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
).
LexOPS can be installed as an R package with:
devtools::install_github("JackEdTaylor/LexOPS@*release")
📖 In-depth walkthrough of the package: https://jackedtaylor.github.io/LexOPSdocs/
🎓 Paper about the package: Taylor, Beith, and Sereno (2020)
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 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 output
stim |>
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 |
The plot_design()
function produces a plot summarising the generated
stimuli.
plot_design(stim)
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 |
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()
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