Friendly Penguins
by Ing. Ernesto Ibanez
2022-08-13
This is an analysis of the palmerpenguins 0.1.1 dataSet.
That I have found so interesting on the Internet.
Takes some time and work but is illustrative.
The emperor penguin (Aptenodytes forsteri).
The Largest flightless seabird is not in this dataSet.
# install.packages("remotes")
# <https://allisonhorst.github.io/palmerpenguins/>
# remotes::install_github("allisonhorst/palmerpenguins")
remotes::install_github("allisonhorst/palmerpenguins", quiet = TRUE)
#library(shiny)
library(dplyr) ##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(magrittr)
library(palmerpenguins)
library(tidyverse) ## ── Attaching packages
## ───────────────────────────────────────
## tidyverse 1.3.2 ──## ✔ ggplot2 3.3.6 ✔ purrr 0.3.4
## ✔ tibble 3.1.8 ✔ stringr 1.4.0
## ✔ tidyr 1.2.0 ✔ forcats 0.5.1
## ✔ readr 2.1.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ tidyr::extract() masks magrittr::extract()
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ✖ purrr::set_names() masks magrittr::set_names()library(bib2df)Basic data analysis with palmerpenguins 0.1.1 dataSet
The Names of variables in palmepenguins DataSet
Here there are the eight (8) variables of palmepenguins DataSet
names(penguins) ## [1] "species" "island" "bill_length_mm"
## [4] "bill_depth_mm" "flipper_length_mm" "body_mass_g"
## [7] "sex" "year"And which of the 8 are categorical and which quantitative variables?
glimpse(penguins)## Rows: 344
## Columns: 8
## $ species <fct> Adelie, Adelie, Adelie, Adelie, Adelie, Adelie, Adel…
## $ island <fct> Torgersen, Torgersen, Torgersen, Torgersen, Torgerse…
## $ bill_length_mm <dbl> 39.1, 39.5, 40.3, NA, 36.7, 39.3, 38.9, 39.2, 34.1, …
## $ bill_depth_mm <dbl> 18.7, 17.4, 18.0, NA, 19.3, 20.6, 17.8, 19.6, 18.1, …
## $ flipper_length_mm <int> 181, 186, 195, NA, 193, 190, 181, 195, 193, 190, 186…
## $ body_mass_g <int> 3750, 3800, 3250, NA, 3450, 3650, 3625, 4675, 3475, …
## $ sex <fct> male, female, female, NA, female, male, female, male…
## $ year <int> 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007…A top view of the data
summary(penguins)## species island bill_length_mm bill_depth_mm
## Adelie :152 Biscoe :168 Min. :32.10 Min. :13.10
## Chinstrap: 68 Dream :124 1st Qu.:39.23 1st Qu.:15.60
## Gentoo :124 Torgersen: 52 Median :44.45 Median :17.30
## Mean :43.92 Mean :17.15
## 3rd Qu.:48.50 3rd Qu.:18.70
## Max. :59.60 Max. :21.50
## NA's :2 NA's :2
## flipper_length_mm body_mass_g sex year
## Min. :172.0 Min. :2700 female:165 Min. :2007
## 1st Qu.:190.0 1st Qu.:3550 male :168 1st Qu.:2007
## Median :197.0 Median :4050 NA's : 11 Median :2008
## Mean :200.9 Mean :4202 Mean :2008
## 3rd Qu.:213.0 3rd Qu.:4750 3rd Qu.:2009
## Max. :231.0 Max. :6300 Max. :2009
## NA's :2 NA's :2The first 6 variables in palmepenguins 0.1.1 DataSet
Here there are first 6 rows of palmepenguins DataSet
head(penguins)## # A tibble: 6 × 8
## species island bill_length_mm bill_depth_mm flipper_l…¹ body_…² sex year
## <fct> <fct> <dbl> <dbl> <int> <int> <fct> <int>
## 1 Adelie Torgersen 39.1 18.7 181 3750 male 2007
## 2 Adelie Torgersen 39.5 17.4 186 3800 fema… 2007
## 3 Adelie Torgersen 40.3 18 195 3250 fema… 2007
## 4 Adelie Torgersen NA NA NA NA <NA> 2007
## 5 Adelie Torgersen 36.7 19.3 193 3450 fema… 2007
## 6 Adelie Torgersen 39.3 20.6 190 3650 male 2007
## # … with abbreviated variable names ¹flipper_length_mm, ²body_mass_gThe last 6 variables in palmepenguins 0.1.1 DataSet
Here there are last 6 rows of palmepenguins DataSet
tail(penguins)## # A tibble: 6 × 8
## species island bill_length_mm bill_depth_mm flipper_le…¹ body_…² sex year
## <fct> <fct> <dbl> <dbl> <int> <int> <fct> <int>
## 1 Chinstrap Dream 45.7 17 195 3650 fema… 2009
## 2 Chinstrap Dream 55.8 19.8 207 4000 male 2009
## 3 Chinstrap Dream 43.5 18.1 202 3400 fema… 2009
## 4 Chinstrap Dream 49.6 18.2 193 3775 male 2009
## 5 Chinstrap Dream 50.8 19 210 4100 male 2009
## 6 Chinstrap Dream 50.2 18.7 198 3775 fema… 2009
## # … with abbreviated variable names ¹flipper_length_mm, ²body_mass_gAnd now the structure of palmepenguins DataSet
str(penguins) ## tibble [344 × 8] (S3: tbl_df/tbl/data.frame)
## $ species : Factor w/ 3 levels "Adelie","Chinstrap",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ island : Factor w/ 3 levels "Biscoe","Dream",..: 3 3 3 3 3 3 3 3 3 3 ...
