In the previous example with complete.cases() function, we considered the rows without any missing values. But that is a) verbose when there are a lot of variables and b) impossible when the variable names are not known (e.g. It's more useful on a data.frame as !is.na(a) would return back a matrix of the same dimensions as data.frame where as complete.cases will return a vector, one for each row of the data.frame.In essence DF[complete.cases(DF), ] will remove all rows with at least 1 NA which is a handy tool. complete.cases checks row-wise for NA, and if present returns FALSE. Using complete.cases() to remove (missing) NA and NaN values. in a function that processes any data.frame). Table 3: Remove Rows by Columns via the complete.cases Function. The resultDF contains rows with none of the values being NA. df1[complete.cases(df1),] so after removing NA and NaN the resultant dataframe will be . I start with. Here is the code that works. complete.cases with a list of all variables works, of course. Pour virer les lignes avec des NA dans un tableau voir du côté de complete.cases. Removing Both Null and missing: By subsetting each column with non NAs and not null is round about way to remove both Null and missing values as shown below # Remove … I'm trying to remove all the NA values from a list of data frames. Basically, I want to remove ALL NA values in age, height, weight, and igf1. Nikhil Muthukrishnan Nikhil Muthukrishnan. is.na will remove actual na values whereas the objective here is to only control for a variable not deal with missing values/na's those which could be legitimate data points . There is a subtle difference between using is.na and complete.cases. Method 2: Remove or Drop rows with NA using complete.cases() function. The only way I have got it to work is by cleaning the data with complete.cases in a for loop. Tu peux aussi te débrouiller avec un apply : En plus ici ce n'est pas l'indexation adéquate pour un data.frame qui est utilisé. R is.na Function Example (remove, replace, count, if else, is not NA) Well, I guess it goes without saying that NA values decrease the quality of our data.. Fortunately, the R programming language provides us with a function that helps us to deal with such missing data: the is.na function. De part la syntaxe utilisée ici pour virer les NA il n'est pas pertinent de l'appliquer quand il n'y a pas de NA dans le tableau. As you can see based on Table 3: All rows with a missing value in X1 are deleted; the row with a missing value in X2 is kept. Is it possible to filter a data.frame for complete cases using dplyr? 1 1 1 bronze badge. If you want to omit rows based on exactly one column, the is.na function works even quicker than complete.cases: One of the variables (age) contains numeric information. I'll know I'm successful when I have 858 observations remaining. share | improve this answer | follow | answered Dec 13 '18 at 21:04. Remove rows of R Dataframe with all NAs. Three of the variables (height, weight, igf1) contain FACTOR type information. But in this example, we will consider rows with NAs but not all NAs. Is there another way of doing this with lapply as I had been trying for a while to no avail.