How to put datasets into an R package

Question

I am creating my own R package and I was wondering what are the possible methods that I can use to add (time-series) datasets to my package. Here are the specifics:

I have created a package subdirectory called data and I am aware that this is the location where I should save the datasets that I want to add to my package. I am also cognizant of the fact that the files containing the data may be. rda , .txt , or .csv files.

Each series of data that I want to add to the package consists of a single column of numbers (eg. of the form 340 or 4.5) and each series of data differs in length.

So far, I have saved all of the datasets into a .txt file. I have also successfully loaded the data using the data() function. Problem not solved, however.

The problem is that each series of data loads as a factor except for the series greatest in length. The series that load as factors contain missing values (of the form '.'). I had to add these missing values in order to make each column of data the same in length. I tried saving the data as unequal columns, but I received an error message after calling data() .

A consequence of adding missing values to get the data to load is that once the data is loaded, I need to remove the NA's in order to get on with my analysis of the data, So. this clearly is not a good way of doing things.

Ideally (I suppose), I would like the data to load as numeric vectors or as a list. In this way, I wouldn't need the NA's appended to the end of each series.

How do I solve this problem? Should I save all of the data into one single file? If so, in what format should I do it? Perhaps I should save the datasets into a number of files? Again, in which format? What is the best practical way of doing this? Any tips would greatly be appreciated.

Answer 1

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 ;)

Answer 2

In addition to saving as rda files you could also choose to load them as numeric with:

 read.table( ... , colClasses="numeric")

Or as non-factor-text:

 read.table( ..., as.is=TRUE) # which does pretty much the same as stringsAsFactors=FALSE
 read.table( ..., colClasses="character")

It also appears that the data function would accept these arguments sinc it is documented to be a simple wrapper for read.table(..., header=TRUE) .

Answer 3

Preferred saving location of your data depends on its format.

As Hadley suggested:

• If you want to store binary data and make it available to the user, put it in data/ . This is the best place to put example datasets.
• If you want to store parsed data, but not make it available to the user, put it in R/sysdata.rda . This is the best place to put data that your functions need.
• If you want to store raw data, put it in inst/extdata .

I suggest you have a look at the linked chapter as it goes into detail about working with data when developing R packages.

Answer 4

TL;DR

1. Create a directory inside your package called /data and place any data in it. Use only .rda and .RData files (recommended here )
2. Save R objects like this (example supposes you have an object in R called 'oceans') - note the object name is the same as the .rda file name as recommended here :

save(oceans, file = "data/oceans.rda", version = 2)

3. Add this line to DESCRIPTION

LazyData: true

4. To load the data use this (it will automatically create an R object with the same name as the object originally saved with save() - in this case called 'oceans' - ie so no need to assign it with <- ):

load("data/oceans.rda")

---

Notes

• If loading data within a function :

oceans <- function() {
  e <- new.env()
  oceans <- load("data/oceans.rda", envir = e)
  e$oceans
}

oceans() # Returns dataset as expected

• version = 2 ensures your data object is available for R versions prior to 3.5.0 Ie it prevents this warning:

WARNING: Added dependency on R >= 3.5.0 because serialized objects in serialize/load version 3 cannot be read in older versions of R.