Week 8: Apply it to your data 7

Import your data

data <- read_excel("../00_data/myData.xlsx", sheet = "nhl_rosters")
data

## # A tibble: 54,883 × 18
##    team_code   season position_type player_id headshot      first_name last_name
##    <chr>        <dbl> <chr>             <dbl> <chr>         <chr>      <chr>    
##  1 ATL       19992000 forwards        8467867 https://asse… Bryan      Adams    
##  2 ATL       19992000 forwards        8445176 https://asse… Donald     Audette  
##  3 ATL       19992000 forwards        8460014 https://asse… Eric       Bertrand 
##  4 ATL       19992000 forwards        8460510 https://asse… Jason      Botterill
##  5 ATL       19992000 forwards        8459596 https://asse… Andrew     Brunette 
##  6 ATL       19992000 forwards        8445733 https://asse… Kelly      Buchberg…
##  7 ATL       19992000 forwards        8460573 https://asse… Hnat       Domenich…
##  8 ATL       19992000 forwards        8459450 https://asse… Shean      Donovan  
##  9 ATL       19992000 forwards        8446675 https://asse… Nelson     Emerson  
## 10 ATL       19992000 forwards        8446823 https://asse… Ray        Ferraro  
## # ℹ 54,873 more rows
## # ℹ 11 more variables: sweater_number <chr>, position_code <chr>,
## #   shoots_catches <chr>, height_in_inches <dbl>, weight_in_pounds <dbl>,
## #   height_in_centimeters <dbl>, weight_in_kilograms <dbl>, birth_date <dttm>,
## #   birth_city <chr>, birth_country <chr>, birth_state_province <chr>

# For reproducible outcome
set.seed(1234)

data_small <- data %>%
    select(team_code, position_code, height_in_inches) %>%
    sample_n(5)

data_small

## # A tibble: 5 × 3
##   team_code position_code height_in_inches
##   <chr>     <chr>                    <dbl>
## 1 DAL       C                           72
## 2 DAL       R                           72
## 3 CHI       D                           68
## 4 PIT       C                           73
## 5 VAN       G                           66

wide to long form

data_wide <- data_small %>%
    
    pivot_wider(names_from = team_code,
                values_from = height_in_inches)

data_wide

## # A tibble: 4 × 5
##   position_code   DAL   CHI   PIT   VAN
##   <chr>         <dbl> <dbl> <dbl> <dbl>
## 1 C                72    NA    73    NA
## 2 R                72    NA    NA    NA
## 3 D                NA    68    NA    NA
## 4 G                NA    NA    NA    66

long to wide form

data_wide <- data_wide %>% 
    
    pivot_longer(cols = -position_code,
                 names_to = "team_code", 
                 values_to = "height_in_inches",
                 values_drop_na = TRUE)

data_wide

## # A tibble: 5 × 3
##   position_code team_code height_in_inches
##   <chr>         <chr>                <dbl>
## 1 C             DAL                     72
## 2 C             PIT                     73
## 3 R             DAL                     72
## 4 D             CHI                     68
## 5 G             VAN                     66

Separating and Uniting

Separate a column

data_sep <- data_small %>%
    separate(col = height_in_inches, into = c("A","B"), sep = 1)

data_sep

## # A tibble: 5 × 4
##   team_code position_code A     B    
##   <chr>     <chr>         <chr> <chr>
## 1 DAL       C             7     2    
## 2 DAL       R             7     2    
## 3 CHI       D             6     8    
## 4 PIT       C             7     3    
## 5 VAN       G             6     6

Unite two columns

data_sep %>%
    
    unite(col = "height_in_inches", c(A,B), sep = "", )

## # A tibble: 5 × 3
##   team_code position_code height_in_inches
##   <chr>     <chr>         <chr>           
## 1 DAL       C             72              
## 2 DAL       R             72              
## 3 CHI       D             68              
## 4 PIT       C             73              
## 5 VAN       G             66

Missing Values