Import your data

data("mtcars")
mtcars <- as_tibble(mtcars)
canada_births_1991_2022 <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2024/2024-01-09/canada_births_1991_2022.csv')
## Rows: 384 Columns: 3
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## dbl (3): year, month, births
## 
## 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_player_births <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2024/2024-01-09/nhl_player_births.csv')
## Rows: 8474 Columns: 9
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (5): first_name, last_name, birth_city, birth_country, birth_state_prov...
## dbl  (3): player_id, birth_year, birth_month
## 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_rosters <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/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.

Repeat the same operation over different columns of a data frame

Case of numeric variables

mtcars %>% 
  map_dbl(.x = ., .f = ~mean(x = .x))
##        mpg        cyl       disp         hp       drat         wt       qsec 
##  20.090625   6.187500 230.721875 146.687500   3.596563   3.217250  17.848750 
##         vs         am       gear       carb 
##   0.437500   0.406250   3.687500   2.812500
mtcars %>% 
  map_dbl(.f = ~mean(x = .x))
##        mpg        cyl       disp         hp       drat         wt       qsec 
##  20.090625   6.187500 230.721875 146.687500   3.596563   3.217250  17.848750 
##         vs         am       gear       carb 
##   0.437500   0.406250   3.687500   2.812500
mtcars %>% 
  map_dbl(mean)
##        mpg        cyl       disp         hp       drat         wt       qsec 
##  20.090625   6.187500 230.721875 146.687500   3.596563   3.217250  17.848750 
##         vs         am       gear       carb 
##   0.437500   0.406250   3.687500   2.812500
# Adding an argument
mtcars %>% 
  map_dbl(.x = ., .f = ~mean(x = .x, trim = 0.1))
##         mpg         cyl        disp          hp        drat          wt 
##  19.6961538   6.2307692 222.5230769 141.1923077   3.5792308   3.1526923 
##        qsec          vs          am        gear        carb 
##  17.8276923   0.4230769   0.3846154   3.6153846   2.6538462
mtcars %>% 
  map_dbl(mean, trim = 0.1)
##         mpg         cyl        disp          hp        drat          wt 
##  19.6961538   6.2307692 222.5230769 141.1923077   3.5792308   3.1526923 
##        qsec          vs          am        gear        carb 
##  17.8276923   0.4230769   0.3846154   3.6153846   2.6538462
mtcars %>% 
  select(.data = ., mpg)
## # A tibble: 32 × 1
##      mpg
##    <dbl>
##  1  21  
##  2  21  
##  3  22.8
##  4  21.4
##  5  18.7
##  6  18.1
##  7  14.3
##  8  24.4
##  9  22.8
## 10  19.2
## # ℹ 22 more rows
mtcars %>% select(mpg)
## # A tibble: 32 × 1
##      mpg
##    <dbl>
##  1  21  
##  2  21  
##  3  22.8
##  4  21.4
##  5  18.7
##  6  18.1
##  7  14.3
##  8  24.4
##  9  22.8
## 10  19.2
## # ℹ 22 more rows

Create your own function

# Double values in columns
double_by_factor <- function(x, factor) {
  x * factor
}

10 %>% double_by_factor(factor = 2)
## [1] 20
mtcars %>% 
  map_dfr(.x = ., .f = ~double_by_factor(x = .x, factor = 10))
## # A tibble: 32 × 11
##      mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb
##    <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
##  1   210    60  1600  1100  39    26.2  165.     0    10    40    40
##  2   210    60  1600  1100  39    28.8  170.     0    10    40    40
##  3   228    40  1080   930  38.5  23.2  186.    10    10    40    10
##  4   214    60  2580  1100  30.8  32.2  194.    10     0    30    10
##  5   187    80  3600  1750  31.5  34.4  170.     0     0    30    20
##  6   181    60  2250  1050  27.6  34.6  202.    10     0    30    10
##  7   143    80  3600  2450  32.1  35.7  158.     0     0    30    40
##  8   244    40  1467   620  36.9  31.9  200     10     0    40    20
##  9   228    40  1408   950  39.2  31.5  229     10     0    40    20
## 10   192    60  1676  1230  39.2  34.4  183     10     0    40    40
## # ℹ 22 more rows
mtcars %>% 
  map_dfr(double_by_factor, factor = 10)
## # A tibble: 32 × 11
##      mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb
##    <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
##  1   210    60  1600  1100  39    26.2  165.     0    10    40    40
##  2   210    60  1600  1100  39    28.8  170.     0    10    40    40
##  3   228    40  1080   930  38.5  23.2  186.    10    10    40    10
##  4   214    60  2580  1100  30.8  32.2  194.    10     0    30    10
##  5   187    80  3600  1750  31.5  34.4  170.     0     0    30    20
##  6   181    60  2250  1050  27.6  34.6  202.    10     0    30    10
##  7   143    80  3600  2450  32.1  35.7  158.     0     0    30    40
##  8   244    40  1467   620  36.9  31.9  200     10     0    40    20
##  9   228    40  1408   950  39.2  31.5  229     10     0    40    20
## 10   192    60  1676  1230  39.2  34.4  183     10     0    40    40
## # ℹ 22 more rows

