# Frequently Asked Questions

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

## Index

- How do I create a libname in R?
- Which data formats does the libname function support?
- Is there a way to filter the datasets in my libname?
- How do I view the variables in my datasets?
- How do I export data to another file format?
- How do I copy a library?
- Can I really do a datastep in R?
- Why is the datastep so slow?
- Can I do “set” and “merge” operations with the datastep?

## Content

### 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:

    mylib$mydataset

To access your datasets.

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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. Note that the sas7bdat file format is read-only at this time.

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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"

      })

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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### Why is the datastep so slow?

Q: I like the `datastep()` function very much. But it seems quite slow. Is there anything I can do to speed it up?

A: Yes. Performance of the `datastep()` is directly related to the size of the input data. The best thing you can do to increase performance is to reduce the input data to only those rows and columns that you need. The Base R `subset()` function and Tidyverse `select()` and `filter()` functions are useful for this purpose. Or you can use the Base R subset brackets ([]) if you are familiar with that syntax. If the datastep performance is still not satisfactory, it is recommended that you explore other R functions to perform your intended operation.

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### Can I do “set” and “merge” operations with the datastep?

Q: In SAS®, I used the datastep frequently to combine two or more datasets. Does the libr datastep support “set” and “merge”?

A: Yes. The `datastep()` function supports both “set” and “merge” operations. The “set” parameter accepts a list of one or more datasets to stack together, and the “merge” parameters are used in almost the same way as SAS®. Here is an example:

    # Subset iris dataset
    dat1 <- subset(mtcars, cyl == 4, c('mpg', 'cyl', 'disp'))[1:5, ]
    dat2 <- subset(mtcars, cyl == 6, c('mpg', 'cyl', 'disp'))[1:5, ]
    dat3 <- mtcars[1:10, c('hp', 'drat', 'wt')]

    # Stack datasets using set operation
    res1 <- datastep(dat1, set = dat2, {})
    #     mpg cyl  disp
    # 1  22.8   4 108.0
    # 2  24.4   4 146.7
    # 3  22.8   4 140.8
    # 4  32.4   4  78.7
    # 5  30.4   4  75.7
    # 6  21.0   6 160.0
    # 7  21.0   6 160.0
    # 8  21.4   6 258.0
    # 9  18.1   6 225.0
    # 10 19.2   6 167.6

    # Merge row by row
    res2 <- datastep(res1, merge = dat3, {})
    #     mpg cyl  disp  hp drat    wt
    # 1  22.8   4 108.0 110 3.90 2.620
    # 2  24.4   4 146.7 110 3.90 2.875
    # 3  22.8   4 140.8  93 3.85 2.320
    # 4  32.4   4  78.7 110 3.08 3.215
    # 5  30.4   4  75.7 175 3.15 3.440
    # 6  21.0   6 160.0 105 2.76 3.460
    # 7  21.0   6 160.0 245 3.21 3.570
    # 8  21.4   6 258.0  62 3.69 3.190
    # 9  18.1   6 225.0  95 3.92 3.150
    # 10 19.2   6 167.6 123 3.92 3.440

The above merge shows how you can append columns even without a key column. If you want to merge by a key, use the “merge_by” and “merge_in” parameters. See the `datastep()` documentation for more information and examples.

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