In R, missing values are represented by NA. You can use the `is.na()` function to identify missing values in a dataset. When performing calculations, functions like `mean()` have an optional argument `na.rm` which, when set to TRUE, will exclude missing values from the calculation. Another approach is to use the `complete.cases()` function to remove rows with missing values. Additionally, libraries like `tidyr` provide functions like `drop_na()` to handle missing values more efficiently.

R provides several functions to handle missing values in a dataset, such as `is.na()` to detect missing values, `na.rm` argument in functions like `mean()` to exclude missing values from calculations, and `na.omit()` to remove rows with missing values. Additionally, you can use packages like `tidyverse` or `dplyr` that offer more convenient ways to work with missing values.

Handling missing values in R involves various techniques. The `is.na()` function can be used to check for missing values, while the `complete.cases()` function can be used to remove rows with missing values. Alternatively, you can use functions like `mean()` with the `na.rm` argument set to TRUE to calculate statistics without considering missing values. Packages like `dplyr` and `tidyverse` offer additional functions such as `na_if()` and `replace_na()` to manipulate missing values in a dataset.