# dUtility data-frame manipulations

#### Klaus Holst & Thomas Scheike

#### 2025-10-21

# Simple data manipulation for data-frames

- Renaming variables, Deleting variables
- Looking at the data
- Making new variales for the analysis
- Making factors (groupings)
- Working with factors
- Making a factor from existing numeric variable and vice versa

Here are some key data-manipulation steps on a data-frame which is how we typically organize our data in R. After having read the data into R it will typically be a data-frame, if not we can force it to be a data-frame. The basic idea of the utility functions is to get a simple and easy to type way of making simple data-manipulation on a data-frame much like what is possible in SAS or STATA.

The functions, say, dcut, dfactor and so on are all functions that basically does what the base R cut, factor do, but are easier to use in the context of data-frames and have additional functionality.

    library(mets)
    data(melanoma)

    is.data.frame(melanoma)
    #> [1] TRUE

Here we work on the melanoma data that is already read into R and is a data-frame.

# dUtility functions

The structure for all functions is

- dfunction(dataframe,y~x|ifcond,…)

to use the function on y in a dataframe grouped by x if condition ifcond is valid. The basic functions are

Data processing * dsort * dreshape * dcut * drm, drename, ddrop, dkeep, dsubset * drelevel * dlag * dfactor, dnumeric

Data aggregation * dby, dby2 * dscalar, deval, daggregate * dmean, dsd, dsum, dquantile, dcor * dtable, dcount

Data summaries * dhead, dtail, * dsummary, * dprint, dlist, dlevels, dunique

A generic function daggregate, daggr, can be called with a function as the argument

- daggregate(dataframe,y~x|ifcond,fun=function,…)

without the grouping variable (x)

- daggregate(dataframe,~y|ifcond,fun=function,…)

A useful feature is that y and x as well as the subset condition can be specified using regular-expressions or by wildcards (default). Here to illustrate this, we compute the means of certain variables.

First just oveall

    dmean(melanoma,~thick+I(log(thick)))
    #>         thick I(log(thick))
    #>    291.985366      5.223341

now only when days>500

    dmean(melanoma,~thick+I(log(thick))|I(days>500))
    #>         thick I(log(thick))
    #>    271.582011      5.168691

and now after sex but only when days>500

    dmean(melanoma,thick+I(log(thick))~sex|I(days>500))
    #>   sex    thick I(log(thick))
    #> 1   0 242.9580      5.060086
    #> 2   1 320.2429      5.353321

and finally after quartiles of days (via the dcut function)

    dmean(melanoma,thick+I(log(thick))~I(dcut(days)))
    #>         I(dcut(days))    thick I(log(thick))
    #> 1       [10,1.52e+03] 482.1731      5.799525
    #> 2 (1.52e+03,2.00e+03] 208.5490      4.987652
    #> 3 (2.00e+03,3.04e+03] 223.2941      4.974759
    #> 4 (3.04e+03,5.56e+03] 250.1961      5.120129

or summary of all variables starting with “s” and that contains “a”

    dmean(melanoma,"s*"+"*a*"~sex|I(days>500))
    #>   sex   status     days
    #> 1   0 1.831933 2399.143
    #> 2   1 1.714286 2169.800

# Renaming, deleting, keeping, dropping variables

    melanoma=drename(melanoma,tykkelse~thick)
    names(melanoma)
    #> [1] "no"       "status"   "days"     "ulc"      "tykkelse" "sex"

Deleting variables

    data(melanoma)
    melanoma=drm(melanoma,~thick+sex)
    names(melanoma)
    #> [1] "no"     "status" "days"   "ulc"

or sas style

    data(melanoma)
    melanoma=ddrop(melanoma,~thick+sex)
    names(melanoma)
    #> [1] "no"     "status" "days"   "ulc"

alternatively we can also keep certain variables

    data(melanoma)
    melanoma=dkeep(melanoma,~thick+sex+status+days)
    names(melanoma)
    #> [1] "thick"  "sex"    "status" "days"

