Skip to content
R-package: Methods 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.
R
Branch: master
Clone or download

README.md

groupdata2

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

CRAN_Status_Badge metacran downloads minimal R version Codecov test coverage Travis build status AppVeyor build status DOI

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.
  • 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 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.
balance() Uses up- and/or downsampling to equalize group sizes. Can balance on ID level.

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.
%primes% Finds remainder for the primes method.
%staircase% Finds remainder for the staircase method.

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

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

group()

Returns the given data as a data frame with added grouping factor made with group_factor(). The data frame is grouped by the grouping factor for easy use in magrittr/dplyr pipelines.

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.

partition()

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

fold()

Creates (optionally) balanced folds for use in cross-validation. Balance folds on one categorical variable and/or one 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.

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.

Grouping Methods

There are currently 9 methods available. They can be divided into 5 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 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 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 can not 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.

Examples

# Attach packages
library(groupdata2)
library(dplyr)
library(knitr)
# Create data frame
df <- data.frame(
  "x" = c(1:12),
  "species" = factor(rep(c('cat', 'pig', 'human'), 4)),
  "age" = sample(c(1:100), 12)
)

group()

# Using group()
group(df, 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
# Using group() with dplyr pipeline to get mean age
df %>%
  group(n = 5, method = 'n_dist') %>% 
  dplyr::summarise(mean_age = mean(age)) %>%
  kable()
.groups mean_age
1 53.50000
2 17.50000
3 48.00000
4 70.50000
5 52.33333
# 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 %>%
  group(n = list("cat", c("pig", 2), "cat"),
        method = 'l_starts',
        starts_col = "species") %>%
  kable()
#> Warning in assign_starts_col(data = data, starts_col = starts_col):
#> 'data[[starts_col]]' is factor. Converting to character.
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

fold()

# Create data frame
df <- 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)),
  "score" = c(10, 24, 15, 35, 24, 14, 24, 40, 30, 
              50, 54, 25, 45, 67, 40, 78, 62, 30))
df <- df %>% arrange(participant)
df$session <- rep(c('1','2', '3'), 6)
# Using fold() 

# First set seed to ensure reproducibility
set.seed(1)

# Use fold() with cat_col, num_col and id_col
df_folded <- fold(df, 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 score session .folds
1 20 a 10 1 1
1 20 a 24 2 1
1 20 a 45 3 1
2 33 b 24 1 1
2 33 b 40 2 1
2 33 b 67 3 1
5 32 b 24 1 2
5 32 b 54 2 2
5 32 b 62 3 2
6 25 a 14 1 2
6 25 a 25 2 2
6 25 a 30 3 2
3 27 a 15 1 3
3 27 a 30 2 3
3 27 a 40 3 3
4 21 b 35 1 3
4 21 b 50 2 3
4 21 b 78 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 2 3
2 a 6 3
2 b 5 3
3 a 3 3
3 b 4 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 26.5 7.120393
2 28.5 3.834058
3 24.0 3.286335

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.

balance()

# Lets first unbalance the dataset by removing some rows
df_b <- df %>% 
  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(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(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
You can’t perform that action at this time.