Help for package hydroToolkit

Type:

Package

Title:

Hydrological Tools for Handling Hydro-Meteorological Data from Argentina and Chile

Version:

0.1.0

Date:

2020-05-07

Author:

Ezequiel Toum <etoum@mendoza-conicet.gob.ar>

Maintainer:

Ezequiel Toum <etoum@mendoza-conicet.gob.ar>

Description:

Read, plot, manipulate and process hydro-meteorological data from Argentina and Chile.

Depends:

R (≥ 2.10)

License:

GPL (≥ 3)

Imports:

ggplot2, plotly, lubridate, utils, methods, readxl, reshape2

Encoding:

UTF-8

LazyData:

true

RoxygenNote:

7.1.0

NeedsCompilation:

Packaged:

2020-05-12 15:38:52 UTC; ezequiel

Repository:

CRAN

Date/Publication:

2020-05-16 10:00:02 UTC

River discharge [m3/s] to volume [hm3]

Description

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

Usage

Qmm_to_Dm(df)

Arguments

df

data frame with class Date in the first column. By default the function converts the second column only. If you have daily or hourly data see agg_serie.

Value

A data frame with two columns: Date and total volume discharge.

Examples

# Create BDHI hydro-met station
guido <- create_hydroMet(class_name = 'BDHI')

# List with meteorological variables (slots in BDHI's object)
cargar <- list('precip', 'Qmd', 'Qmm')

# Now assign as names the files
hydro_files   <- list.files( system.file('extdata', package = "hydroToolkit"), pattern = 'Guido' )
names(cargar) <- hydro_files

# Build the object with the met records
guido <- build_hydroMet(obj = guido, slot_list = cargar, 
               path = system.file('extdata', package = "hydroToolkit") )
               
# Now get mean monthly discharge
Qmm <- get_hydroMet(obj = guido, name = 'Qmm')[[1]]

# Get the monthly water volume
Qmm_vol <- Qmm_to_Dm(df = Qmm)

Aggregate slot data

Description

This method provides common functions to aggregate the data inside a slot.

Usage

agg_hydroMet(
  obj,
  slot_name,
  col_name,
  fun,
  period,
  out_name = NULL,
  start_month = NULL,
  end_month = NULL,
  allow_NA = NULL
)

## S4 method for signature 'hydroMet_BDHI'
agg_hydroMet(
  obj,
  slot_name,
  col_name,
  fun,
  period,
  out_name = NULL,
  start_month = NULL,
  end_month = NULL,
  allow_NA = NULL
)

## S4 method for signature 'hydroMet_DGI'
agg_hydroMet(
  obj,
  slot_name,
  col_name,
  fun,
  period,
  out_name = NULL,
  start_month = NULL,
  end_month = NULL,
  allow_NA = NULL
)

## S4 method for signature 'hydroMet_CR2'
agg_hydroMet(
  obj,
  slot_name,
  col_name,
  fun,
  period,
  out_name = NULL,
  start_month = NULL,
  end_month = NULL,
  allow_NA = NULL
)

## S4 method for signature 'hydroMet_IANIGLA'
agg_hydroMet(
  obj,
  slot_name,
  col_name,
  fun,
  period,
  out_name = NULL,
  start_month = NULL,
  end_month = NULL,
  allow_NA = NULL
)

Arguments

obj

an hydroMet_XXX class object. This method is not allowed for hydroMet_compact class. This is because this class was thought as ready to use, so when building this class you should have already aggregated your data.

slot_name

a single or vector string containing the slot(s) to aggregate.

col_name

a single or vector string with the name of the column to aggregate in slot_name.

fun

a single or vector string containing one of the following functions: mean, min, max or sum.

period

a single or vector string with the period of aggregation: hourly, daily, monthly, annual or climatic. NOTE_1: the 'climatic' option returns the all series annual statistics ('fun'). NOTE_2: if the object is of class hydroMet_IANIGLA you must provide a single period value.

out_name

optional. Single or vector string with the output column name of the variable to aggregate.

start_month

optional. Numeric (or numeric vector) value of the first month. It only makes sense if the period is annual. NOTE: as an example, in case you have just two slots (out of five) that you want to aggregate annually you must provide a vector of length two. Default value is January. NOTE*: if the object is of class hydroMet_IANIGLA you must provide a single start_month value.

end_month

optional. Numeric (or numeric vector) value of the last month. It only makes sense if the period is annual. NOTE: as an example, in case you have just two slots (out of five) that you want to aggregate annually you must provide a vector of length two. Default value es December. NOTE*: if the object is of class hydroMet_IANIGLA you must provide a single end_month value.

allow_NA

optional. Numeric (or numeric vector) value with the maximum allowed number of NA_real_ values. By default the function will not tolerate any NA_real_ in an aggregation period (and will return NA_real_ instead).

Value

An hydroMet_XXX class object with the required slot(s) aggregated.

Functions

agg_hydroMet,hydroMet_BDHI-method: aggregation method for BDHI data
agg_hydroMet,hydroMet_DGI-method: aggregation method for DGI data
agg_hydroMet,hydroMet_CR2-method: aggregation method for CR2 data
agg_hydroMet,hydroMet_IANIGLA-method: aggregation method for IANIGLA data

Examples

# Create BDHI hydro-met station
guido <- create_hydroMet(class_name = 'BDHI')

# List with meteorological variables (slots in BDHI's object)
cargar <- list('precip', 'Qmd', 'Qmm')

# Assign as names the files
hydro_files   <- list.files( system.file('extdata', package = "hydroToolkit"), pattern = 'Guido' )
names(cargar) <- hydro_files

# Build the object with the met records
guido <- build_hydroMet(obj = guido, slot_list = cargar, 
               path = system.file('extdata', package = "hydroToolkit") )

# Aggregrate precipitation serie
guido <- agg_hydroMet(obj = guido, slot_name = 'precip', col_name = 'precip', fun = 'sum',
       period = 'monthly', out_name = 'P_month', allow_NA = 3)

Aggregates a data frame to a larger time period

Description

This is a useful function to easily aggregate your data.

