The goal of ‘wrappedtools’ is to make my (and possibly your) life a
bit easier by a set of convenience functions for many common tasks like
e.g. computation of mean and SD and pasting them with ±. Instead
of

paste(round(mean(x),some_level), round(sd(x),some_level), sep=‘±’)

a simple meansd(x, roundDig = some_level) is enough.

You can install the released version of ‘wrappedtools’ from CRAN or the latest development version from github with:

`::install_github("abusjahn/wrappedtools") devtools`

This is a basic example which shows you how to solve a common problem, that is, describe and test differences in some measures between 2 samples, rounding descriptive statistics to a reasonable precision in the process:

```
# Standard functions to obtain median and quartiles:
median(mtcars$mpg)
#> [1] 19.2
quantile(mtcars$mpg,probs = c(.25,.75))
#> 25% 75%
#> 15.425 22.800
# wrappedtools adds rounding and pasting:
median_quart(mtcars$mpg)
#> [1] "19 (15/23)"
# on a higher level, this logic leads to
compare2numvars(data = mtcars, dep_vars = c('wt','mpg', "disp"),
indep_var = 'am',
gaussian = FALSE,
round_desc = 3)
#> # A tibble: 3 × 5
#> # Groups: Variable [3]
#> Variable desc_all `am 0` `am 1` p
#> <fct> <chr> <chr> <chr> <chr>
#> 1 wt 3.32 (2.53/3.66) 3.52 (3.44/3.84) 2.32 (1.90/2.81) 0.001
#> 2 mpg 19.2 (15.3/22.8) 17.3 (14.8/19.2) 22.8 (20.6/30.4) 0.002
#> 3 disp 196 (121/337) 276 (177/360) 120 (79/160) 0.001
```

To explain the *wrapper’ part of the package name, here is another example, using the ks.test as test for a Normal distribution, where ksnormal simply wraps around the ks.test function:

```
<- rnorm(100)
somedata ks.test(x = somedata, 'pnorm', mean=mean(somedata), sd=sd(somedata))
#>
#> Asymptotic one-sample Kolmogorov-Smirnov test
#>
#> data: somedata
#> D = 0.062488, p-value = 0.8297
#> alternative hypothesis: two-sided
ksnormal(somedata)
#> [1] 0.8297214
```

Saving variable selections: Variables may fall into different groups:
Some are following a Gaussian distribution, others are ordinal or
factorial. There may be several grouping variables like treatment,
gender… To refer to such variables, it is convenient to have their index
and name stored. The name may be needed as character or , symbol,
complex variable names like “size [cm]” may need to be surrounded by
backticks in some function calls but must not have those in
others.

Function FindVars finds columns in tibbles or dataframes, based on name
pattern. This is comparable to the selection helpers in ‘tidyselect’,
but does not select the content of matching variables, but names,
positions, and count:

```
<- FindVars(varnames = c('wt','mpg'),
gaussvars allnames = colnames(mtcars))
gaussvars#> $index
#> [1] 1 6
#>
#> $names
#> [1] "mpg" "wt"
#>
#> $bticked
#> [1] "`mpg`" "`wt`"
#>
#> $count
#> [1] 2
#Exclusion based on pattern
<- FindVars(varnames = c('a','cy'),
factorvars allnames = colnames(mtcars),
exclude = c('t'))
$names
factorvars#> [1] "cyl" "am" "gear" "carb"
```

This should give you the general idea, I’ll try to expand this intro over time…