Creating Vector of Results of Repeated Function Calls in R

Creating vector of results of repeated function calls in R

replicate is your friend. See ?replicate

replicate(10, my_function_call()) # this would be what you're looking for

How to run a function multiple times and write the results to a list?

Combination of above answer, comment, and my own answer. Naturally, I like mine better. Also, I think there is a mistake in the above answer for base R.

n <- 10

# give 1 to gen_mat n-times
lapply(rep(1, n), gen_mat)

# replicate n-times 
replicate(n, gen_mat(1), simplify=FALSE)

# lapply returns error if FUN is not function or 
# the function is not taking an argument. Hence a dummy function.
lapply(seq_len(n), function(x) gen_mat(1))

microbenchmarking the three methods

I used a larger value for n, but the results are similar in my desktop with smaller n as well. For this, replicate takes longer than the other two methods.

set.seed(1)
gen_mat <- function(x) matrix(c(1, 1, 1, x + rnorm(1)), nrow = 2)
n <- 1000 

library(microbenchmark)
library(ggplot2)

mb <- microbenchmark(
  lap1 = {lapply(rep(1, n), gen_mat)},
  repl = {replicate(n, gen_mat(1), simplify=FALSE)},
  lap2 = {lapply(seq_len(n), function(x) gen_mat(1))},
  times = 10000L
)

mb
# Unit: milliseconds
# expr      min       lq     mean   median       uq      max neval cld
# lap1 2.839435 3.494157 4.806954 3.854269 5.611413 103.0111 10000  a 
# repl 3.091829 3.777199 5.140789 4.165856 6.013591 101.4318 10000   b
# lap2 3.131491 3.761274 5.089170 4.140316 5.939075 101.1983 10000   b

autoplot(mb)

Extract elements from a vector that are repeated n times (R)

We can use table if it is based on frequency

v1 <- table(col.vector)
as.integer(names(v1)[v1 == 3])
[1] 3 4

---

If it is based on repeating elements, use rleid

library(data.table)
v1 <- table(rleid(col.vector))
as.integer(names(v1)[v1 == 3])

Alternative ways to create a repetitive vector in R

We can use bitwAnd

> bitwAnd(0:9, 1)
 [1] 0 1 0 1 0 1 0 1 0 1

or kronecker

> kronecker(as.vector(matrix(1, 5)), 0:1)
 [1] 0 1 0 1 0 1 0 1 0 1

> kronecker((1:5)^0, 0:1)
 [1] 0 1 0 1 0 1 0 1 0 1

or outer

> as.vector(outer(0:1, (1:5)^0))
 [1] 0 1 0 1 0 1 0 1 0 1

Is there a way to repeat a function a fixed number of times and save every result as a data frame?

• As mentioned in the comments, the replicate implementation can reach your goal, i.e.,

res <- replicate(100,df[sample(nrow(df), size = 25, replace = FALSE),],simplify = F)

• An alternative is to use sapply (or lapply), i.e.,

res <- sapply(1:100, function(k) df[sample(nrow(df), size = 25, replace = FALSE),],simplify = F)

or

res <- lapply(1:100, function(k) df[sample(nrow(df), size = 25, replace = FALSE),])

How to repeat a code for multiple times and store the output of each iteration in the same dataframe?

In the absence of a reproducible example, the following uses an example from the {Metrics} package documentation to construct your dataframe dp. Augment as appropriate.

Further you need to provide parameters to your function. In this case we supply the data frame dp (which you call in your function).

Lastly, replicate() returns an array/matrix. We reformat this into a "long" format and then coerce it to a data frame.

library(Metrics)

# simulate the data -----------------------------------------
actual <- c(1.1, 1.9, 3.0, 4.4, 5.0, 5.6)
predicted <- c(0.9, 1.8, 2.5, 4.5, 5.0, 6.2)

dp <- data.frame(
    true_norm         = actual
  , dp_threshold_norm = predicted
)

# make function work -----------------------------------------
getValue <- function(dp) {          # we define a parameter dp for the function
     mae <- mae(dp$true_norm, dp$dp_threshold_norm)
     rmse <- rmse(dp$true_norm, dp$dp_threshold_norm)
     per_metrics <- c(mae,rmse)
     return(per_metrics)            # return value 
}

# apply function multiple times with replicate()
# check this to understand the returned data format
replicate(n = 10, expr = getValue(dp))

# result ---------------------------------------------
## store in variable
result <- replicate(n = 10, expr = getValue(dp))

## coerce to "long data frame"  - here we set ncol 2 for 2 variables
result <- matrix(result, ncol = 2)

## coerce to data frame
result <- as.data.frame.array(result)

This yields:

result

          V1        V2
1  0.2500000 0.2500000
2  0.3341656 0.3341656
3  0.2500000 0.2500000
4  0.3341656 0.3341656
5  0.2500000 0.2500000
6  0.3341656 0.3341656
7  0.2500000 0.2500000
8  0.3341656 0.3341656
9  0.2500000 0.2500000
10 0.3341656 0.3341656

You can now rename the columns as required.

---

Related Topics