Authors
+ +Citation
+ Source:DESCRIPTION
+ -
-
-
-
Dylan Beaudette. Maintainer, author. -
-
- -
-
Jay Skovlin. Author. -
-
- -
-
Stephen Roecker. Author. -
-
- -
-
USDA-NRCS Soil Survey Staff. Contributor. -
-
-
Beaudette D, Skovlin J, Roecker S, Brown A (2022). +sharpshootR: A Soil Survey Toolkit. +R package version 2.0, https://CRAN.R-project.org/package=sharpshootR. +
+@Manual{,
+ title = {sharpshootR: A Soil Survey Toolkit},
+ author = {Dylan Beaudette and Jay Skovlin and Stephen Roecker and Andrew Brown},
+ year = {2022},
+ note = {R package version 2.0},
+ url = {https://CRAN.R-project.org/package=sharpshootR},
+}
Authors
--
-
+
@@ -68,133 +67,133 @@
-- - + + diff --git a/docs/reference/dist.along.grad.html b/docs/reference/dist.along.grad.html index 852f8721..c905c25c 100644 --- a/docs/reference/dist.along.grad.html +++ b/docs/reference/dist.along.grad.html @@ -1,67 +1,12 @@ - - - - - - - -- +- - + + diff --git a/docs/reference/dailyWB_SSURGO.html b/docs/reference/dailyWB_SSURGO.html index de751bb3..fe4fbc2d 100644 --- a/docs/reference/dailyWB_SSURGO.html +++ b/docs/reference/dailyWB_SSURGO.html @@ -1,67 +1,12 @@ - - - - - - - -+-
This package contains a 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.packages('sharpshootR', dep=TRUE)Install the development version from Github:
-
-remotes::install_github("ncss-tech/sharpshootR", dependencies=FALSE, upgrade=FALSE, build=FALSE)-- - + + diff --git a/docs/reference/constantDensitySampling.html b/docs/reference/constantDensitySampling.html index 91dc93f5..2ed77a33 100644 --- a/docs/reference/constantDensitySampling.html +++ b/docs/reference/constantDensitySampling.html @@ -1,67 +1,12 @@ - - - - - - - --Website
-http://ncss-tech.github.io/AQP/
+
+ -remotes::install_github("ncss-tech/sharpshootR", dependencies=FALSE, upgrade=FALSE, build=FALSE)-- - + + diff --git a/docs/reference/component.adj.matrix-1.png b/docs/reference/component.adj.matrix-1.png index 16c12e39..a6af4397 100644 Binary files a/docs/reference/component.adj.matrix-1.png and b/docs/reference/component.adj.matrix-1.png differ diff --git a/docs/reference/component.adj.matrix.html b/docs/reference/component.adj.matrix.html index 5730840c..640a5c09 100644 --- a/docs/reference/component.adj.matrix.html +++ b/docs/reference/component.adj.matrix.html @@ -1,67 +1,12 @@ - - - - - - - --Citation
-Dylan Beaudette, Jay Skovlin and Stephen Roecker (2021). sharpshootR: A Soil Survey Toolkit. R package version 1.8. https://CRAN.R-project.org/package=sharpshootR
++-Citation +
+Dylan Beaudette, Jay Skovlin and Stephen Roecker (2021). sharpshootR: A Soil Survey Toolkit. R package version 1.8. https://CRAN.R-project.org/package=sharpshootR
-- - + + diff --git a/docs/reference/colorMixtureVenn-1.png b/docs/reference/colorMixtureVenn-1.png index c5dbe496..a2ae61c3 100644 Binary files a/docs/reference/colorMixtureVenn-1.png and b/docs/reference/colorMixtureVenn-1.png differ diff --git a/docs/reference/colorMixtureVenn.html b/docs/reference/colorMixtureVenn.html index 93753359..00ae4269 100644 --- a/docs/reference/colorMixtureVenn.html +++ b/docs/reference/colorMixtureVenn.html @@ -1,67 +1,12 @@ - - - - - - - --Quick Reference
--- - + + diff --git a/docs/reference/PLSS2LL.html b/docs/reference/PLSS2LL.html index f24ff00b..d862aabf 100644 --- a/docs/reference/PLSS2LL.html +++ b/docs/reference/PLSS2LL.html @@ -1,67 +1,12 @@ - - - - - - - --Vizualization
++- - + + diff --git a/docs/reference/HHM.html b/docs/reference/HHM.html index d4332330..37e306a6 100644 --- a/docs/reference/HHM.html +++ b/docs/reference/HHM.html @@ -1,67 +1,12 @@ - - - - - - - -Quick Reference +
++-Visualization +
-
-
aggregateColorPlot
-aspect.plot
-diagnosticPropertyPlot
-dueling.dendrograms
-plotAvailWater
-plotProfileDendrogram
-plotSoilRelationGraph
-plotSoilRelationChordGraph
-plotTransect
-SoilTaxonomyDendrogram
-vizGeomorphicComponent
-vizHillslopePosition
-vizFlatsPosition
-vizTerracePosition
-vizMountainPosition
-vizAnnualClimate
-plotWB
+aggregateColorPlot
+aspect.plot
+diagnosticPropertyPlot
+plotAvailWater
+plotProfileDendrogram
+plotSoilRelationGraph
+plotSoilRelationChordGraph
+plotTransect
+SoilTaxonomyDendrogram
+vizGeomorphicComponent
+vizHillslopePosition
+vizFlatsPosition
+vizTerracePosition
+vizMountainPosition
+vizAnnualClimate
+plotWB
+plotWB_lines
-- - + + diff --git a/docs/reference/FFD.html b/docs/reference/FFD.html index e71e9389..785f4075 100644 --- a/docs/reference/FFD.html +++ b/docs/reference/FFD.html @@ -1,67 +1,12 @@ - - - - - - - --Climate
+ --- - + + diff --git a/docs/reference/CDECsnowQuery.html b/docs/reference/CDECsnowQuery.html index ef5e3ffd..6e810b53 100644 --- a/docs/reference/CDECsnowQuery.html +++ b/docs/reference/CDECsnowQuery.html @@ -1,67 +1,12 @@ - - - - - - - --Spatial Data
+ --- - + + diff --git a/docs/reference/CDECquery.html b/docs/reference/CDECquery.html index ab9f922d..aee14f39 100644 --- a/docs/reference/CDECquery.html +++ b/docs/reference/CDECquery.html @@ -1,67 +1,12 @@ - - - - - - - --Utility
+ - --@@ -255,5 +266,7 @@-Related Packages
+ --- @@ -203,50 +202,62 @@-Examples
++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)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)Dev status
+ + diff --git a/docs/pkgdown.css b/docs/pkgdown.css index 1273238d..80ea5b83 100644 --- a/docs/pkgdown.css +++ b/docs/pkgdown.css @@ -56,8 +56,10 @@ img.icon { float: right; } -img { +/* Ensure in-page images don't run outside their container */ +.contents img { max-width: 100%; + height: auto; } /* Fix bug in bootstrap (only seen in firefox) */ @@ -78,11 +80,10 @@ dd { /* Section anchors ---------------------------------*/ a.anchor { - margin-left: -30px; - display:inline-block; - width: 30px; - height: 30px; - visibility: hidden; + display: none; + margin-left: 5px; + width: 20px; + height: 20px; background-image: url(./link.svg); background-repeat: no-repeat; @@ -90,17 +91,15 @@ a.anchor { background-position: center center; } -.hasAnchor:hover a.anchor { - visibility: visible; -} - -@media (max-width: 767px) { - .hasAnchor:hover a.anchor { - visibility: hidden; - } +h1:hover .anchor, +h2:hover .anchor, +h3:hover .anchor, +h4:hover .anchor, +h5:hover .anchor, +h6:hover .anchor { + display: inline-block; } - /* Fixes for fixed navbar --------------------------*/ .contents h1, .contents h2, .contents h3, .contents h4 { @@ -264,31 +263,26 @@ table { /* Syntax highlighting ---------------------------------------------------- */ -pre { - word-wrap: normal; - word-break: normal; - border: 1px solid #eee; -} - -pre, code { +pre, code, pre code { background-color: #f8f8f8; color: #333; } +pre, pre code { + white-space: pre-wrap; + word-break: break-all; + overflow-wrap: break-word; +} -pre code { - overflow: auto; - word-wrap: normal; - white-space: pre; +pre { + border: 1px solid #eee; } -pre .img { +pre .img, pre .r-plt { margin: 5px 0; } -pre .img img { +pre .img img, pre .r-plt img { background-color: #fff; - display: block; - height: auto; } code a, pre a { @@ -305,9 +299,8 @@ a.sourceLine:hover { .kw {color: #264D66;} /* keyword */ .co {color: #888888;} /* comment */ -.message { color: black; font-weight: bolder;} -.error { color: orange; font-weight: bolder;} -.warning { color: #6A0366; font-weight: bolder;} +.error {font-weight: bolder;} +.warning {font-weight: bolder;} /* Clipboard --------------------------*/ @@ -365,3 +358,27 @@ mark { content: ""; } } + +/* Section anchors --------------------------------- + Added in pandoc 2.11: https://github.com/jgm/pandoc-templates/commit/9904bf71 +*/ + +div.csl-bib-body { } +div.csl-entry { + clear: both; +} +.hanging-indent div.csl-entry { + margin-left:2em; + text-indent:-2em; +} +div.csl-left-margin { + min-width:2em; + float:left; +} +div.csl-right-inline { + margin-left:2em; + padding-left:1em; +} +div.csl-indent { + margin-left: 2em; +} diff --git a/docs/pkgdown.js b/docs/pkgdown.js index 7e7048fa..6f0eee40 100644 --- a/docs/pkgdown.js +++ b/docs/pkgdown.js @@ -80,7 +80,7 @@ $(document).ready(function() { var copyButton = ""; - $(".examples, div.sourceCode").addClass("hasCopyButton"); + $("div.sourceCode").addClass("hasCopyButton"); // Insert copy buttons: $(copyButton).prependTo(".hasCopyButton"); @@ -91,7 +91,7 @@ // Initialize clipboard: var clipboardBtnCopies = new ClipboardJS('[data-clipboard-copy]', { text: function(trigger) { - return trigger.parentNode.textContent; + return trigger.parentNode.textContent.replace(/\n#>[^\n]*/g, ""); } }); diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml index 2bf7eedd..e7cc5be0 100644 --- a/docs/pkgdown.yml +++ b/docs/pkgdown.yml @@ -1,6 +1,9 @@ -pandoc: 2.7.3 -pkgdown: 1.6.1 +pandoc: 2.17.1.1 +pkgdown: 2.0.7 pkgdown_sha: ~ articles: {} -last_built: 2021-04-25T08:18Z +last_built: 2022-12-29T04:01Z +urls: + reference: https://ncss-tech.github.io/sharpshootR/reference + article: https://ncss-tech.github.io/sharpshootR/articles diff --git a/docs/reference/CDEC.snow.courses.html b/docs/reference/CDEC.snow.courses.html index 40db19e2..1ebd3b3c 100644 --- a/docs/reference/CDEC.snow.courses.html +++ b/docs/reference/CDEC.snow.courses.html @@ -1,67 +1,12 @@ - - - - - - - -CDEC Snow Course List — CDEC.snow.courses • sharpshootR - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -CDEC Snow Course List — CDEC.snow.courses • sharpshootR - - - - + + --- - + + diff --git a/docs/reference/CDEC_StationInfo.html b/docs/reference/CDEC_StationInfo.html index fdcdb570..1f61d537 100644 --- a/docs/reference/CDEC_StationInfo.html +++ b/docs/reference/CDEC_StationInfo.html @@ -1,67 +1,12 @@ - - - - - - - -- - - -+-+-CDEC Snow Course List
@@ -117,70 +52,87 @@CDEC Snow Course List
The CDEC snow course list, updated September 2019
data(CDEC.snow.courses)
+++ ++data(CDEC.snow.courses)++Format
+A data frame with 259 observations on the following 9 variables.
course_number
+course number
+
+ name
+connotative course label
+ id
+course ID
- elev_feet
+course elevation in feet
+
+ latitude
+latitude
+
+ longitude
+longitude
+
+ april.1.Avg.inches
+average inches of snow as of April 1st
+
+ agency
+responsible agency
+
+ watershed
+watershed label
- course_numbercourse number
- nameconnotative course label
- idcourse ID
- elev_feetcourse elevation in feet
- latitudelatitude
- longitudelongitude
- april.1.Avg.inchesaverage inches of snow as of April 1st
- agencyresponsible agency
- watershedwatershed label
-
Format
+A data frame with 259 observations on the following 9 variables.
