myClim: microclimatic data in R

library(myClim)
## Read pre-defined loggers without metadata
# read from Tomst files
tms.f <- mc_read_files(c("data_91184101_0.csv", "data_94184102_0.csv",
                         "data_94184103_0.csv"),
                       dataformat_name = "TOMST", silent = T)

# read from HOBO files
hob.f <- mc_read_files(c("20024354_comma.csv"), 
                       dataformat_name = "HOBO",
                       date_format = "%y.%m.%d %H:%M:%S",
                       silent = T)

# read all Tomst files from current directory
tms.d <- mc_read_files(".", dataformat_name = "TOMST", recursive = F, silent = T)

# read from data.frame
meteo.table <- readRDS("airTmax_meteo.rds") # wide format data frame 
meteo <- mc_read_wide(meteo.table, sensor_id = "T_C", 
                      sensor_name = "airTmax", silent = T)

## Read pre-defined logger with metadata
# provide two tables. Can be csv files or R data.frame
ft <- read.table("files_table.csv", sep=",", header = T)
lt <- read.table("localities_table.csv", sep=",", header = T)

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

# clean runs automatically while reading
tms <- mc_prep_clean(tms.m, silent = T) # clean series
#> Warning in mc_prep_clean(tms.m, silent = T): MyClim object is already cleaned.
#> Repeated cleaning overwrite cleaning informations.
tms.info <- mc_info_clean(tms) # call cleaning log

tms <- mc_prep_solar_tz(tms) # calculate solar time

# provide user defined offset to UTC in minutes 
# for conversion to political time use offset in minutes. 
tms.usertz <- mc_prep_meta_locality(tms,
                                    values = as.list(c(A1E05 = 60,
                                                       A2E32 = 0,
                                                       A6W79 = 120)),
                                    param_name = "tz_offset")

# simulate calibration data (sensor shift/offset to add)
i <- mc_info(tms)
calib_table <- data.frame(serial_number = i$serial_number,
                          sensor_id     = i$sensor_id,
                          datetime      = as.POSIXct("2016-11-29",tz="UTC"),
                          cor_factor    = 0.398,
                          cor_slope     = 0)

## load calibration to myClim metadata 
tms.load <- mc_prep_calib_load(tms, calib_table)

## run calibration for selected sensors
tms <- mc_prep_calib(tms.load, sensors = c("TM_T",
                                           "TMS_T1",
                                           "TMS_T2",
                                           "TMS_T3"))

mc_info_count(tms)
mc_info_clean(tms)
mc_info(tms)

## crop the time-series
start <- as.POSIXct("2021-01-01", tz = "UTC")
end   <- as.POSIXct("2021-03-31", tz = "UTC")

tms   <- mc_prep_crop(tms, start, end)


## simulate another myClim object and rename some localities and sensors
tms1 <- tms
tms1 <- mc_prep_meta_locality(tms1, list(A1E05 = "ABC05", A2E32 = "CDE32"), 
                              param_name = "locality_id") # locality ID

tms1 <- mc_prep_meta_sensor(tms1,
                            values=list(TMS_T1 = "TMS_Tsoil",
                                        TMS_T2 = "TMS_Tair2cm"),
                            localities = "A6W79", param_name = "name") # sensor names

## merge two myClim objects Prep-format
tms.m  <- mc_prep_merge(list(tms, tms1))
tms.im <- mc_info(tms.m) # see info 

## Filtering 
tms.out  <- mc_filter(tms, localities = "A1E05", reverse = T) # exclude one locality.
tms.m    <- mc_filter(tms.m, sensors = c("TMS_T2", "TMS_T3"), reverse = F) # keep only two sensor
tms.if   <- mc_info(tms.m) # see info 