This can also be done with direct asignment

    data(melanoma)
    ddrop(melanoma) <- ~thick+sex
    names(melanoma)
    #> [1] "no"     "status" "days"   "ulc"

The dkeep function can also be used to re-ordering the variables in the data-frame

    data(melanoma)
    names(melanoma)
    #> [1] "no"     "status" "days"   "ulc"    "thick"  "sex"
    melanoma=dkeep(melanoma,~days+status+.)
    names(melanoma)
    #> [1] "days"   "status" "no"     "ulc"    "thick"  "sex"

# Looking at the data

    data(melanoma)
    dstr(melanoma)
    #> 'data.frame':    205 obs. of  6 variables:
    #>  $ no    : int  789 13 97 16 21 469 685 7 932 944 ...
    #>  $ status: int  3 3 2 3 1 1 1 1 3 1 ...
    #>  $ days  : int  10 30 35 99 185 204 210 232 232 279 ...
    #>  $ ulc   : int  1 0 0 0 1 1 1 1 1 1 ...
    #>  $ thick : int  676 65 134 290 1208 484 516 1288 322 741 ...
    #>  $ sex   : int  1 1 1 0 1 1 1 1 0 0 ...

The data can in Rstudio be seen as a data-table but to list certain parts of the data in output window

    dlist(melanoma)
    #>     no  status days ulc thick sex
    #> 1   789 3       10  1    676  1
    #> 2    13 3       30  0     65  1
    #> 3    97 2       35  0    134  1
    #> 4    16 3       99  0    290  0
    #> 5    21 1      185  1   1208  1
    #> ---
    #> 201 317 2      4492 1   706   1
    #> 202 798 2      4668 0   612   0
    #> 203 806 2      4688 0    48   0
    #> 204 606 2      4926 0   226   0
    #> 205 328 2      5565 0   290   0

    dlist(melanoma, ~.|sex==1)
    #>     no  status days ulc thick
    #> 1   789 3       10  1    676
    #> 2    13 3       30  0     65
    #> 3    97 2       35  0    134
    #> 5    21 1      185  1   1208
    #> 6   469 1      204  1    484
    #> ---
    #> 191 445 2      3909 1   806
    #> 195 415 2      4119 0    65
    #> 197 175 2      4207 0    65
    #> 198 493 2      4310 0   210
    #> 201 317 2      4492 1   706

    dlist(melanoma, ~ulc+days+thick+sex|sex==1)
    #>     ulc days thick sex
    #> 1   1    10   676  1
    #> 2   0    30    65  1
    #> 3   0    35   134  1
    #> 5   1   185  1208  1
    #> 6   1   204   484  1
    #> ---
    #> 191 1   3909 806   1
    #> 195 0   4119  65   1
    #> 197 0   4207  65   1
    #> 198 0   4310 210   1
    #> 201 1   4492 706   1

Getting summaries

    dsummary(melanoma)
    #>        no            status          days           ulc            thick
    #>  Min.   :  2.0   Min.   :1.00   Min.   :  10   Min.   :0.000   Min.   :  10
    #>  1st Qu.:222.0   1st Qu.:1.00   1st Qu.:1525   1st Qu.:0.000   1st Qu.:  97
    #>  Median :469.0   Median :2.00   Median :2005   Median :0.000   Median : 194
    #>  Mean   :463.9   Mean   :1.79   Mean   :2153   Mean   :0.439   Mean   : 292
    #>  3rd Qu.:731.0   3rd Qu.:2.00   3rd Qu.:3042   3rd Qu.:1.000   3rd Qu.: 356
    #>  Max.   :992.0   Max.   :3.00   Max.   :5565   Max.   :1.000   Max.   :1742
    #>       sex
    #>  Min.   :0.0000
    #>  1st Qu.:0.0000
    #>  Median :0.0000
    #>  Mean   :0.3854
    #>  3rd Qu.:1.0000
    #>  Max.   :1.0000

or for specfic variables

    dsummary(melanoma,~thick+status+sex)
    #>      thick          status          sex
    #>  Min.   :  10   Min.   :1.00   Min.   :0.0000
    #>  1st Qu.:  97   1st Qu.:1.00   1st Qu.:0.0000
    #>  Median : 194   Median :2.00   Median :0.0000
    #>  Mean   : 292   Mean   :1.79   Mean   :0.3854
    #>  3rd Qu.: 356   3rd Qu.:2.00   3rd Qu.:1.0000
    #>  Max.   :1742   Max.   :3.00   Max.   :1.0000

