LexOPS

LexOPS is an R package for generating matched stimuli for factorial design experiments. You can use the functions on any dataframe, but there is an inbuilt database of example features for English words for psycholinguistics studies in English (LexOPS::lexops).

Installation

LexOPS can be installed as an R package with:

devtools::install_github("JackEdTaylor/LexOPS@*release")

How to Use

📖 In-depth walkthrough of the package: https://jackedtaylor.github.io/LexOPSdocs/

🎓 Paper about the package: Taylor, Beith, and Sereno (2020)

TL;DR

LexOPS makes it easy to generate matched stimuli in a reproducible way. The functions work on any dataframe, but there is an inbuilt dataset, LexOPS::lexops, containing psycholinguistic variables for English words.

The “Generate Pipeline”

The following example pipeline generates 50 words per condition (200 in total), for a study with a 2 x 2, syllables (1, 2) by concreteness (low, high) design. Words are matched by length exactly, and by word frequency within a tolerance of ±0.2 Zipf.

library(LexOPS)
stim<-lexops|>
split_by(Syllables.CMU, 1:1~2:2) |>
split_by(CNC.Brysbaert, 1:2~4:5) |>
control_for(Zipf.SUBTLEX_UK, -0.2:0.2) |>
control_for(Length) |>
generate(n=50, match_null="balanced")

#> Generated 50/50 (100%). 157 total iterations, 0.32 success rate.

A preview of what was generated:

# create a table of the first 5 rows of the outputstim|>
head(5) |>knitr::kable()

item_nr	A1_B1	A1_B2	A2_B1	A2_B2	match_null
1	gist	yank	iffy	tofu	A1_B2
2	oomph	speck	hyper	rabbi	A2_B1
3	worst	voice	lucky	cover	A1_B1
4	suave	stoop	avail	lilac	A2_B2
5	shrewd	starch	bygone	condom	A2_B1

Review Generated Stimuli

The plot_design() function produces a plot summarising the generated stimuli.

plot_design(stim)

Convert to Long Format

The long_format() function coerces the generated stimuli into long format.

# present the same 20 words as in the earlier table
long_format(stim) |>
head(20) |>knitr::kable()

item_nr	condition	match_null	string	Zipf.SUBTLEX_UK	Length	Syllables.CMU	CNC.Brysbaert
1	A1_B1	A1_B2	gist	2.974489	4	1	1.81
1	A1_B2	A1_B2	yank	2.933782	4	1	4.10
1	A2_B1	A1_B2	iffy	2.928732	4	2	1.68
1	A2_B2	A1_B2	tofu	3.045984	4	2	4.86
2	A1_B1	A2_B1	oomph	3.074134	5	1	1.52
2	A1_B2	A2_B1	speck	3.011706	5	1	4.46
2	A2_B1	A2_B1	hyper	3.208953	5	2	2.00
2	A2_B2	A2_B1	rabbi	3.315872	5	2	4.64
3	A1_B1	A1_B1	worst	4.915294	5	1	1.54
3	A1_B2	A1_B1	voice	4.887075	5	1	4.13
3	A2_B1	A1_B1	lucky	5.030973	5	2	1.76
3	A2_B2	A1_B1	cover	4.863260	5	2	4.23
4	A1_B1	A2_B2	suave	2.910580	5	1	1.48
4	A1_B2	A2_B2	stoop	3.045984	5	1	4.63
4	A2_B1	A2_B2	avail	2.877579	5	2	1.33
4	A2_B2	A2_B2	lilac	3.017955	5	2	4.69
5	A1_B1	A2_B1	shrewd	3.244739	6	1	1.92
5	A1_B2	A2_B1	starch	3.291232	6	1	4.29
5	A2_B1	A2_B1	bygone	3.091935	6	2	1.69
5	A2_B2	A2_B1	condom	3.225935	6	2	4.87

Shiny App

The package has an interactive shiny app, which supports most code functionality, with useful additional features like visualising distributions and relationships. It’s a friendly front-end to the package’s functions. A demo version of the LexOPS shiny app is available online at https://jackt.shinyapps.io/lexops/, but it is faster and more reliable to run it locally, with:

LexOPS::run_shiny()

Matching on Custom Dataframes

As well as the built-in dataframe LexOPS::lexops, you can generate matches from any dataframe object.

Here is an example using mtcars. We pick five automatic and five manual models of car, matched for acceleration (within ±5 qsec) and the number of carburetor barrels (carb; exactly).

mtcars|>tibble::as_tibble(rownames="car_id") |>
set_options(id_col="car_id") |>
split_by(am, 0:0~1:1) |>
control_for(qsec, -5:5) |>
control_for(carb, 0:0) |>
generate(5)

#> item_nr A1 A2 match_null
#> 1 1 Merc 280 Ford Pantera L A2
#> 2 2 Merc 230 Volvo 142E A1
#> 3 3 Hornet Sportabout Porsche 914-2 A1
#> 4 4 Cadillac Fleetwood Mazda RX4 A1
#> 5 5 Dodge Challenger Honda Civic A2