Support for R (.RData) files has been introduced in DVN 3.5.
R has been increasingly popular in the research/academic community, owing to the fact that it is free and open-source (unlike SPSS and STATA). Consequently, there is an increasing amount of data available exclusively in R format.
The data must be formatted as an R dataframe (data.frame()). If an .RData file contains multiple dataframes, only the 1st one will be ingested (this may change in the future).
The handling of these types is intuitive and straightforward. The resulting tab file columns, summary statistics and UNF signatures should be identical to those produced by ingesting the same vectors from SPSS and Stata.
Things that are unique to R:
R explicitly supports Missing Values for all of the types above; Missing Values encoded in R vectors will be recognized and preserved in TAB files, counted in the generated summary statistics and data analysis. Please note however that the Dataverse notation for a missing value, as stored in a TAB file, is an empty string, an not “NA” as in R.
In addition to Missing Values, R recognizes “Not a Value” (NaN) and positive and negative infinity for floating point variables. These are now properly supported by the DVN.
Also note, that unlike Stata, that does recognize “float” and “double” as distinct data types, all floating point values in R are in fact doubles.
These are ingested as “Categorical Values” in the DVN.
One thing to keep in mind: in both Stata and SPSS, the actual value of a categorical variable can be both character and numeric. In R, all factor values are strings, even if they are string representations of numbers. So the values of the resulting categoricals in the DVN will always be of string type too.
Another thing to note is that R factors have no builtin support for SPSS or STATA-like descriptive labels. This is in fact potentially confusing, as they also use the word “label”, in R parlance. However, in the context of a factor in R, it still refers to the “payload”, or the data content of its value. For example, if you create a factor with the “labels” of democrat, republican and undecided, these strings become the actual values of the resulting vector. Once ingested in the Dataverse, these values will be stored in the tab-delimited file. The Dataverse DataVariable object representing the vector will be of type “Character” and have 3 VariableCategory objects with the democrat, etc. for both the CategoryValue and CategoryLabel. (In one of the future releases, we are planning to make it possible for the user to edit the CategoryLabel, using it for its intended purpose - as a descriptive, human-readable text text note).
R Boolean (logical) values are supported.
Most noticeably, R lacks a standard mechanism for defining descriptive labels for the data frame variables. In the DVN, similarly to both Stata and SPSS, variables have distinct names and labels; with the latter reserved for longer, descriptive text. With variables ingested from R data frames the variable name will be used for both the “name” and the “label”.
Optional R packages exist for providing descriptive variable labels; in one of the future versions support may be added for such a mechanism. It would of course work only for R files that were created with such optional packages.
Similarly, R categorical values (factors) lack descriptive labels too. Note: This is potentially confusing, since R factors do actually have “labels”. This is a matter of terminology - an R factor’s label is in fact the same thing as the “value” of a categorical variable in SPSS or Stata and DVN; it contains the actual meaningful data for the given observation. It is NOT a field reserved for explanatory, human-readable text, such as the case with the SPSS/Stata “label”.
Ingesting an R factor with the level labels “MALE” and “FEMALE” will produce a categorical variable with “MALE” and “FEMALE” in the values and labels both.
This warrants a dedicated section of its own, because of some unique ways in which time values are handled in R.
R makes an effort to treat a time value as a real time instance. This is in contrast with either SPSS or Stata, where time value representations such as “Sep-23-2013 14:57:21” are allowed; note that in the absence of an explicitly defined time zone, this value cannot be mapped to an exact point in real time. R handles times in the “Unix-style” way: the value is converted to the “seconds-since-the-Epoch” Greenwich time (GMT or UTC) and the resulting numeric value is stored in the data file; time zone adjustments are made in real time as needed.
Things still get ambiguous and confusing when R displays this time value: unless the time zone was explicitly defined, R will adjust the value to the current time zone. The resulting behavior is often counter-intuitive: if you create a time value, for example:
timevalue<-as.POSIXct("03/19/2013 12:57:00", format = "%m/%d/%Y %H:%M:%OS");
on a computer configured for the San Francisco time zone, the value will be differently displayed on computers in different time zones; for example, as “12:57 PST” while still on the West Coast, but as “15:57 EST” in Boston.
If it is important that the values are always displayed the same way, regardless of the current time zones, it is recommended that the time zone is explicitly defined. For example:
timevalue<-as.POSIXct("03/19/2013 12:57:00", format = "%m/%d/%Y %H:%M:%OS", tz="PST");
Now the value will always be displayed as “15:57 PST”, regardless of the time zone that is current for the OS ... BUT ONLY if the OS where R is installed actually understands the time zone “PST”, which is not by any means guaranteed! Otherwise, it will quietly adjust the stored GMT value to the current time zone, yet it will still display it with the “PST” tag attached!** One way to rephrase this is that R does a fairly decent job storing time values in a non-ambiguous, platform-independent manner - but gives you no guarantee that the values will be displayed in any way that is predictable or intuitive.
In practical terms, it is recommended to use the long/descriptive forms of time zones, as they are more likely to be properly recognized on most computers. For example, “Japan” instead of “JST”. Another possible solution is to explicitly use GMT or UTC (since it is very likely to be properly recognized on any system), or the “UTC+<OFFSET>” notation. Still, none of the above guarantees proper, non-ambiguous handling of time values in R data sets. The fact that R quietly modifies time values when it doesn’t recognize the supplied timezone attribute, yet still appends it to the changed time value does make it quite difficult. (These issues are discussed in depth on R-related forums, and no attempt is made to summarize it all in any depth here; this is just to made you aware of this being a potentially complex issue!)
An important thing to keep in mind, in connection with the DVN ingest of R files, is that it will reject an R data file with any time values that have time zones that we can’t recognize. This is done in order to avoid (some) of the potential issues outlined above.
It is also recommended that any vectors containing time values ingested into the DVN are reviewed, and the resulting entries in the TAB files are compared against the original values in the R data frame, to make sure they have been ingested as expected.
Another potential issue here is the UNF. The way the UNF algorithm works, the same date/time values with and without the timezone (e.g. “12:45” vs. “12:45 EST”) produce different UNFs. Considering that time values in Stata/SPSS do not have time zones, but ALL time values in R do (yes, they all do - if the timezone wasn’t defined explicitly, it implicitly becomes a time value in the “UTC” zone!), this means that it is impossible to have 2 time value vectors, in Stata/SPSS and R, that produce the same UNF.
A pro tip: if it is important to produce SPSS/Stata and R versions of the same data set that result in the same UNF when ingested, you may define the time variables as strings in the R data frame, and use the “YYYY-MM-DD HH:mm:ss” formatting notation. This is the formatting used by the UNF algorithm to normalize time values, so doing the above will result in the same UNF as the vector of the same time values in Stata.
Note: date values (dates only, without time) should be handled the exact same way as those in SPSS and Stata, and should produce the same UNFs.