How to split a dataframe based on column class - r

Let's take the iris dataset for example.
I want to create two dataframes from this one. This first one would have the contiuous variables, and the second one the discrete ones.
What I do first is create a list with the category of the column
iris <- iris
a <- lapply(iris, class)
Then I create two empty lists and store the index of both discrete and continuous columns.
cont <- list()
disc <- list()
for (i in 1:length(a)){
if (a[[i]][1] == "numeric")
cont <- append(cont, i)
else
disc <- append(disc, i)
}
But I do not know how to split based on this lists.

Try the following code:
classes_iris <- sapply(iris, class)
split.default(iris, classes_iris)
Output:
$factor
Species
1 setosa
2 setosa
3 setosa
4 setosa
5 setosa
6 setosa
7 setosa
8 setosa
9 setosa
10 setosa
11 setosa
12 setosa
13 setosa
14 setosa
15 setosa
16 setosa
17 setosa
18 setosa
19 setosa
20 setosa
21 setosa
22 setosa
23 setosa
24 setosa
25 setosa
26 setosa
27 setosa
28 setosa
29 setosa
30 setosa
31 setosa
32 setosa
33 setosa
34 setosa
35 setosa
36 setosa
37 setosa
38 setosa
39 setosa
40 setosa
41 setosa
42 setosa
43 setosa
44 setosa
45 setosa
46 setosa
47 setosa
48 setosa
49 setosa
50 setosa
51 versicolor
52 versicolor
53 versicolor
54 versicolor
55 versicolor
56 versicolor
57 versicolor
58 versicolor
59 versicolor
60 versicolor
61 versicolor
62 versicolor
63 versicolor
64 versicolor
65 versicolor
66 versicolor
67 versicolor
68 versicolor
69 versicolor
70 versicolor
71 versicolor
72 versicolor
73 versicolor
74 versicolor
75 versicolor
76 versicolor
77 versicolor
78 versicolor
79 versicolor
80 versicolor
81 versicolor
82 versicolor
83 versicolor
84 versicolor
85 versicolor
86 versicolor
87 versicolor
88 versicolor
89 versicolor
90 versicolor
91 versicolor
92 versicolor
93 versicolor
94 versicolor
95 versicolor
96 versicolor
97 versicolor
98 versicolor
99 versicolor
100 versicolor
101 virginica
102 virginica
103 virginica
104 virginica
105 virginica
106 virginica
107 virginica
108 virginica
109 virginica
110 virginica
111 virginica
112 virginica
113 virginica
114 virginica
115 virginica
116 virginica
117 virginica
118 virginica
119 virginica
120 virginica
121 virginica
122 virginica
123 virginica
124 virginica
125 virginica
126 virginica
127 virginica
128 virginica
129 virginica
130 virginica
131 virginica
132 virginica
133 virginica
134 virginica
135 virginica
136 virginica
137 virginica
138 virginica
139 virginica
140 virginica
141 virginica
142 virginica
143 virginica
144 virginica
145 virginica
146 virginica
147 virginica
148 virginica
149 virginica
150 virginica
$numeric
Sepal.Length Sepal.Width Petal.Length Petal.Width
1 5.1 3.5 1.4 0.2
2 4.9 3.0 1.4 0.2
3 4.7 3.2 1.3 0.2
4 4.6 3.1 1.5 0.2
5 5.0 3.6 1.4 0.2
6 5.4 3.9 1.7 0.4
7 4.6 3.4 1.4 0.3
8 5.0 3.4 1.5 0.2
9 4.4 2.9 1.4 0.2
10 4.9 3.1 1.5 0.1
11 5.4 3.7 1.5 0.2
12 4.8 3.4 1.6 0.2
13 4.8 3.0 1.4 0.1
14 4.3 3.0 1.1 0.1
15 5.8 4.0 1.2 0.2
16 5.7 4.4 1.5 0.4
17 5.4 3.9 1.3 0.4
18 5.1 3.5 1.4 0.3
19 5.7 3.8 1.7 0.3
20 5.1 3.8 1.5 0.3
21 5.4 3.4 1.7 0.2
22 5.1 3.7 1.5 0.4
23 4.6 3.6 1.0 0.2
24 5.1 3.3 1.7 0.5
25 4.8 3.4 1.9 0.2
26 5.0 3.0 1.6 0.2
27 5.0 3.4 1.6 0.4
28 5.2 3.5 1.5 0.2
29 5.2 3.4 1.4 0.2
30 4.7 3.2 1.6 0.2
31 4.8 3.1 1.6 0.2
32 5.4 3.4 1.5 0.4
33 5.2 4.1 1.5 0.1
34 5.5 4.2 1.4 0.2
35 4.9 3.1 1.5 0.2
36 5.0 3.2 1.2 0.2
37 5.5 3.5 1.3 0.2
38 4.9 3.6 1.4 0.1
39 4.4 3.0 1.3 0.2
40 5.1 3.4 1.5 0.2
41 5.0 3.5 1.3 0.3
42 4.5 2.3 1.3 0.3
43 4.4 3.2 1.3 0.2
44 5.0 3.5 1.6 0.6
45 5.1 3.8 1.9 0.4
46 4.8 3.0 1.4 0.3
47 5.1 3.8 1.6 0.2
48 4.6 3.2 1.4 0.2
49 5.3 3.7 1.5 0.2
50 5.0 3.3 1.4 0.2
51 7.0 3.2 4.7 1.4
52 6.4 3.2 4.5 1.5
53 6.9 3.1 4.9 1.5
54 5.5 2.3 4.0 1.3
55 6.5 2.8 4.6 1.5
56 5.7 2.8 4.5 1.3
57 6.3 3.3 4.7 1.6
58 4.9 2.4 3.3 1.0
59 6.6 2.9 4.6 1.3
60 5.2 2.7 3.9 1.4
61 5.0 2.0 3.5 1.0
62 5.9 3.0 4.2 1.5
63 6.0 2.2 4.0 1.0
64 6.1 2.9 4.7 1.4
65 5.6 2.9 3.6 1.3
66 6.7 3.1 4.4 1.4
67 5.6 3.0 4.5 1.5
68 5.8 2.7 4.1 1.0
69 6.2 2.2 4.5 1.5
70 5.6 2.5 3.9 1.1
71 5.9 3.2 4.8 1.8
72 6.1 2.8 4.0 1.3
73 6.3 2.5 4.9 1.5
74 6.1 2.8 4.7 1.2
