Import two related datasets from TidyTuesday Project.
nhl_rosters <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2024/2024-01-09/nhl_rosters.csv')## Rows: 54883 Columns: 18
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (10): team_code, position_type, headshot, first_name, last_name, positi...
## dbl (7): season, player_id, sweater_number, height_in_inches, weight_in_po...
## date (1): birth_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.nhl_teams <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2024/2024-01-09/nhl_teams.csv')## Rows: 59 Columns: 2
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): team_code, full_name
##
## 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 nhl_roster
Data 2
set.seed(1234)
nhl_rosters_small <- nhl_rosters %>% select(team_code, season, position_type) %>% sample_n(10)
nhl_teams_small <- nhl_teams %>% select(team_code, full_name) %>% sample_n(10)
nhl_rosters_small## # A tibble: 10 × 3
## team_code season position_type
## <chr> <dbl> <chr>
## 1 DAL 20122013 forwards
## 2 DAL 20142015 forwards
## 3 CHI 19351936 defensemen
## 4 PIT 19981999 forwards
## 5 VAN 19821983 goalies
## 6 COL 20132014 forwards
## 7 OTT 20232024 forwards
## 8 PHI 19751976 forwards
## 9 PIT 19971998 goalies
## 10 TOR 19561957 forwardsnhl_teams_small## # A tibble: 10 × 2
## team_code full_name
## <chr> <chr>
## 1 EDM Edmonton Oilers
## 2 QUE Quebec Nordiques
## 3 STL St. Louis Blues
## 4 DFL Detroit Falcons
## 5 MTL Montréal Canadiens
## 6 CLE Cleveland Barons
## 7 MNS Minnesota North Stars
## 8 BUF Buffalo Sabres
## 9 SJS San Jose Sharks
## 10 BOS Boston BruinsDescribe the resulting data:
How is it different from the original two datasets? 1 row compared to 10 rows in orginal dataset
nhl_rosters_small %>% inner_join(nhl_teams_small, by = c("team_code"))## # A tibble: 0 × 4
## # ℹ 4 variables: team_code <chr>, season <dbl>, position_type <chr>,
## # full_name <chr>Describe the resulting data:
How is it different from the original two datasets?
nhl_rosters_small %>%
left_join(nhl_teams_small, by = "team_code")## # A tibble: 10 × 4
## team_code season position_type full_name
## <chr> <dbl> <chr> <chr>
## 1 DAL 20122013 forwards <NA>
## 2 DAL 20142015 forwards <NA>
## 3 CHI 19351936 defensemen <NA>
## 4 PIT 19981999 forwards <NA>
## 5 VAN 19821983 goalies <NA>
## 6 COL 20132014 forwards <NA>
## 7 OTT 20232024 forwards <NA>
## 8 PHI 19751976 forwards <NA>
## 9 PIT 19971998 goalies <NA>
## 10 TOR 19561957 forwards <NA>Describe the resulting data:
How is it different from the original two datasets?
nhl_rosters_small %>%
right_join(nhl_teams_small, by = "team_code")## # A tibble: 10 × 4
## team_code season position_type full_name
## <chr> <dbl> <chr> <chr>
## 1 EDM NA <NA> Edmonton Oilers
## 2 QUE NA <NA> Quebec Nordiques
## 3 STL NA <NA> St. Louis Blues
## 4 DFL NA <NA> Detroit Falcons
## 5 MTL NA <NA> Montréal Canadiens
## 6 CLE NA <NA> Cleveland Barons
## 7 MNS NA <NA> Minnesota North Stars
## 8 BUF NA <NA> Buffalo Sabres
## 9 SJS NA <NA> San Jose Sharks
## 10 BOS NA <NA> Boston BruinsDescribe the resulting data:
How is it different from the original two datasets?
nhl_rosters_small %>%
full_join(nhl_teams_small, by = "team_code")## # A tibble: 20 × 4
## team_code season position_type full_name
## <chr> <dbl> <chr> <chr>
## 1 DAL 20122013 forwards <NA>
## 2 DAL 20142015 forwards <NA>
## 3 CHI 19351936 defensemen <NA>
## 4 PIT 19981999 forwards <NA>
## 5 VAN 19821983 goalies <NA>
## 6 COL 20132014 forwards <NA>
## 7 OTT 20232024 forwards <NA>
## 8 PHI 19751976 forwards <NA>
## 9 PIT 19971998 goalies <NA>
## 10 TOR 19561957 forwards <NA>
## 11 EDM NA <NA> Edmonton Oilers
## 12 QUE NA <NA> Quebec Nordiques
## 13 STL NA <NA> St. Louis Blues
## 14 DFL NA <NA> Detroit Falcons
## 15 MTL NA <NA> Montréal Canadiens
## 16 CLE NA <NA> Cleveland Barons
## 17 MNS NA <NA> Minnesota North Stars
## 18 BUF NA <NA> Buffalo Sabres
## 19 SJS NA <NA> San Jose Sharks
## 20 BOS NA <NA> Boston BruinsDescribe the resulting data:
How is it different from the original two datasets?
nhl_rosters_small %>%
semi_join(nhl_teams_small)## Joining with `by = join_by(team_code)`## # A tibble: 0 × 3
## # ℹ 3 variables: team_code <chr>, season <dbl>, position_type <chr>Describe the resulting data:
How is it different from the original two datasets?
nhl_rosters_small %>%
anti_join(nhl_teams_small, by = "team_code") %>%
count(team_code, sort = TRUE)## # A tibble: 8 × 2
## team_code n
## <chr> <int>
## 1 DAL 2
## 2 PIT 2
## 3 CHI 1
## 4 COL 1
## 5 OTT 1
## 6 PHI 1
## 7 TOR 1
## 8 VAN 1