## upload metadata from data frame

# load data frame with metadata (coordinates)
metadata <- readRDS("metadata.rds")

# upload metadata from data.frame
tms.f <- mc_prep_meta_locality(tms.f, values = metadata)

## upload metadata from named list
tms.usertz <- mc_prep_meta_locality(tms, 
                                    values = as.list(c(A1E05 = 57,
                                                            A2E32 = 62,
                                                            A6W79 = 55)),
                                    param_name = "tz_offset")

# one locality with two downloads in time 
data <- mc_load("join_example.rds")

joined_data <- mc_join(data, comp_sensors = c("TMS_T1", "TMS_T2"))

#> Locality: 94184102
#> Problematic interval: 2020-12-01 00:00:00 UTC--2020-12-31 23:45:00 UTC
#> 
#> Older logger TMS 94184102
#> start                 end
#> 2020-10-06 09:15:00   2020-12-31 23:45:00
#> 
#> Newer logger TMS 94184102
#> start                end 
#> 2020-12-01 00:00:00  2021-04-07 11:45:00 
#>
#> Loggers are different. They cannot be joined automatically.
#> 
#> 1: use older logger
#> 2: use newer logger
#> 3: use always older logger
#> 4: use always newer logger
#> 5: exit
#> 
#> Write choice number or start datetime of use newer 
#> logger in format YYYY-MM-DD hh:mm.

## lines
tms.plot <- mc_filter(tms, localities = "A6W79")

p <- mc_plot_line(tms.plot, sensors = c("TMS_T3", "TMS_T1", "TMS_moist"))
p <- p+ggplot2::scale_x_datetime(date_breaks = "1 week", date_labels = "%W")
p <- p+ggplot2::xlab("week")
p <- p+ggplot2::aes(size = sensor_name)
p <- p+ggplot2::scale_size_manual(values = c(1, 1 ,2))
p <- p+ggplot2::guides(size = "none")
p <- p+ggplot2::scale_color_manual(values = c("hotpink", "pink", "darkblue"), name = NULL)

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

# with defaults only convert Raw-format  to Agg-format
tms.ag <- mc_agg(tms.m,fun = NULL, period = NULL)

# aggregate to daily mean, range, coverage, and 95 percentile. 
tms.day <- mc_agg(tms, fun = c("mean", "range", "coverage", "percentile"),
                percentiles = 95, period = "day", min_coverage = 0.95)

# 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 = 0.95)

# aggregate with your custom function. (how many records are below -5°C per month)
tms.all.custom <- mc_agg(tms.out, fun = list(TMS_T3 = "below5"), period = "month",
                         custom_functions = list(below5 = function(x){length(x[x<(-5)])}))
r <- mc_reshape_long(tms.all.custom)

## calculate virtual sensor VWC from raw TMS moisture signal
tms.calc <- mc_calc_vwc(tms.out, soiltype = "loamy sand A")

## virtual sensor with growing and freezing degree days
tms.calc <- mc_calc_gdd(tms.calc, sensor = "TMS_T3",)
tms.calc <- mc_calc_fdd(tms.calc, sensor = "TMS_T3")

## virtual sensor to estimate snow presence from 2 cm air temperature 
tms.calc <- mc_calc_snow(tms.calc, sensor = "TMS_T2")

## summary data.frame of snow estimation
tms.snow <- mc_calc_snow_agg(tms.calc)

##  virtual sensor with VPD
hobo.vpd <- mc_calc_vpd(hob.f)

temp_env  <- mc_env_temp(tms, period = "all", min_coverage = 0.9)
moist_env <- mc_env_moist(tms.calc, period = "all", min_coverage = 0.9)
vpd_env   <- mc_env_vpd(hobo.vpd, period = "all", min_coverage = 0.9)

## wide table of air temperature and soil moisture
tms.wide <- mc_reshape_wide(tms.calc, sensors = c("TMS_T3", "vwc"))

## long table of air temperature and soil moisture
tms.long <- mc_reshape_long(tms.calc, sensors = c("TMS_T3", "vwc"))

tms.long.all <- mc_reshape_long(tms.all)