Converting a Nested For Loop into `sapply()` in R - r

Question

I have been trying to create a series of coplots using a nested for loop but the loop takes too long to run (the original data set is very big). I have looked at similar questions and they suggest using the sapply function but I am still unclear about how to convert between the 2. I understand I need to create a plotting function to use (see below) but what I don't understand is how the i's and j's of the nested for loop into sapply arguements.
I have made some sample data, the nested for loop that I have been using and the plotting function I created that are below. Could someone walk me through how I convert my nested for loop into sapply arguements. I have been doing all of this in R. Many Thanks
y = rnorm(n = 200, mean = 10, sd = 2)
x1 = rnorm(n = 200, mean = 5, sd = 2)
x2 = rnorm(n = 200, mean = 2.5, sd = 2)
x3 = rep(letters[1:4], each = 50)
x4 = rep(LETTERS[1:8], each = 25)
dat = data.frame(y = y, x1 = x1, x2 = x2, x3 = x3, x4 = x4)
for(i in dat[, 2:3]){
for(j in dat[, 4:5]){
coplot(y ~ i | j, rows = 1, data = dat)
}
}
coplop_fun = function(data, x, y, x, na.rm = TRUE){
coplot(.data[[y]] ~ .data[[x]] | .data[[z]], data = data, rows = 1)
}

Answer 1

I think you might be able to use mapply here and not sapply. mapply is similar to sapply but allows for you to pass two inputs instead of one.
y = rnorm(n = 200, mean = 10, sd = 2)
x1 = rnorm(n = 200, mean = 5, sd = 2)
x2 = rnorm(n = 200, mean = 2.5, sd = 2)
x3 = rep(letters[1:4], each = 50)
x4 = rep(LETTERS[1:8], each = 25)
dat = data.frame(y = y, x1 = x1, x2 = x2, x3 = x3, x4 = x4)
for(i in dat[, 2:3]){
for(j in dat[, 4:5]){
coplot(y ~ i | j, rows = 1, data = dat)
}
}
mapply(function(x,j){coplot(dat[["y"]]~x|j,rows =1)}, dat[,2:3],dat[,4:5])

Answer 2

We can use a combination of functions expand.grid, formula and apply to accept character column names into coplot.
# combinations of column names for plotting
vars <- expand.grid(y = "y", x = c("x1", "x2"), z = c("x3", "x4"))
# cycle through column name variations, construct formula for each combination
apply(vars, MARGIN = 1,
FUN = function(x) coplot(
formula = formula(paste(x[1], "~", x[2], "|", x[3])),
data = dat, row = 1
)
)

Answer 3

Here's a tidyverse version of #nya's solution with expand.grid() and apply(). Each row in ds_plot_parameters represents a single plot. The equation variable is the string eventually passed to coplot().
Each equation is passed to purrr::walk(), which then calls coplot()
to produce one graph each. as.equation() converts the string to an equation.
ds_plot_parameters <-
tidyr::expand_grid(
v = c("x1", "x2"),
w = c("x3", "x4")
) |>
dplyr::mutate(
equation = paste0("y ~ ", v, " | ", w),
)
ds_plot_parameters$equation |>
purrr::walk(
\(e) coplot(as.formula(e), rows = 1, data = dat)
)
Gravy:
If you want to more input to the graph, then expand ds_plot_parameters to include other things like graph & axis titles.
ds_plot_parameters <-
tidyr::expand_grid(
v = c("x1", "x2"),
w = c("x3", "x4")
) |>
dplyr::mutate(
equation = paste0("y ~ ", v, " | ", w),
label_y = "Outcome (mL)",
label_x = paste(v, " (log 10)")
)
ds_plot_parameters |>
dplyr::select(
# Make sure this order exactly matches the function signature
equation,
label_x,
label_y,
) |>
purrr::pwalk(
.f = \(equation, label_x, label_y) {
coplot(
formula = as.formula(equation),
xlab = label_x,
ylab = label_y,
rows = 1,
data = dat
)
}
)
ds_plot_parameters
# # A tibble: 4 x 5
# v w equation label_y label_x
# <chr> <chr> <chr> <chr> <chr>
# 1 x1 x3 y ~ x1 | x3 Outcome (mL) x1 (log 10)
# 2 x1 x4 y ~ x1 | x4 Outcome (mL) x1 (log 10)
# 3 x2 x3 y ~ x2 | x3 Outcome (mL) x2 (log 10)
# 4 x2 x4 y ~ x2 | x4 Outcome (mL) x2 (log 10)