Analyze fgeo data

The goal of fgeo.analyze is to analyze fgeo data.

Installation

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

Or install all fgeo packages in one step.

For details on how to install packages from GitHub, see this article.

Example

library(fgeo)
#> -- Attaching packages --------------------------------------------------- fgeo 0.0.0.9002 --
#> v fgeo.x       0.0.0.9000     v fgeo.analyze 0.0.0.9000
#> v fgeo.tool    0.0.0.9004     v fgeo.map     0.0.0.9402
#>

Abundance

Your data may have multiple stems per treeid and even multiple measures per stemid (if trees have buttresses).

# Trees with buttresses may have multiple measurements of a single stem. 
# Main stems have highest `HOM`, then largest `DBH`.
vft <- tribble(
  ~CensusID, ~TreeID, ~StemID, ~DBH, ~HOM,
          1,     "1",   "1.1",   88,  130,
          1,     "1",   "1.1",   10,  160,  # Main stem
          1,     "2",   "2.1",   20,  130,
          1,     "2",   "2.2",   30,  130,  # Main stem
)

Fundamentally, abundance() counts rows. All of these results are the same:

nrow(vft)
#> [1] 4
dplyr::count(vft)
#> # A tibble: 1 x 1
#>       n
#>   <int>
#> 1     4
dplyr::summarize(vft, n = n())
#> # A tibble: 1 x 1
#>       n
#>   <int>
#> 1     4
abundance(vft)
#> Warning: `treeid`: Duplicated values were detected. Do you need to pick
#> main stems?
#> # A tibble: 1 x 1
#>       n
#>   <int>
#> 1     4

But that result is likely not what you expect. Instead, you likely expect this:

summarize(vft, n = n_distinct(TreeID))
#> Warning in summarise_impl(.data, dots): hybrid evaluation forced for
#> `n_distinct`. Please use dplyr::n_distinct() or library(dplyr) to remove
#> this warning.
#> # A tibble: 1 x 1
#>       n
#>   <int>
#> 1     2

As shown above, you can get a correct result by combining summarize() and n_distinct() (from the dplyr package). But abundance() includes some useful additional features (see ?abundance()). This code conveys your intention more clearly, i.e. to calculate tree abundance by counting the number of main stems:

(main_stems <- pick_main_stem(vft))
#> # A tibble: 2 x 5
#>   CensusID TreeID StemID   DBH   HOM
#>      <dbl> <chr>  <chr>  <dbl> <dbl>
#> 1        1 1      1.1       10   160
#> 2        1 2      2.2       30   130
abundance(main_stems)
#> # A tibble: 1 x 1
#>       n
#>   <int>
#> 1     2

If you have data from multiple censuses, then you can compute by census (or any other group).

vft2 <- tibble::tribble(
  ~CensusID, ~TreeID, ~StemID, ~DBH, ~HOM,
          1,     "1",   "1.1",   10,  130,
          1,     "1",   "1.2",   20,  130,  # Main stem
          2,     "1",   "1.1",   12,  130,
          2,     "1",   "1.2",   22,  130   # Main stem
)
by_census <- group_by(vft2, CensusID)
(main_stems_by_census <- pick_main_stem(by_census))
#> # A tibble: 2 x 5
#> # Groups:   CensusID [2]
#>   CensusID TreeID StemID   DBH   HOM
#>      <dbl> <chr>  <chr>  <dbl> <dbl>
#> 1        1 1      1.2       20   130
#> 2        2 1      1.2       22   130
abundance(main_stems_by_census)
#> # A tibble: 2 x 2
#> # Groups:   CensusID [2]
#>   CensusID     n
#>      <dbl> <int>
#> 1        1     1
#> 2        2     1

Often you will need to first subset data (e.g. by status or DBH) and then count.

over20 <- filter(main_stems_by_census, DBH > 20)
abundance(over20)
#> # A tibble: 1 x 2
#> # Groups:   CensusID [1]
#>   CensusID     n
#>      <dbl> <int>
#> 1        2     1

