Hi Nash,
If I understand your question correctly, you want the "mice" package.
Hopefully you have more data than your example.
Jim
On Sat, Dec 19, 2015 at 6:14 AM, Web Web <webweb8...@gmail.com> wrote:
> Hello,
> I need some help in data cleaning using R. my CSV file looks as
> follows.
>
>
> "id","gender","age","category1","category2","category3","category4","category5","category6","category7","category8","category9","category10"1,"Male",22,"movies","music","travel","cloths","grocery",,,,,2,"Male",28,"travel","books","movies",,,,,,,3,"Female",27,"rent","fuel","grocery","cloths",,,,,,4,"Female",22,"rent","grocery","travel","movies","cloths",,,,,5,"Female",22,"rent","online-shopping","utiliy",,,,,,,
>
> I need to reformat as follows.
>
> id gender age category rank1 Male 22 movies
> 11 Male 22 music 21 Male 22 travel
> 31 Male 22 cloths 41 Male 22 grocery
> 51 Male 22 books NA1 Male 22 rent
> NA1 Male 22 fuel NA1 Male 22 utility
> NA1 Male 22 online-shopping NA
> ...................................5 Female 22 movies
> NA5 Female 22 music NA5 Female 22 travel
> NA5 Female 22 cloths NA5 Female 22 grocery
> NA5 Female 22 books NA5 Female 22 rent
> 15 Female 22 fuel NA5 Female 22 utility
> NA5 Female 22 online-shopping 2
>
> So far My efforts are as follows.
>
> mini <- read.csv("~/MS/coding/mini.csv", header=FALSE)
> mini_clean <- mini[-1,]
> df_mini <- melt(df_clean, id.vars=c("V1","V2","V3"))
> sqldf('select * from df_mini order by "V1"')
>
> Now I want to know what is the best way to fill all missing categories for
> all users.
>
> Thanks
> Nash
>
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>
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