Import two related datasets from TidyTuesday Project.
ufo_sightings <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2023/2023-06-20/ufo_sightings.csv')## Rows: 96429 Columns: 12
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (7): city, state, country_code, shape, reported_duration, summary, day_...
## dbl (1): duration_seconds
## lgl (1): has_images
## dttm (2): reported_date_time, reported_date_time_utc
## date (1): posted_date
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.places <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2023/2023-06-20/places.csv')## Rows: 14417 Columns: 10
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (6): city, alternate_city_names, state, country, country_code, timezone
## dbl (4): latitude, longitude, population, elevation_m
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Describe the two datasets:
Data1: UfoSightings
Data 2: Places
set.seed(1234)
ufo_sightings_small <- ufo_sightings %>% select(city, state, shape) %>% sample_n(10)
places_small <- places %>% select(city, state, country_code)
ufo_sightings_small## # A tibble: 10 × 3
## city state shape
## <chr> <chr> <chr>
## 1 Minneapolis MN light
## 2 Danville CA circle
## 3 Avon NY fireball
## 4 Glendale AZ triangle
## 5 Mayville NY teardrop
## 6 Dallas TX triangle
## 7 Mount Horeb WI other
## 8 Scarborough ME orb
## 9 Phoenix AZ other
## 10 Slocomb AL circleplaces_small## # A tibble: 14,417 × 3
## city state country_code
## <chr> <chr> <chr>
## 1 Pinehurst NC US
## 2 Rapid City MI US
## 3 Cleveland OH US
## 4 Bloomington IN US
## 5 Irvine CA US
## 6 Moore OK US
## 7 Short Pump VA US
## 8 Norwalk CT US
## 9 Blayney New South Wales AU
## 10 Greybull WY US
## # ℹ 14,407 more rowsDescribe the resulting data:
How is it different from the original two datasets? 11 rows compared to the 10 in the original data set
ufo_sightings_small %>% left_join(places_small, by = c("city", "state"))## # A tibble: 11 × 4
## city state shape country_code
## <chr> <chr> <chr> <chr>
## 1 Minneapolis MN light US
## 2 Danville CA circle US
## 3 Avon NY fireball US
## 4 Glendale AZ triangle US
## 5 Glendale AZ triangle US
## 6 Mayville NY teardrop US
## 7 Dallas TX triangle US
## 8 Mount Horeb WI other US
## 9 Scarborough ME orb US
## 10 Phoenix AZ other US
## 11 Slocomb AL circle USDescribe the resulting data:
How is it different from the original two datasets? it shared the 11 rows with the second but does not share the same 10 rows as the first.
ufo_sightings_small %>% right_join(places_small, by = c("city", "state"))## # A tibble: 14,417 × 4
## city state shape country_code
## <chr> <chr> <chr> <chr>
## 1 Minneapolis MN light US
## 2 Danville CA circle US
## 3 Avon NY fireball US
## 4 Glendale AZ triangle US
## 5 Glendale AZ triangle US
## 6 Mayville NY teardrop US
## 7 Dallas TX triangle US
## 8 Mount Horeb WI other US
## 9 Scarborough ME orb US
## 10 Phoenix AZ other US
## # ℹ 14,407 more rowsDescribe the resulting data:
How is it different from the original two datasets? Way more rows than the original two data sets. this pulled all the original data from the dataset.
ufo_sightings_small %>% inner_join(places_small, by = c("city", "state"))## # A tibble: 11 × 4
## city state shape country_code
## <chr> <chr> <chr> <chr>
## 1 Minneapolis MN light US
## 2 Danville CA circle US
## 3 Avon NY fireball US
## 4 Glendale AZ triangle US
## 5 Glendale AZ triangle US
## 6 Mayville NY teardrop US
## 7 Dallas TX triangle US
## 8 Mount Horeb WI other US
## 9 Scarborough ME orb US
## 10 Phoenix AZ other US
## 11 Slocomb AL circle USDescribe the resulting data:
How is it different from the original two datasets? this had all the rows of the orignal dataset so this was very different from the first two origninal
ufo_sightings_small %>%
full_join(places_small, by = c("city", "state"))## # A tibble: 14,417 × 4
## city state shape country_code
## <chr> <chr> <chr> <chr>
## 1 Minneapolis MN light US
## 2 Danville CA circle US
## 3 Avon NY fireball US
## 4 Glendale AZ triangle US
## 5 Glendale AZ triangle US
## 6 Mayville NY teardrop US
## 7 Dallas TX triangle US
## 8 Mount Horeb WI other US
## 9 Scarborough ME orb US
## 10 Phoenix AZ other US
## # ℹ 14,407 more rowsDescribe the resulting data:
How is it different from the original two datasets? This showed more similarity to the first dataset where there were 10 rows but eliminated the fourth column
ufo_sightings_small %>%
semi_join(places_small, by = c("city", "state"))## # A tibble: 10 × 3
## city state shape
## <chr> <chr> <chr>
## 1 Minneapolis MN light
## 2 Danville CA circle
## 3 Avon NY fireball
## 4 Glendale AZ triangle
## 5 Mayville NY teardrop
## 6 Dallas TX triangle
## 7 Mount Horeb WI other
## 8 Scarborough ME orb
## 9 Phoenix AZ other
## 10 Slocomb AL circleDescribe the resulting data:
How is it different from the original two datasets? this was very different from the original data set since there was no rows at all after the code was ran
ufo_sightings_small %>%
anti_join(places_small, by = c("city", "state"))## # A tibble: 0 × 3
## # ℹ 3 variables: city <chr>, state <chr>, shape <chr>