Iteration

1. Write a for loop that calculates the mean bill length for each species in the penguins data set (don’t use group_by()) and saves them as species_means.

library(tidyverse)

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library(palmerpenguins)
penguin_species <- unique(penguins$species)
species_means <- NA
for (i in penguin_species) {
  species_means[i] <- penguins |> 
    filter(species == i) |> 
    summarise(mean_bill_length = mean(bill_length_mm, na.rm = TRUE)) |> 
    pull()
}
species_means <- species_means[!is.na(species_means)]

2. Turn #1 into a function called species_mean that lets the user determine which variable to calculate the mean over.

species_mean <- function(var) {
  penguin_species <- unique(penguins$species)
  species_means <- NA
  for (i in penguin_species) {
    species_means[i] <- penguins |> 
      filter(species == i) |> 
      summarise(mean({{var}}, na.rm = TRUE)) |> 
      pull()
  }
  species_means <- species_means[!is.na(species_means)]
  return(species_means)
}
species_mean(bill_length_mm)

   Adelie    Gentoo Chinstrap 
 38.79139  47.50488  48.83382 

3. Create a list penguins_island that separates the penguins data by island.

penguins_island <- penguins |> 
  split(penguins$island)

4. Apply map() to find the number of observations for each year.

map(penguins_island, nrow)

$Biscoe
[1] 168

$Dream
[1] 124

$Torgersen
[1] 52

5. Apply map() to calculate the mean body weight for each island.

map(penguins_island, ~ mean(.x$body_mass_g, na.rm = TRUE))

$Biscoe
[1] 4716.018

$Dream
[1] 3712.903

$Torgersen
[1] 3706.373

6. Rework #5 to return a numeric vector with values rounded to 1 decimal place.

map_dbl(penguins_island, ~ round(mean(.x$body_mass_g, na.rm = TRUE), 1))

   Biscoe     Dream Torgersen 
   4716.0    3712.9    3706.4