## $ bill_length_mm : num [1:344] 39.1 39.5 40.3 NA 36.7 39.3 38.9 39.2 34.1 42 ...
## $ bill_depth_mm : num [1:344] 18.7 17.4 18 NA 19.3 20.6 17.8 19.6 18.1 20.2 ...
## $ flipper_length_mm: int [1:344] 181 186 195 NA 193 190 181 195 193 190 ...
## $ body_mass_g : int [1:344] 3750 3800 3250 NA 3450 3650 3625 4675 3475 4250 ...
## $ sex : Factor w/ 2 levels "female","male": 2 1 1 NA 1 2 1 2 NA NA ...
## $ year : int [1:344] 2007 2007 2007 2007 2007 2007 2007 2007 2007 2007 ...Missing values
It’s a lot missing values NA (19) in this [344 × 8] dataSet, dislike
Iris dataSet by example.
penguins %>%
#group_by(species) %>%
select(everything()) %>%
summarise_all(list(~sum(is.na(.)))) %>%
pivot_longer(cols = 1:8, names_to = 'columns', values_to = 'NA_count') %>%
arrange(desc(NA_count)) %>%
ggplot(aes(y = columns, x = NA_count)) + geom_col(fill = 'darkorange') +
geom_label(aes(label = NA_count)) +
theme_minimal() +
labs(title = 'Penguins - NA Count')
The count of individuals by specie
penguins %>% count(species)## # A tibble: 3 × 2
## species n
## <fct> <int>
## 1 Adelie 152
## 2 Chinstrap 68
## 3 Gentoo 124The Graphic of Penguin mass vs. flipper length
mass_flipper <- ggplot(data = penguins,
aes(x = flipper_length_mm,
y = body_mass_g)) +
geom_point(aes(color = species,
shape = species),
size = 3,
alpha = 0.8) +
scale_color_manual(values = c("darkorange","purple","cyan4")) +
labs(title = "Penguin size, Palmer Station LTER",
subtitle = "Flipper length and body mass for Adelie, Chinstrap and Gentoo Penguins",
x = "Flipper length (mm)",
y = "Body mass (g)",
color = "Penguin species",
shape = "Penguin species") +
theme(legend.position = c(0.2, 0.7),
plot.title.position = "plot",
plot.caption = element_text(hjust = 0, face= "italic"),
plot.caption.position = "plot")
mass_flipper## Warning: Removed 2 rows containing missing values (geom_point).
The Graphic of Flipper length vs. bill length
flipper_bill <- ggplot(data = penguins,
aes(x = flipper_length_mm,
y = bill_length_mm)) +
geom_point(aes(color = species,
shape = species),
size = 3,
alpha = 0.8) +
scale_color_manual(values = c("darkorange","purple","cyan4")) +
labs(title = "Flipper and bill length",
subtitle = "Dimensions for Adelie, Chinstrap and Gentoo Penguins at Palmer Station LTER",
x = "Flipper length (mm)",
y = "Bill length (mm)",
color = "Penguin species",
shape = "Penguin species") +
theme(legend.position = c(0.85, 0.15),
plot.title.position = "plot",
plot.caption = element_text(hjust = 0, face= "italic"),
plot.caption.position = "plot")
flipper_bill## Warning: Removed 2 rows containing missing values (geom_point).
The Graph of Bill length vs. depth
bill_len_dep <- ggplot(data = penguins,
aes(x = bill_length_mm,
y = bill_depth_mm,
group = species)) +
geom_point(aes(color = species,
shape = species),
size = 3,
alpha = 0.8) +
geom_smooth(method = "lm", se = FALSE, aes(color = species)) +
scale_color_manual(values = c("darkorange","purple","cyan4")) +
labs(title = "Penguin bill dimensions",
subtitle = "Bill length and depth for Adelie, Chinstrap and Gentoo Penguins at Palmer Station LTER",
x = "Bill length (mm)",
y = "Bill depth (mm)",
color = "Penguin species",
shape = "Penguin species") +
theme(legend.position = c(0.85, 0.15),
plot.title.position = "plot",
plot.caption = element_text(hjust = 0, face= "italic"),
plot.caption.position = "plot")
bill_len_dep## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 2 rows containing non-finite values (stat_smooth).## Warning: Removed 2 rows containing missing values (geom_point).
A Note at the end.
Even the first approach to palmerpenguins 0.1.1 dataSet, is a lot of code! Of course, I skipped a lot of things in this post, There are many plots, correlations and data cleaning we can do for this dataSet. However, I hope this trial gives inspiration for further data analysis.
Next time it will be go to a R Shiny App!!!!
Thanks.
x <- citation("palmerpenguins")
toBibtex(x) ## @Manual{,
## title = {palmerpenguins: Palmer Archipelago (Antarctica) penguin data},
## author = {Allison Marie Horst and Alison Presmanes Hill and Kristen B Gorman},
## year = {2020},
## note = {R package version 0.1.0},
## doi = {10.5281/zenodo.3960218},
## url = {https://allisonhorst.github.io/palmerpenguins/},
## }