Usage

agg_serie(
  df,
  fun,
  period,
  out_name,
  start_month = NULL,
  end_month = NULL,
  allow_NA = NULL
)

Arguments

df

data frame with class Date or POSIXct in the first column. The function always aggregates the second column.

fun

string containing one of the following functions: mean, min, max or sum.

period

string with the period of aggregation: hourly, daily, monthly, annual or climatic. NOTE: the 'climatic' option returns the all series annual statistics ('fun').

out_name

string with the output column name of the variable to aggregate.

start_month

optional. Numeric value of the first month. It only makes sense if the period is annual.

end_month

optional. Numeric value of the last month. It only makes sense if the period is annual.

allow_NA

optional. Numeric value with the maximum allowed number of NA_real_ values. By default the function will not tolerate any NA_real_ in an aggregation period (and will return NA_real_ instead).

Value

A data frame with to columns: the date and the aggregated variable.

Examples

# Path to file
dgi_path  <- system.file('extdata', package = "hydroToolkit")

toscas <- read_DGI(file = 'Toscas.xlsx', sheet = 'tmean', path = dgi_path)

# Monthly mean temperature
m_toscas <- agg_serie(df = toscas, fun = 'mean', period = 'monthly', out_name = 'T_month')

Automatically load native data files

Description

This method is the recommended one for loading your data-sets (as provided by the agency).

Usage

build_hydroMet(
  obj,
  slot_list,
  path = NULL,
  col_names = NULL,
  start_date = NULL,
  end_date = NULL
)

## S4 method for signature 'hydroMet_BDHI'
build_hydroMet(obj, slot_list, path = NULL)

## S4 method for signature 'hydroMet_CR2'
build_hydroMet(obj, slot_list, path = NULL)

## S4 method for signature 'hydroMet_DGI'
build_hydroMet(obj, slot_list, path = NULL)

## S4 method for signature 'hydroMet_IANIGLA'
build_hydroMet(obj, slot_list, path = NULL)

## S4 method for signature 'hydroMet_compact'
build_hydroMet(
  obj,
  slot_list,
  col_names = NULL,
  start_date = NULL,
  end_date = NULL
)

Arguments

obj

an hydroMet_XXX class object (see create_hydroMet).

slot_list

a list containing (in each element) a vector string with the slot names. The name of the list elements are the native file names (e.g.: Qmd_Guido_BDHI.txt). NOTE: when the obj argument is of class hydroMet_compact, slot_list allows to build from multiple objects. So, in this case you have to provide a list of list: the top list contains as names the objects names (as you read them from Global Environment); then every object (top level) contains another list with slot names as names and the column(s) number(s) to extract as numeric value. E.g.: list(bdhi_obj = list(Qmd = 2, Qmm = c(2, 5)), cr2_obj = list(precip = 4) ).

path

string with the files directory. If not provided, the method will use the current working directory. NOTE: this argument is harmless for an object of class hydroMet_compact.

col_names

it just make sense if 'obj' argument is of hydroMet_compact class. String vector with the names of the column output. Default value (NULL) will return expressive column names.

start_date

it just make sense if 'obj' argument is of hydroMet_compact class. String or POSIXct with the starting date to extract. You can use start_date without end_date. In this case you will subset your data from start_date till the end.

end_date

it just make sense if 'obj' argument is of hydroMet_compact class. String or POSIXct with the last date to extract. You can use end_date without start_date. In this case you will subset your data from the beginning till end_date.

Value

An S4 object of class hydroMet_XXX with the data loaded in each slot.

Functions

build_hydroMet,hydroMet_BDHI-method: build up method for BDHI class
build_hydroMet,hydroMet_CR2-method: build up method for CR2 class
build_hydroMet,hydroMet_DGI-method: build up method for DGI class
build_hydroMet,hydroMet_IANIGLA-method: build up method for IANIGLA class
build_hydroMet,hydroMet_compact-method: build up method for compact class

Examples

# Path to file
dgi_path  <- system.file('extdata', package = "hydroToolkit")
file_name <- list.files(path = dgi_path, pattern = 'Toscas')

# Read Toscas
var_nom <- list(slotNames(x = 'hydroMet_DGI')[2:7])
names(var_nom) <- file_name

# Load Toscas meteo station data
toscas_dgi <- create_hydroMet(class_name = 'DGI')
toscas_dgi <- build_hydroMet(obj = toscas_dgi, slot_list = var_nom,
                 path = dgi_path)

Creates an hydroMet class or subclass.

Description

This function is the constructor of hydroMet class and its subclasses.

Usage

create_hydroMet(class_name = "hydroMet")

Arguments

class_name

string with the name of the class. Valid arguments are: hydroMet, BDHI, CR2, DGI, IANIGLA or compact.

Value

an S4 object of class hydroMet

Examples

# Create class 'hydroMet'
met_station <- create_hydroMet(class_name = 'hydroMet')

# Subclass 'BDHI'
bdhi_station <- create_hydroMet(class_name = 'BDHI')

# Subclass 'DGI'
dgi_station <- create_hydroMet(class_name = 'DGI')

# Subclass 'CR2'
cr2_station <- create_hydroMet(class_name = 'CR2')

# Subclass 'IANIGLA'
ianigla_station <- create_hydroMet(class_name = 'IANIGLA')

Find non-reported dates and fill them with `NA_real_`

Description

This function complete non-reported dates and assign NA_real_ as their value.

Usage

fill_serie(df, colName, timeStep)

Arguments

df

data frame with date and numeric vector as first and second column respectively.

colName

output colname of the numeric variable, e.g.: 'Qmd(m3/s)'.

timeStep

character with a valid time step: 'day', 'month', '4h', 'day/3', 'hour'.

Value

A data frame with missing time steps filled with NA's.

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_serie <- data.frame(dates, met_var)

# Fill serie
met_fill  <- fill_serie(df = met_serie, colName = 'Temp', timeStep = 'day')

Fill a time interval in a data frame with a specific numeric value

Description

Assign specific values to a time interval.

Usage

fill_value(df, col, value, from, to)

Arguments

df

data frame with the first column being the date and the others numeric variables.

col

numeric vector with column(s) number(s) to be filled.

value

numeric or NA_real_. This numeric vector contains the elements to be fill in.

from

character, Date or POSIXct with the first date to be filled.

to

character, Date or POSIXct with the last date to be filled.

Value

A data frame filled with the value in the specified time period.

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_serie <- data.frame(dates, met_var)

# Fill serie
met_fill <- fill_serie(df = met_serie, colName = 'Temp', timeStep = 'day')

# Now fill value
met_fill <- fill_value(df = met_fill, col = 2, value = 10, from = '1990-02-01', to = '1990-02-15')

Get the slot(s) content(s)

Description

Extract the slots that you want from an hydroMet or hydroMet_XXX class.

