Tutorial 4

week4/tutorial/tutorial-04.qmd

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+---
+title: "Tutorial-04"
+author: Kate Saunders
+format: 
+  html:
+    toc: true
+    css: tutorial.css
+    embed-resources: true
+  pdf:
+    toc: true
+editor: visual
+---
+
+## Visualisation in R
+
+### Learning Objectives
+
+-   Practice how to create plots in R.
+
+-   This will involve understanding the grammar of graphics and what each of the different layers are.
+
+-   You will learn to create a range of different plots.
+
+### Preparation
+
+-   Ensure you installed R and RStudio.
+
+-   Ensure you have installed the `ggplot2` package. You can install this package directly, but this package is also part of the `tidyverse` package.
+
+-   Before you get started set yourself up an R project. This will help you to direct R to where your data is installed.\
+
+-   If you need any help doing the above, refer to the Lecture 1 and Tutorial 1 material.
+
+-   Download the dataset from Moodle, `boston_celtics.csv,` and place it in a folder called `data` within your R Project.
+
+### Task
+
+Today you will be creating visualisations in `ggplot2` to analyse sporting statistics from the Boston Celtics NBA basketball team.
+
+```{r echo = FALSE}
+show_solutions = FALSE
+```
+
+### Exercise 1
+
+1.  Read your data set into R and follow the Reading Data Checklist from the lecture.\
+    \
+    We recommend installing the package `here` to help keep things organised when referencing files.
+
+```{r echo = TRUE, warning = FALSE, message = FALSE}
+if(!require(here)){
+  install.packages("here")
+}
+library(here)
+```
+
+```{r echo = TRUE, warning = FALSE, message = FALSE}
+library(tidyverse)
+boston_celtics <- read_csv(here("data", "boston_celtics.csv"))
+```
+
+If you have an trouble run through the check list: 
+```{r echo = TRUE, eval = FALSE, warning = FALSE, message = FALSE}
+
+# Check your working directory
+getwd()  
+
+# Check your data file is where you think it is
+file.exists(here("data","boston_celtics.csv"))
+
+# Read in your data 
+library(tidyverse)
+boston_celtics <- read_csv(here("data", "boston_celtics.csv"))
+
+# Look at your data 
+View(boston_celtics)
+
+# Look at a summary
+summary(boston_celtics)
+```
+
+
+2.  Create a scatter plot that shows the `game_date` and `team_scores`. Change the colour of the points using `team_winner` to show if the team won the game. Make the colour of the points `forestgreen`. Focus on the geometry and aesthetic layer for now.
+
+```{r eval = FALSE}
+    ggplot(data = ???) + 
+      geom_???(aes(x = ???, y = ???), col = ???)
+```
+
+```{r echo = show_solutions, eval = show_solutions}
+ggplot(data = boston_celtics) + 
+  geom_point(aes(x = game_date, y = team_score), col = "forestgreen")
+```  
+
+3.  Colour the points according to if the team won or lost. Use the hex colour codes from the [Boston Celtics team]("https://teamcolorcodes.com/boston-celtics-color-codes/") to set the colour scheme.
+
+```{r eval = FALSE}
+    ggplot(data = ???) + 
+      geom_???(aes(x = ???, y = ???, col = ???))
+```
+
+```{r echo = show_solutions, eval = show_solutions}
+ggplot(data = boston_celtics) + 
+  geom_point(aes(x = game_date, y = team_score, col = team_winner)) + 
+  scale_colour_manual(label = c("Loss", "Win"), values = c("TRUE" = "#007A33", "FALSE" = "#BA9653"))
+```  
+
+4. As there are a lot of points, change the size and the transparency to reduce overplotting. 
+
+```{r eval = FALSE}
+ggplot(data = ???) + 
+  geom_???(
+    aes(x = ???, y = ???, col = ???), 
+    size = ???, alpha = ???
+    )
+```
+
+```{r echo = show_solutions, eval = show_solutions}
+ggplot(data = boston_celtics) + 
+  geom_point(aes(x = game_date, y = team_score, col = team_winner), size = 0.75, alpha = 0.4) + 
+  scale_colour_manual(label = c("Loss", "Win"), values = c("TRUE" = "#007A33", "FALSE" = "#BA9653"))
+```  
+
+5.Look at the [theme options]("https://ggplot2.tidyverse.org/reference/ggtheme.html") and try using a few different ones, before deciding on the theme for your plot.
+
+```{r eval = FALSE}
+ggplot(data = ???) + 
+  geom_???(
+    aes(x = ???, y = ???, col = ???), 
+    size = ???, alpha = ???
