catto_freq: Frequency encoding
In cattonum: Encode Categorical Features
Description
Usage
Arguments
Value
Examples
Description
Frequency encoding
Usage
1
catto_freq(train, ..., test, verbose = TRUE)
Arguments
train
The training data, in a data.frame or tibble.
...
The columns to be encoded. If none are specified, then
all character and factor columns are encoded.
test
The test data, in a data.frame or tibble.
verbose
Should informative messages be printed? Defaults to
TRUE (not yet used).
Value
The encoded dataset in a cattonum_df if no test dataset was
provided, and the encoded datasets in a cattonum_df2 otherwise.
Examples
1
Example output
sh: 1: cannot create /dev/null: Permission denied
sh: 1: cannot create /dev/null: Permission denied
cattonum is seeking a new maintainer; please respond if interested: https://github.com/bfgray3/cattonum/issues/40
# A cattonum_df with the following data:
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 5.1 3.5 1.4 0.2 50
2 4.9 3.0 1.4 0.2 50
3 4.7 3.2 1.3 0.2 50
4 4.6 3.1 1.5 0.2 50
5 5.0 3.6 1.4 0.2 50
6 5.4 3.9 1.7 0.4 50
7 4.6 3.4 1.4 0.3 50
8 5.0 3.4 1.5 0.2 50
9 4.4 2.9 1.4 0.2 50
10 4.9 3.1 1.5 0.1 50
11 5.4 3.7 1.5 0.2 50
12 4.8 3.4 1.6 0.2 50
13 4.8 3.0 1.4 0.1 50
14 4.3 3.0 1.1 0.1 50
15 5.8 4.0 1.2 0.2 50
16 5.7 4.4 1.5 0.4 50
17 5.4 3.9 1.3 0.4 50
18 5.1 3.5 1.4 0.3 50
19 5.7 3.8 1.7 0.3 50
20 5.1 3.8 1.5 0.3 50
21 5.4 3.4 1.7 0.2 50
22 5.1 3.7 1.5 0.4 50
23 4.6 3.6 1.0 0.2 50
24 5.1 3.3 1.7 0.5 50
25 4.8 3.4 1.9 0.2 50
26 5.0 3.0 1.6 0.2 50
27 5.0 3.4 1.6 0.4 50
28 5.2 3.5 1.5 0.2 50
29 5.2 3.4 1.4 0.2 50
30 4.7 3.2 1.6 0.2 50
31 4.8 3.1 1.6 0.2 50
32 5.4 3.4 1.5 0.4 50
33 5.2 4.1 1.5 0.1 50
34 5.5 4.2 1.4 0.2 50
35 4.9 3.1 1.5 0.2 50
36 5.0 3.2 1.2 0.2 50
37 5.5 3.5 1.3 0.2 50
38 4.9 3.6 1.4 0.1 50
39 4.4 3.0 1.3 0.2 50
40 5.1 3.4 1.5 0.2 50
41 5.0 3.5 1.3 0.3 50
42 4.5 2.3 1.3 0.3 50
43 4.4 3.2 1.3 0.2 50
44 5.0 3.5 1.6 0.6 50
45 5.1 3.8 1.9 0.4 50
46 4.8 3.0 1.4 0.3 50
47 5.1 3.8 1.6 0.2 50
48 4.6 3.2 1.4 0.2 50
49 5.3 3.7 1.5 0.2 50
50 5.0 3.3 1.4 0.2 50
51 7.0 3.2 4.7 1.4 50
52 6.4 3.2 4.5 1.5 50
53 6.9 3.1 4.9 1.5 50
54 5.5 2.3 4.0 1.3 50
55 6.5 2.8 4.6 1.5 50
56 5.7 2.8 4.5 1.3 50
57 6.3 3.3 4.7 1.6 50
58 4.9 2.4 3.3 1.0 50
59 6.6 2.9 4.6 1.3 50
60 5.2 2.7 3.9 1.4 50
61 5.0 2.0 3.5 1.0 50
62 5.9 3.0 4.2 1.5 50
63 6.0 2.2 4.0 1.0 50
64 6.1 2.9 4.7 1.4 50
65 5.6 2.9 3.6 1.3 50
66 6.7 3.1 4.4 1.4 50
67 5.6 3.0 4.5 1.5 50
68 5.8 2.7 4.1 1.0 50
