Analyze forest diversity and dynamics

fgeo helps you to install, load, and access the documentation of multiple packages to analyze forest diversity and dynamics. It allows you to manipulate and plot ForestGEO data, and to do common analyses including abundance, demography, and species-habitats associations.

Installation

Expected R environment

R version is recent
All packages are updated (run update.packages())
No other R session is running
Current R session is clean (click Session > Restart R)

Install the latest stable version of fgeo with:

install.packages("devtools")
devtools::install_github("forestgeo/fgeo@*release")

Or install the development version of fgeo with:

install.packages("devtools")
devtools::install_github("forestgeo/fgeo", upgrade = "never")

To upgrade packages see ?devtools::update_packages()
To remove packages see ?remove.packages()

Tips to avoid or fix common installation problems

Alternative installation

If you failed to install fgeo try this instead:

install.packages("devtools")
devtools::install_github("forestgeo/fgeo.install")
fgeo.install::install_fgeo()

If this still fails, follow the advice below and then try to install fgeo again.

Update R, RStudio, and R packages

How?
Why?

Instruct RStudio not to preserve your workspace between sessions

Why?

In RStudio go to Tools > Global Options…

Use RStudio projects (or the here package)

Why?

Restart R many times each day

Press Cmd/Ctrl + Shift + F10 to restart RStudio or go to Session > Restart R.

Increase the rate limit to install from GitHub

If you reach GitHub’s rate limit see usethis::browse_github_pat().

If that was unclear continue reading. This describes the same process but in more detail.

Ensure you have an account on GitHub (https://github.com/).
Generate a GitHub token named exactly GITHUB_PAT by running usethis::browse_github_pat() in R.
You will be sent to GitHub and you should see something like this:

…

Make sure your token description says exactly GITHUB_PAT and click Generate token

…

Store your new token in the environmental variable GITHUB_PAT by running usethis::edit_r_environ() in R.
A file called .Renviron will open. Type the name and value of your GitHub token. Ensure to end this file with a new empty line. Your .Renviron file should now look like this:

…

Save and close .Renviron.

Install package development utilities

Sometimes you may want to install the source version of an R package from CRAN or GitHub. If that package contains a src/ folder you will need to install package development utilities.

How?

Troubleshoot: error: X11 library is missing: install XQuartz …

If you are a mac user, fgeo may fail to install with the error below. Install XQuartz from https://www.xquartz.org/ and try to install fgeo again.

Error : .onLoad failed in loadNamespace() for 'tcltk', details:
  call: fun(libname, pkgname)
  error: X11 library is missing: install XQuartz from xquartz.macosforge.org

Example

library(fgeo)
#> -- Attaching packages ----------------------------------------------- fgeo 0.0.0.9002 --
#> v fgeo.analyze 1.0.0          v fgeo.tool    1.0.0     
#> v fgeo.plot    1.0.0.9000     v fgeo.x       1.0.0.9000
#> -- Conflicts ------------------------------------------------------- fgeo_conflicts() --
#> x fgeo.tool::filter() masks stats::filter()

Explore fgeo

On an interactive session, fgeo_help() and fgeo_browse_reference() help you to search documentation.

if (interactive()) {
  # To search on the viewer; accepts keywords
  fgeo_help()
  # To search on a web browser
  fgeo_browse_reference() 
}

Access and manipulate data

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] "C:/Users/LeporeM/Documents/R/R-3.5.2/library/fgeo.x/extdata/view/vft_4quad.csv"

`read_<table>()`

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>

Importing multiple censuses from a directory into a list

(This and the following section don’t use fgeo because other packages already do this well.)

Combine fs::dir_ls() with purrr::map() to import multiple censuses from a directory into a list:

Use fs::dir_ls() to create the paths to the files you want to import.
Use purrr::map() to iterate over each path and apply a custom function to import them.

library(purrr)
#> 
#> Attaching package: 'purrr'
#> The following object is masked from 'package:fgeo.tool':
#> 
#>     %||%
library(fs)

(rdata_files <- example_path("rdata"))
#> [1] "C:/Users/LeporeM/Documents/R/R-3.5.2/library/fgeo.x/extdata/rdata"
(paths <- fs::dir_ls(rdata_files))
#> C:/Users/LeporeM/Documents/R/R-3.5.2/library/fgeo.x/extdata/rdata/tree5.RData
#> C:/Users/LeporeM/Documents/R/R-3.5.2/library/fgeo.x/extdata/rdata/tree6.RData

# The formula syntax `~ fun(.x)` is a shortcut for `function(.x) fun(.x)`
censuses <- map(paths, ~ get(load(.x)))
censuses
#> $`C:/Users/LeporeM/Documents/R/R-3.5.2/library/fgeo.x/extdata/rdata/tree5.RData`
#> # A tibble: 3 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
#> # ... with 9 more variables: dbh <dbl>, pom <chr>, hom <dbl>,
#> #   ExactDate <date>, DFstatus <chr>, codes <chr>, nostems <dbl>,
#> #   status <chr>, date <dbl>
#> 
#> $`C:/Users/LeporeM/Documents/R/R-3.5.2/library/fgeo.x/extdata/rdata/tree6.RData`
#> # A tibble: 3 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.    582850        6
#> 2    119    158 1001~ 100104  MYRS~ 1021    183.   410.    578696        6
#> 3    180    225 1001~ 100174  CASA~ 921     165.   410.    617049        6
#> # ... with 9 more variables: dbh <dbl>, pom <chr>, hom <dbl>,
#> #   ExactDate <date>, DFstatus <chr>, codes <chr>, nostems <dbl>,
#> #   status <chr>, date <dbl>

Exporting multiple censuses from a list into a directory

Use purrr::walk2() to map over two things in parallel – each census to each path to a file. It is similar to purrr::map2() and base::Map() but prints nothing to the console.