+    ) + 
+  theme_???()
+```
+
+```{r echo = show_solutions, eval = show_solutions}
+ggplot(data = boston_celtics) + 
+  geom_point(aes(x = game_date, y = team_score, col = team_winner), size = 0.75, alpha = 0.4) + 
+  scale_colour_manual(label = c("Loss", "Win"), values = c("TRUE" = "#007A33", "FALSE" = "#BA9653")) + 
+  theme_bw()
+```  
+
+5.  Add appropriate labels to your plot and edit the legend position.
+
+```{r eval = FALSE}
+ggplot(data = ???) + 
+  geom_???(
+    aes(x = ???, y = ???, col = ???), 
+    size = ???, alpha = ???
+    ) + 
+  theme_???() + 
+  labs(title = ???, subtitle = ???, x = ???, y = ???)
+```
+
+```{r echo = show_solutions, eval = show_solutions}
+ggplot(data = boston_celtics) + 
+  geom_point(aes(x = game_date, y = team_score, col = team_winner), size = 0.75, alpha = 0.4) + 
+  scale_colour_manual(label = c("Loss", "Win"), values = c("TRUE" = "#007A33", "FALSE" = "#BA9653")) + 
+  theme_bw() + 
+  labs(title = "Boston Celtics Team Score", 
+       subtitle = "2021 - Current Season", 
+       x = "Date", 
+       y = "Points")
+```  
+6.  Move the legend on the plot to increase the data-density. *(Note the solutions show a more advanced way to move the legend that the lecture notes.)*
+
+```{r eval = FALSE}
+ggplot(data = ???) + 
+  geom_???(
+    aes(x = ???, y = ???, col = ???), 
+    size = ???, alpha = ???
+    ) + 
+  theme_???() + 
+  labs(title = ???, subtitle = ???, x = ???, y = ???)
+  theme(legend.position = ???)
+```
+
+```{r echo = show_solutions, eval = show_solutions}
+ggplot(data = boston_celtics) + 
+  geom_point(aes(x = game_date, y = team_score, col = team_winner), size = 0.75, alpha = 0.4) + 
+  scale_colour_manual(label = c("Loss", "Win"), values = c("TRUE" = "#007A33", "FALSE" = "#BA9653")) + 
+  theme_bw() + 
+  labs(title = "Boston Celtics Team Score", 
+       subtitle = "2021 - Current Season", 
+       x = "Date", 
+       y = "Points") + 
+  theme(legend.position = c(0.8, 1.1), legend.title = element_blank(), legend.direction = "horizontal")
+```  
+### Exercise 2
+
+Now we've mastered the basic layers, take some time to creating some other visualisations that are interesting to you! 
+
+* You may like to produce plots looking at the distribution of some of the key variables. For example, `assists`, `rebounds`, `steals` etc. 
+
+```{r echo = show_solutions, eval = show_solutions}
+boston_celtics |>
+  ggplot(aes(x = assists)) + 
+  geom_histogram(aes(y = ..density..)) + 
+  geom_density()
+
+boston_celtics |>
+  ggplot(aes(x = steals)) + 
+  geom_histogram(aes(y = ..density..)) + 
+  geom_density()
+
+boston_celtics |>
+  ggplot() + 
+  geom_histogram(aes(x = total_rebounds), fill = "blue", alpha = 0.3) +
+  geom_histogram(aes(x = offensive_rebounds), fill = "green", alpha = 0.3) + 
+  geom_histogram(aes(x = defensive_rebounds), fill = "orange", alpha = 0.3)
+```
+
+* Maybe you are interested in how the shot percentage varies between `field_goal_pct` (2 pts) and `three_point_field_goal_pct` (3 pts).
+
+```{r echo = show_solutions, eval = show_solutions}
+boston_celtics |>
+  filter(season == 2025) |> 
+  ggplot() + 
+  geom_point(aes(x = field_goal_pct, y = three_point_field_goal_pct)) 
+```
+
+* Perhaps you want to create a bar chart (`geom_bar`) to see how many times they've played opposing teams this season.
+
+```{r echo = show_solutions, eval = show_solutions}
+boston_celtics |>
+  filter(season == 2025) |> 
+  ggplot() + 
+  geom_bar(aes(y = opponent_team_name))
+```
+### Finishing Up
+
+At the end of the tutorial, share your favourite plot to the discussion forum.
+
+##### Material developed by Dr. Kate Saunders.
+
+##### © Copyright 2024 Monash University