Select (and optionally rename) variables in a data.frame, using a concise mini-language that makes it easy to refer
to variables based on their name (e.g. a:f selects all columns from a on the left to f on the right). You can
also use predicate functions like is.numeric() to select variables based on their properties.
Arguments
- .data
A
data.frame.- ...
<
poor-select> One or more unquoted expressions separated by commas. Variable names can be used as if they were positions in the data frame, so expressions likex:ycan be used to select a range of variables.
Value
An object of the same type as .data. The output has the following properties:
Rows are not affected.
Output columns are a subset of input columns, potentially with a different order. Columns will be renamed if
new_name = old_nameform is used.Data frame attributes are preserved.
Groups are maintained; you can't select off grouping variables.
Details
Overview of selection features
poorman selections implement a dialect of R where operators make it easy to select variables:
:for selecting a range of consecutive variables.!for taking the complement of a set of variables.&and|for selecting the intersection or the union of two sets of variables.c()for combining selections.
In addition, you can use selection helpers. Some helpers select specific columns:
everything(): Matches all variables.last_col(): Select last variable, possibly with an offset.
These helpers select variables by matching patterns in their names:
starts_with(): Starts with a prefix.ends_with(): Ends with a suffix.contains(): Contains a literal string.matches(): Matches a regular expression.num_range(): Matches a numerical range likex01,x02,x03.
These helpers select variables from a character vector:
all_of(): Matches variable names in a character vector. All names must be present, otherwise an out-of-bounds error is thrown.any_of(): Same asall_of(), except that no error is thrown for names that don't exist.
This helper selects variables with a function:
where(): Applies a function to all variables and selects those for which the function returnsTRUE.
Examples
# Here we show the usage for the basic selection operators. See the
# specific help pages to learn about helpers like [starts_with()].
# Select variables by name:
mtcars %>% select(mpg)
#> mpg
#> Mazda RX4 21.0
#> Mazda RX4 Wag 21.0
#> Datsun 710 22.8
#> Hornet 4 Drive 21.4
#> Hornet Sportabout 18.7
#> Valiant 18.1
#> Duster 360 14.3
#> Merc 240D 24.4
#> Merc 230 22.8
#> Merc 280 19.2
#> Merc 280C 17.8
#> Merc 450SE 16.4
#> Merc 450SL 17.3
#> Merc 450SLC 15.2
#> Cadillac Fleetwood 10.4
#> Lincoln Continental 10.4
#> Chrysler Imperial 14.7
#> Fiat 128 32.4
#> Honda Civic 30.4
#> Toyota Corolla 33.9
#> Toyota Corona 21.5
#> Dodge Challenger 15.5
#> AMC Javelin 15.2
#> Camaro Z28 13.3
#> Pontiac Firebird 19.2
#> Fiat X1-9 27.3
#> Porsche 914-2 26.0
#> Lotus Europa 30.4
#> Ford Pantera L 15.8
#> Ferrari Dino 19.7
#> Maserati Bora 15.0
#> Volvo 142E 21.4