Basal area

If trees have buttresses, then you may need to pick the main stemid of each stem so you do not count the same stem more than once.

vft3 <- tribble(
  ~CensusID, ~TreeID, ~StemID, ~DBH, ~HOM,
          1,     "1",   "1.1",   88,  130,
          1,     "1",   "1.1",   10,  160,  # Main stem
          1,     "2",   "2.1",   20,  130,
          1,     "2",   "2.2",   30,  130,  # Main stem
          2,     "1",   "1.1",   98,  130,
          2,     "1",   "1.1",   20,  160,  # Main stem
          2,     "2",   "2.1",   30,  130,
          2,     "2",   "2.2",   40,  130,  # Main stem
)
(main_stemids <- pick_main_stemid(vft3))
#> # A tibble: 6 x 5
#>   CensusID TreeID StemID   DBH   HOM
#>      <dbl> <chr>  <chr>  <dbl> <dbl>
#> 1        1 1      1.1       10   160
#> 2        1 2      2.1       20   130
#> 3        1 2      2.2       30   130
#> 4        2 1      1.1       20   160
#> 5        2 2      2.1       30   130
#> 6        2 2      2.2       40   130
main_stemids
#> # A tibble: 6 x 5
#>   CensusID TreeID StemID   DBH   HOM
#>      <dbl> <chr>  <chr>  <dbl> <dbl>
#> 1        1 1      1.1       10   160
#> 2        1 2      2.1       20   130
#> 3        1 2      2.2       30   130
#> 4        2 1      1.1       20   160
#> 5        2 2      2.1       30   130
#> 6        2 2      2.2       40   130
basal_area(main_stemids)
#> Warning: `stemid`: Duplicated values were detected. Do you need to pick
#> largest `hom` values?
#> Warning: `censusid`: Multiple values were detected. Do you need to group by
#> censusid?
#> # A tibble: 1 x 1
#>   basal_area
#>        <dbl>
#> 1      3377.

basal_area() also allows you to compute by groups.

by_census <- group_by(main_stemids, CensusID)
basal_area(by_census)
#> # A tibble: 2 x 2
#> # Groups:   CensusID [2]
#>   CensusID basal_area
#>      <dbl>      <dbl>
#> 1        1      1100.
#> 2        2      2278.

But if you want to compute on a subset of data, then you need to pick the data first.

ten_to_twenty <- filter(by_census, DBH >= 10, DBH <= 20)
ba <- basal_area(ten_to_twenty)
ba
#> # A tibble: 2 x 2
#> # Groups:   CensusID [2]
#>   CensusID basal_area
#>      <dbl>      <dbl>
#> 1        1       393.
#> 2        2       314.

And if you need to convert units, then you can do so after the fact with fgeo.tool::convert_unit_at().

convert_unit_at(ba, .at = "basal_area", from = "mm2", to = "hectare")
#> # A tibble: 2 x 2
#> # Groups:   CensusID [2]
#>   CensusID   basal_area
#>      <dbl>        <dbl>
#> 1        1 0.0000000393
#> 2        2 0.0000000314

Abundance and basal area aggregated by year

Example data.

vft <- example_byyr
vft
#> # A tibble: 8 x 13
#>   PlotName CensusID TreeID StemID Status   DBH Genus SpeciesName ExactDate 
#>   <chr>       <int>  <int>  <dbl> <chr>  <int> <chr> <chr>       <date>    
#> 1 luq             1      1    1.1 alive     10 Gn    spp         2001-01-01
#> 2 luq             1      1    1.2 dead      NA Gn    spp         2001-01-01
#> 3 luq             1      2    2.1 alive     20 Gn    spp         2001-01-01
#> 4 luq             1      2    2.2 alive     30 Gn    spp         2001-01-01
#> 5 luq             2      1    1.1 alive     20 Gn    spp         2002-01-01
#> 6 luq             2      1    1.2 gone      NA Gn    spp         2002-01-01
#> 7 luq             2      2    2.1 dead      NA Gn    spp         2002-01-01
#> 8 luq             2      2    2.2 dead      NA Gn    spp         2002-01-01
#> # ... with 4 more variables: PlotCensusNumber <int>, Family <chr>,
#> #   Tag <int>, HOM <int>

