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title subtitle author job framework highlighter hitheme widgets ext_widgets mode knit logo license github
Knowledge Transfer
R libraries for alpenv
Johannes Brenner
Institute for Alpine Environment, EURAC
io2012
highlight.js
tomorrow
rCharts
libraries/nvd3
libraries/leaflet
selfcontained
slidify::knit2slides
logo.png
by-nc-sa
user repo
JBrenn
KnowledgeTransferEURAC

Content

all following R libraries are hosted on my GitHub account: https://github.com/JBrenn

  1. DataBaseAlpEnvEURAC - from raw LTER data to database: reading, formating, writing Mazia LTER data; post-process WISKI .rzx files
  2. SMCcalibration - interactive shiny app for calibrating SMC sensors
  3. SpatialInterpol - spatial interpolation using local ordinary kriging and inverse distance weighting
  4. AnalyseGEOtop - GEOtop simulation analysis
  5. TopoSUB - landscape k-means clustering & land-surface modeling with GEOtop
  6. SoilMoisturePattern - mobile campaigns' SMC data (project HiResAlp)
The content of this presentation is covered by the R script "KnowledgTransferEURAC_main.R. You can find it in "//ABZ02FST/alpenv/Präsentationen/BrJ_KnowledgeTransfer".

Intro R & GitHub

  • version control
  • branching
  • R packages in GitHub can be installed with devtools::install_github:
library(devtools)
# install master branch
install_github("JBrenn/DataBaseAlpEnvEURAC")
# install different branch
install_github("JBrenn/SMCcalibration@download")
  • place to gather alpenv scripts?

DataBaseAlpEnvEURAC - get data 1

  • base function is dB_readStationData

  • dB_updatedb: writing, updating SQlite database for specific variables or total data set (multiple stations and .csv output supported) - preperation for LTER database with fixed headers

  • dB_getMETEO: get meteo data

  • dB_getGEOtop: get meteo data and convert to standard GEOtop meteo file format (multiple stations supported)

  • dB_getSWC: get soil moisture data

  • dB_getSoilTemp: get soil temperature data

  • dB_getSWP: get soil water pressure (B2)

--- bg:#EEE

DataBaseAlpEnvEURAC - get data 2

# load libraries
library(DataBaseAlpEnvEURAC); library(dygraphs)

# easy data access, e.g. SWC data station P2
path2data <- "/media/alpenv/Projekte/HiResAlp/06_Workspace/BrJ/02_data/Station_data_Mazia"
P2 <- dB_getSWC(path2data = path2data, station = "P2", aggregation = "h", 
                minVALUE = 0, maxVALUE = 1, write.csv = FALSE, path2write = "./")
# use calibration function
data("calibration"); View(calibration)
P2_cal <- dB_getSWC(path2data = path2data, station = "P2", aggregation = "h", 
                    minVALUE = 0, maxVALUE = 1, calibrate=T)
# compare
P2_merge <- merge(P2[,1], P2_cal[,1])
time(P2_merge) <- as.POSIXct(time(P2_merge))
dygraph(P2_merge) %>%
  dyRangeSelector() %>% dyRoller()
<iframe src="./assets/widgets/dygraph1.html" width=100% height=100% allowtransparency="true"> </iframe>

--- bg:#EEE

DataBaseAlpEnvEURAC - update database

# creating .sqlite database for specific variables or total data
# for SWC .sqlite database is copied in data folder of SMCcalibrate package
# this is needed for downloading data with the calibration shiny app

P <- dB_updatedb(stations = c("P1","P2","P3"), variables = "SWC", 
                 inCloud = "/home/jbre/Schreibtisch/", 
                 write_csv = F, return_data = T)
  • variables: "TOTAL", "METEO", "SWC", "TSoil"
  • multiple stations and variables supported
  • possibility to write .csv for each station
  • handle .sqlite with R package RSQlite or specific software (e.g. Sqliteman)

