Consider the following example data frame in R. Table 1: Exemplifying Data Frame with Missing Values I’m creating some duplicates of the data for the following examples. In R, missing values are often represented by NA or some other value that represents missing values (i.e. An important feature of is.na is that the function can be reversed by simply putting a ! y_NA <- runif(50)
In a vector or column, NA values can be removed as follows: is.na_remove <- data$x_num[!is.na(data$x_num)]. Compare the output with the data table above — The TRUE values are at the same position as before the NA elements. # 213 322 47. Alternatively, pass a function to
In case of characters or factors, it is also possible in R to set NA to blank: is.na_blank_cha <- data$x_cha # Duplicate character column
Either a character vector, or something I’ve shown you the most important ways to use the is.na R function. replace. data: A data frame or vector. is.na_blank_cha[is.na(is.na_blank_cha)] <- "" # Class character to blank
within the if statement or within for loops. #  "Wow, that's awesome" "Wow, that's awesome" "Wow, that's awesome" "Damn, it's NA"
At this point, our problem is outlined, we covered the theory and the function we will use, and we are all ready and equipped to do some applied examples of removing rows with NA in R. Recall our dataset. Sorry for that, I just fixed it. Or do you have a question about the usage of is.na in a specific scenario? cex = 2,
is.na_blank_fac[is.na(is.na_blank_fac)] <- "" # Class character to blank
pass a named vector (c(pattern1 = replacement1)) to Vectorised over string, pattern and replacement. pch = pch_numb,
str_replace_all(string, pattern, replacement). However, there are hundreds of different possibilities to apply is.na in a useful way. This is a translation of the SQL command NULLIF. stri_replace() for the underlying implementation. of multiple columns), the complete.cases function is preferable. In the following example, I’m printing “Damn, it’s NA” to the R Studio console whenever a missing occurs; and “Wow, that’s awesome” in case of an observed value. R Find Missing Values (6 Examples for Data Frame, Column & Vector), Remove All-NA Columns from Data Frame in R (Example), Replace 0 with NA in R (Example) | Changing Zero in Data Frame & Vector, R is.na Function Example (remove, replace, count, if else, is not NA). If we want to count NAs in multiple columns at the same time, we can use the function colSums: colSums(is.na(data))
Thanks for the comment. Generally, # [3,] TRUE TRUE TRUE
If data is a vector, a single value used for replacement.. Additional arguments for methods. This was the reason I wanted to replace the empty cells with NAs in the first place. Note: Our new vector is.na_remove is shorter in comparison to the original column data$x_num, since we use a filter that deletes all missing values. x_fac[rbinom(N, 1, 0.3) == 1] <- NA # 30% missings
is.na_blank_fac <- as.character(is.na_blank_fac) # Convert temporarily to character
pch_numb <- as.character( # Specify plotted numbers
Please accept YouTube cookies to play this video. Do you know any other helpful applications? data <- data.frame(x_num, x_fac, x_cha, # Create data frame
4 1 15 No Control FALSE M 0 2 2 Physical Impairment (Eyes, Ear, Limb) D. 5 4 21 No Control FALSE 25 NA NA D. 6 4 20 No Control NA F 30 2 4 Drinking Alcohol - Impaired D. inj1 PED_STATE st rac1. Required fields are marked *. On this website, I provide statistics tutorials as well as codes in R programming and Python.