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Create a daily aggregation from an hourly dataset

Source: R/tbl_aggregations.R
daily_aggregation.Rd

https://confluence.ecmwf.int/display/CKB/ERA5+family+post-processed+daily+statistics+documentation # nolint: line_length_linter

Usage

daily_aggregation(
  folder_in,
  folder_out,
  pattern = ".txt$",
  from = "2002-01-01 00",
  to = "2021-05-31 23",
  take_out_first_record = TRUE,
  aggregation_function = mean,
  mode = c("agg_fun", "max_min", "last_value")[1],
  na.rm = FALSE
)

Arguments

folder_in

Path of the input files

folder_out

Path where to save the transformed files

pattern

an optional regular expression. Only file names which match the regular expression will be returned.

from

The first date of the series, including the hour part.

to

The last date of the series, including the hour part.

take_out_first_record

Logical. If TRUE, the first record of the input file will be removed. This is useful when the first record is the hour 00:00, that corresponds to the previous day. The length in hour between the from and to must be the same as the length of the hours in the input files.

aggregation_function

The function to use on the hourly groups like mean, sum, mode, etc

mode

The mode of aggregation. The options are agg_fun, max_min or last_value.

na.rm

a logical value indicating whether NA values should be removed before the computation proceeds.

Value

Files with a daily resolution

Details

This function allows to aggregate hourly observations to daily time series. The function for aggregation can be informed in the aggregation_function parameter, this parameter takes a function as argument. The default function is mean, so a daily average is returned.

The function will create a daily aggregation from an hourly dataset. The function for aggregation can be informed in the aggregation_function parameter, this parameter takes a function as argument. The default function is mean, so a daily average is returned. Alternatively, the user can choose the mode parameter to inform the function to use choosing between the agg_fun, max_min, and last_value. The agg_fun will use the function informed in the aggregation_function parameter. The max_min will return the maximum and minimum values of the day. The last_value will return the last value of the day, which is useful for some variables like precipitation where the last value of the day is the accumulated precipitation.