根据多个分层字段和标准对数据进行子集

Question

My data frame has multiple factors. I would like to subset the data in a way that excludes only data that belongs to a specific factor level within another factor level.

I've used the two following approaches, but only one has worked - not sure why. Would appreciate if someone could explain it.

This is a simplified example, where f1 and f2 are the factors:

df = data.frame(f1 = c(rep(2019,4),rep(2018,4),rep(2017,4)), 
           f2 = rep(1:4,3), data = c(0:11))
print (df)

Output:

     f1 f2 data
1  2019  1    0
2  2019  2    1
3  2019  3    2
4  2019  4    3
5  2018  1    4
6  2018  2    5
7  2018  3    6
8  2018  4    7
9  2017  1    8
10 2017  2    9
11 2017  3   10
12 2017  4   11

In this case I want to keep only data that do not belong to level "1" of "factor 2" that are from "2019" in "factor 1".

Method 1:

subs.df = subset (df, f1 != 2019 & f2 != 1)
print (subs.df)

     f1 f2 data
6  2018  2    5
7  2018  3    6
8  2018  4    7
10 2017  2    9
11 2017  3   10
12 2017  4   11

Method 2:

subs.df = subset (df, !(f1 %in% 2019 & f2 %in% 1))
print (subs.df)

     f1 f2 data
2  2019  2    1
3  2019  3    2
4  2019  4    3
5  2018  1    4
6  2018  2    5
7  2018  3    6
8  2018  4    7
9  2017  1    8
10 2017  2    9
11 2017  3   10
12 2017  4   11

WORKED!

Why doesn't method 1 work but method 2 does? What are the differences?

Answer 1

This is a logical issue, the negation of (A and B) is (not A) or (not B)

You just have to replace & by | (or)

subs.df = subset (df, f1 != 2019 | f2 != 1)