CODEWITHSUNDEEP
r
Ayush Agarwal
Ayush Agarwal

Reputation: 57

Remove specific missing values in r

my data set has missing values marked as 'XXX'

I have tried na.omit(mydata)

df <- data.frame(X=factor(c(0.2, "XXX", 0.4, 0.1)), Y=factor(c(0.8, 1, 0.9, "XXX")))

here X and Y are factors. I found that the missing data is encoded as "XXX" by checking the levels of the factor.

I want to remove row "2" and row "4". can someone help, I have been trying for a while now.

Upvotes: 0

Views: 166

Answers (4)

Darren Tsai
Darren Tsai

Reputation: 35739

You don't need to convert "XXX" to NA. Just filter "XXX" directly.

library(dplyr)

df %>% filter(across(everything(), ~ . != "XXX"))

#     X   Y
# 1 0.2 0.8
# 2 0.4 0.9

The corresponding version using filter_all().

df %>% filter_all(all_vars(. != "XXX"))

A base R solution.

df[rowSums(df == "XXX") == 0, ]

Upvotes: 1

Martin Gal
Martin Gal

Reputation: 16998

Another option using tidyverse: 

df %>%
  mutate(across(everything(), str_replace, "XXX", NA_character_)) %>%
  drop_na()

#     X   Y
# 1 0.2 0.8
# 2 0.4 0.9

Upvotes: 1

Matt
Matt

Reputation: 7423

Two base R solutions: 

df <- subset(df, X != "XXX" & Y != "XXX")

or 

df <- df[df$X != "XXX" & df$Y != "XXX",]

dplyr solution:

library(dplyr)

df <- df %>% filter(X != "XXX" & Y != "XXX")

Gives us: 

    X   Y
1 0.2 0.8
3 0.4 0.9

Upvotes: 1

mysteRious
mysteRious

Reputation: 4314

You can also filter for complete cases like this:

library(dplyr)
library(magrittr)
df %>% replace(.=="XXX", NA_character_) %>% filter(complete.cases(.))

The output is:

> df %>% replace(.=="XXX", NA_character_) %>% filter(complete.cases(.))
    X   Y
1 0.2 0.8  
2 0.4 0.9

Upvotes: 2

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