arrange() orders the rows of a data frame by the values of selected
columns.
Unlike other dplyr verbs, arrange() largely ignores grouping; you
need to explicitly mention grouping variables (or use .by_group = TRUE)
in order to group by them, and functions of variables are evaluated
once per data frame, not once per group.
arrange(.data, ..., .by_group = FALSE)A data frame, data frame extension (e.g. a tibble), or a lazy data frame (e.g. from dbplyr or dtplyr). See Methods, below, for more details.
<data-masking> Variables, or functions of
variables. Use desc() to sort a variable in descending order.
If TRUE, will sort first by grouping variable. Applies to
grouped data frames only.
An object of the same type as .data. The output has the following
properties:
All rows appear in the output, but (usually) in a different place.
Columns are not modified.
Groups are not modified.
Data frame attributes are preserved.
The sort order for character vectors will depend on the collating sequence
of the locale in use: see locales().
This function is a generic, which means that packages can provide implementations (methods) for other classes. See the documentation of individual methods for extra arguments and differences in behaviour.
The following methods are currently available in loaded packages:
dplyr (data.frame)
.
arrange(mtcars, cyl, disp)
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
#> Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
#> Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
#> Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
#> Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
#> Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
#> Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
#> Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
#> Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
#> Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
#> Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
#> Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
#> Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
#> Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
#> Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
#> Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
#> Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
#> Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
#> Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
#> Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
#> Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
#> Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
#> AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
#> Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
#> Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
#> Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
#> Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
#> Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
#> Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
#> Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
#> Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
#> Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
arrange(mtcars, desc(disp))
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
#> Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
#> Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
#> Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
#> Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
#> Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
#> Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
#> Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
#> Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
#> AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
#> Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
#> Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
#> Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
#> Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
#> Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
#> Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
#> Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
#> Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
#> Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
#> Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
#> Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
#> Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
#> Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
#> Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
#> Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
#> Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
#> Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
#> Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
#> Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
#> Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
#> Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
#> Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
# grouped arrange ignores groups
by_cyl <- mtcars %>% group_by(cyl)
by_cyl %>% arrange(desc(wt))
#> # A tibble: 32 × 11
#> # Groups: cyl [3]
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 10.4 8 460 215 3 5.42 17.8 0 0 3 4
#> 2 14.7 8 440 230 3.23 5.34 17.4 0 0 3 4
#> 3 10.4 8 472 205 2.93 5.25 18.0 0 0 3 4
#> 4 16.4 8 276. 180 3.07 4.07 17.4 0 0 3 3
#> 5 19.2 8 400 175 3.08 3.84 17.0 0 0 3 2
#> 6 13.3 8 350 245 3.73 3.84 15.4 0 0 3 4
#> 7 15.2 8 276. 180 3.07 3.78 18 0 0 3 3
#> 8 17.3 8 276. 180 3.07 3.73 17.6 0 0 3 3
#> 9 14.3 8 360 245 3.21 3.57 15.8 0 0 3 4
#> 10 15 8 301 335 3.54 3.57 14.6 0 1 5 8
#> # … with 22 more rows
# Unless you specifically ask:
by_cyl %>% arrange(desc(wt), .by_group = TRUE)
#> # A tibble: 32 × 11
#> # Groups: cyl [3]
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 24.4 4 147. 62 3.69 3.19 20 1 0 4 2
#> 2 22.8 4 141. 95 3.92 3.15 22.9 1 0 4 2
#> 3 21.4 4 121 109 4.11 2.78 18.6 1 1 4 2
#> 4 21.5 4 120. 97 3.7 2.46 20.0 1 0 3 1
#> 5 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1
