Mauro Lepore
2018-04-13 17:31:23
This document shows how to map specific species and tree-tags using a ViewFullTable.
Info:
Install.
# General purpose functions for data science
install.packages("tidyverse")
# Allow installation form GitHub
install.packages("remotes")
# More info: https://forestgeo.github.io/fgeo.map/
remotes::install_github("forestgeo/fgeo")
# Example data
remotes::install_github("forestgeo/fgeo.data")library(tidyverse)
#> ── Attaching packages ─────────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
#> ✔ ggplot2 2.2.1 ✔ purrr 0.2.4
#> ✔ tibble 1.4.2 ✔ dplyr 0.7.4
#> ✔ tidyr 0.8.0 ✔ stringr 1.3.0
#> ✔ readr 1.1.1 ✔ forcats 0.3.0
#> ── Conflicts ────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
#> ✖ dplyr::filter() masks stats::filter()
#> ✖ dplyr::lag() masks stats::lag()
library(fgeo)
#> ── Attaching packages ─────────────────────────────────────────────────────────────────────── fgeo 0.0.0.9000 ──
#> ✔ bciex 0.0.0.9000 ✔ fgeo.habitat 0.0.0.9001
#> ✔ fgeo.abundance 0.0.0.9002 ✔ fgeo.map 0.0.0.9203
#> ✔ fgeo.demography 0.0.0.9000 ✔ fgeo.tool 0.0.0.9002
#> Warning in fun(libname, pkgname): couldn't connect to display ":0"
#> ── Conflicts ─────────────────────────────────────────────────────────────────────────────── fgeo_conflicts() ──
#> ✖ dplyr::filter() masks stats::filter()
#> ✖ dplyr::intersect() masks base::intersect()
#> ✖ dplyr::lag() masks stats::lag()
#> ✖ dplyr::setdiff() masks base::setdiff()
#> ✖ dplyr::setequal() masks base::setequal()
#> ✖ dplyr::union() masks base::union()Data must be a ViewFullTable. Get it via ForestGEO's database.
# Example data from Barro Colorado Island
vft <- as.tibble(fgeo.data::bci_vft_random)
# Writing a .csv to demonstrate how to read a file sent by the database
write_csv(vft, "vft_from_database_for_examples.csv")vft <- read_csv("vft_from_database_for_examples.csv")
#> Parsed with column specification:
#> cols(
#> .default = col_integer(),
#> PlotName = col_character(),
#> Family = col_character(),
#> Genus = col_character(),
#> SpeciesName = col_character(),
#> Mnemonic = col_character(),
#> Subspecies = col_character(),
#> QuadratName = col_character(),
#> PX = col_double(),
#> PY = col_double(),
#> QX = col_double(),
#> QY = col_double(),
#> Tag = col_character(),
#> PrimaryStem = col_character(),
#> HOM = col_double(),
#> ExactDate = col_date(format = ""),
#> ListOfTSM = col_character(),
#> LargeStem = col_character(),
#> Status = col_character()
#> )
#> See spec(...) for full column specifications.
# Also `View(vft)`
vft
#> # A tibble: 2,257 x 32
#> DBHID PlotName PlotID Family Genus SpeciesName Mnemonic Subspecies
#> <int> <chr> <int> <chr> <chr> <chr> <chr> <chr>
#> 1 1621522 bci 1 Violace… Hyban… prunifolius hybapr <NA>
#> 2 1619521 bci 1 Violace… Hyban… prunifolius hybapr <NA>
#> 3 1632477 bci 1 Fabacea… Lonch… heptaphyll… loncla <NA>
#> 4 1630644 bci 1 Fabacea… Lonch… heptaphyll… loncla <NA>
#> 5 1077531 bci 1 Laurace… Beils… pendula beilpe <NA>
#> 6 1074697 bci 1 Laurace… Beils… pendula beilpe <NA>
#> 7 826469 bci 1 Violace… Rinor… sylvatica rinosy <NA>
#> 8 823306 bci 1 Violace… Rinor… sylvatica rinosy <NA>
#> 9 1006845 bci 1 Fabacea… Swart… simplex swars2 ochnacea
#> 10 1003843 bci 1 Fabacea… Swart… simplex swars2 ochnacea
#> # ... with 2,247 more rows, and 24 more variables: SpeciesID <int>,
#> # SubspeciesID <int>, 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 <int>, HOM <dbl>, ExactDate <date>,
#> # Date <int>, ListOfTSM <chr>, HighHOM <int>, LargeStem <chr>,
#> # Status <chr>maply_sp()# Optional: Copy `Mnemonic` into `sp` -- a more intuitive name
vft <- vft %>%
mutate(sp = Mnemonic)
vft %>% select(sp, everything())
#> # A tibble: 2,257 x 33
#> sp DBHID PlotName PlotID Family Genus SpeciesName Mnemonic
#> <chr> <int> <chr> <int> <chr> <chr> <chr> <chr>
#> 1 hybapr 1621522 bci 1 Violaceae Hybant… prunifolius hybapr
#> 2 hybapr 1619521 bci 1 Violaceae Hybant… prunifolius hybapr
#> 3 loncla 1632477 bci 1 Fabaceae-p… Loncho… heptaphyll… loncla
#> 4 loncla 1630644 bci 1 Fabaceae-p… Loncho… heptaphyll… loncla
#> 5 beilpe 1077531 bci 1 Lauraceae Beilsc… pendula beilpe
#> 6 beilpe 1074697 bci 1 Lauraceae Beilsc… pendula beilpe
#> 7 rinosy 826469 bci 1 Violaceae Rinorea sylvatica rinosy
#> 8 rinosy 823306 bci 1 Violaceae Rinorea sylvatica rinosy
#> 9 swars2 1006845 bci 1 Fabaceae-p… Swartz… simplex swars2
#> 10 swars2 1003843 bci 1 Fabaceae-p… Swartz… simplex swars2
#> # ... with 2,247 more rows, and 25 more variables: Subspecies <chr>,
#> # SpeciesID <int>, SubspeciesID <int>, 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 <int>,
#> # HOM <dbl>, ExactDate <date>, Date <int>, ListOfTSM <chr>,
#> # HighHOM <int>, LargeStem <chr>, Status <chr>Store chosen species in a vector.