# Select multiple variables by separating them with commas. Note
# how the order of columns is determined by the order of inputs:
mtcars %>% select(disp, gear, am)
#> disp gear am
#> Mazda RX4 160.0 4 1
#> Mazda RX4 Wag 160.0 4 1
#> Datsun 710 108.0 4 1
#> Hornet 4 Drive 258.0 3 0
#> Hornet Sportabout 360.0 3 0
#> Valiant 225.0 3 0
#> Duster 360 360.0 3 0
#> Merc 240D 146.7 4 0
#> Merc 230 140.8 4 0
#> Merc 280 167.6 4 0
#> Merc 280C 167.6 4 0
#> Merc 450SE 275.8 3 0
#> Merc 450SL 275.8 3 0
#> Merc 450SLC 275.8 3 0
#> Cadillac Fleetwood 472.0 3 0
#> Lincoln Continental 460.0 3 0
#> Chrysler Imperial 440.0 3 0
#> Fiat 128 78.7 4 1
#> Honda Civic 75.7 4 1
#> Toyota Corolla 71.1 4 1
#> Toyota Corona 120.1 3 0
#> Dodge Challenger 318.0 3 0
#> AMC Javelin 304.0 3 0
#> Camaro Z28 350.0 3 0
#> Pontiac Firebird 400.0 3 0
#> Fiat X1-9 79.0 4 1
#> Porsche 914-2 120.3 5 1
#> Lotus Europa 95.1 5 1
#> Ford Pantera L 351.0 5 1
#> Ferrari Dino 145.0 5 1
#> Maserati Bora 301.0 5 1
#> Volvo 142E 121.0 4 1
# Rename variables:
mtcars %>% select(MilesPerGallon = mpg, everything())
#> MilesPerGallon cyl disp hp drat wt qsec vs am gear
#> Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4
#> Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4
#> Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4
#> Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3
#> Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3
#> Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3
#> Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3
#> Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4
#> Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4
#> Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4
#> Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4
#> Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3
#> Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3
#> Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3
#> Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3
#> Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3
#> Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3
#> Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4
#> Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4
#> Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4
#> Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3
#> Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3
#> AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3
#> Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3
#> Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3
#> Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4
#> Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5
#> Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5
#> Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5
#> Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5
#> Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5
#> Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4
#> 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
# The `:` operator selects a range of consecutive variables:
select(mtcars, mpg:cyl)
#> mpg cyl
#> Mazda RX4 21.0 6
