Import your data

data("mtcars")
mtcars <- as_tibble(mtcars)
rating <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2022/2022-01-25/ratings.csv', show_col_types = FALSE)
rating
## # A tibble: 21,831 × 10
##      num     id name          year  rank average bayes_average users_rated url  
##    <dbl>  <dbl> <chr>        <dbl> <dbl>   <dbl>         <dbl>       <dbl> <chr>
##  1   105  30549 Pandemic      2008   106    7.59          7.49      108975 /boa…
##  2   189    822 Carcassonne   2000   190    7.42          7.31      108738 /boa…
##  3   428     13 Catan         1995   429    7.14          6.97      108024 /boa…
##  4    72  68448 7 Wonders     2010    73    7.74          7.63       89982 /boa…
##  5   103  36218 Dominion      2008   104    7.61          7.50       81561 /boa…
##  6   191   9209 Ticket to R…  2004   192    7.41          7.30       76171 /boa…
##  7   100 178900 Codenames     2015   101    7.6           7.51       74419 /boa…
##  8     3 167791 Terraformin…  2016     4    8.42          8.27       74216 /boa…
##  9    15 173346 7 Wonders D…  2015    16    8.11          7.98       69472 /boa…
## 10    35  31260 Agricola      2007    36    7.93          7.81       66093 /boa…
## # ℹ 21,821 more rows
## # ℹ 1 more variable: thumbnail <chr>
ratings <- head(rating, 50)
ratings
## # A tibble: 50 × 10
##      num     id name          year  rank average bayes_average users_rated url  
##    <dbl>  <dbl> <chr>        <dbl> <dbl>   <dbl>         <dbl>       <dbl> <chr>
##  1   105  30549 Pandemic      2008   106    7.59          7.49      108975 /boa…
##  2   189    822 Carcassonne   2000   190    7.42          7.31      108738 /boa…
##  3   428     13 Catan         1995   429    7.14          6.97      108024 /boa…
##  4    72  68448 7 Wonders     2010    73    7.74          7.63       89982 /boa…
##  5   103  36218 Dominion      2008   104    7.61          7.50       81561 /boa…
##  6   191   9209 Ticket to R…  2004   192    7.41          7.30       76171 /boa…
##  7   100 178900 Codenames     2015   101    7.6           7.51       74419 /boa…
##  8     3 167791 Terraformin…  2016     4    8.42          8.27       74216 /boa…
##  9    15 173346 7 Wonders D…  2015    16    8.11          7.98       69472 /boa…
## 10    35  31260 Agricola      2007    36    7.93          7.81       66093 /boa…
## # ℹ 40 more rows
## # ℹ 1 more variable: thumbnail <chr>

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

get_fib <- function(n) {
  if(n == 0) 1
  else if(n == 1) 1
  else get_fib(n-1)+get_fib(n-2)
}
get_fib(5)
## [1] 8
mtcars$mpg %>% map(~get_fib(as.integer(.x)))
## [[1]]
## [1] 17711
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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 into a list of data frames
  split(.$cyl) %>%
  # Repeat regression over each frame
  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

ratings$average %>% map(~get_fib(as.integer(.x)))
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