How to split data into training/testing sets using sample function

I've just started using R and I'm not sure how to incorporate my dataset with the following sample code:

sample(x, size, replace = FALSE, prob = NULL)

I have a dataset that I need to put into a training (75%) and testing (25%) set. I'm not sure what information I'm supposed to put into the x and size? Is x the dataset file, and size how many samples I have?

There are numerous approaches to achieve data partitioning. For a more complete approach take a look at the createDataPartition function in the caret package.

Here is a simple example:

data(mtcars)

## 75% of the sample size
smp_size <- floor(0.75 * nrow(mtcars))

## set the seed to make your partition reproducible
set.seed(123)
train_ind <- sample(seq_len(nrow(mtcars)), size = smp_size)

train <- mtcars[train_ind, ]
test <- mtcars[-train_ind, ]

It can be easily done by:

set.seed(101) # Set Seed so that same sample can be reproduced in future also
# Now Selecting 75% of data as sample from total 'n' rows of the data  
sample <- sample.int(n = nrow(data), size = floor(.75*nrow(data)), replace = F)
train <- data[sample, ]
test  <- data[-sample, ]

By using caTools package:

require(caTools)
set.seed(101) 
sample = sample.split(data$anycolumn, SplitRatio = .75)
train = subset(data, sample == TRUE)
test  = subset(data, sample == FALSE)

I would use dplyr for this, makes it super simple. It does require an id variable in your data set, which is a good idea anyway, not only for creating sets but also for traceability during your project. Add it if doesn't contain already.

mtcars$id <- 1:nrow(mtcars)
train <- mtcars %>% dplyr::sample_frac(.75)
test  <- dplyr::anti_join(mtcars, train, by = 'id')