Pivot data from wide to long

Analogous function for pivot_longer in tidyr.

longer_dt(
  data,
  ...,
  gathered_name = "variable",
  gathered_value = "value",
  group_to_keep = NULL,
  negate = FALSE,
  na.rm = FALSE
)

Arguments

data	A data.frame
...	Pattern for unchanged group or unquoted names. Pattern can accept regular expression to match column names. If set `negate = TRUE`, return non-matching columns.
gathered_name	name for the measured variable names column. The default name is 'variable'.
gathered_value	name for the molten data values column(s). The default name is 'value'.
group_to_keep	Group to keep, namely vector of unchanged variables. Can be integer (corresponding id column numbers) or character (id column names) vector.
negate	If `TRUE`, return non-matching elements.
na.rm	If `TRUE`, `NA` values will be removed from the molten data.

Value

A data.table

Examples


## Example 1:
stocks = data.frame(
  time = as.Date('2009-01-01') + 0:9,
  X = rnorm(10, 0, 1),
  Y = rnorm(10, 0, 2),
  Z = rnorm(10, 0, 4)
)

stocks
#>          time            X          Y           Z
#> 1  2009-01-01 -1.400043517 -1.1073988  1.87261768
#> 2  2009-01-02  0.255317055  1.2579641  1.45180502
#> 3  2009-01-03 -2.437263611  4.1300498 -5.21817418
#> 4  2009-01-04 -0.005571287 -3.2619788  2.95110529
#> 5  2009-01-05  0.621552721  1.0248539  7.55401972
#> 6  2009-01-06  1.148411606 -3.7260230 -0.38978042
#> 7  2009-01-07 -1.821817661 -1.0440250 -3.74338941
#> 8  2009-01-08 -0.247325302 -0.1052038 -0.06380125
#> 9  2009-01-09 -0.244199607  1.0859927 -3.30715581
#> 10 2009-01-10 -0.282705449 -1.8281497 -6.04959861

stocks %>%
  longer_dt(time)
#>           time variable        value
#>  1: 2009-01-01        X -1.400043517
#>  2: 2009-01-02        X  0.255317055
#>  3: 2009-01-03        X -2.437263611
#>  4: 2009-01-04        X -0.005571287
#>  5: 2009-01-05        X  0.621552721
#>  6: 2009-01-06        X  1.148411606
#>  7: 2009-01-07        X -1.821817661
#>  8: 2009-01-08        X -0.247325302
#>  9: 2009-01-09        X -0.244199607
#> 10: 2009-01-10        X -0.282705449
#> 11: 2009-01-01        Y -1.107398767
#> 12: 2009-01-02        Y  1.257964084
#> 13: 2009-01-03        Y  4.130049791
#> 14: 2009-01-04        Y -3.261978804
#> 15: 2009-01-05        Y  1.024853900
#> 16: 2009-01-06        Y -3.726022984
#> 17: 2009-01-07        Y -1.044025029
#> 18: 2009-01-08        Y -0.105203820
#> 19: 2009-01-09        Y  1.085992685
#> 20: 2009-01-10        Y -1.828149655
#> 21: 2009-01-01        Z  1.872617682
#> 22: 2009-01-02        Z  1.451805023
#> 23: 2009-01-03        Z -5.218174180
#> 24: 2009-01-04        Z  2.951105285
#> 25: 2009-01-05        Z  7.554019717
#> 26: 2009-01-06        Z -0.389780418
#> 27: 2009-01-07        Z -3.743389414
#> 28: 2009-01-08        Z -0.063801245
#> 29: 2009-01-09        Z -3.307155815
#> 30: 2009-01-10        Z -6.049598605
#>           time variable        value

stocks %>%
  longer_dt("ti")
#>           time variable        value
#>  1: 2009-01-01        X -1.400043517
#>  2: 2009-01-02        X  0.255317055
#>  3: 2009-01-03        X -2.437263611
#>  4: 2009-01-04        X -0.005571287
#>  5: 2009-01-05        X  0.621552721
#>  6: 2009-01-06        X  1.148411606
#>  7: 2009-01-07        X -1.821817661
#>  8: 2009-01-08        X -0.247325302
#>  9: 2009-01-09        X -0.244199607
#> 10: 2009-01-10        X -0.282705449
#> 11: 2009-01-01        Y -1.107398767
#> 12: 2009-01-02        Y  1.257964084
#> 13: 2009-01-03        Y  4.130049791
#> 14: 2009-01-04        Y -3.261978804
#> 15: 2009-01-05        Y  1.024853900
#> 16: 2009-01-06        Y -3.726022984
#> 17: 2009-01-07        Y -1.044025029
#> 18: 2009-01-08        Y -0.105203820
#> 19: 2009-01-09        Y  1.085992685
#> 20: 2009-01-10        Y -1.828149655
#> 21: 2009-01-01        Z  1.872617682
#> 22: 2009-01-02        Z  1.451805023
#> 23: 2009-01-03        Z -5.218174180
#> 24: 2009-01-04        Z  2.951105285
#> 25: 2009-01-05        Z  7.554019717
#> 26: 2009-01-06        Z -0.389780418
#> 27: 2009-01-07        Z -3.743389414
#> 28: 2009-01-08        Z -0.063801245
#> 29: 2009-01-09        Z -3.307155815
#> 30: 2009-01-10        Z -6.049598605
#>           time variable        value

