Usage of ‘wrappedtools’

library(wrappedtools)
#> Loading required package: tidyverse
#> -- Attaching packages --------------------------------------- tidyverse 1.3.1 --
#> v ggplot2 3.3.3     v purrr   0.3.4
#> v tibble  3.1.1     v dplyr   1.0.5
#> v tidyr   1.1.3     v stringr 1.4.0
#> v readr   1.4.0     v forcats 0.5.1
#> -- Conflicts ------------------------------------------ tidyverse_conflicts() --
#> x dplyr::filter() masks stats::filter()
#> x dplyr::lag()    masks stats::lag()
#> Package wrappedtools is still experimental, be warned that there might be dragons

‘wrappedtools’

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.

Installation

You can install the released version of ‘wrappedtools’ from github with:

devtools::install_github("abusjahn/wrappedtools")

Examples

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$mpg)
#> [1] "19 (15/23)"
# on a higher level, this logic leads to
compare2numvars(data = mtcars, dep_vars = c('wt','mpg', "disp"), 
                indep_var = 'am',
                gaussian = F,
                round_desc = 3)
#> # A tibble: 3 x 5
#> # Groups:   Variable [3]
#>   Variable desc_all    `am 0`                        `am 1`                p    
#>   <fct>    <chr>       <chr>                         <chr>                 <chr>
#> 1 wt       3.32 (2.53~ "Error in DESC(x = .$Value, ~ " \n  unbenutztes Ar~ 0.001
#> 2 mpg      19.2 (15.3~ "Error in DESC(x = .$Value, ~ " \n  unbenutztes Ar~ 0.002
#> 3 disp     196 (121/3~ "Error in DESC(x = .$Value, ~ " \n  unbenutztes Ar~ 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 wrapps around the ks.test function:

somedata <- rnorm(100)
ks.test(x = somedata, 'pnorm', mean=mean(somedata), sd=sd(somedata))
#> 
#>  One-sample Kolmogorov-Smirnov test
#> 
#> data:  somedata
#> D = 0.039945, p-value = 0.9972
#> alternative hypothesis: two-sided

ksnormal(somedata)
#> [1] 0.9972476

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