Abundance by year.

abundance_byyr(vft, DBH >= 10, DBH < 20)
#> # A tibble: 1 x 3
#>   species family yr_2001
#>   <chr>   <chr>    <dbl>
#> 1 Gn spp  f            1
abundance_byyr(vft, DBH >= 10)
#> # A tibble: 1 x 4
#>   species family yr_2001 yr_2002
#>   <chr>   <chr>    <dbl>   <dbl>
#> 1 Gn spp  f            2       1

Basal area by year.

basal <- basal_area_byyr(vft, DBH >= 10)
basal
#> # A tibble: 1 x 4
#>   species family yr_2001 yr_2002
#>   <chr>   <chr>    <dbl>   <dbl>
#> 1 Gn spp  f        1100.    314.
# Convert units and standardize by plot size in hectares
years <- c("yr_2001", "yr_2002")
in_he <- convert_unit_at(basal, .at = years, from = "mm2", to = "hectare")
standardize_at(in_he, .at = years, denominator = 50)
#> # A tibble: 1 x 4
#>   species family       yr_2001  yr_2002
#>   <chr>   <chr>          <dbl>    <dbl>
#> 1 Gn spp  f      0.00000000220 6.28e-10

Demography

census1 <- fgeo.x::tree5
census2 <- fgeo.x::tree6

Demography functions output a list that you can convert to a more convenient dataframe with to_df().

recruitment_impl(census1, census2)
#> Detected dbh ranges:
#>   * `census1` = 14-661.
#>   * `census2` = 14.5-676.
#> Using dbh `mindbh = 0` and above.
#> $N2
#> [1] 25
#> 
#> $R
#> [1] 0
#> 
#> $rate
#> [1] 0
#> 
#> $lower
#> [1] 0
#> 
#> $upper
#> [1] 0.03132581
#> 
#> $time
#> [1] 4.529172
#> 
#> $date1
#> [1] 18936.1
#> 
#> $date2
#> [1] 20589.76
to_df(
  recruitment_impl(census1, census2, quiet = TRUE)
)
#> # A tibble: 1 x 8
#>      N2     R  rate lower  upper  time  date1  date2
#>   <dbl> <dbl> <dbl> <dbl>  <dbl> <dbl>  <dbl>  <dbl>
#> 1    25     0     0     0 0.0313  4.53 18936. 20590.

Except if you use split2: This argument creates a complex data structure that to_df() cannot handle.

not_recommended <- recruitment_impl(
  census1, census2, 
  split1 = census1$sp, 
  split2 = census1$quadrat, 
  quiet = TRUE
)
#> Warning: `split2` is deprecated.
#> * Bad: `split1 = x1, split2 = x2`
#> * Good: `split1 = interaction(x1, x2)`
#> This warning is displayed once per session.
# Errs
to_df(not_recommended)
#> Error:   Can't deal with data created with `split2` (deprecated).
#>   * Bad: `split1 = x1, split2 = x2`
#>   * Good: `split1 = interaction(x1, x2)`

Instead, pass the multiple grouping variables to split via interaction(). This approach allows you to use any number of grouping variables and the output always works with to_df().

# Recommended
sp_quadrat <- interaction(census1$sp, census1$quadrat)
recruitment <- recruitment_impl(
  census1, census2, 
  split1 = sp_quadrat, 
  quiet = TRUE
)
to_df(recruitment)
#> # A tibble: 540 x 9
#>    groups         N2     R  rate lower  upper  time date1 date2
#>    <chr>       <dbl> <dbl> <dbl> <dbl>  <dbl> <dbl> <dbl> <dbl>
#>  1 MIRGAR.102      0     0    NA    NA NA     NA    18792    NA
#>  2 PREMON.1110     1     0     0     0  0.411  4.48 18918 20556
#>  3 INGLAU.115      1     0     0     0  0.413  4.46 18934 20564
#>  4 IXOFER.117      1     0     0     0  0.404  4.56 18961 20627
#>  5 BUCTET.1223     1     0     0     0  0.410  4.50 19058 20703
#>  6 PSYBRA.1318     1     0     0     0  0.412  4.48 19011 20646
#>  7 MYRDEF.1321     1     0     0     0  0.410  4.50 19031 20676
#>  8 HIRRUG.1402     1     0     0     0  0.398  4.64 18808 20502
#>  9 MANBID.1502     0     0    NA    NA NA     NA    18812    NA
#> 10 MATDOM.1505     1     0     0     0  0.404  4.56 18854 20521
#> # ... with 530 more rows