#> 6 32.4 4 78.7 66 4.08 2.2 19.5 1 1 4 1
#> 7 26 4 120. 91 4.43 2.14 16.7 0 1 5 2
#> 8 27.3 4 79 66 4.08 1.94 18.9 1 1 4 1
#> 9 33.9 4 71.1 65 4.22 1.84 19.9 1 1 4 1
#> 10 30.4 4 75.7 52 4.93 1.62 18.5 1 1 4 2
#> # … with 22 more rows
# use embracing when wrapping in a function;
# see ?dplyr_data_masking for more details
tidy_eval_arrange <- function(.data, var) {
.data %>%
arrange({{ var }})
}
tidy_eval_arrange(mtcars, mpg)
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
#> Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
#> Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
#> Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
#> Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
#> Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
#> Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
#> AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
#> Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
#> Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
#> Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
#> Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
#> Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
#> Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
#> Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
#> Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
#> Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
#> Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
#> Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
#> Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
#> Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
#> Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
#> Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
#> Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
#> Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
#> Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
#> Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
#> Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
#> Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
#> Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
#> Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
#> Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
# use across() access select()-style semantics
iris %>% arrange(across(starts_with("Sepal")))
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#> 1 4.3 3.0 1.1 0.1 setosa
#> 2 4.4 2.9 1.4 0.2 setosa
#> 3 4.4 3.0 1.3 0.2 setosa
#> 4 4.4 3.2 1.3 0.2 setosa
#> 5 4.5 2.3 1.3 0.3 setosa
#> 6 4.6 3.1 1.5 0.2 setosa
#> 7 4.6 3.2 1.4 0.2 setosa
#> 8 4.6 3.4 1.4 0.3 setosa
#> 9 4.6 3.6 1.0 0.2 setosa
#> 10 4.7 3.2 1.3 0.2 setosa
#> 11 4.7 3.2 1.6 0.2 setosa
#> 12 4.8 3.0 1.4 0.1 setosa
#> 13 4.8 3.0 1.4 0.3 setosa
#> 14 4.8 3.1 1.6 0.2 setosa
#> 15 4.8 3.4 1.6 0.2 setosa
#> 16 4.8 3.4 1.9 0.2 setosa
#> 17 4.9 2.4 3.3 1.0 versicolor
#> 18 4.9 2.5 4.5 1.7 virginica
#> 19 4.9 3.0 1.4 0.2 setosa
#> 20 4.9 3.1 1.5 0.1 setosa
#> 21 4.9 3.1 1.5 0.2 setosa
