Remove rows that contain all NA or certain columns in R?

Remove rows that contain all NA or certain columns in R?, when coming to data cleansing handling NA values is a crucial point.

If we have missing data then sometimes we need to remove the row that contains NA values, or only need to remove if all the column contains NA values or if any column contains NA value need to remove the row.

In this article, we are going to discuss how to remove NA values from a data frame.

How to clean the datasets in R? » janitor Data Cleansing »

Remove rows that contain all NA or certain columns in R?

1. Remove rows from column contains NA

If you want to remove the row contains NA values in a particular column, the following methods can try.

Method 1: Using drop_na()

Create a data frame

df=data.frame(Col1=c("A","B","C","D",
                          "P1","P2","P3")
                   ,Col2=c(7,8,NA,9,10,8,9)
                   ,Col3=c(5,7,6,8,NA,7,8)
                   ,Col4=c(7,NA,7,7,NA,7,7))
df
    Col1 Col2 Col3 Col4
 1    A    7    5    7
 2    B    8    7   NA
 3    C   NA    6    7
 4    D    9    8    7
 5   P1   10   NA   NA
 6   P2    8    7    7
 7   P3    9    8    7
library(tidyr)
df %>% drop_na(Col2)
   Col1 Col2 Col3 Col4
1    A    7    5    7
2    B    8    7   NA
3    D    9    8    7
4   P1   10   NA   NA
5   P2    8    7    7
6   P3    9    8    7

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Method 2: Using is.na()

Create a data frame

df=data.frame(Col1=c("A","B","C","D",
                     "P1","P2","P3")
              ,Col2=c(7,8,NA,9,10,8,9)
              ,Col3=c(5,7,6,8,NA,7,8)
              ,Col4=c(7,NA,7,7,NA,7,7)) 
df[!is.na(df$Col3),]
   Col1 Col2 Col3 Col4
1    A    7    5    7
2    B    8    7   NA
3    C   NA    6    7
4    D    9    8    7
6   P2    8    7    7
7   P3    9    8    7

Method 3:Using complete.cases()

df=data.frame(Col1=c("A","B","C","D",
                     "P1","P2","P3")
              ,Col2=c(7,8,NA,9,10,8,9)
              ,Col3=c(5,7,6,8,NA,7,8)
              ,Col4=c(7,NA,7,7,NA,7,7))
df[complete.cases(df$Col4),]
   Col1 Col2 Col3 Col4
1    A    7    5    7
3    C   NA    6    7
4    D    9    8    7
6   P2    8    7    7
7   P3    9    8    7

Method 4:Using which()

df=data.frame(Col1=c("A","B","C","D",
                     "P1","P2","P3")
              ,Col2=c(7,8,NA,9,10,8,9)
              ,Col3=c(5,7,6,8,NA,7,8)
              ,Col4=c(7,NA,7,7,NA,7,7))
df[-which(is.na(df$Col3)),]
   Col1 Col2 Col3 Col4
1    A    7    5    7
2    B    8    7   NA
3    C   NA    6    7
4    D    9    8    7
6   P2    8    7    7
7   P3    9    8    7

2. Remove Rows with contains some missing NA values

Method 1:Using na.omit() Function

df=data.frame(Col1=c(NA,"B","C","D",
                     "P1","P2","P3")
              ,Col2=c(NA,8,NA,9,10,8,9)
              ,Col3=c(NA,7,6,8,NA,7,8)
              ,Col4=c(NA,NA,7,7,NA,7,7))
df
   Col1 Col2 Col3 Col4
 1    NA   NA   NA  NA
 2    B    8    7   NA
 3    C   NA    6    7
 4    D    9    8    7
 5   P1   10   NA   NA
 6   P2    8    7    7
 7   P3    9    8    7
na.omit(df)
   Col1 Col2 Col3 Col4
4    D    9    8    7
6   P2    8    7    7
7   P3    9    8    7

Method 2:Using complete.cases() Function

df=data.frame(Col1=c(NA,"B","C","D",
                     "P1","P2","P3")
              ,Col2=c(NA,8,NA,9,10,8,9)
              ,Col3=c(NA,7,6,8,NA,7,8)
              ,Col4=c(NA,NA,7,7,NA,7,7))
df[complete.cases(df), ]
   Col1 Col2 Col3 Col4
4    D    9    8    7
6   P2    8    7    7
7   P3    9    8    7

Method 3:Using rowSums() & is.na() Functions

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df=data.frame(Col1=c(NA,"B","C","D",
                     "P1","P2","P3")
              ,Col2=c(NA,8,NA,9,10,8,9)
              ,Col3=c(NA,7,6,8,NA,7,8)
              ,Col4=c(NA,NA,7,7,NA,7,7))
df[rowSums(is.na(df)) == 0, ]
  Col1 Col2 Col3 Col4
4    D    9    8    7
6   P2    8    7    7
7   P3    9    8    7

Method 4:Using Using drop_na() Function

df=data.frame(Col1=c(NA,"B","C","D",
                     "P1","P2","P3")
              ,Col2=c(NA,8,NA,9,10,8,9)
              ,Col3=c(NA,7,6,8,NA,7,8)
              ,Col4=c(NA,NA,7,7,NA,7,7))
library(tidyr)
df %>% drop_na() 
   Col1 Col2 Col3 Col4
1    D    9    8    7
2   P2    8    7    7
3   P3    9    8    7

Method 5:Using remove_empty function from janitor package

df<-df %>% remove_empty(whic=c("rows"))
df<-df %>% remove_empty(whic=c("cols"))

3. Row which contains all column values that are missing

Suppose if you want to remove all column values contains NA then following codes will be handy.

Method 1:Using  is.na(), rowSums() & ncol() Functions

df=data.frame(Col1=c(NA,"B","C","D",
                     "P1","P2","P3")
              ,Col2=c(NA,8,NA,9,10,8,9)
              ,Col3=c(NA,7,6,8,NA,7,8)
              ,Col4=c(NA,NA,7,7,NA,7,7))
df[rowSums(is.na(df)) != ncol(df), ]
Col1 Col2 Col3 Col4
2    B    8    7   NA
3    C   NA    6    7
4    D    9    8    7
5   P1   10   NA   NA
6   P2    8    7    7
7   P3    9    8    7

Method 2:Using  Using filter() Function

df=data.frame(Col1=c(NA,"B","C","D",
                     "P1","P2","P3")
              ,Col2=c(NA,8,NA,9,10,8,9)
              ,Col3=c(NA,7,6,8,NA,7,8)
              ,Col4=c(NA,NA,7,7,NA,7,7))
library("dplyr")
filter(df, rowSums(is.na(df)) != ncol(df))
   Col1 Col2 Col3 Col4
1    B    8    7   NA
2    C   NA    6    7
3    D    9    8    7
4   P1   10   NA   NA
5   P2    8    7    7
6   P3    9    8    7

Conclusion

If you found some other useful function, please mentioned in the comment box.

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4 Responses

  1. Kamran says:

    This is great.

  2. Bill Denney says:

    The janitor package has remove_empty() to simplify removing completely empty rows or columns.

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