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README.md
In unjoin: Separate a Data Frame by Normalization
unjoin
The goal of unjoin is to provide unjoin for data frames. This is
exactly part of what tidyr::nest does, but with two differences:
- the split data frames are not nested, they are split and returned as
two whole tibbles
main and data
- there is an explicit key column added to identify the de-duplicated
rows in
main with the rows in data.
Installation
Install unjoin from CRAN:
install.packages("unjoin")
You can install the development unjoin from github with:
# install.packages("devtools")
devtools::install_github("hypertidy/unjoin")
Example
This is a basic example which shows you how to unjoin a data frame.
library(unjoin)
unjoin(iris)
#> $.idx0
#> # A tibble: 1 x 1
#> .idx0
#> <int>
#> 1 1
#>
#> $data
#> # A tibble: 150 x 6
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species .idx0
#> <dbl> <dbl> <dbl> <dbl> <fct> <int>
#> 1 5.1 3.5 1.4 0.2 setosa 1
#> 2 4.9 3 1.4 0.2 setosa 1
#> 3 4.7 3.2 1.3 0.2 setosa 1
#> 4 4.6 3.1 1.5 0.2 setosa 1
#> 5 5 3.6 1.4 0.2 setosa 1
#> 6 5.4 3.9 1.7 0.4 setosa 1
#> 7 4.6 3.4 1.4 0.3 setosa 1
#> 8 5 3.4 1.5 0.2 setosa 1
#> 9 4.4 2.9 1.4 0.2 setosa 1
#> 10 4.9 3.1 1.5 0.1 setosa 1
#> # … with 140 more rows
#>
#> attr(,"class")
#> [1] "unjoin"
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
iris %>% unjoin(Species)
#> $.idx0
#> # A tibble: 3 x 2
#> Species .idx0
#> <fct> <int>
#> 1 setosa 1
#> 2 versicolor 2
#> 3 virginica 3
#>
#> $data
#> # A tibble: 150 x 5
#> Sepal.Length Sepal.Width Petal.Length Petal.Width .idx0
#> <dbl> <dbl> <dbl> <dbl> <int>
#> 1 5.1 3.5 1.4 0.2 1
#> 2 4.9 3 1.4 0.2 1
#> 3 4.7 3.2 1.3 0.2 1
#> 4 4.6 3.1 1.5 0.2 1
#> 5 5 3.6 1.4 0.2 1
#> 6 5.4 3.9 1.7 0.4 1
#> 7 4.6 3.4 1.4 0.3 1
#> 8 5 3.4 1.5 0.2 1
#> 9 4.4 2.9 1.4 0.2 1
#> 10 4.9 3.1 1.5 0.1 1
#> # … with 140 more rows
#>
#> attr(,"class")
#> [1] "unjoin"
iris %>% unjoin(Species, Petal.Width)
#> $.idx0
#> # A tibble: 27 x 3
#> Species Petal.Width .idx0
#> <fct> <dbl> <int>
#> 1 setosa 0.2 2
#> 2 setosa 0.4 4
#> 3 setosa 0.3 3
#> 4 setosa 0.1 1
#> 5 setosa 0.5 5
#> 6 setosa 0.6 6
#> 7 versicolor 1.4 11
#> 8 versicolor 1.5 12
#> 9 versicolor 1.3 10
#> 10 versicolor 1.6 13
#> # … with 17 more rows
#>
#> $data
#> # A tibble: 150 x 4
#> Sepal.Length Sepal.Width Petal.Length .idx0
#> <dbl> <dbl> <dbl> <int>
#> 1 5.1 3.5 1.4 2
#> 2 4.9 3 1.4 2
#> 3 4.7 3.2 1.3 2
#> 4 4.6 3.1 1.5 2
#> 5 5 3.6 1.4 2
#> 6 5.4 3.9 1.7 4
#> 7 4.6 3.4 1.4 3
#> 8 5 3.4 1.5 2
#> 9 4.4 2.9 1.4 2
#> 10 4.9 3.1 1.5 1
#> # … with 140 more rows
#>
#> attr(,"class")
#> [1] "unjoin"
This is used to build topological data structures, with a kind of
inside-out version of a nested data frame. Whether it’s of broader use
is unclear.
There is a record here of some of the thinking that led to unjoin:
https://github.com/r-gris/babelfish
The function unjoin replaces the method here:
http://rpubs.com/cyclemumner/iout_nest
(d2 <- iris %>% unjoin(Species, Petal.Width))
#> $.idx0
#> # A tibble: 27 x 3
#> Species Petal.Width .idx0
#> <fct> <dbl> <int>
#> 1 setosa 0.2 2
#> 2 setosa 0.4 4
#> 3 setosa 0.3 3
#> 4 setosa 0.1 1
#> 5 setosa 0.5 5
#> 6 setosa 0.6 6
#> 7 versicolor 1.4 11
#> 8 versicolor 1.5 12
#> 9 versicolor 1.3 10
#> 10 versicolor 1.6 13
#> # … with 17 more rows
#>
#> $data
#> # A tibble: 150 x 4
#> Sepal.Length Sepal.Width Petal.Length .idx0
#> <dbl> <dbl> <dbl> <int>
#> 1 5.1 3.5 1.4 2
#> 2 4.9 3 1.4 2
#> 3 4.7 3.2 1.3 2
#> 4 4.6 3.1 1.5 2
#> 5 5 3.6 1.4 2
#> 6 5.4 3.9 1.7 4
#> 7 4.6 3.4 1.4 3
#> 8 5 3.4 1.5 2
#> 9 4.4 2.9 1.4 2
#> 10 4.9 3.1 1.5 1
#> # … with 140 more rows
#>
#> attr(,"class")
#> [1] "unjoin"
We can chain unjoins together, but make sure not to repeat a key_col
in one of these.
