trilnick / sharpshootR

Miscellaneous soil data management, summary, visualization, and conversion utilities to support soil survey.

sharpshootR

This package contains mish-mash of functionality and sample data related to the daily business of soil survey operations with the USDA-NRCS. Many of the functions are highly specialized and inherit default arguments from the names used by the various NCSS (National Cooperative Soil Survey) databases.

Install the stable version from CRAN:

install.packages('sharpshootR', dep=TRUE)

Install the development version from Github:

remotes::install_github("ncss-tech/sharpshootR", dependencies=FALSE, upgrade=FALSE, build=FALSE)

Website

http://ncss-tech.github.io/AQP/

Quick Reference

Vizualization

• aggregateColorPlot
• aspect.plot
• diagnosticPropertyPlot
• dueling.dendrograms
• plotAvailWater
• plotProfileDendrogram
• plotSoilRelationGraph
• plotSoilRelationChordGraph
• plotTransect
• SoilTaxonomyDendrogram
• vizGeomorphicComponent
• vizHillslopePosition
• vizFlatsPosition
• vizTerracePosition
• vizMountainPosition
• vizAnnualClimate
• plotWB

Climate

• monthlyWB
• CDECquery
• CDECsnowQuery
• FFD
• FFDplot
• PCP_plot
• waterDayYear

Spatial Data

• constantDensitySampling
• generateLineHash
• polygonAdjacency
• PLSS2LL
• LL2PLSS
• sample.by.poly
• samplingStability
• sampleRasterStackByMU

Utility

• component.adj.matrix
• dist.along.grad

Misc.

• percentileDemo
• multinominal2logical
• site_photos_kml

Related Packages

• aqp
• soilDB

Examples

library(sharpshootR)
data(loafercreek, package = 'soilDB')

# generalize horizon names using REGEX rules
n <- c('Oi', 'A', 'BA','Bt1','Bt2','Bt3','Cr','R')
p <- c('O', '^A$|Ad|Ap|AB','BA$|Bw', 
       'Bt1$|^B$','^Bt$|^Bt2$','^Bt3|^Bt4|CBt$|BCt$|2Bt|2CB$|^C$','Cr','R')
loafercreek$genhz <- generalize.hz(loafercreek$hzname, n, p)

# remove non-matching generalized horizon names
loafercreek$genhz[loafercreek$genhz == 'not-used'] <- NA
loafercreek$genhz <- factor(loafercreek$genhz)

# aggregate color data, this function is from the `aqp` package
a <- aggregateColor(loafercreek, 'genhz', k = 8)

# plot
par(mar=c(3,4,4,1))
aggregateColorPlot(a, print.n.hz = TRUE)

Dependency Graph