Fast reshape/tranpose of data
fast.reshape(
data,
varying,
id,
num,
sep = "",
keep,
idname = "id",
numname = "num",
factor = FALSE,
idcombine = TRUE,
labelnum = FALSE,
labels,
regex = mets.options()$regex,
dropid = FALSE,
...
)data.frame or matrix
Vector of prefix-names of the time varying variables. Optional for Long->Wide reshaping.
id-variable. If omitted then reshape Wide->Long.
Optional number/time variable
String seperating prefix-name with number/time
Vector of column names to keep
Name of id-variable (Wide->Long)
Name of number-variable (Wide->Long)
If true all factors are kept (otherwise treated as character)
If TRUE and id is vector of several variables, the unique id is combined from all the variables.
Otherwise the first variable is only used as identifier.
If TRUE varying variables in wide format (going from long->wide) are labeled 1,2,3,... otherwise use 'num' variable. In long-format (going from wide->long) varying variables matching 'varying' prefix are only selected if their postfix is a number.
Optional labels for the number variable
Use regular expressions
Drop id in long format (default FALSE)
Optional additional arguments
m <- lava::lvm(c(y1,y2,y3,y4)~x)
d <- lava::sim(m,5)
d
#> y1 y2 y3 y4 x
#> 1 -0.57557601 -0.3326475 -0.4457970 0.77282675 0.06016044
#> 2 -1.05053922 -0.9088874 0.7541443 -0.66245889 -0.58889449
#> 3 1.96377843 0.2523829 0.3169168 0.49386202 0.53149619
#> 4 -2.16909044 -1.0242058 -1.6979506 -2.20005456 -1.51839408
#> 5 0.09917712 0.1292274 0.2063671 -0.01771241 0.30655786
fast.reshape(d,"y")
#> x y id num
#> 1 0.06016044 -0.57557601 1 1
#> 2 0.06016044 -0.33264749 1 2
#> 3 0.06016044 -0.44579702 1 3
#> 4 0.06016044 0.77282675 1 4
#> 5 -0.58889449 -1.05053922 2 1
#> 6 -0.58889449 -0.90888735 2 2
#> 7 -0.58889449 0.75414434 2 3
#> 8 -0.58889449 -0.66245889 2 4
#> 9 0.53149619 1.96377843 3 1
#> 10 0.53149619 0.25238289 3 2
#> 11 0.53149619 0.31691678 3 3
#> 12 0.53149619 0.49386202 3 4
#> 13 -1.51839408 -2.16909044 4 1
#> 14 -1.51839408 -1.02420575 4 2
#> 15 -1.51839408 -1.69795061 4 3
#> 16 -1.51839408 -2.20005456 4 4
#> 17 0.30655786 0.09917712 5 1
#> 18 0.30655786 0.12922738 5 2
#> 19 0.30655786 0.20636712 5 3
#> 20 0.30655786 -0.01771241 5 4
fast.reshape(fast.reshape(d,"y"),id="id")
#> x1 y1 id num1 x2 y2 num2 x3
#> 1 0.06016044 -0.57557601 1 1 0.06016044 -0.3326475 2 0.06016044
#> 2 -0.58889449 -1.05053922 2 1 -0.58889449 -0.9088874 2 -0.58889449
#> 3 0.53149619 1.96377843 3 1 0.53149619 0.2523829 2 0.53149619
#> 4 -1.51839408 -2.16909044 4 1 -1.51839408 -1.0242058 2 -1.51839408
#> 5 0.30655786 0.09917712 5 1 0.30655786 0.1292274 2 0.30655786
#> y3 num3 x4 y4 num4
#> 1 -0.4457970 3 0.06016044 0.77282675 4
#> 2 0.7541443 3 -0.58889449 -0.66245889 4
#> 3 0.3169168 3 0.53149619 0.49386202 4
#> 4 -1.6979506 3 -1.51839408 -2.20005456 4
#> 5 0.2063671 3 0.30655786 -0.01771241 4
##### From wide-format
(dd <- fast.reshape(d,"y"))
#> x y id num
#> 1 0.06016044 -0.57557601 1 1
#> 2 0.06016044 -0.33264749 1 2
#> 3 0.06016044 -0.44579702 1 3
