Big Data/Analytics Zone is brought to you in partnership with:

Mark is a graph advocate and field engineer for Neo Technology, the company behind the Neo4j graph database. As a field engineer, Mark helps customers embrace graph data and Neo4j building sophisticated solutions to challenging data problems. When he's not with customers Mark is a developer on Neo4j and writes his experiences of being a graphista on a popular blog at http://markhneedham.com/blog. He tweets at @markhneedham. Mark is a DZone MVB and is not an employee of DZone and has posted 529 posts at DZone. You can read more from them at their website. View Full User Profile

R: Filter a Data Frame Based on Values in Two Columns

01.27.2013
| 3892 views |
  • submit to reddit
In the most recent assignment of the Computing for Data Analysis course we had to filter a data frame which contained N/A values in two columns to only return rows which had no N/A’s.

I started with a data frame that looked like this:

> data <- read.csv("specdata/002.csv") 
> # we'll just use a few rows to make it easier to see what's going on
> data[2494:2500,]
           Date sulfate nitrate ID
2494 2007-10-30    3.25   0.902  2
2495 2007-10-31      NA      NA  2
2496 2007-11-01      NA      NA  2
2497 2007-11-02    6.56   1.270  2
2498 2007-11-03      NA      NA  2
2499 2007-11-04      NA      NA  2
2500 2007-11-05    6.10   0.772  2

We want to only return the rows which have a value in both the ‘sulfate’ and the ‘nitrate’ columns.

I initially tried to use the Filter function but wasn’t very successful:

> smallData <- data[2494:2500,]
> Filter(function(x) !is.na(x$sulfate), smallData)
Error in x$sulfate : $ operator is invalid for atomic vectors

I’m not sure that Filter is designed to filter data frames – it seems more appropriate for lists or vectors – so I ended up filtering the data frame using what I think is called an extract operation:

> smallData[!is.na(smallData$sulfate) & !is.na(smallData$nitrate),]
           Date sulfate nitrate ID
2494 2007-10-30    3.25   0.902  2
2497 2007-11-02    6.56   1.270  2
2500 2007-11-05    6.10   0.772  2

The code inside the square brackets returns a collection indicating whether or not we should return each row:

> !is.na(smallData$sulfate) & !is.na(smallData$nitrate)
[1]  TRUE FALSE FALSE  TRUE FALSE FALSE  TRUE

which is equivalent to doing this:

> smallData[c(TRUE, FALSE, FALSE, TRUE, FALSE, FALSE, TRUE),]
           Date sulfate nitrate ID
2494 2007-10-30    3.25   0.902  2
2497 2007-11-02    6.56   1.270  2
2500 2007-11-05    6.10   0.772  2

We put a comma after the list of true/false values to indicate that we want to return all the columns otherwise we’d get this error:

> smallData[c(TRUE, FALSE, FALSE, TRUE, FALSE, FALSE, TRUE)]
Error in `[.data.frame`(smallData, c(TRUE, FALSE, FALSE, TRUE, FALSE,  : 
  undefined columns selected

We could filter the columns as well if we wanted to:

> smallData[c(TRUE, FALSE, FALSE, TRUE, FALSE, FALSE, TRUE), c(1,2)]
           Date sulfate
2494 2007-10-30    3.25
2497 2007-11-02    6.56
2500 2007-11-05    6.10

As is no doubt obvious, I don’t know much R so if there’s a better way to do anything please let me know.

The full code is on github if you’re interested in seeing it in context.

Published at DZone with permission of Mark Needham, author and DZone MVB. (source)

(Note: Opinions expressed in this article and its replies are the opinions of their respective authors and not those of DZone, Inc.)