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[R-package on CRAN] General purpose tools for ForestGEO Packages
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README.md

Import and manipulate ForestGEO data

lifecycle Travis build status Coverage status CRAN status

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] "/home/mauro/R/x86_64-pc-linux-gnu-library/3.6/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 x 32
#>     DBHID PlotName PlotID Family Genus SpeciesName Mnemonic Subspecies
#>     <int> <chr>     <int> <chr>  <chr> <chr>       <chr>    <chr>     
#>  1 385164 luquillo      1 Rubia… Psyc… brachiata   PSYBRA   <NA>      
#>  2 385261 luquillo      1 Urtic… Cecr… schreberia… CECSCH   <NA>      
#>  3 384600 luquillo      1 Rubia… Psyc… brachiata   PSYBRA   <NA>      
#>  4 608789 luquillo      1 Rubia… Psyc… berteroana  PSYBER   <NA>      
#>  5 388579 luquillo      1 Areca… Pres… acuminata   PREMON   <NA>      
#>  6 384626 luquillo      1 Arali… Sche… morototoni  SCHMOR   <NA>      
#>  7 410958 luquillo      1 Rubia… Psyc… brachiata   PSYBRA   <NA>      
#>  8 385102 luquillo      1 Piper… Piper glabrescens PIPGLA   <NA>      
#>  9 353163 luquillo      1 Areca… Pres… acuminata   PREMON   <NA>      
#> 10 481018 luquillo      1 Salic… Case… arborea     CASARB   <NA>      
#> # … with 490 more rows, and 24 more variables: SpeciesID <int>,
#> #   SubspeciesID <chr>, 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>

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 x 19
#>    treeID stemID tag   StemTag sp    quadrat    gx    gy MeasureID CensusID
#>     <int>  <int> <chr> <chr>   <chr> <chr>   <dbl> <dbl>     <int>    <int>
#>  1   7624 160987 1089… 175325  TRIP… 722     139.  425.     486675        5
#>  2  19930 117849 1234… 165576  CASA… 425      61.3 496.     471979        5
#>  3  31702  39793 22889 22889   SLOB… 304      53.8  73.8    447307        5
#>  4  35355  44026 27538 27538   SLOB… 1106    203.  110.     449169        5
#>  5  39705  48888 33371 33370   CASS… 1010    184.  194.     451067        5
#>  6  57380 155867 66962 171649  SLOB… 1414    274.  279.     459427        5
#>  7  95656 129113 1315… 131519  OCOL… 402      79.7  22.8    474157        5
#>  8  96051 129565 1323… 132348  HIRR… 1403    278    40.6    474523        5
#>  9  96963 130553 1347… 134707  TETB… 610     114.  182.     475236        5
#> 10 115310 150789 1652… 165286  MANB… 225      24.0 497.     483175        5
#> 11 121424 158579 1707… 170701  CASS… 811     146.  218.     484785        5
#> 12 121689 158871 1712… 171277  INGL… 515      84.2 285.     485077        5
#> 13 121953 159139 1718… 171809  PSYB… 1318    247.  354.     485345        5
#> 14 124522 162698 1742… 174224  CASS… 1411    279.  210.     488386        5
#> 15 125038 163236 1753… 175335  CASS… 822     153.  426.     488924        5
#> 16 126087     NA 1773… <NA>    CASA… 521      89.8 408.         NA       NA
#> 17 126803     NA 1785… <NA>    PSYB… 622     113.  426          NA       NA
#> 18 126934     NA 1787… <NA>    MICR… 324      47   480.         NA       NA
#> # … with 9 more variables: dbh <dbl>, pom <chr>, hom <dbl>,
#> #   ExactDate <date>, DFstatus <chr>, codes <chr>, nostems <dbl>,
#> #   status <chr>, date <dbl>

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 x 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

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 x 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

Get started with fgeo

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