The same applies for other demography functions.

to_df(mortality_impl(census1, census2, split1 = sp_quadrat, quiet = TRUE))
#> # A tibble: 540 x 10
#>    groups          N     D  rate  lower   upper  time date1 date2 dbhmean
#>    <chr>       <dbl> <dbl> <dbl>  <dbl>   <dbl> <dbl> <dbl> <dbl>   <dbl>
#>  1 MIRGAR.102      1     1   Inf 0.0374 Inf      4.61 18792 20475    29.4
#>  2 PREMON.1110     1     0     0 0        0.411  4.48 18918 20556   106  
#>  3 INGLAU.115      1     0     0 0        0.413  4.46 18934 20564    30.5
#>  4 IXOFER.117      1     0     0 0        0.404  4.56 18961 20627    73  
#>  5 BUCTET.1223     1     0     0 0        0.410  4.50 19058 20703   661  
#>  6 PSYBRA.1318     1     0     0 0        0.412  4.48 19011 20646    14  
#>  7 MYRDEF.1321     1     0     0 0        0.410  4.50 19031 20676    30.5
#>  8 HIRRUG.1402     1     0     0 0        0.398  4.64 18808 20502    30.4
#>  9 MANBID.1502     1     1   Inf 0.0371 Inf      4.64 18812 20506    15.7
#> 10 MATDOM.1505     1     0     0 0        0.404  4.56 18854 20521   210  
#> # ... with 530 more rows
growth <- to_df(growth_impl(census1, census2, split1 = sp_quadrat, quiet = TRUE))
growth
#> # A tibble: 540 x 8
#>    groups         rate     N  clim dbhmean  time date1 date2
#>    <chr>         <dbl> <dbl> <dbl>   <dbl> <dbl> <dbl> <dbl>
#>  1 MIRGAR.102   NA         0    NA    NA   NA       NA    NA
#>  2 PREMON.1110   1.56      1    NA   106    4.48 18918 20556
#>  3 INGLAU.115    0.224     1    NA    30.5  4.46 18934 20564
#>  4 IXOFER.117    0.219     1    NA    73    4.56 18961 20627
#>  5 BUCTET.1223   3.33      1    NA   661    4.50 19058 20703
#>  6 PSYBRA.1318   0.447     1    NA    14    4.48 19011 20646
#>  7 MYRDEF.1321   1.07      1    NA    30.5  4.50 19031 20676
#>  8 HIRRUG.1402  -0.474     1    NA    30.4  4.64 18808 20502
#>  9 MANBID.1502  NA         0    NA    NA   NA       NA    NA
#> 10 MATDOM.1505   0.876     1    NA   210    4.56 18854 20521
#> # ... with 530 more rows

A simple way to separate the grouping variables is with tidyr::separate().

tidyr::separate(
  growth, 
  groups, into = c("species", "quadrats")
)
#> # A tibble: 540 x 9
#>    species quadrats    rate     N  clim dbhmean  time date1 date2
#>    <chr>   <chr>      <dbl> <dbl> <dbl>   <dbl> <dbl> <dbl> <dbl>
#>  1 MIRGAR  102       NA         0    NA    NA   NA       NA    NA
#>  2 PREMON  1110       1.56      1    NA   106    4.48 18918 20556
#>  3 INGLAU  115        0.224     1    NA    30.5  4.46 18934 20564
#>  4 IXOFER  117        0.219     1    NA    73    4.56 18961 20627
#>  5 BUCTET  1223       3.33      1    NA   661    4.50 19058 20703
#>  6 PSYBRA  1318       0.447     1    NA    14    4.48 19011 20646
#>  7 MYRDEF  1321       1.07      1    NA    30.5  4.50 19031 20676
#>  8 HIRRUG  1402      -0.474     1    NA    30.4  4.64 18808 20502
#>  9 MANBID  1502      NA         0    NA    NA   NA       NA    NA
#> 10 MATDOM  1505       0.876     1    NA   210    4.56 18854 20521
#> # ... with 530 more rows