-
-
Source
- -Data were scraped from http://cdec.water.ca.gov/misc/SnowCourses.html, 2019.
- -Examples
-
+#> course_number name id elev_feet latitude longitude -#> 2 1 PARKS CREEK PRK 6700 41.367 -122.550 -#> 3 2 LITTLE SHASTA LSH 6200 41.808 -122.195 -#> 4 3 SWEETWATER SWT 5850 41.382 -122.535 -#> 6 5 MIDDLE BOULDER 1 MBL 6600 41.217 -122.807 -#> 7 417 BOX CAMP BXC 6450 41.597 -123.165 -#> 8 311 MIDDLE BOULDER 3 MB3 6200 41.225 -122.811 -#> april.1.Avg.inches agency watershed -#> 2 35.1 Mount Shasta Ranger District SHASTA R -#> 3 16.6 Goosenest Ranger District SHASTA R -#> 4 13.1 Mount Shasta Ranger District SHASTA R -#> 6 31.5 Salmon/Scott River Ranger District SCOTT R -#> 7 35.2 Salmon/Scott River Ranger District SCOTT R -#> 8 27.4 Salmon/Scott River Ranger District SCOTT R++ +Source
+Data were scraped from http://cdec.water.ca.gov/misc/SnowCourses.html, 2019.
++Examples
++data(CDEC.snow.courses) +head(CDEC.snow.courses) +#> course_number name id elev_feet latitude longitude +#> 2 1 PARKS CREEK PRK 6700 41.367 -122.550 +#> 3 2 LITTLE SHASTA LSH 6200 41.808 -122.195 +#> 4 3 SWEETWATER SWT 5850 41.382 -122.535 +#> 6 5 MIDDLE BOULDER 1 MBL 6600 41.217 -122.807 +#> 7 417 BOX CAMP BXC 6450 41.597 -123.165 +#> 8 311 MIDDLE BOULDER 3 MB3 6200 41.225 -122.811 +#> april.1.Avg.inches agency watershed +#> 2 35.1 Mount Shasta Ranger District SHASTA R +#> 3 16.6 Goosenest Ranger District SHASTA R +#> 4 13.1 Mount Shasta Ranger District SHASTA R +#> 6 31.5 Salmon/Scott River Ranger District SCOTT R +#> 7 35.2 Salmon/Scott River Ranger District SCOTT R +#> 8 27.4 Salmon/Scott River Ranger District SCOTT R +CDEC Sensor Details (by Station) — CDEC_StationInfo • sharpshootR - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -CDEC Sensor Details (by Station) — CDEC_StationInfo • sharpshootR - - - - + + ---- - - -++-@@ -117,162 +52,56 @@CDEC Sensor Details (by Station)
- Source:R/CDEC_StationInfo.R+ Source:R/CDEC_StationInfo.RCDEC_StationInfo.RdCDEC Sensor Details (by Station)
Query CDEC Website for Sensor Details
CDEC_StationInfo(s)
- -Arguments
--
+- -s -character, a single CDEC station ID (e.g. 'HHM')
+-+CDEC_StationInfo(s)Value
+++Arguments
+++ +Value
+ +A
+listobject containing site metadata, sensor metadata, and possibly comments about the site.++ -See also
++[CDECquery]Examples
--# \donttest{ -if(requireNamespace("curl") & - curl::has_internet() -) { - - # CDEC API needs a long timeout - options(timeout = 60000) - - CDEC_StationInfo('HHM') - -} -#> $site.meta -#> Station ID River Basin Hydrologic Area Latitude -#> 1 HHM MOKELUMNE R SAN JOAQUIN RIVER 38.49066 -#> Operator Elevation County Nearby City -#> 1 CA Dept of Water Resources/DFM-Hydro-SMN 8700 ALPINE BEAR VALLEY -#> Longitude Maintenance Name -#> 1 119.8056 CA Dept of Water Resources/DFM-Hydro-SMN HIGHLAND MEADOW -#> -#> $sensor.meta -#> sensor_details sensor interval sensor_name -#> 1 PRECIPITATION, ACCUMULATED, INCHES 2 (daily) (RAIN) -#> 2 PRECIPITATION, INCREMENTAL, INCHES 45 (daily) (PPT INC) -#> 3 SNOW, WATER CONTENT, INCHES 3 (daily) (SNOW WC) -#> 4 SNOW, WATER CONTENT(REVISED), INCHES 82 (daily) (SNO ADJ) -#> 5 TEMPERATURE, AIR AVERAGE, DEG F 30 (daily) (TEMP AV) -#> 6 TEMPERATURE, AIR MAXIMUM, DEG F 31 (daily) (TEMP MX) -#> 7 TEMPERATURE, AIR MINIMUM, DEG F 32 (daily) (TEMP MN) -#> 8 ATMOSPHERIC PRESSURE, INCHES 17 (event) (BAR PRE) -#> 9 BATTERY VOLTAGE, VOLTS 14 (event) (BAT VOL) -#> 10 PRECIPITATION, ACCUMULATED, INCHES 2 (event) (RAIN) -#> 11 REFLECTED IRRADIANCE AVG, W/M^2 119 (event) (RIRR AV) -#> 12 RELATIVE HUMIDITY, % 12 (event) (REL HUM) -#> 13 SNOW DEPTH, INCHES 18 (event) (SNOW DP) -#> 14 SNOW, WATER CONTENT, INCHES 3 (event) (SNOW WC) -#> 15 SOIL MOISTR, DEPTH 1, % 197 (event) (SOILMD1) -#> 16 SOIL MOISTR, DEPTH 2, % 198 (event) (SOILMD2) -#> 17 SOIL MOISTR, DEPTH 3, % 199 (event) (SOILMD3) -#> 18 SOIL TEMP, DEPTH 1, DEG F 194 (event) (SOILTD1) -#> 19 SOIL TEMP, DEPTH 2, DEG F 195 (event) (SOILTD2) -#> 20 SOIL TEMP, DEPTH 3, DEG F 196 (event) (SOILTD3) -#> 21 SOLAR RADIATION, W/M^2 26 (event) (SOLAR R) -#> 22 TEMPERATURE, AIR, DEG F 4 (event) (TEMP) -#> 23 WIND, DIRECTION, DEG 10 (event) (WIND DR) -#> 24 WIND, SPEED, MPH 9 (event) (WIND SP) -#> 25 BATTERY VOLTAGE, VOLTS 14 (hourly) (BAT VOL) -#> 26 PRECIPITATION, ACCUMULATED, INCHES 2 (hourly) (RAIN) -#> 27 SNOW, WATER CONTENT, INCHES 3 (hourly) (SNOW WC) -#> 28 TEMPERATURE, AIR, DEG F 4 (hourly) (TEMP) -#> 29 SNOW DEPTH, INCHES 18 (monthly) (SNOW DP) -#> 30 SNOW, WATER CONTENT, INCHES 3 (monthly) (SNOW WC) -#> 31 SNOW, WATER CONTENT(REVISED), INCHES 82 (monthly) (SNO ADJ) -#> 32 SOLAR RADIATION AVG, W/M^2 103 (hourly) (SLRR AV) -#> 33 SOLAR RADIATION MAX, W/M^2 105 (hourly) (SLRR MX) -#> 34 SOLAR RADIATION MIN, W/M^2 104 (hourly) (SLRR MN) -#> collection_method period_of_record -#> 1 COMPUTED 10/01/2002 to present -#> 2 COMPUTED 12/01/1996 to present -#> 3 COMPUTED 11/29/1989 to present -#> 4 COMPUTED 10/21/1989 to present -#> 5 COMPUTED 09/02/1999 to present -#> 6 COMPUTED 09/02/1999 to present -#> 7 COMPUTED 09/02/1999 to present -#> 8 SATELLITE 09/14/2016 to present -#> 9 SATELLITE 09/14/2016 to present -#> 10 SATELLITE 09/14/2016 to present -#> 11 SATELLITE 09/14/2016 to present -#> 12 SATELLITE 09/14/2016 to present -#> 13 SATELLITE 09/14/2016 to present -#> 14 SATELLITE 09/14/2016 to present -#> 15 SATELLITE 09/14/2016 to present -#> 16 SATELLITE 09/14/2016 to present -#> 17 SATELLITE 09/14/2016 to present -#> 18 SATELLITE 09/14/2016 to present -#> 19 SATELLITE 09/14/2016 to present -#> 20 SATELLITE 09/14/2016 to present -#> 21 SATELLITE 09/14/2016 to present -#> 22 SATELLITE 09/14/2016 to present -#> 23 SATELLITE 09/14/2016 to present -#> 24 SATELLITE 09/14/2016 to present -#> 25 SATELLITE 01/01/1995 to present -#> 26 SATELLITE 10/14/1989 to present -#> 27 SATELLITE 10/14/1989 to present -#> 28 SATELLITE 10/13/1989 to present -#> 29 MANUAL ENTRY 05/01/1952 to present -#> 30 MANUAL ENTRY 05/01/1952 to present -#> 31 MANUAL ENTRY 05/01/1952 to present -#> 32 SATELLITE 10/10/2012 to 09/14/2016 -#> 33 SATELLITE 10/10/2012 to 09/14/2016 -#> 34 SATELLITE 10/10/2012 to 09/14/2016 -#> -#> $comments -#> date -#> 1 04/27/2020 -#> 2 12/31/2016 -#> 3 07/23/2014 -#> comment -#> 1 Latitude and longitude updated from information provided by DFM-Statewide Monitoring Network Section. Datum WGS-84 -#> 2 Soil Tmp1, Soil Mois1 @ 16cm , Soil Tmp2, Soil Mois2 @ 31cm, Soil Tmp3, Soil Mois3 @ 62cm -#> 3 Latitude and longitude updated based on information from Hydrology Branch field technicians. -#>- -# } - -Easy Access to the CDEC API — CDECquery • sharpshootR - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -Easy Access to the CDEC API — CDECquery • sharpshootR - - - - + + ---- - - -++@@ -117,210 +52,92 @@-Easy Access to the CDEC API
A (relatively) simple interface to the CDEC website.
CDECquery(id, sensor, interval = "D", start, end)
- -Arguments
--
- -- -id -station ID (e.g. 'spw'), single value or vector of station IDs, see details
- -sensor -the sensor ID, single value or vector of sensor numbers, see details
- -interval -character, 'D' for daily, 'H' for hourly, 'M' for monthly, 'E' for event: see Details.
- -start -starting date, in the format 'YYYY-MM-DD'
- -end -ending date, in the format 'YYYY-MM-DD'
Value
- -A
-data.frameobject with the following fields:datetime,year,month,value.Details
++-+CDECquery(id, sensor, interval = "D", start, end)Sensors that report data on an
-intervalother than monthly ('M'), daily ('D'), or hourly ('H') can be queried with aneventinterval ('E'). Soil moisture and temperature sensors are an example of this type of reporting. See examples below.-
-
- 1.
Station IDs can be found here: http://cdec.water.ca.gov/staInfo.html
-- 2a.
Sensor IDs can be found using this URL: http://cdec.water.ca.gov/dynamicapp/staMeta?station_id=, followed by the station ID.
-- 2b.
Sensor details can be accessed using
CDEC_StationInfo with the station ID.
-- 3.
Resevoir capacities can be found here: http://cdec.water.ca.gov/misc/resinfo.html
-- 4.
A new interactive map of CDEC stations can be found here: http://cdec.water.ca.gov
+ - id +
station ID (e.g. 'spw'), single value or vector of station IDs, see details
-- sensor +
the sensor ID, single value or vector of sensor numbers, see details
- - interval +
character, 'D' for daily, 'H' for hourly, 'M' for monthly, 'E' for event: see Details.
