I need to calculate the population median for several time periods by combining the medians of 10 different samples in each period (dataset Median). Each of the sample median has been obtained by taking a different number of observations (dataset Observation).
Median - dataset
Time1 Time2 Time3 Time4 Time5
Sample1 60000 71139 70000 75000 75000
Sample2 80000 88000 87750 88500 90000
Sample3 66000 73325 73000 78126 75000
Sample4 60000 74000 72000 75500 73000
Sample5 50500 60000 60000 66750 81500
Sample6 60000 70000 72000 78500 80000
Sample7 50000 60000 59999 63000 60000
Sample8 53000 55000 58300 59995 64500
Sample9 92529 111000 115000 120063 118000
Sample10 92500 115000 101000 104100 110075
Observations - dataset
Time1 Time2 Time3 Time4 Time5
Sample1 159 202 174 134 172
Sample2 148 178 148 121 140
Sample3 563 680 652 513 678
Sample4 554 634 518 512 595
Sample5 343 415 347 270 390
Sample6 738 954 769 720 825
Sample7 704 949 863 648 762
Sample8 595 681 640 517 663
Sample9 517 782 610 504 472
Sample10 627 733 621 493 512
I am trying to generate a vector with the Median[1:1] repeated Observations[1:1] times, this vector need to be concatenate to another vector Median[1:2] repeated Observations[1:2] times, then concatenate the vector to another vector Median[1:3] repeated Observations[1:3] times , and so on...
I aim to generate 5 vectors (as many as columns - periods) each of these vectors with a length equal to the aggregate number of sample observations in each time frame.
for (i in 1:ncol(Median)) {
for (j in 1:nrow(Median)) {
vector_median=(seq(as.numeric(Med[i,j]),as.numeric(Med [i,j]),length.out=as.numeric(Observations[i,j])))
}
}
Consider a nested mapply (the multiple-input version of apply family) where you pass both Med and Observations columns in pairwise iteration and then pass each of the columns corresponding Sample values in a pairwise iteration into the rep() function:
Data
txt = " Time1 Time2 Time3 Time4 Time5
Sample1 60000 71139 70000 75000 75000
Sample2 80000 88000 87750 88500 90000
Sample3 66000 73325 73000 78126 75000
Sample4 60000 74000 72000 75500 73000
Sample5 50500 60000 60000 66750 81500
Sample6 60000 70000 72000 78500 80000
Sample7 50000 60000 59999 63000 60000
Sample8 53000 55000 58300 59995 64500
Sample9 92529 111000 115000 120063 118000
Sample10 92500 115000 101000 104100 110075 "
Med = read.table(text=txt, header=TRUE)
txt = "Time1 Time2 Time3 Time4 Time5
Sample1 159 202 174 134 172
Sample2 148 178 148 121 140
Sample3 563 680 652 513 678
Sample4 554 634 518 512 595
Sample5 343 415 347 270 390
Sample6 738 954 769 720 825
Sample7 704 949 863 648 762
Sample8 595 681 640 517 663
Sample9 517 782 610 504 472
Sample10 627 733 621 493 512"
Obs = read.table(text=txt, header=TRUE)
Process
replicate_medians <- function(m,o){
mapply(function(m_sub, o_sub) rep(m_sub, times=o_sub), m, o)
}
output <- mapply(function(x,y) unlist(replicate_medians(x,y)), Med, Obs, SIMPLIFY=FALSE)
# EQUIVALENT WITH Map() WRAPPER
output <- Map(function(x,y) unlist(replicate_medians(x,y)), Med, Obs)
Output (returns a list of 5 named numeric vectors)
str(output)
# List of 5
# $ Time1: int [1:4948] 60000 60000 60000 60000 60000 60000 60000 60000 60000 60000 ...
# $ Time2: int [1:6208] 71139 71139 71139 71139 71139 71139 71139 71139 71139 71139 ...
# $ Time3: int [1:5342] 70000 70000 70000 70000 70000 70000 70000 70000 70000 70000 ...
# $ Time4: int [1:4432] 75000 75000 75000 75000 75000 75000 75000 75000 75000 75000 ...
# $ Time5: int [1:5209] 75000 75000 75000 75000 75000 75000 75000 75000 75000 75000 ...
length(output$Time1[output$Time1==60000])
#[1] 1451 <---- THREE SAMPLES WITH THIS MEDIAN: 159 + 554 + 738 = 1,451
length(output$Time1[output$Time1==80000])
# [1] 148
length(output$Time1[output$Time1==66000])
# [1] 563