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)]) , ])()
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
