Exploring Palmer Penguins
Michelle Twomey
2022-07-12
Data Sources
Data was collected and made available by Dr. Kristen Gorman and the Palmer Station, Antarctica LTER, a member of the Long Term Ecological Research Network. Data is available by CC-0 license in accordance with the Palmer Station LTER Data Policy and the LTER Data Access Policy for Type I data. The palmerpenguins package contains two datasets. Penguins_raw and Penguins. Both datasets contain data for 344 penguins. There are 3 different species of penguins in this dataset, collected from 3 islands in the Palmer Archipelago, Antarctica from 2007- 2009.
Install Packages
install.packages("tidyverse")## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.2'
## (as 'lib' is unspecified)library(tidyverse)## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──## ✔ ggplot2 3.3.6 ✔ purrr 0.3.4
## ✔ tibble 3.1.7 ✔ dplyr 1.0.9
## ✔ tidyr 1.2.0 ✔ stringr 1.4.0
## ✔ readr 2.1.2 ✔ forcats 0.5.1## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()install.packages("dplyr")## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.2'
## (as 'lib' is unspecified)library(dplyr)
install.packages("ggplot2")## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.2'
## (as 'lib' is unspecified)library(ggplot2)
install.packages("palmerpenguins")## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.2'
## (as 'lib' is unspecified)library(palmerpenguins)
data(penguins)Taking a closer look at dataset
head(penguins)## # A tibble: 6 × 8
## species island bill_length_mm bill_depth_mm flipper_length_… body_mass_g sex
## <fct> <fct> <dbl> <dbl> <int> <int> <fct>
## 1 Adelie Torge… 39.1 18.7 181 3750 male
## 2 Adelie Torge… 39.5 17.4 186 3800 fema…
## 3 Adelie Torge… 40.3 18 195 3250 fema…
## 4 Adelie Torge… NA NA NA NA <NA>
## 5 Adelie Torge… 36.7 19.3 193 3450 fema…
## 6 Adelie Torge… 39.3 20.6 190 3650 male
## # … with 1 more variable: year <int>How many penguins of each species?
penguins%>%
count(species, .drop = FALSE)## # A tibble: 3 × 2
## species n
## <fct> <int>
## 1 Adelie 152
## 2 Chinstrap 68
## 3 Gentoo 124ggplot(data = penguins)+ geom_bar(mapping = aes(x=species, fill = species))
How many penguin species on each island?
penguins%>%
count(species, island, .drop = FALSE)## # A tibble: 9 × 3
## species island n
## <fct> <fct> <int>
## 1 Adelie Biscoe 44
## 2 Adelie Dream 56
## 3 Adelie Torgersen 52
## 4 Chinstrap Biscoe 0
## 5 Chinstrap Dream 68
## 6 Chinstrap Torgersen 0
## 7 Gentoo Biscoe 124
## 8 Gentoo Dream 0
## 9 Gentoo Torgersen 0ggplot(data = penguins)+
geom_bar(mapping = aes(x =island, fill = species))+
facet_wrap(~species) +
labs(title = "Island Penguin Species")
Count sex of each penguin species
penguins%>%
count(species, sex, .drop=FALSE)## # A tibble: 8 × 3
## species sex n
## <fct> <fct> <int>
## 1 Adelie female 73
## 2 Adelie male 73
## 3 Adelie <NA> 6
## 4 Chinstrap female 34
## 5 Chinstrap male 34
## 6 Gentoo female 58
## 7 Gentoo male 61
## 8 Gentoo <NA> 5ggplot(data=penguins)+
geom_bar(mapping=aes(x=sex, fill=species))+
facet_wrap(~species)+
labs(title = "Penguin Species Sex")
Group by island and summarize mean bill length
penguins%>%
group_by(island)%>%
drop_na()%>%
summarize(mean_bill_length_mm = mean(bill_depth_mm))## # A tibble: 3 × 2