75 6.4 2.9 4.3 1.3
76 6.6 3.0 4.4 1.4
77 6.8 2.8 4.8 1.4
78 6.7 3.0 5.0 1.7
79 6.0 2.9 4.5 1.5
80 5.7 2.6 3.5 1.0
81 5.5 2.4 3.8 1.1
82 5.5 2.4 3.7 1.0
83 5.8 2.7 3.9 1.2
84 6.0 2.7 5.1 1.6
85 5.4 3.0 4.5 1.5
86 6.0 3.4 4.5 1.6
87 6.7 3.1 4.7 1.5
88 6.3 2.3 4.4 1.3
89 5.6 3.0 4.1 1.3
90 5.5 2.5 4.0 1.3
91 5.5 2.6 4.4 1.2
92 6.1 3.0 4.6 1.4
93 5.8 2.6 4.0 1.2
94 5.0 2.3 3.3 1.0
95 5.6 2.7 4.2 1.3
96 5.7 3.0 4.2 1.2
97 5.7 2.9 4.2 1.3
98 6.2 2.9 4.3 1.3
99 5.1 2.5 3.0 1.1
100 5.7 2.8 4.1 1.3
101 6.3 3.3 6.0 2.5
102 5.8 2.7 5.1 1.9
103 7.1 3.0 5.9 2.1
104 6.3 2.9 5.6 1.8
105 6.5 3.0 5.8 2.2
106 7.6 3.0 6.6 2.1
107 4.9 2.5 4.5 1.7
108 7.3 2.9 6.3 1.8
109 6.7 2.5 5.8 1.8
110 7.2 3.6 6.1 2.5
111 6.5 3.2 5.1 2.0
112 6.4 2.7 5.3 1.9
113 6.8 3.0 5.5 2.1
114 5.7 2.5 5.0 2.0
115 5.8 2.8 5.1 2.4
116 6.4 3.2 5.3 2.3
117 6.5 3.0 5.5 1.8
118 7.7 3.8 6.7 2.2
119 7.7 2.6 6.9 2.3
120 6.0 2.2 5.0 1.5
121 6.9 3.2 5.7 2.3
122 5.6 2.8 4.9 2.0
123 7.7 2.8 6.7 2.0
124 6.3 2.7 4.9 1.8
125 6.7 3.3 5.7 2.1
126 7.2 3.2 6.0 1.8
127 6.2 2.8 4.8 1.8
128 6.1 3.0 4.9 1.8
129 6.4 2.8 5.6 2.1
130 7.2 3.0 5.8 1.6
131 7.4 2.8 6.1 1.9
132 7.9 3.8 6.4 2.0
133 6.4 2.8 5.6 2.2
134 6.3 2.8 5.1 1.5
135 6.1 2.6 5.6 1.4
136 7.7 3.0 6.1 2.3
137 6.3 3.4 5.6 2.4
138 6.4 3.1 5.5 1.8
139 6.0 3.0 4.8 1.8
140 6.9 3.1 5.4 2.1
141 6.7 3.1 5.6 2.4
142 6.9 3.1 5.1 2.3
143 5.8 2.7 5.1 1.9
144 6.8 3.2 5.9 2.3
145 6.7 3.3 5.7 2.5
146 6.7 3.0 5.2 2.3
147 6.3 2.5 5.0 1.9
148 6.5 3.0 5.2 2.0
149 6.2 3.4 5.4 2.3
150 5.9 3.0 5.1 1.8
As you can see it is split based on the classes factor and numeric.

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For example, I have the typical dataframe:
library(tidyverse)
my_data <- as_tibble(iris)
my_data
# A tibble: 150 × 5
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
<dbl> <dbl> <dbl> <dbl> <fct>
1 5.1 3.5 1.4 0.2 setosa
2 4.9 3 1.4 0.2 setosa
3 4.7 3.2 1.3 0.2 setosa
4 4.6 3.1 1.5 0.2 setosa
5 5 3.6 1.4 0.2 setosa
6 5.4 3.9 1.7 0.4 setosa
7 4.6 3.4 1.4 0.3 setosa
8 5 3.4 1.5 0.2 setosa
9 4.4 2.9 1.4 0.2 setosa
10 4.9 3.1 1.5 0.1 setosa
# … with 140 more rows
I just want to reorder the dataset by the column "Species" (which has 3 values: setosa, virginica and versicolor), specifying an exact order of rows. For example: virginica, then setosa, then versicolor.
You can use arrange and match:
library(dplyr)
iris %>%
arrange(match(Species, c("virginica", "setosa", "versicolor")))
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#> 1 6.3 3.3 6.0 2.5 virginica
#> 2 5.8 2.7 5.1 1.9 virginica
#> 3 7.1 3.0 5.9 2.1 virginica
#> 4 6.3 2.9 5.6 1.8 virginica
#> 5 6.5 3.0 5.8 2.2 virginica
#> 6 7.6 3.0 6.6 2.1 virginica
#> 7 4.9 2.5 4.5 1.7 virginica
#> 8 7.3 2.9 6.3 1.8 virginica
#> 9 6.7 2.5 5.8 1.8 virginica
#> 10 7.2 3.6 6.1 2.5 virginica
#> 11 6.5 3.2 5.1 2.0 virginica
#> 12 6.4 2.7 5.3 1.9 virginica
#> 13 6.8 3.0 5.5 2.1 virginica
#> 14 5.7 2.5 5.0 2.0 virginica
#> 15 5.8 2.8 5.1 2.4 virginica
#> 16 6.4 3.2 5.3 2.3 virginica
#> 17 6.5 3.0 5.5 1.8 virginica
#> 18 7.7 3.8 6.7 2.2 virginica
#> 19 7.7 2.6 6.9 2.3 virginica
#> 20 6.0 2.2 5.0 1.5 virginica
#> 21 6.9 3.2 5.7 2.3 virginica
#> 22 5.6 2.8 4.9 2.0 virginica
#> 23 7.7 2.8 6.7 2.0 virginica
#> 24 6.3 2.7 4.9 1.8 virginica
#> 25 6.7 3.3 5.7 2.1 virginica
#> 26 7.2 3.2 6.0 1.8 virginica
#> 27 6.2 2.8 4.8 1.8 virginica
#> 28 6.1 3.0 4.9 1.8 virginica
#> 29 6.4 2.8 5.6 2.1 virginica
#> 30 7.2 3.0 5.8 1.6 virginica
#> 31 7.4 2.8 6.1 1.9 virginica
#> 32 7.9 3.8 6.4 2.0 virginica
#> 33 6.4 2.8 5.6 2.2 virginica
#> 34 6.3 2.8 5.1 1.5 virginica
#> 35 6.1 2.6 5.6 1.4 virginica
#> 36 7.7 3.0 6.1 2.3 virginica
#> 37 6.3 3.4 5.6 2.4 virginica
#> 38 6.4 3.1 5.5 1.8 virginica
#> 39 6.0 3.0 4.8 1.8 virginica
#> 40 6.9 3.1 5.4 2.1 virginica
#> 41 6.7 3.1 5.6 2.4 virginica
#> 42 6.9 3.1 5.1 2.3 virginica
#> 43 5.8 2.7 5.1 1.9 virginica
#> 44 6.8 3.2 5.9 2.3 virginica
#> 45 6.7 3.3 5.7 2.5 virginica
#> 46 6.7 3.0 5.2 2.3 virginica
#> 47 6.3 2.5 5.0 1.9 virginica
#> 48 6.5 3.0 5.2 2.0 virginica
#> 49 6.2 3.4 5.4 2.3 virginica
#> 50 5.9 3.0 5.1 1.8 virginica
#> 51 5.1 3.5 1.4 0.2 setosa
