dUtility data-frame manipulations

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+03,2e+03] 208.5490      4.987652
#> 3    (2e+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 ...
#> Warning in structure(res, ngroupvar = 0, class = c("daggregate", class(res))): Calling 'structure(NULL, *)' is deprecated, as NULL cannot have attributes.
#>   Consider 'structure(list(), *)' instead.

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

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+03,2e+03]    (2e+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+03,2e+03]"    "(2e+03,3.04e+03]"   
#> [4] "(3.04e+03,5.56e+03]"
#> -----------------------------------------

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+03,2e+03]"    "(2e+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 ...
#> Warning in structure(res, ngroupvar = 0, class = c("daggregate", class(res))): Calling 'structure(NULL, *)' is deprecated, as NULL cannot have attributes.
#>   Consider 'structure(list(), *)' instead.
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 4.3.2 (2023-10-31)
#> Platform: x86_64-pc-linux-gnu (64-bit)
#> Running under: Ubuntu 22.04.3 LTS
#> 
#> Matrix products: default
#> BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
#> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0
#> 
#> locale:
#>  [1] LC_CTYPE=C.UTF-8       LC_NUMERIC=C           LC_TIME=C.UTF-8       
#>  [4] LC_COLLATE=C.UTF-8     LC_MONETARY=C.UTF-8    LC_MESSAGES=C.UTF-8   
#>  [7] LC_PAPER=C.UTF-8       LC_NAME=C              LC_ADDRESS=C          
#> [10] LC_TELEPHONE=C         LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C   
#> 
#> time zone: UTC
#> tzcode source: system (glibc)
#> 
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> other attached packages:
#> [1] mets_1.3.4     timereg_2.0.5  survival_3.5-7
#> 
#> loaded via a namespace (and not attached):
#>  [1] Matrix_1.6-1.1      future.apply_1.11.1 jsonlite_1.8.8     
#>  [4] compiler_4.3.2      Rcpp_1.0.12         stringr_1.5.1      
#>  [7] parallel_4.3.2      jquerylib_0.1.4     globals_0.16.2     
#> [10] splines_4.3.2       systemfonts_1.0.5   textshaping_0.3.7  
#> [13] yaml_2.3.8          fastmap_1.1.1       lattice_0.21-9     
#> [16] R6_2.5.1            knitr_1.45          future_1.33.1      
#> [19] desc_1.4.3          bslib_0.6.1         rlang_1.1.3        
#> [22] cachem_1.0.8        stringi_1.8.3       xfun_0.42          
#> [25] fs_1.6.3            sass_0.4.8          memoise_2.0.1      
#> [28] cli_3.6.2           pkgdown_2.0.7       magrittr_2.0.3     
#> [31] digest_0.6.34       grid_4.3.2          mvtnorm_1.2-4      
#> [34] lifecycle_1.0.4     lava_1.7.3          vctrs_0.6.5        
#> [37] evaluate_0.23       glue_1.7.0          listenv_0.9.1      
#> [40] numDeriv_2016.8-1.1 codetools_0.2-19    ragg_1.2.7         
#> [43] parallelly_1.37.0   rmarkdown_2.25      purrr_1.0.2        
#> [46] tools_4.3.2         htmltools_0.5.7

Klaus Holst & Thomas Scheike

2024-02-20