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Here are some key data-manipulation steps on a data frame, which is how we typically organise our data in R. After reading the data into R it will typically be a data frame; if not, we can force it to be one. The basic idea of the utility functions is to provide a simple and easy-to-type way of performing common data manipulations on a data frame, much like what is possible in SAS or Stata.
The functions dcut, dfactor, and so on do
essentially what the base R cut and factor
functions do, but are easier to use in the context of data frames and
have additional functionality.
Here we work on the melanoma data, which has already been read into R and is a data frame.
The structure for all functions is
dfunction(dataframe, y~x|ifcond, ...)to apply the function to y in a data frame, grouped by
x, and only when condition ifcond is
satisfied. 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, ...)or 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
wildcards (default). Here we compute the means of certain variables.
First, overall:
now only when days>500
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 5.353321and 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 5.120129or summary of all variables starting with “s” and that contains “a”
melanoma=drename(melanoma,tykkelse~thick)
names(melanoma)
#> [1] "no" "status" "days" "ulc" "tykkelse" "sex"Deleting variables
or SAS style:
data(melanoma)
melanoma=ddrop(melanoma,~thick+sex)
names(melanoma)
#> [1] "no" "status" "days" "ulc"alternatively, we can 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 assignment
The dkeep function can also be used to re-ordering the variables in the data-frame
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 be viewed as a data table in RStudio, but to list certain parts of the data in the 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 0dlist(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 706dlist(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 1Getting 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. :1.0000or for specific 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. :1.0000Summaries 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. :3.000and 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. :3.000dsummary(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. :3.000or
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. :3.000For more complex conditions, use 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. :3.000Tables between variables
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 43All 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 90and with new variables
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 1and to sort after multiple variables increasing and decreasing
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 6.182085When the above definitions are done using a condition this can be achieved using the dtransform function that extends transform with a possible condition
On the melanoma data, the variable thick gives the
thickness of the melanoma tumour. For some analyses we would like to
create a factor depending on the thickness. This can be done in several
different ways:
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 12With 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]"
#> -----------------------------------------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+03,2e+03]" "(2e+03,3.04e+03]"
#> [4] "(3.04e+03,5.56e+03]"
#> -----------------------------------------This can also be typed out more specifically
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 combining the 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 51Combine 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"
#> -----------------------------------------A numeric variable status with values 1, 2, 3 can be
converted to 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 7A gender factor with values "M", "F" can be
converted to 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 79sessionInfo()
#> R version 4.6.0 (2026-04-24)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.4 LTS
#>
#> Matrix products: default
#> BLAS: /home/kkzh/.asdf/installs/r/4.6.0/lib/R/lib/libRblas.so
#> LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0 LAPACK version 3.12.0
#>
#> locale:
#> [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
#> [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
#> [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
#> [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
#> [9] LC_ADDRESS=C LC_TELEPHONE=C
#> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
#>
#> time zone: Europe/Copenhagen
#> tzcode source: system (glibc)
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] mets_1.3.10
#>
#> loaded via a namespace (and not attached):
#> [1] cli_3.6.6 knitr_1.51 rlang_1.2.0
#> [4] xfun_0.57 otel_0.2.0 jsonlite_2.0.0
#> [7] listenv_0.10.1 future.apply_1.20.2 lava_1.9.1
#> [10] htmltools_0.5.9 sass_0.4.10 rmarkdown_2.31
#> [13] grid_4.6.0 evaluate_1.0.5 jquerylib_0.1.4
#> [16] fastmap_1.2.0 numDeriv_2016.8-1.1 yaml_2.3.12
#> [19] mvtnorm_1.3-7 lifecycle_1.0.5 timereg_2.0.7
#> [22] compiler_4.6.0 codetools_0.2-20 Rcpp_1.1.1-1.1
#> [25] future_1.70.0 lattice_0.22-9 digest_0.6.39
#> [28] R6_2.6.1 parallelly_1.47.0 parallel_4.6.0
#> [31] splines_4.6.0 Matrix_1.7-5 bslib_0.11.0
#> [34] tools_4.6.0 RcppArmadillo_15.2.6-1 globals_0.19.1
#> [37] survival_3.8-6 cachem_1.1.0These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.