#> 52 4.9 3.0 1.4 0.2 setosa
#> 53 4.7 3.2 1.3 0.2 setosa
#> 54 4.6 3.1 1.5 0.2 setosa
#> 55 5.0 3.6 1.4 0.2 setosa
#> 56 5.4 3.9 1.7 0.4 setosa
#> 57 4.6 3.4 1.4 0.3 setosa
#> 58 5.0 3.4 1.5 0.2 setosa
#> 59 4.4 2.9 1.4 0.2 setosa
#> 60 4.9 3.1 1.5 0.1 setosa
#> 61 5.4 3.7 1.5 0.2 setosa
#> 62 4.8 3.4 1.6 0.2 setosa
#> 63 4.8 3.0 1.4 0.1 setosa
#> 64 4.3 3.0 1.1 0.1 setosa
#> 65 5.8 4.0 1.2 0.2 setosa
#> 66 5.7 4.4 1.5 0.4 setosa
#> 67 5.4 3.9 1.3 0.4 setosa
#> 68 5.1 3.5 1.4 0.3 setosa
#> 69 5.7 3.8 1.7 0.3 setosa
#> 70 5.1 3.8 1.5 0.3 setosa
#> 71 5.4 3.4 1.7 0.2 setosa
#> 72 5.1 3.7 1.5 0.4 setosa
#> 73 4.6 3.6 1.0 0.2 setosa
#> 74 5.1 3.3 1.7 0.5 setosa
#> 75 4.8 3.4 1.9 0.2 setosa
#> 76 5.0 3.0 1.6 0.2 setosa
#> 77 5.0 3.4 1.6 0.4 setosa
#> 78 5.2 3.5 1.5 0.2 setosa
#> 79 5.2 3.4 1.4 0.2 setosa
#> 80 4.7 3.2 1.6 0.2 setosa
#> 81 4.8 3.1 1.6 0.2 setosa
#> 82 5.4 3.4 1.5 0.4 setosa
#> 83 5.2 4.1 1.5 0.1 setosa
#> 84 5.5 4.2 1.4 0.2 setosa
#> 85 4.9 3.1 1.5 0.2 setosa
#> 86 5.0 3.2 1.2 0.2 setosa
#> 87 5.5 3.5 1.3 0.2 setosa
#> 88 4.9 3.6 1.4 0.1 setosa
#> 89 4.4 3.0 1.3 0.2 setosa
#> 90 5.1 3.4 1.5 0.2 setosa
#> 91 5.0 3.5 1.3 0.3 setosa
#> 92 4.5 2.3 1.3 0.3 setosa
#> 93 4.4 3.2 1.3 0.2 setosa
#> 94 5.0 3.5 1.6 0.6 setosa
#> 95 5.1 3.8 1.9 0.4 setosa
#> 96 4.8 3.0 1.4 0.3 setosa
#> 97 5.1 3.8 1.6 0.2 setosa
#> 98 4.6 3.2 1.4 0.2 setosa
#> 99 5.3 3.7 1.5 0.2 setosa
#> 100 5.0 3.3 1.4 0.2 setosa
#> 101 7.0 3.2 4.7 1.4 versicolor
#> 102 6.4 3.2 4.5 1.5 versicolor
#> 103 6.9 3.1 4.9 1.5 versicolor
#> 104 5.5 2.3 4.0 1.3 versicolor
#> 105 6.5 2.8 4.6 1.5 versicolor
#> 106 5.7 2.8 4.5 1.3 versicolor
#> 107 6.3 3.3 4.7 1.6 versicolor
#> 108 4.9 2.4 3.3 1.0 versicolor
#> 109 6.6 2.9 4.6 1.3 versicolor
#> 110 5.2 2.7 3.9 1.4 versicolor
#> 111 5.0 2.0 3.5 1.0 versicolor
#> 112 5.9 3.0 4.2 1.5 versicolor
#> 113 6.0 2.2 4.0 1.0 versicolor
#> 114 6.1 2.9 4.7 1.4 versicolor
#> 115 5.6 2.9 3.6 1.3 versicolor
#> 116 6.7 3.1 4.4 1.4 versicolor
#> 117 5.6 3.0 4.5 1.5 versicolor
#> 118 5.8 2.7 4.1 1.0 versicolor
#> 119 6.2 2.2 4.5 1.5 versicolor
#> 120 5.6 2.5 3.9 1.1 versicolor
#> 121 5.9 3.2 4.8 1.8 versicolor
#> 122 6.1 2.8 4.0 1.3 versicolor
#> 123 6.3 2.5 4.9 1.5 versicolor
#> 124 6.1 2.8 4.7 1.2 versicolor
#> 125 6.4 2.9 4.3 1.3 versicolor
#> 126 6.6 3.0 4.4 1.4 versicolor
#> 127 6.8 2.8 4.8 1.4 versicolor
#> 128 6.7 3.0 5.0 1.7 versicolor
#> 129 6.0 2.9 4.5 1.5 versicolor
#> 130 5.7 2.6 3.5 1.0 versicolor
#> 131 5.5 2.4 3.8 1.1 versicolor
#> 132 5.5 2.4 3.7 1.0 versicolor
#> 133 5.8 2.7 3.9 1.2 versicolor
#> 134 6.0 2.7 5.1 1.6 versicolor
#> 135 5.4 3.0 4.5 1.5 versicolor
#> 136 6.0 3.4 4.5 1.6 versicolor
#> 137 6.7 3.1 4.7 1.5 versicolor
#> 138 6.3 2.3 4.4 1.3 versicolor
#> 139 5.6 3.0 4.1 1.3 versicolor
#> 140 5.5 2.5 4.0 1.3 versicolor
#> 141 5.5 2.6 4.4 1.2 versicolor
#> 142 6.1 3.0 4.6 1.4 versicolor
#> 143 5.8 2.6 4.0 1.2 versicolor
#> 144 5.0 2.3 3.3 1.0 versicolor
#> 145 5.6 2.7 4.2 1.3 versicolor
#> 146 5.7 3.0 4.2 1.2 versicolor
#> 147 5.7 2.9 4.2 1.3 versicolor
#> 148 6.2 2.9 4.3 1.3 versicolor
#> 149 5.1 2.5 3.0 1.1 versicolor
#> 150 5.7 2.8 4.1 1.3 versicolor

Sort data frame using last column name in R [duplicate]

This question already has answers here:
Order data frame by the last column with dplyr
(3 answers)
Closed 7 months ago.
I want to automatically sort the data frame based on the last column name.
Since the last column name in my data frame will be dynamic, I cannot specify the column name.
Below is what i want to achieve as an output.
iris %>%
select(-Species) %>%
arrange(desc(Petal.Width))
As suggested in response i tried below option, however it dosen't work. Am i missing something?
iris %>%
select(-Species) %>%
arrange(desc(ncol(.)))
last_col() is only supported in functions that feature tidy selection syntax, which arrange()doesn’t.
ncol() will give you the number of the last column, we can use it to subset the data.frame.
See TarJae‘s answer for another Dplyr option to make tidy selection syntax available inside arrange().
library(dplyr)
iris %>%
select(!Species) %>%
arrange(desc(.[,ncol(.)]))