#> 4 0.06016044 0.77282675 1 4
#> 5 -0.58889449 -1.05053922 2 1
#> 6 -0.58889449 -0.90888735 2 2
#> 7 -0.58889449 0.75414434 2 3
#> 8 -0.58889449 -0.66245889 2 4
#> 9 0.53149619 1.96377843 3 1
#> 10 0.53149619 0.25238289 3 2
#> 11 0.53149619 0.31691678 3 3
#> 12 0.53149619 0.49386202 3 4
#> 13 -1.51839408 -2.16909044 4 1
#> 14 -1.51839408 -1.02420575 4 2
#> 15 -1.51839408 -1.69795061 4 3
#> 16 -1.51839408 -2.20005456 4 4
#> 17 0.30655786 0.09917712 5 1
#> 18 0.30655786 0.12922738 5 2
#> 19 0.30655786 0.20636712 5 3
#> 20 0.30655786 -0.01771241 5 4
## Same with explicit setting new id and number variable/column names
## and seperator "" (default) and dropping x
fast.reshape(d,"y",idname="a",timevar="b",sep="",keep=c())
#> y a num
#> 1 -0.57557601 1 1
#> 2 -0.33264749 1 2
#> 3 -0.44579702 1 3
#> 4 0.77282675 1 4
#> 5 -1.05053922 2 1
#> 6 -0.90888735 2 2
#> 7 0.75414434 2 3
#> 8 -0.66245889 2 4
#> 9 1.96377843 3 1
#> 10 0.25238289 3 2
#> 11 0.31691678 3 3
#> 12 0.49386202 3 4
#> 13 -2.16909044 4 1
#> 14 -1.02420575 4 2
#> 15 -1.69795061 4 3
#> 16 -2.20005456 4 4
#> 17 0.09917712 5 1
#> 18 0.12922738 5 2
#> 19 0.20636712 5 3
#> 20 -0.01771241 5 4
## Same with 'reshape' list-syntax
fast.reshape(d,list(c("y1","y2","y3","y4")),labelnum=TRUE)
#> x y1 id num
#> 1 0.06016044 -0.57557601 1 1
#> 2 0.06016044 -0.33264749 1 2
#> 3 0.06016044 -0.44579702 1 3
#> 4 0.06016044 0.77282675 1 4
#> 5 -0.58889449 -1.05053922 2 1
#> 6 -0.58889449 -0.90888735 2 2
#> 7 -0.58889449 0.75414434 2 3
#> 8 -0.58889449 -0.66245889 2 4
#> 9 0.53149619 1.96377843 3 1
#> 10 0.53149619 0.25238289 3 2
#> 11 0.53149619 0.31691678 3 3
#> 12 0.53149619 0.49386202 3 4
#> 13 -1.51839408 -2.16909044 4 1
#> 14 -1.51839408 -1.02420575 4 2
#> 15 -1.51839408 -1.69795061 4 3
#> 16 -1.51839408 -2.20005456 4 4
#> 17 0.30655786 0.09917712 5 1
#> 18 0.30655786 0.12922738 5 2
#> 19 0.30655786 0.20636712 5 3
#> 20 0.30655786 -0.01771241 5 4
##### From long-format
fast.reshape(dd,id="id")
#> x1 y1 id num1 x2 y2 num2 x3
#> 1 0.06016044 -0.57557601 1 1 0.06016044 -0.3326475 2 0.06016044
#> 2 -0.58889449 -1.05053922 2 1 -0.58889449 -0.9088874 2 -0.58889449
#> 3 0.53149619 1.96377843 3 1 0.53149619 0.2523829 2 0.53149619
#> 4 -1.51839408 -2.16909044 4 1 -1.51839408 -1.0242058 2 -1.51839408
#> 5 0.30655786 0.09917712 5 1 0.30655786 0.1292274 2 0.30655786
#> y3 num3 x4 y4 num4
#> 1 -0.4457970 3 0.06016044 0.77282675 4
#> 2 0.7541443 3 -0.58889449 -0.66245889 4
#> 3 0.3169168 3 0.53149619 0.49386202 4
#> 4 -1.6979506 3 -1.51839408 -2.20005456 4
#> 5 0.2063671 3 0.30655786 -0.01771241 4
## Restrict set up within-cluster varying variables
fast.reshape(dd,"y",id="id")
#> x y1 id num y2 y3 y4
#> 1 0.06016044 -0.57557601 1 1 -0.3326475 -0.4457970 0.77282675
#> 2 -0.58889449 -1.05053922 2 1 -0.9088874 0.7541443 -0.66245889
#> 3 0.53149619 1.96377843 3 1 0.2523829 0.3169168 0.49386202
#> 4 -1.51839408 -2.16909044 4 1 -1.0242058 -1.6979506 -2.20005456
#> 5 0.30655786 0.09917712 5 1 0.1292274 0.2063671 -0.01771241
fast.reshape(dd,"y",id="id",keep="x",sep=".")
