The big picture

Remember that the final goal is to have a GitHub repository where you can showcase your work. Assignment 1 was to create the repository. Assignment two required you to develop one R script to clean some data in that same repository. For this third assignment, you will visualize the data you cleaned last week. Your fourth assignment will require you to work with spatial data. Your final project will leverage the data and visualizations you’ll produce to wrap it all together.

This assignment

Task: Develop one data visualization script that reads the clean data you exported last week, visualizes them, and exports 1-2 figures in .png format.

Your visualization should meet the following criteria (50% of your grade):

(10%) You must use at lest two different geoms to show different visualizations¹.
(10%) You must modify labels as appropriate, and make sure to include units when relevant. Use the caption argument to attribute the source of your data.
(10%) All text appears in Sentence case, as relevant.
(10%) You must use any theme other than the default theme.
(10%) At least one of your figures should have panels, produced either with facet_*() or by combining multiple plots with cowplot::plot_grid() or the patchwork package.

Additionally, your script should have the following (50% of your grade):

(10%) Be called data_visualization.R and be saved inside the scripts/03_content/ folder
(10%) Contain code documentation using comments #
(10%) Clearly indicate all packages and data loaded at the top of the script²
(10%) Uses relative paths to read data and write figures. Figure(s) are exported to the results/img folder.
(10%) I can reproduce your figure without needing to modify any code.

Turning in your assignment

Please share the link to your github repo via Canvas
The deadline for this assignment is Nov 2 by 23:59

Resources

Class material

R4DS

Visualization (Look at section 1.6 for saving your plot)
Layers

Example of a figure that would get 100%

Code

## SET UP ######################################################################
# Load packages
library(EVR628tools)
library(tidyverse)
library(cowplot)

# Load data
data("data_geartypes") # In your case you will load them from your data/processed folder

## PROCESSING ##################################################################

# My data are already clean, but I need to make some final touches for visualization
data_vis <- data_geartypes |> 
  mutate(
    geartype = str_replace_all(geartype, "_", " "), # swap the underscores for spaces
    geartype = str_to_sentence(geartype), # Make them into sentence case
    geartype = fct_lump_n(geartype, 5), # Lump them (5 categories + others)
    geartype = fct_reorder(geartype, effort_hours, .fun = mean))  # Order them based on mean effort

## VISUALIZE ###################################################################

# Build a figure
p1 <- ggplot(data = data_vis,
             mapping = aes(x = effort_hours, y = geartype)) +
  stat_summary(geom = "pointrange",
               fun.data = mean_se,
               pch = 21,
               color = "black",
               fill = "steelblue") +
  labs(title = "Mean fishing effort by gear",
       subtitle = "Catergory 'Other' contains 7 gears combined",
       x = "Fishing effort (hours) [Mean ± SE]",
       y = "Gear",
       caption = "Data come from the `gfwr` package") +
  theme_minimal(base_size = 12) +
  scale_x_continuous(expand = c(0.1, 1))

# Build my second figure
p2 <- data_vis |> 
  group_by(geartype) |> 
  summarize(n_vessels = n_distinct(vessel_id)) |> 
  ggplot(mapping = aes(x = n_vessels, y = fct_reorder(geartype, n_vessels))) +
  geom_col(fill = "cadetblue", color = "black") +
  labs(title = "Fleet capacity by gear",
       subtitle = "Catergory 'Other' contains 7 gears combined",
       x = "Fleet capacity (# of vessels)",
       y = "Gear",
       caption = "Data come from the `gfwr` package") +
  theme_minimal(base_size = 12)


my_plot <- plot_grid(p1, p2,
                     ncol = 1,
                     labels = c("A)", "B)"))

## EXPORT ######################################################################
ggsave(plot = my_plot,
       filename = "results/img/effort_and_capacity.png", # Export my file as png
       width = 8,
       height = 8)

EVR 628- Intro to Environmental Data Science

The big picture

This assignment

Turning in your assignment

Resources

Example of a figure that would get 100%

Footnotes