aggregate formula - differing results

?s 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

 ????*LEIBNIZ-ZENTRUM*
*F?R ARCH?OLOGIE*

*Dr. Ivan CALANDRA*
**Head of IMPALA (IMaging Platform At LeizA)

*MONREPOS* Archaeological Research Centre, Schloss Monrepos
56567 Neuwied, Germany

T: +49 2631 9772 243
T: +49 6131 8885 543
ivan.calandra at leiza.de

leiza.de <http://www.leiza.de/>
<http://www.leiza.de/>
ORCID <https://orcid.org/0000-0003-3816-6359>
ResearchGate
<https://www.researchgate.net/profile/Ivan_Calandra>

LEIZA is a foundation under public law of the State of 
Rhineland-Palatinate and the City of Mainz. Its headquarters are in 
Mainz. Supervision is carried out by the Ministry of Science and Health 
of the State of Rhineland-Palatinate. LEIZA is a research museum of the 
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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

aggregate formula - differing results

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