#> Sepal.Length Sepal.Width Petal.Length Petal.Width
#> 1 6.3 3.3 6.0 2.5
#> 2 7.2 3.6 6.1 2.5
#> 3 6.7 3.3 5.7 2.5
#> 4 5.8 2.8 5.1 2.4
#> 5 6.3 3.4 5.6 2.4
#> 6 6.7 3.1 5.6 2.4
#> 7 6.4 3.2 5.3 2.3
#> 8 7.7 2.6 6.9 2.3
#> 9 6.9 3.2 5.7 2.3
#> 10 7.7 3.0 6.1 2.3
#> 11 6.9 3.1 5.1 2.3
#> 12 6.8 3.2 5.9 2.3
#> 13 6.7 3.0 5.2 2.3
#> 14 6.2 3.4 5.4 2.3
#> 15 6.5 3.0 5.8 2.2
#> 16 7.7 3.8 6.7 2.2
#> 17 6.4 2.8 5.6 2.2
#> 18 7.1 3.0 5.9 2.1
#> 19 7.6 3.0 6.6 2.1
#> 20 6.8 3.0 5.5 2.1
#> 21 6.7 3.3 5.7 2.1
#> 22 6.4 2.8 5.6 2.1
#> 23 6.9 3.1 5.4 2.1
#> 24 6.5 3.2 5.1 2.0
#> 25 5.7 2.5 5.0 2.0
#> 26 5.6 2.8 4.9 2.0
#> 27 7.7 2.8 6.7 2.0
#> 28 7.9 3.8 6.4 2.0
#> 29 6.5 3.0 5.2 2.0
#> 30 5.8 2.7 5.1 1.9
#> 31 6.4 2.7 5.3 1.9
#> 32 7.4 2.8 6.1 1.9
#> 33 5.8 2.7 5.1 1.9
#> 34 6.3 2.5 5.0 1.9
#> 35 5.9 3.2 4.8 1.8
#> 36 6.3 2.9 5.6 1.8
#> 37 7.3 2.9 6.3 1.8
#> 38 6.7 2.5 5.8 1.8
#> 39 6.5 3.0 5.5 1.8
#> 40 6.3 2.7 4.9 1.8
#> 41 7.2 3.2 6.0 1.8
#> 42 6.2 2.8 4.8 1.8
#> 43 6.1 3.0 4.9 1.8
#> 44 6.4 3.1 5.5 1.8
#> 45 6.0 3.0 4.8 1.8
#> 46 5.9 3.0 5.1 1.8
#> 47 6.7 3.0 5.0 1.7
#> 48 4.9 2.5 4.5 1.7
#> 49 6.3 3.3 4.7 1.6
#> 50 6.0 2.7 5.1 1.6
#> 51 6.0 3.4 4.5 1.6
#> 52 7.2 3.0 5.8 1.6
#> 53 6.4 3.2 4.5 1.5
#> 54 6.9 3.1 4.9 1.5
#> 55 6.5 2.8 4.6 1.5
#> 56 5.9 3.0 4.2 1.5
#> 57 5.6 3.0 4.5 1.5
#> 58 6.2 2.2 4.5 1.5
#> 59 6.3 2.5 4.9 1.5
#> 60 6.0 2.9 4.5 1.5
#> 61 5.4 3.0 4.5 1.5
#> 62 6.7 3.1 4.7 1.5
#> 63 6.0 2.2 5.0 1.5
#> 64 6.3 2.8 5.1 1.5
#> 65 7.0 3.2 4.7 1.4
#> 66 5.2 2.7 3.9 1.4
#> 67 6.1 2.9 4.7 1.4
#> 68 6.7 3.1 4.4 1.4
#> 69 6.6 3.0 4.4 1.4
#> 70 6.8 2.8 4.8 1.4
#> 71 6.1 3.0 4.6 1.4
#> 72 6.1 2.6 5.6 1.4
#> 73 5.5 2.3 4.0 1.3
#> 74 5.7 2.8 4.5 1.3
#> 75 6.6 2.9 4.6 1.3
#> 76 5.6 2.9 3.6 1.3
#> 77 6.1 2.8 4.0 1.3
#> 78 6.4 2.9 4.3 1.3
#> 79 6.3 2.3 4.4 1.3
#> 80 5.6 3.0 4.1 1.3
#> 81 5.5 2.5 4.0 1.3
#> 82 5.6 2.7 4.2 1.3
#> 83 5.7 2.9 4.2 1.3
#> 84 6.2 2.9 4.3 1.3
#> 85 5.7 2.8 4.1 1.3
#> 86 6.1 2.8 4.7 1.2
#> 87 5.8 2.7 3.9 1.2
#> 88 5.5 2.6 4.4 1.2
#> 89 5.8 2.6 4.0 1.2
#> 90 5.7 3.0 4.2 1.2
#> 91 5.6 2.5 3.9 1.1
#> 92 5.5 2.4 3.8 1.1
#> 93 5.1 2.5 3.0 1.1
#> 94 4.9 2.4 3.3 1.0
#> 95 5.0 2.0 3.5 1.0
#> 96 6.0 2.2 4.0 1.0
#> 97 5.8 2.7 4.1 1.0
#> 98 5.7 2.6 3.5 1.0
#> 99 5.5 2.4 3.7 1.0
#> 100 5.0 2.3 3.3 1.0
#> 101 5.0 3.5 1.6 0.6
#> 102 5.1 3.3 1.7 0.5
#> 103 5.4 3.9 1.7 0.4
#> 104 5.7 4.4 1.5 0.4
#> 105 5.4 3.9 1.3 0.4
#> 106 5.1 3.7 1.5 0.4
#> 107 5.0 3.4 1.6 0.4
#> 108 5.4 3.4 1.5 0.4
#> 109 5.1 3.8 1.9 0.4
#> 110 4.6 3.4 1.4 0.3
#> 111 5.1 3.5 1.4 0.3
#> 112 5.7 3.8 1.7 0.3
#> 113 5.1 3.8 1.5 0.3
#> 114 5.0 3.5 1.3 0.3
#> 115 4.5 2.3 1.3 0.3
#> 116 4.8 3.0 1.4 0.3
#> 117 5.1 3.5 1.4 0.2
#> 118 4.9 3.0 1.4 0.2
#> 119 4.7 3.2 1.3 0.2
#> 120 4.6 3.1 1.5 0.2
#> 121 5.0 3.6 1.4 0.2
#> 122 5.0 3.4 1.5 0.2
#> 123 4.4 2.9 1.4 0.2
#> 124 5.4 3.7 1.5 0.2
#> 125 4.8 3.4 1.6 0.2
#> 126 5.8 4.0 1.2 0.2
#> 127 5.4 3.4 1.7 0.2
#> 128 4.6 3.6 1.0 0.2
#> 129 4.8 3.4 1.9 0.2
#> 130 5.0 3.0 1.6 0.2
#> 131 5.2 3.5 1.5 0.2
#> 132 5.2 3.4 1.4 0.2
#> 133 4.7 3.2 1.6 0.2
#> 134 4.8 3.1 1.6 0.2
#> 135 5.5 4.2 1.4 0.2
#> 136 4.9 3.1 1.5 0.2
#> 137 5.0 3.2 1.2 0.2
#> 138 5.5 3.5 1.3 0.2
#> 139 4.4 3.0 1.3 0.2
#> 140 5.1 3.4 1.5 0.2
#> 141 4.4 3.2 1.3 0.2
#> 142 5.1 3.8 1.6 0.2
#> 143 4.6 3.2 1.4 0.2
#> 144 5.3 3.7 1.5 0.2
#> 145 5.0 3.3 1.4 0.2
#> 146 4.9 3.1 1.5 0.1
#> 147 4.8 3.0 1.4 0.1
#> 148 4.3 3.0 1.1 0.1
#> 149 5.2 4.1 1.5 0.1
#> 150 4.9 3.6 1.4 0.1
Created on 2022-08-07 by the reprex package (v2.0.1)
We could do it using across:
library(dplyr)
library(psych) # for `headTail()`
iris %>%
select(-Species) %>%
arrange(across(last_col(), desc)) %>%
headTail()
Sepal.Length Sepal.Width Petal.Length Petal.Width
1 6.3 3.3 6 2.5
2 7.2 3.6 6.1 2.5
3 6.7 3.3 5.7 2.5
4 5.8 2.8 5.1 2.4
... ... ... ... ...
