基于满足R中的多列条件删除行

Question

I have a very large dataset that I would like to clean up by deleting rows where I have select columns meet the condition of all entries in these column selection being equal to 0. Here is currently what I have:

df1 <- filter(df,((n)==0 & (n+1)==0 & (n+2)==0 & (n+3)==0 & ......(n+100)==0)

How do I do this so that I delete all row entries that meet this condition for every nth column?

Also, if I wanted to iterate this condition, do I need to state the name of the column?

Here is an example dataset:

 A tibble: 10 x 10
 A B C D E F G H I J
 1 1 1 1 1 0 0 0 0 0
 0 0 0 0 0 0 0 0 0 0
 0 0 1 1 1 0 0 0 1 1
 0 0 0 0 0 0 0 0 0 0
 1 1 1 1 1 1 1 0 1 1
 1 1 1 1 1 0 0 0 0 0 
 0 0 0 0 0 0 0 0 0 0
 0 0 0 0 0 0 0 1 0 0
 0 0 0 0 1 0 0 0 0 0
 0 0 0 0 0 0 0 0 0 0

I want to remove all rows where F, G & H column are equal to 0, where my result will be:

 A tibble: 10 x 2
 A B C D E F G H I J
 1 1 1 1 1 1 1 0 1 1
 0 0 0 0 0 0 0 1 0 0

Answer 1

One option is filter_at

library(dplyr)
df %>%
   filter_at(11:20, any_vars( .  != 0))

A reproducible example

df1 %>% 
   filter_at(vars(`11`:`13`), any_vars(. != 0))
# A tibble: 2 x 4
#   `11`  `12`  `13` grp  
#     <dbl> <dbl> <dbl> <chr>
#1     1     0     4 a    
#2     0     1     0 b    

Or using across from the devel version of dplyr

df1 %>%
    filter(across(cols = matches('^\\d+$'), ~ (.x == 0))) %>% 
    anti_join(df1, .)
# A tibble: 2 x 4
#   `11`  `12`  `13` grp  
#  <dbl> <dbl> <dbl> <chr>
#1     1     0     4 a    
#2     0     1     0 b    

Update

Based on the OP's update, if we have 'n' as some column index and want to filter basedd the columns from that position to the 100 columns after that

n <- 5
df %>%
     filter_at(n:(n+100), any_vars(. != 0))

Update2

df2 %>%
   filter_at(vars(F, G, H), any_vars(. != 0))
# A tibble: 2 x 10
#      A     B     C     D     E     F     G     H     I     J
#  <int> <int> <int> <int> <int> <int> <int> <int> <int> <int>
#1     1     1     1     1     1     1     1     0     1     1
#2     0     0     0     0     0     0     0     1     0     0

Or using base R

df2[rowSums(df2[c("F", "G", "H")] != 0) > 0,]

data

df1 <- tibble(`11` = c(1, 0, 0), `12` = c(0, 1, 0), `13` = c(4,  0, 0), 
  grp = letters[1:3])





df2 <- structure(list(A = c(1L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L), 
    B = c(1L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L), C = c(1L, 
    0L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 0L), D = c(1L, 0L, 1L, 0L, 
    1L, 1L, 0L, 0L, 0L, 0L), E = c(1L, 0L, 1L, 0L, 1L, 1L, 0L, 
    0L, 1L, 0L), F = c(0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L
    ), G = c(0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L), H = c(0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L), I = c(0L, 0L, 1L, 0L, 
    1L, 0L, 0L, 0L, 0L, 0L), J = c(0L, 0L, 1L, 0L, 1L, 0L, 0L, 
    0L, 0L, 0L)), row.names = c(NA, -10L), class = c("tbl_df", 
"tbl", "data.frame"))