(files <- path_file(names(censuses)))
#> tree5.RData tree6.RData
(folder <- tempdir())
#> [1] "C:\\Users\\LeporeM\\AppData\\Local\\Temp\\1\\RtmpU3uA3r"
(paths <- path(folder, files))
#> C:/Users/LeporeM/AppData/Local/Temp/1/RtmpU3uA3r/tree5.RData
#> C:/Users/LeporeM/AppData/Local/Temp/1/RtmpU3uA3r/tree6.RData

walk2(censuses, paths, ~ save(.x, file = .y))

# Confirm that the folder contains the files we just saved
path_file(dir_ls(folder, regexp = "tree"))
#> tree5.RData tree6.RData

`pick_<what>()` and `drop_<what>()`

fgeo is pipe-friendly. You may not use pipes but often they make code easier to read.

Use %>% to emphasise a sequence of actions, rather than the object that the actions are being performed on.

– https://style.tidyverse.org/pipes.html

pick_dbh_under(), drop_status() and friends pick and drop rows from a ForestGEO ViewFullTable or census table.

(census <- censuses[[2]])
#> # A tibble: 3 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.    582850        6
#> 2    119    158 1001~ 100104  MYRS~ 1021    183.   410.    578696        6
#> 3    180    225 1001~ 100174  CASA~ 921     165.   410.    617049        6
#> # ... with 9 more variables: dbh <dbl>, pom <chr>, hom <dbl>,
#> #   ExactDate <date>, DFstatus <chr>, codes <chr>, nostems <dbl>,
#> #   status <chr>, date <dbl>

census %>% 
  pick_dbh_under(100)
#> # A tibble: 2 x 19
#>   treeID stemID tag   StemTag sp    quadrat    gx    gy MeasureID CensusID
#>    <int>  <int> <chr> <chr>   <chr> <chr>   <dbl> <dbl>     <int>    <int>
#> 1    119    158 1001~ 100104  MYRS~ 1021     183.  410.    578696        6
#> 2    180    225 1001~ 100174  CASA~ 921      165.  410.    617049        6
#> # ... 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_<column(s)>()`

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

Plot data

For simplicity, we will focus on only a few species.

stem_2sp <- stem %>% 
  filter(sp %in% c("PREMON", "CASARB"))

autoplot() and friends produce different output depending on the class of input. You can create different input classes, for example, with sp() and sp_elev():

Use sp(census) to plot the column sp of a census dataset – i.e. to plot species distribution.

class(sp(stem_2sp))
#> [1] "sp"         "tbl_df"     "tbl"        "data.frame"

autoplot(sp(stem_2sp))

Use sp_elev(census, elevation) to plot the columns sp and elev of a census and elevation dataset, respectively – i.e. to plot species distribution and topography.

data("elevation")
class(sp_elev(stem_2sp, elevation))
#> [1] "sp_elev" "list"

autoplot(sp_elev(stem_2sp, elevation))

Analyze

Abundance

abundance() and basal_area() calculate abundance and basal area, optionally by groups.

abundance(
  pick_main_stem(census)
)
#> # A tibble: 1 x 1
#>       n
#>   <int>
#> 1     3

by_species <- group_by(census, sp)

basal_area(by_species)
#> # A tibble: 3 x 2
#> # Groups:   sp [3]
#>   sp     basal_area
#>   <chr>       <dbl>
#> 1 CASARB      1669.
#> 2 DACEXC     29865.
#> 3 MYRSPL      1583.

Demography

recruitment_ctfs(), mortality_ctfs(), and growth_ctfs() calculate recruitment, mortality, and growth. They all output a list. as_tibble() converts the output from a list to a more convenient dataframe.

data("tree5")

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.

Species-habitats association

tt_test() runs a torus translation test to determine habitat associations of tree species. as_tibble() converts the output from a list to a more convenient dataframe. summary() helps you to interpret the result.

