2023_SoilTemp_virtual

Introducing myClim an R package for microclimatic data handling.
Developed by Matěj Man, Vojtěch Kalčík, Martin Macek, Jan Wild, Martin Kopecký, Josef Brůna, Lucia Hederová.
Department of Geoecology, Institute of Botany of the Czech Academy of Sciences

• https://github.com/ibot-geoecology/myClim
• http://labgis.ibot.cas.cz/myclim/index.html
• http://labgis.ibot.cas.cz

Install myClim

requiered_packages <- c("stringr", "lubridate", "tibble", "dplyr", "purrr",
                        "ggplot2", "ggforce", "viridis", "runner",
                        "rmarkdown", "knitr", "kableExtra", "tidyr", "plotly", "zoo")
missing_packages <- requiered_packages[!(requiered_packages %in% installed.packages()[,"Package"])]
if(length(missing_packages)) install.packages(missing_packages)

install.packages("http://labgis.ibot.cas.cz/myclim/myClim_latest.tar.gz", repos=NULL, build_vignettes=TRUE)

Reading logger data with metadata

library(myClim)

ft<-read.table("files_table.csv",sep=",",header = T)
lt<-read.table("localities_table.csv",sep=",",header = T)

tms <- mc_read_data(files_table = "files_table.csv",
                      localities_table =lt,
                      silent = T,clean = T)

Plotting

mc_plot_line(tms,sensors = c("TMS_T3","TMS_T1","TMS_TMSmoisture"))

mc_plot_raster(tms,sensors = c("TMS_T3"))

Aggregation in time

# aggregate to daily mean, range, coverage, and 95 percentile. 
tms.week <- mc_agg(tms, fun=c("mean","range","coverage","percentile"),
                percentiles = 95, period = "week",min_coverage = 0.8)

mc_plot_raster(tms.week,sensors = c("TMS_T3_mean"))

# aggregate all time-series, return one value per sensor.
tms.all <- mc_agg(tms, fun=c("mean","range","coverage","percentile"),
                percentiles = 95, period = "all",min_coverage = 0.8)
r<-mc_reshape_long(tms.all)

Standard myClim variables

temp_env <- mc_env_temp(tms,period="all",min_coverage = 0.8)