Is "not null" in Tidyverse?

In the Tidyverse, there isn't a direct function or operator specifically named "not null" like you might find in SQL. However, Tidyverse offers a variety of ways to check for and handle missing data, which effectively achieves the same goal. Missing values in R are represented as `NA` (Not Available).

Here are the common methods to identify non-missing values in Tidyverse:

1. Using `!is.na()`: The `is.na()` function checks for `NA` values, and negating it with `!` effectively checks for values that are "not null" or "not missing".

- Example:

library(dplyr)
df <- tibble(x = c(1, NA, 3, NA, 5))
df_not_null <- df %>% filter(!is.na(x))

This code will filter the DataFrame (`df`) and only keep rows where the value of `x` is not `NA`.

2. Within `dplyr::filter()`: You can directly use `!is.na()` within `dplyr::filter()` to filter non-null values. This is the most straightforward approach for selecting rows where certain columns are not missing.

3. Using `complete.cases()`: This function returns a logical vector indicating which cases are complete, meaning they contain no `NA` values. You can use it to filter rows that have no missing values in the specified columns.

library(dplyr)
df <- tibble(x = c(1, NA, 3, NA, 5), y = c("a", "b", NA, "d", "e"))
df_complete <- df %>% filter(complete.cases(x, y))

This code will keep only the row(s) in the DataFrame `df` where both column `x` and `y` are not `NA`.

4. With `tidyr::drop_na()`: The `drop_na()` function from the `tidyr` package drops rows with any missing values from the specified columns. This is a concise way to remove rows containing `NA` values.

library(dplyr)
library(tidyr)
df <- tibble(x = c(1, NA, 3, NA, 5), y = c("a", "b", NA, "d", "e"))
df_not_null <- df %>% drop_na(x,y)

This will remove any rows where columns 'x' or 'y' have NA values.

In summary, while there isn't a specific function named "not null" in Tidyverse, the combination of `!is.na()`, `complete.cases()`, and `drop_na()` provides equivalent and powerful ways to work with missing data and identify non-missing values.

Learn more about this