# This analysis makes sense only for tree tables
tree <- download_data("luquillo_tree5_random")
data("habitat")
result <- 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()`.

as_tibble(result)
#> # A tibble: 292 x 8
#>    habitat sp     N.Hab Gr.Hab Ls.Hab Eq.Hab Rep.Agg.Neut Obs.Quantile
#>  * <chr>   <chr>  <dbl>  <dbl>  <dbl>  <dbl>        <dbl>        <dbl>
#>  1 1       ALCFLO     2   1443    153      4            0        0.902
#>  2 2       ALCFLO     1    807    778     15            0        0.504
#>  3 3       ALCFLO     0      0    715    885           -1        0    
#>  4 4       ALCFLO     0      0    402   1198           -1        0    
#>  5 1       ALCLAT     0      0    544   1056           -1        0    
#>  6 2       ALCLAT     1   1432    156     12            0        0.895
#>  7 3       ALCLAT     0      0    324   1276           -1        0    
#>  8 4       ALCLAT     0      0    144   1456           -1        0    
#>  9 1       ANDINE     1   1117    466     17            0        0.698
#> 10 2       ANDINE     1   1081    510      9            0        0.676
#> # ... with 282 more rows

summary(result)
#> # 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

Related projects

Additional packages maintained by ForestGEO but not included in fgeo:

fgeo.data: Open datasets of ForestGEO.
fgeo.krig: Analyze soils.

Other packages not maintained by ForestGEO:

CTFS-R Package: The original package of CTFS functions. No longer supported by ForestGEO.
BIOMASS: An R package to estimate above-ground biomass in tropical forests.

R code from recent publications by ForestGEO partners

Data have been made available as required by the journal to enable reproduction of the results presented in the paper. Please do not share these data without permission of the ForestGEO plot Principal Investigators (PIs). If you wish to publish papers based on these data, you are also required to get permission from the PIs of the corresponding ForestGEO plots.

Soil drivers of local-scale tree growth in a lowland tropical forest (Zemunik et al., 2018).
Plant diversity increases with the strength of negative density dependence at the global scale (LaManna et al., 2018)
- Response #1: LaManna et al. 2018. Response to Comment on “Plant diversity increases with the strength of negative density dependence at the global scale” Science Vol. 360, Issue 6391, eaar3824. DOI: 10.1126/science.aar3824
- Response #2: LaManna et al. 2018. Response to Comment on “Plant diversity increases with the strength of negative density dependence at the global scale”. Science Vol. 360, Issue 6391, eaar5245. DOI: 10.1126/science.aar5245

Information

Acknowledgments

Thanks to all partners of ForestGEO for sharing their ideas and code. For feedback on fgeo, special thanks to Gabriel Arellano, Stuart Davies, Lauren Krizel, Sean McMahon, and Haley Overstreet. There are many other people that deserve special acknowledgment; I thank them in the documentation and home page of each individual package that make up the fgeo development.

Name		Name	Last commit message	Last commit date
Latest commit History 660 Commits
.buildignore		.buildignore
.github		.github
R		R
docs		docs
inst		inst
man		man
tests		tests
vignettes/siteonly		vignettes/siteonly
.Rbuildignore		.Rbuildignore
.gitignore		.gitignore
.travis.yml		.travis.yml
DESCRIPTION		DESCRIPTION
LICENSE.md		LICENSE.md
NAMESPACE		NAMESPACE
NEWS.md		NEWS.md
README.Rmd		README.Rmd
README.md		README.md
_pkgdown.yml		_pkgdown.yml
appveyor.yml		appveyor.yml
codecov.yml		codecov.yml
cran-comments.md		cran-comments.md
fgeo.Rproj		fgeo.Rproj

Provide feedback

Saved searches

Use saved searches to filter your results more quickly

Repository files navigation

Analyze forest diversity and dynamics

Installation

Alternative installation

Update R, RStudio, and R packages

Instruct RStudio not to preserve your workspace between sessions

Use RStudio projects (or the here package)

Restart R many times each day

Increase the rate limit to install from GitHub

Install package development utilities

Troubleshoot: error: X11 library is missing: install XQuartz …

Example

Explore fgeo

Access and manipulate data

`read_<table>()`

Importing multiple censuses from a directory into a list

Exporting multiple censuses from a list into a directory

`pick_<what>()` and `drop_<what>()`

`add_<column(s)>()`

Plot data

Analyze

Abundance

Demography

Species-habitats association

Related projects

R code from recent publications by ForestGEO partners

Information

Acknowledgments

About

Uh oh!

Releases 9

Packages

Uh oh!

Contributors

Uh oh!

Languages

Folders and files

Latest commit

History

Repository files navigation

Analyze forest diversity and dynamics

Installation

Alternative installation

Update R, RStudio, and R packages

Instruct RStudio not to preserve your workspace between sessions

Use RStudio projects (or the here package)

Restart R many times each day

Increase the rate limit to install from GitHub

Install package development utilities

Troubleshoot: error: X11 library is missing: install XQuartz …

Example

Explore fgeo

Access and manipulate data

read_<table>()

Importing multiple censuses from a directory into a list

Exporting multiple censuses from a list into a directory

pick_<what>() and drop_<what>()

add_<column(s)>()

Plot data

Analyze

Abundance

Demography

Species-habitats association

Related projects

R code from recent publications by ForestGEO partners

Information

Acknowledgments

About

Topics

Resources

License

Code of conduct

Contributing

Uh oh!

Stars

Watchers

Forks

Releases 9

Packages 0

Uh oh!

Contributors

Uh oh!

Languages

`read_<table>()`

`pick_<what>()` and `drop_<what>()`

`add_<column(s)>()`

Packages