| Title: | Handle Air Quality Data from the European Environment Agency Data Portal |
| Version: | 1.0.1 |
| Description: | This software downloads and manages air quality data from the European Environmental Agency (EEA) dataflow (https://www.eea.europa.eu/data-and-maps/data/aqereporting-9). See the web page https://eeadmz1-downloads-webapp.azurewebsites.net/ for details on the EEA's Air Quality Download Service. The package allows dynamically mapping the stations, summarising and time aggregating the measurements and building spatial interpolation maps. See the web page https://www.eea.europa.eu/en for further information on EEA activities and history. Further details, as well as, an extended vignette of the main functions included in the package, are available at the GitHub web page dedicated to the project. |
| URL: | https://github.com/PaoloMaranzano/EEAaq_R |
| License: | GPL (≥ 3) |
| Depends: | R (≥ 3.5.0) |
| Imports: | arrow, curl, dplyr, ggplot2, ggpubr, gstat, httr, leaflet, lubridate, raster, readr, sf, stats, tibble, tidyr, utils, rlang, ggspatial, tidyselect |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.3.2 |
| Suggests: | knitr, rmarkdown, rvest, readxl, digest, gh, base64enc, stringr, gifski, htmlwidgets, grDevices, tidyverse |
| NeedsCompilation: | no |
| Packaged: | 2025-03-20 18:38:13 UTC; paulm |
| Author: | Paolo Maranzano 
[aut, cre, cph],
Riccardo Borgoni
[aut, cph],
Samir Doghmi [aut, cph],
Agostino Tassan Mazzocco [aut, cph] |
| Maintainer: | Paolo Maranzano <pmaranzano.ricercastatistica@gmail.com> |
| Repository: | CRAN |
| Date/Publication: | 2025-03-20 18:50:02 UTC |
Export and save an EEAaq_df class object
Description
EEAaq_export export an EEAaq_df class object as a .csv or a .txt file.
Usage
EEAaq_export(data, filepath, format)
Arguments
data |
an |
filepath |
character string giving the file path |
format |
character string giving the format of the file. It must be one of 'csv' and 'txt'. |
Value
No return value, called for side effects.
Examples
### Download PM10 data for the province (NUTS-3) of Milano (Italy)
### from January 1st to January 31st, 2023
`%>%` <- dplyr::`%>%`
IDstations <- EEAaq_get_stations(byStation = TRUE, complete = FALSE)
IDstations <- IDstations %>%
dplyr::filter(NUTS3 %in% c("Milano")) %>%
dplyr::pull(AirQualityStationEoICode) %>%
unique()
data <- EEAaq_get_data(IDstations = IDstations, pollutants = "PM10",
from = "2023-01-01", to = "2023-01-31",
verbose = TRUE)
### Export data to csv file
temp <- tempdir()
filepath <- paste0(temp, "/data.csv")
EEAaq_export(data = data, filepath = filepath, format = "csv")
Download air quality data at european level from the EEA download service
Description
This function retrieves air quality datasets at european level, based on station, time and pollutant specifications.
This function generates a data.frame/tibble object of class EEAaq_df.
Usage
EEAaq_get_data(
IDstations = NULL,
pollutants = NULL,
from = NULL,
to = NULL,
verbose = TRUE
)
Arguments
IDstations |
Numeric value. Unique ID of the stations to retrieve. |
pollutants |
the pollutants for which to download data. It may be:
|
from |
character defining the initial date of the period to be retrieved. The format is |
to |
character defining the final date of the period to be retrieved. The format is |
verbose |
logic value (T or F). If |
Details
Recall that stations and sensors are physically managed by national or local environmental protection agencies with their own specificities and rules.
EEA operates as a collector of national environmental protection systems and harmonizes the information received by national offices.
However, data provided can change on a country basis. For instance, time resolution, sampling frequency, spatial coverage, or
the classifications (e.g., urban or rural) can differ country by country. Before downloading the data, we suggest to manage and filter the stations/sensors
of interest through their metadata files (provided by EEAaq_get_stations or EEAaq_get_dataframe). See the examples and the vignette
for practical examples.
Value
A data frame of class EEAaq_df, if zone_name is specified, and of class EEAaq_df_sfc
if whether the parameter quadrant or polygon is specified.