Usage

get_hydroMet(obj, name = NA_character_)

## S4 method for signature 'hydroMet'
get_hydroMet(obj, name = NA_character_)

## S4 method for signature 'hydroMet_BDHI'
get_hydroMet(obj, name = NA_character_)

## S4 method for signature 'hydroMet_DGI'
get_hydroMet(obj, name = NA_character_)

## S4 method for signature 'hydroMet_IANIGLA'
get_hydroMet(obj, name = NA_character_)

## S4 method for signature 'hydroMet_CR2'
get_hydroMet(obj, name = NA_character_)

## S4 method for signature 'hydroMet_compact'
get_hydroMet(obj, name = NA_character_)

Arguments

obj

an hydroMet or hydroMet_XXX class object.

name

a valid single string or vector string with the required slot name(s).

Value

A list with the slot's data.

Functions

get_hydroMet,hydroMet-method: get method for generic hydroMet object
get_hydroMet,hydroMet_BDHI-method: get method for BDHI class
get_hydroMet,hydroMet_DGI-method: get method for DGI class
get_hydroMet,hydroMet_IANIGLA-method: get method for IANIGLA class
get_hydroMet,hydroMet_CR2-method: get method for CR2 class
get_hydroMet,hydroMet_compact-method: get method for compact class

Examples

# Create an IANIGLA object
cuevas <- create_hydroMet(class_name = 'IANIGLA')

# Extract one of its slots
tair <- get_hydroMet(obj = cuevas, name = 'tair')

`hydroMet` superclass object

Description

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

Value

A basic hydroMet class object.

Slots

id: numeric. 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. Altitude 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.

`hydroMet` subclass for BDHI (Base de Datos Hidrologica Integrada) data

Description

An suitable object for store hydro-meteorological data from BDHI.

Value

A hydroMet_BDHI class object.

Slots

Qmd: data.frame from read_BDHI containing daily mean river discharge [m3/s].
Qmm: data.frame from read_BDHI containing monthly mean river discharge [m3/s].
precip: data.frame from read_BDHI containing daily liquid precipitation [mm].
tdb: data.frame from read_BDHI containing subdaily dry bulb temperature [ºC].
tmax: data.frame from read_BDHI containing daily maximum air temperature [ºC].
tmin: data.frame from read_BDHI containing daily minimum air temperature [ºC].
swe: data.frame from read_BDHI containing daily snow water equivalent [mm].
hr: data.frame from read_BDHI containing subdaily relative humidity [%].
wspd: data.frame from read_BDHI containing subdaily wind speed [km/hr].
wdir: data.frame from read_BDHI containing subdaily wind direction [º].
evap: data.frame from read_BDHI containing daily pan-evaporation [mm].
anem: data.frame from read_BDHI containing daily wind speed above the evap tank [km/hr].
patm: data.frame from read_BDHI containing subdaily atmospheric pressure [mbar].

`hydroMet` subclass for CR2 (Explorador Climático) data

Description

A suitable object for store hydro-meteorological data from CR2.

Value

A hydroMet_CR2 class object.

Slots

precip: data.frame from read_CR2 containing daily precipitation [mm].
tmean: data.frame from read_CR2 containing daily mean air temperature [ºC].
tmax: data.frame from read_CR2 containing daily maximum air temperature [ºC].
tmin: data.frame from read_CR2 containing daily minimum air temperature [ºC].

`hydroMet` subclass for DGI (Departamento General de Irrigación) data

Description

A suitable object for store hydro-meteorological data from DGI.

Value

A hydroMet_DGI class object.

Slots

hsnow: data.frame from read_DGI containing daily snow height [m].
swe: data.frame from read_DGI containing daily snow water equivalent [mm].
tmean: data.frame from read_DGI containing daily mean air temperature [ºC].
tmax: data.frame from read_DGI containing daily max. air temperature [ºC].
tmin: data.frame from read_DGI containing daily min. air temperature [ºC].
hr: data.frame from read_DGI containing daily mean relative humidity [%].
patm: data.frame from read_DGI containing daily mean atmospheric pressure [hPa].

`hydroMet` subclass for IANIGLA (Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales) data

Description

A suitable object for store hydro-meteorological data provided by IANIGLA.

Value

A hydroMet_IANIGLA class object.

Slots

date: time serie of dates (class POSIXct or Date).
tair: numeric matrix with air temperature.
hr: numeric matrix with relative humidity.
patm: numeric matrix with atmospheric pressure.
precip: numeric matrix with precipitacion.
wspd: numeric matrix with wind speed.
wdir: numeric matrix with wind direction.
kin: numeric matrix with incoming short-wave radiation.
hsnow: numeric matrix with snow height.
tsoil: numeric matrix with soil temperature.
hwat: numeric matrix with stream water level.

`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 precipitacion 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 modelling). You can also use it to put together variables of the same kind (e.g. precipitation records) to make some regional analysis.

Hydrological year classification

Description

This function allows you to get the hydrological year. The criteria is consistent with the one of Departamento General de Irrigacion (Mendoza - Argentina).

Usage

hydro_year_DGI(df)

Arguments

df

a data frame with total annual volumes discharges created with agg_serie function.

Value

A data frame containing the hydrological classification for each year.

Examples

# Create BDHI hydro-met station
guido <- create_hydroMet(class_name = 'BDHI')

# List with meteorological variables (slots in BDHI's object)
cargar <- list('precip', 'Qmd', 'Qmm')

# Now assign as names the files
hydro_files   <- list.files( system.file('extdata', package = "hydroToolkit"), pattern = 'Guido' )
names(cargar) <- hydro_files

# Build the object with the met records
guido <- build_hydroMet(obj = guido, slot_list = cargar, 
               path = system.file('extdata', package = "hydroToolkit") )
               
# Now get mean monthly discharge
Qmm <- get_hydroMet(obj = guido, name = 'Qmm')[[1]]

# Get the monthly water volume
Qmm_vol <- Qmm_to_Dm(df = Qmm)

# Aggregate data frame to get total annual discharges
AD  <- agg_serie(df = Qmm_vol, fun = 'sum', period = 'annual', out_name = 'Ann_vol',
         start_month = 7, end_month = 6, allow_NA = 2)
         
# Get hydrological year classification
AD_class <- hydro_year_DGI(df = AD)

Interpolation

Description

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

Usage

interpolate(df, miss_table, threshold, method = "linear")

Arguments

df

data frame with two columns: 'Date' or 'POSIXct' class in the first column and a numeric variable in the second one.

miss_table

data frame with three columns: first and last date of interpolation (first and second column respectively). The last and third column, is a numeric with the number of steps to interpolate. See report_miss_data.

