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the runtime is the judge
# Does the delivery method change tooth growth? Let the runtime decide.
tt <- t.test(len ~ supp, data = ToothGrowth)
boxplot(len ~ supp, data = ToothGrowth,
col = c("#e6f0fb", "#c9e0f6"), border = "#3a86d4",
xlab = "Supplement", ylab = "Tooth length",
main = "Tooth growth by supplement")
# jitter the raw points + mark the group means (publication style)
stripchart(len ~ supp, data = ToothGrowth, vertical = TRUE,
method = "jitter", add = TRUE, pch = 19, col = "#3a86d466")
means <- tapply(ToothGrowth$len, ToothGrowth$supp, mean)
points(seq_along(means), means, pch = 18, cex = 1.8, col = "#1e5fa8")
mtext(sprintf("Welch t-test: p = %.3f", tt$p.value), col = "#1e5fa8")
No setup, no upload. webR runs the analysis on your own machine and draws the plot — this is what “the runtime is the judge” means.
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My field is functional genomics, and i have found this site just recently. This is an absolutely terrific resource for cluster analysis using R. Great way to get some insights into ggplot2 too. Thank you very much!Datanovia website is really incredible. They understand the tools relevant to modern advanced scientific analysis--but teach it at the level of someone trying to build these skills from scratch. Incredible attention to detail.Datanovia is excellent, it’s extremely useful. It boasts very good techniques, elegant codes, and is well-written and organised. It’s one of the best of its kind.Because reading isn't doing. Pro is where you apply it to your own data — with Prova running every answer, real projects to build, and a certificate that proves the skill. It's the loop a generic chatbot can't close: the runtime is the judge.
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