- - start +
starting date, in the format 'YYYY-MM-DD'
-if(requireNamespace("curl") &
-curl::has_internet() &
- require(latticeExtra) &
- require(plyr) &
- require(e1071)) {
-
- library(RColorBrewer)
-
- # get daily reservoir storage (ac. ft) from
- # Pinecrest, New Melones and Lyons reservoirs
- pinecrest <- CDECquery(id='swb', sensor=15, interval='D',
- start='2012-09-01', end='2015-01-01')
-
- new.melones <- CDECquery(id='nml', sensor=15, interval='D',
- start='2012-09-01', end='2015-01-01')
-
- lyons <- CDECquery(id='lys', sensor=15, interval='D',
- start='2012-09-01', end='2015-01-01')
-
- # compute storage capacity
- pinecrest$capacity <- pinecrest$value / 18312 * 100
- new.melones$capacity <- new.melones$value / 2400000 * 100
- lyons$capacity <- lyons$value / 6228 * 100
-
- # combine
- g <- make.groups(new.melones, lyons, pinecrest)
-
- # resonable date scale
- r <- range(g$datetime)
- s.r <- seq(from=r[1], to=r[2], by='1 month')
-
- # better colors
- tps <- list(superpose.line=list(lwd=2, col=brewer.pal(n=3, name='Set1')))
-
- # plot
- xyplot(capacity ~ datetime, groups=which, data=g, type='l',
- xlab='', ylab='Capacity (%)', ylim=c(-5, 105),
- scales=list(x=list(at=s.r, labels=format(s.r, "%b\n%Y"))),
- auto.key=list(columns=3, lines=TRUE, points=FALSE),
- par.settings=tps,
- panel=function(...) {
- panel.abline(h=seq(0, 100, by=10), col='grey')
- panel.abline(v=s.r, col='grey')
- panel.xyplot(...)
- })
-
-
- ##
- # New Melones monthly data, retrieve as far back in time as possible
- new.melones.monthly <- CDECquery(id='nml', sensor=15, interval='M',
- start='1900-01-01', end='2015-01-01')
-
- # convert to pct. capacity
- new.melones.monthly$capacity <- new.melones.monthly$value / 2400000 * 100
-
-
- # make a nice color ramp function
- cols <- colorRampPalette(brewer.pal(9, 'Spectral'),
- space='Lab', interpolate='spline')
-
- # plot, each pixel is colored by the total precip by year/month
- levelplot(capacity ~ year * month, data=new.melones.monthly, col.regions=cols, xlab='',
- ylab='', scales=list(x=list(tick.number=20)), main='New Melones Capacity (%)')
-
-
- ##
- # get daily precip totals from Stan Powerhouse
- x <- CDECquery(id='spw', sensor=45, interval='D', start='1900-01-01', end='2019-01-01')
-
- # compute total precip by year/month
- a <- ddply(x, c('year', 'month'), summarize, s=sum(value, na.rm=TRUE))
-
- # convert monthly precipitation values into Z-scores by month
- a.scaled <- ddply(a, 'month', summarize, year=year, scaled.ppt=scale(s))
-
- # make a nice color ramp function, scaled by the skewness of the underlying distribution
- cols <- colorRampPalette(
- brewer.pal(9, 'Spectral'),
- space='Lab', interpolate='spline', bias=skewness(a.scaled$scaled.ppt, na.rm=TRUE))
-
- # plot, each pixel is colored by the total precip by year/month
- levelplot(scaled.ppt ~ year * month, data=a.scaled, col.regions=cols, xlab='',
- ylab='', scales=list(x=list(tick.number=10)),
- main='Monthly Total Precipitation (as z-score) SPW')
-
-
- ##
- # get pre-aggregated monthly data from Sonora RS
- x <- CDECquery(id='sor', sensor=2, interval='M', start='1900-01-01', end='2019-01-01')
-
- # make a nice color ramp function, scaled by the skewness of the underlying distribution
- cols <- colorRampPalette(brewer.pal(9, 'Spectral'), space='Lab',
- interpolate='spline', bias=skewness(x$value, na.rm=TRUE))
-
- # plot
- levelplot(value ~ year * month, data=x, col.regions=cols, xlab='',
- ylab='', scales=list(x=list(tick.number=20)),
- main='Monthly Total Precipitation (inches) SOR')
-
-
- ### query an 'event' type sensor
- # Bryte test site (BYT)
- # single request: air temperature and soil temperature at depth 1 (25cm)
- # measurement interval is 20 minutes
- x <- CDECquery('BYT', c(4, 194), 'E', '2016-01-01', '2017-01-01')
-
- # data are in long format, check number of records for each sensor
- table(x$sensor_type)
-
- # plot grouped data
- xyplot(value ~ datetime, groups=sensor_type, data=x, type=c('g', 'l'),
- auto.key=list(columns=2, points=FALSE, lines=TRUE))
+
+- end +
ending date, in the format 'YYYY-MM-DD'
+
+- 1. +
Station IDs can be found here: http://cdec.water.ca.gov/staInfo.html
+
+- 2a. +
Sensor IDs can be found using this URL: http://cdec.water.ca.gov/dynamicapp/staMeta?station_id=, followed by the station ID.
+
+- 2b. +
Sensor details can be accessed using
CDEC_StationInfo with the station ID.
+- 3. +
Reservoir capacities can be found here: http://cdec.water.ca.gov/misc/resinfo.html
+
+- 4. +
A new interactive map of CDEC stations can be found here: http://cdec.water.ca.gov
+
+
+
+Arguments
+References
+http://cdec.water.ca.gov/queryCSV.html
-See also
- -Author
+D.E. Beaudette
-Examples
--# \donttest{ +++Value
- } -#>#> Warning: package 'latticeExtra' was built under R version 4.0.3#>#>#> Warning: package 'plyr' was built under R version 4.0.3#> -#>#> -#> -#>#>#> Warning: package 'e1071' was built under R version 4.0.5#> Warning: package 'RColorBrewer' was built under R version 4.0.3
-# } ++A
+data.frameobject with the following fields:datetime,year,month,value.++Details
+Sensors that report data on an
+intervalother than monthly ('M'), daily ('D'), or hourly ('H') can be queried with aneventinterval ('E'). Soil moisture and temperature sensors are an example of this type of reporting. See examples below.++References
+ +++ -See also
+ +Get snow survey data (California only) from the CDEC website. — CDECsnowQuery • sharpshootR - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -Get snow survey data (California only) from the CDEC website. — CDECsnowQuery • sharpshootR - - - - + + ---- - - -++-@@ -117,81 +52,68 @@Get snow survey data (California only) from the CDEC website.
- + Source:R/CDECsnowQuery.RCDECsnowQuery.RdGet snow survey data (California only) from the CDEC website.
Get snow survey data (California only) from the CDEC website.
CDECsnowQuery(course, start_yr, end_yr)
- -Arguments
--
- -- -course -integer, course number (e.g. 129)
- -start_yr -integer, the starting year (e.g. 2010)
- -end_yr -integer, the ending year (e.g. 2013)
Details
- -This function downloads data from the CDEC website, therefore an internet connection is required. The `SWE` column contains adjusted SWE if available (`Adjusted` column), otherwise the reported SWE is used (`Water` column).
-Value
++-+CDECsnowQuery(course, start_yr, end_yr)a
-data.frameobject, see examplesReferences
++Arguments
+- course +
integer, course number (e.g. 129)
- - start_yr +
integer, the starting year (e.g. 2010)
- - end_yr +
integer, the ending year (e.g. 2013)
-if(requireNamespace("curl") &
- curl::has_internet()
- ) {
-
- # get data for course numbe 129
- x <- CDECsnowQuery(course=129, start_yr=2010, end_yr=2011)
-
-}
+
http://cdec.water.ca.gov/cgi-progs/snowQuery
-Author
-D.E. Beaudette
-Note
+Snow course locations, ID numbers, and other information can be found here: http://cdec.water.ca.gov/misc/SnowCourses.html
-Examples
-# \donttest{ ++++Value
+ +a
+data.frameobject, see examples++Details
+This function downloads data from the CDEC website, therefore an internet connection is required. The
+SWEcolumn contains adjusted SWE if available (Adjustedcolumn), otherwise the reported SWE is used (Watercolumn). See the tutorial for examples.++Note
+Snow course locations, ID numbers, and other information can be found here: http://cdec.water.ca.gov/misc/SnowCourses.html
+++ -# } -References
+ +Frost-Free Day Evaluation — FFD • sharpshootR - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -Frost-Free Day Evaluation — FFD • sharpshootR - - - - + + ---- - - -++@@ -117,78 +52,104 @@-Frost-Free Day Evaluation
Evaluation frost-free days and related metrics from daily climate records.
FFD(d, returnDailyPr = TRUE, minDays = 165, ...) -FFDplot(s, sub.title = NULL)
- -Arguments
--
- -- -d -data.framewith columns 'datetime' 'year', and 'value'; 'value' being daily minimum temperature, see details- -returnDailyPr -optionally return
listwith daily summaries- -minDays -min number of days / spring|fall required for a reasonable estimate of FFD
- -... -further arguments passed to
frostFreePeriod- -s -object returned by
FFD- -sub.title -override default subtitle
Details
- -The default
- -frostTemp=32argument tofrostFreePeriodis suitable for use with minimum daily temperatures in degrees Fahrenheit. UsefrostTemp=0for temperatures in degrees Celsius.Value
- -a
-listwith the following elements:- summary
FFD summary statistics as a
data.frame- fm
frost matrix
- Pr.frost
Pr(frost|day): daily probability of frost
Author
+++ ++FFD( + d, + returnDailyPr = TRUE, + minDays = 165, + frostTemp = 32, + endSpringDOY = 182, + startFallDOY = 213 +)++Arguments
+- d +
data.framewith columns 'datetime' 'year', and 'value'; 'value' being daily minimum temperature, see details
+
+
+- returnDailyPr +
optionally return
listwith daily summaries
+
+
+- minDays +
min number of days of non-NA data in spring | fall, required for a reasonable estimate of FFD
+
+- frostTemp +
critical temperature that defines "frost" (same units as
d$value)
+
+
+- endSpringDOY +
day of year that marks end of "spring" (typically Jan 1 -- June 30)
+
+
+- startFallDOY +
day of year that marks start of "fall" (typically Aug 1 -- Dec 31)
+
+
++Value
+ + +a
data.framewhen areturnDailyPr=FALSE, otherwise alistwith the following elements:summary: FFD summary statistics as a
data.frame
+fm: frost matrix
+Pr.frost: Pr(frost|day): daily probability of frost
+
++ +Details
+The default
+ +frostTemp=32is suitable for use with minimum daily temperatures in degrees Fahrenheit. UsefrostTemp = 0for temperatures in degrees Celsius.+Examples
+++# 11 years of data from highland meadows +data('HHM', package = 'sharpshootR') +x.ffd <- FFD(HHM, returnDailyPr = FALSE, frostTemp = 32) + +str(x.ffd) +#> 'data.frame': 1 obs. of 10 variables: +#> $ ffd.50 : num 80 +#> $ ffd.80 : num 70 +#> $ ffd.90 : num 60 +#> $ spring.50: num 165 +#> $ spring.80: num 168 +#> $ spring.90: num 169 +#> $ fall.50 : num 245 +#> $ fall.80 : num 238 +#> $ fall.90 : num 228 +#> $ n.yrs : num 6 + +Highland Meadows — HHM • sharpshootR - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -Highland Meadows — HHM • sharpshootR - - - - + + --- - + + diff --git a/docs/reference/HenryTimeLine.html b/docs/reference/HenryTimeLine.html index e1a0964a..adefbcfa 100644 --- a/docs/reference/HenryTimeLine.html +++ b/docs/reference/HenryTimeLine.html @@ -1,67 +1,12 @@ - - - - - - - -- - - -+-+-Highland Meadows
@@ -117,53 +52,72 @@Highland Meadows
11 years of climate data from the Highland Meadows weather station, as maintained by CA DWR.
data("HHM")
++++data("HHM")+Format
+A data frame with 3469 observations on the following 12 variables.
station_id
+a character vector
- dur_code
+a character vector
- station_ida character vector
- dur_codea character vector
- sensor_numa numeric vector
- sensor_typea character vector
- valuea numeric vector
- flaga character vector
- unitsa character vector
- datetimea POSIXct
- yeara numeric vector
- montha factor with levels
JanuaryFebruaryMarchAprilMayJuneJulyAugustSeptemberOctoberNovemberDecember
- water_yeara numeric vector
- water_daya numeric vector
-
-sensor_num
+a numeric vector
+
+ sensor_type
+a character vector
+
+ value
+a numeric vector
+ flag
+a character vector
+
+ units
+a character vector
+
+ datetime
+a POSIXct
+
+ year
+a numeric vector
+
+ month
+a factor with levels
JanuaryFebruaryMarchAprilMayJuneJulyAugustSeptemberOctoberNovemberDecember
+
+ water_year
+a numeric vector
+
+ water_day
+a numeric vector
+
+
+
Format
+A data frame with 3469 observations on the following 12 variables.
