Good day colleagues. Below is a csv file attached which i am using in my
> analysis.
>
>
>
> household.id <http://hh.id>
>
> hd17.perm
>
> hd17employ
>
> health.exp
>
> total.food.exp
>
> total.nfood.exp
>
> 1
>
> 2
>
> yes
>
> 1654
>
> 23654
>
> 23655
>
> 2
>
> 2
>
> yes
>
> NA
>
> NA
>
> 65984
>
> 3
>
> 6
>
> no
>
> 2547
>
> 123311
>
> 52416
>
> 4
>
> 8
>
> NA
>
> 2365
>
> 13648
>
> 12544
>
> 5
>
> 6
>
> NA
>
> 1254
>
> 36549
>
> 12365
>
> 6
>
> 8
>
> yes
>
> 1236
>
> 236541
>
> 26522
>
> 7
>
> 8
>
> no
>
> NA
>
> 13264
>
> 23698
>
>
>
>
>
> So I created a df using the above and its a csv file as follows
>
> wbpractice <- read.csv("world_practice.csv")
>
> Now i am doing data cleaning and trying to replace all missing values with
> the averages of the respective columns.
>
> the dimension of the actual dataset is;
>
> dim(wbpractice)
[1] 31998 6
I used the following script which i executed by i got some error messages
for(i in 1:ncol( wbpractice )){
wbpractice [is.na( wbpractice [,i]), i] <- mean( wbpractice [,i],
na.rm = TRUE)
}
Any help to replace all NAs with average values in my dataframe?
>
>>
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