A dataset containing the hourly pedestrian counts from 2015-01-01 to 2016-12-31 at 4 sensors in the city of Melbourne.
pedestrianA tsibble with 66,071 rows and 5 variables:
Sensor: Sensor names (key)
Date_Time: Date time when the pedestrian counts are recorded (index)
Date: Date when the pedestrian counts are recorded
Time: Hour associated with Date_Time
Counts: Hourly pedestrian counts
library(dplyr)
data(pedestrian)
# make implicit missingness to be explicit ----
pedestrian %>% fill_gaps()
#> # A tsibble: 69,048 x 5 [1h] <Australia/Melbourne>
#> # Key: Sensor [4]
#> Sensor Date_Time Date Time Count
#> <chr> <dttm> <date> <int> <int>
#> 1 Birrarung Marr 2015-01-01 00:00:00 2015-01-01 0 1630
#> 2 Birrarung Marr 2015-01-01 01:00:00 2015-01-01 1 826
#> 3 Birrarung Marr 2015-01-01 02:00:00 2015-01-01 2 567
#> 4 Birrarung Marr 2015-01-01 03:00:00 2015-01-01 3 264
#> 5 Birrarung Marr 2015-01-01 04:00:00 2015-01-01 4 139
#> 6 Birrarung Marr 2015-01-01 05:00:00 2015-01-01 5 77
#> 7 Birrarung Marr 2015-01-01 06:00:00 2015-01-01 6 44
#> 8 Birrarung Marr 2015-01-01 07:00:00 2015-01-01 7 56
#> 9 Birrarung Marr 2015-01-01 08:00:00 2015-01-01 8 113
#> 10 Birrarung Marr 2015-01-01 09:00:00 2015-01-01 9 166
#> # … with 69,038 more rows
# compute daily maximum counts across sensors ----
pedestrian %>%
group_by_key() %>%
index_by(Date) %>% # group by Date and use it as new index
summarise(MaxC = max(Count))
#> # A tsibble: 2,752 x 3 [1D]
#> # Key: Sensor [4]
#> Sensor Date MaxC
#> <chr> <date> <int>
#> 1 Birrarung Marr 2015-01-01 1630
#> 2 Birrarung Marr 2015-01-02 352
#> 3 Birrarung Marr 2015-01-03 226
#> 4 Birrarung Marr 2015-01-04 852
#> 5 Birrarung Marr 2015-01-05 1427
#> 6 Birrarung Marr 2015-01-06 937
#> 7 Birrarung Marr 2015-01-07 708
#> 8 Birrarung Marr 2015-01-08 568
#> 9 Birrarung Marr 2015-01-09 1629
#> 10 Birrarung Marr 2015-01-10 2439
#> # … with 2,742 more rows