Species-habitat associations

tree <- fgeo.data::luquillo_tree5_random
elevation <- fgeo.data::luquillo_elevation
# Pick alive trees, of 10 mm or more
census <- filter(tree, status == "A", dbh >= 10)
# Pick sufficiently abundant species
pick <- filter(add_count(census, sp), n > 50)
species <- unique(pick$sp)
# Use your habitat data or create it from elevation data
habitat <- fgeo.tool::fgeo_habitat(elevation, gridsize = 20, n = 4)
# A list or matrices
tt_lst <- tt_test(census, species, habitat)
#> Using `plotdim = c(320, 500)`. To change this value see `?tt_test()`.
#> Using `gridsize = 20`. To change this value see `?tt_test()`.
tt_lst
#> [[1]]
#>        N.Hab.1 Gr.Hab.1 Ls.Hab.1 Eq.Hab.1 Rep.Agg.Neut.1 Obs.Quantile.1
#> CASARB      35     1508       90        2              0         0.9425
#>        N.Hab.2 Gr.Hab.2 Ls.Hab.2 Eq.Hab.2 Rep.Agg.Neut.2 Obs.Quantile.2
#> CASARB      24      433     1162        5              0       0.270625
#>        N.Hab.3 Gr.Hab.3 Ls.Hab.3 Eq.Hab.3 Rep.Agg.Neut.3 Obs.Quantile.3
#> CASARB      11      440     1157        3              0          0.275
#>        N.Hab.4 Gr.Hab.4 Ls.Hab.4 Eq.Hab.4 Rep.Agg.Neut.4 Obs.Quantile.4
#> CASARB       8      774      824        2              0        0.48375
#> 
#> [[2]]
#>        N.Hab.1 Gr.Hab.1 Ls.Hab.1 Eq.Hab.1 Rep.Agg.Neut.1 Obs.Quantile.1
#> PREMON      94     1511       87        2              0       0.944375
#>        N.Hab.2 Gr.Hab.2 Ls.Hab.2 Eq.Hab.2 Rep.Agg.Neut.2 Obs.Quantile.2
#> PREMON      97     1403      196        1              0       0.876875
#>        N.Hab.3 Gr.Hab.3 Ls.Hab.3 Eq.Hab.3 Rep.Agg.Neut.3 Obs.Quantile.3
#> PREMON      39      212     1386        2              0         0.1325
#>        N.Hab.4 Gr.Hab.4 Ls.Hab.4 Eq.Hab.4 Rep.Agg.Neut.4 Obs.Quantile.4
#> PREMON      15       64     1535        1              0           0.04
#> 
#> [[3]]
#>        N.Hab.1 Gr.Hab.1 Ls.Hab.1 Eq.Hab.1 Rep.Agg.Neut.1 Obs.Quantile.1
#> SLOBER      21      413     1183        4              0       0.258125
#>        N.Hab.2 Gr.Hab.2 Ls.Hab.2 Eq.Hab.2 Rep.Agg.Neut.2 Obs.Quantile.2
#> SLOBER      25      558     1040        2              0        0.34875
#>        N.Hab.3 Gr.Hab.3 Ls.Hab.3 Eq.Hab.3 Rep.Agg.Neut.3 Obs.Quantile.3
#> SLOBER      21     1289      309        2              0       0.805625
#>        N.Hab.4 Gr.Hab.4 Ls.Hab.4 Eq.Hab.4 Rep.Agg.Neut.4 Obs.Quantile.4
#> SLOBER       8      833      764        3              0       0.520625
# A simple summary to help you interpret the results
summary(tt_lst)
#>   Species Habitat_1 Habitat_2 Habitat_3 Habitat_4
#> 1  CASARB   neutral   neutral   neutral   neutral
#> 2  PREMON   neutral   neutral   neutral   neutral
#> 3  SLOBER   neutral   neutral   neutral   neutral
# A combined matrix
Reduce(rbind, tt_lst)
#>        N.Hab.1 Gr.Hab.1 Ls.Hab.1 Eq.Hab.1 Rep.Agg.Neut.1 Obs.Quantile.1
#> CASARB      35     1508       90        2              0       0.942500
#> PREMON      94     1511       87        2              0       0.944375
#> SLOBER      21      413     1183        4              0       0.258125
#>        N.Hab.2 Gr.Hab.2 Ls.Hab.2 Eq.Hab.2 Rep.Agg.Neut.2 Obs.Quantile.2
#> CASARB      24      433     1162        5              0       0.270625
#> PREMON      97     1403      196        1              0       0.876875
#> SLOBER      25      558     1040        2              0       0.348750
#>        N.Hab.3 Gr.Hab.3 Ls.Hab.3 Eq.Hab.3 Rep.Agg.Neut.3 Obs.Quantile.3
#> CASARB      11      440     1157        3              0       0.275000
#> PREMON      39      212     1386        2              0       0.132500
#> SLOBER      21     1289      309        2              0       0.805625
#>        N.Hab.4 Gr.Hab.4 Ls.Hab.4 Eq.Hab.4 Rep.Agg.Neut.4 Obs.Quantile.4
#> CASARB       8      774      824        2              0       0.483750
#> PREMON      15       64     1535        1              0       0.040000
#> SLOBER       8      833      764        3              0       0.520625
# A dataframe
dfm <- to_df(tt_lst)
# Using dplyr to summarize results by species and distribution
summarize(group_by(dfm, sp, distribution), n = sum(stem_count))
#> # A tibble: 3 x 3
#> # Groups:   sp [?]
#>   sp     distribution     n
#>   <chr>  <chr>        <dbl>
#> 1 CASARB neutral         78
#> 2 PREMON neutral        245
#> 3 SLOBER neutral         75