Summaries in different groups (sex)

    dsummary(melanoma,thick+days+status~sex)
    #> sex: 0
    #>      thick             days          status
    #>  Min.   :  10.0   Min.   :  99   Min.   :1.000
    #>  1st Qu.:  97.0   1st Qu.:1636   1st Qu.:2.000
    #>  Median : 162.0   Median :2059   Median :2.000
    #>  Mean   : 248.6   Mean   :2283   Mean   :1.833
    #>  3rd Qu.: 306.0   3rd Qu.:3131   3rd Qu.:2.000
    #>  Max.   :1742.0   Max.   :5565   Max.   :3.000
    #> ------------------------------------------------------------
    #> sex: 1
    #>      thick             days          status
    #>  Min.   :  16.0   Min.   :  10   Min.   :1.000
    #>  1st Qu.: 105.0   1st Qu.:1052   1st Qu.:1.000
    #>  Median : 258.0   Median :1860   Median :2.000
    #>  Mean   : 361.1   Mean   :1946   Mean   :1.722
    #>  3rd Qu.: 484.0   3rd Qu.:2784   3rd Qu.:2.000
    #>  Max.   :1466.0   Max.   :4492   Max.   :3.000

and only among those with thin-tumours or only females (sex==1)

    dsummary(melanoma,thick+days+status~sex|thick<97)
    #> sex: 0
    #>      thick            days          status
    #>  Min.   :10.00   Min.   : 355   Min.   :1.000
    #>  1st Qu.:32.00   1st Qu.:1762   1st Qu.:2.000
    #>  Median :64.00   Median :2227   Median :2.000
    #>  Mean   :51.48   Mean   :2425   Mean   :2.034
    #>  3rd Qu.:65.00   3rd Qu.:3185   3rd Qu.:2.000
    #>  Max.   :81.00   Max.   :4688   Max.   :3.000
    #> ------------------------------------------------------------
    #> sex: 1
    #>      thick            days          status
    #>  Min.   :16.00   Min.   :  30   Min.   :1.000
    #>  1st Qu.:30.00   1st Qu.:1820   1st Qu.:2.000
    #>  Median :65.00   Median :2886   Median :2.000
    #>  Mean   :55.75   Mean   :2632   Mean   :1.875
    #>  3rd Qu.:81.00   3rd Qu.:3328   3rd Qu.:2.000
    #>  Max.   :81.00   Max.   :4207   Max.   :3.000

    dsummary(melanoma,thick+status~+1|sex==1)
    #>      thick            status
    #>  Min.   :  16.0   Min.   :1.000
    #>  1st Qu.: 105.0   1st Qu.:1.000
    #>  Median : 258.0   Median :2.000
    #>  Mean   : 361.1   Mean   :1.722
    #>  3rd Qu.: 484.0   3rd Qu.:2.000
    #>  Max.   :1466.0   Max.   :3.000

or

    dsummary(melanoma,~thick+status|sex==1)
    #>      thick            status
    #>  Min.   :  16.0   Min.   :1.000
    #>  1st Qu.: 105.0   1st Qu.:1.000
    #>  Median : 258.0   Median :2.000
    #>  Mean   : 361.1   Mean   :1.722
    #>  3rd Qu.: 484.0   3rd Qu.:2.000
    #>  Max.   :1466.0   Max.   :3.000

