timbr provides data frames for forest (or tree) data structures. You can create forest data structures from data frames and process them based on their hierarchies.
You can install the development version of timbr from GitHub with:
# the released version from CRAN:
install.packages("timbr")
# the development version from GitHub:
# install.packages("devtools")
devtools::install_github("UchidaMizuki/timbr")timbr provides some tidyverse methods as follows,
mutate()summarise()select() and relocate()rows_update() and rows_patch()modify()library(timbr)
library(dplyr)fr <- tidyr::expand_grid(key1 = letters[1:2],
key2 = letters[1:2],
key3 = letters[1:2]) %>%
mutate(value = row_number()) %>%
forest_by(key1, key2, key3)
fr
#> # A forest: 8 nodes and 1 feature
#> # Groups: key1, key2 [4]
#> # Roots: key3 [8]
#> key1 key2 . value
#> <chr> <chr> <node> <int>
#> 1 a a <key3> a 1
#> 2 a a <key3> b 2
#> 3 a b <key3> a 3
#> 4 a b <key3> b 4
#> 5 b a <key3> a 5
#> 6 b a <key3> b 6
#> 7 b b <key3> a 7
#> 8 b b <key3> b 8
fr_sum <- fr %>%
summarise(value = sum(value)) %>%
summarise(value = sum(value))
fr_sum
#> # A forest: 14 nodes and 1 feature
#> # Roots: key1 [2]
#> . value
#> <node> <int>
#> 1 <key1> a 10
#> 2 <key1> b 26
children(fr_sum)
#> # A forest: 12 nodes and 1 feature
#> # Groups: key1 [2]
#> # Roots: key2 [4]
#> key1 . value
#> <chr> <node> <int>
#> 1 a <key2> a 3
#> 2 a <key2> b 7
#> 3 b <key2> a 11
#> 4 b <key2> b 15
fr_sum %>%
climb(key3)
#> # A forest: 8 nodes and 1 feature
#> # Roots: key3 [8]
#> . value
#> <node> <int>
#> 1 <key3> a 1
#> 2 <key3> b 2
#> 3 <key3> a 3
#> 4 <key3> b 4
#> 5 <key3> a 5
#> 6 <key3> b 6
#> 7 <key3> a 7
#> 8 <key3> b 8