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 59.2240You 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.
If you do not want the plot or you want to save time, you can try
dm8n_np()
dm8n_df = dm8n_np(aqi, colio=6, outputmode = 1)
#> Joining with `by = join_by(temp_datetime)`
#> [1] "2017-05-01"
#> [1] "2017-05-02"
#> [1] "2017-05-03"
#> [1] "2017-05-04"
#> [1] "2017-05-05"
#> Joining with `by = join_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 0.2380136dm8n_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).