To make more complex conditions need to use the I()

    dsummary(melanoma,thick+days+status~sex|I(thick<97 & sex==1))
    #> sex: 1
    #>      thick            days          status
    #>  Min.   :16.00   Min.   :  30   Min.   :1.000
    #>  1st Qu.:30.00   1st Qu.:1820   1st Qu.:2.000
    #>  Median :65.00   Median :2886   Median :2.000
    #>  Mean   :55.75   Mean   :2632   Mean   :1.875
    #>  3rd Qu.:81.00   3rd Qu.:3328   3rd Qu.:2.000
    #>  Max.   :81.00   Max.   :4207   Max.   :3.000

Tables between variables

    dtable(melanoma,~status+sex)
    #>
    #>        sex  0  1
    #> status
    #> 1          28 29
    #> 2          91 43
    #> 3           7  7

All bivariate tables

    dtable(melanoma,~status+sex+ulc,level=2)
    #>
    #>    status
    #> sex  1  2  3
    #>   0 28 91  7
    #>   1 29 43  7
    #>
    #>    status
    #> ulc  1  2  3
    #>   0 16 92  7
    #>   1 41 42  7
    #>
    #>    sex
    #> ulc  0  1
    #>   0 79 36
    #>   1 47 43

All univariate tables

    dtable(melanoma,~status+sex+ulc,level=1)
    #>
    #> status
    #>   1   2   3
    #>  57 134  14
    #>
    #> sex
    #>   0   1
    #> 126  79
    #>
    #> ulc
    #>   0   1
    #> 115  90

and with new variables

    dtable(melanoma,~status+sex+ulc+dcut(days)+I(days>300),level=1)
    #>
    #> status
    #>   1   2   3
    #>  57 134  14
    #>
    #> sex
    #>   0   1
    #> 126  79
    #>
    #> ulc
    #>   0   1
    #> 115  90
    #>
    #> dcut(days)
    #>       [10,1.52e+03] (1.52e+03,2.00e+03] (2.00e+03,3.04e+03] (3.04e+03,5.56e+03]
    #>                  52                  51                  51                  51
    #>
    #> I(days > 300)
    #> FALSE  TRUE
    #>    11   194

# Sorting the data

To sort the data

    data(melanoma)
    mel= dsort(melanoma,~days)
    dsort(melanoma) <- ~days
    head(mel)
    #>    no status days ulc thick sex
    #> 1 789      3   10   1   676   1
    #> 2  13      3   30   0    65   1
    #> 3  97      2   35   0   134   1
    #> 4  16      3   99   0   290   0
    #> 5  21      1  185   1  1208   1
    #> 6 469      1  204   1   484   1

and to sort after multiple variables increasing and decreasing

    dsort(melanoma) <- ~days-status
    head(melanoma)
    #>    no status days ulc thick sex
    #> 1 789      3   10   1   676   1
    #> 2  13      3   30   0    65   1
    #> 3  97      2   35   0   134   1
    #> 4  16      3   99   0   290   0
    #> 5  21      1  185   1  1208   1
    #> 6 469      1  204   1   484   1

# Making new variales for the analysis

To define a bunch of new covariates within a data-frame

    data(melanoma)
    melanoma= transform(melanoma, thick2=thick^2, lthick=log(thick) )
    dhead(melanoma)
    #>    no status days ulc thick sex  thick2   lthick
    #> 1 789      3   10   1   676   1  456976 6.516193
    #> 2  13      3   30   0    65   1    4225 4.174387
    #> 3  97      2   35   0   134   1   17956 4.897840
    #> 4  16      3   99   0   290   0   84100 5.669881
    #> 5  21      1  185   1  1208   1 1459264 7.096721
    #> 6 469      1  204   1   484   1  234256 6.182085

When the above definitions are done using a condition this can be achieved using the dtransform function that extends transform with a possible condition

     melanoma=dtransform(melanoma,ll=thick*1.05^ulc,sex==1)
     melanoma=dtransform(melanoma,ll=thick,sex!=1)
     dmean(melanoma,ll~sex+ulc)
    #>   sex ulc       ll
    #> 1   0   0 173.7342
    #> 2   1   0 197.3611
    #> 3   0   1 374.5532
    #> 4   1   1 523.1198

# Making factors (groupings)

On the melanoma data the variable thick gives the thickness of the melanom tumour. For some analyses we would like to make a factor depending on the thickness. This can be done in several different ways

    melanoma=dcut(melanoma,~thick,breaks=c(0,200,500,800,2000))

New variable is named thickcat.0 by default.

