Package 'hydrotoolbox' reference manual

Title:	Hydrological Tools for Handling Hydro-Meteorological Data Records
Description:	Read, plot, manipulate and process hydro-meteorological data records (with special features for Argentina and Chile data-sets).
Authors:	Ezequiel Toum <[email protected]>
Maintainer:	Ezequiel Toum <[email protected]>
License:	GPL (>= 3)
Version:	1.1.2
Built:	2024-07-24 06:21:19 UTC
Source:	CRAN

Aggregates a data frame to a larger time period

Description

Aggregates a data frame to a larger time period

Usage

agg_table(
  x,
  col_name,
  fun,
  period,
  out_name = NULL,
  allow_na = 0,
  start_month = 1,
  end_month = 12
)
agg_table(
  x,
  col_name,
  fun,
  period,
  out_name = NULL,
  allow_na = 0,
  start_month = 1,
  end_month = 12
)

Arguments

`x`	data frame or tibble with class `Date` or `POSIX*` in the first column.
`col_name`	string with column(s) name(s) to aggregate.
`fun`	string with supported aggregation function name (one per col_name): mean, min, max, sum, last or first.
`period`	string with the aggregation time-step: hourly, daily, monthly, annually or climatic. NOTE: the climatic option returns the all series annual statistics (fun).
`out_name`	optional. String with the output column(s) name(s). Default values coerce the original name plus the fun argument (e.g.: `tair_max`).
`allow_na`	optional. Numeric value with the maximum allowed number of `NA_real_` values. By default the function will not tolerate any `NA_real_` (and will return `NA_real_` instead).
`start_month`	optional. Numeric value defining the first month of the annual period (it just make sense if period is either annually or climatic). Default sets to 1 (January). NOTE: keep in mind that if you choose climatic as period you have to round off a complete year (e.g.: `..., start_month = 6, end_month = 5, ...`)
`end_month`	optional. Numeric value defining the last month of the annual period (it just make sense if period is either annually or climatic). Default sets to 12 (December). NOTE: keep in mind that if you choose climatic as period you have to round off a complete year (e.g.: `..., start_month = 6, end_month = 5, ...`)

Value

A data frame with the Date and the aggregated variable(s).

Examples


# set path to file
path <- system.file('extdata', 'snih_qd_guido.xlsx',
         package = 'hydrotoolbox')

# read and load daily streamflow with default column name
guido_qd <- read_snih(path = path, by = 'day', out_name = 'q(m3/s)')

# aggregate daily to monthly discharge
guido_q_month <- agg_table(x = guido_qd, col_name = 'q(m3/s)',
                          fun = 'mean', period = 'monthly',
                          out_name = 'qm(m3/s)')

# suppose that we are interested on getting the annual maximum
# daily mean discharge for every hydrological year (since this
# station is located at the Mendoza River Basin ~32.9º S, we will
# consider that annual period starts on July)
guido_q_annual <- agg_table(x = guido_qd, col_name = 'q(m3/s)',
                            fun = 'max', period = 'annually',
                            out_name = 'qmax(m3/s)',
                            start_month = 7, end_month = 6)

# now we want the mean, maximum and minimum monthly discharges
guido_q_stats <- agg_table(x = guido_qd, col_name = rep('q(m3/s)', 3),
                           fun = c('mean', 'max', 'min'),
                           period = 'monthly')


# set path to file
path <- system.file('extdata', 'snih_qd_guido.xlsx',
         package = 'hydrotoolbox')

# read and load daily streamflow with default column name
guido_qd <- read_snih(path = path, by = 'day', out_name = 'q(m3/s)')

# aggregate daily to monthly discharge
guido_q_month <- agg_table(x = guido_qd, col_name = 'q(m3/s)',
                          fun = 'mean', period = 'monthly',
                          out_name = 'qm(m3/s)')

# suppose that we are interested on getting the annual maximum
# daily mean discharge for every hydrological year (since this
# station is located at the Mendoza River Basin ~32.9º S, we will
# consider that annual period starts on July)
guido_q_annual <- agg_table(x = guido_qd, col_name = 'q(m3/s)',
                            fun = 'max', period = 'annually',
                            out_name = 'qmax(m3/s)',
                            start_month = 7, end_month = 6)

# now we want the mean, maximum and minimum monthly discharges
guido_q_stats <- agg_table(x = guido_qd, col_name = rep('q(m3/s)', 3),
                           fun = c('mean', 'max', 'min'),
                           period = 'monthly')

Cumulative sum

Description

The function supports NA_real_ values. It could be very useful when dealing with incomplete precipitation series.

Usage

cum_sum(x, col_name, out_name = NULL)
cum_sum(x, col_name, out_name = NULL)

Arguments

`x`	data frame or tibble with class `Date` or `POSIX*` in the first column.
`col_name`	string with column(s) name(s) where to apply the function.
`out_name`	optional. String with new column(s) name(s). If you set it as `NULL`, the function will overwrite the original table.

Value

The same table but with the new series.

Examples

## Not run: 
# set path to file
path <- system.file('extdata', 'ianigla_cuevas.csv',
         package = 'hydrotoolbox')

# read the file and add the new column with cumulative precipitation
cuevas <-
  read_ianigla(path = path) %>%
  cum_sum(col_name = 'Precip_Total', out_name = 'p_cum')

# plot it
plot(x = cuevas[ , 'date', drop = TRUE],
     y = cuevas[ , 'p_cum', drop = TRUE],
     col = 'red', type = 'l',
     xlab = 'Date', ylab = 'Pcum(mm)')

## End(Not run)


## Not run: 
# set path to file
path <- system.file('extdata', 'ianigla_cuevas.csv',
         package = 'hydrotoolbox')

# read the file and add the new column with cumulative precipitation
cuevas <-
  read_ianigla(path = path) %>%
  cum_sum(col_name = 'Precip_Total', out_name = 'p_cum')

# plot it
plot(x = cuevas[ , 'date', drop = TRUE],
     y = cuevas[ , 'p_cum', drop = TRUE],
     col = 'red', type = 'l',
     xlab = 'Date', ylab = 'Pcum(mm)')

## End(Not run)

Find non-reported dates and fill them with `NA_*`

Description

Automatically finds non recorded date periods and fills them with NA_real_ values.

Usage

fill_table(x, col_name = "all", by = NULL)
fill_table(x, col_name = "all", by = NULL)

Arguments

`x`	data frame (or tibble) with class `Date` or `POSIX*` in the first column.
`col_name`	string with column(s) name(s) to fill.
`by`	string with a valid time step (e.g.: `"month", "day", "6 hour", "3 hour", "1 hour", "15 min"`).

Value

A data frame (or tibble) with the date and the filled numeric variable(s).

Examples


# let's use a synthetic example to illustrate the use of the function
dates <- seq.Date(from = as.Date('1980-01-01'),
                 to = as.Date('2020-01-01'), by = 'day' )
var   <- runif(n = length(dates), min = 0, max = 100)

met_var <- data.frame(date = dates, random = var)[-c(50:100, 251, 38) , ]

met_var_fill <- fill_table(x = met_var, by = 'day')


# let's use a synthetic example to illustrate the use of the function
dates <- seq.Date(from = as.Date('1980-01-01'),
                 to = as.Date('2020-01-01'), by = 'day' )
var   <- runif(n = length(dates), min = 0, max = 100)

met_var <- data.frame(date = dates, random = var)[-c(50:100, 251, 38) , ]

met_var_fill <- fill_table(x = met_var, by = 'day')

Aggregates the table inside a slot to a larger time period

Description

This method allows you to get your data temporally aggregated.

Usage

hm_agg(
  obj,
  slot_name,
  col_name,
  fun,
  period,
  out_name = NULL,
  allow_na = 0,
  start_month = 1,
  end_month = 12,
  relocate = NULL
)

## S4 method for signature 'hydromet_station'
hm_agg(
  obj,
  slot_name,
  col_name,
  fun,
  period,
  out_name = NULL,
  allow_na = 0,
  start_month = 1,
  end_month = 12,
  relocate = NULL
)

## S4 method for signature 'hydromet_compact'
hm_agg(
  obj,
  slot_name,
  col_name,
  fun,
  period,
  out_name = NULL,
  allow_na = 0,
  start_month = 1,
  end_month = 12
)
hm_agg(
  obj,
  slot_name,
  col_name,
  fun,
  period,
  out_name = NULL,
  allow_na = 0,
  start_month = 1,
  end_month = 12,
  relocate = NULL
)

## S4 method for signature 'hydromet_station'
hm_agg(
  obj,
  slot_name,
  col_name,
  fun,
  period,
  out_name = NULL,
  allow_na = 0,
  start_month = 1,
  end_month = 12,
  relocate = NULL
)

## S4 method for signature 'hydromet_compact'
hm_agg(
  obj,
  slot_name,
  col_name,
  fun,
  period,
  out_name = NULL,
  allow_na = 0,
  start_month = 1,
  end_month = 12
)

Arguments

`obj`	a valid `hydromet_XXX` class object.
`slot_name`	string with the name of the slot to aggregate.
`col_name`	string with column(s) name(s) to aggregate.
`fun`	string with supported aggregation function name (one per col_name): mean, min, max, sum, last or first.
`period`	string with the aggregation time-step: hourly, daily, monthly, annually or climatic. NOTE 1: the climatic option returns the all series annual statistics (fun). NOTE 2: when using annually as period, the method will return the starting dates in the first slot column.
`out_name`	string with the output column(s) name(s). Default values coerce the original name plus the fun argument (e.g.: `tair_max`).
`allow_na`	optional. Numeric value with the maximum allowed number of `NA_real_` values. By default the function will not tolerate any `NA_real_` (and will return `NA_real_` instead).
`start_month`	optional. Numeric value defining the first month of the annual period (it just make sense if period is either annually or climatic). Default sets to 1 (January). NOTE: keep in mind that if you choose climatic as period you have to round off a complete year (e.g.: `..., start_month = 6, end_month = 5, ...`)
`end_month`	optional. Numeric value defining the last month of the annual period (it just make sense if period is either annually or climatic). Default sets to 12 (December). NOTE: keep in mind that if you choose climatic as period you have to round off a complete year (e.g.: `..., start_month = 6, end_month = 5, ...`)
`relocate`	optional. String with the name of the slot where to allocate the aggregated table. It only make sense for `hydromet_station` class. When using it you must keep in mind that all aggregated series are allocated in a single slot.

Value

A data frame with the Date and the aggregated variable(s) inside the specified slot.

Functions

hm_agg(hydromet_station): temporal aggregation method for station class
hm_agg(hydromet_compact): temporal aggregation method for compact class

Examples

## Not run: 
# cuevas station
path <- system.file('extdata', package = 'hydrotoolbox')

# use the build method
hm_cuevas <-
  hm_create() %>%
  hm_build(bureau = 'ianigla', path = path,
           file_name = 'ianigla_cuevas.csv',
           slot_name = c('tair', 'rh', 'patm',
                         'precip', 'wspd', 'wdir',
                         'kin', 'hsnow', 'tsoil'),
           by = 'hour',
           out_name = c('tair(°C)', 'rh(%)', 'patm(mbar)',
                        'p(mm)', 'wspd(km/hr)', 'wdir(°)',
                        'kin(kW/m2)', 'hsnow(cm)', 'tsoil(°C)' )
          )

# aggregate air temperature data to mean value
hm_agg(obj = hm_cuevas, slot_name = 'tair', col_name = 'tair(°C)',
      fun = 'mean', period = 'daily', out_name = 't_mean') %>%
 hm_show(slot_name = 'tair')

# the previous command overwrites the original slot, so now we are going
# to relocate the agg values
hm_agg(obj = hm_cuevas, slot_name = 'tair',
      col_name = 'tair(°C)',
      fun = 'mean',
      period = 'daily',
      relocate = 'tmean',
      out_name = 'tmean(°C)',
      ) %>%
 hm_show(slot_name = 'tmean')

## End(Not run)


## Not run: 
# cuevas station
path <- system.file('extdata', package = 'hydrotoolbox')

# use the build method
hm_cuevas <-
  hm_create() %>%
  hm_build(bureau = 'ianigla', path = path,
           file_name = 'ianigla_cuevas.csv',
           slot_name = c('tair', 'rh', 'patm',
                         'precip', 'wspd', 'wdir',
                         'kin', 'hsnow', 'tsoil'),
           by = 'hour',
           out_name = c('tair(°C)', 'rh(%)', 'patm(mbar)',
                        'p(mm)', 'wspd(km/hr)', 'wdir(°)',
                        'kin(kW/m2)', 'hsnow(cm)', 'tsoil(°C)' )
          )

# aggregate air temperature data to mean value
hm_agg(obj = hm_cuevas, slot_name = 'tair', col_name = 'tair(°C)',
      fun = 'mean', period = 'daily', out_name = 't_mean') %>%
 hm_show(slot_name = 'tair')

# the previous command overwrites the original slot, so now we are going
# to relocate the agg values
hm_agg(obj = hm_cuevas, slot_name = 'tair',
      col_name = 'tair(°C)',
      fun = 'mean',
      period = 'daily',
      relocate = 'tmean',
      out_name = 'tmean(°C)',
      ) %>%
 hm_show(slot_name = 'tmean')

## End(Not run)

Load native data files automatically

Description

The method allows you to automatically load your native data inside the hydromet_station slots.

