[R] aggregate formula - differing results

Mon Sep 4 13:56:34 CEST 2023

Às 12:51 de 04/09/2023, Ivan Calandra escreveu:
> Thanks Rui for your help; that would be one possibility indeed.
> 
> But am I the only one who finds that behavior of aggregate() completely 
> unexpected and confusing? Especially considering that dplyr::summarise() 
> and doBy::summaryBy() deal with NAs differently, even though they all 
> use mean(na.rm = TRUE) to calculate the group stats.
> 
> Best wishes,
> Ivan
> 
> On 04/09/2023 13:46, Rui Barradas wrote:
>> Às 10:44 de 04/09/2023, Ivan Calandra escreveu:
>>> Dear useRs,
>>>
>>> I have just stumbled across a behavior in aggregate() that I cannot 
>>> explain. Any help would be appreciated!
>>>
>>> Sample data:
>>> my_data <- structure(list(ID = c("FLINT-1", "FLINT-10", "FLINT-100", 
>>> "FLINT-101", "FLINT-102", "HORN-10", "HORN-100", "HORN-102", 
>>> "HORN-103", "HORN-104"), EdgeLength = c(130.75, 168.77, 142.79, 
>>> 130.1, 140.41, 121.37, 70.52, 122.3, 71.01, 104.5), SurfaceArea = 
>>> c(1736.87, 1571.83, 1656.46, 1247.18, 1177.47, 1169.26, 444.61, 
>>> 1791.48, 461.15, 1127.2), Length = c(44.384, 29.831, 43.869, 48.011, 
>>> 54.109, 41.742, 23.854, 32.075, 21.337, 35.459), Width = c(45.982, 
>>> 67.303, 52.679, 26.42, 25.149, 33.427, 20.683, 62.783, 26.417, 
>>> 35.297), PLATWIDTH = c(38.84, NA, 15.33, 30.37, 11.44, 14.88, 13.86, 
>>> NA, NA, 26.71), PLATTHICK = c(8.67, NA, 7.99, 11.69, 3.3, 16.52, 
>>> 4.58, NA, NA, 9.35), EPA = c(78, NA, 78, 54, 72, 49, 56, NA, NA, 56), 
>>> THICKNESS = c(10.97, NA, 9.36, 6.4, 5.89, 11.05, 4.9, NA, NA, 10.08), 
>>> WEIGHT = c(34.3, NA, 25.5, 18.6, 14.9, 29.5, 4.5, NA, NA, 23), RAWMAT 
>>> = c("FLINT", "FLINT", "FLINT", "FLINT", "FLINT", "HORNFELS", 
>>> "HORNFELS", "HORNFELS", "HORNFELS", "HORNFELS")), row.names = c(1L, 
>>> 2L, 3L, 4L, 5L, 111L, 112L, 113L, 114L, 115L), class = "data.frame")
>>>
>>> 1) Simple aggregation with 2 variables:
>>> aggregate(cbind(Length, Width) ~ RAWMAT, data = my_data, FUN = mean, 
>>> na.rm = TRUE)
>>>
>>> 2) Using the dot notation - different results:
>>> aggregate(. ~ RAWMAT, data = my_data[-1], FUN = mean, na.rm = TRUE)
>>>
>>> 3) Using dplyr, I get the same results as #1:
>>> group_by(my_data, RAWMAT) %>%
>>>    summarise(across(c("Length", "Width"), ~ mean(.x, na.rm = TRUE)))
>>>
>>> 4) It gets weirder: using all columns in #1 give the same results as 
>>> in #2 but different from #1 and #3
>>> aggregate(cbind(EdgeLength, SurfaceArea, Length, Width, PLATWIDTH, 
>>> PLATTHICK, EPA, THICKNESS, WEIGHT) ~ RAWMAT, data = my_data, FUN = 
>>> mean, na.rm = TRUE)
>>>
>>> So it seems it is not only due to the notation (cbind() vs. dot). Is 
>>> it a bug? A peculiar thing in my dataset? I tend to think this could 
>>> be due to some variables (or their names) as all notations seem to 
>>> agree when I remove some variables (although I haven't found out 
>>> which variable(s) is (are) at fault), e.g.:
>>>
>>> my_data2 <- structure(list(ID = c("FLINT-1", "FLINT-10", "FLINT-100", 
>>> "FLINT-101", "FLINT-102", "HORN-10", "HORN-100", "HORN-102", 
>>> "HORN-103", "HORN-104"), EdgeLength = c(130.75, 168.77, 142.79, 
>>> 130.1, 140.41, 121.37, 70.52, 122.3, 71.01, 104.5), SurfaceArea = 
>>> c(1736.87, 1571.83, 1656.46, 1247.18, 1177.47, 1169.26, 444.61, 
>>> 1791.48, 461.15, 1127.2), Length = c(44.384, 29.831, 43.869, 48.011, 
>>> 54.109, 41.742, 23.854, 32.075, 21.337, 35.459), Width = c(45.982, 
>>> 67.303, 52.679, 26.42, 25.149, 33.427, 20.683, 62.783, 26.417, 
>>> 35.297), RAWMAT = c("FLINT", "FLINT", "FLINT", "FLINT", "FLINT", 
>>> "HORNFELS", "HORNFELS", "HORNFELS", "HORNFELS", "HORNFELS")), 
>>> row.names = c(1L, 2L, 3L, 4L, 5L, 111L, 112L, 113L, 114L, 115L), 
>>> class = "data.frame")
>>>
>>> aggregate(cbind(EdgeLength, SurfaceArea, Length, Width) ~ RAWMAT, 
>>> data = my_data2, FUN = mean, na.rm = TRUE)
>>>
>>> aggregate(. ~ RAWMAT, data = my_data2[-1], FUN = mean, na.rm = TRUE)
>>>
>>> group_by(my_data2, RAWMAT) %>%
>>>    summarise(across(where(is.numeric), ~ mean(.x, na.rm = TRUE)))
>>>
>>>
>>> Thank you in advance for any hint.
>>> Best wishes,
>>> Ivan
>>>
>>>
>>>
>>>
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>> Hello,
>>
>> You can define a vector of the columns of interest and subset the data 
>> with it. Then the default na.action = na.omit will no longer remove 
>> the rows with NA vals in at least one column and the results are the 
>> same.
>>
>> However, this will not give the mean values of the other numeric 
>> columns, just of those two.
>>
>>
>>
>> # define a vector of columns of interest
>> cols <- c("Length", "Width", "RAWMAT")
>>
>> # 1) Simple aggregation with 2 variables, select cols:
>> aggregate(cbind(Length, Width) ~ RAWMAT, data = my_data[cols], FUN = 
>> mean, na.rm = TRUE)
>>
>> # 2) Using the dot notation - if cols are selected, equal results:
>> aggregate(. ~ RAWMAT, data = my_data[cols], FUN = mean, na.rm = TRUE)
>>
>> # 3) Using dplyr, the results are now the same results as #1 and #2:
>> my_data %>%
>>   select(all_of(cols)) %>%
>>   group_by(RAWMAT) %>%
>>   summarise(across(c("Length", "Width"), ~ mean(.x, na.rm = TRUE)))
>>
>>
>> Hope this helps,
>>
>> Rui Barradas
>>
Hello,

Puzzling at first yes, unexpected no, it's documented behavior.

This is the result of how the aggregate works, by first applying 
na.action to the data and only then applying the function to each column 
in the formula's LHS.

dplyr works column by column, so there is no na.action involved.

Hope this helps,

Rui Barradas