Abstract

This vignette introduces time series windowing with the R package
`groupdata2`

.

`groupdata2`

has a set of methods for easy grouping,
windowing, folding, partitioning, splitting and balancing of data.

For a more extensive description of groupdata2, please see Description of groupdata2

Contact author at r-pkgs@ludvigolsen.dk

When working with time series, `groupdata2`

allows us to
quickly divide them into groups / windows.

```
library(groupdata2)
library(dplyr) # %>%
require(ggplot2, quietly = TRUE) # Attach if installed
library(knitr) # kable
```

We will use the `austres`

dataset for this vignette. It
contains numbers (in thousands) of Australian residents measured
*quarterly* from March 1971 to March 1994.

Let’s load the data and take a look at the first values.

```
<- data.frame('residents' = austres)
timeSeriesFrame
# Show structure of data frame
str(timeSeriesFrame)
#> 'data.frame': 89 obs. of 1 variable:
#> $ residents: Time-Series from 1971 to 1993: 13067 13130 13198 13254 13304 ...
# Show head of data
%>% head(12) %>% kable() timeSeriesFrame
```

residents |
---|

13067.3 |

13130.5 |

13198.4 |

13254.2 |

13303.7 |

13353.9 |

13409.3 |

13459.2 |

13504.5 |

13552.6 |

13614.3 |

13669.5 |

A visualization of the data. We see that the number of residents
increases quite linearly with time.

Let’s say, that instead of having four measures per year, we want 1
measure every 3 years.

We can do this by making groups of 12 elements each with the
`greedy`

method and use the means of each group as our
measurements.

When using the `greedy`

method, we specify the desired
group size. Every group, except the last, is guaranteed to have this
size. The last group gets the elements that are left, i.e. it might be
smaller or of the same size as the other groups.

```
= timeSeriesFrame %>%
ts
# Group data
group(n = 12, method = 'greedy') %>%
# Find means of each group
::summarise(mean = mean(residents))
dplyr
# Show new data
%>% kable() ts
```

.groups | mean |
---|---|

1 | 13376.45 |

2 | 13945.62 |

3 | 14418.36 |

4 | 15022.52 |

5 | 15663.29 |

6 | 16378.30 |

7 | 17151.38 |

8 | 17573.18 |

A visualization of the data.

This procedure has left us with fewer datapoints, which could be
useful if we had a very large `data frame`

to start with, or
if we just wanted to describe the change in residents every 3rd year (or
every year for that matter, by simply changing `n`

to
`4`

).

If we wanted to know which group had the largest increase in residents, we could find the range (difference between the max and min value) within each group instead of taking the mean.

```
<- timeSeriesFrame %>%
ts
# Group data
group(n = 12, method = 'greedy') %>%
# Find range of each group
::summarise(range = diff(range(residents)))
dplyr
# Show new data
%>% kable() ts
```

.groups | range |
---|---|

1 | 602.2 |

2 | 433.0 |

3 | 454.2 |

4 | 650.8 |

5 | 568.0 |

6 | 758.9 |

7 | 614.2 |

8 | 178.9 |

For the fun of it, let’s say we want to make staircased groups inside
the greedy groups, we just created.

When using the `staircase`

method we specify **step
size**. Every group is 1 step larger than the previous group
(e.g. with a step size of 5, group sizes would be 5,10,15,…).

By creating subgroups for every greedy group, the group size will “start over” for each greedy group.

When using the staircase method, the last group might not have the
size of the second last group + step size. We want to make sure that it
does have such size, so we use the helper tool `%staircase%`

to find a step size with a remainder of 0.

```
= 12
main_group_size
# Loop through a list ranging from 1-30
for (step_size in c(1:30)){
# If the remainder is 0
if(main_group_size %staircase% step_size == 0){
# Print the step size
print(step_size)
}
}#> [1] 2
#> [1] 4
#> [1] 12
```

So our step size could be 2, 4 or 12. We pick a step size of 2, because it will yield the most subgroups for the example.

Now we will first make the greedy groups like before, then we will create subgroups with the staircase method.

In order not to overwrite the `.groups`

column from the
first use of `group()`

, we will use the `col_name`

argument in `group()`

. As `group()`

groups the
`data frame`

by the generated grouping factor (what a
sentence!), a second call to it will create subgroups within the initial
groups. Note that versions previous to `v1.3.0`

did not
detect these groupings, why you had to run the second
`group()`

call inside `dplyr`

’s `do()`

function.

```
<- timeSeriesFrame %>%
ts
# Group data
group(n = 12, method = 'greedy') %>%
# Create subgroups
group(n = 2, method = 'staircase', col_name = '.subgroups')
#> 'group()' now detects grouped data.frames and is applied group-wise (since v1.3.0). If this is unwanted, use 'dplyr::ungroup()' before 'group()'.
#> NOTE: This message is displayed once per session.
# Show head of new data
%>% head(24) %>% kable() ts
```

residents | .groups | .subgroups |
---|---|---|

13067.3 | 1 | 1 |

13130.5 | 1 | 1 |

13198.4 | 1 | 2 |

13254.2 | 1 | 2 |

13303.7 | 1 | 2 |

13353.9 | 1 | 2 |

13409.3 | 1 | 3 |

13459.2 | 1 | 3 |

13504.5 | 1 | 3 |

13552.6 | 1 | 3 |

13614.3 | 1 | 3 |

13669.5 | 1 | 3 |

13722.6 | 2 | 1 |

13772.1 | 2 | 1 |

13832.0 | 2 | 2 |

13862.6 | 2 | 2 |

13893.0 | 2 | 2 |

13926.8 | 2 | 2 |

13968.9 | 2 | 3 |

14004.7 | 2 | 3 |

14033.1 | 2 | 3 |

14066.0 | 2 | 3 |

14110.1 | 2 | 3 |

14155.6 | 2 | 3 |

We can get the means of each subgroup. To do this, we first group by
`.groups`

and then `.subgroups`

. Then, we find the
mean number of residents for each subgroup. If we had just grouped by
`.subgroups`

, we would have taken the mean of all the data
points in each subgroup level. This would have left us with (in this
case) 3 means, instead of 1 per subgroup level per main group level.

Now that we are at it, we might as well find the ranges for each subgroup as well.

```
<- ts %>%
ts_means
# Group by first .groups, then .subgroups
group_by(.groups, .subgroups) %>%
# Find the mean and range of each subgroup
::summarise(mean = mean(residents),
dplyrrange = diff(range(residents)))
#> `summarise()` has grouped output by '.groups'. You can override using the
#> `.groups` argument.
# Show head of new data
%>% head(9) %>% kable() ts_means
```

.groups | .subgroups | mean | range |
---|---|---|---|

1 | 1 | 13098.90 | 63.2 |

1 | 2 | 13277.55 | 155.5 |

1 | 3 | 13534.90 | 260.2 |

2 | 1 | 13747.35 | 49.5 |

2 | 2 | 13878.60 | 94.8 |

2 | 3 | 14056.40 | 186.7 |

3 | 1 | 14211.95 | 39.5 |

3 | 2 | 14341.92 | 115.1 |

3 | 3 | 14538.12 | 215.6 |

The differences in range follows the differences in number of measurements per subgroup.

Here is a visualization of the means per subgroup:

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

r-pkgs@ludvigolsen.dk, or open an issue on the github
page https://github.com/LudvigOlsen/groupdata2 so I can make
improvements.