Get started with fgeo

Information

EDIT: Run this chunk then delete it: TODO: Move files to .github/ but refer to (FILE.md), not (.github/FILE.md)

usethis::use_template("SUPPORT.md", package = "fgeo.template")
usethis::use_template("CONTRIBUTING.md", package = "fgeo.template")
usethis::use_template("CODE_OF_CONDUCT.md", package = "fgeo.template")
usethis::use_template("ISSUE_TEMPLATE.md", package = "fgeo.template")

READ AND DELETE THIS SECTION

What is special about using README.Rmd instead of just README.md? You can include R chunks like so:

summary(cars)
#>      speed           dist       
#>  Min.   : 4.0   Min.   :  2.00  
#>  1st Qu.:12.0   1st Qu.: 26.00  
#>  Median :15.0   Median : 36.00  
#>  Mean   :15.4   Mean   : 42.98  
#>  3rd Qu.:19.0   3rd Qu.: 56.00  
#>  Max.   :25.0   Max.   :120.00

You’ll still need to render README.Rmd regularly, to keep README.md up-to-date.

You can also embed plots, for example:

In that case, don’t forget to commit and push the resulting figure files, so they display on GitHub!

Name		Name	Last commit message	Last commit date
Latest commit History 12 Commits
.github		.github
R		R
data-raw		data-raw
data		data
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
README.Rmd		README.Rmd
README.md		README.md
_pkgdown.yml		_pkgdown.yml
fgeo.analyze.Rproj		fgeo.analyze.Rproj

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Analyze fgeo data

Installation

Example

Abundance

Basal area

Abundance and basal area aggregated by year

Demography

Species-habitat associations

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Analyze fgeo data

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Abundance

Basal area

Abundance and basal area aggregated by year

Demography

Species-habitat associations

Information

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