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 as all_of() except it doesn't throw an error.

  • everything(): Matches all variables.

  • last_col(): Select the last variable, possibly with an offset.

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). If TRUE, the default, ignores case when matching names.

vars

character(n). A character vector of variable names. When called from inside selecting functions such as select(), 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 the nth variable from the end of the data.frame.

Value

An integer vector giving the position of the matched variables.

See also

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