{lay}
{lay}
The function lay() – the only function of the package
{lay} – is intended to be used to apply a function on
each row of a data frame or tibble, independently, and across multiple
columns containing values of the same class (e.g. all numeric).
Implementing rowwise operations for tabular data is notoriously
awkward in R. Many options have been proposed, but they tend to be
complicated, inefficient, or both. Instead lay() aims at
reaching a sweet spot between simplicity and efficiency.
The function has been specifically designed to be combined with
functions from {dplyr} and to
feel as if it was part of it (but you can use lay() without
{dplyr}).
There is hardly any code behind lay() (it can be coded
in 3 lines), so this package may just be an interim solution before an
established package fulfills the need… Time will tell.
You can install the development version of {lay} with:
remotes::install_github("courtiol/lay")Consider the following dataset, which contains information about the use of pain relievers for non medical purpose.
library(lay) ## requires to have installed {lay}
drugs
#> # A tibble: 100 × 8
#> caseid hydrocd oxycodp codeine tramadl morphin methdon vicolor
#> <chr> <int> <int> <int> <int> <int> <int> <int>
#> 1 1 0 0 0 0 0 0 0
#> 2 2 0 0 0 0 0 0 0
#> 3 3 0 0 0 0 0 0 0
#> 4 4 0 0 0 0 0 0 0
#> 5 5 0 0 0 0 0 0 0
#> 6 6 0 0 0 0 0 0 0
#> 7 7 0 0 0 0 0 0 0
#> 8 8 0 0 0 0 0 0 0
#> 9 9 0 0 0 0 0 0 1
#> 10 10 0 0 0 0 0 0 0
#> # ℹ 90 more rowsThe dataset is tidy: each row represents one individual and each variable forms a column.
Imagine now that you would like to know if each individual did use any of these pain relievers.
How would you proceed?
lay()This is how you would achieve our goal using lay():
library(dplyr, warn.conflicts = FALSE) ## requires to have installed {dplyr}
drugs_full |>
mutate(everused = lay(pick(-caseid), any))
#> # A tibble: 55,271 × 9
#> caseid hydrocd oxycodp codeine tramadl morphin methdon vicolor everused
#> <chr> <int> <int> <int> <int> <int> <int> <int> <lgl>
#> 1 1 0 0 0 0 0 0 0 FALSE
#> 2 2 0 0 0 0 0 0 0 FALSE
#> 3 3 0 0 0 0 0 0 0 FALSE
#> 4 4 0 0 0 0 0 0 0 FALSE
#> 5 5 0 0 0 0 0 0 0 FALSE
#> 6 6 0 0 0 0 0 0 0 FALSE
#> 7 7 0 0 0 0 0 0 0 FALSE
#> 8 8 0 0 0 0 0 0 0 FALSE
#> 9 9 0 0 0 0 0 0 1 TRUE
#> 10 10 0 0 0 0 0 0 0 FALSE
#> # ℹ 55,261 more rowsWe used mutate() from {dplyr} to
create a new column called everused, and we used
pick() from that same package to remove the column
caseid when laying down each row of the data and applying the
function any().
When combining lay() and {dplyr}, you
should always use pick() or across(). The
functions pick() and across() let you pick
among many selection
helpers from the package {tidyselect},
which makes it easy to specify which columns to consider.
