**Author:** Ludvig R.
Olsen (
r-pkgs@ludvigolsen.dk
)

**License:** MIT

**Started:** October 2016

R package for dividing data into groups.

- Create
**balanced partitions**and cross-validation**folds**. - Perform time series
**windowing**and general**grouping**and**splitting**of data. **Balance**existing groups with**up- and downsampling**.**Collapse**existing groups to fewer, balanced groups.- Finds values, or indices of values, that
**differ**from the previous value by some threshold(s). - Check if two grouping factors have the same groups,
**memberwise**.

Function | Description |
---|---|

`group_factor()` |
Divides data into groups by a wide range of methods. |

`group()` |
Creates grouping factor and adds to the given data frame. |

`splt()` |
Creates grouping factor and splits the data by these groups. |

`partition()` |
Splits data into partitions. Balances a given categorical variable and/or numerical variable between partitions and keeps all data points with a shared ID in the same partition. |

`fold()` |
Creates folds for (repeated) cross-validation. Balances a given categorical variable and/or numerical variable between folds and keeps all data points with a shared ID in the same fold. |

`collapse_groups()` |
Collapses existing groups into a smaller set of groups with categorical, numerical, ID, and size balancing. |

`balance()` |
Uses up- and/or downsampling to equalize
group sizes. Can balance on ID level. See wrappers:
`downsample()` , `upsample()` . |

Function | Description |
---|---|

`all_groups_identical()` |
Checks whether two grouping factors
contain the same groups, memberwise. |

`differs_from_previous()` |
Finds values, or indices of values, that differ from the previous value by some threshold(s). |

`find_starts()` |
Finds values or indices of values that are not the same as the previous value. |

`find_missing_starts()` |
Finds missing starts for the
`l_starts` method. |

`summarize_group_cols()` |
Calculates summary statistics about group
columns (i.e. `factor` s). |

`summarize_balances()` |
Summarizes the balances of numeric, categorical, and ID columns in and between groups in one or more group columns. |

`ranked_balances()` |
Extracts the standard deviations from the
`Summary` data frame from the output of
`summarize_balances()` |

`%primes%` |
Finds remainder for the
`primes` method. |

`%staircase%` |
Finds remainder for the
`staircase` method. |

- groupdata2

CRAN version:

`install.packages("groupdata2")`

Development version:

`install.packages("devtools")`

`devtools::install_github("LudvigOlsen/groupdata2")`

`groupdata2`

contains a number of vignettes with relevant
use cases and descriptions:

`vignette(package = "groupdata2")`

# for an overview

`vignette("introduction_to_groupdata2")`

# begin here

```
# Attach packages
library(groupdata2)
library(dplyr) # %>% filter() arrange() summarize()
library(knitr) # kable()
```

```
# Create small data frame
<- data.frame(
df_small "x" = c(1:12),
"species" = rep(c('cat', 'pig', 'human'), 4),
"age" = sample(c(1:100), 12),
stringsAsFactors = FALSE
)
```

```
# Create medium data frame
<- data.frame(
df_medium "participant" = factor(rep(c('1', '2', '3', '4', '5', '6'), 3)),
"age" = rep(c(20, 33, 27, 21, 32, 25), 3),
"diagnosis" = factor(rep(c('a', 'b', 'a', 'b', 'b', 'a'), 3)),
"diagnosis2" = factor(sample(c('x','z','y'), 18, replace = TRUE)),
"score" = c(10, 24, 15, 35, 24, 14, 24, 40, 30,
50, 54, 25, 45, 67, 40, 78, 62, 30))
<- df_medium %>% arrange(participant)
df_medium $session <- rep(c('1','2', '3'), 6) df_medium
```

Returns a factor with group numbers,
e.g. `factor(c(1,1,1,2,2,2,3,3,3))`

.

This can be used to subset, aggregate, group_by, etc.

Create equally sized groups by setting
`force_equal = TRUE`

Randomize grouping factor by setting
`randomize = TRUE`

```
# Create grouping factor
group_factor(
data = df_small,
n = 5,
method = "n_dist"
)#> [1] 1 1 2 2 3 3 3 4 4 5 5 5
#> Levels: 1 2 3 4 5
```

Creates a grouping factor and adds it to the given data frame. The
data frame is grouped by the grouping factor for easy use in
`magrittr`

(`%>%`

) pipelines.

```
# Use group()
group(data = df_small, n = 5, method = 'n_dist') %>%
kable()
```

x | species | age | .groups |
---|---|---|---|

1 | cat | 68 | 1 |

2 | pig | 39 | 1 |

3 | human | 1 | 2 |

4 | cat | 34 | 2 |

5 | pig | 87 | 3 |

6 | human | 43 | 3 |

7 | cat | 14 | 3 |

8 | pig | 82 | 4 |

9 | human | 59 | 4 |

10 | cat | 51 | 5 |

11 | pig | 85 | 5 |

12 | human | 21 | 5 |

```
# Use group() in a pipeline
# Get average age per group
%>%
df_small group(n = 5, method = 'n_dist') %>%
::summarise(mean_age = mean(age)) %>%
dplyrkable()
```

