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Aggregate () puts multiple output columns in a matrix instead

I have to calculate several quantiles for a specific variable:

> res1 <- aggregate(airquality$Wind, list(airquality$Month), function (x) quantile(x, c(0.9, 0.95, 0.975)))
> head(res1)
  Group.1   x.90%   x.95% x.97.5%
1       5 16.6000 17.5000 18.8250
2       6 14.9000 15.5600 17.3650
3       7 14.3000 14.6000 14.9000
4       8 12.6000 14.0500 14.6000
5       9 14.9600 15.5000 15.8025

The result looks good at first, but the aggregate actually returns it in a very strange way, where the last 3 columns are not data.frame columns, but represent one matrix!

> names(res1)
[1] "Group.1" "x"      
> dim(res1)
[1] 5 2
> class(res1[,2])
[1] "matrix"

This causes a lot of problems during further processing.

A few questions:

  • Why is aggregate () behaving so weird?
  • Is there any way to convince him to make the result that I expect?
  • Or am I possibly using the wrong function for this purpose? Is there any other preferred way to get the desired result?

Of course, I could do some transformation of the output of aggregate (), but I'm looking for a simpler and simpler solution.

+5
1

Q1: ?

?aggregate ( ). , , simplify.

simplify FALSE, aggregate list .

res2 <- aggregate(airquality$Wind, list(airquality$Month), function (x) 
  quantile(x, c(0.9, 0.95, 0.975)), simplify = FALSE)
str(res2)
# 'data.frame':  5 obs. of  2 variables:
#  $ Group.1: int  5 6 7 8 9
#  $ x      :List of 5
#   ..$ 1  : Named num  16.6 17.5 18.8
#   .. ..- attr(*, "names")= chr  "90%" "95%" "97.5%"
#   ..$ 32 : Named num  14.9 15.6 17.4
#   .. ..- attr(*, "names")= chr  "90%" "95%" "97.5%"
#   ..$ 62 : Named num  14.3 14.6 14.9
#   .. ..- attr(*, "names")= chr  "90%" "95%" "97.5%"
#   ..$ 93 : Named num  12.6 14.1 14.6
#   .. ..- attr(*, "names")= chr  "90%" "95%" "97.5%"
#   ..$ 124: Named num  15 15.5 15.8
#   .. ..- attr(*, "names")= chr  "90%" "95%" "97.5%"

matrix, list , , " ", "" "".

, : "" "" "airquality", , (, , quantile).

res3 <- aggregate(cbind(Wind, Temp) ~ Month, airquality, 
                  function (x) quantile(x, c(0.9, 0.95, 0.975)))
res3
#   Month Wind.90% Wind.95% Wind.97.5% Temp.90% Temp.95% Temp.97.5%
# 1     5  16.6000  17.5000    18.8250   74.000   77.500     79.500
# 2     6  14.9000  15.5600    17.3650   87.300   91.100     92.275
# 3     7  14.3000  14.6000    14.9000   89.000   91.500     92.000
# 4     8  12.6000  14.0500    14.6000   94.000   95.000     96.250
# 5     9  14.9600  15.5000    15.8025   91.100   92.550     93.000

matrix - - :

res3$Temp
#       90%   95%  97.5%
# [1,] 74.0 77.50 79.500
# [2,] 87.3 91.10 92.275
# [3,] 89.0 91.50 92.000
# [4,] 94.0 95.00 96.250
# [5,] 91.1 92.55 93.000

Q2: data.frame?

a list matrix . "" matrix , do.call(data.frame, ...):

do.call(data.frame, res1)
#   Group.1 x.90. x.95. x.97.5.
# 1       5 16.60 17.50 18.8250
# 2       6 14.90 15.56 17.3650
# 3       7 14.30 14.60 14.9000
# 4       8 12.60 14.05 14.6000
# 5       9 14.96 15.50 15.8025
str(.Last.value)
# 'data.frame':  5 obs. of  4 variables:
#  $ Group.1: int  5 6 7 8 9
#  $ x.90.  : num  16.6 14.9 14.3 12.6 15
#  $ x.95.  : num  17.5 15.6 14.6 14.1 15.5
#  $ x.97.5.: num  18.8 17.4 14.9 14.6 15.8a

Q3: ?

R, , . data.table, :

library(data.table)
as.data.table(airquality)[, as.list(quantile(Wind, c(.9, .95, .975))), 
                          by = Month]
#    Month   90%   95%   97.5%
# 1:     5 16.60 17.50 18.8250
# 2:     6 14.90 15.56 17.3650
# 3:     7 14.30 14.60 14.9000
# 4:     8 12.60 14.05 14.6000
# 5:     9 14.96 15.50 15.8025
str(.Last.value)
# Classes ‘data.table’ and 'data.frame':  5 obs. of  4 variables:
#  $ Month: int  5 6 7 8 9
#  $ 90%  : num  16.6 14.9 14.3 12.6 15
#  $ 95%  : num  17.5 15.6 14.6 14.1 15.5
#  $ 97.5%: num  18.8 17.4 14.9 14.6 15.8
#  - attr(*, ".internal.selfref")=<externalptr>
+8

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