Our function lay() is quite flexible! For example, you
can pass argument(s) of the function you wish to apply rowwise (here
any()):
drugs_with_NA <- drugs ## create a copy of the dataset
drugs_with_NA[1, 2] <- NA ## introduce a missing value
drugs_with_NA |>
mutate(everused = lay(pick(-caseid), any)) |> ## without additional argument
slice(1) ## keep first row only
#> # A tibble: 1 × 9
#> caseid hydrocd oxycodp codeine tramadl morphin methdon vicolor everused
#> <chr> <int> <int> <int> <int> <int> <int> <int> <lgl>
#> 1 1 NA 0 0 0 0 0 0 NA
drugs_with_NA |>
mutate(everused = lay(pick(-caseid), any, na.rm = TRUE)) |> ## with additional argument
slice(1)
#> # A tibble: 1 × 9
#> caseid hydrocd oxycodp codeine tramadl morphin methdon vicolor everused
#> <chr> <int> <int> <int> <int> <int> <int> <int> <lgl>
#> 1 1 NA 0 0 0 0 0 0 FALSESince one of the backbones of lay() is {rlang}, you can use
the so-called lambda
syntax to define anonymous functions on the fly:
drugs_with_NA |>
mutate(everused = lay(pick(-caseid), ~ any(.x, na.rm = TRUE))) ## same as above, different syntax
#> # A tibble: 100 × 9
#> caseid hydrocd oxycodp codeine tramadl morphin methdon vicolor everused
#> <chr> <int> <int> <int> <int> <int> <int> <int> <lgl>
#> 1 1 NA 0 0 0 0 0 0 FALSE
#> 2 2 0 0 0 0 0 0 0 FALSE
#> 3 3 0 0 0 0 0 0 0 FALSE
#> 4 4 0 0 0 0 0 0 0 FALSE
#> 5 5 0 0 0 0 0 0 0 FALSE
#> 6 6 0 0 0 0 0 0 0 FALSE
#> 7 7 0 0 0 0 0 0 0 FALSE
#> 8 8 0 0 0 0 0 0 0 FALSE
#> 9 9 0 0 0 0 0 0 1 TRUE
#> 10 10 0 0 0 0 0 0 0 FALSE
#> # ℹ 90 more rowsWe can also apply many functions at once, as exemplified with another dataset:
data("world_bank_pop", package = "tidyr") ## requires to have installed {tidyr}
world_bank_pop |>
filter(indicator == "SP.POP.TOTL") |>
mutate(lay(pick(matches("\\d")),
~ tibble(min = min(.x), mean = mean(.x), max = max(.x))), .after = indicator)
#> # A tibble: 266 × 23
#> country indicator min mean max `2000` `2001` `2002` `2003` `2004`
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 ABW SP.POP.TOTL 8.91e4 9.81e4 1.05e5 8.91e4 9.07e4 9.18e4 9.27e4 9.35e4
#> 2 AFE SP.POP.TOTL 4.02e8 5.08e8 6.33e8 4.02e8 4.12e8 4.23e8 4.34e8 4.45e8
#> 3 AFG SP.POP.TOTL 1.95e7 2.73e7 3.56e7 1.95e7 1.97e7 2.10e7 2.26e7 2.36e7
#> 4 AFW SP.POP.TOTL 2.70e8 3.45e8 4.31e8 2.70e8 2.77e8 2.85e8 2.93e8 3.01e8
#> 5 AGO SP.POP.TOTL 1.64e7 2.26e7 3.02e7 1.64e7 1.69e7 1.75e7 1.81e7 1.88e7
#> 6 ALB SP.POP.TOTL 2.87e6 2.96e6 3.09e6 3.09e6 3.06e6 3.05e6 3.04e6 3.03e6
#> 7 AND SP.POP.TOTL 6.61e4 7.32e4 8.02e4 6.61e4 6.78e4 7.08e4 7.39e4 7.69e4
#> 8 ARB SP.POP.TOTL 2.87e8 3.52e8 4.24e8 2.87e8 2.94e8 3.00e8 3.07e8 3.13e8
#> 9 ARE SP.POP.TOTL 3.28e6 6.58e6 9.07e6 3.28e6 3.45e6 3.63e6 3.81e6 3.99e6
#> 10 ARG SP.POP.TOTL 3.71e7 4.05e7 4.40e7 3.71e7 3.75e7 3.79e7 3.83e7 3.87e7
#> # ℹ 256 more rows
#> # ℹ 13 more variables: `2005` <dbl>, `2006` <dbl>, `2007` <dbl>, `2008` <dbl>,
#> # `2009` <dbl>, `2010` <dbl>, `2011` <dbl>, `2012` <dbl>, `2013` <dbl>,
#> # `2014` <dbl>, `2015` <dbl>, `2016` <dbl>, `2017` <dbl>Since the other backbone of lay() is {vctrs}, the
splicing happens automatically (unless the output of the call is used to
create a named column). This is why, in the last chunk of code, three
different columns (min, mean and max) are
directly created.
