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

Question

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)


        

Answer 1

I think this would solve the problem:

df = data.frame(read.csv("data.csv"))
# Split the dataset into 80-20
numberOfRows = nrow(df)
bound = as.integer(numberOfRows *0.8)
train=df[1:bound ,2]
test1= df[(bound+1):numberOfRows ,2]

Answer 2

After looking through all the different methods posted here, I didn't see anyone utilize TRUE/FALSE to select and unselect data. So I thought I would share a method utilizing that technique.

n = nrow(dataset)
split = sample(c(TRUE, FALSE), n, replace=TRUE, prob=c(0.75, 0.25))

training = dataset[split, ]
testing = dataset[!split, ]

Explanation

There are multiple ways of selecting data from R, most commonly people use positive/negative indices to select/unselect respectively. However, the same functionalities can be achieved by using TRUE/FALSE to select/unselect.

Consider the following example.

# let's explore ways to select every other element
data = c(1, 2, 3, 4, 5)


# using positive indices to select wanted elements
data[c(1, 3, 5)]
[1] 1 3 5

# using negative indices to remove unwanted elements
data[c(-2, -4)]
[1] 1 3 5

# using booleans to select wanted elements
data[c(TRUE, FALSE, TRUE, FALSE, TRUE)]
[1] 1 3 5

# R recycles the TRUE/FALSE vector if it is not the correct dimension
data[c(TRUE, FALSE)]
[1] 1 3 5

Answer 3

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')

Answer 4

I can suggest using the rsample package:

# choosing 75% of the data to be the training data
data_split <- initial_split(data, prop = .75)
# extracting training data and test data as two seperate dataframes
data_train <- training(data_split)
data_test  <- testing(data_split)

Answer 5

This is almost the same code, but in more nice look

bound <- floor((nrow(df)/4)*3)         #define % of training and test set

df <- df[sample(nrow(df)), ]           #sample rows 
df.train <- df[1:bound, ]              #get training set
df.test <- df[(bound+1):nrow(df), ]    #get test set

Answer 6

library(caret)
intrain<-createDataPartition(y=sub_train$classe,p=0.7,list=FALSE)
training<-m_train[intrain,]
testing<-m_train[-intrain,]