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

A data frame with date and the interpolated numeric variable.

Examples

# Create BDHI hydro-met station
guido <- create_hydroMet(class_name = 'BDHI')

# List with meteorological variables (slots in BDHI's object)
cargar <- list('precip', 'Qmd', 'Qmm')

# Now assign as names the files
hydro_files   <- list.files( system.file('extdata', package = "hydroToolkit"), pattern = 'Guido' )
names(cargar) <- hydro_files

# Build the object with the met records
guido <- build_hydroMet(obj = guido, slot_list = cargar, 
               path = system.file('extdata', package = "hydroToolkit") )
               
# Get mean daily discharge and report miss data
Qmd  <- get_hydroMet(obj = guido, name = 'Qmd')[[1]]
miss <- report_miss_data(df = Qmd)

# Now interpolate miss values 
Qmd_fill <- interpolate(df = Qmd, miss_table = miss, threshold = 5, method = "linear")

Modify data inside a specific slot

Description

Apply a pre-defined (e.g.: movAvg, fill_value or Qmm_to_Dm) or user defined function to an existing series inside a slot.

Usage

modify_hydroMet(
  obj,
  name = NA_character_,
  colName = NA_character_,
  colNum = 2,
  FUN = NULL,
  ...
)

## S4 method for signature 'hydroMet_BDHI'
modify_hydroMet(
  obj,
  name = NA_character_,
  colName = NA_character_,
  colNum = 2,
  FUN = NULL,
  ...
)

## S4 method for signature 'hydroMet_CR2'
modify_hydroMet(
  obj,
  name = NA_character_,
  colName = NA_character_,
  colNum = 2,
  FUN = NULL,
  ...
)

## S4 method for signature 'hydroMet_DGI'
modify_hydroMet(
  obj,
  name = NA_character_,
  colName = NA_character_,
  colNum = 2,
  FUN = NULL,
  ...
)

## S4 method for signature 'hydroMet_IANIGLA'
modify_hydroMet(
  obj,
  name = NA_character_,
  colName = NA_character_,
  colNum = 1,
  FUN = NULL,
  ...
)

Arguments

obj

hydroMet_XXX subclass object. See hydroMet_BDHI, hydroMet_DGI, hydroMet_IANIGLA or hydroMet_CR2.

name

string with the slot name of the data frame.

colName

string with the new column name (from FUN).

colNum

numeric value with the data frame column where to apply FUN. It must be > 1 (except in 'IANIGLA' subclass).

FUN

the function name.

...

FUN arguments to pass.

Value

The same hydroMet subclass provided in obj with an extra column.

Functions

modify_hydroMet,hydroMet_BDHI-method: modify method for BDHI class
modify_hydroMet,hydroMet_CR2-method: modify method for CR2 class
modify_hydroMet,hydroMet_DGI-method: modify method for DGI class
modify_hydroMet,hydroMet_IANIGLA-method: modify method for IANIGLA class

Examples

# Create BDHI hydro-met station
guido <- create_hydroMet(class_name = 'BDHI')

# List with meteorological variables (slots in BDHI's object)
cargar <- list('precip', 'Qmd', 'Qmm')

# Now assign as names the files
hydro_files   <- list.files( system.file('extdata', package = "hydroToolkit"), pattern = 'Guido' )
names(cargar) <- hydro_files

# Build the object with the met records
guido <- build_hydroMet(obj = guido, slot_list = cargar, 
               path = system.file('extdata', package = "hydroToolkit") )

Moving average windows

Description

Smooth a numeric serie with a moving average windows

Usage

movAvg(df, k, pos)

Arguments

df

data frame with the serie that you want to smooth. By default, t he function uses column 2.

k

numeric value with windows size., e.g.: 5

pos

string with the position of the window:

'izq': left aligned. The output value is on the left, so the function weights the (k - 1) values at the right side.
'der': right aligned. The output value is on the right, so the function weights the (k - 1) values at the left side.
'cen': center. The output value is in the middle of the window.

Value

data frame with the smooth serie.

Examples

# Relative path to raw data
full_path <- system.file('extdata', package = "hydroToolkit")

# Apply function
cuevas <- read_IANIGLA(file = 'Cuevas.csv', path = full_path)   
               
# Get air temperature
cuevas_tair <- cuevas[ , 1:2]

# Create a moving average serie of Tair
Tair_mov <- movAvg(df = cuevas_tair, k = 10, pos = 'izq')

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 object. The plot outputs can be static (ggplot2) or interactive (plotly).

Usage

plot_hydroMet(
  obj,
  slot_name,
  col_number,
  interactive = FALSE,
  line_type = NULL,
  line_color = "dodgerblue",
  x_lab = "Date",
  y_lab = "y",
  title_lab = NULL,
  legend_lab = NULL,
  double_yaxis = NULL,
  list_extra = NULL,
  from = NULL,
  to = NULL,
  scatter = NULL
)

## S4 method for signature 'hydroMet_BDHI'
plot_hydroMet(
  obj,
  slot_name,
  col_number,
  interactive = FALSE,
  line_type = NULL,
  line_color = "dodgerblue",
  x_lab = "Date",
  y_lab = "y",
  title_lab = NULL,
  legend_lab = NULL,
  double_yaxis = NULL,
  list_extra = NULL,
  from = NULL,
  to = NULL
)

## S4 method for signature 'hydroMet_CR2'
plot_hydroMet(
  obj,
  slot_name,
  col_number,
  interactive = FALSE,
  line_type = NULL,
  line_color = "dodgerblue",
  x_lab = "Date",
  y_lab = "y",
  title_lab = NULL,
  legend_lab = NULL,
  double_yaxis = NULL,
  list_extra = NULL,
  from = NULL,
  to = NULL
)

## S4 method for signature 'hydroMet_DGI'
plot_hydroMet(
  obj,
  slot_name,
  col_number,
  interactive = FALSE,
  line_type = NULL,
  line_color = "dodgerblue",
  x_lab = "Date",
  y_lab = "y",
  title_lab = NULL,
  legend_lab = NULL,
  double_yaxis = NULL,
  list_extra = NULL,
  from = NULL,
  to = NULL
)

