These functions allow you to select variables based on their names.
starts_with(): Starts with a prefix.ends_with(): Ends with a prefix.contains(): Contains a literal string.matches(): Matches a regular expression.all_of(): Matches variable names in a character vector. All names must be present, otherwise an error is thrown.any_of(): The same asall_of()except it doesn't throw an error.everything(): Matches all variables.last_col(): Select the last variable, possibly with an offset.
Usage
starts_with(match, ignore.case = TRUE, vars = peek_vars())
ends_with(match, ignore.case = TRUE, vars = peek_vars())
contains(match, ignore.case = TRUE, vars = peek_vars())
matches(match, ignore.case = TRUE, perl = FALSE, vars = peek_vars())
num_range(prefix, range, width = NULL, vars = peek_vars())
all_of(x, vars = peek_vars())
any_of(x, vars = peek_vars())
everything(vars = peek_vars())
last_col(offset = 0L, vars = peek_vars())Arguments
- match
character(n). If length > 1, the union of the matches is taken.- ignore.case
logical(1). IfTRUE, the default, ignores case when matching names.- vars
character(n). A character vector of variable names. When called from inside selecting functions such asselect(), these are automatically set to the names of the table.- perl
logical(1). Should Perl-compatible regexps be used?- prefix
A prefix which starts the numeric range.
- range
integer(n). A sequence of integers, e.g.1:5.- width
numeric(1). Optionally, the "width" of the numeric range. For example, a range of 2 gives "01", a range of three "001", etc.- x
character(n). A vector of column names.- offset
integer(1). Select thenth variable from the end of thedata.frame.
Examples
mtcars %>% select(starts_with("c"))
#> cyl carb
#> Mazda RX4 6 4
#> Mazda RX4 Wag 6 4
#> Datsun 710 4 1
#> Hornet 4 Drive 6 1
#> Hornet Sportabout 8 2
#> Valiant 6 1
#> Duster 360 8 4
#> Merc 240D 4 2
#> Merc 230 4 2
#> Merc 280 6 4
#> Merc 280C 6 4
#> Merc 450SE 8 3
#> Merc 450SL 8 3
#> Merc 450SLC 8 3
#> Cadillac Fleetwood 8 4
#> Lincoln Continental 8 4
#> Chrysler Imperial 8 4
#> Fiat 128 4 1
#> Honda Civic 4 2
#> Toyota Corolla 4 1
#> Toyota Corona 4 1
#> Dodge Challenger 8 2
#> AMC Javelin 8 2
#> Camaro Z28 8 4
#> Pontiac Firebird 8 2
#> Fiat X1-9 4 1
#> Porsche 914-2 4 2
#> Lotus Europa 4 2
#> Ford Pantera L 8 4
#> Ferrari Dino 6 6
#> Maserati Bora 8 8
#> Volvo 142E 4 2
mtcars %>% select(starts_with(c("c", "h")))
#> cyl hp carb
#> Mazda RX4 6 110 4
#> Mazda RX4 Wag 6 110 4
#> Datsun 710 4 93 1
#> Hornet 4 Drive 6 110 1
#> Hornet Sportabout 8 175 2
#> Valiant 6 105 1
#> Duster 360 8 245 4
#> Merc 240D 4 62 2
#> Merc 230 4 95 2
#> Merc 280 6 123 4
#> Merc 280C 6 123 4
#> Merc 450SE 8 180 3
#> Merc 450SL 8 180 3
#> Merc 450SLC 8 180 3
#> Cadillac Fleetwood 8 205 4
#> Lincoln Continental 8 215 4
#> Chrysler Imperial 8 230 4
#> Fiat 128 4 66 1
#> Honda Civic 4 52 2
#> Toyota Corolla 4 65 1