#> Mazda RX4 Wag 21.0 6
#> Datsun 710 22.8 4
#> Hornet 4 Drive 21.4 6
#> Hornet Sportabout 18.7 8
#> Valiant 18.1 6
#> Duster 360 14.3 8
#> Merc 240D 24.4 4
#> Merc 230 22.8 4
#> Merc 280 19.2 6
#> Merc 280C 17.8 6
#> Merc 450SE 16.4 8
#> Merc 450SL 17.3 8
#> Merc 450SLC 15.2 8
#> Cadillac Fleetwood 10.4 8
#> Lincoln Continental 10.4 8
#> Chrysler Imperial 14.7 8
#> Fiat 128 32.4 4
#> Honda Civic 30.4 4
#> Toyota Corolla 33.9 4
#> Toyota Corona 21.5 4
#> Dodge Challenger 15.5 8
#> AMC Javelin 15.2 8
#> Camaro Z28 13.3 8
#> Pontiac Firebird 19.2 8
#> Fiat X1-9 27.3 4
#> Porsche 914-2 26.0 4
#> Lotus Europa 30.4 4
#> Ford Pantera L 15.8 8
#> Ferrari Dino 19.7 6
#> Maserati Bora 15.0 8
#> Volvo 142E 21.4 4
# The `!` operator negates a selection:
mtcars %>% select(!(mpg:qsec))
#> vs am gear carb
#> Mazda RX4 0 1 4 4
#> Mazda RX4 Wag 0 1 4 4
#> Datsun 710 1 1 4 1
#> Hornet 4 Drive 1 0 3 1
#> Hornet Sportabout 0 0 3 2
#> Valiant 1 0 3 1
#> Duster 360 0 0 3 4
#> Merc 240D 1 0 4 2
#> Merc 230 1 0 4 2
#> Merc 280 1 0 4 4
#> Merc 280C 1 0 4 4
#> Merc 450SE 0 0 3 3
#> Merc 450SL 0 0 3 3
#> Merc 450SLC 0 0 3 3
#> Cadillac Fleetwood 0 0 3 4
#> Lincoln Continental 0 0 3 4
#> Chrysler Imperial 0 0 3 4
#> Fiat 128 1 1 4 1
#> Honda Civic 1 1 4 2
#> Toyota Corolla 1 1 4 1
#> Toyota Corona 1 0 3 1
#> Dodge Challenger 0 0 3 2
#> AMC Javelin 0 0 3 2
#> Camaro Z28 0 0 3 4
#> Pontiac Firebird 0 0 3 2
#> Fiat X1-9 1 1 4 1
#> Porsche 914-2 0 1 5 2
#> Lotus Europa 1 1 5 2
#> Ford Pantera L 0 1 5 4
#> Ferrari Dino 0 1 5 6
#> Maserati Bora 0 1 5 8
#> Volvo 142E 1 1 4 2
mtcars %>% select(!ends_with("p"))
#> mpg cyl drat wt qsec vs am gear carb
#> Mazda RX4 21.0 6 3.90 2.620 16.46 0 1 4 4
#> Mazda RX4 Wag 21.0 6 3.90 2.875 17.02 0 1 4 4
#> Datsun 710 22.8 4 3.85 2.320 18.61 1 1 4 1
#> Hornet 4 Drive 21.4 6 3.08 3.215 19.44 1 0 3 1
#> Hornet Sportabout 18.7 8 3.15 3.440 17.02 0 0 3 2
#> Valiant 18.1 6 2.76 3.460 20.22 1 0 3 1
#> Duster 360 14.3 8 3.21 3.570 15.84 0 0 3 4
#> Merc 240D 24.4 4 3.69 3.190 20.00 1 0 4 2
#> Merc 230 22.8 4 3.92 3.150 22.90 1 0 4 2
#> Merc 280 19.2 6 3.92 3.440 18.30 1 0 4 4
#> Merc 280C 17.8 6 3.92 3.440 18.90 1 0 4 4
#> Merc 450SE 16.4 8 3.07 4.070 17.40 0 0 3 3
#> Merc 450SL 17.3 8 3.07 3.730 17.60 0 0 3 3
#> Merc 450SLC 15.2 8 3.07 3.780 18.00 0 0 3 3
#> Cadillac Fleetwood 10.4 8 2.93 5.250 17.98 0 0 3 4
#> Lincoln Continental 10.4 8 3.00 5.424 17.82 0 0 3 4
#> Chrysler Imperial 14.7 8 3.23 5.345 17.42 0 0 3 4
#> Fiat 128 32.4 4 4.08 2.200 19.47 1 1 4 1
#> Honda Civic 30.4 4 4.93 1.615 18.52 1 1 4 2
#> Toyota Corolla 33.9 4 4.22 1.835 19.90 1 1 4 1
#> Toyota Corona 21.5 4 3.70 2.465 20.01 1 0 3 1
#> Dodge Challenger 15.5 8 2.76 3.520 16.87 0 0 3 2
#> AMC Javelin 15.2 8 3.15 3.435 17.30 0 0 3 2
#> Camaro Z28 13.3 8 3.73 3.840 15.41 0 0 3 4
#> Pontiac Firebird 19.2 8 3.08 3.845 17.05 0 0 3 2
#> Fiat X1-9 27.3 4 4.08 1.935 18.90 1 1 4 1
#> Porsche 914-2 26.0 4 4.43 2.140 16.70 0 1 5 2
#> Lotus Europa 30.4 4 3.77 1.513 16.90 1 1 5 2
#> Ford Pantera L 15.8 8 4.22 3.170 14.50 0 1 5 4
#> Ferrari Dino 19.7 6 3.62 2.770 15.50 0 1 5 6