#> 22 4.9 3.6 1.4 0.1 setosa
#> 23 5.0 2.0 3.5 1.0 versicolor
#> 24 5.0 2.3 3.3 1.0 versicolor
#> 25 5.0 3.0 1.6 0.2 setosa
#> 26 5.0 3.2 1.2 0.2 setosa
#> 27 5.0 3.3 1.4 0.2 setosa
#> 28 5.0 3.4 1.5 0.2 setosa
#> 29 5.0 3.4 1.6 0.4 setosa
#> 30 5.0 3.5 1.3 0.3 setosa
#> 31 5.0 3.5 1.6 0.6 setosa
#> 32 5.0 3.6 1.4 0.2 setosa
#> 33 5.1 2.5 3.0 1.1 versicolor
#> 34 5.1 3.3 1.7 0.5 setosa
#> 35 5.1 3.4 1.5 0.2 setosa
#> 36 5.1 3.5 1.4 0.2 setosa
#> 37 5.1 3.5 1.4 0.3 setosa
#> 38 5.1 3.7 1.5 0.4 setosa
#> 39 5.1 3.8 1.5 0.3 setosa
#> 40 5.1 3.8 1.9 0.4 setosa
#> 41 5.1 3.8 1.6 0.2 setosa
#> 42 5.2 2.7 3.9 1.4 versicolor
#> 43 5.2 3.4 1.4 0.2 setosa
#> 44 5.2 3.5 1.5 0.2 setosa
#> 45 5.2 4.1 1.5 0.1 setosa
#> 46 5.3 3.7 1.5 0.2 setosa
#> 47 5.4 3.0 4.5 1.5 versicolor
#> 48 5.4 3.4 1.7 0.2 setosa
#> 49 5.4 3.4 1.5 0.4 setosa
#> 50 5.4 3.7 1.5 0.2 setosa
#> 51 5.4 3.9 1.7 0.4 setosa
#> 52 5.4 3.9 1.3 0.4 setosa
#> 53 5.5 2.3 4.0 1.3 versicolor
#> 54 5.5 2.4 3.8 1.1 versicolor
#> 55 5.5 2.4 3.7 1.0 versicolor
#> 56 5.5 2.5 4.0 1.3 versicolor
#> 57 5.5 2.6 4.4 1.2 versicolor
#> 58 5.5 3.5 1.3 0.2 setosa
#> 59 5.5 4.2 1.4 0.2 setosa
#> 60 5.6 2.5 3.9 1.1 versicolor
#> 61 5.6 2.7 4.2 1.3 versicolor
#> 62 5.6 2.8 4.9 2.0 virginica
#> 63 5.6 2.9 3.6 1.3 versicolor
#> 64 5.6 3.0 4.5 1.5 versicolor
#> 65 5.6 3.0 4.1 1.3 versicolor
#> 66 5.7 2.5 5.0 2.0 virginica
#> 67 5.7 2.6 3.5 1.0 versicolor
#> 68 5.7 2.8 4.5 1.3 versicolor
#> 69 5.7 2.8 4.1 1.3 versicolor
#> 70 5.7 2.9 4.2 1.3 versicolor
#> 71 5.7 3.0 4.2 1.2 versicolor
#> 72 5.7 3.8 1.7 0.3 setosa
#> 73 5.7 4.4 1.5 0.4 setosa
#> 74 5.8 2.6 4.0 1.2 versicolor
#> 75 5.8 2.7 4.1 1.0 versicolor
#> 76 5.8 2.7 3.9 1.2 versicolor
#> 77 5.8 2.7 5.1 1.9 virginica
#> 78 5.8 2.7 5.1 1.9 virginica
#> 79 5.8 2.8 5.1 2.4 virginica
#> 80 5.8 4.0 1.2 0.2 setosa
#> 81 5.9 3.0 4.2 1.5 versicolor
#> 82 5.9 3.0 5.1 1.8 virginica
#> 83 5.9 3.2 4.8 1.8 versicolor
#> 84 6.0 2.2 4.0 1.0 versicolor
#> 85 6.0 2.2 5.0 1.5 virginica
#> 86 6.0 2.7 5.1 1.6 versicolor
#> 87 6.0 2.9 4.5 1.5 versicolor
#> 88 6.0 3.0 4.8 1.8 virginica
#> 89 6.0 3.4 4.5 1.6 versicolor
#> 90 6.1 2.6 5.6 1.4 virginica
#> 91 6.1 2.8 4.0 1.3 versicolor
#> 92 6.1 2.8 4.7 1.2 versicolor
#> 93 6.1 2.9 4.7 1.4 versicolor
#> 94 6.1 3.0 4.6 1.4 versicolor
#> 95 6.1 3.0 4.9 1.8 virginica
#> 96 6.2 2.2 4.5 1.5 versicolor
#> 97 6.2 2.8 4.8 1.8 virginica
#> 98 6.2 2.9 4.3 1.3 versicolor
#> 99 6.2 3.4 5.4 2.3 virginica
#> 100 6.3 2.3 4.4 1.3 versicolor
#> 101 6.3 2.5 4.9 1.5 versicolor
#> 102 6.3 2.5 5.0 1.9 virginica
#> 103 6.3 2.7 4.9 1.8 virginica
#> 104 6.3 2.8 5.1 1.5 virginica
#> 105 6.3 2.9 5.6 1.8 virginica
#> 106 6.3 3.3 4.7 1.6 versicolor
#> 107 6.3 3.3 6.0 2.5 virginica
#> 108 6.3 3.4 5.6 2.4 virginica
#> 109 6.4 2.7 5.3 1.9 virginica
#> 110 6.4 2.8 5.6 2.1 virginica
#> 111 6.4 2.8 5.6 2.2 virginica
#> 112 6.4 2.9 4.3 1.3 versicolor
#> 113 6.4 3.1 5.5 1.8 virginica
#> 114 6.4 3.2 4.5 1.5 versicolor
#> 115 6.4 3.2 5.3 2.3 virginica
#> 116 6.5 2.8 4.6 1.5 versicolor