## S4 method for signature 'hydroMet_IANIGLA'
plot_hydroMet(
  obj,
  slot_name,
  col_number,
  interactive = FALSE,
  line_type = NULL,
  line_color = "dodgerblue",
  x_lab = "Date",
  y_lab = "y",
  title_lab = NULL,
  legend_lab = NULL,
  double_yaxis = NULL,
  list_extra = NULL,
  from = NULL,
  to = NULL
)

## S4 method for signature 'hydroMet_compact'
plot_hydroMet(
  obj,
  slot_name,
  col_number,
  interactive = FALSE,
  line_type = NULL,
  line_color = "dodgerblue",
  x_lab = "x",
  y_lab = "y",
  title_lab = NULL,
  legend_lab = NULL,
  double_yaxis = NULL,
  list_extra = NULL,
  from = NULL,
  to = NULL,
  scatter = NULL
)

Arguments

obj

a valid hydroMet_XXX object.

slot_name

string(s) with the name of the slot(s) to use in plotting.

col_number

numeric (vector) with the column's variable to plot. In case you decide to merge slots you must provide a list in which each element contains the column numbers of the variable to plot.

interactive

logical. Default value, FALSE, will return a ggplot2 class object. Otherwise you will get a plotly one.

line_type

string with line dash type (ggplot2) or mode in plotly case. ggplot2: 'solid' (default value), 'twodash', 'longdash', 'dotted', 'dotdash', 'dashed' or 'blank'. plotly: 'lines' (default value), 'lines+markers' or 'markers'.

line_color

string with a valid color. See 'colors()' or Rcolor document.

x_lab

string with x axis label.

y_lab

string with y axis label. In case you use double_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 with plot label(s) name(s). NOTE: ggplot2 double_yaxis does not support legend_lab in this package version, so giving values to this argument will be harmfulness.

double_yaxis

numeric vector with either 1 (= main axis - left) or 2 (= secondary axis - right) indicating whether the variable should be plotted in either left or right axis. NOTE: in this package version ggplot2 supports just one line plot for each 'y' axis.

list_extra

list with the ggplot2 argument to pass. This argument was design to allow the user to modify ggplot2 arguments (you can find nice examples in ggplot2 - Essentials) NOTE: in this package version this argument doesn't make sense for plotly (except for scatter plot in hydroMet_compact class).

from

string (or POSIXct - valid only in 'BDHI' and 'IANIGLA') with the starting Date. You can use 'from' without 'to'. In this case you will subset your data 'from' till the end.

to

string (or POSIXct - valid only in 'BDHI' and 'IANIGLA') with the ending Date. You can use 'to' without 'from'. In this case you will subset your data from the beginning till 'to'.

scatter

numeric vector of length two with the column number to plot as scatter. The first variable (column number) will be the 'x' variable and the second one the 'y' variable. This argument will work just for class hydroMet_compact.

Value

A ggplot2 or plotly objects to analyze your data.

Functions

plot_hydroMet,hydroMet_BDHI-method: plot method for BDHI class
plot_hydroMet,hydroMet_CR2-method: plot method for CR2 class
plot_hydroMet,hydroMet_DGI-method: plot method for DGI class
plot_hydroMet,hydroMet_IANIGLA-method: plot method for IANIGLA class
plot_hydroMet,hydroMet_compact-method: plot method for compact class

Examples

# Path to file
dgi_path  <- system.file('extdata', package = "hydroToolkit")
file_name <- list.files(path = dgi_path, pattern = 'Toscas')

# Read Toscas
var_nom <- list(slotNames(x = 'hydroMet_DGI')[2:7])
names(var_nom) <- file_name

# Load Toscas meteo station data
toscas_dgi <- create_hydroMet(class_name = 'DGI')
toscas_dgi <- build_hydroMet(obj = toscas_dgi, slot_list = var_nom, path = dgi_path)

# Plot mean air temperature
plot_hydroMet(obj = toscas_dgi, col_number = 2, slot_name = 'tmean',
 legend_lab = 'Tmean(ºC)' )

# Now let's plot an interactive graph
plot_hydroMet(obj = toscas_dgi, col_number = 2, slot_name = 'tmean',
 interactive = TRUE, y_lab = 'Tmean(ºC)' )

Cumulative sum of precipitation series

Description

Returns a data frame with two columns: the date and the cumulative sum of the chosen col_number. This function can deal with NA_real_.

Usage

precip_cumsum(df, col_number = 2, out_name = NULL)

Arguments

df

data frame with Date (or POSIXct) in the first column and numeric variables on the others.

col_number

numeric. The column number of the series where to apply the cumulative sum.

out_name

optional. String value with the column output name. Default is 'cumsum_' plus the original name.

Value

A data frame with two columns: date and the cumulative sum of the series.

Examples

# Load daily precipitation data-set from BDHI
load( paste0(system.file('extdata', package = "hydroToolkit"), '/bdhi_p.rda') )

# Get compact slot
p_bdhi <- get_hydroMet(obj = bdhi_p, name = 'compact')[[1]]

# Apply cumulative precipitation function
p_cum <- precip_cumsum(df = p_bdhi, col_number = 2, out_name = 'cum_guido')

Make homogeneity test or fill gaps in a series

Description

This method can do both: test homogeneity in precipitation series or fill data gaps using regional analysis.

Usage

precip_hydroMet(
  obj,
  col_target = 2,
  fill = FALSE,
  method = "spearman",
  min_value = 0.2
)

## S4 method for signature 'hydroMet_compact'
precip_hydroMet(
  obj,
  col_target = 2,
  fill = FALSE,
  method = "spearman",
  min_value = 0.2
)

Arguments

obj

an hydroMet_compact class object.

col_target

numeric. The column number of the target series (either to test homogeneity or to fill gaps) in compact slot.

fill

logical. By default value (FALSE) you will make an homogeneity test to your target series.

method

string (default is spearman - possible values are: spearman, pearson or kendall). When creating the regional (or master series) the method uses a weighted mean. The weighted values are the correlations coefficients.

min_value

numeric. Series with a correlation value less than min_value are thrown away.

Value

If fill = FALSE the method will return a list with three elements: a data frame with all necessary values to correct your target serie, a plot with p-values and the correlation matrix. When fill = TRUE the list will contain: the data frame with the target series gaps filled and the correlation matrix.

