groupdata2

Author: Ludvig R. Olsen ( r-pkgs@ludvigolsen.dk )
License: MIT
Started: October 2016

Overview

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.

Main functions

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().

Other tools

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. factors).
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.

Table of Contents

• groupdata2
  • Overview
    • Main functions
    • Other tools
  • Installation
  • Vignettes
  • Data for examples
  • Functions
    • group_factor()
    • group()
    • splt()
    • partition()
    • fold()
    • collapse_groups()
    • balance()
  • Grouping Methods
    • Specify group size
    • Specify number of groups
    • Specify list
    • Specify distance between members
    • Specify step size
    • Specify start at
  • Balancing ID Methods

Installation

CRAN version:

install.packages("groupdata2")

Development version:

install.packages("devtools")
devtools::install_github("LudvigOlsen/groupdata2")

Vignettes

groupdata2 contains a number of vignettes with relevant use cases and descriptions:

vignette(package = "groupdata2") # for an overview
vignette("introduction_to_groupdata2") # begin here

Data for examples

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

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

# Create medium data frame
df_medium <- data.frame(
  "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 <- df_medium %>% arrange(participant)
df_medium$session <- rep(c('1','2', '3'), 6)

Functions

group_factor()

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

group()

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') %>% 
  dplyr::summarise(mean_age = mean(age)) %>%
  kable()

.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

splt()

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

partition()

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
df_partitioned <- partition(
  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

fold()

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
df_folded <- fold(
  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.

collapse_groups()

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
df_collapsed <- collapse_groups(
  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
coll_summ <- df_collapsed %>% 
  summarize_balances(group_cols = '.coll_groups',
                     cat_cols = "diagnosis2",
                     num_cols = "score")

coll_summ$Groups %>% 
  kable()

.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

coll_summ$Summary %>% 
  kable()

.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.

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_b <- df_medium %>% 
  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

Grouping Methods

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.

Specify group size

Method: greedy

Divides up the data greedily given a specified group size.

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

Specify number of groups

Method: n_dist (Default)

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

Method: n_fill

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

Method: n_last

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

Method: n_rand

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

Specify list

Method: l_sizes

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

Method: l_starts

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().

Specify distance between members

Method: every

Every nth data point is combined to a group.

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

Specify step size

Method: staircase

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

Specify start at

Method: primes

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

Balancing ID Methods

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

ID method: n_ids

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.

ID method: n_rows_c

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.

ID method: distributed

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.

ID method: nested

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