Usage

hm_build(
  obj,
  bureau,
  path,
  file_name,
  slot_name,
  by,
  out_name = NULL,
  sheet = NULL
)

## S4 method for signature 'hydromet_station'
hm_build(
  obj,
  bureau,
  path,
  file_name,
  slot_name,
  by,
  out_name = NULL,
  sheet = NULL
)
hm_build(
  obj,
  bureau,
  path,
  file_name,
  slot_name,
  by,
  out_name = NULL,
  sheet = NULL
)

## S4 method for signature 'hydromet_station'
hm_build(
  obj,
  bureau,
  path,
  file_name,
  slot_name,
  by,
  out_name = NULL,
  sheet = NULL
)

Arguments

`obj`	a valid `hydromet_station` class object.
`bureau`	string value containing one of the available options: 'aic', 'cr2', 'dgi', 'ianigla', 'mnemos' or 'snih'.
`path`	string vector with the path(s) to the `file_name` argument. If you set a single string it will be recycled for all the files.
`file_name`	string vector with the native file(s) name(s).
`slot_name`	string vector with the slot(s) where to set the file(s) or sheet(s).
`by`	string vector with the time step of the series (e.g.: 'month', 'day', '6 hour', '3 hour', '1 hour', '15 min' ). If you set it as 'none', the function will ignore automatic gap filling. If you set a single string it will be recycled for all the files.
`out_name`	optional. String vector with user defined variable(s) column(s) name(s).
`sheet`	optional. Sheet to read. Either a string vector (the name of a sheet), or an integer vector (the position of the sheet). If neither argument specifies the sheet, defaults to the first sheet. This argument just make sense for: 'aic': you must provide a single name or integer indicating the met-station to read. 'dgi': just keep it as `NULL`. 'mnemos': just keep it as `NULL`.

Value

A hydromet_station object with the required data loaded inside.

Functions

hm_build(hydromet_station): build method for hydromet station object

Examples

## Not run: 
# path to all example files
path <- system.file('extdata', package = 'hydrotoolbox')

# ianigla file
hm_create() %>%
  hm_build(bureau = 'ianigla', path = path,
           file_name = 'ianigla_cuevas.csv',
           slot_name = c('tair', 'rh', 'patm',
                         'precip', 'wspd', 'wdir',
                         'kin', 'hsnow', 'tsoil'),
           by = 'hour',
           out_name = c('tair(°C)', 'rh(%)', 'patm(mbar)',
                        'p(mm)', 'wspd(km/hr)', 'wdir(°)',
                        'kin(kW/m2)', 'hsnow(cm)', 'tsoil(°C)' )
          ) %>%
          hm_show()

# cr2 file
hm_create() %>%
  hm_build(bureau = 'cr2', path = path,
           file_name = 'cr2_tmax_yeso_embalse.csv',
           slot_name = c('tmax'),
           by = 'day',
           out_name = c('tair(°C)' )
          )  %>%
          hm_show()

# dgi file
hm_create() %>%
  hm_build(bureau = 'dgi', path = path,
           file_name = 'dgi_toscas.xlsx',
           slot_name = c('swe', 'tmax',
           'tmin', 'tmean', 'rh', 'patm'),
           by = 'day' ) %>%
          hm_show()

# snih file
hm_create() %>%
  hm_build(bureau = 'snih', path = path,
           file_name = c('snih_hq_guido.xlsx',
           'snih_qd_guido.xlsx'),
           slot_name = c('hq', 'qd'),
           by = c('none', 'day') ) %>%
          hm_show()

# aic    => you have to request for this files to AIC.

# mnemos => the data are the same of snih but generated
#           with MNEMOSIII software.

## End(Not run)

## Not run: 
# path to all example files
path <- system.file('extdata', package = 'hydrotoolbox')

# ianigla file
hm_create() %>%
  hm_build(bureau = 'ianigla', path = path,
           file_name = 'ianigla_cuevas.csv',
           slot_name = c('tair', 'rh', 'patm',
                         'precip', 'wspd', 'wdir',
                         'kin', 'hsnow', 'tsoil'),
           by = 'hour',
           out_name = c('tair(°C)', 'rh(%)', 'patm(mbar)',
                        'p(mm)', 'wspd(km/hr)', 'wdir(°)',
                        'kin(kW/m2)', 'hsnow(cm)', 'tsoil(°C)' )
          ) %>%
          hm_show()

# cr2 file
hm_create() %>%
  hm_build(bureau = 'cr2', path = path,
           file_name = 'cr2_tmax_yeso_embalse.csv',
           slot_name = c('tmax'),
           by = 'day',
           out_name = c('tair(°C)' )
          )  %>%
          hm_show()

# dgi file
hm_create() %>%
  hm_build(bureau = 'dgi', path = path,
           file_name = 'dgi_toscas.xlsx',
           slot_name = c('swe', 'tmax',
           'tmin', 'tmean', 'rh', 'patm'),
           by = 'day' ) %>%
          hm_show()

# snih file
hm_create() %>%
  hm_build(bureau = 'snih', path = path,
           file_name = c('snih_hq_guido.xlsx',
           'snih_qd_guido.xlsx'),
           slot_name = c('hq', 'qd'),
           by = c('none', 'day') ) %>%
          hm_show()

# aic    => you have to request for this files to AIC.

# mnemos => the data are the same of snih but generated
#           with MNEMOSIII software.

## End(Not run)

Load native data files automatically

Description

The method allows you to automatically load your native data inside the hydromet_station slots.

Usage

hm_build_generic(
  obj,
  path,
  file_name,
  slot_name,
  by = "none",
  out_name = NULL,
  sheet = NULL,
  FUN,
  ...
)

## S4 method for signature 'hydromet_station'
hm_build_generic(
  obj,
  path,
  file_name,
  slot_name,
  by = "none",
  out_name = NULL,
  sheet = NULL,
  FUN,
  ...
)
hm_build_generic(
  obj,
  path,
  file_name,
  slot_name,
  by = "none",
  out_name = NULL,
  sheet = NULL,
  FUN,
  ...
)

## S4 method for signature 'hydromet_station'
hm_build_generic(
  obj,
  path,
  file_name,
  slot_name,
  by = "none",
  out_name = NULL,
  sheet = NULL,
  FUN,
  ...
)

Arguments

`obj`	a valid `hydromet_station` class object.
`path`	string vector with the path(s) to the `file_name` argument. If you set a single string it will be recycled for all the files.
`file_name`	string vector with the native file(s) name(s).
`slot_name`	string vector with the slot(s) where to set the file(s) or sheet(s).
`by`	string vector with the time step of the series (e.g.: 'month', day', '6 hour', '3 hour', '1 hour', '15 min' ). If you set it as `"none"`, the function will ignore automatic gap filling. If you set a single string, it will be recycled for all the files.
`out_name`	optional. A list containing string vectors with user defined variable(s) column(s) name(s). The list length should be equal to the `slot_name` length.
`sheet`	Sheet to read (only excel files). Either a string vector (the name of a sheet) or an integer vector (the position of the sheet). This argument just make sense for excel files.
`FUN`	function name for reading the data (e.g.: `read_csv()`). The method will always use the `path + file` as first argument(s) to `FUN`.
`...`	`FUN` arguments to pass.

Value

A hydromet_station object with the required data loaded inside.

Functions

hm_build_generic(hydromet_station): build method for hydromet station object

Examples

## Not run: 
# you can download the data from:
# https://gitlab.com/ezetoum27/hydrotoolbox/-/tree/master/my_data

# set the data path
my_path <- "./home/my_folder/my_data"

#///////////////////////////////
# Rectangular data
# txt, csv, csv2 and others.
# See readr package.
#///////////////////////////////

library(readr)
#* Case 1: single file - many numeric variables

hm_create() %>%
hm_build_generic(path = my_path,
                 file_name = "ianigla_cuevas.csv",
                 slot_name = c("tair", "rh", "patm",
                              "precip", "wspd", "wdir",
                              "kin", "hsnow", "tsoil"),
                 by = c("hour"),
                 FUN = read_csv,
                 col_select = !Est & !YJday & !hh.mm.ss & !bat.Volts
                ) %>%
 hm_show()

#* Case 2: multiple files (one per observation)
hm_create() %>%
 hm_build_generic(path = my_path,
                  file_name = c("h_relativa_cuevas.csv",
                                "p_atm_cuevas.csv",
                                "precip_total_cuevas.csv",
                                "temp_aire_cuevas.csv",
                                "vel_viento_cuevas.csv"),
                  slot_name = c("rh", "patm", "precip",
                                "tair", "wspd"),
                  by = c("hour", "45 min", "30 min", "1 hour", "15 min"),
                  FUN = read_csv  ) %>%
 hm_show()

#///////////////////////////////
# Excel files
# Recommended package => readxl
#///////////////////////////////

library(readxl)


#* Case 1: single file - one sheet - many numeric variables

hm_create() %>%
 hm_build_generic(path = my_path,
                  file_name = "mnemos_guido.xlsx",
                  slot_name = c("qd"),
                  by = c("day"),
                  FUN = read_excel,
                  sheet = 1L,
                  skip = 3,
                  out_name = list("q_m3/s")
 ) %>% hm_show()



#* Case 2: single file - multiple sheets (one per variable)

hm_create() %>%
 hm_build_generic(path = my_path,
                  file_name = "mnemos_guido.xlsx",
                  slot_name = c("qd", "evap", "tair",
                                "tmax", "tmin"),
                  by = c(q = "day", evap =  "day", tair = "6 hour",
                         tmax = "day", tmin = "day"),
                  FUN = read_excel,
                  sheet = c(1L:5L),
                  skip = 3,
                  out_name = list( c("q_m3/s", "flag"),
                                   c("evap_mm", "flag"),
                                   c("tair", "flag"),
                                   c("tmax", "flag"),
                                   c("tmin", "flag")
                  )
 ) %>%
 hm_show()

#* Case 3: multiple files - one sheet per file

hm_create() %>%
  hm_build_generic(path = my_path,
                   file_name = c("discharge_daily.xlsx",
                                 "air_teperature_subdaily.xlsx"),
                   slot_name = c("qd", "tair"),
                   by = c(q = "day", tair = "6 hour"),
                   FUN = read_excel,
                   sheet = c(1L, 1L),
                   skip = 3,
                   out_name = list( c("q_m3/s", "flag"),
                                    c("tair", "flag"))
  ) %>%
  hm_show()


## End(Not run)

## Not run: 
# you can download the data from:
# https://gitlab.com/ezetoum27/hydrotoolbox/-/tree/master/my_data

# set the data path
my_path <- "./home/my_folder/my_data"

#///////////////////////////////
# Rectangular data
# txt, csv, csv2 and others.
# See readr package.
#///////////////////////////////

library(readr)
#* Case 1: single file - many numeric variables

hm_create() %>%
hm_build_generic(path = my_path,
                 file_name = "ianigla_cuevas.csv",
                 slot_name = c("tair", "rh", "patm",
                              "precip", "wspd", "wdir",
                              "kin", "hsnow", "tsoil"),
                 by = c("hour"),
                 FUN = read_csv,
                 col_select = !Est & !YJday & !hh.mm.ss & !bat.Volts
                ) %>%
 hm_show()

#* Case 2: multiple files (one per observation)
hm_create() %>%
 hm_build_generic(path = my_path,
                  file_name = c("h_relativa_cuevas.csv",
                                "p_atm_cuevas.csv",
                                "precip_total_cuevas.csv",
                                "temp_aire_cuevas.csv",
                                "vel_viento_cuevas.csv"),
                  slot_name = c("rh", "patm", "precip",
                                "tair", "wspd"),
                  by = c("hour", "45 min", "30 min", "1 hour", "15 min"),
                  FUN = read_csv  ) %>%
 hm_show()

#///////////////////////////////
# Excel files
# Recommended package => readxl
#///////////////////////////////

library(readxl)


#* Case 1: single file - one sheet - many numeric variables

hm_create() %>%
 hm_build_generic(path = my_path,
                  file_name = "mnemos_guido.xlsx",
                  slot_name = c("qd"),
                  by = c("day"),
                  FUN = read_excel,
                  sheet = 1L,
                  skip = 3,
                  out_name = list("q_m3/s")
 ) %>% hm_show()



#* Case 2: single file - multiple sheets (one per variable)

hm_create() %>%
 hm_build_generic(path = my_path,
                  file_name = "mnemos_guido.xlsx",
                  slot_name = c("qd", "evap", "tair",
                                "tmax", "tmin"),
                  by = c(q = "day", evap =  "day", tair = "6 hour",
                         tmax = "day", tmin = "day"),
                  FUN = read_excel,
                  sheet = c(1L:5L),
                  skip = 3,
                  out_name = list( c("q_m3/s", "flag"),
                                   c("evap_mm", "flag"),
                                   c("tair", "flag"),
                                   c("tmax", "flag"),
                                   c("tmin", "flag")
                  )
 ) %>%
 hm_show()

#* Case 3: multiple files - one sheet per file

hm_create() %>%
  hm_build_generic(path = my_path,
                   file_name = c("discharge_daily.xlsx",
                                 "air_teperature_subdaily.xlsx"),
                   slot_name = c("qd", "tair"),
                   by = c(q = "day", tair = "6 hour"),
                   FUN = read_excel,
                   sheet = c(1L, 1L),
                   skip = 3,
                   out_name = list( c("q_m3/s", "flag"),
                                    c("tair", "flag"))
  ) %>%
  hm_show()


## End(Not run)

Creates an hydromet object.