#> 117 6.5 3.0 5.8 2.2 virginica
#> 118 6.5 3.0 5.5 1.8 virginica
#> 119 6.5 3.0 5.2 2.0 virginica
#> 120 6.5 3.2 5.1 2.0 virginica
#> 121 6.6 2.9 4.6 1.3 versicolor
#> 122 6.6 3.0 4.4 1.4 versicolor
#> 123 6.7 2.5 5.8 1.8 virginica
#> 124 6.7 3.0 5.0 1.7 versicolor
#> 125 6.7 3.0 5.2 2.3 virginica
#> 126 6.7 3.1 4.4 1.4 versicolor
#> 127 6.7 3.1 4.7 1.5 versicolor
#> 128 6.7 3.1 5.6 2.4 virginica
#> 129 6.7 3.3 5.7 2.1 virginica
#> 130 6.7 3.3 5.7 2.5 virginica
#> 131 6.8 2.8 4.8 1.4 versicolor
#> 132 6.8 3.0 5.5 2.1 virginica
#> 133 6.8 3.2 5.9 2.3 virginica
#> 134 6.9 3.1 4.9 1.5 versicolor
#> 135 6.9 3.1 5.4 2.1 virginica
#> 136 6.9 3.1 5.1 2.3 virginica
#> 137 6.9 3.2 5.7 2.3 virginica
#> 138 7.0 3.2 4.7 1.4 versicolor
#> 139 7.1 3.0 5.9 2.1 virginica
#> 140 7.2 3.0 5.8 1.6 virginica
#> 141 7.2 3.2 6.0 1.8 virginica
#> 142 7.2 3.6 6.1 2.5 virginica
#> 143 7.3 2.9 6.3 1.8 virginica
#> 144 7.4 2.8 6.1 1.9 virginica
#> 145 7.6 3.0 6.6 2.1 virginica
#> 146 7.7 2.6 6.9 2.3 virginica
#> 147 7.7 2.8 6.7 2.0 virginica
#> 148 7.7 3.0 6.1 2.3 virginica
#> 149 7.7 3.8 6.7 2.2 virginica
#> 150 7.9 3.8 6.4 2.0 virginica
iris %>% arrange(across(starts_with("Sepal"), desc))
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#> 1 7.9 3.8 6.4 2.0 virginica
#> 2 7.7 3.8 6.7 2.2 virginica
#> 3 7.7 3.0 6.1 2.3 virginica
#> 4 7.7 2.8 6.7 2.0 virginica
#> 5 7.7 2.6 6.9 2.3 virginica
#> 6 7.6 3.0 6.6 2.1 virginica
#> 7 7.4 2.8 6.1 1.9 virginica
#> 8 7.3 2.9 6.3 1.8 virginica
#> 9 7.2 3.6 6.1 2.5 virginica
#> 10 7.2 3.2 6.0 1.8 virginica
#> 11 7.2 3.0 5.8 1.6 virginica
#> 12 7.1 3.0 5.9 2.1 virginica
#> 13 7.0 3.2 4.7 1.4 versicolor
#> 14 6.9 3.2 5.7 2.3 virginica
#> 15 6.9 3.1 4.9 1.5 versicolor
#> 16 6.9 3.1 5.4 2.1 virginica
#> 17 6.9 3.1 5.1 2.3 virginica
#> 18 6.8 3.2 5.9 2.3 virginica
#> 19 6.8 3.0 5.5 2.1 virginica
#> 20 6.8 2.8 4.8 1.4 versicolor
#> 21 6.7 3.3 5.7 2.1 virginica
#> 22 6.7 3.3 5.7 2.5 virginica
#> 23 6.7 3.1 4.4 1.4 versicolor
#> 24 6.7 3.1 4.7 1.5 versicolor
#> 25 6.7 3.1 5.6 2.4 virginica
#> 26 6.7 3.0 5.0 1.7 versicolor
#> 27 6.7 3.0 5.2 2.3 virginica
#> 28 6.7 2.5 5.8 1.8 virginica
#> 29 6.6 3.0 4.4 1.4 versicolor
#> 30 6.6 2.9 4.6 1.3 versicolor
#> 31 6.5 3.2 5.1 2.0 virginica
#> 32 6.5 3.0 5.8 2.2 virginica
#> 33 6.5 3.0 5.5 1.8 virginica
#> 34 6.5 3.0 5.2 2.0 virginica
#> 35 6.5 2.8 4.6 1.5 versicolor
#> 36 6.4 3.2 4.5 1.5 versicolor
#> 37 6.4 3.2 5.3 2.3 virginica
#> 38 6.4 3.1 5.5 1.8 virginica
#> 39 6.4 2.9 4.3 1.3 versicolor
#> 40 6.4 2.8 5.6 2.1 virginica
#> 41 6.4 2.8 5.6 2.2 virginica
#> 42 6.4 2.7 5.3 1.9 virginica
#> 43 6.3 3.4 5.6 2.4 virginica
#> 44 6.3 3.3 4.7 1.6 versicolor
#> 45 6.3 3.3 6.0 2.5 virginica
#> 46 6.3 2.9 5.6 1.8 virginica
#> 47 6.3 2.8 5.1 1.5 virginica
#> 48 6.3 2.7 4.9 1.8 virginica
#> 49 6.3 2.5 4.9 1.5 versicolor
#> 50 6.3 2.5 5.0 1.9 virginica
#> 51 6.3 2.3 4.4 1.3 versicolor
#> 52 6.2 3.4 5.4 2.3 virginica
#> 53 6.2 2.9 4.3 1.3 versicolor
#> 54 6.2 2.8 4.8 1.8 virginica