Functions

precip_hydroMet,hydroMet_compact-method: homogeneity test applied to precipitation data stored in compact class.

Examples

# Load daily precipitation data-set from BDHI
load( paste0(system.file('extdata', package = "hydroToolkit"), '/bdhi_p.rda') )

# Fill gaps in Tupungato station
relleno <- precip_hydroMet(obj = bdhi_p, col_target = 5, fill = TRUE)

Reads data from Base de Datos Hidrológica Integrada (BDHI) - Argentina

Description

Reads files downloaded from the Base de Datos Hidrológica Integrada (BDHI) as a data frame.

Usage

read_BDHI(file, colName, timeStep, is.Wdir = FALSE)

Arguments

file

string with the name (including extension) of the file.

colName

string with variable name. E.g.: Qmd(m3/s)

timeStep

string with time step: 'month', 'day', 'day/3', '4h' or 'hour'.

'day': data recorded once a day
'month': data recorded monthly
'4h': applies to atmospheric pressure time series only
'day/3': applies to wind related variables, relative humidity, and dry bulb temperature'
'hour': in case you have to deal with hourly data.

is.Wdir

a logical value indicating if the variable is wind direction. Default value is set to FALSE.

Value

A data frame with two columns: date and variable. Gaps between dates are filled with NA_real_ and duplicated rows are eliminated automatically.

Examples

# Relative path to raw data
full_path <- system.file('extdata', package = "hydroToolkit")

# Apply function
guido_Qmd <- read_BDHI(file = paste0(full_path, '/Qmd_Mendoza_Guido'), 
                colName = 'Q(m3/s)', timeStep = 'day')

Reads data from Explorador Climático de Chile

Description

Reads data downloaded from Explorador Climatico de Chile (CR2) as a data frame.

Usage

read_CR2(file, colName, path = NULL)

Arguments

file

string with the file name (include extension). The only accepted format is '.csv'.

colName

string with the name of the variable.

path

string with the files directory. If not provided, the function will use the current working directory.

Value

A two column data frame with date and variable. Gaps between dates are filled with NA_real_ and duplicated rows are eliminated automatically.

Examples

# Relative path to raw data
full_path <- system.file('extdata', package = "hydroToolkit")

# Apply function
yeso_tmed <- read_CR2(file = 'Tmed_Yeso_Embalse.csv', 
                colName = 'T(ºC)', path = full_path)

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

Description

Reads the Departamento General de Irrigacion(Mendoza - Argentina) excel sheet.

Usage

read_DGI(file, sheet = NULL, colName = NULL, range = NULL, path = NULL)

Arguments

file

string with the file name ('xlsx' excel files).

sheet

sheet to read. Either a string (the name of a sheet), or an integer (the position of the sheet). Default value is sheet one.

colName

string with the name of the second column (as default first column is Date). If ignored first row excel names are used.

range

string providing cell range to read. E.g.: 'A1:B75'.

path

string with the files directory. If not provided, the function will use the current working directory.

Value

A data frame with two columns: date and variable. Gaps between dates are filled with NA_real_ and duplicated rows are eliminated automatically.

Examples

# Relative path to raw data
full_path <- system.file('extdata', package = "hydroToolkit")

# Apply function
toscas_hr <- read_DGI(file = 'Toscas.xlsx', sheet = 'hr',
                colName = 'RH(%)', path = full_path)

Reads data provided by IANIGLA

Description

Reads the data provided by IANIGLA (Instituto Argentino de Nivologia, Glaciologia y Ciencias Ambientales).

Usage

read_IANIGLA(file, all = FALSE, path = NULL)

Arguments

file

string with the name of the '.csv' file downloaded from the meteo-stations web page.

all

logical value indicating whether the returned data frame contain all the original columns or just the date and data.

path

string with the files directory. If not provided, the function will use the current working directory.

Value

A data frame containing the hourly data measured by the automatic weather stations. Gaps between dates are filled with NA_real_ and duplicated rows are eliminated automatically.

Note

In this package version we only provide functionality for a specific data-set generated in the institute.

Examples

# Relative path to raw data
full_path <- system.file('extdata', package = "hydroToolkit")

# Apply function
cuevas <- read_IANIGLA(file = 'Cuevas.csv', path = full_path)

Object summaries

Description

This method returns a list with two elements: the first one is a data frame with miss data (see also report_miss_data) and the second one is also a data frame with the mean, sd, max and min values.

Usage

report_hydroMet(
  obj,
  slot_name,
  col_name,
  start_date = NULL,
  end_date = NULL,
  Lang = "spanish"
)

## S4 method for signature 'hydroMet_BDHI'
report_hydroMet(
  obj,
  slot_name,
  col_name,
  start_date = NULL,
  end_date = NULL,
  Lang = "spanish"
)

## S4 method for signature 'hydroMet_CR2'
report_hydroMet(
  obj,
  slot_name,
  col_name,
  start_date = NULL,
  end_date = NULL,
  Lang = "spanish"
)

## S4 method for signature 'hydroMet_DGI'
report_hydroMet(
  obj,
  slot_name,
  col_name,
  start_date = NULL,
  end_date = NULL,
  Lang = "spanish"
)

## S4 method for signature 'hydroMet_IANIGLA'
report_hydroMet(
  obj,
  slot_name,
  col_name,
  start_date = NULL,
  end_date = NULL,
  Lang = "spanish"
)

Arguments

obj

an hydroMet_XXX object.

slot_name

a single or vector string containing the slot(s) to report.

col_name

a single or vector string with the name of the column to report in slot_name.

start_date

optional (default is the first Date). Single string or POSIXct with the starting Date to report.

end_date

optional (default is the last Date). Single string or POSIXct with the last Date to report.

Lang

optional (default value is spanish). Single string with the language to report results: spanish or english.

Value

A list containing two data frames: the first one with miss data and the second with the mean, sd, max and min values of the series.

