fgeo 1.2.0 release candidate

fgeo is a collection of R packages to analyze forest diversity and dynamics. We plan to announce it widely in early 2019. Before that, we would love you to give us some feedback, and report issues.

This post highlights the main features. For more details please see the Example and Reference sections.

Setup

To install fgeo see Installation. To use fgeo run library(fgeo) in R.

library(fgeo)

All fgeo packages are now attached.

search()
#>  [1] ".GlobalEnv"           "package:fgeo.x"       "package:fgeo.tool"   
#>  [4] "package:fgeo.plot"    "package:fgeo.analyze" "package:fgeo"        
#>  [7] "package:stats"        "package:graphics"     "package:grDevices"   
#> [10] "package:utils"        "package:datasets"     "package:methods"     
#> [13] "Autoloads"            "tools:callr"          "package:base"

Explore fgeo

Use fgeo_help() to search documentation. It is similar to utils::help.search() but specific for fgeo.

Access and manipulate data

fgeo comes with small datasets for examples. When you use fgeo you can also use many datasets directly.

tree5
#> # A tibble: 30 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   8055  10036 1094… 109482  CECS… 522      94.8 424.     468874        5
#>  3  19930 117849 1234… 165576  CASA… 425      61.3 496.     471979        5
#>  4  23746  29677 14473 14473   PREM… 617     100.  328.     442571        5
#>  5  31702  39793 22889 22889   SLOB… 304      53.8  73.8    447307        5
#>  6  35355  44026 27538 27538   SLOB… 1106    203.  110.     449169        5
#>  7  35891  44634 282   282     DACE… 901     172.   14.7    434266        5
#>  8  39705  48888 33371 33370   CASS… 1010    184.  194.     451067        5
#>  9  50184  60798 5830  5830    MATD… 1007    191.  132.     437645        5
#> 10  57380 155867 66962 171649  SLOB… 1414    274.  279.     459427        5
#> # … with 20 more rows, and 9 more variables: dbh <dbl>, pom <chr>, hom <dbl>,
#> #   ExactDate <date>, DFstatus <chr>, codes <chr>, nostems <dbl>, status <chr>,
#> #   date <dbl>

Other datasets are stored in R’s library. You can access them with example_path().

example_path("rdata")
#> [1] "/home/travis/R/Library/fgeo.x/extdata/rdata"

dir(example_path("rdata"))
#> [1] "tree5.RData" "tree6.RData"

Larger datasets live online. You can download them with download_data().

download_data("luquillo_tree5_random")
#> # A tibble: 1,000 x 19
#>    treeID stemID tag   StemTag sp    quadrat    gx    gy MeasureID CensusID
#>     <int>  <int> <chr> <chr>   <chr> <chr>   <dbl> <dbl>     <int>    <int>
#>  1    104    143 10009 10009   DACE… 113      10.3  245.    439947        5
#>  2    119    158 1001… 100104  MYRS… 1021    183.   410.    466597        5
#>  3    180    225 1001… 100174  CASA… 921     165.   410.    466623        5
#>  4    602    736 1006… 100649  GUAG… 821     149.   414.    466727        5
#>  5    631    775 10069 10069   PREM… 213      38.3  245.    439989        5
#>  6    647    793 1007… 100708  SCHM… 821     143.   411.    466743        5
#>  7   1086   1339 10122 10122   DRYG… 413      68.9  253.    440021        5
#>  8   1144   1410 1012… 101285  SCHM… 920     161.   395.    466889        5
#>  9   1168   1438 10131 10131   DACE… 413      70.6  252.    440031        5
#> 10   1380 114352 1015… 149529  CASA… 820     142.   386.    466957        5
#> # … with 990 more rows, and 9 more variables: dbh <dbl>, pom <chr>, hom <dbl>,
#> #   ExactDate <date>, DFstatus <chr>, codes <chr>, nostems <dbl>, status <chr>,
#> #   date <dbl>

Use read_vft() to read your own ViewFullTable. Compared to utils::read.csv() it is easier to use and safer.

