CODEWITHSUNDEEP
rlapplysapplymapply
Stephen
Stephen

Reputation: 324

Way to do this using apply?

I want to take an average for each row across different data frames. Does anyone know of a more clever way to do this using apply statements? Sorry for the wall of code.

Youl would need a vector of 1000:1006 for each hiXXXX file and then a vector 2:13 for the columns. I have used mapply for something weird like this before so maybe that could do it somehow?

for (i in 1:nrow(subavg)) {
    subavg[i,c(2)] <- mean(c(hi1000[i,c(2)],hi1001[i,c(2)],hi1002[i,c(2)],hi1003[i,c(2)],hi1004[i,c(2)],hi1005[i,c(2)],hi1006[i,c(2)]))
    subavg[i,c(3)] <- mean(c(hi1000[i,c(3)],hi1001[i,c(3)],hi1002[i,c(3)],hi1003[i,c(3)],hi1004[i,c(3)],hi1005[i,c(3)],hi1006[i,c(3)]))
    subavg[i,c(4)] <- mean(c(hi1000[i,c(4)],hi1001[i,c(4)],hi1002[i,c(4)],hi1003[i,c(4)],hi1004[i,c(4)],hi1005[i,c(4)],hi1006[i,c(4)]))
    subavg[i,c(5)] <- mean(c(hi1000[i,c(5)],hi1001[i,c(5)],hi1002[i,c(5)],hi1003[i,c(5)],hi1004[i,c(5)],hi1005[i,c(5)],hi1006[i,c(5)]))
    subavg[i,c(6)] <- mean(c(hi1000[i,c(6)],hi1001[i,c(6)],hi1002[i,c(6)],hi1003[i,c(6)],hi1004[i,c(6)],hi1005[i,c(6)],hi1006[i,c(6)]))
    subavg[i,c(7)] <- mean(c(hi1000[i,c(7)],hi1001[i,c(7)],hi1002[i,c(7)],hi1003[i,c(7)],hi1004[i,c(7)],hi1005[i,c(7)],hi1006[i,c(7)]))
    subavg[i,c(8)] <- mean(c(hi1000[i,c(8)],hi1001[i,c(8)],hi1002[i,c(8)],hi1003[i,c(8)],hi1004[i,c(8)],hi1005[i,c(8)],hi1006[i,c(8)]))
    subavg[i,c(9)] <- mean(c(hi1000[i,c(9)],hi1001[i,c(9)],hi1002[i,c(9)],hi1003[i,c(9)],hi1004[i,c(9)],hi1005[i,c(9)],hi1006[i,c(9)]))
    subavg[i,c(10)] <- mean(c(hi1000[i,c(10)],hi1001[i,c(10)],hi1002[i,c(10)],hi1003[i,c(10)],hi1004[i,c(10)],hi1005[i,c(10)],hi1006[i,c(10)]))
    subavg[i,c(11)] <- mean(c(hi1000[i,c(11)],hi1001[i,c(11)],hi1002[i,c(11)],hi1003[i,c(11)],hi1004[i,c(11)],hi1005[i,c(11)],hi1006[i,c(11)]))
    subavg[i,c(12)] <- mean(c(hi1000[i,c(12)],hi1001[i,c(12)],hi1002[i,c(12)],hi1003[i,c(12)],hi1004[i,c(12)],hi1005[i,c(12)],hi1006[i,c(12)]))
    subavg[i,c(13)] <- mean(c(hi1000[i,c(13)],hi1001[i,c(13)],hi1002[i,c(13)],hi1003[i,c(13)],hi1004[i,c(13)],hi1005[i,c(13)],hi1006[i,c(13)]))
}

Upvotes: 2

Views: 48

Answers (1)

akrun
akrun

Reputation: 887311

As there are only 7 datasets, we can use that as arguments for Map, then cbind it, and get the rowMeans

Map(function(...) rowMeans(cbind(...)), hi1000, hi1001, hi1002, hi1003, 
                     hi1004, hi1005, hi1006)

Or use + with Reduce after getting the datasets in a list and then divide by the total number of datasets, i.e. 7 

Reduce(`+`, mget(paste0("hi", 1000:1006)))/7

The second solution is more compact, but if we have NAs in the dataset, it is better to use the first one as the rowMeans have na.rm argument. By default it is FALSE, but we can set it to TRUE.

Upvotes: 4

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