Palmer Penguin Data Set Analysis

1 What is this report about

This report is an assignment of the Productive R Workflow course.

The goal is to build a Quarto report using a script that loads the palmerpenguins data set and performs an exploratory analysis.

We will load tidyverse, patchwork, TD, knitr, plotly and here in this report.

Note

This report is done to showcase some of the Quarto features and it’s merely academic.

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library(tidyverse)
library(patchwork)
library(knitr)
library(DT)
library(plotly)
library(here)

options(scipen = 100)

2 Data loading

The data is already loaded and prepared in our data folder, as well as some custom functions.

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source(here("R/functions_learn_by_doing.R"))

penguins_data <- read_rds(here("data/data_clean.rds"))

The culmen (bill) length and depth are measured as shown

3 Descriptive analytics

3.1 Summary of the data

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summary(penguins_data)

      species          island    bill_length_mm  bill_depth_mm  
 Adelie   :150   Biscoe   :168   Min.   :32.10   Min.   :13.10  
 Chinstrap: 68   Dream    :124   1st Qu.:39.27   1st Qu.:15.57  
 Gentoo   :124   Torgersen: 50   Median :44.50   Median :17.30  
                                 Mean   :43.96   Mean   :17.14  
                                 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:164   2007:110  
 1st Qu.:190.0     1st Qu.:3550   male  :167   2008:113  
 Median :197.0     Median :4050   NA    : 11   2009:119  
 Mean   :201.0     Mean   :4203                          
 3rd Qu.:213.2     3rd Qu.:4756                          
 Max.   :231.0     Max.   :6300                          
 NA's   :2         NA's   :2

3.2 Average bill length and depth by species

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avg_bill_length <- penguins_data %>%
  group_by(species) %>%
  summarise(mean_bill_length = num(mean(bill_length_mm, na.rm = TRUE), digits = 2))

avg_bill_depth <- penguins_data %>%
  group_by(species) %>%
  summarise(mean_bill_depth = num(mean(bill_depth_mm, na.rm = TRUE), digits = 2))

kable(avg_bill_length)
kable(avg_bill_depth)

species	mean_bill_length
Adelie	38.81
Chinstrap	48.83
Gentoo	47.50

species	mean_bill_depth
Adelie	18.34
Chinstrap	18.42
Gentoo	14.98

3.3 Scatter plot with Bill dimensions (length and depth)

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ggplotly(penguins_data %>%
  na.omit() %>%
  ggplot(aes(x = bill_length_mm, y = bill_depth_mm, color = species, shape = species)) +
  geom_point() + 
  labs(title = "Penguin Bill Dimensions",
       x = "Bill Length (mm)",
       y = "Bill Depth (mm)") +
  scale_shape_manual(values = c("Adelie" = 16,
                                "Chinstrap" = 17,
                                "Gentoo" = 18)) +
  scale_color_manual(values = c("Adelie" = "#ff8100",
                                "Chinstrap" = "#c25ecb",
                                "Gentoo" = "#056e75")))

Length and Depth are negatively correlated when we look at all data points

3.4 Scatterplots with Bill dimensions (length and depth) by species

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p1 <- species_length_depth(penguins_data, "Chinstrap", "#c25ecb")
p2 <- species_length_depth(penguins_data, "Gentoo", "#056e75")
p3 <- species_length_depth(penguins_data, "Adelie", "#ff8100")

p1 + p2 + p3

Length and Depth are positively correlated when we split them by species

4 Take a look at the data

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datatable(penguins_data,
filter = "top")

5 Data source

Horst AM, Hill AP, Gorman KB (2020). palmerpenguins: Palmer Archipelago (Antarctica) penguin data. R package version 0.1.0. https://allisonhorst.github.io/palmerpenguins/. doi: 10.5281/zenodo.3960218.