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README.md
In fgeo.tool: Import and Manipulate 'ForestGEO' Data
Import and manipulate ForestGEO data
fgeo.tool helps you to import and manipulate
ForestGEO data.
Installation
Install the latest stable version of fgeo.tool from CRAN with:
install.packages("fgeo.tool")
Install the development version of fgeo.tool from GitHub with:
# install.packages("devtools")
devtools::install_github("forestgeo/fgeo.tool")
Or install all fgeo packages in one
step.
Example
library(fgeo.tool)
#>
#> Attaching package: 'fgeo.tool'
#> The following object is masked from 'package:stats':
#>
#> filter
# Helps access data for examples
library(fgeo.x)
example_path() allows you to access datasets stored in your R
libraries.
example_path()
#> [1] "csv" "mixed_files" "rdata" "rdata_one"
#> [5] "rds" "taxa.csv" "tsv" "vft_4quad.csv"
#> [9] "view" "weird" "xl"
(vft_file <- example_path("view/vft_4quad.csv"))
#> [1] "/usr/local/lib/R/site-library/fgeo.x/extdata/view/vft_4quad.csv"
read_vft() and read_taxa() import a ViewFullTable and ViewTaxonomy
from .tsv or .csv files.
read_vft(vft_file)
#> # A tibble: 500 × 32
#> DBHID PlotName PlotID Family Genus Speci…¹ Mnemo…² Subsp…³ Speci…⁴ Subsp…⁵
#> <int> <chr> <int> <chr> <chr> <chr> <chr> <chr> <int> <chr>
#> 1 385164 luquillo 1 Rubiace… Psyc… brachi… PSYBRA <NA> 185 <NA>
#> 2 385261 luquillo 1 Urticac… Cecr… schreb… CECSCH <NA> 74 <NA>
#> 3 384600 luquillo 1 Rubiace… Psyc… brachi… PSYBRA <NA> 185 <NA>
#> 4 608789 luquillo 1 Rubiace… Psyc… berter… PSYBER <NA> 184 <NA>
#> 5 388579 luquillo 1 Arecace… Pres… acumin… PREMON <NA> 182 <NA>
#> 6 384626 luquillo 1 Araliac… Sche… moroto… SCHMOR <NA> 196 <NA>
#> 7 410958 luquillo 1 Rubiace… Psyc… brachi… PSYBRA <NA> 185 <NA>
#> 8 385102 luquillo 1 Piperac… Piper glabre… PIPGLA <NA> 174 <NA>
#> 9 353163 luquillo 1 Arecace… Pres… acumin… PREMON <NA> 182 <NA>
#> 10 481018 luquillo 1 Salicac… Case… arborea CASARB <NA> 70 <NA>
#> # … with 490 more rows, 22 more variables: QuadratName <chr>, QuadratID <int>,
#> # PX <dbl>, PY <dbl>, QX <dbl>, QY <dbl>, TreeID <int>, Tag <chr>,
#> # StemID <int>, StemNumber <int>, StemTag <int>, PrimaryStem <chr>,
#> # CensusID <int>, PlotCensusNumber <int>, DBH <dbl>, HOM <dbl>,
#> # ExactDate <date>, Date <int>, ListOfTSM <chr>, HighHOM <int>,
#> # LargeStem <chr>, Status <chr>, and abbreviated variable names ¹SpeciesName,
#> # ²Mnemonic, ³Subspecies, ⁴SpeciesID, ⁵SubspeciesID
#> # ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names
pick_dbh_under(), drop_status() and friends pick and drop rows from
a ForestGEO ViewFullTable or census table.