# Choosing, for example, a few, large trees
chosen_dbh <- 300
sp_chosen <- vft %>%
filter(DBH > chosen_dbh) %>%
select(DBH, sp, everything()) %>%
arrange(sp) %>%
pull(sp) %>%
unique()
sp_chosen
#> [1] "alsebl" "brosal" "cecrin" "cordal" "eugene" "guapst" "heisco"
#> [8] "huracr" "ingama" "jac1co" "poutre" "quaras" "tab2ar" "tri2tu"
#> [15] "virosp"Subset the data to keep the observations that match the chosen species.
vft_chosen <- vft %>%
filter(sp %in% sp_chosen)
vft_chosen
#> # A tibble: 314 x 33
#> DBHID PlotName PlotID Family Genus SpeciesName Mnemonic Subspecies
#> <int> <chr> <int> <chr> <chr> <chr> <chr> <chr>
#> 1 828597 bci 1 Meliace… Trich… tuberculata tri2tu <NA>
#> 2 825434 bci 1 Meliace… Trich… tuberculata tri2tu <NA>
#> 3 1876882 bci 1 Erythro… Heist… concinna heisco <NA>
#> 4 1877443 bci 1 Erythro… Heist… concinna heisco <NA>
#> 5 440509 bci 1 Meliace… Trich… tuberculata tri2tu <NA>
#> 6 439821 bci 1 Meliace… Trich… tuberculata tri2tu <NA>
#> 7 1955821 bci 1 Sapotac… Poute… reticulata poutre <NA>
#> 8 1957474 bci 1 Sapotac… Poute… reticulata poutre <NA>
#> 9 742469 bci 1 Malvace… Quara… asterolepis quaras <NA>
#> 10 740125 bci 1 Malvace… Quara… asterolepis quaras <NA>
#> # ... with 304 more rows, and 25 more variables: SpeciesID <int>,
#> # SubspeciesID <int>, 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 <int>, HOM <dbl>, ExactDate <date>,
#> # Date <int>, ListOfTSM <chr>, HighHOM <int>, LargeStem <chr>,
#> # Status <chr>, sp <chr>Store maps in a list.
maps <- maply_tag(vft_chosen)
#> Warning in do.call(cond, list(customized)): Duplicated values were detected
#> * Likely you want only the last 2 censuses
#> * Detected censuses: 6, 171
# Showing only one map
first(maps)
Print each map in the list onto a .pdf
# Printing all plots of `p` to .pdf, with parameters optimized for size letter
pdf("maps.pdf", paper = "letter", height = 10.5, width = 8)
maps
dev.off()Exploring data by tag number.
