na

I have a data frame containing (in random places) a character value (say "foo" ) that I want to replace with a NA . What's the best way to do so across the whole data frame? c-urchin This: df[ df == "foo" ] <- NA One way to nip this in the bud is to convert that character to NA when you read the data in in the first place. df <- read.csv("file.csv", na.strings = c("foo", "bar")) Another option is is.na<- : is.na(df) <- df == "foo" Note that its use may seem a bit counter-intuitive, but it actually assigns NA values to df at the index on the right hand side. This could be done with dplyr:

Regarding the bounty Ben Bolker 's paste2 -solution produces a "" when the strings that are pasted contains NA 's in the same position. Like this, > paste2(c("a","b", "c", NA), c("A","B", NA, NA)) [1] "a, A" "b, B" "c" "" The fourth element is an "" instead of an NA Like this, [1] "a, A" "b, B" "c" NA I'm offering up this small bounty for anyone who can fix this. Original question I've read the help page ?paste , but I don't understand how to have R ignore NA s. I do the following, foo <- LETTERS[1:4] foo[4] <- NA foo [1] "A" "B" "C" NA paste(1:4, foo, sep = ", ") and get [1] "1, A" "2, B" "3,

There are a lot of posts about replacing NA values. I am aware that one could replace NAs in the following table/frame with the following: x[is.na(x)]<-0 But, what if I want to restrict it to only certain columns? Let's me show you an example. First, let's start with a dataset. set.seed(1234) x <- data.frame(a=sample(c(1,2,NA), 10, replace=T), b=sample(c(1,2,NA), 10, replace=T), c=sample(c(1:5,NA), 10, replace=T)) Which gives: a b c 1 1 NA 2 2 2 2 2 3 2 1 1 4 2 NA 1 5 NA 1 2 6 2 NA 5 7 1 1 4 8 1 1 NA 9 2 1 5 10 2 1 1 Ok, so I only want to restrict the replacement to columns 'a' and 'b'. My

I have a number of large datasets with ~10 columns, and ~200000 rows. Not all columns contain values for each row, although at least one column must contain a value for the row to be present, I would like to set a threshold for how many NA s are allowed in a row. My Dataframe looks something like this: ID q r s t u v w x y z A 1 5 NA 3 8 9 NA 8 6 4 B 5 NA 4 6 1 9 7 4 9 3 C NA 9 4 NA 4 8 4 NA 5 NA D 2 2 6 8 4 NA 3 7 1 32 And I would like to be able to delete the rows that contain more than 2 cells containing NA to get ID q r s t u v w x y z A 1 5 NA 3 8 9 NA 8 6 4 B 5 NA 4 6 1 9 7 4 9 3 D 2 2 6

I have a huge vector which has a couple of NA values, and I'm trying to find the max value in that vector (the vector is all numbers), but I can't do this because of the NA values. How can I remove the NA values so that I can compute the max? Trying ?max , you'll see that it actually has a na.rm = argument, set by default to FALSE . (That's the common default for many other R functions, including sum() , mean() , etc.) Setting na.rm=TRUE does just what you're asking for: d <- c(1, 100, NA, 10) max(d, na.rm=TRUE) If you do want to remove all of the NA s, use this idiom instead: d <- d[!is.na(d)

This question is related to a post with a similar title ( replace NA in an R vector with adjacent values ). I would like to scan a column in a data frame and replace NA's with the value in the adjacent cell. In the aforementioned post, the solution was to replace the NA not with the value from the adjacent vector (e.g. the adjacent element in the data matrix) but was a conditional replace for a fixed value. Below is a reproducible example of my problem: UNIT <- c(NA,NA, 200, 200, 200, 200, 200, 300, 300, 300,300) STATUS <-c('ACTIVE','INACTIVE','ACTIVE','ACTIVE','INACTIVE','ACTIVE','INACTIVE',