Examples
`%>%` <- dplyr::`%>%`
### Download PM10 data for the province (NUTS-3) of Milano (Italy)
### from January 1st to January 31st, 2023
IDstations <- EEAaq_get_stations(byStation = TRUE, complete = FALSE)
IDstations <- IDstations %>%
dplyr::filter(NUTS3 %in% c("Milano")) %>%
dplyr::pull(AirQualityStationEoICode) %>%
unique()
data <- EEAaq_get_data(IDstations = IDstations, pollutants = "PM10",
from = "2023-01-01", to = "2023-01-31",
verbose = TRUE)
EEAaq_get_dataframe
Description
Retrieve one of the metadata (i.e., LAU, NUTS, stations, or pollutant) tables from the EEA and Eurostat dataflows. This function downloads and loads one dataset at a time from a predefined list of available datasets. Ensure that the dataset name is written correctly. See details for further details.
Usage
EEAaq_get_dataframe(dataframe = NULL)
Arguments
dataframe |
name of the
|
Details
The function retrieves information from the EEAaq GitHub folder one of the available metadata.
Since the end of 2024, the data EEA air quality retrieving dataflow is undergoing a major re-organization. In particular, since January 2025, raw data are accessible only through an online platform/dashboard.
While EEAaq is build to explicitly deal with the automatic and constantly-updated system for raw data, the same process is not always possible for the metadata.
Indeed, most of the metadata information are updated and require relevant pre-processing (i.e., data manipulation and cleaning) steps to make them consistent with the main database on pollutants concentrations.
For this reasons, all the metadata files are periodically pre-processed and updated (on GitHub) by the package maintainers. For issues with the data or code, please contact the development team at pmaranzano.ricercastatistica@gmail.com
Value
a dataframe
Examples
LAU <- EEAaq_get_dataframe(dataframe= "LAU")
pollutant <- EEAaq_get_dataframe(dataframe = "pollutant")
stations <- EEAaq_get_dataframe(dataframe = "stations")
NUTS <- EEAaq_get_dataframe(dataframe = "NUTS")
Download EEA measurement station information dataset
Description
Download the updated dataset from EEA, containing measurement station information. For further information about the variables
see stations.
Usage
EEAaq_get_stations(byStation = TRUE, complete = TRUE)
Arguments
byStation |
Logic value (T or F). If |
complete |
Logic value (T or F). If |
Details
Note that, for very small towns or certain countries, such as Turkey or Albania, data may not currently be available in the dataset. This limitation reflects the data unavailability at the the EEA Air Quality Viewer https://discomap.eea.europa.eu/App/AQViewer/index.html?fqn=Airquality_Dissem.b2g.AirQualityStatistics.
Value
A tibble containing the stations information. Further details available here stations.
Examples
EEAaq_get_stations(byStation = TRUE, complete = TRUE)
Build a spatial interpolation map based on the Inverse Distance Weighting technique.
The function EEAaq_idw_map requires as input a EEAaq_taggr_df or a EEAaq_taggr_df_sfc class object and produces a
spatial interpolation map. Depending on the time frequency of the aggregation, multiple maps are generated, one for
each timestamp. Interpolation maps may be exported as pdf, jpeg, png, gif and html.
Description
Build a spatial interpolation map based on the Inverse Distance Weighting technique.
The function EEAaq_idw_map requires as input a EEAaq_taggr_df or a EEAaq_taggr_df_sfc class object and produces a
spatial interpolation map. Depending on the time frequency of the aggregation, multiple maps are generated, one for
each timestamp. Interpolation maps may be exported as pdf, jpeg, png, gif and html.
