Import two related datasets from TidyTuesday Project.
survivalists <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2023/2023-01-24/survivalists.csv')## Rows: 94 Columns: 16
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (10): name, gender, city, state, country, reason_tapped_out, reason_cate...
## dbl (5): season, age, result, days_lasted, day_linked_up
## lgl (1): medically_evacuated
##
## 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.loadouts <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2023/2023-01-24/loadouts.csv')## Rows: 940 Columns: 6
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (4): version, name, item_detailed, item
## dbl (2): season, item_number
##
## 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
Data 2
set.seed(2718)
survivalists_small <- survivalists %>% select(season, name, age) %>% sample_n(10)
loadouts_small <- loadouts %>% select(season, name, item_detailed) %>% sample_n(10)
survivalists_small## # A tibble: 10 × 3
## season name age
## <dbl> <chr> <dbl>
## 1 5 Sam Larson 24
## 2 9 Jessie Krebs 49
## 3 5 Britt Ahart 41
## 4 3 Dave Nessia 49
## 5 9 Tom Garstang 35
## 6 8 Tim Madsen 48
## 7 7 Joe Nicholas 31
## 8 4 Josh Richardson 19
## 9 3 Callie North 27
## 10 8 Nate Weber 47loadouts_small## # A tibble: 10 × 3
## season name item_detailed
## <dbl> <chr> <chr>
## 1 9 Benki Hill Trapping wire
## 2 6 Nikki van Schyndel Trapping wire
## 3 6 Barry Karcher Sleeping bag
## 4 5 Brad Richardson Sleeping bag
## 5 4 Pete Brockdorff Gillnet – 12′ x 4′
## 6 7 Amos Rodriguez Fishing line and hooks
## 7 3 Megan Hanacek Gillnet
## 8 4 Dave Whipple Tarp – 12′ x 12′
## 9 9 Juan Pablo Quinonez Axe
## 10 4 Jesse Bosdell RationsDescribe the resulting data:
How is it different from the original two datasets?
x <- tribble(
~key, ~val_x,
1, "x1",
2, "x2",
3, "x3"
)
y <- tribble(
~key, ~val_y,
1, "y1",
2, "y2",
4, "y3"
)
inner_join(x, y)## Joining with `by = join_by(key)`## # A tibble: 2 × 3
## key val_x val_y
## <dbl> <chr> <chr>
## 1 1 x1 y1
## 2 2 x2 y2Describe the resulting data:
How is it different from the original two datasets?
left_join(x, y)## Joining with `by = join_by(key)`## # A tibble: 3 × 3
## key val_x val_y
## <dbl> <chr> <chr>
## 1 1 x1 y1
## 2 2 x2 y2
## 3 3 x3 <NA>Describe the resulting data:
How is it different from the original two datasets?
right_join(x, y)## Joining with `by = join_by(key)`## # A tibble: 3 × 3
## key val_x val_y
## <dbl> <chr> <chr>
## 1 1 x1 y1
## 2 2 x2 y2
## 3 4 <NA> y3Describe the resulting data:
How is it different from the original two datasets?
full_join(x, y)## Joining with `by = join_by(key)`## # A tibble: 4 × 3
## key val_x val_y
## <dbl> <chr> <chr>
## 1 1 x1 y1
## 2 2 x2 y2
## 3 3 x3 <NA>
## 4 4 <NA> y3Describe the resulting data:
How is it different from the original two datasets?
semi_join(x, y)## Joining with `by = join_by(key)`## # A tibble: 2 × 2
## key val_x
## <dbl> <chr>
## 1 1 x1
## 2 2 x2Describe the resulting data:
How is it different from the original two datasets?
anti_join(x, y)## Joining with `by = join_by(key)`## # A tibble: 1 × 2
## key val_x
## <dbl> <chr>
## 1 3 x3