Import data
# excel file
ufo_sightings <- read_excel("../00_data/myData_Shoals.xlsx")
ufo_sightings
## # A tibble: 96,429 × 13
## reported_date_time reported_date_time_utc posted_date city state
## <dttm> <dttm> <dttm> <chr> <chr>
## 1 2022-08-29 02:03:00 2022-08-29 02:03:00 2022-09-09 00:00:00 Pinehur… NC
## 2 2022-08-19 21:51:00 2022-08-19 21:51:00 2022-10-08 00:00:00 Rapid C… MI
## 3 2022-08-13 01:30:00 2022-08-13 01:30:00 2022-09-09 00:00:00 Clevela… OH
## 4 2022-08-06 17:00:00 2022-08-06 17:00:00 2022-09-09 00:00:00 Bloomin… IN
## 5 2022-08-04 03:40:00 2022-08-04 03:40:00 2022-09-09 00:00:00 Irvine CA
## 6 2022-07-22 12:00:00 2022-07-22 12:00:00 2022-09-09 00:00:00 Moore OK
## 7 2022-07-19 12:27:00 2022-07-19 12:27:00 2022-09-09 00:00:00 Short P… VA
## 8 2022-07-14 14:56:00 2022-07-14 14:56:00 2022-09-09 00:00:00 Norwalk CT
## 9 2022-07-13 15:40:00 2022-07-13 15:40:00 2022-09-09 00:00:00 Blayney New …
## 10 2022-07-13 00:10:00 2022-07-13 00:10:00 2022-09-09 00:00:00 Greybull WY
## # ℹ 96,419 more rows
## # ℹ 8 more variables: country_code <chr>, shape <chr>, reported_duration <chr>,
## # duration_seconds <dbl>, summary <chr>, has_images <lgl>, day_part <chr>,
## # Time <dttm>
Plot data
ufo_sightings <- ufo_sightings %>%
add_count(shape, name = "count")
night_shape <- ufo_sightings %>%
filter(day_part %in% c("night") & shape %in% c("formation", "light", "disk", "flash", "fireball", "star", "orb"))
night_shape
## # A tibble: 21,315 × 14
## reported_date_time reported_date_time_utc posted_date city state
## <dttm> <dttm> <dttm> <chr> <chr>
## 1 2023-03-26 21:05:00 2023-03-26 21:05:00 2023-04-09 00:00:00 Palm Be… FL
## 2 2023-03-21 21:09:00 2023-03-21 21:09:00 2023-04-09 00:00:00 Barnegat NJ
## 3 2023-03-04 20:03:00 2023-03-04 20:03:00 2023-03-06 00:00:00 Myrtle … SC
## 4 2023-02-27 00:15:00 2023-02-27 00:15:00 2023-03-06 00:00:00 Rosedale MD
## 5 2023-02-22 20:15:00 2023-02-22 20:15:00 2023-03-06 00:00:00 Silver … MD
## 6 2023-02-03 05:30:00 2023-02-03 05:30:00 2023-03-06 00:00:00 Ajax ON
## 7 2023-02-02 18:45:00 2023-02-02 18:45:00 2023-03-06 00:00:00 Garfield NJ
## 8 2023-01-26 03:40:00 2023-01-26 03:40:00 2023-03-06 00:00:00 Montcla… CA
## 9 2023-01-25 03:00:00 2023-01-25 03:00:00 2023-03-06 00:00:00 tampa FL
## 10 2023-01-08 04:00:00 2023-01-08 04:00:00 2023-03-06 00:00:00 New Yor… NY
## # ℹ 21,305 more rows
## # ℹ 9 more variables: country_code <chr>, shape <chr>, reported_duration <chr>,
## # duration_seconds <dbl>, summary <chr>, has_images <lgl>, day_part <chr>,
## # Time <dttm>, count <int>
ggplot(data = night_shape) +
geom_bar(mapping = aes(x = shape))
afternoon_shape <- ufo_sightings %>%
