filtering data frame based on NA on multiple columns

We can get the logical index for both columns, use & and subset the rows.

df1[!is.na(df1$type) & !is.na(df1$company),]
# id  type company
#3  3 North    Alex
#5 NA North     BDA

Or use rowSums on the logical matrix (is.na(df1[-1])) to subset.

df1[!rowSums(is.na(df1[-1])),]

Using dplyr, you can also use the filter_at function

library(dplyr)
df_non_na <- df %>% filter_at(vars(type,company),all_vars(!is.na(.)))

all_vars(!is.na(.)) means that all the variables listed need to be not NA.

If you want to keep rows that have at least one value, you could do:

df_non_na <- df %>% filter_at(vars(type,company),any_vars(!is.na(.)))

Related