Usage
EEAaq_idw_map(
data = NULL,
pollutant = NULL,
aggr_fun,
distinct = FALSE,
gradient = TRUE,
idp = 2,
nmax = NULL,
maxdist = NULL,
NUTS_filler = NULL,
NUTS_extborder = NULL,
NUTS_intborder = NULL,
dynamic = FALSE,
tile = "Esri.WorldGrayCanvas",
filepath = NULL,
width = 1280,
height = 720,
res = 144,
delay = 1,
save = NULL,
verbose = TRUE
)
Arguments
data |
an object of class |
pollutant |
vector containing the pollutant for which to build the map. It must be one of the pollutants
contained in |
aggr_fun |
character containing the aggregation function to use for computing the interpolation. It must
be one of the statistics contained in |
distinct |
logic value (T or F). If |
gradient |
logic value (T or F). If |
idp |
numeric value that specify the inverse distance weighting power. For further information see
|
nmax |
numeric value; specify the number of nearest observations that should be
used for the inverse distance weighting computing, where nearest is defined in terms of the
space of the spatial locations. By default, all observations are used. For further information see
|
maxdist |
numeric value; only observations within a distance of |
NUTS_filler |
character containing the NUTS level or LAU for which to aggregate the idw computing, in order to obtain a uniform coloring inside each area at the specified level. Recall that the NUTS classification (Nomenclature of territorial units for statistics) is a hierarchical system for dividing up the economic territory of the EU and the UK. The levels are defined as follows:
For instance if |
NUTS_extborder |
character containing the NUTS level or LAU for which draw external boundaries. Admissible values are 'NUTS0', 'NUTS1', 'NUTS2', 'NUTS3', 'LAU'. |
NUTS_intborder |
character containing the NUTS level or LAU for which draw internal boundaries. Admissible values are 'NUTS0', 'NUTS1', 'NUTS2', 'NUTS3', 'LAU'. |
dynamic |
logic value (T or F). If |
tile |
character representing the name of the provider tile. To see the full list of the providers, run
|
filepath |
a character string giving the file path. |
width, height |
the width and the height of the plot, expressed in pixels (by default |
res |
the nominal resolution in ppi which will be recorded in the bitmap file, if a positive integer
(by default |
delay |
numeric value specifying the time to show each image in seconds, when |
save |
character representing in which extension to save the map. Allowed values are 'jpeg', 'png', 'pdf'
(if |
verbose |
logic value (T or F). If |
Details
EEAaq_idw_map create a spatial interpolation map, based on the Inverse Distance Weighting method (Shepard 1968).
This method starts from the available georeferenced data and estimates the value of the variable in the points
where it's unknown as a weighted average of the known values, where weights are given by an inverse function of the
distance of every point from the fixed stations.
The greater the distance of a point from a station, the smaller the weight assigned to the values of the respective
station for the computing of that unknown point.
Given the sampling plan s_{i} for i=1,...,n, which represent the location of the air quality stations,
the pollutant concentration value Y(s_i)=Y_i represents the value of the pollutant concentration detected
by the site s_i and u is the point for which the value of the concentration in unknown.
\hat{Y}(u) = \sum_{i=1}^{n} Y_i \omega_i(u),
where
\omega_i(u) = \frac{g(d(s_i,u))}{\sum_{i=1}^{n}g(d(s_i,u))}
represent the weights assigned to each location s_i and d(s_i,u) is the distance between u
and s_i.
Value
cosa restituisce la funzione
Examples
## Not run:
`%>%` <- dplyr::`%>%`
### Filter all the stations installed in the city (LAU) of Milano (Italy)
IDstations <- EEAaq_get_stations(byStation = FALSE, complete = FALSE)
IDstations <- IDstations %>%
dplyr::filter(LAU_NAME == "Milano") %>%
dplyr::pull(AirQualityStationEoICode) %>%
unique()
### Download NO2 measurement for the city of Milano from January 1st
### to December 31st, 2023
data <- EEAaq_get_data(IDstations = IDstations, pollutants = "NO2",
from = "2023-01-01", to = "2023-01-31",
verbose = TRUE)
### Monthly aggregation: compute station-specific monthly minimum,
### average, and maximum NO2 concentrations
t_aggr <- EEAaq_time_aggregate(data = data, frequency = "monthly",
aggr_fun = c("mean", "min", "max"))
### Static IDW interpolation of the average NO2 concentrations for the
### whole Lombardy region (NUTS_extborder = "NUTS2"). Interpolated values
### are then aggregated at the provincial level (NUTS_filler = "NUTS3")
EEAaq_idw_map(data = t_aggr, pollutant = "NO2", aggr_fun = "mean",
distinct = TRUE, gradient = FALSE,
dynamic = FALSE,
NUTS_filler = "NUTS3",
NUTS_extborder = "NUTS2")
### Dynamic IDW interpolation map (interactive leafleat) of the average
### NO2 concentrations for the whole Lombardy region
### (NUTS_extborder = "NUTS2"). Interpolated values are then aggregated
### at the municipal level (NUTS_filler = "LAU")
EEAaq_idw_map(data = t_aggr, pollutant = "NO2", aggr_fun = "mean",
distinct = TRUE, gradient = FALSE,
dynamic = TRUE,
NUTS_filler = "LAU",
NUTS_extborder = "NUTS2",
NUTS_intborder = "LAU")
## End(Not run)
Reverse function of EEAaq_export. Reads an EEAaq_df object from a .txt or .csv
file saved through EEAaq_export.