147 4.8 3 1.4 0.1
148 4.3 3 1.1 0.1
149 5.2 4.1 1.5 0.1
150 4.9 3.6 1.4 0.1
Using Base R option
subset(iris , select = -Species) |>
(\(x) x[order(- x[ncol(x)]) , ])()
output
Sepal.Length Sepal.Width Petal.Length Petal.Width
101 6.3 3.3 6.0 2.5
110 7.2 3.6 6.1 2.5
145 6.7 3.3 5.7 2.5
115 5.8 2.8 5.1 2.4
137 6.3 3.4 5.6 2.4
141 6.7 3.1 5.6 2.4
116 6.4 3.2 5.3 2.3
119 7.7 2.6 6.9 2.3
......................................................
40 5.1 3.4 1.5 0.2
43 4.4 3.2 1.3 0.2
47 5.1 3.8 1.6 0.2
48 4.6 3.2 1.4 0.2
49 5.3 3.7 1.5 0.2
50 5.0 3.3 1.4 0.2
10 4.9 3.1 1.5 0.1
13 4.8 3.0 1.4 0.1
14 4.3 3.0 1.1 0.1
33 5.2 4.1 1.5 0.1
38 4.9 3.6 1.4 0.1

Defining groups in aes() to make same groups having the same color in multiple ggplots

Can somebody try to explain the syntax of aes(color=paste("mean", Role), group=Role) in this question. I really wish to learn how this works. This also works on my data but I do not understand the use of paste() and adding mean
The question was very old but it works on my data.
As #Allan Cameron said in the comments, it takes your variable Role and add the word "mean" before the entries and these will be displayed in the legend because of the colour aesthetic. I will give you a reproducibel example using the iris dataset:
The data:
Sepal.Length Sepal.Width Species
1 5.1 3.5 setosa
2 4.9 3.0 setosa
3 4.7 3.2 setosa
4 4.6 3.1 setosa
5 5.0 3.6 setosa
6 5.4 3.9 setosa
7 4.6 3.4 setosa
8 5.0 3.4 setosa
9 4.4 2.9 setosa
10 4.9 3.1 setosa
11 5.4 3.7 setosa
12 4.8 3.4 setosa
13 4.8 3.0 setosa
14 4.3 3.0 setosa
15 5.8 4.0 setosa
16 5.7 4.4 setosa
17 5.4 3.9 setosa
18 5.1 3.5 setosa
19 5.7 3.8 setosa
20 5.1 3.8 setosa
21 5.4 3.4 setosa
22 5.1 3.7 setosa
23 4.6 3.6 setosa
24 5.1 3.3 setosa
25 4.8 3.4 setosa
26 5.0 3.0 setosa
27 5.0 3.4 setosa
28 5.2 3.5 setosa
29 5.2 3.4 setosa
30 4.7 3.2 setosa
31 4.8 3.1 setosa
32 5.4 3.4 setosa
33 5.2 4.1 setosa
34 5.5 4.2 setosa
35 4.9 3.1 setosa
36 5.0 3.2 setosa
37 5.5 3.5 setosa
38 4.9 3.6 setosa
39 4.4 3.0 setosa
40 5.1 3.4 setosa
41 5.0 3.5 setosa
42 4.5 2.3 setosa
43 4.4 3.2 setosa
44 5.0 3.5 setosa
45 5.1 3.8 setosa
46 4.8 3.0 setosa
47 5.1 3.8 setosa
48 4.6 3.2 setosa
49 5.3 3.7 setosa
50 5.0 3.3 setosa
51 7.0 3.2 versicolor
52 6.4 3.2 versicolor
53 6.9 3.1 versicolor
54 5.5 2.3 versicolor
55 6.5 2.8 versicolor
56 5.7 2.8 versicolor
57 6.3 3.3 versicolor
58 4.9 2.4 versicolor
59 6.6 2.9 versicolor
60 5.2 2.7 versicolor
61 5.0 2.0 versicolor
62 5.9 3.0 versicolor
63 6.0 2.2 versicolor
64 6.1 2.9 versicolor
65 5.6 2.9 versicolor
66 6.7 3.1 versicolor
67 5.6 3.0 versicolor
68 5.8 2.7 versicolor
69 6.2 2.2 versicolor
70 5.6 2.5 versicolor
71 5.9 3.2 versicolor
72 6.1 2.8 versicolor
73 6.3 2.5 versicolor
74 6.1 2.8 versicolor
75 6.4 2.9 versicolor
76 6.6 3.0 versicolor
77 6.8 2.8 versicolor
78 6.7 3.0 versicolor
79 6.0 2.9 versicolor
80 5.7 2.6 versicolor
81 5.5 2.4 versicolor
82 5.5 2.4 versicolor
83 5.8 2.7 versicolor
84 6.0 2.7 versicolor
85 5.4 3.0 versicolor
86 6.0 3.4 versicolor
87 6.7 3.1 versicolor
88 6.3 2.3 versicolor
89 5.6 3.0 versicolor
90 5.5 2.5 versicolor
91 5.5 2.6 versicolor
92 6.1 3.0 versicolor
93 5.8 2.6 versicolor
94 5.0 2.3 versicolor
95 5.6 2.7 versicolor
96 5.7 3.0 versicolor
97 5.7 2.9 versicolor
98 6.2 2.9 versicolor
99 5.1 2.5 versicolor
100 5.7 2.8 versicolor
101 6.3 3.3 virginica
102 5.8 2.7 virginica
103 7.1 3.0 virginica
104 6.3 2.9 virginica
105 6.5 3.0 virginica
106 7.6 3.0 virginica
107 4.9 2.5 virginica
108 7.3 2.9 virginica
109 6.7 2.5 virginica
110 7.2 3.6 virginica
111 6.5 3.2 virginica
112 6.4 2.7 virginica
113 6.8 3.0 virginica
114 5.7 2.5 virginica
115 5.8 2.8 virginica
116 6.4 3.2 virginica
117 6.5 3.0 virginica
118 7.7 3.8 virginica
119 7.7 2.6 virginica
120 6.0 2.2 virginica
121 6.9 3.2 virginica
122 5.6 2.8 virginica
123 7.7 2.8 virginica
124 6.3 2.7 virginica
125 6.7 3.3 virginica
126 7.2 3.2 virginica
127 6.2 2.8 virginica
128 6.1 3.0 virginica
129 6.4 2.8 virginica
130 7.2 3.0 virginica
131 7.4 2.8 virginica
132 7.9 3.8 virginica
133 6.4 2.8 virginica
134 6.3 2.8 virginica
135 6.1 2.6 virginica
136 7.7 3.0 virginica
137 6.3 3.4 virginica
138 6.4 3.1 virginica
139 6.0 3.0 virginica
140 6.9 3.1 virginica
141 6.7 3.1 virginica
142 6.9 3.1 virginica
143 5.8 2.7 virginica
144 6.8 3.2 virginica
145 6.7 3.3 virginica
146 6.7 3.0 virginica
147 6.3 2.5 virginica
148 6.5 3.0 virginica
149 6.2 3.4 virginica
150 5.9 3.0 virginica
You can run the following code:
iris %>%
ggplot(aes(x = Sepal.Width, y = Sepal.Length, color = Species)) +
geom_line() +
geom_point() +
stat_summary(fun.y=mean, geom="line", size = 1.5,
linetype="dotted", aes(color=paste("mean", Species)))
Output:
As you can see in the legend of the plot, the species are shown also with "mean" before and those lines are the mean values of those species.

