Getting help and documentation

Install required packages
R’s own help system
- Help system for R functions
- Getting help without knowing the function name
Online documentation
- Search, mailing lists and Q&A sites
- Official documentation
Books
Detach (automatically) loaded packages (if possible)
Get the article source from GitHub

Install required packages

sos

wants <- c("sos")
has   <- wants %in% rownames(installed.packages())
if(any(!has)) install.packages(wants[!has])

R’s own help system

Help system for R functions

help.start()
help(round)
?round
?"/"
# not shown (opens browser window)

args(round)

function (x, digits = 0) 
NULL

example(round)


round> round(.5 + -2:4) # IEEE rounding: -2  0  0  2  2  4  4
[1] -2  0  0  2  2  4  4

round> ( x1 <- seq(-2, 4, by = .5) )
 [1] -2.0 -1.5 -1.0 -0.5  0.0  0.5  1.0  1.5  2.0  2.5  3.0  3.5  4.0

round> round(x1) #-- IEEE rounding !
 [1] -2 -2 -1  0  0  0  1  2  2  2  3  4  4

round> x1[trunc(x1) != floor(x1)]
[1] -1.5 -0.5

round> x1[round(x1) != floor(x1 + .5)]
[1] -1.5  0.5  2.5

round> (non.int <- ceiling(x1) != floor(x1))
 [1] FALSE  TRUE FALSE  TRUE FALSE  TRUE FALSE  TRUE FALSE  TRUE FALSE
[12]  TRUE FALSE

round> x2 <- pi * 100^(-1:3)

round> round(x2, 3)
[1]       0.031       3.142     314.159   31415.927 3141592.654

round> signif(x2, 3)
[1] 3.14e-02 3.14e+00 3.14e+02 3.14e+04 3.14e+06

Getting help without knowing the function name

help.search("mean")
# not shown (opens browser window)

apropos("mean")

 [1] "colMeans"      ".colMeans"     "kmeans"        "mean"         
 [5] "mean.Date"     "mean.default"  "mean.difftime" "mean.POSIXct" 
 [9] "mean.POSIXlt"  "rowMeans"      ".rowMeans"     "weighted.mean"

library(sos)                  # for findFn()
findFn("Petal.Length")
# not shown (opens browser window)

Online documentation

Search, mailing lists and Q&A sites

R mailing lists: Especially R-help, also see the posting guide
R mailing list search and help pages
Stack Overflow: R Q&A site - the technical side of using R
CrossValidated: R Q&A site - the statistical side of using R

Official documentation

Books

Introductory statistics

Dalgaard, P. (2008). Introductory Statistics with R (2nd ed.). London, UK: Springer. URL
Maindonald, J. & Braun, W. J. (2010). Data Analysis and Graphics Using R: An Example-Based Approach (3rd ed.). Cambridge, UK: Cambridge University Press. URL
Wollschlaeger, D. (2026). Grundlagen der Datenanalyse mit R (6th ed.). Berlin: Springer. URL

