Remove rows in dataframe based on multiple columns in another dataframe

Question

Using the iris dataset, I want to remove values that match the below dataframe in that I want to remove any with the species name setosa that has the petal.length of 1.4 and remove any with the species name versicolor that has the petal.length of 4.6. This table can update anytime so the query needs to reference the table.

#dataframe of things to remove
species<-c("setosa","versicolor")
Petal.Length<-c(1.4,4.6)
remove<-data.frame(species,Petal.Length, stringsAsFactors = FALSE)

#iris dataset
iris<-iris

Whats the best way to do this?

Answer 1

use dplyr::anti_join

iris %>% anti_join(remove, by = c("Species" = "species", "Petal.Length" = "Petal.Length"))

    Sepal.Length Sepal.Width Petal.Length Petal.Width    Species
1            4.7         3.2          1.3         0.2     setosa
2            4.6         3.1          1.5         0.2     setosa
3            5.4         3.9          1.7         0.4     setosa
4            5.0         3.4          1.5         0.2     setosa
5            4.9         3.1          1.5         0.1     setosa
6            5.4         3.7          1.5         0.2     setosa
7            4.8         3.4          1.6         0.2     setosa
8            4.3         3.0          1.1         0.1     setosa
9            5.8         4.0          1.2         0.2     setosa
10           5.7         4.4          1.5         0.4     setosa
11           5.4         3.9          1.3         0.4     setosa
12           5.7         3.8          1.7         0.3     setosa
13           5.1         3.8          1.5         0.3     setosa
14           5.4         3.4          1.7         0.2     setosa
15           5.1         3.7          1.5         0.4     setosa
16           4.6         3.6          1.0         0.2     setosa
17           5.1         3.3          1.7         0.5     setosa
18           4.8         3.4          1.9         0.2     setosa
19           5.0         3.0          1.6         0.2     setosa
20           5.0         3.4          1.6         0.4     setosa
21           5.2         3.5          1.5         0.2     setosa
22           4.7         3.2          1.6         0.2     setosa
23           4.8         3.1          1.6         0.2     setosa
24           5.4         3.4          1.5         0.4     setosa
25           5.2         4.1          1.5         0.1     setosa
26           4.9         3.1          1.5         0.2     setosa
27           5.0         3.2          1.2         0.2     setosa
28           5.5         3.5          1.3         0.2     setosa
29           4.4         3.0          1.3         0.2     setosa
30           5.1         3.4          1.5         0.2     setosa
31           5.0         3.5          1.3         0.3     setosa
32           4.5         2.3          1.3         0.3     setosa
33           4.4         3.2          1.3         0.2     setosa
34           5.0         3.5          1.6         0.6     setosa
35           5.1         3.8          1.9         0.4     setosa
36           5.1         3.8          1.6         0.2     setosa
37           5.3         3.7          1.5         0.2     setosa
38           7.0         3.2          4.7         1.4 versicolor
39           6.4         3.2          4.5         1.5 versicolor
40           6.9         3.1          4.9         1.5 versicolor
41           5.5         2.3          4.0         1.3 versicolor
42           5.7         2.8          4.5         1.3 versicolor
43           6.3         3.3          4.7         1.6 versicolor
44           4.9         2.4          3.3         1.0 versicolor
45           5.2         2.7          3.9         1.4 versicolor
46           5.0         2.0          3.5         1.0 versicolor
47           5.9         3.0          4.2         1.5 versicolor
48           6.0         2.2          4.0         1.0 versicolor
49           6.1         2.9          4.7         1.4 versicolor
50           5.6         2.9          3.6         1.3 versicolor
51           6.7         3.1          4.4         1.4 versicolor
52           5.6         3.0          4.5         1.5 versicolor
53           5.8         2.7          4.1         1.0 versicolor
54           6.2         2.2          4.5         1.5 versicolor
55           5.6         2.5          3.9         1.1 versicolor
56           5.9         3.2          4.8         1.8 versicolor
57           6.1         2.8          4.0         1.3 versicolor
58           6.3         2.5          4.9         1.5 versicolor
59           6.1         2.8          4.7         1.2 versicolor
60           6.4         2.9          4.3         1.3 versicolor
61           6.6         3.0          4.4         1.4 versicolor
62           6.8         2.8          4.8         1.4 versicolor
63           6.7         3.0          5.0         1.7 versicolor
64           6.0         2.9          4.5         1.5 versicolor
65           5.7         2.6          3.5         1.0 versicolor
66           5.5         2.4          3.8         1.1 versicolor
67           5.5         2.4          3.7         1.0 versicolor
