Easily move row names to a column and vice-versa without the unwanted
side-effects to object class and attributes. Drop-in replacement for
tibble::rownames_to_column()
and tibble::column_to_rownames()
which
can have undesired side-effects to complex object attributes.
Does not import any external packages, modify the environment, or change
the object (other than the desired column). When using col2rn()
, if
explicit row names exist, they are overwritten with a warning. add_rowid()
does not affect row names, which differs from tibble::rowid_to_column()
.
Usage
rn2col(data, name = ".rn")
col2rn(data, name = ".rn")
has_rn(data)
rm_rn(data)
set_rn(data, value)
add_rowid(data, name = ".rowid")
Arguments
- data
An object that inherits from class
data.frame
. Typically asoma_adat
class object.- name
Character. The name of the column to move.
- value
Character. The new set of names for the data frame. If duplicates exist they are modified on-the-fly via
make.unique()
.
Value
All functions attempt to return an object of the same class as
the input with fully intact and unmodified attributes (aside from those
required by the desired action). has_rn()
returns a scalar logical.
Functions
rn2col()
: moves the row names ofdata
to an explicit column whether they are explicit or implicit.col2rn()
: is the inverse ofrn2col()
. If row names exist, they will be overwritten (with warning).has_rn()
: returns a boolean indicating whether the data frame has explicit row names assigned.rm_rn()
: removes existing row names, leaving only "implicit" row names.set_rn()
: sets (and overwrites) existing row names for data frames only.add_rowid()
: adds a sequential integer row identifier; starting at1:nrow(data)
. It does not remove existing row names currently, but may in the future (please code accordingly).
Examples
df <- data.frame(a = 1:5, b = rnorm(5), row.names = LETTERS[1:5])
df
#> a b
#> A 1 2.0650249
#> B 2 -1.6309894
#> C 3 0.5124269
#> D 4 -1.8630115
#> E 5 -0.5220125
rn2col(df) # default name is `.rn`
#> .rn a b
#> 1 A 1 2.0650249
#> 2 B 2 -1.6309894
#> 3 C 3 0.5124269
#> 4 D 4 -1.8630115
#> 5 E 5 -0.5220125
rn2col(df, "AptName") # pass `name =`
#> AptName a b
#> 1 A 1 2.0650249
#> 2 B 2 -1.6309894
#> 3 C 3 0.5124269
#> 4 D 4 -1.8630115
#> 5 E 5 -0.5220125
# moving columns
df$mtcars <- sample(names(mtcars), 5)
col2rn(df, "mtcars") # with a warning
#> Warning: `df` already has row names. They will be over-written.
#> a b
#> cyl 1 2.0650249
#> disp 2 -1.6309894
#> am 3 0.5124269
#> wt 4 -1.8630115
#> gear 5 -0.5220125
# Move back and forth easily
# Leaves original object un-modified
identical(df, col2rn(rn2col(df)))
#> [1] TRUE
# add "id" column
add_rowid(mtcars)
#> .rowid mpg cyl disp hp drat wt qsec vs am gear
#> Mazda RX4 1 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4
#> Mazda RX4 Wag 2 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4
#> Datsun 710 3 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4
#> Hornet 4 Drive 4 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3
#> Hornet Sportabout 5 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3
#> Valiant 6 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3
#> Duster 360 7 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3
#> Merc 240D 8 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4
#> Merc 230 9 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4
#> Merc 280 10 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4
#> Merc 280C 11 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4
#> Merc 450SE 12 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3
#> Merc 450SL 13 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3
#> Merc 450SLC 14 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3
#> Cadillac Fleetwood 15 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3
#> Lincoln Continental 16 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3
#> Chrysler Imperial 17 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3
#> Fiat 128 18 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4
#> Honda Civic 19 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4
#> Toyota Corolla 20 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4
#> Toyota Corona 21 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3
#> Dodge Challenger 22 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3
#> AMC Javelin 23 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3
#> Camaro Z28 24 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3
#> Pontiac Firebird 25 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3
#> Fiat X1-9 26 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4
#> Porsche 914-2 27 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5
#> Lotus Europa 28 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5
#> Ford Pantera L 29 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5
#> Ferrari Dino 30 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5
#> Maserati Bora 31 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5
#> Volvo 142E 32 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
# remove row names
has_rn(mtcars)
#> [1] TRUE
mtcars2 <- rm_rn(mtcars)
has_rn(mtcars2)
#> [1] FALSE