#> x y.1 id y.2 y.3 y.4
#> 1 0.06016044 -0.57557601 1 -0.3326475 -0.4457970 0.77282675
#> 2 -0.58889449 -1.05053922 2 -0.9088874 0.7541443 -0.66245889
#> 3 0.53149619 1.96377843 3 0.2523829 0.3169168 0.49386202
#> 4 -1.51839408 -2.16909044 4 -1.0242058 -1.6979506 -2.20005456
#> 5 0.30655786 0.09917712 5 0.1292274 0.2063671 -0.01771241
#####
x <- data.frame(id=c(5,5,6,6,7),y=1:5,x=1:5,tv=c(1,2,2,1,2))
x
#> id y x tv
#> 1 5 1 1 1
#> 2 5 2 2 2
#> 3 6 3 3 2
#> 4 6 4 4 1
#> 5 7 5 5 2
(xw <- fast.reshape(x,id="id"))
#> id y1 x1 tv1 y2 x2 tv2
#> 1 5 1 1 1 2 2 2
#> 2 6 3 3 2 4 4 1
#> 3 7 5 5 2 NA NA NA
(xl <- fast.reshape(xw,c("y","x"),idname="id2",keep=c()))
#> y x id2 num
#> 1 1 1 1 1
#> 2 2 2 1 2
#> 3 3 3 2 1
#> 4 4 4 2 2
#> 5 5 5 3 1
#> 6 NA NA 3 2
(xl <- fast.reshape(xw,c("y","x","tv")))
#> id y x tv num
#> 1 5 1 1 1 1
#> 2 5 2 2 2 2
#> 3 6 3 3 2 1
#> 4 6 4 4 1 2
#> 5 7 5 5 2 1
#> 6 7 NA NA NA 2
(xw2 <- fast.reshape(xl,id="id",num="num"))
#> id y1 x1 tv1 y2 x2 tv2
#> 1 5 1 1 1 2 2 2
#> 2 6 3 3 2 4 4 1
#> 3 7 5 5 2 NA NA NA
fast.reshape(xw2,c("y","x"),idname="id")
#> id tv1 tv2 y x num
#> 1 5 1 2 1 1 1
#> 2 5 1 2 2 2 2
#> 3 6 2 1 3 3 1
#> 4 6 2 1 4 4 2
#> 5 7 2 NA 5 5 1
#> 6 7 2 NA NA NA 2
### more generally:
### varying=list(c("ym","yf","yb1","yb2"), c("zm","zf","zb1","zb2"))
### varying=list(c("ym","yf","yb1","yb2")))
##### Family cluster example
d <- mets:::simBinFam(3)
d
#> agem agef ageb1 ageb2 xm xf xb1 xb2 ym yf yb1 yb2 id
#> 1 23.12490 27.68406 13.55082 16.98722 1 1 1 1 1 0 1 0 1
#> 2 24.05690 28.48338 14.53548 15.76694 1 1 0 0 1 1 1 1 2
#> 3 29.96077 31.87501 13.12305 15.21414 1 1 1 0 1 1 1 1 3
fast.reshape(d,var="y")
#> agem agef ageb1 ageb2 xm xf xb1 xb2 id y num
#> 1 23.12490 27.68406 13.55082 16.98722 1 1 1 1 1 1 m
#> 2 23.12490 27.68406 13.55082 16.98722 1 1 1 1 1 0 f
#> 3 23.12490 27.68406 13.55082 16.98722 1 1 1 1 1 1 b1
#> 4 23.12490 27.68406 13.55082 16.98722 1 1 1 1 1 0 b2
#> 5 24.05690 28.48338 14.53548 15.76694 1 1 0 0 2 1 m
#> 6 24.05690 28.48338 14.53548 15.76694 1 1 0 0 2 1 f
#> 7 24.05690 28.48338 14.53548 15.76694 1 1 0 0 2 1 b1
#> 8 24.05690 28.48338 14.53548 15.76694 1 1 0 0 2 1 b2
#> 9 29.96077 31.87501 13.12305 15.21414 1 1 1 0 3 1 m
#> 10 29.96077 31.87501 13.12305 15.21414 1 1 1 0 3 1 f
#> 11 29.96077 31.87501 13.12305 15.21414 1 1 1 0 3 1 b1
