means and SD for columns in a dataframe with NA values

Question

I\'m trying to calculate the mean and standard deviation of several columns (except the first column) in a data.frame with NA values.

I\'ve tried

Answer 1

sapply(df, function(cl) list(means=mean(cl,na.rm=TRUE), sds=sd(cl,na.rm=TRUE)))
      col1     col2     col3     col4     col5    
means 3        8        12.5     18.25    22.5    
sds   1.581139 1.581139 1.290994 1.707825 1.290994

as.data.frame( t(sapply(df, function(cl) list(means=mean(cl,na.rm=TRUE), 
                                              sds=sd(cl,na.rm=TRUE))) ))
     means      sds
col1     3 1.581139
col2     8 1.581139
col3  12.5 1.290994
col4 18.25 1.707825
col5  22.5 1.290994

Answer 2

The functions you should be using (e.g. colMeans) will almost all have a parameter called na.rm which defaults to FALSE. Just do colMeans(x = your_df, na.rm = TRUE) and you'll be good to go. Same with using just mean() if you want to go column by column.

Answer 3

The following example code may prove useful.

# Create a 5 column dataframe that contains some NAs
col1 <- c(1,2,3,4,5)
col2 <- c(6,7,8,9,10)
col3 <- c(11,12,13,14,NA)
col4 <- c(16,NA,18,19,20)
col5 <- c(21,22,23,24,NA)
dataframe <- data.frame(col1,col2,col3,col4,col5)

# Apply the mean() function to all but the first column of the dataframe
apply(dataframe[,2:ncol(dataframe)], 2, function(x) mean(x, na.rm=TRUE))

# Check that the returned values are correct:
mean(col2)
mean(col3, na.rm=TRUE)
mean(col4, na.rm=TRUE)
mean(col5, na.rm=TRUE)

For the standard deviation, replace mean() with sd().