Some times you need to filter a data frame applying the same condition over multiple columns. Obviously you could explicitly write the condition over every column, but that’s not very handy.

For those situations, it is much better to use filter_at in combination with all_vars.

Imagine we have the famous iris dataset with some attributes missing and want to get rid of those observations with any missing value.

# # A tibble: 10 x 6
# rowid Sepal.Length Sepal.Width Petal.Length Petal.Width Species
# <int> <dbl> <dbl> <dbl> <dbl> <fct> 
# 1 1 5.1 NA 1.4 0.2 setosa 
# 2 2 NA 3 1.4 NA setosa 
# 3 3 4.7 3.2 1.3 0.2 setosa 
# 4 4 NA 3.1 1.5 0.2 setosa 
# 5 5 5 3.6 1.4 0.2 setosa 
# 6 6 5.4 3.9 1.7 0.4 setosa 
# 7 7 4.6 3.4 1.4 0.3 setosa 
# 8 8 NA 3.4 1.5 0.2 setosa 
# 9 9 4.4 2.9 1.4 0.2 setosa 
# 10 10 NA NA NA NA setosa 

We could write the condition on every column, but that would cumbersome:

iris %>%
 filter(!is.na(Sepal.Length) & 
 !is.na(Sepal.Width) &
 !is.na(Petal.Length) &
 !is.na(Petal.Width))

Instead, we just have to select the columns we will filter on and apply the condition:

features <- iris %>% names() %>% keep(~ str_detect(.,"[.]"))
iris %>% filter_at(vars(features), all_vars(!is.na(.)))
# # A tibble: 5 x 6
# rowid Sepal.Length Sepal.Width Petal.Length Petal.Width Species
# <int> <dbl> <dbl> <dbl> <dbl> <fct> 
# 1 3 4.7 3.2 1.3 0.2 setosa 
# 2 5 5 3.6 1.4 0.2 setosa 
# 3 6 5.4 3.9 1.7 0.4 setosa 
# 4 7 4.6 3.4 1.4 0.3 setosa 
# 5 9 4.4 2.9 1.4 0.2 setosa 

Here we have used the function all_vars in the predicate to explicit that every feature must satisfy the condition. To be honest, for that purpose it would have been easier to simply use iris %>% na.omit().

But what if we wanted the opposite? Keeping only the rows with all the selected features missing is as easy as changing the predicate part:

iris %>% filter_at(vars(features), all_vars(is.na(.)))

# # A tibble: 1 x 6
# rowid Sepal.Length Sepal.Width Petal.Length Petal.Width Species
# <int> <dbl> <dbl> <dbl> <dbl> <fct> 
# 1 10 NA NA NA NA setosa 

Another option is to apply the condition on any feature. That’s where any_vars comes handy. Here we keep only the observations with at least one missing feature:

iris %>% filter_at(vars(features), any_vars(is.na(.)))

# # A tibble: 5 x 6
# rowid Sepal.Length Sepal.Width Petal.Length Petal.Width Species
# <int> <dbl> <dbl> <dbl> <dbl> <fct> 
# 1 1 5.1 NA 1.4 0.2 setosa 
# 2 2 NA 3 1.4 NA setosa 
# 3 4 NA 3.1 1.5 0.2 setosa 
# 4 8 NA 3.4 1.5 0.2 setosa 
# 5 10 NA NA NA NA setosa 

Also, there are some other fancy ways to manipulate data frames with the filter family. One trick is using contains() or starts_with() to select the variables:

iris %>% filter_at(vars(contains("Length")), all_vars(. >= 1.4))

# # A tibble: 5 x 6
# rowid Sepal.Length Sepal.Width Petal.Length Petal.Width Species
# <int> <dbl> <dbl> <dbl> <dbl> <fct> 
# 1 1 5.1 NA 1.4 0.2 setosa 
# 2 5 5 3.6 1.4 0.2 setosa 
# 3 6 5.4 3.9 1.7 0.4 setosa 
# 4 7 4.6 3.4 1.4 0.3 setosa 
# 5 9 4.4 2.9 1.4 0.2 setosa 

Another example is applying the condition on columns that satisfy certain condition with filter_if (notice the rowid fetaure here):

iris %>% filter_if(is.numeric, any_vars(. > 5))

# # A tibble: 6 x 6
# rowid Sepal.Length Sepal.Width Petal.Length Petal.Width Species
# <int> <dbl> <dbl> <dbl> <dbl> <fct> 
# 1 1 5.1 NA 1.4 0.2 setosa 
# 2 6 5.4 3.9 1.7 0.4 setosa 
# 3 7 4.6 3.4 1.4 0.3 setosa 
# 4 8 NA 3.4 1.5 0.2 setosa 
# 5 9 4.4 2.9 1.4 0.2 setosa 
# 6 10 NA NA NA NA setosa