Important: when using lay() the
function you want to use for the rowwise job must output a scalar
(vector of length 1), or a tibble or data frame with a single row.
We can apply a function that returns a vector of length > 1 by
turning such a vector into a tibble using as_tibble_row()
from {tibble}:
world_bank_pop |>
filter(indicator == "SP.POP.TOTL") |>
mutate(lay(pick(matches("\\d")),
~ as_tibble_row(quantile(.x, na.rm = TRUE))), .after = indicator)
#> # A tibble: 266 × 25
#> country indicator `0%` `25%` `50%` `75%` `100%` `2000` `2001` `2002`
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 ABW SP.POP.TOTL 8.91e4 9.38e4 9.86e4 1.03e5 1.05e5 8.91e4 9.07e4 9.18e4
#> 2 AFE SP.POP.TOTL 4.02e8 4.48e8 5.03e8 5.64e8 6.33e8 4.02e8 4.12e8 4.23e8
#> 3 AFG SP.POP.TOTL 1.95e7 2.38e7 2.69e7 3.13e7 3.56e7 1.95e7 1.97e7 2.10e7
#> 4 AFW SP.POP.TOTL 2.70e8 3.03e8 3.42e8 3.85e8 4.31e8 2.70e8 2.77e8 2.85e8
#> 5 AGO SP.POP.TOTL 1.64e7 1.89e7 2.21e7 2.59e7 3.02e7 1.64e7 1.69e7 1.75e7
#> 6 ALB SP.POP.TOTL 2.87e6 2.90e6 2.94e6 3.02e6 3.09e6 3.09e6 3.06e6 3.05e6
#> 7 AND SP.POP.TOTL 6.61e4 7.11e4 7.21e4 7.55e4 8.02e4 6.61e4 6.78e4 7.08e4
#> 8 ARB SP.POP.TOTL 2.87e8 3.15e8 3.51e8 3.87e8 4.24e8 2.87e8 2.94e8 3.00e8
#> 9 ARE SP.POP.TOTL 3.28e6 4.07e6 7.49e6 8.73e6 9.07e6 3.28e6 3.45e6 3.63e6
#> 10 ARG SP.POP.TOTL 3.71e7 3.88e7 4.05e7 4.21e7 4.40e7 3.71e7 3.75e7 3.79e7
#> # ℹ 256 more rows
#> # ℹ 15 more variables: `2003` <dbl>, `2004` <dbl>, `2005` <dbl>, `2006` <dbl>,
#> # `2007` <dbl>, `2008` <dbl>, `2009` <dbl>, `2010` <dbl>, `2011` <dbl>,
#> # `2012` <dbl>, `2013` <dbl>, `2014` <dbl>, `2015` <dbl>, `2016` <dbl>,
#> # `2017` <dbl>
The first draft of this package has been created by @romainfrancois as a reply
to a tweet I (Alexandre Courtiol) posted under @rdataberlin
in February 2020. At the time I was exploring different ways to perform
rowwise jobs in R and I was experimenting with various ideas on how to
exploit the fact that the newly introduced function
across() from {dplyr} creates
tibbles on which one can easily apply a function. Romain came up with
lay() as the better solution, making good use of {rlang} & {vctrs}.
The verb lay() never made it to be integrated within {dplyr}, but,
so far, I still find lay() superior than most alternatives,
which is why I decided to document and maintain this package.