More Adv. Code

More Adv. Code for Base R Visualization

Now that you know the basics, we can add more details and change things.

I have provided some examples. In the first example, I add three different box plots with different colors and I adjust opacity of the colors.

library(tidyverse)

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.4     ✔ readr     2.1.5
✔ forcats   1.0.0     ✔ stringr   1.5.1
✔ ggplot2   3.5.1     ✔ tibble    3.2.1
✔ lubridate 1.9.3     ✔ tidyr     1.3.1
✔ purrr     1.0.4     
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(RandomData)

## BOX PLOT ##

# Wrangle the Data
thebigthree <- constructors_stats |>
  # Filter for the top 3 constructors since 2016 
  filter((constructor == "Mercedes" | constructor == "Red Bull" | constructor == "Ferrari") &            year > 2016)  |>
  # calculate total wins per year per constructor
  group_by(constructor, year) |> 
  summarize(total_wins = sum(constructor_wins))

`summarise()` has grouped output by 'constructor'. You can override using the
`.groups` argument.

# Create boxplot
boxplot(total_wins ~ constructor, 
        data = thebigthree,
        main = "The Big Three Constructor Wins Since 2016",
        xlab = "Constructor",
        ylab = "Wins",
        col = c(adjustcolor("red", alpha.f = 0.4), # alpha.f adjusts the opacity
                adjustcolor("grey", alpha.f = 0.4),
                adjustcolor("blue", alpha.f = 0.4)))

In my second example, I add two more lines to my time series graph, change their colors, and also add a legend.

## TIME SERIES ##

# Wrangle the Data

# Avg wins for all drivers since 2006
avg_wins <- driver_stats |>
    filter(year > 2006) |>
    group_by(surname, year) |> 
    summarize(
      max_wins = max(driver_wins)) |>
    ungroup() |>
    group_by(year) |>
  summarize(
      total_wins = mean(max_wins))

`summarise()` has grouped output by 'surname'. You can override using the
`.groups` argument.

# Avg wins for Lewis Hamilton since joining the grid in 2007
hamilton <- driver_stats |>
  # Filter for the Lewis Hamilton
  filter(surname == "Hamilton")  |>
  # calculate total wins per year for Hamilton
  group_by(year) |> 
  summarize(total_wins = sum(max(driver_wins)))

# Avg wins for Max Verstappen since joining the grid in 2015
verstappen <- driver_stats |>
  # Filter for Max Verstappen
  filter(surname== "Verstappen")  |>
  # calculate total wins per year for Verstappen
  group_by(year) |> 
  summarize(total_wins = sum(max(driver_wins)))

# Create Time Series Plot
plot(avg_wins$year, avg_wins$total_wins, type = "l",
     col = "grey",
     main = "Total Wins Over Time",
     xlab = "Year",
     ylab = "Total Wins",
     ylim = range(c(avg_wins$total_wins,
                    hamilton$total_wins,
                    verstappen$total_wins
     )))
    

# Add lines for Hamilton and Verstappen
lines(hamilton$year, hamilton$total_wins, col = "turquoise", type = "l")
lines(verstappen$year, verstappen$total_wins, col = "red", type = "l")

# Add a legend to the plot
legend("topleft", legend = c("Avg.", "Hamilton", "Verstappen"),
       col = c("grey", "turquoise", "red"), lty = 1, cex = 0.8)