To see levels of factors in data-frame

    dlevels(melanoma)
    #> thickcat.0 #levels=:4
    #> [1] "[0,200]"     "(200,500]"   "(500,800]"   "(800,2e+03]"
    #> -----------------------------------------

Checking group sizes

    dtable(melanoma,~thickcat.0)
    #>
    #> thickcat.0
    #>     [0,200]   (200,500]   (500,800] (800,2e+03]
    #>         109          64          20          12

With adding to the data-frame directly

    dcut(melanoma,breaks=c(0,200,500,800,2000)) <- gr.thick1~thick
    dlevels(melanoma)
    #> thickcat.0 #levels=:4
    #> [1] "[0,200]"     "(200,500]"   "(500,800]"   "(800,2e+03]"
    #> -----------------------------------------
    #> gr.thick1 #levels=:4
    #> [1] "[0,200]"     "(200,500]"   "(500,800]"   "(800,2e+03]"
    #> -----------------------------------------

new variable is named thickcat.0 (after first cut-point), or to get quartiles with default names thick.cat.4

    dcut(melanoma) <- ~ thick  # new variable is thickcat.4
    dlevels(melanoma)
    #> thickcat.0 #levels=:4
    #> [1] "[0,200]"     "(200,500]"   "(500,800]"   "(800,2e+03]"
    #> -----------------------------------------
    #> gr.thick1 #levels=:4
    #> [1] "[0,200]"     "(200,500]"   "(500,800]"   "(800,2e+03]"
    #> -----------------------------------------
    #> thickcat.4 #levels=:4
    #> [1] "[10,97]"        "(97,194]"       "(194,356]"      "(356,1.74e+03]"
    #> -----------------------------------------

or median groups, here starting again with the original data,

    data(melanoma)
    dcut(melanoma,breaks=2) <- ~ thick  # new variable is thick.2
    dlevels(melanoma)
    #> thickcat.2 #levels=:2
    #> [1] "[10,194]"       "(194,1.74e+03]"
    #> -----------------------------------------

to control new names

    data(melanoma)
    mela= dcut(melanoma,thickcat4+dayscat4~thick+days,breaks=4)
    dlevels(mela)
    #> thickcat4 #levels=:4
    #> [1] "[10,97]"        "(97,194]"       "(194,356]"      "(356,1.74e+03]"
    #> -----------------------------------------
    #> dayscat4 #levels=:4
    #> [1] "[10,1.52e+03]"       "(1.52e+03,2.00e+03]" "(2.00e+03,3.04e+03]"
    #> [4] "(3.04e+03,5.56e+03]"
    #> -----------------------------------------

or

    data(melanoma)
    dcut(melanoma,breaks=4) <- thickcat4+dayscat4~thick+days
    dlevels(melanoma)
    #> thickcat4 #levels=:4
    #> [1] "[10,97]"        "(97,194]"       "(194,356]"      "(356,1.74e+03]"
    #> -----------------------------------------
    #> dayscat4 #levels=:4
    #> [1] "[10,1.52e+03]"       "(1.52e+03,2.00e+03]" "(2.00e+03,3.04e+03]"
    #> [4] "(3.04e+03,5.56e+03]"
    #> -----------------------------------------

This can also be typed out more specifically

    melanoma$gthick = cut(melanoma$thick,breaks=c(0,200,500,800,2000))
    melanoma$gthick = cut(melanoma$thick,breaks=quantile(melanoma$thick),include.lowest=TRUE)

# Working with factors

To see levels of covariates in data-frame

    data(melanoma)
    dcut(melanoma,breaks=4) <- thickcat4~thick
    dlevels(melanoma)
    #> thickcat4 #levels=:4
    #> [1] "[10,97]"        "(97,194]"       "(194,356]"      "(356,1.74e+03]"
    #> -----------------------------------------