#> Maserati Bora 15.0 8 3.54 3.570 14.60 0 1 5 8
#> Volvo 142E 21.4 4 4.11 2.780 18.60 1 1 4 2
# `&` and `|` take the intersection or the union of two selections:
iris %>% select(starts_with("Petal") & ends_with("Width"))
#> Petal.Length Petal.Width Sepal.Width
#> 1 1.4 0.2 3.5
#> 2 1.4 0.2 3.0
#> 3 1.3 0.2 3.2
#> 4 1.5 0.2 3.1
#> 5 1.4 0.2 3.6
#> 6 1.7 0.4 3.9
#> 7 1.4 0.3 3.4
#> 8 1.5 0.2 3.4
#> 9 1.4 0.2 2.9
#> 10 1.5 0.1 3.1
#> 11 1.5 0.2 3.7
#> 12 1.6 0.2 3.4
#> 13 1.4 0.1 3.0
#> 14 1.1 0.1 3.0
#> 15 1.2 0.2 4.0
#> 16 1.5 0.4 4.4
#> 17 1.3 0.4 3.9
#> 18 1.4 0.3 3.5
#> 19 1.7 0.3 3.8
#> 20 1.5 0.3 3.8
#> 21 1.7 0.2 3.4
#> 22 1.5 0.4 3.7
#> 23 1.0 0.2 3.6
#> 24 1.7 0.5 3.3
#> 25 1.9 0.2 3.4
#> 26 1.6 0.2 3.0
#> 27 1.6 0.4 3.4
#> 28 1.5 0.2 3.5
#> 29 1.4 0.2 3.4
#> 30 1.6 0.2 3.2
#> 31 1.6 0.2 3.1
#> 32 1.5 0.4 3.4
#> 33 1.5 0.1 4.1
#> 34 1.4 0.2 4.2
#> 35 1.5 0.2 3.1
#> 36 1.2 0.2 3.2
#> 37 1.3 0.2 3.5
#> 38 1.4 0.1 3.6
#> 39 1.3 0.2 3.0
#> 40 1.5 0.2 3.4
#> 41 1.3 0.3 3.5
#> 42 1.3 0.3 2.3
#> 43 1.3 0.2 3.2
#> 44 1.6 0.6 3.5
#> 45 1.9 0.4 3.8
#> 46 1.4 0.3 3.0
#> 47 1.6 0.2 3.8
#> 48 1.4 0.2 3.2
#> 49 1.5 0.2 3.7
#> 50 1.4 0.2 3.3
#> 51 4.7 1.4 3.2
#> 52 4.5 1.5 3.2
#> 53 4.9 1.5 3.1
#> 54 4.0 1.3 2.3
#> 55 4.6 1.5 2.8
#> 56 4.5 1.3 2.8
#> 57 4.7 1.6 3.3
#> 58 3.3 1.0 2.4
#> 59 4.6 1.3 2.9
#> 60 3.9 1.4 2.7
#> 61 3.5 1.0 2.0
#> 62 4.2 1.5 3.0
#> 63 4.0 1.0 2.2
#> 64 4.7 1.4 2.9
#> 65 3.6 1.3 2.9
#> 66 4.4 1.4 3.1
#> 67 4.5 1.5 3.0
#> 68 4.1 1.0 2.7
#> 69 4.5 1.5 2.2
#> 70 3.9 1.1 2.5
#> 71 4.8 1.8 3.2
#> 72 4.0 1.3 2.8
#> 73 4.9 1.5 2.5
#> 74 4.7 1.2 2.8
#> 75 4.3 1.3 2.9
#> 76 4.4 1.4 3.0
#> 77 4.8 1.4 2.8
#> 78 5.0 1.7 3.0
#> 79 4.5 1.5 2.9
#> 80 3.5 1.0 2.6
#> 81 3.8 1.1 2.4
#> 82 3.7 1.0 2.4
#> 83 3.9 1.2 2.7
#> 84 5.1 1.6 2.7
#> 85 4.5 1.5 3.0
#> 86 4.5 1.6 3.4
#> 87 4.7 1.5 3.1
#> 88 4.4 1.3 2.3
#> 89 4.1 1.3 3.0
#> 90 4.0 1.3 2.5
#> 91 4.4 1.2 2.6
#> 92 4.6 1.4 3.0
#> 93 4.0 1.2 2.6
#> 94 3.3 1.0 2.3
#> 95 4.2 1.3 2.7
#> 96 4.2 1.2 3.0
#> 97 4.2 1.3 2.9
#> 98 4.3 1.3 2.9
#> 99 3.0 1.1 2.5
#> 100 4.1 1.3 2.8
#> 101 6.0 2.5 3.3
#> 102 5.1 1.9 2.7
#> 103 5.9 2.1 3.0
#> 104 5.6 1.8 2.9
#> 105 5.8 2.2 3.0
#> 106 6.6 2.1 3.0
#> 107 4.5 1.7 2.5
#> 108 6.3 1.8 2.9
#> 109 5.8 1.8 2.5
#> 110 6.1 2.5 3.6
#> 111 5.1 2.0 3.2
#> 112 5.3 1.9 2.7
#> 113 5.5 2.1 3.0
#> 114 5.0 2.0 2.5
#> 115 5.1 2.4 2.8
#> 116 5.3 2.3 3.2
#> 117 5.5 1.8 3.0
#> 118 6.7 2.2 3.8
#> 119 6.9 2.3 2.6
#> 120 5.0 1.5 2.2
#> 121 5.7 2.3 3.2
#> 122 4.9 2.0 2.8
#> 123 6.7 2.0 2.8
#> 124 4.9 1.8 2.7
#> 125 5.7 2.1 3.3
#> 126 6.0 1.8 3.2
#> 127 4.8 1.8 2.8
#> 128 4.9 1.8 3.0
#> 129 5.6 2.1 2.8
#> 130 5.8 1.6 3.0
#> 131 6.1 1.9 2.8
#> 132 6.4 2.0 3.8
#> 133 5.6 2.2 2.8
#> 134 5.1 1.5 2.8
#> 135 5.6 1.4 2.6
#> 136 6.1 2.3 3.0
#> 137 5.6 2.4 3.4
#> 138 5.5 1.8 3.1
#> 139 4.8 1.8 3.0
#> 140 5.4 2.1 3.1
#> 141 5.6 2.4 3.1
#> 142 5.1 2.3 3.1
#> 143 5.1 1.9 2.7
#> 144 5.9 2.3 3.2
#> 145 5.7 2.5 3.3
#> 146 5.2 2.3 3.0
#> 147 5.0 1.9 2.5
#> 148 5.2 2.0 3.0
#> 149 5.4 2.3 3.4
#> 150 5.1 1.8 3.0
iris %>% select(starts_with("Petal") | ends_with("Width"))