69 6.2 2.2 4.5 1.5 50
70 5.6 2.5 3.9 1.1 50
71 5.9 3.2 4.8 1.8 50
72 6.1 2.8 4.0 1.3 50
73 6.3 2.5 4.9 1.5 50
74 6.1 2.8 4.7 1.2 50
75 6.4 2.9 4.3 1.3 50
76 6.6 3.0 4.4 1.4 50
77 6.8 2.8 4.8 1.4 50
78 6.7 3.0 5.0 1.7 50
79 6.0 2.9 4.5 1.5 50
80 5.7 2.6 3.5 1.0 50
81 5.5 2.4 3.8 1.1 50
82 5.5 2.4 3.7 1.0 50
83 5.8 2.7 3.9 1.2 50
84 6.0 2.7 5.1 1.6 50
85 5.4 3.0 4.5 1.5 50
86 6.0 3.4 4.5 1.6 50
87 6.7 3.1 4.7 1.5 50
88 6.3 2.3 4.4 1.3 50
89 5.6 3.0 4.1 1.3 50
90 5.5 2.5 4.0 1.3 50
91 5.5 2.6 4.4 1.2 50
92 6.1 3.0 4.6 1.4 50
93 5.8 2.6 4.0 1.2 50
94 5.0 2.3 3.3 1.0 50
95 5.6 2.7 4.2 1.3 50
96 5.7 3.0 4.2 1.2 50
97 5.7 2.9 4.2 1.3 50
98 6.2 2.9 4.3 1.3 50
99 5.1 2.5 3.0 1.1 50
100 5.7 2.8 4.1 1.3 50
101 6.3 3.3 6.0 2.5 50
102 5.8 2.7 5.1 1.9 50
103 7.1 3.0 5.9 2.1 50
104 6.3 2.9 5.6 1.8 50
105 6.5 3.0 5.8 2.2 50
106 7.6 3.0 6.6 2.1 50
107 4.9 2.5 4.5 1.7 50
108 7.3 2.9 6.3 1.8 50
109 6.7 2.5 5.8 1.8 50
110 7.2 3.6 6.1 2.5 50
111 6.5 3.2 5.1 2.0 50
112 6.4 2.7 5.3 1.9 50
113 6.8 3.0 5.5 2.1 50
114 5.7 2.5 5.0 2.0 50
115 5.8 2.8 5.1 2.4 50
116 6.4 3.2 5.3 2.3 50
117 6.5 3.0 5.5 1.8 50
118 7.7 3.8 6.7 2.2 50
119 7.7 2.6 6.9 2.3 50
120 6.0 2.2 5.0 1.5 50
121 6.9 3.2 5.7 2.3 50
122 5.6 2.8 4.9 2.0 50
123 7.7 2.8 6.7 2.0 50
124 6.3 2.7 4.9 1.8 50
125 6.7 3.3 5.7 2.1 50
126 7.2 3.2 6.0 1.8 50
127 6.2 2.8 4.8 1.8 50
128 6.1 3.0 4.9 1.8 50
129 6.4 2.8 5.6 2.1 50
130 7.2 3.0 5.8 1.6 50
131 7.4 2.8 6.1 1.9 50
132 7.9 3.8 6.4 2.0 50
133 6.4 2.8 5.6 2.2 50
134 6.3 2.8 5.1 1.5 50
135 6.1 2.6 5.6 1.4 50
136 7.7 3.0 6.1 2.3 50
137 6.3 3.4 5.6 2.4 50
138 6.4 3.1 5.5 1.8 50
139 6.0 3.0 4.8 1.8 50
140 6.9 3.1 5.4 2.1 50
141 6.7 3.1 5.6 2.4 50
142 6.9 3.1 5.1 2.3 50
143 5.8 2.7 5.1 1.9 50
144 6.8 3.2 5.9 2.3 50
145 6.7 3.3 5.7 2.5 50
146 6.7 3.0 5.2 2.3 50
147 6.3 2.5 5.0 1.9 50
148 6.5 3.0 5.2 2.0 50
149 6.2 3.4 5.4 2.3 50
150 5.9 3.0 5.1 1.8 50
cattonum documentation built on July 2, 2020, 2:26 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Description Usage Arguments Value Examples
Description
Frequency encoding
Usage
1 | catto_freq(train, ..., test, verbose = TRUE)
|
Arguments
train |
The training data, in a |
... |
The columns to be encoded. If none are specified, then all character and factor columns are encoded. |
test |
The test data, in a |
verbose |
Should informative messages be printed? Defaults to
|
Value
The encoded dataset in a cattonum_df if no test dataset was
provided, and the encoded datasets in a cattonum_df2 otherwise.
