#Penguin Activity! Review your wrangling skills!

Install the palmerpenguins package if you do not have it

#install.packages(“palmerpenguins”)

library(tidyverse)

## Warning: package 'tidyverse' was built under R version 4.5.2

## Warning: package 'ggplot2' was built under R version 4.5.2

## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.4     ✔ readr     2.1.5
## ✔ forcats   1.0.0     ✔ stringr   1.5.1
## ✔ ggplot2   4.0.2     ✔ tibble    3.3.0
## ✔ lubridate 1.9.4     ✔ tidyr     1.3.1
## ✔ purrr     1.1.0     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(palmerpenguins) #to load the penguins dataset

## Warning: package 'palmerpenguins' was built under R version 4.5.2

## 
## Attaching package: 'palmerpenguins'
## 
## The following objects are masked from 'package:datasets':
## 
##     penguins, penguins_raw

#load the dataset

penguins

## # A tibble: 344 × 8
##    species island    bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
##    <fct>   <fct>              <dbl>         <dbl>             <int>       <int>
##  1 Adelie  Torgersen           39.1          18.7               181        3750
##  2 Adelie  Torgersen           39.5          17.4               186        3800
##  3 Adelie  Torgersen           40.3          18                 195        3250
##  4 Adelie  Torgersen           NA            NA                  NA          NA
##  5 Adelie  Torgersen           36.7          19.3               193        3450
##  6 Adelie  Torgersen           39.3          20.6               190        3650
##  7 Adelie  Torgersen           38.9          17.8               181        3625
##  8 Adelie  Torgersen           39.2          19.6               195        4675
##  9 Adelie  Torgersen           34.1          18.1               193        3475
## 10 Adelie  Torgersen           42            20.2               190        4250
## # ℹ 334 more rows
## # ℹ 2 more variables: sex <fct>, year <int>

data(“penguins”) # to see it in the environment

Count penguins by species

penguins |> 
  count(species, island)

## # A tibble: 5 × 3
##   species   island        n
##   <fct>     <fct>     <int>
## 1 Adelie    Biscoe       44
## 2 Adelie    Dream        56
## 3 Adelie    Torgersen    52
## 4 Chinstrap Dream        68
## 5 Gentoo    Biscoe      124

Count penguins by species and island

penguins |> 
  count(species, island)

## # A tibble: 5 × 3
##   species   island        n
##   <fct>     <fct>     <int>
## 1 Adelie    Biscoe       44
## 2 Adelie    Dream        56
## 3 Adelie    Torgersen    52
## 4 Chinstrap Dream        68
## 5 Gentoo    Biscoe      124

Filter penguins by species (Adelie)

penguins |>
  filter(species == "Adelie")

## # A tibble: 152 × 8
##    species island    bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
##    <fct>   <fct>              <dbl>         <dbl>             <int>       <int>
##  1 Adelie  Torgersen           39.1          18.7               181        3750
##  2 Adelie  Torgersen           39.5          17.4               186        3800
##  3 Adelie  Torgersen           40.3          18                 195        3250
##  4 Adelie  Torgersen           NA            NA                  NA          NA
##  5 Adelie  Torgersen           36.7          19.3               193        3450
##  6 Adelie  Torgersen           39.3          20.6               190        3650
##  7 Adelie  Torgersen           38.9          17.8               181        3625
##  8 Adelie  Torgersen           39.2          19.6               195        4675
##  9 Adelie  Torgersen           34.1          18.1               193        3475
## 10 Adelie  Torgersen           42            20.2               190        4250
## # ℹ 142 more rows
## # ℹ 2 more variables: sex <fct>, year <int>

Count Adelie penguins by island and sex

penguins |>
  filter(species == "Adelie") |>
  count(island, sex)

## # A tibble: 8 × 3
##   island    sex        n
##   <fct>     <fct>  <int>
## 1 Biscoe    female    22
## 2 Biscoe    male      22
## 3 Dream     female    27
## 4 Dream     male      28
## 5 Dream     <NA>       1
## 6 Torgersen female    24
## 7 Torgersen male      23
## 8 Torgersen <NA>       5

Select specific columns (species, island, flipper_length_mm))

penguins |>
  select(species, island, flipper_length_mm)

## # A tibble: 344 × 3
##    species island    flipper_length_mm
##    <fct>   <fct>                 <int>
##  1 Adelie  Torgersen               181
##  2 Adelie  Torgersen               186
##  3 Adelie  Torgersen               195
##  4 Adelie  Torgersen                NA
##  5 Adelie  Torgersen               193
##  6 Adelie  Torgersen               190
##  7 Adelie  Torgersen               181
##  8 Adelie  Torgersen               195
##  9 Adelie  Torgersen               193
## 10 Adelie  Torgersen               190
## # ℹ 334 more rows

