Include Data Examples in Developing R Packages

Include data examples in developing R packages

Please look at CRAN packages that include data and copy their approach. I just added data to a at-work-only package a few weeks back and it just works...

For what it is worth, the manual has a section 1.1.5 Data in packages which explains it.

Include example using data in documentation of that data (when developing R package)

move the .rda file to a data folder - and enter LazyData: yes in the DESCRIPTION file

How to put datasets into an R package

I'm not sure if I understood your question correctly. But, if you edit your data in your favorite format and save with

save(myediteddata, file="data.rda")

The data should be loaded exactly the way you saw it in R.

To load all files in data directory you should add

LazyData: true

To your DESCRIPTION file, in your package.

If this don't help you could post one of your files and a print of the format you want, this will help us to help you ;)

How to add external data file into developing R package?

You should manually create inst/extdata/file.csv in the base directory for your project (where DESCRIPTION is). You can put all the files you want to access in that directory.

Then to get the files in function examples or your vignette:

files <- lapply(list.files(system.file('extdata', package = 'my_package'), full.names = TRUE), read.csv)

system.file() returns the path to the extdata folder, then list.files() will create a vector of all the files in extdata. Finally, running lapply() with read.csv() should read the contents of all the files into a single list for you.

running all examples in r package

Try the following code to run all examples

devtools::run_examples()

How to use an R package's own data when writing a vignette

I think you are supposed to use

data(my_dataset, package = "my_package")

to load your package's data into the session where your vignette is built.

Could you confirm that your datasets are stored inside the ./data directory of your package as *.rda files

How to make a great R reproducible example

Basically, a minimal reproducible example (MRE) should enable others to exactly reproduce your issue on their machines.

Please do not post images of your data, code, or console output!

tl;dr

A MRE consists of the following items:

a minimal dataset, necessary to demonstrate the problem
the minimal runnable code necessary to reproduce the issue, which can be run on the given dataset
all necessary information on the used librarys, the R version, and the OS it is run on, perhaps a sessionInfo()
in the case of random processes, a seed (set by set.seed()) to enable others to replicate exactly the same results as you do

For examples of good MREs, see section "Examples" at the bottom of help pages on the function you are using. Simply type e.g. help(mean), or short ?mean into your R console.

Providing a minimal dataset

Usually, sharing huge data sets is not necessary and may rather discourage others from reading your question. Therefore, it is better to use built-in datasets or create a small "toy" example that resembles your original data, which is actually what is meant by minimal. If for some reason you really need to share your original data, you should use a method, such as dput(), that allows others to get an exact copy of your data.

Built-in datasets

You can use one of the built-in datasets. A comprehensive list of built-in datasets can be seen with data(). There is a short description of every data set, and more information can be obtained, e.g. with ?iris, for the 'iris' data set that comes with R. Installed packages might contain additional datasets.

Creating example data sets

Preliminary note: Sometimes you may need special formats (i.e. classes), such as factors, dates, or time series. For these, make use of functions like: as.factor, as.Date, as.xts, ... Example:

d <- as.Date("2020-12-30")

where

class(d)
# [1] "Date"

Vectors

x <- rnorm(10)  ## random vector normal distributed
x <- runif(10)  ## random vector uniformly distributed    
x <- sample(1:100, 10)  ## 10 random draws out of 1, 2, ..., 100    
x <- sample(LETTERS, 10)  ## 10 random draws out of built-in latin alphabet

Matrices

m <- matrix(1:12, 3, 4, dimnames=list(LETTERS[1:3], LETTERS[1:4]))
m
#   A B C  D
# A 1 4 7 10
# B 2 5 8 11
# C 3 6 9 12

Data frames

set.seed(42)  ## for sake of reproducibility
n <- 6
dat <- data.frame(id=1:n, 
                  date=seq.Date(as.Date("2020-12-26"), as.Date("2020-12-31"), "day"),
                  group=rep(LETTERS[1:2], n/2),
                  age=sample(18:30, n, replace=TRUE),
                  type=factor(paste("type", 1:n)),
                  x=rnorm(n))
dat
#   id       date group age   type         x
# 1  1 2020-12-26     A  27 type 1 0.0356312
# 2  2 2020-12-27     B  19 type 2 1.3149588
# 3  3 2020-12-28     A  20 type 3 0.9781675
# 4  4 2020-12-29     B  26 type 4 0.8817912
# 5  5 2020-12-30     A  26 type 5 0.4822047
# 6  6 2020-12-31     B  28 type 6 0.9657529

^{Note: Although it is widely used, better do not name your data frame df, because df() is an R function for the density (i.e. height of the curve at point x) of the F distribution and you might get a clash with it.}

Copying original data

If you have a specific reason, or data that would be too difficult to construct an example from, you could provide a small subset of your original data, best by using dput.