Description
Reverse function of EEAaq_export. Reads an EEAaq_df object from a .txt or .csv
file saved through EEAaq_export.
Usage
EEAaq_import(file_data)
Arguments
file_data |
file path of the 'csv' or 'txt' file containing the air quality data to import. |
Value
No return value, called for side effects.
Examples
`%>%` <- dplyr::`%>%`
### Download PM10 data for the province (NUTS-3) of Milano (Italy)
### from January 1st to January 31st, 2023
IDstations <- EEAaq_get_stations(byStation = TRUE, complete = FALSE)
IDstations <- IDstations %>%
dplyr::filter(NUTS3 %in% c("Milano")) %>%
dplyr::pull(AirQualityStationEoICode) %>%
unique()
data <- EEAaq_get_data(IDstations = IDstations, pollutants = "PM10",
from = "2023-01-01", to = "2023-01-31",
verbose = TRUE)
### Export data to csv file
temp <- tempdir()
filepath <- paste0(temp, "/data.csv")
EEAaq_export(data = data, filepath = filepath, format = "csv")
### Import the EEAaq_df object saved in the previous code line
EEAaq_import(file_data = filepath)
Create a static or dynamic (interactive leaflet) map representing the geographical locations of the
stations based on a user-defined input dataset of class EEAaq_df or EEAaq_df_sfc.
Description
Create a static or dynamic (interactive leaflet) map representing the geographical locations of the
stations based on a user-defined input dataset of class EEAaq_df or EEAaq_df_sfc.
Usage
EEAaq_map_stations(
data = NULL,
NUTS_extborder = NULL,
NUTS_intborder = NULL,
color = TRUE,
dynamic = FALSE
)
Arguments
data |
an |
NUTS_extborder |
character containing the NUTS level or LAU for which draw external boundaries. Admissible values are 'NUTS0', 'NUTS1', 'NUTS2', 'NUTS3', 'LAU'. Recall that the NUTS classification (Nomenclature of territorial units for statistics) is a hierarchical system for dividing up the economic territory of the EU and the UK. The levels are defined as follows:
|
NUTS_intborder |
character containing the NUTS level or LAU for which draw internal boundaries. Admissible values are 'NUTS0', 'NUTS1', 'NUTS2', 'NUTS3', 'LAU'. |
color |
logical value (T or F). If |
dynamic |
logical value (T or F). If |
Value
A map representing the specified area and the points representing the location of the stations able to detect the specified pollutants.
Examples
library(sf)
`%>%` <- dplyr::`%>%`
### Retrieve all the stations measuring PM10 in Belgium
IDstations <- EEAaq_get_stations(byStation = FALSE, complete = TRUE)
IDstations <- IDstations %>%
dplyr::filter(ISO %in% c("BE"),
is.na(OperationalActivityEnd),
AirPollutant %in% "PM10") %>%
dplyr::pull(AirQualityStationEoICode) %>%
unique()
### Download the corresponding data from December 1st to December 31st, 2021
data <- EEAaq_get_data(IDstations = IDstations, pollutants = "PM10",
from = "2021-12-01", to = "2021-12-31",
verbose = TRUE)
### Static map of available stations across the whole country.
### External borders are given by the union of the available regions (NUTS-2),
### while municipalities (LAUs) are used as inner borders.
EEAaq_map_stations(data = data,
NUTS_extborder = "NUTS1", NUTS_intborder = "NUTS2",
color = TRUE, dynamic = FALSE)
### Dynamic (interactive leaflet) map of available stations across the whole
### country. External borders are given by the union of the available
### regions (NUTS-2), while provinces (NUTS-3) are used as inner borders.
EEAaq_map_stations(data = data,
NUTS_extborder = "NUTS2", NUTS_intborder = "NUTS3",
color = TRUE, dynamic = TRUE)
Generate an EEAaq_df data summary.
This function must be applied to an EEAaq_df or EEAaq_df_sfc class object and produces a list of data frames,
containing relevant information about the data, such as descriptive statistics, missing values statistics,
gap length and linear correlation.