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#> 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
#> 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
#> 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
#> 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
#> 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
#> 92 6.1 3.0 4.6 1.4 versicolor
#> 93 5.8 2.6 4.0 1.2 versicolor
#> 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
#> 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
#> 103 7.1 3.0 5.9 2.1 virginica
#> 104 6.3 2.9 5.6 1.8 virginica
#> 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
#> 108 7.3 2.9 6.3 1.8 virginica
#> 109 6.7 2.5 5.8 1.8 virginica
#> 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
#> 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
#> 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
#> 125 6.7 3.3 5.7 2.1 virginica
#> 126 7.2 3.2 6.0 1.8 virginica
#> 127 6.2 2.8 4.8 1.8 virginica
#> 128 6.1 3.0 4.9 1.8 virginica
#> 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
#> 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
#> 139 6.0 3.0 4.8 1.8 virginica
#> 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
#> 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
#> 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
# To take the difference between two selections, combine the `&` and
# `!` operators:
iris %>% select(starts_with("Petal") & !ends_with("Width"))
#> Petal.Length
#> 1 1.4
#> 2 1.4
#> 3 1.3
#> 4 1.5
#> 5 1.4
#> 6 1.7
#> 7 1.4
#> 8 1.5
#> 9 1.4
#> 10 1.5
#> 11 1.5
#> 12 1.6
#> 13 1.4
#> 14 1.1
#> 15 1.2
#> 16 1.5
#> 17 1.3
#> 18 1.4
#> 19 1.7
#> 20 1.5
#> 21 1.7
#> 22 1.5
#> 23 1.0
#> 24 1.7
#> 25 1.9
#> 26 1.6
#> 27 1.6
#> 28 1.5
#> 29 1.4
#> 30 1.6
#> 31 1.6
#> 32 1.5
#> 33 1.5
#> 34 1.4
#> 35 1.5
#> 36 1.2
#> 37 1.3
#> 38 1.4
#> 39 1.3
#> 40 1.5
#> 41 1.3
#> 42 1.3
#> 43 1.3
#> 44 1.6
#> 45 1.9
#> 46 1.4
#> 47 1.6
#> 48 1.4
#> 49 1.5
#> 50 1.4
#> 51 4.7
#> 52 4.5
#> 53 4.9
#> 54 4.0
#> 55 4.6
#> 56 4.5
#> 57 4.7
#> 58 3.3
#> 59 4.6
#> 60 3.9
#> 61 3.5
#> 62 4.2
#> 63 4.0
#> 64 4.7
#> 65 3.6
#> 66 4.4
#> 67 4.5
#> 68 4.1
#> 69 4.5
#> 70 3.9
#> 71 4.8
#> 72 4.0
#> 73 4.9
#> 74 4.7
#> 75 4.3
#> 76 4.4
#> 77 4.8
#> 78 5.0
#> 79 4.5
#> 80 3.5
#> 81 3.8
#> 82 3.7
#> 83 3.9
#> 84 5.1
#> 85 4.5
#> 86 4.5
#> 87 4.7
#> 88 4.4
#> 89 4.1
#> 90 4.0
#> 91 4.4
#> 92 4.6
#> 93 4.0
#> 94 3.3
#> 95 4.2
#> 96 4.2
#> 97 4.2
#> 98 4.3
#> 99 3.0
#> 100 4.1
#> 101 6.0
#> 102 5.1
#> 103 5.9
#> 104 5.6
#> 105 5.8
#> 106 6.6
#> 107 4.5
#> 108 6.3
#> 109 5.8
#> 110 6.1
#> 111 5.1
#> 112 5.3
#> 113 5.5
#> 114 5.0
#> 115 5.1
#> 116 5.3
#> 117 5.5
#> 118 6.7
#> 119 6.9
#> 120 5.0
#> 121 5.7
#> 122 4.9
#> 123 6.7
#> 124 4.9
#> 125 5.7
#> 126 6.0
#> 127 4.8
#> 128 4.9
#> 129 5.6
#> 130 5.8
#> 131 6.1
#> 132 6.4
#> 133 5.6
#> 134 5.1
#> 135 5.6
#> 136 6.1
#> 137 5.6
#> 138 5.5
#> 139 4.8
#> 140 5.4
#> 141 5.6
#> 142 5.1
#> 143 5.1
#> 144 5.9
#> 145 5.7
#> 146 5.2
#> 147 5.0
#> 148 5.2
#> 149 5.4
#> 150 5.1