.groups | mean_age |
---|---|

1 | 53.5 |

2 | 17.5 |

3 | 48.0 |

4 | 70.5 |

5 | 52.3 |

```
# Using group() with 'l_starts' method
# Starts group at the first 'cat',
# then skips to the second appearance of "pig" after "cat",
# then starts at the following "cat".
%>%
df_small group(n = list("cat", c("pig", 2), "cat"),
method = 'l_starts',
starts_col = "species") %>%
kable()
```

x | species | age | .groups |
---|---|---|---|

1 | cat | 68 | 1 |

2 | pig | 39 | 1 |

3 | human | 1 | 1 |

4 | cat | 34 | 1 |

5 | pig | 87 | 2 |

6 | human | 43 | 2 |

7 | cat | 14 | 3 |

8 | pig | 82 | 3 |

9 | human | 59 | 3 |

10 | cat | 51 | 3 |

11 | pig | 85 | 3 |

12 | human | 21 | 3 |

Creates the specified groups with `group_factor()`

and
splits the given data by the grouping factor with
`base::split`

. Returns the splits in a list.

```
splt(data = df_small,
n = 3,
method = 'n_dist') %>%
kable()
```

x | species | age |
---|---|---|

1 | cat | 68 |

2 | pig | 39 |

3 | human | 1 |

4 | cat | 34 |

x | species | age | |
---|---|---|---|

5 | 5 | pig | 87 |

6 | 6 | human | 43 |

7 | 7 | cat | 14 |

8 | 8 | pig | 82 |

x | species | age | |
---|---|---|---|

9 | 9 | human | 59 |

10 | 10 | cat | 51 |

11 | 11 | pig | 85 |

12 | 12 | human | 21 |

Creates (optionally) balanced partitions (e.g. training/test sets). Balance partitions on categorical variable(s) and/or a numerical variable. Make sure that all datapoints sharing an ID is in the same partition.

```
# First set seed to ensure reproducibility
set.seed(1)
# Use partition() with categorical and numerical balancing,
# while ensuring all rows per ID are in the same partition
<- partition(
df_partitioned data = df_medium,
p = 0.7,
cat_col = 'diagnosis',
num_col = "age",
id_col = 'participant'
)
%>%
df_partitioned kable()
```

participant | age | diagnosis | diagnosis2 | score | session |
---|---|---|---|---|---|

1 | 20 | a | z | 10 | 1 |

1 | 20 | a | y | 24 | 2 |

1 | 20 | a | x | 45 | 3 |

2 | 33 | b | z | 24 | 1 |

2 | 33 | b | x | 40 | 2 |

2 | 33 | b | x | 67 | 3 |

3 | 27 | a | z | 15 | 1 |

3 | 27 | a | x | 30 | 2 |

3 | 27 | a | z | 40 | 3 |

4 | 21 | b | z | 35 | 1 |

4 | 21 | b | x | 50 | 2 |

4 | 21 | b | z | 78 | 3 |

participant | age | diagnosis | diagnosis2 | score | session |
---|---|---|---|---|---|

5 | 32 | b | y | 24 | 1 |

5 | 32 | b | x | 54 | 2 |

5 | 32 | b | z | 62 | 3 |

6 | 25 | a | x | 14 | 1 |

6 | 25 | a | z | 25 | 2 |

6 | 25 | a | x | 30 | 3 |

Creates (optionally) balanced folds for use in cross-validation. Balance folds on categorical variable(s) and/or a numerical variable. Ensure that all datapoints sharing an ID is in the same fold. Create multiple unique fold columns at once, e.g. for repeated cross-validation.

```
# First set seed to ensure reproducibility
set.seed(1)
# Use fold() with categorical and numerical balancing,
# while ensuring all rows per ID are in the same fold
<- fold(
df_folded data = df_medium,
k = 3,
cat_col = 'diagnosis',
num_col = "age",
id_col = 'participant'
)
# Show df_folded ordered by folds
%>%
df_folded arrange(.folds) %>%
kable()
```

participant | age | diagnosis | diagnosis2 | score | session | .folds |
---|---|---|---|---|---|---|