#> 55 6.2 2.2 4.5 1.5 versicolor
#> 56 6.1 3.0 4.6 1.4 versicolor
#> 57 6.1 3.0 4.9 1.8 virginica
#> 58 6.1 2.9 4.7 1.4 versicolor
#> 59 6.1 2.8 4.0 1.3 versicolor
#> 60 6.1 2.8 4.7 1.2 versicolor
#> 61 6.1 2.6 5.6 1.4 virginica
#> 62 6.0 3.4 4.5 1.6 versicolor
#> 63 6.0 3.0 4.8 1.8 virginica
#> 64 6.0 2.9 4.5 1.5 versicolor
#> 65 6.0 2.7 5.1 1.6 versicolor
#> 66 6.0 2.2 4.0 1.0 versicolor
#> 67 6.0 2.2 5.0 1.5 virginica
#> 68 5.9 3.2 4.8 1.8 versicolor
#> 69 5.9 3.0 4.2 1.5 versicolor
#> 70 5.9 3.0 5.1 1.8 virginica
#> 71 5.8 4.0 1.2 0.2 setosa
#> 72 5.8 2.8 5.1 2.4 virginica
#> 73 5.8 2.7 4.1 1.0 versicolor
#> 74 5.8 2.7 3.9 1.2 versicolor
#> 75 5.8 2.7 5.1 1.9 virginica
#> 76 5.8 2.7 5.1 1.9 virginica
#> 77 5.8 2.6 4.0 1.2 versicolor
#> 78 5.7 4.4 1.5 0.4 setosa
#> 79 5.7 3.8 1.7 0.3 setosa
#> 80 5.7 3.0 4.2 1.2 versicolor
#> 81 5.7 2.9 4.2 1.3 versicolor
#> 82 5.7 2.8 4.5 1.3 versicolor
#> 83 5.7 2.8 4.1 1.3 versicolor
#> 84 5.7 2.6 3.5 1.0 versicolor
#> 85 5.7 2.5 5.0 2.0 virginica
#> 86 5.6 3.0 4.5 1.5 versicolor
#> 87 5.6 3.0 4.1 1.3 versicolor
#> 88 5.6 2.9 3.6 1.3 versicolor
#> 89 5.6 2.8 4.9 2.0 virginica
#> 90 5.6 2.7 4.2 1.3 versicolor
#> 91 5.6 2.5 3.9 1.1 versicolor
#> 92 5.5 4.2 1.4 0.2 setosa
#> 93 5.5 3.5 1.3 0.2 setosa
#> 94 5.5 2.6 4.4 1.2 versicolor
#> 95 5.5 2.5 4.0 1.3 versicolor
#> 96 5.5 2.4 3.8 1.1 versicolor
#> 97 5.5 2.4 3.7 1.0 versicolor
#> 98 5.5 2.3 4.0 1.3 versicolor
#> 99 5.4 3.9 1.7 0.4 setosa
#> 100 5.4 3.9 1.3 0.4 setosa
#> 101 5.4 3.7 1.5 0.2 setosa
#> 102 5.4 3.4 1.7 0.2 setosa
#> 103 5.4 3.4 1.5 0.4 setosa
#> 104 5.4 3.0 4.5 1.5 versicolor
#> 105 5.3 3.7 1.5 0.2 setosa
#> 106 5.2 4.1 1.5 0.1 setosa
#> 107 5.2 3.5 1.5 0.2 setosa
#> 108 5.2 3.4 1.4 0.2 setosa
#> 109 5.2 2.7 3.9 1.4 versicolor
#> 110 5.1 3.8 1.5 0.3 setosa
#> 111 5.1 3.8 1.9 0.4 setosa
#> 112 5.1 3.8 1.6 0.2 setosa
#> 113 5.1 3.7 1.5 0.4 setosa
#> 114 5.1 3.5 1.4 0.2 setosa
#> 115 5.1 3.5 1.4 0.3 setosa
#> 116 5.1 3.4 1.5 0.2 setosa
#> 117 5.1 3.3 1.7 0.5 setosa
#> 118 5.1 2.5 3.0 1.1 versicolor
#> 119 5.0 3.6 1.4 0.2 setosa
#> 120 5.0 3.5 1.3 0.3 setosa
#> 121 5.0 3.5 1.6 0.6 setosa
#> 122 5.0 3.4 1.5 0.2 setosa
#> 123 5.0 3.4 1.6 0.4 setosa
#> 124 5.0 3.3 1.4 0.2 setosa
#> 125 5.0 3.2 1.2 0.2 setosa
#> 126 5.0 3.0 1.6 0.2 setosa
#> 127 5.0 2.3 3.3 1.0 versicolor
#> 128 5.0 2.0 3.5 1.0 versicolor
#> 129 4.9 3.6 1.4 0.1 setosa
#> 130 4.9 3.1 1.5 0.1 setosa
#> 131 4.9 3.1 1.5 0.2 setosa
#> 132 4.9 3.0 1.4 0.2 setosa
#> 133 4.9 2.5 4.5 1.7 virginica
#> 134 4.9 2.4 3.3 1.0 versicolor
#> 135 4.8 3.4 1.6 0.2 setosa
#> 136 4.8 3.4 1.9 0.2 setosa
#> 137 4.8 3.1 1.6 0.2 setosa
#> 138 4.8 3.0 1.4 0.1 setosa
#> 139 4.8 3.0 1.4 0.3 setosa
#> 140 4.7 3.2 1.3 0.2 setosa
#> 141 4.7 3.2 1.6 0.2 setosa
#> 142 4.6 3.6 1.0 0.2 setosa
#> 143 4.6 3.4 1.4 0.3 setosa
#> 144 4.6 3.2 1.4 0.2 setosa
#> 145 4.6 3.1 1.5 0.2 setosa
#> 146 4.5 2.3 1.3 0.3 setosa
#> 147 4.4 3.2 1.3 0.2 setosa
#> 148 4.4 3.0 1.3 0.2 setosa
#> 149 4.4 2.9 1.4 0.2 setosa
#> 150 4.3 3.0 1.1 0.1 setosa