# [R] Missing data

David Carlson dcarlson at tamu.edu
Thu Apr 25 15:42:04 CEST 2013

```Another approach:

x[1:length(x) %% 10 == 0] <- NA

Just replace 10 by the interval you want. Or to add 5 missing values
randomly:

x[sample(1:length(x), 5)] <-NA

-------------------------------------
David L Carlson
Associate Professor of Anthropology
Texas A&M University
College Station, TX 77840-4352

-----Original Message-----
From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-project.org] On
Behalf Of Rainer Schuermann
Sent: Thursday, April 25, 2013 5:45 AM
To: r-help at r-project.org
Cc: Roslina Zakaria
Subject: Re: [R] Missing data

> x <- read.table( "clipboard" )

and renamed the only column

> colnames( x )[1] <- "orig"

With a loop, I created a 2nd column "miss" where in every 10th row the
observation is set to NA:

for( i in 1 : length( x\$orig ) )
{
if( as.integer( rownames( x )[ i ] ) %% 10 == 0 )
{
x\$miss[i] <- NA
} else {
x\$miss[i] <- x\$orig[i]
}
}

This is probably the least elegant of all possible solutions but it works...

Rgds,
Rainer

On Wednesday 24 April 2013 23:41:21 Roslina Zakaria wrote:
> Dear r-users,
>
> I would like to investigate about how to fill in missing data.  I started
with a complete data and try to introduce missing data into the data series.
Then I would use some method to fill in the missing data and then compare
with the original data how good it is.  My question is, how do I introduce
missing data in my complete data systematically like for example every 10th
data will be erased and assumed as missing.  Here are some rainfall data:
>
> 125
> 130.3
> 327.2
> 252.2
> 33.8
> 6.1
> 5.1
> 0.5
> 0.5
> 0
> 2.3
> 0
> 0
> 0
> 0
> 0
> 0
> 0
> 0
> 0
> 0.8
> 5.1
> 0
> 0.3
> 0
> 0
> 0
> 0
> 0
> 0
> 45.7
> 43.4
> 0
> 0
> 0
> 0
> 0
>
> Thank you so much for any help given.  I hope my question is clear.
> 	[[alternative HTML version deleted]]
>

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