CODEWITHSUNDEEP
r
learningcompsci
learningcompsci

Reputation: 193

writing a function to calculate the mean of columns in a dataframe in R

I have to calculate the mean of the columns in a dataframe by writing a function and then applying it. I understand that this is easy to do with mean and apply but I need to write my own function. I have made many attempts but cannot seem to get this right. Below are 3 of my attempts. I am a beginner at R. I would greatly appreciate any suggestions.

mean_fun<-function(x){
  mean_c[i]= sum(x[1:dim(x)],na.rm=TRUE)/length(x[1:dim(x)])
  return(mean_c[i])
}


mean_fun<-function(x){
  for( i in 1:ncol(x)){
    s=sum(x[1:i],na.rm=TRUE)
    l=dim(x[1:i])
    mean_c=s/l
    return (mean_c)
  }


mean_fun<-function(x){
  x=rbind(x,newrow)
  for(i in 1:ncol(x)){
    x[newbottomrownumber,i]=sum[i]/length[i]}
  return(x[1303,])
}

Upvotes: 4

Views: 4792

Answers (3)

tatxif
tatxif

Reputation: 438

Assuming that all the columns in your data frame are numeric, here is a tweak of your first function, where x is a vector (a column in mydataframe). 

mean_fun<-function(x){
    mean_c= sum(x,na.rm=TRUE)/length(!is.na(x))
    return(mean_c)
}

apply(mydataframe,2,mean_fun)

Upvotes: 4

shiny
shiny

Reputation: 3502

Why not to use dplyr?

You can get the mean for all columns in your data.frame using 

summarise_each(funs(mean))

If we apply it to mtcars

library(dplyr)
mtcars %>% summarise_each(funs(mean))

#       mpg    cyl     disp       hp     drat      wt     qsec     vs      am   gear   carb
#1 20.09062 6.1875 230.7219 146.6875 3.596563 3.21725 17.84875 0.4375 0.40625 3.6875 2.8125

Upvotes: 1

d.b
d.b

Reputation: 32558

Here's an example by slightly modifying your second attempt

mean_fun<-function(x){
    mean_c = numeric(0)
    for( i in 1:ncol(x)){
        s = sum(x[,i], na.rm=TRUE)
        l = length(x[,i][is.na(x[,i]) == FALSE])
        mean_c[i] = s/l
    }
    return (mean_c)
}

USAGE

mean_fun(mtcars)
# [1]  20.090625   6.187500 230.721875 146.687500   3.596563   3.217250  17.848750   0.437500   0.406250
#[10]   3.687500   2.812500

Upvotes: 3

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