#> 12 29.96077 31.87501 13.12305 15.21414 1 1 1 0 3 1 b2
fast.reshape(d,varying=list(c("ym","yf","yb1","yb2")))
#> agem agef ageb1 ageb2 xm xf xb1 xb2 id ym num
#> 1 23.12490 27.68406 13.55082 16.98722 1 1 1 1 1 1 ym
#> 2 23.12490 27.68406 13.55082 16.98722 1 1 1 1 1 0 yf
#> 3 23.12490 27.68406 13.55082 16.98722 1 1 1 1 1 1 yb1
#> 4 23.12490 27.68406 13.55082 16.98722 1 1 1 1 1 0 yb2
#> 5 24.05690 28.48338 14.53548 15.76694 1 1 0 0 2 1 ym
#> 6 24.05690 28.48338 14.53548 15.76694 1 1 0 0 2 1 yf
#> 7 24.05690 28.48338 14.53548 15.76694 1 1 0 0 2 1 yb1
#> 8 24.05690 28.48338 14.53548 15.76694 1 1 0 0 2 1 yb2
#> 9 29.96077 31.87501 13.12305 15.21414 1 1 1 0 3 1 ym
#> 10 29.96077 31.87501 13.12305 15.21414 1 1 1 0 3 1 yf
#> 11 29.96077 31.87501 13.12305 15.21414 1 1 1 0 3 1 yb1
#> 12 29.96077 31.87501 13.12305 15.21414 1 1 1 0 3 1 yb2
d <- lava::sim(lava::lvm(~y1+y2+ya),10)
d
#> y1 y2 ya
#> 1 -0.14439960 1.0273924 -0.4302118
#> 2 0.20753834 1.2079084 -0.9261095
#> 3 2.30797840 -1.2313234 -0.1771040
#> 4 0.10580237 0.9838956 0.4020118
#> 5 0.45699881 0.2199248 -0.7317482
#> 6 -0.07715294 -1.4672500 0.8303732
#> 7 -0.33400084 0.5210227 -1.2080828
#> 8 -0.03472603 -0.1587546 -1.0479844
#> 9 0.78763961 1.4645873 1.4411577
#> 10 2.07524501 -0.7660820 -1.0158475
(dd <- fast.reshape(d,"y"))
#> y id num
#> 1 -0.14439960 1 1
#> 2 1.02739244 1 2
#> 3 -0.43021175 1 a
#> 4 0.20753834 2 1
#> 5 1.20790840 2 2
#> 6 -0.92610950 2 a
#> 7 2.30797840 3 1
#> 8 -1.23132342 3 2
#> 9 -0.17710396 3 a
#> 10 0.10580237 4 1
#> 11 0.98389557 4 2
#> 12 0.40201178 4 a
#> 13 0.45699881 5 1
#> 14 0.21992480 5 2
#> 15 -0.73174817 5 a
#> 16 -0.07715294 6 1
#> 17 -1.46725003 6 2
#> 18 0.83037317 6 a
#> 19 -0.33400084 7 1
#> 20 0.52102274 7 2
#> 21 -1.20808279 7 a
#> 22 -0.03472603 8 1
#> 23 -0.15875460 8 2
#> 24 -1.04798441 8 a
#> 25 0.78763961 9 1
#> 26 1.46458731 9 2
#> 27 1.44115771 9 a
#> 28 2.07524501 10 1
#> 29 -0.76608200 10 2
#> 30 -1.01584747 10 a
fast.reshape(d,"y",labelnum=TRUE)
#> ya y id num
#> 1 -0.4302118 -0.14439960 1 1
#> 2 -0.4302118 1.02739244 1 2
#> 3 -0.9261095 0.20753834 2 1
#> 4 -0.9261095 1.20790840 2 2
#> 5 -0.1771040 2.30797840 3 1
#> 6 -0.1771040 -1.23132342 3 2
#> 7 0.4020118 0.10580237 4 1
#> 8 0.4020118 0.98389557 4 2
#> 9 -0.7317482 0.45699881 5 1
#> 10 -0.7317482 0.21992480 5 2
#> 11 0.8303732 -0.07715294 6 1
#> 12 0.8303732 -1.46725003 6 2
