• First of all, create a data.table object with some columns having same name.

  • Then, use ">

    How to find the row variance of columns having same name in data.table object in R?



    To find the row variance of columns having same name in data.table object in R, we can follow the below steps −

    • First of all, create a data.table object with some columns having same name.

    • Then, use tapply along with colnames and var function to find the row variance of columns having same name.

    Example

    Create the data.table object

    Let’s create a data.table object as shown below −

    library(data.table) DT<- data.table(x=rpois(25,5),y=rpois(25,5),x=rpois(25,2),y=rpois(25,1),check.names=FALSE) DT
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    Output

    On executing, the above script generates the below output(this output will vary on your system due to randomization) −

         x y  x y
    1:   2 11 1 1
    2:   4  5 2 1
    3:   5  4 2 1
    4:   5  5 0 0
    5:   5  1 0 1
    6:   8  6 3 2
    7:   4  4 4 1
    8:   4 12 4 2
    9:   4  4 2 0
    10:  8  6 1 3
    11:  9 10 1 0
    12:  5  3 1 0
    13:  7  4 0 0
    14:  6  4 4 0
    15:  8  7 4 1
    16: 10  3 1 0
    17:  3 10 3 1
    18:  3  4 3 2
    19:  8  3 2 3
    20:  4  4 0 1
    21:  5  2 2 3
    22: 10  5 2 2
    23:  7  8 2 0
    24:  7  7 3 0
    25:  7  7 2 0
         x  y x y

    Find the row variance of columns having same name

    Using tapply along with colnames and var function to find the row standard deviation of columns having same name in data.table object DT −

    library(data.table) DT<- data.table(x=rpois(25,5),y=rpois(25,5),x=rpois(25,2),y=rpois(25,1),check.names=FALSE) t(apply(DT,1, function(x) tapply(x,colnames(DT),var)))

    Output

            x   y
    [1,]   0.5 50.0
    [2,]   2.0  8.0
    [3,]   4.5  4.5
    [4,]  12.5 12.5
    [5,]  12.5  0.0
    [6,]  12.5  8.0
    [7,]   0.0  4.5
    [8,]   0.0 50.0
    [9,]   2.0  8.0
    [10,] 24.5  4.5
    [11,] 32.0 50.0
    [12,]  8.0  4.5
    [13,] 24.5  8.0
    [14,]  2.0  8.0
    [15,]  8.0 18.0
    [16,] 40.5  4.5
    [17,]  0.0 40.5
    [18,]  0.0  2.0
    [19,] 18.0  0.0
    [20,]  8.0  4.5
    [21,]  4.5  0.5
    [22,] 32.0  4.5
    [23,] 12.5 32.0
    [24,]  8.0 24.5
    [25,] 12.5 24.5
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