filtering data frame based on NA on multiple columns

Question

I have the following data frame lets call it df, with the following observations:

id   type   company
1    NA      NA
2    NA      ADM
3    Nort         


        

Answer 1

You need AND operator (&), not OR (|) I also strongly suggest the tidyverse approach by using the dplyr function filter() and the pipe operator %>%, from dplyr as well:

library(dplyr)
df_not_na <- df %>% filter(!is.na(company) & !is.na(type))

Answer 2

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(.)))

Answer 3

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])),]

Answer 4

you can use

na.omit(data_frame_name)