Converts an object to geosamples class

Description

Converts an object of class "SoilProfileCollection" or "SpatialPointsDataFrame" to an object of class "geosamples" with all measurements broken into individual records. Geosamples are standardized spatially and temporally referenced samples from the Earth's surface.

Usage

## S4 method for signature 'SoilProfileCollection'
as.geosamples(obj, 
    registry = as.character(NA), sample.area = 1, mxd = 2, TimeSpan.begin, TimeSpan.end)
## S4 method for signature 'SpatialPointsDataFrame'
as.geosamples(obj, 
    registry = as.character(NA), sample.area = 1, mxd = 2, TimeSpan.begin, TimeSpan.end)

Arguments

Value

Returns an object of type "geosamples". Many columns required by the "geosamples" class might be not available and will result in NA values. To ensure compatibility, when building an object of type "SoilProfilesCollection", use some standard naming convention to attach attributes to each measurement (horizons and sites slots in the "SoilProfileCollection-class"):

"locationError"

can be used to attach location errors in meters to each spatial location

"sampleArea"

can be used to attach spatial support to each measurement (usually 1 by 1 meter)

"measurementError"

can be used to attach specific measurement errors to each measurement in both site and horizons table

"IGSN"

can be used to attach the unique identifier (International Geo Sample Number) to each specific observation (corresponds to the "observationid" column)

Author(s)

Tomislav Hengl and Hannes I. Reuter

See Also

geosamples-class, as.data.frame, aqp::SoilProfileCollection

Examples

library(aqp)
library(plyr)
library(rgdal)
library(sp)
# sample profile from Nigeria:
lon = 3.90; lat = 7.50; time = as.POSIXct("1978", format="%Y") 
id = "ISRIC:NG0017"; TAXNFAO8 = "LXp" 
top = c(0, 18, 36, 65, 87, 127) 
bottom = c(18, 36, 65, 87, 127, 181)
ORCDRC = c(18.4, 4.4, 3.6, 3.6, 3.2, 1.2)
methodid = c("TAXNFAO8", "ORCDRC")
description = c("FAO 1988 classification system group", 
    "Method of Walkley-Black (Org. matter = Org. C x 1.72)")
units = c("FAO 1988 classes", "permille")
detectionLimit = c(as.character(NA), "0.1")
# prepare a SoilProfileCollection:
prof1 <- join(data.frame(id, top, bottom, ORCDRC), 
    data.frame(id, lon, lat, time, TAXNFAO8), type='inner')
depths(prof1) <- id ~ top + bottom
site(prof1) <- ~ lon + lat + time + TAXNFAO8 
coordinates(prof1) <- ~ lon + lat + time
proj4string(prof1) <- CRS("+proj=longlat +datum=WGS84")
# add measurement errors:
attr(prof1@horizons$ORCDRC, "measurementError") <- c(1.5, 0.5, 0.5, 0.5, 0.5, 0.5)
attr(prof1@sp@coords, "locationError") <- 1500
# add the metadata:
prof1@metadata <- data.frame(methodid, description, units, detectionLimit)
# convert to geosamples:
x <- as.geosamples(prof1)
x
# print only the sampled values of ORCDRC:
ORCDRC <- subset(x, "ORCDRC")
ORCDRC[,c("sampleid", "altitude", "observedValue")]

# convert object of type SpatialPointsDataFrame:
data(meuse)
# prepare columns:
names(meuse)[which(names(meuse)=="x")] = "longitude"
names(meuse)[which(names(meuse)=="y")] = "latitude"
meuse$altitude = -.15
meuse$time = unclass(as.POSIXct("1992-01-01"))
coordinates(meuse) <- ~ longitude + latitude + altitude + time
proj4string(meuse) <- CRS("+init=epsg:28992")
library(plotKML)
hm <- reproject(meuse[,c("zinc", "copper")])
hm.geo <- as.geosamples(hm)
hm.geo