Examples
1 |
Example output
sh: 1: cannot create /dev/null: Permission denied
sh: 1: cannot create /dev/null: Permission denied
cattonum is seeking a new maintainer; please respond if interested: https://github.com/bfgray3/cattonum/issues/40
# A cattonum_df with the following data:
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 5.1 3.5 1.4 0.2 50
2 4.9 3.0 1.4 0.2 50
3 4.7 3.2 1.3 0.2 50
4 4.6 3.1 1.5 0.2 50
5 5.0 3.6 1.4 0.2 50
6 5.4 3.9 1.7 0.4 50
7 4.6 3.4 1.4 0.3 50
8 5.0 3.4 1.5 0.2 50
9 4.4 2.9 1.4 0.2 50
10 4.9 3.1 1.5 0.1 50
11 5.4 3.7 1.5 0.2 50
12 4.8 3.4 1.6 0.2 50
13 4.8 3.0 1.4 0.1 50
14 4.3 3.0 1.1 0.1 50
15 5.8 4.0 1.2 0.2 50
16 5.7 4.4 1.5 0.4 50
17 5.4 3.9 1.3 0.4 50
18 5.1 3.5 1.4 0.3 50
19 5.7 3.8 1.7 0.3 50
20 5.1 3.8 1.5 0.3 50
21 5.4 3.4 1.7 0.2 50
22 5.1 3.7 1.5 0.4 50
23 4.6 3.6 1.0 0.2 50
24 5.1 3.3 1.7 0.5 50
25 4.8 3.4 1.9 0.2 50
26 5.0 3.0 1.6 0.2 50
27 5.0 3.4 1.6 0.4 50
28 5.2 3.5 1.5 0.2 50
29 5.2 3.4 1.4 0.2 50
30 4.7 3.2 1.6 0.2 50
31 4.8 3.1 1.6 0.2 50
32 5.4 3.4 1.5 0.4 50
33 5.2 4.1 1.5 0.1 50
34 5.5 4.2 1.4 0.2 50
35 4.9 3.1 1.5 0.2 50
36 5.0 3.2 1.2 0.2 50
37 5.5 3.5 1.3 0.2 50
38 4.9 3.6 1.4 0.1 50
39 4.4 3.0 1.3 0.2 50
40 5.1 3.4 1.5 0.2 50
41 5.0 3.5 1.3 0.3 50
42 4.5 2.3 1.3 0.3 50
43 4.4 3.2 1.3 0.2 50
44 5.0 3.5 1.6 0.6 50
45 5.1 3.8 1.9 0.4 50
46 4.8 3.0 1.4 0.3 50
47 5.1 3.8 1.6 0.2 50
48 4.6 3.2 1.4 0.2 50
49 5.3 3.7 1.5 0.2 50
50 5.0 3.3 1.4 0.2 50
51 7.0 3.2 4.7 1.4 50
52 6.4 3.2 4.5 1.5 50
53 6.9 3.1 4.9 1.5 50
54 5.5 2.3 4.0 1.3 50
55 6.5 2.8 4.6 1.5 50
56 5.7 2.8 4.5 1.3 50
57 6.3 3.3 4.7 1.6 50
58 4.9 2.4 3.3 1.0 50
59 6.6 2.9 4.6 1.3 50
60 5.2 2.7 3.9 1.4 50
61 5.0 2.0 3.5 1.0 50
62 5.9 3.0 4.2 1.5 50
63 6.0 2.2 4.0 1.0 50
64 6.1 2.9 4.7 1.4 50
65 5.6 2.9 3.6 1.3 50
66 6.7 3.1 4.4 1.4 50
67 5.6 3.0 4.5 1.5 50
68 5.8 2.7 4.1 1.0 50
69 6.2 2.2 4.5 1.5 50
70 5.6 2.5 3.9 1.1 50
