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
*LEIBNIZ-ZENTRUM*
*FÜR ARCHÄOLOGIE*
*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.calan...@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
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museum of the Leibniz Association.
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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
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