-
-
Sensor Data Timeline from Henry Mount Soil and Water DB — HenryTimeLine • sharpshootR - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -Sensor Data Timeline from Henry Mount Soil and Water DB — HenryTimeLine • sharpshootR - - + + - - --- - + + diff --git a/docs/reference/LL2PLSS.html b/docs/reference/LL2PLSS.html index 8200615e..b1cb4452 100644 --- a/docs/reference/LL2PLSS.html +++ b/docs/reference/LL2PLSS.html @@ -1,67 +1,12 @@ - - - - - - - -- - - -+-+Sensor Data Timeline from Henry Mount Soil and Water DB
- Source:R/HenryTimeLine.R+ Source:R/HenryTimeLine.RHenryTimeLine.Rd--This function generates a simple chart of start/end dates for a set of sensor data returned by
+soilDB::fetchHenry.This function generates a simple chart of start/end dates for non-NA sensor data returned by
soilDB::fetchHenry(). Data are organized according to sensor name + sensor depth.HenryTimeLine(sensor_data, ...)
++-+HenryTimeLine(sensor_data, ...)Arguments
--
+- -sensor_data -soiltemp,soilVWC, or related data returned bysoilDB::fetchHenry()- -... -additional arguments to
latticeExtra::segplot++Arguments
+- sensor_data +
soiltemp,soilVWC, or related data returned bysoilDB::fetchHenry()
- - ... +
additional arguments to
latticeExtra::segplot
-
Value
-a
-latticegraphics objectNote
+This function does not symbolize sections of missing data between the first and last record.
-Author
+++Value
+ +a
+latticegraphics objectLL2PLSS — LL2PLSS • sharpshootR - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -LL2PLSS — LL2PLSS • sharpshootR - - + + - - --- - + + diff --git a/docs/reference/PCP_plot.html b/docs/reference/PCP_plot.html index 5df89104..8e28a5dc 100644 --- a/docs/reference/PCP_plot.html +++ b/docs/reference/PCP_plot.html @@ -1,67 +1,12 @@ - - - - - - - -- - - -+-+--Uses lattitude and longitude coordinates to return the PLSS section geometry from the BLM PLSS web service.
+Uses latitude and longitude coordinates to return the PLSS section geometry from the BLM PLSS web service.
LL2PLSS(x, y, returnlevel = "I")
- -Arguments
--
- -- -x -longitude coordinates
- -y -lattitude coordinates
- -returnlevel -'S' for "Section" or 'I' for "Intersection" (subsections)
Value
- -
-listof of PLSS codes and coordinates.Details
- -This function takes xy coordinates and returns the PLSS section geometry to the quarter-quarter section.
-returnleveloptions are defaulted to 'I' which returns smallest intersected sectional aliquot geometry, 'S' will return the section geometry of the coordinates. See https://gis.blm.gov/arcgis/rest/services/Cadastral/BLM_Natl_PLSS_CadNSDI/MapServer for details.Note
++-+LL2PLSS(x, y, returnlevel = c("I", "S"))This function requires the following packages:
-httr,jsonlite, andsp.See also
+++Arguments
+++Value
+ + +
+sfobject with geometry and PLSS definition.++Details
+This function accepts geographic coordinates and returns the PLSS fabric geometry to the quarter-quarter section.
+returnleveldefaults to 'I' which returns smallest intersected sectional aliquot geometry, 'S' will return the section geometry of the coordinates. See https://gis.blm.gov/arcgis/rest/services/Cadastral/BLM_Natl_PLSS_CadNSDI/MapServer for details.++Note
+This function requires the following packages:
+httr,jsonlite, andsp.++See also
+ +Percentiles of Cumulative Precipitation — PCP_plot • sharpshootR - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -Percentiles of Cumulative Precipitation — PCP_plot • sharpshootR - - - - + + ---- - - -++@@ -117,105 +52,88 @@-Percentiles of Cumulative Precipitation
Generate a plot representing percentiles of cumulative precipitation, given a historic record, and criteria for selecting a year of data for comparison.
PCP_plot(x, this.year, this.day = NULL, method = "exemplar", -q.color = "RoyalBlue", c.color = "firebrick", ...)
- -Arguments
--
- -- -x -result from
CDECquery()for now, will need to generalize to other sources- -this.year -a single water year, e.g. 2020
- -this.day -optional integer representing days since start of selected water year
- -method -'exemplar' or 'daily', currently 'exemplar' is the only method available
- -q.color -color of percentiles cumulative precipitation
- -c.color -color of selected year
- -... -addtional arguments to
plot()Details
- -This is very much a work in progress. Further examples at https://ncss-tech.github.io/AQP/sharpshootR/CDEC.html, and https://ncss-tech.github.io/AQP/sharpshootR/cumulative-PPT.html
-Value
- -Currently nothing is returned.
-Author
++-+PCP_plot( + x, + this.year, + this.day = NULL, + method = "exemplar", + q.color = "RoyalBlue", + c.color = "firebrick", + ... +)D.E. Beaudette
-See also
++Arguments
+- x +
result from
CDECqueryfor now, will need to generalize to other sources
-
- - this.year +
a single water year, e.g. 2020
+
+
+- this.day +
optional integer representing days since start of selected water year
+
+
+- method +
'exemplar' or 'daily', currently 'exemplar' is the only method available
+
+
+- q.color +
color of percentiles cumulative precipitation
+
+
+- c.color +
color of selected year
+
+
+- ... +
additional arguments to
plot
+
+
Examples
-# \donttest{ ++++Value
+ + +nothing, this function is called to create graphical output
+++Details
+This is very much a work in progress. Further examples at https://ncss-tech.github.io/AQP/sharpshootR/CDEC.html, and https://ncss-tech.github.io/AQP/sharpshootR/cumulative-PPT.html.
+++ -if(requireNamespace("curl") & - curl::has_internet() - ) { - - s <- 'SPW' -# get metadata -s.info <- CDEC_StationInfo(s) -# format title for cumulative PPT -title.text <- sprintf("%s [%s]", s.info$site.meta$Name, s) - -# get data -x <- CDECquery(id=s, sensor=45, interval='D', start='2000-01-01', end='2030-01-01') - -## NOTE: requires sharpshootR >= 1.6.1 -# plot -par(mar=c(4.5, 4.5, 2.5, 1.5)) -PCP_plot(x[1:(nrow(x)-60), ], ylab='Cumulative PPT (inches)', main=title.text, this.year = 2020) - -} -See also
+ +
-# } -PLSS2LL — PLSS2LL • sharpshootR - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -PLSS2LL — PLSS2LL • sharpshootR + + - - - - --- - + + diff --git a/docs/reference/Rplot001.png b/docs/reference/Rplot001.png index 83bfeedd..6a62b8bc 100644 Binary files a/docs/reference/Rplot001.png and b/docs/reference/Rplot001.png differ diff --git a/docs/reference/Rplot002.png b/docs/reference/Rplot002.png index 5b25e1f8..3a4624f9 100644 Binary files a/docs/reference/Rplot002.png and b/docs/reference/Rplot002.png differ diff --git a/docs/reference/Rplot003.png b/docs/reference/Rplot003.png index ce05cd40..523d196b 100644 Binary files a/docs/reference/Rplot003.png and b/docs/reference/Rplot003.png differ diff --git a/docs/reference/Rplot004.png b/docs/reference/Rplot004.png index 6459b5fd..d001d834 100644 Binary files a/docs/reference/Rplot004.png and b/docs/reference/Rplot004.png differ diff --git a/docs/reference/Rplot005.png b/docs/reference/Rplot005.png index f3c5aa6d..f12caaf5 100644 Binary files a/docs/reference/Rplot005.png and b/docs/reference/Rplot005.png differ diff --git a/docs/reference/Rplot006.png b/docs/reference/Rplot006.png index 3e7dab6c..84437750 100644 Binary files a/docs/reference/Rplot006.png and b/docs/reference/Rplot006.png differ diff --git a/docs/reference/SoilTaxonomyDendrogram-1.png b/docs/reference/SoilTaxonomyDendrogram-1.png index 5e7a7790..1cd83547 100644 Binary files a/docs/reference/SoilTaxonomyDendrogram-1.png and b/docs/reference/SoilTaxonomyDendrogram-1.png differ diff --git a/docs/reference/SoilTaxonomyDendrogram-2.png b/docs/reference/SoilTaxonomyDendrogram-2.png index 5e7a7790..6f38e48d 100644 Binary files a/docs/reference/SoilTaxonomyDendrogram-2.png and b/docs/reference/SoilTaxonomyDendrogram-2.png differ diff --git a/docs/reference/SoilTaxonomyDendrogram.html b/docs/reference/SoilTaxonomyDendrogram.html index 51e44dc1..15090caf 100644 --- a/docs/reference/SoilTaxonomyDendrogram.html +++ b/docs/reference/SoilTaxonomyDendrogram.html @@ -1,67 +1,12 @@ - - - - - - - -- - - -+-+--Fetch latitude and longitude centroid coordinates for coded PLSS information from the BLM PLSS web service.
+Fetch latitude and longitude (centroid) coordinates for coded PLSS information from the BLM PLSS web service.
PLSS2LL(p, plssid = "plssid")
- -Arguments
--
+- -p -data.frame with chunks of PLSS coordinates
- -plssid -Column name containing PLSS ID (default:
"plssid")+-+PLSS2LL(p, plssid = "plssid")Value
+++Arguments
+- p +
data.framewith chunks of PLSS definition
- - plssid +
column name containing PLSS ID
-
-
A
-data.frameof PLSS codes and coordinates.Note
-This function expects that the dataframe will have a 'plssid' column generated by the
-formatPLSSfunction. Requires the following packages:httr, andjsonlite.See also
+Author
+++Value
+ +A
+data.frameof PLSS codes and coordinates.++Note
+This function expects that the dataframe will have a 'plssid' column generated by the
+formatPLSSfunction. Requires the following packages:httr, andjsonlite.++See also
+ +Soil Taxonomy Dendrogram — SoilTaxonomyDendrogram • sharpshootR - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -Soil Taxonomy Dendrogram — SoilTaxonomyDendrogram • sharpshootR - - + + - - --- - + + diff --git a/docs/reference/aggregateColorPlot-1.png b/docs/reference/aggregateColorPlot-1.png index 0a95ecb9..428a89c7 100644 Binary files a/docs/reference/aggregateColorPlot-1.png and b/docs/reference/aggregateColorPlot-1.png differ diff --git a/docs/reference/aggregateColorPlot.html b/docs/reference/aggregateColorPlot.html index bf2973f2..e324f1f6 100644 --- a/docs/reference/aggregateColorPlot.html +++ b/docs/reference/aggregateColorPlot.html @@ -1,67 +1,12 @@ - - - - - - - -- - - -+-+Soil Taxonomy Dendrogram
- Source:R/SoilTaxonomyDendrogram.R+ Source:R/SoilTaxonomyDendrogram.RSoilTaxonomyDendrogram.Rd--Plot a dendrogram based on the first 4 levels of Soil Taxonomy, with soil profiles hanging below. A dissimilarity matrix is computed using Gower's distance metric for nominal-scale variables, based on order, sub order, great group, and subgroup level taxa. See the Details and Examples sections below for more information.
+Plot a dendrogram based on the first 4 levels of Soil Taxonomy, with soil profiles hanging below. A dissimilarity matrix is computed using Gower's distance metric for nominal (
KST.order = FALSE) or ordinal (KST.order = TRUE) scale variables, based on soil order, suborder, greatgroup, and subgroup taxa.SoilTaxonomyDendrogram( - spc, - name = "hzname", - name.style = "right-center", - rotationOrder = NULL, - max.depth = 150, - n.depth.ticks = 6, - scaling.factor = 0.015, - cex.names = 0.75, - cex.id = 0.75, - axis.line.offset = -4, - width = 0.1, - y.offset = 0.5, - shrink = FALSE, - font.id = 2, - cex.taxon.labels = 0.66, - dend.color = par("fg"), - dend.width = 1, - ... -)
- -Arguments
--
- -- -spc -a
SoilProfileCollectionobject, typically returned bysoilDB::fetchOSD- -name -column name containing horizon names
- -name.style -passed to
aqp::plotSPC(default: "right-center")- -rotationOrder -numeric vector with desired ordering of leaves in the dendrogram from left to right, or character vector matching profile IDs
- -max.depth -depth at which profiles are truncated for plotting
- -n.depth.ticks -suggested number of ticks on the depth axis
- -scaling.factor -scaling factor used to convert depth units into plotting units
- -cex.names -character scaling for horizon names
- -cex.id -character scaling for profile IDs
- -axis.line.offset -horizontal offset for depth axis
- -width -width of profiles
- -y.offset -vertical offset between dendrogram and profiles
- -shrink -logical, should long horizon names be shrunk by 80% ?
