The knitr package is an alternative tool to Sweave based on a different design with more features. This document is not an introduction, but only serves as a placeholder to guide you to the real manuals, which are available on the package website https://yihui.org/knitr/ (e.g. the main manual and the graphics manual ), and remember to read the help pages of functions in this package. There is a book “Dynamic Docuemnts with R and knitr” for this package, too.
Below are code chunk examples:
options(digits = 4)
rnorm(20)
#> [1] 0.83016 -0.39967 0.08787 -0.88639 -0.36881 1.36275 0.17043 2.03971
#> [9] -1.00856 1.62743 0.56613 -0.57689 -0.06896 0.97484 0.31215 1.06589
#> [17] -0.34321 -1.58723 0.25899 -0.28606
fit = lm(dist ~ speed, data = cars)
b = coef(fit)
| Estimate | Std. Error | t value | Pr(>|t|) | |
|---|---|---|---|---|
| (Intercept) | -17.579 | 6.758 | -2.601 | 0.012 |
| speed | 3.932 | 0.416 | 9.464 | 0.000 |
The fitted regression equation is \(Y=-17.6+3.93x\).
par(mar=c(4, 4, 1, .1))
plot(cars, pch = 20)
abline(fit, col = 'red')
1 A scatterplot with a regression line.
References
Xie Y (2024). knitr: A General-Purpose Package for Dynamic Report Generation in R. R package version 1.49.1, https://yihui.org/knitr/.
Xie Y (2015). Dynamic Documents with R and knitr, 2nd edition. Chapman and Hall/CRC, Boca Raton, Florida. ISBN 978-1498716963, https://yihui.org/knitr/.
Xie Y (2014). “knitr: A Comprehensive Tool for Reproducible Research in R.” In Stodden V, Leisch F, Peng RD (eds.), Implementing Reproducible Computational Research. Chapman and Hall/CRC. ISBN 978-1466561595.