71 5.9 3.2 4.8 1.8 50
72 6.1 2.8 4.0 1.3 50
73 6.3 2.5 4.9 1.5 50
74 6.1 2.8 4.7 1.2 50
75 6.4 2.9 4.3 1.3 50
76 6.6 3.0 4.4 1.4 50
77 6.8 2.8 4.8 1.4 50
78 6.7 3.0 5.0 1.7 50
79 6.0 2.9 4.5 1.5 50
80 5.7 2.6 3.5 1.0 50
81 5.5 2.4 3.8 1.1 50
82 5.5 2.4 3.7 1.0 50
83 5.8 2.7 3.9 1.2 50
84 6.0 2.7 5.1 1.6 50
85 5.4 3.0 4.5 1.5 50
86 6.0 3.4 4.5 1.6 50
87 6.7 3.1 4.7 1.5 50
88 6.3 2.3 4.4 1.3 50
89 5.6 3.0 4.1 1.3 50
90 5.5 2.5 4.0 1.3 50
91 5.5 2.6 4.4 1.2 50
92 6.1 3.0 4.6 1.4 50
93 5.8 2.6 4.0 1.2 50
94 5.0 2.3 3.3 1.0 50
95 5.6 2.7 4.2 1.3 50
96 5.7 3.0 4.2 1.2 50
97 5.7 2.9 4.2 1.3 50
98 6.2 2.9 4.3 1.3 50
99 5.1 2.5 3.0 1.1 50
100 5.7 2.8 4.1 1.3 50
101 6.3 3.3 6.0 2.5 50
102 5.8 2.7 5.1 1.9 50
103 7.1 3.0 5.9 2.1 50
104 6.3 2.9 5.6 1.8 50
105 6.5 3.0 5.8 2.2 50
106 7.6 3.0 6.6 2.1 50
107 4.9 2.5 4.5 1.7 50
108 7.3 2.9 6.3 1.8 50
109 6.7 2.5 5.8 1.8 50
110 7.2 3.6 6.1 2.5 50
111 6.5 3.2 5.1 2.0 50
112 6.4 2.7 5.3 1.9 50
113 6.8 3.0 5.5 2.1 50
114 5.7 2.5 5.0 2.0 50
115 5.8 2.8 5.1 2.4 50
116 6.4 3.2 5.3 2.3 50
117 6.5 3.0 5.5 1.8 50
118 7.7 3.8 6.7 2.2 50
119 7.7 2.6 6.9 2.3 50
120 6.0 2.2 5.0 1.5 50
121 6.9 3.2 5.7 2.3 50
122 5.6 2.8 4.9 2.0 50
123 7.7 2.8 6.7 2.0 50
124 6.3 2.7 4.9 1.8 50
125 6.7 3.3 5.7 2.1 50
126 7.2 3.2 6.0 1.8 50
127 6.2 2.8 4.8 1.8 50
128 6.1 3.0 4.9 1.8 50
129 6.4 2.8 5.6 2.1 50
130 7.2 3.0 5.8 1.6 50
131 7.4 2.8 6.1 1.9 50
132 7.9 3.8 6.4 2.0 50
133 6.4 2.8 5.6 2.2 50
134 6.3 2.8 5.1 1.5 50
135 6.1 2.6 5.6 1.4 50
136 7.7 3.0 6.1 2.3 50
137 6.3 3.4 5.6 2.4 50
138 6.4 3.1 5.5 1.8 50
139 6.0 3.0 4.8 1.8 50
140 6.9 3.1 5.4 2.1 50
141 6.7 3.1 5.6 2.4 50
142 6.9 3.1 5.1 2.3 50
143 5.8 2.7 5.1 1.9 50
144 6.8 3.2 5.9 2.3 50
145 6.7 3.3 5.7 2.5 50
146 6.7 3.0 5.2 2.3 50
147 6.3 2.5 5.0 1.9 50
148 6.5 3.0 5.2 2.0 50
149 6.2 3.4 5.4 2.3 50
150 5.9 3.0 5.1 1.8 50
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