Is there a way to calculate an asymmetrical mean (e. g. from percentile 0.05 to 0.5) by group using the aggregate command? R-STUDIO

I am calculating the Tukey outlier detection algorythm on a data set of prices.

The thing is that I need it to be calculated by group (another variable included in the same data set), which works perfectly fine with the aggregate command up until I need to calculate a mean using only the data between percentile 5 to the median and one using only the data from the median to percentile 95.

As far as I know, the command goes this way: aggregate(doc$x, by=list(doc$group), FUN=mean, trim = 0.05), if the mean was trimmed symmetrically taking the upper and lower 5% (total 10%) from the data before printing the result. I don't know how to go through the next steps where I need to calculate the upper and lower mean taking the median as a division point, still keeping the upper and lower 5% off.

medlow <- aggregate(doc1$`rp`, by=list(doc1$`Código Artículo`), FUN=mean,trim =c(0.05,0.5))
medup <- aggregate(doc1$`rp`, by=list(doc1$`Código Artículo`), FUN=mean,trim =c(0.5,0.95))

medtrunc <- aggregate(doc1$`rp`, by=list(doc1$`Código Artículo`), FUN=mean,trim = 0.05)

I expect the output to be the number I need for each group, but it goes

Error in mean.default(X[[i]], ...) : 'trim' must be numeric of length one.

Solution

First, I think you are using aggregate and trim the wrong way. 'trim' must be numeric of length one means that you can only exclude a particular fraction of data from both upper and lower tails of the distribution:

df = data.frame(
  gender = c(
    "male","male","male","male","female","female","female", "female"
    ),
  score = rnorm(8, 10, 2)
  )
aggregate(score ~ gender, data = df, mean, trim = 0.1)

  gender     score
1 female 11.385263
2   male  9.954465

For the splitting based on the median and calculating trimmed mean for the split data, you can easily split your data frame by making a new variable MedianSplit by a simple for loop:

df$MedianSplit <- 0
for (i in 1:nrow(df)) {
  if (df$score[i] <= median(df$score)) {
    df$MedianSplit[i] = "lower" 
  } else {
    df$MedianSplit[i] = "upper"
  }
}

df



gender     score MedianSplit
1   male  7.062605       lower
2   male  9.373052       upper
3   male  6.592681       lower
4   male  7.298971       lower
5 female  7.795813       lower
6 female  7.800914       upper
7 female 12.431028       upper
8 female 10.661753       upper

Then, use aggregate to compute the trimmed means:

For data below than median (i.e., [0, 0.5])

aggregate(
  score ~ gender, 
  data = df[ which(df$MedianSplit == "lower"), ], 
  mean, trim = 0.05
)

  gender    score
1 female 7.795813
2   male 6.984752

and for those above the median (i.e., [0.5, 1]):

aggregate( score ~ gender, data = df[ which(df$MedianSplit == "upper"), ], mean, trim = 0.05 )

  gender     score
1 female 10.297898
2   male  9.373052