In this tutorial, we going to look at Subsetting in R, but this tutorial is part 2 so if you didn’t read part 1, you can read it here.
Subsetting – Remove Missing Values
We learned missing values in the data types tutorial. We can remove missing values, for this, we going to use subsetting.
We created, simple a vector after that, we used is.na() function for finding missing value, we extract missing value with “!”. So you can use the exclamation mark to subtract any value, set, number, or array you want.
Subsetting – Remove Missing Values (Multiple)
We will learn a new function to clear 2 series at once. When working with multiple data sets, you can clear 2 data sets from null values with this function. But, these 2 data sets require data of the same length.
We deleted the empty values in the given two vectors with the complete.cases function.
Subsetting Remove Missing Value – Practice
Now we will do exercises to reinforce the concepts we have learned. Let’s pass the practice.
Subsetting Partial Matching
It is possible to access an index with shortcuts. For example, we want to call the variable in a list, instead of typing the entire name, we can write the initial letter or first 3 letters.
We can also use square brackets instead of the $ symbol. When using square brackets, the “exact” argument should also be used so that partial matching can be made.
CONGRATULATIONS, YOU FINISHED SUBSETTING IN R PROGRAMMING!
