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If you are looking for NA counts for each column in a dataframe then: na_count <-sapply(x, function(y) sum(length(which(is.na(y))))) should give you a list with the counts for each column. na_count <- data.frame(na_count) Should output the data nicely in a dataframe like: Counting Missing Values (NA) in R. This post is also available in Spanish. To check for missing values in R you might be tempted to use the equality operator == with your vector on one side and NA on the other. Don’t! If you insist, you’ll get a useless results. x <- c (1, 5, NA, 3, NA) x == NA. ## [1] NA NA NA NA NA. Counts NAs in an object. Examples # NOT RUN { x <- sample(c(1:10, NA), 30, replace = TRUE) na.count(x) x.df <- do.call(data.frame, lapply(1:4, function(i) sample(c(1 There are a number of ways in R to count NAs (missing values).

A common use case is to count the NAs over multiple columns, ie., a whole dataframe. That’s basically the question “how many NAs are there in each column of my dataframe”? This post demonstrates some ways to answer this question. Way 1: using sapply Counting the NA values in a data frame is a common step when exploring and cleaning data.Code used in this clip:# Load datadf ＜- mtcars# Set some values to N Also, is there a better way to count missing values by group using a native R function? DF <- data.frame (YEAR=c (2000,2000,2000,2001,2001,2001,2001,2002,2002,2002), X=c (1,NA,3,NA,NA,NA,7,8,9,10)) DF aggregate (X ~ YEAR, data=DF, function (x) { sum (is.na (x)) }) with (DF, aggregate (X, list (YEAR), function (x) { sum (is.na (x)) })) aggregate # NOT RUN { x <- sample(c(1:10, NA), 30, replace = TRUE) na.count(x) x.df <- do.call(data.frame, lapply(1:4, function(i) sample(c(1:10, NA), 30, replace = TRUE))) colnames(x.df) <- paste("X", 1:4, sep = "") na.count(x.df) # } As @Roland noticed there are multiple functions for finding and dealing with missing values in R (see help("NA") and here). Example: Create a fake dataset with some NA's: data <- matrix(1:300,,3) data[sample(300, 40)] <- NA Check if there are any missing values: anyNA(data) Columnwise check if there are any missing values: apply(data, 2, anyNA) count_na: Count the number of NAs in each row or in each column; d.eta: Sample data set for eta function examples; d.ngo: NGO Dataset; d.superiority: Student self assessment data; eta: Eta coefficient for nominal/interval data. foo: A function to demonstrate how an R function is defined.

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Description. The row- and column-wise functions take either a matrix or a vector as input. If a vector, then argument dim.

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2019-03-26 You can use apply, which is actually the basis of the rowMeans function. If you are concerned that your row means are not correct because of NA's, just use the na.rm = TRUE argument in rowMeans. 2021-03-05 So the best solution to count TRUE values is to use the sum() function along with the na.rm argument as TRUE.