jedrusiakr: Utilities for Statistics in Psychology

A set of tools for quick exploration and reporting. The main part consists of functions used to calculate basic descriptive statistics and normality checks (mainly opisz() and opisz_by()). The types of descriptives reported are different for different types of measurement scales (nominal, ordinal and continous). The package also includes a set of functions for autoreporting (raport_()) that take a model and returns an example of analysis description in Polish (see Example below). For the time being, the package and its documentation are written only in Polish.

Installation

You can install the development version of jedrusiakr from GitHub with:

# install.packages("devtools")
devtools::install_github("jakub-jedrusiak/jedrusiakr")

Example

Summary statistics:

library(jedrusiakr)
statystyki_opisowe_by(iris, "ilosciowa", Species, where(is.numeric))
#> $setosa
#> $setosa$opisowe
#> # A tibble: 4 × 7
#>   var              N     M    SD      A      K  `NA`
#>   <chr>        <int> <dbl> <dbl>  <dbl>  <dbl> <int>
#> 1 Petal.Length    50 1.46  0.174 0.106   1.02      0
#> 2 Petal.Width     50 0.246 0.105 1.25    1.72      0
#> 3 Sepal.Length    50 5.01  0.352 0.120  -0.253     0
#> 4 Sepal.Width     50 3.43  0.379 0.0412  0.955     0
#> 
#> $setosa$test_shapiro_wilka
#> # A tibble: 4 × 4
#>   var          statistic           p p.signif
#>   <chr>            <dbl>       <dbl> <chr>   
#> 1 Petal.Length     0.955 0.0548      ns      
#> 2 Petal.Width      0.800 0.000000866 ****    
#> 3 Sepal.Length     0.978 0.460       ns      
#> 4 Sepal.Width      0.972 0.272       ns      
#> 
#> 
#> $versicolor
#> $versicolor$opisowe
#> # A tibble: 4 × 7
#>   var              N     M    SD       A       K  `NA`
#>   <chr>        <int> <dbl> <dbl>   <dbl>   <dbl> <int>
#> 1 Petal.Length    50  4.26 0.470 -0.607   0.0479     0
#> 2 Petal.Width     50  1.33 0.198 -0.0312 -0.410      0
#> 3 Sepal.Length    50  5.94 0.516  0.105  -0.533      0
#> 4 Sepal.Width     50  2.77 0.314 -0.363  -0.366      0
#> 
#> $versicolor$test_shapiro_wilka
#> # A tibble: 4 × 4
#>   var          statistic      p p.signif
#>   <chr>            <dbl>  <dbl> <chr>   
#> 1 Petal.Length     0.966 0.158  ns      
#> 2 Petal.Width      0.948 0.0273 *       
#> 3 Sepal.Length     0.978 0.465  ns      
#> 4 Sepal.Width      0.974 0.338  ns      
#> 
#> 
#> $virginica
#> $virginica$opisowe
#> # A tibble: 4 × 7
#>   var              N     M    SD      A       K  `NA`
#>   <chr>        <int> <dbl> <dbl>  <dbl>   <dbl> <int>
#> 1 Petal.Length    50  5.55 0.552  0.549 -0.154      0
#> 2 Petal.Width     50  2.03 0.275 -0.129 -0.602      0
#> 3 Sepal.Length    50  6.59 0.636  0.118  0.0329     0
#> 4 Sepal.Width     50  2.97 0.322  0.366  0.706      0
#> 
#> $virginica$test_shapiro_wilka
#> # A tibble: 4 × 4
#>   var          statistic      p p.signif
#>   <chr>            <dbl>  <dbl> <chr>   
#> 1 Petal.Length     0.962 0.110  ns      
#> 2 Petal.Width      0.960 0.0870 ns      
#> 3 Sepal.Length     0.971 0.258  ns      
#> 4 Sepal.Width      0.967 0.181  ns

Autoreporting in Polish:

library(jedrusiakr)
library(dplyr)
model <- iris %>%
  filter(Species %in% c("setosa", "versicolor")) %>%
  t.test(Petal.Length ~ Species, .)
raport_t(model, "długością płatków u różnych gatunków irysów")
#> [1] "Celem sprawdzenia istotności różnicy między długością płatków u różnych gatunków irysów wykonano test $t$-Studenta dla prób niezależnych. Test wykazał, że różnica w średnich ($ΔM = 2,8$) między grupami jest istotna statystycznie ($t(62,14) = 39,49$; $p < 0,001$)."