- -font.id -font style applied to profile id, default is 2 (bold)
- -cex.taxon.labels -character scaling for taxonomic information
- -dend.color -dendrogram line color
- -dend.width -dendrogram line width
- -... -additional arguments to
aqp::plotSPCValue
- -An invisibly-returned list containing:
-
-
dist: pair-wise dissimilarity matrix
-order: final ordering of hclust leaves
-
Details
- -This function looks for specific site-level attributes named:
-soilorder,suborder,greatgroup, andsubgroup.The
-rotationOrderargument uses (requires) thedendextend::rotate()function to re-order leaves within thehclustrepresentation of the ST hierarchy. Perfect sorting is not always possible.Author
++-+SoilTaxonomyDendrogram( + spc, + KST.order = TRUE, + rotationOrder = NULL, + level = c(soilorder = "soilorder", suborder = "suborder", greatgroup = "greatgroup", + subgroup = "subgroup"), + cluster.method = c("divisive", "agglomerative"), + cluster.args = list(), + name = "hzname", + name.style = "center-center", + id.style = "side", + max.depth = max(spc), + n.depth.ticks = 6, + scaling.factor = 0.015, + cex.names = 0.75, + cex.id = 0.75, + axis.line.offset = -4, + width = 0.1, + y.offset = 0.5, + shrink = FALSE, + font.id = 2, + cex.taxon.labels = 0.66, + dend.color = par("fg"), + dend.width = 1, + ... +)D.E. Beaudette
+++Arguments
+- spc +
a
SoilProfileCollectionobject, typically returned bysoilDB::fetchOSD
- - KST.order +
logical, encode / cluster taxa via ordinal factors, based on ordering within Keys to Soil Taxonomy
+
+
+- rotationOrder +
character vector of profile IDs with desired ordering of leaves in the dendrogram from left to right; exact ordering is not always possible
+
-- level +
character. One or more site-level columns in
spc. Default:"soilorder","suborder","greatgroup"and"subgroup"
+
+
+- cluster.method +
Either "divisive" (
cluster::diana(); default) or "agglomerative" (cluster::agnes())
+
+
+- cluster.args +
Optional: additional arguments for
cluster::diana()orcluster::agnes()cluster methods
+
+
+- name +
column name containing horizon names
+
+
+- name.style +
passed to
aqp::plotSPC
+
+
+- id.style +
passed to
aqp::plotSPC
+
+
+- max.depth +
depth at which profiles are truncated for plotting
+
+
+- n.depth.ticks +
suggested number of ticks on the depth axis
+
+
+- scaling.factor +
scaling factor used to convert depth units into plotting units
+
+
+- cex.names +
character scaling for horizon names
+
+
+- cex.id +
character scaling for profile IDs
+
+
+- axis.line.offset +
horizontal offset for depth axis
+
+
+- width +
width of profiles
+
+
+- y.offset +
vertical offset between dendrogram and profiles
+
+
+- shrink +
logical, should long horizon names be shrunk by 80% ?
+
+
+- font.id +
font style applied to profile id, default is 2 (bold)
+
+
+- cex.taxon.labels +
character scaling for taxonomic information
+
+
+- dend.color +
dendrogram line color
+
+
+- dend.width +
dendrogram line width
+
+
+- ... +
additional arguments to
aqp::plotSPC
+
+
Examples
-
+-# \donttest{ -if(requireNamespace("curl") & - curl::has_internet() & - require(aqp) & - require(soilDB) -) { - - - - # soils of interest - s.list <- c('musick', 'cecil', 'drummer', 'amador', 'pentz', 'reiff', - 'san joaquin','montpellier','grangeville','pollasky','ramona') - - # fetch and convert data into an SPC - h <- fetchOSD(s.list) - - # plot dendrogram + profiles - SoilTaxonomyDendrogram(h) - - # again, this time save the pair-wise dissimilarity matrix - # note that there isn't a lot of discrimination between soils - (d <- SoilTaxonomyDendrogram(h)) - - - # a different set - soils <- c('cecil', 'altavista', 'lloyd', 'wickham', 'wilkes', - 'chewacla', 'congaree') - - # get morphology + extended summaries for sorting of dendrogram - s <- fetchOSD(soils, extended = TRUE) - - # get summary and ignore the figure - res <- vizHillslopePosition(s$hillpos) - - # compare default sorting to soils sorting according to catenary, e.g. - # hillslope position - par(mar=c(0,0,1,1), mfrow=c(2,1)) - - SoilTaxonomyDendrogram(s$SPC, width=0.25) - mtext('default sorting', side = 2, line=-1, font=3, cex=1.25) - - SoilTaxonomyDendrogram(s$SPC, rotationOrder = res$order, width=0.25) - mtext('approx. catenary sorting', side = 2, line=-1, font=3, cex=1.25) - - -# classic chronosequence from the San Joaquin Valley, CA -library(aqp) -library(soilDB) -library(sharpshootR) - -s <- c('tujunga', 'hanford', 'greenfield', 'snelling', 'san joaquin') -osds <- fetchOSD(s) - -idx <- match(toupper(s), profile_id(osds)) - -# encode horizon boundarydistinctness via vertical offset -osds$hd <- hzDistinctnessCodeToOffset( - osds$distinctness, - codes=c('very abrupt', 'abrupt', 'clear', 'gradual', 'diffuse') -) - -# encode horizon boundary topography via vertical offset -osds$hzto <- hzTopographyCodeToOffset( - osds$topography, - codes = c('smooth', 'wavy', 'irregular', 'broken') -) - -# also encode horizon boundary topography las line type -osds$hzto.lty <- hzTopographyCodeToLineType( - osds$topography, - codes = c('smooth', 'wavy', 'irregular', 'broken') -) - -# label data source, used later -site(osds)$source <- 'OSD' - -# concise representation of hz bnd distinctness and topography -# similar to field notes -osds$bnd.code <- sprintf( - "%s%s", - substr(osds$distinctness, 1, 1), - substr(osds$topography, 1, 1) -) - -# remove NA -osds$bnd.code <- gsub('NANA', '', osds$bnd.code) - -par(mar = c(0, 0, 0, 1), bg = 'black', fg = 'white') - -plotSPC( -osds, -plot.order = idx, -width = 0.3, -name.style = 'center-center', -cex.names = 0.66, -plot.depth.axis = FALSE, -hz.depths = TRUE, -shrink = TRUE, -hz.distinctness.offset = 'hd', -hz.topography.offset = 'hzto', -hz.boundary.lty = 'hzto.lty' -) - -legend( -'bottomright', -horiz = TRUE, -legend = c('Smooth', 'Wavy', 'Irregular', 'Broken'), -lty = 1:4, -inset = 0.05, -bty = 'n', -cex = 0.85 -) - -# note that `rotationOrder` uses the ordering of series names (uppercase to match profile IDs) -# to re-order the terminal branches of the dendrogram -SoilTaxonomyDendrogram( -osds, -rotationOrder = toupper(s), -cex.taxon.labels = 0.85, -width = 0.3, -name.style = 'center-center', -cex.names = 0.66, -plot.depth.axis = FALSE, -hz.depths = TRUE, -shrink = TRUE, -hz.distinctness.offset = 'hd', -hz.topography.offset = 'hzto', -hz.boundary.lty = 'hzto.lty' -) - -legend( -'bottomright', -horiz = TRUE, -legend = c('Smooth', 'Wavy', 'Irregular', 'Broken'), -lty = 1:4, -inset = 0.05, -bty = 'n', -cex = 0.85 -) - -} -
#> -#> -#>
-# } +++Value
+ + +An invisibly-returned list containing:
dist: pair-wise dissimilarity matrix
+order: final ordering ofhclustleaves
+
++ + +Details
+This function looks for specific site-level attributes named:
+"soilorder","suborder","greatgroup", and"subgroup", or their NASIS physical column name analogues"taxorder","taxsuborder","taxgrtgroup", and"taxsubgrp". See https://github.com/ncss-tech/sharpshootR/blob/master/misc/soilTaxonomyDendrogram-examples.R for some examples.The
+rotationOrderargument usesape::rotateConstr()to reorder leaves within thehclustrepresentation of the ST hierarchy. Perfect sorting is not always possible.+Examples
+++# built-in data, same as results from soilDB::fetchOSD() +data("OSDexamples") + +# examples using first 8 profiles + +# KST-style ordering +SoilTaxonomyDendrogram( + OSDexamples$SPC[1:8, ], width = 0.3, name.style = 'center-center', + KST.order = TRUE +) +
+
+# classic ordering, based on nominal scale variables (unordered factors)
+SoilTaxonomyDendrogram(
+ OSDexamples$SPC[1:8, ], width = 0.3, name.style = 'center-center',
+ KST.order = FALSE
+)
+
+
+
+Plot aggregate soil color data — aggregateColorPlot • sharpshootR - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -Plot aggregate soil color data — aggregateColorPlot • sharpshootR - - - - + + --- - + + diff --git a/docs/reference/amador.html b/docs/reference/amador.html index db16782d..51a63546 100644 --- a/docs/reference/amador.html +++ b/docs/reference/amador.html @@ -1,67 +1,12 @@ - - - - - - - -- - - -+-+--Generate a plot from summaries generated by aqp::aggregateColor().
+Generate a plot from summaries generated by
aqp::aggregateColor().aggregateColorPlot(x, print.label=TRUE, label.font = 1, -label.cex = 0.65, buffer.pct = 0.02, print.n.hz=FALSE, -rect.border='black', horizontal.borders=FALSE, -horizontal.border.lwd=2, x.axis=TRUE, y.axis=TRUE, -...)
- -Arguments
--
- -- -x -a list, results from
aqp::aggregateColor()- -print.label -print Munsell color labels inside of rectangles, when they fit
- -label.font -font specification for color labels
- -label.cex -font size for color lables
- -buffer.pct -extra space between labels and color rectangles
- -print.n.hz -optionally print the number of horizons
- -rect.border -color for rectangle border
- -horizontal.borders -optionally add horizontal borders between bands of color
- -horizontal.border.lwd -line width for horizontal borders
- -x.axis -logical: add a scale and label to x-axis?
- -y.axis -logical: add group labels to y-axis?
- -... -additional arguments passed to plot
Details
- -Tutorial at http://ncss-tech.github.io/AQP/sharpshootR/aggregate-soil-color.html.
-Author
++-+aggregateColorPlot( + x, + print.label = TRUE, + label.font = 1, + label.cex = 0.65, + label.orientation = c("v", "h"), + buffer.pct = 0.02, + print.n.hz = FALSE, + rect.border = "black", + horizontal.borders = FALSE, + horizontal.border.lwd = 2, + x.axis = TRUE, + y.axis = TRUE, + ... +)D.E. Beaudette
+++Arguments
+- x +
a
list, results fromaqp::aggregateColor()
- - print.label +
logical, print Munsell color labels inside of rectangles, only if they fit
-# 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)
+- label.font +
font specification for color labels
-# aggregate color data, this function is from the `aqp` package
-a <- aggregateColor(loafercreek, 'genhz')
-# plot
-par(mar=c(4,4,1,1))
-aggregateColorPlot(a, print.n.hz = TRUE)
-
-}
-- label.cex +
font size for color labels
+
+
+- label.orientation +
label orientation,
vfor vertical orhfor horizontal
+
+
+- buffer.pct +
extra space between labels and color rectangles
+
+
+- print.n.hz +
optionally print the number of horizons below Munsell color labels
+
+
+- rect.border +
color for rectangle border
+
+
+- horizontal.borders +
optionally add horizontal borders between bands of color
+
+
+- horizontal.border.lwd +
line width for horizontal borders
-# }
-- x.axis +
logical, add a scale and label to x-axis?
+
+
+- y.axis +
logical, add group labels to y-axis?