1 | 20 | a | z | 10 | 1 | 1 |

1 | 20 | a | y | 24 | 2 | 1 |

1 | 20 | a | x | 45 | 3 | 1 |

5 | 32 | b | y | 24 | 1 | 1 |

5 | 32 | b | x | 54 | 2 | 1 |

5 | 32 | b | z | 62 | 3 | 1 |

4 | 21 | b | z | 35 | 1 | 2 |

4 | 21 | b | x | 50 | 2 | 2 |

4 | 21 | b | z | 78 | 3 | 2 |

6 | 25 | a | x | 14 | 1 | 2 |

6 | 25 | a | z | 25 | 2 | 2 |

6 | 25 | a | x | 30 | 3 | 2 |

2 | 33 | b | z | 24 | 1 | 3 |

2 | 33 | b | x | 40 | 2 | 3 |

2 | 33 | b | x | 67 | 3 | 3 |

3 | 27 | a | z | 15 | 1 | 3 |

3 | 27 | a | x | 30 | 2 | 3 |

3 | 27 | a | z | 40 | 3 | 3 |

```
# Show distribution of diagnoses and participants
%>%
df_folded group_by(.folds) %>%
count(diagnosis, participant) %>%
kable()
```

.folds | diagnosis | participant | n |
---|---|---|---|

1 | a | 1 | 3 |

1 | b | 5 | 3 |

2 | a | 6 | 3 |

2 | b | 4 | 3 |

3 | a | 3 | 3 |

3 | b | 2 | 3 |

```
# Show age representation in folds
# Notice that we would get a more even distribution if we had more data.
# As age is fixed per ID, we only have 3 ages per category to balance with.
%>%
df_folded group_by(.folds) %>%
summarize(mean_age = mean(age),
sd_age = sd(age)) %>%
kable()
```

.folds | mean_age | sd_age |
---|---|---|

1 | 26 | 6.57 |

2 | 23 | 2.19 |

3 | 30 | 3.29 |

**Notice**, that the we now have the opportunity to
include the *session* variable and/or use *participant* as
a random effect in our model when doing cross-validation, as any
participant will only appear in one fold.

We also have a balance in the representation of each diagnosis, which could give us better, more consistent results.

Collapses a set of groups into a smaller set of groups while attempting to balance the new groups by specified numerical columns, categorical columns, level counts in ID columns, and/or the number of rows.

```
# We consider each participant a group
# and collapse them into 3 new groups
# We balance the number of levels in diagnosis2 column,
# as this diagnosis is not constant within the participants
<- collapse_groups(
df_collapsed data = df_medium,
n = 3,
group_cols = 'participant',
cat_cols = 'diagnosis2',
num_cols = "score"
)
# Show df_collapsed ordered by new collapsed groups
%>%
df_collapsed arrange(.coll_groups) %>%
kable()
```

participant | age | diagnosis | diagnosis2 | score | session | .coll_groups |
---|---|---|---|---|---|---|

1 | 20 | a | z | 10 | 1 | 1 |

1 | 20 | a | y | 24 | 2 | 1 |

1 | 20 | a | x | 45 | 3 | 1 |

2 | 33 | b | z | 24 | 1 | 1 |

2 | 33 | b | x | 40 | 2 | 1 |

2 | 33 | b | x | 67 | 3 | 1 |

3 | 27 | a | z | 15 | 1 | 2 |

3 | 27 | a | x | 30 | 2 | 2 |

3 | 27 | a | z | 40 | 3 | 2 |

4 | 21 | b | z | 35 | 1 | 2 |

4 | 21 | b | x | 50 | 2 | 2 |

4 | 21 | b | z | 78 | 3 | 2 |

5 | 32 | b | y | 24 | 1 | 3 |

5 | 32 | b | x | 54 | 2 | 3 |

5 | 32 | b | z | 62 | 3 | 3 |

6 | 25 | a | x | 14 | 1 | 3 |

6 | 25 | a | z | 25 | 2 | 3 |

6 | 25 | a | x | 30 | 3 | 3 |

```
# Summarize the balances of the new groups
<- df_collapsed %>%
coll_summ summarize_balances(group_cols = '.coll_groups',
cat_cols = "diagnosis2",
num_cols = "score")
$Groups %>%
coll_summkable()
```

.group_col | .group | # rows | mean(score) | sum(score) | # diag_x | # diag_y | # diag_z |
---|---|---|---|---|---|---|---|

.coll_groups | 1 | 6 | 35.0 | 210 | 3 | 1 | 2 |

.coll_groups | 2 | 6 | 41.3 | 248 | 2 | 0 | 4 |

.coll_groups | 3 | 6 | 34.8 | 209 | 3 | 1 | 2 |

```
$Summary %>%
coll_summkable()
```

.group_col | measure | # rows | mean(score) | sum(score) | # diag_x | # diag_y | # diag_z |
---|---|---|---|---|---|---|---|

