I have data frame like this
class col2 col3 col4 col5 col6
A AA 0 5 4 2 15
B AA 4 10 14 12 25
C AA 19 2 8 5 3
D SS 17 5 5 32 12
E AA 14 2 12 14 55
F II 12 17 1 9 0
G SS 10 37 8 2 17
H II 17 7 5 7 14
I want to remove all columns that have zero values
class col3 col4 col5
A AA 5 4 2
B AA 10 14 12
C AA 2 8 5
D SS 5 5 32
E AA 2 12 14
F II 17 1 9
G SS 37 8 2
H II 7 5 7
So the result I want is just want those columns which do not contain any zeros
Thank you
CodePudding user response：
Based on your description I assume you want to remove rows with zero values, not columns. Here's how you can do it with dplyr:
library(dplyr)
filter(df, across(everything(), ~.!=0))
#> # A tibble: 4 x 6
#> class col2 col3 col4 col5 col6
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 AA 4 10 14 12 25
#> 2 AA 19 2 8 5 3
#> 3 AA 14 2 12 14 55
#> 4 SS 10 37 8 2 17
CodePudding user response：
A possible solution:
df[apply(df == 0, 2, sum) == 0]
#> class col3 col4 col5
#> A AA 5 4 2
#> B AA 10 14 12
#> C AA 2 8 5
#> D SS 5 5 32
#> E AA 2 12 14
#> F II 17 1 9
#> G SS 37 8 2
#> H II 7 5 7
CodePudding user response：
One base R option could be:
df_so[,!sapply(df_so, function(x) any(x == 0))]
# class col3 col4 col5
#A AA 5 4 2
#B AA 10 14 12
#C AA 2 8 5
#D SS 5 5 32
#E AA 2 12 14
#F II 17 1 9
#G SS 37 8 2
#H II 7 5 7
Not my answer, but @user2974951 provided a very fast and straightforward answer as a comment in the Original Post:
df[,colSums(df==0)==0]
CodePudding user response：
With the new dataset:
base R:
In base R we can use Filter and negate any:
Filter(function(x) !any(x %in% 0), df)
class col3 col4 col5
A AA 5 4 2
B AA 10 14 12
C AA 2 8 5
D SS 5 5 32
E AA 2 12 14
F II 17 1 9
G SS 37 8 2
H II 7 5 7