Identify rows based on 2 external vectors

I'm trying to flag rows that have values that match elements in 2 external vectors. I need these values to match, meaning it can't be any case, but something where both conditions are met (i.e. not using an %in% operator).
Here's what I've tried:
widths <- c(1.2, 1.7, 1.8, 1.8, 1.9)
species <- c(rep("versicolor", 3), rep("virginica", 2))
iris_flagged <- iris %>%
filter(Species != "setosa") %>%
mutate(flag = ifelse((Petal.Width == widths & Species == species), "check", "")) %>%
arrange(Species, Petal.Width) #to help with visualization
But when I run this some flags are missed. If you look at the data you'll see that rows 14, 50, 57, 59, 61:64, and 66:71 are missing flags.
A clunky, non-robust method could look like this, but I really want to make it robust and use these vectors to my advantage.
iris_flagged2 <- iris %>%
filter(Species != "setosa") %>%
mutate(flag = ifelse((Species == "versicolor" & Petal.Width == 1.2 |
Species == "versicolor" & Petal.Width == 1.7 |
Species == "versicolor" & Petal.Width == 1.8 |
Species == "virginica" & Petal.Width == 1.8 |
Species == "virginica" & Petal.Width == 1.9),
"check", "")) %>%
arrange(Species, Petal.Width)
Thanks for your help. I tried to search but wasn't sure how to phrase my question.
Here is one way using map2 to pass each pair of condition and reduce to combine them into one.
library(tidyverse)
iris2 <- iris %>% filter(Species != "setosa")
iris2 <- iris2 %>%
mutate(flag = ifelse(map2(species, widths,
~Species == .x & Petal.Width == .y) %>% reduce(`|`), 'check', ''))
iris2
Additional to the comment of #akrun, here is a possible solution for your task:
library(dplyr)
iris %>%
mutate(across(-Species, ~round(.,1)),
flag = ifelse(Petal.Width %in% widths &
Species %in% species, "check",""))
output:
Sepal.Length Sepal.Width Petal.Length Petal.Width Species flag
1 5.1 3.5 1.4 0.2 setosa
2 4.9 3.0 1.4 0.2 setosa
3 4.7 3.2 1.3 0.2 setosa
4 4.6 3.1 1.5 0.2 setosa
5 5.0 3.6 1.4 0.2 setosa
6 5.4 3.9 1.7 0.4 setosa
7 4.6 3.4 1.4 0.3 setosa
8 5.0 3.4 1.5 0.2 setosa
9 4.4 2.9 1.4 0.2 setosa
10 4.9 3.1 1.5 0.1 setosa
11 5.4 3.7 1.5 0.2 setosa
12 4.8 3.4 1.6 0.2 setosa
13 4.8 3.0 1.4 0.1 setosa
14 4.3 3.0 1.1 0.1 setosa
15 5.8 4.0 1.2 0.2 setosa
16 5.7 4.4 1.5 0.4 setosa
17 5.4 3.9 1.3 0.4 setosa
18 5.1 3.5 1.4 0.3 setosa
19 5.7 3.8 1.7 0.3 setosa
20 5.1 3.8 1.5 0.3 setosa
21 5.4 3.4 1.7 0.2 setosa
22 5.1 3.7 1.5 0.4 setosa
23 4.6 3.6 1.0 0.2 setosa
24 5.1 3.3 1.7 0.5 setosa
25 4.8 3.4 1.9 0.2 setosa
26 5.0 3.0 1.6 0.2 setosa
27 5.0 3.4 1.6 0.4 setosa
28 5.2 3.5 1.5 0.2 setosa
29 5.2 3.4 1.4 0.2 setosa
30 4.7 3.2 1.6 0.2 setosa
31 4.8 3.1 1.6 0.2 setosa
32 5.4 3.4 1.5 0.4 setosa
33 5.2 4.1 1.5 0.1 setosa
34 5.5 4.2 1.4 0.2 setosa
35 4.9 3.1 1.5 0.2 setosa
36 5.0 3.2 1.2 0.2 setosa
37 5.5 3.5 1.3 0.2 setosa
38 4.9 3.6 1.4 0.1 setosa
39 4.4 3.0 1.3 0.2 setosa
40 5.1 3.4 1.5 0.2 setosa
41 5.0 3.5 1.3 0.3 setosa
42 4.5 2.3 1.3 0.3 setosa
43 4.4 3.2 1.3 0.2 setosa
44 5.0 3.5 1.6 0.6 setosa
45 5.1 3.8 1.9 0.4 setosa
46 4.8 3.0 1.4 0.3 setosa
47 5.1 3.8 1.6 0.2 setosa
48 4.6 3.2 1.4 0.2 setosa
49 5.3 3.7 1.5 0.2 setosa
50 5.0 3.3 1.4 0.2 setosa
51 7.0 3.2 4.7 1.4 versicolor
52 6.4 3.2 4.5 1.5 versicolor
53 6.9 3.1 4.9 1.5 versicolor
54 5.5 2.3 4.0 1.3 versicolor
55 6.5 2.8 4.6 1.5 versicolor
56 5.7 2.8 4.5 1.3 versicolor
57 6.3 3.3 4.7 1.6 versicolor
58 4.9 2.4 3.3 1.0 versicolor
59 6.6 2.9 4.6 1.3 versicolor
60 5.2 2.7 3.9 1.4 versicolor
61 5.0 2.0 3.5 1.0 versicolor
62 5.9 3.0 4.2 1.5 versicolor
63 6.0 2.2 4.0 1.0 versicolor
64 6.1 2.9 4.7 1.4 versicolor
65 5.6 2.9 3.6 1.3 versicolor
66 6.7 3.1 4.4 1.4 versicolor
67 5.6 3.0 4.5 1.5 versicolor
68 5.8 2.7 4.1 1.0 versicolor
69 6.2 2.2 4.5 1.5 versicolor
70 5.6 2.5 3.9 1.1 versicolor
71 5.9 3.2 4.8 1.8 versicolor check
72 6.1 2.8 4.0 1.3 versicolor
73 6.3 2.5 4.9 1.5 versicolor
74 6.1 2.8 4.7 1.2 versicolor check
75 6.4 2.9 4.3 1.3 versicolor
76 6.6 3.0 4.4 1.4 versicolor
77 6.8 2.8 4.8 1.4 versicolor
78 6.7 3.0 5.0 1.7 versicolor check
79 6.0 2.9 4.5 1.5 versicolor
80 5.7 2.6 3.5 1.0 versicolor
81 5.5 2.4 3.8 1.1 versicolor
82 5.5 2.4 3.7 1.0 versicolor
83 5.8 2.7 3.9 1.2 versicolor check
84 6.0 2.7 5.1 1.6 versicolor
85 5.4 3.0 4.5 1.5 versicolor
86 6.0 3.4 4.5 1.6 versicolor
87 6.7 3.1 4.7 1.5 versicolor
88 6.3 2.3 4.4 1.3 versicolor
89 5.6 3.0 4.1 1.3 versicolor
90 5.5 2.5 4.0 1.3 versicolor