Description

This function is the constructor of hydromet class and its subclass.

Usage

hm_create(class_name = "station")
hm_create(class_name = "station")

Arguments

class_name

string with the name of the class. Valid arguments are: hydromet, station or compact.

Value

An S4 object of class hydromet.

Examples

# create class 'hydromet'
hym_metadata <- hm_create(class_name = 'hydromet')

# subclass 'station'
hym_station <- hm_create(class_name = 'station')

# subclass 'compact'
hym_compact <- hm_create(class_name = 'compact')

# create class 'hydromet'
hym_metadata <- hm_create(class_name = 'hydromet')

# subclass 'station'
hym_station <- hm_create(class_name = 'station')

# subclass 'compact'
hym_compact <- hm_create(class_name = 'compact')

Extract the slot

Description

Get the table (or metadata) that you want from an hydromet or hydromet_XXX class.

Usage

hm_get(obj, slot_name = NA_character_)

## S4 method for signature 'hydromet'
hm_get(obj, slot_name = NA_character_)

## S4 method for signature 'hydromet_station'
hm_get(obj, slot_name = NA_character_)

## S4 method for signature 'hydromet_compact'
hm_get(obj, slot_name = NA_character_)
hm_get(obj, slot_name = NA_character_)

## S4 method for signature 'hydromet'
hm_get(obj, slot_name = NA_character_)

## S4 method for signature 'hydromet_station'
hm_get(obj, slot_name = NA_character_)

## S4 method for signature 'hydromet_compact'
hm_get(obj, slot_name = NA_character_)

Arguments

`obj`	an `hydromet` or `hydromet_XXX` class object.
`slot_name`	string with slot to extract.

Value

The required data frame or metadata.

Functions

hm_get(hydromet): get method for generic hydromet object
hm_get(hydromet_station): get method for station class
hm_get(hydromet_compact): get method for compact class

Examples

## Not run: 
# set path to file
path_file <- system.file('extdata', 'ianigla_cuevas.csv',
             package = 'hydrotoolbox')

# read file
cuevas <-
     read_ianigla(path = path_file,
                  out_name = c('tair(°C)', 'rh(%)', 'patm(mbar)',
                             'p(mm)', 'wspd(km/hr)', 'wdir(°)',
                              'kin(kW/m2)', 'hsnow(cm)', 'tsoil(°C)' ) )

# create and set one the variables
hm_cuevas <-
 hm_create() %>%
 hm_set(tair = cuevas[ , c('date', 'tair(°C)')],
        rh = cuevas[ , c("date", 'rh(%)')])


# now extract the slot of air temperature
head( hm_get(obj = hm_cuevas, slot_name = 'tair') )

# extract multiple data
out_list <- list()
for(i in c("tair", "rh")){
 out_list[[ i ]] <-
   hm_cuevas %>%
   hm_get(slot_name = i)
 }


## End(Not run)

## Not run: 
# set path to file
path_file <- system.file('extdata', 'ianigla_cuevas.csv',
             package = 'hydrotoolbox')

# read file
cuevas <-
     read_ianigla(path = path_file,
                  out_name = c('tair(°C)', 'rh(%)', 'patm(mbar)',
                             'p(mm)', 'wspd(km/hr)', 'wdir(°)',
                              'kin(kW/m2)', 'hsnow(cm)', 'tsoil(°C)' ) )

# create and set one the variables
hm_cuevas <-
 hm_create() %>%
 hm_set(tair = cuevas[ , c('date', 'tair(°C)')],
        rh = cuevas[ , c("date", 'rh(%)')])


# now extract the slot of air temperature
head( hm_get(obj = hm_cuevas, slot_name = 'tair') )

# extract multiple data
out_list <- list()
for(i in c("tair", "rh")){
 out_list[[ i ]] <-
   hm_cuevas %>%
   hm_get(slot_name = i)
 }


## End(Not run)

Melt many objects into an `hydromet_compact` class object

Description

This method allows you merge several tables (inside hydromet_station and/or hydromet_compact class objects) into a single one and set them into the compact slot (hydromet_compact class object).

Usage

hm_melt(obj, melt, slot_name, col_name, out_name = NULL)

## S4 method for signature 'hydromet_compact'
hm_melt(obj, melt, slot_name, col_name, out_name = NULL)
hm_melt(obj, melt, slot_name, col_name, out_name = NULL)

## S4 method for signature 'hydromet_compact'
hm_melt(obj, melt, slot_name, col_name, out_name = NULL)

Arguments

`obj`	a valid `hydromet_compact` class object.
`melt`	string vector containing the `hydromet_xxx` class objects names (as you have in the Global Environment) that you want for melting.
`slot_name`	list (one element per `melt` vector name) with the slot(s) to extract per every `hydromet_xxx` class object (as string vectors).
`col_name`	string vector with the name of the variables to keep. You must comply the following name convention `'melt_slot_variable'` (e.g.: 'guido_qd_Q(m3/s)' - where guido is your object name, qd is the slot with daily mean river discharge and Q(m3/s) is the required column name inside that slot). Another option is to set this argument just with the string `'all'` and the method will preserve all the slot(s) columns. Other minimal option is to choose the string `'last'`: in this case you will get only the last column of each slot(s).
`out_name`	optional. String vector with the output names of the final table. If you use the default value (`NULL`) the method will add the object and slot name (provided in `melt` and `slot_name` argument) at the beginning of every column (e.g.: `'guido_qd_q(m3/s)'`).

Value

An hydromet_compact class object with a data frame inside the compact slot with all variables that you provided in col_name.

Functions

hm_melt(hydromet_compact): plot method for compact class

Note

Remember that all the chosen variables should have the same temporal resolution. The method itself will not warn you about bad entries.

Examples

## Not run: 
# lets say that we want to put together snow water equivalent from Toscas (dgi)
# and daily streamflow discharge from Guido (snih)

# path to all example files
path <- system.file('extdata', package = 'hydrotoolbox')

# on the first place we build the stations
# dgi file
toscas <-
hm_create() %>%
  hm_build(bureau = 'dgi', path = path,
           file_name = 'dgi_toscas.xlsx',
           slot_name = c('swe', 'tmax',
           'tmin', 'tmean', 'rh', 'patm'),
           by = 'day',
           out_name = c('swe', 'tmax',
           'tmin', 'tmean', 'rh', 'patm') )

# snih file
guido <-
hm_create() %>%
  hm_build(bureau = 'snih', path = path,
           file_name = c('snih_hq_guido.xlsx',
           'snih_qd_guido.xlsx'),
           slot_name = c('hq', 'qd'),
           by = c('none', 'day') )

# now we melt the requiered data
hm_create(class_name = 'compact') %>%
     hm_melt(melt = c('toscas', 'guido'),
             slot_name = list(toscas = 'swe', guido = 'qd'),
             col_name = 'all',
             out_name = c('swe(mm)', 'qd(m3/s)')
             ) %>%
       hm_plot(slot_name = 'compact',
               col_name = list( c('swe(mm)', 'qd(m3/s)') ),
               interactive = TRUE,
               line_color = c('dodgerblue', 'red'),
               y_lab = c('q(m3/s)', 'swe(mm)'),
               dual_yaxis = c('right', 'left')
                )

## End(Not run)


## Not run: 
# lets say that we want to put together snow water equivalent from Toscas (dgi)
# and daily streamflow discharge from Guido (snih)

# path to all example files
path <- system.file('extdata', package = 'hydrotoolbox')

# on the first place we build the stations
# dgi file
toscas <-
hm_create() %>%
  hm_build(bureau = 'dgi', path = path,
           file_name = 'dgi_toscas.xlsx',
           slot_name = c('swe', 'tmax',
           'tmin', 'tmean', 'rh', 'patm'),
           by = 'day',
           out_name = c('swe', 'tmax',
           'tmin', 'tmean', 'rh', 'patm') )

# snih file
guido <-
hm_create() %>%
  hm_build(bureau = 'snih', path = path,
           file_name = c('snih_hq_guido.xlsx',
           'snih_qd_guido.xlsx'),
           slot_name = c('hq', 'qd'),
           by = c('none', 'day') )

# now we melt the requiered data
hm_create(class_name = 'compact') %>%
     hm_melt(melt = c('toscas', 'guido'),
             slot_name = list(toscas = 'swe', guido = 'qd'),
             col_name = 'all',
             out_name = c('swe(mm)', 'qd(m3/s)')
             ) %>%
       hm_plot(slot_name = 'compact',
               col_name = list( c('swe(mm)', 'qd(m3/s)') ),
               interactive = TRUE,
               line_color = c('dodgerblue', 'red'),
               y_lab = c('q(m3/s)', 'swe(mm)'),
               dual_yaxis = c('right', 'left')
                )

## End(Not run)

Create, modify and delete columns inside a slot

Description

This method allows you to modify whatever (except 'date' column) you want inside a slot data frame. Since this package was designed with the aim of providing useful objects to store and track changes in hydro-meteorological series, is not recommend to delete or change the original data, but it is upon to you.

Usage

hm_mutate(obj, slot_name, FUN, ...)

## S4 method for signature 'hydromet_station'
hm_mutate(obj, slot_name, FUN, ...)

## S4 method for signature 'hydromet_compact'
hm_mutate(obj, slot_name, FUN, ...)
hm_mutate(obj, slot_name, FUN, ...)

## S4 method for signature 'hydromet_station'
hm_mutate(obj, slot_name, FUN, ...)

## S4 method for signature 'hydromet_compact'
hm_mutate(obj, slot_name, FUN, ...)

Arguments

`obj`	a valid `hydromet_XXX` class object.
`slot_name`	string with the a valid name.
`FUN`	function name. The function output must be a data frame with the first column being the `Date`. Note that `hydrotoolbox` provides common used hydrological functions: see for example mov_avg. An interesting function to use is `mutate` from `dplyr` package.
`...`	`FUN` arguments to pass.

Value

The same object but with the modified slot's data frame

Functions

hm_mutate(hydromet_station): method for station class.
hm_mutate(hydromet_compact): method for compact class.

Examples

## Not run: 
# path to all example files
path <- system.file('extdata', package = 'hydrotoolbox')

# build the snih station file
guido <-
hm_create() %>%
  hm_build(bureau = 'snih', path = path,
           file_name = c('snih_hq_guido.xlsx',
           'snih_qd_guido.xlsx'),
           slot_name = c('hq', 'qd'),
           by = c('none', 'day') ) %>%
  hm_name(slot_name = 'qd',
          col_name = 'q(m3/s)')

 # apply a moving average windows to streamflow records
hm_mutate(obj = guido, slot_name = 'qd',
          FUN = mov_avg, k = 10,
          pos = 'c', out_name = 'mov_avg') %>%
 hm_plot(slot_name = 'qd',
         col_name = list(c('q(m3/s)', 'mov_avg') ),
         interactive = TRUE,
         line_color = c('dodgerblue', 'red3'),
         y_lab = 'Q(m3/s)',
         legend_lab = c('original', 'mov_avg')  )

## End(Not run)


## Not run: 
# path to all example files
path <- system.file('extdata', package = 'hydrotoolbox')

# build the snih station file
guido <-
hm_create() %>%
  hm_build(bureau = 'snih', path = path,
           file_name = c('snih_hq_guido.xlsx',
           'snih_qd_guido.xlsx'),
           slot_name = c('hq', 'qd'),
           by = c('none', 'day') ) %>%
  hm_name(slot_name = 'qd',
          col_name = 'q(m3/s)')

 # apply a moving average windows to streamflow records
hm_mutate(obj = guido, slot_name = 'qd',
          FUN = mov_avg, k = 10,
          pos = 'c', out_name = 'mov_avg') %>%
 hm_plot(slot_name = 'qd',
         col_name = list(c('q(m3/s)', 'mov_avg') ),
         interactive = TRUE,
         line_color = c('dodgerblue', 'red3'),
         y_lab = 'Q(m3/s)',
         legend_lab = c('original', 'mov_avg')  )

## End(Not run)

Set new column names

Description

Change slot's column names.

Usage

hm_name(obj, slot_name, col_name)

## S4 method for signature 'hydromet_station'
hm_name(obj, slot_name, col_name)

## S4 method for signature 'hydromet_compact'
hm_name(obj, slot_name, col_name)
hm_name(obj, slot_name, col_name)

## S4 method for signature 'hydromet_station'
hm_name(obj, slot_name, col_name)

## S4 method for signature 'hydromet_compact'
hm_name(obj, slot_name, col_name)

Arguments

`obj`	a valid `hydromet_*` class object.
`slot_name`	string with the a valid name.
`col_name`	string vector with new column names.

