substracting a vector from a data.frame in R-CodePudding

I have the following dataset, called 'test'

structure(list(time = c(2, 3.9, 5.8, 7.8, 9.7, 11.7, 13.6, 15.5, 
17.5, 19.4, 21.4, 23.3, 25.3, 27.2, 29.2, 31.2, 33.1, 35.1, 37.1, 
39, 41, 42.9, 44.9, 46.8, 48.7, 50.7, 52.6, 54.6, 56.5, 58.5, 
60.4, 62.4, 64.3, 66.2, 68.2, 70.1, 72.1, 74, 76, 77.9, 79.8, 
81.8, 83.7, 85.7, 87.6, 89.5, 91.5, 93.4, 95.4, 97.3), v = c(14.82, 
14.804, 14.82, 14.82, 14.804, 14.82, 14.812, 14.804, 14.8, 14.808, 
14.8, 14.804, 15.844, 15.848, 15.848, 15.852, 15.852, 15.848, 
15.852, 15.852, 15.852, 15.852, 15.856, 15.852, 15.852, 15.856, 
15.856, 15.856, 15.856, 15.856, 15.856, 15.856, 15.852, 15.852, 
15.852, 15.852, 15.856, 15.856, 15.856, 15.86, 15.856, 15.86, 
15.864, 15.856, 15.86, 15.86, 15.86, 15.856, 15.86, 15.856), 
    a = c(1.5, 1.476, 1.5, 1.491, 1.452, 1.476, 1.478, 1.44, 
    1.454, 1.438, 1.442, 1.471, 0.002, 0.002, 0.002, 0.002, 0.002, 
    0.002, 0.002, 0.002, 0.001, 0.002, 0.002, 0.001, 0.001, 0.002, 
    0.002, 0.002, 0.001, 0.001, 0.001, 0.002, 0.002, 0.002, 0.001, 
    0.002, 0.002, 0.002, 0.001, 0.002, 0.002, 0.002, 0.002, 0.002, 
    0.001, 0.002, 0.002, 0.002, 0.001, 0.002), t1 = c(14.61, 
    14.61, 14.61, 14.61, 14.61, 14.61, 14.61, 14.62, 14.62, 14.63, 
    14.64, 14.65, 14.67, 14.7, 14.72, 14.75, 14.78, 14.82, 14.85, 
    14.89, 14.93, 14.97, 15.01, 15.05, 15.09, 15.13, 15.17, 15.2, 
    15.23, 15.26, 15.29, 15.32, 15.34, 15.37, 15.39, 15.41, 15.43, 
    15.45, 15.46, 15.48, 15.49, 15.51, 15.52, 15.53, 15.54, 15.55, 
    15.56, 15.56, 15.57, 15.58), t2 = c(14.63, 14.62, 14.62, 
    14.62, 14.62, 14.62, 14.62, 14.63, 14.63, 14.64, 14.65, 14.67, 
    14.69, 14.71, 14.74, 14.78, 14.82, 14.86, 14.9, 14.95, 15, 
    15.06, 15.11, 15.16, 15.22, 15.27, 15.31, 15.36, 15.4, 15.44, 
    15.48, 15.51, 15.55, 15.58, 15.61, 15.63, 15.65, 15.68, 15.7, 
    15.71, 15.73, 15.75, 15.76, 15.77, 15.78, 15.79, 15.8, 15.81, 
    15.82, 15.83), t3 = c(14.63, 14.63, 14.63, 14.63, 14.63, 
    14.63, 14.63, 14.63, 14.64, 14.65, 14.66, 14.68, 14.7, 14.73, 
    14.75, 14.79, 14.83, 14.86, 14.91, 14.95, 15, 15.04, 15.09, 
    15.13, 15.18, 15.22, 15.26, 15.3, 15.33, 15.37, 15.4, 15.43, 
    15.46, 15.48, 15.5, 15.53, 15.54, 15.56, 15.58, 15.59, 15.61, 
    15.62, 15.63, 15.64, 15.65, 15.66, 15.67, 15.67, 15.68, 15.69
    ), t4 = c(14.65, 14.65, 14.65, 14.65, 14.64, 14.64, 14.65, 
    14.65, 14.66, 14.67, 14.68, 14.7, 14.73, 14.75, 14.79, 14.82, 
    14.86, 14.91, 14.95, 15, 15.05, 15.1, 15.16, 15.21, 15.25, 
    15.3, 15.35, 15.39, 15.43, 15.46, 15.5, 15.53, 15.56, 15.59, 
    15.62, 15.64, 15.66, 15.68, 15.7, 15.72, 15.73, 15.75, 15.76, 
    15.77, 15.78, 15.79, 15.8, 15.81, 15.82, 15.82), t5 = c(14.65, 
    14.65, 14.65, 14.65, 14.65, 14.65, 14.66, 14.66, 14.67, 14.69, 
    14.7, 14.73, 14.75, 14.78, 14.81, 14.85, 14.89, 14.93, 14.97, 
    15.02, 15.06, 15.11, 15.15, 15.2, 15.24, 15.28, 15.32, 15.36, 
    15.4, 15.43, 15.46, 15.49, 15.52, 15.54, 15.57, 15.59, 15.61, 
    15.63, 15.64, 15.66, 15.68, 15.69, 15.7, 15.71, 15.72, 15.73, 
    15.74, 15.75, 15.76, 15.76), t6 = c(14.63, 14.63, 14.63, 
    14.63, 14.63, 14.63, 14.63, 14.64, 14.65, 14.66, 14.67, 14.69, 
    14.72, 14.74, 14.77, 14.8, 14.84, 14.88, 14.91, 14.95, 15, 
    15.04, 15.08, 15.12, 15.16, 15.2, 15.23, 15.27, 15.3, 15.33, 
    15.36, 15.39, 15.41, 15.44, 15.46, 15.48, 15.5, 15.52, 15.54, 
    15.55, 15.57, 15.58, 15.59, 15.61, 15.62, 15.63, 15.64, 15.65, 
    15.65, 15.66), t7 = c(14.64, 14.64, 14.64, 14.64, 14.64, 
    14.64, 14.64, 14.64, 14.65, 14.66, 14.67, 14.68, 14.7, 14.72, 
    14.75, 14.77, 14.8, 14.83, 14.86, 14.9, 14.93, 14.97, 15, 
    15.04, 15.07, 15.11, 15.14, 15.17, 15.21, 15.24, 15.26, 15.29, 
    15.32, 15.34, 15.36, 15.38, 15.41, 15.42, 15.44, 15.46, 15.47, 
    15.49, 15.5, 15.52, 15.53, 15.54, 15.55, 15.56, 15.57, 15.58
    ), t8 = c(14.6, 14.6, 14.6, 14.6, 14.6, 14.6, 14.61, 14.61, 
    14.62, 14.63, 14.64, 14.66, 14.68, 14.71, 14.74, 14.77, 14.8, 
    14.84, 14.88, 14.92, 14.96, 15, 15.03, 15.07, 15.11, 15.14, 
    15.17, 15.21, 15.24, 15.26, 15.29, 15.31, 15.34, 15.36, 15.38, 
    15.39, 15.41, 15.43, 15.44, 15.45, 15.47, 15.48, 15.49, 15.5, 
    15.51, 15.52, 15.52, 15.53, 15.54, 15.54)), row.names = c(NA, 
50L), class = "data.frame")

