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How to standardize multiple columns not all in data.table object in R?
To standardize multiple columns not all in data.table object in R, we can follow the below steps −
First of all, create a data.table object.
Then, subset the columns with single square brackets and use lapply, list and scale function to standardize those columns.
Example
Create the data.table object
Let’s create a data.table object as shown below −
library(data.table) x<-sample(1:50,25) y<-sample(1:50,25) z<-sample(1:50,25) DT<-data.table(x,y,z) DT
Output
On executing, the above script generates the below output(this output will vary on your system due to randomization) −
x y z 1: 31 19 49 2: 4 38 45 3: 25 46 43 4: 21 45 32 5: 50 48 14 6: 48 8 50 7: 32 33 18 8: 36 20 42 9: 15 18 47 10: 49 43 19 11: 30 21 36 12: 10 35 24 13: 47 14 27 14: 1 47 21 15: 5 32 34 16: 20 26 30 17: 42 15 7 18: 26 2 33 19: 33 41 23 20: 22 42 48 21: 44 9 10 22: 23 7 46 23: 28 40 8 24: 16 31 22 25: 9 44 37 x y z
Standardize multiple columns not all
Subset the x and y columns with single square brackets and use lapply, list and scale function to standardize those columns as shown below −
library(data.table)
x<-sample(1:50,25)
y<-sample(1:50,25)
z<-sample(1:50,25)
DT<-data.table(x,y,z)
DT[,c("y","z"):=lapply(list(y,z),scale)]
DTOutput
x y z 1: 31 -0.6845293 1.3518818 2: 4 0.6212997 1.0579944 3: 25 1.1711225 0.9110508 4: 21 1.1023946 0.1028606 5: 50 1.3085782 -1.2196325 6: 48 -1.4405357 1.4253536 7: 32 0.2776605 -0.9257451 8: 36 -0.6158015 0.8375789 9: 15 -0.7532572 1.2049381 10: 49 0.9649390 -0.8522733 11: 30 -0.5470737 0.3967479 12: 10 0.4151162 -0.4849141 13: 47 -1.0281686 -0.2644986 14: 1 1.2398503 -0.7053296 15: 5 0.2089327 0.2498042 16: 20 -0.2034344 -0.0440831 17: 42 -0.9594407 -1.7339353 18: 26 -1.8529027 0.1763324 19: 33 0.8274833 -0.5583860 20: 22 0.8962111 1.2784099 21: 44 -1.3718078 -1.5135198 22: 23 -1.5092635 1.1314663 23: 28 0.7587554 -1.6604635 24: 16 0.1402048 -0.6318578 25: 9 1.0336668 0.4702197 x y z
Updated on: 12-Nov-2021
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