Import and manipulate ForestGEO data
Import and manipulate ForestGEO data
fgeo.tool helps you to import and manipulate ForestGEO data.
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.
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 namespick_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 namespick_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 19add_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 rowsadd_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