tree5 <- fgeo.x::tree5
tree5 %>%
pick_dbh_under(100)
#> # A tibble: 18 × 19
#> treeID stemID tag StemTag sp quadrat gx gy Measu…¹ Censu…² dbh
#> <int> <int> <chr> <chr> <chr> <chr> <dbl> <dbl> <int> <int> <dbl>
#> 1 7624 160987 108958 175325 TRIPAL 722 139. 425. 486675 5 10.9
#> 2 19930 117849 123493 165576 CASARB 425 61.3 496. 471979 5 23.6
#> 3 31702 39793 22889 22889 SLOBER 304 53.8 73.8 447307 5 67
#> 4 35355 44026 27538 27538 SLOBER 1106 203. 110. 449169 5 50
#> 5 39705 48888 33371 33370 CASSYL 1010 184. 194. 451067 5 67
#> 6 57380 155867 66962 171649 SLOBER 1414 274. 279. 459427 5 16.6
#> 7 95656 129113 131519 131519 OCOLEU 402 79.7 22.8 474157 5 23.6
#> 8 96051 129565 132348 132348 HIRRUG 1403 278 40.6 474523 5 12.9
#> 9 96963 130553 134707 134707 TETBAL 610 114. 182. 475236 5 18.6
#> 10 115310 150789 165286 165286 MANBID 225 24.0 497. 483175 5 14.6
#> 11 121424 158579 170701 170701 CASSYL 811 146. 218. 484785 5 20.2
#> 12 121689 158871 171277 171277 INGLAU 515 84.2 285. 485077 5 13.4
#> 13 121953 159139 171809 171809 PSYBRA 1318 247. 354. 485345 5 14
#> 14 124522 162698 174224 174224 CASSYL 1411 279. 210. 488386 5 13.1
#> 15 125038 163236 175335 175335 CASSYL 822 153. 426. 488924 5 14.5
#> 16 126087 NA 177394 <NA> CASARB 521 89.8 408. NA NA NA
#> 17 126803 NA 178513 <NA> PSYBER 622 113. 426 NA NA NA
#> 18 126934 NA 178763 <NA> MICRAC 324 47 480. NA NA NA
#> # … with 8 more variables: pom <chr>, hom <dbl>, ExactDate <date>,
#> # DFstatus <chr>, codes <chr>, nostems <dbl>, status <chr>, date <dbl>, and
#> # abbreviated variable names ¹MeasureID, ²CensusID
#> # ℹ Use `colnames()` to see all variable names
pick_main_stem() and pick_main_stemid() pick the main stem or main
stemid(s) of each tree in each census.
stem <- download_data("luquillo_stem6_random")
dim(stem)
#> [1] 1320 19
dim(pick_main_stem(stem))
#> [1] 1000 19
add_status_tree() adds the column status_tree based on the status of
all stems of each tree.
stem %>%
select(CensusID, treeID, stemID, status) %>%
add_status_tree()
#> # A tibble: 1,320 × 5
#> CensusID treeID stemID status status_tree
#> <int> <int> <int> <chr> <chr>
#> 1 6 104 143 A A
#> 2 6 119 158 A A
#> 3 NA 180 222 G A
#> 4 NA 180 223 G A
#> 5 6 180 224 G A
#> 6 6 180 225 A A
#> 7 6 602 736 A A
#> 8 6 631 775 A A
#> 9 6 647 793 A A
#> 10 6 1086 1339 A A
#> # … with 1,310 more rows
#> # ℹ Use `print(n = ...)` to see more rows
add_index() and friends add columns to a ForestGEO-like dataframe.
stem %>%
select(gx, gy) %>%
add_index()
#> Guessing: plotdim = c(320, 500)
#> * If guess is wrong, provide the correct argument `plotdim`
#> # A tibble: 1,320 × 3
#> gx gy index
#> <dbl> <dbl> <dbl>
#> 1 10.3 245. 13
#> 2 183. 410. 246
#> 3 165. 410. 221
#> 4 165. 410. 221
#> 5 165. 410. 221
#> 6 165. 410. 221
#> 7 149. 414. 196
#> 8 38.3 245. 38
#> 9 143. 411. 196
#> 10 68.9 253. 88
#> # … with 1,310 more rows
#> # ℹ Use `print(n = ...)` to see more rows
Information
Try the fgeo.tool package in your browser
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fgeo.tool documentation built on Sept. 3, 2022, 5:05 p.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.
