edibble

Installation

You can install the package from CRAN:

install.packages("edibble")

You can install the development version with:

# install.packages("remotes")
remotes::install_github("emitanaka/edibble")

Overview

The goal of edibble R-package is to aid in the plan, design and simulation of experiments by mapping fundamental components of experiments to an object oriented system. The edibble system is built on the principle that the system must make it easy to recover experimental context by encouraging the user to be explicit about experimental details in fundamental terms.

Examples

Consider an experiment where you want to know what is an effective way of teaching (flipped or traditional style) for teaching a particular subject and how different forms of exams (take-home, open-book or closed-book) affect student’s marks.

There are four classes for this subject with each class holding 30 students. The teaching style can only be applied to the whole class but exam can be different for individual students.

library(edibble)

set.seed(2020)

des <- design(name = "Effective teaching") %>%
    set_units(class = 4,
              student = nested_in(class, 30)) %>%
    set_trts(style = c("flipped", "traditional"),
             exam = c("take-home", "open-book", "closed-book")) %>%
    allot_trts(style ~ class,
               exam ~ student) %>%
    assign_trts("random")

serve_table(des)
#> # Effective teaching 
#> # An edibble: 120 x 4
#>        class     student       style        exam
#>    <unit(4)> <unit(120)>    <trt(2)>    <trt(3)>
#>  1    class1   student1  traditional closed-book
#>  2    class1   student2  traditional open-book  
#>  3    class1   student3  traditional take-home  
#>  4    class1   student4  traditional closed-book
#>  5    class1   student5  traditional take-home  
#>  6    class1   student6  traditional take-home  
#>  7    class1   student7  traditional open-book  
#>  8    class1   student8  traditional open-book  
#>  9    class1   student9  traditional closed-book
#> 10    class1   student10 traditional closed-book
#> # … with 110 more rows

Before constructing the experiment, you might want to think about what you are recording for which level of unit and what values these variables can be recorded as.

out <- des %>% 
    set_rcrds_of(student = c("exam_mark",
                             "quiz1_mark",
                             "quiz2_mark",
                             "gender"),
              class = c("room",
                        "teacher")) %>%
    expect_rcrds(exam_mark <= 100,
                 exam_mark >= 0,
                 quiz1_mark <= 15L,
                 quiz1_mark >= 0L,
                 quiz2_mark <= 30L,
                 quiz2_mark >= 0L,
                 factor(gender, levels = c("female", "male", "non-binary", "unknown"))) %>%
    serve_table()

out
#> # Effective teaching 
#> # An edibble: 120 x 10
#>        class     student       style        exam exam_m…¹ quiz1…² quiz2…³ gender
#>    <unit(4)> <unit(120)>    <trt(2)>    <trt(3)>   <rcrd>  <rcrd>  <rcrd> <rcrd>
#>  1    class1   student1  traditional closed-book        o       o       o      o
#>  2    class1   student2  traditional open-book          o       o       o      o
#>  3    class1   student3  traditional take-home          o       o       o      o
#>  4    class1   student4  traditional closed-book        o       o       o      o
#>  5    class1   student5  traditional take-home          o       o       o      o
#>  6    class1   student6  traditional take-home          o       o       o      o
#>  7    class1   student7  traditional open-book          o       o       o      o
#>  8    class1   student8  traditional open-book          o       o       o      o
#>  9    class1   student9  traditional closed-book        o       o       o      o
#> 10    class1   student10 traditional closed-book        o       o       o      o
#> # … with 110 more rows, 2 more variables: room <rcrd>, teacher <rcrd>, and
#> #   abbreviated variable names ¹​exam_mark, ²​quiz1_mark, ³​quiz2_mark

When you export the above edibble design using the export_design function, the variables you are recording are constraint to the values you expect, e.g. for factors, the cells have a drop-down menu to select from possible values.

export_design(out, file = "/PATH/TO/FILE.xlsx")

In addition, there is a spreadsheet for every observational level. E.g. here room and teacher is the same for all students in one class so rather than entering duplicate information, these are exported to another sheet for data entry.

