10 Efficient learning
As with any vibrant open source software community, R is fast moving. This can be disorientating because it means that you can never ‘finish’ learning R. On the other hand it can make R a fascinating subject: there is always more to learn. Even experienced R users keep finding new functionality that helps solve problems quicker and more elegantly. Therefore learning how to learn is one of the most important skills to have if you want to learn R in depth. We emphasise depth of learning because it is more efficient to learn something properly than to Google it repeatedly every time we forget how it works.
This chapter equips you with concepts and tips that will accelerate the transition from an R hacker to an R programmer. This inevitably involves effective use of R’s help, reading R source code and use of online material.
10.1 Top 5 tips for efficient learning
- Monitor the R tag at stackoverflow for new questions.
- Read about the latest developments in the R Journal.
- Regularly browse R-bloggers to get an overview of the R eco-system.
- If asking a question, simplify your code and data as much as possible; the question should be reproducible.
- Subscribe (but not necessarily post) to the
R-develmailing list to gain a deeper insight into the R language.
10.2 Using R help
The R-project website contains seven detailed manuals about the R language, development, installation and data import. While the manuals are long, they do contain all necessary information. In particular if you are developing a package and want to submit that package to CRAN, you must confirm that you have read the extension documentation.
All functions have help files. For example to see the help file for
plot, just type:
# Or help("plot") ?plot
The resulting help page is divided into a number of sections. The most helpful section (I find) is the examples (at the bottom of the help page) showing precisely how the function works. You can either copy and paste the code, or actually run the example code using the
When a package is added to CRAN, the example part of the documentation is run on all major platforms. This helps ensure that a package works on multiple systems.
Another useful section in the help file is
See Also:. In the
plot help file, it gives pointers to 3d plotting.
To look for help about a certain topic rather than a specific function use
??topic, which is analogous to
?function. To search for information about regression in all installed packages, for example, use the following command:
# Or help.search("regression") ??regression
To search more specifically for objects the
appropos function can be useful. To search for all objects and functions in the current workspace containing the text string
lm, for example, one would enter:
# Showing the first six results #>  ".__C__anova.glm" ".__C__anova.glm.null" ".__C__diagonalMatrix" #>  ".__C__generalMatrix" ".__C__glm" ".__C__glm.null"
Sometimes a package contains vignettes. To browse any vignettes associated with a particular package, we can use the handy function
browseVignettes(package = "benchmarkme")
10.2.1 Reading R source code
R is open source. This means that we view the underlying source code and examine any function. Of course the code is complex, and diving straight into the source code won’t help that much. However, watching to the github R source code mirror will allow you to monitor small changes that occur. This gives a nice entry point into a complex code base. Likewise examining the source of small functions, such as
NCOL is informative, e.g.
Subscribing to the R NEWS blog is an easy way of keeping track of future changes.
Many R packages are developed in the open on github or r-forge. Select a few well known packages and examine their source. A good package to start with is drat. This is a relatively simple package developed by Dirk Eddelbuettel (author of Rcpp) that only contains a few functions. It gives you an excellent pointer into software development by one of the key R package writers.
10.3 Online resources
There is plenty of online help available. R-bloggers is a blog aggregator of content contributed by bloggers who write about R (in English). It is a great way to get exposed to new and different packages. Similarly monitoring the #rstats twitter tag keeps you up-to-date with the latest news.
There are also mailing lists, Google groups and the Stack Exchange Q & A sites. Before requesting help, read a few other questions to learn the format of the site. Make sure you search previous questions so you are not duplicating work. Perhaps the most important point is that people aren’t under any obligation to answer your question. One of the fantastic things about the open-source community is that you can ask questions and one of core developers may answer your question free; but remember, everyone is busy!
The number one place on the internet for getting help on programming is Stackoverflow. This website provides a platform for asking and answering questions. Through site membership, questions and answers are voted up or down. Users of Stackoverflow earn reputation points when their question or answer is up-voted. Anyone (with enough reputation) can edit a question or answer. This helps the content remain relevant.
Questions are tagged. The R questions can be found under the R tag. The R page contains links to Official documentation, free resources, and various other links. Members of the Stackoverflow R community have tagged, using
r-faq, a few question that often crop up.
10.3.2 Mailing lists and groups.
There are a large number of mailing lists and Google groups focused on R and particular packages. The main list for getting help is
R-help. This is a high volume mailing list, with around a dozen messages per day. A more technical mailing list is
R-devel. This list is intended for questions and discussion about code development in R. The discussion on this list is very technical. However it’s a good place to be introduced to new ideas - but it’s not the place to ask about these ideas! There are many other special interest mailing lists covering topics such as high performance computing to ecology. Many popular packages also have their own mailing list or Google group, e.g. ggplot2 and shiny. The key piece of advice is before mailing a list, read the relevant mailing archive and check that your message is appropriate.
10.3.3 Asking a question
Asking questions on stackoverflow and R-help is hard. Your question should contain just enough information that you problem is clear and can be reproducible, while at the same time avoid unnecessary details. Fortunately there is a SO question - How to make a great R reproducible example? - that provides excellent guidance!
Minimal data set
What is the smallest data set you can construct that will reproduce your issue? Your actual data set may contain \(10^5\) rows and \(10^4\) columns, but to get your idea across you might only need \(4\) rows and \(3\) columns. Making small example data sets is easy. For example, to create a data frame with two numeric columns and a column of characters just use
set.seed(1) example_df = data.frame(x = rnorm(4), y = rnorm(4), z = sample(LETTERS, 4))
Note the call to
set.seed ensures anyone who runs the code will get the same random number stream. Alternatively, you use one of the many data sets that come with R -
If creating an example data set isn’t possible, then use
dput on your actual data set. This will create an ASCII text representation of the object that will enable anyone to recreate the object
dput(example_df) #> structure(list( #> x = c(-0.626453810742332, 0.183643324222082, -0.835628612410047, 1.59528080213779), #> y = c(0.329507771815361, -0.820468384118015, 0.487429052428485, 0.738324705129217), #> z = structure(c(3L, 4L, 1L, 2L), .Label = c("J", "R", "S", "Y"), class = "factor")), #> .Names = c("x", "y", "z"), row.names = c(NA, -4L), class = "data.frame")
What you should not do, is simply copy and paste your entire function into your question. It’s unlikely that your entire function doesn’t work, so just simplify it the bare minimum. The aim is to target your actual issue. Avoid copying and pasting large blocks of code; remove superfluous lines that are not part of the problem. Before asking your question, can you run you code in a clean R environment and reproduce your error?