R/read_fgeo.R
read_vft.Rdread_vft() and read_taxa() help you to read ViewFullTable and
ViewTaxonomy data from text files delivered by the ForestGEO database.
These functions avoid common problems about column separators, missing
values, column names, and column types.
A path to a file.
Single character used to separate fields within a record. The
default (delim = NULL) is to guess between comma or tab ("," or "\t").
Character vector of strings to interpret as missing values. Set this
option to character() to indicate no missing values.
Other arguments passed to readr::read_delim().
A tibble.
Thanks to Shameema Jafferjee Esufali for inspiring the feature that
automatically detects delim (issue 65).
readr::read_delim(), type_vft(), type_taxa().
Other functions to read text files delivered by ForestgGEO's database:
type_vft()
assert_is_installed("fgeo.x")
library(fgeo.x)
example_path()
#> [1] "csv" "mixed_files" "rdata" "rdata_one"
#> [5] "rds" "taxa.csv" "tsv" "vft_4quad.csv"
#> [9] "view" "weird" "xl"
file_vft <- example_path("view/vft_4quad.csv")
read_vft(file_vft)
#> # A tibble: 500 × 32
#> DBHID PlotName PlotID Family Genus Speci…¹ Mnemo…² Subsp…³ Speci…⁴ Subsp…⁵
#> <int> <chr> <int> <chr> <chr> <chr> <chr> <chr> <int> <chr>
#> 1 385164 luquillo 1 Rubiace… Psyc… brachi… PSYBRA NA 185 NA
#> 2 385261 luquillo 1 Urticac… Cecr… schreb… CECSCH NA 74 NA
#> 3 384600 luquillo 1 Rubiace… Psyc… brachi… PSYBRA NA 185 NA
#> 4 608789 luquillo 1 Rubiace… Psyc… berter… PSYBER NA 184 NA
#> 5 388579 luquillo 1 Arecace… Pres… acumin… PREMON NA 182 NA
#> 6 384626 luquillo 1 Araliac… Sche… moroto… SCHMOR NA 196 NA
#> 7 410958 luquillo 1 Rubiace… Psyc… brachi… PSYBRA NA 185 NA
#> 8 385102 luquillo 1 Piperac… Piper glabre… PIPGLA NA 174 NA
#> 9 353163 luquillo 1 Arecace… Pres… acumin… PREMON NA 182 NA
#> 10 481018 luquillo 1 Salicac… Case… arborea CASARB NA 70 NA
#> # … with 490 more rows, 22 more variables: 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>, and abbreviated variable names ¹SpeciesName,
#> # ²Mnemonic, ³Subspecies, ⁴SpeciesID, ⁵SubspeciesID
file_taxa <- example_path("view/taxa.csv")
read_taxa(file_taxa)
#> # A tibble: 163 × 21
#> ViewID SpeciesID Subspec…¹ Family Mnemo…² Genus Speci…³ Rank Subsp…⁴ Autho…⁵
#> <int> <int> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 1 56 NA Fabac… AESAME Aesc… americ… NA NA (Poir.…
#> 2 2 57 NA Eupho… ALCFLO Alch… florib… NA NA (Benth…
#> 3 3 58 NA Eupho… ALCLAT Alch… latifo… NA NA Sw.
#> 4 4 59 NA Fabac… ANDINE Andi… inermis NA NA (W. Wr…
#> 5 5 60 NA Rubia… ANTOBT Sten… obtusi… NA NA (Urb.)…
#> 6 6 61 NA Myrsi… ARDGLA Ardi… glauci… NA NA Urb.
#> 7 7 62 NA Morac… ARTALT Arto… altilis NA NA (Parki…
#> 8 8 63 NA Laura… BEIPEN Beil… pendula NA NA (Sw.) …
#> 9 9 64 NA Solan… BRUPOR Brun… portor… NA NA Krug &…
#> 10 10 65 NA Combr… BUCTET Buch… tetrap… NA NA (Aubl.…
#> # … with 153 more rows, 11 more variables: IDLevel <chr>, subspMnemonic <chr>,
#> # subspAuthority <chr>, FieldFamily <chr>, Lifeform <chr>, Description <chr>,
#> # wsg <dbl>, wsglevel <chr>, ListOfOldNames <chr>, Specimens <chr>,
#> # Reference <chr>, and abbreviated variable names ¹SubspeciesID, ²Mnemonic,
#> # ³SpeciesName, ⁴Subspecies, ⁵Authority