Import and manipulate ForestGEO data
fgeo.tool helps you to import and manipulate ForestGEO data.
Installation
Install the latest stable version of fgeo.tool from CRAN with:
install.packages("fgeo.tool")
Install the development version of fgeo.tool from GitHub with:
# install.packages("devtools")
devtools::install_github("forestgeo/fgeo.tool")
Or install all fgeo packages in one step.
Example
library(fgeo.tool)
#>
#> Attaching package: 'fgeo.tool'
#> The following object is masked from 'package:stats':
#>
#> filter
# Helps access data for examples
library(fgeo.x)
example_path() allows you to access datasets stored in your R
libraries.
example_path()
#> [1] "csv" "mixed_files" "rdata" "rdata_one"
#> [5] "rds" "taxa.csv" "tsv" "vft_4quad.csv"
#> [9] "view" "weird" "xl"
(vft_file <- example_path("view/vft_4quad.csv"))
#> [1] "/usr/local/lib/R/site-library/fgeo.x/extdata/view/vft_4quad.csv"
read_vft() and read_taxa() import a ViewFullTable and ViewTaxonomy
from .tsv or .csv files.
read_vft(vft_file)
#> # A tibble: 500 × 32
#> DBHID PlotName PlotID Family Genus Speci…¹ Mnemo…² Subsp…³ Speci…⁴ Subsp…⁵
#> <int> <chr> <int> <chr> <chr> <chr> <chr> <chr> <int> <chr>
#> 1 385164 luquillo 1 Rubiace… Psyc… brachi… PSYBRA <NA> 185 <NA>
#> 2 385261 luquillo 1 Urticac… Cecr… schreb… CECSCH <NA> 74 <NA>
#> 3 384600 luquillo 1 Rubiace… Psyc… brachi… PSYBRA <NA> 185 <NA>
#> 4 608789 luquillo 1 Rubiace… Psyc… berter… PSYBER <NA> 184 <NA>
#> 5 388579 luquillo 1 Arecace… Pres… acumin… PREMON <NA> 182 <NA>
#> 6 384626 luquillo 1 Araliac… Sche… moroto… SCHMOR <NA> 196 <NA>
#> 7 410958 luquillo 1 Rubiace… Psyc… brachi… PSYBRA <NA> 185 <NA>
#> 8 385102 luquillo 1 Piperac… Piper glabre… PIPGLA <NA> 174 <NA>
#> 9 353163 luquillo 1 Arecace… Pres… acumin… PREMON <NA> 182 <NA>
#> 10 481018 luquillo 1 Salicac… Case… arborea CASARB <NA> 70 <NA>
#> # … with 490 more rows, 22 more variables: QuadratName <chr>, QuadratID <int>,
#> # PX <dbl>, PY <dbl>, QX <dbl>, QY <dbl>, TreeID <int>, Tag <chr>,
#> # StemID <int>, StemNumber <int>, StemTag <int>, PrimaryStem <chr>,
#> # CensusID <int>, PlotCensusNumber <int>, DBH <dbl>, HOM <dbl>,
#> # ExactDate <date>, Date <int>, ListOfTSM <chr>, HighHOM <int>,
#> # LargeStem <chr>, Status <chr>, and abbreviated variable names ¹SpeciesName,
#> # ²Mnemonic, ³Subspecies, ⁴SpeciesID, ⁵SubspeciesID
#> # ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names
pick_dbh_under(), drop_status() and friends pick and drop rows from
a ForestGEO ViewFullTable or census table.
