[R] Issue with Transform function in R

[Ivan Calandra]

This might not be the whole story, but part of the problem is that you 
want to select a _*character string*_ greater/smaller than another. That 
doesn't make much sense!

I am not sure how to best compare two dates, but if you convert the Date 
values into numeric, then that would work. The problem is that it seems 
your Date values are character, and the comparison in your ifelse is 
also a character.

So something like this might work (untested because no reproducible 
example):
transform(Df1, ifelse(as.numeric(Date) > numeric.value1 && 
as.numeric(Date) < numeric.value2, Variable1 ==NA, Variable1))

HTH,
Ivan

--
Ivan Calandra, PhD
Scientific Mediator
University of Reims Champagne-Ardenne
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ivan.calandra at univ-reims.fr
--
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Le 21/07/2016 à 03:00, Bhaskar Mitra a écrit :
> Hello Everyone,
>
>
> I am trying to replace the values in the 2nd column (Variable 1)
> corresponding to certain dates  (Date)
>
>
> with NAs as shown below. Both Date and Variable1 are numeric vectors . I am
> trying to use the transform function
>
>
> as shown below but it doesn’t seem to work even though if I am not getting
> any error
>
>
> Any suggestions/help in this regard?
>
> regards,
>
> ---------------------------------------------------------------------------------------------------------------------------------------------------------
>
>
>
> Df1 <- data.frame(Date, Variable1)
>
>
>
>
> a1 <- transform(Df1, ifelse(Date  > "010301000300 " && Date <
> "010501000300", Variable1 ==NA, Variable1))
>
>
> Original Data frame
>
>
>
>       Date                          Variable1
>
> 010101000300                     1
>
> 010201000300                     2
>
> 010301000300                     3
>
> 010401000300                     4
>
> 010501000300                     5
>
> 010601000300                     6
>
> 010701000300                     7
>
>   .
>
> .
>
> .
>
> ……….
>
>
>
>
> Transformed data frame (i hope to transform)
>
>
>
>        Date                       Variable1
>
> 010101000300                    1
>
> 010201000300                    2
>
> 010301000300                   NA
>
> 010401000300                   NA
>
> 010501000300                    NA
>
> 010601000300                    6
>
> 010701000300                    7
>
>
>
> ……….
>
> 	[[alternative HTML version deleted]]
>
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