There is also support for more complex nesting structures. You can always make the structure using edibble and take the resulting data frame to use in other experimental design software. It’s also possible to bring existing data frame into edibble if you want to take advantage of the exporting feature in edibble.

design("nesting structure") %>% 
  # there are 3 sites labelled A, B, C
  set_units(site = c("A", "B", "C"),
            # each site has 2 blocks except B with 3 sites
            block = nested_in(site, 
                              "B" ~ 3,
                                . ~ 2),
            # levels can be specified by their number instead
            # so for below "block1" has 30 plots, 
            # "block2" and "block3" has 40 plots,
            # the rest of blocks have 20 plots.
            plot = nested_in(block, 
                              1 ~ 30,
                        c(2, 3) ~ 40,
                              . ~ 20)) %>% 
  serve_table()
#> # nesting structure 
#> # An edibble: 190 x 3
#>         site     block        plot
#>    <unit(3)> <unit(7)> <unit(190)>
#>  1         A    block1      plot1 
#>  2         A    block1      plot2 
#>  3         A    block1      plot3 
#>  4         A    block1      plot4 
#>  5         A    block1      plot5 
#>  6         A    block1      plot6 
#>  7         A    block1      plot7 
#>  8         A    block1      plot8 
#>  9         A    block1      plot9 
#> 10         A    block1      plot10
#> # … with 180 more rows

Experimental data

tidyverse is well suited for the data science project workflow as illustrated below in (B) (from Grolemund and Wickham 2017). For experimental data, the statistical aspect begins before obtaining data as depicted below in (A). The focus of edibble is to facilitate work in (A).

The edibble R-package differ considerably to other packages for constructing experimental design with a focus on the whole process and less on the randomisation process (which the other software generally focus and do well on). Some features include:

• declaratively create experimental designs based on experimental components (e.g. units and treatments),
• explicitly specify variables that are to be recorded (e.g. response), and
• set expected values for variables to be recorded which restrict the data entry when the design is exported as an xlsx file,
• simulate values for record variables,
• make classical named designs see Cookbook chapter.

Work-in-progress book on this package can be found here.

Limitations

Currently, edibble:

• expects you to know the number of units available from the start. Unknown numbers will be supported in future versions.
• in theory, edibble should support experiments that are not comparative experiments but this is not tested.
• does not do enough testing so design should be diagnosed after construction (which should be done regardless of how much testing edibble implements).

Related Work

The way that edibble specifies experimental design is largely novel (if I say so myself) and there are no work that resembles it. I’m concurrently working on two extension packages:

• deggust - to visualise the designs constructed from edibble as ggplot2 objects (WIP).
• sizzled - for experiments that require sample size calculation (WIP).

Below are some other related work. You can also have a look at the CRAN Task View for Design of Experiment and Analysis of Experimental Data for a whole collection.

• DeclareDesign for survey or sampling designs
• designr for balanced factorial designs with crossed and nested random and fixed effect to data frame
• dae for functions useful in the design and ANOVA of experiments (this is in fact powering the randomisation in edibble)
• plotdesignr for designing agronomic field experiments

Acknowledgement

edibble is hugely inspired by the work of Tidyverse Team. I’m grateful for the dedication and work by the Tidyverse Team, as well as R Development Core Team that supports the core R ecosystem, that made developing this package possible.

Tidyverse familiarity

The implementation in edibble adopt a similar nomenclature and design philosophy as tidyverse (and where it does not, it’s likely my shortcoming) so that tidyverse users can leverage their familiarity of the tidyverse language when using edibble. Specifically, edibble follows the philosophy:

• main functions do one thing and have a consistent form of <verb>_<noun> (e.g. set_units and set_rcrds) where the nouns are generally plural. Exceptions are when the subject matter is clearly singular (e.g. design and set_context);
• pipable functions;
• all dots arguments are dynamic dots;
• duplicate names repaired with same option as tibble for additions to edibble graph;
• ability for developers to extend certain components. Currently only supported for others to contribute their own classical named experimental designs via prep_classical_;
• the specification of complex nested structure drawing similarity to dplyr::case_when (LHS is character or integer for edibble however).

Code of Conduct

Please note that the edibble project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.