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Create a rolling mean column by displaying means to corresponding values in R data frame.
To create a rolling mean column by displaying means to corresponding values in R data frame, we can use ave function with rep function.
For Example, if we have a data frame called df that contains a numerical column say X then we can create a rolling mean column for every 5 values by using the below mentioned command −
df$Rolling_M_5<-ave(df$X,rep(1:(nrow(df)/5),each=5),FUN=function(x){mean(x)})Example 1
Following snippet creates a sample data frame −
x<-rnorm(20) df1<-data.frame(x) df1
The following dataframe is created
x 1 0.54695282 2 -2.14433689 3 0.60614761 4 1.42172678 5 1.45355367 6 0.27851529 7 0.43483588 8 3.10239061 9 1.26224723 10 -0.04519611 11 -1.22239574 12 1.04256580 13 -0.44032532 14 1.41468774 15 -0.15795517 16 -0.18538886 17 -0.82291711 18 -0.56077655 19 1.75063692 20 0.23353949
To create a rolling mean column in df1 with mean displayed corresponding to each value on the above created data frame, add the following code to the above snippet −
x<-rnorm(20)
df1<-data.frame(x)
df1$Rolling_Mean_4<-ave(df1$x,rep(1:(nrow(df1)/4),each=4),FUN=function(x){mean(x)})
df1Output
If you execute all the above given snippets as a single program, it generates the following Output −
x Rolling_Mean_4 1 0.54695282 0.1076226 2 -2.14433689 0.1076226 3 0.60614761 0.1076226 4 1.42172678 0.1076226 5 1.45355367 1.3173239 6 0.27851529 1.3173239 7 0.43483588 1.3173239 8 3.10239061 1.3173239 9 1.26224723 0.2593053 10 -0.04519611 0.2593053 11 -1.22239574 0.2593053 12 1.04256580 0.2593053 13 -0.44032532 0.1577546 14 1.41468774 0.1577546 15 -0.15795517 0.1577546 16 -0.18538886 0.1577546 17 -0.82291711 0.1501207 18 -0.56077655 0.1501207 19 1.75063692 0.1501207 20 0.23353949 0.1501207
Example 2
Following snippet creates a sample data frame −
y<-rpois(20,10) df2<-data.frame(y) df2
The following dataframe is created
y 1 6 2 11 3 12 4 15 5 5 6 12 7 11 8 15 9 13 10 9 11 9 12 4 13 9 14 10 15 7 16 8 17 6 18 9 19 13 20 11
To create a rolling mean column in df2 with mean displayed corresponding to each value on the above created data frame, add the following code to the above snippet −
y<-rpois(20,10)
df2<-data.frame(y)
df2$Rolling_Mean_2<-ave(df2$y,rep(1:(nrow(df2)/2),each=2),FUN=function(x){mean(x)})
df2Output
If you execute all the above given snippets as a single program, it generates the following Output −
y Rolling_Mean_2 1 6 8.5 2 11 8.5 3 12 13.5 4 15 13.5 5 5 8.5 6 12 8.5 7 11 13.0 8 15 13.0 9 13 11.0 10 9 11.0 11 9 6.5 12 4 6.5 13 9 9.5 14 10 9.5 15 7 7.5 16 8 7.5 17 6 7.5 18 9 7.5 19 13 12.0 20 11 12.0
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