CODEWITHSUNDEEP
rdataframe
Anubhav Dikshit
Anubhav Dikshit

Reputation: 1829

filtering data frame based on NA on multiple columns

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

id   type   company
1    NA      NA
2    NA      ADM
3    North   Alex
4    South   NA
NA   North   BDA
6    NA      CA

I want to retain only the records which do not have NA in column "type" and "company".

id   type   company
3    North   Alex
NA   North   BDA

I tried: 

 df_non_na <- df[!is.na(df$company) || !is.na(df$type), ]

But this did not work.

Thanks in advance

Upvotes: 23

Views: 78912

Answers (6)

Markus Bauer
Markus Bauer

Reputation: 155

The example with dplyr (version >= 1.0.4) and if_all(), since filter_at() is superseded

id <- c(1, 2, 3, 4, NA, 6)
type <- c(NA, NA, "North", "South", "North", NA)
company <- c(NA, "ADM", "Alex", NA, "BDA", "CA")

df <- tibble(id, type, company)

library(dplyr)

df_non_na <- df %>% filter(if_all(c(type,company), ~ !is.na(.)))

Upvotes: 10

gradcylinder
gradcylinder

Reputation: 410

You'll want to use drop_na()

library(dplyr)

new_df <- df %>% 
    drop_na(type, company)

Upvotes: 17

Ricecakes
Ricecakes

Reputation: 781

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(.)))

Upvotes: 45

damianooldoni
damianooldoni

Reputation: 505

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))

Upvotes: 7

user9325029
user9325029

Reputation: 11

you can use 

na.omit(data_frame_name)

Upvotes: -6

akrun
akrun

Reputation: 887851

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])),]

Upvotes: 22

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