A few examples of filtering out missing values in a R data frame

filter_na_example.R

library(tidyverse)

df <- tibble(d13C = c(1, NA, 3, 4, NA, 6),
             d15N = c(1, 2, NA, 4, NA, 6),
             d42 = c(1, NA, NA, 4, 5, NA))

# to remove all rows from `df` that have missing values in `d13C` 
# but keep rows that have missing values in any other variable:
filter(df, !is.na(d13C))

# to remove all rows with NAs in `d13C` and `d15N`, 
# but keep rows that have missing values in any other variable:
# In other words, remove a remove if the value of `d13C` and `d15N` are *both* NA,
# but keep the row if only one of `d13C` and `d15N` is NA, and also keep the row
# if the other variable (`d42`) is NA
filter_at(df, vars(d13C, d15N), any_vars(!is.na(.)))

# But if we want to remove any rows where *either* `d13C` and `d15N` are NA, but
# keep the row if the other variable (`d42`) is NA, then we would do this.
filter_at(df, vars(d13C, d15N), all_vars(!is.na(.)))