#> 13 -1.2080828 -0.33400084 7 1
#> 14 -1.2080828 0.52102274 7 2
#> 15 -1.0479844 -0.03472603 8 1
#> 16 -1.0479844 -0.15875460 8 2
#> 17 1.4411577 0.78763961 9 1
#> 18 1.4411577 1.46458731 9 2
#> 19 -1.0158475 2.07524501 10 1
#> 20 -1.0158475 -0.76608200 10 2
fast.reshape(dd,id="id",num="num")
#> y1 id y2 ya
#> 1 -0.14439960 1 1.0273924 -0.4302118
#> 2 0.20753834 2 1.2079084 -0.9261095
#> 3 2.30797840 3 -1.2313234 -0.1771040
#> 4 0.10580237 4 0.9838956 0.4020118
#> 5 0.45699881 5 0.2199248 -0.7317482
#> 6 -0.07715294 6 -1.4672500 0.8303732
#> 7 -0.33400084 7 0.5210227 -1.2080828
#> 8 -0.03472603 8 -0.1587546 -1.0479844
#> 9 0.78763961 9 1.4645873 1.4411577
#> 10 2.07524501 10 -0.7660820 -1.0158475
fast.reshape(dd,id="id",num="num",labelnum=TRUE)
#> y1 id y2 y3
#> 1 -0.14439960 1 1.0273924 -0.4302118
#> 2 0.20753834 2 1.2079084 -0.9261095
#> 3 2.30797840 3 -1.2313234 -0.1771040
#> 4 0.10580237 4 0.9838956 0.4020118
#> 5 0.45699881 5 0.2199248 -0.7317482
#> 6 -0.07715294 6 -1.4672500 0.8303732
#> 7 -0.33400084 7 0.5210227 -1.2080828
#> 8 -0.03472603 8 -0.1587546 -1.0479844
#> 9 0.78763961 9 1.4645873 1.4411577
#> 10 2.07524501 10 -0.7660820 -1.0158475
fast.reshape(d,c(a="y"),labelnum=TRUE) ## New column name
#> ya a id num
#> 1 -0.4302118 -0.14439960 1 1
#> 2 -0.4302118 1.02739244 1 2
#> 3 -0.9261095 0.20753834 2 1
#> 4 -0.9261095 1.20790840 2 2
#> 5 -0.1771040 2.30797840 3 1
#> 6 -0.1771040 -1.23132342 3 2
#> 7 0.4020118 0.10580237 4 1
#> 8 0.4020118 0.98389557 4 2
#> 9 -0.7317482 0.45699881 5 1
#> 10 -0.7317482 0.21992480 5 2
#> 11 0.8303732 -0.07715294 6 1
#> 12 0.8303732 -1.46725003 6 2
#> 13 -1.2080828 -0.33400084 7 1
#> 14 -1.2080828 0.52102274 7 2
#> 15 -1.0479844 -0.03472603 8 1
#> 16 -1.0479844 -0.15875460 8 2
#> 17 1.4411577 0.78763961 9 1
#> 18 1.4411577 1.46458731 9 2
#> 19 -1.0158475 2.07524501 10 1
#> 20 -1.0158475 -0.76608200 10 2
##### Unbalanced data
m <- lava::lvm(c(y1,y2,y3,y4)~ x+z1+z3+z5)
d <- lava::sim(m,3)
d
#> y1 y2 y3 y4 x z1 z3
#> 1 -1.476059 -0.199160129 -4.1732690 -1.346706 -0.1643758 -1.3702079 -0.3087406
#> 2 -1.959051 0.008613182 0.9196863 -1.591375 0.4206946 0.9878383 -1.2532898
#> 3 2.173273 1.432963646 2.4309376 2.273980 -0.4002467 1.5197450 0.6422413
#> z5
#> 1 -0.04470914
#> 2 -1.73321841
#> 3 0.00213186
fast.reshape(d,c("y","z"))
#> x y z id num
