How to calculate mean value of all columns of datarame [duplicate] - r

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calculate the mean for each column of a matrix in R
(10 answers)
Closed last year.
I have a data frame and I want to calculate the mean of all columns and save it into a new dataframe. I found this solution calculate the mean for each column of a matrix in R however, this is only for matrix and not dataframe
structure(list(TotFlArea = c(1232, 596, 708, 1052, 716), logg_weighted_assess = c(13.7765298160156,
13.1822275291412, 13.328376420438, 13.3076293132057, 13.5164823091252
), TypeDwel1.2.Duplex = c(0, 0, 0, 0, 0), TypeDwelApartment.Condo = c(0,
1, 1, 1, 1), TypeDwelTownhouse = c(1, 0, 0, 0, 0), Age_new.70 = c(0,
0, 0, 0, 0), Age_new0.1 = c(0, 0, 0, 0, 0), Age_new16.40 = c(1,
1, 0, 1, 0), Age_new2.5 = c(0, 0, 0, 0, 0), Age_new41.70 = c(0,
0, 0, 0, 0), Age_new6.15 = c(0, 0, 1, 0, 1), LandFreehold = c(1,
1, 1, 0, 1), LandLeasehold.prepaid = c(0, 0, 0, 1, 0), LandOthers = c(0,
0, 0, 0, 0), cluster_K_mean.1 = c(0, 0, 0, 0, 0)), row.names = c("1",
"2", "3", "4", "5"), class = "data.frame")
Can you please advise how I can do this?
Note: my data frame can have NA values which should be excluded from mean calculation

Answer 1

As #akrun pointed out. Also another alternative
apply(df, 2, mean)
where 2 means by column and 1 is by row.
However, besides its flexibility (e.g. changing from mean to mode or applying to selected columns only apply(df[,c('a', 'b')], 2, mean)) below shows the disadvantage to using apply (in terms of speed)
library(data.table)
library(microbenchmark)
# dummy data
x <- 1e7
df <- data.table(a = 1:x )
y <- letters[2:10]
df[, (y) := lapply(2:10, \(i) a+i)]
# benchmark
z <-
microbenchmark(colMeans = {colMeans(df)}
, apply = {apply(df, 2, mean)}
, times = 30
)
plot(z)