#> Toyota Corona 4 97 1
#> Dodge Challenger 8 150 2
#> AMC Javelin 8 150 2
#> Camaro Z28 8 245 4
#> Pontiac Firebird 8 175 2
#> Fiat X1-9 4 66 1
#> Porsche 914-2 4 91 2
#> Lotus Europa 4 113 2
#> Ford Pantera L 8 264 4
#> Ferrari Dino 6 175 6
#> Maserati Bora 8 335 8
#> Volvo 142E 4 109 2
mtcars %>% select(ends_with("b"))
#> carb
#> Mazda RX4 4
#> Mazda RX4 Wag 4
#> Datsun 710 1
#> Hornet 4 Drive 1
#> Hornet Sportabout 2
#> Valiant 1
#> Duster 360 4
#> Merc 240D 2
#> Merc 230 2
#> Merc 280 4
#> Merc 280C 4
#> Merc 450SE 3
#> Merc 450SL 3
#> Merc 450SLC 3
#> Cadillac Fleetwood 4
#> Lincoln Continental 4
#> Chrysler Imperial 4
#> Fiat 128 1
#> Honda Civic 2
#> Toyota Corolla 1
#> Toyota Corona 1
#> Dodge Challenger 2
#> AMC Javelin 2
#> Camaro Z28 4
#> Pontiac Firebird 2
#> Fiat X1-9 1
#> Porsche 914-2 2
#> Lotus Europa 2
#> Ford Pantera L 4
#> Ferrari Dino 6
#> Maserati Bora 8
#> Volvo 142E 2
mtcars %>% relocate(contains("a"), .before = mpg)
#> drat am gear carb mpg cyl disp hp wt qsec vs
#> Mazda RX4 3.90 1 4 4 21.0 6 160.0 110 2.620 16.46 0
#> Mazda RX4 Wag 3.90 1 4 4 21.0 6 160.0 110 2.875 17.02 0
#> Datsun 710 3.85 1 4 1 22.8 4 108.0 93 2.320 18.61 1
#> Hornet 4 Drive 3.08 0 3 1 21.4 6 258.0 110 3.215 19.44 1
#> Hornet Sportabout 3.15 0 3 2 18.7 8 360.0 175 3.440 17.02 0
#> Valiant 2.76 0 3 1 18.1 6 225.0 105 3.460 20.22 1
#> Duster 360 3.21 0 3 4 14.3 8 360.0 245 3.570 15.84 0
#> Merc 240D 3.69 0 4 2 24.4 4 146.7 62 3.190 20.00 1
#> Merc 230 3.92 0 4 2 22.8 4 140.8 95 3.150 22.90 1
#> Merc 280 3.92 0 4 4 19.2 6 167.6 123 3.440 18.30 1
#> Merc 280C 3.92 0 4 4 17.8 6 167.6 123 3.440 18.90 1
#> Merc 450SE 3.07 0 3 3 16.4 8 275.8 180 4.070 17.40 0
#> Merc 450SL 3.07 0 3 3 17.3 8 275.8 180 3.730 17.60 0
#> Merc 450SLC 3.07 0 3 3 15.2 8 275.8 180 3.780 18.00 0
#> Cadillac Fleetwood 2.93 0 3 4 10.4 8 472.0 205 5.250 17.98 0
#> Lincoln Continental 3.00 0 3 4 10.4 8 460.0 215 5.424 17.82 0
#> Chrysler Imperial 3.23 0 3 4 14.7 8 440.0 230 5.345 17.42 0
#> Fiat 128 4.08 1 4 1 32.4 4 78.7 66 2.200 19.47 1
#> Honda Civic 4.93 1 4 2 30.4 4 75.7 52 1.615 18.52 1
#> Toyota Corolla 4.22 1 4 1 33.9 4 71.1 65 1.835 19.90 1
#> Toyota Corona 3.70 0 3 1 21.5 4 120.1 97 2.465 20.01 1
#> Dodge Challenger 2.76 0 3 2 15.5 8 318.0 150 3.520 16.87 0
#> AMC Javelin 3.15 0 3 2 15.2 8 304.0 150 3.435 17.30 0
#> Camaro Z28 3.73 0 3 4 13.3 8 350.0 245 3.840 15.41 0
#> Pontiac Firebird 3.08 0 3 2 19.2 8 400.0 175 3.845 17.05 0
#> Fiat X1-9 4.08 1 4 1 27.3 4 79.0 66 1.935 18.90 1
#> Porsche 914-2 4.43 1 5 2 26.0 4 120.3 91 2.140 16.70 0
#> Lotus Europa 3.77 1 5 2 30.4 4 95.1 113 1.513 16.90 1
#> Ford Pantera L 4.22 1 5 4 15.8 8 351.0 264 3.170 14.50 0
#> Ferrari Dino 3.62 1 5 6 19.7 6 145.0 175 2.770 15.50 0
#> Maserati Bora 3.54 1 5 8 15.0 8 301.0 335 3.570 14.60 0
#> Volvo 142E 4.11 1 4 2 21.4 4 121.0 109 2.780 18.60 1
iris %>% select(matches(".t."))