Specialized and advanced statistical topics

Regressions models
- Fox J, Weisberg S. 2019. An R Companion to Applied Regression (3rd ed). Thousand Oaks, CA: Sage. URL
- Fox J. 2020. Regression diagnostics (2nd ed). URL
- Harrell Jr FE. 2015. Regression Modeling Strategies (2nd ed). New York: Springer. URL
Multivariate analysis
- Zelterman D. 2015. Applied multivariate statistics with R. New York, NY: Springer.
Linear mixed models
- Meier L. 2022. ANOVA and Mixed Models: A Short Introduction Using R . Boca Raton, FL: Chapman & Hall/CRC. URL
- Pinheiro JC, Bates, DM. 2000. Mixed-Effects Models in S and S-PLUS. New York, NY: Springer.
- West BT, Welch, KB, Galecki AT. 2022. Linear mixed models: A practical guide using statistical software (3rd ed). Boca Raton, FL: Chapman & Hall/CRC. URL
Resampling methods
- Chihara L, Hesterberg T. 2022. Mathematical Statistics with Resampling and R (3. Aufl.). Hoboken, NJ: Wiley. URL
Time series
- Shumway RH, Stoffer DS. 2016. Time series analysis and its applications (5th ed). New York, NY: Springer. URL
- Shumway RH, Stoffer DS. 2019. Time Series: A Data Analysis Approach Using R (2nd ed). Boca Raton, FL: Chapman & Hall/CRC. URL
- Hyndman RJ, Athanasopoulos G. 2019. Forecasting: Principles and practice (3rd ed). Melbourne, Australia: OTexts. URL
Bayes methods
- Kruschke JK. 2015. Doing Bayesian data analysis: A tutorial with R, JAGS, and Stan (2nd ed). Amsterdam: Academic Press. URL
- McElreath R. 2020. Statistical rethinking: A Bayesian course with examples in R and Stan (2nd ed). Boca Raton, FL: Chapman & Hall/CRC. URL
Spatial statistics
- Lovelace R, Nowosad J, Muenchow J. 2025. Geocomputation with R. 2. Aufl. Boca Raton, FL: Chapman & Hall/CRC. URL
- Pebesma E, Bivand RS. 2023. Spatial Data Science With Applications in R. Boca Raton, FL: Chapman & Hall/CRC. URL
R for data science and machine learning:
- James G, Witten D, Hastie T, Tibshirani R. 2021. An introduction to statistical learning with applications in R (2nd ed). New York, NY: Springer. URL
- Wickham H, Grolemund G. 2023. R for data science (2nd ed). Sebastopol, CA: O’Reilly. URL

Diagrams

Murrell, P. (2018). R Graphics (3rd ed.). Boca Raton, FL: Chapman & Hall/CRC. URL
Unwin, A. (2015). Graphical data analysis with R. Boca Raton, FL: Chapman & Hall/CRC. URL
Wickham, H; Sievert C. (2016). ggplot2: Elegant Graphics for Data Analysis. New York, NY: Springer. URL
Chang, W. (2018).R Graphics Cookbook (2nd ed). Sebastopol, CA: O’Reilly. URL
Wilke, C. O. (2019). Fundamentals of data visualization. Sebastopol, CA: O’Reilly. URL

Programming with R

Chambers JM. 2016. Extending R. Boca Raton, FL: Chapman & Hall/CRC.
Gillespie C, Lovelace R. 2017. Efficient R programming. Sebastopol, CA: O’Reilly. URL
Wickham H. 2019. Advanced R (2nd ed). Boca Raton, FL: Chapman & Hall/CRC. URL
Wickham H. 2023. R packages. Sebastopol (2nd ed), CA: O’Reilly. URL

Transition from other statistical software packages

Muenchen, R. A. (2011). R for SAS and SPSS Users (2nd ed.). New York, NY: Springer. URL
Muenchen, R. A. & Hilbe, J. M. (2010). R for Stata Users. New York, NY: Springer. URL

Dynamic documents and reproducible research

Xie Y. 2015. Dynamic documents with R and knitr (2nd ed). Boca Raton, FL: Chapman & Hall/CRC.
Xie Y, Dervieux C, Riederer E. 2020. R Markdown Cookbook. Boca Raton, FL: Chapman & Hall CRC. URL
Xie Y, Allaire JJ, Grolemund G. 2018. R markdown: The definitive guide. Boca Raton, FL: Chapman & Hall/CRC. URL
Gandrud, C. (2020). Reproducible research with R & RStudio (3rd ed). Boca Raton, FL: Chapman & Hall/CRC. URL
Stodden, V., Leisch, F. & Peng, R. D. (2014). Implementing Reproducible Research. Boca Raton, FL: Chapman & Hall/CRC.

Shiny for interactive Web-Apps

Sievert C. 2020. Interactive web-based data visualization with R, plotly, and shiny. Boca Raton, FL: Chapman & Hall/CRC. URL
Wickham H. 2020. Mastering shiny. Sebastopol, CA: O’Reilly. URL

Meta-Analysis

Harrer M, Cuijpers P, Furukawa T, Ebert D. 2021. Doing Meta-Analysis with R: A Hands-On Guide. Boca Raton, FL: Chapman & Hall/CRC. URL
Schwarzer G, Carpenter J, Rücker G. 2015. Meta-Analysis with R. New York, NY: Springer. URL

Detach (automatically) loaded packages (if possible)

try(detach(package:sos))
try(detach(package:brew))

Get the article source from GitHub

R markdown - markdown - R code - all posts