calculate the mean for each column of a matrix in R

I am working on R in R studio. I need to calculate the mean for each column of a data frame.

 cluster1  // 5 by 4 data frame
 mean(cluster1) // 

I got :

  Warning message:
  In mean.default(cluster1) :
  argument is not numeric or logical: returning NA

But I can use

  mean(cluster1[[1]])

to get the mean of the first column.

How to get means for all columns ?

Any help would be appreciated.

You can use colMeans:

### Sample data
set.seed(1)
m <- data.frame(matrix(sample(100, 20, replace = TRUE), ncol = 4))

### Your error
mean(m)
# [1] NA
# Warning message:
# In mean.default(m) : argument is not numeric or logical: returning NA

### The result using `colMeans`
colMeans(m)
#   X1   X2   X3   X4 
# 47.0 64.4 44.8 67.8 

You can use 'apply' to run a function or the rows or columns of a matrix or numerical data frame:

cluster1 <- data.frame(a=1:5, b=11:15, c=21:25, d=31:35)

apply(cluster1,2,mean)  # applies function 'mean' to 2nd dimension (columns)

apply(cluster1,1,mean)  # applies function to 1st dimension (rows)

sapply(cluster1, mean)  # also takes mean of columns, treating data frame like list of vectors

In case you have NA's:

sapply(data, mean, na.rm = T)      # Returns a vector (with names)   
lapply(data, mean, na.rm = T)      # Returns a list  

Remember that "mean" needs numeric data. If you have mixed class data, then use:

numdata<-data[sapply(data, is.numeric)]  
sapply(numdata, mean, na.rm = T)  # Returns a vector
lapply(numdata, mean, na.rm = T)  # Returns a list  

Another way is to use purrr package

# example data like what is said above

@A Handcart And Mohair

set.seed(1)
m <- data.frame(matrix(sample(100, 20, replace = TRUE), ncol = 4))


library(purrr)
means <- map_dbl(m, mean)

> means
#  X1   X2   X3   X4 
#47.0 64.4 44.8 67.8 

You can try this:

mean(as.matrix(cluster1))

try it ! also can calculate NA's data!

df <- data.frame(a1=1:10, a2=11:20)

df %>% summarise_each(funs( mean( .,na.rm = TRUE)))


# a1   a2
# 5.5 15.5

class(mtcars)
my.mean <- unlist(lapply(mtcars, mean)); my.mean



   mpg        cyl       disp         hp       drat         wt       qsec         vs 
 20.090625   6.187500 230.721875 146.687500   3.596563   3.217250  17.848750   0.437500 
        am       gear       carb 
  0.406250   3.687500   2.812500 

colMeans(A, na.rm = FALSE, dims = 1)

https://stat.ethz.ch/R-manual/R-devel/library/base/html/colSums.html

This is in the base class, so no library is required.

The first answer looks like it is using colMeans from the analytics library which is not available in the R version 4.0.2.

For diversity: Another way is to converts a vector function to one that works with data frames by using plyr::colwise()

set.seed(1)
m <- data.frame(matrix(sample(100, 20, replace = TRUE), ncol = 4))

plyr::colwise(mean)(m)


#   X1   X2   X3   X4
# 1 47 64.4 44.8 67.8