imputation in R

Data goes like this:

Df <- data.table( id = c(1,2,3,4, 5,6,7,8,9,10) , age = c(23, 34,26,33,43,13,16,19,21,10) , Death = c(1, 0,NA,1,NA,1,1,0,0,0) , Recover = c(0,1,0,0,NA,0,0,1,NA,1), date = (2013-03-04,NA,2013-04-03,2013-04-03,NA,2013-04-07,2013-04-14,NA,NA,2013-04-28

i want to remove NA s from above table.
class (date) =date
i'm new to R, please help me to clean the table using imputation..

Not totally sure I follow. Could you explain in more detail what you are trying to achieve?

In the example below, the 2nd column is set to NULL.

df <- data.frame(
  c1 = 1:3,
  c2 = 1:3
df$c2 = NULL

#>   c1
#> 1  1
#> 2  2
#> 3  3

#> [1] TRUE

Created on 2021-08-13 by the reprex package (v2.0.0)

Another strategy to consider is to use R's NA, missing value.

can you please check it once again?

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