## island mean_bill_length_mm
## <fct> <dbl>
## 1 Biscoe 15.9
## 2 Dream 18.3
## 3 Torgersen 18.5Group by island and find max bill length
penguins%>%
group_by(island)%>%
drop_na()%>%
summarize(max_bill_length_mm = max(bill_length_mm))## # A tibble: 3 × 2
## island max_bill_length_mm
## <fct> <dbl>
## 1 Biscoe 59.6
## 2 Dream 58
## 3 Torgersen 46Group by species
penguins%>%
group_by(species)%>%
drop_na()%>%
summarize(max_bill_length_mm = max(bill_length_mm))## # A tibble: 3 × 2
## species max_bill_length_mm
## <fct> <dbl>
## 1 Adelie 46
## 2 Chinstrap 58
## 3 Gentoo 59.6Taking a closer look at body mass
penguins%>%
group_by(species)%>%
drop_na()%>%
summarize(max_body_mass_g = max(body_mass_g))## # A tibble: 3 × 2
## species max_body_mass_g
## <fct> <int>
## 1 Adelie 4775
## 2 Chinstrap 4800
## 3 Gentoo 6300Filter Gentoo penguins and arrange body mass and bill length in descending order
penguins%>%
filter(species== "Gentoo")%>%
arrange(-body_mass_g)## # A tibble: 124 × 8
## species island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
## <fct> <fct> <dbl> <dbl> <int> <int>
## 1 Gentoo Biscoe 49.2 15.2 221 6300
## 2 Gentoo Biscoe 59.6 17 230 6050
## 3 Gentoo Biscoe 51.1 16.3 220 6000
## 4 Gentoo Biscoe 48.8 16.2 222 6000
## 5 Gentoo Biscoe 45.2 16.4 223 5950
## 6 Gentoo Biscoe 49.8 15.9 229 5950
## 7 Gentoo Biscoe 48.4 14.6 213 5850
## 8 Gentoo Biscoe 49.3 15.7 217 5850
## 9 Gentoo Biscoe 55.1 16 230 5850
## 10 Gentoo Biscoe 49.5 16.2 229 5800
## # … with 114 more rows, and 2 more variables: sex <fct>, year <int>penguins%>%
filter(species == "Gentoo")%>%
arrange(-bill_length_mm)## # A tibble: 124 × 8
## species island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
## <fct> <fct> <dbl> <dbl> <int> <int>
## 1 Gentoo Biscoe 59.6 17 230 6050
## 2 Gentoo Biscoe 55.9 17 228 5600
## 3 Gentoo Biscoe 55.1 16 230 5850
## 4 Gentoo Biscoe 54.3 15.7 231 5650
## 5 Gentoo Biscoe 53.4 15.8 219 5500
## 6 Gentoo Biscoe 52.5 15.6 221 5450
## 7 Gentoo Biscoe 52.2 17.1 228 5400
## 8 Gentoo Biscoe 52.1 17 230 5550
## 9 Gentoo Biscoe 51.5 16.3 230 5500
## 10 Gentoo Biscoe 51.3 14.2 218 5300
## # … with 114 more rows, and 2 more variables: sex <fct>, year <int>#### Relationship between flipper length and body mass
ggplot(data = penguins)+
geom_smooth(mapping = aes(x= flipper_length_mm, y= body_mass_g, linetype = species))+
geom_point(mapping = aes(x=flipper_length_mm, y= body_mass_g))+
labs(title = "Flipper length v's Body mass")## `geom_smooth()` using method = 'loess' and formula 'y ~ x'## Warning: Removed 2 rows containing non-finite values (stat_smooth).## Warning: Removed 2 rows containing missing values (geom_point).
ggplot(data=penguins, aes(x= flipper_length_mm, y= body_mass_g))+
geom_point(aes(color=species, shape=species))## Warning: Removed 2 rows containing missing values (geom_point).
ggplot(data = penguins)+
geom_point(mapping = aes(x= flipper_length_mm, y= body_mass_g, color= species))+
facet_wrap(~species)+
labs(title = "Relationship between Species, Flipperlength and Body mass")## Warning: Removed 2 rows containing missing values (geom_point).
ggplot(data = penguins)+
geom_point(mapping = aes(x= flipper_length_mm, y= body_mass_g, color= species))+
facet_grid(sex~species)+
labs(title = "Relationship between Sex, Flipper length and Body mass")## Warning: Removed 2 rows containing missing values (geom_point).