This vignette is an introduction to the package
groupdata2 is a set of methods for easy grouping, windowing, folding, partitioning, splitting and balancing of data.
When working with data you sometimes want to divide it into groups and subgroups for processing or descriptive statistics. It can help reduce the amount of information, allowing you to compare measurements on different scales - e.g. income per year instead of per month.
groupdata2 is a set of tools for creating groups from your data. It consists of six, easy to use, main functions, namely
group_factor() is at the heart of it all. It creates the groups and is used by the other functions. It returns a grouping factor with group numbers, i.e. 1s for all elements in group 1, 2s for group 2, etc. So if you ask it to create 2 groups from a
('Hans', 'Dorte', 'Mikkel', 'Leif') it will return a factor
(1, 1, 2, 2).
group() takes in either a
data frame or
vector and returns a
data frame with a grouping factor added to it. The
data frame is grouped by the grouping factor (using
dplyr::group_by), which makes it very easy to use in
If, for instance, you have a column in a
data frame with quarterly measurements, and you would like to see the average measurement per year, you can simply create groups with a size of 4, and take the mean of each group, all within a 3-line pipeline.
splt() takes in either a
data frame or
vector, creates a grouping factor, and splits the given data by this factor using
base::split. Often it will be faster to use
group() instead of
splt(). I also find it easier to work with the output of
partition() creates (optionally) balanced partitions (e.g. train/test sets) from given group sizes. It can balance partitions on one categorical variable and/or one numerical variable. It is able to keep all datapoints with a shared ID in the same partition.
fold() creates (optionally) balanced folds for cross-validation. It can balance folds on one categorical variable and/or one numerical variable. It is able to keep all datapoints with a shared ID in the same fold.
balance() uses up- or downsampling to fix the size of all groups to the min, max, mean, or median group size or to a specific number of rows. Balancing can also happen on the ID level, e.g. to ensure the same number of IDs in each category.
I came up with too many use cases to present them all neatly in one vignette. To give each example more space I instead aim to create vignettes for each of them. For now, these are the available vignettes dealing with each their topic:
Cross-validation with groupdata2
In this vignette, we go through the basics of cross-validation, such as creating balanced train/test sets with
partition() and balanced folds with
fold(). We also write up a simple cross-validation function and compare multiple linear regression models.
Time series with groupdata2
In this vignette, we divide up a time series into groups (windows) and subgroups using
group() with the
staircase methods. We do some basic descriptive stats of each group and use them to reduce the data size.
Automatic groups with groupdata2
In this vignette, we will use the
l_starts method with
group() to allow transferring of information from one dataset to another. We will use the automatic grouping function that finds group starts all by itself.
For a more extensive description of the features in
groupdata2, see Description of groupdata2.
Well done, you made it to the end of this introduction to
groupdata2! If you want to know more about the various methods and arguments, you can read the Description of groupdata2.
If you have any questions or comments to this vignette (tutorial) or
groupdata2, please send them to me at
[email protected], or open an issue on the github page https://github.com/LudvigOlsen/groupdata2 so I can make improvements.