Value

The same object but with new column names.

Functions

hm_name(hydromet_station): set new column name for station class
hm_name(hydromet_compact): set new column name for compact class

Examples

## Not run: 
# path to all example files
path <- system.file('extdata', package = 'hydrotoolbox')

# we first build the snih station file
guido <-
hm_create() %>%
  hm_build(bureau = 'snih', path = path,
           file_name = c('snih_hq_guido.xlsx',
           'snih_qd_guido.xlsx'),
           slot_name = c('hq', 'qd'),
           by = c('none', 'day') )

 guido %>% hm_show(slot_name = 'qd')

# now we can change default names
hm_name(obj = guido, slot_name = 'qd',
        col_name = 'q(m3/s)') %>%
        hm_show(slot_name = 'qd')

## End(Not run)

## Not run: 
# path to all example files
path <- system.file('extdata', package = 'hydrotoolbox')

# we first build the snih station file
guido <-
hm_create() %>%
  hm_build(bureau = 'snih', path = path,
           file_name = c('snih_hq_guido.xlsx',
           'snih_qd_guido.xlsx'),
           slot_name = c('hq', 'qd'),
           by = c('none', 'day') )

 guido %>% hm_show(slot_name = 'qd')

# now we can change default names
hm_name(obj = guido, slot_name = 'qd',
        col_name = 'q(m3/s)') %>%
        hm_show(slot_name = 'qd')

## End(Not run)

Methods to easily use `ggplot2` or `plotly` (interactive)

Description

This method allows you to make plots (using simple and expressive arguments) of the variables contained inside an hydromet_XXX class object. The plot outputs can be static (ggplot2) or dynamic (plotly).

Usage

hm_plot(
  obj,
  slot_name,
  col_name,
  interactive = FALSE,
  line_type = NULL,
  line_color = NULL,
  line_size = NULL,
  line_alpha = NULL,
  x_lab = "date",
  y_lab = "y",
  title_lab = NULL,
  legend_lab = NULL,
  dual_yaxis = NULL,
  from = NULL,
  to = NULL,
  scatter = NULL
)

## S4 method for signature 'hydromet_station'
hm_plot(
  obj,
  slot_name,
  col_name,
  interactive = FALSE,
  line_type = NULL,
  line_color = NULL,
  line_size = NULL,
  line_alpha = NULL,
  x_lab = "date",
  y_lab = "y",
  title_lab = NULL,
  legend_lab = NULL,
  dual_yaxis = NULL,
  from = NULL,
  to = NULL,
  scatter = NULL
)

## S4 method for signature 'hydromet_compact'
hm_plot(
  obj,
  slot_name,
  col_name,
  interactive = FALSE,
  line_type = NULL,
  line_color = NULL,
  line_size = NULL,
  line_alpha = NULL,
  x_lab = "date",
  y_lab = "y",
  title_lab = NULL,
  legend_lab = NULL,
  dual_yaxis = NULL,
  from = NULL,
  to = NULL,
  scatter = NULL
)
hm_plot(
  obj,
  slot_name,
  col_name,
  interactive = FALSE,
  line_type = NULL,
  line_color = NULL,
  line_size = NULL,
  line_alpha = NULL,
  x_lab = "date",
  y_lab = "y",
  title_lab = NULL,
  legend_lab = NULL,
  dual_yaxis = NULL,
  from = NULL,
  to = NULL,
  scatter = NULL
)

## S4 method for signature 'hydromet_station'
hm_plot(
  obj,
  slot_name,
  col_name,
  interactive = FALSE,
  line_type = NULL,
  line_color = NULL,
  line_size = NULL,
  line_alpha = NULL,
  x_lab = "date",
  y_lab = "y",
  title_lab = NULL,
  legend_lab = NULL,
  dual_yaxis = NULL,
  from = NULL,
  to = NULL,
  scatter = NULL
)

## S4 method for signature 'hydromet_compact'
hm_plot(
  obj,
  slot_name,
  col_name,
  interactive = FALSE,
  line_type = NULL,
  line_color = NULL,
  line_size = NULL,
  line_alpha = NULL,
  x_lab = "date",
  y_lab = "y",
  title_lab = NULL,
  legend_lab = NULL,
  dual_yaxis = NULL,
  from = NULL,
  to = NULL,
  scatter = NULL
)

Arguments

`obj`	a valid `hydromet_XXX` class object.
`slot_name`	string vector with the name of the slot(s) to use in plotting.
`col_name`	list containing the column name of the variables to plot. Every element inside the list belongs to the previous defined slot(s).
`interactive`	logical. Default value, `FALSE`, will return a `ggplot2` class object. Otherwise you will get a `plotly` one.
`line_type`	string with the name of the line dash type (`ggplot2`) or mode in the `plotly` case. `ggplot2`: `'solid'` (default value), `'twodash'`, `'longdash'`, `'dotted'`, `'dotdash'`, `'dashed'` or `'blank'`. `plotly`: `'lines'` (default value), `'lines+markers'` or `'markers'`. NOTE: when using `scatter` plot this arguments goes through the `shape` argument (in `geom_point()`) as numeric.
`line_color`	string with a valid `color` name. See `'colors()'` or Rcolor document.
`line_size`	numeric vector containing the size of every line to plot. If you use the `NULL` value it will return the plots with default(s) for either `ggplot2` or `plotly`.
`line_alpha`	numeric vector with line(s) transparency. From 0 (invisible) to 1.
`x_lab`	string with `x axis` label. Default is `'Date'`.
`y_lab`	string with `y axis` label. In case you use `dual_yaxis` argument you must supply both `c('ylab', 'y2lab')`.
`title_lab`	string with the title of the plot. Default is a plot without title.
`legend_lab`	string vector with plot label(s) name(s).
`dual_yaxis`	string vector suggesting which variables are assign either to the `'left'` or `'right'` y axis.
`from`	string value for `'Date'` class or `POSIXct(lt)` class for date-time data with the starting `Date`. You can use `'from'` without `'to'`. In this case you will subset your data `'from'` till the end.
`to`	string value for `'Date'` class or `POSIXct(lt)` class for date-time data with the ending `Date`. You can use `'to'` without `'from'`. In this case you will subset your data from the beginning till `'to'`.
`scatter`	string vector (of length two) suggesting which variables goes in the `'x'` and `'y'` axis respectively. Valid character entries are `'x'` and `'y'`.

Value

A ggplot2 or plotly object.

Functions

hm_plot(hydromet_station): plot method for station class
hm_plot(hydromet_compact): plot method for compact class

Examples

## Not run: 
# lets work with the cuevas station
path <- system.file('extdata', package = 'hydrotoolbox')

# use the build method
hm_cuevas <-
  hm_create() %>%
  hm_build(bureau = 'ianigla', path = path,
           file_name = 'ianigla_cuevas.csv',
           slot_name = c('tair', 'rh', 'patm',
                         'precip', 'wspd', 'wdir',
                         'kin', 'hsnow', 'tsoil'),
           by = 'hour',
           out_name = c('tair(°C)', 'rh(%)', 'patm(mbar)',
                        'p(mm)', 'wspd(km/hr)', 'wdir(°)',
                        'kin(kW/m2)', 'hsnow(cm)', 'tsoil(°C)' )
           )

# let's start by making a single variable static plot
hm_plot(obj = hm_cuevas, slot_name = 'tair',
        col_name = list('tair(°C)') )

# we add labels, change color, line type and we focus
# on specific date range
hm_plot(obj = hm_cuevas, slot_name = 'tair',
        col_name = list('tair(°C)'),
        line_type = 'longdash',
        line_color = 'dodgerblue',
        x_lab = 'Date time', y_lab = 'T(°C)',
        title_lab = 'Hourly temperature at Cuevas',
        legend_lab = 'Tair',
        from = ISOdate(2020, 7, 1),
        to = ISOdate(2020, 7, 5))

# compare air with soil temperature
hm_plot(obj = hm_cuevas, slot_name = c('tair', 'tsoil'),
        col_name = list('tair(°C)', 'tsoil(°C)'),
        line_type = c('longdash', 'solid'),
        line_color = c('dodgerblue', 'tan4'),
        x_lab = 'Date time', y_lab = 'T(°C)',
        title_lab = 'Hourly temperature at Cuevas',
        legend_lab = c('Tair', 'Tsoil'),
        from = ISOdate(2020, 7, 1),
        to = ISOdate(2020, 7, 5))

# let's add relative humidity on the right y-axis
hm_plot(obj = hm_cuevas, slot_name = c('tair', 'tsoil', 'rh'),
        col_name = list('tair(°C)', 'tsoil(°C)', 'rh(%)'),
        line_type = c('longdash', 'solid', 'solid'),
        line_color = c('dodgerblue', 'tan4', 'red'),
        x_lab = 'Date time', y_lab = c('T(°C)', 'RH(%)'),
        title_lab = 'Hourly meteo data at Cuevas',
        legend_lab = c('Tair', 'Tsoil', 'RH'),
        dual_yaxis = c('left', 'left', 'right'),
        from = ISOdate(2020, 7, 1),
        to = ISOdate(2020, 7, 5))

# we decide to analize the previous variables in detail
# with a dynamic plot
hm_plot(obj = hm_cuevas, slot_name = c('tair', 'tsoil', 'rh'),
        col_name = list('tair(°C)', 'tsoil(°C)', 'rh(%)'),
        line_color = c('dodgerblue', 'tan4', 'red'),
        x_lab = 'Date time', y_lab = c('T(°C)', 'RH(%)'),
        title_lab = 'Hourly meteo data at Cuevas',
        legend_lab = c('Tair', 'Tsoil', 'RH'),
        dual_yaxis = c('left', 'left', 'right'),
        interactive = TRUE)
# click on the Zoom icon and play a little...


# suppose now that we want to make a scatter plot to show
# the negative correlation between air temperature and
# relative humidity
hm_plot(obj = hm_cuevas, slot_name = c('tair', 'rh'),
        col_name = list('tair(°C)', 'rh(%)'),
        line_color = 'dodgerblue',
        x_lab = 'Tair', y_lab = 'RH',
        scatter = c('x', 'y') )

## End(Not run)


## Not run: 
# lets work with the cuevas station
path <- system.file('extdata', package = 'hydrotoolbox')

# use the build method
hm_cuevas <-
  hm_create() %>%
  hm_build(bureau = 'ianigla', path = path,
           file_name = 'ianigla_cuevas.csv',
           slot_name = c('tair', 'rh', 'patm',
                         'precip', 'wspd', 'wdir',
                         'kin', 'hsnow', 'tsoil'),
           by = 'hour',
           out_name = c('tair(°C)', 'rh(%)', 'patm(mbar)',
                        'p(mm)', 'wspd(km/hr)', 'wdir(°)',
                        'kin(kW/m2)', 'hsnow(cm)', 'tsoil(°C)' )
           )

# let's start by making a single variable static plot
hm_plot(obj = hm_cuevas, slot_name = 'tair',
        col_name = list('tair(°C)') )

# we add labels, change color, line type and we focus
# on specific date range
hm_plot(obj = hm_cuevas, slot_name = 'tair',
        col_name = list('tair(°C)'),
        line_type = 'longdash',
        line_color = 'dodgerblue',
        x_lab = 'Date time', y_lab = 'T(°C)',
        title_lab = 'Hourly temperature at Cuevas',
        legend_lab = 'Tair',
        from = ISOdate(2020, 7, 1),
        to = ISOdate(2020, 7, 5))

# compare air with soil temperature
hm_plot(obj = hm_cuevas, slot_name = c('tair', 'tsoil'),
        col_name = list('tair(°C)', 'tsoil(°C)'),
        line_type = c('longdash', 'solid'),
        line_color = c('dodgerblue', 'tan4'),
        x_lab = 'Date time', y_lab = 'T(°C)',
        title_lab = 'Hourly temperature at Cuevas',
        legend_lab = c('Tair', 'Tsoil'),
        from = ISOdate(2020, 7, 1),
        to = ISOdate(2020, 7, 5))

# let's add relative humidity on the right y-axis
hm_plot(obj = hm_cuevas, slot_name = c('tair', 'tsoil', 'rh'),
        col_name = list('tair(°C)', 'tsoil(°C)', 'rh(%)'),
        line_type = c('longdash', 'solid', 'solid'),
        line_color = c('dodgerblue', 'tan4', 'red'),
        x_lab = 'Date time', y_lab = c('T(°C)', 'RH(%)'),
        title_lab = 'Hourly meteo data at Cuevas',
        legend_lab = c('Tair', 'Tsoil', 'RH'),
        dual_yaxis = c('left', 'left', 'right'),
        from = ISOdate(2020, 7, 1),
        to = ISOdate(2020, 7, 5))

# we decide to analize the previous variables in detail
# with a dynamic plot
hm_plot(obj = hm_cuevas, slot_name = c('tair', 'tsoil', 'rh'),
        col_name = list('tair(°C)', 'tsoil(°C)', 'rh(%)'),
        line_color = c('dodgerblue', 'tan4', 'red'),
        x_lab = 'Date time', y_lab = c('T(°C)', 'RH(%)'),
        title_lab = 'Hourly meteo data at Cuevas',
        legend_lab = c('Tair', 'Tsoil', 'RH'),
        dual_yaxis = c('left', 'left', 'right'),
        interactive = TRUE)
# click on the Zoom icon and play a little...