This dataset has 8 columns (t1-t8) on which I want to do a correction. This is done by taking the mean of the first 5 rows per column: colmeans = colMeans(test[1:5,4:11]) and substracting those means from the dataset test[, paste0('t', 1:8)] = test[, paste0('t', 1:8)] - colmeans

However, something is going wrong in this substraction, but I can't figure out what. The outcome of the code above doesn't match the outcome when done by hand for a single row: test2[1, paste0('t', 1:8)] - colmeans (test2 is the same as the original test dataset)

The correct outcome of the substraction for the first line is:

  t1    t2 t3    t4 t5 t6 t7 t8
>1  0 0.008  0 0.002  0  0  0  0

But the outcome I get with the code is wrong:

         t1     t2     t3    t4    t5     t6     t7     t8
>1      0.000 -0.018 -0.010 0.028 0.000  0.030  0.010 -0.030

I know the outcome done by hand is correct, So what is wrong with my code?

CodePudding user response：

This is because the computation is done across rows (instead of columns). You could tranpose twice to correct the problem:

t(t(test[, paste0('t', 1:8)]) - colmeans)

     t1     t2   t3     t4   t5   t6   t7   t8
1  0.00  0.008 0.00  0.002 0.00 0.00 0.00 0.00
2  0.00 -0.002 0.00  0.002 0.00 0.00 0.00 0.00
3  0.00 -0.002 0.00  0.002 0.00 0.00 0.00 0.00
4  0.00 -0.002 0.00  0.002 0.00 0.00 0.00 0.00
5  0.00 -0.002 0.00 -0.008 0.00 0.00 0.00 0.00
6  0.00 -0.002 0.00 -0.008 0.00 0.00 0.00 0.00
7  0.00 -0.002 0.00  0.002 0.01 0.00 0.00 0.01
8  0.01  0.008 0.00  0.002 0.01 0.01 0.00 0.01
9  0.01  0.008 0.01  0.012 0.02 0.02 0.01 0.02
10 0.02  0.018 0.02  0.022 0.04 0.03 0.02 0.03