Use masonry, build a (data) structure!

Using a standard interface, create common data results structures, such as from a linear regression or correlation. Design the analysis, add settings and variables, construct the results, and lastly scrub and polish it up.

One of the main goals of mason is to be able to easily implement other analyses to this infrastructure. Since, I’d argue, most statistical methods follow a similar pattern (what are the variables, what options to use for the method, what to select from the results), this can be easily encapsulated into a ‘blueprint -> construction -> scrubbing and polishing’ workflow.

mason was designed to be best used with the magrittr %>% pipes, though it doesn’t need to be. It was also designed to follow the tidy data philosophy, specifically that everything should result in a data frame, within limits. This makes it easier to do further analysis, visualization, and inclusion into report formats. This flow was deliberately chosen so it works well with dplyr, tidyr, ggplot2, and many other excellent packages out there that help make analyses easier.

Installation

The package can be installed from CRAN using:

install.packages("mason")

For the development version, install using:

# install.packages("remotes")
remotes::install_github('lwjohnst86/mason')

Typical usage

The typical usage for this package would flow like this:

library(mason)
design(iris, 'glm') %>%
    add_settings() %>%
    add_variables('yvars', c('Sepal.Length', 'Sepal.Width')) %>%
    add_variables('xvars', c('Petal.Length', 'Petal.Width')) %>%
    construct() %>%
    scrub() %>%
    polish_adjust_pvalue()
#> # A tibble: 8 x 11
#>   Yterms Xterms term  estimate std.error statistic   p.value conf.low conf.high
#>   <chr>  <chr>  <chr>    <dbl>     <dbl>     <dbl>     <dbl>    <dbl>     <dbl>
#> 1 Sepal… Petal… (Int…    4.31     0.0784     54.9  2.43e-100    4.15     4.46  
#> 2 Sepal… Petal… Peta…    0.409    0.0189     21.6  1.04e- 47    0.372    0.446 
#> 3 Sepal… Petal… (Int…    3.45     0.0761     45.4  9.02e- 89    3.31     3.60  
#> 4 Sepal… Petal… Peta…   -0.106    0.0183     -5.77 4.51e-  8   -0.142   -0.0698
#> 5 Sepal… Petal… (Int…    4.78     0.0729     65.5  3.34e-111    4.63     4.92  
#> 6 Sepal… Petal… Peta…    0.889    0.0514     17.3  2.33e- 37    0.788    0.989 
#> 7 Sepal… Petal… (Int…    3.31     0.0621     53.3  1.84e- 98    3.19     3.43  
#> 8 Sepal… Petal… Peta…   -0.209    0.0437     -4.79 4.07e-  6   -0.295   -0.124 
#> # … with 2 more variables: sample.size <int>, adj.p.value <dbl>

Depending on the statistical method being used, each function may have slightly different arguments.

Problems?

If there are problems, create an issue and let me know what the problem is!

Contributing a statistical method

1. Add the method to design
2. Add a new function to the S3 method add_settings following the naming convention add_settings.statmethod_bp and include the appropriate settings to the statistical method.
3. If needed, add another option to the type argument in the add_variables function.
4. Like the add_settings instructions above, do the same for the construct and scrub S3 method.
5. If needed, add another polish_ type function.

Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.