It *sounds* as though you are trying to impute missing data.
There are better approaches than just plugging in means.
You might want to look into CALIBERrfimpute or missForest.

On Tue, 19 Oct 2021 at 01:39, Admire Tarisirayi Chirume
<atchir...@gmail.com> wrote:
>
> 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?
>
>
>
> >
> >>
>
>         [[alternative HTML version deleted]]
>
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> and provide commented, minimal, self-contained, reproducible code.

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