# suppose now that we want to make a scatter plot to show
# the negative correlation between air temperature and
# relative humidity
hm_plot(obj = hm_cuevas, slot_name = c('tair', 'rh'),
        col_name = list('tair(°C)', 'rh(%)'),
        line_color = 'dodgerblue',
        x_lab = 'Tair', y_lab = 'RH',
        scatter = c('x', 'y') )

## End(Not run)

Get a summary report of your data

Description

Returns a list with two elements: the first one contains basic statistics (mean, sd, max and min) values and the second one is a table with summary of miss data (see also report_miss).

Usage

hm_report(obj, slot_name, col_name = "all")

## S4 method for signature 'hydromet_station'
hm_report(obj, slot_name, col_name = "all")

## S4 method for signature 'hydromet_compact'
hm_report(obj, slot_name = "compact", col_name = "all")
hm_report(obj, slot_name, col_name = "all")

## S4 method for signature 'hydromet_station'
hm_report(obj, slot_name, col_name = "all")

## S4 method for signature 'hydromet_compact'
hm_report(obj, slot_name = "compact", col_name = "all")

Arguments

`obj`	a valid `hydromet_XXX` class object.
`slot_name`	string with the name of the slot to report.
`col_name`	string vector with the column(s) name(s) to report. By default the function will do it in all columns inside the slot.

Value

A list summarizing basic statistics and missing data. The missing data table presents a data frame (one per col_name) with three columns: start-date, end-date and number of missing time steps. In the last row of this table you will find the total number of missing measurements (under "time_step" column). The "first" and "last" columns will have a NA_character for this last row.

Functions

hm_report(hydromet_station): report method for station class
hm_report(hydromet_compact): report method for compact class

Examples

## Not run: 
# cuevas station
path <- system.file('extdata', package = 'hydrotoolbox')

# use the build method
hm_cuevas <-
  hm_create() %>%
  hm_build(bureau = 'ianigla', path = path,
           file_name = 'ianigla_cuevas.csv',
           slot_name = c('tair', 'rh', 'patm',
                         'precip', 'wspd', 'wdir',
                         'kin', 'hsnow', 'tsoil'),
           by = 'hour',
           out_name = c('tair(°C)', 'rh(%)', 'patm(mbar)',
                        'p(mm)', 'wspd(km/hr)', 'wdir(°)',
                        'kin(kW/m2)', 'hsnow(cm)', 'tsoil(°C)' )
          )

# report incoming solar radiation
hm_report(obj = hm_cuevas, slot_name = 'kin')

## End(Not run)

## Not run: 
# cuevas station
path <- system.file('extdata', package = 'hydrotoolbox')

# use the build method
hm_cuevas <-
  hm_create() %>%
  hm_build(bureau = 'ianigla', path = path,
           file_name = 'ianigla_cuevas.csv',
           slot_name = c('tair', 'rh', 'patm',
                         'precip', 'wspd', 'wdir',
                         'kin', 'hsnow', 'tsoil'),
           by = 'hour',
           out_name = c('tair(°C)', 'rh(%)', 'patm(mbar)',
                        'p(mm)', 'wspd(km/hr)', 'wdir(°)',
                        'kin(kW/m2)', 'hsnow(cm)', 'tsoil(°C)' )
          )

# report incoming solar radiation
hm_report(obj = hm_cuevas, slot_name = 'kin')

## End(Not run)

Set the data of an `hydromet` object or its subclass

Description

With this method you can set (or change) an specific slot value (change the table).

Usage

hm_set(
  obj = NULL,
  id = NULL,
  agency = NULL,
  station = NULL,
  lat = NULL,
  long = NULL,
  alt = NULL,
  country = NULL,
  province = NULL,
  river = NULL,
  active = NULL,
  basin_area = NULL,
  basin_eff = NULL,
  other_1 = NULL,
  other_2 = NULL,
  ...
)

## S4 method for signature 'hydromet'
hm_set(
  obj = NULL,
  id = NULL,
  agency = NULL,
  station = NULL,
  lat = NULL,
  long = NULL,
  alt = NULL,
  country = NULL,
  province = NULL,
  river = NULL,
  active = NULL,
  basin_area = NULL,
  basin_eff = NULL,
  other_1 = NULL,
  other_2 = NULL,
  ...
)

## S4 method for signature 'hydromet_station'
hm_set(
  obj = NULL,
  id = NULL,
  agency = NULL,
  station = NULL,
  lat = NULL,
  long = NULL,
  alt = NULL,
  country = NULL,
  province = NULL,
  river = NULL,
  active = NULL,
  basin_area = NULL,
  basin_eff = NULL,
  other_1 = NULL,
  other_2 = NULL,
  hq = NULL,
  hw = NULL,
  qh = NULL,
  qd = NULL,
  qa = NULL,
  qm = NULL,
  wspd = NULL,
  wdir = NULL,
  evap = NULL,
  anem = NULL,
  patm = NULL,
  rh = NULL,
  tair = NULL,
  tmax = NULL,
  tmin = NULL,
  tmean = NULL,
  tsoil = NULL,
  precip = NULL,
  rainfall = NULL,
  swe = NULL,
  hsnow = NULL,
  kin = NULL,
  kout = NULL,
  lin = NULL,
  lout = NULL,
  unvar = NULL
)

## S4 method for signature 'hydromet_compact'
hm_set(
  obj = NULL,
  id = NULL,
  agency = NULL,
  station = NULL,
  lat = NULL,
  long = NULL,
  alt = NULL,
  country = NULL,
  province = NULL,
  river = NULL,
  active = NULL,
  basin_area = NULL,
  basin_eff = NULL,
  other_1 = NULL,
  other_2 = NULL,
  compact = NULL
)
hm_set(
  obj = NULL,
  id = NULL,
  agency = NULL,
  station = NULL,
  lat = NULL,
  long = NULL,
  alt = NULL,
  country = NULL,
  province = NULL,
  river = NULL,
  active = NULL,
  basin_area = NULL,
  basin_eff = NULL,
  other_1 = NULL,
  other_2 = NULL,
  ...
)

## S4 method for signature 'hydromet'
hm_set(
  obj = NULL,
  id = NULL,
  agency = NULL,
  station = NULL,
  lat = NULL,
  long = NULL,
  alt = NULL,
  country = NULL,
  province = NULL,
  river = NULL,
  active = NULL,
  basin_area = NULL,
  basin_eff = NULL,
  other_1 = NULL,
  other_2 = NULL,
  ...
)

## S4 method for signature 'hydromet_station'
hm_set(
  obj = NULL,
  id = NULL,
  agency = NULL,
  station = NULL,
  lat = NULL,
  long = NULL,
  alt = NULL,
  country = NULL,
  province = NULL,
  river = NULL,
  active = NULL,
  basin_area = NULL,
  basin_eff = NULL,
  other_1 = NULL,
  other_2 = NULL,
  hq = NULL,
  hw = NULL,
  qh = NULL,
  qd = NULL,
  qa = NULL,
  qm = NULL,
  wspd = NULL,
  wdir = NULL,
  evap = NULL,
  anem = NULL,
  patm = NULL,
  rh = NULL,
  tair = NULL,
  tmax = NULL,
  tmin = NULL,
  tmean = NULL,
  tsoil = NULL,
  precip = NULL,
  rainfall = NULL,
  swe = NULL,
  hsnow = NULL,
  kin = NULL,
  kout = NULL,
  lin = NULL,
  lout = NULL,
  unvar = NULL
)

## S4 method for signature 'hydromet_compact'
hm_set(
  obj = NULL,
  id = NULL,
  agency = NULL,
  station = NULL,
  lat = NULL,
  long = NULL,
  alt = NULL,
  country = NULL,
  province = NULL,
  river = NULL,
  active = NULL,
  basin_area = NULL,
  basin_eff = NULL,
  other_1 = NULL,
  other_2 = NULL,
  compact = NULL
)

Arguments

`obj`	an `hydromet` or `hydromet_XXX` class object.
`id`	ANY. This is the ID assigned by the agency.
`agency`	character. The name of the agency (or institution) that provides the data of the station.
`station`	character. The name of the (hydro)-meteorological station.
`lat`	numeric. Latitude of the station.
`long`	numeric. Longitude of the station
`alt`	numeric. Altitute of the station.
`country`	character. Country where the station is located. Argentina is set as default value.
`province`	character. Name of the province where the station is located. Mendoza is set as default value.
`river`	character. Basin river's name.
`active`	logical. It indicates whether or not the station is currently operated. Default value is `TRUE`.
`basin_area`	numeric. The basin area (km2) of the catchment upstream of the gauge.
`basin_eff`	numeric. The effective area (km2) of the basin upstream of the gauge. In Canada, many basins have variable contributing fractions. In these basins, the effective area of the basin contributes flow to the outlet at least one year in two.
`other_1`	ANY. It is the first free-to-fill slot in order to give you the chance to write extra information about your hydro-met station.
`other_2`	ANY. It is the second free-to-fill slot in order to give you the chance to write extra information about your hydro-met station.
`...`	arguments to be passed to methods. They rely on the slots of the `obj` subclass.
`hq`	water-height vs stream-discharge measurements.
`hw`	water level records.
`qh`	hourly mean river discharge.
`qd`	daily mean river discharge.
`qa`	annual river discharge.
`qm`	monthly mean river discharge.
`wspd`	wind speed.
`wdir`	wind direction.
`evap`	pan-evaporation.
`anem`	anemometer wind speed records (usually installed above the pan-evap tank).
`patm`	atmospheric pressure.
`rh`	relative humidity.
`tair`	air temperature (typically recorded at hourly time-step).
`tmax`	daily maximum recorded air temperature.
`tmin`	daily minimum recorded air temperature.
`tmean`	daily mean air temperature.
`tsoil`	soil temperature.
`precip`	total (snow and rain) precipitation records.
`rainfall`	liquid only precipitation measurements.
`swe`	snow water equivalent (typically recorded on snow pillows).
`hsnow`	snow height from ultrasonic devices.
`kin`	incoming short-wave radiation.
`kout`	outgoing short-wave radiation.
`lin`	incoming long-wave radiation.
`lout`	outgoing long-wave radiation.
`unvar`	reserved for non-considered variables.
`compact`	data frame with Date as first column. All other columns are hydro-meteorological variables.

Value

The hydromet object with the slots set.

Functions

hm_set(hydromet): set method for generic object
hm_set(hydromet_station): set method for station object
hm_set(hydromet_compact): set method for compact object

Examples

## Not run: 
# create an hydro-met station
hm_guido <- hm_create(class_name = 'station')

# assign altitude
hm_guido <- hm_set(obj = hm_guido, alt = 2480)

# now we read streamflow - water height measurements
path_file <- system.file('extdata', 'snih_hq_guido.xlsx',
package = 'hydrotoolbox')
guido_hq  <- read_snih(path = path_file, by = 'none',
             out_name = c('h(m)', 'q(m3/s)',
                            'q_coarse_solid(kg/s)',
                            'q_fine_solid(kg/s)') )

# set the new data frame
 # note: you can do it manually but using the hm_build() method
 #       is stromgly recommended
hm_guido <- hm_set(obj = hm_guido, hq = guido_hq)
hm_show(obj = hm_guido)

## End(Not run)

## Not run: 
# create an hydro-met station
hm_guido <- hm_create(class_name = 'station')

# assign altitude
hm_guido <- hm_set(obj = hm_guido, alt = 2480)

# now we read streamflow - water height measurements
path_file <- system.file('extdata', 'snih_hq_guido.xlsx',
package = 'hydrotoolbox')
guido_hq  <- read_snih(path = path_file, by = 'none',
             out_name = c('h(m)', 'q(m3/s)',
                            'q_coarse_solid(kg/s)',
                            'q_fine_solid(kg/s)') )

# set the new data frame
 # note: you can do it manually but using the hm_build() method
 #       is stromgly recommended
hm_guido <- hm_set(obj = hm_guido, hq = guido_hq)
hm_show(obj = hm_guido)

## End(Not run)

Easy access to see your data

Description

This method shows the 'head', 'tail' or 'all' data from specific slot.

Usage

hm_show(obj, slot_name = "fill", show = "head")

## S4 method for signature 'hydromet'
hm_show(obj, slot_name = "fill", show = "head")

## S4 method for signature 'hydromet_station'
hm_show(obj, slot_name = "fill", show = "head")

## S4 method for signature 'hydromet_compact'
hm_show(obj, slot_name = "compact", show = "head")
hm_show(obj, slot_name = "fill", show = "head")

## S4 method for signature 'hydromet'
hm_show(obj, slot_name = "fill", show = "head")

## S4 method for signature 'hydromet_station'
hm_show(obj, slot_name = "fill", show = "head")

## S4 method for signature 'hydromet_compact'
hm_show(obj, slot_name = "compact", show = "head")

Arguments

`obj`	a valid `hydromet_XXX` class object.
`slot_name`	string vector with the name of the slot(s) to show. Alternatively you can use `'fill'` or `'empty'` to get the data frames with or without data respectively.
`show`	string with either `'head'`, `'tail'` or `'all'`.