Description
Generate an EEAaq_df data summary.
This function must be applied to an EEAaq_df or EEAaq_df_sfc class object and produces a list of data frames,
containing relevant information about the data, such as descriptive statistics, missing values statistics,
gap length and linear correlation.
Usage
EEAaq_summary(data = NULL, verbose = TRUE)
Arguments
data |
an |
verbose |
logic value (T or F). If |
Value
The function EEAaq_summary computes and return a list of summary statistics of the dataset given in
data. In particular the elements of the list are:
Summaryglobal missing count, missing rate, negative count, minimum, maximum, mean and standard deviation, organized by pollutant.Summary_byStatlist of data frames, one for each different station, containing the descriptive statistics (missing count, missing rate, negative count, minimum, maximum, mean and standard deviation), organized by station.gap_lengthone data frame for each pollutant, containing the gap length organized by station.Corr_Matrixifdatacontains more than one pollutant, the correlation matrix between pollutans is provided, organised by station.
Examples
`%>%` <- dplyr::`%>%`
### Download PM10 data for the province (NUTS-3) of Milano (Italy)
### from January 1st to January 31st, 2023
IDstations <- EEAaq_get_stations(byStation = TRUE, complete = FALSE)
IDstations <- IDstations %>%
dplyr::filter(NUTS3 %in% c("Milano")) %>%
dplyr::pull(AirQualityStationEoICode) %>%
unique()
data <- EEAaq_get_data(IDstations = IDstations, pollutants = "PM10",
from = "2023-01-01", to = "2023-01-31",
verbose = TRUE)
### Compute summary statistics
EEAaq_summary(data)
Time aggregation of an EEAaq_df class object.
Description
EEAaq_time_aggregate compute a time aggregation of an EEAaq_df or EEAaq_df_sfc class object,
based on the specified frequency and the aggregation functions aggr_fun.
Usage
EEAaq_time_aggregate(
data = NULL,
frequency = "monthly",
aggr_fun = c("mean", "min", "max")
)
Arguments
data |
an |
frequency |
vector containing the time frequency for which to aggregate the |
aggr_fun |
character vector containing one or more agregation functions. Admissible values are 'mean', 'median', 'min', 'max', 'sd', 'var', 'quantile_pp' (where pp is a number in the range [0,1], representing the required percentile). |
Value
A EEAaq_taggr_df or a EEAaq_taggr_df_sfc class object, which is a tibble containing the
required time aggregation.
Examples
`%>%` <- dplyr::`%>%`
### Filter all the stations installed in the city (LAU) of Milano (Italy)
IDstations <- EEAaq_get_stations(byStation = FALSE, complete = FALSE)
IDstations <- IDstations %>%
dplyr::filter(LAU_NAME == "Milano") %>%
dplyr::pull(AirQualityStationEoICode) %>%
unique()
### Download NO2 measurement for the city of Milano from January 1st
### to December 31st, 2023
data <- EEAaq_get_data(IDstations = IDstations, pollutants = "NO2",
from = "2023-01-01", to = "2023-01-31",
verbose = TRUE)
### Monthly aggregation: compute station-specific monthly minimum,
### average, and maximum NO2 concentrations
t_aggr <- EEAaq_time_aggregate(data = data, frequency = "monthly",
aggr_fun = c("mean", "min", "max"))
### Weekly aggregation: compute station-specific monthly average and
### standard deviation concentrations
t_aggr <- EEAaq_time_aggregate(data = data, frequency = "weekly",
aggr_fun = c("mean", "sd"))
Code_extr
Description
This function extracts the numerical value from NUTS-level strings.
Usage
code_extr(level)
Arguments
level |
A character vector representing NUTS-level codes (e.g., |
Value
A sorted numeric vector containing the extracted NUTS levels.
Get LAU data
Description
Local Administrative Units (LAUs) are the building blocks of the NUTS classification and correspond to the municipalities and communes within the EU To get the final dataframe we combine two dataset: one taken from Eurostat (https://ec.europa.eu/eurostat/web/nuts/local-administrative-units)that includes City names and City IDs, essential for querying and associations. The other one taken from EEA which provides LAU information. The Latter dataset is updated automatically by selecting the most recent shapefile (SHP) available online. While The Eurostat dataset URL needs to be manually updated with the latest download link to ensure the City-related data is current.
