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. 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. |
- 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
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)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 5Creates 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 |
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()
|
|
|
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()
|
|
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.
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.
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 |
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 nth 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.