Value

It prints the data inside the required slot.

Functions

hm_show(hydromet): print method for hydromet class
hm_show(hydromet_station): print method for station class
hm_show(hydromet_compact): print method for compact class

Examples

## Not run: 
# lets work with the cuevas station
path <- system.file('extdata', package = 'hydrotoolbox')

# use the build method
hm_cuevas <-
  hm_create() %>%
  hm_build(bureau = 'ianigla', path = path,
           file_name = 'ianigla_cuevas.csv',
           slot_name = c('tair', 'rh', 'patm',
                         'precip', 'wspd', 'wdir',
                         'kin', 'hsnow', 'tsoil'),
           by = 'hour',
           out_name = c('tair(°C)', 'rh(%)', 'patm(mbar)',
                        'p(mm)', 'wspd(km/hr)', 'wdir(°)',
                        'kin(kW/m2)', 'hsnow(cm)', 'tsoil(°C)' )
          )

# now we want to know which are the slots with data
hm_show(obj = hm_cuevas)

# see the last values of our data
hm_show(obj = hm_cuevas, show = 'tail')

# print the entire tables
hm_show(obj = hm_cuevas, show = "all")

# or maybe we want to know which slot have no data
hm_show(obj = hm_cuevas, slot_name = 'empty')

# focus on specific slots
hm_show(obj = hm_cuevas, slot_name = c('kin', 'rh') )
hm_show(obj = hm_cuevas, slot_name = c('kin', 'rh'), show = 'tail' )

## End(Not run)

## Not run: 
# lets work with the cuevas station
path <- system.file('extdata', package = 'hydrotoolbox')

# use the build method
hm_cuevas <-
  hm_create() %>%
  hm_build(bureau = 'ianigla', path = path,
           file_name = 'ianigla_cuevas.csv',
           slot_name = c('tair', 'rh', 'patm',
                         'precip', 'wspd', 'wdir',
                         'kin', 'hsnow', 'tsoil'),
           by = 'hour',
           out_name = c('tair(°C)', 'rh(%)', 'patm(mbar)',
                        'p(mm)', 'wspd(km/hr)', 'wdir(°)',
                        'kin(kW/m2)', 'hsnow(cm)', 'tsoil(°C)' )
          )

# now we want to know which are the slots with data
hm_show(obj = hm_cuevas)

# see the last values of our data
hm_show(obj = hm_cuevas, show = 'tail')

# print the entire tables
hm_show(obj = hm_cuevas, show = "all")

# or maybe we want to know which slot have no data
hm_show(obj = hm_cuevas, slot_name = 'empty')

# focus on specific slots
hm_show(obj = hm_cuevas, slot_name = c('kin', 'rh') )
hm_show(obj = hm_cuevas, slot_name = c('kin', 'rh'), show = 'tail' )

## End(Not run)

Subset your data by dates

Description

The method will subset the required slot.

Usage

hm_subset(obj, slot_name = "all", from = NULL, to = NULL)

## S4 method for signature 'hydromet_station'
hm_subset(obj, slot_name = "all", from = NULL, to = NULL)

## S4 method for signature 'hydromet_compact'
hm_subset(obj, slot_name = "all", from = NULL, to = NULL)
hm_subset(obj, slot_name = "all", from = NULL, to = NULL)

## S4 method for signature 'hydromet_station'
hm_subset(obj, slot_name = "all", from = NULL, to = NULL)

## S4 method for signature 'hydromet_compact'
hm_subset(obj, slot_name = "all", from = NULL, to = NULL)

Arguments

`obj`	a valid `hydromet_XXX` class object.
`slot_name`	string vector with the name(s) of the slot(s) to subset. If you use 'all' as argument the method will subset all the variables with data.
`from`	string `Date` or `POSIX*` value with the starting date. You can use `from` without `to`. In this case you will subset your data `from` till the end.
`to`	string `Date` or `POSIX*` value with the starting date. You can use `to` without `from`. In this case you will subset your data from the beginning till `to`.

Value

The same hydromet_XXX class object provided in obj but subsetted.

Functions

hm_subset(hydromet_station): subset method for station class
hm_subset(hydromet_compact): subset method for compact class

Examples

## Not run: 
# cuevas station
path <- system.file('extdata', package = 'hydrotoolbox')

# use the build method
hm_cuevas <-
  hm_create() %>%
  hm_build(bureau = 'ianigla', path = path,
           file_name = 'ianigla_cuevas.csv',
           slot_name = c('tair', 'rh', 'patm',
                         'precip', 'wspd', 'wdir',
                         'kin', 'hsnow', 'tsoil'),
           by = 'hour',
           out_name = c('tair(°C)', 'rh(%)', 'patm(mbar)',
                        'p(mm)', 'wspd(km/hr)', 'wdir(°)',
                        'kin(kW/m2)', 'hsnow(cm)', 'tsoil(°C)' )
          )

# subset relative humidity and plot it
hm_subset(obj = hm_cuevas, slot_name = 'rh',
          from = ISOdate(2020, 2, 1),
          to = ISOdate(2020, 4, 1) ) %>%
  hm_plot(slot_name = 'rh',
          col_name = list('rh(%)'),
          interactive = TRUE,
          y_lab = 'RH(%)' )

## End(Not run)

## Not run: 
# cuevas station
path <- system.file('extdata', package = 'hydrotoolbox')

# use the build method
hm_cuevas <-
  hm_create() %>%
  hm_build(bureau = 'ianigla', path = path,
           file_name = 'ianigla_cuevas.csv',
           slot_name = c('tair', 'rh', 'patm',
                         'precip', 'wspd', 'wdir',
                         'kin', 'hsnow', 'tsoil'),
           by = 'hour',
           out_name = c('tair(°C)', 'rh(%)', 'patm(mbar)',
                        'p(mm)', 'wspd(km/hr)', 'wdir(°)',
                        'kin(kW/m2)', 'hsnow(cm)', 'tsoil(°C)' )
          )

# subset relative humidity and plot it
hm_subset(obj = hm_cuevas, slot_name = 'rh',
          from = ISOdate(2020, 2, 1),
          to = ISOdate(2020, 4, 1) ) %>%
  hm_plot(slot_name = 'rh',
          col_name = list('rh(%)'),
          interactive = TRUE,
          y_lab = 'RH(%)' )

## End(Not run)

`hydromet` subclass for compact data

Description

This subclass is useful for storing in a single data frame ready to use hydro-meteorological series or many variables of the same kind (e.g. lets say precipitation series).

Value

A hydromet_compact class object.

Slots

compact: data.frame with Date as first column (class 'Date' or 'POSIXct'). All other columns are the numeric hydro-meteorological variables (double). This subclass was though to join in a single table ready to use data (e.g. in modeling). You can also use it to put together variables of the same kind (e.g. precipitation records) to make some regional analysis.

Examples

## Not run: 
# create an compact station
hm_create(class_name = "compact")

## End(Not run)
## Not run: 
# create an compact station
hm_create(class_name = "compact")

## End(Not run)

`hydromet` subclass for store hydro-meteorological records.

Description

A suitable object for store your hydro-meteorological data.

Value

An hydromet_station class object.

Slots

hq: water-height vs stream-discharge measurements.
hw: water level records.
qh: hourly mean river discharge.
qd: daily mean river discharge.
qm: monthly mean river discharge.
qa: annual river discharge.
wspd: wind speed.
wdir: wind direction.
evap: pan-evaporation.
anem: anemometer wind speed records (usually installed above the pan-evap tank).
patm: atmospheric pressure.
rh: relative humidity.
tair: air temperature (typically recorded at hourly time-step).
tmax: daily maximum recorded air temperature.
tmin: daily minimum recorded air temperature.
tmean: daily mean air temperature.
tsoil: soil temperature.
precip: total (snow and rain) precipitation records.
rainfall: liquid only precipitation measurements.
swe: snow water equivalent (typically recorded on snow pillows).
hsnow: snow height from ultrasonic devices.
kin: incoming short-wave radiation.
kout: outgoing short-wave radiation.
lin: incoming long-wave radiation.
lout: outgoing long-wave radiation.
unvar: reserved for non-considered variables.

Examples

## Not run: 
# create an hydromet station
hm_create(class_name = "station")

## End(Not run)

## Not run: 
# create an hydromet station
hm_create(class_name = "station")

## End(Not run)

`hydromet` superclass object

Description

A suitable object for store basic information about an hydro-meteorological station.

Value

A basic hydromet class object. This class is provided in order to set the meta-data of the station.

Slots

id: ANY. This is the ID assigned by the agency.
agency: string. The name of the agency (or institution) that provides the data of the station.
station: string. The name of the (hydro)-meteorological station.
lat: numeric. Latitude of the station.
long: numeric. Longitude of the station
alt: numeric. Altitude of the station.
country: string. Country where the station is located. Argentina is set as default value.
province: string. Name of the province where the station is located. Mendoza is set as default value.
river: string. Basin river's name.
active: logical. It indicates whether or not the station is currently operated. Default value is TRUE.
basin_area: numeric. The basin area (km2) of the catchment upstream of the gauge.
basin_eff: numeric. The effective area (km2) of the basin upstream of the gauge. In Canada, many basins have variable contributing fractions. In these basins, the effective area of the basin contributes flow to the outlet at least one year in two.
other_1: ANY. It is the first free-to-fill slot in order to give you the chance to write extra information about your hydro-met station.
other_2: ANY. It is the second free-to-fill slot in order to give you the chance to write extra information about your hydro-met station.

Examples

## Not run: 
# create class hydromet
hm_create(class_name = "hydromet")

## End(Not run)


## Not run: 
# create class hydromet
hm_create(class_name = "hydromet")

## End(Not run)

Interpolation

Description

This function applies interpolation to fill in missing (or non-recorded) values.

Usage

interpolate(
  x,
  col_name,
  out_name = NULL,
  miss_table,
  threshold,
  method = "linear"
)
interpolate(
  x,
  col_name,
  out_name = NULL,
  miss_table,
  threshold,
  method = "linear"
)

Arguments

`x`	data frame with class `Date` or `POSIX*` in the first column and numeric on the others.
`col_name`	string with column name of the series to interpolate.
`out_name`	optional. String with new column name. If you set it as `NULL`, the function will overwrite the original data frame.
`miss_table`	data frame with three columns: first and last date of interpolation (first and second column respectively). The last and third column, is of class `numeric` with the number of steps to interpolate. See report_miss.
`threshold`	numeric variable with the maximum number of dates in which to apply the interpolation.
`method`	string with the interpolation method. In this version only `'linear'` method is allowed.

Value

The same data frame but with interpolated values.

Examples


# read cuevas station file
path <- system.file('extdata', 'ianigla_cuevas.csv',
        package = 'hydrotoolbox')

cuevas <- read_ianigla(path = path)

# get the miss_table
miss_data <- report_miss(x = cuevas, col_name = 'Irradiancia')[[1]]

# apply interpolation function when gap is less than 3 hours
cuevas_interpo <- interpolate(x = cuevas,
                              col_name = 'Irradiancia',
                              out_name = 'kin_interpo',
                              miss_table = miss_data,
                              threshold = 3)

report_miss(x = cuevas_interpo,
            col_name = c('Irradiancia', 'kin_interpo'))


# read cuevas station file
path <- system.file('extdata', 'ianigla_cuevas.csv',
        package = 'hydrotoolbox')

cuevas <- read_ianigla(path = path)

# get the miss_table
miss_data <- report_miss(x = cuevas, col_name = 'Irradiancia')[[1]]

# apply interpolation function when gap is less than 3 hours
cuevas_interpo <- interpolate(x = cuevas,
                              col_name = 'Irradiancia',
                              out_name = 'kin_interpo',
                              miss_table = miss_data,
                              threshold = 3)

report_miss(x = cuevas_interpo,
            col_name = c('Irradiancia', 'kin_interpo'))

Moving average windows

Description

Smooth numeric series with a moving average windows.

Usage

mov_avg(
  x,
  col_name = "last",
  k,
  pos = "c",
  out_name = NULL,
  from = NULL,
  to = NULL
)
mov_avg(
  x,
  col_name = "last",
  k,
  pos = "c",
  out_name = NULL,
  from = NULL,
  to = NULL
)

Arguments

`x`	data frame (or tibble) with class `Date` or `POSIX*` in the first column.
`col_name`	string vector with the column(s) name(s) of the series to smooth. The default value uses the `'last'` column. Another single string choice is to use `'all'`. Is important to keep in mind that this argument commands, so if you provide two columns names, `k` and `pos` arguments must be of length two; if not the single value will be recycled.
`k`	numeric vector with the windows size. E.g.: `k = 5`.
`pos`	string vector with the position of the windows: 'c': center (default). The output value is in the middle of the window. 'l': left aligned. The output value is on the left, so the function weights the `(k - 1)` values at the right side. 'r': right aligned. The output value is on the right, so the function weights the `(k - 1)` values at the left side.
`out_name`	optional. String vector with new column names. If you set it as `NULL` the function will overwrite the original series.
`from`	optional. String value for `'Date'` class or `POSIX*` class for date-time data containing the starting `Date`.
`to`	optional. String value for `'Date'` class or `POSIX*` class for date-time data containing the ending `Date`.