Usage
get_LAU(year = "Null")
Arguments
year |
expressed as four digit (YYYY) |
Value
A tibble containing LAUs information with selected columns (e.g., ISO, LAU_ID, NUTS3_ID and geometry ).
Get NUTS
Description
It automatically updates the dataset by identifying the most recent available file, accessing the corresponding page, and downloading the SHP file at the 1:20 Million scale with the EPSG:4326 reference system from this website (https://gisco-services.ec.europa.eu/distribution/v2/nuts/)
Usage
get_NUTS(year = "Null")
Arguments
year |
expressed as four digit (YYYY) |
Value
A tibble containing LAUs information with selected columns (NUTS_ID, LEVL_CODE...)
Get pollutant
Description
Retrieve Pollutant Data from EEA Vocabulary (https://dd.eionet.europa.eu/vocabulary/aq/pollutant) Downloads and processes pollutant data from the EEA (European Environment Agency) vocabulary database. The data includes relevant information such as pollutant names, codes, and descriptions.
Usage
get_pollutants()
Value
A tibble containing pollutant information with selected columns (e.g., URI, notation, and extracted code).
Get Station Data
Description
This function downloads detailed information for each SamplingPointId. It performs a spatial join to merge the spatial information of LAU and NUTS (specifically, the geometries of LAU and the geometry of stations) and fills in the missing data for CITY_NAME and CITY_ID (retrieved from https://discomap.eea.europa.eu/App/AQViewer/index.html?fqn=Airquality_Dissem.b2g.AirQualityStatistics) through a left join based on the AirQualityStationEoICode column. These values are essential for querying the endpoint. The missing_cities file was obtained manually (from 2000 to 2024) because the website did not allow downloading more than 100,000 rows at a time. The data was collected in multiple batches, filtering SamplingPoints using the following criteria:
Filter on data used in AQ Report: yes
Filter on data coverage: yes For each station, the column AirQualityStationEoICode (identical for all sensors at the same station) was used to select the first row containing unique values for CITY_NAME and CITY_ID. No station reported more than one value for this pair of columns. To support future uploads, it is necessary to integrate updated AirQualityStationEoICode values.
Usage
get_stations()
Value
a tibble
Handle Dates based on Dataset Ranges
Description
This function handles dates based on the respective dataset. According to the documentation:
Data from 2024 onwards corresponds to Unverified data transmitted continuously (Up-To-Date/UTD/E2a).
Data from 2013 to the begin of 2023 corresponds to Verified data (E1a) reported by countries by 30 September each year for the previous year.
Data delivered before 2012 corresponds to Historical Airbase data. The range for E1 is extended until 31/12/2023 because the observations are already validated, and no data for 2023 is retrieved when considering E2.
Usage
handle_dates(from, to)
Arguments
from |
StartDate (in "YYYY-MM-DD" format). |
to |
EndDate (in "YYYY-MM-DD" format). |
Value
A list of datasets with associated date ranges and descriptions.
Check if a given object is an EEAaq_df class object
Description
Given an object as input, is_EEAaq_df verify that the given object belongs
to the EEAaq_df class.
Usage
is_EEAaq_df(data)
Arguments
data |
the object for which verify the if it belongs to the |
Value
logical value (T ot F). If TRUE the object given in input is an EEAaq_df object.
If FALSE the object doesn't belong to the EEAaq_df class.
Examples
### Download PM10 data for the province (NUTS-3) of Milano (Italy)
### from January 1st to January 31st, 2023
`%>%` <- dplyr::`%>%`
IDstations <- EEAaq_get_stations(byStation = TRUE, complete = FALSE)
IDstations <- IDstations %>%
dplyr::filter(NUTS3 %in% c("Milano")) %>%
dplyr::pull(AirQualityStationEoICode) %>%
unique()
data <- EEAaq_get_data(IDstations = IDstations, pollutants = "PM10",
from = "2023-01-01", to = "2023-01-31",
verbose = TRUE)
### Check if the imported object belongs to the EEAaq_df class
is_EEAaq_df(data = data)
Aggregate data based on a specific statistic
Description
Given data and the aggregation function desired, this function compute a time aggregation of the data.
Usage
my_summarise(data, fun_aggr)
Arguments
data |
An |
fun_aggr |
Vector character containing the aggregation function for which to time aggregate. |
Value
A tibble with the required aggregation.