R which na

Fortunately, the R programming language provides us with a function that helps us to deal with such missing data: the is. Our data consists of three columns, each of them with a different class: numeric, factor, and character.

This is how the first six lines of our data look like:. Table 1: Example Data for the is. The function produces a matrix, consisting of logical values i.

An important feature of is. We are also able to check whether there is or is not an NA value in a column or vector :. As you have seen, is. We can apply the function to a whole database or to a column no matter which class the vector has. In the following, I have prepared examples for the most important R functions that can be combined with is. Note: Our new vector is.

You can learn more about the removal of NA values from a vector here…. If you want to drop rows with missing values of a data frame i. Learn more…. Based on is. Combined with the R function sum, we can count the amount of NAs in our columns. We can also test, if there is at least 1 missing value in a column of our data. In combination with the which function, is. Missing values have to be considered in our programming routines, e. Note: Within the if statement we use is na instead of equal to — the approach we would usually use in case of observed values e.

You want to learn even more possibilities to deal with NAs in R? Then definitely check out the following video of my YouTuber channel. In the video, I provide further examples for is. I also speak about other functions for the handling of missing data in R data frames.

Please accept YouTube cookies to play this video. To do so we use the replace argument, and specify a named list, which contains the names of the variable and the value it would take to replace with NA. Sometimes you have many of the same value that you want to replace. Below we will now consider some very simple examples of the use of these functions, so that you can better understand how to use them.

This is useful in cases where you want to specify a rule that only affects a selected number of variables. Additionally, you can specify rules as function, for example, make a value NA if the exponent of that number is less than There may be some cases where you can identify variables based on some test - is.

For example,. For data frames, a convenient shortcut to compute the total missing values in each column is to use colSums :. To recode missing values; or recode specific indicators that represent missing values, we can use normal subsetting and assignment operations.

For example, we can recode missing values in vector x with the mean values in x by first subsetting the vector to identify NA s and then assign these elements a value. Similarly, if missing values are represented by another value i. If we want to recode missing values in a single data frame variable we can subset for the missing value in that specific variable of interest and then assign it the replacement value.

For example, here we recode the missing value in col4 with the mean value of col4. We can exclude missing values in a couple different ways.