Mutate new columns for flipper length in cm and inches (cm = mm/10)(in = mm/25.4)

penguins_mutate <-  penguins |>
  mutate(flipper_length_cm = flipper_length_mm/10)|>
  mutate(flipper_length_in = flipper_length_mm/25.4)
penguins_mutate

## # A tibble: 344 × 10
##    species island    bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
##    <fct>   <fct>              <dbl>         <dbl>             <int>       <int>
##  1 Adelie  Torgersen           39.1          18.7               181        3750
##  2 Adelie  Torgersen           39.5          17.4               186        3800
##  3 Adelie  Torgersen           40.3          18                 195        3250
##  4 Adelie  Torgersen           NA            NA                  NA          NA
##  5 Adelie  Torgersen           36.7          19.3               193        3450
##  6 Adelie  Torgersen           39.3          20.6               190        3650
##  7 Adelie  Torgersen           38.9          17.8               181        3625
##  8 Adelie  Torgersen           39.2          19.6               195        4675
##  9 Adelie  Torgersen           34.1          18.1               193        3475
## 10 Adelie  Torgersen           42            20.2               190        4250
## # ℹ 334 more rows
## # ℹ 4 more variables: sex <fct>, year <int>, flipper_length_cm <dbl>,
## #   flipper_length_in <dbl>

Group penguins by species

penguins|>
  group_by(species)

## # A tibble: 344 × 8
## # Groups:   species [3]
##    species island    bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
##    <fct>   <fct>              <dbl>         <dbl>             <int>       <int>
##  1 Adelie  Torgersen           39.1          18.7               181        3750
##  2 Adelie  Torgersen           39.5          17.4               186        3800
##  3 Adelie  Torgersen           40.3          18                 195        3250
##  4 Adelie  Torgersen           NA            NA                  NA          NA
##  5 Adelie  Torgersen           36.7          19.3               193        3450
##  6 Adelie  Torgersen           39.3          20.6               190        3650
##  7 Adelie  Torgersen           38.9          17.8               181        3625
##  8 Adelie  Torgersen           39.2          19.6               195        4675
##  9 Adelie  Torgersen           34.1          18.1               193        3475
## 10 Adelie  Torgersen           42            20.2               190        4250
## # ℹ 334 more rows
## # ℹ 2 more variables: sex <fct>, year <int>

Summarize penguin data by species (basic)

penguins |>
  group_by(species) |>
  summarize(
  n = n()
)

## # A tibble: 3 × 2
##   species       n
##   <fct>     <int>
## 1 Adelie      152
## 2 Chinstrap    68
## 3 Gentoo      124

Summarize penguin data by species (additional statistics)

penguins |>
  group_by(species) |> 
  summarize(
  n = n(),
  mean_mass = mean(body_mass_g),
  max_flipper_length = max(flipper_length_mm),
  percent_female = sum(sex == "female") / n()
)

## # A tibble: 3 × 5
##   species       n mean_mass max_flipper_length percent_female
##   <fct>     <int>     <dbl>              <int>          <dbl>
## 1 Adelie      152       NA                  NA           NA  
## 2 Chinstrap    68     3733.                212            0.5
## 3 Gentoo      124       NA                  NA           NA

Summarize penguin data by species (handling missing values)

penguins |>
  filter(!is.na(body_mass_g), !is.na(flipper_length_mm), !is.na(sex))|>
  group_by(species) |> 
  summarize(
    n = n(),
    mean_mass = mean (body_mass_g),
    max_flipper_length = max(flipper_length_mm),
    percent_female = sum(sex == "female") / n()
  )

## # A tibble: 3 × 5
##   species       n mean_mass max_flipper_length percent_female
##   <fct>     <int>     <dbl>              <int>          <dbl>
## 1 Adelie      146     3706.                210          0.5  
## 2 Chinstrap    68     3733.                212          0.5  
## 3 Gentoo      119     5092.                231          0.487

penguins |>
  group_by(species) |> 
  summarize(
    n = n(),
    mean_mass = mean(body_mass_g,  na.rm = TRUE),
    max_flipper_length = max(flipper_length_mm, na.rm = TRUE),
    percent_female = sum(sex == "female", na.rm = TRUE) / n()
  )

## # A tibble: 3 × 5
##   species       n mean_mass max_flipper_length percent_female
##   <fct>     <int>     <dbl>              <int>          <dbl>
## 1 Adelie      152     3701.                210          0.480
## 2 Chinstrap    68     3733.                212          0.5  
## 3 Gentoo      124     5076.                231          0.468

Penguins Extra Credit

Ricardo Zavaleta

2026-02-23

Install the palmerpenguins package if you do not have it

Count penguins by species

Count penguins by species and island

Filter penguins by species (Adelie)

Count Adelie penguins by island and sex

Select specific columns (species, island, flipper_length_mm))

Mutate new columns for flipper length in cm and inches (cm = mm/10)(in = mm/25.4)

Group penguins by species

Summarize penguin data by species (basic)

Summarize penguin data by species (additional statistics)

Summarize penguin data by species (handling missing values)