R：比这个for-loop更有效的解决方案

Question

I wrote a functioning for loop, but it's slow over thousands of rows and I'm looking for more efficient alternative. Thanks in advance!

The task:

• If column a matches column b , column d becomes NA .
• If column a does not match b , but b matches c , then column e becomes NA .

The for loop:

for (i in 1:nrow(data)) {
     if (data$a[i] == data$b[i]) {data$d[i] <- NA}
     if (!(data$a[i] == data$b[i]) & data$b[i] == data$c[i])
        {data$e[i] <- NA}
}

An example:

a    b    c    d    e
F    G    G    1    10
F    G    F    5    10
F    F    F    2    8

Would become:

a    b    c    d    e
F    G    G    1    NA
F    G    F    5    10
F    F    F    NA    8

Answer 1

If you're concerned about speed and efficiency, I'd recommend data.table (though technically vectorizing a normal data.frame as recommended by @parfait would probably speed things up more than enough)

library(data.table)

DT <- fread("a    b    c    d    e
             F    G    G    1    10
             F    G    F    5    10
             F    F    F    2    8")
print(DT)
#    a b c d  e
# 1: F G G 1 10
# 2: F G F 5 10
# 3: F F F 2  8

DT[a == b, d := NA]
DT[!a == b & b == c, e := NA]

print(DT)
#    a b c  d  e
# 1: F G G  1 NA
# 2: F G F  5 10
# 3: F F F NA  8

Answer 2

Suppose df is your data then:

ab <- with(df, a==b)
bc <- with(df, b==c)

df$d[ab] <- NA
df$e[!ab & bc] <- NA

which would result in

#   a b c  d  e
# 1 F G G  1 NA
# 2 F G F  5 10
# 3 F F F NA  8

Answer 3

We could create a list of quosure and evaluate it

library(tidyverse)
qs <- setNames(quos(d*NA^(a == b), e*NA^((!(a ==b) & (b == c)))), c("d", "e"))
df1 %>%
    mutate(!!! qs)
#  a b c  d  e
#1 F G G  1 NA
#2 F G F  5 10
#3 F F F NA  8