#> 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
mtcars %>% select(last_col())
#> carb
#> Mazda RX4 4
#> Mazda RX4 Wag 4
#> Datsun 710 1
#> Hornet 4 Drive 1
#> Hornet Sportabout 2
#> Valiant 1
#> Duster 360 4
#> Merc 240D 2
#> Merc 230 2
#> Merc 280 4
#> Merc 280C 4
#> Merc 450SE 3
#> Merc 450SL 3
#> Merc 450SLC 3
#> Cadillac Fleetwood 4
#> Lincoln Continental 4
#> Chrysler Imperial 4
#> Fiat 128 1
#> Honda Civic 2
#> Toyota Corolla 1
#> Toyota Corona 1
#> Dodge Challenger 2
#> AMC Javelin 2
#> Camaro Z28 4
#> Pontiac Firebird 2
#> Fiat X1-9 1
#> Porsche 914-2 2
#> Lotus Europa 2
#> Ford Pantera L 4
#> Ferrari Dino 6
#> Maserati Bora 8
#> Volvo 142E 2
# `all_of()` selects the variables in a character vector:
iris %>% select(all_of(c("Petal.Length", "Petal.Width")))
#> Petal.Length Petal.Width
#> 1 1.4 0.2
#> 2 1.4 0.2
#> 3 1.3 0.2
#> 4 1.5 0.2
#> 5 1.4 0.2
#> 6 1.7 0.4
#> 7 1.4 0.3
#> 8 1.5 0.2
#> 9 1.4 0.2
#> 10 1.5 0.1
#> 11 1.5 0.2
#> 12 1.6 0.2
#> 13 1.4 0.1
#> 14 1.1 0.1
#> 15 1.2 0.2
#> 16 1.5 0.4
#> 17 1.3 0.4
#> 18 1.4 0.3
#> 19 1.7 0.3
#> 20 1.5 0.3
#> 21 1.7 0.2
#> 22 1.5 0.4
#> 23 1.0 0.2
#> 24 1.7 0.5
#> 25 1.9 0.2
#> 26 1.6 0.2
#> 27 1.6 0.4
#> 28 1.5 0.2
#> 29 1.4 0.2
#> 30 1.6 0.2
#> 31 1.6 0.2
#> 32 1.5 0.4
#> 33 1.5 0.1
#> 34 1.4 0.2
#> 35 1.5 0.2
#> 36 1.2 0.2
#> 37 1.3 0.2
#> 38 1.4 0.1
#> 39 1.3 0.2
#> 40 1.5 0.2
#> 41 1.3 0.3
#> 42 1.3 0.3
#> 43 1.3 0.2
#> 44 1.6 0.6
#> 45 1.9 0.4
#> 46 1.4 0.3
#> 47 1.6 0.2
#> 48 1.4 0.2
#> 49 1.5 0.2
#> 50 1.4 0.2
#> 51 4.7 1.4
#> 52 4.5 1.5
#> 53 4.9 1.5
#> 54 4.0 1.3
#> 55 4.6 1.5
#> 56 4.5 1.3
#> 57 4.7 1.6
#> 58 3.3 1.0
#> 59 4.6 1.3
#> 60 3.9 1.4
#> 61 3.5 1.0
#> 62 4.2 1.5
#> 63 4.0 1.0
#> 64 4.7 1.4
#> 65 3.6 1.3
#> 66 4.4 1.4
#> 67 4.5 1.5
#> 68 4.1 1.0
#> 69 4.5 1.5
#> 70 3.9 1.1
#> 71 4.8 1.8
#> 72 4.0 1.3
#> 73 4.9 1.5
#> 74 4.7 1.2
#> 75 4.3 1.3
#> 76 4.4 1.4
#> 77 4.8 1.4
#> 78 5.0 1.7
#> 79 4.5 1.5
#> 80 3.5 1.0
#> 81 3.8 1.1
#> 82 3.7 1.0
#> 83 3.9 1.2
#> 84 5.1 1.6
#> 85 4.5 1.5
#> 86 4.5 1.6
#> 87 4.7 1.5
#> 88 4.4 1.3
#> 89 4.1 1.3
#> 90 4.0 1.3
#> 91 4.4 1.2
#> 92 4.6 1.4
#> 93 4.0 1.2
#> 94 3.3 1.0
#> 95 4.2 1.3
#> 96 4.2 1.2
#> 97 4.2 1.3
#> 98 4.3 1.3
#> 99 3.0 1.1
#> 100 4.1 1.3
#> 101 6.0 2.5
#> 102 5.1 1.9
#> 103 5.9 2.1
#> 104 5.6 1.8
#> 105 5.8 2.2
#> 106 6.6 2.1
#> 107 4.5 1.7
#> 108 6.3 1.8
#> 109 5.8 1.8
#> 110 6.1 2.5
#> 111 5.1 2.0
#> 112 5.3 1.9
#> 113 5.5 2.1
#> 114 5.0 2.0
#> 115 5.1 2.4
#> 116 5.3 2.3
#> 117 5.5 1.8
#> 118 6.7 2.2
#> 119 6.9 2.3
#> 120 5.0 1.5
#> 121 5.7 2.3
#> 122 4.9 2.0
#> 123 6.7 2.0
#> 124 4.9 1.8
#> 125 5.7 2.1
#> 126 6.0 1.8
#> 127 4.8 1.8
#> 128 4.9 1.8
#> 129 5.6 2.1
#> 130 5.8 1.6
#> 131 6.1 1.9
#> 132 6.4 2.0
#> 133 5.6 2.2
#> 134 5.1 1.5
#> 135 5.6 1.4
#> 136 6.1 2.3
#> 137 5.6 2.4
#> 138 5.5 1.8
#> 139 4.8 1.8
#> 140 5.4 2.1
#> 141 5.6 2.4
#> 142 5.1 2.3
#> 143 5.1 1.9
#> 144 5.9 2.3
#> 145 5.7 2.5
#> 146 5.2 2.3
#> 147 5.0 1.9
#> 148 5.2 2.0
#> 149 5.4 2.3
#> 150 5.1 1.8
# `all_of()` is strict and will throw an error if the column name isn't found
try({iris %>% select(all_of(c("Species", "Genres")))})
#> Error in all_of(c("Species", "Genres")) :
#> The column Genres does not exist.
# However `any_of()` allows missing variables
iris %>% select(any_of(c("Species", "Genres")))
#> 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