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(GSIF)
GSIF version 0.5-2 (2016-06-25)
URL: http://gsif.r-forge.r-project.org/
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/GSIF/as.geosamples.Rd_%03d_medium.png", width=480, height=480)
> ### Name: as.geosamples
> ### Title: Converts an object to geosamples class
> ### Aliases: as.geosamples as.geosamples,SoilProfileCollection-method
> ###   as.geosamples,SpatialPointsDataFrame-method
> ### Keywords: methods
> 
> ### ** Examples
> 
> library(aqp)
This is aqp 1.9.3
> library(plyr)
> library(rgdal)
Loading required package: sp
rgdal: version: 1.1-10, (SVN revision 622)
 Geospatial Data Abstraction Library extensions to R successfully loaded
 Loaded GDAL runtime: GDAL 1.11.3, released 2015/09/16
 Path to GDAL shared files: /usr/share/gdal/1.11
 Loaded PROJ.4 runtime: Rel. 4.9.2, 08 September 2015, [PJ_VERSION: 492]
 Path to PROJ.4 shared files: (autodetected)
 Linking to sp version: 1.2-3 
> library(sp)
> # sample profile from Nigeria:
> lon = 3.90; lat = 7.50; time = as.POSIXct("1978", format="%Y") 
> id = "ISRIC:NG0017"; TAXNFAO8 = "LXp" 
> top = c(0, 18, 36, 65, 87, 127) 
> bottom = c(18, 36, 65, 87, 127, 181)
> ORCDRC = c(18.4, 4.4, 3.6, 3.6, 3.2, 1.2)
> methodid = c("TAXNFAO8", "ORCDRC")
> description = c("FAO 1988 classification system group", 
+     "Method of Walkley-Black (Org. matter = Org. C x 1.72)")
> units = c("FAO 1988 classes", "permille")
> detectionLimit = c(as.character(NA), "0.1")
> # prepare a SoilProfileCollection:
> prof1 <- join(data.frame(id, top, bottom, ORCDRC), 
+     data.frame(id, lon, lat, time, TAXNFAO8), type='inner')
Joining by: id
> depths(prof1) <- id ~ top + bottom
Warning message:
converting IDs from factor to character 
> site(prof1) <- ~ lon + lat + time + TAXNFAO8 
> coordinates(prof1) <- ~ lon + lat + time
> proj4string(prof1) <- CRS("+proj=longlat +datum=WGS84")
> # add measurement errors:
> attr(prof1@horizons$ORCDRC, "measurementError") <- c(1.5, 0.5, 0.5, 0.5, 0.5, 0.5)
> attr(prof1@sp@coords, "locationError") <- 1500
> # add the metadata:
> prof1@metadata <- data.frame(methodid, description, units, detectionLimit)
> # convert to geosamples:
> x <- as.geosamples(prof1)
> x
  Registry            : NA 
  Variables           : ORCDRC, TAXNFAO8 
  Total samples       : 7 
  Unique locations    : 1 
  Mean location error : 1500 
  Min longitude       : 3.9 
  Max longitude       : 3.9 
  Min latitude        : 7.5 
  Max latitude        : 7.5 
  Total area (app.)   : 0 (square-km) 
> # print only the sampled values of ORCDRC:
> ORCDRC <- subset(x, "ORCDRC")
> ORCDRC[,c("sampleid", "altitude", "observedValue")]
      sampleid altitude observedValue
2 ISRIC:NG0017   -0.090          18.4
3 ISRIC:NG0017   -0.270           4.4
4 ISRIC:NG0017   -0.505           3.6
5 ISRIC:NG0017   -0.760           3.6
6 ISRIC:NG0017   -1.070           3.2
7 ISRIC:NG0017   -1.540           1.2
> 
> # convert object of type SpatialPointsDataFrame:
> data(meuse)
> # prepare columns:
> names(meuse)[which(names(meuse)=="x")] = "longitude"
> names(meuse)[which(names(meuse)=="y")] = "latitude"
> meuse$altitude = -.15
> meuse$time = unclass(as.POSIXct("1992-01-01"))
> coordinates(meuse) <- ~ longitude + latitude + altitude + time
> proj4string(meuse) <- CRS("+init=epsg:28992")
> library(plotKML)
plotKML version 0.5-6 (2016-05-02)
URL: http://plotkml.r-forge.r-project.org/
> hm <- reproject(meuse[,c("zinc", "copper")])
Reprojecting to +proj=longlat +datum=WGS84 ...
> hm.geo <- as.geosamples(hm)
> hm.geo
  Registry            : NA 
  Variables           :  
  Total samples       : 310 
  Unique locations    : 155 
  Mean location error : NA 
  Min longitude       : 5.72319 
  Max longitude       : 5.76304 
  Min latitude        : 50.95661 
  Max latitude        : 50.99156 
  Total area (app.)   : 9.983 (square-km) 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>