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How to calculate weighted mean in R?
Weighted mean is the average which is determined by finding the sum of the products of weights and the values then dividing this sum by the sum of total weights. If the weights are in proportion then the total sum of the weights should be 1. In base R, we have a function weighted.mean to find the weighted mean in which we just need to pass the vector of values and the vector of weights.
Examples
x1<-c(1,2,3,4) x1
Output
[1] 1 2 3 4
Example
Weight_x1<-c(0.25,0.25,0.25,0.25) Weight_x1
Output
[1] 0.25 0.25 0.25 0.25
Example
Weighted_Mean_x1<-weighted.mean(x1,Weight_x1) Weighted_Mean_x1
Output
[1] 2.5
Example
x2<-rpois(10,5) x2
Output
[1] 3 3 4 6 6 10 5 3 3 6
Example
Weight_x2<-rep(0.1,10) Weight_x2
Output
[1] 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1
Example
Weighted_Mean_x2<-weighted.mean(x2,Weight_x2) Weighted_Mean_x2
Output
[1] 4.9
Example
x3<-c(8,6,7) x3
Output
[1] 8 6 7
Example
Weight_x3<-c(0.5,0.3,0.2) Weight_x3
Output
[1] 0.5 0.3 0.2
Example
Weighted_Mean_x3<-weighted.mean(x3,Weight_x3) Weighted_Mean_x3
Output
[1] 7.2
Example
x4<-rnorm(5,1.5) x4
Output
[1] 3.0320687 0.9741989 0.2783473 0.8061909 0.5833953
Example
Weight_x4<-rep(0.2,5) Weight_x4
Output
[1] 0.2 0.2 0.2 0.2 0.2
Example
Weighted_Mean_x4<-weighted.mean(x4,Weight_x4) Weighted_Mean_x4
Output
[1] 1.13484
Example
x5<-c(1,2,5,7) x5
Output
[1] 1 2 5 7
Example
Weight_x5<-c(2,14,8,32) Weight_x5
Output
[1] 2 14 8 32
Example
Weighted_Mean_x5<-weighted.mean(x5,Weight_x5) Weighted_Mean_x5
Output
[1] 5.25
Example
x6<-runif(10,2,4) x6
Output
[1] 2.126860 3.194281 3.051176 3.083388 3.393485 2.139493 2.634237 2.647985 [9] 3.318430 2.765557
Example
Weight_x6<-rep(0.1,10) Weight_x6
Output
[1] 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1
Example
Weighted_Mean_x6<-weighted.mean(x6,Weight_x6) Weighted_Mean_x6
Output
[1] 2.835489
Example
x7<-c(1,2,3,4) x7
Output
[1] 1 2 3 4
Example
Weight_x7<-c(73,378,459,90) Weight_x7
Output
[1] 73 378 459 90
Example
Weighted_Mean_x7 <-weighted.mean(x7,Weight_x7) Weighted_Mean_x7
Output
[1] 2.566
Updated on 08-Sep-2020 11:50:08
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