Helpers for Working With Row Names
rownames.RdEasily 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.- name
character(1). The name of the column to move.- value
character(n). The new set of names for the data frame. If duplicates exist they are modified on-the-fly viamake.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 ofdatato 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 -1.3944642
#> B 2 -0.3984692
#> C 3 0.4623946
#> D 4 -1.1794509
#> E 5 1.3908901
rn2col(df) # default name is `.rn`
#> .rn a b
#> 1 A 1 -1.3944642
#> 2 B 2 -0.3984692
#> 3 C 3 0.4623946
#> 4 D 4 -1.1794509
#> 5 E 5 1.3908901
rn2col(df, "feature") # pass `name =`
#> feature a b
#> 1 A 1 -1.3944642
#> 2 B 2 -0.3984692
#> 3 C 3 0.4623946
#> 4 D 4 -1.1794509
#> 5 E 5 1.3908901
# 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
#> qsec 1 -1.3944642
#> cyl 2 -0.3984692
#> drat 3 0.4623946
#> gear 4 -1.1794509
#> am 5 1.3908901
# 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