[1:3], log))) # Same thing in base R

    head(ftransformv(airquality, 1:3, log, apply = FALSE))
    head(`[<-`(airquality, 1:3, value = log(airquality[1:3])))         # Same thing in base R

    # Using apply = FALSE yields meaningful performance gains with collapse functions
    # This calls fwithin.default, and repeates the grouping by month 3 times:
    head(ftransformv(airquality, 1:3, fwithin, Month))

    # This calls fwithin.data.frame, and only groups one time -> 5x faster!
    head(ftransformv(airquality, 1:3, fwithin, Month, apply = FALSE))

    library(magrittr) # Pipe operators
    # This also works for grouped and panel data frames (calling fwithin.grouped_df)
    airquality %>% fgroup_by(Month) %>%
      ftransformv(1:3, fwithin, apply = FALSE) %>% head

    # But this gives the WRONG result (calling fwithin.default). Need option apply = FALSE!!
    airquality %>% fgroup_by(Month) %>%
      ftransformv(1:3, fwithin) %>% head

    # For grouped modification of single columns in a grouped dataset, we can use GRP():
    airquality %>% fgroup_by(Month) %>%
      ftransform(W_Ozone = fwithin(Ozone, GRP(.)),                 # Grouped centering
                 sd_Ozone_m = fsd(Ozone, GRP(.), TRA = "replace"), # In-Month standard deviation
                 sd_Ozone = fsd(Ozone, TRA = "replace"),           # Overall standard deviation
                 sd_Ozone2 = fsd(Ozone, TRA = "replace_fill"),     # Same, overwriting NA's
                 sd_Ozone3 = fsd(Ozone)) %>% head                  # Same thing (calling alloc())

    rm(airquality)

    ## For more complex mutations we can use ftransform with compound pipes
    airquality %>% fgroup_by(Month) %>%
      ftransform(get_vars(., 1:3) %>% fwithin %>% flag(0:2)) %>% head

    airquality %>% ftransform(STD(., cols = 1:3) %>% replace_NA(0)) %>% head

    # The list argument feature also allows flexible operations creating multiple new columns
    airquality %>% # The variance of Wind and Ozone, by month, weighted by temperature:
      ftransform(fvar(list(Wind_var = Wind, Ozone_var = Ozone), Month, Temp, "replace")) %>% head

    # Same as above using a grouped data frame (a bit more complex)
    airquality %>% fgroup_by(Month) %>%
      ftransform(fselect(., Wind, Ozone) %>% fvar(Temp, "replace") %>% add_stub("_var", FALSE)) %>%
      fungroup %>% head

    # This performs 2 different multi-column grouped operations (need c() to make it one list)
    ftransform(airquality, c(fmedian(list(Wind_Day_median = Wind,
                                          Ozone_Day_median = Ozone), Day, TRA = "replace"),
                             fsd(list(Wind_Month_sd = Wind,
                                      Ozone_Month_sd = Ozone), Month, TRA = "replace"))) %>% head

    ## settransform(v) works like ftransform(v) but modifies a data frame in the global environment..
    settransform(airquality, Ratio = Ozone / Temp, Ozone = NULL, Temp = NULL)
    head(airquality)
    rm(airquality)

    # Grouped and weighted centering
    settransformv(airquality, 1:3, fwithin, Month, Temp, apply = FALSE)
    head(airquality)
    rm(airquality)

    # Suitably lagged first-differences
    settransform(airquality, get_vars(airquality, 1:3) %>% fdiff %>% flag(0:2))
    head(airquality)
    rm(airquality)

    # Same as above using magrittr::`%<>%`
    airquality %<>% ftransform(get_vars(., 1:3) %>% fdiff %>% flag(0:2))
    head(airquality)
    rm(airquality)

    # It is also possible to achieve the same thing via a replacement method (if needed)
    ftransform(airquality) <- get_vars(airquality, 1:3) %>% fdiff %>% flag(0:2)
    head(airquality)
    rm(airquality)

    ## fcompute only returns the modified / computed columns
    head(fcompute(airquality, Ozone = -Ozone))
    head(fcompute(airquality, new = -Ozone, Temp = (Temp-32)/1.8))
    head(fcompute(airquality, new = -Ozone, new2 = 1))

    # Can preserve existing columns, computed ones are added to the right if names are different
    head(fcompute(airquality, new = -Ozone, new2 = 1, keep = 1:3))

    # If given same name as preserved columns, preserved columns are replaced in order...
    head(fcompute(airquality, Ozone = -Ozone, new = 1, keep = 1:3))

    # Same holds for fcomputev
    head(fcomputev(iris, is.numeric, log)) # Same as:
    iris %>% get_vars(is.numeric) %>% dapply(log) %>% head()

    head(fcomputev(iris, is.numeric, log, keep = "Species"))   # Adds in front
    head(fcomputev(iris, is.numeric, log, keep = names(iris))) # Preserve order

    # Keep a subset of the data, add standardized columns
    head(fcomputev(iris, 3:4, STD, apply = FALSE, keep = names(iris)[3:5]))

[Package collapse version 1.7.6 Index]