Author: Ludvig R. Olsen (
[email protected] )
License:
MIT
Started: October
2016
R package for dividing data into groups.
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) crossvalidation. 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. 
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 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') %>%
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()
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
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 
Creates (optionally) balanced folds for use in crossvalidation. 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 crossvalidation.
# 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 crossvalidation, 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 

5  32  b  y  24  1  1 
5  32  b  x  54  2  1 
5  32  b  z  62  3  1 
6  25  a  x  14  1  1 
6  25  a  z  25  2  1 
6  25  a  x  30  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 
1  20  a  z  10  1  3 
1  20  a  y  24  2  3 
1  20  a  x  45  3  3 
2  33  b  z  24  1  3 
2  33  b  x  40  2  3 
2  33  b  x  67  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  34.8  209  3  1  2 
.coll_groups  2  6  41.3  248  2  0  4 
.coll_groups  3  6  35.0  210  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 acrossgroups 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 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.