68           5.8         2.7          3.9         1.2 versicolor
69           6.0         2.7          5.1         1.6 versicolor
70           5.4         3.0          4.5         1.5 versicolor
71           6.0         3.4          4.5         1.6 versicolor
72           6.7         3.1          4.7         1.5 versicolor
73           6.3         2.3          4.4         1.3 versicolor
74           5.6         3.0          4.1         1.3 versicolor
75           5.5         2.5          4.0         1.3 versicolor
76           5.5         2.6          4.4         1.2 versicolor
77           5.8         2.6          4.0         1.2 versicolor
78           5.0         2.3          3.3         1.0 versicolor
79           5.6         2.7          4.2         1.3 versicolor
80           5.7         3.0          4.2         1.2 versicolor
81           5.7         2.9          4.2         1.3 versicolor
82           6.2         2.9          4.3         1.3 versicolor
83           5.1         2.5          3.0         1.1 versicolor
84           5.7         2.8          4.1         1.3 versicolor
85           6.3         3.3          6.0         2.5  virginica
86           5.8         2.7          5.1         1.9  virginica
87           7.1         3.0          5.9         2.1  virginica
88           6.3         2.9          5.6         1.8  virginica
89           6.5         3.0          5.8         2.2  virginica
90           7.6         3.0          6.6         2.1  virginica
91           4.9         2.5          4.5         1.7  virginica
92           7.3         2.9          6.3         1.8  virginica
93           6.7         2.5          5.8         1.8  virginica
94           7.2         3.6          6.1         2.5  virginica
95           6.5         3.2          5.1         2.0  virginica
96           6.4         2.7          5.3         1.9  virginica
97           6.8         3.0          5.5         2.1  virginica
98           5.7         2.5          5.0         2.0  virginica
99           5.8         2.8          5.1         2.4  virginica
100          6.4         3.2          5.3         2.3  virginica
101          6.5         3.0          5.5         1.8  virginica
102          7.7         3.8          6.7         2.2  virginica
103          7.7         2.6          6.9         2.3  virginica
104          6.0         2.2          5.0         1.5  virginica
105          6.9         3.2          5.7         2.3  virginica
106          5.6         2.8          4.9         2.0  virginica
107          7.7         2.8          6.7         2.0  virginica
108          6.3         2.7          4.9         1.8  virginica
109          6.7         3.3          5.7         2.1  virginica
110          7.2         3.2          6.0         1.8  virginica
111          6.2         2.8          4.8         1.8  virginica
112          6.1         3.0          4.9         1.8  virginica
113          6.4         2.8          5.6         2.1  virginica
114          7.2         3.0          5.8         1.6  virginica
115          7.4         2.8          6.1         1.9  virginica
116          7.9         3.8          6.4         2.0  virginica
117          6.4         2.8          5.6         2.2  virginica
118          6.3         2.8          5.1         1.5  virginica
119          6.1         2.6          5.6         1.4  virginica
120          7.7         3.0          6.1         2.3  virginica
121          6.3         3.4          5.6         2.4  virginica
122          6.4         3.1          5.5         1.8  virginica
123          6.0         3.0          4.8         1.8  virginica
124          6.9         3.1          5.4         2.1  virginica
125          6.7         3.1          5.6         2.4  virginica
126          6.9         3.1          5.1         2.3  virginica
127          5.8         2.7          5.1         1.9  virginica
128          6.8         3.2          5.9         2.3  virginica
129          6.7         3.3          5.7         2.5  virginica
130          6.7         3.0          5.2         2.3  virginica
131          6.3         2.5          5.0         1.9  virginica
132          6.5         3.0          5.2         2.0  virginica
133          6.2         3.4          5.4         2.3  virginica
134          5.9         3.0          5.1         1.8  virginica

Answer 2

You can create a unique key by pasting the values of both the dataframes rowwise and removing the rows which match.

iris[!do.call(paste, iris[names(remove)]) %in% do.call(paste, remove), ]

#    Sepal.Length Sepal.Width Petal.Length Petal.Width    Species
#3            4.7         3.2          1.3         0.2     setosa
#4            4.6         3.1          1.5         0.2     setosa
#6            5.4         3.9          1.7         0.4     setosa
#8            5.0         3.4          1.5         0.2     setosa
#10           4.9         3.1          1.5         0.1     setosa
#11           5.4         3.7          1.5         0.2     setosa
#...
#...

data

Corrected the column name of Species so that it matches with iris.

Species<-c("setosa","versicolor")
Petal.Length<-c(1.4,4.6)
remove<-data.frame(Species,Petal.Length, stringsAsFactors = FALSE)