To begin, we’ll load foqat
and show one dataset in
foqat
:
aqi
is a dataset about time series of air quality with
1-second resolution.
library(foqat)
head(aqi)
#> Time NO NO2 CO SO2 O3
#> 1 2017-05-01 01:00:00 0.0376578 2.79326 0.256900 NA 56.5088
#> 2 2017-05-01 01:01:00 0.0341483 2.76094 0.254692 NA 57.0546
#> 3 2017-05-01 01:02:00 0.0310285 2.65239 0.265178 NA 57.6654
#> 4 2017-05-01 01:03:00 0.0357016 2.60257 0.269691 NA 58.7863
#> 5 2017-05-01 01:04:00 0.0337507 2.59527 0.273395 NA 59.0342
#> 6 2017-05-01 01:05:00 0.0238120 2.57260 0.276464 NA 59.2240
You can use dm8n()
to calculate daily maximum-8-hour
ozone.
colid
is the column index of date. colio
is
the column index of ozone. outputMode
have two options:
value 1
will output 1 list which incudes 1 table
(maximum-8-hour ozone); value 2
will output 1 list which
contains 4 tables (8-hour ozone, statistics of the number of effective
hourly concentrations in each 8-hour average concentration, statistics
of the number of effective 8-hour average concentrations in each day,
maximum-8-hour ozone). This function will calculate the average values
of other species at the same time and plot them.
= dm8n(aqi, colio=6, outputmode = 1) dm8n_df
If you do not want the plot or you want to save time, you can try
dm8n_np()
= dm8n_np(aqi, colio=6, outputmode = 1)
dm8n_df #> Joining, by = "temp_datetime"
#> [1] "2017-05-01"
#> [1] "2017-05-02"
#> [1] "2017-05-03"
#> [1] "2017-05-04"
#> [1] "2017-05-05"
#> Joining, by = "date"
dm8n_df#> date O3 NO2 CO SO2 NO
#> 1 2017-05-01 NA NA NA NA NA
#> 2 2017-05-02 NA NA NA NA NA
#> 3 2017-05-03 54.89782 0.4927718 0.2603227 0.427208 0.2720057
#> 4 2017-05-04 94.59790 1.3747043 0.3623108 3.785070 0.1757755
#> 5 2017-05-05 77.99124 2.3113478 0.2567683 1.712878 0.2380136
dm8n_batch()
allows you to calculate daily
maximum-8-hour ozone of multiple sites (or cities, or sensors), which
means that it will calculate daily maximum-8-hour ozone for all columns
except first column (date).