Below are some frequently asked questions about the libr package. Click on the links below to navigate to the full question and answer content.

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### How do I create a libname in R?

Q: I have a directory full of datasets. I need to use several of them in my analysis. In SAS®, I would create a libname so I could access all of them. Is there a way to do something similar in R?

A: With the libr package, you can create a libname in R very much the same way you create a libname in SAS®.

libname(mylib, "c:/mypath/mydata", "csv")

The above statement will create a libname “mylib” from the directory specified on the second parameter. The libname will use the CSV engine. If there are any CSV files in the directory, they will be all loaded into the library. To work directly with the datasets, you can then do:

lib_load(mylib)

This statement will load the datasets into your workspace, where you can begin using them. For instance, you could get summary statistics for a variable like this:

summary(mylib.dat1\$var1)

If you made any changes to the data, and want to keep those changes, remember to save them with:

lib_write(mylib)

When you are done, unload the datasets with:

lib_unload(mylib)

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### Which data formats does the libname function support?

Q: I can see from the examples that the libr package supports CSV and SAS dataset file formats. What other data formats does the package support?

A: The package supports the following data formats: csv, sas7bdat, rds, Rdata, rda, xls, xlsx, xpt, and dbf. The libname() help page has a full list, and a short discussion of some details on each format.

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### Is there a way to filter the datasets in my libname?

Q: I have a directory with over 100 datasets. I want to use the libname() function, but worry about loading all those datasets into memory. Is there a way I can filter the libname, to get only some of the datasets?

A: Yes. The filter parameter on the libname() function allows you to pass a wildcard filter string. For example, the following call will load only those datasets that start with ‘a’:

libname(mylib, "c:/mypath/mydata", "csv", filter = "a*")

If you have a more complicated filter criteria, you can also pass a vector of filter strings. The below example will load only those datasets that start with ‘a’ or ‘b’.

libname(mylib, "c:/mypath/mydata", "csv", filter = c("a*", "b*"))

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### How do I view the variables in my datasets?

Q: I’m doing some analysis with my data, and can’t remember all the variable names. Is there an easy way to view or print out the variables in my datasets?

A: Yes. The dictionary() function from the libr pacakge will return a dataset with all the variables in your dataset, and some interesting attributes for each variable. The dictionary() function works on a single data frame, or an entire library. You can save this dictionary as metadata, print it, or even create a report from it. Here is an example:

# Create libname
libname(mylib, "c:/mypath/mydata", "csv")

# Get dictionary
d <- dictionary(mylib)

# View dictionary
# View(d)

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### How do I export data to another file format?

Q: Let’s say I have some data in one format (sas7bdat), and want to export this data to another format (csv or Excel). How can I do that with the libr package?

A: The lib_export() function was designed for this purpose. You can take an existing library and export the entire thing to another library with a different file format. Like this:

libname(libA, "c:/mypath/mydata1", "sas7bdat")

lib_export(libA, libB, "c:/mypath/mydata2", "csv")

The above statements will take the SAS® datasets in the library “libA”, export them to CSV, place the new CSV files in the directory “c:/mypath/mydata2”, and assign a new libname “libB” to that directory. You now have two libnames, and can continue working with each as desired.

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### How do I copy a library?

Q: I have a directory full of datasets. I want to back up the entire thing to another directory. How can I do that?

A: You can use the lib_copy() function, like this:

# Create libname
libname(lib1, "c:/mypath/mydata1", "csv")

# Copy to a new location
lib_copy(lib1, lib2, "c:/mypath/mydata2")

You will now have a reference to the new libname lib2 at the new location, and can use this libname like any other.

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### Can I really do a datastep in R?

Q: When I first started learning R I searched all over for a way to do a datastep. I was shocked to learn there was nothing similar. Does the libr package really allow me to do a datastep in R?

A: Yes. The libr datastep() function does not have all the capabilities of a SAS® datastep. But it has the most commonly-used functionality. You can loop through the data row by row, examine, and compare variable values for each row. It has basic data shaping, grouping, retain, assigning of attributes, and a datastep array. Here is a simple example showing categorization of an age variable into age groups:

library(dplyr)
library(libr)

# Define data library
libname(dat, "./data", "csv")

# Prepare data
dm_mod <- dat.DM %>%
select(USUBJID, SEX, AGE, ARM) %>%
filter(ARM != "SCREEN FAILURE") %>%
datastep({

if (AGE >= 18 & AGE <= 24)
AGECAT = "18 to 24"
else if (AGE >= 25 & AGE <= 44)
AGECAT = "25 to 44"
else if (AGE >= 45 & AGE <= 64)
AGECAT <- "45 to 64"
else if (AGE >= 65)
AGECAT <- ">= 65"

})

lib_unload(dat)

The datastep example above is part of a dplyr pipeline, but it can also function independently. Notice that, just like a SAS® datastep, you don’t have to declare new variables. You can just assign the new variable a value, and the datastep function will create it automatically.

You can check out the datastep() help page, or the datastep vignette for additional examples and complete documentation.

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