tree5 <- fgeo.x::tree5
tree5 %>%
pick_dbh_under(100)
#> # A tibble: 18 × 19
#> treeID stemID tag StemTag sp quadrat gx gy Measu…¹ Censu…² dbh
#> <int> <int> <chr> <chr> <chr> <chr> <dbl> <dbl> <int> <int> <dbl>
#> 1 7624 160987 108958 175325 TRIPAL 722 139. 425. 486675 5 10.9
#> 2 19930 117849 123493 165576 CASARB 425 61.3 496. 471979 5 23.6
#> 3 31702 39793 22889 22889 SLOBER 304 53.8 73.8 447307 5 67
#> 4 35355 44026 27538 27538 SLOBER 1106 203. 110. 449169 5 50
#> 5 39705 48888 33371 33370 CASSYL 1010 184. 194. 451067 5 67
#> 6 57380 155867 66962 171649 SLOBER 1414 274. 279. 459427 5 16.6
#> 7 95656 129113 131519 131519 OCOLEU 402 79.7 22.8 474157 5 23.6
#> 8 96051 129565 132348 132348 HIRRUG 1403 278 40.6 474523 5 12.9
#> 9 96963 130553 134707 134707 TETBAL 610 114. 182. 475236 5 18.6
#> 10 115310 150789 165286 165286 MANBID 225 24.0 497. 483175 5 14.6
#> 11 121424 158579 170701 170701 CASSYL 811 146. 218. 484785 5 20.2
#> 12 121689 158871 171277 171277 INGLAU 515 84.2 285. 485077 5 13.4
#> 13 121953 159139 171809 171809 PSYBRA 1318 247. 354. 485345 5 14
#> 14 124522 162698 174224 174224 CASSYL 1411 279. 210. 488386 5 13.1
#> 15 125038 163236 175335 175335 CASSYL 822 153. 426. 488924 5 14.5
#> 16 126087 NA 177394 <NA> CASARB 521 89.8 408. NA NA NA
#> 17 126803 NA 178513 <NA> PSYBER 622 113. 426 NA NA NA
#> 18 126934 NA 178763 <NA> MICRAC 324 47 480. NA NA NA
#> # … with 8 more variables: pom <chr>, hom <dbl>, ExactDate <date>,
#> # DFstatus <chr>, codes <chr>, nostems <dbl>, status <chr>, date <dbl>, and
#> # abbreviated variable names ¹MeasureID, ²CensusID
#> # ℹ Use `colnames()` to see all variable names
pick_main_stem() and pick_main_stemid() pick the main stem or main
stemid(s) of each tree in each census.
stem <- download_data("luquillo_stem6_random")
dim(stem)
#> [1] 1320 19
dim(pick_main_stem(stem))
#> [1] 1000 19
add_status_tree() adds the column status_tree based on the status of
all stems of each tree.
stem %>%
select(CensusID, treeID, stemID, status) %>%
add_status_tree()
#> # A tibble: 1,320 × 5
#> CensusID treeID stemID status status_tree
#> <int> <int> <int> <chr> <chr>
#> 1 6 104 143 A A
#> 2 6 119 158 A A
#> 3 NA 180 222 G A
#> 4 NA 180 223 G A
#> 5 6 180 224 G A
#> 6 6 180 225 A A
#> 7 6 602 736 A A
#> 8 6 631 775 A A
#> 9 6 647 793 A A
#> 10 6 1086 1339 A A
#> # … with 1,310 more rows
#> # ℹ Use `print(n = ...)` to see more rows
add_index() and friends add columns to a ForestGEO-like dataframe.
stem %>%
select(gx, gy) %>%
add_index()
#> Guessing: plotdim = c(320, 500)
#> * If guess is wrong, provide the correct argument `plotdim`
#> # A tibble: 1,320 × 3
#> gx gy index
#> <dbl> <dbl> <dbl>
#> 1 10.3 245. 13
#> 2 183. 410. 246
#> 3 165. 410. 221
#> 4 165. 410. 221
#> 5 165. 410. 221
#> 6 165. 410. 221
#> 7 149. 414. 196
#> 8 38.3 245. 38
#> 9 143. 411. 196
#> 10 68.9 253. 88
#> # … with 1,310 more rows
#> # ℹ Use `print(n = ...)` to see more rows
Information
Try the fgeo.tool 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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