91 5.5 2.6 4.4 1.2 versicolor check
92 6.1 3.0 4.6 1.4 versicolor
93 5.8 2.6 4.0 1.2 versicolor check
94 5.0 2.3 3.3 1.0 versicolor
95 5.6 2.7 4.2 1.3 versicolor
96 5.7 3.0 4.2 1.2 versicolor check
97 5.7 2.9 4.2 1.3 versicolor
98 6.2 2.9 4.3 1.3 versicolor
99 5.1 2.5 3.0 1.1 versicolor
100 5.7 2.8 4.1 1.3 versicolor
101 6.3 3.3 6.0 2.5 virginica
102 5.8 2.7 5.1 1.9 virginica check
103 7.1 3.0 5.9 2.1 virginica
104 6.3 2.9 5.6 1.8 virginica check
105 6.5 3.0 5.8 2.2 virginica
106 7.6 3.0 6.6 2.1 virginica
107 4.9 2.5 4.5 1.7 virginica check
108 7.3 2.9 6.3 1.8 virginica check
109 6.7 2.5 5.8 1.8 virginica check
110 7.2 3.6 6.1 2.5 virginica
111 6.5 3.2 5.1 2.0 virginica
112 6.4 2.7 5.3 1.9 virginica check
113 6.8 3.0 5.5 2.1 virginica
114 5.7 2.5 5.0 2.0 virginica
115 5.8 2.8 5.1 2.4 virginica
116 6.4 3.2 5.3 2.3 virginica
117 6.5 3.0 5.5 1.8 virginica check
118 7.7 3.8 6.7 2.2 virginica
119 7.7 2.6 6.9 2.3 virginica
120 6.0 2.2 5.0 1.5 virginica
121 6.9 3.2 5.7 2.3 virginica
122 5.6 2.8 4.9 2.0 virginica
123 7.7 2.8 6.7 2.0 virginica
124 6.3 2.7 4.9 1.8 virginica check
125 6.7 3.3 5.7 2.1 virginica
126 7.2 3.2 6.0 1.8 virginica check
127 6.2 2.8 4.8 1.8 virginica check
128 6.1 3.0 4.9 1.8 virginica check
129 6.4 2.8 5.6 2.1 virginica
130 7.2 3.0 5.8 1.6 virginica
131 7.4 2.8 6.1 1.9 virginica check
132 7.9 3.8 6.4 2.0 virginica
133 6.4 2.8 5.6 2.2 virginica
134 6.3 2.8 5.1 1.5 virginica
135 6.1 2.6 5.6 1.4 virginica
136 7.7 3.0 6.1 2.3 virginica
137 6.3 3.4 5.6 2.4 virginica
138 6.4 3.1 5.5 1.8 virginica check
139 6.0 3.0 4.8 1.8 virginica check
140 6.9 3.1 5.4 2.1 virginica
141 6.7 3.1 5.6 2.4 virginica
142 6.9 3.1 5.1 2.3 virginica
143 5.8 2.7 5.1 1.9 virginica check
144 6.8 3.2 5.9 2.3 virginica
145 6.7 3.3 5.7 2.5 virginica
146 6.7 3.0 5.2 2.3 virginica
147 6.3 2.5 5.0 1.9 virginica check
148 6.5 3.0 5.2 2.0 virginica
149 6.2 3.4 5.4 2.3 virginica
150 5.9 3.0 5.1 1.8 virginica check

Adding a space into every row in a column, with space position dependent on character length

I want to be add a white space into every row of one column in a data frame. This column is character in terms of data type. The position of this white space is dependent on the length of the string in each row.
e.g for all rows in this particular column, all rows with less than 6 characters should have the white space after the second character, whilst those with 7 or more characters should have the white space after the 4th character
As an example, looking at the Iris dataset and the Species column, for the setosa rows I would want a white space after the second character, so "setosa" becomes "se tosa"
I know that this will be an ifelse statement, but I'm not sure how to proceed
You can first determine the number of characters in iris$Species:
iris$Species_char <- nchar(as.character(iris$Species))
Based on this new column you can define an ifelse statement, with iris$Species_char <= 6 as your condition, the insertion of whitespace after the second character as action to be taken if condition evaluates to TRUE, and insertion of whitespace after the fourth character as action to be taken if condition evaluates to FALSE. To make sure the characters before and after the insertion point are recollected we use backreference, with \\1 referring back to the chars before, and \\2 referring back to the chars after the insertion point:
iris$Species <- ifelse(iris$Species_char <= 6,
sub("(\\w{2})(.*)", "\\1 \\2", iris$Species),
sub("(\\w{4})(.*)", "\\1 \\2", iris$Species))
The above two steps in one step:
iris$Species <- ifelse(nchar(iris$Species) <= 6,
sub("(\\w{2})(.*)", "\\1 \\2", iris$Species),
sub("(\\w{4})(.*)", "\\1 \\2", iris$Species))
EDIT: Using dplyr and stringr:
library(dplyr)
library(stringr)
iris %>%
mutate(Species = if_else(str_count(Species) <= 6, sub("(\\w{2})(.*)", "\\1 \\2", Species), sub("(\\w{4})(.*)", "\\1 \\2", Species)))
Result:
iris
Sepal.Length Sepal.Width Petal.Length Petal.Width Species Species_char
1 5.1 3.5 1.4 0.2 se tosa 6
2 4.9 3.0 1.4 0.2 se tosa 6
3 4.7 3.2 1.3 0.2 se tosa 6
4 4.6 3.1 1.5 0.2 se tosa 6
5 5.0 3.6 1.4 0.2 se tosa 6
6 5.4 3.9 1.7 0.4 se tosa 6
7 4.6 3.4 1.4 0.3 se tosa 6
8 5.0 3.4 1.5 0.2 se tosa 6
9 4.4 2.9 1.4 0.2 se tosa 6
10 4.9 3.1 1.5 0.1 se tosa 6
11 5.4 3.7 1.5 0.2 se tosa 6
12 4.8 3.4 1.6 0.2 se tosa 6
13 4.8 3.0 1.4 0.1 se tosa 6
14 4.3 3.0 1.1 0.1 se tosa 6
15 5.8 4.0 1.2 0.2 se tosa 6
16 5.7 4.4 1.5 0.4 se tosa 6
