How can I filter out null values using Tidyverse in R?

Filtering out null values using the Tidyverse package in R is straightforward and efficient. The primary function used for this purpose is drop_na() from the tidyr package, which is part of the Tidyverse ecosystem. Here's how You can do it:

First, make sure You have the Tidyverse package installed. If not, install it using:

install.packages("tidyverse")

Once installed, load the Tidyverse library:

library(tidyverse)

Now, let's consider an example. Suppose You have a data frame named df with some NA values:

df <- data.frame( ID = 1:5, Name = c("Alice", "Bob", NA, "David", "Eve"), Age = c(25, 30, NA, 28, 22), Score = c(85, 92, 78, NA, 88) )

To filter out rows with NA values in any column, You can use drop_na():

df_clean <- df %>% drop_na()

This will remove any row where at least one column has an NA value. If You want to filter based on specific columns, You can specify those columns in drop_na():

df_clean_name_age <- df %>% drop_na(Name, Age)

This will only remove rows where either the "Name" or "Age" column has an NA value, leaving rows with NA in other columns intact.

Using Tidyverse's drop_na() function provides a concise and readable way to handle NA values in Your data frames, enhancing Your data cleaning workflow in R.

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