Functions

report_hydroMet,hydroMet_BDHI-method: report method for BDHI class
report_hydroMet,hydroMet_CR2-method: report method for CR2 class
report_hydroMet,hydroMet_DGI-method: report method for DGI class
report_hydroMet,hydroMet_IANIGLA-method: report method for IANIGLA class

Examples

# Create IANIGLA class
cuevas <- create_hydroMet(class_name = 'IANIGLA')
 
# List with meteorological variables (slots in BDHI's object)
cargar <- list( slotNames(x = 'hydroMet_IANIGLA')[2:11] )

# Assign as names the files
hydro_files   <- list.files( system.file('extdata', package = "hydroToolkit"), pattern = 'Cuevas' )
names(cargar) <- hydro_files

# Build met-station
cuevas <- build_hydroMet(obj = cuevas, slot_list = cargar, 
           path = system.file('extdata', package = "hydroToolkit") )

# Get report
report_hydroMet(obj = cuevas, slot_name = 'kin', col_name = 'kin_1')

Report `NA_real_` values

Description

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

Usage

report_miss_data(df, Lang = "spanish")

Arguments

df

data frame with hydro-meteo data. First column is date and the second the numeric vector to be reported.

Lang

string with output column name language: 'spanish' (default) or 'english'.

Value

A data frame with three columns: start-date, end-date and number of missing time steps.

Examples

# Create BDHI hydro-met station
guido <- create_hydroMet(class_name = 'BDHI')

# List with meteorological variables (slots in BDHI's object)
cargar <- list('precip', 'Qmd', 'Qmm')

# Now assign as names the files
hydro_files   <- list.files( system.file('extdata', package = "hydroToolkit"), pattern = 'Guido' )
names(cargar) <- hydro_files

# Build the object with the met records
guido <- build_hydroMet(obj = guido, slot_list = cargar, 
               path = system.file('extdata', package = "hydroToolkit") )
               
# Get mean daily discharge and report miss data
Qmd  <- get_hydroMet(obj = guido, name = 'Qmd')[[1]]
miss <- report_miss_data(df = Qmd)

Remove spikes

Description

Removes spikes, and sets their value to NA_real_.

Usage

rm_spikes(df, tolerance)

Arguments

df

data frame with date and numeric variable in the first and second column respectively (from read_XXX functions).

tolerance

numeric with maximum tolerance between a number and its successor.

Value

The same data frame but without peaks.

Examples

# Relative path to raw data
full_path <- system.file('extdata', package = "hydroToolkit")

# Read IANIGLA file
cuevas <- read_IANIGLA(file = 'Cuevas.csv', path = full_path)             

# Remove spikes from air temperature series
tair_rm_spikes <- rm_spikes(df = cuevas, tolerance = 10)

Set the data of an `hydroMet` object or its subclasses

Description

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

Usage

set_hydroMet(
  obj = NULL,
  id = NULL,
  agency = NULL,
  station = NULL,
  lat = NULL,
  long = NULL,
  alt = NULL,
  country = NULL,
  province = NULL,
  river = NULL,
  active = NULL,
  ...
)

## S4 method for signature 'hydroMet'
set_hydroMet(
  obj = NULL,
  id = NULL,
  agency = NULL,
  station = NULL,
  lat = NULL,
  long = NULL,
  alt = NULL,
  country = NULL,
  province = NULL,
  river = NULL,
  active = NULL
)

## S4 method for signature 'hydroMet_BDHI'
set_hydroMet(
  obj = NULL,
  id = NULL,
  agency = NULL,
  station = NULL,
  lat = NULL,
  long = NULL,
  alt = NULL,
  country = NULL,
  province = NULL,
  river = NULL,
  active = NULL,
  Qmd = NULL,
  Qmm = NULL,
  precip = NULL,
  tdb = NULL,
  tmax = NULL,
  tmin = NULL,
  swe = NULL,
  hr = NULL,
  wspd = NULL,
  wdir = NULL,
  evap = NULL,
  anem = NULL,
  patm = NULL
)

## S4 method for signature 'hydroMet_DGI'
set_hydroMet(
  obj = NULL,
  id = NULL,
  agency = NULL,
  station = NULL,
  lat = NULL,
  long = NULL,
  alt = NULL,
  country = NULL,
  province = NULL,
  river = NULL,
  active = NULL,
  swe = NULL,
  tmean = NULL,
  tmax = NULL,
  tmin = NULL,
  hr = NULL,
  patm = NULL,
  hsnow = NULL
)

## S4 method for signature 'hydroMet_IANIGLA'
set_hydroMet(
  obj = NULL,
  id = NULL,
  agency = NULL,
  station = NULL,
  lat = NULL,
  long = NULL,
  alt = NULL,
  country = NULL,
  province = NULL,
  river = NULL,
  active = NULL,
  date = NULL,
  tair = NULL,
  hr = NULL,
  patm = NULL,
  precip = NULL,
  wspd = NULL,
  wdir = NULL,
  kin = NULL,
  hsnow = NULL,
  tsoil = NULL,
  hwat = NULL
)

## S4 method for signature 'hydroMet_CR2'
set_hydroMet(
  obj = NULL,
  id = NULL,
  agency = NULL,
  station = NULL,
  lat = NULL,
  long = NULL,
  alt = NULL,
  country = NULL,
  province = NULL,
  river = NULL,
  active = NULL,
  precip = NULL,
  tmean = NULL,
  tmax = NULL,
  tmin = NULL
)

## S4 method for signature 'hydroMet_compact'
set_hydroMet(
  obj = NULL,
  id = NULL,
  agency = NULL,
  station = NULL,
  lat = NULL,
  long = NULL,
  alt = NULL,
  country = NULL,
  province = NULL,
  river = NULL,
  active = NULL,
  compact = NULL
)

Arguments

obj

an hydroMet or hydroMet_XXX class object.

id

numeric. 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.

...

arguments to be passed to methods. They rely on the slots of the obj subclass.

Qmd

daily mean river discharge.

Qmm

monthly mean river discharge.

precip

precipitation.

tdb

dry bulb temperature.

tmax

daily maximum air temperature.

tmin

daily minimum air temperature.

swe

snow water equivalent.

hr

relative humidity.

wspd

wind speed.

wdir

wind direction.

evap

evaporation.

anem

wind speed above the pan-evaporation.

patm

atmospheric pressure.

tmean

daily mean air temperature.

hsnow

snow height.

date

time serie with dates.

tair

air temperature.

kin

incoming shortwave radiation.

tsoil

soil temperature.

hwat

stream water level.

compact

data frame with Date as first column. All other columns are hydro-meteorological variables.

Value

The hydroMet object with the slots setted.