+
+
+- ... +
additional arguments passed to
plot
+
+
Examples
-
+# \donttest{ -if(requireNamespace("curl") & - curl::has_internet() & - require(aqp) & - require(soilDB)) { - - data(loafercreek, package = 'soilDB') +#>#>#> -#>#> -#> -#>#>
+++Value
+ + +nothing, function called for graphical output
+++ + +Details
+Tutorial at http://ncss-tech.github.io/AQP/sharpshootR/aggregate-soil-color.html.
++Examples
+++# \donttest{ + +if(require(aqp) & + require(soilDB)) { + + 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') + + # plot + op <- par(no.readonly = TRUE) + + par(mar=c(4,4,1,1)) + + # vertical labels, the default + aggregateColorPlot(a, print.n.hz = TRUE) + + # horizontal labels + aggregateColorPlot(a, print.n.hz = TRUE, label.orientation = 'h') + + par(op) + +} +#> Loading required package: aqp +#> This is aqp 2.0 +#> Loading required package: soilDB +
+
+
+# }
+SSURGO Data Associated with the Amador Soil Series — amador • sharpshootR - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -SSURGO Data Associated with the Amador Soil Series — amador • sharpshootR - - - - + + --- - + + diff --git a/docs/reference/aspect.plot-1.png b/docs/reference/aspect.plot-1.png index 51a547fd..9f5be733 100644 Binary files a/docs/reference/aspect.plot-1.png and b/docs/reference/aspect.plot-1.png differ diff --git a/docs/reference/aspect.plot.html b/docs/reference/aspect.plot.html index 6cb21f99..c2e470ed 100644 --- a/docs/reference/aspect.plot.html +++ b/docs/reference/aspect.plot.html @@ -1,67 +1,12 @@ - - - - - - - -- - - -+-+-SSURGO Data Associated with the Amador Soil Series
@@ -117,47 +52,49 @@SSURGO Data Associated with the Amador Soil Series
SSURGO Data Associated with the Amador Soil Series
data(amador)
- ++-+data(amador)Format
+++Format
+A subset of data taken from the "component" table of SSURGO
+Source
USDA-NRCS SSURGO Database
+Plot Aspect Data — aspect.plot • sharpshootR - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -Plot Aspect Data — aspect.plot • sharpshootR - - - - + + ---- - - -++@@ -117,137 +52,163 @@-Plot Aspect Data
Plot a graphical summary of multiple aspect measurements on a circular diagram.
aspect.plot(p, q=c(0.05, 0.5, 0.95), p.bins = 60, p.bw = 30, stack=TRUE, -p.axis = seq(0, 350, by = 10), plot.title = NULL, -line.col='RoyalBlue', line.lwd=1, line.lty=2, -arrow.col=line.col, arrow.lwd=1, arrow.lty=1, -arrow.length=0.15, -...)
- -Arguments
--
- -- -p -a vector of aspect angles in degrees, measured clock-wise from North
- -q -a vector of desired quantiles
- -p.bins -number of bins to use for circular histogram
- -p.bw -bandwidth used for circular density estimation
- -stack -TRUE/FALSE, should the individual points be stacked into
p.binsnumber of bins and plotted- -p.axis -a sequence of integers (degrees) describing the circular axis
- -plot.title -an informative title
- -line.col -density line color
- -line.lwd -density line width
- -line.lty -density line line style
- -arrow.col -arrow color
- -arrow.lwd -arrow line width
- -arrow.lty -arrow line style
- -arrow.length -arrow head length
- -... -further arguments passed to
plot.circularDetails
++ +-Spread and central tendency are depicted with a combination of circular histogram and kernel density estimate. The circular mean, and relative confidence in that mean are depicted with an arrow: longer arrow lengths correspond to greater confidence in the mean.
-Author
+++Arguments
+- p +
a vector of aspect angles in degrees, measured clock-wise from North
- - q +
a vector of desired quantiles
+
+
+- p.bins +
number of bins to use for circular histogram
+
+
+- p.bw +
bandwidth used for circular density estimation
+
+
+- stack +
logical, should the individual points be stacked intop.binsnumber of bins and plotted
+
+
+- p.axis +
a sequence of integers (degrees) describing the circular axis
+
+
+- plot.title +
an informative title
+
+
+- line.col +
density line color
+
+
+- line.lwd +
density line width
+
+
+- line.lty +
density line line style
+
+
+- arrow.col +
arrow color
+
+
+- arrow.lwd +
arrow line width
+
+
+- arrow.lty +
arrow line style
+
+
+- arrow.length +
arrow head length
+
+
+- ... +
further arguments passed to
circular::plot.circular
+
+
D.E. Beaudette
-Note
+++Value
+ + +invisibly returns circular stats
+++Details
+Spread and central tendency are depicted with a combination of circular histogram and kernel density estimate. The circular mean, and relative confidence in that mean are depicted with an arrow: longer arrow lengths correspond to greater confidence in the mean.
+++ -Note
Manual adjustment of
+p.bwmay be required in order to get an optimal circular density plot. This function requires the packagecircular, version 0.4-7 or later.Examples
-
+# simulate some data -p.narrow <- runif(n=25, min=215, max=280) -p.wide <- runif(n=25, min=0, max=270) - -# set figure margins to 0, 2-column plot -par(mar=c(0,0,0,0), mfcol=c(1,2)) - -# plot -aspect.plot(p.narrow, p.bw=10, plot.title='Soil A', pch=21, col='black', bg='RoyalBlue') -#> -#> Rayleigh Test of Uniformity -#> General Unimodal Alternative -#> -#> Test Statistic: 0.9431 -#> P-value: 0 -#>aspect.plot(p.wide, p.bw=10, plot.title='Soil B', pch=21, col='black', bg='RoyalBlue') -
#> -#> Rayleigh Test of Uniformity -#> General Unimodal Alternative -#> -#> Test Statistic: 0.3636 -#> P-value: 0.035 -#>+Examples
++# simulate some data +p.narrow <- runif(n=25, min=215, max=280) +p.wide <- runif(n=25, min=0, max=270) + +# set figure margins to 0, 2-column plot +op <- par(no.readonly = TRUE) +par(mar = c(0,0,0,0), mfcol = c(1,2)) + +# plot, save circular stats +x <- aspect.plot(p.narrow, p.bw=10, plot.title='Soil A', pch=21, col='black', bg='RoyalBlue') +y <- aspect.plot(p.wide, p.bw=10, plot.title='Soil B', pch=21, col='black', bg='RoyalBlue') +
+
+# reset output device options
+par(op)
+
+x
+#> Circular Data:
+#> Type = angles
+#> Units = degrees
+#> Template = geographics
+#> Modulo = 2pi
+#> Zero = 1.570796
+#> Rotation = clock
+#> 5% 50% 95%
+#> 274.1839 245.3117 217.7417
+#> attr(,"uniformity")
+#> Rayleigh Uniformity p Value
+#> 9.521243e-01 7.135418e-10
+
+
+Create a Venn Diagram of Simulated Color Mixtures — colorMixtureVenn • sharpshootR - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -Create a Venn Diagram of Simulated Color Mixtures — colorMixtureVenn • sharpshootR + + - - - - ---- - - -++-@@ -117,70 +52,99 @@Create a Venn Diagram of Simulated Color Mixtures
- Source:R/colorMixtureVenn.R+ Source:R/colorMixtureVenn.RcolorMixtureVenn.RdCreate a Venn Diagram of Simulated Color Mixtures
Create a Venn Diagram of Simulated Color Mixtures
colorMixtureVenn( - chips, - mixingMethod = "spectra", - ellipse = FALSE, - labels = TRUE -)
- -Arguments
--
- - -- -chips -character vector of standard Munsell color notation (e.g. "10YR 3/4")
- -mixingMethod -approach used to simulate a mixture: see
aqp::mixMunsellfor details- -ellipse -logical, use alternative ellipse-style (4 or 5 colors only)
- -labels -logical, print mixture labels
Examples
-
+-if(requireNamespace("venn") & requireNamespace("gower")) { - -# "tan" / "dark red" / "dark brown" -chips <- c('10YR 8/1', '2.5YR 3/6', '10YR 2/2') -colorMixtureVenn(chips) - -} -#>#>#>#>#>#>
+ ++ +++Arguments
+- chips +
character vector of standard Munsell color notation (e.g. "10YR 3/4")
+
+
+- w +
vector of proportions, can sum to any number, must be same length as
chips
+
+
+- mixingMethod +
approach used to simulate a mixture: see
aqp::mixMunsellfor details
+
+
+- ellipse +
logical, use alternative ellipse-style (4 or 5 colors only)
+
+
+- labels +
logical, print mixture labels
+
+
+- names +
logical, print names outside of the "sets"
+
+
+- sncs +
scaling factor for set names
+
+
++ +Value
+ + +nothing returned, function is called to create graphical output
++Examples
+++if(requireNamespace("venn") & requireNamespace("gower")) { + +chips <- c('10YR 8/1', '2.5YR 3/6', '10YR 2/2') +names(chips) <- c("tan", "dark red", "dark brown") + +colorMixtureVenn(chips) +colorMixtureVenn(chips, names = TRUE) + +colorMixtureVenn(chips, w = c(1, 1, 1), names = TRUE) +colorMixtureVenn(chips, w = c(10, 5, 1), names = TRUE) + +} +#> Loading required namespace: venn +
+
+
+
+
+Create an adjacency matrix from a data.frame of component data — component.adj.matrix • sharpshootR - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -Create an adjacency matrix from a data.frame of component data — component.adj.matrix • sharpshootR - - - - + + ---- - - -++-@@ -117,100 +52,111 @@Create an adjacency matrix from a data.frame of component data
- + Source:R/component.adj.matrix.Rcomponent.adj.matrix.RdCreate an adjacency matrix from a data.frame of component data
Create an adjacency matrix from SSURGO component data
component.adj.matrix(d, mu='mukey', co='compname', wt='comppct_r', -method='community.matrix', standardization='max', metric='jaccard', -rm.orphans=TRUE, similarity=TRUE, return.comm.matrix=FALSE)
- -Arguments
--
- -- -d -a
data.frame, typically of SSURGO data- -mu -name of the column containing the map unit ID (typically 'mukey')
- -co -name of the column containing the component ID (typically 'compname')
- -wt -name of the column containing the component weight percent (typically 'comppct_r')
- -method -one of either: `community.matrix`, or `occurrence`; see details
- -standardization -community matrix standardization method, passed to
decostand- -metric -community matrix dissimilarity metric, passed to
vegdist- -rm.orphans -logical, should map units with a single component be ommitted? (typically yes)
- -similarity -logical, return a similarity matrix? (if FALSE, a distance matrix is returned)
- -return.comm.matrix -logical, return pseudo-community matrix? (if TRUE no adjacency matrix is created)
Value
- -a similatiy matrix / ajacency matrix suitable for use with
-igraphfunctions or anything else that can accomodate a _similarity_ matrix.Author
++-+component.adj.matrix( + d, + mu = "mukey", + co = "compname", + wt = "comppct_r", + method = c("community.matrix", "occurrence"), + standardization = "max", + metric = "jaccard", + rm.orphans = TRUE, + similarity = TRUE, + return.comm.matrix = FALSE +)D.E. Beaudette
-Details
+++Arguments
+- d +
data.frame, typically of SSURGO data
+
+
+- mu +
name of the column containing the map unit ID (typically 'mukey')
+
+
+- co +
name of the column containing the component ID (typically 'compname')
+
+
+- wt +
name of the column containing the component weight percent (typically 'comppct_r')
+
+
+- method +
one of either:
community.matrix, oroccurrence; see details
+
+
+- standardization +
community matrix standardization method, passed to
vegan::decostand
- - metric +
community matrix dissimilarity metric, passed to
vegan::vegdist
-# convert into adjacency matrix
-m <- component.adj.matrix(amador)
-# plot network diagram, with Amador soil highlighted
-plotSoilRelationGraph(m, s='amador')
-- rm.orphans +
logical, should map units with a single component be omitted? (typically yes)
+
+
+- similarity +
logical, return a similarity matrix? (if
FALSE, a distance matrix is returned)
+
+
+- return.comm.matrix +
logical, return pseudo-community matrix? (if
TRUEno adjacency matrix is created)
+
+
Pending...