To relevel the factor

    dtable(melanoma,~thickcat4)
    #>
    #> thickcat4
    #>        [10,97]       (97,194]      (194,356] (356,1.74e+03]
    #>             56             53             45             51
    melanoma = drelevel(melanoma,~thickcat4,ref="(194,356]")
    dlevels(melanoma)
    #> thickcat4 #levels=:4
    #> [1] "[10,97]"        "(97,194]"       "(194,356]"      "(356,1.74e+03]"
    #> -----------------------------------------
    #> thickcat4.(194,356] #levels=:4
    #> [1] "(194,356]"      "[10,97]"        "(97,194]"       "(356,1.74e+03]"
    #> -----------------------------------------

or to take the third level in the list of levels, same as above,

    melanoma = drelevel(melanoma,~thickcat4,ref=2)
    dlevels(melanoma)
    #> thickcat4 #levels=:4
    #> [1] "[10,97]"        "(97,194]"       "(194,356]"      "(356,1.74e+03]"
    #> -----------------------------------------
    #> thickcat4.(194,356] #levels=:4
    #> [1] "(194,356]"      "[10,97]"        "(97,194]"       "(356,1.74e+03]"
    #> -----------------------------------------
    #> thickcat4.2 #levels=:4
    #> [1] "(97,194]"       "[10,97]"        "(194,356]"      "(356,1.74e+03]"
    #> -----------------------------------------

To combine levels of a factor (first combinining first 3 groups into one)

    melanoma = drelevel(melanoma,~thickcat4,newlevels=1:3)
    dlevels(melanoma)
    #> thickcat4 #levels=:4
    #> [1] "[10,97]"        "(97,194]"       "(194,356]"      "(356,1.74e+03]"
    #> -----------------------------------------
    #> thickcat4.(194,356] #levels=:4
    #> [1] "(194,356]"      "[10,97]"        "(97,194]"       "(356,1.74e+03]"
    #> -----------------------------------------
    #> thickcat4.2 #levels=:4
    #> [1] "(97,194]"       "[10,97]"        "(194,356]"      "(356,1.74e+03]"
    #> -----------------------------------------
    #> thickcat4.1:3 #levels=:2
    #> [1] "[10,97]-(194,356]" "(356,1.74e+03]"
    #> -----------------------------------------

or to combine groups 1 and 2 into one group and 3 and 4 into another

    dkeep(melanoma) <- ~thick+thickcat4
    melanoma = drelevel(melanoma,gthick2~thickcat4,newlevels=list(1:2,3:4))
    dlevels(melanoma)
    #> thickcat4 #levels=:4
    #> [1] "[10,97]"        "(97,194]"       "(194,356]"      "(356,1.74e+03]"
    #> -----------------------------------------
    #> gthick2 #levels=:2
    #> [1] "[10,97]-(97,194]"         "(194,356]-(356,1.74e+03]"
    #> -----------------------------------------

Changing order of factor levels

    dfactor(melanoma,levels=c(3,1,2,4)) <-  thickcat4.2~thickcat4
    dlevel(melanoma,~ "thickcat4*")
    #> thickcat4 #levels=:4
    #> [1] "[10,97]"        "(97,194]"       "(194,356]"      "(356,1.74e+03]"
    #> -----------------------------------------
    #> thickcat4.2 #levels=:4
    #> [1] "(194,356]"      "[10,97]"        "(97,194]"       "(356,1.74e+03]"
    #> -----------------------------------------
    dtable(melanoma,~thickcat4+thickcat4.2)
    #>
    #>                thickcat4.2 (194,356] [10,97] (97,194] (356,1.74e+03]
    #> thickcat4
    #> [10,97]                            0      56        0              0
    #> (97,194]                           0       0       53              0
    #> (194,356]                         45       0        0              0
    #> (356,1.74e+03]                     0       0        0             51