--- bg:#EEE

SMCcalibration

# load libraries
library(SMCcalibration)
library(shiny)

# easy data access
data("SensorVSample")

# data description
?SensorVSample
View(SensorVSample)

# reduce data
data <- unique(data[,-8])

# run shiny app
shinyApp(ui, server)

--- bg:#EEE

DataBaseAlpEnvEURAC - postprocess .zrx

  • dB_readZRX - read ZRX data file, working for single variable and multiple variables in .zrx file
  • intern use by dB_readZRX2station - process ZRX data files, returns .csv file for each station containing available variables and station meta data.
files <- dir("/home/jbre/Schreibtisch/zrx/SouthTyrol", full.names = T)
data <- dB_readZRX2station(files = files, write_csv = F, 
                           multivar = FALSE)

path <- "/home/jbre/Schreibtisch/zrx/Mazia0480"
files <- dir(path, full.names = T)
mazia <- dB_readZRX2station(files = files, write_csv = T, output_path = path, 
                            multivar = TRUE)
# show data.table
library(DT)
matschdata <- read_csv(file.path(path,"st0480_1440.csv"))
datatable(matschdata)

Matsch-0480 data after postprocess

<iframe src="./assets/widgets/dt1.html" width=100% height=100% allowtransparency="true"> </iframe>

Intro Rmarkdown

  • .Rmd files - An R Markdown (.Rmd) file is a record of your research. It contains the code that a scientist needs to reproduce your work along with the narration that a reader needs to understand your work.

  • Reproducible Research - At the click of a button you can rerun the code in an R Markdown file to reproduce your work and export the results as a finished report.

  • Dynamic Docs - You can choose to export the finished report as a html, pdf, MS Word, ODT, RTF, or markdown document; or as a html or pdf based slide show.

Example:

  • Download or clone Git repository DataBaseAlpEnvEURAC
  • In the folder Rmd find the file 01_data_preparation_climate_quality.rmd
  • open/run in RStudio
<style> em { font-style: italic } strong { font-weight: bold; } </style>

--- bg:#EEE

SpatialInterpol

library(SpatialInterpol)
ordkrig100 <- OrdKrig(datafolder = "master")
#ordkrig20  <- OrdKrig(datafolder = "master", npix = 20)
idw     <- OrdKrig(datafolder = "master", inverseDistWeigths = TRUE)
plot(ordkrig100$AdigeVenosta$vario, ordkrig100$AdigeVenosta$vario_fit)

plot of chunk SpatialInterpol1

SpatialInterpol results

<iframe src="./assets/widgets/leaflet1.html" width=100% height=100% allowtransparency="true"> </iframe>

Analyse GEOtop

  • R markdown docs on time series visualisation, GOFs for comparision with observed time series, and simple visual sensitivity

  • read GEOtop 1d simulation output: GEOtop_ReadPointData, GEOtop_readValidationData

  • Create Geotop input maps with RSAGA: GEOtop_CreateInptsMAPS

  • Hydrological Budget for 3d simulations: GEOtop_CheckHydroBudget

  • Output map animation: GEOtop_AnimateMaps

  • Visualisation of pF-Curves: GEOtop_VisSoilWaterRet (base), GEOtop_VisSoilWaterRet_gg (ggplot2)

library(AnalyseGeotop)
GEOtop_VisSoilWaterRet(alpha = 0.02, n = 1.2, theta_sat = 0.52, theta_res = 0.05, 
                       ksat = 0.002, add_ref_curves = T, png = F, accurate = 1)

plot of chunk pF-Curves

TopoSUB

TopoSUB methodology - R functions to use for specific tasks:
(b) + (c) TopoSUB_preprocessor, (d) TopoSUB_read and TopoSUB_remap, (e) shiny app

Vignettes in R

  • long-form documentation for R packages
  • install GitHub packages with vignettes accordingly
library(devtools)

# install GitHub R package & build vignettes
install_github("JBrenn/TopoSUB", build_vignettes = TRUE)

# browse for package vignettes or look them up in package directory ("doc")
library(TopoSUB)
browseVignettes("TopoSUB")

--- { tpl: thankyou, social: [{title: github, href: "https://github.com/JBrenn"}] }

Thank You

For more information you can browse

This presentation is built with slidify THANKS to the developer Ramnath Vaidyanathan.
The content of this presentation is covered by the R script "KnowledgTransferEURAC_main.R". You can find it in "//ABZ02FST/alpenv/Präsentationen/BrJ_KnowledgeTransfer/".
<iframe src=' assets/fig/HiResAlp-1.html ' scrolling='no' frameBorder='0' seamless class='rChart polycharts ' id=iframe- chart119f37881c75 ></iframe> <style>iframe.rChart{ width: 100%; height: 400px;}</style> <style> em { font-style: italic } strong { font-weight: bold; } div.figure { float: right; width: 90%; margin: 0.5em; padding: 0.5em; } div.figure p { text-align: left; font-style: italic; font-size: smaller; text-indent: 0; } </style>