Some love for Base R. Part 2

Where were we? Giving some love to base-R and putting together the idea that it is possible to write R very clearly when using base. Two sets of typical issues:

Subsetting rows and columns

When running analyses we often want to work on a subset of all cases (rows) or variables (columns). People are used to filter() (for rows) and select() (for columns) in the tidyverse but then search how to do that in base and get ugly responses. For example, if we had a number of trials in a data frame called all_trials and we wanted to keep only a single one located in Christchurch we could try using sort of matrix notation, keeping the rows that meet the criterion, and all variables, as we don’t specify criteria for them:

my_trial <– all_trials[all_trials$location == "Christchurch", ]

# better, by using with(). More below
my_trial <- with(all_trials, all_trials[location == "Christchurch", ])

You could have been tempted to use all_trials[location == "Christchurch", ] by itself, but R wouldn't have known to look for location inside all_trials. Much clearer, though, would have been to use the subset() function from base R, which does the job of both filter() and select() in the tidyverse. It works like this:

subset(data_frame, conditions_for_rows, select = conditions_for_columns)

# we keep all columns, as we aren't using select
my_trial <– subset(all_trials, location == "Christchurch")

It is way clearer and pipe ready, as the first argument is the data frame name!

This code can easily be expanded to more complex conditions; for example to include all trees from Christchurch and (&)that are also taller than 10 m:

my_trial <– subset(all_trials, location == "Christchurch" & height > 10)

The dataset contains multiple variables but we only want to keep, say, location, block, height and diameter:

my_trial <– subset(all_trials, location == "Christchurch" & height > 10,
                   select = c(location, block, height, diameter))

with() and pipes

Another one. In the tidyverse functions are designed to receive the name of the data frame as the first argument, as in some_function(data = ..., other arguments). Most of the time in base R data is not the first argument and, in some cases, the functions do not take data = ... as an argument. The first case is not a problems, unless we want to use the base pipe |>. The second leads to either going for $ notation or, god helps us, using attach() to make our variables global. Note: never do this.

Argh! What to do? Here is where with() comes to life, being very useful for these two problematic cases. In essence, with(data_frame, function) is saying "look for the function arguments in the specified data_frame".

For example, this blog post gives a lengthy comparison of the %>% and |> pipes but, in my opinion, it complicates things a lot because is missing the use of with(). The post starts "When I am feeling lazy, I use base R for quick plots plot(mtcars$hp, mtcars$mpg)".

As a start, if I were feeling lazy I would've used plot(mpg ~ hp, mtcars), highlighting that the plot function already takes the data argument. In fact, I'm using it as plot(formula, data). If I needed data in the first place I could have simply used with(), which defaults to a data frame as the first argument:

mtcars |> with(plot(mpg ~ hp))

# This is simply calling
with(mtcars, plot(mpg ~ hp))

Instead, the author chooses to use anonymous (lambda) functions, which do have their place in R, but ends up with nasty looking code:

mtcars |> (\(x) plot(x$mpg ~ x$hp))()
# vs
mtcars |> with(plot(mpg ~ hp))
# or even
mtcars |> with(plot(hp, mpg))

I'm partial to using a formula in plot because I can easily visualise the underlying model in my head.

Some functions, mean() for example, don't take a data frame argument. Again, with() is your friend.

# This produces an error
mtcars |> mean(mpg)

# while this one works
mtcars |> with(mean(mpg))

Both within() (used in part 1) and with() will make your base code mucho moar readable (pun intended) and pipe ready.