Storing R Objects in a relational database

Question

I frequently create nonparametric statistics (loess, kernel densities, etc) on data I pull out of a relational database. To make data management easier I would like to store R output back inside my DB. This is easy with simple data frames of numbers or text, but I have not figured out how to store R objects back in my relational database. So is there a way to store a vector of kernel densities, for example, back into a relational database?

Right now I work around this by saving the R objects to a network drive space so others can load the objects as needed.

Answer 1

An example R variable, that's fairly complex:

library(nlme)
model <- lme(uptake ~ conc + Treatment, CO2, random = ~ 1 | Plant / Type)

The best storage database method for R variables depends upon how you want to use it.

I need to do in-database analytics on the values

In this case, you need to break the object down into values that the database can handle natively. This usually means converting it into one or more data frames. The easiest way to do this is to use the broom package.

library(broom)
coefficients_etc <- tidy(model)
model_level_stats <- glance(model)
row_level_stats <- augment(model)

---

I just want storage

In this case you want to serialize your R variables. That is, converting them to be a string or a binary blob. There are several methods for this.

---

My data has to be accessible by programs other than R, and needs to be human-readable

You should store your data in a cross-platform text format; probably JSON or YAML. JSON doesn't support some important concepts like Inf; YAML is more general but the support in R isn't as mature. XML is also possible, but is too verbose to be useful for storing large arrays.

library(RJSONIO)
model_as_json <- toJSON(model)
nchar(model_as_json) # 17916

library(yaml)
# yaml package doesn't yet support conversion of language objects,
# so preprocessing is needed
model2 <- within(
  model,
  {
     call <- as.character(call)
     terms <- as.character(terms)
  }
)
model_as_yaml <- as.yaml(model2) 
nchar(model_as_yaml) # 14493

---

My data has to be accessible by programs other than R, and doesn't need to be human-readable

You could write your data to an open, cross-platform binary format like HFD5. Currently support for HFD5 files (via rhdf5) is limited, so complex objects are not supported. (You'll probably need to unclass everything.)

library(rhdf5)
h5save(rapply(model2, unclass, how = "replace"), file = "model.h5")
bin_h5 <- readBin("model.h5", "raw", 1e6)
length(bin_h5) # 88291 not very efficient in this case

The feather package let's you save data frames in a format readable by both R and Python. To use this, you would first have to convert the model object into data frames, as described in the broom section earlier in the answer.

library(feather)
library(broom)
write_feather(augment(model), "co2_row.feather")  # 5474 bytes
write_feather(tidy(model), "co2_coeff.feather")   # 2093 bytes
write_feather(glance(model), "co2_model.feather") #  562 bytes

Another alternative is to save a text version of the variable (see previous section) to a zipped file and store its bytes in the database.

writeLines(model_as_json)
tar("model.tar.bz", "model.txt", compression = "bzip2")
bin_bzip <- readBin("model.tar.bz", "raw", 1e6)
length(bin_bzip) # only 42 bytes!

---

My data only needs to be accessible by R, and needs to be human-readable

There are two options for turning a variable into a string: serialize and deparse.

p <- function(x)
{
  paste0(x, collapse = "\n")
}

serialize needs to be sent to a text connection, and rather than writing to file, you can write to the console and capture it.

 model_serialized <- p(capture.output(serialize(model, stdout())))
 nchar(model_serialized) # 23830

Use deparse with control = "all" to maximise the reversibility when re-parsing later.

model_deparsed <- p(deparse(model, control = "all"))
nchar(model_deparsed) # 22036

---

My data only needs to be accessible by R, and doesn't need to be human-readable

The same sorts of techniques shown in the previous sections can be applied here. You can zip a serialized or deparsed variable and re-read it as a raw vector.

serialize can also write variables in a binary format. In this case, it is most easily used with its wrapper saveRDS.

saveRDS(model, "model.rds")
bin_rds <- readBin("model.rds", "raw", 1e6)
length(bin_rds) # 6350

Answer 2

Use the serialization feature to turn any R object into a (raw or character) string, then store that string. See help(serialize).

Reverse this for retrieval: get the string, then unserialize() into a R object.