unjoin(iris, Species, key_col = "vertex") %>% unjoin(Petal.Width, vertex, key_col = "branch")
#> $vertex
#> # A tibble: 3 x 2
#> Species vertex
#> <fct> <int>
#> 1 setosa 1
#> 2 versicolor 2
#> 3 virginica 3
#>
#> $branch
#> # A tibble: 27 x 3
#> Petal.Width vertex branch
#> <dbl> <int> <int>
#> 1 0.2 1 2
#> 2 0.4 1 4
#> 3 0.3 1 3
#> 4 0.1 1 1
#> 5 0.5 1 5
#> 6 0.6 1 6
#> 7 1.4 2 11
#> 8 1.5 2 13
#> 9 1.3 2 10
#> 10 1.6 2 15
#> # … with 17 more rows
#>
#> $data
#> # A tibble: 150 x 4
#> Sepal.Length Sepal.Width Petal.Length branch
#> <dbl> <dbl> <dbl> <int>
#> 1 5.1 3.5 1.4 2
#> 2 4.9 3 1.4 2
#> 3 4.7 3.2 1.3 2
#> 4 4.6 3.1 1.5 2
#> 5 5 3.6 1.4 2
#> 6 5.4 3.9 1.7 4
#> 7 4.6 3.4 1.4 3
#> 8 5 3.4 1.5 2
#> 9 4.4 2.9 1.4 2
#> 10 4.9 3.1 1.5 1
#> # … with 140 more rows
#>
#> attr(,"class")
#> [1] "unjoin"
Also, there’s no escape hatch here, you can’t “unjoin” your way to
normal nirvana, each unjoin needs to carry the last unjoin-key with it,
and you just end up with the big link table with no attributes. It needs
some kind of group-semantic to cut the chain.
Please note that this project is released with a Contributor Code of
Conduct. By
participating in this project you agree to abide by its terms.
Try the unjoin package in your browser
Any scripts or data that you put into this service are public.
unjoin documentation built on July 2, 2020, 4:06 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
unjoin
The goal of unjoin is to provide unjoin for data frames. This is
exactly part of what tidyr::nest does, but with two differences:
- the split data frames are not nested, they are split and returned as
two whole tibbles
mainanddata - there is an explicit key column added to identify the de-duplicated
rows in
mainwith the rows indata.
Installation
Install unjoin from CRAN:
install.packages("unjoin")
You can install the development unjoin from github with:
# install.packages("devtools")
devtools::install_github("hypertidy/unjoin")
Example
This is a basic example which shows you how to unjoin a data frame.
library(unjoin)
unjoin(iris)
#> $.idx0
#> # A tibble: 1 x 1
#> .idx0
#> <int>
#> 1 1
#>
#> $data
#> # A tibble: 150 x 6
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species .idx0
#> <dbl> <dbl> <dbl> <dbl> <fct> <int>
#> 1 5.1 3.5 1.4 0.2 setosa 1
#> 2 4.9 3 1.4 0.2 setosa 1
#> 3 4.7 3.2 1.3 0.2 setosa 1
#> 4 4.6 3.1 1.5 0.2 setosa 1
#> 5 5 3.6 1.4 0.2 setosa 1
#> 6 5.4 3.9 1.7 0.4 setosa 1
#> 7 4.6 3.4 1.4 0.3 setosa 1
#> 8 5 3.4 1.5 0.2 setosa 1
#> 9 4.4 2.9 1.4 0.2 setosa 1
#> 10 4.9 3.1 1.5 0.1 setosa 1
#> # … with 140 more rows
#>
#> attr(,"class")
#> [1] "unjoin"
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
iris %>% unjoin(Species)
#> $.idx0
#> # A tibble: 3 x 2
#> Species .idx0
#> <fct> <int>
#> 1 setosa 1
#> 2 versicolor 2
#> 3 virginica 3
#>
#> $data
#> # A tibble: 150 x 5
#> Sepal.Length Sepal.Width Petal.Length Petal.Width .idx0
#> <dbl> <dbl> <dbl> <dbl> <int>
#> 1 5.1 3.5 1.4 0.2 1
#> 2 4.9 3 1.4 0.2 1
#> 3 4.7 3.2 1.3 0.2 1
#> 4 4.6 3.1 1.5 0.2 1
#> 5 5 3.6 1.4 0.2 1
#> 6 5.4 3.9 1.7 0.4 1
#> 7 4.6 3.4 1.4 0.3 1
#> 8 5 3.4 1.5 0.2 1
#> 9 4.4 2.9 1.4 0.2 1
#> 10 4.9 3.1 1.5 0.1 1
#> # … with 140 more rows
#>