Value

The same data frame but with the smooth series.

Examples


# read guido daily streamflow records
path <- system.file('extdata', 'snih_qd_guido.xlsx',
        package = 'hydrotoolbox')

# read and apply the function
qd_guido <-
  read_snih(path = path, by = 'day', out_name = 'q(m3/s)') %>%
  mov_avg(k = 5, out_name = 'q_smooth')



# read guido daily streamflow records
path <- system.file('extdata', 'snih_qd_guido.xlsx',
        package = 'hydrotoolbox')

# read and apply the function
qd_guido <-
  read_snih(path = path, by = 'day', out_name = 'q(m3/s)') %>%
  mov_avg(k = 5, out_name = 'q_smooth')

Monthly river discharge [m3/s] to volume [hm3]

Description

Converts mean monthly river discharge [m3/s] to total volume discharge [hm3].

Usage

qm_vol(x, col_name, out_name = NULL)
qm_vol(x, col_name, out_name = NULL)

Arguments

`x`	data frame with class Date in the first column and numeric on the others.
`col_name`	string with column(s) name(s) where to apply the function.
`out_name`	optional. String with new column(s) name(s). If you set it as `NULL`, the function will overwrite the original data frame.

Value

The same data frame but with the total volume discharge.

Examples


# read guido daily streamflow records
path <- system.file('extdata', 'snih_qd_guido.xlsx',
        package = 'hydrotoolbox')

# read, aggregate the function to monthly resolution and get the volume
qm_guido <-
  read_snih(path = path, by = 'day', out_name = 'q(m3/s)') %>%
  agg_table(col_name = 'q(m3/s)', fun = 'mean', period = 'monthly',
            out_name = 'qm(m3/s)') %>%
  qm_vol(col_name = 'qm(m3/s)', out_name = 'vm(hm3)')


# read guido daily streamflow records
path <- system.file('extdata', 'snih_qd_guido.xlsx',
        package = 'hydrotoolbox')

# read, aggregate the function to monthly resolution and get the volume
qm_guido <-
  read_snih(path = path, by = 'day', out_name = 'q(m3/s)') %>%
  agg_table(col_name = 'q(m3/s)', fun = 'mean', period = 'monthly',
            out_name = 'qm(m3/s)') %>%
  qm_vol(col_name = 'qm(m3/s)', out_name = 'vm(hm3)')

Reads data from AIC

Description

Reads excel files provided by the AIC.

Usage

read_aic(
  path,
  by = "day",
  out_name = NULL,
  sheet = NULL,
  skip = 12,
  get_sheet = FALSE
)
read_aic(
  path,
  by = "day",
  out_name = NULL,
  sheet = NULL,
  skip = 12,
  get_sheet = FALSE
)

Arguments

`path`	path to the xlsx file.
`by`	string with the time step of the series (e.g.: `'month', 'day', '6 hour', '3 hour', '1 hour', '15 min'`). By default this argument is set to `'day'`. If you set it as `'none'`, the function will ignore automatic gap filling.
`out_name`	optional. String vector with user defined variable(s) column(s) name(s).
`sheet`	optional. Sheet to read. Either a string (the name of a sheet), or an integer (the position of the sheet). If neither argument specifies the sheet, defaults to the first sheet.
`skip`	optional. Minimum number of rows to skip before reading anything, be it column names or data. Leading empty rows are automatically skipped, so this is a lower bound.
`get_sheet`	logical indicating whether you want to print available sheet names (`TRUE`) in the file or not.

Value

A data frame with the data inside the xlsx file. Gaps between dates are filled with NA_real_ and duplicated rows are eliminated automatically.

Examples


# This files are provided by AIC under legal agreement only.

# This files are provided by AIC under legal agreement only.

Reads data from Explorador Climático (CR2 - Chile)

Description

Reads csv files downloaded from the CR2 web page as a data frame.

Usage

read_cr2(path, by = "day", out_name = NULL)
read_cr2(path, by = "day", out_name = NULL)

Arguments

`path`	path to the csv file.
`by`	string with the time step of the series (e.g.: `'month', 'day', '6 hour', '3 hour', '1 hour', '15 min'`). The default and unique possible value is `'day'`.
`out_name`	optional. String vector with user defined variable(s) column(s) name(s).

Value

A data frame with the data inside the csv file. Gaps between dates are filled with NA_real_ and duplicated rows are eliminated automatically.

Examples


# list cr2 files
list.files( system.file('extdata', package = 'hydrotoolbox'), pattern = 'cr2' )

# set path to file
path_tmax <- system.file('extdata', 'cr2_tmax_yeso_embalse.csv',
             package = 'hydrotoolbox')

# read file with default colname
head( read_cr2(path = path_tmax) )

# assign a column name
head( read_cr2(path = path_tmax, out_name = 'tmax(°C)') )

# list cr2 files
list.files( system.file('extdata', package = 'hydrotoolbox'), pattern = 'cr2' )

# set path to file
path_tmax <- system.file('extdata', 'cr2_tmax_yeso_embalse.csv',
             package = 'hydrotoolbox')

# read file with default colname
head( read_cr2(path = path_tmax) )

# assign a column name
head( read_cr2(path = path_tmax, out_name = 'tmax(°C)') )

Reads data from Departamento General de Irrigación - Hydrological Division (DGI - Mendoza - Argentina)

Description

Reads excel files provided by the DGI (Hydrological Division).

Usage

read_dgi(path, by = "day", out_name = NULL, sheet = NULL, get_sheet = FALSE)
read_dgi(path, by = "day", out_name = NULL, sheet = NULL, get_sheet = FALSE)

Arguments

`path`	path to the xlsx file.
`by`	string with the time step of the series (e.g.: `'month', 'day', '6 hour', '3 hour', '1 hour', '15 min'`). By default this argument is set to `'day'`. If you set it as `'none'`, the function will ignore automatic gap filling.
`out_name`	optional. String vector with user defined variable(s) column(s) name(s).
`sheet`	optional. Sheet to read. Either a string (the name of a sheet), or an integer (the position of the sheet). If neither argument specifies the sheet, defaults to the first sheet.
`get_sheet`	logical indicating whether you want to print available sheet names (`TRUE`) in the file or not.

Value

A data frame with the data inside the xlsx file. Gaps between dates are filled with NA_real_ and duplicated rows are eliminated automatically.

Examples


# set path to file
path_file <- system.file('extdata', 'dgi_toscas.xlsx',
             package = 'hydrotoolbox')

# because dgi files has multiple sheets we take a look
# on them
read_dgi(path = path_file, get_sheet = TRUE)

# read swe with default column names
head( read_dgi(path = path_file, sheet = 'swe') )

# assign name
head( read_dgi(path = path_file, sheet = 'swe', out_name = 'swe(mm)') )

# now read relative humidity
head( read_dgi(path = path_file, sheet = 'hr', out_name = 'rh(%)') )


# set path to file
path_file <- system.file('extdata', 'dgi_toscas.xlsx',
             package = 'hydrotoolbox')

# because dgi files has multiple sheets we take a look
# on them
read_dgi(path = path_file, get_sheet = TRUE)

# read swe with default column names
head( read_dgi(path = path_file, sheet = 'swe') )

# assign name
head( read_dgi(path = path_file, sheet = 'swe', out_name = 'swe(mm)') )

# now read relative humidity
head( read_dgi(path = path_file, sheet = 'hr', out_name = 'rh(%)') )

Reads data from Sistema de Monitoreo Meteorológico de Alta Montaña (IANIGLA - Argentina)

Description

Reads csv files downloaded from the Sistema de Monitoreo Meteorológico de Alta Montaña web page as a data frame.

Usage

read_ianigla(path, by = "1 hour", out_name = NULL)
read_ianigla(path, by = "1 hour", out_name = NULL)

Arguments

`path`	path to the csv file.
`by`	string with the time step of the series (e.g.: `'month', 'day', '6 hour', '3 hour', '1 hour', '15 min'`). The default value is `'1 hour'`. If you set it as `'none'`, the function will ignore automatic gap filling.
`out_name`	optional. String vector with user defined variable(s) column(s) name(s).

Value

A data frame with the data inside the csv file. Gaps between dates are filled with NA_real_ and duplicated rows are eliminated automatically.

Examples


# set path to file
path_file <- system.file('extdata', 'ianigla_cuevas.csv',
             package = 'hydrotoolbox')

# read with default names
head( read_ianigla(path = path_file) )

# set column names
head(
read_ianigla(path = path_file,
             out_name = c('tair(°C)', 'rh(%)', 'patm(mbar)',
                           'p(mm)', 'wspd(km/hr)', 'wdir(°)',
                           'kin(kW/m2)', 'hsnow(cm)', 'tsoil(°C)' ) )
)

# set path to file
path_file <- system.file('extdata', 'ianigla_cuevas.csv',
             package = 'hydrotoolbox')

# read with default names
head( read_ianigla(path = path_file) )

# set column names
head(
read_ianigla(path = path_file,
             out_name = c('tair(°C)', 'rh(%)', 'patm(mbar)',
                           'p(mm)', 'wspd(km/hr)', 'wdir(°)',
                           'kin(kW/m2)', 'hsnow(cm)', 'tsoil(°C)' ) )
)

Reads data provided by MNEMOS software (SNIH - Argentina)

Description

Reads xlsx files generated with MNEMOS software.

Usage

read_mnemos(
  path,
  by = "none",
  out_name = NULL,
  sheet = NULL,
  skip = 3,
  get_sheet = FALSE
)
read_mnemos(
  path,
  by = "none",
  out_name = NULL,
  sheet = NULL,
  skip = 3,
  get_sheet = FALSE
)

Arguments

`path`	path to the xlsx file.
`by`	string with the time step of the series (e.g.: `'month', 'day', '6 hour', '3 hour', '1 hour', '15 min'`). If you set it as `'none'`, the function will ignore automatic gap filling.
`out_name`	optional. String vector with user defined variable(s) column(s) name(s).
`sheet`	optional. Sheet to read. Either a string (the name of a sheet), or an integer (the position of the sheet). If neither argument specifies the sheet, defaults to the first sheet.
`skip`	optional. Minimum number of rows to skip before reading anything, be it column names or data. Leading empty rows are automatically skipped, so this is a lower bound.
`get_sheet`	logical indicating whether you want to print available variables (`TRUE`) in every file sheet or not.

Value

A data frame with the data inside the specified sheet. Gaps between dates are filled with NA_real_ and duplicated rows are eliminated automatically. In case you set get_sheet = TRUE the function will return a list with the variables inside each sheet.

Examples


# list mnemos files
list.files( system.file('extdata', package = 'hydrotoolbox'), pattern = 'mnemos' )

# set path
path <- system.file('extdata', 'mnemos_guido.xlsx',  package = 'hydrotoolbox')

# we can see which variables are inside the sheet's file
read_mnemos(path = path, get_sheet = TRUE)

# now we want to read the maximum temperature
tmax_guido <- read_mnemos(path = path, by = 'day',
                          out_name = 'tmax(ºC)', sheet = '11413-016')



# list mnemos files
list.files( system.file('extdata', package = 'hydrotoolbox'), pattern = 'mnemos' )

# set path
path <- system.file('extdata', 'mnemos_guido.xlsx',  package = 'hydrotoolbox')

# we can see which variables are inside the sheet's file
read_mnemos(path = path, get_sheet = TRUE)

# now we want to read the maximum temperature
tmax_guido <- read_mnemos(path = path, by = 'day',
                          out_name = 'tmax(ºC)', sheet = '11413-016')

Reads data from Servicio Nacional de Información Hídrica (SNIH - Argentina)

Description

Reads excel files downloaded from the SNIH web page as a data frame.

Usage

read_snih(path, by, out_name = NULL)
read_snih(path, by, out_name = NULL)

Arguments

`path`	path to the xlsx file.
`by`	string with the time step of the series (e.g.: `'month', 'day', '6 hour', '3 hour', '1 hour', '15 min'`). If you set it as `'none'`, the function will ignore automatic gap filling.
`out_name`	optional. String vector with user defined variable(s) column(s) name(s).

Value

A data frame with the data inside the xlsx file. Gaps between dates are filled with NA_real_ and duplicated rows are eliminated automatically.

Examples


# set path to file
path_file <- system.file('extdata', 'snih_qd_guido.xlsx', package = 'hydrotoolbox')

# read daily streamflow with default column name
head( read_snih(path = path_file, by = 'day') )

# now we use the function with column name
head( read_snih(path = path_file,  by = 'day', out_name = 'qd(m3/s)') )


# set path to file
path_file <- system.file('extdata', 'snih_qd_guido.xlsx', package = 'hydrotoolbox')

# read daily streamflow with default column name
head( read_snih(path = path_file, by = 'day') )

# now we use the function with column name
head( read_snih(path = path_file,  by = 'day', out_name = 'qd(m3/s)') )

Report `NA_real_` values inside a table.

Description

Creates a data frame with reported dates and number of times-step of missing or not recorded data.

