您可以从dplyr使用mutate(across())。

mutate(df,across(all_of(vector),\(v) replace(v,v==1,0)))

一个基本的方法可以是用vector子集化df，然后在df[vector]==1中子集化这个。

df[,vector][df[,vector]==1] <- 0
#df[vector][df[vector]==1] <- 0 #Alternative

df
#  A B C
#1 0 0 2
#2 2 0 2
#3 2 0 1
#4 0 2 2

另一种方法是使用for循环。

for(i in vector) df[[i]][df[[i]]==1] <- 0
#for(i in vector) df[,i][df[,i]==1] <- 0 #Variant

基准

bench::mark(check=FALSE,
langtang = local({df <- dplyr::mutate(df,dplyr::across(all_of(vector),\(v) replace(v,v==1,0)))}),
"Maël" = local({df[, vector] <- replace(df[, vector], df[, vector] == 1, 0)}),
GKi = local({df[,vector][df[,vector]==1] <- 0}),
GKi2 = local(for(i in vector) df[,i][df[,i]==1] <- 0),
GKi3 = local(for(i in vector) df[[i]][df[[i]]==1] <- 0)
)
#  expression      min median itr/s…¹ mem_al…² gc/se…³ n_itr  n_gc total…⁴ result
#  <bch:expr> <bch:tm> <bch:>   <dbl> <bch:by>   <dbl> <int> <dbl> <bch:t> <list>
#1 langtang     2.66ms    3ms    299.   7.89KB    8.37   143     4   478ms <NULL>
#2 Maël       219.56µs  241µs   4017.     280B   12.3   1955     6   487ms <NULL>
#3 GKi        222.48µs  243µs   4013.     280B   12.3   1951     6   486ms <NULL>
#4 GKi2       106.96µs  116µs   8452.     280B   12.3   4119     6   487ms <NULL>
#5 GKi3        60.75µs   65µs  15217.     280B   14.4   7398     7   486ms <NULL>

for循环比其他 base 变体快约3倍，比 dplyr 变体快约50倍。所有 base 变体使用的内存都比 dplyr 变体少。