#> attr(,"class")
#> [1] "unjoin"
iris %>% unjoin(Species, Petal.Width)
#> $.idx0
#> # A tibble: 27 x 3
#> Species Petal.Width .idx0
#> <fct> <dbl> <int>
#> 1 setosa 0.2 2
#> 2 setosa 0.4 4
#> 3 setosa 0.3 3
#> 4 setosa 0.1 1
#> 5 setosa 0.5 5
#> 6 setosa 0.6 6
#> 7 versicolor 1.4 11
#> 8 versicolor 1.5 12
#> 9 versicolor 1.3 10
#> 10 versicolor 1.6 13
#> # … with 17 more rows
#>
#> $data
#> # A tibble: 150 x 4
#> Sepal.Length Sepal.Width Petal.Length .idx0
#> <dbl> <dbl> <dbl> <int>
#> 1 5.1 3.5 1.4 2
#> 2 4.9 3 1.4 2
#> 3 4.7 3.2 1.3 2
#> 4 4.6 3.1 1.5 2
#> 5 5 3.6 1.4 2
#> 6 5.4 3.9 1.7 4
#> 7 4.6 3.4 1.4 3
#> 8 5 3.4 1.5 2
#> 9 4.4 2.9 1.4 2
#> 10 4.9 3.1 1.5 1
#> # … with 140 more rows
#>
#> attr(,"class")
#> [1] "unjoin"
This is used to build topological data structures, with a kind of inside-out version of a nested data frame. Whether it’s of broader use is unclear.
There is a record here of some of the thinking that led to unjoin: https://github.com/r-gris/babelfish
The function unjoin replaces the method here:
http://rpubs.com/cyclemumner/iout_nest
(d2 <- iris %>% unjoin(Species, Petal.Width))
#> $.idx0
#> # A tibble: 27 x 3
#> Species Petal.Width .idx0
#> <fct> <dbl> <int>
#> 1 setosa 0.2 2
#> 2 setosa 0.4 4
#> 3 setosa 0.3 3
#> 4 setosa 0.1 1
#> 5 setosa 0.5 5
#> 6 setosa 0.6 6
#> 7 versicolor 1.4 11
#> 8 versicolor 1.5 12
#> 9 versicolor 1.3 10
#> 10 versicolor 1.6 13
#> # … with 17 more rows
#>
#> $data
#> # A tibble: 150 x 4
#> Sepal.Length Sepal.Width Petal.Length .idx0
#> <dbl> <dbl> <dbl> <int>
#> 1 5.1 3.5 1.4 2
#> 2 4.9 3 1.4 2
#> 3 4.7 3.2 1.3 2
#> 4 4.6 3.1 1.5 2
#> 5 5 3.6 1.4 2
#> 6 5.4 3.9 1.7 4
#> 7 4.6 3.4 1.4 3
#> 8 5 3.4 1.5 2
#> 9 4.4 2.9 1.4 2
#> 10 4.9 3.1 1.5 1
#> # … with 140 more rows
#>
#> attr(,"class")
#> [1] "unjoin"
We can chain unjoins together, but make sure not to repeat a key_col
in one of these.
unjoin(iris, Species, key_col = "vertex") %>% unjoin(Petal.Width, vertex, key_col = "branch")
#> $vertex
#> # A tibble: 3 x 2
#> Species vertex
#> <fct> <int>
#> 1 setosa 1
#> 2 versicolor 2
#> 3 virginica 3
#>
#> $branch
#> # A tibble: 27 x 3
#> Petal.Width vertex branch
#> <dbl> <int> <int>
#> 1 0.2 1 2
#> 2 0.4 1 4
#> 3 0.3 1 3
#> 4 0.1 1 1
#> 5 0.5 1 5
#> 6 0.6 1 6
#> 7 1.4 2 11
#> 8 1.5 2 13
#> 9 1.3 2 10
#> 10 1.6 2 15
#> # … with 17 more rows
#>
#> $data
#> # A tibble: 150 x 4
#> Sepal.Length Sepal.Width Petal.Length branch
#> <dbl> <dbl> <dbl> <int>
#> 1 5.1 3.5 1.4 2
#> 2 4.9 3 1.4 2
#> 3 4.7 3.2 1.3 2
#> 4 4.6 3.1 1.5 2
#> 5 5 3.6 1.4 2
#> 6 5.4 3.9 1.7 4
#> 7 4.6 3.4 1.4 3
#> 8 5 3.4 1.5 2
#> 9 4.4 2.9 1.4 2
#> 10 4.9 3.1 1.5 1
#> # … with 140 more rows
#>
#> attr(,"class")
#> [1] "unjoin"
Also, there’s no escape hatch here, you can’t “unjoin” your way to normal nirvana, each unjoin needs to carry the last unjoin-key with it, and you just end up with the big link table with no attributes. It needs some kind of group-semantic to cut the chain.
Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.
Try the unjoin package in your browser
Any scripts or data that you put into this service are public.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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