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How to find the mean of a column summarized by other column names and common values in those columns in data.table object in R?
To find the mean of a column summarized by other column names and common values in those columns in data.table object in R, we can follow the below steps −
First of all, create a data.table object.
Then, melt the data.table object using melt function from reshape2 package.
After that, use dcast function to find the mean of a column summarized by other column names and common values in those columns.
Example
Create the data.table object
Let’s create a data.table object as shown below −
library(data.table) ID<-LETTERS[1:25] f1<-sample(0:2,25,replace=TRUE) f2<-sample(0:2,25,replace=TRUE) DV<-rnorm(25) DT<-data.table(ID,f1,f2,DV) DT
Output
On executing, the above script generates the below output(this output will vary on your system due to randomization) −
ID f1 f2 DV 1: A 0 2 -0.43728169 2: B 0 2 0.94425821 3: C 2 2 -0.36174159 4: D 2 0 -1.61506759 5: E 0 1 -0.91609245 6: F 0 0 -1.59289758 7: G 1 2 -0.91523489 8: H 0 0 -1.21002004 9: I 0 1 0.84662264 10: J 1 0 1.08053374 11: K 0 0 1.30727422 12: L 1 0 0.66752310 13: M 1 0 -1.06868916 14: N 1 0 -0.04441251 15: O 2 2 1.47602752 16: P 2 0 -1.24926504 17: Q 2 2 1.01722018 18: R 1 2 -1.88543815 19: S 0 0 -2.13536456 20: T 0 0 -0.30234787 21: U 0 1 -0.17677848 22: V 1 1 -0.34633266 23: W 2 2 0.98363925 24: X 0 0 0.78338684 25: Y 0 2 -0.35833690 ID f1 f2 DV
Melt the data.table object
Using melt function from reshape2 package to melt the data.table object DT −
library(data.table)
ID<-LETTERS[1:25]
f1<-sample(0:2,25,replace=TRUE)
f2<-sample(0:2,25,replace=TRUE)
DV<-rnorm(25)
DT<-data.table(ID,f1,f2,DV)
library(reshape2)
DT_new<-melt(DT,id.vars=c("ID","DV"))
DT_newOutput
ID DV variable value 1 A -0.43728169 f1 0 2 B 0.94425821 f1 0 3 C -0.36174159 f1 2 4 D -1.61506759 f1 2 5 E -0.91609245 f1 0 6 F -1.59289758 f1 0 7 G -0.91523489 f1 1 8 H -1.21002004 f1 0 9 I 0.84662264 f1 0 10 J 1.08053374 f1 1 11 K 1.30727422 f1 0 12 L 0.66752310 f1 1 13 M -1.06868916 f1 1 14 N -0.04441251 f1 1 15 O 1.47602752 f1 2 16 P -1.24926504 f1 2 17 Q 1.01722018 f1 2 18 R -1.88543815 f1 1 19 S -2.13536456 f1 0 20 T -0.30234787 f1 0 21 U -0.17677848 f1 0 22 V -0.34633266 f1 1 23 W 0.98363925 f1 2 24 X 0.78338684 f1 0 25 Y -0.35833690 f1 0 26 A -0.43728169 f2 2 27 B 0.94425821 f2 2 28 C -0.36174159 f2 2 29 D -1.61506759 f2 0 30 E -0.91609245 f2 1 31 F -1.59289758 f2 0 32 G -0.91523489 f2 2 33 H -1.21002004 f2 0 34 I 0.84662264 f2 1 35 J 1.08053374 f2 0 36 K 1.30727422 f2 0 37 L 0.66752310 f2 0 38 M -1.06868916 f2 0 39 N -0.04441251 f2 0 40 O 1.47602752 f2 2 41 P -1.24926504 f2 0 42 Q 1.01722018 f2 2 43 R -1.88543815 f2 2 44 S -2.13536456 f2 0 45 T -0.30234787 f2 0 46 U -0.17677848 f2 1 47 V -0.34633266 f2 1 48 W 0.98363925 f2 2 49 X 0.78338684 f2 0 50 Y -0.35833690 f2 2
Find the mean of a column summarized by other column names and common values
Using dcast function to find the mean of DV summarized by column names f1 and f2 and common values 0,1, and 2 in these group columns −
library(data.table)
ID<-LETTERS[1:25]
f1<-sample(0:2,25,replace=TRUE)
f2<-sample(0:2,25,replace=TRUE)
DV<-rnorm(25)
DT<-data.table(ID,f1,f2,DV)
library(reshape2)
DT_new<-melt(DT,id.vars=c("ID","DV"))
dcast(DT_new,variable~value,value.var="DV",fun.aggregate=mean)Output
variable 0 1 2 1 f1 -0.2706315 -0.3588644 0.04180212 2 f2 -0.4482789 -0.1481452 0.05145688
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