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")The main functions provided by timbr are as follows,
children()climb()leaves()traverse()rbind()
timbr provides some tidyverse methods as follows,
mutate()summarise()select()andrelocate()rows_update()androws_patch()
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_sum <- fr |>
summarise(value = sum(value)) |>
summarise(value = sum(value))
fr
#> # A forest: 8 nodes and 1 feature
#> # Groups: key1, key2 [4]
#> # Trees:
#> # 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
#> # A forest: 14 nodes and 1 feature
#> # Trees:
#> # key1 [2]
#> # └─key2 [4]
#> # └─key3 [8]
#> . value
#> <node> <int>
#> 1 <key1> a 10
#> 2 <key1> b 26
children(fr_sum)
#> # A forest: 12 nodes and 1 feature
#> # Groups: key1 [2]
#> # Trees:
#> # key2 [4]
#> # └─key3 [8]
#> 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
#> # Trees:
#> # 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 8fr1 <- tidyr::expand_grid(key1 = letters[1:2],
key2_1 = letters[1:2],
key3_1 = letters[1:2]) |>
mutate(value = row_number()) |>
forest_by(key1, key2_1, key3_1) |>
summarise(value = sum(value))
fr2 <- tidyr::expand_grid(key1 = letters[1:2],
key2_2 = letters[1:2],
key3_2 = letters[1:2]) |>
mutate(value = row_number()) |>
forest_by(key1, key2_2, key3_2) |>
summarise(value = sum(value))
fr <- rbind(fr1, fr2)
fr_sum <- fr |>
summarise(value = sum(value))
fr
#> # A forest: 24 nodes and 1 feature
#> # Groups: key1 [2]
#> # Trees:
#> # key2_1 [4]
#> # └─key3_1 [8]
#> # key2_2 [4]
#> # └─key3_2 [8]
#> key1 . value
#> <chr> <node> <int>
#> 1 a <key2_1> a 3
#> 2 a <key2_1> b 7
#> 3 b <key2_1> a 11
#> 4 b <key2_1> b 15
#> 5 a <key2_2> a 3
#> 6 a <key2_2> b 7
#> 7 b <key2_2> a 11
#> 8 b <key2_2> b 15
fr_sum
#> # A forest: 26 nodes and 1 feature
#> # Trees:
#> # key1 [2]
#> # ├─key2_1 [4]
#> # │ └─key3_1 [8]
#> # └─key2_2 [4]
#> # └─key3_2 [8]
#> . value
#> <node> <int>
#> 1 <key1> a 20
#> 2 <key1> b 52
traverse(fr_sum,
function(x, children) {
x$value <- prod(children$value)
x
})
#> # A forest: 26 nodes and 1 feature
#> # Trees:
#> # key1 [2]
#> # ├─key2_1 [4]
#> # │ └─key3_1 [8]
#> # └─key2_2 [4]
#> # └─key3_2 [8]
#> . value
#> <node> <int>
#> 1 <key1> a 576
#> 2 <key1> b 2822400