Remove rows with all or some NAs (missing values) in data.frame

Question

I\'d like to remove the lines in this data frame that:

a) contain NAs across all columns. Below is my example data frame.

Answer 1

One approach that's both general and yields fairly-readable code is to use the filter() function and the across() helper functions from the {dplyr} package.

library(dplyr)

vars_to_check <- c("rnor", "cfam")

# Filter a specific list of columns to keep only non-missing entries

df %>% 
  filter(across(one_of(vars_to_check),
                ~ !is.na(.x)))

# Filter all the columns to exclude NA
df %>% 
  filter(across(everything(),
                ~ !is.na(.)))

# Filter only numeric columns
df %>%
  filter(across(where(is.numeric),
                ~ !is.na(.)))

Similarly, there are also the variant functions in the dplyr package (filter_all, filter_at, filter_if) which accomplish the same thing:

library(dplyr)

vars_to_check <- c("rnor", "cfam")

# Filter a specific list of columns to keep only non-missing entries
df %>% 
  filter_at(.vars = vars(one_of(vars_to_check)),
            ~ !is.na(.))

# Filter all the columns to exclude NA
df %>% 
  filter_all(~ !is.na(.))

# Filter only numeric columns
df %>%
  filter_if(is.numeric,
            ~ !is.na(.))