filter(day_part %in% c("afternoon") & shape %in% c("formation", "light", "disk", "flash", "fireball", "star", "orb"))
afternoon_shape
## # A tibble: 3,740 × 14
## reported_date_time reported_date_time_utc posted_date city state
## <dttm> <dttm> <dttm> <chr> <chr>
## 1 2023-05-16 22:00:00 2023-05-16 22:00:00 2023-05-19 00:00:00 Tucson AZ
## 2 2023-05-15 16:35:00 2023-05-15 16:35:00 2023-05-19 00:00:00 Fenton MI
## 3 2023-04-17 18:58:00 2023-04-17 18:58:00 2023-05-19 00:00:00 Tomball TX
## 4 2023-04-14 17:18:00 2023-04-14 17:18:00 2023-05-19 00:00:00 temple GA
## 5 2023-03-25 19:30:00 2023-03-25 19:30:00 2023-04-09 00:00:00 alma AR
## 6 2023-02-20 18:00:00 2023-02-20 18:00:00 2023-03-06 00:00:00 Hartford WI
## 7 2022-10-31 15:32:00 2022-10-31 15:32:00 2022-12-22 00:00:00 New Por… FL
## 8 2022-10-09 08:34:00 2022-10-09 08:34:00 2022-12-22 00:00:00 Norwich ENG
## 9 2022-10-01 20:55:00 2022-10-01 20:55:00 2022-12-22 00:00:00 Edmonton AB
## 10 2022-09-24 21:30:00 2022-09-24 21:30:00 2022-10-08 00:00:00 Las Veg… NV
## # ℹ 3,730 more rows
## # ℹ 9 more variables: country_code <chr>, shape <chr>, reported_duration <chr>,
## # duration_seconds <dbl>, summary <chr>, has_images <lgl>, day_part <chr>,
## # Time <dttm>, count <int>
ggplot(data = afternoon_shape) +
geom_bar(mapping = aes(x = shape))
morning_shape <- ufo_sightings %>%
filter(day_part %in% c("morning") & shape %in% c("formation", "light", "disk", "flash", "fireball", "star", "orb"))
morning_shape
## # A tibble: 2,328 × 14
## reported_date_time reported_date_time_utc posted_date city state
## <dttm> <dttm> <dttm> <chr> <chr>
## 1 2023-04-03 12:04:00 2023-04-03 12:04:00 2023-05-19 00:00:00 Glouces… MA
## 2 2023-04-02 07:12:00 2023-04-02 07:12:00 2023-04-09 00:00:00 Leslie MI
## 3 2022-11-23 11:27:00 2022-11-23 11:27:00 2022-12-22 00:00:00 Indepen… OH
## 4 2022-10-02 15:11:00 2022-10-02 15:11:00 2022-10-08 00:00:00 San Ber… CA
## 5 2022-09-25 10:56:00 2022-09-25 10:56:00 2022-10-08 00:00:00 Arlingt… VA
## 6 2022-09-11 08:20:00 2022-09-11 08:20:00 2022-10-08 00:00:00 Anoka MN
## 7 2022-07-20 11:00:00 2022-07-20 11:00:00 2022-09-09 00:00:00 Vero be… FL
## 8 2022-07-08 13:14:00 2022-07-08 13:14:00 2022-12-22 00:00:00 Merida Yuca…
## 9 2022-06-06 14:06:00 2022-06-06 14:06:00 2022-06-22 00:00:00 Sacrame… CA
## 10 2022-05-27 07:30:00 2022-05-27 07:30:00 2022-06-22 00:00:00 Palm Bay FL
## # ℹ 2,318 more rows
## # ℹ 9 more variables: country_code <chr>, shape <chr>, reported_duration <chr>,
## # duration_seconds <dbl>, summary <chr>, has_images <lgl>, day_part <chr>,
## # Time <dttm>, count <int>
ggplot(data = morning_shape) +
geom_bar(mapping = aes(x = shape))