#> 1 -0.1643758 -1.476058703 -1.37020788 1 1
#> 2 -0.1643758 -0.199160129 NA 1 2
#> 3 -0.1643758 -4.173268951 -0.30874057 1 3
#> 4 -0.1643758 -1.346706079 NA 1 4
#> 5 -0.1643758 NA -0.04470914 1 5
#> 6 0.4206946 -1.959051303 0.98783827 2 1
#> 7 0.4206946 0.008613182 NA 2 2
#> 8 0.4206946 0.919686338 -1.25328976 2 3
#> 9 0.4206946 -1.591374775 NA 2 4
#> 10 0.4206946 NA -1.73321841 2 5
#> 11 -0.4002467 2.173273287 1.51974503 3 1
#> 12 -0.4002467 1.432963646 NA 3 2
#> 13 -0.4002467 2.430937614 0.64224131 3 3
#> 14 -0.4002467 2.273979870 NA 3 4
#> 15 -0.4002467 NA 0.00213186 3 5
##### not-varying syntax:
fast.reshape(d,-c("x"))
#> x y z id num
#> 1 -0.1643758 -1.476058703 -1.37020788 1 1
#> 2 -0.1643758 -0.199160129 NA 1 2
#> 3 -0.1643758 -4.173268951 -0.30874057 1 3
#> 4 -0.1643758 -1.346706079 NA 1 4
#> 5 -0.1643758 NA -0.04470914 1 5
#> 6 0.4206946 -1.959051303 0.98783827 2 1
#> 7 0.4206946 0.008613182 NA 2 2
#> 8 0.4206946 0.919686338 -1.25328976 2 3
#> 9 0.4206946 -1.591374775 NA 2 4
#> 10 0.4206946 NA -1.73321841 2 5
#> 11 -0.4002467 2.173273287 1.51974503 3 1
#> 12 -0.4002467 1.432963646 NA 3 2
#> 13 -0.4002467 2.430937614 0.64224131 3 3
#> 14 -0.4002467 2.273979870 NA 3 4
#> 15 -0.4002467 NA 0.00213186 3 5
##### Automatically define varying variables from trailing digits
fast.reshape(d)
#> x y z id num
#> 1 -0.1643758 -1.476058703 -1.37020788 1 1
#> 2 -0.1643758 -0.199160129 NA 1 2
#> 3 -0.1643758 -4.173268951 -0.30874057 1 3
#> 4 -0.1643758 -1.346706079 NA 1 4
#> 5 -0.1643758 NA -0.04470914 1 5
#> 6 0.4206946 -1.959051303 0.98783827 2 1
#> 7 0.4206946 0.008613182 NA 2 2
#> 8 0.4206946 0.919686338 -1.25328976 2 3
#> 9 0.4206946 -1.591374775 NA 2 4
#> 10 0.4206946 NA -1.73321841 2 5
#> 11 -0.4002467 2.173273287 1.51974503 3 1
#> 12 -0.4002467 1.432963646 NA 3 2
#> 13 -0.4002467 2.430937614 0.64224131 3 3
#> 14 -0.4002467 2.273979870 NA 3 4
#> 15 -0.4002467 NA 0.00213186 3 5
##### Prostate cancer example
data(prt)
head(prtw <- fast.reshape(prt,"cancer",id="id"))
#> country time status zyg id cancer1 cancer2
#> 31 Denmark 96.98833 1 DZ 1 0 0
#> 39 Denmark 68.04498 1 DZ 3 0 0
#> 51 Denmark 78.78068 1 DZ 5 0 0
#> 70 Denmark 100.95488 1 MZ 9 0 0
#> 83 Denmark 104.55035 1 DZ 12 0 1
#> 95 Denmark 95.65324 1 DZ 15 0 0
ftable(cancer1~cancer2,data=prtw)
#> cancer1 0 1
#> cancer2
#> 0 13405 349
#> 1 362 106
rm(prtw)