(viewfulltable_file <- example_path("view/vft_4quad.csv"))
#> [1] "/home/travis/R/Library/fgeo.x/extdata/view/vft_4quad.csv"

read_vft(viewfulltable_file)
#> # A tibble: 500 x 32
#>     DBHID PlotName PlotID Family Genus SpeciesName Mnemonic Subspecies SpeciesID
#>     <int> <chr>     <int> <chr>  <chr> <chr>       <chr>    <chr>          <int>
#>  1 385164 luquillo      1 Rubia… Psyc… brachiata   PSYBRA   <NA>             185
#>  2 385261 luquillo      1 Urtic… Cecr… schreberia… CECSCH   <NA>              74
#>  3 384600 luquillo      1 Rubia… Psyc… brachiata   PSYBRA   <NA>             185
#>  4 608789 luquillo      1 Rubia… Psyc… berteroana  PSYBER   <NA>             184
#>  5 388579 luquillo      1 Areca… Pres… acuminata   PREMON   <NA>             182
#>  6 384626 luquillo      1 Arali… Sche… morototoni  SCHMOR   <NA>             196
#>  7 410958 luquillo      1 Rubia… Psyc… brachiata   PSYBRA   <NA>             185
#>  8 385102 luquillo      1 Piper… Piper glabrescens PIPGLA   <NA>             174
#>  9 353163 luquillo      1 Areca… Pres… acuminata   PREMON   <NA>             182
#> 10 481018 luquillo      1 Salic… Case… arborea     CASARB   <NA>              70
#> # … with 490 more rows, and 23 more variables: 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>

Plot

# Using only two species to keep the example small
two_species <-  c("PREMON", "CASARB")

# The pipe operator (`%>%`) can make your code easier to read
census <- download_data("luquillo_stem6_random") %>% 
  filter(sp %in% two_species)

Use autoplot() to plot different classes of ForestGEO data. For example:

• autoplot() species distribution

class(sp(census))
#> [1] "sp"         "tbl_df"     "tbl"        "data.frame"

autoplot(sp(census))

• autoplot() habitats

# Using `data()` just for clarity
data("habitat")

class(habitat)
#> [1] "fgeo_habitat" "tbl_df"       "tbl"          "data.frame"

autoplot(habitat)

Analyze

The available analyses include abundance, mortality, and species-habitat association.

census %>% 
  pick_main_stem() %>% 
  group_by(sp) %>% 
  abundance()
#> # A tibble: 2 x 2
#> # Groups:   sp [2]
#>   sp         n
#>   <chr>  <int>
#> 1 CASARB    94
#> 2 PREMON   252

data("tree5")
data("tree6")

as_tibble(
  mortality_ctfs(tree5, tree6)
)
#> Detected dbh ranges:
#>   * `census1` = 10.9-323.
#>   * `census2` = 10.5-347.
#> Using dbh `mindbh = 0` and above.
#> # A tibble: 1 x 9
#>       N     D    rate   lower  upper  time  date1  date2 dbhmean
#>   <dbl> <dbl>   <dbl>   <dbl>  <dbl> <dbl>  <dbl>  <dbl>   <dbl>
#> 1    27     1 0.00834 0.00195 0.0448  4.52 18938. 20590.    101.

tree <- download_data("luquillo_tree5_random")
data("habitat")

summary(
  tt_test(tree, habitat)
)
#> Using `plotdim = c(320, 500)`. To change this value see `?tt_test()`.
#> Using `gridsize = 20`. To change this value see `?tt_test()`.
#> # A tibble: 292 x 3
#>    sp     habitat association
#>    <chr>  <chr>   <chr>      
#>  1 ALCFLO 1       neutral    
#>  2 ALCFLO 2       neutral    
#>  3 ALCFLO 3       repelled   
#>  4 ALCFLO 4       repelled   
#>  5 ALCLAT 1       repelled   
#>  6 ALCLAT 2       neutral    
#>  7 ALCLAT 3       repelled   
#>  8 ALCLAT 4       repelled   
#>  9 ANDINE 1       neutral    
#> 10 ANDINE 2       neutral    
#> # … with 282 more rows

Get started with fgeo