Repeat the same operation over different elements of a list

When you have a grouping variable (factor)

mtcars %>% 
  lm(formula = mpg ~ wt, data = .)
## 
## Call:
## lm(formula = mpg ~ wt, data = .)
## 
## Coefficients:
## (Intercept)           wt  
##      37.285       -5.344
mtcars %>% 
  distinct(cyl)
## # A tibble: 3 × 1
##     cyl
##   <dbl>
## 1     6
## 2     4
## 3     8
reg_coeff_tbl <- mtcars %>% 

  # Split it into a list of data frames
  split(.$cyl) %>% 

  # Repeat regression over each group
  map(~ lm(formula = mpg ~ wt, data = .x)) %>% 

  # Extract coefficients from regression results
  map(broom::tidy, conf.int = TRUE) %>% 

  # Convert to tibble
  bind_rows(.id = "cyl") %>% 

  # Filter for wt coefficients
  filter(term == "wt")
reg_coeff_tbl %>% 
  mutate(
    estimate = -estimate,
    conf.low = -conf.low,
    conf.high = -conf.high
  ) %>% 
  ggplot(aes(x = estimate, y = cyl)) +
  geom_point() +
  geom_errorbar(aes(xmin = conf.low, xmax = conf.high))

Create your own

Choose either one of the two cases above and apply it to your data

### For each NHL position, how much does weight increase for every extra inch of height
# Merge births + rosters by player_id

nhl <- nhl_rosters %>%
  left_join(nhl_player_births, by = "player_id") %>%
  filter(!is.na(height_in_inches), !is.na(weight_in_pounds)) %>% 
  filter(!is.na(position_code))
## Warning in left_join(., nhl_player_births, by = "player_id"): Detected an unexpected many-to-many relationship between `x` and `y`.
## ℹ Row 37968 of `x` matches multiple rows in `y`.
## ℹ Row 1 of `y` matches multiple rows in `x`.
## ℹ If a many-to-many relationship is expected, set `relationship =
##   "many-to-many"` to silence this warning.
# Quick look
nhl %>% distinct(position_code)
## # A tibble: 5 × 1
##   position_code
##   <chr>        
## 1 L            
## 2 R            
## 3 C            
## 4 D            
## 5 G
# (C, LW, RW, D, G, etc.)

reg_coeff_tbl <- nhl %>% 
 
  split(.$position_code) %>% 
 
  map(~ lm(weight_in_pounds ~ height_in_inches, data = .x)) %>% 

  map(broom::tidy, conf.int = TRUE) %>% 

  bind_rows(.id = "position_code") %>% 
  
  filter(term == "height_in_inches")

# Plot 

reg_coeff_tbl %>% 
  mutate(
    estimate = -estimate,      
    conf.low = -conf.low,
    conf.high = -conf.high
  ) %>% 
  ggplot(aes(x = estimate, y = position_code)) +
  geom_point(size = 3) +
  geom_errorbar(aes(xmin = conf.low, xmax = conf.high), width = 0.25) +
  labs(
    title = "Effect of Player Height on Weight by NHL Position",
    x = "Estimated Slope (Negative Sign Applied)",
    y = "Position"
  ) +
  theme_minimal()