vft %>%
filter(Tag == "260255") %>%
select(Tag, matches("census"), everything())
#> # A tibble: 3 x 33
#> Tag CensusID PlotCensusNumber DBHID PlotName PlotID Family Genus
#> <chr> <int> <int> <int> <chr> <int> <chr> <chr>
#> 1 260255 6 6 1438276 bci 1 Rubiaceae Alse…
#> 2 260255 6 6 1438283 bci 1 Rubiaceae Alse…
#> 3 260255 171 7 1435155 bci 1 Rubiaceae Alse…
#> # ... with 25 more variables: SpeciesName <chr>, Mnemonic <chr>,
#> # Subspecies <chr>, SpeciesID <int>, SubspeciesID <int>,
#> # QuadratName <chr>, QuadratID <int>, PX <dbl>, PY <dbl>, QX <dbl>,
#> # QY <dbl>, TreeID <int>, StemID <int>, StemNumber <int>, StemTag <int>,
#> # PrimaryStem <chr>, DBH <int>, HOM <dbl>, ExactDate <date>, Date <int>,
#> # ListOfTSM <chr>, HighHOM <int>, LargeStem <chr>, Status <chr>,
#> # sp <chr>tags <- c("257260", "260255")
vft %>%
filter(Tag %in% tags) %>%
select(Tag, matches("quadrat"), everything())
#> # A tibble: 5 x 33
#> Tag QuadratName QuadratID DBHID PlotName PlotID Family Genus
#> <chr> <chr> <int> <int> <chr> <int> <chr> <chr>
#> 1 257260 0112 1471 1425858 bci 1 Meliaceae Trichilia
#> 2 257260 0112 1471 1422534 bci 1 Meliaceae Trichilia
#> 3 260255 0001 64 1438276 bci 1 Rubiaceae Alseis
#> 4 260255 0001 64 1438283 bci 1 Rubiaceae Alseis
#> 5 260255 0001 64 1435155 bci 1 Rubiaceae Alseis
#> # ... with 25 more variables: SpeciesName <chr>, Mnemonic <chr>,
#> # Subspecies <chr>, SpeciesID <int>, SubspeciesID <int>, PX <dbl>,
#> # PY <dbl>, QX <dbl>, QY <dbl>, TreeID <int>, StemID <int>,
#> # StemNumber <int>, StemTag <int>, PrimaryStem <chr>, CensusID <int>,
#> # PlotCensusNumber <int>, DBH <int>, HOM <dbl>, ExactDate <date>,
#> # Date <int>, ListOfTSM <chr>, HighHOM <int>, LargeStem <chr>,
#> # Status <chr>, sp <chr>add_sp() with a ViewFullTableChoosing some some species
some_sp <- sample(vft$sp, 3)
some_sp
#> [1] "poular" "hybapr" "swars2"
vft <- filter(vft, sp %in% some_sp)
vft
#> # A tibble: 467 x 33
#> DBHID PlotName PlotID Family Genus SpeciesName Mnemonic Subspecies
#> <int> <chr> <int> <chr> <chr> <chr> <chr> <chr>
#> 1 1621522 bci 1 Violaceae Hyba… prunifolius hybapr <NA>
#> 2 1619521 bci 1 Violaceae Hyba… prunifolius hybapr <NA>
#> 3 1006845 bci 1 Fabaceae… Swar… simplex swars2 ochnacea
#> 4 1003843 bci 1 Fabaceae… Swar… simplex swars2 ochnacea
#> 5 1542121 bci 1 Violaceae Hyba… prunifolius hybapr <NA>
#> 6 1539889 bci 1 Violaceae Hyba… prunifolius hybapr <NA>
#> 7 2017665 bci 1 Violaceae Hyba… prunifolius hybapr <NA>
#> 8 2019766 bci 1 Violaceae Hyba… prunifolius hybapr <NA>
#> 9 1002106 bci 1 Violaceae Hyba… prunifolius hybapr <NA>
#> 10 998978 bci 1 Violaceae Hyba… prunifolius hybapr <NA>
#> # ... with 457 more rows, and 25 more variables: SpeciesID <int>,
#> # SubspeciesID <int>, 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 <int>, HOM <dbl>, ExactDate <date>,
#> # Date <int>, ListOfTSM <chr>, HighHOM <int>, LargeStem <chr>,
#> # Status <chr>, sp <chr>The functions expect census tables (tree and stem) but we can adapt our ViewFullTable accordingly:
map_gx_gy() expects the columns gx and gy. In a ViewFullTable they are called QX and QY but we can fix that.vft <- rename(vft, gx = PX, gy = PY)add_sp() expects the column named sp. In a ViewFullTables there is no sp column but before we created one from Mnemonic. Let's confirm that.select(vft, matches("sp"))
#> # A tibble: 467 x 5
#> SpeciesName Subspecies SpeciesID SubspeciesID sp
#> <chr> <chr> <int> <int> <chr>
#> 1 prunifolius <NA> 494 NA hybapr
#> 2 prunifolius <NA> 494 NA hybapr
#> 3 simplex ochnacea 980 8 swars2
#> 4 simplex ochnacea 980 8 swars2
#> 5 prunifolius <NA> 494 NA hybapr
#> 6 prunifolius <NA> 494 NA hybapr
#> 7 prunifolius <NA> 494 NA hybapr
#> 8 prunifolius <NA> 494 NA hybapr
#> 9 prunifolius <NA> 494 NA hybapr
#> 10 prunifolius <NA> 494 NA hybapr
#> # ... with 457 more rowsvft %>%
map_gx_gy() %>%
limit_gx_gy(xlim = c(0, 1000), ylim = c(0, 500)) %>%
add_sp() %>%
wrap("sp", scales = "fixed")
fgeo.map wraps ggplot2 to provide some handy shortcuts for common actions. But it is wise to learn ggplot2 because with it you will be able to do a lot more.
ggplot(vft, aes(gx, gy)) +
coord_fixed(xlim = c(0, 1000), ylim = c(0, 500)) +
geom_point(aes(color = sp)) +
facet_wrap("sp", scales = "fixed")