# Example 2:
# \donttest{
library(tidyr)
library(tidydt)

billboard %>%
  longer_dt(
    "wk",
    gathered_name = "week",
    gathered_value = "rank",
    na.rm = TRUE,negate = TRUE
  )
#> Warning: 'measure.vars' [wk1, wk2, wk3, wk4, ...] are not all of the same type. By order of hierarchy, the molten data value column will be of type 'double'. All measure variables not of type 'double' will be coerced too. Check DETAILS in ?melt.data.table for more on coercion.
#>             artist                   track date.entered week rank
#>    1:        2 Pac Baby Don't Cry (Keep...   2000-02-26  wk1   87
#>    2:      2Ge+her The Hardest Part Of ...   2000-09-02  wk1   91
#>    3: 3 Doors Down              Kryptonite   2000-04-08  wk1   81
#>    4: 3 Doors Down                   Loser   2000-10-21  wk1   76
#>    5:     504 Boyz           Wobble Wobble   2000-04-15  wk1   57
#>   ---                                                            
#> 5303:        Creed                  Higher   1999-09-11 wk63   50
#> 5304:     Lonestar                  Amazed   1999-06-05 wk63   45
#> 5305:        Creed                  Higher   1999-09-11 wk64   50
#> 5306:     Lonestar                  Amazed   1999-06-05 wk64   50
#> 5307:        Creed                  Higher   1999-09-11 wk65   49

# or use:
 billboard %>%
   longer_dt(
     artist,track,date.entered,
     gathered_name = "week",
     gathered_value = "rank",
     na.rm = TRUE,negate = TRUE
   )
#> Warning: 'measure.vars' [wk1, wk2, wk3, wk4, ...] are not all of the same type. By order of hierarchy, the molten data value column will be of type 'double'. All measure variables not of type 'double' will be coerced too. Check DETAILS in ?melt.data.table for more on coercion.
#>             artist                   track date.entered week rank
#>    1:        2 Pac Baby Don't Cry (Keep...   2000-02-26  wk1   87
#>    2:      2Ge+her The Hardest Part Of ...   2000-09-02  wk1   91
#>    3: 3 Doors Down              Kryptonite   2000-04-08  wk1   81
#>    4: 3 Doors Down                   Loser   2000-10-21  wk1   76
#>    5:     504 Boyz           Wobble Wobble   2000-04-15  wk1   57
#>   ---                                                            
#> 5303:        Creed                  Higher   1999-09-11 wk63   50
#> 5304:     Lonestar                  Amazed   1999-06-05 wk63   45
#> 5305:        Creed                  Higher   1999-09-11 wk64   50
#> 5306:     Lonestar                  Amazed   1999-06-05 wk64   50
#> 5307:        Creed                  Higher   1999-09-11 wk65   49

 # or use:
 billboard %>%
   longer_dt(
     group_to_keep = 1:3,
     gathered_name = "week",
     gathered_value = "rank",
     na.rm = TRUE,negate = TRUE
   )
#> Warning: 'measure.vars' [wk1, wk2, wk3, wk4, ...] are not all of the same type. By order of hierarchy, the molten data value column will be of type 'double'. All measure variables not of type 'double' will be coerced too. Check DETAILS in ?melt.data.table for more on coercion.
#>             artist                   track date.entered week rank
#>    1:        2 Pac Baby Don't Cry (Keep...   2000-02-26  wk1   87
#>    2:      2Ge+her The Hardest Part Of ...   2000-09-02  wk1   91
#>    3: 3 Doors Down              Kryptonite   2000-04-08  wk1   81
#>    4: 3 Doors Down                   Loser   2000-10-21  wk1   76
#>    5:     504 Boyz           Wobble Wobble   2000-04-15  wk1   57
#>   ---                                                            
#> 5303:        Creed                  Higher   1999-09-11 wk63   50
#> 5304:     Lonestar                  Amazed   1999-06-05 wk63   45
#> 5305:        Creed                  Higher   1999-09-11 wk64   50
#> 5306:     Lonestar                  Amazed   1999-06-05 wk64   50
#> 5307:        Creed                  Higher   1999-09-11 wk65   49
# }

Arguments

Value

See also

Examples

Contents