[R] replace_missings

[Thevaraja, Mayooran]

Hello

      You can use the following function,



######################################################

replace_missings <- function(x, replacement) {

  is_missings <- is.na(x)

  x[is_missings] <- replacement

  message(sum(is_missings), " missings replaced by the given value ", replacement)

  x

}



A <- c(0,1,2,3,4,5,6,7,8,9,10)

B <- c(12,15,3,65,NA,12,NA,4,6,25,4)



df <- data.frame(A,B)



replace_missings(df, replacement = 0)

#################################################



Regards

Mayooran









-----Original Message-----

From: R-help <r-help-bounces using r-project.org<mailto:r-help-bounces using r-project.org>> On Behalf Of ani jaya

Sent: Friday, 18 October 2019 2:43 PM

To: r-help <r-help using r-project.org<mailto:r-help using r-project.org>>

Subject: [R] NA value in list of data frame



Dear R-Help,



I have a list of data frame that I import from excel file using read.xlsx command.



sheets <- openxlsx::getSheetNames("rainfall.xlsx")

test <- lapply(sheets,function(i) read.xlsx("rainfall.xlsx", sheet=i, startRow=8, cols=1:2))

names(test) <- sprintf("%i", 1986:2015)



And I got a data.frame with 365 rows and two columns contains date and rainfall data.

There is a value in rainfall data that i want to change as NA (8888). So my question is how to change those value in all the data frame?



test[1]$`1986`

       Date   RR

1   01-01-1986    0

2   02-01-1986    7

3   03-01-1986   72

4   04-01-1986    4

5   05-01-1986   19

6   06-01-1986    4

7   07-01-1986   16

8   08-01-1986   21

9   09-01-1986   34

10  10-01-1986   72

11  11-01-1986   93

12  12-01-1986  178

13  13-01-1986   86

14  14-01-1986   11

15  15-01-1986    0

16  16-01-1986   31

17  17-01-1986   22

18  18-01-1986   18

19  19-01-1986    3

20  20-01-1986    0

21  21-01-1986   31

22  22-01-1986   46

23  23-01-1986    4

24  24-01-1986   40

25  25-01-1986   63

26  26-01-1986  125

27  27-01-1986   33

28  28-01-1986   44

29  29-01-1986    6

30  30-01-1986    0

31  31-01-1986    0

32  01-02-1986    2

33  02-02-1986   71

34  03-02-1986 8888

35  04-02-1986    0

36  05-02-1986    0

37  06-02-1986   56

38  07-02-1986   19

39  08-02-1986    3

40  09-02-1986    7

41  10-02-1986   24

42  11-02-1986   55

43  12-02-1986    0

44  13-02-1986    0

.

.

.



Should I extract the data frame from the list one by one?



Thank you for your help!



Best,

Saat M.





(Sorry for the uncompleted email before).



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