https://github.com/dbosak01/libr
An R package to create data libraries and data dictionaries.
https://github.com/dbosak01/libr
Last synced: 3 months ago
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An R package to create data libraries and data dictionaries.
- Host: GitHub
- URL: https://github.com/dbosak01/libr
- Owner: dbosak01
- Created: 2020-09-23T00:39:42.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2024-04-16T12:49:31.000Z (10 months ago)
- Last Synced: 2024-05-18T17:04:26.124Z (9 months ago)
- Language: R
- Size: 4.68 MB
- Stars: 27
- Watchers: 3
- Forks: 1
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
- Funding: .github/FUNDING.yml
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README
[](https://cran.r-project.org/package=libr)
[](https://cran.r-project.org/package=libr)
[](https://cran.r-project.org/package=libr)
[](https://cran.r-project.org/package=libr)
[](https://github.com/dbosak01/libr/actions/workflows/R-CMD-check.yaml)
[](https://app.codecov.io/gh/dbosak01/libr?branch=master)
# Introduction to **libr**
R is a very flexible and powerful language. But there are some inconveniences
when working with data:
1. **Loading many related datasets**: When you have many data files
that are related in some way, it is troublesome when you want to use
them all in your analysis. Most data import packages make you load each
file individually. And the thing is, *almost all data* comes in sets of related
files. So you would think that there would be an easy mechanism
to load them all at once.
2. **Examining the Attributes of Your Data**: Once you have a set of related
data files loaded into your program, the next problem is how to examine
the structure of *all of them*. R has the `str()` function
if you want to understand the structure of a single dataset. But what if
there are many? There is no simple way to examine the attributes
of many related datasets. And further, some valuable attributes are missing
from the `str()` function output.
3. **Row-By-Row Processing of Data**: R processes data column by column.
In most cases, column-by-column is a perfectly suitable way of processing data.
But in some cases it is desirable to process data row-by-row. Row-by-row
processing is useful when you have many related columns, and want to
perform complex conditional processing across them. It is also very convenient
when you have grouped data, and want to perform conditional processing
within a group.
### Solution
The **libr** package was developed to address all of the above problems. It does so
by introducing three concepts:
* Data Libraries
* Data Dictionaries
* A Data Step
By introducing these concepts, the **libr** package makes working with
data in R much easier and faster. The **libr** package can greatly simplify
your data processing code. And it makes some types of data operations effortless
that are quite tricky to do with **Base R** or even **tidyverse** functions.
### Installation
To install the **libr** package, run the following
command from your R console:
install.packages("libr")
Then put the following line at the top of your program or script:
library(libr)
The **libr** package will then be ready to use! For examples and usage
information, please visit the **libr** documentation site
[here](https://libr.r-sassy.org/articles/libr.html)
### Getting Help
If you need help, the first place
to turn to is the [libr](https://libr.r-sassy.org) web site. The web site
has full documentation on all **libr** functions.
If you want to look at the code for the **libr** package, visit the
github page [here](https://github.com/dbosak01/libr).
If you encounter a bug or have a feature request, please submit an issue
[here](https://github.com/dbosak01/libr/issues).
### See Also
The **libr** package is part of the **sassy** meta-package.
The **sassy** meta-package includes several packages that help make R
easier for everyone, especially people with a background in SAS® software.
You can read more about the **sassy** package
[here](https://r-sassy.org/).