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To drop rows with NA's in some specific columns, you can use the filter() function from the dplyr package and the in.na() function. First, the latter one determines if a value in a column is missing and returns a TRUE or FALSE. Next, the filter function drops all rows with an NA.
To remove all rows having NA, we can use na. omit function. For Example, if we have a data frame called df that contains some NA values then we can remove all rows that contains at least one NA by using the command na. omit(df).
The na. omit() function returns a list without any rows that contain na values. It will drop rows with na value / nan values. This is the fastest way to remove na rows in the R programming language.
Use is.na
DF <- data.frame(x = c(1, 2, 3), y = c(0, 10, NA), z=c(NA, 33, 22))
DF[!is.na(DF$y),]
Hadley's tidyr just got this amazing function drop_na
library(tidyr)
DF %>% drop_na(y)
x y z
1 1 0 NA
2 2 10 33
You could use the complete.cases function and put it into a function thusly:
DF <- data.frame(x = c(1, 2, 3), y = c(0, 10, NA), z=c(NA, 33, 22))
completeFun <- function(data, desiredCols) {
completeVec <- complete.cases(data[, desiredCols])
return(data[completeVec, ])
}
completeFun(DF, "y")
# x y z
# 1 1 0 NA
# 2 2 10 33
completeFun(DF, c("y", "z"))
# x y z
# 2 2 10 33
EDIT: Only return rows with no NAs
If you want to eliminate all rows with at least one NA in any column, just use the complete.cases function straight up:
DF[complete.cases(DF), ]
# x y z
# 2 2 10 33
Or if completeFun is already ingrained in your workflow ;)
completeFun(DF, names(DF))
Use 'subset'
DF <- data.frame(x = c(1, 2, 3), y = c(0, 10, NA), z=c(NA, 33, 22))
subset(DF, !is.na(y))
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