calculate the mean for each column of a matrix in R

Question

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.

Answer 1

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 

Answer 2

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

Answer 3

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  

Answer 4

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 

Answer 5

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

Answer 6

You can try this:

mean(as.matrix(cluster1))