-Examples
-
+# load sample data set -data(amador) +
++ + +Value
+ + +a similarity matrix / adjacency matrix suitable for use with
+igraphfunctions or anything else that can accommodate a similarity matrix.+Examples
++if (requireNamespace("igraph")) { + # load sample data set + data(amador) + + # convert into adjacency matrix + m <- component.adj.matrix(amador) + + # plot network diagram, with Amador soil highlighted + plotSoilRelationGraph(m, s = 'amador') +} +#> Loading required namespace: igraph +
+Constant Density Sampling — constantDensitySampling • sharpshootR - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -Constant Density Sampling — constantDensitySampling • sharpshootR - - - - + + --- - + + diff --git a/docs/reference/dailyWB.html b/docs/reference/dailyWB.html index 0d96ab94..66b41d80 100644 --- a/docs/reference/dailyWB.html +++ b/docs/reference/dailyWB.html @@ -1,67 +1,12 @@ - - - - - - - -- - - -+-+-Constant Density Sampling
@@ -117,84 +52,73 @@Constant Density Sampling
Perform sampling at a constant density over all polygons within a SpatialPolygonsDataFrame object.
constantDensitySampling(x, polygon.id='pID', parallel=FALSE, cores=NULL, -n.pts.per.ac=1, min.samples=5, sampling.type='regular', iterations=10)
- -Arguments
--
- -- -x -a
SpatialPolygonsDataFrameobject in a projected CRS with units of meters- -polygon.id -name of attribute in
xthat contains a unique ID for each polygon- -parallel -invoke parallel back-end
- -cores -number of CPU cores to use for parallel operation
- -n.pts.per.ac -requested sampling density in points per acre (results will be close)
- -min.samples -minimum requested number of samples per polygon
- -sampling.type -sampling type, see
spsample- -iterations -number of tries that
spsamplewill attemptValue
++-+constantDensitySampling(x, polygon.id='pID', parallel=FALSE, cores=NULL, +n.pts.per.ac=1, min.samples=5, sampling.type='regular')a
-SpatialPointsDataFrameobjectAuthor
+++Arguments
+- x +
a
SpatialPolygonsDataFrameobject in a projected CRS with units of meters
- - polygon.id +
name of attribute in
xthat contains a unique ID for each polygon
- - parallel +
invoke parallel back-end
+
+ - cores +
number of CPU cores to use for parallel operation
+
+ - n.pts.per.ac +
requested sampling density in points per acre (results will be close)
-
+ - min.samples +
minimum requested number of samples per polygon
+
+ - sampling.type +
sampling type
+
+
D.E. Beaudette
-Note
+This function expects that
-xhas coordinates associated with a projected CRS and units of meters.See also
+++ +Value
+a
+SpatialPointsDataFrameobject++Note
+This function expects that
+xhas coordinates associated with a projected CRS and units of meters.+See also
+ +Simple Daily Water Balance — dailyWB • sharpshootR - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -Simple Daily Water Balance — dailyWB • sharpshootR - - + + - - --- - - -+++@@ -117,73 +52,84 @@-Simple Daily Water Balance
Simple interface to the hydromad "leaky bucket" soil moisture model, with accommodation for typical inputs from common soil data and climate sources. Critical points along the water retention curve are specified using volumetric water content (VWC): satiation (saturation), field capacity (typically 1/3 bar suction), and permanent wilting point (typically 15 bar suction).
dailyWB(x, daily.data, id, S_0 = 0.5)
++-+dailyWB(x, daily.data, id, MS.style = "default", S_0 = 0.5, M = 0, etmult = 1)Arguments
--
- -- -x -data.frame, required columns include:-
-
sat: VWC at satiation
+ - x +
data.frame, required columns include:sat: VWC at satiationfc: VWC at field capacitypwp: VWC at permanent wilting pointthickness: soil material thickness in cma.ss: recession coefficients for subsurface flow from saturated zone, should be > 0 (range: 0-1)"id"
-
+Arguments
+- -daily.data -data.frame, required columns include:-
-
date:Dateclass representation of dates
+
- daily.data
+data.frame, required columns include:date:Dateclass representation of datesPPT: daily total, precipitation in mmPET: daily total, potential ET in mm
-
- -id -character, name of column in
xthat is used to identify records- -S_0 -fraction of water storage filled at time = 0 (range: 0-1)
Value
- -a
-data.frameReferences
+ + + +- id
+character, name of column in
xthat is used to identify records- MS.style
+moisture state classification style, see
estimateSoilMoistureState- S_0
+fraction of water storage filled at time = 0 (range: 0-1)
- M
+fraction of area covered by deep-rooted vegetation
- etmult
+multiplier for PET
++Value
+ + +a
+ + +data.frame+References
Farmer, D., M. Sivapalan, Farmer, D. (2003). Climate, soil and vegetation controls upon the variability of water balance in temperate and semiarid landscapes: downward approach to water balance analysis. Water Resources Research 39(2), p 1035.
Bai, Y., T. Wagener, P. Reed (2009). A top-down framework for watershed model evaluation and selection under uncertainty. Environmental Modelling and Software 24(8), pp. 901-916.
+Perform daily water balance modeling using SSURGO and DAYMET — dailyWB_SSURGO • sharpshootR - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -Perform daily water balance modeling using SSURGO and DAYMET — dailyWB_SSURGO • sharpshootR - - + + - - --- - + + diff --git a/docs/reference/diagnosticPropertyPlot-1.png b/docs/reference/diagnosticPropertyPlot-1.png index e5ddcdf5..c8941c1c 100644 Binary files a/docs/reference/diagnosticPropertyPlot-1.png and b/docs/reference/diagnosticPropertyPlot-1.png differ diff --git a/docs/reference/diagnosticPropertyPlot-2.png b/docs/reference/diagnosticPropertyPlot-2.png index 4d8d9fe9..f1490e64 100644 Binary files a/docs/reference/diagnosticPropertyPlot-2.png and b/docs/reference/diagnosticPropertyPlot-2.png differ diff --git a/docs/reference/diagnosticPropertyPlot.html b/docs/reference/diagnosticPropertyPlot.html index 35f2ef93..733cf383 100644 --- a/docs/reference/diagnosticPropertyPlot.html +++ b/docs/reference/diagnosticPropertyPlot.html @@ -1,67 +1,12 @@ - - - - - - - -- - - -+-+Perform daily water balance modeling using SSURGO and DAYMET
- Source:R/dailyWB_SSURGO.R+ Source:R/dailyWB_SSURGO.RdailyWB_SSURGO.Rd-+ +Perform daily water balance modeling using SSURGO and DAYMET
+Pending.
++-dailyWB_SSURGO( + x, + cokeys = NULL, + start = 1988, + end = 2018, + modelDepth = 100, + MS.style = "default", + a.ss = 0.1, + S_0 = 0.5, + bufferRadiusMeters = 1 +)dailyWB_SSURGO( - x, - cokeys = NULL, - start = 1988, - end = 2018, - modelDepth = 100, - bufferRadiusMeters = 1, - ... -)
- -Arguments
--
- -- -x -SpatialPointsobject representing a single point- -cokeys -vector of component keys to use
- -start -starting year (limited to DAYMET holdings)
- -end -ending year (limited to DAYMET holdings)
- -modelDepth -soil depth used for water balance, see details
- -bufferRadiusMeters -spatial buffer (meters) applied to
xfor the lookup of SSURGO data- -... -additional arguments to
simpleWBValue
- -
-data.frameof daily water balance resultsReferences
+++Arguments
+- x +
sfobject representing a single point
- - cokeys +
vector of component keys to use
+
+
+- start +
starting year (limited to DAYMET holdings)
+
+
+- end +
ending year (limited to DAYMET holdings)
+
+
+- modelDepth +
soil depth used for water balance, see details
+
+
+- MS.style +
moisture state classification style, see
estimateSoilMoistureState
+
+
+- a.ss +
recession coefficients for subsurface flow from saturated zone, should be > 0 (range: 0-1)
+
+
+- S_0 +
fraction of water storage filled at time = 0 (range: 0-1)
+
+
+- bufferRadiusMeters +
spatial buffer (meters) applied to
xfor the look-up of SSURGO data
+
+
Farmer, D., M. Sivapalan, Farmer, D. (2003). Climate, soil and vegetation controls upon the variability of water balance in temperate and semiarid landscapes: downward approach to water balance analysis. Water Resources Research 39(2), p 1035.
-Author
+++Value
+ + +
+data.frameof daily water balance results++References
+Farmer, D., M. Sivapalan, Farmer, D. (2003). Climate, soil and vegetation controls upon the variability of water balance in temperate and semiarid landscapes: downward approach to water balance analysis. Water Resources Research 39(2), p 1035.
+Diagnostic Property Plot — diagnosticPropertyPlot • sharpshootR - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -Diagnostic Property Plot (base graphics) — diagnosticPropertyPlot • sharpshootR - - - - + + ---- - - -++--@@ -117,125 +52,139 @@Diagnostic Property Plot
- +Diagnostic Property Plot (base graphics)
+ Source:R/diagnosticPropertyPlot.RdiagnosticPropertyPlot.RdDiagnostic Property Plot
Generate a graphical description of the presence/absence of soil diagnostic properties.
diagnosticPropertyPlot(f, v, k, grid.label='pedon_id', -dend.label='pedon_id', sort.vars=TRUE) -diagnosticPropertyPlot2(f, v, k, grid.label='pedon_id', sort.vars=TRUE)
- -Arguments
--
- -- -f -a
SoilProfileCollectionobject- -v -a character vector of site-level attribute names that are boolean (e.g. TRUE/FALSE) data
- -k -an integer, number of groups to highlight
- -grid.label -the name of a site-level attribute (usually unique) annotating the y-axis of the grid
- -dend.label -the name of a site-level attribute (usually unique) annotating dendrogram terminal leaves
- -sort.vars -sort variables according to natural clustering (TRUE), or use supplied ordering in
v(FALSE)Details
++-+diagnosticPropertyPlot( + f, + v, + k, + grid.label = "pedon_id", + dend.label = "pedon_id", + sort.vars = TRUE +)This function attempts to display several pieces of information within a single figure. First, soil profiles are sorted according to the presence/absence of diagnostic features named in
-v. Second, these diagnostic features are sorted according to their distribution among soil profiles. Third, a binary grid is established with row-ordering of profiles based on step 1 and column-ordering based on step 2. Blue cells represent the presence of a diagnostic feature. Soils with similar diagnostic features should 'clump' together. See examples below.Value
+++Arguments
+- f +
SoilProfileCollectionobject
- rda
data.framecontaining IDs and grouping code
- profile.ordera vector containing the order of soil profiles (row-order in figure), according to diagnostic property values
- var.ordera vector containing the order of variables (column-order in figure), according to their distribution among profiles
-- v +
character vector of site-level attribute names of
logicaltype
- - k +
an integer, number of groups to highlight
-
- - grid.label +
the name of a site-level attribute (usually unique) annotating the y-axis of the grid
-if(require(aqp) &
- require(soilDB) &
- require(latticeExtra)
- ) {
-
- # sample data, an SPC
-data(gopheridge, package='soilDB')
-# get depth class
-sdc <- getSoilDepthClass(gopheridge)
-site(gopheridge) <- sdc
+- dend.label +
the name of a site-level attribute (usually unique) annotating dendrogram terminal leaves
-# diagnostic properties to consider, no need to convert to factors
-v <- c('lithic.contact', 'paralithic.contact', 'argillic.horizon',
-'cambic.horizon', 'ochric.epipedon', 'mollic.epipedon', 'very.shallow',
-'shallow', 'mod.deep', 'deep', 'very.deep')
-# base graphics
-x <- diagnosticPropertyPlot(gopheridge, v, k=5)
+- sort.vars +
sort variables according to natural clustering (
TRUE), or use supplied ordering inv
-# lattice graphics
-x <- diagnosticPropertyPlot2(gopheridge, v, k=3)
+rd
+a
data.framecontaining IDs and grouping code
+
+profile.order
+a vector containing the order of soil profiles (row-order in figure), according to diagnostic property values
+var.order
+a vector containing the order of variables (column-order in figure), according to their distribution among profiles
-# }
-
a list is silently returned by this function, containing:
-
-
Author
-D.E. Beaudette and J.M. Skovlin
-See also
+Examples
-
+# \donttest{ ++
#> List of 3 -#> $ rd :'data.frame': 52 obs. of 3 variables: -#> ..$ peiid : chr [1:52] "1137354" "1147151" "1147190" "242808" ... -#> ..$ pedon_id: chr [1:52] "08DWB028" "07RJV098" "07RJV099" "S2007CA009002" ... -#> ..$ g : int [1:52] 1 2 2 1 1 1 1 1 1 1 ... -#> $ profile.order: int [1:52] 1 4 6 7 8 9 15 16 17 21 ... -#> $ var.order : int [1:8] 1 3 5 8 2 4 6 7+a
listis silently returned by this function, containing:++Details
+This function attempts to display several pieces of information within a single figure. First, soil profiles are sorted according to the presence/absence of diagnostic features named in
+v. Second, these diagnostic features are sorted according to their distribution among soil profiles. Third, a binary grid is established with row-ordering of profiles based on step 1 and column-ordering based on step 2. Blue cells represent the presence of a diagnostic feature. Soils with similar diagnostic features should 'clump' together. See examples below.++ + +See also
+ ++Examples
+++# \donttest{ + +if(require(aqp) & + require(soilDB) & + require(latticeExtra) +) { + + # sample data, an SPC + data(gopheridge, package='soilDB') + + # get depth class + sdc <- getSoilDepthClass(gopheridge, name = 'hzname') + site(gopheridge) <- sdc + + # diagnostic properties to consider, no need to convert to factors + v <- c('lithic.contact', 'paralithic.contact', 'argillic.horizon', + 'cambic.horizon', 'ochric.epipedon', 'mollic.epipedon', 'very.shallow', + 'shallow', 'mod.deep', 'deep', 'very.deep') + + # base graphics + x <- diagnosticPropertyPlot(gopheridge, v, k=5) + + # lattice graphics + x <- diagnosticPropertyPlot2(gopheridge, v, k=3) + + # check output + str(x) + +} +#> Loading required package: latticeExtra +#> Loading required package: lattice +
+
+#> List of 3
+#> $ rd :'data.frame': 52 obs. of 3 variables:
+#> ..$ peiid : chr [1:52] "1137354" "1147151" "1147190" "242808" ...
