Package 'DataMetProcess'

Fix the time zone

Description

Allows you to correct the timezone based on a date column and another time column

Usage

adjustDate(data = NULL, col_date = NULL, col_hour = NULL, fuso = NULL)

Arguments

data	Data frame containing the data
col_date	Column containing the dates
col_hour	Column containing the time. It must be in the format "hh", "hh:mm", or "hh:mm:ss"; only the hours "hh" will be used for conversion.
fuso	Time zone for correction. Query OlsonNames()

Value

Data frame with the corrected timezone

Examples

address <-
 base::system.file("extdata",
                    "ex1_inmet.CSV",
                    package = "DataMetProcess")

df <-
  read.table(
    address,
    h=TRUE,
    sep = ";",
    dec = ",",
    skip = 8,
    na.strings = -9999,
    check.names = FALSE
  )

df$Data = as.Date(df$Data,format = "%d/%m/%Y")


df <-
  adjustDate(df,
             colnames(df)[1],
             colnames(df)[2],
             fuso = "America/Bahia")

head(df[1:2])

---

Calculation of daily, monthly and annual scales

Description

Performs data processing on an hourly scale for daily, monthly or annual scales

Usage

calculateDMY(
  data = NULL,
  col_date = NULL,
  col_sum = NULL,
  col_mean = NULL,
  col_max = NULL,
  col_min = NULL,
  n.round = 2,
  type = c("Daily", "Monthly", "Yearly")
)

Arguments

data	Data frame containing the data
col_date	String with the column of data containing the date (R default date: "%Y-%m-%d")
col_sum	String with the column of data to apply the sum process
col_mean	String with the column of data to apply the averaging process
col_max	String with data column to find maximum
col_min	String with data column to find minimum
n.round	Integer, number of decimal places
type	string, receives "Daily", "Monthly" or "Yearly" ("Daily" default). Defines the scale of processing to be performed

Value

Data frame with the defined scale

Examples

address <-
 base::system.file("extdata",
                    "ex1_inmet.CSV",
                    package = "DataMetProcess")

df <-
read.table(
  address,
  h=TRUE,
  sep = ";",
  dec = ",",
  skip = 8,
  na.strings = -9999,
  check.names = FALSE
)

df$Data = as.Date(df$Data,format = "%d/%m/%Y")

df.d <-
  calculateDMY(
    data = df,
    col_date = "Data",
    col_sum = colnames(df)[c(3,7)],
    col_mean = colnames(df)[-c(1,2,3,7)],
    type = "Daily"
  )

df.m <-
  calculateDMY(
    data = df.d,
    col_date = "Date",
    col_sum = colnames(df.d)[c(2)],
    col_mean = colnames(df.d)[-c(1,2)],
    type = "Monthly"
  )

df.a <-
  calculateDMY(
    data = df.m,
    col_date = "Date",
    col_sum = colnames(df.m)[c(2)],
    col_mean = colnames(df.m)[-c(1,2)],
    type = "Yearly"
  )

---

FAO Penman-Monteith method for daily reference evapotranspiration

Description

Calculation of daily reference evapotranspiration using the FAO-56 Penman-Monteith method for a dataset stored in a data.frame.

Usage

calculateETrefPM(
  data,
  lat,
  alt,
  za,
  DAP = 1,
  date,
  Ta = NULL,
  Tmin = NULL,
  Tmax = NULL,
  RH = NULL,
  RHmin = NULL,
  RHmax = NULL,
  Rg,
  AP,
  WS,
  G = NULL,
  Kc = NULL
)

Arguments

data	Data frame containing the data
lat	Numeric, latitude in decimal degrees
alt	Numeric, altitude in meters
za	Numeric, anemometer height in meters
DAP	Numeric, days after planting for the first date
date	String with the column name containing date records
Ta	Optional. String with the column name containing mean air temperature (degC)
Tmin	Optional. String with the column name containing minimum air temperature (degC)
Tmax	Optional. String with the column name containing maximum air temperature (degC)
RH	String with the column name containing mean relative humidity (percent)
RHmin	Optional. String with the column name containing minimum relative humidity (percent)
RHmax	Optional. String with the column name containing maximum relative humidity (percent)
Rg	String with the column name containing global radiation (MJ/m2/day)
AP	String with the column name containing atmospheric pressure (hPa)
WS	String with the column name containing wind speed (m/s)
G	Optional. If NULL, soil heat flux is assumed to be zero (MJ/m2/day)
Kc	Optional. Crop coefficient column name

---

Launch DataMetProcess Shiny Application

Description

The 'DMPshiny' function is used to start the Shiny application of the 'DataMetProcess' package. It allows configuring the host address, port, whether to launch the browser automatically, and the maximum upload size.

Usage

DMPshiny(
  host = "127.0.0.1",
  port = NULL,
  launch.browser = TRUE,
  maxUploadSize = 200
)

Arguments

host	Character. The host address where the application will run. Default is "127.0.0.1".
port	Integer. The port on which the application will run. If NULL, a random port will be used.
launch.browser	Logical. Indicates whether the browser should be launched automatically. Default is TRUE.
maxUploadSize	Numeric. Maximum upload file size in megabytes. Default is 200.

Details

The function sets Shiny options, such as the maximum upload size, and then runs the Shiny application located in the 'DataMetProcess_Shiny/App.R' directory of the package.

Value

This function does not return a value. It starts the Shiny server and opens the application in the specified browser.

Examples

## Not run: 
  DMPshiny()

## End(Not run)

---

List of data available at INMET by year

Description

Collects the available files for the year and returns a list containing: 1) a table containing the addresses of each file inside the zip for later extraction by the down_inmet() function, 2) Yearther structured table with the information available in the file name (e,g, city, station code, year, date of start and end date) and 3) the address of the zip file.

Usage

list_inmet(year = NULL, filename = NULL)

Arguments

year	year for download in the INMET database
filename	string containing the path and name of the file with the extension ".zip", if NULL (default) it will be saved in a temporary file

Value

List containing: 1) a table containing the addresses of each file inside the zip for later extraction by the unzip() function of the utils package, 2) Yearther structured table with the information available in the file name (e,g, city, station code, year, date of start and end date) and 3) the address of the zip file.

Examples

file.down <- tempfile()
file.save <- tempfile()

info.inmet <-
  DataMetProcess::list_inmet(year="2000",file.down)

unzip.file <-
  utils::unzip(
    zipfile = file.down, #or info.inmet$Saved
    exdir = file.save
  )

unzip.file

---