EDA in R

The eda-basic.qmd file groups the penguins by species and sex, summarizes the means of the numeric columns, and plots bill length against body mass.

Expand the section below to view the source file:

View eda-basic.qmd

---
title: "Penguins EDA"
format:
  html:
    code-fold: true
---

## Penguin Size and Mass by Sex and Species

```{r}
library(palmerpenguins)
library(dplyr)
library(ggplot2)

df <- palmerpenguins::penguins
```

```{r}
df %>%
  group_by(species, sex) %>%
  summarise(
    across(
      where(is.numeric), 
      \(x) mean(x, na.rm = TRUE)
      )
    ) %>%
  knitr::kable()
```

## Penguin Size vs Mass by Species

```{r}
df %>%
  ggplot(aes(x = bill_length_mm, y = body_mass_g, color = species)) +
  geom_point() + 
  geom_smooth(method = "lm")
```

I’ve also included the code from the eda-basic.qmd file in the sections below.

Load data

The code below loads palmerpenguins, dplyr, and ggplot2 and stores the penguins data as df:

library(palmerpenguins)
library(dplyr)
library(ggplot2)

df <- palmerpenguins::penguins

Manipulate and summarize

The pipeline below returns a table with the mean of every numeric column by species and sex:

df %>%
  dplyr::group_by(species, sex) %>%
  dplyr::summarise(
    dplyr::across(
      dplyr::where(is.numeric), 
      \(x) mean(x, na.rm = TRUE)
      )
    ) %>%
  knitr::kable()

species	sex	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g	year
Adelie	female	37.25753	17.62192	187.7945	3368.836	2008.055
Adelie	male	40.39041	19.07260	192.4110	4043.493	2008.055
Adelie	NA	37.84000	18.32000	185.6000	3540.000	2007.000
Chinstrap	female	46.57353	17.58824	191.7353	3527.206	2007.971
Chinstrap	male	51.09412	19.25294	199.9118	3938.971	2007.971
Gentoo	female	45.56379	14.23793	212.7069	4679.741	2008.069
Gentoo	male	49.47377	15.71803	221.5410	5484.836	2008.066
Gentoo	NA	45.62500	14.55000	215.7500	4587.500	2008.400

Visualize

The scatter plot below shows bill length against body mass, with a linear fit line for each species:

df %>%
  ggplot2::ggplot(
    ggplot2::aes(
      x = bill_length_mm, 
      y = body_mass_g, 
      color = species)) +
  ggplot2::geom_point() + 
  ggplot2::geom_smooth(method = "lm")

Recap

1. The eda-basic.qmd file loads palmerpenguins::penguins, groups the data by species and sex, and uses dplyr::across() with where(is.numeric) to compute the mean of every numeric column.

2. The scatter plot of bill_length_mm against body_mass_g adds a geom_smooth(method = "lm") line for each species. That linear relationship is what the model in Modeling in Python estimates: body mass increases with bill length, and species shifts the baseline.

Next, Lab 2: Project Architecture loads the penguins data into duckdb and turns the model into a vetiver API.