+#> ..$ pedon_id: chr [1:52] "08DWB028" "07RJV098" "07RJV099" "S2007CA009002" ...
+#> ..$ g : int [1:52] 1 2 2 1 1 1 1 1 1 1 ...
+#> $ profile.order: int [1:52] 1 4 6 7 8 9 15 16 17 21 ...
+#> $ var.order : int [1:8] 1 3 5 8 2 4 6 7
+
+# }
+Compute Euclidean distance along a gradient. — dist.along.grad • sharpshootR - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -Compute Euclidean distance along a gradient. — dist.along.grad • sharpshootR - - - - + + --- - + + diff --git a/docs/reference/dueling.dendrograms.html b/docs/reference/dueling.dendrograms.html index bb5014e7..da641ccb 100644 --- a/docs/reference/dueling.dendrograms.html +++ b/docs/reference/dueling.dendrograms.html @@ -1,67 +1,12 @@ - - - - - - - -- - - -+-+-Compute Euclidean distance along a gradient.
@@ -117,84 +52,91 @@Compute Euclidean distance along a gradient.
This function computes Euclidean distance along points aligned to a given gradient (e.g. elevation).
dist.along.grad(coords, var, grad.order, grad.scaled.min, grad.scaled.max)
- -Arguments
--
- -- -coords -a matrix of x and y coordinates in some projected coordinate system
- -var -a vector of the same length as
coords, describing the gradient of interest- -grad.order -vector of integers that define ordering of coordinates along gradient
- -grad.scaled.min -min value of rescaled gradient values
- -grad.scaled.max -max value of rescaled gradient values
Details
- -This function is primarily intended for use within
-plotTransect.Value
- -A
data.frameobject:-
-
- scaled.grad
scaled gradient values
- - scaled.distance
cumulative distance, scaled to the interval of
0.5, nrow(coords) + 0.5
- - distance
cumulative distance computed along gradient, e.g. transect distance
- - variable
sorted gradient values
- - x
x coordinates, ordered by gradient values
- - y
y coordinate, ordered by gradient values
- - grad.order
a vector index describing the sort order defined by gradient values
-
-
+-+dist.along.grad(coords, var, grad.order, grad.scaled.min, grad.scaled.max)Author
+++Arguments
+- coords +
a matrix of x and y coordinates in some projected coordinate system
- - var +
a vector of the same length as
coords, describing the gradient of interest
- - grad.order +
vector of integers that define ordering of coordinates along gradient
+
+ - grad.scaled.min +
min value of rescaled gradient values
+
+ - grad.scaled.max +
max value of rescaled gradient values
+
+
D.E. Beaudette
-Note
+This function is very much a work in progress, ideas welcome.
-See also
+++Details
+This function is primarily intended for use within
+plotTransect.++ +Value
+A
data.frameobject:- scaled.grad +
scaled gradient values
+
+ - scaled.distance +
cumulative distance, scaled to the interval of
0.5, nrow(coords) + 0.5
+
+ - distance +
cumulative distance computed along gradient, e.g. transect distance
-
+ - variable +
sorted gradient values
+
+ - x +
x coordinates, ordered by gradient values
+
+ - y +
y coordinate, ordered by gradient values
+
+ - grad.order +
a vector index describing the sort order defined by gradient values
+
+
+
++Note
+This function is very much a work in progress, ideas welcome.
++See also
+ +Dueling Dendrograms — dueling.dendrograms • sharpshootR - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -Dueling Dendrograms — dueling.dendrograms • sharpshootR - - - - + + ---- - - -++@@ -117,109 +52,79 @@-Dueling Dendrograms
Graphically compare two related dendrograms
dueling.dendrograms(p.1, p.2, lab.1 = "D1", -lab.2 = "D2", cex.nodelabels=0.75, arrow.length=0.05)
- -Arguments
--
- -- -p.1 -left-hand phylo-class dendrogram
- -p.2 -right-hand phylo-class dendrogram
- -lab.1 -left-hand title
- -lab.2 -right-hand title
- -cex.nodelabels -character expansion size for node labels
- -arrow.length -arrow head size
Author
- -D. E. Beaudette
-Details
++-+dueling.dendrograms( + p.1, + p.2, + lab.1 = "D1", + lab.2 = "D2", + cex.nodelabels = 0.75, + arrow.length = 0.05 +)Connector arrows are used to link nodes from the left-hand dendrogram to the right-hand dendrogram.
++Arguments
+- p.1 +
left-hand phylo-class dendrogram
- - p.2 +
right-hand phylo-class dendrogram
-# compute dissimilarity using different sets of variables
-# note that these are rescaled to the interval [0,1]
-d.1 <- profile_compare(sp3, vars=c('clay', 'cec'), k=0, max_d=100, rescale.result=TRUE)
-d.2 <- profile_compare(sp3, vars=c('clay', 'L'), k=0, max_d=100, rescale.result=TRUE)
-# cluster via divisive hierarchical algorithm
-# convert to 'phylo' class
-p.1 <- as.phylo(as.hclust(diana(d.1)))
-p.2 <- as.phylo(as.hclust(diana(d.2)))
+- lab.1 +
left-hand title
-# graphically compare two dendrograms
-dueling.dendrograms(p.1, p.2, lab.1='clay and CEC', lab.2='clay and L')
-# graphically check the results of ladderize() from ape package
-dueling.dendrograms(p.1, ladderize(p.1), lab.1='standard', lab.2='ladderized')
+- lab.2 +
right-hand title
-# sanity-check: compare something to itself
-dueling.dendrograms(p.1, p.1, lab.1='same', lab.2='same')
-# graphically compare diana() to agnes() using d.2
-dueling.dendrograms(as.phylo(as.hclust(diana(d.2))),
-as.phylo(as.hclust(agnes(d.2))), lab.1='diana', lab.2='agnes')
-}
-- cex.nodelabels +
character expansion size for node labels
+
+
+- arrow.length +
arrow head size
+
+
Examples
-if(require(aqp) & - require(cluster) & - require(latticeExtra) & - require(ape) -) { - - # load sample dataset from aqp package -data(sp3) -# promote to SoilProfileCollection -depths(sp3) <- id ~ top + bottom +#>#> Warning: package 'cluster' was built under R version 4.0.4#>#> Warning: package 'ape' was built under R version 4.0.3#>#>#>#>
++++Value
+ + +nothing is returned, function is called to generate graphical output
+++ -Details
+Connector arrows are used to link nodes from the left-hand dendrogram to the right-hand dendrogram.
+
A very simple estimation of soil moisture state based on volumetric water content
- Source:R/estimateSoilMoistureState.R
+ Source: R/estimateSoilMoistureState.R
estimateSoilMoistureState.RdThis is a very simple classification of volumetric water content (VWC) into 5 "moisture states", based on an interpretation of water retention thresholds. Classification is performed using VWC at satiation, field capacity (typically 1/3 bar suction), permanent wilting point (typically 15 bar suction), and water surplus in mm. The inputs to this function are closely aligned with the assumptions and output from hydromad::hydromad(sma = 'bucket', ...).
Soil moisture classification rules are as follows:
-
-
VWC <=
pwp: "very dry"
-VWC >
pwpand <= (mid-point betweenfcandpwp): "dry"
-VWC > (mid-point between
fcandpwp) and <=fc: "moist"
+ VWC <=
pwp: "very dry"
+VWC >
pwpAND <= (mid-point betweenfcandpwp): "dry"
+VWC > (mid-point between
fcandpwp) AND <=fc: "moist"VWC >
fc: "very moist"
-U(surplus) > 2mm: "saturated / runoff"
-VWC >
fcANDU(surplus) > 4mm: "wet"
+
This is a very simple classification of volumetric water content (VWC) into 5 "moisture states", based on an interpretation of water retention thresholds. Classification is performed using VWC at satiation, field capacity (typically 1/3 bar suction), permanent wilting point (typically 15 bar suction), and water surplus in mm. The inputs to this function are closely aligned with the assumptions and output from hydromad::hydromad(sma = 'bucket', ...).
Soil moisture classification rules are as follows:
estimateSoilMoistureState(
+ VWC,
+ U,
+ sat,
+ fc,
+ pwp,
+ style = c("default", "newhall")
+)estimateSoilMoistureState(VWC, U, sat, fc, pwp)- -
Arguments
-| VWC | -vector of volumetric water content (VWC), range is 0-1 |
-
|---|---|
| U | -vector of surplus water (mm) |
-
| sat | -satiation water content, range is 0-1 |
-
| fc | -field capacity water content, range is 0-1 |
-
| pwp | -permanent wilting point water content, range is 0-1 |
-
Value
- -vector of moisture states (ordered factor)
-Author
+Arguments
+- VWC +
vector of volumetric water content (VWC), range is 0-1
+
+
+- U +
vector of surplus water (mm)
+
+
+- sat +
satiation water content, range is 0-1
+
+- fc +
field capacity water content, range is 0-1
+
+
+- pwp +
permanent wilting point water content, range is 0-1
+
+
+- style +
VWC classification style
+
+
Value
+ + +vector of moisture states (ordered factor)
+Examples
-+-# "very moist" -estimateSoilMoistureState(VWC = 0.3, U = 0, sat = 0.35, fc = 0.25, pwp = 0.15) -#> [1] very moist -#> Levels: very dry < dry < moist < very moist < saturated / runoff-# "very dry" -estimateSoilMoistureState(VWC = 0.15, U = 0, sat = 0.35, fc = 0.25, pwp = 0.15) -#> [1] very dry -#> Levels: very dry < dry < moist < very moist < saturated / runoff-
Examples
+
+# "very moist"
+estimateSoilMoistureState(VWC = 0.3, U = 0, sat = 0.35, fc = 0.25, pwp = 0.15)
+#> [1] very moist
+#> Levels: very dry < dry < moist < very moist < wet
+estimateSoilMoistureState(VWC = 0.3, U = 2, sat = 0.35, fc = 0.25, pwp = 0.15)
+#> [1] very moist
+#> Levels: very dry < dry < moist < very moist < wet
+
+"wet"
+#> [1] "wet"
+estimateSoilMoistureState(VWC = 0.3, U = 5, sat = 0.35, fc = 0.25, pwp = 0.15)
+#> [1] wet
+#> Levels: very dry < dry < moist < very moist < wet
+
+# "very dry"
+estimateSoilMoistureState(VWC = 0.15, U = 0, sat = 0.35, fc = 0.25, pwp = 0.15)
+#> [1] very dry
+#> Levels: very dry < dry < moist < very moist < wet
+
+# "dry"
+estimateSoilMoistureState(VWC = 0.18, U = 0, sat = 0.35, fc = 0.25, pwp = 0.15)
+#> [1] dry
+#> Levels: very dry < dry < moist < very moist < wet
+
+