[R] Descriptive Statistics: useful hacks

Leonard Mada |eo@m@d@ @end|ng |rom @yon|c@eu
Sun Oct 3 00:00:15 CEST 2021

Dear R Users,

I have started to compile some useful hacks for the generation of nice 
descriptive statistics. I hope that these functions & hacks are useful 
to the wider R community. I hope that package developers also get some 
inspiration from the code or from these ideas.

I have started to review various packages focused on descriptive 
statistics - although I am still at the very beginning.

### Hacks / Code
- split table headers in 2 rows;
- split results over 2 rows: view.gtsummary(...);
- add abbreviations as footnotes: add.abbrev(...);

The results are exported as a web page (using shiny) and can be printed 
as a pdf documented. See the following pdf example:


### Example
# currently focused on package gtsummary

mtcars %>%
     # rename2():
     # - see file Tools.Data.R;
     # - behaves in most cases the same as dplyr::rename();
     rename2("HP" = "hp", "Displ" = disp, "Wt (klb)" = "wt", "Rar" = 
drat) %>%
     # as.factor.df():
     # - see file Tools.Data.R;
     # - encode as (ordered) factor;
     as.factor.df("cyl", "Cyl ") %>%
     # the Descriptive Statistics:
     tbl_summary(by = cyl) %>%
     modify_header(update = header) %>%
     add_p() %>%
     add_overall() %>%
     modify_header(update = header0) %>%
     # Hack: split long statistics !!!
     view.gtsummary(view=FALSE, len=8) %>%
         c("Displ", "HP", "Rar", "Wt (klb)" = "Wt"),
         c("Displacement (in^3)", "Gross horsepower", "Rear axle ratio",
         "Weight (1000 lbs)"));

The required functions are on Github:

The functions rename2() & as.factor.df() are only data-helpers and can 
be found also on Github:


1.) The function add.abbrev() operates on the generated html-code:

- the functionality is more generic and could be used easily with other 
packages that export web pages as well;

2.) Split statistics: is an ugly hack. I plan to redesign the 
functionality using xml-technologies. But I have already too many 

3.) as.factor.df(): traditionally, one would create derived data-sets or 
add a new column with the variable as factor (as the user may need the 
numeric values for further analysis). But it looked nicer as a single 
block of code.



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