Functions

set_hydroMet,hydroMet-method: set method for generic object
set_hydroMet,hydroMet_BDHI-method: set method for BDHI object
set_hydroMet,hydroMet_DGI-method: set method for DGI object
set_hydroMet,hydroMet_IANIGLA-method: set method for IANIGLA object
set_hydroMet,hydroMet_CR2-method: set method for CR2 object
set_hydroMet,hydroMet_compact-method: set method for compact object

Examples

# Create BDHI hydro-met station
guido <- create_hydroMet(class_name = 'BDHI')

# Assign altitude
guido <- set_hydroMet(obj = guido, alt = 2480)

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, threshold, case = ">=")

Arguments

x

numeric vector or data frame with a numeric series in the second column.

threshold

numeric value with threshold.

case

string with either '>=' (greater or equal than) or '<=' (lesser or equal than) symbol.

Value

Numeric vector or data frame with values greater (or lesser) or equal than 'threshold' set as NA_real_.

Examples

# Relative path to raw data
full_path <- system.file('extdata', package = "hydroToolkit")

# Read IANIGLA file
cuevas <- read_IANIGLA(file = 'Cuevas.csv', path = full_path)             

# Set threshold from air temperature series
tair_thres <- set_threshold(x = cuevas, threshold = 40)

Subset your data

Description

This method allows you to easily cut the data stored in an hydroMet_XXX class object by dates.

Usage

subset_hydroMet(obj, slot_name, from = NULL, to = NULL)

## S4 method for signature 'hydroMet_BDHI'
subset_hydroMet(obj, slot_name, from = NULL, to = NULL)

## S4 method for signature 'hydroMet_DGI'
subset_hydroMet(obj, slot_name, from = NULL, to = NULL)

## S4 method for signature 'hydroMet_CR2'
subset_hydroMet(obj, slot_name, from = NULL, to = NULL)

## S4 method for signature 'hydroMet_IANIGLA'
subset_hydroMet(obj, slot_name, from = NULL, to = NULL)

## S4 method for signature 'hydroMet_compact'
subset_hydroMet(obj, slot_name, from = NULL, to = NULL)

Arguments

obj

an hydroMet_XXX class object.

slot_name

string vector with the slot(s) name(s) to subset. NOTE: in case you want to subset a hydroMet_IANIGLA object is recommended to consider all the slots with data.

from

string (or POSIXct - valid only in 'BDHI' and 'IANIGLA') with the starting Date. You can use from without to. In this case you will subset your data 'from' till the end.

to

string (or POSIXct - valid only in 'BDHI' and 'IANIGLA') with the ending 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 provided in obj but subsetted.

Functions

subset_hydroMet,hydroMet_BDHI-method: subset method for BDHI data
subset_hydroMet,hydroMet_DGI-method: subset method for DGI data
subset_hydroMet,hydroMet_CR2-method: subset method for CR2 data
subset_hydroMet,hydroMet_IANIGLA-method: subset method for IANIGLA data
subset_hydroMet,hydroMet_compact-method: subset method for compact data

Examples

# Create BDHI hydro-met station
guido <- create_hydroMet(class_name = 'BDHI')

# List with meteorological variables (slots in BDHI's object)
cargar <- list('precip', 'Qmd', 'Qmm')

# Assign as names the files
hydro_files   <- list.files( system.file('extdata', package = "hydroToolkit"), pattern = 'Guido' )
names(cargar) <- hydro_files

# Build the object with the met records
guido <- build_hydroMet(obj = guido, slot_list = cargar, 
               path = system.file('extdata', package = "hydroToolkit") )
               
# Subset daily mean discharge
guido <- subset_hydroMet(obj = guido, slot_name = 'Qmd', from = '2005-01-01',
            to = '2010-12-31')

Snow water equivalent to melt

Description

Converts a snow water equivalent series (from snow pillow) into a melt series.

Usage

swe_to_melt(df)

Arguments

df

data frame with 'swe' serie in the second column. See 'read_XXX' functions.

Value

Data frame containing the numeric vector with melted snow.

Examples

# Relative path to raw data
full_path <- system.file('extdata', package = "hydroToolkit")

# Read swe sheet
toscas_swe <- read_DGI(file = 'Toscas.xlsx', sheet = 'swe',
                colName = 'swe(mm)', path = full_path)

# swe to melt
toscas_melt <- swe_to_melt(df = toscas_swe)

Snow water equivalent to snowfall

Description

Converts a snow water equivalent series (from snow pillow) to a snowfall series.

Usage

swe_to_precip(df)

Arguments

df

data frame with 'swe' series in the second column. See 'read_XXX' functions.

Value

Data frame containing the numeric vector with inferred snowfall.

Examples

# Relative path to raw data
full_path <- system.file('extdata', package = "hydroToolkit")

# Read swe sheet
toscas_swe <- read_DGI(file = 'Toscas.xlsx', sheet = 'swe',
                colName = 'swe(mm)', path = full_path)

# swe to snowfall
toscas_snfall <- swe_to_precip(df = toscas_swe)

River discharge [m3/s] to volume [hm3]

Description

Usage

Arguments

Value

Examples

Aggregate slot data

Description

Usage

Arguments

Value

Functions

Examples

Aggregates a data frame to a larger time period

Description

Usage

Arguments

Value

Examples

Automatically load native data files

Description

Usage

Arguments

Value

Functions

Examples

Creates an hydroMet class or subclass.

Description

Usage

Arguments

Value

Examples

Find non-reported dates and fill them with NA_real_

Description

Usage

Arguments

Value

Examples

Fill a time interval in a data frame with a specific numeric value

Description

Usage

Arguments

Value

Examples

Get the slot(s) content(s)

Description

Usage

Arguments

Value

Functions

Examples

hydroMet superclass object

Description

Value

Slots

hydroMet subclass for BDHI (Base de Datos Hidrologica Integrada) data

Description

Value

Slots

hydroMet subclass for CR2 (Explorador Climático) data

Description

Value

Slots

hydroMet subclass for DGI (Departamento General de Irrigación) data

Description

Value

Slots

hydroMet subclass for IANIGLA (Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales) data

Description

Value

Slots

hydroMet subclass for compact data

Description

Value

Slots

Hydrological year classification

Description

Usage

Arguments

Value

Find non-reported dates and fill them with `NA_real_`

`hydroMet` superclass object

`hydroMet` subclass for BDHI (Base de Datos Hidrologica Integrada) data

`hydroMet` subclass for CR2 (Explorador Climático) data

`hydroMet` subclass for DGI (Departamento General de Irrigación) data

`hydroMet` subclass for IANIGLA (Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales) data

`hydroMet` subclass for compact data

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