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

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猫巷女王i
猫巷女王i 2020-11-22 10:43

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)


        
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  • 2020-11-22 11:08

    I bumped into this one, it can help too.

    set.seed(12)
    data = Sonar[sample(nrow(Sonar)),]#reshufles the data
    bound = floor(0.7 * nrow(data))
    df_train = data[1:bound,]
    df_test = data[(bound+1):nrow(data),]
    
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  • 2020-11-22 11:08

    We can divide data into a particular ratio here it is 80% train and 20% in a test dataset.

    ind <- sample(2, nrow(dataName), replace = T, prob = c(0.8,0.2))
    train <- dataName[ind==1, ]
    test <- dataName[ind==2, ]
    
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  • 2020-11-22 11:09

    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)
    
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  • 2020-11-22 11:09

    Assuming df is your data frame, and that you want to create 75% train and 25% test

    all <- 1:nrow(df)
    train_i <- sort(sample(all, round(nrow(df)*0.75,digits = 0),replace=FALSE))
    test_i <- all[-train_i]
    

    Then to create a train and test data frames

    df_train <- df[train_i,]
    df_test <- df[test_i,]
    
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  • 2020-11-22 11:09
    set.seed(123)
    llwork<-sample(1:length(mydata),round(0.75*length(mydata),digits=0)) 
    wmydata<-mydata[llwork, ]
    tmydata<-mydata[-llwork, ]
    
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  • 2020-11-22 11:10

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