Why use dput()?

dput throws all information needed to exactly reproduce your data on your console. You may simply copy the output and paste it into your question.

Calling dat (from above) produces output that still lacks information about variable classes and other features if you share it in your question. Furthermore, the spaces in the type column make it difficult to do anything with it. Even when we set out to use the data, we won't manage to get important features of your data right.

  id       date group age   type         x
1  1 2020-12-26     A  27 type 1 0.0356312
2  2 2020-12-27     B  19 type 2 1.3149588
3  3 2020-12-28     A  20 type 3 0.9781675

Subset your data

To share a subset, use head(), subset() or the indices iris[1:4, ]. Then wrap it into dput() to give others something that can be put in R immediately. Example

dput(iris[1:4, ]) # first four rows of the iris data set

Console output to share in your question:

structure(list(Sepal.Length = c(5.1, 4.9, 4.7, 4.6), Sepal.Width = c(3.5, 
3, 3.2, 3.1), Petal.Length = c(1.4, 1.4, 1.3, 1.5), Petal.Width = c(0.2, 
0.2, 0.2, 0.2), Species = structure(c(1L, 1L, 1L, 1L), .Label = c("setosa", 
"versicolor", "virginica"), class = "factor")), row.names = c(NA, 
4L), class = "data.frame")

When using dput, you may also want to include only relevant columns, e.g. dput(mtcars[1:3, c(2, 5, 6)])

_{Note: If your data frame has a factor with many levels, the dput output can be unwieldy because it will still list all the possible factor levels even if they aren't present in the subset of your data. To solve this issue, you can use the droplevels() function. Notice below how species is a factor with only one level, e.g. dput(droplevels(iris[1:4, ])). One other caveat for dput is that it will not work for keyed data.table objects or for grouped tbl_df (class grouped_df) from the tidyverse. In these cases you can convert back to a regular data frame before sharing, dput(as.data.frame(my_data)).}

Producing minimal code

Combined with the minimal data (see above), your code should exactly reproduce the problem on another machine by simply copying and pasting it.

This should be the easy part but often isn't. What you should not do:

showing all kinds of data conversions; make sure the provided data is already in the correct format (unless that is the problem, of course)
copy-paste a whole script that gives an error somewhere. Try to locate which lines exactly result in the error. More often than not, you'll find out what the problem is yourself.

What you should do:

add which packages you use if you use any (using library())
test run your code in a fresh R session to ensure the code is runnable. People should be able to copy-paste your data and your code in the console and get the same as you have.
if you open connections or create files, add some code to close them or delete the files (using unlink())
if you change options, make sure the code contains a statement to revert them back to the original ones. (eg op <- par(mfrow=c(1,2)) ...some code... par(op) )

Providing necessary information

In most cases, just the R version and the operating system will suffice. When conflicts arise with packages, giving the output of sessionInfo() can really help. When talking about connections to other applications (be it through ODBC or anything else), one should also provide version numbers for those, and if possible, also the necessary information on the setup.

If you are running R in R Studio, using rstudioapi::versionInfo() can help report your RStudio version.

If you have a problem with a specific package, you may want to provide the package version by giving the output of packageVersion("name of the package").

Seed

Using set.seed() you may specify a seed¹, i.e. the specific state, R's random number generator is fixed. This makes it possible for random functions, such as sample(), rnorm(), runif() and lots of others, to always return the same result, Example:

set.seed(42)
rnorm(3)
# [1]  1.3709584 -0.5646982  0.3631284

set.seed(42)
rnorm(3)
# [1]  1.3709584 -0.5646982  0.3631284

¹_{Note: The output of set.seed() differs between R >3.6.0 and previous versions. Specify which R version you used for the random process, and don't be surprised if you get slightly different results when following old questions. To get the same result in such cases, you can use the RNGversion()-function before set.seed() (e.g.: RNGversion("3.5.2")).}

inst and extdata folders in R Packaging

You were both very close and essentially had this. A formal reference from 'Writing R Extensions' is:

1.1.3 Package subdirectories

[...]

The contents of the inst subdirectory will be copied recursively
to the installation directory. Subdirectories of inst should not
interfere with those used by R (currently, R, data, demo,
exec, libs, man, help, html and Meta, and earlier versions
used latex, R-ex). The copying of the inst happens after src
is built so its Makefile can create files to be installed. Prior to
R 2.12.2, the files were installed on POSIX platforms with the permissions in the package sources, so care should be taken to ensure
these are not too restrictive: R CMD build will make suitable
adjustments. To exclude files from being installed, one can specify a
list of exclude patterns in file .Rinstignore in the top-level
source directory. These patterns should be Perl-like regular
expressions (see the help for regexp in R for the precise details),
one per line, to be matched(10) against the file and directory paths,
e.g. doc/.*[.]png$ will exclude all PNG files in inst/doc based on
the (lower-case) extension.

Include Data Examples in Developing R Packages