Regression Modelling of infectious diseases

Subproject 2 goes here

Introduction

Data

I start it with the penguins dataset from @palmerpenguins package [@corsi2021]

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…

Species

Figure 1 is a scatter plot of species of penguins

Show the code 
ggplot(penguins,
       aes(x = bill_length_mm, y = bill_depth_mm,
           color = species, shape = species)
       )+
  geom_point()+
  theme_minimal()+
  #scale_color_continuous()+
  labs(x= "Bill length (mm)", y="Bill depth (mm)")
Warning: Removed 2 rows containing missing values or values outside the scale range
(`geom_point()`).
A scatter plot
Figure 1: A scatter plot

Penguins

Table 1 below shows first 10 penguins from the dataset.

Show the code 
penguins|>
  slice_head(n=10)|>
  select(species, island, bill_length_mm, bill_depth_mm)|> 
  gt()
Table 1: First 10 penguins
species island bill_length_mm bill_depth_mm
Adelie Torgersen 39.1 18.7
Adelie Torgersen 39.5 17.4
Adelie Torgersen 40.3 18.0
Adelie Torgersen NA NA
Adelie Torgersen 36.7 19.3
Adelie Torgersen 39.3 20.6
Adelie Torgersen 38.9 17.8
Adelie Torgersen 39.2 19.6
Adelie Torgersen 34.1 18.1
Adelie Torgersen 42.0 20.2

Analysis

modelling

References

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