Combine levels but now control factor-level names

    melanoma=drelevel(melanoma,gthick3~thickcat4,newlevels=list(group1.2=1:2,group3.4=3:4))
    dlevels(melanoma)
    #> thickcat4 #levels=:4
    #> [1] "[10,97]"        "(97,194]"       "(194,356]"      "(356,1.74e+03]"
    #> -----------------------------------------
    #> gthick2 #levels=:2
    #> [1] "[10,97]-(97,194]"         "(194,356]-(356,1.74e+03]"
    #> -----------------------------------------
    #> thickcat4.2 #levels=:4
    #> [1] "(194,356]"      "[10,97]"        "(97,194]"       "(356,1.74e+03]"
    #> -----------------------------------------
    #> gthick3 #levels=:2
    #> [1] "group1.2" "group3.4"
    #> -----------------------------------------

# Making a factor from existing numeric variable and vice versa

A numeric variable “status” with values 1,2,3 into a factor by

    data(melanoma)
    melanoma = dfactor(melanoma,~status, labels=c("malignant-melanoma","censoring","dead-other"))
    melanoma = dfactor(melanoma,sexl~sex,labels=c("females","males"))
    dtable(melanoma,~sexl+status.f)
    #>
    #>         status.f malignant-melanoma censoring dead-other
    #> sexl
    #> females                          28        91          7
    #> males                            29        43          7

A gender factor with values “M”, “F” can be converted into numerics by

    melanoma = dnumeric(melanoma,~sexl)
    dstr(melanoma,"sex*")
    #> 'data.frame':    205 obs. of  3 variables:
    #>  $ sex   : int  1 1 1 0 1 1 1 1 0 0 ...
    #>  $ sexl  : Factor w/ 2 levels "females","males": 2 2 2 1 2 2 2 2 1 1 ...
    #>  $ sexl.n: num  2 2 2 1 2 2 2 2 1 1 ...
    dtable(melanoma,~'sex*',level=2)
    #>
    #>          sex
    #> sexl        0   1
    #>   females 126   0
    #>   males     0  79
    #>
    #>       sex
    #> sexl.n   0   1
    #>      1 126   0
    #>      2   0  79
    #>
    #>       sexl
    #> sexl.n females males
    #>      1     126     0
    #>      2       0    79

# SessionInfo

    sessionInfo()
    #> R version 4.5.1 (2025-06-13)
    #> Platform: aarch64-apple-darwin25.0.0
    #> Running under: macOS Tahoe 26.0.1
    #>
    #> Matrix products: default
    #> BLAS:   /Users/kkzh/.asdf/installs/R/4.5.1/lib/R/lib/libRblas.dylib
    #> LAPACK: /Users/kkzh/.asdf/installs/R/4.5.1/lib/R/lib/libRlapack.dylib;  LAPACK version 3.12.1
    #>
    #> locale:
    #> [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
    #>
    #> time zone: Europe/Copenhagen
    #> tzcode source: internal
    #>
    #> attached base packages:
    #> [1] stats     graphics  grDevices utils     datasets  methods   base
    #>
    #> other attached packages:
    #> [1] mets_1.3.8
    #>
    #> loaded via a namespace (and not attached):
    #>  [1] cli_3.6.5           knitr_1.50          rlang_1.1.6
    #>  [4] xfun_0.53           jsonlite_2.0.0      future.apply_1.20.0
    #>  [7] listenv_0.9.1       lava_1.8.1          htmltools_0.5.8.1
    #> [10] sass_0.4.10         rmarkdown_2.30      grid_4.5.1
    #> [13] evaluate_1.0.5      jquerylib_0.1.4     fastmap_1.2.0
    #> [16] numDeriv_2016.8-1.1 yaml_2.3.10         mvtnorm_1.3-3
    #> [19] lifecycle_1.0.4     timereg_2.0.7       compiler_4.5.1
    #> [22] codetools_0.2-20    Rcpp_1.1.0          future_1.67.0
    #> [25] lattice_0.22-7      digest_0.6.37       R6_2.6.1
    #> [28] parallelly_1.45.1   parallel_4.5.1      splines_4.5.1
    #> [31] Matrix_1.7-4        bslib_0.9.0         tools_4.5.1
    #> [34] globals_0.18.0      survival_3.8-3      cachem_1.1.0