EEAaq_summary {EEAaq} | R Documentation |
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:
Summary
global missing count, missing rate, negative count, minimum, maximum, mean and standard deviation, organized by pollutant.Summary_byStat
list 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_length
one data frame for each pollutant, containing the gap length organized by station.Corr_Matrix
ifdata
contains 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)