The goal of pipeline is to generate and process machine-readable study descriptions.
You can install the released version of pipeline from GitHub with:
devtools::install_github("scienceverse/pipeline")
Get the file paths to the json file and data. Load the study parameters and data with the pipeline()
function; this will run the analyses.
library(pipeline)
# find file paths to data included with this package installation
iat_json_path <- system.file("extdata", "iat.json", package = "pipeline")
iat_data_path <- system.file("extdata", "iat.csv", package = "pipeline")
iat_study <- pipeline(iat_json_path, iat_data_path)
Generate a report. Set results='asis'
in the code chunk.
summary(iat_study, "hypotheses", "analyses", "results")
Mean RT will be significantly slower in the incongruent condition compared to the congruent condition.
If all criteria are met, this hypothesis is supported.
Reaction times for congruent and incongruent trials will be signifiantly and positively correlated.
If all criteria are met, this hypothesis is supported.
We will run t.test(x = .data$incongruent, y = .data$congruent, alternative = two.sided, paired = TRUE, var.equal = FALSE, conf.level = 0.95)
We will run cor.test(x = .data$incongruent, y = .data$congruent, alternative = two.sided, paired = TRUE, var.equal = FALSE, conf.level = 0.95)
Mean RT will be significantly slower in the incongruent condition compared to the congruent condition.
Conclusion: Congratulations! All criteria were met, this hypothesis was supported.
Mean RT will be significantly slower in the incongruent condition compared to the congruent condition.
Conclusion: Congratulations! All criteria were met, this hypothesis was supported.
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