.coll_groups | mean | 6 | 37.06 | 222.3 | 2.667 | 0.667 | 2.67 |

.coll_groups | median | 6 | 35.00 | 210.0 | 3.000 | 1.000 | 2.00 |

.coll_groups | SD | 0 | 3.71 | 22.2 | 0.577 | 0.577 | 1.16 |

.coll_groups | IQR | 0 | 3.25 | 19.5 | 0.500 | 0.500 | 1.00 |

.coll_groups | min | 6 | 34.83 | 209.0 | 2.000 | 0.000 | 2.00 |

.coll_groups | max | 6 | 41.33 | 248.0 | 3.000 | 1.000 | 4.00 |

```
# Check the across-groups standard deviations
# This is a measure of how balanced the groups are (lower == more balanced)
# and is especially useful when comparing multiple group columns
%>%
coll_summ ranked_balances() %>%
kable()
```

.group_col | measure | # rows | mean(score) | sum(score) | # diag_x | # diag_y | # diag_z |
---|---|---|---|---|---|---|---|

.coll_groups | SD | 0 | 3.71 | 22.2 | 0.577 | 0.577 | 1.16 |

**Recommended**: By enabling the `auto_tune`

setting, we often get a much better balance.

Uses up- and/or downsampling to fix the group sizes 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.

```
# Lets first unbalance the dataset by removing some rows
<- df_medium %>%
df_b arrange(diagnosis) %>%
filter(!row_number() %in% c(5,7,8,13,14,16,17,18))
# Show distribution of diagnoses and participants
%>%
df_b count(diagnosis, participant) %>%
kable()
```

diagnosis | participant | n |
---|---|---|

a | 1 | 3 |

a | 3 | 2 |

a | 6 | 1 |

b | 2 | 3 |

b | 4 | 1 |

```
# First set seed to ensure reproducibility
set.seed(1)
# Downsampling by diagnosis
balance(
data = df_b,
size = "min",
cat_col = "diagnosis"
%>%
) count(diagnosis, participant) %>%
kable()
```

diagnosis | participant | n |
---|---|---|

a | 1 | 2 |

a | 3 | 1 |

a | 6 | 1 |

b | 2 | 3 |

b | 4 | 1 |

```
# Downsampling the IDs
balance(
data = df_b,
size = "min",
cat_col = "diagnosis",
id_col = "participant",
id_method = "n_ids"
%>%
) count(diagnosis, participant) %>%
kable()
```

diagnosis | participant | n |
---|---|---|

a | 1 | 3 |

a | 3 | 2 |

b | 2 | 3 |

b | 4 | 1 |

There are currently 10 methods available. They can be divided into 6 categories.

*Examples of group sizes are based on a vector with 57
elements.*

Divides up the data greedily given a specified group size.

E.g. group sizes: 10, 10, 10, 10, 10, 7

Divides the data into a specified number of groups and distributes excess data points across groups.

E.g. group sizes: 11, 11, 12, 11, 12

Divides the data into a specified number of groups and fills up groups with excess data points from the beginning.

E.g. group sizes: 12, 12, 11, 11, 11

Divides the data into a specified number of groups. The algorithm finds the most equal group sizes possible, using all data points. Only the last group is able to differ in size.

E.g. group sizes: 11, 11, 11, 11, 13

Divides the data into a specified number of groups. Excess data points are placed randomly in groups (only 1 per group).

E.g. group sizes: 12, 11, 11, 11, 12

Uses a list / vector of group sizes to divide up the data.

Excess data points are placed in an extra group.

E.g. `n = c(11, 11)`

returns group sizes: 11, 11, 35

Uses a list of starting positions to divide up the data.

Starting positions are values in a vector (e.g. column in data frame).
Skip to a specific nth appearance of a value by using
`c(value, skip_to)`

.

E.g. `n = c(11, 15, 27, 43)`

returns group sizes: 10, 4,
12, 16, 15

Identical to `n = list(11, 15, c(27, 1), 43`

where
`1`

specifies that we want the first appearance of 27 after
the previous value 15.

If passing `n = "auto"`

starting positions are
automatically found with `find_starts()`

.

Every `n`

th data point is combined to a group.

E.g. group sizes: 12, 12, 11, 11, 11

Uses step_size to divide up the data. Group size increases with 1 step for every group, until there is no more data.

E.g. group sizes: 5, 10, 15, 20, 7

Creates groups with sizes corresponding to prime numbers.

Starts at `n`

(prime number). Increases to the the next prime
number until there is no more data.

E.g. group sizes: 5, 7, 11, 13, 17, 4

There are currently 4 methods for balancing (up-/downsampling) on ID
level in `balance()`

.

Balances on ID level only. It makes sure there are the same number of IDs in each category. This might lead to a different number of rows between categories.

Attempts to level the number of rows per category, while only removing/adding entire IDs. This is done with repetition and by iteratively picking the ID with the number of rows closest to the lacking/excessive number of rows in the category.

Distributes the lacking/excess rows equally between the IDs. If the number to distribute cannot be equally divided, some IDs will have 1 row more/less than the others.

Balances the IDs within their categories, meaning that all IDs in a category will have the same number of rows.