How to remove line that has missing?

The subset function resolves this directly and clearly, in my opinion. This can be done in conjunction with the is.na function being applied to the variable of interest.

> data.frame(x=1:12, y=rnorm(12), z=c(TRUE, TRUE, NA))
    x           y    z
1   1  1.02572367 TRUE
2   2  0.03988014 TRUE
3   3 -0.33269252   NA
4   4  0.05357787 TRUE
5   5 -0.05166907 TRUE
6   6 -0.68981171   NA
7   7  1.14728375 TRUE
8   8 -0.76820827 TRUE
9   9 -0.45425148   NA
10 10 -0.27369393 TRUE
11 11 -0.12687725 TRUE
12 12 -0.38773276   NA

> df <- data.frame(x=1:12, y=rnorm(12), z=c(TRUE, TRUE, NA))
> subset(df, !is.na(z))
    x          y    z
1   1 -0.2223889 TRUE
2   2 -0.7398008 TRUE
4   4 -1.6382330 TRUE
5   5  1.2596270 TRUE
7   7  1.0555701 TRUE
8   8 -1.5904792 TRUE
10 10 -0.0942284 TRUE
11 11 -0.3278851 TRUE

And you can also include more rules in the filter.

> subset(df, !is.na(z) & x %% 2 == 0)
    x          y    z
2   2 -0.7398008 TRUE
4   4 -1.6382330 TRUE
8   8 -1.5904792 TRUE
10 10 -0.0942284 TRUE

To remove rows without data in R, you must use the function complete.cases ().

For example in a dataset {x}:

y <- x[complete.cases(x),]
str(y)

The complete.cases (x) is a logical vector that will return TRUE for rows with data and FALSE for rows with no data.

Consider the following database:

> dados <- data.frame(
+     var1 = c(NA, 1),
+     var2 = c(1, NA)
+   )
>   
>   dados
  var1 var2
1   NA    1
2    1   NA

You can exclude all rows that have at least one missing using na.omit :

> na.omit(dados)
[1] var1 var2
<0 linhas> (ou row.names de comprimento 0)

Or delete all missing (NA) lines in some variable:

> dados[!is.na(dados$var1),]
  var1 var2
2    1   NA
> dados[!is.na(dados$var2),]
  var1 var2
1   NA    1

To check if a vector element is NA in R, we use the function is.na :

> is.na(NA)
[1] TRUE
> is.na(1)
[1] FALSE

To actually remove missions from the data.frame, you need to overwrite:

dados <- na.omit(dados)

You can also use the filter function of dplyr :

Creating sample data (based on Daniel's data):

dados <- data.frame(var1 = c(NA, 1, 3), var2 = c(1, NA, 3))

Loading dplyr :

library(dplyr)

Remove% with% s only from column NA

dados %>% filter(!is.na(var1))

Remove% with% s only from column var1

dados %>% filter(!is.na(var2))

To remove all% s from% s, use% s of% s. You can easily fit into the piping chain:

# remove todos NAs
dados %>% na.omit