17 5.4 3.9 1.3 0.4 se tosa 6
18 5.1 3.5 1.4 0.3 se tosa 6
19 5.7 3.8 1.7 0.3 se tosa 6
20 5.1 3.8 1.5 0.3 se tosa 6
21 5.4 3.4 1.7 0.2 se tosa 6
22 5.1 3.7 1.5 0.4 se tosa 6
23 4.6 3.6 1.0 0.2 se tosa 6
24 5.1 3.3 1.7 0.5 se tosa 6
25 4.8 3.4 1.9 0.2 se tosa 6
26 5.0 3.0 1.6 0.2 se tosa 6
27 5.0 3.4 1.6 0.4 se tosa 6
28 5.2 3.5 1.5 0.2 se tosa 6
29 5.2 3.4 1.4 0.2 se tosa 6
30 4.7 3.2 1.6 0.2 se tosa 6
31 4.8 3.1 1.6 0.2 se tosa 6
32 5.4 3.4 1.5 0.4 se tosa 6
33 5.2 4.1 1.5 0.1 se tosa 6
34 5.5 4.2 1.4 0.2 se tosa 6
35 4.9 3.1 1.5 0.2 se tosa 6
36 5.0 3.2 1.2 0.2 se tosa 6
37 5.5 3.5 1.3 0.2 se tosa 6
38 4.9 3.6 1.4 0.1 se tosa 6
39 4.4 3.0 1.3 0.2 se tosa 6
40 5.1 3.4 1.5 0.2 se tosa 6
41 5.0 3.5 1.3 0.3 se tosa 6
42 4.5 2.3 1.3 0.3 se tosa 6
43 4.4 3.2 1.3 0.2 se tosa 6
44 5.0 3.5 1.6 0.6 se tosa 6
45 5.1 3.8 1.9 0.4 se tosa 6
46 4.8 3.0 1.4 0.3 se tosa 6
47 5.1 3.8 1.6 0.2 se tosa 6
48 4.6 3.2 1.4 0.2 se tosa 6
49 5.3 3.7 1.5 0.2 se tosa 6
50 5.0 3.3 1.4 0.2 se tosa 6
51 7.0 3.2 4.7 1.4 vers icolor 10
52 6.4 3.2 4.5 1.5 vers icolor 10
53 6.9 3.1 4.9 1.5 vers icolor 10
54 5.5 2.3 4.0 1.3 vers icolor 10
55 6.5 2.8 4.6 1.5 vers icolor 10
56 5.7 2.8 4.5 1.3 vers icolor 10
57 6.3 3.3 4.7 1.6 vers icolor 10
58 4.9 2.4 3.3 1.0 vers icolor 10
59 6.6 2.9 4.6 1.3 vers icolor 10
60 5.2 2.7 3.9 1.4 vers icolor 10
61 5.0 2.0 3.5 1.0 vers icolor 10
62 5.9 3.0 4.2 1.5 vers icolor 10
63 6.0 2.2 4.0 1.0 vers icolor 10
64 6.1 2.9 4.7 1.4 vers icolor 10
65 5.6 2.9 3.6 1.3 vers icolor 10
66 6.7 3.1 4.4 1.4 vers icolor 10
67 5.6 3.0 4.5 1.5 vers icolor 10
68 5.8 2.7 4.1 1.0 vers icolor 10
69 6.2 2.2 4.5 1.5 vers icolor 10
70 5.6 2.5 3.9 1.1 vers icolor 10
71 5.9 3.2 4.8 1.8 vers icolor 10
72 6.1 2.8 4.0 1.3 vers icolor 10
73 6.3 2.5 4.9 1.5 vers icolor 10
74 6.1 2.8 4.7 1.2 vers icolor 10
75 6.4 2.9 4.3 1.3 vers icolor 10
76 6.6 3.0 4.4 1.4 vers icolor 10
77 6.8 2.8 4.8 1.4 vers icolor 10
78 6.7 3.0 5.0 1.7 vers icolor 10
79 6.0 2.9 4.5 1.5 vers icolor 10
80 5.7 2.6 3.5 1.0 vers icolor 10
81 5.5 2.4 3.8 1.1 vers icolor 10
82 5.5 2.4 3.7 1.0 vers icolor 10
83 5.8 2.7 3.9 1.2 vers icolor 10
84 6.0 2.7 5.1 1.6 vers icolor 10
85 5.4 3.0 4.5 1.5 vers icolor 10
86 6.0 3.4 4.5 1.6 vers icolor 10
87 6.7 3.1 4.7 1.5 vers icolor 10
88 6.3 2.3 4.4 1.3 vers icolor 10
89 5.6 3.0 4.1 1.3 vers icolor 10
90 5.5 2.5 4.0 1.3 vers icolor 10
91 5.5 2.6 4.4 1.2 vers icolor 10
92 6.1 3.0 4.6 1.4 vers icolor 10
93 5.8 2.6 4.0 1.2 vers icolor 10
94 5.0 2.3 3.3 1.0 vers icolor 10
95 5.6 2.7 4.2 1.3 vers icolor 10
96 5.7 3.0 4.2 1.2 vers icolor 10
97 5.7 2.9 4.2 1.3 vers icolor 10
98 6.2 2.9 4.3 1.3 vers icolor 10
99 5.1 2.5 3.0 1.1 vers icolor 10
100 5.7 2.8 4.1 1.3 vers icolor 10
101 6.3 3.3 6.0 2.5 virg inica 9
102 5.8 2.7 5.1 1.9 virg inica 9
103 7.1 3.0 5.9 2.1 virg inica 9
104 6.3 2.9 5.6 1.8 virg inica 9
105 6.5 3.0 5.8 2.2 virg inica 9
106 7.6 3.0 6.6 2.1 virg inica 9
107 4.9 2.5 4.5 1.7 virg inica 9
108 7.3 2.9 6.3 1.8 virg inica 9
109 6.7 2.5 5.8 1.8 virg inica 9
110 7.2 3.6 6.1 2.5 virg inica 9
111 6.5 3.2 5.1 2.0 virg inica 9
112 6.4 2.7 5.3 1.9 virg inica 9
113 6.8 3.0 5.5 2.1 virg inica 9
114 5.7 2.5 5.0 2.0 virg inica 9
115 5.8 2.8 5.1 2.4 virg inica 9
116 6.4 3.2 5.3 2.3 virg inica 9
117 6.5 3.0 5.5 1.8 virg inica 9
118 7.7 3.8 6.7 2.2 virg inica 9
119 7.7 2.6 6.9 2.3 virg inica 9
120 6.0 2.2 5.0 1.5 virg inica 9
121 6.9 3.2 5.7 2.3 virg inica 9
122 5.6 2.8 4.9 2.0 virg inica 9
123 7.7 2.8 6.7 2.0 virg inica 9
124 6.3 2.7 4.9 1.8 virg inica 9
125 6.7 3.3 5.7 2.1 virg inica 9
126 7.2 3.2 6.0 1.8 virg inica 9
127 6.2 2.8 4.8 1.8 virg inica 9
128 6.1 3.0 4.9 1.8 virg inica 9
129 6.4 2.8 5.6 2.1 virg inica 9
130 7.2 3.0 5.8 1.6 virg inica 9
131 7.4 2.8 6.1 1.9 virg inica 9
132 7.9 3.8 6.4 2.0 virg inica 9
133 6.4 2.8 5.6 2.2 virg inica 9
134 6.3 2.8 5.1 1.5 virg inica 9
135 6.1 2.6 5.6 1.4 virg inica 9
136 7.7 3.0 6.1 2.3 virg inica 9
137 6.3 3.4 5.6 2.4 virg inica 9
138 6.4 3.1 5.5 1.8 virg inica 9
139 6.0 3.0 4.8 1.8 virg inica 9
140 6.9 3.1 5.4 2.1 virg inica 9
141 6.7 3.1 5.6 2.4 virg inica 9
142 6.9 3.1 5.1 2.3 virg inica 9
143 5.8 2.7 5.1 1.9 virg inica 9
144 6.8 3.2 5.9 2.3 virg inica 9
145 6.7 3.3 5.7 2.5 virg inica 9
146 6.7 3.0 5.2 2.3 virg inica 9
147 6.3 2.5 5.0 1.9 virg inica 9
148 6.5 3.0 5.2 2.0 virg inica 9
149 6.2 3.4 5.4 2.3 virg inica 9
150 5.9 3.0 5.1 1.8 virg inica 9

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