Usage

report_miss(x, col_name = "all")
report_miss(x, col_name = "all")

Arguments

`x`	data frame with hydro-meteo data. First column is date and the second the numeric vector to be reported.
`col_name`	string vector with the column(s) name(s) to report. By default the function will report all numeric columns.

Value

A list containing a data frame (one per col_name) with three columns: start-date, end-date and number of missing time steps. In the last row of the table you will find the total number of missing measurements (under "time_step" column). That's why under start and end-date columns you will find NA.

Examples


# read guido daily streamflow records
path <- system.file('extdata', 'snih_qd_guido.xlsx',
        package = 'hydrotoolbox')

# load raw data
qd_guido <-
  read_snih(path = path, by = 'day', out_name = 'q(m3/s)') %>%
  mov_avg(k = 5, out_name = 'q_smooth')

# get the data report
qd_guido %>%
  report_miss()



# read guido daily streamflow records
path <- system.file('extdata', 'snih_qd_guido.xlsx',
        package = 'hydrotoolbox')

# load raw data
qd_guido <-
  read_snih(path = path, by = 'day', out_name = 'q(m3/s)') %>%
  mov_avg(k = 5, out_name = 'q_smooth')

# get the data report
qd_guido %>%
  report_miss()

Remove spikes

Description

Remove spikes and set their value as NA_real_.

Usage

rm_spike(x, col_name, out_name = NULL, tolerance)
rm_spike(x, col_name, out_name = NULL, tolerance)

Arguments

`x`	data frame or tibble with class Date or POSIX* in the first column.
`col_name`	string with column(s) name(s) where to apply the function.
`out_name`	optional. String with new column(s) name(s). If you set it as `NULL`, the function will overwrite the original table.
`tolerance`	numeric vector with the maximum tolerance between a number and its successor. If you provide a single value it will be recycled.

Value

The same table but with the peaks removed.

Examples


# set path to file
path_file <- system.file('extdata', 'ianigla_cuevas.csv',
             package = 'hydrotoolbox')

# read with default names
cuevas <- read_ianigla(path = path_file,
                       out_name = c('tair(°C)', 'rh(%)', 'patm(mbar)',
                                    'p(mm)', 'wspd(km/hr)', 'wdir(°)',
                                    'kin(kW/m2)', 'hsnow(cm)', 'tsoil(°C)') )

# remove spikes in snow heigh series
cuevas %>%
  rm_spike(col_name = 'hsnow(cm)',
           out_name = 'hsnow',
           tolerance = 50) # 50 cm of snow its OK for this zone


# set path to file
path_file <- system.file('extdata', 'ianigla_cuevas.csv',
             package = 'hydrotoolbox')

# read with default names
cuevas <- read_ianigla(path = path_file,
                       out_name = c('tair(°C)', 'rh(%)', 'patm(mbar)',
                                    'p(mm)', 'wspd(km/hr)', 'wdir(°)',
                                    'kin(kW/m2)', 'hsnow(cm)', 'tsoil(°C)') )

# remove spikes in snow heigh series
cuevas %>%
  rm_spike(col_name = 'hsnow(cm)',
           out_name = 'hsnow',
           tolerance = 50) # 50 cm of snow its OK for this zone

Rolling functions

Description

It provides a generic function to rolling table columns. Internally it is using rollapplyr from package zoo.

Usage

roll_fun(
  x,
  col_name = "last",
  k,
  pos = "c",
  FUN,
  ...,
  out_name = NULL,
  from = NULL,
  to = NULL
)
roll_fun(
  x,
  col_name = "last",
  k,
  pos = "c",
  FUN,
  ...,
  out_name = NULL,
  from = NULL,
  to = NULL
)

Arguments

`x`	data frame (or tibble) with class `Date` or `POSIX*` in the first column.
`col_name`	string vector with the column(s) name(s) of the series to roll. The default value uses the `'last'` column. Another single string choice is to use `'all'`. Is important to keep in mind that this argument commands, so if you provide two columns names, `k` and `pos` arguments must be of length two; if not the single value will be recycled.
`k`	numeric vector with the windows size. E.g.: `k = 5`.
`pos`	string vector with the position of the windows: 'c': center (default). The output value is in the middle of the window. 'l': left aligned. The output value is on the left, so the function weights the `(k - 1)` values at the right side. 'r': right aligned. The output value is on the right, so the function weights the `(k - 1)` values at the left side.
`FUN`	the function to be applied.
`...`	optional arguments to `FUN`.
`out_name`	optional. String vector with new column names. If you set it as `NULL` the function will overwrite the original series.
`from`	optional. String value for `'Date'` class or `POSIX*` class for date-time data containing the starting `Date`.
`to`	optional. String value for `'Date'` class or `POSIX*` class for date-time data containing the ending `Date`.

Value

The same table but with the rolling series.

Examples


# read guido daily streamflow records
path <- system.file('extdata', 'snih_qd_guido.xlsx',
        package = 'hydrotoolbox')

# read and apply the function
qd_guido <-
  read_snih(path = path, by = 'day', out_name = 'q(m3/s)') %>%
  roll_fun(k = 5, FUN = mean, na.rm = TRUE,
   out_name = 'q_smooth')



# read guido daily streamflow records
path <- system.file('extdata', 'snih_qd_guido.xlsx',
        package = 'hydrotoolbox')

# read and apply the function
qd_guido <-
  read_snih(path = path, by = 'day', out_name = 'q(m3/s)') %>%
  roll_fun(k = 5, FUN = mean, na.rm = TRUE,
   out_name = 'q_smooth')

Set a threshold

Description

Set tolerable extreme values (maximum or minimum). Records greater or equal than ('>=') or lesser or equal than ('<=') 'threshold' argument are set to NA_real_.

Usage

set_threshold(x, col_name, out_name = NULL, threshold, case = ">=")
set_threshold(x, col_name, out_name = NULL, threshold, case = ">=")

Arguments

`x`	data frame or tibble with class Date or POSIX* in the first column.
`col_name`	string with column(s) name(s) where to apply the function.
`out_name`	optional. String with new column(s) name(s). If you set it as `NULL`, the function will overwrite the original table.
`threshold`	numeric vector with the threshold value(s). If you provide a single value it will be recycled among `col_name` strings.
`case`	string with either ">=" (greater or equal than) or "<=" (lesser or equal than) symbol. Default string is ">=".

Value

The same data frame but with the threshold set.

Examples


# set path to file
path_file <- system.file('extdata', 'ianigla_cuevas.csv',
             package = 'hydrotoolbox')

# read with default names
cuevas <- read_ianigla(path = path_file,
                       out_name = c('tair(°C)', 'rh(%)', 'patm(mbar)',
                                    'p(mm)', 'wspd(km/hr)', 'wdir(°)',
                                    'kin(kW/m2)', 'hsnow(cm)', 'tsoil(°C)') )

# remove values higher than 1.50 meters
cuevas %>%
  set_threshold(col_name = 'hsnow(cm)',
                out_name = 'hsnow_thres',
                threshold = 150 )


# set path to file
path_file <- system.file('extdata', 'ianigla_cuevas.csv',
             package = 'hydrotoolbox')

# read with default names
cuevas <- read_ianigla(path = path_file,
                       out_name = c('tair(°C)', 'rh(%)', 'patm(mbar)',
                                    'p(mm)', 'wspd(km/hr)', 'wdir(°)',
                                    'kin(kW/m2)', 'hsnow(cm)', 'tsoil(°C)') )

# remove values higher than 1.50 meters
cuevas %>%
  set_threshold(col_name = 'hsnow(cm)',
                out_name = 'hsnow_thres',
                threshold = 150 )

Set user defined values

Description

Specify specific values between dates.

Usage

set_value(x, col_name, out_name = NULL, value, from, to)
set_value(x, col_name, out_name = NULL, value, from, to)

Arguments

`x`	data frame or tibble with class `Date` or `POSIX*` in the first column.
`col_name`	string with column(s) name(s) to set.
`out_name`	optional. String with new column(s) name(s). If you set it as `NULL`, the function will overwrite the original data frame.
`value`	numeric vector with the numeric values to set between dates (`from` and `to`). If you provide a number it will be recycled. When using a multiple dates (i.e.: "date" vector in `from` and `to`) use a list with a numeric vector inside each element.
`from`	string vector for `'Date'` class or `POSIX*` class for date-time data with the starting date.
`to`	string vector for `'Date'` class or `POSIX*` class for date-time data with the ending date.

Value

The same table but with the set numeric values between the dates.

Examples


# create a data frame
dates   <- seq.Date(from = as.Date('1990-01-01'), to = as.Date('1990-12-01'), by = 'm')
met_var <- runif(n = 12, 0, 10)

met_table <- data.frame(dates, met_var)

# set single value recycling
set_value(x = met_table, col_name = 'met_var', value = 10,
 from = '1990-01-01', to = '1990-06-01' )

# set different periods
set_value(x = met_table, col_name = 'met_var', value = list(NA_real_, c(1, 2) ),
 from = c('1990-01-01', '1990-11-01'), to = c('1990-06-01', '1990-12-01') )

# now set as new columns
set_value(x = met_table, col_name = 'met_var', out_name = 'met_set',
 value = list(NA_real_, c(1, 2) ),
 from = c('1990-01-01', '1990-11-01'),
  to = c('1990-06-01', '1990-12-01') )

# create a data frame
dates   <- seq.Date(from = as.Date('1990-01-01'), to = as.Date('1990-12-01'), by = 'm')
met_var <- runif(n = 12, 0, 10)

met_table <- data.frame(dates, met_var)

# set single value recycling
set_value(x = met_table, col_name = 'met_var', value = 10,
 from = '1990-01-01', to = '1990-06-01' )

# set different periods
set_value(x = met_table, col_name = 'met_var', value = list(NA_real_, c(1, 2) ),
 from = c('1990-01-01', '1990-11-01'), to = c('1990-06-01', '1990-12-01') )

# now set as new columns
set_value(x = met_table, col_name = 'met_var', out_name = 'met_set',
 value = list(NA_real_, c(1, 2) ),
 from = c('1990-01-01', '1990-11-01'),
  to = c('1990-06-01', '1990-12-01') )

Snow Water Equivalent to melt or snowfall

Description

Derive melt or snowfall series from snow water equivalent measurements (snow pillows measurements).

Usage

swe_derive(x, col_name, out_name = NULL, case)
swe_derive(x, col_name, out_name = NULL, case)

Arguments

`x`	data frame or tibble with class Date or POSIX* in the first column.
`col_name`	string with column(s) name(s) where to apply the function.
`out_name`	optional. String with new column(s) name(s). If you set it as `NULL`, the function will overwrite the original table.
`case`	string vector with "sf" (meaning snowfall) or "m" (meaning melt).

Value

The same data frame but with the derived series.

Examples


# set path to file
path_file <- system.file('extdata', 'dgi_toscas.xlsx',
             package = 'hydrotoolbox')

# swe table
swe_toscas <- read_dgi(path = path_file,
                       sheet = 'swe',
                       out_name = 'swe(mm)')

# add melt and snowfall
swe_toscas <-
  swe_toscas %>%
  swe_derive(col_name = rep('swe(mm)', 2),
             out_name = c('melt(mm)', 'snowfall(mm)'),
             case = c('m', 'sf') )


# set path to file
path_file <- system.file('extdata', 'dgi_toscas.xlsx',
             package = 'hydrotoolbox')

# swe table
swe_toscas <- read_dgi(path = path_file,
                       sheet = 'swe',
                       out_name = 'swe(mm)')

# add melt and snowfall
swe_toscas <-
  swe_toscas %>%
  swe_derive(col_name = rep('swe(mm)', 2),
             out_name = c('melt(mm)', 'snowfall(mm)'),
             case = c('m', 'sf') )

Package 'hydrotoolbox'

Help Index

Aggregates a data frame to a larger time period

Description

Usage

Arguments

Value

Examples

Cumulative sum

Description

Usage

Arguments

Value

Examples

Find non-reported dates and fill them with NA_*

Description

Usage

Arguments

Value

Examples

Aggregates the table inside a slot to a larger time period

Description

Usage

Arguments

Value

Functions

Examples

Load native data files automatically

Description

Usage

Arguments

Value

Functions

Examples

Load native data files automatically

Description

Usage

Arguments

Value

Functions

Examples

Creates an hydromet object.

Description

Usage

Arguments

Value

Examples

Extract the slot

Description

Usage

Arguments

Value

Functions

Examples

Melt many objects into an hydromet_compact class object

Description

Usage

Arguments

Value

Functions

Note

Examples

Create, modify and delete columns inside a slot

Description

Usage

Arguments

Value

Functions

Examples

Set new column names

Description

Usage

Arguments

Value

Functions

Examples

Methods to easily use ggplot2 or plotly (interactive)

Description

Usage

Arguments

Find non-reported dates and fill them with `NA_*`

Melt many objects into an `hydromet_compact` class object

Methods to easily use `ggplot2` or `plotly` (interactive)

Set the data of an `hydromet` object or its subclass

`hydromet` subclass for compact data

`hydromet` subclass for store hydro-meteorological records.

`hydromet` superclass object